Papers

Successive personal best performances provide a natural measure of progression in an athlete's career. Classical record theory predicts a universal gap distribution, $P(g)\sim 1/g$, for independent and identically distributed (i.i.d.) sequences. However, sporting careers are shaped by learning, aging, changes in ability, and external influences that violate these assumptions. We investigate the statistics of inter-record gaps, defined as the number of innings between successive personal best scores, in cricket. Using career records of leading Test, ODI, and T20 players obtained from ESPN Cricinfo. We find that the empirical distributions are well described by truncated power law $P(g) \propto g^{-\alpha} e^{-\lambda g}$ with exponents in the range (0.799 $\leq \alpha \leq$ 0.843). Much of this deviation disappears when the temporal ordering of innings is destroyed, indicating that career evolution plays a key role in shaping record occurrence. Bootstrap-shuffled careers, which preserve individual score distributions and career lengths while removing temporal ordering, yield significantly larger exponents ($\alpha \approx 0.939\text{--}0.979$). These findings show that the progression of personal best performances retains information about the temporal organization of a player's career and cannot be fully explained by simple stochastic record processes. More generally, they illustrate how record statistics are altered in nonstationary and path-dependent systems.

We report a Monte Carlo study of the magnetocaloric effect (MCE) in two-dimensional ferromagnetic Ising models on square, honeycomb, and triangular lattices with monolayer and bilayer configurations. Using Binder cumulant analysis, we determine the critical temperature ($T_c$) of each structure and find that $T_c$ increases with coordination number, from the honeycomb ($r=3$) to the triangular ($r=6$) lattice. In contrast, the magnetic entropy change ($-\Delta S_M$) decreases with coordination number, reaching its maximum for the honeycomb lattice. After normalization by their peak values and appropriate temperature scaling, both $-\Delta S_M$ and the field exponent $n$ collapse onto universal master curves for different magnetic fields and across all six lattice structures at a fixed low field. This demonstrates universal MCE scaling independent of coordination number and layer count. Critical scaling analysis further supports the observed universality and power-law behavior. Unlike $-\Delta S_M$, the adiabatic temperature change ($\Delta T_{ad}$) increases with coordination number, whereas the magnetic Grüneisen parameter ($\Gamma_M$) follows the same trend as $-\Delta S_M$. Although the peak value of $-\Delta S_M$ decreases with coordination number, the relative cooling power and cooling capacity remain nearly unchanged due to compensating broadening of the $-\Delta S_M$ curves. The field dependence of $-\Delta S_M$, relative cooling power, and cooling capacity follows power laws up to $\sim2.6$ T (assuming $J\approx1$ meV). Hysteresis analysis shows that lattices with lower coordination numbers exhibit a faster reduction in loop width with increasing temperature. These results establish the universal scaling behavior of the magnetocaloric effect in two-dimensional monolayer and bilayer magnetic lattices and provide guidelines for designing magnetic refrigerants.

We introduce a validation process that harnesses engineered many-body relaxation to control and calibrate quantum hardware. On two independently developed neutral-atom processors, we realize the many-body quantum Mpemba effect in an open system for the first time. Initial-state engineering creates fast and slow relaxation pathways: the fast pathway opens access to unreachable mixed-state physics before hardware noise obscures the target dynamics, whereas the slow pathway amplifies hidden imperfections in preparation and control. Computational-basis measurements directly and independently benchmark dynamical reliability, revealing each processor's actual operating window as a many-body simulator. The processors are thus judged by the very dynamics they are built to reproduce. Complementary responses disentangle control errors and drive an adaptive, time-resolved scheme that supplies hardware developers directly actionable corrections, enhancing faithful reproduction of the target dynamics. These results establish many-body relaxation as a transferable validation and calibration tool for programmable quantum processors.

Symmetry is central to modern machine learning and physics: invariances and equivariances improve sample efficiency, robustness, and out-of-distribution generalization, while symmetry principles guide scientific modeling. Yet for stochastic dynamical systems the relevant continuous symmetries are rarely known, and symmetry discovery for SDEs has remained essentially unexplored. We introduce LieStoNet, an end-to-end, template-free framework for discovering Lie-point symmetries of SDEs directly from spatiotemporal trajectories, without prespecifying symmetry groups, templates, or canonical coordinates. Building on the seminal SDE Lie-symmetry theory of Gaeta and Quintero (1999), which formalizes Lie-point SDE symmetries and their relation to Fokker-Planck symmetries, LieStoNet learns neural surrogates for drift and diffusion from increments, then learns projectable generators by enforcing the SDE determining equations, separately regularizing for closure under Lie brackets, adherence to the Lie algebra axioms (bilinearity, antisymmetry, Jacobi), and a non-redundant independent basis. The surrogate also defines an associated Fokker-Planck equation, enabling optional discovery of its Lie-point symmetries in parallel. Across multiple canonical SDEs with known analytic symmetries, LieStoNet recovers generators consistent with the ground-truth symmetry algebra, providing interpretable symmetry discovery for noisy dynamics. Code is available at \href{https://github.com/sumit-sinha-seas/LieStoNet_Final.git}{this link}.