October 2025 arXiv papers — page 54

Abhijit Banerjee, Sujoy Majumder, Nabadwip Sarkar

The objective of the paper is twofold. The first objective is to study the uniqueness problem of meromorphic function $f(z)$ when $f^{(1)}(z)$ shares two distinct finite values $a_1$, $a_2$ and $\infty$ CM with $\Delta_cf(z)$. In this context, we provide a result that resolves the open problem posed by Qi et al. [Comput. Methods Funct. Theory, 18 (2018), 567

Tieguang Zi, Fu-Wen Shu

Space-based gravitational-wave detectors, such as the Laser Interferometer Space Antenna (LISA), provide a platform to probe new fundamental fields through extreme-mass-ratio inspirals (EMRIs), where a compact secondary object carrying scalar or vector charges inspirals into a massive primary. In a theory-agnostic framework, we compute the ultra-light vector

Anum Paracha, Junaid Arshad, Mohamed Ben Farah, Khalid Ismail

Data poisoning attacks are a potential threat to machine learning (ML) models, aiming to manipulate training datasets to disrupt their performance. Existing defenses are mostly designed to mitigate specific poisoning attacks or are aligned with particular ML algorithms. Furthermore, most defenses are developed to secure deep neural networks or binary classif

R. F. Shamoyan, N. M. Makhina

In this expository paper we collect many recent advances in analytic function spaces of several complex variables related with trace problem in tubular domains over symmetric cones and bounded strongly pseudoconvex domains with smooth boundary. We consider various function space of analytic functions of several variables in various domains in $C^n$ and provi

Tu Anh Dinh, Philipp Nicolas Schumacher, Jan Niehues

Large Language Models (LLMs) have advanced rapidly in recent years. One application of LLMs is to support student learning in educational settings. However, prior work has shown that LLMs still struggle to answer questions accurately within university-level computer science courses. In this work, we investigate how incorporating university course materials c

Shanfeng Xiao, Huaxiong Huang, Robert Eisenberg, Zilong Song

Effective metabolic waste clearance and maintaining ionic homeostasis are essential for the health and normal function of the central nervous system. To understand its mechanism and the role of fluid flow, we develop a multidomain electro-osmotic model of optic-nerve microcirculation that couples hydrostatic and osmotic fluid transport with electro-diffusive

Miaomiao Li, Bin Pei, Yong Xu, Xiaole Yue

This paper investigates a class of slow--fast systems of rough partial differential equations defined over a monotone family of interpolation Hilbert spaces. By employing the controlled rough path framework tailored to a monotone family of interpolation spaces, together with a time discretization argument, we demonstrate that the slow component strongly conv

Qiao Li, Jie Li, Yukang Zhang, Lei Tan

Aerial-Ground person re-identification (AG-ReID) is an emerging yet challenging task that aims to match pedestrian images captured from drastically different viewpoints, typically from unmanned aerial vehicles (UAVs) and ground-based surveillance cameras. The task poses significant challenges due to extreme viewpoint discrepancies, occlusions, and domain gap

Weijian Li, Andreas A. Malikopoulos

In this paper, we investigate the infinite-horizon risk-constrained linear quadratic regulator problem (RC-QR), which augments the classical LQR formulation with a statistical constraint on the variability of the system state to incorporate risk awareness, a key requirement in safety-critical control applications. We propose an actor-critic learning algorith

Langwen Huang, Luigi Fusco, Florian Scheidl, Jan Zibell

As the resolution of weather and climate simulations increases, the amount of data produced is growing rapidly from hundreds of terabytes to tens of petabytes. The huge size becomes a limiting factor for broader adoption, and its fast growth rate will soon exhaust all the available storage devices. To address these issues, we present EBCC (Error Bounded Clim

Iliass Ayaou, Denis Cavallucci

Patent text embeddings enable prior art search, technology landscaping, and patent analysis, yet existing benchmarks inadequately capture patent-specific challenges. We introduce PatenTEB, a comprehensive benchmark comprising 15 tasks across retrieval, classification, paraphrase, and clustering, with 2.06 million examples. PatenTEB employs domain-stratified

Heejoon Koo, Miika Toikkanen, Yoon Tae Kim, Soo Yong Kim

Multimodal respiratory sound classification offers promise for early pulmonary disease detection by integrating bioacoustic signals with patient metadata. Nevertheless, current approaches remain vulnerable to spurious correlations from attributes such as age, sex, or acquisition device, which hinder their generalization, especially under distribution shifts

Yike Sun, Yaxuan Gao, Kewei Wang, Jingnan Sun

We present a lateral ventricular brain-computer interface (LV-BCI) that deploys an expandable, flexible electrode into the lateral ventricle through a minimally invasive external ventricular drainage pathway. Inspired by the framework of traditional Chinese lanterns, the electrode expands uniformly within the ventricle and conforms to the ependymal wall. Com

Shireen Kudukkil Manchingal

Machine learning has achieved remarkable successes, yet its deployment in safety-critical domains remains hindered by an inherent inability to manage uncertainty, resulting in overconfident and unreliable predictions when models encounter out-of-distribution data, adversarial perturbations, or naturally fluctuating environments. This thesis, titled Epistemic

Tianhao Zhao, Yiyang Zou, Zihao Mao, Peilun Xiao

Accident anticipation aims to predict potential collisions in an online manner, enabling timely alerts to enhance road safety. Existing methods typically predict frame-level risk scores as indicators of hazard. However, these approaches rely on ambiguous binary supervision (labeling all frames in accident videos as positive) despite the fact that risk varies

Hao Chen, Conghui Xie, Cunsheng Ding

R. W. Hamming published the Hamming codes and the sphere packing bound in 1950. In the past 75 years, infinite families of distance-optimal linear codes over finite fields with minimum distance at most 8 with respect to the sphere packing bound have been reported in the literature. However, it is a 75-year-old open problem in coding theory whether there is a

Tenghui Li, Guoxu Zhou, Xuyang Zhao, Yuning Qiu

As the length of input text increases, the key-value (KV) cache in LLMs imposes prohibitive GPU memory costs and limits long-context inference on resource constrained devices. Existing approaches, such as KV quantization and pruning, reduce memory usage but suffer from numerical precision loss or suboptimal retention of key-value pairs. In this work, Low Ran

Sapir Goldring, Zamir Ben Hur, David Lou Alon, Chad McKell

This paper investigates the performance of Binaural Signal Matching (BSM) methods for near-field sound reproduction using a wearable glasses-mounted microphone array. BSM is a flexible, signal-independent approach for binaural rendering with arbitrary arrays, but its conventional formulation assumes far-field sources. In our previous work, we proposed a near

Berkay Döner, Thorir Mar Ingolfsson, Luca Benini, Yawei Li

Electroencephalography (EEG) offers a non-invasive lens into human brain activity, but building large-scale models is hampered by topological heterogeneity: each public EEG data defines its own electrode layout, limiting generalization. We introduce LUNA (Latent Unified Network Architecture), a self-supervised foundation model that reconciles disparate elect

Xiaoyan Zhao, Ming Yan, Yilun Qiu, Haoting Ni

Large language models (LLMs) have shown remarkable success in recent years, enabling a wide range of applications, including intelligent assistants that support users' daily life and work. A critical factor in building such assistants is personalizing LLMs, as user preferences and needs vary widely. Activation steering, which directly leverages directions re

Eunseop Yoon, Hee Suk Yoon, Jaehyun Jang, SooHwan Eom

Reinforcement Learning with Verifiable Rewards (RLVR) has significantly improved LLM reasoning, but its sparse, outcome-based reward provides no guidance for intermediate steps, slowing exploration. We propose Progressively Ascending Confidence Reward (PACR), a dense, model-intrinsic reward computed directly from the model's evolving belief in the correct an

Daniele Iannotti, Lorenzo Campos Venuti, Alioscia Hamma

The probability distribution of a measure of non-stabilizerness, also known as magic, is investigated for Haar-random pure quantum states. Focusing on the stabilizer R\'enyi entropies, the associated probability density functions (PDFs) are found to display distinct non-analytic features analogous to Van Hove singularities in condensed matter systems. For a

Apratim Shukla, Dootika Vats, Eric C. Chi

Bayesian formulation of modern day signal processing problems has called for improved Markov chain Monte Carlo (MCMC) sampling algorithms for inference. The need for efficient sampling techniques has become indispensable for high dimensional distributions that often characterize many core signal processing problems, e.g., image denoising, sparse signal recov

Imran Khan

Prompt engineering, particularly Chain-of-Thought (CoT) prompting, significantly enhances LLM reasoning capabilities. We introduce "Sculpting," a constrained, rule-based prompting method designed to improve upon standard CoT by reducing errors from semantic ambiguity and flawed common sense. We evaluate three prompting strategies (Zero Shot, standard CoT, an

Jongwan Ko, Woowon Byun, Kwang-Il Seon, Jihun Kim

Low-surface-brightness (LSB) structures play a crucial role in understanding galaxy evolution by providing significant insights into galaxy interactions, the histories of mass assembly, and the distribution of dark matter. Nevertheless, their inherently faint nature, coupled with observational difficulties such as stray light interference and variations in t

Ibuki Nakamura, Yutaro Kashiwa, Bin Lin, Hajimu Iida

Developers often opt for easier but non-optimal implementation to meet deadlines or create rapid prototypes, leading to additional effort known as technical debt to improve the code later. Oftentimes, developers explicitly document the technical debt in code comments, referred to as Self-Admitted Technical Debt (SATD). Numerous researchers have investigated

Aarav Shah

Slow-roll of the inflaton (inflationary field) defines the standard dynamics of the inflationary epoch. However, the inflaton deviates from slow-roll when it encounters an extremely flat region in the inflationary potential, and enters a phase dubbed Ultra Slow Roll (USR). In previous studies, there have been various theories which modify the theory of gener

Tianming Lan

After the industrialization of 5G cellular communications, 6G has increasingly become a research hotspot in the academia. Space-Air-Ground Integrated Network (SAGIN) is a key supporting technology for 6G because of its advantages such as high-speed transmission and expanded coverage. This paper summarizes the motivation to develop the SAGIN-assisted 6G first

Keonhee Lee, Seunghee Lee, C. A. Morales

We introduce the {\em $\mu$-topological stability}. This is a type of stability depending on the measure $\mu$ different from the set-valued approach \cite{lm}. We prove that the map $f$ is $m_p$-topologically stable if and only if $p$ is a topologically stable point ($m_p$ is the Dirac measure supported on $p$). On closed manifolds of dimension $\geq2$ we p

Shu-guo Lei, Cheng-ping Huang

The Cotton-Mouton (CM) effect, referring to linear birefringence induced by a magnetic field, is usually very weak in natural materials. We propose theoretically that a giant CM effect may be achieved in the THz region with the suspension of iron nanorods. The unusual effect stems from the dual nature of the iron nanorods, which exhibit both ferromagnetic an

Shijie Bao, Qi'an Guan, Zhitong Mi, Zheng Yuan

In this article, using key tools including Zhou valuations, Tian functions and a convergence result for relative types, we establish necessary and sufficient conditions for the existence of valuative interpolations on the rings of germs of holomorphic functions and real analytic functions at the origin in $\mathbb{C}^{n}$ and $\mathbb{R}^{n}$, respectively.

Amir Mohammad Khadem Hosseini, Sattar Mirzakuchaki

Semantic segmentation has emerged as a fundamental problem in computer vision, gaining particular importance in real-time applications such as autonomous driving. The main challenge is achieving high accuracy while operating under computational and hardware constraints. In this research, we present an FPGA-based implementation of real-time semantic segmentat

Yutao Wu, Xiao Liu, Yunhao Feng, Jiale Ding

Large Language Models (LLMs) increasingly serve as research assistants, yet their reliability in scholarly tasks remains under-evaluated. In this work, we introduce PaperAsk, a benchmark that systematically evaluates LLMs across four key research tasks: citation retrieval, content extraction, paper discovery, and claim verification. We evaluate GPT-4o, GPT-5

Parthasaarathy Sudarsanam, Sebastian Braun, Hannes Gamper

Neural audio codecs have been widely studied for mono and stereo signals, but spatial audio remains largely unexplored. We present the first discrete neural spatial audio codec for first-order ambisonics (FOA). Building on the WavTokenizer architecture, we extend it to support four-channel FOA signals and introduce a novel spatial consistency loss to preserv

Ryosuke Kurihara, Satoshi Hakamada, Masaki Kondo, Ryuji Okazaki

Since iron-based superconductors have been discovered, many scientists have focused on their characteristic properties, such as an unconventional mechanism and a high upper critical field. Sulphur-substituted FeTe compounds are one of the members of the iron-based superconductors; however, chemical processes, such as O$_2$ annealing, are needed to induce sup

Jahidul Arafat, Sanjaya Poudel

Chromatin sensitive partial wave spectroscopic (csPWS) microscopy enables label free detection of nanoscale chromatin packing alterations that occur before visible cellular transformation. However, manual nuclear segmentation limits population scale analysis needed for biomarker discovery in early cancer detection. The lack of annotated csPWS imaging data pr

Dmitri V. Fursaev

Gravitational shockwaves produce perturbations of field systems. We study classical scalar and electromagnetic fields and gravitational memory effects left after the action on the fields of plane-fronted gravitational shockwaves. The gravitational memory plays a key role for the choice of Cauchy data which determine the perturbations. We demonstrate that fie

Krishna Gurugubelli

Automated dysarthria detection and severity assessment from speech have attracted significant research attention due to their potential clinical impact. Despite rapid progress in acoustic modeling and deep learning, models still fall short of human expert performance. This manuscript provides a comprehensive analysis of the reasons behind this gap, emphasizi

Kunyang Zhou, Yeqin Shao

In this paper, we present a novel diffusion-based model for lane detection, called DiffusionLane, which treats the lane detection task as a denoising diffusion process in the parameter space of the lane. Firstly, we add the Gaussian noise to the parameters (the starting point and the angle) of ground truth lanes to obtain noisy lane anchors, and the model le

Yixiao Nie, Yang Zhang, Yingjie Jin, Zhepeng Wang

The integration of self-driving cars and service robots is becoming increasingly prevalent across a wide array of fields, playing a crucial and expanding role in both industrial applications and everyday life. In parallel, the rapid advancements in Large Language Models (LLMs) have garnered substantial attention and interest within the research community. Th

Lukáš Gáborik, Sascha Kurz, Giuseppe Mazzuoccolo, Jozef Rajník

We investigate multidimensional nowhere-zero flows of bridgeless graphs. By extending the established use of the Euclidean norm, this paper considers the Manhattan and Chebyshev norms, leading to the definition of the flow numbers $\Phi_d^1(G)$ and $\Phi_d^\infty(G)$, respectively. These flow numbers are always rational and in two dimensions, they distinguis

A. M. Rodriguez Zarate, T. Thiemann

Interesting quantum integrable models are rare and one often has to resort to approximation methods. One of these is the Raleigh Ritz method which under certain circumstances allows to approximately compute the lowest energy eigenstate (or ground state) of a given Hamiltonian whose pure point spectrum is bounded from below. The quality of such approximations

Daisuke Hirota

Cooperative systems often remain in persistently suboptimal yet stable states. This paper explains such "rational stagnation" as an equilibrium sustained by a rational adversary whose utility follows the principle of potential loss, $u_{D} = U_{ideal} - U_{actual}$. Starting from the Prisoner's Dilemma, we show that the transformation $u_{i}' = a\,u_{i} + b\

Alireza Kabgani, Masoud Ahookhosh

This paper introduces a first-order majorization-minimization framework based on a high-order majorant for continuous functions, incorporating a non-quadratic regularization term of degree $p>1$. Notably, it is shown to be valid if and only if the function is $p$-paraconcave, thus extending beyond Lipschitz and H\"{o}lder gradient continuity for $p \in (1,2]

Ziqi Diao, Xingyu Zhou, Le Liang, Shi Jin

Channel estimation for massive multiple-input multiple-output (MIMO) systems is fundamentally constrained by excessive pilot overhead and high estimation latency. To overcome these obstacles, recent studies have leveraged deep generative networks to capture the prior distribution of wireless channels. In this paper, we propose a novel estimation framework th

Jeongin Kim, Wonho Bae, YouLee Han, Giyeong Oh

Semantic segmentation demands dense pixel-level annotations, which can be prohibitively expensive - especially under extremely constrained labeling budgets. In this paper, we address the problem of low-budget active learning for semantic segmentation by proposing a novel two-stage selection pipeline. Our approach leverages a pre-trained diffusion model to ex

Guojian Zhan, Likun Wang, Pengcheng Wang, Feihong Zhang

Maximum entropy has become a mainstream off-policy reinforcement learning (RL) framework for balancing exploitation and exploration. However, two bottlenecks still limit further performance improvement: (1) non-stationary Q-value estimation caused by jointly injecting entropy and updating its weighting parameter, i.e., temperature; and (2) short-sighted loca

Zheping Wu, Lingzhen Guo, Haobin Shi, Wei-Wei Zhang

Bosonic codes represent a promising route toward quantum error correction in continuous-variable systems, with direct relevance to experimental platforms such as circuit QED and optomechanics. However, their preparation and stabilization remain highly challenging, requiring ultra-precise control of nonlinear interactions to create entangled superpositions, s

Andrea Medaglia, Lorenzo Pareschi, Mattia Zanella

We present a numerical framework for the simulation of collisional plasma dynamics, based on a coupling between Direct Simulation Monte Carlo (DSMC) and Particle-in-Cell (PIC) methods for the Vlasov-Maxwell-Landau system. The approach extends previously developed DSMC techniques for the homogeneous Landau equation to the fully inhomogeneous, electromagnetic

Gousia Habib, Aniket Bhardwaj, Ritvik Sharma, Shoeib Amin Banday

Secure and reliable medical image classification is crucial for effective patient treatment, but centralized models face challenges due to data and privacy concerns. Federated Learning (FL) enables privacy-preserving collaborations but struggles with heterogeneous, non-IID data and high communication costs, especially in large networks. We propose \textbf{CF

Yu Luo, Nan Huang, Sophie Yu, Hendry Xu

Depression, as a typical mental disorder, has become a prevalent issue significantly impacting public health. However, the prevention and treatment of depression still face multiple challenges, including complex diagnostic procedures, ambiguous criteria, and low consultation rates, which severely hinder timely assessment and intervention. To address these is

Guan-Yan Yang, Farn Wang

The integration of Artificial Intelligence (AI) into safety-critical systems introduces a new reliability paradigm: silent failures, where AI produces confident but incorrect outputs that can be dangerous. This paper introduces the Formal Assurance and Monitoring Environment (FAME), a novel framework that confronts this challenge. FAME synergizes the mathema

Amal Jayawardena, Andrew Hung, Greg Qiao, Neil OBrien-Simpson

Structurally nanoengineered antimicrobial peptide polymers (SNAPPs) are emerging as promising selective agents against bacterial membranes. In this study, we used all atom molecular dynamics simulation techniques to investigate the interaction of a promising cationic SNAPP architecture (Alt-SNAPP with 8 arms made of alternating lysine and valine residues) wi

Xiaoyin Hu, Xin Liu, Kim-Chuan Toh, Nachuan Xiao

In this paper, we consider the nonlinear constrained optimization problem (NCP) with constraint set $\{x \in \mathcal{X}: c(x) = 0\}$, where $\mathcal{X}$ is a closed convex subset of $\mathbb{R}^n$. Building upon the forward-backward envelope framework for optimization over $\mathcal{X}$, we propose a forward-backward semi-envelope (FBSE) approach for solvi

Yumeng Shi, Zhongliang Yang, Yisi Wang, Linna Zhou

In the domain of corporate credit rating, traditional deep learning methods have improved predictive accuracy but still suffer from the inherent 'black-box' problem and limited interpretability. While incorporating non-financial information enriches the data and provides partial interpretability, the models still lack hierarchical reasoning mechanisms, limit

Jialin Song, Yingheng Tang, Pu Ren, Shintaro Takayoshi

Simulating hybrid magnonic quantum systems remains a challenge due to the large disparity between the timescales of the two systems. We present a massively parallel GPU-based simulation framework that enables fully coupled, large-scale modeling of on-chip magnon-photon circuits. Our approach resolves the dynamic interaction between ferromagnetic and electrom

Maurizio Serva

The Swadesh approach for determining the temporal separation between two languages relies on the stochastic process of words replacement (when a complete new word emerges to represent a given concept). It is well known that the basic assumptions of the Swadesh approach are often unrealistic due to various contamination phenomena and misjudgments (horizontal

Tianyi Li

We measure LLMs' output error at pairwise text comparison, noting the probability of error in their preferences. Our method does not rely on the ground truth and supports two scenarios: (i) uniform error rate regardless of the order of comparison, estimated with two comparisons for each text pair with either text placed first; (ii) binary positional bias ass

Chengrui Yang, Weixin Cai, Taotao Qiu

The Pulsar Timing Array (PTA) data of nano-Hertz gravitational waves released in 2023 implies that if such gravitational waves comes from the scalar perturbation induction at the end of inflation, the accompanied primordial black holes (PBHs) will be over-produced, with the fraction exceed the upper bound of unity. This is recognized as the ``overproduction

Yongchuan Cui, Peng Liu, Hui Zhang

Existing deep learning-based models for remote sensing pansharpening exhibit exceptional performance on training datasets. However, due to sensor-specific characteristics and varying imaging conditions, these models suffer from substantial performance degradation when applied to unseen satellite data, lacking generalizability and thus limiting their applicab

Michael Candon, Pier Marzocca, Earl H. Dowell

Numerical simulation of the transonic shock buffet phenomenon remains a formidable challenge due to its inherent nonlinear and unsteady characteristics. These difficulties are further compounded in three-dimensional configurations and when aeroelastic coupling is considered. Consequently, computational studies of aeroelastic shock buffet interactions have la

Juyeon Kim, Geon Lee, Dongwon Choi, Taeuk Kim

Retrieval over visually rich documents is essential for tasks such as legal discovery, scientific search, and enterprise knowledge management. Existing approaches fall into two paradigms: single-vector retrieval, which is efficient but coarse, and multi-vector retrieval, which is accurate but computationally expensive. To address this trade-off, we propose H

Juepeng Zheng, Peifeng Zhang, Yibin Wen, Qingmei Li

Domain Adaptation (DA) provides an effective way to tackle target-domain tasks by leveraging knowledge learned from source domains. Recent studies have extended this paradigm to Multi-Source Domain Adaptation (MSDA), which exploits multiple source domains carrying richer and more diverse transferable information. However, a substantial performance gap still

Yaokun Li, Lihe Ding, Xiao Chen, Guang Tan

Generating dynamic and interactive 3D trees has wide applications in virtual reality, games, and world simulation. However, existing methods still face various challenges in generating structurally consistent and realistic 4D motion for complex real trees. In this paper, we propose DynamicTree, the first framework that can generate long-term, interactive 3D

Maria Korobeynikova, Alessia Battisti, Lukas Fischer, Yingqiang Gao

Current evaluation of German automatic text simplification (ATS) relies on general-purpose metrics such as SARI, BLEU, and BERTScore, which insufficiently capture simplification quality in terms of simplicity, meaning preservation, and fluency. While specialized metrics like LENS have been developed for English, corresponding efforts for German have lagged b

Jiazheng Zhu, Ning Jiang, Yibo Wang, Tinggui Wang

X-ray quasi-periodic eruptions (QPEs) are rare and enigmatic phenomena that increasingly show a connection to tidal disruption events (TDEs). However, the recently discovered QPEs in ZTF19acnskyy ("Ansky") appear to be linked to an active galactic nucleus (AGN) rather than a TDE, as their slow decay and AGN-like variability differ markedly from that of typic

Gwihwan Go, Quan Zhang, Chijin Zhou, Zhao Wei

Automated unit test generation is essential for robust software development, yet existing approaches struggle to generalize across multiple programming languages and operate within real-time development. While Large Language Models (LLMs) offer a promising solution, their ability to generate high coverage test code depends on prompting a concise context of t

Sofoklis Kitharidis, Cor J. Veenman, Thomas Bäck, Niki van Stein

In the context of algorithmic decision-making, fair machine learning methods often yield multiple models that balance predictive fairness and performance in varying degrees. This diversity introduces a challenge for stakeholders who must select a model that aligns with their specific requirements and values. To address this, we propose an interactive framewo

Siddharth Jain, Shyamgopal Karthik, Vineet Gandhi

Pretrained models are ubiquitous in the current deep learning landscape, offering strong results on a broad range of tasks. Recent works have shown that models differing in various design choices exhibit categorically diverse generalization behavior, resulting in one model grasping distinct data-specific insights unavailable to the other. In this paper, we p

Nnamdi Aghanya, Jun Li, Kewei Wang

Large Language Models (LLMs) can achieve near-optimal lossless compression by acting as powerful probability models. We investigate their use in the lossy domain, where reconstruction fidelity is traded for higher compression ratios. This paper introduces Error-Bounded Predictive Coding (EPC), a lossy text codec that leverages a Masked Language Model (MLM) a

Ollie Olby, Andreea Bacalum, Rory Baggott, Namid Stillman

Execution algorithms are vital to modern trading, they enable market participants to execute large orders while minimising market impact and transaction costs. As these algorithms grow more sophisticated, optimising them becomes increasingly challenging. In this work, we present a reinforcement learning (RL) framework for discovering optimal execution strate

Mohammed Alduais, Xinming Li, Qipei Mei

As the demand grows within the construction industry for processes that are not only faster but also safer and more efficient, offsite construction has emerged as a solution, though it brings new safety risks due to the close interaction between workers, machinery, and moving obstacles. Predicting the future trajectories of workers and taking into account so

Chong Zha, Yanshuang Chen, Cheng-Ran Du, Peng Tan

The application of an external field often renders empirical criteria for identifying liquid-gas phase transitions ambiguous. Here, we demonstrate that the finite-size scaling of the density profile provides a definitive criterion to distinguish liquid-gas coexistence from a single fluid phase in field-confined systems. Our scaling method collapses the densi

Christoph Wiederkehr, Christian Heumann, Michael Schomaker

We evaluate the performance of targeted maximum likelihood estimation (TMLE) for estimating the average treatment effect in missing data scenarios under varying levels of positivity violations. We employ model- and design-based simulations, with the latter using undersmoothed highly adaptive lasso on the 'WASH Benefits Bangladesh' dataset to mimic real-world

Minho Park, Kinam Kim, Junha Hyung, Hyojin Jang

Diffusion and flow matching models have emerged as powerful robot policies, enabling Vision-Language-Action (VLA) models to generalize across diverse scenes and instructions. Yet, when trained via imitation learning, their high generative capacity makes them sensitive to noise in human demonstrations: jerks, pauses, and jitter which reduce action coherence.

Meituan LongCat Team, Xunliang Cai, Qilong Huang, Zhuoliang Kang

Video generation is a critical pathway toward world models, with efficient long video inference as a key capability. Toward this end, we introduce LongCat-Video, a foundational video generation model with 13.6B parameters, delivering strong performance across multiple video generation tasks. It particularly excels in efficient and high-quality long video gen

Kunal Bhosikar, Siddharth Katageri, Vivek Madhavaram, Kai Han

Generating realistic full-body motion interacting with objects is critical for applications in robotics, virtual reality, and human-computer interaction. While existing methods can generate full-body motion within 3D scenes, they often lack the fidelity for fine-grained tasks like object grasping. Conversely, methods that generate precise grasping motions ty

Giora Simchoni, Saharon Rosset

We present MMbeddings, a probabilistic embedding approach that reinterprets categorical embeddings through the lens of nonlinear mixed models, effectively bridging classical statistical theory with modern deep learning. By treating embeddings as latent random effects within a variational autoencoder framework, our method substantially decreases the number of

Qingzhu Zhang, Jiani Zhong, Zongsheng Li, Xinke Shen

Task-specific pre-training is essential when task representations diverge from generic pre-training features. Existing task-general pre-training EEG models struggle with complex tasks like emotion recognition due to mismatches between task-specific features and broad pre-training approaches. This work aims to develop a task-specific multi-dataset joint pre-t

Vincent Lu, Aaron Truong, Zeyu Yun, Yubei Chen

Scaling and architectural advances have produced strikingly photorealistic image generative models, yet their mechanisms still remain opaque. Rather than advancing scaling, our goal is to strip away complicated engineering tricks and propose a simple, non-parametric generative model. Our design is grounded in three principles of natural images-(i) spatial no

Akinori Nishino, Naomichi Hatano

We study time-evolving resonant states in an open double quantum-dot system, taking into account spin degrees of freedom as well as both on-dot and interdot Coulomb interactions. We exactly derived a non-Hermite effective Hamiltonian acting on the subspace of two quantum dots, where the non-Hermiticity arises from an effect of infinite external leads connect

Pankaj Jain, Harishyam Kumar

We study the effect of medium on radioactive alpha decay and other similar decays. The initial state in these type of decays is a quasi-bound state with energy greater than zero. Such a state has very large amplitude in the nuclear region and is exponentially suppressed at larger distances. The decay rate of such states is known to decrease rapidly with decr

Yahel Uffenheimer, Omri Weinstein

We study the capability of the Fast Fourier Transform (FFT) to accelerate exact and approximate matrix multiplication without using Strassen-like divide-and-conquer. We present a simple exact algorithm running in $O(n^{2.89})$ time, which only sums a few convolutions (FFTs) in $\mathbb{Z}_{m}^{k}$, building on the work of Cohn, Kleinberg, Szegedy and Umans (

Haoyang Liu, Jie Wang, Yuyang Cai, Xiongwei Han

Optimization modeling is one of the most crucial but technical parts of operations research (OR). To automate the modeling process, existing works have leveraged large language models (LLMs), prompting them to break down tasks into steps for generating variables, constraints, and objectives. However, due to the highly complex mathematical structures inherent

Qi Sheng

Provenance analysis based on system audit data has emerged as a fundamental approach for investigating Advanced Persistent Threat (APT) attacks. Due to the high concealment and long-term persistence of APT attacks, they are only represented as a minimal part of the critical path in the provenance graph. While existing techniques employ behavioral pattern mat

Tatsuru Kikuchi

This paper develops a continuous framework for analyzing financial contagion that incorporates both geographic proximity and interbank network linkages. The framework characterizes stress propagation through a master equation whose solution admits a Feynman-Kac representation as expected cumulative stress along stochastic paths through spatial-network space.

Wei-en Chiu, Chia-Hsien Huang, Yi-Hsien Wu, Hsi-Sheng Goan

In Si/SiGe quantum dots, the decoherence behavior of spin qubits usually comes from the non-Markovian effect of the charge noise. To improve the performance of using the coherent noise models in the decoherence simulation and tomography analysis, here we propose a spin-phonon model derived from the electric dipole spin resonance to characterize the decoheren

Deng-Shan Wang, Yingmin Yang, Liming Zang

This work investigates the long-time asymptotic behaviors of solutions to the initial value problem of the two-component nonlinear Klein-Gordon equation by inverse scattering transform and Riemann-Hilbert formulism. Two reflection coefficients are defined and their properties are analyzed in detail. The Riemann-Hilbert problem associated with the initial val

Tathagata Ghosh, Surhud More

Gravitational waves (GWs) from the compact binary coalescence provide direct measurement of the luminosity distance to the event. However, unlike binary neutron stars, redshift information is not available from GW observations of binary black holes. Consequently, independent redshift measurements of such GW events are necessary to measure $H_0$. In this stud

Cynthia Marcelino, Thomas Pusztai, Stefan Nastic

Serverless computing provides infrastructure management and elastic auto-scaling, therefore reducing operational overhead. By design serverless functions are stateless, which means they typically leverage external remote services to store and exchange data. Transferring data over a network typically involves serialization and deserialization. These operation

Michimasa Morita, Junichiro Shiomi

Understanding thermal transport in silicon nanostructures is crucial for effective thermal management in semiconductor devices. In such nanostructures, boundary scattering can significantly reduce thermal conductivity. Diffusive boundary scattering explains the experimentally observed thickness dependence of thermal conductivity in thin films with thicknesse

Akira Omoto

This work presents a unified framework for estimating both sound-field direction and diffuseness using practical microphone arrays with different spatial configurations. Building on covariance-based diffuseness models, we formulate a velocity-only covariance approach that enables consistent diffuseness evaluation across heterogeneous array geometries without

Chao-Qiang Geng, Xiang-Nan Jin, Chia-Wei Liu, Xin-Yi Liu

We propose to search for $\Xi_{cc}^+$ in the decay $\Lambda_b \to \Xi_{cc}^+ D^-$, which serves as a tagged and reconstructible source of $\Xi_{cc}^+$, providing an experimentally clean environment for its discovery. A possible fully charged final state is $[(pK^- K^- \pi^+)_{\Xi^0_c} \pi^ + ]_{\Xi_{cc}^+} \,(K^+ \pi^- \pi^-)_{D^-}$, where the subscripts ind

Maximilian Bauhofer, Marcus Henninger, Meik Kottkamp, Lucas Giroto

For a wide range of envisioned integrated sensing and communication (ISAC) use cases, it is necessary to incorporate tracking techniques into cellular communication systems. While numerous multi-target tracking (MTT) algorithms exist, they have not yet been applied to real-world ISAC, with its challenges such as clutter and non-optimal hardware with design e

Xuelei Pang, Weiyun Sun, Ning Jing, Wenwen Gong

Autophagy and migrasome formation constitute critical cellular mechanisms for maintaining cellular homeostasis, however, their potential compensatory interplay remains poorly understood. In this study, we identify VPS39, a core component of the HOPS complex, as a molecular switch coordinating these processes. Genetic ablation of VPS39 not only impairs autoph

Saranraj Nambusubramaniyan, Shervin Safavi, Raja Guru, Andreas Knoblauch

Solving the synaptic Credit Assignment Problem(CAP) is central to learning in both biological and artificial neural systems. Finding an optimal solution for synaptic CAP means setting the synaptic weights that assign credit to each neuron for influencing the final output and behavior of neural networks or animals. Gradient-based methods solve this problem in

Tianyu Liu, Somabha Mukherjee, Abhik Ghosh

We consider the problem of learning the interaction strength between the nodes of a network based on dependent binary observations residing on these nodes, generated from a Markov Random Field (MRF). Since these observations can possibly be corrupted/noisy in larger networks in practice, it is important to robustly estimate the parameters of the underlying t

Junho Park, Taehan Kim, Mohammad Ali, Di Liang

As an increasingly powerful technique in integrated photonics, inverse design uses optimization algorithms to automatically create compact, high-performance photonic structures, often yielding non-intuitive layouts far more compact than conventional designs. While adjoint-based inverse design is a prominent optimization method, the resulting free-form layout

Shuge Rong, Yifeng Ding

We present a STIT ('see to it that') logic with discrete temporal operators and deontic operators in which we can formalize and reason about legal concepts such as persistent duty and the dynamic concept of power from Hohfeld. As our main technical contribution, we show that this logic is sound and complete with respect to the semantics based on interpreted

Md. Selim Reza, Mst. Ayesha Siddika, Md. Tofazzal Hossain, Md. Ashad Alam

Cervical (CC) and endometrial cancers (EC) are two common types of gynecological tumors that threaten the health of females worldwide. Since their underlying mechanisms and associations remain unclear, computational bioinformatics analysis is required. In the present study, bioinformatics methods were used to screen for key candidate genes, their functions a