Machine Learning Methods for Attack Detection in the Smart Grid.

Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach.

Obturator hernia: A diagnostic challenge.

Introduction: Obturator hernia is an extremely rare type of hernia with relatively high mortality and morbidity. Its early diagnosis is challenging since the signs and symptoms are non specific.

Presentation Of Case: Here in we present a case of 70 years old women who presented with complaints of intermittent colicky abdominal pain and vomiting.

Regularized variational Bayesian learning of echo state networks with delay&sum readout.

In this work, a variational Bayesian framework for efficient training of echo state networks (ESNs) with automatic regularization and delay&sum (D&S) readout adaptation is proposed. The algorithm uses a classical batch learning of ESNs. By treating the network echo states as fixed basis functions parameterized with delay parameters, we propose a variational Bayesian ESN training scheme.