May 2024 arXiv papers — page 107

Kengbo Lu, Abigail Raz

The Union-Closed Sets Conjecture, often attributed to P\'eter Frankl in 1979, remains an open problem in discrete mathematics. It posits that for any finite family of sets $S\neq\{\emptyset\}$, if the union of any two sets in the family is also in the family, then $\underline{\text{there must exist an element that belongs to at least half of the member sets}

Yurun Gu, Clément Rey

In this article, we focus on computing the quantiles of a random variable $f(X)$, where $X$ is a $[0,1]^d$-valued random variable, $d \in \mathbb{N}^{\ast}$, and $f:[0,1]^d\to \mathbb{R}$ is a deterministic Lipschitz function. We are particularly interested in scenarios where the cost of a single function evaluation is high, while the law of $X$ is assumed t

Haoyi Wu, Kewei Tu

Huge memory consumption has been a major bottleneck for deploying high-throughput large language models in real-world applications. In addition to the large number of parameters, the key-value (KV) cache for the attention mechanism in the transformer architecture consumes a significant amount of memory, especially when the number of layers is large for deep

Qianqian Chen, Shaoyuan Li

This paper investigates an aperiodic distributed model predictive control approach for multi-agent systems (MASs) in which parameterized synchronization constraints is considered and an innovative self-triggered criterion is constructed. Different from existing coordination methodology, the proposed strategy achieves the cooperation of agents through the syn

Omar Hurtado

We extend methods of Ding and Smart from their breakthrough paper in 2020 which showed Anderson localization for certain random Schr\"odinger operators on $\ell^2(\mathbb{Z}^2)$ via a quantitative unique continuation principle and Wegner estimate. We replace the requirement of identical distribution with the requirement of a uniform bound on the essential ra

Lukas Schauer, Thorsten Horstmann, Steffen Druesedow, Michael Rademacher

This paper introduces a novel Wireshark dissector designed to facilitate the analysis of Service-Based Interface (SBI) communication in 5G Core Networks. Our approach involves parsing the OpenAPI schemes provided by the 5G specification to automatically generate the dissector code. Our tool enables the validation of 5G Core Network traces to ensure complianc

Go Soma, Kento Komatsu, Chun Ren, Yoshiaki Nakano

Imaging polarimetry plays an essential role in various fields since it imparts rich information that cannot be obtained through mere intensity and spectral measurements. To retrieve full Stokes parameters, at least four sensor pixels are required, each of which projects incident light to a different polarization state in the Stokes space. Conventional full-S

Rongrong Ma, Guansong Pang, Ling Chen

Graph neural networks (GNNs) have achieved state-of-the-art performance in graph representation learning. Message passing neural networks, which learn representations through recursively aggregating information from each node and its neighbors, are among the most commonly-used GNNs. However, a wealth of structural information of individual nodes and full gra

Lujain Ibrahim, Saffron Huang, Umang Bhatt, Lama Ahmad

Current AI evaluation methods, which rely on static, model-only tests, fail to account for harms that emerge through sustained human-AI interaction. As AI systems proliferate and are increasingly integrated into real-world applications, this disconnect between evaluation approaches and actual usage becomes more significant. In this paper, we propose a shift

Kyuhyeon Choi

This study explores the relationship between the precise asymptotics of the level-two large deviation rate function and the behavior of metastable stochastic systems. Initially identified for overdamped Langevin dynamics (Ges{\`u} et al., SIAM J Math Anal 49(4), 3048-3072, 2017), this connection has been validated across various models, including random walk

Xiaoming Shi, Zeming Liu, Li Du, Yuxuan Wang

This paper surveys and organizes research works on medical dialog systems, which is an important yet challenging task. Although these systems have been surveyed in the medical community from an application perspective, a systematic review from a rigorous technical perspective has to date remained noticeably absent. As a result, an overview of the categories,

Anastasia Voznyuk, Vasily Konovalov

The Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection shared task in the SemEval-2024 competition aims to tackle the problem of misusing collaborative human-AI writing. Although there are a lot of existing detectors of AI content, they are often designed to give a binary answer and thus may not be suitable for more nuan

Juan Diego Arias Espinoza, Philippe Corboz

Infinite projected entangled-pair states (iPEPS) provide a powerful tool to study two-dimensional strongly correlated systems directly in the thermodynamic limit. In this work, we extend the iPEPS toolbox by a method to efficiently evaluate non-equal time two-point correlators, enabling the computation of spectral functions. It is based on an iPEPS ansatz of

Michael Bonitz, Jan Vorberger, Mandy Bethkenhagen, Maximilian Böhme

Accurate knowledge of the properties of hydrogen at high compression is crucial for astrophysics (e.g. planetary and stellar interiors, brown dwarfs, atmosphere of compact stars) and laboratory experiments, including inertial confinement fusion. There exists experimental data for the equation of state, conductivity, and Thomson scattering spectra. However, t

Yudong Li, Yuhao Feng, Wen Zhou, Zhe Zhao

Large Language Models (LLMs) have gained significant attention in the field of natural language processing (NLP) due to their wide range of applications. However, training LLMs for languages other than English poses significant challenges, due to the difficulty in acquiring large-scale corpus and the requisite computing resources. In this paper, we propose C

Jiayun Pang, Ivan Vulić

Transformer-based encoder-decoder models have demonstrated impressive results in chemical reaction prediction tasks. However, these models typically rely on pretraining using tens of millions of unlabelled molecules, which can be time-consuming and GPU-intensive. One of the central questions we aim to answer in this work is: Can FlanT5 and ByT5, the encode-d

Washim Uddin Mondal, Vaneet Aggarwal

We consider a constrained Markov Decision Problem (CMDP) where the goal of an agent is to maximize the expected discounted sum of rewards over an infinite horizon while ensuring that the expected discounted sum of costs exceeds a certain threshold. Building on the idea of momentum-based acceleration, we develop the Primal-Dual Accelerated Natural Policy Grad

Arash Jamshidi, Seyed Mohammad Hosseini, Seyed Mahdi Noormousavi, Mahdi Jafari Siavoshani

We present a new approach to machine learning-powered combinatorial auctions, which is based on the principles of Differential Privacy. Our methodology guarantees that the auction mechanism is truthful, meaning that rational bidders have the incentive to reveal their true valuation functions. We achieve this by inducing truthfulness in the auction dynamics,

Jinchuan Zhang, Ming Sun, Chong Mu, Jinhao Zhang

Temporal Knowledge Graph (TKG) reasoning focuses on predicting events through historical information within snapshots distributed on a timeline. Existing studies mainly concentrate on two perspectives of leveraging the history of TKGs, including capturing evolution of each recent snapshot or correlations among global historical facts. Despite the achieved si

Zhaohuan Zhan, Lisha Yu, Sijie Yu, Guang Tan

In the Vision-and-Language Navigation (VLN) task, the agent is required to navigate to a destination following a natural language instruction. While learning-based approaches have been a major solution to the task, they suffer from high training costs and lack of interpretability. Recently, Large Language Models (LLMs) have emerged as a promising tool for VL

Antoine Ducros

This is a work in progress, far from being in its final form whose purpose is to investigate thoroughly the structure of Berkovich analytic curves and its relation with the semi-stable reduction theorem (of which a new proof is given here, starting from the local study of Berkovich curves) through the formalism of "triangulations". It has been already on the

Guner Dilsad Er, Sebastian Trimpe, Michael Muehlebach

We consider a distributed learning problem, where agents minimize a global objective function by exchanging information over a network. Our approach has two distinct features: (i) It substantially reduces communication by triggering communication only when necessary, and (ii) it is agnostic to the data-distribution among the different agents. We therefore gu

Qianqian Chen, Shaoyuan Li

This paper investigates the distributed model predictive control for an asynchronous nonlinear multi-agent system with external interference via a self-triggered generator and a prediction horizon regulator. First, a shrinking constraint related to the error between the actual state and the predicted state is introduced into the optimal control problem to en

Sebastian Bischof

Let $(W, S)$ be a Coxeter system of rank $n$ and let $p_{(W, S)}(t)$ be its growth function. It is known that $p_{(W, S)}(q^{-1}) < \infty$ holds for all $n \leq q \in \mathbb{N}$. In this paper we will show that this still holds for $q = n-1$, if $(W, S)$ is $2$-spherical. Moreover, we will prove that $p_{(W, S)}(q^{-1}) = \infty$ holds for $q = n-2$, if th

Yixin Ji, Yang Xiang, Juntao Li, Qingrong Xia

In recent years, large language models (LLMs) have driven advances in natural language processing. Still, their growing scale has increased the computational burden, necessitating a balance between efficiency and performance. Low-rank compression, a promising technique, reduces non-essential parameters by decomposing weight matrices into products of two low-

Antonio Niemela, Nicolas Wijsen, Angels Aran, Luciano Rodriguez

Gradual solar energetic particle (SEP) events are associated with shocks driven by coronal mass ejections (CMEs). The merging of two CMEs (so-called Cannibalistic CMEs) and the interaction of their associated shocks, has been linked to some of the most powerful solar storms ever recorded. Multiple studies have focused on the observational aspects of these SE

E. Lalounta, P. -E. Christopoulou, A. Papageorgiou, C. E. Ferreira Lopes

Low mass ratio systems (LMR) are a very interesting class of contact eclipsing binaries challenging the theoretical models of stability. These systems are also considered possible progenitors of the rare low-mass optical transients called red novae. In this study, we present the identification of 7 new totally eclipsing LMR systems from Catalina Sky Surveys

Simon Badger

We present the status of on-going efforts to compute the two-loop virtual corrections to $pp\to t\bar{t} j$ in the leading colour approximation. We review the recent study of the master integrals and their differential equations and present some ideas on modern techniques that can be applied to provide complete evaluation of helicity amplitudes containing in

Sheng Yang, Jiawang Bai, Kuofeng Gao, Yong Yang

Given the power of vision transformers, a new learning paradigm, pre-training and then prompting, makes it more efficient and effective to address downstream visual recognition tasks. In this paper, we identify a novel security threat towards such a paradigm from the perspective of backdoor attacks. Specifically, an extra prompt token, called the switch toke

Remi Desmartin, Omri Isac, Grant Passmore, Ekaterina Komendantskaya

Recent advances in the verification of deep neural networks (DNNs) have opened the way for a broader usage of DNN verification technology in many application areas, including safety-critical ones. However, DNN verifiers are themselves complex programs that have been shown to be susceptible to errors and numerical imprecision; this, in turn, has raised the qu

Zikun Zhou, Wentao Xiong, Li Zhou, Xin Li

The crux of Referring Video Object Segmentation (RVOS) lies in modeling dense text-video relations to associate abstract linguistic concepts with dynamic visual contents at pixel-level. Current RVOS methods typically use vision and language models pretrained independently as backbones. As images and texts are mapped to uncoupled feature spaces, they face the

Jasper Stokman

In a recent joint paper with S. Sahi and V. Venkateswaran (2025), families of actions of the double affine Hecke algebra on spaces of quasi-polynomials were introduced. These so-called quasi-polynomial representations led to the introduction of quasi-polynomial extensions of the nonsymmetric Macdonald polynomials, which reduce to metaplectic Iwahori-Whittake

Irene Ferfoglia, Gaia Saveri, Laura Nenzi, Luca Bortolussi

Deep learning methods for time series have already reached excellent performances in both prediction and classification tasks, including anomaly detection. However, the complexity inherent in Cyber Physical Systems (CPS) creates a challenge when it comes to explainability methods. To overcome this inherent lack of interpretability, we propose ECATS, a concep

Ruigang Zheng, Xiaosheng Zhuang

In this paper, we prove the existence of a spherical $t$-design formed by adding extra points to an arbitrarily given point set on the sphere and, subsequently, deduce the existence of nested spherical designs. Estimates on the number of required points are also given. For the case that the given point set is a spherical $t_1$-design such that $t_1 < t$ and

Haotian Liu, Zhiqing Wei, Jinghui Piao, Huici Wu

In the evolution towards the forthcoming era of sixth-generation (6G) mobile communication systems characterized by ubiquitous intelligence, integrated sensing and communication (ISAC) is in a phase of burgeoning development. However, the capabilities of communication and sensing within single frequency band fall short of meeting the escalating demands. To t

L. Schiltz, B. Escribano, G. M. Muñoz Caro, S. Cazaux

The surfaces of icy moons are primarily composed of water ice that can be mixed with other compounds, such as carbon dioxide. The carbon dioxide (CO$_2)$ stretching fundamental band observed on Europa and Ganymede appears to be a combination of several bands that are shifting location from one moon to another. We investigate the cause of the observed shift i

Chonticha Kritpetch, Nandan Roy, Narayan Banerjee

We investigate the interaction between the dark sectors from the point of view of a dynamical system analysis. A general setup for interacting dark energy models that incorporates both quintessence and phantom fields through a switch parameter, allowing an interaction in the dark sectors, has been considered. In the first part of our analysis, we have not as

Julian Gurs, Mikhail Korobko, Christian Darsow-Fromm, Sebastian Steinlechner

Ultra-stable, quasi-monochromatic laser light forms the basis for high-precision interferometric measurements, e.g. for observing gravitational waves and for time keeping with optical clocks. Optical frequency conversion enables access to wavelengths at which optical materials have the lowest absorption and the lowest mechanical loss. Here we report a 25 % r

Xiaosong Sun, Shuai Zeng

The interest in Danielewski varieties arose from the study of the Cancellation Problem. In this paper, we study the isomorphism classes and stably isomorphisms of double Danielewski varieties, and show that they are counterexamples of the Cancellation Problem.

Alok Laddha, Amit Suthar

Arkani-Hamed, Bai, He, and Yan (ABHY) discovered a convex realisation of the associahedron whose combinatorial and geometric structure generates tree-level amplitudes in bi-adjoint scalar theory. In this paper, we identify S-matrix of Yang-Mills theory with a scalar obtained by contracting the canonical form of ABHY associahedron with a multi-vector field (M

Hua Chen, Xin Liao, Ming Zhang

In this paper, we investigate the existence of weak solutions for a class of degenerate elliptic Dirichlet problems with critical nonlinearity and a logarithmic perturbation

Ray Ganardi, Tulja Varun Kondra, Nelly H. Y. Ng, Alexander Streltsov

A central question since the beginning of quantum information science is how two distant parties can convert one entangled state into another. It has been conjectured that such conversions could be executed reversibly in an asymptotic regime, mirroring the reversible nature of Carnot cycles in classical thermodynamics. While a conclusive proof of this conjec

Mads Vestergaard Schmidt, Erich Schoedl

The rod and slit paradox, as first proposed by R. Shaw, is revisited. In this paradox, a rod of rest length $l_0$ moves parallel to the horizontal x-axis. Simultaneously, a thin sheet parallel to the horizontal xz-plane with a rod-shaped slit of rest length $d < l_0$ moves along the vertical y-axis. The rod passes through the slit due to relativistic effects

Junhong Zou, Xiangyu Zhu, Zhaoxiang Zhang, Zhen Lei

Humans' innate ability to decompose scenes into objects allows for efficient understanding, predicting, and planning. In light of this, Object-Centric Learning (OCL) attempts to endow networks with similar capabilities, learning to represent scenes with the composition of objects. However, existing OCL models only learn through reconstructing the input image

Jiawei Li, Jingshu Peng, Haoyang Li, Lei Chen

Time-series analysis plays a pivotal role across a range of critical applications, from finance to healthcare, which involves various tasks, such as forecasting and classification. To handle the inherent complexities of time-series data, such as high dimensionality and noise, traditional supervised learning methods first annotate extensive labels for time-se

Rudolf Herdt, Louisa Kinzel, Johann Georg Maaß, Marvin Walther

Rodents employ a broad spectrum of ultrasonic vocalizations (USVs) for social communication. As these vocalizations offer valuable insights into affective states, social interactions, and developmental stages of animals, various deep learning approaches have aimed to automate both the quantitative (detection) and qualitative (classification) analysis of USVs

Xingmei Wang, Weiwen Liu, Xiaolong Chen, Qi Liu

Click-Through Rate (CTR) prediction holds a paramount position in recommender systems. The prevailing ID-based paradigm underperforms in cold-start scenarios due to the skewed distribution of feature frequency. Additionally, the utilization of a single modality fails to exploit the knowledge contained within textual features. Recent efforts have sought to mi

Cheng Liu, Xiaofeng Wang, Xiaobing Zhang, Mikhail Kovalev

We present a comprehensive photometric and spectroscopic analysis of the short-period ($\sim$5.32 hours) and low-mass eclipsing binary TMTSJ0803 discovered by Tsinghua-Ma Huateng Telescope for Survey (TMTS). By fitting the light curves and radial velocity data with the Wilson--Devinney code, we find that the binary is composed of two late spotted active M dw

Yury Kochetkov

We consider the space $P$ of generic complex 5-degree polynomials. Critical values of such polynomial, i.e. four points in the complex plane, either are vertices of a convex quadrangle $Q$, or vertices of a triangle $T$ with one point inside $T$. The inverse image of $Q$ is a tree-like connected structure of five ovals (a cactus). The inverse image of $T$ is

Matthieu Vladaj, Quentin Marécat, Bruno Senjean, Matthieu Saubanère

The one-particle reduced density-matrix (1-RDM) functional theory is a promising alternative to density-functional theory (DFT) that uses the 1-RDM rather than the electronic density as a basic variable. However, long-standing challenges such as the lack of Kohn--Sham scheme and the complexity of the pure $N$-representability conditions are still impeding it

Arnav Chavan, Nahush Lele, Deepak Gupta

Low-rank approximations, of the weight and feature space can enhance the performance of deep learning models, whether in terms of improving generalization or reducing the latency of inference. However, there is no clear consensus yet on \emph{how}, \emph{when} and \emph{why} these approximations are helpful for large language models (LLMs). In this work, we

Antonio González-López

Background: Calculating the characteristics of radiation emerging from radiation shielding to determine the level of safety in protected areas has been a widely studied topic in Radiation Protection due to its importance for the protection of the public and professionals. Methods: This work provides spatial distributions of energy spectra of emergent photons

Xin Tan, Wenbin Wu, Zhiwei Zhang, Chaojie Fan

3D occupancy perception holds a pivotal role in recent vision-centric autonomous driving systems by converting surround-view images into integrated geometric and semantic representations within dense 3D grids. Nevertheless, current models still encounter two main challenges: modeling depth accurately in the 2D-3D view transformation stage, and overcoming the

Aditya Bhattacharya, Simone Stumpf, Katrien Verbert

With the increasing adoption of Artificial Intelligence (AI) systems in high-stake domains, such as healthcare, effective collaboration between domain experts and AI is imperative. To facilitate effective collaboration between domain experts and AI systems, we introduce an Explanatory Model Steering system that allows domain experts to steer prediction model

Jianming Chen, Gerui Chen

According to the quantum deformation approach to quantum gravity, the thermodynamical entropy of a quantum-deformed (q-deformed) black hole with horizon area $A$ established by Jalalzadeh is expressed as $S_q = \pi\sin \left( \frac{A}{8G\mathcal N} \right) /\sin\left(\frac{\pi}{2\mathcal N}\right)$, where $\mathcal N=L_q^2/L_{p}^2$ is the q-deformation param

I-Hsiang Chen, Wei-Ting Chen, Yu-Wei Liu, Ming-Hsuan Yang

Crowd counting and localization have become increasingly important in computer vision due to their wide-ranging applications. While point-based strategies have been widely used in crowd counting methods, they face a significant challenge, i.e., the lack of an effective learning strategy to guide the matching process. This deficiency leads to instability in m

Guillaume Garnier

In this article, we consider two different statistical models. First, we focus on the estimation of the jump intensity of a compound Poisson process in the presence of unknown noise. This problem combines both the deconvolution problem and the decompounding problem. More specifically, we observe several independent compound Poisson processes but we assume th

Xinfeng Wang, Jin Cui, Yoshimi Suzuki, Fumiyo Fukumoto

Large language model (LLM)-based recommender models that bridge users and items through textual prompts for effective semantic reasoning have gained considerable attention. However, few methods consider the underlying rationales behind interactions, such as user preferences and item attributes, limiting the reasoning capability of LLMs for recommendations. T

Bhabana Kumar, Mrinal Kumar Das

In this study, we explore the application of the $\Gamma_{3}$ modular group, which is isomorphic to the $A_{4}$ symmetric group in developing a model for neutrino mass. We realized a non-supersymmetric left-right asymmetric model incorporating modular symmetry, where the modular forms consist of both holomorphic and non-holomorphic components and the Yukawa

Omar Mustafa

We investigate Klein-Gordon (KG) oscillators in a G\"{o}% del-type Som-Raychaudhuri spacetime in a mixed magnetic field (given by the vector potential $A_{\mu }=\left( 0,0,A_{\varphi },0\right) $, with $% A_{\varphi }=B_{1}r^{2}/2+B_{2}r$). The resulting KG equation takes a Schr% \"{o}dinger-like form (with an oscillator plus a linear plus a Coulomb-like int

Huiyu Li, Junhua Hu

Stock price prediction has always been a difficult task for forecasters. Using cutting-edge deep learning techniques, stock price prediction based on investor sentiment extracted from online forums has become feasible. We propose a novel hybrid deep learning framework for predicting stock prices. The framework leverages the XLNET model to analyze the sentime

Aditya Bhattacharya, Simone Stumpf, Katrien Verbert

Biases in Artificial Intelligence (AI) or Machine Learning (ML) systems due to skewed datasets problematise the application of prediction models in practice. Representation bias is a prevalent form of bias found in the majority of datasets. This bias arises when training data inadequately represents certain segments of the data space, resulting in poor gener

Wei Zhang, Tsz Fung Poon, Chun Wai Tsang, Wenyan Wang

The kagome metal CsV$_3$Sb$_5$ is an ideal platform to study the interplay between topology and electron correlation. To understand the fermiology of CsV$_3$Sb$_5$, intensive quantum oscillation (QO) studies at ambient pressure have been conducted. However, due to the Fermi surface reconstruction by the complicated charge density wave (CDW) order, the QO spe

Julien Aubert, Luc Lehéricy, Patricia Reynaud-Bouret

We prove oracle inequalities for a penalized log-likelihood criterion that hold even if the data are not independent and not stationary, based on a martingale approach. The assumptions are checked for various contexts: density estimation with independent and identically distributed (i.i.d) data, hidden Markov models, spiking neural networks, adversarial band

Markus Lange-Hegermann, Christoph Zimmer

Experimental exploration of high-cost systems with safety constraints, common in engineering applications, is a challenging endeavor. Data-driven models offer a promising solution, but acquiring the requisite data remains expensive and is potentially unsafe. Safe active learning techniques prove essential, enabling the learning of high-quality models with mi

Eugenia Franco, Juan J. L. Velázquez

In this paper we study a simple stochastic version of the Hopfield-Ninio kinetic proofreading model. The model is characterized by means of two parameters, the unbinding time, which depends on the binding energy between a ligand and a receptor, and the number of times $M \geq 1$ that a ligand attaches to a receptor. We prove that, under suitable assumptions

Francesca De Luca Fornaciari, Begoña Altuna, Itziar Gonzalez-Dios, Maite Melero

In this work, we explore idiomatic language processing with Large Language Models (LLMs). We introduce the Idiomatic language Test Suite IdioTS, a new dataset of difficult examples specifically designed by language experts to assess the capabilities of LLMs to process figurative language at sentence level. We propose a comprehensive evaluation methodology ba

Noslen Hernández, Béatrice B. Roques, Marlène Z. Lacroix, Didier Concordet

The widespread practice of delivering antibiotics through drinking water to livestock leads to considerable variability in exposure levels among animals, raising concerns regarding disease outbreaks and the emergence of antibiotic resistance. This variability is primarily driven by three pivotal factors: fluctuations in drug concentration within water pipes,

Bo Huang, Dongming Wang, Jing Yang

The classical theory of Kosambi-Cartan-Chern (KCC) developed in differential geometry provides a powerful method for analyzing the behaviors of dynamical systems. In the KCC theory, the properties of a dynamical system are described in terms of five geometrical invariants, of which the second corresponds to the so-called Jacobi stability of the system. Diffe

Zhe Huang, Yizhe Zhao, Hao Xiao, Chenyan Wu

Multi-view camera-only 3D object detection largely follows two primary paradigms: exploiting bird's-eye-view (BEV) representations or focusing on perspective-view (PV) features, each with distinct advantages. Although several recent approaches explore combining BEV and PV, many rely on partial fusion or maintain separate detection heads. In this paper, we pr

Jiyue Tao, Yunsong Zhang, Sunil Kumar Rajendran, Feitian Zhang

Robotic systems driven by artificial muscles present unique challenges due to the nonlinear dynamics of actuators and the complex designs of mechanical structures. Traditional model-based controllers often struggle to achieve desired control performance in such systems. Deep reinforcement learning (DRL), a trending machine learning technique widely adopted i

Jonas Kälble, Sascha Wirges, Maxim Tatarchenko, Eddy Ilg

Automated driving fundamentally requires knowledge about the surrounding geometry of the scene. Modern approaches use only captured images to predict occupancy maps that represent the geometry. Training these approaches requires accurate data that may be acquired with the help of LiDAR scanners. We show that the techniques used for current benchmarks and tra

Cheng Niu, Xingguang Wang, Xuxin Cheng, Juntong Song

Dialogue State Tracking (DST) is designed to monitor the evolving dialogue state in the conversations and plays a pivotal role in developing task-oriented dialogue systems. However, obtaining the annotated data for the DST task is usually a costly endeavor. In this paper, we focus on employing LLMs to generate dialogue data to reduce dialogue collection and

Samuel Shuman

We will discuss two approaches to estimating partial derivatives and the metric components; one utilizing past work describing a causal set $\Box$ operator, and one using a construction from linear algebra called the Moore-Penrose inverse. After running numerical tests on a causal diamond in $\mathbb{M}^2$, we find that the approach using the Moore-Penrose i

Weihang Liu, Yu Zhao, Yi Jiao, Xiao Li

The pursuit of coherent radiation generation remains a key direction in the advancement of storage ring light sources. Despite the potential of laser modulation in achieving this goal, it leads to a significant decline in the quality of the electron beam. Efforts to mitigate this decline have resulted in the proposal of demodulation schemes. However, impleme

Frédéric Rousset, Katharina Schratz

A large toolbox of numerical schemes for dispersive equations has been established, based on different discretization techniques such as discretizing the variation-of-constants formula (e.g., exponential integrators) or splitting the full equation into a series of simpler subproblems (e.g., splitting methods). In many situations these classical schemes allow

Rajgowrav Cheenikundil, Zhiwei Lu, Manuel Pereiro, Anna Delin

Magnetic skyrmions have garnered attention for their potential roles in spintronic applications, such as information carriers in computation, data storage, and nano-oscillators due to their small size, topological stability, and the requirement of small electric currents to manipulate them. Two key challenges in harnessing skyrmions are the stabilization req

Yirong Zhou, Chengyan Wang, Mengtian Lu, Kunyuan Guo

In cardiac Magnetic Resonance Imaging (MRI) analysis, simultaneous myocardial segmentation and T2 quantification are crucial for assessing myocardial pathologies. Existing methods often address these tasks separately, limiting their synergistic potential. To address this, we propose SQNet, a dual-task network integrating Transformer and Convolutional Neural

Dario Mazzoleni, Cyrill B. Muratov, Berardo Ruffini

In this paper we introduce a simple variational model describing the ground state of a superconducting charge qubit. The model gives rise to a shape optimization problem that aims at maximizing the number of qubit states at a given gating voltage. We show that for small values of the charge optimal shapes exist and are $C^{2,\alpha}$-nearly spherical sets. I

Gengxian Li, Haotian Chen, Yong Xiao

Kinetic infernal mode (KIM) is an electromagnetic instability driven by thermal ions in weak magnetic shear region with a frequency similar to the kinetic ballooning mode (KBM). Gyrokinetic simulations of KIM using Gyrokinetic Toroidal Code (GTC) found that the electromagnetic instability shows a smooth transition from KBM to KIM in both frequency and growth

Xiaoshuai Hao, Yifan Yang, Hui Zhang, Mengchuan Wei

In this report, we describe the technical details of our submission to the 2024 RoboDrive Challenge Robust Map Segmentation Track. The Robust Map Segmentation track focuses on the segmentation of complex driving scene elements in BEV maps under varied driving conditions. Semantic map segmentation provides abundant and precise static environmental information

Luchu Li, Kangda Zhi, Cunhua Pan

This paper proposes a two-timescale transmission scheme for extremely large-scale (XL)-reconfigurable intelligent surfaces (RIS)-aided massive multi-input multi-output (MIMO) systems considering visibility regions (VRs). The beamforming of base stations (BS) is designed based on rapidly changing instantaneous channel state information (CSI), while the phase

Lydia Harris Serafin, Ethan R. Cluff, Gus L. W. Hart, Eric R. Homer

Solute segregation in materials with grain boundaries (GBs) has emerged as a popular method to thermodynamically stabilize nanocrystalline structures. However, the impact of varied GB crystallographic character on solute segregation has never been thoroughly examined. This work examines Co solute segregation in a dataset of 7272 Al bicrystal GBs that span th

Tao Huang, Yang Zhou, Daqi Lin, Junqiu Zhu

Strand-based hair rendering has become increasingly popular in production for its realistic appearance. However, the prevailing level-of-detail solution employing hair cards for distant hair models introduces a significant discontinuity in dynamics and appearance during the transition from strands to cards. We introduce an innovative real-time framework for

Koushik Mandal

In this paper, the entanglement dynamics of different subsystems such as atom-atom, atom-field and field-field with radiation field in squeezed coherent thermal states for the intensity-dependent double Jaynes-Cummings model (IDDJCM) and double Jaynes-Cummings model (DJCM) are investigated. The effects of squeezed and thermal photons on entanglement are exam

Guy Hay, Nir Sharon

This paper addresses the problem of accurately estimating a function on one domain when only its discrete samples are available on another domain. To answer this challenge, we utilize a neural network, which we train to incorporate prior knowledge of the function. In addition, by carefully analyzing the problem, we obtain a bound on the error over the extrap

Jonas Veenstra, Oleksandr Gamayun, Martin Brandenbourger, Freek van Gorp

Breathing solitons consist of a fast beating wave within a compact envelope of stable shape and velocity. They can propagate and carry information and energy in a variety of contexts such as plasmas, optical fibers and cold atoms, but propagating breathers have remained elusive when energy conservation is broken. Here, we report on the observation of breathi

Jiangyuan Guo, Wei Chen, Yuxuan Sun, Jialong Xu

Although semantic communication (SC) has shown its potential in efficiently transmitting multimodal data such as texts, speeches and images, SC for videos has focused primarily on pixel-level reconstruction. However, these SC systems may be suboptimal for downstream intelligent tasks. Moreover, SC systems without pixel-level video reconstruction present adva

Shijie Liu, Kang Yan, Feiwei Qin, Changmiao Wang

Single image super-resolution (SR) is an established pixel-level vision task aimed at reconstructing a high-resolution image from its degraded low-resolution counterpart. Despite the notable advancements achieved by leveraging deep neural networks for SR, most existing deep learning architectures feature an extensive number of layers, leading to high computa

Jorge Olivares-Vinales

In this work we study the Artin-Mazur zeta function for piecewise monotone functions acting on a compact interval of real numbers. In the case of unimodal maps, Milnor and Thurston gave a characterization for the rationality of the Artin-Mazur zeta function in terms of the orbit of the unique turning point. We show that for multimodal maps, the previous char

Jian-Ming Yan, Tao Zhu, Mustapha Azreg-Aïnou, Mubasher Jamil

A novel class of vacuum metrics expressible in analytical form was recently found for pure $\mathcal R^2$ gravity, based on a groundwork put forth by Buchdahl in 1962. These Buchdahl-inspired solutions offer a practical framework for testing ${\cal R}^2$ gravity through empirical observations. Within a subclass of asymptotically flat Buchdahl-inspired vacuum

Sirry Chen, Shuo Feng, Songsong Liang, Chen-Chen Zong

Social media bot detection is increasingly crucial with the rise of social media platforms. Existing methods predominantly construct social networks as graph and utilize graph neural networks (GNNs) for bot detection. However, most of these methods focus on how to improve the performance of GNNs while neglecting the community structure within social networks

Yongliang Lin, Yongzhi Su, Sandeep Inuganti, Yan Di

Estimating the 6D pose of an object from a single RGB image is a critical task that becomes additionally challenging when dealing with symmetric objects. Recent approaches typically establish one-to-one correspondences between image pixels and 3D object surface vertices. However, the utilization of one-to-one correspondences introduces ambiguity for symmetri

Dishant Goyal, Ashwin Jacob, Kaushtubh Kumar, Diptapriyo Majumdar

We consider structural parameterizations of the fundamental Dominating Set problem and its variants in the parameter ecology program. We give improved FPT algorithms and lower bounds under well-known conjectures for dominating set in graphs that are k vertices away from a cluster graph or a split graph. These are graphs in which there is a set of k vertices

Ziv Abelson, Shimshon Bar-Ad

We demonstrate the generation of non-classical light by destructive interference of identical Kerr squeezed states. Perfect pair-wise cancellation of amplitudes that contribute to odd Fock states results in light with only even Fock states, independent of the strength of the nonlinearity. The observability of this effect is only limited by the quality of the

Ruibo Wang, Song Zhang, Ping Huang, Donghai Zhang

Accurately reconstructing road surfaces is pivotal for various applications especially in autonomous driving. This paper introduces a position encoding Multi-Layer Perceptrons (MLPs) framework to reconstruct road surfaces, with input as world coordinates x and y, and output as height, color, and semantic information. The effectiveness of this method is demon

Zesong Fei, Shuntian Tang, Xinyi Wang, Fanghao Xia

Integrated sensing and communication (ISAC) is regarded as a promising technique for 6G communication network. In this letter, we investigate the Pareto bound of the ISAC system in terms of a unified Kullback-Leibler (KL) divergence performance metric. We firstly present the relationship between KL divergence and explicit ISAC performance metric, i.e., demod

Kris Sankaran

Society's capacity for algorithmic problem-solving has never been greater. Artificial Intelligence is now applied across more domains than ever, a consequence of powerful abstractions, abundant data, and accessible software. As capabilities have expanded, so have risks, with models often deployed without fully understanding their potential impacts. Interpret

Alejandro Torres-Orjuela

Gravitational wave (GW) detection has enabled us to test General Relativity in an entirely new regime. A prominent role in tests of General Relativity takes the detection of the Quasi-normal modes (QNMs) that arise as the highly distorted remnant formed after the merger emits GWs until it becomes a regular Kerr BH. According to the no-hair theorem, the frequ

Tong Chen, Qingcheng Lyu, Long Bai, Erjian Guo

Advances in endoscopy use in surgeries face challenges like inadequate lighting. Deep learning, notably the Denoising Diffusion Probabilistic Model (DDPM), holds promise for low-light image enhancement in the medical field. However, DDPMs are computationally demanding and slow, limiting their practical medical applications. To bridge this gap, we propose a l