Matteo Marsili from The Abdus Salam International Centre for Theoretical Physics with lecture titleTheory of Optimal Learning Machines is now publicy available.

Advances in biological and medical technologies drive continuous generation of large amounts of biomedical Big Data. This will double in less than 3 years if the current rate of growth is sustained! Given the exponential progress in sequencing technology the increase will only get steeper, entailing an intensified demand for experts in NGS data analysis. Big Data requires applying new solution to leverage its potential. Machine Learning (ML) is the answer to the increased complexity of research problems in science, industry and in everyday life.

On June 17th and 18th the project partners has meet at Centre International de Conférences Sorbonne Universités in Paris to kick-start a series of research agenda workshops to leverage EU expectations of a Humane Centered AI in Europe. Delivering a compelling research is crucial to our chances of getting long term significant impact via the new Horizon Europe programme.

High-throughput phenotypic screening, based on high content imaging, is increasingly often used as a tool in the context of drug discovery. Compound screens are used to find hits that produce the desired phenotypes in relevant cellular assays. Genomic screens are used to elucidate the underlying molecular pathways and identify suitable drug targets. Since a wealth of data is produced in the process of high- content screening, data science approaches such as statistics, machine learning and neural networks can play an important role in making the most of the collected data. Much like virtual screening can be performed in more classical chemoinformatic settings by, e.g., learning predictive models for QSAR (quantitative structure-activity relations) from data obtained through compound screens, similar approaches can be taken in the context of high-throughput phenotypic screening.

Videolectures.net is planning to present the TheyBuyforYou project in video lectures format. Initially you are kindly invited to read about its first results. We are happy to announce the first release of the knowledge graph for public procurement, integrating tender and company data. Public procurement affects organisations across all sectors. With tenders amounting to close to 2 trillion euros annually in the EU, it is critical that this market operates fairly and efficiently, supporting competitiveness and accountability.

The intention of the Workshop within the AI4D initiaitve in sub-Saharan Africa is to scope out the African ML/AI landscape, provide inputs for an African AI research roadmap, and support the development of cross-continent cooperation on AI for sustainable development. It is anticipated that the outcome of the workshop will be a Network of Excellence on AI for sub Saharan African researchers who focus on applications and research relating to AI and human development.

Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. However, traditionally machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph. In this talk I will discuss methods that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. I will provide a conceptual review of key advancements in this area of representation learning on graphs, including random-walk based algorithms, and graph convolutional networks.

VideoLectures.NET is part of the H2020 project MOVING, which has been working on developing new and more effective methods for lecture video fragmentation and fragment-level annotation, to allow for fine-grained access to lecture video collections. In the latest MOVING method, developed by CERTH (also a member, and coordinator of the MOVING consortium), automatically-generated speech transcripts of the lecture video are analysed with the help of word embeddings that are generated from pre-trained state-of-the-art neural networks. This lecture video fragmentation method is part of the MOVING platform, and its results are also being ingested in the VideoLectures.NET platform, making it possible for the users of both platforms to access and view specific fragments of lecture videos that cater to their information needs. For now, the fragments are accessible only for some lectures in VideoLectures.NET (testing phase); see for instance the lecture on deep learning. The fragments are presented as "chapters" to the right of the video player window, and can serve as a tool to find particular video parts easier and faster.

The seminar on Speech and Language Technology Tools is organized by HunCLARIN, Juhász Gyula Faculty of Pedagogy at the University of Szeged and the Hungarian Association of Applied Linguists and Language. The aim of the seminar is to provide researchers, teachers and students working in the humanities and social sciences a broad view of the corpora and state-of-the-art software in speech and language processing developed for Hungarian, mostly as part of the activities of HunCLARIN. This seminar is supported by CLARIN ERIC. The seminar took place at the Juhász Gyula Faculty of Education, University of Szeged, Hungary on Friday, 19 October, 2018.

HunCLARIN, Juhász Gyula Faculty of Pedagogy at the University of Szeged and the Hungarian Association of Applied Linguists and Language organized a Seminar on Speech and Language Technology Tools. The aim of the seminar is to provide a broad view of the corpora and state-of-the-art software in speech and language processing developed for Hungarian.