August 2022 arXiv papers — page 91

Katsuhisa Ouchi, Hiroyuki Masuyama

This paper considers the level-increment (LI) truncation approximation of M/G/1-type Markov chains. The LI truncation approximation is useful for implementing the M/G/1 paradigm, which is the framework for computing the stationary distribution of M/G/1-type Markov chains. The main result of this paper is a subgeometric convergence formula for the total varia

Matt-Heun Hong, Lauren A. Marsh, Jessica L. Feuston, Janet Ruppert

Interpretive scholars generate knowledge from text corpora by manually sampling documents, applying codes, and refining and collating codes into categories until meaningful themes emerge. Given a large corpus, machine learning could help scale this data sampling and analysis, but prior research shows that experts are generally concerned about algorithms pote

Yao Zhang, Yun Xiong, Yiheng Sun, Caihua Shan

Risk scoring systems have been widely deployed in many applications, which assign risk scores to users according to their behavior sequences. Though many deep learning methods with sophisticated designs have achieved promising results, the black-box nature hinders their applications due to fairness, explainability, and compliance consideration. Rule-based sy

Geonho Han, Hyuckjin Choi, Ryeong Myeong Kim, Ki Tae Nam

Visible light is a proper spectrum for secure wireless communications because of its high directivity and impermeability in indoor scenarios. However, if an eavesdropper is located very close to a legitimate receiver, secure communications become highly risky. In this paper, to further increase the level of security of visible light communication (VLC) and i

Ren Guan

In this paper we consider a variation of the Vafa-Witten equations on compact, oriented and smooth 4-manifolds, and construct a set of perturbation terms to establish the transversality of that equations. The new perturbed equations provide us a priori estimates of the solutions, while the original reduced Vafa-Witten equations does not. By applying the a pr

Shubham Shakya, Mayank Dave

With the increasing popularity of Android in the last decade, Android is popular among users as well as attackers. The vast number of android users grabs the attention of attackers on android. Due to the continuous evolution of the variety and attacking techniques of android malware, our detection methods should need an update too. Most of the researcher's w

Pengyang Yu, Chaofan Fu, Yanwei Yu, Chao Huang

Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link prediction to node classification. However, most existing works ignore the relation heterogeneity with multiplex network between multi-typed nodes and different importance of relations in meta-p

Yanwei Yu, Ruoshan Lan, Lei Cao, Peng Song

The increasing pervasiveness of object tracking technologies leads to huge volumes of spatiotemporal data collected in the form of trajectory streams. The discovery of useful group patterns from moving objects' movement behaviours in trajectory streams is critical for real-time applications ranging from transportation management to military surveillance. Mot

Peiran Yan, Shengchen Li

Audio captioning is a task that generates description of audio based on content. Pre-trained models are widely used in audio captioning due to high complexity. Unless a comprehensive system is re-trained, it is hard to determine how well pre-trained models contribute to audio captioning system. To prevent the time consuming and energy consuming process of re

Yair Caro, Balázs Patkós, Zsolt Tuza

As a variant of the much studied Tur\'an number, $ex(n,F)$, the largest number of edges that an $n$-vertex $F$-free graph may contain, we introduce the connected Tur\'an number $ex_c(n,F)$, the largest number of edges that an $n$-vertex connected $F$-free graph may contain. We focus on the case where the forbidden graph is a tree. The celebrated conjecture o

Jialiang Wang, Yurong Zhong, Weiling Li

Latent Factor (LF) models are effective in representing high-dimension and sparse (HiDS) data via low-rank matrices approximation. Hessian-free (HF) optimization is an efficient method to utilizing second-order information of an LF model's objective function and it has been utilized to optimize second-order LF (SLF) model. However, the low-rank representatio

Russell Z. Kunes, Mingzhang Yin, Max Land, Doron Haviv

Gradient estimation is often necessary for fitting generative models with discrete latent variables, in contexts such as reinforcement learning and variational autoencoder (VAE) training. The DisARM estimator (Yin et al. 2020; Dong, Mnih, and Tucker 2020) achieves state of the art gradient variance for Bernoulli latent variable models in many contexts. Howev

Chun Liu, Cheng Wang, Steven M. Wise, Xingye Yue

A second order accurate (in time) numerical scheme is proposed and analyzed for the Poisson-Nernst-Planck equation (PNP) system, reformulated as a non-constant mobility $H^{-1}$ gradient flow in the Energetic Variational Approach (EnVarA). The centered finite difference is taken as the spatial discretization. Meanwhile, the highly nonlinear and singular natu

Stephane Durocher, J. Mark Keil, Debajyoti Mondal

Given a set $P$ of points and a set $U$ of axis-parallel unit squares in the Euclidean plane, a minimum ply cover of $P$ with $U$ is a subset of $U$ that covers $P$ and minimizes the number of squares that share a common intersection, called the minimum ply cover number of $P$ with $U$. Biedl et al. [Comput. Geom., 94:101712, 2020] showed that determining th

Xiang Ji, Chun Liu, Pei Liu, Shenggao Zhou

This work proposes a new variational, thermodynamically consistent model to predict thermal electrokinetics in electric double layer capacitors (EDLCs) by using an energetic variational approach. The least action principle and maximum dissipation principle from the non-equilibrium thermodynamics are employed to develop modified Nernst-Planck equations for no

Somayajulu L. N. Dhulipala, Yifeng Che, Michael D. Shields

When sampling for Bayesian inference, one popular approach is to use Hamiltonian Monte Carlo (HMC) and specifically the No-U-Turn Sampler (NUTS) which automatically decides the end time of the Hamiltonian trajectory. However, HMC and NUTS can require numerous numerical gradients of the target density, and can prove slow in practice. We propose Hamiltonian ne

Zhongyan Zhang, Lei Wang, Yang Wang, Luping Zhou

Quality feature representation is key to instance image retrieval. To attain it, existing methods usually resort to a deep model pre-trained on benchmark datasets or even fine-tune the model with a task-dependent labelled auxiliary dataset. Although achieving promising results, this approach is restricted by two issues: 1) the domain gap between benchmark da

Chao Fang, Aojun Zhou, Zhongfeng Wang

The Transformer has been an indispensable staple in deep learning. However, for real-life applications, it is very challenging to deploy efficient Transformers due to immense parameters and operations of models. To relieve this burden, exploiting sparsity is an effective approach to accelerate Transformers. Newly emerging Ampere GPUs leverage a 2:4 sparsity

Khang Nhut Lam, Kim-Ngoc Thi Nguyen, Loc Huu Nguy, Jugal Kalita

This paper discusses a facial expression recognition model and a description generation model to build descriptive sentences for images and facial expressions of people in images. Our study shows that YOLOv5 achieves better results than a traditional CNN for all emotions on the KDEF dataset. In particular, the accuracies of the CNN and YOLOv5 models for emot

Tuyen T. M. Nguyen, Vu A. Le, Tuan A. Nguyen

We consider all connected and simply connected 7-dimensional Lie groups whose Lie algebras have nilradical $\g_{5,2} = \s \{X_1, X_2, X_3, X_4, X_5 \colon [X_1, X_2] = X_4, [X_1, X_3] = X_5\}$ of Dixmier. First, we give a geometric description of the maximal-dimensional orbits in the coadjoint representation of all considered Lie groups. Next, we prove that,

Yanqiu Ruan, Xiaobo Li, Karthyek Murthy, Karthik Natarajan

Given data on the choices made by consumers for different offer sets, a key challenge is to develop parsimonious models that describe and predict consumer choice behavior while being amenable to prescriptive tasks such as pricing and assortment optimization. The marginal distribution model (MDM) is one such model, which requires only the specification of mar

Rohan Sangameswaran

According to the World Health Organization(WHO), malaria is estimated to have killed 627,000 people and infected over 241 million people in 2020 alone, a 12% increase from 2019. Microscopic diagnosis of blood cells is the standard testing procedure to diagnose malaria. However, this style of diagnosis is expensive, time-consuming, and greatly subjective to h

L. Avilés, J. Diaz, D. M. Penafiel, V. C. Orozco

Some time ago, the standard geometric framework of Einstein gravity was extended by gauging the Maxwell algebra as well as the so called AdS-Maxwell algebra. In this letter it is shown that the actions for these four-dimensional extended Einstein gravities can be obtained from the five-dimensional Chern-Simons gravities actions by using the Randall-Sundrum c

Bing Li, Lingmin Liao, Sanju Velani, Evgeniy Zorin

Let $T$ be a $d\times d$ matrix with real coefficients. Then $T$ determines a self-map of the $d$-dimensional torus ${\Bbb T}^d={\mathbb{R}}^d/{\Bbb Z}^d$. Let $ \{E_n \}_{n \in \mathbb{N}} $ be a sequence of subsets of ${\Bbb T}^d$ and let $W(T,\{E_n \})$ be the set of points $\mathbf{x} \in {\Bbb T}^d$ such that $T^n(\mathbf{x})\in E_n $ for infinitely man

Charbel Toumieh, Alain Lambert

Safe Corridors (a series of overlapping convex shapes) have been used recently in multiple state-of-the-art motion planning methods. They allow to represent the free space in the environment in an efficient way for collision avoidance. In this paper, we propose a new framework for generating Safe Corridors. We assume that we have a voxel grid representation

Khang Nhut Lam, Feras Al Tarouti, Jugal Kalita

This paper proposes approaches to automatically create a large number of new bilingual dictionaries for low-resource languages, especially resource-poor and endangered languages, from a single input bilingual dictionary. Our algorithms produce translations of words in a source language to plentiful target languages using available Wordnets and a machine tran

U-Shin Kim, Yoon-Ho Kim

The stationary light pulse (SLP) refers to a zero-group-velocity optical pulse in an atomic ensemble prepared by two counter-propagating driving fields. Despite the uniqueness of an optical pulse trapped within an atomic medium without a cavity, observations of SLP so far have been limited to trapping a single optical pulse due to the stringent SLP phase-mat

Haining Pan, Sankar Das Sarma

Motivated by the fact that the idealized Kitaev chain toy model and the experimental semiconductor-superconductor Majorana nanowire can both host the Majorana zero modes, we theoretically investigate the question to what extent the two models are equivalent or similar, using the perspective of the corresponding dual spin models for both. We start with the du

Khang Nhut Lam, Nguyen Hoang Huynh, Nguyen Bao Ngoc, To Thi Huynh Nhu

The research reported in this paper transforms a normal trash bin into a smarter one by applying computer vision technology. With the support of sensors and actuator devices, the trash bin can automatically classify garbage. In particular, a camera on the trash bin takes pictures of trash, then the central processing unit analyzes and makes decisions regardi

L. Cardenas, J. Diaz, P. Salgado, D. Salgado

We consider the curvatures 2 form asociated with AdSL4 valued one-form gauge connetion, and then we construct a four-dimensional action that generalize the Einstein-Hilbert gravity. It is shown that the Maxwell extension of Einstein gravity can be obtained from AdSL4-gravity making use of the Inonu-Wigner contraction method. In the same way, by gauging the A

Masakazu Kato, Hideo Kubo

The aim of this paper is to determine the critical exponent for the nonlinear wave equations with damping and potential terms of the scale invariant order, by assuming that these terms satisfy a special relation. We underline that our critical exponent is different from the one for related equations such as the nonlinear wave equation without lower order ter

Jingcheng Ni, Nan Zhou, Jie Qin, Qian Wu

Contrastive learning has shown great potential in video representation learning. However, existing approaches fail to sufficiently exploit short-term motion dynamics, which are crucial to various down-stream video understanding tasks. In this paper, we propose Motion Sensitive Contrastive Learning (MSCL) that injects the motion information captured by optica

Khang Nhut Lam, Nam Nhat Le, Jugal Kalita

In this study, we build a chatbot system in a closed domain with the RASA framework, using several models such as SVM for classifying intents, CRF for extracting entities and LSTM for predicting action. To improve responses from the bot, the kNN algorithm is used to transform false entities extracted into true entities. The knowledge domain of our chatbot is

J. A. Rojas, A. Morales, L. Gutiérrez, J. A. Otero

An experimental and theoretical study of the spectral response of coupled visco-elastic bars subject to axial oscillations is done. Novel closed formulas for the envelope function and their width is derived. These formulas explicitly show the role played by energy dissipation. They show that the internal friction does not affect the width of the envelope of

Joel Wolfrath, Abhishek Chandra

Data stream processing is an increasingly important topic due to the prevalence of smart devices and the demand for real-time analytics. Geo-distributed streaming systems, where cloud-based queries utilize data streams from multiple distributed devices, face challenges since wide-area network (WAN) bandwidth is often scarce or expensive. Edge computing allow

Jie You, Jae-Won Chung, Mosharaf Chowdhury

Training deep neural networks (DNNs) is becoming increasingly more resource- and energy-intensive every year. Unfortunately, existing works primarily focus on optimizing DNN training for faster completion, often without considering the impact on energy efficiency. In this paper, we observe that common practices to improve training performance can often lead

Vasiliki Fragkou, Quentin A. Parker, Albert A. Zijlstra, Roberto Vazquez

We report confirmation of a large, evolved, bipolar planetary nebula and its blue, white dwarf central star as a member of the ~500 Myr old Galactic open star cluster M37 (NGC 2099). This is only the third known example of a planetary nebula in a Galactic open cluster and was found via our on-going program of identifying and studying planetary nebulae - open

Junjie Li, Zilei Wang, Yuan Gao, Xiaoming Hu

In unsupervised domain adaptive (UDA) semantic segmentation, the distillation based methods are currently dominant in performance. However, the distillation technique requires complicate multi-stage process and many training tricks. In this paper, we propose a simple yet effective method that can achieve competitive performance to the advanced distillation m

Xiangyu Zhao, Di Zang, Sheng Wang, Zhenrong Shen

Brain network analysis for traumatic brain injury (TBI) patients is critical for its consciousness level assessment and prognosis evaluation, which requires the segmentation of certain consciousness-related brain regions. However, it is difficult to construct a TBI segmentation model as manually annotated MR scans of TBI patients are hard to collect. Data au

Man Ho Chan, Antonino Del Popolo

In the past few decades, some studies pointed out that magnetic field might affect the rotation curves in galaxies. However, the impact is relatively small compared with the effects of dark matter and the baryonic components. In this letter, we revisit the impact of magnetic field on the rotation curve of our Galaxy. We show that the inner Galactic rotation

Hsin-Yu Ko, Marcos F. Calegari Andrade, Zachary M. Sparrow, Ju-an Zhang

High-throughput DFT calculations are key to screening existing/novel materials, sampling potential energy surfaces, and generating quantum mechanical data for machine learning. By including a fraction of exact exchange (EXX), hybrid functionals reduce the self-interaction error in semi-local DFT and furnish a more accurate description of the underlying elect

Tianshu Feng, Zhipu Zhou, Joshi Tarun, Vijayan N. Nair

There are many different methods in the literature for local explanation of machine learning results. However, the methods differ in their approaches and often do not provide same explanations. In this paper, we consider two recent methods: Integrated Gradients (Sundararajan, Taly, & Yan, 2017) and Baseline Shapley (Sundararajan and Najmi, 2020). The origina

Laizhong Cui, Xiaoxin Su, Yipeng Zhou

Recently, blockchain-based federated learning (BFL) has attracted intensive research attention due to that the training process is auditable and the architecture is serverless avoiding the single point failure of the parameter server in vanilla federated learning (VFL). Nevertheless, BFL tremendously escalates the communication traffic volume because all loc

Tao Guo, Yizhu Wang, Jie Han, Huihui Wu

We consider the semantic rate-distortion problem motivated by task-oriented video compression. The semantic information corresponding to the task, which is not observable to the encoder, shows impacts on the observations through a joint probability distribution. The similarities among intra-frame segments and inter-frames in video compression are formulated

Yingting Liu, Chaochao Chen, Jamie Cui, Li Wang

K-means is one of the most widely used clustering models in practice. Due to the problem of data isolation and the requirement for high model performance, how to jointly build practical and secure K-means for multiple parties has become an important topic for many applications in the industry. Existing work on this is mainly of two types. The first type has

Adeilson Antonio da Silva, Mauricio Pamplona Segundo

We investigate how to modify executable files to deceive malware classification systems. This work's main contribution is a methodology to inject bytes across a malware file randomly and use it both as an attack to decrease classification accuracy but also as a defensive method, augmenting the data available for training. It respects the operating system fil

C. You, Y. Zhao, G. Feng, T. Q. S. Quek

Various middleboxes are ubiquitously deployed in networks to perform packet processing functions, such as firewalling, proxy, scheduling, etc., for the flows passing through them. With the explosion of network traffic and the demand for multiple types of network resources, it has never been more challenging for a middlebox to provide Quality-of-Service (QoS)

Mingrui Liu, Yue Hu, A. Lazarian, Siyao Xu

Magnetic fields play an important role in the formation and evolution of a galaxy, but it is challenging to measure them by observation. Here we study the Seyfert galaxy NGC 3627's magnetic field orientations measured from the synchrotron polarization observed with the Very Large Array (VLA) and from the Velocity Gradient Technique (VGT) using spectroscopic

Hyunsik Jeon, Jongjin Kim, Hoyoung Yoon, Jaeri Lee

How can we accurately recommend actions for users to control their devices at home? Action recommendation for smart home has attracted increasing attention due to its potential impact on the markets of virtual assistants and Internet of Things (IoT). However, designing an effective action recommender system for smart home is challenging because it requires h

Xinwu Cao, Wei-Min Gu

Outflows are inevitably driven from the disk if the vertical component of the black hole (BH) gravity cannot resist the radiation force. We derive the mass loss rate in the outflows by solving a dynamical equation for the vertical gas motion in the disk. The structure of a supercritical accretion disk is calculated with the radial energy advection included.

Zhengeng Yang, Hongshan Yu, Wei Sun, Li-Cheng

Deep Learning has greatly advanced the performance of semantic segmentation, however, its success relies on the availability of large amounts of annotated data for training. Hence, many efforts have been devoted to domain adaptive semantic segmentation that focuses on transferring semantic knowledge from a labeled source domain to an unlabeled target domain.

Zhengyi Zhou

We study several aspects of fillings for links of general quotient singularities using Floer theory, including co-fillings, Weinstein fillings, strong fillings, exact fillings and exact orbifold fillings, focusing on non-existence of exact fillings of contact links of isolated terminal quotient singularities. We provide an extensive list of isolated terminal

Arnab Sarkar, Yuanyuan Su, Nhut Truong, Scott Randall

We report results from deep Suzaku and mostly snapshot Chandra observations of four nearby galaxy groups: MKW4, Antlia, RXJ1159+5531, and ESO3060170. Their peak temperatures vary over 2-3 keV, making them the smallest systems with gas properties constrained to their viral radii. The average Fe abundance in the outskirts (R $>$ 0.25R$_{200}$) of their intragr

Rishab Balasubramanian, Kunal Rathore

Contrastive learning is commonly used as a method of self-supervised learning with the "anchor" and "positive" being two random augmentations of a given input image, and the "negative" is the set of all other images. However, the requirement of large batch sizes and memory banks has made it difficult and slow to train. This has motivated the rise of Supervis

Sangeet S. Kumar, Meera M. Parish, Jesper Levinsen

We theoretically investigate the scenario of a semiconductor quantum well in a microcavity, where the band structure is arranged such that optically excited electron-hole pairs cannot form Coulomb-bound excitonic states. However, it is still possible to form exciton polaritons (part-light, part-matter quasiparticles), where the excitons are bound via the exc

Rishab Balasubramanian, Rupashree Dey, Kunal Rathore

Contrastive learning is commonly applied to self-supervised learning, and has been shown to outperform traditional approaches such as the triplet loss and N-pair loss. However, the requirement of large batch sizes and memory banks has made it difficult and slow to train. Recently, Supervised Contrasative approaches have been developed to overcome these probl

Cathryn M. Trott, Rajesh Mondal, Garrelt Mellema, Steven G. Murray

The Multi-frequency Angular Power Spectrum (MAPS) is an alternative to spherically-averaged power spectra, and computes local fluctuations in the angular power spectrum without need for line-of-sight spectral transform. To test different approaches to MAPS and treatment of the foreground contamination, and compare with the spherically-averaged power spectrum

Yi-Jing Liu, Hongyang Du, Dusit Niyato, Gang Feng

As the next-generation Internet paradigm, Web 3.0 encapsulates the expectations of user-centric immersion and interaction experiences in a decentralized manner. Metaverse, a virtual world interacting with the physical world, is becoming one of the most potential technology to push forward with Web 3.0. In the Metaverse, users expect to tailor immersive and i

Clayton Miller, Renee Christensen, Jin Kai Leong, Mahmoud Abdelrahman

This paper describes the adaptation of an open-source ecological momentary assessment smart-watch platform with three sets of micro-survey wellness-related questions focused on i) infectious disease (COVID-19) risk perception, ii) privacy and distraction in an office context, and iii) triggers of various movement-related behaviors in buildings. This platform

Geonuk Kim, Jaemin Son, Kanghyu Lee, Jaesik Min

We present a novel approach to OCR(Optical Character Recognition) of Korean character, Hangul. As a phonogram, Hangul can represent 11,172 different characters with only 52 graphemes, by describing each character with a combination of the graphemes. As the total number of the characters could overwhelm the capacity of a neural network, the existing OCR encod

Wei-Xi Li, Rui Xu, Tong Yang

We consider a Prandtl model derived from MHD in the Prandtl-Hartmann regime that has a damping term due to the effect of the Hartmann boundary layer. A global-in-time well-posedness is obtained in the Gevrey function space with the optimal index $2$. The proof is based on a cancellation mechanism through some auxiliary functions from the study of the Prandtl

Da-Chuan Lu, Zhengdi Sun

We consider the triality fusion category discovered in the $c = 1$ KT theory \cite{Thorngren:2021yso}. We analyze this fusion category using the tools from the group theoretical fusion category and describe how to compute the simple lines, fusion rules and $F$-symbols. We then study the physical implication of this fusion category including deriving the spin

Xusheng Ai, Victor S. Sheng, Chunhua Li, Zhiming Cui

In order to deal with variant-length long videos, prior works extract multi-modal features and fuse them to predict students' engagement intensity. In this paper, we present a new end-to-end method Class Attention in Video Transformer (CavT), which involves a single vector to process class embedding and to uniformly perform end-to-end learning on variant-len

Haimiao Mo, Yuchen Li, Shanlin Yang, Wei Zhang

Early detection of anxiety is crucial for reducing the suffering of individuals with mental disorders and improving treatment outcomes. Utilizing an mHealth platform for anxiety screening can be particularly practical in improving screening efficiency and reducing costs. However, the effectiveness of existing methods has been hindered by differences in mobil

Moustapha Dieye, Amadou Diop, Mamadou Moustapha Mbaye, Mark A. McKibben

When the evolution familiy is hyperbolic and satisfies the Acquistapace-Terreni conditions, the existence and uniquenness of an almost automorphic mild solution and a weighted pseudo almost automorphic mild solution in distribution of mean-filed nonautonomous stochastic evolution equations driven by fractional Brownian motion is proved. Examples illustrating

Robert A. Bridges, Ashley E. Rice, Sean Oesch, Jeff A. Nichols

Modern security operation centers (SOCs) rely on operators and a tapestry of logging and alerting tools with large scale collection and query abilities. SOC investigations are tedious as they rely on manual efforts to query diverse data sources, overlay related logs, and correlate the data into information and then document results in a ticketing system. Sec

Yue Hu, Alex Lazarian

Probing magnetic fields in astrophysical environments is both important and challenging. The Gradient Technique (GT) is a new tool for tracing magnetic fields, rooted in the properties of magnetohydrodynamic (MHD) turbulence and turbulent magnetic reconnection. In this work, we examine the performance of GT when applied to synthetic synchrotron emission and

Xiangzhe Kong, Wenbing Huang, Yang Liu

Antibody design is valuable for therapeutic usage and biological research. Existing deep-learning-based methods encounter several key issues: 1) incomplete context for Complementarity-Determining Regions (CDRs) generation; 2) incapability of capturing the entire 3D geometry of the input structure; 3) inefficient prediction of the CDR sequences in an autoregr

Xu Gan, Caijun Zhong, Chongwen Huang, Zhaohui Yang

Reconfigurable intelligent surface (RIS) can be employed in a cell-free system to create favorable propagation conditions from base stations (BSs) to users via configurable elements. However, prior works on RIS-aided cell-free system designs mainly rely on the instantaneous channel state information (CSI), which may incur substantial overhead due to extremel

Jun-ichiro Kishine, Hiroaki Kusunose, Hiroshi M. Yamamoto

In solid state physics, any symmetry breaking is known to be associated with emergence of an order parameter. However, the order parameter for molecular and crystal chirality, which is a consequence of parity and mirror symmetry breaking, has not been known since its discovery. In this article, the authors show that the order parameter for chirality can be d

Emiliia Nazarenko, Nikolaos Athanasios Anagnostopoulos, Stavros G. Stavrinides, Nico Mexis

This work presents the hardware demonstrator of a secure encryption system based on synchronised Chua chaotic circuits. In particular, the presented encryption system comprises two Chua circuits that are synchronised using a dedicated bidirectional synchronisation line. One of them forms part of the transmitter, while the other of the receiver. Both circuits

Ashish A. Mhaske, Joydeep Bagchi, Bhal Chandra Joshi, Joe Jacob

We have designed a low-cost radio telescope system named the Bose Horn Antenna Radio Telescope (BHARAT) to detect the 21 cm hydrogen line emission from our Galaxy. The system is being used at the Radio Physics Laboratory (RPL), Inter-University Centre for Astronomy and Astrophysics (IUCAA), India, for laboratory sessions and training students and teachers. I

Marija Dimitrijević Ćirić, Nikola Konjik, Andjelo Samsarov

Some noncommutative (NC) theories posses a certain type of dualities that are implicitly built within their structure. In this paper we establish still another example of this kind. More precisely, we show that the noncommutative U(1) gauge theory coupled to a NC scalar field and to a classical geometry of the Reissner Nordstrom (RN) type is completely equiv

Jitao Liu, Yanqing Wang, Yulin Ye

Motivated by the works of Cheskidov, Lopes Filho, Nussenzveig Lopes and Shvydkoy in [8, Commun. Math. Phys. 348: 129-143, 2016] and Chen and Yu in [5, J. Math. Pures Appl. 131: 1-16, 2019], we address how the $L^p$ control of vorticity could influence the energy conservation for the incompressible homogeneous and nonhomogeneous Euler equations in this paper.

Ariane Lizotte, Jean-Gabriel Young, Antoine Allard

The network reconstruction task aims to estimate a complex system's structure from various data sources such as time series, snapshots, or interaction counts. Recent work has examined this problem in networks whose relationships involve precisely two entities-the pairwise case. Here we investigate the general problem of reconstructing a network in which

Minhyun Kim, Taehun Lee

In this paper, given a prescribed measure on $\mathbb{S}^1$ whose density is bounded and positive, we establish a uniform diameter estimate for solutions to the planar $L_p$ dual Minkowski problem when $0<p<1$ and $q\ge 2$. We also prove the uniqueness and positivity of solutions to the $L_p$ Minkowski problem when the density of the measure is sufficiently

O. Lourenço, C. H. Lenzi, T. Frederico, M. Dutra

In this work we study the outcomes related to dimensionless tidal deformability $(\Lambda)$ obtained through a relativistic mean-field (RMF) hadronic model including short-range correlations (SRC) and dark matter (DM) content [Phys. Rev. D 105, 023008 (2022)]. As a dark particle candidate, we use the lightest neutralino interacting with nucleons through the

Ahmed Taha, Yen Nhi Truong Vu, Brent Mombourquette, Thomas Paul Matthews

Medical images come in high resolutions. A high resolution is vital for finding malignant tissues at an early stage. Yet, this resolution presents a challenge in terms of modeling long range dependencies. Shallow transformers eliminate this problem, but they suffer from quadratic complexity. In this paper, we tackle this complexity by leveraging a linear sel

Chaoyu He, Yujie Liao, Tao Ouyang, Huimin Zhang

A new set of lattice-models based on the hexagonal $\sqrt{N}\times\sqrt{N}$ super-cells of the well-known honeycomb lattice with single-hole defect (HL-D-1/2N) are proposed to realize the nontrivial isolated flat-bands. Through performing both tight-binding and density functional theory calculations, we demonstrate that the experimentally realized graphitic

Mariam Rakka, Mohammed E. Fouda, Pramod Khargonekar, Fadi Kurdahi

Mixed-precision Deep Neural Networks achieve the energy efficiency and throughput needed for hardware deployment, particularly when the resources are limited, without sacrificing accuracy. However, the optimal per-layer bit precision that preserves accuracy is not easily found, especially with the abundance of models, datasets, and quantization techniques th

Nantheera Anantrasirichai, Thanarat H. Chalidabhongse, Duangdao Palasuwan, Korranat Naruenatthanaset

Manual examination of faecal smear samples to identify the existence of parasitic eggs is very time-consuming and can only be done by specialists. Therefore, an automated system is required to tackle this problem since it can relate to serious intestinal parasitic infections. This paper reviews the ICIP 2022 Challenge on parasitic egg detection and classific

Xianzhi Du, Wei-Chih Hung, Tsung-Yi Lin

This paper summarizes model improvements and inference-time optimizations for the popular anchor-based detectors in the scenes of autonomous driving. Based on the high-performing RCNN-RS and RetinaNet-RS detection frameworks designed for common detection scenes, we study a set of framework improvements to adapt the detectors to better detect small objects in

Paul Soulos, Sudha Rao, Caitlin Smith, Eric Rosen

Machine translation has seen rapid progress with the advent of Transformer-based models. These models have no explicit linguistic structure built into them, yet they may still implicitly learn structured relationships by attending to relevant tokens. We hypothesize that this structural learning could be made more robust by explicitly endowing Transformers wi

Sorabh Chhabra, Jyotirmay Paul, A. N. Ramaprakash, Avinash Surendran

Accurately simulating the atmospheric turbulence behaviour is always challenging. The well-known FFT based method falls short in correctly predicting both the low and high frequency behaviours. Sub-harmonic compensation aids in low-frequency correction but does not solve the problem for all screen size to outer scale parameter ratios (G/$L_0$). FFT-based sim

A. C. Trapp, Steven R. Furlanetto

One of the most exciting advances of the current generation of telescopes has been the detection of galaxies during the epoch of reionization, using deep fields that have pushed these instruments to their limits. It is essential to optimize our analyses of these fields in order to extract as much information as possible from them. In particular, standard met

Chetraj Pandey, Anli Ji, Rafal A. Angryk, Manolis K. Georgoulis

Solar flare prediction is a central problem in space weather forecasting and has captivated the attention of a wide spectrum of researchers due to recent advances in both remote sensing as well as machine learning and deep learning approaches. The experimental findings based on both machine and deep learning models reveal significant performance improvements

Runxue Bao, Bin Gu, Heng Huang

Sparsity regularized loss minimization problems play an important role in various fields including machine learning, data mining, and modern statistics. Proximal gradient descent method and coordinate descent method are the most popular approaches to solving the minimization problem. Although existing methods can achieve implicit model identification, aka su

Florian Herren, Stefan Höche, Frank Krauss, Daniel Reichelt

We present a simple parton-shower model that replaces the explicit angular ordering of the coherent branching formalism with a differentially accurate simulation of soft-gluon radiation by means of a non-trivial dependence on azimuthal angles. We introduce a global kinematics mapping and provide an analytic proof that it satisfies the criteria for next-to le

Geoff Goehle, Benjamin Cowen, Thomas E. Blanford, J. Daniel Park

A fundamental challenge in acoustic data processing is to separate a measured time series into relevant phenomenological components. A given measurement is typically assumed to be an additive mixture of myriad signals plus noise whose separation forms an ill-posed inverse problem. In the setting of sensing elastic objects using active sonar, we wish to separ

Melody X. Lim, Heinrich M. Jaeger

We present a scheme for generating shape-dependent, specific bonds between millimeter scale particles, using acoustic levitation. We levitate particles in an ultrasonic standing wave, allowing for substrate-free assembly. Secondary scattering generates shape-dependent attractive forces between particles, while driving the acoustic trap above its resonance fr

Yoo Kyung Lee, Hanzhen Lin, Wolfgang Ketterle

Simple, paradigmatic systems are important tools in understanding strongly correlated systems. One such system is the Bose-Hubbard model, which can be realized using atoms in optical lattices with delta-function interactions. We report the first experimental observation of two features of the Bose-Hubbard model: superexchange via virtual molecules in excited

Matthew Malencia, Sandeep Manjanna, M. Ani Hsieh, George Pappas

In this paper, we present an online adaptive planning strategy for a team of robots with heterogeneous sensors to sample from a latent spatial field using a learned model for decision making. Current robotic sampling methods seek to gather information about an observable spatial field. However, many applications, such as environmental monitoring and precisio

Devin Jean, Suk Seo

Assume that a graph $G$ models a detection system for a facility with a possible ``intruder," or a multiprocessor network with a possible malfunctioning processor. We consider the problem of placing detectors at a subset of vertices in $G$ to determine the location of an intruder if there is any. Many types of detection systems have been defined for differen

Sulaiman Aburakhia, Ryan Myers, Abdallah Shami

Vibration-based condition monitoring techniques are commonly used to detect and diagnose failures of rolling bearings. Accuracy and delay in detecting and diagnosing different types of failures are the main performance measures in condition monitoring. Achieving high accuracy with low delay improves system reliability and prevents catastrophic equipment fail

S. M. Farzaneh, Mehdi Hatefipour, William F. Schiela, Neda Lotfizadeh

We investigate the effects of an in-plane magnetic field on the weak antilocalization signature of near-surface quantum wells lacking bulk and inversion symmetry. The measured magnetoconductivity exhibits a strong anisotropy with respect to the direction of the in-plane magnetic field. The two-fold symmetry of the observed magneto-anisotropy originates from

Zejiang Hou, Fei Sun, Yen-Kuang Chen, Yuan Xie

Self-attention based transformer models have been dominating many computer vision tasks in the past few years. Their superb model qualities heavily depend on the excessively large labeled image datasets. In order to reduce the reliance on large labeled datasets, reconstruction based masked autoencoders are gaining popularity, which learn high quality transfe

Taahir Aiyoob Patel, Clement N. Nyirenda

Vehicle hijacking is one of the leading crimes in many cities. For instance, in South Africa, drivers must constantly remain vigilant on the road in order to ensure that they do not become hijacking victims. This work is aimed at developing a map depicting hijacking spots in a city by using Twitter data. Tweets, which include the keyword "hijacking", are obt

Vincenzo Maria Schimmenti, Federico Lanza, Alex Hansen, Silvio Franz

Understanding the flow of yield stress fluids in porous media is a major challenge. In particular, experiments and extensive numerical simulations report a non-linear Darcy law as a function of the pressure gradient. In this letter, we consider a tree-like porous structure for which the problem of the flow can be resolved exactly thanks to a mapping with the

Christopher C. Hogan, Jason Murphy, David Grow

We consider the problem of recovering a spatially-localized cubic nonlinearity in a nonlinear Schr\"odinger equation in dimensions two and three. We prove that solutions with data given by small-amplitude wave packets accrue a nonlinear phase that determines the X-ray transform of the nonlinear coefficient.

Jason Milionis, Ciamac C. Moallemi, Tim Roughgarden, Anthony Lee Zhang

Automated Market Makers (AMMs) are both liquidity sources and investment vehicles for market participants. This paper analyzes the risks and returns of liquidity provision (LP) investments in AMMs. In a continuous-time model, we show that LP returns decompose into a beta-like component reflecting market risk exposure, and an alpha-like component reflecting m