October 2020 arXiv papers — page 69

Jieqi Shi, Peiliang Li, Shaojie Shen

A robust 3D object tracker which continuously tracks surrounding objects and estimates their trajectories is key for self-driving vehicles. Most existing tracking methods employ a tracking-by-detection strategy, which usually requires complex pair-wise similarity computation and neglects the nature of continuous object motion. In this paper, we propose to di

Lei Cai, Zhengyang Wang, Rob Kulathinal, Sudhir Kumar

Predictive modeling is useful but very challenging in biological image analysis due to the high cost of obtaining and labeling training data. For example, in the study of gene interaction and regulation in Drosophila embryogenesis, the analysis is most biologically meaningful when in situ hybridization (ISH) gene expression pattern images from the same devel

Byungjoo Kim, Bryce Chudomelka, Jinyoung Park, Jaewoo Kang

Deep neural networks have achieved state-of-the-art performance in a variety of fields. Recent works observe that a class of widely used neural networks can be viewed as the Euler method of numerical discretization. From the numerical discretization perspective, Strong Stability Preserving (SSP) methods are more advanced techniques than the explicit Euler me

Lei Cai, Jundong Li, Jie Wang, Shuiwang Ji

We consider the graph link prediction task, which is a classic graph analytical problem with many real-world applications. With the advances of deep learning, current link prediction methods commonly compute features from subgraphs centered at two neighboring nodes and use the features to predict the label of the link between these two nodes. In this formali

Xiachong Feng, Xiaocheng Feng, Bing Qin, Ting Liu

Abstractive dialogue summarization is the task of capturing the highlights of a dialogue and rewriting them into a concise version. In this paper, we present a novel multi-speaker dialogue summarizer to demonstrate how large-scale commonsense knowledge can facilitate dialogue understanding and summary generation. In detail, we consider utterance and commonse

R. B. MacKenzie, M. B. Paranjape, U. A. Yajnik

We propose the origins of Dark Energy in a hidden sector with a pair of very light fermions, oppositely charged under an abelian gauge force $U(1)_X$ but of unequal mass. The system is dubbed PAAI, plasma which is abelian, asymmetric and idealised. For a range of the hidden fine structure constant values and the value of mass of the lightest fermion the PAAI

Rabab Abdelfattah, Xiaofeng Wang, Song Wang

Accurate detection and segmentation of transmission towers~(TTs) and power lines~(PLs) from aerial images plays a key role in protecting power-grid security and low-altitude UAV safety. Meanwhile, aerial images of TTs and PLs pose a number of new challenges to the computer vision researchers who work on object detection and segmentation -- PLs are long and t

Shun Uchida

In this paper, we consider a doubly nonlinear parabolic equation $ \partial _t β(u) - \nabla \cdot α(x , \nabla u) \ni f$ with the homogeneous Dirichlet boundary condition in a bounded domain, where $β: \mathbb{R} \to 2 ^{ \mathbb{R} }$ is a maximal monotone graph satisfying $0 \in β(0)$ and $ \nabla \cdot α(x , \nabla u )$ stands for a generalized $p$-Lapla

Venkatesh Choppella, Viswanath Kasturi, Mrityunjay Kumar, Ojas Mohril

The importance of algorithms and data structures in computer science curricula has been amply recognized. For many students, however, gaining a good understanding of algorithms remains a challenge. Because of the automated nature of sequential algorithms. there is an inherent tension in directly applying the `learning by doing' approach. This partly expl

Nabin Kumar Meher, Sudhansu Sekhar Rout

Let $(U_n)_{n\geq 0}$ be a fixed linear recurrence sequence of integers with order at least two, and for any positive integer $\ell$, let $\ell \cdot 2^{\ell} + 1$ be a Cullen number. Recently in \cite{bmt}, generalized Cullen numbers in terms of linear recurrence sequence $(U_n)_{n\geq 0}$ under certain weak assumptions has been studied. However, there is a

Luis Leal, Marvin Castillo, Fernando Juarez, Erick Ramirez

Medical head CT-scan imaging has been successfully combined with deep learning for medical diagnostics of head diseases and lesions[1]. State of the art classification models and algorithms for this task usually are based on 3d convolution layers for volumetric data on a supervised learning setting (1 input volume, 1 prediction per patient) or 2d convolution

Yi-An Li, Song Zhang, Yu-Gang Ma

$α$-clustered structures in light nuclei could be studied through "snapshots" taken by relativistic heavy-ion collisions. A multiphase transport (AMPT) model is employed to simulate the initial structure of collision nuclei and the proceeding collisions at center of mass energy $\sqrt{s_{NN}}$ = 6.37 TeV. This initial structure can finally be reflect

Hai Wang, Wei-Shi Zheng, Ling Yingbiao

Human-object interaction(HOI) detection is an important task for understanding human activity. Graph structure is appropriate to denote the HOIs in the scene. Since there is an subordination between human and object---human play subjective role and object play objective role in HOI, the relations between homogeneous entities and heterogeneous entities in the

Chien-Chuan Su, Ren Wang, Hung-Jin Lin, Yu-Lun Liu

Tone-mapping plays an essential role in high dynamic range (HDR) imaging. It aims to preserve visual information of HDR images in a medium with a limited dynamic range. Although many works have been proposed to provide tone-mapped results from HDR images, most of them can only perform tone-mapping in a single pre-designed way. However, the subjectivity of to

Dongkeun Lee, Wonmin Son

For the identification of non-trivial quantum phase, we exploit a Bell-type correlation that is applied to the one-dimensional spin-1 XXZ chain. It is found that our generalization of bipartite Bell correlation can take a decomposed form of transverse spin correlation together with high-order terms. The formulation of density-matrix renormalisation group is

Sisi Li, Qinghua Zhang, Shan Lin, Xiahan Sang

Low-dimensional quantum materials that remain strongly ferromagnetic down to mono layer thickness are highly desired for spintronic applications. Although oxide materials are important candidates for next generation of spintronic, ferromagnetism decays severely when the thickness is scaled to the nano meter regime, leading to deterioration of device performa

Haohan Wang, Peiyan Zhang, Eric P. Xing

Neural machine translation has achieved remarkable empirical performance over standard benchmark datasets, yet recent evidence suggests that the models can still fail easily dealing with substandard inputs such as misspelled words, To overcome this issue, we introduce a new encoding heuristic of the input symbols for character-level NLP models: it encodes th

Eduardo Mojica-Nava, David Yanguas-Rojas, César A. Uribe

We consider the model of cooperative learning via distributed non-Bayesian learning, where a network of agents tries to jointly agree on a hypothesis that best described a sequence of locally available observations. Building upon recently proposed weak communication network models, we propose a robust cooperative learning rule that allows asynchronous commun

Calvin Kielas-Jensen, Venanzio Cichella

This paper presents a method and an open-source implementation, Bernstein/Bézier Optimal Trajectories (BeBOT), for the generation of trajectories for autonomous system operations. The proposed method is based on infinite dimensional optimal control formulations of trajectory generation problems. By approximating the trajectories using Bernstein polynomials,

Charley Gros, Andreanne Lemay, Olivier Vincent, Lucas Rouhier

ivadomed is an open-source Python package for designing, end-to-end training, and evaluating deep learning models applied to medical imaging data. The package includes APIs, command-line tools, documentation, and tutorials. ivadomed also includes pre-trained models such as spinal tumor segmentation and vertebral labeling. Original features of ivadomed includ

Kanako Oshiro

Given a quandle, we can construct a symmetric quandle called the symmetric double of the quandle. We show that the (co)homology groups of a given quandle are isomorphic to those of its symmetric double. Moreover, quandle coloring numbers and quandle cocycle invariants of oriented links and oriented surface-links can be interpreted by using symmetric quandles

Yuqian Fu, Li Zhang, Junke Wang, Yanwei Fu

Humans can easily recognize actions with only a few examples given, while the existing video recognition models still heavily rely on the large-scale labeled data inputs. This observation has motivated an increasing interest in few-shot video action recognition, which aims at learning new actions with only very few labeled samples. In this paper, we propose

Bing Wang

We localize the entropy functionals of G. Perelman and generalize his no-local-collapsing theorem and pseudo-locality theorem. Our generalization is technically inspired by further development of Li-Yau estimates along the Ricci flow. It has various applications, including to show the continuous dependence of the Ricci flow with respect to the initial metric

Sung-Il Baik, Ratnesh K. Gupta, K. Sharvan Kumar, David N Seidman

A 10 wt.% nickel-steel has been developed for high pressures and low-temperature applications, due to its high strength, excellent toughness, and low ductile-to-brittle transition temperature (DBTT). Under dynamic loading conditions this steel is, however, prone to shear localization that manifests as adiabatic shear bands (ASBs). The temperature increases a

Yi-Fan Song, Zhang Zhang, Caifeng Shan, Liang Wang

One essential problem in skeleton-based action recognition is how to extract discriminative features over all skeleton joints. However, the complexity of the State-Of-The-Art (SOTA) models of this task tends to be exceedingly sophisticated and over-parameterized, where the low efficiency in model training and inference has obstructed the development in the f

Yusuke Hisai, Daisuke Akamatsu, Takumi Kobayashi, Kazumoto Hosaka

We report improved frequency ratio measurement with $^{87}$Sr and $^{171}$Yb optical lattice clocks at the National Metrology Institute of Japan (NMIJ). The $^{87}$Sr optical lattice clock is enhanced with several major modifications and is re-evaluated with a reduced uncertainty of $1.1\times10^{-16}$. We employed a $^{171}$Yb optical lattice clock with an

Danqing Shi, Xinyue Xu, Fuling Sun, Yang Shi

Visual data stories shown in the form of narrative visualizations such as a poster or a data video, are frequently used in data-oriented storytelling to facilitate the understanding and memorization of the story content. Although useful, technique barriers, such as data analysis, visualization, and scripting, make the generation of a visual data story diffic

Tian-Ran Yang, Yu-Yu Zhang, Hui Dong, Libin Fu

To take quantum advantage of collective effects in many-body system, we design an elementary block for building multipartite quantum battery, which enables charging an atomic ensemble with optimal numbers in a common thermal bath. One achieves maximum free energy as the stored energy in the steady state, which is prior to each atom parallel charging independ

Kin Wai Cheuk, Yin-Jyun Luo, Emmanouil Benetos, Dorien Herremans

Most of the state-of-the-art automatic music transcription (AMT) models break down the main transcription task into sub-tasks such as onset prediction and offset prediction and train them with onset and offset labels. These predictions are then concatenated together and used as the input to train another model with the pitch labels to obtain the final transc

Farhad Farokhi, Girish Nair

We consider private function evaluation to provide query responses based on private data of multiple untrusted entities in such a way that each cannot learn something substantially new about the data of others. First, we introduce perfect non-stochastic privacy in a two-party scenario. Perfect privacy amounts to conditional unrelatedness of the query respons

Boris Runov

Einstein-Rosen waves with two polarizations are cylindrically symmetric solutions to vacuum Einstein equations. Einstein equations in this case reduce to an integrable system. In 1971, Geroch has shown that this system admits an infinite-dimensional group of symmetry transformations known as the Geroch group. The phase space of this system can be parametrize

Yu-Lin Chou

We prove that every nonnegative continuous real-valued function on a given compact metric space is the uniform limit of some increasing sequence of nonnegative simple functions being linear combinations of indicators of open sets; here the nontriviality is relative to the standard choice(s) of approximating simple functions for measurable functions, where on

Sayyed Jaffar Ali Raza, Apan Dastider, Mingjie Lin

In this paper we present a Bayesian reinforcement learning framework that allows robotic manipulators to adaptively recover from random mechanical failures autonomously, hence being survivable. To this end, we formulate the framework of Bayesian Policy Morphing (BPM) that enables a robot agent to self-modify its learned policy after the diminution of its man

Dongsoo Lee

Using Freedman and Quinn's result for $\mathbb{Z}$-homology 3-spheres, we show that a 3-dimensional homology handle with trivial Alexander polynomial bounds a homology $S^1\times D^3$. As a consequence, a distinguished homology handle with trivial Alexander polynomial is topologically null $\widetilde{H}$-cobordant.

Ximin Li, Xiaodong Wei, Xiaowei Qin

Keyword Spotting (KWS) plays a vital role in human-computer interaction for smart on-device terminals and service robots. It remains challenging to achieve the trade-off between small footprint and high accuracy for KWS task. In this paper, we explore the application of multi-scale temporal modeling to the small-footprint keyword spotting task. We propose a

W. Horiuchi, Y. Suzuki, T. Uesaka, M. Miwa

Background: Eclipse effect of the neutron and proton in a deuteron target is essential to correctly describe high-energy deuteron scattering. The nucleus-deuteron scattering needs information not only on the nucleus-proton but also the nucleus-neutron interaction, for which no direct measurement of the nucleus-neutron cross sections is available for unstable

Qiong Xu, Jeff Wang, Hiroki Shirato, Lei Xing

This paper presents a dictionary learning-based method with region-specific image patches to maximize the utility of the powerful sparse data processing technique for CT image reconstruction. Considering heterogeneous distributions of image features and noise in CT, region-specific customization of dictionaries is utilized in iterative reconstruction. Thorac

Saeed Ghorbani, Calden Wloka, Ali Etemad, Marcus A. Brubaker

We present a probabilistic framework to generate character animations based on weak control signals, such that the synthesized motions are realistic while retaining the stochastic nature of human movement. The proposed architecture, which is designed as a hierarchical recurrent model, maps each sub-sequence of motions into a stochastic latent code using a va

Ziteng Wang, Tara Murphy, David L. Kaplan, Keith W. Bannister

We discuss observational strategies to detect prompt bursts associated with gravitational wave events using the Australian Square Kilometre Array Pathfinder (ASKAP). Many theoretical models of binary neutron stars mergers predict that bright, prompt radio emission would accompany the merger. The detection of such prompt emission would greatly improve our kno

Sreeja Nag, Philip Dabney, Vinay Ravindra, Cody Anderson

The Earth Observation planning community has access to tools that can propagate orbits and compute coverage of Earth observing imagers with customizable shapes and orientation, model the expected Earth Reflectance at various bands, epochs and directions, generate simplified instrument performance metrics for imagers and radars, and schedule single and multip

Yuheng Xing, Hao Chen, Ning Xu, Xiao Li

Owing to its charge-free property, magnon is highly promising to achieve dissipationless transport without Joule heating and thus potentially applicable to energy-efficient devices. Moreover, a kagome lattice, as stacking layers of many magnon ferromagnets, also exhibits valley structure in quasiparticle spectra, which are likely to add a new dimension to ma

Xun Guo, Hao Wang, Shijun Zhao, Ke Jin

Energy loss of energetic ions in solid is crucial in many field, and accurate prediction of the ion stopping power is a long-time goal. Though great efforts have been made, it is still very difficult to find a universal prediction model to accurately calculate the ion stopping power in distinct target materials. Deep learning algorithm is a newly emerged met

Amarjit Budhiraja, Nicolas Fraiman, Adam Waterbury

We propose two numerical schemes for approximating quasi-stationary distributions (QSD) of finite state Markov chains with absorbing states. Both schemes are described in terms of certain interacting chains in which the interaction is given in terms of the total time occupation measure of all particles in the system and has the impact of reinforcing transiti

Sreeja Nag, Alan S. Li, Vinay Ravindra, Marc Sanchez Net

Small spacecraft now have precise attitude control systems available commercially, allowing them to slew in 3 degrees of freedom, and capture images within short notice. When combined with appropriate software, this agility can significantly increase response rate, revisit time and coverage. In prior work, we have demonstrated an algorithmic framework that c

Shailesh Nirgudkar, Tianyu Ding

This paper describes a methodology to detect sepsis ahead of time by analyzing hourly patient records. The Physionet 2019 challenge consists of medical records of over 40,000 patients. Using imputation and weak ensembler technique to analyze these medical records and 3-fold validation, a model is created and validated internally. The model achieved an accura

Priya Mani, Carlotta Domeniconi, Igor Griva

Manifold regularization methods for matrix factorization rely on the cluster assumption, whereby the neighborhood structure of data in the input space is preserved in the factorization space. We argue that using the k-neighborhoods of all data points as regularization constraints can negatively affect the quality of the factorization, and propose an unsuperv

Faeze Brahman, Alexandru Petrusca, Snigdha Chaturvedi

Automatically generating stories is a challenging problem that requires producing causally related and logical sequences of events about a topic. Previous approaches in this domain have focused largely on one-shot generation, where a language model outputs a complete story based on limited initial input from a user. Here, we instead focus on the task of inte

Ju-Seung Byun, Byungmoon Kim, Huamin Wang

In this paper, we propose a new algorithm PPG (Proximal Policy Gradient), which is close to both VPG (vanilla policy gradient) and PPO (proximal policy optimization). The PPG objective is a partial variation of the VPG objective and the gradient of the PPG objective is exactly same as the gradient of the VPG objective. To increase the number of policy update

A. S. Panasenko

The well-known Formanek's module finiteness theorem states that every unital prime PI-algebra (i.e. a central order in a matrix algebra by Posner's theorem) embeds into a finitely generated module over its center. An analogue of this theorem for alternative and Jordan algebras was earlier proved by V.N.Zhelyabin and the author. In this paper we discu

Aamodh Suresh, Sonia Martinez

This work investigates the design of risk-perception-aware motion-planning strategies that incorporate non-rational perception of risks associated with uncertain spatial costs. Our proposed method employs the Cumulative Prospect Theory (CPT) to generate a perceived risk map over a given environment. CPT-like perceived risks and path-length metrics are then c

An Zou, Sajag Poudel, Manish Gupta, Shalabh C. Maroo

Experiments of water wicking in 1D silicon-dioxide nanochannels of heights 59 nm, 87 nm, 124 nm and 1015 nm are used to estimate the disjoining pressure of water which was found to be as high as ~1.5 MPa while exponentially decreasing with increasing channel height. Such a relation resulting from curve fitting of experimentally-derived data was implemented a

Sai Li, Zijian Guo

Instrumental variable methods are widely used for inferring the causal effect in the presence of unmeasured confounders. Existing instrumental variable methods for nonlinear outcome models require stringent identifiability conditions. This paper considers a flexible semi-parametric potential outcome model that allows for possibly invalid instruments. We prop

Cheng Meng, Jun Yu, Jingyi Zhang, Ping Ma

Sufficient dimension reduction is used pervasively as a supervised dimension reduction approach. Most existing sufficient dimension reduction methods are developed for data with a continuous response and may have an unsatisfactory performance for the categorical response, especially for the binary-response. To address this issue, we propose a novel estimatio

Amirhossein Taghvaei, Prashant G. Mehta, Tryphon T. Georgiou

This paper is concerned with optimality and stability analysis of a family of ensemble Kalman filter (EnKF) algorithms. EnKF is commonly used as an alternative to the Kalman filter for high-dimensional problems, where storing the covariance matrix is computationally expensive. The algorithm consists of an ensemble of interacting particles driven by a feedbac

P. M. Petryakova

We consider a possible ways of the dynamics of a homogeneous and isotropic space described by the FLRW metric in the framework of cubic f(R) gravity in the absence of matter. This article points an method for limiting the parameters of extended gravity models. We propose and develop a method for f(R) gravity models based on the dynamics of metrics for variou

Maciej Besta, Florian Marending, Edgar Solomonik, Torsten Hoefler

Vectorization and GPUs will profoundly change graph processing. Traditional graph algorithms tuned for 32- or 64-bit based memory accesses will be inefficient on architectures with 512-bit wide (or larger) instruction units that are already present in the Intel Knights Landing (KNL) manycore CPU. Anticipating this shift, we propose SlimSell: a vectorizable g

Jameson Graber, Alan Mullenix, Laurent Pfeiffer

We analyze a system of partial differential equations that model a potential mean field game of controls, briefly MFGC. Such a game describes the interaction of infinitely many negligible players competing to optimize a personal value function that depends in aggregate on the state and, most notably, control choice of all other players. A solution of the sys

Yuan Yuan, Kristen M. Altenburger, Farshad Kooti

Randomized experiments, or "A/B" tests, remain the gold standard for evaluating the causal effect of a policy intervention or product change. However, experimental settings, such as social networks, where users are interacting and influencing one another, may violate conventional assumptions of no interference for credible causal inference. Existing solution

Mohammad Sadek

Let $K$ be a number field. Given a polynomial $f(x)\in K[x]$ of degree $d\ge 2$, it is conjectured that the number of preperiodic points of $f$ is bounded by a uniform bound that depends only on $d$ and $[K:\mathbb Q]$. However, the only examples of parametric families of polynomials with no preperiodic points are known when $d$ is divisible by either $2$ or

Sharon Zhang, Amit Moscovich, Amit Singer

We consider problems of dimensionality reduction and learning data representations for continuous spaces with two or more independent degrees of freedom. Such problems occur, for example, when observing shapes with several components that move independently. Mathematically, if the parameter space of each continuous independent motion is a manifold, then thei

Ilya Valmianski, Nave Frost, Navdeep Sood, Yang Wang

Symptom checkers have emerged as an important tool for collecting symptoms and diagnosing patients, minimizing the involvement of clinical personnel. We developed a machine-learning-backed system, SmartTriage, which goes beyond conventional symptom checking through a tight bi-directional integration with the electronic medical record (EMR). Conditioned on EM

Ruiqi Ni, Teseo Schneider, Daniele Panozzo, Zherong Pan

Generating locally optimal UAV-trajectories is challenging due to the non-convex constraints of collision avoidance and actuation limits. We present the first local, optimization-based UAV-trajectory generator that simultaneously guarantees the validity and asymptotic optimality for known environments. \textit{Validity:} Given a feasible initial guess, our a

D. Collomb, S. J. Bending, A. E. Koshelev, M. P. Smylie

In this letter, we describe quantitative magnetic imaging of superconducting vortices in RbEuFe$_4$As$_4$ in order to investigate the unique interplay between the magnetic and superconducting sublattices. Our scanning Hall microscopy data reveal a pronounced suppression of the superfluid density near the magnetic ordering temperature in good qualitative agre

Fredrik Andreassen, Boris Kruglikov

We review computations of joint invariants on a linear symplectic space, discuss variations for an extension of group and space and relate this to other equivalence problems and approaches, most importantly to differential invariants.

Laura E. Liao, Jonathan Carruthers, Sophie J. Smither, CL4 Virology Team

Mathematical modelling has successfully been used to provide quantitative descriptions of many viral infections, but for the Ebola virus, which requires biosafety level 4 facilities for experimentation, modelling can play a crucial role. Ebola modelling efforts have primarily focused on in vivo virus kinetics, e.g., in animal models, to aid the development o

Holger Boche, Yannik Böck, Christian Deppe

Fekete's lemma is a well known result from combinatorial mathematics that shows the existence of a limit value related to super- and subadditive sequences of real numbers. In this paper, we analyze Fekete's lemma in view of the arithmetical hierarchy of real numbers by Zheng and Weihrauch and fit the results into an information-theoretic context. We introduc

Sarala Padi, Dinesh Manocha, Ram D. Sriram

We present a Multi-Window Data Augmentation (MWA-SER) approach for speech emotion recognition. MWA-SER is a unimodal approach that focuses on two key concepts; designing the speech augmentation method and building the deep learning model to recognize the underlying emotion of an audio signal. Our proposed multi-window augmentation approach generates addition

Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu

Data augmentation helps neural networks generalize better by enlarging the training set, but it remains an open question how to effectively augment graph data to enhance the performance of GNNs (Graph Neural Networks). While most existing graph regularizers focus on manipulating graph topological structures by adding/removing edges, we offer a method to augm

Xavier Puig, Tianmin Shu, Shuang Li, Zilin Wang

In this paper, we introduce Watch-And-Help (WAH), a challenge for testing social intelligence in agents. In WAH, an AI agent needs to help a human-like agent perform a complex household task efficiently. To succeed, the AI agent needs to i) understand the underlying goal of the task by watching a single demonstration of the human-like agent performing the sa

Amir Kalev, Itay Hen

The Dyson series is an infinite sum of multi-dimensional time-ordered integrals, which serves as a formal representation of the quantum time-evolution operator in the interaction-picture. Using the mathematical tool of divided differences, we introduce an alternative representation for the series that is entirely free from both time ordering and integrals. I

Skanda Vivek

The rise of connected and autonomous vehicles, combined with the proliferation of IoT and connected surfaces, lead to the emergence of novel complex cyber risks. Lack of encryption and authentication in internal vehicular networks are widely recognized as cause for concern by cybersecurity experts, automobile, and OEM manufacturers. This concern has only bee

Seyone Chithrananda, Gabriel Grand, Bharath Ramsundar

GNNs and chemical fingerprints are the predominant approaches to representing molecules for property prediction. However, in NLP, transformers have become the de-facto standard for representation learning thanks to their strong downstream task transfer. In parallel, the software ecosystem around transformers is maturing rapidly, with libraries like HuggingFa

William J. Keith, Fabrizio Zanello

We investigate the parity of the coefficients of certain eta-quotients, extensively examining the case of $m$-regular partitions. Our theorems concern the density of their odd values, in particular establishing lacunarity modulo 2 for specified coefficients; self-similarities modulo 2; and infinite families of congruences in arithmetic progressions. For all

Felix Kling, Arvind Rajaraman, Freida Liz Rivera

It has been shown that scalar fields can form gravitationally bound compact objects called boson stars. In this study, we analyze boson star configurations where the scalar fields contain a small amount of angular momentum and find two new classes of solutions. In the first case all particles are in the same slowly rotating state and in the second case the m

Hon Yiu So, Urun Erbas Oz, Lauren Griffith, Susan Kirkland

The R software has become popular among researchers due to its flexibility and open-source nature. However, researchers in the fields of public health and epidemiological studies are more customary to commercial statistical softwares such as SAS, SPSS and Stata. This paper provides a comprehensive comparison on analysis of health survey data using the R surv

Cosme Louart

This paper provides a framework to show the concentration of solutions $Y^*$ to convex minimizing problem where the objective function $\phi(X)(Y)$ depends on some random vector $X$ satisfying concentration of measure hypotheses. More precisely, the convex problem translates into a contractive fixed point equation that ensure the transmission of the concentr

Brendan Burns Healy, G. Christopher Hruska

A group $\Gamma$ with a family of subgroups $\mathbb{P}$ is relatively hyperbolic if $\Gamma$ admits a cusp-uniform action on a proper $\delta$--hyperbolic space. We show that any two such spaces for a given group pair are quasi-isometric, provided the spaces have "constant horospherical distortion," a condition satisfied by Groves--Manning's cusped Cayley g

Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry

Ensemble methods which average over multiple neural network predictions are a simple approach to improve a model's calibration and robustness. Similarly, data augmentation techniques, which encode prior information in the form of invariant feature transformations, are effective for improving calibration and robustness. In this paper, we show a surprising pat

R. Michiels, A. C. LaForge, M. Bohlen, C. Callegari

High-intensity extreme ultraviolet (XUV) pulses from a free-electron laser can be used to create a nanoplasma in clusters. In Ref. [Michiels et al. PCCP, 2020; 22: 7828-7834] we investigated the formation of excited states in an XUV-induced nanoplasma in ammonia clusters. In the present article we expand our previous study with a detailed analysis of the nan

Yayun Hu, G. Murthy, S. Rao, J. K. Jain

We develop a density functional treatment of non-interacting abelian anyons, which is capable, in principle, of dealing with a system of a large number of anyons in an external potential. Comparison with exact results for few particles shows that the model captures the behavior qualitatively and semi-quantitatively, especially in the vicinity of the fermioni

Robert Burklund, Jeremy Hahn, Andrew Senger

We prove that the inertia groups of all sufficiently-connected, high-dimensional $(2n)$-manifolds are trivial. This is a key step toward a general classification of manifolds in the metastable range. Specifically, for $m \gg 0$ and $k>5/12$, suppose $M$ is a $\lfloor km \rfloor$-connected, smooth, closed, oriented $m$-manifold and $\Sigma$ is an exotic $m$-s

G. Ky Duong, Nikos I. Kavallaris, Hatem Zaag

In the current paper, we provide a thorough investigation of the blowing up behaviour induced via diffusion of the solution of the following non local problem \begin{equation*} \left\{\begin{array}{rcl} \partial_t u &=& \Delta u - u + \displaystyle{\frac{u^p}{ \left(\mathop{\,\rlap{-}\!\!\int}\nolimits_\Omega u^r dr \right)^\gamma }}\quad\text{in}\quad \Omeg

Dmitry Ryabogin

Let $d\ge 2$ and let $K$ and $L$ be two convex bodies in ${\mathbb R^d}$ such that $L\subset \textrm{int}\,K$ and the boundary of $L$ does not contain a segment. If $K$ and $L$ satisfy the $(d+1)$-equichordal property, i.e., for any line $l$ supporting the boundary of $L$ and the points $\{\zeta_{\pm}\}$ of the intersection of the boundary of $K$ with $l$, $

V. G. Kupriyanov, M. Kurkov, P. Vitale

We construct a non-commutative kappa-Minkowski deformation of U(1) gauge theory, following a general approach, recently proposed in JHEP 2008 (2020) 041. We obtain an exact (all orders in the non-commutativity parameter) expression for both the deformed gauge transformations and the deformed field strength, which is covariant under these transformations. The

Lukas Schmid, Victor Reijgwart, Lionel Ott, Juan Nieto

Exploration is a fundamental problem in robot autonomy. A major limitation, however, is that during exploration robots oftentimes have to rely on on-board systems alone for state estimation, accumulating significant drift over time in large environments. Drift can be detrimental to robot safety and exploration performance. In this work, a submap-based, multi

Burak Soner, Merve Karakas, Utku Noyan, Furkan Sahbaz

Relative vehicle positioning methods can contribute to safer and more efficient autonomous driving by enabling collision avoidance and platooning applications. For full automation, these applications require cm-level positioning accuracy and greater than 50 Hz update rate. Since sensor-based methods (e.g., LIDAR, cameras) have not been able to reliably satis

Daniela Esparza, Gerardo Flores

Commonly, SLAM algorithms are focused on a static environment, however, there are several scenes where dynamic objects are present. This work presents the STDyn-SLAM an image feature-based SLAM system working on dynamic environments using a series of sub-systems, like optic flow, orb features extraction, visual odometry, and convolutional neural networks to

Patrick Schmid, Maciej Besta, Torsten Hoefler

We propose a topology-aware distributed Reader-Writer lock that accelerates irregular workloads for supercomputers and data centers. The core idea behind the lock is a modular design that is an interplay of three distributed data structures: a counter of readers/writers in the critical section, a set of queues for ordering writers waiting for the lock, and a

Siwei Zhang, Zhiwu Huang, Danda Pani Paudel, Luc Van Gool

Human emotions can be inferred from facial expressions. However, the annotations of facial expressions are often highly noisy in common emotion coding models, including categorical and dimensional ones. To reduce human labelling effort on multi-task labels, we introduce a new problem of facial emotion recognition with noisy multi-task annotations. For this n

Anton Peshkov, S. Teitel

We study numerically a system of athermal, overdamped, frictionless spheres, as in a non-Brownian suspension, in two and three dimensions. Compressing the system isotropically at a fixed rate $\dot\epsilon$, we investigate the critical behavior at the jamming transition. The finite compression rate introduces a control timescale, which allows one to probe th

Georgios N. Tsigaridas, Aristides I. Kechriniotis, Christos A. Tsonos, Konstantinos K. Delibasis

In a recent work, we have shown that all solutions to the Weyl equation and a special class of solutions to the Dirac equation are degenerate in the sense that they remain unaltered under the influence of a wide variety of different electromagnetic fields. In this study, our previous work is significantly extended, providing a wide class of degenerate soluti

Leticia Pardo-Simón

It is known that, for many transcendental entire functions in the Eremenko-Lyubich class $\mathcal{B}$, every escaping point can eventually be connected to infinity by a curve of escaping points. When this is the case, we say that the functions are criniferous. In this paper, we extend this result to a new class of maps in $\mathcal{B}$. Furthermore, we show

Georgios N. Tsigaridas, Aristides I. Kechriniotis, Christos A. Tsonos, Konstantinos K. Delibasis

In a recent work we have proven the existence of degenerate solutions to the Dirac equation, corresponding to an infinite number of different electromagnetic fields, providing also some examples regarding massless particles. In the present article our results are extended significantly, providing degenerate solutions to the Dirac equation for particles with

Sruti Bhagavatula, Lujo Bauer, Apu Kapadia

Awareness about security and privacy risks is important for developing good security habits. Learning about real-world security incidents and data breaches can alert people to the ways in which their information is vulnerable online, thus playing a significant role in encouraging safe security behavior. This paper examines 1) how often people read about secu

Sanjai Narain, Emily Mak, Dana Chee, Todd Huster

Central to the design of many robot systems and their controllers is solving a constrained blackbox optimization problem. This paper presents CNMA, a new method of solving this problem that is conservative in the number of potentially expensive blackbox function evaluations; allows specifying complex, even recursive constraints directly rather than as hard-t

Michael Sven Ferguson, Oded Zilberberg, Gianni Blatter

Steady-state observables, such as occupation numbers and currents, are crucial experimental signatures in open quantum systems. The time-convolutionless (TCL) master equation, which is both exact and time-local, is an ideal candidate for the perturbative computation of such observables. We develop a diagrammatic approach to evaluate the steady-state TCL gene

Jason Parker

In this article, we characterize the (covariant) isotropy groups of free, finitely generated racks and quandles. As a consequence, we show that the usual inner automorphisms of such racks and quandles are precisely those automorphisms that are "coherently extendible". We then use this result to compute the global isotropy groups of the categories of

P. P. Abrantes, G. Bastos, D. Szilard, C. Farina

We investigate the resonance energy transfer (RET) rate between two quantum emitters near a suspended graphene sheet in vacuum under the influence of an external magnetic field. We perform the analysis for low and room temperatures and show that, due to the extraordinary magneto-optical response of graphene, it allows for an active control and tunability of

Alexander Veretennikov

This paper discusses several techniques which may be used for applying the coupling method to solutions of stochastic differential equations (SDEs). They all work in dimension $d\ge 1$, although, in $d=1$ the most natural way is to use intersections of trajectories, which requires nothing but strong Markov property and non-degeneracy of the diffusion coeffic

Zexun Chen, Jun Fan, Kuo Wang

Gaussian processes occupy one of the leading places in modern statistics and probability theory due to their importance and a wealth of strong results. The common use of Gaussian processes is in connection with problems related to estimation, detection, and many statistical or machine learning models. With the fast development of Gaussian process application