The AXES research video search system

We will demonstrate a multimedia content information retrieval engine developed for audiovisual digital libraries targeted at academic researchers and journalists. It is the second of three multimedia IR systems being developed by the AXES project1. The system brings together traditional text IR and state-of-the-art content indexing and retrieval technologies to allow users to search and browse digital libraries in novel ways. Key features include: metadata and ASR search and filtering, on-the-fly visual concept classification (categories, faces, places, and logos), and similarity search (instances and faces)

Novel highly luminescent amine-functionalized bridged silsesquioxanes

Amine-functionalized bridged silsesquioxanes (BSs) were synthesized from bis[(3-trimethoxysilyl)propyl] amine via a solvent-mediated route. BS-1 and BS-2 were obtained at neutral pH with sub- and stoichiometric amounts of water, respectively, and high tetrahydrofuran content. BS-3 was prepared with hyperstoichiometric water concentration, high tetrahydrofuran content, and hydrochloric acid. BS-4 was synthesized with hyperstoichiometric water concentration, high ethanol content, and sodium hydroxide. BS-1 and BS-2 were produced as transparent films, whereas BS-3 and BS-4 formed white powders. Face-to-face stacking of flat or folded lamellae yielded quasi-hydrophobic platelets with emission quantum yields of 0.05 ± 0.01 (BS-1 and BS-2) or superhydrophilic onion-like nanoparticles with exciting emission quantum yields of 0.38 ± 0.03 (BS-3) and 0.33 ± 0.04 (BS-4), respectively. The latter two values are the largest ever reported for amine-functionalized siloxane-based hybrids lacking aromatic groups. Fast Grotthus proton hopping between = [Formula: see text]/ = NH groups (BS-3) and = N-/ = NH groups (BS-4), promoted by H+ and OH- ions, respectively, and aided by short amine-amine contacts provided by the onion-like morphology, account for this unique optical behavior.This work was supported by Fundacao para a Ciencia e a Tecnologia (FCT)/MEC and FEDER (contracts UID/QUI/00616/2013, POCI-01-0145-FEDER-007491, and UID/Multi/00709/2013) and when appropriate co-financed by FEDER under the PT2020 Partnership Agreement, project UniRCell (Ref. SAICTPAC/0032/2015, POCI-01-0145-FEDER-016422), and project LUMECD (Ref. PTDC/CTM/NAN/0956/20149 and POCI-01-0145-FEDER-016884). RP and MC acknowledge FCT for grants SFRH/BPD/87759/2012 and SFRH/BD/118466/2016, respectively. SN also acknowledges FCT for grants SFRH/BPD/63152/2009 and Post-PhD Fellowship of LUMECD project. This work was developed within the scope of the project CICECO-Aveiro Institute of Materials, POCI-01-0145-FEDER-007679 (FCT-Ref. UID/CTM/50011/2013), financed by national funds through the FCT/MEC and when appropriate co-financed by FEDER under the PT2020 Partnership Agreement

Manufacturing & Testing of a Cross-Flow Total Heat Exchanger

AXES, Access for Audiovisual Archives, is a research project developing tools for new engaging ways to interact with audiovisual libraries, integrating advanced audio and video analysis technologies. The presented prototype is targeted at academic researchers and journalists. The tool allows them to search and retrieve video segments through metadata, audio analysis, as well as visual concepts and similarity searches. Presented here is a user-based vision on the research-oriented tool provided by AXES

Surprise Language Challenge: Developing a Neural Machine Translation System between Pashto and English in Two Months

In the media industry and the focus of global reporting can shift overnight. There is a compelling need to be able to develop new machine translation systems in a short period of time and in order to more efficiently cover quickly developing stories. As part of the EU project GoURMET and which focusses on low-resource machine translation and our media partners selected a surprise language for which a machine translation system had to be built and evaluated in two months(February and March 2021). The language selected was Pashto and an Indo-Iranian language spoken in Afghanistan and Pakistan and India. In this period we completed the full pipeline of development of a neural machine translation system: data crawling and cleaning and aligning and creating test sets and developing and testing models and and delivering them to the user partners. In this paperwe describe rapid data creation and experiments with transfer learning and pretraining for this low-resource language pair. We find that starting from an existing large model pre-trained on 50languages leads to far better BLEU scores than pretraining on one high-resource language pair with a smaller model. We also present human evaluation of our systems and which indicates that the resulting systems perform better than a freely available commercial system when translating from English into Pashto direction and and similarly when translating from Pashto into English