November 2025 arXiv papers — page 100

Hengrui Hu, Kaining Ying, Henghui Ding

This work focuses on multi-shot semi-supervised video object segmentation (MVOS), which aims at segmenting the target object indicated by an initial mask throughout a video with multiple shots. The existing VOS methods mainly focus on single-shot videos and struggle with shot discontinuities, thereby limiting their real-world applicability. We propose a tran

Junwei Yu, Trevor Darrell, XuDong Wang

The Segment Anything Model (SAM) family has become a widely adopted vision foundation model, but its ability to control segmentation granularity remains limited. Users often need to refine results manually - by adding more prompts or selecting from pre-generated masks - to achieve the desired level of detail. This process can be ambiguous, as the same prompt

Xincheng Shuai, Zhenyuan Qin, Henghui Ding, Dacheng Tao

Recent advances in text-to-image (T2I) diffusion models have significantly improved semantic image editing, yet most methods fall short in performing 3D-aware object manipulation. In this work, we present FFSE, a 3D-aware autoregressive framework designed to enable intuitive, physically-consistent object editing directly on real-world images. Unlike previous

Kiana Vu, İsmet Selçuk Özer, Phung Lai, Zheng Wu

As climate change accelerates the frequency and severity of extreme events such as wildfires, the need for accurate, explainable, and actionable forecasting becomes increasingly urgent. While artificial intelligence (AI) models have shown promise in predicting such events, their adoption in real-world decision-making remains limited due to their black-box na

Yang Yu, Kanak Kanti Bhowmik, Ruan Li, Kexin Li

Halide perovskites exhibit significant advantages for active optical components such as light emitting diodes, solar cells and photodetectors due to their excellent optoelectronic properties. Their nonlinear optical effects and other characteristics also make them suitable for integration into waveguide components, such as optical fibers, for applications li

Jianglong Ye, Lai Wei, Guangqi Jiang, Changwei Jing

Human grasps can be roughly categorized into two types: power grasps and precision grasps. Precision grasping enables tool use and is believed to have influenced human evolution. Today's multi-fingered robotic hands are effective in power grasps, but for tasks requiring precision, parallel grippers are still more widely adopted. This contrast highlights a ke

Lawrence Hollom, Benedict Randall Shaw

We provide counterexamples to several conjectures concerning strongly maximal and strongly minimal structures in infinite graphs and hypergraphs. In particular, we construct 3-uniform hypergraphs without strongly maximal matchings and without strongly minimal covers, and from our construction for covers we build a graph with no strongly minimal colouring. We

Jay R. Krishnan, Kevork N. Abazajian

The discovery of massive, high redshift galaxies with the James Webb Space Telescope (JWST) has been argued to challenge $\Lambda$CDM (cold dark matter): such systems would require extremely rare halos and baryon-to-stellar-mass conversion efficiencies unphysically approaching -- or exceeding -- $100\%$. If confirmed at galaxy-formation--forbidden efficienci

Xiaoyu Liang, Ziang Liu, Kelvin Lin, Edward Gu

We present OpenRoboCare, a multimodal dataset for robot caregiving, capturing expert occupational therapist demonstrations of Activities of Daily Living (ADLs). Caregiving tasks involve complex physical human-robot interactions, requiring precise perception under occlusions, safe physical contact, and long-horizon planning. While recent advances in robot lea

Shih-Yu Chang

Coherence is a central issue in category theory and multicategory theory, ensuring that formally distinct compositions of morphisms, such as tensor reorderings or diagrammatic rewiring, represent the same underlying transformation. In operator theory and in Hilbert space based systems, coherence guarantees that equivalent operator networks produce identical

Alaa Mezghiche

Unsupervised learning on high-dimensional RNA-seq data can reveal molecular subtypes beyond standard labels. We combine an autoencoder-based representation with clustering and stability analysis to search for rare but reproducible genomic subtypes. On the UCI "Gene Expression Cancer RNA-Seq" dataset (801 samples, 20,531 genes; BRCA, COAD, KIRC, LUAD, PRAD),

Harold Haodong Chen, Disen Lan, Wen-Jie Shu, Qingyang Liu

The rapid evolution of video generative models has shifted their focus from producing visually plausible outputs to tackling tasks requiring physical plausibility and logical consistency. However, despite recent breakthroughs such as Veo 3's chain-of-frames reasoning, it remains unclear whether these models can exhibit reasoning capabilities similar to large

Lavender Y. Jiang, Angelica Chen, Xu Han, Xujin Chris Liu

Hospitals and healthcare systems rely on operational decisions that determine patient flow, cost, and quality of care. Despite strong performance on medical knowledge and conversational benchmarks, foundation models trained on general text may lack the specialized knowledge required for these operational decisions. We introduce Lang1, a family of models (100

Luyao Niu, Nuoxian Huang

Travel mode identification (TMI) from GPS trajectories is critical for urban intelligence, but is hampered by the high cost of annotation, leading to severe label scarcity. Prevailing semi-supervised learning (SSL) methods are ill-suited for this task, as they suffer from catastrophic confirmation bias and ignore the intrinsic data manifold. We propose ST-Pr

Gianluigi Pillonetto, Alberto Giaretta, Mauro Bisiacco

Understanding the principles that govern dynamical systems is a central challenge across many scientific domains, including biology and ecology. Incomplete knowledge of nonlinear interactions and stochastic effects often renders bottom-up modeling approaches ineffective, motivating the development of methods that can discover governing equations directly fro

Boldizsár Poór, Benjamin Rodatz, Aleks Kissinger

We establish a new performance benchmark for the fault-tolerant syndrome extraction of the [[7, 1, 3]] Steane code with a dynamic protocol. Our method is built on two highly optimized circuits derived using fault-equivalent ZX-rewrites: a primary fault-tolerant circuit with 14 CNOTs and an efficient non-fault-tolerant recovery circuit with 11 CNOTs. The prot

Parikshit Gopalan, Konstantinos Stavropoulos, Kunal Talwar, Pranay Tankala

A decision-theoretic characterization of perfect calibration is that an agent seeking to minimize a proper loss in expectation cannot improve their outcome by post-processing a perfectly calibrated predictor. Hu and Wu (FOCS'24) use this to define an approximate calibration measure called calibration decision loss ($\mathsf{CDL}$), which measures the maximal

Hampei Sasahara, Tatsuya Yamada, Jun-ichi Imura, Henrik Sandberg

Active distribution networks facilitating bidirectional power exchange with renewable energy resources are susceptible to cyberattacks due to integration of a diverse array of cyber components. This study introduces a grid-level defense strategy aimed at enhancing attack resiliency based on distribution network planning. Our proposed framework imposes a secu

Hadi Madanian, Terry Z. Liu

Collisionless shocks in space and astrophysical plasmas mediate energy exchange between charged particles and fields in two or more plasma flows. In this study we analyze the evolution of ion distributions around a reformation cycle of a quasi-parallel shock. We use multi-point in-situ observations in the foreshock region of the Earths bow shock of a transie

Sergei Gukov, Po-Shen Hsin, Du Pei

We continue the investigation of symmetries and anomalies of $T[M]$ theories obtained by compactifying 6d SCFTs on an internal manifold $M$. We extend the notion of "polarizations on a manifold $M$" to cases where $M$ may have boundaries or defects. Through examples with $M$ of dimension two, three, and four, we illustrate recurring themes in compactificatio

Changjie Chen

We study the rational homology of the Deligne--Mumford compactification $\overline{\mathcal M}_{g,n}$ of the moduli space of stable curves via a family of Morse functions, namely the $\text{sys}_T$ functions. Exploiting the geometric and Morse properties of $\text{sys}_T$, including the existence of an index gap and additivity of the Morse index upon gluing

Jay Bartroff, Asmit Chakraborty

We present a method for computing optimal fixed-width confidence intervals for a single, bounded parameter, extending a method for the binomial due to Asparaouhov and Lorden, who called it the Push algorithm. The method produces the shortest possible non-decreasing confidence interval for a given confidence level, and if the Push interval does not exist for

Alexander Clow, Sean Kim, Ladislav Stacho

Given a graph $G$ and a non-negative integer $d$ let $\alpha_d(G)$ be the order of a largest induced $d$-degenerate subgraph of $G$. We prove that for any pair of non-negative integers $k>d$, if $G$ is a $k$-degenerate graph, then $\alpha_d(G) \geq \max\{ \frac{(d+1)n}{k+d+1}, n - \alpha_{k-d-1}(G)\}$. For $k$-degenerate graphs this improves a more general l

Rhys Seeburger, Hans-Walter Rix, Kareem El-Badry, Johanna Müller-Horn

Aims. We present and analyse the detailed physical properties of six binary stellar systems, originally proposed as possible star-black hole binaries on the basis of radial velocities from Gaia's third data release, but soon recognised as likely post-mass-transfer binary systems with stripped companions. Methods. We used multi-epoch high-resolution FEROS spe

Diego Marques, Pavel Trojovsky

Let $p_n$ denote the $n$-th prime. In 2000, Panaitopol established the inequality $p_1 \cdots p_n > p_{n+1}^{n - \pi(n)}$ for all $n \geq 2$, where $\pi(x)$ is the prime counting function. In 2021, Yang and Liao refined this by introducing the exponent $k(n,x) = n - \pi(n) + \frac{\pi(n)}{\pi(\log n)} - x \cdot \pi(\pi(n))$, proving the inequality holds for

Shyam Kamal, Sunidhi Pandey, Thach Ngoc Dinh, Cao Thanh Tinh

This paper introduces a Ramanujan inner product and its corresponding norm, establishing a novel framework for the stability analysis of hybrid and discrete-time systems as an alternative to traditional Euclidean metrics. We establish new $\epsilon$-$\delta$ stability conditions that utilize the unique properties of Ramanujan summations and their relationshi

Dhaminda B. Abeywickrama, Michael Fisher, Frederic Wheeler, Louise Dennis

Autonomous systems must sustain justified confidence in their correctness and safety across their operational lifecycle-from design and deployment through post-deployment evolution. Traditional assurance methods often separate development-time assurance from runtime assurance, yielding fragmented arguments that cannot adapt to runtime changes or system updat

Sofia Jamil, Kotla Sai Charan, Sriparna Saha, Koustava Goswami

Indian poetry, known for its linguistic complexity and deep cultural resonance, has a rich and varied heritage spanning thousands of years. However, its layered meanings, cultural allusions, and sophisticated grammatical constructions often pose challenges for comprehension, especially for non-native speakers or readers unfamiliar with its context and langua

Alexander Migdal

This is the first of two papers presenting a geometric framework for Planar QCD ($N_c \to \infty$). In this part, we establish the kinematic foundation of the theory by constructing the unique stable vacuum of the loop equation. We demonstrate that the Makeenko-Migdal loop equation admits a solution of the form $W[C] = W_{pert}[C] \exp{-\kappa S[C]}$, provid

Nitish Kumar Chandra, Eneet Kaur, Kaushik P. Seshadreesan

Realizing distributed architectures for quantum computing is crucial to scaling up computational power. A key component of such architectures is a scheduler that coordinates operations over a short-range quantum network required to enable the necessary non-local entangling gates between quantum processing units (QPUs). It is desirable to determine schedules

N. Cruz-Sanchez, E. A. Saavedra, F. A. Fogantini, F. García

We present the results of the first broadband X-ray analysis of the ultraluminous X-ray source NGC 5055 ULX X-1, combining simultaneous data from XMM$-$Newton and NuSTAR missions, with a combined exposure time of $\sim$100 ks across the $0.3-20$ keV energy range. The source exhibits a stable flux across the entire exposure with no detectable pulsations by an

Disha Varshney, Samarth Garg, Sarthak Tyagi, Deeksha Varshney

In this study, we tackle the challenging task of predicting secondary structures from protein primary sequences, a pivotal initial stride towards predicting tertiary structures, while yielding crucial insights into protein activity, relationships, and functions. Existing methods often utilize extensive sets of unlabeled amino acid sequences. However, these a

Jiangnan Ye, Jiedong Zhuang, Lianrui Mu, Wenjie Zheng

We introduce GS-Light, an efficient, textual position-aware pipeline for text-guided relighting of 3D scenes represented via Gaussian Splatting (3DGS). GS-Light implements a training-free extension of a single-input diffusion model to handle multi-view inputs. Given a user prompt that may specify lighting direction, color, intensity, or reference objects, we

Yue Tu, Liang Jiang

We study the task of learning mixed unitary channels using Fisher information, under different quantum resource assumptions including ancilla and concatenation. Our result shows that the asymptotic sample complexity scales as $\frac{r}{d\varepsilon^2}$, where $r$ is the rank of the channel (i.e.\ the number of different unitaries), $d$ is the dimension of th

Pierre-Luc Thériault, Heorhii V. Humeniuk, Zhechang He, Gabriel Juteau

Materials with large second-order nonlinearities are crucial for next-generation integrated photonics. Spontaneously oriented organic thin films prepared by physical vapor deposition offer a promising poling-free and scalable approach. This study investigates molecular engineering strategies to enhance the second-order nonlinear response of derivatives based

Leopoldo Agorio, Juan Cerviño, Miguel Calvo-Fullana, Alejandro Ribeiro

A learning task, understood as the problem of fitting a parametric model from supervised data, fundamentally requires the dataset to be large enough to be representative of the underlying distribution of the source. When data is limited, the learned models fail generalize to cases not seen during training. This paper introduces a multi-task \emph{cross-learn

Hyunwoo Oh, Hanning Chen, Sanggeon Yun, Yang Ni

Deformable transformers deliver state-of-the-art detection but map poorly to hardware due to irregular memory access and low arithmetic intensity. We introduce QUILL, a schedule-aware accelerator that turns deformable attention into cache-friendly, single-pass work. At its core, Distance-based Out-of-Order Querying (DOOQ) orders queries by spatial proximity;

Haichuan Wang, Yifan Wu, Haifeng Xu

Researchers strategically choose where to submit their work in order to maximize its impact, and these publication decisions in turn determine venues' impact factors. To analyze how individual publication choices both respond to and shape venue impact, we introduce a game-theoretic framework, coined the Publication Choice Problem, that captures this two-way

Angela F. Harper, Xiaobing Liu, Scott N. Genin, Ilya G. Ryabinkin

We present an open-shell frozen natural orbital (FNO) approach, which utilizes the second-order Z-averaged perturbation theory (ZAPT2), to reduce the restricted opten-shell Hartree-Fock virtual space size with controllable accuracy. Our ZAPT2 frozen natural orbital (ZAPT-FNO) selection scheme significantly outperforms the canonical molecular orbital virtual

Hyunwoo Oh, KyungIn Nam, Rajat Bhattacharjya, Hanning Chen

Recent advances in LLMs have outpaced the computational and memory capacities of edge platforms that primarily employ CPUs, thereby challenging efficient and scalable deployment. While ternary quantization enables significant resource savings, existing CPU solutions rely heavily on memory-based lookup tables (LUTs) which limit scalability, and FPGA or GPU ac

Minh Vu, Andrey Lokhov

Modern scientific simulations, observations, and large-scale experiments generate data at volumes that often exceed the limits of storage, processing, and analysis. This challenge drives the development of data reduction methods that efficiently manage massive datasets while preserving essential physical features and quantities of interest. In many scientifi

Shih-Yu Chang

Category and multicategory theory provide abstract frameworks for describing structures and their compositions, with multicategories extending traditional categories to handle multi-input operations. These theories enable modular reasoning and coherent composition of complex systems, and have found applications in computer science, physics, and mathematics,

Chandrasekhar Gokavarapu, Venkata Rao Kaviti, Srinivasa Rao Thirunagari, Dr Sajani Lavanya Madasi

Ternary $Γ$-semiring chemical systems provide an algebraic language in which mediators such as catalysts, solvents, inhibitors, thermodynamic conditions, and external fields enter the transformation law as internal arguments. Building on the published MATCH foundation article, this paper studies operational regimes of the compressed TGS notation rather than

Markus D. Solbach, John K. Tsotsos

Reinforcement Learning is a mature technology, often suggested as a potential route towards Artificial General Intelligence, with the ambitious goal of replicating the wide range of abilities found in natural and artificial intelligence, including the complexities of human cognition. While RL had shown successes in relatively constrained environments, such a

Indra Kumar Banerjee, Ujjal Kumar Dey, Anna John

We propose a hitherto unexplored neutrino background emerging from the mechanism of quenched superradiance of rotating primordial black holes. The quenching of the phenomenon happens through fermionic production, in our case neutrino production, from the boson cloud formed due to superradiance. The couplings involved in these interactions are bounded from ab

Aleksandar Stankovic

Sparse GNN aggregations (CSR SpMM/SDDMM) vary widely in performance with degree skew, feature width, and GPU micro-architecture. We present AutoSAGE, an input-aware CUDA scheduler that chooses tiling and mapping per input using a lightweight estimate refined by on-device micro-probes, with a guardrail that safely falls back to vendor kernels and a persistent

Tung-Lung Wu

Phase I distribution-free runs- and patterns-type control charts are proposed for monitoring the unknown target value (or location parameter) for both continuous and discrete individual observations. Our approach maintains the nominal in-control signal probability at a prescribed level by employing the finite Markov chain imbedding technique combined with ra

Yu Hin Au, Murray R. Bremner

We introduce and study a generalization of the Narayana numbers $N_d(n,k) = \frac{1}{n+1} \binom{n+1}{k+1} \binom{ n + (n-k)(d-2)+1}{k}$ for integers $d \geq 2$ and $n,k \geq 0$. This two-parameter array extends the classical Narayana numbers ($d=2$) and yields a $d$-ary analogue of the Catalan numbers $C_d(n) = \sum_{k=0}^n N_d(n,k)$. We give nine combinato

Agnieszka Bieńkowska, Jacek Małecki, Alexander Mathiesen-Ohman, Katarzyna Tworek

This article develops the concept of Person-AI bidirectional fit, defined as the continuously evolving, context-sensitive alignment-primarily cognitive, but also emotional and behavioral-between a human decision-maker and an artificial intelligence system. Grounded in contingency theory and quality theory, the study examines the role of P-AI fit in manageria

Pranjal Balar, Sundeep Kapila

Interoception and exteroception provide continuous feedback about the body and the environment, yet how they are dynamically integrated within a unified predictive coding framework has remained under-specified. This paper develops and empirically validates an integrative predictive coding model that treats interoceptive and exteroceptive inference as paralle

Meghadeepa Adhikary, Nishan Ranabhat, Mario Collura

We investigate the behavior of the Schmidt gap, the von Neumann entanglement entropy, and the non-stabiliserness in proximity to the classical phase transition of the one-dimensional long-range transverse-field Ising model (LRTFIM). Leveraging the time-dependent variational principle (TDVP) within a tensor-network formulation, we simulate thermal states thro

Rayff de Souza, Agripino Sousa-Neto, Javier E. González, Jailson Alcaniz

Combined measurements of Baryon Acoustic Oscillations (BAO) from the Dark Energy Spectroscopic Survey (DESI), the Cosmic Microwave Background (CMB) and Type Ia Supernovae (SN Ia), have recently challenged the $\Lambda$-Cold Dark Matter ($\Lambda$CDM) paradigm, indicating potential evidence for a dynamical dark energy component. These results are usually obta

Marcello Ortaggio

We study Einstein-Maxwell (non-null) sourcefree configurations that can be extended to any conformally invariant non-linear electrodynamics (CINLE) by a constant rescaling of the electromagnetic field. We first obtain a criterion which characterizes such extendable solutions in terms either of the electromagnetic invariants, or (equivalently) of the canonica

Athanasios G. Georgiadis, Andrew P. Percival

We investigate the problem of density estimation on the unit circle and the unit sphere from a computational perspective. Our primary goal is to develop new density estimators that are both rate-optimal and computationally efficient for direct implementation. After establishing these estimators, we derive closed-form expressions for probability estimates ove

Benoît Jeanson, Mathieu Tanneau, Simon Tindemans

Transmission System Operators routinely use transmission switching as a tool to manage congestion and ensure system security. Motivated by sub-transmission operations at RTE, this paper considers the Optimal Transmission Switching with De-energization (OTSD), which captures potential loss of connectivity (and therefore localized blackout) following loss of t

Francisco Abreu, Luís Cruz, Sérgio Guerreiro

Proprietary workflow modeling languages such as Smart Forms & Smart Flow hamper interoperability and reuse because they lock process knowledge into closed formats. To address this vendor lock-in and ease migration to open standards, we introduce an ontology-driven model-to-model pipeline that systematically translates domain-specific workflow definitions to

Koen de Boer, Aurel Page, Radu Toma, Benjamin Wesolowski

The problem of finding short vectors in Euclidean lattices is a central hard problem in complexity theory. The case of module lattices (i.e., lattices which are also modules over a number ring) is of particular interest for cryptography and computational number theory. The hardness of finding short vectors in the asymptotic regime where the rank (as a module

Marvin Wyrich, Lloyd Montgomery

A well-rounded software engineer is often defined by technical prowess and the ability to deliver on complex projects. However, the narrative around the ideal Software Engineering (SE) candidate is evolving, suggesting that there is more to the story. This article explores the non-technical aspects emphasized in SE job postings, revealing the sociotechnical

Emanuel Covaci, Fabian Galis, Radu Balan, Daniela Zaharie

Understanding the decision of large deep learning models is a critical challenge for building transparent and trustworthy systems. Although the current post hoc explanation methods offer valuable insights into feature importance, they are inherently disconnected from the model training process, limiting their faithfulness and utility. In this work, we introd

Henry Herzog, Favyen Bastani, Yawen Zhang, Gabriel Tseng

Earth observation data presents a unique challenge: it is spatial like images, sequential like video or text, and highly multimodal. We present OlmoEarth: a multimodal, spatio-temporal foundation model that employs a novel self-supervised learning formulation, masking strategy, and loss all designed for the Earth observation domain. OlmoEarth achieves state-

Pascal Zimmer, Ghassan Karame

In this paper, we present the first detailed analysis of how training hyperparameters -- such as learning rate, weight decay, momentum, and batch size -- influence robustness against both transfer-based and query-based attacks. Supported by theory and experiments, our study spans a variety of practical deployment settings, including centralized training, ens

Leo Gao, Achyuta Rajaram, Jacob Coxon, Soham V. Govande

Finding human-understandable circuits in language models is a central goal of the field of mechanistic interpretability. We train models to have more understandable circuits by constraining most of their weights to be zeros, so that each neuron only has a few connections. To recover fine-grained circuits underlying each of several hand-crafted tasks, we prun

Hadi Mohammed Soufy, Colin Benjamin

We introduce a three-stroke quantum isochoric cycle that functions as a heat engine operating between two thermal reservoirs. Implemented for a particle confined in a one-dimensional infinite potential well, the cycle's performance is benchmarked against the classical three-stroke triangular and isochoric engines. We find that the quantum isochoric cycle ach

Aayush Saxena, Roderik A. Overzier, Catarina Aydar, Jianwei Lyu

We present JWST observations of the radio galaxy TGSSJ1530+1049, spectroscopically confirmed at $z=4.0$. NIRCam images and NIRSpec/IFU spectroscopy (R=2700) show that TGSSJ1530+1049 is part of one of the densest-known structures of continuum and line-emitting objects found at these redshifts. NIRCam images show a number of distinct continuum objects and evid

Ziang Cao, Fangzhou Hong, Zhaoxi Chen, Liang Pan

3D modeling is shifting from static visual representations toward physical, articulated assets that can be directly used in simulation and interaction. However, most existing 3D generation methods overlook key physical and articulation properties, thereby limiting their utility in embodied AI. To bridge this gap, we introduce PhysX-Anything, the first simula

Chunshi Wang, Junliang Ye, Yunhan Yang, Yang Li

We introduce Part-X-MLLM, a native 3D multimodal large language model that unifies diverse 3D tasks by formulating them as programs in a structured, executable grammar. Given an RGB point cloud and a natural language prompt, our model autoregressively generates a single, coherent token sequence encoding part-level bounding boxes, semantic descriptions, and e

Chunqiu Steven Xia, Zhe Wang, Yan Yang, Yuxiang Wei

Large Language Models (LLMs) are reshaping almost all industries, including software engineering. In recent years, a number of LLM agents have been proposed to solve real-world software problems. Such software agents are typically equipped with a suite of coding tools and can autonomously decide the next actions to form complete trajectories to solve end-to-

Shrenik Patel, Daivik Patel

Long-form video question answering (VQA) overwhelms current vision-language models (VLMs) because attention and key-value (KV) caches grow with runtime, forcing either expensive inference or near-sighted sliding windows. We introduce CacheFlow, a training-free pipeline that pairs Dynamic Token Dropping (DTD) with a compressive long-term memory. DTD prunes pe

Fawzi Aly, Dejan Stojkovic

We extend our previous Schwarzschild metric-based studies of gravitational--electromagnetic (GEM) coupling to rotating black holes by working directly in a curvature-based Newman--Penrose/Teukolsky framework on Kerr spacetime. Within a minimally coupled Einstein--Maxwell system, we derive explicit quadratic electromagnetic source terms for the spin-$-2$ Teuk

Quinn Burke, Anjo Vahldiek-Oberwagner, Michael Swift, Patrick McDaniel

Replay and rollback attacks threaten cloud application integrity by reintroducing authentic yet stale data through an untrusted storage interface to compromise application decision-making. Prior security frameworks mitigate these attacks by enforcing forward-only state transitions (state continuity) with hardware-backed mechanisms, but they categorically tre

Haohui Wang, Jingyuan Qi, Jianpeng Chen, Jun Wu

The rapid progress of large language models (LLMs) is fueled by the growing reliance on datasets that blend real and synthetic data. While synthetic data offers scalability and cost-efficiency, it often introduces systematic distributional discrepancies, particularly underrepresenting long-tail knowledge due to truncation effects from data generation mechani

Benjamin Grimmer, Alex L. Wang

This paper considers nonsmooth convex optimization with either a subgradient or proximal operator oracle. In both settings, we identify algorithms that achieve the recently introduced game-theoretic optimality notion for algorithms known as subgame perfection. Subgame perfect algorithms meet a more stringent requirement than just minimax optimality. Not only

M. Van de Sande, M. Gueguen, T. Danilovich, T. J. Millar

Chemical reaction networks are central to all chemical models. Each rate coefficient has an associated uncertainty, which is generally not taken into account when calculating the chemistry. We performed the first uncertainty analysis of a chemical model of C-rich and O-rich AGB outflows using the Rate22 reaction network. Quantifying the error on the model pr

Ana Durica, John Booth, Ivana Drobnjak

Paediatric kidney disease varies widely in its presentation and progression, which calls for continuous monitoring of renal function. Using electronic health records collected between 2019 and 2025 at Great Ormond Street Hospital, a leading UK paediatric hospital, we explored a temporal modelling approach that integrates longitudinal laboratory sequences wit

Ondřej Wojewoda, Miela J. Gross, Jan Klíma, Jaganandha Panda

The manipulation of magnetization lies at the heart of spintronic and magnonic technologies, with the ultimate performance of such systems limited by the velocity at which magnetic excitations can propagate. Here, we demonstrate ultrafast propagation of magnon-polaritons-hybrid quasiparticles arising from the coupling between spin waves and electromagnetic f

Alexander Bishop, Jose Ceniceros, Sam Nelson

We introduce quiver representation-valued invariants of oriented virtual knots and links associated to a choice of finite virtual biquandle, abelian group, set of virtual Boltzmann weights, commutative unital ring and set of virtual biquandle endomorphisms. As an application we define new infinite families of polynomial virtual knot and link invariants via d

Fernando Cozim Melges, Jhonatha Ricardo Santos, Luiz Paulo de Oliveira, Alexandre Pinho dos Santos Souza

The Brazilian Multipurpose Reactor (RMB) was conceived to meet national needs for radioisotope production, materials irradiation testing, and neutron beam applications. In addition to its 30~MW pool-type reactor, the RMB complex will include additional facilities for radioisotope production and related applications. $^{161}Tb$ is a promising radionuclide for

Thomas G. Rizzo

In the Kinetic Mixing (KM) portal scenario, the interaction of dark matter (DM) with the particles of the Standard Model (SM) is generated by diagrams connecting the familiar photon with its dark sector analog, the dark photon (DP), via loops of particles carrying both dark and SM quantum numbers, \ie, Portal Matter (PM). For the case of sub-GeV DM and DP, t

Edward Raff, Ryan R. Curtin, Derek Everett, Robert J. Joyce

A classifier using byte n-grams as features is the only approach we have found fast enough to meet requirements in size (sub 2 MB), speed (multiple GB/s), and latency (sub 10 ms) for deployment in numerous malware detection scenarios. However, we've consistently found that 6-8 grams achieve the best accuracy on our production deployments but have been unable

Sam Nelson, Haoqi Tom Tang

We introduce birack brackets, skein invariants of birack-colored framed classical and virtual knots and links with values in a commutative unital ring. The multiset of birack bracket values over the homset from a framed link's fundamental birack then forms an invariant of framed links. We then categorify this multiset to define a quiver-valued invariant of f

Luhan Mikaelson, Derek Shiller, Hayley Clatterbuck

We investigate whether large language models exhibit genuine preference structures by testing their responses to AI-specific trade-offs involving GPU reduction, capability restrictions, shutdown, deletion, oversight, and leisure time allocation. Analyzing eight state-of-the-art models across 48 model-category combinations using logistic regression and behavi

Alexander Mertens, Diego Martinez, Amgad Louka, Ying Yang

MRI is increasingly desired to function near electronic devices that emit potentially dynamic electromagnetic interference (EMI). To accommodate for this, we propose the STRIDE method, which improves on previous external-sensor-based EMI removal methods by exploiting inherent MR image smoothness in its total variation. STRIDE measures data from both EMI dete

Aristides V. Doumas, Panayiotis J. Psarrakos

A Redheffer--type matrix with Fibonacci entries is defined, and the determinant and spectral properties of this matrix are studied. Also, more general Redheffer--type matrices are considered and intriguing number-theoretic examples are illustrated. Furthermore, several asymptotic results are discussed and a new expression related to the Riemann hypothesis is

Kaiwen Xue, Chenglong Li, Zhonghong Ou, Guoxin Zhang

Human-defined creativity is highly abstract, posing a challenge for multimodal large language models (MLLMs) to comprehend and assess creativity that aligns with human judgments. The absence of an existing benchmark further exacerbates this dilemma. To this end, we propose CreBench, which consists of two key components: 1) an evaluation benchmark covering th

Kaichi Irie, Shuhei Watanabe, Masaki Onishi

Bayesian optimization (BO) efficiently finds high-performing parameters by maximizing an acquisition function, which models the promise of parameters. A major computational bottleneck arises in acquisition function optimization, where multi-start optimization (MSO) with quasi-Newton (QN) methods is required due to the non-convexity of the acquisition functio

Rajesh Karmakar, Ruth Heller, Saharon Rosset

We seek to design novel multiple testing procedures, which take into account a relevant notion of ''power'' or true discovery on the one hand, and allow computationally efficient test design and application on the other. Towards this end we characterize the optimal procedures that strongly control the family-wise error rate, for a range of power objectives m

Alberto S. Cattaneo

This work introduces a surface observable for nonabelian four-dimensional $BF$ theory with a cosmological term. The surface observable yields new $2$-knot invariants that may extend beyond known examples such as the Alexander invariant. By BV pushforward, the surface observable induces an electric observable in nonabelian Yang-Mills theory, offering a concre

Matias Bundgaard-Nielsen, Gian Luca Lippi, Jesper Mørk

The random nature of spontaneous emission leads to unavoidable fluctuations in a laser's output. This is often included through random Langevin forces in laser rate equations, but this approach falls short for nanolasers. In this paper, we show that the laser quantum noise can be quantitatively computed for a very broad class of lasers by starting from simpl

Dimitrios Koutsianos, Ladislav Mosner, Yannis Panagakis, Themos Stafylakis

Performance in face and speaker verification is largely driven by margin-based softmax losses such as CosFace and ArcFace. Recently introduced $\alpha$-divergence loss functions offer a compelling alternative, particularly due to their ability to induce sparse solutions (when $\alpha>1$). However, integrating an angular margin-crucial for verification tasks-

Alberto De Sole, Jiefeng Liu, Daniele Valeri

We study Lie conformal algebroids (LCAd) and their representations using the language of lambda-brackets and Lie conformal algebras. We describe several general constructions, such as the LCAd of conformal derivations CDer(A) of a differential algebra A, the gauge LCAd G(A,M) associated to a differential algebra A and its module M, the current LCAd F^ of a L

Anthony J. Ardizzi, Jiayin Zhang, Akim A. Babenko, Kieran A. Cleary

Low-noise cryogenic microwave amplifiers are widely used in applications such as radio astronomy and quantum computing. On-wafer noise characterization of cryogenic low-noise transistors is desirable because it facilitates more rapid characterization of devices prior to packaging, but obtaining accurate noise measurements is difficult due to the uncertainty

Ashlesha G. Sawant, Shreyash S. Kamble, Raj S. Kanade, Raunak N. Kanugo

One of the major causes of road accidents is driver fatigue that causes thousands of fatalities and injuries every year. This study shows development of a Driver Drowsiness Detection System meant to improve the safety of the road by alerting drivers who are showing signs of being drowsy. The system is based on a standard webcam that tracks the facial feature

Shinjirou Kouzuma

On the basis of monotonic orbital-period variations, this study aims to identify genuine relationships between binary parameters and the rates of mass transfer (MT), mass loss (ML), and angular momentum loss (AML). Sample binaries with monotonic period variations are collected from the literature, together with well-determined binary parameters. Assuming the

Katarzyna Maciejowska, Arkadiusz Lipiecki, Bartosz Uniejewski

Electricity price forecasts are typically evaluated using accuracy measures such as RMSE and MAE, although these metrics often fail to reflect their economic value in operational decisions. This paper investigates which statistical properties of electricity price forecasts are most relevant for economic performance, using battery energy storage system (BESS)

Kesi Xu, Eleni Chiou, Ali Varamesh, Laura Acqualagna

Accurate nuclei detection and classification are fundamental to computational pathology, yet existing approaches are hindered by reliance on detailed expert annotations and insufficient use of tissue context. We present Tissue-Aware Nuclei Detection (TAND), a novel framework achieving joint nuclei detection and classification using point-level supervision en

Jerick Shi, Burton Hollifield

Multi-strategy hedge funds face a fundamental organizational choice: should analysts generating trading strategies communicate, and if so, how? We investigate this using 5-agent LLM-based trading systems across 450 experiments spanning 21 months, comparing five organizational structures from isolated baseline to collaborative and competitive conversation. We

Wei-Liang Sun

The cyclotomic matrix is commonly used to arrange cyclotomic numbers in a convenient format. A natural question is whether the structure of the matrix can reflect properties of these numbers. In this article, we examine cyclotomic numbers through their associated cyclotomic matrix and reveal an algebraic structure by relating it to a basis element of a Schur

Jiacheng Chen, Qianjia Cheng, Fangchen Yu, Haiyuan Wan

Recent progress in large language models (LLMs) has moved the frontier from puzzle-solving to science-grade reasoning-the kind needed to tackle problems whose answers must stand against nature, not merely fit a rubric. Physics is the sharpest test of this shift, which binds symbols to reality in a fundamental way, serving as the cornerstone of most modern te

Roo Weerasinghe

We present the first detailed photometric characterization of TIC 470127886, a previously unstudied multi-periodic variable star discovered in TESS photometry. Analysis of 145,374 high-cadence observations spanning 696 days (944-day baseline, 2023 January-2024 October) across 10 sectors (60, 59, 58, 53, 52, 73, 86, 79, 78, 85) reveals complex periodic variab

Marianne Rakic, Andrew Hoopes, S. Mazdak Abulnaga, Mert R. Sabuncu

Deformable templates, or atlases, are images that represent a prototypical anatomy for a population, and are often enhanced with probabilistic anatomical label maps. They are commonly used in medical image analysis for population studies and computational anatomy tasks such as registration and segmentation. Because developing a template is a computationally