January 2022 arXiv papers — page 52

Junya Ikemoto, Toshimitsu Ushio

Deep reinforcement learning (DRL) has attracted much attention as an approach to solve optimal control problems without mathematical models of systems. On the other hand, in general, constraints may be imposed on optimal control problems. In this study, we consider the optimal control problems with constraints to complete temporal control tasks. We describe

A. M. Gu'enault, P. V. E. McClintock, M. Poole, R. Schanen

We describe a new type of torsional oscillator, suitable for studies of quantum fluids at frequencies of $\sim$ $100$ Hz, but capable of reaching high velocities of up to several cm\,s$^{-1}$. This requires the oscillator amplitude to exceed $100$ $\mu m$, which is much too large for a conventional capacitor-driven device. We describe the new geometry for th

Hanyu Song, David B. Dunson

There is a need for new models for characterizing dependence in multivariate data. The multivariate Gaussian distribution is routinely used, but cannot characterize nonlinear relationships in the data. Most non-linear extensions tend to be highly complex; for example, involving estimation of a non-linear regression model in latent variables. In this article,

J. Papavassiliou, A. C. Aguilar, M. N. Ferreira

The three-gluon vertex is a fundamental ingredient of the intricate QCD dynamics, being inextricably connected to key nonperturbative phenomena, such as the emergence of a mass scale in the gauge sector of the theory. In this presentation, we review the main theoretical properties of the three-gluon vertex in the Landau gauge, obtained from the fruitful syne

Athar Sefid, C Lee Giles

The summarization literature focuses on the summarization of news articles. The news articles in the CNN-DailyMail are relatively short documents with about 30 sentences per document on average. We introduce SciBERTSUM, our summarization framework designed for the summarization of long documents like scientific papers with more than 500 sentences. SciBERTSUM

Isha Garg, Manish Nagaraj, Kaushik Roy

Advances in Federated Learning and an abundance of user data have enabled rich collaborative learning between multiple clients, without sharing user data. This is done via a central server that aggregates learning in the form of weight updates. However, this comes at the cost of repeated expensive communication between the clients and the server, and concern

D. Baramidze, L. -E. Persson, H. Singh, G. Tephnadze

We prove that there exists a martingale $f\in H_p $ such that the subsequence $\{L_{2^n}f \}$ of N\"orlund logarithmic means with respect to the Walsh system are not bounded in the Lebesgue space $weak-L_p $ for $0<p<1 $. Moreover, we prove that for any $f\in L_p(G),$ $p\geq 1, $ $L_{2^n}f$ converge to $f$ at any Lebesgue point $x$. Some new related inequali

Yongcheng Qi, Yingchao Zhou

Given a random sample of size $n$ from a $p$ dimensional random vector, where both $n$ and $p$ are large, we are interested in testing whether the $p$ components of the random vector are mutually independent. This is the so-called complete independence test. In the multivariate normal case, it is equivalent to testing whether the correlation matrix is an ide

Sergio Rozada, Santiago Paternain, Antonio G. Marques

Value-function (VF) approximation is a central problem in Reinforcement Learning (RL). Classical non-parametric VF estimation suffers from the curse of dimensionality. As a result, parsimonious parametric models have been adopted to approximate VFs in high-dimensional spaces, with most efforts being focused on linear and neural-network-based approaches. Diff

Nikhil Sarin, Paul D. Lasky, Francisco H. Vivanco, Simon P. Stevenson

Short gamma-ray bursts are believed to be produced by both binary neutron star (BNS) and neutron star-black hole (NSBH) mergers. We use current estimates for the BNS and NSBH merger rates to calculate the fraction of observable short gamma-ray bursts produced through each channel. This allows us to constrain merger rates of BNS to $\mathcal{R}_{\rm{BNS}}=384

Emily Gullerud, Rita Johnson, aBa Mbirika

We explore a certain family $\{A_n\}_{n=1}^{\infty}$ of $n \times n$ tridiagonal real symmetric matrices. After deriving a three-term recurrence relation for the characteristic polynomials of this family, we find a closed form solution. The coefficients of these characteristic polynomials turn out to involve the diagonal entries of Pascal's triangle in a tan

Christoph Hotter, Laurin Ostermann, Helmut Ritsch

Ramsey spectroscopy in large, dense ensembles of ultra-cold atoms trapped in optical lattices suffers from dipole-dipole interaction induced shifts and collective superradiance limiting its precision and accuracy. We propose a novel geometry implementing fast signal readout with minimal heating for large atom numbers at lower densities via an optical cavity

M. M. Hedman, P. D. Nicholson, M. El Moutamid, S. Smotherman

Saturn's C ring contains multiple structures that appear to be density waves driven by time-variable anomalies in the planet's gravitational field. Semi-empirical extensions of density wave theory enable the observed wave properties to be translated into information about how the pattern speeds and amplitudes of these gravitational anomalies have changed ove

Mohamed H. Awida

Form factor can be engineered in photonic cavities such that it dramatically increases the scan speed of axion haloscope detectors. Using higher-order modes of a photonic cavity, we can probe axions at larger mass ranges beyond the reach of current detectors that rely on conventional copper cavities. The main idea is to produce a higher-order mode with longi

Clinton DeW. Van Siclen

Fluid infiltration of a permeable brick in contact with a pressurized reservoir of fluid is considered. A stochastic model, informed by Darcy's law and the incompressibility of the fluid, shows how the heterogeneity of the permeability field affects the time evolution of the fluid infiltration. In particular, the cause of anomalous (non-Darcian) advance of a

Volodymyr A. Kuznietsov, Oleh Savchuk, Mark I. Gorenstein, Volker Koch

We study fluctuations of particle number in the presence of critical point by utilizing molecular dynamics simulations of the classical Lennard-Jones fluid in a periodic box. The numerical solution of the $N$-body problem naturally incorporates all correlations, exact conservation laws, and finite size effects, allowing us to study the fluctuation signatures

Nabil Ibtehaz, Muhammad E. H. Chowdhury, Amith Khandakar, Susu M. Zughaier

Raman spectroscopy provides a vibrational profile of the molecules and thus can be used to uniquely identify different kind of materials. This sort of fingerprinting molecules has thus led to widespread application of Raman spectrum in various fields like medical dignostics, forensics, mineralogy, bacteriology and virology etc. Despite the recent rise in Ram

Simon St-Amant

We study the broken non-abelian X-ray transform in Minkowski space. This transform acts on the space of Hermitian connections on a causal diamond and is known to be injective up to an infinite-dimensional gauge. We show a stability estimate that takes into account the gauge, leading to a new proof of the transform's injectivity. Our proof leads us to conside

Sachin Konan, Esmaeil Seraj, Matthew Gombolay

Information sharing is key in building team cognition and enables coordination and cooperation. High-performing human teams also benefit from acting strategically with hierarchical levels of iterated communication and rationalizability, meaning a human agent can reason about the actions of their teammates in their decision-making. Yet, the majority of prior

Marian Kupczynski

Bell inequalities may only be derived, if hidden variables do not depend on the experimental settings. The stochastic independence of hidden and setting variables is called: freedom of choice, free will, measurement independence or no conspiracy. By imbedding the Bell causal structure in a larger causal network the authors correctly prove, that one can expla

Óscar Pimentel, Pablo A. Estévez, Francisco Förster

In astronomical surveys, such as the Zwicky Transient Facility, supernovae (SNe) are relatively uncommon objects compared to other classes of variable events. Along with this scarcity, the processing of multi-band light-curves is a challenging task due to the highly irregular cadence, long time gaps, missing-values, few observations, etc. These issues are pa

Andrew Stolman, Caleb Levy, C. Seshadhri, Aneesh Sharma

Graph representation learning (also called graph embeddings) is a popular technique for incorporating network structure into machine learning models. Unsupervised graph embedding methods aim to capture graph structure by learning a low-dimensional vector representation (the embedding) for each node. Despite the widespread use of these embeddings for a variet

Jérôme Poineau

Consider a Berkovich space over a good Banach ring and the relative projective line over it. (It is a space whose fibers are projective lines over different complete valued fields.) For each polarized endomorphism of this line, we prove that the family of equilibrium measures associated to the restrictions of the endomorphism to the fibers is continuous. The

Steven Duplij

We propose a generalization of the external direct product concept to polyadic algebraic structures which introduces novel properties in two ways: the arity of the product can differ from that of the constituents, and the elements from different multipliers can be \textquotedblleft entangled\textquotedblright\ such that the product is no longer componentwise

Constantin Payerne, Vincent Rossetto

We investigate the statistical distribution of Newtonian gravitational forces acting on a test particle embedded in an infinite, homogeneous, and uncorrelated random gas of point masses. Using order statistics, we derive the probability density functions of distances to the $n$-th nearest neighbors in arbitrary spatial dimensions and analyze their contributi

Qiyu Hu, Shuhan Shi, Yunlong Cai, Guanding Yu

Deep-unfolding neural networks (NNs) have received great attention since they achieve satisfactory performance with relatively low complexity. Typically, these deep-unfolding NNs are restricted to a fixed-depth for all inputs. However, the optimal number of layers required for convergence changes with different inputs. In this paper, we first develop a frame

CMS Collaboration

A search for resonances decaying into a W boson and a radion, where the radion decays into two W bosons, is presented. The data analyzed correspond to an integrated luminosity of 138 fb$^{-1}$ recorded in proton-proton collisions with the CMS detector at $\sqrt{s} =$ 13 TeV. One isolated charged lepton is required, together with missing transverse momentum a

Stefan Abi-Karam, Yuqi He, Rishov Sarkar, Lakshmi Sathidevi

Graph neural networks (GNNs) have recently exploded in popularity thanks to their broad applicability to ubiquitous graph-related problems such as quantum chemistry, drug discovery, and high energy physics. However, meeting demand for novel GNN models and fast inference simultaneously is challenging because of the gap between the difficulty in developing eff

Zhen Xiang, David J. Miller, George Kesidis

Backdoor attacks (BAs) are an emerging threat to deep neural network classifiers. A victim classifier will predict to an attacker-desired target class whenever a test sample is embedded with the same backdoor pattern (BP) that was used to poison the classifier's training set. Detecting whether a classifier is backdoor attacked is not easy in practice, especi

Seema Sangari, Eric Dallal

With an ever evolving cyber domain, delays in reporting incidents are a well-known problem in the cyber insurance industry. Addressing this problem is a requisite to obtaining the true picture of cyber incident rates and to model it appropriately. The proposed algorithm addresses this problem by creating a model of the distribution of reporting delays and us

Jeffrey A. Nichols, Kevin D. Spakes, Cory L. Watson, Robert A. Bridges

In this case study, we describe the design and assembly of a cyber security testbed at Oak Ridge National Laboratory in Oak Ridge, TN, USA. The range is designed to provide agile reconfigurations to facilitate a wide variety of experiments for evaluations of cyber security tools -- particularly those involving AI/ML. In particular, the testbed provides reali

Huiyun Xia, Yijie Mao, Bruno Clerckx, Xiaokang Zhou

As investigations on physical layer security evolve from point-to-point systems to multi-user scenarios, multi-user interference (MUI) is introduced and becomes an unavoidable issue. Different from treating MUI totally as noise in conventional secure communications, in this paper, we propose a rate-splitting multiple access (RSMA)-based secure beamforming de

Suraj Nair, Eugene Yang, Dawn Lawrie, Kevin Duh

The advent of transformer-based models such as BERT has led to the rise of neural ranking models. These models have improved the effectiveness of retrieval systems well beyond that of lexical term matching models such as BM25. While monolingual retrieval tasks have benefited from large-scale training collections such as MS MARCO and advances in neural archit

Upinder Kaur, Haozhe Zhou, Xiaxin Shen, Byung-Cheol Min

Robot systems are increasingly integrating into numerous avenues of modern life. From cleaning houses to providing guidance and emotional support, robots now work directly with humans. Due to their far-reaching applications and progressively complex architecture, they are being targeted by adversarial attacks such as sensor-actuator attacks, data spoofing, m

Taylor Olson, Ken Forbus

To interact with humans, artificial intelligence (AI) systems must understand our social world. Within this world norms play an important role in motivating and guiding agents. However, very few computational theories for learning social norms have been proposed. There also exists a long history of debate on the distinction between what is normal (is) and wh

Elisa Rheaume, Lorenzo Giacomel, Jean-Luc Vay, Axel Huebl

In physics research particle accelerators are highly valued, and extraordinarily expensive, technical instruments. The high cost of particle accelerators results from the immense lengths required to accelerate particles to high energies, using radio frequency cavities. A current promising field of research, laser-driven particle acceleration has the potentia

Muhammad Suleman Saleem, Jelena Mišić, Vojislav B. Mišić

We investigate the use of Android permissions as the vehicle to allow for quick and effective differentiation between benign and malware apps. To this end, we extract all Android permissions, eliminating those that have zero impact, and apply two feature ranking algorithms namely Chi-Square test and Fisher's Exact test to rank and additionally filter them, r

Richard de Grijs

High-level Chinese cartographic developments predate European innovations by several centuries. Whereas European cartographic progress -- and in particular the search for a practical solution to the perennial "longitude problem" at sea -- was driven by persistent economic motivations, Chinese mapmaking efforts responded predominantly to administrative, cadas

Angela Wu

We show that any knot which is smoothly the closure of a 3-braid cannot be Lagrangian concordant to and from the maximum Thurston-Bennequin Legendrian unknot except the unknot itself. Our obstruction comes from drawing the Weinstein handlebody diagrams of particular symplectic fillings of cyclic branched double covers of knots in $S^3$. We use the Legendrian

A. Llera, M. Brammer, B. Oakley, J. Tillmann

An increasing number of large-scale multi-modal research initiatives has been conducted in the typically developing population, as well as in psychiatric cohorts. Missing data is a common problem in such datasets due to the difficulty of assessing multiple measures on a large number of participants. The consequences of missing data accumulate when researcher

Paul Gavrikov, Janis Keuper

We present first empirical results from our ongoing investigation of distribution shifts in image data used for various computer vision tasks. Instead of analyzing the original training and test data, we propose to study shifts in the learned weights of trained models. In this work, we focus on the properties of the distributions of dominantly used 3x3 convo

Mallory Dolorfino, Cordelia Horch, Kelly Jabbusch, Ryan Martinez

A toric code, introduced by Hansen to extend the Reed-Solomon code as a $k$-dimensional subspace of $\mathbb{F}_q^n$, is determined by a toric variety or its associated integral convex polytope $P \subseteq [0,q-2]^n$, where $k=|P \cap \mathbb{Z}^n|$ (the number of integer lattice points of $P$). There are two relevant parameters that determine the quality o

Zewen Zhang, Ming Yuan, Bhuvanesh Sundar, Kaden R. A. Hazzard

Ultracold Rydberg atom arrays are an emerging platform for quantum simulation and computing. However, decoherence in these systems remains incompletely understood. Recent experiments [Guardado-Sanchez et al. Phys. Rev. X 8, 021069 (2018)] observed strong decoherence in the quench and longitudinal-field-sweep dynamics of two-dimensional Ising models realized

Karthik Dinakar, Henry Lieberman

Many datasets represent a combination of different ways of looking at the same data that lead to different generalizations. For example, a corpus with examples generated by different people may be mixtures of many perspectives and can be viewed with different perspectives by others. It isnt always possible to represent the viewpoints by a clean separation, i

S. Nakajima, S. Nakamura, Y. Shimizu

We study the problem of parameter estimation for discretely observed stochastic differential equations driven by small fractional noise. Under some conditions, we obtain strong consistency and rate of convergence of the least square estimator(LSE) when small dispersion coefficient converges to 0 and sample size converges to infty.

Ioannis Agadakos, Manuel Egele, William Robertson

Designing and implementing secure software is inarguably more important than ever. However, despite years of research into privilege separating programs, it remains difficult to actually do so and such efforts can take years of labor-intensive engineering to reach fruition. At the same time, new intra-process isolation primitives make strong data isolation a

Ana Aleksandric, Mercy Jesuloluwa Obasanya, Sarah Melcher, Shirin Nilizadeh

Objective: The aims of the study were to examine the association between social media sentiments surrounding COVID-19 vaccination and the effects on vaccination rates in the United States (US), as well as other contributing factors to the COVID-19 vaccine hesitancy. Method: The dataset used in this study consists of vaccine-related English tweets collected i

Or Litany, Haggai Maron, David Acuna, Jan Kautz

Standard Federated Learning (FL) techniques are limited to clients with identical network architectures. This restricts potential use-cases like cross-platform training or inter-organizational collaboration when both data privacy and architectural proprietary are required. We propose a new FL framework that accommodates heterogeneous client architecture by a

CMS Collaboration

A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons ($\tau_\mathrm{h}$) that originate from genuine tau leptons in the CMS detector against $\tau_\mathrm{h}$ candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a $\ta

Marco Padovani, Shmuel Bialy, Daniele Galli, Alexei V. Ivlev

Cosmic rays (CRs) at sub-TeV energies play a fundamental role in the chemical and dynamical evolution of molecular clouds, as they control the ionisation, dissociation, and excitation of H$_{2}$. Their characterisation is important both for the interpretation of observations and for the development of theoretical models. The methods used so far for estimatin

Habib Larian, Ali Larian, Mahdi Sharifi, Homa Movahednejad

Advancements of the Web technology provide this opportunity for Internet of Things (IoT) to take steps towards Web of Things (WoT). By increasing trend of reusing Web techniques to create a monolithic environment to control, monitor, and compose the smart objects, a mature WoT architecture is finally emerged in four layers to be a solution for IoT-middleware

Nan Wu, Jiwon Lee, Yuan Xie, Cong Hao

Despite the stride made by machine learning (ML) based performance modeling, two major concerns that may impede production-ready ML applications in EDA are stringent accuracy requirements and generalization capability. To this end, we propose hybrid graph neural network (GNN) based approaches towards highly accurate quality-of-result (QoR) estimations with g

Concetta Laurita

An error estimate for the Gauss-Lobatto quadrature formula for integration over the interval $[-1, 1]$, relative to the Jacobi weight function $w^{\alpha,\beta}(t)=(1-t)^\alpha(1+t)^\beta$, $\alpha,\beta>-1$, is obtained. This estimate holds true for functions belonging to some Sobolev-type subspaces of the weighted space $L_{w^{\alpha,\beta}}^1([-1,1])$.

Felix Wilsch

Addressing the ${(g-2)_\mu}$ anomaly and simultaneously being consistent with the tight bounds on ${\mu \to e \gamma}$ requires a precise alignment of the lepton dipole operator in flavor space. We investigate this alignment condition in the Standard Model Effective Field Theory by considering the Renormalization Group mixing of the relevant operators. We fi

Ellen Arteca, Alexi Turcotte

The static properties of code repositories, e.g., lines of code, dependents, dependencies, etc. can be readily scraped from code hosting platforms such as GitHub, and from package management systems such as npm for JavaScript; Although no less important, information related to the dynamic properties of programs, e.g., number of tests in a test suite that pas

Austin van Loon, Salvatore Giorgi, Robb Willer, Johannes Eichstaedt

The word embedding association test (WEAT) is an important method for measuring linguistic biases against social groups such as ethnic minorities in large text corpora. It does so by comparing the semantic relatedness of words prototypical of the groups (e.g., names unique to those groups) and attribute words (e.g., 'pleasant' and 'unpleasant' words). We sho

Yuan Yang, Deepayan Sanyal, Joel Michelson, James Ainooson

Figural analogy problems have long been a widely used format in human intelligence tests. In the past four decades, more and more research has investigated automatic item generation for figural analogy problems, i.e., algorithmic approaches for systematically and automatically creating such problems. In cognitive science and psychometrics, this research can

Hanshang Jin, Jackson Badger, Peter Klavins, Jing-Tai Zhao

Sub-oxides with the anti-perovskite structure constitute a large part of the interstitial stabilized compounds with novel chemical and physical properties, especially in the systems containing rare earth elements and/or early transition metal elements. A new sub-oxide compound, (Ce$_{0.85}$Ti$_{0.15}$)Ge$_3$O$_{0.5}$, is synthesized by flux growth. The compo

Ephrem A. Retta, Richard Sutcliffe, Eiad Almekhlafi, Yosef K. Enku

In this paper, we create EMIR, the first-ever Music Information Retrieval dataset for Ethiopian music. EMIR is freely available for research purposes and contains 600 sample recordings of Orthodox Tewahedo chants, traditional Azmari songs and contemporary Ethiopian secular music. Each sample is classified by five expert judges into one of four well-known Eth

Antoine Francotte, Raphael Esson, Eric Abachin, Melissa Vanhamme

The tetanus neurotoxin (TeNT) is one of the most toxic proteins known to man, which prior to the use of the vaccine against the TeNT producing bacteria Clostridium tetani, resulted in a 20 % mortality rate upon infection. The clinical detrimental effects of tetanus have decreased immensely since the introduction of global vaccination programs, which depend o

Lucie Pansart, Hadrien Cambazard, Nicolas Catusse

We study an elementary path problem which appears in the pricing step of a column generation scheme solving the kidney exchange problem. The latter aims at finding exchanges of donations in a pool of patients and donors of kidney transplantations. Informally, the problem is to determine a set of cycles and chains of limited length maximizing a medical benefi

Yuan Tian, Minghao Han, Chetan Kulkarni, Olga Fink

Prescribing optimal operation based on the condition of the system and, thereby, potentially prolonging the remaining useful lifetime has a large potential for actively managing the availability, maintenance and costs of complex systems. Reinforcement learning (RL) algorithms are particularly suitable for this type of problems given their learning capabiliti

Javier Garcia-Bernardo, Petr Janský

We exploit the new country-by-country reporting data of multinational corporations, with unparalleled country coverage, to reveal the distributional consequences of profit shifting. We estimate that multinational corporations worldwide shifted over \$850 billion in profits in 2017, primarily to countries with effective tax rates below 10\%. Countries with lo

The Genomic Data Science Community Network, Rosa Alcazar, Maria Alvarez, Rachel Arnold

Over the last 20 years, there has been an explosion of genomic data collected for disease association, functional analyses, and other large-scale discoveries. At the same time, there have been revolutions in cloud computing that enable computational and data science research, while making data accessible to anyone with a web browser and an internet connectio

Sangeetha Siddegowda, Marios Fournarakis, Markus Nagel, Tijmen Blankevoort

While neural networks have advanced the frontiers in many machine learning applications, they often come at a high computational cost. Reducing the power and latency of neural network inference is vital to integrating modern networks into edge devices with strict power and compute requirements. Neural network quantization is one of the most effective ways of

Laura Wartschinski, Yannic Noller, Thomas Vogel, Timo Kehrer

Context: Identifying potential vulnerable code is important to improve the security of our software systems. However, the manual detection of software vulnerabilities requires expert knowledge and is time-consuming, and must be supported by automated techniques. Objective: Such automated vulnerability detection techniques should achieve a high accuracy, poin

Abhishek Yenpure, Sudhanshu Sane, Roba Binyahib, David Pugmire

The performance of particle advection-based flow visualization techniques is complex, since computational work can vary based on many factors, including number of particles, duration, and mesh type. Further, while many approaches have been introduced to optimize performance, the efficacy of a given approach can be similarly complex. In this work, we seek to

Soumangsu Chakraborty, Akikazu Hashimoto

We show that the $T \bar T$ deformation of conformal field theories whose entropy grows as $S(E) \sim E^\gamma$ for $\gamma > 1/2$ exhibits negative specific heat in its microcanonical thermodynamic function $S({\cal E})$. We analyze the large $N$ symmetric product CFT as a concrete example of a CFT with this property and compute the thermodynamic functions

Hailey Aronson, Thomas W. Baumgarte, Stuart L. Shapiro

We assess whether gravity darkening, induced by a tidal interaction during a stellar fly-by, might be sufficient to explain the Great Dimming of Betelgeuse. Adopting several simple approximations, we calculate the tidal deformation and associated gravity darkening in a close tidal encounter, as well as the reduction in the radiation flux as seen by a distant

Jamie M. Fitzgerald, Joshua J. P. Thompson, Ermin Malic

Twisted atomically thin semiconductors are characterized by moir\'{e} excitons. Their optical signatures and selection rules are well understood. However, their hybridization with photons in the strong coupling regime for heterostructures integrated in an optical cavity has not been in the focus of research yet. Here, we combine an excitonic density matrix f

Cody Karcher, Robert Haimes

A method of Sequential Log-Convex Programming (SLCP) is constructed that exploits the log-convex structure present in many engineering design problems. The mathematical structure of Geometric Programming (GP) is combined with the ability of Sequential Quadratic Program (SQP) to accommodate a wide range of objective and constraint functions, resulting in a pr

Qiyang Han

In the standard Gaussian linear measurement model $Y=X\mu_0+\xi \in \mathbb{R}^m$ with a fixed noise level $\sigma>0$, we consider the problem of estimating the unknown signal $\mu_0$ under a convex constraint $\mu_0 \in K$, where $K$ is a closed convex set in $\mathbb{R}^n$. We show that the risk of the natural convex constrained least squares estimator (LS

Gabriele Tiboni, Karol Arndt, Ville Kyrki

In recent years, domain randomization over dynamics parameters has gained a lot of traction as a method for sim-to-real transfer of reinforcement learning policies in robotic manipulation; however, finding optimal randomization distributions can be difficult. In this paper, we introduce DROPO, a novel method for estimating domain randomization distributions

Fernando Contreras, Juan Galvis

In this paper, we present numerical procedures to compute solutions of partial differential equations posed on fractals. In particular, we consider the strong form of the equation using standard graph Laplacian matrices and also weak forms of the equation derived using standard length or area measure on a discrete approximation of the fractal set. We then in

Moshe Eisenberg, Ore Gottlieb, Ehud Nakar

The role of relativistic jets in unbinding the stellar envelope during a supernova (SN) associated with a gamma-ray burst (GRB) is unclear. To study that, we explore observational signatures of stellar explosions that are driven by jets. We focus on the final velocity distribution of the outflow in such explosions and compare its observational imprints to SN

He Jia, Christopher J. White, Eliot Quataert, Sean M. Ressler

We study the observational signatures of magnetically arrested black hole accretion with non-rotating inflow onto a rotating black hole; we consider a range of angles between the black hole spin and the initial magnetic field orientation. We compare the results of our General Relativistic Magneto-Hydrodynamic simulations to more commonly used rotating initia

Russell Impagliazzo, Rex Lei, Toniann Pitassi, Jessica Sorrell

We introduce the notion of a reproducible algorithm in the context of learning. A reproducible learning algorithm is resilient to variations in its samples -- with high probability, it returns the exact same output when run on two samples from the same underlying distribution. We begin by unpacking the definition, clarifying how randomness is instrumental in

Erasmo Artur, Rosane Minghim

Multidimensional data analysis has become increasingly important in many fields, mainly due to current vast data availability and the increasing demand to extract knowledge from it. In most applications, the role of the final user is crucial to build proper machine learning models and to explain the patterns found in data. In this paper, we present an open u

Badal Joshi, Gheorghe Craciun

Networks with absolute concentration robustness (ACR) have the property that a translation of a coordinate hyperplane either contains all steady states (static ACR) or attracts all trajectories (dynamic ACR). The implication for the underlying biological system is robustness in the concentration of one of the species independent of the initial conditions as

Mongi Blel, Jamel Benameur

In \cite{CJ}, the authors show that the Cauchy problem of the Navier-Stokes equations with damping $\alpha|u|^{\beta-1}u(\alpha>0,\;\beta\geq1)$ has global weak solutions in $L^2(\R^3)$. In this paper, we prove the uniqueness, the continuity in $L^2$ for $\beta>3$, also the large time decay is proved for $\beta\geq\frac{10}3$. Fourier analysis and standard t

Martin Hairer, Khoa Lê, Tommaso Rosati

We consider the Allen-Cahn equation $\partial_t u- \Delta u=u-u^3$ with a rapidly mixing Gaussian field as initial condition. We show that provided that the amplitude of the initial condition is not too large, the equation generates fronts described by nodal sets of the Bargmann-Fock Gaussian field, which then evolve according to mean curvature flow.

Xiangnan Yin, Liming Chen

Occlusions often occur in face images in the wild, troubling face-related tasks such as landmark detection, 3D reconstruction, and face recognition. It is beneficial to extract face regions from unconstrained face images accurately. However, current face segmentation datasets suffer from small data volumes, few occlusion types, low resolution, and imprecise

Felix Kastner, Andreas Rößler

For the approximation and simulation of twofold iterated stochastic integrals and the corresponding L\'{e}vy areas w.r.t. a multi-dimensional Wiener process, we review four algorithms based on a Fourier series approach. Especially, the very efficient algorithm due to Wiktorsson and a newly proposed algorithm due to Mrongowius and R\"ossler are considered. To

Feng Ding, Robin D. Wordsworth

Detection of water vapor in the atmosphere of temperate rocky exoplanets would be a major milestone on the path towards characterization of exoplanet habitability. Past modeling work has shown that cloud formation may prevent the detection of water vapor on Earth-like planets with surface oceans using the James Webb Space Telescope (JWST). Here we analyze th

Rohit Chhiber, William H. Matthaeus, Arcadi V. Usmanov, Riddhi Bandyopadhyay

Motivated by theoretical, numerical, and observational evidence, we explore the possibility that the critical transition between sub-Alfv\'enic flow and super-Alfv\'enic flow in the solar atmosphere takes place in fragmented and disconnected subvolumes within a general Alfv\'en critical zone. The initial observations of sub-Alfv\'enic periods by Parker Solar

Olasunbo Farinre, Swapnil M. Mhatre, Albert F. Rigosi, Prabhakar Misra

This work reports the modeling and simulation of gas sensors made from pristine graphene nanoplatelets (P-GnPs) using COMSOL Multiphysics software. The mass balance equation was solved while including contributions of electromigration flux. An example GnP-based gas sensor was simulated to undergo exposure to NO2 and CO gases at different concentrations to un

M. A. Jafari, M. Wawrzyniak-Adamczewska, S. Stagraczyński, A. Dyrdal

Vanadium based dichalcogenides, VSe$_2$, are two-dimensional materials in which magnetic Vanadium atoms are arranged in a hexagonal lattice and are coupled ferromagnetically within the plane. However, adjacent atomic planes are coupled antiferromagnetically. This provides new and interesting opportunities for application in spintronics and data storage and p

C. Smith, M. Gordovskyy, P. K. Browning

We present results revealing microwave pulsations produced in a model of a flaring twisted solar coronal loop, without any external oscillatory driver. Two types of oscillations are identified: slowly-decaying oscillations with a period of about 70-75s and amplitude of about 5-10% seen in loops both with and without energetic electrons, and oscillations with

Qing Lyu, Christopher T. Whitlow, Ge Wang

Recently, deep learning has achieved remarkable successes in medical image analysis. Although deep neural networks generate clinically important predictions, they have inherent uncertainty. Such uncertainty is a major barrier to report these predictions with confidence. In this paper, we propose a novel yet simple Bayesian inference approach called SoftDropC

Insu Han, Mike Gartrell, Jennifer Gillenwater, Elvis Dohmatob

A determinantal point process (DPP) on a collection of $M$ items is a model, parameterized by a symmetric kernel matrix, that assigns a probability to every subset of those items. Recent work shows that removing the kernel symmetry constraint, yielding nonsymmetric DPPs (NDPPs), can lead to significant predictive performance gains for machine learning applic

Arpit Bhardwaj, Phanish Suryanarayana

We study the electromechanical response of Janus transition metal dichalcogenide (TMD) nanotubes from first principles. In particular, considering both armchair and zigzag variants of twenty-seven select Janus TMD nanotubes, we determine the change in bandgap and charge carriers' effective mass upon axial and torsional deformations using density functional t

Miao-jung Yvonne Ou, Petr Plecháč, Jiangming Xie

This paper concerns the Time-Domain Full Waveform Inversion (FWI) for dispersive and dissipative poroelastic materials. The forward problem is an initial boundary value problem (IBVP) of the poroelastic equations with a memory term; the FWI is formulated as a minimization problem of a least-square misfit function with the (IBVP) as the constraint. In this pa

Md Shahriar Iqbal, Rahul Krishna, Mohammad Ali Javidian, Baishakhi Ray

Modern computer systems are highly configurable, with the total variability space sometimes larger than the number of atoms in the universe. Understanding and reasoning about the performance behavior of highly configurable systems, over a vast and variable space, is challenging. State-of-the-art methods for performance modeling and analyses rely on predictiv

Tanmay Saha, Arpan Das, Sibasish Ghosh

Collisional models are a category of microscopic framework designed to study open quantum systems. The framework involves a system sequentially interacting with a bath comprised of identically prepared units. In this regard, quantum homogenization is a process where the system state approaches the identically prepared state of bath unit in the asymptotic lim

Gonzalo Arranz, Cayetano Martínez-Muriel, Oscar Flores, Manuel García-Villalba

We present a method for computing fluid-structure interaction problems for multi-body systems. The fluid flow equations are solved using a fractional-step method with the immersed boundary method proposed by Uhlmann [J. Comput Phys. 209 (2005) 448]. The equations of the rigid bodies are solved using recursive algorithms proposed by Felis [Auton. Robot 41 (20

Shiraz Minwalla, Amiya Mishra, Naveen Prabhakar, Tarun Sharma

We demonstrate that the known expressions for the thermal partition function of large $N$ Chern-Simons matter theories admit a simple Hilbert space interpretation as the partition function of an associated ungauged large $N$ matter theory with one additional condition: the Fock space of this associated theory is projected down to the subspace of its \emph{qu

Anastasiia Filimonova, Sam Junius, Laura Lopez Honorez, Susanne Westhoff

Feebly interacting thermal relics are promising dark matter candidates. Among them, scenarios of inelastic Dark Matter evade direct detection by suppressed elastic scattering off atomic nuclei. We introduce inelastic Dirac Dark Matter, a new model with two Dirac fermions in the MeV-GeV mass range. At feeble couplings, dark matter can depart from chemical as

Yayun He, Wei Chen, Tan Luo, Shanshan Cao

Suppression of jet spectra or jet quenching in high-energy heavy-ion collisions is caused by jet energy loss in the dense medium. The azimuthal anisotropy of jet energy loss in non-central heavy-ion collisions can lead to jet anisotropy which in turn can provide insight into the path-length dependence of jet quenching. This is investigated within the Linear

Jim Fuller, Wenbin Lu

The angular momentum (AM) content of massive stellar cores helps to determine the natal spin rates of neutron stars and black holes. Asteroseismic measurements of low-mass stars have proven that stellar cores rotate slower than predicted by most prior work, so revised models are necessary. In this work, we apply an updated AM transport model based on the Tay

Neige Frankel, Annalisa Pillepich, Hans-Walter Rix, Vicente Rodriguez-Gomez

Galactic bars are prominent dynamical structures within disk galaxies whose size, formation time, strength, and pattern speed influence the dynamical evolution of their hosts galaxies. Yet, their formation and evolution in a cosmological context is not well understood, as cosmological simulation studies have been limited by the classic trade off between simu