Published: 2026-04-19 03:49:38
Authors: Jiaqi Zhao, Fengwei Wang
Categories: cs.CR
Abstract:
In the 47th IEEE Symposium on Security and Privacy (IEEE S&P 2026), Gao et al. proposed an efficient and user-friendly secure transformer inference framework, namely Euston. In Euston, a singular value decomposition-based matrix transmission protocol is designed to efficiently transmit input matrices, reducing communication bandwidth by approximately 2.8 times. In this manuscript, we show that this transmission protocol introduces subspace leakage of random masks, enabling the model owner to recover private samples easily. We further validate the effectiveness of the recovery attack through simple experiments on image and language datasets, highlighting a fundamental privacy risk of the protocol design.
Published: 2026-04-19 03:39:37
Authors: Hui Gao, Xinming Wu, Jintao Li, Xiaoming Sun, Jiarun Yang
Categories: physics.geo-ph
Abstract:
Seismic stratigraphic interpretation of shelf-edge clinothems is essential for revealing tectonic evolution, paleoclimate change, depositional dynamic conditions, and hydrocarbon generation and accumulation during basin filling. However, traditional interpretation methods remain labor-intensive, time-consuming, and highly subjective. Although AI-based method offer a potential solution for automated this task, its development has been limited by the scarcity of comprehensive and representative benchmark datasets for shelf-edge clinothems. This limitation primarily arises from limited field data availability, the scarcity of reliable geological labels, and the structural complexity and strong variability of clinothem-dominated systems. To address this gap, we develop a hybrid benchmark dataset through two complementary strategies of field data curation and geological and geophysical forward modeling, ultimately generating 3,000 unlabeled field and 4,000 labeled synthetic seismic data, respectively. We further evaluate several representative baseline deep learning models on these datasets, and the accurate results demonstrate that the curated dataset provides an effective and representative basis for model training, quantitative assessment, and practical application. Finally, we have publicly released this hybrid benchmark dataset (https://doi.org/10.5281/zenodo.18910271) to facilitate the development, validation, and assessment of deep learning methods for automated seismic stratigraphic interpretation.
Published: 2026-04-19 03:31:16
Authors: Dongxiao Zhao, Qiang Zhang
Categories: math.GR
Abstract:
Let $F \ast G$ be a free product of a free group $F$ and a LERF group $G$. In this note, we provide sufficient conditions for a subgroup $H$ of $F \ast G$ to be $\mathcal{A} \cup \mathcal{S}$-separable, that is, for any finite set $\{γ_1, \ldots, γ_n\} \subset (F \ast G) \setminus H$, there is a surjection $f$ from $F \ast G$ to an alternating or symmetric group such that $f(γ_i) \notin f(H)$ for all $i$. As a corollary, any finitely generated infinite-index subgroup of a free group is $\mathcal{A} \cup \mathcal{S}$-separable in the free product of the free group and an arbitrary LERF group, generalizing a result of Wilton.
Published: 2026-04-19 03:31:02
Authors: Badrinath Balasubramaniam, Vignesh Suresh, Benjamin Metcalf, Beiwen Li
Categories: cs.CV, cs.RO
Abstract:
Unrecovered e-waste represents a significant economic loss. Hard disk drives (HDDs) comprise a valuable e-waste stream necessitating robotic disassembly. Automating the disassembly of HDDs requires holistic 3D sensing, scene understanding, and fastener localization, however current methods are fragmented, lack robust 3D sensing, and lack fastener localization. We propose an autonomous vision pipeline which performs 3D sensing using a Fringe Projection Profilometry (FPP) module, with selective triggering of a depth completion module where FPP fails, and integrates this module with a lightweight, real-time instance segmentation network for scene understanding and critical component localization. By utilizing the same FPP camera-projector system for both our depth sensing and component localization modules, our depth maps and derived 3D geometry are inherently pixel-wise aligned with the segmentation masks without registration, providing an advantage over RGB-D perception systems common in industrial sensing. We optimize both our trained depth completion and instance segmentation networks for deployment-oriented inference. The proposed system achieves a box mAP@50 of 0.960 and mask mAP@50 of 0.957 for instance segmentation, while the selected depth completion configuration with the Depth Anything V2 Base backbone achieves an RMSE of 2.317 mm and MAE of 1.836 mm; the Platter Facing learned inference stack achieved a combined latency of 12.86 ms and a throughput of 77.7 Frames Per Second (FPS) on the evaluation workstation. Finally, we adopt a sim-to-real transfer learning approach to augment our physical dataset. The proposed perception pipeline provides both high-fidelity semantic and spatial data which can be valuable for downstream robotic disassembly. The synthetic dataset developed for HDD instance segmentation will be made publicly available.
Published: 2026-04-19 03:27:24
Authors: Ramkishor Sharma, Samarth Majumdar, Divya Sachdeva
Categories: astro-ph.CO, hep-ph
Abstract:
Recently, a mechanism for generating astrophysically relevant magnetic fields via ultralight pseudoscalar dark matter, through the coupling term $g_{φγ} φF_{μν}\tilde{F}^{μν}$ in the Lagrangian density, was proposed in Brandenberger et al (2026) (see Ref. 1). In this scenario, the electromagnetic fields are amplified through the phenomena of parametric resonance due to the oscillatory behaviour of the pseudoscalar field. However, the analysis presented in that work does not account for the effects of a conducting medium. In this paper, we incorporate the finite conductivity of the plasma into the dynamics of the pseudoscalar and electromagnetic fields. We show that, due to the large conductivity relative to the Hubble parameter, the amplification of the electromagnetic fields due to parametric resonance is significantly suppressed. Consequently, we find that, for observationally viable values of the coupling between the electromagnetic field and the ultralight pseudoscalar field, it is not possible to generate magnetic fields of sufficient strength to explain their presence in cosmic voids.
Published: 2026-04-19 03:20:40
Authors: Qingwei Lin
Categories: cs.LG
Abstract:
Conditional depth execution routes a subset of tokens through a lightweight cheap FFN while the remainder execute the standard full FFN at each controlled layer. The central difficulty is gate training: the gate decision must propagate through many layers before it influences the language modeling (LM) loss, so the resulting gradients are weak and noisy. Auxiliary losses are commonly stacked to stabilise training, yet the interactions among them -- particularly between a predictive auxiliary and explicit score supervision -- have not been systematically compared under controlled conditions.
We evaluate two gate designs under a 157.5M-parameter decoder-only model with controller-only training, 50% full-path budget, and 3-seed runs on a fineweb-edu subset. The MLP gate (G1) maps the current hidden state to a utility score; the JEPA-guided gate (G3) adds an action-conditional predictor that forecasts, in a low-dimensional latent space, the outcome of executing full vs. cheap per token, aligned against a fixed target head. Under the standard recipe with oracle-style utility regression and pairwise rank supervision (util/rank), G3 improves early-to-mid optimisation over G1 in 3/3 seeds (lower avg LM, faster threshold hits, ~10.3x lower grad norms), with 20k-step endpoint LM within a 0.005 heuristic reference.
A key finding (ablation A3): jointly removing util/rank improves best/avg LM and threshold-hit speed in 3/3 seeds for both gates, and the early-to-mid advantage of G3 over G1 disappears. We trace this to an off-policy oracle label that assumes all subsequent layers execute full, whereas gated execution routes only a fraction through full -- making util/rank net-negative under the current recipe. Removing util/rank also cuts the training FLOPs proxy from ~1.53x to ~1.07x full-only (2.87h to 1.75h on a V100-32GB, ~39%). Conclusions are scoped to the studied regime.
Published: 2026-04-19 03:06:24
Authors: Zihao Zhang
Categories: math.AP
Abstract:
We address the existence and stability of transonic shocks for the two-dimensional steady rotating Euler system in an almost flat nozzle. Under the influence of the Coriolis force, we first establish a class of special transonic shock solutions in a flat nozzle, whose states depend on the vertical variable. It is shown that these solutions exist if and only if the upstream Mach number satisfies certain conditions, while the shock position is arbitrary. We then determine the shock position and establish the existence of the transonic shock solution under small perturbations of the incoming supersonic flow, the exit pressure, and the upper nozzle wall. The problem is formulated as a free boundary problem for a hyperbolic-elliptic mixed nonlinear system. We decompose the hyperbolic and elliptic modes in terms of the deformation and vorticity, and analyze the solvability condition to determine the admissible shock positions. Starting from the obtained initial approximation of the shock solution, a nonlinear iteration scheme can be constructed to derive a transonic shock solution in which the shock front is close to the initial approximating position.
Published: 2026-04-19 03:03:25
Authors: Jiuyun Jiang, Yuecheng Hong, Bo Yang, Jin Yang, Guangxin Jiang, Xiaomeng Guo, Guang Xiao
Categories: cs.MA, cs.AI
Abstract:
Modeling coordination among generative agents in complex multi-round decision-making presents a core challenge for AI and operations management. Although behavioral experiments have revealed cognitive biases behind supply chain inefficiencies, traditional methods face scalability and control limitations. We introduce a scalable experimental paradigm using Large Language Models (LLMs) to simulate multi-stage supply chain dynamics. Grounded in a Hierarchical Reasoning Framework, this study specifically analyzes the impact of cognitive heterogeneity on agent interactions. Unlike prior homogeneous settings, we employ DeepSeek and GPT agents to systematically vary reasoning sophistication across supply chain tiers. Through rigorously replicated and statistically validated simulations, we investigate how this cognitive diversity influences collective outcomes. Results indicate that agents exhibit myopic and self-interested behaviors that exacerbate systemic inefficiencies. However, we demonstrate that information sharing effectively mitigates these adverse effects. Our findings extend traditional behavioral methods and offer new insights into the dynamics of AI-enabled organizations. This work underscores both the potential and limitations of LLM-based agents as proxies for human decision-making in complex operational environments.
Published: 2026-04-19 02:50:14
Authors: Nwe Ni Win, Jim Basilakis, Steven Thomas, Seyhan Yazar, Laura Pierce, Stephanie Liu, Paul M. Middleton, Nasser Ghadiri, X. Rosalind Wang
Categories: cs.AI
Abstract:
Extracting clinically relevant information from unstructured medical narratives such as admission notes, discharge summaries, and emergency case histories remains a challenge in clinical natural language processing (NLP). Medical Entity Recognition (MER) identifies meaningful concepts embedded in these records. Recent advancements in large language models (LLMs) have shown competitive MER performance; however, evaluations often focus on general entity types, offering limited utility for real-world clinical needs requiring finer-grained extraction. To address this gap, we rigorously evaluated the open-source LLaMA3 model for fine-grained medical entity recognition across 18 clinically detailed categories. To optimize performance, we employed three learning paradigms: zero-shot, few-shot, and fine-tuning with Low-Rank Adaptation (LoRA). To further enhance few-shot learning, we introduced two example selection methods based on token- and sentence-level embedding similarity, utilizing a pre-trained BioBERT model. Unlike prior work assessing zero-shot and few-shot performance on proprietary models (e.g., GPT-4) or fine-tuning different architectures, we ensured methodological consistency by applying all strategies to a unified LLaMA3 backbone, enabling fair comparison across learning settings. Our results showed that fine-tuned LLaMA3 surpasses zero-shot and few-shot approaches by 63.11% and 35.63%, respectivel respectively, achieving an F1 score of 81.24% in granular medical entity extraction.
Published: 2026-04-19 02:47:53
Authors: Zhuo Ouyang, Jixian Liu, Enrique Mallada
Categories: math.OC, eess.SY, stat.ML
Abstract:
Inductive bias refers to restrictions on the hypothesis class that enable a learning method to generalize effectively from limited data. A canonical example in control is linearity, which underpins low sample-complexity guarantees for stabilization and optimal control. For general nonlinear dynamics, by contrast, guarantees often rely on smoothness assumptions (e.g., Lipschitz continuity) which, when combined with covering arguments, can lead to data requirements that grow exponentially with the ambient dimension. In this paper we argue that data-efficient nonlinear control demands exploiting inductive bias embedded in nature itself, namely, structure imposed by physical laws. Focusing on Hamiltonian systems, we leverage symplectic geometry and intrinsic recurrence on energy level sets to solve target reachability problems. Our approach combines the recurrence property with a recently proposed class of policies, called chain policies, which composes locally certified trajectory segments extracted from demonstrations to achieve target reachability. We provide sufficient conditions for reachability under this construction and show that the resulting data requirements depend on explicit geometric and recurrence properties of the Hamiltonian rather than the state dimension.
Published: 2026-04-19 02:43:00
Authors: Yu Zhang, Kaiyuan Shen, Yang Li
Categories: cs.CV
Abstract:
We present EmbodiedHead, a speech-driven talking-head framework that equips LLMs with real-time visual avatars for conversation. A practical embodied avatar must achieve real-time generation, unified listening-speaking behavior, and high rendered visual quality simultaneously. Our framework couples the first Rectified-Flow Diffusion Transformer (DiT) for this task with a differentiable renderer, enabling diverse, high-fidelity generation in as few as four sampling steps. Prior listening-speaking methods rely on dual-stream audio, introducing an interlocutor look-ahead dependency incompatible with causal user--LLM interaction. We instead adopt a single-stream interface with explicit per-frame listening-speaking state conditioning and a Streaming Audio Scheduler, suppressing spurious mouth motion during listening while enabling seamless turn-taking. A two-stage training scheme of coefficient-space pretraining and joint image-domain refinement further closes the gap between motion-level supervision and rendered quality. Extensive experiments demonstrate state-of-the-art visual quality and motion fidelity in both speaking and listening scenarios.
Published: 2026-04-19 02:14:29
Authors: Taha Saeed Khan, Hamidreza Nazaripouya
Categories: eess.SY
Abstract:
This paper establishes a sufficient condition for guaranteeing power flow solvability in distribution grids with inverter-based resources (IBRs) operating under IEEE 1547 compliant Volt-Var control. While designed to improve voltage profiles, reactive power injection can drive the system toward its operational limits. Under these stressed conditions, any further incremental reactive power injection can trigger voltage collapse, the point at which a power flow solution ceases to exist. In this paper, by leveraging a phasor-based voltage representation, the power flow equations with Volt-Var control are developed in the complex fixed point form, enabling a compact formulation and the rigorous application of fixed-point theorems. Addressing the challenges posed by the non-holomorphicity of the complex power flow equations due to the Volt-Var function's dependence on voltage magnitude, the solvability conditions are then developed using the Brouwer fixed-point theorem. The proposed conditions are validated through simulations on distribution test feeders, with a primary focus on their application to real-time decision-making for voltage regulation services.
Published: 2026-04-19 02:09:37
Authors: Kensuke Kamisoyama, Lento Nagano, Koji Terashi
Categories: quant-ph
Abstract:
Various classical machine learning models, including linear regression, kernel methods, and deep neural networks, exhibit double descent, in which the test risk peaks near the interpolation threshold and then decreases in the overparameterized regime. However, this phenomenon has received less attention in the quantum setting. In this work, we investigate the double descent phenomenon in quantum kernel ridge regression (QKRR). By applying deterministic equivalents from random matrix theory (RMT), we derive an asymptotic expression for the test risk of QKRR in the high-dimensional limit. Our analysis rigorously characterizes the interpolation peak and reveals how explicit regularization can effectively suppress it. We corroborate our theoretical results with numerical simulations, demonstrating close agreement even for finite-size quantum systems.
Published: 2026-04-19 02:04:14
Authors: Hongye Liu, Dhanajit Brahma, Ricardo Henao
Categories: cs.CL
Abstract:
Recent advances in summary evaluation are based on model-based metrics to assess quality dimensions, such as completeness, conciseness, and faithfulness. However, these methods often require large language models, and predicted scores are frequently miscalibrated, limiting their reliability. Moreover, evaluating the average quality across different summaries for a single document typically requires access to multiple reference summaries. Here, we propose a general framework that generates individual and average proxy scores without relying on reference summaries, human annotations, or expensive model-based metrics. We also propose group isotonic regression binning (GIRB), a calibration method that adjusts the raw predictions to better align with ground-truth evaluation metrics. While we focus on continuous-value scenarios, such as summarization, the method is applicable to discrete-value tasks, such as question answering. Experiments on seven datasets demonstrate that our approach consistently outperforms existing baselines.
Published: 2026-04-19 01:51:41
Authors: Junjia Huang, Binbin Yang, Pengxiang Yan, Jiyang Liu, Bin Xia, Zhao Wang, Yitong Wang, Liang Lin, Guanbin Li
Categories: cs.CV
Abstract:
Storyboard synthesis plays a crucial role in visual storytelling, aiming to generate coherent shot sequences that visually narrate cinematic events with consistent characters, scenes, and transitions. However, existing approaches are mostly adapted from text-to-image diffusion models, which struggle to maintain long-range temporal coherence, consistent character identities, and narrative flow across multiple shots. In this paper, we introduce DreamShot, a video generative model based storyboard framework that fully exploits powerful video diffusion priors for controllable multi-shot synthesis. DreamShot supports both Text-to-Shot and Reference-to-Shot generation, as well as story continuation conditioned on previous frames, enabling flexible and context-aware storyboard generation. By leveraging the spatial-temporal consistency inherent in video generative models, DreamShot produces visually and semantically coherent sequences with improved narrative fidelity and character continuity. Furthermore, DreamShot incorporates a multi-reference role conditioning module that accepts multiple character reference images and enforces identity alignment via a Role-Attention Consistency Loss, explicitly constraining attention between reference and generated roles. Extensive experiments demonstrate that DreamShot achieves superior scene coherence, role consistency, and generation efficiency compared to state-of-the-art text-to-image storyboard models, establishing a new direction toward controllable video model-driven visual storytelling.
Published: 2026-04-19 01:18:44
Authors: Weibing Zheng, Laurah Turner, Jess Kropczynski, Matthew Kelleher, Murat Ozer, Shane Halse
Categories: cs.SE, cs.AI, cs.ET, cs.HC, cs.MA
Abstract:
As Artificial Intelligence (AI) and Agentic AI become increasingly integrated across sectors such as education and healthcare, it is critical to ensure that Multi-Agent Education System (MAES) is explainable from the early stages of requirements engineering (RE) within the AI software development lifecycle. Explainability is essential to build trust, promote transparency, and enable effective human-AI collaboration. Although personas are well-established in human-computer interaction to represent users and capture their needs and behaviors, their role in RE for explainable MAES remains underexplored. This paper proposes a human-first, persona-driven, explainable MAES RE framework and demonstrates the framework through a MAES for clinical reasoning training. The framework integrates personas and user stories throughout the RE process to capture the needs, goals, and interactions of various stakeholders, including medical educators, medical students, AI patient agent, and clinical agents (physical exam agent, diagnostic agent, clinical intervention agent, supervisor agent, evaluation agent). The goals, underlying models, and knowledge base shape agent interactions and inform explainability requirements that guided the clinical reasoning training of medical students. A post-usage survey found that more than 78\% of medical students reported that MAES improved their clinical reasoning skills. These findings demonstrate that RE based on persona effectively connects technical requirements with non-technical medical students from a human-centered approach, ensuring that explainable MAES are trustworthy, interpretable, and aligned with authentic clinical scenarios from the early stages of the AI system engineering. The partial MAES for the clinical scenario simulator is~\href{https://github.com/2sigmaEdTech/MAS/}{open sourced here}.
Published: 2026-04-19 00:39:15
Authors: Khandoker Ashik Uz Zaman, Mahdi H. Miraz, Mohammed N. M. Ali
Categories: cs.CR, cs.AI, cs.NI
Abstract:
INTRODUCTION: The proliferation of the amalgamation of IoT and edge computing has increased the demand for decentralised trust and security mechanisms capable of operating across heterogeneous and resource-limited devices. Approaches such as federated learning, Zero Trust architectures, lightweight blockchain and distributed neural models offer alternatives to centralised control. OBJECTIVES: This review examines various state-of-the-art decentralised mechanisms and evaluates their effectiveness in terms of securing IoT networks at the edge. METHODS: Thirty recent studies were analysed to compare how decentralised architectures establish trust, support secure communication and enable intrusion and anomaly detection. Frameworks, such as DFGL-LZTA, SecFedDNN and COSIER were assessed. RESULTS: Decentralised designs enhance privacy, reduce single points of failure and improve adaptive threat response, though challenges remain in scalability, efficiency and interoperability. CONCLUSION: The study identifies key considerations and future research needs for building secure and resilient trust-aware IoT edge ecosystems.
Published: 2026-04-18 23:50:54
Authors: Danny Calegari, Ewain Gwynne
Categories: math.PR, gr-qc, math.GT, math.MG
Abstract:
A CaTherine wheel is a space-filling curve $f : S^1\to S^2$ such that for every closed interval $J\subset S^1$, $f(J)$ is homeomorphic to a closed disk and $f(\partial J)$ is contained in $\partial f(J)$. A CaTherine wheel gives rise to a pair of disjoint, dense topological trees in $S^2$ which roughly speaking lie to the left and right of $f$. We give necessary and sufficient conditions for a topological tree in $S^2$ to arise as one of these trees for some CaTherine wheel $f$. We apply this result to show that there is a unique CaTherine wheel corresponding to the geodesic tree rooted at $\infty$ for the $γ$-Liouville quantum gravity (LQG) metric, for $γ\in (0,2)$. In other words, we construct the space-filling curve which is the contour exploration of the LQG geodesic tree.
Published: 2026-04-18 22:52:51
Authors: Bo Ma, Weiqi Yan, Jinsong Wu
Categories: cs.CV
Abstract:
We propose PPEDCRF, a calibrated selective perturbation framework that protects \emph{background-based location privacy} in released video frames against gallery-based retrieval attackers. Even after GPS metadata are stripped, an adversary can geolocate a frame by matching its background visual cues to geo-tagged reference imagery; PPEDCRF mitigates this threat by estimating location-sensitive background regions with a dynamic conditional random field (DCRF), rescaling perturbation strength with a normalized control penalty (NCP), and injecting Gaussian noise only inside the inferred regions via a DP-style calibration rule.
On a controlled paired-scene retrieval benchmark with eight attacker backbones and three noise seeds, PPEDCRF reduces ResNet18 Top-1 retrieval accuracy from 0.667 to $0.361\pm0.127$ at $σ_0=8$ while preserving $36.14\,$dB PSNR -- an ${\approx}6\,$dB quality advantage over global Gaussian noise. Transfer across the eight-backbone seed-averaged benchmark is broadly supportive (23 of 24 backbone-gallery cells show negative $Δ$), while appendix-scale confirmation identifies MixVPR as a remaining adverse-transfer exception. Matched-operating-point analysis shows that PPEDCRF and global Gaussian noise converge in Top-1 privacy at equal utility, so the practical benefit is spatially concentrated perturbation that preserves higher visual quality at any given noise scale rather than stronger matched-utility privacy. Code: https://github.com/mabo1215/PPEDCRF
Published: 2026-04-18 22:37:05
Authors: Kritti Sharma, Elisabeth Krause, Vikram Ravi, Liam Connor, Dhayaa Anbajagane, Pranjal R. S
Categories: astro-ph.CO, astro-ph.GA, astro-ph.HE
Abstract:
Complex astrophysical processes regulate the growth of galaxies by injecting energy and momentum into their surroundings, redistributing baryons across megaparsec scales. The clustering of matter on these scales, as measured via weak lensing and galaxy surveys, encodes critical cosmological information on the dynamical dark energy, the nature of dark matter and the sum of neutrino masses. The suppression of matter clustering due to feedback processes limits the interpretation of cosmological measurements. Multiple probes of the baryon distribution have attempted to quantify the strength of feedback via measurements of suppression in the matter power spectrum. The dispersion measures (DMs) of fast radio bursts (FRBs) have emerged as a powerful new probe of baryons, with the advantage over other probes of being unbiased with respect to density and temperature. Here, we use a sample of 109 FRBs with redshifts and DMs to directly measure the spatial fluctuations in the baryon density field, quantifying the effects of feedback on the matter power spectrum at scales of $k \sim 0.1-3$ h/Mpc, and the gas fraction in galaxy groups and clusters ($10^{13}-10^{15} M_\odot$). We use a halo-model prescription to conduct inference, and find that FRB data reduces the posterior variance at k $\sim$ 1 h/Mpc by a factor of $\sim 8$ relative to the prior. The statistical precision of inferred FRB constraints is similar to other baryon tracers, while probing a complementary redshift regime ($z \lesssim 0.3$). A comparison with several hydrodynamical simulations excludes extreme large-scale feedback scenarios at $\sim 2σ$ confidence. This work establishes FRBs as a sensitive probe of feedback-regulated structure formation. As next-generation experiments deliver orders-of-magnitude larger samples, FRBs are poised to drive the constraints on baryonic physics in the era of precision cosmology.
Published: 2026-04-18 22:10:47
Authors: Yijun Wang, Mihai Bâce, Maria Torres Vega
Categories: cs.HC, cs.LG
Abstract:
The occurrence of cybersickness in virtual reality (VR) significantly impairs users' perception and sense of immersion. Therefore, timely detection of cybersickness and the application of appropriate intervention strategies are crucial for enhancing the user experience. However, existing cybersickness detection methods often suffer from issues such as poor detection reliability across different levels of cybersickness and unnecessary model complexity. Furthermore, while cybersickness exhibits significant inter-user variability, most existing approaches aggregate all data from users and lack user-specific solutions. In this paper, we investigate a lightweight approach for cybersickness detection incorporating an ensemble learning model and user-specific eye and head tracking data. Our experiments using the open-source dataset Simulation 2021 demonstrate that feature engineering and training set construction are critical for determining detection performance. Models trained with data from similar-content segments achieve the best results, attaining detection accuracies of 93% in the cross-user setting and 88% in the user-personalized setting, using only 23-dimensional eye and head features. Moreover, by using user-specific data, well-tuned ensemble learning models with shorter training and inference times can be feasibly applied to real-world cybersickness detection, offering superior time efficiency and outstanding detection performance. This work offers useful evidence toward the development of lightweight and user-adaptive cybersickness detection models for VR applications.
Published: 2026-04-18 22:02:54
Authors: Khemraj Shukla, Zongren Zou, Theo Kaeufer, Michael Triantafyllou, George Em Karniadakis
Categories: cs.LG, physics.comp-ph
Abstract:
Physics-informed neural networks (PINNs) have emerged as a promising framework for solving inverse problems governed by partial differential equations (PDEs), including the reconstruction of turbulent flow fields from sparse data. However, most existing PINN formulations are deterministic and do not provide reliable quantification of epistemic uncertainty, which is critical for ill-posed problems such as data-driven Reynolds-averaged Navier-Stokes (RANS) modeling. In this work, we develop and systematically evaluate a set of probabilistic extensions of PINNs for uncertainty quantification in turbulence modeling. The proposed framework combines (i) Bayesian PINNs with Hamiltonian Monte Carlo sampling and a tempered multi-component likelihood, (ii) Monte Carlo dropout, and (iii) repulsive deep ensembles that enforce diversity in function space. Particular emphasis is placed on the role of ensemble diversity and likelihood tempering in improving uncertainty calibration for PDE-constrained inverse problems. The methods are assessed on a hierarchy of test cases, including the Van der Pol oscillator and turbulent flow past a circular cylinder at Reynolds numbers Re=3,900 (direct numerical simulation data) and Re = 10,000 (experimental particle image velocimetry data). The results demonstrate that Bayesian PINNs provide the most consistent uncertainty estimates across all inferred quantities, while function-space repulsive ensembles offer a computationally efficient approximation with competitive accuracy for primary flow variables. These findings provide quantitative insight into the trade-offs between accuracy, computational cost, and uncertainty calibration in physics-informed learning, and offer practical guidance for uncertainty quantification in data-driven turbulence modeling.
Published: 2026-04-18 21:45:39
Authors: Mateen R Shaikh
Categories: stat.ME
Abstract:
Models with fewer parameters are often easier to interpret and more robust. Parsimony can be achieved through optimizing objectives like the AIC or BIC, which are functions of the the number of free parameters in the model. Optimizing this discrete objective is a challenge, often relying on discrete optimization. We construct smooth functions with optima that reach the same optima of these objectives but permit continuous rather than discrete optimization, relieving some selection burden. Proofs of convergence are provided and a novel method of clustering through explicit overparamterization shows promising results.
Published: 2026-04-18 21:30:56
Authors: Moo K. Chung, D. Vijay Anand, Anass B El-Yaagoubi, Jae-Hun Jung, Anqi Qiu, Hernando Ombao
Categories: q-bio.NC
Abstract:
Classical causal models, such as Granger causality and structural equation modeling, are largely restricted to acyclic interactions and struggle to represent cyclic and higher-order dynamics in complex networks. We introduce a causal framework grounded in a variational principle, interpreting causality as directional energy flow from high- to low-energy states along network connections. Using Hodge theory, network flows are decomposed into dissipative components and a persistent harmonic component that captures stable cyclic interactions. Applied to resting-state fMRI connectivity, our variational framework reveals robust cyclic causal patterns that are not detected by conventional causal models, highlighting the value of variational principles for causality.
Published: 2026-04-18 20:58:46
Authors: Beáta Bényi, Sithembele Nkonkobe
Categories: math.CO
Abstract:
In this paper, we study some combinations of the degenerate and incomplete Stirling numbers of the second kind. We use a combinatorial approach and provide some asymptotic results.
Published: 2026-04-18 20:26:55
Authors: Janna Katharina Behr
Categories: hep-ex
Abstract:
A significant excess of $t\bar{t}$ events near the production threshold was observed in LHC Run-2 data by the ATLAS Collaboration. It is consistent with the formation of $t\bar{t}$ quasi-bound states, which were first hypothesised almost 40 years ago. This contribution summarises the experimental results and outlines a path toward further characterisation of the excess.
Published: 2026-04-18 20:23:27
Authors: Zedong Dan, Zijie Wang, Wei Zhang, Xiangru Lin, Weiming Zhang, Xiao Tan, Jingdong Wang, Liang Lin, Guanbin Li
Categories: cs.CV
Abstract:
Offline vectorized maps constitute critical infrastructure for high-precision autonomous driving and mapping services. Existing approaches rely predominantly on single ego-vehicle trajectories, which fundamentally suffer from viewpoint insufficiency: while memory-based methods extend observation time by aggregating ego-trajectory frames, they lack the spatial diversity needed to reveal occluded regions. Incorporating views from surrounding vehicles offers complementary perspectives, yet naive fusion introduces three key challenges: computational cost from large candidate pools, redundancy from near-collinear viewpoints, and noise from pose errors and occlusion artifacts.
We present OptiMVMap, which reformulates multi-vehicle mapping as a select-then-fuse problem to address these challenges systematically. An Optimal Vehicle Selection (OVS) module strategically identifies a compact subset of helpers that maximally reduce ego-centric uncertainty in occluded regions, addressing computation and redundancy challenges. Cross-Vehicle Attention (CVA) and Semantic-aware Noise Filter (SNF) then perform pose-tolerant alignment and artifact suppression before BEV-level fusion, addressing the noise challenge. This targeted pipeline yields more complete and topologically faithful maps with substantially fewer views than indiscriminate aggregation. On nuScenes and Argoverse2, OptiMVMap improves MapTRv2 by +10.5 mAP and +9.3 mAP, respectively, and surpasses memory-augmented baselines MVMap and HRMapNet by +6.2 mAP and +3.8 mAP on nuScenes. These results demonstrate that uncertainty-guided selection of helper vehicles is essential for efficient and accurate multi-vehicle vectorized mapping. The code is released at https://github.com/DanZeDong/OptiMVMap.
Published: 2026-04-18 20:10:04
Authors: Haoze Guo, Ziqi Wei
Categories: cs.HC
Abstract:
While consent banners and privacy policies invite users to read and choose, many choices are shaped by repeated, low-yield interaction routines rather than deliberation. This paper studies performative scrolling: slow, low-information interaction that can signal attention to consent without substantially improving understanding. We present the Performative Scrolling Index (PSI), a reproducible interface-audit metric for measuring pre-choice burden before a meaningful non-accepting alternative becomes visible and actionable. PSI decomposes burden into four observable components: distance, time, focus loops, and hidden reveals. In this paper, PSI is the primary burden metric, while companion signals such as AAI, CSI, and divergence are used as secondary interpretive audit aids rather than standalone validated scales. We also provide a least-effort audit protocol, design-side invariants, a worked example, and a medium-scale live deployment across desktop and mobile conditions under pointer and keyboard traversal policies. Together, these analyses show how structural choices such as offscreen alternatives, fragmented disclosure, and staged modal flows can increase pre-choice friction without improving meaningful control. PSI is not a measure of comprehension or legal sufficiency; rather, it is a diagnostic of interface-side burden intended to support reproducible audits and redesigns.
Published: 2026-04-18 20:06:33
Authors: Nayan Yadav, Shadi Oveisgharan, Shirin Jalali
Categories: cs.CE
Abstract:
Snow depth plays a central role in seasonal snowpack characterization and the terrestrial water cycle, yet remains challenging to estimate at high spatial resolution. Recent studies have shown that repeat-pass interferometric synthetic aperture radar (InSAR) measurements combined with physics-based models can enable effective snow water equivalent (SWE) retrieval. However, the performance of these methods depends strongly on measurement accuracy and modeling assumptions.
Building on the success of InSAR-based approaches, we develop a robust learning-based model that directly learns the relationship between measured InSAR observables and snow depth. The model is trained on a single SnowEx Idaho site and evaluated across independent years and geographically distinct regions. Results demonstrate strong temporal and spatial transferability. In temporal transfer experiments, the proposed approach achieves a Pearson correlation of 0.81 with lidar snow depth, compared to a correlation of approximately 0.47 reported for physics-based Sentinel-1 SWE retrievals over the same site.
Published: 2026-04-18 19:46:34
Authors: Aditya Shribhagwan Khandelwal, Mohammad Samar Ansari, Asra Aslam
Categories: cs.CV
Abstract:
Breast cancer is a leading cause of cancer-related mortality worldwide, and timely accurate diagnosis is critical to improving survival outcomes. While convolutional neural networks (CNNs) have demonstrated strong performance on histopathology image classification, and machine learning models on structured electronic health records (EHR) have shown utility for clinical risk stratification, most existing work treats these modalities in isolation. This paper presents a systematic multimodal framework that integrates patch-level histopathology features from the BreCaHAD dataset with structured clinical data from MIMIC-IV. We train and evaluate unimodal image models (a simple CNN baseline and ResNet-18 with transfer learning), unimodal tabular models (XGBoost and a multilayer perceptron), and an intermediate-fusion model that concatenates latent representations from both modalities. ResNet-18 achieves near-perfect accuracy (1.000) and AUC (1.000) on three-class patch-level classification, while XGBoost achieves 98% accuracy on the EHR prediction task. The intermediate fusion model yields a macro-average AUC of 0.997, outperforming all unimodal baselines and delivering the largest improvements on the diagnostically critical but class-imbalanced mitosis category (AUC 0.994). Grad-CAM and SHAP interpretability analyses validate that model decisions align with established pathological and clinical criteria. Our results demonstrate that multimodal integration delivers meaningful improvements in both predictive performance and clinical transparency.
Published: 2026-04-18 19:46:30
Authors: Michael C. Mozer, Shoaib Ahmed Siddiqui, Rosanne Liu
Categories: cs.LG, cs.AI
Abstract:
Transformers encode structure in sequences via an expanding contextual history. However, their purely feedforward architecture fundamentally limits dynamic state tracking. State tracking -- the iterative updating of latent variables reflecting an evolving environment -- involves inherently sequential dependencies that feedforward networks struggle to maintain. Consequently, feedforward models push evolving state representations deeper into their layer stack with each new input step, rendering information inaccessible in shallow layers and ultimately exhausting the model's depth. While this depth limit can be bypassed by dynamic depth models and by explicit or latent thinking that externalizes state representations, these solutions are computationally and memory inefficient. In this article, we argue that temporally extended cognition requires refocusing from explicit thought traces to implicit activation dynamics via recurrent architectures. We introduce a taxonomy of recurrent and continuous-thought transformer architectures, categorizing them by their recurrence axis (depth versus step) and their ratio of input tokens to recurrence steps. Finally, we outline promising research directions, including enhanced state-space models and coarse-grained recurrence, to better integrate state tracking into modern foundation models.
Published: 2026-04-18 19:31:38
Authors: Ashiqur Rahman, Md. Abu Sayed, Md Sharjis Ibne Wadud, Md. Abu Asad Al-Hafiz, Adam Mushtak, Muhammad E. H. Chowdhury
Categories: eess.IV, cs.AI, cs.CV
Abstract:
Accurate segmentation of gastrointestinal (GI) organs in magnetic resonance enterography (MRE) is critical for diagnosing inflammatory bowel disease (IBD). However, anatomical variability, class imbalance, and low tissue contrast hinder reliable automation. This study proposes a dual-stage deep learning framework for organ-specific segmentation of GI structures from coronal MRE images to address these challenges.
A publicly available MRE dataset of 3,195 coronal T2-weighted HASTE slices from 114 IBD patients was used. Initially, a DenseNet201-UNet++ model generated coarse masks for ROI extraction. A DenseNet121-SelfONN-UNet model was then trained on organ-specific patches. Extensive data augmentation, normalization, five-fold cross-validation, and class-specific weighting were applied to mitigate severe class imbalance, particularly for the appendix.
The initial stage achieved strong organ localization but underperformed for the appendix; class weighting improved its DSC from 6.76% to 85.76%. The second-stage DenseNet121-SelfONN-UNet significantly enhanced segmentation across all GI structures, with notable DSC gains (cecum +23.62%, sigmoid +18.57%, rectum +17.99%, small intestine +16.06%). Overall, the framework achieved mDSC of 88.99%, mIoU of 84.76%, and mHD95 of 6.94 mm, outperforming all baselines.
This framework demonstrates the effectiveness of a coarse-to-fine, organ-aware segmentation strategy for intestinal MRE. Despite higher computational cost, it shows strong potential for clinical translation and enables anatomically informed diagnostic tools in gastroenterology.
Published: 2026-04-18 19:25:32
Authors: Xuancheng Shao
Categories: math.NT
Abstract:
Let $\mathbb{F}_p$ be a finite field of prime order $p$ and let $A \subset \mathbb{F}_p$ be a subset. In the dense regime when $|A| \geq αp$ for some $α\in (0,1)$, we determine the optimal constant $f(α)$ in the inequality $$ \max(|A+A|, |A\cdot A|) \geq (f(α) - o(1))p. $$ The proof relies on a structural result for sumsets of dense subsets, established via a regularity lemma in general finite abelian groups.
Published: 2026-04-18 19:10:12
Authors: Md Shamim Ahmed, Maja Dusanic, Moritz Nikolai Kirschner, Elisabeth Nyoungui, Jana Zschüntzsch, Lukas Galke Poech, Richard Röttger
Categories: cs.CL
Abstract:
Frontier large language models generate clinically accurate outputs, but their citations are often fabricated. We term this the Provenance Gap. We tested five frontier LLMs across 36 clinician-validated scenarios for three rare neuromuscular disease pairs. No model produced a clinically relevant PubMed identifier without prompting. When explicitly asked to cite, the best model achieved 15.3% relevant PMIDs; the majority resolved to real publications in unrelated fields. We present HEG-TKG (Hierarchical Evidence-Grounded Temporal Knowledge Graphs), a system that grounds clinical claims in temporal knowledge graphs built from 4,512 PubMed records and curated sources with quality-tier stratification and 1,280 disease-trajectory milestones. In a controlled three-arm comparison using the same synthesis model, HEG-TKG matches baseline clinical feature coverage while achieving 100% evidence verifiability with 203 inline citations. Guideline-RAG, given overlapping source documents as raw text, produces zero verifiable citations. LLM judges cannot distinguish fabricated from verified citations without PubMed audit data. Independent clinician evaluation confirms the verifiability advantage (Cohen's d = 1.81, p < 0.001) with no degradation on safety or completeness. A counterfactual experiment shows 80% resistance to injected clinical errors with 100% detectability via citation trace. The system deploys on-premise via open-source models so patient data never leaves institutional infrastructure.
Published: 2026-04-18 18:40:56
Authors: Disen Liao, Freda Shi
Categories: cs.CL
Abstract:
Tokenization is the first step in every language model (LM), yet it never takes the sounds of words into account. We investigate how tokenization influences text-only LMs' ability to represent phonological knowledge. Through a series of probing experiments, we show that subword-based tokenization systematically weakens the encoding of both local (e.g., rhyme) and global (e.g., syllabification) phonological features. To quantify this effect, we introduce the syllabification-tokenization alignment distance (STAD), a metric that measures the misalignment between a model's tokenization and the natural syllable boundaries of words, and find that higher misalignment correlates with poorer phonological representations, providing a simple diagnostic for phonology-aware tokenization. To address these limitations, we propose a lightweight IPA-based fine-tuning method that infuses phonological awareness into LMs, leading to consistent improvements across three phonology-related tasks while largely preserving math and general reasoning ability, with 1.1\% and 0.9\% drops on GSM8K and MMLU, respectively.
Published: 2026-04-18 18:36:53
Authors: R. Mantovan, A. Bozhko, V. Zhurkin, A. Bogach, A. Khanas, S. Zarubin, A. Zenkevich, V. Glushkov
Categories: cond-mat.str-el, cond-mat.mtrl-sci
Abstract:
Disorder in any form is considered to be highly detrimental to the experimental exploration of novel phenomena in quantum materials with non-trivial band topology. Contrary to established belief, clear topological features are reliably detected in the electron transport of polycrystalline 65-nm-thick ε-FeSi films grown via solid-state reaction of Fe deposited on a Si(100) substrate. The observation of temperature-independent anomalous Hall conductivity σ_{xy}^{AHE} \sim const (σ_{xx)) (σ_{xy}^{AHE} \approx 14 uS/sq.) below 200 K firmly proves the anomalous Hall effect in this compound to be intrinsic and originating from a non-trivial Berry phase. The discovered scaling dominates over the nanoscale (\sim 40 nm) polycrystalline texture and is robust to temperature crossover between bulk and surface modes of electron transport. The non-trivial topological state of ε-FeSi is also confirmed by a chiral anomaly both in anisotropic longitudinal magnetoresistance and planar Hall effect specific for Weyl semimetals. Relating scaled anomalous Hall conductance to a "quantized" Hall response of a Weyl semimetal the distance between two Weyl points has been estimated as (k_{+}^{W}-k_{-}^{W})/(2π) \approx 0.36. Our findings confirm the topological origin of electron transport in the polycrystalline ε-FeSi thin films and discover its potential as a new high temperature and noble metal-free Weyl semimetal.
Published: 2026-04-18 18:28:05
Authors: Chenjun Shi, Jitao Zhang
Categories: physics.optics
Abstract:
Confocal Brillouin microscopy enables high-resolution mechanical imaging but has low acquisition speed, partly due to its pixel-by-pixel mapping strategy. Line-scanning Brillouin microscopy (LSBM) significantly improves imaging speed by utilizing a multiplexing approach. However, current method is limited to a single-stage virtually imaged phased array (VIPA) spectrometer with insufficient capability of suppressing noise. Consequently, an absorptive gas chamber is often used to help reject excessive elastically scattered light. This approach requires specific tunable laser sources whose frequencies (e.g., around 780 nm) are locked to the absorption line of the gas chamber. Here, we developed a multiplexed Brillouin spectrometer for LSBM that increased the noise suppression to 57 dB without using any gas chamber. This is achieved by cascading two VIPA etalons with parallel dispersion axes in the spectrometer, where the first VIPA acts as a band-pass filter and the second as spectrum analyzer. We demonstrated its performance by acquiring Brillouin images of bio-printed phantoms with an inverted co-axial LSBM. This gas-chamber-free approach can expand the implementation of LSBM to other wavelengths where Brillouin scattering is more efficient and commercial laser sources are readily available.
Published: 2026-04-18 18:09:37
Authors: Zeng Wang, Minghao Shao, Weimin Fu, Prithwish Basu Roy, Xiaolong Guo, Ramesh Karri, Muhammad Shafique, Johann Knechtel, Ozgur Sinanoglu
Categories: cs.CR
Abstract:
The integration of large language models (LLMs) into electronic design automation (EDA) workflows has introduced powerful capabilities for RTL generation, verification, and design optimization, but also raises critical security concerns. Malicious LLM outputs in this domain pose hardware-level threats, including hardware Trojan insertion, side-channel leakage, and intellectual property theft, that are irreversible once fabricated into silicon. Such requests often exploit semantic disguise, embedding adversarial intent within legitimate engineering language that existing safety mechanisms, trained on general-purpose hazards, fail to detect. No benchmark exists to evaluate LLM vulnerability to such domain-specific threats. We present the HarmChip benchmark to assess jailbreak susceptibility in hardware security, spanning 16 hardware security domains, 120 threats, and 360 prompts at two difficulty levels. Evaluation of state-of-the-art LLMs reveals an alignment paradox: They refuse legitimate security queries while complying with semantically disguised attacks, exposing blind spots in safety guardrails and underscoring the need for domain-aware safety alignment.
Published: 2026-04-18 17:56:07
Authors: Boxuan Zhou, Yuancheng Jing, Chun-Chieh Yu, Haoyang Li, Ran Wang, Xingxu Yan, Xiaoqing Pan, Yu Huang, Wei Xiong, Xiangfeng Duan
Categories: physics.optics
Abstract:
Monolayer transition metal dichalcogenides (e.g., MoS2) exhibit exceptionally large optical nonlinearities for high-order nonlinear light generation (NLG), yet their inherent atomic thickness fundamentally limits light-matter interactions and thus conversion efficiency. Here, we overcome this intrinsic trade-off using a solution-processed bulk monolayer MoS2 (BM-MoS2) architecture composed of electronically decoupled MoS2 monolayers separated by organic interlayers. This layered superstructure preserves the exceptional intrinsic nonlinear susceptibility of monolayer MoS2 while enabling scalable interaction length. In the sub-wavelength regime, the NLG scales nearly quadratically with layer number (N^1.8), confirming the constructive buildup of nonlinear fields across stacked monolayers. As a result, a 100-nm-thick BM-MoS2 thin film exhibits colossal high-order NLG, including four-wave mixing and high harmonic generation nearly two orders of magnitude stronger than those from a 3-mm-thick ZnSe crystal. The generated nonlinear beam is directly visible to the naked eye and exhibits broad spectral tunability spanning more than 1000 nm in the mid-IR, enabling mid-IR-to-visible upconversion spectroscopy for resolving molecular vibrational fingerprints. By uniting monolayer-scale nonlinear susceptibility with bulk interaction length and coherent field buildup, BM-MoS2 establishes a thin-film platform for ultra-compact and substrate-agnostic nonlinear photonic systems beyond the constraints of conventional single crystals.
Published: 2026-04-18 17:52:02
Authors: Jiafei Song, Fengwei Zhou, Jin Qu, Wenjin Jason Li, Tong Wu, Gengjian Xue, Zhikang Zhao, Daomin Wei, Yichao Lu, Bailin Na
Categories: cs.CV, cs.LG
Abstract:
Recent Multimodal Large Language Models (MLLMs) have demonstrated strong performance on vision-language understanding tasks, yet their inference efficiency is often hampered by the large number of visual tokens, particularly in high-resolution or multi-image scenarios. To address this issue, we propose EvoComp, a visual token compression framework that significantly reduces token count while preserving task accuracy. EvoComp introduces a lightweight encoder-only transformer-based compressor that selects the most informative and non-redundant visual tokens by jointly considering visual and textual contexts. A core challenge lies in providing effective supervision for training the compressor. To this end, we design an evolutionary labeling strategy that searches for token subsets minimizing the MLLM's output loss, while enforcing semantic diversity through vocabulary-based token grouping. We further train the compressor using a tailored loss function combining the GHM loss to mitigate class and difficulty imbalance, and a cosine similarity regularization to encourage semantic separation between retained and discarded tokens. Extensive experiments across multiple vision-language benchmarks show that EvoComp outperforms existing methods based on attention or similarity heuristics. Notably, it retains 99.3% of the original accuracy under 3x token compression and delivers up to 1.6x speedup on mobile devices.
Published: 2026-04-18 17:43:59
Authors: Antonio De Santis, Tommaso Bonetti, Andrea Tocchetti, Marco Brambilla
Categories: cs.CL, cs.AI
Abstract:
The interpretation of implicit meanings is an integral aspect of human communication. However, this framework may not transfer to interactions with Large Language Models (LLMs). To investigate this, we introduce the task of Implicit Information Extraction (IIE) and propose an LLM-based IIE pipeline that builds a structured knowledge graph from a context sentence by extracting relational triplets, validating implicit inferences, and analyzing temporal relations. We evaluate two LLMs against crowdsourced human judgments on two datasets. We find that humans agree with most model triplets yet consistently propose many additions, indicating limited coverage in current LLM-based IIE. Moreover, in our experiments, models appear to be more conservative about implicit inferences than humans in socially rich contexts, whereas humans become more conservative in shorter, fact-oriented contexts. Our code is available at https://github.com/Antonio-Dee/IIE_from_LLM.
Published: 2026-04-18 17:42:13
Authors: Heinz H. Bauschke, Yuan Gao
Categories: math.OC, math.FA, math.NA
Abstract:
In 2023, Boţ and Nguyen introduced a new class of accelerated algorithms for finding a fixed point of a nonexpansive operator as the weak limit of a sequence. In this paper, we analyze a particular instance of their algorithm when the nonexpansive operator is assumed to be linear. Surprisingly, the Boţ-Nguyen acceleration then fits naturally into the framework of weighted mean ergodic iterations. This allows us to identify the weak limit as the projection of the starting point onto the fixed point set. Moreover, the weights involved are closely related to the beta-binomial distribution. Finally, when the parameter is equal to 4, then we obtain strong convergence of the iterates.
Published: 2026-04-18 17:36:15
Authors: Michelle Star, Andrew Aquilina, Yu-Ru Lin
Categories: cs.CL
Abstract:
When users seek social support from chatbots, they disclose their situation gradually, yet most evaluations of supportive LLMs rely on single-turn, fully specified prompts. We introduce a multi-turn simulation framework that closes this gap. Support-seeking narratives from five Reddit communities are decomposed into ordered fragments and revealed turn by turn to a language model. Each response is coded with the Social Support Behavior Code (SSBC), an established multi-label taxonomy that captures the composition of support, rather than a single quality score. To ask whether support choices track the model's own construal of user distress, we use linear probes on hidden representations to estimate this internal signal without altering the generation context. Across two mid-scale models (Llama-3.1-8B, OLMo-3-7B) and more than 6,200 turns, support composition shifts systematically with estimated distress: teaching declines as estimated distress rises, a finding that replicates across architectures, while increases in affective and esteem-oriented strategies (such as validation) are suggestive but model-specific and rest on noisier annotations. Community context independently shapes behavior, tracking topic and discourse norms rather than demographic categories. These trajectory-level dynamics, invisible to single-turn evaluation, motivate multi-turn auditing frameworks for socially sensitive applications.
Published: 2026-04-18 17:31:00
Authors: Matteo Bordignon, Paolo Minelli
Categories: math.NT
Abstract:
We consider elliptic Dedekind sums that were introduced by Sczech as generalizations of the classical ones to complex lattices. We prove that these sums -- suitably normalized -- have a Gaussian limiting distribution. As an application, we prove a conjecture due to Ito.
Published: 2026-04-18 17:27:16
Authors: Murad Sarsour
Categories: nucl-ex
Abstract:
Measurements of light hadron production in ultrarelativistic nuclear collisions provide essential insight into final-state effects arising from both hot and cold nuclear matter. They probe collective behavior, hadronization via recombination, and baryon and strangeness enhancement, while their system-size and centrality dependence constrain the role of initial-state geometry and nuclear parton distribution functions. In this talk, we present recent PHENIX measurements of identified charged hadrons ($π/K/p$) at midrapidity ($|y| < 0.35$) and low-mass vector mesons, including $ω$, $ρ$, and $φ$, at forward rapidity ($1.2 < |y| < 2.2$) in $p+p$, $p+$Al, $p/d/^{3}$He+Cu+Au, and Au+Au collisions at $\sqrt{s_{NN}} = 200$ GeV, as well as U+U collisions at $\sqrt{s_{NN}} = 193$ GeV. Tests of various empirical scaling behaviors, together with comparisons to previous measurements and theoretical model calculations, are discussed.
Published: 2026-04-18 17:21:53
Authors: Minghao Zou, Gen Liu, Guanghui Yue, Baoquan Zhao, Zhihua Wang, Paul L. Rosin, Hantao Liu, Wei Zhou
Categories: cs.CV
Abstract:
The rapid advancement of generative models has led to a growing volume of AI-generated videos, making the automatic quality assessment of such videos increasingly important. Existing AI-generated content video quality assessment (AIGC-VQA) methods typically estimate visual quality by analyzing each video independently, ignoring potential relationships among videos. In this work, we revisit AIGC-VQA from an inter-video perspective and formulate it as a reference-aware evaluation problem. Through this formulation, quality assessment is guided not only by intrinsic video characteristics but also by comparisons with related videos, which is more consistent with human perception. To validate its effectiveness, we propose Reference-aware Video Quality Assessment (RefVQA), which utilizes a query-centered reference graph to organize semantically related samples and performs graph-guided difference aggregation from the reference nodes to the query node. Experiments on existing datasets demonstrate that our proposed RefVQA outperforms state-of-the-art methods across multiple quality dimensions, with strong generalization ability validated by cross-dataset evaluation. These results highlight the effectiveness of the proposed reference-based formulation and suggest its potential to advance AIGC-VQA.
Published: 2026-04-18 17:17:34
Authors: Fu Li, Bo Zhao, Vikrant Chaudhary, Shengqiao Wang, Chen Shen, Hao Wang, Hongbin Zhang
Categories: cond-mat.mtrl-sci
Abstract:
Altermagnets provide a promising platform for unconventional transport and optical responses beyond conventional ferromagnets and antiferromagnets. In this work, we develop a medium-throughput first-principles workflow to evaluate transport and optical properties in approximately 150 known altermagnetic compounds collected from the MAGNDATA database. By combining density functional theory, Wannier interpolation, and symmetry analysis, we investigate representative linear and nonlinear responses, including the anomalous Hall effect, magneto-optical Kerr effect, and bulk photovoltaic effect. We find that these responses are strongly constrained by magnetic symmetry and further shaped by spin-orbit coupling, band structure, and inversion symmetry breaking. Representative examples include a finite anomalous Hall response in metallic VNb3S6, giant Kerr rotation in insulating CaIrO3, and large shift current in non-centrosymmetric CuFeS2. These results establish a symmetry-guided route for identifying experimentally accessible fingerprints and functional transport properties in altermagnetic materials.
Published: 2026-04-18 16:59:29
Authors: Ji-Eun Byun, Hyeuk Ryu, Junho Song
Categories: cs.LG, math.PR
Abstract:
Coherent systems are representative of many practical applications, ranging from infrastructure networks to supply chains. Probabilistic evaluation of such systems remains challenging, however, because existing decomposition-based methods scale poorly as the number of components grows. To address this limitation, this study proposes the Reference-state System Reliability (RSR) method. Like existing approaches, RSR characterises the boundary between different system states using reference states in the component-state space. Where it departs from these methods is in how the state space is explored: rather than using reference states to decompose the space into disjoint hypercubes, RSR uses them to classify Monte Carlo samples, making computational cost significantly less sensitive to the number of reference states. To make this classification efficient, samples and reference states are stored as matrices and compared using batched matrix operations, allowing RSR to exploit the advances in high-throughput matrix computing driven by modern machine learning. We demonstrate that RSR evaluates the system-state probability of a graph with 119 nodes and 295 edges within 10~seconds, highlighting its potential for real-time risk assessment of large-scale systems. We further show that RSR scales to problems involving hundreds of thousands of reference states -- well beyond the reach of existing methods -- and extends naturally to multi-state systems. Nevertheless, when the number of boundary reference states grows exceedingly large, RSR's convergence slows down, a limitation shared with existing reference-state-based approaches that motivates future research into learning-based representations of system-state boundaries.
Published: 2026-04-18 16:34:29
Authors: Yiming Wang, Frederick W. B. Li, Jingyun Wang
Categories: cs.CV
Abstract:
Zero-shot action recognition is challenging due to the semantic gap between seen and unseen classes. We present a novel framework that enhances CLIP with disentangled embeddings and semantic-guided interaction. A Motion Separation Module (MSM) separates motion-sensitive and global-static features, while a Motion Aggregation Block (MAB) employs gated cross-attention to refine motion representation without re-coupling redundant information. To facilitate generalization to unseen categories, we enforce semantic alignment between video features and textual representations by aligning projected embeddings with positive textual prompts, while leveraging negative prompts to explicitly model "non-class" semantics. Experiments on standard benchmarks demonstrate that our method consistently outperforms prior CLIP-based approaches, achieving robust zero-shot action recognition across both coarse and fine-grained datasets.
Published: 2026-04-18 16:29:25
Authors: Evgenii Chzhen, Sholom Schechtman
Categories: math.OC
Abstract:
We analyze the constant step size subgradient method on nonsmooth, nonconvex functions. We identify geometric assumptions on the objective function under which i) its domain admits a partition (stratification) into smooth manifolds (strata) on which the function is smooth; ii) a global projection formula for Clarke subgradients holds; and iii) quantitative curvature bounds hold on each stratum. Under these conditions, we prove that the iterates of the subgradient method locally shadow a Riemannian gradient descent on nearby strata, which we use to measure stationarity. We introduce a selection rule for the active stratum and develop a mechanism that assembles local descent inequalities across successive strata into explicit convergence rates. These rates are expressed in terms of the number of dimensions present in the stratification, improve as the number of strata decreases, and recover, up to constants, the classical rates in the smooth case. We show that the stated assumptions follow from the existence of Lipschitz stratifications of semialgebraic sets, and are therefore automatically satisfied for semialgebraic functions and, more generally, for functions definable in polynomially bounded o-minimal structures, yielding the first known convergence rates in these settings. As intermediate results of independent interest, we establish tubular neighborhood estimates for Lipschitz stratifications and a global projection formula for Clarke subgradients. Finally, we show that our framework extends to decreasing step size and recovers, via an alternative argument, the recently announced result of Lai and Song on sequential convergence of the subgradient method with step sizes 1/k.