December 2020 arXiv papers — page 24

Eimear Byrne, Heide Gluesing-Luerssen, Alberto Ravagnani

We study the structure of anticodes in the sum-rank metric for arbitrary fields and matrix blocks of arbitrary sizes. Our main result is a complete classification of optimal linear anticodes. We also compare the cardinality of the ball in the sum-rank metric with that of an optimal linear anticode, showing that the latter is strictly larger over sufficiently

Lingling Chen, Yu Yang, Binglei Wang, Shengyou Yang

Soft elastomers that can exhibit extremely large deformations under the action of an electric field are essential for applications such as soft robotics, stretchable and flexible electronics, energy harvesting among others. The critical limiting factor in conventional electro-actuation of such materials is the occurrence of the so-called pull-in instability.

Sabyasachi Chattopadhyay, Matthew A. Bershady, Marsha J. Wolf, andMichael P. Smith

We have developed an analytic model for generic image transfer using microlens-coupled fibers to determine the telescope input beam speed that optimizes the lenslet clear aperture and minimizes fiber focal-ratio degradation. Assuming fibers are fed at f/3.5 by the lenslets, our study shows that f/11 is the optimum telescope beam speed to feed a lenslet coupl

Simone Camosso

A comparison on some facts concerning the geometric quantization of symplectic manifolds is presented here. Criticism, facts and improvements on the sophisticated theory of geometric quantization are presented touching briefly, all the "salient points of the theory". The unfamiliar reader can consider this as a "soft" introduction to the topic.

Sagar Gubbi, Bharadwaj Amrutur

Natural scene text detection is an important aspect of scene understanding and could be a useful tool in building engaging augmented reality applications. In this work, we address the problem of false positives in text spotting. We propose improving the performace of sliding window text spotters by looking for character pairs (bigrams) rather than single cha

Hasan Karjoun, Abdelaziz Beljadid, Philippe G. LeFloch

We develop a new finite volume method using unstructured mesh-vertex grids for coupled systems modeling shallow water flows and solute transport over complex bottom topography. Novel well-balanced positivity preserving discretization techniques are proposed for the water surface elevation and the concentration of the pollutant. For the hydrodynamic system, t

Filippo Bracci, Łukasz Kosiński, Włodzimierz Zwonek

In this paper we study the following "slice rigidity property": given two Kobayashi complete hyperbolic manifolds $M, N$ and a collection of complex geodesics $\mathcal F$ of $M$, when is it true that every holomorphic map $F:M\to N$ which maps isometrically every complex geodesic of $\mathcal F$ onto a complex geodesic of $N$ is a biholomorphism? Among othe

Sagar Gubbi, Shishir Kolathaya, Bharadwaj Amrutur

Industrial robot manipulators are not able to match the precision and speed with which humans are able to execute contact rich tasks even to this day. Therefore, as a means overcome this gap, we demonstrate generative methods for imitating a peg-in-hole insertion task in a 6-DOF robot manipulator. In particular, generative adversarial imitation learning (GAI

Elisabeth Hoppe, Jens Wetzl, Philipp Roser, Lina Felsner

Continuous protocols for cardiac magnetic resonance imaging enable sampling of the cardiac anatomy simultaneously resolved into cardiac phases. To avoid respiration artifacts, associated motion during the scan has to be compensated for during reconstruction. In this paper, we propose a sampling adaption to acquire 2-D respiration information during a continu

Lam Pham, Huy Phan, Ross King, Alfred Mertins

This paper presents an inception-based deep neural network for detecting lung diseases using respiratory sound input. Recordings of respiratory sound collected from patients are firstly transformed into spectrograms where both spectral and temporal information are well presented, referred to as front-end feature extraction. These spectrograms are then fed in

Sagar Gubbi Venkatesh, Raviteja Upadrashta, Shishir Kolathaya, Bharadwaj Amrutur

Existing architectures for imitation learning using image-to-action policy networks perform poorly when presented with an input image containing multiple instances of the object of interest, especially when the number of expert demonstrations available for training are limited. We show that end-to-end policy networks can be trained in a sample efficient mann

Chen Avin

Motivated by recent developments in optical switching and reconfigurable network design, we study dynamic binary search trees (BSTs) in the matching model. In the classical dynamic BST model, the cost of both link traversal and basic reconfiguration (rotation) is $O(1)$. However, in the matching model, the BST is defined by two optical switches (that represe

Lingming Zhang, Yue Zhao, Deyu Meng, Zhiming Cui

The ability to segment teeth precisely from digitized 3D dental models is an essential task in computer-aided orthodontic surgical planning. To date, deep learning based methods have been popularly used to handle this task. State-of-the-art methods directly concatenate the raw attributes of 3D inputs, namely coordinates and normal vectors of mesh cells, to t

Sagar Gubbi Venkatesh, Nihesh Rathod, Shishir Kolathaya, Bharadwaj Amrutur

Imitation learning is a data-driven approach to acquiring skills that relies on expert demonstrations to learn a policy that maps observations to actions. When performing demonstrations, experts are not always consistent and might accomplish the same task in slightly different ways. In this paper, we demonstrate inherent stochasticity in demonstrations colle

Sayantan Banerjee, Weining Shen

We study the graph signal denoising problem by estimating a piecewise constant signal over an undirected graph. We propose a new Bayesian approach that first converts a general graph to a chain graph via the depth-first search algorithm, and then imposes a heavy-tailed $t$-shrinkage prior on the differences between consecutive signals over the induced chain

Sagar Gubbi Venkatesh, Raviteja Upadrashta, Bharadwaj Amrutur

It is highly desirable for robots that work alongside humans to be able to understand instructions in natural language. Existing language conditioned imitation learning models directly predict the actuator commands from the image observation and the instruction text. Rather than directly predicting actuator commands, we propose translating the natural langua

C. Swastik, Ravinder K. Banyal, Mayank Narang, P. Manoj

Directly imaged planets are self-luminous companions of pre-main sequence and young main sequence stars. They reside in wider orbits ($\sim10\mathrm{s}-1000\mathrm{s}$~AU) and generally are more massive compared to the close-in ($\lesssim 10$~AU) planets. Determining the host star properties of these outstretched planetary systems is important to understand

Sagar Gubbi Venkatesh, Anirban Biswas, Raviteja Upadrashta, Vikram Srinivasan

Robots that can manipulate objects in unstructured environments and collaborate with humans can benefit immensely by understanding natural language. We propose a pipelined architecture of two stages to perform spatial reasoning on the text input. All the objects in the scene are first localized, and then the instruction for the robot in natural language and

Jiayu Bao

Adversarial examples have gained tons of attention in recent years. Many adversarial attacks have been proposed to attack image classifiers, but few work shift attention to object detectors. In this paper, we propose Sparse Adversarial Attack (SAA) which enables adversaries to perform effective evasion attack on detectors with bounded \emph{l$_{0}$} norm per

Jiaxin Zhou, Wangtao Lu

This paper proposes a novel, rigorous and simple Fourier-transformation approach to study resonances in a perfectly conducting slab with finite number of subwavelength slits of width $h\ll 1$. Since regions outside the slits are variable separated, by Fourier transforming the governing equation, we could express field in the outer regions in terms of field d

Sagar Gubbi Venkatesh, Bharadwaj Amrutur

A robot that can operate in novel and unstructured environments must be capable of recognizing new, previously unseen, objects. In this work, a visual cue is used to specify a novel object of interest which must be localized in new environments. An end-to-end neural network equipped with a Siamese network is used to learn the cue, infer the object of interes

Yongxing Dai, Jun Liu, Yan Bai, Zekun Tong

Unsupervised domain adaptive (UDA) person re-identification (re-ID) is a challenging task due to the missing of labels for the target domain data. To handle this problem, some recent works adopt clustering algorithms to off-line generate pseudo labels, which can then be used as the supervision signal for on-line feature learning in the target domain. However

Xin Du, Weihong Cai, Jianquan Liu, Ding Yu

Determining the value of basketball players through analyzing the players' behavior is important for the managers of modern basketball teams. However, conventional methods always utilize isolated statistical data, leading to ineffective and inaccurate evaluations. Existing models based on dynamic network theory offer major improvements to the results of such

Maksim V. Kukushkin

In this paper we consider a norm based on the infinitesimal generator of the shift semigroup in a direction. The relevance of such a focus is guaranteed by an abstract representation of a fractional integro-differential operator by means of a composition of the corresponding infinitesimal generator. The main result of the paper is a theorem establishing equi

Nusrat Binta Nizam, Tohfatul Jinan, Wahida Binte Naz Aurthy, Md. Rakib Hossen

Back pain is one of the leading causes of disability-adjusted life year globally and the most common cause of low back pain is poor sitting posture. There are several actions that can be adopted proactively to avoid poor sitting posture induced back pain including behavioral change, regular exercise, and use of an ergonomic chair. However, these are either e

Mingqiu Li, Qi-Shu Yan, Yongchao Zhang, Zhijie Zhao

The left-right symmetric model (LRSM) is a well-motivated framework to restore parity and implement seesaw mechanisms for the tiny neutrino masses at or above the TeV-scale, and has a very rich phenomenology at both the high-energy and high-precision frontiers. In this paper we examine the phase transition and resultant gravitational waves (GWs) in the minim

Sai Munikoti, Laya Das, Balasubramaniam Natarajan

In the quest to improve efficiency, interdependence and complexity are becoming defining characteristics of modern complex networks representing engineered and natural systems. Graph theory is a widely used framework for modeling such complex networks and to evaluate their robustness to disruptions. Particularly, identification of critical nodes/links in a g

Xiaoying Wu, Dimitri Theodoratos, Nikos Mamoulis

We address the problem of summarizing embedded tree patterns extracted from large data trees. We do so by defining and mining closed and maximal embedded unordered tree patterns from a single large data tree. We design an embedded frequent pattern mining algorithm extended with a local closedness checking technique. This algorithm is called {\em closedEmbTM-

Bin Huang, Jiayong Li, Jianhui Wang

A reasonable node selection criterion (NSC) is crucial for the network reduction in power systems. In contrast to the previous works that only consider structure property, this paper proposes a comprehensive and quantitative NSC considering both structural and electrical properties. The proposed NSC is developed by employing the evidential reasoning approach

Jiongmin Yong, Jianfeng Zhang

For an optimal control problem of an It\^o's type stochastic differential equation, the control process could be taken as open-loop or closed-loop forms. In the standard literature, provided appropriate regularity, the value functions under these two types of controls are equal and are the unique (viscosity) solution to the corresponding (path-dependent) HJB

Qiang He, Xinwen Hou

Offline reinforcement learning (RL), also known as batch RL, aims to optimize policy from a large pre-recorded dataset without interaction with the environment. This setting offers the promise of utilizing diverse, pre-collected datasets to obtain policies without costly, risky, active exploration. However, commonly used off-policy algorithms based on Q-lear

Quanyi Li, Zhenghao Peng, Qihang Zhang, Chunxiao Liu

Over the past few years there is a growing interest in the learning-based self driving system. To ensure safety, such systems are first developed and validated in simulators before being deployed in the real world. However, most of the existing driving simulators only contain a fixed set of scenes and a limited number of configurable settings. That might eas

Zhengyang Liu, Wei Zeng, Xiaolei Ma, Xiang Cheng

We experimentally study density fluctuations and energy spectra of bulk \textit{E. coli} suspensions of different concentrations. Our results verify the predicted scaling law of giant number fluctuations in three-dimensional (3D) wet active fluids. We find that such a scaling behavior persists at small scales even in low concentration suspensions well below

Ayaan Haque

Semi-supervised learning has been gaining attention as it allows for performing image analysis tasks such as classification with limited labeled data. Some popular algorithms using Generative Adversarial Networks (GANs) for semi-supervised classification share a single architecture for classification and discrimination. However, this may require a model to c

Sai Munikoti, Laya Das, Balasubramaniam Natarajan

Identifying critical nodes and links in graphs is a crucial task. These nodes/links typically represent critical elements/communication links that play a key role in a system's performance. However, a majority of the methods available in the literature on the identification of critical nodes/links are based on an iterative approach that explores each node/li

Shao-Pin Chiu, C. C. Tsuei, Sheng-Shiuan Yeh, Fu-Chun Zhang

Unconventional superconductivity and in particular triplet superconductivity have been front and center of topological materials and quantum technology research. Here we report our observation of triplet superconductivity in nonmagnetic CoSi$_2$/TiSi$_2$ heterostructures on silicon. CoSi$_2$ undergoes a sharp superconducting transition at a critical temperat

Ahmed Bakry, Ahmed Alshahrie, Alaa Mahmoud, Hamed Dalir

We introduce modeling and simulation of the noise properties associated with types of modal oscillations induced by scaling the asymmetric gain suppression (AGS) in multimode semiconductor lasers. The study is based on numerical integration of a system of rate equations of 21-oscillating modes taking account of the self- and cross-modal gain suppression mech

Song-Kyoo, Kim

Bigdata is a dataset of which size is beyond the ability of handling a valuable raw material that can be refined and distilled into valuable specific insights. Compact data is a method that optimizes the big dataset that gives best assets without handling complex bigdata. The compact dataset contains the maximum knowledge patterns at fine grained level for e

Yibo Hu, Yuzhe Ou, Xujiang Zhao, Jin-Hee Cho

Traditional deep neural networks (NNs) have significantly contributed to the state-of-the-art performance in the task of classification under various application domains. However, NNs have not considered inherent uncertainty in data associated with the class probabilities where misclassification under uncertainty may easily introduce high risk in decision ma

Moustafa Ahmed, Ahmed Bakry, Ammar Qasem, Hamed Dalir

Here we report on studying the electronic and optical material properties of the technologically-relevant material indium tin oxide (ITO) as a function of thermal annealing. In this work, ITO powder has been prepared utilizing solid-state reaction methods. An electron beam gun technology has been used to prepare a ITO film (325 nm). The ITO window layer has

Neeti Pokhriyal, Abenezer Dara, Benjamin Valentino, Soroush Vosoughi

Researchers have used social media data to estimate various macroeconomic indicators about public behaviors, mostly as a way to reduce surveying costs. One of the most widely cited economic indicator is consumer confidence index (CCI). Numerous studies in the past have focused on using social media, especially Twitter data, to predict CCI. However, the stron

Mark A. Pinsky

Assessment of the degree of boundedness/stability of multidimensional nonlinear systems with time-dependent and nonperiodic coefficients is an important problem in various applied areas which has no adequate resolution yet. Most of the known techniques provide computationally intensive and conservative stability criteria in this field and frequently fail to

Pradeep Fernando, Irina Calciu, Jayneel Gandhi, Aasheesh Kolli

Emerging non-volatile memory (NVM) technologies promise memory speed byte-addressable persistent storage with a load/store interface. However, programming applications to directly manipulate NVM data is complex and error-prone. Applications generally employ libraries that hide the low-level details of the hardware and provide a transactional programming mode

S. K. Biswal, H. C. Das, Ankit Kumar, Bharat Kumar

According to the general theory of relativity, a massive body induces curvature in the surrounding spacetime. In this study, the surface curvature (SC) of neutron stars is computed using various curvature quantities derived from the relativistic mean-field, density-dependent RMF, and Skyrme-Hartree-Fock equations of states. Neutron star properties, including

Victor J. W. Guo, Ji-Cai Liu, Michael J. Schlosser

We prove two supercongruences for specific truncated hypergeometric series. These include an uniparametric extension of a supercongruence that was recently established by Long and Ramakrishna. Our proofs involve special instances of various hypergeometric identities including Whipple's transformation and the Karlsson--Minton summation.

Abdelkader Khouass, Christian Attiogbé, Mohamed Messabihi

Critical and cyber-physical systems (CPS) that exist in large industries, such as nuclear power plants, railway, automotive or aeronautical industries are complex heterogeneous systems. They are complex because they are open, perimeter-less, often built by assembling various heterogeneous and interacting components which are frequently reconfigured due to re

Mengying Sun, Jing Xing, Bin Chen, Jiayu Zhou

Learning with curriculum has shown great effectiveness in tasks where the data contains noisy (corrupted) labels, since the curriculum can be used to re-weight or filter out noisy samples via proper design. However, obtaining curriculum from a learner itself without additional supervision or feedback deteriorates the effectiveness due to sample selection bia

Jiaoyang Huang, Daniel Z. Huang, Qing Yang, Guang Cheng

In this paper, we study the power iteration algorithm for the spiked tensor model, as introduced in [44]. We give necessary and sufficient conditions for the convergence of the power iteration algorithm. When the power iteration algorithm converges, for the rank one spiked tensor model, we show the estimators for the spike strength and linear functionals of

Dat Ngo, Lam Pham, Anh Nguyen, Ben Phan

This paper proposes a robust deep learning framework used for classifying anomaly of respiratory cycles. Initially, our framework starts with front-end feature extraction step. This step aims to transform the respiratory input sound into a two-dimensional spectrogram where both spectral and temporal features are well presented. Next, an ensemble of C- DNN an

Shuntaro Aoki, Masahide Yamaguchi

We study effects of multiple scalar fields (scalar isocurvatons) with the Hubble scale masses on the inflationary bispectrum in the squeezed limit, particular paying attention to the question how to disentangle mass spectra of such fields. We consider two isocurvatons with almost degenerate masses and the coupling of an inflaton to both isocurvatons as an ex

Arman Haghanifar, Mahdiyar Molahasani Majdabadi, Seok-Bum Ko

Dental caries is one of the most chronic diseases involving the majority of the population during their lifetime. Caries lesions are typically diagnosed by radiologists relying only on their visual inspection to detect via dental x-rays. In many cases, dental caries is hard to identify using x-rays and can be misinterpreted as shadows due to different reason

Javier Gonzalez-Rocha, Alejandro J. Sosa, Regina Hanlon, Arthur A. Allen

Ocean hazardous spills and search and rescue incidents are more prevalent as maritime activities increase across all sectors of society. However, emergency response time remains a factor due to a lack of information to accurately forecast the location of small objects. Existing drifting characterization techniques are limited to objects whose drifting proper

Bram van Berlo, Amany Elkelany, Tanir Ozcelebi, Nirvana Meratnia

The increasing bandwidth requirement of new wireless applications has lead to standardization of the millimeter wave spectrum for high-speed wireless communication. The millimeter wave spectrum is part of 5G and covers frequencies between 30 and 300 GHz corresponding to wavelengths ranging from 10 to 1 mm. Although millimeter wave is often considered as a co

Zhiyuan Jiang

In this paper, we adopt the fluid limits to analyze Age of Information (AoI) in a wireless multiaccess network with many users. We consider the case wherein users have heterogeneous i.i.d. channel conditions and the statuses are generate-at-will. Convergence of the AoI occupancy measure to the fluid limit, represented by a Partial Derivative Equation (PDE),

Fan Lyu, Fuyuan Hu, Victor S. Sheng, Zhengtian Wu

In our daily life, the scenes around us are always with multiple labels especially in a smart city, i.e., recognizing the information of city operation to response and control. Great efforts have been made by using Deep Neural Networks to recognize multi-label images. Since multi-label image classification is very complicated, people seek to use the attentio

Rostislav Grigorchuk, Cosmas Kravaris

We develop further Cannon's method of cone types for finding the growth function of a group, which can also be used to find the coordination sequences of certain infinite graphs. We then apply this method to compute the growth functions and series of the wallpaper groups (the 2 dimensional crystallographic groups). The paper has a number of illustrating colo

Nadezhda V. Timofeeva

Admissible pairs $((\widetilde S, \widetilde L), \widetilde E)$ consisting of an $N$-dimensional projective scheme~$\widetilde S$ of certain class with a special ample invertible sheaf $\widetilde L$ and a locally free ${\cal O}_{\widetilde S}$-sheaf $\widetilde E$ are considered. An admissible pair can be produced in the procedure of a transformation (which

Takahiro Orito, Yoshihito Kuno, Ikuo Ichinose

We find that a flat-band fermion system with interactions and without disorders exhibits non-thermalized ergodicity-breaking dynamics, an analog of many-body localization (MBL). In the previous works, we observed flat-band many-body localization (FMBL) in the Creutz ladder model. The origin of FMBL is a compact localized state governed by local integrals of

Susan Amin, Maziar Gomrokchi, Hossein Aboutalebi, Harsh Satija

A major challenge in reinforcement learning is the design of exploration strategies, especially for environments with sparse reward structures and continuous state and action spaces. Intuitively, if the reinforcement signal is very scarce, the agent should rely on some form of short-term memory in order to cover its environment efficiently. We propose a new

Karthik H. Shankar

The controversies around the 2020 US presidential elections certainly casts serious concerns on the efficiency of the current voting system in representing the people's will. Is the naive Plurality voting suitable in an extremely polarized political environment? Alternate voting schemes are gradually gaining public support, wherein the voters rank their choi

Xing Huang, Panpan Ren, Feng-Yu Wang

Due to their intrinsic link with nonlinear Fokker-Planck equations and many other applications, distribution dependent stochastic differential equations (DDSDEs for short) have been intensively investigated. In this paper we summarize some recent progresses in the study of DDSDEs, which include the correspondence of weak solutions and nonlinear Fokker-Planck

Ilya Kapovich, Zachary Simon

Motivated by results about "untangling" closed curves on hyperbolic surfaces, Gupta and Kapovich introduced the primitivity and simplicity index functions for finitely generated free groups, $d_{prim}(g;F_N)$ and $d_{simp}(g;F_N)$, where $1\ne g\in F_N$, and obtained some upper and lower bounds for these functions. In this paper, we study the behavior of the

Zhang Zechuan, Fan Engui

In this article, the inverse scattering transform is considered for the Gerdjikov-Ivanov equation with zero and non-zero boundary conditions by a matrix Riemann-Hilbert (RH) method. The formula of the soliton solutions are established by Laurent expansion to the RH problem. The method we used is different from computing solution with simple poles since the r

Mark A. Pinsky

Estimation of the degree of stability and the bounds of solutions to non-autonomous nonlinear systems present major concerns in numerous applied problems. Yet, current techniques are frequently yield overconservative conditions which are unable to effectively gage these characteristics in time-varying nonlinear systems. This paper develops a novel methodolog

Marek Lassak

We present a survey article about the geometry of convex bodies on the $d$-dimensional sphere $S^d$. We concentrate on the results based on the notion of the width of a convex body $C \subset S^d$ determined by a supporting hemisphere of $C$. Important tools are the lunes containing $C$. The supporting hemispheres take over the role of the supporting half-sp

Masaki Hamada, Hiroshi Hirai

In this paper, we address the noncommutative rank (nc-rank) computation of a linear symbolic matrix \[ A = A_1 x_1 + A_2 x_2 + \cdots + A_m x_m, \] where each $A_i$ is an $n \times n$ matrix over a field $\mathbb{K}$, and $x_i$ $(i=1,2,\ldots,m)$ are noncommutative variables. For this problem, polynomial time algorithms were given by Garg, Gurvits, Oliveira,

Rui Tang

We introduce a new updating rule, the conditional maximum likelihood rule (CML) for updating ambiguous information. The CML formula replaces the likelihood term in Bayes' rule with the maximal likelihood of the given signal conditional on the state. We show that CML satisfies a new axiom, increased sensitivity after updating, while other updating rules do no

Majd Ghrear, Sven E. Vahsen, Cosmin Deaconu

Directional detection of nuclear recoils is broadly desirable in nuclear and particle physics. At low recoil energies, this capability may be used to confirm the cosmological origin of a dark matter signal, to penetrate the so-called neutrino floor, or to distinguish between different neutrino sources. Gas Time Projection Chambers (TPCs) can enable direction

David Nesvorny

The dynamical structure of the Kuiper belt can be used as a clue to the formation and evolution of the Solar System, planetary systems in general, and Neptune's early orbital history in particular. The problem is best addressed by forward modeling where different initial conditions and Neptune's orbital evolutions are tested, and the model predictions are co

Peijie Sun, K. Ramesh Kumar, Meng Lyu, Zhen Wang

How magnetism affects the Seebeck effect is an important issue widely concerned in the thermoelectric community yet remaining elusive. Based on a thermodynamic analysis of spin degrees of freedom on varied $d$-electron based ferro- and anti-ferromagnets, we demonstrate that in itinerant or partially itinerant magnetic compounds there exists a generic spin co

Rupert L. Frank, Michael Loss

This paper presents some results concerning the size of magnetic fields that support zero modes for the three dimensional Dirac equation and related problems for spinor equations. It is a well known fact that for the Schr\"odinger in three dimensions to have a negative energy bound state, the 3/2- norm of the potential has to be greater than the Sobolev cons

Sujan Kumar Gonugondla, Charbel Sakr, Hassan Dbouk, Naresh R. Shanbhag

This paper obtains fundamental limits on the computational precision of in-memory computing architectures (IMCs). An IMC noise model and associated SNR metrics are defined and their interrelationships analyzed to show that the accuracy of IMCs is fundamentally limited by the compute SNR ($\text{SNR}_{\text{a}}$) of its analog core, and that activation, weigh

Priyotosh Bandyopadhyay, Saunak Dutta, Anirban Karan

Though various extensions of the Standard Model with higher gauge group predict the existence of leptoquarks, none of them has been observed yet at any of the colliders. In this paper, we study the prospect of several past and future $e$-$p$ colliders like HERA, LHeC and FCC-he to detect them through radiation amplitude zero. We find that the leptoquarks sho

Rui Luo, Buddhika Nettasinghe, Vikram Krishnamurthy

This paper deals with the modeling and estimation of the sociological phenomena called echo chambers and segregation in social networks. Specifically, we present a novel community-based graph model that represents the emergence of segregated echo chambers as a Markov bridge process. A Markov bridge is a one-dimensional Markov random field that facilitates mo

Gary R. W. Greaves, Joseph W. Iverson, John Jasper, Dustin G. Mixon

We investigate equiangular lines in finite orthogonal geometries, focusing specifically on equiangular tight frames (ETFs). In parallel with the known correspondence between real ETFs and strongly regular graphs (SRGs) that satisfy certain parameter constraints, we prove that ETFs in finite orthogonal geometries are closely aligned with a modular generalizat

Jianwei Zhang

Network coding (NC), when combined with multipath routing, enables a linear programming (LP) formulation for a multi-source multicast with intra-session network coding (MISNC) problem. However, it is still hard to solve using conventional methods due to the enormous scale of variables or constraints. In this paper, we try to solve this problem in terms of th

Zheng Tan, Patrice A. Camati, Guillaume Coeuret Cauquil, Alexia Auffèves

We theoretically derive and experimentally compare several different ways to access entropy production in a quantum process under feedback control. We focus on a bipartite quantum system realizing an autonomous Maxwell's demon scheme reported by Najera-Santos et al. [Phys.~Rev.~Research 2, 032025(R) (2020)], where information encoded in a demon is consumed t

Alvaro Suarez, Mateo Dutra, Martin Monteiro, Arturo C. Marti

Bernoulli's equation, which relates the pressure of an ideal fluid in motion with its velocity and height under certain conditions, is a central topic in General Physics courses for Science and Engineering students. This equation, frequently used both textbooks as in science outreach activities or museums, is often extrapolated to explain situations in which

Marco Favorito

The heterogeneity of tools that support temporal logic formulae poses several challenges in terms of interoperability. In particular, a standard syntax for temporal logic on finite traces, despite similar to the one for infinite traces, is currently missing. This document proposes a standard grammar for several temporal logic formalisms interpreted over fini

Yue Hu, Ao Qu, Dan Work

Event detection has been an important task in transportation, whose task is to detect points in time when large events disrupts a large portion of the urban traffic network. Travel information {Origin-Destination} (OD) matrix data by map service vendors has large potential to give us insights to discover historic patterns and distinguish anomalies. However,

H. Babaei-Aghbolagh, Komeil Babaei Velni, Davood Mahdavian Yekta, H. Mohammadzadeh

We investigate the $T\bar{T}$-like flows for non-linear electrodynamic theories in $D(=\!\!2n)$-dimensional spacetime. Our analysis is restricted to the deformation problem of the classical free action by employing the proposed $T\bar{T}$ operator from a simple integration technique. We show that this flow equation is compatible with $T\bar{T}$ deformation o

Samy Badreddine, Artur d'Avila Garcez, Luciano Serafini, Michael Spranger

Artificial Intelligence agents are required to learn from their surroundings and to reason about the knowledge that has been learned in order to make decisions. While state-of-the-art learning from data typically uses sub-symbolic distributed representations, reasoning is normally useful at a higher level of abstraction with the use of a first-order logic la

Diarmuid Crowley, Thomas Schick, Wolfgang Steimle

We prove that the derivative map $d \colon \mathrm{Diff}_\partial(D^k) \to \Omega^kSO_k$, defined by taking the derivative of a diffeomorphism, can induce a nontrivial map on homotopy groups. Specifically, for $k = 11$ we prove that the following homomorphism is non-zero: $$ d_* \colon \pi_5\mathrm{Diff}_\partial(D^{11}) \to \pi_{5}\Omega^{11}SO_{11} \cong \

Krzysztof Lis, Sina Honari, Pascal Fua, Mathieu Salzmann

Vehicles can encounter a myriad of obstacles on the road, and it is impossible to record them all beforehand to train a detector. Instead, we select image patches and inpaint them with the surrounding road texture, which tends to remove obstacles from those patches. We then use a network trained to recognize discrepancies between the original patch and the i

Huachuan Wang, James Ting-Ho Lo

This paper aims to overcome a fundamental problem in the theory and application of deep neural networks (DNNs). We propose a method to solve the local minimum problem in training DNNs directly. Our method is based on the cross-entropy loss criterion's convexification by transforming the cross-entropy loss into a risk averting error (RAE) criterion. To allevi

I. Y. Chestnov, S. M. Arakelian, A V. Kavokin

The artificial gauge field for electrically neutral exciton polaritons devoid from the polarization degree of freedom can be synthesized by means of applying crossed electric and magnetic fields. The appearance of the gauge potential can be ascribed to the motional (magneto-electric) Stark effect which is responsible for the presence of a linear-in-momentum

S. Orlando, A. Wongwathanarat, H. -T. Janka, M. Miceli

The structure and morphology of supernova remnants (SNRs) reflect the properties of the parent supernovae (SNe) and the characteristics of the inhomogeneous environments through which the remnants expand. Linking the morphology of SNRs to anisotropies developed in their parent SNe can be essential to obtain key information on many aspects of the explosion pr

V. Roman-Rodriguez, B. Brecht, S. Kaali, C. Silberhorn

Configurable and scalable continuous variable quantum networks for measurement-based quantum information protocols or multipartite quantum communication schemes can be obtained via parametric down conversion (PDC) in non-linear waveguides. In this work, we exploit symmetric group velocity matching (SGVM) to engineer the properties of the squeezed modes of th

Ahmadreza Jeddi, Mohammad Javad Shafiee, Alexander Wong

Adversarial Training (AT) with Projected Gradient Descent (PGD) is an effective approach for improving the robustness of the deep neural networks. However, PGD AT has been shown to suffer from two main limitations: i) high computational cost, and ii) extreme overfitting during training that leads to reduction in model generalization. While the effect of fact

Elliot Snider, Nathan Dasenbrock-Gammon, Raymond McBride, Xiaoyu Wang

Room temperature superconductivity has been achieved under high pressure in an organically derived carbonaceous sulfur hydride with a critical superconducting transition temperature (Tc) of 288 kelvin. This development is part of a new class of dense, hydrogen rich materials with remarkably high critical temperatures. Metal superhydrides are a subclass of th

Lauri Lahti

Developing machine learning models to support health analytics requires increased understanding about statistical properties of self-rated expression statements. We analyzed self-rated expression statements concerning the coronavirus COVID-19 epidemic to identify statistically significant differences between groups of respondents and to detect the patient's

A. Mogulkoc, M. Modarresi, A. N. Rudenko

We present a density functional theory (DFT) based study of a two-dimensional phase of chromium bismuthate (CrBi), previously unknown material with exceptional magnetic and magnetooptical characteristics. Monolayer CrBi is a ferromagnetic metal with strong spin-orbit coupling induced by the heavy bismuth atoms, resulting in a strongly anisotropic Ising-type

Anuradha Welivita, Yubo Xie, Pearl Pu

We propose a novel large-scale emotional dialogue dataset, consisting of 1M dialogues retrieved from the OpenSubtitles corpus and annotated with 32 emotions and 9 empathetic response intents using a BERT-based fine-grained dialogue emotion classifier. This work explains the complex pipeline used to preprocess movie subtitles and select good movie dialogues t

Alex Fedorov, Tristan Sylvain, Eloy Geenjaar, Margaux Luck

Sensory input from multiple sources is crucial for robust and coherent human perception. Different sources contribute complementary explanatory factors. Similarly, research studies often collect multimodal imaging data, each of which can provide shared and unique information. This observation motivated the design of powerful multimodal self-supervised repres

Takaaki Tanaka, Tomoyuki Okuno, Hiroyuki Uchida, Hiroya Yamaguchi

In spite of their importance as standard candles in cosmology and as major major sites of nucleosynthesis in the Universe, what kinds of progenitor systems lead to type Ia supernovae (SN) remains a subject of considerable debate in the literature. This is true even for the case of Tycho's SN exploded in 1572 although it has been deeply studied both observati

Francesco Calogero, Farrin Payandeh

The solution $x_n\left(t\right)$, $n=1,2,$ of the \textit{initial-values} problem is reported of the \textit{autonomous} system of $2$ coupled first-order ODEs with \textit{homogeneous cubic polynomial} right-hand sides, \begin{eqnarray} \dot{x}_n = c_{n1} \left(x_1\right)^3 + c_{n2}\left( x_1\right)^2 x_2 + c_{n3} x_1 \left(x_2\right)^2+c_{n4} \left(x_2\rig

Sagar Gubbi Venkatesh, Raviteja Upadrashta, Shishir Kolathaya, Bharadwaj Amrutur

Robots that must operate in novel environments and collaborate with humans must be capable of acquiring new knowledge from human experts during operation. We propose teaching a robot novel objects it has not encountered before by pointing a hand at the new object of interest. An end-to-end neural network is used to attend to the novel object of interest indi

Alex Fedorov, Lei Wu, Tristan Sylvain, Margaux Luck

Introspection of deep supervised predictive models trained on functional and structural brain imaging may uncover novel markers of Alzheimer's disease (AD). However, supervised training is prone to learning from spurious features (shortcut learning) impairing its value in the discovery process. Deep unsupervised and, recently, contrastive self-supervised app

Sepideh Maleki, Udit Agarwal, Martin Burtscher, Keshav Pingali

Hypergraph partitioning is used in many problem domains including VLSI design, linear algebra, Boolean satisfiability, and data mining. Most versions of this problem are NP-complete or NP-hard, so practical hypergraph partitioners generate approximate partitioning solutions for all but the smallest inputs. One way to speed up hypergraph partitioners is to ex

Auwal Tijjani Amshi

There is great significance in evaluating a node's Influence ranking in complex networks. Over the years, many researchers have presented different measures for quantifying node interconnectedness within networks. Therefore, this paper introduces a centrality measure called Tr-centrality which focuses on using the node triangle structure and the node neighbo