September 2019 arXiv papers — page 7

Renato Negrinho, Darshan Patil, Nghia Le, Daniel Ferreira

Neural architecture search methods are able to find high performance deep learning architectures with minimal effort from an expert. However, current systems focus on specific use-cases (e.g. convolutional image classifiers and recurrent language models), making them unsuitable for general use-cases that an expert might wish to write. Hyperparameter optimiza

Zinan Lin, Alankar Jain, Chen Wang, Giulia Fanti

Limited data access is a longstanding barrier to data-driven research and development in the networked systems community. In this work, we explore if and how generative adversarial networks (GANs) can be used to incentivize data sharing by enabling a generic framework for sharing synthetic datasets with minimal expert knowledge. As a specific target, our foc

Xuzhou Zhan, Alexander Dyachenko

In this paper, we associate a class of Hurwitz matrix polynomials with Stieltjes positive definite matrix sequences. This connection leads to an extension of two classical criteria of Hurwitz stability for real polynomials to matrix polynomials: tests for Hurwitz stability via positive definiteness of block-Hankel matrices built from matricial Markov paramet

Tetsuro Habe

We study the refraction effect of electronic wave in hole-doped lateral heterojunctions of metallic and semiconducting transition-metal dichalcogenide monolayers. This effect is theoretically investigated in 2$H$-type MoSe$_2$-NbS$_2$ and WSe$_2$-NbS$_2$ junctions by combining the first-principles calculation and the lattice Green's function method. We show

Ulvi Yurtsever

Thought experiments involving thermodynamic principles have often been used to derive fundamental physical limits to information processing and storage. Similar thought experiments involving black holes can shed further light into such limits.

Charikleia Iakovidou, Ermin Wei

In this work, we consider the problem of a network of agents collectively minimizing a sum of convex functions. The agents in our setting can only access their local objective functions and exchange information with their immediate neighbors. Motivated by applications where computation is imperfect, including, but not limited to, empirical risk minimization

Shuhang Chen, Adithya M. Devraj, Ana Bušić, Sean P. Meyn

The objective in this paper is to obtain fast converging reinforcement learning algorithms to approximate solutions to the problem of discounted cost optimal stopping in an irreducible, uniformly ergodic Markov chain, evolving on a compact subset of $\mathbb{R}^n$. We build on the dynamic programming approach taken by Tsitsikilis and Van Roy, wherein they pr

Amirhossein Taghvaei, Prashant G. Mehta, Sean P. Meyn

Feedback particle filter (FPF) is a numerical algorithm to approximate the solution of the nonlinear filtering problem in continuous-time settings. In any numerical implementation of the FPF algorithm, the main challenge is to numerically approximate the so-called gain function. A numerical algorithm for gain function approximation is the subject of this pap

Andrea Pozzi, Gabriele Ciaramella, Stefan Volkwein, Davide M. Raimondo

Advanced battery management systems rely on mathematical models to guarantee optimal functioning of Lithium-ion batteries. The Pseudo-Two Dimensional (P2D) model is a very detailed electrochemical model suitable for simulations. On the other side, its complexity prevents its usage in control and state estimation. Therefore, it is more appropriate the use of

Hal Schenck, Mike Stillman, Beihui Yuan

For a planar simplicial complex Delta contained in R^2, Schumaker proved that a lower bound on the dimension of the space C^r_k(Delta) of planar splines of smoothness r and polynomial degree at most k on Delta is given by a polynomial P_Delta(r,k), and Alfeld-Schumaker showed this polynomial gives the correct dimension when k >= 4r+1. Examples due to Morgan-

Orlando Romero, Sarthak Chatterjee, Sérgio Pequito

Fractional-order dynamical systems are used to describe processes that exhibit temporal long-term memory and power-law dependence of trajectories. There has been evidence that complex neurophysiological signals like electroencephalogram (EEG) can be modeled by fractional-order systems. In this work, we propose a model-based approach for closed-loop Transcran

Connor G. Bischak, Lucas Q. Flagg, David S. Ginger

Conjugated polymer-based organic electrochemical transistors (OECTs) are being studied for applications ranging from biochemical sensing to neural interfaces. While new conjugated polymers are being developed that can interface digital electronics with the aqueous chemistry of life, the vast majority of high-performance, high-mobility organic transistor mate

Abay M. Kassa, Kundan Kumar, Sarah E. Gasda, Florin A. Radu

In this paper, we consider a non-local (in time) two-phase flow model. The non-locality is introduced through the wettability alteration induced dynamic capillary pressure function. We present a monotone fixed-point iterative linearization scheme for the resulting non-standard model. The scheme treats the dynamic capillary pressure functions semi-implicitly

Caiwen Ding, Shuo Wang, Ning Liu, Kaidi Xu

Deep neural networks (DNNs), as the basis of object detection, will play a key role in the development of future autonomous systems with full autonomy. The autonomous systems have special requirements of real-time, energy-efficient implementations of DNNs on a power-constrained system. Two research thrusts are dedicated to performance and energy efficiency e

Benjamin Busam, Matthieu Hog, Steven McDonagh, Gregory Slabaugh

Whether to attract viewer attention to a particular object, give the impression of depth or simply reproduce human-like scene perception, shallow depth of field images are used extensively by professional and amateur photographers alike. To this end, high quality optical systems are used in DSLR cameras to focus on a specific depth plane while producing visu

Jose Luis Alvarez-Perez

A new series expansion for the the Airy function is presented here that stems from the method of steepest descents and can be related to the Hadamard expansions as presented in prevous works cited in the manuscript, and which is convergent for all values of the complex variable. Hadamard expansions were introduced as an extension of the method of steepest de

Yan Peng

We study stationary scalar field hairy configurations supported by asymptotically flat horizonless compact stars. At the star surface, we impose Neumann boundary conditions for the scalar field. With analytical methods, we obtain bounds on the frequency of scalar fields. For certain discrete frequency satisfying the bounds, we numerically get solutions of sc

Sunil Gandhi, Tim Oates, Tinoosh Mohsenin, Nicholas Waytowich

In this paper, we present a method for learning from video demonstrations by using human feedback to construct a mapping between the standard representation of the agent and the visual representation of the demonstration. In this way, we leverage the advantages of both these representations, i.e., we learn the policy using standard state representations, but

Jayanth Regatti, Gaurav Tendolkar, Yi Zhou, Abhishek Gupta

The performance of fully synchronized distributed systems has faced a bottleneck due to the big data trend, under which asynchronous distributed systems are becoming a major popularity due to their powerful scalability. In this paper, we study the generalization performance of stochastic gradient descent (SGD) on a distributed asynchronous system. The system

Jibril Ben Achour, Etera R. Livine

We show that the simplest FLRW cosmological system consisting in the homogeneous and isotropic massless Einstein-Scalar system enjoys a hidden conformal symmetry under the 1D conformal group $SL(2,\mathbb{R})$ acting as Mobius transformations in proper time. This invariance is made explicit through the mapping of FLRW cosmology onto conformal mechanics. On t

Ezra Y. S. Tjung, Shyamini Kularathna, Krishna Kumar, Kenichi Soga

The Material Point Method (MPM) is a hybrid Eulerian-Lagrangian approach capable of simulating large deformation problems of history-dependent materials. While the MPM can represent complex and evolving material domains by using Lagrangian points, boundary conditions are often applied to the Eulerian nodes of the background mesh nodes. Hence, the use of a st

Ricky X. F. Chen

In this paper, we first obtain some analogues of a formula of Zagier (1995) and Stanley (2011). For instance, we prove that the number of pairs of $n$-cycles whose product has $k$ cycles and has $m$ given elements contained in distinct cycles (or separated) is given by $$ \frac{2 (n-1)! C_m(n+1,k)}{(n+m)(n+1-m)} $$ when $n-k$ is even, where $C_m(n,k)$ is the

Yi Luo, Enea Ceolini, Cong Han, Shih-Chii Liu

Beamforming has been extensively investigated for multi-channel audio processing tasks. Recently, learning-based beamforming methods, sometimes called \textit{neural beamformers}, have achieved significant improvements in both signal quality (e.g. signal-to-noise ratio (SNR)) and speech recognition (e.g. word error rate (WER)). Such systems are generally non

Minh Kha, Peter Kuchment

The classical Riemann-Roch theorem has been extended by N. Nadirashvili and then M. Gromov and M. Shubin to computing indices of elliptic operators on compact (as well as non-compact) manifolds, when a divisor mandates a finite number of zeros and allows a finite number of poles of solutions. On the other hand, Liouville type theorems count the number of sol

Aqib Hasnain, Nibodh Boddupalli, Shara Balakrishnan, Enoch Yeung

This paper describes the optimal selection of a control policy to program the steady state of controlled nonlinear systems with hyperbolic fixed points. This work is motivated by the field of synthetic biology, in which saddle points are common (along with limit cycles), and the aim is to program cells to perform both digital and analog computation, though d

Huaian Diao, Rajesh Jayaram, Zhao Song, Wen Sun

We study the Kronecker product regression problem, in which the design matrix is a Kronecker product of two or more matrices. Given $A_i \in \mathbb{R}^{n_i \times d_i}$ for $i=1,2,\dots,q$ where $n_i \gg d_i$ for each $i$, and $b \in \mathbb{R}^{n_1 n_2 \cdots n_q}$, let $\mathcal{A} = A_1 \otimes A_2 \otimes \cdots \otimes A_q$. Then for $p \in [1,2]$, the

Jonas Hirsch, Michele Marini

We prove that tangent cones at singular boundary points of a two-dimensional current almost area minimizing are unique. Following the ideas exposed by White in [8], the result is achieved by combining a suitable epiperimetric inequality and an almost-monotonicity formula for the mass at boundary points.

Guihua Gong, Huaxin Lin, Z. Niu

A classification theorem is obtained for a class of unital simple separable amenable Z-stable C*-algebras which exhausts all possible values of the Elliott invariant for unital stably finite simple separable amenable Z-stable C*-algebras. Moreover, it contains all unital simple separable amenable C*-algebras which satisfy the UCT and have finite rational tra

Enguerrand Horel, Kay Giesecke, Victor Storchan, Naren Chittar

Many applications from the financial industry successfully leverage clustering algorithms to reveal meaningful patterns among a vast amount of unstructured financial data. However, these algorithms suffer from a lack of interpretability that is required both at a business and regulatory level. In order to overcome this issue, we propose a novel two-steps met

Hamed Nikbakht

Earthquake research in the last few decades has led to considerable advances in seismic hazard and risk modeling across academia, industry, and government. Technological advances such as high performance computing and visualization can further facilitate earthquake hazard and risk research. This work utilizes the CAVE of the Marquette Visualization Laborator

Krishna Kumar, Jeffrey Salmond, Shyamini Kularathna, Christopher Wilkes

In this paper, we describe a new scalable and modular material point method (MPM) code developed for solving large-scale problems in continuum mechanics. The MPM is a hybrid Eulerian-Lagrangian approach, which uses both moving material points and computational nodes on a background mesh. The MPM has been successfully applied to solve large-deformation proble

Agnes Beaudry, Mark Behrens, Prasit Bhattacharya, Dominic Culver

Mahowald proved the height 1 telescope conjecture at the prime 2 as an application of his seminal work on bo-resolutions. In this paper we study the height 2 telescope conjecture at the prime 2 through the lens of tmf-resolutions. To this end we compute the structure of the tmf-resolution for a specifc type 2 complex Z. We find that, analogous to the height

N. E. Britavskiy, A. Z. Bonanos, A. Herrero, M. Cerviño

Increasing the statistics of evolved massive stars in the Local Group enables investigating their evolution at different metallicities. During the late stages of stellar evolution, the physics of some phenomena, such as episodic and systematic mass loss, are not well constrained. For example, the physical properties of red supergiants (RSGs) in different met

Utku Kose

Because of todays technological developments and the influence of digital systems into every aspect of our lives, importance of cyber security improves more and more day-by-day. Projects, educational processes and seminars realized for this aim create and improve awareness among individuals and provide useful tools for growing equipped generations. The aim o

Utku Kose

Artificial intelligence is known as the most effective technological field for rapid developments shaping the future of the world. Even today, it is possible to see intense use of intelligence systems in all fields of the life. Although advantages of the Artificial Intelligence are widely observed, there is also a dark side employing efforts to design hackin

Alexey Györi, Mathis Niederau, Violett Zeller, Volker Stich

It is crucial today that economies harness renewable energies and integrate them into the existing grid. Conventionally, energy has been generated based on forecasts of peak and low demands. Renewable energy can neither be produced on demand nor stored efficiently. Thus, the aim of this paper is to evaluate Deep Learning-based forecasts of energy consumption

Jiacheng Pan, Hongyi Sun, Kecheng Xu, Yifei Jiang

Accurately forecasting the future movements of surrounding vehicles is essential for safe and efficient operations of autonomous driving cars. This task is difficult because a vehicle's moving trajectory is greatly determined by its driver's intention, which is often hard to estimate. By leveraging attention mechanisms along with long short-term memory (LSTM

Wen Kokke, J. Garrett Morris, Philip Wadler

Process calculi based in logic, such as $\pi$DILL and CP, provide a foundation for deadlock-free concurrent programming, but exclude non-determinism and races. HCP is a reformulation of CP which addresses a fundamental shortcoming: the fundamental operator for parallel composition from the $\pi$-calculus does not correspond to any rule of linear logic, and t

Elad Segal, Avia Efrat, Mor Shoham, Amir Globerson

Models for reading comprehension (RC) commonly restrict their output space to the set of all single contiguous spans from the input, in order to alleviate the learning problem and avoid the need for a model that generates text explicitly. However, forcing an answer to be a single span can be restrictive, and some recent datasets also include multi-span quest

Neehar Peri, Neal Gupta, W. Ronny Huang, Liam Fowl

Targeted clean-label data poisoning is a type of adversarial attack on machine learning systems in which an adversary injects a few correctly-labeled, minimally-perturbed samples into the training data, causing a model to misclassify a particular test sample during inference. Although defenses have been proposed for general poisoning attacks, no reliable def

Jiwei Zhang, Shiang-Yu Huang, Zhan-Hong Lin, Jer-Shing Huang

We systematically investigate the generation of optical chirality patterns by applying the superposition of two waves in three scenarios, namely plane waves in free space, evanescent waves of totally reflected light at dielectric interface and propagating surface plasmon waves on a metallic surface. In each scenario, the general analytical solution of the op

Giorgio Venturi, Matteo Viale

This is an introductory paper to a series of results linking generic absoluteness results for second and third order number theory to the model theoretic notion of model companionship. Specifically we develop here a general framework linking Woodin's generic absoluteness results for second order number theory and the theory of universally Baire sets to model

Kartik Chandra, Audrey Xie, Jonathan Ragan-Kelley, Erik Meijer

Working with any gradient-based machine learning algorithm involves the tedious task of tuning the optimizer's hyperparameters, such as its step size. Recent work has shown how the step size can itself be optimized alongside the model parameters by manually deriving expressions for "hypergradients" ahead of time. We show how to automatically compute hypergra

George Glauberman, Justin Lynd

A rigid automorphism of a linking system is an automorphism which restricts to the identity on the Sylow subgroup. A rigid inner automorphism is conjugation by an element in the center of the Sylow subgroup. At odd primes, it is known that each rigid automorphism of a centric linking system is inner. We prove that the group of rigid outer automorphisms of a

Muhammad Mahbubur Rahman, Tim Finin

Understanding and extracting of information from large documents, such as business opportunities, academic articles, medical documents and technical reports, poses challenges not present in short documents. Such large documents may be multi-themed, complex, noisy and cover diverse topics. We describe a framework that can analyze large documents and help peop

Subhrajit Sinha, Umesh Vaidya, Enoch Yeung

In this paper we develop the concept of information transfer between the Borel-measurable sets for a dynamical system described by a measurable space and a non-singular transformation. The concept is based on how Shannon entropy is transferred between the measurable sets, as the dynamical system evolves. We show that the proposed definition of information tr

Nasser Aldaghri, Hessam Mahdavifar

Cryptographic protocols are often implemented at upper layers of communication networks, while error-correcting codes are employed at the physical layer. In this paper, we consider utilizing readily-available physical layer functions, such as encoders and decoders, together with shared keys to provide a threshold-type security scheme. To this end, we first c

Milad Hooshyar, Sara Bonetti, Arvind Singh, Efi Foufoula-Georgiou

The logarithmic mean-velocity profile is a key experimental and theoretical result in wall-bounded turbulence. Similarly, here we show that the topographic surface emerging between parallel zero-elevation boundaries presents an intermediate region with a logarithmic mean-elevation profile. We use model simulations, which account for growth, erosion, and smoo

Maria Jose Llop, Pamela Llop, Maria Soledad Lopez, Andrea Gomez

Leptospiroris, the infectious disease caused by the spirochete bacteria Leptospira interrogans, constitutes an important public health problem all over the world. In Argentina, some regions present climate and geographic characteristics that favors the habitat of the bacteria Leptospira, whose survival strongly depends on climatic factors. For this reason, r

Aitor Muguruza

We present a novel Monte Carlo based LSV calibration algorithm that applies to all stochastic volatility models, including the non-Markovian rough volatility family. Our framework overcomes the limitations of the particle method proposed by Guyon and Henry-Labord\`ere (2012) and theoretically guarantees a variance reduction without additional computational c

Sergey Nechayev, Jörg S. Eismann, Martin Neugebauer, Peter Banzer

Deep sub-wavelength localization and displacement sensing of optical nanoantennas have emerged as extensively pursued objectives in nanometrology, where focused beams serve as high-precision optical rulers while the scattered light provides an optical readout. Here, we show that in these schemes using an optical excitation as a position gauge implies that th

Jonathan Stokes, Steven Weber

Star sampling (SS) is a random sampling procedure on a graph wherein each sample consists of a randomly selected vertex (the star center) and its (one-hop) neighbors (the star points). We consider the use of SS to find any member of a target set of vertices in a graph, where the figure of merit (cost) is either the expected number of samples (unit cost) or t

Javier Ahumada, Walter Weidmann, Marcelo Miller Bertolami, Leila Saker

We present Gemini-South observations of nine faint and extended planetary nebulae. Using direct images taken with the spectrograph GMOS, we built the $(u' - g')$ vs. $(g' - r')$ diagrams of the stars in the observed areas which allowed us, also considering their geometrical positions, to identify the probable central stars of the nebulae. Our stellar spectra

Marcus Carlsson, Daniele Gerosa, Carl Olsson

Low rank recovery problems have been a subject of intense study in recent years. While the rank function is useful for regularization it is difficult to optimize due to its non-convexity and discontinuity. The standard remedy for this is to exchange the rank function for the convex nuclear norm, which is known to favor low rank solutions under certain condit

Jacob Krantz, Maxwell Dulin, Paul De Palma

The identification of syllables within phonetic sequences is known as syllabification. This task is thought to play an important role in natural language understanding, speech production, and the development of speech recognition systems. The concept of the syllable is cross-linguistic, though formal definitions are rarely agreed upon, even within a language

Christoph Kern, Bernd Weiss, Jan-Philipp Kolb

Nonresponse in panel studies can lead to a substantial loss in data quality due to its potential to introduce bias and distort survey estimates. Recent work investigates the usage of machine learning to predict nonresponse in advance, such that predicted nonresponse propensities can be used to inform the data collection process. However, predicting nonrespon

Jung Hoon Lee

Deep neural networks (DNNs) may outperform human brains in complex tasks, but the lack of transparency in their decision-making processes makes us question whether we could fully trust DNNs with high stakes problems. As DNNs' operations rely on a massive number of both parallel and sequential linear/nonlinear computations, predicting their mistakes is nearly

Ali Hatamizadeh, Debleena Sengupta, Demetri Terzopoulos

The Active Contour Model (ACM) is a standard image analysis technique whose numerous variants have attracted an enormous amount of research attention across multiple fields. Incorrectly, however, the ACM's differential-equation-based formulation and prototypical dependence on user initialization have been regarded as being largely incompatible with the recen

Zahra Asghari, Hamid Arian Zad, Hosein Eshghi

This study focuses on enhancing the electrical and thermoelectric properties of tungsten trioxide (WO$_3$) thin films using the spray pyrolysis technique. Three samples, namely A$_1$, A$_2$, and A$_3$, were prepared by depositing the films onto glass substrates at different deposition rates: R$_1=1$ mL/min, R$_2=3$ mL/min, and R$_3=7$ mL/min, respectively. S

S. A. Buterin, M. A. Kuznetsova, V. A. Yurko

We study Sturm-Liouville operators on closed sets of a special structure, which are sometimes referred as time scales and often appear in modelling various real processes. Depending on the set structure, such operators unify both differential and difference operators. We obtain properties of their spectral characteristics and study inverse problems with resp

Felipe Vico, Leslie Greengard, Michael O'Neil, Manas Rachh

In this work we present an algorithm to construct an infinitely differentiable smooth surface from an input consisting of a (rectilinear) triangulation of a surface of arbitrary shape. The original surface can have non-trivial genus and multiscale features, and our algorithm has computational complexity which is linear in the number of input triangles. We us

Eric Lei, Oscar Castañeda, Olav Tirkkonen, Tom Goldstein

Neural networks have been proposed recently for positioning and channel charting of user equipments (UEs) in wireless systems. Both of these approaches process channel state information (CSI) that is acquired at a multi-antenna base-station in order to learn a function that maps CSI to location information. CSI-based positioning using deep neural networks re

Parsa Esfahanian, Mohammad Akhavan

Convolutional Neural Networks (CNNs) have gained a significant attraction in the recent years due to their increasing real-world applications. Their performance is highly dependent to the network structure and the selected optimization method for tuning the network parameters. In this paper, we propose novel yet efficient methods for training convolutional n

Marcin Modrzejewski, Sirous Yourdkhani, Jiri Klimes

The random phase approximation (RPA) has received a considerable interest in the field of modeling systems where noncovalent interactions are important. Its advantages over widely used density functional theory (DFT) approximations are the exact treatment of exchange and the description of long-range correlation. In this work we address two open questions re

Irving Solis, Read Sandström, James Motes, Nancy M. Amato

Multi-Agent Motion Planning (MAMP) is the problem of computing feasible paths for a set of agents given individual start and goal states. Given the hardness of MAMP, most of the research related to multi-agent systems has focused on multi-agent pathfinding (MAPF), which simplifies the problem by assuming a shared discrete representation of the space for all

Kai Gong, V. Ongun Özçelik, Kengran Yang, Claire E. White

Quaternary CaO-MgO-Al2O3-SiO2 glasses are important constituents of the Earth's lower crust and mantle, and they also have important industrial applications such as in metallurgical processes, concrete production and emerging low-CO2 cement technologies. In particular, these applications rely heavily on the composition-structure-reactivity relationships for

Sekhar Ghosh

In this paper, we study the existence of infinitely many weak solutions to a fractional Kirchhoff-Schr\"{o}dinger-Poisson system involving the weak singularity, i.e. when $0<\gamma<1$. Further, we obtain the existence of a solution with the strong singularity, i.e. when $\gamma>1$. We employ variational techniques to prove the existence and multiplicity resu

Trevor M. Leslie

A well-known result of Carrillo, Choi, Tadmor, and Tan states that the 1D Euler Alignment model with smooth interaction kernels possesses a 'critical threshold' criterion for the global existence or finite-time blowup of solutions, depending on the global nonnegativity (or lack thereof) of the quantity $e_0 = \partial_x u_0 + \phi*\rho_0$. In this note, we r

Michael Albert, Vít Jelínek, Michal Opler

For a hereditary permutation class $\mathcal{C}$, we say that two permutations $\pi$ and $\sigma$ of $\mathcal{C}$ are Wilf-equivalent in $\mathcal{C}$, if $\mathcal{C}$ has the same number of permutations avoiding $\pi$ as those avoiding $\sigma$. We say that a permutation class $\mathcal{C}$ exhibits a Wilf collapse if the number of permutations of size $n

Jesse C. Cresswell

In this thesis we apply techniques from quantum information theory to study quantum gravity within the framework of the anti-de Sitter / conformal field theory correspondence (AdS/CFT). Through AdS/CFT, progress has been made in understanding the structure of entanglement in quantum field theories, and in how gravitational physics can emerge from these struc

Sasha Brownsberger, Nicholas Mondrik, Christopher W. Stubbs

As the precision frontier of large-area survey astrophysics advances towards the one millimagnitude level, flux calibration of astronomical instrumentation remains an ongoing challenge. We describe initial testing of silicon solar cells as large-aperture precise calibration photodiodes. We present measurements of dark current, linearity, frequency response,

Antonios Antoniadis, Naveen Garg, Gunjan Kumar, Nikhil Kumar

Given n jobs with release dates, deadlines and processing times we consider the problem of scheduling them on m parallel machines so as to minimize the total energy consumed. Machines can enter a sleep state and they consume no energy in this state. Each machine requires Q units of energy to awaken from the sleep state and in its active state the machine can

Dominik Goeke, Michael Schneider

The standard single picker routing problem (SPRP) seeks the cost-minimal tour to collect a set of given articles in a rectangular single-block warehouse with parallel picking aisles and a dedicated storage policy, i.e, each SKU is only available from one storage location in the warehouse. We present a compact formulation that forgoes classical subtour elimin

Akshay Arora, Arun Nethi, Priyanka Kharat, Vency Verghese

In recent times, machine learning (ML) and artificial intelligence (AI) based systems have evolved and scaled across different industries such as finance, retail, insurance, energy utilities, etc. Among other things, they have been used to predict patterns of customer behavior, to generate pricing models, and to predict the return on investments. But the suc

Ching-Lun Tai, Borching Su, Cai Jia

Generalized frequency division multiplexing (GFDM) is a promising candidate waveform for next-generation wireless communication systems. However, GFDM channel estimation is still challenging due to the inherent interference. In this paper, we formulate a pilot design framework with linear minimum mean square error (LMMSE) channel estimation for GFDM, and pro

José M. M. Veloso

We verify if Gausssian curvature of surfaces and normal curvature of curves in surfaces introduced by Diniz-Veloso arXiv:1210.7110 and by Balogh-Tyson-Vecchi arXiv:1604.00180 to prove Gauss-Bonnet theorems in Heisenberg space $\mathbb H^1$ are equal. The authors in arXiv:1604.00180 utilize a limit of Gaussian and normal curvatures defined in the Riemannian a

Geoffrey Fox, Shantenu Jha

We present a taxonomy of research on Machine Learning (ML) applied to enhance simulations together with a catalog of some activities. We cover eight patterns for the link of ML to the simulations or systems plus three algorithmic areas: particle dynamics, agent-based models and partial differential equations. The patterns are further divided into three actio

Badr-Eddine Chérief-Abdellatif, Pierre Alquier

In some misspecified settings, the posterior distribution in Bayesian statistics may lead to inconsistent estimates. To fix this issue, it has been suggested to replace the likelihood by a pseudo-likelihood, that is the exponential of a loss function enjoying suitable robustness properties. In this paper, we build a pseudo-likelihood based on the Maximum Mea

Shang-Shun Zhang, Hiroaki Ishizuka, Hao Zhang, Gábor B. Halász

By considering an extended double-exchange model with spin-orbit coupling (SOC), we derive a general form of the Berry phase $\gamma$ that electrons pick up when moving around a closed loop. This form generalizes the well-known result valid for SU(2) invariant systems, $\gamma=\Omega/2$, where $\Omega$ is the solid angle subtended by the local magnetic momen

Shuran Sheng, Ruitao Chen, Peng Chen, Xianbin Wang

Technology advancements on sensing, communications, and computing directly accelerate the recent development of Internet of Things (IoT), leading to rich and diverse applications in industry and business processes. For enabling IoT applications, smart devices and networks rely on the capabilities for information collection, processing and tight collaboration

Fatemeh Mirmojarabian, Mehdi Kargarian, Abdollah Langari

In this work we study the phase diagram of Kekul\'{e}-Kitaev model. The model is defined on a honeycomb lattice with bond dependent anisotropic exchange interactions making it exactly solvable in terms of Majorana representation of spins in close analogy to the Kitaev model. However, the energy spectrum of Majorana fermions has a multi-band structure charact

Zakhar Kabluchko

Pick $d+1$ points uniformly at random on the unit sphere in $\mathbb R^d$. What is the expected value of the angle sum of the simplex spanned by these points? Choose $n$ points uniformly at random in the $d$-dimensional ball. What is the expected number of faces of their convex hull? We answer these and some related questions of stochastic geometry. To this

Zhengdao Chen, Jianyu Zhang, Martin Arjovsky, Léon Bottou

We propose Symplectic Recurrent Neural Networks (SRNNs) as learning algorithms that capture the dynamics of physical systems from observed trajectories. An SRNN models the Hamiltonian function of the system by a neural network and furthermore leverages symplectic integration, multiple-step training and initial state optimization to address the challenging nu

Noah Giansiracusa, Xian Wu

The literature on maximal torus orbits in the Grassmannian is vast; in this paper we initiate a program to extend this to diagonal subtori. Our main focus is generalizing portions of Kapranov's seminal work on Chow quotient compactifications of these orbit spaces. This leads naturally to discrete polymatroids, generalizing the matroidal framework underlying

Natalia Tomashenko, Antoine Caubriere, Yannick Esteve, Antoine Laurent

This work investigates spoken language understanding (SLU) systems in the scenario when the semantic information is extracted directly from the speech signal by means of a single end-to-end neural network model. Two SLU tasks are considered: named entity recognition (NER) and semantic slot filling (SF). For these tasks, in order to improve the model performa

B. Yang, E. Jehin, F. J. Pozuelos, Y. Moulane

Main belt comets (MBCs) are a peculiar class of volatile-containing objects with comet-like morphology and asteroid-like orbits. However, MBCs are challenging targets to study remotely due to their small sizes and the relatively large distance they are from the Sun and the Earth. Recently, a number of weakly active short-period comets have been identified th

Ezgi Yıldırım, Payam Azad, Şule Gündüz Öğüdücü

In recent years, deep learning has gained an indisputable success in computer vision, speech recognition, and natural language processing. After its rising success on these challenging areas, it has been studied on recommender systems as well, but mostly to include content features into traditional methods. In this paper, we introduce a generalized neural ne

H. Calcutt, E. R. Willis, J. K. Jørgensen, P. Bjerkeli

Context. Propyne (CH$_3$CCH) has been detected in a variety of environments, from Galactic star-forming regions to extragalactic sources. Such molecules are excellent tracers of the physical conditions in star-forming regions. Aims. This study explores the emission of CH$_3$CCH in the low-mass protostellar binary, IRAS 16293$-$2422, examining the spatial sca

Graciela B. Gelmini, Philip Lu, Volodymyr Takhistov

We discuss how a laboratory detection of a sterile neutrino not only would constitute a fundamental discovery of a new particle, but could also provide an indication of the evolution of the Universe before Big-Bang Nucleosynthesis (BBN), a fundamental discovery in cosmology. These "visible" sterile neutrinos could be detected in experiments such as KATRIN/TR

Álvaro Ibrain Rodríguez, Lara Lloret Iglesias

The evolution of the information and communication technologies has dramatically increased the number of people with access to the Internet, which has changed the way the information is consumed. As a consequence of the above, fake news have become one of the major concerns because its potential to destabilize governments, which makes them a potential danger

Matteo Negri, Davide Bergamini, Carlo Baldassi, Riccardo Zecchina

Generative processes in biology and other fields often produce data that can be regarded as resulting from a composition of basic features. Here we present an unsupervised method based on autoencoders for inferring these basic features of data. The main novelty in our approach is that the training is based on the optimization of the `local entropy' rather th

P. K. Resmi

The precise measurement of the CKM angle $\phi_3$ is important to further test the Standard Model description of $CP$ violation. The small values of the branching fractions of the decays involved in the measurement limits the precision, hence a larger dataset has to be accumulated to improve the precision. The Belle~II experiment at the SuperKEKB asymmetric-

Scott Armstrong, Wei Wu

We consider the $\nabla \phi$ interface model with a uniformly convex interaction potential possessing H\"older continuous second derivatives. Combining ideas of Naddaf and Spencer with methods from quantitative homogenization, we show that the surface tension (or free energy) associated to the model is at least $C^{2,\beta}$ for some $\beta>0$. We also prov

Pietro D'Antuono, Michele Ciavarella

A criterion for the mean stress effect correction in the shift factor approach for variable amplitude life prediction is presented for both smooth and notched specimens. The criterion is applied to the simple idea proposed by the authors in a previous note that Ga{\ss}ner curves can be interpreted as shifted W\"ohler curves. The mean stress correction used h

Godfrey Keller, Sven Rady

We analyze undiscounted continuous-time games of strategic experimentation with two-armed bandits. The risky arm generates payoffs according to a L\'{e}vy process with an unknown average payoff per unit of time which nature draws from an arbitrary finite set. Observing all actions and realized payoffs, plus a free background signal, players use Markov strate

Rongrong Wang, Xiaopeng Zhang

We provide a rigorous mathematical treatment to the crowding issue in data visualization when high dimensional data sets are projected down to low dimensions for visualization. By properly adjusting the capacity of high dimensional balls, our method makes right enough room to prepare for the embedding. A key component of the proposed method is an estimation

Justin Zheng, Kazuhide Okamoto, Panagiotis Tsiotras

This work proposes a new self-driving framework that uses a human driver control model, whose feature-input values are extracted from images using deep convolutional neural networks (CNNs). The development of image processing techniques using CNNs along with accelerated computing hardware has recently enabled real-time detection of these feature-input values

Anastasiya Ivanova, Dmitry Pasechnyuk, Pavel Dvurechensky, Alexander Gasnikov

In this paper, we consider the resource allocation problem in a network with a large number of connections which are used by a huge number of users. The resource allocation problem under discussion is a maximization problem with linear inequality constraints. To solve this problem we construct the dual problem and propose to use the following numerical optim

Johan Broekaert

Mathematical steps are developed to obtain optimised well tempered model "temperaments", based on a musical Well Temperament definition by H. Kelletat, that is derived from A. Werckmeister. This supports objective mathematical ranking of historical temperaments. Historical temperaments that fit best with mathematical models are often installed on organs. Mus

Joshua Frisch, Wade Hann-Caruthers, Pooya Vahidi Ferdowsi

Let $\Sigma$ be a countable alphabet. For $r\geq 1$, an infinite sequence $s$ with characters from $\Sigma$ is called $r$-quasi-regular, if for each $\sigma\in\Sigma$ the ratio of the longest to shortest interval between consecutive occurrences of $\sigma$ in $s$ is bounded by $r$. In this paper, we answer a question asked by Kempe, Schulman, and Tamuz, and