November 2022 arXiv papers — page 47

Xin He, Jiangchao Yao, Yuxin Wang, Zhenheng Tang

One-shot neural architecture search (NAS) substantially improves the search efficiency by training one supernet to estimate the performance of every possible child architecture (i.e., subnet). However, the inconsistency of characteristics among subnets incurs serious interference in the optimization, resulting in poor performance ranking correlation of subne

Young Chun Ahn, Seokhwan Jang, Sungheon Park, Ji-Yeon Kim

The method of neural radiance fields (NeRF) has been developed in recent years, and this technology has promising applications for synthesizing novel views of complex scenes. However, NeRF requires dense input views, typically numbering in the hundreds, for generating high-quality images. With a decrease in the number of input views, the rendering quality of

Mengshou Wang, Gao Li, Liangrong Peng, Liu Hong

EGCG, as a representative of amyloid inhibitors, has shown a promising ability against Abeta fibrillation by directly degradating the mature fibrils. Most previous studies have been focusing on its functional mechanisms, meanwhile its optimal dosage has been seldom considered. To solve this critical issue, we refer to the generalized Logistics model for amyl

Ana Kostovska, Jasmin Bogatinovski, Andrej Treven, Sašo Džeroski

The multi-label classification (MLC) task has increasingly been receiving interest from the machine learning (ML) community, as evidenced by the growing number of papers and methods that appear in the literature. Hence, ensuring proper, correct, robust, and trustworthy benchmarking is of utmost importance for the further development of the field. We believe

Bartosz Dabrowski, Katarzyna Mikula, Pawel Flisek, Christian Vocks

The Sun is the source of different types of radio bursts that are associated with solar flares, for example. Among the most frequently observed phenomena are type III solar bursts. Their radio images at low frequencies (below 100 MHz) are relatively poorly studied due to the limitations of legacy radio telescopes. We study the general characteristics of type

Guodong Yin, Mufeng Zhou, Yiming Chen, Wenjun Tang

Performing data-intensive tasks in the von Neumann architecture is challenging to achieve both high performance and power efficiency due to the memory wall bottleneck. Computing-in-memory (CiM) is a promising mitigation approach by enabling parallel in-situ multiply-accumulate (MAC) operations within the memory with support from the peripheral interface and

Yuu Hariya

In this paper, with the help of a result by Matsumoto--Yor (2000), we prove a Girsanov-type formula for a class of anticipative transforms of Brownian motion which possesses exponential functionals as anticipating factors. Our result unifies existing formulas in earlier works. As an application, we also consider the law of Brownian motion perturbed by a posi

Yuri Levin

We argue that resonant friction has a dramatic effect on a disc whose rotation direction is misaligned with that of its host nuclear star cluster. The disc's gravity causes gravitational perturbation of the cluster that in turn exerts a strong torque back onto the disc. We argue that this torque may be responsible for the observed disruption of the clockwise

Mitsuhiro Nishijima, Kazuhide Nakata

We first provide an inner-approximation hierarchy described by a sum-of-squares (SOS) constraint for the copositive (COP) cone over a general symmetric cone. The hierarchy is a generalization of that proposed by Parrilo (2000) for the usual COP cone (over a nonnegative orthant). We also discuss its dual. Second, we characterize the COP cone over a symmetric

Abhilasha Sancheti, Aparna Garimella, Balaji Vasan Srinivasan, Rachel Rudinger

Legal documents are typically long and written in legalese, which makes it particularly difficult for laypeople to understand their rights and duties. While natural language understanding technologies can be valuable in supporting such understanding in the legal domain, the limited availability of datasets annotated for deontic modalities in the legal domain

Zhe Feng

In the framework of $f\left(R, T, R_{ab}T^{ab}\right)$ gravity theory, the slow-roll approximation of the cosmic inflation is investigated, where $T$ is the trace of the energy-momentum tensor $T^{ab}$, $R$ and $R_{ab}$ are the Ricci scalar and tensor, respectively. After obtaining the equations of motion of the gravitational field from the action principle

Qiang Huang, Yanhao Wang, Anthony K. H. Tung

This paper investigates a new yet challenging problem called Reverse $k$-Maximum Inner Product Search (R$k$MIPS). Given a query (item) vector, a set of item vectors, and a set of user vectors, the problem of R$k$MIPS aims to find a set of user vectors whose inner products with the query vector are one of the $k$ largest among the query and item vectors. We p

Kristóf Bérczi, Bence Mátravölgyi, Tamás Schwarcz

Two pairs of disjoint bases $\mathbf{P}_1=(R_1,B_1)$ and $\mathbf{P}_2=(R_2,B_2)$ of a matroid $M$ are called equivalent if $\mathbf{P}_1$ can be transformed into $\mathbf{P}_2$ by a series of symmetric exchanges. In 1980, White conjectured that such a sequence always exists whenever $R_1\cup B_1=R_2\cup B_2$. A strengthening of the conjecture was proposed b

Cristina Martinez-Lombilla, Sarah Brough, Mireia Montes, Roberto Baena-Galle

We present a pilot study to assess the potential of Hyper Suprime-Cam Public Data Release 2 (HSC-PDR2) images for the analysis of extended faint structures within groups of galaxies. We examine the intra-group light (IGL) of the group 400138 ($M_{\rm{dyn}}= 1.3 \pm 0.5 \times 10^{13} $M$_{\odot}$, $z\sim 0.2$) from the Galaxy And Mass Assembly (GAMA) survey

Guoxi Huang, Adrian G. Bors

Static appearance of video may impede the ability of a deep neural network to learn motion-relevant features in video action recognition. In this paper, we introduce a new concept, Dynamic Appearance (DA), summarizing the appearance information relating to movement in a video while filtering out the static information considered unrelated to motion. We consi

August Y. Chen, Bjarne Schülke

Here we consider the hypergraph Tur\'an problem in uniformly dense hypergraphs as was suggested by Erd\H{o}s and S\'os. Given a $3$-graph $F$, the uniform Tur\'an density $\pi_u(F)$ of $F$ is defined as the supremum over all $d\in[0,1]$ for which there is an $F$-free uniformly $d$-dense $3$-graph, where uniformly $d$-dense means that every linearly sized sub

Xianfeng Wu, Jinhui Qian, Qing Wei, Xianzu Wu

In many vision and robotics applications, it is common that the captured objects are represented by very few points. Most of the existing completion methods are designed for partial point clouds with many points, and they perform poorly or even fail completely in the case of few points. However, due to the lack of detail information, completing objects from

Alina A. Malofeeva, Varvara O. Mikhnevich, Giovanni Carraro, Anton F. Seleznev

In this study, we continue our project to search for unresolved binary and multiple systems in open clusters exploiting the photometric diagram (H-W2)-W1 vs W2-(BP-K) firstly introduced in \citet{Malofeeva+2022}. In particular, here we estimate the binary and multiple star ratios and the distribution of the component mass ratio $q$ in the Galactic clusters A

Jonathan Bader, Joel Witzke, Soeren Becker, Ansgar Lößer

Scientific workflows consist of thousands of highly parallelized tasks executed in a distributed environment involving many components. Automatic tracing and investigation of the components' and tasks' performance metrics, traces, and behavior are necessary to support the end user with a level of abstraction since the large amount of data cannot be analyzed

Abhimanyu Das, Ayush Jain, Weihao Kong, Rajat Sen

We begin the study of list-decodable linear regression using batches. In this setting only an $\alpha \in (0,1]$ fraction of the batches are genuine. Each genuine batch contains $\ge n$ i.i.d. samples from a common unknown distribution and the remaining batches may contain arbitrary or even adversarial samples. We derive a polynomial time algorithm that for

Andrea Aiello

John von Neumann's spectral theorem for self-adjoint operators is a cornerstone of quantum mechanics. Among other things, it also provides a connection between expectation values of self-adjoint operators and expected values of real-valued random variables. This paper presents a plain-spoken formulation of this theorem in terms of Dirac's bra and ket notatio

Pengcheng Li

Given a closed, smooth, connected, orientable $4$-manifold $M$, whose integral homology groups can have $2$-torsion, we determine the homotopy decomposition of the double suspension $\Sigma^2M$ as wedge sums of some elementary $\mathbf{A}_3^3$-complexes, which are $2$-connected finite complexes of dimension at most $6$. Furthermore, we utilize the Postnikov

Fangchen Liu, Hao Liu, Aditya Grover, Pieter Abbeel

We are interested in learning scalable agents for reinforcement learning that can learn from large-scale, diverse sequential data similar to current large vision and language models. To this end, this paper presents masked decision prediction (MaskDP), a simple and scalable self-supervised pretraining method for reinforcement learning (RL) and behavioral clo

Zixian Guo, Bowen Dong, Zhilong Ji, Jinfeng Bai

Prompt tuning has been employed as an efficient way to adapt large vision-language pre-trained models (e.g. CLIP) to various downstream tasks in data-limited or label-limited settings. Nonetheless, visual data (e.g., images) is by default prerequisite for learning prompts in existing methods. In this work, we advocate that the effectiveness of image-text con

Pasin Manurangsi, Warut Suksompong

Fairness and privacy are two important concerns in social decision-making processes such as resource allocation. We study privacy in the fair allocation of indivisible resources using the well-established framework of differential privacy. We present algorithms for approximate envy-freeness and proportionality when two instances are considered to be adjacent

Pierre Chambon, Christian Bluethgen, Jean-Benoit Delbrouck, Rogier Van der Sluijs

Multimodal models trained on large natural image-text pair datasets have exhibited astounding abilities in generating high-quality images. Medical imaging data is fundamentally different to natural images, and the language used to succinctly capture relevant details in medical data uses a different, narrow but semantically rich, domain-specific vocabulary. N

Anshuman S. Pal

In [Pal et al., arXiv:2206.03552], the authors discuss how an unsupported flat annulus contracted at its inner boundary by a factor $\Delta$, buckles into a radial wrinkling pattern that is fully isometric and tension-free. What selects the wavelength in such a pure-bending configuration, in the absence of any competing sources of work? In this paper, with t

Yunjie Tian, Lingxi Xie, Jihao Qiu, Jianbin Jiao

We propose integrally pre-trained transformer pyramid network (iTPN), towards jointly optimizing the network backbone and the neck, so that transfer gap between representation models and downstream tasks is minimal. iTPN is born with two elaborated designs: 1) The first pre-trained feature pyramid upon vision transformer (ViT). 2) Multi-stage supervision to

Z. Lyu, X. Xie, H. Kou

We prove that the category of c-spaces with continuous maps is not cartesian closed. As a corollary the category of locally finitary compact spaces with continuous maps is also not cartesian closed.

Renjue Li, Tianhang Qin, Pengfei Yang, Cheng-Chao Huang

We present a practical verification method for safety analysis of the autonomous driving system (ADS). The main idea is to build a surrogate model that quantitatively depicts the behaviour of an ADS in the specified traffic scenario. The safety properties proved in the resulting surrogate model apply to the original ADS with a probabilistic guarantee. Furthe

Aaron Paul M. Dela Rosa

Online courses, as a pedagogical approach to teaching, boomed during this Coronavirus Disease 2019 pandemic era. Universities shifted from traditional face to face classes to online distance learning due to the cause of the pandemic. This study aimed to determine how effective an online course is in learning a programming course. The study utilized mixed met

Joshua Knights, Kavisha Vidanapathirana, Milad Ramezani, Sridha Sridharan

Many existing datasets for lidar place recognition are solely representative of structured urban environments, and have recently been saturated in performance by deep learning based approaches. Natural and unstructured environments present many additional challenges for the tasks of long-term localisation but these environments are not represented in current

Shurui Lv, Yan Wang, Jun Yu

Computer model calibration is a crucial step in building a reliable computer model. In the face of massive physical observations, a fast estimation for the calibration parameters is urgently needed. To alleviate the computational burden, we design a two-step algorithm to estimate the calibration parameters by employing the subsampling techniques. Compared wi

Oleksii Ivanytskyi, David Blaschke, Tobias Fischer, Andreas Bauswein

We present a relativistic density functional approach to color superconducting quark matter that mimics quark confinement by a fast growth of the quasiparticle selfenergy in the confining region. The approach is shown to be equivalent to a chiral model of quark matter with medium dependent couplings. While the (pseudo)scalar sector of the model is fitted to

Mansi Mishra, M. K. Vemuri

(1) Suppose $\mu$ is a smooth measure on a hypersurface of positive Gaussian curvature in $\R^{2n}$. If $n\ge 2$, then $W(\mu)$, the Weyl transform of $\mu$, is a compact operator, and if $p>n\ge 6$ then $W(\mu)$ belongs to the $p$-Schatten class. (2) There exist Schatten class operators with linearly dependent quantum translates.

Aaron Paul M. Dela Rosa, Luigi Miguel M. Villanueva, John Mardy R. San Miguel, John Emmanuel B. Quinto

This study was focused on the development of a web e-learning application for the database courses taken by Information Technology (IT) students at the College of Information and Communications Technology (CICT) of Bulacan State University (BulSU). The research methodology used in this project was the cross-sectional developmental approach. The Agile Softwar

Fleury Gérard, Lacomme Philippe, Christian Prins

This paper deals with the Stochastic Capacitated Arc Routing Problem (SCARP), obtained by randomizing quantities on the arcs in the CARP. Optimization problems for the SCARP are characterized by decisions that are made without knowing their full consequences. For real-life problems, it is important to create solutions insensitive to variations of the quantit

Hongyang Du, Jiacheng Wang, Dusit Niyato, Jiawen Kang

Semantic communications can reduce the resource consumption by transmitting task-related semantic information extracted from source messages. However, when the source messages are utilized for various tasks, e.g., wireless sensing data for localization and activities detection, semantic communication technique is difficult to be implemented because of the in

Tom M. Kwan, Lixin Dai, Alexander Tchekhovskoy

In this letter, we investigate Bondi-like accretion flows with zero or low specific angular momentum by performing 3D general relativistic magnetohydrodynamic simulations. In order to check if relativistic jets can be launched magnetically from such flows, we insert a large-scale poloidal magnetic field into the accretion flow and consider a rapidly spinning

Aaron Paul M. Dela Rosa

This study was developed to describe the daily operations and encountered problems of the National Labor Relations Commission Regional Arbitration Branch No. IV (NLRC RAB IV) through conducted observations and interviews. These problems were addressed and analyzed to be the features of the developed web-based management information system (MIS) for cases. Th

Nicolas Misdariis, Elif Özcan, Massimo Grassi, Sandra Pauletto

The Audible Universe project aims at making dialogue between two scientific domains investigating two distinct research objects, briefly said, Stars and Sound. It has been instantiated within a collaborative workshop that started to mutually acculturate both communities, by sharing and transmitting respective knowledge, skills and practices. One main outcome

Roy Friedman, Sultan Hassan

Extracting the maximum amount of cosmological and astrophysical information from upcoming large-scale surveys remains a challenge. This includes evaluating the exact likelihood, parameter inference and generating new diverse synthetic examples of the incoming high-dimensional data sets. In this work, we propose the use of normalizing flows as a generative mo

Charanpreet Singh, Sk Jamaluddin, Ashis K. Nandy, Masashi Tokunaga

Owing to the geometrical frustration, Mn$_3 $Sn exhibits a 120$^{\circ}$ in-plane triangular antiferromagnetic (AFM) order with a large anomalous Hall effect (AHE). Here, we present a combined theoretical and experimental study to demonstrate that the in-plane AFM structure in Mn$_3 $Sn can be significantly modified to a tunable noncoplanar magnetic state by

Simón Piga, Marcelo Sales, Bjarne Schülke

Given $\alpha>0$ and an integer $\ell\geq5$, we prove that every sufficiently large $3$-uniform hypergraph $H$ on $n$ vertices in which every two vertices are contained in at least $\alpha n$ edges contains a copy of $C_\ell^{-}$, a tight cycle on $\ell$ vertices minus one edge. This improves a previous result by Balogh, Clemen, and Lidick\'y.

Jiacheng Wang, Hongyang Du, Xiaolong Yang, Dusit Niyato

Recent advances in emerging technologies such as artificial intelligence and extended reality have pushed the Metaverse, a virtual, shared space, into reality. In Metaverse, users can customize virtual avatars to experience a different life. While impressive, avatar construction requires a lot of data that manifest users in the physical world from various pe

Zhetao Jia, Matteo Seclì, Alexander Avdoshkin, Walid Redjem

Complex networks play a fundamental role in understanding phenomena from the collective behavior of spins, neural networks, and power grids to the spread of diseases. Topological phenomena in such networks have recently been exploited to preserve the response of systems in the presence of disorder. We propose and demonstrate topological structurally disorder

Federico Camponovo, Giampiero Passarino

This work is based on a bottom{-}up approach to the standard{-}model effective field theory (SMEFT), resulting in an equiprobable space of Wilson coefficients. The randomly generated Wilson coefficients of the SMEFT (in the Warsaw basis) are treated as pseudo-data and, for each observable, the corresponding probability density function is computed. The goal

Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos

Bayesian deep learning seeks to equip deep neural networks with the ability to precisely quantify their predictive uncertainty, and has promised to make deep learning more reliable for safety-critical real-world applications. Yet, existing Bayesian deep learning methods fall short of this promise; new methods continue to be evaluated on unrealistic test beds

Jiawei Zhan, Jun Liu, Wei Tang, Guannan Jiang

Multi-label image classification, which can be categorized into label-dependency and region-based methods, is a challenging problem due to the complex underlying object layouts. Although region-based methods are less likely to encounter issues with model generalizability than label-dependency methods, they often generate hundreds of meaningless or noisy prop

Jing Zhou, Xinru Jing, Muyu Liu, Hansheng Wang

In this paper, we propose a dictionary screening method for embedding compression in text classification tasks. The key purpose of this method is to evaluate the importance of each keyword in the dictionary. To this end, we first train a pre-specified recurrent neural network-based model using a full dictionary. This leads to a benchmark model, which we then

Bing Han, Feifei Zhao, Yi Zeng, Guobin Shen

Developmental plasticity plays a prominent role in shaping the brain's structure during ongoing learning in response to dynamically changing environments. However, the existing network compression methods for deep artificial neural networks (ANNs) and spiking neural networks (SNNs) draw little inspiration from brain's developmental plasticity mechanisms, thu

Shengcai Liu, Fu Peng, Ke Tang

Attack Ensemble (AE), which combines multiple attacks together, provides a reliable way to evaluate adversarial robustness. In practice, AEs are often constructed and tuned by human experts, which however tends to be sub-optimal and time-consuming. In this work, we present AutoAE, a conceptually simple approach for automatically constructing AEs. In brief, A

Shunyu Liu, Yihe Zhou, Jie Song, Tongya Zheng

Value Decomposition (VD) aims to deduce the contributions of agents for decentralized policies in the presence of only global rewards, and has recently emerged as a powerful credit assignment paradigm for tackling cooperative Multi-Agent Reinforcement Learning (MARL) problems. One of the main challenges in VD is to promote diverse behaviors among agents, whi

Han Zheng, Christopher Kang, Gokul Subramanian Ravi, Hanrui Wang

We propose SnCQA, a set of hardware-efficient variational circuits of equivariant quantum convolutional circuits respective to permutation symmetries and spatial lattice symmetries with the number of qubits $n$. By exploiting permutation symmetries of the system, such as lattice Hamiltonians common to many quantum many-body and quantum chemistry problems, Ou

Weiwei Zhu, Jiangbin Gong, Raditya Weda Bomantara

This short Perspective article presents an overview of the discovery of topological $\pi$ modes as well as their physical significance in quantum computing and the understanding of an exotic phase of matter, i.e., the Floquet time crystal. The recent proposals of $2\pi/k$ modes as the generalizations of $\pi$ modes are further elucidated.

Philipp A. Witte, Russell J. Hewett, Kumar Saurabh, AmirHossein Sojoodi

Solving partial differential equations with deep learning makes it possible to reduce simulation times by multiple orders of magnitude and unlock scientific methods that typically rely on large numbers of sequential simulations, such as optimization and uncertainty quantification. Two of the largest challenges of adopting scientific AI for industrial problem

Ryan Gibara, Nageswari Shanmugalingam

In this paper we fix $1\le p<\infty$ and consider $(\Om,d,\mu)$ be an unbounded, locally compact, non-complete metric measure space equipped with a doubling measure $\mu$ supporting a $p$-Poincar\'e inequality such that $\Om$ is a uniform domain in its completion $\bar\Om$. We realize the trace of functions in the Dirichlet-Sobolev space $D^{1,p}(\Om)$ on th

Neeraj Varshney, Man Luo, Chitta Baral

Recent state-of-the-art open-domain QA models are typically based on a two stage retriever-reader approach in which the retriever first finds the relevant knowledge/passages and the reader then leverages that to predict the answer. Prior work has shown that the performance of the reader usually tends to improve with the increase in the number of these passag

Ayan Chatterjee, Bestoun S. Ahmed, Erik Hallin, Anton Engman

Today, machine learning (ML) is widely used in industry to provide the core functionality of production systems. However, it is practically always used in production systems as part of a larger end-to-end software system that is made up of several other components in addition to the ML model. Due to production demand and time constraints, automated software

Lorenzo Shaikewitz, Yilin Wu, Suneel Belkhale, Jennifer Grannen

Assistance during eating is essential for those with severe mobility issues or eating risks. However, dependence on traditional human caregivers is linked to malnutrition, weight loss, and low self-esteem. For those who require eating assistance, a semi-autonomous robotic platform can provide independence and a healthier lifestyle. We demonstrate an essentia

Hayato Motohashi, Teruaki Suyama

General relativity describes gravitation in terms of the geometry of spacetime. It predicts the existence of gravitational waves (GWs) that stretch and compress spacetime and were detected recently by state-of-the-art interferometer observations. Yet, for those who are not familiar with general relativity, it may be difficult to understand how the GWs actual

Hyeonsik Yeom, Junguk Park, Jinho Choi, Jeongseok Ha

This work proposes a joint power control and access points (APs) scheduling algorithm for uplink cell-free massive multiple-input multiple-output (CF-mMIMO) networks without channel hardening assumption. Extensive studies have done on the joint optimization problem assuming the channel hardening. However, it has been reported that the channel hardening may n

Josh Gardner, Zoran Popović, Ludwig Schmidt

Researchers have proposed many methods for fair and robust machine learning, but comprehensive empirical evaluation of their subgroup robustness is lacking. In this work, we address this gap in the context of tabular data, where sensitive subgroups are clearly-defined, real-world fairness problems abound, and prior works often do not compare to state-of-the-

Jangwon Suh, Jimyeong Kim, Euna Jung, Wonjong Rhee

The performance of cardiac arrhythmia detection with electrocardiograms(ECGs) has been considerably improved since the introduction of deep learning models. In practice, the high performance alone is not sufficient and a proper explanation is also required. Recently, researchers have started adopting feature attribution methods to address this requirement, b

Zixuan Ke, Bing Liu

Continual learning (CL) is a learning paradigm that emulates the human capability of learning and accumulating knowledge continually without forgetting the previously learned knowledge and also transferring the learned knowledge to help learn new tasks better. This survey presents a comprehensive review and analysis of the recent progress of CL in NLP, which

Rien Quirynen, Stefano Di Cairano

Mixed-integer model predictive control (MI-MPC) can be a powerful tool for modeling hybrid control systems. In case of a linear-quadratic objective in combination with linear or piecewise-linear system dynamics and inequality constraints, MI-MPC needs to solve a mixed-integer quadratic program (MIQP) at each sampling time step. This paper presents a collecti

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

We search for an axion-like particle (ALP) $a$ through the process $\psi(3686)\rightarrow\pi^+\pi^-J/\psi$, $J/\psi\rightarrow\gamma a$, $a\rightarrow\gamma\gamma$ in a data sample of $(2.71\pm0.01)\times10^9$ $\psi(3686)$ events collected by the BESIII detector. No significant ALP signal is observed over the expected background, and the upper limits on the

Sercan Kazımoğlu, Kamaljeet Gangania

In this paper, we establish the radius of $\gamma$-Spirallike of order $\alpha$ of certain well-known special functions. The main results of the paper are new and natural extensions of some known results.

Chun Bao, Jie Cao, Yaqian Ning, Yang Cheng

Extensive research works demonstrate that the attention mechanism in convolutional neural networks (CNNs) effectively improves accuracy. Nevertheless, few works design attention mechanisms using large receptive fields. In this work, we propose a novel attention method named Rega-net to increase CNN accuracy by enlarging the receptive field. Inspired by the m

Sercan Kazımoğlu, Kamaljeet Gangania

In this paper our aim is to find the radii of $\gamma$-Spirallike of order $\alpha$ and convex $\gamma$-Spirallike of order $\alpha$ for three different kinds of normalizations of the function $N_\nu(z)=az^2J_\nu^{\prime\prime}(z)+bzJ_\nu^{\prime}(z)+cJ_\nu(z),$ where $J_\nu(z)$ is the Bessel function of the first kind of order $\nu.$ Moreover, the $\mathcal

Yuki Adachi, C. S. Lim, Nobuhito Maru

We perform analytic construction of a sphaleron-like solution in the 4-dimensional (4D) space-time invoking the framework of 5D SU(2) gauge theory. By the sphaleron-like solution we mean a static finite energy solution to the equation of motion, which carries the Chern-Simons number $N_\text{CS}=\frac{1}{2}$. Since we are interested in the static solution in

Komal Kumari, Garima Rajpoot, Sudhir Ranjan Jain

We construct a double-toric surface code by exploiting the planar tessellation using a rhombus-shaped tile. With n data qubits, we are able to encode at least n/3 logical qubits or quantum memories. By a suitable arrangement of the tiles, the code achieves larger distances, leading to significant error-correcting capability. We demonstrate the robustness of

James D. Gaynor, Ashley P. Fidler, Yuki Kobayashi, Yen-Cheng Lin

Attosecond four-wave mixing spectroscopy using an XUV pulse and two noncollinear near-infrared pulses is employed to measure Rydberg wavepacket dynamics resulting from extreme ultraviolet excitation of a 3s electron in atomic argon into a series of autoionizing 3s-1np Rydberg states around 29 eV. The emitted signals from individual Rydberg states exhibit osc

Mark Agranovsky, Alexander Koldobsky, Dmitry Ryabogin, Vladyslav Yaskin

A bounded domain $K \subset \mathbb R^n$ is called polynomially integrable if the $(n-1)$-dimensional volume of the intersection $K$ with a hyperplane $\Pi$ polynomially depends on the distance from $\Pi$ to the origin. It was proved in [7] that there are no such domains with smooth boundary if $n$ is even, and if $n$ is odd then the only polynomially integr

Divyanshi Dwivedi, K. Victor Sam Moses Babu, Pradeep Kumar Yemula, Pratyush Chakraborty

The global generation of renewable energy has rapidly increased, primarily due to the installation of large-scale renewable energy power plants. However, monitoring renewable energy assets in these large plants remains challenging due to environmental factors that could result in reduced power generation, malfunctioning, and degradation of asset life. Theref

Yandi Shen, Yihong Wu

Empirical Bayes (EB) is a popular framework for large-scale inference that aims to find data-driven estimators to compete with the Bayesian oracle that knows the true prior. Two principled approaches to EB estimation have emerged over the years: $f$-modeling, which constructs an approximate Bayes rule by estimating the marginal distribution of the data, and

Masoumeh Ghanbarpour, Axton Isaly, Ricardo G. Sanfelice, Warren E. Dixon

For a broad class of nonlinear systems, we formulate the problem of guaranteeing safety with optimality under constraints. Specifically, we define controlled safety for differential inclusions with constraints on the states and the inputs. Through the use of nonsmooth analysis tools, we show that a continuous optimal control law can be selected from a set-va

Ting-Wei Liu, Fabio Semperlotti

This study explores the possibility and presents a methodology to synthesize a classical mechanical analogue to the quantum mechanical 1D Kitaev model. While being fundamentally different, we will identify significant conceptual similarities between the two models that culminate in the occurrence, in the classical analogue system, of topologically non-trivia

Tzihong Chiueh, Yi-Hsiung Hsu

Soliton in the hostile turbulent wave dark matter ($\Psi$DM) halo of a galaxy agitates with various kinds of excitation, and the soliton even breathes heavily under great stress. A theory of collective excitation for a $\Psi$DM soliton is presented. The collective excitation has different degrees of coupling to negative energy modes, where lower-order excita

Yao Zhou, Zhen-Qiang Yin, Rui-Qiang Wang, Shuang Wang

Quantum key distribution (QKD) allows two remote parties to share information-theoretically secure keys. In recent years, a revolutionary breakthrough called twin-field (TF) QKD has been developed to overcome the linear key-rate constraint and greatly increases the achievable distance. Phase-randomization and subsequent postselection play important roles in

J. Derek Tucker, Drew Yarger

Detecting changepoints in functional data has become an important problem as interest in monitoring of climate phenomenon has increased, where the data is functional in nature. The observed data often contains both amplitude ($y$-axis) and phase ($x$-axis) variability. If not accounted for properly, true changepoints may be undetected, and the estimated unde

Alexander Goldberg, Giulia Fanti, Nihar B. Shah

There are a number of forums where people participate under pseudonyms. One example is peer review, where the identity of reviewers for any paper is confidential. When participating in these forums, people frequently engage in "batching": executing multiple related tasks (e.g., commenting on multiple papers) at nearly the same time. Our empirical analysis sh

Jirong Yi, Qiaosheng Zhang, Zhen Chen, Qiao Liu

As one of the central tasks in machine learning, regression finds lots of applications in different fields. An existing common practice for solving regression problems is the mean square error (MSE) minimization approach or its regularized variants which require prior knowledge about the models. Recently, Yi et al., proposed a mutual information based superv

Sifatul Mostafi, Taghreed Alghamdi, Khalid Elgazzar

Traffic simulation is a great tool to demonstrate complex traffic structures which can be extremely useful for the planning, development, and management of road traffic networks. Current traffic simulators offer limited features when it comes to interactive and adaptive traffic modeling. This paper presents RegTraffic, a novel interactive traffic simulator t

George A. Gontcharov, Maxim Yu. Khovritchev, Aleksandr V. Mosenkov, Vladimir B. Il'in

We present new isochrone fits to the colour-magnitude diagrams of the Galactic globular clusters NGC\,6362 and NGC\,6723. We utilize 22 and 26 photometric filters for NGC\,6362 and NGC\,6723, respectively, from the ultraviolet to mid-infrared using data sets from {\it HST}, {\it Gaia}, unWISE, and other photometric sources. We use models and isochrones from

Tomohiro Ishikawa, Yuki Kawasaki, Kenji Tsuji, Rika Yamada

DECi-hertz Interferometer Gravitational Wave Observatory (DECIGO) is a future mission for a space-borne laser interferometer. DECIGO has 1,000-km-long arm cavities mainly to detect the primordial gravitational waves (PGW) at lower frequencies around 0.1 Hz. Observations in the electromagnetic spectrum have lowered the bounds on the upper limit of PGW energy

Zhipeng Lu, Xianchang Meng

Since the well-known breakthrough of L. Guth and N. Katz on the Erdos distinct distances problem in the plane, mainstream of interest is aroused by their method and the Elekes-Sharir framework. In short words, they study the second moment in the framework. One may wonder if higher moments would be more efficient. In this paper, we show that any higher moment

Maxwell T. West, Sarah M. Erfani, Christopher Leckie, Martin Sevior

Machine learning (ML) methods such as artificial neural networks are rapidly becoming ubiquitous in modern science, technology and industry. Despite their accuracy and sophistication, neural networks can be easily fooled by carefully designed malicious inputs known as adversarial attacks. While such vulnerabilities remain a serious challenge for classical ne

Avijit Shee, Chia-Nan Yeh, Bo Peng, Karol Kowalski

Embedding theories became important approaches used for accurate calculations of both molecules and solids. In these theories, a small chosen subset of orbitals is treated with an accurate method, called an impurity solver, capable of describing higher correlation effects. Ideally, such a chosen fragment should contain multiple orbitals responsible for the c

Anindya Chanda, Sergio Fenley

Quasigeodesic behavior of flow lines is a very useful property in the study of Anosov flows. Not every Anosov flow in dimension three is quasigeodesic. In fact up to orbit equivalence, the only previously known examples of quasigeodesic Anosov flows were suspension flows. In this article, we prove that a new class of examples are quasigeodesic. These are the

Jingchen Hu

On a compact complex affine manifold with a constant coefficient K\"ahler metric $\omega_0$, we introduce a concept: $(S,\omega_0)$-convexity and show that $(S,\omega_0)$-convexity is preserved by geodesics in the space of K\"ahler potentials. This implies that if two potentials are both strictly $(S,\omega_0)$-convex, then the metrics along the geodesic con

Chu-Tak Lee, Qipeng Guo, Xipeng Qiu

Modern language models mostly take sub-words as input, a design that balances the trade-off between vocabulary size, number of parameters, and performance. However, sub-word tokenization still has disadvantages like not being robust to noise and difficult to generalize to new languages. Also, the current trend of scaling up models reveals that larger models

W. H. Matthaeus, S. Adhikari, R. Bandyopadhyay, M. R. Brown

Space plasmas are three-dimensional dynamic entities. Except under very special circumstances, their structure in space and their behavior in time are not related in any simple way. Therefore, single spacecraft in situ measurements cannot unambiguously unravel the full space-time structure of the heliospheric plasmas of interest in the inner heliosphere, in

Stephen W. Bruenn, Andre Sieverding, Eric J. Lentz, Tuguldur Sukhbold

We compare the core-collapse evolution of a pair of 15.8 $M_\odot$ stars with significantly different internal structures, a consequence of bimodal variability exhibited by massive stars during their late evolutionary stages. The 15.78 and 15.79 $M_\odot $ progenitors have core masses of 1.47 and 1.78 $M_\odot$ and compactness parameters $\xi_{1.75}$ of 0.30

Yunfan Liu, Qi Li, Zhenan Sun, Tieniu Tan

One-shot face re-enactment is a challenging task due to the identity mismatch between source and driving faces. Specifically, the suboptimally disentangled identity information of driving subjects would inevitably interfere with the re-enactment results and lead to face shape distortion. To solve this problem, this paper proposes to use 3D Morphable Model (3

Ahmad K. Omar, Hyeongjoo Row, Stewart A. Mallory, John F. Brady

Nonequilibrium phase transitions are routinely observed in both natural and synthetic systems. The ubiquity of these transitions highlights the conspicuous absence of a general theory of phase coexistence that is broadly applicable to both nonequilibrium and equilibrium systems. Here, we present a general mechanical theory for phase separation rooted in idea

Yang Xiao, Dehua Liu, Jizhou He, Yongli Ma

We theoretically prose and investigate a photo-Otto engine that is working with a single-mode radiation field inside an optical cavity and alternatively driven by a hot and a cold reservoir, where the hot reservoir is realized by sending one of a pair of correlated two-level atoms to pass through the optical cavity, and the cold one is made of a collection o

Pengfei Yi, Lipeng Zhu, Zhenyu Xiao, Rui Zhang

In this paper, we propose to deploy multiple unmanned aerial vehicle (UAV) mounted base stations to serve ground users in outdoor environments with obstacles. In particular, the geographic information is employed to capture the blockage effects for air-to-ground (A2G) links caused by buildings, and a realistic blockage-aware A2G channel model is proposed to

Yalin Liao, Junpeng Zhan

Quantum neural networks (QNNs), represented by parameterized quantum circuits, can be trained in the paradigm of supervised learning to map input data to predictions. Much work has focused on theoretically analyzing the expressive power of QNNs. However, in almost all literature, QNNs' expressive power is numerically validated using only simple univariate fu

Sosung Baik, Sung-Ha Hwang

We study the revenue comparison problem of auctions when the seller has a maxmin expected utility preference. The seller holds a set of priors around some reference belief, interpreted as an approximating model of the true probability law or the focal point distribution. We develop a methodology for comparing the revenue performances of auctions: the seller