Desarrollo, implementación y evaluación de redes neuronales recurrentes de tipo LSTM-g

El objetivo de este Proyecto Final de Carrera es la implementación y desarrollo de la redes neuronales recurrentes de tipo Generalized Long Short Term Memory (LSTM-g) y su combinación con un diseño previo para redes de tipo feedforward o multilayer perceptron (MLP) entrenadas mediante Backpropagation. De este modo, se pretende no solamente implementar el algoritmo de entrenamiento de las LSTM-g sino poder utilizar ciertas características de las mismas (puertas "gated") en redes no recurrentes. Otro objetivo es aprovechar el diseño e implementación previas de la versión MLP, una especie de "arma de doble filo" porque nos ofrece partes ya resueltas pero, por otra parte, supone restricciones en el diseño. La memoria de este trabajo realiza un recorrido teórico sobre los distintos tipos de redes neuronales artificiales y sus respectivos algoritmos de aprendizaje abordando las dificultades que poseen. En particular, explicamos la dificultad de entrenar una red neuronal recurrente conforme aumenta la longitud de las secuencias utilizadas, donde se observa el problema del desvanecimiento de errores (Vanishing Gradients)[4] en el proceso de aprendizaje y cómo éste ha sido resuelto en la literatura. Este problema motiva las redes de tipo "long short term memory" que han sido mejoradas en diversos trabajos de la literatura con la aparición de las peephole connections, entre otras. Estas mejoras culmina con la introducción y análisis en profundidad de las redes LSTM y LSTM-g como solución al problema de aprendizaje comentado anteriormente en las redes neuronales recurrentes. A continuación se expone el diseño e implentación eficiente de un caso particular del mecanismo de control de flujo en las redes neuronales recurrentes de tipo LSTM-g como parte de la herramienta April, implementada en C++ y Lua, desarrollada por los codirectores de este proyecto y que, al inicio de este proyecto, disponía de un módulo de entrenamiento de MLPs utilizando el algoritmo de backpropagation. Explicamos cómo la flexibilidad del diseño inicial ha permitido preadaptar al mismo para la incorporación de las LSTM-g que son un tipo particular de red recurrente basada en la manipulación de datos locales (es decir, los datos utilizados durante el entrenamiento y la evaluación de las redes no crece con la longitud de las secuencias a procesar). Finalmente, la memoria presenta las conclusiones alcanzadas y se proponen los trabajos futuros.Assaf Layouss, NG. (2012). Desarrollo, implementación y evaluación de redes neuronales recurrentes de tipo LSTM-g. http://hdl.handle.net/10251/17390.Archivo delegad

A critical examination of deep learningapproaches to automated speech recognition

Recently, deep learning techniques have been successfully applied to automatic speech recognition (ASR) tasks. Most current speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) are exploited to model the emission probability of the HMM. Deep Neural Networks (DNNs) and Deep Belief Networks(DBNs) have recently proven though to outperform GMMs in modeling the probability of emission in HMMs. Deep architectures such as DBNs with many hidden layers are useful for multilevel feature representation thus building a distributed representation at different levels of a certain input. These networks are first pre-trained as a multi-layer generative model of a window of feature vector without making use of any discriminative information in unsupervised mode. Once the generative pre-training is complete, discriminative fine-tuning is performed to adjust the model parameters to make them better at predicting. Our aim is to study different levels of representation for speech acoustic features that are produced by the hidden layers of DBNs. To this end, we estimate phoneme recognition error and use classification accuracy evaluated with Support Vector Machines (SVMs) as a measure of separability between the DBN representations of 61 phoneme classes. In addition, we investigate the relation between different subgroups/categories of phonemes at various representation levels using correlation analysis. The tests have been performed on TIMIT database and simulations have been developed to run on a graphics processing unit (GPU) cluster at PDC/KTH

A critical examination of deep learningapproaches to automated speech recognition

Recently, deep learning techniques have been successfully applied to automatic speech recognition (ASR) tasks. Most current speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) are exploited to model the emission probability of the HMM. Deep Neural Networks (DNNs) and Deep Belief Networks(DBNs) have recently proven though to outperform GMMs in modeling the probability of emission in HMMs. Deep architectures such as DBNs with many hidden layers are useful for multilevel feature representation thus building a distributed representation at different levels of a certain input. These networks are first pre-trained as a multi-layer generative model of a window of feature vector without making use of any discriminative information in unsupervised mode. Once the generative pre-training is complete, discriminative fine-tuning is performed to adjust the model parameters to make them better at predicting. Our aim is to study different levels of representation for speech acoustic features that are produced by the hidden layers of DBNs. To this end, we estimate phoneme recognition error and use classification accuracy evaluated with Support Vector Machines (SVMs) as a measure of separability between the DBN representations of 61 phoneme classes. In addition, we investigate the relation between different subgroups/categories of phonemes at various representation levels using correlation analysis. The tests have been performed on TIMIT database and simulations have been developed to run on a graphics processing unit (GPU) cluster at PDC/KTH

WhatsApp: Improvement tool for surgical team communication

International audienc

WhatsApp: Improvement tool for surgical team communication

International audienc

Local Communities’ Willingness to Accept Compensation for Sustainable Ecosystem Management in Wadi Araba, South of Jordan

In developing countries, like Jordan, climate change and population growth have prompted land-use and land-cover changes that have profoundly affected ESs, especially by poor people living in fragile ecosystems. This study aimed to analyze the attitudes towards ES among households living in Wadi Araba, a study area located in a dry ecosystem with limited natural resources, as well as to determine the value of ES and the main socio-economic and perceptions factors influencing households’ willingness to accept (WTA) compensation according to the families’ priorities. The face-to-face method was used to interview a random sample of 296 residents from the study sites, using a structured questionnaire to capture the accepted level of compensation for conservation by the local community. Additionally, multiple linear regression analysis was applied to determine the main socio-economic factors affecting WTA. More than 91% of the respondents were willing to accept compensation for three different conservation plans that reflect the resident’s priority. For the three priorities, the weighted average of the compensation levels was JOD 436, 339, 261 per household per year, respectively, and the aggregate values were about JOD (1,196,977.8, 930,601.2, and 719,411.8, respectively) (JOD 1 = USD 1.41). The residents’ gender, age, and income were among the most important factors that affect the compensation level. The main policy implications are that the government and non-governmental organizations should strengthen advocacy and education of arid ecological and natural resources protection, besides including the local community in any decisions in establishing differentiated compensation strategies and regulations. Eventually, the conservation and restoration activities will become self-initiated

Management of patients with high-risk and advanced prostate cancer in the Middle East:resource-stratified consensus recommendations

PURPOSE Prostate cancer care in the Middle East is highly variable and access to specialist multidisciplinary management is limited. Academic tertiary referral centers offer cutting-edge diagnosis and treatment; however, in many parts of the region, patients are managed by non-specialists with limited resources. Due to many factors including lack of awareness and lack of prostate-specific antigen (PSA) screening, a high percentage of men present with locally advanced and metastatic prostate cancer at diagnosis. The aim of these recommendations is to assist clinicians in managing patients with different levels of access to diagnostic and treatment modalities. METHODS The first Advanced Prostate Cancer Consensus Conference (APCCC) satellite meeting for the Middle East was held in Beirut, Lebanon, November 2017. During this meeting a consortium of urologists, medical oncologists, radiation oncologist and imaging specialists practicing in Lebanon, Syria, Iraq, Kuwait and Saudi Arabia voted on a selection of consensus questions. An additional workshop to formulate resource-stratified consensus recommendations was held in March 2019. RESULTS Variations in practice based on available resources have been proposed to form resource-stratified recommendations for imaging at diagnosis, initial management of localized prostate cancer requiring therapy, treatment of castration-sensitive/naïve advanced prostate cancer and treatment of castration-resistant prostate cancer. CONCLUSION This is the first regional consensus on prostate cancer management from the Middle East. The following recommendations will be useful to urologists and oncologists practicing in all areas with limited access to specialist multi-disciplinary teams, diagnostic modalities and treatment resources