April 2023 arXiv papers — page 30

Guangyuan Ma, Hongtao Liu, Xing Wu, Wanhui Qian

News recommendation aims to predict click behaviors based on user behaviors. How to effectively model the user representations is the key to recommending preferred news. Existing works are mostly focused on improvements in the supervised fine-tuning stage. However, there is still a lack of PLM-based unsupervised pre-training methods optimized for user repres

Xin Lin, Lijun Zhu

Electrical switching of magnetization via spin-orbit torque (SOT) is of great potential in fast, dense, energy-efficient nonvolatile magnetic memory and logic technologies. Recently, enormous efforts have been stimulated to investigate switching of perpendicular magnetization in van der Waals systems that have unique, strong tunability and spin-orbit couplin

Michael Llordes, Debasis Ganguly, Sumit Bhatia, Chirag Agarwal

Neural retrieval models (NRMs) have been shown to outperform their statistical counterparts owing to their ability to capture semantic meaning via dense document representations. These models, however, suffer from poor interpretability as they do not rely on explicit term matching. As a form of local per-query explanations, we introduce the notion of equival

Van-Duc Le, Tien-Cuong Bui, Sang-Kyun Cha

Citywide Air Pollution Forecasting tries to precisely predict the air quality multiple hours ahead for the entire city. This topic is challenged since air pollution varies in a spatiotemporal manner and depends on many complicated factors. Our previous research has solved the problem by considering the whole city as an image and leveraged a Convolutional Lon

Ebrahimi Seyed-Mohsen, Tuunanen Johanna, Saarela Ville, Honkamo Marja

Recent research shows the eye has a paravascular solute transport pathway driven by physiological pulsations resembling the brain. we developed synchronous multimodal imaging tools aimed at measuring the driving pulsations of the human eye. We used an eye-tracking functional eye camera (FEC) compatible with magnetic resonance imaging (MRI) for measuring eye

Yunpeng Weng, Xing Tang, Liang Chen, Xiuqiang He

Multi-task learning for various real-world applications usually involves tasks with logical sequential dependence. For example, in online marketing, the cascade behavior pattern of $impression \rightarrow click \rightarrow conversion$ is usually modeled as multiple tasks in a multi-task manner, where the sequential dependence between tasks is simply connecte

Feng Guo, Jie Wang

We study a class of polynomial optimization problems with a robust polynomial matrix inequality (PMI) constraint where the uncertainty set itself is defined also by a PMI. These can be viewed as matrix generalizations of semi-infinite polynomial programs, since they involve actually infinitely many PMI constraints in general. Under certain SOS-convexity assu

Stefano Moretti, Muyuan Song

We study the time-honoured decay $H^\pm\to A W^\pm$ but for the first time, we do so for the case of both $A$ and $W^\pm$ being off-shell, therefore computing a $1\to 4$ body decay. We show that the corresponding decay rate not only extends the reach of $H^\pm$ searches to small masses of the latter but also that the results of our implementation differ sign

László Csató

The best-worst method is an increasingly popular approach to solving multi-criteria decision-making problems. However, the usual prioritisation techniques may result in an ordinal violation if the best (worst) alternative identified in the first step does not receive the highest (lowest) weight. The current paper gives two sufficient conditions for the logar

Xing-Yan Fan, Xiang-Ru Xie, Jing-Ling Chen

As one of the most elegant theories in physics, Yang-Mills (YM) theory not only incorporates Maxwell's equations unifying electromagnetism, but also underpins the standard model explaining the electroweak and strong interactions in a succinct way. Whereas the highly nonlinear terms in YM equations involving the interactions between potentials and fields reta

Mizuki Tabata, Kana Kurata, Junichiro Tamamatsu

Estimating the layout of a room from a single-shot panoramic image is important in virtual/augmented reality and furniture layout simulation. This involves identifying three-dimensional (3D) geometry, such as the location of corners and boundaries, and performing 3D reconstruction. However, occlusion is a common issue that can negatively impact room layout e

Zihan Wang, Feiyang Wang, Joseph Vovrosh, Johannes Knolle

The phenomenon of confinement is well known in high-energy physics and can also be realized for low-energy domain-wall excitations in one-dimensional quantum spin chains. A bound state consisting of two domain-walls can behave like a meson, and in a recent work of Vovrosh et al. [PRX Quantum 3, 040309 (2022)] , it was demonstrated that a pair of mesons could

Eugenia Iofinova, Alexandra Peste, Dan Alistarh

Pruning - that is, setting a significant subset of the parameters of a neural network to zero - is one of the most popular methods of model compression. Yet, several recent works have raised the issue that pruning may induce or exacerbate bias in the output of the compressed model. Despite existing evidence for this phenomenon, the relationship between neura

Ayuki Kamada, Hee Jung Kim

Recent analysis on the stellar kinematics of ultra-faint dwarf (UFD) galaxies has put a stringent upper limit on the self-scattering cross section of dark matter, i.e., $\sigma/m<{\cal O}(0.1)\,{\rm cm^2/g}$ at the scattering velocity of ${\cal O}(10)\,{\rm km/s}$. Resonant self-interacting dark matter (rSIDM) is one possibility that can be consistent with t

Junde Wu, Wei Ji, Yuanpei Liu, Huazhu Fu

The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in various segmentation tasks and its prompt-based interface. However, recent studies and individual experiments have shown that SAM underperforms in medical image segmentation, since the lack of the medical specific knowledge

C. Patra, T. Agarwal, Rajeshwari R. Chowdhury, R. P. Singh

Superconducting topological materials have generated considerable interest in condensed matter research due to their unusual gap structures and topological properties. In this study, we have investigated the normal and superconducting characteristics of a potential topological semimetal 1$T$-NbSeTe through comprehensive transport and magnetization measuremen

Van-Duc Le, Tien-Cuong Bui, Wen-Syan Li

An end-to-end machine learning (ML) lifecycle consists of many iterative processes, from data preparation and ML model design to model training and then deploying the trained model for inference. When building an end-to-end lifecycle for an ML problem, many ML pipelines must be designed and executed that produce a huge number of lifecycle versions. Therefore

Rodrigo Castellanos, Andrea Ianiro, Stefano Discetti

The convective heat transfer in a turbulent boundary layer (TBL) on a flat plate is enhanced using an artificial intelligence approach based on linear genetic algorithms control (LGAC). The actuator is a set of six slot jets in crossflow aligned with the freestream. An open-loop optimal periodic forcing is defined by the carrier frequency, the duty cycle and

Chris Halcrow, Egor Babaev

We study ferroelectric domain walls in barium titanate. We search for structurally nontrivial, so-called non-Ising domain walls, where the Polarisation is non-zero along the entire wall. Our approach enables us to find solutions for domain walls in any orientation, and the existence and energy of these walls depend on their particular orientation. We find th

Guozhang Li, De Cheng, Xinpeng Ding, Nannan Wang

Weakly Supervised Temporal Action Localization (WTAL) aims to classify and localize temporal boundaries of actions for the video, given only video-level category labels in the training datasets. Due to the lack of boundary information during training, existing approaches formulate WTAL as a classificationproblem, i.e., generating the temporal class activatio

Lei Shi, Tianyu Gao, Zheng Zhang, Junxing Zhang

Automated medical image segmentation can assist doctors to diagnose faster and more accurate. Deep learning based models for medical image segmentation have made great progress in recent years. However, the existing models fail to effectively leverage Transformer and MLP for improving U-shaped architecture efficiently. In addition, the multi-scale features o

Nesrine Aroua, Mourad Bellassoued

This article deals with the multidimensional Borg-Levinson theorem for perturbed bi-harmonic operator. More precisely, in a bounded smooth domain of $\R^n$, with $n \geq 2$, we prove the stability of the first and zero order coefficients of the bi-harmonic operator from some asymptotic behavior of the boundary spectral data of the corresponding bi-harmonic o

Soumya Chakrabarti

We show that it is possible to steer clear of a spacetime singularity during gravitational collapse by considering the time-variation of a fundamental coupling, in this case, the fine structure constant {\alpha}. We study a spherical distribution of cold dark matter coexisting with other fluid elements, collapsing under its own gravity. Dark matter is writte

Fadi Sun, Jinwu Ye

We study the response of a quantum magnet with spin-orbit coupling (SOC) to a Zeeman field by constructing effective actions and performing Renormalization Group (RG) analysis. There are several novel classes of quantum phase transitions at a low $ h_{c1} $ and an upper critical field $ h_{c2} $ driven by magnon condensations at commensurate (C-) or in-comme

Mohammed Bachir

We give a class of bounded closed sets $C$ in a Banach space satisfying a generalized and stronger form of the Bishop-Phelps property studied by Bourgain in \cite{Bj} for dentable sets. A version of the {\it ``Bishop-Phelps-Bollob\'as"} theorem will be also given. The density and the residuality of bounded linear operators attaining their maximum on $C$ (kno

Rongjian Yang, Zhijie Zhang, Weiguo Zheng, Jeffery Xu Yu

Streaming graphs are drawing increasing attention in both academic and industrial communities as many graphs in real applications evolve over time. Continuous subgraph matching (shorted as CSM) aims to report the incremental matches of a query graph in such streaming graphs. It involves two major steps, i.e., candidate maintenance and incremental match gener

Subhayan De, Patrick T. Brewick

Nonlinear systems, such as with degrading hysteretic behavior, are often encountered in engineering applications. In addition, due to the ubiquitous presence of uncertainty and the modeling of such systems becomes increasingly difficult. On the other hand, datasets from pristine models developed without knowing the nature of the degrading effects can be easi

Lin Dengtian, Ma Yang, Li Yuhong, Song Xuemeng

Illegal live-streaming identification, which aims to help live-streaming platforms immediately recognize the illegal behaviors in the live-streaming, such as selling precious and endangered animals, plays a crucial role in purifying the network environment. Traditionally, the live-streaming platform needs to employ some professionals to manually identify the

Ulrich Bunke

We provide a homotopy theorist's point of view on $KK$- and $E$-theory for $C^{*}$-algebras. We construct stable $\infty$-categories representing these theories through a sequence of Dwyer-Kan localizations of the category of $C^{*}$-algebras. Thereby we will reveal the homotopic theoretic meaning of various classical construction from $C^{*}$-algebra theory

Masoud Kavian, Mohammad Mahdi Mojahedian, Mohammad Hossein Yassaee, Mahtab Mirmohseni

Random binning is a widely used technique in information theory with diverse applications. In this paper, we focus on the output statistics of random binning (OSRB) using the Tsallis divergence $T_\alpha$. We analyze all values of $\alpha \in (0, \infty)\cup\{\infty\}$ and consider three scenarios: (i) the binned sequence is generated i.i.d., (ii) the sequen

Jonelle Angelo S. Cenita, Paul Richie F. Asuncion, Jayson M. Victoriano

The study aimed to evaluate the regression models' performance in predicting the cost of medical insurance. The Three (3) Regression Models in Machine Learning namely Linear Regression, Gradient Boosting, and Support Vector Machine were used. The performance will be evaluated using the metrics RMSE (Root Mean Square), r2 (R Square), and K-Fold Cross-validati

Hao Dong, Zhiyuan Ning, Pengyang Wang, Ziyue Qiao

Temporal knowledge graph (TKG) reasoning aims to predict the future missing facts based on historical information and has gained increasing research interest recently. Lots of works have been made to model the historical structural and temporal characteristics for the reasoning task. Most existing works model the graph structure mainly depending on entity re

Kyungtak Lim, Xavier Garbet, Yanick Sarazin, Etienne Gravier

The effect of toroidal rotation on both turbulent and neoclassical transport of tungsten (W) in tokamaks is investigated using the flux-driven, global, nonlinear 5D gyrokinetic code GYSELA. Nonlinear simulations are carried out with different levels of momentum injection that drive W to the supersonic regime, while the toroidal velocity of the main ions rema

Geordie Williamson

A personal and informal account of what a pure mathematician might expect when using tools from deep learning in their research.

José Antonio Nájera, Carlos Aráoz Alvarado, Celia Escamilla-Rivera

In this paper, we study the constraints on the $f(Q)=Q/(8\pi G) - \alpha \ln(Q/Q_0)$, symmetric teleparallel model using local measurements and gravitational wave mock standard sirens. Using observational local SNIa and BAO data and energy conditions, the logarithmic $f(Q)$ model is capable of explaining the cosmic late-time acceleration by geometrical means

Mohsen Ahmadi, Ahmad Gholizadeh Lonbar, Hajar Kazemi Naeini, Ali Tarlani Beris

This research assesses the performance of two deep learning models, SAM and U-Net, for detecting cracks in concrete structures. The results indicate that each model has its own strengths and limitations for detecting different types of cracks. Using the SAM's unique crack detection approach, the image is divided into various parts that identify the location

Gongyan Liu, Runze Li, Xiaozhou Zhou, Tianrui Sun

Reconstruction and fast prediction of flow fields are important for the improvement of data center operations and energy savings. In this study, an artificial neural network (ANN) and variational autoencoder (VAE) composite model is proposed for the reconstruction and prediction of 3D flowfields with high accuracy and efficiency. The VAE model is trained to

Martin J. Gander, Davide Palitta

Time-parallel time integration has received a lot of attention in the high performance computing community over the past two decades. Indeed, it has been shown that parallel-in-time techniques have the potential to remedy one of the main computational drawbacks of parallel-in-space solvers. In particular, it is well-known that for large-scale evolution probl

Yu Zhang, Lin Zhang

Fracture is one of the main failure modes of engineering structures such as buildings and roads. Effective detection of surface cracks is significant for damage evaluation and structure maintenance. In recent years, the emergence and development of deep learning techniques have shown great potential to facilitate surface crack detection. Currently, most repo

Zhenbin Cao, Changxing Miao, Zijian Wang

Fourier decay of fractal measures on surfaces plays an important role in geometric measure theory and partial differential equations. In this paper, we study the quadratic surfaces of high co-dimensions. Unlike the case of co-dimension 1, quadratic surfaces of high co-dimensions possess some special scaling structures and degenerate characteristics. We will

Kirill P. Kalinin, George Mourgias-Alexandris, Hitesh Ballani, Natalia G. Berloff

Solving optimization problems is challenging for existing digital computers and even for future quantum hardware. The practical importance of diverse problems, from healthcare to financial optimization, has driven the emergence of specialised hardware over the past decade. However, their support for problems with only binary variables severely restricts the

Apurba Das, Ramkrishna Mandal

In this paper, we first introduce the notion of a (relative) averaging operator of any nonzero weight $\lambda$. We show that such operators are intimately related to triassociative algebras introduced by Loday and Ronco. Next, we construct a differential graded Lie algebra and a $L_\infty$-algebra whose Maurer-Cartan elements are respectively relative avera

Han Wang, Jiayuan Zhang, Lipeng Wan, Xingyu Chen

Manipulation relationship detection (MRD) aims to guide the robot to grasp objects in the right order, which is important to ensure the safety and reliability of grasping in object stacked scenes. Previous works infer manipulation relationship by deep neural network trained with data collected from a predefined view, which has limitation in visual dislocatio

Ganning Zhao, Tingwei Shen, Suya You, C. -C. Jay Kuo

Ensuring the realism of computer-generated synthetic images is crucial to deep neural network (DNN) training. Due to different semantic distributions between synthetic and real-world captured datasets, there exists semantic mismatch between synthetic and refined images, which in turn results in the semantic distortion. Recently, contrastive learning (CL) has

Nikolay Bogachev, Sami Douba

The L\"obell polyhedra form an infinite family of compact right-angled hyperbolic polyhedra in dimension $3$. We observe, through both elementary and more conceptual means, that the ``systoles'' of the L\"obell polyhedra approach $0$, so that these polyhedra give rise to particularly straightforward examples of closed hyperbolic $3$-manifolds with arbitraril

Xiangze Jia, Hui Zhou, Xinge Zhu, Yandong Guo

In this paper, we propose a novel self-supervised motion estimator for LiDAR-based autonomous driving via BEV representation. Different from usually adopted self-supervised strategies for data-level structure consistency, we predict scene motion via feature-level consistency between pillars in consecutive frames, which can eliminate the effect caused by nois

Shachar Itzhaky, Sharon Shoham, Yakir Vizel

Hyperproperties govern the behavior of a system or systems across multiple executions, and are being recognized as an important extension of regular temporal properties. So far, such properties have resisted comprehensive treatment by modern software model-checking approaches such as IC3/PDR, due to the need to find not only an inductive invariant but also a

Yongjae Lee, Li Yang, Deliang Fan

Neural radiance field (NeRF) has shown remarkable performance in generating photo-realistic novel views. Among recent NeRF related research, the approaches that involve the utilization of explicit structures like grids to manage features achieve exceptionally fast training by reducing the complexity of multilayer perceptron (MLP) networks. However, storing f

Adam Rupe, Karthik Kashinath, Nalini Kumar, James P. Crutchfield

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically-grounded framework for describing emergent organization that, via data-driven

Daeun Kim, Namyoon Lee

This paper addresses cooperative link scheduling problems for base station (BS) aided device-to-device (D2D) communications using limited channel state information (CSI) at BS. We first derive the analytical form of ergodic sum-spectral efficiency as a function of network parameters, assuming statistical CSI at the BS. However, the optimal link scheduling, w

Ze-Hao Wang, Long-Kun Shan, Tong-Tian Weng, Tian-Long Chen

Optical microscopy image plays an important role in scientific research through the direct visualization of the nanoworld, where the imaging mechanism is described as the convolution of the point spread function (PSF) and emitters. Based on a priori knowledge of the PSF or equivalent PSF, it is possible to achieve more precise exploration of the nanoworld. H

Zeyi Liu, Songqiao Hu, Xiao He

Real-time safety assessment (RTSA) of dynamic systems is a critical task that has significant implications for various fields such as industrial and transportation applications, especially in non-stationary environments. However, the absence of a comprehensive review of real-time safety assessment methods in non-stationary environments impedes the progress a

Weiwei He, Ziming Tang, Qihua Gong, Min Yi

The extraordinary properties of a heterostructure by stacking atom-thick van der Waals (vdW) magnets have been extensively studied. However, the magnetocaloric effect (MCE) of heterostructures that are based on monolayer magnets remains to be explored. Herein, we deliberate MCE of vdW heterostructure composed of a monolayer CrI$_3$ and metal atomic layers (A

Saransh Singh, K. Aditya Mohan

The relationship between the refractive index decrement, $\delta$, and the real part of the atomic form factor, $f^\prime$, is used to derive a simple polynomial functional form for $\delta(E)$ far from the K-edge of the element. The functional form, motivated by the underlying physics, follows an infinite power sum, with most of the energy dependence captur

A. M. Escobar-Ruiz, R. Azuaje

In this study the notion of particular integrability in Classical Mechanics, introduced in [J. Phys. A: Math. Theor. 46 025203, 2013], is revisited within the formalism of symplectic geometry. A particular integral $\cal I$ is a function not necessarily conserved in the whole phase space $T^*Q$ but when restricted to a certain invariant subspace ${\cal W}\su

Nguyen Anh Dao, Anh Nguyen Vu Tien

The main purpose of this paper is to study weak solutions of time-fractional of porous medium equation with nonlocal pressure: \[ \partial^\alpha_t u=\operatorname{div}\left( |u|^{m}\nabla (-\Delta)^{-s} u\right) \,\, \text{in } \mathbb{R}^N\times (0,T) \,, \] with $m\geq 1$, $N\geq 2$, $\frac{1}{2}\leq s<1$, and $\alpha\in(0,1)$. We first prove an existence

Jingwen Fu, Zhizheng Zhang, Dacheng Yin, Yan Lu

This paper explores the connection between learning trajectories of Deep Neural Networks (DNNs) and their generalization capabilities when optimized using (stochastic) gradient descent algorithms. Instead of concentrating solely on the generalization error of the DNN post-training, we present a novel perspective for analyzing generalization error by investig

Kunio Kaneta, Kin-ya Oda

Since electroweak symmetry is generally broken during inflation, the Standard Model Higgs field can become supermassive even after the end of inflation. In this paper, we study the non-thermal phase space distribution of the Higgs field during reheating, focusing in particular on two different contributions: primordial condensate and stochastic fluctuations.

Hyungtae Lim, Daebeom Kim, Beomsoo Kim, Hyun Myung

In recent years, the demand for mapping construction sites or buildings using light detection and ranging~(LiDAR) sensors has been increased to model environments for efficient site management. However, it is observed that sometimes LiDAR-based approaches diverge in narrow and confined environments, such as spiral stairs and corridors, caused by fixed parame

Evangelos Georganas, Dhiraj Kalamkar, Kirill Voronin, Abhisek Kundu

During the past decade, Deep Learning (DL) algorithms, programming systems and hardware have converged with the High Performance Computing (HPC) counterparts. Nevertheless, the programming methodology of DL and HPC systems is stagnant, relying on highly-optimized, yet platform-specific and inflexible vendor-optimized libraries. Such libraries provide close-t

Shimpei Kobayashi

We study geodesics of multivariate normal distributions with respect to the Fisher metric. First it will be shown that a computational formula for geodesics can be understood using the block Cholesky decomposition and a natural Riemannian submersion. Next a mid point algorithm for geodesics will be obtained. And finally a new Toda lattice type Lax pair will

Kai Yan, Ruo-Yu Liu

Diffuse gamma-ray emission (DGE) has been discovered over the Galactic disk in the energy range from sub-GeV to sub-PeV. While it is believed to be dominated by the pionic emission of cosmic ray (CR) hadrons via interactions with interstellar medium, unresolved gamma-ray sources may also be potential contributors. TeV gamma-ray halos around middle-aged pulsa

Simone Lazier, Saravanan Thirumuruganathan, Hadis Anahideh

Machine learning (ML) based approaches are increasingly being used in a number of applications with societal impact. Training ML models often require vast amounts of labeled data, and crowdsourcing is a dominant paradigm for obtaining labels from multiple workers. Crowd workers may sometimes provide unreliable labels, and to address this, truth discovery (TD

Alex Cowan

We give asymptotics for shifted convolutions of the form $$\sum_{n < X} \frac{\sigma_{2u}(n,\chi)\sigma_{2v}(n+k,\psi)}{n^{u+v}}$$ for nonzero complex numbers $u,v$ and nontrivial Dirichlet characters $\chi,\psi$. We use the technique of "automorphic regularization" to find the spectral decomposition of a combination of Eisenstein series which is not obvious

Shuaiying Hou, Hongyu Tao, Hujun Bao, Weiwei Xu

Although part-based motion synthesis networks have been investigated to reduce the complexity of modeling heterogeneous human motions, their computational cost remains prohibitive in interactive applications. To this end, we propose a novel two-part transformer network that aims to achieve high-quality, controllable motion synthesis results in real-time. Our

Leigang Qu, Meng Liu, Wenjie Wang, Zhedong Zheng

Image-text retrieval aims to bridge the modality gap and retrieve cross-modal content based on semantic similarities. Prior work usually focuses on the pairwise relations (i.e., whether a data sample matches another) but ignores the higher-order neighbor relations (i.e., a matching structure among multiple data samples). Re-ranking, a popular post-processing

Antoine Nzeyimana

This paper describes the system entered by the author to the SemEval-2023 Task 12: Sentiment analysis for African languages. The system focuses on the Kinyarwanda language and uses a language-specific model. Kinyarwanda morphology is modeled in a two tier transformer architecture and the transformer model is pre-trained on a large text corpus using multi-tas

Akash Dutta, Jordi Alcaraz, Ali TehraniJamsaz, Eduardo Cesar

Growing heterogeneity and configurability in HPC architectures has made auto-tuning applications and runtime parameters on these systems very complex. Users are presented with a multitude of options to configure parameters. In addition to application specific solutions, a common approach is to use general purpose search strategies, which often might not iden

Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan

Auxiliary tasks improve the representations learned by deep reinforcement learning agents. Analytically, their effect is reasonably well understood; in practice, however, their primary use remains in support of a main learning objective, rather than as a method for learning representations. This is perhaps surprising given that many auxiliary tasks are defin

Yi-Fan Zhang, Xue Wang, Kexin Jin, Kun Yuan

Many recent machine learning tasks focus to develop models that can generalize to unseen distributions. Domain generalization (DG) has become one of the key topics in various fields. Several literatures show that DG can be arbitrarily hard without exploiting target domain information. To address this issue, test-time adaptive (TTA) methods are proposed. Exis

Shujing Miao, Shuchao Li, Wei Wei

A graph $G$ is said to be $k$-extendable if every matching of size $k$ in $G$ can be extended to a perfect matching of $G$, where $k$ is a positive integer. We say $G$ is $1$-excludable if for every edge $e$ of $G$, there exists a perfect matching excluding $e$. In this paper, we first establish a lower bound on the size (resp. the spectral radius) of $G$ to

Ratul Biswas, Arnab Sen

We consider an $N \times N$ random symmetric Toeplitz matrix with an i.i.d. input sequence drawn from a distribution that lies in the domain of attraction of an $\alpha$-stable law for $0 < \alpha < 2$. We show that under an appropriate scaling, its empirical eigenvalue distribution, as $N \to \infty$, converges weakly to a random symmetric probability distr

Torben C. Frost

In astronomy, gravitational lensing of light leads to the formation of multiple images, arcs, Einstein rings, and, most important, the shadow of black holes. Analogously in the vicinity of a massive compact object massive particles, following timelike geodesics, are gravitationally lensed. So far gravitational lensing of massive particles was mainly investig

Jianzhang Zhang, Yiyang Chen, Nan Niu, Yinglin Wang

Recently, various illustrative examples have shown the impressive ability of generative large language models (LLMs) to perform NLP related tasks. ChatGPT undoubtedly is the most representative model. We empirically evaluate ChatGPT's performance on requirements information retrieval (IR) tasks to derive insights into designing or developing more effective r

Yakun Yu, Jiuding Yang, Weidong Guo, Hui Liu

With the widespread popularity of user-generated short videos, it becomes increasingly challenging for content creators to promote their content to potential viewers. Automatically generating appealing titles and covers for short videos can help grab viewers' attention. Existing studies on video captioning mostly focus on generating factual descriptions of a

Ahan Kak, Van-Quan Pham, Huu-Trung Thieu, Nakjung Choi

In recent years, the radio access network (RAN) domain has witnessed a sea change with increasing levels of virtualization and softwarization driven by emerging paradigms such as the Open RAN (O-RAN) movement. However, the fundamental building block of the cellular network, i.e., the base station, remains unchanged and ill-equipped to handle this architectur

Federico Zimmerman, Lucía Pedraza, Joaquín Navajas, Pablo Balenzuela

Political polarization has become a growing concern in democratic societies, as it drives tribal alignments and erodes civic deliberation among citizens. Given its prevalence across different countries, previous research has sought to understand under which conditions people tend to endorse extreme opinions. However, in polarized contexts, citizens not only

Daniel Frolovsky, Sergei V. Ketov

The E-type $\alpha$-attractor models of single-field inflation were generalized further in order to accommodate production of primordial black holes (PBH) via adding a near-inflection point to the inflaton scalar potential at smaller scales, in good agreement with measurements of the cosmic microwave background (CMB) radiation. A minimal number of new parame

Boyuan Zhang, Jiannan Tian, Sheng Di, Xiaodong Yu

Today's large-scale scientific applications running on high-performance computing (HPC) systems generate vast data volumes. Thus, data compression is becoming a critical technique to mitigate the storage burden and data-movement cost. However, existing lossy compressors for scientific data cannot achieve a high compression ratio and throughput simultaneously

Ke Chen, Liangyan Li, Huan Liu, Yunzhe Li

Stereo Image Super-Resolution (stereoSR) has attracted significant attention in recent years due to the extensive deployment of dual cameras in mobile phones, autonomous vehicles and robots. In this work, we propose a new StereoSR method, named SwinFSR, based on an extension of SwinIR, originally designed for single image restoration, and the frequency domai

Jesús Arturo Jiménez González, Andrzej Mróz

Bidirected graphs are multigraphs where every edge has an independent direction at each end. In the paper, with an arbitrary bidirected graph we associate a non-negative integral quadratic form (called the incidence form of the graph), and determine all forms that appear in this way in two main results: first, among non-negative connected unit forms, precise

Yassine Sbai Sassi

We propose a rate optimal estimator for the linear regression model on network data with interacted (unobservable) individual effects. The estimator achieves a faster rate of convergence $N$ compared to the standard estimators' $\sqrt{N}$ rate and is efficient in cases that we discuss. We observe that the individual effects alter the eigenvalue distribution

Zhen Li, Faqiang Yuan

It was demonstrated by Comisso and Asenjo that the magnetic reconnection in the ergosphere is a promising mechanism to extract energy from the rotating Kerr black hole. In this work, we investigate the role of Comisso-Asenjo mechanism in energy extraction from the newly suggested rotating hairy black holes which have an extra hair due to the additional surro

Shaolin Chen, Hidetaka Hamada, Dou Xie

The main aim of this paper is to investigate the Hardy-Littlewood type Theorem and the Heinz type inequality on functions induced by a differential operator. We first prove a more general Hardy-Littlewood type theorem for the Dirichlet solution of a differential operator which depends on $\alpha >0$ over the unit ball $\mathbb{B}^n$ of $\mathbb{R}^n$ with $n

Ruofei Zhao, Songkai Xue, Yuekai Sun

In this paper, we study the convergence of the spectral embeddings obtained from the leading eigenvectors of certain similarity matrices to their population counterparts. We opt to study this convergence in a uniform (instead of average) sense and highlight the benefits of this choice. Using the Newton-Kantorovich Theorem and other tools from functional anal

Xiaoling Zhou, Nan Yang, Ou Wu

Adversarial training is an effective learning technique to improve the robustness of deep neural networks. In this study, the influence of adversarial training on deep learning models in terms of fairness, robustness, and generalization is theoretically investigated under more general perturbation scope that different samples can have different perturbation

Sicong Xie, Binbin Hu, Fengze Li, Ziqi Liu

Aiming at helping users locally discovery retail services (e.g., entertainment and dinning), Online to Offline (O2O) service platforms have become popular in recent years, which greatly challenge current recommender systems. With the real data in Alipay, a feeds-like scenario for O2O services, we find that recurrence based temporal patterns and position bias

Widemberg S. Nobre, Alexandra M. Schmidt, Erica E. M. Moodie, David A. Stephens

We propose and discuss a Bayesian procedure to estimate the average treatment effect (ATE) for multilevel observations in the presence of confounding. We focus on situations where the confounders may be latent (e.g., spatial latent effects). This work is motivated by an interest in determining the causal impact of directly observed therapy (DOT) on the succe

Liang Li, Pengfei Lan, Xiaosong Zhu, Peixiang Lu

High harmonic generation (HHG) from gas phase atoms (or molecules) has opened up a new frontier in ultrafast optics, where attosecond time resolution and Angstrom spatial resolution are accessible. The fundamental physical pictures of HHG are always explained by the laser-induced recollision of particle-like electron motion, which lay the foundation of attos

C. A. Moura, L. Quintino, F. Rossi-Torres

We analyze the expected arrival time spectrum of supernova neutrinos using simulated luminosity and compute the expected number of events in future detectors such as the DUNE Far Detector and Hyper-Kamiokande. We develop a general method using minimum square statistics that can compute the sensitivity to any variable affecting neutrino time of flight. We app

Stavros Garoufalidis, Don Zagier

This is an article about the work of Walter Neumann on hyperbolic geometry, ideal triangulations of 3-manifolds, the volume and Chern-Simons invariants of 3-manifolds and their elements of the the Bloch group. The article focuses on the relations of these classical invariants and the impact of Neumann's work to quantum topology and to mathematical physics.

Yuanhang Zhu, Kenneth Breuer

We experimentally study the aeroelastic instability boundaries and three-dimensional vortex dynamics of pitching swept wings, with the sweep angle ranging from 0 to 25 degrees. The structural dynamics of the wings are simulated using a cyber-physical control system. With a constant flow speed, a prescribed high inertia and a small structural damping, we show

Nuttirudee Charoenruk, Narongrid Asavaroungpipop, Pannee Pattanapradit, Kittiya Ku-kiattikun

The use of registers has been increasingly popular in the field of population census because of its advantages over the traditional census. While the traditional census requires a large amount of fieldwork and data collection, the registered-based census can rely on pre-existing administrative data. As a result, the register-based census can save both time a

Sara Hatami Gazani, Fardad Dadboud, Miodrag Bolic, Iraj Mantegh

Depth completion and object detection are two crucial tasks often used for aerial 3D mapping, path planning, and collision avoidance of Uncrewed Aerial Vehicles (UAVs). Common solutions include using measurements from a LiDAR sensor; however, the generated point cloud is often sparse and irregular and limits the system's capabilities in 3D rendering and safe

Kristopher Brown, David I. Spivak

The category Set_* of sets and partial functions is well-known to be traced monoidal, meaning that a partial function S+U -/-> T+U can be coherently transformed into a partial function S -/-> T. This transformation is generally described in terms of an implicit procedure that must be run. We make this procedure explicit by enriching the traced category in Ca

Xiaofei Guan, Xintong Wang, Hao Wu, Zihao Yang

In this paper, we introduce an innovative approach for addressing Bayesian inverse problems through the utilization of physics-informed invertible neural networks (PI-INN). The PI-INN framework encompasses two sub-networks: an invertible neural network (INN) and a neural basis network (NB-Net). The primary role of the NB-Net lies in modeling the spatial basi

Yuejiang Li, Zhanjiang Chen, H. Vicky Zhao

The emerging social network platforms enable users to share their own opinions, as well as to exchange opinions with others. However, adversarial network perturbation, where malicious users intentionally spread their extreme opinions, rumors, and misinformation to others, is ubiquitous in social networks. Such adversarial network perturbation greatly influen

Jiacheng Wang, Ping Liu, Jingen Liu, Wei Xu

Virtual try-on of eyeglasses involves placing eyeglasses of different shapes and styles onto a face image without physically trying them on. While existing methods have shown impressive results, the variety of eyeglasses styles is limited and the interactions are not always intuitive or efficient. To address these limitations, we propose a Text-guided Eyegla

Andrea Erdas

Using the Schwinger proper time method I calculate exactly the bubble diagram contribution to the thermal neutrino self-energy, obtaining exact results that cannot be achieved using the contact approximation. These results allow me to obtain the dispersion relation for neutrino in a medium under several different conditions.

Weifan Wang, Binbin Hu, Zhicheng Peng, Mingjie Zhong

Recently, the growth of service platforms brings great convenience to both users and merchants, where the service search engine plays a vital role in improving the user experience by quickly obtaining desirable results via textual queries. Unfortunately, users' uncontrollable search customs usually bring vast amounts of long-tail queries, which severely thre