Machine Learning for 5G and Beyond:From ModelBased to Data-Driven Mobile Wireless Networks

During the past few decades,mobile wireless communications have experienced four generations of technological revolution,namely from 1 G to 4 G,and the deployment of the latest 5 G networks is expected to take place in 2019.One fundamental question is how we can push forward the development of mobile wireless communications while it has become an extremely complex and sophisticated system.We believe that the answer lies in the huge volumes of data produced by the network itself,and machine learning may become a key to exploit such information.In this paper,we elaborate why the conventional model-based paradigm,which has been widely proved useful in pre-5 G networks,can be less efficient or even less practical in the future 5 G and beyond mobile networks.Then,we explain how the data-driven paradigm,using state-of-the-art machine learning techniques,can become a promising solution.At last,we provide a typical use case of the data-driven paradigm,i.e.,proactive load balancing,in which online learning is utilized to adjust cell configurations in advance to avoid burst congestion caused by rapid traffic changes.

常压CO2促进的铜催化炔丙醇水解制α-羟基酮（英文）

α-羟基酮是一类非常有用的结构,广泛存在于各种具有生物活性的合成药物和天然产物中.水对炔丙醇的区域选择性加成为合成α-羟基酮提供了一种简单并且直接的方法.然而,目前已经报道的多种用于炔烃水解的方法对于炔丙醇的适用性较差,这是由于羟基的引入降低了炔烃的活性以及炔丙醇易发生Meyer-Schuster重排和Rupe重排等副反应.另一方面,CO2可以作为助催化剂促进炔丙醇水解生成α-羟基酮.具体来讲,炔丙醇与CO2可以发生羧化环化反应得到α-亚烷基环状碳酸酯,亲核性的水与原位产生的α-亚烷基环状碳酸酯反应使其开环,经历脱羧、异构化过程,最终得到α-羟基酮.与炔丙醇的直接水解相比,以CO2为促进剂可以更加高效、高选择性地得到α-羟基酮.然而,目前发展的用于CO2促进的炔丙醇水解合成α-羟基酮的反应中,高压的CO2和过量的碱是不可缺少的.本文发展了廉价且高效的铜催化体系用于CO2助催化的炔丙醇水解生成α-羟基酮的反应.该催化体系可以在不需要过量碱的存在下使得炔丙醇的水解反应在常压CO2条件下顺利进行.控制实验结果表明,铜催化炔丙醇和CO2的羧化环化反应可以在常压CO2条件下进行,而有机碱可以有效促进α-亚烷基环状碳酸酯与水反应生成α-羟基酮,这解释了我们发展的铜催化体系在常压CO2条件下高效进行的原因.相同条件下,本文发展的铜催化体系的催化活性明显高于之前报道的银催化剂,凸显了该催化体系的高效性.在最优反应条件下,一系列具有不同烷基或芳基取代基的炔丙醇都表现出良好的反应活性,能以70%–97%的收率转化为相应的α-羟基酮.在合成应用方面,该方法可应用于克级规模实验,反应能以70%的收率得到相应的α-羟基酮产物;而且,该方法也可以应用于口服药物炔孕酮的进一步衍生化.通过逻辑实验以及对核磁谱图的分析,我们还对该反应提出了一个可能的反应机理.总之,我们使用廉价的过渡金属铜催化剂,成功实现了炔丙醇在常压CO2为助催化剂条件下发生水解反应合成α-羟基酮.该催化体系反应条件温和,底物适用性良好,催化效率高,为合成α-羟基酮提供了一种简单、高效的新方法.

Minimal Gated Unit for Recurrent Neural Networks

Recurrent neural networks （RNN） have been very successful in handling sequence data. However, understanding RNN and finding the best practices for RNN learning is a difficult task, partly because there are many competing and complex hidden units, such as the long short-term memory （LSTM） and the gated recurrent unit （GRU）. We propose a gated unit for RNN, named as minimal gated unit （MCU）, since it only contains one gate, which is a minimal design among all gated hidden units. The design of MCU benefits from evaluation results on LSTM and GRU in the literature. Experiments on various sequence data show that MCU has comparable accuracy with GRU, but has a simpler structure, fewer parameters, and faster training. Hence, MGU is suitable in RNN＇s applications. Its simple architecture also means that it is easier to evaluate and tune, and in principle it is easier to study MGU＇s properties theoretically and empirically.

Crowd intelligence in AI 2.0 era

The Internet based cyber-physical world has profoundly changed the information environment for the development of artificial intelligence(AI), bringing a new wave of AI research and promoting it into the new era of AI 2.0. As one of the most prominent characteristics of research in AI 2.0 era, crowd intelligence has attracted much attention from both industry and research communities. Specifically, crowd intelligence provides a novel problem-solving paradigm through gathering the intelligence of crowds to address challenges. In particular, due to the rapid development of the sharing economy, crowd intelligence not only becomes a new approach to solving scientific challenges, but has also been integrated into all kinds of application scenarios in daily life, e.g., online-tooffline(O2O) application, real-time traffic monitoring, and logistics management. In this paper, we survey existing studies of crowd intelligence. First, we describe the concept of crowd intelligence, and explain its relationship to the existing related concepts, e.g., crowdsourcing and human computation. Then, we introduce four categories of representative crowd intelligence platforms. We summarize three core research problems and the state-of-the-art techniques of crowd intelligence. Finally, we discuss promising future research directions of crowd intelligence.

Structural diversity for decision tree ensemble learning

Decision trees are a kind of off-the-shelf predictive models, and they have been successfully used as the base learners in ensemble learning. To construct a strong classi- fier ensemble, the individual classifiers should be accurate and diverse. However, diversity measure remains a mystery although there were many attempts. We conjecture that a deficiency of previous diversity measures lies in the fact that they consider only behavioral diversity, i.e., how the classifiers behave when making predictions, neglecting the fact that classifiers may be potentially different even when they make the same predictions. Based on this recognition, in this paper, we advocate to consider structural diversity in addition to behavioral diversity, and propose the TMD （tree matching diversity） measure for decision trees. To investigate the usefulness of TMD, we empirically evaluate performances of selective ensemble approaches with decision forests by incorporating different diversity measures. Our results validate that by considering structural and behavioral diversities together, stronger ensembles can be constructed. This may raise a new direction to design better diversity measures and ensemble methods.

Boosting and margin theory

Many researchers have worked on the ex- planation of AdaBoost＇s good experimental results in theory. Some work give an upper bound of generaliza- tion error in terms of the margin distribution function, while Breiman gave a sharper generalization error bound based on minimum margin. He also developed the arc- gv algorithm to maximize the minimum margin, then made the minimum margin larger than AdaBoost. How- ever, its empirical results are even worse than AdaBoost. Therefore, is the minimum margin bound not practi- cal？ This paper gives a new concept called Equilibrium margin （Emargin） and proves a new generalization er- ror bound using Emargin, which is always better than minimum margin bound. In addition, we show Emargin is a good indicator of generalization. Then, we conduct experiments showing that the Emargin of AdaBoost is larger than arc-gv, but the generalization error of Ada- Boost is usually better.

Rehearsal:learning from prediction to decision

Machine learning[1]studies focus mostly on prediction,where a model is built from a set of observational data formaking correct predictions on unseen instances.It has beenaddressed very well by modem techniques such as deeplearming[2],though some issues,c.g,open environmentmachine learning[3],remain to be developed.

Efficient and Recyclable Cobalt(Ⅱ)/Ionic Liquid Catalytic System for CO2 Conversion to Prepare 2-Oxazolinones at Atmospheric Pressure

Summary of main observation and conclusion Converting CO2 into value-added chemicals represents a promising way to alleviate the CO2 derived environmental issues,for which the development of catalysts with high efficiency and recyclability is very desirable.Herein,the catalytic system by combining cobalt source and ionic liquid(IL)has been developed as the efficacious and recyclable catalyst for the carboxylative cyclization of propargylic amine and CO2 to prepare 2-oxazolinones.In this protocol,various propargylic amines were successfully transformed into the corresponding 2-oxazolinones with CoBr2 and diethylimidazolium acetate([EEIM][OAc])as the catalyst under atmospheric CO2 pressure.It is worth noting that the turnover number(TON)of this transformation can be up to 1740,presumably being attributed to the cooperative effect of the cobalt and IL.Furthermore,the existence of IL enables the catalytic system to be easily recycled to 10 times without losing its activity.

Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models

Researchers often summarize their work in the form of scientific posters.Posters provide a coherent and efficient way to convey core ideas expressed in scientific papers.Generating a good scientific poster,however,is a complex and time-consuming cognitive task,since such posters need to be readable,informative,and visually aesthetic.In this paper, for the first time,we study the challenging problem of learning to generate posters from scientific papers.To this end,a data-driven framework,which utilizes graphical models,is proposed.Specifically,given content to display,the key elements of a good poster,including attributes of each panel and arrangements of graphical elements,are learned and inferred from data.During the inference stage,the maximum a posterior (MAP)estimation framework is employed to incorporate some design principles.In order to bridge the gap between panel attributes and the composition within each panel,we also propose a recursive page splitting algorithm to generate the panel layout for a poster.To learn and validate our model,we collect and release a new benchmark dataset,called NJU-Fudan Paper-Poster dataset,which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes.Qualitative and quantitative results indicate the effectiveness of our approach.

Ionic Liquid-Modified Porous Organometallic Polymers as Efficient and Selective Photocatalysts for Visible-Light-Driven CO_(2) Reduction

In the photoreduction of CO_(2) to CO,the competitive H2 evolution is always inevitable due to the approximate reduction potentials of H+/H2 and CO_(2)/CO,which results in poor selectivity for CO production.Herein,imidazolium-type ionic liquid-(IL-)modified rhenium bipyridine-based porous organometallic polymers(Re-POMP-IL)were designed as efficient and selective photocatalysts for visible-light CO_(2) photoreduction to CO based on the affinity of IL with CO_(2).Photoreduction studies demonstrated that CO_(2) photoreduction promoted by Re-POMP-IL functioning as the catalyst exhibits excellent CO selectivity up to 95.5%and generate 40.1 mmol CO/g of Re-POMP-IL1.0(obtained by providing equivalent[(5,5′-divinyl-2,2′-bipyridine)Re(CO)_(3)Cl]and 3-ethyl-1-vinyl-1H-imidazol-3-ium bromide)at 12 h,outperforming that attained with the corresponding Re-POMP analogue without IL,which highlights the crucial role of IL.Notably,CO_(2) adsorption,light harvesting,and transfer of photogenerated charges as key steps for CO_(2)RR were studied by employing POMPs modified with different amounts of IL as photocatalysts,among which the CO_(2) affinity as an important factor for POMPs catalyzed CO_(2) reduction is revealed.Overall,this work provides a practical pathway to improve the CO_(2) photoreduction efficiency and CO selectivity by employing IL as a regulator.

Learnware： on the future of machine learning

Current machine learning techniques have achieved great success; however, there are many deficiencies. First, to train a strong model, a large amount of training examples are required, whereas collecting the data, particularly data with labels, is expensive or even difficult in many real tasks. Second, once a model has been trained, if environment changes, which often happens in real tasks, the model can hardly perform well or even become useless. Third,

Launch of the Excellent Young Computer Scientists Forum

Since Volume 10, 2016, Frontiers of Computer Science established an "NSFC Excellent Young Scholars Forum", which aims to publishing articles from recipients of the NSFC (National Science Foundation of China) Excellent Young Scholars Program. During the past three years, 37 articles have been published and this forum has received very positive feedbacks from readers.

Metatranscriptome of the protistan community in Reticufitermes flaviceps

The hindgut of lower termites harbors various symbiotic protists, which per- form varied functions in lignocellulose decomposition. As termites are social insects, the species and numbers of these flagellated protists in the termite gut vary among the different castes. Juvenile hormones （JHs） can regulate caste differentiation in termites. In this study, we used the juvenile hormone analog fenoxycarh to induce termite workers （Reticulitermesflaviceps） to differentiate into pre-soldiers. A metatranscriptomic investigation of the protistan community was then performed by 454 pyrosequencing. From a thorough analysis based on 597 312 generated reads, we found that the starch and sucrose metabolism pathway was the most abundant pathway across the metatranscriptome. The current study demonstrates that the metatranscriptome of the protistan community in termites contains an abundance of lignocellulase, which plays a vital role in termite nutrition.

Launch of the NSFC Excellent Young Scholars Forum

In 2012, the National Natural Science Foundation of China （NSFC） launched the Excellent Young Scholars （EYS） Pro- gram. As its name suggests, this program aims to recognize and support excellent young scholars in the fields of science and engineering. This program is similar to the NSF Career Award in the United States, and the competition is very tough： only a very limited number of applicants can get through. Each awardee will receive a fund of one million CNY for a three-year term.With such generous support, awardees are expected to do more excellent research and grow up quickly as distinguished young scholars.