November 2024 arXiv papers — page 88

Song-Shan Luo, Zhong-Wen Feng

As an important class of quantum gravity models, the generalized uncertainty principle (GUP) plays an important role in exploring the properties of cosmology and its related problems. In this paper, we explore the influence of the higher-order GUP on the primordial big bang nucleosynthesis (BBN). Firstly, based on a new higher-order GUP, we derived the Fried

Shibin Mei, Hang Wang, Bingbing Ni

The cameras equipped on mobile terminals employ different sensors in different photograph modes, and the transferability of raw domain denoising models between these sensors is significant but remains sufficient exploration. Industrial solutions either develop distinct training strategies and models for different sensors or ignore the differences between sen

Vydeki D, Divyansh Bhandari, Pranav Pratap Patil, Aarush Anand Kulkarni

Dysgraphia is a learning disorder that affects handwriting abilities, making it challenging for children to write legibly and consistently. Early detection and monitoring are crucial for providing timely support and interventions. This study applies deep learning techniques to address the dual tasks of dysgraphia detection and optical character recognition (

Qing-Yu Wang, Shuai Hu, You-He Zhou, Cun Xue

The damage and the critical current density (Jc) degradation of polycrystalline superconductors induced by strain dramatically influence their performance in applications. Unfortunately, the state-of-the-art experimental techniques are unable to detect the damage of internal polycrystalline structures and the microscopic superconductivity in the presence of

Ping Zhang, Lei Su, Jinjie Yang, Xin Chen

Hosting diverse large language model workloads in a unified resource pool through co-location is cost-effective. For example, long-running chat services generally follow diurnal traffic patterns, which inspire co-location of batch jobs to fulfill resource valleys between successive peaks, and thus to saturate resource allocation in cluster-wide scope. These

Michael C. Knaus

Estimators that weight observed outcomes to form effect estimates have a long tradition. Their outcome weights are widely used in established procedures, such as checking covariate balance, characterizing target populations, or detecting and managing extreme weights. This paper introduces a general framework for deriving such outcome weights. It establishes

Muhammad Lawrence Pattersons, Freddy Permana Zen

Rotating neutron stars (NSs) are crucial objects of study, as our understanding of them relies significantly on observational data from these rotating stars. Observations suggest that the magnetic fields of NSs range from approximately $10^{8-15}$ G. In this work, we compute the Kepler frequency and moment of inertia for rotating NSs under the influence of a

Yuxiang Liu, Ligong Wang, Xiaolong Jia

The $Q$-index of graph $G$ is the largest eigenvalue of the signless Laplacian matrix of $G$. Wang [Discrete Appl. Math. 356(2024)] proved the sharp upper bounds on the $Q$-index of leaf-free graphs with given size and characterized the corresponding extremal graphs. A graph is $2$ leaves-free if it has no two pendent vertices. In this paper, we give sharp u

Christian D. Blakely

We propose an error-correcting model for the microprice, a high-frequency estimator of future prices given higher order information of imbalances in the orderbook. The model takes into account a current microprice estimate given the spread and best bid to ask imbalance, and adjusts the microprice based on recent dynamics of higher price rank imbalances. We i

Ekaterina Smolina, Lev Smirnov, Daniel Leykam, Franco Nori

The use of machine learning to predict wave dynamics is a topic of growing interest, but commonly-used deep learning approaches suffer from a lack of interpretability of the trained models. Here we present an interpretable machine learning framework for analyzing the nonlinear evolution dynamics of optical wavepackets in complex wave media. We use sparse reg

Ryuichiro Kitano

We report the result of the numerical lattice computation of the lepton anomalous magnetic moment in QED up to five loops. We concentrate on the contributions from diagrams without lepton loops, which are the most difficult part of the calculation in the Feynman diagram method while the lattice formulation is the easiest. Good agreement with the results of t

Francesca Pelusi, Andrea Scagliarini, Mauro Sbragaglia, Massimo Bernaschi

Emulsions are paramount in various interdisciplinary topical areas, yet a satisfactory understanding of their behavior in buoyancy-driven thermal flows has not been established. In the present work, we unravel the dynamical regimes of thermal convection in emulsions by leveraging a large set of mesoscale numerical simulations. Emulsions are prepared with a g

Qingming Zhao, Wei Wang

The small mass limit of the Langevin equation perturbed by $\alpha$-stable L\'{e}vy noise is considered by rewriting it in the form of slow-fast system, and spliting the fast component into three parts, where $\alpha\in(1,2)$. By exploring the three parts respectively, the approximation equation is derived. The convergence is either in the sense of uniform m

Zhi Wang, Xin Yang, Du Chen, Han Gao

To protect users from data breaches and phishing attacks, service providers typically implement two-factor authentication (2FA) to add an extra layer of security against suspicious login attempts. However, since 2FA can sometimes hinder user experience by introducing additional steps, many websites aim to reduce inconvenience by minimizing the frequency of 2

Yevgeniia Yevgenieva, Alexander Zuyev, Peter Benner

We investigate a nonlinear parabolic partial differential equation whose boundary conditions contain a single control input. This model describes a chemical reaction of the type ``$A \to $ product'', occurring in a dispersed flow tubular reactor. The existence and uniqueness of solutions to the nonlinear Cauchy problem under consideration are established by

Muqsit Azeem, Jan Kretinsky, Maximilian Weininger

Algorithmic analysis of Markov decision processes (MDP) and stochastic games (SG) in practice relies on value-iteration (VI) algorithms. Since the basic version of VI does not provide guarantees on the precision of the result, variants of VI have been proposed that offer such guarantees. In particular, sound value iteration (SVI) not only provides precise lo

Riccardo Riccio

This paper introduces a novel method for real-time exercise classification using a Bidirectional Long Short-Term Memory (BiLSTM) neural network. Existing exercise recognition approaches often rely on synthetic datasets, raw coordinate inputs sensitive to user and camera variations, and fail to fully exploit the temporal dependencies in exercise movements. Th

Bertil Schmidt, Felix Kallenborn, Alexander Wichmann, Alejandro Chacon

The continually increasing volume of DNA sequence data has resulted in a growing demand for fast implementations of core algorithms. Computation of pairwise alignments between candidate haplotypes and sequencing reads using Pair-HMMs is a key component in DNA variant calling tools such as the GATK HaplotypeCaller but can be highly time consuming due to its q

Maxim Kazarian, Zhuoke Yang

We introduce a universal weight system (a function on chord diagrams satisfying the $4$-term relation) taking values in the ring of polynomials in infinitely many variables whose particular specializations are weight systems associated with the Lie algebras $\mathfrak{so}(N)$, $\mathfrak{sp}(2M)$, as well as Lie superalgebras $\mathfrak{osp}(N|2M)$. We exten

Zhe Su, Aron Bencsik, Giacomo Indiveri, Davide Bertozzi

Multi-core neuromorphic processors are becoming increasingly significant due to their energy-efficient local computing and scalable modular architecture, particularly for event-based processing applications. However, minimizing the cost of inter-core communication, which accounts for the majority of energy usage, remains a challenging issue. Beyond optimizin

Yi-Jun Chang, Lyuting Chen, Yanyu Chen, Gopinath Mishra

Bonne and Censor-Hillel (ICALP 2019) initiated the study of distributed subgraph finding in dynamic networks of limited bandwidth. For the case where the target subgraph is a clique, they determined the tight bandwidth complexity bounds in nearly all settings. However, several open questions remain, and very little is known about finding subgraphs beyond cli

Zhendong Liu, Yuanbi Nie, Yingshui Tan, Jiaheng Liu

Benefiting from the powerful capabilities of Large Language Models (LLMs), pre-trained visual encoder models connected to LLMs form Vision Language Models (VLMs). However, recent research shows that the visual modality in VLMs is highly vulnerable, allowing attackers to bypass safety alignment in LLMs through visually transmitted content, launching harmful a

Jared Miller, Jaap Eising, Florian Dörfler, Roy S. Smith

Static structured control refers to the task of designing a state-feedback controller such that the control gain satisfies a subspace constraint. Structured control has applications in control of communication-inhibited dynamical systems, such as systems in networked environments. This work performs $H_2$-suboptimal regulation under a common structured state

Klaus R. Scherer, Felix Burkhardt, Uwe D. Reichel, Florian Eyben

Increasingly frequent publications in the literature report voice quality differences between depressed patients and controls. Here, we examine the possibility of using voice analysis as an early warning signal for the development of emotion disturbances in young adults. As part of a major interdisciplinary European research project in four countries (ECoWeB

Prateek Sharma

How do we explain the simultaneous growth in energy efficiency of cloud computing and its energy consumption? The Jevons paradox provides one perspective of this phenomenon. However, it is not clear or obvious \emph{why} the Jevons paradox exists, and \emph{when} is it applicable. To answer these questions, we seek inspiration from thermodynamics, and model

Mingjie Yang, Guangming Liang, Dongzhu Liu, Lei Zhang

Integrated sensing and communication (ISAC) unifies wireless communication and sensing by sharing spectrum and hardware, which often incurs trade-offs between two functions due to limited resources. However, this paper shifts focus to exploring the synergy between communication and sensing, using WiFi sensing as an exemplary scenario where communication sign

Laura Bazahica, Vesa Kaarnioja, Lassi Roininen

The theoretical development of quasi-Monte Carlo (QMC) methods for uncertainty quantification of partial differential equations (PDEs) is typically centered around simplified model problems such as elliptic PDEs subject to homogeneous zero Dirichlet boundary conditions. In this paper, we present a theoretical treatment of the application of randomly shifted

Antonino Ficarra, Somayeh Moradi, Tim Römer

In this paper, we study the componentwise linearity of symbolic powers of edge ideals. We propose the conjecture that all symbolic powers of the edge ideal of a cochordal graph are componentwise linear. This conjecture is verified for some families of cochordal graphs, including complements of block graphs and complements of proper interval graphs. As a coro

Xincan Yu, Sikun Yang

We propose a novel dynamic network model to capture evolving latent communities within temporal networks. To achieve this, we decompose each observed dynamic edge between vertices using a Poisson-gamma edge partition model, assigning each vertex to one or more latent communities through \emph{nonnegative} vertex-community memberships. Specifically, hierarchi

Jan-Torge Schindler, Joseph F. Hennawi, Frederick B. Davies, Sarah E. I. Bosman

The nature of "Little Red Dots" and their relation to other forms of accreting supermassive black holes remain an open question. Here we report the discovery of a Little Red Dot at $z=7.3$. It is attenuated by moderate amounts of dust, $A_V = {2.79}\,\textrm{mag}$, with an intrinsic bolometric luminosity of $10^{46.6}\,\textrm{erg}\,\textrm{s}^{-1}$ and a SM

Yu Meng, Chuan Liu, Xin-Yu Tuo, Haobo Yan

We calculate the s-wave scattering length in the $0^+$ sector of $\eta_c\eta_c$ and the $2^+$ sector of $J/\psi J/\psi$ using three $N_f=2$ twisted mass gauge ensembles with the lattice spacing $a=0.0667,0.085,0.098$ fm, respectively. The scattering lengths are extracted using the conventional L{\"u}scher finite size method. We observe sizable discretization

Hanxiang Xu, Wei Ma, Ting Zhou, Yanjie Zhao

In recent years, the programming capabilities of large language models (LLMs) have garnered significant attention. Fuzz testing, a highly effective technique, plays a key role in enhancing software reliability and detecting vulnerabilities. However, traditional fuzz testing tools rely on manually crafted fuzz drivers, which can limit both testing efficiency

Viktoriia Chekalina, Anton Razzhigaev, Elizaveta Goncharova, Andrey Kuznetsov

In this paper we present an approach to reduce hallucinations in Large Language Models (LLMs) by incorporating Knowledge Graphs (KGs) as an additional modality. Our method involves transforming input text into a set of KG embeddings and using an adapter to integrate these embeddings into the language model space, without relying on external retrieval process

Shuo Zhang, Jian K. Liu

Protein language models (PLMs) have demonstrated remarkable capabilities in learning relationships between protein sequences and functions. However, finetuning these large models requires substantial computational resources, often with suboptimal task-specific results. This study investigates how parameter-efficient finetuning via LoRA can enhance protein pr

Guy Libourel, Marwane Mokhtari, Vandad-Julien Rohani, Bernard Bourdon

Condensation processes, which are responsible for the main chemical differences between gas and solids in the Galaxy, are the major mechanisms that control the cycle of dust from evolved stars to planetary systems. However, they are still poorly understood, mainly because the thermodynamics and kinetic models of nucleation or grain growth lack experimental d

Charlie Lebarbé, Emilien Flayac, Michel Fournié, Didier Henrion

We use moment-SOS (Sum Of Squares) relaxations to address the optimal control problem of the 1D heat equation perturbed with a nonlinear term. We extend the current framework of moment-based optimal control of PDEs to consider a quadratic cost on the control. We develop a new method to extract a nonlinear controller from approximate moments of the solution.

Faiz Ahmed, Nitin Kumar Jha, Md Faizan

The rapid adoption of e-commerce has transformed how students access goods and resources. However, existing platforms often fail to address the specific needs of campus communities, where students face challenges such as financial constraints, lack of access to affordable goods, and inefficient resource circulation. This research proposes ShareSpace, a local

Lucas E. A. Stehouwer, Cécile X. Yu, Barnaby van Straaten, Alberto Tosato

Disorder in the heterogeneous material stack of semiconductor spin qubit systems introduces noise that compromises quantum information processing, posing a challenge to coherently control large-scale quantum devices. Here, we exploit low-disorder epitaxial strained quantum wells in Ge/SiGe heterostructures grown on Ge wafers to comprehensively probe the nois

Mingda Zhang, Mingli Zhu, Zihao Zhu, Baoyuan Wu

Backdoor attack has been considered as a serious security threat to deep neural networks (DNNs). Poisoned sample detection (PSD) that aims at filtering out poisoned samples from an untrustworthy training dataset has shown very promising performance for defending against data poisoning based backdoor attacks. However, we observe that the detection performance

Zhihao Li, Haoze Song, Di Xiao, Zhilu Lai

Partial Differential Equations (PDEs) underpin many scientific phenomena, yet traditional computational approaches often struggle with complex, nonlinear systems and irregular geometries. This paper introduces the AMG method, a Multi-Graph neural operator approach designed for efficiently solving PDEs on Arbitrary geometries. AMG leverages advanced graph-bas

S. Kato, M. Anzorena, D. Chen, K. Fujita

Motivated by the difference between the fluxes of sub-PeV Galactic diffuse gamma-ray emission (GDE) measured by the Tibet AS$\gamma$ experiment and the LHAASO collaboration, our study constrains the contribution to the GDE flux measured by Tibet AS$\gamma$ from the sub-PeV gamma-ray sources in the first LHAASO catalog plus the Cygnus Cocoon. After removing t

Hemant Prasad, Jan T. Sobczyk, Artur M. Ankowski, J. Luis Bonilla

We present the implementation and results of a new model for the n-particle n-hole ($\it{np-nh}$) contribution in the NuWro event generator, grounded in the theoretical framework established by the Valencia group in 2020. For the $\it{2p2h}$ component, we introduce a novel nucleon sampling function with tunable parameters to approximate correlations in the m

Jonathan Ansari, Eva Lütkebohmert

This paper presents comparison results and establishes risk bounds for credit portfolios within classes of Bernoulli mixture models, assuming conditionally independent defaults that are stochastically increasing with a common risk factor. We provide simple and interpretable conditions for conditional default probabilities that imply a comparison of credit po

Robin Carpentier, Benjamin Zi Hao Zhao, Hassan Jameel Asghar, Dali Kaafar

Interactions with online Large Language Models raise privacy issues where providers can gather sensitive information about users and their companies from the prompts. While textual prompts can be sanitized using Differential Privacy, we show that it is difficult to anticipate the performance of an LLM on such sanitized prompt. Poor performance has clear mone

Jean Vassoyan, Anan Schütt, Jill-Jênn Vie, Arun-Balajiee Lekshmi-Narayanan

Massive Open Online Courses (MOOCs) have greatly contributed to making education more accessible. However, many MOOCs maintain a rigid, one-size-fits-all structure that fails to address the diverse needs and backgrounds of individual learners. Learning path personalization aims to address this limitation, by tailoring sequences of educational content to opti

A. Hammad, Mihoko M Nojiri

In this article, we review recent machine learning methods used in challenging particle identification of heavy-boosted particles at high-energy colliders. Our primary focus is on attention-based Transformer networks. We report the performance of state-of-the-art deep learning networks and further improvement coming from the modification of networks based on

Ziyi Wang, Huang Zhang, Shanfang Huang

Heat pipe is an efficient heat transfer element based on two-phase natural circulation, which has advantages of simple structure, strong heat transfer ability, and good isothermal performance. Heat pipes are widely used in heat transfer and other fields, and especially have important applications in nuclear engineering. One of its most important characterist

Gururaj A. Wagle, Emmanouil Chatzopoulos, Michael J. Baer

In this study, we perform detailed spectroscopic modeling to analyze the interaction of circumstellar material (CSM) with ejecta in both hydrogen-rich and hydrogen-poor superluminous supernovae (SLSNe), systematically varying properties such as CSM density, composition, and geometry to explore their effects on spectral lines and light curve evolution. Using

Sutanu Gayen, Sanket Kale, Sayantan Sen

Learning high-dimensional distributions is a significant challenge in machine learning and statistics. Classical research has mostly concentrated on asymptotic analysis of such data under suitable assumptions. While existing works [Bhattacharyya et al.: SICOMP 2023, Daskalakis et al.: STOC 2021, Choo et al.: ALT 2024] focus on discrete distributions, the cur

Rüveyda Yilmaz, Kaan Keven, Yuli Wu, Johannes Stegmaier

Automated cell segmentation in microscopy images is essential for biomedical research, yet conventional methods are labor-intensive and prone to error. While deep learning-based approaches have proven effective, they often require large annotated datasets, which are scarce due to the challenges of manual annotation. To overcome this, we propose a novel frame

Shuai Li, Michael Burke, Subramanian Ramamoorthy, Juergen Gall

This paper introduces a novel framework to learn data association for multi-object tracking in a self-supervised manner. Fully-supervised learning methods are known to achieve excellent tracking performances, but acquiring identity-level annotations is tedious and time-consuming. Motivated by the fact that in real-world scenarios object motion can be usually

Ankita Vaishnobi Bisoi, Shreyas V, Jose Siguenza, Bharath Ramsundar

Variant calling is a fundamental task in genomic research, essential for detecting genetic variations such as single nucleotide polymorphisms (SNPs) and insertions or deletions (indels). This paper presents an enhancement to DeepChem, a widely used open-source drug discovery framework, through the integration of DeepVariant. In particular, we introduce a var

D. Hazra, A. Moorti

Self-focusing and guiding of ultra-short (pulse duration: L<lambda_P: plasma wavelength), intense laser pulses in underdense plasma relevant to laser wakefield electron acceleration has been studied numerically. The analysis considers contribution of wakefield non-linearities along with relativistic effects. Stable propagation of laser pulse with mild period

Théophile Champion, Marek Grześ, Howard Bowman

Model-based reinforcement learning refers to a set of approaches capable of sample-efficient decision making, which create an explicit model of the environment. This model can subsequently be used for learning optimal policies. In this paper, we propose a temporal Gaussian Mixture Model composed of a perception model and a transition model. The perception mo

Giulia Lafratta, Bernd Porr, Christopher Chandler, Alice Miller

We present a hierarchical framework to solve robot planning as an input control problem. At the lowest level are temporary closed control loops, ("tasks"), each representing a behaviour, contingent on a specific sensory input and therefore temporary. At the highest level, a supervising "Configurator" directs task creation and termination. Here resides "core"

Alexandr Malijevský, Martin Pospíšil

We study bridging transitions that emerge between two sinusoidally-shaped walls of amplitude $A$, wavenumber $k$, and mean separation $L$. The focus is on weakly corrugated walls to examine the properties of bridging transitions in the limit when the walls become flat. The reduction of walls roughness can be achieved in two ways which we show differ qualitat

Chen Gao, Zixin Zhao, Lv Shao, Tong Liu

In e-commerce, Trigger-Induced Recommendation (TIR), recommending items after a user clicks a trigger, is an important task. However, modern platforms rely on a continuous stream of diverse and short-lived promotional scenarios (e.g., for Black Friday), creating a significant challenge. Existing methods are less effective here: they either fall into a trigge

Chuang Yang, Xu Han, Tao Han, Yuejiao SU

Traffic signs play a key role in assisting autonomous driving systems (ADS) by enabling the assessment of vehicle behavior in compliance with traffic regulations and providing navigation instructions. However, current works are limited to basic sign understanding without considering the egocentric vehicle's spatial position, which fails to support further re

Ronaldo F. de Lima, Giuseppe Pipoli

We classify the isoparametric hypersurfaces and the homogeneous hypersurfaces of $\mathbb H^n\times\mathbb R$ and $\mathbb S^n\times\mathbb R$, $n\ge 2$, by establishing that any such hypersurface has constant angle function and constant principal curvatures.

Zhaoqing Wang, Xiaobo Xia, Runnan Chen, Dongdong Yu

This paper presents the Large Vision Diffusion Transformer (LaVin-DiT), a scalable and unified foundation model designed to tackle over 20 computer vision tasks in a generative framework. Unlike existing large vision models directly adapted from natural language processing architectures, which rely on less efficient autoregressive techniques and disrupt spat

Xinyan Guan, Yanjiang Liu, Xinyu Lu, Boxi Cao

The evolution of machine learning has increasingly prioritized the development of powerful models and more scalable supervision signals. However, the emergence of foundation models presents significant challenges in providing effective supervision signals necessary for further enhancing their capabilities. Consequently, there is an urgent need to explore nov

Gabriel Finneran, Laura Cotter, Antonio Martin-Carrillo

More than 60 broad-lined type Ic (Ic-BL) supernovae (SNe) are associated with a long gamma-ray burst (GRB). However, many type Ic-BL SNe exhibit no sign of an associated GRB. On average, the expansion velocities of GRB-associated type Ic-BL SNe (GRB-SNe) are greater than those of type Ic-BL SNe without an associated GRB. This work presents the largest spectr

Pengjun Guo, He Zhu

The Genebass dataset, released by Karczewski et al. (2022), provides a comprehensive resource elucidating associations between genes and 4,529 phenotypes based on nearly 400,000 exomes from the UK Biobank. This extensive dataset enables the evaluation of gene set enrichment across a wide range of phenotypes, facilitating the inference of associations between

Chen Gao, Zixin Zhao, Lv Shao, Tong Liu

E-commerce app users exhibit behaviors that are inherently logically consistent. A series of multi-scenario user behaviors interconnect to form the scene-level all-domain user moveline, which ultimately reveals the user's true intention. Traditional CTR prediction methods typically focus on the item-level interaction between the target item and the historica

Bhagya. R, E. Harikumar

In this paper, we construct $SU(N)$ Yang-Mills theory in the $\kappa$-space-time, valid up to first order in the deformation parameter $a$, using the generalisation of Feynman's approach. Using the $\kappa$-deformed Wong's equation derived, in the Jacobi identity involving velocities and coordinates of $\kappa$-deformed space-time, the $\kappa$-deformed homo

Tristan J. B. Cann, Iain S. Weaver, Hywel T. P. Williams

Online communications, and in particular social media, are a key component of how society interacts with and promotes content online. Collective attention on such content can vary wildly. The majority of breaking topics quickly fade into obscurity after only a handful of interactions, while the possibility exists for content to ``go viral'', seeing sustained

Pei Li, Joo-Ho Choi, Dingyang Zhang, Shuyou Zhang

Accurate prediction of turbine blade fatigue life is essential for ensuring the safety and reliability of aircraft engines. A significant challenge in this domain is uncovering the intrinsic relationship between mechanical properties and fatigue life. This paper introduces Reinforced Symbolic Learning (RSL), a method that derives predictive formulas linking

Funing Xia, Junyuan Wang, Lin Dai

Clustered cell-free networking has been considered as an effective scheme to trade off between the low complexity of current cellular networks and the superior performance of fully cooperative networks. With clustered cell-free networking, the wireless network is decomposed into a number of disjoint parallel operating subnetworks with joint processing adopte

Jan-Ole Koslik

Markov-switching models are powerful tools that allow capturing complex patterns from time series data driven by latent states. Recent work has highlighted the benefits of estimating components of these models nonparametrically, enhancing their flexibility and reducing biases, which in turn can improve state decoding, forecasting, and overall inference. Form

Muhammad Saad Zia, Ashiq Anjum, Lu Liu, Anthony Conway

Physics Informed Machine Learning has emerged as a popular approach for modeling and simulation in digital twins, enabling the generation of accurate models of processes and behaviors in real-world systems. However, existing methods either rely on simple loss regularizations that offer limited physics integration or employ highly specialized architectures th

Chenhang Cui, Gelei Deng, An Zhang, Jingnan Zheng

Recent advances in Large Vision-Language Models (LVLMs) have showcased strong reasoning abilities across multiple modalities, achieving significant breakthroughs in various real-world applications. Despite this great success, the safety guardrail of LVLMs may not cover the unforeseen domains introduced by the visual modality. Existing studies primarily focus

Florian Stäbler, Alioune Gadiaga, Eugene V. Sukhorukov

We develop a theory of heat transport in non-chiral transmission lines (TLs) of quantum Hall edge channels coupled to Ohmic contacts (OCs) that accounts for a dynamical accumulation of charge in the reservoirs. As a consequence, heat transport is driven by charge fluctuations in the heat Coulomb blockade regime. This framework challenges conventional paradig

Alejandro Hernandez, Levin Brinkmann, Ignacio Serna, Nasim Rahaman

While AI models have demonstrated remarkable capabilities in constrained domains like game strategy, their potential for genuine creativity in open-ended domains like art remains debated. We explore this question by examining how AI can transcend human cognitive limitations in visual art creation. Our research hypothesizes that visual art contains a vast une

Kan Hu, Minxian Xu, Kejiang Ye, Chengzhong Xu

Microservices architecture has become the dominant architecture in cloud computing paradigm with its advantages of facilitating development, deployment, modularity and scalability. The workflow of microservices architecture is transparent to the users, who are concerned with the quality of service (QoS). Taking Service Level Objective (SLO) as an important i

Kun Xiang, Zhili Liu, Terry Jingchen Zhang, Yinya Huang

In this paper, we address the challenging task of multimodal reasoning by incorporating the notion of ``slow thinking'' into multimodal large language models (MLLMs). Our core idea is that models can learn to adaptively use different levels of reasoning to tackle questions of varying complexity. We propose a novel paradigm of Self-structured Chain of Thought

Yi Liu

Let $M$ be an oriented closed hyperbolic $3$--manifold. Suppose that $w$ is a rational second cohomology class of $M$ with dual Thurston norm $1$. Upon the existence of certain nonvanishing Alexander polynomials, the author shows that the pullback of $w$ to some finite cover of $M$ is the real Euler class of some transversely oriented taut foliation on that

G. A. Galazutdinov, E. Babina

The rest wavelengths of DIBs are of fundamental importance owing to the lack of unambiguous identification for these mysterious features. Usually the wavelengths of DIBs are estimated using known interstellar lines serving to shift the spectrum to the rest wavelengths velocity scale. However, the latter may not share in fact the same parts of a cloud which c

Langxuan Chen, Jun Feng

We revisit the problem of thermalization process for an Unruh-DeWitt (UDW) detector in de Sitter space. We derive the full dynamics of the detector in the context of open quantum system, neither using Markovian or RWA approximations. We utilize quantum Fisher information (QFI) for Hubble parameter estimation, as a process function to distinguish the thermali

T. X. Hu, D. Wu, J. Zhang

The Bernstein wave is a well-known electrostatic eigen-mode in magnetized plasmas, and it is of broad connection to multiple disciplines, such as controlled nuclear fusions and astrophysics. In this work, we extend the Bernstein mode from classical to quantum plasmas by means of the quantum kinetic theory in a self-consistent manner, and especially the quant

Harry Richman, Farbod Shokrieh, Chenxi Wu

We prove that the principal minors of the distance matrix of a tree satisfy a combinatorial expression involving counts of rooted spanning forests of the underlying tree. This generalizes a result of Graham and Pollak, and refines a result of Graham and Lov\'asz on the coefficients of the characteristic polynomial of the distance matrix. We also give such an

Xinyang Li, Yi Zhang, Yi Xie, Jianfei Yang

Survival prediction is a critical task in pathology. In clinical practice, pathologists often examine multiple cases, leveraging a broader spectrum of cancer phenotypes to enhance pathological assessment. Despite significant advancements in deep learning, current solutions typically model each slide as a sample, struggling to effectively capture comparable a

Leyu Hu, Jiaxin Xie, Xingju Cai, Deren Han

In recent years, a distributed Douglas-Rachford splitting method (DDRSM) has been proposed to tackle multi-block separable convex optimization problems. This algorithm offers relatively easier subproblems and greater efficiency for large-scale problems compared to various augmented-Lagrangian-based parallel algorithms. Building upon this, we explore the exte

Zong Wang, Zhihua Guo, Zhihua Chen, Ming Li

We establish the profound equivalence between measures of genuine multipartite entanglement(GME) and their corresponding coherence measures. Initially we construct two distinct classes of measures for genuine multipartite entanglement utilizing real symmetric concave functions and the convex roof technique. We then demonstrate that all coherence measures for

Paula Pirker-Díaz, Matthew C. Wilson, Sönke Beier, Karoline Wiesner

Uncertainty persists over how and why some countries become democratic and others do not, or why some countries remain democratic and others 'backslide' toward autocracy. Furthermore, while scholars generally agree on the nature of 'democracy' and 'autocracy', the nature of regimes in-between, and changes between them, are much less clear. By applying the sp

Tianyi Zhang, Wenhan Cao, Chang Liu, Tao Zhang

Existing state estimation algorithms for legged robots that rely on proprioceptive sensors often overlook foot slippage and leg deformation in the physical world, leading to large estimation errors. To address this limitation, we propose a comprehensive measurement model that accounts for both foot slippage and variable leg length by analyzing the relative m

Ross G. Pinsky

Let $S_n$ denote the set of permutations of $[n]$ and let $\sigma=\sigma_1\cdots\sigma_n\in S_n$. For a subsequence $\{\sigma_{i_j}\}_{j=1}^k$ of $\{\sigma_i\}_{i=1}^n$ of length $k\ge2$, construct the ``up/down'' sequence $V_1\cdots V_{k-1}$ defined by $$ V_j=\begin{cases} U,\ \text{if}\ \sigma_{i_j+1}-\sigma_{i_j}>0;\\ D,\ \text{if}\ \sigma_{i_j+1}-\sigma_

Antonios Gasteratos, Konstantinos A. Tsintotas, Tobias Fischer, Yiannis Aloimonos

Visual-based recognition, e.g., image classification, object detection, etc., is a long-standing challenge in computer vision and robotics communities. Concerning the roboticists, since the knowledge of the environment is a prerequisite for complex navigation tasks, visual place recognition is vital for most localization implementations or re-localization an

Rajkamal Nailwal, Aljaž Zalar

Given a closed subset $K$ in $\mathbb{R}$, the rational $K$-truncated moment problem ($K$-RTMP) asks to characterize the existence of a positive Borel measure $\mu$, supported on $K$, such that a linear functional $\mathcal{L}$, defined on all rational functions of the form $\frac{f}{q}$, where $q$ is a fixed polynomial with all real zeros of even order and

Jingxuan Li, Yuning Yang, Shengqi Yang, Linfan Zhang

The recent progress in Vision-Language Models (VLMs) has broadened the scope of multimodal applications. However, evaluations often remain limited to functional tasks, neglecting abstract dimensions such as personality traits and human values. To address this gap, we introduce Value-Spectrum, a novel Visual Question Answering (VQA) benchmark aimed at assessi

Kui Ren, Ziqi Yang, Li Lu, Jian Liu

The rapid advancement of AI technology, particularly in generating AI-generated content (AIGC), has transformed numerous fields, e.g., art video generation, but also brings new risks, including the misuse of AI for misinformation and intellectual property theft. To address these concerns, AIGC watermarks offer an effective solution to mitigate malicious acti

Defan Chen, Luchan Zhang

Detecting small objects in complex scenes, such as those captured by drones, is a daunting challenge due to the difficulty in capturing the complex features of small targets. While the YOLO family has achieved great success in large target detection, its performance is less than satisfactory when faced with small targets. Because of this, this paper proposes

Matteo Pistillo, Charlotte Stix

High-impact and potentially dangerous capabilities can and should be broken down into early warning shots long before reaching red lines. Each of these early warning shots should correspond to a precursory capability. Each precursory capability sits on a spectrum indicating its proximity to a final high-impact capability, corresponding to a red line. To mean

Veronica Panizza, Alessandro Roggero, Philipp Hauke, Pietro Faccioli

Lattice models are valuable tools to gain insight into the statistical physics of heteropolymers. We rigorously map the partition function of these models into a vacuum expectation value of a $\mathbb{Z}_2$ lattice gauge theory (LGT), with both fermionic and bosonic degrees of freedom. Because the associated path integral expression is not affected by a sign

Dongseok Shim, Yichun Shi, Kejie Li, H. Jin Kim

Recent advancements in text-to-3D generation, building on the success of high-performance text-to-image generative models, have made it possible to create imaginative and richly textured 3D objects from textual descriptions. However, a key challenge remains in effectively decoupling light-independent and lighting-dependent components to enhance the quality o

Jingqi Zeng, Xiaobin Jia

Traditional Chinese Medicine (TCM) involves complex compatibility mechanisms characterized by multi-component and multi-target interactions, which are challenging to quantify. To address this challenge, we applied graph artificial intelligence to develop a TCM multi-dimensional knowledge graph that bridges traditional TCM theory and modern biomedical science

Frederik Møller, Botond C. Nagy, Márton Kormos, Gábor Takács

We conduct a comprehensive study of anomalous charge transport in the quantum sine--Gordon model. Employing the framework of Generalized Hydrodynamics, we compute Drude weights and Onsager matrices across a wide range of coupling strengths to quantify ballistic and diffusive transport, respectively. We find that charge transport is predominantly diffusive at

Javier Oller-Iscar, Andrés R. Tejedor, Marisol Ripoll, Jorge Ramírez

This work investigates the effects of tangent polar activity on the conformational and dynamic properties of entangled polymer melts through Langevin molecular dynamics simulations. We examine systems composed of all self-propelled, monodisperse linear chains, so that constraint release is considered. The range of activities explored here includes values whe

Yoonki Cho, Jaeyoon Kim, Woo Jae Kim, Junsik Jung

Domain generalizable person re-identification (DG re-ID) aims to learn discriminative representations that are robust to distributional shifts. While data augmentation is a straightforward solution to improve generalization, certain augmentations exhibit a polarized effect in this task, enhancing in-distribution performance while deteriorating out-of-distrib

Pekka Koskela, Riddhi Mishra, Zheng Zhu

In this paper, we study the relationship between Sobolev extension domains and homogeneous Sobolev extension domains. Precisely, we obtain the following results. 1- Let $1\leq q\leq p\leq \infty$. Then a bounded $(L^{1, p}, L^{1, q})$-extension domain is also a $(W^{1, p}, W^{1, q})$-extension domain. 2- Let $1\leq q\leq p<q^\star\leq \infty$ or $n< q \leq p

Haoran Xu, Fredrik Kjolstad

Building a high-performance JIT-capable VM for a dynamic language has traditionally required a tremendous amount of time, money, and expertise. We present Deegen, a meta-compiler that allows users to generate a high-performance JIT-capable VM for their own language at an engineering cost similar to writing a simple interpreter. Deegen takes in the execution