December 2023 arXiv papers — page 72

Jesper Larsson Träff

We give optimally fast $O(\log p)$ time (per processor) algorithms for computing round-optimal broadcast schedules for message-passing parallel computing systems. This affirmatively answers the questions posed in Tr\"aff (2022). The problem is to broadcast $n$ indivisible blocks of data from a given root processor to all other processors in a (subgraph of a)

Feliz Minhós, Sara Perestrelo

We present an existence and localization result for periodic solutions of second-order non-linear coupled planar systems, without requiring periodicity for the non-linearities. The arguments for the existence tool are based on a variation of the Nagumo condition and the Topological Degree Theory. The localization tool is based on a technique of orderless upp

Lilith Zschetzsche, Robert E. Zillich

We investigate self-localization of a polaron in a homogeneous Bose-Einstein condensate in one dimension. This effect, where an impurity is trapped by the deformation that it causes in the surrounding Bose gas, has been first predicted by mean field calculations, but has not been seen in experiments. We study the system in one dimension, where, according to

Martin Colot, Cédric Simar, Mathieu Petieau, Ana Maria Cebolla Alvarez

Electromyograms (EMG)-based hand gesture recognition systems are a promising technology for human/machine interfaces. However, one of their main limitations is the long calibration time that is typically required to handle new users. The paper discusses and analyses the challenge of cross-subject generalization thanks to an original dataset containing the EM

Vassilis Lyberatos, Spyridon Kantarelis, Edmund Dervakos, Giorgos Stamou

In the age of music streaming platforms, the task of automatically tagging music audio has garnered significant attention, driving researchers to devise methods aimed at enhancing performance metrics on standard datasets. Most recent approaches rely on deep neural networks, which, despite their impressive performance, possess opacity, making it challenging t

H. R. Coelho, A. Miglio, T. Morel, N. Lagarde

Photometric time series gathered by space telescopes such as CoRoT and Kepler allow to detect solar-like oscillations in red-giant stars and to measure their global seismic constraints, which can be used to infer global stellar properties (e.g. masses, radii, evolutionary states). Combining such precise constraints with photospheric abundances provides a mea

Jérémy Scanvic, Mike Davies, Patrice Abry, Julián Tachella

Self-supervised methods have recently proved to be nearly as effective as supervised ones in various imaging inverse problems, paving the way for learning-based approaches in scientific and medical imaging applications where ground truth data is hard or expensive to obtain. These methods critically rely on invariance to translations and/or rotations of the i

Weilin Xiao, Ming Xu, Yonggui Lin

The visual feature pyramid has proven its effectiveness and efficiency in target detection tasks. Yet, current methodologies tend to overly emphasize inter-layer feature interaction, neglecting the crucial aspect of intra-layer feature adjustment. Experience underscores the significant advantages of intra-layer feature interaction in enhancing target detecti

Diogo Luvizon, Vladislav Golyanik, Adam Kortylewski, Marc Habermann

Creating a controllable and relightable digital avatar from multi-view video with fixed illumination is a very challenging problem since humans are highly articulated, creating pose-dependent appearance effects, and skin as well as clothing require space-varying BRDF modeling. Existing works on creating animatible avatars either do not focus on relighting at

Nikita Kotelevskii, Samuel Horváth, Karthik Nandakumar, Martin Takáč

In modern federated learning, one of the main challenges is to account for inherent heterogeneity and the diverse nature of data distributions for different clients. This problem is often addressed by introducing personalization of the models towards the data distribution of the particular client. However, a personalized model might be unreliable when applie

Ioannis Contopoulos, Demosthenes Kazanas, Demetrios B. Papadopoulos

We investigate the generation of gravitational waves from the rotation of an orthogonal pulsar magnetosphere in flat space time. We calculate the first order metric perturbation due to the rotation of the non-axisymmetric distribution of electromagnetic energy density around the central star. We show that gravitational waves from a strong magnetic field puls

Yanran Tang, Ruihong Qiu, Yilun Liu, Xue Li

Legal case retrieval is an information retrieval task in the legal domain, which aims to retrieve relevant cases with a given query case. Recent research of legal case retrieval mainly relies on traditional bag-of-words models and language models. Although these methods have achieved significant improvement in retrieval accuracy, there are still two challeng

Tinku Supakar, David Space, Sophy Meija, Rou Yu Tan

Microfluidic valves play a key role within microfluidic systems by regulating fluid flow through distinct microchannels, enabling many advanced applications in medical diagnostics, lab-on-chips, and laboratory automation. While microfluidic systems are often limited to planar structures, 3D printing enables new capabilities to generate complex designs for fl

Merlijn Krale, Thiago D. Simão, Jana Tumova, Nils Jansen

Partial observability and uncertainty are common problems in sequential decision-making that particularly impede the use of formal models such as Markov decision processes (MDPs). However, in practice, agents may be able to employ costly sensors to measure their environment and resolve partial observability by gathering information. Moreover, imprecise trans

Manuel Cossio

This document provides responses to the FDA's request for public comments (Docket No FDA 2023 N 4853) on the role of digital health technologies (DHTs) in detecting prediabetes and undiagnosed type 2 diabetes. It explores current DHT applications in prevention, detection, treatment and reversal of prediabetes, highlighting AI chatbots, online forums, wearabl

Songtao Peng, Yiping Chen, Xincheng Shu, Wu Shuai

In recent years, various international security events have occurred frequently and interacted between real society and cyberspace. Traditional traffic monitoring mainly focuses on the local anomalous status of events due to a large amount of data. BGP-based event monitoring makes it possible to perform differential analysis of international events. For many

Xiaomeng Chen, Shuai Li, Lili Wang, Wendong Wang

Suspensions of aerobic bacteria often develop flows from the interplay of chemotaxis and buoyancy, which is so-called the chemotaxis-Navier-Stokes flow. In 2004, Dombrowski et al. observed that Bacterial flow in a sessile drop related to those in the Boycott effect of sedimentation can carry bioconvective plumes, viewed from below through the bottom of a pet

Pedro Hack, Christian B. Mendl

Elementary thermal operations are thermal operations that act non-trivially on at most two energy levels of a system at the same time. They were recently introduced in order to bring thermal operations closer to experimental feasibility. A key question to address is whether any thermal operation could be realized via elementary ones, that is, whether element

Nicolas Boulanger, Yannick Herfray, Noémie Parrini

In this article we carry out a detailed investigation of the geometric nature of the points at infinity of Minkowski superspace. It turns out that there are several sets of points forming the superconformal boundary of Minkowski superspace: on top of a well-behaved super $\mathscr{I}$, we find other sets that we exhibit and study. We also study the intersect

Livia Betz

This paper addresses an optimal control problem governed by a rate independent evolution involving an integral operator. Its particular feature is that the dissipation potential depends on the history of the state. Because of the non-smooth nature of the system, the application of standard adjoint calculus is excluded. We derive optimality conditions in qual

Nils Melsom Kristensen, Paulina Tedesco, Jean Rabault, Ole Johan Aarnes

Knowledge about statistics for water level variations along the coast due to storm surge is important for the utilization of the coastal zone. An open and freely available storm surge hindcast archive covering the coast of Norway and adjacent sea areas spanning the time period 1979-2022 is presented. The storm surge model is forced by wind stress and mean se

Haochun Ma, Davide Prosperino, Alexander Haluszczynski, Christoph Räth

Identifying and quantifying co-dependence between financial instruments is a key challenge for researchers and practitioners in the financial industry. Linear measures such as the Pearson correlation are still widely used today, although their limited explanatory power is well known. In this paper we present a much more general framework for assessing co-dep

Albin Grataloup, Stefan Jonas, Angela Meyer

Federated learning has recently emerged as a privacy-preserving distributed machine learning approach. Federated learning enables collaborative training of multiple clients and entire fleets without sharing the involved training datasets. By preserving data privacy, federated learning has the potential to overcome the lack of data sharing in the renewable en

A. M. Haider, S. Rjasanow, M. Schanz

The acoustic wave equation is solved in time domain with a boundary element formulation. The time discretisation is performed with the generalised convolution quadrature method and for the spatial approximation standard lowest order elements are used. Collocation and Galerkin methods are applied. In the interest of increasing the efficiency of the boundary e

Baitan Shao, Ying Chen

Offline distillation is a two-stage pipeline that requires expensive resources to train a teacher network and then distill the knowledge to a student for deployment. Online knowledge distillation, on the other hand, is a one-stage strategy that alleviates the requirement with mutual learning and collaborative learning. Recent peer collaborative learning (PCL

He Zhao, Mathias Schultheis, Caixia Qu, Tomaz Zwitter

Diffuse interstellar bands (DIBs) are weak and broad interstellar absorption features in astronomical spectra originating from unknown molecules. To measure DIBs in spectra of late-type stars more accurately and more efficiently, we developed a Random Forest model to isolate the DIB features from the stellar components and applied this method to 780 thousand

S. Jalilvand, S. Sodagar, Z. Noorinejad, H. Karbaschi

We consider an armchair bilayer phosphorene that is connected to two hot and cold leads from both sides and study the thermoelectric properties of such a system with periodic vacancies at the middle of nanoribbon and in the armchair direction. For this purpose, we first analytically show that by creating a vacancy, a localized state is generated around it. T

Hyunseok Kim, Tuoc Phan, Tai-Peng Tsai

We study the Dirichlet problem for a second order linear elliptic equation in a bounded smooth domain $\Omega$ in $\mathbb{R}^n$, $n \ge 3$, with the drift $\mathbf{b} $ belonging to the critical weak space $L^{n,\infty}(\Omega )$. We decompose the drift $\mathbf{b} = \mathbf{b}_1 + \mathbf{b}_2$ in which $\text{div} \mathbf{b}_1 \geq 0$ and $\mathbf{b}_2$ i

Wanying Wang, Yichen Zhu, Yirui Zhou, Chaomin Shen

Generative Adversarial Imitation Learning (GAIL) stands as a cornerstone approach in imitation learning. This paper investigates the gradient explosion in two types of GAIL: GAIL with deterministic policy (DE-GAIL) and GAIL with stochastic policy (ST-GAIL). We begin with the observation that the training can be highly unstable for DE-GAIL at the beginning of

Yiting Qu, Zhikun Zhang, Yun Shen, Michael Backes

To prevent the mischievous use of synthetic (fake) point clouds produced by generative models, we pioneer the study of detecting point cloud authenticity and attributing them to their sources. We propose an attribution framework, FAKEPCD, to attribute (fake) point clouds to their respective generative models (or real-world collections). The main idea of FAKE

Wen-Ying Liu, Hua-Xing Chen

A lot of exotic hadrons were reported in the past twenty years, which bring us the renaissance of the hadron spectroscopy. Most of them can be understood as hadronic molecules, whose interactions are mainly due to the exchange of light mesons, and specifically, light vector mesons through the coupled-channel unitary approach within the local hidden-gauge for

David d'Enterria, Van Dung Le

We perform an extensive survey of rare and exclusive few-body decays -- defined as those with branching fractions $\mathcal{B} \lesssim 10^{-5}$ into two to four final particles -- of the Higgs, Z, W bosons, and the top quark. Such rare decays can probe physics beyond the Standard Model (BSM), constitute a background for exotic decays into new BSM particles,

Kyu Jung Bae, Jongkuk Kim

We consider the axion-mediated scattering processes between dark matter (DM) and nucleus. Substantial contributions are made via the CP-odd gluonic current which induces the spin-dependent process. Since the QCD axion is too feebly coupled to the visible particles, non-QCD axions are necessary for the current DM experiments to accomplish the ample sensitivit

Esin Koyuncu, Timofey Solovyev, Johannes Sauer, Elena Alshina

Learned image compression has a problem of non-bit-exact reconstruction due to different calculations of floating point arithmetic on different devices. This paper shows a method to achieve a deterministic reconstructed image by quantizing only the decoder of the learned image compression model. From the implementation perspective of an image codec, it is be

Nikolaos Dimakis, Kevin Duffy, Alex Giacomini, Alexander Yu. Kamenshchik

We investigate the impact of conformal transformations on the physical properties of solution trajectories in nonmetricity gravity. Specifically, we explore the phase-space and reconstruct the cosmological history of a spatially flat Friedmann-Lema\^{\i}tre-Robertson-Walker universe within scalar-nonmetricity theory in both the Jordan and Einstein frames. A

Boldizsár Balázs, Tamás Vicsek, Gergő Somorjai, Tamás Nepusz

Coordination of local and global aerial traffic has become a legal and technological bottleneck as the number of unmanned vehicles in the common airspace continues to grow. To meet this challenge, automation and decentralization of control is an unavoidable requirement. In this paper, we present a solution that enables self-organization of cooperating autono

Brian Weber, Justyna P. Zwolak

Gate-defined semiconductor quantum dot (QD) arrays are a promising platform for quantum computing. However, presently, the large configuration spaces and inherent noise make tuning of QD devices a nontrivial task and with the increasing number of QD qubits, the human-driven experimental control becomes unfeasible. Recently, researchers working with QD system

Yiyu Guo, Zhijin Qin, Xiaoming Tao, Geoffrey Ye Li

The metaverse is expected to provide immersive entertainment, education, and business applications. However, virtual reality (VR) transmission over wireless networks is data- and computation-intensive, making it critical to introduce novel solutions that meet stringent quality-of-service requirements. With recent advances in edge intelligence and deep learni

Parham Zarghami

In this paper, we will extend the falling and rising factorial transforms \cite{ref. 1} which in this case every arbitrary function can be applied. Then, the properties of these transforms will be investigated and some corollaries will be shown. These transforms have interesting properties that led to new series expansions, representation of functions in ter

Kai Huang, Yongqi Liang

Let g be a positive integer congruent to 1 modulo 4 and K be an arbitrary number field. We construct infinitely many explicit one-parameter algebraic families of degree 4 del Pezzo surfaces and of genus g hyperelliptic curves such that each K-member of the families violates the Hasse principle. In particular, we obtain algebraic families of non-trivial 2-tor

Ilan Hirshberg, N. Christopher Phillips

We construct an uncountable family of pairwise nonisomorphic AH algebras with the same Elliott invariant and same radius of comparison. They can be distinguished by a local radius of comparison function, naturally defined on the positive cone of the K_0 group.

Andrea Schillaci, Kazuto Hasegawa, Carlotta Pipolo, Giacomo Boracchi

In several problems involving fluid flows, Computational Fluid Dynamics (CFD) provides detailed quantitative information, and often allows the designer to successfully optimize the system, by minimizing a cost function. Sometimes, however, one cannot improve the system with CFD alone, because a suitable cost function is not readily available: one notable exa

Rui Cao, Tianrui Wang, Meng Ge, Longbiao Wang

Supervised speech enhancement has gained significantly from recent advancements in neural networks, especially due to their ability to non-linearly fit the diverse representations of target speech, such as waveform or spectrum. However, these direct-fitting solutions continue to face challenges with degraded speech and residual noise in hearing evaluations.

Deborah Hendrych, Mathieu Besançon, Sebastian Pokutta

We tackle the Optimal Experiment Design Problem, which consists of choosing experiments to run or observations to select from a finite set to estimate the parameters of a system. The objective is to maximize some measure of information gained about the system from the observations, leading to a convex integer optimization problem. We leverage Boscia.jl, a re

Sandi Klavžar, Eva Zmazek

A set of vertices $X$ of a graph $G$ is a strong edge geodetic set if to any pair of vertices from $X$ we can assign one (or zero) shortest path between them such that every edge of $G$ is contained in at least one on these paths. The cardinality of a smallest strong edge geodetic set of $G$ is the strong edge geodetic number ${\rm sg_e}(G)$ of $G$. In this

Lanlan Chen, Kai Wu, Jian Lou, Jing Liu

Modeling continuous-time dynamics constitutes a foundational challenge, and uncovering inter-component correlations within complex systems holds promise for enhancing the efficacy of dynamic modeling. The prevailing approach of integrating graph neural networks with ordinary differential equations has demonstrated promising performance. However, they disrega

Huai-Liang Chang, Shuai Guo, Jun Li, Wei-Ping Li

Let $G$ be a reductive group acting on an affine scheme $V$. We study the set of principal $G$-bundles on a smooth projective curve $\mathcal C$ such that the associated $V$-bundle admits a section sending the generic point of $\mathcal C$ into the GIT stable locus $V^{\mathrm{s}}(\theta)$. We show that after fixing the degree of the line bundle induced by t

Zhuangzhuang Tian, Haobo Chang, Xin Lv, Mengna Yang

In the fields of quantum metrology and quantum information processing with the system of optically trapped single neutral atoms, the coherence time of qubit encoded in the electronic states is regarded as one of the most important parameters. Longer coherence time is always pursued for higher precision of measurement and quantum manipulation. The coherence t

Haoyi Wang, Victor Sanchez, Chang-Tsun Li

Cross-age facial images are typically challenging and expensive to collect, making noise-free age-oriented datasets relatively small compared to widely-used large-scale facial datasets. Additionally, in real scenarios, images of the same subject at different ages are usually hard or even impossible to obtain. Both of these factors lead to a lack of supervise

Xizhou Bu, Wenjuan Li, Zhengxiong Liu, Zhiqiang Ma

Imitation learning attracts much attention for its ability to allow robots to quickly learn human manipulation skills through demonstrations. However, in the real world, human demonstrations often exhibit random behavior that is not intended by humans. Collecting high-quality human datasets is both challenging and expensive. Consequently, robots need to have

Yijiong Yu, Yongfeng Huang, Zhixiao Qi, Zhe Zhou

As Large Language Models (LLMs) continue to evolve, more are being designed to handle long-context inputs. Despite this advancement, most of them still face challenges in accurately handling long-context tasks, often showing the "lost in the middle" issue. We identify that insufficient retrieval capability is one of the important reasons for this issue. To t

Ó. Jiménez-Arranz, L. Chemin, M. Romero-Gómez, X. Luri

Context: The Large Magellanic Cloud (LMC) internal kinematics have been studied in unprecedented depth thanks to the excellent quality of the Gaia mission data, revealing the disc's non-axisymmetric structure. Aims: We want to constrain the LMC bar pattern speed using the astrometric and spectroscopic data from the Gaia mission. Methods: We apply three metho

Chang-Yan Wang, Tian-Gang Zhou, Yi-Neng Zhou, Pengfei Zhang

Measuring physical observables requires averaging experimental outcomes over numerous identical measurements. The complete distribution function of possible outcomes or its Fourier transform, known as the full counting statistics, provides a more detailed description. This method captures the fundamental quantum fluctuations in many-body systems and has gain

Yunpeng Song, Yiheng Bian, Yongtao Tang, Guiyu Ma

Mobile task automation is an emerging field that leverages AI to streamline and optimize the execution of routine tasks on mobile devices, thereby enhancing efficiency and productivity. Traditional methods, such as Programming By Demonstration (PBD), are limited due to their dependence on predefined tasks and susceptibility to app updates. Recent advancement

Samuel Yang-Zhao, Kee Siong Ng, Marcus Hutter

Prior approximations of AIXI, a Bayesian optimality notion for general reinforcement learning, can only approximate AIXI's Bayesian environment model using an a-priori defined set of models. This is a fundamental source of epistemic uncertainty for the agent in settings where the existence of systematic bias in the predefined model class cannot be resolved b

Xiaolei Guo, Xi Liu, Yuliang Xin

We report the discovery of energy-dependent morphology for the GeV gamma-ray emission from HESS J1857+026 with more than 13 years of {\it Fermi} Large Area Telescope (LAT) data. The GeV gamma-ray emission from this region is composed of two extended components. The hard component with an index of $1.74 \pm 0.07$ in the energy range of 0.5-500 GeV is spatiall

Yunlong Zang, Yingfei Gu, Shenghan Jiang

Symmetries and quantum anomalies serve as powerful tools for constraining complicated quantum many-body systems, offering valuable insights into low-energy characteristics based on their ultraviolet structure. Nevertheless, their applicability has traditionally been confined to closed quantum systems, rendering them largely unexplored for open quantum system

Itamar Cohen, Paolo Giaccone, Carla Fabiana Chiasserini

In an edge-cloud multi-tier network, datacenters provide services to mobile users, with each service having specific latency constraints and computational requirements. Deploying such a variety of services while matching their requirements with the available computing resources is challenging. In addition, time-critical services may have to be migrated as th

Đorđe Marković, Simon Vandevelde, Linde Vanbesien, Joost Vennekens

Substantial efforts have been made in developing various Decision Modeling formalisms, both from industry and academia. A challenging problem is that of expressing decision knowledge in the context of incomplete knowledge. In such contexts, decisions depend on what is known or not known. We argue that none of the existing formalisms for modeling decisions ar

Felipe Gonçalves

We completely classify Fourier summation formulas, and in particular, all crystalline measures with quadratic decay. Our classification employs techniques from almost periodic functions, Hermite-Biehler functions, de Branges spaces and Poisson representation. We show how our classification generalizes recent results of Kurasov \& Sarnak and Olevskii \& Ulano

Tiantian Cao, Xuan Dong, Chunli Peng, Zhengqing Li

The dual camera system of wide-angle ($\bf{W}$) and telephoto ($\bf{T}$) cameras has been widely adopted by popular phones. In the overlap region, fusing the $\bf{W}$ and $\bf{T}$ images can generate a higher quality image. Related works perform pixel-level motion alignment or high-dimensional feature alignment of the $\bf{T}$ image to the view of the $\bf{W

Paul Thévenin, Stephan Wagner

We study two related probabilistic models of permutations and trees biased by their number of descents. Here, a descent in a permutation $\sigma$ is a pair of consecutive elements $\sigma(i), \sigma(i+1)$ such that $\sigma(i) > \sigma(i+1)$. Likewise, a descent in a rooted tree with labelled vertices is a pair of a parent vertex and a child such that the lab

Vladimir Yu. Protasov, Tatyana Zaitseva

The regularity of refinable functions has been analysed in an extensive literature and is well-understood in two cases: 1) univariate 2) multivariate with an isotropic dilation matrix. The general (non-isotropic) case offered a great resistance. It was done only recently by developing the matrix method. In this paper we make the next step and extend the Litt

Anirban Mukherjee, Dhiraj Tapader, Animesh Hazra, Punyabrata Pradhan

We study a paradigmatic model of absorbing-phase transition - the Oslo model - on a one-dimensional ring of $L$ sites with a fixed global density $\bar{\rho}$; notably, microscopic dynamics conserve both mass and \textit{center of mass (CoM), but lacks time-reversal symmetry}. Despite having highly constrained dynamics due to CoM conservation, the system exh

Léonie Papon

We construct a coupling between a massive GFF and a random curve in which the curve can be interpreted as the level line of the field and has the law of massive SLE$_4$. This coupling is obtained by reweighting the law of the standard coupling GFF-SLE$_4$ and our result can be seen as a conditional version of the path-integral formulation of the massive GFF.

Clara Horvath, Andreas Körner, Corinna Modiz

The thyroid gland, in conjunction with the pituitary and the hypothalamus, forms a regulated system due to their mutual influence through released hormones. The equilibrium point of this system, commonly referred to as the "set point", is individually determined. This means that determining the correct amount of medication to be administered to patients with

Manon Mottier, Gilles chardon, Frédéric Pascal

Detection and identification of emitters provide vital information for defensive strategies in electronic intelligence. Based on a received signal containing pulses from an unknown number of emitters, this paper introduces an unsupervised methodology for deinterleaving RADAR signals based on a combination of clustering algorithms and optimal transport distan

Emil Engström, Eskil Hansen

The Neumann--Neumann method is a commonly employed domain decomposition method for linear elliptic equations. However, the method exhibits slow convergence when applied to semilinear equations and does not seem to converge at all for certain quasilinear equations. We therefore propose two modified Neumann--Neumann methods that have better convergence propert

Ning Liu, Yiming Fan, Xianyi Zeng, Milan Klöwer

Neural operators (NOs) have emerged as effective tools for modeling complex physical systems in scientific machine learning. In NOs, a central characteristic is to learn the governing physical laws directly from data. In contrast to other machine learning applications, partial knowledge is often known a priori about the physical system at hand whereby quanti

Sören Kohnert, Dominik Zoeke, Reinhard Stolle

Classification of road users is important for traffic monitoring. The usability of a height estimate based on the two-ray ground-reflection model as a feature for the classification of vehicles is analyzed in this paper. The four-ray ground-reflection model for fast chirp ramp sequence waveforms of FMCW radars is derived and simplified to the well-known two-

Huai-Liang Chang, Shuai Guo, Jun Li, Wei-Ping Li

The theory of Mixed-Spin-P (MSP) fields was introduced by Chang-Li-Li-Liu for the quintic threefold, aiming at studying its higher-genus Gromov-Witten invariants. Chang-Guo-Li has successfully applied it to prove conjectures including the BCOV Feynman rule, Yamaguchi-Yau's polynomiality conjecture and the Holomorphic Anomaly Equation. Meanwhile, Fan-Jarvis-R

Guillermo J. Amador, Brett Klaassen van Oorschot, Uddalok Sen, Benjamin Karman

Scientific progress within the last few decades has revealed the functional morphology of an insect's sticky footpads -- a soft, sponge-like pad that secretes a thin liquid film. However, the physico-chemical mechanisms underlying their adhesion remain elusive. Here, we explore these underlying mechanisms by simultaneously measuring adhesive force and contac

Jacopo Ulivelli

We introduce functional Wulff shapes based on the classical construction for compact convex sets. With this new tool, we establish a functional version of Aleksandrov's variational lemma in the family of convex functions with compact domain. The resulting formula is then applied to evaluate the first variation of a class of functionals on convex functions. I

Chenlu Zhan, Yufei Zhang, Yu Lin, Gaoang Wang

Medical vision-language pre-training (Med-VLP) models have recently accelerated the fast-growing medical diagnostics application. However, most Med-VLP models learn task-specific representations independently from scratch, thereby leading to great inflexibility when they work across multiple fine-tuning tasks. In this work, we propose UniDCP, a Unified medic

Zijian Liang, Yijia Xu, Joseph T. Iosue, Yu-An Chen

In this paper, we introduce an algorithm for extracting topological data from translation invariant generalized Pauli stabilizer codes in two-dimensional systems, focusing on the analysis of anyon excitations and string operators. The algorithm applies to $\mathbb{Z}_d$ qudits, including instances where $d$ is a nonprime number. This capability allows the id

Reda Khoufache, Mustapha Lebbah, Hanene Azzag, Etienne Goffinet

Dirichlet Process Mixture Models (DPMMs) are widely used to address clustering problems. Their main advantage lies in their ability to automatically estimate the number of clusters during the inference process through the Bayesian non-parametric framework. However, the inference becomes considerably slow as the dataset size increases. This paper proposes a n

Jiahuan He, Chao Kan, Wen Song

In this paper, we focus on the exploration of solution uniqueness, sharpness, and robust recovery in sparse regularization with a gauge $J$. Based on the criteria for the uniqueness of Lagrange multipliers in the dual problem, we give a characterization of the unique solution via the so-called radial cone. We establish characterizations of the isolated calmn

Taishu Kayanoki, Junjie Mao, Yasushi Fukazawa

Active galactic nucleus (AGN) outflows including jets and ionized winds have been key phenomena such as jet collimation and AGN feedback to the host galaxy in astrophysics. Radio galaxies, a type of AGN with misaligned jets, have provided valuable insights into the properties and relationships of these outflows. However, several aspects regarding AGN outflow

Benedikt Brantner, Guillaume de Romemont, Michael Kraus, Zeyuan Li

Two of the many trends in neural network research of the past few years have been (i) the learning of dynamical systems, especially with recurrent neural networks such as long short-term memory networks (LSTMs) and (ii) the introduction of transformer neural networks for natural language processing (NLP) tasks. While some work has been performed on the inter

Wei Liu, Frank F. Deppisch, Zixiang Chen

We discuss the potential of using long-lived particle (LLP) searches for right-handed neutrinos (RHNs) to test resonant leptogenesis and the seesaw mechanism. This is challenging if only RHNs are added to the Standard Model (SM), as naturally the active-sterile mixing strengths $|V_{\ell N}|^2$ are small, for 1 GeV $\lesssim M_N \lesssim 1000$ GeV. Instead,

Hailee E. Hettrick, Begum Cannataro, David W. Miller

Driven by the desire to find positions that satisfy keepout constraints for a space-based telescope mission, this work develops a process for tracing a point in space in the regime of the restricted three-body problem to a halo orbit, characterized by its out-of-plane amplitude, and its position on that halo orbit, denoted by the halo orbit time. This proces

Jonas Wittmann, Daniel Hornung, Korbinian Griesbauer, Daniel Rixen

Nowadays, robots are applied in dynamic environments. For a robust operation, the motion planning module must consider other tasks besides reaching a specified pose: (self) collision avoidance, joint limit avoidance, keeping an advantageous configuration, etc. Each task demands different joint control commands, which may counteract each other. We present a h

Frank Steglich

In the first part of this article, I briefly review early research activities concerning strongly correlated electron systems, beginning with the discovery of superconductivity and the first observation of a resistance minimum in nominally pure Cu-metal, which was explained many years later by Kondo. I will also address the antagonistic behavior of conventio

Silvia Ferro, Matteo Faganello, Francesco Califano, Fabio Bacchini

This study presents three-dimensional (3D) resistive Hall-magnetohydrodynamic simulations of the Kelvin-Helmholtz instability (KHI) dynamics at Earth's magnetospheric flanks during northward interplanetary magnetic field periods. By comparing two simulations with and without initial magnetic shear, we analyze the impact of distinct magnetic field orientation

Fabien Geyer, Thomas Multerer, Paulo Mendes, Dominic Schupke

Advances in wireless localization techniques aiming to exploit context-dependent data has been leading to a growing interest in services able of localizing or tracking targets inside buildings with high accuracy and precision. Hence, the demand for indoor localization services has become a key prerequisite in some markets, such as in the aviation sector. In

Yufei Ge, Weitang Li, Jiajun Ren, Zhigang Shuai

Organic molecular materials are potential high-performance thermoelectric materials. Theoretical understanding of thermoelectric conversion in organic materials is essential for rational molecular design for efficient energy conversion materials. In organic materials, non-local electron-phonon coupling plays a vital role in charge transport and leads to comp

Joel Dyer, Nicholas Bishop, Yorgos Felekis, Fabio Massimo Zennaro

Agent-based simulators provide granular representations of complex intelligent systems by directly modelling the interactions of the system's constituent agents. Their high-fidelity nature enables hyper-local policy evaluation and testing of what-if scenarios, but is associated with large computational costs that inhibits their widespread use. Surrogate mode

Alaeddine Zahir, Khalide Jbilou, Ahmed Ratnani

This study introduces a novel technique for multi-view clustering known as the "Consensus Graph-Based Multi-View Clustering Method Using Low-Rank Non-Convex Norm" (CGMVC-NC). Multi-view clustering is a challenging task in machine learning as it requires the integration of information from multiple data sources or views to cluster data points accurately. The

Matthias Gimperlein, Jasper N. Immink, Michael Schmiedeberg

Using Brownian dynamics simulations we study gel-forming colloid-polymer mixtures. The focus of this article lies on the differences of dense and dilute gel networks in terms of structure formation both on a local and a global level. We apply reduction algorithms and observe that dilute networks and dense gels differ in the way structural properties like the

N. T. Cherpak, A. A. Barannik, Y. -S. He, L. Sun

The paper analyzes the characteristics of the microwave (MW) response of FeSe1-xTex films based on the results of measuring the impedance properties of the films in the X-band for two orientations of the film in the MW magnetic field, perpendicular and parallel. The analysis of the temperature dependence of the microwave response of a film with a perpendicul

Zhi Jin, Sheng Xu, Xiang Zhang, Tianze Ling

De novo peptide sequencing from mass spectrometry (MS) data is a critical task in proteomics research. Traditional de novo algorithms have encountered a bottleneck in accuracy due to the inherent complexity of proteomics data. While deep learning-based methods have shown progress, they reduce the problem to a translation task, potentially overlooking critica

Patrick Bieker, Timo Richarz

We classify all normal Schubert varieties in the affine Grassmannian of a semisimple group over an arbitrary field with special attention to small positive characteristic. The proof is elementary and relies on tangent space calculations for quasi-minuscule Schubert varieties, a refined Levi lemma in positive characteristic and the classification of minimal d

Tao Wang

Natural scene text detection is a significant challenge in computer vision, with tremendous potential applications in multilingual, diverse, and complex text scenarios. We propose a multilingual text detection model to address the issues of low accuracy and high difficulty in detecting multilingual text in natural scenes. In response to the challenges posed

Kun Peng, Lei Jiang, Hao Peng, Rui Liu

Aspect Sentiment Triplet Extraction (ASTE) is an emerging task to extract a given sentence's triplets, which consist of aspects, opinions, and sentiments. Recent studies tend to address this task with a table-filling paradigm, wherein word relations are encoded in a two-dimensional table, and the process involves clarifying all the individual cells to extrac

Noramon Dron, Javier Escudero

Determining the underlying number of components $R$ in tensor decompositions is challenging. Diverse techniques exist for various decompositions, notably the core consistency diagnostic (CORCONDIA) for Canonical Polyadic Decomposition (CPD). Here, we propose a model that intuitively adapts CORCONDIA for rank estimation in Block Term Decomposition (BTD) of ra

T. Valtinos, A. Mandilara, D. Syvridis

Recent advancements in quantum hardware have enabled the realization of high-dimensional quantum states. This work investigates the potential of qutrits in quantum machine learning, leveraging their larger state space for enhanced supervised learning tasks. To that end, the Gell-Mann feature map is introduced which encodes information within an $8$-dimension

Nirbhay Patil, Fabian Aguirre-Lopez, Jean-Philippe Bouchaud

Economic and ecological models can be extremely complex, with a large number of agents/species each featuring multiple interacting dynamical quantities. In an attempt to understand the generic stability properties of such systems, we define and study an interesting new matrix ensemble with extensive correlations, generalising the elliptic ensemble. We determ

Soren Kohnert, Michael Vogt, Reinhard Stolle

Target classification is an important task of automotive radar systems. In this work, a concept for estimating the height of vehicles to allow for a differentiation between passenger cars, trucks, and others, is presented and discussed. Fixed installed radar sensors for traffic monitoring in the 77 GHz band are used to track and analyze radar echoes from ind

Maxime Ligonnière

In this note, we define a bounded variant on the Hilbert projective metric on an infinite dimensional space $E$ and study the contraction properties of the projective maps associated with positive linear operators on $E$. More precisely, we prove that any positive linear operator acts projectively as a $1$-Lipschitz map relatively to this distance. We also s

Yuming Qiu, Aleksandra Pizurica, Qi Ming, Nicolas Nadisic

Automated mark localization in scatter images, greatly helpful for discovering knowledge and understanding enormous document images and reasoning in visual question answering AI systems, is a highly challenging problem because of the ubiquity of overlapping marks. Locating overlapping marks faces many difficulties such as no texture, less contextual informat