This article introduces a weak pseudo-inverse of a monotone function, which is applied to prove that the associativity of a two-place function $T: [0,1]^2\rightarrow [0,1]$ defined by $T(x,y)=t^{[-1]}(F(t(x),t(y)))$ where $F:[0,\infty]^2\rightarrow[0,\infty]$ is an associative function with neutral element in $[0,\infty]$, $t: [0,1]\rightarrow [0,\infty]$ is a left continuous monotone function and $t^{[-1]}:[0,\infty]\rightarrow[0,1]$ is the weak pseudo-inverse of $t$ depends only on properties of the range of $t$.

It is conjectured since long that for any convex body $P\subset \mathbb{R}^n$ there exists a point in its interior which belongs to at least $2n$ normals from different points on the boundary of $P$. The conjecture is proven for $n=2,3,4$. We treat the same problem for convex polytopes in $\mathbb{R}^3$ and prove that each generic simple polytope has a point in its interior with $10$ normals to the boundary. This is an exact bound: there exists a tetrahedron with no more than $10$ normals from a point in its interior.

Let $\Delta= \sum_{m=0}^\infty q^{(2m+1)^2} \in \mathbb{F}_2[[q]]$ be the reduction mod 2 of the $\Delta$ series. A modular form $f$ modulo $2$ of level 1 is a polynomial in $\Delta$. If $p$ is an odd prime, then the Hecke operator $T_p$ transforms $f$ in a modular form $T_p(f)$ which is a polynomial in $\Delta$ whose degree is smaller than the degree of $f$, so that $T_p$ is nilpotent. The order of nilpotence of $f$ is defined as the smallest integer $g=g(f)$ such that, for every family of $g$ odd primes $p_1,p_2,\ldots,p_g$, the relation $T_{p_1}T_{p_2}\ldots T_{p_g}(f)=0$ holds. We show how one can compute explicitly $g(f)$; if $f$ is a polynomial of degree $d\geqslant 1$ in $\Delta$, one finds that $g(f) < \frac 32 \sqrt d$.

We study the Ollivier-Ricci curvature and its modification introduced by Lin, Lu, and Yau on graphs. We provide a complete characterization of all graphs with Lin-Lu-Yau curvature at least one. We then explore the relationship between the Lin-Lu-Yau curvature and the Ollivier-Ricci curvature with vanishing idleness on regular graphs. An exact formula for the difference between these two curvature notions is established, along with an equality condition. This condition allows us to characterize edges that are bone-idle in regular graphs. Furthermore, we demonstrate the non-existence of 3-regular bone-idle graphs and present a complete characterization of all 4-regular bone-idle graphs. We also show that there exist no 5-regular bone-idle graphs that are symmetric or a Cartesian product of a 3-regular and a 2-regular graph.

We extend two known constructions that relate regular subdivisions to initial degenerations of projective toric varieties and Grassmannians. We associate a point configuration $A$ with any homogeneous ideal $I$. We obtain upper and lower bounds on each initial ideal of $I$ in terms of regular subdivisions of $A$. Both bounds can be interpreted categorically via limits over face posets of subdivisions. We also investigate when these bounds are exact. This is an extended abstract of a forthcoming paper.

The Aldous-Hoover Theorem concerns an infinite matrix of random variables whose distribution is invariant under finite permutations of rows and columns. It states that, up to equality in distribution, each random variable in the matrix can be expressed as a function only depending on four key variables: one common to the entire matrix, one that encodes information about its row, one that encodes information about its column, and a fourth one specific to the matrix entry. We state and prove the theorem within a category-theoretic approach to probability, namely the theory of Markov categories. This makes the proof more transparent and intuitive when compared to measure-theoretic ones. A key role is played by a newly identified categorical property, the Cauchy--Schwarz axiom, which also facilitates a new synthetic de Finetti Theorem. We further provide a variant of our proof using the ordered Markov property and the d-separation criterion, both generalized from Bayesian networks to Markov categories. We expect that this approach will facilitate a systematic development of more complex results in the future, such as categorical approaches to hierarchical exchangeability.

A famous conjecture of Chowla on the least primes in arithmetic progressions implies that the abscissa of convergence of the Weil representation zeta function for a procyclic group $G$ only depends on the set $S$ of primes dividing the order of $G$ and that it agrees with the abscissa of the Dedekind zeta function of $\mathbb{Z}[p^{-1}\mid p \not\in S]$. Here we show that these consequences hold unconditionally for random procyclic groups in a suitable model. As a corollary, every real number $1 \leq \beta \leq 2$ is the Weil abscissa of some procyclic group.

In this paper we prove a reverse H\"{o}lder inequality for the variable exponent Muckenhoupt weights $\mathcal{A}_{p(\cdot)}$, introduced by the first author, Fiorenza, and Neugeabauer. All of our estimates are quantitative, showing the dependence of the exponent function on the $\mathcal{A}_{p(\cdot)}$ characteristic. As an application, we use the reverse H\"{o}lder inequality to prove that the matrix $\mathcal{A}_{p(\cdot)}$ weights, introduced in our previous paper, have both a right and left-openness property. This result is new even in the scalar case.

We establish the independence of multipliers for polynomial endomorphisms of $\mathbb C^n$ and endomorphisms of $\mathbb P^n.$ This allows us to extend results about the bifurcation measure and the critical height obtained in \cite{arXiv:2305.02246} to the case of polynomial endomorphisms of $\mathbb C^n$ for $n\geq 3$. An important step in the proof is the irreducibility of the spaces of endomorphisms with $N$ marked periodic points, which is of independent interest.

In this expository article, we study and discuss invariants of vector fields and holomorphic foliations that intertwine the theories of complex analytic singular varieties and singular holomorphic foliations on complex manifolds: two different settings with many points in common.

Let $\alpha(G)$ denote the cardinality of a maximum independent set and $\mu(G)$ be the size of a maximum matching of a graph $G=\left( V,E\right) $. If $\alpha(G)+\mu(G)=\left\vert V\right\vert $, then $G$ is a K\"{o}nig-Egerv\'{a}ry graph, and $G$ is a $1$-K\"{o}nig-Egerv\'{a}ry graph whenever $\alpha(G)+\mu(G)=\left\vert V\right\vert -1$. The corona $H\circ\mathcal{X}$ of a graph $H$ and a family of graphs $\mathcal{X}=\left\{ X_{i}:1\leq i\leq\left\vert V(H)\right\vert \right\} $ is obtained by joining each vertex $v_{i}$ of $H$ to all the vertices of the corresponding graph $X_{i},i=1,2,...,\left\vert V(H)\right\vert $. In this paper we completely characterize graphs whose coronas are $k$-K\"{o}nig-Egerv\'{a}ry graphs, where $k\in\left\{ 0,1\right\} $.

We investigate under which circumstances there exists nonzero {\it{projective}} smooth $\field[G]$-modules, where $\field$ is a field of characteristic $p$ and $G$ is a locally pro-$p$ group. We prove the non-existence of (non-trivial) projective objects for so-called {\it{fair}} groups -- a family including $\bf{G}(\frak{F})$ for a connected reductive group $\bf{G}$ defined over a non-archimedean local field $\frak{F}$. This was proved in \cite{SS24} for finite extensions $\frak{F}/\Bbb{Q}_p$. The argument we present in this note has the benefit of being completely elementary and, perhaps more importantly, adaptable to $\frak{F}=\Bbb{F}_q(\!(t)\!)$. Finally, we elucidate the fairness condition via a criterion in the Chabauty space of $G$.

In this article we identify a sharp ill-posedness/well-posedness threshold for kinetic wave equations (KWE) derived from quasilinear Schr\"{o}dinger models. Our analysis shows that KWE becomes ill-posed in a range where the original quasilinear equation is itself well-posed. We also prove that both the gain-only and full equation share the same well-posedness threshold, thus legitimizing a gain-only approach to solving 4-wave kinetic equations.

We show that there is a topology on the group of loops in euclidean space such that this group is embedded in a Lie group which is simple relative to the loops. An extension of this Lie group gives the structural group of a principal bundle with connection whose holonomy coincides with the Chen signature map. We also give an alternative geometric new proof of Chen signature Theorem.

This article presents an error analysis of the recently introduced Frenet immersed finite element (IFE) method. The Frenet IFE space employed in this method is constructed to be locally conforming to the function space of the associated weak form for the interface problem. This article further establishes a critical trace inequality for the Frenet IFE functions. These features enable us to prove that the Frenet IFE method converges optimally under mesh refinement in both $L^2$ and energy norms.

The following paper provides a multi-band channel measurement analysis on the frequency range (FR)3. This study focuses on the FR3 low frequencies 6.5 GHz and 8.75 GHz with a setup tailored to the context of integrated sensing and communication (ISAC), where the data are collected with and without the presence of a target. A method based on multiple signal classification (MUSIC) is used to refine the delays of the channel impulse response estimates. The results reveal that the channel at the lower frequency 6.5 GHz has additional distinguishable multipath components in the presence of the target, while the one associated with the higher frequency 8.75 GHz has more blockage. The set of results reported in this paper serves as a benchmark for future multi-band studies in the FR3 spectrum.

We give formulas for the conjugated motivic Milnor basis of the mod 2 motivic Steenrod algebra.

In distributed optimization, the communication of model updates can be a performance bottleneck. Consequently, gradient compression has been proposed as a means of increasing optimization throughput. In general, due to information loss, compression introduces a penalty on the number of iterations needed to reach a solution. In this work, we investigate how the iteration penalty depends on the interaction between compression and problem structure, in the context of non-convex stochastic optimization. We focus on linear compression schemes, where compression and decompression can be modeled as multiplication with a random matrix. We consider several distributions of matrices, among them random orthogonal matrices and matrices with random Gaussian entries. We find that in each case, the impact of compression on convergence can be quantified in terms of the norm of the Hessian of the objective, using a norm defined by the compression scheme. The analysis reveals that in certain cases, compression performance is related to low-rank structure or other spectral properties of the problem. In these cases, our bounds predict that the penalty introduced by compression is significantly reduced compared to worst-case bounds that only consider the compression level, ignoring problem data. We verify the theoretical findings on several optimization problems, including fine-tuning an image classification model.

The following Fisher-KPP type equation $$ u_t=Ku_{xx}-Bu^q+Au^p, \quad (x,t)\in\real\times(0,\infty), $$ with $p>q>0$ and $A$, $B$, $K$ positive coefficients, is considered. For both $p>q>1$ and $p>1$, $q=1$, we construct stationary solutions, establish their behavior as $|x|\to\infty$ and prove that they are separatrices between solutions decreasing to zero in infinite time and solutions presenting blow-up in finite time. We also establish decay rates for the solutions that decay to zero as $t\to\infty$.

Some qualitative properties of radially symmetric solutions to the non-homogeneous heat equation with critical density and weighted source $$ |x|^{-2}\partial_tu=\Delta u+|x|^{\sigma}u^p, \quad (x,t)\in\mathbb{R}^N\times(0,T), $$ are obtained, in the range of exponents $p>1$, $\sigma\ge-2$. More precisely, we establish conditions fulfilled by the initial data in order for the solutions to either blow-up in finite time or decay to zero as $t\to\infty$ and, in the latter case, we also deduce decay rates and large time behavior. In the limiting case $\sigma=-2$ we prove the existence of non-trivial, non-negative solutions, in stark contrast to the homogeneous case. A transformation to a generalized Fisher-KPP equation is derived and employed in order to deduce these properties.

We study the advection equation along vector fields singular at the initial time. More precisely, we prove that for divergence-free vector fields in $L^1_{loc}((0, T ]; BV (\mathbb{T}^d;\mathbb{R}^d))\cap L^2((0, T ) \times\mathbb{T}^d;\mathbb{R}^d)$, there exists a unique vanishing diffusivity solution. This class includes the vector field constructed by Depauw, for which there are infinitely many distinct bounded solutions to the advection equation.

A Sidon set $M$ is a subset of $\mathbb{F}_2^t$ such that the sum of four distinct elements of $M$ is never 0. The goal is to find Sidon sets of large size. In this note we show that the graphs of almost perfect nonlinear (APN) functions with high linearity can be used to construct large Sidon sets. Thanks to recently constructed APN functions $\mathbb{F}_2^8\to \mathbb{F}_2^8$ with high linearity, we can construct Sidon sets of size 192 in $\mathbb{F}_2^{15}$, where the largest sets so far had size 152. Using the inverse and the Dobbertin function also gives larger Sidon sets as previously known. Moreover, we improve the upper bound for the linearity of arbitrary APN functions.

Finite-dimensional Reedy algebras form a ring-theoretic analogue of Reedy categories and were recently proved to be quasi-hereditary. We identify Reedy algebras as quasi-hereditary algebras admitting a triangular (or Poincar\'e-Birkhoff-Witt type) decomposition into the tensor product of two oppositely directed subalgebras over a common semisimple subalgebra. This exhibits homological and representation-theoretic structure of the ingredients of the Reedy decomposition and it allows to give a characterisation of Reedy algebras in terms of idempotent ideals occurring in heredity chains, providing an analogue for Reedy algebras of a result of Dlab and Ringel on quasi-hereditary algebras.

The parameter $q(G)$ of an $n$-vertex graph $G$ is the minimum number of distinct eigenvalues over the family of symmetric matrices described by $G$. We show that all $G$ with $e(\overline{G}) = |E(\overline{G})| \leq \lfloor n/2 \rfloor -1$ have $q(G)=2$. We conjecture that any $G$ with $e(\overline{G}) \leq n-3$ satisfies $q(G) = 2$. We show that this conjecture is true if $\overline{G}$ is bipartite and in other sporadic cases. Furthermore, we characterize $G$ with $\overline{G}$ bipartite and $e(\overline{G}) = n-2$ for which $q(G) > 2$.

We provide a complete classification of when the homeomorphism group of a stable surface, $\Sigma$, has the automatic continuity property: Any homomorphism from Homeo$(\Sigma)$ to a separable group is necessarily continuous. This result descends to a classification of when the mapping class group of $\Sigma$ has the automatic continuity property. Towards this classification, we provide a general framework for proving automatic continuity for groups of homeomorphisms. Applying this framework, we also show that the homeomorphism group of any stable second countable Stone space has the automatic continuity property. Under the presence of stability this answers two questions of Mann.

In this paper, we study the Picard group of the Baily-Borel compactification of orthogonal Shimura varieties. As a result, we determine the Picard group of the Baily-Borel compactification of the moduli space of quasi-polarized K3 surfaces. Interestingly, in contrast to the situation observed in the moduli space of curves, we find that the Picard group of the Baily-Borel compactification is isomorphic to $\mathbb{Z}$.

Sufficient conditions are given for a function $F(p)$ to be the Laplace transform of a function $f(t)$ or a distribution $f$. No assumption on $f$ is given a priori. It is not even assumed that $f=0$ for $t<0$.

Mean-field limits have been used now as a standard tool in approximations, including for networks with a large number of nodes. Statistical inference on mean-filed models has attracted more attention recently mainly due to the rapid emergence of data-driven systems. However, studies reported in the literature have been mainly limited to continuous models. In this paper, we initiate a study of statistical inference on discrete mean-field models (or jump processes) in terms of a well-known and extensively studied model, known as the power-of-L, or the supermarket model, to demonstrate how to deal with new challenges in discrete models. We focus on system parameter estimation based on the observations of system states at discrete time epochs over a finite period. We show that by harnessing the weak convergence results developed for the supermarket model in the literature, an asymptotic inference scheme based on an approximate least squares estimation can be obtained from the mean-field limiting equation. Also, by leveraging the law of large numbers alongside the central limit theorem, the consistency of the estimator and its asymptotic normality can be established when the number of servers and the number of observations go to infinity. Moreover, numerical results for the power-of-two model are provided to show the efficiency and accuracy of the proposed estimator.

We study structural and enumerative aspects of pure simplicial complexes and clique complexes. We prove a necessary and sufficient condition for any simplicial complex to be a clique complex that depends only on the list of facets. We also prove a theorem that a class of ``triangle-intersection free" pure clique complexes are uniquely determined up to isomorphism merely from the facet-adjacency matrix. Lastly, we count the number of pure simplicial complexes with a fixed number of facets and find an upper bound to the number of pure clique complexes.

For every $n\geq 4$ we construct infinitely many mutually not homotopic closed manifolds of dimension $n$ which admit a negatively curved Einstein metric but no locally symmetric metric.

V-states are uniformly rotating vortex patches of the incompressible 2D Euler equation and the only known explicit examples are circles and ellipses. In this paper, we prove the existence of non-convex V-states with analytic boundary which are far from the known examples. To prove it, we use a combination of analysis of the linearized operator at an approximate solution and computer-assisted proof techniques.

Let $\mathcal{M}_g$ be the moduli space of hyperbolic surfaces of genus $g$ endowed with the Weil-Petersson metric. We view the regularized determinant $\log \det(\Delta_{X})$ of Laplacian as a function on $\mathcal{M}_g$ and show that there exists a universal constant $E>0$ such that as $g\to \infty$, (1) the expected value of $\left|\frac{\log \det(\Delta_{X})}{4\pi(g-1)}-E \right|$ over $\mathcal{M}_g$ has rate of decay $g^{-\delta}$ for some uniform constant $\delta \in (0,1)$; (2) the expected value of $\left|\frac{\log \det(\Delta_{X})}{4\pi(g-1)}\right|^\beta$ over $\mathcal{M}_g$ approaches to $E^\beta$ whenever $\beta \in [1,2)$.

The mathematical modeling of crowds is complicated by the fact that crowds possess the behavioral ability to develop and adapt moving strategies in response to the context. For example, in emergency situations, people tend to alter their walking strategy in response to fear. To be able to simulate these situations, we consider a kinetic model of crowd dynamics that features the level of stress as a parameter and propose to estimate this key parameter by solving an inverse crowd dynamics problem. This paper states the mathematical problem and presents a method for its numerical solution. We show some preliminary results based on a synthetic data set, i.e., test cases where the exact stress level is known and the crowd density data are generated numerically by solving a forward crowd dynamics problem.

In this paper, we will construct formulas and bounds for Neighborhood Degree-based indices of graphs and describe graphs that attain the bounds. Furthermore, we will establish a lower bound for the spectral radius of any graph.

In this paper we introduce some recent progresses on the convergence rate in Wasserstein distance for empirical measures of Markov processes. For diffusion processes on compact manifolds possibly with reflecting or killing boundary conditions, the sharp convergence rate as well as renormalization limits are presented in terms of the dimension of the manifold and the spectrum of the generator. For general ergodic Markov processes, explicit estimates are presented for the convergence rate by using a nice reference diffusion process, which are illustrated by some typical examples. Finally, some techniques are introduced to estimate the Wasserstein distance of empirical measures.

It is known that if a plane graph is graceful (resp. near-graceful), then its semidual is conservative (resp. near-conservative). In this work we prove that the semidual of a plane graph of size $M$ consisting of two nested cycles is conservative if $M \equiv 0,3 \pmod 4$, and near-conservative otherwise. We also show that for a given integer $m_1 \geq 3$, there exists $m^* > m_1$ such that for $m_2 \geq m^*$, if $m_1+m_2 \equiv 0,3 \pmod 4$ (resp. $m_1+m_2 \equiv 1,2 \pmod 4$), then there exists a graceful (resp. near-graceful) plane graph consisting of two nested cycles with sizes $m_1$ and $m_2$, respectively.

Over-the-air federated learning (FL), i.e., AirFL, leverages computing primitively over multiple access channels. A long-standing challenge in AirFL is to achieve coherent signal alignment without relying on expensive channel estimation and feedback. This paper proposes NCAirFL, a CSI-free AirFL scheme based on unbiased non-coherent detection at the edge server. By exploiting binary dithering and a long-term memory based error-compensation mechanism, NCAirFL achieves a convergence rate of order $\mathcal{O}(1/\sqrt{T})$ in terms of the average square norm of the gradient for general non-convex and smooth objectives, where $T$ is the number of communication rounds. Experiments demonstrate the competitive performance of NCAirFL compared to vanilla FL with ideal communications and to coherent transmission-based benchmarks.

Twisted torus knots are a generalization of torus knots, obtained by introducing additional full twists to adjacent strands of the torus knots. In this article, we present an explicit formula for the Alexander polynomial of twisted torus knots. Our approach utilizes a presentation of the knot group of twisted torus knots combined with Fox's free differential calculus. As applications, we provide a lower bound for the genus of certain families of twisted torus knots and identify families of twisted torus knots that are not $L$-space knots.

We introduce a simplified model of planar first passage percolation where weights along vertical edges are deterministic. We show that the limit shape has a flat edge in the vertical direction if and only if the random distribution of the horizontal edges has an atom at the infimum of its support. Furthermore, we present bounds on the upper and lower derivative of the time constant.

Using the properties of generalized Fibonacci numbers, we determine the automorphism groups of some K3 surfaces with Picard number 2. Conversely, using the automorphisms of K3 surfaces with Picard number 2, we prove the criterion for a given integer n is to be a generalized Fibonacci number. Moreover, we show that the generalized k-th Fibonacci number divides the generalized q-th Fibonacci number if and only if k divides q.

We prove that, when $n$ goes to infinity, Kostant's problem has negative answer for almost all simple highest weight modules in the principal block of the BGG category $\mathcal{O}$ for the Lie algebra $\mathfrak{sl}_n(\mathbb{C})$.

We establish a central limit theorem for the sum of $\epsilon$-independent random variables, extending both the classical and free probability setting. Central to our approach is the use of graphon limits to characterize the limiting distribution, which depends on the asymptotic structure of the underlying graphs governing $\epsilon$-independence. This framework yields a range of exotic limit laws, interpolating between free and classical cases and allowing for mixtures such as free and classical convolutions of the semi-circle and Gaussian distributions. We provide a complete characterization of the limiting law, captured as the distribution of an operator on the full Fock space. We extend our main result to the multivariate setting as well as the non-identically distributed case. The proof provides insights into the combinatorial structure of $\epsilon$-independence, shedding light on a natural connection between the graph of crossings of the partitions involved and the graphon limit of the graphs of $\epsilon$-independence.

We construct canonical measures, referred to as Hilbert measures, on orbit spaces of classical coregular representations of the orthogonal groups $\operatorname{O}_m$. We observe that the measures have singularities along non-principal strata of the orbit space if and only if the number of copies of the defining representation of $\operatorname{O}_m$ is equal to $m$.

In this paper, we improve the original Lagrange multiplier approach \cite{ChSh22,ChSh_II22} and introduce a new energy correction approach to construct a class of robust, positivity/bound-preserving, mass conserving and energy dissipative schemes for Keller-Segel equations which only need to solve several linear Poisson like equations. To be more specific, we use a predictor-corrector approach to construct a class of positivity/bound-preserving and mass conserving schemes which can be implemented with negligible cost. Then a energy correction step is introduced to construct schemes which are also energy dissipative, in addition to positivity/bound-preserving and mass conserving. This new approach is not restricted to any particular spatial discretization and can be combined with various time discretization to achieve high-order accuracy in time. We show stability results for mass-conservative, positivity/bound-preserving and energy dissipative schemes for two different Keller-Segel systems. A error analysis is presented for a second-order, bound-preserving, mass-conserving and energy dissipative scheme for the second-type of Keller-Segel equations. Ample numerical experiments are shown to validate the stability and accuracy of our approach.

We study the max-type recursive model introduced by Hu and Shi (J. Stat. Phys., 2018), which generalizes the model of Derrida and Retaux (J. Stat. Phys., 2014). We show that the class of geometric-type distributions are preserved by the model with geometric offspring distribution. The key result is a characterization for the long-time asymptotic behavior of the marginal distributions. From this result, we derive the conditional limit law and the asymptotics of the sustainability probability and the first moment in the critical case.

We introduce the concept of generalized almost plastic structure, and, on a pseudo-Riemannian manifold endowed with two $(1,1)$-tensor fields satisfying some compatibility conditions, we construct a family of generalized almost plastic structures and characterize their integrability with respect to a given affine connection on the manifold.

From a category $\mathcal{A}$ with an involution $\varrho$, we introduce $\varrho$-complexes, which are a generalization of (bounded) complexes, periodic complexes and modules of $\imath$quiver algebras. The homological properties of the category $\mathcal{C}_\varrho(\mathcal{A})$ of $\varrho$-complexes are given to make the machinery of semi-derived Ringel-Hall algebras applicable. The $\imath$Hall algebra of the weighted projective line $\mathbb{X}$ is the twisted semi-derived Ringel-Hall algebra of $\mathcal{C}_\varrho({\rm coh}(\mathbb{X}))$, where $\varrho$ is an involution of ${\rm coh}(\mathbb{X})$. This $\imath$Hall algebra is used to realize the quasi-split $\imath$quantum loop algebra, which is a generalization of the $\imath$quantum group arising from the quantum symmetric pair of quasi-split affine type ADE in its Drinfeld type presentation.

In this paper we propose and study a class of nonparametric, yet interpretable measures of association between two random vectors $X$ and $Y$ taking values in $\mathbb{R}^{d_1}$ and $\mathbb{R}^{d_2}$ respectively ($d_1, d_2\ge 1$). These nonparametric measures -- defined using the theory of reproducing kernel Hilbert spaces coupled with optimal transport -- capture the strength of dependence between $X$ and $Y$ and have the property that they are 0 if and only if the variables are independent and 1 if and only if one variable is a measurable function of the other. Further, these population measures can be consistently estimated using the general framework of geometric graphs which include $k$-nearest neighbor graphs and minimum spanning trees. Additionally, these measures can also be readily used to construct an exact finite sample distribution-free test of mutual independence between $X$ and $Y$. In fact, as far as we are aware, these are the only procedures that possess all the above mentioned desirable properties. The correlation coefficient proposed in Dette et al. (2013), Chatterjee (2021), Azadkia and Chatterjee (2021), at the population level, can be seen as a special case of this general class of measures.

We establish a group-action version of the Szemer\'edi-Trotter theorem over any field, extending Bourgain's result for the group $\mathrm{SL}_2(k)$. As an Elekes-Szab\'o-type application, we obtain quantitative bounds on the number of collinear triples on reducible cubic surfaces in $\mathbb{P}^3(k)$, where $k = \mathbb{F}_{q}$ and $k = \mathbb{C}$, thereby improving a recent result by Bays, Dobrowolski, and the second author.

Building on our recent work we construct the Penrose transformations of the cohomology groups of homogenous line bundles on flag domains $D=G_\R/T$ with $G_\R$ of Hermitian type. We give sufficient conditions for the injectivity of the Penrose transformations, and conditions under which the Penrose transformation of the automorphic cohomology groups on compact quotients of flag domains is an isomorphism. Finally we prove that the higher automorphic cohomology groups of certain homogeneous line bundles are isomorphic to the groups of automorphic forms on the Hermitian symmetric domain.

Let $K$ be a field and let $R = K[X_1, \ldots, X_m]$ with $m \geq 2$. Give $R$ the standard grading. Let $I$ be a homogeneous ideal of height $g$. Assume $1 \leq g \leq m -1$. Suppose $H^i_I(R) \neq 0$ for some $i \geq 0$. We show (1) $H^i_I(R)_n \neq 0$ for all $n \leq -m$. (2) if Supp $H^i_I(R) \neq \{ (X_1, \ldots, X_m)\}$ then $H^i_I(R)_n \neq 0$ for all $n \in \mathbb{Z}$. Furthermore if char $K = 0$ then $\dim_K H^i_I(R)_n$ is infinite for all $n \in \mathbb{Z}$. (3) $\dim_K H^g_I(R)_n$ is infinite for all $n \in \mathbb{Z}$. In fact we prove our results for $\mathcal{T}(R)$ where $\mathcal{T}(-)$ is a large sub class of graded Lyubeznik functors

In this article we study the spectral, linear and nonlinear stability of stationary shock profile solutions to the Lax-Wendroff scheme for hyperbolic conservation laws. We first clarify the spectral stability of such solutions depending on the convexity of the flux for the underlying conservation law. The main contribution of this article is a detailed study of the so-called Green's function for the linearized numerical scheme. As evidenced on numerical simulations, the Green's function exhibits a highly oscillating behavior ahead of the leading wave before this wave reaches the shock location. One of our main results gives a quantitative description of this behavior. Because of the existence of a one-parameter family of stationary shock profiles, the linearized numerical scheme admits the eigenvalue 1 that is embedded in its continuous spectrum, which gives rise to several contributions in the Green's function. Our detailed analysis of the Green's function describes these contributions by means of a so-called activation function. For large times, the activation function describes how the mass of the initial condition accumulates along the eigenvector associated with the eigenvalue 1 of the linearized numerical scheme. We can then obtain sharp decay estimates for the linearized numerical scheme in polynomially weighted spaces, which in turn yield a nonlinear orbital stability result for spectrally stable stationary shock profiles. This nonlinear result is obtained despite the lack of uniform ${\ell}$ 1 estimates for the Green's function of the linearized numerical scheme, the lack of such estimates being linked with the dispersive nature of the numerical scheme. This dispersive feature is in sharp contrast with previous results on the orbital stability of traveling waves or discrete shock profiles for parabolic perturbations of conservation laws.

In this work, we investigate the compactness and the long time behavior of killed Feynman-Kac semigroups of various processes arising from statistical physics with very general singular Schr{\"o}dinger potentials. The processes we consider cover a large class of processes used in statistical physics, with strong links with quantum mechanics and (local or not) Schr{\"o}dinger operators (including e.g. fractional Laplacians). For instance we consider solutions to elliptic differential equations, L{\'e}vy processes, the kinetic Langevin process with locally Lipschitz gradient fields, and systems of interacting L{\'e}vy particles. Our analysis relies on a Perron-Frobenius type theorem derived in a previous work [A. Guillin, B. Nectoux, L. Wu, 2020 J. Eur. Math. Soc.] for Feller kernels and on the tools introduced in [L. Wu, 2004, Probab. Theory Relat. Fields] to compute bounds on the essential spectral radius of a bounded nonnegative kernel.

For $f\in \mathcal{S}$, the class univalent functions in the unit disk $\mathbb{D}$ and given by $f(z)=z+\sum_{n=2}^{\infty} a_n z^n$ for $z\in \mathbb{D}$, we improve previous bounds for the second and third Hankel determinants in case when either $a_2=0,$ or $a_3=0$. We also improve an upper bound for the coefficient difference $|a_4|-|a_3|$ when $f\in \mathcal{S}$.

This paper explores the optimal investment problem of a renewal risk model with generalized Erlang distributed interarrival times. We assume that the phases of the interarrival time can be observed. The price of the risky asset is driven by the CEV model and the insurer aims to maximize the exponential utility of the terminal wealth by asset allocation. By solving the corresponding Hamilton-Jacobi-Bellman equation, when the interest rate is zero, the concavity of the solution as well as the the explicit expression of the investment policy is shown. When the interest rate is not zero, the explicit expression of the optimal investment strategy is shown, the structure as well as the concavity of the value function is proved.

We introduce a generalisation of norm relations in the group algebra $\mathbb{Q}[G]$, where $G$ is a finite group. We give some properties of these relations, and use them to obtain relations between the $S$-unit groups of different subfields of the same Galois extension of $\mathbb{Q}$, of Galois group $G$. Then we deduce an algorithm to compute the class groups of some number fields by reducing the problem to fields of lower degree. We compute the class groups of some large number fields.

In this article, we consider nonlocal Hamilton-Jacobi Equations on networks with Kirchhoff type conditions for the interior vertices and Dirichlet boundary conditions for the boundary ones: our aim is to provide general existence and comparison results in the case when the integro-differential operators are of order strictly less than 1. The main originality of these results is to allow these nonlocal terms to have contributions on several different edges of the network. The existence of Lipschitz continuous solutions is proved in two ways: either by using the vanishing viscosity method or by the usual Perron's method. The comparison proof relies on arguments introduced by Lions and Souganidis. We also introduce a notion of flux-limited solution, nonlocal analog to the one introduced by Imbert and Monneau, and prove that the solutions of the Kirchhoff problem are flux-limited solutions for a suitable flux-limiter. After treating in details the case when we only have one interior vertex, we extend our approach to treat general networks.

We define linear and radial stretch maps in the affine-additive group, and prove that they are minimizers of the mean quasiconformal distortion functional. For the proofs we use a method based on the notion of modulus of a curve family and the minimal stretching property (MSP) of the afore-mentioned maps. MSP relies on certain given curve families compatible with the respective geometric settings of the strech maps.

This paper presents an investigation into the high-order asymptotic expansion for 2D and 3D cubic nonlinear Klein-Gordon equations in the non-relativistic limit regime. There are extensive numerical and analytic results concerning that the solution of NLKG can be approximated by first-order modulated Schr\"odinger profiles in terms of $e^{i\frac t {\varepsilon^2}}v + c.c. $, where $v$ is the solution of related NLS and ``$c.c.$" denotes the complex conjugate. Particularly, the best analytic result up to now is given in \cite{lei}, which proves that the $L_x^2$ norm of the error can be controlled by $\varepsilon^2 +(\varepsilon^2t)^{\frac \alpha 4}$ for $H^\alpha_x$-data, $\alpha \in [1, 4]$. As for the high-order expansion, to our best knowledge, there are only numerical results, while the theoretical one is lacking. In this paper, we extend this study further and give the first high-order analytic result. We introduce the high-order expansion inspired by the numerical experiments in \cite{schratz2020, faou2014a}: \[ e^{i\frac t {\varepsilon^2}}v +\varepsilon^2 \Big( \frac 18 e^{3i\frac t {\varepsilon^2} }v^3 +e^{i\frac t {\varepsilon^2}} w \Big) +c.c., \] where $w$ is the solution to some specific Schr\"odinger-type equation. We show that the $L_x^2$ estimate of the error is of higher order $\varepsilon^4+\left(\varepsilon^2t\right)^\frac{\alpha}{4}$ for $H^\alpha_x$-data, $\alpha \in [4, 8]$.

Suppose that $h$ is an instance of the Gaussian free field (GFF) on a simply connected domain $D \subseteq {\mathbf C}$ and $x,y \in \partial D$ are distinct. Fix $\kappa \in (0,4)$ and for each $\theta \in {\mathbf R}$ let $\eta_\theta$ be the flow line of $h$ from $x$ to $y$. Recall that for $\theta_1 < \theta_2$ the fan ${\mathbf F}(\theta_1,\theta_2)$ of flow lines of $h$ from $x$ to $y$ is the closure of the union of $\eta_\theta$ as $\theta$ varies in any fixed countable dense subset of $[\theta_1,\theta_2]$. We show that the adjacency graph of components of $D \setminus {\mathbf F}(\theta_1,\theta_2)$ is a.s. connected, meaning it a.s. holds that for every pair $U,V$ of components there exist components $U_1,\ldots,U_n$ so that $U_1 = U$, $U_n = V$, and $\partial U_i \cap \partial U_{i+1} \neq \emptyset$ for each $1 \leq i \leq n-1$. We further show that ${\mathbf F}(\theta_1,\theta_2)$ a.s. determines the flow lines used in its construction. That is, for each $\theta \in [\theta_1,\theta_2]$ we prove that $\eta_\theta$ is a.s. determined by ${\mathbf F}(\theta_1,\theta_2)$ as a set.

The paper focuses on studying strong geodetic sets and numbers in the context of corona-type products of graphs. Our primary focus is on three variations of the corona products: the generalized corona, generalized edge corona, and generalized neighborhood corona products. A strong geodetic set is a minimal subset of vertices that covers all vertices in the graph through unique geodesics connecting pairs from this subset. We obtain the strong geodetic set and number of the corona-type product graph using the strong 2-geodetic set and strong 2-geodetic number of the initial arbitrary graphs. We analyze how the structural properties of these corona products affect the strong geodetic number, providing new insights into geodetic coverage and the relationships between graph compositions. This work contributes to expanding research on the geodetic parameters of product graphs.

We computed the first Whittaker coefficient of an Eisenstein series on a global metaplectic group induced from the torus and related the result with a Weyl group multiple Dirichlet series attached to the (dual) root system of the group under a mild assumption on the root system and the degree of the metaplectic cover. This confirms a conjecture of Brubaker-Bump-Friedberg.

The Erd\"{o}s-Moser equation $ \sum_{i=1}^{m - 1} i^k = m^k $ is a longstanding problem in number theory, with the only known solution in positive integers being $ (k, m) = (1, 3) $. This paper investigates the possibility of other solutions by employing approximation methods based on the Euler-MacLaurin formula to extend the discrete sum $ S(m - 1, k) $ to a continuous function $ S_{\mathbb{R}}(m - 1, k) $. Analyzing the approximate polynomial $ P_{\mathbb{R}}(m) = S_{\mathbb{R}}(m - 1, k) - m^k $, we apply the rational root theorem to search for potential integer solutions. Our investigation confirms that for $ k = 1 $, the only solution is $ m = 3 $. For $ k \geq 2 $, the approximation suggests that no additional positive integer solutions exist. However, we acknowledge the limitations of using approximation methods in the context of Diophantine equations, where exactness is crucial. The omission of correction terms in the approximation may overlook valid solutions. Despite these limitations, our work provides insights into the behavior of the Erd\"{o}s-Moser equation and highlights the challenges in finding solutions using analytical methods. We discuss the implications of our findings and suggest directions for future research, emphasizing the need for exact analytical techniques to conclusively address the conjecture.

The method of fragments was recently proposed, and its effectiveness has been empirically shown for three specialised pickup and delivery problems. We propose an enhanced fragment algorithm that for the first time, effectively solves the Pickup and Delivery Problem with Time Windows. Additionally, we describe the approach in general terms to exemplify its theoretical applicability to vehicle routing problems without pickup and delivery requirements. We then apply it to the Truck-Based Drone Delivery Routing Problem Problem with Time Windows. The algorithm uses a fragment formulation rather than a route one. The definition of a fragment is problem specific, but generally, they can be thought of as enumerable segments of routes with a particular structure. A resource expanded network is constructed from the fragments and is iteratively updated via dynamic discretization discovery. Additionally, we introduce two new concepts called formulation leveraging and column enumeration for row elimination that are crucial for solving difficult problems. These use the strong linear relaxation of the route formulation to strengthen the fragment formulation. We test our algorithm on instances of the Pickup and Delivery Problem with Time Windows and the Truck-Based Drone Delivery Routing Problem with Time Windows. Our approach is competitive with, or outperforms the state-of-the-art algorithm for both.

We prove that the Kloosterman sum $\text{Kl}(1,q)$ changes sign infinitely many times, as $q\rightarrow +\infty$ with at most two prime factors. As a consequence, our result is unconditional compared with Drappeau and Maynard's (Proc. Amer. Math. Soc., 2019), in which the existence of Laudau-Siegel zeros is required. Our arguments contain the Selberg sieve method, spectral theory and distribution of Kloosterman sums along with previous works by Fouvry, Matom\"{a}ki, Michel, Sivak-Fischler, and Xi.

The 1D Schr\"odinger equation closed with the transparent boundary conditions(TBCs) is known as a successful model for describing quantum effects, and is usually considered with a self-consistent Poisson equation in simulating quantum devices. We introduce discrete fourth order transparent boundary conditions(D4TBCs), which have been proven to be essentially non-oscillating when the potential vanishes, and to share the same accuracy order with the finite difference scheme used to discretize the 1D Schr\"odinger equation. Furthermore, a framework of analytic discretization of TBCs(aDTBCs) is proposed, which does not introduce any discretization error, thus is accurate. With the accurate discretizations, one is able to improve the accuracy of the discretization for the 1D Schr\"odinger problem to arbitrarily high levels. As numerical tools, two globally fourth order compact finite difference schemes are proposed for the 1D Schr\"odinger-Poisson problem, involving either of the D4TBCs or the aDTBCs, respectively, and the uniqueness of solutions of both discrete Schr\"odinger problems are rigorously proved. Numerical experiments, including simulations of a resistor and two nanoscale resonant tunneling diodes, verify the accuracy order of the discretization schemes and show potential of the numerical algorithm introduced for the 1D Schr\"odinger-Poisson problem in simulating various quantum devices.

In this paper we formulate the almost invariant subspaces theorems of backward shift operators in terms of the ranges or kernels of product of Toeplitz and Hankel operators. This approach simplifies and gives more explicit forms of these almost invariant subspaces which are derived from related nearly backward shift invariant subspaces with finite defect. Furthermore, this approach also leads to the surprising result that the almost invariant subspaces of backward shift operators are the same as the almost invariant subspaces of forward shift operators which were treated only briefly in literature.

We propose a universal matrix Capelli identity and explain how to derive Capelli identities for all quantum immanants in the Reflection Equation algebra and in the universal enveloping algebra U(gl_(M|N)).

In a recent study by Tenner, the concept of the interval poset of a permutation is introduced to effectively represent all intervals and their inclusions within a permutation. This research presents a new geometric viewpoint on these interval posets. We establish a one-to-one correspondence between the set of interval posets for permutations of size $n$ and a specific subset of dissections of a convex polygon with $n+1$ sides. Through this correspondence, we investigate various intriguing subsets of interval posets and uncover their connections to particular polygon dissections.

In this paper, we demonstrate how a class of advanced matrix concentration inequalities, introduced in \cite{brailovskaya2024universality}, can be used to eliminate the dimensional factor in the convergence rate of matrix completion. This dimensional factor represents a significant gap between the upper bound and the minimax lower bound, especially in high dimension. Through a more precise spectral norm analysis, we remove the dimensional factors for five different estimators in various settings, thereby establishing their minimax rate optimality.

In this paper, we propose a bistatic sensing-assisted beam tracking method for simultaneous communication and tracking of user vehicles navigating arbitrary-shaped road trajectories. Prior work on simultaneous communication and tracking assumes a colocated radar receiver at the transmitter for sensing measurements using the reflected Integrated Sensing and Communication (ISAC) signals in the mmWave band. Full isolation between transmitter and receiver is required here to avoid self-interference. We consider the bistatic setting where the sensing receivers are not colocated and can be realized in practice using traditional half-duplex transmit or receive nodes. First, we process the echoes reflected from the vehicle at multiple multi-antenna nodes at various locations, facilitating estimation of the vehicle's current position. Then, we propose selection criteria for the estimates and a maximum likelihood (ML) fusion scheme to fuse these selected estimates based on the estimated error covariance matrices of these measurements. This fusion scheme is important in bistatic and multistatic settings as the localization error depends significantly on the geometry of the transmitter, target, and receiver locations. Finally, we predict the vehicle's next location using a simple kinematic equation-based model. Through extensive simulation, we study the average spectral efficiency of communication with a moving user using the proposed simultaneous communication and tracking scheme. The proposed fusion-based scheme achieves almost the same average spectral efficiency as an ideal scheme that knows the exact trajectory. We also show that the proposed scheme can be easily extended to systems with Hybrid Digital-Analog architectures and performs similarly even in these systems.

Given a connected graph $G=(V,E)$ and a crossing family $\mathcal{C}$ over ground set $V$ such that $|\delta_G(U)|\geq 2$ for every $U\in \mathcal{C}$, we prove there exists a strong orientation of $G$ for $\mathcal{C}$, i.e., an orientation of $G$ such that each set in $\mathcal{C}$ has at least one outgoing and at least one incoming arc. This implies the main conjecture in Chudnovsky et al. (Disjoint dijoins. Journal of Combinatorial Theory, Series B, 120:18--35, 2016). In particular, in every minimal counterexample to the Edmonds-Giles conjecture where the minimum weight of a dicut is $2$, the arcs of nonzero weight must be disconnected.

In this paper we survey some results on Ricci flowing non-smooth initial data. Among other things, we give a non-exhaustive list of various weak initial data which can be evolved with the Ricci flow. We also survey results which show that various curvature lower bounds will, possibly up to a constant, be preserved, if we start with such possibly non-smooth initial data. Some proofs/proof sketches are given in certain cases. A list of some open problems related to these areas is given in the last section of the paper.

In this paper we consider the coin billiards introduced by M. Bialy. It is a family of maps of the annulus $\mathbb A = \mathbb T \times (0,\pi)$ given by the composition of the classical billiard map on a convex planar table $\Gamma$ with the geodesic flow on the lateral surface of a cylinder (coin) of given height having as bases two copies of $\Gamma$. We prove the following three main theorems: in two different scenarios (when the height of the coin is small, or when the coin is near-circular) there is a family of KAM curves close to, but not accumulating on, the boundary $\partial \mathbb A$; for any non-circular coin, if the height of the coin is sufficiently large, there is a neighbourhood of $\partial \mathbb A$ through which there passes no invariant essential curve; for many noncircular coins, there are Birkhoff zones of instability. These results provide partial answers to questions of Bialy. Finally, we describe the results of some numerical experiments on the elliptical coin billiard.

The entropy regularization is inspired by information entropy from machine learning and the ideas of exploration and exploitation in reinforcement learning, which appears in the control problem to design an approximating algorithm for the optimal control. This paper is concerned with the optimal exploratory control for backward stochastic system, generated by the backward stochastic differential equation and with the entropy regularization in its cost functional. We give the theoretical depict of the optimal relaxed control so as to lay the foundation for the application of such a backward stochastic control system to mathematical finance and algorithm implementation. For this, we first establish the stochastic maximum principle by convex variation method. Then we prove sufficient condition for the optimal control and demonstrate the implicit form of optimal control. Finally, the existence and uniqueness of the optimal control for backward linear-quadratic control problem with entropy regularization is proved by decoupling techniques.

In this short note, we introduce cospectral graphons, paralleling the notion of cospectral graphs. As in the graph case, we give three equivalent definitions: by equality of spectra, by equality of cycle densities, and by a unitary transformation. We also give an example of two cospectral graphons that cannot be approximated by two sequences of cospectral graphs in the cut distance.

Let $Y \to E \stackrel{p}{\to} B$ be a fibration and let $f: E \to E$ be a fiber map over $B$. In this work, we study the geometric and algebraic Reidemeister classes of the iterates of $f$ and introduce a Nielsen-type periodic number over $B$, denoted by $N_B P_n(f)$. When $B$ is a point, then $N_B P_n(f)$ coincides with the classical Nielsen periodic number.

The development of extremum seeking (ES) has progressed, over the past hundred years, from static maps, to finite-dimensional dynamic systems, to networks of static and dynamic agents. Extensions from ODE dynamics to maps and agents that incorporate delays or even partial differential equations (PDEs) is the next natural step in that progression through ascending research challenges. This paper reviews results on algorithm design and theory of ES for such infinite-dimensional systems. Both hyperbolic and parabolic dynamics are presented: delays or transport equations, heat-dominated equation, wave equations, and reaction-advection-diffusion equations. Nash equilibrium seeking (NES) methods are introduced for noncooperative game scenarios of the model-free kind and then specialized to single-agent optimization. Even heterogeneous PDE games, such as a duopoly with one parabolic and one hyperbolic agent, are considered. Several engineering applications are touched upon for illustration, including flow-traffic control for urban mobility, oil-drilling systems, deep-sea cable-actuated source seeking, additive manufacturing modeled by the Stefan PDE, biological reactors, light-source seeking with flexible-beam structures, and neuromuscular electrical stimulation.

We investigate (in)stabilities of periodic patterns under stochastic forcing in reaction-diffusion equations exhibiting a so-called Busse balloon. Specifically, we used a one-dimensional Klausmeier model for dryland vegetation patterns. Using numerical methods, we can accurately describe the transient dynamics of the stochastic solutions and compare several notions of stability. In particular, we show that stochastic stability heavily depends on the model parameters, the intensity of the noise and the location of the wavenumber of the periodic pattern within the deterministic Busse balloon. Furthermore, the boundary of the Busse balloon becomes blurred under the stochastic perturbations.

In this paper, we develop and implement an efficient asymptotic-preserving (AP) scheme to solve the gas mixture of Boltzmann equations, under the so-called "relaxation time scale" relevant to the epochal relaxation phenomenon. The disparity in molecular masses, ranging across several orders of magnitude, leads to significant challenges in both the evaluation of collision operators and designing of efficient numerical schemes in order to capture the multi-scale nature of the dynamics. A direct implementation by using the spectral method faces prohibitive computational costs as the mass ratio decreases due to the need to resolve vastly different thermal velocities. Different from [I. M. Gamba, S. Jin, and L. Liu, Commun. Math. Sci., 17 (2019), pp. 1257-1289], we propose an alternative approach by conducting asymptotic expansions for the collision operators, which can significantly reduce the computational complexity and works well for uniformly small $\varepsilon$. By incorporating the separation of three time scales in the model's relaxation process [P. Degond and B. Lucquin-Desreux, Math. Models Methods Appl. Sci., 6 (1996), pp. 405-436], we design an AP scheme that is able to capture the epochal relaxation phenomenon of disparage mass mixtures while maintaining the computational efficiency. Numerical experiments will demonstrate the effectiveness of our proposed scheme in handling large mass ratios of heavy and light species, in addition to validating the AP properties.

A holomorphic pre-foliation $\mathscr{F}=\mathcal{C}\boxtimes\mathcal{F}$ on $\mathbb{P}^{2}_{\mathbb{C}}$ is the data of a reduced complex projective curve $\mathcal{C}$ of $\mathbb{P}^{2}_{\mathbb{C}}$ and a holomorphic foliation $\mathcal{F}$ on $\mathbb{P}^{2}_{\mathbb{C}}$. When the foliation $\mathcal{F}$ is convex (resp. reduced convex) and the curve $\mathcal{C}$ is invariant by $\mathcal{F}$, we say that the pre-foliation $\mathscr{F}=\mathcal{C}\boxtimes\mathcal{F}$ is convex (resp. reduced convex). We prove that the dual web of a reduced convex pre-foliation on $\mathbb{P}^{2}_{\mathbb{C}}$ is flat. This generalizes our previous result obtained in the case where the associated curve consists only of invariant lines.

Determining the maximal density $m_1(\mathbb{R}^2)$ of planar sets without unit distances is a fundamental problem in combinatorial geometry. This paper investigates lower bounds for this quantity. We introduce a novel approach to estimating $m_1(\mathbb{R}^2)$ by reformulating the problem as a Maximal Independent Set (MIS) problem on graphs constructed from flat torus, focusing on periodic sets with respect to two non-collinear vectors. Our experimental results supported by theoretical justifications of proposed method demonstrate that for a sufficiently wide range of parameters this approach does not improve the known lower bound $0.22936 \le m_1(\mathbb{R}^2)$. The best discrete sets found are approximations of Croft's construction. In addition, several open source software packages for MIS problem are compared on this task.

For any $a\in\mathbb{C}$, the zeros of $\zeta(s)-a$, denoted by $\rho_a=\beta_a+i\gamma_a$, are called $a$-points of the Riemann zeta function $\zeta(s)$. In this paper, we reformulate some basic results about the $a$-points of $\zeta(s)$ shown by Garunk\v{s}tis and Steuding. We then deduce an asymptotic of the sum \[S_T(a,\delta)=\sum_{\tau<\gamma_a\leqslant T}\zeta'(\rho_a+i\delta)X^{\rho_a},\quad T\to\infty,\] where $0\ne\delta=\frac{2\pi\alpha}{\log\frac{T}{2\pi X}}\ll 1$, and $X>0$ and $\tau\geqslant|\delta|+1$ are fixed. We also find the interesting varied behavior of $S_T(a,\delta)$ in different $X$ ranges, which is more complicated than those described before by Gonek and Pearce-Crump.

We consider models for inference which involve observers which may have multiple copies, such as in the Sleeping Beauty problem. We establish a framework for describing these problems on a probability space satisfying Kolmogorov's axioms, and this enables the main competing solutions to be compared precisely.

We introduce the Lebesgue--H\"{o}lder--Dini and Lebesgue--H\"{o}lder spaces $L^p(\mathbb{R};{\mathcal C}_{\vartheta,\varsigma}^{\alpha,\rho}({\mathbb R}^n))$ ($\vartheta\in \{l,b\}, \, \varsigma\in \{d,s,c,w\}$, $p\in (1,+\infty]$ and $\alpha\in [0,1)$), and then use a vector-valued Calder\'{o}n--Zygmund theorem to establish the maximal Lebesgue--H\"{o}lder--Dini and Lebesgue--H\"{o}lder regularity for a class of parabolic equations. As an application, we obtain the unique strong solvability of the following stochastic differential equation \begin{eqnarray*} X_{s,t}(x)=x+\int\limits_s^tb(r,X_{s,r}(x))dr+W_t-W_{s}, \ \ t\in [s,T], \ x\in \mathbb{R}^n, \ s\in [0,T], \end{eqnarray*} for the low regularity growing drift in critical Lebesgue--H\"{o}lder--Dini spaces $L^p([0,T];{\mathcal C}^{\frac{2}{p}-1,\rho}_{l,d}({\mathbb R}^n;{\mathbb R}^n))$ ($p\in (1,2]$), where $\{W_t\}_{0\leq t\leq T}$ is a $n$-dimensional standard Wiener process. In particular, when $p=2$ we give a partially affirmative answer to a longstanding open problem, which was proposed by Krylov and R\"{o}ckner for $b\in L^2([0,T];L^\infty({\mathbb R}^n;{\mathbb R}^n))$ based upon their work ({\em Probab. Theory Relat. Fields 131(2): 154--196, 2005}).

Inexact proximal augmented Lagrangian methods (pALMs) are particularly appealing for tackling convex constrained optimization problems because of their elegant convergence properties and strong practical performance. To solve the associated pALM subproblems, efficient methods such as Newton-type methods are essential. Consequently, the effectiveness of the inexact pALM hinges on the error criteria used to control the inexactness when solving these subproblems. However, existing inexact pALMs either rely on absolute-type error criteria (which may complicate implementation by necessitating the pre-specification of an infinite sequence of error tolerance parameters) or require an additional correction step when using relative error criteria (which can potentially slow down the convergence of the pALM). To address this deficiency, this paper proposes ripALM, a relative-type inexact pALM, which can simplify practical implementation while preserving the appealing convergence properties of the classical absolute-type inexact pALM. We emphasize that ripALM is the first relative-type inexact version of the vanilla pALM with provable convergence guarantees. Numerical experiments on quadratically regularized optimal transport (OT) problems demonstrate the competitive efficiency of the proposed method compared to existing methods. As our analysis can be extended to a more general convex constrained problem setting, including other regularized OT problems, the proposed ripALM may provide broad applicability and has the potential to serve as a basic optimization tool.

Obtaining explicit stability estimates in classical functional inequalities like the Sobolev inequality has been an essentially open question for 30 years, after the celebrated but non-constructive result of G. Bianchi and H. Egnell in 1991. Recently, new methods have emerged which provide some clues on these fascinating questions. The goal of the course is to give an introduction to the topic for some fundamental functional inequalities and present several methods that can be used to obtain explicit estimates.

In this paper we develop the homological version of $\Sigma$-theory for locally compact Hausdorff groups, leaving the homotopical version for another paper. Both versions are connected by a Hurewicz-like theorem. They can be thought of as directional versions of type $\mathrm{CP}_m$ and type $\mathrm{C}_m$, respectively. And classical $\Sigma$-theory is recovered if we equip an abstract group with the discrete topology. This paper provides criteria for type $\mathrm{CP}_m$ and homological locally compact $\Sigma^m$. Given a short exact sequence with kernel of type $\mathrm{CP}_m$, we can derive $\Sigma^m$ of the extension on the sphere that vanishes on the kernel from the quotient and likewise. Given a short exact sequence with abelian quotient, $\Sigma$-theory on the extension can tell if the kernel is of type $\mathrm{CP}_m$.

In this work we study the behavior of the forward-backward (FB) algorithm when the proximity operator is replaced by a sub-iterative procedure to approximate a Gaussian denoiser, in a Plug-and-Play (PnP) fashion. In particular, we consider both analysis and synthesis Gaussian denoisers within a dictionary framework, obtained by unrolling dual-FB iterations or FB iterations, respectively. We analyze the associated minimization problems as well as the asymptotic behavior of the resulting FB-PnP iterations. In particular, we show that the synthesis Gaussian denoising problem can be viewed as a proximity operator. For each case, analysis and synthesis, we show that the FB-PnP algorithms solve the same problem whether we use only one or an infinite number of sub-iteration to solve the denoising problem at each iteration. To this aim, we show that each "one sub-iteration" strategy within the FB-PnP can be interpreted as a primal-dual algorithm when a warm-restart strategy is used. We further present similar results when using a Moreau-Yosida smoothing of the global problem, for an arbitrary number of sub-iterations. Finally, we provide numerical simulations to illustrate our theoretical results. In particular we first consider a toy compressive sensing example, as well as an image restoration problem in a deep dictionary framework.

Inverse problems of partial differential equations are ubiquitous across various scientific disciplines and can be formulated as statistical inference problems using Bayes' theorem. To address large-scale problems, it is crucial to develop discretization-invariant algorithms, which can be achieved by formulating methods directly in infinite-dimensional space. We propose a novel normalizing flow based infinite-dimensional variational inference method (NF-iVI) to extract posterior information efficiently. Specifically, by introducing well-defined transformations, the prior in Bayes' formula is transformed into post-transformed measures that approximate the true posterior. To circumvent the issue of mutually singular probability measures, we formulate general conditions for the employed transformations. As guiding principles, these conditions yield four concrete transformations. Additionally, to minimize computational demands, we have developed a conditional normalizing flow variant, termed CNF-iVI, which is adept at processing measurement data of varying dimensions while requiring minimal computational resources. We apply the proposed algorithms to two typical inverse problems governed by a simple smooth equation and the steady-state Darcy flow equation. Numerical results confirm our theoretical findings, illustrate the efficiency of our algorithms, and verify the discretization-invariant property.

We investigate compatibility of gradings for an almost Koszul or Koszul algebra $R$ that is also the higher preprojective algebra $\Pi_{n+1}(A)$ of an $n$-hereditary algebra $A$. For an $n$-representation finite algebra $A$, we show that $A$ must be Koszul if $\Pi_{n+1}(A)$ can be endowed with an almost Koszul grading. For a basic $n$-representation infinite algebra $A$ such that $\Pi_{n+1}(A)$ is graded coherent, we show that $A$ must be Koszul if $\Pi_{n+1}(A)$ can be endowed with a Koszul grading. From this we deduce that a higher preprojective grading of an (almost) Koszul algebra $R = \Pi_{n+1}(A)$ is, in both cases, isomorphic to a cut of the (almost) Koszul grading. Up to a further assumption on the tops of the degree $0$ subalgebras for the different gradings, we also show a similar result without the basic assumption in the $n$-representation infinite case. As an application, we show that $n$-APR tilting preserves the property of being Koszul for $n$-representation infinite algebras that have graded coherent higher preprojective algebras.

In this paper, for any $\lambda \geq 1, R_\lambda^2$ is the Bana\'s-Fr\k{a}czek space. The exact value of $L_{\mathrm{YJ}}(\xi, \eta, X)$ for this space will be calculated. Specifically, $L_{\mathrm{YJ}}\left(\xi, \eta, R_\lambda^2\right)=1+\frac{2 \xi \eta}{\xi^2+\eta^2}\left(1-\frac{1}{\lambda^2}\right)$ is the result thereafter through meticilous computation.

We carry out a detailed quantitative analysis on the geometry of invariant manifolds for smooth dissipative systems in dimension two. We begin by quantifying the regularity of any orbit (finite or infinite) in the phase space with a set of explicit inequalities. Then we relate this directly to the quasi-linearization of the local dynamics on regular neighborhoods of this orbit. The parameters of regularity explicitly determine the sizes of the regular neighborhoods and the smooth norms of the corresponding regular charts. As a corollary, we establish the existence of smooth stable and center manifolds with uniformly bounded geometries for regular orbits independently of any pre-existing invariant measure. This provides us with the technical background for the renormalization theory of H\'enon-like maps developed in the sequel papers.

We quantify the topological expansion properties of bounded degree simplicial complexes in terms of a family of sublinear functions, in analogy with the separation profile of Benjamini-Schramm-Tim\'ar for classical expansion of bounded degree graphs. We prove that, like the separation profile, these new invariants are monotone under regular maps between complexes satisfying appropriate higher connectivity assumptions. In the dimension $1$ case, we recover the cutwidth profile of Huang-Hume-Kelly-Lam. We also prove the seemingly new result that any $1$-dimensional topological expander necessarily contains a graphical expander. In higher dimensions, we give full calculations of these new invariants for Euclidean spaces, which are natural analogues of waist and width-volume inequalities due to Gromov and Guth respectively. We present several other methods of obtaining upper bounds including na\"ive (yet useful) direct product and fibring theorems, and show how lower bounds can be obtained via thick embeddings of complexes, in analogy with previous work of Barrett-Hume. Using this, we find lower bounds for $k$-expansion of $(k+1)$-fold horocyclic products of trees, and for rank $k$ symmetric spaces of non-compact type. As a further application, we prove that for every $k\geq 2$ there is no coarse embedding (and more generally, no regular map) from the $k$-fold horocyclic product of $3$-regular trees to either any product $(\mathbb{H}^2)^{k-2}\times H \times D$ where $\mathbb{H}^2$ is the real hyperbolic plane, $H$ is a bounded degree hyperbolic graph and $D$ is a doubling metric space, or to any symmetric space whose non-compact factor has corank (dimension minus rank) is strictly less than $k$.

We consider cylindrical algebraic decomposition (CAD) and the key concept of delineability which underpins CAD theory. We introduce the novel concept of projective delineability which is easier to guarantee computationally. We prove results about this which can allow reduced CAD computations.

We prove an abstract Birkhoff normal form theorem for Hamiltonian partial differential equations on torus. The normal form is complete up to arbitrary finite order. The proof is based on a valid non-resonant condition and a suitable norm of Hamiltonian function. Then as two examples, we apply this theorem to nonlinear wave equation in one dimension and nonlinear Schr\"{o}dinger equation in high dimension. Consequently, the polynomially long time stability is proved in Sobolev spaces $H^s$ with the index $s$ being much smaller than before. Further, by taking the iterative steps depending on the size of initial datum, we prove sub-exponentially long time stability for these two equations.

This study investigates air pollution in central Taiwan, focusing on key pollutants, including SO$_2$, NO$_2$, PM$_{10}$, and PM$_{2.5}$. We use non-negative matrix factorization (NMF) to reduce data dimensionality, followed by wind direction analysis and speed to trace pollution sources. Our findings indicate that PM$_{2.5}$ and NO$_2$ levels are primarily influenced by local sources, while SO$_2$ levels are more affected by transboundary factors. For PM$_{10}$, contributions from domestic and transboundary sources are nearly equal.

In the present note we focus on dynamics on the Gehman dendrite $\mathcal{G}$. It is well-known that the set of its endpoints is homeomorphic to a standard Cantor ternary set. For any given surjective Cantor system $\mathcal{C}$ we provide constructions of (i) a mixing but not exact and (ii) an exact map on $\mathcal{G}$, such that in both cases the subsystem formed by $\text{End}(\mathcal{G})$ is conjugate to the initially chosen system on $\mathcal{C}$.

We present a novel isogeometric collocation method for solving the Poisson's and the biharmonic equation over planar bilinearly parameterized multi-patch geometries. The proposed approach relies on the use of a modified construction of the C^s-smooth mixed degree isogeometric spline space [20] for s=2 and s=4 in case of the Poisson's and the biharmonic equation, respectively. The adapted spline space possesses the minimal possible degree p=s+1 everywhere on the multi-patch domain except in a small neighborhood of the inner edges and of the vertices of patch valency greater than one where a degree p=2s+1 is required. This allows to solve the PDEs with a much lower number of degrees of freedom compared to employing the C^s-smooth spline space [29] with the same high degree p=2s+1 everywhere. To perform isogeometric collocation with the smooth mixed degree spline functions, we introduce and study two different sets of collocation points, namely first a generalization of the standard Greville points to the set of mixed degree Greville points and second the so-called mixed degree superconvergent points. The collocation method is further extended to the class of bilinear-like G^s multi-patch parameterizations [26], which enables the modeling of multi-patch domains with curved boundaries, and is finally tested on the basis of several numerical examples.

We present a novel architecture for learning geometry-aware preconditioners for linear partial differential equations (PDEs). We show that a deep operator network (Deeponet) can be trained on a simple geometry and remain a robust preconditioner for problems defined by different geometries without further fine-tuning or additional data mining. We demonstrate our method for the Helmholtz equation, which is used to solve problems in electromagnetics and acoustics; the Helmholtz equation is not positive definite, and with absorbing boundary conditions, it is not symmetric.

We derive the Helmholtz--Korteweg equation, which models acoustic waves in Korteweg fluids. We further derive a nematic variant of the Helmholtz-Korteweg equation, which incorporates an additional orientational term in the stress tensor. Its dispersion relation coincides with that arising in Virga's analysis of the Euler-Korteweg equations, which we extend to consider imaginary wave numbers and the effect of boundary conditions. In particular, our extensions allow us to analyze the effect of nematic orientation on the penetration depth of evanescent plane waves, and on the scattering of sound waves by obstacles. Furthermore, we make new, experimentally-verifiable predictions for the effect of boundary conditions for a modification of the Mullen-L\"uthi-Stephen experiment, and for the scattering of acoustic waves in nematic-Korteweg fluids by a circular obstacle.

Digital twins (DTs) of wireless environments can be utilized to predict the propagation channel and reduce the overhead of required to estimate the channel statistics. However, direct channel prediction requires data-intensive calibration of the DT to capture the environment properties relevant for propagation of electromagnetic signals. We introduce a framework that starts from a satellite image of the environment to produce an uncalibrated DT, which has no or imprecise information about the materials and their electromagnetic properties. The key idea is to use the uncalibrated DT to implicitly provide a geometric prior for the environment. This is utilized to inform a Gaussian process (GP), which permits the use of few channel measurements to attain an accurate prediction of the channel statistics. Additionally, the framework is able to quantify the uncertainty in channel statistics prediction and select rate in ultra-reliable low-latency communication (URLLC) that complies with statistical guarantees. The efficacy of the proposed geometry-informed GP is validated using experimental data obtained through a measurement campaign. Furthermore, the proposed prediction framework is shown to provide significant improvements compared to the benchmarks where i) direct channel statistics prediction is obtained using an uncalibrated DT and (ii) the GP predicts channel statistics using information about the location.

We classify all connected and Lagrangian \'etale algebras in the Drinfeld center $\mathscr{Z}_1(\mathbf{2Vect}^\pi_G)$, where $G$ is a finite group and $\pi$ is a 4-cocycle on $G$. By D\'ecoppet's result every bosonic fusion 2-category $\mathfrak{C}$ has its Drinfeld center equivalent to $\mathscr{Z}_1(\mathbf{2Vect}^\pi_G)$ for some $G$ and $\pi$. Combining this fact with classification of Lagrangian algebras in $\mathscr{Z}_1(\mathbf{2Vect}^\pi_G)$, we obtain a classification of bosonic fusion 2-categories.

The fundamental quasisymmetric functions in superspace are a generalization of the fundamental quasisymmetric functions involving anticommuting variables. We obtain the action of the product, coproduct, and antipode on the fundamental quasisymmetric functions in superspace. We also extend to superspace the well known expansion of the Schur functions in terms of fundamental quasisymmetric functions.

The Generalized Hamming weights and their relative version, which generalize the minimum distance of a linear code, are relevant to numerous applications, including coding on the wire-tap channel of type II, $t$-resilient functions, bounding the cardinality of the output in list decoding algorithms, ramp secret sharing schemes, and quantum error correction. The generalized Hamming weights have been determined for some families of codes, including Cartesian codes and Hermitian one-point codes. In this paper, we determine the generalized Hamming weights of decreasing norm-trace codes, which are linear codes defined by evaluating monomials that are closed under divisibility on the rational points of the extended norm-trace curve given by $x^{u} = y^{q^{s - 1}} + y^{q^{s - 2}} + \cdots + y$ over the finite field of cardinality $q^s$, where $u$ is a positive divisor of $\frac{q^s - 1}{q - 1}$. As a particular case, we obtain the weight hierarchy of one-point norm-trace codes and recover the result of Barbero and Munuera (2001) giving the weight hierarchy of one-point Hermitian codes. We also study the relative generalized Hamming weights for these codes and use them to construct impure quantum codes with excellent parameters.

The log canonical threshold (lct) is a fundamental invariant in birational geometry, essential for understanding the complexity of singularities in algebraic varieties. Its real counterpart, the real log canonical threshold (rlct), also known as the learning coefficient, has become increasingly relevant in statistics and machine learning, where it plays a critical role in model selection and error estimation for singular statistical models. In this paper, we investigate the rlct and its multiplicity for real (not necessarily reduced) hyperplane arrangements. We derive explicit combinatorial formulas for these invariants, generalizing earlier results that were limited to specific examples. Moreover, we provide a general algebraic theory for real log canonical thresholds, and present a SageMath implementation for efficiently computing the rlct and its multiplicity in the case or real hyperplane arrangements. Applications to examples are given, illustrating how the formulas also can be used to analyze the asymptotic behavior of high-dimensional volume integrals.

Bi-level optimization problems, where one wishes to find the global minimizer of an upper-level objective function over the globally optimal solution set of a lower-level objective, arise in a variety of scenarios throughout science and engineering, machine learning, and artificial intelligence. In this paper, we propose and investigate, analytically and experimentally, consensus-based bi-level optimization (CB$^2$O), a multi-particle metaheuristic derivative-free optimization method designed to solve bi-level optimization problems when both objectives may be nonconvex. Our method leverages within the computation of the consensus point a carefully designed particle selection principle implemented through a suitable choice of a quantile on the level of the lower-level objective, together with a Laplace principle-type approximation w.r.t. the upper-level objective function, to ensure that the bi-level optimization problem is solved in an intrinsic manner. We give an existence proof of solutions to a corresponding mean-field dynamics, for which we first establish the stability of our consensus point w.r.t. a combination of Wasserstein and $L^2$ perturbations, and consecutively resort to PDE considerations extending the classical Picard iteration to construct a solution. For such solution, we provide a global convergence analysis in mean-field law showing that the solution of the associated nonlinear nonlocal Fokker-Planck equation converges exponentially fast to the unique solution of the bi-level optimization problem provided suitable choices of the hyperparameters. The practicability and efficiency of our CB$^2$O algorithm is demonstrated through extensive numerical experiments in the settings of constrained global optimization, sparse representation learning, and robust (clustered) federated learning.

Around the early 2000-s, Bourgain, Katz and Tao introduced an arithmetic approach to study Kakeya-type problems. They showed that the Euclidean Kakeya conjecture follows from a natural problem in additive combinatorics, now referred to as the `Arithmetic Kakeya Conjecture'. We consider a higher dimensional variant of this problem and prove an upper bound using a certain iterative argument. The main new ingredient in our proof is a general way to strengthen the sum-difference inequalities of Katz and Tao which might be of independent interest. As a corollary, we obtain a new lower bound for the Minkowski dimension of $(n, d)$-Besicovitch sets.

We prove that the stationary power-law vortex $\overline{\omega}(x) = \beta |x|^{-\alpha}$, which explicitly solves the incompressible Euler equations in $\mathbb{R}^2$, is linearly stable in self-similar coordinates with the natural scaling.

In this paper, we study the computation of sensitivities with respect to spot of path dependent financial derivatives by means of path weighting. We propose explicit path weighting formula and variance reduction adjustment in order to address the large variance happening when the first simulation time step is small. We also propose a covariance inflation technique to addresses the degenerator case when the covariance matrix is singular. The stock dynamics we consider is given in a general functional form, which includes the classical Black-Scholes model, the implied distribution model, and the local volatility model.

This paper investigates methods for calculating the chromatic symmetric function (CSF) of a graph in chromatic-bases and the $m_\lambda$-basis. Our key contributions include a novel approach for calculating the CSF in chromatic-bases constructed from forests and an efficient method for determining the CSF in the $m_\lambda$-basis. As applications, we present combinatorial proofs for two known theorems that were originally established using algebraic techniques. Additionally, we demonstrate that an algorithm introduced by Gonzalez, Orellana, and Tomba arises as a special case of our proposed method.

We study quantitative aspects of the following fact: For every graph $F$, there exists a graph $G$ with the property that any $2$-coloring of the triangles of $G$ yields an induced copy of $F$, in which all triangles are monochromatic. We define the Ramsey number $R_{\text{ind}}^{\Delta}(F)$ as the smallest size of such a graph $G$. Although this fact has several proofs, all of them provide tower-type bounds. We study the number $R_{\text{ind}}^{\Delta}(F)$ for some particular classes of graphs $F$.

We consider a generalization of the forest fire model on $\mathbb{Z}_+$ with ignition at zero only, studied in [arXiv:0907.1821]. Unlike that model, we allow delays in the spread of the fires as well as the non-zero burning time of individual ``trees''. We obtain some general properties for this model, which cover, among others, the phenomena of an ``infinite fire'', not present in the original model.

We continue the program initiated in [J. Funct. Anal. 260 (1), 76-116 (2011)] and [arXiv:2404.00293] and study $L^1$-type functional inequalities for some probability measures defined in terms of homogeneous norms on a stratified Lie group, our goal being to obtain isoperimetric inequalities beyond the nondegenerate setting of probability measures defined in terms of the Carnot-Carath\'eodory distance. We then provide some examples on and beyond stratified Lie groups.

In this paper we obtain bounds on a very general class of distance-based topological indices of graphs, which includes the Wiener index, defined as the sum of the distances between all pairs of vertices of the graph, and most generalisations of the Wiener index, including the Harary index and the hyper-Wiener index. Our results imply several new bounds on well-studied topological indices, among those sharp lower bounds on the Harary index and sharp upper bounds on the hyper-Wiener index for (i) graphs of given order and size (which resolves a problem in the monograph [The Harary index of a graph, Xu, Das, Trinajsti\'{c}, Springer (2015)], (ii) for $\kappa$-connected graphs, where $\kappa$ is even, (iii) for maximal outerplanar graphs and for Apollonian networks (a subclass of maximal planar graphs), and (iv) for trees in which all vertices have odd degree.

Investigation of the generalized trigonometric and hyperbolic functions containing two parameters has been a very active research area over the last decade. We believe, however, that their monotonicity and convexity properties with respect to parameters have not been thoroughly studied. In this paper, we make an attempt to fill this gap. Our results are not complete; for some functions, we manage to establish (log)-convexity/concavity in parameters, while for others, we only managed the prove monotonicity, in which case we present necessary and sufficient conditions for convexity/concavity. In the course of the investigation, we found two hypergeometric representations for the generalized cosine and hyperbolic cosine functions which appear to be new. In the last section of the paper, we present four explicit integral evaluations of combinations of generalized trigonometric/hyperbolic functions in terms of hypergeometric functions.

This paper presents a novel approach for nonlinear assimilation called score-based sequential Langevin sampling (SSLS) within a recursive Bayesian framework. SSLS decomposes the assimilation process into a sequence of prediction and update steps, utilizing dynamic models for prediction and observation data for updating via score-based Langevin Monte Carlo. An annealing strategy is incorporated to enhance convergence and facilitate multi-modal sampling. The convergence of SSLS in TV-distance is analyzed under certain conditions, providing insights into error behavior related to hyper-parameters. Numerical examples demonstrate its outstanding performance in high-dimensional and nonlinear scenarios, as well as in situations with sparse or partial measurements. Furthermore, SSLS effectively quantifies the uncertainty associated with the estimated states, highlighting its potential for error calibration.

The paper is concerned with a scalar conservation law with discontinuous gradient-dependent flux. Namely, the flux is described by two different functions $f(u)$ or $g(u)$, when the gradient $u_x$ of the solution is positive or negative, respectively. We study here the unstable case where $f(u)>g(u)$ for all $u\in {\mathbb R}$. Assuming that both $f$ and $g$ are strictly convex, solutions to the Riemann problem are constructed. Even for smooth initial data, examples show that infinitely many solutions can occur. For an initial data which is piecewise monotone, i.e., increasing or decreasing on a finite number of intervals, a global solution of the Cauchy problem is obtained. It is proved that such solution is unique under the additional requirement that the number of interfaces, where the flux switches between $f$ and $g$, remains as small as possible.

We prove a linearization result for quasistatic fracture evolution in nonlinear elasticity. As the stiffness of the material tends to infinity, we show that rescaled displacement fields and their associated crack sets converge to a solution of quasistatic crack growth in linear elasticity without any a priori assumptions on the geometry of the crack set. This result corresponds to the evolutionary counterpart of the static linearization result by the first author, where a Griffith model for nonsimple brittle materials has been considered featuring an elastic energy which also depends suitably on the second gradient of the deformations. The proof relies on a careful study of unilateral global minimality, as determined by the nonlinear evolutionary problem, and its linearization together with a variant of the jump transfer lemma in GSBD.

We study a variety of questions centered around the computation of cohomology of line bundles on the incidence correspondence (the partial flag variety parametrizing pairs consisting of a point in projective space and a hyperplane containing it). Over a field of characteristic zero, this problem is resolved by the Borel-Weil-Bott theorem. In positive characteristic, we give recursive formulas for cohomology, generalizing work of Donkin and Liu in the case of the 3-dimensional flag variety. In characteristic 2, we provide non-recursive formulas describing the cohomology characters in terms of truncated Schur polynomials and Nim symmetric polynomials. The main technical ingredient in our work is the recursive description of the splitting type of vector bundles of principal parts on the projective line. We also discuss properties of the structure constants in the graded Han-Monsky representation ring, and explain how our cohomology calculation characterizes the Weak Lefschetz Property for Artinian monomial complete intersections.

For positive integers $r > \ell \geq 1$, an $\ell$-cycle in an $r$-uniform hypergraph is a cycle where each edge consists of $r$ vertices and each pair of consecutive edges intersect in $\ell$ vertices. We show that for $\ell \geq 2$, a random $r$-uniform hypergraph contains a Hamilton $\ell$-cycle with high probability whenever the expected number of such cycles tends to infinity. Moreover, for $\ell = 2$, we show that the normalized number of Hamilton $2$-cycles converges to a lognormal distribution. This determines the exact threshold for the appearance of non-linear Hamilton cycles in random hypergraphs, confirming a conjecture of Narayanan and Schacht.

Let $R$ and $S$ be commutative rings with identity, $f:R\to S$ a ring homomorphism and $J$ an ideal of $S$. Then the subring $R\bowtie^fJ:=\{(r,f(r)+j)\mid r\in R$ and $j\in J\}$ of $R\times S$ is called the amalgamation of $R$ with $S$ along $J$ with respect to $f$. In this paper, we generalize and improve recent results on the computation of the diameter of the zero-divisor graph of amalgamated algebras and obtain new results. In particular, we provide new characterizations for completeness of the zero-divisor graph of amalgamated algebra, as well as, a complete description for the diameter of the zero-divisor graph of amalgamations in the special case of finite rings.

Let $R$ and $S$ be commutative rings with unity, $f:R\to S$ a ring homomorphism and $J$ an ideal of $S$. Then the subring $R\bowtie^fJ:=\{(a,f(a)+j)\mid a\in R$ and $j\in J\}$ of $R\times S$ is called the amalgamation of $R$ with $S$ along $J$ with respect to $f$. In this paper, we determine when $R\bowtie^fJ$ is a (generalized) filter ring.

We provide a general theory of height-offset variables and their properties for nearest-neighbor integer-valued gradient models on trees. This notion goes back to Sheffield [25], who realized that such tail-measurable variables can be used to associate to gradient Gibbs measures also proper Gibbs measures, via the procedure of pinning at infinity. On the constructive side, our theory incorporates the existence of height-offset variables, regularity properties of their Lebesgue densities and concentration properties of the associated Gibbs measure. On the pathological side, we show that pinning at infinity necessarily comes at a cost. This phenomenon will be analyzed on the levels of translation invariance, the tree-indexed Markov chain property, and extremality. The scope of our theory incorporates free measures, and also height-periodic measures of period 2, assuming only finite second moments of the transfer operator which encodes the nearest neighbor interaction. Our proofs are based on investigations of the respective martingale limits, past and future tail-decompositions, and infinite product representations for moment generating functions.

We investigate several properties of Kronecker (direct, tensor) products of graphs that are planar and $3$-connected (polyhedral, $3$-polytopal). This class of graphs was recently characterised and constructed by the second author [15]. Our main result is that cancellation holds for the Kronecker product of graphs when the product is planar and $3$-connected (it is known that Kronecker cancellation may fail in general). Equivalently, polyhedral graphs are Kronecker products in at most one way. This is a special case of the deep and interesting question, open in general, of Kronecker product cancellation for simple graphs: when does $A\wedge C\simeq B\wedge C$ imply $A\simeq B$? We complete our investigation on simultaneous products by characterising and constructing the planar graphs that are Cartesian products in two distinct ways, and the planar, $3$-connected graphs that are both Kronecker and Cartesian products. The other type of results we obtain are in extremal graph theory. We classify the polyhedral Kronecker products that are either face-regular or vertex-regular graphs. The face-regular ones are certain quadrangulations of the sphere, while the vertex-regular ones are certain cubic graphs (duals of maximal planar graphs). We also characterise, and iteratively construct, the face-regular subclass of graphs minimising the number of vertices of degree $3$.

Inspired by the work of Ulrich and Huneke-Ulrich, we describe a pattern to show that the ideals of certain opposite embedded Schubert varieties defined by this pattern arise by taking residual intersections of two geometrically linked opposite Schubert varieties. This pattern is uniform for the ADE types. Some of the free resolutions of the Schubert varieties in question are important for the structure of finite free resolutions. Our proof is representation theoretical and uniform for our pattern, however it is possible to derive our results using case-by-case analysis and the aid of a computer.

We present a contravariant adjunction between compact $T_1$-spaces and a class of distributive lattices which recomprises key portions of Stone's duality and of Isbell's duality among its instantiations. This brings us to focus on $T_1$-spaces, rather than sober spaces, and to identify points in them with minimal prime filters on some base for a $T_1$-topology (which is what Stone's duality does on the base of clopen sets of compact $0$-dimensional spaces), in spite of completely prime filters on the topology (which is what Isbell's duality does on a sober space). More precisely our contravariant adjunction produces a contravariant, faithful and full embedding of the category of compact $T_1$-spaces with arrows given by closed continuous map as a reflective subcategory of a category $\mathsf{SbfL} $ whose objects are the bounded distributive lattices isomorphic to some base of a $T_1$-topological space (e.g. subfits, when the lattices are frames) and whose arrows are given by (what we call) set-like-morphisms (a natural class of morphisms characterized by a first order expressible constraint). Furthermore this contravariant adjunction becomes a duality when one restricts on the topological side to the category of compact $T_2$-spaces with arbitrary continuous maps, and on the lattice-theoretic side to the category of compact, complete, and normal lattices. A nice by-product of the above results is a lattice-theoretic reformulation of the Stone-\v{C}ech compactification theorem which we have not been able to trace elsewhere in the literature.

We prove that if $D$ is a digraph of maximum outdegree and indegree at least $k$, and minimum semidegree at least $k/2$ that contains no oriented $4$-cycles, then $D$ contains each oriented tree $T$ with~$k$ arcs. This can be slightly improved if $T$ is either antidirected or an arborescence.

We discuss and present some statements for flows related to the Krygin-Atkinson theorem on the recurrence of cylindrical cascades.

In this paper an asymptotic expansion of the global error on the stepsize for partitioned linear multistep methods is proved. This provides a tool to analyse the behaviour of these integrators with respect to error growth with time and conservation of invariants. In particular, symmetric partitioned linear multistep methods with no common roots in their first characteristic polynomials, except unity, appear as efficient methods to approximate non-separable Hamiltonian systems since they can be explicit and show good long term behaviour at the same time. As a case study, a thorough analysis is given for small oscillations of the double pendulum problem, which is illustrated by numerical experiments.

In 1948, Shannon used a probabilistic argument to show the existence of codes achieving a maximal rate defined by the channel capacity. In 1954, Muller and Reed introduced a simple deterministic code construction, based on polynomial evaluations, conjectured shortly after to achieve capacity. The conjecture led to decades of activity involving various areas of mathematics and the recent settlement by [AS23] using flower set boosting. In this paper, we provide an alternative proof of the weak form of the capacity result, i.e., that RM codes have a vanishing local error at any rate below capacity. Our proof relies on the recent Polynomial Freiman-Ruzsa conjecture's proof [GGMT23] and an entropy extraction approach similar to [AY19]. Further, a new additive combinatorics conjecture is put forward which would imply the stronger result with vanishing global error. We expect the latter conjecture to be more directly relevant to coding applications.

In this article we study different extensions of the celebrated Hopf's boundary lemma within the context of a family of nonlocal, nonlinear and nonstandard growth operators. More precisely, we examine the behavior of solutions of the fractional $a-$Laplacian operator near the boundary of a domain satisfying the interior ball condition. Our approach addresses problems involving both constant-sign and sign-changing potentials.

We introduce the notions of weighted extremal K{\"a}hler twins together with the related notion of extremal Sasaki twins. In the K\"ahler setting this leads to a generalization of the twinning phenomenon appearing among LeBrun's strongly Hermitian solutions to the Einstein-Maxwell equations on the first Hirzebruch surface \cite{Leb16} to weighted extremal metrics on Hirzebruch surfaces in general. We discover that many twins appear and that this can be viewed in the Sasaki setting as a case where we have more than one extremal ray in the Sasaki cone even when we do not allow changes within the isotopy class. We also study extremal Sasaki twins directly in the Sasaki setting with a main focus on the toric Sasaki case.

We study the capacity of loop-erased random walk (LERW) on $\mathbb{Z}^d$. For $d\geq4$, we prove a strong law of large numbers and give explicit expressions for the limit in terms of the non-intersection probabilities of a simple random walk and a two-sided LERW. Along the way, we show that four-dimensional LERW is ergodic. For $d=3$, we show that the scaling limit of the capacity of LERW is random. We show that the capacity of the first $n$ steps of LERW is of order $n^{1/\beta}$, with $\beta$ the growth exponent of three-dimensional LERW. We express the scaling limit of the capacity of LERW in terms of the capacity of Kozma's scaling limit of LERW.

The existence of all small-amplitude Wilton ripple solutions of the Kawahara equation is proven. These are periodic, traveling-wave solutions that bifurcate from a two-dimensional nullspace spanned by two distinct, co-propagating cosine waves. In contrast with previous results, the proof, which relies on a carefully constructed Lyapunov-Schmidt reduction, implies the existence of all small-amplitude Wilton ripples of the Kawahara equation, of which there are countably infinite. Though this result pertains only to the Kawahara equation, the method of proof likely extends to most nonlinear dispersive equations admitting Wilton ripple solutions.

We study and solve several problems in two closely related settings: set families in $2^{[n]}$ with many disjoint pairs of sets and low rank matrices with many zero entries. - More than 40 years ago, Daykin and Erd\H{o}s asked for the maximum number of disjoint pairs of sets in a family $F\subseteq 2^{[n]}$ of size $2^{(1/2+\delta)n}$ and conjectured it contains at most $o(|F|^2)$ such pairs. This was proven by Alon and Frankl in 1985. In this paper we completely resolve this problem, proving an optimal dependence of the number of disjoint pairs on the size of family $F$. We also prove the natural variant of the Daykin-Erd\H{o}s conjecture in which disjoint pairs are replaced by pairs with intersection $\lambda\neq 0$. - Motivated by a conjecture of Lovett related to the famous log-rank conjecture, Singer and Sudan asked to show that for two families $A, B \subseteq 2^{[n]}$ with a positive constant fraction of set pairs $(a,b)\in A\times B$ being disjoint, there are $R\subset A$ and $S\subset B$ such that all set pairs $(r, s)\in R\times S$ are disjoint, and $|R|\geq 2^{-O(\sqrt{n})}|A|$ and $|S|\geq 2^{-O(\sqrt{n})}|B|$. We prove this conjecture in a strong quantitative form. - We prove the following generalizations of the best known bounds for the log-rank conjecture. If $M$ is an $n\times n$ non-negative integer matrix of rank $r$ in which the average of the entries is $\varepsilon\leq 1/2$, then $M$ contains an all-zero submatrix of size at least $2^{-O(\sqrt{\varepsilon r})}n$. Unlike the known bounds for the log-rank conjecture, this result is optimal. Moreover, using similar methods, we also prove that any $n\times n$ matrix of rank $r$ with entries from $\{0,\dots,t\}$ contains a constant submatrix of size at least $2^{-O(t\sqrt{r})}n$. Our proofs use probabilistic, entropy and discrepancy methods and explore connections to additive combinatorics and coding theory.

We construct an uncountable subring of $\mathbb R$ with Hausdorff dimension zero (and hence of Lebesgue measure zero).

We give a formula with explicit error term for the number of $K$-rational points $P$ satisfying $H(f(P)) \le X$ as $X \to \infty$, where $f$ is a nonconstant morphism between projective spaces defined over a number field $K$ and $H$ is the absolute multiplicative Weil height. This yields formulae for the counting functions of $f(\mathbb{P}^m(K))$ with respect to the Weil height as well as of $\mathbb{P}^m(K)$ with respect to the Call-Silverman canonical height.

The method of constructing approximate solutions of the first boundary value problem for linear differential equations based on incomplete (even and odd) trigonometric splines is considered. The theoretical positions are illustrated by numerical examples.

In this paper we study the density and distribution of CM elliptic curves over $\mathbb{Q}$. In particular, we prove that the natural density of CM elliptic curves over $\mathbb{Q}$, when ordered by naive height, is zero. Furthermore, we analyze the distribution of these curves among the thirteen possible CM orders of class number one. Our results show that asymptotically, $100\%$ of them have complex multiplication by the order $\mathbb{Z}\left[\frac{-1 + \sqrt{-3}}{2} \right]$, that is, have $j$-invariant 0. We conduct this analysis within two different families of representatives for the $\mathbb{Q}$-isomorphism classes of CM elliptic curves: one commonly used in the literature and another constructed using the theory of twists. As part of our proofs, we give asymptotic formulas for the number of elliptic curves with a given $j$-invariant and bounded naive height.

Most model reduction methods are space-only in that they reduce the spatial dimension of the solution but not the temporal one. These methods integrate an encoding of the state of the nonlinear dynamical system forward in time. We propose a space-time method -- one that solves a system of algebraic equations for the encoding of the trajectory, i.e., the solution on a time interval $[0,T]$. The benefit of this approach is that with the same total number of degrees of freedom, a space-time encoding can leverage spatiotemporal correlations to represent the trajectory far more accurately than a space-only one. We use spectral proper orthogonal decomposition (SPOD) modes, a spatial basis at each temporal frequency tailored to the structures that appear at that frequency, to represent the trajectory. These modes have a number of properties that make them an ideal choice for space-time model reduction. We derive an algebraic system involving the SPOD coefficients that represent the solution, as well as the initial condition and the forcing. The online phase of the method consists of solving this system for the SPOD coefficients given the initial condition and forcing. We test the model on a Ginzburg-Landau system, a $1 + 1$ dimensional nonlinear PDE. We find that the proposed method is $\sim 2$ orders of magnitude more accurate than POD-Galerkin at the same number of modes and CPU time for all of our tests. In fact, the method is substantially more accurate even than the projection of the solution onto the POD modes, which is a lower bound for the error of any space-only Petrov-Galerkin method.

For a domain $\Omega$ in a finite-dimensional space $E$, we consider the space $M=(\Omega,d)$ where $d$ is the intrinsic distance in $\Omega$. We obtain an isometric representation of the space $\mathrm{Lip}_{0}(M)$ as a subspace of $L^{\infty}(\Omega;E^{*})$ and we use this representation in order to obtain the corresponding isometric representation for the Lipschitz-free space $\mathcal{F}(M)$ as a quotient of the space $L^{1}(\Omega;E)$. We compare our result with those existent in the literature for bounded domains with Lipschitz boundary, and for convex domains, which can be then deduced as a corollaries of our result.

In this paper we prove that the Gromov--Hausdorff distance between $\mathbb{R}^n$ and its subset $A$ is finite if and only if $A$ is an $\varepsilon$-net in $\mathbb{R}^n$ for some $\varepsilon>0$. For infinite-dimensional Euclidean spaces this is not true. The proof is essentially based on upper estimate of the Euclidean Gromov--Hausdorff distance by means of the Gromov-Hausdorff distance.

Nowadays, there is an increasing concern about the unsustainability of the take-make-dispose paradigm upon which traditional production and consumption systems are built. The concept of circular economy is gaining attention as a potential solution, but it is an emerging field still lacking analytical and methodological dynamics approaches. Hence, in this paper, firstly we propose a quantitative definition of circularity, namely, $\lambda$, predicated on compartmental dynamical thermodynamics, and then, we use it to state the optimization of the circularity $\lambda$ as an arg-max problem. By leveraging the derivation of Lagrange's equations of motion from the first law of thermodynamics, we apply the analytical mechanics approaches to circularity. Three examples illustrate the calculation of $\lambda$ for different settings of two compartmental networks. In particular, hypothesizing a repair stage followed by product reuse we highlight the memory property of $\lambda$. Finally, robotic repair is proposed within this framework to pave the way for circular robotics as a new area of research in which the performance of a robotic system is measured against $\lambda$.

This paper presents the Random-Key Optimizer (RKO), a versatile and efficient stochastic local search method tailored for combinatorial optimization problems. Using the random-key concept, RKO encodes solutions as vectors of random keys that are subsequently decoded into feasible solutions via problem-specific decoders. The RKO framework is able to combine a plethora of classic metaheuristics, each capable of operating independently or in parallel, with solution sharing facilitated through an elite solution pool. This modular approach allows for the adaptation of various metaheuristics, including simulated annealing, iterated local search, and greedy randomized adaptive search procedures, among others. The efficacy of the RKO framework, implemented in C++, is demonstrated through its application to three NP-hard combinatorial optimization problems: the alpha-neighborhood p-median problem, the tree of hubs location problem, and the node-capacitated graph partitioning problem. The results highlight the framework's ability to produce high-quality solutions across diverse problem domains, underscoring its potential as a robust tool for combinatorial optimization.

Using the worldline quantum field theory formalism, we compute the radiation-reacted impulse, scattering angle, radiated energy and recoil of a classical black hole (or neutron star) scattering event at fifth post-Minkowskian and sub-leading self-force orders (5PM-1SF). This state-of-the-art four-loop computation employs advanced integration-by-parts and differential equation technology, and is considerably more challenging than the conservative 5PM-1SF counterpart. As compared with the conservative 5PM-1SF, in the radiation sector Calabi-Yau three-fold periods appear and contribute to the radiated energy and recoil observables. We give an extensive exposition of the canonicalization of the differential equations and provide details on boundary integrations, Feynman rules, and integration-by-parts strategies. Comparisons to numerical relativity are also performed.

A new class of shell models is proposed, where the shell variables are defined on a recurrent sequence of integer wave-numbers such as the Fibonacci or the Padovan series, or their variations including a sequence made of square roots of Fibonacci numbers rounded to the nearest integer. Considering the simplest model, which involves only local interactions, the interaction coefficients can be generalized in such a way that the inviscid invariants, such as energy and helicity, can be conserved even though there is no exact self-similarity. It is shown that these models basically have identical features with standard shell models, and produce the same power law spectra, similar spectral fluxes and analogous deviation from self-similar scaling of the structure functions implying comparable levels of turbulent intermittency. Such a formulation potentially opens up the possibility of using shell models, or their generalizations along with discretized regular grids, such as those found in direct numerical simulations, either as diagnostic tools, or subgrid models. It also allows to develop models where the wave-number shells can be interpreted as sparsely decimated sets of wave-numbers over an initially regular grid. In addition to conventional shell models with local interactions that result in forward cascade, a particular helical shell model with long range interactions is considered on a similarly recurrent sequence of wave numbers, corresponding to the Fibonacci series, and found to result in the usual inverse cascade.

We consider estimation of a linear functional of the treatment effect using adaptively collected data. This task finds a variety of applications including the off-policy evaluation (\textsf{OPE}) in contextual bandits, and estimation of the average treatment effect (\textsf{ATE}) in causal inference. While a certain class of augmented inverse propensity weighting (\textsf{AIPW}) estimators enjoys desirable asymptotic properties including the semi-parametric efficiency, much less is known about their non-asymptotic theory with adaptively collected data. To fill in the gap, we first establish generic upper bounds on the mean-squared error of the class of AIPW estimators that crucially depends on a sequentially weighted error between the treatment effect and its estimates. Motivated by this, we also propose a general reduction scheme that allows one to produce a sequence of estimates for the treatment effect via online learning to minimize the sequentially weighted estimation error. To illustrate this, we provide three concrete instantiations in (\romannumeral 1) the tabular case; (\romannumeral 2) the case of linear function approximation; and (\romannumeral 3) the case of general function approximation for the outcome model. We then provide a local minimax lower bound to show the instance-dependent optimality of the \textsf{AIPW} estimator using no-regret online learning algorithms.

We look at a family of 3d $\mathcal{N}=4$ rank-0 orthosymplectic quiver gauge theories. We define a superconformal field theory (SCFT) to be rank-0 if either the Higgs branch or Coulomb branch is trivial. This family of non-linear orthosymplectic quivers has Coulomb branches that can be factorized into products of known moduli spaces. More importantly, the Higgs branches are all trivial. Consequently, the full moduli space of the smallest member is simply $\mathrm{(one-}F_4 \; \mathrm{instanton}) \times \mathrm{(one-}F_4 \; \mathrm{instanton})$. Although the $3d$ mirror is non-Lagrangian, it can be understood through the gauging of topological symmetries of Lagrangian theories. Since the 3d mirror possesses a trivial Coulomb branch, we discuss some implications for rank-0 4d $\mathcal{N}=2$ SCFTs and symplectic duality.

We propose an information-theoretic framework to measure narratives, providing a formalism to understand pivotal moments, cliffhangers, and plot twists. This approach offers creatives and AI researchers tools to analyse and benchmark human- and AI-created stories. We illustrate our method in TV shows, showing its ability to quantify narrative complexity and emotional dynamics across genres. We discuss applications in media and in human-in-the-loop generative AI storytelling.

Nearest neighbor (NN) algorithms have been extensively used for missing data problems in recommender systems and sequential decision-making systems. Prior theoretical analysis has established favorable guarantees for NN when the underlying data is sufficiently smooth and the missingness probabilities are lower bounded. Here we analyze NN with non-smooth non-linear functions with vast amounts of missingness. In particular, we consider matrix completion settings where the entries of the underlying matrix follow a latent non-linear factor model, with the non-linearity belonging to a \Holder function class that is less smooth than Lipschitz. Our results establish following favorable properties for a suitable two-sided NN: (1) The mean squared error (MSE) of NN adapts to the smoothness of the non-linearity, (2) under certain regularity conditions, the NN error rate matches the rate obtained by an oracle equipped with the knowledge of both the row and column latent factors, and finally (3) NN's MSE is non-trivial for a wide range of settings even when several matrix entries might be missing deterministically. We support our theoretical findings via extensive numerical simulations and a case study with data from a mobile health study, HeartSteps.

This article addresses the challenge of parameter calibration in stochastic models where the likelihood function is not analytically available. We propose a gradient-based simulated parameter estimation framework, leveraging a multi-time scale algorithm that tackles the issue of ratio bias in both maximum likelihood estimation and posterior density estimation problems. Additionally, we introduce a nested simulation optimization structure, providing theoretical analyses including strong convergence, asymptotic normality, convergence rate, and budget allocation strategies for the proposed algorithm. The framework is further extended to neural network training, offering a novel perspective on stochastic approximation in machine learning. Numerical experiments show that our algorithm can improve the estimation accuracy and save computational costs.

For any $\{0,1\}$-valued function $f$, its \emph{$n$-folded XOR} is the function $f^{\oplus n}$ where $f^{\oplus n}(X_1, \ldots, X_n) = f(X_1) \oplus \cdots \oplus f(X_n)$. Given a procedure for computing the function $f$, one can apply a ``naive" approach to compute $f^{\oplus n}$ by computing each $f(X_i)$ independently, followed by XORing the outputs. This approach uses $n$ times the resources required for computing $f$. In this paper, we prove a strong XOR lemma for \emph{information complexity} in the two-player randomized communication model: if computing $f$ with an error probability of $O(n^{-1})$ requires revealing $I$ bits of information about the players' inputs, then computing $f^{\oplus n}$ with a constant error requires revealing $\Omega(n) \cdot (I - 1 - o_n(1))$ bits of information about the players' inputs. Our result demonstrates that the naive protocol for computing $f^{\oplus n}$ is both information-theoretically optimal and asymptotically tight in error trade-offs.

We introduce a stochastic model of a population with overlapping generations and arbitrary levels of self-fertilization versus outcrossing. We study how the global graph of reproductive relationships, or pedigree, influences the genealogical relationships of a sample of two gene copies at a genetic locus. Specifically, we consider a diploid Moran model with constant population size $N$ over time, in which a proportion of offspring are produced by selfing. We show that the conditional distribution of the pairwise coalescence time at a single locus given the random pedigree converges to a limit law as $N$ tends to infinity. This limit law generally differs from the corresponding traditional result obtained by averaging over all possible pedigrees. We describe three different behaviors in the limit depending on the relative strengths, from large to small, of selfing versus outcrossing: partial selfing, limited outcrossing, and negligible outcrossing. In the case of partial selfing, coalescence times are related to the Kingman coalescent, similar to what is found in traditional analyses. In the the case of limited outcrossing, the retained pedigree information forms a random graph coming from a fragmentation-coagulation process, with the limiting coalescence times given by the meeting times of random walks on this graph. In the case of negligible outcrossing, which represents complete or nearly complete selfing, coalescence times are determined entirely by the fixed times to common ancestry of diploid individuals in the pedigree.

Recent work on supervised learning [GKR+22] defined the notion of omnipredictors, i.e., predictor functions $p$ over features that are simultaneously competitive for minimizing a family of loss functions $\mathcal{L}$ against a comparator class $\mathcal{C}$. Omniprediction requires approximating the Bayes-optimal predictor beyond the loss minimization paradigm, and has generated significant interest in the learning theory community. However, even for basic settings such as agnostically learning single-index models (SIMs), existing omnipredictor constructions require impractically-large sample complexities and runtimes, and output complex, highly-improper hypotheses. Our main contribution is a new, simple construction of omnipredictors for SIMs. We give a learner outputting an omnipredictor that is $\varepsilon$-competitive on any matching loss induced by a monotone, Lipschitz link function, when the comparator class is bounded linear predictors. Our algorithm requires $\approx \varepsilon^{-4}$ samples and runs in nearly-linear time, and its sample complexity improves to $\approx \varepsilon^{-2}$ if link functions are bi-Lipschitz. This significantly improves upon the only prior known construction, due to [HJKRR18, GHK+23], which used $\gtrsim \varepsilon^{-10}$ samples. We achieve our construction via a new, sharp analysis of the classical Isotron algorithm [KS09, KKKS11] in the challenging agnostic learning setting, of potential independent interest. Previously, Isotron was known to properly learn SIMs in the realizable setting, as well as constant-factor competitive hypotheses under the squared loss [ZWDD24]. As they are based on Isotron, our omnipredictors are multi-index models with $\approx \varepsilon^{-2}$ prediction heads, bringing us closer to the tantalizing goal of proper omniprediction for general loss families and comparators.

Label Distribution Learning (LDL) is an effective approach for handling label ambiguity, as it can analyze all labels at once and indicate the extent to which each label describes a given sample. Most existing LDL methods consider the number of labels to be static. However, in various LDL-specific contexts (e.g., disease diagnosis), the label count grows over time (such as the discovery of new diseases), a factor that existing methods overlook. Learning samples with new labels directly means learning all labels at once, thus wasting more time on the old labels and even risking overfitting the old labels. At the same time, learning new labels by the LDL model means reconstructing the inter-label relationships. How to make use of constructed relationships is also a crucial challenge. To tackle these challenges, we introduce Incremental Label Distribution Learning (ILDL), analyze its key issues regarding training samples and inter-label relationships, and propose Scalable Graph Label Distribution Learning (SGLDL) as a practical framework for implementing ILDL. Specifically, in SGLDL, we develop a New-label-aware Gradient Compensation Loss to speed up the learning of new labels and represent inter-label relationships as a graph to reduce the time required to reconstruct inter-label relationships. Experimental results on the classical LDL dataset show the clear advantages of unique algorithms and illustrate the importance of a dedicated design for the ILDL problem.

Herd immunity is a critical concept in epidemiology, describing a threshold at which a sufficient proportion of a population is immune, either through infection or vaccination, thereby preventing sustained transmission of a pathogen. In the classic Susceptible-Infectious-Recovered (SIR) model, which has been widely used to study infectious disease dynamics, the achievement of herd immunity depends on key parameters, including the transmission rate ($\beta$) and the recovery rate ($\gamma$), where $\gamma$ represents the inverse of the mean infectious period. While the transmission rate has received substantial attention, recent studies have underscored the significant role of $\gamma$ in determining the timing and sustainability of herd immunity. Additionally, it is becoming increasingly evident that assuming $\gamma$ as a constant parameter might oversimplify the dynamics, as variations in recovery times can reflect diverse biological, social, and healthcare-related factors. In this paper, we investigate how heterogeneity in the recovery rate affects herd immunity. We show empirically that the mean of the recovery rate is not a reliable metric for determining the achievement of herd immunity. Furthermore, we provide a theoretical result demonstrating that it is instead the mean recovery time, which is the mean of the inverse $1/\gamma$ of the recovery rate that is critical in deciding whether herd immunity is achievable within the SIR framework. A similar result is proved for the SEIR model. These insights have significant implications for public health interventions and theoretical modeling of epidemic dynamics.

Given a graph $G=(V,E)$, a function $f:V\to \{0,1,2\}$ is said to be a \emph{Roman Dominating function} (RDF) if for every $v\in V$ with $f(v)=0$, there exists a vertex $u\in N(v)$ such that $f(u)=2$. A Roman Dominating function $f$ is said to be an \emph{Independent Roman Dominating function} (IRDF), if $V_1\cup V_2$ forms an independent set, where $V_i=\{v\in V~\vert~f(v)=i\}$, for $i\in \{0,1,2\}$. The total weight of $f$ is equal to $\sum_{v\in V} f(v)$, and is denoted as $w(f)$. The \emph{Roman Domination Number} (resp. \emph{Independent Roman Domination Number}) of $G$, denoted by $\gamma_R(G)$ (resp. $i_R(G)$), is defined as min$\{w(f)~\vert~f$ is an RDF (resp. IRDF) of $G\}$. For a given graph $G$, the problem of computing $\gamma_R(G)$ (resp. $i_R(G)$) is defined as the \emph{Roman Domination problem} (resp. \emph{Independent Roman Domination problem}). In this paper, we examine structural parameterizations of the (Independent) Roman Domination problem. We propose fixed-parameter tractable (FPT) algorithms for the (Independent) Roman Domination problem in graphs that are $k$ vertices away from a cluster graph. These graphs have a set of $k$ vertices whose removal results in a cluster graph. We refer to $k$ as the distance to the cluster graph. Specifically, we prove the following results when parameterized by the deletion distance $k$ to cluster graphs: we can find the Roman Domination Number (and Independent Roman Domination Number) in time $4^kn^{O(1)}$. In terms of lower bounds, we show that the Roman Domination number can not be computed in time $2^{\epsilon k}n^{O(1)}$, for any $0<\epsilon <1$ unless a well-known conjecture, SETH fails. In addition, we also show that the Roman Domination problem parameterized by distance to cluster, does not admit a polynomial kernel unless NP $\subseteq$ coNP$/$poly.

The Bethe-Salpeter equation has been extensively employed to compute the two-body electron-hole propagator and its poles which correspond to the neutral excitation energies of the system. Through a different time-ordering, the two-body Green's function can also describe the propagation of two electrons or two holes. The corresponding poles are the double ionization potentials and double electron affinities of the system. In this work, a Bethe-Salpeter equation for the two-body particle-particle propagator is derived within the linear-response formalism using a pairing field and anomalous propagators. This framework allows us to compute kernels corresponding to different self-energy approximations ($GW$, $T$-matrix, and second-Born) as in the usual electron-hole case. The performance of these various kernels is gauged for singlet and triplet valence double ionization potentials using a set of 23 small molecules. The description of double core hole states is also analyzed.

Averaging iterations of Stochastic Gradient Descent (SGD) have achieved empirical success in training deep learning models, such as Stochastic Weight Averaging (SWA), Exponential Moving Average (EMA), and LAtest Weight Averaging (LAWA). Especially, with a finite weight averaging method, LAWA can attain faster convergence and better generalization. However, its theoretical explanation is still less explored since there are fundamental differences between finite and infinite settings. In this work, we first generalize SGD and LAWA as Finite Weight Averaging (FWA) and explain their advantages compared to SGD from the perspective of optimization and generalization. A key challenge is the inapplicability of traditional methods in the sense of expectation or optimal values for infinite-dimensional settings in analyzing FWA's convergence. Second, the cumulative gradients introduced by FWA introduce additional confusion to the generalization analysis, especially making it more difficult to discuss them under different assumptions. Extending the final iteration convergence analysis to the FWA, this paper, under a convexity assumption, establishes a convergence bound $\mathcal{O}(\log\left(\frac{T}{k}\right)/\sqrt{T})$, where $k\in[1, T/2]$ is a constant representing the last $k$ iterations. Compared to SGD with $\mathcal{O}(\log(T)/\sqrt{T})$, we prove theoretically that FWA has a faster convergence rate and explain the effect of the number of average points. In the generalization analysis, we find a recursive representation for bounding the cumulative gradient using mathematical induction. We provide bounds for constant and decay learning rates and the convex and non-convex cases to show the good generalization performance of FWA. Finally, experimental results on several benchmarks verify our theoretical results.

We introduce and analyze a natural game formulated as follows. In this one-person game, the player is given a random permutation $A=(a_1,\dots, a_n)$ of a multiset $M$ of $n$ reals that sum up to $0$, where each of the $n!$ permutation sequences is equally likely. The player only knows the value of $n$ beforehand. The elements of the sequence are revealed one by one and the player can stop the game at any time. Once the process stops, say, after the $i$th element is revealed, the player collects the amount $\sum_{j=i+1}^{n} a_j$ as his/her payoff and the game is over (the payoff corresponds to the unrevealed part of the sequence). Three online algorithms are given for maximizing the expected payoff in the binary case when $M$ contains only $1$'s and $-1$'s. $\texttt{Algorithm 1}$ is slightly suboptimal, but is easier to analyze. Moreover, it can also be used when $n$ is only known with some approximation. $\texttt{Algorithm 2}$ is exactly optimal but not so easy to analyze on its own. $\texttt{Algorithm 3}$ is the simplest of all three. It turns out that the expected payoffs of the player are $\Theta(\sqrt{n})$ for all three algorithms. In the end, we address the general problem and deal with an arbitrary zero-sum multiset, for which we show that our $\texttt{Algorithm 3}$ returns a payoff proportional to $\sqrt{n}$, which is worst case-optimal.

In wildfires, burning pieces of ember-firebrands-are carried downstream by wind. At the time of landing, these firebrands can start secondary fires far away from the main burning unit. This phenomenon is called spotting and the secondary fires are referred to as spot fires. Here, we first present numerical evidence that atmospheric traveling waves can increase the spotting distance by at least an order of magnitude compared to unidirectional wind conditions. We then present theoretical results explaining this numerical observation. In particular, we show that the firebrand's motion can synchronize with the traveling wave, leading to a surf-like motion for some firebrand particles. This delays the firebrand's landing, making extreme spotting distances possible. This physical phenomena helps explain the discrepancy between previous theoretical estimates of maximum spotting distance and much larger spotting distances observed empirically. We derive new analytical expressions for the landing time and landing distance of the firebrands.

An analyst observes the frequency with which a decision maker (DM) takes actions, but does not observe the frequency of actions conditional on the payoff-relevant state. We ask when can the analyst rationalize the DM's choices as if the DM first learns something about the state before taking action. We provide a support function characterization of the triples of utility functions, prior beliefs, and (marginal) distributions over actions such that the DM's action distribution is consistent with information given the agent's prior and utility function. Assumptions on the cardinality of the state space and the utility function allow us to refine this characterization, obtaining a sharp system of finitely many inequalities the utility function, prior, and action distribution must satisfy. We apply our characterization to study comparative statics and ring-network games, and to identify conditions under which a data set is consistent with a public information structure in first-order Bayesian persuasion games. We characterize the set of distributions over posterior beliefs that are consistent with the DM's choices. Assuming the first-order approach applies, we extend our results to settings with a continuum of actions and/or states.%

In [arXiv:2311.08980], it was shown that if the limit of the free energy in a non-convex vector spin glass model exists, it must be a critical value of a certain functional. In this work, we extend this result to multi-species spin glass models with non-convex interactions, where spins from different species may lie in distinct vector spaces. Since the species proportions may be irrational and the existence of the limit of the free energy is not generally known, non-convex multi-species models cannot be approximated by vector spin models in a straightforward manner, necessitating more careful treatment.

Phylogenetic networks are directed acyclic graphs that depict the genomic evolution of related taxa. Reticulation nodes in such networks (nodes with more than one parent) represent reticulate evolutionary events, such as recombination, reassortment, hybridization, or horizontal gene transfer. Typically, the complexity of a phylogenetic network is expressed in terms of its level, i.e., the maximum number of edges that are required to be removed from each biconnected component of the phylogenetic network to turn it into a tree. Here, we study the relationship between the level of a phylogenetic network and another popular graph complexity parameter - treewidth. We show a $\frac{k+3}{2}$ upper bound on the treewidth of level-$k$ phylogenetic networks and an improved $(1/3 + \delta) k$ upper bound for large $k$. These bounds imply that many computational problems on phylogenetic networks, such as the small parsimony problem or some variants of phylogenetic diversity maximization, are polynomial-time solvable on level-$k$ networks with constant $k$. Our first bound is applicable to any $k$, and it allows us to construct an explicit tree decomposition of width $\frac{k+3}{2}$ that can be used to analyze phylogenetic networks generated by tools like SNAQ that guarantee bounded network level. Finally, we show a $k/13$ lower bound on the maximum treewidth among level-$k$ phylogenetic networks for large enough $k$ based on expander graphs.

In data-based control, dissipativity can be a powerful tool for attaining stability guarantees for nonlinear systems if that dissipativity can be inferred from data. This work provides a tutorial on several existing methods for data-based dissipativity estimation of nonlinear systems. The interplay between the underlying assumptions of these methods and their sample complexity is investigated. It is shown that methods based on delta-covering result in an intractable trade-off between sample complexity and robustness. A new method is proposed to quantify the robustness of machine learning-based dissipativity estimation. It is shown that this method achieves a more tractable trade-off between robustness and sample complexity. Several numerical case studies demonstrate the results.

We study the complexity of sampling, rounding, and integrating arbitrary logconcave functions. Our new approach provides the first complexity improvements in nearly two decades for general logconcave functions for all three problems, and matches the best-known complexities for the special case of uniform distributions on convex bodies. For the sampling problem, our output guarantees are significantly stronger than previously known, and lead to a streamlined analysis of statistical estimation based on dependent random samples.

Reconfigurable electromagnetic structures (REMSs), such as reconfigurable reflectarrays (RRAs) or reconfigurable intelligent surfaces (RISs), hold significant potential to improve wireless communication and sensing systems. Even though several REMS modeling approaches have been proposed in recent years, the literature lacks models that are both computationally efficient and physically consistent. As a result, algorithms that control the reconfigurable elements of REMSs (e.g., the phase shifts of an RIS) are often built on simplistic models that are inaccurate. To enable physically accurate REMS-parameter tuning, we present a new framework for efficient and physically consistent modeling of general REMSs. Our modeling method combines a circuit-theoretic approach with a new formalism that describes a REMS's interaction with the electromagnetic (EM) waves in its far-field region. Our modeling method enables efficient computation of the entire far-field radiation pattern for arbitrary configurations of the REMS reconfigurable elements once a single full-wave EM simulation of the non-reconfigurable parts of the REMS has been performed. The predictions made by the proposed framework align with the physical laws of classical electrodynamics and model effects caused by inter-antenna coupling, non-reciprocal materials, polarization, ohmic losses, matching losses, influence of metallic housings, noise from low-noise amplifiers, and noise arising in or received by antennas. In order to validate the efficiency and accuracy of our modeling approach, we (i) compare our modeling method to EM simulations and (ii) conduct a case study involving a planar RRA that enables simultaneous multiuser beam- and null-forming using a new, computationally efficient, and physically accurate parameter tuning algorithm.

We relate the so-called powercone models of mixed non-deterministic and probabilistic choice proposed by Tix, Keimel, Plotkin, Mislove, Ouaknine, Worrell, Morgan, and McIver, to our own models of previsions. Under suitable topological assumptions, we show that they are isomorphic. We rely on Keimel's cone-theoretic variants of the classical Hahn-Banach separation theorems, using functional analytic methods, and on the Schr\"oder-Simpson Theorem. Lemma 3.4 in the original 2017 version, published at MSCS, had a wrong proof, and we prove a repaired, albeit slightly less general version here.

Erasures are the primary type of errors in physical systems dominated by leakage errors. While quantum error correction (QEC) using stabilizer codes can combat these error, the question of achieving near-capacity performance with explicit codes and efficient decoders remains a challenge. Quantum decoding is a classical computational problem that decides what the recovery operation should be based on the measured syndromes. For QEC, using an accurate decoder with the shortest possible runtime will minimize the degradation of quantum information while awaiting the decoder's decision. We examine the quantum erasure decoding problem for general stabilizer codes and present decoders that not only run in linear-time but are also accurate. We achieve this by exploiting the symmetry of degenerate errors. Numerical evaluations show near maximum-likelihood decoding for various codes, achieving capacity performance with topological codes and near-capacity performance with non-topological codes. We furthermore explore the potential of our decoders to handle other error models, such as mixed erasure and depolarizing errors, and also local deletion errors via concatenation with permutation invariant codes.