Miracle-dz/UniFAR-FTUs huggingface dataset:
Deblending and classifying astronomical sources with Mask R-CNN deep learning

We apply a new deep learning technique to detect, classify, and deblend sources in multiband astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask Region-based Convolutional Neural Network image processing framework, a general code for efficient object detection, classification, and instance segmentation. After evaluating the performance of our network against simulated ground truth images for star and galaxy classes, we find a precision of 92 per cent at 80 per cent recall for stars and a precision of 98 per cent at 80 per cent recall for galaxies in a typical field with ∼30 galaxies arcmin−2. We investigate the deblending capability of our code, and find that clean deblends are handled robustly during object masking, even for significantly blended sources. This technique, or extensions using similar network architectures, may be applied to current and future deep imaging surveys such as Large Synoptic Survey Telescope and Wide-Field Infrared Survey Telescope. Our code, astro r-cnn, is publicly available at https://github.com/burke86/astro_rcnn.
3 things they don’t tell you about star clusters

Abstract Dense stellar systems in general and star clusters in particular have recently regained the interest of the extragalactic and even cosmology communities, due to the role they could play as actors and probes of re-ionization, galactic archeology and the dark matter content of galaxies, among many others. In the era of the exploitation and the preparation of large stellar surveys (Gaia, APOGEE, 4MOST, WEAVE), of the detection of gravitational waves mostly originating from dense regions like the cores of clusters (Ligo, LISA), and in an always more holistic view of galaxy formation (HARMONI, Euclid, LSST†), a complete theory on the formation and evolution of clusters is needed to interpret the on-going and forthcoming data avalanche. In this context, the community carries an effort to model the aspects of star cluster formation and evolution in galactic and even cosmological context. However, it is not always easy to understand the caveats and the shortcuts taken in theories and simulations, and their implications on the conclusions drawn. I take the opportunity of this document to highlight three of these topics and discuss why some shortcuts taken by the community are or could be misleading.
Byzantine Approximate Agreement on Graphs

Consider a distributed system with $n$ processors out of which $f$ can be Byzantine faulty. In the approximate agreement task, each processor $i$ receives an input value $x_i$ and has to decide on an output value $y_i$ such that - the output values are in the convex hull of the non-faulty processors' input values, - the output values are within distance $d$ of each other. Classically, the values are assumed to be from an $m$-dimensional Euclidean space, where $m \ge 1$. In this work, we study the task in a discrete setting, where input values with some structure expressible as a graph. Namely, the input values are vertices of a finite graph $G$ and the goal is to output vertices that are within distance $d$ of each other in $G$, but still remain in the graph-induced convex hull of the input values. For $d=0$, the task reduces to consensus and cannot be solved with a deterministic algorithm in an asynchronous system even with a single crash fault. For any $d \ge 1$, we show that the task is solvable in asynchronous systems when $G$ is chordal and $n > (\omega+1)f$, where $\omega$ is the clique number of~$G$. In addition, we give the first Byzantine-tolerant algorithm for a variant of lattice agreement. For synchronous systems, we show tight resilience bounds for the exact variants of these and related tasks over a large class of combinatorial structures.
Option Pricing Under Power Laws: A Robust Heuristic

We build a heuristic that takes a given option price in the tails with strike K and extends (for calls, all strikes > K, for put all strikes < K) assuming the continuation falls into what we define as "Karamata Constant" over which the strong Pareto law holds. The heuristic produces relative prices for options, with for sole parameter the tail index alpha under some mild arbitrage constraints. Usual restrictions such as finiteness of variance are not required. The heuristic allows us to scrutinize the volatility surface and test theories of relative tail option mispricing and overpricing usually built on thin tailed models and modification of the Black-Scholes formula.
Multi-Robot Path Deconfliction through Prioritization by Path Prospects

This work deals with the problem of planning conflict-free paths for mobile robots in cluttered environments. Since centralized, coupled planning algorithms are computationally intractable for large numbers of robots, we consider decoupled planning, in which robots plan their paths sequentially in order of priority. Choosing how to prioritize the robots is a key consideration. State-of-the-art prioritization heuristics, however, do not model the coupling between a robot’s mobility and its environment. This is particularly relevant when prioritizing between robots with different degrees of mobility. In this paper, we propose a prioritization rule that can be computed online by each robot independently, and that provides consistent, conflict-free path plans. Our innovation is to formalize a robot’s path prospects to reach its goal from its current location. To this end, we consider the number of homology classes of trajectories, which capture distinct prospects of paths for each robot. This measure is used as a prioritization rule, whenever any robots enter negotiation to deconflict path plans. We perform simulations with heterogeneous robot teams and compare our method to five benchmarks. Our method achieves the highest success rate, and strikes a good balance between makespan and flowtime objectives.
DpgMedia2019: A Dutch News Dataset for Partisanship Detection

We present a new Dutch news dataset with labeled partisanship. The dataset contains more than 100K articles that are labeled on the publisher level and 776 articles that were crowdsourced using an internal survey platform and labeled on the article level. In this paper, we document our original motivation, the collection and annotation process, limitations, and applications.
Valence-quark distributions of pions and kaons in a nuclear medium

In-medium valence-quark distributions of $\pi^+$ and $K^+$ mesons in symmetric nuclear matter are studied by combining the Nambu--Jona-Lasinio model and the quark-meson coupling model. The in-medium properties of the current quarks, which are used as inputs for studying the in-medium pion and kaon properties in the Nambu--Jona-Lasinio model, are calculated within the quark-meson coupling model. The light-quark condensates, light-quark dynamical masses, pion and kaon decay constants, and pion- and kaon-quark coupling constants are found to decrease as nuclear density increases. The obtained valence quark distributions in vacuum for both the $\pi^+$ and $K^+$ could reasonably describe the available experimental data over a wide range of Bjorken-$x$. The in-medium valence $u$-quark distribution in the $\pi^+$ at $Q^2=16~\mbox{GeV}^2$ is found to be almost unchanged compared to the in-vacuum case. However, the in-medium to in-vacuum ratios of both the valence $u$-quark and valence $s$-quark distributions of the $K^+$ meson at $Q^2=16~\mbox{GeV}^2$ increase with nuclear matter density, but show different $x$-dependence. Namely, the ratio for the valence $u$-quark distribution increases with $x$, while that for the valence $s$ quark decreases with $x$. These features are enhanced at higher density regions.
Unified Simulation and Test Platform for Control Systems of Unmanned Vehicles

Control systems on unmanned vehicles are safety-critical systems whose requirements on reliability and safety are ever-increasing. Currently, testing a complex autonomous control system is an expensive and time-consuming process, which requires massive repeated experimental testing during the whole development stage. This paper presents a unified simulation and test platform for vehicle autonomous control systems aiming to significantly improve the development speed and safety level of unmanned vehicles. First, a unified modular modeling framework compatible with different types of vehicles is proposed with methods to ensure modeling credibility. Then, the simulation software system is developed by the model-based design framework, whose modular programming methods and automatic code generation functions ensure the efficiency, credibility, and standardization of the system development process. Finally, an FPGA-based real-time hardware-in-the-loop simulation platform is proposed to ensure the comprehensiveness and credibility of the simulation and test results. In the end, the proposed platform is applied to a multicopter control system. By comparing with experimental results, the accuracy and credibility of the simulation testing results are verified by using the simulation credibility assessment method proposed in our previous work. To verify the practicability of the proposed platform, several successful applications are presented for the multicopter rapid prototyping, estimation algorithm verification, autonomous flight testing, and automatic safety testing with automatic fault injection and result evaluation of unmanned vehicles.
Cheetah: Mixed Low-Precision Hardware & Software Co-Design Framework for DNNs on the Edge

Low-precision DNNs have been extensively explored in order to reduce the size of DNN models for edge devices. Recently, the posit numerical format has shown promise for DNN data representation and compute with ultra-low precision in [5.8]-bits. However, previous studies were limited to studying posit for DNN inference only. In this paper, we propose the Cheetah framework, which supports both DNN training and inference using posits, as well as other commonly used formats. Additionally, the framework is amenable for different quantization approaches and supports mixed-precision floating point and fixed-point numerical formats. Cheetah is evaluated on three datasets: MNIST, Fashion MNIST, and CIFAR-10. Results indicate that 16-bit posits outperform 16-bit floating point in DNN training. Furthermore, performing inference with [5.8]-bit posits improves the trade-off between performance and energy-delay-product over both [5.8]-bit float and fixed-point.
Quaternary compounds Ag2XYSe4 (X  =  Ba, Sr; Y  =  Sn, Ge) as novel potential thermoelectric materials

Experimental results have shown that the quaternary compound Cu2ZnSnSe4 is an excellent thermoelectric (TE) material. This inspires us to seek the other quaternary compounds with similar chemical formula to Cu2ZnSnSe4 as TE materials. In this paper, we use the first-principle method to systematically explore the electronic and phonon structures, mechanical, thermal and TE properties of p- and n-type Ag2XYSe4 (X = Ba, Sr; Y = Sn, Ge). It is found that the ZT maximum for n-type Ag2SrGeSe4 can reach up to 1.22 at 900 K, and those for p-type Ag2SrSnSe4, Ag2SrGeSe4 and Ag2BaSnSe4 can reach up to 1.20, 1.13 and 1.12, respectively. Our work not only shows that Ag2XYSe4 (X = Ba, Sr; Y = Sn, Ge) are a kind of potential TE material, but also can inspire more theoretical and experimental research on the TE properties of quaternary compounds.
Maximum likelihood convolutional beamformer for simultaneous denoising and dereverberation

This article describes a probabilistic formulation of a Weighted Power minimization Distortionless response convolutional beamformer (WPD). The WPD unifies a weighted prediction error based dereverberation method (WPE) and a minimum power distortionless response beamformer (MPDR) into a single convolutional beamformer, and achieves simultaneous dereverberation and denoising in an optimal way. However, the optimization criterion is obtained simply by combining existing criteria without any clear theoretical justification. This article presents a generative model and a probabilistic formulation of a WPD, and derives an optimization algorithm based on a maximum likelihood estimation. We also describe a method for estimating the steering vector of the desired signal by utilizing WPE within the WPD framework to provide an effective and efficient beamformer for denoising and dereverberation.
LRG-BEASTS: Transmission Spectroscopy and Retrieval Analysis of the Highly Inflated Saturn-mass Planet WASP-39b

We present a ground-based transmission spectrum and comprehensive retrieval analysis of the highly inflated Saturn-mass planet WASP-39b. We obtained low-resolution spectra (R ≈ 400) of a transit of WASP-39b using the ACAM instrument on the 4.2 m William Herschel Telescope as part of the LRG-BEASTS survey. Our transmission spectrum is in good agreement with previous ground- and space-based observations of WASP-39b, and covers a wavelength range of 4000–9000 Å. Previous analyses of this exoplanet have retrieved water abundances that span more than four orders of magnitude, which in turn lead to conclusions of a subsolar or highly supersolar atmospheric metallicity. In order to determine the cause of the large discrepancies in the literature regarding WASP-39b’s atmospheric metallicity, we performed retrieval analyses of all literature data sets. Our retrievals, which assume equilibrium chemistry, recovered highly supersolar metallicities for all data sets. When running our retrievals on a combined spectrum, spanning 0.3–5 μm, we recovered an atmospheric metallicity of × solar. We find that stellar activity has a negligible effect on the derived abundances and instead conclude that different assumptions made during retrieval analyses lead to the reported water abundances that differ by orders of magnitude. This in turn has significant consequences for the conclusions we draw. This is the fourth planet to be observed as part of the LRG-BEASTS survey, which is demonstrating that 4 m class telescopes can obtain low-resolution transmission spectra with precisions of around one atmospheric scale height.
Towards a Comprehensive Bibliography for SETI

In this work, we motivate, describe, and announce a living bibliography for academic papers and other works published in the Search for Extraterrestrial Intelligence (SETI). The bibliography makes use of bibliographic groups (bibgroups) in the NASA Astrophysics Data System (ADS), allowing it to be accessed and searched by any interested party, and is composed only of works which have a presence on the ADS. We establish criteria that describe the scope of our bibliography, which we define as any academic work which broadly: 1) advances knowledge within SETI, 2) deals with topics that are fundamentally related to or about SETI, or 3) is useful for the better understanding of SETI, and which has a presence on ADS. We discuss the future work needed to continue the development of the bibliography. The bibliography can be found by using the bibgroup field (bibgroup: SETI) in the ADS search engine.
Open Dataset of Phishing and Tor Hidden Services Screen-captures

Security analysts need to classify, search and correlate numerous images. Automatic classification tools improve the efficiency of such tasks. However, the main resources to develop these tools are datasets, which are introduced and provided by the present paper, for the specific cases of visual correlation of phishing and onion websites. CIRCL's Open-Source tools are the sources of these screenshots, which had been manually verified against personal information leaks. Usage examples of these datasets are proposed in the current paper. These researches directions are, however, not the main contribution of the paper. The main contribution is the availability of the two datasets.
Rogue quantum gravitational waves

In this paper, we propose the existence and discuss the properties of rogue quantum gravitational waves. More specifically, we numerically solve the Schr\"odinger-Newton system of equations using a spectral scheme with a $4^{th}$ order Runge-Kutta time integrator and show that noise either imposed on wave function $\Psi$, or the gravitational field $\Phi$, triggers the modulation instability which turns the monochromatic wave fields into chaotic ones exhibiting high and unexpected waves. Such waves can be named as rogue quantum gravitational waves. We discuss the characteristics and probabilities of occurrences of such rogue waves in the frame of the Schr\"odinger-Netwon equations. We suggest alternative methods for studying rogue quantum gravitational waves and rogue gravitational waves.
An Algorithm for Graph-Fused Lasso Based on Graph Decomposition

This work proposes a new algorithm for solving the graph-fused lasso (GFL), a method for parameter estimation that operates under the assumption that the signal tends to be locally constant over a predefined graph structure. The proposed method applies the alternating direction method of multipliers (ADMM) algorithm and is based on the decomposition of the objective function into two components. While ADMM has been widely used in this problem, existing works such as network lasso decompose the objective function into the loss function component and the total variation penalty component. In comparison, this work proposes to decompose the objective function into two components, where one component is the loss function plus part of the total variation penalty, and the other component is the remaining total variation penalty. Compared with the network lasso algorithm, this method has a smaller computational cost per iteration and converges faster in most simulations numerically.
Proximal Point Methods for Optimization with Nonconvex Functional Constraints

Nonconvex optimization is becoming more and more important in machine learning and operations research. In spite of recent progresses, the development of provably efficient algorithm for optimization with nonconvex functional constraints remains open. Such problems have potential applications in risk-averse machine learning, semisupervised learning and robust optimization among others. In this paper, we introduce a new proximal point type method for solving this important class of nonconvex problems by transforming them into a sequence of convex constrained subproblems. We establish the convergence and rate of convergence of this algorithm to the KKT point under different types of constraint qualifications. In particular, we prove that our algorithm will converge to an $\epsilon$-KKT point in $O(1/\epsilon)$ iterations under a properly defined condition. For practical use, we present inexact variants of this approach, in which approximate solutions of the subproblems are computed by either primal or primal-dual type algorithms, and establish their associated rate of convergence. To the best of our knowledge, this is the first time that proximal point type method is developed for nonlinear programing with nonconvex functional constraints, and most of the convergence and complexity results seem to be new in the literature.
Scalable Inference of System-level Models from Component Logs

Behavioral software models play a key role in many software engineering tasks; unfortunately, these models either are not available during software development or, if available, they quickly become outdated as the implementations evolve. Model inference techniques have been proposed as a viable solution to extract finite-state models from execution logs. However, existing techniques do not scale well when processing very large logs, such as system-level logs obtained by combining component-level logs. Furthermore, in the case of component-based systems, existing techniques assume to know the definitions of communication channels between components. However, this information is usually not available in the case of systems integrating 3rd-party components with limited documentation. In this paper, we address the scalability problem of inferring the model of a component-based system from the individual component-level logs, when the only available information about the system are high-level architecture dependencies among components and a (possibly incomplete) list of log message templates denoting communication events between components. Our model inference technique, called SCALER, follows a divide and conquer approach. The idea is to first infer a model of each system component from the corresponding logs; then, the individual component models are merged together taking into account the dependencies among components, as reflected in the logs. We evaluated SCALER in terms of scalability and accuracy, using a dataset of logs from an industrial system; the results show that SCALER can process much larger logs than a state-of-the-art tool, while yielding more accurate models.
Multiplex graph matching matched filters

We consider the problem of detecting a noisy induced multiplex template network in a larger multiplex background network. Our approach, which extends the graph matching matched filter framework of Sussman et al. (IEEE Trans Pattern Anal Mach Intell 42(11):2887–2900, 2019) to the multiplex setting, utilizes a multiplex analogue of the classical graph matching problem to use the template as a matched filter for efficiently searching the background for candidate template matches. The effectiveness of our approach is demonstrated both theoretically and empirically, with particular attention paid to the potential benefits of considering multiple channels in the multiplex framework.
Leader–Follower Network Aggregative Game With Stochastic Agents’ Communication and Activeness

This article presents a leader–follower scheme for network aggregative games. The followers and leader are selfish cost minimizing agents. The cost function of each follower is affected by its own strategy, the strategy of leader and the aggregate strategy of its neighbors through a communication graph. Also, the leader's cost function depends on its own strategy and the aggregate strategy of all the followers. The leader infinitely often wakes up, receives the aggregate strategy of the followers, updates its decision value, and broadcasts it to all the followers. Then, the followers apply the updated strategy of the leader into their cost functions. The establishment of information exchange between each neighboring pair of followers, and also, decision updating by a follower at each iteration, that is to say the activeness of the follower in that iteration, are both considered to be drawn from two arbitrary distributions. Moreover, a distributed algorithm based on subgradient method is proposed for updating the strategies of leader and followers. The convergence of the proposed algorithm to the unique Stackelberg equilibrium point of the game is proven in both almost sure and mean square senses.
Application of Virtual Reality Simulators and Virtual Labs in Medical Education

Application of Virtual Reality Simulators and Virtual Labs in Medical Education Majid Zare Bidaki Social Determinants of Health Research Center, Medical Microbiology Department, Faculty of Paramedical Sciences, Birjand University of Medical Sciences, Birjand, Iran Corresponding author: Majid Zare Bidaki, Social Determinants of Health Research Center, Medical Microbiology Department, Faculty of Paramedical Sciences, Birjand University of Medical Sciences, Birjand, Iran. E-mail: m.zare@live.co.uk