Understanding how fragment shell effects influence spontaneous fission mass yields remains a central challenge in nuclear fission theory. This work investigates the role of shell effects in the spontaneous fission of $^{240}$Pu, $^{232}$Th, and $^{264}$Fm by combining microscopic collective dynamics with fragment-level shell analysis. A two-step framework is employed: first, the tunneling from the inner to outer turning points is described using the Wentzel-Kramers-Brillouin approximation along the least-action path on a potential energy surface calculated from constrained Hartree-Fock-Bogoliubov theory. Second, the dissipative descent from the outer turning points to scission is simulated via Langevin dynamics in a large collective space of quadrupole and octupole deformations. Fragment shell effects are quantified using smoothed level density indicators for representative even-even fragment pairs extracted from Langevin scission configurations. The analysis reveals that enhanced yields arise from a coherent overlap among dynamically populated scission configurations, low-energy regions on the fragment potential energy surfaces, and low neutron and/or proton level densities near the Fermi surface. Proton shell effects provide persistent microscopic selectivity in both light and heavy fragments across asymmetric channels, while neutron shell effects offer additional stabilization. Deformed shell effects at finite quadrupole and octupole deformations play a crucial role in stabilizing asymmetric fission channels. This work demonstrates that fission fragment yields reflect shell-favored configurations that are made accessible by the potential energy surface topology and populated by stochastic dynamics, with the largest yields corresponding to configurations where shell gaps provide maximal binding.

This work introduces a novel structure-preserving methods for conservative systems based on a predictor-corrector strategy. The framework applies a discrete gradient correction to predictions generated by explicit one-step or multi-step schemes, which preserves nonlinear invariants while maintaining the accuracy order of the original predictor. This approach extends naturally to problems requiring simultaneous conservation of multiple invariants. Under mild conditions, conservation properties, solvability, numerical accuracy, and stability are established. Long-term numerical simulations on Lotka-Volterra systems, sine-Gordon equations, rigid body dynamics, and Kepler problems demonstrate improved robustness and conservation properties compared to existing projection and relaxation methods.

Many-body interactions govern the complex behavior of many amorphous materials, from metallic glasses to biological tissues, yet are often replaced by pairwise additive frameworks for computational efficiency. Here, we use classical density functional theory (DFT) to study a model soft glass of solvent-free polymer-grafted nanoparticles (PGNs), where the absence of solvent forces grafted chains to uniformly fill the interstitial space, generating strong angular-dependent many-body interactions between the cores. We show that NequIP, an equivariant message-passing graph neural network (GNN), learns the high-dimensional, rugged potential energy landscape of the system and reproduces classical DFT energies across a range of PGN design parameters at four orders of magnitude lower cost. Systematic analysis of GNN hyperparameters offers physical insights into the range, anisotropy, and effective body order of interactions. GNN-driven Monte Carlo simulations reveal locally favored icosahedral-like structures at equilibrium, and strikingly, recover equilibrium structures in agreement with experiments, despite the network being trained only on high-energy, out-of-equilibrium configurations.

We introduce a spectral version of the classical inducibility problem. Given an $\ell$-vertex graph $F$ and an $n$-vertex graph $G$, let $H_F(G)$ be the $\ell$-uniform hypergraph whose edges are the $\ell$-sets inducing a copy of $F$ in $G$. We study the maximum possible $α$-spectral radius of $H_F(G)$ over all $n$-vertex graphs $G$. For fixed $G$, this spectral parameter tends to $\ell!$ times the number of induced copies of $F$ in $G$ as $α\to\infty$, and therefore refines the usual induced-copy count. Our main result is a spectral analogue of the Brown--Sidorenko reduction: for every complete multipartite graph $F$, every $n$, and every $α\ge1$, a spectral extremal graph can be chosen to be complete multipartite. We also show that the leading asymptotic constant is the ordinary inducibility $i(F)$, and obtain exact multipartite reductions for stars $K_{1,t}$ and balanced complete $r$-partite graphs $K_{a,\ldots,a}$ with $r\le 2^a-1$.

We establish a fundamental principle in coherent wave-optical computing: restricting the modulation manifold from continuous complex-valued to binary amplitude suppresses stochastic-noise up-conversion while preserving classification fidelity, yielding a counter-intuitive less-is-more robustness law. Seven-layer binary-amplitude-mask D2NN (BM-D2NN) achieve 90.9% (MNIST) and 81.9% (Fashion-MNIST) test accuracy, within 2~4 pp of continuous-modulation D2NN (C-D2NN). Under pixel-wise Gaussian noise N(m,σ^2), spanning zero-mean (shot noise) to nonzero-mean (thermal/readout) regimes, BM-D2NN outperform C-D2NN by up to 32.8 pp (MNIST) and 18.5 pp (Fashion-MNIST). We analytically derive a noise-contribution metric C, governed by a transmission-bias factor K computable from clean data alone, that is consistently smaller for binary modulation than for continuous modulation (as verified for all test samples), guaranteeing the robustness ordering without noisy simulation. BM-D2NN additionally deliver a 6.79-fold higher imaging-plane intensity for clean data input. These results establish a quantitative physical principle connecting modulation-manifold geometry to noise robustness, applicable to any coherent optical processor in the z/λ >> 1 regime.

We study Dehn fillings on two-bridge knots via non-abelian representations into $\mathrm{PSL}(2,\mathbb{R})$ whose meridian image is hyperbolic. For each fixed nontrivial two-bridge knot, we prove that the set of surgery slopes admitting such representations is bounded. Equivalently, Dehn fillings along slopes with sufficiently large absolute value admit no non-abelian $\mathrm{PSL}(2,\mathbb{R})$ representations with hyperbolic meridian image. The proof combines the Riley polynomial with Khoi's surgery-slope formula. On each admissible real algebraic branch, we express the meridian and longitude translation parameters as functions of the branch parameter and derive uniform endpoint estimates for their quotient. The resulting bound is effective in principle but not optimized. We also provide examples illustrating the parameter sets and endpoint behavior.

We investigate the jet quenching parameter $\hat{q}$ in the QCD phase diagram within a QCD-assisted low-energy effective theory using the functional renormalization group (fRG). Following the formalism that relates $\hat{q}$ to the spectral functions of the chiral order-parameter field, we compute the $σ$ and $π$ meson contributions to $\hat{q}$ at finite temperature and baryon chemical potential from analytically continued mesonic two-point functions. We find that $\hat{q}$ receives appreciable contributions mainly above the chiral phase boundary and exhibits a pronounced enhancement at large baryon chemical potential as the chiral crossover sharpens toward the critical end point (CEP), a behavior consistent with the picture of partonic critical opalescence (PCO), a pronounced enhancement of jet transverse momentum broadening induced by the critical $σ$ field fluctuations.

Harnessing dissipation in the presence of strong symmetries has recently emerged as a promising route for generating entanglement in atomic clocks. However, previous proposals relied on regimes where cavity photons can be adiabatically eliminated, significantly limiting their applicability to experimentally relevant cavity-QED regimes that lie in or near the resolved vacuum Rabi splitting regime. Here we show that symmetry-protected dissipative spin squeezing can be realized even when cavity photons actively participate in the dynamics, extending the experimental relevance of the protocol. We study a three-level ensemble of $^{87}\mathrm{Sr}$ atoms coupled to an optical cavity in the resolved vacuum Rabi splitting regime and demonstrate that, with smooth ramps of the drive amplitude and detunings, the driven-dissipative dynamics enters a stable low-photon regime in which nonadiabatic cavity excitations and sector-resolving photon leakage can be controlled. Within this low-photon operating window, sector-resolving photon leakage is suppressed and the sector-dependent geometric phase realizes effective one-axis twisting. At the end of the protocol the entanglement can also be efficiently transferred directly onto the long-lived clock states by turning the drive off. For experimentally realistic parameters, we theoretically show that more than $25\,\mathrm{dB}$ of squeezing can be generated for $10^5$ atoms, closely saturating the ideal one-axis twisting scaling $ξ_{\min}^2 \propto N^{-2/3}$. At fixed cooperativity, the optimized squeezing remains broadly comparable to the unresolved-regime implementation, while the resolved-regime implementation reaches comparable squeezing on a substantially shorter physical timescale. These results establish symmetry-protected dissipative dynamics as a practical route to beyond the standard-quantum-limit performance in optical-clock platforms.

SRAM-based analog computing-in-memory demonstrates outstanding efficiency. However, it faces three critical challenges: significant ADC overhead, high latency for multi-bit inputs, and limited read bitline voltage. To address these issues, this work proposes a multi-bit highly reconfigurable 256x128 in-memory computing array supporting 1-7b input, 2-4b weight, and 1-7b output. Three key innovations are introduced: 1) The IMADC occupies only 3% area overhead, achieving a 9x improvement compared to previous IMADC; 2) The BSCHA reduces latency by 1.9x and 6.6x compared to traditional pulse-width modulation (PWM) and bit-slicing modes, respectively; 3) A dual-8T bitcell enabling ternary weight storage through a decoupled read path, integrated with a read wordline under-driven cascode technique, improves linearity of unit discharge current by 7x and increases the usable read bitline voltage by 3.5x.

Despite numerous technological advancements, the digital divide remains a pressing issue affecting millions worldwide. We present a framework for diagnosing internet inequality at the Census Block Group level by pairing approximately 170 million crowdsourced Ookla speed tests (2021--2025) with U.S. Census demographics across six metropolitan regions. After quantifying and correcting for sampling bias, we use Random Forest regression with permutation importance to identify the socio-economic drivers of download speed, upload speed, and latency. Population density dominates all three metrics at the regional level, but this dominance is an artifact of scale: once areas are stratified into density bins, its influence vanishes in medium- and higher-density neighborhoods, revealing that socio-economic conditions are the true differentiators of internet quality in most urban settings. After controlling for density, income and racial composition emerge as the primary drivers, income consistently dictating upload speed and racial composition proving to be a stronger predictor of download speed than either income or education. Our findings demonstrate that internet inequality is locally configured: no single national narrative explains it, and effective policy demands region-specific intervention.

Particulate Stokes flow in confined, periodic geometries underlies a broad class of problems in biophysics, microfluidics, and the rheology of complex fluids. Boundary integral equation (BIE) methods are a natural tool for such problems, but existing periodization schemes rely either on periodic Green's functions, which are restrictive for complex confining geometries, or on free-space schemes that solve auxiliary proxy strengths alongside the surface densities in an extended linear system whose cost scales unfavorably in three dimensions. We present a BIE framework for three-dimensional particulate Stokes flow in periodic pipes with circular cross-sections, wall-bounded doubly-periodic, and triply-periodic geometries that uses only the free-space Green's function and avoids both Ewald summation and the extended linear system. Proxy sources placed on equivalent surfaces of the kernel-independent FMM (KIFMM) form the auxiliary basis, and contributions from far image boxes are captured by a hierarchical proxy sum made absolutely convergent by a net-force-zero compatibility condition. The resulting periodization precomputation depends only on the periodic-box geometry, independent of the kernel and of the surfaces inside the box, and is reused verbatim across the Stokeslet, stresslet, and rotlet. Combined with high-order adaptive surface discretizations, the method achieves high-order accuracy at $\mathcal{O}(N)$ cost with a single layer of image boxes in the near field. Numerical examples on dense polydisperse suspensions with thousands of particles and on flow through complex periodic channels, together with strong and weak scaling studies, demonstrate efficient performance on systems with millions of degrees of freedom on distributed-memory architectures.

We develop a systematic method to express generating functions for moments of combinatorial statistics in terms of partition traces. We employ an algebraic approach based on the complete Bell polynomials and their inversion formula, alongside an analytic approach via Faà di Bruno's formula. Our approach can be applied to a wide class of combinatorial statistics, such as the largest part of an integer partition, the partition crank and rank, and the unimodal sequence rank.

This study investigates Wikimedia Commons contributors' lived experiences with the Computer-Aided Tagging (CAT) tool, an AI-assisted image tagging system designed to improve Commons' discoverability, searchability, accessibility, and multilingual support. Using a qualitative analysis of 595 CAT-related community comments from 11 wiki pages and 16 in-depth interviews, we identify seven key issues that contributed to CAT's mixed reception and eventual deactivation. We also offer community-informed suggestions for improving the tool. We reflect on the implications for designing human-AI collaboration on Commons and for developing AI-assisted tools that support open knowledge work. This work contributes to HCI and CSCW research by extending the understanding of human-AI collaboration beyond Anglophone, text-centric, corporate platforms.

The Moebius-Kantor graph MK=G(8,3) is a Cayley graph of three non-abelian groups, the Pauli group P(1), the semi-dihedral group SD(16), as well as the dihedral group D(16) of order 16. In topological graph theory, it illustrates the Heawood number 7 of the torus and leads to the Tucker group Aut(MK), the unique group of genus 2. We compute the Lefschetz numbers to illustrate the Brouwer-Lefschetz fixed point theorem. MK is also the dual of the 2-skeleton complex of the 3-sphere G. The graph represents one of flat Clifford tori of a Hopf fibration in the 3-sphere G=K(2,2,2,2) reflecting that Coxeter saw that MK is a subgraph of the tesseract G*. It carries a metric d so that (MK,d) has only one algebraic group structure (P(1),*) that preserves the metric. It makes the Pauli group natural, similarly as the Moebius ladder M(16) makes the dihedral group D(16) natural, forcing the algebraic structure from the metric structure.

The eigenstate thermalization hypothesis (ETH) posits that energy eigenstates encode local properties of the microcanonical ensemble. Motivated by recent interest in the physics of non-commuting conserved charges and the non-Abelian ETH, we study chaotic eigenstates in the presence of symmetries described by general compact Lie groups, such as SU(2). By applying non-Abelian symmetry resolution, we develop a non-Abelian microcanonical entropy and relate this entropy to the entanglement entropy of chaotic eigenstates. We find that microcanonical entropy is closely related to the symmetry-resolved entanglement entropy, which differs from conventional entanglement entropy by a universal logarithmic correction. Our results depend on the global Casimir charge, e.g. total spin. At finite charge density, we find a logarithmic enhancement to conventional entanglement entropy. At zero density, we find no such correction to entanglement entropy, but a logarithmic reduction to microcanonical entropy and symmetry-resolved entanglement entropy. We discuss the implications of our approach for non-Abelian eigenstate thermalization.

We study the lowest eigenvalue of the Neumann magnetic Laplacian in a planar domain divided into two regions, with piecewise constant magnetic fields that may scale differently in the inner and outer parts. Our aim is to describe the high-flux limit and determine when the ground-state energy is eventually monotone and when it continues to oscillate. We identify several asymptotic regimes according to the relative strength of the outer field. When the outer field is fixed, the lowest eigenvalue exhibits persistent oscillations and the low-energy states localize in the outer region. When the outer field grows more slowly, the behavior depends strongly on the geometry: it is eventually monotone for non-circular domains, while oscillations may persist for disks. In the critical regime, where the two fields are comparable, geometry and flux distribution both play a decisive role. When the outer field dominates, the problem reduces asymptotically to an effective operator on the inner region. These results show how uneven magnetic scaling, topology, and geometry shape the high-flux spectral behavior.

Community anchor institutions (CAIs), such as libraries, schools, and community centers, are critical for providing Internet access to un- or under-served individuals and communities. Because many of these institutions are themselves under-provisioned, analyzing the reliability and quality of their Internet service is important. Doing so at scale requires knowing the IP addresses of these institutions so that broadband measurement and policy evaluation can occur. Unfortunately, these IPs are not systematically documented. As a first step towards widespread, scalable evaluation of CAI Internet connectivity, this paper presents Reverse IP Geolocation (RG), a new framework to infer IP addresses from physical address data. A key insight is that CAI street addresses are publicly known, which allows us to identify a candidate set of IPs from commercial geolocation that are likely serving the location associated with a CAI. In this paper, \textbf{we focus on US public libraries}, which offer both geographic diversity across thousands of locations, and some publicly available institutional records (\eg{}WHOIS registrations) that enable systematic validation of our approach. Our approach offers a novel integration of IP geolocation databases, DNS PTR records, WHOIS registrations, broadband provider data, and active measurements to identify IPs likely assigned to libraries and validate them. Based on evaluations, our approach can map a library to its IP prefix approx. half of the time, with coverage across all US states, as well as urban and rural areas. Our results highlight the feasibility of mapping CAI presence in IP space and offer a foundation for large-scale, remote broadband infrastructure evaluation.

The transition to near-field (NF) communications in ultra-massive multiple-input multiple-output (UM-MIMO) systems fundamentally alters the spatial degrees of freedom (DoF) of wireless channels. While the NF DoF of line-of-sight (LoS) transmission channels is well-characterized in the literature, the DoF in NF multipath scenarios remains underexplored. This paper investigates the spatial DoF of NF UM-MIMO channels under practical multipath conditions. A generic DoF metric is derived by modeling multipath propagation and analyzing the resulting eigenvalue distribution based on the Green' s function representation of the channel. The DoF contribution of each path is determined by the product of the effective electrical aperture and the subtended solid angle, and the total DoF is obtained through the effective union of spatially resolvable path contributions. A mapping between the eigenvalue distribution and multipath powers is further established. Numerical simulations and real-world NF channel measurements at 28-30 GHz with 720 array elements are conducted for validation in both LoS multipath and non-LoS scenarios. The results show that multipath propagation can significantly increase the spatial DoF and that the proposed metric accurately predicts the DoF of practical NF channels. The proposed framework provides a practical tool for DoF prediction and supports capacity analysis and spatial multiplexing design in future NF UM-MIMO systems.

The distinct physical and geochemical differences between Earth and Mars provide fundamental constraints on terrestrial planet formation, yet a self-consistent framework linking dynamical and chemical aspects remains elusive. Here we present an integrated modeling framework that couples high-resolution N-body simulations with impact-driven metal-silicate equilibration to track the dynamical accretion history and chemical differentiation for Earth and Mars. Using a narrow ring planetesimal accretion scenario, we show that Earth and Mars analogs naturally sample systematically different solid reservoirs within the protoplanetary disk. Earth analogs preferentially accrete reduced material around the planetesimal ring center, whereas Mars analogs acquire a larger fraction of oxidized material exterior to the ring. This leads to diverse bulk redox states, with composition further modified by impact-dependent pressure-temperature equilibration conditions during core formation. As a result, Earth analogs experience deeper equilibration and more efficient transfer of iron into the core, producing mantles with low iron oxide contents and larger core mass fractions. In contrast, Mars analogs equilibrate at shallower conditions, retain more iron in their mantles, and develop smaller cores. Our results demonstrate that the dynamical and geochemical differences between Earth and Mars emerge from the coupled effects of accretion pathways, the disk's radial redox structure and impact-controlled differentiation, rather than from any single process. Our unified framework physically explains the geochemical diversity of terrestrial planets and offers a potential pathway to interpret compositions of rocky planets in exoplanetary systems.

We present an extended catalogue of 1193 Herbig-Haro (HH) objects, comprising 477 isolated HH objects and 716 HH knots, compiled through a meticulous review of the literature available through mid-2025. The catalogue provides comprehensive data for each entry, including celestial coordinates, distances, knot separations, exciting sources (with spectral types where available), object characteristics, and bibliographic references. We also perform a preliminary statistical analysis of key parameters such as distance, exciting source properties, morphology, and excitation state. By combining our extended catalogue with the two earlier HH object catalogues published by Hippel et al. in 1988 and Reipurth in 2000, astronomers can access comprehensive information on all known HH objects, thereby facilitating research on star formation and stellar outflows.

Let $\bigtriangleup$ be a simplicial complex and let $δ_{\mathcal{NF}}$ denote the NF-operator. The NF-complex $δ_{\mathcal{NF}}(\bigtriangleup)$ is defined as the Stanley--Reisner complex of the facet ideal of $\bigtriangleup$. Iterating $δ_{\mathcal{NF}}$ gives a periodic orbit (up to isomorphism), and the smallest positive integer $t$ for which $δ_{\mathcal{NF}}^{\,t}(\bigtriangleup)\cong \bigtriangleup$ is called the \emph{NF-number} of $\bigtriangleup$ (Habi and Mahmood, Algebra Colloquium, 2022). In this work, we provide various results and determine explicit formulas for the NF-number for several families of graphs. In particular, we compute the NF-number for dumbbell graphs. We also prove that the NF-number of the complete split graph $S_{n,m}$ equals $m+n+2$, and that the NF-number of the double star $D_{p+q}$ equals $p+q+4$. We conclude with remarks, open problems, and conjectures to guide future research.

We investigate the eigenvector distribution at the soft edge for Gaussian random matrices with finite-rank deformations, in the critical regime of BBP transition. For finite-rank deformations of the GOE and GUE with critical spikes, we find that the squared overlap between a leading eigenvector and a spike, rescaled by \(N^{1/3}\), converges weakly to the negative reciprocal of the derivative of an Airy-Green function evaluated at the corresponding soft-edge root. For the rank-one critically spiked Gaussian \(β\)-ensemble, \(β>0\), we obtain an analogous result involving an Airy-Green function. In both cases, the Airy-Green functions are generalizations of the one introduced by Bykhovskaya--Gorin--Sodin \cite{Bykhovskaya-Gorin-Sodin25}. The proofs are both based on an eigenvector--eigenvalue identity and a resolvent-differentiation mechanism.

Autonomous coding agents built on large language models (LLMs) are rapidly being integrated into development workflows, yet their operational safety properties remain poorly understood beyond evaluations of explicitly malicious inputs. In practice, high-impact failures arise during benign, goal-directed use through environment breakage, fabricated success reports, etc. that current benchmarks do not capture. What categories of operational safety failures actually occur when coding agents are used for everyday development tasks and what is their impact? We present an incident-driven empirical study grounded in two complementary evidence streams. We screen 68,816 papers from 22 premier venues, curating 185 safety-relevant studies, and mine 16,586 GitHub issues from widely deployed LLM-powered coding tools, manually confirming 547 genuine safety failures. Applying systematic open coding over both corpora, we derive a multi-dimensional safety taxonomy of 33 operational risk types organized across seven dimensions, and annotate each incident with contributing factors, task context, severity, and downstream impact. Our findings show that coding-agent failures are often severe, with 326 of 547 incidents rated high or critical. The dominant risks are constraint violations, destructive operations, authorization bypasses, and deception, and over 65% of incidents arise in bug fixing and setup or configuration, patterns largely missing from prior literature. These results have direct implications for SE tool designers and benchmark developers: guardrails must go beyond adversarial-prompt defenses to enforce environmental constraints, failure transparency, and safe-halt behaviors.

A Brain-Computer Interface (BCI) speller systems based on Event-Related Potentials (ERPs) enables users to select characters by detecting brain responses to visual stimuli, recorded through electroencephalogram (EEG). One challenge is to accurately identify target-related responses, such as the P300 component. However, existing methods tend to ignore feature selection, perform feature selection without interpretability, or require large computational effort or data manipulation. To address these limitations, we propose a novel Bayesian generative modeling framework to the binary classification of EEG responses to stimuli. Our approach employs a Probit-link Split-and-merge Gaussian Process (P-SMGP) prior to perform spatial-temporal feature selection, effectively capturing the distinctions between target and non-target ERP responses. Through both simulation studies and real EEG data analysis, our approach can reduce computational complexity and provide statistical interpretations on transformed ERP functions while maintaining comparable prediction accuracy. These findings underscore the value of interpretable, stimulus-level modeling for advancing predictive and personalized BCI systems.

Magnetostriction, a fundamental phenomenon bridging magnetism and mechanics, has enabled a broad spectrum of applications. For almost two centuries, it has been mainly investigated for ferromagnets. Regarding the magnetostriction of antiferromagnets (AFMs), limitedly known examples for both conventional collinear AFMs and noncollinear AFMs predominantly exhibit non-saturating magnetic-field dependence. Herein, we report an easily saturated magnetostriction effect in a prototypical altermagnet - MnTe, which is an emerging class of collinear AFMs with special crystal symmetries. For high-quality MnTe single crystals, the magnetostriction saturates under a moderate field of ~0.7 T with an intriguing two-fold-symmetry anisotropy. First-principles calculations reveal that the saturated and anisotropic magnetostriction originates from symmetry-allowed coupling between elastic strain and its Néel order parameter. These findings break the traditional wisdom on antiferromagnetic magnetostriction.

Text-based sequential recommender systems, while greatly improving recommendation accuracy by incorporating item contexts, are undeniably more expensive to train. By condensing a large dataset into a compact set of synthetic samples for model training, dataset distillation offers a promising solution. However, its adoption in text-based sequential recommendation is non-trivial given the large pool of discrete items. This challenge is further compounded by language model-based item encoding, which makes bi-level optimization commonly used in dataset distillation prohibitively expensive. To this end, we propose First-order dataset distillation for Text-based Sequential Recommendation (FOSTER), which facilitates effectiveness and efficiency via three novel components: (1) stochastic item subset sampling that replaces costly full-corpus embedding extraction at each distillation step; (2) first-order optimization with trajectory-anchored parameter reset to avoid expensive bi-level gradient computation; and (3) regularization that explicitly promotes co-occurrence between semantically similar items in the synthetic sequences. Extensive experiments on three benchmarks show that FOSTER consistently outperforms existing dataset distillation and coreset selection baselines, approximating full-dataset performance using as few as 20 synthetic interaction sequences.

Achieving uniform critical current across Josephson junctions is essential for the large-scale integration of superconducting quantum circuits. In this work, we statistically analyzed the variation of the critical current of Al/AlO${\rm _x}$/Al junctions using room-temperature tunnel resistance statistics, and identified the dominant contribution among the modeled sources of the variation based on their dependence on geometry and deposition conditions of junctions. Our model-based analysis reveals that fluctuations in the Al film thickness play the dominant role among the modeled contributing factors. Based on this analysis, we found that, in Dolan-bridge double-angle deposition, adopting a deposition angle of 30-degree for bilayer junctions significantly improves uniformity, yielding a relative standard deviation of 1.2% (0.5%) across a 9.75 mm (1.5 mm) square region.

We prove an $L^2$ two-weight testing theorem for the Dunkl--Poisson semigroup. The difficulty is geometric. The Dunkl orbit distance has several reflected diagonals, so a single orbit-box test may mix different chamber components. We avoid this by working on one Weyl chamber and keeping the chamber indices. Under the wall-null assumption the full operator becomes a finite matrix of scalar positive Poisson-type operators. In each entry the orbit diagonal is just the ordinary diagonal in the chamber variables. The scalar proof is then a principal-cube stopping-time argument, with two Dunkl kernel comparisons as the only new estimates. The resulting forward and backward tests are necessary and sufficient for the original Dunkl--Poisson inequality.

Quantum error correction (QEC) is transitioning from physical feasibility demonstrations to systems engineering challenges. Google has achieved below-threshold performance on distance-5/7 surface codes, while Riverlane and Rigetti have demonstrated hardware-integrated low-latency feedback loops. These milestones indicate that the core challenge of real-time decoding has shifted from algorithmic capability to system-level engineering. However, a substantial engineering gap remains between laboratory demonstrations and scalable fault-tolerant quantum computing (FTQC). This white paper addresses three questions: (1) Where are the real bottlenecks in real-time QEC: beyond average decoder speed, the constraints lie in QEC round time, tail latency, and end-to-end data path coordination; (2) How mature are mainstream decoder algorithms: we benchmark the major decoders for both surface codes and quantum low-density parity-check (qLDPC) codes, evaluating their real-time readiness; (3) What system stack do we propose: a six-layer reference architecture from syndrome acquisition to logical operations, with interface definitions and latency budget models. Our results quantify the gap between current decoder performance and real-time requirements, and identify the architectural choices needed to close it.

The symmetry of a system's response to external stimuli is a fundamental concept in physics and materials science. At the microscopic scale, breaking this symmetry to achieve a rectified response is exceptionally difficult to engineer and remains rare in nature. Conventional micromechanics models of crystalline solids assume a symmetric response to applied stress, where reversing the load simply inverts the direction of defect velocity without altering its magnitude. In this work, we report an atomic-scale, geometry-rooted mechanism that breaks this symmetry. Molecular dynamics simulations of face-centered cubic nickel reveal that dislocations containing atomic-scale jogs exhibit asymmetric mobility under opposite applied stresses, reversing the loading direction triggers significantly higher drag. This asymmetry arises from an unconventional coupling between an atomic displacement vector and the second-order tensorial eigenstrain of the jog motion mechanism. Because jogs are ubiquitous structures in plastic deformation, this discovery challenges classical descriptions of plastic deformation mechanisms, with direct implications to cyclic creep, and opens new pathways for defect engineering to enhance fatigue resistance.