March 2024 arXiv papers — page 91

Hosen Nator, Uzi Pereg

The optimal coordination rates are determined in three primary settings of multi-user quantum networks, thus characterizing the minimal resources required in order to simulate a joint quantum state among multiple parties. We study the following models: (1) a cascade network with rate-limited entanglement, (2) a broadcast network, which consists of a single s

S. Jamal Seyedmohammadi, S. Kawa Atapour, Jamshid Abouei, Arash Mohammadi

Federated Learning (FL) has emerged as a prominent alternative to the traditional centralized learning approach. Generally speaking, FL is a decentralized approach that allows for collaborative training of Machine Learning (ML) models across multiple local nodes, ensuring data privacy and security while leveraging diverse datasets. Conventional FL, however,

William Theisen, Walter Scheirer

The social media landscape of conflict dynamics has grown increasingly multi-modal. Recent advancements in model architectures such as CLIP have enabled researchers to begin studying the interplay between the modalities of text and images in a shared latent space. However, CLIP models fail to handle situations on social media when modalities present in a pos

Saeed Bagherinejad, Thomas Jacobsen, Nuno K. Pratas, Ramoni O. Adeogun

In-body subnetworks (IBS) are envisioned to support reliable wireless connectivity for emerging applications including extended reality (XR) in the human body. As the deployment of in-body sub-networks is uncontrollable by nature, the dynamic radio resource allocation scheme in place becomes of the uttermost importance for the performance of the in-body sub-

Junbo Peng, Chih-Wei Chang, Richard L. J. Qiu, Tonghe Wang

Background: Dual-energy imaging on cone-beam CT (CBCT) scanners has great potential in different clinical applications, including image-guided surgery and adaptive proton therapy. However, the clinical practice of dual-energy CBCT (DE-CBCT) has been hindered by the requirement of sophisticated hardware components. Purpose: In this work, we aim to propose a p

Alexandr Malijevský, Martin Pospíšil

We study bridging transitions between a pair of non-planar surfaces. We show that the transition can be described using a generalized Kelvin equation by mapping the system to a slit of finite length. The proposed equation is applied to analyze the asymptotic behaviour of the growth of the bridging film, which occurs when the confining walls are gradually fla

Khan Reaz, Gerhard Wunder

The existing high-friction device onboarding process hinders the promise and potentiality of Internet of Things (IoT). Even after several attempts by various device manufacturers and working groups, no widely adopted standard solution came to fruition. The latest attempt by Fast Identity Online (FIDO) Alliance promises a zero touch solution for mass market I

Keyu Li, Hanxue Gu, Roy Colglazier, Robert Lark

Scoliosis, a prevalent condition characterized by abnormal spinal curvature leading to deformity, requires precise assessment methods for effective diagnosis and management. The Cobb angle is a widely used scoliosis quantification method that measures the degree of curvature between the tilted vertebrae. Yet, manual measuring of Cobb angles is time-consuming

Peter Bradshaw, Jinghan A Zeng

The online list coloring game is a two-player graph-coloring game played on a graph $G$ as follows. On each turn, a Lister reveals a new color $c$ at some subset $S \subseteq V(G)$ of uncolored vertices, and then a Painter chooses an independent subset of $S$ to which to assign $c$. As the game is played, the revealed colors at each vertex $v \in V(G)$ form

Xiangyu Chen, Jing Liu, Ye Wang, Pu Perry Wang

Low-rank adaptation (LoRA) and its variants are widely employed in fine-tuning large models, including large language models for natural language processing and diffusion models for computer vision. This paper proposes a generalized framework called SuperLoRA that unifies and extends different LoRA variants, which can be realized under different hyper-parame

Xiang Huang, Sitao Cheng, Shanshan Huang, Jiayu Shen

Employing Large Language Models (LLMs) for semantic parsing has achieved remarkable success. However, we find existing methods fall short in terms of reliability and efficiency when hallucinations are encountered. In this paper, we address these challenges with a framework called QueryAgent, which solves a question step-by-step and performs step-wise self-co

D. Pugliese, Z. Stuchlík

Comparing different dark matter (DM) models, we explore the DM influence on black hole (BH) accretion disk physics, considering corotating and counterrotating thick accretion tori orbiting a central spinning BH. Our results identify accretion onto a central BH as a good indicator of DM presence, signaling possible DM tracers in accretion physics. We analyze

Selene Mor, Valentina Gosetti, Vadim F. Agekyan, Claudio Giannetti

The light-mediated interaction of fermionic and bosonic excitations governs the optoelectronic properties of condensed matter systems. In photoexcited semiconductors, the coupling of electron-hole pairs (excitons) to coherent optical phonons enables a modulation of the excitonic resonance that is phase-locked to the frequency of the coupled vibrational mode.

Kai Zhang, Riccardo Zuliani, Efe C. Balta, John Lygeros

This work introduces the Data-Enabled Predictive iteRative Control (DeePRC) algorithm, a direct data-driven approach for iterative LTI systems. The DeePRC learns from previous iterations to improve its performance and achieves the optimal cost. By utilizing a tube-based variation of the DeePRC scheme, we propose a two-stage approach that enables safe active

Liang Xu, Yizhou Zhou, Yichao Yan, Xin Jin

Humans constantly interact with their surrounding environments. Current human-centric generative models mainly focus on synthesizing humans plausibly interacting with static scenes and objects, while the dynamic human action-reaction synthesis for ubiquitous causal human-human interactions is less explored. Human-human interactions can be regarded as asymmet

Roni Kroll, Yoav Tsori

We describe theoretically ``electrolubrication'' in liquid mixtures, the phenomenon where an electric field applied transverse to the confining surfaces leads to concentration gradients that alter the flow profile significantly. When the more polar liquid is the less viscous one, the stress in the liquid falls on two electric-field-induced thin lubrication l

Wen-Hao Zhong, Wei-Lin Li, Yong-Chang Chen, Xue-Jia Yu

The long-range interaction can fundamentally alter properties in gapped topological phases such as emergent massive edge modes. However, recent research has shifted attention to topological nontrivial critical points or phases, and it is natural to explore how long-range interaction influences them. In this work, we investigate the topological behavior and p

Tobias Hallmen, Fabian Deuser, Norbert Oswald, Elisabeth André

In this research, we introduce a novel methodology for assessing Emotional Mimicry Intensity (EMI) as part of the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild. Our methodology utilises the Wav2Vec 2.0 architecture, which has been pre-trained on an extensive podcast dataset, to capture a wide array of audio features that include bot

Jiaxiang Tang, Ruijie Lu, Xiaokang Chen, Xiang Wen

Text-to-texture synthesis has become a new frontier in 3D content creation thanks to the recent advances in text-to-image models. Existing methods primarily adopt a combination of pretrained depth-aware diffusion and inpainting models, yet they exhibit shortcomings such as 3D inconsistency and limited controllability. To address these challenges, we introduc

Andreas Bott, Mario Beykirch, Florian Steinke

Thermal power flow (TPF) is an important task for various control purposes in 4 Th generation district heating grids with multiple decentral heat sources and meshed grid structures. Computing the TPF, i.e., determining the grid state consisting of temperatures, pressures, and mass flows for given supply and demand values, is classically done by solving the n

Shubhra Aich, Wenshan Wang, Parv Maheshwari, Matthew Sivaprakasam

High-speed off-road navigation requires long-range, high-resolution maps to enable robots to safely navigate over different surfaces while avoiding dangerous obstacles. However, due to limited computational power and sensing noise, most approaches to off-road mapping focus on producing coarse (20-40cm) maps of the environment. In this paper, we propose Futur

Jakub Mandula, Jonas Kühne, Luca Pascarella, Michele Magno

Unmanned Aerial Vehicles (UAVs) are gaining popularity in civil and military applications. However, uncontrolled access to restricted areas threatens privacy and security. Thus, prevention and detection of UAVs are pivotal to guarantee confidentiality and safety. Although active scanning, mainly based on radars, is one of the most accurate technologies, it c

Adrian Lutsch, Muhammad El-Hindi, Matthias Heinrich, Daniel Ritter

Trusted Execution Environments (TEEs), such as Intel's Software Guard Extensions (SGX), are increasingly being adopted to address trust and compliance issues in the public cloud. Intel SGX's second generation (SGXv2) addresses many limitations of its predecessor (SGXv1), offering the potential for secure and efficient analytical cloud DBMSs. We assess this p

Yifei Yuan, Chen Shi, Runze Wang, Liyi Chen

Generative query rewrite generates reconstructed query rewrites using the conversation history while rely heavily on gold rewrite pairs that are expensive to obtain. Recently, few-shot learning is gaining increasing popularity for this task, whereas these methods are sensitive to the inherent noise due to limited data size. Besides, both attempts face perfor

V Hewes, Adam Aurisano, Giuseppe Cerati, Jim Kowalkowski

Liquid Argon Time Projection Chamber (LArTPC) detector technology offers a wealth of high-resolution information on particle interactions, and leveraging that information to its full potential requires sophisticated automated reconstruction techniques. This article describes NuGraph2, a Graph Neural Network (GNN) for low-level reconstruction of simulated neu

Marie-Charlotte Brandenburg, Georg Loho, Guido Montúfar

We consider a binary classifier defined as the sign of a tropical rational function, that is, as the difference of two convex piecewise linear functions. The parameter space of ReLU neural networks is contained as a semialgebraic set inside the parameter space of tropical rational functions. We initiate the study of two different subdivisions of this paramet

Meilin Wang, Yexing Song, Pengxu Wei, Xiaoyu Xian

Deep learning technologies have demonstrated their effectiveness in removing cloud cover from optical remote-sensing images. Convolutional Neural Networks (CNNs) exert dominance in the cloud removal tasks. However, constrained by the inherent limitations of convolutional operations, CNNs can address only a modest fraction of cloud occlusion. In recent years,

Yi Chu, David Grace, Josh Shackleton, Andy White

Arxiv is acting weird and throwing error: "Bad character(s) in field Abstract." for no reason. Please refer to the manuscript.

Yuxuan Wang, Xuanyu Yi, Zike Wu, Na Zhao

The advent of 3D Gaussian Splatting (3DGS) has revolutionized 3D editing, offering efficient, high-fidelity rendering and enabling precise local manipulations. Currently, diffusion-based 2D editing models are harnessed to modify multi-view rendered images, which then guide the editing of 3DGS models. However, this approach faces a critical issue of multi-vie

Lars C. Reining, Thomas S. A. Wallis

Mooney images can contribute to our understanding of the processes involved in visual perception, because they allow a dissociation between image content and image understanding. Mooney images are generated by first smoothing and subsequently thresholding an image. In most previous studies this was performed manually, using subjective criteria for generation

Haralambos Panagopoulos, Ettore Vicari

We study the out-of-equilibrium behavior of statistical systems along critical relaxational flows arising from instantaneous quenches of the temperature $T$ to the critical point $T_c$, starting from equilibrium conditions at time $t=0$. In the case of soft quenches, i.e. when the initial temperature $T$ is assumed sufficiently close to $T_c$ (to keep the sy

Ayush Thakur, Rashmi Vashisth

This paper presents Loops On Retrieval Augmented Generation (LoRAG), a new framework designed to enhance the quality of retrieval-augmented text generation through the incorporation of an iterative loop mechanism. The architecture integrates a generative model, a retrieval mechanism, and a dynamic loop module, allowing for iterative refinement of the generat

Mert Özer, Maximilian Weiherer, Martin Hundhausen, Bernhard Egger

Neural Radiance Fields (NeRFs) quickly evolved as the new de-facto standard for the task of novel view synthesis when trained on a set of RGB images. In this paper, we conduct a comprehensive evaluation of neural scene representations, such as NeRFs, in the context of multi-modal learning. Specifically, we present four different strategies of how to incorpor

F. Hinterleitner

Loop quantum gravity methods are applied to a symmetry-reduced model with homogeneity in two dimensions, derived from a Gowdy model [5,6]. The conditions for propagation of unidirectional plane gravitational waves at exactly the speed of light are set up in form of null Killing equations in terms of Ashtekar variables and imposed as operators on quantum stat

Xiyu Deng, Quan Khanh Luu, Anh Van Ho, Yorie Nakahira

Autonomous control systems face significant challenges in performing complex tasks in the presence of latent risks. To address this, we propose an integrated framework that combines Large Language Models (LLMs), numerical optimization, and optimization-based control to facilitate efficient subtask learning while ensuring safety against latent risks. The fram

Parham Dehghani, Mariana Frank, Benjamin Fuks

We investigate the impact of incorporating vector-like leptons into the Standard Model, aiming to address longstanding puzzles related to the anomalous magnetic moments of the muon and electron while maintaining consistency with neutrino masses and mixings. We find that among the various representations of vector-like leptons permitted by the Standard Model

Rathish Das, Omrit Filtser, Matthew J. Katz, Joseph S. B. Mitchell

We propose precise notions of what it means to guard a domain "robustly", under a variety of models. While approximation algorithms for minimizing the number of (precise) point guards in a polygon is a notoriously challenging area of investigation, we show that imposing various degrees of robustness on the notion of visibility coverage leads to a more tracta

Ilias Willems, Sara Rutten, Gilles Crommen, Ingrid Van Keilegom

This paper addresses the problem of identifying and estimating the causal effect of a treatment in the presence of unmeasured confounding and various types of right-censoring. Examples of these censoring mechanisms are administrative censoring, competing risks and dependent censoring (e.g. loss to follow-up). Different parametric transformations are applied

Zvonimir Hartl, Ante Đerek

Single Sign-On (SSO) protocols streamline user authentication with a unified login for multiple online services, improving usability and security. One of the most common SSO protocol frameworks - the Security Assertion Markup Language V2.0 (SAML) Web SSO Profile - has been in use for more than two decades, primarily in government, education and enterprise en

Shanglong Yang, Zhipeng Yuan, Shunbao Li, Ruoling Peng

In the rapidly evolving field of artificial intelligence (AI), the application of large language models (LLMs) in agriculture, particularly in pest management, remains nascent. We aimed to prove the feasibility by evaluating the content of the pest management advice generated by LLMs, including the Generative Pre-trained Transformer (GPT) series from OpenAI

Keqiang Yan, Cong Fu, Xiaofeng Qian, Xiaoning Qian

Crystal structures are characterized by atomic bases within a primitive unit cell that repeats along a regular lattice throughout 3D space. The periodic and infinite nature of crystals poses unique challenges for geometric graph representation learning. Specifically, constructing graphs that effectively capture the complete geometric information of crystals

Michiel Sandra, Christian Nelson, Xuhong Li, Xuesong Cai

Channel sounding is a vital step in understanding wireless channels for the design and deployment of wireless communication systems. In this paper, we present the design and implementation of a coherent distributed massive MIMO channel sounder operating at 5-6 GHz with a bandwidth of 400 MHz based on the NI USRP X410. Through the integration of transceiver c

Chiara Damiolini, Angela Gibney, Daniel Krashen

Through the introduction of new ideals, and with the assistance of the $d$-th mode transition algebras $\mathfrak{A}_d$, for $d\in \mathbb{N}$, we show how Zhu's associative algebra $\mathsf{A}$, conventionally valued for tracking information about the degree $0$ part of an $\mathbb{N}$-graded module over a vertex operator algebra $V$, also contains informat

Ashesh Ashesh, Florian Jug

In this work, we present denoiSplit, a method to tackle a new analysis task, i.e. the challenge of joint semantic image splitting and unsupervised denoising. This dual approach has important applications in fluorescence microscopy, where semantic image splitting has important applications but noise does generally hinder the downstream analysis of image conte

Khan Reaz, Gerhard Wunder

The classical combinatorics-based password strength formula provides a result in tens of bits, whereas the NIST Entropy Estimation Suite give a result between 0 and 1 for Min-entropy. In this work, we present a newly developed metric -- Expectation entropy that can be applied to estimate the strength of any random or random-like password. Expectation entropy

Morten Lüders

We prove the Gersten conjecture for $p$-adic \'etale Tate twists for a smooth scheme $X$ in mixed characteristic in the Nisnevich topology. Our main observation is that, while $p$-adic \'etale Tate twists are not $\mathbb A^1$-invariant, for the proof of the Gersten conjecture it suffices that they satisfy the $\mathbb P^1$-bundle formula. This fits nicely w

Shlok Nahar, Devashish Tupkary, Yuming Zhao, Norbert Lütkenhaus

The postselection technique is an important proof technique for proving the security of quantum key distribution protocols against coherent attacks. In this work, we go through multiple steps to rigorously apply the postselection technique to optical quantum key distribution protocols. First, we place the postselection technique on a rigorous mathematical fo

Hewan Zhang, Kun Ding

Strong coupling between vacuum fields and quantum matter occurs at the nanoscale and broadens the horizon of light-matter interaction. Nanoscale Casimir force, as an exhibition of vacuum fields, inevitably experiences the influence of surface electrons due to their quantum character, which are ignorable in micron Casimir force. Here, we develop a three-dimen

Ziying Song, Lei Yang, Shaoqing Xu, Lin Liu

Integrating LiDAR and camera information into Bird's-Eye-View (BEV) representation has emerged as a crucial aspect of 3D object detection in autonomous driving. However, existing methods are susceptible to the inaccurate calibration relationship between LiDAR and the camera sensor. Such inaccuracies result in errors in depth estimation for the camera branch,

Sebastian Franz, Natalia Kopteva

Time-fractional parabolic equations with a Caputo time derivative of order $\alpha\in(0,1)$ are discretized in time using continuous collocation methods. For such discretizations, we give sufficient conditions for existence and uniqueness of their solutions. Two approaches are explored: the Lax-Milgram Theorem and the eigenfunction expansion. The resulting s

M. P. Ariza, S. Conti, M. Ortiz

When used in bulk applications, mechanical metamaterials set forth a multiscale problem with many orders of magnitude in scale separation between the micro and macro scales. However, mechanical metamaterials fall outside conventional homogenization theory on account of the flexural, or bending, response of their members, including torsion. We show that homog

Hao Wu, Fan Xu, Yifan Duan, Ziwei Niu

This paper proposes a two-stage framework named ST-PAD for spatio-temporal fluid dynamics modeling in the field of earth sciences, aiming to achieve high-precision simulation and prediction of fluid dynamics through spatio-temporal physics awareness and parameter diffusion guidance. In the upstream stage, we design a vector quantization reconstruction module

Yoav Tsori

We describe the ``electrolubrication'' occurring in liquid mixtures confined between two charged surfaces. For a mixture of two liquids, the effective viscosity decreases markedly in the presence of a field. The origin of this reduction is field-induced phase separation, leading to the formation of two low-viscosity lubrication layers at the surfaces. These

Wei Wang, Dongdong Zou, Zhenpeng Wu, Qi Sui

Coherent technology inherent with more availabledegrees of freedom is deemed a competitive solution for nextgeneration ultra-high-speed short-reach optical interconnects.However, the fatal barriers to implementing the conventiona.coherent system in short-reach optical interconnect are the costfootprint, and power consumption. Self-homodyne coherentsystem exh

Juan Elenter, Luiz F. O. Chamon, Alejandro Ribeiro

With the widespread adoption of machine learning systems, the need to curtail their behavior has become increasingly apparent. This is evidenced by recent advancements towards developing models that satisfy robustness, safety, and fairness requirements. These requirements can be imposed (with generalization guarantees) by formulating constrained learning pro

Adnan Theerens, Chris Cornelis

This paper introduces a novel Choquet distance using fuzzy rough set based measures. The proposed distance measure combines the attribute information received from fuzzy rough set theory with the flexibility of the Choquet integral. This approach is designed to adeptly capture non-linear relationships within the data, acknowledging the interplay of the condi

Chunjiang Shi, Ying Chen, Xiangyu Jiang, Ming Gong

We calculate the form factor $M(q^2)$ for the Dalitz decay $J/\psi\to \gamma^*(q^2)\eta_{(N_f=1)}$ with $\eta_{(N_f)}$ being the SU($N_f$) flavor singlet pseudoscalar meson. The difference among the partial widths $\Gamma(J/\psi\to \gamma \eta_{(N_f)})$ at different $N_f$ can be attributed in part to the $\mathbf{U}_A(1)$ anomaly that induces a $N_f$ scaling

Danyang Wang, Chengchun Shi, Shikai Luo, Will Wei Sun

In real-world scenarios, datasets collected from randomized experiments are often constrained by size, due to limitations in time and budget. As a result, leveraging large observational datasets becomes a more attractive option for achieving high-quality policy learning. However, most existing offline reinforcement learning (RL) methods depend on two key ass

Jason L. Harman, Jaelle Scheuerman

This paper describes a generalizable model evaluation method that can be adapted to evaluate AI/ML models across multiple criteria including core scientific principles and more practical outcomes. Emerging from prediction competitions in Psychology and Decision Science, the method evaluates a group of candidate models of varying type and structure across mul

Kelson Kaj, Ian Hammock, Chunxu Chen, Xiaoguang Zhao

We investigate light-matter coupling in the cuprate superconductor La$_{1.85}$Sr$_{0.15}$Cu0$_4$ (LSCO), accomplished by adhering metamaterial resonator arrays (MRAs) to a c-axis oriented single crystal. The resonators couple to the Josephson Plasma Mode (JPM) which manifests as a plasma edge in the terahertz reflectivity in the superconducting state. Terahe

Yi Luo, Zhenghao Lin, Yuhao Zhang, Jiashuo Sun

Large Language Models (LLMs) exhibit impressive capabilities but also present risks such as biased content generation and privacy issues. One of the current alignment techniques includes principle-driven integration, but it faces challenges arising from the imprecision of manually crafted rules and inadequate risk perception in models without safety training

Pere Ara, Ken Goodearl, Pace P. Nielsen, Enrique Pardo

We provide two new formulations of the separativity problem. First, it is known that separativity (and strong separativity) in von Neumann regular (and exchange) rings is tightly connected to unit-regularity of certain kinds of elements. By refining this information, we characterize separative regular rings in terms of a special type of inner inverse operati

Amirreza Silani, Simon H. Tindemans

The rapid increase of photovoltaic cells, batteries, and Electric Vehicles (EVs) in electric grids can result in congested distribution networks. An alternative to enhancing network capacity is a redispatch market, allowing Distribution System Operators (DSOs) to alleviate congested networks by asking energy consumers to change their consumption schedules. H

Sha Zhang, Di Huang, Jiajun Deng, Shixiang Tang

The ability to understand and reason the 3D real world is a crucial milestone towards artificial general intelligence. The current common practice is to finetune Large Language Models (LLMs) with 3D data and texts to enable 3D understanding. Despite their effectiveness, these approaches are inherently limited by the scale and diversity of the available 3D da

Sungjun Han, Sebastian Padó

According to the principle of compositional generalization, the meaning of a complex expression can be understood as a function of the meaning of its parts and of how they are combined. This principle is crucial for human language processing and also, arguably, for NLP models in the face of out-of-distribution data. However, many neural network models, inclu

Javad Rafiei Asl, Mohammad H. Rafiei, Manar Alohaly, Daniel Takabi

Machine learning models are vulnerable to maliciously crafted Adversarial Examples (AEs). Training a machine learning model with AEs improves its robustness and stability against adversarial attacks. It is essential to develop models that produce high-quality AEs. Developing such models has been much slower in natural language processing (NLP) than in areas

Xing-Jian Lv, Xiao-Jun Bi, Kun Fang, Yi-Qing Guo

Recently LHAASO Collaboration gives precise measurements of cosmic rays (CR) all particle energy spectrum and mean logarithmic mass $\left \langle \ln A \right \rangle$ from 0.3 PeV to 30 PeV. Combining the CR measurements by AMS-02 and DAMPE in space and that by LHAASO and Auger on the ground we construct a model to recover all these measurements from tens

Lingzhe Zhao, Peng Wang, Peidong Liu

While neural rendering has demonstrated impressive capabilities in 3D scene reconstruction and novel view synthesis, it heavily relies on high-quality sharp images and accurate camera poses. Numerous approaches have been proposed to train Neural Radiance Fields (NeRF) with motion-blurred images, commonly encountered in real-world scenarios such as low-light

Andrea Venturi, Dario Stabili, Mirco Marchetti

Machine Learning (ML) algorithms have become increasingly popular for supporting Network Intrusion Detection Systems (NIDS). Nevertheless, extensive research has shown their vulnerability to adversarial attacks, which involve subtle perturbations to the inputs of the models aimed at compromising their performance. Recent proposals have effectively leveraged

László Kovács, Ali Jlidi

One of the hot topics in machine learning is the field of GNN. The complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. This paper represents a survey, providing a comprehensive overview of Graph Neural Networks (GNNs). We

Hossein Movasati

In this article we prove that linear differential equations with only algebraic solutions have zero $m$-curvature modulo $p^k$ for all except a finite number of primes $p$ and all $k,m\in\mathbb N$ with ${\rm ord}_pm!\geq k$. This provides us with a reformulation of Grothendieck-Katz conjecture with stronger hypothesis.

Manuel Ratz, Miguel A. Mendez

We propose a method to obtain superresolution of turbulent statistics for three-dimensional ensemble particle tracking velocimetry (EPTV). The method is ''meshless'' because it does not require the definition of a grid for computing derivatives, and it is ''binless'' because it does not require the definition of bins to compute local statistics. The method c

Daniel Aleksander Krause, Archontis Politis, Annamaria Mesaros

Sound Event Detection and Localization (SELD) is a combined task of identifying sound events and their corresponding direction-of-arrival (DOA). While this task has numerous applications and has been extensively researched in recent years, it fails to provide full information about the sound source position. In this paper, we overcome this problem by extendi

Rameswar Sahu

With the advent of advanced machine learning techniques, boosted object tagging has witnessed significant progress. In this article, we take this field further by introducing novel architectural modifications compatible with a wide array of Graph Neural Network (GNN) architectures. Our approach advocates for integrating capsule layers, replacing the conventi

Gonzalo Contreras-Aso, Regino Criado, Miguel Romance

The study of hypergraphs has received a lot of attention over the past few years, however up until recently there has been no interest in systems where higher order interactions are not undirected. In this article we introduce the notion of heterogeneous hypergraphs from an algebraic point of view, which have traditional directed hypergraphs as a particular

Laurence Carassus, Massinissa Ferhoune

We study a general robust utility maximization problem in a discrete-time frictionless market. The investor is assumed to have a possibly infinite, random, nonconcave, and nondecreasing utility function defined on the whole real line. She also faces model ambiguity on her beliefs about the market, which is modelled through a set of priors. We assume that the

Sudip Karan, Gurmeet Singh Punia, Surajit Biswas

The leading quantum-gravitational correction to the black hole entropy is known to be a universal logarithmic term. In this study, we investigate the logarithmic corrections for the black holes in the STU supergravity models, which are a bosonic truncation into a specific class of $U(1)^2$-charged Einstein-Maxwell-dilaton theory. We demonstrate how the entir

Weizheng Cai, Xinyi He, Tian-Nan Ye, Xinmeng Hu

Two-dimensional (2D) heterostructuring is a versatile methodology for designing nanoarchitecture catalytic systems that allow for reconstruction and modulation of interfaces and electronic structures. However, catalysts with such structures are extremely scarce due to limited synthetic strategies. Here, we report a highly ordered 2D Ru/Si nano-heterostructur

Sebastian Hartwig, Dominik Engel, Leon Sick, Hannah Kniesel

AI-based text-to-image models do not only excel at generating realistic images, they also give designers more and more fine-grained control over the image content. Consequently, these approaches have gathered increased attention within the computer graphics research community, which has been historically devoted towards traditional rendering techniques, that

Danial Majidi, Justin P. Bergfield, Ville Maisi, Johannes Höfer

In this perspective, we discuss thermal imbalance and the associated electron-mediated thermal transport in quantum electronic devices at very low temperatures. We first present the theoretical approaches describing heat transport in nanoscale conductors at low temperatures, in which quantum confinement and interactions play an important role. We then discus

Julius Fergy Tiongson Rabago, Lekbir Afraites, Hirofumi Notsu

A non-conventional shape optimization approach is introduced to address the identification of an obstacle immersed in a fluid described by the Stokes equation within a larger bounded domain, relying on boundary measurements on the accessible surface. The approach employs tools from shape optimization, utilizing the coupled complex boundary method to transfor

Hui Lu, Albert Ali Salah, Ronald Poppe

A key challenge in continuous sign language recognition (CSLR) is to efficiently capture long-range spatial interactions over time from the video input. To address this challenge, we propose TCNet, a hybrid network that effectively models spatio-temporal information from Trajectories and Correlated regions. TCNet's trajectory module transforms frames into al

Sha Zhang, Jiajun Deng, Lei Bai, Houqiang Li

We present a hybrid-view-based knowledge distillation framework, termed HVDistill, to guide the feature learning of a point cloud neural network with a pre-trained image network in an unsupervised manner. By exploiting the geometric relationship between RGB cameras and LiDAR sensors, the correspondence between the two modalities based on both image-plane vie

Hussein Sibai, Sacha Huriot, Tyler Martin, Murat Arcak

We propose an efficient symbolic control synthesis algorithm for equivariant continuous-time dynamical systems to satisfy reach-avoid specifications. The algorithm exploits dynamical symmetries to construct lean abstractions to avoid redundant computations during synthesis. Our proposed algorithm adds another layer of abstraction over the common grid-based d

Qingcang Shui, Shu Zhang, Pengju Wang, Alexander Mushtukov

We present a detailed analysis of the X-ray luminosity (Lx) dependence of the cyclotron absorption line energy (Ecyc) for the X-ray binary pulsar 1A 0535+262 during its 2020 giant outburst based on pulse-to-pulse analysis. By applying this technique to high cadence observations of Insight-HXMT, we reveal the most comprehensive Ecyc-Lx correlation across a br

Chuhan Tang, Zhiqiang Tian, Tao Ouyang, Anlian Pan

Going beyond the bistability paradigm of the charge polarizations in ferroelectrics is highly desired for ferroelectric (FE) memory devices toward ultra-high-density information storage. Here, we propose to build multistates by combining the intrinsic and sliding-induced polarizations. The physics is that there is at least one order of magnitude difference i

Yuqi Zhang, Guanying Chen, Jiaxing Chen, Shuguang Cui

We present a neural radiance field method for urban-scale semantic and building-level instance segmentation from aerial images by lifting noisy 2D labels to 3D. This is a challenging problem due to two primary reasons. Firstly, objects in urban aerial images exhibit substantial variations in size, including buildings, cars, and roads, which pose a significan

Md. Musfiqur Rahman Sanim, Safrunnesa Saira, Fatin Faiaz Ahsan, Rajon Bardhan

Given a n points in two dimensional space, a Manhattan Network G is a network that connects all n points with either horizontal or vertical edges, with the property that for any two point in G should be connected by a Manhattan path and distance between this two points is equal to Manhattan Distance. The Minimum Manhattan Network problem is to find a Manhatt

Xiaoyu Tong, Rochelle Choenni, Martha Lewis, Ekaterina Shutova

Metaphors in natural language are a reflection of fundamental cognitive processes such as analogical reasoning and categorisation, and are deeply rooted in everyday communication. Metaphor understanding is therefore an essential task for large language models (LLMs). We release the Metaphor Understanding Challenge Dataset (MUNCH), designed to evaluate the me

Shicong Liu, Xianghao Yu, Zhen Gao, Jie Xu

Future sixth-generation (6G) systems are expected to leverage extremely large-scale multiple-input multiple-output (XL-MIMO) technology, which significantly expands the range of the near-field region. The spherical wavefront characteristics in the near field introduce additional degrees of freedom (DoFs), namely distance and angle, into the channel model, wh

Wangbo Zhao, Jiasheng Tang, Yizeng Han, Yibing Song

Existing parameter-efficient fine-tuning (PEFT) methods have achieved significant success on vision transformers (ViTs) adaptation by improving parameter efficiency. However, the exploration of enhancing inference efficiency during adaptation remains underexplored. This limits the broader application of pre-trained ViT models, especially when the model is co

Jen-tse Huang, Eric John Li, Man Ho Lam, Tian Liang

Decision-making is a complex process requiring diverse abilities, making it an excellent framework for evaluating Large Language Models (LLMs). Researchers have examined LLMs' decision-making through the lens of Game Theory. However, existing evaluation mainly focus on two-player scenarios where an LLM competes against another. Additionally, previous benchma

Yiping Zuo, Jiajia Guo, Biyun Sheng, Chen Dai

In the evolving environment of mobile edge computing (MEC), optimizing system performance to meet the growing demand for low-latency computing services is a top priority. Integrating fluidic antenna (FA) technology into MEC networks provides a new approach to address this challenge. This letter proposes an FA-enabled MEC scheme that aims to minimize the tota

Wangsong Yin, Mengwei Xu, Yuanchun Li, Xuanzhe Liu

Being more powerful and intrusive into user-device interactions, LLMs are eager for on-device execution to better preserve user privacy. In this work, we propose a new paradigm of mobile AI: LLM as a system service on mobile devices (LLMaaS). Unlike traditional DNNs that execute in a stateless manner, such a system service is stateful: LLMs execution often n

Tim D. Pearce

'Debris disks' are collections of small bodies around stars, such as the Asteroid Belt and Kuiper Belt in our Solar System. These disks are composed of objects smaller than planets, including asteroids, comets, dust, and dwarf planets. We detect debris disks around a significant fraction of stars, and these disks appear to be common components of planetary s

Qian Dai, Dong Wei, Hong Liu, Jinghan Sun

Most existing federated learning (FL) methods for medical image analysis only considered intramodal heterogeneity, limiting their applicability to multimodal imaging applications. In practice, it is not uncommon that some FL participants only possess a subset of the complete imaging modalities, posing inter-modal heterogeneity as a challenge to effectively t

Mingyang Song, Mao Zheng, Xuan Luo

Despite recent efforts to develop large language models with robust long-context capabilities, the lack of long-context benchmarks means that relatively little is known about their performance. To alleviate this gap, in this paper, we propose \textbf{Counting-Stars}, a multi-evidence, position-aware, and scalable benchmark designed to evaluate the multi-evid

Mathias M. Claus, Marcus Wyss, Dirk Schüler, Martino Poggio

Many bacteria share the fascinating ability to sense Earth's magnetic field -- a process known as magnetotaxis. These bacteria synthesize magnetic nanoparticles, called magnetosomes, within their own cell body and arrange them to form a linear magnetic chain. The chain, which behaves like a compass needle, aligns the microorganisms with the geomagnetic field

Aharon Davidson, Nadav Barkai

Let a physical event constitute a simple loop in spacetime. This in turn calls for a generalized loop line element (= distance$^2$ between two neighboring loops) capable of restoring, at the shrinking loop limit, the special relativistic line element (= distance$^2$ between the two neighboring centers-of-mass, respectively). Sticking at first stage to a flat

Hendrik Schrautzer, Moritz Sallermann, Pavel F. Bessarab, Hannes Jónsson

A method for locating first order saddle points on the energy surface of a magnetic system is described and several applications presented where the mechanism of various magnetic transitions is identified. The starting point for the iterative search algorithm can be anywhere, even close to a local energy minimum representing an initial state of the system, a