19,187 research outputs found

    Atypical developmental trajectories of white matter microstructure in prenatal alcohol exposure: Preliminary evidence from neurite orientation dispersion and density imaging

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    IntroductionFetal alcohol spectrum disorder (FASD), a life-long condition resulting from prenatal alcohol exposure (PAE), is associated with structural brain anomalies and neurobehavioral differences. Evidence from longitudinal neuroimaging suggest trajectories of white matter microstructure maturation are atypical in PAE. We aimed to further characterize longitudinal trajectories of developmental white matter microstructure change in children and adolescents with PAE compared to typically-developing Controls using diffusion-weighted Neurite Orientation Dispersion and Density Imaging (NODDI).Materials and methodsParticipants: Youth with PAE (n = 34) and typically-developing Controls (n = 31) ages 8–17 years at enrollment. Participants underwent formal evaluation of growth and facial dysmorphology. Participants also completed two study visits (17 months apart on average), both of which involved cognitive testing and an MRI scan (data collected on a Siemens Prisma 3 T scanner). Age-related changes in the orientation dispersion index (ODI) and the neurite density index (NDI) were examined across five corpus callosum (CC) regions defined by tractography.ResultsWhile linear trajectories suggested similar overall microstructural integrity in PAE and Controls, analyses of symmetrized percent change (SPC) indicated group differences in the timing and magnitude of age-related increases in ODI (indexing the bending and fanning of axons) in the central region of the CC, with PAE participants demonstrating atypically steep increases in dispersion with age compared to Controls. Participants with PAE also demonstrated greater increases in ODI in the mid posterior CC (trend-level group difference). In addition, SPC in ODI and NDI was differentially correlated with executive function performance for PAE participants and Controls, suggesting an atypical relationship between white matter microstructure maturation and cognitive function in PAE.DiscussionPreliminary findings suggest subtle atypicality in the timing and magnitude of age-related white matter microstructure maturation in PAE compared to typically-developing Controls. These findings add to the existing literature on neurodevelopmental trajectories in PAE and suggest that advanced biophysical diffusion modeling (NODDI) may be sensitive to biologically-meaningful microstructural changes in the CC that are disrupted by PAE. Findings of atypical brain maturation-behavior relationships in PAE highlight the need for further study. Further longitudinal research aimed at characterizing white matter neurodevelopmental trajectories in PAE will be important

    Aplicación móvil basada en técnicas de clasificación de machine learning como apoyo en el reconocimiento de emociones en textos de estudiantes universitarios

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    Los estudiantes universitarios están expuestos a distintos factores económicos, sociales y académicos que afectan su estado emocional, adicionalmente estos tienden a ignorar su salud mental lo que es perjudicial a largo plazo. Debido a esto, en la presente investigación se pretende construir una aplicación móvil a través de la cual los escolares puedan llevar un control de su estado anímico con tan solo responder unas simples preguntas. Para poder identificar estas emociones en las respuestas se desarrolló un modelo con una técnica de Machine Learning denominada redes neuronales de tipo Transformer y se desplegó en un servicio web. Este modelo tiene la capacidad de clasificar texto en 6 emociones diferentes como son: tristeza, alegría, enojo, miedo, amor y sorpresa. En la validación se alcanzó una exactitud de 93%, un promedio en la precisión de 89% y en el promedio del puntaje F1 un 88%. Así mismo se creó una aplicación móvil para los estudiantes y una plataforma web de administración en donde se pueda observar el historial de las emociones registradas

    Learning disentangled speech representations

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    A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody. The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions. In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks. This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically

    Parents as Teachers external evaluation FY16-17 to FY18-19

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    The current evaluation of Parents As Teachers Program was designed to examine program reach and impacts on key factors related to school success using quantitative data from FY16-17 through FY18-19 and gives recommendations for the future

    Effects of Consumer‑Wearable Activity Tracker‑Based Programs on Objectively Measured Daily Physical Activity and Sedentary Behavior Among School‑Aged Children: A Systematic Review and Meta‑analysis

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    Carolina Casado-Robles is supported by a research grant from the Spanish Ministry of Universities [Grant Number: FPU16/03314]. This publication is part of the WearFit project (Reference B-SEJ-029-UGR18), funded by the FEDER/Regional Government of Andalusia-Ministry of Economy and Knowledge. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Background: The popularity of consumer-wearable activity trackers has led the scientific community to conduct an increasing number of intervention studies integrating them to promote physical activity (PA) and to reduce sedentary behavior (SB) levels among school-aged children. Therefore, the aim of the present study was to estimate the effects of consumer-wearable activity tracker-based programs on daily objectively measured PA and SB among apparently healthy school-aged children, as well as to compare the influence of participants’ and programs’ characteristics. Methods: Eligibility criteria were: (1) participants: apparently healthy school-aged children (< 18 years old); (2) intervention: aimed to promote PA and/or to reduce SB incorporating consumer-wearable activity trackers; (3) comparator: baseline measurements and/or a control/traditional group; (4) outcomes: objectively measured daily PA and/or SB levels; (5) study design: pre-experimental, quasi-experimental, and true-experimental trials. Relevant studies were searched from eight databases up to December 2020, as well as from four alternative modes of searching. Based on the Cochrane Risk-of-bias tool 2, the risk of bias was assessed following four domains: (1) randomization process; (2) missing outcome data; (3) measurement of the outcomes; and (4) selection of the reported results. Based on a comprehensive systematic review, meta-analyses of the Cohen’s standardized mean difference (d) and 95% confidence interval (CI) with a random-effects model were conducted to estimate the overall effects, as well as the within- and between-study subgroups analyses effects, of the programs on daily total steps, moderate-to-vigorous PA (MVPA), total PA and SB. Results: Forty-four publications (i.e., 45 studies) were included in the systematic review (5,620 unique participants; mean age = 12.85 ± 2.84 years) and 40 publications (i.e., 41 studies) in the meta-analysis. Programs had a mean length of 11.78 ± 13.17 weeks and most used a waist-worn consumer-wearable activity tracker (77.78% waist-worn; 22.22% wrist-worn). Programs characteristics were: goal-setting strategies (64.06%); participants’ logbooks (56.25%); counseling sessions (62.50%); reminders (28.13%); motivational strategies (42.19%); and exercise routine (17.19%). Results showed a statistically significant moderate favorable effect on daily total steps (d = 0.612, 95% CI 0.477–0.746), small favorable effect on daily MVPA (d = 0.220, 95% CI 0.134–0.307), trivial favorable effect on daily total PA (d = 0.151, 95% CI 0.038–0.264) and trivial unfavorable effect on daily SB (d = 0.172, 95% CI 0.039–0.305). Subgroups analyses showed a higher effect for daily total steps and daily MVPA levels in females and the physically inactive for daily total steps (p = 0.003–0.044). Programs with educational counseling and/or goal-setting strategies, as well as a greater number of strategies, were more effective for improving children’s daily total steps, and wrist-worn activity trackers were more effective than waist-worn trackers for improving their daily MVPA levels (p = 0.001–0.021). Conclusions: Consumer-wearable activity tracker-based programs seem to be effective in promoting school-aged children’s daily total steps and MVPA levels, especially for females and those that are physically inactive. These programs should include specific goal-setting, educational counseling, and wrist-worn trackers as especially effective strategies. However, due to the certainty of evidence being from “low” to “moderate”, future well-designed primary research studies about the topic are needed.Spanish Government FPU16/03314FEDER/Regional Government of Andalusia-Ministry of Economy and Knowledge B-SEJ-029-UGR1

    Building body identities - exploring the world of female bodybuilders

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    This thesis explores how female bodybuilders seek to develop and maintain a viable sense of self despite being stigmatized by the gendered foundations of what Erving Goffman (1983) refers to as the 'interaction order'; the unavoidable presentational context in which identities are forged during the course of social life. Placed in the context of an overview of the historical treatment of women's bodies, and a concern with the development of bodybuilding as a specific form of body modification, the research draws upon a unique two year ethnographic study based in the South of England, complemented by interviews with twenty-six female bodybuilders, all of whom live in the U.K. By mapping these extraordinary women's lives, the research illuminates the pivotal spaces and essential lived experiences that make up the female bodybuilder. Whilst the women appear to be embarking on an 'empowering' radical body project for themselves, the consequences of their activity remains culturally ambivalent. This research exposes the 'Janus-faced' nature of female bodybuilding, exploring the ways in which the women negotiate, accommodate and resist pressures to engage in more orthodox and feminine activities and appearances

    Data-to-text generation with neural planning

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    In this thesis, we consider the task of data-to-text generation, which takes non-linguistic structures as input and produces textual output. The inputs can take the form of database tables, spreadsheets, charts, and so on. The main application of data-to-text generation is to present information in a textual format which makes it accessible to a layperson who may otherwise find it problematic to understand numerical figures. The task can also automate routine document generation jobs, thus improving human efficiency. We focus on generating long-form text, i.e., documents with multiple paragraphs. Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or its variants. These models generate fluent (but often imprecise) text and perform quite poorly at selecting appropriate content and ordering it coherently. This thesis focuses on overcoming these issues by integrating content planning with neural models. We hypothesize data-to-text generation will benefit from explicit planning, which manifests itself in (a) micro planning, (b) latent entity planning, and (c) macro planning. Throughout this thesis, we assume the input to our generator are tables (with records) in the sports domain. And the output are summaries describing what happened in the game (e.g., who won/lost, ..., scored, etc.). We first describe our work on integrating fine-grained or micro plans with data-to-text generation. As part of this, we generate a micro plan highlighting which records should be mentioned and in which order, and then generate the document while taking the micro plan into account. We then show how data-to-text generation can benefit from higher level latent entity planning. Here, we make use of entity-specific representations which are dynam ically updated. The text is generated conditioned on entity representations and the records corresponding to the entities by using hierarchical attention at each time step. We then combine planning with the high level organization of entities, events, and their interactions. Such coarse-grained macro plans are learnt from data and given as input to the generator. Finally, we present work on making macro plans latent while incrementally generating a document paragraph by paragraph. We infer latent plans sequentially with a structured variational model while interleaving the steps of planning and generation. Text is generated by conditioning on previous variational decisions and previously generated text. Overall our results show that planning makes data-to-text generation more interpretable, improves the factuality and coherence of the generated documents and re duces redundancy in the output document

    Generación de textos en ruso mediante técnicas de Aprendizaje Automático para la industria del lenguaje

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    [ES] Hoy en día los avances en el área del Procesamiento del Lenguaje Natural y el Aprendizaje Automático permiten el análisis, la comprensión y la generación de texto automáticamente cada vez más precisa y fluida. El objetivo de este trabajo final de grado es la creación automática de ejemplos de texto en ruso, a partir de datos de texto ya existentes mediante técnicas de aprendizaje automático. Se han empleado redes neuronales y recursos lingüísticos para la generación automática de texto en ruso. Para el desarrollo del trabajo se han utilizado datos de dominio público. El sistema genera nuevos textos utilizando información de embeddings entrenadas con una ingente cantidad de datos en modelos de lenguaje neuronales. La generación de dichos textos incrementa el corpus utilizado para el entrenamiento de modelos para tareas del Procesamiento del Lenguaje Natural como la traducción automática. También podría aplicarse a otras tareas como la generación de resúmenes automáticos o parafraseadores de textos. Por último, se ha realizado un análisis de los resultados obtenidos evaluando la calidad de los textos generados y se han añadido al entrenamiento de modelos de traducción automática neuronal. Estos modelos se han comparado realizando un análisis cuantitativo, comparando los distintos métodos mediante varias métricas automáticas típicas utilizadas en traducción automática y se han medido los tiempos empleados y la cantidad de texto generado para un buen uso en la industria del lenguaje, y un análisis cualitativo, donde se han expuesto ejemplos de traducción generados por los modelos de traducción entrenados y se han comparado entre sí.[EN] Current progress in the areas of Natural Language Processing and Machine Learning allows for the analysis, understanding and automatic generation of increasingly accurate and fluid text. The objective of this final degree project is automatically creating text examples in Russian from existing text data using machine learning techniques. Neural networks and linguistic resources have been used for the automatic generation of text in Russian. To develop this project, data from the public domain have been used. The system generates new texts using information from embeddings trained with a huge amount of data in neural language models. The generation of these texts increases the corpus used to train models for several Natural Language Processing tasks, for instance, machine translation. It could also be applied to other tasks such as generating automatic summaries or to text paraphrasers. Finally, an analysis of the results obtained evaluating the quality of generated texts has been carried out and those texts have been added to the training process of neural machine translation models. On the one hand, these models have been compared by performing a quantitative analysis, comparing the different methods by means of several typical automatic metrics used in machine translation and measuring the times spent and the amount of text generated for good use in the language industry. On the other hand, they have been compared through a qualitative analysis, where examples of translation generated by the trained translation models have been exposed and compared with each other.[CA] Hui dia, els avanços en l’àrea del Processament del Llenguatge Natural i l’Aprenentatge Automàtic permeten l’anàlisi, la comprensió i la generació automàtica de text cada vegada més precís i fluid. L’objectiu d’aquest treball final de grau és la creació automàtica d’exemples de text en rus a partir de dades de text ja existents mitjançant tècniques d’aprenentatge automàtic. S’han emprat xarxes neuronals i recursos lingüístics per a la generació automàtica de text en rus. Per al desenvolupament del treball s’han utilitzat dades de domini públic. El sistema genera nous textos utilitzant informació d’embeddings entrenades amb una ingent quantitat de dades en models de llenguatge neuronals. La generació d’aquests textos incrementa el corpus utilitzat a l’entrenament de models per a tasques de Processament del Llenguatge Natural com ara la traducció automàtica. També podria aplicar-se a d’altres tasques com, per exemple, la generació de resums automàtics o als parafrasejadors de textos. Finalment, s’ha realitzat una anàlisi dels resultats obtinguts mitjançant l’avaluació de la qualitat dels textos generats, els quals s’han afegit a l’entrenament de models de traducció automàtica neuronal. Aquests models s’han comparat realitzant, d’una banda, una anàlisi quantitativa amb la comparació dels diferents mètodes mitjançant diverses mètriques automàtiques típiques utilitzades en traducció automàtica, així com el mesurament dels temps emprats i la quantitat de text generat per un bon ús en la indústria del llenguatge i, d’altra banda, una anàlisi qualitativa, on s’han exposat exemples de traducció generats pels models de traducció entrenats i s’han comparat entre ells.Gregoryev, M. (2022). Generación de textos en ruso mediante técnicas de Aprendizaje Automático para la industria del lenguaje. Universitat Politècnica de València. http://hdl.handle.net/10251/182213TFG
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