Person re-identification (Re-ID) aims at matching images of the same person across disjoint camera views, which is a challenging problem in multimedia analysis, multimedia editing and content-based media retrieval communities. The major challenge lies in how to preserve similarity of the same person across video footages with large appearance variations, while discriminating different individuals. To address this problem, conventional methods usually consider the pairwise similarity between persons by only measuring the point to point (P2P) distance. In this paper, we propose to use deep learning technique to model a novel set to set (S2S) distance, in which the underline objective focuses on preserving the compactness of intra-class samples for each camera view, while maximizing the margin between the intra-class set and inter-class set. The S2S distance metric is consisted of three terms, namely the class-identity term, the relative distance term and the regularization term. The class-identity term keeps the intra-class samples within each camera view gathering together, the relative distance term maximizes the distance between the intra-class class set and inter-class set across different camera views, and the regularization term smoothness the parameters of deep convolutional neural network (CNN). As a result, the final learned deep model can effectively find out the matched target to the probe object among various candidates in the video gallery by learning discriminative and stable feature representations. Using the CUHK01, CUHK03, PRID2011 and Market1501 benchmark datasets, we extensively conducted comparative evaluations to demonstrate the advantages of our method over the state-of-the-art approaches.

There is a pressing need to build an architecture that could subsume these networks undera unified framework that achieves both higher performance and less overhead. To this end, two fundamental issues are yet to be addressed. The first one is how to implement the back propagation when neuronal activations are discrete. The second one is how to remove the full-precision hidden weights in the training phase to break the bottlenecks of memory/computation consumption. To address the first issue, we present a multistep neuronal activation discretization method and a derivative approximation technique that enable the implementing the back propagation algorithm on discrete DNNs. While for the second issue, we propose a discrete state transition (DST) methodology to constrain the weights in a discrete space without saving the hidden weights. In this way, we build a unified framework that subsumes the binary or ternary networks as its special cases.More particularly, we find that when both the weights and activations become ternary values, the DNNs can be reduced to gated XNOR networks (or sparse binary networks) since only the event of non-zero weight and non-zero activation enables the control gate to start the XNOR logic operations in the original binary networks. This promises the event-driven hardware design for efficient mobile intelligence. We achieve advanced performance compared with state-of-the-art algorithms. Furthermore,the computational sparsity and the number of states in the discrete space can be flexibly modified to make it suitable for various hardware platforms.

One of the scariest statements I ever heard came from a young relative of mine: "All the science I know I learned from your books!" To which I replied, gasping a little, "But you know I make it up, right?" But not entirely--which raises a critical question. While much has been written about how to use science to create, plot, or enhance one's fiction, not as much has been written about how speculative fiction impacts our understanding of science. Consider the following: Haijun Yao, editor of China's major SF magazine, Science Fiction World, told me last year that the Chinese government, which banned SF during the Cultural Revolution, is now very enthusiastic about its publication.

The response to Musk's comments about the need for Artificial Intelligence (AI) regulation by experts in has been almost like a knee-jerk reaction. The reaction has been prevalently along the lines of not being able to identify areas that require regulation. I suspect that most AI researchers have not really made a serious effort with regards to the big picture. I am deliberately avoid discussing here the "Why?" of AI regulation. Rather I will discuss the questions of "What?" and "How?".

Autonomous vehicles can add a new member to their ranks--the self-driving wheelchair. This summer, two robotic wheelchairs made headlines: one at a Singaporean hospital and another at a Japanese airport. The Singapore-MIT Alliance for Research and Technology, or SMART, developed the former, first deployed in Singapore's Changi General Hospital in September 2016, where it successfully navigated the hospital's hallways. It is the latest in a string of autonomous vehicles made by SMART, including a golf cart, electric taxi and, most recently, a scooter that zipped more than 100 MIT visitors around on tours in 2016. The SMART self-driving wheelchair has been in development for about a year and a half, since January 2016, says Daniela Rus, director of MIT's Computer Science and Artificial Intelligence Laboratory and a principal investigator in the SMART Future Urban Mobility research group.

Tom Siebel is a legend in enterprise software, having sold his company, Siebel Software, to Larry Ellison's Oracle (ORCL) in 2006 for $3.4 billion. You might not immediately suspect that about him in person. His mop of curly hair and his thoughtful expression give him the air of a Bob Dylan of technology, an artist, while his youthful enthusiasm suggests he's still on the Silicon Valley startup road trip, even though at age 64, he's seen four decades of the tech world's evolution. Unlike many very accomplished people, he deflects from his own ego and endows his peers in tech, such as Ellison, with heaps of praise, calling them "brilliant" "very, very smart," or "so, so smart." Siebel recently swung by the Barron's offices to chat about C3 IoT, his next company, which he claims has already surpassed established firms such as IBM (IBM) and GE (GE) in the Internet of Things.

Demand for artificial intelligence and machine learning specialists in the country are expected to see a 60 per cent rise by 2018 due to increasing adoption of automation, says KellyOCG India. According to Francis Padamadan, Country Director, KellyOCG India, a talent management solutions provider, although AI and machine adoption is on the rise in India, there is negligible talent with experience in technologies like deep learning and neutral networks.

Seen it, seen it, seen it, seen it, seen it. Most of us instinctively know whether objects are familiar or unfamiliar. Now we may know how we know. It turns out monkeys have a cluster of neurons in their brains that decides whether or not they have seen objects before. The primary visual area, at the back of the brain, does most of the early work in perceiving an object, especially its physical attributes, such as what direction it is moving.

A video that shows an automatic bathroom soap dispenser failing to detect the hand of a dark-skinned man has gone viral and raised questions about racism in technology, as well as the lack of diversity in the industry that creates it. The now-viral video was uploaded to Twitter on Wednesday by Chukwuemeka Afigbo, Facebook's head of platform partnerships in the Middle east and Africa. He tweeted: 'If you have ever had a problem grasping the importance of diversity in tech and its impact on society, watch this video.' The video begins with a white man waving his hand under the dispenser and instantly getting soap on his first try. Then, a darker skinned man waves his hand under the dispenser in various directions for ten seconds, with soap never being released.

The Bios Urn mixes cremains with soil and seedlings. It automatically waters and cares for the memorial sapling, sending updates to a smartphone app. The Bios Urn mixes cremains with soil and seedlings. It automatically waters and cares for the memorial sapling, sending updates to a smartphone app. Earlier this summer, a modest little startup in Barcelona, Spain, unveiled its newest product -- a biodegradable, Internet-connected funeral urn that turns the ashes of departed loved ones into an indoor tree.