November 2025 arXiv papers — page 167

Milena Trajanoska, Riste Stojanov, Dimitar Trajanov

Enterprises often maintain multiple databases for storing critical business data in siloed systems, resulting in inefficiencies and challenges with data interoperability. A key to overcoming these challenges lies in integrating disparate data sources, enabling businesses to unlock the full potential of their data. Our work presents a novel approach for integ

Leyan Xue, Changqing Zhang, Kecheng Xue, Xiaohong Liu

Although multimodal fusion has made significant progress, its advancement is severely hindered by the lack of adequate evaluation benchmarks. Current fusion methods are typically evaluated on a small selection of public datasets, a limited scope that inadequately represents the complexity and diversity of real-world scenarios, potentially leading to biased e

Jian'an Zhang

We propose ARBITER, a risk-neutral neural operator for learning joint SPX-VIX term structures under no-arbitrage constraints. ARBITER maps market states to an operator that outputs implied volatility and variance curves while enforcing static arbitrage (calendar, vertical, butterfly), Lipschitz bounds, and monotonicity. The model couples operator learning wi

Seulgi Kim, Kiran Kokilepersaud, Mohit Prabhushankar, Ghassan AlRegib

Multi-modal fusion methods often suffer from two types of representation collapse: feature collapse where individual dimensions lose their discriminative power (as measured by eigenspectra), and modality collapse where one dominant modality overwhelms the other. Applications like human action anticipation that require fusing multifarious sensor data are hind

Zhicheng Cai, Xinyuan Guo, Yu Pei, Jiangtao Feng

Autonomous agents driven by Large Language Models (LLMs) have revolutionized reasoning and problem-solving but remain static after training, unable to grow with experience as intelligent beings do during deployment. We introduce Forward Learning with EXperience (FLEX), a gradient-free learning paradigm that enables LLM agents to continuously evolve through a

Qibing Ren, Zhijie Zheng, Jiaxuan Guo, Junchi Yan

In this work, we study the risks of collective financial fraud in large-scale multi-agent systems powered by large language model (LLM) agents. We investigate whether agents can collaborate in fraudulent behaviors, how such collaboration amplifies risks, and what factors influence fraud success. To support this research, we present MultiAgentFraudBench, a la

Mert Yazan, Frederik Bungaran Ishak Situmeang, Suzan Verberne

Conversational search interfaces, like ChatGPT, offer an interactive, personalized, and engaging user experience compared to traditional search. On the downside, they are prone to cause overtrust issues where users rely on their responses even when they are incorrect. What aspects of the conversational interaction paradigm drive people to adopt it, and how i

Bohan Yu, Wei Huang, Kang Liu

This paper proposes SR-KI, a novel approach for integrating real-time and large-scale structured knowledge bases (KBs) into large language models (LLMs). SR-KI begins by encoding KBs into key-value pairs using a pretrained encoder, and injects them into LLMs' KV cache. Building on this representation, we employ a two-stage training paradigm: first locating a

John H. Black

The interdisciplinary field of astrochemistry arose during the 1970s as observations in previously unexplored parts of the electromagnetic spectrum began to reveal the extent of a molecular component of interstellar matter with a surprisingly rich chemistry. Astrochemistry expanded further in order to explain the role of atomic and molecular processes in a b

Zinuo You, Jin Zheng, John Cartlidge

Existing graph neural networks typically rely on heuristic choices for hidden dimensions and propagation depths, which often lead to severe information loss during propagation, known as over-squashing. To address this issue, we propose Channel Capacity Constrained Estimation (C3E), a novel framework that formulates the selection of hidden dimensions and dept

Weiguo Yin, A. M. Tsvelik

The prohibition of finite-temperature phase transition in one-dimensional (1D) Ising models and 1D/2D quantum Heisenberg models with short-range interactions fundamentally constrains the application potentials of low-dimensional magnetic materials. Recently, ultranarrow phase crossover (UNPC), which can approach a transition at a desirable finite temperature

Snehadri Das, Nicolas Abgrall, Mark Bandstra, Reynold Cooper

Networks of radiation detectors provide a platform for real-time radioactive source detection and identification in urban environments. Detection algorithms in these systems must adapt to naturally-occurring changes in background, which requires well-characterized relationships between precipitation events and their corresponding radiological signature. We p

Mayank Saini, Arit Kumar Bishwas

As AI moves beyond text, large language models (LLMs) increasingly power vision, audio, and document understanding; however, their high inference costs hinder real-time, scalable deployment. Conversely, smaller open-source models offer cost advantages but struggle with complex or multimodal queries. We introduce a unified, modular framework that intelligentl

Yingjie Xu, Xuesong Cai, Ali Al-Ameri, Sara Willhammar

This paper presents a comprehensive study on the 3D positioning capabilities in distributed multiple-input multiple-output (MIMO) systems. Unlike previous studies that mainly rely on idealized isotropic antenna models, we adopt a polarimetric model that takes advantage of effective aperture distribution functions to characterize realistic antenna patterns, p

Ritwik Mukherjee, Siddhartha Mukherjee, I. V. Kolokolov, V. V. Lebedev

Equal-time scaling exponents in fully developed turbulence typically exhibit non anomalous scaling in the inverse cascade of two-dimensional (2D) turbulence and anomalous scaling in three dimensions. We demonstrate that multiscaling is not confined to longitudinal, scalar velocity increments, but also emerges in increments associated with the magnitude and o

Dipendra Prasad

Some question about representations of $p$-adic groups are discussed.

Abhishek More, Anthony Zhang, Nicole Bonilla, Ashvik Vivekan

Chain-of-thought (CoT) prompting enables Large Language Models to solve complex problems, but deploying these models safely requires reliable confidence estimates, a capability where existing methods suffer from poor calibration and severe overconfidence on incorrect predictions. We propose Enhanced Dirichlet and Topology Risk (EDTR), a novel decoding strate

Yuan Gao, Eilon Reisin-Tzur, Yin Tian, Tianyu Yuan

We show that the higher-dimensional Heegaard Floer homology between tuples of cotangent fibers and the conormal bundle of a homotopically nontrivial simple closed curve on $T^2$ recovers the polynomial representation of double affine Hecke algebra of type A. We also give a topological interpretation of Cherednik's inner product on the polynomial representati

Ekta Tiwari

Let $G$ denote the unramified quasi-split unitary group $\mathbb{U}(1,1)(F)$ over a $p$-adic field $F$ with residual characteristic $p \neq 2$. In this paper, we first construct a large family of irreducible representations of the maximal compact subgroup $\mathcal{K} = \mathbb{U}(1,1)(\mathcal{O}_F)$ of $G$. We then describe the branching rules for all prin

Wenkang Hu, Xincheng Tang, Yanzhi E, Yitong Li

While there has been significant progress to use simulated data to learn robotic manipulation of rigid objects, applying its success to deformable objects has been hindered by the lack of both deformable object models and realistic non-rigid body simulators. In this paper, we present Real Garment Benchmark (RGBench), a comprehensive benchmark for robotic man

Sungrae Hong, Sol Lee, Jisu Shin, Jiwon Jeong

With the increasing demand for histopathological specimen examination and diagnostic reporting, Multiple Instance Learning (MIL) has received heightened research focus as a viable solution for AI-centric diagnostic aid. Recently, to improve its performance and make it work more like a pathologist, several MIL approaches based on the use of multiple-resolutio

Yujin Tong

In Aganagic's Fukaya category of the Coulomb branch of quiver gauge theory, the $T_\theta$-brane algebra gives a symplectic realization of the Khovanov-Lauda-Rouquier-Webster (KLRW) algebra, where each standard module is known to admit two Lagrangian realizations: the 'U'-shaped $T$-brane and the step $I$-brane. We show that the latter arises as the infinite

Peyman Hosseini, Ondrej Bohdal, Taha Ceritli, Ignacio Castro

Test-time Reinforcement Learning (TTRL) has shown promise in adapting foundation models for complex tasks at test-time, resulting in large performance improvements. TTRL leverages an elegant two-phase sampling strategy: first, multi-sampling derives a pseudo-label via majority voting, while subsequent downsampling and reward-based fine-tuning encourages the

Felipe Castaño, Constantinos Patsakis, Francesco Zola, Fran Casino

LockBit has evolved from an obscure Ransomware-as-a-Service newcomer in 2019 to the most prolific ransomware franchise of 2024. Leveraging a recently leaked MySQL dump of the gang's management panel, this study offers an end-to-end reconstruction of LockBit's technical, behavioral, and financial apparatus. We recall the family's version timeline and map its

Lifeng Han, David Lindevelt, Sander Puts, Erik van Mulligen

Metaphors and metaphorical language (MLs) play an important role in healthcare communication between clinicians, patients, and patients' family members. In this work, we focus on Dutch language data from cancer patients. We extract metaphors used by patients using two data sources: (1) cancer patient storytelling interview data and (2) online forum data, inc

Brian B. Avants, Nicholas J. Tustison, James R Stone

Interpretable representation learning is a central challenge in modern machine learning, particularly in high-dimensional settings such as neuroimaging, genomics, and text analysis. Current methods often struggle to balance the competing demands of interpretability and model flexibility, limiting their effectiveness in extracting meaningful insights from com

Amit Vaisman, Guy Ohayon, Hila Manor, Michael Elad

While zero-shot diffusion-based compression methods have seen significant progress in recent years, they remain notoriously slow and computationally demanding. This paper presents an efficient zero-shot diffusion-based compression method that runs substantially faster than existing methods, while maintaining performance that is on par with the state-of-the-a

S. K. Rithvik

We present a systematic evaluation of large language models on quantum mechanics problem-solving. Our study evaluates 15 models from five providers (OpenAI, Anthropic, Google, Alibaba, DeepSeek) spanning three capability tiers on 20 tasks covering derivations, creative problems, non-standard concepts, and numerical computation, comprising 900 baseline and 75

Reem Al-Saidi, Erman Ayday, Ziad Kobti

This study investigates embedding reconstruction attacks in large language models (LLMs) applied to genomic sequences, with a specific focus on how fine-tuning affects vulnerability to these attacks. Building upon Pan et al.'s seminal work demonstrating that embeddings from pretrained language models can leak sensitive information, we conduct a comprehensive

Filippo Morbiato, Luca Romano, Alessandro Persona

Visual hallucination, where Multimodal Large Language Models fabricate details inconsistent with image content, critically undermines their reliability. Existing fine-tuning methods offer limited improvement, failing to deeply intervene in factual reasoning. This paper introduces Grounded Visual Factualization (GVF) Finetuning, a novel approach to systematic

Jie Qiao, Shu-Hao Zhang, Jing-Bo Qin, Xiao-Long Zhao

The nature of the intermediate ground-state phase in the spin-1/2 frustrated square lattice model has long been debated. Using cluster density matrix embedding theory, we investigate the phase diagram of this model. The Neel phase is directly identified for J2<0.45 and the collinear phase for J2>0.65 based on the ground state. Although no direct evidence of

Tao Liu, Kan Ren, Qian Chen

With the rapid growth of the low-altitude economy, unmanned aerial vehicles (UAVs) have become key platforms for measurement and tracking in intelligent patrol systems. However, in GNSS-denied environments, localization schemes that rely solely on satellite signals are prone to failure. Cross-view image retrieval-based localization is a promising alternative

Xiao-Bin Sui, Jing Liu, Rong-Gen Cai

We propose a novel mechanism where a first-order phase transition modulates the decay rate of a massive field. This modulation, even if the scalar field has negligible energy density, subsequently generates an observable stochastic gravitational-wave background. The stochastic nature of bubble nucleation leads to the asynchrony of phase transitions, generati

Jingyu Hu, Shu Yang, Xilin Gong, Hongming Wang

Large Reasoning Models (LRMs) suffer from sycophantic behavior, where models tend to agree with users' incorrect beliefs and follow misinformation rather than maintain independent reasoning. This behavior undermines model reliability and poses societal risks. Mitigating LRM sycophancy requires monitoring how this sycophancy emerges during the reasoning traje

Sunil Mohan, Theofanis Karaletsos

Two scientific fields showing increasing interest in pre-trained large language models (LLMs) are drug development / repurposing, and personalized medicine. For both, LLMs have to demonstrate factual knowledge as well as a deep understanding of drug mechanisms, so they can recall and reason about relevant knowledge in novel situations. Drug mechanisms of act

Xiangwu Guo, Difei Gao, Mike Zheng Shou

Recent advancements in GUI agents have significantly expanded their ability to interpret natural language commands to manage software interfaces. However, acquiring GUI data remains a significant challenge. Existing methods often involve designing automated agents that browse URLs from the Common Crawl, using webpage HTML to collect screenshots and correspon

Nicholas Felstead

Frontier AI labs face intense commercial competitive pressure to develop increasingly powerful systems, raising the risk of a race to the bottom on safety. Voluntary coordination among labs - including by way of joint safety testing, information sharing, and resource pooling - could reduce catastrophic and existential risks. But the risk of antitrust scrutin

Dian Jin, Jeremy Coulson

Data-driven control methods based on subspace representations are powerful but are often limited to linear time-invariant systems where the model order is known. A key challenge is developing online data-driven control algorithms for time-varying systems, especially when the system's complexity is unknown or changes over time. To address this, we propose a n

Isaac Cinzori

A diffeological space is a set equipped with a smooth structure, known as a diffeology, which allows us to extend certain notions from manifolds to these more general spaces. We study a generalized notion of tangent space to a point of a manifold, namely the internal tangent space to a point of a diffeological space. In particular, we study these internal ta

Yeor Hafouta

We obtain non-uniform Edgeworth expansions for several classes of weakly dependent (non-stationary) sequences of random variables, including uniformly elliptic inhomogeneous Markov chains, random and time-varying (partially) hyperbolic or expanding dynamical systems, products of random matrices and some classes of local statistics. To the best of our knowled

Moussa Atwi, Benjamin Berkels

Transmission Electron Microscopy enables high-resolution imaging of materials, but the resulting images are difficult to interpret directly. One way to address this is exit wave reconstruction, i.e., the recovery of the complex-valued electron wave at the specimen's exit plane from intensity-only measurements. This is an inverse problem with a nonlinear forw

Hamayoon Behmanush, Freshta Akhtari, Roghieh Nooripour, Ingmar Weber

Designing impactful educational technologies in contexts of socio-political instability requires a nuanced understanding of educational aspirations. Currently, scalable metrics for measuring aspirations are limited. This study adapts, translates, and evaluates Snyder's Hope Scale as a metric for measuring aspirations among 136 women learning programming onli

Bowoo Kang

We prove a comparison principle for the pluripotential complex Monge-Amp\`ere flows for the right-hand side of the form $dt \wedge d\mu$ where $d\mu$ is dominated by a Monge-Amp\`ere measure of a bounded plurisubharmonic function. As a consequence, we obtain the uniqueness of the weak solution to the pluripotential Cauchy-Dirichlet problem. We also study the

Zhi Zheng, Yu Gu, Wei Liu, Yee Whye Teh

The soft-thinking paradigm for Large Language Model (LLM) reasoning can outperform the conventional discrete-token Chain-of-Thought (CoT) reasoning in some scenarios, underscoring its research and application value. However, while the discrete-token CoT reasoning pattern can be reinforced through policy optimization algorithms such as group relative policy o

Amin Faghih

This study discusses a class of linear systems of fractional differential equations with non-constant coefficients, with a particular focus on problems exhibiting highly oscillatory and non-smooth behavior. We first establish the regularity properties of the solutions under specific conditions on the input data. A spectral Galerkin method based on M\"{u}ntz-

Vishal Cholapadi Ravindra

Sensor selection is an often under-appreciated aspect of state estimator or Kalman filter design. The basic minimum requirement for the choice of a sensor set while designing Kalman filters is that all states are observable. In addition, the sensors should be chosen with a view towards estimating the states with a desired accuracy. Often observability is tre

Zhengyu Zou, Jingfeng Li, Hao Li, Xiaolei Hou

Neural Radiance Fields (NeRFs) implicitly model continuous three-dimensional scenes using a set of images with known camera poses, enabling the rendering of photorealistic novel views. However, existing NeRF-based methods encounter challenges in applications such as autonomous driving and robotic perception, primarily due to the difficulty of capturing accur

Takashi Hayakawa, Satoshi Asai

Hierarchical Bayesian models based on Gaussian processes are considered useful for describing complex nonlinear statistical dependencies among variables in real-world data. However, effective Monte Carlo algorithms for inference with these models have not yet been established, except for several simple cases. In this study, we show that, compared with the sl

Shuyuan Liu, Jiawei Chen, Xiao Yang, Hang Su

With the widespread application of large language models (LLMs) in various fields, the security challenges they face have become increasingly prominent, especially the issue of jailbreak. These attacks induce the model to generate erroneous or uncontrolled outputs through crafted inputs, threatening the generality and security of the model. Although existing

Shuo Yang, Yinghui Xing, Shizhou Zhang, Zhilong Niu

Infrared and visible object detection (IVOD) is essential for numerous around-the-clock applications. Despite notable advancements, current IVOD models exhibit notable performance declines when confronted with incomplete modality data, particularly if the dominant modality is missing. In this paper, we take a thorough investigation on modality incomplete IVO

Dongsheng Wang, Shen Gao, Chengrui Huang, Yuxi Huang

Next Point-of-Interest (POI) recommendation is a fundamental task in location-based services. While recent advances leverage Large Language Model (LLM) for sequential modeling, existing LLM-based approaches face two key limitations: (i) strong reliance on the contextual completeness of user histories, resulting in poor performance on out-of-history (OOH) sce

Zihang Fu, Yunchao Wang, Chenyu Huang, Guodao Sun

Infographics are widely used in social media to convey complex information, yet how they influence users' affects remains underexplored due to the scarcity of relevant datasets. To address this gap, we introduce a 3.5k-sample affect-annotated InfoAffect dataset, which combines textual content with real-world infographics. We first collected the raw data from

Kumar Ghosh

Replica symmetry breaking (RSB) underlies the complex organization of disordered systems, yet quantitative validation beyond $N \sim 100$ spins has remained computationally challenging. We use quantum annealing to access ground states of the Sherrington-Kirkpatrick model up to $N = 4000$ spins, enabling the most extensive test of Parisi's Nobel Prize-winning

Lionel Z. Wang, Shihan Ben, Yulu Huang, Simeng Qin

Sugar dating-related content has rapidly proliferated on mainstream social media platforms, giving rise to serious societal and regulatory concerns, including commercialization of intimate relationships and the normalization of transactional relationships.~Detecting such content is highly challenging due to the prevalence of subtle euphemisms, ambiguous ling

Yağmur Bülbül, Ilmar Gahramanov, Ali Mert Yetkin, Reyhan Yumuşak

We construct the lens hyperbolic modular double, a new algebraic structure whose intertwining operator produces a lens hyperbolic hypergeometric solution of the Yang--Baxter equation.

G. J. M. Luna, A. Dobrotka, M. Orio

We analyzed TESS archival data of three novae after recent outbursts, searching the orbital and white dwarf (WD) rotation period and possible variations of these periods. In V1405 Cas, we detected a period of $\sim$116.88 seconds, which we identified as due to the WD spin, and measured a rate of increase of 0.00165$\pm0.000006\, {\rm s\, d}^{-1}$, one of the

Leloko J. Lepolesa, Kayode E. Adetunji, Khmaies Ouahada, Zhenqing Liu

The transition from the Internal Combustion Engine Vehicles (ICEVs) to the Electric Vehicles (EVs) is globally recommended to combat the unfavourable environmental conditions caused by reliance on fossil fuels. However, it has been established that the charging of EVs can destabilize the grid when they penetrate the market in large numbers, especially in gri

Cong Wen, Yunfei Li, Kexin Liu, Yixin Qiu

Wheeled bipedal robots have garnered increasing attention in exploration and inspection. However, most research simplifies calculations by ignoring leg dynamics, thereby restricting the robot's full motion potential. Additionally, robots face challenges when traversing uneven terrain. To address the aforementioned issue, we develop a complete dynamics model

Dachuan Lin, Guobin Shen, Zihao Yang, Tianrong Liu

Safety evaluation of large language models (LLMs) increasingly relies on LLM-as-a-judge pipelines, but strong judges can still be expensive to use at scale. We study whether structured multi-agent debate can improve judge reliability while keeping backbone size and cost modest. To do so, we introduce HAJailBench, a human-annotated jailbreak benchmark with 11

Hung D. Nguyen, Jeongseok Ha

This paper investigates the use of an unmanned aerial vehicle (UAV) to assist covert communication between a low-Earth orbit (LEO) satellite and a ground user under the surveillance of a passive warden. The UAV simultaneously serves its own ground network and acts as a friendly jammer to enhance the covertness of satellite transmissions. We derive a closed-f

Xiang Zhang, Geng Wu, Wenbin Huang, Daoyong Fu

ROI selective encryption, as an efficient privacy protection technique, encrypts only the key regions in the video, thereby ensuring security while minimizing the impact on coding efficiency. However, existing ROI-based video encryption methods suffer from insufficient flexibility and lack of a unified evaluation system. To address these issues, we propose a

Pragati Sahu, Swadesh Chand, Gulab C. Dewangan, Andrzej A. Zdziarski

We perform broadband spectral and timing studies of the Galactic low-mass black hole candidate AT2019wey using quasi-simultaneous NICER, Swift, and NuSTAR observations obtained in 2022. The long-term MAXI light curve, along with the hardness-intensity diagram (HID), indicates that the source remained in the hard state and did not switch to the soft state. Sp

Felix Finster, Claudio F. Paganini

It is shown that the collapse model derived from the theory of causal fermion systems does not give rise to a heating of the probe, up to higher order corrections in the Planck length. Experimental consequences are worked out. The connections to the continuous spontaneous localization (CSL) model, models involving a second-quantized background and the events

Irina Proskurina, Marc-Antoine Carpentier, Julien Velcin

Optimization of offensive content moderation models for different types of hateful messages is typically achieved through continued pre-training or fine-tuning on new hate speech benchmarks. However, existing benchmarks mainly address explicit hate toward protected groups and often overlook implicit or indirect hate, such as demeaning comparisons, calls for

Suqing Wang, Ziyang Ma, Li Xinyi, Zuchao Li

Large Language Models (LLMs) are widely adopted, but their high training cost leads many developers to fine-tune existing open-source models. While most adhere to open-source licenses, some falsely claim original training despite clear derivation from public models, raising pressing concerns about intellectual property protection and the need to verify model

Mengxue Guan, Xiaodong Zhou, Jingyi Duan, Chaoxi Cui

Light-matter interaction in frustrated Kagome metals enables access to hidden quantum states, yet the microscopic origin of symmetry breaking under ultrafast excitation remains elusive. Here, we uncover a microscopic mechanism for laser-induced symmetry breaking in RbV3Sb5 through first-principles real-time simulations. Selective excitation of a single-QM ph

Kunrong Li, Zhu Sun, Kwan Hui Lim

We propose HyMoERec, a novel sequential recommendation framework that addresses the limitations of uniform Position-wise Feed-Forward Networks in existing models. Current approaches treat all user interactions and items equally, overlooking the heterogeneity in user behavior patterns and diversity in item complexity. HyMoERec initially introduces a hybrid mi

Sejin Kim

We study the inverse problem of holographic entanglement entropy in AdS$_3$ using a data-driven generative model. Training data consist of randomly generated geometries and their holographic entanglement entropies using the Ryu--Takayanagi formula. After training, the Transformer reconstructs the blackening function within our metric ansatz from previously u

Michael Coons, Jan Mazáč, Ari Pincus-Kazmar, Adam Stout

Recently, Baake and Coons proved several results on the average size of the autocorrelations of the Thue--Morse sequence. They also considered the absolute value of the autocorrelations, and showed that the average value of the autocorrelations is zero. In particular, they showed that $\sum_{n\leqslant x}|\eta(n)|=o(x^\alpha)$ for any $\alpha>\log(3)/\log(4)

Jian Guo, Lihong Pei, Wenchao Xue, Yanlong Zhao

This paper studies the problem of online parameter estimation for cyber-physical systems with binary outputs that may be subject to adversarial data tampering. Existing methods are primarily offline and unsuitable for real-time learning. To address this issue, we first develop a first-order gradient-based algorithm that updates parameter estimates recursivel

Ralf Römer, Julian Balletshofer, Jakob Thumm, Marco Pavone

Diffusion policies (DPs) achieve state-of-the-art performance on complex manipulation tasks by learning from large-scale demonstration datasets, often spanning multiple embodiments and environments. However, they cannot guarantee safe behavior, requiring external safety mechanisms. These, however, alter actions in ways unseen during training, causing unpredi

Myeong-Hwan Mun, Jubin Park, Chang Ho Hyun, Shung-Ichi Ando

We analyze the elastic $\alpha$-$^{12}$C scattering including the contribution of resonance states below the $p$-$^{15}$N breakup threshold energy. We use the cluster effective field theory in which scattering amplitude is expanded in terms of the effective range expansion parameters for the angular momentum states from $l=0$ to $l=6$. The amplitude contains

Qiuxiang Zhang, Yan Zhu, Xiaofang Peng, Weiguang Yang

We report combined experimental and first principles investigations of hydrogen absorption in epitaxial vanadium films on MgO(001) with nominal thicknesses of 10 nm and 50 nm. In - situ optical transmission and four - probe resistance isotherms show that the 50 nm film reproduces bulk like behavior with a clear first order alpha-beta hydride transition, the

Chen He, Xun Jiang, Lei Wang, Hao Yang

Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of reasoning tasks. Recent methods have further improved LLM performance in complex mathematical reasoning. However, when extending these methods beyond the domain of mathematical reasoning to tasks involving complex domain-specific knowledge, we observe a consistent fa

Marc Weber, John Paul Strachan, Christian Ebenbauer

To bridge the gap between idealised communication models and the stochastic reality of networked systems, we introduce a framework for embedding asynchronous communication directly into algorithm dynamics using stochastic differential equations (SDE) driven by Poisson Jumps. We apply this communication-aware design to the continuous-time gradient flow, yield

Prajval Kumar Murali, Mohsen Kaboli

Robots operating in real-world environments frequently encounter unknown objects with complex structures and articulated components, such as doors, drawers, cabinets, and tools. The ability to perceive, track, and manipulate these objects without prior knowledge of their geometry or kinematic properties remains a fundamental challenge in robotics. In this wo

Gerhard Hiermann, Joana Ji, Ana Moreno, Rolf Moeckel

Epidemics expose critical tensions between protecting public health and maintaining essential urban mobility. Public transport systems face this dilemma most acutely: they enable access to jobs, education, and services, yet also facilitate close contact among travelers. We develop an integrated modeling framework that couples agent-based epidemic simulation

Qian Ma, Ruoxiang Xu, Yongqiang Cai

Numerous studies have demonstrated that the Transformer architecture possesses the capability for in-context learning (ICL). In scenarios involving function approximation, context can serve as a control parameter for the model, endowing it with the universal approximation property (UAP). In practice, context is represented by tokens from a finite set, referr

Magnus Fries

We extend the relative index theorem on non-compact manifolds to encompass a wide variety of hypoelliptic differential operators of arbitrary order, demonstrating that the change in index when changing a differential operator locally can be calculated locally. We also show that the notion of invertibility at infinity (and coercive at infinity) is not only su

Mang Li, Wei Lyu

The one-epoch overfitting problem has drawn widespread attention, especially in CTR and CVR estimation models in search, advertising, and recommendation domains. These models which rely heavily on large-scale sparse categorical features, often suffer a significant decline in performance when trained for multiple epochs. Although recent studies have proposed

Rajat Bharadwaj, Parvathy Gireesan, Harikrishnan Sundaresan, Chithra H Sharma

Solid-state quantum technologies such as quantum dot qubits and quantum electrical metrology circuits rely on quantum phenomena at ultra-low energies, making them highly sensitive to various forms of environmental noise. Conventional passive filtering schemes can reduce high-frequency noise but are often ineffective against low-frequency interference, like p

Saeed Razavikia, Deniz Gündüz, Carlo Fischione

Over-the-air computation (OAC) has emerged as a key technique for efficient function computation over multiple-access channels (MACs) by exploiting the waveform superposition property of the wireless domain. While conventional OAC methods rely on analog amplitude modulation, their performance is often limited by noise sensitivity and hardware constraints, mo

Yingnan Zhao, Xinmiao Wang, Dewei Wang, Xinzhe Liu

Humanoid robots are promising to learn a diverse set of human-like locomotion behaviors, including standing up, walking, running, and jumping. However, existing methods predominantly require training independent policies for each skill, yielding behavior-specific controllers that exhibit limited generalization and brittle performance when deployed on irregul

Muhittin Evren Aydın, Adela Mihai, Cihan Özgür

In this paper, we study orientable hypersurfaces $N$ in Riemannian manifolds $(M,\langle , \rangle)$ for which the inner product $\langle U, \mathcal{V} \rangle$ is constant, where $U$ is the unit normal vector field to $N$ and $\mathcal{V}$ is a globally defined torse-forming vector field on $M$, called the axis of $N$. When $\mathcal{V}$ is a unit torse-fo

Wonseok Choi, Jeongjae Lee, Songnam Hong

We study downlink multi-group multicast (MGM) transmission in overloaded millimeter-wave (mmWave) systems, where the number of users exceeds the number of transmit antennas. We first show that, under realistic line-of-sight (LoS)-dominant user geometries, the conventional single-slot MGM scheme suffers from a fundamental collapse of the max-min fairness degr

Hideki Nishizawa, Toru Mano, Kazuya Anazawa, Tatsuya Matsumura

With the widespread adoption of AI, machine-to-machine communications are rapidly increasing, reshaping the requirements for optical networks. Recent advances in Gaussian noise modeling for digital coherent transmission have raised expectations for digital-twin-based operation. However, unlike digital twins in wireless communication, which are already well e

Sara Mahdavi Hezavehi, Danny Weyns, Paris Avgeriou

In this essay, we provide an overview of methodological considerations necessary to lay out the foundation for our PhD research on uncertainty and risk-aware adaptation.

Haojun Tang, Qiwei Lin, Tongda Xu, Lida Huang

Attention injection-based style transfer has achieved remarkable progress in recent years. However, existing methods often suffer from content leakage, where the undesired semantic content of the style image mistakenly appears in the stylized output. In this paper, we propose V-Shuffle, a zero-shot style transfer method that leverages multiple style images f

Yash Palan

Imaginary-time evolution (ITE) is one of the most widely used numerical techniques for obtaining ground states of many-body Hamiltonians. In this work, we compare ITE with gradient descent (GD) within the framework of Gaussian wavefunction ansatze. We show that while ITE and GD are formally equivalent for fermionic systems, GD exhibits consistently faster co

Rathin Chandra Shit, Sharmila Subudhi

The optimization of urban traffic is threatened by the complexity of achieving a balance between transport efficiency and the maintenance of privacy, as well as the equitable distribution of traffic based on socioeconomically diverse neighborhoods. Current centralized traffic management schemes invade user location privacy and further entrench traffic dispar

Anastasiia Birillo, Aleksei Rostovskii, Yaroslav Golubev, Hieke Keuning

Automated feedback generation plays a crucial role in enhancing personalized learning experiences in computer science education. Among different types of feedback, next-step hint feedback is particularly important, as it provides students with actionable steps to progress towards solving programming tasks. This study investigates how students interact with a

Tianyu Geng, Feng Ji, Wee Peng Tay

Conventional image sensors have limited dynamic range, causing saturation in high-dynamic-range (HDR) scenes. Modulo cameras address this by folding incident irradiance into a bounded range, yet require specialized unwrapping algorithms to reconstruct the underlying signal. Unlike HDR recovery, which extends dynamic range from conventional sampling, modulo r

Yan Gao, Yazheng Yang, Zhibin Lan, Yidong Chen

Code-switching (CS) speech translation (ST) aims to translate speech that alternates between multiple languages into a target language text, posing significant challenges due to the complexity of semantic modeling and the scarcity of CS data. Previous studies mainly rely on the models themselves to implicitly learn semantic representations and resort to cost

Qi Shi, Pavel Naumov

A law in a multiagent system is a set of constraints imposed on agents' behaviours to avoid undesirable outcomes. The paper considers two types of laws: useful laws that, if followed, completely eliminate the undesirable outcomes and gap-free laws that guarantee that at least one agent can be held responsible each time an undesirable outcome occurs. In both

Guolong Wang, Heng Huang, Zhiqiang Zhang, Wentian Li

Perceiving and producing aesthetic judgments is a fundamental yet underexplored capability for multimodal large language models (MLLMs). However, existing benchmarks for image aesthetic assessment (IAA) are narrow in perception scope or lack the diversity needed to evaluate systematic aesthetic production. To address this gap, we introduce AesTest, a compreh

Jiacheng Wang, Changyuan Zhao, Dusit Niyato, Geng Sun

The increasing saturation of terrestrial resources has driven economic activities into low-altitude airspace. These activities, such as air taxis, rely on low-altitude wireless networks, and one key enabling technology is integrated sensing and communication (ISAC). However, in low-altitude airspace, ISAC is vulnerable to channel-access attacks, thereby degr

Nassim Athmouni

We establish explicit convergence radii for the Baker--Campbell--Hausdorff (BCH) series in special Banach--Malcev algebras of shifts-those embeddable into a Banach alternative algebra. Under the continuity estimate $\|[x,y]\|\leq B\|x\|\|y\|$, the series converges absolutely whenever $B(\|x\|+\|y\|)<1/(4K)$, where $K\geq1$ bounds the absolute BCH coefficient

Runhan Shi, Letian Chen, Gufeng Yu, Yang Yang

Chemical reaction prediction remains a fundamental challenge in organic chemistry, where existing machine learning models face two critical limitations: sensitivity to input permutations (molecule/atom orderings) and inadequate modeling of substructural interactions governing reactivity. These shortcomings lead to inconsistent predictions and poor generaliza

Sara Mahdavi Hezavehi, Danny Weyns, Paris Avgeriou

In this essay, we introduce the basic concepts necessary to lay out the foundation for our PhD research on uncertainty and risk-aware adaptation, and discuss relevant related research.

Dennis Prangle, Cecilia Viscardi, Sammy Ragy

A popular method for likelihood-free inference is approximate Bayesian computation sequential Monte Carlo (ABC-SMC) algorithms. These approximate the posterior using a population of particles, which are updated using Markov kernels. Several such kernels have been proposed. In this paper we review these, highlighting some less well known choices, and proposin