arXiv papers from November 2020

James Evans

Given a $k$-self similar set $X\subset [0,1]^{d}$ we calculate both its Hausdorff dimension and its entropy, and show that these two quantities are in fact equal. This affirmatively resolves a conjecture of Adamczewski and Bell.

Dineshan Subramoney, Clement N. Nyirenda

This work presents a comparative evaluation of four population-based optimization algorithms for workflow scheduling in cloud-fog environments. These algorithms are as follows: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE) and GA-PSO. This work also provides the motivational groundwork for the weighted sum objective f

Vishesh Jain, Ashwin Sah, Mehtaab Sawhney

Let $A$ be an $n\times n$ random matrix whose entries are i.i.d. with mean $0$ and variance $1$. We present a deterministic polynomial time algorithm which, with probability at least $1-2\exp(-\Omega(\epsilon n))$ in the choice of $A$, finds an $\epsilon n \times \epsilon n$ sub-matrix such that zeroing it out results in $\widetilde{A}$ with \[\|\widetilde{A

Andrew Gelman, Aki Vehtari

We review the most important statistical ideas of the past half century, which we categorize as: counterfactual causal inference, bootstrapping and simulation-based inference, overparameterized models and regularization, Bayesian multilevel models, generic computation algorithms, adaptive decision analysis, robust inference, and exploratory data analysis. We

Raghav Kansal, Javier Duarte, Breno Orzari, Thiago Tomei

We develop a graph generative adversarial network to generate sparse data sets like those produced at the CERN Large Hadron Collider (LHC). We demonstrate this approach by training on and generating sparse representations of MNIST handwritten digit images and jets of particles in proton-proton collisions like those at the LHC. We find the model successfully

Maxwell Van Gelder, Mitchell Wortsman, Kiana Ehsani

Although sparse neural networks have been studied extensively, the focus has been primarily on accuracy. In this work, we focus instead on network structure, and analyze three popular algorithms. We first measure performance when structure persists and weights are reset to a different random initialization, thereby extending experiments in Deconstructing Lot

Takashi J. Moriya, Ji-an Jiang, Naoki Yasuda, Mitsuru Kokubo

Some supernovae such as pair-instability supernovae are predicted to have the duration of more than a year in the observer frame. To constrain the rates of supernovae lasting for more than a year, we conducted a long-term deep transient survey using Hyper Suprime-Cam (HSC) on the 8.2m Subaru telescope. HSC is a wide-field (a 1.75 deg2 field-of-view) camera a

Gianluca Calcagni, Sachiko Kuroyanagi

Among all cosmological quantum-gravity or quantum-gravity-inspired scenarios, only very few predict a blue-tilted primordial tensor spectrum. We explore five of them and check whether they can generate a stochastic gravitational-wave background detectable by present and future interferometers: non-local quantum gravity, string-gas cosmology, new ekpyrotic sc

Yuhan Yao, S. R. Kulkarni, Kevin B. Burdge, Ilaria Caiazzo

AT2019wey (SRGA J043520.9+552226, SRGE J043523.3+552234) is a transient first reported by the ATLAS optical survey in 2019 December. It rose to prominence upon detection, three months later, by the Spektrum-Roentgen-Gamma (SRG) mission in its first all-sky survey. X-ray observations reported in Yao et al. suggest that AT2019wey is a Galactic low-mass X-ray b

Satya Narayan Shukla, Benjamin M. Marlin

Irregularly sampled time series data arise naturally in many application domains including biology, ecology, climate science, astronomy, and health. Such data represent fundamental challenges to many classical models from machine learning and statistics due to the presence of non-uniform intervals between observations. However, there has been significant pro

B. Pinheiro da Silva, B. A. D. Marques, R. B. Rodrigues, P. H. Souto Ribeiro

We developed a method to characterize arbitrary superpositions of light orbital angular momentum (OAM) with high fidelity by using astigmatic tomography and machine learning processing. In order to define each superposition unequivocally, we combine two intensity measurements. The first one is the direct image of the input beam, which cannot distinguish betw

Jorge Munzenmayer, Derek Frydel

This article examines the suggestion made in Ref. [EPL, 115 (2016) 60001] that a solution to a particle in an infinite spherical well model, if it is square-integrable, is a physically valid solution, even if at the precise location of the singularity there is no underlying physical cause, therefore, the divergence would have to be a nonlocal phenomenon caus

Bahador Bahmani, WaiChing Sun

We present a hybrid model/model-free data-driven approach to solve poroelasticity problems. Extending the data-driven modeling framework originated from Kirchdoerfer and Ortiz (2016), we introduce one model-free and two hybrid model-based/data-driven formulations capable of simulating the coupled diffusion-deformation of fluid-infiltrating porous media with

Eric Wim Flesch

The SDSS-DR16 Quasar Superset and pipeline catalogues are searched for undeclared quasars which were concealed by or confused with other objects, usually due to incomplete deblending. Forty such quasars with redshifts are found and herewith presented. Also, 82 entries in the SDSS-DR16Q main quasar catalogue are shown to be non-quasars, some also due to incom

Yulia T. Peet, Matthew M. Peet

We present a new analytical and numerical framework for solution of Partial Differential Equations (PDEs) that is based on an exact transformation that moves the boundary constraints into the dynamics of the corresponding governing equation. The framework is based on a Partial Integral Equation (PIE) representation of PDEs, where a PDE equation is transforme

David Hruška

We prove a stronger version of a conjecture stated in a paper from 2017 by J. M. Ash and S. Catoiu concerning relations between various notions of the Lipschitz property and differentiability in the Euclidean plane. We also provide an improved version of the main result of that paper.

Pratheep Bondalapati, Abhishek Tiwari, Mustafa Emin Sahin, Qi Tang

In this article we introduce a novel solution called SuperCell, which can improve the return on investment (ROI) for rural area network coverage. SuperCell offers two key technical features: it uses tall towers with high-gain antennas for wide coverage and high-order sectorization for high capacity. We show that a solution encompassing a high-elevation platf

Yuhan Yao, S. R. Kulkarni, K. C. Gendreau, Gaurava K. Jaisawal

Here, we present MAXI, SWIFT, NICER, NuSTAR and Chandra observations of the X-ray transient AT2019wey (SRGA J043520.9+552226, SRGE J043523.3+552234). From spectral and timing analyses we classify it as a Galactic low-mass X-ray binary (LMXB) with a black hole (BH) or neutron star (NS) accretor. AT2019wey stayed in the low/hard state (LHS) from 2019 December

Benoît Collins, Félix Parraud

Given a random subspace $H_n$ chosen uniformly in a tensor product of Hilbert spaces $V_n\otimes W$, we consider the collection $K_n$ of all singular values of all norm one elements of $H_n$ with respect to the tensor structure. A law of large numbers has been obtained for this random set in the context of $W$ fixed and the dimension of $H_n$ and $V_n$ tendi

Benjamin Y. Cho, Jeageun Jung, Mattan Erez

DL inference queries play an important role in diverse internet services and a large fraction of datacenter cycles are spent on processing DL inference queries. Specifically, the matrix-matrix multiplication (GEMM) operations of fully-connected MLP layers dominate many inference tasks. We find that the GEMM operations for datacenter DL inference tasks are me

Subekshya Bidari, Zachary P Kilpatrick

Honey bees make decisions regarding foraging and nest-site selection in groups ranging from hundreds to thousands of individuals. To effectively make these decisions bees need to communicate within a spatially distributed group. However, the spatiotemporal dynamics of honey bee communication have been mostly overlooked in models of collective decisions, focu

Carlotta Pittori

We give an overview of the AGILE gamma-ray satellite scientific highlights. AGILE is an Italian Space Agency (ASI) mission devoted to observations in the 30 MeV - 50 GeV gamma-ray energy range, with simultaneous X-ray imaging in the 18-60 keV band. Launched in April 2007, the AGILE satellite has completed its tenth year of operations in orbit, and it is subs

Abu Jahid, Mohammed H. Alsharif, Trevor J. Hall

Optical wireless communication (OWC) covering an ultra-wide range of unlicensed spectrum has emerged as an extent efficient solution to mitigate conventional RF spectrum scarcity ranging from communication distances from nm to several kilometers. Free space optical (FSO) systems operating near IR (NIR) band in OWC links has received substantial attention for

Kiarash Gordiz, Sokseiha Muy, Wolfgang G. Zeier, Yang Shao-Horn

Ion diffusion is important in a variety of applications, yet fundamental understanding of the diffusive process in solids is still missing, especially considering the interaction of lattice vibrations (phonons) and the mobile species. In this work, we introduce two formalisms that determine the individual contributions of normal modes of vibration (phonons)

M. H. McDuffie, P. Graham, J. L. Eppele, J. T. Gruenwald

Knowledge of the decay rates (or half-lives) of radioisotopes is critical in many fields, including medicine, archeology, and nuclear physics, to name just a few. Central to the many uses of radioisotopes is the belief that decay rates are fundamental constants of nature, just as the masses of the radioisotopes themselves are. Recently, the belief that decay

Pedro Domingos

Deep learning's successes are often attributed to its ability to automatically discover new representations of the data, rather than relying on handcrafted features like other learning methods. We show, however, that deep networks learned by the standard gradient descent algorithm are in fact mathematically approximately equivalent to kernel machines, a lear

Sobhan Goudarzi, Amir Asif, Hassan Rivaz

Beamforming in plane-wave imaging (PWI) is an essential step in creating images with optimal quality. Adaptive methods estimate the apodization weights from echo traces acquired by several transducer elements. Herein, we formulate plane-wave beamforming as a blind source separation problem. The output of each transducer element is considered as a non-indepen

Hanchen Xie, Mohamed E. Hussein, Aram Galstyan, Wael Abd-Almageed

Deep neural networks are powerful, massively parameterized machine learning models that have been shown to perform well in supervised learning tasks. However, very large amounts of labeled data are usually needed to train deep neural networks. Several semi-supervised learning approaches have been proposed to train neural networks using smaller amounts of lab

Yernat M. Assylbekov, Maarten V. de Hoop

We present a comprehensive mathematical study of the Magneto-Telluric (MT) method, on bounded domain in $\mathbb{R}^3$. We show that electrical conductivity and magnetic permeability, assumed to be $C^2$, can be uniquely recovered from MT data measured on the boundary of the domain. The proof is based on the construction of complex geometric optics solutions

Tatyana Ivanova

Contact algebra is one of the main tools in region-based theory of space. In \cite{dmvw1, dmvw2,iv,i1} it is generalized by dropping the operation Boolean complement. Furthermore we can generalize contact algebra by dropping also the operation meet. Thus we obtain structures, called contact join-semilattices (CJS) and structures, called distributive contact

Mina Aziziha, Saeed Akbarshahi, Suresh Pittala, Sayandeep Ghosh

Delafossites are promising candidates for photocatalysis applications because of their chemical stability and absorption in the solar region of the electromagnetic spectrum. For example, CuAlO2 has good chemical stability but has a large indirect bandgap (~3 eV), so that efforts to improve its absorption in the solar region through alloying are investigated.

Deidre A. Hunter, Bruce G. Elmegreen, Esther Goldberger, Hannah Taylor

The stellar, gaseous and young stellar disks in the LITTLE THINGS sample of nearby dIrrs are fitted with functions to search for correlations between the parameters. We find that the HI radial profiles are generally flatter in the center and fall faster in the outer regions than the V-band profiles, while young stars are more centrally concentrated, especial

Kathlen Kohn, Rosa Winter, Yuhan Jiang

We study the maximum likelihood degree of linear concentration models in algebraic statistics. We relate the geometry of the reciprocal variety to that of semidefinite programming. We show that the Zariski closure in the Grassmanian of the set of linear spaces that do not attain their maximal possible maximum likelihood degree coincides with the Zariski clos

Gergo Merkely, Alireza Borjali, Molly Zgoda, Evan M. Farina

Background: MRI is the modality of choice for cartilage imaging; however, its diagnostic performance is variable and significantly lower than the gold standard diagnostic knee arthroscopy. In recent years, deep learning has been used to automatically interpret medical images to improve diagnostic accuracy and speed. Purpose: The primary purpose of this study

Divyansh Agarwal, Yuta Baba, Pratik Sachdeva, Tanya Tandon

Residents of developing countries are disproportionately susceptible to displacement as a result of humanitarian crises. During such crises, language barriers impede aid workers in providing services to those displaced. To build resilience, such services must be flexible and robust to a host of possible languages. \textit{Tarjimly} aims to overcome the barri

Umair Mohammad, Sameh Sorour, Mohamed Hefeida

This paper extends the paradigm of "mobile edge learning (MEL)" by designing an optimal task allocation scheme for training a machine learning model in an asynchronous manner across mutiple edge nodes or learners connected via a resource-constrained wireless edge network. The optimization is done such that the portion of the task allotted to each learner is

Daniel Sinambela

We present a large-amplitude existence theory for two-dimensional solitary waves propagating through a two layer body of water. The domain of the fluid is bounded below by an impermeable flat ocean floor and above by a free boundary at constant pressure. For any piecewise smooth upstream density distribution and laminar background current, we construct a glo

Marcin Stawiski

Call a colouring of a graph distinguishing if the only automorphism which preserves it is the identity. We investigate the role of the Axiom of Choice in the existence of certain proper or distinguishing colourings in both vertex and edge variants with special emphasis on locally finite connected graphs. We show that every locally finite connected graph has

Lingjing Jiang, Niina Haiminen, Anna-Paola Carrieri, Shi Huang

Feature selection is indispensable in microbiome data analysis, but it can be particularly challenging as microbiome data sets are high-dimensional, underdetermined, sparse and compositional. Great efforts have recently been made on developing new methods for feature selection that handle the above data characteristics, but almost all methods were evaluated

J. Ted Mackereth, Andrea Miglio, Yvonne Elsworth, Benoit Mosser

The NASA-$\it{TESS}$ mission presents a treasure trove for understanding the stars it observes and the Milky Way, in which they reside. We present a first look at the prospects for Galactic and stellar astrophysics by performing initial asteroseismic analyses of bright ($G < 11$) red giant stars in the $\it{TESS}$ Southern Continuous Viewing Zone (SCVZ). Usi

Davis Gilton, Gregory Ongie, Rebecca Willett

Deep neural networks have been applied successfully to a wide variety of inverse problems arising in computational imaging. These networks are typically trained using a forward model that describes the measurement process to be inverted, which is often incorporated directly into the network itself. However, these approaches are sensitive to changes in the fo

Jiaqi Li, Ross Drummond, Stephen R. Duncan

With the rise of smartphones and the internet-of-things, data is increasingly getting generated at the edge on local, personal devices. For privacy, latency and energy saving reasons, this shift is causing machine learning algorithms to move towards decentralisation with the data and algorithms stored, and even trained, locally on devices. The device hardwar

Andrew D. Thrapp, Michael R. Hughes

Significance: Confocal laser scanning enables optical sectioning in fiber bundle endomicroscopy but limits the frame rate. To be able to better explore tissue morphology it is useful to stitch sequentially acquired frames into a mosaic. However, low frame rates limit the maximum probe translation speed. Line-scanning confocal endomicroscopy provides higher f

Dmitry Churochkin, Fernando Lund

We study the coherent propagation and incoherent diffusion of in-plane elastic waves in a two dimensional continuum populated by many, randomly placed and oriented, edge dislocations. Because of the Peierls-Nabarro force the dislocations can oscillate around an equilibrium position with frequency $\omega_0$. The coupling between waves and dislocations is giv

Harry Halpin

Due to the widespread COVID-19 pandemic, there has been a push for `immunity passports' and even technical proposals. Although the debate about the medical and ethical problems of immunity passports has been widespread, there has been less inspection of the technical foundations of immunity passport schemes. These schemes are envisaged to be used for sharing

Yingtai Xiao, Zeyu Ding, Yuxin Wang, Danfeng Zhang

In practice, differentially private data releases are designed to support a variety of applications. A data release is fit for use if it meets target accuracy requirements for each application. In this paper, we consider the problem of answering linear queries under differential privacy subject to per-query accuracy constraints. Existing practical frameworks

Hatim Labrigui, Samir Kabbaj

In this work, we introduce a new concept of integral $K$-operator frame for the set of all adjointable operators from Hilbert $C^{\ast}$-modules $\mathcal{H}$ to it self noted $End_{\mathcal{A}}^{\ast}(\mathcal{H}) $. We give some propertis relating some construction of integral $K$-operator frame and operators preserving integral $K$-operator frame and we e

Vijay Ravi, Yile Gu, Ankur Gandhe, Ariya Rastrow

End-to-end automatic speech recognition (ASR) systems, such as recurrent neural network transducer (RNN-T), have become popular, but rare word remains a challenge. In this paper, we propose a simple, yet effective method called unigram shallow fusion (USF) to improve rare words for RNN-T. In USF, we extract rare words from RNN-T training data based on unigra

Siyang Li, George F. Smoot

We characterized the S14160-3050HS Multi-Pixel Photon Counter (MPPC), a high efficiency, single channel silicon photomultiplier manufactured by Hamamatsu Photonics K.K. All measurements were performed at a room temperature of (23.0 $\pm$ 0.3) $^{\circ}$C. We obtained an I-V curve and used relative derivatives to find a breakdown voltage of 38.88 V. At a 3 V

Sutanay Choudhury, Jenna A. Bilbrey, Logan Ward, Sotiris S. Xantheas

Intermolecular and long-range interactions are central to phenomena as diverse as gene regulation, topological states of quantum materials, electrolyte transport in batteries, and the universal solvation properties of water. We present a set of challenge problems for preserving intermolecular interactions and structural motifs in machine-learning approaches

Joseph W. Fowler, Galen C. O'Neil, Bradley K. Alpert, Douglas A. Bennett

We use an array of transition-edge sensors, cryogenic microcalorimeters with 4 eV energy resolution, to measure L x-ray emission-line profiles of four elements of the lanthanide series: praseodymium, neodymium, terbium, and holmium. The spectrometer also surveys numerous x-ray standards in order to establish an absolute-energy calibration traceable to the In

Zhidong Lu, Florian Holzapfel

Incremental Nonlinear Dynamic Inversion (INDI) control has attracted increasing research attention for it retains the high-performance of NDI and has enhanced robustness. However, when actual elements of the flight control system and real-world phenomena (such as actuator dynamics, sensor noise, time delay, etc.) are considered, the INDI control may have deg

Guosheng Fu, Wenzheng Kuang

We present a novel monolithic divergence-conforming HDG scheme for a linear fluid-structure interaction (FSI) problem with a thick structure. A pressure-robust optimal energy-norm estimate is obtained for the semidiscrete scheme. When combined with a Crank-Nicolson time discretization, our fully discrete scheme is energy stable and produces an exactly diverg

William Kuszmaul, Alek Westover

The problem of scheduling tasks on $p$ processors so that no task ever gets too far behind is often described as a game with cups and water. In the $p$-processor cup game on $n$ cups, there are two players, a filler and an emptier, that take turns adding and removing water from a set of $n$ cups. In each turn, the filler adds $p$ units of water to the cups,

Aiad El Gourari, Allal Ghanmi, Ilham Rouchdi

In this paper, we are concerned with the bicomplex analog of the well-known result asserting that real-valued harmonic functions, on simply connected domains, are the real parts of holomorphic functions. We show that this assertion, word for word, fails for bc-harmonic functions and we provide a complete characterization of bc-harmonic functions that are the

Kanthashree Mysore Sathyendra, Samridhi Choudhary, Leah Nicolich-Henkin

In this paper, we propose and experiment with techniques for extreme compression of neural natural language understanding (NLU) models, making them suitable for execution on resource-constrained devices. We propose a task-aware, end-to-end compression approach that performs word-embedding compression jointly with NLU task learning. We show our results on a l

Yujia Xie, Yixiu Mao, Simiao Zuo, Hongteng Xu

We consider a variant of regression problem, where the correspondence between input and output data is not available. Such shuffled data is commonly observed in many real world problems. Taking flow cytometry as an example, the measuring instruments may not be able to maintain the correspondence between the samples and the measurements. Due to the combinator

Tatiana Lopez-Guevara, Michael Burke, Nicholas K. Taylor, Kartic Subr

Model-free reinforcement learning (RL) is a powerful tool to learn a broad range of robot skills and policies. However, a lack of policy interpretability can inhibit their successful deployment in downstream applications, particularly when differences in environmental conditions may result in unpredictable behaviour or generalisation failures. As a result, t

Justine Falque

Three-dimensional Catalan numbers are a variant of the classical (bidimensional) Catalan numbers, that count, among other interesting objects, the standard Young tableaux of shape (n,n,n). In this paper, we present a structural bijection between two three-dimensional Catalan objects: 1234-avoiding up-down permutations, and a class of weighted Dyck paths.

S. Selenu

In this article it is introduced a theoretical model made in order to perform calculations of the quantum heat of a body that could be acquired or delivered during a thermal transformation of its quantum states. Here the model is mainly targeted to the electronic structure of matter[1] at the nano and micro scale where DFT models have been frequently develop

Griffin M. Kearney, Kevin F. Palmowski, Michael Robinson

In this paper, we use tools from sheaf theory to model and analyze optimal network control problems and their associated discrete relaxations. We consider a general problem setting in which pieces of equipment and their causal relations are represented as a directed network, and the state of this equipment evolves over time according to known dynamics and th

Xin Xing, Gongbo Liang, Hunter Blanton, Muhammad Usman Rafique

We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer's disease classification. Training a 3D convolutional neural network (CNN) is time-consuming and computationally expensive. We make use of approximate rank pooling to transform the 3D MRI image volume into a 2D image to use as input to a 2D CNN. We show our proposed CNN model achieves $9.5\%

G. Michele Pinna

The execution of an event in a complex and distributed system where the dependencies vary during the evolution of the system can be represented in many ways, and one of them is to use Context-Dependent Event structures. Event structures are related to Petri nets. The aim of this paper is to propose what can be the appropriate kind of Petri net corresponding

Yue Chen, Matthew R. Carver, Steven K. Morley, Andrew S. Hoover

We use long-term electron and proton in-situ measurements made by the CXD particle instruments, developed by Los Alamos National Laboratory and carried on board GPS satellites, to determine total ionizing dose (TID) values and daily/yearly dose rate (DR) values in medium Earth orbits (MEOs) caused by the natural space radiation environment. Here measurement-

Matthias Schäfer, Martin Strohmeier, Mauro Leonardi, Vincent Lenders

The use of wireless signals for purposes of localization enables a host of applications relating to the determination and verification of the positions of network participants, ranging from radar to satellite navigation. Consequently, it has been a longstanding interest of theoretical and practical research in mobile networks and many solutions have been pro

Ahmed Jaber, Pascal Lafon, Rafic Younes

Multi-Objective Mixed-Integer Non-Linear Programming problems (MO-MINLPs) appear in several real-world applications, especially in the mechanical engineering field. To determine a good approximated Pareto front for this type of problems, we propose a general hybrid approach based on a Multi-Criteria Branch-and-Bound (MCBB) and Non-dominated Sorting Genetic A

Gaetano Lambiase, Mairi Sakellariadou, Antonio Stabile

Using recent experimental results of detection of gravitational waves from the binary black hole signals by Advanced LIGO and Advanced Virgo, we investigate the propagation of gravitational waves in the context of fourth order gravity nonminimally coupled to a massive scalar field. Gravitational radiation admits extra massive modes of oscillation and we assu

Michail Tsagris

The paper proposes a new hybrid Bayesian network learning algorithm, termed Forward Early Dropping Hill Climbing (FEDHC), devised to work with either continuous or categorical variables. Further, the paper manifests that the only implementation of MMHC in the statistical software \textit{R}, is prohibitively expensive and a new implementation is offered. Fur

S. Selenu

In this article it will be introduced a new theorem, can be considered a generalization of Hellmann-Feynman theorem[1]. The latter used in conjunction with the quantization of the free energy[2] of a quantum system allows to derive strightly the electronic Heat variations of a quantum electronic system, in its condensed phase of eigenstates, showing its agre

Digvijay Wadekar, Francisco Villaescusa-Navarro, Shirley Ho, Laurence Perreault-Levasseur

Upcoming 21cm surveys will map the spatial distribution of cosmic neutral hydrogen (HI) over unprecedented volumes. Mock catalogues are needed to fully exploit the potential of these surveys. Standard techniques employed to create these mock catalogs, like Halo Occupation Distribution (HOD), rely on assumptions such as the baryonic properties of dark matter

Jeffrey Chan, Andrew C. Miller, Emily B. Fox

Modern wearable devices are embedded with a range of noninvasive biomarker sensors that hold promise for improving detection and treatment of disease. One such sensor is the single-lead electrocardiogram (ECG) which measures electrical signals in the heart. The benefits of the sheer volume of ECG measurements with rich longitudinal structure made possible by

Allan Bai, Peter Erdos, Charles Semple, Mike Steel

Rooted phylogenetic networks provide a more complete representation of the ancestral relationship between species than phylogenetic trees when reticulate evolutionary processes are at play. One way to reconstruct a phylogenetic network is to consider its `ancestral profile' (the number of paths from each ancestral vertex to each leaf). In general, this infor

Lucas Gagnon

Describing the conjugacy classes of the unipotent upper triangular groups $\mathrm{UT}_{n}(\mathbb{F}_{q})$ uniformly (for all or many values of $n$ and $q$) is a nearly impossible task. This paper takes on the related problem of describing the normal subgroups of $\mathrm{UT}_{n}(\mathbb{F}_{q})$. For $q$ a prime, a bijection will be established between the

Hanumantha Rao Vutukuri, Maciej Lisicki, Eric Lauga, Jan Vermant

Active systems such as microorganisms and self-propelled particles show a plethora of collective phenomena, including swarming, clustering, and phase separation. Control over the propulsion direction and switchability of the interactions between the individual self-propelled units may open new avenues in designing of materials from within. Here, we present a

Ivoline C. Ngong, Krystal Maughan, Joseph P. Near

Group fairness metrics can detect when a deep learning model behaves differently for advantaged and disadvantaged groups, but even models that score well on these metrics can make blatantly unfair predictions. We present smooth prediction sensitivity, an efficiently computed measure of individual fairness for deep learning models that is inspired by ideas fr

Aqeeb Iqbal Arka, Biresh Kumar Joardar, Ryan Gary Kim, Dae Hyun Kim

Heterogeneous manycore architectures are the key to efficiently execute compute- and data-intensive applications. Through silicon via (TSV)-based 3D manycore system is a promising solution in this direction as it enables integration of disparate computing cores on a single system. However, the achievable performance of conventional through-silicon-via (TSV)-

Abhinav Anand, Matthias Degroote, Alán Aspuru-Guzik

Natural evolutionary strategies (NES) are a family of gradient-free black-box optimization algorithms. This study illustrates their use for the optimization of randomly-initialized parametrized quantum circuits (PQCs) in the region of vanishing gradients. We show that using the NES gradient estimator the exponential decrease in variance can be alleviated. We

Edgard G. Rivera-Valentín, Vincent F. Chevrier, Alejandro Soto, Germán Martínez

Special Regions on Mars are defined as environments able to host liquid water that meets certain temperature and water activity requirements that allow known terrestrial organisms to replicate, and therefore could be habitable. Such regions would be a concern for planetary protection policies owing to the potential for forward contamination (biological conta

Elise Darragh-Ford, Ethan O. Nadler, Sean McLaughlin, Risa H. Wechsler

We present a wavelet-based algorithm to identify dwarf galaxies in the Milky Way in ${\it Gaia}$ DR2 data. Our algorithm detects overdensities in 4D position--proper motion space, making it the first search to explicitly use velocity information to search for dwarf galaxy candidates. We optimize our algorithm and quantify its performance by searching for moc

Andrew Kosenko

We study a game of strategic information design between a sender, who chooses state-dependent information structures, a mediator who can then garble the signals generated from these structures, and a receiver who takes an action after observing the signal generated by the first two players. We characterize sufficient conditions for information revelation, co

Krzysztof Święcicki

In this paper we prove that $L_{p}$ does not admit an equivariant coarse embedding into $\ell_p$ i.e there is no proper, affine, isometric action of $L_{p}$, viewed as a group under addition with the standard metric $|| . ||_p$, on $\ell_p$. This is done by showing that representations of $L_{p}$ into $ Isom(\ell_p)$ has to be trivial, which allows us to red

Amish Mittal, Sourav Sahoo, Arnhav Datar, Juned Kadiwala

Reliable detection of the prodromal stages of Alzheimer's disease (AD) remains difficult even today because, unlike other neurocognitive impairments, there is no definitive diagnosis of AD in vivo. In this context, existing research has shown that patients often develop language impairment even in mild AD conditions. We propose a multimodal deep learning met

P. G. J. Persoon, R. N. A. Bekkers, F. Alkemade

Technological cumulativeness is considered one of the main mechanisms for technological progress, yet its exact meaning and dynamics often remain unclear. To develop a better understanding of this mechanism we approach a technology as a body of knowledge consisting of interlinked inventions. Technological cumulativeness can then be understood as the extent t

Jürgen Dieber, Sabrina Kirrane

When it comes to complex machine learning models, commonly referred to as black boxes, understanding the underlying decision making process is crucial for domains such as healthcare and financial services, and also when it is used in connection with safety critical systems such as autonomous vehicles. As such interest in explainable artificial intelligence (

Barbara I. Mahler

Contagion maps exploit activation times in threshold contagions to assign vectors in high-dimensional Euclidean space to the nodes of a network. A point cloud that is the image of a contagion map reflects both the structure underlying the network and the spreading behaviour of the contagion on it. Intuitively, such a point cloud exhibits features of the netw

Valtteri Lindholm, Alexis Finoguenov, Johan Comparat, Charles C. Kirkpatrick

Aims. We analyze the autocorrelation function of a large contiguous sample of galaxy clusters, the Constrain Dark Energy with X-ray (CODEX) sample, in which we take particular care of cluster definition. These clusters were X-ray selected using the RASS survey and then identified as galaxy clusters using the code redMaPPer run on the photometry of the SDSS.

Jorge Kysnney Santos Kamassury, Danilo Silva

In this letter, we introduce a new syndrome-based decoder where a deep neural network (DNN) estimates the error pattern from the reliability and syndrome of the received vector. The proposed algorithm works by iteratively selecting the most confident positions to be the error bits of the error pattern, updating the vector received when a new position of the

Qihao Liu, Weichao Qiu, Weiyao Wang, Gregory D. Hager

We propose an unsupervised vision-based system to estimate the joint configurations of the robot arm from a sequence of RGB or RGB-D images without knowing the model a priori, and then adapt it to the task of category-independent articulated object pose estimation. We combine a classical geometric formulation with deep learning and extend the use of epipolar

Fabio Cermelli, Massimiliano Mancini, Yongqin Xian, Zeynep Akata

Semantic segmentation models have two fundamental weaknesses: i) they require large training sets with costly pixel-level annotations, and ii) they have a static output space, constrained to the classes of the training set. Toward addressing both problems, we introduce a new task, Incremental Few-Shot Segmentation (iFSS). The goal of iFSS is to extend a pret

Federica Cecchetto, Vera Traub, Rico Zenklusen

We consider the Connectivity Augmentation Problem (CAP), a classical problem in the area of Survivable Network Design. It is about increasing the edge-connectivity of a graph by one unit in the cheapest possible way. More precisely, given a $k$-edge-connected graph $G=(V,E)$ and a set of extra edges, the task is to find a minimum cardinality subset of extra

Otavio Henrique Perez, Paulo Ricardo da Silva

We present a theorem of resolution of singularities for real analytic constrained differential systems $A(x)\dot{x} = F(x)$ defined on a 2-manifold with corners having impasse set $\{x; \det A(x) = 0\}$. This result can be seen as a generalization of the classical one for 2-dimensional real analytic vector fields. Our proof is based on weighted blow-ups and

Roberto G. Ramírez-Chavarría, Celia Sánchez-Pérez, Luisa Romero-Ornelas, Eva Ramón-Gallegos

Impedance measurement is a common technique to characterize and detect the electrical properties of biological cells. However, to decode the underlying physical processes, it requires complex electrical models alongside prior knowledge of the sample under study. In this work, we introduce an attractive label-free method for sensing biological cells in suspen

A. P. Isaev, A. A. Provorov

Explicit formulae for the projectors onto invariant subspaces of the $\operatorname{ad}^{\otimes 2}$ representation of the Lie algebras $so(N)$ and $sp(2r)$ have been found by means of the split Casimir operator. These projectors have also been considered from the viewpoint of the universal complex simple Lie algebra description by using the Vogel parametris

Jay Roberts, Theodoros Tsiligkaridis

Diagnosis of COVID-19 at point of care is vital to the containment of the global pandemic. Point of care ultrasound (POCUS) provides rapid imagery of lungs to detect COVID-19 in patients in a repeatable and cost effective way. Previous work has used public datasets of POCUS videos to train an AI model for diagnosis that obtains high sensitivity. Due to the h

Saulo Martiello Mastelini, Andre Carlos Ponce de Leon Ferreira de Carvalho

A central aspect of online decision tree solutions is evaluating the incoming data and enabling model growth. For such, trees much deal with different kinds of input features and partition them to learn from the data. Numerical features are no exception, and they pose additional challenges compared to other kinds of features, as there is no trivial strategy

Lingxiao Wang, Tian Xu, Till Hannes Stoecker, Horst Stoecker

As the COVID-19 pandemic continues to ravage the world, it is of critical significance to provide a timely risk prediction of the COVID-19 in multi-level. To implement it and evaluate the public health policies, we develop a framework with machine learning assisted to extract epidemic dynamics from the infection data, in which contains a county-level spatiot

Stephanie Lieggi, Ivo Jimenez, Jeff LeFevre, Carlos Maltzahn

The incubator and research projects sponsored by the Center for Research in Open Source Software (CROSS, cross.ucsc.edu) at UC Santa Cruz have been very effective at promoting the professional and technical development of research software engineers. Carlos Maltzahn founded CROSS in 2015 with a generous gift of $2,000,000 from UC Santa Cruz alumnus Dr. Sage

Florian Meinfelder, Jannik Schaller

Data fusion describes the method of combining data from (at least) two initially independent data sources to allow for joint analysis of variables which are not jointly observed. The fundamental idea is to base inference on identifying assumptions, and on common variables which provide information that is jointly observed in all the data sources. A popular c

Stephen R. Kane, Tiffany Jansen, Thomas Fauchez, Franck Selsis

Transiting compact multi-planet systems provide many unique opportunities to characterize the planets, including studies of size distributions, mean densities, orbital dynamics, and atmospheric compositions. The relatively short orbital periods in these systems ensure that events requiring specific orbital locations of the planets (such as primary transit an

Eduard Eiben, Robert Ganian, Dušan Knop, Sebastian Ordyniak

Recently a strong connection has been shown between the tractability of integer programming (IP) with bounded coefficients on the one side and the structure of its constraint matrix on the other side. To that end, integer linear programming is fixed-parameter tractable with respect to the primal (or dual) treedepth of the Gaifman graph of its constraint matr