November 2020 arXiv papers — page 30

Ronghuai Qi, Amir Khajepour, William W. Melek

This paper presents redundancy resolution and disturbance rejection via torque optimization in Hybrid Cable-Driven Robots (HCDRs). To begin with, we initiate a redundant HCDR for nonlinear whole-body system modeling and model reduction. Based on the reduced dynamic model, two new methods are proposed to solve the redundancy resolution problem: joint-space to

Christiane Rousseau

When are two germs of analytic systems conjugate or orbitally equivalent under an analytic change of coordinates in the neighborhood of a singular point? A way to answer is to use normal forms. But there are large classes of dynamical systems for which the change of coordinates to a normal form diverges. In this paper we discuss the case of singularities for

Roger D. Maddux

The Theorems of Pappus and Desargues are generalized by two special formulas that hold in the three-dimensional vector space over a field.

Zidi Xiu, Junya Chen, Ricardo Henao, Benjamin Goldstein

Dealing with severe class imbalance poses a major challenge for real-world applications, especially when the accurate classification and generalization of minority classes is of primary interest. In computer vision, learning from long tailed datasets is a recurring theme, especially for natural image datasets. While existing solutions mostly appeal to sampli

Li Yang, Wenjun Hu, Li Li

Deep neural networks have been shown as a potentially powerful ansatz in variational Monte Carlo for solving quantum many-body problems. We propose two improvements in this direction. The first is graph neural ansatz (GNA), which is a variational wavefunction universal to arbitrary geometry. GNA results in accurate ground-state energies on 2D Kagome lattices

Evelia R. García Barroso, Marcelo E. Hernandes, M. Fernando Hernández Iglesias

We characterize nondicrital generalized curve foliations with fixed reduced separatrix. Moreover, we give suficient conditions when a plane analytic curve is its reduced separatrix. For that, we introduce a distinguished expression for a given 1-form, called {\it Weierstrass form}. Then, using Weierstrass forms, we characterize the nondicritical generalized

Justin Anguiano

This study showcases the approaches towards lepton identification and $\gamma \gamma$ mitigation at center-of-mass energy $\sqrt{s} = 500$ GeV for semileptonic WW decays at the ILC. The analysis is performed using fully simulated Standard Model Monte Carlo events with the ILD detector concept and emphasizes the measurement of the W mass. The mass measurement

Peize Sun, Rufeng Zhang, Yi Jiang, Tao Kong

We present Sparse R-CNN, a purely sparse method for object detection in images. Existing works on object detection heavily rely on dense object candidates, such as $k$ anchor boxes pre-defined on all grids of image feature map of size $H\times W$. In our method, however, a fixed sparse set of learned object proposals, total length of $N$, are provided to obj

Evan S. Gawlik

We construct fast, structure-preserving iterations for computing the sign decomposition of a unitary matrix $A$ with no eigenvalues equal to $\pm i$. This decomposition factorizes $A$ as the product of an involutory matrix $S = \operatorname{sign}(A) = A(A^2)^{-1/2}$ times a matrix $N = (A^2)^{1/2}$ with spectrum contained in the open right half of the compl

Ziyi Gong, Paul Munro

Vital to primary visual processing, retinal circuitry shows many similar structures across a very broad array of species, both vertebrate and non-vertebrate, especially functional components such as lateral inhibition. This surprisingly conservative pattern raises a question of how evolution leads to it, and whether there is any alternative that can also pro

Peter Werner, Alexander K. Hartmann

We study numerically the behavior of RNA secondary structures under influence of a varying external force. This allows to measure the work $W$ during the resulting fast unfolding and refolding processes. Here, we investigate a medium-size hairpin structure. Using a sophisticated large-deviation algorithm, we are able to measure work distributions with high p

Agelos Kratimenos, Georgios Pavlakos, Petros Maragos

Independent Sign Language Recognition is a complex visual recognition problem that combines several challenging tasks of Computer Vision due to the necessity to exploit and fuse information from hand gestures, body features and facial expressions. While many state-of-the-art works have managed to deeply elaborate on these features independently, to the best

John Gertz

Arguments are reviewed in support of the hypothesis that ET would more likely send physical probes to surveil our Solar System and communicate with Earth than to communicate from afar with interstellar radio, infrared or laser beacons. Although the standard SETI practice of targeting individual stars or galaxies with powerful telescopes might detect a foregr

Jing Yang, Chun Ouyang, Wil M. P. van der Aalst, Arthur H. M. ter Hofstede

Providing appropriate structures around human resources can streamline operations and thus facilitate the competitiveness of an organization. To achieve this goal, modern organizations need to acquire an accurate and timely understanding of human resource grouping while faced with an ever-changing environment. The use of process mining offers a promising way

Shan Wu, Yu Song, Yu He, Alex Frano

Interactions between nematic fluctuations, magnetic order and superconductivity are central to the physics of iron-based superconductors. Here we report on in-plane transverse acoustic phonons in hole-doped Sr$_{1-x}$Na$_x$Fe$_2$As$_2$ measured via inelastic X-ray scattering, and extract both the nematic susceptibility and the nematic correlation length. By

Kyle Tilbury, Jesse Hoey

Machine learning has the potential to aid in mitigating the human effects of climate change. Previous applications of machine learning to tackle the human effects in climate change include approaches like informing individuals of their carbon footprint and strategies to reduce it. For these methods to be the most effective they must consider relevant social-

Daniel Berkowitz

We propose two higher order generalized uncertainty principles(GUPs) which predict a minimum uncertainty in momentum and apply the deformations that they entail of the Heisenberg algebra to one half of the phase space of the LRS Bianchi I models. After numerically solving the resultant Wheeler Dewitt equations we analyze our solutions and provide evidence th

Zymantas Darbenas, Rein van der Hout, Marcel Oliver

We study the question of uniqueness of weak solution to the fast reaction limit of the Keller and Rubinow model for Liesegang rings as introduced by Hilhorst et al. (J. Stat. Phys. 135, 2009, pp. 107-132). The model is characterized by a discontinuous reaction term which can be seen as an instance of spatially distributed non-ideal relay hysteresis. In gener

Skylar Sutherland, Bernhard Egger, Joshua Tenenbaum

We propose a method for constructing generative models of 3D objects from a single 3D mesh. Our method produces a 3D morphable model that represents shape and albedo in terms of Gaussian processes. We define the shape deformations in physical (3D) space and the albedo deformations as a combination of physical-space and color-space deformations. Whereas previ

Spyros Angelopoulos, Shahin Kamali

Contract scheduling is a general technique that allows to design a system with interruptible capabilities, given an algorithm that is not necessarily interruptible. Previous work on this topic has largely assumed that the interruption is a worst-case deadline that is unknown to the scheduler. In this work, we study the setting in which there is a potentially

Natalia Neverova, David Novotny, Vasil Khalidov, Marc Szafraniec

In this work, we focus on the task of learning and representing dense correspondences in deformable object categories. While this problem has been considered before, solutions so far have been rather ad-hoc for specific object types (i.e., humans), often with significant manual work involved. However, scaling the geometry understanding to all objects in natu

Dimitrios Tanoglidis, Aleksandra Ćiprijanović, Alex Drlica-Wagner

Searches for low-surface-brightness galaxies (LSBGs) in galaxy surveys are plagued by the presence of a large number of artifacts (e.g., objects blended in the diffuse light from stars and galaxies, Galactic cirrus, star-forming regions in the arms of spiral galaxies, etc.) that have to be rejected through time consuming visual inspection. In future surveys,

Shane Gilroy, John O'Dwyer, Lucas Bortoleto

The applications of automotive cameras in Advanced Driver-Assistance Systems (ADAS) are growing rapidly as automotive manufacturers strive to provide 360 degree protection for their customers. Vision systems must capture high quality images in both daytime and night-time scenarios in order to produce the large informational content required for software anal

Jabari Hastings, Amy Kanne, Ray Li, Mary Wootters

We define wedge-lifted codes, a variant of lifted codes, and we study their locality properties. We show that (taking the trace of) wedge-lifted codes yields binary codes with the $t$-disjoint repair property ($t$-DRGP). When $t = N^{1/2d}$, where $N$ is the block length of the code and $d \geq 2$ is any integer, our codes give improved trade-offs between re

Ryosuke Hirai, Philipp Podsiadlowski, Stanley P. Owocki, Fabian R. N. Schneider

$\eta$ Carinae is an extraordinary massive star famous for its 19th century Great Eruption and the surrounding Homunculus nebula ejected in that event. The cause of this eruption has been the centre of a long-standing mystery. Recent observations, including light-echo spectra of the eruption, suggest that it most likely resulted from a stellar merger in an u

Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan

We study the problem of heavy-tailed mean estimation in settings where the variance of the data-generating distribution does not exist. Concretely, given a sample $\mathbf{X} = \{X_i\}_{i = 1}^n$ from a distribution $\mathcal{D}$ over $\mathbb{R}^d$ with mean $\mu$ which satisfies the following \emph{weak-moment} assumption for some ${\alpha \in [0, 1]}$: \b

Matej Klemen, Luka Krsnik, Marko Robnik-Šikonja

Deep learning approaches are superior in NLP due to their ability to extract informative features and patterns from languages. The two most successful neural architectures are LSTM and transformers, used in large pretrained language models such as BERT. While cross-lingual approaches are on the rise, most current NLP techniques are designed and applied to En

Yoji Yamato

When using heterogeneous hardware, barriers of technical skills such as OpenMP, CUDA and OpenCL are high. Based on that, I have proposed environment-adaptive software that enables automatic conversion, configuration. However, including existing technologies, there has been no research to properly and automatically offload the mixed offloading destination env

Yan Xia, Yusheng Xu, Shuang Li, Rui Wang

We tackle the problem of place recognition from point cloud data and introduce a self-attention and orientation encoding network (SOE-Net) that fully explores the relationship between points and incorporates long-range context into point-wise local descriptors. Local information of each point from eight orientations is captured in a PointOE module, whereas l

Mohamed Y. Elwazir, Zeynettin Akkus, Didem Oguz, Jae K. Oh

Echocardiography (echo) is an indispensable tool in a cardiologist's diagnostic armamentarium. To date, almost all echocardiographic parameters require time-consuming manual labeling and measurements by an experienced echocardiographer and exhibit significant variability, owing to the noisy and artifact-laden nature of echo images. For example, mitral inflow

Jihyeon Lee, Joseph Z. Xu, Kihyuk Sohn, Wenhan Lu

To respond to disasters such as earthquakes, wildfires, and armed conflicts, humanitarian organizations require accurate and timely data in the form of damage assessments, which indicate what buildings and population centers have been most affected. Recent research combines machine learning with remote sensing to automatically extract such information from s

Shane Gilroy

Vision Systems are quickly becoming a large component of Active Automotive Safety Systems. In order to be effective in critical safety applications these systems must produce high quality images in both daytime and night-time scenarios in order to provide the large informational content required for software analysis in applications such as lane departure, p

Maria Refinetti, Stéphane d'Ascoli, Ruben Ohana, Sebastian Goldt

Direct Feedback Alignment (DFA) is emerging as an efficient and biologically plausible alternative to the ubiquitous backpropagation algorithm for training deep neural networks. Despite relying on random feedback weights for the backward pass, DFA successfully trains state-of-the-art models such as Transformers. On the other hand, it notoriously fails to tra

Luiz A. Zanlorensi, Rayson Laroca, Diego R. Lucio, Lucas R. Santos

Recently, ocular biometrics in unconstrained environments using images obtained at visible wavelength have gained the researchers' attention, especially with images captured by mobile devices. Periocular recognition has been demonstrated to be an alternative when the iris trait is not available due to occlusions or low image resolution. However, the periocul

Kuan-Sen Lin, Barry Bradlyn

Since the discovery of the Harper-Hofstadter model, it has been known that condensed matter systems with periodic modulations can be promoted to non-trivial topological states with emergent gauge fields in higher dimensions. In this work, we develop a general procedure to compute the gauge fields in higher dimensions associated to low-dimensional systems wit

Pranav Aggarwal, Ajinkya Kale

There has been a recent spike in interest in multi-modal Language and Vision problems. On the language side, most of these models primarily focus on English since most multi-modal datasets are monolingual. We try to bridge this gap with a zero-shot approach for learning multi-modal representations using cross-lingual pre-training on the text side. We present

Yves Gingras, Kristoff Talin

This research presents the results of a comparative analysis of the links between religious practices and beliefs and levels of scientific knowledge. Based on secondary analyses of survey data in the European Union (Eurobarometers 2005 and 2010) and the United States (Pew Research Center 2018), we show that, regardless of the country, correlations suggest th

Yang Li

Artificial neural networks are often very complex and too deep for a human to understand. As a result, they are usually referred to as black boxes. For a lot of real-world problems, the underlying pattern itself is very complicated, such that an analytic solution does not exist. However, in some cases, laws of physics, for example, the pattern can be describ

Manon Césaire, Lucas Schott, Hatem Hajri, Sylvain Lamprier

This paper introduces stochastic sparse adversarial attacks (SSAA), standing as simple, fast and purely noise-based targeted and untargeted attacks of neural network classifiers (NNC). SSAA offer new examples of sparse (or $L_0$) attacks for which only few methods have been proposed previously. These attacks are devised by exploiting a small-time expansion i

Joanne Truong, Sonia Chernova, Dhruv Batra

Deep reinforcement learning models are notoriously data hungry, yet real-world data is expensive and time consuming to obtain. The solution that many have turned to is to use simulation for training before deploying the robot in a real environment. Simulation offers the ability to train large numbers of robots in parallel, and offers an abundance of data. Ho

Michael R. Klug

Rochlin proved that a closed 4-dimensional connected smooth oriented manifold $X^4$ with vanishing second Stiefel-Whitney class has signature $\sigma(X)$ divisible by 16. This was generalized by Kervaire and Milnor to the statement that if $\xi \in H_2(X;\mathbb{Z})$ is an integral lift of an element in $H_2(X; \mathbb{Z}/2\mathbb{Z})$ that is dual to $w_2(X

J. -P. Tetienne, L. P. McGuinness, V. Jacques

Studies of individual quantum systems, which have led to considerable progress in our understanding of quantum physics, have traditionally been associated with atomic gases. In the last decades however, the emphasis has shifted towards solid-state systems, which are much more practical for applications. In particular, a new field has recently emerged that is

Li Chen, Nathaniel Josephs, Lizhen Lin, Jie Zhou

In this paper, we propose a new spectral-based approach to hypothesis testing for populations of networks. The primary goal is to develop a test to determine whether two given samples of networks come from the same random model or distribution. Our test statistic is based on the trace of the third order for a centered and scaled adjacency matrix, which we pr

Corbyn Jones, Mauricio Gomez, Ryan M. Muoio, Alex Vidal

We study the stochastic force dynamics of a model microswimmer (Chlamydomonas reinhardtii), using a combined experimental, theoretical, and numerical approach. While swimming dynamics have been extensively studied using hydrodynamic approaches, which infer forces from the viscous flow field, we directly measure the stochastic forces generated by the microswi

N. Aggarwal, O. D. Aguiar, A. Bauswein, G. Cella

The first direct measurement of gravitational waves by the LIGO and Virgo collaborations has opened up new avenues to explore our Universe. This white paper outlines the challenges and gains expected in gravitational wave searches at frequencies above the LIGO/Virgo band, with a particular focus on Ultra High-Frequency Gravitational Waves (UHF-GWs), covering

Matthew Li, Laurent Demanet, Leonardo Zepeda-Núñez

We introduce an end-to-end deep learning architecture called the wide-band butterfly network (WideBNet) for approximating the inverse scattering map from wide-band scattering data. This architecture incorporates tools from computational harmonic analysis, such as the butterfly factorization, and traditional multi-scale methods, such as the Cooley-Tukey FFT a

V. G. Kogan, R. Prozorov

We study the effects of anisotropic order parameters on the temperature dependence of London penetration depth anisotropy $\gamma_\lambda(T)$. After MgB$_2$, this dependence is commonly attributed to distinct gaps on multi-band Fermi surfaces in superconductors. We have found, however, that the anisotropy parameter may depend on temperature also in one-band

Nelson Darkwah Oppong, Giulio Pasqualetti, Oscar Bettermann, Philip Zechmann

Constraints in the dynamics of quantum many-body systems can dramatically alter transport properties and relaxation timescales even in the absence of static disorder. Here, we report on the observation of such constrained dynamics arising from the distinct mobility of two species in the one-dimensional mass-imbalanced Fermi-Hubbard model, realized with ultra

Andi Rabia, Francesco Tumino, Alberto Milani, Valeria Russo

Graphdiyne, atomically-thin 2D carbon nanostructure based on sp-sp2 hybridization, is an appealing system potentially showing outstanding mechanical and optoelectronic properties. Surface-catalyzed coupling of halogenated sp-carbon-based molecular precursors represents a promising bottom-up strategy to fabricate extended 2D carbon systems with engineered str

Eleonore Faber, Martina Juhnke-Kubitzke, Haydee Lindo, Claudia Miller

We generalize Buchsbaum and Eisenbud's resolutions for the powers of the maximal ideal of a polynomial ring to resolve powers of the homogeneous maximal ideal over graded Koszul algebras. Our approach has the advantage of producing resolutions that are both more explicit and minimal compared to those previously discovered by Green and Mart\'{\i}nez-Villa \ci

Sahand Hajifar, Hongyue Sun

Accurate evaluation of liver viability during its procurement is a challenging issue and has traditionally been addressed by taking invasive biopsy on liver. Recently, people have started to investigate on the non-invasive evaluation of liver viability during its procurement using the liver surface thermal images. However, existing works include the backgrou

Tadele Mengesha, James M. Scott

In this paper we prove a fractional analogue of the classical Korn's first inequality. The inequality makes it possible to show the equivalence of a function space of vector field characterized by a Gagliardo-type seminorm with 'projected difference' with that of a corresponding fractional Sobolev space. As an application, we will use it to obtain a Cacciopp

Anjian Li, Liting Sun, Wei Zhan, Masayoshi Tomizuka

Safety is an important topic in autonomous driving since any collision may cause serious injury to people and damage to property. Hamilton-Jacobi (HJ) Reachability is a formal method that verifies safety in multi-agent interaction and provides a safety controller for collision avoidance. However, due to the worst-case assumption on the cars future behaviours

Christopher Hawthorne

Fix an abelian group $\Gamma$ and an injective endomorphism $F \colon \Gamma \to \Gamma$. Improving on the results of Bell and Moosa, new characterizations are here obtained for the existence of spanning sets, $F$-automaticity, and $F$-sparsity. The model theoretic status of these sets is also investigated, culminating with a combinatorial description of the

Guillermo Terrén-Serrano, Manel Martínez-Ramón

The energy available in Micro Grid (MG) that is powered by solar energy is tightly related to the weather conditions in the moment of generation. Very short-term forecast of solar irradiance provides the MG with the capability of automatically controlling the dispatch of energy. To achieve this, we propose a method for statistical quantification of cloud fea

Karl J. Friston, Guillaume Flandin, Adeel Razi

This technical report describes the rationale and technical details for the dynamic causal modelling of mitigated epidemiological outcomes based upon a variety of timeseries data. It details the structure of the underlying convolution or generative model (at the time of writing on 6-Nov-20). This report is intended for use as a reference that accompanies the

I. Gross, W. Akhtar, V. Garcia, L. J. Martínez

While ferromagnets are at the heart of daily life applications, their large magnetization and resulting energy cost for switching bring into question their suitability for reliable low-power spintronic devices. Non-collinear antiferromagnetic systems do not suffer from this problem and often possess remarkable extra functionalities: non-collinear spin order

Anthony Kelly

A flexible discriminative image denoiser is introduced in which multi-task learning methods are applied to a densoising FCN based on U-Net. The activations of the U-Net model are modified by affine transforms that are a learned function of conditioning inputs. The learning procedure for multiple noise types and levels involves applying a distribution of nois

Xiao Zang, Sebastian Kurtek, Oksana Chkrebtii, J. Derek Tucker

We propose a new method for clustering of functional data using a $k$-means framework. We work within the elastic functional data analysis framework, which allows for decomposition of the overall variation in functional data into amplitude and phase components. We use the amplitude component to partition functions into shape clusters using an automated appro

Mahdi A. Shalchi, Marcelo T. Yamashita, Tobias Frederico, Lauro Tomio

Atom-dimer exchange and dissociation reaction rates are predicted for different combinations of two $^4$He atoms and one of the alkaline species among $^{6}$Li, $^{7}$Li and $^{23}$Na, by using three-body scattering formalism with short-range two-body interactions. Our study was concerned with low-energy reaction rates in which the $s-$, $p-$ and $d-$ wave c

Lucas Brivadis, Jean-Paul Gauthier, Ludovic Sacchelli, Ulysse Serres

We address the problem of dynamic output feedback stabilization at an unobservable target point. The challenge lies in according the antagonistic nature of the objective and the properties of the system: the system tends to be less observable as it approaches the target. We illustrate two main ideas: well chosen perturbations of a state feedback law can yiel

L Lavoine, T Bourdel

The existence of quantum droplets in binary Bose-Einstein condensate mixtures rely on beyondmean field effects, competing with mean-field effects. Interestingly, the beyond-mean field effect is changing from repulsive in 3D to attractive in 1D leading to drastically different behaviors. We study in detail the crossover between these two regimes in a quasi-1D

Yuri Gurevich, Vladimir Vovk

We explain, on the example of Wigner's quasiprobability distribution, how negative probabilities may be used in the foundations of probability.

Gersende Fort, Eric Moulines, Hoi-To Wai

The Expectation Maximization (EM) algorithm is a key reference for inference in latent variable models; unfortunately, its computational cost is prohibitive in the large scale learning setting. In this paper, we propose an extension of the Stochastic Path-Integrated Differential EstimatoR EM (SPIDER-EM) and derive complexity bounds for this novel algorithm,

Vivek Dixit, Raja Selvarajan, Tamer Aldwairi, Yaroslav Koshka

We present a real-world application that uses a quantum computer. Specifically, we train a RBM using QA for cybersecurity applications. The D-Wave 2000Q has been used to implement QA. RBMs are trained on the ISCX data, which is a benchmark dataset for cybersecurity. For comparison, RBMs are also trained using CD. CD is a commonly used method for RBM training

Marc Olive, Nicolas Auffray

The piezoelectricity law is a constitutive model that describes how mechanical andelectric fields are coupled within a material. In its linear formulation this law comprises threeconstitutive tensors of increasing order: the second order permittivity tensor S, the third orderpiezoelectricity tensor P and the fourth-order elasticity tensor C. In a first part

Régis Houssou, Stephan Robert-Nicoud

This paper proposes a method to detect bank frauds using a mixed approach combining a stochastic intensity model with the probability of fraud observed on transactions. It is a dynamic unsupervised approach which is able to predict financial frauds. The fraud prediction probability on the financial transaction is derived as a function of the dynamic intensit

Qiang Sun

Based on recent studies on hydrophobic interactions, it is devoted to investigate the directional nature of hydrophobic interactions. It means that the hydrophobic interactions are dependent on the relative orientations as the solutes tend to be aggregated in water. In H1w process, they are attracted to approach each other in the specific direction with lowe

Wenqiang Yi, Wenjuan Yu, Yuanwei Liu, Chuan Heng Foh

This paper proposes a tractable solution for integrating non-orthogonal multiple access (NOMA) into massive machine-type communications (mMTC) to increase the uplink connectivity. Multiple transmit power levels are provided at the user end to enable open-loop power control, which is absent from the traditional uplink NOMA with the fixed transmit power. The b

Chiara Amorino, Charlotte Dion, Arnaud Gloter, Sarah Lemler

In this paper, we consider a one-dimensional diffusion process with jumps driven by a Hawkes process. We are interested in the estimations of the volatility function and of the jump function from discrete high-frequency observations in a long time horizon which remained an open question until now. First, we propose to estimate the volatility coefficient. For

Marco Frasca

We provide a study of quantum chromodynamics with the technique of Dyson-Schwinger equations in differential form. In this way, we are able to approach the non-perturbative limit and recover, with some approximations, the 't Hooft limit of the theory. Quark mass in the propagator term goes off-shell at low-energies signaling confinement. A condition for such

Thomas Matheson, Carl Stubens, Nicholas Wolf, Chien-Hsiu Lee

We describe the Arizona-NOIRLab Temporal Analysis and Response to Events System (ANTARES), a software instrument designed to process large-scale streams of astronomical time-domain alerts. With the advent of large-format CCDs on wide-field imaging telescopes, time-domain surveys now routinely discover tens of thousands of new events each night, more than can

Sijie Zhu, Taojiannan Yang, Matias Mendieta, Chen Chen

This paper presents A3D, an adaptive 3D network that can infer at a wide range of computational constraints with one-time training. Instead of training multiple models in a grid-search manner, it generates good configurations by trading off between network width and spatio-temporal resolution. Furthermore, the computation cost can be adapted after the model

Elena Cherkaev, Fernando Guevara Vasquez, China Mauck, Milo Prisbrey

We theoretically show that a superposition of plane waves causes small (compared to the wavelength) particles dispersed in a fluid to assemble in quasiperiodic two or three dimensional patterns. We experimentally demonstrate this theory by using ultrasound waves to assemble quasiperiodic patterns of carbon nanoparticles in water using an octagonal arrangemen

Christian Bayer, Denis Belomestny, Paul Hager, Paolo Pigato

Least squares Monte Carlo methods are a popular numerical approximation method for solving stochastic control problems. Based on dynamic programming, their key feature is the approximation of the conditional expectation of future rewards by linear least squares regression. Hence, the choice of basis functions is crucial for the accuracy of the method. Earlie

Dániel B. Nagy, Philippe Claudin, Tamás Börzsönyi, Ellák Somfai

The rheology of a 3-dimensional granular system consisting of frictional elongated particles was investigated by means of discrete element model (DEM) calculations. A homogenous shear flow of frictional spherocyliders was simulated, and a number of rheological quantities were calculated. In the framework of the $\mu(I)$ rheology, the effective friction was f

Walid Hafiane, Joel Legrand, Yannick Toussaint, Adrien Coulet

Relation extraction (RE) consists in identifying and structuring automatically relations of interest from texts. Recently, BERT improved the top performances for several NLP tasks, including RE. However, the best way to use BERT, within a machine learning architecture, and within a transfer learning strategy is still an open question since it is highly depen

Claudia Shi, Victor Veitch, David Blei

The defining challenge for causal inference from observational data is the presence of `confounders', covariates that affect both treatment assignment and the outcome. To address this challenge, practitioners collect and adjust for the covariates, hoping that they adequately correct for confounding. However, including every observed covariate in the adjustme

Qiyao Wang, Haiyan Wang, Chetan Gupta, Aniruddha Rajendra Rao

In the last few decades, building regression models for non-scalar variables, including time series, text, image, and video, has attracted increasing interests of researchers from the data analytic community. In this paper, we focus on a multivariate time series regression problem. Specifically, we aim to learn mathematical mappings from multiple chronologic

Chunmei Wang

A new primal-dual weak Galerkin (PDWG) finite element method is introduced and analyzed for the ill-posed elliptic Cauchy problems with ultra-low regularity assumptions on the exact solution. The Euler-Lagrange formulation resulting from the PDWG scheme yields a system of equations involving both the primal equation and the adjoint (dual) equation. The optim

M. Ivory, W. J. Setzer, N. Karl, H. McGuinness

We report on the characterization of heating rates and photo-induced electric charging on a microfabricated surface ion trap with integrated waveguides. Microfabricated surface ion traps have received considerable attention as a quantum information platform due to their scalability and manufacturability. Here we characterize the delivery of 435 nm light thro

B. Czerny, M. L. Martínez-Aldama, G. Wojtkowska, M. Zajaček

Recent measurements of the parameters of the Concordance Cosmology Model ($\Lambda$CDM) done in the low-redshift Universe with Supernovae Ia/Cepheids, and in the distant Universe done with Cosmic Microwave Background (CMB) imply different values for the Hubble constant (67.4 $\pm$ 0.5 km s$^{-1}$ Mpc$^{-1}$ from Planck vs 74.03 $\pm$ 1.42 km s$^{-1}$ Mpc$^{-

Sesuai Y. Madanha

There has been some interest on how the average character degree affects the structure of a finite group. We define, and denote by $ \mathrm{anz}(G) $, the average number of zeros of characters of a finite group $ G $ as the number of zeros in the character table of $ G $ divided by the number of irreducible characters of $ G $. We show that if $ \mathrm{anz

CMS Collaboration

A search for nonresonant production of Higgs boson pairs via gluon-gluon and vector boson fusion processes in final states with two bottom quarks and two photons is presented. The search uses data from proton-proton collisions at a center-of-mass energy of $\sqrt{s} =$ 13 TeV recorded with the CMS detector at the LHC, corresponding to an integrated luminosit

Will Price, Dima Damen

In this paper, we introduce an attribution method for explaining action recognition models. Such models fuse information from multiple frames within a video, through score aggregation or relational reasoning. We break down a model's class score into the sum of contributions from each frame, fairly. Our method adapts an axiomatic solution to fair reward distr

Alexander Bauman

We consider, for a finite graph $G$, when the surjective map $\mathrm{Conf}_{n+1}(G) \rightarrow \mathrm{Conf}_n(G)$ of configuration spaces admits a section. We study when the answer depends only on the homotopy type of $G$, and give a complete answer. We also provide basic techniques for construction of sections.

Shishir Agrawal, Matthias Strauch

Let $G$ be a $p$-adic reductive group and $\mathfrak{g}$ its Lie algebra. We construct a functor from the extension closure of the Bernstein-Gelfand-Gelfand category $\mathcal{O}$ associated to $\mathfrak{g}$ into the category of locally analytic representations of $G$, thereby expanding on an earlier construction of Orlik-Strauch. A key role in this new con

Daniel Felisberto Traciná Filho, Claudia Marcela Justel

In this paper, some subclasses of block graphs are considered in order to analyze Fiedler vector of its members. Two families of block graphs with cliques of fixed size, the block-path and block-starlike graphs, are introduced. Cases A and B of classification for both families were considered, as well as the behavior of the algebraic connectivity for particu

Siddhant Siddhant, Indranil Chakraborty, Sayan Kar

Memory effects are studied in the simplest scalar-tensor theory, the Brans--Dicke (BD) theory. To this end, we introduce, in BD theory, novel Kundt spacetimes (without and with gyratonic terms), which serve as backgrounds for the ensuing analysis on memory. The BD parameter $\omega$ and the scalar field ($\phi$) profile, expectedly, distinguishes between dif

Clayton Thomas

We study the strategic simplicity of stable matching mechanisms where one side has fixed preferences, termed priorities. Specifically, we ask which priorities are such that the strategyproofness of deferred acceptance (DA) can be recognized by agents unable to perform contingency reasoning, that is, \emph{when is DA obviously strategyproof} (Li, 2017)? We an

Sesuai Y. Madanha

Let $ G$ be a finite group and $p$ be a prime. Let $ \mathrm{Vo}(G) $ denote the set of the orders of vanishing elements, $\mathrm{Vo}_{p} (G)$ be the subset of $ \mathrm{Vo}(G) $ consisting of those orders of vanishing elements divisible by $p$ and $\mathrm{Vo}_{p'} (G) $ be the subset of $ \mathrm{Vo}(G) $ consisting of those orders of vanishing elements n

Wenyun Ju, Horacio Silva-Saravia, Neeraj Nayak, Wenxuan Yao

This letter proposes a new approach for online detection of low-quality synchrophasor data under both normal and event conditions. The proposed approach utilizes the features of synchrophasor data in time and frequency domains to distinguish multiple regional PMU signals and detect low-quality synchrophasor data. The proposed approach does not require any of

S. W. Hancock, S. Zahedpour, H. M. Milchberg

We present a technique for the single-shot measurement of the space- and time-resolved spatiotemporal amplitude and phase of an ultrashort laser pulse. The method, transient-grating single-shot supercontinuum spectral interferometry (TG- SSSI), is demonstrated by the space-time imaging of short pulses carrying spatiotemporal optical vortices (STOVs). TG-SSSI

Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner, Anthony L. Caterini

Multi-goal reaching is an important problem in reinforcement learning needed to achieve algorithmic generalization. Despite recent advances in this field, current algorithms suffer from three major challenges: high sample complexity, learning only a single way of reaching the goals, and difficulties in solving complex motion planning tasks. In order to addre

Mitchell Black, Ehsan Arabi, Dimitra Panagou

We present a novel technique for solving the problem of safe control for a general class of nonlinear, control-affine systems subject to parametric model uncertainty. Invoking Lyapunov analysis and the notion of fixed-time stability (FxTS), we introduce a parameter adaptation law which guarantees convergence of the estimates of unknown parameters in the syst

Yifei Sun, Hang Zou, Samson Lasaulce, Michel Kieffer

The conventional approach to pre-process data for compression is to apply transforms such as the Fourier, the Karhunen-Lo\`{e}ve, or wavelet transforms. One drawback from adopting such an approach is that it is independent of the use of the compressed data, which may induce significant optimality losses when measured in terms of final utility (instead of bei

Ignacio Carlucho, Mariano De Paula, Gerardo G. Acosta, Corina Barbalata

In marine operations underwater manipulators play a primordial role. However, due to uncertainties in the dynamic model and disturbances caused by the environment, low-level control methods require great capabilities to adapt to change. Furthermore, under position and torque constraints the requirements for the control system are greatly increased. Reinforce

Tian Carey, Adrees Arbab, Luca Anzi, Helen Bristow

Air-stable semiconducting inks suitable for complementary logic are key to create low-power printed integrated circuits (ICs). High-performance printable electronic inks with two-dimensional materials have the potential to enable the next generation of high performance, low-cost printed digital electronics. Here we demonstrate air-stable, low voltage (< 5 V)

Gaia Pozzoli, Mattia Radice, Manuele Onofri, Roberto Artuso

We consider a continuous-time random walk which is the generalization, by means of the introduction of waiting periods on sites, of the one-dimensional nonhomogeneous random walk with a position-dependent drift known in the mathematical literature as Gillis random walk. This modified stochastic process allows to significantly change local, non-local and tran

Moriah Elkin, Peter Webb

We compute explicitly the submodule structure of the Young modules for symmetric groups $S_n$ over fields of characteristic 2, when $n \le 7$. We use this information to compute the submodule structure of indecomposable projectives for the corresponding Schur algebras when $n \le 5$, and we give give partial information when n=6,7, including the Gabriel quiv