October 2023 arXiv papers — page 134

Felix Finster, Niky Kamran

A new inequality for a nonlinear surface layer integral is proved for minimizers of causal variational principles. This inequality is applied to obtain a new proof of the positive mass theorem with volume constraint. Next, a positive mass theorem without volume constraint is stated and proved by introducing and using the concept of asymptotic alignment. More

Jonathan D. Victor, Guillermo Aguilar, Suniyya A. Waraich

Characterizing judgments of similarity within a perceptual or semantic domain, and making inferences about the underlying structure of this domain from these judgments, has an increasingly important role in cognitive and systems neuroscience. We present a new framework for this purpose that makes limited assumptions about how perceptual distances are convert

Riddhiman Bhattacharya, Tiefeng Jiang

Sampling from distributions play a crucial role in aiding practitioners with statistical inference. However, in numerous situations, obtaining exact samples from complex distributions is infeasible. Consequently, researchers often turn to approximate sampling techniques to address this challenge. Fast approximate sampling from complicated distributions has g

Ioannis P. A. Papadopoulos, Timon S. Gutleb, Richard M. Slevinsky, Sheehan Olver

We discuss computing with hierarchies of families of (potentially weighted) semiclassical Jacobi polynomials which arise in the construction of multivariate orthogonal polynomials. In particular, we outline how to build connection and differentiation matrices with optimal complexity and compute analysis and synthesis operations in quasi-optimal complexity. W

Mariia Churilova, Martin Kološ, Zdeněk Stuchlík

String loop vibrations in a central plane of a Schwarzschild black hole are investigated for various string equations of state. We discuss string loop stability and derive frequencies of vibrational modes. Using the vibrating string loop model we fit the quasi-periodic oscillation (QPO) observed in X-ray signal coming from some compact sources. We demonstrat

Pertti Mattila

This paper extends some results of [M5] and [M3], in particular, removing assumptions of positive lower density. We give conditions on a general family $P_{\lambda}:\mathbb{R}^{n}\to\mathbb{R}^{m}, \lambda \in \Lambda,$ of orthogonal projections which guarantee that the Hausdorff dimension formula $\dim A\cap P_{\lambda}^{-1}\{u\}=s-m$ holds generically for

Reginald Christian Bernardo, Kin-Wang Ng

The recent astronomical milestone by the pulsar timing arrays (PTA) has revealed galactic-size gravitational waves (GW) in the form of a stochastic gravitational wave background (SGWB), correlating the radio pulses emitted by millisecond pulsars. This draws the outstanding questions toward the origin and the nature of the SGWB; the latter is synonymous to te

Camillo Brena, Nicola Gigli

It is known that on $\mathrm{RCD}$ spaces one can define a distributional Ricci tensor ${\bf Ric}$. Here we give a fine description of this object by showing that it admits the polar decomposition $${\bf Ric}=\omega\,|{\bf Ric}|$$ for a suitable non-negative measure $|{\bf Ric}|$ and unitary tensor field $\omega$. The regularity of both the mass measure and

Shreyas Havaldar, Jatin Chauhan, Karthikeyan Shanmugam, Jay Nandy

Covariate shift in the test data is a common practical phenomena that can significantly downgrade both the accuracy and the fairness performance of the model. Ensuring fairness across different sensitive groups under covariate shift is of paramount importance due to societal implications like criminal justice. We operate in the unsupervised regime where only

Karam Dawoud, Wojciech Samek, Peter Eisert, Sebastian Lapuschkin

In the ever-evolving field of Artificial Intelligence, a critical challenge has been to decipher the decision-making processes within the so-called "black boxes" in deep learning. Over recent years, a plethora of methods have emerged, dedicated to explaining decisions across diverse tasks. Particularly in tasks like image classification, these methods typica

Masanori Hanada, Hiromasa Watanabe

We describe how the general mechanism of partial deconfinement applies to large-$N$ QCD and the partially-deconfined phase inevitably appears between completely-confined and completely-deconfined phases. Furthermore, we propose how the partial deconfinement can be observed in the real-world QCD with the SU(3) gauge group. For this purpose, we employ lattice

Arghya Choudhury, Arpita Mondal, Subhadeep Mondal, Subhadeep Sarkar

In this work we have studied a multi-lepton final state arising from sneutrino and left-handed slepton production at the high luminosity and high energy LHC in the context of R-parity violating supersymmetry when only the lepton number violating $\lambda_{121}$ and/or $\lambda_{122}$ couplings are non-zero. We have taken into account both pair production and

Francesco Turci, Robert L. Jack, Nigel B. Wilding

We study wetting droplets formed of active Brownian particles in contact with a repulsive potential barrier, in a wedge geometry. Our numerical results demonstrate a transition between partially wet and completely wet states, as a function of the barrier height, analogous to the corresponding surface phase transition in passive fluids. We analyse partially w

Jiaming Hu, Shu Zhao, Wenbin Li, Hua Wang

In this article, we systematically explore several key properties of electronic states in helical materials systems, including the inheritance of orbital angular momentum (OAM) from local atomic orbitals to the entire helical structure, the conservation of helical momentum, and the emergence of helical-induced spin-orbit coupling (hSOC). We then apply this c

Stefan Appel, Viviana Villafane, Jonathan J. Finley, Kai Müller

Radial Bragg gratings are commonly used to enhance light extraction from quantum emitters, but lack a well-suited, fast simulation method for optimization beyond periodic designs. To overcome this limitation, we propose and demonstrate an algorithm based on the transfer matrix model (TMM) to calculate the free-space emission of such gratings. Using finite di

Zhan Yu, Qiuhao Chen, Yuling Jiao, Yinan Li

Parameterized quantum circuits (PQCs) have emerged as a promising approach for quantum neural networks. However, understanding their expressive power in accomplishing machine learning tasks remains a crucial question. This paper investigates the expressivity of PQCs for approximating general multivariate function classes. Unlike previous Universal Approximat

Lalitha Sairam, Amaury H. M. J. Triaud, Thomas A. Baycroft, Jerome Orosz

Ongoing ground-based radial-velocity observations seeking to detect circumbinary planets focus on single-lined binaries even though over nine in every ten binary systems in the solar-neighbourhood are double-lined. Double-lined binaries are on average brighter, and should in principle yield more precise radial-velocities. However, as the two stars orbit one

Xiaorong Zhang, Sahar Zeinali, Georg Schildbach

This paper addresses the problem of traffic prediction and control of autonomous vehicles on highways. A modified Interacting Multiple Model Kalman filter algorithm is applied to predict the motion behavior of the traffic participants by considering their interactions. A scenario generation component is used to produce plausible scenarios of the vehicles bas

Jianwei Liu, Shirui Lyu, Denis Hadjivelichkov, Valerio Modugno

Legged robots, particularly quadrupeds, offer promising navigation capabilities, especially in scenarios requiring traversal over diverse terrains and obstacle avoidance. This paper addresses the challenge of enabling legged robots to navigate complex environments effectively through the integration of data-driven path-planning methods. We propose an approac

Jing Qiu, Weijun Fang, Fang-Wei Fu

Cyclic codes are an important class of linear codes. Bounding the minimum distance of cyclic codes is a long-standing research topic in coding theory, and several well-known and basic results have been developed on this topic. Recently, locally repairable codes (LRCs) have attracted much attention due to their repair efficiency in large-scale distributed sto

Frederik Benirschke

We study algebraic subvarieties of strata of differentials in genus zero satisfying algebraic relations among periods. The main results are Ax-Schanuel and Andr\'e-Oort-type theorems in genus zero. As a consequence, one obtains several equivalent characterizations of bi-algebraic varieties. It follows that bi-algebraic varieties in genus zero are foliated by

Adrian Hayler, Felix Wimbauer, Dominik Muhle, Christian Rupprecht

3D semantic scene understanding is a fundamental challenge in computer vision. It enables mobile agents to autonomously plan and navigate arbitrary environments. SSC formalizes this challenge as jointly estimating dense geometry and semantic information from sparse observations of a scene. Current methods for SSC are generally trained on 3D ground truth base

Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, Xiangru Tang

This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As LLMs find applications across diverse domains, the reliability and accuracy of their outputs become vital. We define the Factuality Issue as the probability of LLMs to produce content inconsistent with established facts. We first delve into the implications of these ina

Sebastián Reyes-Carocca, Pietro Speziali

In this article we consider compact Riemann surfaces that are uniquely determined by the property of possessing a group of automorphisms of a prescribed order, strengthening uniqueness results proved by Nakagawa. More precisely, we deal with the cases in which such an order is $3g$ and $3g+3,$ where $g$ is the genus. We prove that if $g$ is odd (respectively

Siddhant Manna, Shantanu Desai

Galaxy clusters could produce gamma-rays from inverse Compton scattering of cosmic ray electrons or hadronic interactions of cosmic ray protons with the intracluster medium. It is still an open question on whether gamma-ray emission ($>$ GeV energies) has been detected from galaxy clusters. We carry out a systematic search for gamma-ray mission based on 300

Mirco Mutti, Riccardo De Santi, Marcello Restelli, Alexander Marx

Posterior sampling allows exploitation of prior knowledge on the environment's transition dynamics to improve the sample efficiency of reinforcement learning. The prior is typically specified as a class of parametric distributions, the design of which can be cumbersome in practice, often resulting in the choice of uninformative priors. In this work, we propo

Yaru Chen, Ruohao Guo, Xubo Liu, Peipei Wu

Audio-visual video parsing is the task of categorizing a video at the segment level with weak labels, and predicting them as audible or visible events. Recent methods for this task leverage the attention mechanism to capture the semantic correlations among the whole video across the audio-visual modalities. However, these approaches have overlooked the impor

Sadasivan Shankar

Estimates of energy usage in layers of computing from devices to algorithms have been determined and analyzed. Building on the previous analysis [3], energy needed from single devices and systems including three large-scale computing applications such as Artificial Intelligence (AI)/Machine Learning for Natural Language Processing, Scientific Simulations, an

Jose A. R. Cembranos, Luis J. Garay, Álvaro Parra-López, Jose M. Sánchez Velázquez

Gravitational particle production of spectator fields due to the expansion universe during the inflationary and reheating phases of the early universe is of particular interest in the context of dark matter, since it allows to constrain the properties of the dark candidate by comparing the density of particles produced with the observed dark matter abundance

Nan Zhang, Daniel Hörcher, Prateek Bansal, Daniel J. Graham

Urban metro systems move vast numbers of passengers with a high level of efficiency in resource use, but frequently experience disruptions that result in delays, crowding, and deterioration in passenger satisfaction and patronage. To quantify these adverse consequences, this paper presents a novel, data-driven causal inference framework to measure metro resi

Sara Murciano, Filiberto Ares, Israel Klich, Pasquale Calabrese

Entanglement asymmetry is a quantity recently introduced to measure how much a symmetry is broken in a part of an extended quantum system. It has been employed to analyze the non-equilibrium dynamics of a broken symmetry after a global quantum quench with a Hamiltonian that preserves it. In this work, we carry out a comprehensive analysis of the entanglement

Vittorio Coti Zelati, Margherita Nolasco

We prove the existence of a normalized, stationary solution $\Psi \colon \mathbb{R}^{3} \to \mathbb{C}^{4}$ with frequency $w > 0$ of the nonlinear Dirac equation. The result covers the case in which the nonlinearity is the gradient of a function of the form \begin{equation*} F(\Psi) = a|(\Psi, \gamma^{0}\Psi)|^{\frac{\alpha}{2}} + b|(\Psi, \gamma^{1}\gamma^

Jingtao Li, Xinyu Wang, Hengwei Zhao, Liangpei Zhang

Remote sensing anomaly detector can find the objects deviating from the background as potential targets for Earth monitoring. Given the diversity in earth anomaly types, designing a transferring model with cross-modality detection ability should be cost-effective and flexible to new earth observation sources and anomaly types. However, the current anomaly de

Zhiming Qian

To mimic human vision with the way of recognizing the diverse and open world, foundation vision models are much critical. While recent techniques of self-supervised learning show the promising potentiality of this mission, we argue that signals from labelled data are also important for common-sense recognition, and properly chosen pre-text tasks can facilita

ALICE Collaboration

Collective behavior has been observed in high-energy heavy-ion collisions for several decades. Collectivity is driven by the high particle multiplicities that are produced in these collisions. At the CERN Large Hadron Collider (LHC), features of collectivity have also been seen in high-multiplicity proton-proton collisions that can attain particle multiplici

Ziliang Yang, Jiabao Su, Mingzheng Sun

Suppose that $G=(V, E)$ be a locally finite and connected graph with symmetric weight and uniformly positive measure, where $V$ denotes the vertex set and $E$ denotes the edge set. We are concered with the following problem $$ \begin{cases}-\Delta u+h u=f(x, u), & \text { in } \Omega, \\ u=0, & \text { on } \partial \Omega,\end{cases} $$ on the graph, where

Yuanhang Liu, Weijia Wu, Donghui Yang, Xu Zhang

This study aims to investigate the functional properties of weak solution spaces and their compact embedding properties in relation to the Dirichlet problem associated with a specific class of degenerate elliptic equations. To expand the scope of analysis for degenerate elliptic problems, we employ weighted Sobolev inequalities and compact embedding techniqu

Haizhong Zheng, Jiachen Sun, Shutong Wu, Bhavya Kailkhura

Given a real-world dataset, data condensation (DC) aims to synthesize a small synthetic dataset that captures the knowledge of a natural dataset while being usable for training models with comparable accuracy. Recent works propose to enhance DC with data parameterization, which condenses data into very compact parameterized data containers instead of images.

R. Casadio, R. da Rocha, A. Giusti, P. Meert

We study different entropies for coherent states representing the geometry of spherically symmetric compact systems. We show that the thermodynamic entropy reproduces the Bekenstein-Hawking result in the presence of thermal modes at the Hawking temperature if the object is a black hole and saturates the Bekenstein bound for more general compact objects. We a

Weijie Gan, Qiuchen Zhai, Michael Thompson McCann, Cristina Garcia Cardona

Ptychography is an imaging technique that captures multiple overlapping snapshots of a sample, illuminated coherently by a moving localized probe. The image recovery from ptychographic data is generally achieved via an iterative algorithm that solves a nonlinear phase retrieval problem derived from measured diffraction patterns. However, these iterative appr

Samuele Mosso, Marc Calaf, Ivana Stiperski

Monin-Obukhov similarity theory (MOST) is used in virtually every Earth System Model (ESM) to parameterize the near-surface turbulent exchanges, however there is high uncertainty in the literature about the appropriate parameterizations to be used. In addition, MOST has limitations in very stable and unstable regimes, over heterogeneous terrain and complex o

P. Mroz, R. Poleski

The number of exoplanets detected using gravitational microlensing technique is currently larger than 200, which enables population studies. Microlensing is uniquely sensitive to low-mass planets orbiting at separations of several astronomical units, a parameter space that is not accessible to other planet-detection techniques, as well as free-floating plane

Kalyan Chakraborty, Azizul Hoque

We exhibit some new families of cyclotomic fields which have non-trivial plus parts of their class numbers. We also prove the $3$ - divisibility of the plus part of the class number of another family consisting of infinitely many cyclotomic fields. At the end, we provide some numerical examples supporting our results.

Alexander R. Miller, Danny Scheinerman

This is a brief report on some recent large-scale Monte Carlo simulations for approximating the density of zeros in character tables of large symmetric groups. Previous computations suggested that a large fraction of zeros cannot be explained by classical vanishing results. Our computations eclipse previous ones and suggest that the opposite is true. We find

Qiyue Yang, Yue Gao, Shaoyuan Li

Inspired by biological motion generation, central pattern generators (CPGs) is frequently employed in legged robot locomotion control to produce natural gait pattern with low-dimensional control signals. However, the limited adaptability and stability over complex terrains hinder its application. To address this issue, this paper proposes a terrain-adaptive

Harold Erbin, Riccardo Finotello, Bio Wahabou Kpera, Vincent Lahoche

Signal detection is one of the main challenges of data science. As it often happens in data analysis, the signal in the data may be corrupted by noise. There is a wide range of techniques aimed at extracting the relevant degrees of freedom from data. However, some problems remain difficult. It is notably the case of signal detection in almost continuous spec

Yongmin Yoon, Jae-Woo Kim, Jongwan Ko

We study the mass-size relation of quiescent galaxies across various environments, with a particular focus on its environmental dependence at the low-mass part of $\log(M_\mathrm{star}/M_{\odot})\lesssim10.0$. Our sample consists of 13,667 quiescent galaxies with $\log(M_\mathrm{star}/M_{\odot})\ge9.4$ and $0.01<z<0.04$ from the Sloan Digital Sky Survey. We

Minh Ngoc Luu, Minh-Duong Nguyen, Ebrahim Bedeer, Van Duc Nguyen

An intelligent Real-Time Sensing (RTS) system must continuously acquire, update, integrate, and apply knowledge to adapt to real-world dynamics. Managing distributed intelligence in this context requires Federated Continual Learning (FCL). However, effectively capturing the diverse characteristics of RTS data in FCL systems poses significant challenges, incl

Marcio A. Jorge Silva, To Fu Ma

The purpose of this paper is twofold. Firstly, we conduct an in-depth analysis of mathematical modeling concerning thermal-mechanical curved beams, by taking into consideration three primary forces widely accepted in the literature: axial load, shear force, and bending moment. Additionally, we examine their appropriate thermal couplings, shedding light on th

Olivier Lai, Mark Chun, Stefan Kuiper, Niek Doelman

Adaptive optics is a technique mostly used on large telescopes. It turns out to be challenging for smaller telescopes (0.5~2m) due to the small isoplanatic angle, small subapertures and high correction speeds needed at visible wavelengths, requiring bright stars for guiding, severely limiting the sky coverage. NGS SCAO is ideal for planetary objects but rema

Sara Santoni, Marco De Petris, Antonio Ferragamo, Gustavo Yepes

Galaxy clusters and their filamentary outskirts reveal useful laboratories to test cosmological models and investigate Universe composition and evolution. Their environment, in particular the filaments of the Cosmic Web to which they are connected, plays an important role in shaping the properties of galaxy clusters. In this project, we analyse the gas filam

Finn Rietz, Johannes Andreas Stork

Discovering all useful solutions for a given task is crucial for transferable RL agents, to account for changes in the task or transition dynamics. This is not considered by classical RL algorithms that are only concerned with finding the optimal policy, given the current task and dynamics. We propose a simple method for discovering all possible solutions of

Renyang Liu, Wei Zhou, Tianwei Zhang, Kangjie Chen

Existing black-box attacks have demonstrated promising potential in creating adversarial examples (AE) to deceive deep learning models. Most of these attacks need to handle a vast optimization space and require a large number of queries, hence exhibiting limited practical impacts in real-world scenarios. In this paper, we propose a novel black-box attack str

Mandani Ntekouli, Gerasimos Spanakis, Lourens Waldorp, Anne Roefs

Through Ecological Momentary Assessment (EMA) studies, a number of time-series data is collected across multiple individuals, continuously monitoring various items of emotional behavior. Such complex data is commonly analyzed in an individual level, using personalized models. However, it is believed that additional information of similar individuals is likel

ALICE Collaboration

Measurements of charged-particle production in pp, p$-$Pb, and Pb$-$Pb collisions in the toward, away, and transverse regions with the ALICE detector are discussed. These regions are defined event-by-event relative to the azimuthal direction of the charged trigger particle, which is the reconstructed particle with the largest transverse momentum ($p_{\mathrm

Luca Dieci, Daniyar Omarov

In this work, we propose a novel implementation of Newton's method for solving semi-discrete optimal transport (OT) problems for cost functions which are a positive combination of $p$-norms, $1<p<\infty$. It is well understood that the solution of a semi-discrete OT problem is equivalent to finding a partition of a bounded region in Laguerre cells, and we pr

Jiayi Fu, Lei Lin, Xiaoyang Gao, Pengli Liu

Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning. In this report, we introduce the KwaiYiiMath which enhances the mathematical reasoning abilities of KwaiYiiBase1, by applying Supervi

V. S. D. S. Mahesh Akavarapu, Arnab Bhattacharya

Phonological reconstruction is one of the central problems in historical linguistics where a proto-word of an ancestral language is determined from the observed cognate words of daughter languages. Computational approaches to historical linguistics attempt to automate the task by learning models on available linguistic data. Several ideas and techniques draw

Massimo Bernaschi, Isidoro González-Adalid Pemartín, Víctor Martín-Mayor, Giorgio Parisi

Quantum annealers are commercial devices aiming to solve very hard computational problems named spin glasses. Just like in metallurgic annealing one slowly cools a ferrous metal, quantum annealers seek good solutions by slowly removing the transverse magnetic field at the lowest possible temperature. The field removal diminishes quantum fluctuations but forc

Paul Schwerdtner, Philipp Schulze, Jules Berman, Benjamin Peherstorfer

This work focuses on the conservation of quantities such as Hamiltonians, mass, and momentum when solution fields of partial differential equations are approximated with nonlinear parametrizations such as deep networks. The proposed approach builds on Neural Galerkin schemes that are based on the Dirac--Frenkel variational principle to train nonlinear parame

Edwin Peter Lobo, Jef Pauwels, Stefano Pironio

Losses in the transmission channel, which increase with distance, pose a major obstacle to photonics demonstrations of quantum nonlocality and its applications. Recently, Chaturvedi, Viola, and Pawlowski (CVP) [arXiv:2211.14231] introduced a variation of standard Bell experiments with the goal of extending the range over which quantum nonlocality can be demo

Dawei Shen

The global stability of Minkowski spacetime, a milestone in the field, has been proven in the celebrated work of Christodoulou and Klainerman \cite{Ch-Kl} in 1993. In 2007, Bieri \cite{Bieri} has extended the result of \cite{Ch-Kl} under lower decay and regularity assumptions on the initial data. In this paper, we extend the result of \cite{Bieri} to minimal

William J. Wolf, Pedro G. Ferreira

There is compelling evidence that the Universe is undergoing a late phase of accelerated expansion. One of the simplest explanations for this behaviour is the presence of dark energy. A plethora of microphysical models for dark energy have been proposed. The hope is that, with the ever increasing precision of cosmological surveys, it will be possible to prec

Long Cui, Xin Hao, Liu Zhao

In this study, we investigate the linear transport of neutral system within the framework of relativistic kinetic theory. Under the relaxation time approximation, we obtain an iterative solution to the relativistic Boltzmann equation in generic stationary spacetime. This solution provides a scheme to study non-equilibrium system order by order. Our calculati

Joydeep Pal, Deepak Choudhary, Nithish Krishnabharathi Gnani, Chandramani Singh

Time-Aware Shaper (TAS) is a time-triggered scheduling mechanism that ensures bounded latency for time-critical Scheduled Traffic (ST) flows. The Linux kernel implementation (a.k.a TAPRIO) has limited capabilities due to varying CPU workloads and thus does not offer tight latency bound for the ST flows. Also, currently only higher cycle times are possible. O

Henrik B. Lassen, William V. Carstensen, Leonid Iliushyn, Timothy J. Booth

As photonic and electronic technologies approach nanometre length scales and terahertz operating speeds, electrical conductivity can no longer be treated as a purely local material parameter. In this regime, charge transport becomes intrinsically nonlocal, with conductivity depending on both frequency and momentum, $\sigma(\omega,q)$, fundamentally limiting

Minji Yoon, Jing Yu Koh, Bryan Hooi, Ruslan Salakhutdinov

Multimodal learning combines multiple data modalities, broadening the types and complexity of data our models can utilize: for example, from plain text to image-caption pairs. Most multimodal learning algorithms focus on modeling simple one-to-one pairs of data from two modalities, such as image-caption pairs, or audio-text pairs. However, in most real-world

Hangyu Wang, Ting Long, Liang Yin, Weinan Zhang

Computerized Adaptive Testing(CAT) refers to an online system that adaptively selects the best-suited question for students with various abilities based on their historical response records. Most CAT methods only focus on the quality objective of predicting the student ability accurately, but neglect concept diversity or question exposure control, which are

Felipe de Morais, Diógines Goldoni, Tiago Kautzmann, Rodrigo da Silva

Emotions and other affective states play a pivotal role in cognition and, consequently, the learning process. It is well-established that computer-based learning environments (CBLEs) that can detect and adapt to students' affective states can enhance learning outcomes. However, practical constraints often pose challenges to the deployment of sensor-based aff

Alberto Ambrosetti, Pier Luigi Silvestrelli, Luca Salasnich

Superfluidity is a well-characterized quantum phenomenon which entails frictionless-motion of mesoscopic particles through a superfluid, such as $^4$He or dilute atomic-gases at very low temperatures. As shown by Landau, the incompatibility between energy- and momentum-conservation, which ultimately stems from the spectrum of the elementary excitations of th

Shavika Rastogi, Nik Dennler, Michael Schmuker, André van Schaik

Gas concentration detection is important for applications such as gas leakage monitoring. Metal Oxide (MOx) sensors show high sensitivities for specific gases, which makes them particularly useful for such monitoring applications. However, how to efficiently sample and further process the sensor responses remains an open question. Here we propose a simple an

Adolfo Ballester-Bolinches, Ramón Esteban-Romero, Maria Ferrara, Vicent Pérez-Calabuig

Nipotency of skew braces is related to certain types of solutions of the Yang-Baxter equation. This paper delves into the study of centrally nilpotent skew braces. In particular, we study their torsion theory (Section 4.1) and we introduce an "index" for subbraces (Section 4.2), but we also show that the product of centrally nilpotent ideals need not be cent

Xinyu Sun, Peihao Chen, Jugang Fan, Thomas H. Li

Learning to navigate to an image-specified goal is an important but challenging task for autonomous systems. The agent is required to reason the goal location from where a picture is shot. Existing methods try to solve this problem by learning a navigation policy, which captures semantic features of the goal image and observation image independently and last

Zhiyu Lin, Mark Riedl

The term co-creativity has been used to describe a wide variety of human-AI assemblages in which human and AI are both involved in a creative endeavor. In order to assist with disambiguating research efforts, we present an ontology of co-creative systems, focusing on how responsibilities are divided between human and AI system and the information exchanged b

Francesc Wilhelmi, Nima Afraz, Elia Guerra, Paolo Dini

Blockchain promises to enhance distributed machine learning (ML) approaches such as federated learning (FL) by providing further decentralization, security, immutability, and trust, which are key properties for enabling collaborative intelligence in next-generation applications. Nonetheless, the intrinsic decentralized operation of peer-to-peer (P2P) blockch

Niccolò Cocciaglia, Massimo Cencini, Angelo Vulpiani

The statistical properties of turbulent flows are fundamentally different from those of systems at equilibrium due to the presence of an energy flux from the scales of injection to those where energy is dissipated by the viscous forces: a scenario dubbed "direct energy cascade". From a statistical mechanics point of view, the cascade picture prevents the exi

Yu-Mei Wu, Zu-Cheng Chen, Yan-Chen Bi, Qing-Guo Huang

The recently detected stochastic signal by several pulsar timing array collaborations, offers an opportunity to scrutinize the fundamental properties of gravity, including the potential mass of the graviton. In this study, we analyze the NANOGrav 15-year data set to search for a stochastic gravitational wave background with modified Hellings-Downs correlatio

F. Rösch, P. Benke, M. Kadler, E. Ros

The origin of high-energy cosmic neutrinos detected by the IceCube observatory is a hotly debated topic in astroparticle physics. There is growing evidence that some of these neutrinos can be associated with active galactic nuclei (AGN) and especially with blazars. Several recent studies have revealed a statistical correlation between radio-bright AGN sample

Francesc Wilhelmi, Dariush Salami, Gianluca Fontanesi, Lorenzo Galati-Giordano

Enterprise Wi-Fi networks can greatly benefit from Artificial Intelligence and Machine Learning (AI/ML) thanks to their well-developed management and operation capabilities. At the same time, AI/ML-based traffic/load prediction is one of the most appealing data-driven solutions to improve the Wi-Fi experience, either through the enablement of autonomous oper

Bojan Kuzma, Mitja Mastnak, Heydar Radjavi, Matjaž Omladič

We introduce the notions of $\varepsilon$-approximate fixed point and weak $\varepsilon$-approximate fixed point. We show that for a group of unitary matrices even the existence of a nontrivial weak $\varepsilon$-approximate fixed point for sufficiently small $\varepsilon$ gives an actual nontrivial common eigenvector. We give estimates for $\varepsilon$ in

Debojyoti Bhattacharya, Subhabrata Paul

Let $G=(V,E)$ be a graph. For an edge $e=xy\in E$, the closed neighbourhood of $e$, denoted by $N_G[e]$ or $N_G[xy]$, is the set $N_G[x]\cup N_G[y]$. A vertex set $L\subseteq V$ is liar's vertex-edge dominating set of a graph $G=(V,E)$ if for every $e_i\in E$, $|N_G[e_i]\cap L|\geq 2$ and for every pair of distinct edges $e_i$ and $e_j$, $|(N_G[e_i]\cup N_G[

Zijie Fang, Yihan Liu, Yifeng Wang, Xiangyang Zhang

Biomarker detection is an indispensable part of the diagnosis and treatment of low-grade glioma (LGG). However, current LGG biomarker detection methods rely on expensive and complex molecular genetic testing, for which professionals are required to analyze the results, and intra-rater variability is often reported. To overcome these challenges, we propose an

Gabriel Ott, Yannik Schaubelt, Juan Miguel Lopez Alcaraz, Wilhelm Haverkamp

Cardiovascular diseases remain the leading global cause of mortality. Age is an important covariate whose effect is most easily investigated in a healthy cohort to properly distinguish the former from disease-related changes. Traditionally, most of such insights have been drawn from the analysis of electrocardiogram (ECG) feature changes in individuals as th

Matteo Lucchini, Marina Ten Have, Jingyi Wang, Jeroen Homan

During the outbursts of black hole X-ray binaries (BHXRBs), their accretion flows transition through several states. The source luminosity rises in the hard state, dominated by non-thermal emission, before transitioning to the blackbody-dominated soft state. As the luminosity decreases, the source transitions back into the hard state and fades to quiescence.

Teeratorn Kadeethum, Stephen J. Verzi, Hongkyu Yoon

Geological carbon and energy storage are pivotal for achieving net-zero carbon emissions and addressing climate change. However, they face uncertainties due to geological factors and operational limitations, resulting in possibilities of induced seismic events or groundwater contamination. To overcome these challenges, we propose a specialized machine-learni

M. K. Volkov, A. A. Pivovarov, K. Nurlan

The $\tau$ lepton decays $\tau \to [K^- K^0 \pi^0, K^- K^+ \pi^-, K ^0 \bar{K^0} \pi^-] \nu_{\tau}$ are described in the $U(3) \times U(3)$ NJL quark model. The contact channel and intermediate channels with axial-vector, vector and pseudoscalar mesons are taken into account. It is shown that the strange scalar meson $K^*_0$ plays an important role in these

Łukasz Chomienia

The paper concerns the theory of parabolic equations on a broad class of closed subsets of Euclidean space possessing a kind of tangent structure. A necessary framework for considering evolutionary problems is developed, and fundamental results about the existence and uniqueness of solutions are established. The presented approach is based on applying the se

Jeremy McDade, Allison Jing, Andrew Cunningham

In this poster, we present RealityDrop, a novel multimodal framework that uses Mixed Reality (MR) technology to manipulate, display, and transfer virtual content across different display systems. Employing MR as the centre of control, RealityDrop affords concise information dissemination among diverse collaborators, through varied representations that best f

Nabin Bhatta, Djordje Minic, Tatsu Takeuchi

Nambu Quantum Mechanics, proposed in Phys. Lett. B536, 305 (2002), is a deformation of canonical Quantum Mechanics in which the manifold over which the "phase" of an energy eigenstate time evolves is modified. This generalization affects oscillation and interference phenomena through the introduction of two deformation parameters that quantify the extent of

Minkun Kim

The problem of overdispersed claim counts and mismeasured covariates is common in insurance. On the one hand, the presence of overdispersion in the count data violates the homogeneity assumption, and on the other hand, measurement errors in covariates highlight the model risk issue in actuarial practice. The consequence can be inaccurate premium pricing whic

Laia Domingo

Reservoir computing is a novel machine learning algorithm that uses a nonlinear dynamical system to efficiently learn complex temporal patterns from data. The objective of this thesis is to investigate the principles of reservoir computing and develop state-of-the-art variants capable of addressing diverse applications in machine learning. The research demon

Jaume Llibre, Gabriel Rondón

A difficult classical problem in the qualitative theory of differential systems in the plane $\mathbb{R}^2$ is the center-focus problem, i.e. to distinguish between a focus and a center. Another difficult problem is to distinguish inside a family of centers the ones which are global. A global center is a center $p$ such that $\mathbb{R}^2\setminus\{p\}$ is f

Yu-Dong Zhang, Xiao-Wei Bai, Feng Feng, Wen-Long Sang

In the framework of nonrelativistic QCD (NRQCD) factorization, we compute both the polarized and the unpolarized decay widths for the processes $\eta_b(\chi_{bJ})\to J/\psi J/\psi$, accurate up to next-to-next-to-leading-order (NNLO) in $\alpha_s$. For the first time, we confirm that the NRQCD factorization does hold at NNLO for the process involving triple

Debojyoti Bhattacharya, Subhabrata Paul

Let $G=(V,E)$ be a simple undirected graph. The open neighbourhood of a vertex $v$ in $G$ is defined as $N_G(v)=\{u\in V~|~ uv\in E\}$; whereas the closed neighbourhood is defined as $N_G[v]= N_G(v)\cup \{v\}$. For an integer $k$, a subset $D\subseteq V$ is called a $k$-vertex-edge dominating set of $G$ if for every edge $uv\in E$, $|(N_G[u]\cup N_G[v]) \cap

Tatsuya Miura, Kensuke Yoshizawa

A new stabilization phenomenon induced by degenerate diffusion is discovered in the context of pinned planar $p$-elasticae. It was known that in the non-degenerate regime $p\in(1,2]$, including the classical case of Euler's elastica, there are no local minimizers other than unique global minimizers. Here we prove that, in stark contrast, in the degenerate re

Sergey S. Siaber, Jonathan Gratus, Rebecca Seviour, Steven P. Jamison

\begin{abstract} We show that it is possible to design corrugated waveguides where phase and group velocities coincide at an inflection point of the dispersion relation, allowing an extended regime of interaction with a charge particle beam. This provides a basis for designing travelling slow-wave structures with a broadband interaction between relativistic

Jia-Wang Bian, Wenjing Bian, Victor Adrian Prisacariu, Philip Torr

Neural surface reconstruction is sensitive to the camera pose noise, even if state-of-the-art pose estimators like COLMAP or ARKit are used. More importantly, existing Pose-NeRF joint optimisation methods have struggled to improve pose accuracy in challenging real-world scenarios. To overcome the challenges, we introduce the pose residual field (PoRF), a nov

Ryan E. Dougherty, Dylan N. Green, Grace M. Kim

Factors within a large-scale software system that simultaneously interact and strongly impact the system's response under a configuration are often difficult to identify. Although screening such a system for the existence of such interactions is important, determining their location is more useful for system engineers. Combinatorial interaction testing (CIT)

Tomasz Klimsiak

We address an open problem posed by H. Brezis, M. Marcus and A.C. Ponce in: Nonlinear elliptic equations with measures revisited. In: Mathematical Aspects of Nonlinear Dispersive Equations (J. Bourgain, C. Kenig, S. Klainerman, eds.), Annals of Mathematics Studies, 163 (2007). We prove that for any bounded Borel measure $\mu$ on a smooth bounded domain $D\su

Jiawen Zhang, Xumeng Wen, Zhenwei Zhang, Shun Zheng

Delivering precise point and distributional forecasts across a spectrum of prediction horizons represents a significant and enduring challenge in the application of time-series forecasting within various industries. Prior research on developing deep learning models for time-series forecasting has often concentrated on isolated aspects, such as long-term poin