November 2019 arXiv papers — page 60

Jairo Inga, Esther Bischoff, Florian Köpf, Sören Hohmann

We consider the inverse problem of dynamic games, where cost function parameters are sought which explain observed behavior of interacting players. Maximum entropy inverse reinforcement learning is extended to the N-player case in order to solve inverse dynamic games with continuous-valued state and control spaces. We present methods for identification of co

D. N. Voskresensky

After reminding of properties of the condensate of the complex scalar field in the external uniform magnetic field $H$ focus is made on the study of phases of the complex neutral vector boson fields coupled with magnetic field by the Zeeman coupling and phases of the charged vector boson fields. These systems may behave as nonmagnetic and ferromagnetic super

Joakim Björk, Karl Henrik Johansson

In this work, fundamental control limitations for rotor angle stability are considered. Limitations are identified by characterizing open-loop transfer function zeros for input-output combinations of certain power system configurations. Of particular interest are non-minimum phase (NMP) zeros that limit the achievable performance of the closed-loop system. B

Colin MacLaurin

We examine length measurement in curved spacetime, based on the 1+3-splitting of a local observer frame. This situates extended objects within spacetime, in terms of a given coordinate which serves as an external reference. The radar metric is shown to coincide with the spatial projector, but these only give meaningful results on the observer's 3-space, wher

Oscar Hernan Madrid Padilla, Yi Yu, Carey E. Priebe

In this paper, we study the offline change point localization problem in a sequence of dependent nonparametric random dot product graphs. To be specific, assume that at every time point, a network is generated from a nonparametric random dot product graph model \citep[see e.g.][]{athreya2017statistical}, where the latent positions are generated from unknown

Chengjie Yu

In this paper, we introduce the notion of developments of curves with respect to symmetric tensors and use it to prove the existence of isometric immersions into a general ambient space with prescribed second fundamental form. Our method provides a geometric construction of such an isometric immersion.

Thomas M. Brenner, Christian Gehrmann, Roman Korobko, Tsachi Livneh

Basic understanding of the driving forces of ion conduction in solids is critical to the development of new solid-state ion conductors. Physical understanding of ion conduction is limited due to strong deviations from harmonic vibrational dynamics in these systems that are difficult to characterize experimentally and theoretically. We overcome this challenge

Martin Bohata

The paper deals with spectral order isomorphisms in the framework of AW*-algebras. We establish that every spectral order isomorphism between sets of all self-adjoint operators (or between sets of all effects, or between sets of all positive operators) in AW*-factors of Type I has a canonical form induced by a continuous function calculus and an isomorphism

Bartosz Bieganowski, Simone Secchi

We consider the nonlinear fractional problem \begin{align*} (-\Delta)^{s} u + V(x) u = f(x,u) &\quad \hbox{in $\mathbb{R}^N$} \end{align*} We show that ground state solutions converge (along a subsequence) in $L^2_{\mathrm{loc}} (\mathbb{R}^N)$, under suitable conditions on $f$ and $V$, to a weak solution of the local problem as $s \to 1^-$.

Biswarup Biswas, Harish Kumar

In this article, we present entropy stable discontinuous Galerkin numerical schemes for equations of special relativistic hydrodynamics with the ideal equation of state. The numerical schemes use the summation by parts (SBP) property of Gauss-Lobatto quadrature rules. To achieve entropy stability for the scheme, we use two-point entropy conservative numerica

Carlos Galindo, Fernando Hernando, Carlos Munuera

A locally recoverable (LRC) code is a code over a finite field $\mathbb{F}_q$ such that any erased coordinate of a codeword can be recovered from a small number of other coordinates in that codeword. We construct LRC codes correcting more than one erasure, which are subfield-subcodes of some $J$-affine variety codes. For these LRC codes, we compute localitie

Sijie Mai, Haifeng Hu, Songlong Xing

Learning joint embedding space for various modalities is of vital importance for multimodal fusion. Mainstream modality fusion approaches fail to achieve this goal, leaving a modality gap which heavily affects cross-modal fusion. In this paper, we propose a novel adversarial encoder-decoder-classifier framework to learn a modality-invariant embedding space.

Nil Şahin

In this article, standard bases of some toric ideals associated to 4-generated pseudo symmetric semigroups with not Cohen-Macaulay tangent cones at the origin are computed. As the tangent cones are not Cohen-Macaulay, non-decreasingness of the Hilbert function of the local ring was not guaranteed. Therefore, using these standard bases, Hilbert functions are

Tonci Novkovic, Remi Pautrat, Fadri Furrer, Michel Breyer

Object finding in clutter is a skill that requires perception of the environment and in many cases physical interaction. In robotics, interactive perception defines a set of algorithms that leverage actions to improve the perception of the environment, and vice versa use perception to guide the next action. Scene interactions are difficult to model, therefor

Rudranil Basu, Augniva Ray

We find the exact quantum gravity partition function on the static patch of 3d de Sitter spacetime. We have worked in the Chern Simons Formulation of 3d Gravity. To obtain a non-perturbative result, we supersymmetrized the Chern Simons action and used the technique of supersymmetric localization. We have obtained an exact non-perturbative result for the spin

François Générau

We show that, on a smooth riemannian manifold, the laplacian of the distance function to a point $b$ is $-\infty$ in the sense of barriers, at every point of the cut locus with respect to $b$.

Ioan Bucataru, Dan Gregorian Fodor

In this paper we prove that a Finsler metrics has constant flag curvature if and only if the curvature of the induced nonlinear connection satisfies an algebraic identity with respect to some arbitrary second rank tensors. Such algebraic identity appears as an obstruction to the formal integrability of some operators in Finsler geometry, [4,7]. This algebrai

Devin Pelser, Hugh Murrell

Recent work in automated sarcasm detection has placed a heavy focus on context and meta-data. Whilst certain utterances indeed require background knowledge and commonsense reasoning, previous works have only explored shallow models for capturing the lexical, syntactic and semantic cues present within a text. In this paper, we propose a deep 56 layer network,

Tiancheng Wen, Shenqi Lai, Xueming Qian

Knowledge distillation (KD) is widely used for training a compact model with the supervision of another large model, which could effectively improve the performance. Previous methods mainly focus on two aspects: 1) training the student to mimic representation space of the teacher; 2) training the model progressively or adding extra module like discriminator.

Deng Cai, Wai Lam

The dominant graph-to-sequence transduction models employ graph neural networks for graph representation learning, where the structural information is reflected by the receptive field of neurons. Unlike graph neural networks that restrict the information exchange between immediate neighborhood, we propose a new model, known as Graph Transformer, that uses ex

Marc Hellmuth, Carsten R. Seemann, Peter F. Stadler

Fitch graphs $G=(X,E)$ are digraphs that are explained by $\{\emptyset, 1\}$-edge-labeled rooted trees $T$ with leaf set $X$: there is an arc $(x,y) \in E$ if and only if the unique path in $T$ that connects the last common ancestor $\mathrm{lca}(x,y)$ of $x$ and $y$ with $y$ contains at least one edge with label "1". In practice, Fitch graphs represent xeno

Daniella B. Deutz, Sybrand van der Zwaag, Pim Groen

As demand rises for flexible electronics, traditionally prepared sintered ceramic sensors must be transformed into fully new sensor materials that can bend and flex in use and integration. Negative temperature coefficient of resistance (NTC) ceramic thermistors are preferred temperature sensors for their high accuracy and excellent stability, yet their high

Jerome Benoit, Saif Eddin Jabari

Urban street networks of unplanned or self-organized cities typically exhibit astonishing scale-free patterns. This scale-freeness can be shown, within the maximum entropy formalism (MaxEnt), as the manifestation of a fluctuating system that preserves on average some amount of information. Monte Carlo methods that can further this perspective are cruelly mis

Mudra Jadav, S. P. Bhatnagar

Frequency dependant complex magnetic permeability is used to understand RF-microwave behaviour of magnetic nanoparticles in the frequency range 250 MHz to 3 GHz. The stable dispersions of Fe3O4 nanoparticles with mean size varying between 11 to 16 nm are prepared for this purpose. The effect of mean particle size and external static magnetic field over micro

Qing-Bo Liu, Yuting Qian, Hua-Hua Fu, Zhijun Wang

In spinful electronic systems, time-reversal symmetry makes that all Kramers pairs at the time-reversal-invariant momenta are Weyl points (WPs) in chiral crystals. Here, we find that such symmetry-enforced WPs can also emerge in bosonic systems ($e.g.$ phonons and photons) due to nonsymmorphic symmetries. We demonstrate that for some nonsymmorphic chiral spa

Ilja Manakov, Markus Rohm, Volker Tresp

In this paper, we present an in-depth investigation of the convolutional autoencoder (CAE) bottleneck. Autoencoders (AE), and especially their convolutional variants, play a vital role in the current deep learning toolbox. Researchers and practitioners employ CAEs for a variety of tasks, ranging from outlier detection and compression to transfer and represen

Ryan Tan, Xiansong Xu, Dario Poletti

We study the interplay between interactions and finite-temperature dephasing baths. We consider a double well with strongly interacting bosons coupled, via the density, to a bosonic bath. Such a system, when the bath has infinite temperature and instantaneous decay of correlations, relaxes with an emerging algebraic behavior with exponent 1/2. Here we show t

Samuel G. G. Johnston, Joscha Prochno

Fa\`a di Bruno's formula gives an expression for the derivatives of the composition of two real-valued functions. In this paper we prove a multivariate and synthesized version of Fa\`a di Bruno's formula in higher dimensions, providing a combinatorial expression for the derivatives of chain compositions $F^{(1)} \circ \ldots \circ F^{(m)}$ of functions $F^{(

Yanmin Dai

For a slightly generalised version of the Jimbo quantum group associated with any finite dimensional simple Lie algebra $\mathfrak{g}$, we show that its centre is a polynomial algebra. We construct a set of algebraically independent central elements, each associated with a fundamental weight of $\mathfrak{g}$, and prove that they generate the entire centre o

L. Foschini, M. L. Lister, T. Hovatta, Y. Y. Kovalev

There are several methods to calculate the radiative and kinetic power of relativistic jets, but their results can differ by one or two orders of magnitude. Therefore, it is necessary to perform a calibration of the jet power, to understand the reasons for these differences (whether wrong hypotheses or intrinsic source variability), and if it is possible to

Yoshito Ishiki

We introduce the notion of pseudo-cones of metric spaces as a generalization of both of the tangent cones and the asymptotic cones. We prove that the Assouad dimension of a metric space is bounded from below by that of any pseudo-cone of it. We exhibit a example containing all compact metric spaces as pseudo-cones, and examples containing all proper length s

Zhi Tian, Hao Chen, Chunhua Shen

We propose the first direct end-to-end multi-person pose estimation framework, termed DirectPose. Inspired by recent anchor-free object detectors, which directly regress the two corners of target bounding-boxes, the proposed framework directly predicts instance-aware keypoints for all the instances from a raw input image, eliminating the need for heuristic g

Juncheng Li, Xin Wang, Siliang Tang, Haizhou Shi

Visual navigation is a task of training an embodied agent by intelligently navigating to a target object (e.g., television) using only visual observations. A key challenge for current deep reinforcement learning models lies in the requirements for a large amount of training data. It is exceedingly expensive to construct sufficient 3D synthetic environments a

Junfeng Li, Sameer G. Kulkarni, K. K. Ramakrishnan, Dan Li

Serverless computing is increasingly popular because of the promise of lower cost and the convenience it provides to users who do not need to focus on server management. This has resulted in the availability of a number of proprietary and open-source serverless solutions. We seek to understand how the performance of serverless computing depends on a number o

Yujing Wang, Qinyang Bao

Docker images are composed of multiple layers, each of which contains a set of instructions, and an archive of files. Layers allow Docker to separate a large build task into smaller ones, such that when a part of the program is changed, only the corresponding layer needs to be changed. Yet the current implementation has major inefficiencies that make the reb

Ali Parsa Sirat, Hossein Mehdipourpicha, Niloofar Zendehdel, Hamid Mozafari

Loss minimization in distribution networks (DN) is of great significance since the trend to the distributed generation (DG) requires the most efficient operating scenario possible for economic viability variations. Moreover, voltage instability in DNs is a critical phenomenon and can lead to a major blackout in the system. The decreasing voltage stability le

Shintaro Yoshiura, Keitaro Takahashi, Tomo Takahashi

We argue that the global signal of neutral hydrogen 21cm line can be a powerful probe of primordial power spectrum on small scales. Since the amplitude of small scale primordial fluctuations is important to determine the early structure formation and the timing when the sources of Lyman $α$ photons are produced, they in turn affect the neutral hydrogen 21cm

Muhammet Bastan, Hao-Yu Wu, Tian Cao, Bhargava Kota

We present an open-set logo detection (OSLD) system, which can detect (localize and recognize) any number of unseen logo classes without re-training; it only requires a small set of canonical logo images for each logo class. We achieve this using a two-stage approach: (1) Generic logo detection to detect candidate logo regions in an image. (2) Logo matching

Beom-Seok Han, Kyeong-Hun Kim, Daehan Park

We present a weighted $L_{q}(L_{p})$-theory ($p,q\in(1,\infty)$) with Muckenhoupt weights for the equation $$ \partial_{t}^αu(t,x)=Δu(t,x) +f(t,x), \quad t>0, x\in \mathbb{R}^d. $$ Here, $α\in (0,2)$ and $\partial_{t}^α$ is the Caputo fractional derivative of order $α$. In particular we prove that for any $p,q\in (1,\infty)$, $w_{1}(x)\in A_p$ and $w_{2}(t)\

Takuya Morita, Jiro Soda

It is of fundamental importance to know the mass of gravitons. A simple method for constraining the graviton mass is to compare the arrival time of light and that of gravitational waves provided that both waves are simultaneously emitted from the same source. To date, from observations of gravitational waves by the LIGO, the upper bound on the graviton mass

Bin Li, Shuseng Wang, Jun Zhang, Xainbin Cao

Massive multiple-input multiple-output (MIMO) radar, enabled by millimeter-wave virtual MIMO techniques, provides great promises to the high-resolution automotive sensing and target detection in unmanned ground/aerial vehicles (UGA/UAV). As a long-established problem, however, existing subspace methods suffer from either high complexity or low accuracy. In t

Kun Wang, Xin Wang, Mark M. Wilde

Entanglement is a striking feature of quantum mechanics, and it has a key property called unextendibility. In this paper, we present a framework for quantifying and investigating the unextendibility of general bipartite quantum states. First, we define the unextendible entanglement, a family of entanglement measures based on the concept of a state-dependent

Ghouthi Boukli Hacene, Vincent Gripon, Nicolas Farrugia, Matthieu Arzel

In this paper, we tackle the problem of incrementally learning a classifier, one example at a time, directly on chip. To this end, we propose an efficient hardware implementation of a recently introduced incremental learning procedure that achieves state-of-the-art performance by combining transfer learning with majority votes and quantization techniques. Th

Cheol-hwan Yoo, Seo-won Ji, Yong-goo Shin, Seung-wook Kim

3D hand pose estimation from a single depth image plays an important role in computer vision and human-computer interaction. Although recent hand pose estimation methods using convolution neural network (CNN) have shown notable improvements in accuracy, most of them have a limitation that they rely on a complex network structure without fully exploiting the

XiaoQian Li, Jie Liu, ShuWu Zhang, GuiXuan Zhang

At present, multi-oriented text detection methods based on deep neural network have achieved promising performances on various benchmarks. Nevertheless, there are still some difficulties for arbitrary shape text detection, especially for a simple and proper representation of arbitrary shape text instances. In this paper, a pixel-based text detector is propos

Tong Che, Xiaofeng Liu, Site Li, Yubin Ge

AI Safety is a major concern in many deep learning applications such as autonomous driving. Given a trained deep learning model, an important natural problem is how to reliably verify the model's prediction. In this paper, we propose a novel framework -- deep verifier networks (DVN) to verify the inputs and outputs of deep discriminative models with deep gen

Li-Li Li, Fu-Hu Liu, Muhammad Waqas, Rasha Al-Yusufi

Transverse momentum (mass) spectra of positively and negatively charged pions, positively and negatively charged kaons, protons and antiprotons produced at mid-(pseudo)rapidity in various collisions at high energies are analyzed in this work. The experimental data measured in central gold-gold, central lead-lead, and inelastic proton-proton collisions by sev

Ping Gao, Daniel Louis Jafferis

In this paper, we propose a concrete teleportation protocol in the SYK model based on a particle traversing a wormhole. The required operations for the communication, and insertion and extraction of the qubit, are all simple operators in terms of the basic qubits. We determine the effectiveness of this protocol, and find a version achieves almost perfect fid

Shan Su, Cheng Peng, Jianbo Shi, Chiho Choi

Predicting an agent's future trajectory is a challenging task given the complicated stimuli (environmental/inertial/social) of motion. Prior works learn individual stimulus from different modules and fuse the representations in an end-to-end manner, which makes it hard to understand what are actually captured and how they are fused. In this work, we borr

Boris L. Altshuler

Attempts to resolve the longstanding Fermion mass hierarchy (FMH) problem in frames of the AdS/CFT correspondence demand the knowledge of bulk fermion masses. The approach of the "old" conformal bootstrap in AdS context permitted to calculate bulk masses of scalar fields [1], [2]. In the present paper this approach is extended to physically more inte

Lucas Liebenwein, Cenk Baykal, Harry Lang, Dan Feldman

We present a provable, sampling-based approach for generating compact Convolutional Neural Networks (CNNs) by identifying and removing redundant filters from an over-parameterized network. Our algorithm uses a small batch of input data points to assign a saliency score to each filter and constructs an importance sampling distribution where filters that highl

Sai Kiran Kadambari, Sundeep Prabhakar Chepuri

In this paper, we focus on learning the underlying product graph structure from multidomain training data. We assume that the product graph is formed from a Cartesian graph product of two smaller factor graphs. We then pose the product graph learning problem as the factor graph Laplacian matrix estimation problem. To estimate the factor graph Laplacian matri

Aleksey Fedoseev, Nikita Chernyadev, Dzmitry Tsetserukou

Today there is a high variety of haptic devices capable of providing tactile feedback. Although most of existing designs are aimed at realistic simulation of the surface properties, their capabilities are limited in attempts of displaying shape and position of virtual objects. This paper suggests a new concept of distributed haptic display for realistic inte

Leonid Positselski

This book contains a detailed exposition of the nonhomogeneous Koszul duality theory in the relative situation over a noncentral, noncommutative, nonsemisimple base ring, as announced in Section 0.4 of arXiv:0708.3398. We prove the Poincare-Birkhoff-Witt theorem in this context and construct the triangulated equivalences of derived Koszul duality. The dualit

Zhenxing Mi, Yiming Luo, Wenbing Tao

Existing learning-based surface reconstruction methods from point clouds are still facing challenges in terms of scalability and preservation of details on large-scale point clouds. In this paper, we propose the SSRNet, a novel scalable learning-based method for surface reconstruction. The proposed SSRNet constructs local geometry-aware features for octree v

Laurence Boxer

We study the approximate fixed point property (AFPP) for continuous single-valued functions and for continuous multivalued functions in digital topology. We extend what is known about these notions and discuss errors that have appeared in the literature.

Yun-Hao Cao, Jianxin Wu, Hanchen Wang, Joan Lasenby

The random subspace method, known as the pillar of random forests, is good at making precise and robust predictions. However, there is not a straightforward way yet to combine it with deep learning. In this paper, we therefore propose Neural Random Subspace (NRS), a novel deep learning based random subspace method. In contrast to previous forest methods, NRS

Tatsuhiro Misumi, Toshiaki Fujimori, Etsuko Itou, Muneto Nitta

We report the results of the lattice simulation of the ${\mathbb C} P^{N-1}$ sigma model on $S_{s}^{1}$(large) $\times$ $S_{\tau}^{1}$(small). We take a sufficiently large ratio of the circumferences to approximate the model on ${\mathbb R} \times S^1$. For periodic boundary condition imposed in the $S_{\tau}^{1}$ direction, we show that the expectation valu

Yunyi Li, Li Liu, Yu Zhao, Xiefeng Cheng

Group sparse representation (GSR) based method has led to great successes in various image recovery tasks, which can be converted into a low-rank matrix minimization problem. As a widely used surrogate function of low-rank, the nuclear norm based convex surrogate usually leads to over-shrinking problem, since the standard soft-thresholding operator shrinks a

Emanuel Reinecke

Full level-n structures on smooth, complex curves are trivializations of the n-torsion points of their Jacobians. We give an algebraic proof that the etale cohomology of the moduli space of smooth, complex curves of genus at least 2 with "infinite level structure" vanishes in degrees above 4g-5. This yields a new perspective on a result of Harer who

Nurali Virani, Naresh Iyer, Zhaoyuan Yang

With the advent of Deep Learning, the field of machine learning (ML) has surpassed human-level performance on diverse classification tasks. At the same time, there is a stark need to characterize and quantify reliability of a model's prediction on individual samples. This is especially true in application of such models in safety-critical domains of industri

Wenqian Liu, Runze Li, Meng Zheng, Srikrishna Karanam

Recent advances in Convolutional Neural Network (CNN) model interpretability have led to impressive progress in visualizing and understanding model predictions. In particular, gradient-based visual attention methods have driven much recent effort in using visual attention maps as a means for visual explanations. A key problem, however, is these methods are d

Shiva Heidari, Reza Asgari

In this paper, the chiral Hall effect of strained Weyl semimetals without any external magnetic field is proposed. Electron-phonon coupling emerges in the low-energy fermionic sector through a pseudogauge potential. We show that, by using chiral kinetic theory, the chiral Hall effect appears as a response to a real time-varying electric field in the presence

Blake C. Stacey

The interpretation of quantum mechanics known as QBism developed out of efforts to understand the probabilities arising in quantum physics as Bayesian in character. But this development was neither easy nor without casualties. Many ideas voiced, and even committed to print, during earlier stages of Quantum Bayesianism turn out to be quite fallacious when see

Shuang Chen, Jun Shen

In this paper, we study the dynamical behaviors of neutral differential equations with small delays. We first establish the existence and smoothness of the global inertial manifolds for these equations. Then we further prove the smoothness of inertial manifolds with respect to small delays for a certain class of neutral differential equations. The method can

Zehua Cheng, Weiyang Wang, Yan Pan, Thomas Lukasiewicz

Low precision training is one of the most popular strategies for deploying the deep model on limited hardware resources. Fixed point implementation of DCNs has the potential to alleviate complexities and facilitate potential deployment on embedded hardware. However, most low precision training solution is based on a mixed precision strategy. In this paper, w

Peter J. Baddoo, Rozhin Hajian, Justin W. Jaworski

We extend unsteady thin aerofoil theory to model aerofoils with generalised chordwise porosity distributions. The analysis considers a linearised porosity boundary condition where the seepage velocity through the aerofoil is related to the local pressure jump across the aerofoil surface and to the unsteady characteristics of the porous medium. Application of

Meng Zheng, Srikrishna Karanam, Terrence Chen, Richard J. Radke

While there has been substantial progress in learning suitable distance metrics, these techniques in general lack transparency and decision reasoning, i.e., explaining why the input set of images is similar or dissimilar. In this work, we solve this key problem by proposing the first method to generate generic visual similarity explanations with gradient-bas

Parikshit Gopalan, Roie Levin, Udi Wieder

Say that we are given samples from a distribution $\psi$ over an $n$-dimensional space. We expect or desire $\psi$ to behave like a product distribution (or a $k$-wise independent distribution over its marginals for small $k$). We propose the problem of enumerating/list-decoding all large subcubes where the distribution $\psi$ deviates markedly from what we

Dan-Virgil Voiculescu

We extend to the case of a threshold ideal our result with J. Bourgain about the essential centre of the commutant mod a diagonalization ideal for a n-tuple of commuting Hermitian operators . We also compute the $K_0$-group of the commutant mod trace-class of a unitary operator with spectrum equal to its essential spectrum. We present the problem of computin

Chong Ye, Quansheng Zhang, Yu-Yuan Chen, Yong Li

Based on the four-level double-$Δ$ model composed of two degenerated (left- and right-handed) chiral ground states and two achiral excited states, we propose a purely optical method for enantio-conversion of chiral mixture. By choosing appropriate parameters, the original four-level model will be simplified to two effective two-level sub-systems with each of

Luca Franci, Julia E. Stawarz, Emanuele Papini, Petr Hellinger

Magnetospheric Multiscale (MMS) observations of plasma turbulence generated by a Kelvin-Helmholtz (KH) event at the Earth's magnetopause are compared with a high-resolution two-dimensional (2D) hybrid direct numerical simulation (DNS) of decaying plasma turbulence driven by large-scale balanced Alfv\'enic fluctuations. The simulation, set up with four observ

Michaela Cully-Hugill, Timothy Trudgian

We give explicit bounds on sums of $d(n)^2$ and $d_4(n)$, where $d(n)$ is the number of divisors of $n$ and $d_4(n)$ is the number of ways of writing $n$ as a product of four numbers. In doing so we make a slight improvement on the upper bound for class numbers of quartic number fields.

Munther Abualkibash

Machine learning has more and more effect on our every day's life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly programmed. Machine Learning algorithms apply ma

Aniket Singha

I propose a design strategy to enhance the performance of heat engine via absorption of thermal energy from the channel region. The absorption of thermal energy can be actuated by inelastic processes and may be accomplished by an energy restrictive flow of electrons into the channel. The proposed design strategy employs dual energy filters to inject and extr

Jonathan Baxter

A form of generalisation error known as Off Training Set (OTS) error was recently introduced in [Wolpert, 1996b], along with a theorem showing that small training set error does not guarantee small OTS error, unless assumptions are made about the target function. Here it is shown that the applicability of this theorem is limited to models in which the distri

Douglas Aberdeen, Jonathan Baxter

Generalised matrix-matrix multiplication forms the kernel of many mathematical algorithms. A faster matrix-matrix multiply immediately benefits these algorithms. In this paper we implement efficient matrix multiplication for large matrices using the floating point Intel Pentium SIMD (Single Instruction Multiple Data) architecture. A description of the issues

V. V. Flambaum, I. B. Samsonov

In atoms and molecules, the electrons screen the nucleus from the external electric field. However, if the frequency of the electric field reaches the energy of atomic or molecular transition, the electric field at the nucleus may be resonantly enhanced by many orders in magnitude. In this paper, we study the mechanisms of screening or enhancement of electri

Ngoc C. Lê, Ngoc-Yen Nguyen, Anh-Duong Trinh

Named entity recognition (NER) plays an important role in text-based information retrieval. In this paper, we combine Bidirectional Long Short-Term Memory (Bi-LSTM) \cite{hochreiter1997,schuster1997} with Conditional Random Field (CRF) \cite{lafferty2001} to create a novel deep learning model for the NER problem. Each word as input of the deep learning model

Ali Oskooei, Sophie Mai Chau, Jonas Weiss, Arvind Sridhar

In this work we perform a study of various unsupervised methods to identify mental stress in firefighter trainees based on unlabeled heart rate variability data. We collect RR interval time series data from nearly 100 firefighter trainees that participated in a drill. We explore and compare three methods in order to perform unsupervised stress detection: 1)

Jong-Ping Hsu

A confining quantum chromodynamics (QCD) model is formulated on the basis of a new general Yang-Mills $SU_3$ symmetry. The general Yang-Mills transformations involve arbitrary vector gauge functions $ω_μ(x)$ and Hamilton's characteristic phase functions. We derive fourth-order equations for new `phase fields', which predicts dual linear and Coulomb-l

Jacob Rafati, David C. Noelle

Efficient exploration for automatic subgoal discovery is a challenging problem in Hierarchical Reinforcement Learning (HRL). In this paper, we show that intrinsic motivation learning increases the efficiency of exploration, leading to successful subgoal discovery. We introduce a model-free subgoal discovery method based on unsupervised learning over a limite

Hang Yu, David Monniaux

Polyhedral projection is a main operation of the polyhedron abstract domain.It can be computed via parametric linear programming (PLP), which is more efficient than the classic Fourier-Motzkin elimination method.In prior work, PLP was done in arbitrary precision rational arithmetic.In this paper, we present an approach where most of the computation is perfor

Yongcai Geng, Yachun Li, Shengguo Zhu

In the recent paper, the global-in-time inviscid limit of the three-dimensional (3D) isentropic compressible Navier-Stokes equations is considered. First, when viscosity coefficients are given as a constant multiple of density's power ($(ρ^ε)^δ$ with $δ>1$), for regular solutions to the corresponding Cauchy problem, via introducing one "quasi-symmetr

Ke He, Bo Liu, Yu Zhang, Andrew Ling

Deep learning and Convolutional Neural Network (CNN) have becoming increasingly more popular and important in both academic and industrial areas in recent years cause they are able to provide better accuracy and result in classification, detection and recognition areas, compared to traditional approaches. Currently, there are many popular frameworks in the m

Gian Paolo Beretta, Enzo Zanchini

From a new rigorous formulation of the general axiomatic foundations of thermodynamics we derive an operational definition of entropy that responds to the emergent need in many technological frameworks to understand and deploy thermodynamic entropy well beyond the traditional realm of equilibrium states of macroscopic systems. The new treatment starts from a

L. Besombes, H. Boukari, V. Tiwari, A. Lafuente-Sampietro

We demonstrate that the spin of a Cr atom in a quantum dot (QD) can be controlled optically and we discuss the main properties of this single spin system. The photoluminescence of individual Cr-doped QDs and their evolution in magnetic field reveal a large magnetic anisotropy of the Cr spin induced by local strain. This results in a splitting of the Cr spin

Adam Kortylewski, Qing Liu, Huiyu Wang, Zhishuai Zhang

Compositional convolutional networks are generative compositional models of neural network features, that achieve state of the art results when classifying partially occluded objects, even when they have not been exposed to occluded objects during training. In this work, we study the performance of CompositionalNets at localizing occluders in images. We show

Rangrang Zhang

In this paper, we establish a small time large deviation principles for the quasilinear parabolic stochastic partial differential equations with multiplicative noise, which are neither monotone nor locally monotone.

Michael Diodato, Yu Li, Antonia Lovjer, Minsu Yeom

Predicting vehicle trajectories, angle and speed is important for safe and comfortable driving. We demonstrate the best predicted angle, speed, and best performance overall winning the top three places of the ICCV 2019 Learning to Drive challenge. Our key contributions are (i) a general neural network system architecture which embeds and fuses together multi

Praveen Kumar Bodigutla, Lazaros Polymenakos, Spyros Matsoukas

An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversation is intrusive and sparse. Current models to estimate user satisfaction use limited feature sets and employ annotation schemes with limited generalizability to conversations spa

Yibei Li

We generalise the result of Tent and Ziegler to homogeneous structures that have a stationary independence relation without the symmetry axiom. We apply our result to prove simplicity of the automorphism group of some asymmetric examples due to Cherlin.

Alexander I. Bobenko, Alexander Y. Fairley

In the projective plane, we consider congruences of straight lines with the combinatorics of the square grid and with all elementary quadrilaterals possessing touching inscribed conics. The inscribed conics of two combinatorially neighbouring quadrilaterals have the same touching point on their common edge-line. We suggest that these nets are a natural proje

Ozgur Basturk, Ekrem M. Esmer, Seyma Torun, Selcuk Yalcinkaya

Transiting planets provide a unique opportunity to search for unseen additional bodies gravitationally bound to a system. It is possible to detect the motion of the center-of-mass of the observed transiting planet-host star duo due to the gravitational tugs of the unseen bodies from the Roemer delay. In order to achieve the goal, determination of the mid-tim

Ozgur Basturk, Selcuk Yalcinkaya, Burak Keten

We have been carrying out a program for over five years to observe transits of selected exoplanets with 1-meter Turkish Telescope, T100 (Baştürk et al. 2014, 2015), by making use of the well-established defocusing technique (Southworth et al. 2009) to achieve high photometric precision. In this contribution, we review the results of our observing program in

Houdun Zeng, Yuliang Xin, Qi Fu, Siming Liu

The spectra fits to a sample of 34 supernova remnants (Zeng et al., 2019) are updated. $γ$-ray spectra of 20 supernova remnants (SNRs) with a soft TeV spectrum are further analyzed. We found that 17 of them can be fitted in the hadronic scenario with a single power-law ion distribution with an index of $\sim$ 2.6, which is significantly softer than the ion d

M. Sharif, Arfa Waseem

This paper studies the effects of charge on a peculiar stellar object, recognized as gravastar, under the influence of $f(R,T)$ gravity by considering the conjecture of Mazur and Mottola in general relativity. The gravastar is also known as an alternative to a black hole and is expressed by three distinct domains named as (i) the interior domain, (ii) the in

Christos Diou, Ioannis Sarafis, Vasileios Papapanagiotou, Ioannis Ioakimidis

The way we eat and what we eat, the way we move and the way we sleep significantly impact the risk of becoming obese. These aspects of behavior decompose into several personal behavioral elements including our food choices, eating place preferences, transportation choices, sleeping periods and duration etc. Most of these elements are highly correlated in a c

Pedro Carrilho

In this thesis, we discuss several instances in which non-linear behaviour affects cosmological evolution in the early Universe. We begin by reviewing the standard cosmological model and the tools used to understand it theoretically and to compute its observational consequences. This includes a detailed exposition of cosmological perturbation theory and the

Fahad Alhasoun, Marta Gonzalez

The classification of streets on road networks has been focused on the vehicular transportational features of streets such as arterials, major roads, minor roads and so forth based on their transportational use. City authorities on the other hand have been shifting to more urban inclusive planning of streets, encompassing the side use of a street combined wi