Uber has unveiled the terms of a hotly anticipated stock market float which it hopes will value the ride-hailing service at more than $91bn (£70bn). While the target is $10bn less than some bankers suggested the 10-year-old firm might be worth, the valuation is more than double the value of the 116-year-old carmaker Ford and would be the largest float by a US tech company since Facebook's in 2012. Its Wall Street debut will gauge investors' excitement about the prospects of a company that has expanded rapidly from taxi services into food delivery and is now investing billions in developing driverless cars. If it hits the mark, Uber will raise around $9bn in new funds and some early investors will make big profits. Despite the scale of ts ambition, Uber lost $1.8bn last year even while its revenues surged by more than 40% to $11.3bn.

Women belonging to the socially disadvantaged caste-groups in India have historically been engaged in labour-intensive, blue-collar work. We study whether there has been any change in the ability to predict a woman's work-status and work-type based on her caste by interpreting machine learning models using feature attribution. We find that caste is now a less important determinant of work for the younger generation of women compared to the older generation. Moreover, younger women from disadvantaged castes are now more likely to be working in white-collar jobs.

With the rapid development of knowledge bases(KBs),question answering(QA)based on KBs has become a hot research issue. In this paper,we propose two frameworks(i.e.,pipeline framework,an end-to-end framework)to focus answering single-relation factoid question. In both of two frameworks,we study the effect of context information on the quality of QA,such as the entity's notable type,out-degree. In the end-to-end framework,we combine char-level encoding and self-attention mechanisms,using weight sharing and multi-task strategies to enhance the accuracy of QA. Experimental results show that context information can get better results of simple QA whether it is the pipeline framework or the end-to-end framework. In addition,we find that the end-to-end framework achieves results competitive with state-of-the-art approaches in terms of accuracy and take much shorter time than them.

Feature space can change or evolve when learning with streaming data. Several recent works have studied feature evolvable learning. They usually assume that features would not vanish or appear in an arbitrary way. For example, when knowing the battery lifespan, old features and new features represented by data gathered by sensors will disappear and emerge at the same time along with the sensors exchanging simultaneously. However, different sensors would have different lifespans, and thus the feature evolution can be unpredictable. In this paper, we propose a novel paradigm: Prediction with Unpredictable Feature Evolution (PUFE). We first complete the unpredictable overlapping period into an organized matrix and give a theoretical bound on the least number of observed entries. Then we learn the mapping from the completed matrix to recover the data from old feature space when observing the data from new feature space. With predictions on the recovered data, our model can make use of the advantage of old feature space and is always comparable with any combinations of the predictions on the current instance.

In this letter, we address the problem of controlling energy storage systems (ESSs) for arbitrage in real-time electricity markets under price uncertainty. We first formulate this problem as a Markov decision process, and then develop a deep reinforcement learning based algorithm to learn a stochastic control policy that maps a set of available information processed by a recurrent neural network to ESSs' charging/discharging actions. Finally, we verify the effectiveness of our algorithm using real-time electricity prices from PJM.

Construction work on a moonbase could begin within the next decade as China reveals its timeline for future missions to the lunar surface. Zhang Kejian, the administrator of the China National Space Administration (CNSA), announced the plans in a recent speech. The research facility will be located near the moon's ice-rich south pole and will be shared with multiple countries, Mr Zhang said. Ice will be needed on the moon to provide water for both human consumption and as a component for rocket fuel. The small step of a lunar base could serve not only as a platform for research but also as a refuelling station for giant leaps out into the solar system.

I needed some explosive chemicals from the old sawmill on the edge of town, but hundreds of freakers were gathered there, feasting on a mass grave. So I set explosive traps around the building's edges, planned a course through its twists and turns, then tossed a napalm-filled molotov cocktail into the building. They were on me in an instant -- hundreds of hungry monsters ready to rip me limb from limb. I ran for it, hoping not to blow myself up with my own bombs or become the freakers' next meal. After an hour of fighting, dying, and trying again, I was finally victorious. I was out of ammo, explosives and medical supplies, but the horde was dead.

Management actions promoted economic prosperity (positive material contributions) and agricultural abandonment (negative material contributions). The park provides nonmaterial and regulating contributions (e.g., opportunities for nature experiences, habitat creation and maintenance).

When people think of Artificial Intelligence (AI), the major image that pops up in their heads is that of a robot gliding around and giving mechanical replies. There are many forms of AI but humanoid robots are one of the most popular forms. They have been depicted in several Hollywood movies and if you are a fan of science fiction, you might have come across a few humanoids. One of the earliest forms of humanoids was created in 1495 by Leonardo Da Vinci. It was an armor suit and it could perform a lot of human functions such as sitting, standing and walking.

In China, blockchain technology is increasingly employed to settle court cases, local news outlet Global Times reported on April 25. Speaking at the 2019 Forum on China Intellectual Property Protection, Zhang Wen, president of the Beijing Internet Court -- which was established in September 2018, and has since processed 14,904 cases -- reportedly said that the court employs technologies such as artificial intelligence (AI) and blockchain to render judgement. Zhang reportedly told the Global Times that "of the 41 cases concluded [with blockchain technology] so far, parties chose to settle out of court rather than litigate in 40 cases with compelling evidence from blockchain. He also noted that the court had deployed blockchain in 58 cases to collect and provide evidence. "In the current use of AI as an assistant to make rulings, efficiency is prioritized over accuracy.