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We construct a sample of 70 clusters using data from XMM-Newton and Planck to investigate the $Y_{\rm SZ,Planck}-Y_{\rm SZ, XMM}$ scaling relation and the cool-core influences on the relation. $Y_{\rm SZ,XMM}$ is calculated by accurate de-projected temperature and electron number density profiles derived from XMM-Newton. $Y_{\rm SZ,Planck}$ is the latest Planck data restricted to our precise X-ray size $\theta_{\rm 500}$. To study the cool-core influences on$Y_{\rm SZ,Planck}-Y_{\rm SZ, XMM}$ scaling relation, we apply two criteria, limits of central cooling time and classic mass deposition rate, to distinguish cool-core clusters (CCCs) from non-cool-core clusters (NCCCs). We also use $Y_{\rm SZ,Planck}$ from other papers, which are derived from different methods, to confirm our results. The intercept and slope of the$Y_{\rm SZ,Planck}-Y_{\rm SZ, XMM}$ scaling relation are $A=-0.86 \pm 0.30$, $B=0.83 \pm 0.06$. The intrinsic scatter is $\sigma_{\rm ins}= 0.14 \pm 0.03$. The ratio of \mbox{$Y_{\rm SZ,Planck}/Y_{\rm SZ, XMM}$} is $1.03 \pm 0.05$, which is perfectly agreed with unity. Discrepancies of $Y_{\rm SZ,Planck}-Y_{\rm SZ, XMM}$ scaling relation between CCCs and NCCCs are found in observation. They are independent of cool-core classification criteria and $Y_{\rm SZ,Planck}$ calculation methods, although discrepancies are more significant under the classification criteria of classic mass deposition rate. The intrinsic scatter of CCCs (0.04) is quite small compared to that of NCCCs (0.27). The ratio of $Y_{\rm SZ,Planck}/Y_{\rm SZ, XMM}$ for CCCs is $0.89 \pm 0.05$, suggesting that CCCs' $Y_{\rm SZ,XMM}$ may overestimate SZ signal. By contrast, the ratio of $Y_{\rm SZ,Planck}/Y_{\rm SZ, XMM}$ for NCCCs is $1.14 \pm 0.12$, which indicates that NCCCs' $Y_{\rm SZ,XMM}$ may underestimate SZ signal., Comment: 16 pages, 5 figures, accepted by RAA

The design of multitarget rendezvous missions requires a method to quickly and accurately approximate the optimal transfer between any two rendezvous targets. In this paper, a deep neural network (DNN)-based method is proposed for quickly approximating optimal perturbed long-duration impulsive transfers. This kind of transfer is divided into three types according to the variation trend of the right ascension of the ascending node (RAAN) difference between the departure body and the rendezvous target. An efficient database generation method combined with a reliable optimization approach is developed. Three regression DNNs are trained individually and applied to approximate the corresponding types of transfers. The simulation results show that the well-trained DNNs are capable of quickly estimating the optimal velocity increments with a relative error of less than 3% for all the three types of transfers. The tests on the debris chains with the total velocity increments of several thousand m/s show that the estimated results can be very close to the optimized ones with a final estimation error of less than 10 m/s., Comment: arXiv admin note: text overlap with arXiv:1902.03738

The design of low-thrust-based multitarget interplanetary missions requires a method to quickly and accurately evaluate the low-thrust transfer between any two visiting targets. Complete evaluation of the low-thrust transfer includes not only the estimation of the optimal fuel consumption but also the judgment of transfer feasibility. In this paper, a deep neural network (DNN)-based method is proposed for quickly evaluating low-thrust transfer. An efficient database generation method is developed for obtaining both the infeasible and optimal transfers. A classification DNN and a regression DNN are trained based on the infeasible and optimal transfers to judge the transfer feasibility and estimate the optimal fuel consumption, respectively. The simulation results show that the well-trained DNNs are capable of quickly determining the transfer feasibility with a correct rate of greater than 98% and approximating the optimal transfer fuel consumption with a relative estimation error of less than 0.4%. The tests on two asteroid chains further show the superiority of the DNN-based method for application to the design of low-thrust-based multitarget interplanetary missions

MapReduce speculation plays an important role in finding potential task stragglers and failures. But a tacit dichotomy exists in MapReduce due to its inherent two-phase (map and reduce) management scheme in which map tasks and reduce tasks have distinctly different execution behaviors, yet reduce tasks are dependent on the results of map tasks. We reveal that speculation policies for fault handling in MapReduce do not recognize this dichotomy between map and reduce tasks, which leads to an issue of speculation myopia for MapReduce fault recovery. These issues cause significant performance degradation upon network and node failures. To address the speculation myopia caused by MapReduce dichotomy, we introduce a new scheme called binocular speculation to help MapReduce increase its assessment scope for speculation. As part of the scheme, we also design three component techniques including neighborhood glance, collective speculation and speculative rollback. Our evaluation shows that, with these techniques, binocular speculation can increase the coordination of map and reduce phases, and enhance the efficiency of MapReduce fault recovery., Comment: 10 pages, 9 figures

Chromosome classification is critical for karyotyping in abnormality diagnosis. To expedite the diagnosis, we present a novel method named Varifocal-Net for simultaneous classification of chromosome's type and polarity using deep convolutional networks. The approach consists of one global-scale network (G-Net) and one local-scale network (L-Net). It follows three stages. The first stage is to learn both global and local features. We extract global features and detect finer local regions via the G-Net. By proposing a varifocal mechanism, we zoom into local parts and extract local features via the L-Net. Residual learning and multi-task learning strategies are utilized to promote high-level feature extraction. The detection of discriminative local parts is fulfilled by a localization subnet of the G-Net, whose training process involves both supervised and weakly-supervised learning. The second stage is to build two multi-layer perceptron classifiers that exploit features of both two scales to boost classification performance. The third stage is to introduce a dispatch strategy of assigning each chromosome to a type within each patient case, by utilizing the domain knowledge of karyotyping. Evaluation results from 1909 karyotyping cases showed that the proposed Varifocal-Net achieved the highest accuracy per patient case (%) 99.2 for both type and polarity tasks. It outperformed state-of-the-art methods, demonstrating the effectiveness of our varifocal mechanism, multi-scale feature ensemble, and dispatch strategy. The proposed method has been applied to assist practical karyotype diagnosis., Comment: This paper is accepted to IEEE TMI for future publication. 13 pages, 11 figures, 9 tables

The Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) , composed of two small satellites, is a new mission to monitor the Gamma-Ray Bursts (GRBs) coincident with Gravitational Wave (GW) events with a FOV of 100% all-sky.Each GECAM satellite detects and localizes GRBs using 25 compact and novel Gamma-Ray Detectors (GRDs) in 6 keV-5 MeV. Each GRD module is comprised of LaBr3:Ce scintillator, SiPM array and preamplifier. A large dynamic range of GRD is achieved by the high gain and low gain channels of the preamplifier. The energy response of GRD prototype was evaluated using radioactive sources in the range of 5.9-1332.5 keV. A energy resolution of 5.3% at 662 keV was determined from the 137Cs pulse height spectra, which meets the GECAM requirement (< 8% at 662 keV). Energy to channel conversion was evaluated and a nonlinearity correction was performed to reduce the residuals (< 1.5%). Also, a Geant4-based simulated in-flight background and a measured GRD LaBr3:Ce intrinsic activity were used to evaluate the capability of in-flight calibration. These results demonstrate the design of GRD., Comment: 17 pages, 9 figures