February 2019 arXiv papers — page 108

Andrej Dujella, Vinko Petričević

For a nonzero integer $n$, a set of $m$ distinct nonzero integers $\{a_1,a_2,...,a_m\}$ such that $a_ia_j+n$ is a perfect square for all $1 \leq i < j \leq m$, is called a $D(n)$-$m$-tuple. In this paper, we show that there infinitely many essentially different quadruples which are simultaneously $D(n_1)$-quadruples and $D(n_2)$-quadruples with $n_1\neq n_2$

Yuqian Zhang, Jelena Bradic

A fundamental challenge in semi-supervised learning lies in the observed data's disproportional size when compared with the size of the data collected with missing outcomes. An implicit understanding is that the dataset with missing outcomes, being significantly larger, ought to improve estimation and inference. However, it is unclear to what extent this is

David Kazhdan, Tamar Ziegler

We use tools of additive combinatorics for the study of subvarieties defined by {\it high rank} families of polynomials in high dimensional $\mathbb{F} _q$-vector spaces. In the first, analytic part of the paper we prove a number properties of high rank systems of polynomials. In the second, we use these properties to deduce results in Algebraic Geometry, su

Andreas Haier, Ilya Molchanov

The family of admissible positions in a transaction costs model is a random closed set, which is convex in case of proportional transaction costs. However, the convexity fails, e.g. in case of fixed transaction costs or when only a finite number of transfers are possible. The paper presents an approach to measure risks of such positions based on the idea of

Petr Zapletal, Andreas Nunnenkamp

Optomechanics offers a natural way to implement synthetic dynamical gauge fields, leading to synthetic electric fields for phonons and, as a consequence, to unidirectional light transport. Here we investigate the quantum dynamics of synthetic gauge fields in the minimal setup of two optical modes coupled by phonon-assisted tunneling where the phonon mode is

Léo Bigorgne

This paper is concerned with the asymptotic behavior of small data solutions to the three-dimensional Vlasov-Maxwell system in the exterior of a light cone. The plasma does not have to be neutral and no compact support assumptions are required on the data. In particular, the initial decay in the velocity variable of the particle density is optimal and we onl

Paolo Aluffi, Leonardo C. Mihalcea, Jörg Schürmann, Changjian Su

Let $X$ be a complex nonsingular variety with globally generated tangent bundle. We prove that the signed Segre-MacPherson (SM) class of a constructible function on $X$ with effective characteristic cycle is effective. This observation has a surprising number of applications to positivity questions in classical situations, unifying previous results in the li

Shreyas S. Shivakumar, Ty Nguyen, Ian D. Miller, Steven W. Chen

In this paper we propose a convolutional neural network that is designed to upsample a series of sparse range measurements based on the contextual cues gleaned from a high resolution intensity image. Our approach draws inspiration from related work on super-resolution and in-painting. We propose a novel architecture that seeks to pull contextual cues separat

Aziz Azizov, Vladimir Chilin, Semyon Litvinov

Let $\mathcal H$ be an infinite-dimensional Hilbert space, and let $\mathcal B(\mathcal H)$ ($\mathcal K(\mathcal H)$) be the $C^*$-algebra of bounded (respectively, compact) linear operators in $\mathcal H$. Let $(E,\|\cdot\|_E)$ be a fully symmetric sequence space. If $\{s_n(x)\}_{n=1}^\infty$ are the singular values of $x\in\mathcal K(\mathcal H)$, let $\

Antoine Perasso, Quentin Richard, Irene Azzali, Ezio Venturino

In this work, we consider a reaction-diffusion system, modeling the interaction between nutrients, phytoplanktons and zooplanktons. Using a semigroup approach in $L^2$, we prove global existence, uniqueness and positivity of the solutions. The Holling type 2 nonlinearities, i.e of rational type with singularity, are handled by providing estimates in $L^\inft

Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone

Fine-tuning large pre-trained models is an effective transfer mechanism in NLP. However, in the presence of many downstream tasks, fine-tuning is parameter inefficient: an entire new model is required for every task. As an alternative, we propose transfer with adapter modules. Adapter modules yield a compact and extensible model; they add only a few trainabl

Yuting Yang, Juan Cao, Mingyan Lu, Jintao Li

Rapid development of Internet technologies promotes traditional newspapers to report news on social networks. However, people on social networks may have different needs which naturally arises the question: whether can we analyze the influence of writing style on news quality automatically and assist writers in improving news quality? It&#39;s challenging du

Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo

The sample mean is among the most well studied estimators in statistics, having many desirable properties such as unbiasedness and consistency. However, when analyzing data collected using a multi-armed bandit (MAB) experiment, the sample mean is biased and much remains to be understood about its properties. For example, when is it consistent, how large is i

Mircea Cimpoeas

Let $r\geq 1$ be an integer, $\mathbf a=(a_1,\ldots,a_r)$ a vector of positive integers and let $D\geq 1$ be a common multiple of $a_1,\ldots,a_r$. In a continuation of a previous paper we prove that, if $D=1$ or $D$ is a prime number, the restricted partition function $p_{\mathbf a}(n): = $ the number of integer solutions $(x_1,\dots,x_r)$ to $\sum_{j=1}^r

Haowei He, Gao Huang, Yang Yuan

Despite the non-convex nature of their loss functions, deep neural networks are known to generalize well when optimized with stochastic gradient descent (SGD). Recent work conjectures that SGD with proper configuration is able to find wide and flat local minima, which have been proposed to be associated with good generalization performance. In this paper, we

Zhouyu Li, Pan Liu, Pengcheng Niu

We prove several Liouville type results for the stationary MHD and Hall-MHD equations. In particular, we show that the velocity and magnetic field, belonging to some Lorentz spaces or satisfying a priori decay assumption, must be zero.

Ronno Das

We compute the rational cohomology of the universal family of smooth cubic surfaces using Vassiliev&#39;s method of simplicial resolution. Modulo embedding, the universal family has cohomology isomorphic to that of $\mathbb{P}^2$. A consequence of our theorem is that over the finite field $\mathbb{F}_q$, away from finitely many characteristics, the average n

Fabienne Comte, Nicolas Marie

For $n$ independent random variables having the same Hölder continuous density, this paper deals with controls of the Wolverton-Wagner&#39;s estimator MSE and MISE. Then, for a bandwidth $h_n(β)$, estimators of $β$ are obtained by a Goldenshluger-Lepski type method and a Lacour-Massart-Rivoirard type method. Some numerical experiments are provided for this l

Michael Mitzenmacher

In many traditional job scheduling settings, it is assumed that one knows the time it will take for a job to complete service. In such cases, strategies such as shortest job first can be used to improve performance in terms of measures such as the average time a job waits in the system. We consider the setting where the service time is not known, but is pred

Alberto Abbondandolo, Carsten Haug, Felix Schlenk

We construct an action selector on aspherical symplectic manifolds that are closed or convex. Such selectors have been constructed by Matthias Schwarz using Floer homology. The construction we present here is simpler and uses only Gromov compactness.

Su Hu, Min-Soo Kim, Yan Li

In this paper, we obtain a formula for the special value of Euler-Dirichlet $L$-function $L_E(s,χ)$ at $s=1$. This leads to another class number formula of $\mathbb{Q}(μ_{m})^{+}$, the maximal real subfield of $m$th cyclotomic field. From this formula, we construct a new type of cyclotomic units in $\mathbb{Q}(μ_{p^{n}})$, which implies a similar Kummer&#39;

Shouling Ji, Qinchen Gu, Haiqin Weng, Qianjun Liu

In this paper, we study the privacy of online health data. We present a novel online health data De-Anonymization (DA) framework, named De-Health. De-Health consists of two phases: Top-K DA, which identifies a candidate set for each anonymized user, and refined DA, which de-anonymizes an anonymized user to a user in its candidate set. By employing both candi

Luiz G. A. Alves, Alberto Aleta, Francisco A. Rodrigues, Yamir Moreno

Tremendous advances have been made in our understanding of the properties and evolution of complex networks. These advances were initially driven by information-poor empirical networks and theoretical analysis of unweighted and undirected graphs. Recently, information-rich empirical data complex networks supported the development of more sophisticated models

Yu Lei, Wenjie Li

In this paper, we study a multi-step interactive recommendation problem, where the item recommended at current step may affect the quality of future recommendations. To address the problem, we develop a novel and effective approach, named CFRL, which seamlessly integrates the ideas of both collaborative filtering (CF) and reinforcement learning (RL). More sp

Alexis Kirke

Of all novel computing methods, quantum computation (QC) is currently the most likely to move from the realm of the unconventional into the conventional. As a result some initial work has been done on applications of QC outside of science: for example music. The small amount of arts research done in hardware or with actual physical systems has not utilized a

Shouling Ji, Haiqin Weng, Yiming Wu, Qinming He

Motivated by many existing security and privacy applications, e.g., network traffic attribution, linkage attacks, private web search, and feature-based data de-anonymization, in this paper, we study the Feature-based Data Inferability (FDI) quantification problem. First, we conduct the FDI quantification under both naive and general data models from both a f

Flavio C. D. Moraes, Ana Lia Leonel, Pedro H. C. Torres, Pedro R. Jacobi

The complexity of emergent wicked problems, such as climate change, culminates in a reformulation of how we think about society and mobilize scientists from various disciplines to seek solutions and perspectives on the problem. From an epistemological point of view, it is essential to evaluate how such topics can be developed inside the academic arena but, t

Burcu Ungor

In this paper, we are interested in a class of modules partaking in the hierarchy of injective and cotorsion modules, so-called Harmanci injective modules, which turn out by the motivation of relations among the concepts of injectivity, flatness and cotorsionness. We give some characterizations and properties of this class of modules. It is shown that the cl

Asaf Cohen, Virginia R. Young

We analyze the probability of ruin for the {\it scaled} classical Cramér-Lundberg (CL) risk process and the corresponding diffusion approximation. The scaling, introduced by Iglehart \cite{I1969} to the actuarial literature, amounts to multiplying the Poisson rate $\la$ by $n$, dividing the claim severity by $\sqrtn$, and adjusting the premium rate so that n

Hani Abdelwahab, Antonio Jesús Calderón, Ivan Kaygorodov

The paper is devoted to give the complete algebraic classification of nilpotent binary Lie algebras of dimension $\leq 6$ over an arbitrary base field ${\mathbb{F}}$ of characteristic not $2$ and the complete geometric classification of nilpotent binary Lie algebras of dimension $6$ over $\mathbb C.$ As an application, we have the algebraic and geometric cla

Stefano Fioravanzo, Giovanni Iacca

Constrained optimization problems are often characterized by multiple constraints that, in the practice, must be satisfied with different tolerance levels. While some constraints are hard and as such must be satisfied with zero-tolerance, others may be soft, such that non-zero violations are acceptable. Here, we evaluate the applicability of MAP-Elites to &#

Vahid Kamali

The evolution of the universe started from a hot and dense Big Bang point. Temperature fluctuation map of cosmic microwave background (CMB) radiation and initial seeds of large scale structures (LSS) are explained by an inflationary period in a very early time. Inflaton as quanta of inflation field is responsible for the accelerated expansion of the universe

Hamed Masoumi, Mohammad Javad Emadi

We study an uplink scenario of a cell-free massive multiple-input multiple-output (CF-mMIMO) system with limited capacity fronthaul links (LC-FHLs) connecting each access point (AP) to the central unit (CU), where user equipments and APs are subject to hardware impairments. Therefore, to efficiently use the capacity of FHLs to maximize the achievable rate, w

Isarapong Eksinchol, John F. Rudge, John Maclennan

Observations of the time lag between the last deglaciation and a surge in volcanic activity in Iceland constrain the average melt ascent velocity to be $\geq50$ $\mathrm{m/yr}$. Although existing theoretical work has explained why the surge in eruption rates increased $5$-$30$ fold from the steady-state rates during the last deglaciation, they cannot account

Abdul Muntakim Rafi, Thamidul Islam Tonmoy, Uday Kamal, Q. M. Jonathan Wu

Camera model identification (CMI) has gained significant importance in image forensics as digitally altered images are becoming increasingly commonplace. In this paper, a novel convolutional neural network (CNN) architecture is proposed for CMI with emphasis given on the preprocessing task considered to be inevitable for removing the scene content that heavi

Santiago Mazuelas, Andrea Zanoni, Aritz Perez

Conventional techniques for supervised classification constrain the classification rules considered and use surrogate losses for classification 0-1 loss. Favored families of classification rules are those that enjoy parametric representations suitable for surrogate loss minimization, and low complexity properties suitable for overfitting control. This paper

Chon-Fai Kam, Ren-Bao Liu

Majorana stars, the $2j$ spin coherent states that are orthogonal to a spin-$j$ state, offer a visualization of general quantum states and may disclose deep structures in quantum states and their evolutions. In particular, the genuine tripartite entanglement - the three-tangle of a symmetric three-qubit state, which can be mapped to a spin-3/2 state, is meas

Sébastien Bubeck, Mark Sellke

We address online combinatorial optimization when the player has a prior over the adversary&#39;s sequence of losses. In this framework, Russo and Van Roy proposed an information-theoretic analysis of Thompson Sampling based on the information ratio, resulting in optimal worst-case regret bounds. In this paper we introduce three novel ideas to this line of w

Mathias Kloss, Thomas Kirschstein, Steffen Liebscher, Martin Petrick

Sources of bias in empirical studies can be separated in those coming from the modelling domain (e.g. multicollinearity) and those coming from outliers. We propose a two-step approach to counter both issues. First, by decontaminating data with a multivariate outlier detection procedure and second, by consistently estimating parameters of the production funct

Itumeleng Monageng, Malcolm Coe, Lee Townsend, David Buckley

SXP4.78 was originally discovered in 2000 as a pulsar in the Small Magellanic Cloud (SMC) by the Rossi X-ray Timing Explorer (RXTE) but it was not spatially located at that time. A new detection in 2018 with the Neil Gehrels Swift Observatory during a Type II outburst permitted its position to be accurately located and its optical counterpart identified. We

Zheng Zhou, Weiming Zhang, Nenghai Yu

Radio-frequency identification(RFID) technology is widely applied in daily human life. The RFID cards are seen everywhere, from entrance guard to consumption. The information security of RFID cards, such as data confidentiality, tag anonymity, mutual authentication etc, has been fully studied. In the paper, using the RFID cards in MIFARE Classic and DESFire

Shigehiro Yasui, Chandrasekhar Chatterjee, Muneto Nitta

We discuss neutron $^{3}P_{2}$ phases in the core of neutron stars in strong magnetic field (magnetars). The neutron $^{3}P_{2}$ pairing provides a wide variety of condensates, such as the uniaxial nematic and (D$_{2}$ and D$_{4}$) biaxial nematic, with different symmetries stemming from the combinations of spin and momentum. Based on the spin-orbital angula

H. H. Fang, B. Han, C. Robert, M. A. Semina

Optical properties of atomically thin transition metal dichalcogenides are controlled by robust excitons characterized by a very large oscillator strength. Encapsulation of monolayers such as MoSe$_2$ in hexagonal boron nitride (hBN) yields narrow optical transitions approaching the homogenous exciton linewidth. We demonstrate that the exciton radiative rate

Duc-Viet Vu

We extend the Dinh-Sibony notion of densities of currents to the setting where the ambient manifold is not necessarily Kähler and study the intersection of analytic sets from the point of view of densities of currents. As an application, we introduce the notion of exotic periodic points of a meromorphic self-map. We then establish the expected asymptotic for

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

The Born cross section for the process $e^+ e^- \to p \bar p $ is measured using the initial state radiation technique with an undetected photon. This analysis is based on datasets corresponding to an integrated luminosity of 7.5 fb$^{-1}$, collected with the BESIII detector at the BEPCII collider at center of mass energies between 3.773 and 4.600 GeV. The B

Eiji Inoue

We introduce mu-scalar curvature for a K&#34;ahler metric with a moment map mu and start up a study on constant mu-scalar curvature K&#34;ahler metric as a generalization of both cscK metric and K&#34;ahler-Ricci soliton and as a continuity path to extremal metric. We study some fundamental constraints to the existence of constant mu-scalar curvature K&#34;a

Tolgahan Cakaloglu, Xiaowei Xu

Deep language models learning a hierarchical representation proved to be a powerful tool for natural language processing, text mining and information retrieval. However, representations that perform well for retrieval must capture semantic meaning at different levels of abstraction or context-scopes. In this paper, we propose a new method to generate multi-r

Raffaello Bianco, Ion Errea, Lorenzo Monacelli, Matteo Calandra

At ambient pressure, bulk 2H-NbS$_2$ displays no charge density wave instability at odds with the isostructural and isoelectronic compounds 2H-NbSe$_2$, 2H-TaS$_2$ and 2H-TaSe$_2$, and in disagreement with harmonic calculations. Contradictory experimental results have been reported in supported single layers, as 1H-NbS$_2$ on Au(111) does not display a charg

Thai-Son Nguyen, Sebastian Stueker, Alex Waibel

Acoustic-to-word (A2W) models that allow direct mapping from acoustic signals to word sequences are an appealing approach to end-to-end automatic speech recognition due to their simplicity. However, prior works have shown that modelling A2W typically encounters issues of data sparsity that prevent training such a model directly. So far, pre-training initiali

Pedro Cisneros-Velarde, Kevin S. Chan, Francesco Bullo

Much recent research on social networks has focused on the modeling and analysis of how opinions evolve as a function of interpersonal relationships. It is also of great interest to model and understand the implications of friendly and antagonistic relationships. In this paper, we propose a new, simple and intuitive model that incorporates the socio-psycholo

Changwei Xiong

Let $M^n=[0,R)\times \mathbb{S}^{n-1}$ be an $n$-dimensional ($n\geq 2$) smooth Riemannian manifold equipped with the warped product metric $g=dr^2+h^2(r)g_{\mathbb{S}^{n-1}}$ and diffeomorphic to a Euclidean ball. Assume that $M$ has strictly convex boundary. First, for the classical Steklov eigenvalue problem, we obtain an optimal lower (upper, respectivel

Dmitry Berdinsky, Phongpitak Trakuldit

In this paper we address the problem of quantitative classification of Cayley automatic groups in terms of a certain numerical characteristic which we earlier introduced for this class of groups. For this numerical characteristic we formulate and prove a fellow traveler property, show its relationship with the Dehn function and prove its invariance with resp

Ziqiang Shi, Huibin Lin, Liu Liu, Rujie Liu

Deep gated convolutional networks have been proved to be very effective in single channel speech separation. However current state-of-the-art framework often considers training the gated convolutional networks in time-frequency (TF) domain. Such an approach will result in limited perceptual score, such as signal-to-distortion ratio (SDR) upper bound of separ

Jayan Mukherjee, Debaditya Raychaudhury

We prove some results on effective very ampleness and projective normality for some varieties with trivial canonical bundle. In the first part we prove an effective projective normality result for an ample line bundle on regular smooth four-folds with trivial canonical bundle. More precisely we show that for a regular smooth fourfold with trivial canonical b

Sho Sonoda

An infinitely wide model is a weighted integration $\int φ(x,v) d μ(v)$ of feature maps. This model excels at handling an infinite number of features, and thus it has been adopted to the theoretical study of deep learning. Kernel quadrature is a kernel-based numerical integration scheme developed for fast approximation of expectations $\int f(x) d p(x)$. In

Chongbin Tang, Sen Chen, Lingling Fan, Lihua Xu

While there have been various studies towards Android apps and their development, there is limited discussion of the broader class of apps that fall in the fake area. Fake apps and their development are distinct from official apps and belong to the mobile underground industry. Due to the lack of knowledge of the mobile underground industry, fake apps, their

Hongbing Qiu, Linlin Sun

In this paper, we firstly prove that every hyper-Lagrangian submanifold $L^{2n} (n > 1)$ in a hyperkähler $4n$-manifold is a complex Lagrangian submanifold. Secondly, we demonstrate an optimal rigidity theorem with the condition on the complex phase map of self-shrinking surfaces in $\mathbb{R}^4$. Last but not least, by using the previous rigidity result, w

Jinhyun So, Basak Guler, A. Salman Avestimehr

How to train a machine learning model while keeping the data private and secure? We present CodedPrivateML, a fast and scalable approach to this critical problem. CodedPrivateML keeps both the data and the model information-theoretically private, while allowing efficient parallelization of training across distributed workers. We characterize CodedPrivateML&#

Xinshi Chen, Hanjun Dai, Le Song

We present a particle flow realization of Bayes&#39; rule, where an ODE-based neural operator is used to transport particles from a prior to its posterior after a new observation. We prove that such an ODE operator exists. Its neural parameterization can be trained in a meta-learning framework, allowing this operator to reason about the effect of an individu

Shi Hu, Yongguan Ke, Yuangang Deng, Chaohong Lee

In Thouless pumping, although non-flat band has no effects on the quantization of particle transport, it induces wave-packet dispersion which hinders the practical applications of Thouless pumping. Indeed, we find that the dispersion mainly arises from the dynamical phase difference between individual Bloch states. Here we propose two efficient schemes to su

Yuanyuan Feng, Tingran Gao, Lei Li, Jian-Guo Liu

Diffusion approximation provides weak approximation for stochastic gradient descent algorithms in a finite time horizon. In this paper, we introduce new tools motivated by the backward error analysis of numerical stochastic differential equations into the theoretical framework of diffusion approximation, extending the validity of the weak approximation from

Daniel Hexner, Pierfrancesco Urbani, Francesco Zamponi

We consider zero temperature packings of soft spheres, that undergo a jamming to unjamming transition as a function of packing fraction. We compare differences in the structure, as measured from the contact statistics, of a finite subsystem of a large packing to a whole packing with periodic boundaries of an equivalent size and pressure. We find that the flu

Belhal Karimi, Blazej Miasojedow, Eric Moulines, Hoi-To Wai

Stochastic approximation (SA) is a key method used in statistical learning. Recently, its non-asymptotic convergence analysis has been considered in many papers. However, most of the prior analyses are made under restrictive assumptions such as unbiased gradient estimates and convex objective function, which significantly limit their applications to sophisti

Shuyang Bai, Takashi Owada, Yizao Wang

A functional limit theorem is established for the partial-sum process of a class of stationary sequences which exhibit both heavy tails and long-range dependence. The stationary sequence is constructed using multiple stochastic integrals with heavy-tailed marginal distribution. Furthermore, the multiple stochastic integrals are built upon a large family of d

Benjamin I. Albert

The Dupont homotopy, a classical construction in the algebraic topology of triangulated smooth manifolds, has been revived in the last decade in the construction of an effective field theory where it appears as a propagator. In this paper, we ask a question of relevance to the renormalization group of this theory: is Dupont&#39;s construction compatible with

Arash Sarshar, Steven Roberts, Adrian Sandu

Alternating Directions Implicit (ADI) integration is an operator splitting approach to solve parabolic and elliptic partial differential equations in multiple dimensions based on solving sequentially a set of related one-dimensional equations. Classical ADI methods have order at most two, due to the splitting errors. Moreover, when the time discretization of

Jian Li, Xuanyuan Luo, Mingda Qiao

Generalization error (also known as the out-of-sample error) measures how well the hypothesis learned from training data generalizes to previously unseen data. Proving tight generalization error bounds is a central question in statistical learning theory. In this paper, we obtain generalization error bounds for learning general non-convex objectives, which h

Rahman Anisur, Wenqia Xu, Kunhang Li, Hua-Shu Dou

The stability of the flow under the magnetic force is one of the classical problems in fluid mechanics. In this paper, the flow in a rectangular duct with different Hartmann (Ha) number is simulated. The finite volume method and the SIMPLE algorithm are used to solve a system of equations and the energy gradient theory is then used to study the (associated)

Chi Jin, Praneeth Netrapalli, Michael I. Jordan

Minimax optimization has found extensive applications in modern machine learning, in settings such as generative adversarial networks (GANs), adversarial training and multi-agent reinforcement learning. As most of these applications involve continuous nonconvex-nonconcave formulations, a very basic question arises---&#34;what is a proper definition of local

Zhicheng Ding, Zhixin Lai, Siyang Li, Panfeng Li

Real-time object tracking necessitates a delicate balance between speed and accuracy, a challenge exacerbated by the computational demands of deep learning methods. In this paper, we propose Confidence-Triggered Detection (CTD), an innovative approach that strategically bypasses object detection for frames closely resembling intermediate states, leveraging t

Mengdi Zhao, Kejie Fang

Bound states in the continuum (BICs), an emerging type of long-lived resonances different from the cavity-based ones, have been explored in several classical systems, including photonic crystals and surface acoustic waves. Here, we reveal symmetry-protected mechanical BICs in the structure of slab-on-substrate optomechanical crystals. Using a group theory ap

Baekjin Kim, Ambuj Tewari

We investigate the optimality of perturbation based algorithms in the stochastic and adversarial multi-armed bandit problems. For the stochastic case, we provide a unified regret analysis for both sub-Weibull and bounded perturbations when rewards are sub-Gaussian. Our bounds are instance optimal for sub-Weibull perturbations with parameter 2 that also have

Andrea Tononi

We derive the two-dimensional equation of state for a bosonic system of ultracold atoms interacting with a finite-range effective interaction. Within a functional integration approach, we employ an hydrodynamic parametrization of the bosonic field to calculate the superfluid equations of motion and the zero-temperature pressure. The ultraviolet divergences,

Yulong Lu, Jonathan C. Mattingly

This paper is concerned with the long time behavior of Langevin dynamics of {\em Coulomb gases} in $\mathbf{R}^d$ with $d\geq 2$, that is a second order system of Brownian particles driven by an external force and a pairwise repulsive Coulomb force. We prove that the system converges exponentially to the unique Boltzmann-Gibbs invariant measure under a weigh

Priscila Leal da Silva, Igor Leite Freire

In this paper we consider a four-parameter equation including the Camassa-Holm and the Dulling-Gottwald-Holm equations, among others. We prove the existence and uniqueness of solutions to a Cauchy problem involving the equation using Kato&#39;s approach. Conservation laws of the equation up to second order are also investigated. From these conservation laws

Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov

Graphical models are useful tools for describing structured high-dimensional probability distributions. Development of efficient algorithms for learning graphical models with least amount of data remains an active research topic. Reconstruction of graphical models that describe the statistics of discrete variables is a particularly challenging problem, for w

Jeanne N. Clelland, Yuhao Hu, Matthew W. Stackpole

To each dynamic equivalence of two control systems is associated an infinite permutation matrix. We investigate how such matrices are related to the existence of dynamic equivalences.

Charles T. Sebens

One can interpret the Dirac equation either as giving the dynamics for a classical field or a quantum wave function. Here I examine whether Maxwell&#39;s equations, which are standardly interpreted as giving the dynamics for the classical electromagnetic field, can alternatively be interpreted as giving the dynamics for the photon&#39;s quantum wave function

V. Fischer, L. Pagani, L. Pickard, A. Couture

The use of argon as a detection and shielding medium for neutrino and dark matter experiments has made the precise knowledge of the cross section for neutron capture on argon an important design and operational parameter. Since previous measurements were averaged over thermal spectra and have significant disagreements, a differential measurement has been per

Michael Hahn, Frank Keller, Yonatan Bisk, Yonatan Belinkov

Intuitively, human readers cope easily with errors in text; typos, misspelling, word substitutions, etc. do not unduly disrupt natural reading. Previous work indicates that letter transpositions result in increased reading times, but it is unclear if this effect generalizes to more natural errors. In this paper, we report an eye-tracking study that compares

Chia-Hao Liu, Yunzhe Tao, Daniel Hsu, Qiang Du

We present a method for predicting the space group of a structure given a calculated or measured atomic pair distribution function (PDF) from that structure. The method utilizes machine learning models trained on more than 100,000 PDFs calculated from structures in the 45 most heavily represented space groups. In particular, we present a convolutional neural

Hengjie Yang, Richard D. Wesel

In this paper, we consider the problem of sequential transmission over the binary symmetric channel (BSC) with full, noiseless feedback. Naghshvar et al. proposed a one-phase encoding scheme, for which we refer to as the small-enough difference (SED) encoder, which can achieve capacity and Burnashev&#39;s optimal error exponent for symmetric binary-input cha

Yijiang Lian, Zhijie Chen, Jinlong Hu, Kefeng Zhang

In this paper, we present a generative retrieval method for sponsored search engine, which uses neural machine translation (NMT) to generate keywords directly from query. This method is completely end-to-end, which skips query rewriting and relevance judging phases in traditional retrieval systems. Different from standard machine translation, the target spac

Mingyong Zhou

In this position paper, we present a discussion on neuromorphic computing and especially the learning/training algorithm to design a series of brains with different memristive values to solve complex ill-posed inverse problems based on a Finite Element(FE) method. First, the neuromorphic computing is addressed and we focus on a type of memristive circuit com

Hicham Zoubeir

Our aim in this paper is to prove, under some growth conditions on the datas, the solvability in a Gevrey class of a polynomially nonlinear functional differential equation.

Guy Lansley, Michael de Smith, Michael Goodchild, Paul Longley

Perhaps one of the mostly hotly debated topics in recent years has been the question of &#34;GIS and Big Data&#34;. Much of the discussion has been about the data: huge volumes of 2D and 3D spatial data and spatio-temporal data are now being collected and stored; so how they can be accessed? and how can we map and interpret massive datasets in an effective m

Stefan Hackstein, Franco Vazza, Marcus Brüggen, Jenny G. Sorce

We simulate the propagation of cosmic rays at ultra-high energies, $\gtrsim 10^{18}$ eV, in models of extragalactic magnetic fields in constrained simulations of the local Universe. We investigate the impact of different magneto-genesis scenarios, both, primordial and astrophysical, on the propagation of cosmic rays. Our study shows that different scenarios

Hacene Mellah, Kamel Eddoine Hemsas, Rachid Taleb, carlo CECATI

In this paper, a sensorless speed and armature resistance and temperature estimator for Brushed (B) DC machines is proposed, based on a Cascade-Forward Neural Network (CFNN) and Quasi-Newton BFGS backpropagation (BP). Since we wish to avoid the use of a thermal sensor, a thermal model is needed to estimate the temperature of the BDC machine. Previous studies

Sergey N. Andrianov, Rinat A. Daishev, Sergey M. Kozyrev, Boris P. Pavlov

We suppose that our Universe is closed manifold in real embedding higher dimensional space. This model well describes expanding character of Universe where each point becomes more far from any other point with time. We have derived Klein-Gordon equation using the symmetry of Universe expansion. Comparing it with squared Dirac-Fock-Ivanenko equation we have d

Ward Struyve

Semi-classical gravity is an approximation to quantum gravity where gravity is treated classically and matter quantum mechanically. Matter is described by quantum field theory on curved space-time, whereas gravity is described by a space-time metric which satisfies Einstein&#39;s field equations. In the usual approach to semi-classical gravity, the matter so

Hamada Rizk, Moustafa Youssef

Among many techniques for indoor localization, fingerprinting has been shown to provide a higher accuracy compared to the alternative techniques. Fingerprinting techniques require an initial calibration phase during which site surveyors visit virtually every location in the area of interest to manually collect the fingerprint data. However, this process is l

Y. Kenedy Meitei, T. Ibungochouba Singh, I. Ablu Meitei

Using the adiabatic invariant action and applying Bohr-Sommerfeld quantization rule and first law of black hole thermodynamics a study of quantization of entropy and horizon area of Kerr-Newman-de Sitter black hole is carried out. The same entropy spectrum is obtained in two different coordinate systems. It is also observed that the spacing of the entropy sp

Alathea Jensen

Many hard problems in the computational sciences are equivalent to counting the leaves of a decision tree, or, more generally, summing a cost function over the nodes. These problems include calculating the permanent of a matrix, finding the volume of a convex polyhedron, and counting the number of linear extensions of a partially ordered set. Many approximat

K. M. Anandu, Muhammad Shaharukh, Bikash K. Behera, Prasanta K. Panigrahi

Quantum error correcting codes (QECC) are the key ingredients both for fault-tolerant quantum computation and quantum communication. Teleportation-based error correction (TEC) helps in detecting and correcting operational and erasure errors by performing X and Z measurements during teleportation. Here we demonstrate the TEC protocol for the detection and cor

Yuanyuan Han, Rui Tang, Yi Gu, Alex Ce Zhang

Increasing demand for understanding the vast heterogeneity of cellular phenotypes has driven the development of imaging flow cytometry (IFC), that combines features of flow cytometry with fluorescence and bright field microscopy. IFC combines the throughput and statistical advantage of flow cytometry with the ability to discretely measure events based on a r

András Gilyén, Tongyang Li

A fundamental problem in statistics and learning theory is to test properties of distributions. We show that quantum computers can solve such problems with significant speed-ups. In particular, we give fast quantum algorithms for testing closeness between unknown distributions, testing independence between two distributions, and estimating the Shannon / von

Achilles A. Beros, Bjørn Kjos-Hanssen, Daylan Kaui Yogi

We show that the digraph of a nondeterministic finite automaton witnessing the automatic complexity of a word can always be taken to be planar. In the case of total transition functions studied by Shallit and Wang, planarity can fail. Let $s_q(n)$ be the number of binary words $x$ of length $n$ having nondeterministic automatic complexity $A_N(x)=q$. We show

D. J. Mullan, R. R. Paudel

Type II radio bursts are observed in the Sun in association with many coronal mass ejections (CME&#39;s. In view of this association, there has been an expectation that, by scaling from solar flares to the flares which are observed on M dwarfs, radio emission analogous to solar Type II bursts should be detectable in association with M dwarf flares. However,

Jayant Gupchup, Douglas Carlson, Răzvan Musăloiu-E., Alex Szalay

Harsh deployment environments and uncertain run-time conditions create numerous challenges for postmortem time reconstruction methods. For example, motes often reboot and thus lose their clock state, considering that the majority of mote platforms lack a real-time clock. While existing time reconstruction methods for long-term data gathering networks rely on

Martina Rovelli

We introduce the notion of weighted limit in an arbitrary quasi-category, suitably generalizing ordinary limits in a quasi-category, and classical weighted limits in an ordinary category. This is accomplished by generalizing Joyal&#39;s approach: we identify a meaningful construction for the quasi-category of weighted cones over a diagram in a quasi-category