July 2022 arXiv papers — page 99

Ri-Hua Zheng, Wen Ning, Ye-Hong Chen, Jia-Hao Lü

Superradiant phase transitions (SPTs) are important for understanding light-matter interactions at the quantum level, and play a central role in criticality-enhanced quantum sensing. So far, SPTs have been observed in driven-dissipative systems, but the emergent light fields did not show any nonclassical characteristic due to the presence of strong dissipati

I. Gutierrez-Sagredo, D. Iglesias Ponte, J. C. Marrero, E. Padrón

In this paper, we discuss several relations between the existence of invariant volume forms for Hamiltonian systems on Poisson-Lie groups and the unimodularity of the Poisson-Lie structure. In particular, we prove that Hamiltonian vector fields on a Lie group endowed with a unimodular Poisson-Lie structure preserve a multiple of any left-invariant volume on

Yang Tan, Enming Zhang, Yang Li, Shao-Lun Huang

We propose two novel transferability metrics F-OTCE (Fast Optimal Transport based Conditional Entropy) and JC-OTCE (Joint Correspondence OTCE) to evaluate how much the source model (task) can benefit the learning of the target task and to learn more transferable representations for cross-domain cross-task transfer learning. Unlike the existing metric that re

Denise Lanzieri, François Lanusse, Jean-Luc Starck

We present a new scheme to compensate for the small-scales approximations resulting from Particle-Mesh (PM) schemes for cosmological N-body simulations. This kind of simulations are fast and low computational cost realizations of the large scale structures, but lack resolution on small scales. To improve their accuracy, we introduce an additional effective f

Jan Schlüter, Gerald Gutenbrunner

In audio classification, differentiable auditory filterbanks with few parameters cover the middle ground between hard-coded spectrograms and raw audio. LEAF (arXiv:2101.08596), a Gabor-based filterbank combined with Per-Channel Energy Normalization (PCEN), has shown promising results, but is computationally expensive. With inhomogeneous convolution kernel si

Yongchuan Yu, Yujie Zhu, Chi Zhang, Oskar J. Haidn

Obtaining high quality particle distribution representing clean geometry in pre-processing is essential for the simulation accuracy of the particle-based methods. In this paper, several level-set based techniques for cleaning up `dirty' geometries automatically and generating homogeneous particle distributions are presented. First, a non-resolved structure i

Petr Zemlyanukha, Igor I. Zinchenko, Evgeny Dombek, Lev E. Pirogov

The environment of S187, a nearby H II region (1.4$\pm$0.3 kpc), is analyzed. A surrounding shell has been studied in the H I line, molecular lines, and also in infrared and radio continua. We report the first evidence of a clumpy HI environment in its photodissociation region. A background radio galaxy enables the estimation of the properties of cold atomic

Théo Lepage, Réda Dehak

State-of-the-art speaker verification systems are inherently dependent on some kind of human supervision as they are trained on massive amounts of labeled data. However, manually annotating utterances is slow, expensive and not scalable to the amount of data available today. In this study, we explore self-supervised learning for speaker verification by learn

Xin-Zhen Weng, Shi-Lin Zhu

We systematically study the mass spectra of the fully heavy dibaryons in an extended chromomagnetic model, which includes both the colorelectric and chromomagnetic interactions. We find no stable state below the corresponding baryon-baryon thresholds. Besides the masses, we also estimate the relative width ratios of the two-body decay channels. We hope our s

Andrei Neguţ

We introduce a quantum loop group associated to a general symmetric Cartan matrix, by imposing just enough relations between the usual generators $\{e_{i,k}, f_{i,k}\}_{i \in I, k \in \mathbb{Z}}$ in order for the natural Hopf pairing between the positive and negative halves of the quantum loop group to be perfect. As an application, we describe the localize

Jonas Marko, Gerd Wachsmuth

We investigate local optimality conditions of first and second order for integer optimal control problems with total variation regularization via a finite-dimensional switching point problem. We show the equivalence of local optimality for both problems, which will be used to derive conditions concerning the switching points of the control function. A non-lo

Mats H. Lamann, Jochen Gemmer

The perturbation theory based on typicality introduced in Ref. [1] and further refined in Refs. [2, 3] provides a powerful tool since it is intended to be applicable to a wide range of scenarios while relying only on a few parameters. Even though the authors present various examples to demonstrate the effectiveness of the theory, the conditions used in its d

Jiashi Li, Xin Xia, Wei Li, Huixia Li

Due to the complex attention mechanisms and model design, most existing vision Transformers (ViTs) can not perform as efficiently as convolutional neural networks (CNNs) in realistic industrial deployment scenarios, e.g. TensorRT and CoreML. This poses a distinct challenge: Can a visual neural network be designed to infer as fast as CNNs and perform as power

Jiashuo Yu, Jinyu Liu, Ying Cheng, Rui Feng

Weakly-supervised audio-visual violence detection aims to distinguish snippets containing multimodal violence events with video-level labels. Many prior works perform audio-visual integration and interaction in an early or intermediate manner, yet overlooking the modality heterogeneousness over the weakly-supervised setting. In this paper, we analyze the mod

Longzhen Zhang, Lingxu Meng, Senlin Wu

The maximum of the Banach-Mazur distance $d_{BM}^M(X,\ell_\infty^n)$, where $X$ ranges over the set of all $n$-dimensional real Banach spaces, is difficult to compute. In fact, it is already not easy to get the maximum of $d_{BM}^M(\ell_p^n,\ell_\infty^n)$ for all $p\in [1,\infty]$. We prove that $d_{BM}^M(\ell_p^3,\ell_\infty^3)\leq 9/5,~\forall p\in[1,\inf

Rodolfo Zevallos, Luis Camacho, Nelsi Melgarejo

The Huqariq corpus is a multilingual collection of speech from native Peruvian languages. The transcribed corpus is intended for the research and development of speech technologies to preserve endangered languages in Peru. Huqariq is primarily designed for the development of automatic speech recognition, language identification and text-to-speech tools. In o

Bo Ju, Zhikang Zou, Xiaoqing Ye, Minyue Jiang

3D object detection task from lidar or camera sensors is essential for autonomous driving. Pioneer attempts at multi-modality fusion complement the sparse lidar point clouds with rich semantic texture information from images at the cost of extra network designs and overhead. In this work, we propose a novel semantic passing framework, named SPNet, to boost t

M. Soldani, L. Bandiera, L. Bomben, C. Brizzolari

Scintillating homogeneous detectors represent the state of the art in electromagnetic calorimetry. Moreover, the currently neglected crystalline nature of the most common inorganic scintillators can be exploited to achieve an outstanding performance boost in terms of compactness and energy resolution. In fact, it was recently demonstrated by the AXIAL/ELIOT

Nicolas Sanchis-Gual, Miguel Zilhão, Vitor Cardoso

We explore the dynamics of boson stars in the presence of axionic couplings through nonlinear evolutions of Einstein's field equations. We show that, for large axionic couplings, isolated boson stars become unstable, and decay via a large burst of electromagnetic radiation, becoming less massive and more dilute. Our full nonlinear results are in good agreeme

Hao Xing, Darius Burschka

In skeleton-based action recognition, Graph Convolutional Networks model human skeletal joints as vertices and connect them through an adjacency matrix, which can be seen as a local attention mask. However, in most existing Graph Convolutional Networks, the local attention mask is defined based on natural connections of human skeleton joints and ignores the

S. P. Sikhosana, K. Knowles, M. Hilton, K. Moodley

The Bullet cluster (1E 0657-55.8) is a massive merging system at redshift $z$=0.296, known to host a powerful radio halo and a relic. Here we present high fidelity MeerKAT L-band (0.9-1.7 GHz) observations of the Bullet cluster, in which we trace a larger extent of both the radio halo and relic in comparison to previous studies. The size of the recovered rad

Gaspar Cano, Rui Dilão

We show that the action potential signals generated inside axons are reaction-diffusion solitons or reaction-diffusion waves, refuting the Hodgkin and Huxley hypothesis that action potentials propagate along axons with an elastic wave mechanism. Reaction-diffusion action potential wavefronts and solitons annihilate at collision and boundaries of axons, in co

Miaomiao Ren, Xianzhong Zhao, Mikhail V. Volkov

Let ${\bf Sr}(n, 1)$ denote the ai-semiring variety defined by the identity $x^n\approx x$, where $n>1$. We characterize all subdirectly irreducible members of a semisimple subvariety of ${\bf Sr}(n, 1)$. Based on this result, we prove that ${\bf Sr}(n, 1)$ is hereditarily finitely based (resp., hereditarily finitely generated) if and only if $n<4$ and that

Matteo Brachetta, Giorgia Callegaro, Claudia Ceci, Carlo Sgarra

We investigate the optimal reinsurance problem when the loss process exhibits jump clustering features and the insurance company has restricted information about the loss process. We maximize expected exponential utility of terminal wealth and show that an optimal solution exists. By exploiting both the Kushner-Stratonovich and Zakai approaches, we provide t

Telikepalli Kavitha

Let $G = (A \cup B,E)$ be a bipartite graph where the set $A$ consists of agents or main players and the set $B$ consists of jobs or secondary players. Every vertex has a strict ranking of its neighbors. A matching $M$ is popular if for any matching $N$, the number of vertices that prefer $M$ to $N$ is at least the number that prefer $N$ to $M$. Popular matc

Peter Frankl, Jian Wang

We consider $k$-graphs on $n$ vertices, that is, $\mathcal{F}\subset \binom{[n]}{k}$. A $k$-graph $\mathcal{F}$ is called intersecting if $F\cap F'\neq \emptyset$ for all $F,F'\in \mathcal{F}$. In the present paper we prove that for $k\geq 7$, $n\geq 2k$, any intersecting $k$-graph $\mathcal{F}$ with covering number at least three, satisfies $|\mathcal{F}|\l

Maximilian Welsch

The Belle~II Collaboration presents four new analyses: The measurement of $V_{ub}$ from $B \to \pi e \nu_e$ decays with a fit to the differential $q^2$ spectrum, the determination of $V_{cb}$ using a fit to the differential $w$ distribution from $B^0 \to D^{\ast -} \ell^+ \nu_\ell$ decays, and the measurement of $q^2$ moments in inclusive $B \to X_c \ell \nu

N. Werner, J. Řípa, F. Münz, F. Hroch

We present a near-UV space telescope on a ~70kg micro-satellite with a moderately fast repointing capability and a near real-time alert communication system that has been proposed in response to a call for an ambitious Czech national mission. The mission, which has recently been approved for Phase 0, A, and B1 study shall measure the brightness evolution of

Shuntaro Aoki, Hyun Min Lee

We consider the embedding of Higgs inflation with a non-minimal coupling into the Weyl gravity. In this model, the effective current-current interactions from the heavy Weyl gauge field cancel the non-canonical Higgs kinetic term in Einstein frame, so the unitarity problem of the original Higgs inflation becomes less severe. For a simple case where the coupl

Siyu Ren, Yiming Zeng, Junhui Hou, Xiaodong Chen

Motivated by the intuition that the critical step of localizing a 2D image in the corresponding 3D point cloud is establishing 2D-3D correspondence between them, we propose the first feature-based dense correspondence framework for addressing the image-to-point cloud registration problem, dubbed CorrI2P, which consists of three modules, i.e., feature embeddi

Cillian Harney, Alasdair I. Fletcher, Stefano Pirandola

Future quantum networks will be hybrid structures, constructed from complex architectures of quantum repeaters interconnected by quantum channels that describe a variety of physical domains; predominantly optical-fiber and free-space links. In this hybrid setting, the interplay between the channel quality within network sub-structures must be carefully consi

Cillian Harney, Stefano Pirandola

The optimal performance of a communication network is limited not only by the quality of point-to-point channels, but by the efficacy of its constituent technologies. Understanding the limits of quantum networks requires an understanding of both the ultimate capacities of quantum channels and the efficiency of imperfect quantum repeaters. In this work, using

Mhairi Dunion, Trevor McInroe, Kevin Sebastian Luck, Josiah P. Hanna

Reinforcement Learning (RL) agents are often unable to generalise well to environment variations in the state space that were not observed during training. This issue is especially problematic for image-based RL, where a change in just one variable, such as the background colour, can change many pixels in the image. The changed pixels can lead to drastic cha

Weijun Fang, Bin Chen, Shu-Tao Xia, Fang-Wei Fu

Constructions of optimal locally repairable codes (LRCs) achieving Singleton-type bound have been exhaustively investigated in recent years. In this paper, we consider new bounds and constructions of Singleton-optimal LRCs with minmum distance $d=6$, locality $r=3$ and minimum distance $d=7$ and locality $r=2$, respectively. Firstly, we establish equivalent

Miguel A. Pozo, Justo Puerto, Alberto Torrejon

In this paper, we propose the Ordered Median Tree Location Problem (OMT). The OMT is a single-allocation facility location problem where p facilities must be placed on a network connected by a non-directed tree. The objective is to minimize the sum of the ordered weighted averaged allocation costs plus the sum of the costs of connecting the facilities in the

Guoxia Wang, Xiaomin Fang, Zhihua Wu, Yiqun Liu

Accurate protein structure prediction can significantly accelerate the development of life science. The accuracy of AlphaFold2, a frontier end-to-end structure prediction system, is already close to that of the experimental determination techniques. Due to the complex model architecture and large memory consumption, it requires lots of computational resource

Yao Chen, Yuanhan Mo, Aimee Readie, Gregory Ligozio

Manual annotation of vertebrae on spinal X-ray imaging is costly and time-consuming due to bone shape complexity and image quality variations. In this study, we address this challenge by proposing an ensemble method called VertXNet, to automatically segment and label vertebrae in X-ray spinal images. VertXNet combines two state-of-the-art segmentation models

Vinod Patidar, Gurpreet Kaur

In this paper, we propose a novel conservative chaotic standard map-driven dynamic DNA coding (encoding, addition, subtraction and decoding) for the image encryption. The proposed image encryption algorithm is a dynamic DNA coding algorithm i.e., for the encryption of each pixel different rules for encoding, addition/subtraction, decoding etc. are randomly s

Jingfu Zhang, Dieter Suter

We show that a single electron spin can serve as a sensor for radio-frequency (RF) magnetic fields. The longitudinal and transverse components of the RF field can be extracted from the phase acquired during free evolution of the spin coherence. In our experimental demonstration, a single electron spin of an NV center in diamond serves as an atomic-size of tw

Matteo Zambra, Alberto Testolin, Marco Zorzi

Deep belief networks (DBNs) are stochastic neural networks that can extract rich internal representations of the environment from the sensory data. DBNs had a catalytic effect in triggering the deep learning revolution, demonstrating for the very first time the feasibility of unsupervised learning in networks with many layers of hidden neurons. These hierarc

Stephan Kuschel, Phay J. Ho, Andre Al Haddad, Felix Zimmermann

Diffraction-before-destruction imaging with single ultrashort X-ray pulses has the potential to visualise non-equilibrium processes, such as chemical reactions, at the nanoscale with sub-femtosecond resolution in the native environment without the need of crystallization. Here, a nanospecimen partially diffracts a single X-ray flash before sample damage occu

Yingsong Huang, Bing Bai, Shengwei Zhao, Kun Bai

Learning against label noise is a vital topic to guarantee a reliable performance for deep neural networks. Recent research usually refers to dynamic noise modeling with model output probabilities and loss values, and then separates clean and noisy samples. These methods have gained notable success. However, unlike cherry-picked data, existing approaches oft

Chau Yi Li, Andrea Cavallaro

The appearance of objects in underwater images is degraded by the selective attenuation of light, which reduces contrast and causes a colour cast. This degradation depends on the water environment, and increases with depth and with the distance of the object from the camera. Despite an increasing volume of works in underwater image enhancement and restoratio

Martin Flechl, Shou-Chun Yin, Junho Park, Peter Skala

De-identification of data used for automatic speech recognition modeling is a critical component in protecting privacy, especially in the medical domain. However, simply removing all personally identifiable information (PII) from end-to-end model training data leads to a significant performance degradation in particular for the recognition of names, dates, l

Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais

Despite their advantages, normalizing flows generally suffer from several shortcomings including their tendency to generate unrealistic data (e.g., images) and their failing to detect out-of-distribution data. One reason for these deficiencies lies in the training strategy which traditionally exploits a maximum likelihood principle only. This paper proposes

Kareem T. Elgindy, Hareth M. Refat

We present a novel direct integral pseudospectral (PS) method (a direct IPS method) for solving a class of continuous-time infinite-horizon optimal control problems (IHOCs). The method transforms the IHOCs into finite-horizon optimal control problems (FHOCs) in their integral forms by means of certain parametric mappings, which are then approximated by finit

Bruno Veloso, João Gama, Rita P. Ribeiro, Pedro M. Pereira

The paper describes the MetroPT data set, an outcome of a eXplainable Predictive Maintenance (XPM) project with an urban metro public transportation service in Porto, Portugal. The data was collected in 2022 that aimed to evaluate machine learning methods for online anomaly detection and failure prediction. By capturing several analogic sensor signals (press

Lucia S. Ramirez, Maxi San Miguel, Tobias Galla

We investigate the time evolution of the density of active links and of the entropy of the distribution of agents among opinions in multi-state voter models with all-to-all interaction and on uncorrelated networks. Individual realisations undergo a sequence of eliminations of opinions until consensus is reached. After each elimination the population remains

Tom Eulenfeld, Christoph Heubeck

The angular momentum of the Earth-Moon system was initially dominated by Earth's rotation with a short solar day of around 5 hours duration. Since then, Earth gradually transferred angular momentum through tidal friction to the orbit of the Moon, resulting in an increasing orbital radius and a deceleration of Earth's rotation. Geologic observations of tidal

Jonah Edmonds, Zahraa S. Abdallah

Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Compared with intrusive load monitoring, NILM (Non-intrusive load monitoring) is low cost, easy to deploy, and flexible. In this paper, we propose a new method, coined IMG-NILM, that utilises convolutional neur

Jannik Zwartscholten, Christian Rehtanz

The large-scale integration of Distributed Energy Resources (DERs) into the electric power system offers new opportunities to ensure stability. For example, Active Distribution Networks (ADNs) can be used in (sub-)transmission systems in the emergency state, as far as high robustness and performance of the ADN control are guaranteed. This paper presents an a

M. Hossein Attar, Omid Taghizadeh, Kaxin Chang, Ramez Askar

This paper presents a kernel-based adaptive filter that is applied for the digital domain self-interference cancellation (SIC) in a transceiver operating in full-duplex (FD) mode. In FD, the benefit of simultaneous transmission and receiving of signals comes at the price of strong self-interference (SI). In this work, we are primarily interested in suppressi

Bing-Lan Wu, Ai-Min Guo, Zhi-Qiang Zhang, Hua Jiang

We study the quantized charge pumping of higher-order topological insulators (HOTIs) with edge-corner correspondences based on the combination of the rotation of in-plane magnetic field and the quantum spin Hall effect. A picture of a specific charge pumping process is uncovered with the help of the non-equilibrium Green's function method. Significantly, we

Walt van Amstel, Jan Harm van der Walt

If $K$ is a compact Hausdorff space so that the Banach lattice $C(K)$ is isometrically lattice isomorphic to a dual of some Banach lattice, then $C(K)$ can be decomposed as the $\ell^\infty$-direct sum of the carriers of a maximal singular family of order continuous functionals on $C(K)$. In order to generalise this result to the vector lattice $C(X)$ of con

Matías Chávez, Matthias Ernst

Interaction frames play an important role in describing and understanding experimental schemes in magnetic resonance. They are often used to eliminate dominating parts of the spin Hamiltonian, e.g., the Zeeman Hamiltonian in the usual (Zeeman) rotating frame, or the radio-frequency-field (rf) Hamiltonian to describe the efficiency of decoupling or recoupling

Jun Lai, Zuoli Xiao, Lian-Ping Wang

In order to treat immiscible two-phase flows at large density ratios and high Reynolds numbers, a three-dimensional code based on the discrete unified gas kinetic scheme (DUGKS) is developed, incorporating two major improvements. First, the particle distribution functions at cell interfaces are reconstructed using a weighted essentially non-oscillatory schem

Decheng Liu, Weijie He, Chunlei Peng, Nannan Wang

Face attribute evaluation plays an important role in video surveillance and face analysis. Although methods based on convolution neural networks have made great progress, they inevitably only deal with one local neighborhood with convolutions at a time. Besides, existing methods mostly regard face attribute evaluation as the individual multi-label classifica

Paolo Luzzatto-Fegiz

We examine the problem of performing simultaneous and coplanar Particle Image Velocimetry (PIV) and Laser-Induced Fluorescence (LIF) measurements in a stratified fluid initially at rest. Our focus is on enabling detailed velocity and density measurements in long internal waves and gravity currents, through relatively small modifications of typical existing P

Danilo Bazzanella, Andrea Gangemi

Proof-of-Work (PoW) is a popular consensus protocol used by Bitcoin since its inception. PoW has the well-known flaw of assigning all the reward to the single miner (or pool) that inserts the new block. This has the consequence of making the variance of the reward and thus the mining enterprise risk extremely high. To address this problem, Shi in 2016 propos

Michal Botur, Jan Paseka, Richard Smolka

Motivated by the classical work of Halmos on functional monadic Boolean algebras we derive three basic sup-semilattice constructions, among other things the so-called powersets and powerset operators. Such constructions are extremely useful and can be found in almost all branches of modern mathematics, including algebra, logic and topology. Our three constru

Luca Fabbri

We present the theory of Dirac spinors in the formulation given by Bohm on the idea of de Broglie: the quantum relativistic matter field is equivalently re-written as a special type of classical fluid and in this formulation it is shown how a relativistic environment can host the non-local aspects of the above-mentioned hidden-variables theory. Sketches for

Patrick Finnerty, Yoshiki Kawanishi, Tomio Kamada, Chikara Ohta

In this article we present our relocatable distributed collections library. Building on top of the AGPAS for Java library, we provide a number of useful intra-node parallel patterns as well as the features necessary to support the distributed nature of the computation through clearly identified methods. In particular, the transfer of distributed collections'

Inês Valentim, Nuno Lourenço, Nuno Antunes

NeuroEvolution automates the generation of Artificial Neural Networks through the application of techniques from Evolutionary Computation. The main goal of these approaches is to build models that maximize predictive performance, sometimes with an additional objective of minimizing computational complexity. Although the evolved models achieve competitive res

Matthias Reitzner, Tim Römer, Mandala von Westenholz

Asymptotic properties of a vector of length power functionals of random geometric graphs are investigated. More precisely, its asymptotic covariance matrix is studied as the intensity of the underlying homogeneous Poisson point process increases. This includes a systematic discussion of matrix properties like rank, definiteness, determinant, eigenspaces or d

Fernando Alonso-Fernandez, Julian Fierrez-Aguilar, Javier Ortega-Garcia

Fingerprint image quality affects heavily the performance of fingerprint recognition systems. This paper reviews existing approaches for fingerprint image quality computation. We also implement, test and compare a selection of them using the MCYT database including 9000 fingerprint images. Experimental results show that most of the algorithms behave similarl

Tomáš Kepka, Miroslav Korbelář, Günter Landsmann

We provide a classification of congruence-simple semirings with a multiplicatively absorbing element and without non-trivial nilpotent elements.

Julian Fierrez-Aguilar, Luis-Miguel Muñoz-Serrano, Fernando Alonso-Fernandez, Javier Ortega-Garcia

The effect of image quality degradation on the verification performance of automatic fingerprint recognition is investigated. We study the performance of two fingerprint matchers based on minutiae and ridge information under varying fingerprint image quality. The ridge-based system is found to be more robust to image quality degradation than the minutiae-bas

Kamalika Bhattacharjee, Subrata Paul, Sukanta Das

This work introduces a new problem, named as, affinity classification problem which is a generalization of the density classification problem. To solve this problem, we introduce temporally stochastic cellular automata where two rules are stochastically applied in each step on all cells of the automata. Our model is defined on 2-dimensional grid having affec

Florian Fischer

We study energy functionals associated with quasi-linear Schr\"odinger operators on infinite graphs, and develop characterisations of (sub-)criticality via Green's functions, harmonic functions of minimal growth and capacities. We proof a quasi-linear version of the Agmon-Allegretto-Piepenbrink theorem, which says that the energy functional is non-negative i

Jiehong Lin, Zewei Wei, Changxing Ding, Kui Jia

It is difficult to precisely annotate object instances and their semantics in 3D space, and as such, synthetic data are extensively used for these tasks, e.g., category-level 6D object pose and size estimation. However, the easy annotations in synthetic domains bring the downside effect of synthetic-to-real (Sim2Real) domain gap. In this work, we aim to addr

Ajay Chandra, Ilya Chevyrev

We study the gauge field marginal of an Abelian Higgs model with Villain action defined on a 2D lattice in finite volume. Our first main result, which holds for gauge theories on arbitrary finite graphs and does not assume that the structure group is Abelian, is a loop expansion of the Radon--Nikodym derivative of the law of the gauge field marginal with res

Yiye Jiang, Jérémie Bigot

This paper is focused on the statistical analysis of data consisting of a collection of multiple series of probability measures that are indexed by distinct time instants and supported over a bounded interval of the real line. By modeling these time-dependent probability measures as random objects in the Wasserstein space, we propose a new auto-regressive mo

Mateusz Kula

We answer negatively a question: if $\mathcal F$ is a family of $n\geqslant 3$ non-vertical, pairwise non-parallel lines on the plane and $\bigcap \mathcal F=\emptyset$, is there a vertical line $L$ such that $L\cap\bigcup \mathcal F$ has exactly $n-1$ or $n-2$ points?

Benedikt Pfülb

Abstract knowledge is deeply grounded in many computer-based applications. An important research area of Artificial Intelligence (AI) deals with the automatic derivation of knowledge from data. Machine learning offers the according algorithms. One area of research focuses on the development of biologically inspired learning algorithms. The respective machine

Roberto Casalbuoni, Daniele Dominici, Massimo Mazzoni

The history of the Institute of Physics at the University of Florence is traced from the beginning of the 20th century, with the arrival of Antonio Garbasso as Director (1913), to the 1960s. Thanks to Garbasso's expertise, not only did the Institute gain new premises on Arcetri hill, where the Astronomical Observatory was already located, but it also formed

Amrik Singh Nimbran

The paper examines the structure of the periodic continued fraction for $\sqrt{d}$ and gives formulae for the central term as well as the repeated partial quotients occurring in its period.

De-Xiu Qiu, Fude Li, K. Xue, X. X. Yi

Unidirectional reflectionlessness is investigated in a waveguide quantum electrodynamics system that consists of a cavity and a $\Lambda$-type three-level quantum dot coupled to a one-dimensional plasmonic waveguide. Analytical expressions of transmission and reflection coefficients are derived and discussed for both resonant and off-resonant couplings. By a

Paweł Pasteczka

e study the properties of the mean-type mappings ${\bf M}\colon I^p \to I^p$ of the form $${\bf M}(x_1,\dots,x_p):=\big(M_1(x_{\alpha_{1,1}},\dots,x_{\alpha_{1,d_1}}),\dots,M_p(x_{\alpha_{p,1}},\dots,x_{\alpha_{p,d_p}})\big),$$ where $p$ and $d_i$-s are positive integers, each $M_i$ is a $d_i$-variable mean on an interval $I \subset \mathbb{R}$, and $\alpha_

Martins Zubkins, Janis Timoshenko, Jevgenijs Gabrusenoks, Kaspars Pudzs

Zinc-iridium oxide (Zn-Ir-O) thin films have been demonstrated as a p-type conducting material. However, the stability of p-type conductivity with respect to chemical composition or temperature is still unclear. In this study we discuss the local atomic structure and the electrical properties of Zn-Ir-O films in the large Ir concentration range. The films ar

Cheng Chen, Canzhe Zhao, Shuai Li

Online learning to rank (OLTR) interactively learns to choose lists of items from a large collection based on certain click models that describe users' click behaviors. Most recent works for this problem focus on the stochastic environment where the item attractiveness is assumed to be invariant during the learning process. In many real-world scenarios, howe

Xiaofan Zhou, Simon Yusuf Enoch, Dong Seong Kim

It is challenging for a security analyst to detect or defend against cyber-attacks. Moreover, traditional defense deployment methods require the security analyst to manually enforce the defenses in the presence of uncertainties about the defense to deploy. As a result, it is essential to develop an automated and resilient defense deployment mechanism to thwa

Kazuki Ikeda

We propose a concept of quantum extensive-form games, which is a quantum extension of classical extensive-form games. Extensive-form games is a general concept of games such as Go, Shogi, and chess, which have triggered the recent AI revolution, and is the basis for many important game theoretic models in economics. Quantum transitions allow for pairwise ann

Jin-Bo Fu, Wei-Min Gu, Zhi-Xiang Zhang, Tuan Yi

We search for compact objects in binaries based on Gaia DR3. A sample of ten targets is derived under the conditions: radial velocity variable, low temperature ($T_{\rm eff} < 6000$ K), high mass function ($f(M_2) > 1 M_\odot$), and ellipsoidal-like light curves. Two targets have LAMOST spectroscopic observations, one of which is a double-lined spectroscopic

W. W. Ahmed, M. Farhat, P. -Y. Chen, X. Zhang

We propose and demonstrate a generative deep learning approach for the shape recognition of an arbitrary object from its acoustic scattering properties. The strategy exploits deep neural networks to learn the mapping between the latent space of a two-dimensional acoustic object and the far-field scattering amplitudes. A neural network is designed as an Adver

Yun-Hao Cao, Peiqin Sun, Yechang Huang, Jianxin Wu

With the success of self-supervised learning (SSL), it has become a mainstream paradigm to fine-tune from self-supervised pretrained models to boost the performance on downstream tasks. However, we find that current SSL models suffer severe accuracy drops when performing low-bit quantization, prohibiting their deployment in resource-constrained applications.

Pere Izquierdo Gómez, Alberto Barragan Moreno, Jun Lin, Tomislav Dragičević

The rapid growth of the electric vehicle (EV) sector is giving rise to many infrastructural challenges. One such challenge is its requirement for the widespread development of EV charging stations which must be able to provide large amounts of power in an on-demand basis. This can cause large stresses on the electrical and electronic components of the chargi

Haolin Wang, Jiawei Zhang, Ming Liu, Xiaohe Wu

Image retouching, aiming to regenerate the visually pleasing renditions of given images, is a subjective task where the users are with different aesthetic sensations. Most existing methods deploy a deterministic model to learn the retouching style from a specific expert, making it less flexible to meet diverse subjective preferences. Besides, the intrinsic d

Yunfei Song

In this paper, we obtain sufficient and necessary conditions of some classical convex sets as positively invariant sets for a continuous dynamical system, namely positive invariance conditions. The approach is based on Nagumo Theorem by deriving the tangent cones of these sets. We also propose approaches using optimization theory and models to verify the exi

Al-Akhir Nayan, Boonserm Kijsirikul, Yuji Iwahori

During a pandemic, early prognostication of patient infected rates can reduce the death by ensuring treatment facility and proper resource allocation. In recent months, the number of death and infected rates has increased more distinguished than before in Bangladesh. The country is struggling to provide moderate medical treatment to many patients. This study

Robin de Jong, Stefan van der Lugt

We define and study a natural system of tautological rings on the moduli spaces of marked curves at the level of differential forms. We show that certain 2-forms obtained from the natural normal functions on these moduli spaces are tautological. Also we show that rings of tautological forms are always finite dimensional. Finally we characterize the Kawazumi-

Markus Held, Matthias Wiesenberger

We present the first simulations of a reduced magnetized plasma model that incorporates both arbitrary wavelength polarization and non-Oberbeck-Boussinesq effects. Significant influence of these two effects on the density, electric potential and ExB vorticity and non-linear dynamics of interchange blobs are reported. Arbitrary wavelength polarization implica

Angelo Iollo, Giulia Sambataro, Tommaso Taddei

We propose a component-based (CB) parametric model order reduction (pMOR) formulation for parameterized nonlinear elliptic partial differential equations (PDEs) based on overlapping subdomains. Our approach reads as a constrained optimization statement that penalizes the jump at the components' interfaces subject to the approximate satisfaction of the PDE in

Junegone Chay, Taehyun Kwon

We study the $N$-jettiness in the electroweak high-energy process for the final muon jet pairs, $e^- e^+ \rightarrow \mu^+ \ \mathrm{jet} + \mu^- \mathrm{jet}$. Compared to QCD, the main difference is that there exist additional gauge nonsinglet contributions in the weak interaction, which make the factorization more elaborate. Especially the nonsinglet cont

Fumiaki Nakai, Yuichi Masubuchi, Yuya Doi, Takato Ishida

Diffusivity in some soft matter and biological systems changes with time, called the fluctuating diffusivity. In this work, we propose a novel origin for fluctuating diffusivity based on stochastic simulations of binary gas mixtures. In this system, the fraction of one component is significantly small, and the mass of the minor component molecule is differen

Christian Ikenmeyer, Igor Pak, Greta Panova

We prove that deciding the vanishing of the character of the symmetric group is $C_=P$-complete. We use this hardness result to prove that the the square of the character is not contained in $\#P$, unless the polynomial hierarchy collapses to the second level. This rules out the existence of any (unsigned) combinatorial description for the square of the char

Thuy C. Nguyen, Nam LH. Phan, Son T. Nguyen

Designing an automatic checkout system for retail stores at the human level accuracy is challenging due to similar appearance products and their various poses. This paper addresses the problem by proposing a method with a two-stage pipeline. The first stage detects class-agnostic items, and the second one is dedicated to classify product categories. We also

Dongyang Li, Dmitry Ignatyev, Antonios Tsourdos, Zhongyuan Wang

A general numerical method using sum of squares programming is proposed to address the problem of estimating the region of attraction (ROA) of an asymptotically stable equilibrium point of a nonlinear polynomial system. The method is based on Lyapunov theory, and a shape function is defined to enlarge the provable subset of a local Lyapunov function. In cont

Jihao Liu, Xin Huang, Guanglu Song, Hongsheng Li

Recently, transformer and multi-layer perceptron (MLP) architectures have achieved impressive results on various vision tasks. However, how to effectively combine those operators to form high-performance hybrid visual architectures still remains a challenge. In this work, we study the learnable combination of convolution, transformer, and MLP by proposing a

Gilles Carron, Ilaria Mondello, David Tewodrose

We show two stability results for a closed Riemannian manifold whose Ricci curvature is small in the Kato sense and whose first Betti number is equal to the dimension. The first one is a geometric stability result stating that such a manifold is Gromov-Hausdorff close to a flat torus. The second one states that, under a stronger assumption, such a manifold i

Gabi Shalev, Gal-Lev Shalev, Joseph Keshet

Image captioning research achieved breakthroughs in recent years by developing neural models that can generate diverse and high-quality descriptions for images drawn from the same distribution as training images. However, when facing out-of-distribution (OOD) images, such as corrupted images, or images containing unknown objects, the models fail in generatin