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This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Magazine on IEEE XPlore.Deep learning in remote sensing has become an international hype, but it is mostly limited to the evaluation of optical data. Although deep learning has been introduced in Synthetic Aperture Radar (SAR) data processing, despite successful first attempts, its huge potential remains locked. In this paper, we provide an introduction to the most relevant deep learning models and concepts, point out possible pitfalls by analyzing special characteristics of SAR data, review the state-of-the-art of deep learning applied to SAR in depth, summarize available benchmarks, and recommend some important future research directions. With this effort, we hope to stimulate more research in this interesting yet under-exploited research field and to pave the way for use of deep learning in big SAR data processing workflows.

Abstract:In this article, we conducted the evaluation of artificial intelligence research from 1990-2014 by using bibliometric analysis. We introduced spatial analysis and social network analysis as geographic information retrieval methods for spatially-explicit bibliometric analysis. This study is based on the analysis of data obtained from database of the Science Citation Index Expanded (SCI-Expanded) and Conference Proceedings Citation Index-Science (CPCI-S). Our results revealed scientific outputs, subject categories and main journals, author productivity and geographic distribution, international productivity and collaboration, and hot issues and research trends. The growth of article outputs in artificial intelligence research has exploded since the 1990s, along with increasing collaboration, reference, and citations. Computer science and engineering were the most frequently-used subject categories in artificial intelligence studies. The top twenty productive authors are distributed in countries with a high investment of research and development. The United States has the highest number of top research institutions in artificial intelligence, producing most single-country and collaborative articles. Although there is more and more collaboration among institutions, cooperation, especially international ones, are not highly prevalent in artificial intelligence research as expected. The keyword analysis revealed interesting research preferences, confirmed that methods, models, and application are in the central position of artificial intelligence. Further, we found interesting related keywords with high co-occurrence frequencies, which have helped identify new models and application areas in recent years. Bibliometric analysis results from our study will greatly facilitate the understanding of the progress and trends in artificial intelligence, in particular, for those researchers interested in domain-specific AI-driven problem-solving. This will be of great assistance for the applications of AI in alternative fields in general and geographic information science, in particular.

This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) offload their computation tasks to multiple edge servers and one cloud server. Considering different real-time computation tasks at different WDs, every task is decided to be processed locally at its WD or to be offloaded to and processed at one of the edge servers or the cloud server. In this paper, we investigate low-complexity computation offloading policies to guarantee quality of service of the MEC network and to minimize WDs’ energy consumption. Specifically, both a linear programing relaxation-based (LR-based) algorithm and a distributed deep learning-based offloading (DDLO) algorithm are independently studied for MEC networks. We further propose a heterogeneous DDLO to achieve better convergence performance than DDLO. Extensive numerical results show that the DDLO algorithms guarantee better performance than the LR-based algorithm. Furthermore, the DDLO algorithm generates an offloading decision in less than 1 millisecond, which is several orders faster than the LR-based algorithm.

Background: This study investigated the effects of various doses of S-ketamine on depression and pain management of cervical carcinoma patients with mild/moderate depression. Material/Methods: This randomized, double-blind, controlled study included 417 cervical carcinoma patients who received laparoscopic modified radical hysterectomy from April 2015 to July 2018 and who also had mild/moderate depression symptoms based on HAMD-17 scores (8~24). All patients were randomized into 4 groups: 1) the control group, 2) the racemic ketamine group, 3) the high-dose S-ketamine group; and 4) the low-dose S-ketamine group. Pain was assessed using the Visual Analogue Score (VAS), and depression was assessed using theHAMD-17 score. Serum levels of BDNF and 5-HT were measured. Results: The 4 groups of patients showed no significant differences in operation time, bleeding volume, hospitalization duration, or complications. The high-dose S-ketamine group showed significantly lower VAS and HAMD-17 scores than all other groups at 1 day and 3 days postoperatively, but no differences were observed in the lowdose S-ketamine group and the racemic ketamine group. The high-dose S-ketamine group showed significantly higher serum BDNF and 5-HT levels at 1 day and 3 days after surgery. However, 1 week after surgery, no difference was observed in any of the treatment groups. Conclusions: At subanesthetic dose, both 0.5 mg/kg and 0.25 mg/kg S-ketamine improved short-term depression and pain for cervical carcinoma patients after surgery, and the effects were better than with the same dose of racemic ketamine.

This meta-analysis found that KLT injection in combination with chemotherapy was associated with improved response rate, quality of life, and symptoms, and a reduced incidence of AEs compared with chemotherapy alone in patients with NSCLC. These findings should be viewed with caution because of the low quality of the included trials.