October 2020 arXiv papers — page 104

Matthias Bolten, Sven-Erik Ekström, Isabella Furci, Stefano Serra-Capizzano

A powerful tool for analyzing and approximating the singular values and eigenvalues of structured matrices is the theory of GLT sequences. By the GLT theory one can derive a function, which describes the singular value or the eigenvalue distribution of the sequence, the latter under precise assumptions. However, for small values of the matrix size of the con

Chaoqing Xu, Tyson Neuroth, Takanori Fujiwara, Ronghua Liang

Diffusion tensor imaging (DTI) has been used to study the effects of neurodegenerative diseases on neural pathways, which may lead to more reliable and early diagnosis of these diseases as well as a better understanding of how they affect the brain. We introduce an intelligent visual analytics system for studying patient groups based on their labeled DTI fib

Tianqiao Liu, Qiang Fang, Wenbiao Ding, Hang Li

There is an increasing interest in the use of mathematical word problem (MWP) generation in educational assessment. Different from standard natural question generation, MWP generation needs to maintain the underlying mathematical operations between quantities and variables, while at the same time ensuring the relevance between the output and the given topic.

Svitlana Vakulenko, Shayne Longpre, Zhucheng Tu, Raviteja Anantha

The dependency between an adequate question formulation and correct answer selection is a very intriguing but still underexplored area. In this paper, we show that question rewriting (QR) of the conversational context allows to shed more light on this phenomenon and also use it to evaluate robustness of different answer selection approaches. We introduce a s

Naoki Wake, Machiko Sato, Kazuhiro Sasabuchi, Minako Nakamura

Mapping spoken text to gestures is an important research topic for robots with conversation capabilities. According to studies on human co-speech gestures, a reasonable solution for mapping is using a concept-based approach in which a text is first mapped to a semantic cluster (i.e., a concept) containing texts with similar meanings. Subsequently, each conce

Pedram Zamirai, Jian Zhang, Christopher R. Aberger, Christopher De Sa

State-of-the-art generic low-precision training algorithms use a mix of 16-bit and 32-bit precision, creating the folklore that 16-bit hardware compute units alone are not enough to maximize model accuracy. As a result, deep learning accelerators are forced to support both 16-bit and 32-bit floating-point units (FPUs), which is more costly than only using 16

Moh. Adhib Ulil Absor, Fumiyuki Ishii

It has been generally accepted that the spin-orbit coupling effect in noncentrosymmetric materials leads to the band splitting and non-trivial spin polarization in the momentum space. However, in some cases, zero net spin polarization in the split bands may occurs, dubbed as the band splitting with vanishing spin polarization (BSVSP) effect, protected by non

Zhengbao Jiang, Antonios Anastasopoulos, Jun Araki, Haibo Ding

Language models (LMs) have proven surprisingly successful at capturing factual knowledge by completing cloze-style fill-in-the-blank questions such as "Punta Cana is located in _." However, while knowledge is both written and queried in many languages, studies on LMs' factual representation ability have almost invariably been performed on English

Alessio Netti, Daniele Tafani, Michael Ott, Martin Schulz

Modern High-Performance Computing (HPC) and data center operators rely more and more on data analytics techniques to improve the efficiency and reliability of their operations. They employ models that ingest time-series monitoring sensor data and transform it into actionable knowledge for system tuning: a process known as Operational Data Analytics (ODA). Ho

Yuki Yamazaki, Shingo Kobayashi, Ai Yamakage

We study an intrinsic relation between the topology of bulk electronic states and magnetic responses of Majorana Kramers pairs, Kramers pairs of Majorana fermions, on a surface of time-reversal-invariant topological superconductors. Majorana Kramers pairs respond to an applied magnetic field anisotropically due to the interplay between time-reversal and crys

Waleed Abdallah, Raj Gandhi, Samiran Roy

We show that one of the simplest extensions of the Standard Model, the addition of a second Higgs doublet, when combined with a dark sector singlet scalar, allows us to: $i)$ explain the long-standing anomalies in the Liquid Scintillator Neutrino Detector (LSND) and MiniBooNE (MB) while maintaining compatibility with the null result from KARMEN, $ii)$ obtain

Naonori S. Sugiyama, Shun Saito, Florian Beutler, Hee-Jong Seo

We establish a practical method for the joint analysis of anisotropic galaxy two- and three-point correlation functions (2PCF and 3PCF) on the basis of the decomposition formalism of the 3PCF using tri-polar spherical harmonics. We perform such an analysis with MultiDark Patchy mock catalogues to demonstrate and understand the benefit of the anisotropic 3PCF

Ankan Banerjee, Manojit Ghosh, Lekha Sharma, Pinaki Pal

We present the results of our investigation on nonlinear overstable rotating magnetoconvection (RMC) in presence of vertical external magnetic field. We focus on the dynamics appearing near the onset of convection by varying the system control parameters, namely, the Taylor number ($\mathrm{Ta}$), the Chandrasekhar number ($\mathrm{Q}$) and the Prandtl numbe

Mauricio Gonzalez-Soto, Ivan R. Feliciano-Avelino, L. Enrique Sucar, Hugo J. Escalante Balderas

A Random Graph is a random object which take its values in the space of graphs. We take advantage of the expressibility of graphs in order to model the uncertainty about the existence of causal relationships within a given set of variables. We adopt a Bayesian point of view in order to capture a causal structure via interaction and learning with a causal env

Yichi Zhang, Qingcheng Liao, Le Ding, Jicong Zhang

Recently, deep convolutional neural networks have achieved great success for medical image segmentation. However, unlike segmentation of natural images, most medical images such as MRI and CT are volumetric data. In order to make full use of volumetric information, 3D CNNs are widely used. However, 3D CNNs suffer from higher inference time and computation co

Oliver Anagnostou, Michele Trenti, Andrew Melatos

The gravitational wave event GW190521 involves the merger of two black holes of $\sim 85\text{M}_\odot$ and $\sim 66\text{M}_\odot$ forming an intermediate-mass black hole (IMBH) of mass $\sim 142\text{M}_\odot$. Both progenitors are challenging to explain within standard stellar evolution as they are within the upper black-hole mass gap. We propose a dynami

Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang

Despite significant advances, deep networks remain highly susceptible to adversarial attack. One fundamental challenge is that small input perturbations can often produce large movements in the network's final-layer feature space. In this paper, we define an attack model that abstracts this challenge, to help understand its intrinsic properties. In our model

Kenji Tsutsui, Kazuya Shinjo, Takami Tohyama

Motivated by the recent development of time-resolved resonant-inelastic x-ray scattering (TRRIXS) in photoexcited antiferromagnetic Mott insulators, we numerically investigate momentum-dependent transient spin dynamics in a half-filled Hubbard model on a square lattice. After turning off a pumping photon pulse, the intensity of a dynamical spin structure fac

Daniel Mork, Ander Wilson

In studies of maternal exposure to air pollution a children's health outcome is regressed on exposures observed during pregnancy. The distributed lag nonlinear model (DLNM) is a statistical method commonly implemented to estimate an exposure-time-response function when it is postulated the exposure effect is nonlinear. Previous implementations of the DLN

Vitaly Bergelson, Rigoberto Zelada

We prove that any strongly mixing action of a countable abelian group on a probability space has higher order mixing properties. This is achieved via introducing and utilizing $\mathcal R$-limits, a notion of convergence which is based on the classical Ramsey Theorem. $\mathcal R$-limits are intrinsically connected with a new combinatorial notion of largenes

He Zhao, Thanh Nguyen, Trung Le, Paul Montague

Deep neural network image classifiers are reported to be susceptible to adversarial evasion attacks, which use carefully crafted images created to mislead a classifier. Many adversarial attacks belong to the category of dense attacks, which generate adversarial examples by perturbing all the pixels of a natural image. To generate sparse perturbations, sparse

Kwansik Park, Jiwon Seo

The vulnerability of Global Positioning System (GPS) receivers to jammers is a major concern owing to the extremely weak received signal power of GPS. Researches have been conducted on a variety of antenna array techniques to be used as countermeasures to GPS jammers, and their antijamming performance is known to be greater than that of single antenna method

Qiongling Li, Takuro Mochizuki

This paper is the second part of our study on the Toda equations and the cyclic Higgs bundles associated to $r$-differentials over non-compact Riemann surfaces. We classify all the solutions up to boundedness around the isolated singularity of an $r$-differential under the assumption that the $r$-differential is meromorphic or has some type of essential sing

Xiaoxia Zhang, Youjun Lu, Taotao Fang

The existence of $\gtrsim10^9M_\odot$ supermassive black holes (SMBHs) at redshift $z>6$ raises the problem of how such SMBHs can grow up within the cosmic time ($<1$\,Gyr) from small seed BHs. In this letter, we use the observations of $14$ Quasars at $z>6.5$ with mass estimates to constrain their seeds and early growth, by self-consistently considering the

Yang Chen, Alan Ritter

Transformers that are pre-trained on multilingual corpora, such as, mBERT and XLM-RoBERTa, have achieved impressive cross-lingual transfer capabilities. In the zero-shot transfer setting, only English training data is used, and the fine-tuned model is evaluated on another target language. While this works surprisingly well, substantial variance has been obse

Dingning Li, Kai Zhang

Scattering methods are widely used in many research areas to analyze and resolve material structures. Given the importance, a large number of full textbooks are devoted to this topic. However, technical details in experiments and disconnection between explanations from different perspectives often confuse and frustrate beginner students and researchers. To c

Hossein Lamei Ramandi

We show it is consistent that there is a Souslin tree $S$ such that after forcing with $S$, $S$ is Kurepa and for all clubs $C \subset \omega_1$, $S\upharpoonright C$ is rigid. This answers Fuchs's questions in Club degrees of rigidity and almost Kurepa trees. Moreover, we show it is consistent with $\diamondsuit$ that for every Souslin tree there is a dense

S. James Gates,, Gabriel Hannon, Rui Xian Siew, Kory Stiffler

We explore the relationship between holoraumy and Hodge duality beyond four dimensions. We find this relationship to be ephemeral beyond six dimensions: it is not demanded by the structure of such supersymmetrical theories. In four dimensions for the case of the vector-tensor $\cal N$ = 4 multiplet, however, we show that such a linkage is present. Reduction

Jinho Baik, Elizabeth Collins-Woodfin, Pierre Le Doussal, Hao Wu

We analyze the free energy and the overlaps in the 2-spin spherical Sherrington Kirkpatrick spin glass model with an external field for the purpose of understanding the transition between this model and the one without an external field. We compute the limiting values and fluctuations of the free energy as well as three types of overlaps in the setting where

Han Xu, Xiaorui Liu, Yaxin Li, Anil K. Jain

Adversarial training algorithms have been proved to be reliable to improve machine learning models&#39; robustness against adversarial examples. However, we find that adversarial training algorithms tend to introduce severe disparity of accuracy and robustness between different groups of data. For instance, a PGD adversarially trained ResNet18 model on CIFAR

Qingyun Wang, Qi Zeng, Lifu Huang, Kevin Knight

To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison. A good review needs to be knowledgeable, namely that the comments should be constructive and informative to help improve the paper; and explainable by providing detailed evid

Yuanhe Tian, Yan Song, Fei Xia

Supertagging is conventionally regarded as an important task for combinatory categorial grammar (CCG) parsing, where effective modeling of contextual information is highly important to this task. However, existing studies have made limited efforts to leverage contextual features except for applying powerful encoders (e.g., bi-LSTM). In this paper, we propose

Taekho You, Oh-Hyun Kwon, Jisung Yoon, Woo-Sung Jung

Describing the evolution of science is a salient work not only for revealing the scientific trend but also for establishing a scientific classification system. In this paper, we investigate the evolution of science by observing the structure and change of keyword co-occurrence networks. Starting from seven target physics fields and their initial keywords sel

Lei Gu, Ruqian Wu

The Raman exponent of single-molecular magnetic relaxation may take various unexpected values because of rich phonon spectrum and spin-phonon coupling of molecular crystals. We systematically examine the origins of different abnormalities, and clarify misunderstandings in the past, particularly the appropriateness of the fitting procedures for the exponents.

Boris Goncharov, D. J. Reardon, R. M. Shannon, Xing-Jiang Zhu

Pulsar timing array projects measure the pulse arrival times of millisecond pulsars for the primary purpose of detecting nanohertz-frequency gravitational waves. The measurements include contributions from a number of astrophysical and instrumental processes, which can either be deterministic or stochastic. It is necessary to develop robust statistical and p

Charles Kemp, Duane W. Hamacher, Daniel R. Little, Simon J. Cropper

Cultures around the world organise stars into constellations, or asterisms, and these groupings are often considered to be arbitrary and culture-specific. Yet there are striking similarities in asterisms across cultures, and groupings such as Orion, the Big Dipper, the Pleiades and the Southern Cross are widely recognized across many different cultures. Psyc

Xiaoman Zhang, Shixiang Feng, Yuhang Zhou, Ya Zhang

Automatic and accurate tumor segmentation on medical images is in high demand to assist physicians with diagnosis and treatment. However, it is difficult to obtain massive amounts of annotated training data required by the deep-learning models as the manual delineation process is often tedious and expertise required. Although self-supervised learning (SSL) s

Movaffaq Kateb, Jon Tomas Gudmundsson, Snorri Ingvarsson

We present deposition and characterization of multilayers consisting of 20 repetitions of 15 $Å$ thick permalloy Ni$_{80}$Fe$_{20}$ at. \% (Py) and 5 $Å$ Pt. The samples were prepared by two different sputter deposition methods, namely dc magnetron sputtering (dcMS) and high power impulse magnetron sputtering (HiPIMS), that represent low and moderate ionized

Hua Liu, Songhua Tan, Qianqian Zhu

This paper investigates the quasi-maximum likelihood inference including estimation, model selection and diagnostic checking for linear double autoregressive (DAR) models, where all asymptotic properties are established under only fractional moment of the observed process. We propose a Gaussian quasi-maximum likelihood estimator (G-QMLE) and an exponential q

Xiaofei Huang, Nihang Fu, Shuangjun Liu, Sarah Ostadabbas

Infant motion analysis is a topic with critical importance in early childhood development studies. However, while the applications of human pose estimation have become more and more broad, models trained on large-scale adult pose datasets are barely successful in estimating infant poses due to the significant differences in their body ratio and the versatili

Sadataka Furui, Serge Dos Santos

Identification of crack positions or anomalies in materials using the time reversal based nonlinear elastic wave spectroscopy (TR-NEWS) is an established method. We propose a system using transducers which emit forward propagating solitonic wave and time-reversed propagating solitonic wave produced by memristers placed on a side of a rectangle and scattered

G. Lugones, A. G. Grunfeld

Droplets of absolutely stable strange quark matter (strangelets) immersed in a lepton background may be the energetically preferred composition of strange star crusts and of the interior of a new class of stars known as strangelet dwarfs. In this work we calculate the surface tension $σ$ and the curvature coefficient $γ$ of charged strangelets as a function

Feihu Huang, Shangqian Gao

In the paper, we study a class of useful minimax problems on Riemanian manifolds and propose a class of effective Riemanian gradient-based methods to solve these minimax problems. Specifically, we propose an effective Riemannian gradient descent ascent (RGDA) algorithm for the deterministic minimax optimization. Moreover, we prove that our RGDA has a sample

Sunny Verma, Chen Wang, Liming Zhu, Wei Liu

The principal component analysis network (PCANet) is an unsupervised parsimonious deep network, utilizing principal components as filters in its convolution layers. Albeit powerful, the PCANet consists of basic operations such as principal components and spatial pooling, which suffers from two fundamental problems. First, the principal components obtain info

Nima Moini

The purpose of this paper is to study the evolution of moving interacting particles on the mesoscopic scale. We will introduce an uncertainty principle and a new priori bound for the evolution of particles subject to a general mesoscopic interaction and use this setting to demonstrate evidence for the universal dispersion of particles in the absence of the c

Zi-Xiang Li, Steven A. Kivelson, Dung-Hai Lee

We present a theoretical framework for understanding the behavior of the normal and superconducting states of overdoped cuprate high temperature superconductors in the vicinity of the doping-tuned quantum superconductor-to-metal transition. The key ingredients on which we focus are d-wave pairing, a flat antinodal dispersion, and disorder. Even for homogeneo

Mauricio Cataldo, P. A. González, Joel Saavedra, Yerko Vázquez

In this work we study thermodynamics of 2+1-dimensional static black holes with a nonlinear electric field. Besides employing the standard thermodynamic approach, we investigate the black hole thermodynamics by studying its thermodynamic geometry. We compute the Weinhold and Ruppeiner metrics and compare the thermodynamic geometry with the standard descripti

Yash Kant, Abhinav Moudgil, Dhruv Batra, Devi Parikh

Recent Visual Question Answering (VQA) models have shown impressive performance on the VQA benchmark but remain sensitive to small linguistic variations in input questions. Existing approaches address this by augmenting the dataset with question paraphrases from visual question generation models or adversarial perturbations. These approaches use the combined

Stephen Guth, Themistoklis P. Sapsis

The rainflow counting algorithm for material fatigue is both simple to implement and extraordinarily successful for predicting material failure times. However, it neglects memory effects and time-ordering dependence, and therefore runs into difficulties dealing with highly intermittent or transient stochastic loads with heavy tailed distributions. Such loads

Shima Beigi

Washing hands, social distancing and staying at home are the preventive measures set in place to contain the spread of the COVID-19, a disease caused by SARS-CoV-2. These measures, although straightforward to follow, highlight the tip of an imbalanced socio-economic and socio-technological iceberg. Here, a System Dynamic (SD) model of COVID-19 preventive mea

M. Y. Hua, C. A. Bravo, A. T. MacDonald, J. D. Hutchinson

In this work, Rossi-alpha measurements were simultaneously performed with a $^3$He-based detection system and an organic scintillator-based detection system. The assembly is 15 kg of plutonium (93 wt$\%$ $^{239}$Pu) reflected by copper and moderated by lead. The goal of Rossi-alpha measurements is to estimate the prompt neutron decay constant, alpha. Simulat

Mariana Y. Noguti, Eduardo Vellasques, Luiz S. Oliveira

In recent years, there has been an increased interest in the application of Natural Language Processing (NLP) to legal documents. The use of convolutional and recurrent neural networks along with word embedding techniques have presented promising results when applied to textual classification problems, such as sentiment analysis and topic segmentation of doc

Yuma Kinoshita, Hitoshi Kiya

In this paper, we propose a novel convolutional neural network (CNN) that never causes checkerboard artifacts, for image enhancement. In research fields of image-to-image translation problems, it is well-known that images generated by usual CNNs are distorted by checkerboard artifacts which mainly caused in forward-propagation of upsampling layers. However,

Tsung-Ying Lu, Hsu-Hsun Chin, Hsin-I Wu, Ren-Song Tsay

In this paper, we propose a very compact embedded CNN processor design based on a modified logarithmic computing method using very low bit-width representation. Our high-quality CNN processor can easily fit into edge devices. For Yolov2, our processing circuit takes only 0.15 mm2 using TSMC 40 nm cell library. The key idea is to constrain the activation and

Aleksandar Miladinović, Miloš Ajčević, Agostino Accardo

Motor-imagery based brain-computer interfaces (MI-BCI) have the potential to become ground-breaking technologies for neurorehabilitation, the reestablishment of non-muscular communication and commands for patients suffering from neuronal disorders and disabilities, but also outside of clinical practice, for video game control and other entertainment purposes

Lixi Zhou, Zijie Wang, Amitabh Das, Jia Zou

In recent, deep learning has become the most popular direction in machine learning and artificial intelligence. However, preparation of training data is often a bottleneck in the lifecycle of deploying a deep learning model for production or research. Reusing models for inferencing a dataset can greatly save the human costs required for training data creatio

Francisco Gonzalez Ledesma, Matthew Mewes

The connection between spherical harmonics and symmetric tensors is explored. For each spherical harmonic, a corresponding traceless symmetric tensor is constructed. These tensors are then extended to include nonzero traces, providing an orthonormal angular-momentum eigenbasis for symmetric tensors of any rank. The relationship between the spherical-harmonic

Eduardo H. M. Cruz, João M. Maciel, Camila Clozato, Matheus S. Serpa

During the COVID-19 pandemic, many countries opted for strict public health measures, including closing schools. They have now started relaxing some of those restrictions. To avoid overwhelming health systems, predictions for the number of new COVID-19 cases need to be considered when choosing a school reopening strategy. Using computer simulation, we analyz

Krzysztof Urbanowski

Analyzing general uncertainty relations one can find that there can exist such pairs of non-commuting observables $A$ and $B$ and such vectors that the lower bound for the product of standard deviations $ΔA$ and $ΔB$ calculated for these vectors is zero: $ΔA\,\cdot\,ΔB \geq 0$. Here we discuss examples of such cases and some other inconsistencies which can b

Hannes Albers, Tobias Kluth, Tobias Knopp

Magnetic nanoparticles and their magnetization dynamics play an important role in many applications. We focus on magnetization dynamics in large ensembles of single domain nanoparticles being characterized by either Brownian or Néel rotation mechanisms. Simulations of the respective behavior are obtained by solving advection-diffusion equations on the sphere

Anurag Sarkar, Zhihan Yang, Seth Cooper

Prior research has shown variational autoencoders (VAEs) to be useful for generating and blending game levels by learning latent representations of existing level data. We build on such models by exploring the level design affordances and applications enabled by conditional VAEs (CVAEs). CVAEs augment VAEs by allowing them to be trained using labeled data, t

Nikita Johnson, Youcef Gheraibia, Tim Kelly

Safety Security Assurance Framework applied to two standards IEC 61508 and Common Criteria - ISO 15408

Daniel C. Elton, Peter D. Spencer

Pathological science occurs when well-intentioned scientists spend extended time and resources studying a phenomena that isn&#39;t real. Researchers who get caught up in pathological science are usually following the scientific method and performing careful experiments, but they get tricked by nature. The study of water has had several protracted episodes of

Ju Long, Hung-Ying Tai, Shen-Ta Hsieh, Michael Juntao Yuan

FaaS (Function as a Service) allows developers to upload and execute code in the cloud without managing servers. FaaS offerings from leading public cloud providers are based on system microVM or application container technologies such as Firecracker or Docker. In this paper, we demonstrate that lightweight high-level runtimes, such as WebAssembly, could offe

Loai Shakaa, Yusuf Abu Muhanna

We prove that the Bohr&#39; radius for large functions is $e^{-π}.$

M. Y. Hua, C. A. Bravo, R. M. Marchie, J. D. Hutchinson

The prompt neutron period (the negative reciprocal of the prompt neutron decay constant) can be estimated using the Rossi-alpha technique that is predicated on fitting Rossi-alpha histograms and of interest in nuclear criticality safety and nonproliferation [1, 2, 3]. The histograms are traditionally fit with a one-exponential model; however, recent work has

Javier Maroto, Clément Vignac, Pascal Frossard

Current state of the art algorithms for recommender systems are mainly based on collaborative filtering, which exploits user ratings to discover latent factors in the data. These algorithms unfortunately do not make effective use of other features, which can help solve two well identified problems of collaborative filtering: cold start (not enough data is av

Dimitrios Metaxas

I consider an Abelian gauge theory, with a complex scalar field and massless, chiral fermions, in the limit where the couplings and the interactions induce radiative symmetry breaking, and I derive and verify the Nielsen identities that ensure the gauge independence of the physical fermion mass that emerges in the broken phase of the effective action.

Mohammed Berkani

In this paper, we define an analytical index for continuous families of Fredholm operators parameterized by a topological space $\mathbb{X}$ into a Banach space $X.$ We also consider the Weyl spectrum for continuous families of bounded linear operators and we study its continuity.

Mikhail Khenner, Victor Henner

Evolution of composition patterns in the annealed, single-crystal surface alloy film is considered in the presence of the spinodal decomposition, the compositional stress and the diffusion anisotropy. While the former two effects contribute to overall phase separation, the anisotropy, correlated with the surface crystallographic orientation, guides the in-pl

Felipe Gomez-Cuba, Tommaso Zugno, Junseok Kim, Michele Polese

This paper analyzes Hybrid Beamforming (HBF) and Multi-User Multiple-Input Multiple-Output (MU-MIMO) in millimeter wave (mmWave) 5th generation (5G) cellular networks considering the full protocol stack with TCP/IP traffic and MAC scheduling. Prior work on HBF and MU-MIMO has assumed full-buffer transmissions and studied link-level performance. We report non

Marcus J. Daum, Arun Ramanathan, Alexander I. Kolesnikov, Stuart A. Calder

Thermomagnetic and inelastic neutron scattering measurements on Na2PrO3 are reported. This material is an antiferromagnetic honeycomb magnet based on the tetravalent lanthanide Pr4+ and has been proposed to host dominant antiferromagnetic Kitaev interactions. These measurements reveal magnetic fluctuations in Na2PrO3 below an energy of 2 meV as well as cryst

David A Benson, Diogo Bolster, Stephen Pankavich, Michael J Schmidt

Traditional interpolation techniques for particle tracking include binning and convolutional formulas that use pre-determined (i.e., closed-form, parameteric) kernels. In many instances, the particles are introduced as point sources in time and space, so the cloud of particles (either in space or time) is a discrete representation of the Green&#39;s function

Pulkit Sharma, Shezan Rohinton Mirzan, Apurva Bhandari, Anish Pimpley

Understanding predictions made by Machine Learning models is critical in many applications. In this work, we investigate the performance of two methods for explaining tree-based models- Tree Interpreter (TI) and SHapley Additive exPlanations TreeExplainer (SHAP-TE). Using a case study on detecting anomalies in job runtimes of applications that utilize cloud-

Claudia Malvenuto, Christophe Reutenauer

The character theory of symmetric groups, and the theory of symmetric functions, both make use of the combinatorics of Young tableaux, such as the Robinson-Schensted algorithm, Schuetzenberger&#39;s &#34;jeu de taquin&#34;, and evacuation. In 1995 Poirier and the second author introduced some algebraic structures, different from the plactic monoid, which ind

Romildo Pina, Ilton Menezes

In this paper we consider $ρ$-Einstein solitons of type $M= \left(B^n, g^{*}\right) \times (F^m,g_F)$, where $\left(B^n,g^{*}\right)$ is conformal to a pseudo-Euclidean space and invariant under the action of the pseudo-orthogonal group, and $\left(F^m,g_{F}\right)$ is an Einstein manifold. We provide all the solutions for the gradient Schouten soliton case.

Muhao Chen, Hongming Zhang, Haoyu Wang, Dan Roth

This paper studies a new cognitively motivated semantic typing task, multi-axis event process typing, that, given an event process, attempts to infer free-form type labels describing (i) the type of action made by the process and (ii) the type of object the process seeks to affect. This task is inspired by computational and cognitive studies of event underst

Behzat Ergun, Qianyu Hao, Andrew Neitzke, Fei Yan

It is known that some theories of class $S$ are actually factorized into multiple decoupled nontrivial four-dimensional $N=2$ theories. We propose a way of constructing examples of this phenomenon using the physics of half-BPS surface defects, and check that it works in one simple example: it correctly reproduces a known realization of two copies of $N=2$ su

Xiangyu Zhang, Rohit Chintala, Andrey Bernstein, Peter Graf

In this paper, we develop a grid-interactive multi-zone building controller based on a deep reinforcement learning (RL) approach. The controller is designed to facilitate building operation during normal conditions and demand response events, while ensuring occupants comfort and energy efficiency. We leverage a continuous action space RL formulation, and dev

Oleg Vasilyev, Vedant Dharnidharka, Nicholas Egan, Charlene Chambliss

We explore the sensitivity of a document summary quality estimator, BLANC, to human assessment of qualities for the same summaries. In our human evaluations, we distinguish five summary qualities, defined by how fluent, understandable, informative, compact, and factually correct the summary is. We make the case for optimal BLANC parameters, at which the BLAN

Liam Fowl, Micah Goldblum, Arjun Gupta, Amr Sharaf

Generative models are increasingly able to produce remarkably high quality images and text. The community has developed numerous evaluation metrics for comparing generative models. However, these metrics do not effectively quantify data diversity. We develop a new diversity metric that can readily be applied to data, both synthetic and natural, of any type.

Jiaxin Huang, Yiqing Xie, Yu Meng, Yunyi Zhang

Taxonomy is not only a fundamental form of knowledge representation, but also crucial to vast knowledge-rich applications, such as question answering and web search. Most existing taxonomy construction methods extract hypernym-hyponym entity pairs to organize a &#34;universal&#34; taxonomy. However, these generic taxonomies cannot satisfy user&#39;s specific

Lizxandra Flores-Rivera, Mario Flock, Ryohei Nakatani

In recent years hydrodynamical (HD) models have become important to describe the gas kinematics in protoplanetary disks, especially in combination with models of photoevaporation and/or magnetic-driven winds. We focus on diagnosing the the vertical extent of the VSI at 203 cells per scale height and allude at what resolution per scale height we obtain conver

Jorge A. V. Tohalino, Diego R. Amancio

This manuscript provides a short and practical introduction to the topic of language networks. This text aims at assisting researchers with no practical experience in text and/or network analysis. We provide a practical tutorial on how to model and characterize texts using network-based features. In this tutorial, we also include examples of pre-processing a

Xingyu Zhou, Jian Tan

Motivated by the increasing concern about privacy in nowadays data-intensive online learning systems, we consider a black-box optimization in the nonparametric Gaussian process setting with local differential privacy (LDP) guarantee. Specifically, the rewards from each user are further corrupted to protect privacy and the learner only has access to the corru

Howard E. Bond

I report the discovery that the 9th-magnitude Galactic-halo star BD+14$^\circ$3061 is a member of the rare class of luminous metal-poor &#34;yellow post-AGB&#34; stars. Its Gaia DR2 parallax implies an absolute magnitude of $M_V=-3.44\pm0.27$, and it is a very high-velocity star moving in a retrograde Galactic orbit. BD+14$^\circ$3061 is a field analog of th

Haoran Zhang, Adney Cardoza, Peter Baile Chen, Sebastian Angel

This paper introduces Beldi, a library and runtime system for writing and composing fault-tolerant and transactional stateful serverless functions. Beldi runs on existing providers and lets developers write complex stateful applications that require fault tolerance and transactional semantics without the need to deal with tasks such as load balancing or main

Jiaxin Huang, Yu Meng, Fang Guo, Heng Ji

Aspect-based sentiment analysis of review texts is of great value for understanding user feedback in a fine-grained manner. It has in general two sub-tasks: (i) extracting aspects from each review, and (ii) classifying aspect-based reviews by sentiment polarity. In this paper, we propose a weakly-supervised approach for aspect-based sentiment analysis, which

Lorenzo Marino

We establish global Schauder estimates for integro-partial differential equations (IPDE) driven by a possibly degenerate Lévy Ornstein-Uhlenbeck operator, both in the elliptic and parabolic setting, using some suitable anisotropic Hölder spaces. The class of operators we consider is composed by a linear drift plus a Lévy operator that is comparable, in a sui

Woojoo Lee, Chi-Ruei Pan, Hyoungdo Nam, Mei-Yin Chou

We use angle resolved photoemission spectroscopy (ARPES) to investigate the coupling of electron quantum well states (QWS) in epitaxial thin Pb and Ag films. More specifically, we investigate the Ag/Si, Pb/Si, and Pb/Ag/Si systems. We found that the parallel momentum plays a very profound role determining how two adjacent quantum wells are coupled electronic

Hongru Chen, Nicolas Rambaux, Jérémie Vaubaillon

Aims. The knowledge of the orbits and origins derived from meteors is important for the study of meteoroids and of the early solar system. With an increase in nano-satellite projects dedicated to Earth observations or directly to meteor observations (e.g., the Meteorix CubeSat), we investigate the stereoscopic measurement of meteor positions using a pair of

A. Deur

Observations indicate that the baryonic matter of galaxies is surrounded by vast dark matter halos, which nature remains unknown. This document details the analysis of the results published in MNRAS 438, 2, 1535 (2014) reporting an empirical correlation between the ellipticity of elliptical galaxies and their dark matter content. Large and homogeneous sample

Youssef Anzarmou, Abdallah Mkhadri, Karim Oualkacha

Accounting for important interaction effects can improve prediction of many statistical learning models. Identification of relevant interactions, however, is a challenging issue owing to their ultrahigh-dimensional nature. Interaction screening strategies can alleviate such issues. However, due to heavier tail distribution and complex dependence structure of

Radja Boughezal, Chien-Yi Chen, Frank Petriello, Daniel Wiegand

We discuss how four-lepton decays of the Z-boson probe currently unconstrained flat directions in the parameter space of the Standard Model Effective Field Theory (SMEFT). We derive the constraints from these decays on four-lepton operators in the SMEFT and show how the LHC data for this process complements probes from neutrino-trident production. Future dif

You Li, Clément Le Bihan, Txomin Pourtau, Thomas Ristorcelli

LiDARs are usually more accurate than cameras in distance measuring. Hence, there is strong interest to apply LiDARs in autonomous driving. Different existing approaches process the rich 3D point clouds for object detection, tracking and recognition. These methods generally require two initial steps: (1) filter points on the ground plane and (2) cluster non-

A. C. Raga, A. Rodriguez-Gonzalez, L. Hernande-Martinez, J. Canto

We present a model of a hypersonic, collimated, &#34;single pulse&#34; outflow, produced by an event with an ejection velocity that first grows, reaches a peak, and then decreases again to zero velocity in a finite time (simultaneously, the ejection density can have an arbitrary time-variability). We obtain a flow with a leading &#34;head&#34; and a trailing

Y. Zhao, D. Feng, Y. Hu, S. Guo

We numerically study the entanglement dynamics of free fermions on a cubic lattice with potential disorder following a quantum quench. We focus, in particular, on the metal-insulator transition at a critical disorder strength and compare the results to the putative many-body localization (MBL) transition in interacting one-dimensional systems. We find that a

Jeff Delaune, David S. Bayard, Roland Brockers

xVIO is a range-visual-inertial odometry algorithm implemented at JPL. It has been demonstrated with closed-loop controls on-board unmanned rotorcraft equipped with off-the-shelf embedded computers and sensors. It can operate at daytime with visible-spectrum cameras, or at night time using thermal infrared cameras. This report is a complete technical descrip

Yixin Gao, Noah D. Stein, Chieh-Chi Kao, Yunliang Cai

Wake word (WW) spotting is challenging in far-field due to the complexities and variations in acoustic conditions and the environmental interference in signal transmission. A suite of carefully designed and optimized audio front-end (AFE) algorithms help mitigate these challenges and provide better quality audio signals to the downstream modules such as WW s

Pritam Chattopadhyay, Tanmoy Pandit, Ayan Mitra, Goutam Paul

Quantum heat cycles and quantum refrigerators are analyzed using various quantum systems as their working mediums. For example, to evaluate the efficiency and the work done of the Carnot cycle in the quantum regime, one can consider the harmonic oscillator as it&#39;s working medium. For all these well-defined working substances (which are analyzed in commut