December 2025 arXiv papers — page 72

Rayne Holland

Identifying heavy hitters in data streams is a fundamental problem with widespread applications in modern analytics systems. These streams are often derived from sensitive user activity, making update-level privacy guarantees necessary. While recent work has adapted the classical heavy hitter algorithm Misra-Gries to satisfy differential privacy in the strea

Vaibhav Gautam, Atsushi Matsuo, Masahito Yamazaki

We advocate the sparsification of bosonic SYK models as a promising arena for the exploration of quantum advantage. We initiate the study of quantum simulations of the models, both in classical simulators and on quantum devices implemented using superconducting qubits. We point out subtleties in the quantum simulations of highly chaotic systems, which should

June Young Yi, Hyeongju Kim, Juheon Lee

This paper presents a lightweight text-to-speech (TTS) system developed for the WildSpoof Challenge TTS Track. Our approach fine-tunes the recently released open-weight TTS model, \textit{Supertonic}\footnote{\url{https://github.com/supertone-inc/supertonic}}, with Self-Purifying Flow Matching (SPFM) to enable robust adaptation to in-the-wild speech. SPFM mi

Cuixin Yang, Rongkang Dong, Kin-Man Lam

Many image restoration (IR) tasks require both pixel-level fidelity and high-level semantic understanding to recover realistic photos with fine-grained details. However, previous approaches often struggle to effectively leverage both the visual and linguistic knowledge. Recent efforts have attempted to incorporate Vision-language models (VLMs), which excel a

Anandu Kalleri Madhu, Chi-Kwong Li, Jami Rönkkö, Mikio Nakahara

This paper introduces a novel edge-based encoding technique for solving the Traveling Salesman Problem (TSP) on a quantum computer, reducing the required number of qubits. For implementation in real quantum devices, we applied the subspace reduction encoding to further reduce the dimension of the TSP solution space. We attack the TSP for 4-, 5-, and 6-city i

Yu Xie, Rikuto Oiwa, Satoru Hayami

From a symmetry perspective, ferroaxial order belongs to the same symmetry as time-reversal-even pseudovectors. Experimentally, ${\rm K_2Zr(PO_4)_2}$ is known to undergo a displacive-type phase transition from a non-ferroaxial to a ferroaxial phase. To identify the key microscopic ingredients driving this transition, we carry out a quantitative analysis comb

G. M. Refatul Islam, Safwan Shaheer, Yaseen Nur, Mohammad Rafid Hamid

Natural Language Processing (NLP) is one of the most revolutionary technologies today. It uses artificial intelligence to understand human text and spoken words. It is used for text summarization, grammar checking, sentiment analysis, and advanced chatbots and has many more potential use cases. Furthermore, it has also made its mark on the education sector.

J. Skowronski, E. Masha, D. Piatti, M. Aliotta

The $^{13}$C(p,$\gamma$)$^{14}$N reaction is the second reaction of the CNO cycle. This cycle takes place in our Sun and fuels massive, Red, and Asymptotic Giant Branch stars. The $^{13}$C(p,$\gamma$)$^{14}$N rate affects the final abundances of $^{12,13}$C and $^{19}$F nuclides, with impact on our understanding of the i- and s-process, giant star nucleosynt

Anton Kirch, Joan Ràfols-Ribé, Kumar Saumya, Thushar Salkod Mahabaleshwar

Efficient charge-carrier injection from air-stable electrodes into organic semiconductors (OSCs) is essential for fabricating solution-processed organic optoelectronic devices under ambient conditions. Today, this is typically achieved by incorporating doped OSC interlayers, introducing self-assembled dipole monolayers, or adding mobile ions to the active ma

Lizhou Liu, Xiaohui Chen, Wenyi Zhang

The convergence of artificial intelligence (AI) and sixth-generation (6G) wireless technologies is driving an urgent need for large-scale, high-fidelity, and reproducible radio frequency (RF) datasets. Existing resources, such as CKMImageNet, primarily provide preprocessed and image-based channel representations, which conceal the fine-grained physical chara

Aaditya Singh, Adam Fry, Adam Perelman, Adam Tart

This is the system card published alongside the OpenAI GPT-5 launch, August 2025. GPT-5 is a unified system with a smart and fast model that answers most questions, a deeper reasoning model for harder problems, and a real-time router that quickly decides which model to use based on conversation type, complexity, tool needs, and explicit intent (for example,

Juan J. Font, Sergio Macario

In this paper we study the best approximation of a fixed fuzzy-number-valued continuous function to a subset of fuzzy-number-valued continuous functions. We also introduce a method to measure the distance between a fuzzy-number-valued continuous function and a real-valued one. Then we prove the existence of the best approximation of a fuzzy-number-valued con

Szymon Głąb, Franciszek Prus-Wiśniowski

We survey recent developments in the theory of achievement sets and present a substantial collection of open problems.

Azhar Iqbal, James M. Chappell, Derek Abbott

We develop an analytical Stackelberg game framework for optimal resource allocation in a sequential attacker--defender setting with a finite set of assets and probabilistic attacks. The defender commits to a mixed protection strategy, after which the attacker best-responds via backward induction. Closed-form expressions for equilibrium protection and attack

Shashank Srivastava, Omkar Kulkarni, Arushi, Deepak Singh

Superconductivity with non-trivial band topology provides a novel platform for exploring topological superconductivity and its quantum applications. A detailed microscopic understanding of the superconducting ground state in such materials is crucial. Here, we report the results of a muon spin rotation/relaxation study ($\mu$SR) of the topologically non-triv

Ioannis Stylianou, Achintya kr. Sarkar, Nauman Dawalatabad, James Glass

Robust Voice Activity Detection (VAD) remains a challenging task, especially under noisy, diverse, and unseen acoustic conditions. Beyond algorithmic development, a key limitation in advancing VAD research is the lack of large-scale, systematically controlled, and publicly available datasets. To address this, we introduce LibriVAD - a scalable open-source da

Zehui Lin, Luyi Han, Xin Wang, Ying Zhou

IMPORTANCE: Modern ultrasound systems are universal diagnostic tools capable of imaging the entire body. However, current AI solutions remain fragmented into single-task tools. This critical gap between hardware versatility and software specificity limits workflow integration and clinical utility. OBJECTIVE: To evaluate the diagnostic accuracy, versatility,

Guoping Cai, Houjin Chen, Yanfeng Li, Jia Sun

Breast ultrasound (BUS) image segmentation plays a vital role in assisting clinical diagnosis and early tumor screening. However, challenges such as speckle noise, imaging artifacts, irregular lesion morphology, and blurred boundaries severely hinder accurate segmentation. To address these challenges, this work aims to design a robust and efficient model cap

Saeed Ebrahimi, Weijie Jiang, Jaewon Yang, Olafur Gudmundsson

Pinterest is a leading visual discovery platform where recommender systems (RecSys) are key to delivering relevant, engaging, and fresh content to our users. In this paper, we study the problem of improving RecSys model predictions for cold-start (CS) items, which appear infrequently in the training data. Although this problem is well-studied in academia, fe

Alireza Moayedikia, Sara Fin

Machine learning approaches for Alzheimer's disease (AD) diagnosis face a fundamental challenges. Clinical assessments are expensive and invasive, leaving ground truth labels available for only a fraction of neuroimaging datasets. We introduce Multi view Adaptive Transport Clustering for Heterogeneous Alzheimer's Disease (MATCH-AD), a semi supervised framewo

BESIII Colaboration, :, M. Ablikim, M. N. Achasov

Using $e^+e^-$ collision data collected with the BESIII detector corresponding to an integrated luminosity of 44.2 fb$^{-1}$, we measure the Born cross sections for the process $e^+e^- \to \Xi(1530)^{0} \bar{\Xi}^{0} + c.c.$ at forty-eight center-of-mass energies between 3.51 and 4.95 GeV. The potential signal from non-$D\bar{D}$ decays for $\psi(3770)$, i.e

Farinaz Mostajeran, Aruzhan Tleubek, Salah A Faroughi

Many physical systems exhibit nonlocal spatiotemporal behaviors described by integro-differential equations (IDEs). Classical methods for solving IDEs require repeatedly evaluating convolution integrals, whose cost increases quickly with kernel complexity and dimensionality. Existing neural solvers can accelerate selected instances of these computations, yet

Evgeny Korotyaev

We consider a first order operator with a smooth periodic 3x3 matrix potential on the real line. It is the Lax operator for the periodic vector NLS equation. Its spectrum covers the real line and it is union of the spectral bands of multiplicity 3, separated by intervals (gaps) of multiplicity 1. We prove and describe the following: \\ $\cdot$ The geometry o

Hongliang Wu, Yuchen Han, Zhengtao Wang, Dezhi Zheng

We present a novel quantum sensing approach to search for axion-electron interactions around the axion mass of 6 \mueV. In this region, laboratory searches are relatively scarce, and our direct experiment measuring the axion-electron coupling constant reaches the sensitivity of 8 \times 10^{-6} GeV^{-1}. The method, based on an organic molecular maser establ

Yongqi Li, Hao Lang, Fei Huang, Tieyun Qian

Role-playing models (RPMs) are widely used in real-world applications but underperform when deployed in the wild. This degradation can be attributed to distribution shifts, including user, character, and dialogue compositional shifts. Existing methods like LLM-as-a-judge fall short in providing a fine-grained diagnosis of how these shifts affect RPM generali

Yifei Ouyang, Ping Zhu, Chung-Sang Ng

In this work, the asymptotic state of nonlinear Landau damping in one-dimensional plasma has been examined using a quasi-linear model and a second-order symplectic integrator. The dispersion relation of the plateau distribution function for the steady-state solution of the quasi-linear mode is extended to the complex plane and compared with the nonlinear sim

Matthias Bentert, Fedor v. Fomin, Petr A. Golovach, Souvik Saha

We study extensions of the classic \emph{Line Cover} problem, which asks whether a set of $n$ points in the plane can be covered using $k$ lines. Line Cover is known to be NP-hard, and we focus on two natural generalizations. The first is \textbf{Line Clustering}, where the goal is to find $k$ lines minimizing the sum of squared distances from the input poin

Michael J. Ryan, Yanzhe Zhang, Amol Salunkhe, Yi Chu

Evaluating user-facing AI applications remains a central challenge, especially in open-ended domains such as travel planning, clinical note generation, or dialogue. The gold standard is user feedback (e.g., thumbs up/down) or behavioral signals (e.g., retention), but these are often scarce in prototypes and research projects, or too-slow to use for system op

Hanyu Li, Jiangshan Duo, Bofei Gao, Hailin Zhang

Chain-of-thought reasoning in large language models can trigger an "overthinking trap": longer rollouts raise cost and latency yet often yield unreliable accuracy gains. Existing methods use global, static controls that may suppress needed reasoning. We propose mastery-gated, sample-level, soft reinforcement learning compression that penalizes long rollouts

Miru Hong, Minho Lee, Geonhee Jo, Jae-Hee So

Transfers play a pivotal role in shaping a football club's success, yet forecasting whether a transfer will succeed remains difficult due to the strong context-dependence of on-field performance. Existing evaluation practices often rely on static summary statistics or post-hoc value models, which fail to capture how a player's contribution adapts to a new ta

Zhenyu Tao, Wei Xu, Xiaohu You

The bisimulation metric (BSM) is a powerful tool for analyzing state similarities within a Markov decision process (MDP), revealing that states closer in BSM have more similar optimal value functions. While BSM has been successfully utilized in reinforcement learning (RL) for tasks like state representation learning and policy exploration, its application to

Yuming Xu, Qianxi Zhang, Qi Chen, Baotong Lu

Scaling Approximate Nearest Neighbor Search (ANNS) to billions of vectors requires distributed indexes that balance accuracy, latency, and throughput. Yet existing index designs struggle with this tradeoff. This paper presents SPIRE, a scalable vector index based on two design decisions. First, it identifies a balanced partition granularity that avoids read-

Zifei Dong, Wenjie Wu, Jinkui Hao, Tianqi Chen

Robust anatomical segmentation of chest X-rays (CXRs) remains challenging due to the scarcity of comprehensive annotations and the substantial variability of real-world acquisition conditions. We propose AnyCXR, a unified framework that enables generalizable multi-organ segmentation across arbitrary CXR projection angles using only synthetic supervision. The

Suraj Kumar, Arvind Kumar, Soumi Chattopadhyay

Dependable service-oriented computing relies on multiple Quality of Service (QoS) parameters that are essential to assess service optimality. However, real-world QoS data are extremely sparse, noisy, and shaped by hierarchical dependencies arising from QoS interactions, and geographical and network-level factors, making accurate QoS prediction challenging. E

Francesc Perera, Hannes Thiel, Eduard Vilalta

We show that a separable C*-algebra $A$ is $\mathcal{Z}$-stable if and only if its uncorrected central sequence algebra $A' \cap A_{\mathcal{U}}$ is pure, if and only if Kirchberg's central sequence algebra $F(A)$ is pure. More generally, we show that a C*-algebra $A$ is separably $\mathcal{Z}$-stable if and only if the relative central sequence algebra $B'

Nicolas Bueno, Luis F. Ayala, Yashar Mehmani

Partially miscible bubble populations trapped in porous media are ubiquitous in subsurface applications such as underground hydrogen storage (UHS), where cyclic injections fragment gas into numerous bubbles with distributions of sizes and compositions. These bubbles exchange mass through Ostwald ripening, driven by differences in composition and interfacial

Jiangjie Chen, Wenxiang Chen, Jiacheng Du, Jinyi Hu

Large language models have recently made significant progress to generate rigorous mathematical proofs. In contrast, utilizing LLMs for theorem proving in formal languages (such as Lean) remains challenging and computationally expensive, particularly when addressing problems at the undergraduate level and beyond. In this work, we present \textbf{Seed-Prover

Gunho Park, Jeongin Bae, Byeongwook Kim, Baeseong park

Weight-only quantization is widely used to mitigate the memory-bound nature of LLM inference. Codebook-based methods extend this trend by achieving strong accuracy in the extremely low-bit regime (e.g., 2-bit). However, current kernels rely on dequantization, which repeatedly fetches centroids and reconstructs weights, incurring substantial latency and cache

Abhivansh Gupta

As LLM-based agents grow more autonomous and multi-modal, ensuring they remain controllable, auditable, and faithful to deployer intent becomes critical. Prior benchmarks measured the propensity for misaligned behavior and showed that agent personalities and tool access significantly influence misalignment. Building on these insights, we propose a Verifiabil

Ferdi, Edy Tri Baskoro, Nobuaki Obata, Aditya Purwa Santika

The quadratic embedding constant (QEC) of a connected graph is defined to be the maximum of the quadratic function associated with its distance matrix on a certain unit sphere of codimension two. In this paper we derive a formula for the QEC of a corona graph $G\odot H$. It is shown that $\mathrm{QEC}(G\odot H)=\psi_{H*}^{-1}(\mathrm{QEC}(G))$ holds under so

Sel Ly, Rufan Yang, Ninad Dixit, Hung Dinh Nguyen

Lithium-ion batteries are the major type of battery used in a variety of everyday applications, including electric vehicles (EVs), mobile devices, and energy storage systems. Predicting the Remaining Useful Life (RUL) of Li-ion batteries is crucial for ensuring their reliability, safety, and cost-effectiveness in battery-powered systems. The materials used f

Iason Kyriakopoulos, Yannis Theodoridis

With the growing popularity of electric vehicles as a means of addressing climate change, concerns have emerged regarding their impact on electric grid management. As a result, predicting EV charging demand has become a timely and important research problem. While substantial research has addressed energy load forecasting in transportation, relatively few st

Shakir Ali, Atif Ahmad Khan, Abhishek Kesarwani, Susanta Samanta

Let $p$ be a prime and $s,m,n$ be positive integers. This paper studies quasi-recursive MDS matrices over Galois rings $GR(p^{s}, p^{sm})$ and proposes various direct construction methods for such matrices. The construction is based on skew polynomial rings $GR(p^{s}, p^{sm})[X;\sigma]$, whose rich factorization properties and enlarged class of polynomials a

Quan Do, Caroline Ahn, Leah Bakst, Michael Pascale

Humans excel at solving novel reasoning problems from minimal exposure, guided by inductive biases, assumptions about which entities and relationships matter. Yet the computational form of these biases and their neural implementation remain poorly understood. We introduce a framework that combines Graph Theory and Graph Neural Networks (GNNs) to formalize in

Baolei Zhang, Minghong Fang, Zhuqing Liu, Biao Yi

Federated Learning (FL) allows multiple clients to collaboratively train a model without sharing their private data. However, FL is vulnerable to Byzantine attacks, where adversaries manipulate client models to compromise the federated model, and privacy inference attacks, where adversaries exploit client models to infer private data. Existing defenses again

Yiren Song, Cheng Liu, Weijia Mao, Mike Zheng Shou

Learning directly from human demonstration videos is a key milestone toward scalable and generalizable robot learning. Yet existing methods rely on intermediate representations such as keypoints or trajectories, introducing information loss and cumulative errors that harm temporal and visual consistency. We present Mitty, a Diffusion Transformer that enables

Aashwin Mishra, Daniel Ratner, Quynh Nguyen

Understanding and manipulating two-dimensional materials for real-world applications remains challenging due to a lack of effective and high-throughput characterization techniques. Soft X-ray time-of-flight photoemission electron microscopy (XPEEM) provides element- and depth-sensitive information of materials and buried interfaces. However, chromatic and sp

Madhava Gaikwad

Large language models are exposed to risks of extraction, distillation, and unauthorized fine-tuning. Existing defenses use watermarking or monitoring, but these act after leakage. We design AlignDP, a hybrid privacy lock that blocks knowledge transfer at the data interface. The key idea is to separate rare and non-rare fields. Rare fields are shielded by PA

Ziyang Lin, Zixuan Sun, Sanhorn Chen, Xiaoyang Chen

Real-time sequential control agents are often bottlenecked by inference latency. Even modest per-step planning delays can destabilize control and degrade overall performance. We propose a speculation-and-correction framework that adapts the predict-then-verify philosophy of speculative execution to model-based control with TD-MPC2. At each step, a pretrained

Yuqi Ping, Junwei Wu, Bofeng Zheng, Fan Liu

In this letter, we present an uncertainty-aware single-anchor Ultra-Wideband (UWB)-based 3D tracking framework. Specifically, a mobile Unmanned Aerial Vehicle (UAV) maintains a desired standoff distance to a moving target using range and 3D bearing measurements from a multi-antenna UWB anchor rigidly mounted on the UAV. To enhance the stability and safety un

SeongJin Kwon, Kyung-Hwan Jin, Jong Eun Han, Siwon Lee

Quantum condensed states in solids often reveal their fundamental nature via interactions with impurities, as epitomized by Yu-Shiba-Rusinov (YSR) bound states at magnetic impurities in superconductors. Although analogous YSR bound states were predicted within quantum condensates of excitons several decades ago, their existence has been elusive. Here, we dir

Aritra Dey, R. Bhuvaneswari, Sourav Chowdhury, Souvik Banerjee

Spin chirality provides a powerful route to control magnetic and topological phases in materials, enabling next-generation spintronic and quantum technologies. Coplanar noncollinear antiferromagnets with Kagome lattice spin geometries host vector spin chirality (VSC), the handedness of spin arrangement, and offer an excellent platform for chirality-driven ph

Shinobu Miya

Large Language Models (LLMs) frequently produce hallucinated statements that are assigned high likelihood by the model itself, exposing a fundamental limitation of probability-based verification. This suggests that hallucination is often not a low-confidence phenomenon, but a failure of structural consistency. In this work, we reformulate the verification of

Zahra Rahmani, Hossein Sameti

Automatic Speech Recognition (ASR) systems suffer significant performance degradation in noisy environments, a challenge that is especially severe for low-resource languages such as Persian. Even state-of-the-art models such as Whisper struggle to maintain accuracy under varying signal-to-noise ratios (SNRs). This study presents a robust noise-sensitive ASR

Rongxiang Zhang, Bo Li, Jinghua Li, Yuguang Song

With the rapid development of distributed renewable energy, multi-microgrids play an increasingly important role in improving the flexibility and reliability of energy supply. Reinforcement learning has shown great potential in coordination strategies due to its model-free nature. Current methods lack explicit quantification of the relationship between indiv

Deriyan Senjaya, Stephen Ekaputra Limantoro

Understanding atomic structures is crucial, yet amorphous materials remain challenging due to their irregular and non-periodic nature. The Wavelet Transform Radial Distribution Function (WT-RDF) offers a physics-based framework for analyzing amorphous structures, reliably reconstructing the first and second Radial Distribution Function (RDF) peaks and overal

Lei Chen, Cheryl Praeger

We extend the notion of an $H$-normal quotient digraph of an $H$-vertex-transitive digraph to that of an $H$-subnormal quotient digraph. Using these concepts, together with bipartite halves of bipartite digraphs, we show that, for each finite connected $H$-vertex-transitive, $(H,s)$-arc-transitive digraph with $s\geqslant6$, either some $H$-normal quotient i

Paul X. McCarthy, Xian Gong, Marian-Andrei Rizoiu, Paolo Boldi

The global aid system functions as a complex and evolving ecosystem; yet widespread understanding of its structure remains largely limited to aggregate volume flows. Here we map the network topology of global aid using a dataset of unprecedented scale: over 10 million transaction records connecting 2,456 publishing organisations across 230 countries between

Chuanhong Gao, Chuang Yang, Tetvui Chong, Deyou Chen

In this paper, we investigate Lyapunov exponents of chaos for both massless and charged particles around a non-linear electrodynamics black hole, and explore their relationships with a phase transition and a chaos bound of this black hole. Our results indicate that these exponents can effectively reveal the phase transition. Specifically, during the phase tr

Suraj Nukala, Meera Sushma, Leimin Tian, Akansel Cosgun

The growing use of service robots in hospitality highlights the need to understand how to effectively communicate with pre-occupied customers. This study investigates the efficacy of commonly used communication modalities by service robots, namely, acoustic/speech, visual display, and micromotion gestures in capturing attention and communicating intention wi

Chitrak Bhowmik, Aparna Baskaran, Sriram Ramaswamy

We study the segregation of motile semiflexible filaments from a background of similar but non-motile filaments. Our Langevin dynamics simulations reveal a wide range of emergent structures governed by filament flexibility and activity, i.e., self-propulsion strength. The system segregates at low activities, while at high activities it undergoes remixing whi

Jun'ichi Ozaki, Ryosuke Susuta, Takuhiro Moriyama, Yohei Shida

Urban mobility data are indispensable for urban planning, transportation demand forecasting, pandemic modeling, and many other applications; however, individual mobile phone-derived Global Positioning System traces cannot generally be shared with third parties owing to severe re-identification risks. Aggregated records, such as origin-destination (OD) matric

Aprup Kale, Rucha Kulkarni, Navya Garg

We study the question of existence and fast computation of fair and efficient allocations of indivisible resources among agents with additive valuations. As such allocations may not exist for arbitrary instances, we ask if they exist for \textit{typical} or \textit{random} instances, meaning when the utility values of agents for the resources are drawn from

Dylan Langharst, Michael Roysdon

Recently, Haddad, Jim\'enez, and Montenegro introduced the affine $p$-Laplace operator, $p>1$, and studied associated affine versions of the isoperimetric inequalities for the first eigenvalue of the affine $p$-Laplace operator, including the affine Faber-Krahn inequality and affine Talenti inequality. In this work, we introduce the $m$th-order $p$-Laplace o

Hibiki Nakada, Shoya Ogawa, Yoshiki Chazono, Kazuyuki Ogata

The ratios of the one-proton knockout cross sections by a deuteron to those by a proton are about 1.5, indicating that using deuteron is more efficient than proton in yielding large knockout cross sections. However, this ratio differs from the intuitive expectation, and its underlying mechanism remains unclear. The purpose of this study is to clarify the mec

Daisuke Takegami, Haruki Takei, Masato Yoshimura, Takuro Katsufuji

Here we investigate the electronic structure of the tetragonal tungsten bronze Ba$_{3-x}$Yb$_x$Ta$_{5}$O$_{15}$ by making use of hard x-ray photoemission spectroscopy. The core level spectroscopy shows that the substitution with Yb ions in the series first occurs on the compact S1 site. For $x\leq1$, Yb is found to be dominantly Yb$^{2+}$ with a small mixing

Chuanting Zhang, Haixia Zhang, Jingping Qiao, Zongzhang Li

The growing demand for intelligent, adaptive resource management in next-generation wireless networks has underscored the importance of accurate and scalable wireless traffic prediction. While recent advancements in deep learning and foundation models such as large language models (LLMs) have demonstrated promising forecasting capabilities, they largely over

Yuhang Zheng, Yang Zhao, Xiuting Zou, Chunyu Zhao

Wave-physics-based intelligent sensing has driven multidisciplinary applications from smart industries to decision-making systems. Traditional sensing paradigms transform physical waveforms into human-understandable intermediate representations through preprocessing. Such transformations inherently cause information loss owing to data processing inequality (

Robert Hickingbotham, Gwenaël Joret

Given a graph $G$ and $\mathcal{A}\subseteq V(G)$, a classical theorem of Gallai (1964) states that for every positive integer $k$, the graph $G$ contains $k$ pairwise vertex-disjoint $\mathcal{A}$-paths, or a set $Z\subseteq V(G)$ of size at most $2(k-1)$ such that $G-Z$ contains no $\mathcal{A}$-paths. We generalise Gallai's theorem to the induced sett

Antonio De Felice, Takehiro Ogura, Shinji Tsujikawa, Kimiko Yamashita

We study dark photon dark matter $X$ associated with a dark $U(1)_X$ gauge symmetry. To evade laboratory and cosmological constraints on kinetic mixing with the Standard Model $U(1)_Y$, we assign a $Z_2$-odd dark parity to $X$ that forbids such mixing. The leading interactions then arise from gauge-invariant dimension-6 Higgs-portal operators, including both

Tianyang Li, Yunfei Fan, Anping Huang, Baoyi Chen

In non-central relativistic heavy-ion collisions, the produced quark-gluon plasma (QGP) behaves approximately as a rotating fluid due to the system's initial angular momentum. In this rotating fluid, the spins of quarks become polarized due to the coupling between spin and angular momentum, as well as random spin-spin interactions. Since the Landau-Lifshitz

Henghui Du, Chunjie Zhang, Xi Chen, Chang Zhou

Long Video Question-Answering (LVQA) presents a significant challenge for Multi-modal Large Language Models (MLLMs) due to immense context and overloaded information, which could also lead to prohibitive memory consumption. While existing methods attempt to address these issues by reducing visual tokens or extending model's context length, they may miss usef

Pawan Kumar, Aditi Gupta

Modern large language models (LLMs) place extraordinary pressure on memory and compute budgets, making principled compression indispensable for both deployment and continued training. We present Hierarchical Sparse Plus Low-Rank (HSS) compression, a two-stage scheme that (i) removes the largest-magnitude weights into a sparse matrix S and (ii) applies a recu

Chung-Ta Huang, Connie Cheng, Vealy Lai

Most digital music tools emphasize precision and control, but often lack support for tactile, improvisational workflows grounded in environmental interaction. Lumia addresses this by enabling users to "compose through looking"--transforming visual scenes into musical phrases using a handheld, camera-based interface and large multimodal models. A vision-langu

Siqi Yang, Zilve Gao, Haibo Qiu, Fanfan Liu

Multimodal Large Language Models (MLLMs) demonstrate significant potential but remain brittle in complex, long-chain visual reasoning tasks. A critical failure mode is "visual forgetting", where models progressively lose visual grounding as reasoning extends, a phenomenon aptly described as "think longer, see less". We posit this failure stems from current t

Son Tung Nguyen, Alejandro Fontan, Michael Milford, Tobias Fischer

Recent learning-based visual localization methods use global descriptors to disambiguate visually similar places, but existing approaches often derive these descriptors from geometric cues alone (e.g., covisibility graphs), limiting their discriminative power and reducing robustness in the presence of noisy geometric constraints. We propose an aggregator mod

Dimitrios Bachtis, David S. Berman, Arabella Schelpe

We use a $\phi^{4}$ quantum field theory with inhomogeneous couplings and explicit symmetry-breaking to model an ensemble of financial time series from the S$\&$P 500 index. The continuum nature of the $\phi^4$ theory avoids the inaccuracies that occur in Ising-based models which require a discretization of the time series. We demonstrate this using the exam

Xuyang Li, Chenyu Li, Danfeng Hong

Optical satellites, with their diverse band layouts and ground sampling distances, supply indispensable evidence for tasks ranging from ecosystem surveillance to emergency response. However, significant discrepancies in band composition and spatial resolution across different optical sensors present major challenges for existing Remote Sensing Foundation Mod

Hiroto Imaeda, Tsunehiro Takeuchi, Hiroyuki Awano, Kenji Tanabe

We conducted a comprehensive study on the compositional dependence of the anomalous Nernst effect (ANE) in amorphous (amo.) Tb-Fe-Co thin films. The anomalous Nernst coefficient strongly depends not only on the Tb composition but also on the transition metal composition, reaching a maximum of 1.8 uV/K for amo. Tb11.0(Fe50.0Co50.0) 89.0. By evaluating the ele

Pengzi Miao

We derive monotone properties of positive harmonic functions on three dimensional manifolds with nonnegative scalar curvature, with an asymptotically flat end. Rigidity characterization of spatial Schwarzschild manifolds with two ends is also given.

Brandon Huang, Hang Hua, Zhuoran Yu, Trevor Darrell

While Vision-language models (VLMs) have demonstrated remarkable performance across multi-modal tasks, their choice of vision encoders presents a fundamental weakness: their low-level features lack the robust structural and spatial information essential for document understanding and web agents. To bridge this gap, we introduce DAVE, a vision encoder purpose

Ahmad Jafar Arifi, Parada. T. P. Hutauruk, Terry Mart, Chalis Setyadi

Hadronic physics has gradually emerged as one of the growing research frontiers in Indonesia, driven by efforts to better understand the properties of the strong interaction and the internal structure of hadrons from the fundamental principles of Quantum Chromodynamics. In the last few decades, Indonesian researchers have made significant contributions to de

Wisnu Uriawan, Imany Fauzy Rahman, Muhamad Zidan, Irma Rohmatillah

The rapid development of deepfake technology powered by AI has raised global concerns regarding the manipulation of information, the usurpation of digital identities, and the erosion of public trust in the authenticity of online content. These challenges extend beyond technical issues and involve complex moral dimensions, rendering conventional, technologica

Hiroki Shibata, Dominik Köppl

The Lempel--Ziv 78 (LZ78) factorization is a well-studied technique for data compression. It and its derivatives are used in compression formats such as "compress" or "gif". Although most research focuses on the factorization of plain data, not much research has been conducted on indexing the data for fast LZ78 factorization. Here, we study the LZ78 factoriz

Medet Jumadildayev

We obtain a generating function for the degree sequences and colors of rooted multipartite labeled series-reduced trees. As an application of this result, we determine the number of symbolic ultrametrics (introduced by B\"ocker and Dress) and increasingly labeled processes. We also find that the number of multipartite labeled series-reduced trees and the col

Yan Gao, Jiliang Wang, Minghan Wang, Xiaohua Chen

In the field of gas pipeline location, existing pipeline location methods mostly rely on pipeline location instruments. However, when faced with complex and curved pipeline scenarios, these methods often fail due to problems such as cable entanglement and insufficient equipment flexibility. To address this pain point, we designed a self-propelled pipeline ro

Peng Fan, Guofei Pang

Convolutional neural operator is a CNN-based architecture recently proposed to enforce structure-preserving continuous-discrete equivalence and enable the genuine, alias-free learning of solution operators of PDEs. This neural operator was demonstrated to outperform for certain cases some baseline models such as DeepONet, Fourier neural operator, and Galerki

Lei Zhang, Alessandro Ridolfi, Di Li, Erbil Gugercinoglu

Located at the centres of supernova remnants, central compact objects (CCOs) are among the most puzzling neutron stars. CCOs are bright in thermal X-rays, yet have evaded detection by major radio telescopes for decades, giving rise to the view that they are intrinsically radio-quiet and possess exceptionally weak magnetic fields. Here we show that the protot

Xiao Liang, Yuxuan An, Di Wang, Jiawei Hu

Medical Vision-Language Models (VLMs) are prone to hallucinations, compromising clinical reliability. While reinforcement learning methods like Group Relative Policy Optimization (GRPO) offer a low-cost alignment solution, their reliance on sparse, outcome-based rewards inadvertently encourages models to "overthink" -- generating verbose, convoluted, and unv

James W. Anderson, Ara Basmajian, Ruben Hidalgo, Perry Susskind

This is an expanded version of the Maskit memorial tribute that appeared in the August 2025 issue of the Notices of the AMS.

Yan Gao, Jiliang Wang, Ming Cheng, Tianyun Huang

In pipeline inspection, traditional tethered inspection robots are severely constrained by cable length and weight, which greatly limit their travel range and accessibility. To address these issues, this paper proposes a self-propelled pipeline robot design based on force analysis and dynamic simulation, with a specific focus on solving core challenges inclu

Wenbo Zhou, Yuke Zhang, Pengfei Zhang

Understanding the non-equilibrium dynamics of quantum many-body systems remains one of the grand challenges of modern physics. In particular, increasing attention has been devoted to the emergence of non-equilibrium universality classes that have no equilibrium counterparts. A prominent example is the Kardar-Parisi-Zhang universality class realized in dissip

Keisuke Toyama, Zhi Zhong, Akira Takahashi, Shusuke Takahashi

In music information retrieval (MIR) research, the use of pretrained foundational audio encoders (FAEs) has recently become a trend. FAEs pretrained on large amounts of music and audio data have been shown to improve performance on MIR tasks such as music tagging and automatic music transcription. However, their use for music structure analysis (MSA) remains

Yu-Peng E, Chun-Chun Zhu, Yu-Xiao Liu

We systematically investigate the quasinormal modes of thick branes in $f(R)$ gravity by numerically solving the Schr\"odinger-like perturbation equation of gravitational perturbations. To ensure the reliability of the results, we employ three complementary methods: the asymptotic iteration method, the direct integration of the wave equation, and the time-do

Rujiao Long, Yang Li, Xingyao Zhang, Weixun Wang

Exploration capacity shapes both inference-time performance and reinforcement learning (RL) training for large (vision-) language models, as stochastic sampling often yields redundant reasoning paths with little high-level diversity. This paper proposes Reasoning Palette, a novel latent-modulation framework that endows the model with a stochastic latent vari

Sanjay Mallick, Ripan Saha

In the paper, concerning a question of Yi [23], we study general criterion for the uniqueness of an L-function and a general meromorphic function. Our results improve and extend all the existing results in this direction [23, 18, 17, 4] to the most general setting. Moreover, we have exhibited a handsome number of examples to justify our claims as well as to

Ethan Silver, Plamen Krastev, Edo Berger

Since the first detection of gravitational waves in 2015 by LIGO from the binary black hole merger GW150914, gravitational-wave astronomy has developed significantly, with over 200 compact binary merger events cataloged. The use of neural networks has the potential to significantly speed up the detection, classification, and especially parameter estimation f

Jiwoo Song, Daning Huang, John Harlim

In this work, we propose a simple kernel ridge regression (KRR) framework with a dynamic-aware validation strategy for long-term prediction of complex dynamical systems. By employing a data-driven kernel derived from diffusion maps, the proposed Diffusion Maps Kernel Ridge Regression (DM-KRR) method implicitly adapts to the intrinsic geometry of the system's

Kai Liu, Zeli Lin, Weibo Wang, Linghe Kong

Pansharpening is a significant image fusion task that fuses low-resolution multispectral images (LRMSI) and high-resolution panchromatic images (PAN) to obtain high-resolution multispectral images (HRMSI). The development of the diffusion models (DM) and the end-to-end models (E2E model) has greatly improved the frontier of pansharping. DM takes the multi-st

Tian-Yong Cao, Shi-jie Zheng, Shu-Xu Yi, Ming-Yu Ge

We present constraints on the nanohertz gravitational wave background (GWB) using X-ray pulsar timing data from the Neutron Star Interior Composition Explorer(\textit{NICER}). By analyzing six millisecond pulsars over a six-year observational baseline, we employed a Bayesian framework to model noise components and search for a common red signal consistent wi