April 2019 arXiv papers — page 92

Hannah M. Christensen

Stochastic parametrisations are used in weather and climate models to improve the representation of unpredictable unresolved processes. When compared to a deterministic model, a stochastic model represents `model uncertainty', i.e., sources of error in the forecast due to the limitations of the forecast model. We present a technique for systematically de

Zijian Zhao, Su Zhu, Kai Yu

Spoken language understanding (SLU) systems can be trained on two types of labelled data: aligned or unaligned. Unaligned data do not require word by word annotation and is easier to be obtained. In the paper, we focus on spoken language understanding from unaligned data whose annotation is a set of act-slot-value triples. Previous works usually focus on imp

Stephan Amann, Max von Witzleben, and Stefan Breuer

Digital holographic microscopy is an emerging potentially low-cost alternative to conventional light microscopy for micro-object imaging on earth, underwater and in space. Immediate access to micron-scale objects however requires a well-balanced system design and sophisticated reconstruction algorithms, that are commercially available, however not accessible

A. F. Lanza, Y. Netto, A. S. Bonomo, H. Parviainen

The study of young Sun-like stars is of fundamental importance to understand the magnetic activity and rotational evolution of the Sun. Space-borne photometry by the Kepler telescope provides unprecedented datasets to investigate these phenomena in Sun-like stars. We present a new analysis of the entire Kepler photometric time series of the moderately young

Raffael Hickisch, Timothy Hodgetts, Paul J. Johnson, Claudio Sillero

Conservation planning needs reliable information on spatial patterns of biodiversity. However, existing data sets are skewed: some habitats, taxa, and locations are under-represented. Here, we map geographic publication density at the sub-national scale of individual 'provinces'. We query the Web of Science catalogues SCI and SSCI for biodiversity-re

Jiang Zhu

The recent work `A unified Bayesian inference framework for generalized linear models' \cite{meng1} shows that the GLM can be solved via iterating between the standard linear module (SLM) (running with standard Bayesian algorithm) and the minimum mean squared error (MMSE) module. The proposed framework utilizes expectation propagation and corresponds to

Byung-Yoon Park, Won-Gi Paeng, Vicente Vento

It was predicted qualitatively in ref.[1] that skyrmion matter at low density is stable in an inhomogeneous phase where skyrmions condensate into lumps while the remaining space is mostly empty. The aim of this paper is to proof quantitatively this prediction. In order to construct an inhomogeneous medium we distort the original FCC crystal to produce a phas

Fanzi Wu, Linchao Bao, Yajing Chen, Yonggen Ling

We address the problem of recovering the 3D geometry of a human face from a set of facial images in multiple views. While recent studies have shown impressive progress in 3D Morphable Model (3DMM) based facial reconstruction, the settings are mostly restricted to a single view. There is an inherent drawback in the single-view setting: the lack of reliable 3D

Deekshith P K, K Gautam Shenoy, Vinod Sharma

We consider a block fading additive white Gaussian noise (AWGN) channel with perfect channel state information (CSI) at the transmitter and the receiver. First, for a given codeword length and non-vanishing average probability of error, we obtain lower and upper bounds on the maximum transmission rate. We derive bounds for three kinds of power constraints in

Zhengjie Miao, Sudeepa Roy, Jun Yang

For testing the correctness of SQL queries, e.g., evaluating student submissions in a database course, a standard practice is to execute the query in question on some test database instance and compare its result with that of the correct query. Given two queries $Q_1$ and $Q_2$, we say that a database instance $D$ is a counterexample (for $Q_1$ and $Q_2$) if

Zhaorong Zhang, Minyue Fu

This paper studies the convergence properties the well-known message-passing algorithm for convex optimisation. Under the assumption of pairwise separability and scaled diagonal dominance, asymptotic convergence is established and a simple bound for the convergence rate is provided for message-passing. In comparison with previous results, our results do not

Zeyuan Wang, Josiah Poon, Shiding Sun, Simon Poon

One way to extract patterns from clinical records is to consider each patient record as a bag with various number of instances in the form of symptoms. Medical diagnosis is to discover informative ones first and then map them to one or more diseases. In many cases, patients are represented as vectors in some feature space and a classifier is applied after to

Alireza Ebrahimvandi, Niyousha Hosseinichimeh

Preterm birth rate (PBR) stands out as a major public health concern in the U.S. However, effective policies for mitigating the problem is largely unknown. The complexities of the problem raise critical questions: Why is PBR increasing despite the massive investment for reducing it? What policies can decrease it? To address these questions, we develop a caus

Chengliang Lin

Theoretical modelling of ionization potential depression and the related ionization equilibrium in dense plasmas, in particular in warm/hot dense matter, represents a significant challenge due to ionic coupling and electronic degeneracy effects. We present a quantum statistical model based on dynamical structure factors for the ionization potential depressio

Jin Hu, Su-Yang Xu, Ni Ni, Zhiqiang Mao

Three-dimensional (3D) topological semimetals represent a new class of topological matters. The study of this family of materials has been at the frontiers of condensed matter physics, and many breakthroughs have been made. Several topological semimetal phases, including Dirac semimetals (DSMs), Weyl semimetals (WSMs), nodal-line semimetals (NLSMs), and trip

Guangting Wang, Chong Luo, Zhiwei Xiong, Wenjun Zeng

The greatest challenge facing visual object tracking is the simultaneous requirements on robustness and discrimination power. In this paper, we propose a SiamFC-based tracker, named SPM-Tracker, to tackle this challenge. The basic idea is to address the two requirements in two separate matching stages. Robustness is strengthened in the coarse matching (CM) s

Jacopo Simoni, Jérôme Daligault

We present first-principles calculations of the rate of energy exchanges between electrons and ions in nonequilibrium warm dense plasmas, liquid metals and hot solids, a fundamental property for which various models offer diverging predictions. To this end, a Kubo relation for the electron-ion coupling parameter is introduced, which includes self-consistentl

Zhou Wei, Zhichun Yang, Jen-Chih Yao

In classical analysis, Lebesgue first proved that $\mathbb{R}$ has the property that each Riemann integrable function from $[a,b]$ into $\mathbb{R}$ is continuous almost everywhere. This property is named as the Lebesgue property. Though the Lebesgue property may be breakdown in many infinite dimensional spaces including Banach or quasi Banach spaces, to det

Bin Liu, Ruiming Tang, Yingzhi Chen, Jinkai Yu

Click-Through Rate prediction is an important task in recommender systems, which aims to estimate the probability of a user to click on a given item. Recently, many deep models have been proposed to learn low-order and high-order feature interactions from original features. However, since useful interactions are always sparse, it is difficult for DNN to lear

Wenxiang Jiao, Haiqin Yang, Irwin King, Michael R. Lyu

In this paper, we address three challenges in utterance-level emotion recognition in dialogue systems: (1) the same word can deliver different emotions in different contexts; (2) some emotions are rarely seen in general dialogues; (3) long-range contextual information is hard to be effectively captured. We therefore propose a hierarchical Gated Recurrent Uni

Z. Araghi Rostami, M. Parvizi, P. Niroomand

In this paper we extend the notion of CP covers for groups to the field of Lie algebras, and show that despite the case of groups, all CP covers of a Lie algebra are isomorphic. Finally we show that CP covers of groups and Lie rings which are in Lazard correspondence, are in Lazard correspondence too, and the Bogomolov multipliers are isomorphic.

Hui Zeng, Lida Li, Zisheng Cao, Lei Zhang

Image cropping aims to improve the composition as well as aesthetic quality of an image by removing extraneous content from it. Existing image cropping databases provide only one or several human-annotated bounding boxes as the groundtruth, which cannot reflect the non-uniqueness and flexibility of image cropping in practice. The employed evaluation metrics

Yun-Wei Yu, Aming Chen, Zi-Gao Dai, Shao-Ze Li

The formation of neutron stars (NSs), both from collapses of massive stars and mergers of compact objects, can be usually indicated by bright transients emitted from explosively-ejected material. In particular, if the newborn NSs can rotate at a millisecond period and have a sufficiently high magnetic field, then the spin-down of the NSs would provide a rema

M. Paicu, P. Zhang, Z. Zhang

In this paper, we first prove the global well-posedness of a scaled anisotropic Navier-Stokes system and the hydrostatic Navier-Stokes system in a 2-D striped domain with small analytic data in the tangential variable. Then we justify the limit from the anisotropic Navier-Stokes system to the hydrostatic Navier-Stokes system with analytic data.

Xiaoya Zhang, Hui Zhang, Wei Peng

In this paper we study the problems of minimizing the sum of two nonconvex functions: one is differentiable and satisfies smooth adaptable property. The smooth adaptable property, also named relatively smooth condition, is weaker than the globally gradient Lipschitz continuity. We analyze an inertial version of the Bregman Proximal Gradient (BPG) algorithm a

Jinsheng Wen, Shun-Li Yu, Shiyan Li, Weiqiang Yu

In condensed matter physics, there is a novel phase termed "quantum spin liquid", in which strong quantum fluctuations prevent the long-range magnetic order from being established, and so the electron spins do not form an ordered pattern but remain "liquid" like even at absolute zero temperature. Such a phase is not involved with any spontane

Yinpeng Dong, Hang Su, Baoyuan Wu, Zhifeng Li

Face recognition has obtained remarkable progress in recent years due to the great improvement of deep convolutional neural networks (CNNs). However, deep CNNs are vulnerable to adversarial examples, which can cause fateful consequences in real-world face recognition applications with security-sensitive purposes. Adversarial attacks are widely studied as the

Xiaoyu Chen, Shugong Xu, Xudong Chen, Shan Cao

Transmission control protocol (TCP) congestion control is one of the key techniques to improve network performance. TCP congestion control algorithm identification (TCP identification) can be used to significantly improve network efficiency. Existing TCP identification methods can only be applied to limited number of TCP congestion control algorithms and foc

Chaojing Duan, Siheng Chen, Jelena Kovacevic

We present a neural-network-based architecture for 3D point cloud denoising called neural projection denoising (NPD). In our previous work, we proposed a two-stage denoising algorithm, which first estimates reference planes and follows by projecting noisy points to estimated reference planes. Since the estimated reference planes are inevitably noisy, multi-p

Jun-Jie Jin, Yi-Nan Zhu, Xian-Min Meng, Feng-Jie Lei

We investigate the relationship between host galaxies&#39; stellar content and active galactic nuclei (AGN) for optically selected QSOs with z$<$0.5. There are total 82 QSOs we select from Sloan Digital Sky Survey (SDSS) . These 82 QSOs both have Wide-field Infrared Survey Explorer (WISE) data and measurable stellar content. With the help of the stellar popu

Yu Zhang, Qian Yang, Lin He

Phonons, the fundamental vibrational modes of a crystal lattice, play a crucial role in determining electronic properties of materials through electron-phonon interaction. However, it has proved difficult to directly probe the phonon modes of materials in electrical measurements. Here, we report the observation of giant quantized phonon peaks of the K and K

Masahiro N. Machida, Shantanu Basu

Starting from a prestellar core with a size of $1.2\times10^4$ AU, we calculate the evolution of a gravitationally collapsing core until $\sim2000$ yr after protostar formation using a three-dimensional resistive magnetohydrodynamic simulation, in which the protostar is resolved with a spatial resolution of $5.6\times10^{-3}$ AU. Following protostar formatio

Maria S. Kirsanova, Dmitri S. Wiebe

High-resolution ALMA images towards the Orion Bar show no discernible offset between the peak of H2 emission in the photodissociation region (PDR) and the CO(3-2) and HCO+(4-3) emission in the molecular region. This implies that positions of H2 and CO dissociation fronts are indistinguishable in the limit of ALMA resolution. We use the chemo-dynamical model

Cong Hao, Xiaofan Zhang, Yuhong Li, Sitao Huang

While embedded FPGAs are attractive platforms for DNN acceleration on edge-devices due to their low latency and high energy efficiency, the scarcity of resources of edge-scale FPGA devices also makes it challenging for DNN deployment. In this paper, we propose a simultaneous FPGA/DNN co-design methodology with both bottom-up and top-down approaches: a bottom

Tingfung Lau, Nathan Ng, Julian Gingold, Nina Desai

To optimize clinical outcomes, fertility clinics must strategically select which embryos to transfer. Common selection heuristics are formulas expressed in terms of the durations required to reach various developmental milestones, quantities historically annotated manually by experienced embryologists based on time-lapse EmbryoScope videos. We propose a new

Yixin Guo, Haozhao Liang

By following the conventional similarity renormalization group (SRG) expansion of the Dirac equation developed in [J.-Y. Guo, Phys. Rev. C \textbf{85}, 021302 (2012)], we work out the analytic expression of the ${1}/{M^4}$ order and verify the convergence of this method. As a step further, the reconstituted SRG method is proposed by using the re-summation te

Angelo Bella, Alan Dow, Rodrigo Hernández-Gutiérrez

In this paper we compare the concepts of pseudoradial spaces and the recently defined strongly pseudoradial spaces in the realm of compact spaces. We show that $\mathrm{MA}+\mathfrak{c}=ω_2$ implies that there is a compact pseudoradial space that is not strongly pseudoradial. We essentially construct a compact, sequentially compact space $X$ and a continuous

Zhigang Song, Weijun Fan, Chuanseng Tan, Qijie Wang

A full-zone 30-band $k$$\cdot$$p$ model is developed as an efficient and reliable tool to compute electronic band structure in Ge$_{1-x}$Sn$_{x}$ alloy. The model was first used to reproduce the electronic band structures in Ge and $α$-Sn obtained with empirical tight binding and \textit{ab initio} methods. Input parameters for the 30-band $k$$\cdot$$p$ mode

Palle Jorgensen, Myung-Sin Song, Feng Tian

We show that an idea, originating initially with a fundamental recursive iteration scheme (usually referred as &#34;the&#34; Kaczmarz algorithm), admits important applications in such infinite-dimensional, and non-commutative, settings as are central to spectral theory of operators in Hilbert space, to optimization, to large sparse systems, to iterated funct

Bas Peters, Eldad Haber, Justin Granek

Geological interpretation of seismic images is a visual task that can be automated by training neural networks. While neural networks have shown to be effective at various interpretation tasks, a fundamental challenge is the lack of labeled data points in the subsurface. For example, the interpolation and extrapolation of well-based lithology using seismic i

David S. Dummit, Hershy Kisilevsky

We consider the signature rank of the units in real multiquadratic fields. When the three quadratic subfields of a real biquadratic field $K$ either (a) all have signature rank 2 (that is, fundamental units of norm $-1$), or (b) all have signature rank 1 (that is, have totally positive fundamental units), we provide explicit examples to show there exist infi

Dong-O Kang, Eungil Ko, Ji Eun Lee

In this paper, we study complex symmetry of Toeplitz operators and block Toeplitz operators. In particular, we give a characterization of complex symmetric block Toeplitz operators with the special conjugation on the vector-valued Hardy space $H_{{\mathbb C}^2}^2$. As some applications, we provide examples of such operators.

Warin Jetsadawisut, Sunan Kitjaruwankul, Panisak Boonamnaj, Pornthep Sompornpisut

Using a coarse-grained model, self-organized assembly of proteins (e.g. CorA and its inner segment iCorA) is studied by examining quantities such as contact profile, radius of gyration, and structure factor as a function of protein concentration at a range of low (native phase) to high (denature phase) temperatures. Visual inspections show distinct structure

Chitraang Murdia, Yasunori Nomura, Pratik Rath, Nico Salzetta

The Ryu-Takayanagi (RT) formula has been a key ingredient in our understanding of holography. Recent work on TT deformations has also boosted our understanding of holography away from the conformal boundary of AdS. In this short note, we aim to refine some recent work demonstrating the success of the RT formula in TT deformed theories. We emphasize general a

Ngoc Truong, Adam A. Monroe, Christopher R. Glein, Ariel D. Anbar

To test the potential of using amino acid abundances as a biosignature at icy ocean worlds, we investigate whether primordial amino acids (accreted or formed by early aqueous processes) could persist until the present time. By examining the decomposition kinetics of amino acids in aqueous solution based on existing laboratory rate data, we find that all four

Mahmudul Hasan, Sujoy Paul, Anastasios I. Mourikis, Amit K. Roy-Chowdhury

Activity recognition is a challenging problem with many practical applications. In addition to the visual features, recent approaches have benefited from the use of context, e.g., inter-relationships among the activities and objects. However, these approaches require data to be labeled, entirely available beforehand, and not designed to be updated continuous

Jianwei Yang, Zhile Ren, Mingze Xu, Xinlei Chen

Passive visual systems typically fail to recognize objects in the amodal setting where they are heavily occluded. In contrast, humans and other embodied agents have the ability to move in the environment, and actively control the viewing angle to better understand object shapes and semantics. In this work, we introduce the task of Embodied Visual Recognition

Nikolaj Tatti

Discovering the underlying structure of a given graph is one of the fundamental goals in graph mining. Given a graph, we can often order vertices in a way that neighboring vertices have a higher probability of being connected to each other. This implies that the edges form a band around the diagonal in the adjacency matrix. Such structure may rise for exampl

Moonjeong Park, Jun-Gi Jang, Lee Sael

Given a large tensor, how can we decompose it to sparse core tensor and factor matrices such that it is easier to interpret the results? How can we do this without reducing the accuracy? Existing approaches either output dense results or give low accuracy. In this paper, we propose VeST, a tensor factorization method for partially observable data to output a

Ran Xin, Anit Kumar Sahu, Usman A. Khan, Soummya Kar

In this paper, we study distributed stochastic optimization to minimize a sum of smooth and strongly-convex local cost functions over a network of agents, communicating over a strongly-connected graph. Assuming that each agent has access to a stochastic first-order oracle ($\mathcal{SFO}$), we propose a novel distributed method, called $\mathcal{S}$-$\mathca

Marko Jereminov, Bryan Hooi, Amritanshu Pandey, Hyun-Ah Song

Aggregated load models, such as PQ and ZIP, are used to represent the approximated load demand at specific buses in grid simulation and optimization problems. In this paper we examine the impact of model choice on the optimal power flow solution and demonstrate that it is possible for different load models to represent the same amount of real and reactive po

Danny Hermes

In computer aided geometric design a polynomial is usually represented in Bernstein form. This paper presents a family of compensated algorithms to accurately evaluate a polynomial in Bernstein form with floating point coefficients. The principle is to apply error-free transformations to improve the traditional de Casteljau algorithm. At each stage of comput

Roland Pulch, Florian Augustin

In uncertainty quantification, critical parameters of mathematical models are substituted by random variables. We consider dynamical systems composed of ordinary differential equations. The unknown solution is expanded into an orthogonal basis of the random space, e.g., the polynomial chaos expansions. A Galerkin method yields a numerical solution of the sto

Sebastian Krämer

Tree tensor networks such as the tensor train format are a common tool for high dimensional problems. The associated multivariate rank and accordant tuples of singular values are based on different matricizations of the same tensor. While the behavior of such is as essential as in the matrix case, here the question about the $\textit{feasibility}$ of specifi

Lars Grasedyck, Sebastian Krämer

Low rank tensor completion is a highly ill-posed inverse problem, particularly when the data model is not accurate, and some sort of regularization is required in order to solve it. In this article we focus on the calibration of the data model. For alternating optimization, we observe that existing rank adaption methods do not enable a continuous transition

Sonja Cox, Martin Hutzenthaler, Arnulf Jentzen, Jan van Neerven

We show that if a sequence of piecewise affine linear processes converges in the strong sense with a positive rate to a stochastic process which is strongly Hölder continuous in time, then this sequence converges in the strong sense even with respect to much stronger Hölder norms and the convergence rate is essentially reduced by the Hölder exponent. Our fir

Felipe S. Abrahão, Ítala M. Loffredo D&#39;Ottaviano, Klaus Wehmuth, Francisco Antônio Dória

This article presents a theoretical investigation of computation beyond the Turing barrier from emergent behavior in distributed systems. In particular, we present an algorithmic network that is a mathematical model of a networked population of randomly generated computable systems with a fixed communication protocol. Then, in order to solve an undecidable p

Jeremy Kepner, Kenjiro Cho, KC Claffy, Vijay Gadepally

The Internet is transforming our society, necessitating a quantitative understanding of Internet traffic. Our team collects and curates the largest publicly available Internet traffic data containing 50 billion packets. Utilizing a novel hypersparse neural network analysis of &#34;video&#34; streams of this traffic using 10,000 processors in the MIT SuperClo

Dawei Gao, Qinghua Guo, Jun Tong, Nan Wu

This work concerns receiver design for light emitting diode (LED) communications where the LED nonlinearity can severely degrade the performance of communications. We propose extreme learning machine (ELM) based non-iterative receivers and iterative receivers to effectively handle the LED nonlinearity and memory effects. For the iterative receiver design, we

A. Arroyo Garcia, M. Hentschinski, K. Kutak

We investigate photo-production of vector mesons J/Psi and Upsilon measured both at HERA and LHC, using 2 particular fits of inclusive unintegrated gluon distributions, based on non-linear Balitsky-Kovchegov evolution (Kutak-Sapeta gluon; KS) and next-to-leading order Balitsky-Fadin-Kuraev-Lipatov evolution (Hentschinski-Sabio Vera-Salas gluon; HSS). We find

George Fulton, Artem Lunev

Tungsten, a plasma-facing material for future fusion reactors, may be exposed to air during abnormal operation or accidents. Only limited information is available on the evolution of related oxide phases. This work addresses the effect of substrate orientation on structural variations of tungsten oxides. Annealing experiments in an argon-oxygen atmosphere ha

Yue Hu, Ka Ho Yuen, A. Lazarian, Laura M. Fissel

Probing magnetic fields in Giant Molecular Clouds is often challenging. Fortunately, recently simulations show that analysis of velocity gradients (the Velocity Gradient Technique) can be used to map out the magnetic field morphology of different physical layers within molecular clouds when applied CO isotopologues with different optical depths. Here, we tes

Max D. Porter, Aaron Barr, Ariel Barr, L. E. Reichl

Systems with space-periodic Hamiltonians have unique scattering properties. The discrete translational symmetry associated with periodicity of the Hamiltonian creates scattering channels that govern the scattering process. We consider a two-dimensional scattering system in which one dimension is a periodic lattice and the other is localized in space. The sca

Trevor K. Karn, Steven Petrone, Christopher Griffin

In this paper we develop a kernel density estimation (KDE) approach to modeling and forecasting recurrent trajectories on a compact manifold. For the purposes of this paper, a trajectory is a sequence of coordinates in a phase space defined by an underlying hidden dynamical system. Our work is inspired by earlier work on the use of KDE to detect shipping ano

Laurent Freidel, Florian Hopfmüller, Aldo Riello

We present a systematic procedure to renormalize the symplectic potential of the electromagnetic field at null infinity in Minkowski space. We work in $D\geq6$ spacetime dimensions as a toy model of General Relativity in $D\geq4$ dimensions. Total variation counterterms as well as corner counterterms are both subtracted from the symplectic potential to make

L. D. Edholm, J. D. McNeal

Sobolev irregularity of the Bergman projection on a family of domains containing the Hartogs triangle is shown. On the Hartogs triangle itself, a sub-Bergman projection is shown to satisfy better Sobolev norm estimates than its Bergman projection.

Jiaxuan You, Yichen Wang, Aditya Pal, Pong Eksombatchai

Recommender systems that can learn from cross-session data to dynamically predict the next item a user will choose are crucial for online platforms. However, existing approaches often use out-of-the-box sequence models which are limited by speed and memory consumption, are often infeasible for production environments, and usually do not incorporate cross-ses

Yuqi Gu, Gongjun Xu

Structured latent attribute models (SLAMs) are a special family of discrete latent variable models widely used in social and biological sciences. This paper considers the problem of learning significant attribute patterns from a SLAM with potentially high-dimensional configurations of the latent attributes. We address the theoretical identifiability issue, p

Victor Croisfelt Rodrigues, Jose Carlos Marinello Filho, Taufik Abrao

To exploit the benefits of massive multiple-input multiple-output (M-MIMO) technology in scenarios where base stations (BSs) need to be cheap and equipped with simple hardware, the computational complexity of classical signal processing schemes for spatial multiplexing of users shall be reduced. This calls for suboptimal designs that perform well the combini

Rodolfo Valiente, Mahdi Zaman, Sedat Ozer, Yaser P. Fallah

A fundamental challenge in autonomous vehicles is adjusting the steering angle at different road conditions. Recent state-of-the-art solutions addressing this challenge include deep learning techniques as they provide end-to-end solution to predict steering angles directly from the raw input images with higher accuracy. Most of these works ignore the tempora

Hong Xuan, Abby Stylianou, Robert Pless

Deep metric learning seeks to define an embedding where semantically similar images are embedded to nearby locations, and semantically dissimilar images are embedded to distant locations. Substantial work has focused on loss functions and strategies to learn these embeddings by pushing images from the same class as close together in the embedding space as po

Jose Felix Salazar, Thomas Zannias

The recent monumental detection of gravitational waves by LIGO, the subsequent detection by the LIGO/VIRGO observatories of a binary neutron star merger seen in the gravitational wave signal $GW170817$,the first photo of the event horizon of the supermassive black hole at the center of the $M87$ galaxy released by the EHT telescope and the ongoing experiment

Michael Chen, Mike D&#39;Arcy, Alisa Liu, Jared Fernandez

Commonsense reasoning is a critical AI capability, but it is difficult to construct challenging datasets that test common sense. Recent neural question answering systems, based on large pre-trained models of language, have already achieved near-human-level performance on commonsense knowledge benchmarks. These systems do not possess human-level common sense,

Masaki Okawa, Takuya Saito, Naoki Sawada, Hiromitsu Nishizaki

This study investigated the waveform representation for audio signal classification. Recently, many studies on audio waveform classification such as acoustic event detection and music genre classification have been published. Most studies on audio waveform classification have proposed the use of a deep learning (neural network) framework. Generally, a freque

Huijie Zheng, Zhiyin Sun, Georgios Chatzidrosos, Chen Zhang

Sensing vector magnetic fields is critical to many applications in fundamental physics, bioimaging, and material science. Magnetic-field sensors exploiting nitrogen-vacancy (NV) centers are particularly compelling as they offer high sensitivity and spatial resolution even at nanoscale. Achieving vector magnetometry has, however, often required applying micro

Daniel R. Reid, Nidhi Pashine, Alec S. Bowen, Sidney R. Nagel

Auxetic materials are characterized by a negative Poisson's ratio, $\mathrm{\nu}$. As the Poisson's ratio becomes negative and approaches the lower isotropic mechanical limit of $\mathrm{\nu = -1}$, materials show enhanced resistance to impact and shear, making them suitable for applications ranging from robotics to impact mitigation. Past experimental effor

Neslisah Torosdagli, Syed Anwar, Payal Verma, Denise K Liberton

Purpose: We perform anatomical landmarking for craniomaxillofacial (CMF) bones without explicitly segmenting them. Towards this, we propose a new simple yet efficient deep network architecture, called \textit{relational reasoning network (RRN)}, to accurately learn the local and the global relations among the landmarks in CMF bones; specifically, mandible, m

A. Pieres, L. Girardi, E. Balbinot, B. Santiago

We present MWFitting, a method to fit the stellar components of the Galaxy by comparing Hess Diagrams (HDs) from TRILEGAL models to real data. We apply MWFitting to photometric data from the first three years of the Dark Energy Survey (DES). After removing regions containing known resolved stellar systems such as globular clusters, dwarf galaxies, nearby gal

Oanh Nguyen, Van Vu

The number of real roots has been a central subject in the theory of random polynomials and random functions since the fundamental papers of Littlewood-Offord and Kac in the 1940s. The main task here is to determine the limiting distribution of this random variable. In 1974, Maslova famously proved a central limit theorem (CLT) for the number of real roots o

Paritosh Parmar, Brendan Tran Morris

Can performance on the task of action quality assessment (AQA) be improved by exploiting a description of the action and its quality? Current AQA and skills assessment approaches propose to learn features that serve only one task - estimating the final score. In this paper, we propose to learn spatio-temporal features that explain three related tasks - fine-

Thomas Kruse, Mikhail Urusov

The time at which a one-dimensional continuous strong Markov process attains a boundary point of its state space is a discontinuous path functional and it is, therefore, unclear whether the exit time can be approximated by hitting times of approximations of the process. We prove a functional limit theorem for approximating weakly both the paths of the Markov

Eduardo V. Castro, Raphael de Gail, M. Pilar López-Sancho, María A. H. Vozmediano

We analyze the localization properties of two dimensional systems based on partite lattices with a basis. Contrary to standard results, we find that a band of critical states emerges for systems in the unitary class A preserving spin symmetry when disorder is unevenly distributed over the basis atoms. The critical metal arises when the less disordered sublat

Heda Song, Mercedes Torres Torres, Ender Özcan, Isaac Triguero

Few-shot learning focuses on learning a new visual concept with very limited labelled examples. A successful approach to tackle this problem is to compare the similarity between examples in a learned metric space based on convolutional neural networks. However, existing methods typically suffer from meta-level overfitting due to the limited amount of trainin

Omar Shehab, Kevin A. Landsman, Yunseong Nam, Daiwei Zhu

We report results for simulating an effective field theory to compute the binding energy of the deuteron nucleus using a hybrid algorithm on a trapped-ion quantum computer. Two increasingly complex unitary coupled-cluster ansaetze have been used to compute the binding energy to within a few percent for successively more complex Hamiltonians. By increasing th

Shahbaz Rezaei, Xin Liu

Due to insufficient training data and the high computational cost to train a deep neural network from scratch, transfer learning has been extensively used in many deep-neural-network-based applications. A commonly used transfer learning approach involves taking a part of a pre-trained model, adding a few layers at the end, and re-training the new layers with

Rosalyn Koscica, Nilanjan Banerjee, Georg Heinz Hoffstaetter, William Lou

In a multipass energy recovery linac (ERL), each cavity must regain all energy expended from beam acceleration during beam deceleration, and the beam should achieve specific energy targets during each loop that returns it to the linac. For full energy recovery, and for every returning beam to meet loop energy requirements, we must specify and maintain the ph

Sven Bachmann, Richard Froese, Eric N Cytrynbaum

In growing plant cells, parallel ordering of microtubules (MTs) along the inner surface of the cell membrane influences the direction of cell expansion and thereby plant morphology. For correct expansion of organs that primarily grow by elongating, such as roots and stems, MTs must bend in the high-curvature direction along the cylindrically shaped cell memb

Weinan E, Chao Ma, Lei Wu

A fairly comprehensive analysis is presented for the gradient descent dynamics for training two-layer neural network models in the situation when the parameters in both layers are updated. General initialization schemes as well as general regimes for the network width and training data size are considered. In the over-parametrized regime, it is shown that gr

Emilio N. M. Cirillo, Matteo Colangeli, Ronald Dickman

In particle systems subject to a nonuniform drive, particle migration is observed from the driven to the non--driven region and vice--versa, depending on details of the hopping dynamics, leading to apparent violations of Fick&#39;s law and of steady--state thermodynamics. We propose and discuss a very basic model in the framework of independent random walker

Chau Tran, Kaylea Champion, Andrea Forte, Benjamin Mako Hill

User-generated content sites routinely block contributions from users of privacy-enhancing proxies like Tor because of a perception that proxies are a source of vandalism, spam, and abuse. Although these blocks might be effective, collateral damage in the form of unrealized valuable contributions from anonymity seekers is invisible. One of the largest and mo

Yan Luo, Yongkang Wong, Mohan Kankanhalli, Qi Zhao

Intra-class compactness and inter-class separability are crucial indicators to measure the effectiveness of a model to produce discriminative features, where intra-class compactness indicates how close the features with the same label are to each other and inter-class separability indicates how far away the features with different labels are. In this work, w

Syed Tahir Amin, Bruno Mera, Nikola Paunković, Vítor R. Vieira

The Uhlmann connection is a mixed state generalisation of the Berry connection. The latter has a very important role in the study of topological phases at zero temperature. Closely related, the quantum fidelity is an information theoretical quantity which is a measure of distinguishability of quantum states. Moreover, it has been extensively used in the anal

Guillaume Dubach, Yuval Peled

We consider words $G_{i_1} \cdots G_{i_m}$ involving i.i.d. complex Ginibre matrices, and study tracial expressions of their eigenvalues and singular values. We show that the limit distribution of the squared singular values of every word of length $m$ is a Fuss-Catalan distribution with parameter $m+1$. This generalizes previous results concerning powers of

Miguel Fernandes, Luís A. Alexandre

A blockchain, during its lifetime, records large amounts of data, that in a common usage its kept on its entirety. In a robotics environment, the old information is useful for human evaluation, or oracles interfacing with the blockchain but it is not useful for the robots that require only current information in order to continue their work. This causes a st

Feng Wan, Rashid Shaisultanov, Yan-Fei Li, Karen Z. Hatsagortsyan

Relativistic spin-polarized positron beams are indispensable for future electron-positron colliders to test modern high-energy physics theory with high precision. However, present techniques require very large scale facilities for those experiments. We put forward a novel efficient way for generating ultrarelativistic polarized positron beams employing curre

Antoine Pauthier, Arnd Scheel

We analyze the effect of nonlinear boundary conditions on an advection-diffusion equation on the half-line. Our model is inspired by models for crystal growth where diffusion models diffusive relaxation of a displacement field, advection is induced by apical growth, and boundary conditions incorporate non-adiabatic effects on displacement at the boundary. Th

K. E. Heintz, T. Zafar, A. De Cia, S. D. Vergani

We present a study of the extinction and depletion-derived dust properties of gamma-ray burst (GRB) absorbers at $1<z<3$ showing the presence of neutral carbon (\ion{C}{I}). By modelling their parametric extinction laws, we discover a broad range of dust models characterizing the GRB \ion{C}{I} absorption systems. In addition to the already well-established

Zhou Gang, Shengwen Wang

In the present paper we study a type of generic singularity of mean curvature flow modelled on the bubble-sheet $\mathbb S^1\times\mathbb R^3$ , and we derive an asymptotic profile for a neighborhood of singularity.

George M. Bergman

This is a collection of questions for inclusion in future editions of the Kourovka Notebook of open questions in group theory. Most are questions I raised in papers between 1981 and 2021. I welcome feedback.

Dali Zhang, Yu Luo, Xi Kang

Observational work have shown that the two brightest satellite galaxies of the Milky Way (MW), the Large Magellanic Cloud (LMC) and the Small Magellanic Cloud (SMC), are rare amongst MW analogues. It is then interesting to know whether the presence of massive satellite has any effect on the whole satellite population in MW analogues. In this article, we inve