December 2020 arXiv papers — page 91

Justin Solomon, Kristjan Greenewald, Haikady N. Nagaraja

We introduce $k$-variance, a generalization of variance built on the machinery of random bipartite matchings. $K$-variance measures the expected cost of matching two sets of $k$ samples from a distribution to each other, capturing local rather than global information about a measure as $k$ increases; it is easily approximated stochastically using sampling an

Zifeng Wang, Batool Salehi, Andrey Gritsenko, Kaushik Chowdhury

We study an Open-World Class Discovery problem in which, given labeled training samples from old classes, we need to discover new classes from unlabeled test samples. There are two critical challenges to addressing this paradigm: (a) transferring knowledge from old to new classes, and (b) incorporating knowledge learned from new classes back to the original

Zifeng Wang, Tong Jian, Kaushik Chowdhury, Yanzhi Wang

In lifelong learning, we wish to maintain and update a model (e.g., a neural network classifier) in the presence of new classification tasks that arrive sequentially. In this paper, we propose a learn-prune-share (LPS) algorithm which addresses the challenges of catastrophic forgetting, parsimony, and knowledge reuse simultaneously. LPS splits the network in

Y. Rajesh, M. S. S. Bharati, S. Venugopal Rao, M. Ghanashyam Krishna

In this work, ZnO nanowire arrays decorated with titanium nitride (TiN) nanoparticles as surface enhanced Raman scattering (SERS) substrates is demonstrated. ZnO nanowires were grown by hydrothermal synthesis while ~100 nm TiN nanoparticles were obtained by grinding commercial powders for several hours. They were then decorated on the ZnO nanowire arrays usi

Dmitry Kazhdan, Botty Dimanov, Mateja Jamnik, Pietro Liò

Recurrent Neural Networks (RNNs) have achieved remarkable performance on a range of tasks. A key step to further empowering RNN-based approaches is improving their explainability and interpretability. In this work we present MEME: a model extraction approach capable of approximating RNNs with interpretable models represented by human-understandable concepts

Richard Evan Schwartz

This paper gives another proof of the key lemma in my recent paper which solves the optimal paper Moebius band conjecture of Halpern and Weaver, namely Lemma T. The proof here is longer but it offers more geometric intuition about what is going on.

Indradyumna Roy, Abir De, Soumen Chakrabarti

After observing a snapshot of a social network, a link prediction (LP) algorithm identifies node pairs between which new edges will likely materialize in future. Most LP algorithms estimate a score for currently non-neighboring node pairs, and rank them by this score. Recent LP systems compute this score by comparing dense, low dimensional vector representat

Chen Wang

There is an increasing need in solving high-dimensional optimization problems under non-deterministic environment. The simultaneous perturbation stochastic approximation (SPSA) algorithm has recently attracted considerable attention for solving high-dimensional optimization problems where the analytical formula cannot be attained. SPSA is designed to estimat

Qi Qi, Yi Xu, Rong Jin, Wotao Yin

In this paper, we present a simple yet effective provable method (named ABSGD) for addressing the data imbalance or label noise problem in deep learning. Our method is a simple modification to momentum SGD where we assign an individual importance weight to each sample in the mini-batch. The individual-level weight of sampled data is systematically proportion

Kerem Y. Camsari, Mustafa Mert Torunbalci, William A. Borders, Hideo Ohno

Naturally random devices that exploit ambient thermal noise have recently attracted attention as hardware primitives for accelerating probabilistic computing applications. One such approach is to use a low barrier nanomagnet as the free layer of a magnetic tunnel junction (MTJ) whose magnetic fluctuations are converted to resistance fluctuations in the prese

Fernanda D. de Melo Hernandez, César A. Hernández Melo, Horacio Tapia-Recillas

Considering $\mathbb{Z}_n$ the ring of integers modulo $n$, the classical Fermat-Euler theorem establishes the existence of a specific natural number $\varphi(n)$ satisfying the following property: $ x^{\varphi(n)}=1%\hspace{1.0cm}\text{for all}\hspace{0.2cm}x\in \mathbb{Z}_n^*, $ for all $x$ belonging to the group of units of $\mathbb{Z}_n$. In this manuscr

Michael Zhang, Xiaotian Cheng, Daniel Copeland, Arjun Desai

Open, or non-laparoscopic surgery, represents the vast majority of all operating room procedures, but few tools exist to objectively evaluate these techniques at scale. Current efforts involve human expert-based visual assessment. We leverage advances in computer vision to introduce an automated approach to video analysis of surgical execution. A state-of-th

Bai Cui, Ahmed Zamzam, Andrey Bernstein

Increasing integration of distributed energy resources (DERs) within distribution feeders provides unprecedented flexibility at the distribution-transmission interconnection. To exploit this flexibility and to use the capacity potential of aggregate DERs, feasible substation power injection trajectories need to be efficiently characterized. This paper provid

Jianfeng Wang, Xiaowei Hu, Pengchuan Zhang, Xiujun Li

Recent vision-language (VL) studies have shown remarkable progress by learning generic representations from massive image-text pairs with transformer models and then fine-tuning on downstream VL tasks. While existing research has been focused on achieving high accuracy with large pre-trained models, building a lightweight model is of great value in practice

Sibasish Laha, Christopher S. Reynolds, James Reeves, Gerard Kriss

Outflows from active galactic nuclei (AGN) are one of the fundamental mechanisms by which the central supermassive black hole interacts with its host galaxy. Detected in $\ge 50\%$ of nearby AGN, these outflows have been found to carry kinetic energy that is a significant fraction of AGN power, and thereby give negative feedback to their host galaxies. To un

T. A. Sobral, V. H. de Holanda, F. C. B. Leal, T. T. Saraiva

The injection of a long flexible rod into a two-dimensional domain yields a complex pattern commonly studied through elasticity theory, packing analysis, and fractal geometries. "Loop" is a one-vertex entity that is naturally formed in this system. The role of the elastic features of each loop in 2D packing has not yet been discussed. In this work, we point

Snehasish Mukherjee, Phaniram Sayapaneni, Shankar Subramanya

The ubiquity of smart voice assistants has made conversational shopping commonplace. This is especially true for low consideration segments like grocery. A central problem in conversational grocery is the automatic generation of short product titles that can be read out fast during a conversation. Several supervised models have been proposed in the literatur

Bo Lin, Qianxiao Li, Weiqing Ren

The quasipotential is a natural generalization of the concept of energy functions to non-equilibrium systems. In the analysis of rare events in stochastic dynamics, it plays a central role in characterizing the statistics of transition events and the likely transition paths. However, computing the quasipotential is challenging, especially in high dimensional

Filánder A. Sequeira, Helen Guillén-Oviedo

In the present paper we describe the computational implementation of some integral terms that arise from mixed virtual element methods (mixed-VEM) in two-dimensional pseudostress-velocity formulations. The implementation presented here consider any polynomial degree $k \geq 0$ in a natural way by building several local matrices of small size through the matr

Jean-Pierre Magnot

In this article, we consider algebras $\mathcal{A}$ of non-formal pseudodifferential operators over $S^1$ which contain $C^\infty(S^1),$ understood as multiplication operators. We apply a construction of Chern-Weil type forms in order to get $2k-$closed cocycles. For $k=1,$ we obtain a cocycle on the algebra of (maybe non classical) pseudodifferential operat

Jinsong Zhang, Xingzi Liu, Kun Li

Human pose transfer, as a misaligned image generation task, is very challenging. Existing methods cannot effectively utilize the input information, which often fail to preserve the style and shape of hair and clothes. In this paper, we propose an adaptive human pose transfer network with two hierarchical deformation levels. The first level generates human se

Jonathan W. Richardson, Ohkyung Kwon, H. Richard Gustafson, Craig Hogan

Precision measurements are reported of the cross-spectrum of rotationally-induced differential position displacements in a pair of colocated 39 m long, high power Michelson interferometers. One arm of each interferometer is bent $90^{\circ}$ near its midpoint to obtain sensitivity to rotations about an axis normal to the plane of the instrument. The instrume

V. N. Krishnachandran

This paper presents a brief account of the important milestones in the historical development of the theory of differential equations. The paper begins with a discussion on the date of birth of differential equations and then touches upon Newton's approach to differential equations. Then the development of the various methods for solving the first order diff

Linhua Jiang, Shu Wang, Bing Zhang, Nobunari Kashikawa

In the optical sky, minutes-duration transients from cosmological distances are rare. Known objects that give rise to such transients include gamma-ray bursts (GRBs), the most luminous explosions in the universe that have been detected at redshift as high as z ~ 9.4. These high-redshift GRBs and their associated emission can be used to probe the star formati

Linhua Jiang, Nobunari Kashikawa, Shu Wang, Gregory Walth

GN-z11 was photometrically selected as a luminous star-forming galaxy candidate at redshift z > 10 based on Hubble Space Telescope (HST) imaging data. Follow-up HST near-infrared grism observations detected a continuum break that was explained as the Ly-alpha break corresponding to z = 11.09 (+0.08-0.12). However, its accurate redshift remained unclear. Here

Yuya Kurebayashi, Hiroki Oshiyama, Naokazu Shibata

We study the ground state of the one-dimensional "$t$-$J_s$-$J_{\tau}$ model," which is a variant of the $t$-$J$ model with additional channel degree of freedom. The model is not only a generalization of the $t$-$J$ model but also an effective model of the two-channel Kondo lattice model in the strong-coupling region. The low-energy excitations and correlati

R. Pattnaik, K. Sharma, K. Alabarta, D. Altamirano

Low Mass X-ray binaries (LMXBs) are binary systems where one of the components is either a black hole or a neutron star and the other is a less massive star. It is challenging to unambiguously determine whether a LMXB hosts a black hole or a neutron star. In the last few decades, multiple observational works have tried, with different levels of success, to a

Xingchi Mu, Jian Zhou

We elucidate light-induced orbital and spin current through nonlinear response theory, which generalizes the well-known bulk photovoltaic effect in centrosymmetric broken materials from charge to the spin and orbital degrees of freedom. We use two-dimensional nonmagnetic ferroelectric materials (such as GeS and its analogues) to illustrate this bulk orbital/

Masahiro Nomura, Shuhei Watanabe, Youhei Akimoto, Yoshihiko Ozaki

Hyperparameter optimization (HPO), formulated as black-box optimization (BBO), is recognized as essential for automation and high performance of machine learning approaches. The CMA-ES is a promising BBO approach with a high degree of parallelism, and has been applied to HPO tasks, often under parallel implementation, and shown superior performance to other

Roger Casals, Eugene Gorsky, Mikhail Gorsky, José Simental

In this manuscript we study braid varieties, a class of affine algebraic varieties associated to positive braids. Several geometric constructions are presented, including certain torus actions on braid varieties and holomorphic symplectic structures on their respective quotients. We also develop a diagrammatic calculus for correspondences between braid varie

Guillermo Terrén-Serrano, Manel Martínez-Ramón

The increasing penetration of photovoltaic systems in the power grid makes it vulnerable to cloud shadow projection. Real-time cloud segmentation in ground-based infrared images is important to reduce the noise in intra-hour global solar irradiance forecasting. We present a comparison between discriminative and generative models for cloud segmentation. The p

Luis Silva, Parag Gupta, David MacTaggart, Radostin D. Simitev

The relative importance of the helicity and cross-helicity electromotive dynamo effects for self-sustained magnetic field generation by chaotic thermal convection in rotating spherical shells is investigated as a function of shell thickness. Two distinct branches of dynamo solutions are found to coexist in direct numerical simulations for shell aspect ratios

Mark Colarusso, William Q. Erickson, Jeb F. Willenbring

The Littlewood-Richardson coefficients $c^{\lambda}_{\mu\nu}$ give the multiplicity of an irreducible polynomial ${\rm GL}_n$-representation $F^{\lambda}_n$ in the tensor product of polynomial representations $F^{\mu}_n\otimes F^{\nu}_n$. In this paper, we generalize these coefficients to an $r$-fold tensor product of rational representations, and give a new

Alexandra J. Tetarenko, Harriet Parsons, Sarah Graves, Jessica Dempsey

In the age of Large Programs and Big Data a key component in project planning for ground-based astronomical observatories is understanding how to balance users demands and telescope capabilities. In particular, future planning for operations requires us to asses the impact of a complex set of parameters, such as right ascension, instrument, and sky condition

Ilyas Khan

In this paper, we prove that any mean curvature flow translator $\Sigma^2 \subset \mathbb{R}^3$ with finite total curvature and one end must be a plane. We also prove that if the translator $\Sigma$ has multiple ends, they are asymptotic to a plane $\Pi$ containing the direction of translation and can be written as graphs over $\Pi$. Finally, we determine th

Leighton Wilson, Nathan Vaughn, Robert Krasny

We present the barycentric Lagrange dual tree traversal (BLDTT) fast summation method for particle interactions. The scheme replaces well-separated particle-particle interactions by adaptively chosen particle-cluster, cluster-particle, and cluster-cluster approximations given by barycentric Lagrange interpolation at proxy particles on a Chebyshev grid in eac

Camille Carter, Jacob Murri, David Reber, Benjamin Webb

In this paper we introduce the notion of a patient first-mean stable system. Such systems are switched systems that are first-mean stable meaning that they converge to a globally attracting fixed point on average. They are also patient so that they do not lose their first-mean stability when time-delays are introduced into the system. As time-delays are, in

Marko Kabić, Gabriel Duque López, Daniel Keller

Collaborative filtering tries to predict the ratings of a user over some items based on opinions of other users with similar taste. The ratings are usually given in the form of a sparse matrix, the goal being to find the missing entries (i.e. ratings). Various approaches to collaborative filtering exist, some of the most popular ones being the Singular Value

Xuebin Zheng, Bingxin Zhou, Yu Guang Wang, Xiaosheng Zhuang

Graph representation learning has many real-world applications, from super-resolution imaging, 3D computer vision to drug repurposing, protein classification, social networks analysis. An adequate representation of graph data is vital to the learning performance of a statistical or machine learning model for graph-structured data. In this paper, we propose a

Gang Chen

Most of the neural networks (NNs) learned via state-of-the-art machine learning techniques are black-box models. For a widespread success of machine learning in science and engineering, it is important to develop new NN architectures to effectively extract high-level mathematical knowledge from complex datasets. Motivated by this understanding, this paper de

Junjun Yin, Guangqing Chi

People's daily activities in the urban environment are complex and vary by individuals. Existing studies using mobile phone data revealed distinct and recurrent transitional activity patterns, known as mobility motifs, in people's daily lives. However, the limitation in using only a few inferred activity types hinders our ability to examine general patterns

Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker

The presence of uncertainty in policy evaluation significantly complicates the process of policy ranking and selection in real-world settings. We formally consider offline policy selection as learning preferences over a set of policy prospects given a fixed experience dataset. While one can select or rank policies based on point estimates of their policy val

Kuntal Sengupta, Rana Zibakhsh, Eric Chitambar, Gilad Gour

Bell nonlocality describes a manifestation of quantum mechanics that cannot be explained by any local hidden variable model. Its origin lies in the nature of quantum entanglement, although understanding the precise relationship between nonlocality and entanglement has been a notorious open problem. In this paper, we resolve this problem by developing a dynam

Aditya Singh, Alessandro Bay, Andrea Mirabile

Humans rely heavily on shapes as a primary cue for object recognition. As secondary cues, colours and textures are also beneficial in this regard. Convolutional neural networks (CNNs), an imitation of biological neural networks, have been shown to exhibit conflicting properties. Some studies indicate that CNNs are biased towards textures whereas, another set

Kungang Zhang, Anh T. Bui, Daniel W. Apley

Supervised learning models are one of the most fundamental classes of models. Viewing supervised learning from a probabilistic perspective, the set of training data to which the model is fitted is usually assumed to follow a stationary distribution. However, this stationarity assumption is often violated in a phenomenon called concept drift, which refers to

Piotr Frąckiewicz

Over the last twenty years of research on quantum game theory have given us many ideas of how quantum games could be played. One of the most prominent ideas in the field is a model of quantum playing a 2x2 game introduced by J. Eisert, M. Wilkens and M. Lewenstein. The scheme assumes that players' strategies are unitary operations the players act on the maxi

Yinan Wang, Kaiwen Wang, Wenjun Cai, Xiaowei Yue

Finite element analysis (FEA) has been widely used to generate simulations of complex and nonlinear systems. Despite its strength and accuracy, the limitations of FEA can be summarized into two aspects: a) running high-fidelity FEA often requires significant computational cost and consumes a large amount of time; b) FEA is a deterministic method that is insu

Loïc Marrec, Irene Lamberti, Anne-Florence Bitbol

A key question in evolution is how likely a mutant is to take over. This depends on natural selection and on stochastic fluctuations. Population spatial structure can impact mutant fixation probabilities. We introduce a model for structured populations on graphs that generalizes previous ones by making migrations independent of birth and death. We demonstrat

Nicolae Strungaru

Given a cut and project scheme and a pre-compact Borel window we show that almost surely all positions of the window give rise to point sets with Besicovitch almost periodic Dirac combs. In particular, all those positions lead to pure point diffractive point sets with autocorrelation given by the covariogram of the window and diffraction given by the square

Luigi Delle Rose, Shaaban Khalil, Stefano Moretti

We explain anomalies currently present in various data samples used for the measurement of the anomalous magnetic moment of electron ($a_e$) and muon ($a_\mu$) in terms of an Aligned 2-Higgs Doublet Model with right-handed neutrinos. The explanation is driven by one and two-loop topologies wherein a very light CP-odd neutral Higgs state ($A$) contributes sig

Aleksandra Burashnikova, Marianne Clausel, Charlotte Laclau, Frack Iutzeller

In this paper, we propose a theoretically founded sequential strategy for training large-scale Recommender Systems (RS) over implicit feedback, mainly in the form of clicks. The proposed approach consists in minimizing pairwise ranking loss over blocks of consecutive items constituted by a sequence of non-clicked items followed by a clicked one for each user

Ivan L. Andronov, Kateryna D. Andrych, Lidia L. Chinarova, Dmytro E. Tvardovskyi

The software MAVKA is described, which was elaborated for statistically optimal determination of the characteristics of the extrema of 1000+ variable stars of different types, mainly eclipsing and pulsating. The approximations are phenomenological, but not physical. As often, the discovery of a new variable star is made on time series of a single-filter (sin

Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu

The computer vision world has been re-gaining enthusiasm in various pre-trained models, including both classical ImageNet supervised pre-training and recently emerged self-supervised pre-training such as simCLR and MoCo. Pre-trained weights often boost a wide range of downstream tasks including classification, detection, and segmentation. Latest studies sugg

Guruprasad Raghavan, Matt Thomson

The geometry of weight spaces and functional manifolds of neural networks play an important role towards 'understanding' the intricacies of ML. In this paper, we attempt to solve certain open questions in ML, by viewing them through the lens of geometry, ultimately relating it to the discovery of points or paths of equivalent function in these spaces. We pro

Wang Zhou, Levente J. Klein, Siyuan Lu

An automated machine learning framework for geospatial data named PAIRS AutoGeo is introduced on IBM PAIRS Geoscope big data and analytics platform. The framework simplifies the development of industrial machine learning solutions leveraging geospatial data to the extent that the user inputs are minimized to merely a text file containing labeled GPS coordina

Louis Omenyi, McSylvester Omaba

Filtration of quantifiable objects by smoothing kernels on Riemannian manifolds for visualisation is an ongoing research. However, using common filters created for linear domains on manifolds with non-Euclidean topologies can yield misleading results. While there is a lot of ongoing research on convolution of quantifiable functions with smoothing kernels on

Latévi M. Lawson

Fring and al in their paper entitled "Strings from position-dependent noncommutativity" have introduced a new set of noncommutative space commutation relations in two space dimensions. It had been shown that any fundamental objects introduced in this space-space non-commutativity are string-like. Taking this result into account, we generalize the seminal wor

Oleg I. Morozov

We consider integrability structures of the generalized Hunter--Saxton equation. In particular, we obtain the Lax representation with nonremovable spectral parameter, find local recursion operators for symmetries and cosymmetries, generate an infinite-dimensional Lie algebra of higher symmetries, and prove existence of infinite number of cosymmetries of high

Oleg I. Morozov

We derive new four-dimensional partial differential equation with the isospectral Lax representation by shrinking the symmetry algebra of the reduced quasi-classical self-dual Yang--Mills equation. Then we find a recursion operator for the obtained equation and construct B{\"a}cklund transformations between this equation and the reduced quasi-classical self-

Jason Garver

"Rigor" is an often sought after but ill-defined concept in education. This work reviews several models of rigor from current literature before proposing a tool which is used to analyze science education throughout history. The 20\textsuperscript{th} century science education in the United States was subject to changing sociopolitical motivations about the u

Riccardo Falcone, Daniela D. Doneva, Kostas D. Kokkotas, Stoytcho S. Yazadjiev

The aim of this paper is to study the stability of soliton-like static solutions via non-linear simulations in the context of a special class of massive tensor-multi-scalar-theories of gravity whose target space metric admits Killing field(s) with a periodic flow. We focused on the case with two scalar fields and maximally symmetric target space metric, as t

Yao Zhou, Jianpeng Xu, Jun Wu, Zeinab Taghavi Nasrabadi

Recommender systems are popular tools for information retrieval tasks on a large variety of web applications and personalized products. In this work, we propose a Generative Adversarial Network based recommendation framework using a positive-unlabeled sampling strategy. Specifically, we utilize the generator to learn the continuous distribution of user-item

T. H. Dang, J. Hawecker, E. Rongione, G. Baez Flores

Spintronic structures are extensively investigated for their spin orbit torque properties, required for magnetic commutation functionalities. Current progress in these materials is dependent on the interface engineering for the optimization of spin transmission. Here, we advance the analysis of ultrafast spin-charge conversion phenomena at ferromagnetic-tran

Ksenia Konyushkova, Konrad Zolna, Yusuf Aytar, Alexander Novikov

In offline reinforcement learning (RL) agents are trained using a logged dataset. It appears to be the most natural route to attack real-life applications because in domains such as healthcare and robotics interactions with the environment are either expensive or unethical. Training agents usually requires reward functions, but unfortunately, rewards are sel

Jonathan Frankle

We revisit and extend the experiments of Goodfellow et al. (2014), who showed that - for then state-of-the-art networks - "the objective function has a simple, approximately convex shape" along the linear path between initialization and the trained weights. We do not find this to be the case for modern networks on CIFAR-10 and ImageNet. Instead, although los

Mikhail Ignatyev

Our studies concern some aspects of scattering theory of the singular differential systems $ y'-x^{-1}Ay-q(x)y=\rho By, \ x>0 $ with $n\times n$ matrices $A,B, q(x), x\in(0,\infty)$, where $A,B$ are constant and $\rho$ is a spectral parameter. We concentrate on the important special case when $q(\cdot)$ is smooth and $q(0)=0$ and derive a formula that expres

Mufan Sang, Wei Xia, John H. L. Hansen

Despite speaker verification has achieved significant performance improvement with the development of deep neural networks, domain mismatch is still a challenging problem in this field. In this study, we propose a novel framework to disentangle speaker-related and domain-specific features and apply domain adaptation on the speaker-related feature space solel

K. Azizi, U. Özdem

We calculate the gravitational form factors of the excited $N(1535)$ state with the quantum numbers $I(J^P)=\frac{1}{2}(\frac{1}{2}^-)$ via light cone QCD sum rules (LCSR). To this end, we consider the quark part of the energy-momentum tensor (EMT) current and use the general form of the nucleon's interpolating field as well as the distribution amplitudes (D

Vincent Corlay, Joseph J. Boutros, Philippe Ciblat, Loïc Brunel

This paper investigates the decoding of a remarkable set of lattices: We treat in a unified framework the Leech lattice in dimension 24, the Nebe lattice in dimension 72, and the Barnes-Wall lattices. A new interesting lattice is constructed as a simple application of single parity-check principle on the Leech lattice. The common aspect of these lattices is

Emmanuel Jordy Menvouta, Sven Serneels, Tim Verdonck

As its name suggests, sufficient dimension reduction (SDR) targets to estimate a subspace from data that contains all information sufficient to explain a dependent variable. Ample approaches exist to SDR, some of the most recent of which rely on minimal to no model assumptions. These are defined according to an optimization criterion that maximizes a nonpara

Róbert Németh, Zoltán Kaufmann, József Cserti

In the ballistic regime we study both semiclassically and quantum mechanically the electron's dynamics in two-dimensional electron gas (2DEG) in the presence of an inhomogeneous magnetic field applied perpendicular to the plane. The magnetic field is constant inside four separate circular regions which are located at the four corners of a square of side leng

Toufik Mansour, Reza Rastegar

We call $i$ a fixed point of a given sequence if the value of that sequence at the $i$-th position coincides with $i$. Here, we enumerate fixed points in the class of restricted growth sequences. The counting process is conducted by calculation of generating functions and leveraging a probabilistic sampling method.

J. M. Ilnytskyi

We consider the SEIRS compartment epidemiology model suitable for predicting the evolution of the COVID-19 pandemy in the extreme limiting case of no acquired immunity. The disease-free and endemic fixed points are found and their stability is analysed. The expression for the basic reproduction ratio is obtained and discussed, emphasizing on its dependence o

Miguel A. Barja

We prove the equivalence between Clifford-Severi inequalities for good classes of varieties of maximal Albanese dimension and Slope Inequalities for fibrations of such varieties over curves. This provides a big set of new Slope Inequalities and characterizes the limit cases. It also gives a machinery to automatically obtain other higher dimensional Slope and

CMS Collaboration

The performance is presented of the reconstruction and identification algorithms for electrons and photons with the CMS experiment at the LHC. The reported results are based on proton-proton collision data collected at a center-of-mass energy of 13 TeV and recorded in 2016-2018, corresponding to an integrated luminosity of 136 fb$^{-1}$. Results obtained fro

Huahong Zhang, Ipek Oguz

Lesion segmentation is a core task for quantitative analysis of MRI scans of Multiple Sclerosis patients. The recent success of deep learning techniques in a variety of medical image analysis applications has renewed community interest in this challenging problem and led to a burst of activity for new algorithm development. In this survey, we investigate the

Mitra Amiri, Ali Rejali

Takahasi and Hatori introduced a class of commutative Banach algebras which satisfy a Bochner-Schoenberg-Eberlein-type inequality. Baised on their results we introduced a class of commutative Fr\'echet algebras which satisfy this property. We show that Fr\'echet C*-algebras and uniform Fr\'echet algebras are BSE-algebras.

G. Fedorovich, D. Kornovan, A. Poddubny, M. Petrov

We study theoretically the competition between directional asymmetric coupling and disorder in a one-dimensional array of quantum emitters chirally coupled through a waveguide mode. Our calculation reveals highly nontrivial phase diagram for the eigenstates spatial profile, nonmonotonously depending on the disorder and directionality strength. The increase o

Jin Lei, Angela Bonaccorso

The full quantum mechanical (QM) model of inclusive breakup of Ichimura-Austern-Vincent (IAV) is implemented in this paper to calculate breakup from heavy radioactive nuclei on a $^9$Be target at intermediate energies. So far it had been implemented and applied only to low energy reactions with light projectiles. The IAV model is successful in predicting abs

Mordechai Guri

In this paper, we show that attackers can exfiltrate data from air-gapped computers via Wi-Fi signals. Malware in a compromised air-gapped computer can generate signals in the Wi-Fi frequency bands. The signals are generated through the memory buses - no special hardware is required. Sensitive data can be modulated and secretly exfiltrated on top of the sign

Mohammad N. Murshed, Zarin Subah, M. Monir Uddin

Data Assimilation (DA) is a computational tool that uses value from the model and the real measurement to arrive to an optimally acceptable value. Rather, this technique relies on the idea of Kalman gain. We point out that DA has two different perspectives based on the type of problem. In this paper, we look into two problem types: one that does not rely on

Nicklas Ramberg, Luca Visinelli

The North American Nanohertz Observatory for Gravitational Waves (NANOGrav) collaboration has recently reported strong evidence for a stochastic process affecting the 12.5 yr dataset of pulsar timing residuals. We show that the signal can be interpreted in terms of a stochastic gravitational wave background emitted from a network of axionic strings in the ea

Muhammad Fayaz, Wenqiang Yi, Yuanwei Liu, Arumugam Nallanathan

Grant-free non-orthogonal multiple access (GF-NOMA) is a potential multiple access framework for short-packet internet-of-things (IoT) networks to enhance connectivity. However, the resource allocation problem in GF-NOMA is challenging due to the absence of closed-loop power control. We design a prototype of transmit power pool (PP) to provide open-loop powe

Alexey Lichkunov, Artem Popov, Alexander Studenikin

We develop the approach to the problem of neutrino oscillations in a magnetic field introduced in \cite{Popov:2019nkr} and extend it to the case of three neutrino generations. The theoretical framework suitable for computation of the Dirac neutrino spin, flavour and spin-flavour oscillations probabilities in a magnetic field is given. It is shown that there

Ismail Aydin, Cihan Unal

By applying Ricceri's variational principle, we demonstrate the existence of solutions for the following Robin problem \begin{equation*}\left\{ \begin{array}{cc}-\func{div}\left( \omega _{1}(x)\left\vert \nabla u\right\vert^{p(x)-2}\nabla u\right) =\lambda \omega _{2}(x)f(x,u), & x\in \Omega \\ \omega _{1}(x)\left\vert \nabla u\right\vert ^{p(x)-2}\frac{\par

Fernando Darío Almeida García, Andrea Carolina Flores Rodriguez, Gustavo Fraidenraich

In this paper, we derive a highly accurate approximation for the probability of detection (PD) of a non-coherent detector operating with Weibull fluctuation targets. To do so, we assume a pulse-to-pulse decorrelation during the coherent processing interval (CPI). Specifically, the proposed approximation is given in terms of: i) a closed-form expression deriv

Tom McClain

In this paper I give overviews of the polysymplectic approach to covariant Hamiltonian field theory and the simplest geometric quantization of classical particle theories. I then give a synopsis of a recently proposed toy model for applying this geometric quantization map to polysymplectic field theory. I show that no special difficulties arise when this toy

Mohammad Reza Rahmati

We study invariant jet differentials in the framework of complex hyperbolicity, focusing on the algebra of invariants for the non--reductive reparametrization group $G_k = \mathbb{C}^{\ast} \ltimes U_k$. The paper develops a uniform, representation--theoretic, and graded--algebraic strategy for the $\rho$--action of $G_k$ on $J_k\mathbb{C}^n$, establishing i

Utkarsh Uppal, Bharat Giddwani

The vulnerability of models to data aberrations and adversarial attacks influences their ability to demarcate distinct class boundaries efficiently. The network's confidence and uncertainty play a pivotal role in weight adjustments and the extent of acknowledging such attacks. In this paper, we address the trade-off between the accuracy and calibration poten

Jianan Chen, Helen M. C. Cheung, Laurent Milot, Anne L. Martel

Colorectal cancer is one of the most common and lethal cancers and colorectal cancer liver metastases (CRLM) is the major cause of death in patients with colorectal cancer. Multifocality occurs frequently in CRLM, but is relatively unexplored in CRLM outcome prediction. Most existing clinical and imaging biomarkers do not take the imaging features of all mul

Franz J. Brandenburg

A map is a partition of the sphere into regions that are labeled as countries or holes. The vertices of a map graph are the countries of a map. There is an edge if and only if the countries are adjacent and meet in at least one point. For a k-map graph, at most k countries meet in a point. A graph is k-planar if it can be drawn in the plane with at most k cr

Chun-Hung Chao, Hsien-Tzu Cheng, Tsung-Ying Ho, Le Lu

Gross tumor volume (GTV) delineation on tomography medical imaging is crucial for radiotherapy planning and cancer diagnosis. Convolutional neural networks (CNNs) has been predominated on automatic 3D medical segmentation tasks, including contouring the radiotherapy target given 3D CT volume. While CNNs may provide feasible outcome, in clinical scenario, dou

Reza Goldouzian, Jeong Han Kim, Kevin Lannon, Adam Martin

In this paper, we explore the impact of extra radiation on predictions of $pp \to t\bar{t}X, X = h/W^{\pm}/Z$ processes within the dimension-6 SMEFT framework. While full next-to-leading order calculations are of course preferred, they are not always practical, and so it is useful to be able to capture the impacts of extra radiation using leading-order matri

Shivam Kumar Mishra, Sudip Sengupta

We report an exact solution of the Hartemann-Luhmann equation of motion for a charged particle interacting with an intense electromagnetic wave/pulse. It is found that the radiation reaction force has a significant affect on the charged particle dynamics and the particle shows, on average, a net energy gain over a period of time. Further, using a MATHEMATICA

Ludvig af Klinteberg, Chiara Sorgentone, Anna-Karin Tornberg

The quadrature error associated with a regular quadrature rule for evaluation of a layer potential increases rapidly when the evaluation point approaches the surface and the integral becomes nearly singular. Error estimates are needed to determine when the accuracy is insufficient and a more costly special quadrature method should be utilized. The final resu

Grigoris Panotopoulos, Ilidio Lopes, Angel Rincon

We show that the extended cosmological equation-of-state developed starting from a Chaplygin equation-of-state, recently applied to stellar modeling, is a viable dark energy model consistent with standard scalar potentials. Moreover we find a Lagrangian formulation based on a canonical scalar field with the appropriate self-interaction potential. Finally, we

A. Almoalem, I. Silber, S. Sandik, M. Lotem

Topological superconductivity is an exotic phase of matter in which the fully gapped superconducting bulk hosts gapless Majorana surface states protected by topology. Intercalation of copper, strontium or niobium between the quintuple layers of the topological insulator Bi$_2$Se$_3$ increases the carrier density and leads to superconductivity that is suggest

Arsha Nagrani, Joon Son Chung, Jaesung Huh, Andrew Brown

We held the second installment of the VoxCeleb Speaker Recognition Challenge in conjunction with Interspeech 2020. The goal of this challenge was to assess how well current speaker recognition technology is able to diarise and recognize speakers in unconstrained or `in the wild' data. It consisted of: (i) a publicly available speaker recognition and diarisat

Wilfried Meidl, Alexandr A. Polujan, Alexander Pott

In this paper we consider further applications of $(n,m)$-functions for the construction of 2-designs. For instance, we provide a new application of the extended Assmus-Mattson theorem, by showing that linear codes of APN functions with the classical Walsh spectrum support 2-designs. On the other hand, we use linear codes and combinatorial designs in order t

Zhaowei Zhu, Xiang Lan, Tingting Zhao, Yangming Guo

Cardiovascular disease is a major threat to health and one of the primary causes of death globally. The 12-lead ECG is a cheap and commonly accessible tool to identify cardiac abnormalities. Early and accurate diagnosis will allow early treatment and intervention to prevent severe complications of cardiovascular disease. In the PhysioNet/Computing in Cardiol