Hemophagocytic lymphohistiocytosis: a rare and fatal manifestation of dengue fever

Authors describe a rare case of dengue fever manifesting as hemophagocytic lymphohistiocytosis. A 26-year-old man presented with acute gastroenteritis along with high grade fever, leukopenia, thrombocytopenia, acute kidney injury, liver dysfunction. Further work-up revealed elevated serum ferritin and LDH levels, and bone marrow biopsy showed hemophagocytes. As dengue fever is in rising trend all over the world, especially in Asian countries, clinicians should look out for this rare but potentially fatal complication of dengue fever

automatic community creation for abstractive spoken conversations summarization

n/

Predicting Post-Editor Profiles from the Translation Process

The purpose of the current investigation is to predict post-editor profiles based on user be- haviour and demographics using machine learning techniques to gain a better understanding of post-editor styles. Our study extracts process unit features from the CasMaCat LS14 database from the CRITT Translation Process Research Database (TPR-DB). The analysis has two main research goals: We create n-gram models based on user activity and part-of-speech sequences to automatically cluster post-editors, and we use discriminative classifier models to character- ize post-editors based on a diverse range of translation process features. The classification and clustering of participants resulting from our study suggest this type of exploration could be used as a tool to develop new translation tool features or customization possibilities

E2E Spoken Entity Extraction for Virtual Agents

This paper rethink some aspects of speech processing using speech encoders, specifically about extracting entities directly from speech, without intermediate textual representation. In human-computer conversations, extracting entities such as names, street addresses and email addresses from speech is a challenging task. In this paper, we study the impact of fine-tuning pre-trained speech encoders on extracting spoken entities in human-readable form directly from speech without the need for text transcription. We illustrate that such a direct approach optimizes the encoder to transcribe only the entity relevant portions of speech ignoring the superfluous portions such as carrier phrases, or spell name entities. In the context of dialog from an enterprise virtual agent, we demonstrate that the 1-step approach outperforms the typical 2-step approach which first generates lexical transcriptions followed by text-based entity extraction for identifying spoken entities.Comment: Updated version to be shared late