November 2018 arXiv papers — page 6

Yuheng Bu, Kevin Small

While recommendation systems generally observe user behavior passively, there has been an increased interest in directly querying users to learn their specific preferences. In such settings, considering queries at different levels of granularity to optimize user information acquisition is crucial to efficiently providing a good user experience. In this work,

Jeffrey M. Hokanson, Caleb C. Magruder

Rational approximation appears in many contexts throughout science and engineering, playing a central role in linear systems theory, special function approximation, and many others. There are many existing methods for solving the rational approximation problem, from fixed point methods like the Sanathanan-Koerner iteration and Vector Fitting, to partial inte

Beau Norgeot, Dmytro Lituiev, Benjamin S. Glicksberg, Atul J. Butte

Clinical data for ambulatory care, which accounts for 90% of the nations healthcare spending, is characterized by relatively small sample sizes of longitudinal data, unequal spacing between visits for each patient, with unequal numbers of data points collected across patients. While deep learning has become state-of-the-art for sequence modeling, it is unkno

Panyue Zhou, Bin Zhu

Let $\mathcal{C}$ be a triangulated category with shift functor $[1]$ and $\mathcal{R}$ a rigid subcategory of $\mathcal{C}$. We introduce the notions of two-term $\mathcal{R}[1]$-rigid subcategories, two-term (weak) $\mathcal{R}[1]$-cluster tilting subcategories and two-term maximal $\mathcal{R}[1]$-rigid subcategories, and discuss relationship between them

Dmitry Golovaty, Michael Novack, Peter Sternberg, Raghavendra Venkatraman

We analyze a model problem based on highly disparate elastic constants that we propose in order to understand corners and cusps that form on the boundary between the nematic and isotropic phases in a liquid crystal. For a bounded planar domain $Ω$ we investigate the $\varepsilon \to 0$ asymptotics of the variational problem \[\inf \frac{1}{2}\int_Ω\left( \fr

Marco M. Dias e Costa, J. M. Toledo, V. B. Bezerra

We obtain the solution corresponding to a static and spherically symmetric black hole with a cloud of strings (Letelier spacetime) immersed in a quintessential fluid. We discuss some aspects of its thermodynamics. We also present a discussion about Hawking radiation of particles, in the background under consideration.

Lars Martin Sektnan

We prove a version of the Arezzo-Pacard-Singer blow-up theorem in the setting of Poincaré type metrics. We apply this to give new examples of extremal Poincaré type metrics. A key feature is an additional obstruction which has no analogue in the compact case. This condition is conjecturally related to ensuring the metrics remain of Poincaré type.

Bret Nestor, Matthew B. A. McDermott, Geeticka Chauhan, Tristan Naumann

Machine learning for healthcare often trains models on de-identified datasets with randomly-shifted calendar dates, ignoring the fact that data were generated under hospital operation practices that change over time. These changing practices induce definitive changes in observed data which confound evaluations which do not account for dates and limit the gen

Harleigh C. Marsh, Mark Karpenko, Qi Gong

Historically, solving optimal control problems with high index differential algebraic equations (DAEs) has been considered extremely hard. Computational experience with Runge-Kutta (RK) methods confirms the difficulties. High index DAE problems occur quite naturally in many practical engineering applications. Over the last two decades, a vast number of real-

Nicholas R. Beaton, Jeremy W. Eng, Christine E. Soteros

We study several related models of self-avoiding polygons in a tubular subgraph of the simple cubic lattice, with a particular interest in the asymptotics of the knotting statistics. Polygons in a tube can be characterised by a finite transfer matrix, and this allows for the derivation of pattern theorems, calculation of growth rates and exact enumeration. W

Heping Dong, Jun Lai, Peijun Li

This paper concerns an inverse elastic scattering problem which is to determine the location and the shape of a rigid obstacle from the phased or phaseless far-field data for a single incident plane wave. By introducing the Helmholtz decomposition, the model problem is reduced to a coupled boundary value problem of the Helmholtz equations. The relation is es

Kai Du, Rui Peng, Ningkui Sun

It is known that a species dies out in the long run for small initial data if its evolution obeys a reaction of bistable nonlinearity. Such a phenomenon, which is termed as the strong Allee effect, is well supported by numerous evidence from ecosystems, mainly due to the environmental pollution as well as unregulated harvesting and hunting. To save an endang

Xiunan Yang, Hua Tong, Weihua Wang, Ke Chen

We rejuvenate well-aged quasi-2D binary colloidal glasses by thermal cycling, and systematically measure both the statistical responses and particle-level structural evolutions during rejuvenation. While the moduli and boson peak are continuously rejuvenated with increasing number of cycles, the mean square displacement (MSD) fluctuates significantly between

Siye Wu

Kapustin and Witten showed that a twisted version of N=4 gauge theory in four dimensions compactifies to a two-dimensional sigma-model whose target space is the Hitchin moduli space. In this talk, I consider the reduction of the gauge theory on a four dimensional orientable spacetime manifold which is not a global product of two surfaces but contains embedde

Kailas Vodrahalli, Ke Li, Jitendra Malik

Modern computer vision algorithms often rely on very large training datasets. However, it is conceivable that a carefully selected subsample of the dataset is sufficient for training. In this paper, we propose a gradient-based importance measure that we use to empirically analyze relative importance of training images in four datasets of varying complexity.

Chandra Chekuri, Kent Quanrud

We consider parallel, or low adaptivity, algorithms for submodular function maximization. This line of work was recently initiated by Balkanski and Singer and has already led to several interesting results on the cardinality constraint and explicit packing constraints. An important open problem is the classical setting of matroid constraint, which has been i

Juhan Bae, Guodong Zhang, Roger Grosse

Variational Bayesian neural networks combine the flexibility of deep learning with Bayesian uncertainty estimation. However, inference procedures for flexible variational posteriors are computationally expensive. A recently proposed method, noisy natural gradient, is a surprisingly simple method to fit expressive posteriors by adding weight noise to regular

Idel Waisberg, Jason Dexter, Pierre Olivier-Petrucci, Guillaume Dubus

The microquasar SS 433 is well-known for its precessing, relativistic baryonic jets. Depending on their heating mechanism, the optical jet bullets may serve as a probe of the collimated radiation coming from the inner region close to the compact object. The optical interferometer VLTI/GRAVITY has allowed to spatially resolved the optical jets in SS 433 for t

Tianqi Zhao

Videos have become ubiquitous on the Internet. And video analysis can provide lots of information for detecting and recognizing objects as well as help people understand human actions and interactions with the real world. However, facing data as huge as TB level, effective methods should be applied. Recurrent neural network (RNN) architecture has wildly been

Zlatko Drmač, Igor Mezić, Ryan Mohr

This paper proposes a new computational method for solving structured least squares problems that arise in the process of identification of coherent structures in fluid flows. It is deployed in combination with dynamic mode decomposition (DMD) which provides a non-orthogonal set of modes --- corresponding to particular temporal frequencies --- a subset of wh

Z. L. Tu, F. Y. Wang

In this paper, we study the correlation between isotropic energy and duration of gamma-ray bursts (GRBs) for the first time. The correlation is found to be $T_d \propto {E_{iso}}^{0.34\pm 0.03}$ from the {\em Swift} GRB sample. After comparing with solar flares from {\em RHESSI} and stellar superflares from {\em Kepler} satellite, we find that the correlatio

Idel Waisberg, Jason Dexter, Pierre Olivier-Petrucci, Guillaume Dubus

The microquasar SS 433 is the only known steady supercritical accretor in the Galaxy. It is well-known for its relativistic baryonic jets, but the system also drives equatorial outflows. These have been routinely detected in radio images, and components associated with a circumbinary disk have also been suggested in optical emission lines. We wish to spatial

Aleksandra Malysheva, Tegg Taekyong Sung, Chae-Bong Sohn, Daniel Kudenko

Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domains. In this paper, we propose a novel approach, called MAGnet, to multi-agent reinforcement learning (MARL) that utilizes a relevance graph representation of the environment obtaine

MohammadHossein Bateni, MohammadTaghi Hajiaghayi, Saeed Seddighin, Cliff Stein

The \Problem{knapsack} problem is a fundamental problem in combinatorial optimization. It has been studied extensively from theoretical as well as practical perspectives as it is one of the most well-known NP-hard problems. The goal is to pack a knapsack of size $t$ with the maximum value from a collection of $n$ items with given sizes and values. Recent evi

Soheil Mohseni, Seyed Masoud Moghaddas-Tafreshi

In this paper, a novel intelligent method based on a multi-agent system (MAS) is applied to the problem of optimal sizing in a stand-alone office complex microgrid such that the electricity demand of the office building and the charging demand of the plug-in hybrid electric vehicle (PHEV) charging station are met. The proposed MAS-based architecture consists

Kyoung-Woong Moon, Jungbum Yoon, Changsoo Kim, Chanyong Hwang

A magnetic skyrmion isusually refers to a twisted spin texture surrounded by uniformly aligned out-of-plane spinsin the background of a uniformly magnetized state. The invariance of the magnetic skyrmion conserves its topological charge under any continuous transformations of the spin textures, leads to which represents the robustness of a magnetic skyrmion

W. H. Brito, G. Kotliar

We investigate the electronic structure of the highly anisotropic $β$ phase of metallic plutonium, within the combination of density functional theory (DFT) and dynamical mean field theory (DMFT). Its crystal structure gives rise to site and orbital selective electronic correlations, with coherent Pu-5$f_{5/2}$ states and very incoherent Pu-5$f_{7/2}$ states

Shira Chapman, Dongsheng Ge, Giuseppe Policastro

We explore the two holographic complexity proposals for the case of a 2d boundary CFT with a conformal defect. We focus on a Randall-Sundrum type model of a thin AdS$_2$ brane embedded in AdS$_3$. We find that, using the "complexity=volume" proposal, the presence of the defect generates a logarithmic divergence in the complexity of the full boundary

Jun-Ho Choi, Jun-Hyuk Kim, Manri Cheon, Jong-Seok Lee

Recently, several deep learning-based image super-resolution methods have been developed by stacking massive numbers of layers. However, this leads too large model sizes and high computational complexities, thus some recursive parameter-sharing methods have been also proposed. Nevertheless, their designs do not properly utilize the potential of the recursive

Octavio Arizmendi, Mauricio Salazar, Jiun-Chau Wang

Two different aspects of parabolic iteration in the complex upper half-plane are considered here. First, from a noncommutative probability perspective, a Berry-Esseen type estimate for the convergence speed of the monotone central limit theorem is proved. Secondly, if the underlying measure in this central limit process is singular to the Lebesgue measure on

Yang Liu, Sebastien Blandin, Samitha Samaranayake

This article considers the stochastic on-time arrival problem in transit networks where both the travel time and the waiting time for transit services are stochastic. A specific challenge of this problem is the combinatorial solution space due to the unknown ordering of transit line arrivals. We propose a network structure appropriate to the online decision-

Longhao Yuan, Jianting Cao, Qiang Wu, Qibin Zhao

The problem of incomplete data is common in signal processing and machine learning. Tensor completion algorithms aim to recover the incomplete data from its partially observed entries. In this paper, taking advantages of high compressibility and flexibility of recently proposed tensor ring (TR) decomposition, we propose a new tensor completion approach named

Monimoy Bujarbaruah, Xiaojing Zhang, H. Eric Tseng, Francesco Borrelli

This paper proposes an Adaptive Robust Model Predictive Control strategy for lateral control in lane keeping problems, where we continuously learn an unknown, but constant steering angle offset present in the steering system. Longitudinal velocity is assumed constant. The goal is to minimize the outputs, which are distance from lane center line and the stead

Longhao Yuan, Qibin Zhao, Lihua Gui, Jianting Cao

Tensor train (TT) decomposition has drawn people's attention due to its powerful representation ability and performance stability in high-order tensors. In this paper, we propose a novel approach to recover the missing entries of incomplete data represented by higher-order tensors. We attempt to find the low-rank TT decomposition of the incomplete data w

Giovanni S. Alberti, Habib Ammari, Francisco Romero, Timothée Wintz

We consider the dynamical super-resolution problem consisting in the recovery of positions and velocities of moving particles from low-frequency static measurements taken over multiple time steps. The standard approach to this issue is a two-step process: first, at each time step some static reconstruction method is applied to locate the positions of the par

Adrian Šošić, Elmar Rueckert, Jan Peters, Abdelhak M. Zoubir

Advances in the field of inverse reinforcement learning (IRL) have led to sophisticated inference frameworks that relax the original modeling assumption of observing an agent behavior that reflects only a single intention. Instead of learning a global behavioral model, recent IRL methods divide the demonstration data into parts, to account for the fact that

Michael Holst, Yuwen Li, Adam Mihalik, Ryan Szypowski

Finite Element Exterior Calculus (FEEC) was developed by Arnold, Falk, Winther and others over the last decade to exploit the observation that mixed variational problems can be posed on a Hilbert complex, and Galerkin-type mixed methods can then be obtained by solving finite-dimensional subcomplex problems. Chen, Holst, and Xu (Math. Comp. 78 (2009) 35-53) e

Rickard Brüel-Gabrielsson, Vignesh Ganapathi-Subramanian, Primoz Skraba, Leonidas J. Guibas

We present an approach to inform the reconstruction of a surface from a point scan through topological priors. The reconstruction is based on basis functions which are optimized to provide a good fit to the point scan while satisfying predefined topological constraints. We optimize the parameters of a model to obtain likelihood function over the reconstructi

Alejandro Parada-Mayorga, Daniel L. Lau, Jhony H. Giraldo, Gonzalo R. Arce

In the area of graph signal processing, a graph is a set of nodes arbitrarily connected by weighted links; a graph signal is a set of scalar values associated with each node; and sampling is the problem of selecting an optimal subset of nodes from which a graph signal can be reconstructed. This paper proposes the use of spatial dithering on the vertex domain

Yao Cui, Zhehan Yi, Jiajun Duan, Di Shi

This paper proposes a resilient-backpropagation-neural-network-(Rprop-NN) based algorithm for Photovoltaic (PV) maximum power point tracking (MPPT). A supervision mechanism is proposed to calibrate the Rprop-NN-MPPT reference and limit short-circuit current caused by incorrect prediction. Conventional MPPT algorithms (e.g., perturb and observe (P&O), hill cl

Xinyi Chen-Lin, Luca V. Delacrétaz, Sean A. Hartnoll

The recently developed effective field theory of fluctuations around thermal equilibrium is used to compute late-time correlation functions of conserved densities. Specializing to systems with a single conservation law, we find that the diffusive pole is shifted in the presence of non-linear hydrodynamic self-interactions, and that the density-density Green&

Jiajun Duan, Zhehan Yi, Di Shi, Hao Xu

In this paper, a neural-network (NN)-based online optimal control method (NN-OPT) is proposed for ultra-capacitors (UCs) energy storage system (ESS) in hybrid AC/DC microgrids involving multiple distributed generations (e.g., Photovoltaic (PV) system, battery storage, diesel generator). Conventional control strategies usually produce large disturbances to bu

Mario Graff, Sabino Miranda-Jiménez, Eric S. Tellez, Daniela Moctezuma

Sentiment analysis (SA) is a task related to understanding people's feelings in written text; the starting point would be to identify the polarity level (positive, neutral or negative) of a given text, moving on to identify emotions or whether a text is humorous or not. This task has been the subject of several research competitions in a number of langua

Gautham Narayan, Thomas Matheson, Abhijit Saha, Tim Axelrod

We have established a network of 19 faint (16.5 mag $< V < $19 mag) northern and equatorial DA white dwarfs as spectrophotometric standards for present and future wide-field observatories. Our analysis infers SED models for the stars that are tied to the three CALSPEC primary standards. Our SED models are consistent with panchromatic Hubble Space Telescope (

Philippe Landry, Reed Essick

We develop a non-parametric method for inferring the universal neutron star (NS) equation of state (EOS) from gravitational wave (GW) observations. Many different possible realizations of the EOS are generated with a Gaussian process conditioned on a set of nuclear-theoretic models. These synthetic EOSs are causal and thermodynamically stable by construction

Bertie Ancona, Monika Henzinger, Liam Roditty, Virginia Vassilevska Williams

The diameter, radius and eccentricities are natural graph parameters. While these problems have been studied extensively, there are no known dynamic algorithms for them beyond the ones that follow from trivial recomputation after each update or from solving dynamic All-Pairs Shortest Paths (APSP), which is very computationally intensive. This is the situatio

Qingyuan Jiang, Naichung Conan Leung

In this paper, we prove a generalization of Orlov&#39;s projectivization formula for the derived category $D^b_{\rm coh} (\mathbb{P}(\mathscr{E}))$, where $\mathscr{E}$ does not need to be a vector bundle; Instead, $\mathscr{E}$ is a coherent sheaf which locally admits two-step resolutions. As a special case, this also gives Orlov&#39;s generalized universal

David Dudal, Caroline Felix, Leticia Palhares, François Rondeau

In this proceeding, $SU(N)$ Yang-Mills theory is quantized in the linear covariant gauges, while taking into account the issue of Gribov copies and we construct the one-loop effective potential for a set of mass dimension 2 condensates, including the Gribov parameter, that refines the infrared region of the Gribov-Zwanziger theory, whilst respecting renormal

FASER Collaboration, Akitaka Ariga, Tomoko Ariga, Jamie Boyd

FASER,the ForwArd Search ExpeRiment,is a proposed experiment dedicated to searching for light, extremely weakly-interacting particles at the LHC. Such particles may be produced in the LHC&#39;s high-energy collisions and travel long distances through concrete and rock without interacting. They may then decay to visible particles in FASER, which is placed 480

Daniel Jönsson, Peter Steneteg, Erik Sundén, Rickard Englund

The complexity of today&#39;s visualization applications demands specific visualization systems tailored for the development of these applications. Frequently, such systems utilize levels of abstraction to improve the application development process, for instance by providing a data flow network editor. Unfortunately, these abstractions result in several iss

Morgan Jones, Matthew M. Peet

We consider a general class of Dynamic Programming (DP) problems with non-separable objective functions. We show that for any problem in this class, there exists an augmented-state DP problem which satisfies the Principle of Optimality and the solutions to which yield solutions to the original problem. Furthermore, we identify a subclass of DP problems with

Miljan Dašić, Igor Stanković, Konstantinos Gkagkas

We present a theoretical study of the influence of the molecular geometry of the cation on the response of ionic liquid (IL) to confinement and mechanical strain. The so-called {\it tailed} model includes a large spherical anion and asymmetric cation consisting of a charged head and neutral tail. Despite its simplicity, this model recovers a wide range of st

Jean Serra, Jesus Angulo, B Ravi Kiran

Consider a family $Z=\{\boldsymbol{x_{i}},y_{i}$,$1\leq i\leq N\}$ of $N$ pairs of vectors $\boldsymbol{x_{i}} \in \mathbb{R}^d$ and scalars $y_{i}$ that we aim to predict for a new sample vector $\mathbf{x}_0$. Kriging models $y$ as a sum of a deterministic function $m$, a drift which depends on the point $\boldsymbol{x}$, and a random function $z$ with zer

Yingda Xia, Fengze Liu, Dong Yang, Jinzheng Cai

While making a tremendous impact in various fields, deep neural networks usually require large amounts of labeled data for training which are expensive to collect in many applications, especially in the medical domain. Unlabeled data, on the other hand, is much more abundant. Semi-supervised learning techniques, such as co-training, could provide a powerful

I. -G. Shin, Y. -H. Ryu, J. C. Yee, A. Gould

We report two microlensing events, KMT-2017-BLG-1038 and KMT-2017-BLG-1146 that are caused by planetary systems. These events were discovered by KMTNet survey observations from the $2017$ bulge season. The discovered systems consist of a planet and host star with mass ratios, $5.3_{-0.4}^{+0.2} \times 10^{-3}$ and $2.0_{-0.1}^{+0.6} \times 10^{-3}$, respecti

Joel Fotso Tachago, Hubert Nnang, Elvira Zappale

An integral representation result is obtained for the variational limit of the family functionals $\int_Ωf\left(\frac{x}{\varepsilon}, Du\right)dx$, as $\varepsilon \to 0$, when the integrand $f = f (x,v)$ is a Carathéodory function, periodic in $x$, convex in $v$ and with nonstandard growth.

Lei Wang, Svetlana Tlupova, Robert Krasny

The Stokeslet and stresslet kernels are commonly used in boundary element simulations and singularity methods for slow viscous flow. Evaluating the velocity induced by a collection of Stokeslets and stresslets by direct summation requires $O(N^2)$ operations, where $N$ is the system size. The present work develops a treecode algorithm for 3D Stokeslets and s

Dushyant Mehta, Kwang In Kim, Christian Theobalt

We investigate filter level sparsity that emerges in convolutional neural networks (CNNs) which employ Batch Normalization and ReLU activation, and are trained with adaptive gradient descent techniques and L2 regularization or weight decay. We conduct an extensive experimental study casting our initial findings into hypotheses and conclusions about the mecha

Che-Yu Chen, Mariam Bouhmadi-López, Pisin Chen

The possibility of testing gravity theories with the help of gravitational wave detections has become an interesting arena of recent research. In this paper, we follow this direction by investigating the quasinormal modes (QNMs) of the axial perturbations for charged black holes in the Palatini-type theories of gravity, specifically ($i$) the Palatini $f(R)$

Pedro H. C. Sant&#39;Anna, Jun B. Zhao

This article proposes doubly robust estimators for the average treatment effect on the treated (ATT) in difference-in-differences (DID) research designs. In contrast to alternative DID estimators, the proposed estimators are consistent if either (but not necessarily both) a propensity score or outcome regression working models are correctly specified. We als

Mikhail Zhitlukhin

We consider a stochastic game-theoretic model of an investment market in continuous time with short-lived assets and study strategies, called survival, which guarantee that the relative wealth of an investor who uses such a strategy remains bounded away from zero. The main results consist in obtaining a sufficient condition for a strategy to be survival and

Helmut Abels, Johannes Kampmann

We rigorously prove the convergence of weak solutions to a model for lipid raft formation in cell membranes which was recently proposed by Garcke et al. to weak (varifold) solutions of the corresponding sharp-interface problem for a suitable subsequence. In the system a Cahn-Hilliard type equation on the boundary of a domain is coupled to a diffusion equatio

Vasilii Duzhin, Nikolay Vassiliev

There exists a well-known hook-length formula for calculating the dimensions of 2D Young diagrams. Unfortunately, the analogous formula for 3D case is unknown. We introduce an approach for calculating the estimations of dimensions of three-dimensional Young diagrams also known as plane partitions. The most difficult part of this task is the calculation of co

Weimin Yi, M. Vivek, W. N. Brandt, T. Wang

We report the discovery of disappearance of Mg ii, Al iii, C iv, and Si iv broad absorption lines (BALs) at the same velocity, accompanied by a new Civ BAL emerging at a higher velocity, in the quasar J0827+4252 at z = 2.038. This is the first report of BAL disappearance (i) over Mg ii, Al iii, C iv, and Si iv ions and (ii) in a weak emission-line quasar (WL

E. Aprile, J. Aalbers, F. Agostini, M. Alfonsi

We present first results on the scalar WIMP-pion coupling from 1 t$\times$yr of exposure with the XENON1T experiment. This interaction is generated when the WIMP couples to a virtual pion exchanged between the nucleons in a nucleus. In contrast to most non-relativistic operators, these pion-exchange currents can be coherently enhanced by the total number of

Zhuo Hui, Ayan Chakrabarti, Kalyan Sunkavalli, Aswin C. Sankaranarayanan

We present a method to separate a single image captured under two illuminants, with different spectra, into the two images corresponding to the appearance of the scene under each individual illuminant. We do this by training a deep neural network to predict the per-pixel reflectance chromaticity of the scene, which we use in conjunction with a previous flash

Kostiantyn Ralchenko, Georgiy Shevchenko

For a class of non-autonomous parabolic stochastic partial differential equations defined on a bounded open subset $D\subset \mathbb {R}^d$ and driven by an $L^2(D)$-valued fractional Brownian motion with the Hurst index $H>1/2$, a new result on existence and uniqueness of a mild solution is established. Compared to the existing results, the uniqueness in a

Sylvain Crovisier, Dawei Yang, Jinhua Zhang

In this paper, we study the limit measures of the empirical measures of Lebesgue almost every point in the basin of a partially hyperbolic attractor. They are strongly related to a notion named Gibbs u-state, which can be defined in a large class of diffeomorphisms with less regularity and which is the same as Pesin-Sinai&#39;s notion for partially hyperboli

Dömötör Pálvölgyi, Gábor Tardos

In this note we disprove a conjecture of Kuzmin and Warmuth claiming that every family whose VC-dimension is at most d admits an unlabeled compression scheme to a sample of size at most d. We also study the unlabeled compression schemes of the joins of some families and conjecture that these give a larger gap between the VC-dimension and the size of the smal

Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, Seraphin Calo

Federated learning distributes model training among a multitude of agents, who, guided by privacy concerns, perform training using their local data but share only model parameter updates, for iterative aggregation at the server. In this work, we explore the threat of model poisoning attacks on federated learning initiated by a single, non-colluding malicious

Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan

Sensitive statistics are often collected across sets of users, with repeated collection of reports done over time. For example, trends in users&#39; private preferences or software usage may be monitored via such reports. We study the collection of such statistics in the local differential privacy (LDP) model, and describe an algorithm whose privacy cost is

Luis Fredes, Amitai Linker, Daniel Remenik

We investigate the effect on survival and coexistence of introducing forest fire epidemics to a certain two-species competition model. The model is an extension of the one introduced by Durrett and Remenik [DR09], who studied a discrete time particle system running on a random 3-regular graph where occupied sites grow until they become sufficiently dense so

Moeness G. Amin, Zhengxin Zeng, Tao Shan

We introduce a simple but effective technique in automatic hand gesture recognition using radar. The proposed technique classifies hand gestures based on the envelopes of their micro-Doppler signatures. These envelopes capture the distinctions among different hand movements and their corresponding positive and negative Doppler frequencies which are generated

Adam R White, Li Bai

This paper builds upon the current methods to increase their capability and automation for 3D surface construction from noisy and potentially sparse point clouds. It presents an analysis of an artificial neural network surface regression and mapping method, describing caveats, improvements and justification for the different approach.

Aditya Gangrade, Praveen Venkatesh, Bobak Nazer, Venkatesh Saligrama

We propose and analyze the problems of \textit{community goodness-of-fit and two-sample testing} for stochastic block models (SBM), where changes arise due to modification in community memberships of nodes. Motivated by practical applications, we consider the challenging sparse regime, where expected node degrees are constant, and the inter-community mean de

G. B. Mainland, Bernard Mulligan

Particle-antiparticle pairs are predicted by quantum field theory to appear as vacuum fluctuations. The model of the vacuum used here is postulated to have the following properties: To minimize the violation of conservation energy allowed by the Heisenberg uncertainty principle and to avoid violating conservation of angular momentum, vacuum fluctuations of c

Philip F. Hopkins, Michael Y. Grudic, Andrew R. Wetzel, Dusan Keres

Radiative feedback (RFB) from stars plays a key role in galaxies, but remains poorly-understood. We explore this using high-resolution, multi-frequency radiation-hydrodynamics (RHD) simulations from the Feedback In Realistic Environments (FIRE) project. We study ultra-faint dwarf through Milky Way mass scales, including H+He photo-ionization; photo-electric,

David Gontier, Christian Hainzl, Mathieu Lewin

The Hartree-Fock ground state of the Homogeneous Electron Gas is never translation invariant, even at high densities. As proved by Overhauser, the (paramagnetic) free Fermi Gas is always unstable under the formation of spin or charge density waves. We give here the first explicit bound on the energy gain due to the breaking of translational symmetry. Our bou

Savvas Varsamopoulos, Koen Bertels, Carmen G. Almudever

Matching algorithms can be used for identifying errors in quantum systems, being the most famous the Blossom algorithm. Recent works have shown that small distance quantum error correction codes can be efficiently decoded by employing machine learning techniques based on neural networks (NN). Various NN-based decoders have been proposed to enhance the decodi

Rafia Malik, Mai Vu

This paper investigates wireless information and power transfer in a full-duplex MIMO relay channel where the self-sustained relay harvests energy from both source transmit signal and self-interference signal to decode and forward source information to a destination. We present a novel technique to jointly optimize power splitting at the relay and precoding

Francesco Malandrino, Carla-Fabiana Chiasserini, Claudio Casetti, Luca Chiaraviglio

Climate changes brought about by global warming as well as man-made environmental changes are often the cause of sever natural disasters. ICT, which is itself responsible for global warming due to its high carbon footprint, can play a role in alleviating the consequences of such hazards by providing reliable, resilient means of communication during a disaste

Manuel Drees, Meng Shi, Zhongyi Zhang

In this study, we apply LHC data to constrain the extension of the Standard Model by an anomaly-free $U(1)_{L_μ-L_τ}$ gauge group; this model contains a new gauge boson ($Z^\prime$) and a scalar dark matter particle ($ϕ_{\rm DM}$). We recast a large number of LHC analyses from ATLAS and CMS of multi-lepton final states. We find that for $10$ GeV $< m_{Z^\pri

Yannis Pantazis, Michail Tsagris, Andrew T. A. Wood

Compositional data consists of vectors of proportions whose components sum to 1. Such vectors lie in the standard simplex, which is a manifold with boundary. One issue that has been rather controversial within the field of compositional data analysis is the choice of metric on the simplex. One popular possibility has been to use the metric implied by logtran

Berkan Dulek, Cuneyd Ozturk, Sinan Gezici

The problem of simple $M-$ary hypothesis testing under a generic performance criterion that depends on arbitrary functions of error probabilities is considered. Using results from convex analysis, it is proved that an optimal decision rule can be characterized as a randomization among at most two deterministic decision rules, of the form reminiscent to Bayes

Rong Ge, Holden Lee, Andrej Risteski

A key task in Bayesian machine learning is sampling from distributions that are only specified up to a partition function (i.e., constant of proportionality). One prevalent example of this is sampling posteriors in parametric distributions, such as latent-variable generative models. However sampling (even very approximately) can be #P-hard. Classical results

Manuel Gessner, Augusto Smerzi, Luca Pezzè

The well known metrological linear squeezing parameters (such as quadrature or spin squeezing) efficiently quantify the sensitivity of Gaussian states. Yet, these parameters are insufficient to characterize the much wider class of highly sensitive non-Gaussian states. Here, we introduce a class of metrological nonlinear squeezing parameters obtained by analy

Sandeep Silwal

In this short paper we present an elementary proof of the infinitude of primes. Our proof is similar in spirit to Euler&#39;s proof that the reciprocals of primes diverges and only uses tools from elementary number theory and calculus. In addition, our proof highlights an interesting use of generating functions.

Tirthankar Banerjee, Niladri Sarkar, John Toner, Abhik Basu

We show that inversion-asymmetric tethered membranes exhibit a new double-spiral phase with long range orientational order not present in symmetric membranes. We calculate the universal algebraic spiral shapes of these membranes in this phase. Asymmetry can trigger the crumpling of these membranes as well. In-vitro experiments on lipid, red blood cell membra

Jack Richings, Carlos Frenk, Adrian Jenkins, Andrew Robertson

N-body simulations make unambiguous predictions for the abundance of substructures within dark matter halos. However, the inclusion of baryons in the simulations changes the picture because processes associated with the presence of a large galaxy in the halo can destroy subhalos and substantially alter the mass function and velocity distribution of subhalos.

Jiaqi Jiang, David Sell, Stephan Hoyer, Jason Hickey

A key challenge in metasurface design is the development of algorithms that can effectively and efficiently produce high performance devices. Design methods based on iterative optimization can push the performance limits of metasurfaces, but they require extensive computational resources that limit their implementation to small numbers of microscale devices.

Zuxuan Wu, Caiming Xiong, Chih-Yao Ma, Richard Socher

We present AdaFrame, a framework that adaptively selects relevant frames on a per-input basis for fast video recognition. AdaFrame contains a Long Short-Term Memory network augmented with a global memory that provides context information for searching which frames to use over time. Trained with policy gradient methods, AdaFrame generates a prediction, determ

Yuri Kondratiev, Oleksandr Kutoviy, Sergey Pirogov, Elena Zhizhina

We study invariant measures of continuous contact model in small dimensional spaces ($d =1,2$). Under general conditions we prove that in the critical regime this system has the one-parameter set of invariant measures parametrized by the spatial density of particles. Also for broad class of initial states we prove the convergence to one of these invariant me

Wen Chen, Odysseas Kanavetas, Michael N. Katehakis

In this paper we introduce the Ameso optimization problem, a special class of discrete optimization problems. We establish its basic properties and investigate the relation between Ameso optimization and the convex optimization. Further, we design an algorithm to solve a multi-dimensional Ameso problem by solving a sequence of one-dimensional Ameso problems.

Tamaghna Hazra, Nishchhal Verma, Mohit Randeria

Understanding the material parameters that control the superconducting transition temperature $T_c$ is a problem of fundamental importance. In many novel superconductors, phase fluctuations determine $T_c$, rather than the collapse of the pairing amplitude. We derive rigorous upper bounds on the superfluid phase stiffness for multi-band systems, valid in any

João L. Costa, José Natário

We study the free boundary problem for the &#34;hard phase&#34; material introduced by Christodoulou, both for rods in (1+1)-dimensional Minkowski spacetime and for spherically symmetric balls in (3+1)-dimensional Minkowski spacetime. Unlike Christodoulou, we do not consider a &#34;soft phase&#34;, and so we regard this material as an elastic medium, capable

Jonathan Squire, Alexander A. Schekochihin, Eliot Quataert, Matthew W. Kunz

We propose that pressure anisotropy causes weakly collisional turbulent plasmas to self-organize so as to resist changes in magnetic-field strength. We term this effect &#34;magneto-immutability&#34; by analogy with incompressibility (resistance to changes in pressure). The effect is important when the pressure anisotropy becomes comparable to the magnetic p

Emmanuel Flurin, Leigh S. Martin, Shay Hacohen-Gourgy, Irfan Siddiqi

At its core, Quantum Mechanics is a theory developed to describe fundamental observations in the spectroscopy of solids and gases. Despite these practical roots, however, quantum theory is infamous for being highly counterintuitive, largely due to its intrinsically probabilistic nature. Neural networks have recently emerged as a powerful tool that can extrac

Chrysoula Markou, Felix J. Rudolph, Angnis Schmidt-May

This work proposes a new gravitational theory formulated in terms of the vierbein field. The vierbein contains components which can be shifted by local Lorentz transformations and therefore do not show up in the spacetime metric. These components are given dynamics and become physical in our setup. They enter the massless theory in the form of an antisymmetr

Dario Tamascelli, Andrea Smirne, James Lim, Susana F. Huelga

Chain-mapping techniques in combination with the time-dependent density matrix renormalization group are a powerful tool for the simulation of open-system quantum dynamics. For finite-temperature environments, however, this approach suffers from an unfavorable algorithmic scaling with increasing temperature. We prove that the system dynamics under thermal en

D. Rosado-Belza, U. Lisenfeld, J. Hibbard, K. Kniermann

UGC 10214 is a minor merger in which a dwarf galaxy has interacted with a large spiral galaxy $\sim$250 Myr ago and produced a perturbed disk and a giant tidal tail. We use a multiwavelength dataset in order to study the present and past star formation rate (SFR) and its relation to the gas and stellar mass at a spatial resolution down to 4 kpc. UGC 10214 is