It is intuitive that NLP tasks for logographic languages like Chinese should benefit from the use of the glyph information in those languages. However, due to the lack of rich pictographic evidence in glyphs and the weak generalization ability of standard computer vision models on character data, an effective way to utilize the glyph information remains to be found. In this paper, we address this gap by presenting the Glyce, the glyph-vectors for Chinese character representations. We make three major innovations: (1) We use historical Chinese scripts (e.g., bronzeware script, seal script, traditional Chinese, etc) to enrich the pictographic evidence in characters; (2) We design CNN structures tailored to Chinese character image processing; and (3) We use image-classification as an auxiliary task in a multi-task learning setup to increase the model's ability to generalize. For the first time, we show that glyph-based models are able to consistently outperform word/char ID-based models in a wide range of Chinese NLP tasks. Using Glyce, we are able to achieve the state-of-the-art performances on 13 (almost all) Chinese NLP tasks, including (1) character-Level language modeling, (2) word-Level language modeling, (3) Chinese word segmentation, (4) name entity recognition, (5) part-of-speech tagging, (6) dependency parsing, (7) semantic role labeling, (8) sentence semantic similarity, (9) sentence intention identification, (10) Chinese-English machine translation, (11) sentiment analysis, (12) document classification and (13) discourse parsing

We consider privacy-preserving algorithms for deep reinforcement learning. State-of-the-art methods that guarantee differential privacy are not extendable to very large state spaces because the noise level necessary to ensure privacy would scale to infinity. We address the problem of providing differential privacy in Q-learning where a function approximation through a neural network is used for parametrization. We develop a rigorous and efficient algorithm by inspecting the reproducing kernel Hilbert space in which the neural network is embedded. Our approach uses functional noise to guarantee privacy, while the noise level scales linearly with the complexity of the neural network architecture. There are no known theoretical guarantees on the performance of deep reinforcement learning, but we gain some insight by providing a utility analysis under the discrete space setting.

Model-free reinforcement learning relies heavily on a safe yet exploratory policy search. Proximal policy optimization (PPO) is a prominent algorithm to address the safe search problem, by exploiting a heuristic clipping mechanism motivated by a theoretically-justified "trust region" guidance. However, we found that the clipping mechanism of PPO could lead to a lack of exploration issue. Based on this finding, we improve the original PPO with an adaptive clipping mechanism guided by a "trust region" criterion. Our method, termed as Trust Region-Guided PPO (TRPPO), improves PPO with more exploration and better sample efficiency, while maintains the safe search property and design simplicity of PPO. On several benchmark tasks, TRPPO significantly outperforms the original PPO and is competitive with several state-of-the-art methods.

Geometrical and appearance quality requirements set the limits of the current industrial performance in injection molding. To guarantee the product's quality, it is necessary to adjust the process settings in a closed loop. Those adjustments cannot rely on the final quality because a part takes days to be geometrically stable. Thus, the final part geometry must be predicted from measurements on hot parts. In this paper, we use recent success of Generative Adversarial Networks (GAN) with the pix2pix network architecture to predict the final part geometry, using only hot parts thermographic images, measured right after production. Our dataset is really small, and the GAN learns to translate thermography to geometry. We firstly study prediction performances using different image similarity comparison algorithms. Moreover, we introduce the innovative use of Discrete Modal Decomposition (DMD) to analyze network predictions. The DMD is a geometrical parameterization technique using a modal space projection to geometrically describe surfaces. We study GAN performances to retrieve geometrical parameterization of surfaces.

Thanks to voice control technology, guests can just tell the room that they want the lights dimmed, the room a little warmer, the music a little softer, and order up some champagne and oysters, keeping their hands free for whatever they want. There are only two hotels equipped with the AI Smart Rooms for now, but InterContinental Hotels plans to roll out the smart service to a total of 100 AI-powered suites across China within the year. InterContinental isn't the only brand rushing to embrace technology in the hopes of wowing business travelers and wooing millennials. Marriott is piloting a new facial recognition check-in program and high-tech showers, while Hilton is taking a phone-based approach to smart rooms.

SEPANG, 28 January 2019 - AirAsia today unveiled a website and mobile app facelift, including a chatbot named AVA (AirAsia Virtual Allstar) powered by artificial intelligence. The updates are designed with guests in mind, and are aimed at delivering a more seamless, user-friendly experience to airasia.com and the app's 3.3 million monthly active users, from flight bookings to browsing for deals to online shopping and customer support. New comprehensive homepage designed for easier and faster navigation, including upcoming flight notice, search shortcut and recent searches, links to the best hotels, travel, duty-free shopping and activity deals, recommended destinations and link to BigPay, AirAsia's money app. Meet AVA, an AI chatbot available on the new live chat feature to respond to guest inquiries instantly. AVA currently speaks eight languages - English, Bahasa Malaysia, Thai, Bahasa Indonesia, Vietnamese, Korean, Simplified Chinese and Traditional Chinese.

Scientists have made a huge breakthrough that allows them to convert radio signals into power. The discovery could allow for phones and other devices that don't use batteries – as well as entirely new ways of using smart technologies. Scientists in the US developed the device, known as a "rectenna", from a semiconductor just a few atoms thick. Wi-fi signals captured by an integrated antenna are transformed into a DC current suitable for electronic circuits. The device could be used to provide battery-less power for smartphones, laptops, medical devices and wearable technology, according to the US-led team.

Toshiba unveiled a remote-controlled robot with tongs on Monday that it hopes will be able to probe the inside of one of the three damaged reactors at Japan's tsunami-hit Fukushima nuclear plant and grip chunks of highly radioactive melted fuel. The device is designed to slide down an extendable 11-meter (36-foot) long pipe and touch melted fuel inside the Unit 2 reactor's primary containment vessel. The reactor was built by Toshiba and GE. An earlier probe carrying a camera captured images of pieces of melted fuel in the reactor last year, and robotic probes in the two other reactors have detected traces of damaged fuel, but the exact location, contents and other details remain largely unknown. Toshiba unveiled the device carrying tongs that comes out of a long telescopic pipe for an internal probe in one of three damaged reactor chambers at Japan's tsunami-hit Fukushima nuclear plant - this time to touch chunks of melted fuel Toshiba's energy systems unit said experiments with the new probe planned in February are key to determining the proper equipment and technologies needed to remove the fuel debris, the most challenging part of the decommissioning process expected to take decades.

India's new industrial policy which will be rolled out in the coming months will have a deep focus on technology and innovation to boost manufacturing and economic growth. Sources in Niti Aayog, the government's policy thinktank, told FactorDaily that while Make In India was focussed largely on programs and schemes, the industrial policy will look at the long-term development of manufacturing and create employment. The industrial policy, which the Department of Industrial Policy and Promotion and Niti Aayog is working on will be built in a way that Indian manufacturing is future ready, and that cannot be done without technology. "The idea is to prepare for future industries. For example, the way fintech, artificial intelligence and internet of things are converging (towards) manufacturing," said one source.