arXiv papers from May 2018

Madhurima Bhattacharjee, Shinji Mukohyama, Mew-Bing Wan, Anzhong Wang

We numerically study the gravitational collapse of a massless scalar field with spherical symmetry in Einstein-aether theory, and show that apparent, spin-0 and dynamical universal horizons (dUHs) can be all formed. The spacetime and the aether field are well-behaved and regular, including regions nearby these horizons (but away from the center of spherical

David d'Enterria, Cynthia Yan

The forward-backward asymmetry of $b$-quarks measured at LEP in $e^+e^-$ collisions at the Z pole, $A_{FB}^{0,b}|^{\rm exp} = 0.0992\pm0.0016$, remains today the electroweak precision observable with the largest disagreement (2.8$\sigma$) with the Standard Model theoretical prediction, $A_{FB}^{0,b}|^{\rm th} = 0.1037\pm0.0008$. The dominant systematic uncer

Atiyeh Zarifi, Birgit Stiller, Moritz Merklein, Yang Liu

Recent advances in design and fabrication of photonic-phononic waveguides have enabled stimulated Brillouin scattering (SBS) in silicon-based platforms, such as under-etched silicon waveguides and hybrid waveguides. Due to the sophisticated design and more importantly high sensitivity of the Brillouin resonances to geometrical variations in micro- and nano-s

Bharath Ramsundar, Roger Chen, Alok Vasudev, Rob Robbins

We formalize the construction of decentralized data markets by introducing the mathematical construction of tokenized data structures, a new form of incentivized data structure. These structures both specialize and extend past work on token curated registries and distributed data structures. They provide a unified model for reasoning about complex data struc

Vamsi K. Amalladinne, Avinash Vem, Dileep Kumar Soma, Krishna R. Narayanan

This article introduces a novel paradigm for the unsourced multiple-access communication problem. This divide-and-conquer approach leverages recent advances in compressive sensing and forward error correction to produce a computationally efficient algorithm. Within the proposed framework, every active device first partitions its data into several sub-blocks,

A. R. G. Santos, T. L. Campante, W. J. Chaplin, M. S. Cunha

In the Sun, the frequencies of the acoustic modes are observed to vary in phase with the magnetic activity level. These frequency variations are expected to be common in solar-type stars and contain information about the activity-related changes that take place in their interiors. The unprecedented duration of Kepler photometric time-series provides a unique

Morteza Hasanvand

Let $G$ be a graph with $X\subseteq V(G)$ and let $l$ be an intersecting supermodular subadditive integer-valued function on subsets of $V(G)$. The graph $G$ is said to be $l$-partition-connected, if for every partition $P$ of $V(G)$, $e_G(P)\ge \sum_{A\in P} l(A)-l(V(G))$, where $e_G(P)$ denotes the number of edges of $G$ joining different parts of $P$. Let

Holger F. Hofmann

Quantum interferences between non-orthogonal states are the best approximation of a joint realization of the non-commuting physical properties represented by the two states. As I have shown recently, such interferences can be used to demonstrate that quantum physics deviates from classical causality in the limit of small action. Here, I point out that this p

Matthew Welborn, Lixue Cheng, Thomas F. Miller

We present a machine learning (ML) method for predicting electronic structure correlation energies using Hartree-Fock input.The total correlation energy is expressed in terms of individual and pair contributions from occupied molecular orbitals, and Gaussian process regression is used to predict these contributions from a feature set that is based on molecul

Emma K. Towlson, Lori Sheetz, Ralucca Gera, Jon Roginski

Our educational systems must prepare students for an increasingly interconnected future, and teachers require equipping with modern tools, such as network science, to achieve this. We held a Networks in Classroom Education (NiCE) workshop for a group of 21 K-12 teachers with various disciplinary backgrounds. The explicit aim of this was to introduce them to

Wenjun Liu, Dmitry Krasnov, Jörg Schumacher

Three-dimensional turbulent magnetoconvection at a Rayleigh number of $Ra=10^7$ in liquid gallium at a Prandtl number $Pr=0.025$ is studied in a closed square cell for very strong external vertical magnetic fields $B_0$ in direct numerical simulations which apply the quasistatic approximation. As $B_0$ or equivalently the Hartmann number $Ha$ are increased,

Somnath Santra, Shubhadeep Mandal, Aditya Bandopadhyay, Suman Chakraborty

The deflection of liquid droplet driven through a liquid medium under the combined action of transverse electric field and pressure driven flow has been studied in the present analysis. The present experimental and numerical analysis identifies the domain confinement as a key parameter for transverse migration of the droplets in the presence of a transverse

Sanjin J. Gutić, Dževad Kozlica, Fehim Korać, Danica Bajuk-Bogdanović

Increasing energy demands of modern society requires deep understanding of the properties of energy storage materials as well as their performance tuning. We show that the capacitance of graphene oxide (GO) can be precisely tuned using a simple electrochemical reduction route. In situ resistance measurements, combined with cyclic voltammetry measurement and

Katsuya Yonehara

A six-dimensional muon ionization cooling in a helical magnet channel has been studied. The cooling performance which is analytically evaluated by solving the exact Hamiltonian is reproduced in numerical simulation. One of the key beam elements for the helical channel is a dense-hydrogen gas-filled RF cavity which realizes a compact cooling channel. Besides,

Emma K. Towlson, Petra E. Vértes, Ulrich Müller, Sebastian E. Ahnert

The study of brain networks, including derived from functional neuroimaging data, attracts broad interest and represents a rapidly growing interdisciplinary field. Comparing networks of healthy volunteers with those of patients can potentially offer new, quantitative diagnostic methods, and a framework for better understanding brain and mind disorders. We ex

Tiancheng Liu, Yuchen Qian, Xi Chen, Xiaobai Sun

This work extends the personalized PageRank model invented by Brin and Page to a family of PageRank models with various damping schemes. The goal with increased model variety is to capture or recognize a larger number of types of network activities, phenomena and propagation patterns. The response in PageRank distribution to variation in damping mechanism is

Jingyi Wang, Askar B. Abdikamalov, Dimitry Ayzenberg, Cosimo Bambi

Signatures of X-ray reprocessing (reflection) out of an accretion disk are commonly observed in the high-energy spectrum of accreting black holes, and can be used to probe the strong gravity region around these objects. In this paper, we extend previous work in the literature and we employ a full emission model for relativistic reflection in non-Kerr spaceti

Hoi-To Wai, Wei Shi, Cesar A. Uribe, Angelia Nedich

This paper studies an acceleration technique for incremental aggregated gradient ({\sf IAG}) method through the use of \emph{curvature} information for solving strongly convex finite sum optimization problems. These optimization problems of interest arise in large-scale learning applications. Our technique utilizes a curvature-aided gradient tracking step to

Yueqi Li, Marius Buerkle, Guangfeng Li, Ali Rostamian

Quantum interference can profoundly affect charge transport in single molecules, but experiments can usually measure only the conductance at the Fermi energy. Because in general the most pronounced features of the quantum interference are not located at the Fermi energy, it is highly desirable to probe charge transport in a broader energy range. Here by the

Keisuke Inomata, Masahiro Kawasaki, Alexander Kusenko, Louis Yang

We study the effect of large baryonic isocurvature perturbations on the abundance of deuterium (D) synthesized in big bang nucleosynthesis (BBN). We found that large baryonic isocurvature perturbations existing at the BBN epoch ($T\sim 0.1\,$MeV) change the D abundance by the second order effect, which, together with the recent precise D measurement, leads t

Shuai Zhang, Fionn D. Malone, Miguel A. Morales

Auxiliary-field quantum Monte Carlo (AFQMC) has repeatedly demonstrated itself as one of the most accurate quantum many-body methods, capable of simulating both real and model systems. In this article we investigate the application of AFQMC to realistic strongly correlated materials in periodic Gaussian basis sets. Using nickel oxide (NiO) as an example, we

Amrita Ghosal, Mauro Conti

Smart Grids are evolving as the next generation power systems that involve changes in the traditional ways of generation, transmission and distribution of power. Advanced Metering Infrastructure (AMI) is one of the key components in smart grids. An AMI comprises of systems and networks, that collects and analyzes data received from smart meters. In addition,

Martin Burger, Jan Haskovec, Peter Markowich, Helene Ranetbauer

We introduce a mesoscopic model for natural network formation processes, acting as a bridge between the discrete and continuous network approach proposed by Hu and Cai. The models are based on a common approach where the dynamics of the conductance network is subject to pressure force effects. We first study topological properties of the discrete model and w

Paweł Tecmer, Cristina E. González-Espinoza

We present a comprehensive relativistic coupled cluster study of the electronic structures of the ThO and ThS molecules in the spinor basis. Specifically, we use the single-reference coupled cluster and the multi-reference Fock Space Coupled Cluster (FSCC) methods to model their ground and electronically-excited states. Two variants of the FSCC method have b

Christoph Redl

Answer Set Programming (ASP) is a well-known problem solving approach based on nonmonotonic logic programs. HEX-programs extend ASP with external atoms for accessing arbitrary external information, which can introduce values that do not appear in the input program. In this work we consider inconsistent ASP- and HEX-programs, i.e., programs without answer set

Yihong Wu, Jiaming Xu

Over the past few years, insights from computer science, statistical physics, and information theory have revealed phase transitions in a wide array of high-dimensional statistical problems at two distinct thresholds: One is the information-theoretical (IT) threshold below which the observation is too noisy so that inference of the ground truth structure is

S. M. Thomas, Xiaxin Ding, F. Ronning, V. Zapf

SmB6 is a candidate topological Kondo insulator that displays surface conduction at low temperatures. Here, we perform torque magnetization measurements as a means to detect de Haas-van Alphen (dHvA) oscillations in SmB6 crystals grown by aluminum flux. We find that dHvA oscillations occur in single crystals containing embedded aluminum, originating from the

Young-Hoon Kiem, Jun Li

We generalize the cosection localized Gysin map to intersection homology and Borel-Moore homology, which provides us with a purely topological construction of the Fan-Jarvis-Ruan-Witten invariants and some GLSM invariants.

Fabienne Comte, Nicolas Marie

This paper deals with the consistency and a rate of convergence for a Nadaraya-Watson estimator of the drift function of a stochastic differential equation driven by an additive fractional noise. The results of this paper are obtained via both some long-time behavior properties of Hairer and some properties of the Skorokhod integral with respect to the fract

Yulin Zhang, Dylan A. Shell

We examine the problem of target tracking whilst simultaneously preserving the target's privacy as epitomized by the robotic panda tracking scenario, which O'Kane introduced at the 2008 Workshop on the Algorithmic Foundations of Robotics in order to elegantly illustrate the utility of ignorance. The present paper reconsiders his formulation and the tracking

Akshay Seshadri, Vaibhav Madhok, Arul Lakshminarayan

Many-body states that are invariant under particle relabelling, the permutation symmetric states, occur naturally when the system dynamics is described by symmetric processes or collective spin operators. We derive expressions for the reduced density matrix for arbitrary subsystem decomposition for these states and study properties of permutation symmetric s

Ian Abraham, Anastasia Mavrommati, Todd D. Murphey

We develop an algorithm to explore an environment to generate a measurement model for use in future localization tasks. Ergodic exploration with respect to the likelihood of a particular class of measurement (e.g., a contact detection measurement in tactile sensing) enables construction of the measurement model. Exploration with respect to the information de

Jonathan Vacher, Pascal Mamassian, Ruben Coen-Cagli

Visual segmentation is a key perceptual function that partitions visual space and allows for detection, recognition and discrimination of objects in complex environments. The processes underlying human segmentation of natural images are still poorly understood. In part, this is because we lack segmentation models consistent with experimental and theoretical

Jaime F. Fisac, Andrea Bajcsy, Sylvia L. Herbert, David Fridovich-Keil

In order to safely operate around humans, robots can employ predictive models of human motion. Unfortunately, these models cannot capture the full complexity of human behavior and necessarily introduce simplifying assumptions. As a result, predictions may degrade whenever the observed human behavior departs from the assumed structure, which can have negative

José Burillo, Brita Nucinkis, Lawrence Reeves

The purpose of this paper is to study the properties of the irrational-slope Thompson's group $F_\tau$ introduced by Cleary in 1995. We construct presentations, both finite and infinite and we describe its combinatorial structure using binary trees. We show that its commutator group is simple. Finally, inspired by the case of Thompson's group F, we define a

Dimitrios Krommydas

We study violations of the Null Energy Condition (NEC) in Quantum Field Theory (QFT) and their implications. For the first part of the project, we examine these violations for classes of already known and novel (first discussed here) QFT states. Next, we discuss the implications of these violations focusing on the example of Wormhole Traversability. After re

Hector Alonzo Barriga-Acosta, Fernando Hernández-Hernández

We show that for any cardinal $\omega<\kappa \leq \mathfrak{c}$ with $cf(\kappa) > \omega$, there are $\mathfrak{c}$ many AD families whose $\Psi$-spaces are pairwise non-homeomorphic and they can be Luzin families or branch families of $2^\omega$.

Renaud Dessalles, Maria D'Orsogna, Tom Chou

Stochastic models that incorporate birth, death and immigration (also called birth-death and innovation models) are ubiquitous and applicable to many research topics such as quantifying species sizes in ecological populations, describing gene family sizes, modeling lymphocyte evolution in the body, and modeling the evolution of firm sizes. Many of these appl

Yuan Yao, Yasamin Jafarian, Hyun Soo Park

This paper presents MONET -- an end-to-end semi-supervised learning framework for a keypoint detector using multiview image streams. In particular, we consider general subjects such as non-human species where attaining a large scale annotated dataset is challenging. While multiview geometry can be used to self-supervise the unlabeled data, integrating the ge

O. J. Oluwadare, K. J. Oyewumi

The approximate bound state solutions of the Schrodinger equation with shifted Deng-Fan potential was obtained via proper quantization rule. The energy spectra for the homogenous diatomic molecules (H2, I2); the heterogeneous diatomic molecules (CO, HCl, LiH); the neutral transition metal hydrides (ScH, TiH, VH, CrH); the transition-metal lithide (CuLi); the

Guannan Zhao, Bo Zhou, Kaiwen Wang, Rui Jiang

The convolutional neural network (CNN) has become a powerful tool for various biomedical image analysis tasks, but there is a lack of visual explanation for the machinery of CNNs. In this paper, we present a novel algorithm, Respond-weighted Class Activation Mapping (Respond-CAM), for making CNN-based models interpretable by visualizing input regions that ar

Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton

Deep generative models can learn to generate realistic-looking images, but many of the most effective methods are adversarial and involve a saddlepoint optimization, which requires a careful balancing of training between a generator network and a critic network. Maximum mean discrepancy networks (MMD-nets) avoid this issue by using kernel as a fixed adversar

Marco Drewes, Jan Hajer, Juraj Klaric, Gaia Lanfranchi

The sensitivity of beam dump experiments to heavy neutrinos depends on the relative size of their mixings with the lepton flavours in the Standard Model. We study the impact of present neutrino oscillation data on these mixing angles in the minimal type I seesaw model. We find that current data significantly constrains the allowed heavy neutrino flavour mixi

D. Méndez Fernández, W. Böhm, A. Vogelsang, J. Mund

Artefacts play a vital role in software and systems development processes. Other terms like documents, deliverables, or work products are widely used in software development communities instead of the term artefact. In the following, we use the term `artefact' including all these other terms. Despite its relevance, the exact denotation of the term `artefact'

L. Schmidtobreick, E. Mason, S. B. Howell, K. S. Long

Context. In the context of a large campaign to determine the system parameters of high mass transfer cataclysmic variables, we found VY Scl in a low state in 2008. Aims. Making use of this low state, we study the stellar components of the binary with little influence of the normally dominating accretion disc. Methods. Time-resolved spectroscopy and photometr

M. A. Acero, P. Adamson, L. Aliaga T. Alion, V. Allakhverdian

We present updated results from the NOvA experiment for $\nu_\mu\rightarrow\nu_\mu$ and $\nu_\mu\rightarrow\nu_e$ oscillations from an exposure of $8.85\times10^{20}$ protons on target, which represents an increase of 46% compared to our previous publication. The results utilize significant improvements in both the simulations and analysis of the data. A joi

Rory Conboye

Using a recently developed piecewise flat method, numerical evolutions of the Ricci flow are computed for a number of manifolds, using a number of different mesh types, and shown to converge to the expected smooth behaviour as the mesh resolution is increased. The manifolds were chosen to have varying degrees of homogeneity, and include Nil and Gowdy manifol

Ibrahim Alsolami, Wolfgang Heidrich

This paper addresses the problem of imaging in the presence of diffraction-photons. Diffraction-photons arise from the low contrast ratio of DMDs ($\sim\,1000:1$), and very much degrade the quality of images captured by SPAD-based systems. Herein, a joint illumination-deconvolution scheme is designed to overcome diffraction-photons, enabling the acquisition

Robin Singh, Peter Su, Lionel Kimerling, Anu Agarwal

We propose an on-chip mid-infrared (MIR) photonic spectroscopy platform for aerosol characterization to obtain highly discriminatory information on the chemistry of aerosol particles. Sensing of aerosols is crucial for various environmental, climactic, warfare threat detection, and pulmonary healthcare applications. Further, there are a number of unintended

Yousef El-Laham, Victor Elvira, Monica F. Bugallo

Importance sampling (IS) is a Monte Carlo methodology that allows for approximation of a target distribution using weighted samples generated from another proposal distribution. Adaptive importance sampling (AIS) implements an iterative version of IS which adapts the parameters of the proposal distribution in order to improve estimation of the target. While

Tom Pitts, Nikitas I. Gidopoulos, Nektarios N. Lathiotakis

In the constrained minimization method of Gidopoulos and Lathiotakis (J. Chem. Phys. 136, 224109), the Hartree exchange and correlation Kohn-Sham potential of a finite $N$-electron system is replaced by the electrostatic potential of an effective charge density that is everywhere positive and integrates to a charge of $N-1$ electrons. The optimal effective c

Katarzyna Pichór, Ryszard Rudnicki

We consider a generational and continuous-time two-phase model of the cell cycle. The first model is given by a stochastic operator, and the second by a piecewise deterministic Markov process. In the second case we also introduce a stochastic semigroup which describes the evolution of densities of the process. We study long-time behaviour of these models. In

Takashi J. Moriya, Matt Nicholl, James Guillochon

The energy liberated by fallback accretion has been suggested as a possible engine to power hydrogen-poor superluminous supernovae. We systematically investigate this model using the Bayesian light-curve fitting code MOSFiT (Modular Open Source Fitter for Transients), fitting the light curves of 37 hydrogen-poor superluminous supernovae assuming a fallback a

Chaomei Chen

Digital Science's Dimensions is envisaged as a next-generation research and discovery platform for a better and more efficient access to cross-referenced scholarly publications, grants, patents, and clinical trials. As a new addition to the growing open citation resources, it offers opportunities that may benefit a wide variety of stakeholders of scientific

Jan Svoboda, Jonathan Masci, Federico Monti, Michael M. Bronstein

Deep learning systems have become ubiquitous in many aspects of our lives. Unfortunately, it has been shown that such systems are vulnerable to adversarial attacks, making them prone to potential unlawful uses. Designing deep neural networks that are robust to adversarial attacks is a fundamental step in making such systems safer and deployable in a broader

E. Biegert, B. Vowinckel, E. Meiburg

We develop a framework for analyzing the momentum balance of laminar particle-laden flows based on immersed boundary methods, which solve the Navier-Stokes equations and resolve the particle surfaces. This framework differs from previous studies by explicitly accounting for the fluid inside the particles, which is a by-product of the immersed boundary method

Partha Ghosh, Arpan Losalka, Michael J Black

Susceptibility of deep neural networks to adversarial attacks poses a major theoretical and practical challenge. All efforts to harden classifiers against such attacks have seen limited success. Two distinct categories of samples to which deep networks are vulnerable, "adversarial samples" and "fooling samples", have been tackled separately so far due to the

Ryan Flanagan, Lucas Lacasa, Emma K. Towlson, Sang Hoon Lee

Schizophrenia, a mental disorder that is characterized by abnormal social behavior and failure to distinguish one's own thoughts and ideas from reality, has been associated with structural abnormalities in the architecture of functional brain networks. Using various methods from network analysis, we examine the effect of two classical therapeutic antipsychot

Jason Cantarella, Kyle Chapman, Philipp Reiter, Clayton Shonkwiler

In this paper, we consider fixed edgelength $n$-step random walks in $\mathbb{R}^d$. We give an explicit construction for the closest closed equilateral random walk to almost any open equilateral random walk based on the geometric median, providing a natural map from open polygons to closed polygons of the same edgelength. Using this, we first prove that a n

Michal Hrbek

We classify all compactly generated t-structures in the unbounded derived category of an arbitrary commutative ring, generalizing the result of [ATLJS10] for noetherian rings. More specifically, we establish a bijective correspondence between the compactly generated t-structures and infinite filtrations of the Zariski spectrum by Thomason subsets. Moreover,

Hongjie Dong, N. V. Krylov

We prove weighted and mixed-norm Sobolev estimates for fully nonlinear elliptic and parabolic equations in the whole space under a relaxed convexity condition with almost VMO dependence on space-time variables. The corresponding interior and boundary estimates are also obtained.

Md. Ali Azam, Dhritiman Bhattacharya, Damien Querlioz, Jayasimha Atulasimha

In the brain, the membrane potential of many neurons oscillates in a subthreshold damped fashion and fire when excited by an input frequency that nearly equals their eigen frequency. In this work, we investigate theoretically the artificial implementation of such "resonate-and-fire" neurons by utilizing the magnetization dynamics of a fixed magnetic skyrmion

Azadeh Nematzadeh, Nathaniel Rodriguez, Alessandro Flammini, Yong-Yeol Ahn

In this chapter, we apply the theoretical framework introduced in the previous chapter to study how the modular structure of the social network affects the spreading of complex contagion. In particular, we focus on the notion of optimal modularity, that predicts the occurrence of global cascades when the network exhibits just the right amount of modularity.

Keren Jiang, Faheem Khan, Javix Thomas, Arindam Phani

A double-stranded DNA unravels thermally through intermediate denatured bubble segments. Intrinsically, fluctuations ensue at the bubble boundaries from non-equilibrium (NE) energy exchanges with the environment. However, such details gets obscured by large population kinetics at the macroscale, associating equilibrium pathway to the unravelling landscape. I

J-G. Caputo, I. Khames, A. Knippel

We characterize all graphs for which there are eigenvectors of the graph Laplacian having all their components in {-1,+1} or {-1,0,+ 1}. Graphs having eigenvectors with components in {-1,+1} are called bivalent and are shown to be the regular bipartite graphs and their extensions obtained by adding edges between vertices with the same value for the given eig

Cheuk Ting Li, Xiugang Wu, Ayfer Ozgur, Abbas El Gamal

The classical problem of supervised learning is to infer an accurate predictor of a target variable $Y$ from a measured variable $X$ by using a finite number of labeled training samples. Motivated by the increasingly distributed nature of data and decision making, in this paper we consider a variation of this classical problem in which the prediction is perf

Paul Müller, Petra Schwille, Thomas Weidemann

Scanning fluorescence correlation spectroscopy (SFCS) with a scan path perpendicular to the membrane plane was introduced to measure diffusion and interactions of fluorescent components in free standing biomembranes. Using a confocal laser scanning microscope (CLSM) the open detection volume is moved laterally with kHz frequency through the membrane and the

Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa

There has recently been a surge of work in explanatory artificial intelligence (XAI). This research area tackles the important problem that complex machines and algorithms often cannot provide insights into their behavior and thought processes. XAI allows users and parts of the internal system to be more transparent, providing explanations of their decisions

Ramón Ramos, David Spierings, Shreyas Potnis, Aephraim M. Steinberg

By employing the equivalent of a knife-edge measurement for matter-waves, we are able to characterize ultra-low momentum widths. We measure a momentum width corresponding to an effective temperature of 0.9 $\pm$ 0.2 nK, limited only by our cooling performance. To achieve similar resolution using standard methods would require hundreds of milliseconds of expa

Yunping Zhang, Omar Dary, Alexey Shashurin

An approach to utilize Low Energy Surface Flashover (LESF) for triggering the discharge in electric propulsion systems has been demonstrated. LESF uses conventional surface flashover mechanism with limited duration of high-current stage of the flashover below <100-200 ns. This eliminates the damage to the LESF assembly and allows robust operation of the same

D. Vainchtein, X. -J. Zhang, A. V. Artemyev, D. Mourenas

Resonant electron interaction with whistler-mode chorus waves is recognized as one of the main drivers of radiation belt dynamics. For moderate wave intensity, this interaction is well described by quasi-linear theory. However, recent statistics of parallel propagating chorus waves have demonstrated that 5-20% of the observed waves are sufficiently intense t

Naman Agarwal, Nicolas Boumal, Brian Bullins, Coralia Cartis

Adaptive regularization with cubics (ARC) is an algorithm for unconstrained, non-convex optimization. Akin to the popular trust-region method, its iterations can be thought of as approximate, safe-guarded Newton steps. For cost functions with Lipschitz continuous Hessian, ARC has optimal iteration complexity, in the sense that it produces an iterate with gra

Zhun Liu, Ying Shen, Varun Bharadhwaj Lakshminarasimhan, Paul Pu Liang

Multimodal research is an emerging field of artificial intelligence, and one of the main research problems in this field is multimodal fusion. The fusion of multimodal data is the process of integrating multiple unimodal representations into one compact multimodal representation. Previous research in this field has exploited the expressiveness of tensors for

Jiachun Liao, Oliver Kosut, Lalitha Sankar, Flavio P. Calmon

We study the problem of data disclosure with privacy guarantees, wherein the utility of the disclosed data is ensured via a \emph{hard distortion} constraint. Unlike average distortion, hard distortion provides a deterministic guarantee of fidelity. For the privacy measure, we use a tunable information leakage measure, namely \textit{maximal $\alpha$-leakage

Nina Stiesdal, Jan Kumlin, Kevin Kleinbeck, Philipp Lunt

We report on the experimental observation of non-trivial three-photon correlations imprinted onto initially uncorrelated photons through interaction with a single Rydberg superatom. Exploiting the Rydberg blockade mechanism, we turn a cold atomic cloud into a single effective emitter with collectively enhanced coupling to a focused photonic mode which gives

S. R. G. Joyce, M. A. Barstow, S. L. Casewell, M. R. Burleigh

Observational tests of the white dwarf mass-radius relationship have always been limited by the uncertainty in the available distance measurements. Most studies have focused on Balmer line spectroscopy because these spectra can be obtained from ground based observatories, while the Lyman lines are only accessible to space based UV telescopes. We present resu

Eszter Feher, Timothy J. Healey, Andras A. Sipos

Recent work demonstrates that finite-deformation nonlinear elasticity is essential in the accurate modeling of wrinkling in highly stretched thin films. Geometrically exact models predict an isola-center bifurcation, indicating that for a bounded interval of aspect ratios only, stable wrinkles appear and then disappear as the macroscopic strain is increased.

Uwe Trittmann

Two-dimensional QCD with adjoint fermions has many attractive features, yet its single-particle content remains largely unknown. To lay the foundation for a crucially improved approximation of the theory's spectrum, we developed a method to find the basis of eigenstates using the symmetry structure of the asymptotic theory where pair production is disallowed

Solomon Barkley, Thomas G. Dimiduk, Jerome Fung, David M. Kaz

A holographic microscope captures interference patterns, or holograms, that encode three-dimensional (3D) information about the object being viewed. Computation is essential to extracting that 3D information. By wrapping low-level scattering codes and taking advantage of Python's data analysis ecosystem, HoloPy makes it easy for experimentalists to use moder

Simon A. Haine

I consider the role of detection noise in quantum-enhanced metrology in collective spin systems, and derive a fundamental bound for the maximum obtainable sensitivity for a given level of added detection noise. I then present an interaction-based readout utilising the commonly used one-axis twisting scheme that approaches this bound for states generated via

Alberto Arenas, Óscar Ciaurri, Edgar Labarga

In this paper we commence the study of discrete harmonic analysis associated with Jacobi orthogonal polynomials of order $(\alpha,\beta)$. Particularly, we give the solution $W^{(\alpha,\beta)}_t$, $t\ge 0$, and some properties of the heat equation related to the operator $J^{(\alpha,\beta)}-I$, where $J^{(\alpha,\beta)}$ is the three-term recurrence relatio

Sourya Dey, Diandian Chen, Zongyang Li, Souvik Kundu

We demonstrate an FPGA implementation of a parallel and reconfigurable architecture for sparse neural networks, capable of on-chip training and inference. The network connectivity uses pre-determined, structured sparsity to significantly reduce complexity by lowering memory and computational requirements. The architecture uses a notion of edge-processing, le

Nikolai A. Zarkevich, Hao Chen, Valery I. Levitas, Duane D. Johnson

Density functional theory (DFT) was employed to study the stress-strain behavior, elastic instabilities, and metallization during a solid-solid phase transformation (PT) between semiconducting Si I (cubic A4) and metallic Si II (tetragonal A5 structure) when subjected to a general stress tensor. With normal stresses ($\sigma_1$, $\sigma_2$, $\sigma_3$) actin

Taesung Lee, Benjamin Edwards, Ian Molloy, Dong Su

Machine learning models are vulnerable to simple model stealing attacks if the adversary can obtain output labels for chosen inputs. To protect against these attacks, it has been proposed to limit the information provided to the adversary by omitting probability scores, significantly impacting the utility of the provided service. In this work, we illustrate

Jing Lei, Joseph B. Kadane

We show that there is a non-empty class of finitely additive probabilities on $\mathbb N^2$ such that for each member of the class, each set with limiting relative frequency $p$ has probability $p$. Hence, in that context the probability that two random integers are coprime is $6/\pi^2$. We also show that two other interpretations of "random integer," namely

Daniel Avila, Mauricio Junca

We consider a Markov control model in discrete time with countable both state space and action space. Using the value function of a suitable long-run average reward problem, we study various reachability/controllability problems. First, we characterize the domain of attraction and escape set of the system, and a generalization called $p$-domain of attraction

Biao He, Hamid Jafarkhani

The highly sparse nature of propagation channels and the restricted use of radio frequency (RF) chains at transceivers limit the performance of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. Introducing reconfigurable antennas to mmWave can offer an additional degree of freedom on designing mmWave MIMO systems. This paper provides a

Andrew Cotter, Maya Gupta, Heinrich Jiang, James Muller

We propose learning flexible but interpretable functions that aggregate a variable-length set of permutation-invariant feature vectors to predict a label. We use a deep lattice network model so we can architect the model structure to enhance interpretability, and add monotonicity constraints between inputs-and-outputs. We then use the proposed set function t

R. de la Fuente Marcos, C. de la Fuente Marcos

2MASS J06562998+3002455 or PSS 309-6 is a high proper-motion star that was discovered during a survey with the 2.1 m telescope at Kitt Peak National Observatory. Here, we reevaluate the status of this interesting star using Gaia DR2. Our results strongly suggest that PSS 309-6 could be a Population II star as the value of its V component is close to -220 km/

Zachary del Rosario, Richard W. Fenrich, Gianluca Iaccarino

Statistical uncertainties complicate engineering design -- confounding regulated design approaches, and degrading the performance of reliability efforts. The simplest means to tackle this uncertainty is double loop simulation; a nested Monte Carlo method that, for practical problems, is intractable. In this work, we introduce a flexible, general approximatio

Valts Blukis, Nataly Brukhim, Andrew Bennett, Ross A. Knepper

We introduce a method for following high-level navigation instructions by mapping directly from images, instructions and pose estimates to continuous low-level velocity commands for real-time control. The Grounded Semantic Mapping Network (GSMN) is a fully-differentiable neural network architecture that builds an explicit semantic map in the world reference

Martin B. Sweatman, Alistair Coombs

A consistent interpretation is provided for Neolithic Gobekli Tepe and Catalhoyuk as well as European Palaeolithic cave art. It appears they all display the same method for recording dates based on precession of the equinoxes, with animal symbols representing an ancient zodiac. The same constellations are used today in the West, although some of the zodiacal

Emre Çelebi, Orhan Çakır, Görkem Türemen, Gökhan Ünel

DEMIRCI software aims to aid RFQ design efforts by making the process easy, fast and accurate. In this report, DEMIRCI 8-term potential results are compared with the results provided by other commercially available simulation software. Computed electric fields are compared to the results from simulations of a recently produced 352 MHz RFQ. Recent development

Subhojeet Pramanik, Aman Hussain

We perform text normalization, i.e. the transformation of words from the written to the spoken form, using a memory augmented neural network. With the addition of dynamic memory access and storage mechanism, we present a neural architecture that will serve as a language-agnostic text normalization system while avoiding the kind of unacceptable errors made by

Dario Zappala

The presence of isotropic Lifshitz points for a U(1) symmetric scalar theory is investigated with the help of the Functional Renormalization Group at the conjectured lower critical dimension d=4. To this aim, a suitable truncation in the expansion of the effective action in powers of the field is considered and, consequently, the Renormalization Group flow i

Jordi Garra Ticó

Neutral meson mixing and $CP$ violation are very well known weak processes that involve decays to meson states that are, in general, a superposition of flavor eigenstates. This paper describes a mathematical interpretation of the time-dependent mixing amplitudes as a complex hyperbolic rotation of the time evolution of those amplitudes without mixing, which

Ming Li, Liang Gao, Jie Wang

The concordance $\Lambda$CDM cosmology predicts tens of satellite galaxies distributed in the inner region ($<40\ \lkpc$) of the Milky Way (MW), yet at most $12$ were discovered at present day, including 3 discovered very recently by Dark Energy Survey (DES) and 5 from other surveys (e.g. PanSTARRS, MagLiteS). We use $5$ ultra-high resolution simulations of

Ilias Diakonikolas, Weihao Kong, Alistair Stewart

We study the problem of high-dimensional linear regression in a robust model where an $\epsilon$-fraction of the samples can be adversarially corrupted. We focus on the fundamental setting where the covariates of the uncorrupted samples are drawn from a Gaussian distribution $\mathcal{N}(0, \Sigma)$ on $\mathbb{R}^d$. We give nearly tight upper bounds and co

Andy Edmonds, Chris Woods, Ana Juan Ferrer, Juan Francisco Ribera

Edge environments offer a number of advantages for software developers including the ability to create services which can offer lower latency, better privacy, and reduced operational costs than traditional cloud hosted services. However large technical challenges exist, which prevent developers from utilising the Edge; complexities related to the heterogeneo

Raphaël Clouâtre, Christopher Ramsey

We study non-selfadjoint operator algebras that can be entirely understood via their finite-dimensional representations. In contrast with the elementary matricial description of finite-dimensional $\mathrm{C}^*$-algebras, in the non-selfadjoint setting we show that an additional level of flexibility must be allowed. Motivated by this peculiarity, we consider