The commercialization of autonomous machines is a thriving sector, and likely to be the next major computing demand driver, after PC, cloud computing, and mobile computing. Nevertheless, a suitable computer architecture for autonomous machines is mis
sing, and many companies are forced to develop ad hoc computing solutions that are neither scalable nor extensible. In this article, we analyze the demands of autonomous machine computing, and argue for the promise of dataflow architectures in autonomous machines.
In the fourth paper of this series, we present the metallicity-dependent Sloan Digital Sky Survey (SDSS) stellar color loci of red giant stars, using a spectroscopic sample of red giants in the SDSS Stripe 82 region. The stars span a range of 0.55 --
1.2 mag in color g-i, -0.3 -- -2.5 in metallicity [Fe/H], and have values of surface gravity log g smaller than 3.5 dex. As in the case of main-sequence (MS) stars, the intrinsic widths of loci of red giants are also found to be quite narrow, a few mmag at maximum. There are however systematic differences between the metallicity-dependent stellar loci of red giants and MS stars. The colors of red giants are less sensitive to metallicity than those of MS stars. With good photometry, photometric metallicities of red giants can be reliably determined by fitting the u-g, g-r, r-i, and i-z colors simultaneously to an accuracy of 0.2 -- 0.25 dex, comparable to the precision achievable with low-resolution spectroscopy for a signal-to-noise ratio of 10. By comparing fitting results to the stellar loci of red giants and MS stars, we propose a new technique to discriminate between red giants and MS stars based on the SDSS photometry. The technique achieves completeness of ~ 70 per cent and efficiency of ~ 80 per cent in selecting metal-poor red giant stars of [Fe/H] $le$ -1.2. It thus provides an important tool to probe the structure and assemblage history of the Galactic halo using red giant stars.
Basing on the large volume textit{Gaia} Early Data Release 3 and LAMOST Data Release 5 data, we estimate the bias-corrected binary fractions of the field late G and early K dwarfs. A stellar locus outlier method is used in this work, which works well
for binaries of various periods and inclination angles with single epoch data. With a well-selected, distance-limited sample of about 90 thousand GK dwarfs covering wide stellar chemical abundances, it enables us to explore the binary fraction variations with different stellar populations. The average binary fraction is 0.42$pm$0.01 for the whole sample. Thin disk stars are found to have a binary fraction of 0.39$pm$0.02, thick disk stars own a higher one of 0.49$pm$0.02, while inner halo stars possibly own the highest binary fraction. For both the thin and thick disk stars, the binary fractions decrease toward higher [Fe/H], [$alpha$/H], and [M/H] abundances. However, the suppressing impacts of the [Fe/H], [$alpha$/H], and [M/H] are more significant for the thin disk stars than those for the thick disk stars. For a given [Fe/H], a positive correlation between [$alpha$/Fe] and the binary fraction is found for the thin disk stars. However, this tendency disappears for the thick disk stars. We suspect that it is likely related to the different formation histories of the thin and thick disks. Our results provide new clues for theoretical works on binary formation.
Researchers are increasingly focusing on intelligent games as a hot research area.The article proposes an algorithm that combines the multi-attribute management and reinforcement learning methods, and that combined their effect on wargaming, it solve
s the problem of the agents low rate of winning against specific rules and its inability to quickly converge during intelligent wargame training.At the same time, this paper studied a multi-attribute decision making and reinforcement learning algorithm in a wargame simulation environment, and obtained data on red and blue conflict.Calculate the weight of each attribute based on the intuitionistic fuzzy number weight calculations. Then determine the threat posed by each opponents chess pieces.Using the red side reinforcement learning reward function, the AC framework is trained on the reward function, and an algorithm combining multi-attribute decision-making with reinforcement learning is obtained. A simulation experiment confirms that the algorithm of multi-attribute decision-making combined with reinforcement learning presented in this paper is significantly more intelligent than the pure reinforcement learning algorithm.By resolving the shortcomings of the agents neural network, coupled with sparse rewards in large-map combat games, this robust algorithm effectively reduces the difficulties of convergence. It is also the first time in this field that an algorithm design for intelligent wargaming combines multi-attribute decision making with reinforcement learning.Attempt interdisciplinary cross-innovation in the academic field, like designing intelligent wargames and improving reinforcement learning algorithms.
The decays $K^{ast}(892)rightarrow K_{S,L}^{0}pi$ can be used to study CP violation and CPT violation. The $K^{ast}(892)$ meson can be produced via $J/psi$ decays at BESIII. In this paper, we study CP violation and CPT violation in $K^{ast}(892)right
arrow K_{S,L}^{0}pi$ decays at BESIII. Basing on two cases: the samples of $10^{10}$ and $10^{12}$ $J/psi$ events, we calculate the expected numbers of the observed signal events on the CP violation in $J/psi$ decays with $K^{ast}(892)$ meson in the final states, we find that the BESIII experiment may be able to unambiguously observe CP violation for each of these two cases. Under the assumption that the observed event on CPT violation is absent, we discuss the upper limits on the absolute value of the CPT violation parameter $Re(z)$ in $J/psi$ decays involving $K^{ast}(892)$ meson in the final states with $10^{10}$ $J/psi$ events and $10^{12}$ $J/psi$ events, respectively. By using the accumulated $10^{10}$ $J/psi$ events, the upper limits on $left|Re(z)right|$ can be obtained at the $90%$ confidence level of $10^{-4}-10^{-5}$, which is competitive with the current best result, if the detection efficiency $varepsilon_{K^0_{L}}$ is assumed to be at the level of $8times 10^{-3}$ at BESIII. And besides, the upper limits on $left|Re(z)right|$ will be improved by about two orders of magnitude compared with the current best result with $10^{12}$ $J/psi$ events.
A self-interacting dark matter halo can experience gravothermal collapse, resulting in a central core with an ultrahigh density. It can further contract and collapse into a black hole, a mechanism proposed to explain the origin of supermassive black
holes. We study dynamical instability of the core in general relativity. We use a truncated Maxwell-Boltzmann distribution to model the dark matter distribution and solve the Tolman-Oppenheimer-Volkoff equation. For given model parameters, we obtain a series of equilibrium configurations and examine their dynamical instability based on considerations of total energy, binding energy, fractional binding energy, and adiabatic index. The numerical results from our semi-analytical method are in good agreement with those from fully relativistic N-body simulations. We further show for the instability to occur in the classical regime, the boundary temperature of the core should be at least $10%$ of the mass of dark matter particles; for a $10^9~{rm M_odot}$ seed black hole, the particle mass needs to be larger than a few keV. These results can be used to constrain different collapse models, in particular, those with dissipative dark matter interactions.
Understanding the origins of small-scale flats of CCDs and their wavelength-dependent variations plays an important role in high-precision photometric, astrometric, and shape measurements of astronomical objects. Based on the unique flat data of 47 n
arrow-band filters provided by JPAS-{it Pathfinder}, we analyze the variations of small-scale flats as a function of wavelength. We find moderate variations (from about $1.0%$ at 390 nm to $0.3%$ at 890 nm) of small-scale flats among different filters, increasing towards shorter wavelengths. Small-scale flats of two filters close in central wavelengths are strongly correlated. We then use a simple physical model to reproduce the observed variations to a precision of about $pm 0.14%$, by considering the variations of charge collection efficiencies, effective areas and thicknesses between CCD pixels. We find that the wavelength-dependent variations of small-scale flats of the JPAS-{it Pathfinder} camera originate from inhomogeneities of the quantum efficiency (particularly charge collection efficiency) as well as the effective area and thickness of CCD pixels. The former dominates the variations in short wavelengths while the latter two dominate at longer wavelengths. The effects on proper flat-fielding as well as on photometric/flux calibrations for photometric/slit-less spectroscopic surveys are discussed, particularly in blue filters/wavelengths. We also find that different model parameters are sensitive to flats of different wavelengths, depending on the relations between the electron absorption depth, the photon absorption length and the CCD thickness. In order to model the wavelength-dependent variations of small-scale flats, a small number (around ten) of small-scale flats with well-selected wavelengths are sufficient to reconstruct small-scale flats in other wavelengths.
The publics attitudes play a critical role in the acceptance, purchase, use, and research and development of autonomous vehicles (AVs). To date, the publics attitudes towards AVs were mostly estimated through traditional survey data with high labor c
osts and a low quantity of samples, which also might be one of the reasons why the influencing factors on the publics attitudes of AVs have not been studied from multiple aspects in a comprehensive way yet. To address the issue, this study aims to propose a method by using large-scale social media data to investigate key factors that affect the publics attitudes and acceptance of AVs. A total of 954,151 Twitter data related to AVs and 53 candidate independent variables from seven categories were extracted using the web scraping method. Then, sentiment analysis was used to measure the public attitudes towards AVs by calculating sentiment scores. Random forests algorithm was employed to preliminarily select candidate independent variables according to their importance, while a linear mixed model was performed to explore the impacting factors considering the unobserved heterogeneities caused by the subjectivity level of tweets. The results showed that the overall attitude of the public on AVs was slightly optimistic. Factors like drunk, blind spot, and mobility had the largest impacts on public attitudes. In addition, people were more likely to express positive feelings when talking about words such as lidar and Tesla that relate to high technologies. Conversely, factors such as COVID-19, pedestrian, sleepy, and highway were found to have significantly negative effects on the publics attitudes. The findings of this study are beneficial for the development of AV technologies, the guidelines for AV-related policy formulation, and the publics understanding and acceptance of AVs.
The large data sample of the $B_c$ meson collected at the LHC experiment and the HL-LHC experiment provides us the opportunity to study the $B_c$ decays and the related physics. In this paper, we investigate the effect of $K^0-bar{K}^0$ mixing on the
the branching ratios, CP violations and CPT violations in the $B_{c}^{pm}rightarrow B^{pm} K_{S,L}^{0}$ decays. We find that some of the $B_c^{pm}rightarrow B^{pm} K_{S,L}^0rightarrow f_{B^{pm}} f_{K_{S,L}^0}$ decay chains have large branching ratios, whose maximum value can exceed the order of $10^{-6}$, the minimum number of $B_c^pm$ events times efficiency for observing the decays at three standard deviations (3$sigma$) level is about $ 10^6$. We study the CP asymmetries in the $B_c^{pm}rightarrow B^{pm} K_{S,L}^0$ decays and find that the CP asymmetries can exceed the order of $10^{-3}$, which are dominated by $K^0-bar{K}^0$ mixing. We give the most promising processes to observe the CP violations and the ranges of the numbers of $B_c^pm$ events-times-efficiency needed to observe the CP asymmetries at a significance of 3$sigma$ in these decays. We investigate the possibility to constraint the CPT violation parameter $Re(z)$ in the $B_c^{pm}rightarrow B^{pm} K_{S,L}^0rightarrow f_{B^{pm}} f_{K_{S,L}^0}$ decays and give the most promising processes to extract the parameter $Re(z)$. We find that the sensitivity for the measurement of parameter $Re(z)$ can reach below $10^{-3}$ in the most promising decays if we assume the selection efficiency is $10^{-3}$ and the total number of $B_c^{pm}$ events collected by the LHCb experiment is $10^{12}$ in the HL-LHC era.
In modern astronomy, machine learning as an raising realm for data analysis, has proved to be efficient and effective to mine the big data from the newest telescopes. By using support vector machine (SVM), we construct a supervised machine learning a
lgorithm, to classify the objects in the Javalambre-Photometric Local Universe Survey (J-Plus). The sample is featured with 12-waveband, and magnitudes is labeled with spectrum-based catalogs, including Sloan Digital Sky Survey spectroscopic data, Large Sky Area Multi-Object Fiber Spectroscopic Telescope, and VERONCAT - Veron Catalog of Quasars & AGN. The performance of the classifier is presented with the applications of blind test validations based on RAdial Velocity Extension, Kepler Input Catalog, 2 MASS Redshift Survey, and UV-bright Quasar Survey. The accuracies of the classifier are 96.5% in blind test and 97.0% in training cross validation. The F_1-scores are 95.0% for STAR, 92.9% for GALAXY and 87.0% for QSO. In the classification for J-Plus catalog, we develop a new method to constrain the potential extrapolation.
