November 2025 arXiv papers — page 135

Haitian Wang, Tingting Shi, Xiaoling Cui

We investigate tetratomic and hexatomic bound states of ultracold molecules dressed by an elliptic microwave field. We show that these bound states can be accurately described by effective one-dimensional (1D) models incorporating high-order angular fluctuations, despite the physical system is in three-dimensional (3D) free space. By comparing with exact sol

James Jin Kang, Dang Bui, Thanh Pham, Huo-Chong Ling

The growing use of large language models in sensitive domains has exposed a critical weakness: the inability to ensure that private information can be permanently forgotten. Yet these systems still lack reliable mechanisms to guarantee that sensitive information can be permanently removed once it has been used. Retraining from the beginning is prohibitively

Yidan Sun, Mengying Zhu, Feiyue Chen, Yangyang Wu

Large language models (LLMs) have demonstrated impressive performance in text generation tasks; however, their embedding spaces often suffer from the isotropy problem, resulting in poor discrimination of domain-specific terminology, particularly in legal and financial contexts. This weakness in terminology-level representation can severely hinder downstream

Dianzhi Yu, Conghao Xiong, Yankai Chen, Wenqian Cui

Survival prediction of cancers is crucial for clinical practice, as it informs mortality risks and influences treatment plans. However, a static model trained on a single dataset fails to adapt to the dynamically evolving clinical environment and continuous data streams, limiting its practical utility. While continual learning (CL) offers a solution to learn

Chao Zhang, Yulin Lu

Since the Industrial Revolution, the world economy has experienced rapid development, and China's economy has also achieved an unprecedented takeoff in the past. Behind the economic growth, population surge, and continuous improvement of people's living standards lies the enormous consumption of fossil energy and environmental pollution. This kind of polluti

Gourab Das, Saptarshi Saha, Rangeet Bhattacharyya

Many-body quantum systems, under suitable conditions, exhibit time-translation symmetry breaking and settle in a discrete time crystalline (DTC) phase -- an out-of-equilibrium quantum phase of matter. The defining feature of DTC is a robust subharmonic response. However, the DTC phase is fragile in the presence of environmental dissipation. Here, we propose

Brendon Madison

We present work on quantifying the minimum requirements for beam polarization precision at future $e^+e^-$ Higgs factories. We find that, under the assumption of a high electron beam polarization ($P_-$) that the positron polarization ($P_+$) is of key importance but for reasons both known and newly discovered. We have discovered that improved positron polar

Tao An

GRB 250702B shows ultra-long, episodic prompt activity (three hard gamma-ray episodes over ~ 3.2 h with quasi-regular spacing P~ 2825 s preceded by a soft X-ray flare about one day earlier. We interpret these phenomena with a unified scenario in which a stellar-mass black hole accretes from a massive, misaligned debris disk and launches a magnetically domina

Soichiro Fujii, Keisuke Hoshino, Yuki Maehara

We study $\omega$-equifibrations between weak $\omega$-categories in the sense of Batanin--Leinster. We define $\omega$-equifibrations as a natural weak $\omega$-categorical analogue of isofibrations between categories, and show that they can be characterised via the right lifting property with respect to a suitable set $J$ of strict $\omega$-functors. The d

Jack S. Calcut, Samantha E. Nieman

We study theta-curves embedded in a standard torus in the 3-sphere. We show that each nontrivial torus knot together with an essential arc determines a prime theta-curve, yielding explicit infinite families of prime theta-curves. We compute their constituent knots and identify the structure governing these embeddings, which leads to a complete classification

Brandon Choi, Matteo Ugliotti, Mateo Reynoso, Daniel R. Gurevich

This paper introduces a novel data driven framework for constructing accurate and general equivariant models of multiscale phenomena which does not rely on specific assumptions about the underlying physics. This framework is illustrated using incompressible fluid turbulence as an example that is representative, practically important, reasonably simple, and e

Mehedi Hasan Raju, Oleg V. Komogortsev

Gaze-based interaction is emerging as a standard input modality on consumer extended-reality (XR) devices. Yet each gaze input constitutes an involuntary biometric disclosure, as the same signal that selects a button can also identify the user who produced it. Reducing identity information without degrading interaction is difficult: most prior methods are va

Cac Phan, Kai Wang

This work provides the first finite-time convergence guarantees for linearly constrained stochastic bilevel optimization using only first-order methods, requiring solely gradient information without any Hessian computations or second-order derivatives. We address the unprecedented challenge of simultaneously handling linear constraints, stochastic noise, and

Frédéric Berdoz, Peer Rheinboldt, Roger Wattenhofer

Speculative decoding accelerates language model inference by separating generation into fast drafting and parallel verification. Its main limitation is drafter-verifier misalignment, which limits token acceptance and reduces overall effectiveness. While small drafting heads trained from scratch compensate with speed, they struggle when verification dominates

Daniela Martin, Jinsu Hong, Connor O'Brien, Valmir P Moraes Filho

Space weather at Earth, driven by the solar activity, poses growing risks to satellites around our planet as well as to critical ground-based technological infrastructure. Major space weather contributors are the solar wind and coronal mass ejections whose variable density, speed, temperature, and magnetic field make the automated classification of those str

Bingbing Hu, Jakob Nogler, Barna Saha

String Edit Distance is a more-than-classical problem whose behavior in the dynamic setting, where the strings are updated over time, is well studied. A single-character substitution, insertion, or deletion can be processed in time $\tilde{\mathcal{O}}(n w)$ when operation costs are positive integers bounded by $w$ [Charalampopoulos, Kociumaka, Mozes, CPM 20

Giovanni Ferrannini, Dario di Gregorio, Federico Fissore

Nash equilibria are crucial for understanding game behavior and systems in economics, physics, biology, and computer science. A significant application arises from the connection between Nash equilibria and optimization problems . However, finding Nash equilibria is challenging due to its NP-Hard complexity, specifically within the PPAD class. By exploiting

Raj Sai Sohel Bandari, Amod Ashtekar, Omar Ibrahim, Mohammed E. Eltayeb

Millimeter-wave (mmWave) communication enables multi-gigabit-per-second data rates but is highly susceptible to path loss and blockage, especially indoors. Many indoor settings, however, include naturally occurring specular surfaces such as glass, glossy metal panels, and signage, that reflect both light and mmWave signals. Exploiting this dual reflectivity,

Xiaomeng Ding, Simon Weidenholzer, Boyu Zhang

We study evolutionary dynamics in which firms endogenously revise the behavioral rules that govern strategy revisions in symmetric Cournot oligopoly. Specifically, we consider two principles that guide rule revision, No-Birth and Survival-of-the-Fittest, both grounded in imitation-based heuristics. We show that, under these principles, all firms eventually a

Deepika Yadav, Spyros Stathopoulos, Patrick Foster, Andreas Tsiamis

Tuneable capacitors are vital for adaptive and reconfigurable electronics, yet existing approaches require continuous bias or mechanical actuation. Here we demonstrate a voltage-programmable ferroelectric memcapacitor based on HfZrO that achieves more than eight stable, reprogrammable capacitance states (3-bit encoding) within a non-volatile window of 24~pF.

Lu Zhao, Rong Shi, Shaoqing Zhang, Shangchao Su

The exponential growth in LLM scales, with parameters soaring from billions to trillions, has necessitated distributed pretraining across large clusters comprising thousands to tens of thousands of devices. While hybrid parallelization strategies enable such pretraining, the vast combinatorial strategy space introduces significant optimization challenges. Tr

Kaleb Ben Naveed, Utkrisht Sahai, Anouck Girard, Dimitra Panagou

Autonomous robots are increasingly deployed for information-gathering tasks in environments that vary across space and time. Planning informative and safe trajectories in such settings is challenging because information decays when regions are not revisited. Most existing planners model information as static or uniformly decaying, ignoring environments where

Misako Tatsuuma, Satoshi Okuzumi, Akimasa Kataoka, Hidekazu Tanaka

Modeling the contact surfaces formed by pebble collisions is crucial to understanding the formation process of comets, which are thought to be composed of pebbles. In this paper, we develop a new model to estimate the contact surface radius and the number of contact points as functions of collision velocity, and examine the formation process of comet 67P/Chu

Xuntao Lyu, Ching-Chi Lin, Abdullah Al Arafat, Georg von der Brüggen

Adversarial patch attacks inject localized perturbations into images to mislead deep vision models. These attacks can be physically deployed, posing serious risks to real-world applications. In this paper, we propose CertMask, a certifiably robust defense that constructs a provably sufficient set of binary masks to neutralize patch effects with strong theore

Lequan Lin, Dai Shi, Andi Han, Feng Chen

Supervised learning relies on high-quality labeled data, but obtaining such data through human annotation is both expensive and time-consuming. Recent work explores using large language models (LLMs) for annotation, but LLM-generated labels still fall short of human-level quality. To address this problem, we propose the Annotation with Critical Thinking (ACT

Ilias Diakonikolas, Mingchen Ma, Lisheng Ren, Christos Tzamos

Learning intersections of halfspaces is a central problem in Computational Learning Theory. Even for just two halfspaces, it remains a major open question whether learning is possible in polynomial time with respect to the margin $\gamma$ of the data points and their dimensionality $d$. The best-known algorithms run in quasi-polynomial time $d^{O(\log(1/\gam

Neo Wang, Sonit Singh

The course forums are increasingly significant and play vital role in facilitating student discussions and answering their questions related to the course. It provides a platform for students to post their questions related to the content and admin issues related to the course. However, there are several challenges due to the increase in the number of studen

Leszek Sliwko, Vladimir Getov

This paper presents a strategy to allocate services on a Cloud system without overloading nodes and maintaining the system stability with minimum cost. We specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. A prototype meta-heuristic load balancer is demons

Md Saiful Islam, Rahul Bhadani

We present a load frequency control strategy deploying a decentralized robust global integral terminal sliding mode control (GITSMC) method to maintain stable frequency and tie-line power in multi-area interconnected microgrids with aggregated uncertainties. To achieve this, firstly, we have developed a mathematical model of the multi-area interconnected sys

Jiahuan Long, Tingsong Jiang, Hanqing Liu, Chao Ma

Adversarial patches have emerged as a popular privacy-preserving approach for resisting AI-driven surveillance systems. However, their conspicuous appearance makes them difficult to deploy in real-world scenarios. In this paper, we propose a thermally activated adversarial wearable designed to ensure adaptability and effectiveness in complex real-world envir

Mingkun Yang, Ran Zhu, Qing Wang, Jie Yang

Split Federated Learning is a system-efficient federated learning paradigm that leverages the rich computing resources at a central server to train model partitions. Data heterogeneity across silos, however, presents a major challenge undermining the convergence speed and accuracy of the global model. This paper introduces Step-wise Momentum Fusion (SMoFi),

Jiao Chen, Haoyi Wang, Jianhua Tang, Junyi Wang

Low-altitude Unmanned Aerial Vehicle (UAV) networks rely on robust semantic segmentation as a foundational enabler for distributed sensing-communication-control co-design across heterogeneous agents within the network. However, segmentation foundation models deteriorate quickly under weather, lighting, and viewpoint drift. Resource-limited UAVs cannot run gr

Aymen Mir, Jian Wang, Riza Alp Guler, Chuan Guo

We present a novel framework for animating humans in 3D scenes using 3D Gaussian Splatting (3DGS), a neural scene representation that has recently achieved state-of-the-art photorealistic results for novel-view synthesis but remains under-explored for human-scene animation and interaction. Unlike existing animation pipelines that use meshes or point clouds a

Yu Zhao, Li You, Jinke Tang, Mengyu Qian

Massive multiple-input multiple-output - orthogonal frequency division multiplexing (MIMO-OFDM) systems face the challenge of high channel acquisition overhead while providing significant spectral efficiency (SE). Adjustable phase shift pilots (APSPs) are an effective technique to acquire channels with low overhead by exploiting channel sparsity. In this pap

Daniel Chan, Adam Nyman

Let $k$ denote an algebraically closed field of characteristic zero and let $X$ denote a smooth elliptic curve over $k$. In this paper, motivated by work in \cite{CN}, we think of two-periodic elliptic helices as noncommutative analogues of degree two line bundles over $X$. We classify and study two-periodic elliptic helices in order to generalize the theory

Nick Bezhanishvili, Antonio Maria Cleani

We introduce pre-filtration and pre-stable canonical rules for the Kuznetsov-Muravitsky system of intuitionistic modal logic and provide a new proof of the Kuznetsov-Muravitsky isomorphism, along with several preservation results. The proofs employ these rules and a duality between modal (Heyting) algebras and their corresponding order-topological spaces.

Woojung Bae, Dongrak Choi, Jun Yan, Sangwook Kang

The semiparametric accelerated failure time (AFT) model offers a direct and interpretable alternative to the Cox proportional hazards model, yet practical diagnostic tools for this framework remain limited. We introduce afttest, an R package that implements martingale-residual-based goodness-of-fit procedures for semiparametric AFT models. In addition to the

Christina Schenk, Miguel Hernández-del-Valle, Luis Calero-Lumbreras, Marcus Noack

Device-to-device variability in experimental noise critically impacts reproducibility, especially in automated, high-throughput systems like additive manufacturing farms. While manageable in small labs, such variability can escalate into serious risks at larger scales, such as architectural 3D printing, where noise may cause structural or economic failures.

Yi-Ping Chen, Derick Suarez, Ying-Kuan Tsai, Vispi Karkaria

Digital Twin (DT) technologies are transforming manufacturing by enabling real-time prediction, monitoring, and control of complex processes. Yet, applying DT to deformation-based metal forming remains challenging because of the strongly coupled spatial-temporal behavior and the nonlinear relationship between toolpath and material response. For instance, she

John Machacek, George D. Nasr

Using Postnikov's Le-diagrams, decorated permutations, and Grassmann necklaces, we classify which positroids are sparse paving matroids. This allows us to enumerate sparse paving positroids, making connections to a known sequence involving the golden ratio and to the Lucas numbers.

Syed Mumtahin Mahmud, Mahdi Mohd Hossain Noki, Prothito Shovon Majumder, Abdul Mohaimen Al Radi

Image deblurring is vital in computer vision, aiming to recover sharp images from blurry ones caused by motion or camera shake. While deep learning approaches such as CNNs and Vision Transformers (ViTs) have advanced this field, they often struggle with complex or high-resolution blur and computational demands. We propose a new dual-domain architecture that

Shahar Hod

The physical and mathematical properties of the composed Reissner-Nordström-black-hole-massive-scalar-field system are studied {\it analytically} in the dimensionless large-mass $Mμ\gg1$ regime [here $\{M,μ\}$ are respectively the mass of the central black hole and the proper mass of the scalar field]. It is proved that, for a given value ${\bar Q}\equiv Q/M

Chian Yeh Goh, Daniel Brito Matehuala, Guillaume Blanquart

The dynamics of self-similar Rayleigh-Taylor (RT) mixing layers are investigated across a broad range of Atwood and Reynolds numbers using the statistically stationary Rayleigh-Taylor (SRT) flow configuration - a computational framework that enables simulation of self-similar RT flows at reduced cost compared to conventional temporally growing mixing layers.

Vincenzo Carletti, Pasquale Foggia, Carlo Mazzocca, Giuseppe Parrella

One of the key advantages of Federated Learning (FL) is its ability to collaboratively train a Machine Learning (ML) model while keeping clients' data on-site. However, this can create a false sense of security. Despite not sharing private data increases the overall privacy, prior studies have shown that gradients exchanged during the FL training remain

Joel Wendin, Claudio Altafini

The paper surveys recent progresses in understanding the dynamics and loss landscape of the gradient flow equations associated to deep linear neural networks, i.e., the gradient descent training dynamics (in the limit when the step size goes to 0) of deep neural networks missing the activation functions and subject to quadratic loss functions. When formulate

Alex J. Vernon, Konstantin Y. Bliokh

Geometric phases play an enormous role in optics and are generally associated with the evolution of light's polarization state on the Poincaré sphere, or its spin on the sphere of spin directions. Here we put forward a new kind of optical geometric phase that appears exclusively in nonparaxial light, resulting from cyclic changes to the relative amplitud

S. S. Afonin, A. V. Sarantsev, A. M. Tsymbal

We discuss the $(L,n)$-classification of excited light non-strange mesons, where $L$ and $n$ are orbital and radial quantum numbers. The selection of true non-strange quark-antiquark excited states and assigning to them definite $L$ and $n$ is a notoriously confusing problem. Three guiding principles for selection of correct observed states are formulated. T

Pieter Belmans, Wendy Lowen, Shinnosuke Okawa, Andrea T. Ricolfi

We develop the deformation-obstruction calculus for morphisms of complexes with a fixed lift of the codomain, to derived categories of flat nilpotent deformations of abelian categories. As an application, we give an alternative proof that semiorthogonal decompositions deform uniquely in smooth proper families of schemes.

Vladimir Bobkov, Mieko Tanaka

Let $Ω$ be a bounded open set and $p,q,r>1$. The main observation of the present work is the following: $W_0^{1,p}(Ω)$-solutions of the equation $-Δ_p u = μ|u|^{q-2}u + |u|^{r-2}u$ parameterized by $μ$ are in bijection with properly normalized critical points of the $0$-homogeneous Rayleigh type quotient $R_α(u)=\|\nabla u\|_p^p/ (\|u\|_q^{αp} \|u\|_r^{p-αp}

Takehiro Tottori, Tetsuya J. Kobayashi

Organisms adapt to volatile environments by integrating sensory information with internal memory, yet their information processing is constrained by resource limitations. Such limitations can fundamentally alter optimal estimation strategies in biological systems. For example, recent experiments suggest that organisms exhibit nonmonotonic phase transitions b

Jianfeng Bi, Masaki Minamikawa, Ruige Dong, DongJun Kang

Two-dimensional (2D) van der Waals (vdW) moiré superlattices have provided a powerful knob to engineer a plethora of new quantum states. However, extending such moiré engineering to one-dimensional (1D) vdW systems has remained challenging. Here we report the moiré-engineered electronic insulating states in a new 1D moiré superlattice, by crystallographicall

Pilsung Kang

Classical approaches often treat interaction as engineered product terms or as emergent patterns in flexible models, offering little control over how synergy or antagonism arises. We take a quantum-inspired view: following the Born rule (probability as squared amplitude), \emph{coherent} aggregation sums complex amplitudes before squaring, creating an interf

Ulrike Schmidt-Kraepelin, Warut Suksompong, Steven Wijaya

Apportionment refers to the well-studied problem of allocating legislative seats among parties or groups with different entitlements. We present a multi-level generalization of apportionment where the groups form a hierarchical structure, which gives rise to stronger versions of the upper and lower quota notions. We show that running Adams' method level-

Chong Lin, Shijun Liao

Permanent gravity waves propagating in deep water, spanning amplitudes from infinitesimal to their theoretical limiting values, remain a classical yet challenging problem due to its inherent nonlinear complexities. Traditional analytical and numerical methods encounter substantial difficulties near the limiting wave condition due to singularities at sharp wa

Zubia Naz, Farhan Asghar, Muhammad Ishfaq Hussain, Yahya Hadadi

Automated medical image captioning translates complex radiological images into diagnostic narratives that can support reporting workflows. We present a Swin-BART encoder-decoder system with a lightweight regional attention module that amplifies diagnostically salient regions before cross-attention. Trained and evaluated on ROCO, our model achieves state-of-t

Marcel Rojahn, Marcus Grum

Across the Artificial Intelligence (AI) lifecycle - from hardware to development, deployment, and reuse - burdens span energy, carbon, water, and embodied impacts. Cloud provider tools improve transparency but remain heterogeneous and often omit water and value chain effects, limiting comparability and reproducibility. Addressing these multi dimensional burd

Shen Chen, Yanlong Li, Jiamin Cui, Wei Yao

A common approach to digital system design involves transforming a continuous-time (s-domain) transfer function into the discrete-time (z-domain) using methods such as Euler or Tustin. These transformations are shown to be specific cases of the Generalized Bilinear Transformation (GBT), characterized by a design parameter, $α$, whose physical interpretation

Ha-Hieu Pham, Minh Le, Han Huynh, Nguyen Quoc Khanh Le

Semi-supervised semantic segmentation (SSSS) is vital in computational pathology, where dense annotations are costly and limited. Existing methods often rely on pixel-level consistency, which propagates noisy pseudo-labels and produces fragmented or topologically invalid masks. We propose Topology Graph Consistency (TGC), a framework that integrates graph-th

Simone Calogero

A nonlinear Lorentz invariant kinetic diffusion equation is introduced, which is consistent with the conservation laws of particles number, energy and momentum. The equilibrium solution converges to the Maxwellian density in the Newtonian limit, but it is not given by the Jüttner distribution commonly employed in relativistic kinetic theory. The nonlinear ki

Sojun Ono, Kazuyuki Sugimura

We present a neural-network emulator for the thermal and chemical evolution in Population III star formation. The emulator accurately reproduces the thermochemical evolution over a wide density range spanning 21 orders of magnitude (10$^{-3}$-10$^{18}$ cm$^{-3}$), tracking six primordial species: H, H$_2$, e$^{-}$, H$^{+}$, H$^{-}$, and H$_2^{+}$. To handle

José Antonio Carrillo, Sondre Tesdal Galtung

We study distributional solutions of pressureless Euler systems on the line. In particular we show that Lagrangian solutions, introduced by Brenier, Gangbo, Savaré and Westdickenberg, and entropy solutions, studied by Nguyen and Tudorascu for the Euler--Poisson system, are equivalent. For the Euler--Poisson system this can be seen as a generalization to seco

Ljudmila Kamenova, Christian Lehn

We prove non-hyperbolicity of primitive symplectic varieties with $b_2 \geq 5$ that satisfy the rational SYZ conjecture. If in addition $b_2 \geq 7$, we establish that the Kobayashi pseudometric vanishes identically. This in particular applies to all currently known examples of irreducible symplectic manifolds and thereby completes the results by Kamenova--L

Jun Woo Chung, Yingjie Lao, Weijie Zhao

Gradient Boosting Decision Trees (GBDTs) are widely used in industry and academia for their high accuracy and efficiency, particularly on structured data. However, watermarking GBDT models remains underexplored compared to neural networks. In this work, we present the first robust watermarking framework tailored to GBDT models, utilizing in-place fine-tuning

Jeongho Min, Dongyoung Kim, Jaehyup Lee

Cross-view image retrieval, particularly street-to-satellite matching, is a critical task for applications such as autonomous navigation, urban planning, and localization in GPS-denied environments. However, existing approaches often require supervised training on curated datasets and rely on panoramic or UAV-based images, which limits real-world deployment.

Ngaiming Kwok

The proliferation of metaphor-based metaheuristics has often been accompanied by issues of symbolic inflation, benchmarking opacity, and statistical misuse. This study presents a diagnostic critique of the recently proposed Exponential Trigonometric Optimizer (ETO), exposing fundamental flaws in its algorithmic structure and the statistical reporting of its

Rahul Soni, Basem Suleiman, Sonit Singh

This paper aims to address the challenge of selecting relevant courses for students by proposing the design and development of a course recommendation system. The course recommendation system utilises a combination of data analytics techniques and machine learning algorithms to recommend courses that align with current industry trends and requirements. In or

Hanzhou Liu, Peng Jiang, Jia Huang, Mi Lu

Restoring 3D scenes with low-light conditions is challenging, and most existing methods depend on precomputed camera poses and scene-specific optimization, which greatly restricts their application to real-world scenarios. To overcome these limitations, we propose Lumos3D, a pose-free single-forward framework for 3D low-light scene restoration. First, we dev

Abdelhay Benmoussa

We derive a normal ordering formula for the operator \((xI)^n\), where \(I\) denotes the Volterra operator. The resulting coefficients are shown to coincide with the Bessel numbers. We also present two applications, along with a generalization of the main result.

Prasit Bhattacharya, Alex Waugh, Mingcong Zeng, Foling Zou

We introduce the notion of $\mathrm{R}$-Eulerian sequences for any $\mathcal{N}_\infty$-ring spectrum $\mathrm{R}$ of finite orientation order. We prove that each $\mathrm{R}$-Eulerian sequence determines a stable $\mathrm{R}$-cohomology operation. Furthermore, we show that the collection of $\mathrm{R}$-Eulerian sequences carries a natural additive and a mu

Yuki Murakami, Takumi Hattori, Kohsuke Kubota

The simultaneous application of multiple treatments is increasingly common in many fields, such as healthcare and marketing. In such scenarios, it is important to estimate the single treatment effects and the interaction treatment effects that arise from treatment combinations. Previous studies have proposed using independent outcome networks with subnetwork

Gabrielle M Gauthier, Eesha Ali, Amna Asim, Sarah Cornell-Maier

Human content moderators (CMs) routinely review distressing digital content at scale. Beyond exposure, the work context (e.g., workload, team structure, and support) may shape mental health outcomes. We examined a cross sectional international CM sample (N = 166) and a U.S. prospective CM sample, including a comparison group of data labelers or tech support

Marry Kong, Rina Buoy, Sovisal Chenda, Nguonly Taing

Compared to English and other high-resource languages, spellchecking for Khmer remains an unresolved problem due to several challenges. First, there are misalignments between words in the lexicon and the word segmentation model. Second, a Khmer word can be written in different forms. Third, Khmer compound words are often loosely and easily formed, and these

Geofrey Nyabere, Hunter Ellis, Miguel Gomez, Wei Jia

Periodic metallic gratings on substrates can support a range of electromagnetic modes, such as leaky waveguide, guided-resonant, and Fabry-Perot (FP) cavity modes, which can strongly modulate optical transmission under resonant excitation. Here, we investigate how this coupling can be dynamically manipulated through charge-density control in a laterally patt

Arthur Castello Branco de Oliveira, Dhruv Jatkar, Eduardo Sontag

This paper investigates how the compositional structure of neural networks shapes their optimization landscape and training dynamics. We analyze the gradient flow associated with overparameterized optimization problems, which can be interpreted as training a neural network with linear activations. Remarkably, we show that the global convergence properties ca

Konstantinos M. Dafnis, Dimitris N. Metaxas

Vision-Language Models (VLMs) excel at zero-shot inference but often degrade under test-time domain shifts. For this reason, episodic test-time adaptation strategies have recently emerged as powerful techniques for adapting VLMs to a single unlabeled image. However, existing adaptation strategies, such as test-time prompt tuning, typically require backpropag

Ting Cai, Kirthevasan Kandasamy

Best arm identification (BAI) aims to identify the highest-performance arm among a set of $K$ arms by collecting stochastic samples from each arm. In real-world problems, the best arm needs to satisfy additional feasibility constraints. While there is limited prior work on BAI with feasibility constraints, they typically assume the performance and constraint

Alberto González-Sanz, Eustasio del Barrio, Marcel Nutz

It is well known that optimal transport suffers from the curse of dimensionality: when the prescribed marginals are approximated by i.i.d. samples, the convergence of the empirical optimal transport problem to the population counterpart slows exponentially with increasing dimension. Entropically regularized optimal transport (EOT) has become the standard bea

James Watt, Christoph Federrath, Claudius Birke, Christian Klingenberg

Magnetohydrodynamic (MHD) simulations of subsonic (Mach number~$<1$) turbulence are crucial to our understanding of several processes including oceanic and atmospheric flows, the amplification of magnetic fields in the early universe, accretion discs, and stratified flows in stars. In this work, we demonstrate that conventional numerical schemes are excessiv

Albert Leonardo Aguilar Suarez, Gregory Beroza

The recent boom in artificial intelligence and machine learning has been powered by large datasets with accurate labels, combined with algorithmic advances and efficient computing. The quality of data can be a major factor in determining model performance. Here, we detail observations of commonly occurring errors in popular seismological machine learning dat

Eric Xie, Danielle Waterfield, Michael Kennedy, Aidong Zhang

Large Language Models (LLMs) have shown immense potential in education, automating tasks like quiz generation and content summarization. However, generating effective presentation slides introduces unique challenges due to the complexity of multimodal content creation and the need for precise, domain-specific information. Existing LLM-based solutions often f

Yufeng Wang, Lu wei, Haibin Ling

Retrieval-Augmented Generation (RAG) improves factuality but retrieving for every query often hurts quality while inflating tokens and latency. We propose Training-free Adaptive Retrieval Gating (TARG), a single-shot policy that decides when to retrieve using only a short, no-context draft from the base model. From the draft's prefix logits, TARG computes li

Parinaz Binandeh Dehaghani, Danilo Pena, A. Pedro Aguiar

This paper explores the impact of dimensionality reduction and pooling methods for Environmental Sound Classification (ESC) using lightweight CNNs. We evaluate Sparse Salient Region Pooling (SSRP) and its variants, SSRP-Basic (SSRP-B) and SSRP-Top-K (SSRP-T), under various hyperparameter settings and compare them with Principal Component Analysis (PCA). Expe

Salvish Goomanee, Andi Han, Pratik Jawanpuria, Bamdev Mishra

This work extends the recently introduced Alpha-Procrustes family of Riemannian metrics for symmetric positive definite (SPD) matrices by incorporating generalized versions of the Bures-Wasserstein (GBW), Log-Euclidean, and Wasserstein distances. While the Alpha-Procrustes framework has unified many classical metrics in both finite- and infinite- dimensional

Jian-Guo Liu, Robert L. Pego

In cosmology, a basic explanation of the observed concentration of mass in singular structures is provided by the Zeldovich approximation, which takes the form of free-streaming flow for perturbations of a uniform Einstein-de Sitter universe in co-moving coordinates. The adhesion model suppresses multi-streaming by introducing viscosity. We study mass flow i

Lyes Smaili, Soulaimane Berkane

This paper addresses the problem of safe autonomous navigation in unknown obstacle-filled environments using only local sensory information. We propose a smooth feedback controller derived from an unconstrained penalty-based formulation that guarantees safety by construction. The controller modifies an arbitrary nominal input through a closed-form expression

Mohamed El Guide, Khalide Jbilou, Kamal Lachhab, Driss Ouazar

Meshless collocation with multiquadric radial basis functions (MQ-RBFs) delivers high accuracy for the three-dimensional Helmholtz equation but produces dense, severely ill-conditioned linear systems. We develop and evaluate three complementary methods that embed regularization in Krylov projections to overcome this instability at scale: (i) an inexpensive T

Kerry A. Nice, Mark Stevenson

To measure access to social services (primary health care, early childhood care/education, and public transport), we created two social service access indexes (SSPT and SSI) for Australian capital cities. We show that only two cities, Melbourne and Sydney, have some limited characteristics of a compact or 15-minute city, but only in the city centres and inne

Rocco Tripodi, Xiaoyu Liu

Cross-lingual Natural Language Processing (NLP) has gained significant traction in recent years, offering practical solutions in low-resource settings by transferring linguistic knowledge from resource-rich to low-resource languages. This field leverages techniques like annotation projection and model transfer for language adaptation, supported by multilingu

Wasique Islam Shafin, Md Nakhla Rafi, Zhenhao Li, Tse-Hsun Chen

Modern software systems require code that is not only functional but also maintainable and well-structured. Although Large Language Models (LLMs) are increasingly used to automate software development, most studies focus on isolated, single-agent function-level generation. This work examines how process structure and role specialization shape multi-agent LLM

Eric Ghysels, Jack Morgan

Classical shadows are an efficient method for constructing an approximate classical description of a quantum state using very few measurements. In the paper we propose to enhance classical shadow methods using bootstrap resampling methods. We apply nonparametric bootstrapping to assess the variability and accuracy of estimators by repeatedly sampling with re

Tianmeng Hu, Yongzheng Cui, Rui Tang, Biao Luo

Value decomposition is a central approach in multi-agent reinforcement learning (MARL), enabling centralized training with decentralized execution by factorizing the global value function into local values. To ensure individual-global-max (IGM) consistency, existing methods either enforce monotonicity constraints, which limit expressive power, or adopt softe

Siddeshwar Raghavan, Jiangpeng He, Fengqing Zhu

Exemplar-Free Continual Learning (EFCL) restricts the storage of previous task data and is highly susceptible to catastrophic forgetting. While pre-trained models (PTMs) are increasingly leveraged for EFCL, existing methods often overlook the inherent imbalance of real-world data distributions. We discovered that real-world data streams commonly exhibit dual

Parth Ashokbhai Shiroya, Amod Ashtekar, Swarnagowri Shashidhar, Mohammed E. Eltayeb

Future intelligent indoor wireless environments require fast and reliable beam alignment to sustain high-throughput links under mobility and blockage. Exhaustive beam training achieves optimal performance but is prohibitively costly. In indoor settings, dense scatterers and transceiver hardware imperfections introduce multipath and sidelobe leakage, producin

Zhijie Qiao, Zhong Cao, Henry X. Liu

End-to-end (E2E) autonomous driving heavily relies on closed-loop simulation, where perception, planning, and control are jointly trained and evaluated in interactive environments. Yet, most existing datasets are collected from the real world under non-interactive conditions, primarily supporting open-loop learning while offering limited value for closed-loo

Eshika Pathak, Ahmed Aboudonia, Sandeep Banik, Naira Hovakimyan

Dynamical system (DS)-based learning from demonstration (LfD) is a powerful tool for generating motion plans in the operation (&#39;task&#39;) space of robotic systems. However, realizing generated motion plans is often compromised by a &#34;task-execution mismatch&#34;, where unmodeled dynamics, persistent disturbances, and system latency cause the robot&#3

Mohammad Mahdi Kamani, Zhongwei Cheng, Lin Chen

The massive growth in the utilization of edge AI has made the applications of machine learning models ubiquitous in different domains. Despite the computation and communication efficiency of these systems, due to limited computation resources on edge devices, relying on more computationally rich systems on the cloud side is inevitable in most cases. Cloud in

Mar Canet Sola, Varvara Guljajeva

This position paper argues for the importance of open small AI models in creative independence for interactive art practices. Deployable locally, these models offer artists vital control over infrastructure and code, unlike dominant large, closed-source corporate systems. Such centralized platforms function as opaque black boxes, imposing severe limitations

Briggs Damman, Jarett LeVan, Scott Baalrud

Molecular dynamics (MD) simulations are used to calculate transport coefficients in a two-component plasma interacting through a repulsive Coulomb potential. The thermal conductivity, electrical conductivity, electrothermal coefficient, thermoelectric coefficient, and shear viscosity are computed using the Green-Kubo formalism over a broad range of Coulomb c

Jarett LeVan, Scott D. Baalrud

Mean force kinetic theory is used to evaluate the electrical conductivity, thermal conductivity, electrothermal coefficient, thermoelectric coefficient, and shear viscosity of a two-component (ion-electron) plasma. Results are compared with molecular dynamics simulations. These simulations are made possible by assuming a repulsive Coulomb force for all inter

Bakhtawar Ahtisham, Kirk Vanacore, Jinsook Lee, Zhuqian Zhou

Large Language Models (LLMs) are increasingly used to annotate learning interactions, yet concerns about reliability limit their utility. We test whether verification-oriented orchestration-prompting models to check their own labels (self-verification) or audit one another (cross-verification)-improves qualitative coding of tutoring discourse. Using transcri