Identifying Activity

Identification of active constraints in constrained optimization is of interest from both practical and theoretical viewpoints, as it holds the promise of reducing an inequality-constrained problem to an equality-constrained problem, in a neighborhood of a solution. We study this issue in the more general setting of composite nonsmooth minimization, in which the objective is a composition of a smooth vector function c with a lower semicontinuous function h, typically nonsmooth but structured. In this setting, the graph of the generalized gradient of h can often be decomposed into a union (nondisjoint) of simpler subsets. "Identification" amounts to deciding which subsets of the graph are "active" in the criticality conditions at a given solution. We give conditions under which any convergent sequence of approximate critical points finitely identifies the activity. Prominent among these properties is a condition akin to the Mangasarian-Fromovitz constraint qualification, which ensures boundedness of the set of multiplier vectors that satisfy the optimality conditions at the solution.Comment: 16 page

Brain Activity Mapping from MEG Data via a Hierarchical Bayesian Algorithm with Automatic Depth Weighting

A recently proposed iterated alternating sequential (IAS) MEG inverse solver algorithm, based on the coupling of a hierarchical Bayesian model with computationally efficient Krylov subspace linear solver, has been shown to perform well for both superficial and deep brain sources. However, a systematic study of its ability to correctly identify active brain regions is still missing. We propose novel statistical protocols to quantify the performance of MEG inverse solvers, focusing in particular on how their accuracy and precision at identifying active brain regions. We use these protocols for a systematic study of the performance of the IAS MEG inverse solver, comparing it with three standard inversion methods, wMNE, dSPM, and sLORETA. To avoid the bias of anecdotal tests towards a particular algorithm, the proposed protocols are Monte Carlo sampling based, generating an ensemble of activity patches in each brain region identified in a given atlas. The performance in correctly identifying the active areas is measured by how much, on average, the reconstructed activity is concentrated in the brain region of the simulated active patch. The analysis is based on Bayes factors, interpreting the estimated current activity as data for testing the hypothesis that the active brain region is correctly identified, versus the hypothesis of any erroneous attribution. The methodology allows the presence of a single or several simultaneous activity regions, without assuming that the number of active regions is known. The testing protocols suggest that the IAS solver performs well with both with cortical and subcortical activity estimation

A POSSIBLE MODEL FOR ANALYSING THE PRACTICAL NEEDS OF STUDENTS IN ECONOMICS-PRACTEAM MODEL

Data presented in this paper are part of the activities of the PRACTeam project "Practice of students in economics. Inter-regional partnership between universities and the labor market" project co-financed by European Social Fund Operational Programme Human Resources Development 2007-2013 -" Invest in people! "Contract no. POSDRU/90/2.1/S/64150. Identifying the needs of practice activity had as research tools: focus group and questionnaires. Research subjects were third-year students who have completed the practical work from all three partners: Oradea, Timisoara and Suceava. The results obtained in this research were the basis for discussions during the workshop PRACTeam between student representatives, tutors and practice coordinators. Based on the central elements and highlighted problems were developed materials for both tutors and students. The specific objectives of identifying needs for practical training were: to determine administrative and organizational elements deemed most appropriate for students in terms of practical training, identifying methods of communication between all stakeholders (students, coordinators and tutors of practice) the most suitable in terms of training students, identifying the strengths and weaknesses in relation to the conduct of practical training Presentation integrates the results with emphasis on elements that can be improved, structured around the following areas: evaluation of the internship, access into the practice, conduct practical work (satisfaction with the relationship with the tutor, satisfaction with relationship with practice coordinator, student satisfaction with the activity, satisfaction with knowledge, skills acquired in satisfaction with the practice, satisfaction with communication with colleagues) positive, negative aspects, students' views on improving practice activity.

Developing A Theory of Change

This is a best practice to obtain clarity about what needs to happen to achieve and sustain the changes, or outcomes, that want to be seen (mapping the outcome pathways to success) and to identify who (people or institutions) can influence these outcomes positively or negatively (mapping the activity ecosystem). It sets the framework for identifying impact, intermediary outcome and process indicators