Direct mining of subjectively interesting relational patterns

Data is typically complex and relational. Therefore, the development of relational data mining methods is an increasingly active topic of research. Recent work has resulted in new formalisations of patterns in relational data and in a way to quantify their interestingness in a subjective manner, taking into account the data analyst's prior beliefs about the data. Yet, a scalable algorithm to find such most interesting patterns is lacking. We introduce a new algorithm based on two notions: (1) the use of Constraint Programming, which results in a notably shorter development time, faster runtimes, and more flexibility for extensions such as branch-and-bound search, and (2), the direct search for the most interesting patterns only, instead of exhaustive enumeration of patterns before ranking them. Through empirical evaluation, we find that our novel bounds yield speedups up to several orders of magnitude, especially on dense data with a simple schema. This makes it possible to mine the most subjectively-interesting relational patterns present in databases where this was previously impractical or impossible

Feeling Good about Giving: The Benefits (and Costs) of Self-Interested Charitable Behavior

In knowledge-intensive settings such as product or software development, fluid teams of individuals with different sets of experience are tasked with projects that are critical to the success of their organizations. Although building teams from individuals with diverse prior experience is increasingly necessary, prior work examining the relationship between experience and performance fails to find a consistent effect of diversity in experience on performance. The problem is that diversity in experience improves a team's information processing capacity and knowledge base, but also creates coordination challenges. We hypothesize that team familiarity - team members' prior experience working with one another - is one mechanism that helps teams leverage the benefits of diversity in team member experience by alleviating coordination problems that diversity creates. We use detailed project- and individual-level data from an Indian software services firm to examine the effects of team familiarity and diversity in experience on performance for software development projects. We find the interaction of team familiarity and diversity in experience has a complementary effect on a project being delivered on time and on budget. In team familiarity, we identify one mechanism for capturing the performance benefits of diversity in experience and provide insight into how the management of experience accumulation affects team performance.Diversity, Experience, Knowledge, Software, Team Familiarity

When is giving an impulse? An ERP investigation of intuitive prosocial behavior

Human prosociality is often assumed to emerge from exerting reflective control over initial, selfish impulses. However, recent findings suggest that prosocial actions can also stem from processes that are fast, automatic and intuitive. Here, we attempt to clarify when prosocial behavior may be intuitive by examining prosociality as a form of reward seeking. Using event-related potentials (ERPs), we explored whether a neural signature that rapidly encodes the motivational salience of an event\u2014the P300\u2014can predict intuitive prosocial motivation. Participants allocated varying amounts of money between themselves and charities they initially labelled as high- or low-empathy targets under conditions that promoted intuitive or reflective decision making. Consistent with our predictions, P300 amplitude over centroparietal regions was greater when giving involved high-empathy targets than low-empathy targets, but only when deciding under intuitive conditions. Reflective conditions, alternatively, elicited an earlier frontocentral positivity related to response inhibition, regardless of target. Our findings suggest that during prosocial decision making, larger P300 amplitude could (i) signal intuitive prosocial motivation and (ii) predict subsequent engagement in prosocial behavior. This work offers novel insight into when prosociality may be driven by intuitive processes and the roots of such behaviors

Noiseless Independent Factor Analysis with mixing constraints in a semi-supervised framework. Application to railway device fault diagnosis.

International audienceIn Independent Factor Analysis (IFA), latent components (or sources) are recovered from only their linear observed mixtures. Both the mixing process and the source densities (that are assumed to be gener- ated according to mixtures of Gaussians) are learned from observed data. This paper investigates the possibility of estimating the IFA model in its noiseless setting when two kinds of prior information are incorporated: constraints on the mixing process and partial knowledge on the cluster membership of some examples. Semi-supervised or partially supervised learning frameworks can thus be handled. These two proposals have been initially motivated by a real-world application that concerns fault diag- nosis of a railway device. Results from this application are provided to demonstrate the ability of our approach to enhance estimation accuracy and remove indeterminacy commonly encountered in unsupervised IFA such as source permutations