March 2020 arXiv papers — page 125

Ryota Kyokawa, Hajime Moriya, Hiroshi Tamura

We consider an open Dicke model comprising a single infinite-component vector spin and a single-mode harmonic oscillator which are connected by Jaynes--Cummings-type interaction between them. This open quantum model is referred to as the OISD (Open Infinite-component Spin Dicke) model. The algebraic structure of the OISD Liouvillian is studied in terms of su

Pingping Zhu, Chang Liu, Silvia Ferrari

This paper presents an adaptive online distributed optimal control approach that is applicable to optimal planning for very-large-scale robotics systems in highly uncertain environments. This approach is developed based on the optimal mass transport theory. It is also viewed as an online reinforcement learning and approximate dynamic programming approach in

Sadegh Raeisi

The Shannon's bound for compression is one of the key restrictions for the compression of quantum information. Here we show that the unitarity of the compression operation imposes new bounds on the compression that are more limiting than Shannon's compression bound. This translates to a no-go theorem for the purification of quantum states. For a spec

Mir Faizal, Hrishikesh Patel

It is known that probing gravity in the submillimeter-micrometer range is difficult due to the relative weakness of the gravitational force. We intend to overcome this challenge by using extreme temporal precision to monitor transient events in a gravitational field. We propose a compressed ultrafast photography system called T-CUP to serve this purpose. We

Suho Ryu, Jounghun Lee

A new diagnostics to break the degeneracy between the total neutrino mass ($M_ν$) and the primordial power spectrum amplitude ($σ_{8}$) by using the drifting coefficient of the field cluster mass function is presented. Analyzing the data from the Cosmological Massive Neutrino Simulations, we first determine the numerical mass functions of the field clusters

Howard Heaton, Xiaohan Chen, Zhangyang Wang, Wotao Yin

Applications abound in which optimization problems must be repeatedly solved, each time with new (but similar) data. Analytic optimization algorithms can be hand-designed to provably solve these problems in an iterative fashion. On one hand, data-driven algorithms can "learn to optimize" (L2O) with much fewer iterations and similar cost per iteration

Sen Liu, Branden B. Kappes, Behnam Amin-ahmadi, Othmane Benafan

Machine learning (ML) is shown to predict new alloys and their performances in a high dimensional, multiple-target-property design space that considers chemistry, multi-step processing routes, and characterization methodology variations. A physics-informed featured engineering approach is shown to enable otherwise poorly performing ML models to perform well

Li Sun, Daniel Adolfsson, Martin Magnusson, Henrik Andreasson

This paper proposes a novel approach for global localisation of mobile robots in large-scale environments. Our method leverages learning-based localisation and filtering-based localisation, to localise the robot efficiently and precisely through seeding Monte Carlo Localisation (MCL) with a deep-learned distribution. In particular, a fast localisation system

Fanyi Xiao, Ling Pei, Lei Chu, Danping Zou

Sensor-based human activity recognition (HAR) is now a research hotspot in multiple application areas. With the rise of smart wearable devices equipped with inertial measurement units (IMUs), researchers begin to utilize IMU data for HAR. By employing machine learning algorithms, early IMU-based research for HAR can achieve accurate classification results on

Bowen Gang, Gourab Mukherjee, Wenguang Sun

We consider the problem of simultaneous estimation of a sequence of dependent parameters that are generated from a hidden Markov model. Based on observing a noise contaminated vector of observations from such a sequence model, we consider simultaneous estimation of all the parameters irrespective of their hidden states under square error loss. We study the r

Jae Sung Lee, Jong-Min Park, Hyun-Myung Chun, Jaegon Um

An engine producing a finite power at the ideal (Carnot) efficiency is a dream engine, which is not prohibited by the thermodynamic second law. Some years ago, a two-terminal heat engine with {\em asymmetric} Onsager coefficients in the linear response regime was suggested by Benenti, Saito, and Casati [Phys. Rev. Lett. {\bf 106}, 230602 (2011)], as a protot

Shinji Hara, Tetsuya Iwasaki, Yutaka Hori

This paper is concerned with robust instability analysis of linear feedback systems subject to a dynamic uncertainty. The work is motivated by, and provides a basic foundation for, a more challenging problem of analyzing persistence of oscillations in nonlinear dynamical systems. We first formalize the problem for SISO LTI systems by introducing a notion of

Tian-Peng Tang, Hang Zhou, Ning Liu

Top squark (stop) is a crucial part of supersymmetric models (SUSY) to understand the naturalness problem. Other than the traditional stop pair production, the single production via electroweak interaction provides signals with distinctive features which could help confirm the existence of the top squark. In this paper, we investigate the observability of st

Eduardo Pavez, Benjamin Girault, Antonio Ortega, Philip A. Chou

We introduce the Region Adaptive Graph Fourier Transform (RA-GFT) for compression of 3D point cloud attributes. The RA-GFT is a multiresolution transform, formed by combining spatially localized block transforms. We assume the points are organized by a family of nested partitions represented by a rooted tree. At each resolution level, attributes are processe

Esdras Medeiros, Jorge Lira, Romildo Silva, Caio Azevedo

In this paper, we apply the Logistic PCA (LPCA) as a dimensionality reduction tool for visualizing patterns and characterizing the relevance of mathematics abilities from a given population measured by a large-scale assessment. We establish an equivalence of parameters between LPCA, Inner Product Representation (IPR) and the two paramenter logistic model (2P

Cayo Dória, Plinio G. P. Murillo

In this article we construct a sequence $\{M_i\}$ of non compact finite volume hyperbolic $3$-manifolds whose kissing number grows at least as $\mathrm{vol}(M_i)^{\frac{31}{27}-ε}$ for any $ε>0$. This extends a previous result due to Schmutz in dimension $2$.

Hongxiang Qiu, Alex Luedtke, Marco Carone

Suppose that we wish to estimate a finite-dimensional summary of one or more function-valued features of an underlying data-generating mechanism under a nonparametric model. One approach to estimation is by plugging in flexible estimates of these features. Unfortunately, in general, such estimators may not be asymptotically efficient, which often makes these

Geon Lee, Jihoon Ko, Kijung Shin

Hypergraphs naturally represent group interactions, which are omnipresent in many domains: collaborations of researchers, co-purchases of items, joint interactions of proteins, to name a few. In this work, we propose tools for answering the following questions in a systematic manner: (Q1) what are structural design principles of real-world hypergraphs? (Q2)

Taisuke Kobayashi

A variational autoencoder (VAE) derived from Tsallis statistics called q-VAE is proposed. In the proposed method, a standard VAE is employed to statistically extract latent space hidden in sampled data, and this latent space helps make robots controllable in feasible computational time and cost. To improve the usefulness of the latent space, this paper focus

Parker C. Lusk, Xiaoyi Cai, Samir Wadhwania, Aleix Paris

Reliance on external localization infrastructure and centralized coordination are main limiting factors for formation flying of vehicles in large numbers and in unprepared environments. While solutions using onboard localization address the dependency on external infrastructure, the associated coordination strategies typically lack collision avoidance and sc

Paul Pu Liang, Jeffrey Chen, Ruslan Salakhutdinov, Louis-Philippe Morency

Several recent works have found the emergence of grounded compositional language in the communication protocols developed by mostly cooperative multi-agent systems when learned end-to-end to maximize performance on a downstream task. However, human populations learn to solve complex tasks involving communicative behaviors not only in fully cooperative settin

Weonyoung Joo, Dongjun Kim, Seungjae Shin, Il-Chul Moon

Estimating the gradients of stochastic nodes in stochastic computational graphs is one of the crucial research questions in the deep generative modeling community, which enables the gradient descent optimization on neural network parameters. Stochastic gradient estimators of discrete random variables are widely explored, for example, Gumbel-Softmax reparamet

Hidekazu Furusho, Nao Komiyama

We introduce the Kashiwara-Vergne bigraded Lie algebra associated with a finite abelian group and give its mould theoretic reformulation. By using the mould theory, we show that it includes Goncharov's dihedral Lie algebra, which generalizes the result of Raphael and Schneps.

Quentin Changeat, Ahmed Al-Refaie, Lorenzo V. Mugnai, Billy Edwards

In this work, we present Alfnoor, a dedicated tool optimised for population studies of exoplanet atmospheres. Alfnoor combines the latest version of the retrieval algorithm TauREx 3, with the instrument noise simulator ArielRad and enables the simultaneous retrieval analysis of a large sample of exo-atmospheres. We applied this tool to the Ariel list of plan

Ramsey Shadfan, Shen Wang, Sebastian A. Nugroho, Fengxin Chen

Drinking water distribution networks (WDN) are large-scale, dynamic systems spanning large geographic areas. Water networks include various components such as junctions, reservoirs, tanks, pipes, pumps, and valves. Hydraulic models for these components depicting mass and energy balance form nonlinear algebraic differential equations (NDAE). While control the

Andrea Bacigalupo, Luigi Gambarotta

The paper is focused on the dynamic homogenization of lattice-like materials with lumped mass at the nodes to obtain energetically consistent models providing accurate descriptions of the acoustic behavior of the discrete system. The equation of motion of the lattice is transformed according to a unitary approach aimed to identify equivalent non-local contin

Qi Wang, Jiefeng Liu, Yunhe Sheng

In this paper, we introduce the notion of Koszul-Vinberg-Nijenhuis structures on a left-symmetric algebroid as analogues of Poisson-Nijenhuis structures on a Lie algebroid, and show that a Koszul-Vinberg-Nijenhuis structure gives rise to a hierarchy of Koszul-Vinberg structures. We introduce the notions of ${\rm KVΩ}$-structures, pseudo-Hessian-Nijenhuis str

Mohammad Javad Shafiee, Ahmadreza Jeddi, Amir Nazemi, Paul Fieguth

This paper analyzes the robustness of deep learning models in autonomous driving applications and discusses the practical solutions to address that.

Francesco Bardozzo, Borja De La Osa, Lubomira Horanska, Javier Fumanal-Idocin

Adaptive binarization methodologies threshold the intensity of the pixels with respect to adjacent pixels exploiting the integral images. In turn, the integral images are generally computed optimally using the summed-area-table algorithm (SAT). This document presents a new adaptive binarization technique based on fuzzy integral images through an efficient de

Al Amin Hosain, Panneer Selvam Santhalingam, Parth Pathak, Huzefa Rangwala

American Sign Language recognition is a difficult gesture recognition problem, characterized by fast, highly articulate gestures. These are comprised of arm movements with different hand shapes, facial expression and head movements. Among these components, hand shape is the vital, often the most discriminative part of a gesture. In this work, we present an a

Michael Brunner, Clemens Sauerwein, Michael Felderer, Ruth Breu

Information security management aims at ensuring proper protection of information values and information processing systems (i.e. assets). Information security risk management techniques are incorporated to deal with threats and vulnerabilities that impose risks to information security properties of these assets. This paper investigates the current state of

Chi-Chun Zhou, Wu-Sheng Dai

Beyond Bose and Fermi statistics, there still exist various kinds of generalized quantum statistics. Two ways to approach generalized quantum statistics: (1) in quantum mechanics, generalize the permutation symmetry of the wave function and (2) in statistical mechanics, generalize the maximum occupation number of quantum statistics. The connection between th

Umberto d'Ortona, Nathalie Thomas

Dry granular material flowing on rough inclines can experience a self-induced Rayleigh-Taylor (RT) instability followed by the spontaneous emergence of convection cells. For this to happen, particles are different in size and density, the larger particles are the denser but still segregate toward the surface. When the flow is, as usual, initially made of two

Jakob Kruse

Mixture Density Networks are a tried and tested tool for modelling conditional probability distributions. As such, they constitute a great baseline for novel approaches to this problem. In the standard formulation, an MDN takes some input and outputs parameters for a Gaussian mixture model with restrictions on the mixture components' covariance. Since co

M. Lotfizadeh

It has been constructed the quantum super fuzzy Dirac and chirality operators on q-deformed super fuzzy sphere. Using the quantum super fuzzy Ginsparg-Wilson algebra, it has been studied the q-deformed super gauged fuzzy Dirac and chirality operators in instanton sector. It has been showed that they have correct commutative limit in the limit case when nonco

Xiao Xu, John T. Foster

The influence function in peridynamic material models has a large effect on the dynamic behavior of elastic waves and in turn can greatly effect dynamic simulations of fracture propagation and material failure. Typically, the influence functions that are used in peridynamic models are selected for their numerical properties without regard to physical conside

Muhammad Asif, Oleksiy Krupin, Wei Ru Wong, Zohreh Hirbodvash

Biosensors exploiting long-range surface plasmon polariton (LRSPP) waveguides comprised of Au stripes embedded in Cytop with integrated and encapsulated microfluidic channels are fabricated and demonstrated. A fabrication approach was devised where the lower cladding and recessed Au stripes are fabricated on a Si substrate, and the upper cladding and microfl

Junchao Zhang

Image interpolation is a special case of image super-resolution, where the low-resolution image is directly down-sampled from its high-resolution counterpart without blurring and noise. Therefore, assumptions adopted in super-resolution models are not valid for image interpolation. To address this problem, we propose a novel image interpolation model based o

Hirshikesh, Emilio Martínez-Pañeda, Sundararajan Natarajan

In this work, we extend the recently proposed adaptive phase field method to model fracture in orthotropic functionally graded materials (FGMs). A recovery type error indicator combined with quadtree decomposition is employed for adaptive mesh refinement. The proposed approach is capable of capturing the fracture process with a localized mesh refinement that

Jun Chen, Yong Liu, Hao Zhang, Shengnan Hou

The quantized neural networks (QNNs) can be useful for neural network acceleration and compression, but during the training process they pose a challenge: how to propagate the gradient of loss function through the graph flow with a derivative of 0 almost everywhere. In response to this non-differentiable situation, we propose a novel Asymptotic-Quantized Est

Israel Perez, Tareik Netro, Mario Vazquez, Jose Elizalde

We have designed and constructed a compact rotary substrate heater for the temperature range from 25 $^\circ$C to 700 $^\circ$C. The heater can be implemented in any deposition system where crystalline samples are needed. Its main function is to provide a heat treatment in situ during film growth. The temperature is monitored and controlled by a temperature

Luis Ramirez Camargo, Johannes Schmidt

Modelling long time series of photovoltaic electricity generation in high temporal resolution using reanalysis data has become a commonly used alternative to assess the viability of systems with high shares of renewables, their risks of failure and probability of extreme events. While there is a considerable amount of literature evaluating the accuracy of th

Lennart Fernandes, Jacques Tempere

Empirical distributions of wealth and income can be reproduced using simplified agent-based models of economic interactions, analogous to microscopic collisions of gas particles. Building upon these models of freely interacting agents, we explore the effect of a segregated economic network in which interactions are restricted to those between agents of simil

Suat Gumussoy, Marc Millstone, Michael L. Overton

We report on our experience with strong stabilization using HIFOO, a toolbox for H-infinity fixed-order controller design. We applied HIFOO to 21 fixed-order stable H-infinity controller design problems in the literature, comparing the results with those published for other methods. The results show that HIFOO often achieves good H-infinity performance with

Anthony Rizzi

A simple way, accessible to undergraduates, is given to understand measurements in quantum mechanics. The ensemble interpretation of quantum mechanics is natural and provides this simple access to the measurement problem. This paper explains measurement in terms of this relatively young interpretation, first made rigorous by L. Ballentine starting in the 197

Ian Mathews, Bolun Xu, Wei He, Vanessa Barreto

While the use of energy storage combined with grid-scale photovoltaic power plants continues to grow, given current lithium-ion battery prices, there remains uncertainty about the profitability of these solar-plus-storage projects. At the same time, the rapid proliferation of electric vehicles is creating a fleet of millions of lithium-ion batteries that wil

S. Son

A scheme of THz radiation using two moderately intense lasers and a moderately relativistic electron beam is proposed. In the scheme, a laser encounters a co-propagating relativistic electron beam, and excites plasmons via the two-plasmon decay. The excited plasmons will emit the THz radiations, interacting with the second laser via the Raman scattering. Our

S. Son

A scheme of soft x-ray lasers is proposed. The backward Raman scattering between an intense visible-light laser and a relativistic electron beam results in sofr x-ray via the Doppler shift. One of the most intense soft x-ray light sources is contemplated.

S. Son

A new scheme of hard x-ray or gamma ray light is considered. The excitation of the Langmuir wave in an ultra dense electron beamm via the two-stream instabilities and the interaction of the excited Langmuir wave with the visible light-laser results in hard x-ray or gamma ray via three-wave interaction. The analysis suggests that the hard x-ray with the wave-

Spencer Axani

The IceCube Neutrino Observatory is capable of performing a unique search for sterile neutrinos through the exploitation of a matter enhanced resonant neutrino oscillation phenomena. As atmospheric muon neutrinos pass the dense material within the Earth, neutral current elastic forward scattering is predicted to induce a transition into a sterile state. This

Tao Hu, Lichao Huang, Han Shen

Recent works in multiple object tracking use sequence model to calculate the similarity score between the detections and the previous tracklets. However, the forced exposure to ground-truth in the training stage leads to the training-inference discrepancy problem, i.e., exposure bias, where association error could accumulate in the inference and make the tra

Hangyu Zhu, Yaochu Jin

Federated learning is a distributed machine learning approach to privacy preservation and two major technical challenges prevent a wider application of federated learning. One is that federated learning raises high demands on communication, since a large number of model parameters must be transmitted between the server and the clients. The other challenge is

Søren Toxvaerd

In 1687 Isaac Newton published PHILOSOPHIÆ\ NATURALIS PRINCIPIA MATHEMATICA, where the classical analytic dynamics was formulated. But Newton also formulated a discrete dynamics, which is the central difference algorithm, known as the Verlet algorithm. In fact Newton used the central difference to derive his second law. The central difference algorithm is us

Luoyi Zhang, Ming Xu

Unsupervised homogeneous network embedding (NE) represents every vertex of networks into a low-dimensional vector and meanwhile preserves the network information. Adjacency matrices retain most of the network information, and directly charactrize the first-order proximity. In this work, we devote to mining valuable information in adjacency matrices at a deep

Ion Matei, Johan de Kleer, Alexander Feldman, Rahul Rai

Reduced-order models that accurately abstract high fidelity models and enable faster simulation is vital for real-time, model-based diagnosis applications. In this paper, we outline a novel hybrid modeling approach that combines machine learning inspired models and physics-based models to generate reduced-order models from high fidelity models. We are using

Hiroshi Watanabe, Ken Shirakawa

In this paper, we consider a system of initial boundary value problems for parabolic equations, as a generalized version of the "$ ϕ$-$ η$-$ θ$ model" of grain boundary motion, proposed by Kobayashi [16]. The system is a coupled system of: an Allen--Cahn type equation as in (1.1) with a given temperature source; and a phase-field model of grain bound

Yusuke Tampo, Masaomi Tanaka, Keiichi Maeda, Naoki Yasuda

Rapidly evolving transients form a new class of transients which show shorter timescales of the light curves than those of typical core-collapse and thermonuclear supernovae. We performed a systematic search for rapidly evolving transients using the deep data taken with the Hyper Suprime-Cam Subaru Strategic Program Transient Survey. By measuring the timesca

Yi-Zheng Fan, Yi Wang, Jiang-Chao Wan

Among all uniform hypergraphs with even uniformity, the odd-transversal or odd-bipartite hypergraphs are more close to bipartite simple graphs from the viewpoint of both structure and spectrum. A hypergraph is called minimal non-odd-transversal if it is non-odd-transversal but deleting any edge results in an odd-transversal hypergraph. In this paper we give

Xinglong Liang, Jun Xu

ReLU (rectified linear units) neural network has received significant attention since its emergence. In this paper, a univariate ReLU (UReLU) neural network is proposed to both modelling the nonlinear dynamic system and revealing insights about the system. Specifically, the neural network consists of neurons with linear and UReLU activation functions, and th

Mousomi Bhakta, Patrizia Pucci

This paper deals with existence and multiplicity of positive solutions to the following class of nonlocal equations with critical nonlinearity: \begin{equation} \tag{$\mathcal E$} (-Δ)^s u = a(x) |u|^{2^*_s-2}u+f(x)\;\;\text{in}\;\mathbb{R}^{N}, \quad u \in \dot{H}^s(\mathbb{R}^{N}), \end{equation} where $s \in (0,1)$, $N>2s$, $2_s^*:=\frac{2N}{N-2s}$, $0< a

Konstantinos Gaitanas

In the current note, we present a new, short proof of the famous AM-GM-HM inequality using only induction and basic calculus.

Miquel Oliu-Barton

In this paper, we solve the constant-payoff conjecture formulated by Sorin, Venel and Vigeral (2010), for absorbing games with an arbitrary evaluation of the stage rewards. That is, the existence of a pair of asymptotically optimal strategies, indexed by the evaluation of the stage rewards, so that the average rewards are constant on any fraction of the game

Friedemann Queisser, Ralf Schützhold

In their article [Phys.\ Rev.\ C {\bf 100}, 064610 (2019)], Lv, Duan, and Liu study the enhancement of deuterium-tritium fusion reactions by the electromagnetic field of an x-ray free-electron laser (XFEL). While we support the general idea (which was put forward earlier in our rapid communication [Phys.\ Rev.\ C {\bf 100}, 041601(R) (2019)]), we find that t

Francesco Farina, Giuseppe Notarstefano

The recently developed Distributed Block Proximal Method, for solving stochastic big-data convex optimization problems, is studied in this paper under the assumption of constant stepsizes and strongly convex (possibly non-smooth) local objective functions. This class of problems arises in many learning and classification problems in which, for example, stron

Javier Vera, Felipe Urbina, Wenceslao Palma

Zipf&#39;s law establishes a scaling behavior for word-frequencies in large text corpora. The appearance of Zipfian properties in human language has been previously explained as an optimization problem for the interests of speakers and hearers. On the other hand, human-like vocabularies can be viewed as bipartite graphs. The aim here is double: within a bipa

Cheng-Hao Cai, Jing Sun, Gillian Dobbie

The B method has facilitated the development of software by specifying the design of software as abstract machines and formally verifying the correctness of the abstract machines. The quality of B abstract machines can significantly impact the quality of final software products. In this paper, we propose a set of criteria for measuring the quality of B abstr

Jieliang Luo, Hui Li

We present a novel technique called Dynamic Experience Replay (DER) that allows Reinforcement Learning (RL) algorithms to use experience replay samples not only from human demonstrations but also successful transitions generated by RL agents during training and therefore improve training efficiency. It can be combined with an arbitrary off-policy RL algorith

Jens Behley, Andres Milioto, Cyrill Stachniss

Panoptic segmentation is the recently introduced task that tackles semantic segmentation and instance segmentation jointly. In this paper, we present an extension of SemanticKITTI, which is a large-scale dataset providing dense point-wise semantic labels for all sequences of the KITTI Odometry Benchmark, for training and evaluation of laser-based panoptic se

Philip M. Singer, Arjun Valiya Parambathu, Xinglin Wang, Dilip Asthagiri

The mechanism behind the $^1$H NMR frequency dependence of $T_1$ and the viscosity dependence of $T_2$ for polydisperse polymers and bitumen remains elusive. We elucidate the matter through NMR relaxation measurements of polydisperse polymers over an extended range of frequencies ($f_0 = 0.01 \leftrightarrow$ 400 MHz) and viscosities ($η= 385 \leftrightarrow

Byung Hoon Ahn, Jinwon Lee, Jamie Menjay Lin, Hsin-Pai Cheng

Recent advances demonstrate that irregularly wired neural networks from Neural Architecture Search (NAS) and Random Wiring can not only automate the design of deep neural networks but also emit models that outperform previous manual designs. These designs are especially effective while designing neural architectures under hard resource constraints (memory, M

David Berthelot, Peyman Milanfar, Ian Goodfellow

Generating realistic images is difficult, and many formulations for this task have been proposed recently. If we restrict the task to that of generating a particular class of images, however, the task becomes more tractable. That is to say, instead of generating an arbitrary image as a sample from the manifold of natural images, we propose to sample images f

Ramón E. R. González, P. H. Figueirêdo, S. Coutinho

We propose a time-parameterized analogy between the thermodynamic behavior of a 3-level energy system and the progression of the HIV infection described by the cell population evolution generated by an appropriated cellular automata model. The development of internal energy and its fluctuations, and of the entropy of the 3-level energy system allows the iden

Tingting Zheng

In this paper, we consider the nonisentropic ideal polytropic Navier-Stokes equations to the Cauchy problem. The asymptotic stability of contact discontinuity is established under the condition that the initial perturbations are partly small but the strength of contact discontinuity can be suitably large. With this conditions, the bounds of density and tempe

Nicholas Galioto, Alex Gorodetsky

We evaluate the robustness of a probabilistic formulation of system identification (ID) to sparse, noisy, and indirect data. Specifically, we compare estimators of future system behavior derived from the Bayesian posterior of a learning problem to several commonly used least squares-based optimization objectives used in system ID. Our comparisons indicate th

Christopher H. Bennett, T. Patrick Xiao, Can Cui, Naimul Hassan

Machine learning implements backpropagation via abundant training samples. We demonstrate a multi-stage learning system realized by a promising non-volatile memory device, the domain-wall magnetic tunnel junction (DW-MTJ). The system consists of unsupervised (clustering) as well as supervised sub-systems, and generalizes quickly (with few samples). We demons

David C. McKay, Andrew W. Cross, Christopher J. Wood, Jay M. Gambetta

To improve the performance of multi-qubit algorithms on quantum devices it is critical to have methods for characterizing non-local quantum errors such as crosstalk. To address this issue, we propose and test an extension to the analysis of simultaneous randomized benchmarking data -- correlated randomized benchmarking. We fit the decay of correlated polariz

Paul A. Parker, Scott H. Holan, Nalini Ravishanker

Time series classification using novel techniques has experienced a recent resurgence and growing interest from statisticians, subject-domain scientists, and decision makers in business and industry. This is primarily due to the ever increasing amount of big and complex data produced as a result of technological advances. A motivating example is that of Goog

Natália P. Rezende, Alisson R. Cadore, Andreij C. Gadelha, Cíntia L. Pereira

This work presents a detailed experimental investigation of the interaction between molecular hydrogen (H$_2$) and monolayer MoS$_2$ field effect transistors (MoS$_2$ FET), aiming for sensing application. The MoS$_2$ FET exhibits a response to H$_2$ that covers a broad range of concentration (0.1 - 90%) at a relatively low operating temperature range (300-47

João Paulo Pinheiro, C. A. de S. Pires

It is well known that the Standard Model of the Electroweak interactions rests on a metastable vacuum. This can only be fixed by means of new physics. Presently neutrino physics provides the most intriguing framework to formulate new physics. This is so because, in addition to the problem of the lightness of the active standard neutrinos, currently MiniBooNE

Daniele Bonadiman, Alessandro Moschitti

An essential task of most Question Answering (QA) systems is to re-rank the set of answer candidates, i.e., Answer Sentence Selection (A2S). These candidates are typically sentences either extracted from one or more documents preserving their natural order or retrieved by a search engine. Most state-of-the-art approaches to the task use huge neural models, s

Mihalis Panteris, Simon Manschitz, Sylvain Calinon

This study proposes a novel imitation learning approach for the stochastic generation of human-like rhythmic wave gestures and their modulation for effective non-verbal communication through a probabilistic formulation using joint angle data from human demonstrations. This is achieved by learning and modulating the overall expression characteristics of the g

C. L. Pereira, A. R. Cadore, N. P. Rezende, A. Gadelha

In this work, we present an investigation regarding how and why molecular hydrogen changes the electronic properties of graphene field effect transistors. We demonstrate that interaction with H2 leads to local doping of graphene near of the graphene-contact heterojunction. We also show that such interaction is strongly dependent on the characteristics of the

Guillaume Gautier, Rémi Bardenet, Michal Valko

We study sampling algorithms for $β$-ensembles with time complexity less than cubic in the cardinality of the ensemble. Following Dumitriu & Edelman (2002), we see the ensemble as the eigenvalues of a random tridiagonal matrix, namely a random Jacobi matrix. First, we provide a unifying and elementary treatment of the tridiagonal models associated to the thr

Abhijit Chakraborty, Yuichi Ikeda

We study on topological properties of global supply chain network in terms of degree distribution, hierarchical structure, and degree-degree correlation in the global supply chain network. The global supply chain data is constructed by collecting various company data from the web site of Standard & Poor&#39;s Capital IQ platform in 2018. The in- and out-degr

Jacob R. Lindale, Shannon L. Eriksson, Christian P. N. Tanner, Warren S. Warren

Many important applications in biochemistry, materials science, and catalysis sit squarely at the interface between quantum and statistical mechanics: coherent evolution is interrupted by discrete events, such as binding of a substrate or isomerization. Theoretical models for such dynamics usually truncate the incorporation of these events to the linear-resp

Emily Diller, Jason Parker

Glioblastoma multiform carries a dismal prognosis with poor response to gold standard treatment. Innovative data analysis methods have been developed to characterize tumor genomic expression with histologic features. In a clinical setting, biopsy selection methods may be constrained by time and financial burden to the patient. Thus, we investigate the impact

Amit Kachroo, Sabit Ekin, Ali Imran

In this paper, we present the case of utilizing interference temperature (IT) as a dynamic quantity rather than as a fixed quantity in an orthogonal frequency division multiple access (OFDMA) based spectrum sharing systems. The fundamental idea here is to reflect the changing capacity demand of primary user (PU) over time in setting the interference power th

Iu. A. Skorodumina, G. V. Fedotov, R. W. Gothe

This study introduces common parameterizations of the quasi-elastic peak in the electron scattering spectrum off deuterium and provides the comparison of the parameterized cross sections with published experimental data. The comparison is performed in the wide $Q^{2}$ range from $\sim$0.3 GeV$^{2}$ to $\sim$4 GeV$^{2}$. In this way the performance of the par

Luís M. S. Russo, Ana D. Correia, Gonzalo Navarro, Alexandre P. Francisco

Lempel-Ziv is an easy-to-compute member of a wide family of so-called macro schemes; it restricts pointers to go in one direction only. Optimal bidirectional macro schemes are NP-complete to find, but they may provide much better compression on highly repetitive sequences. We consider the problem of approximating optimal bidirectional macro schemes. We descr

E. J. Polzin, R. P. Breton, B. Bhattacharyya, D. Scholte

We present a comparative study of the low-frequency eclipses of spider (compact, irradiating binary) PSRs B1957+20 and J1816+4510. Combining these data with those of three other eclipsing systems we study the frequency dependence of the eclipse duration. PSRs B1957+20 and J1816+4510 have similar orbital properties, but the companions to the pulsars have mass

Parisa Golbayani, Dan Wang, Ionut Florescu

Recent literature implements machine learning techniques to assess corporate credit rating based on financial statement reports. In this work, we analyze the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor&#39;s. We analyze companies from the energy, financial and h

Andrew D. Shiner, Mohammad E. Mousa-Pasandi, Meng Qiu, Michael A. Reimer

A method for in-service OSNR measurement with a coherent transceiver is presented and experimentally verified. A neural network is employed to identify and remove the nonlinear noise contribution to the estimated OSNR.

David N. Carvalho, Fabio Biancalana

Recent techniques have allowed transition metal dichalcogenides (TMD) monolayers to be grown and adequately characterised. Of particular interest, their nonlinear optical response presents many promising opportunities for future nanophotonic devices and technology. The dispersion of the carriers is trigonally-warped, leading to an anisotropic Fermi surface f

Tomasz Klimsiak

We propose a new definition of renormalized solution to linear equation with self-adjoint operator generating a Markov semigroup and bounded Borel measure on the right-hand side. We give a uniqueness result and study the structure of solutions to truncated equations.

Sandra Cerrai, Jan Wehr, Yichun Zhu

We study the small mass limit of the equation describing planar motion of a charged particle of a small mass $μ$ in a force field, containing a magnetic component, perturbed by a stochastic term. We regularize the problem by adding a small friction of intensity $\e>0$. We show that for all small but fixed frictions the small mass limit of $q_{μ, \e}$ gives t

G. Galazutdinov, A. Bondar, Byeong-Cheol Lee, R. Hakalla

This paper considers a very special set of a few interstellar features --- broad diffuse interstellar bands (DIBs) at 4430, 4882, 5450, 5779 and 6175 ÅÅ. The set is small, and measurements of equivalent widths of these DIBs are challenging because of severe stellar, interstellar, and sometimes, also telluric contaminations inside their broad profiles. Nevert

Yimeng Li, Jana Kosecka

The advances in deep reinforcement learning recently revived interest in data-driven learning based approaches to navigation. In this paper we propose to learn viewpoint invariant and target invariant visual servoing for local mobile robot navigation; given an initial view and the goal view or an image of a target, we train deep convolutional network control

D. M. Gaslac Gallardo, S. M. Giuliatti Winter, G. Madeira, M. A. Muñoz-Gutiérrez

The ring system and small satellites of Neptune were discovered during Voyager 2 flyby in 1989 (Smith et al.1989). In this work we analyse the diffusion maps which can give an overview of the system. As a result we found the width of unstable and stable regions close to each satellite. The innermost Galle ring, which is further from the satellites, is locate

Edgar Fajardo, Matevz Tadel, Justas Balcas, Alja Tadel

The University of California system has excellent networking between all of its campuses as well as a number of other Universities in CA, including Caltech, most of them being connected at 100 Gbps. UCSD and Caltech have thus joined their disk systems into a single logical xcache system, with worker nodes from both sites accessing data from disks at either s

Benjamin Geisler, Rossitza Pentcheva

The origin of the $3 \times 1$ reconstruction observed in epitaxial LaCoO$_{3}$ films on SrTiO$_3(001)$ is assessed by using first-principles calculations including a Coulomb repulsion term. We compile a phase diagram as a function of the oxygen pressure, which shows that ($3 \times 1$)-ordered oxygen vacancies (LaCoO$_{2.67}$) are favored under commonly use