April 2023 arXiv papers — page 86

Koki Mizuno, Ai Yamakage

Nonsymmorphic crystals can host characteristic double surface Dirac cones with fourfold degeneracy on the Dirac points, called wallpaper fermion, protected by wallpaper group symmetry. We clarify the charge and spin Hall effect of wallpaper fermions in the presence of the (anti)ferromagnetism.Based on a four-sublattice model, we construct the effective Hamil

Alessandro Giovagnoli, Yunpu Ma, Volker Tresp

Quantum Machine Learning (QML) is a recent and rapidly evolving field where the theoretical framework and logic of quantum mechanics are employed to solve machine learning tasks. Various techniques with different levels of quantum-classical hybridization have been proposed. Here we focus on variational quantum circuits (VQC), which emerged as the most promis

Binzhe Yuan, Liangtao Dai, Xin Lou

This paper proposes a parametric error analysis method for Goldschmidt floating point division, which reveals how the errors of the intermediate results accumulate and propagate during the Goldschmidt iterations. The analysis is developed by separating the error terms with and without convergence to zero, which are the key parts of the iterative approximate

Yasunori Mawatari

Kinetic inductances of superconducting nanostrips with a meander pattern are theoretically investigated based on the London model, and the effect of the current crowding at the turns of the nanostrips is considered. The complex current approach is developed for analytical investigation of the kinetic inductance of nanostrips with turns for thin $d<\lambda$ a

Joshua T. Y. Tse, Shunsuke Murai, Katsuhisa Tanaka

Surface lattice resonance supported on nanoparticle arrays is a promising candidate in enhancing fluorescent effects in both absorption and emission. The optical enhancement provided by surface lattice resonance is primarily through the light confinement beyond the diffraction limit, where the nanoparticle arrays can enhance light-matter interaction for incr

Fredrik Eriksson, Erik Fransson, Christopher Linderälv, Zheyong Fan

It has recently been demonstrated that MoS2 with irregular interlayer rotations can achieve an extreme anisotropy in the lattice thermal conductivity (LTC), which is for example of interest for applications in waste heat management in integrated circuits. Here, we show by atomic scale simulations based on machine-learned potentials that this principle extend

Shu Nakamura, Yasutomo Kawanishi, Shohei Nobuhara, Ko Nishino

In this paper, we realize automatic visual recognition and direction estimation of pointing. We introduce the first neural pointing understanding method based on two key contributions. The first is the introduction of a first-of-its-kind large-scale dataset for pointing recognition and direction estimation, which we refer to as the DP Dataset. DP Dataset con

Dianzhao Li, Ostap Okhrin

Deep Reinforcement Learning (DRL) has shown remarkable success in solving complex tasks across various research fields. However, transferring DRL agents to the real world is still challenging due to the significant discrepancies between simulation and reality. To address this issue, we propose a robust DRL framework that leverages platform-dependent percepti

Wenyang Liu, Yi Wang, Kim-Hui Yap, Lap-Pui Chau

In this paper, we study a real-world JPEG image restoration problem with bit errors on the encrypted bitstream. The bit errors bring unpredictable color casts and block shifts on decoded image contents, which cannot be resolved by existing image restoration methods mainly relying on pre-defined degradation models in the pixel domain. To address these challen

Haochun Wang, Chi Liu, Nuwa Xi, Zewen Qiang

Large Language Models (LLMs), such as the LLaMA model, have demonstrated their effectiveness in various general-domain natural language processing (NLP) tasks. Nevertheless, LLMs have not yet performed optimally in biomedical domain tasks due to the need for medical expertise in the responses. In response to this challenge, we propose HuaTuo, a LLaMA-based m

Bulat Khaertdinov, Stylianos Asteriadis

In recent years, self-supervised learning (SSL) frameworks have been extensively applied to sensor-based Human Activity Recognition (HAR) in order to learn deep representations without data annotations. While SSL frameworks reach performance almost comparable to supervised models, studies on interpreting representations learnt by SSL models are limited. Neve

Ryo Takahashi, Tomoki Ozawa

We propose an unconventional bulk-edge correspondence for two-dimensional Stiefel-Whitney insulators and Euler insulators, which are topological insulators protected by the $PT$ symmetry. We find that, although the energy spectrum under the open boundary condition is generally gapped, the entanglement spectrum is gapless when the Stiefel-Whitney or Euler cla

Hugo Parlier

This note is about variations on a theorem of Bers about short pants decompositions of surfaces. It contains a version for surfaces with boundary but also a slight improvement on the best known bound for closed surfaces.

Yanfang Lyu, Xiaoyu Zhao, Zhiqiang Gong, Xiao Kang

Data-driven prediction of fluid flow and temperature distribution in marine and aerospace engineering has received extensive research and demonstrated its potential in real-time prediction recently. However, usually large amounts of high-fidelity data are required to describe and accurately predict the complex physical information, while in reality, only lim

Bowen Zheng, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan

Learning new classes without forgetting is crucial for real-world applications for a classification model. Vision Transformers (ViT) recently achieve remarkable performance in Class Incremental Learning (CIL). Previous works mainly focus on block design and model expansion for ViTs. However, in this paper, we find that when the ViT is incrementally trained,

Qijie Bai, Jiawen Guo, Haiwei Zhang, Changli Nie

Temporal heterogeneous information network (temporal HIN) embedding, aiming to represent various types of nodes of different timestamps into low dimensional spaces while preserving structural and semantic information, is of vital importance in diverse real-life tasks. Researchers have made great efforts on temporal HIN embedding in Euclidean spaces and got s

Jinlong Fan, Jing Zhang, Dacheng Tao

Neural radiance field (NeRF) has become a popular 3D representation method for human avatar reconstruction due to its high-quality rendering capabilities, e.g., regarding novel views and poses. However, previous methods for editing the geometry and appearance of the avatar only allow for global editing through body shape parameters and 2D texture maps. In th

Jayrald Empino, Jean Allyson Junsay, Mary Grace Verzon, Mideth Abisado

Since its establishment in 1999, the Metro Rail Transit Line 3 (MRT3) has served as a transportation option for numerous passengers in Metro Manila, Philippines. The Philippine government's transportation department records more than a thousand people using the MRT3 daily and forecasting the daily passenger count may be rather challenging. The MRT3's daily r

Katharina Fogelberg, Sireesha Chamarthi, Roman C. Maron, Julia Niebling

The limited ability of Convolutional Neural Networks to generalize to images from previously unseen domains is a major limitation, in particular, for safety-critical clinical tasks such as dermoscopic skin cancer classification. In order to translate CNN-based applications into the clinic, it is essential that they are able to adapt to domain shifts. Such ne

Frédéric Marin, Eugene Churazov, Ildar Khabibullin, Riccardo Ferrazzoli

The center of the Milky Way Galaxy hosts a $\sim$4 million solar mass black hole (Sgr A$^*$) that is currently very quiescent with a luminosity many orders of magnitude below those of active galactic nuclei. Reflection of X-rays from Sgr A$^*$ by dense gas in the Galactic Center region offers a means to study its past flaring activity on times scales of hund

Mayank Poddar, Akash Mishra, Mohit Kewlani, Haoyang Pei

Depth Estimation has wide reaching applications in the field of Computer vision such as target tracking, augmented reality, and self-driving cars. The goal of Monocular Depth Estimation is to predict the depth map, given a 2D monocular RGB image as input. The traditional depth estimation methods are based on depth cues and used concepts like epipolar geometr

Chiara Boiti, David Jornet, Alessandro Oliaro

Given a function $f\in L^2(\mathbb R)$, we consider means and variances associated to $f$ and its Fourier transform $\hat{f}$, and explore their relations with the Wigner transform $W(f)$, obtaining a simple new proof of Shapiro's mean-dispersion principle. Uncertainty principles for orthonormal sequences in $L^2(\mathbb R)$ involving linear partial differen

Federico Spada

I present a re-analysis of the available observational constraints on the trajectory of 'Oumuamua, the first confirmed interstellar object discovered in the solar system. 'Oumuamua passed through the inner solar system on a hyperbolic (i.e., unbound) trajectory. Its discovery occurred after perihelion passage, and near the time of its closest approach to Ear

Haoran Zhu, Xiaolin Chang, Jelena Mišić, Vojislav B. Mišić

Stubborn mining attack greatly downgrades Bitcoin throughput and also benefits malicious miners (attackers). This paper aims to quantify the impact of block receiving delay on stubborn mining attack severity in imperfect Bitcoin networks. We develop an analytic model and derive formulas of both relative revenue and system throughput, which are applied to stu

Chenkai Ma

Prompt engineering and calibration make large language models excel at reasoning tasks, including multiple choice commonsense reasoning. From a practical perspective, we investigate and evaluate these strategies on smaller language models. Through experiments on five commonsense reasoning benchmarks, we find that each strategy favors certain models, but thei

Qijie Bai, Changli Nie, Haiwei Zhang, Dongming Zhao

Temporal link prediction, aiming to predict future edges between paired nodes in a dynamic graph, is of vital importance in diverse applications. However, existing methods are mainly built upon uniform Euclidean space, which has been found to be conflict with the power-law distributions of real-world graphs and unable to represent the hierarchical connection

Yasunori Okumura

We consider the social welfare function a la Arrow, where some voters are not qualified to evaluate some alternatives. Thus, the inputs of the social welfare function are the preferences of voters on the alternatives that they are qualified to evaluate only. Our model is a generalization of the peer rating model, where each voter evaluates the other voters (

Pengfei Shi, Xinyu Zhang, Wei Zhong

We propose a model averaging approach, combined with a partition and matching method to estimate the conditional average treatment effects under heteroskedastic error settings. The proposed approach has asymptotic optimality and consistency of weights and estimator. Numerical studies show that our method has good finite-sample performances.

Chunyan Xiong, Mengli Lu, Xiaotong Yu, Jian Cao

Soft-thresholding has been widely used in neural networks. Its basic network structure is a two-layer convolution neural network with soft-thresholding. Due to the network's nature of nonlinearity and nonconvexity, the training process heavily depends on an appropriate initialization of network parameters, resulting in the difficulty of obtaining a globally

Matyas Barczy, Miguel González, Pedro Martín-Chávez, Inés del Puerto

Branching processes form an important family of stochastic processes that have been successfully applied in many fields. In this paper, we focus our attention on controlled multi-type branching processes (CMBPs). A Feller-type diffusion approximation is derived for some critical CMBPs. Namely, we consider a sequence of appropriately scaled random step functi

Rémi Boutin, Pierre Latouche, Charles Bouveyron

Numerical interactions leading to users sharing textual content published by others are naturally represented by a network where the individuals are associated with the nodes and the exchanged texts with the edges. To understand those heterogeneous and complex data structures, clustering nodes into homogeneous groups as well as rendering a comprehensible vis

Jie Guo, Qimeng Wang, Yan Gao, Xiaolong Jiang

CLIP (Contrastive Language-Image Pretraining) is well-developed for open-vocabulary zero-shot image-level recognition, while its applications in pixel-level tasks are less investigated, where most efforts directly adopt CLIP features without deliberative adaptations. In this work, we first demonstrate the necessity of image-pixel CLIP feature adaption, then

Decio Levi, Miguel A. Rodríguez

Determining if an (1+1)-differential-difference equation is integrable or not (in the sense of possessing an infinite number of symmetries) can be reduced to the study of the dependence of the equation on the lattice points, according to Yamilov's theorem. We shall apply this result to a class of differential-difference equations obtained as partial continuo

Christoph Angermann, Simon Göppel, Markus Haltmeier

Reconstructing an image from noisy and incomplete measurements is a central task in several image processing applications. In recent years, state-of-the-art reconstruction methods have been developed based on recent advances in deep learning. Especially for highly underdetermined problems, maintaining data consistency is a key goal. This can be achieved eith

Lan Chen, Xi Chen, Shiyu Wu, Yaqi Yang

As a phenomenal large language model, ChatGPT has achieved unparalleled success in various real-world tasks and increasingly plays an important role in our daily lives and work. However, extensive concerns are also raised about the potential ethical issues, especially about whether ChatGPT-like artificial general intelligence (AGI) will replace human jobs. T

Jeongsu Jang, Joonho Kim, Dongchul Sung, Jong Hyuk Kim

Group-IV monochalcogenides have recently shown great potential for their thermoelectric, ferroelectric, and other intriguing properties. The electrical properties of group-IV monochalcogenides exhibit a strong dependence on the chalcogen type. For example, GeTe exhibits high doping concentration, whereas S/Se-based chalcogenides are semiconductors with sizab

Raed Alharbi, Sylvia Chan-Olmsted, Huan Chen, My T. Thai

Understanding the COVID-19 vaccine hesitancy, such as who and why, is very crucial since a large-scale vaccine adoption remains as one of the most efficient methods of controlling the pandemic. Such an understanding also provides insights into designing successful vaccination campaigns for future pandemics. Unfortunately, there are many factors involving in

Graham Frederick, Yaswant T, Brintha Therese A

Hypertension is a medical condition characterized by high blood pressure, and classifying it into its various stages is crucial to managing the disease. In this project, a novel method is proposed for classifying stages of hypertension using Photoplethysmography (PPG) signals and deep learning models, namely AvgPool_VGG-16. The PPG signal is a non-invasive m

Suresh Chandra Baral, P. Maneesha, E. G. Rini, Somaditya Sen

Double perovskites R2NiMnO6 (R= Rare earth element) (RNMO) are a significant class of materials owing to their Multifunctional properties with structural modifications. In particular, multifunctional double perovskite oxides La2NiMnO6 (LNMO) which possess both electric and magnetic orderings, chemical flexibility, versatility, and indispensable properties li

Julien Hambuckers, Marie Kratz, Antoine Usseglio-Carleve

We introduce a method to estimate simultaneously the tail and the threshold parameters of an extreme value regression model. This standard model finds its use in finance to assess the effect of market variables on extreme loss distributions of investment vehicles such as hedge funds. However, a major limitation is the need to select ex ante a threshold below

Thai Bui

Given a graph $G(V, E)$ and a positive integer $k$ ($k \geq 1$), a simple path on $k$ vertices is a sequence of $k$ vertices in which no vertex appears more than once and each consecutive pair of vertices in the sequence are connected by an edge. This paper provides an overview of current research on the existence and counting of k-paths in graphs.

Denny Lane B. Sombillo, Neris I. Sombillo

We revisit the quantum correction to the classical time of arrival to address the unphysical instantaneous arrival in the limit of zero initial momentum. In this study, we show that the vanishing of arrival time is due to the contamination of the causality-violating component of the initial wave packet. Motivated by this observation, we propose to update the

Céline Lichtensteiger, Marios Hadjimichael, Edoardo Zatterin, Chia-Ping Su

We study the complex ferroelastic/ferroelectric domain structure in the prototypical ferroelectric PbTiO3 epitaxially strained on (110)o-oriented DyScO3 substrates, using a combination of atomic force microscopy, laboratory and synchrotron x-ray diffraction and high resolution scanning transmission electron microscopy. We observe that the anisotropic strain

Tuo Zhang, Lei Gao, Sunwoo Lee, Mi Zhang

In cross-device Federated Learning (FL) environments, scaling synchronous FL methods is challenging as stragglers hinder the training process. Moreover, the availability of each client to join the training is highly variable over time due to system heterogeneities and intermittent connectivity. Recent asynchronous FL methods (e.g., FedBuff) have been propose

Nuo Chen, Kangping Lou, Yalong Yu, Xuanjian He

Thin-film lithium niobate is a promising platform owing to its large electro-optic coefficients and low propagation loss. However, the large footprints of devices limit their application in large-scale integrated optical systems. A crucial challenge is how to maintain the performance advantage given the design space restrictions in this situation. This artic

Dimuthu D. K. Arachchige, Tanmay Varshney, Umer Huzaifa, Iyad Kanj

Legged locomotion is a highly promising but under-researched subfield within the field of soft robotics. The compliant limbs of soft-limbed robots offer numerous benefits, including the ability to regulate impacts, tolerate falls, and navigate through tight spaces. These robots have the potential to be used for various applications, such as search and rescue

Francesco Sgherzi, Marco Siracusa, Ivan Fernandez, Adrià Armejach

Sparse matrix computation is crucial in various modern applications, including large-scale graph analytics, deep learning, and recommender systems. The performance of sparse kernels varies greatly depending on the structure of the input matrix, making it difficult to gain a comprehensive understanding of sparse computation and its relationship to inputs, alg

Qingsen Yan, Weiye Chen, Song Zhang, Yu Zhu

Mapping Low Dynamic Range (LDR) images with different exposures to High Dynamic Range (HDR) remains nontrivial and challenging on dynamic scenes due to ghosting caused by object motion or camera jitting. With the success of Deep Neural Networks (DNNs), several DNNs-based methods have been proposed to alleviate ghosting, they cannot generate approving results

Longfei Fang, Huiqiu Lin, Yongtang Shi

Given a planar graph family $\mathcal{F}$, let ${\rm ex}_{\mathcal{P}}(n,\mathcal{F})$ and ${\rm spex}_{\mathcal{P}}(n,\mathcal{F})$ be the maximum size and maximum spectral radius over all $n$-vertex $\mathcal{F}$-free planar graphs, respectively. Let $tC_{\ell}$ be the disjoint union of $t$ copies of $\ell$-cycles, and $t\mathcal{C}$ be the family of $t$ v

Abhisek Kundu, Naveen K. Mellempudi, Dharma Teja Vooturi, Bharat Kaul

Sparse training is emerging as a promising avenue for reducing the computational cost of training neural networks. Several recent studies have proposed pruning methods using learnable thresholds to efficiently explore the non-uniform distribution of sparsity inherent within the models. In this paper, we propose Gradient Annealing (GA), where gradients of mas

Colin Scheibner, Hillel Ori, Adam E. Cohen, Vincenzo Vitelli

Excitable media, ranging from bioelectric tissues and chemical oscillators to forest fires and competing populations, are nonlinear, spatially extended systems capable of spiking. Most investigations of excitable media consider situations where the amplifying and suppressing forces necessary for spiking coexist at every point in space. In this case, spiking

Wanrong Zhu, Jack Hessel, Anas Awadalla, Samir Yitzhak Gadre

In-context vision and language models like Flamingo support arbitrarily interleaved sequences of images and text as input. This format not only enables few-shot learning via interleaving independent supervised (image, text) examples, but also, more complex prompts involving interaction between images, e.g., "What do image A and image B have in common?" To su

Yunhong Li, Zuo Quan Xu, Xun Yu Zhou

We study a continuous-time expected utility maximization problem in which the investor at maturity receives the value of a contingent claim in addition to the investment payoff from the financial market. The investor knows nothing about the claim other than its probability distribution, hence an ``intractable claim''. In view of the lack of necessary informa

Tianshu Kuai, Akash Karthikeyan, Yash Kant, Ashkan Mirzaei

Animating an object in 3D often requires an articulated structure, e.g. a kinematic chain or skeleton of the manipulated object with proper skinning weights, to obtain smooth movements and surface deformations. However, existing models that allow direct pose manipulations are either limited to specific object categories or built with specialized equipment. T

Ton de Kok, Mirjam S. Meijer

We consider the classical discrete time lost-sales model under stationary continuous demand and linear holding and penalty costs and positive constant lead time. To date the optimal policy structure is only known implicitly by solving numerically the Bellman equations. In this paper we derive an optimality equation for the lost-sales model. We propose a fixe

Youngsub Yoon, Atanu Guha

We show that Hossenfelder's covariant formulation of Verlinde's emergent gravity predicts inflation and the late-time acceleration at the same time, without assuming a separate field such as inflaton, whose sole purpose is producing inflation. In particular, for the current deceleration parameter $q=-0.95$ to $-0.55$, we obtained $\lambda^2$, the mass of the

Alexander Demin, Shashi Gowda

We present Groebner.jl, a Julia package for computing Groebner bases with the F4 algorithm. Groebner.jl is an efficient, portable, and open-source software. Groebner.jl works over integers modulo a prime and over the rationals, supports basic multi-threading, and specializes in computation in the degree reverse lexicographical monomial ordering. The implemen

K. Poojitha, A. Sai Charish, M. Arun Kuamr Reddy, S. Ayyasamy

The abstract outlines the problem of toxic comments on social media platforms, where individuals use disrespectful, abusive, and unreasonable language that can drive users away from discussions. This behavior is referred to as anti-social behavior, which occurs during online debates, comments, and fights. The comments containing explicit language can be clas

Hongxu Chen, Chanwoo Kim

We consider the Boltzmann equation in convex domain with non-isothermal boundary of diffuse reflection. For both unsteady/steady problems, we construct solutions belong to $W^{1,p}_x$ for any $p<3$. We prove that the unsteady solution converges to the steady solution in the same Sobolev space exponentially fast as $t \rightarrow \infty$.

Nathaniel Gallup

With the goal of computing the Grothendieck group of certain multigraded infinite polynomial rings and the $K$-series of infinite matrix Schubert spaces, we introduce a new type of $\Gamma$-graded $k$-algebra (which we call a PDCF algebra) and a new type of graded module (a BDF module) over said algebra. Since infinite polynomial rings are not Noetherian and

Zhipeng Deng, Luyang Luo, Hao Chen

Federated learning (FL) has been introduced to the healthcare domain as a decentralized learning paradigm that allows multiple parties to train a model collaboratively without privacy leakage. However, most previous studies have assumed that every client holds an identical label set. In reality, medical specialists tend to annotate only diseases within their

Yangguang Wang, Xiang Zhang, Mingyuan Lin, Lei Yu

Scene Dynamic Recovery (SDR) by inverting distorted Rolling Shutter (RS) images to an undistorted high frame-rate Global Shutter (GS) video is a severely ill-posed problem due to the missing temporal dynamic information in both RS intra-frame scanlines and inter-frame exposures, particularly when prior knowledge about camera/object motions is unavailable. Co

Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu

In this article, we present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP), with a focus of advancing DP's deployment in real-world applications. Key points and high-level contents of the article were originated from the discussions from "Differential Privacy (DP): Challenges Towards the Ne

Shaozhe Hao, Kai Han, Kwan-Yee K. Wong

We tackle the issue of generalized category discovery (GCD). GCD considers the open-world problem of automatically clustering a partially labelled dataset, in which the unlabelled data may contain instances from both novel categories and labelled classes. In this paper, we address the GCD problem with an unknown category number for the unlabelled data. We pr

Xingjia Cheng, Wen Xu, Hua Wen, Jing Zhang

Bilayer (BL) molybdenum disulfide (MoS$_2$) is one of the most important electronic structures not only in valleytronics but also in realizing twistronic systems on the basis of the topological mosaics in Moir\'e superlattices. In this work, BL MoS$_2$ on sapphire substrate with 2$H$-stacking structure is fabricated. We apply the terahertz (THz) time-domain

Gang-Feng Guo, Xi-Xi Bao, Han-Jie Zhu, Xiao-Ming Zhao

Non-Hermitian skin effect, the localization of an extensive number of eigenstates at the ends of the system, has greatly expanded the frontier of physical laws. It has long been believed that the present of skin modes is equivalent to the topologically nontrivial point gap of complex eigenvalues under periodic boundary conditions, and vice versa. However, we

Li Zhu, Jiahui Xiong, Feng Xiong, Hanzheng Hu

Unmanned Aerial Vehicles (UAVs), specifically drones equipped with remote sensing object detection technology, have rapidly gained a broad spectrum of applications and emerged as one of the primary research focuses in the field of computer vision. Although UAV remote sensing systems have the ability to detect various objects, small-scale objects can be chall

Dan-Radu Grigore

We consider the general framework of perturbative quantum field theory for the pure Yang-Mills model developped in [10] and consider the coupling with spin 3/2 particles. We will derive the most general form of the interaction with pure Yang-Mills particles and with the spin $2$ field. The expressions for the interactions are obtained, as in the pure Yang-Mi

N Wedasinghe, NT Sirisoma, APR Wickramarachchi

Visual-impaired communities are one of the hindrances groups to accessing web content access in the world. The obstacles encountered by this community in their current practices and to develop best practice guidelines to overcome the digital divide in Sri Lanka become gap filling of this domain. A preliminary survey indicated five main problems including acc

Dianhao Zhang, Mien Van, Pantelis Sopasakis, Seán McLoone

To enable safe and effective human-robot collaboration (HRC) in smart manufacturing, seamless integration of sensing, cognition, and prediction into the robot controller is critical for real-time awareness, response, and communication inside a heterogeneous environment (robots, humans, and equipment). The proposed approach takes advantage of the prediction c

Chong Zheng

In this paper, we study some useful properties of persistent pairs in a discrete Morse function on a simplicial complex $K$. In case of $\dim K=1$ (i.e., a graph), by using the properties, we characterize strongly connectedness of critical simplices between two distinct discrete Morse functions, and relate the number of such pairs to the Euler characteristic

Guang-Zhao Xu, Wei-Jun Zhang, Li-Xing You, Yu-Ze Wang

The effective and convenient detection of single photons via advanced detectors with a large active area is becoming significant for quantum and classical applications. This work demonstrates the fabrication of a superconducting microstrip single-photon detector (SMSPD) with a millimeter-scale active area via the use of ultraviolet (UV) photolithography. The

Weizhu Bao, Yong Lu, Zhifei Zhang

In this paper, we study the nonrelativistic limit of the cubic nonlinear Klein-Gordon equation in $\mathbb{R}^{3}$ with a small parameter $0<\varepsilon \ll 1$, which is inversely proportional to the speed of light. We show that the cubic nonlinear Klein-Gordon equation converges to the cubic nonlinear Schr\"odinger equation with a convergence rate of order

Harshit Gupta, Ajay Kumar Bharti

Fog computing is an emerging technology in the field of network services where data transfer from one device to another to perform some kind of activity. Fog computing is an extended concept of cloud computing. It works in-between the Internet of Things (IoT) and cloud data centers and reduces the communication gaps. Fog computing has made possible to have d

Jingyuan Wang, Yufan Wu, Mingxuan Li, Xin Lin

While having achieved great success in rich real-life applications, deep neural network (DNN) models have long been criticized for their vulnerability to adversarial attacks. Tremendous research efforts have been dedicated to mitigating the threats of adversarial attacks, but the essential trait of adversarial examples is not yet clear, and most existing met

Shunya Saito

In this paper, we classify several subcategories of the category of coherent sheaves on a noetherian divisorial scheme (e.g. a quasi-projective scheme over a commutative noetherian ring). More precisely, we classify the torsionfree (resp. torsion) classes closed under tensoring with line bundles by the subsets (resp. specialization-closed subsets) of the sch

Aiyu Cui, Svetlana Lazebnik

The goal of human stylization is to transfer full-body human photos to a style specified by a single art character reference image. Although previous work has succeeded in example-based stylization of faces and generic scenes, full-body human stylization is a more complex domain. This work addresses several unique challenges of stylizing full-body human imag

Shuying Wang, Stephen G. Walker

The paper considers a Cox process where the stochastic intensity function for the Poisson data model is itself a non-homogeneous Poisson process. We show that it is possible to obtain the marginal data process, namely a non-homogeneous count process exhibiting over-dispersion. While the intensity function is non-decreasing, it is straightforward to transform

Bingren Chen, Hanqing Wu, Haomu Yuan, Lei Wu

This paper proposes a quasi-binary encoding based algorithm for solving a specific quadratic optimization models with discrete variables, in the quantum approximate optimization algorithm (QAOA) framework. The quadratic optimization model has three constraints: 1. Discrete constraint, the variables are required to be integers. 2. Bound constraint, each varia

Qingsen Yan, Song Zhang, Weiye Chen, Hao Tang

Generating a high-quality High Dynamic Range (HDR) image from dynamic scenes has recently been extensively studied by exploiting Deep Neural Networks (DNNs). Most DNNs-based methods require a large amount of training data with ground truth, requiring tedious and time-consuming work. Few-shot HDR imaging aims to generate satisfactory images with limited data.

Jingrun Chen, Weinan E, Yixin Luo

We present a framework for solving time-dependent partial differential equations (PDEs) in the spirit of the random feature method. The numerical solution is constructed using a space-time partition of unity and random feature functions. Two different ways of constructing the random feature functions are investigated: feature functions that treat the spatial

Ha-Thanh Nguyen, Randy Goebel, Francesca Toni, Kostas Stathis

We examine how well the state-of-the-art (SOTA) models used in legal reasoning support abductive reasoning tasks. Abductive reasoning is a form of logical inference in which a hypothesis is formulated from a set of observations, and that hypothesis is used to explain the observations. The ability to formulate such hypotheses is important for lawyers and lega

Siming Yan, Yuqi Yang, Yuxiao Guo, Hao Pan

Masked autoencoders (MAE) have recently been introduced to 3D self-supervised pretraining for point clouds due to their great success in NLP and computer vision. Unlike MAEs used in the image domain, where the pretext task is to restore features at the masked pixels, such as colors, the existing 3D MAE works reconstruct the missing geometry only, i.e, the lo

Soumya Dutta, Sriram Ganapathy

Emotion recognition in conversations is challenging due to the multi-modal nature of the emotion expression. We propose a hierarchical cross-attention model (HCAM) approach to multi-modal emotion recognition using a combination of recurrent and co-attention neural network models. The input to the model consists of two modalities, i) audio data, processed thr

Xinhong Dai, Bin Duo, Xiaojun Yuan, Marco Di Renzo

The rapid development of unmanned aerial vehicle (UAV) technology provides flexible communication services to terrestrial nodes. Energy efficiency is crucial to the deployment of UAVs, especially rotary-wing UAVs whose propulsion power is sensitive to the wind effect. In this paper, we first derive a three-dimensional (3D) generalised propulsion energy consu

Dingcheng Yang, Wenjian Yu, Zihao Xiao, Jiaqi Luo

Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples. Moreover, the transferability of the adversarial examples has received broad attention in recent years, which means that adversarial examples crafted by a surrogate model can also attack unknown models. This phenomenon gave birth to the transfer-based adversarial attacks, w

Yu-Qi Yang, Yu-Xiao Guo, Jian-Yu Xiong, Yang Liu

The use of pretrained backbones with fine-tuning has been successful for 2D vision and natural language processing tasks, showing advantages over task-specific networks. In this work, we introduce a pretrained 3D backbone, called {\SST}, for 3D indoor scene understanding. We design a 3D Swin transformer as our backbone network, which enables efficient self-a

Lothar Moeller

We report tidal-induced latency variations on a transpacific subsea cable. Week-long recordings with a precision phase meter suggest length changes in the sub-meter range caused by the Poisson effect. The described method adds to the toolbox for the new field >>optical oceanic seismology<<.

Andrew B. Pace, Sergey E. Koposov, Matthew G. Walker, Nelson Caldwell

We present Magellan/M2FS spectroscopy of four recently discovered Milky Way star clusters (Gran 3/Patchick~125, Gran 4, Garro 01, LP 866) and two newly discovered open clusters (Gaia 9, Gaia 10) at low Galactic latitudes. We measure line-of-sight velocities and stellar parameters ([Fe/H], $\log{g}$, $T_{\rm eff}$, [Mg/Fe]) from high resolution spectroscopy c

Guillaume Braun

In this work, we focus on the Bipartite Stochastic Block Model (BiSBM), a popular model for bipartite graphs with a community structure. We consider the high dimensional setting where the number $n_1$ of type I nodes is far smaller than the number $n_2$ of type II nodes. The recent work of Braun and Tyagi (2022) established a sufficient and necessary conditi

Junpeng Hu, Shi Jin, Lei Zhang

Partial differential equation (PDE) models with multiple temporal/spatial scales are prevalent in several disciplines such as physics, engineering, and many others. These models are of great practical importance but notoriously difficult to solve due to prohibitively small mesh and time step sizes limited by the scaling parameter and CFL condition. Another c

Arindam Ray, Balaji Padmanabhan, Lina Bouayad

Machine learning algorithms are increasingly used to make or support decisions in a wide range of settings. With such expansive use there is also growing concern about the fairness of such methods. Prior literature on algorithmic fairness has extensively addressed risks and in many cases presented approaches to manage some of them. However, most studies have

Jiayi Deng, Danyang Huang, Xiangyu Chang, Bo Zhang

Identifying the number of communities is a fundamental problem in community detection, which has received increasing attention recently. However, rapid advances in technology have led to the emergence of large-scale networks in various disciplines, thereby making existing methods computationally infeasible. To address this challenge, we propose a novel subsa

Christopher Sneden, Ann Merchant Boesgaard, John J. Cowan, Ian U. Roederer

We have derived new detailed abundances of Mg, Ca, and the Fe-group elements Sc through Zn (Z = 21-30) for 37 main sequence turnoff very metal-poor stars ([Fe/H] <= -2.1). We analyzed Keck HIRES optical and near-UV high signal-to-noise spectra originally gathered for a beryllium abundance survey. Using typically about 400 Fe-group lines with accurate laborat

Vivekananda Bal, Rajdip Bandyopadhyaya

Droplet-based interphase synthesis provides means to produce nanoparticles with low polydispersity by controlling mass transport through droplet dynamics. An experimentally validated model, based on coupled computational fluid dynamics with population balance equation is proposed. The model incorporates: (i) hydrodynamics and thermal-transport of droplet-lad

Nicholas J. Pritchard, Andreas Wicenec, Mohammed Bennamoun, Richard Dodson

Neuromorphic computing and spiking neural networks aim to leverage biological inspiration to achieve greater energy efficiency and computational power beyond traditional von Neumann architectured machines. In particular, spiking neural networks hold the potential to advance artificial intelligence as the basis of third-generation neural networks. Aided by de

Yuefeng Zhang, Chuanmin Jia, Jiannhui Chang, Siwei Ma

In this age of information, images are a critical medium for storing and transmitting information. With the rapid growth of image data amount, visual compression and visual data perception are two important research topics attracting a lot attention. However, those two topics are rarely discussed together and follow separate research path. Due to the compact

Sen Yang, Wen-Di Guo, Qin Tan, Yu-Xiao Liu

We study the axial gravitational quasinormal modes of a self-dual black hole in loop quantum gravity. Considering the axial perturbation of the background spacetime, we obtain the Schr\"{o}dinger-like master equation. Then we calculate the quasinormal frequencies with the Wentzel-Kramers-Brillouin approximation and the asymptotic iteration method. We also in

Hyunsu Lee

Based on the predictive map theory of spatial learning in animals, this study delves into the dynamics of Successor Feature (SF) and Predecessor Feature (PF) algorithms within noisy environments. Utilizing Q-learning and Q($\lambda$) learning as benchmarks for comparative analysis, our investigation yielded unexpected outcomes. Contrary to prevailing expecta

Chengchun Hao, Siqi Yang

In this paper, we prove the existence of smooth initial data for the two-dimensional free boundary incompressible viscous magnetohydrodynamics (MHD) equations, for which the interface remains regular but collapses into a splash singularity (self-intersects in at least one point) in finite time. The existence of the splash singularities is guaranteed by a loc