November 2018 arXiv papers — page 113

Sophia S. Chabysheva, John R. Hiller

We use the interpolating coordinates studied by Hornbostel to investigate a transition from equal-time quantization to light-front quantization, in the context of two-dimensional $ϕ^4$ theory. A consistent treatment is found to require careful consideration of vacuum bubbles, in a nonperturbative extension of the analysis by Collins. Numerical calculations o

Matteo Brogi, Michael R Line

Hi-resolution spectroscopy (R > 25,000) has recently emerged as one of the leading methods to detect atomic and molecular species in the atmospheres of exoplanets. However, it has so far been lacking in a robust method to extract quantitative constraints on temperature structure and molecular/atomic abundances. In this work we present a novel Bayesian atmosp

Louise Budzynski, Federico Ricci-Tersenghi, Guilhem Semerjian

The typical complexity of Constraint Satisfaction Problems (CSPs) can be investigated by means of random ensembles of instances. The latter exhibit many threshold phenomena besides their satisfiability phase transition, in particular a clustering or dynamic phase transition (related to the tree reconstruction problem) at which their typical solutions shatter

Luke Weisenbach, Paul Schechter, Joachim Wambsganss

Studies of the inner regions of micro-lensed AGN during caustic crossing events have often relied upon the approximation that the magnification near a fold caustic is inversely proportional to the square root of the source-caustic distance. We examine here the behavior of the individual micro-images (one a micro-minimum, the other a micro-saddle) that emerge

Bounghun Bock, Michael Damron, C. M. Newman, Vladas Sidoravicius

In independent bond percolation on $\mathbb{Z}^d$ with parameter $p$, if one removes the vertices of the infinite cluster (and incident edges), for which values of $p$ does the remaining graph contain an infinite cluster? Grimmett-Holroyd-Kozma used the triangle condition to show that for $d \geq 19$, the set of such $p$ contains values strictly larger than

David Diego, Kristian Agasøster Haaga, Bjarte Hannisdal

We propose a method for computing the transfer entropy between time series using Ulam's approximation of the Perron-Frobenius (transfer) operator associated with the map generating the dynamics. Our method differs from standard transfer entropy estimators in that the invariant measure is estimated not directly from the data points but from the invariant

Akinari Hoshi, Aiichi Yamasaki

We classify stably/retract rational norm one tori in dimension $p-1$ where $p$ is a prime number and in dimension up to ten with some minor exceptions.

Alkida Balliu, Sebastian Brandt, Yi-Jun Chang, Dennis Olivetti

Consider a computer network that consists of a path with $n$ nodes. The nodes are labeled with inputs from a constant-sized set, and the task is to find output labels from a constant-sized set subject to some local constraints---more formally, we have an LCL (locally checkable labeling) problem. How many communication rounds are needed (in the standard LOCAL

I. Rousochatzakis, S. Kourtis, J. Knolle, R. Moessner

Quantum spin liquids are long-range entangled states of matter with emergent gauge fields and fractionalized excitations. While candidate materials, such as the Kitaev honeycomb ruthenate $α$-RuCl$_3$, show magnetic order at low temperatures $T$, here we demonstrate numerically a dynamical crossover from magnon-like behavior at low $T$ and frequencies $ω$ to

Claire Maïza, Valentin Touzeau, David Monniaux, Jan Reineke

For applications in worst-case execution time analysis and in security, it is desirable to statically classify memory accesses into those that result in cache hits, and those that result in cache misses. Among cache replacement policies, the least recently used (LRU) policy has been studied the most and is considered to be the most predictable. The state-of-

Gernot Münster, Raimar Wulkenhaar

According to the Leutwyler-Smilga relation, in Quantum Chromodynamics (QCD) the topological susceptibility vanishes linearly with the quark masses. Calculations of the topological susceptibility in the context of lattice QCD, extrapolated to zero quark masses, show a remnant non-zero value as a lattice artefact. Employing the Atiyah-Singer theorem in the fra

Dalal Al Ghanim, Ronnie Loeffen, Alex Watson

We introduce two models of taxation, the latent and natural tax processes, which have both been used to represent loss-carry-forward taxation on the capital of an insurance company. In the natural tax process, the tax rate is a function of the current level of capital, whereas in the latent tax process, the tax rate is a function of the capital that would ha

Huaian Diao, Xinlin Cao, Hongyu Liu

This paper is concerned with the intrinsic geometric structures of conductive transmission eigenfunctions. The geometric properties of interior transmission eigenfunctions were first studied in [9]. It is shown in two scenarios that the interior transmission eigenfunction must be locally vanishing near a corner of the domain with an interior angle less than

Pedro J. Villasana T., Stanislaw Gorlow

In this paper, we extend the $\beta$-CNMF to two dimensions and derive exact multiplicative updates for its factors. The new updates generalize and correct the nonnegative matrix factor deconvolution previously proposed by Schmidt and M{\o}rup. We show by simulation that the updates lead to a monotonically decreasing $\beta$-divergence in terms of the mean a

Askar A. Iliasov, Mikhail I. Katsnelson, Shengjun Yuan

In this article we investigate the energy spectrum statistics of fractals at the quantum level. We show that the energy-level distribution of a fractal follows a power-law behaviour, if its energy spectrum is a limit set of piece-wise linear functions. We propose that such a behaviour is a general feature of fractals, which can not be described properly by r

Łukasz Nakonieczny

In the presented paper we tackle the problem of the effective field theory in curved spacetime (cEFT) construction. To this end, we propose to use the heat kernel method. After introducing the general formalism based on the well established formulas known from the application of the heat kernel method to deriving the one-loop effective action in curved space

Tianfang Qi, Su Hu

Let $\mathbb{A}=\mathbb{F}_{q}[T]$ be the polynomial ring over finite field $\mathbb{F}_{q}$, and $\mathbb{A}_{+}$ be the set of monic polynomials in $\mathbb{A}$. In this paper, we show that a large class of arithmetic functions in multi-variables over $\mathbb{A}_{+}$ can be expanded through the polynomial Ramanujan sums and the unitary polynomial Ramanuja

Peter Ebenfelt, Ilya Kossovskiy, Bernhard Lamel

We develop a classification theory for real-analytic hypersurfaces in $\mathbb C^2$ in the case when the hypersurface is of {\em infinite type} at the reference point. This is the remaining, not yet understood case in $\mathbb C^2$ in the {\it Problème local}, formulated by H.\,Poincaré in 1907 and asking for a complete biholomorphic classification of real h

P. Phukan, M. Lalung, J. K. Sarma

We suggest a modified form of a unitarized BFKL equation imposing the so-called kinematic constraint on the gluon evolution in multi-Regge kinematics. The underlying nonlinear effects on the gluon evolution are investigated by solving the unitarized BFKL equation analytically. We obtain an equation of the critical boundary between dilute and dense partonic s

Michiya Mori

We give a complete description of order isomorphisms between operator intervals in general von Neumann algebras. For the description, we use Jordan $^*$-isomorphisms and locally measurable operators. Our results generalize several works by L. Molnár and P. Šemrl on type I factors.

Alkida Balliu, Juho Hirvonen, Dennis Olivetti, Jukka Suomela

A graph is weakly $2$-colored if the nodes are labeled with colors black and white such that each black node is adjacent to at least one white node and vice versa. In this work we study the distributed computational complexity of weak $2$-coloring in the standard LOCAL model of distributed computing, and how it is related to the distributed computational com

José Juan Carreño, José Antonio Martínez, María Luz Puertas

In this paper we present a lower bound for the domination number of the Cartesian product of a path and a cycle, that is tight if the length of the cycle is a multiple of five. This bound improves the natural lower bound obtained by using the domination number of the Cartesian product of two paths, that is the best one known so far.

Yuma Koizumi, Noboru Harada, Yoichi Haneda

This study proposes a trainable adaptive window switching (AWS) method and apply it to a deep-neural-network (DNN) for speech enhancement in the modified discrete cosine transform domain. Time-frequency (T-F) mask processing in the short-time Fourier transform (STFT)-domain is a typical speech enhancement method. To recover the target signal precisely, DNN-b

Yanjie Zhang, Xiao Wang, Jinqiao Duan

We use the mean exit time to quantify macroscopic dynamical behaviors of stochastic dynamical systems driven by tempered Lévy fluctuations, which are solutions of nonlocal elliptic equations. Firstly, we construct a new numerical scheme to compute and solve the mean exit time associated with the one dimensional stochastic system. Secondly, we extend the anal

Qingyin Ma, John Stachurski

Some approaches to solving challenging dynamic programming problems, such as Q-learning, begin by transforming the Bellman equation into an alternative functional equation, in order to open up a new line of attack. Our paper studies this idea systematically, with a focus on boosting computational efficiency. We provide a characterization of the set of valid

Pasin Manurangsi, Warut Suksompong

We consider a fair division setting in which $m$ indivisible items are to be allocated among $n$ agents, where the agents have additive utilities and the agents' utilities for individual items are independently sampled from a distribution. Previous work has shown that an envy-free allocation is likely to exist when $m=\Omega(n\log n)$ but not when $m=n+o(n)$

Dario Mazzoleni, Benedetta Pellacci, Gianmaria Verzini

We study the optimization of the positive principal eigenvalue of an indefinite weighted problem, associated with the Neumann Laplacian in a box $Ω\subset\mathbb{R}^N$, which arises in the investigation of the survival threshold in population dynamics. When trying to minimize such eigenvalue with respect to the weight, one is lead to consider a shape optimiz

Djamel Djenouri, Roufaida Laidi, Cherif Zizoua

Energy consumption in residential and commercial buildings has increased dramatically worldwide in the last decade, due to the constant population and economic growth, the proliferation of electronic and consumer appliances. This has dramatic footprint on the environment in terms of carbon emission, in addition to the economic impact. Green and smart buildin

J. Refsgaard, J. Büscher, A. Arokiaraj, H. O. U. Fynbo

The shape and normalisation of the beta-delayed alpha spectrum from 11Be was measured by implanting 11Be ions in a segmented Si detector. The spectrum is found to be dominated by a well-known transition to the 3/2+ state at Ex = 9.87MeV in 11B. A significant increase in the observed decay strength towards the higher end of the Q window means, however, that t

Mohamd Saleem Lone

A homothetical surface arises as a graph of a function $z = φ_1(v_1) φ_2(v_2)$. In this paper, we study the homothetical surfaces in three dimensional psuedo-Galilean space$\left(\mathbb{G}_3^1\right)$ satisfying the conditions $Δ^{II}\textbf{x}_i=λ_i\textbf{x}_i,$ where $Δ^{II}$ is the Laplacian with respect to second fundamental form. In particular, we sho

Ana Prlić

The notion of algebraic Dirac induction was introduced by P. Pandžić and D. Renard. This is a construction which gives representations with prescribed Dirac cohomology. They proved that all holomorphic discrete series representations can be constructed via Dirac induction. All discrete series representations of the group $SO_{e}(4,1)$ are nonholomorphic. In

Hirokazu Kameoka, Kou Tanaka, Damian Kwasny, Takuhiro Kaneko

This paper proposes a voice conversion (VC) method using sequence-to-sequence (seq2seq or S2S) learning, which flexibly converts not only the voice characteristics but also the pitch contour and duration of input speech. The proposed method, called ConvS2S-VC, has three key features. First, it uses a model with a fully convolutional architecture. This is par

H. L. Dao, Parinya Karndumri

We study five-dimensional $N=4$ gauged supergravity coupled to five vector multiplets with compact and non-compact gauge groups $U(1)\times SU(2)\times SU(2)$ and $U(1)\times SO(3,1)$. For $U(1)\times SU(2)\times SU(2)$ gauge group, we identify $N=4$ $AdS_5$ vacua with $U(1)\times SU(2)\times SU(2)$ and $U(1)\times SU(2)_{\textrm{diag}}$ symmetries and analy

Milan Žukovič, Michal Borovský, Matúš Lach, Dionissios T. Hristopulos

A novel Gibbs Markov random field for spatial data on Cartesian grids based on the modified planar rotator (MPR) model of statistical physics has been recently introduced for efficient and automatic interpolation of big data sets, such as satellite and radar images. The MPR model does not rely on Gaussian assumptions. Spatial correlations are captured via ne

Nicolas Tholozan, Jérémy Toulisse

We prove that some relative character varieties of the fundamental group of a punctured sphere into the Hermitian Lie groups $\mathrm{SU}(p,q)$ admit compact connected components. The representations in these components have several counter-intuitive properties. For instance, the image of any simple closed curve is an elliptic element. These results extend a

Nikhil Bansal

We give a general method for rounding linear programs that combines the commonly used iterated rounding and randomized rounding techniques. In particular, we show that whenever iterated rounding can be applied to a problem with some slack, there is a randomized procedure that returns an integral solution that satisfies the guarantees of iterated rounding and

Mariko Takagishi, Michel van de Velden

In multiple correspondence analysis, both individuals (observations) and categories can be represented in a biplot that jointly depicts the relationships across categories or individuals, as well as the associations between them. Additional information about the individuals can enhance interpretation capacities, such as by including categorical variables for

Heiko Gimperlein, Ceyhun Oezdemir, David Stark, Ernst P. Stephan

Solutions to the wave equation in the exterior of a polyhedral domain or a screen in $\mathbb{R}^3$ exhibit singular behavior from the edges and corners. We present quasi-optimal $hp$-explicit estimates for the approximation of the Dirichlet and Neumann traces of these solutions for uniform time steps and (globally) quasi-uniform meshes on the boundary. The

Yiyang Wang, Risheng Liu, Long Ma, Xiaoliang Song

We in this paper propose a realizable framework TECU, which embeds task-specific strategies into update schemes of coordinate descent, for optimizing multivariate non-convex problems with coupled objective functions. On one hand, TECU is capable of improving algorithm efficiencies through embedding productive numerical algorithms, for optimizing univariate s

Kieran F. Thomas, Matthew J. Davis, Karen. V. Kheruntsyan

We study the nonequilibrium dynamics of the quantum Newton's cradle in a one-dimensional (1D) Bose gas in the weakly-interacting quasicondensate regime. This is the opposite regime to the original quantum Newton's cradle experiment of Kinoshita et al. [Nature 440, 900 (2006)], which was realized in the strongly interacting 1D Bose gas. Using finite t

M. Redies, F. R. Lux, J. -P. Hanke, P. M. Buhl

While chiral magnetic skyrmions have been attracting significant attention in the past years, recently, a new type of a chiral particle emerging in thin films $-$ a chiral bobber $-$ has been theoretically predicted and experimentally observed. Here, based on theoretical arguments, we provide a clear pathway to utilizing chiral bobbers for the purposes of fu

Tomoyuki Arakawa

We give a functorial construction of the genus zero chiral algebras of class $\mathcal{S}$, that is, the vertex algebras corresponding to the theory of class $\mathcal{S}$ associated with genus zero pointed Riemann surfaces via the 4d/2d duality discovered by Beem, Lemos, Liendo, Peelaers, Rastelli and van Rees in physics. We show that there is a unique fami

Jean-Baptiste Bayle, Marc Lilley, Antoine Petiteau, Hubert Halloin

The Laser Interferometer Space Antenna (LISA) is a European Space Agency mission that aims to measure gravitational waves in the millihertz range. Laser frequency noise enters the interferometric measurements and dominates the expected gravitational signals by many orders of magnitude. Time-delay interferometry (TDI) is a technique that reduces this laser no

Jörn Warnecke, Sven Bingert

The hot loop structures in the solar corona can be well modeled by three dimensional magnetohydrodynamic simulations, where the corona is heated by field line braiding driven at the photosphere. To be able to reproduce the emission comparable to observations, one has to use realistic values for the Spitzer heat conductivity, which puts a large constraint on

Mohsen Yavartanoo, Eu Young Kim, Kyoung Mu Lee

We propose an efficient Stereographic Projection Neural Network (SPNet) for learning representations of 3D objects. We first transform a 3D input volume into a 2D planar image using stereographic projection. We then present a shallow 2D convolutional neural network (CNN) to estimate the object category followed by view ensemble, which combines the responses

Nicolas Moutal, Ivan I. Maximov, Denis S. Grebenkov

Since the seminal paper by Mitra et al., diffusion MR has been widely used in order to estimate surface-to-volume ratios. In the present work we generalize Mitra's formula for arbitrary diffusion encoding waveforms, including recently developed q-space trajectory encoding sequences. We show that surface-to-volume ratio can be significantly misestimated u

Naftali R. Smith, Baruch Meerson

We study a Brownian excursion on the time interval $\left|t\right|\leq T$, conditioned to stay above a moving wall $x_{0}\left(t\right)$ such that $x_0\left(-T\right)=x_0\left(T\right)=0$, and $x_{0}\left(\left|t\right|<T\right)>0$. For a whole class of moving walls, typical fluctuations of the conditioned Brownian excursion are described by the Ferrari-Spoh

Nikolas Ioannou, Celestine Dünner, Kornilios Kourtis, Thomas Parnell

In this paper we analyze, evaluate, and improve the performance of training generalized linear models on modern CPUs. We start with a state-of-the-art asynchronous parallel training algorithm, identify system-level performance bottlenecks, and apply optimizations that improve data parallelism, cache line locality, and cache line prefetching of the algorithm.

Chin-Chia Michael Yeh

The last decade has seen a flurry of research on all-pairs-similarity-search (or, self-join) for text, DNA, and a handful of other datatypes, and these systems have been applied to many diverse data mining problems. Surprisingly, however, little progress has been made on addressing this problem for time series subsequences. In this thesis, we have introduced

Carlos A. Benavides-Gallego, A. A. Abdujabbarov, Cosimo Bambi

In this work we derived a rotating and non-linear magnetic-charged black hole surrounded by quintessence using the Newman-Janis algorithm. Considering the state parameter $ω_q=-3/2$, we studied the event horizons, the ergosphere, and the ZAMO. We found that the existence of the outer horizon is constrained by the values of the charge $Q$. Furthermore, we fou

Chin-Chia Michael Yeh, Yan Zhu, Evangelos E. Papalexakis, Abdullah Mueen

Since its introduction, unsupervised representation learning has attracted a lot of attention from the research community, as it is demonstrated to be highly effective and easy-to-apply in tasks such as dimension reduction, clustering, visualization, information retrieval, and semi-supervised learning. In this work, we propose a novel unsupervised representa

Yisheng Chai, Peipei Lu, Haifeng Du, Jianxin Shen

We have developed a sensitive technique to probe the magnetic skyrmion phases and dynamics by employing the interfacial coupling effect in a magnetoelectric composite configuration. The study on a MnSi single crystal sample using this technique provides clear evidences for the skyrmion lattice phase and coexistence of skyrmion and conical phase. Above the Cu

M. N. Chernodub, V. A. Goy, A. V. Molochkov

We study properties of SU(2) Yang-Mills theory on a four-dimensional Euclidean spacetime in which two directions are compactified into a finite two-dimensional torus ${\mathbb T}^2$ while two others constitute a large ${\mathbb R}^2$ subspace. This Euclidean ${\mathbb T}^2 \times {\mathbb R}^2$ manifold corresponds simultaneously to two systems in a (3+1) di

Dongliang He, Zhichao Zhou, Chuang Gan, Fu Li

Despite the success of deep learning for static image understanding, it remains unclear what are the most effective network architectures for the spatial-temporal modeling in videos. In this paper, in contrast to the existing CNN+RNN or pure 3D convolution based approaches, we explore a novel spatial temporal network (StNet) architecture for both local and g

Yijun Yuan, Sören Schwertfeger

Mapping is an essential task for mobile robots and topological representation often works as a basis for the various applications. In this paper, a novel framework that can build topological maps incrementally is proposed. The algorithm is using a distance map, and in our framework the topological map can grow as we append more sensor data to the map. To dem

P. Guo, K. Wang, X. L. Zhou

In this work, a non-gradient descent learning (NGDL) scheme was proposed for deep feedforward neural networks (DNN). It is known that an autoencoder can be used as the building blocks of the multi-layer perceptron (MLP) DNN, the MLP is taken as an example to illustrate the proposed scheme of pseudoinverse learning algorithm for autoencoder (PILAE) in this pa

Jian-Hua Liu, Dong-Yang Jing, Lin Zhuang, Wei Quan

The hybrid optical pumping spin exchange relaxation free (HOPSERF) atomic co-magnetometers make ultrahigh sensitivity measurement of inertia achievable. The wall relaxation rate has a big effect on the polarization and fundamental sensitivity for the co-magnetometer, but it is often neglected in the experiments. However, there is almost no work about the sys

A. S. Mishchenko

The Hochschild homology and cohomology group can be described in terms of the homology and cohomology of the classifying space of the groupoid of the adjoint action of the group under the suitable assumption of the finiteness of the supports of cohomology groups. The difference between homology and cohomology leads to a correction of the results in the book

Tom Bachmann, Maria Yakerson

We study the interplay of the homotopy coniveau tower, the Rost-Schmid complex of a strictly homotopy invariant sheaf, and homotopy modules. For a strictly homotopy invariant sheaf $M$, smooth $k$-scheme $X$ and $q \geqslant 0$ we construct a novel cycle complex $C^*(X, M, q)$ and we prove that in favorable cases, $C^*(X, M, q)$ is equivalent to the homotopy

Claire Mathieu, Simon Mauras

A top-list is a possibly incomplete ranking of elements: only a subset of the elements are ranked, with all unranked elements tied for last. Top-list aggregation, a generalization of the well-known rank aggregation problem, takes as input a collection of top-lists and aggregates them into a single complete ranking, aiming to minimize the number of upsets (pa

David Boozer

We describe a scheme for constructing generating sets for Kronheimer and Mrowka's singular instanton knot homology for the case of knots in lens spaces. The scheme involves Heegaard-splitting a lens space containing a knot into two solid tori. One solid torus contains a portion of the knot consisting of an unknotted arc, as well as holonomy perturbations of

Sungho Shin, Youngmin Jo, Jungwook Choi, Swagath Venkataramani

Deep neural networks (DNNs) have emerged as successful solutions for variety of artificial intelligence applications, but their very large and deep models impose high computational requirements during training. Multi-GPU parallelization is a popular option to accelerate demanding computations in DNN training, but most state-of-the-art multi-GPU deep learning

Efthymios Tzinis, Shrikant Venkataramani, Paris Smaragdis

We present a monophonic source separation system that is trained by only observing mixtures with no ground truth separation information. We use a deep clustering approach which trains on multi-channel mixtures and learns to project spectrogram bins to source clusters that correlate with various spatial features. We show that using such a training process we

D. B. Schaeffer, W. Fox, R. K. Follett, G. Fiksel

We present the first laboratory observations of time-resolved electron and ion velocity distributions in forming, magnetized collisionless shocks. Thomson scattering of a probe laser beam was used to observe the interaction of a laser-driven, supersonic piston plasma expanding through a magnetized ambient plasma. From the Thomson-scattered spectra we measure

Marwan, Andonowati, N. Karjanto

This paper aims to describe a deterministic generation of extreme waves in a typical towing tank. Such a generation involves an input signal to be provided at the wavemaker in such a way that at a certain position in the wave tank, say at a position of a tested object, a large amplitude wave emerges. For the purpose, we consider a model called a spatial nonl

Dan Romik, Robert Scherer

We consider alternative orders of summation for the conditionally convergent series defining the weight-2 Eisenstein series G2 and the Weierstrass p-function. The resulting sums differ from the standard ones by a residual term that can be thought of as a function of the shapes with respect to which we sum. We compute this residual function explicitly and giv

M. Zhang, Y. Wang

Gravity gradient is known as a serious systematic effect in atomic tests of the universality of free fall, where the initial central position and velocity of atoms need to be exactly controlled. In this paper, we study quantum free fall with high-order gravity gradients. It is shown that the cubic terms in the Newtonian potential shall generate a new phase s

Yuan Ke, Stanislav Minsker, Zhao Ren, Qiang Sun

We offer a survey of recent results on covariance estimation for heavy-tailed distributions. By unifying ideas scattered in the literature, we propose user-friendly methods that facilitate practical implementation. Specifically, we introduce element-wise and spectrum-wise truncation operators, as well as their $M$-estimator counterparts, to robustify the sam

Cătălin Gherghe

We study a functional that derives from the classical Yang-Mills functional and Born-Infeld theory. We establish its first variation formula and prove the existence of critical points. We also obtain the second variation formula.

Tanwi Bandyopadhyay, Ujjal Debnath

A review on spatially flat D-dimensional Friedmann-Robertson-Walker (FRW) model of the universe has been performed. Some standard parameterizations of the equation of state parameter of the Dark Energy models are proposed and the possibilities of finite time future singularities are investigated. It is found that certain types of these singularities may appe

Aniket Patra, Boris L. Altshuler, Emil A. Yuzbashyan

We study the dynamics of two ensembles of atoms (or equivalently, atomic clocks) coupled to a bad cavity and pumped incoherently by a Raman laser. Our main result is the nonequilibrium phase diagram for this experimental setup in terms of two parameters - detuning between the clocks and the repump rate. There are three main phases - trivial steady state (Pha

Emmanuel A. Gonzalez

This paper discusses and summarizes some results on complex variables that are very useful in fractional-order systems analysis and design, specifically when the system is analyzed in the frequency domain. The author hopes that this document will serve as a handy reference when performing computations with complex variables, especially when working within th

Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depic

Bidisha Chakrabarty, Soumyadeep Chaudhuri, R. Loganayagam

We consider a quantum Brownian particle interacting with two harmonic baths, which is then perturbed by a cubic coupling linking the particle and the baths. This cubic coupling induces non-linear dissipation and noise terms in the influence functional/master equation of the particle. Its effect on the Out-of-Time-Ordered Correlators (OTOCs) of the particle c

Takashi Morita, Hiroki Koda

A pervasive belief with regard to the differences between human language and animal vocal sequences (song) is that they belong to different classes of computational complexity, with animal song belonging to regular languages, whereas human language is superregular. This argument, however, lacks empirical evidence since superregular analyses of animal song ar

Rui-Juan Jing, Marc Moreno-Maza, Delaram Talaashrafi

In this paper, we propose a new method for removing all the redundant inequalities generated by Fourier-Motzkin elimination. This method is based on an improved version of Balas&#39; work and can also be used to remove all the redundant inequalities in the input system. Moreover, our method only uses arithmetic operations on matrices and avoids resorting to

Kazuo Fujikawa, Anca Tureanu

It is shown that the specific &#34;charge conjugation&#34; transformation used to define the Majorana fermions in the conventional seesaw mechanism, namely $(ν_{R})^{C}=C\bar{ν_{R}}^{T}$ for a chiral fermion $ν_{R}$ (and similarly for $ν_{L}$), is a hidden symmetry associated with CP symmetry, and thus it formally holds independently of the P- and C-violatin

Connie Kou, Hwee Kuan Lee, Jorge Sanz, Teck Khim Ng

There is emerging interest in performing regression between distributions. In contrast to prediction on single instances, these machine learning methods can be useful for population-based studies or on problems that are inherently statistical in nature. The recently proposed distribution regression network (DRN) has shown superior performance for the distrib

Siran Li, Marshall Slemrod

Direct linkages between regular or irregular isometric embeddings of surfaces and steady compressible or incompressible fluid dynamics are investigated in this paper. For a surface $(M,g)$ isometrically embedded in $\mathbb{R}^3$, we construct a mapping which sends the second fundamental form of the embedding to the density, velocity, and pressure of steady

Lijun Zhao, Huihui Bai, Anhong Wang, Yao Zhao

In this paper, we propose a deep multiple description coding framework, whose quantizers are adaptively learned via the minimization of multiple description compressive loss. Firstly, our framework is built upon auto-encoder networks, which have multiple description multi-scale dilated encoder network and multiple description decoder networks. Secondly, two

Chunmao Huang, Xin Wang, Xiaoqiang Wang

We consider a $\mathbb{R}^d$-valued branching random walk with a stationary and ergodic environment $ξ=(ξ_n)$ indexed by time $n\in\mathbb{N}$. Let $Z_n$ be the counting measure of particles of generation $n$. With the help of the uniform convergence of martingale and the multifractal analysis, we establish a large deviation result for the measures $Z_n$ as

Ashwin Sah

Extending results of Linial (1984) and Aigner (1985), we prove a uniform lower bound on the balance constant of a poset $P$ of width $2$. This constant is defined as $δ(P) = \max_{(x, y)\in P^2}\min\{\mathbb{P}(x\prec y), \mathbb{P}(y\prec x)\}$, where $\mathbb{P}(x\prec y)$ is the probability $x$ is less than $y$ in a uniformly random linear extension of $P

Huan-Yu Bi, Ren-You Zhang, Wen-Gan Ma, Yi Jiang

The precision study of $W^-W^+H$ production with subsequent $W^{\pm} \rightarrow l^{\pm} \overset{ _{(-)}}{ν_{l}}$ and $H \rightarrow b\bar{b}$ decays at the LHC can help us to study the Higgs gauge couplings and to search for new physics beyond the SM. In this paper, we calculate the shower-matched NLO QCD correction and the EW corrections from the $q\bar{q

Yosuke Morita

In a previous paper, we obtained a cohomological obstruction to the existence of compact manifolds locally modelled on a homogeneous space. In this paper, we give a classification of the semisimple symmetric spaces to which this obstruction is applicable.

Zheting Dong, Christine Escher, Catherine Searle

We extend the equivariant classification results of Escher and Searle for closed, simply connected, non-negatively curved Riemannian $n$-manifolds admitting isometric isotropy-maximal torus actions to the class of such manifolds admitting isometric strictly almost isotropy-maximal torus actions. In particular, we prove that such manifolds are equivariantly d

Wenyu Wang, Yang Xu

Based on the relationship between proper distance and coordinate distance, the geometrical phenomenon caused by the passing gravitational waves can not be observed locally. The electromagnetic wave equations in the background gravitational waves are studied. We find that the expansion and contraction of wave lengths are always synchronous with the objects it

Yoshihito Kuno

The $N$-flavor interacting Su-Schrieffer-Heeger (i-SSH) model realizable in cold-atoms in an optical lattice is studied. We clarify the relationship between the i-SSH model and the Chiral-Gross-Neveu-Wilson (CGNW) model. Following the previous study of the CGNW model in the high-energy physics community, the groundstate phases of the i-SSH model are investig

Jack Collins, David Howard, Jürgen Leitner

We quantify the accuracy of various simulators compared to a real world robotic reaching and interaction task. Simulators are used in robotics to design solutions for real world hardware without the need for physical access. The `reality gap&#39; prevents solutions developed or learnt in simulation from performing well, or at at all, when transferred to real

Jongwook Choi, Yijie Guo, Marcin Moczulski, Junhyuk Oh

This paper investigates whether learning contingency-awareness and controllable aspects of an environment can lead to better exploration in reinforcement learning. To investigate this question, we consider an instantiation of this hypothesis evaluated on the Arcade Learning Element (ALE). In this study, we develop an attentive dynamics model (ADM) that disco

Benjamin Howard

We prove an invariance property of intersections of Kudla-Rapoport divisors on a unitary Rapoport-Zink space.

Michael Grabowecky, Gilad Gour

Given a finite dimensional pure state transformation restricted by entanglement assisted local operations and classical communication (ELOCC), we derive minimum and maximum bounds on the entanglement of an ancillary catalyst that allows that transformation. These bounds are non-trivial even when the Schmidt number of both the original and ancillary states be

Xiao-Hui Hu, Zhen-Xing Zhao

FCNC processes offer important tools to test the Standard Model (SM), and to search for possible new physics. In this work, we investigate the $s\to dν\barν$ rare hyperon decays in SM and beyond. We find that in SM the branching ratios for these rare hyperon decays range from $10^{-14}$ to $10^{-11}$. When all the errors in the form factors are included, we

Mohammad Saeed Shafiee, Mohammad Javad Shafiee, Alexander Wong

While deep neural networks extract rich features from the input data, the current trade-off between depth and computational cost makes it difficult to adopt deep neural networks for many industrial applications, especially when computing power is limited. Here, we are inspired by the idea that, while deeper embeddings are needed to discriminate difficult sam

Sandipan Banerjee, Walter J. Scheirer, Kevin W. Bowyer, Patrick J. Flynn

We propose an algorithm to generate realistic face images of both real and synthetic identities (people who do not exist) with different facial yaw, shape and resolution.The synthesized images can be used to augment datasets to train CNNs or as massive distractor sets for biometric verification experiments without any privacy concerns. Additionally, law enfo

Zhong-Ying Fan, Minyong Guo

There are several different proposals, relating holographic complexity to the gravitational objects defined on the Wheeler-DeWitt patch. In this paper, we investigate the evolution of complexity following a global quantum quench for these proposals. We find that surprisingly they all reproduce known properties of complexity, such as the switchback effect. Ho

Bo Jiang, Tianyi Lin, Shuzhong Zhang

In this paper, we propose a unified two-phase scheme to accelerate any high-order regularized tensor approximation approach on the smooth part of a composite convex optimization model. The proposed scheme has the advantage of not needing to assume any prior knowledge of the Lipschitz constants for the gradient, the Hessian and/or high-order derivatives. This

Rana Jafari, Travis Jones, Rick Trebino

We demonstrate a novel algorithmic approach for the second-harmonic-generation (SHG) frequency-resolved optical gating (FROG) ultrashort-pulse-measurement technique that always converges and, for complex pulses, is also much faster. It takes advantage of the Paley-Wiener Theorem to retrieve the precise pulse spectrum (half the desired information) directly f

Ke Sun

A basic problem in machine learning is to find a mapping $f$ from a low dimensional latent space $\mathcal{Y}$ to a high dimensional observation space $\mathcal{X}$. Modern tools such as deep neural networks are capable to represent general non-linear mappings. A learner can easily find a mapping which perfectly fits all the observations. However, such a map

Faiq Khalid, Imran Hafeez Abbassi, Semeen Rehman, Awais Mehmood Kamboh

Security vulnerability analysis of Integrated Circuits using conventional design-time validation and verification techniques (like simulations, emulations, etc.) is generally a computationally intensive task and incomplete by nature, especially under limited resources and time constraints. To overcome this limitation, we propose a novel methodology based on

Imran Hafeez Abbassi, Faiq Khalid, Semeen Rehman, Awais Mehmood Kamboh

Conventional Hardware Trojan (HT) detection techniques are based on the validation of integrated circuits to determine changes in their functionality, and on non-invasive side-channel analysis to identify the variations in their physical parameters. In particular, almost all the proposed side-channel power-based detection techniques presume that HTs are dete