arXiv papers from February 2020

Yu-Wei Fan, Kuan-Wen Lai

Are Fourier-Mukai equivalent cubic fourfolds birationally equivalent? We obtain an affirmative answer to this question for very general cubic fourfolds of discriminant 20, where we produce birational maps via the Cremona transformation defined by the Veronese surface. By studying how these maps act on the cubics known to be rational, we surprisingly found ne

Demetris Avraam, Vasiliki Bitsouni, Katerina Kaouri, Alessandra Micheletti

This report summarises the work and results produced at the 146th European Study Group with Industry/co-creation event with society on the challenge \textit{Breaking barriers for women in Science}. The aim of this challenge, proposed by the Cyprus-based non-profit AIPFE Cyprus-Women of Europe, was to quantify the barriers that women face in science so that e

Semih Cayci, Atilla Eryilmaz, R. Srikant

We consider a budget-constrained bandit problem where each arm pull incurs a random cost, and yields a random reward in return. The objective is to maximize the total expected reward under a budget constraint on the total cost. The model is general in the sense that it allows correlated and potentially heavy-tailed cost-reward pairs that can take on negative

Tra Huynh, Amali Priyanka Jambuge, Hien Khong, James T. Laverty

Inchargeness is associated with one's authority in driving the activity in collaboration. We study how inchargeness changes within a collaborative group when its members have differing expertise. We present a case study of a group of three students working in an upper division undergraduate physics laboratory. One of them has less on-task expertise than her

Tra Huynh, Adrian M Madsen, Eleanor C Sayre

Personalized undergraduate research programs can help increase undergraduate students' participation in research. Personas of undergraduate researchers are a powerful means of encapsulating the richness of students' various goals, motivations, and experiences in research. Student personas can support the design of research programs with student-centered appr

Boris Bukh, Oleksandr Rudenko

Let $a_1,\dotsc,a_n$ be a permutation of $[n]$. Two disjoint order-isomorphic subsequences are called \emph{twins}. We show that every permutation of $[n]$ contains twins of length $\Omega(n^{3/5})$ improving the trivial bound of $\Omega(n^{1/2})$. We also show that a random permutation contains twins of length $\Omega(n^{2/3})$, which is sharp.

Johannes Buck, Claudia Klüppelberg

Recursive max-linear vectors model causal dependence between node variables by a structural equation model, expressing each node variable as a max-linear function of its parental nodes in a directed acyclic graph (DAG) and some exogenous innovation. For such a model, there exists a unique minimum DAG, represented by the Kleene star matrix of its edge weight

Zoe Gonzalez Izquierdo, Tameem Albash, Itay Hen

Motivated by recent experiments in which specific thermal properties of complex many-body systems were successfully reproduced on a commercially available quantum annealer, we examine the extent to which quantum annealing hardware can reliably sample from the thermal state in a specific basis associated with a target quantum Hamiltonian. We address this ques

Adam N. Elmachtoub, Jason Cheuk Nam Liang, Ryan McNellis

We consider the use of decision trees for decision-making problems under the predict-then-optimize framework. That is, we would like to first use a decision tree to predict unknown input parameters of an optimization problem, and then make decisions by solving the optimization problem using the predicted parameters. A natural loss function in this framework

Xiao Xu, Fang Dong, Yanghua Li, Shaojian He

A contextual bandit problem is studied in a highly non-stationary environment, which is ubiquitous in various recommender systems due to the time-varying interests of users. Two models with disjoint and hybrid payoffs are considered to characterize the phenomenon that users' preferences towards different items vary differently over time. In the disjoint payo

Robert Coquereaux

The present contribution is the written counterpart of a talk given in Yerevan at the SQS'2019 International Workshop "Supersymmetries and Quantum Symmetries" (SQS'2019, 26 August - August 31, 2019). After a short presentation of various pictographs (O-blades, metric honeycombs) that one can use in order to calculate SU(n) multiplicities (Littlewood-Richards

Sylvain Marsat, John G. Baker, Tito Dal Canton

The space-based gravitational wave detector LISA will observe mergers of massive black hole binary systems (MBHBs) to cosmological distances, as well as inspiralling stellar-origin (or stellar-mass) binaries (SBHBs) years before they enter the LIGO/Virgo band. Much remains to be explored for the parameter recovery of both classes of systems. Previous MBHB an

Adane K. Geremew, Sergey Rumyantsev, Roger Lake, Alexander A. Balandin

We report on current oscillations in quasi two-dimensional (2D) 1T-TaS2 charge-density-wave devices. The MHz-frequency range of the oscillations and the linear dependence of the frequency of the oscillations on the current resemble closely the "narrow band noise," which was often observed in the classical bulk quasi-one-dimensional (1D) trichalcogenide charg

Xia Wu, Haiyuan Liu, Ziqi Liu, Mingdong Chen

Many researchers have identified robotics as a potential solution to the aging population faced by many developed and developing countries. If so, how should we address the cognitive acceptance and ambient control of elderly assistive robots through design? In this paper, we proposed an explorative design of an ambient SuperLimb (Supernumerary Robotic Limb)

Paidamoyo Chapfuwa, Chunyuan Li, Nikhil Mehta, Lawrence Carin

Conventional survival analysis approaches estimate risk scores or individualized time-to-event distributions conditioned on covariates. In practice, there is often great population-level phenotypic heterogeneity, resulting from (unknown) subpopulations with diverse risk profiles or survival distributions. As a result, there is an unmet need in survival analy

Linhan Yang, Fang Wan, Haokun Wang, Xiaobo Liu

Inspired by widely used soft fingers on grasping, we propose a method of rigid-soft interactive learning, aiming at reducing the time of data collection. In this paper, we classify the interaction categories into Rigid-Rigid, Rigid-Soft, Soft-Rigid according to the interaction surface between grippers and target objects. We find experimental evidence that th

Andrey A Popov, Adrian Sandu, Elias D. Nino-Ruiz, Geir Evensen

The Ensemble Kalman Filters (EnKF) employ a Monte-Carlo approach to represent covariance information, and are affected by sampling errors in operational settings where the number of model realizations is much smaller than the model state dimension. To alleviate the effects of these errors EnKF relies on model-specific heuristics such as covariance localizati

Yalin Liao, Aleksandar Vakanski, Min Xian, David Paul

Recent advances in data analytics and computer-aided diagnostics stimulate the vision of patient-centric precision healthcare, where treatment plans are customized based on the health records and needs of every patient. In physical rehabilitation, the progress in machine learning and the advent of affordable and reliable motion capture sensors have been cond

Wei-Hung Weng, Yu-An Chung, Schrasing Tong

In the era of clinical information explosion, a good strategy for clinical text summarization is helpful to improve the clinical workflow. The ideal summarization strategy can preserve important information in the informative but less organized, ill-structured clinical narrative texts. Instead of using pure statistical learning approaches, which are difficul

Aikaterini Aretaki, Efthymios N. Karatzas

This work investigates an elliptic optimal control problem defined on uncertain domains and discretized by a fictitious domain finite element method and cut elements. Key ingredients of the study are to manage cases considering the usually computationally "forbidden" combination of poorly conditioned equation system matrices due to challenging geometries, op

Nicolae-Catalin Ristea, Liviu Cristian Dutu, Anamaria Radoi

Emotion recognition has become an important field of research in the human-computer interactions domain. The latest advancements in the field show that combining visual with audio information lead to better results if compared to the case of using a single source of information separately. From a visual point of view, a human emotion can be recognized by ana

Razvan V. Marinescu

In order to find effective treatments for Alzheimer's disease (AD), we need to identify subjects at risk of AD as early as possible. To this end, recently developed disease progression models can be used to perform early diagnosis, as well as predict the subjects' disease stages and future evolution. However, these models have not yet been applied to rare ne

A. Ricottone, M. S. Rudner, W. A. Coish

We extend non-Hermitian topological quantum walks on a Su-Schrieffer-Heeger (SSH) lattice [M. S. Rudner and L. Levitov, Phys. Rev. Lett. 102, 065703 (2009)] to the case of non-Markovian evolution. This non-Markovian model is established by coupling each unit cell in the SSH lattice to a reservoir formed by a quasi-continuum of levels. We find a topological t

Mirjam Weilenmann, Roger Colbeck

Self-testing usually refers to the task of taking a given set of observed correlations that are assumed to arise via a process that is accurately described by quantum theory, and trying to infer the quantum state and measurements. In other words it is concerned with the question of whether we can tell what quantum black-box devices are doing by looking only

Iztok Fister, Iztok Fister

Association Rule Mining is a machine learning method for discovering the interesting relations between the attributes in a huge transaction database. Typically, algorithms for Association Rule Mining generate a huge number of association rules, from which it is hard to extract structured knowledge and present this automatically in a form that would be suitab

Ketan Mittal, Som Dutta, Paul Fischer

We develop a multirate timestepper for semi-implicit solutions of the unsteady incompressible Navier-Stokes equations (INSE) based on a recently-developed multidomain spectral element method (SEM). For {\em incompressible} flows, multirate timestepping (MTS) is particularly challenging because of the tight coupling implied by the incompressibility constraint

Michal Zamboj

The Hopf fibration mapping circles on a 3-sphere to points on a 2-sphere is well known to topologists. While the 2-sphere is embedded in 3-space, four-dimensional Euclidean space is needed to visualize the 3-sphere. Visualizing objects in 4-space using computer graphics based on their analytical representations has become popular in recent decades. For purel

Zeyi Yang, Sheng Ge, Fang Wan, Yujia Liu

Robotic fingers made of soft material and compliant structures usually lead to superior adaptation when interacting with the unstructured physical environment. In this paper, we present an embedded sensing solution using optical fibers for an omni-adaptive soft robotic finger with exceptional adaptation in all directions. In particular, we managed to insert

E. T. Davletov, V. V. Tsyganok, V. A. Khlebnikov, D. A. Pershin

Bose-Einstein condensation (BEC) is a powerful tool for a wide range of research activities, a large fraction of which are related to quantum simulations. Various problems may benefit from different atomic species, but cooling down novel species interesting for quantum simulations to BEC temperatures requires a substantial amount of optimization and is usual

Dongchan Lee, Konstantin Turitsyn, Jean-Jacques Slotine

We present an algorithm for robust model predictive control with consideration of uncertainty and safety constraints. Our framework considers a nonlinear dynamical system subject to disturbances from an unknown but bounded uncertainty set. By viewing the system as a fixed point of an operator acting over trajectories, we propose a convex condition on control

Fang Wan, Haokun Wang, Jiyuan Wu, Yujia Liu

The engineering design of robotic grippers presents an ample design space for optimization towards robust grasping. In this paper, we adopt the reconfigurable design of the robotic gripper using a novel soft finger structure with omni-directional adaptation, which generates a large number of possible gripper configurations by rearranging these fingers. Such

Ergun Yukseltan, Ahmet Yucekaya, Ayse Humeyra Bilge, Esra Agca Aktunc

Due to expensive infrastructure and the difficulties in storage, supply conditions of natural gas are different from those of other traditional energy sources like petroleum or coal. To overcome these challenges, supplier countries require take-or-pay agreements for requested natural gas quantities. These contracts have many pre-clauses; if they are not met

Ruwan Wickramarachchi, Cory Henson, Amit Sheth

The autonomous driving (AD) industry is exploring the use of knowledge graphs (KGs) to manage the vast amount of heterogeneous data generated from vehicular sensors. The various types of equipped sensors include video, LIDAR and RADAR. Scene understanding is an important topic in AD which requires consideration of various aspects of a scene, such as detected

Sangdon Park, Osbert Bastani, James Weimer, Insup Lee

Reliable uncertainty estimates are an important tool for helping autonomous agents or human decision makers understand and leverage predictive models. However, existing approaches to estimating uncertainty largely ignore the possibility of covariate shift--i.e., where the real-world data distribution may differ from the training distribution. As a consequenc

Hongzhuo Liang, Chuangchuang Zhou, Shuang Li, Xiaojian Ma

Robust and accurate estimation of liquid height lies as an essential part of pouring tasks for service robots. However, vision-based methods often fail in occluded conditions while audio-based methods cannot work well in a noisy environment. We instead propose a multimodal pouring network (MP-Net) that is able to robustly predict liquid height by conditionin

Yuriy A. Spirichev

It is shown that invariants and relativistically invariant laws of conservation of physical quantities in Minkowski space follow from 4-tensors of the second rank, which are four-dimensional derivatives of 4-vectors, tensor products of 4-vectors and inner products of 4-tensors of the second rank. Two forms of the system of equations of conservation laws for

Raghav G. Jha

We consider one-plaquette unitary matrix model at finite $N$ using exact expression of the partition function for both SU($N$) and U($N$) groups.

Alon Z. Shapira, Hannes Uecker, Arik Yochelis

Stationary periodic patterns are widespread in natural sciences, ranging from nano-scale electrochemical and amphiphilic systems to mesoscale fluid, chemical and biological media and to macro-scale vegetation and cloud patterns. Their formation is usually due to a primary symmetry breaking of a uniform state to stripes, often followed by secondary instabilit

Mohammed Hichem Mortad

In this note, we give the most natural (perhaps the simplest ever) generalization of the Fuglede-Putnam theorem where all operators involved are unbounded.

Ahmed Saoudi

In this paper, we introduce the notion of Weinstein two-wavelet and we define the two-wavelet localization operators in the setting of the Weinstein theory. Then we give a host of sufficient conditions for the boundedness and compactness of the two-wavelet localization operator on $L^{p}_{\alpha}(\mathbb{R}^{d+1}_+)$ for all $1\leq p\leq \infty$, in terms of

Danijela Damjanovic, James Tanis

In this paper we prove a perturbative result for a class of $\mathbb Z^2$ actions on Heisenberg nilmanifolds, which have Diophantine properties. Along the way we prove cohomological rigidity and obtain a tame splitting for the cohomology with coefficients in smooth vector fields for such actions.

Eliya Nachmani, Yossi Adi, Lior Wolf

We present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the

Martin Bridgeman, Jeffrey Brock, Kenneth Bromberg

In this paper, we use the Weil-Petersson gradient flow for renormalized volume to study the space $CC(N;S,X)$ of convex cocompact hyperbolic structures on the relatively acylindrical 3-manifold $(N;S)$. Among the cases of interest are the deformation space of an acylindrical manifold and the Bers slice of quasi-Fuchsian space associated to a fixed surface. T

Shrinu Kushagra

The standard proof of NP-Hardness of 3DM provides a power-$4$ reduction of 3SAT to 3DM. In this note, we provide a linear-time reduction. Under the exponential time hypothesis, this reduction improves the runtime lower bound from $2^{o(\sqrt[4]{m})}$ (under the standard reduction) to $2^{o(m)}$.

Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge Belongie

Recent advances in deep representation learning on Riemannian manifolds extend classical deep learning operations to better capture the geometry of the manifold. One possible extension is the Fr\'echet mean, the generalization of the Euclidean mean; however, it has been difficult to apply because it lacks a closed form with an easily computable derivative. I

Nian Yao, Zhiqiu Li, Zhichao Ling, Junfeng Lin

In this paper, we study the asymptotic behaviors of implied volatility of an affine jump-diffusion model. Let log stock price under risk-neutral measure follow an affine jump-diffusion model, we show that an explicit form of moment generating function for log stock price can be obtained by solving a set of ordinary differential equations. A large-time large

Xinyang Zhou, Yue Chen, Zhiyuan Liu, Changhong Zhao

Solving optimal power flow (OPF) problems for large distribution networks incurs high computational complexity. We consider a large multi-phase distribution network of tree topology with a deep penetration of active devices. We divide the network into collaborating areas featuring subtree topology and subareas featuring subsubtree topology. We design a multi

Biagio Ricceri

Here is one of the results obtained in this paper: Let $\Omega\subset {\bf R}^n$ be a smooth bounded domain, let $q>1$, with $q<{{n+2}\over {n-2}}$ if $n\geq 3$ and let $\lambda_1$ be the first eigenvalue of the problem $$\cases{-\Delta u=\lambda u & in $\Omega$ \cr & \cr u=0 & on $\partial\Omega$\ .\cr}$$ Then, for every $\lambda>\lambda_1$ and for every co

Michael Whittaker, Neil Giridharan, Adriana Szekeres, Joseph M. Hellerstein

There is no shortage of state machine replication protocols. From Generalized Paxos to EPaxos, a huge number of replication protocols have been proposed that achieve high throughput and low latency. However, these protocols all have two problems. First, they do not scale. Many protocols actually slow down when you scale them, instead of speeding up. For exam

Luis C. Lamb, Artur Garcez, Marco Gori, Marcelo Prates

Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNN) have been widely used in relational and symbolic domains, with widespread application of GNNs in combinatorial optimization, constraint satisfaction, relational reasoning and other scientific domains. The need for

Edward Verenich, Alvaro Velasquez, M. G. Sarwar Murshed, Faraz Hussain

The integration of artificial intelligence capabilities into modern software systems is increasingly being simplified through the use of cloud-based machine learning services and representational state transfer architecture design. However, insufficient information regarding underlying model provenance and the lack of control over model evolution serve as an

Tiago Azevedo, Luca Passamonti, Pietro Liò, Nicola Toschi

The characterisation of the brain as a "connectome", in which the connections are represented by correlational values across timeseries and as summary measures derived from graph theory analyses, has been very popular in the last years. However, although this representation has advanced our understanding of the brain function, it may represent an oversimplif

Rashik Shadman, M. G. Sarwar Murshed, Edward Verenich, Alvaro Velasquez

The use of transfer learning with deep neural networks has increasingly become widespread for deploying well-tested computer vision systems to newer domains, especially those with limited datasets. We describe a transfer learning use case for a domain with a data-starved regime, having fewer than 100 labeled target samples. We evaluate the effectiveness of c

Gerhard Mayer

Mass spectrometry has experienced a rapid development since its first application for protein analysis in the 1980s. While the most common use of mass spectrometry for protein analysis is identification and quantification workflows on peptides (digested from their parent protein), there is also a rapidly growing use of mass spectrometry for structural proteo

M. G. Sarwar Murshed, Edward Verenich, James J. Carroll, Nazar Khan

Supermarkets need to ensure clean and safe environments for both shoppers and employees. Slips, trips, and falls can result in injuries that have a physical as well as financial cost. Timely detection of hazardous conditions such as spilled liquids or fallen items on supermarket floors can reduce the chances of serious injuries. This paper presents EdgeLite,

Gerhard Mayer

The structure of proteins is essential for its function. The determination of protein structures is possible by experimental or predicted by computational methods, but also a combination of both approaches is possible. Here, first an overview about experimental structure determination methods with their pros and cons is given. Then we describe how mass spect

Gerhard Mayer

First we shortly review the different kinds of network modelling methods for systems biology with an emphasis on the different subtypes of logical models, which we review in more detail. Then we show the advantages of Boolean networks models over more mechanistic modelling types like differential equation techniques. Then follows an overlook about connection

Marc Canellas, Rachel Haga

Walter Huang, a 38-year-old Apple Inc. engineer, died on March 23, 2018, after his Tesla Model X crashed into a highway barrier in Mountain View, California. Tesla immediately disavowed responsibility for the accident. "The fundamental premise of both moral and legal liability is a broken promise, and there was none here: [Mr. Huang] was well aware that the

Fabio Botelho

The first part of this article develops a variational formulation for relativistic mechanics. The results are established through standard tools of variational analysis and differential geometry. The novelty here is that the main motion manifold has a $n+1$ dimensional range. It is worth emphasizing in a first approximation we have neglected the self-interac

Matthew Macauley, Nora Youngs

Though it goes without saying that linear algebra is fundamental to mathematical biology, polynomial algebra is less visible. In this article, we will give a brief tour of four diverse biological problems where multivariate polynomials play a central role -- a subfield that is sometimes called "algebraic biology." Namely, these topics include biochemical rea

Dejan Govc, Ran Levi, Jason P. Smith

Complete digraphs are referred to in the combinatorics literature as tournaments. We consider a family of semi-simplicial complexes, that we refer to as "tournaplexes", whose simplices are tournaments. In particular, given a digraph $\mathcal{G}$, we associate with it a "flag tournaplex" which is a tournaplex containing the directed flag complex of $\mathcal

Pieter Verding, Wim Deferme, Werner Steffen

We have developed a combination of light scattering techniques to study and characterize droplets of a ultrasonic spray printer or coater in flight. For this economically relevant printer there is so far no reliable technique to systematically adjust the experimental parameters. We have combined photon correlation spectroscopy and turbidimetry to determine s

P. Y. Wen, O. V. Ivakhnenko, M. A. Nakonechnyi, B. Suri

We investigate the Landau-Zener-Stuckelberg-Majorana interferometry of a superconducting qubit in a semi-infinite transmission line terminated by a mirror. The transmon-type qubit is at the node of the resonant electromagnetic (EM) field, hiding from the EM field. "Mirror, mirror" briefly describes this system, because the qubit acts as another mirror. We mo

Qingjie Meng, Daniel Rueckert, Bernhard Kainz

Deep learning models exhibit limited generalizability across different domains. Specifically, transferring knowledge from available entangled domain features(source/target domain) and categorical features to new unseen categorical features in a target domain is an interesting and difficult problem that is rarely discussed in the current literature. This prob

Tao Wang, Zheng Gong, Alexey Arefiev

A laser-driven azimuthal plasma magnetic field is known to facilitate electron energy gain from the irradiating laser pulse. The enhancement is due to changes in the orientation between the laser electric field and electron velocity caused by magnetic field deflections. Transverse electron confinement is critical for realizing this concept experimentally. We

Mark Kamsma

We introduce the framework of AECats (abstract elementary categories), generalising both the category of models of some first-order theory and the category of subsets of models. Any AEC and any compact abstract theory ("cat", as introduced by Ben-Yaacov) forms an AECat. In particular, we find applications in positive logic and continuous logic: the category

Djordje Minic

We review our recent work on quantum foundations of quantum mechanics, quantum field theory and quantum gravity (formulated as metastring theory) and various implications for the problems of dark matter and dark energy. The first point concerns the new understanding of quantum theory via the concept of quantum (modular) spacetime endowed with manifest non-lo

Mohammadreza F. Imani, David R. Smith, Philipp del Hougne

Achieving the very special condition of perfect absorption (PA) in a complex scattering enclosure promises to enable a wealth of applications in secure communication, precision sensing, wireless power transfer, analog signal processing and random lasing. Consequently, a lot of recent research effort was dedicated to proposing wave-front shaping protocols to

Ziping Liu, Joyce C. Mhlanga, Richard Laforest, Paul-Robert Derenoncourt

Tumor segmentation in oncological PET is challenging, a major reason being the partial-volume effects that arise due to low system resolution and finite voxel size. The latter results in tissue-fraction effects, i.e. voxels contain a mixture of tissue classes. Conventional segmentation methods are typically designed to assign each voxel in the image as belon

Mohsen Sadatsafavi, Paramita Saha-Chaudhuri, John Petkau

The performance of risk prediction models is often characterized in terms of discrimination and calibration. The Receiver Operating Characteristic (ROC) curve is widely used for evaluating model discrimination. When evaluating the performance of a risk prediction model in a new sample, the shape of the ROC curve is affected by both case-mix and the postulate

Jiaju Zhang, Pasquale Calabrese, Marcello Dalmonte, M. A. Rajabpour

We carry out a comprehensive comparison between the exact modular Hamiltonian and the lattice version of the Bisognano-Wichmann (BW) one in one-dimensional critical quantum spin chains. As a warm-up, we first illustrate how the trace distance provides a more informative mean of comparison between reduced density matrices when compared to any other Schatten $

J. Maurice Rojas, Yuyu Zhu

We reveal a complexity chasm, separating the trinomial and tetranomial cases, for solving univariate sparse polynomial equations over certain local fields. First, for any fixed field $K\in\{\mathbb{Q}_2,\mathbb{Q}_3,\mathbb{Q}_5,\ldots\}$, we prove that any polynomial $f\in\mathbb{Z}[x]$ with exactly $3$ monomial terms, degree $d$, and all coefficients havin

Perry T. Mahon, J. E. Sipe

We extend a field theoretic approach for the investigation of the electronic charge-current density response of crystalline systems to arbitrary applied electromagnetic fields. The approach leads to the introduction of microscopic polarization and magnetization fields, as well as free charge and current densities, the dynamics of which are described by a lat

Fabio Pusateri, Avy Soffer

We propose an approach to nonlinear evolution equations with large and decaying external potentials that addresses the question of controlling globally-in-time the nonlinear interactions of localized waves in this setting. This problem arises when studying localized perturbations around (possibly non-decaying) special solutions of evolution PDEs, and trying

Lawrence Reeves, Peter Scott, Gadde Swarup

Analogues of JSJ decompositions were developed for Poincar\'e duality pairs in [19]. These decompositions depend only on the group. Our focus will be on describing the edge splittings of these decompositions more precisely. We use our results to compare these decompositions with two other closely related decompositions.

Andrey V. Panov

The nonlinear optical properties of the high index nanoparticles are boosted at the anapole state. Researchers intensively study this phenomenon as promising for various applications. In this work, the nonlinear optical Kerr effect of disordered and square lattice metasurfaces of GaP nanodisks is investigated as a function of the disk size in the vicinity of

Yanan Li, Nathaniel P. Smith, William Rexhausen, Marvin A Schofield

Metal intercalation into layered topological insulator materials such as the binary chalcogenide Bi2X3 (X=Te or Se) has yielded novel two-dimensional electron-gas physics, phase transitions to superconductivity, as well as interesting magnetic ground states. Of recent interest is the intercalation-driven interplay between lattice distortions, density wave or

Ibrahim Jawarneh, Nesreen Alsharman

In this paper, different categories of the arch fingerprint are set up in a general dynamical system model using ordinary differential equations. We study its global dynamics and analyze the existence and stability of equilibria. Numerical simulations using Maple show the matching between real images of categories of arch fingerprint and phase portraits of t

Chaoyue Liu, Libin Zhu, Mikhail Belkin

The success of deep learning is due, to a large extent, to the remarkable effectiveness of gradient-based optimization methods applied to large neural networks. The purpose of this work is to propose a modern view and a general mathematical framework for loss landscapes and efficient optimization in over-parameterized machine learning models and systems of n

Boris Muzellec, Kanji Sato, Mathurin Massias, Taiji Suzuki

Gradient Langevin dynamics (GLD) and stochastic GLD (SGLD) have attracted considerable attention lately, as a way to provide convergence guarantees in a non-convex setting. However, the known rates grow exponentially with the dimension of the space. In this work, we provide a convergence analysis of GLD and SGLD when the optimization space is an infinite dim

Duanbing Chen, Tao Zhou

We proposed a Monte-Carlo method to estimate temporal reproduction number without complete information about symptom onsets of all cases. Province-level analysis demonstrated the huge success of Chinese control measures on COVID-19, that is, provinces' reproduction numbers quickly decrease to <1 by just one week after taking actions.

Woojay Jeon, Leo Liu, Henry Mason

We propose a method to reduce false voice triggers of a speech-enabled personal assistant by post-processing the hypothesis lattice of a server-side large-vocabulary continuous speech recognizer (LVCSR) via a neural network. We first discuss how an estimate of the posterior probability of the trigger phrase can be obtained from the hypothesis lattice using k

Shengyu Zhang, Tan Jiang, Qinghao Huang, Ziqi Tan

In this paper, we present an approach, namely Lexical Semantic Image Completion (LSIC), that may have potential applications in art, design, and heritage conservation, among several others. Existing image completion procedure is highly subjective by considering only visual context, which may trigger unpredictable results which are plausible but not faithful

Berker Peköz, Mohammed Hafez, Selçuk Köse, Hüseyin Arslan

Practical multiple-input-multiple-output (MIMO) systems depend on a predefined set of precoders to provide spatial multiplexing gain. This limitation on the flexibility of the precoders affects the overall performance. Here, we propose a transmission scheme that can reduce the effect of mismatch between users' channels and precoders. The scheme uses the chan

Gautam Krishna, Co Tran, Mason Carnahan, Yan Han

In this paper we demonstrate predicting electroencephalograpgy (EEG) features from acoustic features using recurrent neural network (RNN) based regression model and generative adversarial network (GAN). We predict various types of EEG features from acoustic features. We compare our results with the previously studied problem on speech synthesis using EEG and

Massimo Bartoletti, Maurizio Murgia, Roberto Zunino

BitML is a process calculus to express smart contracts that can be run on Bitcoin. One of its current limitations is that, once a contract has been stipulated, the participants cannot renegotiate its terms: this prevents expressing common financial contracts, where funds have to be added by participants at run-time. In this paper, we extend BitML with a new

Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett

Federated learning is a distributed machine learning paradigm in which a large number of clients coordinate with a central server to learn a model without sharing their own training data. Standard federated optimization methods such as Federated Averaging (FedAvg) are often difficult to tune and exhibit unfavorable convergence behavior. In non-federated sett

Suat Mercan, Enes Erdin, Kemal Akkaya

Blockchain-based cryptocurrencies received a lot of attention recently for their applications in many domains. IoT domain is one of such applications, which can utilize cryptocur-rencies for micro payments without compromising their payment privacy. However, long confirmation times of transactions and relatively high fees hinder the adoption of cryptoccurenc

Paul Irofti, Andra Băltoiu

We investigate the possibilities of employing dictionary learning to address the requirements of most anomaly detection applications, such as absence of supervision, online formulations, low false positive rates. We present new results of our recent semi-supervised online algorithm, TODDLeR, on a anti-money laundering application. We also introduce a novel u

Pantelis Sopasakis, Emil Fresk, Panagiotis Patrinos

We present Optimization Engine (OpEn): an open-source code generation tool for real-time embedded nonconvex optimization, which implements a novel numerical method. OpEn combines the proximal averaged Newton-type method for optimal control (PANOC) with the penalty and augmented Lagrangian methods to compute approximate stationary points of nonconvex problems

Ki-Seok Kim

We derive an effective dual holographic Einstein-Maxwell theory, applying renormalization group transformations to interacting Dirac fermions in a recursive way. In particular, we show how both background metric tensor and U(1) gauge fields become dynamical to describe the renormalization group flows of coupling functions and order parameter fields of the co

Gus Smith, Zachary Tatlock, Luis Ceze

A core problem in hardware-software codesign is in the sheer size of the design space. Without a set ISA to constrain the hardware-software interface, the design space explodes. This work presents a strategy for managing the massive hardware-software design space within the domain of machine learning inference workloads and accelerators. We first propose Eng

David R. Patton, Kieran D. Wilson, Colin J. Metrow, Sara L. Ellison

We use the IllustrisTNG cosmological hydrodynamical simulations to investigate how the specific star formation rates (sSFRs) of massive galaxies $(M_* > 10^{10} M_\odot)$ depend on the distance to their closest companions. We estimate sSFR enhancements by comparing with control samples that are matched in redshift, stellar mass, local density and isolation,

Alessandro Di Siena, Alejandro Banon Navarro, Frank Jenko

Turbulent transport is known to limit the plasma confinement of present-day optimized stellarators. To address this issue, a novel method to strongly suppress turbulence in such devices is proposed, namely the resonant wave-particle interaction of supra-thermal particles - e.g., from ion-cyclotron-resonance-frequency (ICRF) heating - with turbulence-driving

Xiaoyang Guo, Anuj Srivastava

Past approaches for statistical shape analysis of objects have focused mainly on objects within the same topological classes, e.g., scalar functions, Euclidean curves, or surfaces, etc. For objects that differ in more complex ways, the current literature offers only topological methods. This paper introduces a far-reaching geometric approach for analyzing sh

Zack Carson, Kent Yagi

Gravitational waves from extreme gravity events such as the coalescence of two black holes in a binary system fill our observable universe, bearing with them the underlying theory of gravity driving their process. One compelling alternative theory of gravity -- known as Einstein-dilaton Gauss-Bonnet gravity motivated by string theory -- describes the presenc

David Arnas

This work introduces a linearized analytical model for the study of the dynamic of satellites in near circular orbits under the effects of the atmospheric drag. This includes the evaluation of the station keeping required for each satellite subjected to a control box strategy, and also the study of the dynamic of tandem formations between two or more satelli

Congcong Li, Peng Shi, Luping Du, Xiaocong Yuan

Optical spin angular momenta in a confined electromagnetic field exhibit remarkable difference with their free space counterparts, in particular, the optical transverse spin that is locked with the energy propagating direction lays the foundation for many intriguing physical effects such as unidirectional transportation, quantum spin Hall effect, photonic Sk

Stavros Garoufalidis, Roland van der Veen

We identify the q-series associated to an 1-efficient ideal triangulation of a cusped hyperbolic 3-manifold by Frohman and Kania-Bartoszynska with the 3D-index of Dimofte-Gaiotto-Gukov. This implies the topological invariance of the $q$-series of Frohman and Kania-Bartoszynska for cusped hyperbolic 3-manifolds. Conversely, we identify the tetrahedron index o

Dian-Wu Yue, Ha H. Nguyen, Yu Sun

As a means to control wireless propagation environments, the use of emerging and novel intelligent reflecting surfaces (IRS) is envisioned to enhance and broaden many applications in future wireless networks. This paper is concerned with a point-to-point IRS-assisted millimeter-wave (mmWave) system in which the IRS consists of multiple subsurfaces, each havi