Published online 30 May 2017 [31] Perez JC (2017) The "Master Code of DNA": Towards the Discovery of the SNPs Function (Single-Nucleotide Polymorphism).

A drone flying above a camel race in Al Batinah South, Oman, captured stunning images of the ancient sport as it flew above the racetrack. Camels have long played an important role in many aspects of desert life. They're used as transportation, food, the focus of festivals, and--in the case of the centuries old practice of camel racing--entertainment. The camels, one-humped dromedaries, used in these races can reach speeds of up to 40 miles per hour along designated tracks. They're expensive to own, and no betting is allowed during the races, so the events are often watched only by people who have skin in the game, including sheikhs, handlers, and owners.

Business people, not to mention the public on a global basis, are getting increasingly excited, as well as concerned, about the potential of artificial intelligence (A.I.)--so much so that China's growing involvement in A.I., and the vast quantity of data that China is capable of generating on a daily basis, has many wondering if the U.S. will be a leader or follower in this important technology category as the future unfolds. Data is the fuel that feeds A.I. The more data you have, the more A.I. can learn and adapt. Most feel it's all about the quantity of data. I have been sharing both in my international speeches and consulting that data quantity is good but not if the quality is bad, and this concern should be forthright for anyone involved in A.I.

Drones are a controversial tech gadget to say the least. They can pose a risk to aircraft, cause potential privacy issues, and are being used to smuggle contraband into prisons. Despite their bad reputation, a lot of research is being put into the use of unmanned aerial vehicles (UAVs) within emergency missions. At New York University's Abu Dhabi campus, Professor of Electrical and Computer Engineering, Antonios Tzes, has been manning a project across five different universities in the US, Sweden, Switzerland, Netherlands, and Greece, to develop drones for use inside buildings, particularly in fire situations. After designing ground vehicles for rescue operations, Tzes and his team were looking for a way to move away from the ground. "We needed to go up into the air, in confined spaces, and drones were the logical way to do it," he tells the Standard.

India is the latest country to announce a national AI initiative. But AI's leading countries are unlikely to let another player muscle in on their turf. The plan: A task force established by the Indian government has released a report on artificial intelligence that calls for the country to boost investment and focus on deploying the technology in manufacturing, health care, agriculture, education, and public utilities. The challenge: India is on course to become the third-largest economy in the world (by GDP) within the next few years. But the country may find it hard to kick-start its own AI revolution. India is playing catch-up with China and the West in terms of technology, research prowess, investment, and--crucially--data, the lifeblood of AI.

India has ambitions to fire up its artificial intelligence capabilities -- but experts say that it's unlikely to catch up with the U.S. and China, which are fiercely competing to be the world leader in the field. An Indian government-appointed task force has released a comprehensive plan with recommendations to boost the AI sector in the country for at least the next five years -- from developing AI technologies and infrastructure, to data usage and research. The task force, appointed by India's Ministry of Commerce and Industry, proposes that the government work with the private sector to develop technologies, with a focus on smart cities and the country's power and water infrastructure. It recommends a network of infrastructure -- a testing facility, and six centers focusing on research in generating AI technologies, such as robotics, autonomous trucks and advanced financial technology. A data center could be set up to "develop an autonomous AI machine that can work on multiple data streams in real time," the plan said. Calling data the "fuel that powers AI," the report said data marketplaces and exchanges could allow the "free flow of data."

Just because Netflix's films got shut out of competition at Cannes doesn't mean the streaming titan stayed home. The company won a worldwide (except for China) distribution deal worth $30 million for the animated film Next Gen with a star-studded cast about a pair of "two unlikely friends in a world filled with robots," according to Deadline. Not much else is known about the film, although Deadline surmised it's one of the biggest deals to come out of the film festival thus far. The voice cast includes Charlyne Yi, Michael Peña, Constance Wu, Jason Sudeikis and David Cross. It's unclear when Netflix will distribute the film, but we know when it's coming out in China: Alibaba and Wanda, which won rights to distribute in the country, will give it a wide release this summer.

There are many important regression problems in real-world brain-computer interface (BCI) applications, e.g., driver drowsiness estimation from EEG signals. This paper considers offline analysis: given a pool of unlabeled EEG epochs recorded during driving, how do we optimally select a small number of them to label so that an accurate regression model can be built from them to label the rest? Active learning is a promising solution to this problem, but interestingly, to our best knowledge, it has not been used for regression problems in BCI so far. This paper proposes a novel enhanced batch-mode active learning (EBMAL) approach for regression, which improves upon a baseline active learning algorithm by increasing the reliability, representativeness and diversity of the selected samples to achieve better regression performance. We validate its effectiveness using driver drowsiness estimation from EEG signals. However, EBMAL is a general approach that can also be applied to many other offline regression problems beyond BCI.

Active learning (AL) [33], a subfield of machine learning, considers the following problem: if the learning algorithm can choose the training data, then which training samples should it choose to maximize the learning performance, under a fixed budget, e.g., the maximum number of labeled training samples? As an example, consider emotion estimation in affective computing [28]. Emotions can be represented as continuous numbers in the 2D space of arousal and valence [30], or in the 3D space of arousal, valence, and dominance [26]. However, emotions are very subjective, subtle, and uncertain. So, usually multiple human assessors are needed to obtain the groundtruth emotion values for each affective sample (video, audio, image, physiological signal, etc). For example, 14-16 assessors were used to evaluate each video clip in the DEAP dataset [21], six to 17 assessors for each utterance in the VAM (Vera am Mittag in German, Vera at Noon in English) spontaneous speech corpus [16], and at least 110 assessors for each sound in the IADS-2 (International Affective Digitized Sounds 2nd Edition) dataset [4]. This is very time-consuming and labor-intensive. How should we optimally select the affective samples to label so that an accurate regression model can be built with the minimum cost (i.e., the minimum number of labeled samples)?

Analogical reasoning is effective in capturing linguistic regularities. This paper proposes an analogical reasoning task on Chinese. After delving into Chinese lexical knowledge, we sketch 68 implicit morphological relations and 28 explicit semantic relations. A big and balanced dataset CA8 is then built for this task, including 17813 questions. Furthermore, we systematically explore the influences of vector representations, context features, and corpora on analogical reasoning. With the experiments, CA8 is proved to be a reliable benchmark for evaluating Chinese word embeddings.