March 2023 arXiv papers — page 112

G. K. Jaisawal, G. Vasilopoulos, S. Naik, C. Maitra

We report comprehensive spectral and temporal properties of the Be/X-ray binary pulsar SMC X-2 using X-ray observations during the 2015 and 2022 outbursts. The pulse profile of the pulsar is unique and strongly luminosity dependent. It evolves from a broad-humped into a double-peaked profile above luminosity 3$\times$10$^{38}$ ergs s$^{-1}$. The pulse fracti

Yubin Hu, Yuze He, Yanghao Li, Jisheng Li

Video semantic segmentation (VSS) is a computationally expensive task due to the per-frame prediction for videos of high frame rates. In recent work, compact models or adaptive network strategies have been proposed for efficient VSS. However, they did not consider a crucial factor that affects the computational cost from the input side: the input resolution.

Haoran Chen, Zuxuan Wu, Xintong Han, Menglin Jia

Current research on continual learning mainly focuses on relieving catastrophic forgetting, and most of their success is at the cost of limiting the performance of newly incoming tasks. Such a trade-off is referred to as the stability-plasticity dilemma and is a more general and challenging problem for continual learning. However, the inherent conflict betwe

Eduardo Abi Jaber, Nathan De Carvalho

We reconcile rough volatility models and jump models using a class of reversionary Heston models with fast mean reversions and large vol-of-vols. Starting from hyper-rough Heston models with a Hurst index $H \in (-1/2,1/2)$, we derive a Markovian approximating class of one dimensional reversionary Heston-type models. Such proxies encode a trade-off between a

Xin Zhou, Kisub Kim, Bowen Xu, DongGyun Han

Code review is an effective software quality assurance activity; however, it is labor-intensive and time-consuming. Thus, a number of generation-based automatic code review (ACR) approaches have been proposed recently, which leverage deep learning techniques to automate various activities in the code review process (e.g., code revision generation and review

Wei Tao, Zhen-Jun Xiao, Ruilin Zhu

Within the framework of non-relativistic QCD (NRQCD) factorization, we compute the matching coefficients between full Quantum Chromodynamics (QCD) and NRQCD for the heavy flavor-changing vector, axial-vector, scalar and pseudo-scalar currents up to next-to-next-to-next-to-leading order (N$^3$LO). We accomplish the analytical expressions for the three-loop re

R. Dutta, P. D. Wurzner, F. Castles

We use a classical, microscopic model of point-like dipolarizable entities (a model that is standard in the case of positive polarizability) and investigate its behavior for simple cubic (sc), body-centered cubic (bcc), and face-centered cubic (fcc) crystals with one entity per primitive cell when the static polarizability of the entities is negative and the

H. Korbmacher, G. A. Domínguez-Castro, W. Li, J. Zakrzewski

Polar lattice gases are usually assumed to have an inter-site interaction that decays with the inter-particle distance $r$ as $1/r^3$. However, a loose-enough transversal confinement may strongly modify the dipolar decay in one-dimensional lattices. We show that this modification alters significantly the ground-state properties of hard-core dipolar bosons. F

Mohamed Akrout, Amal Feriani, Faouzi Bellili, Amine Mezghani

Data-driven machine learning (ML) is promoted as one potential technology to be used in next-generations wireless systems. This led to a large body of research work that applies ML techniques to solve problems in different layers of the wireless transmission link. However, most of these applications rely on supervised learning which assumes that the source (

Zesen Cheng, Kehan Li, Peng Jin, Xiangyang Ji

Unified visual grounding pursues a simple and generic technical route to leverage multi-task data with less task-specific design. The most advanced methods typically present boxes and masks as vertex sequences to model referring detection and segmentation as an autoregressive sequential vertex generation paradigm. However, generating high-dimensional vertex

Morgan Barnes, Fan Feng, John S. Biggins

Liquid crystal elastomers (LCEs) are soft phase-changing solids that exhibit large reversible contractions upon heating, Goldstone-like soft modes and resultant microstructural instabilities. We heat a planar LCE slab to isotropic, clamp the lower surface then cool back to nematic. Clamping prevents macroscopic elongation, producing compression and microstru

Sophie Rosu, Edwin A. Quintero, Gregor Rauw, Philippe Eenens

HD165052 is a short-period massive eccentric binary system that undergoes apsidal motion. As the rate of apsidal motion is directly related to the internal structure constants of the binary components, its study allows to get insight into the internal structure of the stars. We use medium- and high-resolution spectroscopic observations of HD165052 to provide

Juan Londoño, Christian Olivera

The purpose of this paper is to establish the equivalence between Lagrangian and classical formulations for the stochastic incompressible Euler equations, the proof is based in Ito-Wentzell-Kunita formula and stochastic analysis techniques. Moreover, we prove a local existence result for the Lagrangian formulation in suitable Sobolev Spaces.

Shijie Huang, Jinlong Lei, Yiguang Hong

We consider distributed learning problem in games with an unknown cost-relevant parameter, and aim to find the Nash equilibrium while learning the true parameter. Inspired by the social learning literature, we propose a distributed non-Bayesian rule to learn the parameter (each agent maintains a belief on the parameter and updates the belief according to the

Marco Dalai, Stefano Della Fiore, Adele A. Rescigno, Ugo Vaccaro

In this paper, we study upper bounds on the minimum length of frameproof codes introduced by Boneh and Shaw to protect copyrighted materials. A $q$-ary $(k,n)$-frameproof code of length $t$ is a $t \times n$ matrix having entries in $\{0,1,\ldots, q-1\}$ and with the property that for any column $\mathbf{c}$ and any other $k$ columns, there exists a row wher

J. Andreas Bærentzen, Rasmus Emil Christensen, Emil Toftegaard Gæde, Eva Rotenberg

In this paper we give a new, efficient algorithm for computing curve skeletons, based on local separators. Our efficiency stems from a multilevel approach, where we solve small problems across levels of detail and combine these in order to quickly obtain a skeleton. We do this in a highly modular fashion, ensuring complete flexibility in adapting the algorit

Aidan J. Hughes, Paul Gardner, Keith Worden

The prospect of informed and optimal decision-making regarding the operation and maintenance (O&M) of structures provides impetus to the development of structural health monitoring (SHM) systems. A probabilistic risk-based framework for decision-making has already been proposed. However, in order to learn the statistical models necessary for decision-making,

Zhengqi Gao, Duane S. Boning

The utilization of Bayesian methods has been widely acknowledged as a viable solution for tackling various challenges in electronic integrated circuit (IC) design under stochastic process variation, including circuit performance modeling, yield/failure rate estimation, and circuit optimization. As the post-Moore era brings about new technologies (such as sil

Harshal Kulkarni, Stuart Raby

An additional generation of quarks and leptons and their SUSY counterparts, which are vector-like under the Standard Model gauge group but are chiral with respect to the new U(1)$_{3-4}$ gauge symmetry, are added to the Minimal Supersymmetric Standard Model (MSSM). We show that this model is a GUT and unifies the three SM gauge couplings and also the additio

Kashin Sugishita, Naoki Masuda

Manga, Japanese comics, has been popular on a global scale. Social networks among characters, which are often called character networks, may be a significant contributor to their popularity. We collected data from 162 popular manga that span over 70 years and analyzed their character networks. First, we found that many of static and temporal properties of th

G. V. Nivarti, J. B. Marston, S. M. Tobias

In recent years, the Generalised Quasilinear (GQL) approximation has been developed and its efficacy tested against purely quasilinear (QL) approximations. GQL systematically interpolates between QL and fully non-linear dynamics by employing a generalised Reynolds decomposition. Here, we examine an exact statistical closure for the GQL equations on the doubl

Caroline Leboeuf, Margarida Carvalho, Yan Kestens, Benoît Thierry

The recent promotion of sustainable urban planning combined with a growing need for public interventions to improve well-being and health have led to an increased collective interest for green spaces in and around cities. In particular, parks have proven a wide range of benefits in urban areas. This also means inequities in park accessibility may contribute

Obaidullah Zaland, Muhammad Abulaish, Mohd. Fazil

Vector-based word representations help countless Natural Language Processing (NLP) tasks capture the language's semantic and syntactic regularities. In this paper, we present the characteristics of existing word embedding approaches and analyze them with regard to many classification tasks. We categorize the methods into two main groups - Traditional approac

N. J. Abdulameer, U. Acharya, C. Aidala, Y. Akiba

Reported here are transverse single-spin asymmetries ($A_{N}$) in the production of charged hadrons as a function of transverse momentum ($p_T$) and Feynman-$x$ ($x_F$) in polarized $p^{\uparrow}$+$p$, $p^{\uparrow}$+Al, and $p^{\uparrow}$+Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV. The measurements have been performed at forward and backward rapidity ($1.4

N. J. Abdulameer, U. Acharya, C. Aidala, Y. Akiba

Presented are the first measurements of the transverse single-spin asymmetries ($A_N$) for neutral pions and eta mesons in $p$+Au and $p$+Al collisions at $\sqrt{s_{_{NN}}}=200$ GeV in the pseudorapidity range $|\eta|<$0.35 with the PHENIX detector at the Relativistic Heavy Ion Collider. The asymmetries are consistent with zero, similar to those for midrapid

Tina Dorosti, Manuel Schultheiss, Felix Hofmann, Johannes Thalhammer

We aim to optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by exploring manually adjusted versus automated window-setting optimization (WSO) on computed tomography (CT) images. 7,194 CT images (3,597 with COPD; 3,597 healthy controls) from 78 subject

John Smillie, Peter Smillie, Barak Weiss, Florent Ygouf

We consider the horospherical foliation on any invariant subvariety in the moduli space of translation surfaces. This foliation can be described dynamically as the strong unstable foliation for the geodesic flow on the invariant subvariety, and geometrically, it is induced by the canonical splitting of $\mathbb{C}$-valued cohomology into its real and imagina

Sofia Bobadilla, Richard Glassey, Alexandre Bergel, Martin Monperrus

Recent research has shown the great potential of automatic feedback in education. This paper presents SOBO, a bot we designed to automatically provide feedback on code quality to undergraduate students. SOBO has been deployed in a course at the KTH Royal Institute of Technology in Sweden with 130+ students. Overall, SOBO has analyzed 1687 GitHub repositories

Bastian Pätzold, Andre Rochow, Michael Schreiber, Raphael Memmesheimer

Haptic perception is highly important for immersive teleoperation of robots, especially for accomplishing manipulation tasks. We propose a low-cost haptic sensing and rendering system, which is capable of detecting and displaying surface roughness. As the robot fingertip moves across a surface of interest, two microphones capture sound coupled directly throu

Meir Friedenberg, Joseph Y. Halpern

For over 25 years, common belief has been widely viewed as necessary for joint behavior. But this is not quite correct. We show by example that what can naturally be thought of as joint behavior can occur without common belief. We then present two variants of common belief that can lead to joint behavior, even without standard common belief ever being achiev

Yunjie Huang, Xiaozhuang Song, Yuanshao Zhu, Shiyao Zhang

In modern traffic management, one of the most essential yet challenging tasks is accurately and timely predicting traffic. It has been well investigated and examined that deep learning-based Spatio-temporal models have an edge when exploiting Spatio-temporal relationships in traffic data. Typically, data-driven models require vast volumes of data, but gather

Guilherme Migliato Marega, Hyun Goo Ji, Zhenyu Wang, Mukesh Tripathi

Led by the rise of the internet of things, the world is experiencing exponential growth of generated data. Data-driven algorithms such as signal processing and artificial neural networks are required to process and extract meaningful information from it. They are, however, seriously limited by the traditional von-Neuman architecture with physical separation

Teng Wu, Bruno Vallet, Cédric Demonceaux

Mobile mapping, in particular, Mobile Lidar Scanning (MLS) is increasingly widespread to monitor and map urban scenes at city scale with unprecedented resolution and accuracy. The resulting point cloud sampling of the scene geometry can be meshed in order to create a continuous representation for different applications: visualization, simulation, navigation,

Malgorzata J. Krawczyk, Krzysztof Malarz

Scientific cooperation on an international level has been well studied in the literature. However, much less is known about this cooperation on the intercontinental level. In this paper, we address this issue by creating a collection of approximately 13.8 million publications around the papers by one of the highly cited author working in complex networks and

Tim Puphal, Malte Probst, Julian Eggert

Risk assessment is a central element for the development and validation of Autonomous Vehicles (AV). It comprises a combination of occurrence probability and severity of future critical events. Time Headway (TH) as well as Time-To-Contact (TTC) are commonly used risk metrics and have qualitative relations to occurrence probability. However, they lack theoret

Chengliang Liu, Jie Wen, Xiaoling Luo, Yong Xu

As we all know, multi-view data is more expressive than single-view data and multi-label annotation enjoys richer supervision information than single-label, which makes multi-view multi-label learning widely applicable for various pattern recognition tasks. In this complex representation learning problem, three main challenges can be characterized as follows

Stefano Gogioso, Nicola Pinzani

We present an exhaustive classification of the 2644 causally complete spaces of input histories on 3 events with binary inputs, together with the algorithm used to find them. This paper forms the supplementary material for a trilogy of works: spaces of input histories, our dynamical generalisation of causal orders, are introduced in "The Combinatorics of Cau

Nicolas Grelier, Stéphane Kaufmann

As a novel and fast-changing field, the video game industry does not have a fixed and well-defined vocabulary. In particular, game genres are of interest: No two experts seem to agree on what they are and how they relate to each other. We use the user-generated tags of the video game digital distribution service Steam to better understand how players think a

Diego Córdoba, Luis Martínez-Zoroa

In this work we construct global unique solutions of the dissipative Surface quasi-geostrophic equation ($\alpha$-SQG) that lose regularity instantly when there is super-critical fractional diffusion.

Neelakantan Padmanabhan

In this work, a numerical simulation of 1D Burgers' equation is developed using finite difference method and a reduced order model (ROM) of the simulation is developed using proper orthogonal decomposition (POD). The objective of this work is to provide an introduction of the POD method to researchers interested in computational fluid dynamics (CFD). This wo

Alexander Mielke

The theory of slow-fast gradient systems leads in a natural way to non-equilibrium steady states, because on the slow time scale the fast subsystem stays in steady states that are controlled by the interaction with the slow system. Using the theory of convergence of gradient systems depending on a small paramter $\varepsilon$ (here the ratio between the slow

Akhil Uniyal, Sayan Chakrabarti, Reggie C. Pantig, Ali Övgün

In this paper, we explore the effect of non-linear electrodynamics (NLED) parameters and magnetic charges on various aspects of black holes, like how they bend light, how they emit radiation, and how they appear as a shadow by considering a thin accretion disk model. We initially examine the overall behavior of the photonsphere and the corresponding shadow s

Claudio Guarcello, Alessandro Braggio, Francesco Giazotto, Roberta Citro

Thermoelectrical properties are frequently used to characterize the materials and endow the free energy from wasted heat for useful purposes. Here, we show that linear thermoelectric effects in tunnel junctions with Fe-based superconductors, not only address the dominance between particle and hole states, but even provide information about the superconductin

Ivana Kajić, Aida Nematzadeh

The ability to discriminate between large and small quantities is a core aspect of basic numerical competence in both humans and animals. In this work, we examine the extent to which the state-of-the-art neural networks designed for vision exhibit this basic ability. Motivated by studies in animal and infant numerical cognition, we use the numerical bisectio

Jaismeen Kaur, Marie Ouillé, Dan Levy, Louis Daniault

We report on the first simultaneous measurement of high-order harmonics, relativistic electrons and low divergence proton beams generated from plasma mirrors driven at kHz repetition rate by relativistic-intensity milliJoule-energy femtosecond laser pulses. This setup enables detailed parametric studies of the particle and radiation spatio-spectral beam prop

V. P. Nagraj, Stephen D. Turner

R packages are the fundamental units of reproducible code in R, providing a mechanism for distributing user-developed code, documentation, and data. Docker is a virtualization technology that allows applications and their dependencies to be distributed and run reproducibly across platforms. The pracpac package provides an interface to create Docker images th

Pascal Pernot

The practice of uncertainty quantification (UQ) validation, notably in machine learning for the physico-chemical sciences, rests on several graphical methods (scattering plots, calibration curves, reliability diagrams and confidence curves) which explore complementary aspects of calibration, without covering all the desirable ones. For instance, none of thes

Axel von Arnim, Jules Lecomte, Naima Elosegui Borras, Stanislaw Wozniak

Optical identification is often done with spatial or temporal visual pattern recognition and localization. Temporal pattern recognition, depending on the technology, involves a trade-off between communication frequency, range and accurate tracking. We propose a solution with light-emitting beacons that improves this trade-off by exploiting fast event-based c

Tino Laidin, Thomas Rey

This short note presents an extension of the hybrid, model-adaptation method introduced in [T.~Laidin, \textit{arXiv 2202.03696}, 2022] for linear collisional kinetic equations in a diffusive scaling to the nonlinear mean-field Vlasov-Poisson-BGK model. The aim of the approach is to reduce the computational cost by taking advantage of the lower dimensionalit

Max Welz, Andreas Alfons

Questionnaires in the behavioral sciences tend to be lengthy. However, literature suggests that survey length is a contributing factor to careless responding, with longer questionnaires yielding higher probability that participants start responding carelessly. Consequently, in long surveys a large number of participants may engage in careless responding, pos

Aran Carmon, Lior Wolf

In the task of automatic program synthesis, one obtains pairs of matching inputs and outputs and generates a computer program, in a particular domain-specific language (DSL), which given each sample input returns the matching output. A key element is being able to perform an efficient search in the space of valid programs. Here, we suggest a variant of MCTS

J. M. A. Paixão, L. P. R. Ospedal, M. J. Neves, J. A. Helayël-Neto

We pursue the investigation of a generic non-linear extension of axionic electrodynamics in a Carroll-Field-Jackiw (CFJ) scenario that implements Lorentz-symmetry violation (LSV). The model we inspect consists of an arbitrary non-linear electrodynamic action coupled to the axion field in presence of an anisotropy four-vector that realizes the breaking of Lor

Alexander Logunov, Lakshmi Priya, Andrea Sartori

This paper focuses on a relation between the growth of harmonic functions and the Hausdorff measure of their zero sets. Let $u$ be a real-valued harmonic function in $\mathbb{R}^n$ with $u(0)=0$ and $n\geq 3$. We prove $$\mathcal{H}^{n-1}(\{u=0\} \cap B(0,2)) \gtrsim_{\varepsilon} N^{1-\varepsilon},$$ where the doubling index $N$ is a notion of growth define

Stefano Bolognesi, Sven Bjarke Gudnason, Giacomo Santoni

We consider certain aspects of the electroweak Skyrmion (EWS). We discuss the case of EWS with dynamical Higgs and find numerical solutions for various values of the cutoff scale. Our results are qualitatively similar to the ones present in the literature, but we find a considerable lower mass than previous studies. We discuss the quantization of the light d

Thierry Mora, Aleksandra M. Walczak

A cornerstone of the classical view of tolerance is the elimination of self-reactive T cells during negative selection in the thymus. However, high-throughput T-cell receptor sequencing data has so far failed to detect substantial signatures of negative selection in the observed repertoires. In addition, quantitative estimates as well as recent experiments s

Dahmane Dechicha, Marjolaine Puel

In this paper, we extend the spectral method developed [Dechicha and Puel, 2023] to any dimension $d\geqslant 1$, in order to construct an eigen-solution for the Fokker-Planck operator with heavy tail equilibria, of the form $(1+|v|^2)^{-\frac{\beta}{2}}$, in the range $\beta \in ]d,d+4[$. The method developed in dimension 1 was inspired by the work of H. Ko

Gaia Forghieri, Andrea Secchi, Andrea Bertoni, Paolo Bordone

Hole-spin qubits in semiconductors represent a mature platform for quantum technological applications. Here we consider their use as quantum sensors, and specifically for inferring the presence and estimating the distance from the qubit of a remote charge. Different approaches are considered - based on the use of single or double quantum dots, ground and out

Jaeyoung Cha, Jaewook Lee, Chulhee Yun

We study convergence lower bounds of without-replacement stochastic gradient descent (SGD) for solving smooth (strongly-)convex finite-sum minimization problems. Unlike most existing results focusing on final iterate lower bounds in terms of the number of components $n$ and the number of epochs $K$, we seek bounds for arbitrary weighted average iterates that

Dahmane Dechicha, Marjolaine Puel

This paper is devoted to the construction of an \emph{eigen-solution} for the Fokker-Planck operator with heavy tail equilibrium. We propose an \textit{alternative} method in dimension 1, which will be generalizable in higher dimension. The later method is inspired by the work of H. Koch on non-linear KdV equation \cite{Koch}. As a consequence of this constr

Vincent Knibbeler

We develop an elementary method to compute spaces of equivariant maps from a homogeneous space $G/H$ of a Lie group $G$ to a module of this group. The Lie group is not required to be compact. More generally, we study spaces of invariant sections in homogeneous vector bundles, and take a special interest in the case where the fibres are algebras. These latter

Chaofeng Guan, Ruihu Li, Yiting Liu, Zhi Ma

The additive codes may have better parameters than linear codes. However, it is still a challenging problem to efficiently construct additive codes that outperform linear codes, especially those with greater distances than linear codes of the same lengths and dimensions. This paper focuses on constructing additive codes that outperform linear codes based on

Salman Beigi, Saleh Rahimi-Keshari

We compute the quantum maximal correlation for bipartite Gaussian states of continuous-variable systems. Quantum maximal correlation is a measure of correlation with the monotonicity and tensorization properties that can be used to study whether an arbitrary number of copies of a resource state can be locally transformed into a target state without classical

Yun-Da Tsai, Tzu-Hsien Tsai, Shou-De Lin

This paper targets a variant of the stochastic multi-armed bandit problem called good arm identification (GAI). GAI is a pure-exploration bandit problem with the goal to output as many good arms using as few samples as possible, where a good arm is defined as an arm whose expected reward is greater than a given threshold. In this work, we propose DGAI - a di

Zihao Guo, Yueying Cao

Convolutional neural networks (CNNs) are a representative class of deep learning algorithms including convolutional computation that perform translation-invariant classification of input data based on their hierarchical architecture. However, classical convolutional neural network learning methods use the steepest descent algorithm for training, and the lear

T. Tony Cai, Yichen Wang, Linjun Zhang

Achieving optimal statistical performance while ensuring the privacy of personal data is a challenging yet crucial objective in modern data analysis. However, characterizing the optimality, particularly the minimax lower bound, under privacy constraints is technically difficult. To address this issue, we propose a novel approach called the score attack, whic

Jonas Oppenlaender, Rhema Linder, Johanna Silvennoinen

We are witnessing a novel era of creativity where anyone can create digital content via prompt-based learning (known as prompt engineering). This paper investigates prompt engineering as a novel creative skill for creating AI art with text-to-image generation. In three consecutive studies, we explore whether crowdsourced participants can 1) discern prompt qu

Olivier Parisot, Thomas Tamisier

Reproducible images preprocessing is important in the field of computer vision, for efficient algorithms comparison or for new images corpus preparation. In this paper, we propose a method to obtain an explicit and ordered sequence of transformations that improves a given image: the computation is performed via a nature-inspired optimization algorithm based

Tamir Shor, Tomer Weiss, Dor Noti, Alex Bronstein

Dynamic Magnetic Resonance Imaging (MRI) is known to be a powerful and reliable technique for the dynamic imaging of internal organs and tissues, making it a leading diagnostic tool. A major difficulty in using MRI in this setting is the relatively long acquisition time (and, hence, increased cost) required for imaging in high spatio-temporal resolution, lea

Feihu Liu, Guoce Xin

Let $A=(a_1, a_2, ..., a_n)$ be relative prime positive integers with $a_i\geq 2$. The Frobenius number $g(A)$ is the greatest integer not belonging to the set $\big\{ \sum_{i=1}^na_ix_i\ |x_i\in \mathbb{N}\big\}$. The general Frobenius problem includes the determination of $g(A)$ and the related Sylvester number $n(A)$ and Sylvester sum $s(A)$. We present a

Stefano Gogioso, Nicola Pinzani

We provide a unified operational framework for the study of causality, non-locality and contextuality, in a fully device-independent and theory-independent setting. Our work has its roots in the sheaf-theoretic framework for contextuality by Abramsky and Brandenburger, which it extends to include arbitrary causal orders (be they definite, dynamical or indefi

Surajit Chakraborty, Vishnu V. Krishnan, Kabir Ramola, Smarajit Karmakar

We show through simulations of amorphous solids prepared in open boundary conditions that they possess significantly fewer low-frequency vibrational modes compared to their periodic boundary counterparts. Specifically, using measurements of the vibrational density of states, we find that the $D(\omega) \sim \omega^4$ law changes to $D(\omega) \sim \omega^\de

Nick Papoulias

We present a new AI task and baseline solution for Inter-Subjective Reasoning. We define inter-subjective information, to be a mixture of objective and subjective information possibly shared by different parties. Examples may include commodities and their objective properties as reported by IR (Information Retrieval) systems, that need to be cross-referenced

Steffen Gracla, Alea Schröder, Maik Röper, Carsten Bockelmann

Direct Low Earth Orbit satellite-to-handheld links are expected to be part of a new era in satellite communications. Space-Division Multiple Access precoding is a technique that reduces interference among satellite beams, therefore increasing spectral efficiency by allowing cooperating satellites to reuse frequency. Over the past decades, optimal precoding s

A. E. O. Ferreira, J. L. B. de Araújo, W. P. Ferreira, J. S. de Sousa

Stressed soft materials commonly present viscoelastic signatures in the form of power-law or exponential decay. Understanding the origins of such rheologic behaviors is crucial to find proper technological applications. Using an elastic network model of macromolecules immersed in a viscous fluid, we numerically reproduce those characteristic viscoelastic rel

Julian Wechsler, Srikanth Raj Chetupalli, Wolfgang Mack, Emanuël A. P. Habets

We consider the task of region-based source separation of reverberant multi-microphone recordings. We assume pre-defined spatial regions with a single active source per region. The objective is to estimate the signals from the individual spatial regions as captured by a reference microphone while retaining a correspondence between signals and spatial regions

Benjamin Clavié, Alexandru Ciceu, Frederick Naylor, Guillaume Soulié

This case study investigates the task of job classification in a real-world setting, where the goal is to determine whether an English-language job posting is appropriate for a graduate or entry-level position. We explore multiple approaches to text classification, including supervised approaches such as traditional models like Support Vector Machines (SVMs)

Jiajun Fu, Yonghao Dang, Ruoqi Yin, Shaojie Zhang

This technical report describes our first-place solution to the pose estimation challenge at ECCV 2022 Visual Perception for Navigation in Human Environments Workshop. In this challenge, we aim to estimate human poses from in-the-wild stitched panoramic images. Our method is built based on Faster R-CNN for human detection, and HRNet for human pose estimation

Jan Haskovec, Mauro Rodriguez Cartabia

We prove that asymptotic global consensus is always reached in the Hegselmann-Krause model with finite speed of information propagation $\mathfrak{c}>0$ under minimal (i.e., necessary) assumptions on the influence function. In particular, we assume that the influence function is globally positive, which is necessary for reaching global consensus, and such th

Lena Schmid, Moritz Roidl, Markus Pauly

Many planning and decision activities in logistics and supply chain management are based on forecasts of multiple time dependent factors. Therefore, the quality of planning depends on the quality of the forecasts. We compare various forecasting methods in terms of out of the box forecasting performance on a broad set of simulated time series. We simulate var

Zijian Feng, Xin Chen, Zijian Lv, Peiyuan Sun

Deep learning (DL) algorithms have been widely applied to short-term voltage stability (STVS) assessment in power systems. However, transferring the knowledge learned in one power grid to other power grids with topology changes is still a challenging task. This paper proposed a transferable DL-based model for STVS assessment by constructing the topology-awar

Gero Friesecke, Maximilian Penka

The recently introduced Genetic Column Generation (GenCol) algorithm has been numerically observed to efficiently and accurately compute high-dimensional optimal transport plans for general multi-marginal problems, but theoretical results on the algorithm have hitherto been lacking. The algorithm solves the OT linear program on a dynamically updated low-dime

Jessica O. de Almeida, Matthias Kleinmann, Gael Sentís

The quantum state associated to an unknown experimental preparation procedure can be determined by performing quantum state tomography. If the statistical uncertainty in the data dominates over other experimental errors, then a tomographic reconstruction procedure must express this uncertainty. A rigorous way to accomplish this is via statistical confidence

Michael Nestler, Axel Voigt

Approximating PDEs on surfaces by the diffuse interface approach allows us to use standard numerical tools to solve these problems. This makes it an attractive numerical approach. We extend this approach to vector-valued surface PDEs and explore their convergence properties. In contrast to the well-studied case of scalar-valued surface PDEs, the optimal orde

Mikhail Gorsky, Hiroyuki Nakaoka, Yann Palu

In this article, we initiate the study of hereditary extriangulated categories. Many important categories arising in representation theory in connection with various theories of mutation are hereditary extriangulated. Special cases include homotopy categories of 2-term complexes with projective components, which are related to silting mutation, and cluster c

Michael Hutchings

We consider an area-preserving diffeomorphism of a compact surface, which is assumed to be an irrational rotation near each boundary component. A finite set of periodic orbits of the diffeomorphism gives rise to a braid in the mapping torus. We show that under some nondegeneracy hypotheses, the isotopy classes of braids that arise from finite sets of periodi

Hayden Hunter

A classic result of Milnor shows that any 3-dimensional unimodular metric Lie algebra admits an orthonormal frame with at most three nontrivial structure constants. These frames are referred to as Milnor frames. We define extensions of Milnor frames into higher dimensions and refer to these higher dimensional analogues as Lie algebras with Milnor frames. We

Anton S. Albino, Otto M. Pires, Mauro Q. Nooblath, Erick G. S. Nascimento

Effective feature selection is essential for enhancing the performance of artificial intelligence models. It involves identifying feature combinations that optimize a given metric, but this is a challenging task due to the problem's exponential time complexity. In this study, we present an innovative heuristic called Evolutionary Quantum Feature Selection (E

M. Kuich, M. Ćwiok, W. Dominik, A. Fijałkowska

A setup designed to study photonuclear reactions at astrophysical energies - an active target Time Projection Chamber was developed and constructed at the Faculty of Physics, University of Warsaw. The device was successfully employed in two experiments at the Institute of Nuclear Physics Polish Academy of Sciences in Cracow, in which {\gamma}- and neutron-in

Anand Thyagachandran, Hema A Murthy

Computed Tomography (CT) scans provide a detailed image of the lungs, allowing clinicians to observe the extent of damage caused by COVID-19. The CT severity score (CTSS) based scoring method is used to identify the extent of lung involvement observed on a CT scan. This paper presents a domain knowledge-based pipeline for extracting regions of infection in C

Hao Wen, Yuanchun Li, Zunshuai Zhang, Shiqi Jiang

Deep learning models are increasingly deployed to edge devices for real-time applications. To ensure stable service quality across diverse edge environments, it is highly desirable to generate tailored model architectures for different conditions. However, conventional pre-deployment model generation approaches are not satisfactory due to the difficulty of h

Wen Zhou

With the rapid development of the computer industry and computer software, the risk of software vulnerabilities being exploited has greatly increased. However, there are still many shortcomings in the existing mining techniques for leakage source research, such as high false alarm rate, coarse-grained detection, and dependence on expert experience. In this p

Alex Bihlo

We show that the error achievable using physics-informed neural networks for solving systems of differential equations can be substantially reduced when these networks are trained using meta-learned optimization methods rather than to using fixed, hand-crafted optimizers as traditionally done. We choose a learnable optimization method based on a shallow mult

Zdravko Marinov, Simon Reiß, David Kersting, Jens Kleesiek

Positron Emission Tomography (PET) and Computer Tomography (CT) are routinely used together to detect tumors. PET/CT segmentation models can automate tumor delineation, however, current multimodal models do not fully exploit the complementary information in each modality, as they either concatenate PET and CT data or fuse them at the decision level. To comba

Tom Nuno Wolf, Sebastian Pölsterl, Christian Wachinger

Alzheimer's disease (AD) has a complex and multifactorial etiology, which requires integrating information about neuroanatomy, genetics, and cerebrospinal fluid biomarkers for accurate diagnosis. Hence, recent deep learning approaches combined image and tabular information to improve diagnostic performance. However, the black-box nature of such neural networ

Avijit Saha, Abhishek Y. Deshmukh, Temistocle Grenga, Heinz Pitsch

Electrofuels (e-fuels) produced from renewable electricity and carbon sources have gained significant attention in recent years as promising alternatives to fossil fuels for the transportation sector. However, the highly volatile e-fuels, such as short-chain oxymethylene ethers are prone to flash vaporization phenomena, which is associated with the formation

Sanqing Qu, Yingwei Pan, Guang Chen, Ting Yao

Deep neural networks (DNNs) usually fail to generalize well to outside of distribution (OOD) data, especially in the extreme case of single domain generalization (single-DG) that transfers DNNs from single domain to multiple unseen domains. Existing single-DG techniques commonly devise various data-augmentation algorithms, and remould the multi-source domain

Javier Sanchez-Rivero, Daniel Talaván, Jose Garcia-Alonso, Antonio Ruiz-Cortés

Grover's algorithm is a well-known contribution to quantum computing. It searches one value within an unordered sequence faster than any classical algorithm. A fundamental part of this algorithm is the so-called oracle, a quantum circuit that marks the quantum state corresponding to the desired value. A generalization of it is the oracle for Amplitude Amplif

Andrea Barontini, Alessandro Candido, Juan M. Cruz-Martinez, Felix Hekhorn

Continuously comparing theory predictions to experimental data is a common task in analysis of particle physics such as fitting parton distribution functions (PDFs). However, typically, both the computation of scattering amplitudes and the evolution of candidate PDFs from the fitting scale to the process scale are non-trivial, computing intesive tasks. We de

Ruihuan Fang, Chengyin Han, Bo Lu, Jiahao Huang

Coherent population trapping (CPT) is a multi-level quantum coherence phenomenon of promising applications in atomic clocks and magnetometers. Particularly, multi-pulse CPT-Ramsey interferometry is a powerful tool for improving the performance of CPT atomic clocks. Most studies on multi-pulse CPT-Ramsey interferometry consider periodic pulse sequence and tim

Nian Wu, Miaomiao Zhang

This paper presents NeurEPDiff, a novel network to fast predict the geodesics in deformation spaces generated by a well known Euler-Poincar\'e differential equation (EPDiff). To achieve this, we develop a neural operator that for the first time learns the evolving trajectory of geodesic deformations parameterized in the tangent space of diffeomorphisms(a.k.a