Teelt van blauwmaanzaad

De teelt van blauwmaanzaad voor de goed doorlatende, kalkrijke klei- en zavelgronden met een goed humusgehalte

Automatic detection and correction of context-dependent dt-mistakes using neural networks

We introduce a novel approach to correcting context-dependent dt-mistakes, one of the most frequent spelling errors in the Dutch language. We show that by using a neural network to estimate the probability distribution of a verb's suffix conditioned jointly on its stem and context, we obtain large improvements over state-of-the-art spell checkers on three different benchmarking datasets, achieving a perfect score on a verb spelling test from \emph{de Standaard}, a Flemish newspaper. The method is unsupervised and only relies on basic preprocessing tools to tokenize the text and identify verbs, which enables training on millions of sentences. Furthermore, we propose a method to determine which words in a sentence cause the system to make corrections, which is valuable for providing feedback to the user

Learning unsupervised multilingual word embeddings with incremental multilingual hubs

Recent research has discovered that a shared bilingual word embedding space can be induced by projecting monolingual word embedding spaces from two languages using a self-learning paradigm without any bilingual supervision. However, it has also been shown that for distant language pairs such fully unsupervised self-learning methods are unstable and often get stuck in poor local optima due to reduced isomorphism between starting monolingual spaces. In this work, we propose a new robust framework for learning unsupervised multilingual word embeddings that mitigates the instability issues. We learn a shared multilingual embedding space for a variable number of languages by incrementally adding new languages one by one to the current multilingual space. Through the gradual language addition our method can leverage the interdependencies between the new language and all other languages in the current multilingual hub/space. We find that it is beneficial to project more distant languages later in the iterative process. Our fully unsupervised multilingual embedding spaces yield results that are on par with the state-of-the-art methods in the bilingual lexicon induction (BLI) task, and simultaneously obtain state-of-the-art scores on two downstream tasks: multilingual document classification and multilingual dependency parsing, outperforming even supervised baselines. This finding also accentuates the need to establish evaluation protocols for cross-lingual word embeddings beyond the omnipresent intrinsic BLI task in future work

Detection of genetically modified plant products by protein strip testing: an evaluation of real-life samples

The determination of the presence of genetically modified plant material by the detection of expressed genetically engineered proteins using lateral flow protein strip tests has been evaluated in different matrices. The presence of five major genetically engineered proteins (CP4-EPSPS, CryIAb, Cry9C, PAT/pat and PAT/bar protein) was detected at low levels in seeds, seed/leaf powder and leaf tissue from genetically modified soy, maize or oilseed rape. A comparison between &quot;protein strip test&quot; (PST) and &quot;polymerase chain reaction&quot; (PCR) analysis of genetically modified food/feed samples demonstrates complementarities of both techniques. -® Springer-Verlag 2007</p

Aesthetic Experience in Virtual Museums: A Postphenomenological Perspective

This article explores the impact of the digitization of traditional works of art on the aesthetic experience from a philosophical point of view. Presenting and making use of a recent approach in the philosophy of technology, initiated by the American philosopher Don Ihde, called postphenomenology. This hybrid form of phenomenology builds on traditional phenomenology and combines it with a pragmatic approach in order to focus on the mediating roles of technology. Concrete technologies and applications such as screens and virtual museums are the starting point for our examination of the specific character of these digital media, which are then compared with their physical referents. Following Ihde’s arguments, we show that digital image technologies, and digital images themselves, are not merely functional, but shape perceptions and experiences. Although currently the positive effects and opportunities of these new applications are emphasized in the field – for collection management, the democratization and accessibility of art, possibilities to interact and intervene in the image, efficient marketing, etc. – they do have a significant impact on the way in which art is experienced

Disentangling taxonomy within the <i>Rhabditis (Pellioditis) marina</i> (Nematoda, Rhadbitidae) species complex using molecular and morphological tools

Correct taxonomy is a prerequisite for biological research, but currently it is undergoing a serious crisis, resulting in the neglect of many highly diverse groups of organisms. In nematodes, species delimitation remains problematic due to their high morphological variability. Evolutionary approaches using DNA sequences can potentially overcome the problems caused by morphology, but they are also affected by flaws. A holistic approach with a combination of morphological and molecular methods can therefore produce a straightforward delimitation of species. The present study investigates the taxonomic status of some highly divergent mitochondrial haplotypes in the Rhabditis (Pellioditis) marina species complex by using a combination of molecular and morphological tools. We used concordance among phylogenetic trees of three molecular markers (COI, ITS, D2D3) to infer molecular lineages. Subsequently, morphometric data from nearly all lineages were analysed with multivariate techniques. The results showed that highly divergent genotypic clusters were accompanied by morphological differences, and we created a graphical polytomous key for future identifications. This study indisputably demonstrates that R. (P.) marina and R. (P.) mediterranea belong to a huge species complex and that biodiversity in free-living marine nematodes may seriously be underestimated

Bilingual lexicon induction by learning to combine word-level and character-level representations

We study the problem of bilingual lexicon induction (BLI) in a setting where some translation resources are available, but unknown translations are sought for certain, possibly domain-specific terminology. We frame BLI as a classification problem for which we design a neural network based classification architecture composed of recurrent long short-term memory and deep feed forward networks. The results show that word- and character-level representations each improve state-of-the-art results for BLI, and the best results are obtained by exploiting the synergy between these word- and character-level representations in the classification model