March 2025 arXiv papers — page 139

Ruggero G. Bettinardi, Mohamed Rahmouni, Ulysse Gimenez

Electroencephalography (EEG) serves as an essential diagnostic tool in neurology; however, its accurate manual interpretation is a time-intensive process that demands highly specialized expertise, which remains relatively scarce and not consistently accessible. To address these limitations, the implementation of automated pre-screening and analysis systems f

Anastasios Avgoustidis, Edmund J. Copeland, Adam Moss, Juhan Raidal

We study the stochastic gravitational wave background sourced by a network of cosmic superstrings and demonstrate that incorporating higher-mass string species, beyond the fundamental string, is crucial for accurately modelling the resulting gravitational wave spectrum across frequencies ranging from nanohertz to kilohertz. Using the multi-tension velocity-d

Zhichao Zhang

Time-frequency concentration and resolution of the Cohen's class time-frequency distribution (CCTFD) has attracted much attention in time-frequency analysis. A variety of uncertainty principles of the CCTFD is therefore derived, including the weak Heisenberg type, the Hardy type, the Nazarov type, and the local type. However, the standard Heisenberg type sti

Juntao Huang, Jiangping Hu, Zhesen Yang

In this study, we systematically explore the non-Hermitian skin effect (NHSE) and its associated complex-frequency detection in the context of a frequency-dependent non-Hermitian Hamiltonian. This Hamiltonian arises from the self-energy correction of the subsystem and can be calculated exactly within our theoretical model, without the need for any approximat

Zirun Guo, Tao Jin

Diffusion customization methods have achieved impressive results with only a minimal number of user-provided images. However, existing approaches customize concepts collectively, whereas real-world applications often require sequential concept integration. This sequential nature can lead to catastrophic forgetting, where previously learned concepts are lost.

Viktor Moskvoretskii, Alina Lobanova, Ekaterina Neminova, Chris Biemann

This paper explores the feasibility of using text-to-image models in a zero-shot setup to generate images for taxonomy concepts. While text-based methods for taxonomy enrichment are well-established, the potential of the visual dimension remains unexplored. To address this, we propose a comprehensive benchmark for Taxonomy Image Generation that assesses mode

Toni Schneidereit, Stefan Gohrenz, Michael Breuß

AI-based object detection, and efforts to explain and investigate their characteristics, is a topic of high interest. The impact of, e.g., complex background structures with similar appearances as the objects of interest, on the detection accuracy and, beforehand, the necessary dataset composition are topics of ongoing research. In this paper, we present a s

Mizuki Mori, Kouichi Takemura

Sch\"afke and Schmidt established that the asymptotics of the coefficients of the local solution to some linear differential equation is related to global structures of solutions. The Heun class equations have the accessory parameters, and we investigate the polynomials whose variable is the accessory parameter which appears as the coefficients of the local

Nevidu Jayatilleke, Ruvan Weerasinghe

Automatic patent summarization approaches that help in the patent analysis and comprehension procedure are in high demand due to the colossal growth of innovations. The development of natural language processing (NLP), text mining, and deep learning has notably amplified the efficacy of text summarization models for abundant types of documents. Summarizing p

Abdullah Tokmak, Kiran G. Krishnan, Thomas B. Schön, Dominik Baumann

Popular safe Bayesian optimization (BO) algorithms learn control policies for safety-critical systems in unknown environments. However, most algorithms make a smoothness assumption, which is encoded by a known bounded norm in a reproducing kernel Hilbert space (RKHS). The RKHS is a potentially infinite-dimensional space, and it remains unclear how to reliabl

Sinuo Liu, Chenyang Lyu, Minghao Wu, Longyue Wang

Recent advances in Large Reasoning Models (LRMs), particularly those leveraging Chain-of-Thought reasoning (CoT), have opened brand new possibility for Machine Translation (MT). This position paper argues that LRMs substantially transformed traditional neural MT as well as LLMs-based MT paradigms by reframing translation as a dynamic reasoning task that requ

Ali Salar, Qing Liu, Yingli Tian, Guoying Zhao

The rapid growth of social media has led to the widespread sharing of individual portrait images, which pose serious privacy risks due to the capabilities of automatic face recognition (AFR) systems for mass surveillance. Hence, protecting facial privacy against unauthorized AFR systems is essential. Inspired by the generation capability of the emerging diff

Sukkeun Kim, Sangwoo Moon, Ivan Petrunin, Hyo-Sang Shin

This study proposes a new Gaussian Mixture Filter (GMF) to improve the estimation performance for the autonomous robotic radio signal source search and localization problem in unknown environments. The proposed filter is first tested with a benchmark numerical problem to validate the performance with other state-of-the-practice approaches such as Particle Fi

Folkert Kuipers

The fact that quantum theory is non-differentiable, while general relativity is built on the assumption of differentiability sources an incompatibility between quantum theory and gravity. Higher order geometry addresses this issue directly by extending differential geometry, such that it can be applied to theories that are non-differentiable, but have a cert

Ning Xie, Fa Peng Huang

Through the black hole (BH) superradiance, ultralight bosons can form dense clouds around rotating Kerr BHs. Certain ultralight bosons, such as axions and axion-like particles (promising dark matter candidates), naturally possess self-interactions, and thus may significantly modify the dynamics of the superradiance process. Previous studies on the detection

Ji-Yu Song, Jing-Zhao Qi, Jing-Fei Zhang, Xin Zhang

We use 47 gravitational-wave (GW) standard sirens from the third Gravitational-Wave Transient Catalog to calibrate distances in the strong gravitational lensing (SGL) system RXJ1131-1231 and constrain the Hubble constant ($H_0$) via the distance sum rule, without assuming a specific cosmological model. For $\Omega_K = 0$, we obtain $H_0 = 73.22^{+5.95}_{-5.4

Bowen Wang, Matteo Zecchin, Osvaldo Simeone

Online conformal prediction enables the runtime calibration of a pre-trained artificial intelligence model using feedback on its performance. Calibration is achieved through set predictions that are updated via online rules so as to ensure long-term coverage guarantees. While recent research has demonstrated the benefits of incorporating prior knowledge into

Halil Mutuk

We present a comprehensive reappraisal of the in-medium properties of the rho meson using the inverse QCD sum rules (QCDSR) formalism, offering a novel, model-independent approach to studying hadronic modifications in nuclear matter. Unlike conventional QCDSR, which rely on a predefined pole+continuum structure, the inverse method reconstructs the spectral f

Qi Zhao, Zhan Ma, Pan Zhou

Recent developments in generative diffusion models have turned many dreams into realities. For video object insertion, existing methods typically require additional information, such as a reference video or a 3D asset of the object, to generate the synthetic motion. However, inserting an object from a single reference photo into a target background video rem

Aron Harder, Amar Kulkarni, Madhur Behl

The field of high-speed autonomous racing has seen significant advances in recent years, with the rise of competitions such as RoboRace and the Indy Autonomous Challenge providing a platform for researchers to develop software stacks for autonomous race vehicles capable of reaching speeds in excess of 170 mph. Ensuring the safety of these vehicles requires t

Mingyu Huang, Ji Guan, Wang Fang, Mingsheng Ying

In the current NISQ (Noisy Intermediate-Scale Quantum) era, simulating and verifying noisy quantum circuits is crucial but faces challenges such as quantum state explosion and complex noise representations, constraining simulation and equivalence checking to circuits with a limited number of qubits. This paper introduces an approximation algorithm for simula

Jie Yu, Yuan-Sen Ting, Luca Casagrande, Fan Liu

Chemical abundance anomalies in twin stars have recently been considered tell-tale signs of interactions between stars and planets. While such signals are prevalent, their nature remains a subject of debate. On one hand, exoplanet formation may induce chemical depletion in host stars by locking up refractory elements. On the other hand, exoplanet engulfment

Wen Xu, Mao-Sheng Li, Bo Li, Gui-Mei Jiao

A quantum channel is usually represented as a sum of Kraus operators. The recent study [Phys. Rev. A 98, 032328 (2018)] has shown that applying a perturbation to the Kraus operators in qubit Pauli channels, the dynamical maps exhibit interesting properties, such as non-Markovianity, singularity. This has sparked our interest in studying the properties of oth

Vivek Chari, Guanghui Qin, Benjamin Van Durme

Sequence-to-sequence tasks often benefit from long contexts, but the quadratic complexity of self-attention in standard Transformers renders this non-trivial. During generation, temporary representations -stored in the so-called KV cache-account for a large portion of GPU memory usage and scale linearly with context length. We introduce KV-Distill, a Transfo

Roy Friedman, Noa Moriel, Matthew Ricci, Guy Pelc

Characterizing the long term behavior of dynamical systems given limited measurements is a common challenge throughout the physical and biological sciences. This is a challenging task due to the sparsity and noise inherent to empirical observations, as well as the variability of possible long-term dynamics. We address this by introducing smooth prototype equ

Lun Li, Hamidreza Kasaei

In agricultural automation, inherent occlusion presents a major challenge for robotic harvesting. We propose a novel imitation learning-based viewpoint planning approach to actively adjust camera viewpoint and capture unobstructed images of the target crop. Traditional viewpoint planners and existing learning-based methods, depend on manually designed evalua

Yanis Basso-Bert, Anca Molnos, Romain Lemaire, William Guicquero

In dynamic environments where new concepts continuously emerge, Deep Neural Networks (DNNs) must adapt by learning new classes while retaining previously acquired ones. This challenge is addressed by Class-Incremental Learning (CIL). This paper introduces Generative Binary Memory (GBM), a novel CIL pseudo-replay approach which generates synthetic binary pseu

L. A. Barinov, F. Ya. Khalili

The double-pass interferometer scheme was proposed in Ref.\,[Light Sci. Appl. {\bf 7}, 11 (2018)] as the method of implementation of the quantum speed meter concept in future laser gravitational-wave (GW) detectors. Later it was shown in Ref.\,[Phys. Rev. D {\bf 110}, 062006 (2024)] that it allows to implement the new type of the optical spring that does not

Maxim Popov, Regina Kurkova, Mikhail Iumanov, Jaafar Mahmoud

Open Semantic Mapping (OSM) is a key technology in robotic perception, combining semantic segmentation and SLAM techniques. This paper introduces a dynamically configurable and highly automated LLM/LVLM-powered pipeline for evaluating OSM solutions called OSMa-Bench (Open Semantic Mapping Benchmark). The study focuses on evaluating state-of-the-art semantic

Peng Rao, Roderich Moessner, Johannes Knolle

Motivated by the appearance of Majorana fermions in a broad range of correlated and topological electronic systems, we develop a general method to compute the dynamical response of interacting Majorana fermions in the random-phase approximation (RPA). This can be applied self-consistently on top of Majorana mean-field theory (MFT) backgrounds, thereby in par

Daisy R H Smith, Silpa Muralidharan, Roland Hablutzel, Georgina Croft

Trapped-ion technology is a leading approach for scalable quantum computing. A key element of ion trapping is reliable loading of atomic sources into the trap. While thermal atomic ovens have traditionally been used for this purpose, laser ablation has emerged as a viable alternative in recent years, offering the advantages of faster and more localized loadi

Patrick Schmidt, Pavel Osinenko, Stefan Streif

This work studies robustness to system disturbance and measurement noise of some popular general practical stabilization techniques, namely, Dini aiming, optimization-based stabilization and inf-convolution stabilization. Common to all these techniques is the explicit usage of a (general nonsmooth) control Lyapunov function, thus allowing to see them as a ki

Davide Ferri, Youichi Shibukawa

Solutions to the quiver-theoretic quantum Yang-Baxter equation are associated with structure categories and structure groupoids. We prove that the structure groupoids of involutive non-degenerate solutions are Garside. This generalises a well-known result about the structure groups of set-theoretic solutions, due to Chouraqui. We also construct involutive no

Jessica Braudo, Amir Michaelis, Muhammad Akashi, Noam Soker

We conduct three-dimensional hydrodynamical simulations of core-collapse supernovae by launching several pairs of jets into a collapsing core model and show that the jittering jets explosion mechanism (JJEM) can form a point-symmetric morphology that accounts for observed morphologies of about a dozen core-collapse supernovae (CCSN) remnants. Point-symmetric

Luyao Gao, Jianchun Liu, Hongli Xu, Xichong Zhang

Speculative inference is a promising paradigm employing small speculative models (SSMs) as drafters to generate draft tokens, which are subsequently verified in parallel by the target large language model (LLM). This approach enhances the efficiency of inference serving by reducing LLM inference latency and costs while preserving generation quality. However,

Yuhao Wang, Yongfeng Lv, Pingping Zhang, Huchuan Lu

Multi-modal object Re-IDentification (ReID) aims to retrieve specific objects by utilizing complementary information from various modalities. However, existing methods focus on fusing heterogeneous visual features, neglecting the potential benefits of text-based semantic information. To address this issue, we first construct three text-enhanced multi-modal o

Samuel Santos-Pérez, Isabel Cordero-Carrión, Pablo Cerdá-Durán

Einstein equations can be written in the so-called Fully Constrained Formulation (FCF). This formulation has two different sectors: the elliptic sector, formed by the Hamiltonian and Momentum constraints together with the equations derived from the gauge choice; and the hyperbolic sector, formed by the evolution of the rest of the spacetime metric variables,

Haoxuan Li, Sixu Yan, Yuhan Li, Xinggang Wang

Vision Language Models (VLMs) pretrained on Internet-scale vision-language data have demonstrated the potential to transfer their knowledge to robotic learning. However, the existing paradigm encounters three critical challenges: (1) expensive inference cost resulting from large-scale model parameters, (2) frequent domain shifts caused by mismatched data mod

Julian Wykowski

We prove that finitely generated free metabelian groups $\Psi_n$ are profinitely rigid in the absolute sense: they are distinguished by their finite quotients among all finitely generated residually finite groups. The proof is based on a previous result of the author governing profinite rigidity for modules over Noetherian domains, as well as a homological c

Luca Manzoni, Luca Mariot, Giuliamaria Menara

Cellular Automata (CA) are commonly investigated as a particular type of dynamical systems, defined by shift-invariant local rules. In this paper, we consider instead CA as algebraic systems, focusing on the combinatorial designs induced by their short-term behavior. Specifically, we review the main results published in the literature concerning the construc

Serban Belinschi, Bartosz Kołodziejek, Kamil Szpojankowski

We study the free analogue of the classical affine fixed-point (or perpetuity) equation \[ \mathbb{X} \stackrel{d}{=} \mathbb{A}^{1/2}\mathbb{X}\,\mathbb{A}^{1/2} + \mathbb{B}, \] where $\mathbb{X}$ is assumed to be $*$-free from the pair $(\mathbb{A},\mathbb{B})$, with $\mathbb{A}\ge 0$ and $\mathbb{B}=\mathbb{B}^*$. Our analysis covers both the subcritical

Duc Kien Doan, Bang Giang Le, Viet Cuong Ta

In safe reinforcement learning, agent needs to balance between exploration actions and safety constraints. Following this paradigm, domain transfer approaches learn a prior Q-function from the related environments to prevent unsafe actions. However, because of the large number of false positives, some safe actions are never executed, leading to inadequate ex

Giorgi Asatiani, Arthur Gautheron, Laurent Mahieu-Williame, Hélène Ratiney

Glioblastoma exhibits significant metabolic alterations, making tracking energy metabolism important for its characterization. This could be crucial for glioblastoma resection in neurosurgery. We link two NADH monitoring methods, showing linear dependence on phantom concentrations.

Kapila W. S. Palitharathna, Christodoulos Skouroumounis, Ioannis Krikidis

In this paper, we consider a tunable liquid convex lens-assisted imaging receiver for indoor multiple-input multiple-output (MIMO) visible light communication (VLC) systems. In contrast to existing MIMO VLC receivers that rely on fixed optical lenses, the proposed receiver leverages the additional degrees of freedom offered by liquid lenses via adjusting bot

Xiaozhao Chen, Xiaofu Lü, Xiurong Guo, Zonghua Shi

In experiments exotic meson resonance $T_{c\bar c}(4020)$ lies above the $D^{*}\bar{D}^{*}$ threshold, and in principle one can not explain $T_{c\bar c}(4020)$ as a meson-meson bound state because meson-meson bound state must lie below the $D^{*}\bar{D}^{*}$ threshold. In this work, exotic resonance $T_{c\bar c}(4020)$ is considered as an unstable meson-meso

Nima Azizi, Wolfgang Dornisch

In this paper, we propose a geometrically nonlinear spectral shell element based on Reissner--Mindlin kinematics using a rotation-based formulation with additive update of the discrete nodal rotation vector. The formulation is provided in matrix notation in detail. The use of a director vector, as opposed to multi-parameter shell models, significantly reduce

Arpan Kanrar, Charlotte Roelants, Manoj K. Yadav

The aim of this article is to advance the knowledge on the theory of skew left braces. We introduce a subclass of skew left braces, which we denote by $\mathcal{I}_n$, $n \ge 1$, such that elements of the annihilator and lower central series' interact `nicely' with respect to commutation. That allows us to define a concept of $n$-isoclinism of skew left brac

Theo Di Piazza, Loic Boussel

Early detection of cervical cancer is crucial for improving patient outcomes and reducing mortality by identifying precancerous lesions as soon as possible. As a result, the use of pap smear screening has significantly increased, leading to a growing demand for automated tools that can assist cytologists managing their rising workload. To address this, the P

Yoshihito Kuno, Takahiro Orito, Ikuo Ichinose

Discovering and categorizing quantum orders in mixed many-body systems are currently one of the most important problems. Target model in this study is an extended version of the cluster model in one dimension with $Z_2\otimes Z_2$ symmetry, and we investigate effects of decoherence applied to the ground state of the model, focusing on the symmetry aspect. By

Shin Yoo, Robert Feldt, Somin Kim, Naryeong Kim

ML-based systems are software systems that incorporates machine learning components such as Deep Neural Networks (DNNs) or Large Language Models (LLMs). While such systems enable advanced features such as high performance computer vision, natural language processing, and code generation, their internal behaviour remain largely opaque to traditional dynamic a

Elspeth K. H. Lee

Observational data provided by JWST instruments continue to challenge theories and models of cloud formation in sub-stellar atmospheres, requiring more sophisticated approaches in an effort to understand their spatial complexity. However, to date, most cloud microphysical models using the moment method for sub-stellar atmospheres have assumed a monodisperse

Hansveer Singh, Romain Vasseur, Andrew C. Potter, Sarang Gopalakrishnan

We consider learnability transitions in monitored quantum systems that undergo noisy evolution, subject to a global strong symmetry -- i.e., in addition to the measuring apparatus, the system can interact with an unobserved environment, but does not exchange charge with it. As in the pure-state setting, we find two information-theoretic phases -- a sharp (fu

Georgy Ponimatkin, Martin Cífka, Tomáš Souček, Médéric Fourmy

We seek to extract a temporally consistent 6D pose trajectory of a manipulated object from an Internet instructional video. This is a challenging set-up for current 6D pose estimation methods due to uncontrolled capturing conditions, subtle but dynamic object motions, and the fact that the exact mesh of the manipulated object is not known. To address these c

Tolgahan Bardakci, Serge Demeyer, Mutlu Beyazit

REST APIs (Representational State Transfer Application Programming Interfaces) are an indispensable building block in today's cloud-native applications, so testing them is critically important. However, writing automated tests for such REST APIs is challenging because one needs strong and readable tests that exercise the boundary values of the protocol embed

Emil Mededovic, Yuli Wu, Henning Konermann, Marcin Kopaczka

Analyzing animal behavior from video recordings is crucial for scientific research, yet manual annotation remains labor-intensive and prone to subjectivity. Efficient segmentation methods are needed to automate this process while maintaining high accuracy. In this work, we propose a novel pipeline that utilizes eye-tracking data from Aria glasses to generate

Zhiyu Mou, Miao Xu, Rongquan Bai, Zhuoran Yang

Auto-bidding has become a cornerstone of modern online advertising platforms, enabling many advertisers to automate bidding at scale and optimize campaign performance. However, prevailing industrial systems rely on single-agent auto-bidding methods that are scalable but overlook the strategic interdependence among advertisers' bids, leading to unstable or su

Yury A. Budkov, Nikolai N. Kalikin, Petr E. Brandyshev

Accurately describing liquids and their mixtures beyond equilibrium remains a significant challenge in modern chemical physics and physical chemistry, especially regarding the calculation of transport properties in liquid-phase systems. This paper introduces a phenomenological nonequilibrium theory specifically designed for multicomponent liquid-phase soluti

Shuvro Chowdhury, Navid Anjum Aadit, Andrea Grimaldi, Eleonora Raimondo

Recent demonstrations on specialized benchmarks have reignited excitement for quantum computers, yet whether they can deliver an advantage for practical real-world problems remains an open question. Here, we show that probabilistic computers (p-computers), when co-designed with hardware to implement powerful Monte Carlo algorithms, provide a compelling and s

Moreno La Quatra, Juan Rafael Orozco-Arroyave, Marco Sabato Siniscalchi

This work aims to tackle the Parkinson's disease (PD) detection problem from the speech signal in a bilingual setting by proposing an ad-hoc dual-head deep neural architecture for type-based binary classification. One head is specialized for diadochokinetic patterns. The other head looks for natural speech patterns present in continuous spoken utterances. On

José Galaz, Maria Kazolea, Antoine Rousseau

We derive a new approach to analyze the coupling of linear Boussinesq and Saint-Venant shallow water wave equations in the case where the interface remains at a constant position in space. We propose a one-way coupling model as a reference, which allows us to obtain an analytical solution, prove the well-posedness of the original coupled model and compute wh

Zhe Liu, Hao Xu, Xiang Liu

In this work, we systematically study the interactions of the $S$-wave $D^{(*)}\bar{B}^{(*)}$ systems within the framework of chiral effective field theory in heavy hadron formalism. We calculate the $D^{(*)}\bar{B}^{(*)}$ effective potentials up to next-to-leading order, explore the bound state formations, and investigate the $D^{(*)}\bar{B}^{(*)}$ scatteri

Md Mamunur Rahaman, Ewan K. A. Millar, Erik Meijering

Zero-shot learning holds tremendous potential for histopathology image analysis by enabling models to generalize to unseen classes without extensive labeled data. Recent advancements in vision-language models (VLMs) have expanded the capabilities of ZSL, allowing models to perform tasks without task-specific fine-tuning. However, applying VLMs to histopathol

Peyman Neshaastegaran, Ming Jian

Generative models, including denoising diffusion models (DM), are gaining attention in wireless applications due to their ability to learn complex data distributions. In this paper, we propose CoDiPhy, a novel framework that leverages conditional denoising diffusion models to address a wide range of wireless physical layer problems. A key challenge of using

Longfei Han, Klaus Kefferpütz, Jürgen Beyerer

Object tracking is an essential task for autonomous systems. With the advancement of 3D sensors, these systems can better perceive their surroundings using effective 3D Extended Object Tracking (EOT) methods. Based on the observation that common road users are symmetrical on the right and left sides in the traveling direction, we focus on the side view profi

Dejan Milojevic, Gioele Zardini, Miriam Elser, Andrea Censi

This paper discusses the integration challenges and strategies for designing mobile robots, by focusing on the task-driven, optimal selection of hardware and software to balance safety, efficiency, and minimal usage of resources such as costs, energy, computational requirements, and weight. We emphasize the interplay between perception and motion planning in

Jia Zhou, Jørgen Bang-Jensen, Jin Yan

A directed graph (digraph) $ D $ is $ k $-linked if $ |D| \geq 2k $, and for any $ 2k $ distinct vertices $ x_1, \ldots, x_k, y_1, \ldots, y_k $ of $ D $, there exist vertex-disjoint paths $ P_1, \ldots, P_k $ such that $ P_i $ is a path from $ x_i $ to $ y_i $ for each $ i \in [k] $. In 1980, Thomassen conjectured that there exists a function $ f(k) $ such

Hsuvas Borkakoty, Luis Espinosa-Anke

Automated content moderation for collaborative knowledge hubs like Wikipedia or Wikidata is an important yet challenging task due to multiple factors. In this paper, we construct a database of discussions happening around articles marked for deletion in several Wikis and in three languages, which we then use to evaluate a range of LMs on different tasks (fro

Nenad Milošević, Daniel Cason, Zarko Milošević, Robert Soulé

Synchronous consensus protocols offer a significant advantage over their asynchronous and partially synchronous counterparts by providing higher fault tolerance -- an essential benefit in distributed systems, like blockchains, where participants may have incentives to act maliciously. However, despite this advantage, synchronous protocols are often met with

Weiyun Wang, Zhangwei Gao, Lianjie Chen, Zhe Chen

We introduce VisualPRM, an advanced multimodal Process Reward Model (PRM) with 8B parameters, which improves the reasoning abilities of existing Multimodal Large Language Models (MLLMs) across different model scales and families with Best-of-N (BoN) evaluation strategies. Specifically, our model improves the reasoning performance of three types of MLLMs and

Saikat Nandi, Monika Jawale, Sanjay Bachhar, Rahul Kumar

Spin-1/2 Heisenberg antiferromagnetic frustrated spin chain systems display exotic ground states with unconventional excitations and distinct quantum phase transitions as the ratio of next-nearest-neighbor to nearest-neighbor coupling is tuned. We present a comprehensive investigation of the structural, magnetic, and thermodynamics properties of the spin-1/2

Emily C. Ehrhardt, Hanno Gottschalk, Tobias J. Riedlinger

NeuralODE is one example for generative machine learning based on the push forward of a simple source measure with a bijective mapping, which in the case of NeuralODE is given by the flow of a ordinary differential equation. Using Liouville's formula, the log-density of the push forward measure is easy to compute and thus NeuralODE can be trained based on th

Zebin He, Mingxin Yang, Shuhui Yang, Yixuan Tang

Physically-based rendering (PBR) has become a cornerstone in modern computer graphics, enabling realistic material representation and lighting interactions in 3D scenes. In this paper, we present MaterialMVP, a novel end-to-end model for generating PBR textures from 3D meshes and image prompts, addressing key challenges in multi-view material synthesis. Our

Davide Bernardi, Giorgio Nicoletti, Prajwal Padmanabha, Samir Suweis

We develop a theoretical framework to understand the persistence and coexistence of competitive species in a spatially explicit metacommunity model with a heterogeneous dispersal kernel. Our analysis, based on methods from the physics of disordered systems and non-Gaussian dynamical mean field theory, reveals that species coexistence is governed by a single

Hao Zhou, Xiaobao Guo, Yuzhe Zhu, Adams Wai-Kin Kong

Propelled by the breakthrough in deep generative models, audio-to-image generation has emerged as a pivotal cross-modal task that converts complex auditory signals into rich visual representations. However, previous works only focus on single-source audio inputs for image generation, ignoring the multi-source characteristic in natural auditory scenes, thus l

Zhiqi Li, Chengrui Dong, Yiming Chen, Zhangchi Huang

We present VicaSplat, a novel framework for joint 3D Gaussians reconstruction and camera pose estimation from a sequence of unposed video frames, which is a critical yet underexplored task in real-world 3D applications. The core of our method lies in a novel transformer-based network architecture. In particular, our model starts with an image encoder that ma

Peter B Sorensen, Anders Nielsen, Peter E Holm, Poul L Bjerg

Accurate prediction of expected concentrations is essential for effective catchment management, requiring both extensive monitoring and advanced modeling techniques. However, due to limitations in the equation solving capacity, the integration of monitoring and modeling has been suffering suboptimal statistical approaches. This limitation results in models t

Zhen Zhang, Meihan Liu, Bingsheng He

Graph domain adaptation has emerged as a promising approach to facilitate knowledge transfer across different domains. Recently, numerous models have been proposed to enhance their generalization capabilities in this field. However, there is still no unified library that brings together existing techniques and simplifies their implementation. To fill this ga

Morimichi Kawasaki, Mitsuaki Kimura, Shuhei Maruyama, Takahiro Matsushita

Given a closed connected symplectic manifold $(M,\omega)$, we construct an alternating $\mathbb{R}$-bilinear form $\mathfrak{b}=\mathfrak{b}_{\mu_{\mathrm{Sh}}}$ on the real first cohomology of $M$ from Shelukhin's quasimorphism $\mu_{\mathrm{Sh}}$. Here $\mu_{\mathrm{Sh}}$ is defined on the universal cover of the group of Hamiltonian diffeomorphisms on $(M,

Samih Karroum, Saad Mazhar

This paper explores the application of bandit algorithms in both stochastic and adversarial settings, with a focus on theoretical analysis and practical applications. The study begins by introducing bandit problems, distinguishing between stochastic and adversarial variants, and examining key algorithms such as Explore-Then-Commit (ETC), Upper Confidence Bou

Shinnosuke Koyama, Joji Nasu

The spin Nernst effect, an antisymmetric response of a spin current to a temperature gradient, has attracted attention as spin transport phenomenon arising from the topologically nontrivial band structure of carriers. This effect can occur not only in itinerant electron systems but also in localized electron systems that emerge due to electronic correlations

Esben Kran, Hieu Minh "Jord" Nguyen, Akash Kundu, Sami Jawhar

We introduce DarkBench, a comprehensive benchmark for detecting dark design patterns--manipulative techniques that influence user behavior--in interactions with large language models (LLMs). Our benchmark comprises 660 prompts across six categories: brand bias, user retention, sycophancy, anthropomorphism, harmful generation, and sneaking. We evaluate models

Ali Elkeshawy, Haifa Fares, Amor Nafkha

Massive MIMO is considered a key enabler to support massive machine-type communication (mMTC). While massive access schemes have been extensively analyzed for co-located massive MIMO arrays, this paper explores activity detection in grant-free random access for mMTC within the context of cell-free massive MIMO systems, employing distributed antenna arrays. T

Nicholas Krämer, Filip Tronarp

This article proposes numerically robust algorithms for Gaussian state estimation with singular observation noise. Our approach combines a series of basis changes with Bayes' rule, transforming the singular estimation problem into a nonsingular one with reduced state dimension. In addition to ensuring low runtime and numerical stability, our proposal facilit

Thomas Cory, Wolf Rieder, Julia Krämer, Philip Raschke

Ensuring transparency of data practices related to personal information is a core requirement of the General Data Protection Regulation (GDPR). However, large-scale compliance assessment remains challenging due to the complexity and diversity of privacy policy language. Manual audits are labour-intensive and inconsistent, while current automated methods ofte

Aristides Moustakas, Shiri Zemah-Shamir, Mirela Tase, Savvas Zotos

Islands are diversity hotspots and vulnerable to environmental degradation, climate variations, land use changes and societal crises. These factors can exhibit interactive impacts on ecosystem services. The study reviewed a large number of papers on the climate change-islands-ecosystem services topic worldwide. Potential inclusion of land use changes and oth

Fengchun Liu, Linghan Cai, Zhikang Wang, Zhiyuan Fan

Histo-genomic multimodal survival prediction has garnered growing attention for its remarkable model performance and potential contributions to precision medicine. However, a significant challenge in clinical practice arises when only unimodal data is available, limiting the usability of these advanced multimodal methods. To address this issue, this study pr

Juan Cumbrado, Ricardo Puebla

The interconnection of quantum nodes holds great promise for scaling up quantum computing units and enabling information processing across long-distance quantum registers. Such quantum networks can be realized using superconducting qubits linked by waveguides, which facilitate fast and robust on-demand quantum information exchange via traveling single photon

S. Stalin, M. Lakshmanan

In recent times, bound soliton states have often been referred to as soliton molecules in the nonlinear optics literature. The striking analogies between photonic bound states and matter molecular structures in chemistry and physics have intensified studies on optical soliton molecules in both conservative and dissipative systems. In this paper, we demonstra

Yangfan He, Zhichao Zhang

This paper devotes to combine the chirp basis function transformation and symplectic coordinates transformation to yield a novel Wigner distribution (WD) associated with the linear canonical transform (LCT), named as the symplectic WD in the LCT domain (SWDL). It incorporates the merits of the symplectic WD (SWD) and the WD in the LCT domain (WDL), achieving

Kui Li, Mengyao Liu, Jianfeng Wu

We study the weighted elliptic equation \begin{equation} -div(|x|^{-2a}\nabla u)=|x|^{-bp}|u|^{p-2}u~~~\mbox{in}~\mathbb{R}^N ~~~~~~~~~~~~~~~~~~~~(0.1)\end{equation} with $N\geq 2$, which arises from the Caffarelli-Kohn-Nirenberg inequalities. Under the assumptions of finite energy and $a_1+a_2=N-2$, for nonnegative solutions we prove the equivalence between

Michal Bechny, Marco Scutari, Julia van der Meer, Francesca Faraci

Chronic Fatigue Syndrome (CFS) and Fibromyalgia (FM) often co-occur as medically unexplained conditions linked to disrupted physiological regulation, including altered sleep. Building on the work of Kishi et al. (2011), who identified differences in sleep-stage transitions in women with CFS and CFS+FM, we exploited the same strictly controlled clinical cohor

Zexuan Yan, Yue Ma, Chang Zou, Wenteng Chen

Inversion-based image editing is rapidly gaining momentum while suffering from significant computation overhead, hindering its application in real-time interactive scenarios. In this paper, we rethink that the redundancy in inversion-based image editing exists in both the spatial and temporal dimensions, such as the unnecessary computation in unedited region

Wassim Bouaziz, El-Mahdi El-Mhamdi, Nicolas Usunier

Protecting the use of audio datasets is a major concern for data owners, particularly with the recent rise of audio deep learning models. While watermarks can be used to protect the data itself, they do not allow to identify a deep learning model trained on a protected dataset. In this paper, we adapt to audio data the recently introduced data taggants appro

Yanzhe Qiu, Zhen He, Mei Lu, Yiduo Xu

A graph $G$ is said to be $F$-free, if $G$ does not contain any copy of $F$. $G$ is said to be $F$-semi-saturated, if the addition of any nonedge $e \not \in E(G)$ would create a new copy of $F$ in $G+e$. $G$ is said to be $F$-saturated, if $G$ is $F$-free and $F$-semi-saturated. The saturation number $sat(n,F)$ (resp. semi-saturation number $ssat(n,F)$) is

Laurie Burchell, Ona de Gibert, Nikolay Arefyev, Mikko Aulamo

Training state-of-the-art large language models requires vast amounts of clean and diverse textual data. However, building suitable multilingual datasets remains a challenge. In this work, we present HPLT v2, a collection of high-quality multilingual monolingual and parallel corpora, extending prior work of the HPLT project. The monolingual portion of the da

Edoh Katchekpele, Issa Cherif Geraldo, Tchilabalo Abozou Kpanzou

Transmutation is a technique for extending classical probability distributions in order to give them more flexibility. In this paper, we are interested in cubic transmutations of the Pareto distribution. We establish a general formula that unifies existing cubic transmutations of the Pareto distribution and facilitates the derivation of new cubic transmutati

Chang Han Low, Ziyue Wang, Tianyi Zhang, Zhu Zhuo

Robotic-assisted surgery (RAS) is central to modern surgery, driving the need for intelligent systems with accurate scene understanding. Most existing surgical AI methods rely on isolated, task-specific models, leading to fragmented pipelines with limited interpretability and no unified understanding of RAS scene. Vision-Language Models (VLMs) offer strong z

Chi-Lan Yang, Alarith Uhde, Naomi Yamashita, Hideaki Kuzuoka

While peer review enhances writing and research quality, harsh feedback can frustrate and demotivate authors. Hence, it is essential to explore how critiques should be delivered to motivate authors and enable them to keep iterating their work. In this study, we explored the impact of appending an automatically generated positive summary to the peer reviews o

Christian Sendlinger, Jonas Kellerer, Felix Spanier

A new parallelized simulation code is presented, which uses a Monte Carlo method to determine particle spectra in the KATRIN source. Reaction chains are generated from the decay of tritium within the source. The code includes all relevant processes: elastic scattering, ionization, excitation (electric, vibrational, rotational), recombination and various clus

Barış Büyüktaş, Gencer Sumbul, Begüm Demir

Federated learning (FL) enables the collaborative training of deep neural networks across decentralized data archives (i.e., clients) without sharing the local data of the clients. Most of the existing FL methods assume that the data distributed across all clients is associated with the same data modality. However, remote sensing (RS) images present in diffe