The paper presents an interesting and smart way of performing covariate shift by aiming to make the two distributions indistinguishable by minimizing MMD. The paper however could benefit of more clarity and completeness so it can make impact. In terms of the applicability of this approach, the authors talk about the importance of being able to perform statistical tests and not just optimize the performance of a classifier. The bounds that they derive for their statistical test is useful to know how big the sample size should be to perform an appropriate shift. However, this doesn't say much on how it affects the scientific questions asked in the experiment, which need different statistical test.

Advances in artificial intelligence, bioengineering and synthetic biology are powering a new breed of odor sensors, some using living cells.

From anywhere and with just a mobile phone, anyone can become an air traffic controller, or at least a virtual air traffic controller. One can follow the world traffic flow of airplanes live and find out where an aircraft is coming from and where it is headed. One just has to take advantage of the millions of pieces of data that fly across the Internet. This is the magic power of Big Data. Artificial intelligence then enters the picture to find patterns and give meaning to the massive and heterogeneous information stream.

From an AI perspective, finding effective treatments for cancer is a high-dimensional search problem characterized by many molecularly distinct cancer subtypes, many potential targets and drug combinations, and a dearth of highquality data to connect molecular subtypes and treatments to responses. The broadening availability of molecular diagnostics and electronic medical records presents both opportunities and challenges to apply AI techniques to personalize and improve cancer treatment. We discuss these in the context of Cancer Commons, a "rapid learning" community where patients, physicians, and researchers collect and analyze the molecular and clinical data from every cancer patient and use these results to individualize therapies. Research opportunities include adaptively planning and executing individual treatment experiments across the whole patient population, inferring the causal mechanisms of tumors, predicting drug response in individuals, and generalizing these findings to new cases. The goal is to treat each patient in accord with the best available knowledge and to continually update that knowledge to benefit subsequent patients.

Our group's work in medical decision making has led us to formulate a framework for expert system design, in particular about how the domain knowledge may be decomposed into substructures We propose that there exist different problem-solving types, i e, uses of knowledge, and corresponding to each is a separate substructure specializing in that1 type of problem-solving Each substructure is in turn further decomposed into a hierarchy of specialists which differ from each other not in the type of problem-solving, but in the conceptual content of their knowledge; e g, one of them may specialize in "heart disease," while another may do so in "liver," though both of them are doing the same type of problem-solving Thus ultimately all the knowledge in the system is distributed among problem-solvers which know how to use that knowledge This is in contrast to the currently dominant expert system paradigm which proposes a common knowledge base accessed by knowledge-free problem-solvers of various kinds In our framework there is no distinction between knowledge bases and problem-solvers: each knowledge source is a problem-solver We have so far had occasion to deal with three generic problem-solving types in expert clinical reasoning: diagnosis (classification), data retrieval and organization, and reasoning about consequences of actions In a novice, these expert structures are often incomplete, and other knowledge structures and learning processes are needed to construct and complete them This is a revised and extended version of an invited talk entitled, "Decomposition of Domain Knowledge Into Knowledge Sources: The MDX Approach," delivered at the IV National Conference of the Canadian Society for Computational Studies of Intelligence, May 17-19, 1982, Saskatchewan For the past few years our research group has been investigating the issues of problem-solving as well as knowledge organization and representation in medical decision making. In parallel with this investigation we have also been building and extending a cluster of systems for various aspects of medical reasoning. MDX, which is a diagnostic system, i.e., its role is to arrive RADEX is a Though in a sense RADEX and PATREC can both be viewed as "intelligent" data base specialists, RADEX has some additional features of interest due to the perceptual nature of some of its knowledge. However, for the purpose of this paper, it is not necessary to go into RADEX in much detail, and we can view PATREC as prototypical of this class of auxiliary systems. Our aim in this paper is to outline a point of view about how a domain gets naturally decomposed into substructures each of which specializes in one type of problem-solving.

Gene-expression microarrays, commonly called gene chips, make it possible to simultaneously measure the rate at which a cell or tissue is expressing--translating into a protein--each of its thousands of genes. One can use these comprehensive snapshots of biological activity to infer regulatory pathways in cells; identify novel targets for drug design; and improve the diagnosis, prognosis, and treatment planning for those suffering from disease. However, the amount of data this new technology produces is more than one can manually analyze. Hence, the need for automated analysis of microarray data offers an opportunity for machine learning to have a significant impact on biology and medicine. This article describes microarray technology, the data it produces, and the types of machine learning tasks that naturally arise with these data.

These diseases can affect people (West Nile virus, HIV, Ebola, Bovine Spongiform Encephalitis), animals (foot-and-mouth disease), and plants (citrus canker in Florida). More recently, the potential of biological terrorism has become a very real threat. On 11 September 2001, the Center for Disease Control alerted states and local public health agencies to monitor for any unusual disease patterns, including the effects of chemical and biological agents (figure 1). In addition to possible disruption and loss of life, bioterrorism could foment political instability, given the panic that fast-moving plagues have historically engendered. Appropriate response to disease outbreaks and emerging threats depends on obtaining reliable and up-to-date information, which often means monitoring many news sources, particularly local news sources, in many languages worldwide.

Origins The idea of developing a tutoring program from the MYCIN knowledge base was first described by Ted Shortliffe (1974). In fact, it was the mixed-initiative dialogue of the SCHOLAR teaching program (Carbonell, 1970) that inspired Shortliffe to produce the consultation dialogue of MYCIN. He conceived of it as a question-answer program in SCHOLAR's style, using a semantic network of disease knowledge. Shortly after I joined the MYCIN project in early 1975, Bruce Buchanan and I decided that developing a tutoring program would be my thesis project. The GUIDON program was operational in early 1979.

Decision analysis and knowledge-based expert systems share some common goals. Both technologies are designed to improve human decision making; they attempt to do this by formalizing human expert knowledge so that it is amenable to mechanized reasoning. However, the technologies are based on rather different principles. Decision analysis is the application of the principles of decision theory supplemented with insights from the psychology of judgment. Expert systems, at least as we use this term here, involve the application of various logical and computational techniques of AI to the representation of human knowledge for automated inference.

Despite some difficult problems in such places, a period of enormous technologydriven change has created new opportunities to address poor management of resources and improve human wellbeing. As just one example of the possibilities, however, take road traffic in cities. The chaotic and spectacular road congestion that is characteristic of developing-world cities is a microcosm of opportunities for applying AI methods. The problems are mainly caused by inadequate infrastructure (for example, road layouts that have not changed significantly despite decades of economic growth, unsealed or pothole-strewn roads), and a lack of resources to monitor or control traffic (for example, scarce and possibly corrupt traffic police, rolling blackouts affecting traffic lights). Any such solution must take into account the unique nature of traffic in these places, where the assumptions made in developed-world intelligent transport systems -- for example, that drivers travel in the correct direction, and only on the road -- might not be valid.