November 2024 arXiv papers — page 22

Riza Velioglu, Petra Bevandic, Robin Chan, Barbara Hammer

This paper introduces Virtual Try-Off (VTOFF), a novel task generating standardized garment images from single photos of clothed individuals. Unlike Virtual Try-On (VTON), which digitally dresses models, VTOFF extracts canonical garment images, demanding precise reconstruction of shape, texture, and complex patterns, enabling robust evaluation of generative

Jun-Ting Ye, Rui Wang, Si-Pei Wang, Lie-Wen Chen

The recently developed nuclear effective interaction based on the so-called N3LO Skyrme pseudopotential is extended to include the hyperon-nucleon and hyperon-hyperon interactions by assuming the similar density, momentum, and isospin dependence as for the nucleon-nucleon interaction. The parameters in these interactions are determined from either experiment

Shalin Jose, Akshay Kannan Sairam, Anil Shaji

A path for efficient classical simulation of the DQC1 circuit that estimates the trace of an implementable unitary under the zero discord condition [Phys. Rev. Lett. 105, 190502 (2010)] is presented. This result reinforces the status of non-classical correlations quantified by quantum discord and related measures as the key resource enabling exponential spee

Siyuan Li, Yuekang Li, Zuxin Chen, Chaopeng Dong

Code reuse in software development frequently facilitates the spread of vulnerabilities, making the scope of affected software in CVE reports imprecise. Traditional methods primarily focus on identifying reused vulnerability code within target software, yet they cannot verify if these vulnerabilities can be triggered in new software contexts. This limitation

Lars Fischer, Bianca Giaccone, Ivan Gonin, Anna Grassellino

Heterodyne detection using microwave cavities is a promising method for detecting high-frequency gravitational waves or ultralight axion dark matter. In this work, we report on studies conducted on a spherical 2-cell cavity developed by the MAGO collaboration for high-frequency gravitational waves detection. Although fabricated around 20 years ago, the cavit

Sergey Krivonos, Armen Nersessian

In the present paper we constructed the supercharges and Hamiltonians for all variants of superconformal mechanics associated with the superalgebras $osp(8|2), {\mathfrak F(4)}, osp(4^\star |4)$, and $su(1,1|4)$. The fermionic and bosonic fields involved were arranged into generators spanning $so(8), so(7), so(5)\oplus su(2)$ and $su(4) \oplus u(1)$ $R$-symm

Gang Liao, Shirou Wang

In the study of non-equilibrium statistical mechanics, Ruelle derived explicit formulae for entropy production of smooth dynamical systems. The vanishing or strict positivity of entropy production is determined by the {\it entropy formula of folding type} \[h_{\mu}(f)= F_{\mu}(f)-\displaystyle\int\sum\nolimits_{\lambda_i(x)<0} \lambda_i(x)d\mu(x), \] which r

Julius Schlegel, Martin Evers, Ulrich Nowak

Spin noise spectroscopy is a technique to measure magnetization fluctuations, a subject of increasing relevance in ultrafast spintronics. We investigate numerically the equilibrium spin noise of ferro- and antiferromagnets within an atomistic spin model. The aim is to predict the possible outcomes of ultrafast spin-noise spectroscopy measurements and demonst

Jan Wingenbach, David Bauch, Xuekai Ma, Robert Schade

In this work, we introduce PHOENIX, a highly optimized explicit open-source solver for two-dimensional nonlinear Schr\"odinger equations with extensions. The nonlinear Schr\"odinger equation and its extensions (Gross-Pitaevskii equation) are widely studied to model and analyze complex phenomena in fields such as optics, condensed matter physics, fluid dynami

Mats Boij, Anthony Iarrobino, Leila Khatami

The authors here show that the partition $P_{k,l}(Q)$ in the table $\mathcal T(Q)$ of partitions having maximal nilpotent commutator a given stable partition $Q$, defined in [IKVZ2], is identical to the analogous partition $P_{k,l}^Q$ defined by the authors in [BIK] using the Burge correspondence.

Esfandiar Nava-Yazdani

We propose a natural intrinsic extension of ridge regression from Euclidean spaces to general Riemannian manifolds for time-series prediction. Our approach combines Riemannian least-squares fitting via B\'ezier curves, empirical covariance on manifolds, and Mahalanobis distance regularization. A key technical contribution is an explicit formula for the gradi

Akihisa Koga, Shiro Sakai

We investigate magnetic properties of the ferromagnetic Ising model on square-triangle tilings to explore how the hyperuniformity, which characterizes long-range behavior of the point pattern, influences critical phenomena where long-range correlations play a crucial role. The square-triangle tilings are spatially random structures in two dimensions construc

T. G. D. K. Sumanathilaka, Nicholas Micallef, Julian Hough

Ambiguous words are often found in modern digital communications. Lexical ambiguity challenges traditional Word Sense Disambiguation (WSD) methods, due to limited data. Consequently, the efficiency of translation, information retrieval, and question-answering systems is hindered by these limitations. This study investigates the use of Large Language Models (

Tobias Black

We consider an initial-boundary value problem for the chemotaxis-Navier--Stokes system \begin{align*} \left\{ \begin{array}{c@{\quad}l@{\quad}l@{\,}c} n_{t}+u\cdot\nabla n=\nabla\cdot\big(D(n)\nabla n-nS(x,n,c)\cdot\nabla c\big),\ &x\in\Omega,& t>0,\\ c_{t}+u\cdot\nabla c=\Delta c-cn,\ &x\in\Omega,& t>0,\\ u_{t}+(u\cdot\nabla)u=\Delta u+\nabla P+n\nabla\Phi,

Mehdi Zayene, Jannik Endres, Albias Havolli, Charles Corbière

Despite progress in stereo depth estimation, omnidirectional imaging remains underexplored, mainly due to the lack of appropriate data. We introduce Helvipad, a real-world dataset for omnidirectional stereo depth estimation, featuring 40K video frames from video sequences across diverse environments, including crowded indoor and outdoor scenes with various l

Florent Afsa

The aim of this article is to study certain categorical-algebraic frameworks for basic homological algebra, introduced in arXiv:2404.15896, with the aim of better understanding the differences between them. We focus on homological self-duality, preservation of normal maps by dinversion and diexactness, finding counterexamples that separate any two of these c

Naim Ahmad, Chirag Kalouni, Abhay Singh Rajawat, Waseem Akhtar

Skyrmions as well as skyrmion bags in magnetic thin films are promising candidates for future high-density memory devices. The observation of skyrmion bags in liquid crystals and their predicted existence in ferromagnetic films has sparked theoretical studies on current induced dynamics of these topological charges. Here using micromagnetism, we study the im

Reinhard K. Kremer, Sebastian Bette, Jürgen Nuss, Pascal Puphal

By single crystal diffraction we characterize the chemo-structural disorder introduced by Zn-Cu site mixing in the kagom\'e spin $S$-1/2 systems herbertsmithite ZnCu$_3$(OH)$_6$Cl$_2$ and YCu$_3$(OH)$_{6}$Br$_{2}$[Br$_x$(OH)$_{1-x}$]. For an untwinned single crystal of herbertsmithite of composition Zn$_{0.95(1)}$Cu$_{2.99(3)}$O$_{5.9(1)}$H$_{5.8(1)}$Cl$_2$

Tim Thyzel

The resolution of low-frequency resistance noise measurements can be increased by amplitude modulation, shifting the spectrum of the resistance fluctuations away from the 1/f noise contributed by measurement instruments. However, commercial lock-in amplifiers used for de-modulating the fluctuations exhibit a problematic 1/f noise contribution, which imposes

Jian Shi, Xuan Wang, Chang Meng, Weikang Qian

Approximate computing is a new computing paradigm. One important area of it is designing approximate circuits for FPGA. Modern FPGAs support dual-output LUT, which can significantly reduce the area of FPGA designs. Several existing works explored the use of dual-output in approximate computing. However, they are limited to small-scale arithmetic circuits. To

Jiayi Cong, Guoliang Cheng, Changsheng You, Xinyu Huang

In this paper, we investigate a resource allocation and model retraining problem for dynamic wireless networks by utilizing incremental learning, in which the digital twin (DT) scheme is employed for decision making. A two-timescale framework is proposed for computation resource allocation, mobile user association, and incremental training of user models. To

Meiqi Cao, Xiangbo Shu, Jiachao Zhang, Rui Yan

Event-based Action Recognition (EAR) possesses the advantages of high-temporal resolution capturing and privacy preservation compared with traditional action recognition. Current leading EAR solutions typically follow two regimes: project unconstructed event streams into dense constructed event frames and adopt powerful frame-specific networks, or employ lig

Thomas Jakobsche, Florina M. Ciorba

HPC systems face security and compliance challenges, particularly in preventing waste and misuse of computational resources by unauthorized or malicious software that deviates from allocation purpose. Existing methods to classify applications based on job names or resource usage are often unreliable or fail to capture applications that have different behavio

Nicolas Billot, Stephan Hellmich, Willy Benz, Andrea Fortier

The CHaracterising ExOPlanet Satellite (CHEOPS) is a partnership between the European Space Agency and Switzerland with important contributions by 10 additional ESA member States. It is the first S-class mission in the ESA Science Programme. CHEOPS has been flying on a Sun-synchronous low Earth orbit since December 2019, collecting millions of short-exposure

Debika Debnath, Paramita Dutta

We investigate chiral quantum dot (QD)-based Josephson junction and show the correlation-induced Josephson diode effect (JDE) in it. The presence of electron-electron interaction spontaneously creates an imbalance between up- and down-spin electrons during the non-equilibrium transport making the QD effectively magnetic. The simultaneous presence of the chir

Luis Eduardo Pessoa, Cristovao Freitas Iglesias, Claudio Miceli

Designing resilient Internet of Things (IoT) systems requires i) identification of IoT Critical Objects (ICOs) such as services, devices, and resources, ii) threat analysis, and iii) mitigation strategy selection. However, the traditional process for designing resilient IoT systems is still manual, leading to inefficiencies and increased risks. In addition,

Jian-Peng Wang, Qin Qin, Fu-Sheng Yu

The era of baryon physics is on the horizon with the accumulation of increasing data by collaborations such as LHCb, Belle II, and BESIII. Despite the wealth of data, one of the critical issues in flavor physics, namely CP violation in baryon decays, still awaits experimental confirmation. It is evident that the development of formulas and phenomenological a

Mathurin Videau, Alessandro Leite, Marc Schoenauer, Olivier Teytaud

Mixture-of-Experts (MoE) models have shown promising potential for parameter-efficient scaling across domains. However, their application to image classification remains limited, often requiring billion-scale datasets to be competitive. In this work, we explore the integration of MoE layers into image classification architectures using open datasets. We cond

Lara Scavuzzo, Karen Aardal, Neil Yorke-Smith

Modern Mixed Integer Linear Programming (MILP) solvers use the Branch-and-Bound algorithm together with a plethora of auxiliary components that speed up the search. In recent years, there has been an explosive development in the use of machine learning for enhancing and supporting these algorithmic components. Within this line, we propose a methodology for p

Geoffrey Tyndall, Kurniawati Azizah, Dipta Tanaya, Ayu Purwarianti

Continual learning for automatic speech recognition (ASR) systems poses a challenge, especially with the need to avoid catastrophic forgetting while maintaining performance on previously learned tasks. This paper introduces a novel approach leveraging the machine speech chain framework to enable continual learning in ASR using gradient episodic memory (GEM).

Yiming Lei, Federico De Marchi, Jialong Li, Raj Joshi

Optical data center networks (DCNs) are emerging as a promising design for cloud infrastructure. However, existing optical DCN architectures operate as closed ecosystems, tying software solutions to specific optical hardware. We introduce OpenOptics, an open research framework that decouples software from hardware, allowing them to evolve independently. Open

Julius P. J. Krebbekx, Roland Tóth, Amritam Das

Scaled Relative Graphs (SRGs) provide a novel graphical frequency-domain method for the analysis of nonlinear systems. However, we show that the current SRG analysis suffers from a pitfall that limit its applicability in analyzing practical nonlinear systems. We overcome this pitfall by modifying the SRG of a linear time invariant operator, combining the SRG

Brycen D. Pearl, Logan P. Gold, Hang Woon Lee

Tropical cyclones (TCs) are highly dynamic natural disasters that travel vast distances and occupy a large spatial scale, leading to loss of life, economic strife, and destruction of infrastructure. The severe impact of TCs makes them crucial to monitor such that the collected data contributes to forecasting their trajectory and severity, as well as the prov

Ángel Luis Muñoz Castañeda, Noemí Decastro-García, Miguel V. Carriegos

Observable convolutional codes defined over Zpr with the Predictable Degree Property admit minimal input/state/output representations that preserve structural properties under scalar restriction. We make use of this fact to present Rosenthal's decoding algorithm for these convolutional codes. When combined with the Greferath-Vellbinger algorithm and a modifi

Sylwia Gutowska, Karolina Górnicka, Paweł Wójcik, Tomasz Klimczuk

This paper presents theoretical and experimental studies on the superconductivity of Pb${_{0.64}}$Bi$_{0.36}$ alloy, which is a prototype of strongly coupled superconductors and exhibits one of the strongest coupling under ambient pressure among the materials studied so far. The critical temperature, the specific heat in the superconducting state, and the ma

Chaoyi Tan, Xiangtian Li, Xiaobo Wang, Zhen Qi

Thispaperaimstoresearchandimplementa real-timevideotargettrackingalgorithmbasedon ConvolutionalNeuralNetworks(CNN),enhancingthe accuracyandrobustnessoftargettrackingincomplex scenarios.Addressingthelimitationsoftraditionaltracking algorithmsinhandlingissuessuchastargetocclusion,morphologicalchanges,andbackgroundinterference,our approachintegratestargetdetect

Francesco Toppan

In this paper I present the state of the art concerning the theoretical detectability (and the open challenges for the experimental detectability) of a special class of paraparticles beyond bosons and fermions. The particles under considerations, obeying a parastatistics, are mutually exchanged via the permutation group and can exist in any space dimension (

Shucheng Chi, Ran Duan, Benyu Wang, Tianle Xie

Given a graph $G=(V,E)$ and two vertices $s,t\in V$, the $f$-fault replacement path ($f$FRP) problem computes for every set of edges $F$ where $|F|\leq f$, the distance from $s$ to $t$ when edges in $F$ fail. A recent result shows that 2FRP in directed graphs can be solved in $\tilde{O}(n^3)$ time [arXiv:2209.07016]. In this paper, we show a 3FRP algorithm i

Jakub Szymkowiak, Weronika Jakubowska, Dawid Malarz, Weronika Smolak-Dyżewska

In computer graphics and vision, recovering easily modifiable scene appearance from image data is crucial for applications such as content creation. We introduce a novel method that integrates 3D Gaussian Splatting with an implicit surface representation, enabling intuitive editing of recovered scenes through mesh manipulation. Starting with a set of input i

Antti Vaaranta, Marco Cattaneo, Paolo Muratore-Ginanneschi

The Jaynes-Cummings and quantum Rabi models are fundamental to cavity and circuit quantum electrodynamics, as they describe the simplest form of light-matter interaction, where a single qubit is coupled to a single bosonic mode. A scenario that is commonly encountered in the experimental practice arises when the bosonic mode interacts with an external dissip

Xiwei Deng, Xianchun He, Jianfeng Bao, Yudan Zhou

CT report generation (CTRG) aims to automatically generate diagnostic reports for 3D volumes, relieving clinicians' workload and improving patient care. Despite clinical value, existing works fail to effectively incorporate diagnostic information from multiple anatomical views and lack related clinical expertise essential for accurate and reliable diagnosis.

Shunyu Mao, Jiajun Luo, Yixin Li, Jiapeng Zhou

Compute Express Link (CXL) is a promising technology that addresses memory and storage challenges. Despite its advantages, CXL faces performance threats from external interference when co-existing with current memory and storage systems. This interference is under-explored in existing research. To address this, we develop CXL-Interplay, systematically charac

Yingjie Xu, Michiel Sandra, Xuesong Cai, Sara Willhammar

Aiming for the sixth generation (6G) wireless communications, distributed massive multiple-input multiple-output (MIMO) systems hold significant potential for spatial multiplexing. In order to evaluate the ability of a distributed massive MIMO system to spatially separate closely spaced users, this paper presents an indoor channel measurement campaign. The m

Natsumi S. Shokida, Diego Kozlowski, Vincent Larivière

The multidisciplinary and socially anchored nature of Feminist Studies presents unique challenges for bibliometric analysis, as this research area transcends traditional disciplinary boundaries and reflects discussions from feminist and LGBTQIA+ social movements. This paper proposes a novel approach for identifying gender/sex related publications scattered a

Esmaeel Mohammadi, Daniel Ortiz-Arroyo, Aviaja Anna Hansen, Mikkel Stokholm-Bjerregaard

Wastewater treatment plants face unique challenges for process control due to their complex dynamics, slow time constants, and stochastic delays in observations and actions. These characteristics make conventional control methods, such as Proportional-Integral-Derivative controllers, suboptimal for achieving efficient phosphorus removal, a critical component

Chi Lu, Xiaoyu Wu, Wenjun Wen, Xiao-song Ma

Photons' frequency degree of freedom is promising to realize large-scale quantum information processing. Quantum frequency combs (QFCs) generated in integrated nonlinear microresonators can produce multiple frequency modes with narrow linewidth. Here, we utilize polarization-entangled QFCs to generate discrete frequency-bin entangled states. Fourteen pairs o

Wenjie Zhuo, Fan Ma, Hehe Fan

We present InfiniDreamer, a novel framework for arbitrarily long human motion generation. InfiniDreamer addresses the limitations of current motion generation methods, which are typically restricted to short sequences due to the lack of long motion training data. To achieve this, we first generate sub-motions corresponding to each textual description and the

Xinyao Zhang

In this article, we study the relation between the universal deformation rings and big Hecke algebras in the residually reducible case. Following the strategy of Skinner-Wiles and Pan's proof of the Fontaine-Mazur conjecture, we prove a pro-modularity result. Based on this result, we also give a conditional big $R=\mathbb{T}$ theorem over some totally real f

Xiyan Jiang, Xiaocong Zhao, Yiru Liu, Zirui Li

The driving interaction-a critical yet complex aspect of daily driving-lies at the core of autonomous driving research. However, real-world driving scenarios sparsely capture rich interaction events, limiting the availability of comprehensive trajectory datasets for this purpose. To address this challenge, we present InterHub, a dense interaction dataset der

Tianyi Wei, Dongdong Chen, Yifan Zhou, Xingang Pan

Representing the cutting-edge technique of text-to-image models, the latest Multimodal Diffusion Transformer (MMDiT) largely mitigates many generation issues existing in previous models. However, we discover that it still suffers from subject neglect or mixing when the input text prompt contains multiple subjects of similar semantics or appearance. We identi

Sudip Halder, S. D. Odintsov, Supriya Pan, Tapan Saha

We perform a detailed investigation of interacting phantom cosmology, by applying the powerful method of dynamical system analysis. We consider two well-studied interaction forms, namely one global and one local one, while the novel ingredient of our work is the examination of new potentials for the phantom field. Our analysis shows the existence of saddle m

Anagha P. Nitindala, Alexandra Veledina, Juri Poutanen

X-ray polarimetry is a fine tool to probe the accretion geometry and physical processes operating in the proximity of compact objects, black holes and neutron stars. Recent discoveries made by the Imaging X-ray Polarimetry Explorer put our understanding of the accretion picture in question. The observed high levels of X-ray polarization in X-ray binaries and

Alberto F. Boix, Santiago Zarzuela

The main goal of this paper is to obtain upper bounds for the regularity of graded deficiency modules in the spirit of the one obtained by Kumini--Murai in the monomial case building upon the spectral sequence formalism developed by \`Alvarez Montaner, Boix and Zarzuela. This spectral sequence formalism allows us not only to recover Kumini--Murai's upper bou

Emil Björnson, Özlem Tuğfe Demir

Reconfigurable intelligent surfaces (RISs) can improve the capacity of wireless communication links by passively beamforming the impinging signals in desired directions. This feature has been demonstrated both analytically and experimentally for conventional RISs, consisting of independently reflecting elements. To further enhance reconfigurability, a new ar

Libin Liu, Shen Chen, Sen Jia, Jingzhe Shi

Spatial intelligence is foundational to AI systems that interact with the physical world, particularly in 3D scene generation and spatial comprehension. Current methodologies for 3D scene generation often rely heavily on predefined datasets, and struggle to adapt dynamically to changing spatial relationships. In this paper, we introduce GraphCanvas3D, a prog

Hiroki Takahasi

We investigate the distribution of the largest digit for a wide class of infinite parabolic Iterated Function Systems (IFSs) of the unit interval. Due to the recurrence to parabolic (neutral) fixed points, the dimension analysis of these systems become more delicate than that of uniformly contracting IFSs. We show that the Hausdorff dimensions of level sets

Zengxi Zhang, Zhiying Jiang, Long Ma, Jinyuan Liu

Underwater images are often affected by light refraction and absorption, reducing visibility and interfering with subsequent applications. Existing underwater image enhancement methods primarily focus on improving visual quality while overlooking practical implications. To strike a balance between visual quality and application, we propose a heuristic invert

Zuguang Li, Shaohua Wu, Liang Li, Songge Zhang

In this letter, we propose an energy-efficient split learning (SL) framework for fine-tuning large language models (LLMs) using geo-distributed personal data at the network edge, where LLMs are split and alternately across massive mobile devices and an edge server. Considering the device heterogeneity and channel dynamics in edge networks, a \underline{C}ut

Danil Belov, Artem Erkhov, Farit Khabibullin, Elisaveta Pestova

This paper is dedicated to the development of a novel adaptive torsion spring mechanism for optimizing energy consumption in legged robots. By adjusting the equilibrium position and stiffness of the spring, the system improves energy efficiency during cyclic movements, such as walking and jumping. The adaptive compliance mechanism, consisting of a torsion sp

Šimon Sedláček, Santosh Kesiraju, Alexander Polok, Jan Černocký

This paper investigates a novel approach to end-to-end speech translation (ST) based on aligning frozen pre-trained automatic speech recognition (ASR) and machine translation (MT) models via a small connector module (Q-Former, our Subsampler-Transformer Encoder). This connector bridges the gap between the speech and text modalities, transforming ASR encoder

Xu Chen, Keke He, Junwei Zhu, Yanhao Ge

Face swapping aims to generate results that combine the identity from the source with attributes from the target. Existing methods primarily focus on image-based face swapping. When processing videos, each frame is handled independently, making it difficult to ensure temporal stability. From a model perspective, face swapping is gradually shifting from gener

Satoru Iwata, Hirota Kinoshita

Given an undirected graph $G = (V,E)$ with a set of terminals $T\subseteq V$ partitioned into a family $\mathcal{S}$ of disjoint blocks, find the maximum number of vertex-disjoint paths whose endpoints belong to two distinct blocks while no other internal vertex is a terminal. This problem is called Mader's $\mathcal{S}$-path packing. It has been of remarkab

Peter Keevash

We give a new proof of the existence of designs, which is much shorter and gives better bounds.

Zi Li, Ying Chen, Zeli Chen, Yanzhou Su

In the radiation therapy of nasopharyngeal carcinoma (NPC), clinicians typically delineate the gross tumor volume (GTV) using non-contrast planning computed tomography to ensure accurate radiation dose delivery. However, the low contrast between tumors and adjacent normal tissues necessitates that radiation oncologists manually delineate the tumors, often re

Minheng Ni, Lei Zhang, Zihan Chen, Kaixin Bai

Unthinking execution of human instructions in robotic manipulation can lead to severe safety risks, such as poisonings, fires, and even explosions. In this paper, we present responsible robotic manipulation, which requires robots to consider potential hazards in the real-world environment while completing instructions and performing complex operations safely

Chen Zhou, Peng Cheng, Junfeng Fang, Yifan Zhang

Multispectral object detection, utilizing RGB and TIR (thermal infrared) modalities, is widely recognized as a challenging task. It requires not only the effective extraction of features from both modalities and robust fusion strategies, but also the ability to address issues such as spectral discrepancies, spatial misalignment, and environmental dependencie

Alexandre Guernut, Christophe Vuillot

We propose and simulate the performance of a set of fault-tolerant and constant-depth logical gates on 2D toric codes. This set combines fold-transversal gates, Dehn twists and single-shot logical Pauli measurements and generates the full Clifford group.

Xinyu Su, Feng Liu, Yanchuan Chang, Egemen Tanin

Traffic forecasting is crucial for transportation systems optimisation. Current models minimise the mean forecasting errors, often favouring periodic events prevalent in the training data, while overlooking critical aperiodic ones like traffic incidents. To address this, we propose DualCast, a dual-branch framework that disentangles traffic signals into intr

Swaroop Gaddikere Nagaraja, Thomas Antretter, Clara Schuecker

This article investigates the effect of using isotropic and anisotropic plastic response functions in the analysis of the elastic-plastic response of unidirectional fibre composites on the meso-scale. Three model problems that use a Drucker-Prager-type pressure-dependent yield function are considered to simulate the non-linearities exhibited by a composite m

Qing-Yu Zhai, Raquel Molina, Eulogio Oset, Li-Sheng Geng

We have studied the $N D^* \bar{K}^*$ system in the framework of the Fixed Center Approximation to the Faddeev equations, taking the exotic $D^* \bar{K}^* $ system as the cluster and allowing the N to interact with the components of the cluster. Previous studies have determined the existence of three states of spin $0,1,2$ for the $D^* \bar{K}^* $ system, th

Audrey Blondé, Khushboo Pandey, Bruno Schuermans, Nicolas Noiray

This paper presents an experimental and numerical study of aeroacoustic instabilities at the interface between neighbouring combustion chambers in modern heavy-duty gas turbines. A simplified laboratory-scale geometry of the gap separating the outlet of these chambers, just upstream of the turbine inlet in can-annular combustor architectures, is considered.

D. G. Yakovlev, I. E. Fedorov

Magnetars are neutron stars with superstrong magnetic fields. Some of them (soft-gamma repeaters, SGRs) demonstrate gigantic flares which nature is still unclear. At decay phase of such flares one often observes quasi-periodic oscillations (QPOs) which are treated as stellar oscillations triggered by the flares. We study, for the first time, magneto-elastic

Haopeng Fang, Di Qiu, Binjie Mao, He Tang

Recent advancements in personalized Text-to-Video (T2V) generation have made significant strides in synthesizing character-specific content. However, these methods face a critical limitation: the inability to perform fine-grained control over motion intensity. This limitation stems from an inherent entanglement of action semantics and their corresponding mag

Chen Chen, Yuchen Sun, Xueluan Gong, Jiaxin Gao

Large language models (LLMs) have seen significant advancements, achieving superior performance in various Natural Language Processing (NLP) tasks, from understanding to reasoning. However, they remain vulnerable to backdoor attacks, where models behave normally for standard queries but generate harmful responses or unintended output when specific triggers a

Chaoyun Zhang, Shilin He, Jiaxu Qian, Bowen Li

GUIs have long been central to human-computer interaction, providing an intuitive and visually-driven way to access and interact with digital systems. The advent of LLMs, particularly multimodal models, has ushered in a new era of GUI automation. They have demonstrated exceptional capabilities in natural language understanding, code generation, and visual pr

Mayank Sharma, Garen Avedissian, Witold Skowroński, Junhyeon Jo

The discovery of van der Waals magnets has established a new domain in the field of magnetism, opening novel pathways for the electrical control of magnetic properties. In this context, Fe3GeTe2 (FGT) emerges as an exemplary candidate owing to its intrinsic metallic properties, which facilitate the interplay of both charge and spin degrees of freedom. Here,

Shun Hu

In the era of 5G communication, the knowledge of channel state information (CSI) is crucial for enhancing network performance. This paper explores the utilization of language models for spatial CSI prediction within MIMO-OFDM systems. We begin by outlining the significance of accurate CSI in enabling advanced functionalities such as adaptive modulation. We r

Tianyuan Zhang, Lu Wang, Xinwei Zhang, Yitong Zhang

Vision-language models (VLMs) have significantly advanced autonomous driving (AD) by enhancing reasoning capabilities. However, these models remain highly vulnerable to adversarial attacks. While existing research has primarily focused on general VLM attacks, the development of attacks tailored to the safety-critical AD context has been largely overlooked. I

Semen Molokov, Vadim Vologodsky

We prove that singular cohomology of the underlying space of Berkovich's analytification of a scheme $X$ locally of finite type over a trivially-valued field $k$ of characteristic $0$ is isomorphic to cdh-cohomology with integer coefficients which is also isomorphic to the weight zero motivic cohomology $H^*(X, \mathbb{Z})$. Using this isomorphism, we demons

Li Luo, Zheming Xu, Yang Yang

We provide the geometric construction of a series of generalized Schur algebras of any type via Borel-Moore homologies and equivariant K-groups of generalized Steinberg varieties. As applications, we obtain a Schur algebra analogue of the local geometric Langlands correspondence of any type, provide an equivariant K-theoretic realization of quasi-split $\ima

Shubham Pande, Sai Sukruth Bezugam, Tinish Bhattacharya, Ewelina Wlazlak

Neuromorphic systems that employ advanced synaptic learning rules, such as the three-factor learning rule, require synaptic devices of increased complexity. Herein, a novel neoHebbian artificial synapse utilizing ReRAM devices has been proposed and experimentally validated to meet this demand. This synapse features two distinct state variables: a neuron coup

Junyi Yang, Ruibin Mao, Mingrui Jiang, Yichuan Cheng

Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using physics for computing in parallel resistive memory arrays. However, recurrent neural networks (RNN) that are widely used for speech-recognition and natural language processing have

Joongwon Chae, Zhenyu Wang, Lian Zhang, Dongmei Yu

Recent advances in multimodal models have demonstrated impressive capabilities in object recognition and scene understanding. However, these models often struggle with precise spatial localization - a critical capability for real-world applications. Inspired by how humans use grid-based references like chess boards and maps, we propose introducing explicit v

Xueluan Gong, Yuji Wang, Shuaike Li, Mengyuan Sun

Federated Learning (FL) emerged as a paradigm for conducting machine learning across broad and decentralized datasets, promising enhanced privacy by obviating the need for direct data sharing. However, recent studies show that attackers can steal private data through model manipulation or gradient analysis. Existing attacks are constrained by low theft quant

Bingyan Nie, Wulin Xie, Jiang Long, Xiaohuan Lu

Recently, multi-view and multi-label classification have become significant domains for comprehensive data analysis and exploration. However, incompleteness both in views and labels is still a real-world scenario for multi-view multi-label classification. In this paper, we seek to focus on double missing multi-view multi-label classification tasks and propos

Chenyu Tang, Shuo Gao, Cong Li, Wentian Yi

Wearable silent speech systems hold significant potential for restoring communication in patients with speech impairments. However, seamless, coherent speech remains elusive, and clinical efficacy is still unproven. Here, we present an AI-driven intelligent throat (IT) system that integrates throat muscle vibrations and carotid pulse signal sensors with larg

Habiba Farzand, Shaun Macdonald, Karola Marky, Mohamed Khamis

Shoulder surfing has been studied extensively, however, it remains unexplored whether and how it impacts users. Understanding this is important as it determines whether shoulder surfing poses a significant concern and, if so, how best to address it. By surveying smartphone users in the UK, we explore how shoulder surfing impacts a) the privacy perceptions of

Peter H. van der Kamp

We associate parametric classes of $n$-component Lotka-Volterra systems which admit $k$ additional linear Darboux polynomials, with admissible loopless hypergraphs of order $n$ and size $k$. We study the equivalence relation on admissible hypergraphs induced by linear transformations of the associated LV-systems, for $n\leq 5$. We present a new 13-parameter

Linwei Dong, Qingnan Fan, Yihong Guo, Zhonghao Wang

Pre-trained text-to-image diffusion models are increasingly applied to real-world image super-resolution (Real-ISR) task. Given the iterative refinement nature of diffusion models, most existing approaches are computationally expensive. While methods such as SinSR and OSEDiff have emerged to condense inference steps via distillation, their performance in ima

Xiaohan Yu, Li Zhang, Xin Zhao, Yue Wang

The recent breakthrough of large language models (LLMs) in natural language processing has sparked exploration in recommendation systems, however, their limited domain-specific knowledge remains a critical bottleneck. Specifically, LLMs lack key pieces of information crucial for sequential recommendations, such as user behavior patterns. To address this crit

Mohit Apte, Ketan Kale, Pranav Datar, Pratiksha Deshmukh

This paper explores the application of a reinforcement learning (RL) framework using the Q-Learning algorithm to enhance dynamic pricing strategies in the retail sector. Unlike traditional pricing methods, which often rely on static demand models, our RL approach continuously adapts to evolving market dynamics, offering a more flexible and responsive pricing

Joanne Boisson, Arif Mehmood, Jose Camacho-Collados

The metaphor studies community has developed numerous valuable labelled corpora in various languages over the years. Many of these resources are not only unknown to the NLP community, but are also often not easily shared among the researchers. Both in human sciences and in NLP, researchers could benefit from a centralised database of labelled resources, easi

L. Klochko, M. d'Aquin, A. Togo, L. Chaput

Machine learning promises to accelerate the material discovery by enabling high-throughput prediction of desirable macro-properties from atomic-level descriptors or structures. However, the limited data available about precise values of these properties have been a barrier, leading to predictive models with limited precision or the ability to generalize. Thi

Jia-Wen Li, Xinwu Cao

The magnetically arrested disks (MADs) have attracted much attention in recent years. The formation of MADs are usually attributed to the accumulation of a sufficient amount of dynamically significant poloidal magnetic flux. In this work, the magnetic flux transport within an advection dominated accretion flow and the formation of a MAD are investigated. The

Jinhui Chen, Feng-Kun Guo, Yu-Gang Ma, Cheng-Ping Shen

Exotic hadrons beyond the conventional quark model have been discovered in the past two decades. Investigations of these states can lead to deep understanding of nonperturbative dynamics of the strong interaction. In this concise review, we focus on the productions of exotic hadrons in $pp$, $p\bar p$, and nuclear collisions. Experimental observations of lig

Bidyut Hazarika, Prabwal Phukon

In this work, we propose a novel formalism for the thermodynamics of flat black holes, inspired by the Restricted Phase Space Thermodynamics (RPST) framework. Our construction is motivated by the observed similarities in the thermodynamic behavior of flat black holes within the R\'enyi entropy framework and that of AdS black holes described by the Bekenstein

Samy Aoulad Lafkih, Nils A. Nilsson, Marie-Christine Angonin, Christophe Le Poncin-Lafitte

In this paper, we present static and spherically symmetric vacuum solutions to the mass-dimension $d\leq 4$ action of an effective-field theory, choosing the diffeomorphism symmetry to be broken explicitly. By using the reduced-action method with a Schwarzschild seed-solution, we find static and spherically symmetric black hole solutions to the field equatio

Marius Tacke, Matthias Busch, Kevin Linka, Christian J. Cyron

Datasets often incorporate various functional patterns related to different aspects or regimes, which are typically not equally present throughout the dataset. We propose a novel, general-purpose partitioning algorithm that utilizes competition between models to detect and separate these functional patterns. This competition is induced by multiple models ite

Melda Yeghaian, Zuhir Bodalal, Daan van den Broek, John B A G Haanen

Purpose: Immunotherapies have revolutionized the landscape of cancer treatments. However, our understanding of response patterns in advanced cancers treated with immunotherapy remains limited. By leveraging routinely collected noninvasive longitudinal and multimodal data with artificial intelligence, we could unlock the potential to transform immunotherapy f

Gourab Ghatak

We introduce a novel metric for stochastic geometry based analysis of automotive radar networks called target {\it tracking probability}. Unlike the well-investigated detection probability (often termed as the success or coverage probability in stochastic geometry), the tracking probability characterizes the event of successive successful target detection wi