March 2023 arXiv papers — page 115

Kun Zhou, Wenbo Li, Yi Wang, Tao Hu

Neural radiance fields (NeRF) show great success in novel view synthesis. However, in real-world scenes, recovering high-quality details from the source images is still challenging for the existing NeRF-based approaches, due to the potential imperfect calibration information and scene representation inaccuracy. Even with high-quality training frames, the syn

Florian Maudet, Charlotte Van Dijck, Muhammad Hamid Raza, Catherine Dubourdieu

Spectroscopic ellipsometry is a powerful method with high surface sensitivity that can be used to monitor the growth of even sub-monolayer film. However, the analysis of ultrathin films is complicated by the correlation of the dielectric constant and the thickness. This problem is usually resolved by fixing one or the other value, limiting the information th

Carl Johan Casselgren

We consider extensions of Brooks' classic theorem on vertex coloring where some colors cannot be used on certain vertices. In particular we prove that if $G$ is a connected graph with maximum degree $\Delta(G) \geq 4$ that is not a complete graph and $P \subseteq V(G)$ is a set of vertices where either (i) at most $\Delta(G)-2$ colors are forbidden for every

Miha Brešar, Aleksandar Mijatović, Andrew Wade

We quantify the asymptotic behaviour of multidimensional drifltess diffusions in domains unbounded in a single direction, with asymptotically normal reflections from the boundary. We identify the critical growth/contraction rates of the domain that separate stability, null recurrence and transience. In the stable case we prove existence and uniqueness of the

Xiaoxiang Zhou, Yongkai Li, Zhe Liu, Jiahao Hao

The transport and optical properties of the Nb-doped Cs(V$_{1-x}$Nb$_{x}$)$_{3}$Sb$_{5}$ with x = 0.03 and 0.07 have been investigated and compared with those of the undoped CsV$_{3}$Sb$_{5}$. Upon Nb doping, the charge-density wave (CDW) transition temperature $T_{\text{CDW}}$ is suppressed, and the superconducting temperature $T_{c}$ rises. The residual re

Ksheera Sagar, Jyotishka Datta, Sayantan Banerjee, Anindya Bhadra

Sparse structure learning in high-dimensional Gaussian graphical models is an important problem in multivariate statistical signal processing; since the sparsity pattern naturally encodes the conditional independence relationship among variables. However, maximum a posteriori (MAP) estimation is challenging under hierarchical prior models, and traditional nu

Maciej Kościelski

This work presents a theoretical study of a protocol for dynamical generation and storage of the durable, highly entangled Greenberger-Horne-Zeilinger (GHZ) state in a system composed of bosonic atoms loaded into a one-dimensional optical lattice potential. A method of indicating entanglement in the system is also presented. The system ground-state can be ei

Hongyu Li, Shanpu Shen, Bruno Clerckx

This work focuses on the synergy of rate-splitting multiple access (RSMA) and beyond diagonal reconfigurable intelligent surface (BD-RIS) to enlarge the coverage, improve the performance, and save on antennas. Specifically, we employ a multi-sector BD-RIS modeled as a prism, which can achieve highly directional full-space coverage, in a multiuser multiple in

Montek Singh Gill

We study the smooth path spaces of Euclidean spaces $\mathbb{R}^N$, as diffeological spaces. We show that the tangent spaces of the free path space $\mathscr{P}$ are isomorphic to $\mathscr{P}$ itself, and that the tangent spaces of the space $\mathscr{P}_{\mathbf{p}, \mathbf{q}}$ of paths with fixed endpoints $\mathbf{p}$ and $\mathbf{q}$ are isomorphic to

Yutong Feng, Biao Gong, Jianwen Jiang, Yiliang Lv

Foundation models are pre-trained on massive data and transferred to downstream tasks via fine-tuning. This work presents Vision Middleware (ViM), a new learning paradigm that targets unified transferring from a single foundation model to a variety of downstream tasks. ViM consists of a zoo of lightweight plug-in modules, each of which is independently learn

Zhe Xue, Yuan Zhang, Zhennan Zhou, Min Tang

It has been noticed that when the waiting time distribution exhibits a transition from an intermediate time power law decay to a long-time exponential decay in the continuous time random walk model, a transition from anomalous diffusion to normal diffusion can be observed at the population level. However, the mechanism behind the transition of waiting time d

R. L. Becerra, E. Troja, A. M. Watson, B. O'Connor

GRB~210704A is a burst of intermediate duration ($T_{90} \sim 1-4$~s) followed by a fading afterglow and an optical excess that peaked about 7 days after the explosion. Its properties, and in particular those of the excess, do not easily fit into the well established classification scheme of GRBs as being long or short, leaving the nature of its progenitor u

Wenxiao Wang, Wei Chen, Qibo Qiu, Long Chen

While features of different scales are perceptually important to visual inputs, existing vision transformers do not yet take advantage of them explicitly. To this end, we first propose a cross-scale vision transformer, CrossFormer. It introduces a cross-scale embedding layer (CEL) and a long-short distance attention (LSDA). On the one hand, CEL blends each t

Nafiseh Jabbari Tofighi, Mohamed Hedi Elfkir, Nevrez Imamoglu, Cagri Ozcinar

Omnidirectional images, aka 360 images, can deliver immersive and interactive visual experiences. As their popularity has increased dramatically in recent years, evaluating the quality of 360 images has become a problem of interest since it provides insights for capturing, transmitting, and consuming this new media. However, directly adapting quality assessm

Alexander Ivanov

This collection bundles the following memos dedicated to the so called exact base-21 (EBTO) and quasi base-21 (QBTO) serial transport codes: [1] "Base-21 Scrambling" (discusses about EBTO codes, present at pp. 1-4 in the bundle); [2] "Base-21 Word Alignment and Boundary Detection" (EBTO, pp. 5-8); [3] "Quasi Base-21 Words" (QBTO, pp. 9-12); [4] "Quasi Base-2

Sixiang Chen, Tian Ye, Jun Shi, Yun Liu

Varicolored haze caused by chromatic casts poses haze removal and depth estimation challenges. Recent learning-based depth estimation methods are mainly targeted at dehazing first and estimating depth subsequently from haze-free scenes. This way, the inner connections between colored haze and scene depth are lost. In this paper, we propose a real-time transf

Digbalay Bose, Rajat Hebbar, Krishna Somandepalli, Shrikanth Narayanan

The process of human affect understanding involves the ability to infer person specific emotional states from various sources including images, speech, and language. Affect perception from images has predominantly focused on expressions extracted from salient face crops. However, emotions perceived by humans rely on multiple contextual cues including social

Liang Xu, Mingti Zhou, Runxia Tao, Zhipeng Zhong

Sequential weak measurements allow the direct extraction of individual density-matrix elements instead of globally reconstructing the whole density matrix, opening a new avenue for the characterization of quantum systems. Nevertheless, the requirement of multiple coupling for each qudit of quantum systems and the lack of appropriate precision evaluation cons

Yudan Su, Jiaming Le, Junying Ma, Long Cheng

The surface/interface species in perovskite oxides play an essential role in many novel emergent physical phenomena and chemical processes. With low eigen-energy in the terahertz region, such species at buried interfaces remain poorly understood due to the lack of feasible experimental techniques. Here, we show that vibrational resonances and two-dimensional

Ziqiao Zhang, Ailin Xie, Jihong Guan, Shuigeng Zhou

Contrastive learning have been widely used as pretext tasks for self-supervised pre-trained molecular representation learning models in AI-aided drug design and discovery. However, exiting methods that generate molecular views by noise-adding operations for contrastive learning may face the semantic inconsistency problem, which leads to false positive pairs

Alemayehu Nana Koya, Wei Li

Charge transfer plasmon (CTP) modes arise when metallic nanoparticles are connected by a conductive junction. These ultatunable plasmonic modes can be actively tuned and broadly modulated from visible to IR regimes, implying their potentials for applications in sensing. This review showcases recent developments in theory and applications of charge transfer p

S. M. Koksbang

The redshift drift is computed along light rays propagating through a simulated universe based on the Newtonian N-body simulation code GADGET-2 combined with a perturbed Friedmann-Lemaitre-Robertson-Walker metric in the Newtonian gauge. It is found that the mean redshift drift is equal to the drift of the mean redshift to the precision of the numerical compu

Qian Zhang

In this paper we study global nonlinear stability for a system of semilinear wave and Klein-Gordon equations with quadratic nonlinearities. We consider nonlinearities of the type of wave-Klein-Gordon interactions where there are no derivatives on the wave component. The initial data are assumed to have a suitable polynomial decay at infinity, but are not nec

Haibo Chen, Yucai Su, Yukun Xiao

In this paper, we construct a large class of new simple modules over the twisted $N=2$ superconformal algebra. These new simple modules are restricted modules based on the simple modules over certain finite-dimensional solvable Lie superalgebras, including various versions of Whittaker modules. We elaborate that they are also the twisted modules for the univ

Qian Zhang

We are interested in the cubic Dirac equation in two space dimensions. We establish the small data global existence and sharp pointwise decay results for general cubic nonlinearities without additional structure. We also prove the scattering of the Dirac equation for certain classes of nonlinearities. In all the above results we do not require the initial da

Rajkumar Buyya, Satish N. Srirama, Redowan Mahmud, Mohammad Goudarzi

The Internet of Things (IoT) paradigm is drastically changing our world by making everyday objects an integral part of the Internet. This transformation is increasingly being adopted in the healthcare sector, where Smart Hospitals are now relying on IoT technologies to track staff, patients, devices, and equipment, both within a hospital and beyond. This par

Yichuan Deng, Zhihang Li, Zhao Song

Matrix sensing is a problem in signal processing and machine learning that involves recovering a low-rank matrix from a set of linear measurements. The goal is to reconstruct the original matrix as accurately as possible, given only a set of linear measurements obtained by sensing the matrix [Jain, Netrapalli and Shanghavi, 2013]. In this work, we focus on a

P. Blair Blakie

In this work, we investigate the ground state properties and collective excitations of a dipolar Bose-Einstein condensate that self-binds into a quantum droplet, stabilized by quantum fluctuations. We demonstrate that a sum rule approach can accurately determine the frequency of the low energy axial excitation, using properties of the droplet obtained from t

Kuang-Yu Ding, Xin-Yee Lam, Kim-Chuan Toh

We design inexact proximal augmented Lagrangian based decomposition methods for convex composite programming problems with dual block-angular structures. Our methods are particularly well suited for convex quadratic programming problems arising from stochastic programming models. The algorithmic framework is based on the application of the abstract inexact p

Akshay Gaikwad, Gayatri Singh, Kavita Dorai, Arvind

In this paper, we present an efficient weak measurement-based scheme for direct quantum state tomography (DQST) and direct quantum process tomography (DQPT), and experimentally implement it on an NMR ensemble quantum information processor without involving any projective measurements. We develop a generalized quantum circuit that enables us to directly measu

Lin Tian, Xiuzhen Zhang, Jey Han Lau

State-sponsored trolls are the main actors of influence campaigns on social media and automatic troll detection is important to combat misinformation at scale. Existing troll detection models are developed based on training data for known campaigns (e.g.\ the influence campaign by Russia's Internet Research Agency on the 2016 US Election), and they fall shor

Tsukasa Iwabuchi, Dáithí Ó hAodha

We discuss optimal estimates of solutions to the compressible Navier-Stokes equations in Besov norms. In particular, we consider the estimate of the curl-free part of the solution to the linearised equations, in the homogeneous case. We prove that our estimate is optimal in the $L^\infty$-norm by showing that the norm is bounded from below by the same decay

Somsukla Maiti, Akshansh Gupta

Development of human machine interface has become a necessity for modern day machines to catalyze more autonomy and more efficiency. Gaze driven human intervention is an effective and convenient option for creating an interface to alleviate human errors. Facial landmark detection is very crucial for designing a robust gaze detection system. Regression based

Zhao-Yun Chen, Cheng Xue, Xi-Ning Zhuang, Tai-Ping Sun

Program implementation and simulation are essential for research in the field of quantum algorithms. However, complex and large-scale quantum algorithms can pose challenges for existing quantum programming languages and simulators. Here, we present a scalable program implementation of the quantum walk on a sparse matrix and the quantum linear solver based on

Tianshuo Yang

We propose an algorithm using the Gaussian elimination method to find the minimal Hamming distance and decode received messages of linear codes. This algorithm is easy to implement as it requires no Gr\"obner bases to compute solutions for systems of polynomial equations

Motofumi Aoki, Tsukasa Iwabuchi

We consider the Cauchy problem for compressible Navier--Stokes equations of the ideal gas in the three-dimensional spaces. It is known that the Cauchy problem in the scaling critical spaces of the homogeneous Besov spaces $\dot B^{\frac{3}{p}}_{p,1}\times\dot B^{-1+\frac{3}{p}}_{p,1}\times\dot B^{-2+\frac{3}{p}}_{p,1}$ is uniquely solvable for all $1 < p<3$

Yudai Awashima, Yuki Fuseya

This study calculated the magnetoresistance (MR) in the Dirac electron system, Dressellhaus-Kip-Kittel (DKK) model, and nodal-line semimetals based on the semiclassical Boltzmann theory, with particular focus on the detailed energy dispersion structure. The negative off-diagonal effective-mass was found to induce negative transverse MR owing to the energy di

Jan Brezina, Eduard Feireisl

We consider the complete system of equations governing the motion of a general compressible, viscous, electrically and heat conductive fluid driven by non-conservative boundary conditions. We show the existence of a bounded absorbing set in the energy space and asymptotic compactness of trajectories. As a corollary, the set of all entire globally bounded sol

Zhixin Wang, Xiaoyun Zhang, Ziying Zhang, Huangjie Zheng

Blind face restoration usually synthesizes degraded low-quality data with a pre-defined degradation model for training, while more complex cases could happen in the real world. This gap between the assumed and actual degradation hurts the restoration performance where artifacts are often observed in the output. However, it is expensive and infeasible to incl

Zhaoyang Xia, Youquan Liu, Xin Li, Xinge Zhu

Training deep models for semantic scene completion (SSC) is challenging due to the sparse and incomplete input, a large quantity of objects of diverse scales as well as the inherent label noise for moving objects. To address the above-mentioned problems, we propose the following three solutions: 1) Redesigning the completion sub-network. We design a novel co

David Baraglia

We completely determine the mod $2$ Seiberg-Witten invariants for any spin structure on any closed, oriented, smooth $4$-manifold $X$. Our computation confirms the validity of the simple type conjecture mod $2$ for spin structures. Our proof also works for families of spin $4$-manifolds and thus computes the mod $2$ Seiberg-Witten invariants for spin familie

Jing Hou, Yonglu Shu

we study the hypercyclic and chaotic properties of the time varying weighted backward shift operator $(Tx)(t)=w(t)x(t+a)$ in $L_p(0,\infty)(1\leq p<\infty)$ and $C_0[0,\infty)$. And we also analyse the spectral structure of the operators if the spaces are complex.

Chencan Fu, Lin Li, Jianbiao Mei, Yukai Ma

Place recognition is a challenging but crucial task in robotics. Current description-based methods may be limited by representation capabilities, while pairwise similarity-based methods require exhaustive searches, which is time-consuming. In this paper, we present a novel coarse-to-fine approach to address these problems, which combines BEV (Bird's Eye View

Bo Zhang, Jiakang Yuan, Botian Shi, Tao Chen

Current 3D object detection models follow a single dataset-specific training and testing paradigm, which often faces a serious detection accuracy drop when they are directly deployed in another dataset. In this paper, we study the task of training a unified 3D detector from multiple datasets. We observe that this appears to be a challenging task, which is ma

Liang Liu, Ling Tian, Zhao Kang, Tianqi Wan

Spacecraft faces various situations when carrying out exploration missions in complex space, thus monitoring the anomaly status of spacecraft is crucial to the development of \textcolor{blue}{the} aerospace industry. The time series telemetry data generated by on-orbit spacecraft \textcolor{blue}{contains} important information about the status of spacecraft

Binbin Du, Rui Deng, Yingxin Zhang

This paper introduces the system submitted by dun_oscar team for the ICPR MSR Challenge. Three subsystems for task1-task3 are descripted respectively. In task1, we develop a visual system which includes a OCR model, a text tracker, and a NLP classifier for distinguishing subtitles and non-subtitles. In task2, we employ an ASR system which includes an AM with

Tianyun Yang, Danding Wang, Fan Tang, Xinying Zhao

Despite the remarkable progress in generative technology, the Janus-faced issues of intellectual property protection and malicious content supervision have arisen. Efforts have been paid to manage synthetic images by attributing them to a set of potential source models. However, the closed-set classification setting limits the application in real-world scena

Sourya Sengupta, Mark A. Anastasio

Interpretability is highly desired for deep neural network-based classifiers, especially when addressing high-stake decisions in medical imaging. Commonly used post-hoc interpretability methods have the limitation that they can produce plausible but different interpretations of a given model, leading to ambiguity about which one to choose. To address this pr

Yao Li, Chuang Li, Jiandong Zhang, Ying Dong

Optically levitated microspheres are an excellent candidate for force and acceleration sensing. Here, we propose an acceleration sensing protocol based on an optically levitated microsphere array (MSA). The system consists of an $N$-microsphere array levitated in a driven optical cavity via holographic optical tweezers. By positioning the microspheres suitab

Leopoldo Mejía, Jia Yin, David R. Reichman, Roi Baer

We present a real-time second-order Green's function (GF) method for computing excited states in molecules and nanostructures, with a computational scaling of $O(N_{\rm e}^3$), where $N_{\rm e}$ is the number of electrons. The cubic scaling is achieved by adopting the stochastic resolution of the identity to decouple the 4-index electron repulsion integrals

Tiancheng Lin, Zhimiao Yu, Hongyu Hu, Yi Xu

Multi-instance learning (MIL) is an effective paradigm for whole-slide pathological images (WSIs) classification to handle the gigapixel resolution and slide-level label. Prevailing MIL methods primarily focus on improving the feature extractor and aggregator. However, one deficiency of these methods is that the bag contextual prior may trick the model into

Jieun Lee, Hakjun Lee, Jiyong Oh

As technology advances in autonomous mobile robots, mobile service robots have been actively used more and more for various purposes. Especially, serving robots have been not surprising products anymore since the COVID-19 pandemic. One of the practical problems in operating a serving robot is that it often fails to estimate its pose on a map that it moves ar

Nacime Bouziani, David A. Ham

Partial differential equations (PDEs) are central to describing and modelling complex physical systems that arise in many disciplines across science and engineering. However, in many realistic applications PDE modelling provides an incomplete description of the physics of interest. PDE-based machine learning techniques are designed to address this limitation

Yun-Hao Cao, Peiqin Sun, Shuchang Zhou

We propose universally slimmable self-supervised learning (dubbed as US3L) to achieve better accuracy-efficiency trade-offs for deploying self-supervised models across different devices. We observe that direct adaptation of self-supervised learning (SSL) to universally slimmable networks misbehaves as the training process frequently collapses. We then discov

Biao Qian, Yang Wang, Richang Hong, Meng Wang

Data-free quantization (DFQ) recovers the performance of quantized network (Q) without the original data, but generates the fake sample via a generator (G) by learning from full-precision network (P), which, however, is totally independent of Q, overlooking the adaptability of the knowledge from generated samples, i.e., informative or not to the learning pro

Fangyu Zuo, Peiguang Jing, Jinglin Sun, Jizhong

Alzheimer's Disease (AD) causes a continuous decline in memory, thinking, and judgment. Traditional diagnoses are usually based on clinical experience, which is limited by some realistic factors. In this paper, we focus on exploiting deep learning techniques to diagnose AD based on eye-tracking behaviors. Visual attention, as typical eye-tracking behavior, i

Yicheng Hsu, Mingsian Bai

A three-stage approach is proposed for speaker counting and speech separation in noisy and reverberant environments. In the spatial feature extraction, a spatial coherence matrix (SCM) is computed using whitened relative transfer functions (wRTFs) across time frames. The global activity functions of each speaker are estimated from a simplex constructed using

Tyler Houston, Darren J. Croton, Manodeep Sinha

In this letter, we explore the quiescent lives of central galaxies using the SAGE galaxy model and Uchuu dark matter simulation. We ask three questions: (1) How much of a galaxy's life is spent in quiescence? (2) How often do galaxies transition off the main sequence? (3) What is the typical duration of a quiescent phase? We find low and high-mass galaxies s

Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li

The high computational and memory requirements of large language model (LLM) inference make it feasible only with multiple high-end accelerators. Motivated by the emerging demand for latency-insensitive tasks with batched processing, this paper initiates the study of high-throughput LLM inference using limited resources, such as a single commodity GPU. We pr

V P Abidha, Pradeesha Ashok, Avi Tomar, Dolly Yadav

The \emph{Square Colouring} of a graph $G$ refers to colouring of vertices of a graph such that any two distinct vertices which are at distance at most two receive different colours. In this paper, we initiate the study of a related colouring problem called the \emph{subset square colouring} of graphs. Broadly, the subset square colouring of a graph studies

Dongfang Zhao

Private set intersection (PSI) aims to allow users to find out the commonly shared items among the users without revealing other membership information. The most recently proposed approach to PSI in the database community was Prism, which is built upon secret sharing and the assumption that multiple non-colluding servers are available. One limitation of Pris

Tianyi Chen, Luming Liang, Tianyu Ding, Zhihui Zhu

The existing model compression methods via structured pruning typically require complicated multi-stage procedures. Each individual stage necessitates numerous engineering efforts and domain-knowledge from the end-users which prevent their wider applications onto broader scenarios. We propose the second generation of Only-Train-Once (OTOv2), which first auto

James Dannatt, Ian Petersen

In this paper we study the problem of determining the largest degree of stability that can be achieved for SISO systems using negative imaginary state feedback control. A state feedback result is given for synthesising a controller for a plant such that a given closed-loop transfer function is strictly negative imaginary with a prescribed degree of stability

Zeqi Shen, Shuo Zhang, Zhuhao Zhang, Qihua Chen

The Light Field (LF) deblurring task is a challenging problem as the blur images are caused by different reasons like the camera shake and the object motion. The single image deblurring method is a possible way to solve this problem. However, since it deals with each view independently and cannot effectively utilize and maintain the LF structure, the restora

Xin Li, Bingchen Li, Xin Jin, Cuiling Lan

In recent years, we have witnessed the great advancement of Deep neural networks (DNNs) in image restoration. However, a critical limitation is that they cannot generalize well to real-world degradations with different degrees or types. In this paper, we are the first to propose a novel training strategy for image restoration from the causality perspective,

Xin Chen, Jorge I. Poveda, Na Li

This paper introduces a class of model-free feedback methods for solving generic constrained optimization problems where the specific mathematical forms of the objective and constraint functions are not available. The proposed methods, termed Projected Zeroth-Order (P-ZO) dynamics, incorporate projection maps into a class of continuous-time model-free dynami

Charissa Poon, Muhammad Febrian Rachmadi, Michal Byra, Matthias Schlachter

We present the first automated pipeline to create an atlas of in situ hybridization gene expression in the adult marmoset brain in the same stereotaxic space. The pipeline consists of segmentation of gene expression from microscopy images and registration of images to a standard space. Automation of this pipeline is necessary to analyze the large volume of d

Wonhyeok Choi, Sunghoon Im

In this paper, we present a new MTL framework that searches for structures optimized for multiple tasks with diverse graph topologies and shares features among tasks. We design a restricted DAG-based central network with read-in/read-out layers to build topologically diverse task-adaptive structures while limiting search space and time. We search for a singl

Gang Chen

Given a basic block of instructions, finding a schedule that requires the minimum number of registers for evaluation is a well-known problem. The problem is NP-complete when the dependences among instructions form a directed-acyclic graph instead of a tree. We are striving to find efficient approximation algorithms for this problem not simply because it is a

Wenhan Yang, Jingdong Gao, Baharan Mirzasoleiman

Contrastive vision-language representation learning has achieved state-of-the-art performance for zero-shot classification, by learning from millions of image-caption pairs crawled from the internet. However, the massive data that powers large multimodal models such as CLIP, makes them extremely vulnerable to various types of targeted data poisoning and back

Junda He, Zhou Xin, Bowen Xu, Ting Zhang

The tremendous success of Stack Overflow has accumulated an extensive corpus of software engineering knowledge, thus motivating researchers to propose various solutions for analyzing its content.The performance of such solutions hinges significantly on the selection of representation model for Stack Overflow posts. As the volume of literature on Stack Overfl

Wan Liu, Qi Lu, ZhiZheng Zhuo, Yaou Liu

Deep learning based methods have achieved state-of-the-art performance for automated white matter (WM) tract segmentation. In these methods, the segmentation model needs to be trained with a large number of manually annotated scans, which can be accumulated throughout time. When novel WM tracts, i.e., tracts not included in the existing annotated WM tracts,

R Sri Prakash, Nikhil Karamchandani, Sharayu Moharir

We consider the problem of service hosting where a service provider can dynamically rent edge resources via short term contracts to ensure better quality of service to its customers. The service can also be partially hosted at the edge, in which case, customers' requests can be partially served at the edge. The total cost incurred by the system is modeled as

Aihua Fan, Hervé Queffélec, Martine Quffélec

This paper is a complement to our previous paper [21]. It surveys the works on the Furstenberg set $S=\{2^{m}3^{n}: n\ge 0, m\ge 0\}$ and its random version $T$. We also present some new results. For example, it is proved that $T$ almost surely contains a subset of positive lower density which is $\frac{4}{3}$-Rider. It is also proved that a class of random

Tingfang Chen, Cunsheng Ding, Chengju Li, Zhonghua Sun

Cyclic codes are an interesting type of linear codes and have wide applications in communication and storage systems due to their efficient encoding and decoding algorithms. Inspired by the recent work on binary cyclic codes published in IEEE Trans. Inf. Theory, vol. 68, no. 12, pp. 7842-7849, 2022, and the arXiv paper arXiv:2301.06446, the objectives of thi

Mir Alimuddin, Ananya Chakraborty, Govind Lal Sidhardh, Ram Krishna Patra

Hardy's argument constitutes an elegant proof of quantum nonlocality. In this work, we report an exotic application of Hardy's nonlocal correlations in two-party communication setup. We come up with a task, wherein a positive payoff can be through an $1$ bit of communication from the sender to the receiver if and only if the communication channel is assisted

Chang-Yong Liu

In this paper, we study the problem of trace anomaly for a chiral fermion. To find whether there exists a parity-odd term (Pontryagin term), we use a modified Breitenlohner-Maison-'t Hooft-Veltman regularization and Fujikawa's method by a new Dirac mass term to calculate the trace anomaly for a Weyl fermion coupled to an abelian gauge field and a gravity sep

Yuheng Jia, Jiawei Tang, Jiahao Jiang

Label distribution learning (LDL) is an effective method to predict the label description degree (a.k.a. label distribution) of a sample. However, annotating label distribution (LD) for training samples is extremely costly. So recent studies often first use label enhancement (LE) to generate the estimated label distribution from the logical label and then ap

Aditya Jain, Pavithran Iyer, Stephen D. Bartlett, Joseph Emerson

Current hardware for quantum computing suffers from high levels of noise, and so to achieve practical fault-tolerant quantum computing will require powerful and efficient methods to correct for errors in quantum circuits. Here, we explore the role and effectiveness of using noise tailoring techniques to improve the performance of error correcting codes. Nois

Zhenyuan Lu, Burcu Ozek, Sagar Kamarthi

Pain is a serious worldwide health problem that affects a vast proportion of the population. For efficient pain management and treatment, accurate classification and evaluation of pain severity are necessary. However, this can be challenging as pain is a subjective sensation-driven experience. Traditional techniques for measuring pain intensity, e.g. self-re

Kijung Lee

This study presents a secondary data analysis of the survey data collected as part of the American Trends Panel series by the Pew Research Center. A logistic regression was performed to ascertain the effects of the perceived risk of sharing, perceived problems on Twitter, and motivation of using Twitter on the likelihood that participants regret sharing on T

Simion Breaz

We prove that an object $U$ in a triangulated category with coproducts is silting if and only if it is a (weak) generator of the category, the orthogonal class $U^{\perp_{>0}}$ contains $U$, and $U^{\perp_{>0}}$ is closed under direct sums. The proof can be dualized to provide a characterization for cosilting objects in triangulated categories with products.

Bowen Jiang, Camillo J. Taylor

This paper presents a finding that leveraging the hierarchical structures among labels for relationships and objects can substantially improve the performance of scene graph generation systems. The focus of this work is to create an informative hierarchical structure that can divide object and relationship categories into disjoint super-categories in a syste

Zhengxiang Wang

The paper studies the capabilities of Recurrent-Neural-Network sequence to sequence (RNN seq2seq) models in learning four transduction tasks: identity, reversal, total reduplication, and quadratic copying. These transductions are traditionally well studied under finite state transducers and attributed with increasing complexity. We find that RNN seq2seq mode

Zixiang Zhao, Haowen Bai, Yuanzhi Zhu, Jiangshe Zhang

Multi-modality image fusion aims to combine different modalities to produce fused images that retain the complementary features of each modality, such as functional highlights and texture details. To leverage strong generative priors and address challenges such as unstable training and lack of interpretability for GAN-based generative methods, we propose a n

Roberto Vila, Narayanaswamy Balakrishnan, Raul Matsushita

This work sheds some light on the relationship between a distribution's standard deviation and its range, a topic that has been discussed extensively in the literature. While many previous studies have proposed inequalities or relationships that depend on the shape of the population distribution, the approach here is built on a family of bounded probability

Billy Jin, Katya Scheinberg, Miaolan Xie

Several classical adaptive optimization algorithms, such as line search and trust region methods, have been recently extended to stochastic settings where function values, gradients, and Hessians in some cases, are estimated via stochastic oracles. Unlike the majority of stochastic methods, these methods do not use a pre-specified sequence of step size param

Hampei Sasahara

This study investigates the vulnerability of direct data-driven control to adversarial attacks in the form of a small but sophisticated perturbation added to the original data. The directed gradient sign method (DGSM) is developed as a specific attack method, based on the fast gradient sign method (FGSM), which has originally been considered in image classif

Qinghai Zheng, Jihua Zhu, Haoyu Tang

In this work, we focus on the challenging problem of Label Enhancement (LE), which aims to exactly recover label distributions from logical labels, and present a novel Label Information Bottleneck (LIB) method for LE. For the recovery process of label distributions, the label irrelevant information contained in the dataset may lead to unsatisfactory recovery

Qingnan An, Zhichao Liu

In this paper, we exhibit two unital, separable, nuclear ${\rm C}^*$-algebras of stable rank one and real rank zero with the same ordered scaled total K-theory, but they are not isomorphic with each other, which forms a counterexample to Elliott Classification Conjecture for real rank zero setting. Thus, we introduce an additional normal condition and give a

Shuangping Jin, Bingbing Yu, Minhao Jing, Yi Zhou

RGB-NIR fusion is a promising method for low-light imaging. However, high-intensity noise in low-light images amplifies the effect of structure inconsistency between RGB-NIR images, which fails existing algorithms. To handle this, we propose a new RGB-NIR fusion algorithm called Dark Vision Net (DVN) with two technical novelties: Deep Structure and Deep Inco

Parshin Shojaee, Kazem Meidani, Amir Barati Farimani, Chandan K. Reddy

Symbolic regression (SR) is a challenging task in machine learning that involves finding a mathematical expression for a function based on its values. Recent advancements in SR have demonstrated the effectiveness of pre-trained transformer-based models in generating equations as sequences, leveraging large-scale pre-training on synthetic datasets and offerin

SP Choi, Jihun Lee, Hyeongseok Ahn, Sanghee Jung

ODIN is an innovative approach that addresses the problem of dataset constraints by integrating generative AI models. Traditional zero-shot learning methods are constrained by the training dataset. To fundamentally overcome this limitation, ODIN attempts to mitigate the dataset constraints by generating on-demand datasets based on user requirements. ODIN con

Matthew Bravo, Jen-Tsung Hsiang, Bei-Lok Hu

In this third of a series on quantum radiation, we explore the feasibility of using the memories kept in a quantum field to decipher certain information about the early universe. As a model study, we let a massless quantum field be subjected to a parametric process for a finite time interval such that the mode frequency of the field transits from one constan

Yun-Mei Li

Helical edge states in topological insulators give counterpropagating spin current on the two parallel edges. We here propose anti-helical edge states of magnons in patterned antiferromagnetic thin films, which host copropagating spin current on the two parallel edges, where the two magnon modes with opposite chirality act like the spin. The embedded heavy m

Ruxi Liang, Pengyu Qin, Yonglu Shu

In this paper, based on the work of Vijay K. Srivastava and Harish Chandra, we give a characterization of the unbounded hypercyclic weighted pseudo-shift operator $wC_{\varphi}$ on $\ell^p$ or $c_0$. Moreover we use the hypercyclicity criterion of B\`es, Chan, and Seubert to give a necessary and sufficient condition in order that $wC_{\varphi}$ be a chaotic

Taekbeom Lee, Youngseok Jang, H. Jin Kim

Existence of symmetric objects, whose observation at different viewpoints can be identical, can deteriorate the performance of simultaneous localization and mapping(SLAM). This work proposes a system for robustly optimizing the pose of cameras and objects even in the presence of symmetric objects. We classify objects into three categories depending on their

Zihan Zhang, Shimin Zhang, Mingshuai Liu, Yanhong Leng

This paper describes a Two-step Band-split Neural Network (TBNN) approach for full-band acoustic echo cancellation. Specifically, after linear filtering, we split the full-band signal into wide-band (16KHz) and high-band (16-48KHz) for residual echo removal with lower modeling difficulty. The wide-band signal is processed by an updated gated convolutional re

William Gilpin

Chaos and unpredictability are traditionally synonymous, yet large-scale machine learning methods recently have demonstrated a surprising ability to forecast chaotic systems well beyond typical predictability horizons. However, recent works disagree on whether specialized methods grounded in dynamical systems theory, such as reservoir computers or neural ord

Aishwarya Mandyam, Didong Li, Jiayu Yao, Diana Cai

Inverse reinforcement learning (IRL) methods infer an agent's reward function using demonstrations of expert behavior. A Bayesian IRL approach models a distribution over candidate reward functions, capturing a degree of uncertainty in the inferred reward function. This is critical in some applications, such as those involving clinical data. Typically, Bayesi