January 2022 arXiv papers — page 135

Bruce Knuteson

The world's stock markets display a decades-long pattern of overnight and intraday returns seemingly consistent with only one explanation: one or more large, long-lived quant firms tending to expand its portfolio early in the day (when its trading moves prices more) and contract its portfolio later in the day (when its trading moves prices less), losing

Xianliang Gong, Yulin Pan

In this work, we develop a multi-fidelity Bayesian experimental design framework to efficiently quantify the extreme-event statistics of an input-to-response (ItR) system with given input probability and expensive function evaluations. The key idea here is to leverage low-fidelity samples whose responses can be computed with a cost of a certain fraction of t

Dani Kiyasseh, Rasheed El-Bouri

Large-scale image databases remain largely biased towards objects and activities encountered in a select few cultures. This absence of culturally-diverse images, which we refer to as the hidden tail, limits the applicability of pre-trained neural networks and inadvertently excludes researchers from under-represented regions. To begin remedying this issue, we

Ievgenii Afanasiev

The paper is concerned with the correlation functions of the characteristic polynomials of random matrices with independent complex entries. We investigate how the asymptotic behavior of the correlation functions depends on the second moment of the common probability law of the matrix entries, a sort of ``reality measure'' of the entries. It is shown

Vitaly V. Chaban

Global warming is seen as a drastic environmental problem nowadays. Carbon dioxide (CO 2 ) concentration in the Earth's atmosphere is linked to the average temperature on the surface of the planet. Carbon capture and storage is an important technological endeavor aiming to improve the ecology. The present work investigates reaction paths that are respons

Hao Liu, Haizhao Yang, Minshuo Chen, Tuo Zhao

Learning operators between infinitely dimensional spaces is an important learning task arising in wide applications in machine learning, imaging science, mathematical modeling and simulations, etc. This paper studies the nonparametric estimation of Lipschitz operators using deep neural networks. Non-asymptotic upper bounds are derived for the generalization

Chung-I Tan

In a contribution to the volume {\it A Passion for Physics}, a collection of essays in honor of Geoffrey Chew's sixtieth birthday, I wrote, together with A. Capella, Uday Sukhatme, and Tran Thanh Van {\it The Pomeron Story.} This is a follow-up to that contribution. This sequel also serves as an opportunity to acknowledge my gratitude to Geoff as a PhD s

Narges Fathalian, Seyedeh Somayeh Hosseini Rad, Nasibeh Alipour, Hossein Safari

Here, we study the temperature structure of flaring and non-flaring coronal loops, using extracted loops from images taken in six extreme ultraviolet (EUV) channels recorded by Atmospheric Imaging Assembly (AIA)/ Solar Dynamic Observatory (SDO). We use data for loops of X2.1-class-flaring active region (AR11283) during 22:10UT till 23:00UT, on 2011, Septembe

Julien Gargani

The estimation of resilience and the determination of inequality growth after a disaster is often difficult. In this study, specific indicators were developed to analyze resilience, and the trajectories of resilience were compared. Energy production is relevant for describing variations in social and economic activities. This indicator was applied to a case

Lynet Allan, George O Amolo, Julius Mwabora, Silas Mureramanzi

Titanium-based oxides are abundant, chemically stable, non-toxic, and highly versatile materials, with applications ranging from photovoltaics to catalysis. For rutile and anatase phases of Titanium dioxide (TiO2), the bandgap ranges from 3.0-3.2 eV, which are too large to absorb in the visible range (400 nm - 700 nm), resulting in poor photo-catalytic effic

Shuang Qiao, Yu-Ning Wu, Xiaolan Yan, Bartomeu Monserrat

Defects are crucial in determining the overall physical properties of semiconductors. Generally, the charge-state transition level (TEL), one of the key physical quantities that determines the dopability of defects in semiconductors, is temperature dependent. However, little is known about the temperature dependence of TEL, and, as a result, almost all exist

Vassily Olegovich Manturov

We construct an invariant of virtual knots which is a sliceness obstruction and sensitive to the $Δ$-move. This invariants works if $\Z_{2}\oplus \Z_{2}$-index of chords is present.

Yecheng Shao, Yongbin Jin, Xianwei Liu, Weiyan He

Gaits and transitions are key components in legged locomotion. For legged robots, describing and reproducing gaits as well as transitions remain longstanding challenges. Reinforcement learning has become a powerful tool to formulate controllers for legged robots. Learning multiple gaits and transitions, nevertheless, is related to the multi-task learning pro

Farshad Noravesh, Kristiaan Kerstens

In this paper, different approaches to portfolio optimization having higher moments such as skewness and kurtosis are classified so that the reader can observe different paradigms and approaches in this field of research which is essential for practitioners in Hedge Funds in particular. Several methods based on different paradigms such as utility approach an

Goldwyn Millar, Michel Kulhandjian, Ayse Alaca, Saban Alaca

NOMA) technique based on an algebraic design is studied. We propose an improved low-density spreading (LDS) sequence design based on projective geometry. In terms of its bit error rate (BER) performance, our proposed improved LDS code set outperforms the existing LDS designs over the frequency nonselective Rayleigh fading and additive white Gaussian noise (A

Michel Kulhandjian, Gunes Karabulut Kurt, Hovannes Kulhandjian, Halim Yanikomeroglu

An improved mean squared error (MSE) minimization solution based on eigenvector decomposition approach is conceived for wideband non-orthogonal multiple-access based computation over multi-access channel (NOMA-CoMAC) framework. This work aims at further developing NOMA-CoMAC for next-generation multimodal sensor networks, where a multimodal sensor monitors s

Raghunath Sahoo, Tapan Kumar Nayak

Our universe was born about 13.8 billion years ago from an extremely hot and dense singular point, in a process known as the Big Bang. The hot and dense matter which dominated the system within a few microseconds of its birth was in the form of a soup of elementary quarks and gluons, known as the quark-gluon plasma (QGP). Signatures compatible with the forma

Baraffe I, Constantino T, Clarke J, Le Saux A

Recent hydrodynamical simulations of convection in a solar-like model suggest that penetrative convective flows at the boundary of the convective envelope modify the thermal background in the overshooting layer. Based on these results, we implement in one-dimensional stellar evolution codes a simple prescription to modify the temperature gradient below the c

Radostin Cholakov, Todor Kolev

There is an increasing interest in the application of deep learning architectures to tabular data. One of the state-of-the-art solutions is TabTransformer which incorporates an attention mechanism to better track relationships between categorical features and then makes use of a standard MLP to output its final logits. In this paper we propose multiple modif

Julien Gargani

Intraplate volcanic islands are often considered as stable relief with constant vertical motion and used for relative sea-level curves reconstruction. This study shows that large landslides cause non-negligible isostatic adjustment. The vertical motion that occurred after landslide is quantified using a modelling approach. We show that a giant landslide caus

Jayden L. Macklin-Cordes, Erich R. Round

Phylogenetic comparative methods are new in our field and are shrouded, for most linguists, in at least a little mystery. Yet the path that led to their discovery in comparative biology is so similar to the methodological history of balanced sampling, that it is only an accident of history that they were not discovered by a typologist. Here we clarify the es

Shanjun Zhang, Mingzhen Li, Hailong Yang, Yi Liu

Deploying various deep learning (DL) models efficiently has boosted the research on DL compilers. The difficulty of generating optimized tensor codes drives DL compiler to ask for the auto-tuning approaches, and the increasing demands require increasing auto-tuning efficiency and quality. Currently, the DL compilers partition the input DL models into several

Zhou Yang, Jieke Shi, Muhammad Hilmi Asyrofi, David Lo

Deep neural networks (DNN) have been widely applied in modern life, including critical domains like autonomous driving, making it essential to ensure the reliability and robustness of DNN-powered systems. As an analogy to code coverage metrics for testing conventional software, researchers have proposed neuron coverage metrics and coverage-driven methods to

Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan

Image restoration is the task of recovering a clean image from a degraded version. In most cases, the degradation is spatially varying, and it requires the restoration network to both localize and restore the affected regions. In this paper, we present a new approach suitable for handling the image-specific and spatially-varying nature of degradation in imag

Sanjiva Prasad, R. Madhukar Yerraguntla, Subodh Sharma

Synchronous reactive data flow is a paradigm that provides a high-level abstract programming model for embedded and cyber-physical systems, including the locally synchronous components of IoT systems. Security in such systems is severely compromised due to low-level programming, ill-defined interfaces and inattention to security classification of data. By in

Adriano Pellegri

A classical derivation of Betz's law is first presented along with some insights. The extended Betz's theory is deduced for a rotor with axis orthogonal to the direction of an ideal fluid in uniform motion. The conceptual design used to demonstrate the generality of the aerodynamic aspects of energy conversion - starting from a suggestive approach to

Mohammadhossein Ghahramani, Mengchu Zhou, Anna Molter, Francesco Pilla

The Internet of Things (IoT) is a paradigm characterized by a network of embedded sensors and services. These sensors are incorporated to collect various information, track physical conditions, e.g., waste bins' status, and exchange data with different centralized platforms. The need for such sensors is increasing; however, proliferation of technologies

Prasad Mani, Chris Hanson, Shravan Hanasoge

The technique of normal-mode coupling is a powerful tool with which to seismically image non-axisymmetric phenomena in the Sun. Here we apply mode coupling in the Cartesian approximation to probe steady, near-surface flows in the Sun. Using Doppler cubes obtained from the Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory, we perform inv

Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan

Image inpainting methods have shown significant improvements by using deep neural networks recently. However, many of these techniques often create distorted structures or blurry textures inconsistent with surrounding areas. The problem is rooted in the encoder layers' ineffectiveness in building a complete and faithful embedding of the missing regions.

Jakob E. Björnberg, Benjamin Lees

We elucidate connections between four models in statistical physics and probability theory: (1) the toric code model of Kitaev, (2) the uniform eight-vertex model, (3) random walk on a hypercube, and (4) a classical Ising model with four-body interaction. As a consequence of our analysis (and of the GKS-inequalities for the Ising model) we obtain correlation

Wencai Liu, W. -M. Wang

It is classical, following Furstenberg's theorem on positive Lyapunov exponent for products of random SL$(2, \mathbb R)$ matrices, that the one dimensional random Schrödinger operator has Anderson localization at arbitrary disorder. This paper proves a nonlinear analogue, thereby establishing a KAM-type persistence result for a non-integrable system.

M. I. Naher, S. H. Naqib

In the present study we have investigated the structural properties, electronic band dispersion, elastic constants, acoustic behavior, phonon spectrum, optical properties, and a number of thermophysical parameters of binary topological semimetal BaGa2 in details via first-principles calculations using the density functional theory (DFT) based formalisms. The

Jian-wei Liu, Hao-jie Xie, Run-kun Lu, Xiong-lin Luo

Multi-view learning can cover all features of data samples more comprehensively, so multi-view learning has attracted widespread attention. Traditional subspace clustering methods, such as sparse subspace clustering (SSC) and low-ranking subspace clustering (LRSC), cluster the affinity matrix for a single view, thus ignoring the problem of fusion between vie

Maitreya Suin, A. N. Rajagopalan

This paper tackles the challenging problem of video deblurring. Most of the existing works depend on implicit or explicit alignment for temporal information fusion which either increase the computational cost or result in suboptimal performance due to wrong alignment. In this study, we propose a factorized spatio-temporal attention to perform non-local opera

Jian-wei Liu, Xi-hao Ding, Run-kun Lu, Xionglin Luo

Multi-view learning attempts to generate a model with a better performance by exploiting the consensus and/or complementarity among multi-view data. However, in terms of complementarity, most existing approaches only can find representations with single complementarity rather than complementary information with diversity. In this paper, to utilize both compl

Haoxu Wang, Yan Jia, Zeqing Zhao, Xuyang Wang

Wake-up word detection models are widely used in real life, but suffer from severe performance degradation when encountering adversarial samples. In this paper we discuss the concept of confusing words in adversarial samples. Confusing words are commonly encountered, which are various kinds of words that sound similar to the predefined keywords. To enhance t

F. Schillaci, G. A. P. Cirrone, G. Cuttone, M. Maggiore

A system of permanent magnet quadrupoles (PMQs) is going to be realized by INFNLNS to be used as a collection system for the injection of laser driven ion beams up to 60 AMeV in an energy selector based on four resistive dipoles. This system is the first element of the ELIMED (ELI-Beamlines MEDical and Multidisciplinary applications) beam transport, dosimetr

Raphael Yuster

Let $H_r(n,p)$ denote the maximum number of Hamiltonian cycles in an $n$-vertex $r$-graph with density $p \in (0,1)$. The expected number of Hamiltonian cycles in the random $r$-graph model $G_r(n,p)$ is $E(n,p)=p^n(n-1)!/2$ and in the random graph model $G_r(n,m)$ with $m=p\binom{n}{r}$ it is, in fact, slightly smaller than $E(n,p)$. For graphs, $H_2(n,p)$

David Giofrè, Enrica Donolato, Irene C. Mammarella

Objectives: Several studies have focused on the role of working memory (WM) in predicting mathematical and reading literacy. Alternative models of WM have been proposed and a modality-dependent model of WM, distinguishing between verbal and visuospatial WM modalities, has been advanced. In addition, the relationship between verbal and visuospatial WM and aca

Moi Hoon Yap, Connah Kendrick, Neil D. Reeves, Manu Goyal

This paper provides conceptual foundation and procedures used in the development of diabetic foot ulcer datasets over the past decade, with a timeline to demonstrate progress. We conduct a survey on data capturing methods for foot photographs, an overview of research in developing private and public datasets, the related computer vision tasks (detection, seg

G. Symeonidis, E. Nerantzis, A. Kazakis, G. A. Papakostas

This paper is an overview of the Machine Learning Operations (MLOps) area. Our aim is to define the operation and the components of such systems by highlighting the current problems and trends. In this context, we present the different tools and their usefulness in order to provide the corresponding guidelines. Moreover, the connection between MLOps and Auto

Thitithep Sitthiyot, Kanyarat Holasut

The pursuit of having an appropriate level of income inequality should be viewed as one of the biggest challenges facing academic scholars as well as policy makers. Unfortunately, research on this issue is currently lacking. This study is the first to introduce the theoretical concept of targeted level of income inequality for a given size of population. By

Aviv Aroch, Shimshon Kallush, Ronnie Kosloff

Controlling the dynamics of quantum systems is a crucial task in quantum science and technology. Obtaining the driving field that transforms the quantum systems to its objective is a typical control task. This task is hard, scaling unfavorably with the size of Hilbert space. To tackle this issue we employ typicality to assist in finding the control field for

Claudia Bucur, Daniele Cassani, Cristina Tarsi

We consider the $N$-Laplacian Schrödinger equation strongly coupled with higher order fractional Poisson's equations. When the order of the Riesz potential $α$ is equal to the Euclidean dimension $N$, and thus it is a logarithm, the system turns out to be equivalent to a nonlocal Choquard type equation. On the one hand, the natural function space setting

Yan Gu, Xue-Min Bai, Xiao-Lei Hao, J. -Q. Liang

We study in this paper the time evolution of $PT$-symmetric non-Hermitian Hamiltonian consisting of periodically driven $SU(1,1)$ generators. A non-Hermitian invariant operator is adopted to solve the Schrödinger equation, since the time-dependent Hamiltonian is no longer a conserved quantity. We propose a scheme to construct the non-Hermitian invariant with

Fei Wang, Wenyu Wang, Jin Min Yang, Yang Zhang

This study provides a brief overview of low-energy supersymmetry (SUSY) in light of current experimental constraints, such as collider searches, dark matter searches, and muon $g-2$ measurements. In addition, we survey a variety of low energy supersymmetric models: the phenomenological minimal supersymmetric model (MSSM); the supersymmetric models with cut-o

Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan

This paper tackles the problem of dynamic scene deblurring. Although end-to-end fully convolutional designs have recently advanced the state-of-the-art in non-uniform motion deblurring, their performance-complexity trade-off is still sub-optimal. Existing approaches achieve a large receptive field by a simple increment in the number of generic convolution la

Gregory Seregin

In the paper, a new {\it slightly supercritical} condition, providing {\it local} regularity of axially symmetric solutions to the non-stationary 3D Navier-Stokes equations, is discussed. It generalises almost all known results in the local regularity theory of weak axisymmetric solutions.

Qinqi Wu, Hui Xu, Xiangdong Ye

For any minimal system $(X,T)$ and $d\geq 1$ there is an associated minimal system $(N_{d}(X), \mathcal{G}_{d}(T))$, where $\mathcal{G}_{d}(T)$ is the group generated by $T\times\cdots\times T$ and $T\times T^2\times\cdots\times T^{d}$ and $N_{d}(X)$ is the orbit closure of the diagonal under $\mathcal{G}_{d}(T)$. It is known that the maximal $d$-step pro-ni

Klaudia Kowalczyk, Ewa L. Lokas

Dwarf spheroidal (dSph) galaxies are believed to be strongly dark matter dominated and thus are considered perfect objects to study dark matter distribution and test theories of structure formation. They possess resolved, multiple stellar populations that offer new possibilities for modeling. A promising tool for the dynamical modeling of these objects is th

Hao Yang, Min Wang, Zhengfei Yu, Yun Zhou

It is well-known that deep neural networks (DNNs) have shown remarkable success in many fields. However, when adding an imperceptible magnitude perturbation on the model input, the model performance might get rapid decrease. To address this issue, a randomness technique has been proposed recently, named Stochastic Neural Networks (SNNs). Specifically, SNNs i

Yuyang Chen, Kaiming Bi, Chih-Hang J. Wu, David Ben-Arieh

Bayesian Optimization algorithm has become a promising approach for nonlinear global optimization problems and many machine learning applications. Over the past few years, improvements and enhancements have been brought forward and they have shown some promising results in solving the complex dynamic problems, systems of ordinary differential equations where

Ahmed G. Ghallab, Ian R. Petersen

Negative imaginary (NI) systems theory is a well-established system theoretic framework for analysis and design of linear-time-invariant (LTI) control systems. In this paper, we aim to generalize negative imaginary systems theory to a class of nonlinear systems. Based on the time domain interpretation of the NI property for LTI systems, a formal definition i

Diancong Jin, Ziheng Chen, Tau Zhou

We utilize the weak convergence method to establish the Freidlin--Wentzell large deviations principle (LDP) for stochastic delay differential equations (SDDEs) with super-linearly growing coefficients, which covers a large class of cases with non-globally Lipschitz coefficients. The key ingredient in our proof is the uniform moment estimate of the controlled

M. Sharif, Amal Majid

The aim of this work is to formulate two new solutions by decoupling the field equations via a minimal geometric deformation in the context of self-interacting Brans-Dicke gravity. We introduce an extra source in the anisotropic fluid distribution to generate new analogs of existing solutions. The radial metric function is transformed to decouple the field e

Yingqiang Ge, Xiaoting Zhao, Lucia Yu, Saurabh Paul

The issue of fairness in recommendation is becoming increasingly essential as Recommender Systems touch and influence more and more people in their daily lives. In fairness-aware recommendation, most of the existing algorithmic approaches mainly aim at solving a constrained optimization problem by imposing a constraint on the level of fairness while optimizi

Yao Li, Ming Yan

The convex minimization of $f(\mathbf{x})+g(\mathbf{x})+h(\mathbf{A}\mathbf{x})$ over $\mathbb{R}^n$ with differentiable $f$ and linear operator $\mathbf{A}: \mathbb{R}^n\rightarrow \mathbb{R}^m$, has been well-studied in the literature. By considering the primal-dual optimality of the problem, many algorithms are proposed from different perspectives such as

Dongkun Han, Hejun Huang

Recent advances in learning techniques have enabled the modelling of unknown dynamical systems directly from data. However, in many contexts, these learning-based methods are short of safety guarantee and strict stability verification. To address this issue, this paper first approximates the partially unknown nonlinear systems by using a learned state space

Zi-Yi Dou, Nanyun Peng

Commonsense question answering (CQA) aims to test if models can answer questions regarding commonsense knowledge that everyone knows. Prior works that incorporate external knowledge bases have shown promising results, but knowledge bases are expensive to construct and are often limited to a fixed set of relations. In this paper, we instead focus on better ut

Bharat Adsul, Milind Sohoni, K V Subrahmanyam

Let $G$ be a connected reductive group acting on a complex vector space $V$ and projective space ${\mathbb P}V$. Let $x\in V$ and ${\cal H}\subseteq {\cal G}$ be the Lie algebra of its stabilizer. Our objective is to understand points $[y]$, and their stabilizers which occur in the vicinity of $[x]$. We construct an explicit ${\cal G}$-action on a suitable n

Y. B. Kudasov, M. V. Logunov, R. V. Kozabaranov, I. V. Makarov

A two-sublattice ferrimagnet undergoes a transition from a collinear to canted magnetic phase at magnetic field oriented along an easy magnetization direction. In this work, we study the transition by means of the magneto-optical Faraday effect in a thin film of compensated iron garnet (Lu$_{3-{\rm{x}}}$Bi$_{\rm{x}}$)(Fe$_{5-{\rm{y}}-{\rm{z}}}$Ga$_{\rm{y}}$A

Yoshihiro Okamura, Takahiro Morimoto, Naoki Ogawa, Yoshio Kaneko

Photodetection is an indispensable function of optoelectronic devices in modern communication and sensing systems. Contrary to the near-infrared/visible regions, the fast and sensitive photodetectors operated at room temperature for the far-infrared/terahertz regions are not well developed despite a possibly vast range of applications. The bulk photovoltaic

Bao Hieu Tran, Thanh Le-Cong, Huu Manh Nguyen, Duc Anh Le

In the last decades, scene text recognition has gained worldwide attention from both the academic community and actual users due to its importance in a wide range of applications. Despite achievements in optical character recognition, scene text recognition remains challenging due to inherent problems such as distortions or irregular layout. Most of the exis

Debadrita Ghosh, Thomas Jennewein, Urbasi Sinha

Higher dimensional quantum systems (qudits) present a potentially more efficient means, compared to qubits, for implementing various information theoretic tasks. One of the ubiquitous resources in such explorations is entanglement. Entanglement Monotones (EMs) are of key importance, particularly for assessing the efficacy of a given entangled state as a reso

Nisha Geng, Tiange Bi, Eva Zurek

Evolutionary structure searches revealed a plethora of stable and low-enthalpy metastable phases in the S-P-H ternary phase diagram under pressure. A wide variety of crystalline structure types were uncovered ranging from those possessing one-dimensional chains, two-dimensional sheets based on S-H or S-P-H square lattices as well as S-H or P-H honeycombs, an

Dung Nguyen, Alix Boc, Abdoulaye Banire Diallo, Vladimir Makarenkov

Phages are one of the most present groups of organisms in the biosphere. Their identification continues and their taxonomies are divergent. However, due to their evolution mode and the complexity of their species ecosystem, their classification is not complete. Here, we present a new approach to the phages classification that combines the methods of horizont

K. Mahesh Krishna

Motivated from two decades old famous Feichtinger conjectures for frames, $R_\varepsilon$-conjecture and Weaver's conjecture for Hilbert spaces (and their solution by Marcus, Spielman, and Srivastava), we formulate Feichtinger conjectures for p-approximate Schauder frames, $R_\varepsilon$-conjecture, Weaver's conjectures and Akemann-Weaver conjecture

Nahian Ibn Hasan

Identification of bird species from audio records is one of the challenging tasks due to the existence of multiple species in the same recording, noise in the background, and long-term recording. Besides, choosing a proper acoustic feature from audio recording for bird species classification is another problem. In this paper, a hybrid method is represented c

Sophia S. Chabysheva, John R. Hiller

We develop a method by which vacuum transitions may be included in light-front calculations. This allows tadpole contributions which are important for symmetry-breaking effects and yet are missing from standard light-front calculations. These transitions also dictate a nontrivial vacuum and contributions from vacuum bubbles to physical states. In nonperturba

Bashdar Rahman Pirot, Nzar Rauf Abdullah, Andrei Manolescu, Vidar Gudmundsson

We study thermoelectric transport through a serial double quantum dot (DQD) coupled to two metallic leads with different thermal energies. We take into account the electron sequential and cotunneling effects via different master equation approaches. In the absence of intra- and inter-dot Coulomb interactions, a small peak in thermoelectric and heat currents

Arnab Chakrabarti, Rituparna Sen

In this paper, the estimation of the Integrated Covariance matrix from high-frequency data, for high dimensional stock price process, is considered. The Hayashi-Yoshida covolatility estimator is an improvement over Realized covolatility for asynchronous data and works well in low dimensions. However it becomes inconsistent and unreliable in the high dimensio

Duy-Hoa Ngo, Madonna Kemp, Donna Truran, Bevan Koopman

Finding concepts in large clinical ontologies can be challenging when queries use different vocabularies. A search algorithm that overcomes this problem is useful in applications such as concept normalisation and ontology matching, where concepts can be referred to in different ways, using different synonyms. In this paper, we present a deep learning based a

Thanh Le-Cong, Xuan Bach D. Le, Quyet-Thang Huynh, Phi-Le Nguyen

With the recent explosive growth of mobile devices such as smartphones or tablets, guaranteeing consistent web appearance across all environments has become a significant problem. This happens simply because it is hard to keep track of the web appearance on different sizes and types of devices that render the web pages. Therefore, fixing the inconsistent app

Nzar Rauf Abdullah, Botan Jawdat Abdullah, Chi-Shung Tang, Vidar Gudmundsson

The optical properties of a hexagonal Boron Nitride (BN) monolayer across the UV spectrum are studied by tuning its planar buckling. The strong $σ\text{-}σ$ bond through sp$^2$ hybridization of a flat BN monolayer can be changed to a stronger $σ\text{-}π$ bond through sp$^3$ hybridization by increasing the planar buckling. This gives rise to the $s$- and $p$

Thanh-Dat Nguyen, Thanh Le-Cong, ThanhVu H. Nguyen, Xuan-Bach D. Le

Graph Neural Networks (GNNs) have recently emerged as a robust framework for graph-structured data. They have been applied to many problems such as knowledge graph analysis, social networks recommendation, and even Covid19 detection and vaccine developments. However, unlike other deep neural networks such as Feed Forward Neural Networks (FFNNs), few analyses

Hongsong Feng, Kaifu Gao, Dong Chen, Alfred J Robison

Cocaine addiction is a psychosocial disorder induced by the chronic use of cocaine and causes a large of number deaths around the world. Despite many decades' effort, no drugs have been approved by the Food and Drug Administration (FDA) for the treatment of cocaine dependence. Cocaine dependence is neurological and involves many interacting proteins in t

Nzar Rauf Abdullah, Botan Jawdat Abdullah, Vidar Gudmundsson

The electronic and the optical properties of metallic nitride (MN) monolayers are studied using a DFT formalism. In most of these monolayers, the electron density of the metallic atoms is much higher than that of the nitride atoms, and ionic, covalent, and metallic bonds are found in M-N bonds, resulting in fascinating electronic and optical properties. The

Andrew Luo, Tianqin Li, Wen-Hao Zhang, Tai Sing Lee

Recent advances in deep generative models have led to immense progress in 3D shape synthesis. While existing models are able to synthesize shapes represented as voxels, point-clouds, or implicit functions, these methods only indirectly enforce the plausibility of the final 3D shape surface. Here we present a 3D shape synthesis framework (SurfGen) that direct

Eun Som Jeon, Anirudh Som, Ankita Shukla, Kristina Hasanaj

Deep neural networks are parametrized by several thousands or millions of parameters, and have shown tremendous success in many classification problems. However, the large number of parameters makes it difficult to integrate these models into edge devices such as smartphones and wearable devices. To address this problem, knowledge distillation (KD) has been

Enhui Shi, Hui Xu, Ziqi Yu

Let $X$ be a compact metric space and let $f:X\rightarrow X$ be a homeomorphism on $X$. We show that if $f$ is both pointwise recurrent and expansive, then the dynamical system $(X, f)$ is topologically conjugate to a subshift of some symbolic system. Moreover, if $f$ is pointwise positively recurrent, then the subshift is semisimple; a counterexample is giv

Yiming Bing, Bright Hu, Ronni Hu, Rhianna Li

We explicitly extend the standard permutation action of the Mathieu group $M_{23}$ on a 23 element set $C=C_{23}$ contained in a finite field of $2^{11}$ elements $\mathbb{F}_{2^{11}}$ to additive functions on this finite field. That is we represent $M_{23}$ as functions $φ:\mathbb{F}_{2^{11}}\to \mathbb{F}_{2^{11}}$ such that $φ(x+y)=φ(x)+φ(y)$ and $φ|_{C}$

Pengfei Wang, Changxing Ding, Zhiyin Shao, Zhibin Hong

Occlusion poses a major challenge for person re-identification (ReID). Existing approaches typically rely on outside tools to infer visible body parts, which may be suboptimal in terms of both computational efficiency and ReID accuracy. In particular, they may fail when facing complex occlusions, such as those between pedestrians. Accordingly, in this paper,

Ze-Hao Wu, Hua-Cheng Zhou, Feiqi Deng, Bao-Zhu Guo

In this paper, a novel control strategy namely disturbance observer-based control is first applied to stabilization and disturbance rejection for an anti-stable stochastic heat equation with Neumann boundary actuation and unknown boundary external disturbance generated by an exogenous system. A disturbance observer-based boundary control is designed based on

Peiliang Huang, Junwei Han, De Cheng, Dingwen Zhang

Zero-shot object detection aims at incorporating class semantic vectors to realize the detection of (both seen and) unseen classes given an unconstrained test image. In this study, we reveal the core challenges in this research area: how to synthesize robust region features (for unseen objects) that are as intra-class diverse and inter-class separable as the

Aaron J. Fillo, Jonathan Bonebrake, David L. Blunck

Large hydrocarbon fuels are used for ground and air transportation and will be for the foreseeable future. Despite their extensive use, turbulent combustion of large hydrocarbon fuels, remains relatively poorly understood and difficult to predict. A key parameter when burning these fuels is the turbulent consumption speed; the velocity at which fuel and air

Di Wu, Tongxin Zhang, Yuan Zhong, Fanghua Jiang

Preliminary mission design requires an efficient and accurate approximation to the low-thrust rendezvous trajectories, which might be generally three-dimensional and involve multiple revolutions. In this paper, a new shaping method using cubic spline functions is developed for the analytical approximation, which shows advantages in the optimality and computa

Xiaoqiang Wang, Lei Zhu, Siliang Tang, Huazhu Fu

Training deep models for RGB-D salient object detection (SOD) often requires a large number of labeled RGB-D images. However, RGB-D data is not easily acquired, which limits the development of RGB-D SOD techniques. To alleviate this issue, we present a Dual-Semi RGB-D Salient Object Detection Network (DS-Net) to leverage unlabeled RGB images for boosting RGB

Megan C. Engel, Jamie A. Smith, Michael P. Brenner

Controlling the evolution of nonequilibrium systems to minimize dissipated heat or work is a key goal for designing nanodevices, both in nanotechnology and biology. Progress in computing optimal protocols has thus far been limited to either simple systems or near-equilibrium evolution. Here, we present an approach for computing optimal protocols based on aut

Yexin Duan, Junhua Zou, Xingyu Zhou, Wu Zhang

Deep neural networks are vulnerable to adversarial examples, which can fool deep models by adding subtle perturbations. Although existing attacks have achieved promising results, it still leaves a long way to go for generating transferable adversarial examples under the black-box setting. To this end, this paper proposes to improve the transferability of adv

Mohamed Amine Kerkouri, Marouane Tliba, Aladine Chetouani, Mohamed Sayeh

This paper introduces a new framework to predict visual attention of omnidirectional images. The key setup of our architecture is the simultaneous prediction of the saliency map and a corresponding scanpath for a given stimulus. The framework implements a fully encoder-decoder convolutional neural network augmented by an attention module to generate represen

Siddharth Chandrasekaran, Jeffrey Matthew Reginald, Wei Wang, Ting Zhu

An improvement in technology is linearly related to time and time-relevant problems. It has been seen that as time progresses, the number of problems humans face also increases. However, technology to resolve these problems tends to improve as well. One of the earliest existing problems which started with the invention of vehicles was parking. The ease of re

Maksim Aizenshtein, Niklas Smal, Morgan McGuire

Order-independent transparency schemes rely on low-order approximations of transmittance as a function of depth. We introduce a new wavelet representation of this function and an algorithm for building and evaluating it efficiently on a GPU. We then extend the order-independent Phenomenological Transparency algorithm to our representation and introduce a new

Alex Sheng, Derek He

Meta-learning traditionally relies on backpropagation through entire tasks to iteratively improve a model's learning dynamics. However, this approach is computationally intractable when scaled to complex tasks. We propose a distributed evolutionary meta-learning strategy using Tensor Processing Units (TPUs) that is highly parallel and scalable to arbitra

Julia Cen

A key feature of integrable systems is that they can be solved to obtain exact analytical solutions. We show how new models can be constructed through generalisations of some well known nonlinear partial differential equations with PT-symmetries whilst preserving integrability. Subsequently, we develop new methods from well-known ones to obtain exact analyti

Flor B. Quiroga, David Hinojosa-Romero, Alexander Valladares, Renela M. Valladares

Materials may behave in non-expected ways when subject to unexpected conditions. For example, when Bi was turned into an amorphous phase (\textit{a}-Bi) unexpectedly it became a superconductor at temperatures below $10$ K. We provided an explanation as to why \textit{a}-Bi superconducts and the crystalline (\textit{c}-Bi) had not been found to do so: we comp

Isaías Rodríguez, Renela María Valladares, David Hinojosa-Romero, Alexander Valladares

Magnetism is a very relevant subject that permeates our everyday lives. However, magnetism keeps taking us from surprise to surprise which seems to indicate that it is a phenomenon not well understood. For example, we found that bulk amorphous palladium becomes magnetic; so, naturally one should ask, could defective palladium develop magnetism? In particular

Fei Shi, Mao-Sheng Li, Lin Chen, Xiande Zhang

A set of multipartite orthogonal product states is strongly nonlocal if it is locally irreducible in every bipartition, which shows the phenomenon of strong quantum nonlocality without entanglement. It is known that unextendible product bases (UPBs) can show the phenomenon of quantum nonlocality without entanglement. Thus it is interesting to investigate the

Taira Watanabe, Kensuke Tanioka, Satoru Hiwa, Tomoyuki Hiroyasu

Endoscopic images typically contain several artifacts. The artifacts significantly impact image analysis result in computer-aided diagnosis. Convolutional neural networks (CNNs), a type of deep learning, can removes such artifacts. Various architectures have been proposed for the CNNs, and the accuracy of artifact removal varies depending on the choice of ar

Zahra Ghadiri, Milad Ranjbar, Fakhteh Ghanbarnejad, Sadegh Raeisi

Over the past decade, fake news and misinformation have turned into a major problem that has impacted different aspects of our lives, including politics and public health. Inspired by natural human behavior, we present an approach that automates the detection of fake news. Natural human behavior is to cross-check new information with reliable sources. We use

M. N. Tarabishy

Gyroscopic motion explanation in texts is relatively long and requires reasonable level of comfort with the mathematical tools used. On the other hand, popular explanation outside academic courses does not explain the phenomenon and only describes it leaving many to think that it is so weird that it defies physics. In this paper we offer a simplified and mat