ISLAMABAD – Over the years, Pakistan's Imran Khan has been known for his anti-American rhetoric, once even suggesting he might, as prime minister, order the shooting down of U.S. drones targeting al-Qaida figures along the Pakistan-Afghan border. Now that Khan is poised to become nuclear-armed Pakistan's leader, Washington will be watching closely for signs of whether he will follow a path of confrontation or continue with the conciliatory tone he struck in his election victory speech. His attitude toward the United States and President Donald Trump -- to whom Khan has often been compared as a populist shaking up the established political order -- could determine the future of a crucial but fraught relationship. Officially allies in fighting terrorism, Pakistan and the United States have a complicated relationship, bound by Washington's dependence on Pakistan to supply its troops in Afghanistan but plagued by accusations that Islamabad is playing a double game. Tensions have grown over U.S. complaints that the Afghan Taliban and al-Haqqani network that target American troops in Afghanistan are allowed to shelter on Pakistani soil.

China has been the hottest story in robotics for the past year. With the Made in China 2025 plan, Xi Jinping's government literally came up with a roadmap for dominating the global robotics industry. An executive guide to the technology and market drivers behind the $135 billion robotics market. But is the hype warranted? Have recent trade tensions with the U.S. affected China's automation push?

Invest India and the UAE Minister for Artificial Intelligence (AI) signed a Memorandum of Understanding (MoU) for India – UAE Artificial Intelligence Bridge in New Delhi. This partnership will generate an estimated USD 20 billion in economic benefits during the next decade for both countries. The MoU will spur development across areas like Blockchain, AI and Analytics as data and processing will be a catalyst for innovation and business growth and serve as the backbone of more effective and efficient service delivery systems. By 2035 AI can potentially add USD 957 billion to the Indian economy. The MoU was signed in the presence of Minister of Commerce & Industry and Civil Aviation, Suresh Prabhu and H.E. Ahmad Sultan Al Falahi, Minister Plenipotentiary – Commercial Attache, UAE Embassy at the India leg of GovHack series of World Government Summit.

The other drivers wouldn't have noticed anything unusual as the two sleek limousines with German license plates joined the traffic on France's Autoroute 1. But what they were witnessing -- on that sunny, fall day in 1994 -- was something many of them would have dismissed as just plain crazy. It had taken a few phone calls from the German car lobby to get the French authorities to give the go-ahead. But here they were: two gray Mercedes 500 SELs, accelerating up to 130 kilometers per hour, changing lanes and reacting to other cars -- autonomously, with an onboard computer system controlling the steering wheel, the gas pedal and the brakes. Decades before Google, Tesla and Uber got into the self-driving car business, a team of German engineers led by a scientist named Ernst Dickmanns had developed a car that could navigate French commuter traffic on its own. The story of Dickmann's invention, and how it came to be all but forgotten, is a neat illustration how technology sometimes progresses: not in small steady steps, but in booms and busts, in unlikely advances and inevitable retreats --"one step forward and three steps back," as one AI researcher put it. It's also a warning of sorts, about the expectations we place on artificial intelligence and the limits of some of the data-driven approaches being used today.

China's social e-commerce startup Pinduoduo broke away from the pack with its successful IPO this week in New York, raising $1.6 billion and valuing the company at nearly $30 billion. Pinduoduo, best known of the newly public Chinese tech companies, was one of three that went public in a frenzy of IPO action this past Thursday in New York -- and in the midst of a U.S.-China trade war and growing restrictions on Chinese investment in the U.S. The two other new publicly traded Chinese companies in New York are Chinese unicorn and mobile data provider Jiguang and automotive transaction service platform Cango. Interestingly, both Pinduoduo and Cango have Tencent as backers. I'll write about the two lesser known ones of the trio since so much info is already out there about the three-year-old Pinduoduo, seen as Alibaba's biggest rival. And the others are almost as interesting as Pinduoduo, which has been likened to Groupon meets Dollar Store.

Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decompose the series into seasonal and trend components. Then, predicting the seasonal and trend components respectively, and combining their predictions together as the heat demand prediction is a possible way to predict heat demand. In this paper, STL-ENN-ARIMA (SEA), a combined model, was proposed based on the combination of the Elman neural network (ENN) and the autoregressive integrated moving average (ARIMA) model, which are commonly applied to heat demand prediction. ENN and ARIMA are used to predict seasonal and trend components, respectively. Experimental results demonstrate that the proposed SEA model has a promising performance.

In this paper, we study the problem of recovering a group sparse vector from a small number of linear measurements. In the past the common approach has been to use various "group sparsity-inducing" norms such as the Group LASSO norm for this purpose. By using the theory of convex relaxations, we show that it is also possible to use $\ell_1$-norm minimization for group sparse recovery. We introduce a new concept called group robust null space property (GRNSP), and show that, under suitable conditions, a group version of the restricted isometry property (GRIP) implies the GRNSP, and thus leads to group sparse recovery. When all groups are of equal size, our bounds are less conservative than known bounds. Moreover, our results apply even to situations where where the groups have different sizes. When specialized to conventional sparsity, our bounds reduce to one of the well-known "best possible" conditions for sparse recovery. This relationship between GRNSP and GRIP is new even for conventional sparsity, and substantially streamlines the proofs of some known results. Using this relationship, we derive bounds on the $\ell_p$-norm of the residual error vector for all $p \in [1,2]$, and not just when $p = 2$. When the measurement matrix consists of random samples of a sub-Gaussian random variable, we present bounds on the number of measurements, which are less conservative than currently known bounds.

One fundamental issue in managing bike sharing systems is the bike flow prediction. Due to the hardness of predicting the flow for a single station, recent research works often predict the bike flow at cluster-level. While such studies gain satisfactory prediction accuracy, they cannot directly guide some fine-grained bike sharing system management issues at station-level. In this paper, we revisit the problem of the station-level bike flow prediction, aiming to boost the prediction accuracy leveraging the breakthroughs of deep learning techniques. We propose a new multi-graph convolutional neural network model to predict the bike flow at station-level, where the key novelty is viewing the bike sharing system from the graph perspective. More specifically, we construct multiple inter-station graphs for a bike sharing system. In each graph, nodes are stations, and edges are a certain type of relations between stations. Then, multiple graphs are constructed to reflect heterogeneous relationships (e.g., distance, ride record correlation). Afterward, we fuse the multiple graphs and then apply the convolutional layers on the fused graph to predict station-level future bike flow. In addition to the estimated bike flow value, our model also gives the prediction confidence interval so as to help the bike sharing system managers make decisions. Using New York City and Chicago bike sharing data for experiments, our model can outperform state-of-the-art station-level prediction models by reducing 25.1% and 17.0% of prediction error in New York City and Chicago, respectively.

In fair division of indivisible goods, using sequences of sincere choices (or picking sequences) is a natural way to allocate the objects. The idea is as follows: at each stage, a designated agent picks one object among those that remain. Another intuitive way to obtain an allocation is to give objects to agents in the first place, and to let agents exchange them as long as such "deals" are beneficial. This paper investigates these notions, when agents have additive preferences over objects, and unveils surprising connections between them, and with other efficiency and fairness notions. In particular, we show that an allocation is sequenceable iff it is optimal for a certain type of deals, namely cycle deals involving a single object. Furthermore, any Paretooptimal allocation is sequenceable, but not the converse. Regarding fairness, we show that an allocation can be envy-free and non-sequenceable, but that every competitive equilibrium with equal incomes is sequenceable. To complete the picture, we show how some domain restrictions may affect the relations between these notions. Finally, we experimentally explore the links between the scales of efficiency and fairness. Keywords: Multiagent Resource Allocation, Fair Division, Efficiency, Distributed Resource Allocation 1. Introduction In this paper, we investigate fair division of indivisible goods.

Semiconductor Engineering sat down with Rob Aitken, an Arm fellow; Raik Brinkmann, CEO of OneSpin Solutions; Patrick Soheili, vice president of business and corporate development at eSilicon; and Chris Rowen, CEO of Babblelabs. What follows are excerpts of that conversation. To view part one, click here. SE: Is the industry's knowledge of machine learning keeping up with the pace of development? Rowen: It's clear that more theories will help us understand what is really possible and some things about what kinds of network designs will be better than others. At the same time, many of our biggest technological advancements have been when deployments got well ahead of theories.