March 2025 arXiv papers — page 61

Biwen Meng, Xi Long, Wanrong Yang, Ruochen Liu

Deep learning has made significant progress in addressing challenges in various fields including computational pathology (CPath). However, due to the complexity of the domain shift problem, the performance of existing models will degrade, especially when it comes to multi-domain or cross-domain tasks. In this paper, we propose a Test-time style transfer (T3s

Tapender, Labh Singh, Surender Verma

We investigate a minimal Type-III scotogenic model featuring two inert scalar doublets and a hyperchargeless triplet fermion. The scalar sector, in addition to the Standard Model Higgs, includes a rich spectrum of dark scalars comprising two CP-even, two CP-odd, and two charged states. This framework gives rise to two viable dark matter candidates: the light

Abdoul Majid O. Thiombiano, Brahim Hnich, Ali Ben Mrad, Mohamed Wiem Mkaouer

The current era of Natural Language Processing (NLP) is dominated by Transformer models. However, novel architectures relying on recurrent mechanisms, such as xLSTM and Mamba, have been proposed as alternatives to attention-based models. Although computation is done differently than with the attention mechanism mechanism, these recurrent models yield good re

Kai Yuan, Qi Wang, Rongquan Feng, Yan Wang

A hypergraph is linear if each pair of distinct vertices appears in at most one common edge. We say $\varGamma=(V,E)$ is an associated graph of a linear hypergraph $\mathcal{H}=(V, X)$ if for any $x\in X$, the induced subgraph $\varGamma[x]$ is a cycle, and for any $e\in E$, there exists a unique edge $y\in X$ such that $e\subseteq y$. A linear hypermap $\ma

Gerold Alsmeyer, Viet Hung Hoang

In branching process theory, linear-fractional distributions are commonly used to model individual reproduction, especially when the goal is to obtain more explicit formulas than those derived under general model assumptions. In this article, we explore a generalization of these distributions, first introduced by Sagitov and Lindo, which offers similar advan

Nariman Naderi, Seyed Amir Ahmad Safavi-Naini, Thomas Savage, Zahra Atf

This study evaluated self-reported response certainty across several large language models (GPT, Claude, Llama, Phi, Mistral, Gemini, Gemma, and Qwen) using 300 gastroenterology board-style questions. The highest-performing models (GPT-o1 preview, GPT-4o, and Claude-3.5-Sonnet) achieved Brier scores of 0.15-0.2 and AUROC of 0.6. Although newer models demonst

Patrik Jansson, Nicola Botta, Tim Richter

One of the most ubiquitous problems in optimization is that of finding all the elements of a finite set at which a function $f$ attains its minimum (or maximum). When the codomain of $f$ is equipped with a total order, it is easy to specify, implement, and verify generic solutions to this problem. But what if $f$ is affected by uncertainties? What if one see

Uwe Hassler, Marc-Oliver Pohle, Tanja Zahn

Sample autocorrelograms typically come with significance (non-rejection) bands for the null hypothesis of no temporal correlation. These bands have three shortcomings. First, they build on pointwise intervals and suffer from joint undercoverage (overrejection) under the null hypothesis. Second, in cases where this null is clearly violated one would rather pr

Takashi Isobe, He Cui, Dong Zhou, Mengmeng Ge

Text-to-Video (T2V) generation has attracted significant attention for its ability to synthesize realistic videos from textual descriptions. However, existing models struggle to balance computational efficiency and high visual quality, particularly on resource-limited devices, e.g.,iGPUs and mobile phones. Most prior work prioritizes visual fidelity while ov

Bui Xuan Hai, Huynh Viet Khanh

Let $E$ be a graph and $K$ a field. In this paper we prove that the multiplicative group of a unital noncommutative Leavitt path algebra $L_K(E)$ contains non-cyclic free subgroups provided $K$ is of characteristic $0$. Further, we provide a description of the generators of such free subgroups in term of the graph $E$.

Rafia Rahim, Samuel Woerz, Andreas Zell

Recently, end-to-end deep networks based stereo matching methods, mainly because of their performance, have gained popularity. However, this improvement in performance comes at the cost of increased computational and memory bandwidth requirements, thus necessitating specialized hardware (GPUs); even then, these methods have large inference times compared to

Bin Li, Dehong Gao, Yeyuan Wang, Linbo Jin

Despite the significant success of Large Vision-Language models(LVLMs), these models still suffer hallucinations when describing images, generating answers that include non-existent objects. It is reported that these models tend to over-focus on certain irrelevant image tokens that do not contain critical information for answering the question and distort th

Sofia Brenner, Linda Kleist, Torsten Mütze, Christian Rieck

In 1984, Winkler conjectured that every simple Venn diagram with $n$ curves can be extended to a simple Venn diagram with $n+1$ curves. His conjecture is equivalent to the statement that the dual graph of any simple Venn diagram has a Hamilton cycle. In this work, we construct counterexamples to Winkler's conjecture for all $n\geq 6$. As part of this proof,

Zihao Chen, Hsuanyu Wu, Chi-Hsi Kung, Yi-Ting Chen

Traffic Atomic Activity which describes traffic patterns for topological intersection dynamics is a crucial topic for the advancement of intelligent driving systems. However, existing atomic activity datasets are collected from an egocentric view, which cannot support the scenarios where traffic activities in an entire intersection are required. Moreover, ex

Qiang Qu, Ming Li, Xiaoming Chen, Tongliang Liu

Conditional human animation traditionally animates static reference images using pose-based motion cues extracted from video data. However, these video-derived cues often suffer from low temporal resolution, motion blur, and unreliable performance under challenging lighting conditions. In contrast, event cameras inherently provide robust and high temporal-re

Eoin Quinn, Ghassene Jebali, Maxime Seince, Oliver Bent

We explore a framework for protein sequence representation learning that decomposes the task between manifold learning and distributional modelling. Specifically we present a Latent Space Diffusion architecture which combines a protein sequence autoencoder with a denoising diffusion model operating on its latent space. We obtain a one-parameter family of lea

Emiram Kablo, Yorick Last, Patricia Arias Cabarcos, Melanie Volkamer

As Virtual Reality (VR) expands into fields like healthcare and education, ensuring secure and user-friendly authentication becomes essential. Traditional password entry methods in VR are cumbersome and insecure, making password managers (PMs) a potential solution. To explore this field, we conducted a user study (n=126 VR users) where participants expressed

Xiaolong Yin, Xingyu Lu, Jiahang Shen, Jingzhe Ni

A CAD command sequence is a typical parametric design paradigm in 3D CAD systems where a model is constructed by overlaying 2D sketches with operations such as extrusion, revolution, and Boolean operations. Although there is growing academic interest in the automatic generation of command sequences, existing methods and datasets only support operations such

Lubnaa Abdur Rahman, Ioannis Papathanail, Lorenzo Brigato, Stavroula Mougiakakou

Food recognition models often struggle to distinguish between seen and unseen samples, frequently misclassifying samples from unseen categories by assigning them an in-distribution (ID) label. This misclassification presents significant challenges when deploying these models in real-world applications, particularly within automatic dietary assessment systems

Ata Khalili, Robert Schober

We investigate resource allocation for a movable antenna (MA) enabled integrated sensing and communication (ISAC) system scanning a sector for sensing and simultaneously serving multiple communication users using multiple variable-length snapshots. To tackle the critical challenges of slow antenna movement speed, dynamic radar cross section (RCS) variation,

Yaroslav Marchukov, Luis Montano

In this demo work we develop a method to plan and coordinate a multi-agent team to gather information on demand. The data is periodically requested by a static Operation Center (OC) from changeable goals locations. The mission of the team is to reach these locations, taking measurements and delivering the data to the OC. Due to the limited communication rang

Yaroslav Marchukov, Luis Montano

In the present work we address the problem of deploying a team of robots in a scenario where some locations of interest must be reached. Thus, a planning for a deployment is required, before sending the robots. The obstacles, the limited communication range, and the need of communicating to a base station, constrain the connectivity of the team and the deplo

Rafia Rahim, Samuel Woerz, Andreas Zell

Knowledge distillation has been quite popular in vision for tasks like classification and segmentation however not much work has been done for distilling state-of-the-art stereo matching methods despite their range of applications. One of the reasons for its lack of use in stereo matching networks is due to the inherent complexity of these networks, where a

Emanuele Venieri, Simone Manoni, Gabriele Ceccolini, Giacomo Madella

Many RISC-V (RV) platforms and SoCs have been announced in recent years targeting the HPC sector, but only a few of them are commercially available and engineered to fit the HPC requirements. The Monte Cimone project targeted assessing their capabilities and maturity, aiming to make RISC-V a competitive choice when building a datacenter. Nowadays, Systems-on

Nathan Clarke, Gaseb Alotibi, Dany Joy, Fudong Li

In todays landscape of increasing electronic crime, network forensics plays a pivotal role in digital investigations. It aids in understanding which systems to analyse and as a supplement to support evidence found through more traditional computer based investigations. However, the nature and functionality of the existing Network Forensic Analysis Tools (NFA

Kangli Wang, Wei Gao

Learning-based point cloud compression methods have made significant progress in terms of performance. However, these methods still encounter challenges including high complexity, limited compression modes, and a lack of support for variable rate, which restrict the practical application of these methods. In order to promote the development of practical poin

Guneet Mutreja, Philipp Schuegraf, Ksenia Bittner

Recent advances in self-supervised learning have led to the development of foundation models that have significantly advanced performance in various computer vision tasks. However, despite their potential, these models often overlook the crucial role of high-resolution digital surface models (DSMs) in understanding urban environments, particularly for buildi

Ashenafi Zebene Woldaregay, Jørgen Aarmo Lund, Phuong Dinh Ngo, Mariyam Tayefi

Background: Clinical natural language processing (NLP) refers to the use of computational methods for extracting, processing, and analyzing unstructured clinical text data, and holds a huge potential to transform healthcare in various clinical tasks. Objective: The study aims to perform a systematic review to comprehensively assess and analyze the state-of-t

N. Blind, N. Restori, B . Chazelas, C. Lovis

Context. Proxima Cen b is the prime target for the search of life around a nearby exoplanet by characterizing its atmosphere in reflected light. Due to the very high star/companion contrast (<1E-6), High Dispersion Coronagraphy is the most promising technique to perform such a characterization. Aims. With a maximum separation of 37 mas, Proxima b can be obse

Andrea Capozzi, Magnus Karlsson, Yupeng Zhao, Jan Kilund

Hyperpolarized 13C Magnetic Resonance Spectroscopic Imaging (HP 13C-MRSI) has the potential to greatly improve diagnostic radiology thanks to its unique capability to detect, noninvasively, a wide range of diseases entailing aberrant metabolism. Nevertheless, it struggles to enter everyday clinical practice as an alternative and/or complement to Positron Emi

Erjian Guo, Zhen Zhao, Zicheng Wang, Tong Chen

Medical Visual Question Answering (Med-VQA) systems benefit the interpretation of medical images containing critical clinical information. However, the challenge of noisy labels and limited high-quality datasets remains underexplored. To address this, we establish the first benchmark for noisy labels in Med-VQA by simulating human mislabeling with semantical

M. Fabbrichesi, R. Floreanini, L. Marzola

High-energy colliders enable the testing of quantum mechanics at its most fundamental level, in the presence of strong and electroweak interactions, with systems that consist of qubits (fermions) and qutrits (massive spin-1 bosons). Quantum state tomography at colliders enables the witnessing of entanglement and Bell non-locality, two defining characteristic

Sakineh Mizani, Martin Oettel, Péter Gurin, Szabolcs Varga

We investigate the orientational ordering and close-packing behavior of a quasi-one-dimensional (q1D) system of hard superellipses, where the centers of the particles are confined to a line, but they can rotate freely within a two-dimensional plane. The particle shape is tuned between an ellipse and a rectangle by varying the deformation parameter (n). The e

Dawei Yan, Yang Li, Qing-Guo Chen, Weihua Luo

Compared to single-turn dialogue, multi-turn dialogue involving multiple images better aligns with the needs of real-world human-AI interactions. Additionally, as training data, it provides richer contextual reasoning information, thereby guiding the model to achieve better performance. However, existing vision-language models (VLMs) primarily rely on single

I. L. Buchbinder, A. S. Budekhina, E. A. Ivanov, K. V. Stepanyantz

Using the harmonic superspace approach, we perform a comprehensive study of the structure of divergences in the higher-derivative $6D$, ${\cal N}=(1,0)$ supersymmetric Yang--Mills theory coupled to the hypermultiplet in the adjoint representation. The effective action is constructed in the framework of the superfield background field method with the help of

Octi Zhang, Quanquan Peng, Rosario Scalise, Bryon Boots

Developing robotic agents that can perform well in diverse environments while showing a variety of behaviors is a key challenge in AI and robotics. Traditional reinforcement learning (RL) methods often create agents that specialize in narrow tasks, limiting their adaptability and diversity. To overcome this, we propose a preliminary, evolution-inspired frame

Angela Montanaro, Francesco Valiera, Francesca Giusti, Francesca Fassioli

Excitons in a monolayer transition metal dichalcogenide (1L-TMD) are highly bound states characterized by a Rydberg-like spectrum of discrete energy levels. Among these, states with odd-parity are known as dark excitons due to selection rules, which make their stationary and transient characterization challenging using linear optical techniques. Here, we dem

Claudio Bonanno, Matteo Giordano

We study localization of the low Dirac modes in 3+1 dimensional pure $\mathrm{SU}(3)$ gauge theory at zero and nonzero imaginary $\theta$ angle, with the aim of better characterizing the relation between low-mode localization and topological features of gauge theories. We show that the mobility edge observed in the deconfined phase at $\theta=0$ is present a

Moein Sorkhei, Christos Matsoukas, Johan Fredin Haslum, Emir Konuk

How well can one expect transfer learning to work in a new setting where the domain is shifted, the task is different, and the architecture changes? Many transfer learning metrics have been proposed to answer this question. But how accurate are their predictions in a realistic new setting? We conducted an extensive evaluation involving over 42,000 experiment

Soulaimene Turki, Daniel Panangian, Houda Chaabouni-Chouayakh, Ksenia Bittner

Three-dimensional urban reconstruction of buildings from single-view images has attracted significant attention over the past two decades. However, recent methods primarily focus on rooftops from aerial images, often overlooking essential geometrical details. Additionally, there is a notable lack of datasets containing complete 3D point clouds for entire bui

Raúl Ortega, José Manuel Gómez-Pérez

We present SciClaims, an interactive web-based system for end-to-end scientific claim analysis in the biomedical domain. Designed for high-stakes use cases such as systematic literature reviews and patent validation, SciClaims extracts claims from text, retrieves relevant evidence from PubMed, and verifies their veracity. The system features a user-friendly

Yufeng Zhong, Chengjian Feng, Feng Yan, Fanfan Liu

In language-guided visual navigation, agents locate target objects in unseen environments using natural language instructions. For reliable navigation in unfamiliar scenes, agents should possess strong perception, planning, and prediction capabilities. Additionally, when agents revisit previously explored areas during long-term navigation, they may retain ir

Mark Spiller, Emilia Isbono, Philipp Schitz

Enforcing multiple constraints based on the concept of control barrier functions (CBFs) is a remaining challenge because each of the CBFs requires a condition on the control inputs to be satisfied which may easily lead to infeasibility problems. The problem becomes even more challenging with input constraints and disturbances. In this paper, we consider enfo

Jiachen Sun, Yin Xia

The quantification of treatment effects plays an important role in a wide range of applications, including policy making and bio-pharmaceutical research. In this article, we study the quantile treatment effect (QTE) while addressing two specific types of heterogeneities: (a) personalized heterogeneity, which captures the varying treatment effects for differe

Nele Callebaut, Blanca Hergueta

We apply an ADM deparametrization strategy to radial canonical $\Lambda < 0$ gravity in three dimensions. It gives rise to a concise notation for previous holographic interpretations in terms of an identified radial 'volume time' and 'true' ADM degrees of freedom. We further discuss York time and conformal boundary conditions in this context, and construct a

Allan Andre Do Nascimento, Han Wang, Antonis Papachristodoulou, Kostas Margellos

In this work, we propose a Model Predictive Control (MPC) formulation incorporating two distinct horizons: a prediction horizon and a constraint horizon. This approach enables a deeper understanding of how constraints influence key system properties such as suboptimality, without compromising recursive feasibility and constraint satisfaction. In this directi

Ryan L. Acosta Babb, Andrew Rout

In this article, we consider Hartree equations generalised to $2p+1$ order nonlinearities. These equations arise in the study of the mean-field limits of Bose gases with $p$-body interactions. We study their well-posedness properties in $H^{s_c}(\mathbb{T}^3)$, where $\mathbb{T}^3$ is the three dimensional torus and $s_c = 3/2 - 1/p$ is the scaling-critical

Davi B. Costa, Masaru Hongo, Dam Thanh Son

Using an effective field-theoretical approach, we investigate the properties of weakly bound two-neutron halo nuclei (also known as Borromean nuclei) that do not support a low-energy $s$-wave core-neutron resonance. Extending the recently formulated effective field theory for weakly bound Borromean nuclei, we incorporate corrections arising from the effectiv

Ayreena Bakhtawar, Dong Han Kim, Seul Bee Lee

Dirichlet's uniform approximation theorem is a fundamental result in Diophantine approximation that gives an optimal rate of approximation with a given bound. We study uniform Diophantine approximation properties on the Hecke group $\mathbf H_4$. For a given real number $\alpha$, we characterize the sequence of $\mathbf H_4$-best approximations of $\alpha$ a

Sebastian Garcia-Saenz, Yizhou Lu, Sébastien Renaux-Petel

We study loop corrections in the effective field theory of inflation with imaginary speed of sound, which has been shown to provide an effective description of multi-field inflationary models characterized by strongly non-geodesic motion and heavy entropic perturbations. We focus on the one-loop corrections to the scalar and tensor power spectra, taking into

Emilia L. K. Blåsten, Tapio Helin, Antti Kujanpää, Lauri Oksanen

We consider the following inverse problem: Suppose a $(1+1)$-dimensional wave equation on $\mathbb{R}_+$ with zero initial conditions is excited with a Neumann boundary data modelled as a white noise process. Given also the Dirichlet data at the same point, determine the unknown first order coefficient function of the system. We first establish that direct p

Aliaume Lopez, Rafał Stefański

We introduce a high-level language with Python-like syntax for string-to-string, polyregular, first-order definable transductions. This language features function calls, boolean variables, and nested for-loops. We devise and implement a complete decision procedure for the verification of such programs against a first-order specification. The decision procedu

Xiaoyu Zhang, Weihong Pan, Chong Bao, Xiyu Zhang

Humans perceive and comprehend their surroundings through information spanning multiple frequencies. In immersive scenes, people naturally scan their environment to grasp its overall structure while examining fine details of objects that capture their attention. However, current NeRF frameworks primarily focus on modeling either high-frequency local views or

Leheng Zhang, Weiyi You, Kexuan Shi, Shuhang Gu

Diffusion-based image super-resolution methods have demonstrated significant advantages over GAN-based approaches, particularly in terms of perceptual quality. Building upon a lengthy Markov chain, diffusion-based methods possess remarkable modeling capacity, enabling them to achieve outstanding performance in real-world scenarios. Unlike previous methods th

Fei Zhu, Yujing Liu, Wenzhuo Liu, Zhaoxiang Zhang

Continual learning, which aims to learn multiple tasks sequentially, has gained extensive attention. However, most existing work focuses on empirical studies, and the theoretical aspect remains under-explored. Recently, a few investigations have considered the theory of continual learning only for linear regressions, establishes the results based on the stri

Amos Y. -A. Chen, Tomotsugu Goto, Cossas K. -W. Wu, Chih-Teng Ling

Brown dwarfs are failed stars with very low mass (13 to 75 $M_J$), and an effective temperature lower than 2500 K. Thus, they play a key role in understanding the gap in the mass function between stars and planets. However, due to their faint nature, previous searches are inevitably limited to the solar neighbourhood (20 pc). To improve our knowledge of the

Robert Krauthgamer, Nir Petruschka, Shay Sapir

Metric embedding is a powerful tool used extensively in mathematics and computer science. We devise a new method of using metric embeddings recursively, which turns out to be particularly effective in $\ell_p$ spaces, $p>2$, yielding state-of-the-art results for Lipschitz decomposition, for Nearest Neighbor Search, and for embedding into $\ell_2$. In a nutsh

Luca Zanella, Massimiliano Mancini, Willi Menapace, Sergey Tulyakov

Recent video-language alignment models are trained on sets of videos, each with an associated positive caption and a negative caption generated by large language models. A problem with this procedure is that negative captions may introduce linguistic biases, i.e., concepts are seen only as negatives and never associated with a video. While a solution would b

Abhijit Roy, Akhandanand Shukla, Aditya Tripathi

Purpose: The study examines the Open Access (OA) landscape of Indian state agricultural universities, focusing on OA growth, leading institutions, prolific authors, preferred sources, funding, APC usage, and trending topics. It aims to identify research gaps, guide future research, and support policymakers in developing effective OA policies Design/methodolo

Meng Li, Lining Wang, Yiming Wang

Existing studies on the convergence of numerical methods for curvature flows primarily focus on first-order temporal schemes. In this paper, we establish a novel error analysis for parametric finite element approximations of genus-1 axisymmetric mean curvature flow, formulated using two classical second-order time-stepping methods: the Crank-Nicolson method

A. López Ariste, Q. Pilate, A. Lavail, Ph. Mathias

Spectropolarimetry of atomic lines in the spectra of Betelgeuse, and other Red SuperGiants (RSG), presents broad line profiles in linear polarization, but narrow profiles in intensity. Recent observations of the Red SuperGiant RW Cep show, on the other hand, broad intensity profiles, comparable to those in linear polarization. This observation hints that thi

Jiate Li, Meng Pang, Yun Dong, Binghui Wang

Graph neural networks (GNNs) are becoming the de facto method to learn on the graph data and have achieved the state-of-the-art on node and graph classification tasks. However, recent works show GNNs are vulnerable to training-time poisoning attacks -- marginally perturbing edges, nodes, or/and node features of training graph(s) can largely degrade GNNs' tes

Andrés García-Silva, José Manuel Gómez-Pérez

Knowledge base population KBP plays a crucial role in populating and maintaining knowledge bases up-to-date in organizations by leveraging domain corpora. Motivated by the increasingly large context windows supported by large language models, we propose to fine-tune an autoregressive language model for end-toend KPB. Our case study involves the population of

Andy Wathen

We consider real non-symmetric matrices and their factorisation as a product of real symmetric matrices. The number of complex eigenvalues of the original matrix reveals restrictions on such factorisations as we shall prove.

Yujing Liu, Zhixin Liu, Lei Guo

Mixed linear regression (MLR) has attracted increasing attention because of its great theoretical and practical importance in capturing nonlinear relationships by utilizing a mixture of linear regression sub-models. Although considerable efforts have been devoted to the learning problem of such systems, i.e., estimating data labels and identifying model para

Claus Kiefer

Modern cosmological theories invoke the idea that all structure in the Universe originates from quantum fluctuations. Understanding the quantum-to-classical transition for these fluctuations is of central importance not only for the foundations of quantum theory, but also for observational astronomy. In my contribution, I review the essential features of thi

Himani Rautela, Shiladitya Sengupta, Vishwas V. Vasisht

We investigate the dynamical properties of liquid and supercooled liquid silicon, modeled using the Stillinger-Weber (SW) potential, to examine the validity of the Stokes-Einstein (SE) relation. Towards this end, we examine the relationship among various dynamical quantities, including (i) the macroscopic transport coefficients - self diffusion coefficient $

Stefan Rass, Martin Dallinger

Artificial intelligence models trained from data can only be as good as the underlying data is. Biases in training data propagating through to the output of a machine learning model are a well-documented and well-understood phenomenon, but the machinery to prevent these undesired effects is much less developed. Efforts to ensure data is clean during collecti

Laura Grigori, Zhipeng Xue

We discuss a randomized strong rank-revealing QR factorization that effectively reveals the spectrum of a matrix $\textbf{M}$. This factorization can be used to address problems such as selecting a subset of the columns of $\textbf{M}$, computing its low-rank approximation, estimating its rank, or approximating its null space. Given a random sketching matrix

Agastya Raj, Devika Dass, Daniel C. Kilper, Marco Ruffini

We present a framework to identify and mitigate rogue OOK signals and user-generated power interference in a multi-user Optical-Spectrum-as-a-Service network. Experimental tests on the OpenIreland-testbed achieve up to 89% detection rate within 10 seconds of an interference event.

Hao-Yuan Chen, Cheng-Pong Huang, Jui-Ming Yao

The emergence of large language models and their applications as AI agents have significantly advanced state-of-the-art code generation benchmarks, transforming modern software engineering tasks. However, even with test-time computed reasoning models, these systems still struggle with complex software engineering challenges. This work introduces CURA, a code

Adam Paksok, Nipen Saikia

Amdeberhan et al. (2024) introduced the notion of a generalized overcubic partition function $\overline a_c (n)$ and proved an infinite family of congruences modulo a prime $p\ge 3$ and some Ramanujan type congruences. In this paper, we show that $\overline a_{2^\lambda m+t}(n) \equiv \overline a_t (n) \pmod {2^{\lambda+1}}$, where $\lambda \geq1, m\geq0,$ a

Jungjae Lee, Dongjae Lee, Chihun Choi, Youngmin Im

Large Foundation Models (LFMs) have unlocked new possibilities in human-computer interaction, particularly with the rise of mobile Graphical User Interface (GUI) Agents capable of interacting with mobile GUIs. These agents allow users to automate complex mobile tasks through simple natural language instructions. However, the inherent probabilistic nature of

Shuo Yang, Siwen Luo, Soyeon Caren Han, Eduard Hovy

Visual Question Answering (VQA) requires reasoning across visual and textual modalities, yet Large Vision-Language Models (LVLMs) often lack integrated commonsense knowledge, limiting their robustness in real-world scenarios. To address this, we introduce MAGIC-VQA, a novel framework that enhances VQA by systematically integrating commonsense knowledge with

Dhritimalya Roy, Ayanendu Dutta, Subenoy Chakraborty

In this study, the geodesic motion of a test particle along with its confinement is investigated within Cylindrically Symmetric Wormhole spacetime admitting to Closed Timelike Curves. The confinement of particles with or without angular momentum is also investigated. It is found that particles with positive angular momentum that co-rotates with the spacetime

Thilagaraj Ravi, Heramb Vivek Bhusane, Rajnandan Choudhury Das, Samir Khan

Rydberg excitation using blue and IR transition is an advantageous path for quantum computation in alkali elements. Aiming to stabilize the IR laser for quantum computation, we study electromagnetically induced transparency (EIT) spectrum using Rydberg state in V+inverted $\Xi$ system (${5S_{1/2}}$ $\rightarrow$ ${5P_{3/2}}$ and ${5S_{1/2}}$ $\rightarrow$ ${

Xinggong Zhang, Haotian Meng, Qingyang Li, Yunpeng Tan

Malicious traffic detection is a pivotal technology for network security to identify abnormal network traffic and detect network attacks. Large Language Models (LLMs) are trained on a vast corpus of text, have amassed remarkable capabilities of context-understanding and commonsense knowledge. This has opened up a new door for network attacks detection. Resea

Takehiro Imamura, Yuka Hashizume, Wen-Chin Huang, Tomoki Toda

This paper proposes music similarity representation learning (MSRL) based on individual instrument sounds (InMSRL) utilizing music source separation (MSS) and human preference without requiring clean instrument sounds during inference. We propose three methods that effectively improve performance. First, we introduce end-to-end fine-tuning (E2E-FT) for the C

Dawit Ketema Gete, Bedru Yimam Ahmed, Tadesse Destaw Belay, Yohannes Ayana Ejigu

This work explores fine-tuning OpenAI's Whisper automatic speech recognition (ASR) model for Amharic, a low-resource language, to improve transcription accuracy. While the foundational Whisper model struggles with Amharic due to limited representation in its training data, we fine-tune it using datasets like Mozilla Common Voice, FLEURS, and the BDU-speech d

Raphael S. Steiner, Mirko De Vita, Endri Bezati

We present several algorithms to generate tables for asymmetric numeral systems and prove that they are optimal in terms of discrepancy. In turn, this gives rise to the strongest proven bound on entropy loss. We further give improved theoretical bounds for the entropy loss in tabled asymmetric numeral systems and a brief empirical evaluation of the stream va

Junyuan Gao, Jiahe Song, Jiang Wu, Runchuan Zhu

Evaluating the multilingual capabilities of Large Vision-Language Models (LVLMs) remains challenging because most benchmarks rely on non-parallel corpora, making it unclear whether cross-lingual performance gaps reflect model limitations or dataset inconsistencies. To address this, we introduce PM4Bench, the first multimodal, multilingual, multi-task benchma

Zequn Zeng, Yudi Su, Jianqiao Sun, Tiansheng Wen

Concept-based models can map black-box representations to human-understandable concepts, which makes the decision-making process more transparent and then allows users to understand the reason behind predictions. However, domain-specific concepts often impact the final predictions, which subsequently undermine the model generalization capabilities, and preve

Jian Wang, Xiaodong Zhang, Lei Ye, Xingyuan Xu

Second-order Volume-preserving algorithms (VPAs) for simulating charged particle motion in electromagnetic fields have been generalized to a rotating angle formulation by using the matrix decomposition methods. Based on this method, the phase stability of this class of VPAs has been analyzed by using the Discrete Fourier Transformations (DFT) technique. It i

Nicolò Drago, Sonia Mazzucchi, Andrea Pinamonti

This paper investigates the application of the classical Chernoff's theorem to construct explicit solutions for the heat and Schr\"odinger equations on the Heisenberg group $\mathbb{H}^d$. Using semigroup approximation techniques, we obtain analytically tractable and numerically implementable representations of fundamental solutions. In particular, we establ

Abdullah Guvendi, Omar Mustafa

We present a rigorous analysis of the relativistic dynamics of vector bosons propagating in a $(2+1)$-dimensional Bonnor-Melvin magnetic spacetime, characterized by an out-of-plane aligned magnetic field and a nonzero cosmological constant $\Lambda$. To achieve this, we derive the exact solution of the fully covariant vector boson equation corresponding to t

Elisabeth Sola, Pierre-Alain Duc, Mathias Urbano, Felix Richards

Hierarchical galactic evolution models predict that mergers drive galaxy growth, producing low surface brightness (LSB) tidal features that trace galaxies' late assembly. These faint structures encode information about past mergers and are sensitive to the properties and environment of the host galaxy. We investigated the relationships between LSB features a

Johannes Müller, Dennis Philipp, Matthias Günther

This paper introduces a novel CUDA-enabled PyTorch-based framework designed for the gradient-based optimization of such reconfigurable electromagnetic structures with electrically tunable parameters. Traditional optimization techniques for these structures often rely on non-gradient-based methods, limiting efficiency and flexibility. Our framework leverages

Xiangrui Liu, Yan Shu, Zheng Liu, Ao Li

Despite advanced token compression techniques, existing multimodal large language models (MLLMs) still struggle with hour-long video understanding. In this work, we propose Video-XL-Pro, an efficient method for extremely long video understanding, built upon Reconstructive Compression of Tokens (ReCoT), a learnable module that leverages self-supervised learni

Irina Pettersson, Antonina Rybalko, Volodymyr Rybalko

We present a derivation of a multidomain model for the electric potential in bundles of randomly distributed axons with different radii. The FitzHugh-Nagumo dynamics is assumed on the axons' membrane, and the conductivity depends nonlinearly on the electric field. Under ergodicity conditions, we study the asymptotic behavior of the potential in the bundle wh

Wei Deng, Mengshi Qi, Huadong Ma

Large Vision-Language Models (VLMs), such as GPT-4, have achieved remarkable success across various fields. However, there are few studies on 3D indoor scene generation with VLMs. This paper considers this task as a planning problem subject to spatial and layout common sense constraints. To solve the problem with a VLM, we propose a new global-local tree sea

Hoang Vu, Henrik Leopold, Han van der Aa

Many organizations strive to increase the level of automation in their business processes. While automation historically was mainly concerned with automating physical labor, current automation efforts mostly focus on automation in a digital manner, thus targeting work that is related to the interaction between humans and computers. This type of automation, c

Cameron S. Vojvodin, Sean T. Holmes, Christine E. A. Kirschhock, David A. Hirsh

New mechanochemical preparations of three multicomponent crystals (MCCs) of the form MCl:urea.nH2O (M = Li+, Na+, and Cs+) are reported. Their structures were determined by an NMR crystallography approach, combining Rietveld refinement of synchrotron powder X-ray diffraction data (PXRD), multinuclear (35Cl, 7Li, 23Na, and 133Cs) solid-state NMR (SSNMR) spect

Itai Boneh, Shiri Chechik, Shay Golan, Shay Mozes

We present a labeling scheme that assigns labels of size $\tilde O(1)$ to the vertices of a directed weighted planar graph $G$, such that for any fixed $\varepsilon>0$ from the labels of any three vertices $s$, $t$ and $f$ one can determine in $\tilde O(1)$ time a $(1+\varepsilon)$-approximation of the $s$-to-$t$ distance in the graph $G\setminus\{f\}$. For

Konstantinos Tsoupos, Stylianos Tzelepis, Georgios Sklavenitis, Dimitrios Stoupis

AcubeSAT is an open-source CubeSat mission aiming to explore the effects of microgravity and radiation on eukaryotic cells using a compact microfluidic lab-on-a-chip platform. It is developed by SpaceDot, a volunteer, interdisciplinary student team at the Aristotle University of Thessaloniki and supported by the "Fly Your Satellite! 3" program of the Europea

Mark A. Santcroos, Walter A. Kosters, Mihai Lefter, Jeroen F. J. Laros

Accurate variant descriptions are of paramount importance in the field of genomics. The domain is confronted with increasingly complex variants, e.g., combinations of multiple indels, making it challenging to generate proper variant descriptions directly from chromosomal sequences. We present a graph based on all minimal alignments that is a complete represe

Calvin Bao, Yow-Ting Shiue, Marine Carpuat, Joel Chan

Scholars often explore literature outside of their home community of study. This exploration process is frequently hampered by field-specific jargon. Past computational work often focuses on supporting translation work by removing jargon through simplification and summarization; here, we explore a different approach that preserves jargon as useful bridges to

Zhenyu Pan, Han Liu

We present MetaSpatial, the first reinforcement learning (RL)-based framework designed to enhance 3D spatial reasoning in vision-language models (VLMs), enabling real-time 3D scene generation without the need for hard-coded optimizations. MetaSpatial addresses two core challenges: (i) the lack of internalized 3D spatial reasoning in VLMs, which limits their

Daniel Yang

With the growing practical interest in vision-based tasks for autonomous systems, the need for efficient and complex methods becomes increasingly larger. In the rush to develop new methods with the aim to outperform the current state of the art, an analysis of the underlying theory is often neglected and simply replaced with empirical evaluations in simulate

Hao Ni, Lianli Gao, Pengpeng Zeng, Heng Tao Shen

Real-world surveillance systems are dynamically evolving, requiring a person Re-identification model to continuously handle newly incoming data from various domains. To cope with these dynamics, Lifelong ReID (LReID) has been proposed to learn and accumulate knowledge across multiple domains incrementally. However, LReID models need to be trained on large-sc

Clara E. Leitgeb, Robert D. Parsons, Andrew Taylor, Kenneth Ragan

The identification of gamma-ray induced air showers with Cherenkov telescopes suffers from contamination with a specific class of cosmic ray induced air showers. The predictions for this background show strong discrepancies between the available event generators. In this study, we identify collision events of cosmic rays with atmospheric nuclei in which a la