We extended the higher-dimensional quantum Oppenheimer-Snyder model to the case with a cosmological constant. For AdS case, we discuss its thermodynamic properties in extend phase space formalism and make comparison with classical black holes. For quantum-corrected small black holes in AdS spacetime, the temperature no longer diverges but tends to zero. Additionally, the heat capacity exhibits characteristic behavior indicative of an extra phase transition induced by quantum corrections, highlighting the profound impact of quantum effects on black hole thermodynamics.

Recently, with the impossibility of eradicating the vulnerabilities of information systems, we must prepare for the occurrence of the security incident by the multi-layer defense called the Defense-in-Depth strategy. In the multi-layer defense, it is important to authorize accesses in fine-grained granularity to compose each layer effectively, and many access control models are proposed to follow them. However, policy description languages proposed so far cannot express the models appropriately in proper granularity. In this paper, we propose a policy description language which can designate many kinds of conditions for access control, such as the dynamic status of an application process, as an element of decision data, and implement it in Datalog. Using the proposed language, we compose the policy of SELinux, which is a major implementation achieving the multi-layer defense, and we confirm the advantages of the proposed language by evaluating its validity and expressiveness.

Existing access control methods grant access requests based on the combinations of applications as subject and files as objects. Therefore intents of applications and the possible effects caused by granting the access requests have not been taken into consideration. In this paper, we propose a new access control method based on application history and intents. With our access control method, system administrators can reduce the risks caused by malicious access attempts and wrong operations. In this paper, the concept and implementation design will be explained as well as the brief evaluation report of TOMOYO Linux, our implementation of the new access control method to Linux.

Cloud storage revolutionizes data management but raises conflicts between functionality and privacy. Public Key Encryption with Equality Test (PKEET), an advanced cryptographic technique, can enable multi-user searchable encryption (SE) through cross-key ciphertext comparison without shared keys. However, existing PKEET-based SE schemes lack ciphertext-file-level authorization, public verifiability, or SE-level support. This paper first proposes a novel PKEET scheme, AVPKEET (Authorized and Verifiable PKEET). It enables non-transferable and non-replayable authorization of ciphertext files, while supporting public verifiability, all without the need for trusted third parties. Then we propose an AVPKEET-based SE scheme, denoted as AVSE (Authorized and Verifiable SE), featuring one-time non-transferable tokens bound to users and nonces, batch operations, and fine-grained access control (ALL, PARTIAL, SINGLE). We prove OW-CCA2 security, token unforgeability, and verification soundness under standard assumptions. Experiment results demonstrate that AVSE achieves the most compact token size (168 bytes) while uniquely providing both ciphertext-file-level authorization and public verification, with acceptable overhead for cloud storage deployment.

We show that the logarithmic Sobolev inequality of the Boolean cube is stable under random monotone censoring. More precisely, if $A_n\subseteq \{0,1\}^n$ is chosen uniformly among all monotone subsets, then the logarithmic Sobolev constant of the censored walk on $A_n$ is of order $n$ with high probability. As a consequence, several analytic and probabilistic properties of the Boolean cube persist for a typical monotone subset: the censored semigroup is hypercontractive, the uniform measure on $A_n$ satisfies Gaussian concentration for Lipschitz observables, and the associated walk mixes in time $O(n\log n)$. The latter proves a conjectured mixing bound of Ding and Mossel for almost all monotone sets. The result is genuinely typical rather than universal. We construct monotone sets of density bounded away from zero whose logarithmic Sobolev constant is of order $n^2$. To prove the result, we establish a sharp logarithmic Sobolev inequality for Hamming caps and combine it with a harmonic extension argument transferring this inequality to monotone sets lying between nearby caps, together with a structural theorem of Korshunov on random monotone sets.

Platform content interventions in recommendation systems are typically evaluated as static "nudges", ignoring that the systems adaptively learn from the resulting user behavior. We investigate this dynamic through a large-scale field experiment on a short-video platform. The experiment involves a "sleep reminder" campaign designed to reduce late-night usage. Paradoxically, the intervention increased late-night engagement by 14.75% and overall platform usage by 2.18%, and the effects persisted for weeks even after the experiment. We explain this through a forced-exploration mechanism, showing that by revealing high latent demand for the promoted content, the intervention triggers a recommendation policy update that routine user behavior would not produce. The data generated by the intervention induced the algorithm to update its post-campaign policy, reinforcing the very engagement loops the campaign aimed to mitigate. Our findings demonstrate that user-facing interventions can effectively retrain the underlying algorithm, triggering durable, system-wide shifts in content distribution that challenge standard evaluation metrics in platform governance and social responsibility initiatives.

We study a kinematical consequence of the Hopf-algebraic momentum composition law in $κ$-Minkowski spacetime. The same curved momentum space can be described in different coordinates. In the bicrossproduct basis the ordered-plane-wave labels are the translation-generator eigenvalues, so the relevant map is one-to-one. In the classical basis, instead, the translation eigenvalues $P_μ$ are nonlinearly related to the ordered-plane-wave labels $p_μ$. This relation can fail to be globally one-to-one in a high-momentum region. When a given classical-basis four-momentum admits more than one real auxiliary preimage, the branch-sensitive quantity $P_+\equiv P_0+P_4=κe^{p_0/κ}$ enters the coproduct and resolves the branches in two-particle states. Imposing the vanishing total-momentum constraint therefore gives branch-dependent $κ$-deformed back-to-back momentum correlations. In a single-branch regime this is just a deformed correlated product, while in a multibranch regime a state specified only by $P_μ$ can be expanded into distinct auxiliary branches. If $P_μ$ are taken as the directly meaningful momenta, the physical content is the resulting deformed correlation pattern. If the auxiliary variables $p_μ$ are assigned operational meaning, the same constrained state can be interpreted as a superposition over different auxiliary branches. We also compare this structure with standard regular self-adjoint nonrelativistic minimal-length models and find no analogous smooth local two-real-branch inversion on their physical domains.

Asteroid-neutron star collision models have been proposed as possible sources of high-energy transients, such as gamma-ray bursts (GRBs) and fast radio bursts (FRBs). The sequence of events following the impact of the asteroid and finally dissolving into the neutron star can have several other observable consequences. We propose that due to the development of the off-diagonal moment of inertia (MI) components, the merger's aftermath can lead to the wobbling of the pulsar (assuming the neutron star happens to be a pulsar). Using sample values of various parameters, viz., size, shape, the locations of the deposits, and the pre-existing pulsar deformation parameter ($η$), we calculate the detailed pulse profile modulation of the pulsar. We observe a distinct pattern of pulse profile modulation on a characteristic timescale enhanced by a factor of $1/η$ compared to the pulse timing. Importantly, even small changes in the MI components, of order $ε$, can produce large pulse profile modulations of order $ε/η$ (depending on the relative location of asteroid material deposition). Thus, if an asteroid-neutron star collision is responsible for a high-energy transient, the associated pulse profile modulation may serve as a falsifiable observational signature of such an event.

Andrews investigated parity conditions in the Rogers-Ramanujan-Gordon theorem. Under the conditions that even parts or odd parts appear an even number of times, Andrews discovered two Rogers-Ramanujan-Gordon type partition theorems and derived corresponding generating functions. In the Rogers-Ramanujan-Gordon theorem, there are two parameters $k$ and $a$, where $k-1$ is the maximum number of consecutive parts $l$ and $l+1$, and $a-1$ is the maximum number of parts equal to $1$. Andrews' first theorem deals with the case $k\equiv a \;(\rm{mod}\;2)$, while the second theorem concerns the case where $k$ is even and $a$ is odd. These two partition identities have different infinite product forms on the right-hand side. In this paper, we consider the case $k\not\equiv a \;(\rm{mod}\;2)$ and use Bailey's lemma to obtain an Andrews-Gordon type identity whose right-hand side coincides with that of Andrews' identity for the case $k\equiv a \;(\rm{mod}\;2)$. By fixing the number of peaks of the corresponding lattice paths, we also derive a recurrence system whose solution agrees with the product-side generating function. We were unable to find a suitable combinatorial interpretation of the infinite sum form of this expression in terms of partitions, but with the help of lattice paths, we provide an appropriate combinatorial interpretation. Finally, we prove this identity analytically by applying Bailey's lemma.

The FC-Gram algorithm constructs high-order trigonometric approximations of nonperiodic functions by periodically extending them to a larger interval, with the quality of the blending continuation of Gram polynomials over the extension interval directly governing the approximation accuracy. We introduce GenFC, a generalized FC-Gram framework in which the continuation of each Gram polynomial is shaped by a cutoff function satisfying prescribed boundary flatness conditions. We establish a convergence theorem showing that for any such family the GenFC approximation error satisfies $O(n^{-\min(r+β,\,d)})$ in the supremum norm on the original interval, where $f \in C^r([0,1])$ has an integrable $(r+1)$th derivative, $d$ is the number of Gram polynomials, and $β\in [0,1]$ is the Fourier decay exponent of $f^{(r+1)}$. The modified FC-Gram algorithm, recently introduced by the authors, is recovered as a special case, and several explicit families satisfying these conditions are constructed in the paper. Numerical experiments across smooth, limited-regularity, and rapidly oscillating test cases confirm the theoretical predictions. The framework is further applied to high-order solvers for linear ODEs and parabolic PDEs via backward differentiation formulae (BDF) time-stepping, demonstrating high-order accuracy throughout.

We present the first combined oscillation analysis of multiple atmospheric neutrino datasets, featuring data from Super-Kamiokande, IceCube-DeepCore, and KM3NeT/ORCA together with reactor data from Daya Bay. Such combinations have long been considered infeasible outside experimental collaborations; we demonstrate that a unified physics model can simultaneously describe all datasets with no significant parameter tensions. Fitting 839048 events across 1536 bins with 91 parameters, our combined analysis yields competitive measurements of the neutrino mixing parameters, and prefers the Normal over the Inverted Mass Ordering.

We study nearest zero--critical point distances for the Gaussian SU(2) polynomial $p_n$. For each zero $z_i$ of $p_n$, let $D_n(z_i)$ denote its Fubini--Study distance to the nearest critical point. We prove that the empirical measure of the rescaled distances $nD_n(z_i)$ converges weakly in probability to the law of $2/|Z|$, where $Z$ is distributed according to the Fubini--Study probability measure in the affine chart.

Multimodal generative retrieval formulates multimodal retrieval as discrete identifier generation, eliminating the need for explicit similarity search over external embeddings. Existing approaches construct identifiers via residual quantization and decode them with trie-constrained beam search. This combination introduces an indexing-decoding gap: identifier learning objectives, including reconstruction and contrastive losses, do not explicitly enforce prefix discriminability during decoding. As a result, even well-optimized identifiers can be irreversibly pruned early in beam search due to low-rank prefixes. We theoretically characterize this gap and derive a survival bound that relates prefix retention to three controllable factors in indexing and decoding. Building on this bound, we propose PRO, prefix retention optimization, a unified framework comprising three mechanisms: (i) prefix ranking distillation aligns quantized prefix rankings with those induced by pre-quantization embeddings using a listwise loss; (ii) vocabulary scheduling increases codebook sizes from shallow to deep residual quantization levels to reduce early competition from non-target prefixes; and (iii) geometric score fusion vectorizes each candidate prefix and incorporates its similarity to the query into beam search scoring, further reducing the indexing-decoding mismatch. Experiments on nine multimodal retrieval tasks show that PRO improves retention of target identifier prefixes and outperforms existing multimodal generative retrieval baselines.

In this paper, we complete the explicit description of rational nilpotent orbits in wavefront sets for irreducible representations of classical groups over a finite field $\mathbbm{k}$ of characteristic $p>3(h-1)$, where $h$ is the Coxeter number of the corresponding root system. In particular, for unipotent representations, we describe the rational nilpotent orbits in their wavefront sets in terms of Lusztig canonical quotients and establish the Alvis-Curtis duality for these orbits. Our approach is based on the descent method. We develop a new composition law for nilpotent orbits, which reduces the computation of rational wavefront sets to the finite Gan-Gross-Prasad problem established in our previous work.

We study Fourier coefficients of the Siegel-Eisenstein series $E_{m+1}^{(2m)}(Z)$ for $m\equiv1\pmod4$ and $m\geq5$. Using the Fourier expansion formula due to Mizumoto, we determine the constant term and the exceptional non-zero Fourier coefficients. The result gives a higher-degree analogue of the degree-two formulas of Kohnen and Nagaoka, which were later rederived by Haruki.

We develop a unified framework for linear-cost optimal control, finite-time optimal steering, dissipativity analysis, and zero-sum differential games for linear impulsive systems whose state is a Hermitian matrix evolving in $\mathbb{H}^{n+m}_{\succeq0}$, a class that encompasses continuous- and discrete-time linear systems and switched systems as degenerate cases, and includes the second-order moment dynamics of linear (stochastic) hybrid systems. The entire theory rests on three tools: a single \emph{key identity} relating cost, trajectory, and a dual variable, an Extended Schur complement lemma, and a Schur inner-product decomposition, applied identically to the flow integral and to each jump. These yield structurally uniform sufficient and necessary conditions, dual linear matrix inequality (LMI) characterizations, and explicit optimal policies for every problem class, on both finite and infinite horizons under time-varying assumptions (without time invariance or periodicity), together with causal dwell-time policies for the problems that admit them.

Large Language Models (LLMs) are rapidly reshaping academic research across the natural sciences, social sciences, and humanities, yet the scientific community lacks a comprehensive, cross-disciplinary account of how these tools are being integrated, what they deliver, and where they fall short. This paper addresses that gap by mapping their current state and outlining an agenda for their responsible integration into scientific research. Our analysis reveals a consistent pattern: LLMs meaningfully accelerate research workflows -- from hypothesis generation and literature synthesis to data analysis and scientific writing -- while introducing serious challenges related to hallucination, reproducibility, dataset bias, and model opacity. Beyond technical limitations, we identify ten underexplored challenges, including the erosion of researcher autonomy, AI-driven confirmation bias, authorship ambiguity, and unequal access to these technologies -- systemic risks that demand interdisciplinary governance frameworks, robust validation standards, and expanded explainability research.

Generative retrieval (GR) has become a scalable approach to candidate generation: each item is assigned a short hierarchical token sequence called a Semantic ID (SID), and the next item's SID is decoded autoregressively. A practical limitation is that the decoder's beam search optimizes the likelihood of token sequences, not the relevance of the underlying items. These objectives diverge when sequence likelihood is poorly calibrated due to beam search error accumulation, and when several items collapse onto a single SID and receive identical scores. We introduce Gryphon, an encoder-decoder generative recommendation architecture that adds a jointly trained item-level scoring component alongside SID generation, reusing the encoder's user representation computed in a single forward pass. Instead of ranking SIDs by accumulated token likelihood, Gryphon resolves each generated SID to its concrete items and re-scores those items directly, which sidesteps miscalibrated sequence scores and separates items that collide on the same identifier. On an industrial music service, with item-level scoring trained under a next-item-prediction objective, Gryphon attains the highest item-level Recall@1000, above the strongest baselines (+3.7% over vanilla GR and +2.5% over collision-resolved GR) at comparable parameter count and latency. Gryphon's item-level ranking also surpasses its beam-likelihood ranking of the same candidates (+4.2% gain), demonstrating the benefit of item-level scoring in GR. Deployed as the sole candidate source in a 7-day A/B test, Gryphon produced no statistically significant change in total listening time (+0.25%) while replacing a pipeline of more than 15 candidate generators and a separate preranking stage, substantially simplifying the candidate-generation system.

This note is written for the special issue of OSID dedicated to the 50th anniversary of the Gorini--Kossakowski--Lindblad--Sudarshan equation. Its purpose is not to give a comprehensive historical review, but rather to reconstruct, in a self-contained way, one logical route leading to the GKLS generator. The emphasis is placed on Kossakowski's structural insight: the combination of Markovianity, complete positivity, trace preservation, and the infinitesimal structure of quantum dynamical semigroups naturally leads to the celebrated form of the generator. I also include a few personal recollections of Andrzej Kossakowski, under whose guidance I spent one year as a postdoctoral researcher in 2003--2004.

Real-world asset (RWA) tokenization has emerged as a prominent application of blockchain technology, enabling off-chain financial and non-financial assets to be represented through blockchain-based instruments. However, deployed RWA systems remain difficult to compare because legal claims, custody arrangements, token mechanics, verification processes, and on-chain integrations are often described separately. This paper develops a systems-level taxonomy of RWA tokenization to classify how off-chain assets are legally, economically, and technically represented on-chain. Following an iterative taxonomy-development method, we organize twenty-three dimensions into five components: governance, asset structure, token properties, distributed ledger technology, and economy. We apply the taxonomy to twenty major RWA systems selected by market capitalization and compare their design choices across asset classes and implementation models. The classification shows that current RWA tokenization is predominantly implemented through hybrid architectures: blockchain tokens support representation, transfer control, redemption workflows, pricing, and composability, while core legal guarantees remain anchored in off-chain legal wrappers, custodial arrangements, compliance processes, and verification mechanisms. The analysis also reveals recurring documentation gaps concerning voting rights, dispute forums, burn mechanics, supply constraints, and reserve verification. Overall, the taxonomy provides a structured basis for comparing RWA systems, identifying design patterns and limitations, and supporting future research on blockchain-based financial infrastructure.

In this paper, we study the nested Hilbert scheme $(\mathbb{A}^2)^{[n,n+1]}=\mathrm{Hilb}^{n,n+1}(\mathbb{A}^2)$ from a combination of deformation theory, torus actions, and Young diagram combinatorics. We first recall the scheme theory and functor basics needed to define Hilbert schemes. We then use a classic result on first-order deformations to identify $T_I(\mathbb{A}^2)^{[n]}\cong \mathrm{Hom}_{\mathbb{C}[x,y]}(I,\mathbb{C}[x,y]/I)$. For a nested pair $I\subset J$, with $\dim_{\mathbb{C}}\mathbb{C}[x,y]/I=n+1$ and $\dim_{\mathbb{C}}\mathbb{C}[x,y]/J=n$, the tangent space becomes a compatibility kernel $T_{(I,J)}(\mathbb{A}^2)^{[n,n+1]}\cong \ker(\mathrm{Hom}(I,R/I)\oplus \mathrm{Hom}(J,R/J)\to \mathrm{Hom}(I,R/J))$. The torus-fixed points are indexed by a partition $λ\vdash n+1$ together with a removable corner $c$ of its Young diagram. This corner is not only combinatorial, but also the monomial form of a one dimensional socle direction in $R/I_λ$. The blow-up map to $(\mathbb{A}^2)^{[n]}\times \mathbb{A}^2$ has fibres given by projective spaces of one-dimensional quotients of $J/\mathfrak m_pJ$, whose torus-fixed points are addable boxes of the smaller diagram. These two local fibres explain how the universal family, the blow-up geometry, and Young diagram combinatorics come together in the study of the local geometry of the nested Hilbert scheme of points. Finally, we derive the tangent weight formula at a fixed point $(I_λ,I_{λ\setminus c})$ in the torus convention used in the paper. Using the standard arrow basis, we show in the proof how the arm-leg weights are modified by the compatibility kernel through a shortening rule determined by $c$. A Macaulay2 verification computes the compatibility kernel from monomial syzygies and checks the weight formula for all partitions of size at most $16$.

Path planning in environments containing obstacles has numerous practical applications. The problem is challenging because it is inherently nonlinear and nonconvex. Consequently, a variety of techniques have been developed to address this problem, among which machine learning and optimal control (or optimization) have emerged as two prominent approaches. In general, machine learning methods do not require a high-fidelity model, and a trained agent can often generate a feasible path in real time. However, the resulting path is not necessarily optimal with respect to performance objectives such as minimizing path length or travel time. In contrast, optimal control and optimization methods typically rely on high-fidelity models and often require computational effort that may not satisfy real-time constraints. Nevertheless, these methods are more likely to produce optimal or near-optimal solutions. To overcome the limitations of each approach while exploiting their respective strengths, this paper proposes a framework that combines reinforcement learning with an arc-search interior-point method for path planning. Numerical simulations demonstrate that the proposed approach effectively integrates the real-time decision-making capability of reinforcement learning with the optimization performance of the arc-search interior-point method, resulting in improved path-planning performance.

Nonspherical gas bubble dynamics in viscoelastic materials influence the stress transmission and energy dissipation of their surroundings and are difficult to predict. Their accurate prediction is essential in applications ranging from biomedical procedures to high-strain-rate rheological measurements. However, existing models do not sufficiently capture the nonspherical rotational dynamics. We formulate and superpose a rotational contribution to the perturbed deformation with a potential contribution. Linearised forward and inverse coordinate maps are formulated based on the deformation field which are used to compute velocities, accelerations, and stresses. The addition of the rotational degree of freedom satisfies the momentum balance equations and stress continuity at the bubble surface. The material surrounding the bubble is modelled with a Kelvin-Voigt constitutive model with Newtonian viscosity and quadratic strain-stiffening neo-Hookean elasticity. The model agrees with previous viscous fluids models when elastic effects are neglected and radial oscillations are small. When viscous effects are small relative to elastic, shear waves radiate from the bubble surface into the material. The resulting strain energy is delocalised and increases damping of the perturbation amplitude in time relative to potential-based models. We show agreement between the stability of the shape modes with previous ultrasound forced experiments and temporal evolution of different shape modes with previous laser-induced cavitation experimental data.

Coupling identical quantum emitters to a photonic integrated circuit (PIC) is a key step for scaling up emitter-photon interfaces for quantum science and information processing. Neutral atoms are attractive candidates due to their indistinguishability and controllability. However, experimental realizations of efficient atom trapping on a PIC while achieving strong single atom-photon coupling has so-far remained elusive. Here, we demonstrate near-deterministic single-atom loading on a microring resonator circuit, reaching single-atom cooperativity parameter C > 1 for strong coupling in cavity quantum electrodynamics. We utilize a precision optical conveyor belt, formed by a moving optical lattice in an optical tweezer, to steadily deliver trapped atoms onto a PIC. By continuously monitoring the transmission of probe photons through the circuit, which is sensitive to the proximity of single atoms near a microring resonator, we detect mean occupancy of 1.5 from 70 occupied lattice sites in a conveyor-belt transport of 4 nm position reproducibility. Based upon real-time feedback, we deterministically transfer the delivered atoms into a stationary trap on the microring, achieving 82% (18%) probability of single-(two-)atom transfer. Our technique can be extended to deterministic, highly efficient atom array assembly, providing a scalable route for neutral atom integration with PICs of complex functionalities.

The win ratio is increasingly used to analyze prioritized composite endpoints in clinical trials, but standard implementations rely on deterministic pairwise comparisons and can perform poorly in the presence of censoring and endpoint-specific missingness. In such settings, unresolved comparisons are often treated as ties, leading to loss of efficiency and potentially biased inference, particularly when lower-priority outcomes are incompletely observed. We propose the probabilistic win ratio (PWR), a framework for estimating the classical win ratio under coarsened observation. The PWR replaces deterministic pairwise decisions with conditional probabilities of win, loss, or tie given the observed data, allowing partially observed comparisons to contribute fractionally while being explicitly penalized according to their uncertainty. Comparisons with greater coarsening receive smaller effective weight, whereas fully observed comparisons contribute as in the classical analysis, preserving the clinical priority structure. When outcomes are fully observed, the PWR reduces exactly to the standard win ratio estimator. Simulation studies show that the PWR maintains low bias and mean squared error across a range of censoring and missingness scenarios. Two clinical trial case studies illustrate complementary data regimes, demonstrating calibration in near-complete data and stability under substantial right censoring.

Heisenberg's uncertainty principle is a cornerstone of quantum mechanics, marking a decisive departure from classical physics. Conceived almost a century ago through a thought experiment showing that measuring an electron's position inevitably disturbs its momentum, it began as a deceptively simple idea that sparked countless studies and grew into the rich research field it is today. This review traces its development into a spectrum of mathematical formulations -- known as uncertainty relations -- and explores their interconnections and wide-ranging applications. We highlight its central role in quantum metrology, where it underpins strategies for extracting information from quantum systems with ever-increasing precision, and its links to multiparameter estimation and squeezed states. This review, dedicated to the centenary of the uncertainty principle, reflects on how it has deepened our understanding of quantum theory and driven practical advances, and looks ahead to a century poised for further surprising and transformative discoveries.

The ability to estimate and predict pathogen variant dynamics can inform public health responses, including planning for increased transmission or severity, shifts in population immunity, or changes to vaccine or therapeutic effectiveness. The COVID-19 pandemic demonstrated the importance of monitoring SARS-CoV-2 variant evolution through viral genome sequencing, enabling predictive models to estimate variant frequencies in the recent past, present, and short-term future. Collaborative forecasting Hubs provided a valuable way to centralize predictive modeling of epidemiological indicators such as cases, hospitalizations, and deaths during the pandemic; however, none existed for variant dynamics. Here, we discuss the creation of the United States SARS-CoV-2 Variant Nowcast Hub, designed to solicit estimates of the relative abundance of a specified set of SARS-CoV-2 variants at the U.S. state level. We discuss the design decisions and challenges in building the Hub and its scoring procedures. Using submissions from the Hub's first respiratory virus season (nowcast dates October 9th, 2024 to June 4th, 2025), we evaluate five individual models and a baseline model. We found that the baseline model, which pools sequences across the U.S., performs well overall, with most individual models performing similarly or slightly worse. Locations with lower sequencing volumes exhibited greater variability in model performance. Models submitted for a single location outperformed those submitted for all locations, potentially due to greater timeliness and magnitude of local data. Much remains to be investigated regarding relative model performance across different phases of variant emergence, and we conclude by proposing future directions within and beyond this Hub.

AI coding agents such as Claude Code and Gemini CLI increasingly extend themselves with third-party skills: markdown packages bundling natural-language instructions, executable scripts, and tool permissions. Because a skill is at once code and agent-facing instruction, it introduces a supply chain dependency whose risk is neither pure code nor pure prompt. Detection tools have never been measured against verified ground truth spanning this hybrid space, leaving their effectiveness unknown and wild-only evaluations biased. We present MalSkillBench, the first runtime-verified benchmark of malicious agent skills: 3,944 malicious skills labeled along a three-dimensional taxonomy of 108 cells. Of these, 3,214 come from a closed-loop Generate-Verify-Feedback pipeline admitting only samples whose malicious behavior fires inside a Docker sandbox under system-call monitoring and an LLM judge; we add 703 in-the-wild and 4,000 matched benign skills. Our measurements are consistent: code injection reaches 94.5% verification yield but prompt injection only 75.8%, the same fragility that later makes it hard to detect; the wild sample is narrow, dominated by one cryptocurrency-theft campaign (86.6% one behavior, 81% from two accounts) with a small but architecturally new tail attacking the agent control plane; the strongest skill-specific detector reaches 98.4% recall on code injection yet collapses on prompt-injection and agent-control attacks, and wild-only scoring swings the ranking by up to 66 recall points; supply-chain scanners and prompt-injection defenses each see only half of a skill, and no combination recovers the code-instruction relationship. Detecting malicious skills therefore requires reasoning jointly over task intent, code, and instructions. We release the dataset, pipeline, baselines, and results.

Understanding the dynamic evolution of opinions, such as U.S. public attitudes toward China, is essential for assessing geopolitical risks. However, existing LLM-based multiagent simulators predominantly rely on static rules and fixed datasets, limiting their ability to capture the dynamic, event-driven nature of macro-level opinion shifts in real-world settings. To address this limitation, we propose an Event-Steered Multi-Agent Simulator (ES-MAS), in which significant events and daily news continuously drive opinion evolution through dynamic interactions among agents. We first construct the China-U.S. Relation Evolution (CURE) dataset, covering 20 quarters from 2021 to 2025, including 258 major events and over 14,000 daily news articles, and providing a comprehensive temporal foundation for modeling opinion dynamics. Building upon the CURE dataset, we propose a Dual-Stream Data Integration Engine (DSDIE) that aligns simulations with historical timelines via macro-level events while enabling personalized information exposure based on individual agent profiles and contextual signals. Furthermore, we design a News-Driven Dynamic Interaction (NDDI) module, which adaptively groups agents with shared news interests into localized interaction contexts, facilitating bottom-up consensus formation while mitigating the risk of isolated information cocoons. Experimental results on the CURE dataset demonstrate that ES-MAS substantially outperforms existing simulators in reproducing real-world historical trends, offering a scalable and effective framework for modeling dynamic opinion evolution.

In this paper, we develop a Constraint Energy Minimizing Generalized Multiscale Finite Element Method (CEM-GMsFEM) for solving linear elasticity problems in heterogeneous perforated domains. The presence of numerous perforations introduces multiple scales into the computational domain, making direct fine-grid simulations computationally expensive. The proposed method follows the standard offline--online decomposition of CEM-GMsFEM. In the offline stage, local spectral problems are solved on coarse elements to construct auxiliary spaces, and localized energy-minimizing basis functions are then computed on oversampled regions to capture fine-scale geometric information induced by the perforations. In the online stage, residual-driven basis functions are constructed in enlarged coarse neighborhoods to incorporate source-term information and improve the accuracy of the multiscale approximation adaptively. We establish convergence results for both the offline and online stages. In particular, we derive error estimates for the localized multiscale approximation and prove the convergence of the adaptive online enrichment algorithm. Moreover, we show that the oversampling regions used in the online stage can be determined locally, leading to a reduction in computational cost while maintaining convergence properties. Numerical experiments on perforated media with different geometric configurations demonstrate the accuracy and efficiency of the proposed method.