March 2024 arXiv papers — page 73

Zoltán Lóránt Nagy

An internal or friendly partition of a vertex set $V(G)$ of a graph $G$ is a partition to two nonempty sets $A\cup B$ such that every vertex has at least as many neighbours in its own class as in the other one. Motivated by Diwan's existence proof on internal partitions of graphs with high girth, we give constructive proofs for the existence of internal part

Cody D. Schimming, C. J. O. Reichhardt, C. Reichhardt

We numerically investigate the effect of a periodic array of asymmetric obstacles in a two-dimensional active nematic. We find that activity in conjunction with the asymmetry leads to a ratchet effect or unidirectional flow of the fluid along the asymmetry direction. The directional flow is still present even in the active turbulent phase when the gap betwee

Ivan Pogorelov, Friederike Butt, Lukas Postler, Christian D. Marciniak

Quantum error correction is a crucial tool for mitigating hardware errors in quantum computers by encoding logical information into multiple physical qubits. However, no single error-correcting code allows for an intrinsically fault-tolerant implementation of all the gates needed for universal quantum computing [1-3]. One way to tackle this problem is to swi

Abraham P. Vinod, Avishai Weiss, Stefano Di Cairano

We study the problem of computing robust controllable sets for discrete-time linear systems with additive uncertainty. We propose a tractable and scalable approach to inner- and outer-approximate robust controllable sets using constrained zonotopes, when the additive uncertainty set is a symmetric, convex, and compact set. Our least-squares-based approach us

Luca Giamattei, Matteo Biagiola, Roberto Pietrantuono, Stefano Russo

In a recent study, Reinforcement Learning (RL) used in combination with many-objective search, has been shown to outperform alternative techniques (random search and many-objective search) for online testing of Deep Neural Network-enabled systems. The empirical evaluation of these techniques was conducted on a state-of-the-art Autonomous Driving System (ADS)

Xudong Sun, Nutan Chen, Alexej Gossmann, Matteo Wohlrapp

A probabilistic graphical model is proposed, modeling the joint model parameter and multiplier evolution, with a hypervolume based likelihood, promoting multi-objective descent in structural risk minimization. We address multi-objective model parameter optimization via a surrogate single objective penalty loss with time-varying multipliers, equivalent to onl

Milan Rakic, Andrew F. Ho, Derek K. K. Lee

We study a supersolid in the context of a Gross-Pitaevskii theory with a non-local effective potential. We employ a homogenisation technique which allows us to calculate the elastic moduli, supersolid fraction and other state variables of the system. Our methodology is verified against numerical simulations of elastic deformations. We can also verify that th

Amanda Montejano

For positive integers $t$ and $n$ let $C_t^n$ be the $n$-cube over $t$ elements, that is, the set of ordered $n$-tuples over the alphabet $\{0,\dots, t-1\}$. We address the question of whether a balanced finite coloring of $C_t^n$ guarantees the presence of a rainbow geometric or combinatorial line. For every even $t\geq 4$ and every $n$, we provide a $\left

Luis E. Candelaria, Yichong Zhang

This paper introduces a methodology to conduct robust inference in bipartite networks under local misspecification. We focus on a class of dyadic network models with misspecified conditional moment restrictions. The framework of misspecification is local, as the effect of misspecification varies with the sample size. We utilize this local asymptotic approach

Yifan Chen, Mark Goldstein, Mengjian Hua, Michael S. Albergo

We propose a framework for probabilistic forecasting of dynamical systems based on generative modeling. Given observations of the system state over time, we formulate the forecasting problem as sampling from the conditional distribution of the future system state given its current state. To this end, we leverage the framework of stochastic interpolants, whic

Paul K. H. Yeung, Shiu-Hang Lee, Tsunefumi Mizuno, Aya Bamba

Based on the 13.7~yr Fermi-LAT data, Yeung et al. (2023) claimed detection of two $\gamma$-ray sources (Src-NE and Src-NW) associated with the supernova remnant (SNR) G298.6$-$0.0, and interpreted it as an old GeV SNR interacting with molecular clouds (MCs). In this follow-up study, we refine the flux measurements below 2~GeV with Fermi-LAT event types of be

Irena Spasojevic, Zheng Ma, Aleix Barrera, Federica Celegato

Rapid progress in information technologies has spurred the need for innovative memory concepts, for which advanced data-processing methods and tailor-made materials are required. Here we introduce a previously unexplored nanoscale magnetic object: an analog magnetic vortex controlled by electric-field-induced ion motion, termed magneto-ionic vortex or "vorti

Abdulhalim Dandoush, Viswanath Kumarskandpriya, Mueen Uddin, Usman Khalil

Network slicing, a cornerstone technology for future networks, enables the creation of customized virtual networks on a shared physical infrastructure. This fosters innovation and agility by providing dedicated resources tailored to specific applications. However, current orchestration and management approaches face limitations in handling the complexity of

Robert Frontczak, Karol Gryszka

In this paper, using a generating function approach, we derive several new convolution sum identities involving Fibonacci m-step numbers. As special instances of the results derived herein, we will get many new and known results involving Fibonacci, Tribonacci, Tetranacci and Pentanacci numbers. In addition, we establish some general results providing insigh

Leonardo Fiore, Jeffrey Yelton

Given a Galois cover $Y \to X$ of smooth projective geometrically connected curves over a complete discrete valuation field $K$ with algebraically closed residue field, we define a semistable model of $Y$ over the ring of integers of a finite extension of $K$ which we call the \emph{relatively stable model} $\Yrst$ of $Y$, and we discuss its properties, focu

Wataru Nakata, Kazuki Yamauchi, Dong Yang, Hiroaki Hyodo

We present UTDUSS, the UTokyo-SaruLab system submitted to Interspeech2024 Speech Processing Using Discrete Speech Unit Challenge. The challenge focuses on using discrete speech unit learned from large speech corpora for some tasks. We submitted our UTDUSS system to two text-to-speech tracks: Vocoder and Acoustic+Vocoder. Our system incorporates neural audio

Brian Hsuan-Cheng Liao, Chih-Hong Cheng, Hasan Esen, Alois Knoll

This paper presents Ego-Centric Intersection-over-Union (EC-IoU), addressing the limitation of the standard IoU measure in characterizing safety-related performance for object detectors in navigating contexts. Concretely, we propose a weighting mechanism to refine IoU, allowing it to assign a higher score to a prediction that covers closer points of a ground

Marcin Pietron, Rafał Olszowski, Jakub Gomułka

Argument mining (AM) is defined as the task of automatically identifying and extracting argumentative components (e.g. premises, claims, etc.) and detecting the existing relations among them (i.e., support, attack, no relations). Deep learning models enable us to analyze arguments more efficiently than traditional methods and extract their semantics. This pa

Glenn D. Hibbard, John Çamkıran

We call for a theory of the particle-scale structure of materials that is based on the general notion of information rather than its special case of symmetry. An inherent limitation to the symmetry-based understanding of structure is described. The rapid decay in interaction strength with interparticle distance is used to argue for the representability of a

Emiliano Liwski, Fatemeh Mohammadi

We study paving matroids, their realization spaces, and their closures, along with matroid varieties and circuit varieties. Within this context, we introduce three distinct methods for generating polynomials within the associated ideals of these varieties across any dimension. Additionally, we explain the relationship between polynomials constructed using th

Ajay D. Kshemkalyani, Manish Kumar, Anisur Rahaman Molla, Gokarna Sharma

Leader election is one of the fundamental and well-studied problems in distributed computing. In this paper, we initiate the study of leader election using mobile agents. Suppose $n$ agents are positioned initially arbitrarily on the nodes of an arbitrary, anonymous, $n$-node, $m$-edge graph $G$. The agents relocate themselves autonomously on the nodes of $G

Elena Maini

Given a multi-EGS-group $K$ acting on the $p$-adic rooted tree, where $p$ is any prime number, we compute the exponent of the congruence quotient $K_n= K/ \St_K(n)$ for all $n\ge 1$. The formula that we obtain for $\exp(K_n)$ only depends on $p$, $n$ and the periodicity of $K$.

Yuxuan Zhou, Xingxing Li, Shengyu Li, Xuanbin Wang

Visual simultaneous localization and mapping (VSLAM) has broad applications, with state-of-the-art methods leveraging deep neural networks for better robustness and applicability. However, there is a lack of research in fusing these learning-based methods with multi-sensor information, which could be indispensable to push related applications to large-scale

MAGIC Collaboration

Flat spectrum radio quasars (FSRQs) constitute a class of jetted active galaxies characterized by a very luminous accretion disk, prominent and rapidly moving line-emitting cloud structures (Broad Line Region, BLR), and a surrounding dense dust structure known as dusty torus. The intense radiation field of the accretion disk strongly determines the observati

Charalampos Anastasiou, Julia Karlen, George Sterman, Aniruddha Venkata

The computation of two-loop amplitudes for the production of multiple Higgs and electroweak gauge bosons via gluon fusion with exact dependence on quark masses relies primarily on numerical methods. We propose a framework that enables their numerical evaluation in momentum space. The method is inspired by the factorization of infrared divergences in QCD scat

Niklas Krieger, Sandro Speth, Steffen Becker

Creating suitable diagrams for technical and scientific publications is challenging and time-consuming, as manual control over the layout is required to communicate information effectively. Existing diagramming tools usually allow modeling the diagrams via a textual domain-specific language (DSL) that can be rendered and auto-layouted or via a graphical edit

Jared Rovny, Sarang Gopalakrishnan, Ania C. Bleszynski Jayich, Patrick Maletinsky

Nitrogen vacancy (NV) centre quantum sensors provide unique opportunities in studying condensed matter systems: they are quantitative, noninvasive, physically robust, offer nanoscale resolution, and may be used across a wide range of temperatures. These properties have been exploited in recent years to obtain nanoscale resolution measurements of static magne

Nastassia Grimm, Camille Bonvin, Isaac Tutusaus

We combine measurements of galaxy velocities from galaxy surveys with measurements of the Weyl potential from the Dark Energy Survey to test the consistency of General Relativity at cosmological scales. Taking the ratio of two model-independent observables - the growth rate of structure and the Weyl potential - we obtain new measurements of the $E_G$ statist

Aram Aguilar, Celia Escamilla-Rivera, Jackson Levi Said, Jurgen Mifsud

There have been several works that have studied scalar cosmological perturbations in $f(T)$ teleparallel gravity theories to understand early cosmic times dynamics. In this direction, the perturbations presented have been performed by considering $f(T)$ extensions as an effective fluid-like scheme, where the equation-of-state contains extra terms due to the

Giovanni Gino Zanvettor, Marco Casini, Antonio Vicino

Renewable energy communities are legal entities involving the association of citizens, organizations and local businesses aimed at contributing to the green energy transition and providing social, environmental and economic benefits to their members. This goal is pursued through the cooperative efforts of the community actors and by increasing the local ener

Hassan Maissoro, Valentin Patilea, Myriam Vimond

The local regularity of functional time series is studied under $L^p-m-$appro\-ximability assumptions. The sample paths are observed with error at possibly random design points. Non-asymptotic concentration bounds of the regularity estimators are derived. As an application, we build nonparametric mean and autocovariance functions estimators that adapt to the

Aske Plaat

Search algorithms are often categorized by their node expansion strategy. One option is the depth-first strategy, a simple backtracking strategy that traverses the search space in the order in which successor nodes are generated. An alternative is the best-first strategy, which was designed to make it possible to use domain-specific heuristic information. By

Abhinab Bhattacharjee, Andrey A. Popov, Arash Sarshar, Adrian Sandu

The Adam optimizer, often used in Machine Learning for neural network training, corresponds to an underlying ordinary differential equation (ODE) in the limit of very small learning rates. This work shows that the classical Adam algorithm is a first-order implicit-explicit (IMEX) Euler discretization of the underlying ODE. Employing the time discretization p

Danqing Ma, Shaojie Li, Bo Dang, Hengyi Zang

Transmission line detection technology is crucial for automatic monitoring and ensuring the safety of electrical facilities. The YOLOv5 series is currently one of the most advanced and widely used methods for object detection. However, it faces inherent challenges, such as high computational load on devices and insufficient detection accuracy. To address the

Václav Blažej, Boris Klemz, Felix Klesen, Marie Diana Sieper

The problem Level Planarity asks for a crossing-free drawing of a graph in the plane such that vertices are placed at prescribed y-coordinates (called levels) and such that every edge is realized as a y-monotone curve. In the variant Constrained Level Planarity (CLP), each level $y$ is equipped with a partial order $\prec_y$ on its vertices and in the desire

Alina Böhm, Tim Schneider, Boris Belousov, Alap Kshirsagar

This paper explores active sensing strategies that employ vision-based tactile sensors for robotic perception and classification of fabric textures. We formalize the active sampling problem in the context of tactile fabric recognition and provide an implementation of information-theoretic exploration strategies based on minimizing predictive entropy and vari

Reynald Affeldt, Alessandro Bruni, Ekaterina Komendantskaya, Natalia Ślusarz

For performance and verification in machine learning, new methods have recently been proposed that optimise learning systems to satisfy formally expressed logical properties. Among these methods, differentiable logics (DLs) are used to translate propositional or first-order formulae into loss functions deployed for optimisation in machine learning. At the sa

Tran-Vu La, Minh-Tan Pham, Marco Chini

Ship detection from satellite imagery using Deep Learning (DL) is an indispensable solution for maritime surveillance. However, applying DL models trained on one dataset to others having differences in spatial resolution and radiometric features requires many adjustments. To overcome this issue, this paper focused on the DL models trained on datasets that co

Maxim Goncharov

In the paper, we introduce the notion of a Rota-Baxter operator of a non-scalar weight. As a motivation, we show that there is a natural connection between Rota-Baxter operators of this type and structures of quasitriangular Lie bialgebras on a quadratic finite-dimensional Lie algebra. Moreover, we show that some classical results on Lie bialgebra structure

Ju Gao, Fang Shen

Our study reveals electron spin in a cavity as a stable circulating current density, characterized by a torus topology. This current density circulates concentrically beyond the cavity boundary, illustrating the concept of evanescent wave spin. While the interaction with a uniform magnetic field aligns with established spin-field observations, our analysis o

Gustavo A. Fernandez-Alcober, Matteo Pintonello

Let $w=w(x_1,\ldots,x_r)$ be an outer commutator word. We show that the word $w(u_1,\ldots,u_r)$ is concise whenever $u_1,\ldots,u_r$ are non-commutator words in disjoint sets of variables. This applies in particular to words of the form $w(x_1^{n_1},\ldots,x_r^{n_r})$, where the $n_i$ are non-zero integers. Our approach is via the study of values of $w$ on

Shakti Deo Kumar, Sudhanshu Tripathi, Krishna Ujjwal, Sarvada Sakshi Jha

Locomotion mechanics of legged robots are suitable when pacing through difficult terrains. Recognising terrains for such robots are important to fully yoke the versatility of their movements. Consequently, robotic terrain classification becomes significant to classify terrains in real time with high accuracy. The conventional classifiers suffer from overfitt

Fabian Mauthe, Christopher Braun, Julian Raible, Peter Zeiler

Central to the efficacy of prognostics and health management methods is the acquisition and analysis of degradation data, which encapsulates the evolving health condition of engineering systems over time. Degradation data serves as a rich source of information, offering invaluable insights into the underlying degradation processes, failure modes, and perform

Ziyang Wang, Bin Miao, Yuming Wang, Chenglong Shen

Since November 2021, Tianwen-1 started its scientific instrument Mars Ion and Neutral Particle Analyzer (MINPA) to detect the particles in the Martian space. To evaluate the reliability of the plasma parameters from the MINPA measurements, in this study, we analyze and reduce the background signal (or noise) appearing in the MINPA data, and then calculate th

Anna M. Krol, Zaid Al-Ars

This paper proposes a new optimized quantum block-ZXZ decomposition method [7,8,10] that results in more optimal quantum circuits than the quantum Shannon decomposition (QSD)[27], which was introduced in 2006 by Shende et al. The decomposition is applied recursively to generic quantum gates, and can take advantage of existing and future small-circuit optimiz

Nicola de Nitti, David Meyer, Christian Seis

We analyze the optimal regularity that is exactly propagated by a transport equation driven by a velocity field with BMO gradient. As an application, we study the 2D Euler equations in case the initial vorticity is bounded. The sharpness of our result for the Euler equations follows from a variation of Bahouri and Chemin's vortex patch example.

Arun Krishnavajjala, SM Hasan Mansur, Justin Jose, Kevin Moran

Recent research has begun to examine the potential of automatically finding and fixing accessibility issues that manifest in software. However, while recent work makes important progress, it has generally been skewed toward identifying issues that affect users with certain disabilities, such as those with visual or hearing impairments. However, there are oth

Erik Zimmermann, Abdur Rehman Jalil, Michael Schleenvoigt, Jan Karthein

Nanoscale superconducting quantum interference devices (SQUIDs) are fabricated in-situ from a single Bi$_{0.26}$Sb$_{1.74}$Te$_{3}$ nanoribbon that is defined using selective-area growth and contacted with superconducting Nb electrodes via a shadow mask technique. We present $h/(2e)$ magnetic flux periodic interference in both, fully and non-fully proximitiz

Thabo Samakhoana, Benjamin Grimmer

Recent works have developed new projection-free first-order methods based on utilizing linesearches and normal vector computations to maintain feasibility. These oracles can be cheaper than orthogonal projection or linear optimization subroutines but have the drawback of requiring a known strictly feasible point to do these linesearches with respect to. In t

Ankit Anand, Ruben Campos Delgado

Barrow proposed that quantum gravity effects might introduce fractal corrections to the area of the event horizon of black holes. The area law gets modified as $S \propto A^{1+\Delta/2}$, with $0\leq\Delta\leq 1$. It was so far unclear whether this assumption could lead to meaningful quantum gravity theories beyond general relativity. In this paper, we argue

Boxaun Ma, Li Chen, Shin'ichi Konomi

The integration of ChatGPT as a supportive tool in education, notably in programming courses, addresses the unique challenges of programming education by providing assistance with debugging, code generation, and explanations. Despite existing research validating ChatGPT's effectiveness, its application in university-level programming education and a detailed

Zijian Xu

A $k$-modal sequence is a sequence of real numbers that can be partitioned into $k+1$ (possibly empty) monotone sections such that adjacent sections have opposite monotonicities. For every positive integer $k$, we prove that any sequence of $n$ pairwise distinct real numbers contains a $k$-modal subsequence of length at least $\sqrt{(2k+1)(n-\frac14)} - \fra

Changbin Im, Björn Kirchhoff, Dariusz Mitoraj, Igor Krivtsov

Two-dimensional (2D) materials have attracted considerable attention due to their unique physicochemical properties and significant potential in energy-related applications. Polymeric carbon nitrides (PCNs) with 2D stacked architecture show promise as photocatalysts for solar-to-fuel conversion and as versatile 2D semiconductors. However, the lack of a clear

Hongjun Wang, Sagar Vaze, Kai Han

Generalized Category Discovery (GCD) aims to classify unlabelled images from both `seen' and `unseen' classes by transferring knowledge from a set of labelled `seen' class images. A key theme in existing GCD approaches is adapting large-scale pre-trained models for the GCD task. An alternate perspective, however, is to adapt the data representation itself fo

Chen Zhao, Tong Zhang, Zheng Dang, Mathieu Salzmann

Determining the relative pose of a previously unseen object between two images is pivotal to the success of generalizable object pose estimation. Existing approaches typically predict 3D translation utilizing the ground-truth object bounding box and approximate 3D rotation with a large number of discrete hypotheses. This strategy makes unrealistic assumption

Ziyao Liu, Huanyi Ye, Chen Chen, Yongsen Zheng

Machine Unlearning (MU) has recently gained considerable attention due to its potential to achieve Safe AI by removing the influence of specific data from trained Machine Learning (ML) models. This process, known as knowledge removal, addresses AI governance concerns of training data such as quality, sensitivity, copyright restrictions, and obsolescence. Thi

Mitodru Niyogi, Eric Gaussier, Arnab Bhattacharya

Decoder-only Large Language Models (LLMs) are currently the model of choice for many Natural Language Processing (NLP) applications. Through instruction fine-tuning and prompting approaches, such LLMs have been efficiently used to solve both general and domain-specific tasks. However, they are costly to train and, to a certain extent, costly to use as well,

Yiwei Lyu, Sung Jik Cha, Cheng Jiang, Asadur Chowdury

High-quality, high-resolution medical imaging is essential for clinical care. Raman-based biomedical optical imaging uses non-ionizing infrared radiation to evaluate human tissues in real time and is used for early cancer detection, brain tumor diagnosis, and intraoperative tissue analysis. Unfortunately, optical imaging is vulnerable to image degradation du

Hongzhan Chen, Hehong Chen, Ming Yan, Wenshen Xu

Large language models (LLMs) have advanced the development of various AI conversational agents, including role-playing conversational agents that mimic diverse characters and human behaviors. While prior research has predominantly focused on enhancing the conversational capability, role-specific knowledge, and stylistic attributes of these agents, there has

Jun Yu, Zerui Zhang, Zhihong Wei, Gongpeng Zhao

Leveraging the synergy of both audio data and visual data is essential for understanding human emotions and behaviors, especially in in-the-wild setting. Traditional methods for integrating such multimodal information often stumble, leading to less-than-ideal outcomes in the task of facial action unit detection. To overcome these shortcomings, we propose a n

Yuyang Shu, Michael E. Bain

Humans see low spatial frequency components before high spatial frequency components. Drawing on this neuroscientific inspiration, we investigate the effect of introducing patches from different spatial frequencies into Vision Transformers (ViTs). We name this model Retina Vision Transformer (RetinaViT) due to its inspiration from the human visual system. Ou

Jai Bardhan, Tanumoy Mandal, Subhadip Mitra, Cyrin Neeraj

We review the main applications of machine learning models that are not fully supervised in particle physics, i.e., clustering, anomaly detection, detector simulation, and unfolding. Unsupervised methods are ideal for anomaly detection tasks -- machine learning models can be trained on background data to identify deviations if we model the background data pr

Yubo Qi, Weiyi Gong, Qimin Yan

This work presents a physics-informed neural network approach bridging deep-learning force field and electronic structure simulations, illustrated through twisted two-dimensional large-scale material systems. The deep potential molecular dynamics model is adopted as the backbone, and electronic structure simulation is integrated. Using Wannier functions as t

Zengqi Peng, Xiao Zhou, Lei Zheng, Yubin Wang

In this work, we present a reward-driven automated curriculum reinforcement learning approach for interaction-aware self-driving at unsignalized intersections, taking into account the uncertainties associated with surrounding vehicles (SVs). These uncertainties encompass the uncertainty of SVs' driving intention and also the quantity of SVs. To deal with thi

Ashish Chauniyal, Pascal Thome, Markus Stricker

Materials characterization using electron backscatter diffraction (EBSD) requires indexing the orientation of the measured region from Kikuchi patterns. The quality of Kikuchi patterns can degrade due to pattern overlaps arising from two or more orientations, in the presence of defects or grain boundaries. In this work we employ constrained non-negative matr

Guang Yang, Gen Tatara

We propose a dynamic mechanism for the reversible regulation of photosynthesis in varying light environments. We employ a three-level quantum model to take into account the correlations between charge donors and charge acceptors immediately before photoexcitation, and show that under continuous illumination, the transfer efficiency of a single charge is inve

Paulami Banerjee, Mohan Padmanabha, Chaitanya Sanghavi, Isabel Michel

Meshfree simulation methods are emerging as compelling alternatives to conventional mesh-based approaches, particularly in the fields of Computational Fluid Dynamics (CFD) and continuum mechanics. In this publication, we provide a comprehensive overview of our research combining Machine Learning (ML) and Fraunhofer's MESHFREE software (www.meshfree.eu), a po

Jaret Heise

The Sanford Underground Research Facility (SURF) has been operating for more than 15 years as an international facility dedicated to advancing compelling multidisciplinary underground scientific research in rare-process physics, as well as offering research opportunities in other disciplines. SURF laboratory facilities include a Surface Campus as well as cam

Nadezhda Zh Bunzarova, Nina C Pesheva, Alexander M Povolotsky

The totally asymmetric simple exclusion process with generalized update is a version of the discrete time totally asymmetric exclusion process with an additional inter-particle interaction that controls the degree of particle clustering. Though the model was shown to be integrable on the ring and on the infinite lattice, on the open chain it was studied main

Régis Mélin, Asmaul Smitha Rashid, Morteza Kayyalha

A Josephson junction, formed between two phase-biased superconductors and a normal metal, hosts a discrete spectrum of Andreev bound states (ABS). In this paper, we develop a theory for long ballistic Andreev interferometers in two-dimensional metals. We consider three frameworks in our theoretical analysis: (i) perturbation theory in the tunneling amplitude

Junhua Liu, Justin Albrethsen, Lincoln Goh, David Yau

Resource allocation in tactical ad-hoc networks presents unique challenges due to their dynamic and multi-hop nature. Accurate prediction of future network connectivity is essential for effective resource allocation in such environments. In this paper, we introduce the Spatial-Temporal Graph Encoder-Decoder (STGED) framework for Tactical Communication Networ

James Farre, Beatrice Pozzetti, Gabriele Viaggi

We study the geometry of hyperconvex representations of hyperbolic groups in ${\rm PSL}(d,\mathbb{C})$ and establish two structural results: a group admitting a hyperconvex representation is virtually isomorphic to a Kleinian group, and its hyperconvex limit set in the appropriate flag manifold has Hausdorff dimension strictly smaller than $2$.

Keegan Hines, Gary Lopez, Matthew Hall, Federico Zarfati

Large Language Models (LLMs), while powerful, are built and trained to process a single text input. In common applications, multiple inputs can be processed by concatenating them together into a single stream of text. However, the LLM is unable to distinguish which sections of prompt belong to various input sources. Indirect prompt injection attacks take adv

Zixuan Wang, Jia Jia, Shikun Sun, Haozhe Wu

Choreographers determine what the dances look like, while cameramen determine the final presentation of dances. Recently, various methods and datasets have showcased the feasibility of dance synthesis. However, camera movement synthesis with music and dance remains an unsolved challenging problem due to the scarcity of paired data. Thus, we present DCM, a ne

Gorka Azkune, Ander Salaberria, Eneko Agirre

This paper shows that text-only Language Models (LM) can learn to ground spatial relations like "left of" or "below" if they are provided with explicit location information of objects and they are properly trained to leverage those locations. We perform experiments on a verbalized version of the Visual Spatial Reasoning (VSR) dataset, where images are couple

Angelina Senchukova, Felipe Uribe, Lassi Roininen

Many inverse problems focus on recovering a quantity of interest that is a priori known to exhibit either discontinuous or smooth behavior. Within the Bayesian approach to inverse problems, such structural information can be encoded using Markov random field priors. We propose a class of priors that combine Markov random field structure with Student's t dist

Anton Glushchenko, Konstantin Lastochkin

A new adaptive observer is proposed for a certain class of nonlinear systems with bounded unknown input and parametric uncertainty. Unlike most existing solutions, the proposed approach ensures asymptotic convergence of the unknown parameters, state and perturbation estimates to an arbitrarily small neighborhood of the equilibrium point. The solution is base

Shijie Zhang, Boyan Jiang, Keke He, Junwei Zhu

Pixel2Mesh (P2M) is a classical approach for reconstructing 3D shapes from a single color image through coarse-to-fine mesh deformation. Although P2M is capable of generating plausible global shapes, its Graph Convolution Network (GCN) often produces overly smooth results, causing the loss of fine-grained geometry details. Moreover, P2M generates non-credibl

Qin Liu, Xiaoning Wu, Xiao Zhang

We investigate the critical phenomena of Kerr-AdS black holes in the modified first law of thermodynamics. The modified black hole thermodynamics exhibits the van der Waals-like phase structure. All the critical exponents are calculated, and the swallowtail diagram of free energy is plotted. Comparing with existing results, the main difference is the corresp

Irakli Titvinidze, Julian Stobbe, Alexey N. Rubtsov, Georg Rohringer

We present a method to study the time evolution of the single impurity Anderson model which exploits a mean field decoupling of the interacting impurity and the non-interacting bath (in form of a chain). This is achieved by the introduction of a pair of auxiliary Majorana fermions between the impurity and the chain. After decoupling, we obtain a self-consist

Jianhao Xie, Ruofan Liao, Ziang Zhang, Sida Yi

Detecting polyps through colonoscopy is an important task in medical image segmentation, which provides significant assistance and reference value for clinical surgery. However, accurate segmentation of polyps is a challenging task due to two main reasons. Firstly, polyps exhibit various shapes and colors. Secondly, the boundaries between polyps and their no

R. Gnana Praveen, Jahangir Alam

Though multimodal emotion recognition has achieved significant progress over recent years, the potential of rich synergic relationships across the modalities is not fully exploited. In this paper, we introduce Recursive Joint Cross-Modal Attention (RJCMA) to effectively capture both intra- and inter-modal relationships across audio, visual, and text modaliti

Mohammod N. I. Suvon, Prasun C. Tripathi, Wenrui Fan, Shuo Zhou

Recent advancements in non-invasive detection of cardiac hemodynamic instability (CHDI) primarily focus on applying machine learning techniques to a single data modality, e.g. cardiac magnetic resonance imaging (MRI). Despite their potential, these approaches often fall short especially when the size of labeled patient data is limited, a common challenge in

Bin Wang, Fei Deng, Peifan Jiang

Electroencephalogram (EEG) signals play a pivotal role in clinical medicine, brain research, and neurological disease studies. However, susceptibility to various physiological and environmental artifacts introduces noise in recorded EEG data, impeding accurate analysis of underlying brain activity. Denoising techniques are crucial to mitigate this challenge.

Benjamin D. Horne, Maurício Gruppi

Pink slime news outlets automatically produce low-quality, often partisan content that is framed as authentic local news. Given that local news is trusted by Americans and is increasingly shutting down due to financial distress, pink slime news outlets have the potential to exploit local information voids. Yet, there are gaps in understanding of pink slime p

Yuming Jiang

Network calculus (NC), particularly its min-plus branch, has been extensively utilized to construct service models and compute delay bounds for time-sensitive networks (TSNs). This paper provides a revisit to the fundamental results. In particular, counterexamples to the most basic min-plus service models, which have been proposed for TSNs and used for compu

Rebecca Pelke, Felix Staudigl, Niklas Thomas, Nils Bosbach

Resistive Random Access Memory (ReRAM) is a promising candidate for implementing Computing-in-Memory (CIM) architectures and neuromorphic circuits. ReRAM cells exhibit significant variability across different memristive devices and cycles, necessitating further improvements in the areas of devices, algorithms, and applications. To achieve this, understanding

Guillermo Aparicio-Estrems, Abel Gargallo-Peiró, Xevi Roca

We present a specific-purpose globalized and preconditioned Newton-CG solver to minimize a metric-aware curved high-order mesh distortion. The solver is specially devised to optimize curved high-order meshes for high polynomial degrees with a target metric featuring non-uniform sizing, high stretching ratios, and curved alignment -- exactly the features that

Florian Strohm, Mihai Bâce, Andreas Bulling

Reusable embeddings of user behaviour have shown significant performance improvements for the personalised saliency prediction task. However, prior works require explicit user characteristics and preferences as input, which are often difficult to obtain. We present a novel method to extract user embeddings from pairs of natural images and corresponding salie

Hiroki Azuma, Yusuke Matsui, Atsuto Maki

Deep learning models achieve high accuracy in segmentation tasks among others, yet domain shift often degrades the models' performance, which can be critical in real-world scenarios where no target images are available. This paper proposes a zero-shot domain adaptation method based on diffusion models, called ZoDi, which is two-fold by the design: zero-shot

Lynn Fayed, Gustav Nilsson, Nikolas Geroliminis

With the increasing popularity of ride-hailing services, new modes of transportation are having a significant impact on the overall performance of transportation networks. As a result, there is a need to ensure that both the various transportation alternatives and the spatial network resources are used efficiently. In this work, we analyze a network configur

Münevver Pınar Eroğlu, Tsiu-Kwen Lee, Jheng-Huei Lin

Let $D$ be a noncommutative division ring. In a recent paper, Lee and Lin proved that if $\text{char}\, D\ne 2$, the only solution of additive maps $f, g$ on $D$ satisfying the identity $f(x) = x^n g(x^{-1})$ on $D\setminus \{0\}$ with $n\ne 2$ a positive integer is the trivial case, that is, $f=0$ and $g=0$. Applying Hua's identity and the theory of functio

Emmanouil Giannakakis, Sina Khajehabdollahi, Anna Levina

Developing reliable mechanisms for continuous local learning is a central challenge faced by biological and artificial systems. Yet, how the environmental factors and structural constraints on the learning network influence the optimal plasticity mechanisms remains obscure even for simple settings. To elucidate these dependencies, we study meta-learning via

Hongyi Li, Jun Xu, Qianchuan Zhao

Many countries are facing energy shortage today and most of the global energy is consumed by HVAC systems in buildings. For the scenarios where the energy system is not sufficiently supplied to HVAC systems, a priority-based allocation scheme based on distributed model predictive control is proposed in this paper, which distributes the energy rationally base

Alfonso Lanuza, Dominik Schneble

Waveguide quantum electrodynamics constitutes a modern paradigm for the interaction of light and matter, in which strong coupling, bath structure, and propagation delays can break the radiative conditions that quantum emitters typically encounter in free space. These characteristics intertwine the excitations of quantum emitters and guided radiation modes to

Junjie Chen, Jiebin Yan, Yuming Fang, Li Niu

Category-agnostic pose estimation (CAPE) aims to predict keypoints for arbitrary classes given a few support images annotated with keypoints. Existing methods only rely on the features extracted at support keypoints to predict or refine the keypoints on query image, but a few support feature vectors are local and inadequate for CAPE. Considering that human c

Lukas Abel, Janusz Ginster, Barbara Zwicknagl

A static variational model for shape formation in heteroepitaxial crystal growth is considered. The energy functional takes into account surface energy, elastic misfit-energy and nucleation energy of dislocations. A scaling law for the infimal energy is proven. The results quantify the expectation that in certain parameter regimes, island formation or topolo

Kåre von Geijer, Philippas Tsigas

The sequential semantics of many concurrent data structures, such as stacks and queues, inevitably lead to memory contention in parallel environments, thus limiting scalability. Semantic relaxation has the potential to address this issue, increasing the parallelism at the expense of weakened semantics. Although prior research has shown that improved performa

Francis Doyon-D'Amour, Carly Stalder, Timothy Hodges, Michel Stephan

Micro-electro-mechanical system (MEMS) microphones (mics) with two acoustic ports are currently receiving considerable interest, with the promise of achieving higher directional sensitivity compared to traditional one-port architectures. However, measuring pressure differences in two-port microphones typically commands sensing elements that are softer than i

Renkai Wu, Yinghao Liu, Pengchen Liang, Qing Chang

In the field of medical image segmentation, variant models based on Convolutional Neural Networks (CNNs) and Visual Transformers (ViTs) as the base modules have been very widely developed and applied. However, CNNs are often limited in their ability to deal with long sequences of information, while the low sensitivity of ViTs to local feature information and