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江宁警方调查得知，谢某诈骗100万元现金后，辗转于各城市的夜场，有时一夜挥霍高达一两万元，连眼睛都不眨一下。可能是她“坚信”，背后有李先生这么一个“取款机”，随时要随时有钱到。而民警找到她时，这百万元巨款也已经被大手大脚花得精光。与此同时，谢某还甚至相信，自己录有两人的视频，李先生应该不敢报警。目前，谢某因涉嫌诈骗罪已被南京江宁警方刑事拘留。案件正在进一步审理之中。

这份白皮书还指出了一个潜在消费趋势，中国亲子消费市场规模已超过全国餐饮市场，突破了4.5万亿元。“亲子产品可比门店的消费热度同比增长是最高的，能达到10%，并且未来的增长空间和潜力依然不可小视。”沈嘉颖总结道。当然，从客流中转化的实际销售额来看，线下商业还是远不如线上电商。阿里巴巴和京东早已经创造一年万亿的零售销售额。

征集“民间高手”的报名通知得到了北京、上海、广州、深圳、海口、昆明、长沙、成都、南京等地高尔夫爱好者的关注和咨询，在短短一周的时间内，最终来自北京的红之队（男队）、来自海南的海之南球友队（男队+女队）、来自云南的七彩云南球友队（男队）搭上了全运会高尔夫资格赛的幸运末班车。

据新华网5月18日报道，为进一步落实房地产市场调控城市主体责任，住建部在4月19日对6个城市进行预警提示的基础上，又对近3个月新建商品住宅、二手住宅价格指数累计涨幅较大的佛山、苏州、大连、南宁4个城市进行了预警提示。随后的5月21日和6月10日，南宁召开了关于促进房地产市场健康稳定发展会议，研究讨论南宁市房地产市场健康稳定发展存在的问题及解决措施。

In terms of academic accomplishments, Geoff has more than 250,000 citations, with more than half in the last five years. He has an astoundingly high H-index of 142. He was the co-inventor of the seminal work on Boltzmann Machines and backpropagation using gradient descent (published in 1983 with Terry Sejnowski, and the Nature paper with David Rumelhart in 1986). This work introduced the idea of hidden layers in neural networks, along with a mathematically elegant and computational tractable way to train their affiliated parameters. Hidden layers freed the software from “human control” (such as expert systems) and back propagation allowed non-linear combination to essentially discover prominent features (in a more goal-directed way than humans) in the process. However, it turned out that these ideas were before their time, as there were not enough data or computing power to enable these theoretical approaches to solve real-world problems or beat other approaches in competitions. The early-1980's were dominated by expert systems, which became discredited in by late-1980's when they were proven to be brittle and unscalable. What displaced expert systems was not Geoff's proposals (which were too early), but simplified versions of neural networks which were compromised to work with less data and computation. My Ph.D. thesis (using hidden Markov models) was among them, and these simplified approaches were able to make some contributions with some applications, but like expert systems, they were not able to scale to the hardest problems (such as playing Go, human-level speech or vision).

From 1985 to 2015, the amount of data and computation increased tremendously. For example, my 1988 Ph.D. thesis used the largest speech database at the time, which was only 100 MB. Today, the best speech recognition systems are trained on 100 TB of data – a million-fold increase. And with that much increase in data size, Geoff's approach (later re-branded deep learning) finally shined, as it could increase the number of layers from one to thousands, and deep learning systems continued to improve as the data size and the complexity of the model increased.