Social marginalization, cultural diversity and education: teachers' representations of Greek-Gypsy identity

Διατυπώνεται κοινωνιολογικό θεωρητικό πλαίσιο σχετικό με την κοινωνική και πολιτισμική ταυτότητα των τσιγγάνων και οι αναπαραστάσεις των εκπαιδευτικών για τους τσιγγάνους

Safe Exploration for Optimization with Gaussian Processes

We consider sequential decision problems under uncertainty, where we seek to optimize an unknown function from noisy samples. This requires balancing exploration (learning about the objective) and exploitation (localizing the maximum), a problem well-studied in the multi-armed bandit literature. In many applications, however, we require that the sampled function values exceed some prespecified "safety" threshold, a requirement that existing algorithms fail to meet. Examples include medical applications where patient comfort must be guaranteed, recommender systems aiming to avoid user dissatisfaction, and robotic control, where one seeks to avoid controls causing physical harm to the platform. We tackle this novel, yet rich, set of problems under the assumption that the unknown function satisfies regularity conditions expressed via a Gaussian process prior. We develop an efficient algorithm called SafeOpt, and theoretically guarantee its convergence to a natural notion of optimum reachable under safety constraints. We evaluate SafeOpt on synthetic data, as well as two real applications: movie recommendation, and therapeutic spinal cord stimulation

Systematic Testing for Detecting Concurrency Errors in Erlang Programs

We present the techniques used in Concuerror, a systematic testing tool able to find and reproduce a wide class of concurrency errors in Erlang programs. We describe how we take advantage of the characteristics of Erlang's actor model of concurrency to selectively instrument the program under test and how we subsequently employ a stateless search strategy to systematically explore the state space of process interleaving sequences triggered by unit tests. To ameliorate the problem of combinatorial explosion, we propose a novel technique for avoiding process blocks and describe how we can effectively combine it with preemption bounding, a heuristic algorithm for reducing the number of explored interleaving sequences. We also briefly discuss issues related to soundness, completeness and effectiveness of techniques used by Concuerror

Test-Driven Development of Concurrent Programs using Concuerror

This paper advocates the test-driven development of concurrent Erlang programs in order to detect early and eliminate the vast majority of concurrency-related errors that may occur in their execution. To facilitate this task we have developed a tool, called Concuerror, that exhaustively explores process interleaving (possibly up to some preemption bound) and presents detailed interleaving information of any errors that occur. We describe in detail the use of Concuerror on a non-trivial concurrent Erlang program that we develop step by step in a test-driven fashion

Closed-Loop Statistical Verification of Stochastic Nonlinear Systems Subject to Parametric Uncertainties

This paper proposes a statistical verification framework using Gaussian processes (GPs) for simulation-based verification of stochastic nonlinear systems with parametric uncertainties. Given a small number of stochastic simulations, the proposed framework constructs a GP regression model and predicts the system's performance over the entire set of possible uncertainties. Included in the framework is a new metric to estimate the confidence in those predictions based on the variance of the GP's cumulative distribution function. This variance-based metric forms the basis of active sampling algorithms that aim to minimize prediction error through careful selection of simulations. In three case studies, the new active sampling algorithms demonstrate up to a 35% improvement in prediction error over other approaches and are able to correctly identify regions with low prediction confidence through the variance metric.Comment: 8 pages, submitted to ACC 201

Safe Exploration for Optimization with Gaussian Processes

We consider sequential decision problems under uncertainty, where we seek to optimize an unknown function from noisy samples. This requires balancing exploration (learning about the objective) and exploitation (localizing the maximum), a problem well-studied in the multi-armed bandit literature. In many applications, however, we require that the sampled function values exceed some prespecified "safety" threshold, a requirement that existing algorithms fail to meet. Examples include medical applications where patient comfort must be guaranteed, recommender systems aiming to avoid user dissatisfaction, and robotic control, where one seeks to avoid controls causing physical harm to the platform. We tackle this novel, yet rich, set of problems under the assumption that the unknown function satisfies regularity conditions expressed via a Gaussian process prior. We develop an efficient algorithm called SafeOpt, and theoretically guarantee its convergence to a natural notion of optimum reachable under safety constraints. We evaluate SafeOpt on synthetic data, as well as two real applications: movie recommendation, and therapeutic spinal cord stimulation

Adaptive Path Planning for Depth Constrained Bathymetric Mapping with an Autonomous Surface Vessel

This paper describes the design, implementation and testing of a suite of algorithms to enable depth constrained autonomous bathymetric (underwater topography) mapping by an Autonomous Surface Vessel (ASV). Given a target depth and a bounding polygon, the ASV will find and follow the intersection of the bounding polygon and the depth contour as modeled online with a Gaussian Process (GP). This intersection, once mapped, will then be used as a boundary within which a path will be planned for coverage to build a map of the Bathymetry. Methods for sequential updates to GP's are described allowing online fitting, prediction and hyper-parameter optimisation on a small embedded PC. New algorithms are introduced for the partitioning of convex polygons to allow efficient path planning for coverage. These algorithms are tested both in simulation and in the field with a small twin hull differential thrust vessel built for the task.Comment: 21 pages, 9 Figures, 1 Table. Submitted to The Journal of Field Robotic

Along the Spectrum of Women\u27s Rights Advocacy: A Cross-Cultural Comparison of Sexual Harassment Law in the United States and India

This Comment compares the development of sexual harassment law in the United States and India. It strives to contribute to this global feminist debate by highlighting the successes and failures of each country\u27s respective anti-harassment protections. It also compares the United States\u27 and India\u27s legal approaches to the problem of workplace sexual harassment. The Comment also discusses the successes and failures of the U.S. and Indian protections in a manner that attempts to minimize the problems present in cross-cultural studies