July 2019 arXiv papers — page 96

Ygor C. N. Sousa, Hansenclever F. Bassani

This paper introduces an incremental semantic mapping approach, with on-line unsupervised learning, based on Self-Organizing Maps (SOM) for robotic agents. The method includes a mapping module, which incrementally creates a topological map of the environment, enriched with objects recognized around each topological node, and a module of places categorization

Jintao Wang, Lu Yang, Jinqiao Duan

We consider recurrent solutions of the nonautonomous modified Swift-Hohenberg equation $$u_t+Δ^2u+2Δu+au+b|\nabla u|^2+u^3=g(t,x).$$ We employ Conley index theory to show that, if the forcing $g:\mathbb{R}\rightarrow L^2(Ω)$ is a recurrent function, then there are at least two recurrent solutions in $H_0^2(Ω)$ under appropriate assumptions on the parameters

Kyle Boone

Upcoming astronomical surveys such as the Large Synoptic Survey Telescope (LSST) will rely on photometric classification to identify the majority of the transients and variables that they discover. We present a set of techniques for photometric classification that can be applied even when the training set of spectroscopically-confirmed objects is heavily bia

Christian Le Merdy, Safoura Zadeh

Let $T\colon L^p({\mathcal M})\to L^p({\mathcal N})$ be a bounded operator between two noncommutative $L^p$-spaces, $1\leq p<\infty$. We say that $T$ is $\ell^1$-bounded (resp. $\ell^1$-contractive) if $T\otimes I_{\ell^1}$ extends to a bounded (resp. contractive) map from $L^p({\mathcal M};\ell^1)$ into $L^p({\mathcal N};\ell^1)$. We show that Yeadon&#39;s

Youwei Zeng, Dan Wu, Jie Xiong, Enze Yi

The past few years have witnessed the great potential of exploiting channel state information retrieved from commodity WiFi devices for respiration monitoring. However, existing approaches only work when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away. On the other hand, most home environmen

Olivier Arrigoni, Christian Le Merdy

We introduce and investigate $H^\infty$-functional calculus for commuting finite families of Ritt operators on Banach space $X$. We show that if either $X$ is a Banach lattice or $X$ or $X^*$ has property $(α)$, then a commuting $d$-tuple $(T_1,\ldots, T_d)$ of Ritt operators on $X$ has an $H^\infty$ joint functional calculus if and only if each $T_k$ admits

Zhenyu Tang, Lianwu Chen, Bo Wu, Dong Yu

We present an efficient and realistic geometric acoustic simulation approach for generating and augmenting training data in speech-related machine learning tasks. Our physically-based acoustic simulation method is capable of modeling occlusion, specular and diffuse reflections of sound in complicated acoustic environments, whereas the classical image method

A. Biekert, S. A. Hertel, E. Huebler, J. Lin

We investigate scintillation linearity of a commercial high pressure $^4$He gas detector using monoenergetic 2.8 MeV neutrons from a deuterium-deuterium fusion neutron generator. The scintillation response of the detector was measured for a range of recoil energies between 83 keV and 626 keV by tagging neutrons scattering into fixed angles with a far-side or

Kento Yamamoto

In this paper, we define syntomic complex for modulus pair (X,D), where X is regular semi-stable family and D is an effective Cartier divisor on X. We compute its cohomology sheaves.

Roberto Iaconi, Keiichi Maeda, Orsola De Marco, Takaya Nozawa

We investigate the common envelope binary interaction, that leads to the formation of compact binaries, such as the progenitor of Type Ia supernovae or of mergers that emit detectable gravitational waves. In this work we diverge from the classic numerical approach that models the dynamic in-spiral. We focus instead on the asymptotic behaviour of the common e

Ali Ebrahimian, Mehrdad Dadsetani, Reza Asgari

Discovery of the new two-dimensional (2D) Dirac semimetals incorporating both superconductivity and the topological band structure has provided a novel platform for realizing the intriguing applications of Dirac fermions and Majorana quasiparticles, ranging from high-speed quantum devices at the nanoscale to topological quantum computations. In this work, ut

Hongyi Chu, Rune Haugseng

Many homotopy-coherent algebraic structures can be described by Segal-type limit conditions determined by an &#34;algebraic pattern&#34;, bywhich we mean an $\infty$-category equipped with a factorization system and a collection of &#34;elementary&#34; objects. Examples of structures that occur as such &#34;Segal $\mathcal{O}$-spaces&#34; for an algebraic pa

Daniel S. Brown, Wonjoon Goo, Scott Niekum

The performance of imitation learning is typically upper-bounded by the performance of the demonstrator. While recent empirical results demonstrate that ranked demonstrations allow for better-than-demonstrator performance, preferences over demonstrations may be difficult to obtain, and little is known theoretically about when such methods can be expected to

Isao Tomita, Shinichi Saito

Flat bands form in a 3D Hopf-linked graphene crystal or a 3D carbon allotrope named Hopfene, which qualitatively differ from bands of only graphenes. This paper discusses carbon-hexagon deformation on the level shift of a flat band via density-functional-theoretical (DFT) analysis to set the flat-band level to the Fermi level, viz., to utilize its large dens

Yo Nakawake, Kosuke Sato

Cultural learning is a unique human capacity essential for a wide range of adaptations. Researchers have argued that folktales have the pedagogical function of transmitting the essential information for the environment. The most important knowledge for foraging and pastoral society is folk-zoological knowledge, such as the predator-prey relationship among wi

Wentao Ouyang, Xiuwu Zhang, Shukui Ren, Li Li

Click-through rate (CTR) prediction is a critical task in online advertising systems. Models like Deep Neural Networks (DNNs) are simple but stateless. They consider each target ad independently and cannot directly extract useful information contained in users&#39; historical ad impressions and clicks. In contrast, models like Recurrent Neural Networks (RNNs

Hugo Germain, Guillaume Bourmaud, Vincent Lepetit

We propose a novel approach to feature point matching, suitable for robust and accurate outdoor visual localization in long-term scenarios. Given a query image, we first match it against a database of registered reference images, using recent retrieval techniques. This gives us a first estimate of the camera pose. To refine this estimate, like previous appro

K. Niranjan Kumar, Irfan Essa, Sehoon Ha, C. Karen Liu

We explore the problem of estimating the mass distribution of an articulated object by an interactive robotic agent. Our method predicts the mass distribution of an object by using the limited sensing and actuating capabilities of a robotic agent that is interacting with the object. We are inspired by the role of exploratory play in human infants. We take th

Brian Brubach, Nathaniel Grammel, Will Ma, Aravind Srinivasan

We study generalizations of online bipartite matching in which each arriving vertex (customer) views a ranked list of offline vertices (products) and matches to (purchases) the first one they deem acceptable. The number of products that the customer has patience to view can be stochastic and dependent on the products seen. We develop a framework that views t

Taiki Yamae, Naoki Takeuchi, Nobuyuki Yoshikawa

Adiabatic quantum-flux-parametron (AQFP) logic is an energy-efficient superconductor logic. It operates with zero static power dissipation and very low dynamic power dissipation owing to adiabatic switching. In previous numerical studies, we have evaluated the energy dissipation of basic AQFP logic gates and demonstrated sub-kBT switching energy, where kB is

Xinshuo Weng, Jianren Wang, David Held, Kris Kitani

3D multi-object tracking (MOT) is an essential component for many applications such as autonomous driving and assistive robotics. Recent work on 3D MOT focuses on developing accurate systems giving less attention to practical considerations such as computational cost and system complexity. In contrast, this work proposes a simple real-time 3D MOT system. Our

Jiahui Li, Zhiqiang Hu, Shuang Yang

Nuclear segmentation is important and frequently demanded for pathology image analysis, yet is also challenging due to nuclear crowdedness and possible occlusion. In this paper, we present a novel bottom-up method for nuclear segmentation. The concepts of Center Mask and Center Vector are introduced to better depict the relationship between pixels and nuclea

Drew A. Hudson, Christopher D. Manning

We introduce the Neural State Machine, seeking to bridge the gap between the neural and symbolic views of AI and integrate their complementary strengths for the task of visual reasoning. Given an image, we first predict a probabilistic graph that represents its underlying semantics and serves as a structured world model. Then, we perform sequential reasoning

David Baraglia

We obtain constraints on the topology of families of smooth $4$-manifolds arising from a finite dimensional approximation of the families Seiberg-Witten monopole map. Amongst other results these constraints include a families generalisation of Donaldson&#39;s diagonalisation theorem and Furuta&#39;s $10/8$ theorem. As an application we construct examples of

Isao Tomita, Shinichi Saito

Electronic and structural properties of a 3D carbon allotrope made of Hopf-linked graphenes, which we call a Hopfene - a type of topological crystal, are examined by semi-empirical molecular-orbital and density-functional-theoretical methods, where band-structure analyses reveal very different properties from those of 2D graphenes. Furthermore, the analyses

Brian Lawrence, Daniel Litt

Let $Σ_{g,n}$ be the orientable genus $g$ surface with $n$ punctures, where $2-2g-n<0$. Let $$ρ: π_1(Σ_{g,n})\to GL_m(\mathbb{C})$$ be a representation. Suppose that for each finite covering map $f: Σ_{g&#39;, n&#39;}\to Σ_{g, n}$, the orbit of (the isomorphism class of) $f^*(ρ)$ under the mapping class group $MCG(Σ_{g&#39;,n&#39;})$ of $Σ_{g&#39;,n&#39;}$ i

Guangyu An, Jun He, Jiankui Li

Let $\mathcal{A}$ be a $*$-algebra and $\mathcal{M}$ be a $*$-$\mathcal A$-bimodule, we study the local properties of $*$-derivations and $*$-Jordan derivations from $\mathcal{A}$ into $\mathcal{M}$ under the following orthogonality conditions on elements in $\mathcal A$: $ab^*=0$, $ab^*+b^*a=0$ and $ab^*=b^*a=0$. We characterize the mappings on zero product

Satoshi Kashiwaya, Kohta Saitoh, Hiromi Kashiwaya, Masao Koyanagi

Sr$_{2}$RuO$_{4}$ is one of the most promising candidates of a topological superconductor with broken time-reversal symmetry, because a number of experiments have revealed evidences for a spin-triplet chiral $p$-wave superconductivity. In order to clarify the time-reversal symmetry of Sr$_{2}$RuO$_{4}$, we introduce a novel test that examines the invariance

Jiequn Han, Chao Ma, Zheng Ma, Weinan E

A new framework is introduced for constructing interpretable and truly reliable reduced models for multiscale problems in situations without scale separation. Hydrodynamic approximation to the kinetic equation is used as an example to illustrate the main steps and issues involved. To this end, a set of generalized moments are constructed first to optimally r

Zengji Yue, Weiyao Zhao, David Cortie, Zhi Li

We study magnetotransport in a rare earth doped topological insulator, Sm0.1Sb1.9Te3 single crystals, under magnetic fields up to 14 T. It is found that that the crystals exhibit Shubnikov de Haas oscillations in their magneto-transport behaviour at low temperatures and high magnetic fields. The SdH oscillations result from the mixed contributions of bulk an

Mohsen Joneidi

Analysis of data from functional magnetic resonance imaging (fMRI) results in constructing functional brain networks. Principal component analysis (PCA) and independent component analysis (ICA) are widely used to generate functional brain networks. Moreover, dictionary learning and sparse representation provide some latent patterns that rules brain activitie

Ahmed BaniMustafa, Nigel Hardy

This work demonstrates the execution of a novel process model for knowledge discovery and data mining for metabolomics (MeKDDaM). It aims to illustrate MeKDDaM process model applicability using four different real-world applications and to highlight its strengths and unique features. The demonstrated applications provide coverage for metabolite profiling, ta

Chulhee Yun, Suvrit Sra, Ali Jadbabaie

Recent results in the literature indicate that a residual network (ResNet) composed of a single residual block outperforms linear predictors, in the sense that all local minima in its optimization landscape are at least as good as the best linear predictor. However, these results are limited to a single residual block (i.e., shallow ResNets), instead of the

Matthew Brehmer, Bongshin Lee, Petra Isenberg, Eun Kyoung Choe

We compare the efficacy of animated and small multiples variants of scatterplots on mobile phones for comparing trends in multivariate datasets. Visualization is increasingly prevalent in mobile applications and mobile-first websites, yet there is little prior visualization research dedicated to small displays. In this paper, we build upon previous experimen

Yanlong Huang, Fares J. Abu-Dakka, João Silvério, Darwin G. Caldwell

As a promising branch of robotics, imitation learning emerges as an important way to transfer human skills to robots, where human demonstrations represented in Cartesian or joint spaces are utilized to estimate task/skill models that can be subsequently generalized to new situations. While learning Cartesian positions suffices for many applications, the end-

Mahdi Ammar

Along the Ricci flow, we study the polyhomogeneity of complete Riemannian metrics endowed with &#34;a Lie structure fibred at infinity&#34;, that is, a class of Lie structures at infinity that induce in a precise way a fibre bundle structure on a certain compactification by a manifold with corners. When the compactification is a manifold with boundary, this

Andrei Alexandru, Paulo F. Bedaque, Neill C. Warrington

We compute with lattice field theory the vector and axial static structure factors of the unitary gas for arbitrary temperature above the superfluid transition and for fugacities 0.1 < z < 1.0. Using the lattice formulation, we calculate beyond the validity of the virial expansion, a commonly used technique in many-body physics. We find qualitative differenc

Enes Krijestorac, Samer Hanna, Danijela Cabric

In recent years, unmanned aerial vehicles (UAVs) have been considered for telecommunications purposes as relays, caches, or IoT data collectors. In addition to being easy to deploy, their maneuverability allows them to adjust their location to optimize the capacity of the link to the user equipment on the ground or of the link to the basestation. The majorit

Alexander N. Pchelintsev, Andrey A. Polunovskiy, Irina Yu. Yukhanova

We consider the harmonic balance method for finding approximate periodic solutions of the Lorenz system. When developing software that implements the described method, the math package Maxima was chosen. The drawbacks of symbolic calculations for obtaining a system of nonlinear algebraic equations with respect to the cyclic frequency, free terms and amplitud

Alexander Makarenko

Since the introduction of strong anticipation by D.~Dubois the numerous investigations of concrete systems have been proposed. In proposed paper the new examples of discrete dynamical systems with anticipation are considered. The mathematical formulation of problems, possible analytical formulas for solutions and numerical examples of presumable solutions ar

Srinivas Desamsetti, Hari Prasad Dasari, Sabique Langodan, Edriss S Titi

Continuous data assimilation (CDA) is successfully implemented for the first time for efficient dynamical downscaling of a global atmospheric reanalysis. A comparison of the performance of CDA with the standard grid and spectral nudging techniques for representing long- and short-scale features in the downscaled fields using the Weather Research and Forecast

Bin Guo, Yasan Ding, Yueheng Sun, Shuai Ma

The wide spread of fake news in social networks is posing threats to social stability, economic development and political democracy etc. Numerous studies have explored the effective detection approaches of online fake news, while few works study the intrinsic propagation and cognition mechanisms of fake news. Since the development of cognitive science paves

Lars Brenna, Håvard D. Johansen, Dag Johansen

Nutritional assessment is key in order to make decisions about the nature and cause of nutrition related health issues that affect an individual. The systematic process of collecting and interpreting relevant nutrition information, however, is still in its technological infancy. Despite technological advances in storage and analysis of nutritional data, meth

Indranil Banik, Pavel Kroupa

The Galaxy is conventionally thought to be surrounded by a massive dark matter (DM) halo. As the Sun goes through this halo, it excites a DM wake behind it. This local asymmetry in the DM distribution would gravitationally affect the motions of Solar System planets, potentially allowing the DM wake to be detected or ruled out. Hernandez (2019) recently calcu

Majid Ali Choudhary

Let M be a compact pseudo-umbilical submanifold of the unit sphere S. In the present note, it is shown that if the normal curvature, scalar curvature S and square of the length of second fundamental form satisfy certain conditions, then M is totally geodesic.

Zhao You, Dan Su, Dong Yu

In many automatic speech recognition (ASR) tasks, an ideal model has to be applicable over multiple domains. In this paper, we propose to teach an all-rounder with experts in different domains. Concretely, we build a multi-domain acoustic model by applying the teacher-student training framework. First, for each domain, a teacher model (domain-dependent model

Ankit Gupta

According to the WHO, Cerebrovascular Stroke, or CS, is the second largest cause of death worldwide. Current diagnosis of CS relies on labor and cost intensive neuroimaging techniques, unsuitable for areas with inadequate access to quality medical facilities. Thus, there is a great need for an efficient diagnosis alternative. StrokeSave is a platform for use

Hainan Zhang, Yanyan Lan, Jiafeng Guo, Jun Xu

Chinese input recommendation plays an important role in alleviating human cost in typing Chinese words, especially in the scenario of mobile applications. The fundamental problem is to predict the conditional probability of the next word given the sequence of previous words. Therefore, statistical language models, i.e.~n-grams based models, have been extensi

Hainan Zhang, Yanyan Lan, Liang Pang, Jiafeng Guo

In multi-turn dialogue generation, response is usually related with only a few contexts. Therefore, an ideal model should be able to detect these relevant contexts and produce a suitable response accordingly. However, the widely used hierarchical recurrent encoderdecoder models just treat all the contexts indiscriminately, which may hurt the following respon

Ran Wang, Haibo Su, Chunye Wang, Kailin Ji

The introduction of pre-trained language models has revolutionized natural language research communities. However, researchers still know relatively little regarding their theoretical and empirical properties. In this regard, Peters et al. perform several experiments which demonstrate that it is better to adapt BERT with a light-weight task-specific head, ra

Laurent El Shafey, Hagen Soltau, Izhak Shafran

Speech applications dealing with conversations require not only recognizing the spoken words, but also determining who spoke when. The task of assigning words to speakers is typically addressed by merging the outputs of two separate systems, namely, an automatic speech recognition (ASR) system and a speaker diarization (SD) system. The two systems are traine

Mojtaba Nayyeri, Xiaotian Zhou, Sahar Vahdati, Hamed Shariat Yazdi

Translation-based embedding models have gained significant attention in link prediction tasks for knowledge graphs. TransE is the primary model among translation-based embeddings and is well-known for its low complexity and high efficiency. Therefore, most of the earlier works have modified the score function of the TransE approach in order to improve the pe

Biao Qian, Yang Wang

1 bit deep neural networks (DNNs), of which both the activations and weights are binarized , are attracting more and more attention due to their high computational efficiency and low memory requirement . However, the drawback of large accuracy dropping also restrict s its application. In this paper, we propose a novel Targeted Acceleration and Compression (T

Leszek Pecyna, Angelo Cangelosi

In this paper a neuro-robotics model capable of counting using gestures is introduced. The contribution of gestures to learning to count is tested with various model and training conditions. Two studies were presented in this article. In the first, we combine different modalities of the robot&#39;s neural network, in the second, a novel training procedure fo

Shuai Ma, Jia Yuan Yu

Variance plays a crucial role in risk-sensitive reinforcement learning, and most risk measures can be analyzed via variance. In this paper, we consider two law-invariant risks as examples: mean-variance risk and exponential utility risk. With the aid of the state-augmentation transformation (SAT), we show that, the two risks can be estimated in Markov decisi

Ivan Nourdin, David Nualart, Giovanni Peccati

In this paper we prove an estimate for the total variation distance, in the framework of the Breuer-Major theorem, using the Malliavin-Stein method, assuming the underlying function $g$ to be once weakly differentiable with $g$ and $g&#39;$ having finite moments of order four with respect to the standard Gaussian density. This result is proved by a combinati

Pier Franco Nali

The purpose of this paper is to prove directly, by an elementary method, the Poisson probability law. This proof is offered as an alternative to the more usual derivation from binomial distribution in the limit of small probabilities. The same proof is then applied to the solution of a problem in statistical mechanics. --- Lo scopo di questo articolo è dimos

Wen-Bo Xie, Yan-Li Lee, Cong Wang, Duan-Bing Chen

Clustering is a fundamental analysis tool aiming at classifying data points into groups based on their similarity or distance. It has found successful applications in all natural and social sciences, including biology, physics, economics, chemistry, astronomy, psychology, and so on. Among numerous existent algorithms, hierarchical clustering algorithms are o

Tobias Kluth, Patryk Szwargulski, Tobias Knopp

The image reconstruction problem of the tomographic imaging technique magnetic particle imaging (MPI) requires the solution of a linear inverse problem. One prerequisite for this task is that the imaging operator that describes the mapping between the tomographic image and the measured signal is accurately known. For 2D and 3D excitation patterns, it is comm

C. Liaskos, G. Pirialakos, A. Pitilakis, S. Abadal

Metasurfaces constitute effective media for manipulating and transforming impinging EM waves. Related studies have explored a series of impactful MS capabilities and applications in sectors such as wireless communications, medical imaging and energy harvesting. A key-gap in the existing body of work is that the attributes of the EM waves to-be-controlled (e.

Wang Donglin, Raja R. Sattiraju, Qiu Anjie, Sanket Partani

At the time of the development, standardization, and further improvement are vital to the modern cellular systems such as the next generation wireless communication (5G). Simulations are essential to test and optimize algorithms and procedures prior to their implementation process of the equipment manufactures. In order to evaluate system performance at diff

A. G. Ghazaryan, Kh. V. Sedrakian

The quantum theory of multiphoton stimulated bremsstrahlung of charged carriers on an arbitrary electrostatic potential of impurity ion in doped bilayer graphene at the presence of coherent electromagnetic radiation is developed. A terahertz wave field is considered exactly, while the electrostatic potential of doped ions as a perturbation. The essentially n

Mohammadreza Chamanbaz, Giuseppe Notarstefano, Francesco Sasso, Roland Bouffanais

In this paper, we consider a network of processors aiming at cooperatively solving mixed-integer convex programs subject to uncertainty. Each node only knows a common cost function and its local uncertain constraint set. We propose a randomized, distributed algorithm working under asynchronous, unreliable and directed communication. The algorithm is based on

Chuanzhong Li

As a generalization of the integrable extended Toda hierarchy and a reduction of the extended multicomponent Toda hierarchy, from the point of a commutative subalgebra of $gl(2,\mathbb{C})$, we construct a strongly coupled extended Toda hierarchy(SCETH) which will be proved to possess a Virasoro type additional symmetry by acting on its tau-function. Further

Chuanzhong Li, Jipeng Cheng

In this paper, we construct the multicomponent modified KP hierarchy and its additional symmetries. The additional symmetries constitute an interesting multi-folds quantum torus type Lie algebra. By a reduction, we also construct the constrained multicomponent modified KP hierarchy and its Virasoro type additional symmetries.

Takayuki Kobayashi, Masashi Misawa, Kazuyuki Tsuda

Asymptotic profile for diffusion wave terms of solutions to the compressible Navier-Stokes-Korteweg system is studied on $R^2$. The diffusion wave with time decay estimate is studied by Hoff and Zumbrun (1995, 1997), Kobayashi and Shibata (2002) and Kobayashi and Tsuda (2018) for the compressible Navier-Stokes system and the compressible Navier-Stokes-Kortew

Javier Navarro, Christian Wagner

Recently, there has been much research into modelling of uncertainty in human perception through Fuzzy Sets (FSs). Most of this research has focused on allowing respondents to express their (intra) uncertainty using intervals. Here, depending on the technique used and types of uncertainties being modelled different types of FSs can be obtained (e.g., Type-1,

Vladimir Chilin, Dogan Comez, Semyon Litvinov

Given a $σ$-finite infinite measure space $(Ω,μ)$, it is shown that any Dunford-Schwartz operator $T:\,\mathcal L^1(Ω)\to\mathcal L^1(Ω)$ can be uniquely extended to the space $\mathcal L^1(Ω)+\mathcal L^\infty(Ω)$. This allows to find the largest subspace $\mathcal R_μ$ of $\mathcal L^1(Ω)+\mathcal L^\infty(Ω)$ such that the ergodic averages $\frac1n\sum\li

Ramesh Bhandari

Within the framework of perturbation theory, we explore in detail the mixing of orbital angular momentum(OAM) modes due to a fiber bend in a step-index multimode fiber. Using scalar wave equation, we develop a complete set of analytic expressions for mode-mixing, including those for the $2π$ walk-off length, which is the distance traveled within the bent fib

Hyunjin Lee, Young Jin Suh

In this paper, we first introduce the full express of the Riemannian curvature tensor of a real hypersurface $M$ in complex quadric $Q^{m}$ from the equation of Gauss. Next we derive a formula for the structure Jacobi operator $R_ξ$ and its derivative under the Levi-Civita connection of $M$. We give a complete classification of Hopf real hypersurfaces with R

Jihyun Park, Kexin Zhao, Kainan Peng, Wei Ping

In this work, we extend ClariNet (Ping et al., 2019), a fully end-to-end speech synthesis model (i.e., text-to-wave), to generate high-fidelity speech from multiple speakers. To model the unique characteristic of different voices, low dimensional trainable speaker embeddings are shared across each component of ClariNet and trained together with the rest of t

Przemyslaw Biecek

Predictive modeling has an increasing number of applications in various fields. High demand for predictive models drives creation of tools that automate and support work of data scientist on the model development. To better understand what can be automated we need first a description of the model life-cycle. In this paper we propose a generic Model Developme

Gurtina Besla, Daniela Huppenkothen, Nicole Lloyd-Ronning, Evan Schneider

The current disparity in computational knowledge is a critical hindrance to the diversity and success of the field. Recommendations are outlined for policies and funding models to enable the growth and retention of a new generation of computational researchers that reflect the demographics of the undergraduate population in Astronomy and Physics.

Sowmya Kamath, Joshua E. Meyers, Patricia R. Burchat

Galaxy color gradients - i.e., spectral energy distributions that vary across the galaxy profile - will impact galaxy shape measurements when the modeled point spread function (PSF) corresponds to that for a galaxy with spatially uniform color. This paper describes the techniques and results of a study of the expected impact of galaxy color gradients on weak

Andrei V. Malyutin

We show that if $K$ is a nontrivial knot then the proportion of satellites of $K$ among all of the prime non-split links of $n$ or fewer crossings does not converge to $0$ as $n$ approaches infinity. This implies in particular that the proportion of hyperbolic links among all of the prime non-split links of $n$ or fewer crossings does not converge to 1 as $n

Nicholas Collins, Hanna Kurniawati

We propose a neural network architecture, called TransNet, that combines planning and model learning for solving Partially Observable Markov Decision Processes (POMDPs) with non-uniform system dynamics. The past decade has seen a substantial advancement in solving POMDP problems. However, constructing a suitable POMDP model remains difficult. Recently, neura

Utsav Banerjee, Andrew Wright, Chiraag Juvekar, Madeleine Waller

This paper presents the first hardware implementation of the Datagram Transport Layer Security (DTLS) protocol to enable end-to-end security for the Internet of Things (IoT). A key component of this design is a reconfigurable prime field elliptic curve cryptography (ECC) accelerator, which is 238x and 9x more energy-efficient compared to software and state-o

Tom Sutton

We show that that classical rational homotopy theory in the sense of Sullivan [6] can be extended compactly supported setting. This presents a simplicial version of the compactly supported de Rham complex in characteristic zero, and proving that it models singular compactly supported cohomology. This presents new avenues of possible study in proper homotopy

Z. Faidon Brotzakis, Peter G. Bolhuis

Transition path sampling (TPS) is a powerful technique for investigating rare transitions, especially when the mechanism is unknown and one does not have access to the reaction coordinate. Straightforward application of TPS does not directly provide the free energy landscape nor the kinetics, which motivated the development of path sampling extensions, such

Songtao Lu, Meisam Razaviyayn, Bo Yang, Kejun Huang

This paper proposes low-complexity algorithms for finding approximate second-order stationary points (SOSPs) of problems with smooth non-convex objective and linear constraints. While finding (approximate) SOSPs is computationally intractable, we first show that generic instances of the problem can be solved efficiently. More specifically, for a generic prob

Saannidhya Rawat

Through this paper, an attempt has been made to quantify the underlying relationships between the leading macroeconomic indicators. More clearly, an effort has been made in this paper to assess the cointegrating relationships and examine the error correction behavior revealed by macroeconomic variables using econometric techniques that were initially develop

David Griffiths, Jan Boehm

Over the past decade deep learning has driven progress in 2D image understanding. Despite these advancements, techniques for automatic 3D sensed data understanding, such as point clouds, is comparatively immature. However, with a range of important applications from indoor robotics navigation to national scale remote sensing there is a high demand for algori

James. R. A. Davenport

Traditional searches for extraterrestrial intelligence (SETI) or &#34;technosignatures&#34; focus on dedicated observations of single stars or regions in the sky to detect excess or transient emission from intelligent sources. The newest generation of synoptic time domain surveys enable an entirely new approach: spatio-temporal SETI, where technosignatures m

Gaston Giribet

The insertion of the spectral flow operator in a string scattering amplitude on AdS$_3\times \mathcal{N}$ produces a change in the winding number of one of the incoming (or outgoing) states, making it possible to compute amplitudes of processes in which winding number in AdS$_3$ is not conserved. The insertion of such operator, however, might seem artificial

Cai Zhang, Mohsen Razavi, Zhiwei Sun, Qiong Huang

We present a secure multi-party quantum summation protocol based on quantum teleportation, in which a malicious, but non-collusive, third party (TP) helps compute the summation. In our protocol, TP is in charge of entanglement distribution and Bell states are shared between participants. Users encode the qubits in their hand according to their private bits a

Fabio Miranda, Harish Doraiswamy, Marcos Lage, Luc Wilson

Large scale shadows from buildings in a city play an important role in determining the environmental quality of public spaces. They can be both beneficial, such as for pedestrians during summer, and detrimental, by impacting vegetation and by blocking direct sunlight. Determining the effects of shadows requires the accumulation of shadows over time across di

Mark Hagen

Let $F$ be a free group of positive, finite rank and let $Φ\in Aut(F)$ be a polynomial-growth automorphism. Then $F\rtimes_Φ\mathbb Z$ is strongly thick of order $η$, where $η$ is the rate of polynomial growth of $ϕ$. This fact is implicit in work of Macura, but her work predates the notion of thickness. Therefore, in this note, we make the relationship betw

Peter Crooks, Markus Röser

This work is concerned with Mishchenko and Fomenko&#39;s celebrated theory of completely integrable systems on a complex semisimple Lie algebra $\mathfrak{g}$. Their theory associates a maximal Poisson-commutative subalgebra of $\mathbb{C}[\mathfrak{g}]$ to each regular element $a\in\mathfrak{g}$, and one can assemble free generators of this subalgebra into

Nikhil Chawla, Arvind Singh, Monodeep Kar, Saibal Mukhopadhyay

The proliferation of ubiquitous computing requires energy-efficient as well as secure operation of modern processors. Side channel attacks are becoming a critical threat to security and privacy of devices embedded in modern computing infrastructures. Unintended information leakage via physical signatures such as power consumption, electromagnetic emission (E

Chethan Kumar Anjinappa, You Zhou, Yavuz Yapici, Dror Baron

In this paper, we tackle channel estimation in millimeter-wave hybrid multiple-input multiple-output systems by considering off-grid effects. In particular, we assume that spatial parameters can take any value in the angular domain, and need not fall on predefined discretized angles. Instead of increasing the number of discretized points to combat off-grid e

Md. Kamrul Hasan, Tajwar Abrar Aleef

Mammography is the most widely used gold standard for screening breast cancer, where, mass detection is considered as the prominent step. Detecting mass in the breast is, however, an arduous problem as they usually have large variations between them in terms of shape, size, boundary, and texture. In this literature, the process of mass detection is automated

Chethan Kumar Anjinappa, Ali Cafer Gurbuz, Yavuz Yapici, Ismail Guvenc

The spectrum scarcity at sub-6 GHz spectrum has made millimeter-wave (mmWave) frequency band a key component of the next-generation wireless networks. While mmWave spectrum offers extremely large transmission bandwidths to accommodate ever-increasing data rates, unique characteristics of this new spectrum need special consideration to achieve the promised ne

Gunduz Caginalp, Mark DeSantis

The methodology presented provides a quantitative way to characterize investor behavior and price dynamics within a particular asset class and time period. The methodology is applied to a data set consisting of over 250,000 data points of the S&P 100 stocks during 2004-2018. Using a two-way fixed-effects model, we uncover trader motivations including evidenc

Dominic M. Bowman, Daniel L. Holdsworth

Context. Modern space telescopes are currently providing high-precision light curves for a large fraction of the sky, such that many new variable stars are being discovered. However, some stars have periodic variability with periods of order minutes and require high-cadence photometry to probe the physical mechanisms responsible. A cadence of less than a min

Ivan Vitev

These proceedings are dedicated to Miklos Gyulassy&#39;s 70$^{\rm th}$ birthday. In his long and distinguished career he has made seminal contributions to many areas of heavy ion theory, including early papers on quarkonium phenomenology in fixed-target heavy ion experiments. Theoretical and experimental studies of the $J/ψ$ and $Υ$ states have evolved consi

Pasqua D&#39;Ambra, Panayot S. Vassilevski

This paper proposes improving the solve time of a bootstrap AMG designed previously by the authors. This is achieved by incorporating the information, set of algebraically smooth vectors, generated by the bootstrap algorithm, in a single hierarchy by using sufficiently large aggregates, and these aggregates are compositions of aggregates already built throug

Douglas R. Stinson, Shannon Veitch

An $\ell$-good sequencing of an STS$(v)$ is a permutation of the points of the design such that no $\ell$ consecutive points in this permutation contain a block of the design. We prove that, for every integer $\ell \geq 3$, there is an $\ell$-good sequencing of any STS$(v)$ provided that $v$ is sufficiently large. We also prove some new nonexistence results

H. Serhat Tetikol, M. Irsadi Aksun

Highly lossy nature of metals has severely limited the scope of practical applications of plasmonics. The conventional approach to circumvent this limitation has been to search for new materials with more favorable dielectric properties (e.g., reduced loss), or to incorporate gain media to overcome the inherent loss. In this study, however, we turn our atten

Chandra Chekuri, Kent Quanrud, Zhao Zhang

Partial Set Cover (PSC) is a generalization of the well-studied Set Cover problem (SC). In PSC the input consists of an integer $k$ and a set system $(U,S)$ where $U$ is a finite set, and $S \subseteq 2^U$ is a collection of subsets of $U$. The goal is to find a subcollection $S&#39; \subseteq S$ of smallest cardinality such that sets in $S&#39;$ cover at le

Nicholas J. Kuhn

Let A and B be two connected graded commutative k-algebras of finite type, where k is a perfect field of positive characteristic p. We prove that the quasi--shuffle algebras generated by A and B are isomorphic as Hopf algebras if and only if A and B are isomorphic as graded k-vector spaces equipped with a Frobenius (pth-power) map. For the hardest part of th