May 2023 arXiv papers — page 125

Dandan Zhao, Zhe Zhang, Dongdong Lu, Jian Kang

Ship target recognition is a vital task in synthetic aperture radar (SAR) imaging applications. Although convolutional neural networks have been successfully employed for SAR image target recognition, surpassing traditional algorithms, most existing research concentrates on the amplitude domain and neglects the essential phase information. Furthermore, sever

Guido Bacciagaluppi

Specker's principle, the condition that pairwise orthogonal propositions must be jointly orthogonal, has been much investigated recently within the programme of finding physical principles to characterise quantum mechanics. It largely appears, however, to lack a transparent justification. In this paper, I provide a derivation of Specker's principle from thre

Ryo Ishizuka

We show that perfectoidization can be (almost) calculated by using $p$-root closure in certain cases, including the semiperfectoid case. To do this, we focus on the universality of perfectoidization and uniform completion, as well as the $p$-root closed property of integral perfectoid rings. Through this calculation, we establish a connection between a class

Alex Kehagias, Kostas D. Kokkotas, Antonio Riotto, John Taskas

The Weak Gravity Conjecture predicts that in quantum gravity there should exist overcharged states, that is states with charge larger than their mass. Extending this to large masses and charges, we are expecting similar overcharged classical solutions. This has been demonstrated in higher-derivative extensions of General Relativity. In this paper we investig

Yunlong Xiao, Yuxiang Yang, Ximing Wang, Qing Liu

Heisenberg's uncertainty principle implies fundamental constraints on what properties of a quantum system can we simultaneously learn. However, it typically assumes that we probe these properties via measurements at a single point in time. In contrast, inferring causal dependencies in complex processes often requires interactive experimentation - multiple ro

Jingyuan Zhang, Xian'an Jin, Weigen Yan

Let $G=(V,E)$ be a digraph having no loops and no multiple arcs, with vertex set $V=\{v_1,v_2,\ldots,v_n\}$ and arc set $E=\{e_1,e_2,\ldots,e_m\}$. Denote the adjacency matrix and the vertex in-degree diagonal matrix of $G$ by $A=(a_{ij})_{n\times n}$ and $D=diag(d^+(v_1),d^+(v_2),\cdots,d^+(v_n))$, where $a_{ij}=1$ if $(v_i,v_j)\in E(G)$ and $a_{ij}=0$ othe

Wenjie Xu, Ben Liu, Miao Peng, Xu Jia

Temporal Knowledge graph completion (TKGC) is a crucial task that involves reasoning at known timestamps to complete the missing part of facts and has attracted more and more attention in recent years. Most existing methods focus on learning representations based on graph neural networks while inaccurately extracting information from timestamps and insuffici

Tal Lancewicki, Aviv Rosenberg, Dmitry Sotnikov

Policy Optimization (PO) is one of the most popular methods in Reinforcement Learning (RL). Thus, theoretical guarantees for PO algorithms have become especially important to the RL community. In this paper, we study PO in adversarial MDPs with a challenge that arises in almost every real-world application -- \textit{delayed bandit feedback}. We give the fir

Han Fang, Zhifei Yang, Xianghao Zang, Chao Ban

Recently, masked video modeling has been widely explored and significantly improved the model's understanding ability of visual regions at a local level. However, existing methods usually adopt random masking and follow the same reconstruction paradigm to complete the masked regions, which do not leverage the correlations between cross-modal content. In this

Victor Lazzarini, Joseph Timoney

Frequency modulation (FM) and phase modulation (PM) are well-known synthesis methods, which have been deployed widely in musical instruments. More recently, some synthesisers have implemented direct forms of FM (as opposed to PM), allowing, at least as part of their design, for higher-order modulation topologies. However, such implementations are affected by

Louis Andreoli, Stéphane Chrétien, Xavier Porte, Daniel Brunner

Hardware implementation of neural network are an essential step to implement next generation efficient and powerful artificial intelligence solutions. Besides the realization of a parallel, efficient and scalable hardware architecture, the optimization of the system's extremely large parameter space with sampling-efficient approaches is essential. Here, we a

Iryna Banakh, Taras Banakh, Maria Kolinko, Alex Ravsky

A metric space $(X,d)$ is called a $subline$ if every 3-element subset $T$ of $X$ can be written as $T=\{x,y,z\}$ for some points $x,y,z$ such that $d(x,z)=d(x,y)+d(y,z)$. By a classical result of Menger, every subline of cardinality $\ne 4$ is isometric to a subspace of the real line. A subline $(X,d)$ is called an $n$-$subline$ for a natural number $n$ if

Woong-Bae G. Zee, Sanjaya Paudel, Jun-Sung Moon, Suk-Jin Yoon

We aim to unravel the interplay between bars, star formation (SF), and active galactic nuclei (AGNs) in barred galaxies. To this end, we utilize the SDSS DR12 to select a sample of nearby (0.02 < z < 0.06) disk galaxies that are suitable for bar examination ($M_r < -20.12$ and inclination $\lesssim$ 53$^{\circ}$). We identify 3662 barred galaxies and measure

Iryna Banakh, Taras Banakh, Maria Kolinko, Alex Ravsky

A subset $X$ of an Abelian group $G$ is called $semiaf\!fine$ if for every $x,y,z\in X$ the set $\{x+y-z,x-y+z\}$ intersects $X$. We prove that a subset $X$ of an Abelian group $G$ is semiaffine if and only if one of the following conditions holds: (1) $X=(H+a)\cup (H+b)$ for some subgroup $H$ of $G$ and some elements $a,b\in X$; (2) $X=(H\setminus C)+g$ for

Yu Zhang, Siqi Chen, Mingdao Wang, Xianlin Zhang

Video colorization task has recently attracted wide attention. Recent methods mainly work on the temporal consistency in adjacent frames or frames with small interval. However, it still faces severe challenge of the inconsistency between frames with large interval.To address this issue, we propose a novel video colorization framework, which combines semantic

Chad Brown, Adam Pease, Josef Urban

We describe a translation from a fragment of SUMO (SUMO-K) into higher-order set theory. The translation provides a formal semantics for portions of SUMO which are beyond first-order and which have previously only had an informal interpretation. It also for the first time embeds a large common-sense ontology into a very secure interactive theorem proving sys

Nouhaila Innan, Muhammad Al-Zafar Khan, Mohamed Bennai

The computation of electronic structure properties at the quantum level is a crucial aspect of modern physics research. However, conventional methods can be computationally demanding for larger, more complex systems. To address this issue, we present a hybrid Classical-Quantum computational procedure that uses the Variational Quantum Eigensolver (VQE) algori

Ashish Mishra, Suresh Jagannathan

Parser combinators are a well-known mechanism used for the compositional construction of parsers, and have shown to be particularly useful in writing parsers for rich grammars with data-dependencies and global state. Verifying applications written using them, however, has proven to be challenging in large part because of the inherently effectful nature of th

Lei Xu, Ling-Xiao Wang, Guang-Jie Chen, Liang Chen

Trapped atoms on photonic structures inspire many novel quantum devices for quantum information processing and quantum sensing. Here, we have demonstrated a hybrid photonic-atom chip platform based on a GaN-on-sapphire chip and the transport of an ensemble of atoms from free space towards the chip with an optical conveyor belt. The maximum transport efficien

Masaya Takahashi, Hiroaki Nishioka, Masahiro Hirai, Hidetaka Takano

From the perspective of global warming, efficiency improvement of power grids is a pressing issue. Power grids have many switching devices to control the flow of electricity. Since there is a slight resistance in the wires and power consumption is proportional to the square of the current, the value of power loss on the wires changes depending on the combina

Alessio Maritan, Ganesh Sharma, Luca Schenato, Subhrakanti Dey

This paper considers the problem of distributed multi-agent learning, where the global aim is to minimize a sum of local objective (empirical loss) functions through local optimization and information exchange between neighbouring nodes. We introduce a Newton-type fully distributed optimization algorithm, Network-GIANT, which is based on GIANT, a Federated l

Joao Marques-Silva

The advances in Machine Learning (ML) in recent years have been both impressive and far-reaching. However, the deployment of ML models is still impaired by a lack of trust in how the best-performing ML models make predictions. The issue of lack of trust is even more acute in the uses of ML models in high-risk or safety-critical domains. eXplainable artificia

Hossein Dolatkhah, Zahra Atlasbaf

In this paper, a new composite right/left-handed (CRLH) Leaky Wave Antenna (LWA) based on substrate-integrated waveguide (SIW) structures is proposed. The suggested antenna has a continuous beam scanning from backward to forward versus frequency and can be used for wideband applications. The compact composite right/left-handed (C-CRLH) unit cell for the ment

Ping Zhou, Han Jia, Yafeng Bi, Yunhan Yang

Efficient sound transmission across the water-air interface has always been expected in the field of ocean exploration. However, the existing researches are mainly concentrated on the narrow-band transmission based on resonance, which greatly limits the transmission capacity and efficiency. Here, we combined the air-based and water-based metafluids to realiz

Yuliang Liu, Zhang Li, Mingxin Huang, Biao Yang

Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks remains relatively unexplored. In this paper, we conducted a comprehensive evaluation of Large Multimodal Models, such as GPT4V and Gemini, in various text-related visual tasks inc

Domenico Iuso, Soumick Chatterjee, Sven Cornelissen, Dries Verhees

Additive Manufacturing (AM) has emerged as a manufacturing process that allows the direct production of samples from digital models. To ensure that quality standards are met in all manufactured samples of a batch, X-ray computed tomography (X-CT) is often used combined with automated anomaly detection. For the latter, deep learning (DL) anomaly detection tec

Nicola Lanatà

Establishing the underlying links between the diverse landscape of theoretical frameworks for simulating strongly correlated matter is crucial for advancing our understanding of these systems. In this work, we focus on the Ghost Gutzwiller Approximation (gGA), an extension of the Gutzwiller Approximation (GA) based on the variational principle. We derive a f

Mohammad Abdous, Poorya Piroozfar, Behrouz Minaei Bidgoli

One of the components of natural language processing that has received a lot of investigation recently is semantic textual similarity. In computational linguistics and natural language processing, assessing the semantic similarity of words, phrases, paragraphs, and texts is crucial. Calculating the degree of semantic resemblance between two textual pieces, p

Jun Shu, Xiang Yuan, Deyu Meng, Zongben Xu

Meta learning recently has been heavily researched and helped advance the contemporary machine learning. However, achieving well-performing meta-learning model requires a large amount of training tasks with high-quality meta-data representing the underlying task generalization goal, which is sometimes difficult and expensive to obtain for real applications.

Zhanwei Yu, Yi Zhao, Di Yuan

Performance degradation due to target deviation by, for example, drift or jitter, presents a significant issue to inter-satellite laser communications. In particular, with periodic acquisition for positioning the satellite receiver, deviation may arise in the time period between two consecutive acquisition operations. One solution to mitigate the issue is to

Michael Ruderman, Alessandro Pisano, Elio Usai

The second-order sub-optimal sliding mode control (SMC), known in the literature for the last two decades, is extended by a control-off mode which allows for saving energy during the finite time convergence. The systems with relative degree two between the sliding variable and switching control with bounded actuation are considered, while the matched upper-b

Zhaoxia Yin, Heng Yin, Hang Su, Xinpeng Zhang

Typically, foundation models are hosted on cloud servers to meet the high demand for their services. However, this exposes them to security risks, as attackers can modify them after uploading to the cloud or transferring from a local system. To address this issue, we propose an iterative decision-based fragile watermarking algorithm that transforms normal tr

Pengcheng Hao, Oktay Karakuş, Alin Achim

Motivated by filtering tasks under a linear system with non-Gaussian heavy-tailed noise, various robust Kalman filters (RKFs) based on different heavy-tailed distributions have been proposed. Although the sub-Gaussian $\alpha$-stable (SG$\alpha$S) distribution captures heavy tails well and is applicable in various scenarios, its potential has not yet been ex

Chawit Kaewnuratchadasorn, Jiaji Wang, Chul-Woo Kim

Infusing deep learning with structural engineering has received widespread attention for both forward problems (structural simulation) and inverse problems (structural health monitoring). Based on Fourier Neural Operator, this study proposes VINO (Vehicle-bridge Interaction Neural Operator) to serve as the digital twin of bridge structures. VINO learns mappi

Han Gao, Kaican Li, Weiyan Xie, Zhi Lin

Domain generalization (DG) is about training models that generalize well under domain shift. Previous research on DG has been conducted mostly in single-source or multi-source settings. In this paper, we consider a third, lesser-known setting where a training domain is endowed with a collection of pairs of examples that share the same semantic information. S

Jelena Jovanovic, Ebrahim Bagheri

Peer review is an integral component of scientific research. The quality of peer review, and consequently the published research, depends to a large extent on the ability to recruit adequate reviewers for submitted papers. However, finding such reviewers is an increasingly difficult task due to several factors, such as the continuous increase both in the pro

Yingpu Deng

In his famous book ``Basic Number Theory", Weil proved several theorems about the existence of norm-orthogonal bases in finite-dimensional vector spaces and lattices over local fields. In this paper, we transform Weil's proofs into algorithms for finding out various norm-orthogonal bases. These algorithms are closely related to the recently introduced closes

Nicolas Perez-Nieves, Dan F. M Goodman

In recent years, newly developed methods to train spiking neural networks (SNNs) have rendered them as a plausible alternative to Artificial Neural Networks (ANNs) in terms of accuracy, while at the same time being much more energy efficient at inference and potentially at training time. However, it is still unclear what constitutes a good initialisation for

Vladimir Spokoiny

This note describes the concentration phenomenon for a high dimensional sub-gaussian vector \( X \). In the Gaussian case, for any linear operator \( Q \), it holds \( P\bigl( \| Q X \|^{2} - tr (B) > 2 \sqrt{x\, tr(B^{2})} + 2 \| B \| x \bigr) \leq e^{-x} \) and \( P\bigl( \| Q X \|^{2} - tr (B) < - 2 \sqrt{x \, tr(B^{2})} \bigr) \leq e^{-x} \) with \( B =

G. L. Klimchitskaya, V. M. Mostepanenko

It has been known that in the nanometer interaction range the available experimental data do not exclude the Yukawa-type corrections to Newton's gravitational law which exceed the Newtonian gravitational force by many orders of magnitude. The strongest constraints on the parameters of Yukawa-type interaction in this interaction range follow from the experime

Shuai Wang, Zipei Yan, Daoan Zhang, Zhongsen Li

Deep neural networks (DNNs) achieve promising performance in visual recognition under the independent and identically distributed (IID) hypothesis. In contrast, the IID hypothesis is not universally guaranteed in numerous real-world applications, especially in medical image analysis. Medical image segmentation is typically formulated as a pixel-wise classifi

Alexei Grinbaum, Laurynas Adomaitis

We suggest the implementation of the Dual Use Research of Concern (DURC) framework, originally designed for life sciences, to the domain of generative AI, with a specific focus on Large Language Models (LLMs). With its demonstrated advantages and drawbacks in biological research, we believe the DURC criteria can be effectively redefined for LLMs, potentially

Shuai Wang, Daoan Zhang, Zipei Yan, Shitong Shao

Source-free domain adaptation aims to adapt deep neural networks using only pre-trained source models and target data. However, accessing the source model still has a potential concern about leaking the source data, which reveals the patient's privacy. In this paper, we study the challenging but practical problem: black-box source-free domain adaptation wher

Omer Ben-Neria, Siiri Kivimäki, Menachem Magidor, Jouko Väänänen

Assuming the consistency of a weakly compact cardinal above a regular uncountable cardinal $\mu$, we prove the consistency of the existence of a wide $\mu^+$-Aronszajn tree, i.e. a tree of height and cardinality $\mu^+$ with no branches of length $\mu^+$, into which every wide $\mu^+$-Aronszajn tree can be embedded.

Robbie G. Hunt, Kévin J. A. Franke, Paul S. Keatley, Philippa M. Shepley

Artificial multiferroics consist of two types of ferroic materials, typically a ferroelectric and ferromagnet, often coupled interfacially by magnetostriction induced by the lattice elongations in the ferroelectric. In BaTiO3 the magnitude of strain induced by these elongations is heavily temperature dependent, varying greatly between each of the polar cryst

N Bidargaddi, J. C. L. Looi

The profound intertwining of digital interactions with traditional human interactions has spawned a novel mode of human interactions at both individual and population levels, carrying substantial ramifications for health. Digital mediation refers to the process by which digital interactions influence and facilitate human interactions. While digital mediation

Tom Schrijvers, Birthe van den Berg, Fabrizio Riguzzi

Automatic differentiation (AD) is a range of algorithms to compute the numeric value of a function's (partial) derivative, where the function is typically given as a computer program or abstract syntax tree. AD has become immensely popular as part of many learning algorithms, notably for neural networks. This paper uses Prolog to systematically derive gradie

Gregor Gunčar, Matjaž Kukar, Tim Smole, Sašo Moškon

The growing threat of antibiotic resistance necessitates accurate differentiation between bacterial and viral infections for proper antibiotic administration. In this study, a Virus vs. Bacteria machine learning model was developed to distinguish between these infection types using 16 routine blood test results, C-reactive protein concentration (CRP), biolog

Shahroz Khan, Stefano Gaggero, Panagiotis Kaklis, Giuliano Vernengo

Simulation-driven shape optimisation (SDSO) of marine propellers is often obstructed by high-dimensional design spaces stemming from its complex geometry and baseline parameterisation, which leads to the notorious curse of dimensionality. In this study, we propose using the shape-supervised dimension reduction (SSDR) approach to expedite the SDSO of marine p

Marc Seidel, Simon Lang, Frank Allgöwer

This paper considers control systems with failures in the feedback channel, that occasionally lead to loss of the control input signal. A useful approach for modeling such failures is to consider window-based constraints on possible loss sequences, for example that at least r control attempts in every window of s are successful. A powerful framework to model

Raffaele Rani, Toby J. T. Moore, David J. Eden, Andrew J. Rigby

The growing range of automated algorithms for the identification of molecular clouds and clumps in large observational datasets has prompted the need for the direct comparison of these procedures. However, these methods are complex and testing for biases is often problematic: only a few of them have been applied to the same data set or calibrated against a c

Yagub N. Aliyev

In the paper, some geometric properties of the plane interception curve defined by a nonlinear ordinary differential equation are discussed. Its parametric representation is used to find the limits of some triangle elements associated with the curve. These limits have some connections with the lemniscate constants A,B and Gauss's constant G, which were used

Chengpei Wu, Yang Lou, Lin Wang, Junli Li

This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks. This kind of network robustness is typically measured by the time-consuming attack simulation, which returns a sequence of values that record the remaining connectivity and controllability after a sequence of node- or edge-removal attac

Sahan Bulathwela, Hamze Muse, Emine Yilmaz

The automatic generation of educational questions will play a key role in scaling online education, enabling self-assessment at scale when a global population is manoeuvring their personalised learning journeys. We develop \textit{EduQG}, a novel educational question generation model built by adapting a large language model. Our extensive experiments demonst

Lei Liu, Yuhao Chi, Ying Li, Zhaoyang Zhang

The generalized linear system (GLS) has been widely used in wireless communications to evaluate the effect of nonlinear preprocessing on receiver performance. Generalized approximation message passing (AMP) is a state-of-the-art algorithm for the signal recovery of GLS, but it was limited to measurement matrices with independent and identically distributed (

Rong Ma, Ni Li

It is significant to study congruences involving multiple harmonic sums. Let $p$ be an odd prime, in recent years, the following curious congruence $$\sum_{\substack{i+j+k=p \\ i, j, k>0}} \frac{1}{i j k} \equiv-2 B_{p-3}\pmod p$$ has been generalized along different directions, where $B_n$ denote the $n$th Bernoulli number. In this paper, we obtain several

Davut Emre Tasar, Ceren Ocal Tasar

The rapid proliferation of information in the digital era underscores the importance of accurate historical representation and interpretation. While artificial intelligence has shown promise in various fields, its potential for historical fact-checking and gap-filling remains largely untapped. This study evaluates the performance of three large language mode

Jianchang Wu, Jiyun Zhang, Manman Hu, Patrick Reiser

High-throughput synthesis of solution-processable structurally variable small-molecule semiconductors is both an opportunity and a challenge. A large number of diverse molecules provide a possibility for quick material discovery and machine learning based on experimental data. However, the diversity of molecular structure leads to the complexity of molecular

Chunxu Zhang, Guodong Long, Tianyi Zhou, Zijjian Zhang

The federated recommendation system is an emerging AI service architecture that provides recommendation services in a privacy-preserving manner. Using user-relation graphs to enhance federated recommendations is a promising topic. However, it is still an open challenge to construct the user-relation graph while preserving data locality-based privacy protecti

Nian Guo, Shansuo Liang, Wei Han

We investigate power allocation for the base matrix of a spatially coupled sparse regression code (SC-SPARC) for reliable communications over an additive white Gaussian noise channel. A conventional SC-SPARC allocates power uniformly to the non-zero entries of its base matrix. Yet, to achieve the channel capacity with uniform power allocation, the coupling w

Marco Cilibrasi, Mario Flock, Judit Szulágyi

We study a Jupiter-mass planet formation for the first time in radiative magneto-hydrodynamics (MHD) simulations and compare it with pure hydrodynamical simulations, as well as to different isothermal configurations. We found that the meridional circulation is the same in every setup. The planetary spiral wakes drive a vertical stirring inside the protoplane

Lachlan Gibson, Marcus Hoerger, Dirk Kroese

Solving decision problems in complex, stochastic environments is often achieved by estimating the expected outcome of decisions via Monte Carlo sampling. However, sampling may overlook rare, but important events, which can severely impact the decision making process. We present a method in which a Normalizing Flow generative model is trained to simulate samp

Zhenchang Liu, Mingrui Hao

This paper studies a heterogeneous Unmanned Aerial Vehicles (UAVs) cooperative search approach suitable for complex environments. In the application, a fixed-wing UAV drops rotor UAVs to deploy the cluster rapidly. Meanwhile, the fixed-wing UAV works as a communication relay node to improve the search performance of the cluster further. The distributed model

Chao Wei, Youren Yu, Ziyun Wang, Lin Jiang

With the advantages of large electro-optical coefficient, wide transparency window, and strong optical confinement, thin-film lithium niobate (TFLN) technique has enabled the development of various high-performance optoelectronics devices, ranging from the ultra-wideband electro-optic modulators to the high-efficient quantum sources. However, the TFLN platfo

Kunyu Guo, Dilong Li, Qi Zhou

In this paper, we consider the first Szeg\H{o} limit theorems on $d$-torus $\mathbb{T}^d$ for $1\leq d\leq +\infty$. It is shown that for any F{\o}lner sequence $\{\sigma_N\}$ of $\mathbb{Z}^d$ and $\varphi\in L^1_+(\mathbb{T}^d)$, it holds that $$ \lim_{N\rightarrow \infty}\left(\det T_{\sigma_N}\varphi\right)^{\frac{1}{|\sigma_N|}}=\exp\left(\int_{\mathbb{

Subhashis Hazarika, Haruki Hirasawa, Sookyung Kim, Kalai Ramea

Clouds have a significant impact on the Earth's climate system. They play a vital role in modulating Earth's radiation budget and driving regional changes in temperature and precipitation. This makes clouds ideal for climate intervention techniques like Marine Cloud Brightening (MCB) which refers to modification in cloud reflectivity, thereby cooling the sur

Jean-Yves Thibon, David G. L. Wang

We obtain the Schur positivity of spider graphs of the forms $S(a,2,1)$ and $S(a,4,1)$, which are considered to have the simpliest structures for which the Schur positivity was unknown. The proof outline has four steps. First, we find noncommutative analogs for the chromatic symmetric functions of the spider graphs $S(a,b,1)$. Secondly, we expand the analogs

Changsuk Oh, H. Jin Kim

The objective of the image inpainting task is to fill missing regions of an image in a visually plausible way. Recently, deep-learning-based image inpainting networks have generated outstanding results, and some utilize their models as object removers by masking unwanted objects in an image. However, while trying to better remove objects using their networks

Yoshihiro Yamazaki, Shousuke Ohmori

Dynamical properties of limit cycles in a tropically discretized negative feedback model are numerically investigated. This model has a controlling parameter $\tau$, which corresponds to time interval for the time evolution of phase in the limit cycles. By considering $\tau$ as a bifurcation parameter, we find that ultradiscrete state emerges due to phase lo

Bin Zhang, Hangyu Mao, Lijuan Li, Zhiwei Xu

Asynchronous action coordination presents a pervasive challenge in Multi-Agent Systems (MAS), which can be represented as a Stackelberg game (SG). However, the scalability of existing Multi-Agent Reinforcement Learning (MARL) methods based on SG is severely constrained by network structures or environmental limitations. To address this issue, we propose the

Ryosuke Sawata, Naoya Takahashi, Stefan Uhlich, Shusuke Takahashi

This paper presents the crossing scheme (X-scheme) for improving the performance of deep neural network (DNN)-based music source separation (MSS) with almost no increasing calculation cost. It consists of three components: (i) multi-domain loss (MDL), (ii) bridging operation, which couples the individual instrument networks, and (iii) combination loss (CL).

Anushiya Arunan, Yan Qin, Xiaoli Li, Chau Yuen

Data-driven industrial health prognostics require rich training data to develop accurate and reliable predictive models. However, stringent data privacy laws and the abundance of edge industrial data necessitate decentralized data utilization. Thus, the industrial health prognostics field is well suited to significantly benefit from federated learning (FL),

Hao Zhuang, Xinjie Huang, Kuanxu Hou, Delei Kong

Event cameras offer promising properties, such as high temporal resolution and high dynamic range. These benefits have been utilized into many machine vision tasks, especially optical flow estimation. Currently, most existing event-based works use deep learning to estimate optical flow. However, their networks have not fully exploited prior hidden states and

Yuto Urano, Xue Mengsong, Kenji Watanabe, Takashi Taniguchi

We investigate the valley coherence in high and low-quality monolayer MoSe2 by polarization-resolved photoluminescence spectroscopy. The observed valley coherence is on the order of 10 % regardless of the sample quality, proving that the suppression of extrinsic effects does not improve the valley coherence. The valley decoherence time estimated based on the

Adrián Irles

The LUXE (Laser Und XFEL Experiment) aims at studying high-field QED in electron-laser and photon-laser interactions, with the 16.5 GeV electron beam of the European XFEL and a laser beam with a power of up to 350 TW. The experiment will measure the spectra of electrons, positrons and photons in expected ranges of $10^{-3}$ to $10^9$ per 1 Hz bunch crossing,

Gaurav Prasanna, John Rohit Ernest, Lalitha G, Sathiya Narayanan

Deep Learning based techniques have gained significance over the past few years in the field of medicine. They are used in various applications such as classifying medical images, segmentation and identification. The existing architectures such as UNet, Attention UNet and Attention Residual UNet are already currently existing methods for the same application

Morteza Hassanvand, Hamid Reza Salimi Moghaddam

In this paper, we consider a Lie group $G$ equipped with two left-invariant Riemannian metrics $g^1$ and $g^2$. Using these two left-invariant Riemannian metrics we define a left-invariant Riemannian metric $\tilde{g}$ on the tangent Lie group $TG$. The Levi-Civita connection, tensor curvature, and sectional curvature of $(TG,\tilde{g})$ in terms of $g^1$ an

Stefan Georgiev, Agnès Lèbre, Eric Josselin, Philippe Mathias

We present the surface magnetic field conditions of the brightest pulsating RV Tauri star, R Sct. Our investigation is based on the longest spectropolarimetric survey ever performed on this variable star. The analysis of high resolution spectra and circular polarization data give sharp information on the dynamics of the atmosphere and the surface magnetism,

Quoc-Huy Trinh

In recent years, polyp segmentation has gained significant importance, and many methods have been developed using CNN, Vision Transformer, and Transformer techniques to achieve competitive results. However, these methods often face difficulties when dealing with out-of-distribution datasets, missing boundaries, and small polyps. In 2022, Meta-Former was intr

Peng Xu, Jun Zhang

Dynamical decoupling (DD) is an efficient method to decouple systems from environmental noises and to prolong the coherence time of systems. In contrast to discrete and continuous DD protocols in the presence of bias field, we propose a Floquet DD at zero bias to perfectly suppress both the zeroth and first orders of noises according to the Floquet theory. S

Elias Chaibub Neto

In the field of statistical disclosure control, the tradeoff between data confidentiality and data utility is measured by comparing disclosure risk and information loss metrics. Distance based metrics such as the mean absolute error (MAE), mean squared error (MSE), mean variation (IL1), and its scaled alternative (IL1s) are popular information loss measures

Tailin Zhou, Zehong Lin, Jun Zhang, Danny H. K. Tsang

Model averaging is a widely adopted technique in federated learning (FL) that aggregates multiple client models to obtain a global model. Remarkably, model averaging in FL yields a superior global model, even when client models are trained with non-convex objective functions and on heterogeneous local datasets. However, the rationale behind its success remai

Michael Gimelfarb, Michael Jong Kim

We study parameterized MDPs (PMDPs) in which the key parameters of interest are unknown and must be learned using Bayesian inference. One key defining feature of such models is the presence of "uninformative" actions that provide no information about the unknown parameters. We contribute a set of assumptions for PMDPs under which Thompson sampling guarantees

H. Zhang, J. J. Pilgram, C. G. Constantin, L. Rovige

We present the first two-dimensional (2D) optical Thomson scattering measurements of electron density and temperature in laser-produced plasmas. The novel instrument directly measures $n_e(x,y)$ and $T_e(x,y)$ in two dimensions over large spatial regions (cm$^2$) with sub-mm spatial resolution, by automatically translating the scattering volume while the pla

Jasmine Roberts

A comprehensive repository of software development libraries, platforms, and tools specifically to the domains of augmented, virtual, and mixed reality.

Gregory Patchell

In this article we investigate the primeness of generalized wreath product II$_1$ factors using deformation/rigidity theory techniques. We give general conditions relating tensor decompositions of generalized wreath products to stabilizers of the associated group action and use this to find new examples of prime II$_1$ factors.

Yunsheng Ma, Wenqian Ye, Xu Cao, Amr Abdelraouf

Driver intention prediction seeks to anticipate drivers' actions by analyzing their behaviors with respect to surrounding traffic environments. Existing approaches primarily focus on late-fusion techniques, and neglect the importance of maintaining consistency between predictions and prevailing driving contexts. In this paper, we introduce a new framework ca

Seungyub Han, Yeongmo Kim, Seokhyeon Ha, Jungwoo Lee

We propose a fine-tuning algorithm for brain tumor segmentation that needs only a few data samples and helps networks not to forget the original tasks. Our approach is based on active learning and meta-learning. One of the difficulties in medical image segmentation is the lack of datasets with proper annotations, because it requires doctors to tag reliable a

Cheril Shah, Yashashree Chandak, Manan Suri

Understanding the representations of different languages in multilingual language models is essential for comprehending their cross-lingual properties, predicting their performance on downstream tasks, and identifying any biases across languages. In our study, we analyze the geometry of three multilingual language models in Euclidean space and find that all

Bogdan Armaselu

In this paper, we consider the Maximum-Profit Routing Problem (MPRP), introduced in \cite{Armaselu-PETRA}. In MPRP, the goal is to route the given fleet of vehicles to pickup goods from specified sites in such a way as to maximize the profit, i.e., total quantity collected minus travelling costs. Although deterministic approximation algorithms are known for

Liqun Qi, Rui Yan, Ziyan Luo, Hong Yan

The T-product method based upon Discrete Fourier Transformation (DFT) has found wide applications in engineering, in particular, in image processing. In this paper, we propose variable T-product, and apply the Zero-Padding Discrete Fourier Transformation (ZDFT), to convert third order tensor problems to the variable Fourier domain. An additional positive int

N. Aizawa, Ren Ito

We investigate the possibilities of integration on the minimal $\mathbb{Z}_2^2$-superspace. Two definitions are taken from the works by Poncin and Schouten and we examine their generalizations. It is shown that these definitions impose some restrictions on the integrable functions. We then introduce a new definition of integral, which is inspired by our prev

Jian Sang, Mingyong Zhou, Jifeng Lan, Boning Gao

Reconfigurable intelligent surface (RIS)-empowered communication, has been considered widely as one of the revolutionary technologies for next generation networks. However, due to the novel propagation characteristics of RISs, underlying RIS channel modeling and measurement research is still in its infancy and not fully investigated. In this paper, we conduc

Alexander G Tartakovsky, Valentin Spivak

Modern information systems generate large volumes of data with anomalies that occur at unknown points in time and have to be detected quickly and reliably with low false alarm rates. The paper develops a general theory of quickest multistream detection in non-i.i.d. stochastic models when a change may occur in a set of multiple data streams. The first part o

Hai E Zhang, Gen Qi Xu, Zhong Jie Han

In this paper, the asymptotic behavior of abstract strongly coupled hyperbolic equations with one infinite memory term is investigated, one specific case of which is the model for describing the dynamical behaviour of magnetic effected piezoelectric beams. A fractional operator is involved in the memory term depending on the parameter $a\in [0,1)$. By means

Xiangxing Tao, Guoen Hu

Let $\Omega$ be homogeneous of degree zero, integrable on $S^{d-1}$ and have mean value zero, $T_{\Omega}$ be the homogeneous singular integral operator with kernel $\frac{\Omega(x)}{|x|^d}$ and $T_{\Omega}^*$ be the maximal operator associated to $T_{\Omega}$. In this paper, the authors prove that if $\Omega\in L^{\infty}(S^{d-1})$, then for all $r\in (1,\,

Zhun-Yong Ong

The spatial structure of the inhomogeneity in a disordered medium determines how waves scatter and propagate in it. We present a theoretical model of how the Fourier components of the disorder control wave scattering in a two-dimensional disordered medium, by analyzing the disordered Green's function for scalar waves. By selecting a set of Fourier components

Ertem Nusret Tas, Runchao Han, David Tse, Fisher Yu

Fourteen years after the invention of Bitcoin, there has been a proliferation of many permissionless blockchains. Each such chain provides a public ledger that can be written to and read from by anyone. In this multi-chain world, a natural question arises: what is the optimal security an existing blockchain, a consumer chain, can extract by only reading and

Jun Cheng, Honglei Su, Jari Korhonen

With the rapid development of 3D vision applications based on point clouds, point cloud quality assessment(PCQA) is becoming an important research topic. However, the prior PCQA methods ignore the effect of local quality variance across different areas of the point cloud. To take an advantage of the quality distribution imbalance, we propose a no-reference p

Kota Dohi, Keisuke Imoto, Noboru Harada, Daisuke Niizumi

We present the task description of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2023 Challenge Task 2: ``First-shot unsupervised anomalous sound detection (ASD) for machine condition monitoring''. The main goal is to enable rapid deployment of ASD systems for new kinds of machines without the need for hyperparameter tuning. In the p