Reversible inhibition of the division of Crithidia luciliae by hydroxyurea and its use for obtaining synchronized cultures

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

MITOCHONDRIA ASSOCIATED WITH THE KINETONUCLEUS OF TRYPANOSOMA MEGA

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Teardrops on My Face: Automatic Weeping Detection from Nonverbal Behavior

Human emotional tears are a powerful socio-emotional signal. Yet, they have received relatively little attention in empirical research compared to facial expressions or body posture. While humans are highly sensitive to others' tears, to date, no automatic means exist for detecting spontaneous weeping. This paper employed facial and postural features extracted using four pre-trained classifiers (FACET, Affdex, OpenFace, OpenPose) to train a Support Vector Machine (SVM) to distinguish spontaneous weepers from non-weepers. Results showed that weeping can be accurately inferred from nonverbal behavior. Importantly, this distinction can be made before the appearance of visible tears on the face. However, features from at least two classifiers need to be combined, with the best models blending three or four classifiers to achieve near-perfect performance (97% accuracy). We discuss how direct and indirect tear detection methods may help to yield important new insights into the antecedents and consequences of emotional tears and how affective computing could benefit from the ability to recognize and respond to this uniquely human signal

Designing the future: strategy, design and the 4th Revolution: an introduction

This is an introduction to the special issue of California Management Review on Design Thinking (DT). This special issue joins the growing body of work exploring the idea of DT and whether DT makes a difference in terms enhancing or augmenting the impact of technology—and, as a result, innovation—in a positive way. We have chosen an interesting, relevant, and useful array of papers that provide different approaches, views, and interpretations of applied design thinking. These articles provide both management and scholarly readers with insights in how DT is used, as well as its impact and usefulness in a variety of contexts

Embedding structure matters: Comparing methods to adapt multilingual vocabularies to new languages

Pre-trained multilingual language models underpin a large portion of modern NLP tools outside of English. A strong baseline for specializing these models for specific languages is Language-Adaptive Pre-Training (LAPT). However, retaining a large cross-lingual vocabulary and embedding matrix comes at considerable excess computational cost during adaptation. In this study, we propose several simple techniques to replace a cross-lingual vocabulary with a compact, language-specific one. Namely, we address strategies for re-initializing the token embedding matrix after vocabulary specialization. We then provide a systematic experimental comparison of our techniques, in addition to the recently-proposed Focus method. We demonstrate that: 1) Embedding-replacement techniques in the monolingual transfer literature are inadequate for adapting multilingual models. 2) Replacing cross-lingual vocabularies with smaller specialized ones provides an efficient method to improve performance in low-resource languages. 3) Simple embedding re-initialization techniques based on script-wise sub-distributions rival techniques such as Focus, which rely on similarity scores obtained from an auxiliary model