March 2023 arXiv papers — page 100

Naoya Arakawa, Kenji Yonemitsu

Nonequilibrium quantum states can be controlled via the driving field in periodically driven systems. Such control, which is called Floquet engineering, has opened various phenomena, such as the light-induced anomalous Hall effect. There are expected to be some essential differences between the anomalous Hall and spin Hall effects of periodically driven syst

Luca Fabbri, Camilla Bordoni, Pedro Barquinha, Jerome Crocco

Amorphous oxide semiconductors are receiving significant attention due to their relevance for large area electronics. Their disordered microscopic structure causes the formation of band tails in the density of states (DOS) that strongly affect charge transport properties. Bandtail properties are crucial to understand for optimizing thin film device performan

Giovanni Falcone, Giuseppe Filippone

For an (imaginary) hyperelliptic curve $ \mathcal{H} $ of genus $g$, with a Weierstrass point $\Omega$, taken as the point at infinity, we determine a basis of the Riemann-Roch space $\mathcal{L}(\Delta + m \Omega)$, where $\Delta$ is of degree zero, directly from the Mumford representation of $\Delta$. This provides in turn a generating matrix of a Goppa co

Jinxiang Lai, Siqian Yang, Wenlong Wu, Tao Wu

Recent Few-Shot Learning (FSL) methods put emphasis on generating a discriminative embedding features to precisely measure the similarity between support and query sets. Current CNN-based cross-attention approaches generate discriminative representations via enhancing the mutually semantic similar regions of support and query pairs. However, it suffers from

Suhyeon Lee, Hyungjin Chung, Minyoung Park, Jonghyuk Park

Diffusion models have become a popular approach for image generation and reconstruction due to their numerous advantages. However, most diffusion-based inverse problem-solving methods only deal with 2D images, and even recently published 3D methods do not fully exploit the 3D distribution prior. To address this, we propose a novel approach using two perpendi

Liang Shi, Jie Zhang, Shiguang Shan

The emergence of deepfake technologies has become a matter of social concern as they pose threats to individual privacy and public security. It is now of great significance to develop reliable deepfake detectors. However, with numerous face manipulation algorithms present, it is almost impossible to collect sufficient representative fake faces, and it is har

Zhirui Gao, Renjiao Yi, Zheng Qin, Yunfan Ye

Template matching is a fundamental task in computer vision and has been studied for decades. It plays an essential role in manufacturing industry for estimating the poses of different parts, facilitating downstream tasks such as robotic grasping. Existing methods fail when the template and source images have different modalities, cluttered backgrounds or wea

M. Kachelriess, S. Ostapchenko, J. Tjemsland

Light (anti-) nuclei are a powerful tool both in collider physics and astrophysics. In searches for new and exotic physics, the expected small astrophysical backgrounds at low energies make these antinuclei ideal probes for, e.g., dark matter. At the same time, their composite structure and small binding energies imply that they can be used in collider exper

Charles Duquet, Christian Le Merdy

Let $M$ be a von Neumann algebra equipped with a normal semi-finite faithful trace (nsf trace in short) and let $T\colon M\to M$ be a contraction. We say that $T$ is absolutely dilatable if there exist another von Neumann algebra $M'$ equipped with a nsf trace, a $w^*$-continuous trace preserving unital $*$-homomorphim $J\colon M\to M'$ and a trace preservin

Guojin Chen, Zehua Pei, Haoyu Yang, Yuzhe Ma

Lithography is fundamental to integrated circuit fabrication, necessitating large computation overhead. The advancement of machine learning (ML)-based lithography models alleviates the trade-offs between manufacturing process expense and capability. However, all previous methods regard the lithography system as an image-to-image black box mapping, utilizing

Hang Zhang, Rongguang Wang, Renjiu Hu, Jinwei Zhang

Chronic active multiple sclerosis lesions, also termed as rim+ lesions, can be characterized by a hyperintense rim at the edge of the lesion on quantitative susceptibility maps. These rim+ lesions exhibit a geometrically simple structure, where gradients at the lesion edge are radially oriented and a greater magnitude of gradients is observed in contrast to

Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu

Mixup, a simple data augmentation method that randomly mixes two data points via linear interpolation, has been extensively applied in various deep learning applications to gain better generalization. However, the theoretical underpinnings of its efficacy are not yet fully understood. In this paper, we aim to seek a fundamental understanding of the benefits

Ramsès Fernàndez-València

This article presents the application of homomorphic authenticators, replication encodings to be precise, to multigroup fully homomorphic encryption schemes. Following the works of Gennaro and Wichs on homomorphic authenticators in combination with the work of multigroup schemes by Kwak et al. we present a verifiable solution for a fully homomorphic primitiv

Yinbin Han, Meisam Razaviyayn, Renyuan Xu

Nonlinear control systems with partial information to the decision maker are prevalent in a variety of applications. As a step toward studying such nonlinear systems, this work explores reinforcement learning methods for finding the optimal policy in the nearly linear-quadratic regulator systems. In particular, we consider a dynamic system that combines line

Alberto Enciso, Michał Wrochna, Gunther Uhlmann

We prove that the Dirichlet-to-Neumann map of the linear wave equation determines the topological, differentiable and conformal structure of the underlying Lorentzian manifold, under mild technical assumptions. With more stringent geometric assumptions, the full Lorentzian structure of the manifold can be recovered as well. The key idea of the proof is to sh

Manuel Müller, Johannes Weber, Fabian Engelhardt, Victor A. S. V. Bittencourt

Magnomechanical devices, in which magnetic excitations couple to mechanical vibrations, have been discussed as efficient and broadband microwave signal transducers in the classical and quantum limit. We experimentally investigate the magnetoelastic coupling between the ferromagnetic resonance (FMR) modes in a metallic Co$_{25}$Fe$_{75}$ thin film, featuring

Cheng Tan, Zhengqiang Zhang, Haoting Sui, Wing Shing Wong

In this paper, we investigate the mean-square stabilization for discrete-time stochastic systems that endure both multiple input delays and multiplicative control-dependent noises. For such multi-delay stochastic systems, we for the first time put forward two stabilization criteria: Riccati type and Lyapunov type. On the one hand, we adopt a reduction method

K. S. Trehaeven, V. Parekh, N. Oozeer, B. Hugo

Radio mini-halos are clouds of diffuse, low surface brightness synchrotron emission that surround the Brightest Cluster Galaxy (BCG) in massive cool-core galaxy clusters. In this paper, we use third generation calibration (3GC), also called direction-dependent (DD) calibration, and point source subtraction on MeerKAT extragalactic continuum data. We calibrat

Jingrui Li, Fang Pan, Guo-Xu Zhang, Zenghui Liu

Structural disorder is common in metal-halide perovskites and important for understanding the functional properties of these materials. First-principles methods can address structure variation on the atomistic scale, but they are often limited by the lack of structure-sampling schemes required to characterize the disorder. In this work, structural disorder i

Qinyu Wu, Zhixing Ling, Chen Zhang, Quan Zhou

In recent years, scientific CMOS (sCMOS) sensors have been vigorously developed and have outperformed CCDs in several aspects: higher readout frame rate, higher radiation tolerance, and higher working temperature. For silicon image sensors, image lag will occur when the charges of an event are not fully transferred inside pixels. It can degrade the image qua

Kenji Itao, Kunihiko Kaneko

Several tiers of social organization with varying economic and social disparities have been observed. However, a quantitative characterization of the types and the causal mechanisms for the transitions have hardly been explained. While anthropologists have emphasized that gift exchange, rather than market exchange, prevails in traditional societies and shape

Li Chen, Wei Liu, Yunfei Chen, Weidong Wang

Decentralized federated learning (DFL) is a variant of federated learning, where edge nodes only communicate with their one-hop neighbors to learn the optimal model. However, as information exchange is restricted in a range of one-hop in DFL, inefficient information exchange leads to more communication rounds to reach the targeted training loss. This greatly

S. Tiwari, N. K. Chakradhari, D. K. Sahu, G. C. Anupama

We present optical photometric and spectroscopic studies of three supernovae (SNe) SN 2013bz, PSN J0910+5003 and ASASSN-16ex. UV-optical photometric data of ASASSN-16ex obtained with Swift-UVOT are also analyzed. These objects were initially classified as 09dc-like type Ia SNe. The decline rate parameters ($\Delta m_{15}(B)_{true}$) are derived as 0.92 $\pm$

Daniel M. Jones, Frank van Kann, John J. McFerran

We have carried absolute frequency measurements of the $(6s^{2})\,^{1}S_{0}$ $-$ $(6s6p)\,^{3}P_{1}$ transition in $^{171}$Yb (the intercombination line), where the spin-1/2 isotope yields two hyperfine lines. The measurements rely on sub-Doppler spectroscopy to yield a discriminator to which a 556 nm laser is locked. The frequency reference for the optical

Xiyue Guo, Junjie Hu, Hujun Bao, Guofeng Zhang

Performing accurate localization while maintaining the low-level communication bandwidth is an essential challenge of multi-robot simultaneous localization and mapping (MR-SLAM). In this paper, we tackle this problem by generating a compact yet discriminative feature descriptor with minimum inference time. We propose descriptor distillation that formulates t

Jun-Hwa Kim, Namho Kim, Chee Sun Won

Facial expression recognition is an essential task for various applications, including emotion detection, mental health analysis, and human-machine interactions. In this paper, we propose a multi-modal facial expression recognition method that exploits audio information along with facial images to provide a crucial clue to differentiate some ambiguous facial

Heung-Chang Lee, Jeonggeun Song

The paper proposes a new algorithm called SymBa that aims to achieve more biologically plausible learning than Back-Propagation (BP). The algorithm is based on the Forward-Forward (FF) algorithm, which is a BP-free method for training neural networks. SymBa improves the FF algorithm's convergence behavior by addressing the problem of asymmetric gradients cau

Upanya Khandelwal, Qikai Guo, Beatriz Noheda, Pavan Nukala

Active memristor elements, also called neuristors, are self-oscillating devices that are very good approximations to biological neuronal functionality and are crucial to the development of low-power neuromorphic hardware. Materials that show conduction mechanisms that depend superlinearly with temperature can lead to negative differential resistance (NDR) re

Hiranya Kishore Dey, Soumyajit Saha

Urschel introduced a notion of nodal partitioning to prove an upper bound on the number of nodal decomposition of discrete Laplacian eigenvectors. The result is an analogue to the well-known Courant's nodal domain theorem on continuous Laplacian. In this article, using the same notion of partitioning, we discuss the lower bound (or lack thereof) on the numbe

Han Yang, Qiuli Wang, Yue Zhang, Zhulin An

Radiologists possess diverse training and clinical experiences, leading to variations in the segmentation annotations of lung nodules and resulting in segmentation uncertainty.Conventional methods typically select a single annotation as the learning target or attempt to learn a latent space comprising multiple annotations. However, these approaches fail to l

Charles O'Neill

Rice is a staple food in the world's diet, and yet huge percentages of crop yields are lost each year to disease. To combat this problem, people have been searching for ways to automate disease diagnosis. Here, we extend on previous modelling work by analysing how disease-classification accuracy is sensitive to both model architecture and common computer vis

Zhuo Su, Matti Pietikäinen, Li Liu

LBP is a successful hand-crafted feature descriptor in computer vision. However, in the deep learning era, deep neural networks, especially convolutional neural networks (CNNs) can automatically learn powerful task-aware features that are more discriminative and of higher representational capacity. To some extent, such hand-crafted features can be safely ign

Grigore Calugareanu, Horia F. Pop, Adrian Vasiu

We introduce the class E2 (resp. SE2) of commutative rings R with the property that each unimodular 2 x 2 matrix with entries in R extends to an invertible 3 x 3 matrix (resp. invertible 3 x 3 matrix whose (3, 3) entry is 0). Among noetherian domains of dimension 1, polynomial rings over Z or Hermite rings, only EDRs belong to the class. Using this, stable r

Woocheol Choi, Jimyeong Kim

In this work, we are concerned with the decentralized optimization problem: \begin{equation*} \min_{x \in \Omega}~f(x) = \frac{1}{n} \sum_{i=1}^n f_i (x), \end{equation*} where $\Omega \subset \mathbb{R}^d$ is a convex domain and each $f_i : \Omega \rightarrow \mathbb{R}$ is a local cost function only known to agent $i$. A fundamental algorithm is the decent

Junwei Ji, Dongyuan Shi, Zhengding Luo, Xiaoyi Shen

By assigning the massive computing tasks of the traditional multichannel active noise control (MCANC) system to several distributed control nodes, distributed multichannel active noise control (DMCANC) techniques have become effective global noise reduction solutions with low computational costs. However, existing DMCANC algorithms simply complete the distri

The POLARBEAR Collaboration, Shunsuke Adachi, Tylor Adkins, Kam Arnold

Very light pseudoscalar fields, often referred to as axions, are compelling dark matter candidates and can potentially be detected through their coupling to the electromagnetic field. Recently a novel detection technique using the cosmic microwave background (CMB) was proposed, which relies on the fact that the axion field oscillates at a frequency equal to

Xiaohan Wang, Wenguan Wang, Jiayi Shao, Yi Yang

Recently, visual-language navigation (VLN) -- entailing robot agents to follow navigation instructions -- has shown great advance. However, existing literature put most emphasis on interpreting instructions into actions, only delivering "dumb" wayfinding agents. In this article, we devise LANA, a language-capable navigation agent which is able to not only ex

Shunsuke Kaji, Muneya Matsui

We study a first passage time of a L\'evy process over a positive constant level. In the spectrally negative case we give conditions for absolutely continuity of the distributions of the first passage times. The tail asymptotics of their densities are also clarified, where the asymptotics depend on tail behaviour of the corresponding L\'evy measures. We appl

Yuwei Zhu, Xingjian Zhang, Xiongfeng Ma

Nonlocality, manifested by the violation of Bell inequalities, indicates entanglement within a joint quantum system. A natural question is how much entanglement is required for a given nonlocal behavior. Here, we explore this question by quantifying entanglement using a family of generalized Clauser-Horne-Shimony-Holt-type Bell inequalities. Given a Bell-ine

Yizhou Xu, YuHao Liu, ShanSuo Liang, Tingyi Wu

Sparse regression codes (SPARCs) are a promising coding scheme that can approach the Shannon limit over Additive White Gaussian Noise (AWGN) channels. Previous works have proven the capacity-achieving property of SPARCs with Gaussian design matrices. We generalize these results to right orthogonally invariant ensembles that allow for more structured design m

Adam Singor, Liam H. Scarlett, Mark C. Zammit, Igor Bray

Photoionization cross sections and rate coefficients have been calculated for all bound vibrational levels of the 1s$\sigma_{\mathrm{g}}$ state of H$_{2}^{+}$, HD$^{+}$, and D$_{2}^{+}$. The Born-Oppenheimer approximation is employed in our calculation of vibrationally-resolved photoionization cross sections. Vibrationally-resolved and local thermal equilibr

Jimy Encomendero, Vladimir Protasenko, Debdeep Jena, Huili Grace Xing

Resonant tunneling transport in polar heterostructures is intimately connected to the polarization fields emerging from the geometric Berry-phase. In these structures, quantum confinement results not only in a discrete electronic spectrum, but also in built-in polarization charges exhibiting a broken inversion symmetry along the transport direction. Thus, el

Sungwon Han, Seungeon Lee, Fangzhao Wu, Sundong Kim

Algorithmic fairness has become an important machine learning problem, especially for mission-critical Web applications. This work presents a self-supervised model, called DualFair, that can debias sensitive attributes like gender and race from learned representations. Unlike existing models that target a single type of fairness, our model jointly optimizes

T. Fukui, T. Yoshida, Y. Hatsugai

A heat conduction equation on a lattice composed of nodes and bonds is formulated assuming the Fourier law and the energy conservation law. Based on this equation, we propose a higher-order topological heat conduction model on the breathing kagome lattice. We show that the temperature measurement at a conner node can detect the corner state which causes rapi

Zipeng Qi, Hao Chen, Chenyang Liu, Zhenwei Shi

The mainstream CNN-based remote sensing (RS) image semantic segmentation approaches typically rely on massive labeled training data. Such a paradigm struggles with the problem of RS multi-view scene segmentation with limited labeled views due to the lack of considering 3D information within the scene. In this paper, we propose ''Implicit Ray-Transformer (IRT

Bhanuday Sharma, Rakesh Kumar

Fluid flows are typically studied by solving the Navier--Stokes equation. One of the fundamental assumptions of this equation is Stokes' hypothesis. This hypothesis assumes bulk viscosity, to be identically zero. The Stokes' hypothesis is a reasonable approximation for commonly observed fluid flows; therefore, Navier--Stokes equation gives satisfactory resul

Chao Zhang, Anzhong Wang, Tao Zhu

The Einstein-$\AE$ther theory has drawn a lot of attentions in recent years. As a representative case of gravitational theories that break the Lorentz symmetry, it plays an important role in testing the Lorentz-violating effects and shedding light on the attempts to construct quantum gravity. Since the first detection to the gravitational wave, the event GW1

Saeideh Yousefzadeh, Hamidreza Pourreza, Hamidreza Mahyar

Content-based image retrieval is the process of retrieving a subset of images from an extensive image gallery based on visual contents, such as color, shape or spatial relations, and texture. In some applications, such as localization, image retrieval is employed as the initial step. In such cases, the accuracy of the top-retrieved images significantly affec

Xiaoyi Shen, Dongyuan Shi, Zhengding Luo, Junwei Ji

Active noise control (ANC) has been widely utilized to reduce unwanted environmental noise. The primary objective of ANC is to generate an anti-noise with the same amplitude but the opposite phase of the primary noise using the secondary source. However, the effectiveness of the ANC application is impacted by the speaker's output saturation. This paper propo

Suyash Mahar, Hao Wang, Wei Shu, Abhishek Dhanotia

Hyperscalars run services across a large fleet of servers, serving billions of users worldwide. These services, however, behave differently than commonly available benchmark suites, resulting in server architectures that are not optimized for cloud workloads. With datacenters becoming a primary server processor market, optimizing server processors for cloud

Ken-ichi Sasaki

In scenarios where electrons are confined to a flat surface, such as graphene, quantizing electrodynamics reveals intriguing insights. We find that one of Maxwell's equations manifests as part of the Hamiltonian, leading to novel constraints on physical states due to residual gauge invariance. We identify two quantum states with zero energy expectation value

Srinath Kailasa, Tingyu Wang, Lorena A. Barba, Timo Betcke

Numba is a game-changing compiler for high-performance computing with Python. It produces machine code that runs outside of the single-threaded Python interpreter and that fully utilizes the resources of modern CPUs. This means support for parallel multithreading and auto vectorization if available, as with compiled languages such as C++ or Fortran. In this

Kerdaris Kurbah, Sukanta Deb, Shashi M. Kanbur, Susmita Das

We present an analysis of the theoretical and observed light curve parameters of the fundamental mode (FU) classical Cepheids in the Magellanic Clouds in $V$- and $I$- photometric bands. The state-of-the-art 1D non-linear radial stellar pulsation (RSP) code in MESA (\textsc{mesa-rsp}) has been utilized to generate the theoretical light curves using four sets

Bruno Hideki Fukushima-Kimura, Noe Kawamoto, Eitaro Noda, Akira Sakai

The Digital Annealer is a CMOS hardware designed by Fujitsu Laboratories for high-speed solving of Quadratic Unconstrained Binary Optimization (QUBO) problems that could be difficult to solve by means of existing general-purpose computers. In this paper, we present a mathematical description of the first-generation Digital Annealer's Algorithm from the Marko

Nanxiang Wang, Haobo Dai

A natural number $n$ is $y$-smooth if the greatest prime factor of $n$ does not exceed $y$. Let $s_{1}$ and $s_{2}$ are $y$-smooth numbers. We consider sums of smooth squares of the binary Titchmarsh divisor problem and give asymptotic formulae for $\sum_{s_{1}^{2}+s_{2}^{2}\le x}\tau(s_{1}^{2}+s_{2}^{2}+1)$ for $(\log x)^{K}\le y<x^{\frac{1}{2}}$, where $K$

Haonan Zhong, Jiamin Chang, Ziyue Yang, Tingmin Wu

Generative AI (e.g., Generative Adversarial Networks - GANs) has become increasingly popular in recent years. However, Generative AI introduces significant concerns regarding the protection of Intellectual Property Rights (IPR) (resp. model accountability) pertaining to images (resp. toxic images) and models (resp. poisoned models) generated. In this paper,

Carlos Contreras Peña, Gregory J. Herczeg, Mizna Ashraf, Jessy Jose

Mid-infrared (mid-IR) variability in young stellar objects (YSOs) is driven by several physical mechanisms, which produce a variety of amplitudes and light curve shapes. One of these mechanisms, variable disk accretion is predicted by models of episodic accretion to drive secular variability, including in the mid-IR. Because the largest accretion bursts are

Yongil Kim, Yerin Hwang, Hyeongu Yun, Seunghyun Yoon

Vulnerability to lexical perturbation is a critical weakness of automatic evaluation metrics for image captioning. This paper proposes Perturbation Robust Multi-Lingual CLIPScore(PR-MCS), which exhibits robustness to such perturbations, as a novel reference-free image captioning metric applicable to multiple languages. To achieve perturbation robustness, we

Lin He, Meicun Hou, Zhiyuan Li, Shuai Feng

High-resolution X-ray observations offer a unique tool for probing the still elusive connection between galaxy mergers and active galactic nuclei (AGNs). We present an analysis of nuclear X-ray emission in an optically selected sample of 92 close galaxy pairs (with projected separations $\lesssim 20$ kpc and line-of-sight velocity offsets $<$ 500 km s$^{-1}$

Sheng-Yang Chiu, Yu-Ting Huang, Chieh-Ting Lin, Yu-Chee Tseng

Due to the COVID-19 epidemic, video conferencing has evolved as a new paradigm of communication and teamwork. However, private and personal information can be easily leaked through cameras during video conferencing. This includes leakage of a person's appearance as well as the contents in the background. This paper proposes a novel way of using online low-re

Sangjun Noh, Raeyoung Kang, Taewon Kim, Seunghyeok Back

Object placement is a fundamental task for robots, yet it remains challenging for partially observed objects. Existing methods for object placement have limitations, such as the requirement for a complete 3D model of the object or the inability to handle complex shapes and novel objects that restrict the applicability of robots in the real world. Herein, we

Hwanchul Jung, Dongsung T. Park, Seokyeong Lee, Uhjin Kim

The resemblance between electrons and optical waves has strongly driven the advancement of mesoscopic physics. However, electron waves have yet to be understood in open cavity structures which have provided contemporary optics with rich insight towards non-Hermitian systems and complex interactions between resonance mode. Here, we report the realization of a

Lilac Atassi

Denoising Diffusion Probabilistic models have emerged as simple yet very powerful generative models. Unlike other generative models, diffusion models do not suffer from mode collapse or require a discriminator to generate high-quality samples. In this paper, a diffusion model that uses a binomial prior distribution to generate piano rolls is proposed. The pa

Qiaole Dong, Chenjie Cao, Yanwei Fu

Optical flow estimation is a challenging problem remaining unsolved. Recent deep learning based optical flow models have achieved considerable success. However, these models often train networks from the scratch on standard optical flow data, which restricts their ability to robustly and geometrically match image features. In this paper, we propose a rethink

Jimy Encomendero, Vladimir Protasenko, Farhan Rana, Debdeep Jena

The recent demonstration of resonant tunneling transport in nitride semiconductors has led to an invigorated effort to harness this quantum transport regime for practical applications. In polar semiconductors, however, the interplay between fixed polarization charges and mobile free carriers leads to asymmetric transport characteristics. Here, we investigate

Marek Biskup

We study the asymptotic distribution of random walks on $\mathbb Z^d$ ($d\ge1$) in deterministic reversible environments defined by an assignment of a positive conductance to each edge of $\mathbb Z^d$. We identify a deterministic set of conductance configurations for which the walk obeys an Invariance Principle; i.e., converges in law to a non-degenerate Br

Toru Sasahara

We prove that a surface in Euclidean $3$-space has Maslovian normal bundle if and only if it is a part of a round sphere, a circular cylinder, or a circular cone.

Hailong Liu, Takatsugu Hirayama

External human-machine interface (eHMI) is considered as a new explicit communication method for pedestrian-AV interactions, particularly in encounter scenarios. Pedestrians without prior negotiation experience with eHMI may misinterpret the driving intentions of AV, leading to confusion and unpredictable behavior. To address this, our study suggests providi

Ee-Leng Tan, Santi Peksi, Woon-Seng Gan

Head-related transfer function (HRTF) is an essential component to create an immersive listening experience over headphones for virtual reality (VR) and augmented reality (AR) applications. Metaverse combines VR and AR to create immersive digital experiences, and users are very likely to interact with virtual objects in the near-field (NF). The HRTFs of such

Jingwei Long, Yumeng Yang, Shuyuan Shi, Xuanyao Fong

Fast domain wall motion in systems with perpendicular magnetization is necessary for many novel applications such as the racetrack memory, domain wall logic devices and artificial synapses. The domain wall speed has been greatly improved after the demonstration of current driven domain wall motion using the spin transfer torque, and later achieved another le

Tom Ichibha, Sangmoon Yoon, Jong Mok Ok, Mina Yoon

PdCrO$_2$ films are synthesized on CuCrO$_2$ buffer layers on Al$_2$O$_3$ substrates. This synthesis is accompanied by impurity phase segregation, which hampers the synthesis of high quality PdCrO$_2$ films. The potential causes of impurity phase segregation were studied by using a combination of experiments and ab initio calculations. X-ray diffraction and

Hao Xu, Chia-Min Chung, Mingpu Qin, Ulrich Schollwöck

Combining the complementary capabilities of two of the most powerful modern computational methods, we find superconductivity in both the electron- and hole-doped regimes of the two-dimensional Hubbard model (with next nearest neighbor hopping). In the electron-doped regime, superconductivity is weaker and is accompanied by antiferromagnetic N\'eel correlatio

Abdeladim El Akri, Khalide Jbilou, Ahmed Ratnani

This paper presents a study of large linear systems resulting from the regular $B$-splines finite element discretization of the $\bm{curl}-\bm{curl}$ and $\bm{grad}-div$ elliptic problems on unit square/cube domains. We consider systems subject to both homogeneous essential and natural boundary conditions. Our objective is to develop a preconditioning strate

Quentin Anthony, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He

In recent years, the training requirements of many state-of-the-art Deep Learning (DL) models have scaled beyond the compute and memory capabilities of a single processor, and necessitated distribution among processors. Training such massive models necessitates advanced parallelism strategies to maintain efficiency. However, such distributed DL parallelism s

Lei Liu, Wen-Rong Sun, Boris A. Malomed, P. G. Kevrekidis

In this work we report on the emergence of a novel type of solitary waves, viz., time-localized solitons in integrable and non-integrable variants of the massive Thirring models and in the three-wave resonant-interaction system, which are models broadly used in plasmas, nonlinear optics and hydrodynamics. An essential finding is that the condition for the ex

Chenda Li, Yao Qian, Zhuo Chen, Dongmei Wang

Automatic target sound extraction (TSE) is a machine learning approach to mimic the human auditory perception capability of attending to a sound source of interest from a mixture of sources. It often uses a model conditioned on a fixed form of target sound clues, such as a sound class label, which limits the ways in which users can interact with the model to

Muhammad Jahanzeb Khan, Rui Hu, Mohammad Sadoghi, Dongfang Zhao

Federated Learning (FL) is a machine learning approach that allows multiple clients to collaboratively learn a shared model without sharing raw data. However, current FL systems provide an all-in-one solution, which can hinder the wide adoption of FL in certain domains such as scientific applications. To overcome this limitation, this paper proposes a decoup

Liangchen Song, Zhong Li, Xuan Gong, Lele Chen

Neural Radiance Fields (NeRF) have led to breakthroughs in the novel view synthesis problem. Positional Encoding (P.E.) is a critical factor that brings the impressive performance of NeRF, where low-dimensional coordinates are mapped to high-dimensional space to better recover scene details. However, blindly increasing the frequency of P.E. leads to overfitt

John Peter J. Nunez, Vaibhav Sharma, Jessika V. Rojas, Radhika Barua

(MnNiSi)1-(Fe2Ge)x composition (x=0.34) alloy was prepared by arc melting, crushed, and sieved to approximately <32 microns. They were utilized in examining the possible magnetic and structural changes when exposed to a dosage of a continuous sweeping rate of ~>120 Gy/min and an absorbed dose of 35 kGy of X-ray radiation. This study reports observable trends

Md Imrul Hasan, Mohammad Saquib

Multiple-input multiple-output (MIMO) systems play an essential role in direction-of-arrival (DOA) estimation. A large number of antennas used in a MIMO system imposes a huge complexity burden on the popular DOA estimation algorithms, such as MUSIC and ESPRIT due to the implementation of eigenvalue decomposition. This renders those algorithms impractical in

Yao Liu, Zesheng Ye, Rui Wang, Binghao Li

Tremendous efforts have been put forth on predicting pedestrian trajectory with generative models to accommodate uncertainty and multi-modality in human behaviors. An individual's inherent uncertainty, e.g., change of destination, can be masked by complex patterns resulting from the movements of interacting pedestrians. However, latent variable-based generat

Shaolun Ruan, Ribo Yuan, Qiang Guan, Yanna Lin

Visualizations have played a crucial role in helping quantum computing users explore quantum states in various quantum computing applications. Among them, Bloch Sphere is the widely-used visualization for showing quantum states, which leverages angles to represent quantum amplitudes. However, it cannot support the visualization of quantum entanglement and su

Kun Li, Zhichun Li, Yuetao Chen, Zixuan Wang

Stencil computation is one of the most important kernels in various scientific computing. Nowadays, most Stencil-driven scientific computing still relies heavily on supercomputers, suffering from expensive access, poor scalability, and duplicated optimizations. This paper proposes Tetris, the first system for high-performance Stencil on heterogeneous CPU+GPU

Junbong Jang, Kwonmoo Lee, Tae-Kyun Kim

Analyzing the dynamic changes of cellular morphology is important for understanding the various functions and characteristics of live cells, including stem cells and metastatic cancer cells. To this end, we need to track all points on the highly deformable cellular contour in every frame of live cell video. Local shapes and textures on the contour are not ev

Narayan G. Sabhahit, Akanksha S. Khurd, Sarika Jalan

The inclusion of inertia in the Kuramoto model has been long reported to change the nature of phase transition, providing a fertile ground to model the dynamical behaviors of interacting units. More recently, higher-order interactions have been realized as essential for the functioning of real-world complex systems ranging from the brain to disease spreading

Hafsa Gulzar, Jiyun Li, Arslan Manzoor, Sadaf Rehmat

With the development of computer -systems that can collect and analyze enormous volumes of data, the medical profession is establishing several non-invasive tools. This work attempts to develop a non-invasive technique for identifying respiratory sounds acquired by a stethoscope and voice recording software via machine learning techniques. This study suggest

Su Wang, Seyyedali Hosseinalipour, Vaneet Aggarwal, Christopher G. Brinton

Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks. Traditional implementations of FL have largely neglected the potential for inter-network cooperation, treating edge/fog devices and other infrastructure participating in ML as separate processing elements. Consequently, FL has be

Youcai Zhang, Yuzhuo Qin, Hengwei Liu, Yanhao Zhang

Knowledge distillation (KD) has been extensively studied in single-label image classification. However, its efficacy for multi-label classification remains relatively unexplored. In this study, we firstly investigate the effectiveness of classical KD techniques, including logit-based and feature-based methods, for multi-label classification. Our findings ind

Tangyou Liu, Tinghua Zhang, Jay Katupitiya, Jiaole Wang

Many robotic surgical systems have been developed with micro-sized forceps for tissue manipulation. However, these systems often lack force sensing at the tool side and the manipulation forces are roughly estimated and controlled relying on the surgeon's visual perception. To address this challenge, we present a vision-based module to enable the micro-sized

Chengliang Liu, Jie Wen, Xiaoling Luo, Chao Huang

In recent years, multi-view multi-label learning has aroused extensive research enthusiasm. However, multi-view multi-label data in the real world is commonly incomplete due to the uncertain factors of data collection and manual annotation, which means that not only multi-view features are often missing, and label completeness is also difficult to be satisfi

Byeong-Cheol Lee, Jae-Rim Koo, Gwanghui Jeong, Myeong-Gu Park

We have been conducting a exoplanet search survey using Bohyunsan Observatory Echelle Spectrograph (BOES) for the last 18 years. We present the detection of exoplanet candidate in orbit around HD 18438 from high-precision radial velocity (RV) mesurements. The target was already reported in 2018 (Bang et al. 2018). They conclude that the RV variations with a

Weiwei Zhou, Jiada Lu, Zhaolong Xiong, Weifeng Wang

Human emotion recognition plays an important role in human-computer interaction. In this paper, we present our approach to the Valence-Arousal (VA) Estimation Challenge, Expression (Expr) Classification Challenge, and Action Unit (AU) Detection Challenge of the 5th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW). Specifically, we p

Tao Liu, Zhi Wang, Hui He, Wei Shi

The conventional machine learning (ML) and deep learning approaches need to share customers' sensitive information with an external credit bureau to generate a prediction model that opens the door to privacy leakage. This leakage risk makes financial companies face an enormous challenge in their cooperation. Federated learning is a machine learning setting t

Belle II Collaboration, F. Abudinén, I. Adachi, K. Adamczyk

We report measurements of the branching fraction and $\it CP$ asymmetry in $B^{0} \to \pi^{0} \pi^{0}$ decays reconstructed at Belle II in an electron-positron collision sample containing $198 \times 10^{6}$ $B\overline{B}$ pairs. We measure a branching fraction $\mathcal{B}(\Bpipi) = (1.38 \pm 0.27 \pm 0.22) \times 10^{-6}$ and a $\it CP$ asymmetry $\Acp(\B

Tomáš Opatrný, Kunal K. Das

We study the general quantum Hamiltonian that can be realized with two species of mutually interacting degenerate ultracold atoms in a ring-shaped trap, with the options of rotation and an azimuthal lattice. We examine the spectrum and the states with a collective spin picture in a Dicke state basis. The system can generate states with a high degree of entan

Jimy Encomendero, Vladimir Protasenko, Berardi Sensale-Rodriguez, Patrick Fay

The phenomenon of resonant tunneling transport through polar double-barrier heterostructures is systematically investigated using a combined experimental and theoretical approach. On the experimental side, GaN/AlN RTDs are grown by MBE. In-situ electron diffraction is employed to monitor the number of monolayers incorporated into each tunneling barrier. Usin

Christopher Chang, Pat Scott, Tomás E. Gonzalo, Felix Kahlhoefer

Global fits explore different parameter regions of a given model and apply constraints obtained at many energy scales. This makes it challenging to perform global fits of simplified models, which may not be valid at high energies. In this study, we derive a unitarity bound for a simplified vector dark matter model with an $s$-channel vector mediator, and app

Xi Hu, Lin Tang

In this paper, we prove Wiener's criterion for parabolic equations with singular and degenerate coefficients. To be precise, we study the problem of the regularity of boundary points for the Dirichlet problem for degenerate parabolic equations, and give a geometric characterization of those boundary points that are regular.

Sunil Arya, Guilherme D. da Fonseca, David M. Mount

Coverings of convex bodies have emerged as a central component in the design of efficient solutions to approximation problems involving convex bodies. Intuitively, given a convex body $K$ and $\epsilon> 0$, a covering is a collection of convex bodies whose union covers $K$ such that a constant factor expansion of each body lies within an $\epsilon$ expansion

Peng Mi, Jianghang Lin, Yiyi Zhou, Yunhang Shen

In this paper, we study teacher-student learning from the perspective of data initialization and propose a novel algorithm called Active Teacher(Source code are available at: \url{https://github.com/HunterJ-Lin/ActiveTeacher}) for semi-supervised object detection (SSOD). Active Teacher extends the teacher-student framework to an iterative version, where the