March 2020 arXiv papers — page 52

Matteo Mazzarini, Andreas Just, Andrea V. Macciò, Reza Moetazedian

We study the distribution of the Milky Way satellites stellar and dark matter debris. For the first time we address the question of the tidal disruption of satellites in simulations by utilising simultaneously a) a realistic set of orbits extracted from cosmological simulations, b) a three component host galaxy with live halo, disc and bulge components, and

Yi Dai, Sukanta Basu, Björn Maronga, Stephan R de Roode

In this study, we have identified certain fundamental limitations of a mixing length parameterization used in a popular turbulent kinetic energy-based subgrid-scale model. Replacing this parameterization with a more physically realistic one significantly improves the overall quality of the large-eddy simulations (LESs) of stable boundary layers. For the rang

H. Cheraghi, S. Mahdavifar

Ergodicity sits at the heart of the connection between statistical mechanics and dynamics of a physical system. By fixing the initial state of the system into the ground state of the Hamiltonian at zero temperature and tuning a control parameter, we consider the occurrence of the ergodicity with quench dynamics in the one-dimensional (1D) spin-1/2 XY model i

Maximilian Augustin, Alexander Meinke, Matthias Hein

Neural networks have led to major improvements in image classification but suffer from being non-robust to adversarial changes, unreliable uncertainty estimates on out-distribution samples and their inscrutable black-box decisions. In this work we propose RATIO, a training procedure for Robustness via Adversarial Training on In- and Out-distribution, which l

Denise Rava, Ronghui Xu

We study explained variation under the additive hazards regression model for right-censored data. We consider different approaches for developing such a measure, and focus on one that estimates the proportion of variation in the failure time explained by the covariates. We study the properties of the measure both analytically, and through extensive simulatio

Katrin Mang, Mirjam Walloth, Thomas Wick, Winnifried Wollner

This work presents a new adaptive approach for the numerical simulation of a phase-field model for fractures in nearly incompressible solids. In order to cope with locking effects, we use a recently proposed mixed form where we have a hydro-static pressure as additional unknown besides the displacement field and the phase-field variable. To fulfill the fract

Steven Finch

The Cantor distribution is obtained from bitstrings; the Cantor-solus distribution (a new name) admits only strings without adjacent 1 bits. We review moments and order statistics associated with these. The Cantor-multus distribution is introduced -- which instead admits only strings without isolated 1 bits -- and more complicated formulas emerge.

Niny Arcila-Maya, Candace Bethea, Morgan Opie, Kirsten Wickelgren

We show the $\mathbb{A}^{1}$-Euler characteristic of a smooth, projective scheme over a characteristic $0$ field is represented by its Hochschild complex together with a canonical bilinear form, and give an exposition of the compactly supported $\mathbb{A}^{1}$-Euler characteristic $\chi^{c}_{\mathbb{A}^{1}}: K_0(\mathbf{Var}_{k}) \to \text{GW}(k)$ from the

Sudarshan Ghonge, Katerina Chatziioannou, James A. Clark, Tyson Littenberg

We present a systematic comparison of the binary black hole (BBH) signal waveform reconstructed by two independent and complementary approaches used in LIGO and Virgo source inference: a template-based analysis, and a morphology-independent analysis. We apply the two approaches to real events and to two sets of simulated observations made by adding simulated

Matej Kanduč, Won Kyu Kim, Rafael Roa, Joachim Dzubiella

The uptake and sorption of charged molecules by responsive polymer membranes and hydrogels in aqueous solutions is of key importance for the development of soft functional materials. Here we investigate the partitioning of simple monoatomic (Na$^+$, K$^+$, Cs$^+$, Cl$^-$, I$^-$) and one molecular ion (4-nitrophenolate; NP$^-$) within a dense, electroneutral

Paulo Hubert

In this work we cast the problem of binary classification in terms of estimating a partition on Bernoulli data. When the explanatory variables are all categorical, the problem can be modelled using the language of boolean functions. We offer a probabilistic analysis of the problem, and propose two algorithms for learning boolean functions from binary data.

Filippo Bonchi, Jens Seeber, Pawel Sobocinski

Relational structures are emerging as ubiquitous mathematical machinery in the semantics of open systems of various kinds. Cartesian bicategories are a well-known categorical algebra of relations that has proved especially useful in recent applications. The passage between a category and its bicategory of relations is an important question that has been wide

Nicole E. Drakos, James E. Taylor, Andrew J. Benson

The ability to accurately predict the evolution of tidally stripped haloes is important for understanding galaxy formation and testing the properties of dark matter. Most studies of substructure evolution make predictions based on empirical models of tidal mass loss that are calibrated using numerical simulations. This approach can be accurate in the cases c

Xiaohan Fu, Lo-Bin Chang, Dongbin Xiu

We present a general numerical approach for constructing governing equations for unknown dynamical systems when only data on a subset of the state variables are available. The unknown equations for these observed variables are thus a reduced system of the complete set of state variables. Reduced systems possess memory integrals, based on the well known Mori-

Nikolai G. Lehtinen

We propose a new approach to unambiguous determination of parameters of positive and negative electric streamer discharges. From hydrodynamic equations, in the assumption of a solution in the shape of a streamer, it is possible to derive several relations between streamer parameters, which form a system of algebraic equations (SAE). Because of the made appro

Carlos A. S. Soares

In this paper we study the maximal subspaces of continuous n-homogeneous polynomials on complex and real non separable Banach spaces. In the real case we will prove that if P is a 2-homogeneous polynomial and if there exist a k-dimensional P-maximal subspace then every P-maximal subspace is k-dimensional.

Francisco J. Palomo

Lightlike Cartan geometries are introduced as Cartan geometries modelled on the future lightlike cone in Lorentz-Minkowski spacetime. Then, we provide an approach to the study of lightlike manifolds from this point of view. It is stated that every lightlike Cartan geometry on a manifold $N$ provides a lightlike metric $h$ with radical distribution globally s

Tim Aschenbruck, Willem Esterhuizen, Murali Padmanabha, Stefan Streif

Controlled environment agriculture (CEA) is used for efficient food production. Efficiency can be increased further by interconnecting different CEA systems (e.g. plants and insect larvae or fish and larvae), using products and by-products of one system in the other. These interconnected systems define an overall system that can be described by models of int

Nancy Nayak, Thulasi Tholeti, Muralikrishnan Srinivasan, Sheetal Kalyani

This paper introduces an incremental training framework for compressing popular Deep Neural Network (DNN) based unfolded multiple-input-multiple-output (MIMO) detection algorithms like DetNet. The idea of incremental training is explored to select the optimal depth while training. To reduce the computation requirements or the number of FLoating point OPerati

Wujie Shi, Wenze Yang

In this paper we study the finite groups in which every element has prime power order, briefly them EPPO-groups. The classification of EPPO-groups is given including the cases of solvable, non-solvable and simple EPPO-groups. This paper is published in Journal of Yunnan Education College, no.1(1986), p.2-10 (in Chinese). Translate it to English is helpful fo

Vitaly Volpert, Malay Banerjee, Sergei Petrovskii

Attempts to curb the spread of coronavirus by introducing strict quarantine measures apparently have different effect in different countries: while the number of new cases has reportedly decreased in China and South Korea, it still exhibit significant growth in Italy and other countries across Europe. In this brief note, we endeavour to assess the efficiency

Tristan Karch, Cédric Colas, Laetitia Teodorescu, Clément Moulin-Frier

This paper investigates the idea of encoding object-centered representations in the design of the reward function and policy architectures of a language-guided reinforcement learning agent. This is done using a combination of object-wise permutation invariant networks inspired from Deep Sets and gated-attention mechanisms. In a 2D procedurally-generated worl

Sourangsu Banerji, Apratim Majumder, Alex Hamrick, Rajesh Menon

To date, various optimization algorithms have been employed to design and improve the performance of nanophotonic structures. Here, we propose to utilize a machine-learning algorithm viz. binary-Additive Reinforcement Learning Algorithm (b-ARLA) coupled with finite-difference time-domain (FDTD) simulations to design ultra-compact and efficient on-chip integr

Facundo Rodriguez, Manuel Merchán

Galaxy groups provide the means for a great diversity of studies that contribute to a better understanding of the structure of the universe on a large scale and allow the properties of galaxies to be linked to those of the host halos. However, the identification of galaxy systems is a challenging task and therefore it is necessary to improve the techniques i

Amare Abebe, Mudhahir Al Ajmi, Maye Elmardi, Hemwati Nandan

In this work, we revisit the shear-free conjecture of general relativity and show the violation of the well-known shear-free condition for perfect-fluid spacetimes. It had been shown in previous investigations that, in the general relativistic framework, the matter congruences of shear-free perfect fluid spacetimes should be either expansion-free or rotation

Marta Latorre, Francescantonio Oliva, Francesco Petitta, Sergio Segura de León

We study the Dirichlet problem for an elliptic equation involving the $1$-Laplace operator and a reaction term, namely: $$ \left\{\begin{array}{ll} \displaystyle -\Delta_1 u =h(u)f(x)&\hbox{in }\Omega\,,\\ u=0&\hbox{on }\partial\Omega\,, \end{array}\right. $$ where $ \Omega \subset \mathbb{R}^N$ is an open bounded set having Lipschitz boundary, $f\in L^1(\Om

Tariq Bdair, Benedikt Wiestler, Nassir Navab, Shadi Albarqouni

Medical image segmentation is one of the major challenges addressed by machine learning methods. Yet, deep learning methods profoundly depend on a large amount of annotated data, which is time-consuming and costly. Though, semi-supervised learning methods approach this problem by leveraging an abundant amount of unlabeled data along with a small amount of la

Qiuhao Hu, Mohammad Reza Amini, Yiheng Feng, Zhen Yang

Connected vehicles (CVs) have situational awareness that can be exploited for control and optimization of the powertrain system. While extensive studies have been carried out for energy efficiency improvement of CVs via eco-driving and planning, the implication of such technologies on the thermal responses of CVs has not been fully investigated. One of the k

Dagoberto S. Freitas

The generation of nonclassical states of a radiation field has become increasingly important in the past years given its various applications in quantum communication. The feasibility of generating such nonclassical states has been established in several branches of physics such as cavity electrodynamics, trapped ions, quantum dots, atoms inside cavities and

Nicholas Vieira, John J. Ruan, Daryl Haggard, Maria R. Drout

We present a wide-field optical imaging search for electromagnetic counterparts to the likely neutron star - black hole (NS-BH) merger GW190814/S190814bv. This compact binary merger was detected through gravitational waves by the LIGO/Virgo interferometers, with masses suggestive of a NS-BH merger. We imaged the LIGO/Virgo localization region using the MegaC

Anh Tran, Mike Eldred, Tim Wildey, Scott McCann

High-fidelity complex engineering simulations are highly predictive, but also computationally expensive and often require substantial computational efforts. The mitigation of computational burden is usually enabled through parallelism in high-performance cluster (HPC) architecture. In this paper, an asynchronous constrained batch-parallel Bayesian optimizati

Jewel Kumar Ghosh, Elias Kiritsis, Francesco Nitti, Lukas T. Witkowski

We employ gauge-gravity duality to study the backreaction effect of 4-dimensional large-$N$ quantum field theories on constant-curvature backgrounds, and in particular de Sitter space-time. The field theories considered are holographic QFTs, dual to RG flows between UV and IR CFTs. We compute the holographic QFT contribution to the gravitational effective ac

Mancho Manev

Ricci-like solitons on Sasaki-like almost contact B-metric manifolds are the object of study. Cases, where the potential of the Ricci-like soliton is the Reeb vector field or pointwise collinear to it, are considered. In the former case, the properties for a parallel or recurrent Ricci-tensor are studied. In the latter case, it is shown that the potential of

Yunxiang Liao, Donovan Buterakos, Mike Schecter, Sankar Das Sarma

The electron self-energy for long-range Coulomb interactions plays a crucial role in understanding the many-body physics of interacting electron systems (e.g. in metals and semiconductors), and has been studied extensively for decades. In fact, it is among the oldest and the most-investigated many body problems in physics. However, there is a lack of an anal

Thomas P. Wytock, Adilson E. Motter

The relationship between microscopic observations and macroscopic behavior is a fundamental open question in biophysical systems. Here, we develop a unified approach that---in contrast with existing methods---predicts cell type from macromolecular data even when accounting for the scale of human tissue diversity and limitations in the available data. We achi

Avraham Klein, Yoni Schattner, Erez Berg, Andrey V. Chubukov

We study the effects of finite temperature on normal state properties of a metal near a quantum critical point to an antiferromagnetic or Ising-nematic state. At $T = 0$ bosonic and fermionic self-energies are traditionally computed within Eliashberg theory and obey scaling relations with characteristic power-laws. Quantum Monte Carlo (QMC) simulations have

Christopher David White, Sascha Heußen, Gil Refael

One challenge of studying the many-body localization transition is defining the length scale that diverges upon the transition to the ergodic phase. In this manuscript we explore the localization properties of a ring with onsite disorder subject to an imaginary magnetic flux. We connect the imaginary flux which delocalizes single-particle orbitals of an Ande

Andrey Gromov, Andrew Lucas, Rahul M. Nandkishore

We introduce new classes of hydrodynamic theories inspired by the recently discovered fracton phases of quantum matter. Fracton phases are characterized by elementary excitations (fractons) with restricted mobility. The hydrodynamic theories we introduce describe thermalization in systems with fracton-like mobility constraints, including fluids where charge

Sebastian Fischetti, Lucas Wallis, Toby Wiseman

We examine the renormalized free energy of the free Dirac fermion and the free scalar on a (2+1)-dimensional geometry $\mathbb{R} \times \Sigma$, with $\Sigma$ having spherical topology and prescribed area. Using heat kernel methods, we perturbatively compute this energy when $\Sigma$ is a small deformation of the round sphere, finding that at any temperatur

Tiara Hung, Ryan J. Foley, Enrico Ramirez-Ruiz, Jane L. Dai

We present the multi-wavelength analysis of the tidal disruption event (TDE) AT~2018hyz (ASASSN-18zj). From follow-up optical spectroscopy, we detect the first unambiguous case of resolved double-peaked Balmer emission in a TDE. The distinct line profile can be well-modelled by a low eccentricity ($e\approx0.1$) accretion disk extending out to $\sim$100 $R_{

Pavel Fileviez Perez, Elliot Golias, Clara Murgui, Alexis D. Plascencia

The Higgs boson could provide the key to discover new physics at the Large Hadron Collider. We investigate novel decays of the Standard Model (SM) Higgs boson into leptophobic gauge bosons which can be light in agreement with all experimental constraints. We study the associated production of the SM Higgs and the leptophobic gauge boson that could be crucial

Laerte Xavier, Fabio Ferreira, Rodrigo Brito, Marco Tulio Valente

Self-admitted technical debt (SATD) is a particular case of Technical Debt (TD) where developers explicitly acknowledge their sub-optimal implementation decisions. Previous studies mine SATD by searching for specific TD-related terms in source code comments. By contrast, in this paper we argue that developers can admit technical debt by other means, e.g., by

Moritz Schubotz, André Greiner-Petter, Norman Meuschke, Olaf Teschke

This poster summarizes our contributions to Wikimedia's processing pipeline for mathematical formulae. We describe how we have supported the transition from rendering formulae as course-grained PNG images in 2001 to providing modern semantically enriched language-independent MathML formulae in 2020. Additionally, we describe our plans to improve the accessib

Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Dacheng Tao

Noise in quantum information processing is often viewed as a disruptive and difficult-to-avoid feature, especially in near-term quantum technologies. However, noise has often played beneficial roles, from enhancing weak signals in stochastic resonance to protecting the privacy of data in differential privacy. It is then natural to ask, can we harness the pow

Leslie S. Smith

Terry Sejnowski's 2020 paper [arXiv:2002.04806] is entitled "The unreasonable effectiveness of deep learning in artificial intelligence". However, the paper doesn't attempt to answer the implied question of why Deep Convolutional Neural Networks (DCNNs) can approximate so many of the mappings that they have been trained to model. While there are detailed mat

Kirill P. Kalinin, Alberto Amo, Jacqueline Bloch, Natalia G. Berloff

Gain-dissipative systems of various physical origin have recently shown the ability to act as analogue minimisers of hard combinatorial optimisation problems. Whether or not these proposals will lead to any advantage in performance over the classical computations depends on the ability to establish controllable couplings for sufficiently dense short- and lon

J. Sedghi Moghaddam, A. Najati, Y. Khedmati

Dynamical sampling deals with frames of the form $\{T^n\varphi\}_{n=0}^\infty$, where $T \in B(\mathcal{H})$ belongs to certain classes of linear operators and $\varphi\in\mathcal{H}$. The purpose of this paper is to investigate a new representation, namely, Fibonacci representation of sequences $\{f_n\}_{n=1}^\infty$ in a Hilbert space $\mathcal{H}$; having

Sergey Bravyi, Dmitri Maslov

The Clifford group plays a central role in quantum randomized benchmarking, quantum tomography, and error correction protocols. Here we study the structural properties of this group. We show that any Clifford operator can be uniquely written in the canonical form $F_1HSF_2$, where $H$ is a layer of Hadamard gates, $S$ is a permutation of qubits, and $F_i$ ar

Leonardo Santilli, Richard J. Szabo, Miguel Tierz

We revisit the duality between five-dimensional supersymmetric gauge theories and deformations of two-dimensional Yang-Mills theory from a new perspective. We give a unified treatment of supersymmetric gauge theories in three and five dimensions using cohomological localization techniques and the Atiyah-Singer index theorem. We survey various known results i

Eduardo M. Gutiérrez, Gustavo E. Romero, Florencia L. Vieyro

NGC 253 is a nearby starburst galaxy in the Sculptor group located at a distance of 3.5 Mpc that has been suggested by some authors as a potential site for cosmic-ray acceleration up to ultra-high energies. Its nuclear region is heavily obscured by gas and dust, which prevents establishing whether or not the galaxy harbours a supermassive black hole coexisti

Gabriela Araujo-Pardo, Juan Carlos Díaz-Patiño, Christian Rubio-Montiel

Complete colorings have the property that any two color classes has at least an edge between them. Parameters such as the Grundy, achromatic and pseudoachromatic numbers comes from complete colorings, with some additional requirement. In this paper, we estimate these numbers in the Kneser graph $K(n,k)$ for some values of $n$ and $k$. We give the exact value

Amit Das, Shounak Ghosh, Debabrata Deb, Farook Rahaman

In the present paper we propose a stellar model under the $f(\mathbb{T})$ gravity following the conjecture of Mazur-Mottola~[Report number: LA-UR-01-5067 (2001); Proc. Natl. Acad. Sci. USA 101 (2004) 9545] known in literature as {\it gravastar}, a viable alternative to the black hole. This gravastar has three different regions, viz., (A) Interior core region

Qinkai Zheng, Han Qiu, Gerard Memmi, Isabelle Bloch

This is the report for the PRIM project in Telecom Paris. This report is about applications based on spatial-frequency transform and deep learning techniques. In this report, there are two main works. The first work is about the enhanced JPEG compression method based on deep learning. we propose a novel method to highly enhance the JPEG compression by transm

Yiran Xu, Xiaoyin Yang, Lihang Gong, Hsuan-Chu Lin

A new paradigm is proposed for autonomous driving. The new paradigm lies between the end-to-end and pipelined approaches, and is inspired by how humans solve the problem. While it relies on scene understanding, the latter only considers objects that could originate hazard. These are denoted as action-inducing, since changes in their state should trigger vehi

Insaf Setitra, Noureddine Aouaa, Abdelkrim Meziane, Afef Benrabia

Children diagnosed with a scoliosis pathology are exposed during their follow up to ionic radiations in each X-rays diagnosis. This exposure can have negative effects on the patient's health and cause diseases in the adult age. In order to reduce X-rays scanning, recent systems provide diagnosis of scoliosis patients using solely RGB images. The output of su

Tony Lelièvre, Gabriel Stoltz, Wei Zhang

We propose new Markov Chain Monte Carlo algorithms to sample probability distributions on submanifolds, which generalize previous methods by allowing the use of set-valued maps in the proposal step of the MCMC algorithms. The motivation for this generalization is that the numerical solvers used to project proposed moves to the submanifold of interest may fin

Oscar Lima, Michael Cashmore, Daniele Magazzeni, Andrea Micheli

In order to ensure the robust actuation of a plan, execution must be adaptable to unexpected situations in the world and to exogenous events. This is critical in domains in which committing to a wrong ordering of actions can cause the plan failure, even when all the actions succeed. We propose an approach to the execution of a task plan that permits some ada

Matteo Rinaldi

Double parton distribution functions (dPDFs), entering the double parton scattering (DPS) cross section, are unknown fundamental quantities encoding new interesting properties of hadrons. Here, the pion dPDFs are investigated within different holographic QCD quark models in order to access their basic features. Results of the calculation,s obtained within th

Dan Timotin

Using the tools of Sz.-Nagy--Foias theory of contractions, we describe in detail the invariant subspaces of the operator $ S\oplus S^* $, where $ S $ is the unilateral shift on a Hilbert space. This answers a question of C\^amara and Ross.

Gabriel Kalweit, Maria Huegle, Moritz Werling, Joschka Boedecker

In many real world applications, reinforcement learning agents have to optimize multiple objectives while following certain rules or satisfying a list of constraints. Classical methods based on reward shaping, i.e. a weighted combination of different objectives in the reward signal, or Lagrangian methods, including constraints in the loss function, have no g

Adilbek Kairzhan, Robert Marangell, Dmitry E. Pelinovsky, Ke Liang Xiao

A flower graph consists of a half line and $N$ symmetric loops connected at a single vertex with $N \geq 2$ (it is called the tadpole graph if $N = 1$). We consider positive single-lobe states on the flower graph in the framework of the cubic nonlinear Schrodinger equation. The main novelty of our paper is a rigorous application of the period function for se

Nikolaos Chatzikonstantinou

We define the manifold of configurations to be the quotient set of $k$ points in Euclidean space identified under congruence, and prove that compact subsets of $\mathbb{R}^d, d \geq 2$, of large Hausdorff dimension have a non-null set of configurations in them. Our method simplifies previous work in arXiv:1708.05919 and achieves a better dimensional threshol

Xuefei Ning, Guangjun Ge, Wenshuo Li, Zhenhua Zhu

With the fast evolvement of embedded deep-learning computing systems, applications powered by deep learning are moving from the cloud to the edge. When deploying neural networks (NNs) onto the devices under complex environments, there are various types of possible faults: soft errors caused by cosmic radiation and radioactive impurities, voltage instability,

Nicolas Behr, Jean Krivine

The Kappa biochemistry and the M{\O}D organo-chemistry frameworks are amongst the most intensely developed applications of rewriting theoretical methods in the life sciences to date. A typical feature of these types of rewriting theories is the necessity to implement certain structural constraints on the objects to be rewritten (a protein is empirically foun

Teresa Maiorano, Adriana Settino, Francesco Malara, Oreste Pezzi

The evolution of a linearly-polarized, long-wavelength Alfv\'en wave --propagating in a collisionless magnetized plasma with a sheared parallel-directed velocity flow-- is here studied by means of two-dimensional hybrid Vlasov-Maxwell (HVM) simulations. The unperturbed sheared flow has been represented by an exact solution of the HVM set of equations (Malara

Daniel A. Dale, Kristin R. Anderson, Louis M. Bran, Isaiah S. Cox

The spatially resolved star formation histories are studied for 32 normal star-forming galaxies drawn from the the Spitzer Extended Disk Galaxy Exploration Science survey. At surface brightness sensitivities fainter than 28 mag arcsec$^{-2}$, the new optical photometry is deep enough to complement archival ultraviolet and infrared imaging and to explore the

Dairui Liu, Jingxiang Sun, Shen Wang

With the rise of big data technologies, many smart transportation applications have been rapidly developed in recent years including bus arrival time predictions. This type of applications help passengers to plan trips more efficiently without wasting unpredictable amount of waiting time at bus stops. Many studies focus on improving the prediction accuracy o

Leo Puyo, Michel Paques, Michael Atlan

Laser Doppler holography (LDH) is a full-field interferometric imaging technique recently applied in ophthalmology to measure blood flow, a parameter of high clinical interest. From the temporal fluctuations of digital holograms acquired at ultrafast frame rates, LDH reveals retinal and choroidal blood flow with a few milliseconds of temporal resolution. How

Vinay Verma, Deepak Singh, Nitin Khanna

Forged images have a ubiquitous presence in today's world due to ease of availability of image manipulation tools. In this letter, we propose a deep learning-based novel approach which utilizes the inherent relationship between DCT coefficient histograms and corresponding quantization step sizes to distinguish between original and forged regions in a JPEG im

Yansong Tang, Jiwen Lu, Jie Zhou

Thanks to the substantial and explosively inscreased instructional videos on the Internet, novices are able to acquire knowledge for completing various tasks. Over the past decade, growing efforts have been devoted to investigating the problem on instructional video analysis. However, the most existing datasets in this area have limitations in diversity and

Lei Tian, Yongqiang Tang, Liangchen Hu, Zhida Ren

Domain adaptation has been a fundamental technology for transferring knowledge from a source domain to a target domain. The key issue of domain adaptation is how to reduce the distribution discrepancy between two domains in a proper way such that they can be treated indifferently for learning. In this paper, we propose a novel domain adaptation approach, whi

Anirban Das, Manfred Denker, Anna Levina, Lucia Tabacu

We propose a nonparametric parameter estimation of confidence intervals when the underlying has large or infinite variance. We explain the method by a simple numerical example and provide an application to estimate the coupling strength in neuronal networks.

Vivina Barutello, Rafael Ortega, Gianmaria Verzini

The goal of the paper is to develop a method that will combine the use of variational techniques with regularization methods in order to study existence and multiplicity results for the periodic and the Dirichlet problem associated to the perturbed Kepler system \[ \ddot x = -\frac{x}{|x|^3} + p(t), \quad x \in \mathbb{R}^d, \] where $d\geq 1$, and $p:\mathb

Andrew J. Landahl, Daniel S. Lobser, Benjamin C. A. Morrison, Kenneth M. Rudinger

QSCOUT is the Quantum Scientific Computing Open User Testbed, a trapped-ion quantum computer testbed realized at Sandia National Laboratories on behalf of the Department of Energy's Office of Science and its Advanced Scientific Computing (ASCR) program. Jaqal, for Just Another Quantum Assembly Language, is the programming language used to specify programs ex

Subrata Nandi, Srinivasan Krishnaswamy, Behrouz Zolfaghari, Pinaki Mitra

In this paper, we propose and evaluate a method for generating key-dependent feedback configurations (KDFC) for $\sigma$-LFSRs. $\sigma$-LFSRs with such configurations can be applied to any stream cipher that uses a word-based LFSR. Here, a configuration generation algorithm uses the secret key(K) and the initialization vector (IV) to generate a feedback con

Sara Brofferio, Marc Peigné, Thi Da Cam Pham

We fix $d \geq 2$ and denote $\mathcal S$ the semi-group of $d \times d$ matrices with non negative entries. We consider a sequence $(A_n, B_n)_{n \geq 1} $ of i. i. d. random variables with values in $\mathcal S\times \mathbb R_+^d$ and study the asymptotic behavior of the Markov chain $(X_n)_{n \geq 0}$ on $ \mathbb R_+^d$ defined by: \[ \forall n \geq 0,

Steven Kleinegesse, Christopher Drovandi, Michael U. Gutmann

Bayesian experimental design (BED) is a framework that uses statistical models and decision making under uncertainty to optimise the cost and performance of a scientific experiment. Sequential BED, as opposed to static BED, considers the scenario where we can sequentially update our beliefs about the model parameters through data gathered in the experiment.

Martino Ciaperoni, Edoardo Galimberti, Francesco Bonchi, Ciro Cattuto

Temporal networks are widely used to represent a vast diversity of systems, including in particular social interactions, and the spreading processes unfolding on top of them. The identification of structures playing important roles in such processes remains largely an open question, despite recent progresses in the case of static networks. Here, we consider

Philipp Terhörst, Jan Niklas Kolf, Naser Damer, Florian Kirchbuchner

Face image quality is an important factor to enable high performance face recognition systems. Face quality assessment aims at estimating the suitability of a face image for recognition. Previous work proposed supervised solutions that require artificially or human labelled quality values. However, both labelling mechanisms are error-prone as they do not rel

Anshuman Suri, David Evans

Despite vast research in adversarial examples, the root causes of model susceptibility are not well understood. Instead of looking at attack-specific robustness, we propose a notion that evaluates the sensitivity of individual neurons in terms of how robust the model's output is to direct perturbations of that neuron's output. Analyzing models from this pers

Wenda Zhao, Abhishek Goudar, Jacopo Panerati, Angela P. Schoellig

Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks. Ultra-wideband (UWB) ranging is a promising solution which is low-cost, lightweight, and computationally inexpensive compared to alternative state-of-the-art approaches such as simultaneous localization and mapping, mak

Indresh Chaudhary, Piyush Garg, Ganesh Subramanian, Viswanathan Shankar

A modal stability analysis shows that pressure-driven pipe flow of an Oldroyd-B fluid is linearly unstable to axisymmetric perturbations, in stark contrast to its Newtonian counterpart which is linearly stable at all Reynolds numbers. The dimensionless groups that govern stability are the Reynolds number, the elasticity number, and the ratio of solvent to so

Winston Heap, Junxian Li, Jing Zhao

We prove lower bounds for the discrete negative $2k$th moment of the derivative of the Riemann zeta function for all fractional $k\geqslant 0$. The bounds are in line with a conjecture of Gonek and Hejhal. Along the way, we prove a general formula for the discrete twisted second moment of the Riemann zeta function. This agrees with a conjecture of Conrey and

Louise Laage

This paper studies a class of linear panel models with random coefficients. We do not restrict the joint distribution of the time-invariant unobserved heterogeneity and the covariates. We investigate identification of the average partial effect (APE) when fixed-effect techniques cannot be used to control for the correlation between the regressors and the tim

Fotis Farakos

In this contribution we revisit higher curvature N=1 supergravity and discuss the quintessence phase that can appear due to the $R^4$ terms. In particular we focus on the bosonic supersymmetric completion within the old-minimal and the new-minimal formulations.

Xuan Li, Yuchen Lu, Christian Desrosiers, Xue Liu

In this paper, we study the problem of out-of-distribution detection in skin disease images. Publicly available medical datasets normally have a limited number of lesion classes (e.g. HAM10000 has 8 lesion classes). However, there exists a few thousands of clinically identified diseases. Hence, it is important if lesions not in the training data can be diffe

Diana Geneva, Georgi Shopov, Stoyan Mihov

We present a construction for the composition of subsequential transducers (representing conditional probabilistic models) with subsequential failure transducers (representing probabilistic models). Under certain conditions, satisfied by the corresponding transduction devices, a more efficient construction is applicable that avoids the creation of unnecessar

Dan Alistarh, Nikita Koval, Giorgi Nadiradze

Several classic problems in graph processing and computational geometry are solved via incremental algorithms, which split computation into a series of small tasks acting on shared state, which gets updated progressively. While the sequential variant of such algorithms usually specifies a fixed (but sometimes random) order in which the tasks should be perfor

John C. Urschel

The Lanczos method is one of the most powerful and fundamental techniques for solving an extremal symmetric eigenvalue problem. Convergence-based error estimates depend heavily on the eigenvalue gap. In practice, this gap is often relatively small, resulting in significant overestimates of error. One way to avoid this issue is through the use of uniform erro

Giannis Delimpaltadakis, Manuel Mazo

In previous work, linear time-invariant event-triggered control (ETC) systems were abstracted to finite-state systems that capture the original systems' sampling behaviour. It was shown that these abstractions can be employed for scheduling of communication traffic in networks of ETC loops. In this paper, we extend this framework to the class of nonlinear ho

Salvador Rodriguez-Gomez Balestra, Jose Manuel Vicent-Luna, Sofia Calero, Shuxia Tao

Metal halide perovskites (MHPs) are nowadays one of the most studied semiconductors due to their exceptional performance as active layers in solar cells. Although MHPs are excellent solid-state semiconductors, they are also ionic compounds, where ion migration plays a decisive role in their formation, their photovoltaic performance and their long-term stabil

Xiang-Dong Shi, Sheng-Bang Qian, Lin-Jia, Wei-Wei Na

ASAS J174406+2446.8 was originally found as a $\delta$ Scuti-type pulsating star with the period P=0.189068 $days$ by ASAS survey. However, the LAMOST stellar parameters reveal that it is far beyond the red edge of pulsational instability strip on the $\log g-T$ diagram of $\delta$ Scuti pulsating stars. To understand the physical properties of the variable

Miguel A. Alejo, Claudio Muñoz, José M. Palacios

We consider the sine-Gordon (SG) equation in 1+1 dimensions. The kink is a static, non-symmetric exact solution to SG, stable in the energy space $H^1\times L^2$. It is well-known that the linearized operator around the kink has a simple kernel and no internal modes. However, it possesses an odd resonance at the bottom of the continuum spectrum, deeply relat

Cameron Buckner

Deep learning is currently the most widespread and successful technology in artificial intelligence. It promises to push the frontier of scientific discovery beyond current limits. However, skeptics have worried that deep neural networks are black boxes, and have called into question whether these advances can really be deemed scientific progress if humans c

T. J. Davidge

We discuss near-infrared (NIR) slit spectra of six early-type disk galaxies in the Virgo Cluster. While the depth of the CO(2,0) band is consistent with the centers of these galaxies having solar or slightly sub-solar luminosity-weighted metallicities, galaxy-to-galaxy variations in the depth of the NaI 2.21um doublet are found, with NaI2.21um lines in three

Dmytro Matvieievskyi

Let $\mathfrak{g}$ be a simple classical Lie algebra over $\mathbb{C}$ and $G$ be the adjoint group. Consider a nilpotent element $e\in \mathfrak{g}$, and the adjoint orbit $\mathbb{O}=Ge$. The formal slices to the codimension $2$ orbits in the closure $\overline{\mathbb{O}}\subset \mathfrak{g}$ are well-known due to the work of Kraft and Procesi. In this pa

Advait Madhavan, Mark D. Stiles

We extend the reach of temporal computing schemes by developing a memory for multi-channel temporal patterns or "wavefronts." This temporal memory re-purposes conventional one-transistor-one-resistor (1T1R) memristor crossbars for use in an arrival-time coded, single-event-per-wire temporal computing environment. The memristor resistances and the associated

Varun Tolani, Somil Bansal, Aleksandra Faust, Claire Tomlin

Real world visual navigation requires robots to operate in unfamiliar, human-occupied dynamic environments. Navigation around humans is especially difficult because it requires anticipating their future motion, which can be quite challenging. We propose an approach that combines learning-based perception with model-based optimal control to navigate among hum

L. D. Woodham, R. T. Wicks, D. Verscharen, J. M. TenBarge

We use fluctuating magnetic helicity to investigate the polarisation properties of Alfv\'enic fluctuations at ion-kinetic scales in the solar wind as a function of $\beta_p$, the ratio of proton thermal pressure to magnetic pressure, and $\theta_{vB}$, the angle between the proton flow and local mean magnetic field, $\mathbf{B}_0$. Using almost 15 years of \

Jacopo De Simoi, Martin Leguil, Kurt Vinhage, Yun Yang

Given an integer $k \geq 5$, and a $C^k$ Anosov flow $\Phi$ on some compact connected $3$-manifold preserving a smooth volume, we show that the measure of maximal entropy (MME) is the volume measure if and only if $\Phi$ is $C^{k-\varepsilon}$-conjugate to an algebraic flow, for $\varepsilon>0$ arbitrarily small. Besides the rigidity, we also study the entro