April 2023 arXiv papers — page 113

Chaitanya Joshi, Mathew Q. Giso, Jean-François Louf, Sujit S. Datta

We recast the problem of hydrogel swelling under physical constraints as an energy optimization problem. We apply this approach to compute equilibrium shapes of hydrogel spheres confined within a jammed matrix of rigid beads, and interpret the results to determine how confinement modifies the mechanics of swollen hydrogels. In contrast to the unconfined case

Ahmed Zahari, Bouzid Mosbahi, Imed Basdouri

In this paper we study the structure and the algebraic varieties of associative trialgebras. We provide a classification of n-dimensional associative trialgebras for n $\leq$ 4. Using the classification result of associative trialgebras, we describe the derivations and centroids of low-dimensional associative trialgebras. We review some proprieties of the ce

Sheshera Mysore, Mahmood Jasim, Andrew McCallum, Hamed Zamani

Methods for making high-quality recommendations often rely on learning latent representations from interaction data. These methods, while performant, do not provide ready mechanisms for users to control the recommendation they receive. Our work tackles this problem by proposing LACE, a novel concept value bottleneck model for controllable text recommendation

Ashwin K Seshadri

In the geosciences, a recurring problem is one of estimating spatial means of a physical field using weighted averages of point observations. An important variant is when individual observations are counted with some probability less than one. This can occur in different contexts: from missing data to estimating the statistics across subsamples. In such situ

Ziyue Zhu, Qiang Meng, Xiao Wang, Ke Wang

This paper explores the potential of curriculum learning in LiDAR-based 3D object detection by proposing a curricular object manipulation (COM) framework. The framework embeds the curricular training strategy into both the loss design and the augmentation process. For the loss design, we propose the COMLoss to dynamically predict object-level difficulties an

Shimon Levit

These are extended lecture notes of the quantum mechanics course which I am teaching in the Weizmann Institute of Science graduate physics program. They cover the topics listed below. The first four chapter are posted here. Their content is detailed on the next page. The other chapters are planned to be added in the coming months. 1. Motion in External Elect

Olivier Fischer, Raphael Steiner

Given a graph $H$, let us denote by $f_\chi(H)$ and $f_\ell(H)$, respectively, the maximum chromatic number and the maximum list chromatic number of $H$-minor-free graphs. Hadwiger's famous coloring conjecture from 1943 states that $f_\chi(K_t)=t-1$ for every $t \ge 2$. In contrast, for list coloring it is known that $2t-o(t) \le f_\ell(K_t) \le O(t (\log \l

Avy Soffer, Xiaoxu Wu

We consider the Schr\"odinger equation with a general interaction term, which is localized in space, for radially symmetric initial data in $n$ dimensions, $n\geq5$. The interaction term may be space-time dependent and nonlinear. Assuming that the solution is bounded in $H^1(\mathbb{R}^n)$ uniformly in time, we prove soliton resolution conjecture (Asymptotic

Xinyang Li, Yuliang Jin

Can liquid-like and gas-like states be distinguished beyond the critical point, where the liquid-gas phase transition no longer exists and conventionally only a single supercritical fluid phase is defined? Recent experiments and simulations report strong evidence of dynamical crossovers above the critical temperature and pressure. Despite using different cri

Thien Hoang

The study of Mahonian statistics dated back to 1915 when MacMahon showed that the major index and the inverse number have the same distribution on a set of permutations with length n. Since then, many Mahonian statistics have been discovered and much effort have been done to find the equidistribution between two Mahonian statistics on permutations avoiding l

Yury Eliyashev

We construct an analog of the Hodge theory on complex manifolds on tropical curves. We use the analytical approach to the problem, it is based on language of tropical differential forms and methods of $L^2-$cohomologies.

Alberto Baccini, Cristina Re

This study investigates the role of editorial board members as gatekeepers in science, creating and utilizing a database of 1,516 active economics journals in 2019, which includes more than 44,000 scholars from over 6,000 institutions and 142 countries. The composition of these editorial boards is explored in terms of geographic affiliation, institutional af

Dimitrios Katsinis, Georgios Pastras, Nikolaos Tetradis

The entanglement entropy of a free scalar field in its ground state is dominated by an area law term. It is noteworthy, however, that the study of entanglement in scalar field theory has not advanced far beyond the ground state. In this paper, we extend the study of entanglement of harmonic systems, which include free scalar field theory as a continuum limit

Junhao Chen, Xueli wang

In this article, we strengthen the proof methods of some previously weakly consistent variants of random forests into strongly consistent proof methods, and improve the data utilization of these variants, in order to obtain better theoretical properties and experimental performance. In addition, based on the multinomial random forest (MRF) and Bernoulli rand

Jumpei F. Yamagishi, Kunihiko Kaneko

A simple cell model consisting of a catalytic reaction network with intermediate complex formation is numerically studied. As nutrients are depleted, the transition from the exponential growth phase to the growth-arrested dormant phase occurs along with hysteresis and a lag time for growth recovery. This transition is caused by the accumulation of intermedia

A. N. Ikot, U. S. Okorie, I. B. Okon, P. O. Amadi

A 2D Schrodinger equation with interacting Mobius square potential model is solved using Nikiforov-Uvarov Functional Analysis (NUFA) formalism. The energy spectra and the corresponding wave function for the linearly and exponentially varying quantum magnetic flux are obtained analytically in a closed form. The evaluated energy spectra are used to obtain an e

Linhao Qu, Minghong Duan, Zhiwei Yang, Manning Wang

Existing super-resolution models for pathology images can only work in fixed integer magnifications and have limited performance. Though implicit neural network-based methods have shown promising results in arbitrary-scale super-resolution of natural images, it is not effective to directly apply them in pathology images, because pathology images have special

Xuran Pan, Tianzhu Ye, Zhuofan Xia, Shiji Song

Self-attention mechanism has been a key factor in the recent progress of Vision Transformer (ViT), which enables adaptive feature extraction from global contexts. However, existing self-attention methods either adopt sparse global attention or window attention to reduce the computation complexity, which may compromise the local feature learning or subject to

Anindya Bhattacharya, Anirban Kar, Alita Nandi

In this paper we explore two intertwined issues. First, using primary data we examine the impact of asymmetric networks, built on rich relational information on several spheres of living, on access to workfare employment in rural India. We find that unidirectional relations, as opposed to reciprocal relations, and the concentration of such unidirectional rel

G. E. Volovik

The theory of quantum gravity, in which tetrads emerge as the bilinear combinations of the fermionic field, suggests that in general relativity the interval $ds$ is dimensionless. Several other approaches to quantum gravity, including the model of superplastic vacuum and $BF$-theories of gravity support this suggestion. The important consequence of such metr

Tengfei Xu, Dachuan Liu, Peng Hao, Bo Wang

Neural operators as novel neural architectures for fast approximating solution operators of partial differential equations (PDEs), have shown considerable promise for future scientific computing. However, the mainstream of training neural operators is still data-driven, which needs an expensive ground-truth dataset from various sources (e.g., solving PDEs' s

Sicong Cao, Biao He, Xiaobing Sun, Yu Ouyang

Java deserialization vulnerability is a severe threat in practice. Researchers have proposed static analysis solutions to locate candidate vulnerabilities and fuzzing solutions to generate proof-of-concept (PoC) serialized objects to trigger them. However, existing solutions have limited effectiveness and efficiency. In this paper, we propose a novel hybrid

Mostafa Emara, Nour Kouzayha, Hesham ElSawy, Tareq Y. Al-Naffouri

Feedback transmissions are used to acknowledge correct packet reception, trigger erroneous packet re-transmissions, and adapt transmission parameters (e.g., rate and power). Despite the paramount role of feedback in establishing reliable communication links, the majority of the literature overlooks its impact by assuming genie-aided systems relying on flawle

Dingkang Liang, Jiahao Xie, Zhikang Zou, Xiaoqing Ye

Supervised crowd counting relies heavily on costly manual labeling, which is difficult and expensive, especially in dense scenes. To alleviate the problem, we propose a novel unsupervised framework for crowd counting, named CrowdCLIP. The core idea is built on two observations: 1) the recent contrastive pre-trained vision-language model (CLIP) has presented

Eyal Buks

We experimentally study a fiber loop laser with an integrated Erbium doped fiber (EDF). The output optical spectrum is measured as a function of the EDF temperature. We find that below a critical temperature of about 10K the measured optical spectrum exhibits a sequence of narrow and unequally-spaced peaks. Externally injected light and filtering are employe

Rui Chen, Shun-Qing Shen

In topological insulators, massive surface bands resulting from local symmetry breaking are believed to exhibit a half-quantized Hall conductance. However, such scenarios are obviously inconsistent with the Thouless-Kohmoto-Nightingale-Nijs theorem, which states that a single band in a lattice with a finite Brillouin zone can only have an integer-quantized H

Yanru Xiao, Cong Wang, Xing Gao

The vulnerability in the algorithm supply chain of deep learning has imposed new challenges to image retrieval systems in the downstream. Among a variety of techniques, deep hashing is gaining popularity. As it inherits the algorithmic backend from deep learning, a handful of attacks are recently proposed to disrupt normal image retrieval. Unfortunately, the

Jun Chen, Deyao Zhu, Kilichbek Haydarov, Xiang Li

Video captioning aims to convey dynamic scenes from videos using natural language, facilitating the understanding of spatiotemporal information within our environment. Although there have been recent advances, generating detailed and enriched video descriptions continues to be a substantial challenge. In this work, we introduce Video ChatCaptioner, an innova

Jin-tao Wu, Ming-jun Feng, Jian-hong Ruan

Electron(positron), proton and nuclei can be accelerated to very high energy by local supernova remnants (SNR). The famous excesses of electron and proton (nuclei) potentially come from such kind of local sources. Recently, the DAMPE experiment measured the electron spectrum (including both electrons and positrons) of cosmic rays with high-accuracy. It provi

Saikat Roy, Gregor Koehler, Michael Baumgartner, Constantin Ulrich

Owing to success in the data-rich domain of natural images, Transformers have recently become popular in medical image segmentation. However, the pairing of Transformers with convolutional blocks in varying architectural permutations leaves their relative effectiveness to open interpretation. We introduce Transformer Ablations that replace the Transformer bl

Anas El Hachimi, Khalide Jbilou, Ahmed Ratnani, Lothar Reichel

The tensor t-product, introduced by Kilmer and Martin [26], is a powerful tool for the analysis of and computation with third-order tensors. This paper introduces eigentubes and eigenslices of third-order tensors under the t-product. The eigentubes and eigenslices are analogues of eigenvalues and eigenvectors for matrices. Properties of eigentubes and eigens

Arapat Ablimit, Feng-Hua Ren, Run-Hong He, Yang-Yang Xie

Control of the dynamics of an open quantum system is crucial in quantum information processing. Basically there are two ways: one is the control on the system and the other is tuning the bath parameters. In this paper, we use the latter to analyze the non-Markovian dynamics of the open system. The model is that the system is immersed in non-Markovian squeeze

Xuanqi Gao, Juan Zhai, Shiqing Ma, Chao Shen

Due to the model aging problem, Deep Neural Networks (DNNs) need updates to adjust them to new data distributions. The common practice leverages incremental learning (IL), e.g., Class-based Incremental Learning (CIL) that updates output labels, to update the model with new data and a limited number of old data. This avoids heavyweight training (from scratch)

R. Conceição, P. J. Costa, L. Gibilisco, M. Pimenta

In this article, it is shown that the $C_k$ and $LCm$ variables, recently introduced as an effective way to discriminate gamma and proton-induced showers in large wide-field gamma-ray observatories, can be generalised to be used in arrays of different detectors and variable fill factors. In particular, the $C_k$ profile discrimination capabilities are evalua

Taeho Kim, Pierre Chausse, Matteo Bottai, Gheorghe Doros

This paper studies predictor functions motivated by maximizing a measure of agreement with the predictand. Specifically, it examines distributional properties and predictive performance of the estimated maximum agreement linear predictor (MALP), the linear predictor maximizing Lin's concordance correlation coefficient (CCC) between the predictor and the pred

Silvio Giancola, Anthony Cioppa, Julia Georgieva, Johsan Billingham

Association football is a complex and dynamic sport, with numerous actions occurring simultaneously in each game. Analyzing football videos is challenging and requires identifying subtle and diverse spatio-temporal patterns. Despite recent advances in computer vision, current algorithms still face significant challenges when learning from limited annotated d

Andronikos Paliathanasis

We study the evolution of the physical variables in $f\left( Q\right) $-gravity for two families of symmetric and flat connections in a spatially flat Friedmann--Lema\^{\i}tre--Robertson--Walker geometry where the equation of motion for the nonmetricity scalar is not trivially identity. From the analysis of dynamics we found that the de Sitter universe is al

Lei Guo, Chunxiao Wang, Xinhua Wang, Lei Zhu

Cross-domain Recommendation (CR) has been extensively studied in recent years to alleviate the data sparsity issue in recommender systems by utilizing different domain information. In this work, we focus on the more general Non-overlapping Cross-domain Sequential Recommendation (NCSR) scenario. NCSR is challenging because there are no overlapped entities (e.

Ming-Feng Li, Min Sun

In modern fulfillment warehouses, agents traverse the map to complete endless tasks that arrive on the fly, which is formulated as a lifelong Multi-Agent Path Finding (lifelong MAPF) problem. The goal of tackling this challenging problem is to find the path for each agent in a finite runtime while maximizing the throughput. However, existing methods encounte

Zhaohui Qin

In this paper, a second-order accurate method was developed for calculating fluid flows in complex geometries. This method uses cut-Cartesian cell mesh in finite volume framework. Calculus is employed to relate fluxes and gradients along curved surfaces to cell-averaged values. The resultant finite difference equations are sparse diagonal systems of equation

Michael Verhage, Pantelis Bampoulis, Marco D. Preuss, Ivo Filot

The chirality induced spin selectivity (CISS) effect is a fascinating phenomena correlating molecular structure with electron spin-polarisation in excited state measurements. Experimental procedures to quantify the spin-filtering magnitude relies generally on averaging data sets, especially those from magnetic field dependent conductive-AFM. We investigate t

Xiao Xue, Hua-Dong Yao, Lars Davidson

The synthetic turbulence generator (STG) lies at the interface of the Reynolds averaged Navier-Stokes (RANS) simulation and large eddy simulation (LES). This paper presents a STG for the multiple-relaxation-time(MRT) lattice Boltzmann method(LBM) framework at high friction Reynolds numbers, with consideration of near wall modeling. The Reichardt wall law, in

Shoki Sugimoto, Joscha Henheik, Volodymyr Riabov, László Erdős

We prove the Eigenstate Thermalisation Hypothesis (ETH) for local observables in a typical translation invariant system of quantum spins with mean field interaction. This mathematically verifies the observation made in [L.Santos and M.Rigol, Phys.Rev.E 82, 031130 (2010, https://journals.aps.org/pre/abstract/10.1103/PhysRevE.82.031130)] that ETH may hold for

David Beauchemin, Richard Khoury

This paper presents RISC, an open-source Python package data generator (https://github.com/GRAAL-Research/risc). RISC generates look-alike automobile insurance contracts based on the Quebec regulatory insurance form in French and English. Insurance contracts are 90 to 100 pages long and use complex legal and insurance-specific vocabulary for a layperson. Hen

Jian Shi, Ni Zhang

Anomaly detection suffered from the lack of anomalies due to the diversity of abnormalities and the difficulties of obtaining large-scale anomaly data. Semi-supervised anomaly detection methods are often used to solely leverage normal data to detect abnormalities that deviated from the learnt normality distributions. Meanwhile, given the fact that limited an

Ze-Yan Hao, Yan Wang, Jia-Kun Li, Yu Xiang

Einstein-Podolsky-Rosen (EPR) steering, a fundamental concept of quantum nonlocality, describes one observer's capability to remotely affect another distant observer's state by local measurements. Unlike quantum entanglement and Bell nonlocality, both associated with the symmetric quantum correlation, EPR steering depicts the unique asymmetric property of qu

Yan Yin, Qihua Gong, Min Yi, Wanlin Guo

The advancement of two-dimensional polar metals tends to be limited by the incompatibility between electric polarity and metallicity as well as dimension reduction. Here, we report polar and metallic Janus monolayers of MoSi$_2$N$_4$ family by breaking the out-of-plane (OOP) structural symmetry through Z (P/As) substitution of N. Despite the semiconducting n

H. K. Avetissian, S. Sukiasyan, H. H. Matevosyan, G. F. Mkrtchian

The objective of this article is to investigate the profound nonlinear optical response exhibited by inversion symmetric fullerene molecules under the influence of different types of disorders described by the Anderson model. Our aim is to elucidate the localization effects on the spectra of high harmonic generation in such molecules. We show that the disord

Yu Chen, Yongjian Xu, Ning Xu

As the feature size of semiconductor technology shrinks to 10 nm and beyond, the multiple patterning lithography (MPL) attracts more attention from the industry. In this paper, we model the layout decomposition of MPL as a generalized graph coloring problem, which is addressed by a distribution evolutionary algorithm based on a population of probabilistic mo

Themba Dube, Amartya Goswami

The aim of this paper is to study some distinguished classes of $k$-ideals of semirings, which include $k$-prime, $k$-semiprime, $k$-radical, $k$-irreducible, and $k$-strongly irreducible ideals. We discuss some of the properties of $k$-ideals and their products, intersections, and ideal quotients under semiring homomorphisms.

Jiawei Feng, Ancong Wu, Wei-Shi Zheng

Due to the modality gap between visible and infrared images with high visual ambiguity, learning \textbf{diverse} modality-shared semantic concepts for visible-infrared person re-identification (VI-ReID) remains a challenging problem. Body shape is one of the significant modality-shared cues for VI-ReID. To dig more diverse modality-shared cues, we expect th

Linlin Zhu, Guanghui Hu

Suppose that a plane wave is incident onto an impenetrable grating profile of Dirichlet or Impedance type or a penetrable grating. The grating interface is assumed to be given by a Lipschitz function in two dimensions. We derive stability estimate of the grating diffraction problem via variational method with an explicit dependence of solutions on the incide

Delong Liu, Shichao Li, Tianyi Shi, Zhu Meng

Among numerous studies for driver state detection, wearable physiological measurements offer a practical method for real-time monitoring. However, there are few driver physiological datasets in open-road scenarios, and the existing datasets suffer from issues such as poor signal quality, small sample sizes, and short data collection periods. Therefore, in th

Anders Johansson, Anders Öberg, Mark Pollicott

In this article we address a well known problem at the intersection of ergodic theory and statistical mechanics. We prove that there exists a continuous eigenfunction for the transfer operator corresponding to pair potentials that satisfy a square summability condition on the variations, when the inverse temperature is subcritical. As a corollary we obtain a

Nikolaos S. Papageorgiou, Dušan D. Repovš, Calogero Vetro

We consider an anisotropic $(p,2)$-equation, with a parametric and superlinear reaction term. We show that for all small values of the parameter the problem has at least five nontrivial smooth solutions, four with constant sign and the fifth nodal (sign-changing). The proofs use tools from critical point theory, truncation and comparison techniques, and crit

Bruce Allen, Sanjeev Dhurandhar, Yashwant Gupta, Maura McLaughlin

Pulsar timing arrays (PTAs) provide a way to detect gravitational waves at nanohertz frequencies. In this band, the most likely signals are stochastic, with a power spectrum that rises steeply at lower frequencies. Indeed, the observation of a common red noise process in pulsar-timing data suggests that the first credible detection of nanohertz-frequency gra

Changjie Qiu, Zhiyong Wang, Xiuhong Lin, Yu Zang

Existing point cloud modeling datasets primarily express the modeling precision by pose or trajectory precision rather than the point cloud modeling effect itself. Under this demand, we first independently construct a set of LiDAR system with an optical stage, and then we build a HPMB dataset based on the constructed LiDAR system, a High-Precision, Multi-Bea

Verya Monjezi, Ashutosh Trivedi, Gang Tan, Saeid Tizpaz-Niari

The deep feedforward neural networks (DNNs) are increasingly deployed in socioeconomic critical decision support software systems. DNNs are exceptionally good at finding minimal, sufficient statistical patterns within their training data. Consequently, DNNs may learn to encode decisions -- amplifying existing biases or introducing new ones -- that may disadv

Guohua Liu, Yan Peng

We investigate distributions of matter fields outside spherically symmetric ultra-compact objects in the asymptotically flat background. Based on the dominant energy condition and the non-negative trace condition, we analytically find a no long hair behavior, which states that the effective radius of matter field hairs cannot extend beyond the outermost null

Pei Li, Péter Udvarhelyi, Song Li, Bing Huang

Defect qubits in 4H-SiC are outstanding candidates for numerous applications in the rapidly emerging field of quantum technology. Carbon clusters can act as emission sources that may appear after thermal oxidation of 4H-SiC or during irradiation which kicks out carbon atoms from their sites. These fluorescent carbon clusters could interfere with the already

Mohammad Heydari, Ashkan Khalifeh

The present paper is concerned with a recursive algorithm as a preprocessing step to find the convex hull of $n$ random points uniformly distributed in the plane. For such a set of points, it is shown that eliminating all but $O(\log n)$ of points can derive the same convex hull as the input set. Finally it will be shown that the running time of the algorith

Terence Eden

A prototype Metaverse experience was created in which users could explore hierarchical cybersecurity data. A small group of participants were surveyed on their attitudes to the Metaverse. They then completed a short series of tasks in the environment. Questions were asked to assess if they were suffering from Cybersickness. After completing further tasks, th

Benben Jiang, Yixing Wang, Zhenghua Ma, Qiugang Lu

Fast charging has attracted increasing attention from the battery community for electrical vehicles (EVs) to alleviate range anxiety and reduce charging time for EVs. However, inappropriate charging strategies would cause severe degradation of batteries or even hazardous accidents. To optimize fast-charging strategies under various constraints, particularly

M. Farasat Shamir, Eesha Meer

The main goal of this work is to provide a comprehensive study of relativistic structures in the context of recently proposed {$\mathcal{R}+ \alpha \mathcal{A}$} gravity, where $\mathcal{R}$ is the Ricci scalar, and $\mathcal{A}$ is the anti-curvature scalar. For this purpose, we examine a new classification of embedded class-I solutions of compact stars. To

Haopeng Zhang, Xiao Liu, Jiawei Zhang

Extractive summarization is a crucial task in natural language processing that aims to condense long documents into shorter versions by directly extracting sentences. The recent introduction of large language models has attracted significant interest in the NLP community due to its remarkable performance on a wide range of downstream tasks. This paper first

Demetris Chrysostomou, Jose Luis Rueda Torres, Jochen Lorenz Cremer

Power electronic interfaced devices progressively enable the increasing provision of flexible operational actions in distribution networks. The feasible flexibility these devices can effectively provide requires estimation and quantification so the network operators can plan operations close to real-time. Existing approaches estimating the distribution netwo

Jiajun Hu, Jian Xiao

We explore the applications of Lorentzian polynomials to the fields of algebraic geometry, analytic geometry and convex geometry. In particular, we establish a series of intersection theoretic inequalities, which we call rKT property, with respect to $m$-positive classes and Schur classes. We also study its convexity variants -- the geometric inequalities fo

Ye Jiang

This paper describes the participation of team QUST in the SemEval2023 task 3. The monolingual models are first evaluated with the under-sampling of the majority classes in the early stage of the task. Then, the pre-trained multilingual model is fine-tuned with a combination of the class weights and the sample weights. Two different fine-tuning strategies, t

Youssef Azouzi

In our work, we provide a constructive proof of a generalized version of Cantor's diagonal argument for nets. This result expands the well-known technique beyond sequences, allowing it to be applied to a broader context. This result has significant potential applications in various fields, and we demonstrate a few of these in our work. One such application i

Qi Wu, David Bauer, Yuyang Chen, Kwan-Liu Ma

Implicit Neural Representations (INRs) have recently exhibited immense potential in the field of scientific visualization for both data generation and visualization tasks. However, these representations often consist of large multi-layer perceptrons (MLPs), necessitating millions of operations for a single forward pass, consequently hindering interactive vis

Ye Jiang, Xiaomin Yu, Yimin Wang, Xiaoman Xu

The standard paradigm for fake news detection mainly utilizes text information to model the truthfulness of news. However, the discourse of online fake news is typically subtle and it requires expert knowledge to use textual information to debunk fake news. Recently, studies focusing on multimodal fake news detection have outperformed text-only methods. Rece

M. P. Liul, A. I. Ryzhov, S. N. Shevchenko

We theoretically describe a driven two-electron four-level double-quantum dot (DQD) tunnel coupled to a fermionic sea by using the rate-equation formalism. This approach allows to find occupation probabilities of each DQD energy level in a relatively simple way, compared to other methods. Calculated dependencies are compared with the experimental results. Th

Yinhao Li, Jinrong Yang, Jianjian Sun, Han Bao

Bounded by the inherent ambiguity of depth perception, contemporary multi-view 3D object detection methods fall into the performance bottleneck. Intuitively, leveraging temporal multi-view stereo (MVS) technology is the natural knowledge for tackling this ambiguity. However, traditional attempts of MVS has two limitations when applying to 3D object detection

Jan-Henrik Metsch

These notes provide a self-contained introduction to Schauder theory on manifolds. First, we derive Schauder estimates for a fourth-order parabolic linear problem with a first- and third-order boundary condition on a smooth compact manifold M. Then, applying these, we prove the existence of solutions and study the associated elliptic problem.

Mehdi Hassani

We obtain global explicit numerical bounds, with best possible constants, for the differences $\frac{1}{n}\sum_{k\leq n}\omega(k)-\log\log n$ and$ \frac{1}{n}\sum_{k\leq n}\Omega(k)-\log\log n$, where $\omega(k)$ and $\Omega(k)$ refer to the number of distinct prime divisors, and the total number of prime divisors of $k$, respectively.

Shuai Li, Ziqi Chen, Hongtu Zhu, Christina Dan Wang

The conditional randomization test (CRT) was recently proposed to test whether two random variables X and Y are conditionally independent given random variables Z. The CRT assumes that the conditional distribution of X given Z is known under the null hypothesis and then it is compared to the distribution of the observed samples of the original data. The aim

Jie Liu

Let $k$ be an algebraically closed field. The generalized or $n$-Kronecker quiver $K(n)$ is the quiver with two vertices, called a source and a sink, and $n$ arrows from source to sink. Given a finite-dimensional module $M$ of the path algebra $kK(n)=\mathcal{K}_n$, we consider its dimension vector $\underline{dim} M=(\dim_k M_1, \dim_k M_2)$. Let $\mathbf{F

Avdhesh Kumar, Berndt Müller, Di-Lun Yang

We explain how spin alignment of vector mesons can be induced by background fields, such as electromagnetic fields or soft gluon fields. Our study is based on the quantum kinetic theory of spinning quarks and antiquarks and incorporates the relaxation of the dynamically generated spin polarization. The spin density matrix of vector mesons is obtained by quar

Manas Chakrabortty, Nayem Sk, Abhik Kumar Sanyal

Unlike F(R) gravity, pure metric F(T) gravity in the vacuum dominated era, ends up with an imaginary action and is therefore not feasible. This eerie situation may only be circumvented by associating a scalar field, which can also drive inflation in the very early universe. We show that, despite diverse claims, F(T) theory admits Noether symmetry only in the

Yulu Gao, Chonghao Sima, Shaoshuai Shi, Shangzhe Di

With the prevalence of multimodal learning, camera-LiDAR fusion has gained popularity in 3D object detection. Although multiple fusion approaches have been proposed, they can be classified into either sparse-only or dense-only fashion based on the feature representation in the fusion module. In this paper, we analyze them in a common taxonomy and thereafter

Apurba Das, Abdenacer Makhlouf

The notion of embedding tensors and the associated tensor hierarchies form an effective tool for the construction of supergravity and higher gauge theories. Embedding tensors and related structures are extensively studied also in the mathematics literature. On the other hand, Hom-Lie algebras were introduced in the study of $q$-deformations of Witt and Viras

Andrea Grimaldi, Luciano Mazza, Eleonora Raimondo, Pietro Tullo

The commercial and industrial demand for the solution of hard combinatorial optimization problems push forward the development of efficient solvers. One of them is the Ising machine which can solve combinatorial problems mapped to Ising Hamiltonians. In particular, spintronic hardware implementations of Ising machines can be very efficient in terms of area a

Wei Lu, Xia Wu

In this paper, we completely determine the number of solutions to $ \operatorname{Tr}^{q^2}_q(bx+b)+c=0, x\in \mu_{q+1}\backslash \{-1\}$ for all $b\in \mathbb{F}_{q^2}, c\in\mathbb{F}_{q}$. As an application, we can give the weight distributions of a class of linear codes, and give a completely answer to a recent conjecture about a class of NMDS codes propo

Tianjiao Li, Lin Geng Foo, Ping Hu, Xindi Shang

Learning with large-scale unlabeled data has become a powerful tool for pre-training Visual Transformers (VTs). However, prior works tend to overlook that, in real-world scenarios, the input data may be corrupted and unreliable. Pre-training VTs on such corrupted data can be challenging, especially when we pre-train via the masked autoencoding approach, wher

Wenbao Ai, Wei Liang, Jianhua Yuan

In this paper, we consider the problem of minimizing a general homogeneous quadratic function, subject to three real or four complex homogeneous quadratic inequality or equality constraints. For this problem, we present a sufficient and necessary test condition to detect whether its typical semidefinite programming (SDP) relaxation is tight or not. This test

Minh Le

This paper investigates the global existence of solutions to Keller-Segel systems with sub-logistic sources using the test function method. Prior work demonstrated that sub-logistic sources $f(u)=ru -\mu \frac{u^2}{\ln^p(u+e)}$ with $p\in(0,1)$ can prevent blow-up solutions for the 2D minimal Keller-Segel chemotaxis model. Our study extends this result by sh

Tehila Dahan, Kfir Y. Levy

We consider stochastic convex optimization problems where the objective is an expectation over smooth functions. For this setting we suggest a novel gradient estimate that combines two recent mechanism that are related to notion of momentum. Then, we design an SGD-style algorithm as well as an accelerated version that make use of this new estimator, and demo

Weiwei Sun, Lingyong Yan, Zheng Chen, Shuaiqiang Wang

Conventional document retrieval techniques are mainly based on the index-retrieve paradigm. It is challenging to optimize pipelines based on this paradigm in an end-to-end manner. As an alternative, generative retrieval represents documents as identifiers (docid) and retrieves documents by generating docids, enabling end-to-end modeling of document retrieval

Yechan Park, Nakahiro Yoshida

In bandit algorithms, the randomly time-varying adaptive experimental design makes it difficult to apply traditional limit theorems to off-policy evaluation of the treatment effect. Moreover, the normal approximation by the central limit theorem becomes unsatisfactory for lack of information due to the small sample size of the inferior arm. To resolve this i

Tehila Dahan, Kfir Y. Levy

We consider distributed learning scenarios where M machines interact with a parameter server along several communication rounds in order to minimize a joint objective function. Focusing on the heterogeneous case, where different machines may draw samples from different data-distributions, we design the first local update method that provably benefits over th

Beini Xie, Heng Chang, Ziwei Zhang, Xin Wang

Graph Neural Networks (GNNs) obtain tremendous success in modeling relational data. Still, they are prone to adversarial attacks, which are massive threats to applying GNNs to risk-sensitive domains. Existing defensive methods neither guarantee performance facing new data/tasks or adversarial attacks nor provide insights to understand GNN robustness from an

Akshay Gaikwad, Omkar Bihani, Arvind, Kavita Dorai

In this study we employ a feed-forward artificial neural network (FFNN) architecture to perform tomography of quantum states and processes obtained from noisy experimental data. To evaluate the performance of the FFNN, we use a heavily reduced data set and show that the density and process matrices of unknown quantum states and processes can be reconstructed

Xunzhao Yu, Yan Wang, Ling Zhu, Dimitar Filev

Optimization algorithms are very different from human optimizers. A human being would gain more experiences through problem-solving, which helps her/him in solving a new unseen problem. Yet an optimization algorithm never gains any experiences by solving more problems. In recent years, efforts have been made towards endowing optimization algorithms with some

Hugues Beauchesne, Cheng-Wei Chiang

Many collider searches have attempted to detect the Higgs boson decaying to a photon and an invisible massless dark photon. For the branching ratio to this channel to be realistically observable at the LHC, there must exist new mediators that interact with both the standard model and the dark photon. In this paper, we study experimental and theoretical const

Janamejaya Channegowda, Vageesh Maiya, Chaitanya Lingaraj

Recent surge in the number of Electric Vehicles have created a need to develop inexpensive energy-dense Battery Storage Systems. Many countries across the planet have put in place concrete measures to reduce and subsequently limit the number of vehicles powered by fossil fuels. Lithium-ion based batteries are presently dominating the electric automotive sect

Kang Wei, Jun Li, Chuan Ma, Ming Ding

Federated learning (FL) enables distributed clients to collaboratively train a machine learning model without sharing raw data with each other. However, it suffers the leakage of private information from uploading models. In addition, as the model size grows, the training latency increases due to limited transmission bandwidth and the model performance degra

Puguang An, Peng Yang, Xianbin Cao, Kun Guo

The integration of a near-space information network (NSIN) with the reconfigurable intelligent surface (RIS) is envisioned to significantly enhance the communication performance of future wireless communication systems by proactively altering wireless channels. This paper investigates the problem of deploying a RIS-integrated NSIN to provide energy-efficient

Shunfeng Chu, Jun Li, Kang Wei, Yuwen Qian

Hierarchical Federated Learning (HFL) is a distributed machine learning paradigm tailored for multi-tiered computation architectures, which supports massive access of devices' models simultaneously. To enable efficient HFL, it is crucial to design suitable incentive mechanisms to ensure that devices actively participate in local training. However, there are

Zanoby N. Khan

COVID19 is a highly contagious disease infected millions of people worldwide. With limited testing components, screening tools such as chest radiography can assist the clinicians in the diagnosis and assessing the progress of disease. The performance of deep learning-based systems for diagnosis of COVID-19 disease in radiograph images has been encouraging. T

Huayue Zhang, Xueyan Tang, Hongxia Yuan, Xin Long

In this paper, we study the nonlinear optical bistability (OB) in a symmetrical multilayer structure. This structure is constructed by embedding a nonlinear three-dimensional Dirac semimetal (3D DSM) into a solution filled one-dimensional photonic crystal Fabry-Perot cavity. OB stems from the third order nonlinear conductivity of 3D DSM and the local field o