December 2020 arXiv papers — page 119

K. Djamaa, A. Mohamed-Meziani

We propose an implementation of ZZ, ZZj and ZZjj productions in MadGraph5_aMC@NLO framework at $\sqrt{s}$ = 14 TeV. We calculate these processes at leading order and next-to-leading order with QCD corrections and we present a theoretical prediction of their total cross sections with different cuts in transverse momentum of jets, including gluon fusion contri

T. E. Boult, P. A. Grabowicz, D. S. Prijatelj, R. Stern

Managing inputs that are novel, unknown, or out-of-distribution is critical as an agent moves from the lab to the open world. Novelty-related problems include being tolerant to novel perturbations of the normal input, detecting when the input includes novel items, and adapting to novel inputs. While significant research has been undertaken in these areas, a

Shuyu Kong, You Li, Jia Wang, Amin Rezaei

Supervised learning on Deep Neural Networks (DNNs) is data hungry. Optimizing performance of DNN in the presence of noisy labels has become of paramount importance since collecting a large dataset will usually bring in noisy labels. Inspired by the robustness of K-Nearest Neighbors (KNN) against data noise, in this work, we propose to apply deep KNN for labe

Bradley E. Schaefer, Howard E. Bond, Kailash C. Sahu

The planetary nebula (PN) called the Stingray (PN G331.3$-$12.1) suddenly turned on in the 1980s, and its central star (V839 Ara) started a fast evolution with large amplitudes in magnitude, surface temperature, and surface gravity, perhaps as part of a late thermal pulse causing a loop in the Hertzsprung-Russell (HR) diagram. With these fast changes, we hav

Xueqing Deng, Yi Zhu, Yuxin Tian, Shawn Newsam

Land-cover classification using remote sensing imagery is an important Earth observation task. Recently, land cover classification has benefited from the development of fully connected neural networks for semantic segmentation. The benchmark datasets available for training deep segmentation models in remote sensing imagery tend to be small, however, often co

Mythreyi Velmurugan, Chun Ouyang, Catarina Moreira, Renuka Sindhgatta

Predictive process analytics focuses on predicting the future states of running instances of a business process. While advanced machine learning techniques have been used to increase accuracy of predictions, the resulting predictive models lack transparency. Current explainable machine learning methods, such as LIME and SHAP, can be used to interpret black b

Yuzhen Qin, Tommaso Menara, Danielle S. Bassett, Fabio Pasqualetti

Phase-amplitude coupling (PAC) describes the phenomenon where the power of a high-frequency oscillation evolves with the phase of a low-frequency one. We propose a model that explains the emergence of PAC in two commonly-accepted architectures in the brain, namely, a high-frequency neural oscillation driven by an external low-frequency input and two interact

Angie Peng, Jeff Naecker, Ben Hutchinson, Andrew Smart

How should we decide which fairness criteria or definitions to adopt in machine learning systems? To answer this question, we must study the fairness preferences of actual users of machine learning systems. Stringent parity constraints on treatment or impact can come with trade-offs, and may not even be preferred by the social groups in question (Zafar et al

Yash Mehta, Dev Patel, Manik Lal Das

A unique identification for citizens can lead to effective governance to manage and provide citizen-centric services. While ensuring this service, privacy of the citizens needs to be preserved. Aadhaar, the identification system by UIDAI has faced some critics regarding its privacy preserving feature. This paper discusses those concerns in Aadhaar system and

Amir-Salar Esteki, Solmaz S. Kia

In this paper we consider the problem of privacy preservation in the static average consensus problem. This problem normally is solved by proposing privacy preservation augmentations for the popular first order Laplacian-based algorithm. These mechanisms however come with computational overhead, may need coordination among the agents to choose their paramete

Ritam Mallick, Shailendra Singh, Rana Nandi

This article studies the maximum mass limit of the quark star formed after the shock-induced phase transition of a cold neutron star. By employing hadronic and quark equation of state that satisfies the current mass bound, we use combustion adiabat conditions to find such a limit. The combustion adiabat condition results in a local or a global maximum pressu

Ahmet Inci, Evgeny Bolotin, Yaosheng Fu, Gal Dalal

With deep reinforcement learning (RL) methods achieving results that exceed human capabilities in games, robotics, and simulated environments, continued scaling of RL training is crucial to its deployment in solving complex real-world problems. However, improving the performance scalability and power efficiency of RL training through understanding the archit

Sanchari Bhattacharjee, Sandip Khan

We have used molecular dynamics simulations to study the effect of water on the wetting behavior and the interfacial structure of ionic liquid (IL) 1-ethyl-3-methylimidazolium boron tetrafluoride[EMIM][BF4] droplets on graphite surfaces which, is a prerequisite for the new IL-based applications in the field of chemical engineering. A slight decrement in the

Zhenqi Hao, Changwei Zou, Xiangyu Luo, Yu Ji

We use scanning tunneling microscopy to investigate Bi2Sr2Ca2Cu3O10+δ trilayer cuprates from the optimally doped to overdoped regime. We find that the two distinct superconducting gaps from the inner and outer CuO2 planes both decrease rapidly with doping, in sharp contrast to the nearly constant Tc. Spectroscopic imaging reveals the absence of quasiparticle

Esther Ezra, Orit E. Raz, Micha Sharir, Joshua Zahl

We show that the maximum number of pairwise non-overlapping $k$-rich lenses (lenses formed by at least $k$ circles) in an arrangement of $n$ circles in the plane is $O\left(\frac{n^{3/2}\log{(n/k^3)}}{k^{5/2}} + \frac{n}{k} \right)$, and the sum of the degrees of the lenses of such a family (where the degree of a lens is the number of circles that form it) i

Yuqi Kong, Fanchao Meng, Benjamin Carterette

Comparing document semantics is one of the toughest tasks in both Natural Language Processing and Information Retrieval. To date, on one hand, the tools for this task are still rare. On the other hand, most relevant methods are devised from the statistic or the vector space model perspectives but nearly none from a topological perspective. In this paper, we

Liam Jolliffe

We investigate $p$-ary $t$-designs which are simultaneously designs for all $t$, which we call universal $p$-ary designs. Null universal designs are well understood due to Gordon James via the representation theory of the symmetric group. We study non-null designs and determine necessary and sufficient conditions on the coefficients for such a design to exis

Ivan Kukanov, Janne Karttunen, Hannu Sillanpää, Ville Hautamäki

Since the invention of cinema, the manipulated videos have existed. But generating manipulated videos that can fool the viewer has been a time-consuming endeavor. With the dramatic improvements in the deep generative modeling, generating believable looking fake videos has become a reality. In the present work, we concentrate on the so-called deepfake videos,

Sanchari Bhattacharjee, Sandip Khan

Molecular dynamics simulations were employed to study the wetting behavior of nanoscale aqueous hydrophilic and hydrophobic Imidazolium based ionic liquid (IL) droplets on a solid graphite substrate subjected to the perpendicular electric field. Imminent transformation in the droplet configuration was observed at E = 0.08 V/Å both for hydrophobic ILs [EMIM][

Xuerong Xiao, Swetava Ganguli, Vipul Pandey

Training robust supervised deep learning models for many geospatial applications of computer vision is difficult due to dearth of class-balanced and diverse training data. Conversely, obtaining enough training data for many applications is financially prohibitive or may be infeasible, especially when the application involves modeling rare or extreme events.

Zewei Chu, Karl Stratos, Kevin Gimpel

Dataless text classification is capable of classifying documents into previously unseen labels by assigning a score to any document paired with a label description. While promising, it crucially relies on accurate descriptions of the label set for each downstream task. This reliance causes dataless classifiers to be highly sensitive to the choice of label de

Pengfei Chen, Junjie Ye, Guangyong Chen, Jingwei Zhao

For multi-class classification under class-conditional label noise, we prove that the accuracy metric itself can be robust. We concretize this finding's inspiration in two essential aspects: training and validation, with which we address critical issues in learning with noisy labels. For training, we show that maximizing training accuracy on sufficiently

Brittany Nicholls, Mariama Adangwa, Rachel Estes, Hugues Nelson Iradukunda

The purpose of this paper is to examine how resource usage of an analytic is affected by the different underlying datatypes of Spark analytics - Resilient Distributed Datasets (RDDs), Datasets, and DataFrames. The resource usage of an analytic is explored as a viable and preferred alternative of benchmarking big data analytics instead of the current common b

Xingchen Ji, Jae K. Jang, Utsav D. Dave, Mateus Corato-Zanarella

Low propagation loss in high confinement waveguides is critical for chip-based nonlinear photonics applications. Sophisticated fabrication processes which yield sub-nm roughness are generally needed to reduce scattering points at the waveguide interfaces in order to achieve ultralow propagation loss. Here, we show ultralow propagation loss by shaping the mod

S. P. Glasby, E. Pierro, Cheryl E. Praeger

We discuss recent progress on the problem of classifying point-primitive generalised polygons. In the case of generalised hexagons and generalised octagons, this has reduced the problem to primitive actions of almost simple groups of Lie type. To illustrate how the natural geometry of these groups may be used in this study, we show that if $\mathcal{S}$ is a

Binghui Wang, Ang Li, Hai Li, Yiran Chen

Graph-based semi-supervised node classification (GraphSSC) has wide applications, ranging from networking and security to data mining and machine learning, etc. However, existing centralized GraphSSC methods are impractical to solve many real-world graph-based problems, as collecting the entire graph and labeling a reasonable number of labels is time-consumi

Minjie Lu, Hao Chen, Glenn Agnolet

Low temperature scanning tunneling spectroscopy of HfNiSn shows a V^m(m < 1) zero bias anomaly around the Fermi level. This local density of states with a fractional power law shape is well known to be a consequence of electronic correlations. For comparison, we have also measured the tunneling conductances of other half-Heusler compounds with 18 valence ele

Tongxin Zhang, Lilin Wang, Zhijun Wang, Junjie Li

Ice growth has attracted great attention for its capability of fabricating hierarchically porous microstructure. However, the formation of tilted lamellar microstructure during freezing needs to be reconsidered due to the limited control of ice orientation with respect to thermal gradient during in-situ observations, which can greatly enrich our insight into

Hyun Jin Jang, Kiseop Lee, Kyungsub Lee

We propose the Hawkes flocking model that assesses systemic risk in high-frequency processes at the two perspectives -- endogeneity and interactivity. We examine the futures markets of WTI crude oil and gasoline for the past decade, and perform a comparative analysis with conditional value-at-risk as a benchmark measure. In terms of high-frequency structure,

Sang-Hoon Han, Seung-Gyo Jeong, Sun-Woo Kim, Tae-Hwan Kim

Topological phases and their topological features are enriched by the fundamental time-reversal, particle-hole, and chiral as well as crystalline symmetries. While one-dimensional (1D) generalized Su-Schrieffer-Heeger (SSH) systems show various topological phenomena such as topological solitons and topological charge pumping, it remains unclear how such symm

Pengyu Zhang, Dong Wang, Huchuan Lu

Visual object tracking, as a fundamental task in computer vision, has drawn much attention in recent years. To extend trackers to a wider range of applications, researchers have introduced information from multiple modalities to handle specific scenes, which is a promising research prospect with emerging methods and benchmarks. To provide a thorough review o

Jaime Cisternas, Paula Mellado, Felipe Urbina, Cristóbal Portilla

In classical mechanics, solutions can be classified according to their stability. Each of them is part of the possible trajectories of the system. However, the signatures of unstable solutions are hard to observe in an experiment, and most of the times if the experimental realization is adiabatic, they are considered just a nuisance. Here we use a small numb

Xiaoyun Li, Zibin Zheng, Hong-Ning Dai

Services computing can offer a high-level abstraction to support diverse applications via encapsulating various computing infrastructures. Though services computing has greatly boosted the productivity of developers, it is faced with three main challenges: privacy and security risks, information silo, and pricing mechanisms and incentives. The recent advance

Jiahua Dong, Yang Cong, Gan Sun, Yunsheng Yang

Weakly-supervised learning has attracted growing research attention on medical lesions segmentation due to significant saving in pixel-level annotation cost. However, 1) most existing methods require effective prior and constraints to explore the intrinsic lesions characterization, which only generates incorrect and rough prediction; 2) they neglect the unde

David Q. Sun, Hadas Kotek, Christopher Klein, Mayank Gupta

This paper develops and implements a scalable methodology for (a) estimating the noisiness of labels produced by a typical crowdsourcing semantic annotation task, and (b) reducing the resulting error of the labeling process by as much as 20-30% in comparison to other common labeling strategies. Importantly, this new approach to the labeling process, which we

Francisco Crespo, Salomón Rebollo-perdomo, Jorge L. Zapata

This work establishes the existence of addition theorems and double-angle formulas for Ck real scalar functions. Moreover, we determine necessary and sufficient conditions for a bivariate function to be an addition formula for a Ck real function. The double-angle formulas allow us to generate a duplication algorithm, which can be used as an alternative to th

Bedangadas Mohanty

We present the measurements related to the spin alignment of $\mathrm{K^{*0}}$ and $\mathrmϕ$ vector mesons at mid-rapidity for Pb--Pb collisions at $\sqrt{s_{\mathrm{NN}}}$ = 2.76 TeV using the ALICE detector at the LHC. The second diagonal spin density matrix element ($ρ_{00}$) is measured from the angular distribution of the decay daughters of the vector

Sicong Wang, Naveen Karunanayake, Tham Nguyen, Suranga Seneviratne

Traditional spam classification requires the end-user to reveal the content of its received email to the spam classifier which violates the privacy. Spam classification over encrypted emails enables the classifier to classify spam email without accessing the email, hence protects the privacy of email content. In this paper, we construct a spam classification

Yan-Tong Bian, Guang-Hua Liu, Sheng-Hui Qian, Xin-Xin Ding

In this letter, the effect of four types of defects (ONout, ONin, VNout and VNin) on the structural, electronic and magnetic properties of MoSi2N4 monolayer were investigated using first-principles calculations. The calculated results reveal that all the four types of defects lead to structural distortions around the O-dopant or N-vacancy, and thereby change

Sahin Lale, Oguzhan Teke, Babak Hassibi, Anima Anandkumar

In many computational tasks and dynamical systems, asynchrony and randomization are naturally present and have been considered as ways to increase the speed and reduce the cost of computation while compromising the accuracy and convergence rate. In this work, we show the additional benefits of randomization and asynchrony on the stability of linear dynamical

Mason Haberle, Jane Wang

An open question in the study of dilation surfaces is to determine the typical dynamical behavior of the directional flow on a fixed dilation surface. We show that on any one-holed dilation torus, in all but a measure zero Cantor set of directions, the directional flow has an attracting periodic orbit, is minimal, or is completely periodic. We further show t

Hailiang Zhao, Shuiguang Deng, Zijie Liu, Zhengzhe Xiang

Edge computing is naturally suited to the applications generated by Internet of Things (IoT) nodes. The IoT applications generally take the form of directed acyclic graphs (DAGs), where vertices represent interdependent functions and edges represent data streams. The status quo of minimizing the makespan of the DAG motivates the study on optimal function pla

Huaiqian You, Yue Yu, Stewart Silling, Marta D&#39;Elia

We show that machine learning can improve the accuracy of simulations of stress waves in one-dimensional composite materials. We propose a data-driven technique to learn nonlocal constitutive laws for stress wave propagation models. The method is an optimization-based technique in which the nonlocal kernel function is approximated via Bernstein polynomials.

R. Vogt, J. Randrup

Background: The role of angular momentum in fission has long been discussed but the observable effects are difficult to quantify. Purpose: We discuss a variety of effects associated with angular momentum in fission and present quantitative illustrations. Methods: We employ the fission simulation model $\mathtt{FREYA}$ which is well suited for this purpose be

Sadat Shaik, Bernadette Bucher, Nephele Agrafiotis, Stephen Phillips

Style analysis of artwork in computer vision predominantly focuses on achieving results in target image generation through optimizing understanding of low level style characteristics such as brush strokes. However, fundamentally different techniques are required to computationally understand and control qualities of art which incorporate higher level style c

Yaofeng Xie, Yu Li, Zhiping Yin, Rui Zhang

We use neutron scattering to investigate spin excitations in Sr(Co$_{1-x}$Ni$_{x})_2$As$_2$, which has a $c$-axis incommensurate helical structure of the two-dimensional (2D) in-plane ferromagnetic (FM) ordered layers for $0.013\leq x \leq 0.25$. By comparing the wave vector and energy dependent spin excitations in helical ordered Sr(Co$_{0.9}$Ni$_{0.1}$)$_2

Keegan Yao, Walter O. Krawec, Jiadong Zhu

It has been shown recently that the framework of quantum sampling, as introduced by Bouman and Fehr, can lead to new entropic uncertainty relations highly applicable to finite-key cryptographic analyses. Here we revisit these so-called sampling-based entropic uncertainty relations, deriving newer, more powerful, relations and applying them to source-independ

Annette Lopez, Patrick Kelly, Kaelyn Dauer, Ettore Vitali

From flow without dissipation of energy to the formation of vortices when placed within a rotating container, the superfluid state of matter has proven to be a very interesting physical phenomenon. Here we present the key mechanisms behind superfluidity in fermionic systems and apply our understanding to an exotic system found deep within the universe -- the

Andrew Kresch, Yuri Tschinkel

We study arithmetic properties of equivariant birational types introduced by Kontsevich, Pestun, and the second author.

Raiyan Abdul Baten, Ehsan Hoque

A person&#39;s appearance, identity, and other nonverbal cues can substantially influence how one is perceived by a negotiation counterpart, potentially impacting the outcome of the negotiation. With recent advances in technology, it is now possible to alter such cues through real-time video communication. In many cases, a person&#39;s physical presence can

Dhruva Kartik, Neeraj Sood, Urbashi Mitra, Tara Javidi

The problem of adaptive sampling for estimating probability mass functions (pmf) uniformly well is considered. Performance of the sampling strategy is measured in terms of the worst-case mean squared error. A Bayesian variant of the existing upper confidence bound (UCB) based approaches is proposed. It is shown analytically that the performance of this Bayes

Johannes N. Hendriks, Fredrik K. Gustafsson, Antônio H. Ribeiro, Adrian G. Wills

This paper is directed towards the problem of learning nonlinear ARX models based on system input--output data. In particular, our interest is in learning a conditional distribution of the current output based on a finite window of past inputs and outputs. To achieve this, we consider the use of so-called energy-based models, which have been developed in all

Shuvo Kumar Paul, Pourya Hoseini, Mircea Nicolescu, Monica Nicolescu

Detecting image correspondences by feature matching forms the basis of numerous computer vision applications. Several detectors and descriptors have been presented in the past, addressing the efficient generation of features from interest points (keypoints) in an image. In this paper, we investigate eight binary descriptors (AKAZE, BoostDesc, BRIEF, BRISK, F

Diganta Bhattacharjee, Kamesh Subbarao

In this paper, a set-membership filtering-based leader-follower synchronization protocol for discrete-time linear multi-agent systems is proposed wherein the aim is to make the agents synchronize with a leader. The agents, governed by identical high-order discrete-time linear dynamics, are subject to unknown-but-bounded input disturbances. In terms of its ow

M. MacDonald, L. Chan, D. Chung, N. Hutchins

We investigate rough-wall turbulent flows through direct numerical simulations of flow over three-dimensional transitionally rough sinusoidal surfaces. The roughness Reynolds number is fixed at $k^+=10$, where $k$ is the sinusoidal semi-amplitude, and the sinusoidal wavelength is varied, resulting in the roughness solidity, $Λ$ (frontal area divided by plan

Michael MacDonald, Marcin J. Kurowski, João Teixeira

We investigate the effects of condensation and liquid water loading on the stably stratified surface layer, with an eye towards understanding the influence of turbulent mixing on fog formation. Direct numerical simulations (DNS) of dry and moist open channel flows are conducted, where in both a constant cooling rate is applied at the ground to mimic longwave

Egecan Cogulu, Nahuel N. Statuto, Yang Cheng, Fengyuan Yang

We report the direct observation of switching of the Néel vector of antiferromagnetic (AFM) domains in response to electrical pulses in micron-scale Pt/$α$-Fe$_2$O$_3$ Hall bars using photoemission electron microscopy. Current pulses lead to reversible and repeatable switching, with the current direction determining the final state, consistent with Hall effe

Michael MacDonald, João Teixeira

We present a turbulent kinetic energy (TKE) closure scheme for the stably stratified atmosphere in which the mixing lengths for momentum and heat are not parameterized in the same manner. The key difference is that, while the mixing length for heat tends towards the stability independent mixing length for momentum in neutrally stratified conditions, it tends

David Lenz, Oana Marin, Vijay Mahadevan, Raine Yeh

Fitting B-splines to discrete data is especially challenging when the given data contain noise, jumps, or corners. Here, we describe how periodic data sets with these features can be efficiently and robustly approximated with B-splines by analyzing the Fourier spectrum of the data. Our method uses a collection of spectral filters to produce different indicat

Evan S. Gawlik, François Gay-Balmaz

We construct a structure-preserving finite element method and time-stepping scheme for inhomogeneous, incompressible magnetohydrodynamics (MHD). The method preserves energy, cross-helicity (when the fluid density is constant), magnetic helicity, mass, total squared density, pointwise incompressibility, and the constraint $\operatorname{div} B = 0$ to machine

CMS Collaboration

Measurements are presented of differential cross sections for the production of Z bosons in association with at least one jet initiated by a charm quark in pp collisions at $\sqrt{s} =$ 13 TeV. The data recorded by the CMS experiment at the LHC correspond to an integrated luminosity of 35.9 fb$^{-1}$. The final states that contain a pair of electrons or muon

Victor A. E. Farias, Felipe T. Brito, Cheryl Flynn, Javam C. Machado

Differential privacy is the state-of-the-art formal definition for data release under strong privacy guarantees. A variety of mechanisms have been proposed in the literature for releasing the output of numeric queries (e.g., the Laplace mechanism and smooth sensitivity mechanism). Those mechanisms guarantee differential privacy by adding noise to the true qu

Brittanny V. Holden, Shan He, Anil V. Rao

The problem of minimum-time, low-thrust, Earth-to-Mars interplanetary orbital trajectory optimization is considered. The minimum-time orbital transfer problem is modeled as a four-phase optimal control problem where the four phases correspond to planetary alignment, Earth escape, heliocentric transfer, and Mars capture. The four-phase optimal control problem

Guillermo Valle-Pérez, Ard A. Louis

Generalization in deep learning has been the topic of much recent theoretical and empirical research. Here we introduce desiderata for techniques that predict generalization errors for deep learning models in supervised learning. Such predictions should 1) scale correctly with data complexity; 2) scale correctly with training set size; 3) capture differences

Evgeny Hershkovitch Neiterman, Michael Klyuchka, Gil Ben-Artzi

Existing methods for enhancing dark images captured in a very low-light environment assume that the intensity level of the optimal output image is known and already included in the training set. However, this assumption often does not hold, leading to output images that contain visual imperfections such as dark regions or low contrast. To facilitate the trai

Christopher Chamberland, Kyungjoo Noh, Patricio Arrangoiz-Arriola, Earl T. Campbell

We present a comprehensive architectural analysis for a proposed fault-tolerant quantum computer based on cat codes concatenated with outer quantum error-correcting codes. For the physical hardware, we propose a system of acoustic resonators coupled to superconducting circuits with a two-dimensional layout. Using estimated physical parameters for the hardwar

Graziela Fonseca, Grasiela Martini, Leonardo Silva

In this paper we determine all partial actions and partial coactions of Taft and Nichols Hopf algebras on their base fields. Furthermore, we prove that all such partial (co)actions are symmetric.

Robin Nicole, Aleksandra Alorić, Peter Sollich

Technological advancement has lead to an increase in number and type of trading venues and diversification of goods traded. These changes have re-emphasized the importance of understanding the effects of market competition: does proliferation of trading venues and increased competition lead to dominance of a single market or coexistence of multiple markets?

Dipen Kumar, Anju Goyal, Alan Truhan, Gary Abrams

In this chapter, we will give a brief overview of fundus photography, SLO, and OCT while discussing photoacoustic imaging potential as the next major ocular imaging modality.

James Walsh, Oluwafunmilola Kesa, Andrew Wang, Mihai Ilas

During the COVID-19 pandemic, policy makers at the Greater London Authority, the regional governance body of London, UK, are reliant upon prompt and accurate data sources. Large well-defined heterogeneous compositions of activity throughout the city are sometimes difficult to acquire, yet are a necessity in order to learn 'busyness' and consequently make saf

Charith Peris, Gokmen Oz, Khadige Abboud, Venkata sai Varada

Current voice assistants typically use the best hypothesis yielded by their Automatic Speech Recognition (ASR) module as input to their Natural Language Understanding (NLU) module, thereby losing helpful information that might be stored in lower-ranked ASR hypotheses. We explore the change in performance of NLU associated tasks when utilizing five-best ASR h

Mohammad Mehedi Hasan Akash, Nilotpal Chakraborty, Saikat Basu

Tracking and characterizing the blood uptake process within solid pancreatic tumors and the subsequent spatio-temporal distribution of red blood cells are critical to the clinical diagnosis of the cancer. This systematic computational study of physical factors, affecting the percolation and penetration of blood into a solid tumor, can assist in the developme

Juan Diego Jaramillo

New sequences of hyperoperations \cite{BE15,HI26,ACK28,GO47,TAR69} are presented together with their local algebraic properties. The commutative hyperoperations reported by Bennet \cite{BE15} are presented as a sequence of monoids. After identifying the semirings along the sequence, the corresponding fields are constructed via inverse completion.

Milan Studeny

The paper deals with conditional linear information inequalities valid for entropy functions induced by discrete random variables. Specifically, the so-called conditional Ingleton inequalities are in the center of interest: these are valid under conditional independence assumptions on the inducing random variables. We discuss five inequalities of this partic

Daniel Drimbe

We provide a new large class of countable icc groups $\mathcal A$ for which the product rigidity result from [CdSS15] holds: if $\Gamma_1,\dots,\Gamma_n\in\mathcal A$ and $\Lambda$ is any group such that $L(\Gamma_1\times\dots\times\Gamma_n)\cong L(\Lambda)$, then there exists a product decomposition $\Lambda=\Lambda_1\times\dots\times \Lambda_n$ such that $

Lvzhou Chen, Nicolaus Heuer

We establish a spectral gap for stable commutator length (scl) of integral chains in right-angled Artin groups (RAAGs). We show that this gap is not uniform, i.e. there are RAAGs and integral chains with scl arbitrarily close to zero. We determine the size of this gap up to a multiplicative constant in terms of the opposite path length of the defining graph.

Daniel Turizo, Daniel K. Molzahn

The admittance matrix encodes the network topology and electrical parameters of a power system in order to relate the current injection and voltage phasors. Since admittance matrices are central to many power engineering analyses, their characteristics are important subjects of theoretical studies. This paper focuses on the key characteristic of \emph{invert

Yang-Hui He, Kyu-Hwan Lee, Thomas Oliver

We show that standard machine-learning algorithms may be trained to predict certain invariants of low genus arithmetic curves. Using datasets of size around one hundred thousand, we demonstrate the utility of machine-learning in classification problems pertaining to the BSD invariants of an elliptic curve (including its rank and torsion subgroup), and the an

Sihan Huang, Haolei Weng, Yang Feng

One of the fundamental problems in network analysis is detecting community structure in multi-layer networks, of which each layer represents one type of edge information among the nodes. We propose integrative spectral clustering approaches based on effective convex layer aggregations. Our aggregation methods are strongly motivated by a delicate asymptotic a

Xinran Li, Alvaro E. Chavarria, Snezana Bogdanovich, Cristiano Galbiati

We performed a measurement of the ionization response of 200 $μ$m-thick amorphous selenium (aSe) layers under drift electric fields of up to 50 V/$μ$m. The aSe target was exposed to ionizing radiation from a $^{57}$Co radioactive source and the ionization pulses were recorded with high resolution. Using the spectral line from the photoabsorption of 122 keV $

Benjamin M Moran, Cheyenne Payne, Quinn Langdon, Daniel L Powell

In the past decade, advances in genome sequencing have allowed researchers to uncover the history of hybridization in diverse groups of species, including our own. Although the field has made impressive progress in documenting the extent of natural hybridization, both historical and recent, there are still many unanswered questions about its genetic and evol

Vishnu Rajendran, Gautham Prasad, Lutz Lampe, Gus Vos

Device-to-device communication (D2D) is a key enabler for connecting devices together to form the Internet of Things (IoT). A growing issue with IoT networks is the increasing number of IoT devices congesting the spectral resources of the cellular bands. Operating D2D in unlicensed band alleviates this issue by offloading network traffic from the licensed ba

James Halverson, Cody Long, Anindita Maiti, Brent Nelson

Dark Yang-Mills sectors, which are ubiquitous in the string landscape, may be reheated above their critical temperature and subsequently go through a confining first-order phase transition that produces stochastic gravitational waves in the early universe. Taking into account constraints from lattice and from Yang-Mills (center and Weyl) symmetries, we use a

Marianna A. Shubov, Madeline M. Edwards

The present research is devoted to the problem of stability of the fluid flow moving in a channel with flexible walls and interacting with the walls, which are subject to traveling waves. Experimental data shows that the energy of the flowing fluid can be transferred and consumed by the structure (the walls), which induces &#34;traveling wave flutter.&#34; T

Pavel Panteleev, Gleb Kalachev

We give a construction of quantum LDPC codes of dimension $\Theta(\log N)$ and distance $\Theta(N/\log N)$ as the code length $N\to\infty$. Using a product of chain complexes this construction also provides a family of quantum LDPC codes of distance $\Omega(N^{1-\alpha/2}/\log N)$ and dimension $\Omega(N^\alpha \log N)$, where $0 \le \alpha < 1$. We also int

Xinyu Zhang, Yirui Wang, Chi-Tung Cheng, Le Lu

Object detection methods are widely adopted for computer-aided diagnosis using medical images. Anomalous findings are usually treated as objects that are described by bounding boxes. Yet, many pathological findings, e.g., bone fractures, cannot be clearly defined by bounding boxes, owing to considerable instance, shape and boundary ambiguities. This makes bo

Evgeny A. Polyakov

In this work we provide a complete description of the lifecycle of entanglement during the real-time motion of open quantum systems. The quantum environment can have arbitrary (e.g. structured) spectral density. The entanglement can be seen constructively as a Lego: its bricks are the modes of the environment. These bricks are connected to each other via ope

Rudrajit Das, Anish Acharya, Abolfazl Hashemi, Sujay Sanghavi

We propose \texttt{FedGLOMO}, a novel federated learning (FL) algorithm with an iteration complexity of $\mathcal{O}(\epsilon^{-1.5})$ to converge to an $\epsilon$-stationary point (i.e., $\mathbb{E}[\|\nabla f(\bm{x})\|^2] \leq \epsilon$) for smooth non-convex functions -- under arbitrary client heterogeneity and compressed communication -- compared to the

Andrey Kurenkov, Roberto Martín-Martín, Jeff Ichnowski, Ken Goldberg

Searching for objects in indoor organized environments such as homes or offices is part of our everyday activities. When looking for a target object, we jointly reason about the rooms and containers the object is likely to be in; the same type of container will have a different probability of having the target depending on the room it is in. We also combine

Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro

Advances in NLP have yielded impressive results for the task of machine reading comprehension (MRC), with approaches having been reported to achieve performance comparable to that of humans. In this paper, we investigate whether state-of-the-art MRC models are able to correctly process Semantics Altering Modifications (SAM): linguistically-motivated phenomen

Toru Kitagawa, Guanyi Wang

How to allocate vaccines over heterogeneous individuals is one of the important policy decisions in pandemic times. This paper develops a procedure to estimate an individualized vaccine allocation policy under limited supply, exploiting social network data containing individual demographic characteristics and health status. We model spillover effects of the

Louis Garrigue

We analyze the map from potentials to the ground state in static many-body quantum mechanics. We first prove that the space of binding potentials is path-connected. Then we show that the map is locally weak-strong continuous and that its differential is compact. In particular, this implies the ill-posedness of the Kohn-Sham inverse problem.

Liyu Chen, Haipeng Luo, Chen-Yu Wei

We study the stochastic shortest path problem with adversarial costs and known transition, and show that the minimax regret is $\widetilde{O}(\sqrt{DT^\star K})$ and $\widetilde{O}(\sqrt{DT^\star SA K})$ for the full-information setting and the bandit feedback setting respectively, where $D$ is the diameter, $T^\star$ is the expected hitting time of the opti

Sokratis Zikas

We construct and study Sarkisov links obtained by blowing up smooth space curves lying on smooth cubic surfaces. We restrict our attention to the case where the blowup is not weak Fano. Together with the results of arXiv:1106.3716 which cover the weak Fano case, we provide a classification of all such curves. This is achieved by computing all curves which sa

Mohammad Pirhooshyaran, Tamas Terlaky

This article explores search strategies for the design of parameterized quantum circuits. We propose several optimization approaches including random search plus survival of the fittest, reinforcement learning both with classical and hybrid quantum classical controllers and Bayesian optimization as decision makers to design a quantum circuit in an automated

Samuel J. Ryskamp, Mark A. Hoefer, Gino Biondini

The interaction of an oblique line soliton with a one-dimensional dynamic mean flow is analyzed using the Kadomtsev-Petviashvili II (KPII) equation. Building upon previous studies that examined the transmission or trapping of a soliton by a slowly varying rarefaction or oscillatory dispersive shock wave in one space and one time dimension, this paper allows

Audun D. Myers, Firas A. Khasawneh, Brittany T. Fasy

We introduce a novel method for Additive Noise Analysis for Persistence Thresholding (ANAPT) which separates significant features in the sublevel set persistence diagram of a time series based on a statistics analysis of the persistence of a noise distribution. Specifically, we consider an additive noise model and leverage the statistical analysis to provide

Yuqing Shi

We work out the details of a correspondence observed by Goodwillie between cosimplicial spaces and good functors from a category of open subsets of the interval to the category of compactly generated weak Hausdorff spaces. Using this, we compute the first page of the integral Bousfield--Kan homotopy spectral sequence of the tower of fibrations given by the T

Raphael J. L. Townshend, Martin Vögele, Patricia Suriana, Alexander Derry

Computational methods that operate on three-dimensional molecular structure have the potential to solve important questions in biology and chemistry. In particular, deep neural networks have gained significant attention, but their widespread adoption in the biomolecular domain has been limited by a lack of either systematic performance benchmarks or a unifie

Kevin Keiler, Simeon I. Mistakidis, Peter Schmelcher

We unravel the polaronic properties of impurities immersed in a correlated trapped one-dimensional (1D) Bose-Bose mixture. This setup allows for the impurities to couple either attractively or repulsively to a specific host, thus offering a highly flexible platform for steering the emergent polaronic properties. Specifically, the polaronic residue peak and s