July 2023 arXiv papers — page 137

Michael Zacharias, Anita Reimer, Catherine Boisson, Andreas Zech

Blazars emit across all electromagnetic wavelengths. While the so-called one-zone model has described well both quiescent and flaring states, it cannot explain the radio emission and fails in more complex data sets, such as AP Librae. In order to self-consistently describe the entire electromagnetic spectrum emitted by the jet, extended radiation models are

Hongming Tang, Shiyu Yue, Zijun Wang, Jizhe Lai

Radio galaxy morphological classification is one of the critical steps when producing source catalogues for large-scale radio continuum surveys. While many recent studies attempted to classify source radio morphology from survey image data using deep learning algorithms (i.e., Convolutional Neural Networks), they concentrated on model robustness most time. I

Jeongmin Park, Zaid Qureshi, Vikram Mailthody, Andrew Gacek

Data compression and decompression have become vital components of big-data applications to manage the exponential growth in the amount of data collected and stored. Furthermore, big-data applications have increasingly adopted GPUs due to their high compute throughput and memory bandwidth. Prior works presume that decompression is memory-bound and have dedic

Andrey Asadchev, Edward F. Valeev

We present a high-performance evaluation method for 4-center 2-particle integrals over Gaussian atomic orbitals with high angular momenta ($l\geq4$) and arbitrary contraction degrees on graphical processing units (GPUs) and other accelerators. The implementation uses the matrix form of McMurchie-Davidson recurrences. Evaluation of the 4-center integrals over

Joowon Lee, Donggil Lee, Junsoo Kim, Hyungbo Shim

In this paper, we present a method to encrypt dynamic controllers that can be implemented through most homomorphic encryption schemes, including somewhat, leveled fully, and fully homomorphic encryption. To this end, we represent the output of the given controller as a linear combination of a fixed number of previous inputs and outputs. As a result, the encr

Zhen Zeng, Kai Sun, Rui Chen, Mengshan Suo

Two-dimensional square ice in graphene nanocapillaries at room temperature is a fascinating phenomenon and has been confirmed experimentally. Instead of temperature for bulk ice, the high van der Waals pressure becomes an all-important factor to induce the formation of square ice and needs to be studied further. By all-atom molecular dynamics simulations of

Zizheng Yan, Yushuang Wu, Yipeng Qin, Xiaoguang Han

In this paper, we introduce a realistic and challenging domain adaptation problem called Universal Semi-supervised Model Adaptation (USMA), which i) requires only a pre-trained source model, ii) allows the source and target domain to have different label sets, i.e., they share a common label set and hold their own private label set, and iii) requires only a

David Scheer, Fabian Hassler

A single Josephson junction in the phase-slip regime exhibits Bloch oscillations in the voltage when biased with a DC current $I_\text{DC}$. The frequency of the oscillation is given by $\pi I_\text{DC}/e$, with $e$ the elementary charge, linking the current to the frequency via fundamental constants of nature. If an additional AC drive is applied, the Bloch

Zichao Wang, Richard Baraniuk

We study the new problem of automatic question generation (QG) from multi-modal sources containing images and texts, significantly expanding the scope of most of the existing work that focuses exclusively on QG from only textual sources. We propose a simple solution for our new problem, called MultiQG-TI, which enables a text-only question generator to proce

Roger J. A. Laeven, Emanuela Rosazza Gianin, Marco Zullino

This paper establishes characterization results for dynamic return and star-shaped risk measures induced via backward stochastic differential equations (BSDEs). We first characterize a general family of static star-shaped functionals in a locally convex Fr\'echet lattice. Next, employing the Pasch-Hausdorff envelope, we build a suitable family of convex driv

Patrick Schnider, Simon Weber

Schaefer's dichotomy theorem [Schaefer, STOC'78] states that a boolean constraint satisfaction problem (CSP) is polynomial-time solvable if one of six given conditions holds for every type of constraint allowed in its instances. Otherwise, it is NP-complete. In this paper, we analyze boolean CSPs in terms of their topological complexity, instead of their com

Ruochun Zhang, Colin Vanden Heuvel, Alexander Schepelmann, Arno Rogg

We discuss the use of the Discrete Element Method (DEM) to simulate the dynamics of granular systems made up of elements with nontrivial geometries. The DEM simulator is GPU accelerated and can handle elements whose shape is defined as the union with overlap of diverse sets of spheres with user-specified radii. The simulator can also handle complex materials

Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon

Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time series analytics is therefore crucial to unlocking the wealth of information implicit in available data. With the recent advancements in graph neural networks (GNNs), there has be

Guobiao Li, Sheng Li, Meiling Li, Zhenxing Qian

With the widespread applications of the deep neural network (DNN), how to covertly transmit the DNN models in public channels brings us the attention, especially for those trained for secret-learning tasks. In this paper, we propose deep network steganography for the covert communication of DNN models. Unlike the existing steganography schemes which focus on

I. Pihlajamaa, V. E. Debets, C. C. L. Laudicina, L. M. C. Janssen

The mode-coupling theory of the glass transition (MCT) has been at the forefront of fundamental glass research for decades, yet the theory's underlying approximations remain obscure. Here we quantify and critically assess the effect of each MCT approximation separately. Using Brownian dynamics simulations, we compute the memory kernel predicted by MCT after

Cong Ding

The notion of admissible pairs of rational homogeneous spaces of Picard number one and their rigidity in terms of the geometric substructures was formulated by Mok and Zhang. The rigidity is known for subdiagram type. While when the admissible pair is of deletion type, the rigidity no longer holds and we need additional conditions. Mok gave a general criteri

Wangbo Yu, Yanbo Fan, Yong Zhang, Xuan Wang

3D facial avatar reconstruction has been a significant research topic in computer graphics and computer vision, where photo-realistic rendering and flexible controls over poses and expressions are necessary for many related applications. Recently, its performance has been greatly improved with the development of neural radiance fields (NeRF). However, most e

Xuesong Wang, Dongsheng Zhang, Zheng Zhang

With the development of CNC machine tools toward high speed and high precision, the traditional static design methods can hardly meet the demand. Hence, in this paper, the dynamics matching design methods of existing CNC machine tool feed systems were investigated and analyzed. Further, sub-system coupling mechanisms and optimization design studies were carr

Miloslav Znojil

It is well known that the unitary evolution of a closed $M-$level quantum system can be generated by a non-Hermitian Hamiltonian $H$ with real spectrum. Its Hermiticity can be restored via an amended inner-product metric $\Theta$. In Hermitian cases the evaluation of the spectrum (i.e., of the bound-state energies) is usually achieved by the diagonalization

Abdul Karim Gizzini, Marwa Chafii

Doubly-selective channel estimation represents a key element in ensuring communication reliability in wireless systems. Due to the impact of multi-path propagation and Doppler interference in dynamic environments, doubly-selective channel estimation becomes challenging. Conventional symbol-by-symbol (SBS) and frame-by-frame (FBF) channel estimation schemes e

L. Kauffman, V. O. Manturov, I. M. Nikonov, S. Kim

In the present paper we develop the techniques suggested in \cite{ManturovNikonov} and the photography principle \cite{ManturovWan} for constructing an invariant of 3-manifolds based on Ptolemy relation. We show that a direct implementation of the techniques leads to a trivial invariant and discuss how this approach can be improved to circumvent the difficul

Zicheng Zhang, Hao Chen, Xun Cao, Zhan Ma

Content providers increasingly replace traditional constant bitrate with variable bitrate (VBR) encoding in real-time video communication systems for better video quality. However, VBR encoding often leads to large and frequent bitrate fluctuation, inevitably deteriorating the efficiency of existing adaptive bitrate (ABR) methods. To tackle it, we propose th

Matteo Boschini, Davide Gerosa, Vijay Varma, Cristobal Armaza

Numerical-relativity surrogate models for both black-hole merger waveforms and remnants have emerged as important tools in gravitational-wave astronomy. While producing very accurate predictions, their applicability is limited to the region of the parameter space where numerical-relativity simulations are available and computationally feasible. Notably, this

Evan Miller

In this paper, we introduce the Fourier-restricted Euler and hypodissipative Navier--Stokes equations. These equations are analogous to the Euler and hypodissipative Navier--Stokes equations respectively, but with the Helmholtz projection replaced by a projection onto a more restrictive constraint space; the $(u\cdot\nabla)u$ nonlinearity is otherwise unchan

Gianluca Galeotti, Fabrizio De Marchi, Ehsan Hamzehpoor, Oliver MacLean

2D materials with high charge carrier mobility and tunable electronic band gaps have attracted intense research effort for their potential use as active components in nanoelectronics. 2D-conjugated polymers (2DCP) constitute a promising sub-class due to the fact that the electronic band structure can be manipulated by varying the molecular building blocks, w

U. Rozikov, R. Khakimov, M. T. Makhammadaliev

In this paper, we focus on studying non-probability Gibbs measures for a Hard Core (HC) model on a Cayley tree of order $k\geq 2$, where the set of integers $\mathbb Z$ is the set of spin values. It is well-known that each Gibbs measure, whether it be a gradient or non-probability measure, of this model corresponds to a boundary law. A boundary law can be th

Hiroshi Nagaoka, Akio Fujiwara

In this paper we study the autoparallelity w.r.t. the e-connection for an information-geometric structure called the SLD structure, which consists of a Riemannian metric and mutually dual e- and m-connections, induced on the manifold of strictly positive density operators. Unlike the classical information geometry, the e-connection has non-vanishing torsion,

Sohail Ahmed, Asma Javaid, Hui Jing, Farhan Saif

We explain optical and mirrors displacement bistability in a hybrid optomechanical system in the presence of a strong laser driving field and a weak probe field. External modulating fields are applied selectively on movable mirrors. We show that the optomechanical coupling, electromechanical Coulomb coupling and, amplitude & phase of external modulating fiel

Max Dupré la Tour, Monika Henzinger, David Saulpic

We consider the problem of clustering privately a dataset in $\mathbb{R}^d$ that undergoes both insertion and deletion of points. Specifically, we give an $\varepsilon$-differentially private clustering mechanism for the $k$-means objective under continual observation. This is the first approximation algorithm for that problem with an additive error that dep

Muneya Matsui, Simos Meintanis

We define data transformations that leave certain classes of distributions invariant, while acting in a specific manner upon the parameters of the said distributions. It is shown that under such transformations the maximum likelihood estimators behave in exactly the same way as the parameters being estimated. As a consequence goodness--of--fit tests based on

G. Kitagawa

Smoothing algorithms for state-space models, i.e., fixed-interval smoothing, fixed-lag smoothing, and two-filter formula for smoothing, are examined using real examples. For linear and Gaussian state-space models, it is observed that similar posterior distributions can be obtained by properly defining the inverse filter. In the case of linear non-Gaussian st

Mingyuan Meng, Lei Bi, Michael Fulham, Dagan Feng

Survival prediction is crucial for cancer patients as it provides early prognostic information for treatment planning. Recently, deep survival models based on deep learning and medical images have shown promising performance for survival prediction. However, existing deep survival models are not well developed in utilizing multi-modality images (e.g., PET-CT

Mashari Alatawi, Nitesh Saxena

End-to-End Encryption (E2EE) aims to make all messages impossible to read by anyone except you and your intended recipient(s). Many well-known and widely used Instant-Messaging (IM) applications (such as Signal, WhatsApp, and Apple's iMessage) claim to provide E2EE. However, a recent technique called client-side scanning (CSS) makes these E2EE claims grandio

Yinghan Guan, Haoran Dai, Zekuan Yu, Shouyu Wang

Multimodal fusion detection always places high demands on the imaging system and image pre-processing, while either a high-quality pre-registration system or image registration processing is costly. Unfortunately, the existing fusion methods are designed for registered source images, and the fusion of inhomogeneous features, which denotes a pair of features

Jean Fasel, Nanjun Yang

Smooth projective $\mathbb{G}_m$-varieties with isolated rational fixed points admit Tate Milnor-Witt motives. Over Euclidean fields, we give a splitting formula of such motives, which reduces the computation of their Chow-Witt groups to that of their Chow groups and cohomologies of Witt sheaf.

Shuaikai Shi, Lijun Zhang, Jie Chen

Hyperspectral images (HSI) have a large amount of spectral information reflecting the characteristics of matter, while their spatial resolution is low due to the limitations of imaging technology. Complementary to this are multispectral images (MSI), e.g., RGB images, with high spatial resolution but insufficient spectral bands. Hyperspectral and multispectr

Mengyao Xue, Weiwei Zhu, Xiangping Wu, Renxin Xu

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) has discovered more than 650 new pulsars, which account for 20% of our known Galactic pulsar population. In this paper, we estimate the prospect of a pulsar survey with a radio telescope array to be planned -- the FAST Array (FASTA), consists of six "FAST-type" telescopes. Such a sensitive radi

Mingyuan Meng, Lei Bi, Michael Fulham, Dagan Feng

Image registration is a fundamental requirement for medical image analysis. Deep registration methods based on deep learning have been widely recognized for their capabilities to perform fast end-to-end registration. Many deep registration methods achieved state-of-the-art performance by performing coarse-to-fine registration, where multiple registration ste

Shatrughna Kumar, Pengfei Li, Boris A. Malomed

The recent creation of Townes solitons (TSs) in binary Bose-Einstein condensates and experimental demonstration of spontaneous symmetry breaking (SSB) in solitons propagating in dual-core optical fibers draw renewed interest to the TS and SSB phenomenology in these and other settings. In particular, stabilization of TSs, which are always unstable in free spa

Simon Geerkens, Christian Sieberichs, Alexander Braun, Thomas Waschulzik

The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper we introduce a novel approach that supports the

Jeremy Lovejoy, Robert Osburn

We prove general fomulas for the deviations of two overpartition ranks from the average. These formulas are in terms of Appell--Lerch series and sums of quotients of theta functions and can be used, among other things, to recover any of the numerous overpartition rank difference identities in the literature. We give two illustrations.

Wei Wang

Let $\mathrm{Diff}_{\partial}(D^{n})$ be the topological group of diffeomorphisms of $D^{n}$ which agree with the identity near the boundary. In this short note, we compute the fundamental groups $\pi_1 \mathrm{Diff}_{\partial}(D^{4k})$ for $k\geq 3$.

Tianqi Li, Guansong Pang, Xiao Bai, Jin Zheng

Zero-Shot Learning (ZSL) focuses on classifying samples of unseen classes with only their side semantic information presented during training. It cannot handle real-life, open-world scenarios where there are test samples of unknown classes for which neither samples (e.g., images) nor their side semantic information is known during training. Open-Set Recognit

Oliver Eales, Steven Riley

The time-varying reproduction number $R(t)$ measures the number of new infections per infectious individual and is closely correlated with the time series of infection incidence by definition. The timings of actual infections are rarely known, and analysis of epidemics usually relies on time series data for other outcomes such as symptom onset. A common impl

Huiqian Wang, Li Liang, Xiaohui Wang, Xiaoyu Wang

The modulation of the valley structure in two-dimensional valley materials is vital in the field of valleytronics. The multiferroicity provides possibility for multiple modulations of the valley, including the magnetic and electric means. Based on the first-principle calculations, we study the valley properties and associated manipulations of multiferroic Co

Jaeheyoung Jeon, Jung Hyun Ryu, Jewoong Cho, Myungjoo Kang

This paper presents a solution to the challenges faced by contrastive learning in sequential recommendation systems. In particular, it addresses the issue of false negative, which limits the effectiveness of recommendation algorithms. By introducing an advanced approach to contrastive learning, the proposed method improves the quality of item embeddings and

Shuaikai Shi, Lijun Zhang, Yoann Altmann, Jie Chen

Hyperspectral images (HSI) with abundant spectral information reflected materials property usually perform low spatial resolution due to the hardware limits. Meanwhile, multispectral images (MSI), e.g., RGB images, have a high spatial resolution but deficient spectral signatures. Hyperspectral and multispectral image fusion can be cost-effective and efficien

Xiaokai Huo, Ansgar Jüngel

A model of vascular network formation is analyzed in a bounded domain, consisting of the compressible Navier-Stokes equations for the density of the endothelial cells and their velocity, coupled to a reaction-diffusion equation for the concentration of the chemoattractant, which triggers the migration of the endothelial cells and the blood vessel formation.

Tiehua Zhang, Yuze Liu, Zhishu Shen, Xingjun Ma

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this paper, we propose a hype

Shuo-Chieh Huang, Ruey S. Tsay

Feature-distributed data, referred to data partitioned by features and stored across multiple computing nodes, are increasingly common in applications with a large number of features. This paper proposes a two-stage relaxed greedy algorithm (TSRGA) for applying multivariate linear regression to such data. The main advantage of TSRGA is that its communication

Živa Urbančič, Jeffrey Giansiracusa

The output of persistent homology is an algebraic object called a persistence module. This object admits a decomposition into a direct sum of interval persistence modules described entirely by the barcode invariant. In this paper we investigate when a morphism $\Phi \colon V \to W$ of persistence modules admits an analogous direct sum decomposition. Jacquard

Pranay Jain, Cheng Ding, Cynthia Rudin, Xiao Hu

Smart watches and other wearable devices are equipped with photoplethysmography (PPG) sensors for monitoring heart rate and other aspects of cardiovascular health. However, PPG signals collected from such devices are susceptible to corruption from noise and motion artifacts, which cause errors in heart rate estimation. Typical denoising approaches filter or

Bjørn Christian Dueholm, Jesper de Claville Christiansen, Benny Endelt, Nikolaj Kristensen

This paper investigates a common rail cylinder lubrication system for large two-stroke marine diesel engines using electronically controlled injectors. The system is studied using the Bosch rate of injection measurement technique The common rail injector has a buildup of mass flow of approximately 1 ms as the injector opens until the nozzle is choked from ca

Dahyun Kang, Piotr Koniusz, Minsu Cho, Naila Murray

We address the task of weakly-supervised few-shot image classification and segmentation, by leveraging a Vision Transformer (ViT) pretrained with self-supervision. Our proposed method takes token representations from the self-supervised ViT and leverages their correlations, via self-attention, to produce classification and segmentation predictions through se

Zilai Zeng, Ce Zhang, Shijie Wang, Chen Sun

Recent work has demonstrated the effectiveness of formulating decision making as supervised learning on offline-collected trajectories. Powerful sequence models, such as GPT or BERT, are often employed to encode the trajectories. However, the benefits of performing sequence modeling on trajectory data remain unclear. In this work, we investigate whether sequ

Junho Choe, Sijong Kwak, Jinhyung Park

The purpose of this paper is to prove that one can read off the gonality sequence of a smooth projective curve from syzygies of secant varieties of the curve embedded by a line bundle of sufficiently large degree. More precisely, together with Ein-Niu-Park's theorem, our main result shows that the gonality sequence of a smooth projective curve completely det

Andreas L. Teigen, Yeonsoo Park, Annette Stahl, Rudolf Mester

The widespread adoption of Neural Radiance Fields (NeRFs) have ensured significant advances in the domain of novel view synthesis in recent years. These models capture a volumetric radiance field of a scene, creating highly convincing, dense, photorealistic models through the use of simple, differentiable rendering equations. Despite their popularity, these

Durgesh Kusuru, Anish C. Turlapaty, Mainak Thakur

Recent literature suggests that the surface electromyography (sEMG) signals have non-stationary statistical characteristics specifically due to random nature of the covariance. Thus suitability of a statistical model for sEMG signals is determined by the choice of an appropriate model for describing the covariance. The purpose of this study is to propose a C

Loc X. Nguyen, Ye Lin Tun, Yan Kyaw Tun, Minh N. H. Nguyen

Semantic communication has gained significant attention from researchers as a promising technique to replace conventional communication in the next generation of communication systems, primarily due to its ability to reduce communication costs. However, little literature has studied its effectiveness in multi-user scenarios, particularly when there are varia

Takahiro Yabe, Kota Tsubouchi, Toru Shimizu, Yoshihide Sekimoto

Modeling and predicting human mobility trajectories in urban areas is an essential task for various applications. The recent availability of large-scale human movement data collected from mobile devices have enabled the development of complex human mobility prediction models. However, human mobility prediction methods are often trained and tested on differen

Jin Matsumoto, Tomoya Takiwaki, Kei Kotake

We investigate the influence of magnetic field amplification on the core-collapse supernovae in highly magnetized progenitors through three-dimensional simulations. By considering rotating models, we observe a strong correlation between the exponential growth of the magnetic field in the gain region and the initiation of shock revival, with a faster onset co

Huiyu Li, Pei Liang, Junhua Hu

Recommender systems have been acknowledged as efficacious tools for managing information overload. Nevertheless, conventional algorithms adopted in such systems primarily emphasize precise recommendations and, consequently, overlook other vital aspects like the coverage, diversity, and novelty of items. This approach results in less exposure for long-tail it

Wenmiao Hu, Yichen Zhang, Yuxuan Liang, Yifang Yin

Street-view imagery provides us with novel experiences to explore different places remotely. Carefully calibrated street-view images (e.g. Google Street View) can be used for different downstream tasks, e.g. navigation, map features extraction. As personal high-quality cameras have become much more affordable and portable, an enormous amount of crowdsourced

Yingxiang Wang, Tanda Li, Shaolan Bi, Timothy R. Bedding

This paper reports estimated stellar parameters of 1,153 Kepler red giant branch stars determined with asteroseismic modeling. We use radial-mode oscillation frequencies, gravity-mode period spacings, Gaia luminosities, and spectroscopic data to characterize these stars. Compared with previous studies, we find that the two additional observed constraints, i.

Wojciech Roga, Baptiste Chevalier, Masahiro Takeoka

In this paper we present a supervised machine learning quantum classifier. It consists of a quantum data re-uploading classifier with binary trainable parameters, the optimal values of which are found by a quantum search algorithm. We show that we can reach the quadratic speed-up in optimization trainable parameters compared to classical brute force search.

Govind Lal Sidhardh, Manik Banik

We present a cryptographic-inspired framework for modeling Bell nonlocal correlations. Drawing inspiration from the renowned De Broglie-Bohm theory, we conceptualize nonlocal boxes as realistic systems featuring instantaneous signaling at the hidden variable level. By introducing randomness into the distribution of the hidden variable the superluminal signal

Kepeng Xu, Li Xu, Gang He, Xianyun Wu

In this study, we address the emerging necessity of converting Standard Dynamic Range Television (SDRTV) content into High Dynamic Range Television (HDRTV) in light of the limited number of native HDRTV content. A principal technical challenge in this conversion is the exacerbation of coding artifacts inherent in SDRTV, which detrimentally impacts the qualit

Zhikai Chen, Haitao Mao, Hang Li, Wei Jin

Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic unders

Bristi Ghosh, Sushanta Dattagupta, Malay Bandyopadhyay

We present here a general formulation for the interband dynamical optical conductivity in the nonlinear regime of graphene in the presence of a quantum bath comprising phonons and electrons. Our main focus is the relaxation behavior of the quantum solid of graphene perturbed by an oscillatory electric field. Considering the optical range of the frequency and

Sung-Yeon Kim, Ngaiming Mok, Aeryeong Seo

In this paper we study the rigidity of proper holomorphic maps $f\colon \Omega\to\Omega'$ between irreducible bounded symmetric domains $\Omega$ and $\Omega'$ with small rank differences: $2\leq \text{rank}(\Omega')< 2\,\text{rank}(\Omega)-1$. More precisely, if either $\Omega$ and $\Omega'$ have the same type or $\Omega$ is of type~III and $\Omega'$ is of t

Dongsheng Li, Chen shen

In this paper, a three-machine equivalent method applicable to asymmetrical faults is proposed considering the operating wind speed and fault severity. Firstly, direct-driven permanent magnet synchronous generator wind turbines (PMSGs) are clustered based on their different active power response characteristics considering the wind speed, the fault severity,

Nhi Kieu, Kien Nguyen, Sridha Sridharan, Clinton Fookes

The advent of high-resolution multispectral/hyperspectral sensors, LiDAR DSM (Digital Surface Model) information and many others has provided us with an unprecedented wealth of data for Earth Observation. Multimodal AI seeks to exploit those complementary data sources, particularly for complex tasks like semantic segmentation. While specialized architectures

Pu Yang, Xiang-Gen Xia, Qingyue Qu, Han Wang

In-band full-duplex relay (FDR) has attracted much attention as an effective solution to improve the coverage and spectral efficiency in wireless communication networks. The basic problem for FDR transmission is how to eliminate the inherent self-interference and re-use the residual self-interference (RSI) at the relay to improve the end-to-end performance.

Jaydeb Saker, Sayma Sultana, Steven R. Wilson, Amiangshu Bosu

Background: The existence of toxic conversations in open-source platforms can degrade relationships among software developers and may negatively impact software product quality. To help mitigate this, some initial work has been done to detect toxic comments in the Software Engineering (SE) domain. Aims: Since automatically classifying an entire text as toxic

Angel Felipe Magnossão de Paula, Giulia Rizzi, Elisabetta Fersini, Damiano Spina

With the increasing influence of social media platforms, it has become crucial to develop automated systems capable of detecting instances of sexism and other disrespectful and hateful behaviors to promote a more inclusive and respectful online environment. Nevertheless, these tasks are considerably challenging considering different hate categories and the a

Tatsuo Kobayashi, Morimitsu Tanimoto

We review the modular flavor symmetric models of quarks and leptons focusing on our works. We present some flavor models of quarks and leptons by using finite modular groups and discuss the phenomenological implications. The modular flavor symmetry gives interesting phenomena at the fixed point of modulus. As a representative, we show the successful texture

P. A. Krachkov, A. I. Milstein

The effect of screening by atomic electrons on the behavior of electron and positron wave functions in the continuous spectrum at small distances is studied. It is shown that these asymptotics are affected not only by the static potential of atomic electrons, but also by the polarization potential, as well as by the exchange interaction, which is essential f

Brendan Gould, Philip Brown

Vehicle-to-Vehicle (V2V) communication is intended to improve road safety through distributed information sharing; however, this type of system faces a design challenge: it is difficult to predict and optimize how human agents will respond to the introduction of this information. Bayesian games are a standard approach for modeling such scenarios; in a Bayesi

Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, Kangwook Lee

Large language models like GPT-4 exhibit emergent capabilities across general-purpose tasks, such as basic arithmetic, when trained on extensive text data, even though these tasks are not explicitly encoded by the unsupervised, next-token prediction objective. This study investigates how small transformers, trained from random initialization, can efficiently

Reem Alrawili, Ali Abdullah S. AlQahtani, Muhammad Khurram Khan

This paper conducts an extensive review of biometric user authentication literature, addressing three primary research questions: (1) commonly used biometric traits and their suitability for specific applications, (2) performance factors such as security, convenience, and robustness, and potential countermeasures against cyberattacks, and (3) factors affecti

Jinqiang Yu, Alexey Ignatiev, Peter J. Stuckey

Recent years have witnessed the widespread use of artificial intelligence (AI) algorithms and machine learning (ML) models. Despite their tremendous success, a number of vital problems like ML model brittleness, their fairness, and the lack of interpretability warrant the need for the active developments in explainable artificial intelligence (XAI) and forma

Rodrigue de Schaetzen, Alessandro Sestini

This short paper presents an efficient path following solution for ground vehicles tailored to game AI. Our focus is on adapting established techniques to design simple solutions with parameters that are easily tunable for an efficient benchmark path follower. Our solution pays particular attention to computing a target speed which uses quadratic Bezier curv

Lauren A. Sgro, Thomas M. Esposito, Guillaume Blaclard, Sebastian Gomez

We present highly sampled photometry of the supernova (SN) 2023ixf, a Type II SN in M101, beginning 2 days before its first known detection. To gather these data, we enlisted the global Unistellar Network of citizen scientists. These 252 observations from 115 telescopes show the SN's rising brightness associated with shock emergence followed by gradual decay

Bruce W. Lee, BongSeok Yang, Jason Hyung-Jong Lee

Though discourse parsing can help multiple NLP fields, there has been no wide language model search done on implicit discourse relation classification. This hinders researchers from fully utilizing public-available models in discourse analysis. This work is a straightforward, fine-tuned discourse performance comparison of seven pre-trained language models. W

Angel Felipe Magnossão de Paula, Paolo Rosso, Damiano Spina

This paper proposes a novelty approach to mitigate the negative transfer problem. In the field of machine learning, the common strategy is to apply the Single-Task Learning approach in order to train a supervised model to solve a specific task. Training a robust model requires a lot of data and a significant amount of computational resources, making this sol

Yunqiu Lv, Jing Zhang, Nick Barnes, Yuchao Dai

Unsupervised object discovery (UOD) refers to the task of discriminating the whole region of objects from the background within a scene without relying on labeled datasets, which benefits the task of bounding-box-level localization and pixel-level segmentation. This task is promising due to its ability to discover objects in a generic manner. We roughly cate

Shuai Li, Rui Tian, Min Liu, Maksims Arzamasovs

There is growing interest to search the chiral Majorana fermions that could arise as the quasi-particle edge state of a two-dimensional topological state of matter. Here we propose a new platform, i.e., a two-dimensional chiral odd Chern number lattice supersolid state, for supporting multiple number-tunable chiral Majorana fermions from a single component R

Ammar Sherif, Abubakar Abid, Mustafa Elattar, Mohamed ElHelw

Multi-Task Learning (MTL) is a powerful technique that has gained popularity due to its performance improvement over traditional Single-Task Learning (STL). However, MTL is often challenging because there is an exponential number of possible task groupings, which can make it difficult to choose the best one because some groupings might produce performance de

Chunhui Zhang, Xin Sun, Yiqian Yang, Li Liu

Current mainstream vision-language (VL) tracking framework consists of three parts, \ie a visual feature extractor, a language feature extractor, and a fusion model. To pursue better performance, a natural modus operandi for VL tracking is employing customized and heavier unimodal encoders, and multi-modal fusion models. Albeit effective, existing VL tracker

Ke Wang, Rong Li, Bo-Chao Liu

In this work, we study the role of triangle singularity in the $J/\psi \to \gamma \bar{p} \Delta$ decay. We find that through a triangle mechanism, involving a triangle loop composed by $\omega$, $\pi$ and $p$, this decay may develop a triangle singularity and produce a visible peak in the invariant mass $M_{\gamma\Delta}$ around 1.73 GeV with a width of 0.0

Junjie Huang, Win-bin Huang, Yi Bu, Qi Cao

With an exponential increase in submissions to top-tier Computer Science (CS) conferences, more and more conferences have introduced a rebuttal stage to the conference peer review process. The rebuttal stage can be modeled as social interactions between authors and reviewers. A successful rebuttal often results in an increased review score after the rebuttal

Chang-Yin Ji, Xiao-Ping Li, Zheng Tang, Di Zhou

Three-dimensional nodal line semimetals (NLSMs) provide remarkable importance for both enrich topological physics and wave management. However, NLSMs realized in acoustic systems are twofold bands degenerate, which are called Weyl NLSMs. Here, we first report on the experimental observation of novel Dirac NLSMs with fourfold degenerate in sonic crystals. We

Qian-Qian Zhang, Ping Zhou, Yang Chen, Xiao Zhang

Galactic supernova remnants (SNRs) play an important role in our understanding of supernovae and their feedback on the interstellar environment. SNR G352.7$-$0.1 is special for its thermal composite morphology and double-ring structure. We have performed spectroscopic mapping in $^{12}$CO and $^{13}$CO $J=2$-1 lines toward G352.7$-$0.1 with the Atacama Pathf

Hanning Su, Qinglong Bao, Jiameng Pan, Fucheng Guo

The inadequate adaptability to flexible interference scenarios remains an unresolved challenge in the majority of techniques utilized for mitigating interrupted-sampling repeater jamming (ISRJ). Matched filtering system based methods is desirable to incorporate anti-ISRJ measures based on prior ISRJ modeling, either preceding or succeeding the matched filter

Liyang Liao, Jorge Puebla, Kei Yamamoto, Junyeon Kim

Phonons and magnons are engineered by periodic potential landscapes in phononic and magnonic crystals, and their combined studies may enable valley phonon transport tunable by the magnetic field. Through nonreciprocal surface acoustic wave transmission, we demonstrate valley-selective phonon-magnon scattering in magnetoelastic superlattices. The lattice symm

Shubham Jadhav, Rudra Pratap

This paper details the development of high-quality, c-axis oriented AlN thin films up to 2 {\mu}m thick, using sputtering on platinum-coated SOI substrates for use in piezoelectric MEMS. Our comprehensive studies illustrate how important growth parameters such as the base Pt electrode quality, deposition temperature, power, and pressure, can influence film q

Qiongling Li, Takuro Mochizuki

Let $X$ be an arbitrary non-compact hyperbolic Riemann surface, that is, not $\mathbb C$ or $\mathbb C^*$. Given a tuple of holomorphic differentials $\boldsymbol q=(q_2,\cdots,q_n)$ on $X$, one can define a Higgs bundle $(\mathbb{K}_{X,n},\theta(\boldsymbol q))$ in the Hitchin section. We show there exists a harmonic metric $h$ on $(\mathbb{K}_{X,n},\theta(

Luke McDermott, Daniel Cummings

This work introduces a novel approach to pruning deep learning models by using distilled data. Unlike conventional strategies which primarily focus on architectural or algorithmic optimization, our method reconsiders the role of data in these scenarios. Distilled datasets capture essential patterns from larger datasets, and we demonstrate how to leverage thi

Yuyuan Li, Jiaming Zhang, Yixiu Liu, Chaochao Chen

Privacy concerns associated with machine learning models have driven research into machine unlearning, which aims to erase the memory of specific target training data from already trained models. This issue also arises in federated learning, creating the need to address the federated unlearning problem. However, federated unlearning remains a challenging tas

Yuening Zhang, Brian C. Williams

When agents collaborate on a task, it is important that they have some shared mental model of the task routines -- the set of feasible plans towards achieving the goals. However, in reality, situations often arise that such a shared mental model cannot be guaranteed, such as in ad-hoc teams where agents may follow different conventions or when contingent con