The proliferation of multi-media applications and increased computing power of mobile devices have led to the development of personalized artificial intelligent (AI) applications that utilize the massive user-information residing on them. However, the traditional centralized training paradigm is not applicable in this scenario due to potential privacy risks and high communication overhead. Federated learning (FL) provides an option to these applications. Nevertheless, the heterogeneity of computing and communication latency among devices have posed great challenges to building efficient learning frameworks. Existing optimizations on FL either fail to speed up training on heterogeneous devices or suffer from poor communication efficiency. In this paper, we propose FedStar, an efficient FL framework that supports decentralized asynchronous training on heterogeneous communication networks. Considering the heterogeneous computing power in the network, FedStar supports running heterogeneity-aware local steps on each device. What’s more, considering the heterogeneous communication latency and possibly unreachable communication path between some devices, FedStar generates a decentralized communication topology that can achieve maximal training throughput. Finally, it adopts weighted aggregation to guarantee high convergence accuracy of global model. Theoretical analysis results show the convergence behaviour of FedStar under non-convex settings. Experimental results show that FedStar can achieve a speedup of 4.81× than the state-of-the-art FL schemes with high convergence accuracy.

Brightness temperature (TB) derived from the Chang’e-2 microwave radiometer (MRM) data has provided a useful way to study the thermal and dielectric properties of the subsurface deposits on the Moon. However, the obvious TB change with the latitude, named latitude effect, has highly limited the application of the MRM data. To solve this problem, a new TB mapping method, named normalized TB (nTB) mapping method, is developed, which is defined as the ratio between the TB and the standard TB at the same point. Based on the newly derived global nTB maps, we identified and classified four types of subsurface deposits with distinct dielectric properties, two of which indicate the abnormally high heat flux or the existence of the granitic systems and the existence of the surface rocks, respectively. Moreover, the nTB at the daytime demonstrates a strong correlation with both the TiO2 and FeO abundances of subsurface deposits, the latter of which has been severely underestimated by the previous studies directly using MRM data. This work is significant to improve the understanding of the basaltic volcanism and thermal evolution of the Moon.

To ensure the user experience of mobile systems, the foreground application can be differentiated to minimize the impact of background applications. However, this article observes that system services in the kernel and framework layer, instead of background applications, are now the major resource competitors. Specifically, these service tasks tend to be quiet when people rarely interact with the foreground application and active when interactions become frequent, and this high overlap of busy times leads to contention for resources. This article proposes iAware, an interaction-aware task scheduling framework in mobile systems. The key insight is to make use of the previously ignored idle period and schedule service tasks to run at that period. iAware quantify the interaction characteristic based on the screen touch event, and successfully stagger the periods of frequent user interactions. With iAware, service tasks tend to run when few interactions occur, for example, when the device’s screen is turned off, instead of when the user is frequently interacting with it. iAware is implemented on real smartphones. Experimental results show that the user experience is significantly improved with iAware. Compared to the state-of-the-art, the application launching speed and frame rate are enhanced by 38.89% and 7.97% separately, with no more than 1% additional battery consumption.

Near-surface temperatures of permanently shadowed regions (PSRs) on the Moon provide fundamental information for water ice exploration. Seasonal temperature variations of PSRs are found in both Chang’E-2 microwave radiometer data and Diviner Lunar radiometer observations. Furthermore, unusual microwave brightness temperature variations between February 2011 and May 2011 of double-shaded PSRs are shown in the Chang’E-2 observational data, i.e., that the minimum microwave brightness temperature occurs before the time when the infrared brightness temperature reaches the minimum in double-shaded PSRs. To interpret this phenomenon, the 1-D thermal model and the microwave radiation transfer model are used. In the thermal model, the reradiation energy from the illuminated area is estimated by effective solar irradiance, which is an analytic solution for the radiative equilibrium temperature in the shadowed area of a spherical bowl-shaped crater. In the simulation, an assumed internal 0.4 W/m2heat flow beneath the lunar surface made a plausible fit to the unusual variations during some lunations. However, this is a huge value compared with the well-known heat flow value of about 0.018 W/m2. Furthermore, it is difficult to obtain this extra heat energy by lateral conduction below the surface in a large impact crater due to the small thermal conductivity of the lunar regolith. Finally, the unusual microwave brightness temperature (TB) changes are concluded to be caused by a calibration problem after excluding other possible reasons. In addition, a statistical correction method is applied to revise the problematic TB data to obtain the proper variation trend of the brightness temperature.

The cause of the hot anomaly is one of the important scientific aims of the current lunar studies. In this study, aimed at a rock-related hot anomaly revealed by the Diviner thermal infrared data, the brightness temperature (TB) derived from the Chang'e-2 microwave sounder (CELMS) data is employed to evaluate its thermophysical features of the regolith. According to the TB performances, the ilmenite-rich material is identified in the region. Thus, we denied the rock influence and ascribed the region as a potential cryptomare. This study provides a new way to evaluate the thermophysical features of the lunar regolith with the CELMS data.

Mare Moscoviense (148°E, 27°N) is one of the few large maria on the lunar farside. In this paper, the China Chang'E-2 Microwave Sounder (CELMS) data were employed to study the microwave thermal emission features of Moscoviense basin. The findings are as follows. (1) The four basaltic units present distinctly different TBperformances at noon and midnight; (2) A new understanding about the basaltic units is made according to the classification results using the maximum likelihood method; (3) The substrate temperature of Moscoviense basin is likely much higher than what we know. The results will be of great significances to understand the mare volcanism on the lunar farside.

In this study, the Chang'E-2 microwave radiometer (CELMS) data are employed to study the thermophysical features of the regolith in the western lunar farside, including Giordano Bruno - the youngest lunar crater of its size. The results are as follows. Firstly, the distribution of the rocks with depth is different in Bruno, King, and Necho craters. Secondly, abundant cold anomalies and one hidden hot anomaly are discovered. Thirdly, at least seven hidden linear structures are discovered, some of which hinting the impacting process of Bruno crater.