May 2020 arXiv papers — page 4

Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Klaus-Robert Müller

Though anomaly detection (AD) can be viewed as a classification problem (nominal vs. anomalous) it is usually treated in an unsupervised manner since one typically does not have access to, or it is infeasible to utilize, a dataset that sufficiently characterizes what it means to be "anomalous." In this paper we present results demonstrating that this

Vu Dinh, Lam Si Tung Ho

One main obstacle for the wide use of deep learning in medical and engineering sciences is its interpretability. While neural network models are strong tools for making predictions, they often provide little information about which features play significant roles in influencing the prediction accuracy. To overcome this issue, many regularization procedures f

D. Marinho Rodrigues, R. da Rocha

Pomeron resonances in AdS/QCD are here studied using the configurational entropy (CE). The concept of CE Regge trajectories, associating the CE of the pomeron resonances with both their spin $J^{PC}$ and to their mass spectra, is used to derive the mass spectra of higher $J^{PC}$ pomeron resonances. For it, the linear, the exponential modified and the anomal

Ti Bai, Dan Nguyen, Biling Wang, Steve Jiang

Despite the indispensable role of X-ray computed tomography (CT) in diagnostic medicine field, the associated ionizing radiation is still a major concern considering that it may cause genetic and cancerous diseases. Decreasing the exposure can reduce the dose and hence the radiation-related risk, but will also induce higher quantum noise. Supervised deep lea

Azur Hodžić, Clara M. Velte

The similarity of the two-point correlation tensor along the streamwise direction in the axi-symmetric jet far-field is analyzed, herein its utility in spectral theory. A separable two-point correlation coefficient has been the basis for the argument that the energy-optimized basis functions along the streamwise direction are Fourier modes (from the approach

Julio César Hernández Arzusa

In this paper we give conditions under which a topological semigroup can be embedded algebraically and topologically into a compact topological group. We prove that every feebly compact regular first countable cancellative commutative topological semigroup with open shifts is a topological group, as well as every connected locally compact Hausdorff cancellat

Kasidit Toprasertpong, Kento Tahara, Mitsuru Takenaka, Shinichi Takagi

In this study, we propose a measurement technique for evaluating ferroelectric polarization characteristics in ferroelectric field-effect transistors (FeFETs). Different from standard metal/ferroelectric/metal capacitors, the depletion and inversion phenomena in semiconductor substrates have to be carefully taken into account when evaluating the ferroelectri

Oluwasegun Ayokunle Somefun, Kayode Akingbade, Folasade Dahunsi

The proportional-integral-derivative (PID) control law is often overlooked as a computational imitation of the critic control in human decision. This paper provides a formulation to remedy this problem. Further, based on the characteristic settling-behaviour of dynamical systems, the "closed PID-loop model" following control (CPLMFC) method is introd

Jiyou Li, Xiang Yu

We obtain asymptotic formulas for sums over arithmetic progressions of coefficients of polynomials of the form $$\prod_{j=1}^n\prod_{k=1}^{p-1}(1-q^{pj-k})^s,$$ where $p$ is an odd prime and $n, s$ are positive integers. Let us denote by $a_i$ the coefficient of $q^i$ in the above polynomial and suppose that $b$ is an integer. We prove that $$\Big|\sum_{i\eq

Ahmed Raoof, Chung-Horng Lung, Ashraf Matrawy

The current standard of Routing Protocol for Low Power and Lossy Networks (RPL) incorporates three modes of security: the Unsecured Mode (UM), Preinstalled Secure Mode (PSM), and the Authenticated Secure Mode (ASM). While the PSM and ASM are intended to protect against external routing attacks and some replay attacks (through an optional replay protection me

N. M . Kawahala, F. C. D. Moraes, G. M. Gusev, A. K. Bakarov

Spin transport was studied in a two-dimensional electron gas hosted in a wide GaAs quantum well occupying two subbands. Using space and time Kerr rotation microscopy to image drifting spin packets under an in-plane accelerating electric field, optical injection and detection of spin polarization were achieved in a pump-probe configuration. The experimental d

Shiv Gehlot, Anubha Gupta, Ritu Gupta

Acute lymphoblastic leukemia (ALL) is a pervasive pediatric white blood cell cancer across the globe. With the popularity of convolutional neural networks (CNNs), computer-aided diagnosis of cancer has attracted considerable attention. Such tools are easily deployable and are cost-effective. Hence, these can enable extensive coverage of cancer diagnostic fac

Yixun Xue, Mohammad Shahidehpour, Zhaoguang Pan, Bin Wang

Massive adoptions of combined heat and power (CHP) units necessitate the coordinated operation of power system and district heating system (DHS). Exploiting the reconfigurable property of district heating networks (DHNs) provides a cost-effective solution to enhance the flexibility of the power system by redistributing heat loads in DHS. In this paper, a uni

Mahsa Taheri, Fang Xie, Johannes Lederer

Neural networks have become standard tools in the analysis of data, but they lack comprehensive mathematical theories. For example, there are very few statistical guarantees for learning neural networks from data, especially for classes of estimators that are used in practice or at least similar to such. In this paper, we develop a general statistical guaran

Praful D. Mankar, Mohamed A. Abd-Elmagid, Harpreet S. Dhillon

This paper considers a large-scale wireless network consisting of source-destination (SD) pairs, where the sources send time-sensitive information, termed status updates, to their corresponding destinations in a time-slotted fashion. We employ Age of information (AoI) for quantifying the freshness of the status updates measured at the destination nodes for t

Narjes Kheirabadi, Azizollah Shafiekhani

The current rechargeable battery technologies have a failure in their performance at high pressure and temperature. In this article, we have brought theoretical insights on using boron nitride flakes as a protecting layer for a lithium-ion battery device and extended its application for a spin-dependent photon emission device. Hence, the electronic propertie

Yasuhiro Yamaguchi, Nodoka Yamanaka

We evaluate the electric dipole moment of charged leptons in the standard model, where the complex phase of the Cabibbo-Kobayashi-Maskawa matrix is the only source of CP violation. We first prove that, at the quark-gluon level, it is suppressed by a factor of $m_b^2 m_c^2 m_s^2$ at all orders of perturbation due to the Glashow-Iliopoulos-Maiani mechanism. We

Alexis Derumigny, Johannes Schmidt-Hieber

It is a common phenomenon that for high-dimensional and nonparametric statistical models, rate-optimal estimators balance squared bias and variance. Although this balancing is widely observed, little is known whether methods exist that could avoid the trade-off between bias and variance. We propose a general strategy to obtain lower bounds on the variance of

Esther S. Daus, Mariya Ptashnyk, Claudia Raithel

In this article a fractional cross-diffusion system is derived as the rigorous many-particle limit of a multi-species system of moderately interacting particles that is driven by Lévy noise. The form of the mutual interaction is motivated by the porous medium equation with fractional potential pressure. Our approach is based on the techniques developed by Oe

Liuyuan Chen, Kanglei Zhou, Junchang Jing, Haiju Fan

The twin support vector machine and its extensions have made great achievements in dealing with binary classification problems. However, it suffers from difficulties in effective solution of multi-classification and fast model selection. This work devotes to the fast regularization parameter tuning algorithm for the twin multi-class support vector machine. S

Jiawei Zhao, Yifan Zhao, Jia Li, Xiaowu Chen

Salient object detection (SOD) is a crucial and preliminary task for many computer vision applications, which have made progress with deep CNNs. Most of the existing methods mainly rely on the RGB information to distinguish the salient objects, which faces difficulties in some complex scenarios. To solve this, many recent RGBD-based networks are proposed by

Chengjian Sun, Changyang She, Chenyang Yang

Deep neural networks (DNNs) have been introduced for designing wireless policies by approximating the mappings from environmental parameters to solutions of optimization problems. Considering that labeled training samples are hard to obtain, unsupervised deep learning has been proposed to solve functional optimization problems with statistical constraints re

Hyung Park, Eva Petkova, Thaddeus Tarpey, R. Todd Ogden

This paper focuses on the problem of modeling and estimating interaction effects between covariates and a continuous treatment variable on an outcome, using a single-index regression approach. The primary motivation is to estimate an optimal individualized dose rule in an observational study. To model possibly nonlinear interaction effects between patients&#

Hyung Park, Eva Petkova, Thaddeus Tarpey, R. Todd Ogden

A novel functional additive model is proposed which is uniquely modified and constrained to model nonlinear interactions between a treatment indicator and a potentially large number of functional and/or scalar pretreatment covariates. The primary motivation for this approach is to optimize individualized treatment rules based on data from a randomized clinic

Doriann Albertin, Vincent Pilaud, Julian Ritter

The associahedron is classically constructed as a removahedron, i.e. by deleting inequalities in the facet description of the permutahedron. This removahedral construction extends to all permutreehedra (which interpolate between the permutahedron, the associahedron and the cube). Here, we investigate removahedra constructions for all quotientopes (which real

Cecilia Cavaterra, Serena Dipierro, Zu Gao, Enrico Valdinoci

We provide global gradient estimates for solutions to a general type of nonlinear parabolic equations, possibly in a Riemannian geometry setting. Our result is new in comparison with the existing ones in the literature, in light of the validity of the estimates in the global domain, and it detects several additional regularity effects due to special paraboli

Sosuke Nishikawa, Ryokan Ri, Yoshimasa Tsuruoka

Unsupervised cross-lingual word embedding (CLWE) methods learn a linear transformation matrix that maps two monolingual embedding spaces that are separately trained with monolingual corpora. This method relies on the assumption that the two embedding spaces are structurally similar, which does not necessarily hold true in general. In this paper, we argue tha

Hanna Le Jeannic, Tomás Ramos, Signe F. Simonsen, Tommaso Pregnolato

Coherent photon-emitter interfaces offer a way to mediate efficient nonlinear photon-photon interactions, much needed for quantum information processing. Here we experimentally study the case of a two-level emitter, a quantum dot, coupled to a single optical mode in a nanophotonic waveguide. We carry out few-photon transport experiments and record the statis

Helge Glockner

For suitable finite-dimensional smooth manifolds M (possibly with various kinds of boundary or corners), locally convex topological vector spaces F and non-negative integers k, we construct continuous linear operators S_n from the space of F-valued k times continuously differentiable functions on M to the corresponding space of smooth functions such that S_n

Yuekun Yao, Barry Haddow

For spoken language translation (SLT) in live scenarios such as conferences, lectures and meetings, it is desirable to show the translation to the user as quickly as possible, avoiding an annoying lag between speaker and translated captions. In other words, we would like low-latency, online SLT. If we assume a pipeline of automatic speech recognition (ASR) a

Benita S. Mackay, Matthew Praeger, James A. Grant-Jacob, Janos Kanczler

The response of adult human bone marrow stromal stem cells to surface topographies generated through femtosecond laser machining can be predicted by a deep neural network. The network is capable of predicting cell response to a statistically significant level, including positioning predictions with a probability P < 0.001, and therefore can be used as a mode

Fanghui Liu, Xiaolin Huang, Yingyi Chen, Johan A. K. Suykens

In this paper, we attempt to solve a long-lasting open question for non-positive definite (non-PD) kernels in machine learning community: can a given non-PD kernel be decomposed into the difference of two PD kernels (termed as positive decomposition)? We cast this question as a distribution view by introducing the \emph{signed measure}, which transforms posi

Luca Banetta, Alessio Zaccone

The pair correlation function of charge stabilized colloidal particles under strongly sheared conditions is studied using the analytical intermediate asymptotics method recently developed in [L. Banetta and A. Zaccone, Phys. Rev. E 99, 052606 (2019)] to solve the steady-state Smoluchowski equation for medium to high values of the Péclet number; the analytica

Aritra Banerjee, Haiying Cai, Lavinia Heisenberg, Eoin Ó Colgáin

Local determinations of the Hubble constant $H_0$ favour a higher value than Planck based on CMB and $Λ$CDM. Through a model-independent expansion, we show that low redshift ($z \lesssim 0.7$) data comprising baryon acoustic oscillations (BAO), cosmic chronometers and Type Ia supernovae has a preference for Quintessence models that lower $H_0$ relative to $Λ

E. Mompó, M. A. López-Castaño, A. Torrente, F. Vega Reyes

We study a granular gas of viscoelastic particles (kinetic energy loss upon collision is a function of the particles&#39; relative velocities at impact) subject to a stochastic thermostat. We show that the system displays anomalous cooling and heating rates during thermal relaxation processes, this causing the emergence of thermal memory. In particular, a si

Wen-Chiao Tsai, Chieh-Fang Teng, Han-Mo Ou, An-Yeu Wu

Recently, deep learning-assisted communication systems have achieved many eye-catching results and attracted more and more researchers in this emerging field. Instead of completely replacing the functional blocks of communication systems with neural networks, a hybrid manner of BCJRNet symbol detection is proposed to combine the advantages of the BCJR algori

Luigi Appolloni, Simone Secchi

We investigate the existence of solutions to the fractional nonlinear Schrödinger equation $(-Δ)^s u = f(u)$ with prescribed $L^2$-norm $\int_{\mathbb{R}^N} |u|^2 \, dx =m$ in the Sobolev space $H^s(\mathbb{R}^N)$. Under fairly general assumptions on the nonlinearity $f$, we prove the existence of a ground state solution and a multiplicity result in the radi

W. N. Faugno, J. K. Jain, Ajit C. Balram

We consider a non-Abelian candidate state at filling factor $ν=3/7$ state belonging to the parton family. We find that, in the second Landau level of GaAs (i.e. at filling factor $ν=2+3/7$), this state is energetically superior to the standard Jain composite-fermion state and also provides a very good representation of the ground state found in exact diagona

Fan Zhang, MinChao Yan, Chen Hu, Jun Ni

The land cover classification has played an important role in remote sensing because it can intelligently identify things in one huge remote sensing image to reduce the work of humans. However, a lot of classification methods are designed based on the pixel feature or limited spatial feature of the remote sensing image, which limits the classification accura

Thi Kim Thoa Thieu, Adrian Muntean

We study the well-posedness of a coupled system of Skorohod-like stochastic differential equations with reflecting boundary condition. The setting describes the evacuation dynamics of a mixed crowd composed of both active and passive pedestrians moving through a domain with obstacles, fire and smoke. As main working techniques, we use compactness methods and

Wensha Gou, Siyu Huang, Qinghua Chen, Jiawei Chen

Cross-border migration brings economic and cultural impacts to the origin and destination, and is also a key to reflect the international relations of related countries. In fact, the migration relationships of countries are complex and multilateral, but most traditional migration models are bilateral. Network theories could provide a better description of gl

Fahri Aydos, A. Murat Özbayoğlu, Yahya Şirin, M. Fatih Demirci

In this paper, we introduce a novel method that combines multiple neural network results to decide the class of the input. This is the first study which used the method for web pages classification. In our model, each element is represented by multiple descriptive images. After the training process of the neural network model, each element is classified by c

Mark A. Hughes, Naitik A. Panjwani, Matias Urdampilleta, Nafsika Theodoropoulou

Erbium implanted silicon as a quantum technology platform has both telecommunications and integrated circuit (IC) processing compatibility. The electron spin coherence time of Er implanted Si with an Er concentration of 3X1017 cm-3 is measured to be ~10 μs at 5 K and the spin echo decay profile displays strong modulation due to super-hyperfine interaction wi

Naoto Iguchi, Yoichi Itoh

We carried out optical high-resolution spectroscopy of T Tauri stars using the Subaru Telescope. Using archived data from the Keck Telescope and the Very Large Telescope, we detected forbidden lines of [S II] at 4069 A, in addition to those of [O I] at 5577 A and 6300 A, for 13 T Tauri stars. We consider that low-velocity components of these forbidden lines

Catarina Moreira, Matheus Hammes, Rasim Serdar Kurdoglu, Peter Bruza

This paper provides the foundations of a unified cognitive decision-making framework (QulBIT) which is derived from quantum theory. The main advantage of this framework is that it can cater for paradoxical and irrational human decision making. Although quantum approaches for cognition have demonstrated advantages over classical probabilistic approaches and b

Dag Normann, Sam Sanders

The Axiom of Choice (AC for short) is the most (in)famous axiom of the usual foundations of mathematics, ZFC set theory. The (non-)essential use of AC in mathematics has been well-studied and thoroughly classified. Now, fragments of countable AC not provable in ZF have recently been used in Kohlenbach&#39;s higher-order Reverse Mathematics to obtain equivale

Shanshan Wang, Lei Zhang

Existing adversarial domain adaptation methods mainly consider the marginal distribution and these methods may lead to either under transfer or negative transfer. To address this problem, we present a self-adaptive re-weighted adversarial domain adaptation approach, which tries to enhance domain alignment from the perspective of conditional distribution. In

Imanol Perez Arribas, Cristopher Salvi, Lukasz Szpruch

Mathematical models, calibrated to data, have become ubiquitous to make key decision processes in modern quantitative finance. In this work, we propose a novel framework for data-driven model selection by integrating a classical quantitative setup with a generative modelling approach. Leveraging the properties of the signature, a well-known path-transform fr

Takafumi Inoue, Shunsuke Inenaga, Hideo Bannai

The longest square subsequence (LSS) problem consists of computing a longest subsequence of a given string $S$ that is a square, i.e., a longest subsequence of form $XX$ appearing in $S$. It is known that an LSS of a string $S$ of length $n$ can be computed using $O(n^2)$ time [Kosowski 2004], or with (model-dependent) polylogarithmic speed-ups using $O(n^2

A. Borghese, F. Coti Zelati, N. Rea, P. Esposito

A few years after its discovery as a magnetar, SGR J1935+2154 started a new burst-active phase on 2020 April 27, accompanied by a large enhancement of its X-ray persistent emission. Radio single bursts were detected during this activation, strengthening the connection between magnetars and fast radio bursts. We report on the X-ray monitoring of SGR J1935+215

Denis V. Vasilyev, Andrey Grankin, Mikhail A. Baranov, Lukas M. Sieberer

We discuss monitoring the time evolution of an analog quantum simulator via a quantum non-demolition (QND) coupling to an auxiliary `clock&#39; qubit. The QND variable of interest is the `energy&#39; of the quantum many-body system, represented by the Hamiltonian of the quantum simulator. We describe a physical implementation of the underlying QND Hamiltonia

Ce Xu

Recently, a new kind of multiple zeta value level two $T({\bf k})$ (which is called multiple $T$-values) was introduced and studied by Kaneko and Tsumura. In this paper, we define a kind of alternating version of multiple $T$-values, and study several duality formulas of weighted sum formulas about alternating multiple $T$-values by using the methods of iter

Chao Chen, Zhihong Chen, Xinyu Jin, Lanjuan Li

Bone age assessment (BAA) is clinically important as it can be used to diagnose endocrine and metabolic disorders during child development. Existing deep learning based methods for classifying bone age use the global image as input, or exploit local information by annotating extra bounding boxes or key points. However, training with the global image underuti

Manaka Okuyama, Masayuki Ohzeki

Geometric Brownian motion (GBM) is a standard model in stochastic differential equations. In this study, we consider a matrix-valued GBM with non-commutative matrices. Introduction of non-commutative matrices into the matrix-valued GBM makes it difficult to obtain an exact solution because the existence of noise terms prevents diagonalization. However, we sh

S. Chandankumar

In the proposed work, we establish a total of six new $P$--$Q$ modular equations involving theta--function $f(-q)$ with moduli of orders 1, 3, 7 and 21.These equations can be regarded as modular identities in the alternate theory of signature 3. As a consequence, several values of quotients of theta--function are evaluated.

Hengjia Wei, Moshe Schwartz

We study whether an asymmetric limited-magnitude ball may tile $\mathbb{Z}^n$. This ball generalizes previously studied shapes: crosses, semi-crosses, and quasi-crosses. Such tilings act as perfect error-correcting codes in a channel which changes a transmitted integer vector in a bounded number of entries by limited-magnitude errors. A construction of latti

Akishi Matsugatani, Seishiro Ono, Yusuke Nomura, Haruki Watanabe

Bloch wavefunctions in solids form a representation of crystalline symmetries. Recent studies revealed that symmetry representations in band structure can be used to diagnose the topological properties of weakly interacting materials. In this work, we introduce an open-source program qeirreps that computes the representation characters in a band structure ba

Chun-Hung Liu, Robin Thomas

Robertson and Seymour&#39;s celebrated Graph Minor Theorem states that graphs are well-quasi-ordered by the minor relation. Unlike the minor relation, the topological minor relation does not well-quasi-order graphs in general. Among all known infinite antichains with respect to the topological containment, subdivisions of a graph obtained from an arbitrarily

Gerardo Acosta, David Fernández-Bretón

If $X$ is a finite tree and $f \colon X \longrightarrow X$ is a map, as the Main Theorem of this paper we find eight conditions, each of which is equivalent to the fact that $f$ is equicontinuous. To name just a few of the results obtained: the equicontinuity of $f$ is equivalent to the fact that there is no arc $A \subseteq X$ satisfying $A \subsetneq f^n[A

Lipu Zhou, Daniel Koppel, Hui Ju, Frank Steinbruecker

This paper presents an efficient algorithm for the least-squares problem using the point-to-plane cost, which aims to jointly optimize depth sensor poses and plane parameters for 3D reconstruction. We call this least-squares problem \textbf{Planar Bundle Adjustment} (PBA), due to the similarity between this problem and the original Bundle Adjustment (BA) in

Yevgeny V. Stadnik

We point out that in models of macroscopic topological defects composed of one or more scalar fields that interact with standard-model fields via scalar-type couplings, the back-action of ambient matter on the scalar field(s) produces an environmental dependence of the fundamental constants of nature, as well as spatial variations of the fundamental constant

Yen-Cheng Liu, Junjiao Tian, Nathaniel Glaser, Zsolt Kira

While significant advances have been made for single-agent perception, many applications require multiple sensing agents and cross-agent communication due to benefits such as coverage and robustness. It is therefore critical to develop frameworks which support multi-agent collaborative perception in a distributed and bandwidth-efficient manner. In this paper

Vaibhav Wasnik

In this work we present symmetry transformations relating bosons to fermions which cannot be represented as a supersymmetric algebra. We present a symmetry transformation relating a complex scalar and a fermion in four dimensions and construct a theory defined by an action that respects the symmetry quantum mechanically. We next invoke gauge symmetry by addi

J. Caravaca, B. J. Land, M. Yeh, G. D. Orebi Gann

This paper presents measurements of the scintillation light yield and time profile for a number of concentration of water-based liquid scintillator, formulated from linear alkylbenzene (LAB) and 2,5-diphenyloxazole (PPO). We find that the scintillation light yield is linear with the concentration of liquid scintillator in water between 1 and 10% with a slope

Haimiao Zhang, Baodong Liu, Hengyong Yu, Bin Dong

X-ray Computed Tomography (CT) is widely used in clinical applications such as diagnosis and image-guided interventions. In this paper, we propose a new deep learning based model for CT image reconstruction with the backbone network architecture built by unrolling an iterative algorithm. However, unlike the existing strategy to include as many data-adaptive

Yan-Qing Zhu, Nathan Goldman, Giandomenico Palumbo

Quantum anomalies offer a useful guide for the exploration of transport phenomena in topological semimetals. In this work, we introduce a model describing a semimetal in four spatial dimensions, whose nodal points act like tensor monopoles in momentum space. This system is shown to exhibit monopole-to-monopole phase transitions, as signaled by a change in th

Ashraf Tantawy, Sherif Abdelwahed, Abdelkarim Erradi

Safety risk assessment is an essential process to ensure a dependable Cyber-Physical System (CPS) design. Traditional risk assessment considers only physical failures. For modern CPS, failures caused by cyber attacks are on the rise. The focus of latest research effort is on safety-security lifecycle integration and the expansion of modeling formalisms for r

Yuqi Gao, Hang Hua, Jiebo Luo

In recent months, COVID-19 has become a global pandemic and had a huge impact on the world. People under different conditions have very different attitudes toward the epidemic. Due to the real-time and large-scale nature of social media, we can continuously obtain a massive amount of public opinion information related to the epidemic from social media. In pa

John D. Clemens

We introduce a notion of relative primeness for equivalence relations, strengthening the notion of non-reducibility, and show for many standard benchmark equivalence relations that non-reducibility may be strengthened to relative primeness. We introduce several analogues of cardinal properties for Borel equivalence relations, including the notion of a prime

Yuan Yuan, Hui Chen

Ghost imaging (GI) is an unconventional imaging method that retrieves the image of an object by correlating a series of known illumination patterns with the total reflected (or transmitted) intensity. We here demonstrate a scheme which can remove the basic requirement of knowing the incident patterns on the object, enabling GI to non-invasively image objects

Ryohei Kobayashi

We discuss a way to construct a commuting projector Hamiltonian model for a (3+1)d topological superconductor in class DIII. The wave function is given by a sort of string net of the Kitaev wire, decorated on the time reversal (T) domain wall. Our Hamiltonian is provided on a generic 3d manifold equipped with a discrete form of the spin structure. We will se

Gaik Ambartsoumian, Mohammad Javad Latifi Jebelli, Rohit Kumar Mishra

We study the inverse problem of recovering a vector field in $\mathbb{R}^2$ from a set of new generalized $V$-line transforms in three different ways. First, we introduce the longitudinal and transverse $V$-line transforms for vector fields in $\mathbb{R}^2$. We then give an explicit characterization of their respective kernels and show that they are complem

Hiroshi Hirai, Ryuhei Mizutani

For a metric $μ$ on a finite set $T$, the minimum 0-extension problem 0-Ext$[μ]$ is defined as follows: Given $V\supseteq T$ and $\ c:{V \choose 2}\rightarrow \mathbf{Q_+}$, minimize $\sum c(xy)μ(γ(x),γ(y))$ subject to $γ:V\rightarrow T,\ γ(t)=t\ (\forall t\in T)$, where the sum is taken over all unordered pairs in $V$. This problem generalizes several class

Min Qiu, Yu-Chih Huang, Jinhong Yuan

The two-user Gaussian interference channel (G-IC) is revisited, with a particular focus on practically amenable discrete input signalling and treating interference as noise (TIN) receivers. The corresponding deterministic interference channel (D-IC) is first investigated and coding schemes that can achieve the entire capacity region of D-IC under TIN are pro

Travis Hee Wai, Michael T. Young, Adam A. Szpiro

We introduce random spatial forests, a method of bagging regression trees allowing for spatial correlation. Our main contribution is the development of a computationally efficient tree building algorithm which selects each split of the tree adjusting for spatial correlation. We evaluate two different approaches for estimation of random spatial forests, a pse

Rozenn Robinel, Stéphane Le Mouélic, Gabriel Tobie, Marion Massé

Between 2004 and 2017, spectral observations have been gathered by the Visual and Infrared Mapping Spectrometer (VIMS) on-board Cassini (Brown et al., 2004) during 23 Enceladus close encounters, in addition to more distant surveys. The objective of the present study is to produce a global hyperspectral mosaic of the complete VIMS data set of Enceladus in ord

Xue Li, Yuanzhi Cheng

The mechanism of message passing in graph neural networks (GNNs) is still mysterious. Apart from convolutional neural networks, no theoretical origin for GNNs has been proposed. To our surprise, message passing can be best understood in terms of power iteration. By fully or partly removing activation functions and layer weights of GNNs, we propose subspace p

Jun Yu, Mengyan Li, Xinlong Hao, Guochen Xie

Automatic kinship verification aims to determine whether some individuals belong to the same family. It is of great research significance to help missing persons reunite with their families. In this work, the challenging problem is progressively addressed in two respects. First, we propose a deep siamese network to quantify the relative similarity between tw

Wei Chen, Jian Sun, Weishuo Li, Dapeng Zhao

Obstacle avoidance is one of the essential and indispensable functions for autonomous mobile robots. Most of the existing solutions are typically based on single condition constraint and cannot incorporate sensor data in a real-time manner, which often fail to respond to unexpected moving obstacles in dynamic unknown environments. In this paper, a novel real

Anh Tran, Julien Tranchida, Tim Wildey, Aidan P. Thompson

We present a scale-bridging approach based on a multi-fidelity (MF) machine-learning (ML) framework leveraging Gaussian processes (GP) to fuse atomistic computational model predictions across multiple levels of fidelity. Through the posterior variance of the MFGP, our framework naturally enables uncertainty quantification, providing estimates of confidence i

Shalini Divya, Thomas Nann

Four different forms of carbon: activated carbon (AC) from human hair, AC from hemp fibers, a carbon fullerene extract consisting of C$_{60}$ and C$_{70}$ fullerene (CFEx) and Super-P carbon black were tested and compared as cathodes for non-aqueous aluminium-ion batteries (AIBs). These materials differ in their general structure, porosity and morphology. Fu

Silvia Amabilino, Lars A. Bratholm, Simon J. Bennie, Michael B. O&#39;Connor

The ability to understand and engineer molecular structures relies on having accurate descriptions of the energy as a function of atomic coordinates. Here we outline a new paradigm for deriving energy functions of hyperdimensional molecular systems, which involves generating data for low-dimensional systems in virtual reality (VR) to then efficiently train a

Fahui Wang, Yujie Hu, Shuai Wang, Xiaojuan Li

Most existing point-based colocation methods are global measures (e.g., join count statistic, cross K function, and global colocation quotient). Most recently, a local indicator such as the local colocation quotient is proposed to capture the variability of colocation across areas. Our research advances this line of work by developing a simulation-based stat

Ahmed Abdalazeez, Ira Didenkulova, Denys Dutykh, Céline Labart

The runup of initial Gaussian narrow-banded and wide-banded wave fields and its statistical characteristics are investigated using direct numerical simulations, based on the nonlinear shallow water equations. The bathymetry consists of the section of a constant depth, which is matched with the beach of constant slope. To address different levels of nonlinear

Haoliang Jiang, Zhenguo Nie, Roselyn Yeo, Amir Barati Farimani

Using deep learning to analyze mechanical stress distributions has been gaining interest with the demand for fast stress analysis methods. Deep learning approaches have achieved excellent outcomes when utilized to speed up stress computation and learn the physics without prior knowledge of underlying equations. However, most studies restrict the variation of

C. N. Parajara, G. M. Platt, F. D. Moura Neto, M. Escobar

In this work, we propose a new approach to the problem of critical point calculation, based on the formulation of Heidemann and Khalil (1980). This leads to a $2 \times 2$ system of nonlinear algebraic equations in temperature and molar volume, which makes possible the prediction of critical points of the mixture through an adaptation of the technique of inv

Xiang Fu

This document is the specification of the CC-Light instantiation of executable QASM (eQASM), a quantum instruction set architecture (QISA) developed in QuTech targeting to control a seven-qubit superconducting quantum processor. This document can serve as a reference manual for low-level programmers, compiler backend developers, and microarchitecture impleme

Andrew Critch, David Krueger

Framed in positive terms, this report examines how technical AI research might be steered in a manner that is more attentive to humanity&#39;s long-term prospects for survival as a species. In negative terms, we ask what existential risks humanity might face from AI development in the next century, and by what principles contemporary technical research might

Sudip Vhaduri, Temiloluwa Prioleau

Personal health devices can enable continuous monitoring of health parameters. However, the benefit of these devices is often directly related to the frequency of use. Therefore, adherence to personal health devices is critical. This paper takes a data mining approach to study continuous glucose monitor use in diabetes management. We evaluate two independent

Kyongsik Yun, Thomas Lu, Alexander Huyen

Paramedics often have to make lifesaving decisions within a limited time in an ambulance. They sometimes ask the doctor for additional medical instructions, during which valuable time passes for the patient. This study aims to automatically fuse voice and text data to provide tailored situational awareness information to paramedics. To train and test speech

Joanna Henzel, Marek Sikora

In the paper, a problem of forecasting promotion efficiency is raised. The authors propose a new approach, using the gradient boosting method for this task. Six performance indicators are introduced to capture the promotion effect. For each of them, within predefined groups of products, a model was trained. A description of using these models for forecasting

Yunkai Xiao, Gabriel Zingle, Qinjin Jia, Harsh R. Shah

Effective peer assessment requires students to be attentive to the deficiencies in the work they rate. Thus, their reviews should identify problems. But what ways are there to check that they do? We attempt to automate the process of deciding whether a review comment detects a problem. We use over 18,000 review comments that were labeled by the reviewees as

CheeWhye Chin

This is an English translation of the monograph &#34;Die Klassenkörper der komplexen Multiplikation&#34; by Max Deuring, published in 1958 as Enzyklopädie der Mathematischen Wissenschaften, Band I-2, Heft 10, Teil II by B.G. Teuber Verlagsgesellschaft, Stuttgart. It gives a systematic exposition of the analytic method in the theory of complex multiplication

Shoujin Wang, Longbing Cao, Liang Hu, Shlomo Berkovsky

A transaction-based recommender system (TBRS) aims to predict the next item by modeling dependencies in transactional data. Generally, two kinds of dependencies considered are intra-transaction dependency and inter-transaction dependency. Most existing TBRSs recommend next item by only modeling the intra-transaction dependency within the current transaction

Babita Rani, Vladimir Bubanja, Vijay K. Jindal

Inspired by a recent experimental and theoretical study [Yang et al., 2017], wherein protrusions in graphene have been proposed as an effective strategy to enhance the performance of sodium ion batteries, a comprehensive study of the effects of phosphorus doping in graphene on adsorption and diffusion behaviour of Li is carried out by using density functiona

Yujie Hu, Harvey J Miller, Xiang Li

Capabilities for collecting and storing data on mobile objects have increased dramatically over the past few decades. A persistent difficulty is summarizing large collections of mobile objects. This paper develops methods for extracting and analyzing hotspots or locations with relatively high levels of mobility activity. We use kernel density estimation (KDE

Yujie Hu, Fahui Wang, Chester Wilmot

Residential segregation recently has shifted to more class or income-based in the United States, and neighborhoods are undergoing significant changes such as commuting patterns over time. To better understand the commuting inequality across neighborhoods of different income levels, this research analyzes commuting variability (in both distance and time) acro

Yujie Hu, Yu Zhang, Kyle Shelton

Pedestrians and cyclists are vulnerable road users. They are at greater risk for being killed in a crash than other road users. The percentage of fatal crashes that involve a pedestrian or cyclist is higher than the overall percentage of total trips taken by both modes. Because of this risk, finding ways to minimize problematic street environments is critica

Emna Jabri

This work is done as part of a research master&#39;s thesis project. The goal is to generate SPARQL queries based on user-supplied keywords to query RDF graphs. To do this, we first transformed the input ontology into an RDF graph that reflects the semantics represented in the ontology. Subsequently, we stored this RDF graph in the Neo4j graphical database t

Yujie Hu, Fahui Wang

Based on the 1990-2010 CTPP data in Baton Rouge, this research analyzes the temporal trends of commuting patterns in both time and distance. In comparison to previous work, commuting length is calibrated more accurately by Monte Carlo based simulation of individual journey-to-work trips to mitigate the zonal effect. First, average commute distance kept climb