August 2022 arXiv papers — page 118

Fan Guo, Xiaocan Li, Omar French, William Daughton

Recently, Sironi (PRL, 128, 145102; S22) reported the correlation between particles accelerated into high energy and their crossings of regions with electric field larger than magnetic field (E>B regions) in kinetic simulations of relativistic magnetic reconnection. They claim that electric fields in E>B regions (for a vanishing guide field) dominate in acce

J. Serrano Bell, R. F. Díaz

SOPHIE+ is a echelle spectrograph located in Haute-Provence Observatory, France. It can reach a precision of near 1 m s$^{-1}$ by simultaneus calibration. However, the zero point shows a low frequency drift of a few m s$^{-1}$ that must be corrected to achieve the needed precision for the current exoplanet search programs. To this end, four radial velocity s

Jiazhuo Cheng, Qiru Wang

This paper concerns the initial-boundary value problem for a mixed pseudo-parabolic $p$-Laplacian type equation. By constructing a family of potential wells, we first present the explicit expression for the depth of potential well, and then prove the existence, uniqueness and decay estimate of global solutions and the blowup phenomena of solutions with subcr

Tongkuen Chang, Bum Ja Jin

In this study, we investigate the global existence of weak solutions of non-Newtonian incompressible fluids governed by (1.1). When $u_0 \in \dot B^{\alpha-\frac{2}{p}}_{p,q}({\mathbb R}^{n}_+) \, \cap \,\dot B^{ 1 -\frac4{n+2}}_{\frac{n+2}2,\frac{n+2}2}({\mathbb R}^{n}_+) \,\cap \, \dot B^{1 +\frac{n}p}_{p,1} (\mathbb{R}_+)$ is given, we will find the weak

Zhongwei Qiu, Qiansheng Yang, Jian Wang, Dongmei Fu

Video 3D human pose estimation aims to localize the 3D coordinates of human joints from videos. Recent transformer-based approaches focus on capturing the spatiotemporal information from sequential 2D poses, which cannot model the contextual depth feature effectively since the visual depth features are lost in the step of 2D pose estimation. In this paper, w

Anjul Tyagi, Tyler Estro, Geoff Kuenning, Erez Zadok

The axes ordering in PCP presents a particular story from the data based on the user perception of PCP polylines. Existing works focus on directly optimizing for PCP axes ordering based on some common analysis tasks like clustering, neighborhood, and correlation. However, direct optimization for PCP axes based on these common properties is restrictive becaus

Zhengchang Kou, Qi You, Jihun Kim, Zhijie Dong

Ultrafast ultrasound imaging is essential for advanced ultrasound imaging techniques such as ultrasound localization microscopy (ULM) and functional ultrasound (fUS). Current ultrafast ultrasound imaging is challenged by the ultrahigh data bandwidth associated with the radio frequency (RF) signal, and by the latency of the computationally expensive beamformi

Brenden R. Ortiz, Paul M. Sarte, Alon H. Avidor, Stephen D. Wilson

The combination of geometric frustration, extended hopping, spin-orbit coupling, and a disordered magnetic ground state make NaRuO$_{2}$ an attractive Heisenberg-Kitaev candidate material. Historically, NaRuO$_2$ has been a challenging material to produce, even in polycrystalline form. Here we present synthetic efforts that identify a propensity for Na$_\tex

Lubin Chang, Yarong Luo

Through assembling the navigation parameters as matrix Lie group state, the corresponding inertial navigation system (INS) kinematic model possesses a group-affine property. The Lie logarithm of the navigation state estimation error satisfies a log-linear autonomous differential equation. These log-linear models are still applicable even with arbitrarily lar

G. Timár, S. N. Dorogovtsev, J. F. F. Mendes

The nonbacktracking matrix, and the related nonbacktracking centrality (NBC) play a crucial role in models of percolation-type processes on networks, such as non-recurrent epidemics. Here we study the localization of NBC in infinite sparse networks that contain an arbitrary finite subgraph. Assuming the local tree-likeness of the enclosing network, and that

Jisheng Bai, Jianfeng Chen, Mou Wang, Muhammad Saad Ayub

Anomalous sound detection for machine condition monitoring has great potential in the development of Industry 4.0. However, these anomalous sounds of machines are usually unavailable in normal conditions. Therefore, the models employed have to learn acoustic representations with normal sounds for training, and detect anomalous sounds while testing. In this a

Kaibo Hu

The report is based on an extended abstract for the MFO workshop "Hilbert Complexes: Analysis, Applications, and Discretizations", held at Oberwolfach during 19-25 June 2022. The aim is to provide an overview of some aspects of discretization of Hilbert complexes with an emphasis on conforming finite elements.

Asim Bashir Khajwal, Chih-Shen Cheng, Arash Noshadravan

This study aims to enable more reliable automated post-disaster building damage classification using artificial intelligence (AI) and multi-view imagery. The current practices and research efforts in adopting AI for post-disaster damage assessment are generally (a) qualitative, lacking refined classification of building damage levels based on standard damage

Sara A. Rosborough, Andrew Robinson, Trent Seelig

The most luminous quasars are created by major, gas-rich mergers and E1821+643, an optically luminous quasar situated at the center of a cool-core cluster, appears to be in the late stages of the post-merger blowout phase. This quasar is also identified as a gravitational recoil candidate, in which the supermassive black hole (SMBH) has received a recoil kic

David Luong, Bhashyam Balaji, Sreeraman Rajan

Noise radars have the same mathematical description as a type of quantum radar known as quantum two-mode squeezing radar. Although their physical implementations are very different, this mathematical similarity allows us to analyze them collectively. We may consider the two types of radars as forming a single class of radars, called noise-type radars. The ta

Anirudh Hari, Kento Katagiri, Wanghui Li, Dorian P. Luccioni

Extreme pressures and temperatures create conditions that allow even hard and brittle materials to deform plastically. Despite extensive research, the upper limits of flow strength, the resistance to plastic flow, remain uncertain, and the mechanisms driving deformation at the relevant stresses are a subject of debate. Using femtosecond in situ X-ray diffrac

J. Bulin, J. Hamaekers, M. P. Ariza, M. Ortiz

We present a Data-Driven (DD) paradigm that enables molecular dynamics calculations to be performed directly from sampled force-field data such as obtained, e.g., from ab initio calculations, thereby eschewing the conventional step of modeling the data by empirical interatomic potentials entirely. The data required by the DD solvers consists of local atomic

Khondhaker Al Momin, Saurav Barua, Md. Shahreer Jamil, Omar Faruqe Hamim

The research examined predicting short-duration traffic flow counts with the Kalman filtering technique (KFT), a computational filtering method. Short-term traffic prediction is an important tool for operation in traffic management and transportation system. The short-term traffic flow value results can be used for travel time estimation by route guidance an

Nicholas J. Harmon, Michael E. Flatté

A pure spin current is predicted to occur when an external magnetic field and a linearly inhomogeneous spin-only field are appropriately aligned. Under these conditions (such as originate from nuclear contact hyperfine fields that do not affect orbital motion) a linear, spin-dependent dispersion for free electrons emerges from the Landau Hamiltonian. The res

Takanori Shirokura, Pham Nam Hai

Half-Heusler alloy topological semimetal YPtBi is a promising candidate for an efficient spin source material having both large spin Hall angle ${\theta}$$_{SH}$ and high thermal stability. However, high-quality YPtBi thin films with low bulk carrier density are usually grown at 600${\deg}$C, which exceeds the limitation of 400${\deg}$C for back end of line

Nusrat Zahan, Parth Kanakiya, Brian Hambleton, Shohanuzzaman Shohan

The OpenSSF Scorecard project is an automated tool to monitor the security health of open-source software. This study evaluates the applicability of the Scorecard tool and compares the security practices and gaps in the npm and PyPI ecosystems.

Mitre C. Dourado, Rodolfo A. Oliveira, Vitor Ponciano, Alessandra B. Queiróz

Given a graph $G$ such that each vertex $v_i$ has a value $f(v_i)$, the expanded-clique graph $H$ is the graph where each vertex $v_i$ of $G$ becomes a clique $V_i$ of size $f(v_i)$ and for each edge $v_iv_j \in E(G)$, there is a vertex of $V_i$ adjacent to an exclusive vertex of $V_j$. In this work, among the results, we present two characterizations of the

Siddharth H. Nair

Recent results in control systems and numerical integration literature utilize invariant set theory to lift dynamical systems evolving on nonlinear manifolds to those evolving on vector spaces. We leverage this technique to propose an algorithm to solve a class of constrained optimization problems as unconstrained problems.

Claudio Bonizzoni, Mirco Tincani, Fabio Santanni, Marco Affronte

Machine Learning finds application in the quantum control and readout of qubits. In this work we apply Artificial Neural Networks to assist the manipulation and the readout of a prototypical molecular spin qubit - an Oxovanadium(IV) moiety - in two experiments designed to test the amplitude and the phase recognition, respectively. We first successfully use a

Dihong Jiang, Guojun Zhang, Mahdi Karami, Xi Chen

Modern machine learning systems achieve great success when trained on large datasets. However, these datasets usually contain sensitive information (e.g. medical records, face images), leading to serious privacy concerns. Differentially private generative models (DPGMs) emerge as a solution to circumvent such privacy concerns by generating privatized sensiti

Anh-Tu Nguyen, Thao Nguyen, Huy-Khiem Le, Huy-Hieu Pham

Sleep apnea (SA) is a type of sleep disorder characterized by snoring and chronic sleeplessness, which can lead to serious conditions such as high blood pressure, heart failure, and cardiomyopathy (enlargement of the muscle tissue of the heart). The electrocardiogram (ECG) plays a critical role in identifying SA since it might reveal abnormal cardiac activit

Muhammad Usman, Youcheng Sun, Divya Gopinath, Rishi Dange

Deep neural network (DNN) models, including those used in safety-critical domains, need to be thoroughly tested to ensure that they can reliably perform well in different scenarios. In this article, we provide an overview of structural coverage metrics for testing DNN models, including neuron coverage (NC), k-multisection neuron coverage (kMNC), top-k neuron

Miriam Fischer, Akshay Gupte

We present multilinear and mixed-integer multilinear programs to find a Nash equilibrium in multi-player noncooperative games. We compare the formulations to common algorithms in Gambit, and conclude that a multilinear feasibility program finds a Nash equilibrium faster than any of the methods we compare it to, including the quantal response equilibrium meth

Christian Adamcewicz, Eric Thrane

The formation history of binary black hole systems is imprinted on the distribution of their masses, spins, and eccentricity. While much has been learned studying these parameters in turn, recent studies have explored the joint distribution of binary black hole parameters in two or more dimensions. Most notably, it has recently been argued that binary black

Chow-Choong Ngeow, Anupam Bhardwaj, Jing-Yi Henderson, Matthew J. Graham

We present the first gri-band period-luminosity (PL) and period-Wesenheit (PW) relations for 37 Type II Cepheids (hereafter TIIC) located in 18 globular clusters based on photometric data from the Zwicky Transient Facility. We also updated BV IJHK-band absolute magnitudes for 58 TIIC in 24 globular clusters using the latest homogeneous distances to the globu

Dat T. Ngo, Thao T. B. Nguyen, Hieu T. Nguyen, Dung B. Nguyen

The rapid development in representation learning techniques such as deep neural networks and the availability of large-scale, well-annotated medical imaging datasets have to a rapid increase in the use of supervised machine learning in the 3D medical image analysis and diagnosis. In particular, deep convolutional neural networks (D-CNNs) have been key player

Benjamin L. Gerard, Javier Perez-Soto, Vincent Chambouleyron, Maaike A. M. van Kooten

Ground-based high contrast imaging (HCI) and extreme adaptive optics (AO) technologies have advanced to the point of enabling direct detections of gas-giant exoplanets orbiting beyond the snow lines around nearby young star systems. However, leftover wavefront errors using current HCI and AO technologies, realized as "speckles" in the coronagraphic science i

Pierros Ntelis

In this paper, we describe a mathematical formalism for a $(D_\tau,D_x)$-dimensional manifold with $N$-correlators of $N_t$ types of objects, with cross correlations and contaminants. In particular, we build this formalism using simple notions of mathematical physics, field theory, topology, algebra, statistics n-correlators and Fourier transform. We discuss

Arnab Sarkar, Scott Randall, Yuanyuan Su, Gabriella E. Alvarez

We report the first unambiguous detection of an axial merger shock in the early-stage merging cluster Abell 98 using deep (227 ks) Chandra observations. The shock is about 420 kpc south from the northern subcluster of Abell 98, in between the northern and central subclusters, with a Mach number of M $\approx$ 2.3 $\pm$ 0.3. Our discovery of the axial merger

Shih-Yu Chang

Gaussian processes can be treated as subsets of a standard Hilbert space, however, the volume size relation between the underlying index space of random processes and its convex hull is not clear. The understanding of such volume size relations can help us to establish a majorizing measure theorem geometrically. In this paper, we assume that the underlying i

Li Yang, Abdallah Shami, Gary Stevens, Stephen De Rusett

Modern vehicles, including autonomous vehicles and connected vehicles, have adopted an increasing variety of functionalities through connections and communications with other vehicles, smart devices, and infrastructures. However, the growing connectivity of the Internet of Vehicles (IoV) also increases the vulnerabilities to network attacks. To protect IoV s

Shih-Yu Chang

Gaussian processes can be considered as subsets of a standard Hilbert space, but the geometric understanding that would relate the size of a set with the size of its convex hull is still lacking. In this work, we adopt a geometric approach to the majorizing measure problem by identifying the covering number relationships between a given space $T$ and its con

Andreas Bauer

Walker-Wang models are fixed-point models of topological order in $3+1$ dimensions constructed from a braided fusion category. For a modular input category $\mathcal M$, the model itself is invertible and is believed to be in a trivial topological phase, whereas its standard boundary is supposed to represent a $2+1$-dimensional chiral phase. In this work we

Jonathan Kim, Brian J. Sandri, Raghavendra B. Rao, Eric F. Lock

We develop a Bayesian approach to predict a continuous or binary outcome from data that are collected from multiple sources with a multi-way (i.e.. multidimensional tensor) structure. As a motivating example we consider molecular data from multiple 'omics sources, each measured over multiple developmental time points, as predictors of early-life iron deficie

Dana S. Balser, Trey V. Wenger, T. M. Bania

Standard stellar evolution models that only consider convection as a physical process to mix material inside of stars predict the production of significant amounts of 3He in low-mass stars (M < 2 Msun), with peak abundances of 3He/H ~ few x 10-3 by number. Over the life-time of the Galaxy, this ought to produce 3He/H abundances that diminish with increasing

Daniel Alpay, Paula Cerejeiras, Uwe Kaehler, Trevor Kling

Given a weighted $\ell^2$ space with weights associated to an entire function, we consider pairs of weighted shift operators, whose commutators are diagonal operators, when considered as operators over a general Fock space. We establish a calculus for the algebra of these commutators and apply it to the general case of Gelfond-Leontiev derivatives. This gene

Dirk Padfield, Daniel J. Liebling

Diarization partitions an audio stream into segments based on the voices of the speakers. Real-time diarization systems that include an enrollment step should limit enrollment training samples to reduce user interaction time. Although training on a small number of samples yields poor performance, we show that the accuracy can be improved dramatically using a

Ashish Rauniyar, Desta Haileselassie Hagos, Debesh Jha, Jan Erik Håkegård

With the advent of the IoT, AI, ML, and DL algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However,

Gavin Stewart

We consider a periodic nonlinear Schr\"odinger equation with white noise dispersion and a power nonlinearity given by \begin{equation*} idu = \Delta u \circ dW_t + |u|^{p-1}u\;dt \end{equation*} By proving stochastic Strichartz estimates, we are able to prove almost sure global wellposedness of this equation with $L^2$ initial data for nonlinearities with ex

Dana Kirchem, Wolf-Peter Schill

The use of green hydrogen can support the decarbonization of sectors which are difficult to electrify, such as industry or heavy transport. Yet, the wider power sector effects of providing green hydrogen are not well understood so far. We use an open-source electricity sector model to investigate potential power sector interactions of three alternative suppl

David Freeman, Dimitrios Giannakis, Joanna Slawinska

We propose a scheme for data-driven parameterization of unresolved dimensions of dynamical systems based on the mathematical framework of quantum mechanics and Koopman operator theory. Given a system in which some components of the state are unknown, this method involves defining a surrogate system in a time-dependent quantum state which determines the fluxe

Michael J. Kane, Owais Gilani, Simon Urbanek

Human mobility describes physical patterns of movement of people within a spatial system. Many of these patterns, including daily commuting, are cyclic and quantifiable. These patterns capture physical phenomena tied to processes studied in epidemiology, and other social, behavioral, and economic sciences. This paper advances human mobility research by propo

Sean E. Sullivan, Hwijong Lee, Annie Weathers, Li Shi

Heavy metal (HM)/magnet bilayers host many magnetoresistances (MR) and spin caloritronic effects. Here we show that the spin Peltier effect and electron-phonon scattering produce much larger unidirectional MR of an HM on a magnetic insulator than existing theories that neglect the interplay between MR and spin caloritronic effects. By accounting for local no

Raphaël Hardy, Andrew Cumming, Paul Charbonneau

The atmosphere of a hot jupiter may be subject to a thermo-resistive instability, in which the increasing electrical conductivity with temperature leads to runaway Ohmic heating. We introduce a simplified model of the local dynamics in the equatorial region of a hot jupiter that incorporates the back reaction on the atmospheric flow as the increasing electri

Ashraf Tadross, Eslam Elhosseiny

A photometric and astrometric study of the two open star clusters Gulliver 18 and Gulliver 58 was carried out for the first time using the early third data release of the Gaia space observatory (Gaia-EDR3). By studying the proper motions, parallaxes, and color-magnitude diagrams of the two clusters, we determined their actual cluster membership. Therefore, a

Mehmet Fatih Ozkan, Yao Ma

Trust is essential for automated vehicles (AVs) to promote and sustain technology acceptance in human-dominated traffic scenarios. However, computational trust dynamic models describing the interactive relationship between the AVs and surrounding human drivers in traffic rarely exist. This paper aims to fill this gap by developing a quantitative trust dynami

Antonino Favano, Luca Barletta, Alex Dytso

This paper studies secrecy-capacity of an $n$-dimensional Gaussian wiretap channel under a peak-power constraint. This work determines the largest peak-power constraint $\bar{\mathsf{R}}_n$ such that an input distribution uniformly distributed on a single sphere is optimal; this regime is termed the low amplitude regime. The asymptotic of $\bar{\mathsf{R}}_n

G. S. Ilha, R. A. Riffel, T. V. Ricci, S. B. Rembold

Red Geysers are quiescent galaxies with galactic scale ionised outflows, likely due to low-luminosity Active Galactic Nuclei (AGN). We used Gemini GMOS-IFU observations of the inner $\sim 1.0-3.0$ kpc of nine Red Geysers selected from the MaNGA survey to study the gas ionisation and kinematics. The emission-line ratios suggest the presence of Seyfert/LINER (

Jitesh Jain, Yuqian Zhou, Ning Yu, Humphrey Shi

Deep image inpainting has made impressive progress with recent advances in image generation and processing algorithms. We claim that the performance of inpainting algorithms can be better judged by the generated structures and textures. Structures refer to the generated object boundary or novel geometric structures within the hole, while texture refers to hi

Sheyu Li, Valentyn Litvin, Charles F. Manski

Medical journals have adhered to a reporting practice that seriously limits the usefulness of published trial findings. Medical decision makers commonly observe many patient covariates and seek to use this information to personalize treatment choices. Yet standard summaries of trial findings only partition subjects into broad subgroups, typically into binary

Andrei Chertkov, Gleb Ryzhakov, Ivan Oseledets

Surrogate models can reduce computational costs for multivariable functions with an unknown internal structure (black boxes). In a discrete formulation, surrogate modeling is equivalent to restoring a multidimensional array (tensor) from a small part of its elements. The alternating least squares (ALS) algorithm in the tensor train (TT) format is a widely us

Akhil Jakatdar, Baqiao Liu, Tandy Warnow, George Chacko

Through discovery of meso-scale structures, community detection methods contribute to the understanding of complex networks. Many community finding methods, however, rely on disjoint clustering techniques, in which node membership is restricted to one community or cluster. This strict requirement limits the ability to inclusively describe communities since s

Pierre-Antoine Graham, Simon Bertrand, Michaël Bédard, Robin Durand

Van Roosbroeck's equations constitute a versatile tool to determine the dynamics of electrons under time- and space-dependent perturbations. Extensively utilized in ordinary semiconductors, their potential to model devices made from topological materials remains untapped. Here, we adapt van Roosbroeck's equations to theoretically study the bulk response of a

Rodolfo Batista Negri, Antônio Fernando Bertachini de Almeida Prado, Ronan Arraes Jardim Chagas, Rodolpho Vilhena de Moraes

The increasing number of space missions may overwhelm ground support infrastructure, prompting the need for autonomous deep-space guidance, navigation, and control (GN\&C) systems. These systems offer sustainable and cost-effective solutions, particularly for asteroid missions that deal with uncertain environments. This study proposes a paradigm shift from t

Dmitry Gorbunov, Dmitry Kalashnikov, Pavel Pakhlov, Timofey Uglov

Hypothetical particles with tiny electric charges (millicharged particles or MCPs) can be produced in electron-positron annihilation if kinematically allowed. Typical searches for them at $e^+e^-$ colliders exploit a signature of a single photon with missing energy carried away by the undetected MCP pair. We put forward an idea to look alternatively for MCP

Elhousni Mahdi, Huang Xinming

As the autonomous driving industry is slowly maturing, visual map localization is quickly becoming the standard approach to localize cars as accurately as possible. Owing to the rich data returned by visual sensors such as cameras or LiDARs, researchers are able to build different types of maps with various levels of details, and use them to achieve high lev

John M Yelton

This is an experimentalist's view of the recent results on, and prospects for, CP Violation in charmed and bottom baryons

Aleksandar Stanić, Yujin Tang, David Ha, Jürgen Schmidhuber

Reinforcement learning agents must generalize beyond their training experience. Prior work has focused mostly on identical training and evaluation environments. Starting from the recently introduced Crafter benchmark, a 2D open world survival game, we introduce a new set of environments suitable for evaluating some agent's ability to generalize on previously

Louis Mozart Kamdem, Ernest Fokoue

Estimating the importance of variables is an essential task in modern machine learning. This help to evaluate the goodness of a feature in a given model. Several techniques for estimating the importance of variables have been developed during the last decade. In this paper, we proposed a computational and theoretical exploration of the emerging methods of va

Maximiliano Isi

We review the formalism underlying the modeling of gravitational wave (GW) polarizations, and the coordinate frames used to define them. In the process, we clarify the notion of "polarization angle" and identify three conceptually distinct definitions. We describe how those are related and how they arise in the practice of GW data analysis, explaining in det

Michael Q. May, Hong Qin

The physics of many closed, conservative systems can be described by both classical and quantum theories. The dynamics according to classical theory is symplectic and admits linear instabilities which would initially seem at odds with a unitary quantum description. Using the example of three-wave interactions, we describe how a time-independent, finite-dimen

Emerson Melo

In this paper we study a rational inattention model in environments where the decision maker faces uncertainty about the true prior distribution over states. The decision maker seeks to select a stochastic choice rule over a finite set of alternatives that is robust to prior ambiguity. We fully characterize the distributional robustness of the rational inatt

Satyabrata Majee, Amit Maji

This paper presents Wold-type decomposition for various pairs of twisted contractions on Hilbert spaces. As a consequence, we obtain Wold-type decomposition for pairs of doubly twisted isometries and in particular, new and simple proof of S\l{}o\'{c}inski's theorem for pairs of doubly commuting isometries are provided. We also achieve an explicit decompositi

Sharan Mourya, SaiDhiraj Amuru, Kiran Kumar Kuchi

Channel State Information (CSI) Feedback plays a crucial role in achieving higher gains through beamforming. However, for a massive MIMO system, this feedback overhead is huge and grows linearly with the number of antennas. To reduce the feedback overhead several compressive sensing (CS) techniques were implemented in recent years but these techniques are of

Zeki C. Seskir, Steven Umbrello, Christopher Coenen, Pieter E. Vermaas

As quantum technologies (QT) advance, their potential impact on and relation with society has been developing into an important issue for exploration. In this paper, we investigate the topic of democratization in the context of QT, particularly quantum computing. The paper contains three main sections. First, we briefly introduce different theories of democr

Hang Hu, Zhao Song, Runzhou Tao, Zhaozhuo Xu

Online bipartite matching is a fundamental problem in online algorithms. The goal is to match two sets of vertices to maximize the sum of the edge weights, where for one set of vertices, each vertex and its corresponding edge weights appear in a sequence. Currently, in the practical recommendation system or search engine, the weights are decided by the inner

Matthew Mastroeni, Jason McCullough, Andrew Osborne, Joshua Rice

Edge ideals of finite simple graphs are well-studied over polynomial rings. In this paper, we initiate the study of edge ideals over exterior algebras, specifically focusing on the depth and singular varieties of such ideals. We prove an upper bound on the depth of the edge ideal associated to a general graph and a more refined bound for bipartite graphs, an

James P. Sethna

Getting the most from power-law-type data can be challenging. James Sethna points out some of the pitfalls in studying power laws arising from emergent scale invariance, as well as important opportunities.

Roi Ronen, Shahar Tsiper, Oron Anschel, Inbal Lavi

In recent years, the dominant paradigm for text spotting is to combine the tasks of text detection and recognition into a single end-to-end framework. Under this paradigm, both tasks are accomplished by operating over a shared global feature map extracted from the input image. Among the main challenges that end-to-end approaches face is the performance degra

Rafe Abdulali, Lauren E. Altman, David G. Grier

Holographic particle characterization uses quantitative analysis of holographic microscopy data to precisely and rapidly measure the diameter and refractive index of individual colloidal spheres in their native media. When this technique is applied to inhomogeneous or aspherical particles, the measured diameter and refractive index represent properties of an

Heinz Isliker, Andres Cathey, Matthias Hoelzl, Stanislas Pamela

We present test-particle simulations of electrons during a nonlinear MHD simulation of a type-I edge localized mode (ELM) to explore the effect of an eruptive plasma filament on the kinetic level. The electrons are moderately heated and accelerated during the filamentary eruption on a fast time scale of the order of 0.5 ms. A clearly non-thermal tail is form

Marco Capolli, Andrea Pinamonti, Gareth Speight

We investigate the connection between maximal directional derivatives and differentiability for Lipschitz functions defined on Laakso space. We show that maximality of a directional derivative for a Lipschitz function implies differentiability only for a $\sigma$-porous set of points. On the other hand, the distance to a fixed point is differentiable everywh

Alma Eguizabal, Ozan Öktem, Mats U. Persson

Photon-counting CT (PCCT) offers improved diagnostic performance through better spatial and energy resolution, but developing high-quality image reconstruction methods that can deal with these large datasets is challenging. Model-based solutions incorporate models of the physical acquisition in order to reconstruct more accurate images, but are dependent on

John R. Klauder

The usual particle in a box is turned into a field theory, and its behavior is examined using canonical and affine quantizations. The resulting leads to a valid affine quantization of the particle in a box field theory, which points toward further valid quantizations of more realistic field theory models.

Emilio Porcu, Philip A. White, Marc G. Genton

The advent of data science has provided an increasing number of challenges with high data complexity. This paper addresses the challenge of space-time data where the spatial domain is not a planar surface, a sphere, or a linear network, but a generalized network (termed a graph with Euclidean edges). Additionally, data are repeatedly measured over different

Marawan Elbatel, Christina Bornberg, Manasi Kattel, Enrique Almar

In-vitro tests are an alternative to animal testing for the toxicity of medical devices. Detecting cells as a first step, a cell expert evaluates the growth of cells according to cytotoxicity grade under the microscope. Thus, human fatigue plays a role in error making, making the use of deep learning appealing. Due to the high cost of training data annotatio

Matthew P Young

We prove an essentially optimal large sieve inequality for self-dual Eisenstein series of varying levels. This bound can alternatively be interpreted as a large sieve inequality for rationals ordered by height. The method of proof is recursive, and has some elements in common with Heath-Brown's quadratic large sieve, and the asymptotic large sieve of Conrey,

Lingzhi Zhang, Yuqian Zhou, Connelly Barnes, Sohrab Amirghodsi

Image inpainting is an essential task for multiple practical applications like object removal and image editing. Deep GAN-based models greatly improve the inpainting performance in structures and textures within the hole, but might also generate unexpected artifacts like broken structures or color blobs. Users perceive these artifacts to judge the effectiven

Leonardo Bennun, Maria Natalia Piol, Cristina Vazquez

In this work we have studied the limitations of the TXRF spectroscopy in the upper limit of validity of the technique, when the analyzed specimen ceases to be a thin film. We have evaluated the non-linear effects in spectra obtained from samples made ad-hoc, which were acquired in a pre-established sequence of dilutions. So, the spectra were obtained in cond

Rachel Bricker, Mikhail Nesterenko, Gokarna Sharma

We consider blockchain in dynamic networks. We define the Blockchain Decision Problem. It requires miners that maintain the blockchain to confirm whether a particular block is accepted. We establish the necessary conditions for the existence of a solution. We, however, prove that the solution, even under these necessary conditions is, in general, impossible.

Patsorn Sangkloy, Wittawat Jitkrittum, Diyi Yang, James Hays

We address the problem of retrieving images with both a sketch and a text query. We present TASK-former (Text And SKetch transformer), an end-to-end trainable model for image retrieval using a text description and a sketch as input. We argue that both input modalities complement each other in a manner that cannot be achieved easily by either one alone. TASK-

Keisuke Fujii, Tilman Enss

We consider two-component fermions with a zero-range interaction both in two and three dimensions and calculate the bulk viscosity for an arbitrary scattering length in the high-temperature regime. We evaluate the Kubo formula for the bulk viscosity using an expansion with respect to the fugacity, which acts as a small parameter at high temperatures. In the

Caterina Mazzetti, Alessandro Sarti, Giovanna Citti

In this paper we propose a neurogeometrical model of the behaviour of cells of the arm area of the primary motor cortex (M1). We will mathematically express as a fiber bundle the hypercolumnar organization of this cortical area, first modelled by Georgopoulos in \cite{georgopoulos1982relations, georgopoulos2015columnar}. On this structure, we will consider t

Federico Rossi, Dylan Shell

We study planning problems faced by robots operating in uncertain environments with incomplete knowledge of state, and actions that are noisy and/or imprecise. This paper identifies a new problem sub-class that models settings in which information is revealed only intermittently through some exogenous process that provides state information periodically. Sev

Bhoj Raj Pandit, Abeer Alsadoon, P. W. C. Prasad, Sarmad Al Aloussi

Background and Purpose: Convolutional neural network is widely used for image recognition in the medical area at nowadays. However, overall accuracy in predicting lung tumor is low and the processing time is high as the error occurred while reconstructing the CT image. The aim of this work is to increase the overall prediction accuracy along with reducing pr

Yoichiro Mori, Laurel Ohm

We consider a classical elastohydrodynamic model of an inextensible filament undergoing planar motion in $\mathbb{R}^3$. The hydrodynamics are described by resistive force theory, and the fiber elasticity is governed by Euler-Bernoulli beam theory. Our aim is twofold: (1) Serve as a starting point for developing the mathematical analysis of filament elastohy

Nikolas Martelaro

The rapid move to remote work due to COVID-19 social distancing policies has slowed or stopped most in-person qualitative user research activities. The limitation on in-person activities has pushed many user researchers to consider how they can continue their research remotely and presents an opportunity for user research teams to (re)design their research p

M. Baluktsian, L. Loetgering, G. Dogan, U. Sanli

In the visible spectrum vortex beams have found various applications, ranging from optical tweezers to super-resolution imaging. Recently, these beams have been demonstrated using X-rays and electron beams. However, so far, no in-depth discussion has been carried out on the vortex quality, which could become essential for a variety of vortex applications. He

Zach Meisel

Evidence has accumulated for an as-yet unaccounted for source of heat located at shallow depths within the accreted neutron star crust. However, the nature of this heat source is unknown. I demonstrate that the inferred depth of carbon ignition in X-ray superbursts can be used as an additional constraint for the magnitude and depth of shallow heating. The in

Menno Yap, Oded Cats

Urban metro and tram networks are regularly subject to planned disruptions, including closures, resulting from the need to maintain and renew infrastructure. In this study, we first empirically analyse the passenger demand response to planned public transport disruptions based on individual passenger travel behaviour, based on which we infer generalised jour

Fransisca Susan, Negin Golrezaei, Ehsan Emamjomeh-Zadeh, David Kempe

We study the problem of actively learning a non-parametric choice model based on consumers' decisions. We present a negative result showing that such choice models may not be identifiable. To overcome the identifiability problem, we introduce a directed acyclic graph (DAG) representation of the choice model. This representation provably encodes all the infor

Jingyi Shen, Haoyu Li, Jiayi Xu, Ayan Biswas

Deep learning based latent representations have been widely used for numerous scientific visualization applications such as isosurface similarity analysis, volume rendering, flow field synthesis, and data reduction, just to name a few. However, existing latent representations are mostly generated from raw data in an unsupervised manner, which makes it diffic

Reetam Majumder, Brian J. Reich, Benjamin A. Shaby

Quantifying changes in the probability and magnitude of extreme flooding events is key to mitigating their impacts. While hydrodynamic data are inherently spatially dependent, traditional spatial models such as Gaussian processes are poorly suited for modeling extreme events. Spatial extreme value models with more realistic tail dependence characteristics ar

Mohsen Sadatsafavi, Tae Yoon Lee, Laure Wynants, Andrew Vickers

Background: Before being used to inform patient care, a risk prediction model needs to be validated in a representative sample from the target population. The finite size of the validation sample entails that there is uncertainty with respect to estimates of model performance. We apply value-of-information methodology as a framework to quantify the consequen

Marmik Shrestha, Omar Hisham Alsadoon, Abeer Alsadoon, Thair Al-Dala'in

Type 2 Diabetes is one of the most major and fatal diseases known to human beings, where thousands of people are subjected to the onset of Type 2 Diabetes every year. However, the diagnosis and prevention of Type 2 Diabetes are relatively costly in today's scenario; hence, the use of machine learning and deep learning techniques is gaining momentum for predi