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We have computed the deflection angles caused by 195 objects in the solar system, including 177 satellites and eight asteroids. Twenty-one satellites and six asteroids can bend light from distant compact extragalactic sources by more than 0.1 $\mu$as, and fourteen satellites and the asteroid Ceres can deflect light by more than 1.0 $\mu$as. We calculated the zones and durations of perturbations posed by the gravitational fields of five planets (excluding Earth, Jupiter, and Saturn), Pluto, and Ceres, where the perturbations would affect astrometry measured with the Squared Kilometre Array (SKA). Perturbed zones with deflection angles larger than 0.1 and 1.0 $\mu$as appear as ribbons. Their widths range from dozens of degrees for Uranus, Neptune, and Venus to several degrees or less for other objects at 0.1 $\mu$as, and from $\sim$ 16$^{\circ}$ for Venus to several degrees or less for other objects at 1.0 $\mu$as. From the calculated perturbation durations, the influence of the gravitational fields of selected objects can be divided into four levels: hardly affect SKA astrometry (I), may have little effect (II), may have a great effect (III) on single-epoch astrometry, and may greatly affect both single- and multi-epoch astrometry (IV). The objects corresponding to these levels are Ceres (I), Pluto (II), Mercury and Mars (III), and other objects (IV)., Comment: 10 figures, 5 tables, accepted by ApJ

Matchmaking systems are vital for creating fair matches in online multiplayer games, which directly affects players' satisfactions and game experience. Most of the matchmaking systems largely rely on precise estimation of players' game skills to construct equitable games. However, the skill rating of a novice is usually inaccurate, as current matchmaking rating algorithms require considerable amount of games for learning the true skill of a new player. Using these unreliable skill scores at early stages for matchmaking usually leads to disparities in terms of team performance, which causes negative game experience. This is known as the ''cold-start'' problem for matchmaking rating algorithms. To overcome this conundrum, this paper proposes QuickSKill, a deep learning based novice skill estimation framework to quickly probe abilities of new players in online multiplayer games. QuickSKill extracts sequential performance features from initial few games of a player to predict his/her future skill rating with a dedicated neural network, thus delivering accurate skill estimation at the player's early game stage. By employing QuickSKill for matchmaking, game fairness can be dramatically improved in the initial cold-start period. We conduct experiments in a popular mobile multiplayer game in both offline and online scenarios. Results obtained with two real-world anonymized gaming datasets demonstrate that proposed QuickSKill delivers precise estimation of game skills for novices, leading to significantly lower team skill disparities and better player game experience. To the best of our knowledge, proposed QuickSKill is the first framework that tackles the cold-start problem for traditional skill rating algorithms., Comment: Accepted by CIKM 2022 Applied Research Track

Using the cross-matched data of Gaia EDR3 and the 2MASS Point Source Catalog, a sample of RC stars with parallax accuracies better than 20% is identified and used to reveal the nearby spiral pattern traced by old stars. As shown in the overdensity distribution of RC stars, there is an arc-like feature extended from $l~\sim$ 90$^\circ$ to $\sim$ 243$^\circ$, which passed close to the Sun. This feature is probably an arm segment traced by old stars, indicating the Galaxy potential in the vicinity of the Sun. By comparing to the spiral arms depicted by young objects, we found that there are considerable offsets between the two different components of Galactic spiral arms. The spiral arm traced by RC stars tends to have a larger pitch angle, hence a more loose wound pattern., Comment: 10 pages, 6 figures, accepted for publication in ApJ

Diffractive optical neural networks have shown promising advantages over electronic circuits for accelerating modern machine learning (ML) algorithms. However, it is challenging to achieve fully programmable all-optical implementation and rapid hardware deployment. Furthermore, understanding the threat of adversarial ML in such system becomes crucial for real-world applications, which remains unexplored. Here, we demonstrate a large-scale, cost-effective, complex-valued, and reconfigurable diffractive all-optical neural networks system in the visible range based on cascaded transmissive twisted nematic liquid crystal spatial light modulators. With the assist of categorical reparameterization, we create a physics-aware training framework for the fast and accurate deployment of computer-trained models onto optical hardware. Furthermore, we theoretically analyze and experimentally demonstrate physics-aware adversarial attacks onto the system, which are generated from a complex-valued gradient-based algorithm. The detailed adversarial robustness comparison with conventional multiple layer perceptrons and convolutional neural networks features a distinct statistical adversarial property in diffractive optical neural networks. Our full stack of software and hardware provides new opportunities of employing diffractive optics in a variety of ML tasks and enabling the research on optical adversarial ML., Comment: 34 pages, 4 figures

Recent years have seen increasing employment of decision intelligence in electronic design automation (EDA), which aims to reduce the manual efforts and boost the design closure process in modern toolflows. However, existing approaches either require a large number of labeled data and expensive training efforts, or are limited in practical EDA toolflow integration due to computation overhead. This paper presents a generic end-to-end sequential decision making framework FlowTune for synthesis tooflow optimization, with a novel high-performance domain-specific, multi-stage multi-armed bandit (MAB) approach. This framework addresses optimization problems on Boolean optimization problems such as a) And-Inv-Graphs (# nodes), b) Conjunction Normal Form (CNF) minimization (# clauses) for Boolean Satisfiability; logic synthesis and technology mapping problems such as c) post static timing analysis (STA) delay and area optimization for standard-cell technology mapping, and d) FPGA technology mapping for 6-in LUT architectures. Moreover, we demonstrate the high extnsibility and generalizability of the proposed domain-specific MAB approach with end-to-end FPGA design flow, evaluated at post-routing stage, with two different FPGA backend tools (OpenFPGA and VPR) and two different logic synthesis representations (AIGs and MIGs). FlowTune is fully integrated with ABC [1], Yosys [2], VTR [3], LSOracle [4], OpenFPGA [5], and industrial tools, and is released publicly. The experimental results conducted on various design stages in the flow all demonstrate that our framework outperforms both hand-crafted flows [1] and ML explored flows [6], [7] in quality of results, and is orders of magnitude faster compared to ML-based approaches., Comment: 13 pages

We measured the relative positions between two pairs of compact extragalactic sources (CESs), J1925-2219 \& J1923-2104 (C1--C2) and J1925-2219 \& J1928-2035 (C1--C3) on 2020 October 23--25 and 2021 February 5 (totaling four epochs), respectively, using the Very Long Baseline Array (VLBA) at 15 GHz. Accounting for the deflection angle dominated by Jupiter, as well as the contributions from the Sun, planets other than Earth, the Moon and Ganymede (the most massive of the solar system's moons), our theoretical calculations predict that the dynamical ranges of the relative positions across four epochs in R.A. of the C1--C2 pair and C1--C3 pair are 841.2 and 1127.9 $\mu$as, respectively. The formal accuracy in R.A. is about 20 $\mu$as, but the error in Decl. is poor. The measured standard deviations of the relative positions across the four epochs are 51.0 and 29.7 $\mu$as in R.A. for C1--C2 and C1--C3, respectively. These values indicate that the accuracy of the post-Newtonian relativistic parameter, $\gamma$, is $\sim 0.061$ for C1--C2 and $\sim 0.026$ for C1--C3. Combining the two CES pairs, the measured value of $\gamma$ is $0.984 \pm 0.037$, which is comparable to the latest published results for Jupiter as a gravitational lens reported by Fomalont \& Kopeikin, i.e., $1.01 \pm 0.03$., Comment: 22 pages, 7 figures, 3 tables, accepted for publication in ApJ