May 2022 arXiv papers — page 136

Song Wang, Yushun Dong, Xiao Huang, Chen Chen

Few-shot graph classification aims at predicting classes for graphs, given limited labeled graphs for each class. To tackle the bottleneck of label scarcity, recent works propose to incorporate few-shot learning frameworks for fast adaptations to graph classes with limited labeled graphs. Specifically, these works propose to accumulate meta-knowledge across

Che-Yu Chen, Hsu-Wen Chiang, Jie-Shiun Tsao

The geometric optics approximation provides an interpretation for eikonal correspondence that, in black-hole-containing spacetimes, connects high-frequency black hole quasinormal modes with closed photon orbits around said black hole. This correspondence has been identified explicitly for Schwarzschild, Reissner-Nordström, Kerr, and Kerr-Newman black holes,

Dae Woong Ham, Jiaze Qiu

Testing whether a variable of interest affects the outcome is one of the most fundamental problem in statistics and is often the main scientific question of interest. To tackle this problem, the conditional randomization test (CRT) is widely used to test the independence of variable(s) of interest (X) with an outcome (Y) holding other variable(s) (Z) fixed.

Shushen Qin, Marcus Cramer, Christiane P. Koch, Alessio Serafini

The ability to characterise a Hamiltonian with high precision is crucial for the implementation of quantum technologies. In addition to the well-developed approaches utilising optimal probe states and optimal measurements, the method of optimal control can be used to identify time-dependent pulses applied to the system to achieve higher precision in the esti

Guanzhou Li, Jianping Wu, Yujing He

As an emerging technology, Connected Autonomous Vehicles (CAVs) are believed to have the ability to move through intersections in a faster and safer manner, through effective Vehicle-to-Everything (V2X) communication and global observation. Autonomous intersection management is a key path to efficient crossing at intersections, which reduces unnecessary slow

Mahdi Chehimi, Christina Chaccour, Christo Kurisummoottil Thomas, Walid Saad

Quantum communication networks (QCNs) utilize quantum mechanics for secure information transmission, but the reliance on fragile and expensive photonic quantum resources renders QCN resource optimization challenging. Unlike prior QCN works that relied on blindly compressing direct quantum embeddings of classical data, this letter proposes a novel quantum sem

Kun Xu, Fan Lin, Anping Huang, Mei Huang

The global polarization of $Λ/{\bar Λ}$ and the splitting of ${\bar Λ}-Λ$ polarization induced by rotation and magnetic field has been investigated in a dynamical quark model by taking into account the axial vector interaction and the anomalous magnetic moment of quarks. It is found that the rotation leads to the spin polarization of quarks and anti-quarks w

Huan Meng

In this article, a quasi-sliding mode control (QSMC) based on MPC is proposed for the constrained continuous-time nonlinear system with external disturbances. The MPC problem is formulated relating to the design of QSMC, to generate the control input, which can imitate the control process of QSMC and guarantee the satisfaction of state and input constraints.

Haotian Wu, Yulin Shao, Krystian Mikolajczyk, Deniz Gündüz

We present a learning-based channel-adaptive joint source and channel coding (CA-JSCC) scheme for wireless image transmission over multipath fading channels. The proposed method is an end-to-end autoencoder architecture with a dual-attention mechanism employing orthogonal frequency division multiplexing (OFDM) transmission. Unlike the previous works, our app

Ling-Yun Huang, Tian-Yu Ye

In this paper, a kind of quantum dialogue (QD) protocols without information leakage assisted by quantum operation is proposed. The participant in charge of preparation can automatically know the collapsed states after quantum operation performed on the prepared quantum states. The other participant is able to know the collapsed states derived from the prepa

Shuyu Zhou, Chen Chen, Xuemei Jia, Ying Wang

We propose a method to characterize the ultra-narrow momentum distribution of atomic gases by employing a standing-wave light-pulse sequences beam-splitter. The mechanism of beam splitting is analyzed in detail, and the influence of a finite-width momentum distribution on the population of each diffraction order is given. The temperature of ultracold atomic

Avijit Ghosh, Matthew Jagielski, Christo Wilson

In this work we explore the intersection fairness and robustness in the context of ranking: when a ranking model has been calibrated to achieve some definition of fairness, is it possible for an external adversary to make the ranking model behave unfairly without having access to the model or training data? To investigate this question, we present a case stu

Wei Xie, Keqi Wang, Hua Zheng, Ben Feng

Driven by the critical needs of biomanufacturing 4.0, we introduce a probabilistic knowledge graph hybrid model characterizing the risk- and science-based understanding of bioprocess mechanisms. It can faithfully capture the important properties, including nonlinear reactions, partially observed state, and nonstationary dynamics. Given very limited real proc

Xiaowen Li, Ze-Fang Jiang, Shanshan Cao, Jian Deng

Extremely large angular orbital momentum can be produced in non-central heavy-ion collisions, leading to a strong transverse polarization of partons that scatter through the quark-gluon plasma (QGP) due to spin-orbital coupling. We develop a perturbative approach to describe the formation and spacetime evolution of quark polarization inside the QGP. Polariza

Nathaniel Bohm, Patrick K. Schelling

Ballistic transport and resonance phenomena are elucidated in the one-dimensional $α$-Fermi-Pasta-Ulam-Tsingou (FPUT) model using an approach of computing thermal response functions. The existence of periodic oscillations in spatially sinusoidal temperature profiles seen in previous studies is confirmed. However, the results obtained using response functions

Mathieu Couillard, Pablo Bianucci

Long cylinders, such as optical fibers, are some of the most widely used photonic devices. The radius and refractive index of these fibers are therefore fundamentally important parameters in determining their performance. We have developed a method to determine the absolute radius, refractive index, and chromatic dispersion of a long cylinder using only the

Daniela Di Donato

We introduce a definition of intrinsically quasi-symmetric sections in metric spaces and we prove the Ahlfors-David regularity for this class of sections. We follow a recent result by Le Donne and the author where we generalize the notion of intrinsically Lipschitz graphs in the sense of Franchi, Serapioni and Serra Cassano. We do this by focusing our attent

Zhengjie Sun, Leevan Ling

We propose a meshless conservative Galerkin method for solving Hamiltonian wave equations. We first discretize the equation in space using radial basis functions in a Galerkin-type formulation. Differ from the traditional RBF Galerkin method that directly uses nonlinear functions in its weak form, our method employs appropriate projection operators in the co

Semih Barutcu, Doğa Gürsoy, Aggelos K. Katsaggelos

Ptychography is a well-established coherent diffraction imaging technique that enables non-invasive imaging of samples at a nanometer scale. It has been extensively used in various areas such as the defense industry or materials science. One major limitation of ptychography is the long data acquisition time due to mechanical scanning of the sample; therefore

Xiaopeng Wang, Wei Kou, Gang Xie, Yaping Xie

Exclusive vector meson production is an excellent probe for describing the structure of proton. In this paper, based on dipole model, the differential cross sections, total cross sections and the ratios of the longitudinal to transverse cross sections of $J/ψ$ and $ρ^0$ productions are calculated with the analytical solution of Balitsky-Kovchegov (BK) equati

Carolyn E. Wood, Magdalena Zych

A simple but powerful particle detector model consists of a two-level system coupled to a field, where the detected particles are the field excitations. This is known as the Unruh-DeWitt detector. Research using this model has often focused on either a completely classical description of the external degrees of freedom of the detector, or a full field-theore

Anca Radulescu, Danae Evans, Amani-Daisa Augustin, Anthony Cooper

In continuation of prior work, we investigate ties between a network's connectivity and ensemble dynamics. This relationship is notoriously difficult to approach mathematically in natural, complex networks. In our work, we aim to understand it in a canonical framework, using complex quadratic node dynamics, coupled in networks which we call complex quadr

Julia Kiseleva, Ziming Li, Mohammad Aliannejadi, Shrestha Mohanty

Human intelligence has the remarkable ability to quickly adapt to new tasks and environments. Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions. To facilitate research in this direction, we propose \emph{IGLU: Interactive

Eric J. Baxter, Jason Kumar, Aleczander D. Paul, Jack Runburg

We consider gamma-ray signals of dark matter annihilation in extragalactic halos in the case where dark matter annihilates from a $p$-wave or $d$-wave state. In these scenarios, signals from extragalactic halos are enhanced relative to other targets, such as the Galactic Center or dwarf spheroidal galaxies, because the typical relative speed of the dark matt

Yang Lu, En-Kun Li, Yi-Ming Hu, Jian-dong Zhang

The space-borne gravitational wave detectors will observe a large population of double white dwarf binaries in the Milky Way. However, the search for double white dwarfs in the gravitational wave data will be time-consuming due to the large number of templates involved and antenna response calculation. In this paper, we implement an iterative combinatorial a

Ruochen Tai, Liyong Lin, Rong Su

This work investigates the problem of synthesizing obfuscated supervisors against covert actuator attackers. For a non-resilient supervisor S, for which there exist some covert actuator attackers that are capable of inflicting damage, we propose an algorithm to compute all the obfuscated supervisors, with the requirements that: 1) any obfuscated supervisor S

J. P. C. Greenlees, J. D. Quigley

We describe the distribution of infinite groups in the $RO(G)$-graded stable homotopy groups of spheres for a finite group $G$.

Hong Qin, Yichen Fu

A theoretical framework is developed to describe the Topological Langmuir-Cyclotron Wave (TLCW), a recently identified topological surface excitation in magnetized plasmas. As a topological wave, the TLCW propagates unidirectionally without scattering in complex boundaries. The TLCW is studied theoretically as a spectral flow of the Hamiltonian Pseudo-Differ

Yingping Mou, Rubem Mondaini, Richard T. Scalettar

The bilayer Hubbard model describes the antiferromagnet to spin singlet transition and, potentially, aspects of the physics of unconventional superconductors. Despite these important applications, significant aspects of its `phase diagram' in the interplane hopping $t_\perp$ versus on-site interaction $U$ parameter space, at half filling, are largely in

Nathan K. Long, Daniel Sgarioto, Matthew Garratt, Karl Sammut

The use of the `ship as a wave buoy analogy' (SAWB) provides a novel means to estimate sea states, where relationships are established between causal wave properties and vessel motion response information. This study focuses on a model-free machine learning approach to SAWB-based sea state estimation (SSE), using neural networks (NNs) to map vessel response

Victor Lecomte, Prasanna Ramakrishnan, Li-Yang Tan

We study the complexity of computing majority as a composition of local functions: \[ \text{Maj}_n = h(g_1,\ldots,g_m), \] where each $g_j :\{0,1\}^{n} \to \{0,1\}$ is an arbitrary function that queries only $k \ll n$ variables and $h : \{0,1\}^m \to \{0,1\}$ is an arbitrary combining function. We prove an optimal lower bound of \[ m \ge Ω\left( \frac{n}{k}

P. D. Gujrati

We show that any external intervention (insertion or removal of a partition) that destroys the equilibrium or brings it in a system always requires work and heat to ensure that the first law is obeyed, a fact that has been completely overlooked in the literature. As a consequence, there is no second law violation. We discuss the ramifications of our finding

Negar Arabzadeh, Ali Ahmadvand, Julia Kiseleva, Yang Liu

The recent increase in the volume of online meetings necessitates automated tools for managing and organizing the material, especially when an attendee has missed the discussion and needs assistance in quickly exploring it. In this work, we propose a novel end-to-end framework for generating interactive questionnaires for preference-based meeting exploration

Michał Lesiuk

In this paper we extend the rank-reduced coupled-cluster formalism to the calculation of non-iterative energy corrections due to quadruple excitations. There are two major components of the proposed formalism. The first is an approximate compression of the quadruple excitation amplitudes using the Tucker format. The second is a modified functional used for e

Anirudh Venkatesh, Vishal Anand, Vivek Narsimhan

We investigate the transient, fluid structure interaction (FSI) of a non-Newtonian fluid peeling two linearly elastic sheets at low Reynolds numbers. Two different non-Newtonian fluids are considered; a simplified sPTT model, and an inelastic fluid with shear thinning viscosity (generalized Newtonian fluid). In the limit of small gap between the sheets, we i

Eugenio Pescimoro, Matteo Icardi, Giovanni Porta, Marco Bianchi

We present a numerical simulation study of advective-diffusive scalar transport in three-dimensional high-contrast discontinuous permeability fields, generated with a truncated pluri-Gaussian geostatistical approach. A range of permeability contrasts, correlation lengths, and Péclet numbers are studied to characterise the transition to non-Fickian transport

Boyang Li, Qing Lu, Weiwen Jiang, Taeho Jung

In most popular public accessible cryptocurrency systems, the mining pool plays a key role because mining cryptocurrency with the mining pool turns the non-profitable situation into profitable for individual miners. In many recent novel blockchain consensuses, the deep learning training procedure becomes the task for miners to prove their workload, thus the

Jorge Pinochet, Walter Bussenius Cortada

Teaching the noncommutativity of the product of matrices to high school or college level students is a difficult task when approached from a purely formal perspective. The aim of this paper is to present a simple experimental activity for teaching the noncommutativity of the matrix product, based on the Jones calculus, a mathematical formalism for describing

Idir Sadani

In this paper, we investigate the generalized Hyers-Ulam stability of the following reciprocal type functional equation \begin{equation*}f(2x+y)+f(2x-y)=\frac{2f(x)f(y)\displaystyle{\sum_{\substack{k=0\\ \text{$k$ is even}}}^{ l}2^{l-k}\binom{l}{k}f(x)^{\frac{k}{l}}f(y)^{\frac{l-k}{l}}}}{\left(4f(y)^{\frac{2}{l}}-f(x)^{\frac{2}{l}}\right)^l}\end{equation*} i

Abdallah Moubayed, Abdallah Shami, Abbas Ibrahim

The growing deployment efforts of 5G networks globally has led to the acceleration of the businesses/services' digital transformation. This growth has led to the need for new communication technologies that will promote this transformation. 6G is being proposed as the set of technologies and architectures that will achieve this target. Among the main use

Zhenning Zhang, Boxin Du, Hanghang Tong

Bundle recommendation is an emerging research direction in the recommender system with the focus on recommending customized bundles of items for users. Although Graph Neural Networks (GNNs) have been applied in this problem and achieve superior performance, existing methods underexplore the graph-level GNN methods, which exhibit great potential in traditiona

Aneta Lisowska, Szymon Wilk, Mor Peleg

Designing theory-driven digital health interventions is a challenging task that needs support. We created a guide for the incomers in the field on how to design digital health interventions with case studies from the Cancer Better Life Experience (CAPABLE) European project. The guide explains how behaviour change theories can inform customisation and persona

Dávid Szeghy, Mahmoud Aslan, Áron Fóthi, Balázs Mészáros

While deep neural networks are sensitive to adversarial noise, sparse coding using the Basis Pursuit (BP) method is robust against such attacks, including its multi-layer extensions. We prove that the stability theorem of BP holds upon the following generalizations: (i) the regularization procedure can be separated into disjoint groups with different weights

Ashwin Srinivasan, Michael Bain, Enrico Coiera

Recent engineering developments in specialised computational hardware, data-acquisition and storage technology have seen the emergence of Machine Learning (ML) as a powerful form of data analysis with widespread applicability beyond its historical roots in the design of autonomous agents. However -- possibly because of its origins in the development of agent

Jessie J. Smith, Saleema Amershi, Solon Barocas, Hanna Wallach

Transparency around limitations can improve the scientific rigor of research, help ensure appropriate interpretation of research findings, and make research claims more credible. Despite these benefits, the machine learning (ML) research community lacks well-developed norms around disclosing and discussing limitations. To address this gap, we conduct an iter

Zhi Qi, Hong Xie, Ye Li, Jian Tan

This paper proposes LPC-AD, a fast and accurate multivariate time series (MTS) anomaly detection method. LPC-AD is motivated by the ever-increasing needs for fast and accurate MTS anomaly detection methods to support fast troubleshooting in cloud computing, micro-service systems, etc. LPC-AD is fast in the sense that its reduces the training time by as high

Tongtao Zhang, Biswadip Dey, Krishna Veeraraghavan, Harshad Kulkarni

Computational fluid dynamics (CFD) simulations, a critical tool in various engineering applications, often require significant time and compute power to predict flow properties. The high computational cost associated with CFD simulations significantly restricts the scope of design space exploration and limits their use in planning and operational control. To

Mario Lino, Stati Fotiadis, Anil A. Bharath, Chris Cantwell

Numerical simulation is an essential tool in many areas of science and engineering, but its performance often limits application in practice or when used to explore large parameter spaces. On the other hand, surrogate deep learning models, while accelerating simulations, often exhibit poor accuracy and ability to generalise. In order to improve these two fac

Hongjun Wang, Jiyuan Chen, Zipei Fan, Zhiwen Zhang

Recently, forecasting the crowd flows has become an important research topic, and plentiful technologies have achieved good performances. As we all know, the flow at a citywide level is in a mixed state with several basic patterns (e.g., commuting, working, and commercial) caused by the city area functional distributions (e.g., developed commercial areas, ed

Elnaz Yousefzadeh Barri, Steven Farber, Anna Kramer, Hadi Jahanshahi

Transportation equity advocates recommend improving public transit in low-income neighbourhoods to alleviate socio-spatial inequalities and increase quality of life. However, transportation planners often overlook transit investments in neighbourhoods with "transit-captive" populations because they are assumed to result in less mode-shifting, congest

Fabian Thiemann, Germán Sciaini, Alexander Kassen, Ulrich Hagemann

In order to determine the role of non-thermal transport of hot carriers which is decisive for the dissipation of energy in condensed matter we performed time-resolved broadband femtosecond transient reflectivity measurements on $7-197 \mathrm{nm}$ thick Bi(111) films epitaxially grown on Si(111). We monitored the behavior of the Fourier amplitude and the cen

Martin Lara

The main effects of the Earth's oblateness on the motion of artificial satellites are usually derived from the variation of parameters equations of an average representation of the oblateness disturbing function. Rather, we approach their solution under the strict mathematical assumptions of Picard's iterative method. Our approach recovers the known

Nikos Mantzakouras, Eteri Biragova

In this paper, we explain a new Iterative Method-Fixed Point and develop its convergence theory for finding approximate solutions of nonlinear equations in the setting of Banach spaces. First, we discuss the convergence analysis of our method by separating the equation into functions from which it is derived and the remaining part of the equation, according

Mortaza Bayat, Hossein Teimoori Faal

In this paper, we first give a simple combinatorial proof of Tepper's identity. Then, as a by product of this interesting identity we present another proof of the well-known Wilson's identity in number theory. Finally, we obtain a generalization of Tepper's identity for any polynomial with real coefficients.

Niraj Hirachan, Anita Mathews, Julio Romero, Raul Fernandez Rojas

This study aims to identify a set of indicators to estimate cognitive workload using a multimodal sensing approach and machine learning. A set of three cognitive tests were conducted to induce cognitive workload in twelve participants at two levels of task difficulty (Easy and Hard). Four sensors were used to measure the participants' physiological chang

Duleabom An, Sophie N. Parragh, Markus Sinnl, Fabien Tricoire

Many real-world optimisation problems involve multiple objectives. When considered concurrently, they give rise to a set of optimal trade-off solutions, also known as efficient solutions. These solutions have the property that neither objective can be improved without deteriorating another objective. Motivated by the success of matheuristics in the single-ob

Gaohang Yu, Shaochun Wan, Liqun Qi, Yanwei Xu

Tensor train (TT) factorization and corresponding TT rank, which can well express the low-rankness and mode correlations of higher-order tensors, have attracted much attention in recent years. However, TT factorization based methods are generally not sufficient to characterize low-rankness along each mode of third-order tensor. Inspired by this, we generaliz

E. Nkadimeng, R. Mckenzie, S. Moayedi, S. Nemecek

The ATLAS detector is set to undergo a substantial upgrade termed the "Phase-II" upgrade during the Long-Shutdown in preparation for the start of operation of the High Luminosity Large Hadron Collider (HL-LHC). This paper describes the development and implementation of the Phase-II upgrade low voltage power supply transformer-coupled buck converter (

Jianwei Wu, Jining Yu, David Shepherd, James Clause

To better understand the shortcomings of class-level documentation, we conducted a survey of 167 experienced software developers. The survey focused on the participants' programming-related information needs and how often class-level documentation satisfies such needs.

Qi Li, Liang Hu, Jinbo Zhang, Jianping Chen

We report on the realization of a long-haul radio frequency (RF) transfer scheme by using multiple-access relay stations (MARSs). The proposed scheme with independent link noise compensation for each fiber sub-link effectively solves the limitation of compensation bandwidth for long-haul transfer. The MARS can have the capability to share the same modulated

Tian-Yu Ye

In this paper, two fault tolerant channel-encrypting quantum dialogue (QD) protocols against collective noise are presented. One is against collective-dephasing noise, while the other is against collective-rotation noise. The decoherent-free states, each of which is composed of two physical qubits, act as traveling states combating collective noise. Einstein

Tian-Yu Ye

In this paper, a secure quantum dialogue protocol via cavity QED is suggested by using the evolution law of atom in cavity QED. The present protocol employs both the two-step transmission and the unitary operation encoding. Two security checks are adopted to ensure its transmission security against the active attacks from an outside eavesdropper. The present

Hasan Naji, Nicolae Goga, Ammar Karkar, Iuliana Marin

Technology adoption in healthcare services has resulted in advancing care delivery services and improving the experiences of patients. This paper presents research that aims to find the important requirements for a remote monitoring system for patients with COVID-19. As this pandemic is growing more and more, there is a critical need for such systems. In thi

Davood Karimi, Ali Gholipour

Deep learning has a great potential for estimating biomarkers in diffusion weighted magnetic resonance imaging (dMRI). Atlases, on the other hand, are a unique tool for modeling the spatio-temporal variability of biomarkers. In this paper, we propose the first framework to exploit both deep learning and atlases for biomarker estimation in dMRI. Our framework

Ivan De Oliveira Nunes, Seoyeon Hwang, Sashidhar Jakkamsetti, Gene Tsudik

There are many well-known techniques to secure sensed data in IoT/CPS systems, e.g., by authenticating communication end-points, encrypting data before transmission, and obfuscating traffic patterns. Such techniques protect sensed data from external adversaries while assuming that the sensing device itself is secure. Meanwhile, both the scale and frequency o

Kwasi Opoku, Subash Pokharel, Aleksandar Dimitrovski

Superimposed sequence quantities have been relied on for years to provide pure fault components for various applications in protection schemes. In more recent times, they have been employed in various solutions to the challenges introduced by integration of distributed generation in distribution systems. To improve the overall reliability of protection schem

Xinyue Wei, Minghua Liu, Zhan Ling, Hao Su

Approximate convex decomposition aims to decompose a 3D shape into a set of almost convex components, whose convex hulls can then be used to represent the input shape. It thus enables efficient geometry processing algorithms specifically designed for convex shapes and has been widely used in game engines, physics simulations, and animation. While prior works

Hiromasa Nakatsuka, Soichiro Morisaki, Tomohiro Fujita, Jun'ya Kume

Ultralight bosonic particles are fascinating candidates of dark matter (DM). It behaves as classical waves in our Galaxy due to its large number density. There have been various methods proposed to search for the wave-like DM, such as methods utilizing interferometric gravitational-wave detectors. Understanding the characteristics of DM signals is crucial to

Chiao-An Yang, Cheng-Yo Tan, Wan-Cyuan Fan, Cheng-Fu Yang

In this paper, we address the task of semantics-guided image outpainting, which is to complete an image by generating semantically practical content. Different from most existing image outpainting works, we approach the above task by understanding and completing image semantics at the scene graph level. In particular, we propose a novel network of Scene Grap

Aranya Lahiri, Claus Sorensen

In this paper we view pro-$p$ Iwahori subgroups $I$ as rigid analytic groups $\Bbb{I}$ for large enough $p$. This is done by endowing $I$ with a natural $p$-valuation, and thereby generalizing results of Lazard for $\text{GL}_n$. We work with a general connected reductive split group over some $p$-adic field (with simply connected derived group) and study th

Gunnar Kudrjavets, Jeff Thomas, Nachiappan Nagappan

Satisfactory software performance is essential for the adoption and the success of a product. In organizations that follow traditional software development models (e.g., waterfall), Software Performance Engineering (SPE) involves time-consuming experimental modeling and performance testing outside the actual production environment. Such existing SPE methods,

Houssem Sifaou, Geoffrey Ye Li

Federated learning (FL) is a promising solution to enable many AI applications, where sensitive datasets from distributed clients are needed for collaboratively training a global model. FL allows the clients to participate in the training phase, governed by a central server, without sharing their local data. One of the main challenges of FL is the communicat

Victor Ayala, Adriano Da Silva, Alejandro Otero Robles

This article describes the control behavior of any linear control systems on the group of proper motions $SE(2)$. It characterizes the controllability property and the control sets of the system.

Peter Nwilo, Johanson Onyegbula, Chukwuma Okolie, Olagoke Daramola

The 20m SPOT DEM (Digital Elevation Model) was acquired by the Office of the Surveyor-General of the Federation (OSGoF) in Nigeria for use in topographic mapping. A localized assessment of the DEM is needed to validate its stated accuracies as it is well known that the vertical accuracy of global DEMs varies in different landscape contexts. Hence, this study

Mingming Nie, Shu-Wei Huang

We analytically and numerically study the direct soliton generation in a cw-pumped doubly resonant degenerate optical parametric oscillator via pump frequency scanning. By means of bifurcation and linear stability analysis of the homogeneous solutions, we discriminate distinctive soliton forming mechanisms and corresponding dynamics, depending on pump and si

Pan Ji, Yuan Tian, Qingan Yan, Yuxin Ma

We present a robust visual-inertial SLAM system that combines the benefits of Convolutional Neural Networks (CNNs) and planar constraints. Our system leverages a CNN to predict the depth map and the corresponding uncertainty map for each image. The CNN depth effectively bootstraps the back-end optimization of SLAM and meanwhile the CNN uncertainty adaptively

Zheng-Qiang Cheng, Wei Tan, Jun-Jiang Xiong

This paper aims to numerically study the pre-fatigue, low-velocity impact (LVI) and fatigue progressive damage behaviours of a full-scale composite helicopter tail structure under multipoint coordinated loading spectrum. First, a fatigue progressive damage model (PDM) incorporating multiaxial fatigue residual strength degradation rule, fatigue failure criter

Qingan Yan, Pan Ji, Nitin Bansal, Yuxin Ma

In this paper, we deal with the problem of monocular depth estimation for fisheye cameras in a self-supervised manner. A known issue of self-supervised depth estimation is that it suffers in low-light/over-exposure conditions and in large homogeneous regions. To tackle this issue, we propose a novel ordinal distillation loss that distills the ordinal informa

Jianfeng Lu, Min Wang

In this paper, we propose and study neural network based methods for solutions of high-dimensional quadratic porous medium equation (QPME). Three variational formulations of this nonlinear PDE are presented: a strong formulation and two weak formulations. For the strong formulation, the solution is directly parameterized with a neural network and optimized b

Evgeny A. Grines, Alexey O. Kazakov, Igor R. Sataev

We study chaotic dynamics in a system of four differential equations describing the dynamics of five identical globally coupled phase oscillators with biharmonic coupling. We show that this system exhibits strange spiral attractors (Shilnikov attractors) with two zero (indistinguishable from zero in numerics) Lyapunov exponents in a wide region of the parame

Shereen Elsayed, Lukas Brinkmeyer, Lars Schmidt-Thieme

In fashion-based recommendation settings, incorporating the item image features is considered a crucial factor, and it has shown significant improvements to many traditional models, including but not limited to matrix factorization, auto-encoders, and nearest neighbor models. While there are numerous image-based recommender approaches that utilize dedicated

Noah Flemens, Jeffrey Moses

While non-Hermitian systems are normally constructed through incoherent coupling to a larger environment, recent works have shown that under certain conditions coherent couplings can be used to similar effect. We show that this new paradigm enables the behavior associated with the PT- symmetric phase of a non-Hermitian subsystem to control the containing Her

Paola Pozzi, Björn Stinner

Elastic flow for closed curves can involve significant deformations. Mesh-based approximation schemes require tangentially redistributing vertices for long-time computations. We present and analyze a method that uses the Dirichlet energy for this purpose. The approach effectively also penalizes the length of the curve, and equilibrium shapes are equivalent t

Jingyi Xu, Hieu Le

Few-shot learning (FSL) aims to learn new categories with a few visual samples per class. Few-shot class representations are often biased due to data scarcity. To mitigate this issue, we propose to generate visual samples based on semantic embeddings using a conditional variational autoencoder (CVAE) model. We train this CVAE model on base classes and use it

Marco De Corato, Ignacio Pagonabarraga

Similar to cells, bacteria, and other microorganisms, synthetic chemically-active colloids can harness the energy from their environment through a surface chemical reaction and use its energy to self-propel in fluidic environments. In this paper, we study the chemo-mechanical coupling that leads to the self-propulsion of chemically active colloids. The coupl

Lucas Ângelo da Silveira, Thaynara Arielly de Lima, Mauricio Ayala-Rincón

Heterogeneous Parallel Island Models (HePIMs) run different bio-inspired algorithms (BAs) in their islands. From a variety of communication topologies and migration policies fine-tuned for homogeneous PIMs (HoPIMs), which run the same BA in all their islands, previous work introduced HePIMs that provided competitive quality solutions regarding the best-adapt

Alexi Block Gorman, Christian Schulz

This paper seeks to build on the extensive connections that have arisen between automata theory, combinatorics on words, fractal geometry, and model theory. Results in this paper establish a characterization for the behavior of the fractal geometry of "$k$-automatic" sets, subsets of $[0,1]^d$ that are recognized by Büchi automata. The primary tools

Haochen Shi, Huazhe Xu, Zhiao Huang, Yunzhu Li

Modeling and manipulating elasto-plastic objects are essential capabilities for robots to perform complex industrial and household interaction tasks (e.g., stuffing dumplings, rolling sushi, and making pottery). However, due to the high degree of freedom of elasto-plastic objects, significant challenges exist in virtually every aspect of the robotic manipula

Iztok Banic, Goran Erceg, Rene Gril Rogina, Judy Kennedy

We introduce dynamical systems $(X,G)$ with closed relations $G$ on compact metric spaces $X$ and discuss different types of minimality of such dynamical systems, all of them generalizing minimal dynamical systems $(X,f)$ with continuous function $f$ on a compact metric space $X$.

Noam Aigerman, Kunal Gupta, Vladimir G. Kim, Siddhartha Chaudhuri

This paper introduces a framework designed to accurately predict piecewise linear mappings of arbitrary meshes via a neural network, enabling training and evaluating over heterogeneous collections of meshes that do not share a triangulation, as well as producing highly detail-preserving maps whose accuracy exceeds current state of the art. The framework is b

R. L. P. G. Amaral, V. E. R. Lemes, O. S. Ventura, L. C. Q. Vilar

After the phase transition of a Grand Unified Theory (GUT), its single coupling splits into the couplings of the basic interactions. On the other side, the measurement of the Weinberg mixing angle in the eletroweak theory determines that the couplings associated to the SU(2) and U(1) symmetries were already distinct in its symmetric phase, before the electro

Rambod Mojgani, Maciej Balajewicz, Pedram Hassanzadeh

Physics-informed neural networks (PINNs) leverage neural-networks to find the solutions of partial differential equation (PDE)-constrained optimization problems with initial conditions and boundary conditions as soft constraints. These soft constraints are often considered to be the sources of the complexity in the training phase of PINNs. Here, we demonstra

F. E. Garbuzov, A. V. Belashov, A. A. Zhikhoreva, Y. M. Beltukov

In this paper we present thorough experimental observation of the process of shock wave transformation into a bulk strain solitary wave in a nonlinearly elastic solid bar made of polystyrene. A theoretical model based on the describing propagation of a plane elastic wave in a bar is developed with account made for material nonlinearity and viscosity. Numeric

Anamaria Crisan, Margaret Drouhard, Jesse Vig, Nazneen Rajani

Deep learning models for natural language processing (NLP) are increasingly adopted and deployed by analysts without formal training in NLP or machine learning (ML). However, the documentation intended to convey the model's details and appropriate use is tailored primarily to individuals with ML or NLP expertise. To address this gap, we conduct a design

Arash Negahdari Kia, Finbarr Murphy, Zahra Dehghani Mohammadabadi, Parisa Shamsi

In this paper, we uncover the essential features of websites that allow intelligent models to distinguish between phishing and legitimate sites. Phishing websites are those that are made with a similar user interface and a near similar address to trustworthy websites in order to persuade users to input their private data for potential future misuse by attack

David Emin, Aria Fallah, Noriko Salamon, Gary Mathern

Epileptogenic lesions have higher concentrations of sodium than does normal brain tissue. Such lesions are palpably recognized by a surgeon and then excised in order to eliminate epileptic seizures with their associated abnormal electrical behavior. Here we study the frequency-dependent electrical conductivities of lesion-laden tissues excised from the brain

Osman Semih Kayhan, Jan C. van Gemert

Which object detector is suitable for your context sensitive task? Deep object detectors exploit scene context for recognition differently. In this paper, we group object detectors into 3 categories in terms of context use: no context by cropping the input (RCNN), partial context by cropping the featuremap (two-stage methods) and full context without any cro

Thiago Felicissimo

Dedukti is a very expressive logical framework which unlike most frameworks, such as the Edinburgh Logical Framework (LF), allows for the representation of computation alongside deduction. However, unlike LF encodings, Dedukti encodings proposed until now do not feature an adequacy theorem -- i.e., a bijection between terms in the encoded system and in its e

Chenru Duan, Adriana J. Ladera, Julian C. -L. Liu, Michael G. Taylor

Accurate virtual high-throughput screening (VHTS) of transition metal complexes (TMCs) remains challenging due to the possibility of high multi-reference (MR) character that complicates property evaluation. We compute MR diagnostics for over 5,000 ligands present in previously synthesized transition metal complexes in the Cambridge Structural Database (CSD).

David Emin

Polarons are composite quasiparticles comprising electronic charge carriers taken together with the alterations they induce in surrounding condensed matter. Strong-coupling polarons form when electronic charge carriers become self-trapped: bound within potential wells stabilized by carriers presence. Distinctively, exciting these bound carriers generates bro