We consider the problem of metric learning subject to a set of constraints on relative-distance comparisons between the data items. Such constraints are meant to reflect side-information that is not expressed directly in the feature vectors of the data items. The relative-distance constraints used in this work are particularly effective in expressing structures at finer level of detail than must-link (ML) and cannot-link (CL) constraints, which are most commonly used for semi-supervised clustering. Relative-distance constraints are thus useful in settings where providing an ML or a CL constraint is difficult because the granularity of the true clustering is unknown. Our main contribution is an efficient algorithm for learning a kernel matrix using the log determinant divergence --- a variant of the Bregman divergence --- subject to a set of relative-distance constraints. The learned kernel matrix can then be employed by many different kernel methods in a wide range of applications. In our experimental evaluations, we consider a semi-supervised clustering setting and show empirically that kernels found by our algorithm yield clusterings of higher quality than existing approaches that either use ML/CL constraints or a different means to implement the supervision using relative comparisons.

Community detection in networks is an important problem in many applied areas. In this paper, we investigate this in the presence of node covariates. Recently, an emerging body of theoretical work has been focused on leveraging information from both the edges in the network and the node covariates to infer community memberships. However, so far the role of the network and that of the covariates have not been examined closely. In essence, in most parameter regimes, one of the sources of information provides enough information to infer the hidden cluster labels, thereby making the other source redundant. To our knowledge, this is the first work which shows that when the network and the covariates carry "orthogonal" pieces of information about the cluster memberships, one can get improved clustering accuracy by using them both, even if each of them fails individually.

BROSSARD, QUEBEC--(Marketwired - Dec. 2, 2016) - (TSX VENTURE:ADK) - Diagnos Inc. ("DIAGNOS" or "the Company") a leader in the use of artificial intelligence and advanced knowledge extraction techniques is pleased to announce a grant of 500,000 stock options to one of its directors. The exercise price of these options has been established at $0.09 per share. The expiry date to which these options can be exercised has been fixed to December 2, 2021. All monies quoted in this press release shall be stated in lawful money of Canada. Founded in 1998, DIAGNOS is a publicly traded Canadian corporation with a mission to commercialize technologies combining contextual imaging and traditional data mining thereby improving decision making processes.

BROSSARD, QUEBEC--(Marketwired - Dec 2, 2016) - (TSX VENTURE:ADK) - Diagnos Inc. ("DIAGNOS" or "the Company") a leader in the use of artificial intelligence and advanced knowledge extraction techniques is pleased to announce a grant of 500,000 stock options to one of its directors. The exercise price of these options has been established at $0.09 per share. The expiry date to which these options can be exercised has been fixed to December 2, 2021. All monies quoted in this press release shall be stated in lawful money of Canada. Founded in 1998, DIAGNOS is a publicly traded Canadian corporation with a mission to commercialize technologies combining contextual imaging and traditional data mining thereby improving decision making processes.

DON'T make fun weekend plans: Researchers say too much planning will kill your enjoyment A researcher from The Ohio State University found that it is common for people to lose excitement over plans they scheduled in advance. Not just for lonely men: Sex robots will let couples have... From a dazzling supermoon to a stunning meteor shower: Here... Are these the legs of Queen Nefertari? Mystery mummified... Britain's ancient multicultural heritage revealed: Roman... Not just for lonely men: Sex robots will let couples have... From a dazzling supermoon to a stunning meteor shower: Here... Are these the legs of Queen Nefertari? Mystery mummified... Britain's ancient multicultural heritage revealed: Roman... Across 13 studies, researchers found that the simple act of scheduling makes otherwise fun tasks feel more like work.

In a recent survey by Tech Pro Research, only 28 percent of respondents, most of whom were in IT leadership positions, said they have firsthand experience with AI or machine learning. However, if the survey results hold true, the majority of respondents will be using the technologies at work in the next few years. Another interesting findings from this survey was that while 42 percent of respondents said their technical staff lack the skills to implement and support AI and machine learning, 41 percent said that all the work in this area would be done in-house. Thirty-nine percent of respondents said their companies were also still working on selecting AI and machine learning vendors. More findings from the survey are shown below. To get all the data and analysis, download the full report: AI and machine learning in the enterprise: Uses, organizational readiness and vendor choices.

As in the global survey, we are concerned with platform business models and the design choices that allow these business models to grow. We find the term platform, which is well-established in economic and management literature, offers a more useful and accurate term than some of the terms that have been used such as "share economy companies," "internet companies" or, even more broadly, "tech companies." Network effects are a key characteristic that distinguish platforms from other business models. As more users engage with a platform, the more attractive the platform becomes to potential new users. When more users attract more users, a dynamic is created that in turn triggers a self-reinforcing cycle of growth.

Several years ago, I attended a Buddhist retreat in which I was introduced to the idea of the "retinue," a constellation of influential and supportive people whom one imagines in an enveloping cloud as one meditates. I took the concept one step further and decided to create an actual photo montage that I could hang on the wall above my desk: my childhood piano teacher, my high school English teacher, my rabbi, mentors in science, writers who encouraged me--in all, 20 people who had profoundly influenced me. Some members of my retinue were still living, some not. In some cases I could find the photographs myself. In others, I had to contact the mentors. When I finally tracked down William Gerace, who introduced me to physics nearly 50 years ago, he was puzzled as to why I should desire such a montage. We had not spoken for decades. Reluctantly, he sent me an old, out-of-focus photo of himself, dating back to the days when I knew him. Now, Gerace is a professor of science education at the University of North Carolina at Greensboro, after a 30-year career as a professor of physics at the University of Massachusetts Amherst, during which time he made the transition from theoretical nuclear physicist to leader in science education and co-founder of the Scientific Reasoning Research Institute at the University of Massachusetts, Amherst. When I knew him, in the late 1960s, he was a lowly instructor in physics at Princeton, where he had recently received his Ph.D. I was an undergraduate. The photo shows a man in his late 20s, about 5 feet 6, slight in build, dark hair beginning to thin, dressed in a button-down shirt and blue sweater, and a Mona Lisa smile. Each new mathematical technique Bill taught us was offered with the enthusiasm of a 12-year-old boy showing his friend a strange new butterfly. I first met Bill Gerace during a physics lab my sophomore year.

Biologically based computational modeling promises virtual characters capable of face-to-face human interaction. Of all the experiences we have in life, face-to-face interaction fills many of our most meaningful moments. The complex interplay of facial expressions, eye gaze, head movements, and vocalizations in quickly evolving "social interaction loops" has enormous influence on how a situation will unfold. From birth, these interactions are a fundamental element of learning and lay the foundation for successful social and emotional functioning through life. What are the underlying processes from which this most human form of interaction emerges? Will we be able to interact with computers in a face-to-face way that feels natural? This article discusses the unique challenges of realistically simulating the appearance and behavior of the face to create interactive autonomous virtual human models that support naturalistic learning and have the "illusion of life." We describe our recent progress toward this goal with "BabyX," an autonomously animated psycho-biological model of a virtual infant. While we explore drivers of facial behavior, we also expect this foundational approach has the potential for more "human" computer interfaces. We also describe our work on our "Auckland Face Simulator" we are developing to broaden this work beyond infants and give a more realistic face and a greater biological basis to adult conversational agents. Simulating the face has great potential for human-computer interaction (HCI), as it increases the available communication channels between humans and machines in an intuitive, accessible way. But it is also a vehicle with which to explore our own nature.

A full-body virtual autopsy of an ancient Egyptian mummy showed visitors he was likely murdered. By combining visualization techniques with interactive multi-touch tables and intuitive user interfaces, visitors to museums and science centers can conduct self-guided tours of large volumetric image data. In an interactive learning experience, visitors become the explorers of otherwise invisible interiors of unique artifacts and subjects. Here, we take as our starting point the state of the art in scanning technologies, then discuss the latest research on high-quality interactive volume rendering and how it can be tailored to meet the specific demands of public venues. We then describe our approach to the creation of interactive stories and the design principles on which they are based and interaction with domain experts. The article is based on experience from several application domains but uses a 2012 public installation of an ancient mummy at the British Museum as its primary example. We also present the results of an evaluation of the installation showing the utility of the developed solutions. Visitors walk into Gallery 64, the Early Egypt Gallery at the British Museum, eager to see and learn about one of the most famous and oldest mummies in the collection. Known as the Gebelein Man, he was buried in a crouched position in a shallow grave during the late pre-dynastic period at the site of Gebelein in Upper Egypt.