ZHUHAI, CHINA – A Chinese state-owned company says it is developing a stealth combat drone in the latest sign of the country's growing aerospace prowess. The CH-7 unmanned aerial vehicle also underscores China's growing competitiveness in the expanding global market for drones. China has won sales in the Middle East and elsewhere by offering drones at lower prices and without the political conditions attached by the U.S. The CH-7's chief designer, Shi Wen, says the aircraft can "fly long hours, scout and strike the target when necessary." "Very soon, I believe, in the next one to two years, (we) can see the CH-7 flying in the blue skies, gradually being a practical and usable product in the future," Shi told The Associated Press. Shi said manufacturer Chinese Aerospace Science and Technology Corporation plans to test fly the drone next year and begin mass production by 2022.

The announcement was one of several at the Fujitsu Forum in Munich to feature AI developments. Fujitsu Intelligence Technology brings together the company's AI work in Japan and around the world to run it from Vancouver, Canada. The area has many research institutions such as the University of Toronto engaged in AI and quantum computing research, as well as startup technology companies. The government there is pushing the country towards AI. "In Vancouver and across British Columbia, Fujitsu will have the opportunity to collaborate with our state-of-the-art universities and research facilities to discover new ways that artificial intelligence can help solve local and global challenges," said John Horgan, Premier of the Province of British Columbia.

The paper develops a formal theory of the degree of justification of arguments, which relies solely on the structure of an argumentation framework, and which can be successfully interfaced with approaches to instantiated argumentation. The theory is developed in three steps. First, the paper introduces a graded generalization of the two key notions underpinning Dung's semantics: self-defense and conflict-freeness. This leads to a natural generalization of Dung's semantics, whereby standard extensions are weakened or strengthened depending on the level of self-defense and conflict-freeness they meet. The paper investigates the fixpoint theory of these semantics, establishing existence results for them. Second, the paper shows how graded semantics readily provide an approach to argument rankings, offering a novel contribution to the recently growing research programme on ranking-based semantics. Third, this novel approach to argument ranking is applied and studied in the context of instantiated argumentation frameworks, and in so doing is shown to account for a simple form of accrual of arguments within the Dung paradigm. Finally, the theory is compared in detail with existing approaches.

Data parallelism can boost the training speed of convolutional neural networks (CNN), but could suffer from significant communication costs caused by gradient aggregation. To alleviate this problem, several scalar quantization techniques have been developed to compress the gradients. But these techniques could perform poorly when used together with decentralized aggregation protocols like ring all-reduce (RAR), mainly due to their inability to directly aggregate compressed gradients. In this paper, we empirically demonstrate the strong linear correlations between CNN gradients, and propose a gradient vector quantization technique, named GradiVeQ, to exploit these correlations through principal component analysis (PCA) for substantial gradient dimension reduction. GradiVeQ enables direct aggregation of compressed gradients, hence allows us to build a distributed learning system that parallelizes GradiVeQ gradient compression and RAR communications. Extensive experiments on popular CNNs demonstrate that applying GradiVeQ slashes the wall-clock gradient aggregation time of the original RAR by more than 5X without noticeable accuracy loss, and reduces the end-to-end training time by almost 50%. The results also show that GradiVeQ is compatible with scalar quantization techniques such as QSGD (Quantized SGD), and achieves a much higher speed-up gain under the same compression ratio.

Because combat environments change over time and technology upgrades are widespread for ground vehicles, a large number of vehicles and equipment become quickly obsolete. A possible solution for the U.S. Army is to develop fleets of modular military vehicles, which are built by interchangeable substantial components also known as modules. One of the typical characteristics of module is their ease of assembly and disassembly through simple means such as plug-in/pull-out actions, which allows for real-time fleet reconfiguration to meet dynamic demands. Moreover, military demands are time-varying and highly stochastic because commanders keep reacting to enemy's actions. To capture these characteristics, we formulated an intelligent agent-based model to imitate decision making process during fleet operation, which combines real-time optimization with artificial intelligence. The agents are capable of inferring enemy's future move based on historical data and optimize dispatch/operation decisions accordingly. We implement our model to simulate an attacker-defender game between two adversarial and intelligent players, representing the commanders from modularized fleet and conventional fleet respectively. Given the same level of combat resources and intelligence, we highlight the tactical advantages of fleet modularity in terms of win rate, unpredictability and suffered damage.

Distributed training of deep nets is an important technique to address some of the present day computing challenges like memory consumption and computational demands. Classical distributed approaches, synchronous or asynchronous, are based on the parameter server architecture, i.e., worker nodes compute gradients which are communicated to the parameter server while updated parameters are returned. Recently, distributed training with AllReduce operations gained popularity as well. While many of those operations seem appealing, little is reported about wall-clock training time improvements. In this paper, we carefully analyze the AllReduce based setup, propose timing models which include network latency, bandwidth, cluster size and compute time, and demonstrate that a pipelined training with a width of two combines the best of both synchronous and asynchronous training. Specifically, for a setup consisting of a four-node GPU cluster we show wall-clock time training improvements of up to 5.4x compared to conventional approaches.

Four-dimensional scanning transmission electron microscopy (4D-STEM) of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomic structure and atomic electric fields. However, efficient processing and interpretation of large volumes of data remain challenging, especially for two-dimensional or light materials because the diffraction signal recorded on the pixelated arrays is weak. Here we employ data-driven manifold leaning approaches for straightforward visualization and exploration analysis of the 4D-STEM datasets, distilling real-space neighboring effects on atomically resolved deflection patterns from single-layer graphene, with single dopant atoms, as recorded on a pixelated detector. These extracted patterns relate to both individual atom sites and sublattice structures, effectively discriminating single dopant anomalies via multi-mode views. We believe manifold learning analysis will accelerate physics discoveries coupled between data-rich imaging mechanisms and materials such as ferroelectric, topological spin and van der Waals heterostructures.

Tooth-like scales of sharks and chicken feathers are created by the same process and explained by a theory from the legendary code-breaker Alan Turing. His reaction-diffusion theory is widely accepted as the way in which many animals get unique patterns in their feathers, fur, teeth and teeth. It has now been extended to include the development of shark scales - a group of animals that are very distantly related to the other known animals. The findings help explain how the scales of a shark evolved to reduce drag and be more energy efficient while swimming. Scientists believe this patterning could help in designing shark-inspired materials to improve energy efficiency.

China has begun rolling out new surveillance software capable of recognising people simply by the way that they walk. The "gait recognition" technology, developed by Chinese artificial intelligence firm Watrix, is capable of identifying individuals from the shape and movement of their silhouette from up to 50 metres away, even if their face is hidden. The system is currently being used by police in Beijing and Shanghai and adds to the country's formidable surveillance network that includes an estimated 170 million CCTV cameras. The software can be used on footage from standard surveillance cameras, however it does not currently work in real-time. Instead, the footage is analysed once it is recorded, which takes approximately 10 minutes.

India is a country in South Asia. It is the seventh-largest country by area, the second-most populous country (with over 1.2 billion people), and the most populous democracy in the world. It is bounded by the Indian Ocean on the south, the Arabian Sea on the southwest, and the Bay of Bengal on the southeast. It shares land borders with Pakistan to the west; China, Nepal, and Bhutan to the northeast; and Bangladesh and Myanmar to the east. In the Indian Ocean, India is in the vicinity of Sri Lanka and the Maldives. According to the International Monetary Fund (IMF), the Indian economy in 2017 was nominally worth US$2.611