Convolutional Neural Networks for Epileptic Seizure Prediction

Epilepsy is the most common neurological disorder and an accurate forecast of seizures would help to overcome the patient's uncertainty and helplessness. In this contribution, we present and discuss a novel methodology for the classification of intracranial electroencephalography (iEEG) for seizure prediction. Contrary to previous approaches, we categorically refrain from an extraction of hand-crafted features and use a convolutional neural network (CNN) topology instead for both the determination of suitable signal characteristics and the binary classification of preictal and interictal segments. Three different models have been evaluated on public datasets with long-term recordings from four dogs and three patients. Overall, our findings demonstrate the general applicability. In this work we discuss the strengths and limitations of our methodology.Comment: accepted for MLESP 201

Generating Semantic Snapshots of Newscasts Using Entity Expansion

textabstractTV newscasts report about the latest event-related facts occurring in the world. Relying exclusively on them is, however, insufficient to fully grasp the context of the story being reported. In this paper, we propose an approach that retrieves and analyzes related documents from the Web to automatically generate semantic annotations that provide viewers and experts comprehensive information about the news. We detect named entities in the retrieved documents that further disclose relevant concepts that were not explicitly mentioned in the original newscast. A ranking algorithm based on entity frequency, popularity peak analysis, and domain experts’ rules sorts those annotations to generate what we call Semantic Snapshot of a Newscast (NSS). We benchmark this method against a gold standard generated by domain experts and assessed via a user survey over five BBC newscasts. Results of the experiments show the robustness of our approach holding an Average Normalized Discounted Cumulative Gain of 66.6%

Augmenting TV Newscasts via Entity Expansion

htmlabstractWe present an approach that leverages on the knowledge present on the Web for identifying and enriching relevant items inside a News video and displaying them in a timely and user friendly fashion. This second screen prototype (i) collects and offers information about persons, locations, organizations and concepts occurring in the newscast, and (ii) combines them for enriching the underlying story along five main dimensions: expert’s opinions, timeline, in depth, in other sources, and geo-localized comments from other viewers. Starting from preliminary insights coming from the named entities spotted on the subtitles, we expand this initial context to a broader event representation by relying in the knowledge of other Web documents talking about the same fact. An online demo of the proposed solution is available at http://linkedtv. project.cwi.nl/news

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements