March 2025 arXiv papers — page 140

Luisa Neubig, Deirdre Larsen, Takeshi Ikuma, Markus Kopp

Our study focuses on isolating swallowing dynamics from interfering patient motion in videofluoroscopy, an X-ray technique that records patients swallowing a radiopaque bolus. These recordings capture multiple motion sources, including head movement, anatomical displacements, and bolus transit. To enable precise analysis of swallowing physiology, we aim to e

Yunpeng Qu, Kun Yuan, Qizhi Xie, Ming Sun

Video Quality Assessment (VQA), which intends to predict the perceptual quality of videos, has attracted increasing attention. Due to factors like motion blur or specific distortions, the quality of different regions in a video varies. Recognizing the region-wise local quality within a video is beneficial for assessing global quality and can guide us in adop

A. C. LaForge, J. D. Asmussen, B. Bastian, M. Bonanomi

Superfluid helium nanodroplets are often considered as transparent and chemically inert nanometer-sized cryo-matrices for high-resolution or time-resolved spectroscopy of embedded molecules and clusters. On the other hand, when the helium nanodroplets are resonantly excited with XUV radiation, a multitude of ultrafast processes are initiated, such as relaxat

Zeyi Xu, Jinfan Liu, Kuangxu Chen, Ye Chen

Accurately and efficiently simulating complex fluid dynamics is a challenging task that has traditionally relied on computationally intensive methods. Neural network-based approaches, such as convolutional and graph neural networks, have partially alleviated this burden by enabling efficient local feature extraction. However, they struggle to capture long-ra

Yeonjin Chang, Erqun Dong, Seunghyeon Seo, Nojun Kwak

While the quality of novel-view images has improved dramatically with 3D Gaussian Splatting, extracting specific objects from scenes remains challenging. Isolating individual 3D Gaussian primitives for each object and handling occlusions in scenes remains far from being solved. We propose a novel object extraction method based on two key principles: (1) obje

Hanna Bogucka, Marcin Hoffmann, Paweł Kryszkiewicz, Łukasz Kułacz

This paper presents the Open Radio Access Net-work (O-RAN) testbed for secure radio access. We discuss radio-originating attack detection and mitigation methods based on anomaly detection and how they can be implemented as specialized applications (xApps) in this testbed. We also pre-sent illustrating results of the methods applied in real-world scenarios an

Roham Koohestani, Maliheh Izadi

In the rapidly evolving world of software development, the surge in developers' reliance on AI-driven tools has transformed Integrated Development Environments into powerhouses of advanced features. This transformation, while boosting developers' productivity to unprecedented levels, comes with a catch: increased hardware demands for software development. Mo

Han Wan, Qi Wang, Yuan Mi, Rui Zhang

Deep learning has shown strong potential in modeling complex spatiotemporal dynamics. However, most existing methods depend on densely and uniformly sampled data, which is often unavailable in practice due to sensor and cost limitations. In many real-world settings, such as mobile sensing and physical experiments, data are burst-sampled with short high-frequ

Zhi Chen, Zecheng Zhao, Jingcai Guo, Jingjing Li

Zero-shot learning (ZSL) aims to recognize unseen classes without labeled training examples by leveraging class-level semantic descriptors such as attributes. A fundamental challenge in ZSL is semantic misalignment, where semantic-unrelated information involved in visual features introduce ambiguity to visual-semantic interaction. Unlike existing methods tha

A. Brignoli, P. Deucher, C. Eckardt, F. Faller

The baseline technology for the Surrounding Background Tagger of the recently approved SHiP experiment relies on liquid scintillator composed of linear alkylbenzene and 2,5-diphenyloxazole as active detector material. The primary scintillation photons are collected by Wavelength-shifting Optical Modules, and the secondary photons are guided by total reflecti

Andrew Ronan

In this paper, we explain how the more general context of generalised equivariant bundles allows for a simple inductive proof of the ECHP. We also make clear the link between the ECHP and the theory of Hurewicz fibrations.

Idan Horowitz, Ori Plonsky

We investigate the choice patterns of Large Language Models (LLMs) in the context of Decisions from Experience tasks that involve repeated choice and learning from feedback, and compare their behavior to human participants. We find that on the aggregate, LLMs appear to display behavioral biases similar to humans: both exhibit underweighting rare events and c

Zhijie Zhu, Lei Fan, Maurice Pagnucco, Yang Song

Classifying images with an interpretable decision-making process is a long-standing problem in computer vision. In recent years, Prototypical Part Networks has gained traction as an approach for self-explainable neural networks, due to their ability to mimic human visual reasoning by providing explanations based on prototypical object parts. However, the qua

Samuel Pawel, Małgorzata Roos, Leonhard Held

The two-trials rule in drug regulation requires statistically significant results from two pivotal trials to demonstrate efficacy. However, it is unclear how the effect estimates from both trials should be combined to quantify the drug effect. Fixed-effect meta-analysis is commonly used but may yield confidence intervals that exclude the value of no effect e

Mohd. Faizuddin Faruqui, Ratnangshu Das, Ravi Kumar L, Pushpak Jagtap

This study presents a multi-agent negotiation-based framework to obtain collision-free paths while performing prescribed-time reach-avoid-stay (RAS) tasks for agents with unknown dynamics and bounded disturbance. By employing spatiotemporal tubes to generate time-varying state constraints, we ensure that all agents adhere to RAS specifications using synthesi

Masoumeh Karimi Kisomi

Lithium-ion batteries are extensively utilized as the primary power source for electric vehicles due to their high energy density, environmental friendliness and lightweight nature. However, their performance and safety are highly dependent on operating temperature. Therefore, a battery thermal management system (BTMS) is essential to ensure the reliable ope

Trinh Tuan

Using Kakichev's classical concept and extending Yakubovich-Britvina's approach (\textit{Results. Math.} 55(1-2):175-197, 2009) and (\textit{Integral Transforms Spec. Funct.} 21(4):259--276, 2010) for setting up Kontorovich-Lebedev convolution operators, this paper proposes a new polyconvolution structure associated with the KL-transform and Fourier integral

Magali Hersant, Yves Thomas

Elementary school students sometimes exhibit deplorable behaviors when faced with arithmetic problems. These behaviors may result from certain teaching practices and could be mitigated by a few actions that are relatively easy to implement in the classroom. We propose four avenues focusing on the choice and formulation of problems and the clarification of el

Shaun Khoo, Gabriel Chua, Rachel Shong

Large Language Models (LLMs) are rapidly entering children's lives - through parent-driven adoption, schools, and peer networks - yet current AI ethics and safety research do not adequately address content-related risks specific to minors. In this paper, we highlight these gaps with a real-world case study of an LLM-based chatbot deployed in a middle school

Chenpeng Wu, Qiqi Gu, Heng Shi, Jianguo Yao

The escalating size of Mixture-of-Experts (MoE) based Large Language Models (LLMs) presents significant computational and memory challenges, necessitating innovative solutions to enhance efficiency without compromising model accuracy. Structured sparsity emerges as a compelling strategy to address these challenges by leveraging the emerging sparse computing

Dimitri Ognibene, Sabrina Patania, Luca Annese, Cansu Koyuturk

Multimodal information-gathering settings, where users collaborate with AI in dynamic environments, are increasingly common. These involve complex processes with textual and multimodal interactions, often requiring additional structural information via cost-incurring requests. AI helpers lack access to users' true goals, beliefs, and preferences and struggle

Bogdan Chornomaz, Shay Moran, Tom Waknine

We introduce and study the spherical dimension, a natural topological relaxation of the VC dimension that unifies several results in learning theory where topology plays a key role in the proofs. The spherical dimension is defined by extending the set of realizable datasets (used to define the VC dimension) to the continuous space of realizable distributions

Sajjad Hussain, Md Saad, Almas Baimagambetov, Khizer Saeed

Energy efficiency and motion smoothness are essential in trajectory planning for high-degree-of-freedom robots to ensure optimal performance and reduce mechanical wear. This paper presents a novel framework integrating sinusoidal trajectory generation with velocity scaling to minimize energy consumption while maintaining motion accuracy and smoothness. The f

Yifeng Cai, Ziqi Zhang, Mengyu Yao, Junlin Liu

Super-apps have emerged as comprehensive platforms integrating various mini-apps to provide diverse services. While super-apps offer convenience and enriched functionality, they can introduce new privacy risks. This paper reveals a new privacy leakage source in super-apps: mini-app interaction history, including mini-app usage history (Mini-H) and operation

Denis Berger, Mouad Lemoudden, William J Buchanan

Shor's and Grover's algorithms' efficiency and the advancement of quantum computers imply that the cryptography used until now to protect one's privacy is potentially vulnerable to retrospective decryption, also known as \emph{harvest now, decrypt later} attack in the near future. This dissertation proposes an overview of the cryptographic schemes used by To

Ji Li, Chao Wang

The pretrained diffusion model as a strong prior has been leveraged to address inverse problems in a zero-shot manner without task-specific retraining. Different from the unconditional generation, the measurement-guided generation requires estimating the expectation of clean image given the current image and the measurement. With the theoretical expectation

Elie Azeraf, Audrey Wagner, Emilie Bialic, Samia Mellah

Air pollution remains one of the most pressing environmental challenges of the modern era, significantly impacting human health, ecosystems, and climate. While traditional air quality monitoring systems provide critical data, their high costs and limited spatial coverage hinder effective real-time pollutant identification. Recent advancements in micro-sensor

Murray Stokely, Neel Nadgir, Jack Peele, Orestis Kostakis

Cloud providers have introduced pricing models to incentivize long-term commitments of compute capacity. These long-term commitments allow the cloud providers to get guaranteed revenue for their investments in data centers and computing infrastructure. However, these commitments expose cloud customers to demand risk if expected future demand does not materia

Yang Liu, Anna Baumeister, Antonia Wachter-Zeh

In this paper, we establish the list-decoding capacity theorem for sum-rank metric codes. This theorem implies the list-decodability theorem for random general sum-rank metric codes: Any random general sum-rank metric code with a rate not exceeding the list-decoding capacity is $\left(\rho,O\left(1/\epsilon\right)\right)$-list-decodable with high probability

Samira Zangooei, Amirhossein Darmani, Hossein Farahmand Nezhad, Laya Mahmoudi

The increasing volume of textual data poses challenges in reading and comprehending large documents, particularly for scholars who need to extract useful information from research articles. Automatic text summarization has emerged as a powerful tool to condense lengthy documents into concise and informative summaries. Depending on the approach used, text sum

Tomek Diederen, Nicola Zamboni

We present a framework for modeling complex, high-dimensional distributions on convex polytopes by leveraging recent advances in discrete and continuous normalizing flows on Riemannian manifolds. We show that any full-dimensional polytope is homeomorphic to a unit ball, and our approach harnesses flows defined on the ball, mapping them back to the original p

José-Luis Vilchis-Medina

In this article, we present a novel method for assessing the similarity of information within knowledge-bases using a logical point of view. This proposal introduces the concept of a similarity property space $\Xi$P for each knowledge K, offering a nuanced approach to understanding and quantifying similarity. By defining the similarity knowledge space $\Xi$K

Yafei Zhang, Murray Wang, Yu Wang, Xiaohui Wang

Matching job descriptions (JDs) with suitable talent requires models capable of understanding not only textual similarities between JDs and candidate resumes but also contextual factors such as geographical location and academic seniority. To address this challenge, we propose a two-stage training framework for large language models (LLMs). In the first stag

Gabriel Merlin

Lucatelli Nunes obtained a 2-categorical version of the adjoint triangle theorem of Dubuc using the descent object of a specific diagram. In some cases, such a diagram can be filled with an extra cell. We show then how to obtain a biadjoint as an inverter of this additional datum (under suitable hypotheses). The problem addressed here is slightly different h

Julian Schelb, Orr Borin, David Garcia, Andreas Spitz

Generative language models are increasingly being subjected to psychometric questionnaires intended for human testing, in efforts to establish their traits, as benchmarks for alignment, or to simulate participants in social science experiments. While this growing body of work sheds light on the likeness of model responses to those of humans, concerns are war

Andi Nika, Jonathan Nöther, Debmalya Mandal, Parameswaran Kamalaruban

We study data poisoning attacks in learning from human preferences. More specifically, we consider the problem of teaching/enforcing a target policy $\pi^\dagger$ by synthesizing preference data. We seek to understand the susceptibility of different preference-based learning paradigms to poisoned preference data by analyzing the number of samples required by

Matej Hrmo, Samuel Kováčik, Patrik Rusnák, Juraj Tekel

The fuzzy onion model formed by connecting a set of concentric fuzzy spheres of increasing radius is motivated by studies of quantum space but can also be used to study standard physics. The main feature of the model is that functions in three-dimensional space -- like scalar fields or wavefunctions -- are expressed in terms of Hermitian matrices of a certai

Po-Hao Chou, Chung-Yu Mou, Chung-Hou Chung, Sungkit Yip

We develop a gauge-invariant renormalized mean-field theory (RMFT) to reliably find the quantum spin liquid (QSL) states and their field response for realistic Kitaev materials under strong magnetic fields and described by the generalized Kitaev $J$-$K$-$\Gamma$-$\Gamma'$ model. Remarkably, while our RMFT reproduces previous results based on using more compl

Zhixuan Li, Hyunse Yoon, Sanghoon Lee, Weisi Lin

Amodal segmentation aims to infer the complete shape of occluded objects, even when the occluded region's appearance is unavailable. However, current amodal segmentation methods lack the capability to interact with users through text input and struggle to understand or reason about implicit and complex purposes. While methods like LISA integrate multi-modal

S. Tchuiaga, P. Bikorimana

We establish a cosymplectic counterpart of Banyaga's theorem by proving that the group of weakly Hamiltonian diffeomorphisms, $\Ham_{\eta,\omega}(M)$, is simple on any closed cosymplectic manifold. A key structural result, derived from Lie group theory, provides the foundation for our argument: the Reeb flow on any closed cosymplectic manifold is always peri

Ting-Rui Liao, Xin Zhao, Hui Zeng, Zhang-Yu Nie

In this work, the phase structure of a holographic s+d model with quartic potential terms from the 4D Einstein-Gauss-Bonnet gravity is studied in the probe limit. We first show the $q_d-\mu$ phase diagram with a very small value of the Gauss-Bonnet coefficient $\alpha=1\times10^{-7}$ and in absence of the quartic terms to locate the suitable choice of the va

Claude Inserra, Cyril Mauger, Philippe Blanc-Benon, Alexander A. Doinikov

An exact solution is developed for the bubble-induced acoustic microstreaming in the case of a gas bubble undergoing asymmetric oscillations. The modeling is based on the decomposition of the solenoidal, first- and second-order, vorticity fields into poloidal and toroidal components. The result is valid for small amplitude bubble oscillations without restric

Shaoshuai Chu, Alexander Kurganov, Ruixiao Xin

We develop new more efficient A-WENO schemes for both hyperbolic systems of conservation laws and nonconservative hyperbolic systems. The new schemes are a very simple modification of the existing A-WENO schemes: They are obtained by a more efficient evaluation of the high-order correction terms. We conduct several numerical experiments to demonstrate the pe

Cyriel Mallart, Andrew Simpkin, Nicolas Ballier, Paula Lissón

This article addresses Second Language (L2) writing development through an investigation of new grammatical and structural complexity metrics. We explore the paradigmatic production in learner English by linking language functions to specific grammatical paradigms. Using the EFCAMDAT as a gold standard and a corpus of French learners as an external test set,

Kunwoo Na, Junghyun Lee, Se-Young Yun, Sungbin Lim

Recent advances in infinite-dimensional diffusion models have demonstrated their effectiveness and scalability in function generation tasks where the underlying structure is inherently infinite-dimensional. To accelerate inference in such models, we derive, for the first time, an analog of the probability-flow ODE (PF-ODE) in infinite-dimensional function sp

Yifeng Cai, Ziqi Zhang, Ding Li, Yao Guo

Modern Federated Learning (FL) has become increasingly essential for handling highly heterogeneous mobile devices. Current approaches adopt a partial model aggregation paradigm that leads to sub-optimal model accuracy and higher training overhead. In this paper, we challenge the prevailing notion of partial-model aggregation and propose a novel "full-weight

Shilong Wang, Jianchun Liu, Hongli Xu, Jiaming Yan

Fine-tuning plays a crucial role in enabling pre-trained LLMs to evolve from general language comprehension to task-specific expertise. To preserve user data privacy, federated fine-tuning is often employed and has emerged as the de facto paradigm. However, federated fine-tuning is prohibitively inefficient due to the tension between LLM complexity and the r

Kaixiang Yang, Xin Li, Qiang Li, Zhiwei Wang

Anticipating and recognizing surgical workflows are critical for intelligent surgical assistance systems. However, existing methods rely on deterministic decision-making, struggling to generalize across the large anatomical and procedural variations inherent in real-world surgeries.In this paper, we introduce an innovative framework that incorporates stochas

Benjamin Heymann

AI alignment, the challenge of ensuring AI systems act in accordance with human values, has emerged as a critical problem in the development of systems such as foundation models and recommender systems. Still, the current dominant approach, reinforcement learning with human feedback (RLHF) faces known theoretical limitations in aggregating diverse human pref

Zhiwu Wang, Yichen Wu, Renzhen Wang, Haokun Lin

Class-Incremental Learning (CIL) aims to prevent catastrophic forgetting of previously learned classes while sequentially incorporating new ones. The more challenging Few-shot CIL (FSCIL) setting further complicates this by providing only a limited number of samples for each new class, increasing the risk of overfitting in addition to standard CIL challenges

Wen-Chao Dai, Ou-Yang Luo, Bing Chen, Xun Chen

Using Kolmogorov-Arnold Networks (KANs), we construct a holographic model informed by lattice QCD data. This neural network approach enables the derivation of an analytical solution for the deformation factor $w(r)$ and the determination of a constant $g$ related to the string tension. Within the KANs-based holographic framework, we further analyze heavy qua

Teng Xu, Taotao Zhou, Youjia Wang, Peng Yang

Analyzing animal behavior is crucial in advancing neuroscience, yet quantifying and deciphering its intricate dynamics remains a significant challenge. Traditional machine vision approaches, despite their ability to detect spontaneous behaviors, fall short due to limited interpretability and reliance on manual labeling, which restricts the exploration of the

Henglyu Liu, Andong Chen, Kehai Chen, Xuefeng Bai

Recent advancement of large language models (LLMs) has led to significant breakthroughs across various tasks, laying the foundation for the development of LLM-based speech translation systems. Existing methods primarily focus on aligning inputs and outputs across modalities while overlooking deeper semantic alignment within model representations. To address

Jialong Wu, Marco Braun, Dominic Spata, Matthias Rottmann

Scene flow provides crucial motion information for autonomous driving. Recent LiDAR scene flow models utilize the rigid-motion assumption at the instance level, assuming objects are rigid bodies. However, these instance-level methods are not suitable for sparse radar point clouds. In this work, we present a novel Traffic-Aware Radar Scene-Flow (TARS) estimat

Quentin Berger, Alexandre Legrand, Rémy Poudevigne, Christophe Sabot

In this paper, we study the transient phase of the Vertex Reinforced Jump Process (VRJP) in dimension $d\geq 3$. In Sabot, Zeng (2019), the authors introduce a positive martingale and show that the VRJP is recurrent if and only if that martingale converges to $0$. On $\mathbb{Z}^d$, $d\ge 3$, with constant conductances $W$, it can be shown that there is a cr

Alexey V. Bolsinov, Andrey Yu. Konyaev, Vladimir S. Matveev

We explore the Jordan-Chevalley decomposition problem for an operator field in small dimensions. In dimensions three and four, we find tensorial conditions for an operator field $L$, similar to a nilpotent Jordan block, to possess local coordinates in which $L$ takes a strictly upper triangular form. We prove the Tempesta-Tondo conjecture for higher order br

Fabian Segatz, Muhammad Ihsan Al Hafiz

Kyber, an IND-CCA2-secure lattice-based post-quantum key-encapsulation mechanism, is the winner of the first post-quantum cryptography standardization process of the US National Institute of Standards and Technology. In this work, we provide an efficient implementation of Kyber on ESP32, a very popular microcontroller for Internet of Things applications. We

David Scholz

A graph $G$ is perfectly divisible if, for every induced subgraph $H$ of $G$, either $V(H)$ is a stable set or admits a partition into two sets $X_1$ and $X_2$ such that $\omega(H[X_1]) < \omega(H)$ and $H[X_2]$ is a perfect graph. In this article, we propose the following generalisation of perfectly divisible graphs. A graph $G$ is perfectly $1$-divisible i

Anthony Couthures, Vineeth S. Varma, Samson Lasaulce, Irinel-Constantin Morarescu

The paper addresses the synchronization of multi-agent systems with continuous-time dynamics interacting through a very general class of monotonic continuous signal functions that covers estimation biases, approximation of discrete quantization, or state-dependent estimation. Our analysis reveals that, in the setup under consideration, synchronization equili

Nahual Sobrino, Unai Aseginolaza, Joaquim Jornet-Somoza, Juan Borge

Accurate assessment and management of errors is indispensable for extracting useful results from noisy intermediate-scale quantum (NISQ) devices. In this work, we propose the qubit error probability (QEP), a device specific metric that combines relaxation, dephasing, gate, and measurement contributions into a single per qubit figure of merit computable befor

Yukai Zheng, Qingna Li

In this paper, we consider the computational protein design (CPD) problem, which is usually modeled as 0/1 programming and is extremely challenging due to its combinatorial properties. As a quadratic semi-assignment problem (QSAP), the CPD problem has been proved to be equivalent to its continuous relaxation problem (RQSAP), in terms of sharing the same opti

A. Tomonaga, H. Mukai, K. Mizuno, J. S. Tsai

The extraction of transition frequencies from a spectrum has conventionally relied on empirical methods, and particularly in complex systems, it is a time-consuming and cumbersome process. To address this challenge, we establish a semi-automated efficient and precise spectrum analysis method. It first employs image processing methods to extract transition fr

Thanasis Bouganis, Jolanta Marzec-Ballesteros

It is well known that there is a deep relationship between codes and lattices. Concepts from coding theory are related to concepts of lattice theory as, for example, weight enumerators to theta series, MacWilliams identity to Jacobi identity, and Gleason's theorem to Hecke's theorem. In this framework, higher-genus (or multiple) weight enumerators are relate

Boyu Chen, Zhengrong Yue, Siran Chen, Zikang Wang

Existing MLLMs encounter significant challenges in modeling the temporal context within long videos. Currently, mainstream Agent-based methods use external tools to assist a single MLLM in answering long video questions. Despite such tool-based support, a solitary MLLM still offers only a partial understanding of long videos, resulting in limited performance

Ruibiao Song, Liying Zhang

The Bayesian inversion method demonstrates significant potential for solving inverse problems, enabling both point estimation and uncertainty quantification (UQ). However, Bayesian maximum a posteriori (MAP) estimation may become unstable when handling data from diverse distributions (e.g., solutions of stochastic partial differential equations (SPDEs)). Add

Xiangjie Kong, Zhenghao Chen, Weiyao Liu, Kaili Ning

Time series forecasting (TSF) has long been a crucial task in both industry and daily life. Most classical statistical models may have certain limitations when applied to practical scenarios in fields such as energy, healthcare, traffic, meteorology, and economics, especially when high accuracy is required. With the continuous development of deep learning, n

Shuan Chen, Kye Sung Park, Taewan Kim, Sunkyu Han

Accurately predicting chemical reaction outcomes and potential byproducts is a fundamental task of modern chemistry, enabling the efficient design of synthetic pathways and driving progress in chemical science. Reaction mechanism, which tracks electron movements during chemical reactions, is critical for understanding reaction kinetics and identifying unexpe

Lun Ji, Hang Li, Chunmei Su

In this paper, we establish error estimates for a fully discrete, filtered Lie splitting scheme applied directly to the Zakharov system -- a model whose solutions may exhibit extremely low regularity in arbitrary dimensions. Remarkably, we find that the scheme exhibits an \emph{approximately structure-preserving} behavior in the fully discrete setting. Our e

Hongze Sun, Jun Wang, Wuque Cai, Duo Chen

Spiking Neural Networks (SNNs) have emerged as a promising tool for event-based optical flow estimation tasks due to their ability to leverage spatio-temporal information and low-power capabilities. However, the performance of SNN models is often constrained, limiting their application in real-world scenarios. In this work, we address this gap by proposing a

Davide Pradovera, Ralf Hiptmair, Ilaria Perugia

This paper describes novel algorithms for the identification of (almost-)resonant behavior in scattering problems. Our methods, relying on rational approximation, aim at building surrogate models of what we call "field amplification", defined as the norm of the solution operator of the scattering problem, which we express through boundary-integral equations.

Fernando García-Castaño, Miguel Ángel Melguizo-Padial

This paper deals with $\varepsilon$-efficient and $\varepsilon$-properly efficient points with respect to a co-radiant set in vector optimization problems. In the first part of the paper, we establish a new nonlinear separation theorem for co-radiant sets in normed spaces. Subsequently, we obtain necessary and sufficient conditions, via scalarization, for bo

Miguel Romero-Arjona, Pablo Valle, Juan C. Alonso, Ana B. Sánchez

The battle for AI leadership is on, with OpenAI in the United States and DeepSeek in China as key contenders. In response to these global trends, the Spanish government has proposed ALIA, a public and transparent AI infrastructure incorporating small language models designed to support Spanish and co-official languages such as Basque. This paper presents the

Brian Pulfer, Yury Belousov, Slava Voloshynovskiy

Recently, large pre-trained foundation models have become widely adopted by machine learning practitioners for a multitude of tasks. Given that such models are publicly available, relying on their use as backbone models for downstream tasks might result in high vulnerability to adversarial attacks crafted with the same public model. In this work, we propose

Zoltán Buczolich, Yann Demichel, Stéphane Seuret

In a famous paper published in 1904, Helge von Koch introduced the curve that still serves nowadays as an iconic representation of fractal shapes. In fact, von Koch's main goal was the construction of a continuous but nowhere differentiable function, very similar to the snowflake, using elementary geometric procedures, and not analytical formulae. We prove t

Ken Judai, Satoshi Shibuta, Kazuki Furukawa

The Brownian motion in water-ethanol mixtures exhibits abnormally large displacements. Using falling-ball viscometry applied to colloidal particles, we experimentally verified that no anomaly exists in the viscosity coefficient of the solution. Our findings reveal that the anomalous Brownian motion can be attributed to the memory effect arising from the coup

Dimitri Colferai

I present the renormalization group improved collinear resummation of the photon-gluon impact factors. We construct the resummed cross section for virtual photon-photon ($\gamma^*\gamma^*$) scattering which incorporates the impact factors and BFKL gluon Green's function up to the next-to-leading logarithmic accuracy in energy. The impact factors include impo

Wei-Jiang Zou, Yi-Long Yang, Shihang Shen, Jie Meng

The simultaneous description for nuclear matter and finite nuclei has been a long-standing challenge in nuclear ab initio theory. With the success for nuclear matter, the relativistic Brueckner-Hartree-Fock (RBHF) theory with covariant chiral interactions is a promising ab initio approach to describe both nuclear matter and finite nuclei. In the description

Dan Leonte, Aamal Hussain, Raphael Huser, Francesco Belardinelli

Beyond specific settings, many multi-agent learning algorithms fail to converge to an equilibrium solution, instead displaying complex, non-stationary behaviours such as recurrent or chaotic orbits. In fact, recent literature suggests that such complex behaviours are likely to occur when the number of agents increases. In this paper, we study Q-learning dyna

Lukas Aumayr, Zeta Avarikioti, Dimitris Karakostas, Karl Kreder

Following the publication of Bitcoin&#39;s arguably most famous attack, selfish mining, various works have introduced mechanisms to enhance blockchain systems&#39; game-theoretic resilience. The only proof-of-work reward rule with a Nash-equilibrium guarantee, FruitChains, demands reward finality on the order of days. The rules that settle in minutes have no

Fernando García-Castaño, Christian Günther, M. A. Melguizo-Padial, Christiane Tammer

In this paper, we study relationships between symmetric and non-symmetric separation of (not necessarily convex) cones by using separating cones of Bishop-Phelps type in real normed spaces. Besides extending some known results for the non-symmetric cone separation approach, we propose a new symmetric cone separation approach and establish cone separation res

Shunqi Mao, Chaoyi Zhang, Weidong Cai

Existing vision-language models (VLMs) often suffer from visual hallucination, where the generated responses contain inaccuracies that are not grounded in the visual input. Efforts to address this issue without model finetuning primarily mitigate hallucination by contrastively reducing language biases or amplifying the weights of visual embedding during deco

Mikhail Shkolnikov, Peter Petrov

The paper is based on a talk given by the first author at the G\"okova Geometry \& Topology conference in May 2024. The subject is an interplay between the ideas of tropical geometry and two-by-two matrices with an intention to explore new types of geometries. More concretely, the article gives a preliminary account for a non-abelian version of phase tropica

Niklas Götz, Iurii Karpenko, Hannah Elfner

This study aims to apply statistical learning, specifically Bayesian inference, to the (3+1)D SMASH-vHLLE-hybrid model using initial conditions generated by the SMASH transport code itself, with the objective of constraining model parameters and gaining deeper insight on the temperature and baryochemical potential dependence of both the shear and the bulk vi

Rui Li, Nicola R. Napolitano, Giuseppe D Ago, Vyacheslav N. Shalyapin

We report the spectroscopic confirmation of a bright blue Einstein ring in the Kilo Degree Survey (KiDS) footprint: the Einstein ``blue eye''. Spectroscopic data from X-Shooter at the Very Large Telescope (VLT) show that the lens is a typical early-type galaxy (ETG) at $z_l=0.9906$, while the background source is a Ly$\alpha$ emitter at $z_s=2.823$. The refe

Jiayun He, Lei Yang, Jiajun Zhan

In this paper, two efficient and magnetization norm preserving numerical schemes based on the scalar auxiliary variable (SAV) method are developed for calculating the ground state in micromagnetic structures. The first SAV scheme is based on the original SAV method for the gradient flow model, while the second scheme features an updated scalar auxiliary vari

M. Atif Sultan, Wei Hao, E. S. Swanson, Lei Chang

An open question in hadronic phenomenology concerns the ``unquenching" effects of higher Fock space components on the leading Fock space description of hadrons. We address this by making a comparison of the bottomonium spectrum as computed with the relativized Godfrey-Isgur quark model and an unquenched coupled channel model driven by the ``$^3P_0$" mechanis

Yirong Sun, Yanjun Chen

We propose PRISM, a novel framework designed to overcome the limitations of 2D-based Preference-Based Reinforcement Learning (PBRL) by unifying 3D point cloud modeling and future-aware preference refinement. At its core, PRISM adopts a 3D Point Cloud-Language Model (3D-PC-LLM) to mitigate occlusion and viewpoint biases, ensuring more stable and spatially con

Yuta Sato

We prove the uniform Lyndon interpolation property (ULIP) of some extensions of the pure logic of necessitation $\mathbf{N}$. For any $m, n \in \mathbb{N}$, $\mathbf{N}^+\mathbf{A}_{m,n}$ is the logic obtained from $\mathbf{N}$ by adding a single axiom $\Box^n \varphi \to \Box^m \varphi$, $\Diamond$-free modal reduction principle, together with a rule $\frac

Léa Fabre, Caroline Bayart, Alexandre Nicolas, Patrick Bonnel

To continuously monitor mobility flows aboard public transportation, low-cost data collection methods based on the passive detection of Wi-Fi signals are promising technological solutions, but they yield uncertain results. We assess the accuracy of these results in light of a three-month experimentation conducted aboard buses equipped with Wi-Fi sensors in a

Maximilian Pierer von Esch, Andreas Völz, Knut Graichen

This paper presents a novel sensitivity-based distributed programming (SBDP) approach for non-convex, large-scale nonlinear programs (NLP). The algorithm relies on first-order sensitivities to cooperatively solve the central NLP in a distributed manner with only neighbor-to-neighbor communication and parallelizable local computations. The decoupling of the s

Bowen Kang, Xi Xia, Tao Guo

The Born-Oppenheimer approximation is one of the very successful tools for solving the hydrogen atom problem. The experimental discovery of hidden heavy flavor tetraquarks, $Q\bar{Q}q \bar{q}$ ($Q=c,b$ and $q=u,d,s$), provides great possibilities for the hydrogen-bond-like structure of the Quantum Chromodynamics version. In this work, considering that the co

Naiyu Jiang, Wendi Bao, Lili Xing, Weiguo Li

In this paper, for solving nonlinear systems we propose two pseudoinverse-free greedy block methods with momentum by combining the residual-based weighted nonlinear Kaczmarz and heavy ball methods. Without the full column rank assumptions on Jacobi matrices of nonlinear systems, we provide a thorough convergence analysis, and derive upper bounds for the conv

Qiuhao Wang, Xu Yang, Yiwei Liu, Saiyu Qi

Graph databases have garnered extensive attention and research due to their ability to manage relationships between entities efficiently. Today, many graph search services have been outsourced to a third-party server to facilitate storage and computational support. Nevertheless, the outsourcing paradigm may invade the privacy of graphs. PeGraph is the latest

Jianheng Liu, Yunfei Wan, Bowen Wang, Chunran Zheng

Digital twins are fundamental to the development of autonomous driving and embodied artificial intelligence. However, achieving high-granularity surface reconstruction and high-fidelity rendering remains a challenge. Gaussian splatting offers efficient photorealistic rendering but struggles with geometric inconsistencies due to fragmented primitives and spar

Harry Richman, Cheng Zhang, Frederick A. Matsen

As part of work to connect phylogenetics with machine learning, there has been considerable recent interest in vector encodings of phylogenetic trees. We present a simple new "ordered leaf attachment" (OLA) method for uniquely encoding a binary, rooted phylogenetic tree topology as an integer vector. OLA encoding and decoding take linear time in the number o

Xiaojuan Liao, Yuehan Xu, Qijun Zhang, Peng Huang

The advent of quantum computers has significantly challenged the security of traditional cryptographic systems, prompting a surge in research on quantum key distribution (QKD). Among various QKD approaches, continuous-variable QKD (CVQKD) offers superior resilience against background noise. However, the local local oscillator (LLO) CVQKD scheme faces substan

Hyunbin Jin, Je Won Yeom, Seunghyun Bae, Taesup Kim

Large language models (LLMs) exhibit strong reasoning abilities, often attributed to few-shot or zero-shot chain-of-thought (CoT) prompting. While effective, these methods require labor-intensive prompt engineering, raising the question of whether reasoning can be induced without reliance on explicit prompts. In this work, we unlock the reasoning capabilitie

Pengfei Luo, Jingbo Zhou, Tong Xu, Yuan Xia

With the proliferation of images in online content, language-guided image retrieval (LGIR) has emerged as a research hotspot over the past decade, encompassing a variety of subtasks with diverse input forms. While the development of large multimodal models (LMMs) has significantly facilitated these tasks, existing approaches often address them in isolation,

Jiayu Han

In this paper, we introduce a finite element method employing the Ned\'el\'ec element space for solving the Maxwell's transmission eigenvalue problem in anisotropic media. The well-posedness of the source problems are derived using $\mathbb T$-coercivity approach. We discuss the discrete compactness property of the finite element space under the case of anis

Zihan Liu, Yuan-Hua Ni

This paper addresses the safety challenges in impulsive systems, where abrupt state jumps introduce significant complexities into system dynamics. A unified framework is proposed by integrating Quadratic Programming (QP), Control Barrier Functions (CBFs), and adaptive gain mechanisms to ensure system safety during impulsive events. The CBFs are constructed t