September 2019 arXiv papers — page 23

D. V. Pavlov, S. O. Gurbatov, S. I. Kudryashov, E. L. Gurevich

Nanoscale hydrodynamic instability of ring-like molten rims around ablative microholes produced in nanometer-thick gold films by tightly focused nanosecond-laser pulses was experimentally explored in terms of laser pulse energy and film thickness. These parametric dependencies of basic instability characteristics - order and period of the resulting nanocrown

Jeremy K. Mason, Srikanth Patala

With the increasing availability of experimental and computational data concerning the properties and distribution of grain boundaries in polycrystalline materials, there is a corresponding need to efficiently and systematically express functions on the grain boundary space. A grain boundary can be described by the rotations applied to two grains on either s

Rong Ge, Runzhe Wang, Haoyu Zhao

It has been observed \citep{zhang2016understanding} that deep neural networks can memorize: they achieve 100\% accuracy on training data. Recent theoretical results explained such behavior in highly overparametrized regimes, where the number of neurons in each layer is larger than the number of training samples. In this paper, we show that neural networks ca

Anqi Li, Davin Raiha, Kenneth W. Shotts

We develop a model of electoral accountability with mainstream and alternative media. In addition to regular high- and low-competence types, the incumbent may be an aspiring autocrat who controls the mainstream media and will subvert democracy if retained in office. A truthful alternative media can help voters identify and remove these subversive types while

Ulrich Aïvodji, Sébastien Gambs, Timon Ther

Recent works have demonstrated that machine learning models are vulnerable to model inversion attacks, which lead to the exposure of sensitive information contained in their training dataset. While some model inversion attacks have been developed in the past in the black-box attack setting, in which the adversary does not have direct access to the structure

Tian Zhao, Yaqi Zhang, Kunle Olukotun

Recurrent Neural Network (RNN) applications form a major class of AI-powered, low-latency data center workloads. Most execution models for RNN acceleration break computation graphs into BLAS kernels, which lead to significant inter-kernel data movement and resource underutilization. We show that by supporting more general loop constructs that capture design

Jie Li, Chengyi Xia, Gaoxi Xiao, Yamir Moreno

The emergence and evolution of real-world systems have been extensively studied in the last few years. However, equally important phenomena are related to the dynamics of systems' collapse, which has been less explored, especially when they can be cast into interdependent systems. In this paper, we develop a dynamical model that allows scrutinizing the colla

Chenguang Zhu, Michael Zeng, Xuedong Huang

Dialogue state tracking is an important component in task-oriented dialogue systems to identify users' goals and requests as a dialogue proceeds. However, as most previous models are dependent on dialogue slots, the model complexity soars when the number of slots increases. In this paper, we put forward a slot-independent neural model (SIM) to track dialogue

Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini

Clustering using deep autoencoders has been thoroughly investigated in recent years. Current approaches rely on simultaneously learning embedded features and clustering the data points in the latent space. Although numerous deep clustering approaches outperform the shallow models in achieving favorable results on several high-semantic datasets, a critical we

Sarah Goodhill, Adam M. Lowrance, Valeria Munoz Gonzales, Jessica Rattray

Using region crossing changes, we define a new invariant called the multi-region index of a knot. We prove that the multi-region index of a knot is bounded from above by twice the crossing number of the knot. In addition, we show that the minimum number of generators of the first homology of the double branched cover of $S^3$ over the knot is strictly less t

Vitaly Kurin, Saad Godil, Shimon Whiteson, Bryan Catanzaro

We present Graph-$Q$-SAT, a branching heuristic for a Boolean SAT solver trained with value-based reinforcement learning (RL) using Graph Neural Networks for function approximation. Solvers using Graph-$Q$-SAT are complete SAT solvers that either provide a satisfying assignment or proof of unsatisfiability, which is required for many SAT applications. The br

Benjamin N. L. Sharkey, Vishnu Reddy, Juan A. Sanchez, Matthew R. M. Izawa

We report near-infrared (0.7-2.5 micron) reflectance spectra for each of the six target asteroids of the forthcoming NASA Discovery-class mission Lucy. Five Jupiter Trojans (the binary (617) Patroclus system, (3548) Eurybates, (21900) Orus, (11351) Leucus, and (15094) Polymele) are well-characterized, with measurable spectral differences. We also report a su

M. Ablikim, M. N. Achasov, P. Adlarson, S. Ahmed

Using E1 radiative transitions $\psi(3686) \to \gamma\chi_{cJ}$ from a sample of $(448.1 \pm 2.9)\times10^{6}$ $\psi(3686)$ events collected with the BESIII detector, the decays $\chi_{cJ}\to \Sigma^{+}\bar{p}K_{S}^{0}+c.c.~(J = 0, 1, 2)$ are studied. The decay branching fractions are measured to be $\mathcal{B}(\chi_{c0}\to \Sigma^{+}\bar{p}K_{S}^{0}+c.c.)

Vivekananda Roy

Markov chain Monte Carlo (MCMC) is one of the most useful approaches to scientific computing because of its flexible construction, ease of use and generality. Indeed, MCMC is indispensable for performing Bayesian analysis. Two critical questions that MCMC practitioners need to address are where to start and when to stop the simulation. Although a great amoun

Jinniu Hu, Peiyu Wei, Ying Zhang

The truncation errors in equations of state (EOSs) of nuclear matter derived from the chiral nucleon-nucleon ($NN$) potentials at different expansion orders are analyzed by a Bayesian model. These EOSs are expanded as functions of a dimensionless parameter, $Q$, which is determined by Fermi momentum, $k_F$ and breakdown scale, $\Lambda_b$. The degree-of-beli

Yu Sun, Eric Tzeng, Trevor Darrell, Alexei A. Efros

This paper addresses unsupervised domain adaptation, the setting where labeled training data is available on a source domain, but the goal is to have good performance on a target domain with only unlabeled data. Like much of previous work, we seek to align the learned representations of the source and target domains while preserving discriminability. The way

Jianming Zheng, Fei Cai, Honghui Chen, Maarten de Rijke

Text representation can aid machines in understanding text. Previous work on text representation often focuses on the so-called forward implication, i.e., preceding words are taken as the context of later words for creating representations, thus ignoring the fact that the semantics of a text segment is a product of the mutual implication of words in the text

Gowtham Muniraju, Cihan Tepedelenlioglu, Andreas Spanias

A novel distributed algorithm for estimating the maximum of the node initial state values in a network, in the presence of additive communication noise is proposed. Conventionally, the maximum is estimated locally at each node by updating the node state value with the largest received measurements in every iteration. However, due to the additive channel nois

MirSaleh Bahavarnia, Hossein K. Mousavi

We design resilient sparse state-feedback controllers for a linear time-invariant (LTI) control system while attaining a pre-specified guarantee on ${\mathcal{H}}_\infty$ performance measure. We leverage a technique from non-fragile control theory to identify a region of resilient state-feedback controllers. Afterward, we explore the region to identify a spa

L. Wang, D. Fullmer, F. Liu, A. S. Morse

A solution is given to the basic distributed feedback control problem for a multi-channel linear system assuming only that the system is jointly controllable, jointly observable and has an associated neighbor graph which is strongly connected. The solution is an observer-based control system which is implemented in a distributed manner. Using these ideas, a

Adam Rupe, Nalini Kumar, Vladislav Epifanov, Karthik Kashinath

Extracting actionable insight from complex unlabeled scientific data is an open challenge and key to unlocking data-driven discovery in science. Complementary and alternative to supervised machine learning approaches, unsupervised physics-based methods based on behavior-driven theories hold great promise. Due to computational limitations, practical applicati

Yueh-Hua Wu, Ting-Han Fan, Peter J. Ramadge, Hao Su

Model-based reinforcement learning (MBRL) aims to learn a dynamic model to reduce the number of interactions with real-world environments. However, due to estimation error, rollouts in the learned model, especially those of long horizons, fail to match the ones in real-world environments. This mismatching has seriously impacted the sample complexity of MBRL.

Masoud Badiei Khuzani, Liyue Shen, Shahin Shahrampour, Lei Xing

We propose a novel supervised learning method to optimize the kernel in the maximum mean discrepancy generative adversarial networks (MMD GANs), and the kernel support vector machines (SVMs). Specifically, we characterize a distributionally robust optimization problem to compute a good distribution for the random feature model of Rahimi and Recht. Due to the

Daniel A. Lazar, Samuel Coogan, Ramtin Pedarsani

When people pick routes to minimize their travel time, the total experienced delay, or social cost, may be significantly greater than if people followed routes assigned to them by a social planner. This effect is accentuated when human drivers share roads with autonomous vehicles. When routed optimally, autonomous vehicles can make traffic networks more effi

Sarira Sahu, Carlos E. Lopez Fortin, Miguel E. Iglesias Martinez, Shigehiro Nagataki

The high energy blazar, Markarian 501 was observed as a part of multi-instrument and multiwavelength campaign spanning the whole electromagnetic spectrum for 4.5 months during March 15 to August 1, 2009. On May 1, Whipple 10m telescope observed a very strong $\gamma$-ray flare in a time interval of about 0.5 h. Apart from this flare, high state and low state

Michael Newman, Leonardo Andreta de Castro, Kenneth R. Brown

Measurement-based quantum computing (MBQC) is a promising alternative to traditional circuit-based quantum computing predicated on the construction and measurement of cluster states. Recent work has demonstrated that MBQC provides a more general framework for fault-tolerance that extends beyond foliated quantum error-correcting codes. We systematically expan

Hajime Sotani, Kohsuke Sumiyoshi

We examine the frequencies of the fundamental ($f$-) and the gravity ($g_i$-) mode in gravitational waves from accreting protoneutron stars (PNSs) toward black hole formation. For this purpose, we analyze numerical results of gravitational collapse of massive stars for two different progenitors with three equations of state. We adopt profiles of central obje

Hao Guo, Peter Hochs, Varghese Mathai

Consider a proper, isometric action by a unimodular, locally compact group $G$ on a complete Riemannian manifold $M$. For equivariant elliptic operators that are invertible outside a cocompact subset of $M$, we show that a localised index in the $K$-theory of the maximal group $C^*$-algebra of $G$ is well-defined. The approach is based on the use of maximal

Alexander Tsymbaliuk

We show that the Lusztig integral form is dual to the RTT integral form of the type A quantum loop algebra with respect to the new Drinfeld pairing, by utilizing the shuffle algebra realization of the former and the PBWD bases of the latter obtained in arXiv:1808.09536.

Andrea Dittadi, Ole Winther

We propose a probabilistic generative model for unsupervised learning of structured, interpretable, object-based representations of visual scenes. We use amortized variational inference to train the generative model end-to-end. The learned representations of object location and appearance are fully disentangled, and objects are represented independently of e

Farhad Farokhi

For evolving datasets with continual reports, the composition rule for differential privacy (DP) dictates that the scale of DP noise must grow linearly with the number of the queries, or that the privacy budget must be split equally between all the queries, so that the privacy budget across all the queries remains bounded and consistent with the privacy guar

Jiarong Lin, Fu Zhang

This paper presents a loop closure method to correct the long-term drift in LiDAR odometry and mapping (LOAM). Our proposed method computes the 2D histogram of keyframes, a local map patch, and uses the normalized cross-correlation of the 2D histograms as the similarity metric between the current keyframe and those in the map. We show that this method is fas

Antonio Ginart, Maxim Naumov, Dheevatsa Mudigere, Jiyan Yang

Embedding representations power machine intelligence in many applications, including recommendation systems, but they are space intensive -- potentially occupying hundreds of gigabytes in large-scale settings. To help manage this outsized memory consumption, we explore mixed dimension embeddings, an embedding layer architecture in which a particular embeddin

Giuseppe Negro, Livio Nicola Carenza, Antonio Lamura, Adriano Tiribocchi

The rheological behaviour of an emulsion made of an active polar component and an isotropic passive fluid is studied by lattice Boltzmann methods. Different flow regimes are found by varying the values of shear rate and extensile activity (occurring, e.g., in microtubule-motor suspensions). By increasing activity, a first transition occurs from linear flow r

Jean-Baptiste Gramain, Rishi Nath, James A. Sellers

A tremendous amount of research has been done in the last two decades on $(s,t)$-core partitions when $s$ and $t$ are positive integers with no common divisor. Here we change perspective slightly and explore properties of $(s,t)$-core and $(\bar{s},\bar{t})$-core partitions for $s$ and $t$ with nontrivial common divisor $g$. We begin by revisiting work by D.

A. Chiavassa, M. Brogi

Context. Spectroscopy of exoplanet atmospheres at high resolving powers is rapidly gaining popularity to measure the presence of atomic and molecular species. While this technique is robust against contaminant absorption in the Earth's atmosphere, the non stationary stellar spectrum creates a non-negligible source of noise that can alter or even prevent dete

Zhi-Song Zhang, Li-Chun Zhu, Wei Tang, Xin-Yi Li

The 500-meter Aperture Spherical Radio Telescope(FAST) has an active reflector. During the observation, the reflector will be deformed into a paraboloid of 300-meters. To improve its surface accuracy, we propose a scheme for photogrammetry to measure the positions of 2226 nodes on the reflector. And the way to detect the nodes in the photos is the key proble

Jeffrey M. Moore, Tyler N. Thompson, Matthew A. Glaser, Meredith D. Betterton

In active matter systems, self-propelled particles can self-organize to undergo collective motion, leading to persistent dynamical behavior out of equilibrium. In cells, cytoskeletal filaments and motor proteins self-organize into complex structures important for cell mechanics, motility, and division. Collective dynamics of cytoskeletal systems can be recon

Hassan Alshehri, Qing Ni, Sydney Taylor, Ryan McBurney

Concentrating solar power, particularly parabolic trough system with solar concentrations less than 50, requires spectrally selective solar absorbers that are thermally stable at high temperatures of 400degC above to achieve high efficiency. In this work, the solar-thermal performance of a selective multilayer metafilm absorber is characterized along with a

Dimitrios Kollias, Stefanos Zafeiriou

Affective computing has been largely limited in terms of available data resources. The need to collect and annotate diverse in-the-wild datasets has become apparent with the rise of deep learning models, as the default approach to address any computer vision task. Some in-the-wild databases have been recently proposed. However: i) their size is small, ii) th

Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer

We present function preserving projections (FPP), a scalable linear projection technique for discovering interpretable relationships in high-dimensional data. Conventional dimension reduction methods aim to maximally preserve the global and/or local geometric structure of a dataset. However, in practice one is often more interested in determining how one or

Sujith Ranasinghe, Denis Leahy

A multipole expansion analysis is applied to 1420 MHz radio continuum images of supernova remnants (SNRs) in order to compare Type Ia and core collapse (CC) SNRs. Because the radio synchrotron emission is produced at the outer shock between the SNR and the ISM, we are investigating whether the ISM interaction of SNRs is different between Type Ia and CC SNRs.

A. Fukui, D. Suzuki, N. Koshimoto, E. Bachelet

We report the analysis of additional multiband photometry and spectroscopy and new adaptive optics (AO) imaging of the nearby planetary microlensing event TCP J05074264+2447555 (Kojima-1), which was discovered toward the Galactic anticenter in 2017 (Nucita et al.). We confirm the planetary nature of the light-curve anomaly around the peak while finding no ad

Bob Holdom

We provide more evidence of not quite black holes at LIGO. We update and streamline our previous search strategy and apply it to the ten black hole merger events and the one neutron star merger event. The strategy is aimed at the evenly spaced resonance spectrum expected from not quite black holes, given that at low frequencies the radial wave equation descr

Yi Shi, Kemal Davaslioglu, Yalin E. Sagduyu, William C. Headley

Dynamic spectrum access (DSA) benefits from detection and classification of interference sources including in-network users, out-network users, and jammers that may all coexist in a wireless network. We present a deep learning based signal (modulation) classification solution in a realistic wireless network setting, where 1) signal types may change over time

Qixuan Wang

Optimal gait design is important for micro-organisms and micro-robots that propel themselves in a fluid environment in the absence of external force or torque. The simplest models of shape changes are those that comprise a series of linked-spheres that can change their separation and their sizes. We examine the dynamics of three existing linked-sphere types

Adam Li, Ronan Perry, Chester Huynh, Tyler M. Tomita

Decision forests (Forests), in particular random forests and gradient boosting trees, have demonstrated state-of-the-art accuracy compared to other methods in many supervised learning scenarios. In particular, Forests dominate other methods in tabular data, that is, when the feature space is unstructured, so that the signal is invariant to a permutation of t

Yuxiao Chen, Mohamadreza Ahmadi, Aaron D. Ames

This paper considers the synthesis of optimal safe controllers based on density functions. We present an algorithm for robust constrained optimal control synthesis using the duality relationship between the density function and the value function. The density function follows the Liouville equation and is the dual of the value function, which satisfies Bellm

David M. Paganin, Alexander Kozlov, Timur E. Gureyev

Imaging is an important means by which information is gathered regarding the physical world. Spatial resolution and signal-to-noise ratio are underpinning concepts. There is a paucity of rigorous definitions for these quantities, which are general enough to be useful in a broad range of imaging problems, while being also sufficiently specific to enable preci

Terrance D. Savitsky, Matthew R. Williams, Jingchen Hu

We propose a Bayesian pseudo posterior mechanism to generate record-level synthetic databases equipped with an $(\epsilon,\delta)-$ probabilistic differential privacy (pDP) guarantee, where $\delta$ denotes the probability that any observed database exceeds $\epsilon$. The pseudo posterior mechanism employs a data record-indexed, risk-based weight vector wit

Jo Schlemper, Jinming Duan, Cheng Ouyang, Chen Qin

We present simple reconstruction networks for multi-coil data by extending deep cascade of CNN's and exploiting the data consistency layer. In particular, we propose two variants, where one is inspired by POCSENSE and the other is calibration-less. We show that the proposed approaches are competitive relative to the state of the art both quantitatively and q

Takaaki Koike, Marius Hofert

We propose a novel framework of estimating systemic risk measures and risk allocations based on Markov chain Monte Carlo (MCMC) methods. We consider a class of allocations whose jth component can be written as some risk measure of the jth conditional marginal loss distribution given the so-called crisis event. By considering a crisis event as an intersection

John Palowitch, Bryan Perozzi

Are Graph Neural Networks (GNNs) fair? In many real world graphs, the formation of edges is related to certain node attributes (e.g. gender, community, reputation). In this case, standard GNNs using these edges will be biased by this information, as it is encoded in the structure of the adjacency matrix itself. In this paper, we show that when metadata is co

Alfred Z. Msezane, Zineb Felfli

The Regge-pole calculated low-energy electron elastic total cross sections (TCSs) for Cm and No, characterized generally by negative-ion formation, shape resonances and Ramsauer-Townsend(R-T) minima, exhibit atomic and fullerene molecular behavior near threshold. Also, a polarization-induced metastable cross section with a deep R-T minimum near threshold is

Joel A. Rosenfeld, Benjamin Russo, Rushikesh Kamalapurkar, Taylor T. Johnson

This manuscript presents a novel approach to nonlinear system identification leveraging densely defined Liouville operators and a new "kernel" function that represents an integration functional over a reproducing kernel Hilbert space (RKHS) dubbed an occupation kernel. The manuscript thoroughly explores the concept of occupation kernels in the contexts of RK

Sajad Mousavi, Atiyeh Fotoohinasab, Fatemeh Afghah

This study proposes a deep learning model that effectively suppresses the false alarms in the intensive care units (ICUs) without ignoring the true alarms using single- and multimodal biosignals. Most of the current work in the literature are either rule-based methods, requiring prior knowledge of arrhythmia analysis to build rules, or classical machine lear

Chapman Siu

We show that Residual Networks (ResNet) is equivalent to boosting feature representation, without any modification to the underlying ResNet training algorithm. A regret bound based on Online Gradient Boosting theory is proved and suggests that ResNet could achieve Online Gradient Boosting regret bounds through neural network architectural changes with the ad

Shokhrukh Yu. Kholmatov, Mardon Pardabaev

We consider the family $$ \hat {\bf h}_\mu:=\hat\varDelta\hat \varDelta - \mu \hat {\bf v},\qquad\mu\in\mathbb{R}, $$ of discrete Schr\"odinger-type operators in one-dimensional lattice $\mathbb{Z}$, where $\hat \varDelta$ is the discrete Laplacian and $\hat{\bf v}$ is of zero-range. We prove that for any $\mu\ne0$ the discrete spectrum of $\hat {\bf h}_\mu$

Jorge E. Miraglia

Total ionization cross sections of H, He, Li, Be, B, C, N, O, F, Ne, P, S and Ar neutral atoms by impact of antiprotons, H^{+}, He^{2+}, Be^{4+}, % C^{6+} and O^{8+}. were calculated using the CDWEIS (continuum distorted wave -Eikonal Initial state) theoretical method. Cross section depending on of initial the quantum numbers n and l are reported in Tables f

Ryan Blair, Patricia Cahn, Alexandra Kjuchukova, Jeffrey Meier

We show that any 4-manifold admitting a $(g;k_1,k_2,0)$-trisection is an irregular 3-fold cover of the 4-sphere whose branching set is a surface in $S^4$, smoothly embedded except for one singular point which is the cone on a link. A 4-manifold admits such a trisection if and only if it has a handle decomposition with no 1-handles; it is conjectured that all

Takafumi Kita

We study the anomalous dimension $\eta$ of homogeneous interacting single-component Bose-Einstein condensates at finite temperatures for $d\lesssim 4$ dimensions. This $\eta$ is defined in terms of the one-particle density matrix $\rho({\bf r})\equiv \langle \hat\psi^\dagger({\bf r}_1)\hat\psi({\bf r}_1+{\bf r})\rangle$ through its asymptotic behavior $\rho(

Nilesh A. Ahuja, Ibrahima Ndiour, Trushant Kalyanpur, Omesh Tickoo

We present a principled approach for detecting out-of-distribution (OOD) and adversarial samples in deep neural networks. Our approach consists in modeling the outputs of the various layers (deep features) with parametric probability distributions once training is completed. At inference, the likelihoods of the deep features w.r.t the previously learnt distr

M. G. M. Moreno, Samuraí Brito, Ranieri V. Nery, Rafael Chaves

Bell nonlocality, the fact that local hidden variable models cannot reproduce the correlations obtained by measurements on entangled states, is a cornerstone in our modern understanding of quantum theory. Apart from its fundamental implications, nonlocality is also at the core of device-independent quantum information processing, which successful implementat

Nikolaus Umlauf, Nadja Klein, Thorsten Simon, Achim Zeileis

Over the last decades, the challenges in applied regression and in predictive modeling have been changing considerably: (1) More flexible model specifications are needed as big(ger) data become available, facilitated by more powerful computing infrastructure. (2) Full probabilistic modeling rather than predicting just means or expectations is crucial in many

Vasileios Tzoumas, Ali Jadbabaie, George J. Pappas

Emerging applications of control, estimation, and machine learning, ranging from target tracking to decentralized model fitting, pose resource constraints that limit which of the available sensors, actuators, or data can be simultaneously used across time. Therefore, many researchers have proposed solutions within discrete optimization frameworks where the o

Corentin Morice, Thilo Kopp, Arno P. Kampf

At the heart of the study of topological insulators lies a fundamental dichotomy: topological invariants are defined in infinite systems, but surface states as their main footprint only exist in finite systems. In the slab geometry, namely infinite in two planar directions and finite in the perpendicular direction, the 2D topological invariant was shown to d

Juan D. Gonzalez, Edmundo F. Lavia, Silvia Blanc, Martin Maas

A model for computing acoustic scattering by a swimbladdered fish with coupling to surrounding fish tissue that is assumed to behave as a homogeneous fluid, is presented. Mathematically, this corresponds to considering the problem of two penetrable scatterers immersed in a homogeneous medium, one of which is wholly embedded in the other. The model is formula

Jan Krzywda, Łukasz Cywiński

Controlled adiabatic transfer of a single electron through a chain of quantum dots has been recently achieved in GaAs and Si/SiGe based quantum dots, opening prospects for turning stationary spin qubits into mobile ones, and solving in this way the problem of long-distance communication between quantum registers in a scalable quantum computing architecture b

Y. A. Garnica, R. Martinez

We propose a non-universal $U(1)_{X}$ extension to the Standard Model with three families and an additional global anomala Peccei-Quinn (PQ) symmetry. The breaking of the former allows us to give masses to the exotic fermionic sector and the later generates the necessary zeros in the mass matrices to explain the fermionic mass hierarchy. In addition, the lar

Zhuolun Xiang, Bolin Ding, Xi He, Jingren Zhou

Local differential privacy (LDP) enables private data sharing and analytics without the need for a trusted data collector. Error-optimal primitives (for, e.g., estimating means and item frequencies) under LDP have been well studied. For analytical tasks such as range queries, however, the best known error bound is dependent on the domain size of private data

Joaquin Luna-Torres

In analogy with the classical theory of topological groups, for finitely complete categories enriched with Grothendieck topologies, we provide the concepts of localized G-topological space, initial Grothendieck topologies and continuous morphisms, in order to obtain the concepts of G-topological monoid and G-topological group objects.

Julien Petit, Renaud Lambiotte, Timoteo Carletti

Graph-limit theory focuses on the convergence of sequences of graphs when the number of nodes becomes arbitrarily large. This framework defines a continuous version of graphs allowing for the study of dynamical systems on very large graphs, where classical methods would become computationally intractable. Through an approximation procedure, the standard syst

Zhujing Xu, Zhang-qi Yin, Qinkai Han, Tongcang Li

Electron and nuclear spins of diamond nitrogen-vacancy (NV) centers are good candidates for quantum information processing as they have long coherence time and can be initialized and read out optically. However, creating a large number of coherently coupled and individually addressable NV centers for quantum computing has been a big challenge. Here we propos

Ran Xin, Usman A. Khan, Soummya Kar

In this paper, we study decentralized empirical risk minimization problems, where the goal is to minimize a finite-sum of smooth and strongly-convex functions available over a network of nodes. In this Part I, we propose \textbf{\texttt{GT-SAGA}}, a decentralized stochastic first-order algorithm based on gradient tracking \cite{DSGT_Pu,DSGT_Xin} and a varian

David Pollard, Dana Yang

The Metropolis-Hastings method is often used to construct a Markov chain with a given $\pi$ as its stationary distribution. The method works even if $\pi$ is known only up to an intractable constant of proportionality. Polynomial time convergence results for such chains (rapid mixing) are hard to obtain for high dimensional probability models where the size

Chang Liu, Yanan Xu, Yanmin Zhu

In recent years, dock-less shared bikes have been widely spread across many cities in China and facilitate people's lives. However, at the same time, it also raises many problems about dock-less shared bike management due to the mismatching between demands and real distribution of bikes. Before deploying dock-less shared bikes in a city, companies need to ma

Ana López-Sepulcre, Nadia Balucani, Cecilia Ceccarelli, Claudio Codella

Formamide (NH$_2$CHO) has been identified as a potential precursor of a wide variety of organic compounds essential to life, and many biochemical studies propose it likely played a crucial role in the context of the origin of life on our planet. The detection of formamide in comets, which are believed to have --at least partially-- inherited their current ch

Ramis Movassagh, Jeffrey Schenker

Any discrete quantum process is represented by a sequence of quantum channels. We consider ergodic quantum processes obtained by a map that takes the points along the trajectory of a discrete ergodic dynamical system to the space of quantum channels. Under natural irreducibility conditions, we obtain a theorem showing that the state under such a process conv

Spencer H. Bryngelson, Tim Colonius

Humpback whales can generate intricate bubbly regions, called bubble nets, via their blowholes. They appear to exploit these bubble nets for feeding via loud vocalizations. A fully-coupled phase-averaging approach is used to model the flow, bubble dynamics, and corresponding acoustics. A previously hypothesized waveguiding mechanism is assessed for varying a

Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun

Adversarial training, which minimizes the maximal risk for label-preserving input perturbations, has proved to be effective for improving the generalization of language models. In this work, we propose a novel adversarial training algorithm, FreeLB, that promotes higher invariance in the embedding space, by adding adversarial perturbations to word embeddings

Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing

Current deep neural networks can achieve remarkable performance on a single task. However, when the deep neural network is continually trained on a sequence of tasks, it seems to gradually forget the previous learned knowledge. This phenomenon is referred to as \textit{catastrophic forgetting} and motivates the field called lifelong learning. Recently, episo

Derek Schafer, Sheikh Ghafoor, Daniel Holmes, Martin Ruefenacht

Composability is one of seven reasons for the long-standing and continuing success of MPI. Extending MPI by composing its operations with user-level operations provides useful integration with the progress engine and completion notification methods of MPI. However, the existing extensibility mechanism in MPI (generalized requests) is not widely utilized and

Peter Grassberger

We consider the growth of clusters in disordered media at zero temperature, as exemplified by supercritical generalized percolation and by the random field Ising model. We show that the morphology of such clusters and of their surfaces can be of different types: They can be standard compact clusters with rough or smooth surfaces, but there exists also a comp

Hector O. Silva, Masato Minamitsuji

The canonical scalar-tensor theory model which exhibits spontaneous scalarization in the strong-gravity regime of neutron stars has long been known to predict a cosmological evolution for the scalar field which generically results in severe violations of present-day Solar System constraints on deviations from general relativity. We study if this tension can

Tianyuan Xu, Shanming Ji, Ming Mei, Jingxue Yin

For the classical reaction diffusion equation, the priori speed of fronts is determined exactly in the pioneering paper (R.D. Benguria and M.C. Depassier, {\em Commun. Math. Phys.} 175:221--227, 1996) by variational characterization method. In this paper, we model the dispersal process using a density-dependent diffusion equation with time delay. We show the

Carlos E. Álvarez, Nicolás Afanador, Gabriel Téllez

We compute the average work done by an external agent, driving a piston at constant speed, over a single particle gas going through an adiabatic compression and expansion process. To do so, we get the analytical expression relating the number of collisions between the piston and the particle with the position of the piston during the process. The ergodicity

Edgar Gasperin, Shalabh Gautam, David Hilditch, Alex Vañó-Viñuales

One method for the numerical treatment of future null-infinity is to decouple coordinates from the tensor basis and choose each in a careful manner. This dual-frame approach is hampered by logarithmically divergent terms that appear in a naive choice of evolved variables. Here we consider a system of wave equations that satisfy the weak-null condition and se

Yihang Wang, Joao Marcelo Lamim Ribeiro, Pratyush Tiwary

Molecular dynamics (MD) has become a powerful tool for studying biophysical systems, due to increasing computational power and availability of software. Although MD has made many contributions to better understanding these complex biophysical systems, there remain methodological difficulties to be surmounted. First, how to make the deluge of data generated i

Tianyuan Xu, Shanming Ji, Ming Mei, Jingxue Yin

We consider the non-monotone degenerate diffusion equation with time delay. Different from the linear diffusion equation, the degenerate equation allows for semi-compactly supported traveling waves. In particular, we discover sharp-oscillating waves with sharp edges and non-decaying oscillations. The degenerate diffusion and the effect of time delay cause us

Kristian Uldall Kristiansen, Peter Szmolyan

In this paper, we describe a novel type of relaxation oscillations occurring in a model of substrate-depletion oscillators. Using geometric singular perturbation theory, with blow-up as a key technical tool, we show that the oscillations in this planar model are produced by a complicated interplay between two stable nodes and a discontinuity set in the singu

Akash Choudhary, T. Renganathan, S. Pushpavanam

Janus particles propel themselves by generating concentration gradients along their active surface. This induces a flow near the surface, known as the diffusio-osmotic slip, which propels the particle even in the absence of externally applied concentration gradients. In this work, we study the influence of viscoelasticity and shear-thinning (described by the

Anup Bhattacharya, Dishant Goyal, Ragesh Jaiswal, Amit Kumar

We give a 3-pass, polylog-space streaming PTAS for the constrained binary $k$-means problem and a 4-pass, polylog-space streaming PTAS for the binary $\ell_0$-low rank approximation problem. The connection between the above two problems has recently been studied. We design a streaming PTAS for the former and use this connection to obtain streaming PTAS for t

Eric A. Bergshoeff, Wout Merbis, Paul K. Townsend

A variant of the ADT method for the determination of gravitational charges as integrals at infinity is applied to "Chern-Simons-like" theories of 3D gravity, and the result is used to find the mass and angular momentum of the BTZ black hole considered as a solution of a variety of massive 3D gravity field equations. The results agree with many obtained previ

Rogemar A. Riffel, Nadia L. Zakamska, Rogerio Riffel

In most galaxies, the fluxes of rotational H2 lines strongly correlate with star formation diagnostics (such as polycyclic aromatic hydrocarbons, PAH), suggesting that H2 emission from warm molecular gas is a minor byproduct of star formation. We analyse the optical properties of a sample of 309 nearby galaxies derived from a parent sample of 2,015 objects o

Dražen Glavan, Gerasimos Rigopoulos

We examine scalar quantum electrodynamics in power-law inflation space-time, and compute the one-loop correction to electric and magnetic field correlators at superhorizon separations. The effect at one-loop descends from the coupling of the vector to the charged scalar current which is greatly enhanced due to gravitational particle production. We conclude t

Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy

Joint image-text embedding is the bedrock for most Vision-and-Language (V+L) tasks, where multimodality inputs are simultaneously processed for joint visual and textual understanding. In this paper, we introduce UNITER, a UNiversal Image-TExt Representation, learned through large-scale pre-training over four image-text datasets (COCO, Visual Genome, Conceptu

Jonathan Rubin

The homotopy category of $N_\infty$ operads is equivalent to a finite lattice, and as the ambient group varies, there are various image constructions between these lattices. In this paper, we explain how to lift this algebraic structure back to the operad level. We show that lattice joins and meets correspond to derived operadic coproducts and products, and

Daichi Kozawa, Ananth Govind Rajan, Sylvia Xin Li, Takeo Ichihara

Atomic vacancy defects in single unit cell thick hexagonal boron nitride are of significant interest because of their photophysical properties, including single-photon emission, and promising applications in quantum communication and computation. The spectroscopic assignment of emission energies to specific atomic vacancies within the triangular lattice is c

Jian Yang, Christian Poellabauer, Pramita Mitra, Cynthia Neubecker

As an emerging technology with exceptional low energy consumption and low-latency data transmissions, Bluetooth Low Energy (BLE) has gained significant momentum in various application domains, such as Indoor Positioning, Home Automation, and Wireless Personal Area Network (WPAN) communications. With various novel protocol stack features, BLE is finding use o

R. V. Tagirov, A. I. Shapiro, N. A. Krivova, Y. C. Unruh

Context. Solar spectral irradiance (SSI) variability is one of the key inputs to models of the Earth's climate. Understanding solar irradiance fluctuations also helps to place the Sun among other stars in terms of their brightness variability patterns and to set detectability limits for terrestrial exo-planets. Aims. One of the most successful and widely use