Search results for "EURA"

2015

The clarification of complete cell lineages, which are produced by specific stem cells, is fundamental for understanding mechanisms, controlling the generation of cell diversity and patterning in an emerging tissue. In the developing Central Nervous System (CNS) of Drosophila, neural stem cells (neuroblasts) exhibit two periods of proliferation: During embryogenesis they produce primary lineages, which form the larval CNS. After a phase of mitotic quiescence, a subpopulation of them resumes proliferation in the larva to give rise to secondary lineages that build up the CNS of the adult fly. Within the ventral nerve cord (VNC) detailed descriptions exist for both primary and secondary lineag…

Impact of ethanol on the perception of wine odorant mixtures

2007

International audience; Several studies have focused on perceptual interactions in binary odor mixtures, but few on more complex mixtures. The aroma of wine is an example of a complex odor mixture. Our aim was to assess the impact of ethanol on the perception of mixtures of Woody (whiskey lactone) and Fruity (isoamyl acetate) odorants commonly found, physico-chemically and perceptually, in wine. Physico-chemically, reduced whiskey lactone volatility was observed in hydro-alcoholic solutions. Perceptually, a synergy effect by the Woody on the Fruity odor was observed in aqueous solutions, which disappeared with the addition of ethanol. Conversely, the Woody odor was masked in both aqueous an…

Improving Speaker-Independent Lipreading with Domain-Adversarial Training

2017

We present a Lipreading system, i.e. a speech recognition system using only visual features, which uses domain-adversarial training for speaker independence. Domain-adversarial training is integrated into the optimization of a lipreader based on a stack of feedforward and LSTM (Long Short-Term Memory) recurrent neural networks, yielding an end-to-end trainable system which only requires a very small number of frames of untranscribed target data to substantially improve the recognition accuracy on the target speaker. On pairs of different source and target speakers, we achieve a relative accuracy improvement of around 40% with only 15 to 20 seconds of untranscribed target speech data. On mul…

Deep Neural Network Frontend for Continuous EMG-Based Speech Recognition

2016

Attention-based Model for Evaluating the Complexity of Sentences in English Language

2020

The automation of text complexity evaluation (ATCE) is an emerging problem which has been tackled by means of different methodologies. We present an effective deep learning- based solution which leverages both Recurrent Neural and the Attention mechanism. The developed system is capable of classifying sentences written in the English language by analysing their syntactical and lexical complexity. An accurate test phase has been carried out, and the system has been compared with a baseline tool based on the Support Vector Machine. This paper represents an extension of a previous deep learning model, which allows showing the suitability of Neural Networks to evaluate sentence complexity in tw…

Deep neural attention-based model for the evaluation of italian sentences complexity

2020

In this paper, the Automatic Text Complexity Evaluation problem is modeled as a binary classification task tackled by a Neural Network based system. It exploits Recurrent Neural Units and the Attention mechanism to measure the complexity of sentences written in the Italian language. An accurate test phase has been carried out, and the system has been compared with state-of-art tools that tackle the same problem. The computed performances proof the model suitability to evaluate sentence complexity improving the results achieved by other state-of-the-art systems.

Multi-class Text Complexity Evaluation via Deep Neural Networks

2019

Automatic Text Complexity Evaluation (ATE) is a natural language processing task which aims to assess texts difficulty taking into account many facets related to complexity. A large number of papers tackle the problem of ATE by means of machine learning algorithms in order to classify texts into complex or simple classes. In this paper, we try to go beyond the methodologies presented so far by introducing a preliminary system based on a deep neural network model whose objective is to classify sentences into more of two classes. Experiments have been carried out on a manually annotated corpus which has been preprocessed in order to make it suitable for the scope of the paper. The results sho…

Matching research and practice: Prediction of individual patient progress and dropout risk for basic routine outcome monitoring.

2021

OBJECTIVE Despite evidence showing that systematic outcome monitoring can prevent treatment failure, the practical conditions that allow for implementation are seldom met in naturalistic psychological services. In the context of limited time and resources, session-by-session evaluation is rare in most clinical settings. This study aimed to validate innovative prediction methods for individual treatment progress and dropout risk based on basic outcome monitoring. METHODS Routine data of a naturalistic psychotherapy outpatient sample were analyzed (N = 3902). Patients were treated with cognitive behavioral therapy with up to 95 sessions (M = 39.19, SD = 16.99) and assessment intervals of 5-15…

How do people talk decades later about their crisis that we call psychosis? : A qualitative study of the personal meaning-making process

2019

Psychosis refers to a severe mental state that often significantly affects the individual’s life course. However, it remains unclear how people with the lived experiences themselves view these phenomena, as part of their life story. In order to evaluate this personal meaning-making process, we conducted in-depth life-story interviews with 20 people who had been diagnosed with non-affective psychosis 10 to 23 years previously in one catchment area. 35% of them were still receiving mental health treatment, and 55% of them were diagnosed with schizophrenia. Only a minority named their experiences as psychosis. On the basis of narrative analysis, two types of stories appeared to encompass how m…

Dropping out of a transdiagnostic online intervention: A qualitative analysis of client's experiences

2017

Introduction An important concern in Internet-based treatments (IBTs) for emotional disorders is the high dropout rate from these protocols. Although dropout rates are usually reported in research studies, very few studies qualitatively explore the experiences of patients who drop out of IBTs. Examining the experiences of these clients may help to find ways to tackle this problem. Method A Consensual Qualitative Research study was applied in 10 intentionally-selected patients who dropped out of a transdiagnostic IBT. Results 22 categories were identified within 6 domains. Among the clients an undeniable pattern arose regarding the insufficient support due to the absence of a therapist and t…