Lexical Diversity in Kinship Across Languages and Dialects

Languages are known to describe the world in diverse ways. Across lexicons, diversity is pervasive, appearing through phenomena such as lexical gaps and untranslatability. However, in computational resources, such as multilingual lexical databases, diversity is hardly ever represented. In this paper, we introduce a method to enrich computational lexicons with content relating to linguistic diversity. The method is verified through two large-scale case studies on kinship terminology, a domain known to be diverse across languages and cultures: one case study deals with seven Arabic dialects, while the other one with three Indonesian languages. Our results, made available as browseable and downloadable computational resources, extend prior linguistics research on kinship terminology, and provide insight into the extent of diversity even within linguistically and culturally close communities

The Taboo Challenge Competition

Games have always been a popular domain of AI research, and they have been used for many recent competitions. However, reaching human-level performance often either focuses on comprehensive world knowledge or solving decision-making problems with unmanageable solution spaces. Building on the popular Taboo board game, the Taboo Challenge Competition addresses a different problem — that of bridging the gap between the domain knowledge of heterogeneous agents trying to jointly identify a concept without making reference to its most salient features. The competition, which was run for the first time at IJCAI 2017, aims to provide a simple testbed for diversity-aware AI where the focus is on integrating independently engineered AI components, while offering a scenario that is challenging yet simple enough to not require mastering general commonsense knowledge or natural language understanding. We describe the design and preparation of the competition, discuss results, and lessons learned

Ontology-Driven Cross-Domain Transfer Learning.

The aim of transfer learning is to reuse learnt knowledge across different contexts. In the particular case of cross-domain transfer (also known as domain adaptation), reuse happens across different but related knowledge domains. While there have been promising first results in combining learning with symbolic knowledge to improve cross-domain transfer results, the singular ability of ontologies for providing classificatory knowledge has not been fully exploited so far by the machine learning community. We show that ontologies, if properly designed, are able to support transfer learning by improving generalization and discrimination across classes. We propose an architecture based on direct attribute prediction for combining ontologies with a transfer learning framework, as well as an ontology-based solution for cross-domain generalization based on the integration of top-level and domain ontologies. We validate the solution on an experiment over an image classification task, demonstrating the system's improved classification performance

Synthesis and Antiproliferative Activity of Steroidal Diaryl Ethers

Novel 13α-estrone derivatives have been synthesized via direct arylation of the phenolic hydroxy function. Chan–Lam couplings of arylboronic acids with 13α-estrone as a nucleophilic partner were carried out under copper catalysis. The antiproliferative activities of the newly synthesized diaryl ethers against a panel of human cancer cell lines (A2780, MCF-7, MDA-MB 231, HeLa, SiHa) were investigated by means of MTT assays. The quinoline derivative displayed substantial antiproliferative activity against MCF-7 and HeLa cell lines with low micromolar IC50 values. Disturbance of tubulin polymerization has been confirmed by microplate-based photometric assay. Computational calculations reveal significant interactions of the quinoline derivative with the taxoid binding site of tubulin

DNA barcode reference libraries for the monitoring of aquatic biota in Europe: Gap-analysis and recommendations for future work

Effective identification of species using short DNA fragments (DNA barcoding and DNA metabarcoding) requires reliable sequence reference libraries of known taxa. Both taxonomically comprehensive coverage and content quality are important for sufficient accuracy. For aquatic ecosystems in Europe, reliable barcode reference libraries are particularly important if molecular identification tools are to be implemented in biomonitoring and reports in the context of the EU Water Framework Directive (WFD) and the Marine Strategy Framework Directive (MSFD). We analysed gaps in the two most important reference databases, Barcode of Life Data Systems (BOLD) and NCBI GenBank, with a focus on the taxa most frequently used in WFD and MSFD. Our analyses show that coverage varies strongly among taxonomic groups, and among geographic regions. In general, groups that were actively targeted in barcode projects (e.g. fish, true bugs, caddisflies and vascular plants) are well represented in the barcode libraries, while others have fewer records (e.g. marine molluscs, ascidians, and freshwater diatoms). We also found that species monitored in several countries often are represented by barcodes in reference libraries, while species monitored in a single country frequently lack sequence records. A large proportion of species (up to 50%) in several taxonomic groups are only represented by private data in BOLD. Our results have implications for the future strategy to fill existing gaps in barcode libraries, especially if DNA metabarcoding is to be used in the monitoring of European aquatic biota under the WFD and MSFD. For example, missing species relevant to monitoring in multiple countries should be prioritized for future collaborative programs. We also discuss why a strategy for quality control and quality assurance of barcode reference libraries is needed and recommend future steps to ensure full utilisation of metabarcoding in aquatic biomonitoring.This paper is a deliverable of the European Cooperation in Science and Technology (COST) Action DNAqua-Net (CA15219) Working Group 1, led by Torbjørn Ekrem and Fedor Čiampor. Thanks to the University of Minho and University of Pécs for hosting workshops and working group meetings. We also thank staff at National Environment Agencies and others that provided national checklists of taxa used in biomonitoring, and otherwise assisted with checklist proof-reading: Jarmila Makovinská and Emília Mišíková Elexová (Slovakia); Steinar Sandøy and Dag Rosland (Norway); Mišel Jelič (Croatia); Marlen Vasquez (Cyprus); Adam Petrusek (Czech Republic); Kristel Panksep (Estonia); Panagiotis Kaspiditis (Greece); Matteo Montagna (Italy); Marija Katarzyte (Lithuania); Ana Rotter (Slovenia); Rosa Trabajo (Spain); Florian Altermatt (Switzerland); Kristian Meissner (Finland), Rigers Bakiu (Albania), Valentina Stamenkovic and Jelena Hinic (Macedonia); Patricia Mergen (Belgium); Gael Denys & the French Biodiversity Agency (France); Mary Kelly-Quinn (Ireland); Piotr Panek and Andrzej Zawal (Poland); Cesare Mario Puzzi (Italy); Carole Fitzpatrick (United Kingdom); Simon Vitecek (Austria); Ana Filipa Filipe (Portugal); Peter Anton Stæhr & Anne Winding (Denmark); Michael Monaghan (Germany); Alain Dohet, Lionel L'Hoste, Nora Welschbillig & Luc Ector (Luxembourg), Lujza Keresztes, (Romania). The authors also want to thank Dirk Steinke for providing the original European ERMS list for marine taxa and Florian Malard for comments on the manuscript. The preparation of the AMBI checklist was carried out in the scope of a Short-term Scientific Mission (ECOST-STSM-CA15219-150217- 082111) granted to SD visiting AZTI, Spain. ZC was supported by grants EFOP-3.6.1.-16-2016-00004 and 20765-3/2018/FEKUTSTRAT. TE was supported by the NorBOL-grant (226134/F50) from the Research Coun cil of Norway. BR, FL and MFG contributed through support from the GBOL project, which is generously funded by the German Federal Min istry of Education and Research (FKZ 01LI1101 and 01LI1501). MG contributed through support of the Polish National Science Centre, grants N N303 5794 39 and 2014/15/B/NZ8/00266. SF was funded by the project PORBIOTA - Portuguese E-Infrastructure for Information and Research on Biodiversity (POCI-01-0145-FEDER-022127), supported by Operational Thematic Program for Competitiveness and Internationalization (POCI), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (FEDER)