November 2025 arXiv papers — page 8

Benjamin D. Ballyk, Ankit Gupta, Sujay Konda, Kavitha Subramanian

Data privacy is a critical challenge in modern medical workflows as the adoption of electronic patient records has grown rapidly. Stringent data protection regulations limit access to clinical records for training and integrating machine learning models that have shown promise in improving diagnostic accuracy and personalized care outcomes. Synthetic data of

Joyentanuj Das, Sumit Mohanty

Let $G$ be a connected graph with vertex set $\{v_1, v_2, \ldots, v_\mathbf{n}\}$. As a variant of the classical distance matrix, the \emph{exponential distance matrix} was introduced independently by Yan and Yeh, and by Bapat et al. For a nonzero indeterminate $q$, the exponential distance matrix $\mathscr{F} = (\mathscr{F}_{ij})_{\mathbf{n} \times \mathbf{

Magdalena Musat

We give an overview of results tying together a circle of problems connected to the Connes Embedding Problem, Kirchberg's reformulations thereof, Tsirelson's conjecture and its relation to quantum information theory, and a class of quantum channels, called factorizable, introduced by Anantharaman-Delaroche. While parts of the article are more expository, the

Krishna Kaushik Yanamundra, Chandler C. Benjamin, Kumbakonam Ramamani Rajagopal

The flow of a colloidal solution between two parallel disks rotating with the same angular velocity about two non-coincident axes was studied. The problem has been approached from two perspectives, the first wherein the stress is expressed in terms of a power-law of kinematical quantities, and the second wherein we consider a non-standard model where the sym

Kaijian Sha, Xiaoming Wang

Within the Darcy-Boussinesq framework for convection in multilayered porous media, we investigate the singular limit in which the thickness of one layer tends to zero. We establish that the solution of the full system converges to that of the corresponding limiting model with one fewer layer. The convergence is established in two complementary senses: (i) st

Gökçen Dilaver, Selma Altinok

Let $R$ be a commutative ring with identity and $G$ a graph. An extending generalized spline on $G$ is a vertex labeling $f \in \prod_{v} M_v$, where for each edge $e=uv$ there exists an $R$-module $M_{uv}$ together with homomorphisms $ \varphi_u : M_u \to M_{uv}$ and $ \varphi_v : M_v \to M_{uv}$ such that $\varphi_u(f_u)=\varphi_v(f_v).$ Extending generali

Jiachuan Peng, Kyle Lam, Jianing Qiu

We trained a classifier with synthetic chest X-ray (CXR) images generated by Nano Banana, the latest AI model for image generation and editing, released by Google. When directly applied to real-world CXRs having only been trained with synthetic data, the classifier achieved an AUROC of 0.923 (95% CI: 0.919 - 0.927), and an AUPR of 0.900 (95% CI: 0.894 - 0.90

Mengting Yu, Shuiying Xiang, Changjian Xie, Yonghang Chen

Robotic continuous control tasks impose stringent demands on the energy efficiency and latency of computing architectures due to their high-dimensional state spaces and real-time interaction requirements. Conventional electronic computing platforms face computational bottlenecks, whereas the fusion of photonic computing and spiking reinforcement learning (RL

Yuchen Shi, Jihong Wang, Jiwei Zhang

In this paper, asymptotic compatibility error estimates of a finite element discretization is presented for 2D nonlocal Poisson problems with Neumann boundary conditions. To this end, we begin with deriving two kind of nonlocal Neumann boundary operators based on nonlocal Green's identities, and establish the corresponding weak convergence to the classical N

Minh-Quan Le, Yuanzhi Zhu, Vicky Kalogeiton, Dimitris Samaras

Recent video diffusion models can synthesize visually compelling clips, yet often violate basic physical laws-objects float, accelerations drift, and collisions behave inconsistently-revealing a persistent gap between visual realism and physical realism. We propose $\texttt{NewtonRewards}$, the first physics-grounded post-training framework for video generat

Nthenya Kyatha, Jay Taneja

Public transport in sub-Saharan Africa (SSA) often operates in overcrowded conditions where existing automated systems fail to capture reliable passenger flow data. Leveraging onboard CCTV already deployed for security, we present a baseline pipeline that combines YOLOv12 detection, BotSORT tracking, OSNet embeddings, OCR-based timestamping, and telematics-b

Akari Ishida, Manabu Machida

Nonlinear inverse problems have complicated landscapes. Hence the calculation with naive iterative schemes (e.g., Gauss-Newton or conjugate gradients) is trapped in local minima. The (first) Born approximation can avoid this trapping but linearization is required. Nonlinear inverse problems can be solved without linearization by means of the inverse Born ser

Yingxuan You, Chen Zhao, Hantao Zhang, Ming Xu

Existing generative models for 3D shapes can synthesize high-fidelity and visually plausible shapes. For certain classes of shapes that have undergone an engineering design process, the realism of the shape is tightly coupled with the underlying physical properties, e.g., aerodynamic efficiency for automobiles. Since existing methods lack knowledge of such p

Mulin Tian, Ajitesh Srivastava

Epidemic outcomes have a complex interplay with human behavior and beliefs. Most of the forecasting literature has focused on the task of predicting epidemic signals using simple mechanistic models or black-box models, such as deep transformers, that ingest all available signals without offering interpretability. However, to better understand the mechanisms

Jonas D. Rockbach, Sven Fuchs, Maren Bennewitz

Due to the progress in artificial intelligence, it is important to understand how capable artificial agents should be used when interacting with humans, since high level authority and responsibility often remain with the human agent. However, integrated frameworks are lacking that can account for heterogeneous agents and draw on different scientific fields,

Shuiying Xiang, Yahui Zhang, Shangxuan Shi, Haowen Zhao

There exists a significant scale gap between photonic neural network integrated chips and neural networks, which hinders the deployment and application of photonic neural network. Here, we propose hardware-aware lightweight spiking neural networks (SNNs) architecture tailored to our photonic neuromorphic chips, and conducts hardware-software collaborative co

Sung Park

Significant Others (SOs) stabilize identity, regulate emotion, and support narrative meaning-making, yet many people today lack access to such relational anchors. Recent advances in large language models and memory-augmented AI raise the question of whether artificial systems could support some of these functions. Existing empathic AIs, however, remain react

Abdelhay Benmoussa

We investigate the algebra generated by the operators $x$ and $\mathrm{I} = \int_0^x$, which satisfy the commutation relation \[ [\mathrm{I},x] = \mathrm{I}x - x\mathrm{I} = - \mathrm{I}^2. \] We develop a combinatorial framework for the normal ordering of words in this algebra and show that any word can be written in the form \[ w = \sum_{i,j} c(i,j) \, x^i

Haney Kang, Eduard Marin, Myoungsung You, Diego Perino

This paper introduces BeaCon, a novel tool for the automated generation of adjustable container security policies. Unlike prior approaches, BeaCon leverages dynamic analysis to simulate realistic environments, uncovering container execution paths that may remain hidden during the profiling phase. To address the challenge of exploring vast profiling spaces, w

Ji Gan, Lingxu Chen, Jiaxu Leng, Xinbo Gao

Artistic font generation (AFG) can assist human designers in creating innovative artistic fonts. However, most previous studies primarily focus on 2D artistic fonts in flat design, leaving personalized 3D-AFG largely underexplored. 3D-AFG not only enables applications in immersive 3D environments such as video games and animations, but also may enhance 2D-AF

Jiawei Chen, Yang Yang, Chao Yu, Yu Tian

Large Reasoning Models (LRMs) have emerged as a powerful advancement in multi-step reasoning tasks, offering enhanced transparency and logical consistency through explicit chains of thought (CoT). However, these models introduce novel safety and reliability risks, such as CoT-hijacking and prompt-induced inefficiencies, which are not fully captured by existi

G. A. Bobkov, I. V. Bobkova, A. M. Bobkov, K. B. Polevoy

We develop a theoretical framework for planar quasi-ballistic Josephson junctions, where multiple superconducting leads are coupled through a large, nearly ballistic normal metal crystal. Our approach, based on quasiclassical Eilenberger equations, accounts for the dominant role of electron reflections from the crystal surfaces or single impurities, a mechan

Hongzong Li, Luwei Liao, Xiangguang Dai, Yuming Feng

Multi-UAV cooperative path planning (MUCPP) is a fundamental problem in multi-agent systems, aiming to generate collision-free trajectories for a team of unmanned aerial vehicles (UAVs) to complete distributed tasks efficiently. A key challenge lies in achieving both efficiency, by minimizing total mission cost, and fairness, by balancing the workload among

K. B. Polevoy, G. A. Bobkov, D. S. Kalashnikov, A. G. Shishkin

We report on the realization of a planar, quasi-ballistic Josephson junction array using a Au micron-sized single-crystal. The system exhibits a nonlocal, multiterminal Josephson effect, where the supercurrent between any two superconducting leads is governed by the phase coherence across the entire crystal. Key evidence includes a non-monotonic dependence o

Lingdong Wang, Guan-Ming Su, Divya Kothandaraman, Tsung-Wei Huang

Traditional video codecs optimized for pixel fidelity collapse at ultra-low bitrates and produce severe artifacts. This failure arises from a fundamental misalignment between pixel accuracy and human perception. We propose a semantic video compression framework named DiSCo that transmits only the most meaningful information while relying on generative priors

Bhavesh R. Sarode, Harshwardhan H. Katkar

We revisit the Fokker-Planck based theory of driven polymer translocation through a narrow nanopore. A bead-spring model of a uniformly charged polyelectrolyte chain translocating through a semi-implicit model of a nanopore embedded in a membrane are used to gain insights into the underlying free energy landscape and kinetics of translocation. The free energ

Pengyu Zeng, Yuqin Dai, Jun Yin, Jing Zhong

Building design directly affects human well-being and carbon emissions, yet generating spatial-functional and energy-compliant floorplans remains manual, costly, and non-scalable. Existing methods produce visually plausible layouts but frequently violate key constraints, yielding invalid results due to the absence of automated evaluation. We present GreenPla

Zeyu Xu, Xiaojie Mao, Hao Mei, Yue Liu

In many decision-making problems, the primary outcome is expensive, time-consuming, or difficult to observe, so individualized treatment rules (ITRs) may be instead learned from surrogate endpoints. However, a surrogate that is highly associated with the primary outcome, or even satisfies existing surrogate criteria, may not necessarily induce a treatment ru

Krishna Kaushik Yanamundra, Lorenzo Fusi

This paper examines the linearized stability of plane Couette flow for stress-power law fluids, which exhibit non-monotonic stress-strain rate behavior. The constitutive model is derived from a thermodynamic framework using a non-convex rate of dissipation potential. Under velocity boundary conditions, the system may admit three steady-state solutions. Linea

Xiao Wu, Ting-Zhu Huang, Liang-Jian Deng, Xiaobing Yu

Scientific discovery increasingly entails long-horizon exploration of complex hypothesis spaces, yet most existing approaches emphasize final performance while offering limited insight into how scientific exploration unfolds over time, particularly balancing efficiency-diversity trade-offs and supporting reproducible, human-in-the-loop discovery workflows. W

Hui Zhong, Jiachen Shen, Lei Fan, Xinyue Zhang

Distributed quantum computing (DQC) is widely regarded as a promising approach to overcome quantum hardware limitations. A major challenge in DQC lies in reducing the communication cost introduced by remote CNOT gates, which are significantly slower and more resource-consuming than local operations. Existing DQC approaches treat the three essential component

Xinkui Zhao, Yifan Zhang, Haidan Zhao, Hao Zhang

The growing complexity of embedded systems creates tension between rich functionality and strict resource and real-time constraints. Traditional monolithic operating system and hypervisor designs suffer from resource bloat and unpredictable scheduling, making them unsuitable for time-critical workloads where low latency and low jitter are essential. We propo

Luca Attolico

Macroeconomic nowcasting sits at the intersection of traditional econometrics, data-rich information systems, and AI applications in business, economics, and policy. Machine learning (ML) methods are increasingly used to nowcast quarterly GDP growth, but adoption in high-stakes settings requires that predictive accuracy be matched by interpretability and rob

Bingzheng Xia, Zujie Ren, Kuang Ma, Xiaoqian Li

With the increasing time and frequency resolution of modern radio telescopes and the exponential growth in observational data volumes, real-time single-pulse detection has become a critical requirement for time-domain radio astronomy. Heimdall, as a representative GPU-accelerated single-pulse search tool, offers substantial performance advantages over CPU-ba

Qiming Zhang, Xiucheng Wang, Nan Cheng, Zhisheng Yin

Radio maps (RMs) provide a spatially continuous description of wireless propagation, enabling cross-layer optimization and unifying communication and sensing for integrated sensing and communications (ISAC). However, constructing high-fidelity RMs at operational scales is difficult, since physics-based solvers are time-consuming and require precise scene mod

Mehdi Dagdoug, Clement Dombry, Jean-Jil Duchamps

Random Forests and Gradient Boosting are among the most effective algorithms for supervised learning on tabular data. Both belong to the class of tree-based ensemble methods, where predictions are obtained by aggregating many randomized regression trees. In this paper, we develop a theoretical framework for analyzing such methods through Reproducing Kernel H

Andrea Procopio, Marco Esposito, Sara Raggiunto, Andrey Gizdov

We study on-device time-series analysis for gait detection in Parkinson's disease (PD) from short windows of triaxial acceleration, targeting resource-constrained wearables and edge nodes. We compare magnitude thresholding to three 1D CNNs for time-series analysis: a literature baseline (separable convolutions) and two ultra-light models - one purely separab

Jiazhen Liu, Mingkuan Feng, Long Chen

Integrating segmentation into Multimodal Large Language Models (MLLMs) presents a core trilemma: simultaneously preserving dialogue ability, achieving high segmentation performance, and ensuring fast inference. Prevailing paradigms are forced into a compromise. Embedding prediction methods introduce a conflicting pixel-level objective that degrades the MLLM'

Johanna Huhtasaari, Joyal Jain Palakulam, Awse Salha, Per Hyldgaard

Producing large-area single-crystalline graphene is key to realizing its full potential in advanced applications, including twistronics. Yet, controlling graphene growth kinetics to avoid grain boundaries or multilayer growth remains challenging. Here, we demonstrate single-crystalline graphene free from multilayer domains via one-step delamination of epitax

Ganghui Cao, Xunyuan Yin

This paper investigates the problem of consensus-based distributed control of linear time-invariant multi-channel systems subject to unknown inputs. A distributed observer-based control framework is proposed, within which observer nodes and controller nodes collaboratively perform state estimation and control tasks. Consensus refers to a distributed cooperat

Damian Heywood, Joseph Andrew Carrier, Kyu-Hong Hwang

This study describes the development of an AI-assisted error analysis system designed to identify, categorize, and correct writing errors in English. Utilizing Large Language Models (LLMs) like Claude 3.5 Sonnet and DeepSeek R1, the system employs a detailed taxonomy grounded in linguistic theories from Corder (1967), Richards (1971), and James (1998). Error

Zhikang Chen, Sen Cui, Deheng Ye, Min Zhang

Model merging has emerged as a practical paradigm for integrating multiple independently trained models into a single model without joint retraining. Previous studies have demonstrated the effectiveness of combining parameters through strategies such as parameter decomposition, coefficient optimization, and subspace learning, significantly reducing the need

Nuo Chen, Hanpei Fang, Jiqun Liu, Wilson Wei

Recent research has explored LLMs as scalable tools for relevance labeling, but studies indicate they are susceptible to priming effects, where prior relevance judgments influence later ones. Although psychological theories link personality traits to such biases, it is unclear whether simulated personalities in LLMs exhibit similar effects. We investigate ho

Deep Patel, Emmanouil-Vasileios Vlatakis-Gkaragkounis

Many emerging applications - such as adversarial training, AI alignment, and robust optimization - can be framed as zero-sum games between neural nets, with von Neumann-Nash equilibria (NE) capturing the desirable system behavior. While such games often involve non-convex non-concave objectives, empirical evidence shows that simple gradient methods frequentl

J. Okamoto, C. Y. Mou, H. Y. Huang, G. Channagowdra

Chirality characterizes the asymmetry between a structure and its mirror image and underlies a wide range of chiral functionalities. In crystallographically chiral materials, phonons with non-zero linear momentum $\textbf{k}$ can acquire a $k$-induced longitudinal magnetization, giving rise to chiral phonons. Helical spin order, with its proper screw-type co

Kaihang Pan, Weile Chen, Haiyi Qiu, Qifan Yu

Recent image editing models boast next-level intelligent capabilities, facilitating cognition- and creativity-informed image editing. Yet, existing benchmarks provide too narrow a scope for evaluation, failing to holistically assess these advanced abilities. To address this, we introduce WiseEdit, a knowledge-intensive benchmark for comprehensive evaluation

Tarika Mane, Amine Boukardagha

We examine the Langevin diffusion confined to a closed, convex domain $D\subset\mathbb{R}^d$, represented as a reflected stochastic differential equation. We introduce a sequence of penalized stochastic differential equations and prove that their invariant measures converge, in Wasserstein-2 distance and with explicit polynomial rate, to the invariant measur

Louis Geist, Loic Landrieu, Damien Robert

Superpoint-based pipelines provide an efficient alternative to point- or voxel-based 3D semantic segmentation, but are often bottlenecked by their CPU-bound partition step. We propose a learnable, fully GPU partitioning algorithm that generates geometrically and semantically coherent superpoints 13$\times$ faster than prior methods. Our module is compact (un

Shitong Luo, Connor W. Coley

The constrained nature of synthesizable chemical space poses a significant challenge for sampling molecules that are both synthetically accessible and possess desired properties. In this work, we present PrexSyn, an efficient and programmable model for molecular discovery within synthesizable chemical space. PrexSyn is based on a decoder-only transformer tra

Jianhai Su, Jinzhu Luo, Qi Zhang

We take the novel perspective of incorporating offline RL algorithms as subroutines of tabula rasa online RL. This is feasible because an online learning agent can repurpose its historical interactions as offline dataset. We formalize this idea into a framework that accommodates several variants of offline RL incorporation such as final policy recommendation

Dong Li, HuaLiang Lin, JiaYu Li

The weak-texture nature of facial skin regions presents significant challenges for local descriptor matching in applications such as facial motion analysis and 3D face reconstruction. Although deep learning-based descriptors have demonstrated superior performance to traditional hand-crafted descriptors in many applications, the scarcity of pore-scale image p

Mingwei Liu, Zheng Pei, Yanlin Wang, Zihao Wang

In low-resource framework development (e.g., HarmonyOS), large language models (LLMs) often lack sufficient pre-training exposure, resulting in poor code generation performance. Although they generally preserve programming logic across languages, they frequently fail on framework-specific APIs and syntax, revealing a gap between learned algorithmic knowledge

Anas Aziz Khan, Md Shah Fahad, Priyanka, Ramesh Chandra

Accurate prediction of enzyme kinetic parameters is crucial for drug discovery, metabolic engineering, and synthetic biology applications. Current computational approaches face limitations in capturing complex enzyme-substrate interactions and often focus on single parameters while neglecting the joint prediction of catalytic turnover numbers (Kcat) and Mich

Nikit Phadke

We establish a precise mathematical equivalence between witness-based similarity systems (REWA) and Shannon's information theory. We prove that witness overlap is mutual information, that REWA bit complexity bounds arise from channel capacity limitations, and that ranking-preserving encodings obey rate-distortion constraints. This unification reveals that fi

Ajit Kumar, IndraPrakash Jha

Protein language models (PLMs) have transformed sequence-based protein analysis, yet most applications rely only on final-layer embeddings, which may overlook biologically meaningful information encoded in earlier layers. We systematically evaluate all 33 layers of ESM-2 for kinase functional prediction using both unsupervised clustering and supervised class

Arkadiusz Czuba

Machine learning researchers strive to develop better and better algorithms to solve computer vision problems, such as image classification. In recent years, the classification of micro-Doppler spectrograms has also benefited from these findings. Convolutional neural networks (CNNs) became the gold standard for these tasks. Unfortunately, CNNs can work on fi

Paolo A. Sossi, Remco C. Hin, Thorsten Kleine, Alessandro Morbidelli

Despite the fact that the terrestrial planets formed from the protoplanetary disk, their compositions show marked departures from that of solar nebula condensates. Metallic cores fix oxygen fugacities ($f$O$_2$s) of the planets to 5 (Mercury) to 1 log units below the iron-w\"ustite (IW) buffer, orders of magnitude higher than the nebular gas. Their oxidised

Zhiqiang Li, Hanyun Zheng

We show that a class of quasiregular Latt\`es maps, called orthotopic Latt\`es maps, are cellular Markov maps. This provides examples of expanding Thurston-type maps that are also uniformly quasiregular, and whose visual metrics are quasisymmetrically equivalent to the Riemannian distance.

Swadesh Sistla, Max Kleiman-Weiner

Large Language Models' (LLMs) programming capabilities enable their participation in open-source games: a game-theoretic setting in which players submit computer programs in lieu of actions. These programs offer numerous advantages, including interpretability, inter-agent transparency, and formal verifiability; additionally, they enable program equilibria, s

Zheqi Hu, Yiwen Hu, Hanwu Li

Efficient inventory management and accurate sales forecasting are critical challenges in large-scale e-commerce platforms such as Amazon, where stockouts and overstocking can lead to substantial financial losses and operational inefficiencies. Traditional single-task forecasting models, which focus solely on sales or inventory, often fail to capture the comp

Wenshuo Chen, Haosen Li, Shaofeng Liang, Lei Wang

The Inversion-Denoising Paradigm, which is based on diffusion models, excels in diverse image editing and restoration tasks. We revisit its mechanism and reveal a critical, overlooked factor in reconstruction degradation: the approximate noise error. This error stems from approximating the noise at step t with the prediction at step t-1, resulting in severe

Jian Zhu

Multi-View Clustering (MVC) has garnered increasing attention in recent years. It is capable of partitioning data samples into distinct groups by learning a consensus representation. However, a significant challenge remains: the problem of untrustworthy fusion. This problem primarily arises from two key factors: 1) Existing methods often ignore the presence

Aparajitha Allamraju, Maitreya Prafulla Chitale, Hiranmai Sri Adibhatla, Rahul Mishra

Document chunking is a crucial component of Retrieval-Augmented Generation (RAG), as it directly affects the retrieval of relevant and precise context. Conventional fixed-length and recursive splitters often produce arbitrary, incoherent segments that fail to preserve semantic structure. Although semantic chunking has gained traction, its influence on genera

Wenshuo Wang, Yaomin Shen, Yingjie Tan, Yihao Chen

Spatiotemporal forecasting often relies on computationally intensive models to capture complex dynamics. Knowledge distillation (KD) has emerged as a key technique for creating lightweight student models, with recent advances like frequency-aware KD successfully preserving spectral properties (i.e., high-frequency details and low-frequency trends). However,

Andrey Gizdov, Andrea Procopio, Yichen Li, Daniel Harari

Human physical reasoning relies on internal "body" representations - coarse, volumetric approximations that capture an object's extent and support intuitive predictions about motion and physics. While psychophysical evidence suggests humans use such coarse representations, their internal structure remains largely unknown. Here we test whether vision models t

Omer Eldadi, Gershon Tenenbaum, Abraham Loeb

Vickers et al. (2025) established that 58.20% of astrobiology experts believe intelligent extraterrestrial life likely exists, providing the first empirical baseline for public comparison. We surveyed 6,114 highly educated and scientifically engaged individuals (77.60% bachelor's degree+; 67.99% high-to-very-high scientific engagement) to assess their belief

Jianhong Han, Yupei Wang, Yuan Zhang, Liang Chen

Multimodal remote sensing object detection aims to achieve more accurate and robust perception under challenging conditions by fusing complementary information from different modalities. However, existing approaches that rely on attention-based or deformable convolution fusion blocks still struggle to balance performance and lightweight design. Beyond fusion

Qing Li, Haikuan Dong, Penghua Ying, Zheyong Fan

Two-dimensional fullerene networks have recently attracted increasing interest due to their diverse bonding topologies and mechanically robust architectures. In this work, we develop an accurate machine-learned potential NEP-C$_{24}$ for both the quasi-hexagonal phase (qHP) and the quasi-tetragonal phase (qTP) C$_{24}$ monolayers, based on the neuroevolution

Miguel Tierz

We obtain closed-form expressions for the $ST^nS$ modular kernels of non-rational Virasoro CFTs and use them to construct fully analytic modular-bootstrap functionals. At rational width $\tau$, the Mordell integrals in these kernels reduce to finite quadratic Gauss sums of $\operatorname{sech}/\sec$ profiles with explicit Weil phases, furnishing a canonical

Zhijian Yang, Edwin Setiadi Sugeng, Mhedine Alicherif, Tat Loon Chng

Machine learning (ML) models have recently been used to reconstruct electric field distributions from EFISH signal profiles-the 'inverse EFISH problem'. This addresses the line-of-sight EFISH inaccuracy caused by the Gouy phase shift in focused beams. A key benefit of this approach is that the accuracy of the reconstructed profile can be directly checked via

Na Li, Hangguan Shan, Wei Ni, Wenjie Zhang

A critical challenge for reinforcement learning (RL) is making decisions based on incomplete and noisy observations, especially in perturbed and partially observable Markov decision processes (P$^2$OMDPs). Existing methods fail to mitigate perturbations while addressing partial observability. We propose \textit{Causal State Representation under Asynchronous

Tingwei Xie, Tianyi Zhou, Yonghong Song

We present PharmaShip, a real-world Chinese dataset of scanned pharmaceutical shipping documents designed to stress-test pre-trained text-layout models under noisy OCR and heterogeneous templates. PharmaShip covers three complementary tasks-sequence entity recognition (SER), relation extraction (RE), and reading order prediction (ROP)-and adopts an entity-ce

Fan Yang, Xingyu Li

Quantized transport not only exist in gapped topological states but also in metallic states. Recently, Kane proposed a quantized nonlinear conductance in ballistic metals whose value is determined by the Euler characteristic of the Fermi sea [Phys. Rev. Lett. 128, 076801 (2022)]. In this paper, we consider two-dimensional noninteracting fermionic systems who

Junqiao Fan, Pengfei Liu, Haocong Rao

With intelligent room-side sensing and service robots widely deployed, human motion prediction (HMP) is essential for safe, proactive assistance. However, many existing HMP methods either produce a single, deterministic forecast that ignores uncertainty or rely on probabilistic models that sacrifice kinematic plausibility. Diffusion models improve the accura

Ruotong Zhang

We construct centered rarefaction wave solutions given background solutions to the compressible Euler equations. The flow considered in this article is the homentropic flow of perfect gas governed by compressible Euler equations and the gamma-law equation of state in 3-D spherical symmetry. We prove the existence of local in time rarefactions for general bac

Na Li, Zewu Zheng, Wei Ni, Hangguan Shan

Multi-agent reinforcement learning (MARL), as a thriving field, explores how multiple agents independently make decisions in a shared dynamic environment. Due to environmental uncertainties, policies in MARL must remain robust to tackle the sim-to-real gap. We focus on robust two-player zero-sum Markov games (TZMGs) in offline settings, specifically on tabul

Na Li, Yuchen Jiao, Hangguan Shan, Shefeng Yan

The thriving field of multi-agent reinforcement learning (MARL) studies how a group of interacting agents make decisions autonomously in a shared dynamic environment. Existing theoretical studies in this area suffer from at least two of the following obstacles: memory inefficiency, the heavy dependence of sample complexity on the long horizon and the large s

Ruirui Huang, Jiacheng Li

We introduce MedCondDiff, a diffusion-based framework for multi-organ medical image segmentation that is efficient and anatomically grounded. The model conditions the denoising process on semantic priors extracted by a Pyramid Vision Transformer (PVT) backbone, yielding a semantically guided and lightweight diffusion architecture. This design improves robust

Yanhui Li, Qi Zhou, Zhihong Xu, Huizhong Guo

Large vision-language models (LVLMs) are increasingly used for tasks where detecting multimodal harmful content is crucial, such as online content moderation. However, real-world harmful content is often camouflaged, relying on nuanced text-image interplay, such as memes or images with embedded malicious text, to evade detection. This raises a key question:

Mareike Dressler, Hongzhi Liao, Vera Roshchina

We provide a complete and explicit characterization of the exposed extreme rays of the cone of sums of nonnegative circuit (SONC) polynomials. The criterion we derive is purely combinatorial and depends only on the existence of certain circuits within the ground set and on the nature of the corresponding extreme ray. Our constructive proofs also yield explic

Hiroki Yamamichi

Turnpike theorems state that if an investor's utility is asymptotically equivalent to a power utility, then the optimal investment strategy converges to the CRRA strategy as the investment horizon tends to infinity. This paper aims to derive the convergence rates of the turnpike theorem for optimal feedback functions in stochastic factor models. In these mod

Junqiao Fan, Haocong Rao, Jiarui Zhang, Jianfei Yang

Existing Human Motion Prediction (HMP) methods based on RGB-D cameras are sensitive to lighting conditions and raise privacy concerns, limiting their real-world applications such as firefighting and healthcare. Motivated by the robustness and privacy-preserving nature of millimeter-wave (mmWave) radar, this work introduces radar as a novel sensing modality f

Naifan Zhang, Ruihan Sun, Ruixi Su, Shiqi Ma

The LLM field has spent a year perfecting RL for tasks machines already excel at, math, code, and deterministic reasoning, while completely sidestepping the domain that actually defines human intelligence: subjective, emotionally grounded, personality sensitive conversation. This space has often been regarded as inherently subjective and challenging to forma

Zhongqi Wang, Jie Zhang, Shiguang Shan, Xilin Chen

Vision-language pretrained models (VLPs) such as CLIP have achieved remarkable success, but are also highly vulnerable to backdoor attacks. Given a model fine-tuned by an untrusted third party, determining whether the model has been injected with a backdoor is a critical and challenging problem. Existing detection methods usually rely on prior knowledge of t

Haizheng Li, Lei Guo

Real-world intelligence systems usually operate by combining offline learning and online adaptation with highly correlated and non-stationary system data or signals, which, however, has rarely been investigated theoretically in the literature. This paper initiates a theoretical investigation on the prediction performance of a two-stage learning framework com

Zhiyuan Wang, Shengcai Liu, Shaofeng Zhang, Ke Tang

Evolutionary Algorithms (EAs) are widely used general-purpose optimization methods due to their domain independence. However, under a limited number of function evaluations (#FEs), the performance of EAs is quite sensitive to the quality of the initial population. Obtaining a high-quality initial population without problem-specific knowledge remains a signif

Shubhashish Pati, Sonali Srotaswini Pradhan, Abhay Pandey, Nikita Sharma

The interplay between real and reciprocal space topology yields intrinsically linked transport phenomena in magnetic Weyl systems, wherein the broken time-reversal symmetry, strong Dzyaloshinskii-Moriya interaction, and pronounced uniaxial anisotropy stabilize the momentum-space Berry-curvature monopoles (Weyl nodes) and real-space chiral spin textures. We p

Kuiyue Liu, Shanshan Chen

In this paper, we investigate a two-species competition model in a landscape consisting of a finite number of adjacent patches. For the two-patch scenario, by treating edge behavior at the interface as a strategy, it has been shown that there exists an ideal free distribution (IFD) strategy, which is a globally evolutionarily stable strategy. Specifically, w

Dehao Dai, Yunyi Zhang

The analysis of non-real-valued data, such as binary time series, has attracted great interest in recent years. This manuscript proposes a post-selection estimator for estimating the coefficient matrices of a high-dimensional generalized binary vector autoregressive process and establishes a Gaussian approximation theorem for the proposed estimator. Furtherm

Sunil Kumar Pasupulati

We study Euclidean ideal classes in real biquadratic fields and obtain unconditional existence results via genus theory. Lenstra showed (assuming the Generalized Riemann Hypothesis) that a number field with unit rank at least one admits a Euclidean ideal precisely when its class group is cyclic; subsequent work has aimed to remove the GRH hypothesis in speci

Takuto Ando, Yu Eto, Ayumu Takeuchi, Yasuhiko Nakashima

Large Language Models (LLMs) demand substantial computational resources, resulting in high energy consumption on GPUs. To address this challenge, we focus on Coarse-Grained Reconfigurable Arrays (CGRAs) as an effective alternative that provides a trade-off between energy efficiency and programmability. This paper presents the first comprehensive, end-to-end

Rui Li

The low-energy effective Hamiltonian of a cylindrical HgTe nanowire grown along the [001] crystallographic direction is constructed by using the perturbation theory. Both the anisotropic term and the bulk inversion asymmetry term of the Kane model are taken into account. Although the anisotropic term has converted the crossing between the $E_{1}$ and $H_{1}$

Ayush Maheshwari, Kaushal Sharma, Vivek Patel, Aditya Maheshwari

While large language models excel on high-resource multilingual tasks, low- and extremely low-resource Indic languages remain severely under-evaluated. We present IndicParam, a human-curated benchmark of over 13,000 multiple-choice questions covering 11 such languages (Nepali, Gujarati, Marathi, Odia as low-resource; Dogri, Maithili, Rajasthani, Sanskrit, Bo

Daud Waqas, Aaryamaan Golthi, Erika Hayashida, Huanzhi Mao

Multi-turn tool-calling LLMs (models capable of invoking external APIs or tools across several user turns) have emerged as a key feature in modern AI assistants, enabling extended dialogues from benign tasks to critical business, medical, and financial operations. Yet implementing multi-turn pipelines remains difficult for many safety-critical industries due

Jongbae Hong

Coherent peaks arising in the differential conductance of quantum dot (QD) and quantum point contact (QPC) devices are classified into two categories, normal and anomalous Kondo peaks, according to the underlying spin dynamics and the form of the scaling function to which the scaled temperature-dependent linear conductance collapses. The zero-bias peaks (ZBP

Ashish Thanga, Vibhu Dixit, Abhilash Shankarampeta, Vivek Gupta

Temporal reasoning over evolving semi-structured tables poses a challenge to current QA systems. We propose a SQL-based approach that involves (1) generating a 3NF schema from Wikipedia infoboxes, (2) generating SQL queries, and (3) query execution. Our central finding challenges model scaling assumptions: the quality of schema design has a greater impact on

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

The first observation of the decay $B^{+}\rightarrow p \bar{\it{\Lambda}}$ is presented, using proton-proton collision data collected by the LHCb experiment between 2016 and 2018 at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of $5.4\,\mathrm{fb}^{-1}$. The signal significance exceeds seven standard deviations. Using the $B^{

Chenqi Zhu, Levi Burner, Yiannis Aloimonos

Visual odometry techniques typically rely on feature extraction from a sequence of images and subsequent computation of optical flow. This point-to-point correspondence between two consecutive frames can be costly to compute and suffers from varying accuracy, which affects the odometry estimate's quality. Attempts have been made to bypass the difficulties or

Qianjie Wu, Tianyi Zhang, Hong Jia, Simon D'Alfonso

Loneliness is a critical mental health issue among university students, yet traditional monitoring methods rely primarily on retrospective self-reports and often lack real-time behavioral context. This study explores the use of passive smartphone sensing data to predict loneliness levels, addressing the limitations of existing approaches in capturing its dyn

Shaira Sadia Karim, Abrar Mahmud Rahim, Lamia Alam, Ishmam Tashdeed

Bug reports are often unstructured and verbose, making it challenging for developers to efficiently comprehend software issues. Existing summarization approaches typically rely on surface-level textual cues, resulting in incomplete or redundant summaries, and they frequently ignore associated code snippets, which are essential for accurate defect diagnosis.