November 2022 arXiv papers — page 88

Kewen Ding

Multi-objective reinforcement learning (MORL) is a relatively new field which builds on conventional Reinforcement Learning (RL) to solve multi-objective problems. One of common algorithm is to extend scalar value Q-learning by using vector Q values in combination with a utility function, which captures the user's preference for action selection. This study

Kang Fu, Jianwei Hu, Seydou Keita, Hao Liu

The stochastic block model is a popular tool for detecting community structures in network data. Detecting the difference between two community structures is an important issue for stochastic block models. However, the two-sample test has been a largely under-explored domain, and too little work has been devoted to it. In this article, based on the maximum e

Luca Belli, Kyra Yee, Uthaipon Tantipongpipat, Aaron Gonzales

We report on the outcome of an audit of Twitter's Home Timeline ranking system. The goal of the audit was to determine if authors from some racial groups experience systematically higher impression counts for their Tweets than others. A central obstacle for any such audit is that Twitter does not ordinarily collect or associate racial information with its us

Taojiannan Yang, Linjie Yang, Xiaojie Jin, Chen Chen

Recent neural architecture search (NAS) works proposed training-free metrics to rank networks which largely reduced the search cost in NAS. In this paper, we revisit these training-free metrics and find that: (1) the number of parameters (\#Param), which is the most straightforward training-free metric, is overlooked in previous works but is surprisingly eff

Gezi Chong, Ying Fu

Consideration here is a higher-order $\mu$-Camassa-Holm equation, which is a higher-order extension of the $\mu$-Camassa-Holm equation and retains some properties of the $\mu$-Camassa-Holm equation and the modified $\mu$-Camassa-Holm equation. By utilizing the inequalities with the maximum and minimum of the solution related to the first three conservation l

Lauren Wszolek, Wilfredo O. Urbina

The ternary Cantor set $C$, constructed by George Cantor in 1883, is probably the best-known example of a perfect nowhere-dense set in the real line, but as we will see later, it is not the only one. The present article will delve into the richness and the peculiarities of $C$ through the exploration of several variants and generalizations and will provide a

Dmitry Badziahin

We construct continued fraction expansions for several families of the Laurent series in $\mathbb{Q}[[t^{-1}]]$. To the best of the author's knowledge, this is the first result of this kind since Gauss derived the continued fraction expansion for $(1+t)^r$, $r\in\mathbb{Q}$ in 1813. As an application, we apply an analogue of the hypergeometric method to one

Simon C. Harris, Emma Horton, Ellen Powell, Andreas E. Kyprianou

We provide a many-to-few formula in the general setting of non-local branching Markov processes. This formula allows one to compute expectations of k-fold sums over functions of the population at k different times. The result generalises [14] to the non-local setting, as introduced in [11] and [8]. As an application, we consider the case when the branching p

Rakshitha Godahewa, Geoffrey I. Webb, Daniel Schmidt, Christoph Bergmeir

Threshold Autoregressive (TAR) models have been widely used by statisticians for non-linear time series forecasting during the past few decades, due to their simplicity and mathematical properties. On the other hand, in the forecasting community, general-purpose tree-based regression algorithms (forests, gradient-boosting) have become popular recently due to

Kelly J. Neubauer, Feng Ye, Yue Shi, Paul Malinowski

The anomalous Hall effect (AHE), typically observed in ferromagnetic (FM) metals with broken time-reversal symmetry, depends on electronic and magnetic properties. In Co$_{3}$Sn$_{2-x}$In$_{x}$S$_{2}$, a giant AHE has been attributed to Berry curvature associated with the FM Weyl semimetal phase, yet recent studies report complicated magnetism. We use neutro

Fan Wang, Bin Cheng, Zi-Wei Cui, Man-Hong Yung

Continuous-time quantum walk is one of the alternative approaches to quantum computation, where a universal set of quantum gates can be achieved by scattering a quantum walker on some specially-designed structures embedded in a sparse graph [Childs, Phys. Rev. Lett. 2009]. Recent advances in femtosecond laser-inscribed optical waveguides represent a promisin

Zhanghao Sun, Wei Ye, Jinhui Xiong, Gyeongmin Choe

Direct time-of-flight (dToF) sensors are promising for next-generation on-device 3D sensing. However, limited by manufacturing capabilities in a compact module, the dToF data has a low spatial resolution (e.g., $\sim 20\times30$ for iPhone dToF), and it requires a super-resolution step before being passed to downstream tasks. In this paper, we solve this sup

Fan Li, Hang Zhou, Huafeng Li, Yafei Zhang

Person text-image matching, also known as text based person search, aims to retrieve images of specific pedestrians using text descriptions. Although person text-image matching has made great research progress, existing methods still face two challenges. First, the lack of interpretability of text features makes it challenging to effectively align them with

Long Chen, Xuehai Huang

In the field of solving partial differential equations (PDEs), Hilbert complexes have become highly significant. Recent advances focus on creating new complexes using the Bernstein-Gelfand-Gelfand (BGG) framework, as shown by Arnold and Hu [Complexes from complexes. {\em Found. Comput. Math.}, 2021]. This paper extends their approach to three-dimensional fin

Belamfedel Alaoui Sadek, Saharsh, Pushpak Jagtap, Adnane Saoud

This paper formulates and studies the concepts of approximate (alternating) bisimulation relations characterizing equivalence relations between interconnected systems and their abstractions. These equivalence relations guarantee that the symbolic model conserves the original model's dynamics. We develop a compositional approach for abstraction-based controll

Lesego E. Moloko, Pavel M. Bokov, Xu Wu, Kostadin N. Ivanov

Artificial Neural Networks (ANNs) have been successfully used in various nuclear engineering applications, such as predicting reactor physics parameters within reasonable time and with a high level of accuracy. Despite this success, they cannot provide information about the model prediction uncertainties, making it difficult to assess ANN prediction credibil

Supriti Vijay, Aman Priyanshu

Social media has become a platform for people to stand up and raise their voices against social and criminal acts. Vocalization of such information has allowed the investigation and identification of criminals. However, revealing such sensitive information may jeopardize the victim's safety. We propose #maskUp, a safe method for information communication in

Yunzhe Li, Juhee Lee, Athanasios Kottas

We develop a flexible Erlang mixture model for survival analysis. The model for the survival density is built from a structured mixture of Erlang densities, mixing on the integer shape parameter with a common scale parameter. The mixture weights are constructed through increments of a distribution function on the positive real line, which is assigned a Diric

Jodie Crocker, Krishna Kumar, Brady R. Cox

We present a novel method of using explainability techniques to design physics-aware neural networks. We demonstrate our approach by developing a convolutional neural network (CNN) for solving an inverse problem for shallow subsurface imaging. Although CNNs have gained popularity in recent years across many fields, the development of CNNs remains an art, as

Yaxian Xia, Yi Cao, Sihao Hu, Tong Liu

E-commerce platforms provide entrances for customers to enter mini-apps that can meet their specific shopping requirements. Trigger items displayed on entrance icons can attract more entering. However, conventional Click-Through-Rate (CTR) prediction models, which ignore user instant interest in trigger item, fail to be applied to the new recommendation scen

Jingwen Zhang, Yifang Chen, Amandeep Singh

The deployment of Multi-Armed Bandits (MAB) has become commonplace in many economic applications. However, regret guarantees for even state-of-the-art linear bandit algorithms (such as Optimism in the Face of Uncertainty Linear bandit (OFUL)) make strong exogeneity assumptions w.r.t. arm covariates. This assumption is very often violated in many economic con

You Peng, Zhuo Ma, Wenjie Zhang, Xuemin Lin

The shortest-path distance is a fundamental concept in graph analytics and has been extensively studied in the literature. In many real-world applications, quality constraints are naturally associated with edges in the graphs and finding the shortest distance between two vertices $s$ and $t$ along only valid edges (i.e., edges that satisfy a given quality co

Jianping Xiong, Chao Liu, Jiao Li, Jiadong Li

High-precision stellar mass and radius measured directly from binaries can effectively calibrate the stellar models. However, such a database containing full spectral types and large range of metallicity is still not fully established. A continuous effort of data collecting and analysis are requested to complete the database. In this work, we provide a catal

Haobo Zhang, Hongliang Zhang, Boya Di, Lingyang Song

Integrated sensing and communication (ISAC) has attracted much attention as a promising approach to alleviate spectrum congestion. However, traditional ISAC systems rely on phased arrays to provide high spatial diversity, where enormous power-consuming components such as phase shifters are used, leading to the high power consumption of the system. In this ar

Chao Zhang, Siqi Han, Milin Zhang

It is easy for the electroencephalogram (EEG) signal to be incomplete due to packet loss, electrode falling off, etc. This paper proposed a Cascade Transformer architecture and a loss weighting method for the single-channel EEG completion, which reduced the Normalized Root Mean Square Error (NRMSE) by 2.8% and 8.5%, respectively. With the percentage of the m

Linjiang Guo, Zijian Feng, Yuxue Chi, Mingzhu Wang

While COVID-19 has impacted humans for a long time, people search the web for pandemic-related information, causing anxiety. From a theoretic perspective, previous studies have confirmed that the number of COVID-19 cases can cause negative emotions, but how statistics of different dimensions, such as the number of imported cases, the number of local cases, a

Yejia Zhang, Nishchal Sapkota, Pengfei Gu, Yaopeng Peng

Understanding of spatial attributes is central to effective 3D radiology image analysis where crop-based learning is the de facto standard. Given an image patch, its core spatial properties (e.g., position & orientation) provide helpful priors on expected object sizes, appearances, and structures through inherent anatomical consistencies. Spatial corresponde

Fangzhou Zhao, Mark E. Turiansky, Audrius Alkauskas, Chris G. Van de Walle

Trap-assisted nonradiative recombination is known to limit the efficiency of optoelectronic devices, but the conventional multi-phonon emission (MPE) process fails to explain the observed loss in wide-band-gap materials. Here we highlight the role of trap-assisted Auger-Meitner (TAAM) recombination, and present a first-principles methodology to determine TAA

Patrick Huber, Kate Scholberg, Elizabeth Worcester, Jonathan Asaadi

This report summarizes the current status of neutrino physics and the broad and exciting future prospects identified for the Neutrino Frontier as part of the 2021 Snowmass Process.

Anqi Qiu, Ian Chesser, Elizabeth Holm

Recent grain growth experiments have revealed that the same type of grain boundary can have very different mobilities depending on its local microstructure. In this work, we use molecular dynamics simulations to quantify uncertainty in the reduced mobility of curved grain boundaries for different types of boundary conditions and over a range of initial veloc

Haoxing Chen, Zhangxuan Gu, Yaohui Li, Jun Lan

Image harmonization is a critical task in computer vision, which aims to adjust the foreground to make it compatible with the background. Recent works mainly focus on using global transformations (i.e., normalization and color curve rendering) to achieve visual consistency. However, these models ignore local visual consistency and their huge model sizes limi

Xingyu Guo, Chen-Te Ma

Our study employs a connected correlation matrix to quantify Quantum Entanglement. The matrix encompasses all necessary measures for assessing the degree of entanglement between particles. We begin with a three-qubit state and involve obtaining a mixed state by performing partial tracing over one qubit. Our goal is to exclude the non-connected sector by focu

Chiara Sabatti, Qian Zhao

Universities have been expanding undergraduate data science programs. Involving graduate students in these new opportunities can foster their growth as data science educators. We describe two programs that employ a near-peer mentoring structure, in which graduate students mentor undergraduates, to (1) strengthen their teaching and mentoring skills and (2) pr

Andres S. Chavez Armijos, Anni Li, Christos G. Cassandras, Yasir K. Al-Nadawi

We derive optimal control policies for a Connected Automated Vehicle (CAV) and cooperating neighboring CAVs to carry out a lane change maneuver consisting of a longitudinal phase where the CAV properly positions itself relative to the cooperating neighbors and a lateral phase where it safely changes lanes. In contrast to prior work on this problem, where the

Akash Doshi, Jeffrey G. Andrews

As future wireless systems trend towards higher carrier frequencies and large antenna arrays, receivers with one-bit analog-to-digital converters (ADCs) are being explored owing to their reduced power consumption. However, the combination of large antenna arrays and one-bit ADCs makes channel estimation challenging. In this paper, we formulate channel estima

Md Nasir, Tina Sederholm, Anshu Sharma, Sundeep Reddy Mallu

Vulnerability and risk assessment of neighborhoods is essential for effective disaster preparedness. Existing traditional systems, due to dependency on time-consuming and cost-intensive field surveying, do not provide a scalable way to decipher warnings and assess the precise extent of the risk at a hyper-local level. In this work, machine learning was used

Raphael Falque, Teresa Vidal-Calleja, Alen Alempijevic

Keypoint annotation in point clouds is an important task for 3D reconstruction, object tracking and alignment, in particular in deformable or moving scenes. In the context of agriculture robotics, it is a critical task for livestock automation to work toward condition assessment or behaviour recognition. In this work, we propose a novel approach for semantic

Dominik Macháček, Ondřej Bojar, Raj Dabre

There have been several meta-evaluation studies on the correlation between human ratings and offline machine translation (MT) evaluation metrics such as BLEU, chrF2, BertScore and COMET. These metrics have been used to evaluate simultaneous speech translation (SST) but their correlations with human ratings of SST, which has been recently collected as Continu

Changhao Liu, You Wu, Songlin Zhou, Fan Yang

Reconfigurable reflectarray antennas (RRAs) have rapidly developed with various prototypes proposed in recent literatures. However, designing wideband, multiband, or high-frequency RRAs faces great challenges, especially the lengthy simulation time due to the lack of systematic design guidance. The current scattering viewpoint of the RRA element, which coupl

Kazuyuki Kanaya, Ryo Ashikawa, Shinji Ejiri, Masakiyo Kitazawa

We study phase structure and critical point of finite-temperature QCD in the heavy-quark region applying the hopping parameter expansion (HPE). We first study finite-size scaling on the critical point on $N_t=4$ lattices with large spatial volumes taking the leading order (LO) and the next-to-leading order (NLO) effects of the HPE, and find that the critical

Qi'an Guan, Zhitong Mi, Zheng Yuan

In this note, we present an optimal $L^2$ extension theorem for holomorphic vector bundles with smooth hermitian metrics for continuous gain on weakly pseudoconvex K\"{a}hler manifolds, which is a unified version of the optimal $L^2$ extension theorems for holomorphic vector bundles with smooth hermitian metrics of Guan-Zhou and Zhou-Zhu.

Nisar Ahmad, Ping Zhu, Chao Shen, Ahmad Ali

In this paper, we study the effects of viscosity on the evolution of double tearing mode (DTM) in a pair of adjacent Harris sheets based on the resistive MHD model in the NIMROD code. Similar to the tearing mode in the conventional single Harris sheet, a transition is observed in the generation of both normal and monster plasmoids at Pr = 1. In the Pr < 1 re

Fatemeh Karimi, Zohre Amoozgar, Reza Reiazi, Mehdi Hosseinzadeh

Smartwatches (SWs) can continuously and autonomously monitor vital signs, including heart rates and physical activities involving wrist movement. The monitoring capability of SWs has several key health benefits arising from their role in preventive and diagnostic medicine. Current research, however, has not explored many of these opportunities, including lon

M. P. Roriz, C. B. Pereira, S. Junqueira, M. Lugaro

We present detailed chemical compositions of four stars on the first-ascent red giant branch that are classified as chemically peculiar, but lack comprehensive analyses at high spectral resolution. For BD+03{\deg}2688, HE 0457-1805, HE 1255-2324, and HE 2207-1746, we derived metallicities [Fe/H] $=-1.21$, $-0.19$, $-0.31$, and $-0.55$, respectively, indicati

Zhi Yu, Chao Feng, Yong Zeng, Teng Li

Wireless communication using fully passive metal reflectors is a promising technique for coverage expansion, signal enhancement, rank improvement and blind-zone compensation, thanks to its appealing features including zero energy consumption, ultra low cost, signaling- and maintenance-free, easy deployment and full compatibility with existing and future wire

Sarthak Kapoor, Harsh Vardhan, Umesh Timalsina, Sumit Kumar

High fidelity design evaluation processes such as Computational Fluid Dynamics and Finite Element Analysis are often replaced with data driven surrogates to reduce computational cost in engineering design optimization. However, building accurate surrogate models still requires a large number of expensive simulations. To address this challenge, we introduce e

Ranran Cai, Igor Žutić, Wei Han

The interplay between superconductivity and ferromagnetism in the superconductor/ferromagnet (SC/FM) heterostructures generates many interesting physical phenomena, including spin-triplet superconductivity, superconducting order parameter oscillation, and topological superconductivity. The unique physical properties make the SC/FM heterostructures as promisi

Kuan-Lin Chen, Daniel D. E. Wong, Ke Tan, Buye Xu

Most speech enhancement (SE) models learn a point estimate and do not make use of uncertainty estimation in the learning process. In this paper, we show that modeling heteroscedastic uncertainty by minimizing a multivariate Gaussian negative log-likelihood (NLL) improves SE performance at no extra cost. During training, our approach augments a model learning

George Stan, George H. Lorimer, D. Thirumalai

Chaperonins are biological nanomachines that help newly translated proteins to fold by rescuing them from kinetically trapped misfolded states. Protein folding assistance by the chaperonin machinery is obligatory in vivo for a subset of proteins in the bacterial proteome. Chaperonins are large oligomeric complexes, with unusual seven fold symmetry (group I)

Shuo Liu, Nirupam Gupta, Nitin H. Vaidya

This report considers the problem of resilient distributed optimization and stochastic learning in a server-based architecture. The system comprises a server and multiple agents, where each agent has its own local cost function. The agents collaborate with the server to find a minimum of the aggregate of the local cost functions. In the context of stochastic

John M Robinson, Maya Miklos, Yee Ming Tso, Colin J. Kennedy

Building scalable quantum systems that demonstrate genuine performance enhancement based on entanglement is a major scientific goal for fields including computing, networking, simulations, and metrology. The tremendous challenge arises from the fragility of entanglement in increasingly larger sized quantum systems. Optical atomic clocks utilizing a large num

Hui Jiang, Zhao-Han Zhang, Yang Li, Camilo Ruiz

A universal mechanism of ultrafast two-electron orbital swap is discovered through two-photon sequential double ionization of Li. After a $1s$ electron in Li is ionized by absorbing an EUV photon, the other two bound electrons located on two different shells have either parallel or antiparallel spin orientations. In the latter case, these two electrons are i

Toshiyasu Arai

In arXiv:2208.12944 it is shown that an ordinal $\sup_{N<\omega}\psi_{\Omega_{1}}(\varepsilon_{\Omega_{\mathbb{S}+N}+1})$ is an upper bound for the proof-theoretic ordinal of a set theory ${\sf KP}\ell^{r}+(M\prec_{\Sigma_{1}}V)$. In this paper we show that a second order arithmetic $\Sigma^{1-}_{2}\mbox{-CA}+\Pi^{1}_{1}\mbox{-CA}_{0}$ proves the wellfounded

Nikola Milićević, Vladimir Itskov

We describe the combinatorics of equilibria and steady states of neurons in threshold-linear networks that satisfy Dale's law. The combinatorial code of a Dale network is characterized in terms of two conditions: (i) a condition on the network connectivity graph, and (ii) a spectral condition on the synaptic matrix. We find that in the weak coupling regime t

Kentaro Ohara, Xichao Zhang, Yinling Chen, Satoshi Kato

Skyrmions and bimerons are versatile topological spin textures that can be used as information bits for both classical and quantum computing. The transformation between isolated skyrmions and bimerons is an essential operation for computing architecture based on multiple different topological bits. Here we report the creation of isolated skyrmions and their

Michael Zshornack

Given a number field $K$, we show that certain $K$-integral representations of closed surface groups can be deformed to being Zariski dense while preserving many useful properties of the original representation. This generalizes a method due to Long and Thistlethwaite who used it to show that thin surface groups in $\operatorname{SL}(2k+1,\mathbf{Z})$ exist

C. Yuce

We consider a non-interacting gas under the inverted harmonic potential and present infinitely degenerate non-stationary orthogonal states. We discuss that it has an infinite entropy at the absolute zero temperature. We show that uncertainty in position of a particle under the inverted harmonic potential can be zero as there exists a solution which asymptote

Yan Ju, Shan Jia, Jialing Cai, Haiying Guan

With the rapid development of deep generative models (such as Generative Adversarial Networks and Diffusion models), AI-synthesized images are now of such high quality that humans can hardly distinguish them from pristine ones. Although existing detection methods have shown high performance in specific evaluation settings, e.g., on images from seen models or

Paul Seurin, Olusola Olabanjo, Joseph Wiggins, Lorien Pratt

Hydrogen is poised to play a major role in decarbonizing the economy. The need to discover, develop, and understand low-cost, high-performance, durable materials that can help maximize the cost of electrolysis as well as the need for an intelligent tool to make evidence-based Hydrogen research funding decisions relatively easier warranted this study.In this

Theodoros D. Bouloumis, Domna G. Kotsifaki, Sile Nic Chormaic

The pursuit for efficient nanoparticle trapping with low powers has led to optical tweezers technology moving from the conventional free-space configuration to advanced plasmonic tweezers systems. However, trapping nanoparticles smaller than 10 nm still remains a challenge even for plasmonic tweezers. Proper nanocavity design and excitation has given rise to

Hongyu Wu, Ri He, Yi Lu, Zhicheng Zhong

Quantum effects of lattice vibration play a major role in many physical properties of condensed matter systems, including thermal properties such as specific heat, structural phase transition, as well as phenomena such as quantum crystal and quantum paraelectricity that are closely related to zero-point fluctuations. However, realizing atomistic simulations

Michael S. Fusco, Benjamin L. Davis, Julia Kennefick, Daniel Kennefick

We present an analysis of the pitch angle distribution function (PADF) for nearby galaxies and its resulting black hole mass function (BHMF) via the well-known relationship between pitch angle and black hole mass. Our sample consists of a subset of 74 spiral galaxies from the Carnegie-Irvine Galaxy Survey with absolute $B$-band magnitude $\mathfrak{M}_{B}>-1

Heng Yu, Koichiro Niinuma, Laszlo A. Jeni

Neural Radiance Fields (NeRF) are compelling techniques for modeling dynamic 3D scenes from 2D image collections. These volumetric representations would be well suited for synthesizing novel facial expressions but for two problems. First, deformable NeRFs are object agnostic and model holistic movement of the scene: they can replay how the motion changes ove

Sehwan Choi, Jungho Kim, Junyong Yun, Jun Won Choi

Predicting the future motion of dynamic agents is of paramount importance to ensuring safety and assessing risks in motion planning for autonomous robots. In this study, we propose a two-stage motion prediction method, called R-Pred, designed to effectively utilize both scene and interaction context using a cascade of the initial trajectory proposal and traj

Fatemeh Karimi, Amir Mehrpanah, Reza Rawassizadeh

Advances in neural networks enable tackling complex computer vision tasks such as depth estimation of outdoor scenes at unprecedented accuracy. Promising research has been done on depth estimation. However, current efforts are computationally resource-intensive and do not consider the resource constraints of autonomous devices, such as robots and drones. In

Amr Ahmadain, Aron C. Wall

We explain why Tseytlin's off-shell formulation of string theory is well-defined. Although quantizing strings on an off-shell background requires an arbitrary choice of Weyl frame, this choice is not physically significant since it can be absorbed into a field redefinition of the target space fields. The off-shell formalism is particularly subtle at tree-lev

Soobin Cho, Panki Kim, Renming Song, Zoran Vondraček

The goal of this paper is to establish sharp two-sided estimates on the heat kernels of two types of purely discontinuous symmetric Markov processes in the upper half-space of $\mathbb R^d$ with jump kernels degenerate at the boundary. The jump kernels are of the form $J(x,y)=\mathcal B(x,y)|x-y|^{-\alpha-d}$, $\alpha\in (0,2)$, where the function $\mathcal

Daniel Paul-Pena, C. Seshadhri

Counting the number of homomorphisms of a pattern graph H in a large input graph G is a fundamental problem in computer science. There are myriad applications of this problem in databases, graph algorithms, and network science. Often, we need more than just the total count. Especially in large network analysis, we wish to compute, for each vertex v of G, the

Jiho Choi, Junghoon Park, Woocheol Kim, Jin-Hyeok Park

The recent advent of play-to-earn (P2E) systems in massively multiplayer online role-playing games (MMORPGs) has made in-game goods interchangeable with real-world values more than ever before. The goods in the P2E MMORPGs can be directly exchanged with cryptocurrencies such as Bitcoin, Ethereum, or Klaytn via blockchain networks. Unlike traditional in-game

Wenjun Ma, Jun Zhang, Kaikai Feng, Haoyun Xing

Data-driven discovery of governing equations is of great significance for helping us understand intrinsic mechanisms and build physical models. Recently, numerous highly innovative algorithms have emerged, aimed at inversely discovering the underlying governing equations from data, such as sparse regression-based methods and symbolic regression-based methods

Kinjal Bhar, He Bai, Jemin George, Carl Busart

We present a practical asynchronous data fusion model for networked agents to perform distributed Bayesian learning without sharing raw data. Our algorithm uses a gossip-based approach where pairs of randomly selected agents employ unadjusted Langevin dynamics for parameter sampling. We also introduce an event-triggered mechanism to further reduce communicat

Xuezheng Wang, Wu Jiang, Zhiqiang Shen, Lei Huang

Observational studies of inner-most regions of the edge-on jets in nearby active galactic nuclei (AGN) are crucial to understand their kinematics and morphology. For the inner jet of the nearby low luminosity AGN in M 84, we present new high-sensitivity observations with very long baseline interferometry since 2019, as well as archival Very Long Baseline Arr

Vishal Katariya, Narayan Bhusal, Chenglong You

The guesswork is an information-theoretic quantity which can be seen as an alternate security criterion to entropy. Recent work has established the theoretical framework for guesswork in the presence of quantum side information, which we extend both theoretically and experimentally. We consider guesswork when the side information consists of the BB84 states

Andreas A. Malikopoulos

The article provides an overview of the latest developments on control approaches for connected and automated vehicles along with their effectiveness on energy and transportation efficiency.

Asher Elmquist, Radu Serban, Dan Negrut

Modeling cameras for the simulation of autonomous robotics is critical for generating synthetic images with appropriate realism to effectively evaluate a perception algorithm in simulation. In many cases though, simulated images are produced by traditional rendering techniques that exclude or superficially handle processing steps and aspects encountered in t

Luiz C. B. da Silva, Tal Bar, Efi Efrati

The geometry and interactions between the constituents of a liquid crystal, which are responsible for inducing the partial order in the fluid, may locally favor an attempted phase that could not be realized in $\mathbb{R}^3$. While states that are incompatible with the geometry of $\mathbb{R}^3$ were identified more than 50 years ago, the collection of compa

Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell

SketchySGD improves upon existing stochastic gradient methods in machine learning by using randomized low-rank approximations to the subsampled Hessian and by introducing an automated stepsize that works well across a wide range of convex machine learning problems. We show theoretically that SketchySGD with a fixed stepsize converges linearly to a small ball

Petar Bakic, Marcela Hanzer

The Adams conjecture predicts that the local theta correspondence should respect Arthur packets. In this paper, we revisit the Adams conjecture for the symplectic--even orthogonal dual pair. Our results provide a precise description of all situations in which the conjecture holds.

Wu Wang

Powerspaces of directed spaces play an important role in modeling the semantics of nondeterministic functional programming languages. The notions of upper,lower and convex powerspace of a directed space are defined by the way of free algebras[25]. In this paper, We study the continuity of power structures of directed spaces and show that the directed lower p

Lilian W. Bialokozowicz

We present the new Orthogonal Polynomials Approximation Algorithm (OPAA), a parallelizable algorithm that estimates probability distributions using functional analytic approach: first, it finds a smooth functional estimate of the probability distribution, whether it is normalized or not; second, the algorithm provides an estimate of the normalizing weight; a

Yasuhiro Asa, Takeshi Kato, Ryuji Mine

In solving today's social issues, it is necessary to determine solutions that are acceptable to all stakeholders and collaborate to apply them. The conventional technology of "permissive meeting analysis" derives a consensusable choice that falls within everyone's permissible range through mathematical analyses; however, it tends to be biased toward the majo

Arun Kumar Awasthi, Rui Liu, Tingyu Gou

Supra-arcade downflows (SADs) are dark voids descending through plasma above the post-flare arcade. Although they are generally viewed as byproducts of flare reconnections in the corona, the nature of SADs is under debate. Here we investigated six distinct episodes of SADs observed in the post-maximum phase of an M-class flare of April 11, 2013. Differential

Jaimie Greasley, Patrick Hosein

Powder X-ray diffraction analysis is a critical component of materials characterization methodologies. Discerning characteristic Bragg intensity peaks and assigning them to known crystalline phases is the first qualitative step of evaluating diffraction spectra. Subsequent to phase identification, Rietveld refinement may be employed to extract the abundance

Maurício Hippert, Emily Dillingham, Hung Tan, David Curtin

The mirror twin Higgs model is a candidate for (strongly-interacting) complex dark matter, which mirrors SM interactions with heavier quark masses. A consequence of this model are mirror neutron stars -- exotic stars made entirely of mirror matter, which are significantly smaller than neutron stars and electromagnetically dark. This makes mergers of two mirr

Natalie Behague, Tom Johnston, Shoham Letzter, Natasha Morrison

Given a graph $H$, we say that an edge-coloured graph $G$ is $H$-rainbow saturated if it does not contain a rainbow copy of $H$, but the addition of any non-edge in any colour creates a rainbow copy of $H$. The rainbow saturation number $\text{rsat}(n,H)$ is the minimum number of edges among all $H$-rainbow saturated edge-coloured graphs on $n$ vertices. We

Juan Zha, Zheng Li, Ying Wei, Yu Zhang

Few-Shot Text Classification (FSTC) imitates humans to learn a new text classifier efficiently with only few examples, by leveraging prior knowledge from historical tasks. However, most prior works assume that all the tasks are sampled from a single data source, which cannot adapt to real-world scenarios where tasks are heterogeneous and lie in different dis

Aadhy Parthasarathy, Theresa Saxton-Fox

A flat plate turbulent boundary layer (TBL) is experimentally subjected to a family of 22 favorable-adverse pressure gradients (FAPGs) by using a ceiling panel of variable convex curvature. We define a FAPG as a sequence of streamwise pressure gradients in the order of favorable followed by adverse, similar to the pressure gradient sequence over the suction

Tommaso Salvatori, Yuhang Song, Yordan Yordanov, Beren Millidge

Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience. Training such models, however, is quite inefficient and unstable. In this work, we show how by simply changing the temporal scheduling of the update rule for the synaptic weights leads to an algorithm that is much more efficient and stable tha

Khashayar Gatmiry, Thomas Kesselheim, Sahil Singla, Yifan Wang

The Prophet Inequality and Pandora's Box problems are fundamental stochastic problem with applications in Mechanism Design, Online Algorithms, Stochastic Optimization, Optimal Stopping, and Operations Research. A usual assumption in these works is that the probability distributions of the $n$ underlying random variables are given as input to the algorithm. S

Joseph L. Briones, Tishya Chhabra, Joshua J. Daymude, Andréa W. Richa

Over three decades of scientific endeavors to realize programmable matter, a substance that can change its physical properties based on user input or responses to its environment, there have been many advances in both the engineering of modular robotic systems and the corresponding algorithmic theory of collective behavior. However, while the design of modul

Nader Zare, Omid Amini, Aref Sayareh, Mahtab Sarvmaili

Soccer Simulation 2D League is one of the major leagues of RoboCup competitions. In a Soccer Simulation 2D (SS2D) game, two teams of 11 players and one coach compete against each other. Several base codes have been released for the RoboCup soccer simulation 2D (RCSS2D) community that have promoted the application of multi-agent and AI algorithms in this fiel

Zhihong Chen, Maya Varma, Xiang Wan, Curtis Langlotz

Radiology report summarization (RRS) is a growing area of research. Given the Findings section of a radiology report, the goal is to generate a summary (called an Impression section) that highlights the key observations and conclusions of the radiology study. However, RRS currently faces essential limitations.First, many prior studies conduct experiments on

Hiroki Naganuma, Kartik Ahuja, Shiro Takagi, Tetsuya Motokawa

Modern deep learning systems do not generalize well when the test data distribution is slightly different to the training data distribution. While much promising work has been accomplished to address this fragility, a systematic study of the role of optimizers and their out-of-distribution generalization performance has not been undertaken. In this study, we

Lauritz van Luijk, Niklas Galke, Alexander Hahn, Daniel Burgarth

We provide state-dependent error bounds for strongly continuous unitary representations of connected Lie groups. That is, we bound the difference of two unitaries applied to a state in terms of the energy with respect to a reference Hamiltonian associated to the representation and a left-invariant metric distance on the group. Our method works for any connec

Giuliano Angelone, Elisa Ercolessi, Paolo Facchi, Davide Lonigro

We perform a reduction from three to two spatial dimensions of the physics of a spin-1/2 fermion coupled to the electromagnetic field, by applying Hadamard's method of descent. We consider first the free case, in which motion is determined by the Dirac equation, and then the coupling with a dynamical electromagnetic field, governed by the Dirac-Maxwell equat

Julien Chhor, Rajarshi Mukherjee, Subhabrata Sen

Given a heterogeneous Gaussian sequence model with unknown mean $\theta \in \mathbb R^d$ and known covariance matrix $\Sigma = \operatorname{diag}(\sigma_1^2,\dots, \sigma_d^2)$, we study the signal detection problem against sparse alternatives, for known sparsity $s$. Namely, we characterize how large $\epsilon^*>0$ should be, in order to distinguish with h

Souvik Bose, Bart De Pontieu, Viggo Hansteen, Alberto Sainz Dalda

It remains unclear which physical processes are responsible for the dramatic increase with height of the temperature in stellar atmospheres, known as the chromospheric ($\sim$10,000 K) and coronal (several million K) heating problems. Statistical studies of sun-like stars reveal that chromospheric and coronal emissions are correlated on a global scale, const

Hailiang Liu, Jia-Hao He, Xuping Tian

Anderson acceleration (AA) as an efficient technique for speeding up the convergence of fixed-point iterations may be designed for accelerating an optimization method. We propose a novel optimization algorithm by adapting Anderson acceleration to the energy adaptive gradient method (AEGD) [arXiv:2010.05109]. The feasibility of our algorithm is examined in li

Hongyi Pan, Xin Zhu, Salih Atici, Ahmet Enis Cetin

In this paper, we propose a novel Discrete Cosine Transform (DCT)-based neural network layer which we call DCT-perceptron to replace the $3\times3$ Conv2D layers in the Residual neural Network (ResNet). Convolutional filtering operations are performed in the DCT domain using element-wise multiplications by taking advantage of the Fourier and DCT Convolution

Zamiul Alam, Stephen P. Martin

The Standard Model can be defined quantitatively by running parameters in a mass-independent renormalization scheme at a fixed reference scale. We provide a set of simple interpolation formulas that give the fundamental Lagrangian parameters in the $\bar{\rm MS}$ scheme at a renormalization scale of 200 GeV, safely above the top-quark mass and suitable for m