December 2020 arXiv papers — page 92

Francesca Dominici, Falco J. Bargagli-Stoffi, Fabrizia Mealli

This paper discusses the fundamental principles of causal inference - the area of statistics that estimates the effect of specific occurrences, treatments, interventions, and exposures on a given outcome from experimental and observational data. We explain the key assumptions required to identify causal effects, and highlight the challenges associated with t

Volodymyr Takhistov

While neutrino physics enters precision era, several important unknowns remain. Atmospheric neutrinos allow to simultaneously test key oscillation parameters, with Super-Kamiokande experiment playing a central role. We discuss results from atmospheric neutrino oscillation analysis of the full dataset from Super-Kamiokande I-IV phases. Further, we discuss tes

Francesco De Lellis, Giovanni Russo, Mario di Bernardo

We introduce a control-tutored reinforcement learning (CTRL) algorithm. The idea is to enhance tabular learning algorithms by means of a control strategy with limited knowledge of the system model. By tutoring the learning process, the learning rate can be substantially reduced. We use the classical problem of stabilizing an inverted pendulum as a benchmark

Kenneth D. Harris

We describe a family of conservative statistical tests for independence of two autocorrelated time series. The series may take values in any sets, and one of them must be stationary. A user-specified function quantifying the association of a segment of the two series is compared to an ensemble obtained by time-shifting the stationary series -N to N steps. If

Paolo Lipari

The shape of the longitudinal development of the showers generated in the atmosphere by very high energy cosmic ray particles encodes information about the mass composition of the flux, and about the properties of hadronic interactions that control the shower development. Studies of the energy dependence of the average and width of the depth of maximum distr

Yung-Lin Hsu, Chen-Feng Liu, Sumudu Samarakoon, Hung-Yu Wei

This work studies a real-time environment monitoring scenario in the industrial Internet of things, where wireless sensors proactively collect environmental data and transmit it to the controller. We adopt the notion of risk-sensitivity in financial mathematics as the objective to jointly minimize the mean, variance, and other higher-order statistics of the

Savas Ozkan, Gozde Bozdagi Akar

This study proposes a novel framework for spectral unmixing by using 1D convolution kernels and spectral uncertainty. High-level representations are computed from data, and they are further modeled with the Multinomial Mixture Model to estimate fractions under severe spectral uncertainty. Furthermore, a new trainable uncertainty term based on a nonlinear neu

David Mallasén Quintana, Alberto Antonio del Barrio García, Manuel Prieto Matías

Automatic digitization of chess games using computer vision is a significant technological challenge. This problem is of much interest for tournament organizers and amateur or professional players to broadcast their over-the-board (OTB) games online or analyze them using chess engines. Previous work has shown promising results, but the recognition accuracy a

H-S. Chan, M-C. Chu, S-C. Leung, L-M. Lin

We present simulations of thermonuclear supernovae admixed with an extended component of fermionic cold dark matter. We consider the explosion of a Chandrasekhar-mass white dwarf using the deflagration model with deflagration-detonation transition with spherical symmetry. The dark matter component is comparable in size with that of the normal matter, and so

Ioannis P. Antoniades, Leonidas P. Karakatsanis, Evgenios G. Pavlos

We perform non-linear analysis on stock market indices using time-dependent extended Tsallis statistics. Specifically, we evaluate the q-triplet for particular time periods with the purpose of demonstrating the temporal dependence of the extended characteristics of the underlying market dynamics. We apply the analysis on daily close price timeseries of four

Sarah Allahmoradi, Mohsen Parsa Moghaddam, Salah Bahramara, Pouria Sheikhahmadi

Regarding the variability of renewable energy sources (RESs), especially in the operation time periods, their high penetration faces the net load pattern of the power system with a major ramp rate challenge. Although employing a market-based mechanism by the independent system operator (ISO) can address this challenge, it may not be possible to handle this c

Samaneh Esfandiarpour, J. Todd Hastings

Focused electron beam induced deposition of pure materials from aqueous solutions has been of interest in recent years. However, controlling the liquid film in partial vacuum is challenging. Here we modify the substrate to increase control over the liquid layer in order to conduct a parametric study of copper deposition in an environmental SEM. We identified

Saleh Rahimi-Keshari, Mohammad Mehboudi, Dario De Santis, Daniel Cavalcanti

Measurement incompatibility is a distinguishing property of quantum physics and an essential resource for many quantum information processing tasks. We introduce an approach to verify the joint measurability of measurements based on phase-space quasiprobability distributions. Our results therefore establish a connection between two notions of non-classicalit

Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang

Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user intent lying in the item transitions plays a pivotal role. Recent graph neural networks (GNNs) based SBR methods regard the item transitions as pairwise relations, which neglect the comp

J. A. Saenz, D. Aslangil, D. Livescu

We investigate relationships between statistics obtained from filtering and from ensemble or Reynolds-averaging turbulence flow fields as a function of length scale. Generalized central moments in the filtering approach are expressed as inner products of generalized fluctuating quantities, $q'(\xi,x)=q(\xi)-\overline q(x)$, representing fluctuations of a fie

Yifan Xu, Pan Xu

Several scientific studies have reported the existence of the income gap among rideshare drivers based on demographic factors such as gender, age, race, etc. In this paper, we study the income inequality among rideshare drivers due to discriminative cancellations from riders, and the tradeoff between the income inequality (called fairness objective) with the

Sedigheh Jahedi, Vahid Keshavarz

In this paper, we introduce the concept of j-hom-derivation, $j\in\{1,2\}$ and solve the new generalized additive-quadratic functional equations in the sense of ternary Banach algebras. Moreover, using the fixed point method, we prove its Hyers-Ulam stability.

Arnaud Liehrmann, Guillem Rigaill, Toby Dylan Hocking

Motivation: Histone modification constitutes a basic mechanism for the genetic regulation of gene expression. In early 2000s, a powerful technique has emerged that couples chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq). This technique provides a direct survey of the DNA regions associated to these modifications. In order to realize

Zana Bucinca, Yucel Yemez, Engin Erzin, Metin Sezgin

Due to its expressivity, natural language is paramount for explicit and implicit affective state communication among humans. The same linguistic inquiry (e.g., How are you?) might induce responses with different affects depending on the affective state of the conversational partner(s) and the context of the conversation. Yet, most dialog systems do not consi

Alessio Benavoli, Dario Azzimonti, Dario Piga

Skew-Gaussian processes (SkewGPs) extend the multivariate Unified Skew-Normal distributions over finite dimensional vectors to distribution over functions. SkewGPs are more general and flexible than Gaussian processes, as SkewGPs may also represent asymmetric distributions. In a recent contribution we showed that SkewGP and probit likelihood are conjugate, w

Yifan Xu, Pan Xu, Jianping Pan, Jun Tao

With the popularity of the Internet, traditional offline resource allocation has evolved into a new form, called online resource allocation. It features the online arrivals of agents in the system and the real-time decision-making requirement upon the arrival of each online agent. Both offline and online resource allocation have wide applications in various

J. Douady, A. Simon, M. Rapacioli, F. Calvo

Molecular clusters of 1,3-butadiene were theoretically investigated using a variety of approaches, encompassing classical force fields and different quantum chemical (QC) methods, as well as density-functional based tight-binding (DFTB) in its self-consistent-charge (SCC) version. Upon suitable reparametrization, SCC-DFTB reproduces the energy difference and

Can Zhang, Hong Liu, Wei Guo, Mang Ye

RGB-Infrared person re-identification (RGB-IR Re-ID) aims to match persons from heterogeneous images captured by visible and thermal cameras, which is of great significance in the surveillance system under poor light conditions. Facing great challenges in complex variances including conventional single-modality and additional inter-modality discrepancies, mo

Neelima Agarwal, Ayan Mukhopadhyay, Sourav Pal, Anurag Tripathi

Event shapes are classical tools for the determination of the strong coupling and for the study of hadronization effects in electron-positron annihilation. In the context of analytical studies, hadronization corrections take the form of power-suppressed contributions to the cross-section, which can be extracted from the perturbative ambiguity of Borel-resumm

Christian Gaetz, Yibo Gao

We reinterpret an inequality, due originally to Sidorenko, for linear extensions of posets in terms of convex subsets of the symmetric group $\mathfrak{S}_n$. We conjecture that the analogous inequalities hold in arbitrary (not-necessarily-finite) Coxeter groups $W$, and prove this for the hyperoctahedral groups $B_n$ and all right-angled Coxeter groups. Our

Luke Schaeffer, Jeffrey Shallit

We show that it is decidable, given an automatic sequence $\bf s$ and a constant $c$, whether all prefixes of $\bf s$ have a string attractor of size $\leq c$. Using a decision procedure based on this result, we show that all prefixes of the period-doubling sequence of length $\geq 2$ have a string attractor of size $2$. We also prove analogous results for o

Mehdi Assanioussi, Lisa Mickel

In this article, we introduce a new effective model for the Kantowski-Sachs spacetime in the context of loop quantum gravity, and we use it to evaluate departures from general relativity in the case of Schwarzschild black hole interior. The model is based on an effective Hamiltonian constructed via the regularized Thiemann identities in the $\bar \mu$-scheme

Almabrok Essa, Vijayan Asari

Derived from a general definition of texture in a local neighborhood, local directional pattern (LDP) encodes the directional information in the small local 3x3 neighborhood of a pixel, which may fail to extract detailed information especially during changes in the input image due to illumination variations. Therefore, in this paper we introduce a novel feat

Ali Frotanpour, Justin Woods, Barry Farmer, Amrit P. Kaphle

We present results of ferromagnetic resonance (FMR) experiments and micromagnetic simulations for a distorted, 2D Kagome artificial spin ice. The distorted structure is created by continuously modulating the 2D primitive lattice translation vectors of a periodic honeycomb lattice, according to an aperiodic Fibonacci sequence used to generate 1D quasicrystals

Emanuele Parisi, Francesco Barchi, Andrea Bartolini, Giuseppe Tagliavini

The analysis of source code through machine learning techniques is an increasingly explored research topic aiming at increasing smartness in the software toolchain to exploit modern architectures in the best possible way. In the case of low-power, parallel embedded architectures, this means finding the configuration, for instance in terms of the number of co

Sayantani Lahiri, Sergio Gimeno-Soler, José A. Font, Alejandro Mus Mejías

We present stationary solutions of magnetized, viscous thick accretion disks around a Schwarzschild black hole. We assume that the tori are not self-gravitating, are endowed with a toroidal magnetic field, and obey a constant angular momentum law. Our study focuses on the role of the black hole curvature in the shear viscosity tensor and in their potential c

Duc Van Le, Rongrong Wang, Yingbo Liu, Rui Tan

Air free-cooled data centers (DCs) have not existed in the tropical zone due to the unique challenges of year-round high ambient temperature and relative humidity (RH). The increasing availability of servers that can tolerate higher temperatures and RH due to the regulatory bodies' prompts to raise DC temperature setpoints sheds light upon the feasibility of

G. I. Kokhirova, O. V. Ivanova, F. Dzh. Rakhmatullaeva, A. M. Buriev

Astrometric and photometric observations of the comet 29P/Schwassmann-Wachmann 1 were performed at the Zeiss-1000 telescope of the International Astronomical Observatory Sanglokh (IAOS) of the Institute of Astrophysics, Academy of Sciences of the Republic of Tajikistan in July and August 2017. Although the comet has a short period of revolution it is regarde

Tielei Zhu, Jiaqing Yang

Consider the two-dimensional inverse elastic wave scattering by an infinite rough surface with a Dirichlet boundary condition. A non-interative sampling technique is proposed for detecting the rough surface by taking elastic wave measurements on a bounded line segment above the surface, based on reconstructing a modified near-field equation associated with a

Konstantinos Dimopoulos, Samuel Sánchez López

We investigate in detail a family of quintessential inflation models in the context of $R+R^2$ Palatini modified gravity. We find that successful inflation and quintessence are obtained with an inflaton scalar potential that is approximately quadratic in inflation and inverse quartic in quintessence. We show that corrections for the kination period due to Pa

Jingxin Zhang, Hao Chen, Songhang Chen, Xia Hong

An improved mixture of probabilistic principal component analysis (PPCA) has been introduced for nonlinear data-driven process monitoring in this paper. To realize this purpose, the technique of a mixture of probabilistic principal component analysers is utilized to establish the model of the underlying nonlinear process with local PPCA models, where a novel

Molla Basir Ahamed, Vasudevarao Allu, Himadri Halder

Let $ \mathcal{H} $ be the class of harmonic functions $ f=h+\bar{g} $ in the unit disk $\mathbb{D}:=\{z\in\mathbb{C} : |z|<1\}$, where $ h $ and $ g $ are analytic in $ \mathbb{D} $. Let $$\mathcal{P}_{\mathcal{H}}^{0}(\alpha)=\{f=h+\overline{g} \in \mathcal{H} : \real (h^{\prime}(z)-\alpha)>|g^{\prime}(z)|\; \mbox{with}\; 0\leq\alpha<1,\; g^{\prime}(0)=0,\

Ioannis Dimitriou

This work deals with the stationary analysis of two-dimensional partially homogeneous nearest-neighbour random walks. Such type of random walks are characterized by the fact that the one-step transition probabilities are functions of the state-space. We show that its stationary behaviour is investigated by solving a finite system of linear equations, two mat

Jian-Feng Cai, Jae Kyu Choi, Jingyang Li, Ke Wei

Recently, mapping a signal/image into a low rank Hankel/Toeplitz matrix has become an emerging alternative to the traditional sparse regularization, due to its ability to alleviate the basis mismatch between the true support in the continuous domain and the discrete grid. In this paper, we introduce a novel structured low rank matrix framework to restore pie

Lei Yi, Shuai Li, Hechuan Jiang, Detlef Lohse

The transient cavity dynamics during water entry of a heavy, non-rotating sphere impacting a rotating pool of liquid is studied experimentally, numerically, and theoretically. We show that the pool rotation advances the transition of the cavity type - from deep seal to surface seal - marked by a reduction in the transitional Froude number. The role of the di

Luca Bottero, Francesco Calisto, Giovanni Graziano, Valerio Pagliarino

The aim of this work is to evaluate the feasibility of re-implementing some key parts of the widely used Weather Research and Forecasting WRF-SFIRE simulator by replacing its core differential equations numerical solvers with state-of-the-art physics-informed machine learning techniques to solve ODEs and PDEs, in order to transform it into a real-time simula

Benoit Baudry, Zimin Chen, Khashayar Etemadi, Han Fu

Software bugs are common and correcting them accounts for a significant part of costs in the software development and maintenance process. This calls for automatic techniques to deal with them. One promising direction towards this goal is gaining repair knowledge from historical bug fixing examples. Retrieving insights from software development history is pa

C. Banerjee, K. Rode, G. Atcheson, S. Lenne

All-optical re-switching has been investigated in the half-metallic Heusler ferrimagnet Mn2Ru0.9Ga, where Mn atoms occupy two inequivalent sites in the XA-type structure. The effect of a second 200 fs 800 nm pump pulse that follows a first pulse, when both are above the threshold for switching, is studied as a function of t12, the time between them. The aims

Markus Borg, Raja Ben Abdessalem, Shiva Nejati, Francois-Xavier Jegeden

The increasing levels of software- and data-intensive driving automation call for an evolution of automotive software testing. As a recommended practice of the Verification and Validation (V&V) process of ISO/PAS 21448, a candidate standard for safety of the intended functionality for road vehicles, simulation-based testing has the potential to reduce both r

Michael Schmitz, André Streicher

Everybody knows from school how to solve a quadratic equation of the form $x^2-px+q=0$ graphically. But this method can become tedious if several equations ought to be solved, as for each pair $(p,q)$ a new parabola has to be drawn. Stunningly, there is one single curve that can be used to solve every quadratic equation via drawing tangent lines through a gi

Alexander Jurisch

We straight-forwardly derive the Onsager-Machlup Lagrangian from the Fokker-Planck equation and show that friction and dissipation are a natural property of the equation of motion. We develop a method to calculate the local variance $\sigma_{2}\,b(q)^{2}$ and identify this function as a Helmholtz-factor. In both meanings the function $b(q)$ describes propert

Marco A Aquino-López, Nicole K. Sanderson, Maarten Blaauw, Joan-Albert Sanchez-Cabeza

The increasing interest in understanding anthropogenic impacts on the environment have led to a considerable number of studies focusing on sedimentary records for the last $\sim$ 100 - 200 years. Dating this period is often complicated by the poor resolution and large errors associated with radiocarbon (14C) ages, which is the most popular dating technique.

O. Ogulcan Tuncer, Ismail Gok

This paper introduces pedal curves of spacelike frontals in the hyperbolic 2-space. We mainly investigate the singularities of these hyperbolic pedal curves of spacelike frontals for non-singular and singular dual curve germs. We then show that for non-singular dual curve germs, the singularities of a pedal curve are dependent on the singularities of the fir

Tomasz Jakubowski, Karol Szczypkowski

We investigate whether a fundamental solution of the Schr\"odinger equation $\partial_t u =(\Delta +V)\, u$ has local in time sharp Gaussian estimates. We compare that class with the class of $V$ for which local in time plain Gaussian estimates hold. We concentrate on $V$ that have fixed sign and we present certain conclusions for $V$ in the Kato class.

Fangqi Li

Diffusion models simulate the propagation of influence in networks. The design and evaluation of diffusion models has been subjective and empirical. When being applied to a network represented by a graph, the diffusion model generates a sequence of edges on which the influence flows, such sequence forms a temporal network. In most scenarios, the statistical

Bin Yan, Xinyu Zhang, Dong Wang, Huchuan Lu

Visual object tracking aims to precisely estimate the bounding box for the given target, which is a challenging problem due to factors such as deformation and occlusion. Many recent trackers adopt the multiple-stage tracking strategy to improve the quality of bounding box estimation. These methods first coarsely locate the target and then refine the initial

Hossein T. Dinani, Enrique Muñoz, Jeronimo R. Maze

Chemical sensors with high sensitivity that can be used in extreme conditions and can be miniaturized are of high interest in science and industry. The Nitrogen-vacancy (NV) center in diamond is an ideal candidate as a nanosensor due to the long coherence time of its electron spin and its optical accessibility. In this theoretical work, we propose to use an

Yu Zhang, Tao Zhou, Wei Wu, Hua Xie

Accurate electroencephalogram (EEG) pattern decoding for specific mental tasks is one of the key steps for the development of brain-computer interface (BCI), which is quite challenging due to the considerably low signal-to-noise ratio of EEG collected at the brain scalp. Machine learning provides a promising technique to optimize EEG patterns toward better d

Bogdan Guster, Miguel Pruneda, Pablo Ordejón, Enric Canadell

NbSe$_3$ and monoclinic-TaS$_3$ ($m$-TaS$_3$) are quasi-1D metals containing three different types of chains and undergoing two different charge density wave (CDW) Peierls transitions at T$_{P_1}$ and T$_{P_2}$. The nature of these transitions is discussed on the basis of first-principles DFT calculation of their electron-hole Lindhard response function. As

Usha N. Bhosle

Let $Y$ be an integral nodal projective curve of arithmetic genus $g\ge 2$ with $m$ nodes defined over an algebraically closed field $k$ and $x$ a nonsingular closed point of $Y$. Let $n$ and $d$ be coprime integers with $n\ge 2$. Fix a line bundle $L$ of degree $d$ on $Y$. Let $U_Y(n,d,L)$ denote the (compactified) "fixed determinant moduli space". We prove

Alberto Blanco-Justicia, Josep Domingo-Ferrer, Sergio Martínez, David Sánchez

Federated learning (FL) allows a server to learn a machine learning (ML) model across multiple decentralized clients that privately store their own training data. In contrast with centralized ML approaches, FL saves computation to the server and does not require the clients to outsource their private data to the server. However, FL is not free of issues. On

Laijin Meng, Xinghao Jiang, Zhenzhen Zhang, Zhaohong Li

Coverless steganography has been a great interest in recent years, since it is a technology that can absolutely resist the detection of steganalysis by not modifying the carriers. However, most existing coverless steganography algorithms select images as carriers, and few studies are reported on coverless video steganography. In fact, video is a securer and

Paolo Leonetti, Michele Caprio

Let $\Phi$ be a correspondence from a normed vector space $X$ into itself, let $u: X\to \mathbf{R}$ be a function, and $\mathcal{I}$ be an ideal on $\mathbf{N}$. Also, assume that the restriction of $u$ on the fixed points of $\Phi$ has a unique maximizer $\eta^\star$. Then, we consider feasible paths $(x_0,x_1,\ldots)$ with values in $X$ such that $x_{n+1}

Hongzhe Zhou, Eric G. Blackman

Despite spatial and temporal fluctuations in turbulent astrophysical systems, mean-field theories can be used to describe their secular evolution. However, observations taken over time scales much shorter than dynamical time scales capture a system in a single state of its turbulence ensemble. Comparing with mean-field theory can falsify the latter only if t

Ning Jiang, Tinggui Wang, Liming Dou, Xinwen Shu

The optical time-domain astronomy has grown rapidly in the past decade but the dynamic infrared sky is rarely explored. Aiming to construct a sample of mid-infrared outburst in nearby galaxies (MIRONG), we have conducted a systematical search of low-redshift ($z<0.35$) SDSS spectroscopic galaxies that have experienced recent MIR flares using their Wide-field

Wesley Joon-Wie Tann, Jackie Tan Jin Wei, Joanna Purba, Ee-Chien Chang

DDoS attacks are simple, effective, and still pose a significant threat even after more than two decades. Given the recent success in machine learning, it is interesting to investigate how we can leverage deep learning to filter out application layer attack requests. There are challenges in adopting deep learning solutions due to the ever-changing profiles,

Manas Bhatnagar, Hailiang Liu

We propose and study a one-dimensional $2\times 2$ hyperbolic Eulerian system with local relaxation from critical threshold phenomena perspective. The system features dynamic transition between strictly and weakly hyperbolic. For different classes of relaxation we identify intrinsic critical thresholds for initial data that distinguish global regularity and

Yujia Wang, Tong Wang, Jiae Yang, Xuebo Yang

Parameter selection is one of the most important parts for nearly all the control strategies. Traditionally, controller parameters are chosen by utilizing trial and error, which is always tedious and time consuming. Moreover, such method is highly dependent on the experience of researchers, which means that it is hard to be popularized. In this light, this p

Li Zeng, Yan Jiang, Weixin Lu, Lei Zou

Subgraph isomorphism is a well-known NP-hard problem which is widely used in many applications, such as social network analysis and knowledge graph query. Its performance is often limited by the inherent hardness. Several insightful works have been done since 2012, mainly optimizing pruning rules and matching orders to accelerate enumerating all isomorphic s

Masahiro Futaki, Hiroshige Kajiura

This is a sequel to our paper arXiv:2008.13462, where we proposed a definition of the Morse homotopy of the moment polytope of toric manifolds. Using this as the substitute of the Fukaya category of the toric manifolds, we proved a version of homological mirror symmetry for the projective spaces and their products via Strominger-Yau-Zaslow construction of th

Srinivas Anumasa, P. K. Srijith

Neural ordinary differential equations (NODEs) treat computation of intermediate feature vectors as trajectories of ordinary differential equation parameterized by a neural network. In this paper, we propose a novel model, delay differential neural networks (DDNN), inspired by delay differential equations (DDEs). The proposed model considers the derivative o

Qing Han, Xumin Jiang, Weiming Shen

We study asymptotic behaviors of solutions to the Loewner-Nirenberg problem in finite cones and establish optimal asymptotic expansions in terms of the corresponding solutions in infinite cones. The spherical domains over which cones are formed are allowed to have singularities. An elliptic operator on such spherical domains with coefficients singular on bou

Ryo Takahashi

Let A be a commutative noetherian ring. Let H(A) be the quotient of the Grothendieck group of finitely generated A-modules by the subgroup generated by pseudo-zero modules. Suppose that the real vector space H(A)_R = H(A) \otimes_Z R has finite dimension. Let C(A) (resp. C_r(A)) be the convex cone in H(A)_R spanned by maximal Cohen-Macaulay A-modules (resp.

Lucas Backes, Davor Dragičević

Given a nonautonomous and nonlinear differential equation \begin{equation}\label{DE} x'=A(t)x+f(t,x) \quad t\geq 0, \end{equation} on an arbitrary Banach space $X$, we formulate very general conditions for the associated linear equation $x'=A(t)x$ and for the nonlinear term $f:[0,+\infty)\times X\to X$ under which the above system satisfies an appropriate ve

Lorenzo Dello Schiavo, Eva Kopfer, Karl-Theodor Sturm

We study random perturbations of Riemannian manifolds $(\mathsf{M},\mathsf{g})$ by means of so-called Fractional Gaussian Fields, which are defined intrinsically by the given manifold. The fields $h^\bullet: \omega\mapsto h^\omega$ will act on the manifolds via conformal transformation $\mathsf{g}\mapsto \mathsf{g}^\omega\colon\!\!= e^{2h^\omega}\,\mathsf{g}

Francesco Sinigaglia, Francisco-Shu Kitaura, Andrés Balaguera-Antolínez, Kentaro Nagamine

This paper investigates the hierarchy of baryon physics assembly bias relations obtained from state-of-the-art hydrodynamic simulations with respect to the underlying cosmic web spanned by the dark matter field. Using the Bias Assignment Method (BAM) we find that non-local bias plays a central role. We classify the cosmic web based on the invariants of the c

Samuel M. Corson

It is shown that the fundamental group of the Griffiths double cone space is isomorphic to that of the triple cone. More generally if $\kappa$ is a cardinal such that $2 \leq \kappa \leq 2^{\aleph_0}$ then the $\kappa$-fold cone has the same fundamental group as the double cone. The isomorphisms produced are non-constructive, and no isomorphism between the f

Weijian Deng, Joshua Marsh, Stephen Gould, Liang Zheng

Motivated by the desire to exploit patterns shared across classes, we present a simple yet effective class-specific memory module for fine-grained feature learning. The memory module stores the prototypical feature representation for each category as a moving average. We hypothesize that the combination of similarities with respect to each category is itself

Alberto Raffero, Luigi Vezzoni

Motivated by M\"uller-Haslhofer results on the dynamical stability and instability of Ricci-flat metrics under the Ricci flow, we obtain dynamical stability and instability results for pairs of Ricci-flat metrics and vanishing 3-forms under the generalized Ricci flow.

Charilaos Mylonas, George Tsialiamanis, Keith Worden, Eleni N. Chatzi

A common shortcoming of vibration-based damage localization techniques is that localized damages, i.e. small cracks, have a limited influence on the spectral characteristics of a structure. In contrast, even the smallest of defects, under particular loading conditions, cause localized strain concentrations with predictable spatial configuration. However, the

Johan Mazoyer, Pauline Arriaga, Justin Hom, Maxwell A. Millar-Blanchaer

Because of bright starlight leakage in coronagraphic raw images, faint astrophysical objects such as exoplanets can only be detected using powerful point spread function (PSF) subtraction algorithms. However, these algorithms have strong effects on faint objects of interest, and often prevent precise spectroscopic analysis and scattering property measurement

Saisubramaniam Gopalakrishnan, Pranshu Ranjan Singh, Haytham Fayek, Savitha Ramasamy

Deep neural networks have shown promise in several domains, and the learned data (task) specific information is implicitly stored in the network parameters. Extraction and utilization of encoded knowledge representations are vital when data is no longer available in the future, especially in a continual learning scenario. In this work, we introduce {\em flas

Umang Bhaskar, A. R. Sricharan, Rohit Vaish

We study the fair allocation of undesirable indivisible items, or chores. While the case of desirable indivisible items (or goods) is extensively studied, with many results known for different notions of fairness, less is known about the fair division of chores. We study the envy-free division of chores, and make three contributions. First, we show that dete

Yunus Camgözlü, Yakup Kutlu

With the use of deep learning in many areas, how to improve this technology or how to develop the structure used more effectively and in a shorter time is an issue that is of interest to many people working in this field. Many studies are carried out on this subject, it is aimed to reduce the duration of the operation and the processing power required, excep

Inna Kaganowa, Boris Kryzhanovsky, Leonid Litinskii

The previously developed n-vicinity method allows us to calculate accurately critical values of inverse temperatures for Ising models with short-range interaction. We generalize the method to the case of long-range interactions in spin systems and obtain theoretical formulas for the inverse temperatures in terms of the spin interaction constants. The compari

Erbol Zhanpeisov

We present a blow-up rate estimate for a solution to the parabolic Gross-Pitaevskii and related systems on entire space with Sobolev subcritical nonlinearity. We extend the results of [Y. Giga, S. Matsui and S. Sasayama, Indiana Univ. Math. J. {53} (2004), 483--514] to the parabolic systems.

Matthieu Lin, Chuming Li, Xingyuan Bu, Ming Sun

Pedestrian detection in crowd scenes poses a challenging problem due to the heuristic defined mapping from anchors to pedestrians and the conflict between NMS and highly overlapped pedestrians. The recently proposed end-to-end detectors(ED), DETR and deformable DETR, replace hand designed components such as NMS and anchors using the transformer architecture,

Lorenzo Fassina, Alessandro Faragli, Francesco Paolo Lo Muzio, Sebastian Kelle

Heart failure (HF) affects at least 26 million people worldwide, so predicting adverse events in HF patients represents a major target of clinical data science. However, achieving large sample sizes sometimes represents a challenge due to difficulties in patient recruiting and long follow-up times, increasing the problem of missing data. To overcome the issu

Fernando Albiac, Jose L. Ansorena

We provide a new extension of Pitt's theorem for compact operators between quasi-Banach lattices, which permits to describe unconditional bases of finite direct sums of Banach spaces $\mathbb{X}_{1}\oplus\dots\oplus\mathbb{X}_{n}$ as direct sums of unconditional bases of its summands. The general splitting principle we obtain yields, in particular, that if e

Manali Saini, Udit Satija, Madhur Deo Upadhayay

Mental task identification and classification using single/limited channel(s) electroencephalogram (EEG) signals in real-time play an important role in the design of portable brain-computer interface (BCI) and neurofeedback (NFB) systems. However, the real-time recorded EEG signals are often contaminated with noises such as ocular artifacts (OAs) and muscle

S. Aljbaae, D. M. Sanchez, A. F. B. A. Prado, J. Souchay

We aim at providing a preliminary approach on the dynamics of a spacecraft in orbit about the asteroid (99942) Apophis during its Earth close approach. The physical properties from the polyhedral shape of the target are derived assigning each tetrahedron to a point mass in its center. That considerably reduces the computation processing time compared to prev

Fuzhao Xue, Aixin Sun, Hao Zhang, Eng Siong Chng

Relation Extraction (RE) is to predict the relation type of two entities that are mentioned in a piece of text, e.g., a sentence or a dialogue. When the given text is long, it is challenging to identify indicative words for the relation prediction. Recent advances on RE task are from BERT-based sequence modeling and graph-based modeling of relationships amon

Olaf Beyersdorff, Joshua Blinkhorn, Meena Mahajan, Tomáš Peitl

We prove the first genuine QBF proof size lower bounds for the proof system Merge Resolution (MRes [Olaf Beyersdorff et al., 2020]), a refutational proof system for prenex quantified Boolean formulas (QBF) with a CNF matrix. Unlike most QBF resolution systems in the literature, proofs in MRes consist of resolution steps together with information on countermo

Leonid Litinskii, Boris Kryzhanovsky

In the present paper, we examine Ising systems on d-dimensional hypercube lattices and solve an inverse problem where we have to determine interaction constants of an Ising connection matrix when we know a spectrum of it eigenvalues. In addition, we define restrictions allowing a random number sequence to be a connection matrix spectrum. We use the previousl

Berk Kaya, Suryansh Kumar, Carlos Oliveira, Vittorio Ferrari

This paper presents an uncalibrated deep neural network framework for the photometric stereo problem. For training models to solve the problem, existing neural network-based methods either require exact light directions or ground-truth surface normals of the object or both. However, in practice, it is challenging to procure both of this information precisely

Paula Reichert

We analyze the points of total collision of the Newtonian gravitational system on shape space (the relational configuration space of the system). While the Newtonian equations of motion, formulated with respect to absolute space and time, are singular at the point of total collision due to the singularity of the Newton potential at that point, this need not

Jun Dai, Haisheng Ji, Leping Li, Jun Zhang

We present the formation and eruption of a sigmoidal filament driven by rotating network magnetic fields (RNFs) near the center of the solar disk, which was observed by the one-meter aperture New Vacuum Solar Telescope (NVST) at Fuxian Solar Observatory (FSO) on 2018 July 12. Counterclockwise RNFs twist two small-scale filaments at their northeastern foot-po

Andrea Di Domenico, Gianluca Perna, Martino Trevisan, Luca Vassio

Cloud gaming is a new class of services that promises to revolutionize the videogame market. It allows the user to play a videogame with basic equipment while using a remote server for the actual execution. The multimedia content is streamed through the network from the server to the user. This service requires low latency and a large bandwidth to work prope

Thomas G. Bisbas, Jonathan C. Tan, Kei E. I. Tanaka

We present three-dimensional astrochemical simulations and synthetic observations of magnetised, turbulent, self-gravitating molecular clouds. We explore various galactic interstellar medium environments, including cosmic-ray ionization rates in the range of $\zeta_{\rm CR}=10^{-17}$-$10^{-14}\,{\rm s}^{-1}$, far-UV intensities in the range of $G_0=1$-$10^3$

Radu Horaud, Miles Hansard, Georgios Evangelidis, Clement Menier

Time-of-flight (TOF) cameras are sensors that can measure the depths of scene-points, by illuminating the scene with a controlled laser or LED source, and then analyzing the reflected light. In this paper, we will first describe the underlying measurement principles of time-of-flight cameras, including: (i) pulsed-light cameras, which measure directly the ti

Awadelrahman M. A. Ali Ahmed

This paper aims to contribute in bench-marking the automatic polyp segmentation problem using generative adversarial networks framework. Perceiving the problem as an image-to-image translation task, conditional generative adversarial networks are utilized to generate masks conditioned by the images as inputs. Both generator and discriminator are convolution

Jatin Sharma, Shobha Lata

Deep Neural Networks are the basic building blocks of modern Artificial Intelligence. They are increasingly replacing or augmenting existing software systems due to their ability to learn directly from the data and superior accuracy on variety of tasks. Existing Software Development Life Cycle (SDLC) methodologies fall short on representing the unique capabi

Jean-Marc Ginoux

Slow-fast dynamical systems, i.e., singularly or non-singularly perturbed dynamical systems possess slow invariant manifolds on which trajectories evolve slowly. Since the last century various methods have been developed for approximating their equations. This paper aims, on the one hand, to propose a classification of the most important of them into two gre

Georgios D. Evangelidis, Miles Hansard, Radu Horaud

This paper addresses the problem of range-stereo fusion, for the construction of high-resolution depth maps. In particular, we combine low-resolution depth data with high-resolution stereo data, in a maximum a posteriori (MAP) formulation. Unlike existing schemes that build on MRF optimizers, we infer the disparity map from a series of local energy minimizat

Nicolas Capitelli, Melina Privitelli

In this note we discuss a theory of combinatorial games that involve transmitting the moves through a noisy channel that can introduce errors during the transmission. Players are aware of this interference and incorporate this variable into the game: the valid move is the received one, regardless of whether it is the other player's sent move (as long as it i