December 2025 arXiv papers — page 23

Haoyu Wang, Zhi Sun, Shuangfeng Han, Xiaoyun Wang

Deep learning is promising to enhance the accuracy and reduce the overhead of channel state information (CSI) feedback, which can boost the capacity of frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. Nevertheless, the generalizability of current deep learning-based CSI feedback algorithms cannot be guaranteed in unseen

Shreeman Auromahima, Sitangshu Bikas Santra, Biplab Bose

In this work, we investigate a simple nonequilibrium system with many interconnected, open subsystems, each exchanging a globally conserved resource with an external reserve. The system is represented by a random graph, where nodes represent the subsystems connected through edges. At each time step, a randomly selected node gains a token (i.e, a resource) fr

PengCheng Hong, RongGang Ping, WeiMin Song

We present a systematic analysis of Bell nonlocality and entanglement in $\chi_{cJ}$($J=0,1,2$) decays into baryon pair($B\bar{B}$), with particular emphasis on their production via the process $e^+e^- \to \psi(2S) \to \gamma \chi_{cJ}$ at BESIII. From the baryon-antibaryon spin density matrix, we construct measurable Bell observables and concurrence, reveal

Vitaliy Golomoziy

In this paper, we consider a modified version of a well-known submartingale condition fortheweak convergence of probabilitymeasures, adapted to the semi-Markov case. In this setting, it is convenient to work with an embedded Markov chain and the filtration generated by jump times. We demonstrate that a straightforward restatement of the classical result is n

Xin Wei, Xiande Zhang, Gennian Ge

For a prime $p \equiv 2 \pmod 3$, it is well known that the largest sum-free subsets of $\mathbb{F}_p^n$ have size $\frac{p+1}{3} p^{n-1}$, and the extremal sets must be a cuboid of the form $\{\frac{p+1}{3}, \frac{p+1}{3}+1, \ldots, \frac{2p-1}{3}\} \times \mathbb{F}_p^{n-1}$ up to isomorphism. Recently, Reiner and Zotova proved a Hilton--Milner type stabil

Terkaa Victor Targema, Kazuharu Bamba, Riasat Ali, Usman Zafar

Chaotic motion near black holes has recently been examined through the lens of the Maldacena-Shenker-Stanford (MSS) chaos-bound, but reported violations remain contradictory. A significant source of ambiguity stems from treating the particle angular momentum as an independently adjustable parameter instead of as a quantity fixed by the circular-orbit conditi

Joyful E. Mdhluli, IAU Office of Astronomy for Development

Astronomy, often perceived as a distant or luxury science, holds immense potential as a driver for sustainable local socio-economic development. This paper explores how astronomy can create tangible benefits for communities through education, tourism, technology transfer, and capacity building. Using case studies from South Africa, Chile, Indonesia, and Indi

Zhenbao Yu, Shirong Ye, Ronghe Jin, Shunkun Liang

In this paper, we aim to estimate the relative pose and focal length between two views with known intrinsic parameters except for an unknown focal length from two affine correspondences (ACs). Cameras are commonly used in combination with inertial measurement units (IMUs) in applications such as self-driving cars, smartphones, and unmanned aerial vehicles. T

Cuiling Wu, Yaozhong Gan, Junliang Xing, Ying Fu

We propose Multi Agent Reflective Policy Optimization (MARPO) to alleviate the issue of sample inefficiency in multi agent reinforcement learning. MARPO consists of two key components: a reflection mechanism that leverages subsequent trajectories to enhance sample efficiency, and an asymmetric clipping mechanism that is derived from the KL divergence and dyn

Endre Süli, Dennis Trautwein

In this work, we develop a class of stable and convergent numerical methods for the approximate solution of the viscoelastic Giesekus model in two space dimensions. The model couples the incompressible Navier--Stokes equations with an evolution equation for an additional stress tensor accounting for elastic effects. This coupled evolution equation is stated

Zirui Zhou, Junfeng Ni, Shujie Zhang, Yixin Chen

Change detection plays a vital role in scene monitoring, exploration, and continual reconstruction. Existing 3D change detection methods often exhibit spatial inconsistency in the detected changes and fail to explicitly separate pre- and post-change states. To address these limitations, we propose SCaR-3D, a novel 3D scene change detection framework that ide

Elias Aravantinos

This study introduces the Digital Competitiveness Index for Trade (DCIT), a composite metric integrating ICT readiness, broadband adoption, GDP per capita, foreign direct investment, government effectiveness, and trade volume to assess countries' digital trade competitiveness. The index captures the enabling conditions -- ICT innovation capacity, broadband d

Hai-Jun Li

In this work, we investigate the effective parameter space associated with the axion mass and the axion decay constant in both the light and heavy QCD axion scenarios. We initiate our discussion by considering the simplest case of two axions, quantitatively analyzing the parameter space in these two distinct scenarios. We find that the axion mass ratios exhi

Juha Park, Ian P. Roberts, Wonjae Shin

High-gain beamforming (BF) is essential for low Earth orbit (LEO) satellite communications to overcome severe path loss, but this requires acquiring precise satellite positions. Conventional satellite acquisition typically relies on time-domain beam sweeping, which incurs substantial overhead and latency. In this correspondence, we propose an efficient one-s

Abu Syed

The rapid integration of AI into education has prioritized capability over trustworthiness, creating significant risks. Real-world deployments reveal that even advanced models are insufficient without extensive architectural scaffolding to ensure reliability. Current evaluation frameworks are fragmented: institutional policies lack technical verification, pe

Aryan Tyagi, Soumyajyoti Biswas, Anirban Chakraborti

The Parallel Minority Game (PMG) is a synchronous adaptive multi-agent model that exhibits active-absorbing transitions characteristic of non-equilibrium statistical systems. We perform a comprehensive numerical study of the PMG under two families of microscopic decision rules: (i) agents update their choices based on instantaneous population in their altern

Ke Wang, Chan-Tong Lam, Benjamin K. Ng, Yue Liu

This paper investigates mutual coupling between phase-dependent amplitudes (PDAs) and designed phase shifts within pixels of near-field (NF) reconfigurable intelligent surfaces (RISs) in the presence of phase errors (PEs). In contrast to existing research that treats phase shifts with errors (PSEs) and the PDAs separately, we introduce a remaining power (RP)

Gaurav Chaudhary, Laxmidhar Behera

Reinforcement Learning (RL) has achieved significant success in solving single-goal tasks. However, uniform goal selection often results in sample inefficiency in multi-goal settings where agents must learn a universal goal-conditioned policy. Inspired by the adaptive and structured learning processes observed in biological systems, we propose a novel Studen

Sameer Sitoula, Tej Bahadur Shahi, Laxmi Prasad Bhatt, Anisha Pokhrel

Social media (SM) platforms (e.g. Facebook, Twitter, and Reddit) are increasingly leveraged to share opinions and emotions, specifically during challenging events, such as natural disasters, pandemics, and political elections, and joyful occasions like festivals and celebrations. Among the SM platforms, Reddit provides a unique space for its users to anonymo

Chenyu Li, Danfeng Hong, Bing Zhang, Zhaojie Pan

The highly nonlinear degradation process, complex physical interactions, and various sources of uncertainty render single-image Super-resolution (SR) a particularly challenging task. Existing interpretable SR approaches, whether based on prior learning or deep unfolding optimization frameworks, typically rely on black-box deep networks to model latent variab

Yi Hu, Yongki Lee, Shijun Zheng

In this paper we give an analytical proof of the ``$\log$-$\log$'' blowup rate for mass-critical nonlinear Schr\"odinger equation (NLS) with a rotation ($\Omega \neq 0$) and a repulsive harmonic potential $V_{\gamma}(x) = \textrm{sgn}(\gamma) \gamma^2 |x|^2$, $\gamma < 0$ when the initial data has a mass slightly above that of $Q$, the ground state solution

Guang Shi, D. Thirumalai

Chromatin is repeatedly deformed in vivo during transcription, nuclear remodeling, and confined migration - yet how mechanical response varies from locus to locus, and how it relates to epigenetic state, remains unclear. We develop a theory to infer locus-specific viscoelasticity from three-dimensional genome organization. Using chromatin structures derived

Hualie Jiang, Ziyang Song, Zhiqiang Lou, Rui Xu

Panoramic depth estimation provides a comprehensive solution for capturing complete $360^\circ$ environmental structural information, offering significant benefits for robotics and AR/VR applications. However, while extensively studied in indoor settings, its zero-shot generalization to open-world domains lags far behind perspective images, which benefit fro

Ross Chu

This paper examines how loss aversion affects wages offered by employers and accepted by job seekers. I introduce a behavioral search model with monopsonistic firms making wage offers to job seekers who experience steeper disutility from pay cuts than utility from equivalent pay raises. Employers strategically reduce pay cuts to avoid offer rejections, and t

Sedi Bartz, Heinz H. Bauschke, Yuan Gao

The Krasnoselskii-Mann iteration is an important algorithm in optimization and variational analysis for finding fixed points of nonexpansive mappings. In the general case, it produces a sequence converging \emph{weakly} to a fixed point provided the parameter sequence satisfies a divergent-series condition. In this paper, we show that \emph{strong} convergen

Grigorios Giotopoulos, Hisham Sati

This is the second in a series of papers that aim to develop rigorous and most encompassing foundations for field theory, where in the first installment, we laid out the natural formulation of bosonic variational field theory via the functorial geometry of smooth sets. Here, we extend this to the category ThickenedSmoothSets of infinitesimally thickened smoo

Shyam Sunder Lakesar, Raj Ganesh S. Pala, K P Rajeev

Electrochemically induced nuclear activity in hydrogen and deuterium-absorbing metals has been reported intermittently, yet a direct observation of nuclear signatures remains challenging. We electrolyzed light water with nickel cathodes under half-wave rectified RMS potentials of 5 V and 20 V and subsequently analyzed them using a Peltier-cooled diffusion-ty

Scott A. Martin, Noah Brenowitz, Dale Durran, Michael Pritchard

Accurate long-range weather forecasting remains a major challenge for AI models, both because errors accumulate over autoregressive rollouts and because reanalysis datasets used for training offer a limited sample of the slow modes of climate variability underpinning predictability. Most AI weather models are autoregressive, producing short lead forecasts th

Mengyao Dai, Xin Zhang

The rainbow number ${\rm rb}(G, H)$ is the minimum number of colors $k$ for which any edge-coloring of $G$ with at least $k$ colors guarantees a rainbow subgraph isomorphic to $H$. The rainbow number has many applications in diverse fields such as wireless communication networks, cryptography, bioinformatics, and social network analysis. In this paper, we de

Hong Song Fang, Ly Ly Nguyen Thi, Shu-Chun Chu

A metalens is composed of an array of artificially designed meta-atoms which can manipulate the phase, polarization and amplitude of light making it an excellent component for wavefront modulation. In this study, we design an transmittive achromatic metalens (NA equals to 0.05) composed of sapphire substrate and cross-shape silica meta-atom array operating a

Qiao Wang, Pengyue Zhao

Deformable and flexible electronics have garnered significant attention due to their distinctive properties; however, their current applications are primarily limited to the thermoelectric domain. Expanding the range of these electronics and their application scope represents a pivotal trend in their development. In this work, a plastic inorganic semiconduct

Ross Chu, Sohee Jeon, Hyun Seung Lee, Tammy Lee

Using detailed data on workplace benefits linked with administrative registers in Korea, we analyze patterns of separations and job transitions to study how parents sort into family-friendly firms after childbirth. We examine two quasi-experimental case studies: 1) staggered compliance with providing onsite childcare, and 2) mandated enrollment into paternit

Xin Zhang, Dezhi Zou

Motivated by frequency assignment problems in wireless broadcast networks, Goddard, Hedetniemi, Hedetniemi, Harris, and Rall introduced the notion of $S$-packing coloring in 2008. Given a non-decreasing sequence $S = (s_1, s_2, \ldots, s_k)$ of positive integers, an $S$-packing coloring of a graph $G$ is a partition of its vertex set into $k$ subsets $\{V_1,

Libo Zhang, Zekun Li, Tianyu Li, Zeyu Cao

Humans exhibit adaptive, context-sensitive responses to egocentric visual input. However, faithfully modeling such reactions from egocentric video remains challenging due to the dual requirements of strictly causal generation and precise 3D spatial alignment. To tackle this problem, we first construct the Human Reaction Dataset (HRD) to address data scarcity

Yuki Seo, Shuhei Wada, Takeaki Yamazaki

In this paper, we consider a two-variable operator function that includes two weighted spectral geometric means, and show fundamental properties of the operator function. Moreover, it satisfies the Ando-Hiai type inequality under some restricted conditions. As an application, we show the log-majorization relations and norm inequalities for the spectral geome

Kaustubh Shivshankar Shejole, Sourabh Deoghare, Pushpak Bhattacharyya

Neural Machine Translation (NMT) systems rely heavily on explicit punctuation cues to resolve semantic ambiguities in a source sentence. Inputting user-generated sentences, which are likely to contain missing or incorrect punctuation, results in fluent but semantically disastrous translations. This work attempts to highlight and address the problem of punctu

Jorge I. Poveda, Mahmoud Abdelgalil

We study sufficient conditions for stability and recurrence in a class of singularly perturbed stochastic hybrid dynamical systems. The systems considered combine multi-time-scale deterministic continuous-time dynamics, modeled by constrained differential inclusions, with discrete-time dynamics described by constrained difference inclusions subject to random

Yuting Wang, Xin Zhang

A proper conflict-free coloring of a graph is a proper vertex coloring wherein each non-isolated vertex's open neighborhood contains at least one color appearing exactly once. For a non-negative integer $k$, a graph $G$ is said to be proper conflict-free (degree+$k$)-choosable if given any list assignment $L$ for $G$ where $|L(v)| = d(v) + k$ holds for every

Bor-Yiing Su, Peter Dykas, Mike Chrzanowski, Jatin Chhugani

Mixed-precision training is a crucial technique for scaling deep learning models, but successful mixedprecision training requires identifying and applying the right combination of training methods. This paper presents our preliminary study on Mixture-of-Representations (MoR), a novel, per-tensor and sub-tensor level quantization framework that dynamically an

Dan Mikulincer, Youngtak Sohn

We study the mixing time of Glauber dynamics for Ising models in which the interaction matrix contains a single negative spectral outlier. This class includes the anti-ferromagnetic Curie-Weiss model, the anti-ferromagnetic Ising model on expander graphs, and the Sherrington-Kirkpatrick model with disorder of negative mean. Existing approaches to rapid mixin

Amirhossein Tighkhorshid, Zahra Dehghanian, Gholamali Aminian, Chengchun Shi

Distillation addresses the slow sampling problem in diffusion models by creating models with smaller size or fewer steps that approximate the behavior of high-step teachers. In this work, we propose a reinforcement learning based distillation framework for diffusion models. Instead of relying on fixed reconstruction or consistency losses, we treat the distil

Po-Chih Wu

Open-vocabulary object detection enables models to localize and recognize objects beyond a predefined set of categories and is expected to achieve recognition capabilities comparable to human performance. In this study, we aim to evaluate the performance of existing models on open-vocabulary object detection tasks under low-quality image conditions. For this

Bin Liu, Wenyan Tian, Huangxin Fu, Zizheng Li

3D reconstruction of medical images is a key technology in medical image analysis and clinical diagnosis, providing structural visualization support for disease assessment and surgical planning. Traditional methods are computationally expensive and prone to structural discontinuities and loss of detail in sparse slices, making it difficult to meet clinical a

Jingchao Wang, Kaiwen Zhou, Zhijian Wu, Kunhua Ji

Vision-Language Tracking aims to continuously localize objects described by a visual template and a language description. Existing methods, however, are typically limited to local search, making them prone to failures under viewpoint changes, occlusions, and rapid target movements. In this work, we introduce the first global tracking framework based on Multi

Zongkun Zheng

We prove a new mean value theorem on the distribution of primes in two simultaneous arithmetic progressions. Our approach builds on previous arguments of Bombieri, Fouvry, Friedlander, and Iwaniec appealing to spectral theory of Kloosterman sums, as well as the $q$-analogue of van der Corput method. In particular, we need estimates for exponential sums comin

Ross Chu, Yuting Huang

This paper develops AI agents that help job seekers write effective requests for job referrals in a professional online community. The basic workflow consists of an improver agent that rewrites the referral request and an evaluator agent that measures the quality of revisions using a model trained to predict the probability of receiving referrals from other

Masaki Fukuda, Tommy Shu

The established equivalence between 2-crossed modules and Gray 3-groups [M. Sarikaya and E. Ulualan, 2024] serves as a benchmark for higher-dimensional algebraic models. However, to the best of our knowledge, the established definitions of 3-crossed modules [Z. Arvasi, T. S. Kuzpinari, and E. \"O. Uslu, 2009] are not clearly suited for extending this equival

Benjamin Avanzi, Matthew Lambrianidis, Greg Taylor, Bernard Wong

The use of neural networks trained on individual claims data has become increasingly popular in the actuarial reserving literature. We consider how to best input historical payment data in neural network models. Additionally, case estimates are also available in the format of a time series, and we extend our analysis to assessing their predictive power. In t

Ruoyu Wang, Ziyu Li, Beier Zhu, Liangyu Yuan

Diffusion models (DMs) have achieved state-of-the-art generative performance but suffer from high sampling latency due to their sequential denoising nature. Existing solver-based acceleration methods often face significant image quality degradation under a low-latency budget, primarily due to accumulated truncation errors arising from the inability to captur

Risha Surana, Adrian Law, Sunwoo Kim, Rishab Sridhar

Clinical notes are often stored in unstructured or semi-structured formats after extraction from electronic medical record (EMR) systems, which complicates their use for secondary analysis and downstream clinical applications. Reliable identification of section boundaries is a key step toward structuring these notes, as sections such as history of present il

Risha Surana, Cameron Saidock, Hugo Chacon

MixRx uses Large Language Models (LLMs) to classify drug combination interactions as Additive, Synergistic, or Antagonistic, given a multi-drug patient history. We evaluate the performance of 4 models, GPT-2, Mistral Instruct 2.0, and the fine-tuned counterparts. Our results showed a potential for such an application, with the Mistral Instruct 2.0 Fine-Tuned

Minh Bui, Simon Monckton, Mo Chen

Reach-avoid (RA) games have significant applications in security and defense, particularly for unmanned aerial vehicles (UAVs). These problems are inherently challenging due to the need to consider obstacles, consider the adversarial nature of opponents, ensure optimality, and account for nonlinear dynamics. Hamilton-Jacobi (HJ) reachability analysis has eme

Shuai Chen, Yang Song, Chen Wang, Ziran Wang

Electronic nose (E-nose) systems face two interconnected challenges in open-set gas recognition: feature distribution shift caused by signal drift and decision boundary failure induced by unknown gas interference. Existing methods predominantly rely on Euclidean distance or conventional classifiers, failing to account for anisotropic feature distributions an

Sergey K. Tolpygo, Evan B. Golden, Vasili K. Semenov

We study energy dissipation and propagation of information encoded by Josephson vortices in two types of circular shift register: a) a uniform register composed of sections of discrete Josephson transmission lines (JTL) forming a closed loop with a flux pump allowing to change the number of moving fluxon; b) a nonuniform register composed of sections of the

Geoff Kimm, Linus Tan

Large Language Models (LLMs) are increasingly used in complex knowledge work, yet linear transcript interfaces limit support for reflection. Schon's Reflective Practice distinguishes between reflection-in-action (during a task) and reflection-on-action (after a task), both benefiting from non-linear, revisitable representations of dialogue. ChatGraPhT is an

Ying Li, Wenjun Qiu, Faysal Hossain Shezan, Kunlin Cai

Recent privacy regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) have established legal requirements for obtaining user consent regarding the collection, use, and sharing of personal data. These regulations emphasize that consent must be informed, freely given, specific, and unambiguous. However,

Zhelun Chen

We consider the limiting behaviour of the archimedean height pairing for homologically trivial algebraic cycles in a degenerating one-parameter family of smooth projective complex varieties. We conjecture that the limit is controlled by the non-archimedean geometric height pairing of the cycles on the generic fiber and verify this for algebraically trivial c

Zhou Yang, Edward Dougherty, Chen Zhang, Zhenhe Pan

A comprehensive retrospective analysis of public health interventions, such as large scale testing, quarantining, and contact tracing, can help identify mechanisms most effective in mitigating COVID-19. We investigate China based SARS-CoV-2 transmission patterns (e.g., infection type and likely transmission source) using publicly released tracking data. We c

Özhan Bingöl

Predefined-time stability enables convergence within a user-specified time independent of initial conditions. Existing results are predominantly based on autonomous Lyapunov inequalities, where the predefined-time is realized through integral bounds on state-dependent decay and therefore acts as an upper bound rather than a structurally enforced deadline. Th

Kiarash Vaziri, Lucine L. Oganesian, HyeongChan Jo, Roberto M. C. Vera

Dynamical modeling of multisite human intracranial neural recordings is essential for developing neurotechnologies such as brain-computer interfaces (BCIs). Linear dynamical models are widely used for this purpose due to their interpretability and their suitability for BCIs. In particular, these models enable flexible real-time inference, even in the presenc

Mikhail Erementchouk, Aditya Shukla, Pinaki Mazumder

Dynamical Ising machines are continuous dynamical systems that evolve from a generic initial state to a state strongly related to the ground state of the classical Ising model. We show that such a machine driven by the V${}_2$ dynamical model can solve exactly discrete tomography problems about reconstructing a binary image from the pixel sums along a discre

Henry Froland, Dorota M. Grabowska, Zhiyao Li

Quantum simulations of many-body systems offer novel methods for probing the dynamics of the Standard Model and its constituent gauge theories. Extracting low-energy predictions from such simulations rely on formulating systematically-improvable representations of lattice gauge theory Hamiltonians that are efficient at all values of the gauge coupling. One s

Hollis Williams

The impact of solid intruders into granular media is commonly described by a combination of quasi-static resistance and an inertial drag force proportional to the square of the impact speed. While intruder geometry is known to influence force magnitudes, its role in controlling the onset of inertial drag has remained largely unexplored. Here we present syste

Zhicheng Liao, Baoliang Chen, Hanwei Zhu, Lingyu Zhu

Existing AGIQA models typically estimate image quality by measuring and aggregating the similarities between image embeddings and text embeddings derived from multi-grade quality descriptions. Although effective, we observe that such similarity distributions across grades usually exhibit multimodal patterns. For instance, an image embedding may show high sim

Yuan Ren, Yong Zheng, Ruixue Liu, Boyang Nan

The development of sophisticated integrated photonic circuits demands microresonators that combine exceptional optical confinement with dynamic operational flexibility. Here, we demonstrate a racetrack resonator on the thin-film lithium niobate platform that achieves an electro-optically tunable coupling strength while maintaining a stable, high intrinsic Q

Muhammad Zain Ali, Bernhard Pfahringer, Tony Smith

Misinformation on social media is a widely acknowledged issue, and researchers worldwide are actively engaged in its detection. However, low-resource languages such as Urdu have received limited attention in this domain. An obvious approach is to utilize a multilingual pretrained language model and fine-tune it for a downstream classification task, such as m

S. A. Bogatyi, E. A. Reznichenko, A. A. Tuzhilin

A topological space is said to be cardinality homogeneous if every nonempty open subset has the same cardinality as the space itself. Let $X$ and $Y$ be cardinality homogeneous metric spaces of the same cardinality. If there exists a $\delta$-surjective $d$-isometry between such equicardinal cardinality homogeneous metric spaces $X$ and $Y$, then there exist

Drishti Gupta, Arun Thalapillil

We present the first systematic resurgent analysis of the Euler-Heisenberg Lagrangian in spinor and scalar quantum electrodynamics for the most general constant background field configuration. In contrast to the extensively studied single-field cases, the two-field case exhibits unique asymptotic structures, leading to a substantially richer pattern of singu

Truong Son Nguyen

We introduce \textbf{Schr\"{o}dinger AI}, a unified machine learning framework inspired by quantum mechanics. The system is defined by three tightly coupled components: (1) a {time-independent wave-energy solver} that treats perception and classification as spectral decomposition under a learned Hamiltonian; (2) a {time-dependent dynamical solver} governing

Julia Gaudio, Andrew Jin

Community detection is the problem of identifying dense communities in networks. Motivated by transitive behavior in social networks ("thy friend is my friend"), an emerging line of work considers spatially-embedded networks, which inherently produce graphs containing many triangles. In this paper, we consider the problem of exact label recovery in the Geome

Xuyan Li, Jie Wang, Zheng Yan

Dynamic graphs are widely used to represent evolving real-world networks. Temporal Graph Neural Networks (TGNNs) have emerged as a powerful tool for processing such graphs, but the lack of transparency and explainability limits their practical adoption. Research on TGNN explainability is still in its early stages and faces several key issues: (i) Current met

Yiqian Li, Wen Jiang, Kostas Daniilidis

Understanding semantics and dynamics has been crucial for embodied agents in various tasks. Both tasks have much more data redundancy than the static scene understanding task. We formulate the view selection problem as an active learning problem, where the goal is to prioritize frames that provide the greatest information gain for model training. To this end

Naoki Kitamura, Yuichi Sudo, Koichi Wada

The computational power of autonomous mobile robots under the Look-Compute-Move (LCM) model has been widely studied through an extensive hierarchy of robot models defined by the presence of memory, communication, and synchrony assumptions. While the general n-robot landscape has been largely established, the exact structure for two robots has remained unreso

Michael Ruofan Zeng

We study the Grothendieck group of the variety $X_{n,k}$ of spanning line configurations introduced by Pawlowski--Rhoades [arXiv:1711.08301] as a geometric model for the generalized coinvariant algebra $R_{n,k}$. Our first result is a localization statement in $K$-theory for the complements of cell closures in smooth cellular varieties. Combining with the Fu

Nikhil Ghosh, Denny Wu, Alberto Bietti

The growing scale of deep learning models has rendered standard hyperparameter (HP) optimization prohibitively expensive. A promising solution is the use of scale-aware hyperparameters, which can enable direct transfer of optimal HPs from small-scale grid searches to large models with minimal performance loss. To understand the principles governing such tran

Minghao Dong, Xinyang Luo, Xujian Ouyang, Yongshun Xiao

Compton cameras (CCs) are a kind of gamma cameras which are designed to determine the directions of incident gammas based on the Compton scatter. However, the reconstruction of CCs face problems of severe artifacts and deformation due to the fundamental reconstruction principle of back-projection of Compton cones. Besides, a part of systematic errors origina

Yijun Ran, Si-Yuan Liu, Junjie Huang, Tao Jia

Signed networks, encoding both positive and negative interactions, are essential for modeling complex systems in social and financial domains. Sign prediction, which infers the sign of a target link, has wide-ranging practical applications. Traditional motif-based Na\"ive Bayes models assume that all neighboring nodes contribute equally to a target link's si

Wei-Tse Cheng, Yen-Jen Chiou, Yuan-Fu Yang

We introduce RGS-SLAM, a robust Gaussian-splatting SLAM framework that replaces the residual-driven densification stage of GS-SLAM with a training-free correspondence-to-Gaussian initialization. Instead of progressively adding Gaussians as residuals reveal missing geometry, RGS-SLAM performs a one-shot triangulation of dense multi-view correspondences derive

Alexis Nikolakopoulos, Raúl González-Jiménez

We point out that, under certain conditions, the nuclear currents that couple to vector bosons can be written as the trace of the product of two matrices. One contains `nucleon dynamics', e.g. form factors, the other contains the overlaps of nuclear wavefunctions. This factorized form may always be obtained and viewed as a `local' approximation, in which par

Xinyi Che, Xiangyu Lyu, Changfu Shi

Dynamical Chern-Simons gravity, a parity-violating modification of general relativity, is regarded as a low-energy effective theory arising from string theory. Gravitational waves provide a powerful probe for testing its predictions. However, current gravitational wave observations are unable to place meaningful constraints on this theory through phase measu

Qianqian Liu, Ajit A. Diwan, Heping Zhang

For a graph $G$ with order $2n$ and a perfect matching, let $f(G)$ and $F(G)$ denote the minimum and maximum forcing number of $G$ respectively. Then $0\leq f(G)\leq F(G)\leq n-1$. Liu and Zhang [10] ever proposed a conjecture: $e(G)\geq \frac{n^2}{n-F(G)}$, where $e(G)$ denotes the number of edges of $G$. In this paper we confirm this conjecture and obtain

Yunge Li, Lanyu Xu

Vision Transformers (ViTs) have achieved remarkable success in visual recognition tasks, but redundant token representations limit their computational efficiency. Existing token merging and pruning strategies often overlook spatial continuity and neighbor relationships, resulting in the loss of local context. This paper proposes novel neighbor-aware token re

Yijun Ran, Jingjing Xiao, Xiao-Ke Xu

Identifying social bots has become a critical challenge due to their significant influence on social media ecosystems. Despite advancements in detection methods, most topology-based approaches insufficiently account for the heterogeneity of neighborhood preferences and lack a systematic theoretical foundation, relying instead on intuition and experience. Her

Eswarasanthosh Kumar Mamillapalli, Nishtha Sharma

Childhood obesity remains a major public health challenge in the United States, strongly influenced by a combination of individual-level, household-level, and environmental-level risk factors. Traditional epidemiological studies typically analyze these levels independently, limiting insights into how structural environmental conditions interact with individu

Zheng Qiu, Chih-Yuan Chiu, Glen Chou

We present an iterative active constraint learning (ACL) algorithm, within the learning from demonstrations (LfD) paradigm, which intelligently solicits informative demonstration trajectories for inferring an unknown constraint in the demonstrator's environment. Our approach iteratively trains a Gaussian process (GP) on the available demonstration dataset to

K. S. Babu, Rahool Kumar Barman, Dorival Gonçalves

We investigate the prospects for observing lepton number violation (LNV) by two units, $|\Delta L| = 2$, at the LHC within the leptoquark variant of the Zee Model, where Majorana neutrino masses arise radiatively at one-loop. The model features an $SU(2)_L$ doublet and singlet leptoquarks, whose interactions produce a distinctive same-sign dilepton plus jets

Hayato Morimura

The wrapped Fukaya category of a Liouville sector is defined via an axiomatic construction from the associated abstract wrapped Floer setup. In this paper, we propose a modified axiomatic construction, removing the irrelevant choices and the factorization axiom from the abstract wrapped Floer setup. Based on our modification, we reformulate and then prove a

Yuli Tan, Junling Zhou

The metric space $\mathcal{H}_{q}(n,w)$ is the set of all words of length $n$ with weight $w$ over the alphabet $\mathbb{Z}_{q}$, under the Hamming distance metric. A $q$-ary constant-weight code, as a nonempty subset of $\mathcal{H}_{q}(n,w)$, has always been a fundamental topic in coding theory. This paper investigates the tiling problem of $\mathcal{H}_{q

Moustapha Awwalou Diouf, Maimouna Tamah Diao, Iyiola Emmanuel Olatunji, Abdoul Kader Kaboré

LLMs democratize software engineering by enabling non-programmers to create applications, but this same accessibility fundamentally undermines security assumptions that have guided software engineering for decades. We show in this work how publicly available LLMs can be socially engineered to transform novices into capable attackers, challenging the foundati

Yoshiaki Sofue, Steven N. Longmore, Daniel Walker, Adam Ginsburg

We investigated the gravitational potential and mass distribution in the Galactic Center by examining the morphology and kinematics of the circumnuclear gaseous disk revealed by the molecular line data from the ALMA CMZ Exploration Survey (ACES). We obtain an estimate of the shape of the potential {within the central $\sim 20$ pc} to reproduce the observed p

Jiachen Jin, Kangkang Deng, Hongxia Wang

We study a class of nonsmooth stochastic optimization problems on Riemannian manifolds. In this work, we propose MARS-ADMM, the first stochastic Riemannian alternating direction method of multipliers with provable near-optimal complexity guarantees. Our algorithm incorporates a momentum-based variance-reduced gradient estimator applied exclusively to the smo

Hao Zhang, Mengsi Lyu, Bo Huang, Yulong Ao

Large Multimodal Models (LMMs) have proven effective on various tasks. They typically encode visual inputs into Original Model sequences of tokens, which are then concatenated with textual tokens and jointly processed by the language model. However, the growing number of visual tokens greatly increases inference cost. Visual token pruning has emerged as a pr

Jaclyn Ocumpaugh, Zhanlan Wei, Amanda Barany, Xiner Liu

Data that contextualizes student interactions with online learning systems can be challenging to obtain. This study looks at the rhetorical strategies of a novel method for conducting in-the-moment Data-Driven Classroom Interviews (DDCIs). By using Ordered Network Analysis (ONA) to reanalyze data from Wei et al.'s (2025) Epistemic Network Analysis, we better

Qiuyuan Zhong, Charudatta Datar, Wei Liu, Gan Liu

Achieving fast and continuous fabrication of large-scale complex 3D structures is key to unlocking industrial-scale adoption of two-photon lithography (TPL). Despite substantial improvement in peak optical patterning rates enabled by recent parallel exposure strategies, the practical fabrication rate of TPL for large structures remains low. This gap is prima

Yongzhen Hu, Yihui Yang, Haotong Lin, Yifan Wang

This paper addresses the problem of decomposed 4D scene reconstruction from multi-view videos. Recent methods achieve this by lifting video segmentation results to a 4D representation through differentiable rendering techniques. Therefore, they heavily rely on the quality of video segmentation maps, which are often unstable, leading to unreliable reconstruct

Rihong Qiu, Zhibang Yang, Xinke Jiang, Weibin Liao

Text-to-SQL translates natural language questions into SQL statements grounded in a target database schema. Ensuring the reliability and executability of such systems requires validating generated SQL, but most existing approaches focus only on syntactic correctness, with few addressing semantic validation (detecting misalignments between questions and SQL).

Ertza Warraich, Ali Imran, Annus Zulfiqar, Shay Vargaftik

As distributed machine learning (ML) workloads scale to thousands of GPUs connected by high-speed interconnects, tail latency in collective communication has become a major bottleneck. Existing RDMA transports, such as RoCE, IRN, SRNIC, and Falcon, enforce strict reliability and in-order delivery, relying on retransmissions and packet sequencing to ensure co

Hanze Meng, Jianhao Cao, Rachel Pottinger

Column Type Annotation (CTA) is a fundamental step towards enabling schema alignment and semantic understanding of tabular data. Existing encoder-only language models achieve high accuracy when fine-tuned on labeled columns, but their applicability is limited to in-domain settings, as distribution shifts in tables or label spaces require costly re-training f

Dongning Rao, Yunbiao Zeng, Zhihua Jiang, Jujian Lv

Human-interaction-involved applications underscore the need for Multi-modal Sentiment Analysis (MSA). Although many approaches have been proposed to address the subtle emotions in different modalities, the power of explanations and temporal alignments is still underexplored. Thus, this paper proposes the Text-routed sparse mixture-of-Experts model with eXpla

Sungwoo Kang

Multi-task learning (MTL) in materials science relies on the assumption that physically related properties share learnable representations. We challenge this assumption using a 54,028-sample metal alloy dataset exhibiting extreme task-level imbalance. Our results reveal a striking dichotomy: MTL significantly degrades regression performance for resistivity a