March 2023 arXiv papers — page 47

Huantong Li, Xiangmiao Wu, Fanbing Lv, Daihai Liao

Zero-shot quantization (ZSQ) is promising for compressing and accelerating deep neural networks when the data for training full-precision models are inaccessible. In ZSQ, network quantization is performed using synthetic samples, thus, the performance of quantized models depends heavily on the quality of synthetic samples. Nonetheless, we find that the synth

Zhiyang Guo, Wengang Zhou, Min Wang, Li Li

We propose a novel framework to reconstruct accurate appearance and geometry with neural radiance fields (NeRF) for interacting hands, enabling the rendering of photo-realistic images and videos for gesture animation from arbitrary views. Given multi-view images of a single hand or interacting hands, an off-the-shelf skeleton estimator is first employed to p

Benfeng Xu, Quan Wang, Zhendong Mao, Yajuan Lyu

In-Context Learning (ICL), which formulates target tasks as prompt completion conditioned on in-context demonstrations, has become the prevailing utilization of LLMs. In this paper, we first disclose an actual predicament for this typical usage that it can not scale up with training data due to context length restriction. Besides, existing works have shown t

Zhu-yao Jin, Jun Jing

A hybrid system established by the direct interaction between a magnon mode and a superconducting transmon qubit is used to realize a high-degree blockade for magnon. It is a fundamental way toward quantum manipulation at the level of a single magnon and preparation of single magnon sources. Through weakly driving the magnon and probing the qubit, our magnon

T. A. Movsessian, T. Yu. Magakian, A. S. Rastorguev, H. R. Andreasyan

Two new Herbig-Haro flows were found in a study of the isolated Dobashi 5006 dark cloud (l$= 216^\circ.7$, b= $-$13$^\circ.9$): one certain (HH 1179) and one presumable, associated with the infrared sources 2MASS 06082284$-$0936139 and 2MASS 06081525$-$0933490, correspondingly. Judging from their spectral energy distributions, these sources may be Class 1 ob

Jiguo Li, Xiaobin Liu, Lirong Zheng

Prior works about text-to-image synthesis typically concatenated the sentence embedding with the noise vector, while the sentence embedding and the noise vector are two different factors, which control the different aspects of the generation. Simply concatenating them will entangle the latent factors and encumber the generative model. In this paper, we attem

Fuyang Tay, Swati Chaudhary, Jiaming He, Nicolas Marquez Peraca

We have studied the terahertz response of a bulk single crystal of La$_{0.875}$Sr$_{0.125}$MnO$_3$ at around its Curie temperature, observing large changes in the real and imaginary parts of the optical conductivity as a function of magnetic field. The terahertz resistance and capacitance extracted from the optical conductivity rapidly increased with increas

Lin Song, Yangge Li, Sheng Cheng, Pan Zhao

$L_1$ adaptive control ($L_1$AC) is a control design technique that can handle a broad class of system uncertainties and provide transient performance guarantees. In this work-in-progress abstract, we discuss how existing formal verification tools can be applied to check the performance of $L_1$AC systems. We show that the theoretical transient performance a

Yiheng Xiong, Jingsong Liu, Kamilia Zaripova, Sahand Sharifzadeh

The extraction of structured clinical information from free-text radiology reports in the form of radiology graphs has been demonstrated to be a valuable approach for evaluating the clinical correctness of report-generation methods. However, the direct generation of radiology graphs from chest X-ray (CXR) images has not been attempted. To address this gap, w

Zhe Jun Tang, Tat-Jen Cham, Haiyu Zhao

Neural Radiance Field (NeRF) is a popular method in representing 3D scenes by optimising a continuous volumetric scene function. Its large success which lies in applying volumetric rendering (VR) is also its Achilles' heel in producing view-dependent effects. As a consequence, glossy and transparent surfaces often appear murky. A remedy to reduce these artef

Bo Zhang, Boyu Jiang, Rong Zheng, Xiaoping Zhang

Continuous monitoring of human vital signs using non-contact mmWave radars is attractive due to their ability to penetrate garments and operate under different lighting conditions. Unfortunately, most prior research requires subjects to stay at a fixed distance from radar sensors and to remain still during monitoring. These restrictions limit the application

Antonio de França, Irina Sviridova

It is well known that the ring radical theory can be approached via language of modules. In this work, we present some generalizations of classical results from module theory, in the two-sided and graded sense. Let $\mathsf{G}$ be a group, $\mathbb{F}$ an algebraically closed field with $\mathsf{char}(\mathbb{F})=0$, $\mathfrak{A}$ a finite dimensional $\mat

Ashwin Prakash, Thejaswin S, Athira Nambiar, Alexandre Bernardino

Biometrics plays a significant role in vision-based surveillance applications. Soft biometrics such as gait is widely used with face in surveillance tasks like person recognition and re-identification. Nevertheless, in practical scenarios, classical fusion techniques respond poorly to changes in individual users and in the external environment. To this end,

Hongjun Wang, Yisen Wang

Deep neural networks obtained by standard training have been constantly plagued by adversarial examples. Although adversarial training demonstrates its capability to defend against adversarial examples, unfortunately, it leads to an inevitable drop in the natural generalization. To address the issue, we decouple the natural generalization and the robust gene

Eugene Goldberg

We study partial quantifier elimination (PQE) for propositional CNF formulas with existential quantifiers. PQE is a generalization of quantifier elimination where one can limit the set of clauses taken out of the scope of quantifiers to a small subset of clauses. The appeal of PQE is that many verification problems (e.g. equivalence checking and model checki

Xinwen Liu, Jing Wang, S. Kevin Zhou, Craig Engstrom

Deep neural networks have been widely studied for predicting a medical condition, such as total knee replacement (TKR). It has shown that data of different modalities, such as imaging data, clinical variables and demographic information, provide complementary information and thus can improve the prediction accuracy together. However, the data sources of vari

Qingyu Lu, Baopu Qiu, Liang Ding, Kanjian Zhang

Generative large language models (LLMs), e.g., ChatGPT, have demonstrated remarkable proficiency across several NLP tasks, such as machine translation, text summarization. Recent research (Kocmi and Federmann, 2023) has shown that utilizing LLMs for assessing the quality of machine translation (MT) achieves state-of-the-art performance at the system level bu

Kinal Mehta, Anuj Mahajan, Pawan Kumar

Recent advances in Reinforcement Learning (RL) have led to many exciting applications. These advancements have been driven by improvements in both algorithms and engineering, which have resulted in faster training of RL agents. We present marl-jax, a multi-agent reinforcement learning software package for training and evaluating social generalization of the

Hansheng Guo, Juncheng Li, Guangwei Gao, Zhi Li

Stereo image super-resolution aims to boost the performance of image super-resolution by exploiting the supplementary information provided by binocular systems. Although previous methods have achieved promising results, they did not fully utilize the information of cross-view and intra-view. To further unleash the potential of binocular images, in this lette

Xusheng Zhu, Wen Chen, Zhendong Li, Qingqing Wu

In this letter, we investigate a novel quadrature spatial scattering modulation (QSSM) transmission technique based on millimeter wave (mmWave) systems, in which the transmitter generates two orthogonal beams targeting candidate scatterers in the channel to carry the real and imaginary parts of the conventional signal, respectively. Meanwhile, the maximum li

Jinguang Tong, Sundaram Muthu, Fahira Afzal Maken, Chuong Nguyen

In this paper, we define a new problem of recovering the 3D geometry of an object confined in a transparent enclosure. We also propose a novel method for solving this challenging problem. Transparent enclosures pose challenges of multiple light reflections and refractions at the interface between different propagation media e.g. air or glass. These multiple

Zhiyu Liang, Chen Liang, Zheng Liang, Hongzhi Wang

Machine learning has emerged as a powerful tool for time series analysis. Existing methods are usually customized for different analysis tasks and face challenges in tackling practical problems such as partial labeling and domain shift. To improve the performance and address the practical problems universally, we develop UniTS, a novel framework that incorpo

Yihao Zhao, Xin Liu, Shufan Liu, Xiang Li

Large-scale GPU clusters are widely-used to speed up both latency-critical (online) and best-effort (offline) deep learning (DL) workloads. However, most DL clusters either dedicate each GPU to one workload or share workloads in time, leading to very low GPU resource utilization. We present MuxFlow, the first production cluster system that supports efficient

Yong Li, Yuanzhi Wang, Zhen Cui

Human multimodal emotion recognition (MER) aims to perceive human emotions via language, visual and acoustic modalities. Despite the impressive performance of previous MER approaches, the inherent multimodal heterogeneities still haunt and the contribution of different modalities varies significantly. In this work, we mitigate this issue by proposing a decou

Adib Mosharrof, Moghis Fereidouni, A. B. Siddique

Slot filling is one of the critical tasks in modern conversational systems. The majority of existing literature employs supervised learning methods, which require labeled training data for each new domain. Zero-shot learning and weak supervision approaches, among others, have shown promise as alternatives to manual labeling. Nonetheless, these learning parad

Jiahao Zhang, Anoop Cherian, Yanbin Liu, Yizhak Ben-Shabat

Multimodal alignment facilitates the retrieval of instances from one modality when queried using another. In this paper, we consider a novel setting where such an alignment is between (i) instruction steps that are depicted as assembly diagrams (commonly seen in Ikea assembly manuals) and (ii) video segments from in-the-wild videos; these videos comprising a

Yosef S. Razin, Karen M. Feigh

A significant challenge to measuring human-automation trust is the amount of construct proliferation, models, and questionnaires with highly variable validation. However, all agree that trust is a crucial element of technological acceptance, continued usage, fluency, and teamwork. Herein, we synthesize a consensus model for trust in human-automation interact

Mohammadreza Sharif, Kiran Mohan, Sarath Suvarna

In recent years, floor plan segmentation has gained significant attention due to its wide range of applications in floor plan reconstruction and robotics. In this paper, we propose a novel 2D floor plan segmentation technique based on a down-sampling approach. Our method employs continuous down-sampling on a floor plan to maintain its structural information

A. B. Siddique, M. H. Maqbool, Kshitija Taywade, Hassan Foroosh

Task-oriented dialog systems enable users to accomplish tasks using natural language. State-of-the-art systems respond to users in the same way regardless of their personalities, although personalizing dialogues can lead to higher levels of adoption and better user experiences. Building personalized dialog systems is an important, yet challenging endeavor an

Wenjia Wang, Yongtao Ge, Haiyi Mei, Zhongang Cai

As it is hard to calibrate single-view RGB images in the wild, existing 3D human mesh reconstruction (3DHMR) methods either use a constant large focal length or estimate one based on the background environment context, which can not tackle the problem of the torso, limb, hand or face distortion caused by perspective camera projection when the camera is close

Rui Wang

This paper characterizes point identification results of the local average treatment effect (LATE) using two imperfect instruments. The classical approach (Imbens and Angrist (1994)) establishes the identification of LATE via an instrument that satisfies exclusion, monotonicity, and independence. However, it may be challenging to find a single instrument tha

Hongjian Song, Yuki Kashiwaba, Shuai Wu, Canming Wang

Image matching is a classic and fundamental task in computer vision. In this paper, under the hypothesis that the areas outside the co-visible regions carry little information, we propose a matching key-points crop (MKPC) algorithm. The MKPC locates, proposes and crops the critical regions, which are the co-visible areas with great efficiency and accuracy. F

Rafael Frongillo, Manuel Lladser, Anish Thilagar, Bo Waggoner

Beginning with Witkowski et al. [2022], recent work on forecasting competitions has addressed incentive problems with the common winner-take-all mechanism. Frongillo et al. [2021] propose a competition mechanism based on follow-the-regularized-leader (FTRL), an online learning framework. They show that their mechanism selects an $\epsilon$-optimal forecaster

John R. Klauder, Riccardo Fantoni

Affine quantization is a relatively new procedure, and it can solve many new problems. This essay reviews this new, and novel, procedure for particle problems, as well as those of fields and gravity. New quantization tools, which are extremely close to, and even constructed from, the tools of canonical quantization, are able to fully solve selected problems

Manuela Mendiolar, Jerzy A. Filar, Wen-Hsi Yang, Susannah Leahy

Environmental scientists frequently rely on time series of explanatory variables to explain their impact on an important response variable. However, sometimes, researchers are less interested in raw observations of an explanatory variable than in derived indices induced by episodes embedded in its time series. Often these episodes are intermittent, occur wit

Andreas Meuleman, Yu-Lun Liu, Chen Gao, Jia-Bin Huang

We present an algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video. The task poses two core challenges. First, most existing radiance field reconstruction approaches rely on accurate pre-estimated camera poses from Structure-from-Motion algorithms, which frequently fail on in-the-wild videos. Second, us

Chia-Yuan Chang, Jiayi Yuan, Sirui Ding, Qiaoyu Tan

Clinical trials are indispensable in developing new treatments, but they face obstacles in patient recruitment and retention, hindering the enrollment of necessary participants. To tackle these challenges, deep learning frameworks have been created to match patients to trials. These frameworks calculate the similarity between patients and clinical trial elig

Yuji Ando, Tomoya Suda

For some classical solutions $\Psi_\mathrm{sol}$ in Witten's bosonic string field theory, it was proven that energy of the solution is proportional to the Ellwood invariant $\mathrm{Tr}(\mathcal{V}\Psi_\mathrm{sol})$ with $\mathcal{V}=c\bar{c}\partial X^0\bar{\partial}X^0$. We examine the relation for solutions involving $X^0$ variables. As a result, we obta

Minjie Hua, Yibing Nan, Shiguo Lian

Person counting is considered as a fundamental task in video surveillance. However, the scenario diversity in practical applications makes it difficult to exploit a single person counting model for general use. Consequently, engineers must preview the video stream and manually specify an appropriate person counting model based on the scenario of camera shot,

Xuezhi Bian, Zhen Tao, Yanze Wu, Jonathan Rawlinson

We prove both analytically and numerically that the total angular momentum of a molecular system undergoing adiabatic Born-Oppenheimer dynamics is conserved only when pseudo-magnetic Berry forces are taken into account. This finding sheds light on the nature of Berry forces for molecular systems with spin-orbit coupling and highlights how ab initio Born-Oppe

Zhengzhe Liu, Peng Dai, Ruihui Li, Xiaojuan Qi

In this paper, we present a new text-guided 3D shape generation approach DreamStone that uses images as a stepping stone to bridge the gap between text and shape modalities for generating 3D shapes without requiring paired text and 3D data. The core of our approach is a two-stage feature-space alignment strategy that leverages a pre-trained single-view recon

Yangqiuting Li, Chandralekha Singh

Structural equation modeling (SEM) is a statistical method widely used in educational research to investigate relationships between variables. SEM models are typically constructed based on theoretical foundations and assessed through fit indices. However, a well-fitting SEM model alone is not sufficient to verify the causal inferences underlying the proposed

D. R. Johnston, O. Ramare, T. S. Trudgian

We consider Dirichlet $L$-functions $L(s, \chi)$ where $\chi$ is a non-principal quadratic character to the modulus $q$. We make explicit a result due to Pintz and Stephens by showing that $|L(1, \chi)|\leq \frac{1}{2}\log q$ for all $q\geq 2\cdot 10^{23}$ and $|L(1, \chi)|\leq \frac{9}{20}\log q$ for all $q\geq 5\cdot 10^{50}$.

Xu Yang, Lei Tan, Wu-Ming Liu

A multi-channel quantum router with four nodal cavities is constructed by two coupled-resonator waveguides and four single cavities. We can achieve directional routing by adjusting the probability of photon exiting from the specified port to close to 100% based on multiple pathways between the photon from the incident port to the outgoing port in this hybrid

Rikpratik Sengupta, Shounak Ghosh, B C Paul, Mehedi Kalam

A non-singular Emergent Universe (EU) scenario within the realm of standard Relativistic physics requires a generalization of the Equation of State (EoS) connecting the pressure and energy density. This generalized EoS is capable of describing a composition of exotic matter, dark energy and cosmological dust matter. Since the EU scenario is known to violate

Yiming Cui, Jiajia Guo, Chao-Kai Wen, Shi Jin

Deep learning (DL)-based channel state information (CSI) feedback has received significant research attention in recent years. However, previous research has overlooked the potential privacy disclosure problem caused by the transmission of CSI datasets during the training process. In this work, we introduce a federated edge learning (FEEL)-based training fra

Eloise Moore, Bruce Gendre, N. Brice Orange, Fiona H. Panther

Ultra-high energy cosmic rays are the most extreme energetic particles detected on Earth, however, their acceleration sites are still mysterious. We explore the contribution of low-luminosity gamma-ray bursts to the ultra-high energy cosmic ray flux, since they form the bulk of the nearby population. We analyse a representative sample of these bursts detecte

Keqin Peng, Liang Ding, Qihuang Zhong, Li Shen

ChatGPT shows remarkable capabilities for machine translation (MT). Several prior studies have shown that it achieves comparable results to commercial systems for high-resource languages, but lags behind in complex tasks, e.g., low-resource and distant-language-pairs translation. However, they usually adopt simple prompts which can not fully elicit the capab

Aneeshan Sain, Ayan Kumar Bhunia, Subhadeep Koley, Pinaki Nath Chowdhury

This paper advances the fine-grained sketch-based image retrieval (FG-SBIR) literature by putting forward a strong baseline that overshoots prior state-of-the-arts by ~11%. This is not via complicated design though, but by addressing two critical issues facing the community (i) the gold standard triplet loss does not enforce holistic latent space geometry, a

Jason J. Ford, Justin M. Kennedy, Caitlin Tompkins, Jasmin James

This paper establishes that an exactly optimal rule for Bayesian Quickest Change Detection (QCD) of Markov chains is a threshold test on the no change posterior. We also provide a computationally efficient scalar filter for the no change posterior whose effort is independent of the dimension of the chains. We establish that an (undesirable) weak practical su

Jianchuan Chen, Wentao Yi, Liqian Ma, Xu Jia

In this work, we focus on synthesizing high-fidelity novel view images for arbitrary human performers, given a set of sparse multi-view images. It is a challenging task due to the large variation among articulated body poses and heavy self-occlusions. To alleviate this, we introduce an effective generalizable framework Generalizable Model-based Neural Radian

Andreia F. Silva, Francisco Pimenta, Manuel A. Alves, Mónica S. N. Oliveira

This work discusses the rheology of several vitreous humour (VH) substitutes used in eye surgery (perfluorocarbons and silicone oils) and their flow behaviour when subjected to saccadic eye movements. Shear rheology experiments revealed that all fluids tested exhibit a constant shear viscosity, while extensional rheological experiments showed that Siluron 20

Sandeep Polisetty, Juelin Liu, Kobi Falus, Yi Ren Fung

Graph neural networks (GNNs), an emerging class of machine learning models for graphs, have gained popularity for their superior performance in various graph analytical tasks. Mini-batch training is commonly used to train GNNs on large graphs, and data parallelism is the standard approach to scale mini-batch training across multiple GPUs. Data parallel appro

Yifu Chen, Qianqian Ren, Liu Yong

Graph augmentations are essential for graph contrastive learning. Most existing works use pre-defined random augmentations, which are usually unable to adapt to different input graphs and fail to consider the impact of different nodes and edges on graph semantics. To address this issue, we propose a framework called Hybrid Augmented Automated Graph Contrasti

Yu-Hui Zhou, Wen-Jia Wang, Li-Ye Xiao, Xian-Hui Zhong

In this work, we systematically study the OZI-allowed two-body strong decay properties of $1P$- and $1D$-wave $\Sigma_c$ baryons within the $j $-$j$ coupling scheme in the framework of the quark pair creation model. For a comparison, we also give the predictions of the chiral quark model. Some model dependencies can be found in the predictions of two models.

Bruno Machado Pacheco, Laio Oriel Seman, Cezar Antonio Rigo, Eduardo Camponogara

This study investigates how to schedule nanosatellite tasks more efficiently using Graph Neural Networks (GNNs). In the Offline Nanosatellite Task Scheduling (ONTS) problem, the goal is to find the optimal schedule for tasks to be carried out in orbit while taking into account Quality-of-Service (QoS) considerations such as priority, minimum and maximum acti

Seokcheon Lee

The Robertson-Walker (RW) metric allows us to apply general relativity to model the behavior of the Universe as a whole (i.e., cosmology). We can properly interpret various cosmological observations, like the cosmological redshift, the Hubble parameter, geometrical distances, and so on, if we identify fundamental observers with individual galaxies. That is t

Jiayi Yuan, Ruixiang Tang, Xiaoqian Jiang, Xia Hu

The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care. However, current approaches face challenges such as data standardization, ethical considerations, and a lack of interoperability between Electronic Health Records (EHRs) and clinical trial criteria. In this paper, we explore

Parastoo Sadeghi, Chien-Hung Chien

This paper explores analytical connections between the perturbation methodology of the Australian Bureau of Statistics (ABS) and the differential privacy (DP) framework. We consider a single static counting query function and find the analytical form of the perturbation distribution with symmetric support for the ABS perturbation methodology. We then analyti

Zibin Zheng, Neng Zhang, Jianzhong Su, Zhijie Zhong

Smart contracts are programs deployed on a blockchain and are immutable once deployed. Reentrancy, one of the most important vulnerabilities in smart contracts, has caused millions of dollars in financial loss. Many reentrancy detection approaches have been proposed. It is necessary to investigate the performance of these approaches to provide useful guideli

Wenteng Liang, Feng Xue, Yihao Liu, Guofeng Zhong

The recently proposed open-world object and open-set detection have achieved a breakthrough in finding never-seen-before objects and distinguishing them from known ones. However, their studies on knowledge transfer from known classes to unknown ones are not deep enough, resulting in the scanty capability for detecting unknowns hidden in the background. In th

Ren Habara, Katsunori Wakabayashi

Nonlinear optical (NLO) effect plays a crucial role to engineer optical angular frequency and symmetry of electronic system. Metallic transition-metal dichalcogenide (TMDC) is one of two-dimensional (2D) materials, which has no inversion symmetry for odd-number-layer. In particular, odd-number-layered NbSe$_2$ has spin splitting owing to Ising-type spin-orbi

Yunfan Lu, Zipeng Wang, Minjie Liu, Hongjian Wang

Event cameras sense the intensity changes asynchronously and produce event streams with high dynamic range and low latency. This has inspired research endeavors utilizing events to guide the challenging video superresolution (VSR) task. In this paper, we make the first attempt to address a novel problem of achieving VSR at random scales by taking advantages

Yukun Li, Liet Vo, Guanqian Wang

In this paper, we consider a new approach for semi-discretization in time and spatial discretization of a class of semi-linear stochastic partial differential equations (SPDEs) with multiplicative noise. The drift term of the SPDEs is only assumed to satisfy a one-sided Lipschitz condition and the diffusion term is assumed to be globally Lipschitz continuous

Jiwoo Seo, Jaewon Lee, Hongjun An

We measured temporal and emission properties of quiescent magnetars using archival Chandra and XMM-Newton data, produced a list of the properties for 17 magnetars, and revisited previously suggested correlations between the properties. Our studies carried out with a larger sample, better spectral characterizations, and more thorough analyses not only confirm

Jinrui Xing, Hui Yuan, Raouf Hamzaoui, Hao Liu

In recent years, point clouds have become increasingly popular for representing three-dimensional (3D) visual objects and scenes. To efficiently store and transmit point clouds, compression methods have been developed, but they often result in a degradation of quality. To reduce color distortion in point clouds, we propose a graph-based quality enhancement n

Taiqiang Wu, Zhe Zhao, Jiahao Wang, Xingyu Bai

Distilling high-accuracy Graph Neural Networks (GNNs) to low-latency multilayer perceptions (MLPs) on graph tasks has become a hot research topic. However, conventional MLP learning relies almost exclusively on graph nodes and fails to effectively capture the graph structural information. Previous methods address this issue by processing graph edges into ext

Dongxu Chang, Zhiming Ma, Guanghui Wang, Guiying Yan

In this study, an optimization model for offline scheduling policy of low-density parity-check (LDPC) codes is proposed to improve the decoding efficiency of the belief propagation (BP). The optimization model uses the number of messages passed (NMP) as a metric to evaluate complexity, and two metrics, average entropy (AE), and gap to maximum a posteriori (G

Hanyu Zhou, Yi Chang, Gang Chen, Luxin Yan

Optical flow estimation has made great progress, but usually suffers from degradation under adverse weather. Although semi/full-supervised methods have made good attempts, the domain shift between the synthetic and real adverse weather images would deteriorate their performance. To alleviate this issue, our start point is to unsupervisedly transfer the knowl

Jun Wang, Xiangyu Ding, Qianqian Zhang, Ying-Chang Liang

Symbiotic radio (SR) has emerged as a spectrum- and energy-efficient paradigm to support massive Internet of Things (IoT) connections. Two multiple access schemes are proposed in this paper to facilitate the massive IoT connections using the cellular network based on the SR technique, namely, the simultaneous access (SA) scheme and the selection diversity ac

Michael D. Barrus, Jean Guillaume, Benjamin Lantz

The distinguishing chromatic number of a graph $G$ is the smallest number of colors needed to properly color the vertices of $G$ so that the trivial automorphism is the only symmetry of $G$ that preserves the coloring. We investigate the distinguishing chromatic number for Hamiltonian circulant graphs with maximum degree at most 4.

Kyung Kiu Kim, Seoktae Koh, Gansukh Tumurtushaa

In this work, we study cosmological solutions of the 8-dimensional Einstein Yang-Mills theory coupled to a perfect-fluid matter. A Yang-Mills instanton of extra dimensions causes a 4-dimensional expanding universe with dynamical compactification of the extra dimensions. To construct physically reliable situations, we impose the null energy condition on the m

Zulong Liu, Kejia-Chen, Zheng Liu

Graph machine learning (GML) has made great progress in node classification, link prediction, graph classification and so on. However, graphs in reality are often structurally imbalanced, that is, only a few hub nodes have a denser local structure and higher influence. The imbalance may compromise the robustness of existing GML models, especially in learning

Zhenyu Xie, Zaiyu Huang, Xin Dong, Fuwei Zhao

Image-based Virtual Try-ON aims to transfer an in-shop garment onto a specific person. Existing methods employ a global warping module to model the anisotropic deformation for different garment parts, which fails to preserve the semantic information of different parts when receiving challenging inputs (e.g, intricate human poses, difficult garments). Moreove

Cong Wei, Brendan Duke, Ruowei Jiang, Parham Aarabi

Vision Transformers (ViT) have shown their competitive advantages performance-wise compared to convolutional neural networks (CNNs) though they often come with high computational costs. To this end, previous methods explore different attention patterns by limiting a fixed number of spatially nearby tokens to accelerate the ViT's multi-head self-attention (MH

Sina Malakpour Estalaki, Tengfei Luo, Khachatur V. Manukyan

Spontaneous crystallization of metals under extreme conditions is a unique phenomenon occurring under far-from-equilibrium conditions that could enable the development of revolutionary and disruptive metastable metals with unusual properties. In this work, the formation of the hexagonal close-packed Nickel (hcp-Ni) metastable phase during spontaneous crystal

Meiqi Hu, Chen Wu, Bo Du

Hyperspectral change detection plays an essential role of monitoring the dynamic urban development and detecting precise fine object evolution and alteration. In this paper, we have proposed an original Efficient Multi-temporal Self-attention Network (EMS-Net) for hyperspectral change detection. The designed EMS module cuts redundancy of those similar and co

Evelyn Chee, Mong Li Lee, Wynne Hsu

Class-incremental continual learning is a core step towards developing artificial intelligence systems that can continuously adapt to changes in the environment by learning new concepts without forgetting those previously learned. This is especially needed in the medical domain where continually learning from new incoming data is required to classify an expa

Irene I. Onnis, Bárbara C. Valério, José Antonio M. Vilhena

In this paper, we construct a one-parameter family of minimal surfaces in the Euclidean $3$-space of arbitrarily high genus and with three ends. Each member of this family is immersed, complete and with finite total curvature. Another interesting property is that the symmetry group of the genus $k$ surfaces $\Sigma_{k,x}$ is the dihedral group with $4(k+1)$

Qian Tao, Zhen Wang, Wenyuan Yu, Yaliang Li

In recent years, a plethora of spectral graph neural networks (GNN) methods have utilized polynomial basis with learnable coefficients to achieve top-tier performances on many node-level tasks. Although various kinds of polynomial bases have been explored, each such method adopts a fixed polynomial basis which might not be the optimal choice for the given gr

Ping Shen, Wen-Biao Han, Chen Zhang, Shu-Cheng Yang

The popular waveform templates of extreme-mass-ratio inspirals usually omit the mass-ratio corrections in the conservative dynamics, and employ adiabatic approximation to include the radiation reaction. With the help of effective-one-body dynamics, we investigate the influence of mass-ratio corrections in the conservative part on the gravitational waves. We

Elijah Pelofske, Georg Hahn, Hristo Djidjev

Quantum annealing is a specialized type of quantum computation that aims to use quantum fluctuations in order to obtain global minimum solutions of combinatorial optimization problems. Programmable D-Wave quantum annealers are available as cloud computing resources which allow users low level access to quantum annealing control features. In this paper, we ar

Shuaijie Yuan, Xinxing Zhou, Yu Chen, Yuhan Zhong

Effective-medium theory pertains to the theoretical modelling of homogenization, which aims to replace an inhomogeneous structure of subwavelength-scale constituents with a homogeneous effective medium. The effective-medium theory is fundamental to various realms, including electromagnetics and material science, since it can largely decrease the complexity i

Botond B Antal, Anthony G Chesebro, Helmut H Strey, Lilianne R Mujica-Parodi

All fields of science depend on mathematical models. Occam's razor refers to the principle that good models should exclude parameters beyond those minimally required to describe the systems they represent. This is because redundancy can lead to incorrect estimates of model parameters from data, and thus inaccurate or ambiguous conclusions. Here, we show how

Shikhar Tuli, Niraj K. Jha

Automated design of efficient transformer models has recently attracted significant attention from industry and academia. However, most works only focus on certain metrics while searching for the best-performing transformer architecture. Furthermore, running traditional, complex, and large transformer models on low-compute edge platforms is a challenging pro

Haomiao Ni, Changhao Shi, Kai Li, Sharon X. Huang

Conditional image-to-video (cI2V) generation aims to synthesize a new plausible video starting from an image (e.g., a person's face) and a condition (e.g., an action class label like smile). The key challenge of the cI2V task lies in the simultaneous generation of realistic spatial appearance and temporal dynamics corresponding to the given image and conditi

Vishal Vinod, Tanmay Shah, Dmitry Lagun

Recent work in Neural Fields (NFs) learn 3D representations from class-specific single view image collections. However, they are unable to reconstruct the input data preserving high-frequency details. Further, these methods do not disentangle appearance from geometry and hence are not suitable for tasks such as texture transfer and editing. In this work, we

Carlos Wiechers, J. L. Lucio, Xóchitl Sánchez-Lozano, Rafael Gómez-Medina

Afterpulsing is a factor limiting the distance over which discrete-variable quantum key distribution systems are secure, and a common feature in single-photon detectors. The relevance of this phenomenon stems from its stochastic, self-interacting nature and the fact that its rate rises with the number of avalanche events, which increases the quantum bit erro

Seulong Kim, Kihong Kim

We investigate the influence of the temporal variations of various medium parameters on the propagation of Dirac-type waves in materials where the quasiparticles are described by a generalized version of the pseudospin-1/2 Dirac equation. Our considerations also include the propagation of electromagnetic waves in metamaterials with the Dirac-type dispersion.

Raúl Rojas

In 1837, the first computer program in history was sketched by the renowned mathematician and inventor Charles Babbage. It was a program for the Analytical Engine. The program consists of a sequence of arithmetical operations and the necessary variable addresses (memory locations) of the arguments and the result, displayed in tabular fashion, like a program

Yulin Luo, Rui Zhao, Xiaobao Wei, Jinwei Chen

Adverse weather removal tasks like deraining, desnowing, and dehazing are usually treated as separate tasks. However, in practical autonomous driving scenarios, the type, intensity,and mixing degree of weather are unknown, so handling each task separately cannot deal with the complex practical scenarios. In this paper, we study the blind adverse weather remo

Heinz H. Bauschke, Theo Bendit, Walaa M. Moursi

Projection operators are fundamental algorithmic operators in Analysis and Optimization. It is well known that these operators are firmly nonexpansive; however, their composition is generally only averaged and no longer firmly nonexpansive. In this note, we introduce the modulus of averagedness and provide an exact result for the composition of two linear pr

Fei Lu, Miao-Jung Yvonne Ou

Regularization is a long-standing challenge for ill-posed linear inverse problems, and a prototype is the Fredholm integral equation of the first kind with additive Gaussian measurement noise. We introduce a new RKHS regularization adaptive to measurement data and the underlying linear operator. This RKHS arises naturally in a variational approach, and its c

Ba Phi Nguyen, Kihong Kim

We present a numerical study of the transport and localization properties of excitations in one-dimensional lattices with diagonal disordered mosaic modulations. The model is characterized by the modulation period $\kappa$ and the disorder strength $W$. We calculate the disorder averages $\langle T\rangle$, $\langle \ln T\rangle$, and $\langle P\rangle$, whe

Junyu Chen, Norihiro Yoshida, Hiroaki Takada

Dataset licensing is currently an issue in the development of machine learning systems. And in the development of machine learning systems, the most widely used are publicly available datasets. However, since the images in the publicly available dataset are mainly obtained from the Internet, some images are not commercially available. Furthermore, developers

Andreas-Stephan Elsenhans

The computation of the normaliser of a permutation group in the full symmetric group is an important and hard problem in computational group theory. This article reports on an algorithm that builds a descending chain of overgroups to determine the normaliser. A detailed performance test in magma shows the improvement for large examples of intransitive and im

Taeyoung Kim, Yunhee Jang, Chanjong Lee, Hyungjoon Koo

Smart contracts are self-executing programs on a blockchain to ensure immutable and transparent agreements without the involvement of intermediaries. Despite the growing popularity of smart contracts for many blockchain platforms like Ethereum, smart contract developers cannot prevent copying their smart contracts from competitors due to the absence of techn

Hui Zhang, Xuexin An, Qiang He, Yudong Yao

Gene regulatory relationships can be abstracted as a gene regulatory network (GRN), which plays a key role in characterizing complex cellular processes and pathways. Recently, graph neural networks (GNNs), as a class of deep learning models, have emerged as a useful tool to infer gene regulatory relationships from gene expression data. However, deep learning

Yaqing Shi, Wei Wang, Gang Zhao, Meng Zhai

Hot Jupiters, particularly those with temperature higher than 2000 K are the best sample of planets that allow in-depth characterization of their atmospheres. We present here a thermal emission study of the ultra hot Jupiter WASP-103 b observed in two secondary eclipses with CFHT/WIRCam in J and Ks bands. By means of high precision differential photometry, w

Yiran Li, Junpeng Wang, Xin Dai, Liang Wang

Vision transformer (ViT) expands the success of transformer models from sequential data to images. The model decomposes an image into many smaller patches and arranges them into a sequence. Multi-head self-attentions are then applied to the sequence to learn the attention between patches. Despite many successful interpretations of transformers on sequential