May 2020 arXiv papers — page 11

Byoung S. Ham

Nonlocal correlation is the key concept in quantum information processing, where quantum entanglement provides such a nonclassical property. Since the first proposal of noninterfering interferometer-based two-photon intensity correlation by Franson (Phys. Rev. Lett. 62, 2205 (1989)), the particle nature of photons has been intensively studied for nonlocal co

Sotiris Tsioutsiouliklis, Evaggelia Pitoura, Panayiotis Tsaparas, Ilias Kleftakis

Algorithmic fairness has attracted significant attention in the past years. Surprisingly, there is little work on fairness in networks. In this work, we consider fairness for link analysis algorithms and in particular for the celebrated PageRank algorithm. We provide definitions for fairness, and propose two approaches for achieving fairness. The first modif

Jie Hu, Qing Wang, Kun Yang

Full-spectrum ranging from sub 6 GHz to THz and visible light will be exploited in 6G in order to reach unprecedented key-performance-indicators (KPIs). However, extraordinary amount of energy will be consumed by network infrastructure, while functions of massively deployed Internet of Everything (IoE) devices are limited by embedded batteries. Therefore, en

Florio M. Ciaglia, Fabio Di Cosmo, Alberto Ibort, Giuseppe Marmo

The analysis of the covariant brackets on the space of functions on the solutions to a variational problem in the framework of contact geometry initiated in the companion letter Ref.19 is extended to the case of the multisymplectic formulation of the free Klein-Gordon theory and of the free Schr\"{o}dinger equation.

Michael Unser

The statements on the BIBO stability of continuous-time convolution systems found in engineering textbooks are often either too vague (because of lack of hypotheses) or mathematically incorrect. What is more troubling is that they usually exclude the identity operator. The purpose of this note is to clarify the issue while presenting some fixes. In particula

Raymond Leung, Alexander Lowe, Anna Chlingaryan, Arman Melkumyan

This paper presents a Bayesian framework for manipulating mesh surfaces with the aim of improving the positional integrity of the geological boundaries that they seek to represent. The assumption is that these surfaces, created initially using sparse data, capture the global trend and provide a reasonable approximation of the stratigraphic, mineralisation an

Spencer Frei, Yuan Cao, Quanquan Gu

We consider the problem of learning the best-fitting single neuron as measured by the expected square loss $\mathbb{E}_{(x,y)\sim \mathcal{D}}[(\sigma(w^\top x)-y)^2]$ over some unknown joint distribution $\mathcal{D}$ by using gradient descent to minimize the empirical risk induced by a set of i.i.d. samples $S\sim \mathcal{D}^n$. The activation function $\

Igor Protasov, Ksenia Protasova

For a bornology $\mathcal B$ on a cardinal $\kappa$, we prove that the $\mathcal B$-macrocube is normal if and only if $\mathcal B$ has a linearly ordered base. As a corollary, we get that the hyperballean of bounded subsets of an ultradiscrete ballean is not normal. These answer Question 1 from \cite{b2} and Question 14.4 from \cite{b1}.

Sahasrajit Sarmasarkar, Kota Srinivas Reddy, Nikhil Karamchandani

We consider the problem of identifying the subset $\mathcal{S}^{\gamma}_{\mathcal{P}}$ of elements in the support of an underlying distribution $\mathcal{P}$ whose probability value is larger than a given threshold $\gamma$, by actively querying an oracle to gain information about a sequence $X_1, X_2, \ldots$ of $i.i.d.$ samples drawn from $\mathcal{P}$. We

Mao Ye, Chengyue Gong, Qiang Liu

State-of-the-art NLP models can often be fooled by human-unaware transformations such as synonymous word substitution. For security reasons, it is of critical importance to develop models with certified robustness that can provably guarantee that the prediction is can not be altered by any possible synonymous word substitution. In this work, we propose a cer

S. Ghasemi, M. Alihosseini, F. Peymanirad, H. Jalali

The intercalated water into nanopores exhibits anomalous properties such as ultralow dielectric constant.~Multi-scale modeling and simulations are used to investigate the dielectric properties of various crystalline two-dimensional ices and bulk ices. Although, the structural properties of two-dimensional (2D-) ices have been extensively studied, much less i

Zhong-Kang Han, Debalaya Sarker, Runhai Ouyang, Yi Gao

Single-atom metal alloy catalysts (SAACs) have recently become a very active new frontier in catalysis research. The simultaneous optimization of both facile dissociation of reactants and a balanced strength of intermediates' binding make them highly efficient and selective for many industrially important reactions. However, discovery of new SAACs is hindere

Arunima Bhattacharya

In this paper, we derive a priori interior Hessian estimates for Lagrangian mean curvature equation if the Lagrangian phase is supercritical and has bounded second derivatives.

Arunima Bhattacharya

In this paper, we solve the Dirichlet problem with continuous boundary data for the Lagrangian mean curvature equation on a uniformly convex, bounded domain in $\mathbb{R}^n$.

Olivier Buffet, Olivier Pietquin, Paul Weng

Reinforcement learning (RL) is a general framework for adaptive control, which has proven to be efficient in many domains, e.g., board games, video games or autonomous vehicles. In such problems, an agent faces a sequential decision-making problem where, at every time step, it observes its state, performs an action, receives a reward and moves to a new state

O. V. Bugaiko, E. V. Gorbar, P. O. Sukhachov

Surface plasmon polaritons in a strained slab of a Weyl semimetal with broken time-reversal symmetry are investigated. It is found that the strain-induced axial gauge field reduces frequencies of these collective modes for intermediate values of the wave vector. Depending on the relative orientation of the separation of Weyl nodes in momentum space, the surf

J. A. Oller

We review a series of unitarization techniques that have been used during the last decades, many of them in connection with the advent and development of current algebra and later of Chiral Perturbation Theory. Several methods are discussed like the generalized effective-range expansion, K-matrix approach, Inverse Amplitude Method, Pad\'e approximants and th

Kazuo Ghoroku, Kouji Kashiwa, Yoshimasa Nakano, Motoi Tachibana

We extend a bottom up holographic model, which has been used in studying the color superconductivity in QCD, to the imaginary chemical potential ($\mu_I$) region, and the phase diagram is studied on the $\mu_I$-temperature (T) plane. The analysis is performed for the case of the probe approximation and for the background where the back reaction from the flav

Guangfeng Lin, Ying Yang, Yindi Fan, Xiaobing Kang

Few-shot learning can find the latent structure information between the prior knowledge and the queried data by the similarity metric of meta-learning to construct the discriminative model for recognizing the new categories with the rare labeled samples. Most existing methods try to model the similarity relationship of the samples in the intra tasks, and gen

Jinhan Kim, Jeongil Ju, Robert Feldt, Shin Yoo

Deep Neural Networks (DNNs) are rapidly being adopted by the automotive industry, due to their impressive performance in tasks that are essential for autonomous driving. Object segmentation is one such task: its aim is to precisely locate boundaries of objects and classify the identified objects, helping autonomous cars to recognise the road environment and

Romain Lebrun, Andrew Ross, Olena Gomonay, Vincent Baltz

Antiferromagnetic materials can host spin-waves with polarizations ranging from circular to linear depending on their magnetic anisotropies. Until now, only easy-axis anisotropy antiferromagnets with circularly polarized spin-waves were reported to carry spin-information over long distances of micrometers. In this article, we report long-distance spin-transp

Mohammed Bahutair, Athman Bouguettaya, Azadeh Ghari Neiat

We propose just-in-time memoryless trust for crowdsourced IoT services. We leverage the characteristics of the IoT service environment to evaluate their trustworthiness. A novel framework is devised to assess a service's trust without relying on previous knowledge, i.e., memoryless trust. The framework exploits service-session-related data to offer a trust v

J. Kluson

In this short note we perform canonical analysis of geometrical action for Dp-brane. We also discuss tachyon condensation in case of the geometrical action for unstable D(p+1)-brane.

Zhe Xing, Rui Wang, Jun Wu, Erwu Liu

Intelligent reflecting surface (IRS) is envisioned as a promising hardware solution to hardware cost and energy consumption in the fifth-generation (5G) mobile communication network. It exhibits great advantages in enhancing data transmission, but may suffer from performance degradation caused by inherent hardware impairment (HWI). For analysing the achievab

Lucia Schuler, Somaya Jamil, Niklas Kühl

Serverless computing has emerged as a compelling new paradigm of cloud computing models in recent years. It promises the user services at large scale and low cost while eliminating the need for infrastructure management. On cloud provider side, flexible resource management is required to meet fluctuating demand. It can be enabled through automated provisioni

Ben J. Marafino, Alejandro Schuler, Vincent X. Liu, Gabriel J. Escobar

Clinical predictive algorithms are increasingly being used to form the basis for optimal treatment policies--that is, to enable interventions to be targeted to the patients who will presumably benefit most. Despite taking advantage of recent advances in supervised machine learning, these algorithms remain, in a sense, blunt instruments--often being developed

Deepak Muralidharan, Joel Ruben Antony Moniz, Sida Gao, Xiao Yang

Named Entity Recognition (NER) and Entity Linking (EL) play an essential role in voice assistant interaction, but are challenging due to the special difficulties associated with spoken user queries. In this paper, we propose a novel architecture that jointly solves the NER and EL tasks by combining them in a joint reranking module. We show that our proposed

Alexander P. Kartun-Giles, Konstantinos Koufos, Xiao Lu, Dusit Niyato

In this paper, we compute the expected number of vehicles with at least one two-hop path to a fixed roadside unit (RSU) in a multi-hop, one-dimensional vehicular ad hoc network (VANET) where other cars can act as relays. The pairwise channels experience Rayleigh fading in the random connection model, and so exist, with a probability given by a function of th

Gautam Krishna, Co Tran, Mason Carnahan, Ahmed Tewfik

In this paper we demonstrate that it is possible to generate more meaningful electroencephalography (EEG) features from raw EEG features using generative adversarial networks (GAN) to improve the performance of EEG based continuous speech recognition systems. We improve the results demonstrated by authors in [1] using their data sets for for some of the test

Marcus Hoerger, Hanna Kurniawati, Alberto Elfes

Motion planning under uncertainty is essential for reliable robot operation. Despite substantial advances over the past decade, the problem remains difficult for systems with complex dynamics. Most state-of-the-art methods perform search that relies on a large number of forward simulations. For systems with complex dynamics, this generally require costly num

Komal Chugh, Parul Gupta, Abhinav Dhall, Ramanathan Subramanian

We propose detection of deepfake videos based on the dissimilarity between the audio and visual modalities, termed as the Modality Dissonance Score (MDS). We hypothesize that manipulation of either modality will lead to dis-harmony between the two modalities, eg, loss of lip-sync, unnatural facial and lip movements, etc. MDS is computed as an aggregate of di

John Rhodes, Anne Schilling

We review the recent approach to Markov chains using the Karnofksy-Rhodes and McCammond expansions in semigroup theory by the authors and illustrate them by two examples.

Shivam Mahajan, Michael Tennenbaum, Sudhir N. Pathak, Devontae Baxter

When grains are added to a cylinder, the weight at the bottom is smaller than the total weight of the column, which is partially supported by the lateral walls through wall/grain frictional forces. This is known as the Janssen effect. Via a combined experimental and numerical investigation, here we demonstrate a reverse Jansen effect whereby the fraction of

Guangfeng Lin, Xiaobing Kang, Kaiyang Liao, Fan Zhao

Graph learning (GL) can dynamically capture the distribution structure (graph structure) of data based on graph convolutional networks (GCN), and the learning quality of the graph structure directly influences GCN for semi-supervised classification. Existing methods mostly combine the computational layer and the related losses into GCN for exploring the glob

Dan Huang, Shanshan Chen

In this paper, we consider the diffusive Nicholson's blowflies model in spatially heterogeneous environment when the diffusion rate is large. We show that the ratio of the average of the maximum per capita egg production rate to that of the death rate affects the dynamics of the model. The unique positive steady state is locally asymptotically stable if the

Damian Bogunowicz, Aleksandr Rybnikov, Komal Vendidandi, Fedor Chervinskii

Even though the peg-hole insertion is one of the well-studied problems in robotics, it still remains a challenge for robots, especially when it comes to flexibility and the ability to generalize. Successful completion of the task requires combining several modalities to cope with the complexity of the real world. In our work, we focus on the visual aspect of

Jin-Fan Hu, Ting-Zhu Huang, Liang-Jian Deng, Tai-Xiang Jiang

Hyperspectral images are of crucial importance in order to better understand features of different materials. To reach this goal, they leverage on a high number of spectral bands. However, this interesting characteristic is often paid by a reduced spatial resolution compared with traditional multispectral image systems. In order to alleviate this issue, in t

Gautam Krishna, Co Tran, Mason Carnahan, Ahmed Tewfik

The electroencephalography (EEG) signals recorded in parallel with speech are used to perform isolated and continuous speech recognition. During speaking process, one also hears his or her own speech and this speech perception is also reflected in the recorded EEG signals. In this paper we investigate whether it is possible to separate out this speech percep

Gautam Krishna, Co Tran, Mason Carnahan, Ahmed Tewfik

In [1,2] authors provided preliminary results for synthesizing speech from electroencephalography (EEG) features where they first predict acoustic features from EEG features and then the speech is reconstructed from the predicted acoustic features using griffin lim reconstruction algorithm. In this paper we first introduce a deep learning model that takes ra

T. Okudaira, T. Oku, T. Ino, H. Hayashida

We are developing a neutron polarizer with polarized $^3$He gas, referred to as a $^3$He spin filter, based on the Spin Exchange Optical Pumping (SEOP) for polarized neutron scattering experiments at Materials and Life Science Experimental Facility (MLF) of Japan Proton Accelerator Research Complex (J-PARC). A $^3$He gas-filling station was constructed at J-

Sarouyeh Khoshkholgh, Andrea Zunino, Klaus Mosegaard

Any search or sampling algorithm for solution of inverse problems needs guidance to be efficient. Many algorithms collect and apply information about the problem on the fly, and much improvement has been made in this way. However, as a consequence of the the No-Free-Lunch Theorem, the only way we can ensure a significantly better performance of search and sa

Łukasz Maślanka, Mikołaj Marciniak, Piotr Śniady

We consider the Robinson-Schensted-Knuth algorithm applied to a random input and investigate the shape of the bumping route (in the vicinity of the $y$-axis) when a specified number is inserted into a large Plancherel-distributed tableau. We show that after a projective change of the coordinate system the bumping route converges in distribution to the Poisso

Ao Huang, Sho Komukai, Tim Friede, Satoshi Hattori

Prospective registration of study protocols in clinical trial registries is a useful way to minimize the risk of publication bias in meta-analysis, and several clinical trial registries are available nowadays. However, they are mainly used as a tool for searching studies and information submitted to the registries has not been utilized as efficiently as it c

Rui Kou, Ying Zhong, Qingyang Wang, Jeongmin Kim

Polymer-air multilayer (PAM) was developed to decrease the heat loss through window glass panes. A PAM consists of a few polymer films separated from each other by air gaps. Thanks to the excellent optical properties of the polymer films, the visual transmittance of PAM is higher than 70%, and the haze is less than 2%. PAM not only has mechanisms to reduce t

Shun Kumagai

Nontrivial examples of Teichm\"uller curves have been studied systematically with notions of combinatorics invariant under affine homeomorphisms. An origami (square-tiled surface) induces a Teichm\"uller curve for which the absolute Galois group acts on the embedded curve in the moduli space. In this paper, we study general origamis not admitting pure half-t

Priyanka Sinha, Shuichi Murakami, Saurabh Basu

Here we comprehensively investigate Landau levels, Hofstadter butterfly and transport properties of a semi-Dirac nanoribbon in a perpendicular magnetic field using a recently developed real-space implementation of the Kubo formula based on Kernel Polynomial Method. A Dirac ribbon is considered to compare and contrast our results for a semi-Dirac system. We f

Xiaofeng Xue, Linjie Zhao

We consider the one dimensional symmetric simple exclusion process with a slow bond. In this model, particles cross each bond at rate $N^2$, except one particular bond, the slow bond, where the rate is $N$. Above, $N$ is the scaling parameter. This model has been considered in the context of hydrodynamic limits, fluctuations and large deviations. We investig

Kashin Sugishita, Naoki Masuda

Changes in air transport networks over time may be induced by competition among carriers, changes in regulations on airline industry, and socioeconomic events such as terrorist attacks and epidemic outbreaks. Such network changes may reflect corporate strategies of each carrier. In the present study, we propose a framework for analyzing evolution patterns in

Nikhil Das, Michael C. Yip

Evaluating distance to collision for robot manipulators is useful for assessing the feasibility of a robot configuration or for defining safe robot motion in unpredictable environments. However, distance estimation is a timeconsuming operation, and the sensors involved in measuring the distance are always noisy. A challenge thus exists in evaluating the expe

Harim Lee, Myeung Un Kim, Yeongjun Kim, Hyeonsu Lyu

The robot market has been growing significantly and is expected to become 1.5 times larger in 2024 than what it was in 2019. Robots have attracted attention of security companies thanks to their mobility. These days, for security robots, unmanned aerial vehicles (UAVs) have quickly emerged by highlighting their advantage: they can even go to any hazardous pl

B. Miao, L. Feder, J. E. Shrock, A. Goffin

We demonstrate a new highly tunable technique for generating meter-scale low density plasma waveguides. Such guides can enable electron acceleration to tens of GeV in a single stage. Plasma waveguides are imprinted in hydrogen gas by optical field ionization induced by two time-separated Bessel beam pulses: The first pulse, a J_0 beam, generates the core of

Sundara Rajan Srinivasavaradhan, Michelle Du, Suhas Diggavi, Christina Fragouli

Recent advances in DNA sequencing technology and DNA storage systems have rekindled the interest in deletion channels. Multiple recent works have looked at variants of sequence reconstruction over a single and over multiple deletion channels, a notoriously difficult problem due to its highly combinatorial nature. Although works in theoretical computer scienc

Manasvi Lingam, Abraham Loeb

Aquatic biospheres reliant on oxygenic photosynthesis are expected to play an important role on Earth-like planets endowed with large-scale oceans insofar as carbon fixation (i.e., biosynthesis of organic compounds) is concerned. We investigate the properties of aquatic biospheres comprising Earth-like biota for habitable rocky planets orbiting Sun-like star

Ruotian Luo, Greg Shakhnarovich

We develop and evaluate captioning models that allow control of caption length. Our models can leverage this control to generate captions of different style and descriptiveness.

Rui Kou, Ying Zhong, Yu Qiao

Corona charging a free-standing polymer film can produce a quasi-permanent potential difference across the film thickness, while the absolute amplitude of surface voltage may be highly sensitive to the free charges. To precisely control the voltage distribution, we investigated the flow electrification technology, by exposing corona-charged polyethylene tere

Yangyang Cheng, Yifan Jing, Lina Li, Guanghui Wang

For a set of positive integers $A \subseteq [n]$, an $r$-coloring of $A$ is rainbow sum-free if it contains no rainbow Schur triple. In this paper we initiate the study of the rainbow Erd\H{o}s-Rothchild problem in the context of sum-free sets, which asks for the subsets of $[n]$ with the maximum number of rainbow sum-free $r$-colorings. We show that for $r=

Keisuke Sugiura, Hiroki Matsutani

An efficient hardware implementation for Simultaneous Localization and Mapping (SLAM) methods is of necessity for mobile autonomous robots with limited computational resources. In this paper, we propose a resource-efficient FPGA implementation for accelerating scan matching computations, which typically cause a major bottleneck in 2D LiDAR SLAM methods. Scan

Alireza Vahid, Shih-Chun Lin, I-Hsiang Wang

Achievable data rates in wireless systems rely heavily on the available channel state information (CSI) throughout the network. However, feedback links, which provide this information, are scarce, unreliable, and subject to security threats. In this work, we study the impact of having intermittent feedback links on the capacity region of the canonical two-us

Hiroshi Yano, Yudai Suzuki, Kohei M. Itoh, Rudy Raymond

Recent days have witnessed significant interests in applying quantum-enhanced techniques for solving a variety of machine learning tasks. Variational methods that use quantum resources of imperfect quantum devices with the help of classical computing techniques are popular for supervised learning. Variational quantum classification (VQC) is one of such metho

Toshinori Hayashi, Yasushi Suto

A number of ongoing surveys are likely to discover star-black hole binaries in our Galaxy in the near future. A fraction of them may be triple systems comprising an inner binary, instead of a single black hole, which might be progenitors of binary black holes (BBHs) routinely discovered now from the gravitational wave. We extend our previous proposal to loca

Kiattikun Chobtham, Anthony C. Constantinou

Latent variables may lead to spurious relationships that can be misinterpreted as causal relationships. In Bayesian Networks (BNs), this challenge is known as learning under causal insufficiency. Structure learning algorithms that assume causal insufficiency tend to reconstruct the ancestral graph of a BN, where bi-directed edges represent confounding and di

Matthew B. James, Graeme L. White, Stephen G. Bosi, Rod R. Letchford

We compared several techniques for calibrating angular separation between wide (>1 arcsec) pairs. These techniques are (i) reference pair calibration using {\alpha} Cen AB orbital parameters, (ii) the video drift method, and (iii) the utilisation of an aperture diffraction grating with red filters of different passbands. Separations of 62 pairs were determin

Ken Kamano, Tomokazu Onozuka

Ohno's relation is a well-known relation on the field of the multiple zeta values and has an interpolation to complex function. In this paper, we call its complex function Ohno function and study it. We consider the region of absolute convergence, give some new expressions, and show new relations of the function. We also give a direct proof of the interpolat

Scott E. Hoffmann

The aim of this paper is to critically examine claims that it is possible to construct point-localized state vectors for the photon. We supply a brief proof of the impossibility of this. Then it is found that the authors making these claims use a non-standard scalar product, not equal to the quantum-mechanical one. This alternative scalar product is found to

Takanobu Hara

We consider the existence of positive solutions to weighted quasilinear elliptic differential equations of the type \[ \begin{cases} - \Delta_{p, w} u = \sigma u^{q} & \text{in $\Omega$}, \\ u = 0 & \text{on $\partial \Omega$} \end{cases} \] in the sub-natural growth case $0 < q < p - 1$, where $\Omega$ is a bounded domain in $\mathbb{R}^{n}$, $\Delta_{p, w}

Rongfang Wang, Fan Ding, Licheng Jiao, Jia-Wei Chen

Recently, many Convolution Neural Networks (CNN) have been successfully employed in bitemporal SAR image change detection. However, most of the existing networks are too heavy and occupy a large volume of memory for storage and calculation. Motivated by this, in this paper, we propose a lightweight neural network to reduce the computational and spatial compl

Biswajit Paul, Kaushiki Mukherjee

Steerability is a characteristic of quantum correlations lying in between entanglement and Bell nonlocality. Understanding how these steering correlations can be shared between different parties has profound applications in ensuring security of quantum communication protocols. Here we show that at most two bipartite reduced states of a three qubit state can

Keian Noori, Nicholas Lin Quan Cheng, Fengyuan Xuan, Su Ying Quek

Two-dimensional (2D) materials are increasingly being used as active components in nanoscale devices. Many interesting properties of 2D materials stem from the reduced and highly non-local electronic screening in two dimensions. While electronic screening within 2D materials has been studied extensively, the question still remains of how 2D substrates screen

Chao Liu, Xin Xia, David Lo, Zhiwei Liu

To accelerate software development, developers frequently search and reuse existing code snippets from a large-scale codebase, e.g., GitHub. Over the years, researchers proposed many information retrieval based models for code search, but they fail to connect the semantic gap between query and code. An early successful deep learning based model DeepCS solved

J. Derek Tucker, Lyndsay Shand, Kenny Chowdhary

Functional data registration is a necessary processing step for many applications. The observed data can be inherently noisy, often due to measurement error or natural process uncertainty, which most functional alignment methods cannot handle. A pair of functions can also have multiple optimal alignment solutions, which is not addressed in current literature

Un-Gi Jong, Chol-Jun Yu, Yun-Hyok Kye, Song-Nam Hong

In spite of intensive studies on the chalcogenides as conventional thermoelectrics, it remains a challenge to find a proper material with high electrical but low thermal conductivities. In this work, we introduced a new class of thermoelectrics, Ge-based inorganic halide perovskites \ce{CsGeX3} (X = I, Br, Cl), which were already known as a promising candida

Jun-Hao Chen, Samuel Yen-Chi Chen, Yun-Cheng Tsai, Chih-Shiang Shur

Deep learning (DL) has been applied extensively in a wide range of fields. However, it has been shown that DL models are susceptible to a certain kinds of perturbations called \emph{adversarial attacks}. To fully unlock the power of DL in critical fields such as financial trading, it is necessary to address such issues. In this paper, we present a method of

Xin Wang, Wei Xiong

During powder production, the pre-alloyed powder composition often deviates from the target composition leading to undesirable properties of additive manufacturing (AM) components. Therefore, we developed a method to perform high-throughput calculation and uncertainty quantification by using a CALPHAD-based ICME framework (CALPHAD: calculations of phase diag

Deok-Kyeong Jang, Sung-Hee Lee

This paper presents a novel recurrent neural network-based method to construct a latent motion manifold that can represent a wide range of human motions in a long sequence. We introduce several new components to increase the spatial and temporal coverage in motion space while retaining the details of motion capture data. These include new regularization term

Sully Billingsley

Petabytes of data are generated at the Atlas experiment at the Large Hadron Collider however not all of it is necessarily interesting, so what do we do with all of this data and how do we find these interesting needles in an uninteresting haystack. This problem can possibly be solved through the process of anomaly detection. In this document, Investigation I

I. Y. Dodin, E. A. Startsev

Quantum computing is gaining increased attention as a potential way to speed up simulations of physical systems, and it is also of interest to apply it to simulations of classical plasmas. However, quantum information science is traditionally aimed at modeling linear Hamiltonian systems of a particular form that is found in quantum mechanics, so extending th

S. C. Das, K. Mandal, N. Khamaru, S. Pramanick

The structural and magnetic behavior of Mn-site doped intermetallic manganese silicide alloys of nominal compositions Mn$_{5-x}$A$_x$Si$_3$ ($x$ = 0.05, 0.1, 0.2 and A = Ni, Cr) have been investigated with a focus to the inverted hysteresis behavior and thermomagnetic irreversibility. Room temperature x-ray powder diffraction data confirm that all the doped

Ricardo Burity, Stefan Tohaneanu

In this paper we give full classification of rank 3 line arrangements in $\mathbb P^2$ (over a field of characteristic 0) that have a minimal logarithmic derivation of degree 3. The classification presents their defining polynomials, up to a change of variables, with their corresponding affine pictures. We also analyze the shape of such a logarithmic derivat

M. Bailes, A. Jameson, F. Abbate, E. D. Barr

We describe system verification tests and early science results from the pulsar processor (PTUSE) developed for the newly-commissioned 64-dish SARAO MeerKAT radio telescope in South Africa. MeerKAT is a high-gain (~2.8 K/Jy) low-system temperature (~18 K at 20cm) radio array that currently operates from 580-1670 MHz and can produce tied-array beams suitable

Denisse Martinez Mejorado, Razieh Saremi, Ye Yang, Jose E. Ramirez-Marquez

Context: The success of software crowdsourcing depends on steady tasks supply and active worker pool. Existing analysis reveals an average task failure ratio of 15.7% in software crowdsourcing market. Goal: The objective of this study is to empirically investigate patterns and effect of task diversity in software crowdsourcing platform in order to improve th

Everett W. Howe

We show that for a large class of rings $R$, the number of principally polarized abelian varieties over a finite field in a given simple ordinary isogeny class and with endomorphism ring $R$ is equal either to 0, or to a ratio of class numbers associated to $R$, up to some small computable factors. This class of rings includes the maximal order of the CM fie

Keisuke Kitayama, Masahito Mochizuki

The emergence of photo-induced topological phases and their phase transitions are theoretically predicted in organic salt $\alpha$-(BEDT-TTF)$_2$I$_3$, which possesses inclined Dirac cones in its band structure. By analyzing a photo-driven tight-binding model describing conduction electrons in the BEDT-TTF layer using the Floquet theorem, we demonstrate that

Jordan Urbaczek, Razieh Saremi, Mostaan Lotfalian Saremi, Julian Togelius

Context: Highly dynamic and competitive crowdsourcing software development (CSD) marketplaces may experience task failure due to unforeseen reasons, such as increased competition over shared supplier resources, or uncertainty associated with a dynamic worker supply. Existing analysis reveals an average task failure ratio of 15.7\% in software crowdsourcing m

Haytham M. Fayek, Anurag Kumar

Recognizing sounds is a key aspect of computational audio scene analysis and machine perception. In this paper, we advocate that sound recognition is inherently a multi-modal audiovisual task in that it is easier to differentiate sounds using both the audio and visual modalities as opposed to one or the other. We present an audiovisual fusion model that lear

Aditi Jha, Joshua Peterson, Thomas L. Griffiths

Deep neural networks are increasingly being used in cognitive modeling as a means of deriving representations for complex stimuli such as images. While the predictive power of these networks is high, it is often not clear whether they also offer useful explanations of the task at hand. Convolutional neural network representations have been shown to be predic

H. -F. Wang, Y. Huang, H. -W. Zhang, M. López-Corredoira

We revisit the diagonal ridge feature (diagonal distributions in the $R, v_{\phi}$ plane) found in $Gaia$ and present timing analysis for it between Galactocentric distances of $R=7.5$ and 12 \,kpc, using Main-Sequence-Turn-Off and OB stars selected from the LAMOST Galactic spectroscopic surveys. We recover the ridge pattern in the $R$--$v_{\phi}$ plane colo

Konrad Gajewski, Sebastian Ferrando, Pablo Olivares

The shortcomings of the popular Black-Scholes-Merton (BSM) model have led to models which could more accurately model the behavior of the underlying assets in energy markets, particularly in electricity and future oil prices. In this paper we consider a class of regime switching time-changed Levy processes, which builds upon the BSM model by incorporating ju

TG Ritto, FA Rochinha

This work is interested in digital twins, and the development of a simplified framework for them, in the context of dynamical systems. Digital twin is an ingenious concept that helps on organizing different areas of expertise aiming at supporting engineering decisions related to a specific asset; it articulates computational models, sensors, learning, real t

Yan Min, Mao Ye, Liang Tian, Yulin Jian

Feature selection is a widely used dimension reduction technique to select feature subsets because of its interpretability. Many methods have been proposed and achieved good results, in which the relationships between adjacent data points are mainly concerned. But the possible associations between data pairs that are may not adjacent are always neglected. Di

Qing Lu, Weizhe Zheng

Let $\mathbf{F}_q$ be a finite field of $q$ elements. We show that the normalized Jacobi sum $q^{-(m-1)/2}J(\chi_1,\dots,\chi_m)$ ($\chi_1\dotsm \chi_m$ nontrivial) is asymptotically equidistributed on the unit circle, when $\chi_1\in \mathcal{A}_1,\dots, \chi_m\in \mathcal{A}_m$ run through arbitrary sets of nontrivial multiplicative characters of $\mathbf{

A. Martinez Torres, K. P. Khemchandani, L. Roca, E. Oset

We present a work which is meant to inspire the few-body practitioners to venture into the study of new, more exotic, systems and to hadron physicists, working mostly on two-body problems, to move in the direction of studying related few-body systems. For this purpose we devote the discussions in the introduction to show how the input two-body amplitudes can

Aleks J. Gurfinkel, Per Arne Rikvold

Centrality, which quantifies the "importance" of individual nodes, is among the most essential concepts in modern network theory. Most prominent centrality measures can be expressed as an aggregation of influence flows between pairs of nodes. As there are many ways in which influence can be defined, many different centrality measures are in use. Parametrized

Dong Yang, Holger Roth, Xiaosong Wang, Ziyue Xu

Object segmentation plays an important role in the modern medical image analysis, which benefits clinical study, disease diagnosis, and surgery planning. Given the various modalities of medical images, the automated or semi-automated segmentation approaches have been used to identify and parse organs, bones, tumors, and other regions-of-interest (ROI). Howev

Shengxue He

The unreal high flows may appear on the actually congested links in the result when a monotonically increasing link travel time function of flow volume is adopted in traffic assignment. The fixed link flow results of a static traffic assignment model (TAM) make it nearly impossible to investigate and make use of the actual evolution of congested zones over t

Zhengzhong Tu, Yilin Wang, Neil Birkbeck, Balu Adsumilli

Recent years have witnessed an explosion of user-generated content (UGC) videos shared and streamed over the Internet, thanks to the evolution of affordable and reliable consumer capture devices, and the tremendous popularity of social media platforms. Accordingly, there is a great need for accurate video quality assessment (VQA) models for UGC/consumer vide

Shengxue He

A linear programming (LP) model is proposed to improve the performance of a controlled freeway during an emergency evacuation. Based on reasonable assumptions, the main relationships among key factors are kept without the uncertain impact of subordinate factors in this model. Three vital issues related to optimal freeway control during an emergency evacuatio

W. Horiuchi, T. Inakura

Background: Nuclear radius is one of the most important and basic properties of atomic nuclei and its evolution is closely related to the saturation of the nuclear density in the internal region but the systematics of the nuclear radii for the neutron-rich unstable nuclei is not well known. Purpose: Motivated by the recent interaction cross section measureme

Chris Blake, Alexandra Amon, Marika Asgari, Maciej Bilicki

The physics of gravity on cosmological scales affects both the rate of assembly of large-scale structure, and the gravitational lensing of background light through this cosmic web. By comparing the amplitude of these different observational signatures, we can construct tests that can distinguish general relativity from its potential modifications. We used th

Pablo Olivares

The objective of the paper is to price weather contracts using temperature as the underlying process when the later follows a mean-reverting dynamics driven by a time-changed Brownian motion coupled to a Gamma Levy subordinator and time-dependent deterministic volatility. This type of model captures the complexity of the temperature dynamic providing a more

Gustavo Terra Bastos

The use of permutation polynomials has appeared, along to their compositional inverses, as a good choice in the implementation of cryptographic systems. Hence, there has been a demand for constructions of these polynomials which coefficients belong to a finite field. As a particular case of permutation polynomial, involution is highly desired since its compo