May 2024 arXiv papers — page 41

Logan Murphy, Kaiyu Yang, Jialiang Sun, Zhaoyu Li

Autoformalization involves automatically translating informal math into formal theorems and proofs that are machine-verifiable. Euclidean geometry provides an interesting and controllable domain for studying autoformalization. In this paper, we introduce a neuro-symbolic framework for autoformalizing Euclidean geometry, which combines domain knowledge, SMT s

Guillaume Valette

Given a bounded subanalytic submanifold of $\mathbb{R}^n$, possibly admitting singularities within its closure, we study the cohomology of $L^p$ differential forms having an $L^p$ exterior differential (in the sense of currents) and satisfying Dirichlet or Neumann condition. We show an $L^p$ Hodge decomposition theorem, an $L^p$ de Rham theorem, as well as a

M. Spyropoulou, J. G. Hopker, J. E. Griffin

Athletic performance follows a typical pattern of improvement and decline during a career. This pattern is also often observed within-seasons, as an athlete aims for their performance to peak at key events such as the Olympic Games or World Championships. A Bayesian hierarchical model is developed to analyse the evolution of athletic sporting performance thr

Jing Kong, Yongling Zhao, Kai Gao, Dominik Strebel

Urban heat island (UHI) in conjunction with heatwave (HW) leads to exacerbation of thermal stress in urban areas. Prior research on UHI and HW has predominantly concentrated on examining the thermal conditions at the surface and near-surface, with few investigations extending to the radiative and dynamical interactions of UHI and HW, particularly with a focu

Tong-Yu He, Jia-Jun Yin, Zhen-Yu Wang, Zhan-Wen Han

We present a new Hubble parameterization method and employ observational data from Hubble, Pantheon, and Baryon Acoustic Oscillations to constrain model parameters. The proposed method is thoroughly validated against these datasets, demonstrating a robust fit to the observational data. The obtained best-fit values are $H_0 = 67.5^{+1.3}_{-1.6}$ $\text{km s}^

Shuhao Cao, Francesco Brarda, Ruipeng Li, Yuanzhe Xi

Recent advancements in operator-type neural networks have shown promising results in approximating the solutions of spatiotemporal Partial Differential Equations (PDEs). However, these neural networks often entail considerable training expenses, and may not always achieve the desired accuracy required in many scientific and engineering disciplines. In this p

Marc Kaufmann, Raghu Raman Ravi, Ulysse Schaller

The introduction of geometry has proven instrumental in the efforts towards more realistic models for real-world networks. In Geometric Inhomogeneous Random Graphs (GIRGs), Euclidean Geometry induces clustering of the vertices, which is widely observed in networks in the wild. Euclidean Geometry in multiple dimensions however restricts proximity of vertices

P. Suárez, F. Alcántara-Ávila, J. Rabault, A. Miró

Designing active-flow-control (AFC) strategies for three-dimensional (3D) bluff bodies is a challenging task with critical industrial implications. In this study we explore the potential of discovering novel control strategies for drag reduction using deep reinforcement learning. We introduce a high-dimensional AFC setup on a 3D cylinder, considering Reynold

A. Krolewski, J. Yu, A. J. Ross, S. Penmetsa

The large scale structure catalogs within DESI Data Release 1 (DR1) use nearly 6 million galaxies and quasars as tracers of the large-scale structure of the universe to measure the expansion history with baryon acoustic oscillations and the growth of structure with redshift-space distortions. In order to take advantage of DESI's unprecedented statistical pow

Manuel Crespo-Ballesteros, Misha Sumetsky

Surface Nanoscale Axial Photonic (SNAP) microresonators are fabricated on silica optical fibers, leveraging silica's outstanding material and mechanical properties. These properties allow for precise control over the microresonator dimension, shape, and mode structure, a key feature for reconfigurable photonic circuits. Such circuits find applications in hig

Babita, Abhash Kumar Jha, Bibekananda Maji, Manidipa Pal

Utilizing inverse Mellin transform of the symmetric square $L$-function attached to Ramanujan tau function, Hafner and Stopple proved a conjecture of Zagier, which states that the constant term of the automorphic function $y^{12}|\Delta(z)|^2$ i.e., the Lambert series $y^{12}\sum_{n=1}^\infty \tau(n)^2 e^{-4 \pi n y}$ can be expressed in terms of the non-tri

Jinchao Pan, Jijun Liu

For boundary value problem of an elliptic equation with variable coefficients describing the physical field distribution in inhomogeneous media, the Levi function can represent the solution in terms of volume and surface potentials, with the drawback that the volume potential involving in the solution expression requires heavy computational costs as well as

Felipe Maia Polo, Ronald Xu, Lucas Weber, Mírian Silva

Most popular benchmarks for comparing LLMs rely on a limited set of prompt templates, which may not fully capture the LLMs' abilities and can affect the reproducibility of results on leaderboards. Many recent works empirically verify prompt sensitivity and advocate for changes in LLM evaluation. In this paper, we consider the problem of estimating the perfor

Xiangyu Dong, Xingyi Zhang, Yanni Sun, Lei Chen

The smoothing issue in graph learning leads to indistinguishable node representations, posing significant challenges for graph-related tasks. However, our experiments reveal that this problem can uncover underlying properties of node anomaly detection (NAD) that previous research has missed. We introduce Individual Smoothing Patterns (ISP) and Neighborhood S

Jin Wang, Shichao Dong, Yapeng Zhu, Kelu Yao

Compositional reasoning capabilities are usually considered as fundamental skills to characterize human perception. Recent studies show that current Vision Language Models (VLMs) surprisingly lack sufficient knowledge with respect to such capabilities. To this end, we propose to thoroughly diagnose the composition representations encoded by VLMs, systematica

Hongyu Yang, Liyang He, Min Hou, Shuanghong Shen

Code Community Question Answering (CCQA) seeks to tackle programming-related issues, thereby boosting productivity in both software engineering and academic research. Recent advancements in Reinforcement Learning from Human Feedback (RLHF) have transformed the fine-tuning process of Large Language Models (LLMs) to produce responses that closely mimic human b

Jiayu Qiu, Hai Zhang

This paper investigates interface modes in a square lattice of photonic crystal composed of gyromagnetic particles with $C_{4v}$ point group symmetry. The study shows that Dirac or linear degenerate points cannot occur at the three high-symmetry points in the Brillouin zone where two Bloch bands touch. Instead, a touch point at the M-point has a quadratic de

Rui Dai, Giulio Evangelisti, Sandra Hirche

Friction modeling has always been a challenging problem due to the complexity of real physical systems. Although a few state-of-the-art structured data-driven methods show their efficiency in nonlinear system modeling, deterministic passivity as one of the significant characteristics of friction is rarely considered in these methods. To address this issue, w

Sheng Yang, Peihan Liu, Cengiz Pehlevan

Hyperbolic spaces have increasingly been recognized for their outstanding performance in handling data with inherent hierarchical structures compared to their Euclidean counterparts. However, learning in hyperbolic spaces poses significant challenges. In particular, extending support vector machines to hyperbolic spaces is in general a constrained non-convex

Yuvraj, Hong G. Im, Swetaprovo Chaudhuri

Understanding how intrinsically fast hydrogen-air premixed flames can be rendered much faster in turbulence is crucial for systematically developing hydrogen-based gas turbines and spark ignition engines. Here, we present fundamental insights into the variation of flame displacement speeds by investigating how the disrupted flame structure affects speed and

Jianfeng Zhang

It is well known that a non-cooperative game may have multiple equilibria. In this paper we consider the efficiency of games, measured by the ratio between the aggregate payoff over all Nash equilibria and that over all admissible controls. Such efficiency operator is typically unstable with respect to small perturbation of the game. This seemingly bad prope

Benny Avelin, Tuomo Kuusi, Patrik Nummi, Eero Saksman

We study periodic solutions to the following divergence-form stochastic partial differential equation with Wick-renormalized gradient on the $d$-dimensional flat torus $\mathbb{T}^d$, \[ -\nabla\cdot\left(e^{\diamond (- \beta X) }\diamond\nabla U\right)=\nabla \cdot (e^{\diamond (- \beta X)} \diamond \mathbf{F}), \] where $X$ is the log-correlated Gaussian f

Erwan Lanneau, Livio Liechti

We show that for $g\ge 2$, all integers $1 \le d \le 3g-3$ arise as trace field degrees of pseudo-Anosov mapping classes in the Torelli group of the closed orientable surface of genus $g$. Our method uses the Thurston-Veech construction of pseudo-Anosov maps, and we provide examples where the stretch factor has algebraic degree any even number between two an

Yueji Ma, Dong Xiao, Zuoqiang Shi, Bin Wang

Unoriented surface reconstructions based on the Gauss formula have attracted much attention due to their elegant mathematical formulation and excellent performance. However, the isotropic characteristics of the formulation limit their capacity to leverage the anisotropic information within the point cloud. In this work, we propose a novel anisotropic formula

Karthik Ramanathan, Brandon J. Sandoval, John E. Parker, Lalit M. Joshi

The next generation of rare-event searches, such as those aimed at determining the nature of particle dark matter or in measuring fundamental neutrino properties, will benefit from particle detectors with thresholds at the meV scale, 100-1000x lower than currently available. Quantum parity detectors (QPDs) are a class of proposed quantum devices, extending r

Yin Wu, Xuejuan Yang

Using data from the Chandra X-Ray Observatory, we revisited the reverse shock in the supernova remnant (SNR) Cassiopeia A.Based on the spectroscopic of a series of annuli in the northwest (NW) and southeast (SE), we get the radial profiles of the S/Si K-alpha line flux ratio and Fe K-alpha line centroid energy. They both show monotonic increase, confirming t

Baoren Xiao, Hao Ni, Weixin Yang

Generative adversarial networks (GANs) have emerged as a powerful tool for generating high-fidelity data. However, the main bottleneck of existing approaches is the lack of supervision on the generator training, which often results in undamped oscillation and unsatisfactory performance. To address this issue, we propose an algorithm called Monte Carlo GAN (M

Pradip Kunwar, Kshitiz Aryal, Maanak Gupta, Mahmoud Abdelsalam

The introduction of transformers has been an important breakthrough for AI research and application as transformers are the foundation behind Generative AI. A promising application domain for transformers is cybersecurity, in particular the malware domain analysis. The reason is the flexibility of the transformer models in handling long sequential features a

Piergiorgio Castioni, Sergio Gòmez, Clara Granell, Alex Arenas

In this study, we explore the dynamic interplay between the timing of vaccination campaigns and the trajectory of disease spread in a population. Through comprehensive data analysis and modeling, we have uncovered a counter-intuitive phenomenon: initiating a vaccination process at an inopportune moment can paradoxically result in a more pronounced second pea

Jian Zhao, Lei Jin, Jianshu Li, Zheng Zhu

The SkatingVerse Workshop & Challenge aims to encourage research in developing novel and accurate methods for human action understanding. The SkatingVerse dataset used for the SkatingVerse Challenge has been publicly released. There are two subsets in the dataset, i.e., the training subset and testing subset. The training subsets consists of 19,993 RGB video

Yiming Li, Zehong Wang, Yue Wang, Zhiding Yu

Humans naturally retain memories of permanent elements, while ephemeral moments often slip through the cracks of memory. This selective retention is crucial for robotic perception, localization, and mapping. To endow robots with this capability, we introduce 3D Gaussian Mapping (3DGM), a self-supervised, camera-only offline mapping framework grounded in 3D G

Fengxiang Zhao, Jiashan Zheng, Kaiqiang Li

In this paper, we consider the following parabolic-parabolic-elliptic system } \begin{align*} \left\{\aligned & u_t=\Delta u-\nabla\cdot(u\nabla v)+\xi\nabla\cdot(u\nabla w)+au-\mu u^{\alpha}, && x\in\Omega, t>0,\\ & v_t=\Delta v+\nabla\cdot(v\nabla w)-v+u,&& x\in\Omega, t>0,\\ & 0=\Delta w-w+u,&& x\in\Omega, t>0\\ \endaligned\right. \end{align*} on a bounde

Petr Ourednik, Dinh Tuan Nguyen, Michael Feiginov

We present a model for an accurate description of the large-signal resonant-tunneling-diode (RTD) dynamics, which allows for a simple and intuitive analysis in terms of dynamical trajectories in a phase space. We show that the RTD admittance can be accurately described by a simple RLRC equivalent circuit, which has a universal configuration, but with differe

Simon Cloutier, Robert H. Cameron, Laurent Gizon

The difference between individual solar cycles in the magnetic butterfly diagram can mostly be ascribed to the stochasticity of the emergence process. We aim to obtain the expectation value of the butterfly diagram from observations of four cycles. This allows us to further determine the generation rate of the surface radial magnetic field. We use data from

Jochen L. Cremer, Adrian Kelly, Ricardo J. Bessa, Milos Subasic

Advanced control, operation, and planning tools of electrical networks with ML are not straightforward. 110 experts were surveyed to show where and how ML algorithms could advance. This paper assesses this survey and research environment. Then, it develops an innovation roadmap that helps align our research community with a goal-oriented realisation of the o

Xin-Yuan Gao, D. Blume, Yangqian Yan

While the thermodynamics for bosonic systems with weak $s$-wave interactions has been known for decades, a general and systematic extension to higher partial waves has not yet been reported. We provide closed-form expressions for the equations of state for weakly interacting systems with arbitrary partial waves in the normal phase. Thermodynamics, including

Raphaël Romero, Maarten Buyl, Tijl De Bie, Jefrey Lijffijt

Dynamic Link Prediction (DLP) addresses the prediction of future links in evolving networks. However, accurately portraying the performance of DLP algorithms poses challenges that might impede progress in the field. Importantly, common evaluation pipelines usually calculate ranking or binary classification metrics, where the scores of observed interactions (

Sheng Yang, Jacob A. Zavatone-Veth, Cengiz Pehlevan

The vulnerability of neural network classifiers to adversarial attacks is a major obstacle to their deployment in safety-critical applications. Regularization of network parameters during training can be used to improve adversarial robustness and generalization performance. Usually, the network is regularized end-to-end, with parameters at all layers affecte

Luca Cavenaghi, Sandro Erba, Claudio Zandron

Liquid Marbles are liquid droplets encapsulated by hydrophobic powder particles. They offer an efficient approach to handling liquids due to their non-wetting nature. In this work, starting from the interaction gate proposed in the literature, we describe how the logic gates AND, XOR, OR, NOT, NAND, and NOR could be realized. Given the irreversibility and no

Sullivan Marafico, Jonathan Biteau, Antonio Condorelli, Olivier Deligny

Arrival directions of ultra-high-energy cosmic rays (UHECRs) observed above $4\times10^{19}\,$eV provide evidence of localized excesses that are key to identifying their sources. We leverage the 3D matter distribution from optical and infrared surveys as a density model of UHECR sources, which are considered to be transient. Agreement of the sky model with U

Duarte Gonçalves, Bruno A. Furtado

This paper tackles challenges in pricing and revenue projections due to consumer uncertainty. We propose a novel data-based approach for firms facing unknown consumer type distributions. Unlike existing methods, we assume firms only observe a finite sample of consumers' types. We introduce \emph{empirically optimal mechanisms}, a simple and intuitive class o

Torsten Weber, Jarod Tall, Fabian Haneder, Juan Diego Urbina

The duality of Jackiw-Teitelboim (JT) gravity and a double scaled matrix integral has led to studies of the canonical spectral form factor (SFF) in the so called $\tau-$scaled limit of large times, $t \to \infty$, and fixed temperature in order to demonstrate agreement with universal random matrix theory (RMT). Though this has been established for the unitar

Yuqing Zhang, Yuan Liu, Zhiyu Xie, Lei Yang

2D diffusion model, which often contains unwanted baked-in shading effects and results in unrealistic rendering effects in the downstream applications. Generating Physically Based Rendering (PBR) materials instead of just RGB textures would be a promising solution. However, directly distilling the PBR material parameters from 2D diffusion models still suffer

Jiashan Zheng, Kaiqiang Li

In this paper, we investigate the effects exerted by the interplay among Laplacian diffusion, chemotaxis cross diffusion and the fluid dynamic mechanism on global existence and boundedness of the solutions. The mathematical model considered herein appears as \begin{align}\left\{ \begin{array}{l} n_t+u\cdot\nabla n=\Delta n-\nabla\cdot( nS(n)\nabla c)-nm,\qua

Hongbo Zeng, Chuangxia Huang, Bingwen Liu

In this paper we investigate the iteration problem for several chaos in non-autonomous discrete system. Firstly, we prove that the Li-Yorke chaos of a non-autonomous discrete dynamical system is preserved under iterations when $f_{1,\infty}$ converges to $f$, which weakens the condition in the literature that $f_{1,\infty}$ uniformly converges to $f$. Beside

Adrian Dumitrescu, János Pach

A \emph{complete geometric graph} consists of a set $P$ of $n$ points in the plane, in general position, and all segments (edges) connecting them. It is a well known question of Bose, Hurtado, Rivera-Campo, and Wood, whether there exists a positive constant $c<1$, such that every complete geometric graph on $n$ points can be partitioned into at most $cn$ pla

Mohammad Reza Kolani, Stavros Nousias, André Borrmann

Utilizing robotic systems in the construction industry is gaining popularity due to their build time, precision, and efficiency. In this paper, we introduce a system that allows the coordination of multiple manipulator robots for construction activities. As a case study, we chose robotic brick wall assembly. By utilizing a multi robot system where arm manipu

Elvys Linhares Pontes, Mohamed Benjannet, Raymond Yung

Understanding the business cycle is crucial for building economic stability, guiding business planning, and informing investment decisions. The business cycle refers to the recurring pattern of expansion and contraction in economic activity over time. Economic analysis is inherently complex, incorporating a myriad of factors (such as macroeconomic indicators

Parinya Karndumri

We study supersymmetric Janus solutions from matter-coupled $F(4)$ gauged supergravity coupled to three vector multiplets and $SO(4)\sim SO(3)\times SO(3)$ gauge group. There are two supersymmetric $AdS_6$ vacua preserving all supersymmetries with $SO(3)\times SO(3)$ and $SO(3)_{\textrm{diag}}$ symmetries dual to $N=2$ SCFTs in five dimensions. We consider a

Zhen-Hui Qin, Shu-Mao Wu, Chen-Bei Hao, Hua-Yang Chen

This work proposes a double-layer thin-film lithium niobate (LiNbO3) longitudinally excited shear wave resonator with a theoretical electromechanical coupling coefficient exceeding 60%, RaR close to 28%, and no spurious modes. This ultra-large electromechanical coupling coefficient, which is close to the upper limit of LiNbO3, is much larger than all microwa

J. S. Dowker

A recent numerical evaluation of the spherical universal log coefficient in the Maxwell free--energy and its decomposition into bulk and edge contributions via a bounded hyperbolic geometry mode calculation is shown to be equivalent to an existing compact formulation for $p$--forms, at $p=1$, on a conically deformed sphere. The edge mode seems to be a ghost

Wenhao Zhang, Bin Huang, Shuyue Chen, Xiaoling Xu

Low-dose computed tomography (LDCT) plays a vital role in clinical applications by mitigating radiation risks. Nevertheless, reducing radiation doses significantly degrades image quality. Concurrently, common deep learning methods demand extensive data, posing concerns about privacy, cost, and time constraints. Consequently, we propose a few-shot low-dose CT

Clemens Stiewe, Alice Lixuan Xu, Anselm Eicke, Lion Hirth

The average revenue, or market value, of wind and solar energy tends to fall with increasing market shares, as is now evident across European electricity markets. At the same time, these markets have become more interconnected. In this paper, we empirically study the multiple cross-border effects on the value of renewable energy: on one hand, interconnection

Esmaeil Peyghan, Leila Nourmohammadifar, Damianos Iosifidis

The classification of conformal Killing vector fields for FLRW space-time from Riemannian point of view was done by Maartens-Maharaj in \cite{Maartens1986}. In this paper, we introduce conformal Killing vector fields from a new point of view for the FLRW space-time. In particular, we consider three cases for the conformal factor. Then, it is shown that there

Maximilian Granz, Manuel Heurich, Tim Landgraf

Recent advances in out-of-distribution (OOD) detection on image data show that pre-trained neural network classifiers can separate in-distribution (ID) from OOD data well, leveraging the class-discriminative ability of the model itself. Methods have been proposed that either use logit information directly or that process the model's penultimate layer activat

Simon Heilig, Alessio Gravina, Alessandro Trenta, Claudio Gallicchio

The dynamics of information diffusion within graphs is a critical open issue that heavily influences graph representation learning, especially when considering long-range propagation. This calls for principled approaches that control and regulate the degree of propagation and dissipation of information throughout the neural flow. Motivated by this, we introd

A. Grishkov, D. Logachev

Let $M$ be an uniformizable Anderson t-motive and $L(M)$ its lattice. First, we prove by an explicit construction that for the non-mixed $M$ the lattice map $M\mapsto L(M)$ is not injective. Second, we show that some lattices which do not belong to the set $L(M)$ of pure $M$, are lattices of non-pure $M$. This is a result towards surjectivity of the lattice

Grégoire Pierra

Gravitational waves (GWs) from compact binary coalescences (CBCs) offer insights into the universe expansion. The spectral siren method, used without electromagnetic counterparts (EMC), infers cosmic expansion (Hubble constant) by relating detector and source frame masses of black hole (BH) mergers. However, heuristic mass models (broken power law, power law

Laura M. S. dos Santos, Jonathas M. de Oliveira, Sendy M. S. do Nascimento, Artur F. Sonsin

In tissue engineering, 3D scaffolds and chemical treatments are often used for providing a cell-friendly surface for improving cell adhesion and tissue growth. Indeed, the cell adhesion degree can be controlled by physical-chemical changes in the surface of substrates, such as wettability, surface charges and roughness. In this work, we describe the synthesi

Vagrant Gautam, Arjun Subramonian, Anne Lauscher, Os Keyes

Personal names simultaneously differentiate individuals and categorize them in ways that are important in a given society. While the natural language processing community has thus associated personal names with sociodemographic characteristics in a variety of tasks, researchers have engaged to varying degrees with the established methodological problems in d

Manuel Serra Nunes, Atabak Dehban, Yiannis Demiris, José Santos-Victor

Despite the significant advances in Deep Reinforcement Learning (RL) observed in the last decade, the amount of training experience necessary to learn effective policies remains one of the primary concerns in both simulated and real environments. Looking to solve this issue, previous work has shown that improved efficiency can be achieved by separately model

Yong Liu, Hang Dong, Jinshan Pan, Qingji Dong

While diffusion models significantly improve the perceptual quality of super-resolved images, they usually require a large number of sampling steps, resulting in high computational costs and long inference times. Recent efforts have explored reasonable acceleration schemes by reducing the number of sampling steps. However, these approaches treat all regions

F. Aljuaydi, S. N. Almutairi, A. -B. A. Mohamed

In this work, the Milburn intrinsic decoherence model is used to investigate the role of spin-spin Heisenberg XYZ interaction supported by spin-orbit Dzyaloshinsky Moriya (DM) interactions of x and y directions together in the non-local correlation (NLC) dynamics of Local quantum Fisher information (LQFI), local quantum uncertainty (LQU), and Log-negativity'

Jia-Shu Pan, Yuan-Sen Ting, Yang Huang, Jie Yu

Analyzing time series of fluxes from stars, known as stellar light curves, can reveal valuable information about stellar properties. However, most current methods rely on extracting summary statistics, and studies using deep learning have been limited to supervised approaches. In this research, we investigate the scaling law properties that emerge when learn

S. Sulis, I. J. M. Crossfield, A. Santerne, M. Saillenfest

Context. Planets with radii of between 2-4 RE closely orbiting solar-type stars are of significant importance for studying the transition from rocky to giant planets. Aims. Our goal is to determine the mass of a transiting planet around the very bright F6 star HD 73344 . This star exhibits high activity and has a rotation period that is close to the orbital

Eric O. D. Andriantiana, Zekhaya B. Shozi

Let $G=(V(G),E(G))$ be a graph with set of vertices $V(G)$ and set of edges $E(G)$. A subset $S$ of $E(G)$ is called a $k$-nearly independent edge subsets if there are exactly $k$ pairs of elements of $S$ that share a common end. $Z_k(G)$ is the number of such subsets. This paper studies $Z_1$. Various properties of $Z_1$ are discussed. We characterise the t

Mahir Hadžić, Gerhard Rein, Matthew Schrecker, Christopher Straub

We prove quantitative decay estimates of macroscopic quantities generated by the solutions to linear transport equations driven by a general family of Hamiltonians. The associated particle trajectories are all trapped in a compact region of phase-space and feature a non-degenerate elliptic stagnation point. The analysis covers a large class of Hamiltonians g

Jingqing Ruan, Ziyue Li, Hua Wei, Haoyuan Jiang

Effective multi-intersection collaboration is pivotal for reinforcement-learning-based traffic signal control to alleviate congestion. Existing work mainly chooses neighboring intersections as collaborators. However, quite an amount of congestion, even some wide-range congestion, is caused by non-neighbors failing to collaborate. To address these issues, we

Ming Ying, Xiaoming Chen, Qiao Qi, Wolfgang Gerstacker

Low earth orbit (LEO) satellite internet of things (IoT) is a promising way achieving global Internet of Everything, and thus has been widely recognized as an important component of sixth-generation (6G) wireless networks. Yet, due to high-speed movement of the LEO satellite, it is challenging to acquire timely channel state information (CSI) and design effe

Yaohua Zha, Naiqi Li, Yanzi Wang, Tao Dai

The pre-trained point cloud model based on Masked Point Modeling (MPM) has exhibited substantial improvements across various tasks. However, these models heavily rely on the Transformer, leading to quadratic complexity and limited decoder, hindering their practice application. To address this limitation, we first conduct a comprehensive analysis of existing

Zhihao Jiang, Dongkyu Lee, Alfred J. H. Jones, Youngju Park

Superlattices from twisted graphene mono- and bi-layer systems give rise to on-demand many-body states such as Mott insulators and unconventional superconductors. These phenomena are ascribed to a combination of flat bands and strong Coulomb interactions. However, a comprehensive understanding is lacking because the low-energy band structure strongly changes

Haiwei Dong, Shuang Xie

The rapid advancement of Large Language Models (LLMs) has significantly impacted human-computer interaction, epitomized by the release of GPT-4o, which introduced comprehensive multi-modality capabilities. In this paper, we first explored the deployment strategies, economic considerations, and sustainability challenges associated with the state-of-the-art LL

Juan C. Pérez, Alejandro Pardo, Mattia Soldan, Hani Itani

This study investigates whether Compressed-Language Models (CLMs), i.e. language models operating on raw byte streams from Compressed File Formats~(CFFs), can understand files compressed by CFFs. We focus on the JPEG format as a representative CFF, given its commonality and its representativeness of key concepts in compression, such as entropy coding and run

Eyal Buks

Multistability cannot be derived from any theoretical model that is based on a monostable master equation. On the other hand, multistability is experimentally-observed in a variety of quantum systems. A master equation having a nonlinear term that gives rise to disentanglement has been recently proposed . The dynamics governed by this master equation is expl

Volodymyr Multian, Fan Wu, Dirk van der Marel, Nicolas Ubrig

Two-dimensional van der Waals magnets show strong interconnection between their electrical, magnetic, and structural properties. Here we reveal the emergence of a luminescent transition upon crossing the N\'eel transition temperature of CrPS$_4$, a layered antiferromagnetic semiconductor. This luminescent transition occurs above the lowest absorption level.

Gautam Sridhar, Massimo Vergassola, Joao C. Marques, Michael B. Orger

Animals chain movements into long-lived motor strategies, exhibiting variability across scales that reflects the interplay between internal states and environmental cues. To reveal structure in such variability, we build Markov models of movement sequences that bridges across time scales and enables a quantitative comparison of behavioral phenotypes among in

Benjamin Sorkin, Haim Diamant, Gil Ariel, Tomer Markovich

Materials that are constantly driven out of thermodynamic equilibrium, such as active and living systems, typically violate the Einstein relation. This may arise from active contributions to particle fluctuations which are unrelated to the dissipative resistance of the surrounding medium. We show that in these cases the widely used relation between informati

Xiaohong Fan, Ke Chen, Huaming Yi, Yin Yang

X-ray Computed Tomography (CT) is one of the most important diagnostic imaging techniques in clinical applications. Sparse-view CT imaging reduces the number of projection views to a lower radiation dose and alleviates the potential risk of radiation exposure. Most existing deep learning (DL) and deep unfolding sparse-view CT reconstruction methods: 1) do no

Yong-Qiang Mao, Hanbo Bi, Liangyu Xu, Kaiqiang Chen

Research on multi-view stereo based on remote sensing images has promoted the development of large-scale urban 3D reconstruction. However, remote sensing multi-view image data suffers from the problems of occlusion and uneven brightness between views during acquisition, which leads to the problem of blurred details in depth estimation. To solve the above pro

Cristian Rodriguez-Opazo, Ehsan Abbasnejad, Damien Teney, Hamed Damirchi

Contrastive Language-Image Pretraining (CLIP) stands out as a prominent method for image representation learning. Various architectures, from vision transformers (ViTs) to convolutional networks (ResNets) have been trained with CLIP to serve as general solutions to diverse vision tasks. This paper explores the differences across various CLIP-trained vision b

Eugenio Marinelli, Yiqing Yan, Virginie Magnone, Pascal Barbry

The surge in demand for cost-effective, durable long-term archival media, coupled with density limitations of contemporary magnetic media, has resulted in synthetic DNA emerging as a promising new alternative. Despite its benefits, storing data on DNA poses several challenges as the technology used for reading/writing data and achieving random access on DNA

Kangye Ji, Fei Cheng, Zeqing Wang, Qichang Zhang

Sample selection is a straightforward technique to combat noisy labels, aiming to prevent mislabeled samples from degrading the robustness of neural networks. However, existing methods mitigate compounding selection bias either by leveraging dual-network disagreement or additional forward propagations, leading to multiplied training overhead. To address this

Tomohiro Hayase, Sacha Braun, Hikari Yanagawa, Itsuki Orito

Social VR platforms enable social, economic, and creative activities by allowing users to create and share their own virtual spaces. In social VR, photography within a VR scene is an important indicator of visitors' activities. Although automatic identification of photo spots within a VR scene can facilitate the process of creating a VR scene and enhance the

Franz Motzkus, Georgii Mikriukov, Christian Hellert, Ute Schmid

Ensuring the quality of black-box Deep Neural Networks (DNNs) has become ever more significant, especially in safety-critical domains such as automated driving. While global concept encodings generally enable a user to test a model for a specific concept, linking global concept encodings to the local processing of single network inputs reveals their strength

Renaud Gauthier

We highlight the presence of a ground state in the $\infty$-categorical Grothendieck construction of Lurie, further developed by Arakawa, in which both straightened and unstraightened pictures coexist.

Miao Liang, Shi-Ping Ding, Ming Wu, Chen Zhao

The long-awaited fractional quantum anomalous Hall (FQAH) effect recently has been observed in the twisted $\mathrm{MoTe_2}$ homobilayers, causing a great sensation. Here, we theoretically investigate the moir\'e band structures of a closely related system, the alternating twisted multilayer $\mathrm{MoTe_2}$ (ATML-$\mathrm{MoTe_2}$), where the adjacent laye

Elmar Große-Klönne

Let $F/{\mathbb Q}_p$ be a finite unramified extension, let $k$ be a finite extension of the residue field of $F$. We provide explicit constructions of integral structures for all rank two \'{e}tale Lubin-Tate $(\varphi,{\mathcal O}_F^{\times})$-modules over $k$. We construct algebraic families of such integral structures and show that these comprehensively

Chunjing Gan, Binbin Hu, Bo Huang, Ziqi Liu

Online service platforms offering a wide range of services through miniapps have become crucial for users who visit these platforms with clear intentions to find services they are interested in. Aiming at effective content delivery, cross-domain recommendation are introduced to learn high-quality representations by transferring behaviors from data-rich scena

Jasper Riebesehl, Holger R. Heebøll, Aleksandr Razumov, Michael Galili

Performing noise characterizations of lasers and optical frequency combs on sampled data offers numerous advantages compared to analog measurement techniques. One of the main advantages is that the measurement setup is greatly simplified. Only a balanced detector followed by an analog-to-digital converter is needed, allowing all the complexity to be moved to

Enes Altinisik, Safa Messaoud, Husrev Taha Sencar, Hassan Sajjad

Adversarial Training (AT) impacts different architectures in distinct ways: vision models gain robustness but face reduced generalization, encoder-based models exhibit limited robustness improvements with minimal generalization loss, and recent work in latent-space adversarial training (LAT) demonstrates that decoder-based models achieve improved robustness

Long Cheng, Qihao Shao, Christine Zhao, Sheng Bi

Cross-lingual emotion detection allows us to analyze global trends, public opinion, and social phenomena at scale. We participated in the Explainability of Cross-lingual Emotion Detection (EXALT) shared task, achieving an F1-score of 0.6046 on the evaluation set for the emotion detection sub-task. Our system outperformed the baseline by more than 0.16 F1-sco

Daniel A. Marostica, Rubens E. G. Machado, E. Athanassoula, T. Manos

Barred galaxies constitute about two thirds of observed disc galaxies. Bars affect not only the mass distribution of gas and stars, but also that of the dark matter. An elongation of the inner dark matter halo is known as the halo bar. We aim to characterise the structure of the halo bars, with the goal of correlating them with the properties of the stellar

Jiangpeng Hu, Fan Yang, Fang Nan, Marco Hutter

Autonomous navigation across unstructured terrains, including forests and construction areas, faces unique challenges due to intricate obstacles and the element of the unknown. Lacking pre-existing maps, these scenarios necessitate a motion planning approach that combines agility with efficiency. Critically, it must also incorporate the robot's kinematic con

Elvise Berchio, Matteo Bonforte, Gabriele Grillo

We study nonnegative solutions to the Cauchy problem for the Fractional Fast Diffusion Equation on a suitable class of connected, noncompact Riemannian manifolds. This parabolic equation is both singular and nonlocal: the diffusion is driven by the (spectral) fractional Laplacian on the manifold, while the nonlinearity is a concave power that makes the diffu

Ali Maleki, Moritz B. Heindl, Yongbao Xin, Robert W. Boyd

Graphene has unique properties paving the way for groundbreaking future applications. Its large optical nonlinearity and ease of integration in devices notably makes it an ideal candidate to become a key component for all-optical switching and frequency conversion applications. In the terahertz (THz) region, various approaches have been independently demonst

Victor Chéron, Berend van Wachem

This paper presents novel correlations to predict the drag, lift, and torque coefficients of axi-symmetric non-spherical rod-like particles in a wall-bounded linear shear flow. The particle position and orientation relative to the wall are varied to systematically investigate the influence of the wall on the hydrodynamic forces. The newly derived correlation

Oliver Fürst

We calculate the Witten index of a class of (non-Fredholm) Dirac-Schr\"odinger operators over $\mathbb{R}^{d+1}$ for $d\geq 3$ odd, and thus generalize known results for the case $d=1$. For a concrete example of the potential, we give a more explicit index formula, showing that the Witten index assumes any real number on this class of operators.

Jian Li, Mark T. Mitchison, Saulo V. Moreira

In slowly driven classical systems, work is a stochastic quantity and its probability distribution is known to satisfy the work fluctuation-dissipation relation, which states that the mean and variance of the dissipated work are linearly related. Recently, it was shown that generation of quantum coherence in the instantaneous energy eigenbasis leads to a cor

Zachary Chase, Bogdan Chornomaz, Steve Hanneke, Shay Moran

This work studies embedding of arbitrary VC classes in well-behaved VC classes, focusing particularly on extremal classes. Our main result expresses an impossibility: such embeddings necessarily require a significant increase in dimension. In particular, we prove that for every $d$ there is a class with VC dimension $d$ that cannot be embedded in any extrema

Subhrajit Sahoo, Praveen C. Srivastava

The isoscalar and isovector components and their contributions to $M1$ transitions are discussed in the odd-$A$, $T=1/2$ mirror nuclei with mass number ranging from $A=23$ to 37. The orbital contributions in various $M1$ transitions and ground state magnetic moments are calculated by comparing analogous $M1$ and Gamow-Teller transitions between mirror pairs.