While the effects of solar mass change can be neglected in studies of solar periapsis shifts, the influence of a black hole's dynamic mass variation on the periapsis shift of test particles demands careful investigation. Using a frame-by-frame adiabatic approximation, we investigate whether the periapsis shift in the extremal limit can serve as a consistency test for the Weak Gravity Conjecture (WGC). We analyze the periapsis shift under Aschenbach-like conditions, where a stable photon sphere generates non-monotonic orbital velocity profiles. Finally, we synthesize the combined effects of extremality and a stable photon sphere, revealing profound modifications including prograde-to-retrograde transitions and triple-regime orbital structures. Our results demonstrate that the periapsis shift-not merely its magnitude but its qualitative orbital behavior-provides a meaningful experimental probe for the WGC in strong-field regimes.

When liquids are cooled and crystallization is avoided, their dynamics slow dramatically and the material eventually solidifies into an amorphous glass. Experiments show that chemically distinct glass forming liquids share universal features in both the spectral shape and the temperature dependence of the primary structural relaxation. We introduce Randium, a generic, energetically coarse-grained minimal model of viscous liquids. The model, inspired by results from atomistic molecular-dynamics simulations, is implemented on a two-dimensional lattice with Gaussian-distributed nearest-neighbor interactions. Temperature is the only control parameter, and at low temperatures, dynamic facilitation and dynamical heterogeneity emerge from simple nearest-neighbor rearrangements. The relaxation spectra obey time-temperature superposition, and they reproduce shapes observed experimentally for chemically distinct systems. The temperature dependence of the structural-relaxation time follows parabolic scaling, and the relaxation time grows exponentially with the heterogeneity length scale. The absence of elasticity-induced facilitation in Randium shows that this is not required for universal viscous-liquid dynamics. Other explanations for universal relaxation are discussed in light of Randium.

The fluctuations of the passage time in first passage percolation are of great interest. We show that the non-random fluctuations in planar FPP are at least of order $\sqrt{\log n}$ under some conditions that are known to be met for a large class of absolutely continuous edge weight distributions. This improves the ${\log(\log(n))}$ bound proven by Nakajima and is the first result showing divergence of the fluctuations for arbitrary directions. Our proof is an application of recent work by Dembin, Elboim and Peled on the BKS midpoint problem and the development of Mermin-Wagner type estimates.

The battle for a more secure Internet is waged on many fronts, including the most basic of networking protocols. Our focus is the IPv4 Identifier (IPID), an IPv4 header field as old as the Internet with an equally long history as an exploited side channel for scanning network properties, inferring off-path connections, and poisoning DNS caches. This article taxonomizes the 25-year history of IPID-based exploits and the corresponding changes to IPID selection methods. By mathematically analyzing these methods' correctness and security and empirically evaluating their performance, we reveal recommendations for best practice as well as shortcomings of current operating system implementations, emphasizing the value of systematic evaluations in network security.

We study the cosmology of axions in the post-inflationary scenario, where random initial conditions and the ensuing string-domain-wall network generate an isocurvature power spectrum. Axions radiated from strings behave as warm, wave-like dark matter: when they constitute the full dark matter abundance, free streaming sets the strongest bounds on their mass. For subdominant fractions, despite being warm, they still lead to an overall enhancement of structure growth in the dominant component, seeded by the axion white-noise fluctuations. We search for this effect using the ultraviolet luminosity function (UVLF) of galaxies at $z=4$-$10$, probing $k\simeq0.5$-$10\,\mathrm{Mpc}^{-1}$. Combining the UVLF analysis with Lyman-$α$ and CMB data yields the leading cosmological limits on post-inflationary axion dark matter, sensitive to tiny fractions for $m_a\lesssim10^{-21}\,\mathrm{eV}$. As a byproduct, we obtain new constraints on generic white-noise power spectra from the UVLF. These results apply broadly to scenarios that generate similar isocurvature perturbations, linking early-universe field dynamics to high-redshift structure formation.

Poly(ethylene terephthalate) (PET), a widely used thermoplastic in packaging, textiles, and engineering applications, is valued for its strength, clarity, and chemical resistance. Increasing environmental impact concerns and regulatory pressures drive the search for alternatives with comparable or superior performance. We present an AI-driven polymer design pipeline employing virtual forward synthesis (VFS) to generate PET-replacement copolymers. Inspired by the esterification route of PET synthesis, we systematically combined a down-selected set of Toxic Substances Control Act (TSCA)-listed monomers to create 12,100 PET-like polymers. Machine learning models predicted glass transition temperature (Tg), band gap, and tendency to crystallize, for all designs. Multi-objective screening identified 1,108 candidates predicted to match or exceed PET in $T_{\rm g}$ and band gap, including the ``rediscovery'' of other known commercial PET-alternate polymers (e.g., PETG, Tritan, Ecozen) that provide retrospective validation of our design pipeline, demonstrating a capability to rapidly design experimentally feasible polymers at a scale. Furthermore, selected, entirely new (previously unknown) candidates designed here have been synthesized and characterized, providing a definitive validation of the design framework.

Magecart skimming attacks have emerged as a significant threat to client-side security and user trust in online payment systems. This paper addresses the challenge of achieving robust and explainable detection of Magecart attacks through a comparative study of various Machine Learning (ML) models with a real-world dataset. Tree-based, linear, and kernel-based models were applied, further enhanced through hyperparameter tuning and feature selection, to distinguish between benign and malicious scripts. Such models are supported by a Behavior Deterministic Finite Automaton (DFA) which captures structural behavior patterns in scripts, helping to analyze and classify client-side script execution logs. To ensure robustness against adversarial evasion attacks, the ML models were adversarially trained and evaluated using attacks from the Adversarial Robustness Toolbox and the Adaptative Perturbation Pattern Method. In addition, concise explanations of ML model decisions are provided, supporting transparency and user trust. Experimental validation demonstrated high detection performance and interpretable reasoning, demonstrating that traditional ML models can be effective in real-world web security contexts.

Painter cohort analysis has long been regarded as a key lens for studying how painting artistic styles develop and transmit across generations. Through a two-year collaboration with art historians, we identify key challenges in traditional painter cohort research: the unstructured characteristic of painter features, the entangled complexity of inheritance relationships, and the cognitively demanding nature of cohort definition and validation. To solve these challenges, we propose HPC-Vis, a visual analytics system for interactive exploration of historical painter cohorts. An improved cohort analytical workflow is designed to integrate structured feature construction, visualization-assisted exploration, algorithm-based recommendation, and unified cohort management. Based on this workflow, we develop three core computational modules: a multi-scale artistic feature construction method that leverages LLMs to extract and organize hierarchical style features from unstructured historical texts, an inheritance reconstruction algorithm that transforms the entangled multi-parent inheritance network into a clear hierarchical forest structure, and a recommendation model that identifies core features of the cohort and recommends cohort members via painter relevance assessment. To support smooth interactive exploration, we further design a set of novel visualizations with multidimensional collaboration, especially an inheriting mountain view inspired by traditional Chinese landscape paintings, and a foldable doughnut chart for hierarchical artistic style labels. HPC-Vis is evaluated and validated through case studies, user studies, and technical evaluations, demonstrating its effectiveness in supporting painter cohort exploration and in providing visual insights for art historical research.

Ionization by a sequence of extreme ultraviolet pulses is investigated based on the rigorous numerical solution of the time-dependent Schrödinger equation, when the driving laser field is treated exactly. This goes beyond the typically used first-order nondipole approximation and reveals the effects of radiation pressure to its full extent. Specifically, we observe the comb structures in both the momentum and the energy distributions of photoelectrons. The comb peaks are shifted, however, depending on the emission angle of electrons. While similar effect is observed already in the first-order nondipole approximation, with increasing the laser field strength the discrepancy with our exact results becomes more pronounced. Also, we observe the additional substructure of the comb peaks arising in the angle-integrated energy distributions of photoelectrons. Finally, as our numerical calculations account for the atomic potential in the entire interaction region, we observe the loss of coherence of comb structures with increasing the number of laser pulses, that we attribute to rescattering.

Kronecker observed that either all roots or only one root of a solvable irreducible equation of odd prime degree with integer coefficients are real. This gives a possibility to construct specific examples of equations not solvable by radicals. A relatively elementary proof without using the full power of Galois theory is due to Weber. We give a rather short proof of Kronecker's theorem with a slightly different argument from Weber's. Several modern presentations of Weber's proof contain inaccuracies, which can be traced back to an error in the original proof. We discuss this error and how it can be corrected.

We consider an integral transform given by $T_ν f(s) := π\int_0^\infty rs J_ν(r s)^2 f(r) \, dr$, where $J_ν$ denotes the Bessel function of the first kind of order $ν$. As shown by Walther (2002, doi:10.1006/jfan.2001.3863), this transform plays an essential role in the study of optimal constants of smoothing estimates for the free Schrödinger equations on $\mathbb{R}^d$. On the other hand, Bez et al. (2015, doi:10.1016/j.aim.2015.08.025) studied these optimal constants using a different method, and obtained a certain alternative expression for $T_ν f$ involving the $d$-dimensional Fourier transform of $x \mapsto f(\lvert x \rvert)$ when $ν= k + d/2 - 1$ for $k \in \mathbb{N}$. The aims of this paper are to extend their identity for non-integer indices and to derive several inequalities from it.

Predicting particle transport in complex flows is traditionally achieved by solving the Navier-Stokes equations. While various numerical and experimental methods exist, they typically require deep physical insights and incur high computational costs. Machine learning offers an alternative by learning predictive patterns directly from data, avoiding explicit physical modeling. However, purely data-driven approaches often lack interpretability, physical consistency, and generalizability in sparse data regimes. To this end, we propose TrajectoryFlowNet, a Lagrangian-Eulerian physics-informed neural network architecture, for fluid flow velocimetry and imaging via learning to predict spatiotemporal flow fields and long-range particle trajectories. The salient features of our model include its ability to handle complex flow patterns with irregular boundaries, predict the full-field flows, image the long-range flow trajectory of any arbitrary particle, and ensure physical consistency in predictions based only on very scarce measurement of flow trajectories. We validate TrajectoryFlowNet via both numerical examples (e.g., lid-driven cavity flow and complex cylinder flow) and experimental test cases (e.g., aortic and ventricle blood flows) across diverse flow scenarios. The results demonstrate our model's effectiveness in capturing intricate particle-laden flow dynamics, enabling long-range tracking of particles and accurate construction of flow fields in real-world applications.

We appeal to the theory of Jouanolou torsors to model the coherent cohomology of configuration spaces of points in d-dimensional affine space. Using this model, we develop the operadic notion of chiral operations, thus generalizing the notion of chiral algebras of Beilinson and Drinfeld to higher dimensions. To produce examples, we use a higher-dimensional conceptualization of the residue which is inspired by Feynman graph integrals. One of our main results is the realization, using higher chiral operations, of the higher-dimensional Kac--Moody and Virasoro algebras.

$gwsnr$ is a Python package for efficient signal-to-noise ratio and detectability calculations for compact-binary gravitational-wave sources. It is designed for large simulated populations where repeated evaluation of the optimal signal-to-noise ratio, $ρ_{\rm opt}$, and the probability of detection, $P_{\rm det}$, becomes computationally expensive with direct noise-weighted inner-product calculations. The package provides multiple calculation pathways under a unified interface, including multiprocessing-based inner-product evaluation, partial-scaling interpolation for non-spinning and aligned-spin systems, JAX and MLX backends for accelerated array execution, and artificial neural network based detectability estimation for more complex waveform settings. It also supports statistical modelling of the observed signal-to-noise ratio, $ρ_{\rm obs}$, under stationary Gaussian noise assumptions, threshold estimation from injection catalogues, hybrid recalculation of marginal events near the detection boundary, and horizon-distance calculations. By combining fast numerical methods with configurable detector, waveform, and population settings, $gwsnr$ enables efficient selection-effect modelling, rate estimation, detector-sensitivity studies, and large-scale compact-binary population simulations. The package is publicly available with documentation, validation examples, and reproducible workflows.

We prove the vanishing of bounded cohomology with separable dual coefficients for many groups of interest in geometry, dynamics, and algebra. These include compactly supported structure-preserving diffeomorphism groups of certain manifolds; the group of interval exchange transformations of the half line; piecewise linear and piecewise projective groups of the line, giving strong answers to questions of Calegari and Navas; direct limit linear groups of relevance in algebraic $K$-theory, thereby answering a question by Kastenholz and Sroka and a question of two of the authors and L{ö}h; and certain subgroups of big mapping class groups, such as the stable braid group and the stable mapping class group, proving a conjecture of Bowden. Moreover, we prove that in the recently introduced framework of enumerated groups, the generic group has vanishing bounded cohomology with separable dual coefficients. At the heart of our approach is an elementary algebraic criterion called the commuting cyclic conjugates condition that is readily verifiable for the aforementioned large classes of groups.

In mirror symmetry, symplectic Landau-Ginzburg models are mirror to a large class of examples, in particular to Fano varieties and hypersurfaces of many Calabi-Yau and Fano varieties. When studying their Fukaya categories on the A-model in homological mirror symmetry, one needs to calculate the weights of pseudo-holomorphic discs bounded by Lagrangian branes. While these calculations simplify for exact and Lefschetz fibrations, we generalize the machinery for computing these weights by dropping the exact and Lefschetz assumptions. For a general symplectic Landau-Ginzburg model, a singular symplectic fibration, we prove that the weights and Lagrangian gradings split into base and fiber components. This is used in many calculations of Fukaya-Seidel categories to provide evidence of Kontsevich's homological mirror symmetry conjecture.

Altermagnets host intrinsically chirality-splitting spin waves, which offer an ideal platform for chirality-based computing with low energy consumption and fast dynamics. However, achieving precise and efficient control over spin-wave chirality remains a challenge. Here, we propose a mechanism to switch the chirality of spin waves in altermagnets via electrically induced Rashba spin-orbit coupling (SOC), which is free of tuning external fields. For in-plane spin polarization, SOC introduces a splitting effect opposite to the altermagnetism, leading to spin inversion in the electronic energy bands and chirality reversal in the spin-wave dispersion. By tuning SOC strength, the chirality splitting of spin waves can be controllably modified, enabling chirality switching at fixed resonance conditions, which results in the reversal of transverse spin susceptibility. We further design an experimental setup based on an altermagnet/antiferromagnet heterostructure to realize this mechanism. Our work establish a pathway toward efficient electrical control of spin-wave chirality in altermagnets, facilitating the development of chirality-based spintronic devices.

We present optical, ultraviolet, and X-ray observations of supernova (SN) 2024iss, a Type IIb SN that shows a prominent double-peaked light curve. We modeled the first peak with a semianalytical shock-cooling model and the X-ray emission with a free-free model. We compare the envelope radius and mass-loss rate with other Type IIb SNe to explore the relationships between the progenitor envelope and the circumstellar material (CSM). The shock-cooling peak in the $V$-band light curve reached $M_V = -17.33\pm 0.26$mag, while the $^{56}$Ni-powered second peak attained $M_V = -17.43\pm 0.26$mag. Early spectra show an photospheric velocity of $\sim19,400\,km\,s^{-1}$ at 3.82days from the H$α$ P~Cygni profile. The Balmer lines persist at least +87 days after the explosion, characterizing hydrogen-rich ejecta. Modeling the first light-curve peak suggests an extended envelope with a mass of $0.11\pm0.04\,M_{\odot}$ and a radius of $244\pm43~R_{\odot}$. Fitting the second light-curve peak with an Arnett-like model indicates a typical $^{56}$Ni mass of $ 0.117\pm0.013~M_{\odot}$ and a relatively low ejecta mass of $1.272\pm0.343\,M_{\odot}$. X-ray observations reveal bright thermal bremsstrahlung emission and indicate a mass-loss rate of $1.6\times10^{-5}\ M_{\odot} \ \rm{yr}^{-1}$. SN 2024iss occupies a transitional position between the two subclasses of extended (eIIb) and compact (cIIb) Type IIb SNe. Its envelope radius and pre-explosion mass-loss rate appear to be correlated as theoretically predicted. The observational properties of SN 2024iss are compatible with a binary interaction scenario being the dominant mechanism for envelope stripping. Furthermore, the low column density of neutral hydrogen suggests a compact CSM with an outer radius of $\lesssim1.3\times10^{14}$ cm, indicating that the progenitor star experienced eruptive mass loss within $\sim4\,yr$ of its terminal explosion.

In this paper, we derive new results on the asymptotic behavior of eigenvalues of perturbed one-dimensional massive Dirac operators in the weak coupling limit. Two classes of potentials are considered. For bounded Hermitian potentials $V$ satisfying $|V(x)| \lesssim |x|^{-1}$ for large $|x|$, we recover the leading term, which may include a logarithmic correction if $V(x) \sim |x|^{-1}$ at infinity. For possibly non-Hermitian $L^1$ potentials satisfying a suitable moment condition, we obtain the second term in the asymptotic expansion. The first result is based on a min-max principle adapted to the non-relativistic limit, while the second result is obtained via the Birman-Schwinger principle and resolvent expansions.

We measure the mass difference between $D_{s}^{*+}$ and $D_{s}^{+}$, $Δm_s$, using the decay chain $D_{s}^{*+} \to D_{s}^{+}(\to K^{+} K^{-} π^{+})π^{0}$, utilizing $e^+e^-$ annihilation data corresponding to an integrated luminosity of 3.19 fb$^{-1}$ collected at a center-of-mass energy of 4.178 GeV with the BESIII detector. The measured value of $Δm_s = [144\,201.9 \pm 44.2({\rm stat.}) \pm 29.9({\rm syst.}) \pm 15.0({\rm PDG})]$ keV/$c^2$ is about seven times more precise than the current Particle Data Group average, where the last uncertainty is from the Particle Data Group average of the $D^{*+} - D^{+}$ mass difference.

We investigate whether combining gas and stellar kinematic maps provides measurable advantages in recovering galaxy mass profiles, compared to using single-component maps alone. While traditional methods struggle to integrate multi-tracer data effectively, we test whether deep learning models can leverage this joint information. We develop a probabilistic convolutional neural network (CNN) framework trained and tested on mock galaxy kinematic maps from multiple cosmological simulation suites. Our model is trained on gas-only, stars-only, and combined gas+stellar velocity maps, allowing direct comparison of performance across tracers. To assess robustness, we include simulations with differing feedback models and galaxy properties. Combining gas and stellar maps reduces the dispersion in the inferred mass profiles by up to a factor of $\sim$1.5 compared to models using either tracer independently. The CNN architecture effectively captures complementary information from the two components. However, we find limitations in generalizing between simulation suites, with reduced performance when applying models trained on one suite to galaxies from another.

The Population III.1 theory for supermassive black hole (SMBH) formation predicts a very early ($z\sim20-25$) transient phase, the ``Pop III.1 Flash'', of cosmic reionization powered by supermassive stars that are SMBH progenitors. The universe then quickly recombined to become mostly neutral, with this state persisting until galaxies begin to reionize intergalactic gas again at $z\sim 10$. The overall Thomson scattering optical depth, $τ$, from the Pop III.1 Flash has been shown to be $τ_{\rm PopIII.1}\sim0.03$, leading to a total $τ\sim0.08-0.09$. Such a value, while significantly larger than that previously inferred from {\it Planck} observations of the low-$l$ $EE$ polarization power spectrum of the CMB, can help relieve several ``tensions'' faced by the standard $Λ$CDM cosmological model, especially the preference for negative neutrino masses and dynamic dark energy. Here we compute $EE$ power spectra of example models of the Pop III.1 Flash. We find that, because of its very high redshift, the contribution to $l\lesssim\:$6 modes is dramatically reduced compared to usual low-$z$ reionization models for the same value of $τ$, while the power at $l\gtrsim\:$6 is boosted. Thus the Pop III.1 reionization scenario provides a natural way to increase $τ$, while remaining closer to the latest CMB low-$l$ polarization observations.

A rigorous analysis is presented for the entanglement spectrum of quantum many-body states possessing a higher-form group-representation symmetry generated by topological Wilson loops, which is generally non-invertible. A general framework based on elementary algebraic topology and category theory is developed to determine the block structure of reduced density matrices for arbitrary bipartite manifolds on which the states are defined. Within this framework, we scrutinize the impact of topology on the entanglement structure for low-dimensional manifolds, including especially the torus, the Klein bottle, and lens spaces. By further incorporating gauge invariance, we refine our framework to determine the entanglement structure for topological gauge theories in arbitrary dimensions. In particular, in two dimensions, it is shown for the Kitaev quantum double model that not only the topological entanglement entropy can be reproduced, but also the Li-Haldane conjecture concerning the full entanglement spectrum holds exactly.

We show that non-trivial stringy excitations in Lorentzian three dimensional de Sitter spacetime can be created self-consistently from gravitational memory in the infinite past. In addition to demonstrating that the Nambu-Goto equations for the string emerge from the two-way gravitational junction conditions, we establish the existence of well-behaved solutions corresponding to transient fluctuations of a closed string about the equator which are both borne out of and dissolve to distinct gravitational memory in the infinite past and future, respectively. The memory at infinite past, which uniquely characterizes such a solution, is a single function giving the relative angular shift at the junction gluing two two-dimensional hemispheres. This reveals that a clock dynamically emerges in the presence of a gravitational junction without the need of any external observer. We also show that our results generalize to the $n$-way gravitational junctions with $n\geq 3$, which are captured by Nambu-Goto-Monge-Ampère equations for coupled $n-1$ strings -- these degrees of freedom exist even in the tensionless limit. Furthermore, for $n\geq 3$, $n-1$ correlated clocks dynamically emerge without the need of external observers in the tensionless limit, revealing a novel feature of pure three-dimensional gravity.

We examine how an anomalous Josephson current influences odd-frequency superconducting correlations in two Josephson junction geometries. The first consists of two ferromagnetic layers between conventional $s$-wave superconductors, with magnetizations along the $x$- and $y$-axes, forming a bilayer junction. The second contains three ferromagnetic layers between two $s$-wave superconductors, with magnetizations along the $x$-, $y$-, and $z$-axes, forming a trilayer junction. Both systems are analyzed in the short and long junction limits. In the bilayer case, where no anomalous Josephson current is present, odd-frequency equal-spin triplet correlations develop pronounced peaks at finite magnetizations in the short junction limit for both tunneling and transparent interfaces. The odd-frequency mixed-spin triplet correlations also exhibit peaks at finite magnetizations for tunneling interfaces, whereas for transparent interfaces they display both peaks and zeros. In the trilayer case, where an anomalous Josephson current exists, similar peaks in both equal- and mixed-spin odd-frequency triplet correlations occur at finite magnetizations for tunneling and transparent interfaces. The spatial profiles of these correlations remain largely unaffected by the anomalous current. The Josephson diode efficiency is finite and reaches its maximum at magnetizations corresponding to the peaks of the anomalous current. Overall, our results show that the anomalous Josephson current has only a marginal influence on odd-frequency spin-triplet pairing. This indicates that the emergence of odd-frequency correlations and the Josephson diode effect are largely independent phenomena, contrary to earlier conjectures. Analysis of the long junction limit leads to the same qualitative conclusions for both configurations.

In density functional theory, the SCAN (Strongly Constrained and Appropriately Normed) and r2SCAN functionals significantly improve over generalized gradient approximation functionals such as PBE (Perdew-Burke-Ernzerhof) in predicting electronic, magnetic, and structural properties across various materials, including transition-metal compounds. However, there remain puzzling cases where SCAN and r2SCAN underperform, such as in calculating the band structure of graphene, the magnetic moment of Fe, the potential energy curve of the Cr2 molecule, and the bond length of VO2. This research identifies a common characteristic among these challenging materials: non-compact covalent bonding through s-s, p-p, or d-d electron hybridization. While SCAN and r2SCAN excel at capturing electron localization at local atomic sites, they struggle to accurately describe electron localization in non-compact covalent bonds, resulting in a biased improvement. To address this issue, we propose the r2SCAN+V approach as a practical modification that improves accuracy across all the tested materials. The parameter V is 4 eV for metallic Fe, but substantially lower for the other cases. Our findings provide valuable insights for the future development of advanced functionals.

We prove the stability and global convergence of a coupled actor-critic gradient flow for infinite-horizon and entropy-regularised Markov decision processes (MDPs) in continuous state and action space with linear function approximation under Q-function realisability. We consider a version of the actor critic gradient flow where the critic is updated using temporal difference (TD) learning while the policy is updated using a policy mirror descent method on a separate timescale. For general action spaces, the relative entropy regularizer is unbounded and thus it is not clear a priori that the actor-critc flow does not suffer from finite-time blow-up. Therefore we first demonstrate stability which in turn enables us obtain a convergence rate of the actor critic flow to the optimal regularised value function. The arguments presented show that timescale separation is crucial for stability and convergence in this setting.

Solid interfaces resist sliding up to a threshold shear force, called static friction, beyond which they start moving and their resistance drops to the kinetic friction. Static friction at rough interfaces has long been described empirically using system-specific coefficients tabulated in engineering handbooks. Here, through nanometer-resolution sliding experiments, we show that it is set by a friction overshoot during the onset of sliding. We demonstrate that this overshoot originates from the collective configurational evolution of surface asperities under shear, and derive a minimal differential equation governing this evolution. Our theory predicts that such overshoots generically emerge when an athermal frictional system evolves smoothly toward a unique steady-state kinetic friction. These results show that static friction is not an intrinsic material property, but an emergent consequence of collective asperity dynamics.

The advent of Stage IV galaxy redshift surveys such as DESI and Euclid marks the beginning of an era of precision cosmology, with one key objective being the detection of primordial non-Gaussianities (PNG), potential signatures of inflationary physics. In particular, constraining the amplitude of local-type PNG, parameterised by $f_{\rm NL}$, with $σ_{f_{\rm NL}} \sim 1$, would provide a critical test of single versus multi-field inflation scenarios. While current large-scale structure and cosmic microwave background analyses have achieved $σ_{f_{\rm NL}} \sim 5$-$9$, further improvements demand novel data compression strategies. We propose a hybrid estimator that hierarchically combines standard $2$-point and $3$-point statistics with a field-level neural summary, motivated by recent theoretical work that shows that such a combination is nearly optimal, disentangling primordial from late-time non-Gaussianity. We employ PatchNet, a convolutional neural network that extracts small-scale information from sub-volumes (patches) of the halo number density field while large-scale information is retained via the power spectrum and bispectrum. Using Quijote-PNG simulations, we evaluate the Fisher information of this combined estimator across various redshifts, halo mass cuts, and scale cuts. Our results demonstrate that the inclusion of patch-based field-level compression always enhances constraints on $f_{\rm NL}$, reaching gains of $30$-$45\%$ at low $k_{\rm max}$ ($\sim 0.1 \, h \, \text{Mpc}^{-1}$), and capturing information beyond the bispectrum. This approach offers a computationally efficient and scalable pathway to tighten the PNG constraints from forthcoming survey data.

Nitrogen-vacancy (NV) centers in diamond are optically addressable and versatile light-matter interfaces with practical application in magnetic field sensing, offering the ability to operate at room temperature and reach sensitivities below pT/$\sqrt{\mathrm{Hz}}$. We propose an approach to simultaneously probe all of the magnetically sensitive states using a broadband microwave field and demonstrate that it can be used to measure the external DC magnetic field strength with sensitivities on the order of 10~pT/$\sqrt{\mathrm{Hz}}$. We develop tools for analyzing the temporal signatures in the transmitted broadband microwaves to estimate the magnetic field, comparing maximum likelihood estimation based on minimizing the Kullback-Leibler divergence to various neural network models, and both methods independently reach practical sensitivities. These results are achieved without optimizing parameters such as the bandwidth, and shape of the probing microwave field such that further improvements in sensitivity can be envisioned. Our results motivate novel implementations of NV-based magnetic sensors with the potential for vectorial magnetic field detection at 1-10 kHz update rates and improved sensitivities without requiring a bias magnetic field.