The Lights Out Puzzle represents a cellular automaton based on a grid of squares where clicking a square changes its state and the states of surrounding squares. A "quiet pattern" is a way to click such that in the end, no change is effected. We introduce a way to "evolve" quiet patterns in smaller grids into ones in $p$ times larger grids when the number of possible states of a square is a prime $p$. Using elliptic curves, we also find that an inverse "de-evolution" exists for most $p$. We also describe the only ways to click a grid of squares such that only 5 (the minimum) number of squares have a nonzero state.

In cognition, the perception of external stimuli and the self-referential awareness of one's own perceptual process are two distinct but interacting operations. We propose a quantum-inspired framework in which both the self state and the perception state are treated as coupled open quantum systems evolving across two different timescales. The fast perceptual subsystem captures adaptive sensing under coherent and dissipative influences, while the self subsystem evolves on a slower timescale, integrating perceptual feedback into a stable internal state. Their mutual coupling forms a closed informational loop, where the self-state biases perception, and perception continually reshapes the self. A macroscopic collective order emerges from the interplay of feedback, dissipation, and coherence. Although the Lindblad formalism rigorously captures microscopic quantum dynamics, the Bloch representation offers a far more tractable and intuitive description by compressing the evolution into observable quantities such as polarization, alignment, and coherence decay. Within this framework, we further identify several meaningful dynamical indicators, such as the collective order parameter, the degree of self-coherence, and the volitional inertia inferred from hysteresis-like loops, which together provide a quantitative characterization of emergent coordination and adaptation in a self-perception coupled system. Unlike traditional models of active matter that rely on instantaneous interaction rules, the introduction of an internal, slow-evolving self-subsystem integrates the history of perceptual interactions to capture adaptive and memory-dependent behavior.

The different forms of the tetrahedron equation appear when all possible ways to label the scattering process of infinitely long straight lines are considered in three dimensional spacetime. This is expected to lead to three dimensional integrability, analogous to the Yang-Baxter equation. Among the three possibilities, we consider two of them and their variants. We show that Clifford algebras solve both the constant and the spectral parameter dependent versions of all of them. We also present a scheme for canonically solving higher simplex equations using tetrahedron solutions.

As a quantification of the main bottleneck to flow over a graph, the network property of conductance plays an important role in the process of synchronization of network-coupled dynamical systems. Diffusive coupling terms serve not only to exchange information between nodes within a networked system, but ultimately to dissipate the entropy of the collective dynamic state down toward that which can be associated with a single dynamic node when the synchronization manifold is stable. While the graph conductance can characterize the coupling strength that is required to maintain widespread synchronization across a majority of the nodes in such a system, it offers no guarantee for a stable synchronization manifold, which involves all nodes in the system. We define a new measure called the synchronization bottleneck of a graph, which we denote by $\Xi$; this new network property provides a quantification of the limiting bottleneck of the flow between any subset of nodes (regardless of its order) and the rest of the networked system. This quantity does control the coupling strength required for a stable synchronization manifold for a large class of dynamical systems. Solving for this quantity is combinatorial, as is the case with conductance, but heuristics based on this optimization problem can guide decentralized strategies for improving global synchronizability.

We study quantum tunneling in an asymmetric double-well potential using a dynamical systems-based approach rooted in the Ehrenfest formalism. In this framework, the time evolution of a Gaussian wave packet is governed by a hierarchy of coupled equations linking lower- and higher-order position moments. An approximate closure, required to render the system tractable, yields a reduced dynamical system for the mean and variance, with skewness entering explicitly due to the potential's asymmetry. Stability analysis of this system identifies energy thresholds for detectable tunneling across the barrier and reveals regimes where tunneling, though theoretically allowed, remains practically undetectable. Comparison with full numerical solutions of the time-dependent Schrödinger equation shows that, beyond reproducing key tunneling features, the dynamical systems approach provides an interpretable description of quantum transport through tunneling in an effective asymmetric two-level system.

Understanding how individual learning behavior and structural dynamics interact is essential to modeling emergent phenomena in socioeconomic networks. While bounded rationality and network adaptation have been widely studied, the role of heterogeneous learning rates both at the agent and network levels remains under explored. This paper introduces a dual-learning framework that integrates individualized learning rates for agents and a rewiring rate for the network, reflecting real-world cognitive diversity and structural adaptability. Using a simulation model based on the Prisoner's Dilemma and Quantal Response Equilibrium, we analyze how variations in these learning rates affect the emergence of large-scale network structures. Results show that lower and more homogeneously distributed learning rates promote scale-free networks, while higher or more heterogeneously distributed learning rates lead to the emergence of core-periphery topologies. Key topological metrics including scale-free exponents, Estrada heterogeneity, and assortativity reveal that both the speed and variability of learning critically shape system rationality and network architecture. This work provides a unified framework for examining how individual learnability and structural adaptability drive the formation of socioeconomic networks with diverse topologies, offering new insights into adaptive behavior, systemic organization, and resilience.

Supersolidity in a dipolar Bose-Einstein condensate (BEC), which is the coexistence of crystalline density modulation and global phase coherence, emerges from the interplay of contact interactions, long-range dipole-dipole forces, and quantum fluctuations. Although realized experimentally, stabilizing this phase at zero temperature often requires high peak densities. Here we chart the finite-temperature phase behavior of a harmonically trapped dipolar BEC using an extended mean-field framework that incorporates both quantum (Lee-Huang-Yang) and thermal fluctuation effects. We find that finite temperature can act constructively: it shifts the supersolid phase boundary toward larger scattering lengths, lowers the density threshold for the onset of supersolidity, and broadens the stability window of modulated phases. Real-time simulations reveal temperature-driven pathways (crystallization upon heating and melting upon cooling) demonstrating the dynamical accessibility and path dependence of supersolid order. Moreover, moderate thermal fluctuations stabilize single-droplet states that are unstable at zero temperature, expanding the experimentally accessible parameter space. These results identify temperature as a key control parameter for engineering and stabilizing supersolid phases, offering realistic routes for their observation and control in dipolar quantum gases.

Recent research has shown that memory, in the form of slow degrees of freedom, can induce a phase of long-range order (LRO) in locally-coupled fast degrees of freedom, producing power-law distributions of avalanches. In fact, such memory-induced LRO (MILRO) arises in a wide range of physical systems. Here, we show that MILRO can be transferred to coupled systems that have no memory of their own. As an example, we consider a stack of layers of spins with local feedforward couplings: only the first layer contains memory, while downstream layers are memory-free and locally interacting. Analytical arguments and simulations reveal that MILRO can indeed drag across the layers, enabling downstream layers to sustain intra-layer LRO despite having neither memory nor long-range interactions. This establishes a simple, yet generic mechanism for propagating collective activity through media without fine tuning to criticality, with testable implications for neuromorphic systems and laminar information flow in the brain cortex.

We consider generalisations of the elliptic Calogero--Moser systems associated to complex crystallographic groups in accordance to [1]. In our previous work [2], we proposed these systems as candidates for Seiberg--Witten integrable systems of certain SCFTs. Here we examine that proposal for complex crystallographic groups of rank one. Geometrically, this means considering elliptic curves $T^2$ with $\mathbb{Z}_m$-symmetries, $m=2,3,4,6$, and Poisson deformations of the orbifolds $(T^2\times\mathbb{C})/\mathbb{Z}_m$. The $m=2$ case was studied in [2], while $m=3,4,6$ correspond to Seiberg--Witten integrable systems for the rank 1 Minahan--Nemeshansky SCFTs of type $E_{6,7,8}$. This allows us to describe the corresponding elliptic fibrations and the Seiberg--Witten differential in a compact elegant form. This approach also produces quantum spectral curves for these SCFTs, which are given by Fuchsian ODEs with special properties.

Symmetry of non-Hermitian matrices underpins many physical phenomena. In particular, chaotic open quantum systems exhibit universal bulk spectral correlations classified on the basis of time-reversal symmetry$^{\dagger}$ (TRS$^{\dagger}$), coinciding with those of non-Hermitian random matrices in the same symmetry class. Here, we analytically study the spectral correlations of non-Hermitian random matrices in the presence of TRS$^{\dagger}$ with signs $+1$ and $-1$, corresponding to symmetry classes AI$^{\dagger}$ and AII$^{\dagger}$, respectively. Using the fermionic replica non-linear sigma model approach, we derive $n$-fold integral expressions for the $n$th moment of the one-point and two-point characteristic polynomials. Performing the replica limit $n\to 0$, we qualitatively reproduce the density of states and level-level correlations of non-Hermitian random matrices with TRS$^{\dagger}$.

A novel decoupling limit of the membrane is proposed, leading to the $(1+2)$-dimensional classically integrable model originally introduced by Manakov, Zakharov, and Ward. This limit is the large-wrapping regime of a membrane propagating toy background of the form $\mathbb{R}_t \times T^2 \times G$ subject to scaling limit, where $G$ is a Lie group and the geometry is supported by a four-form flux. Such toy backgrounds can arise from consistent eleven-dimensional supergravity solutions, exemplified by the uplift of the pure NSNS AdS$_3 \times$ S$^3 \times$ T$^4$ background. The scaling limit can be interpreted as similtaneous small tension and non- or hyper-relativistic limit.