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Gottfried Wilhelm Osann (1796–1866) was a German scientist most renowned for his work in chemistry and physics. However, inspired by Goethe’s work on color, he published a paper on simultaneous color contrast in 1833 using a method that is similar to that of later authors: reflection of an achromatic spot from an angled piece of glass. He wrote at least four more papers on color contrasts, in 1836 using essentially the same method as credited to others. We provide a description and translation of Osann’s 1833 paper and the relevant part of his 1836 paper, say why these papers are interesting and important, give some biographical information about Osann, comment on the fate of Osann’s papers, and describe Osann’s other papers on color.

Multilingual question answering tasks typically assume answers exist in the same language as the question. Yet in practice, many languages face both information scarcity -- where languages have few reference articles -- and information asymmetry -- where questions reference concepts from other cultures. This work extends open-retrieval question answering to a cross-lingual setting enabling questions from one language to be answered via answer content from another language. We construct a large-scale dataset built on questions from TyDi QA lacking same-language answers. Our task formulation, called Cross-lingual Open Retrieval Question Answering (XOR QA), includes 40k information-seeking questions from across 7 diverse non-English languages. Based on this dataset, we introduce three new tasks that involve cross-lingual document retrieval using multi-lingual and English resources. We establish baselines with state-of-the-art machine translation systems and cross-lingual pretrained models. Experimental results suggest that XOR QA is a challenging task that will facilitate the development of novel techniques for multilingual question answering. Our data and code are available at https://nlp.cs.washington.edu/xorqa. Comment: Published as a conference paper at NAACL-HLT 2021 (long)

Grounded semantics is typically learnt from utterance-level meaning representations (e.g., successful database retrievals, denoted objects in images, moves in a game). We explore learning word and utterance meanings by continuous observation of the actions of an instruction follower (IF). While an instruction giver (IG) provided a verbal description of a configuration of objects, IF recreated it using a GUI. Aligning these GUI actions to sub-utterance chunks allows a simple maximum entropy model to associate them as chunk meaning better than just providing it with the utterance-final configuration. This shows that semantics useful for incremental (word-by-word) application, as required in natural dialogue, might also be better acquired from incremental settings.

Abstract In ‘Blindness’ José Saramago transports us into a society in which an unexpected epidemic of blindness spreads quickly, disseminating chaos between citizens and institutions. This new reality acts as a magnifying glass highlighting the weaknesses and (few) forces of individuals and community. From this masterpiece we can learn many insights useful to understand the SARS-CoV-2 pandemic.

Despite considerable research interest, it is still an open issue as to how morphologically complex words such as "car+s" are represented and processed in the brain. We studied the neural correlates of the processing of inflected nouns in the morphologically rich Finnish language. Previous behavioral studies in Finnish have yielded a robust inflectional processing cost, i.e., inflected words are harder to recognize than otherwise matched morphologically simple words. Theoretically this effect could stem either from decomposition of inflected words into a stem and a suffix at input level and/or from subsequent recombination at the semantic-syntactic level to arrive at an interpretation of the word. To shed light on this issue, we used magnetoencephalography to reveal the time course and localization of neural effects of morphological structure and frequency of written words. Ten subjects silently read high- and low-frequency Finnish words in inflected and monomorphemic form. Morphological complexity was accompanied by stronger and longer-lasting activation of the left superior temporal cortex from 200 ms onwards. Earlier effects of morphology were not found, supporting the view that the well-established behavioral processing cost for inflected words stems from the semantic-syntactic level rather than from early decomposition. Since the effect of morphology was detected throughout the range of word frequencies employed, the majority of inflected Finnish words appears to be represented in decomposed form and only very high-frequency inflected words may acquire full-form representations.

ABSTRACTIn June of 2018, AMC debuted the series Dietland, a television show about a woman of size named Plum who plans to have weight loss surgery until she begins a journey of self-acceptance. Whi...

ABSTRACTMicroscopic use-wear is present on all four burins studied. The four heavily patinated chert artifacts from the Upper Paleolithic Baradostian horizon, radiocarbon date between 28,000 and 33,000 B.P. Burin usage originates and extends from the trihedral bit to adjacent facets; probable haft wear traces are present on three of the four tools.

After finishing my undergrad studies, I did what many newly minted electrical engineers do and moved to Silicon Valley. Working at Apple, I was living my dream of contributing to the very products I had always admired. Yet, after a couple of years, I began to feel as though something was missing. I

Background A quantitative trait locus (QTL) is a genomic region that correlates with a phenotype. Most of the experimental information about QTL mapping studies is described in tables of scientific publications. Traditional text mining techniques aim to extract information from unstructured text rather than from tables. We present QTLTableMiner++ (QTM), a table mining tool that extracts and semantically annotates QTL information buried in (heterogeneous) tables of plant science literature. QTM is a command line tool written in the Java programming language. This tool takes scientific articles from the Europe PMC repository as input, extracts QTL tables using keyword matching and ontology-based concept identification. The tables are further normalized using rules derived from table properties such as captions, column headers and table footers. Furthermore, table columns are classified into three categories namely column descriptors, properties and values based on column headers and data types of cell entries. Abbreviations found in the tables are expanded using the Schwartz and Hearst algorithm. Finally, the content of QTL tables is semantically enriched with domain-specific ontologies (e.g. Crop Ontology, Plant Ontology and Trait Ontology) using the Apache Solr search platform and the results are stored in a relational database and a text file. Results The performance of the QTM tool was assessed by precision and recall based on the information retrieved from two manually annotated corpora of open access articles, i.e. QTL mapping studies in tomato (Solanum lycopersicum) and in potato (S. tuberosum). In summary, QTM detected QTL statements in tomato with 74.53% precision and 92.56% recall and in potato with 82.82% precision and 98.94% recall. Conclusion QTM is a unique tool that aids in providing QTL information in machine-readable and semantically interoperable formats. Electronic supplementary material The online version of this article (10.1186/s12859-018-2165-7) contains supplementary material, which is available to authorized users.