July 2023 arXiv papers — page 87

Yuankai Zhou, Huanyu Li

Non-core drilling has gradually become the primary exploration method in geological exploration engineering, and well logging curves have increasingly gained importance as the main carriers of geological information. However, factors such as geological environment, logging equipment, borehole quality, and unexpected events can all impact the quality of well

Nasrin Razmi, Bho Matthiesen, Armin Dekorsy, Petar Popovski

The emergence of mega-constellations of interconnected satellites has a major impact on the integration of cellular wireless and non-terrestrial networks, while simultaneously offering previously inconceivable data gathering capabilities. This paper studies the problem of running a federated learning (FL) algorithm within low Earth orbit satellite constellat

Stefan Evans, Ralf Schützhold

We consider birefringent (i.e., polarization changing) scattering of x-ray photons at the superposition of two optical laser beams of ultra-high intensity and study the resonant contributions of axions or axion-like particles, which could also be short-lived. Applying the specifications of the Helmholtz International Beamline for Extreme Fields (HIBEF), we f

Christian Herglotz, Mohammadreza Jamali, Stéphane Coulombe, Carlos Vazquez

This paper presents an efficient method for encoding common projection formats in 360$^\circ$ video coding, in which we exploit inactive regions. These regions are ignored in the reconstruction of the equirectangular format or the viewport in virtual reality applications. As the content of these pixels is irrelevant, we neglect the corresponding pixel values

Tianming Bai, Aretha L. Teckentrup, Konstantinos C. Zygalakis

This work is concerned with the use of Gaussian surrogate models for Bayesian inverse problems associated with linear partial differential equations. A particular focus is on the regime where only a small amount of training data is available. In this regime the type of Gaussian prior used is of critical importance with respect to how well the surrogate model

Ross J. Turner, Stanislav S. Shabala

Analytical models describing the dynamics of lobed radio sources are essential for interpretation of the tens of millions of radio sources that will be observed by the Square Kilometre Array and pathfinder instruments. We propose that historical models can be grouped into two classes in which the forward expansion of the radio source is driven by either the

Donatella Darsena, Giacinto Gelli, Ivan Iudice, Francesco Verde

Communication between satellites in low-Earth orbit (LEO) constellations takes place through inter-satellite links (ISLs). Unlike intra-plane ISLs, which interconnect satellites belonging to the same orbital plane with fixed relative distance, inter-plane ISLs experience significant Doppler frequency shifts, since satellites belonging to different orbital pl

Huawei Sun, Hao Feng, Georg Stettinger, Lorenzo Servadei

Accurate and robust object detection is critical for autonomous driving. Image-based detectors face difficulties caused by low visibility in adverse weather conditions. Thus, radar-camera fusion is of particular interest but presents challenges in optimally fusing heterogeneous data sources. To approach this issue, we propose two new radar preprocessing tech

Christian Herglotz, Stéphane Coulombe, Ahmad Vakili, André Kaup

This paper proposes a method to evaluate and model the power consumption of modern virtual reality playback and streaming applications on smartphones. Due to the high computational complexity of the virtual reality processing toolchain, the corresponding power consumption is very high, which reduces operating times of battery-powered devices. To tackle this

Yuzhuo Wang, Kai Li

Under the data-driven research paradigm, research software has come to play crucial roles in nearly every stage of scientific inquiry. Scholars are advocating for the formal citation of software in academic publications, treating it on par with traditional research outputs. However, software is hardly consistently cited: one software entity can be cited as d

Christian Herglotz, André Kaup, Stéphane Coulombe, Ahmad Vakili

This paper derives optimal spatial scaling and rate control parameters for power-efficient wireless video streaming on portable devices. A video streaming application is studied, which receives a high-resolution and high-quality video stream from a remote server and displays the content to the end-user.We show that the resolution of the input video can be ad

Jesus Tordesillas, Victor Klemm, Jonathan P. How, Marco Hutter

Despite the numerous applications of convex constraints in Robotics, enforcing them within learning-based frameworks remains an open challenge. Existing techniques either fail to guarantee satisfaction at all times, or incur prohibitive computational costs. This paper presents RAYEN, a framework for imposing hard convex constraints on the output or latent va

Pablo Galve, Alejandro Lopez-Montes, Jose M Udias, Stephen C Moore

Positron Emission Tomography (PET) scanners are usually designed with the goal to obtain the best compromise between sensitivity, resolution, field-of-view size, and cost. Therefore, it is difficult to improve the resolution of a PET scanner with hardware modifications, without affecting some of the other important parameters. Iterative image reconstruction

Bobo Hua, Florentin Münch, Haohang Zhang

Inspired by asymptotically flat manifolds, we introduce the concept of asymptotically flat graphs and define the discrete ADM mass on them. We formulate the discrete positive mass conjecture based on the scalar curvature in the sense of Ollivier curvature, and prove the positive mass theorem for asymptotically flat graphs that are combinatorially isomorphic

Laura Ares, Alfredo Luis

How to manage coherence as a continuous variable quantum resource is still an open question. We face this situation from the very definition of incoherent states in quadrature basis. We apply several measures of coherence for some physical states of light relative to a quadrature basis. We examine the action on the coherence of several transformations such a

Qianqian Liu, Yaxian Zhang, Heping Zhang

In a region R consisting of unit squares, a (domino) tiling is a collection of dominoes (the union of two adjacent squares) which pave fully the region. The flip graph of R is defined on the set of all tilings of R where two tilings are adjacent if we change one from the other by a flip (a 90-degree rotation of a pair of side-by-side dominoes). If R is simpl

Noam Ben-Moshe, Shany Biton, Kenta Tsutsui, Mahmoud Suleiman

Introduction: The presence of fibrillatory waves (f-waves) is important in the diagnosis of atrial fibrillation (AF), which has motivated the development of methods for f-wave extraction. We propose a novel approach to benchmarking methods designed for single-lead ECG analysis, building on the hypothesis that better-performing AF classification using feature

Hai-Qing Cao, Mao-Sheng Li, Hui-Juan Zuo

The nonlocal set has received wide attention over recent years. Shortly before, Li and Wang arXiv:2202.09034 proposed the concept of a locally stable set: the only possible orthogonality preserving measurement on each subsystem is trivial. Locally stable sets present stronger nonlocality than those sets that are just locally indistinguishable. In this work,

Jean-Michel Coron, Joachim Krieger, Shengquan Xiang

Continuing the investigations started in the recent work [Krieger-Xiang, 2022] on semi-global controllability and stabilization of the $(1+1)$-dimensional wave maps equation with spatial domain $\mathbb{S}^1$ and target $\mathbb{S}^k$, where {\it semi-global} refers to the $2\pi$-energy bound, we prove global exact controllability of the same system for $k>1

Umara Noor, Sawera Shahid, Rimsha Kanwal, Zahid Rashid

Cyber threat attribution is the process of identifying the actor of an attack incident in cyberspace. An accurate and timely threat attribution plays an important role in deterring future attacks by applying appropriate and timely defense mechanisms. Manual analysis of attack patterns gathered by honeypot deployments, intrusion detection systems, firewalls,

S. B. Rutkevich

We study the tuning effect of a transverse magnetic field on the confinement of spinons in the infinite XXZ spin-1/2 chain. The spinon confinement in this model takes place in the gapped antiferromagnetic phase upon application of a staggered longitudinal magnetic field. The tuning transverse magnetic field has mutually orthogonal uniform and staggered compo

Prathyusha Devabhakthini, Sasmita Parida, Raj Mani Shukla, Suvendu Chandan Nayak

Adversarial attacks are a type of attack on machine learning models where an attacker deliberately modifies the inputs to cause the model to make incorrect predictions. Adversarial attacks can have serious consequences, particularly in applications such as autonomous vehicles, medical diagnosis, and security systems. Work on the vulnerability of deep learnin

Emre Oral, Ria Chawla, Michel Wijkstra, Narges Mahyar

In the face of complex decisions, people often engage in a three-stage process that spans from (1) exploring and analyzing pertinent information (intelligence); (2) generating and exploring alternative options (design); and ultimately culminating in (3) selecting the optimal decision by evaluating discerning criteria (choice). We can fairly assume that all g

Svetlana Kuznetsova, Yves Auregan, Vincent Pagneux

A method of higher-order modes filtering in an air-filled waveguide using a resistive layer is proposed. An analogue of Cremer's criterion is discussed and used to obtain the optimal modal attenuation of the non-planar waves while the plane wave is preserved. Numerical validation of the concept is performed for a straight waveguide and an abrupt expansion in

Yanna Jiang, Baihe Ma, Xu Wang, Guangsheng Yu

As a distributed learning, Federated Learning (FL) faces two challenges: the unbalanced distribution of training data among participants, and the model attack by Byzantine nodes. In this paper, we consider the long-tailed distribution with the presence of Byzantine nodes in the FL scenario. A novel two-layer aggregation method is proposed for the rejection o

Xiaohui Zhang, Mangui Liang, Zhengkun Tian, Jiangyan Yi

End-to-end model, especially Recurrent Neural Network Transducer (RNN-T), has achieved great success in speech recognition. However, transducer requires a great memory footprint and computing time when processing a long decoding sequence. To solve this problem, we propose a model named time-sparse transducer, which introduces a time-sparse mechanism into tra

Yanqing Wang, Wei Wei, Gnag Wu, Yulin Ye

In this paper, we are concerned with the minimal regularity of weak solutions implying the law of balance for both energy and helicity in the incompressible Euler equations. In the spirit of recent works due to Berselli [5] and Berselli-Georgiadis [6], it is shown that the energy of weak solutions is invariant if $v\in L^{p}(0,T;B^{\frac1p}_{\frac{2p}{p-1},c

Cong Jiang, Xiaolei Yang

Legal syllogism is a form of deductive reasoning commonly used by legal professionals to analyze cases. In this paper, we propose legal syllogism prompting (LoT), a simple prompting method to teach large language models (LLMs) for legal judgment prediction. LoT teaches only that in the legal syllogism the major premise is law, the minor premise is the fact,

Davide De Biasio

After an introductory chapter on the quantum supersymmetric string, in which particular attention will be devoted to the techniques via which phenomenologically viable models can be obtained from the ultraviolet microscopic degrees of freedom, and a brief review of the swampland program, the technical tools required to deal with geometric flows will be outli

Juan F Poyatos

Complexity in biology is often described using a multi-map architecture, where the genotype, representing the encoded information, is mapped to the functional level, known as the phenotype, which is then connected to a latent phenotype we refer to as fitness. This underlying architecture governs the processes that drive evolution. Moreover, natural selection

Kai Katsumata, Duc Minh Vo, Tatsuya Harada, Hideki Nakayama

Label-noise or curated unlabeled data is used to compensate for the assumption of clean labeled data in training the conditional generative adversarial network; however, satisfying such an extended assumption is occasionally laborious or impractical. As a step towards generative modeling accessible to everyone, we introduce a novel conditional image generati

Ron Keuth, Mattias Heinrich, Martin Eichenlaub, Marian Himstedt

Purpose: Navigation guidance is a key requirement for a multitude of lung interventions using video bronchoscopy. State-of-the-art solutions focus on lung biopsies using electromagnetic tracking and intraoperative image registration w.r.t. preoperative CT scans for guidance. The requirement of patient-specific CT scans hampers the utilisation of navigation g

Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang

Existing face forgery detection models try to discriminate fake images by detecting only spatial artifacts (e.g., generative artifacts, blending) or mainly temporal artifacts (e.g., flickering, discontinuity). They may experience significant performance degradation when facing out-domain artifacts. In this paper, we propose to capture both spatial and tempor

Tengfei Liang, Yi Jin, Wu Liu, Tao Wang

Visible-Infrared person Re-IDentification (VI-ReID) is a challenging cross-modality image retrieval task that aims to match pedestrians' images across visible and infrared cameras. To solve the modality gap, existing mainstream methods adopt a learning paradigm converting the image retrieval task into an image classification task with cross-entropy loss and

Hongxiao Li, Wanling Gao, Lei Wang, Jianfeng Zhan

\textsc{Lara} is a key-value algebra that aims at unifying linear and relational algebra with three types of operation abstraction. The study of \textsc{Lara}'s expressive ability reports that it can represent relational algebra and most linear algebra operations. However, several essential computations, such as matrix inversion and determinant, cannot be ex

Benedikt Mersch, Tiziano Guadagnino, Xieyuanli Chen, Ignacio Vizzo

Mobile robots that navigate in unknown environments need to be constantly aware of the dynamic objects in their surroundings for mapping, localization, and planning. It is key to reason about moving objects in the current observation and at the same time to also update the internal model of the static world to ensure safety. In this paper, we address the pro

Sergey Troitsky

GRB 221009A was the brightest gamma-ray burst ever detected on Earth. In its early afterglow phase, photons with exceptional energies above 10 TeV were observed by LHAASO, and a photon-like air shower above 200 TeV was detected by Carpet-2. Gamma rays of very high energies can hardly reach us from the distant GRB because of pair production on cosmic backgrou

Marian Rockenhäuser, Felix Kogel, Einius Pultinevicius, Tim Langen

Currently, there is great interest in laser cooling of barium monofluoride (BaF) molecules for precision tests of fundamental symmetries. We use high-resolution absorption spectroscopy to characterize several as yet imprecisely known transition frequencies required to realize such cooling. We extract an improved set of molecular constants for the bosonic 138

Saif Ahmad, Jochem Baltussen, Pauline Kergus, Zohra Kader

In this paper, the problem of electric vehicle (EV) charging at the workplace is addressed via a two-layer predictive algorithm. We consider a time of use (TOU) pricing model for energy drawn from the grid and try to minimize the charging cost incurred by the EV charging station (EVCS) operator via an economic layer based on dynamic programming (DP) approach

Gerrit Schierholz

The axion, originally postulated by Peccei and Quinn to solve the strong CP problem, has become of great interest in particle and astroparticle phenomenology. Yet it has a problem. It is widely assumed that the axion leaves the nonperturbative features of QCD unscathed. This is, however, not the case. On the contrary, the axion extension is found to be in co

Matteo Boffa, Rodolfo Vieira Valentim, Luca Vassio, Danilo Giordano

The collection of security-related logs holds the key to understanding attack behaviors and diagnosing vulnerabilities. Still, their analysis remains a daunting challenge. Recently, Language Models (LMs) have demonstrated unmatched potential in understanding natural and programming languages. The question arises whether and how LMs could be also useful for s

Yan-Jie Zhou, Wei Liu, Yuan Gao, Jing Xu

Skin diseases are among the most prevalent health issues, and accurate computer-aided diagnosis methods are of importance for both dermatologists and patients. However, most of the existing methods overlook the essential domain knowledge required for skin disease diagnosis. A novel multi-task model, namely DermImitFormer, is proposed to fill this gap by imit

M. J. Vasli, K. Babaei Velni, M. R. Mohammadi Mozaffar, A. Mollabashi

We investigate various aspects of the Lanczos coefficients in a family of free Lifshitz scalar theories, characterized by their integer dynamical exponent, at finite temperature. In this non-relativistic setup, we examine the effects of mass, finite ultraviolet cutoff, and finite lattice spacing on the behavior of the Lanczos coefficients. We also investigat

Victor E. Ambruş, Sören Schlichting, Clemens Werthmann

We simulate the space-time dynamics of high-energy collisions based on a microscopic kinetic description, in order to determine the range of applicability of an effective description in relativistic viscous hydrodynamics. We find that hydrodynamics provides a quantitatively accurate description of collective flow when the average inverse Reynolds number $\ma

Han Wu, Bo Hu, Fei Peng, Zinan Wang

Ghost imaging in the time domain allows for reconstructing fast temporal objects using a slow photodetector. The technique involves correlating random or pre-programmed probing temporal intensity patterns with the integrated signal measured after modulation by the temporal object. However, the implementation of temporal ghost imaging necessitates ultrafast d

Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, David Huber

We assume to be given structural equations over discrete variables inducing a directed acyclic graph, namely, a structural causal model, together with data about its internal nodes. The question we want to answer is how we can compute bounds for partially identifiable counterfactual queries from such an input. We start by giving a map from structural casual

Zhiyuan Peng, Xuyang Wu, Qifan Wang, Yi Fang

Dense retrieval (DR) converts queries and documents into dense embeddings and measures the similarity between queries and documents in vector space. One of the challenges in DR is the lack of domain-specific training data. While DR models can learn from large-scale public datasets like MS MARCO through transfer learning, evidence shows that not all DR models

Li Shen, Yuning Wei, Yangzhu Wang

This paper shows that time series forecasting Transformer (TSFT) suffers from severe over-fitting problem caused by improper initialization method of unknown decoder inputs, esp. when handling non-stationary time series. Based on this observation, we propose GBT, a novel two-stage Transformer framework with Good Beginning. It decouples the prediction process

Lukas Brechtel, Christof A. O. Rauber, Christoph Fischer

In this paper, the authors present a Radio Access Network (RAN) concept for future mobile communication systems beyond 5G. The concept is based on knowledge of the environment. The three conceptual applications RAN authentication, beam steering, and channel estimation are presented and their added value with respect to 6G development goals is outlined. The c

Mingyang Zhang, Xinyi Yu, Haodong Zhao, Linlin Ou

One-shot Neural architecture search (One-shot NAS) has been proposed as a time-efficient approach to obtain optimal subnet architectures and weights under different complexity cases by training only once. However, the subnet performance obtained by weight sharing is often inferior to the performance achieved by retraining. In this paper, we investigate the p

Kangyang Luo, Kunkun Zhang, Shengbo Zhang, Xiang Li

Decentralized learning (DL) has recently employed local updates to reduce the communication cost for general non-convex optimization problems. Specifically, local updates require each node to perform multiple update steps on the parameters of the local model before communicating with others. However, most existing methods could be highly sensitive to data he

Uta Mohring, Melvin Drent, Ivo Adan, Willem van Jaarsveld

We develop a parsimonious model of an e-commerce fulfillment center that offers time-dependent shipment options and corresponding fees to utility-maximizing customers arriving according to a Poisson process. For any such policy, we provide an exact steady-state analysis using the underlying periodic Markov chain to characterize system performance. Because sh

Sofia Brenner

For $k, \ell \in \mathbb{N}$, we introduce the concepts of $k$-ultrahomogeneity and $\ell$-tuple regularity for finite groups. Inspired by analogous concepts in graph theory, these form a natural generalization of homogeneity, which was studied by Cherlin and Felgner and Li as well as automorphism transitivity, which was investigated by Zhang. Additionally,

Lili Hao, Rui Chang, Xiaokai Hou, Jun He

Narrow-linewidth lasers have high spectral purity, long coherent length and low phase noise, so they have important applications in cold atom physics, quantum communication, quantum information processing and optical precision measurement. We inject transmitted laser from a narrow-linewidth (15 kHz) flat-concave Fabry-Perot (F-P) cavity made of ultra-low exp

Remo Pareschi

This study evaluates the GPT-4 Large Language Model's abductive reasoning in complex fields like medical diagnostics, criminology, and cosmology. Using an interactive interview format, the AI assistant demonstrated reliability in generating and selecting hypotheses. It inferred plausible medical diagnoses based on patient data and provided potential causes a

Christof A. O. Rauber, Lukas Brechtel, Hans D. Schotten

The introduction of new types of frequency spectrum in 6G technology facilitates the convergence of conventional mobile communications and radar functions. Thus, the mobile network itself becomes a versatile sensor system. This enables mobile network operators to offer a sensing service in addition to conventional data and telephony services. The potential b

Fanze Kong, Chen-Chih Lai, Juncheng Wei

To describe the cellular self-aggregation phenomenon, some strongly coupled PDEs named as Keller-Segel (KS) and Patlak-Keller-Segel (PKS) systems were proposed in 1970s. Since KS and PKS systems possess relatively simple structures but admit rich dynamics, plenty of scholars have studied them and obtained many significant results. However, the cells in gener

Stepan Perminov, Ivan Kalinov, Dzmitry Tsetserukou

Numerous mobile robots with mounted Ultraviolet-C (UV-C) lamps were developed recently, yet they cannot work in the same space as humans without irradiating them by UV-C. This paper proposes a novel modular and scalable Human-Aware Genetic-based Coverage Path Planning algorithm (GHACPP), that aims to solve the problem of disinfecting of unknown environments

Vojtěch Trávníček, Jan Roik, Karol Bartkiewicz, Antonín Černoch

In this paper, we present a supervised learning technique that utilizes artificial neural networks to design new collective entanglement witnesses for two-qubit and qubit-qutrit systems. Machine-designed collective entanglement witnesses allow for continuous tuning of their sensitivity and selectivity. These witnesses are, thus, a conceptually novel instrume

Pierre Le Jeune, Anissa Mokraoui

In Few-Shot Object Detection (FSOD), detecting small objects is extremely difficult. The limited supervision cripples the localization capabilities of the models and a few pixels shift can dramatically reduce the Intersection over Union (IoU) between the ground truth and predicted boxes for small objects. To this end, we propose Scale-adaptive Intersection o

Ozay Gurtug, Mustafa Halilsoy, Mert Mangut

The non-trivial naked singularities that possess directional behavior in the charged and uncharged Zipoy-Voorhees (ZV) spacetimes, known as {\gamma} - metrics are investigated within the context of quantum mechanics. Classically singular spacetime is understood as a geodesic incompleteness with respect to a particle probe, while quantum singularity is unders

Luca Didaci, Sara Maria Pani, Claudio Frongia, Matteo Fraschini

EEG-based biometric represents a relatively recent research field that aims to recognize individuals based on their recorded brain activity by means of electroencephalography (EEG). Among the numerous features that have been proposed, connectivity-based approaches represent one of the more promising methods tested so far. In this paper, we investigate how th

Huimin Wang, Wai-Chung Kwan, Kam-Fai Wong, Yefeng Zheng

Automatic diagnosis (AD), a critical application of AI in healthcare, employs machine learning techniques to assist doctors in gathering patient symptom information for precise disease diagnosis. The Transformer-based method utilizes an input symptom sequence, predicts itself through auto-regression, and employs the hidden state of the final symptom to deter

Wei-Jian Guo, Hu Zou, Victoria Anne Fawcett, Rebecca Canning

Changing-look Active Galactic Nuclei (CL AGN) can be generally confirmed by the emergence (turn-on) or disappearance (turn-off) of broad emission lines, associated with a transient timescale (about $100\sim5000$ days) that is much shorter than predicted by traditional accretion disk models. We carry out a systematic CL AGN search by cross-matching the spectr

Jisong Kim, Minjae Seong, Geonho Bang, Dongsuk Kum

While LiDAR sensors have been successfully applied to 3D object detection, the affordability of radar and camera sensors has led to a growing interest in fusing radars and cameras for 3D object detection. However, previous radar-camera fusion models were unable to fully utilize the potential of radar information. In this paper, we propose Radar-Camera Multi-

Yannan Li, Jingbo Wang, Chao Wang

Data poisoning aims to compromise a machine learning based software component by contaminating its training set to change its prediction results for test inputs. Existing methods for deciding data-poisoning robustness have either poor accuracy or long running time and, more importantly, they can only certify some of the truly-robust cases, but remain inconcl

François Doré, Enrico Formenti

This paper provides a linear time algorithm in the number of edges that, given a simple 3-connected non-planar graph G with a Klein bottle rotation system, outputs a straight line drawing of G with no crossings on the flat Klein bottle.

Zhanpeng Zhou, Yongyi Yang, Xiaojiang Yang, Junchi Yan

Recent work has revealed many intriguing empirical phenomena in neural network training, despite the poorly understood and highly complex loss landscapes and training dynamics. One of these phenomena, Linear Mode Connectivity (LMC), has gained considerable attention due to the intriguing observation that different solutions can be connected by a linear path

Jiaze Gao, Zhihuan Zhou, Minghui Du, Rui Zou

The Hubble constant ${H}_0$ is a crucial parameter in cosmology. However, different cosmic observations have resulted in varying posterior results for ${H}_0$, leading to what is known as the ${H}_0$ tension. In order to address this issue, it is beneficial to use other dataset to constrain ${H}_0$. In this paper, via the cosmographic approach based on the F

Zhao Wang

This study employs molecular dynamics simulations to examine the physisorption behavior of hydrocarbon molecules on a covalent graphene-nanotube hybrid nanostructure. The results indicate that the adsorbed molecules undergo self-diffusion into the nanotubes without the need for external driving forces, primarily driven by significant variations in binding en

Tianyang Hu, Fei Chen, Haonan Wang, Jiawei Li

In generative modeling, numerous successful approaches leverage a low-dimensional latent space, e.g., Stable Diffusion models the latent space induced by an encoder and generates images through a paired decoder. Although the selection of the latent space is empirically pivotal, determining the optimal choice and the process of identifying it remain unclear.

Nicholas Bermingham, Vanessa Robins, Katharine Turner

Symmetry is ubiquitous throughout nature and can often give great insights into the formation, structure and stability of objects studied by mathematicians, physicists, chemists and biologists. However, perfect symmetry occurs rarely so quantitative techniques must be developed to identify approximate symmetries. To facilitate the analysis of an independent

Marco Bertuletti, Samuel Riedel, Yichao Zhang, Alessandro Vanelli-Coralli

Synchronization is likely the most critical performance killer in shared-memory parallel programs. With the rise of multi-core and many-core processors, the relative impact on performance and energy overhead of synchronization is bound to grow. This paper focuses on barrier synchronization for TeraPool, a cluster of 1024 RISC-V processors with non-uniform me

Yannan Li, Jingbo Wang, Chao Wang

We propose a method for certifying the fairness of the classification result of a widely used supervised learning algorithm, the k-nearest neighbors (KNN), under the assumption that the training data may have historical bias caused by systematic mislabeling of samples from a protected minority group. To the best of our knowledge, this is the first certificat

Ruiqi Li, Leyang Cui, Songtuan Lin, Patrik Haslum

Action models, which take the form of precondition/effect axioms, facilitate causal and motivational connections between actions for AI agents. Action model acquisition has been identified as a bottleneck in the application of planning technology, especially within narrative planning. Acquiring action models from narrative texts in an automated way is essent

Franz Achleitner, Anton Arnold, Volker Mehrmann, Eduard A. Nigsch

The concept of hypocoercivity for linear evolution equations with dissipation is discussed and equivalent characterizations that were developed for the finite-dimensional case are extended to separable Hilbert spaces. Using the concept of a hypocoercivity index, quantitative estimates on the short-time and long-time decay behavior of a hypocoercive system ar

Wen Yan, Bernard Chiu, Ziyi Shen, Qianye Yang

One of the distinct characteristics in radiologists' reading of multiparametric prostate MR scans, using reporting systems such as PI-RADS v2.1, is to score individual types of MR modalities, T2-weighted, diffusion-weighted, and dynamic contrast-enhanced, and then combine these image-modality-specific scores using standardised decision rules to predict the l

Svetlana Pavlitska, Nico Lambing, J. Marius Zöllner

Traffic sign recognition is an essential component of perception in autonomous vehicles, which is currently performed almost exclusively with deep neural networks (DNNs). However, DNNs are known to be vulnerable to adversarial attacks. Several previous works have demonstrated the feasibility of adversarial attacks on traffic sign recognition models. Traffic

Zhisen Meng, Yong Zhang, Enwei Liang, Zhao Wang

Machine learning feature importance calculations are used to determine the molecular substructures that are responsible for mid and far-infrared (IR) emission features of neutral polycyclic aromatic hydrocarbons (PAHs). Using the extended-connectivity fingerprint as a descriptor of chemical structure, a random forest model is trained on the spectra of 14,124

Subba Reddy Oota, Zijiao Chen, Manish Gupta, Raju S. Bapi

Can artificial intelligence unlock the secrets of the human brain? How do the inner mechanisms of deep learning models relate to our neural circuits? Is it possible to enhance AI by tapping into the power of brain recordings? These captivating questions lie at the heart of an emerging field at the intersection of neuroscience and artificial intelligence. Our

Siyi Guo, Zihao He, Ashwin Rao, Eugene Jang

The rich and dynamic information environment of social media provides researchers, policy makers, and entrepreneurs with opportunities to learn about social phenomena in a timely manner. However, using this data to understand social behavior is difficult due heterogeneity of topics and events discussed in the highly dynamic online information environment. To

Fu-Lai Wang, Xiang Liu

The discoveries of the $P_{\psi s}^\Lambda(4459)$ and $P_{\psi s}^\Lambda(4338)$ as the potential $\Xi_c\bar D^{(*)}$ molecules have sparked our curiosity in exploring a novel class of molecular $P_{\psi s}^{\Lambda/\Sigma}$ pentaquarks. In this study, we carry out an investigation into the higher molecular pentaquarks, specifically focusing on the $P_{\psi

Zaohui Zhang, Teng Man, Herbert E. Huppert, Sergio Andres Galindo-Torres

In this letter, following an extensive experimental validation, we perform constant-volume shearing simulations of non-Brownian granular suspensions using the discrete element method coupled with the lattice Boltzmann method. We choose a wide range of solid fractions, shear rates, fluid viscosities, particle sizes, and inter-particle frictional coefficients

Ekansh Sharma, Christoph Henke, Alex Mitrevski, Paul G. Plöger

Loading of shipping containers for dairy products often includes a press-fit task, which involves manually stacking milk cartons in a container without using pallets or packaging. Automating this task with a mobile manipulator can reduce worker strain, and also enhance the efficiency and safety of the container loading process. This paper proposes an approac

Yakun Liu, Fanrui Hu, Guoyi Shi, Hyunsoo Yang

Generating out-of-plane spins in sputtered materials holds immense potential for achieving field-free spin-orbit torque switching in practical applications and mass production. In this work, we present the detection of out-of-plane spins from single-layer ferromagnetic Co layers, which are visualized through helicity-dependent photomapping techniques. Our ex

Xuan-Quy Dao, Ngoc-Bich Le

This study examines the efficacy of two SOTA large language models (LLMs), namely ChatGPT and Microsoft Bing Chat (BingChat), in catering to the needs of Vietnamese students. Although ChatGPT exhibits proficiency in multiple disciplines, Bing Chat emerges as the more advantageous option. We conduct a comparative analysis of their academic achievements in var

Ya-Bing Zuo, Hong-Yao Jin, Jing-Ying Tian, Jia Yi

The form factors of $B_{(s)}$ decays into P-wave excited charmed mesons (including $D^*_0(2300)$, $D_1(2430)$, $D_1(2420)$, $D^*_2(2460)$ and their strange counterparts, denoted generically as $D^{**}_{(s)}$) are systematically calculated via the QCD sum rules in the framework of heavy quark effective field theory (HQEFT). We consider contributions up to the

Zeev Nutov

A set family ${\cal F}$ is $uncrossable$ if $A \cap B,A \cup B \in {\cal F}$ or $A \setminus B,B \setminus A \in {\cal F}$ for any $A,B \in {\cal F}$. A classic result of Williamson, Goemans, Mihail, and Vazirani [STOC 1993:708-717] states that the problem of covering an uncrossable set family by a min-cost edge set admits approximation ratio $2$, by a prima

P. Risse, V. Bertone, T. Ježo, M. Klasen

Mass-dependent quark contributions are of great importance to DIS processes. The simplified-ACOT-scheme includes these effects over a wide range of momentum transfers up to next-to-leading order in QCD. In recent years an improvement in the case of neutral current DIS has been achieved by using zero-mass contributions up to next-to-next-to-leading order (NNL

Ke Yan, Xiaoli Yin, Yingda Xia, Fakai Wang

Liver tumor segmentation and classification are important tasks in computer aided diagnosis. We aim to address three problems: liver tumor screening and preliminary diagnosis in non-contrast computed tomography (CT), and differential diagnosis in dynamic contrast-enhanced CT. A novel framework named Pixel-Lesion-pAtient Network (PLAN) is proposed. It uses a

Narutaka Ozawa

We prove the following two results. First, the isometry semigroup of a unital properly infinite nuclear C*-algebra is right amenable. Second, the unitary group of a unital simple monotracial C*-algebra whose tracial GNS representation is hyperfinite is skew-amenable in the weak topology. This answers in part a conjecture of Alekseev, Schmidt, and Thom and a

Bo Bai, Yu Gao, Jie Ma, Yuze Wu

We consider the symmetric difference of two graphs on the same vertex set $[n]$, which is the graph on $[n]$ whose edge set consists of all edges that belong to exactly one of the two graphs. Let $\mathcal{F}$ be a class of graphs, and let $M_{\mathcal{F}}(n)$ denote the maximum possible cardinality of a family $\mathcal{G}$ of graphs on $[n]$ such that the

Qi Mao, Tinghan Yang, Yinuo Zhang, Zijian Wang

Recent advances in generative compression methods have demonstrated remarkable progress in enhancing the perceptual quality of compressed data, especially in scenarios with low bitrates. However, their efficacy and applicability to achieve extreme compression ratios ($<0.05$ bpp) remain constrained. In this work, we propose a simple yet effective coding fram

Sourav Manna, Ankur Das

Shot noise at a conductance plateau in a quantum point contact (QPC) can be explained by considering equilibrations at the quantum Hall edges. The indication from recent experiments is that the charge equilibration length is much shorter than the thermal equilibration length. We discuss how this discovery gives rise to different thermal equilibration regimes

Jun-Sang Yoo, Hongjae Lee, Seung-Won Jung

This paper presents a novel framework called HST for semi-supervised video object segmentation (VOS). HST extracts image and video features using the latest Swin Transformer and Video Swin Transformer to inherit their inductive bias for the spatiotemporal locality, which is essential for temporally coherent VOS. To take full advantage of the image and video

Minwoo Seong, Jeongseok Oh, SeungJun Kim

The increasing use of artificial intelligence (AI) technology in turn-based sports, such as badminton, has sparked significant interest in evaluating strategies through the analysis of match video data. Predicting future shots based on past ones plays a vital role in coaching and strategic planning. In this study, we present a Multi-Layer Multi-Input Transfo

Bo Wang, Yang Zhang

We study the perturbations up to the 2nd-order for a power-law inflation driven by a scalar field in synchronous coordinates. We present the 1st-order solutions, and analytically solve the 2nd-order perturbed Einstein equation and scalar field equation, give the 2nd-order solutions for all the scalar, vector, and tensor metric perturbations, as well as the p

Fardin Ahsan Sakib, Saadat Hasan Khan, A. H. M. Rezaul Karim

Large-scale language models (LLMs) have emerged as a groundbreaking innovation in the realm of question-answering and conversational agents. These models, leveraging different deep learning architectures such as Transformers, are trained on vast corpora to predict sentences based on given queries. Among these LLMs, ChatGPT, developed by OpenAI, has ushered i

Jiayin Wang, Weizhi Ma, Chumeng Jiang, Min Zhang

In the era of information explosion, numerous items emerge every day, especially in feed scenarios. Due to the limited system display slots and user browsing attention, various recommendation systems are designed not only to satisfy users' personalized information needs but also to allocate items' exposure. However, recent recommendation studies mainly focus

Wei Chen, Yihui Ren, Ai Kagawa, Matthew R. Carbone

Fast screening of drug molecules based on the ligand binding affinity is an important step in the drug discovery pipeline. Graph neural fingerprint is a promising method for developing molecular docking surrogates with high throughput and great fidelity. In this study, we built a COVID-19 drug docking dataset of about 300,000 drug candidates on 23 coronaviru