Graphical Gaussian models have proven to be useful tools for exploring network structures based on multivariate data. Applications to studies of gene expression have generated substantial interest in these models, and resulting recent progress includes the development of fitting methodology involving penalization of the likelihood function. In this paper we advocate the use of multivariate $t$-distributions for more robust inference of graphs. In particular, we demonstrate that penalized likelihood inference combined with an application of the EM algorithm provides a computationally efficient approach to model selection in the $t$-distribution case. We consider two versions of multivariate $t$-distributions, one of which requires the use of approximation techniques. For this distribution, we describe a Markov chain Monte Carlo EM algorithm based on a Gibbs sampler as well as a simple variational approximation that makes the resulting method feasible in large problems.

For each utterance of interest, the set of points corresponding to the location of people at the time of that The Human Speechome Project corpus (Roy 2009), (Roy et utterance are added to the appropriate bin(s) of the histogram al. 2006) is a typical large, unstructured dataset.

In this paper, we introduce new capabilities to our existing microsimulation framework, Simulacron. These new capabilities add the modelling of behaviours based on motivations and improve our existing non-deterministic movement capacity. We then discuss the application of these new features to a simple, synthetic, proof of concept, scenario involving the transit of people through a corridor and how an induced panic affects their throughput. Finally we describe a more complex scenario, which is currently under development, involving the detonation of an explosive device in a major metropolitan transport hub at peak hour and the analysis of subsequent reaction.

Smart environments offer a vision of unobtrusive interaction with our surroundings, interpreting and anticipating our needs. One key aspect for making environments smart is the ability to recognize the current context. However, like any human space, smart environments are subject to changes and mutations of their purposes and their composition as people shape their living places according to their needs. In this paper we present an approach for recognizing context situations in smart environments that addresses this challenge. We propose a formalism for describing and sharing context states (or situations) and an architecture for gradually introducing contextual knowledge to an environment, where the current context is determined on sensing people's usage of devices and sound analysis.

Human computing promises new capabilities that cannot be easily provided by computing machinery. However, humans are less disciplined than their mechanical counterparts and hence are liable to produce accidental or deliberate mistakes. As we start to develop regimes for identifying and correcting errors in human computation, we find an important model in the computing groups that operated at the start of the 20th century.

Despite a dramatic growth of power consumption inhouseholds, less attention has been paid to monitoring,analyzing and predicting energy usage. In this paper,we propose a framework to mine raw energy data bytransforming time series energy data into a symbol se-quence, and then extend a suffix tree data structure asan efficient representation to analyze global structuralpatterns. Then, we use a clustering algorithm to detectenergy pattern outliers which are far from their clustercentroids. To validate our approach, we use real powerdata collected from a smart apartment testbed duringtwo months.

Frustration has been generally viewed in a negative light and its potential role in learning neglected. We propose a new approach to intrinsically motivated learning where frustration is a key factor that allows to dynamically balance exploration and exploitation. Moreover, based on the result obtained from our experiment with older infants, we propose that a temporary decrease in learning from negative feedback can also be beneficial in fine-tuning a newly learned behavior. We suggest that this temporal indifference to the outcome of an action may be related to the sense of control, and results from the state of elation, that is the experience of overcoming a very difficult task after prolonged frustration. Our preliminary simulation results serve as a proof-of-concept for our approach.

Data centers today are heterogeneous: they have servers from multiple generations and multiple vendors; server machines have multiple cores that are capable of running at difference speeds, and some have general purpose graphics processing units (GPGPU). Hardware trends indicate that future processors will have heterogeneous cores with different speeds and capabilities. This environment enables new advances in power saving and application optimization. It also poses new challenges, as current systems software is ill-suited for heterogeneity. In this position paper, we focus on interactive applications and outline some of the techniques to embrace heterogeneity. We show that heterogeneity can be exploited to deliver interactive services in an energy-efficient manner. For example, our initial study suggests that neither high-end nor low-end servers alone are very effective in servicing a realistic workload, which typically has requests with varying service demands. High-end servers achieve good throughput but the energy costs are high. Low-end servers are energy-efficient for short requests, but they may not be able to serve long requests at the desired quality of service. In this work, we show that a heterogeneous system can be a better choice than an equivalent homogeneous system to deliver interactive services in a cost-effective manner, transforming heterogeneity from a resource management nightmare to an asset. We highlight some of the challenges and opportunities and the role of AI and machine learning techniques for hosting large interactive services in data centers.

Having discovered a decision principle for a well-known prima facie duty theory in biomedical ethics to resolve particular cases of a common type of ethical dilemma, we developed three applications: a medical ethics advisor system, a medication reminder system and an instantiation of this system in a Nao robot. We are now developing a general, automated method for generating from scratch the ethics needed for a machine to function in a particular domain, without making the assumptions used in our prototype systems.

We describe a framework for rapidly prototyping applications which require intelligent visual processing, but for which reliable algorithms do not yet exist, or for which engineering those algorithms is too costly. The framework, CrowdSight, leverages the power of crowdsourcing to offload intelligent processing to humans, and enables new applications to be built quickly and cheaply, affording system builders the opportunity to validate a concept before committing significant time or capital. Our service accepts requests from users either via email or simple mobile applications, and handles all the communication with a backend human computation platform. We build redundant requests and data aggregation into the system freeing the user from managing these requirements. We validate our framework by building several test applications and verifying that prototypes can be built more easily and quickly than would be the case without the framework.