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Le tableau vivant: Una forma de trasvase entre pintura y cine. El caso de La ronda de noche (1642) de Rembrandt van Rijn y La ronda de noche (2007) de Peter Greenaway

Giovanni De J. Orozco-Abarca
<span title="2016-07-11">2016</span> <i title="Universidad de Costa Rica"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xaajz6tqnndejffgibjhcylkqy" style="color: black;">Escena. Revista de las artes</a> </i> &nbsp;
relación con el óleo La ronda de noche (Nachwacht, 1642) de Rembrandt van Rijn, con el objetivo de visualizar la configuración de una propuesta estética novedosa en el campo cinematográfico a partir del  ...  La presente investigación estudia un punto de encuentro entre el cine y la pintura mediante la representación del tableau vivant en el filme La ronda de noche (Nightwatching, 2007) de Peter Greenaway en  ...  Esta acción conduce al pintor a un posterior Figura 1 Rembrandt van Rijn. La ronda de noche (1642), detalle. Figura 2 Rembrandt van Rijn. La ronda de noche (1642), detalle.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.15517/es.v75i2.25575">doi:10.15517/es.v75i2.25575</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/urtnkgqfyvdc5cmrltonknorgq">fatcat:urtnkgqfyvdc5cmrltonknorgq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170922140803/https://revistas.ucr.ac.cr/index.php/escena/article/viewFile/25575/pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5e/36/5e36f45f4c94ea51a856fd88afcf881fffa70bb4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.15517/es.v75i2.25575"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Discrimination in lexical decision

Petar Milin, Laurie Beth Feldman, Michael Ramscar, Peter Hendrix, R. Harald Baayen, Hedderik van Rijn
<span title="2017-02-24">2017</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
In this study we present a novel set of discrimination-based indicators of language processing derived from Naive Discriminative Learning (NDL) theory. We compare the effectiveness of these new measures with classical lexical-distributional measures-in particular, frequency counts and form similarity measures-to predict lexical decision latencies when a complete morphological segmentation of masked primes is or is not possible. Data derive from a re-analysis of a large subset of decision
more &raquo; ... es from the English Lexicon Project, as well as from the results of two new masked priming studies. Results demonstrate the superiority of discrimination-based predictors over lexical-distributional predictors alone, across both the simple and primed lexical decision tasks. Comparable priming after masked CORNER and CORNEA type primes, across two experiments, fails to support early obligatory segmentation into morphemes as predicted by the morpho-orthographic account of reading. Results fit well with NDL theory, which, in conformity with Word and Paradigm theory, rejects the morpheme as a relevant unit of analysis. Furthermore, results indicate that readers with greater spelling proficiency and larger vocabularies make better use of orthographic priors and handle lexical competition more efficiently. and syllable frequencies [11] . Counts can also be refined in various ways. For instance, counts of neighbors can be weighted for proximity or interconnectedness within similarity neighborhoods [12, 13] . Count measures-whether counts of neighbors or straightforward counts of occurrenceshave proved quite useful for predicting lexical decision reaction times and accuracy. However, the use of counts linked to lexical units such as words raises the question of how these units are discriminated from other similar units. Models, ranging from the interactive activation model of [14] to the Bayesian Reader of [15] , account for the identification of word units by means of a hierarchy of sublexical units, such as letter features and letters, and algorithms sorting out the evidence provided by lower-level units for higher-level units. A very different approach is explored by [16] . They constructed a two-layer network with weights estimated by application of the simplest possible error-driven learning rule, originally proposed by [17] . As input units they used letter pairs, and as output units, semantic units. In what follows, we refer to these units, which are pointers to locations in a high-dimensional semantic vector space [18] [19] [20] [21] [22] , as lexomes. What [16] observed is that the network, when trained on 20 million words from the British National Corpus, produced activations for target words that shared many properties with observed reaction times in the visual lexical decision task. For instance, reaction times are predictable to a considerable extent from measures such as whole word frequency, constituent frequency, family size, and inflectional entropy, and the same holds for the activations produced by the network. Remarkably, even relative effect sizes were closely matched between empirical and simulated reaction times. Crucial to understanding the predictions of error-driven learning is to realize that while correct prediction leads to strengthening of the associations (weights) between features (henceforth cues) and discriminated categories (henceforth outcomes), misprediction results in weakened association strength. [23] provides a telling example from vision. When a visual prime (a picture of a grand piano) precedes a target stimulus (a picture of a table) which has to be named, naming times are delayed compared to unrelated prime pictures. This phenomenen, named anti-priming by Marsolek, arises as a consequence of error-driven learning. When recognizing the grand piano, weights from features such as "having legs" and "having a large flat horizontal surface" are strengthened to the grand piano, but at the same time weakened to "table", even though tables have legs and large flat surfaces. Precisely because these cues have just been downgraded as valid cues for tables after having recognized a grand piano, subsequent interpreting and naming the picture of a table takes more time. This example illustrates the continuous tug of war between input cues competing for outcome categories-a tug of war that resists precise quantification by means of simple counts. Crucially, the association strength of a given cue to a given outcome is co-determined not only by how often this cue and this outcome co-occurs, but also by how often this cue co-occurs with other outcomes. This important insight is taken into account by, e.g., the statistics for two-by-two contingency tables proposed by [24] [25] [26] . The intricacies of error-driven learning, however, are not well captured even by these highlevel statistical measures. This is because the association strength between a cue c i and an outcome o j not only depends on how often c i co-occurs with other outcomes, but also on how often other cues c j that are present in the visual input together with c i co-occur with other cues and other outcomes. This continuous between-cue calibration across learning histories gives rise to, e.g., phenomena such as the secondary family size effect reported by [27] . As a consequence, a proper estimate of the weight on the connection from c i and o j actually depends on the complete history of events in which c i was present and weights were adjusted as a function of whether outcomes were predicted correctly or incorrectly. Thus, this approach characterizes Discrimination in lexical decision PLOS ONE | the mental lexicon as a dynamic system in which seemingly unrelated constellations of cues far back in learning history can have unexpected consequences for current processing. A first goal of the present study is to examine in further detail whether measures derived from principles of discrimination learning might outperform classical measures based on counts. In this endeavor, we depart from the previous study by Baayen et al. (2011) in several ways. First, as cues, we use letter trigrams instead of letter bigrams, as we have found that for English this systematically gives rise to more precise prediction. For other languages, letter bigrams may outperform letter trigrams, see [28] for the case of Vietnamese. Second, we extend the activation measure of the previous study with several new network statistics, and test their predictive value for unprimed and primed lexical decision times. Below, we present these new additional measures in further detail. Third, the original model of Baayen et al. (2011) was in fact a decompositional model at the level of lexomes. It did not include lexomes for complex words such as works, worker, and workforce, and posited that the evidence for a complex word is obtained by integrating over the evidences for its constituents. However, subsequent work [28, 29] has shown that excellent predictions are obtained when complex words are entered into the model with their own lexomes. From a semantic perspective, this has the advantage of doing justice to the often unpredictable shades of meanings of complex words. For instance, English worker, although often described in theoretical treatises as meaning 'someone who works', in actual language use is found to denote someone employed for manual or industrial labor, to denote a member of the working class, or to denote a usually sterile member of a colony of bees that carries out most of the labor. It turns out that both whole-word and constituent frequency effects emerge for free when complex words are granted their own lexomes [28] . Furthermore, since morpheme frequency effects emerged in the network of Pham and Baayen in the absence of form representations for morphemes, the revised model fits well with recent approaches in theoretical morphology such as word and paradigm morphology [30] , which eschew the morpheme as theoretical construct. This brings us to the second goal of the present study, the vexed issue of blind morphoorthographic segmentation in visual word recognition. According to [31] , it is the orthographic and not the semantic properties of morphemes that have profound consequences early in visual word recognition. Rastle and colleagues argue that the visual system parses corner into corn and er, even though corner is semantically unrelated to corn. Conversely, for a word like brothel, segmentation into broth and el is said not to take place. Even though el is a frequent letter sequence in English, appearing in words such as level, angel, personnel, apparel, wheel, barrel, jewel, and many others, it is not a true morpheme of this language. Different priming outcomes for prime-target pairs such as corner-corn and brothel-broth have been taken as support for the importance to the earliest stages of visual processing of the morpheme-as-form, a purely orthographic unit devoid of semantics. The theory of morpho-orthographic segmentation is incompatible with linguistic theories such as word and paradigm morphology, and it is also incompatible with discriminative learning theory. The present study confronts these opposing perspectives on reading with data from both unprimed and primed lexical decision. Generalized additive mixed models (GAMMs) with predictors grounded in discrimination learning are compared with GAMMs using classical lexical distributional covariates. In what follows, we first discuss the issues surrounding the theory of morpho-orthographic segmentation. We then introduce in more detail our theory of discrimination learning and associated measures, after which we proceed to discuss experimental data from unprimed and primed lexical decision. Discrimination in lexical decision PLOS ONE | Fig 2. The results of three 'linear' classification algorithms applied to the simulated data, using 1-hot encoding for column and row membership. Left panel: standard logistic regression, middle panel: logistic regression with lasso regularization, right panel: standard Rescorla-Wagner learning. Red pixels indicate predicted class A responses, blue pixels indicate true class A responses.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0171935">doi:10.1371/journal.pone.0171935</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28235015">pmid:28235015</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5325216/">pmcid:PMC5325216</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/57uelwioprgd7priy4egsn4nee">fatcat:57uelwioprgd7priy4egsn4nee</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171012080900/http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0171935&amp;type=printable" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/50/f6/50f6d056061acb3db37f7c9385d6291d18e3ff68.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0171935"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5325216" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Sensitive capacitive pressure sensors based on graphene membrane arrays [article]

Makars Šiškins, Martin Lee, Dominique Wehenkel, Richard van Rijn, Tijmen W. de Jong, Johannes R. Renshof, Berend C. Hopman, Willemijn S. J. M. Peters, Dejan Davidovikj, Herre S. J. van der Zant, Peter G. Steeneken
<span title="2020-03-19">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
., most of the gas permeation in graphene drums occurs along the van der Waals interface between the 2D material and the substrate 2 .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2003.08869v1">arXiv:2003.08869v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gvs6ualnxbafpagpayxlafyyd4">fatcat:gvs6ualnxbafpagpayxlafyyd4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200828084012/https://arxiv.org/pdf/2003.08869v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a1/2b/a12b062c70744e4fa3cfa8e3d6c532e88c57c107.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2003.08869v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Characterizing outdoor recreation user groups: A typology of peri-urban recreationists in the Kromme Rijn area, the Netherlands

Franziska Komossa, Emma H. van der Zanden, Peter H. Verburg
<span title="">2019</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4noua7kwzraonns4r6kyllohbe" style="color: black;">Land Use Policy</a> </i> &nbsp;
The typology is defined on case-study level in the Dutch Kromme Rijn area.  ...  Van den Berg and Koole (2006) for example found that elderly people as a recreational user group are less attracted to wild natural landscapes than younger generations.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.landusepol.2018.10.017">doi:10.1016/j.landusepol.2018.10.017</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zyzp74qpcbbg5pu4qjubd7t74i">fatcat:zyzp74qpcbbg5pu4qjubd7t74i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210428002048/https://research.vu.nl/ws/files/118514893/Characterizing_outdoor_recreation_user_groups.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/59/90/599036d580bcc4d3ae88b568531e5afdeee5ddae.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.landusepol.2018.10.017"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Towards evidence-based pharmacotherapy in children

Elles Marleen Kemper, Maruschka Merkus, Peter C. Wierenga, Petra C. Van Rijn, Desirée Van der Werff, Loraine Lie-A-Huen, Martin Offringa
<span title="2010-12-28">2010</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bhaaslzv7ngqtawni2ztiofore" style="color: black;">Pediatric Anaesthesia</a> </i> &nbsp;
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1111/j.1460-9592.2010.03493.x">doi:10.1111/j.1460-9592.2010.03493.x</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/21199133">pmid:21199133</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rc6joaqbhbcfjjkj2gstm7nbzq">fatcat:rc6joaqbhbcfjjkj2gstm7nbzq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180725043033/https://hal.archives-ouvertes.fr/hal-00604055/document" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/59/b1/59b1032fab28b130bdeb147e80f45884bd4f31f6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1111/j.1460-9592.2010.03493.x"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Does Feature Selection Improve Classification? A Large Scale Experiment in OpenML [chapter]

Martijn J. Post, Peter van der Putten, Jan N. van Rijn
<span title="">2016</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
It is often claimed that data pre-processing is an important factor contributing towards the performance of classification algorithms. In this paper we investigate feature selection, a common data preprocessing technique. We conduct a large scale experiment and present results on what algorithms and data sets benefit from this technique. Using meta-learning we can find out for which combinations this is the case. To complement a large set of meta-features, we introduce the Feature Selection
more &raquo; ... markers, which prove useful for this task. All our experimental results are made publicly available on OpenML.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-46349-0_14">doi:10.1007/978-3-319-46349-0_14</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5sppnyj4mrbtdbl7wr7swo563y">fatcat:5sppnyj4mrbtdbl7wr7swo563y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190226060447/http://pdfs.semanticscholar.org/6d5d/f96724ccb36ff56d99d6be6d872062f29b77.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/6d/5d/6d5df96724ccb36ff56d99d6be6d872062f29b77.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-46349-0_14"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Phenylketonuria: tyrosine supplementation in phenylalanine-restricted diets

Francjan J van Spronsen, Margreet van Rijn, Jolita Bekhof, Richard Koch, Peter GA Smit
<span title="2001-02-01">2001</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xrdm7tm6kzdl7kbhsmmrmk2yxy" style="color: black;">American Journal of Clinical Nutrition</a> </i> &nbsp;
Treatment of phenylketonuria (PKU) consists of restriction of natural protein and provision of a protein substitute that lacks phenylalanine but is enriched in tyrosine. Large and unexplained differences exist, however, in the tyrosine enrichment of the protein substitutes. Furthermore, some investigators advise providing extra free tyrosine in addition to the tyrosine-enriched protein substitute, especially in the treatment of maternal PKU. In this article, we discuss tyrosine concentrations
more &raquo; ... blood during low-phenylalanine, tyrosine-enriched diets and the implications of these blood tyrosine concentrations for supplementation with tyrosine. We conclude that the present method of tyrosine supplementation during the day is far from optimal because it does not prevent low blood tyrosine concentrations, especially after an overnight fast, and may result in largely increased blood tyrosine concentrations during the rest of the day. Both high tyrosine enrichment of protein substitutes and extra free tyrosine supplementation may not be as safe as considered at present, especially to the fetus of a woman with PKU. The development of dietary compounds that release tyrosine more slowly could be beneficial. We advocate decreasing the tyrosine content of protein substitutes to Ϸ6% by wt (6 g/100 g protein equivalent) at most and not giving extra free tyrosine without knowing the diurnal variations in the blood tyrosine concentration and having biochemical evidence of a tyrosine deficiency. We further advocate that a better daily distribution of the protein substitute be achieved by improving the palatability of these products. Am J Clin Nutr 2001;73:153-7.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/ajcn/73.2.153">doi:10.1093/ajcn/73.2.153</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/11157309">pmid:11157309</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qwjszskcibcu5eji2rmobiulfa">fatcat:qwjszskcibcu5eji2rmobiulfa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180729011256/https://watermark.silverchair.com/153.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAZ8wggGbBgkqhkiG9w0BBwagggGMMIIBiAIBADCCAYEGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMR_TVNRBYjUGu9CimAgEQgIIBUhmjAmwp682QX7S6JPW_V611XEiBqgmcMcAbQqj8NFC3RFnR1FrY6Jd5ziwxATJO7GilTEO1mXnJdfBIdrrDdGdpuLiarOO5XIEe655oPxnn_4sQjon6KK9BAiLVUaNnTVGuhShFdlcEeamZnCdnKL5GwgTSkBc-rTU1hr9Wj7V75MPe1ttFk0uuggs9G1-bUOURtFqmQNkoFSF_y8LlaHcvJBC2U69NET1s5BUhcjkZxy2F4TIN0Iqas62e8DZNDKZN6QNEXAsLjfl8vxv_Q3Y5PgiKe0Ev9OySiujnvMLQ0NUCcbB6e-w5jh8ALE31xLqkaEspXkMgztBZlC72qfFTSd6IO7gbIhn-BP4aMZAJZ4q-r2UiesGAzwAY55g6N8UdESF_0pIHKfM6fkje7-dTY-U7Zo6LObmVgIMQTXUNN7d4iv633Ji1o02JsO4UhZUw" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c4/41/c4416a383c360ec74a5e638b0f7192576b5cc36a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/ajcn/73.2.153"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> oup.com </button> </a>

Gibbs Sampling with People [article]

Peter M. C. Harrison, Raja Marjieh, Federico Adolfi, Pol van Rijn, Manuel Anglada-Tort, Ofer Tchernichovski, Pauline Larrouy-Maestri, Nori Jacoby
<span title="2020-11-02">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
A core problem in cognitive science and machine learning is to understand how humans derive semantic representations from perceptual objects, such as color from an apple, pleasantness from a musical chord, or seriousness from a face. Markov Chain Monte Carlo with People (MCMCP) is a prominent method for studying such representations, in which participants are presented with binary choice trials constructed such that the decisions follow a Markov Chain Monte Carlo acceptance rule. However, while
more &raquo; ... MCMCP has strong asymptotic properties, its binary choice paradigm generates relatively little information per trial, and its local proposal function makes it slow to explore the parameter space and find the modes of the distribution. Here we therefore generalize MCMCP to a continuous-sampling paradigm, where in each iteration the participant uses a slider to continuously manipulate a single stimulus dimension to optimize a given criterion such as 'pleasantness'. We formulate both methods from a utility-theory perspective, and show that the new method can be interpreted as 'Gibbs Sampling with People' (GSP). Further, we introduce an aggregation parameter to the transition step, and show that this parameter can be manipulated to flexibly shift between Gibbs sampling and deterministic optimization. In an initial study, we show GSP clearly outperforming MCMCP; we then show that GSP provides novel and interpretable results in three other domains, namely musical chords, vocal emotions, and faces. We validate these results through large-scale perceptual rating experiments. The final experiments use GSP to navigate the latent space of a state-of-the-art image synthesis network (StyleGAN), a promising approach for applying GSP to high-dimensional perceptual spaces. We conclude by discussing future cognitive applications and ethical implications.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.02595v2">arXiv:2008.02595v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mzru52syojcgzf3qajk272qjwe">fatcat:mzru52syojcgzf3qajk272qjwe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201106010406/https://arxiv.org/pdf/2008.02595v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/bf/4a/bf4a6182a1cea55227b1cf0164365856be78cb95.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.02595v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Exploring emotional prototypes in a high dimensional TTS latent space [article]

Pol van Rijn, Silvan Mertes, Dominik Schiller, Peter M. C. Harrison, Pauline Larrouy-Maestri, Elisabeth André, Nori Jacoby
<span title="2021-05-05">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Recent TTS systems are able to generate prosodically varied and realistic speech. However, it is unclear how this prosodic variation contributes to the perception of speakers' emotional states. Here we use the recent psychological paradigm 'Gibbs Sampling with People' to search the prosodic latent space in a trained GST Tacotron model to explore prototypes of emotional prosody. Participants are recruited online and collectively manipulate the latent space of the generative speech model in a
more &raquo; ... entially adaptive way so that the stimulus presented to one group of participants is determined by the response of the previous groups. We demonstrate that (1) particular regions of the model's latent space are reliably associated with particular emotions, (2) the resulting emotional prototypes are well-recognized by a separate group of human raters, and (3) these emotional prototypes can be effectively transferred to new sentences. Collectively, these experiments demonstrate a novel approach to the understanding of emotional speech by providing a tool to explore the relation between the latent space of generative models and human semantics.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2105.01891v1">arXiv:2105.01891v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/m2ollyspszda7bs4u2xxdmobje">fatcat:m2ollyspszda7bs4u2xxdmobje</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210507021230/https://arxiv.org/pdf/2105.01891v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ab/f4/abf443e48497c5b8d51f1c3a1902fdac031e590e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2105.01891v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Sensitive capacitive pressure sensors based on graphene membrane arrays

Makars Šiškins, Martin Lee, Dominique Wehenkel, Richard van Rijn, Tijmen W. de Jong, Johannes R. Renshof, Berend C. Hopman, Willemijn S. J. M. Peters, Dejan Davidovikj, Herre S. J. van der Zant, Peter G. Steeneken
<span title="2020-11-16">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bbulciqfbvewnpn6akaezg6jlm" style="color: black;">Microsystems &amp; Nanoengineering</a> </i> &nbsp;
., most of the gas permeation in graphene drums occurs along the van der Waals interface between the 2D material and the substrate 2 .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41378-020-00212-3">doi:10.1038/s41378-020-00212-3</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34567711">pmid:34567711</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8433463/">pmcid:PMC8433463</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2k2osahtxvdb7jx35qduihyun4">fatcat:2k2osahtxvdb7jx35qduihyun4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210428044343/https://repository.tudelft.nl/islandora/object/uuid%3A05e4f501-6109-443f-8743-bc3cb5801eeb/datastream/OBJ/download" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/11/12/111243b230136560333d38bc7225457f66822cdf.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41378-020-00212-3"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8433463" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Rapid detection of genetic variability in chrysanthemum (Dendranthema grandiflora Tzvelev) using random primers

Kirsten Wolff, Jenny Peters-van Rijn
<span title="">1993</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ecz4fjpldjgh3lvpx7tho6iwam" style="color: black;">Heredity</a> </i> &nbsp;
We thank Robert Campbell, Annelies Hofman, Albert Kamping and Ed van der Meyden and an anonymous reviewer  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/hdy.1993.147">doi:10.1038/hdy.1993.147</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/8270426">pmid:8270426</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/naqsnilgrfefxfjgaal47or3te">fatcat:naqsnilgrfefxfjgaal47or3te</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200316140103/https://www.nature.com/articles/hdy1993147.pdf?error=cookies_not_supported&amp;code=3223be69-3e12-4a8b-9708-66d89c3b18b7" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/26/db/26db7fc49cf80d78c158a98563900aa661e1ee95.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/hdy.1993.147"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

One-Dimensional 13C NMR Is a Simple and Highly Quantitative Method for Enantiodiscrimination

Peter Lankhorst, Jozef van Rijn, Alexander Duchateau
<span title="2018-07-20">2018</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dstyyzbt45gknhqqjsh45p55h4" style="color: black;">Molecules</a> </i> &nbsp;
The discrimination of enantiomers of mandelonitrile by means of 1D 13C NMR and with the aid of the chiral solvating agent (S)-(+)-1-(9-anthryl)-2,2,2-trifluoroethanol (TFAE) is presented. 1H NMR fails for this specific compound because proton signals either overlap with the signals of the chiral solvating agent or do not show separation between the (S)-enantiomer and the (R)-enantiomer. The 13C NMR method is validated by preparing artificial mixtures of the (R)-enantiomer and the racemate, and
more &raquo; ... t is shown that with only 4 mg of mandelonitrile a detection limit of the minor enantiomer of 0.5% is obtained, corresponding to an enantiomeric excess value of 99%. Furthermore, the method shows high linearity, and has a small relative standard deviation of only 0.3% for the minor enantiomer when the relative abundance of this enantiomer is 20%. Therefore, the 13C NMR method is highly suitable for quantitative enantiodiscrimination. It is discussed that 13C NMR is preferred over 1H NMR in many situations, not only in molecules with more than one chiral center, resulting in complex mixtures of many stereoisomers, but also in the case of molecules with overlapping multiplets in the 1H NMR spectrum, and in the case of molecules with many quaternary carbon atoms, and therefore less abundant protons.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/molecules23071785">doi:10.3390/molecules23071785</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30036942">pmid:30036942</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/w4cqadcr7jam7glw3pav2le4jq">fatcat:w4cqadcr7jam7glw3pav2le4jq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191129050045/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC6100457&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/74/ff/74ff33078501050d69548199d438fefc926a4970.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/molecules23071785"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Functional multiplex reporter assay using tagged Gaussia luciferase

Sjoerd van Rijn, Jonas Nilsson, David P. Noske, W. Peter Vandertop, Bakhos A. Tannous, Thomas Würdinger
<span title="2013-01-10">2013</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tnqhc2x2aneavcd3gx5h7mswhm" style="color: black;">Scientific Reports</a> </i> &nbsp;
We are thankful to Priscilla Jainandunsing, Laura Jonkman, Stephanie van Hoppe, Tonny Lagerweij and Laurine Wedekind for technical support.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/srep01046">doi:10.1038/srep01046</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/23308339">pmid:23308339</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3541509/">pmcid:PMC3541509</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4rezkbcbzbefdbo27e6cdi66pe">fatcat:4rezkbcbzbefdbo27e6cdi66pe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190309010909/http://pdfs.semanticscholar.org/f13e/2e8fa2658830eb3af1f370d8718b55f9f4a3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f1/3e/f13e2e8fa2658830eb3af1f370d8718b55f9f4a3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/srep01046"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3541509" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Human regulatory T cells at the maternal-fetal interface show functional site-specific adaptation with tumor-infiltrating-like features [article]

Judith Wienke, Laura Brouwers, Leone M van der Burg, Michal Mokry, Rianne C Scholman, Peter GJ Nikkels, Bas B van Rijn, Femke van Wijk
<span title="2019-11-05">2019</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Objectives: Regulatory T cells (Tregs) are crucial for maintaining immune tolerance against the semi-allogeneic fetus during pregnancy. Since their functional profile at the human maternal-fetal interface is still elusive, we investigated the transcriptional profile and functional adaptation of human uterine Tregs (uTregs) during pregnancy. Methods: Blood and uterine biopsies from the placental bed (=maternal-fetal interface) and incision site (=control), were obtained from women with
more &raquo; ... pregnancies undergoing primary Caesarean section. Tregs and CD4+ non-Tregs (Tconv) were isolated for transcriptomic profiling by Cel-Seq2. Results were validated on protein and single cell level by flow cytometry. Results: Placental bed uterine Tregs (uTregs) showed elevated expression of Treg signature markers compared to blood Tregs, including FOXP3, CTLA4 and TIGIT. The uTreg transcriptional profile was indicative of late-stage effector Treg differentiation and chronic activation with high expression of immune checkpoints GITR, TNFR2, OX-40, 4-1BB, genes associated with suppressive capacity (CTLA4, HAVCR2, IL10, IL2RA, LAYN, PDCD1), activation (HLA-DR, LRRC32), and transcription factors MAF, PRDM1, BATF, and VDR. uTregs mirrored uTconv Th1 polarization, and characteristics indicating tissue-residency, including high CD69, CCR1, and CXCR6. The particular transcriptional signature of placental bed uTregs overlapped strongly with the specialized profile of human tumor-infiltrating Tregs, and, remarkably, was more pronounced at the placental bed than uterine control site. Conclusion: uTregs at the maternal-fetal interface acquire a highly differentiated effector Treg profile similar to tumor-infiltrating Tregs, which is locally enriched compared to a distant uterine site. This introduces the novel concept of site-specific transcriptional adaptation of human Tregs within one organ.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/820753">doi:10.1101/820753</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jgla3mcldfbfninkookpr3osoe">fatcat:jgla3mcldfbfninkookpr3osoe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200711022553/https://www.biorxiv.org/content/biorxiv/early/2019/11/05/820753.full.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/18/ae/18ae4b7c50a989a9ee91bbe9b4f2c33012690464.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/820753"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

Effects of Exergaming in People with Dementia: Results of a Systematic Literature Review

Joeke van Santen, Rose-Marie Dröes, Marije Holstege, Olivier Blanson Henkemans, Annelies van Rijn, Ralph de Vries, Annemieke van Straten, Franka Meiland, Peter Whitehouse
<span title="2018-04-24">2018</span> <i title="IOS Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/uux5o5kf5zaednhl4637xcmbte" style="color: black;">Journal of Alzheimer&#39;s Disease</a> </i> &nbsp;
Physical exercise benefits functioning, health, and well-being. However, people living with dementia in particular hardly engage in exercise. Exergaming (exercise and gaming) is an innovative, fun, and relatively safe way of exercising in a virtual reality or gaming environment. It may help people living with dementia overcome barriers they can experience regarding regular exercise activities. Objective: This systematic literature review aims to provide an overview of the cost-effectiveness of
more &raquo; ... xergaming and its effects on physical, cognitive, emotional, and social functioning, as well as the quality of life in people living with dementia. Methods: PubMed, Embase, Cinahl, PsycINFO, the Cochrane Library, and the Web of Science Core Collection were searched. Selection of studies was carried out by at least two independent researchers. Results: Three studies were found to be eligible and were included in this review. Two of these showed some statistically significant effects of exergaming on physical, cognitive, and emotional functioning in people living with dementia, although based on a very small sample. No articles were found about the cost-effectiveness of exergaming. Conclusion: Only a few controlled studies have been conducted into the effectiveness of exergaming, and these show very little significant benefits. More well-designed studies are necessary to examine the effects of exergaming. such as increased mortality rates [1], as well as an increase in health-related costs [2] , and a decrease in general well-being and quality of life [3] . On the other hand, exercise positively influences physical fitness, cognition, daily functioning, general health, and wellbeing in older people [3] [4] [5] [6] [7] [8] [9] . These effects presumably apply to older people living with or living without dementia [10-18]. Physical activity and exercise are
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3233/jad-170667">doi:10.3233/jad-170667</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29689716">pmid:29689716</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5929299/">pmcid:PMC5929299</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ldx66mcgnndufiawfieisqk5eq">fatcat:ldx66mcgnndufiawfieisqk5eq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190306212732/http://pdfs.semanticscholar.org/d567/1d40c236fcaaa4a88746879c8b840fed78d7.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d5/67/d5671d40c236fcaaa4a88746879c8b840fed78d7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3233/jad-170667"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5929299" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>
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