bluuebunny/crossref_metadata_2025 huggingface dataset:
Composite Binary Decomposition Networks
Authors: Qiaoben You , Wang Zheng , Li Jianguo , Dong Yinpeng , Jiang Yu-Gang , Zhu Jun
Published: September 2019

Binary neural networks have great resource and computing efficiency, while suffer from long training procedure and non-negligible accuracy drops, when comparing to the fullprecision counterparts. In this paper, we propose the composite binary decomposition networks (CBDNet), which first compose real-valued tensor of each layer with a limited number of binary tensors, and then decompose some conditioned binary tensors into two low-rank binary tensors, so that the number of parameters and operations are greatly reduced comparing to the original ones. Experiments demonstrate the effectiveness of the proposed method, as CBDNet can approximate image classification network ResNet-18 using 5.25 bits, VGG-16 using 5.47 bits, DenseNet-121 using 5.72 bits, object detection networks SSD300 using 4.38 bits, and semantic segmentation networks SegNet using 5.18 bits, all with minor accuracy drops.1
Nearest-Neighbour-Induced Isolation Similarity and Its Impact on Density-Based Clustering
Authors: Qin Xiaoyu , Ting Kai Ming , Zhu Ye , Lee Vincent CS
Published: September 2019

A recent proposal of data dependent similarity called Isolation Kernel/Similarity has enabled SVM to produce better classification accuracy. We identify shortcomings of using a tree method to implement Isolation Similarity; and propose a nearest neighbour method instead. We formally prove the characteristic of Isolation Similarity with the use of the proposed method. The impact of Isolation Similarity on densitybased clustering is studied here. We show for the first time that the clustering performance of the classic density-based clustering algorithm DBSCAN can be significantly uplifted to surpass that of the recent density-peak clustering algorithm DP. This is achieved by simply replacing the distance measure with the proposed nearest-neighbour-induced Isolation Similarity in DBSCAN, leaving the rest of the procedure unchanged. A new type of clusters called mass-connected clusters is formally defined. We show that DBSCAN, which detects density-connected clusters, becomes one which detects mass-connected clusters, when the distance measure is replaced with the proposed similarity. We also provide the condition under which mass-connected clusters can be detected, while density-connected clusters cannot.
Explicitly Imposing Constraints in Deep Networks via Conditional Gradients Gives Improved Generalization and Faster Convergence
Authors: Ravi Sathya N. , Dinh Tuan , Lokhande Vishnu Suresh , Singh Vikas
Published: September 2019

A number of results have recently demonstrated the benefits of incorporating various constraints when training deep architectures in vision and machine learning. The advantages range from guarantees for statistical generalization to better accuracy to compression. But support for general constraints within widely used libraries remains scarce and their broader deployment within many applications that can benefit from them remains under-explored. Part of the reason is that Stochastic gradient descent (SGD), the workhorse for training deep neural networks, does not natively deal with constraints with global scope very well. In this paper, we revisit a classical first order scheme from numerical optimization, Conditional Gradients (CG), that has, thus far had limited applicability in training deep models. We show via rigorous analysis how various constraints can be naturally handled by modifications of this algorithm. We provide convergence guarantees and show a suite of immediate benefits that are possible — from training ResNets with fewer layers but better accuracy simply by substituting in our version of CG to faster training of GANs with 50% fewer epochs in image inpainting applications to provably better generalization guarantees using efficiently implementable forms of recently proposed regularizers.
Regularized Evolution for Image Classifier Architecture Search
Authors: Real Esteban , Aggarwal Alok , Huang Yanping , Le Quoc V.
Published: September 2019

The effort devoted to hand-crafting neural network image classifiers has motivated the use of architecture search to discover them automatically. Although evolutionary algorithms have been repeatedly applied to neural network topologies, the image classifiers thus discovered have remained inferior to human-crafted ones. Here, we evolve an image classifier— AmoebaNet-A—that surpasses hand-designs for the first time. To do this, we modify the tournament selection evolutionary algorithm by introducing an age property to favor the younger genotypes. Matching size, AmoebaNet-A has comparable accuracy to current state-of-the-art ImageNet models discovered with more complex architecture-search methods. Scaled to larger size, AmoebaNet-A sets a new state-of-theart 83.9% top-1 / 96.6% top-5 ImageNet accuracy. In a controlled comparison against a well known reinforcement learning algorithm, we give evidence that evolution can obtain results faster with the same hardware, especially at the earlier stages of the search. This is relevant when fewer compute resources are available. Evolution is, thus, a simple method to effectively discover high-quality architectures.
Covariate Shift Adaptation on Learning from Positive and Unlabeled Data
Authors: Sakai Tomoya , Shimizu Nobuyuki
Published: September 2019

The goal of binary classification is to identify whether an input sample belongs to positive or negative classes. Usually, supervised learning is applied to obtain a classification rule, but in real-world applications, it is conceivable that only positive and unlabeled data are accessible for learning, which is called learning from positive and unlabeled data (PU learning). Furthermore, in practice, data distributions are likely to differ between training and testing due to, for example, time variation and domain shift. The covariate shift is a dataset shift situation, where distributions of covariates (inputs) differ between training and testing, but the input-output relation is the same. In this paper, we address the PU learning problem under the covariate shift. We propose an importanceweighted PU learning method and reveal in which situations the importance-weighting is necessary. Moreover, we derive the convergence rate of the proposed method under mild conditions and experimentally demonstrate its effectiveness.
Unsupervised Learning with Contrastive Latent Variable Models
Authors: Severson Kristen A. , Ghosh Soumya , Ng Kenney
Published: September 2019

In unsupervised learning, dimensionality reduction is an important tool for data exploration and visualization. Because these aims are typically open-ended, it can be useful to frame the problem as looking for patterns that are enriched in one dataset relative to another. These pairs of datasets occur commonly, for instance a population of interest vs. control or signal vs. signal free recordings. However, there are few methods that work on sets of data as opposed to data points or sequences. Here, we present a probabilistic model for dimensionality reduction to discover signal that is enriched in the target dataset relative to the background dataset. The data in these sets do not need to be paired or grouped beyond set membership. By using a probabilistic model where some structure is shared amongst the two datasets and some is unique to the target dataset, we are able to recover interesting structure in the latent space of the target dataset. The method also has the advantages of a probabilistic model, namely that it allows for the incorporation of prior information, handles missing data, and can be generalized to different distributional assumptions. We describe several possible variations of the model and demonstrate the application of the technique to de-noising, feature selection, and subgroup discovery settings.
MEAL: Multi-Model Ensemble via Adversarial Learning
Authors: Shen Zhiqiang , He Zhankui , Xue Xiangyang
Published: September 2019

Often the best performing deep neural models are ensembles of multiple base-level networks. Unfortunately, the space required to store these many networks, and the time required to execute them at test-time, prohibits their use in applications where test sets are large (e.g., ImageNet). In this paper, we present a method for compressing large, complex trained ensembles into a single network, where knowledge from a variety of trained deep neural networks (DNNs) is distilled and transferred to a single DNN. In order to distill diverse knowledge from different trained (teacher) models, we propose to use adversarial-based learning strategy where we define a block-wise training loss to guide and optimize the predefined student network to recover the knowledge in teacher models, and to promote the discriminator network to distinguish teacher vs. student features simultaneously. The proposed ensemble method (MEAL) of transferring distilled knowledge with adversarial learning exhibits three important advantages: (1) the student network that learns the distilled knowledge with discriminators is optimized better than the original model; (2) fast inference is realized by a single forward pass, while the performance is even better than traditional ensembles from multi-original models; (3) the student network can learn the distilled knowledge from a teacher model that has arbitrary structures. Extensive experiments on CIFAR-10/100, SVHN and ImageNet datasets demonstrate the effectiveness of our MEAL method. On ImageNet, our ResNet-50 based MEAL achieves top-1/5 21.79%/5.99% val error, which outperforms the original model by 2.06%/1.14%.
Evaluating Recommender System Stability with Influence-Guided Fuzzing
Authors: Shriver David , Elbaum Sebastian , Dwyer Matthew B. , Rosenblum David S.
Published: September 2019

Recommender systems help users to find products or services they may like when lacking personal experience or facing an overwhelming set of choices. Since unstable recommendations can lead to distrust, loss of profits, and a poor user experience, it is important to test recommender system stability. In this work, we present an approach based on inferred models of influence that underlie recommender systems to guide the generation of dataset modifications to assess a recommender’s stability. We implement our approach and evaluate it on several recommender algorithms using the MovieLens dataset. We find that influence-guided fuzzing can effectively find small sets of modifications that cause significantly more instability than random approaches.
Transferable Curriculum for Weakly-Supervised Domain Adaptation
Authors: Shu Yang , Cao Zhangjie , Long Mingsheng , Wang Jianmin
Published: September 2019

Domain adaptation improves a target task by knowledge transfer from a source domain with rich annotations. It is not uncommon that “source-domain engineering” becomes a cumbersome process in domain adaptation: the high-quality source domains highly related to the target domain are hardly available. Thus, weakly-supervised domain adaptation has been introduced to address this difficulty, where we can tolerate the source domain with noises in labels, features, or both. As such, for a particular target task, we simply collect the source domain with coarse labeling or corrupted data. In this paper, we try to address two entangled challenges of weaklysupervised domain adaptation: sample noises of the source domain and distribution shift across domains. To disentangle these challenges, a Transferable Curriculum Learning (TCL) approach is proposed to train the deep networks, guided by a transferable curriculum informing which of the source examples are noiseless and transferable. The approach enhances positive transfer from clean source examples to the target and mitigates negative transfer of noisy source examples. A thorough evaluation shows that our approach significantly outperforms the state-of-the-art on weakly-supervised domain adaptation tasks.
Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents
Authors: Siddhant Aditya , Goyal Anuj , Metallinou Angeliki
Published: September 2019

User interaction with voice-powered agents generates large amounts of unlabeled utterances. In this paper, we explore techniques to efficiently transfer the knowledge from these unlabeled utterances to improve model performance on Spoken Language Understanding (SLU) tasks. We use Embeddings from Language Model (ELMo) to take advantage of unlabeled data by learning contextualized word representations. Additionally, we propose ELMo-Light (ELMoL), a faster and simpler unsupervised pre-training method for SLU. Our findings suggest unsupervised pre-training on a large corpora of unlabeled utterances leads to significantly better SLU performance compared to training from scratch and it can even outperform conventional supervised transfer. Additionally, we show that the gains from unsupervised transfer techniques can be further improved by supervised transfer. The improvements are more pronounced in low resource settings and when using only 1000 labeled in-domain samples, our techniques match the performance of training from scratch on 10-15x more labeled in-domain data.
Safe Policy Improvement with Baseline Bootstrapping in Factored Environments
Authors: Simão Thiago D. , Spaan Matthijs T. J.
Published: September 2019

We present a novel safe reinforcement learning algorithm that exploits the factored dynamics of the environment to become less conservative. We focus on problem settings in which a policy is already running and the interaction with the environment is limited. In order to safely deploy an updated policy, it is necessary to provide a confidence level regarding its expected performance. However, algorithms for safe policy improvement might require a large number of past experiences to become confident enough to change the agent’s behavior. Factored reinforcement learning, on the other hand, is known to make good use of the data provided. It can achieve a better sample complexity by exploiting independence between features of the environment, but it lacks a confidence level. We study how to improve the sample efficiency of the safe policy improvement with baseline bootstrapping algorithm by exploiting the factored structure of the environment. Our main result is a theoretical bound that is linear in the number of parameters of the factored representation instead of the number of states. The empirical analysis shows that our method can improve the policy using a number of samples potentially one order of magnitude smaller than the flat algorithm.
Diversity-Driven Extensible Hierarchical Reinforcement Learning
Authors: Song Yuhang , Wang Jianyi , Lukasiewicz Thomas , Xu Zhenghua , Xu Mai
Published: September 2019

Hierarchical reinforcement learning (HRL) has recently shown promising advances on speeding up learning, improving the exploration, and discovering intertask transferable skills. Most recent works focus on HRL with two levels, i.e., a master policy manipulates subpolicies, which in turn manipulate primitive actions. However, HRL with multiple levels is usually needed in many real-world scenarios, whose ultimate goals are highly abstract, while their actions are very primitive. Therefore, in this paper, we propose a diversitydriven extensible HRL (DEHRL), where an extensible and scalable framework is built and learned levelwise to realize HRL with multiple levels. DEHRL follows a popular assumption: diverse subpolicies are useful, i.e., subpolicies are believed to be more useful if they are more diverse. However, existing implementations of this diversity assumption usually have their own drawbacks, which makes them inapplicable to HRL with multiple levels. Consequently, we further propose a novel diversity-driven solution to achieve this assumption in DEHRL. Experimental studies evaluate DEHRL with nine baselines from four perspectives in two domains; the results show that DEHRL outperforms the state-of-the-art baselines in all four aspects.
Network Structure and Transfer Behaviors Embedding via Deep Prediction Model
Authors: Sun Xin , Song Zenghui , Dong Junyu , Yu Yongbo , Plant Claudia , Böhm Christian
Published: September 2019

Network-structured data is becoming increasingly popular in many applications. However, these data present great challenges to feature engineering due to its high non-linearity and sparsity. The issue on how to transfer the link-connected nodes of the huge network into feature representations is critical. As basic properties of the real-world networks, the local and global structure can be reflected by dynamical transfer behaviors from node to node. In this work, we propose a deep embedding framework to preserve the transfer possibilities among the network nodes. We first suggest a degree-weight biased random walk model to capture the transfer behaviors of the network. Then a deep embedding framework is introduced to preserve the transfer possibilities among the nodes. A network structure embedding layer is added into the conventional Long Short-Term Memory Network to utilize its sequence prediction ability. To keep the local network neighborhood, we further perform a Laplacian supervised space optimization on the embedding feature representations. Experimental studies are conducted on various real-world datasets including social networks and citation networks. The results show that the learned representations can be effectively used as features in a variety of tasks, such as clustering, visualization and classification, and achieve promising performance compared with state-of-the-art models.
Learning Vine Copula Models for Synthetic Data Generation
Authors: Sun Yi , Cuesta-Infante Alfredo , Veeramachaneni Kalyan
Published: September 2019

A vine copula model is a flexible high-dimensional dependence model which uses only bivariate building blocks. However, the number of possible configurations of a vine copula grows exponentially as the number of variables increases, making model selection a major challenge in development. In this work, we formulate a vine structure learning problem with both vector and reinforcement learning representation. We use neural network to find the embeddings for the best possible vine model and generate a structure. Throughout experiments on synthetic and real-world datasets, we show that our proposed approach fits the data better in terms of loglikelihood. Moreover, we demonstrate that the model is able to generate high-quality samples in a variety of applications, making it a good candidate for synthetic data generation.
Matrix Completion for Graph-Based Deep Semi-Supervised Learning
Authors: Taherkhani Fariborz , Kazemi Hadi , Nasrabadi Nasser M.
Published: September 2019

Convolutional Neural Networks (CNNs) have provided promising achievements for image classification problems. However, training a CNN model relies on a large number of labeled data. Considering the vast amount of unlabeled data available on the web, it is important to make use of these data in conjunction with a small set of labeled data to train a deep learning model. In this paper, we introduce a new iterative Graph-based Semi-Supervised Learning (GSSL) method to train a CNN-based classifier using a large amount of unlabeled data and a small amount of labeled data. In this method, we first construct a similarity graph in which the nodes represent the CNN features corresponding to data points (labeled and unlabeled) while the edges tend to connect the data points with the same class label. In this graph, the missing label of unsupervised nodes is predicted by using a matrix completion method based on rank minimization criterion. In the next step, we use the constructed graph to calculate triplet regularization loss which is added to the supervised loss obtained by initially labeled data to update the CNN network parameters.
Nuevos instrumentos de financiación para el sector no lucrativo: el reto del crowdfunding social
Authors: Sajardo Moreno Antonia
Published: September 2019

Propósito: Exponer la manera en que la crisis económica en España ha venido haciendo especialmente notoria la debilidad financiera del Sector No Lucrativo, tradicionalmente dependiente del apoyo público. El agotamiento de las arcas públicas, el receso en las donaciones privadas, y un incremento de la reticencia del recurso financiero por la banca tradicional, sitúan al Sector ante la disyuntiva de un progresivo deterioro como fuerza económica y social. Descripción: En este contexto, el Sector No Lucrativo español se ha visto impelido a explorar nuevas formas de financiación, tornando una nueva mirada hacia su mismo origen, hacia su base social, la ciudadanía. Punto de vista: Es de destacar que la ciudadanía, en este siglo, se halla inmersa en un nuevo paradigma tecnológico, el paradigma digital, el cual viene a conformarse como el instrumento de primer orden para una nueva forma de solidaridad social. Conclusiones: Uno de los principales exponentes de la nueva solidaridad social lo configura el crowdfunding o financiación en masa a través de internet, de cuyo análisis y potencialidades se ocupa este trabajo.
Innovación del usuario e identificación de usuarios innovadores – Un estudio exploratorio en Colombia
Authors: Sánchez Perdomo Sandra Jennina
Published: September 2019

Introducción: En países latinoamericanos, los estudios sobre innovación del usuario son incipientes.  En Colombia por ejemplo, no se han encontrado evidencias de investigación formal sobre innovación del usuario.  Para llenar este vacío, este estudio exploratorio tiene como objetivo medir el porcentaje de innovación del usuario en la muestra analizada y determinar las características de estos usuarios innovadores. Metodología: Esta investigación es de tipo descriptivo con corte transversal y naturaleza exploratoria. Se aplicó la metodología adoptada en anteriores estudios realizados por en Japón, el Reino Unido y los Estados Unidos. Se aplicó un instrumento de medición para identificar usuarios innovadores sobre una muestra de 433 individuos Bogotá, Colombia. Resultados: Se encontró que existen actividades de innovación del usuario a nivel de consumidor, el porcentaje de innovación del usuario en la muestra analizada es de 4.8% y la tendencia a innovar fue más pronunciada entre los adultos jóvenes menores  a 30 años, que terminaron sus estudios de secundaria y se encuentran cursando estudios universitarios. Conclusiones: Los resultados de este estudio exploratorio constituyen un importante punto de partida para  una mejor comprensión de las actividades de innovación del usuario en países emergentes o en vía de desarrollo en Latinoamérica.
AGROMEMORIA EN ESCENARIOS DE POSTCONFLICTO Usos del suelo y café en Aguadas, Pensilvania y Samaná (Caldas Colombia)
Authors: Acosta – Nates Paola Andrea , Martínez –Calderón Camila Andrea , Vásquez- Valencia Andrés Felipe
Published: September 2019

Propósito: el trabajo parte de establecer las implicaciones de la transición de cultivos de tradición (café) como estrategia de los pobladores para permanecer en sus territorios defendiendo lo que hemos denominado la agromemoria, que implica un trabajo real en términos del uso del suelo y el ecosistema. Metodología: la metodología descriptiva utilizada busca especificar las características de los actores locales apoyándose en la entrevista como instrumento de interrelación para conocer la interpretación y variación en los usos del suelo en lo que la gente local reconoce como el antes, el durante y el después del conflicto armado. Resultados:de lo anterior se infiriere que a pesar de que los pobladores locales establecen diversas estrategias para no ser desplazados de sus territorios, las transformaciones territoriales evidentes principalmente en el cambio de los usos del suelo, tiene serias implicaciones sobre los ecosistemas y por lo tanto en los medios económicos del decidir quedarse a largo plazo. Conclusiones: las transformaciones territoriales generadas por el conflicto armado y por factores de tipo medioambiental impactan de manera directa tanto en los referentes socioculturales como en los sistemas productivos tradicionales de los actores locales
Relación entre percepción de dominio del ambiente y autoeficacia en estudiantes universitarios
Authors: Freire Rodríguez Carlos , Ferradás Canedo María Del Mar
Published: September 2019

La percepción de dominio del ambiente se erige una de las dimensiones esenciales para el bienestar psicológico de los seres humanos, en la medida en que alude al grado en que la persona se siente responsable de los hechos que ocurren en su vida y, por ende, se percibe competente para elegir y crear entornos propicios para satisfacer sus propias necesidades de desarrollo personal. En el presente trabajo se pretende estudiar la relación entre esta dimensión del bienestar psicológico y la conducta motivada de los estudiantes universitarios. Específicamente, se analiza si el grado (bajo, medio o alto) de dominio del ambiente percibido por los estudiantes influye en sus juicios de autoeficacia. En el estudio participaron 630 estudiantes (427 mujeres y 203 hombres) de la Universidade da Coruña (Medad = 21.17; DT = 3.31). Los niveles (bajo, medio y alto) de dominio del ambiente se determinaron con base en los percentiles 33 y 66, que fueron tomados como puntos de corte. Las diferencias de autoeficacia entre los tres niveles de dominio del ambiente percibido se analizaron mediante un ANCOVA. La titulación, el curso y el género fueron tomadas como covariables. Como prueba de contrastes a posteriori se utilizó la de Games-Howell. Se estimó el tamaño del efecto de las diferencias mediante el estadístico d de Cohen. Los resultados obtenidos muestran una relación positiva entre el nivel de dominio del ambiente y la autoeficacia, de tal forma que el grupo de dominio del ambiente autopercibido más alto evidencia niveles de autoeficacia significativamente más elevados que los grupos medio y bajo. Habida cuenta de la importancia de la autoeficacia para favorecer la implicación y el esfuerzo de los estudiantes, los resultados de este trabajo enfatizan la conveniencia de fomentar ambientes de aprendizaje que favorezcan la percepción de dominio.
¿Puede la formación en altas capacidades afectar las actitudes de los maestros en educación primaria?
Authors: Seade Mejía Carolina , Encalada Glenda , Peñaherrera Vélez Ma. José , Dávila Pontón Yolanda , Vélez Calvo Ximena
Published: September 2019

Investigaciones han fortalecido el argumento de que las actitudes de los docentes influyen de manera directa en sus emociones y prácticas educativas. Es primordial identificar estas actitudes para mejorar la formación y consecuentemente la práctica profesional. Con este antecedente, el objetivo de esta investigación es analizar el efecto que la formación en altas capacidades tiene en las actitudes de maestros de educación primaria hacia estos estudiantes. Se realizó un estudio cuasi-experimental pre y post test, con 15 profesores que asistieron a la Maestría de Educación Inclusiva en una universidad privada en Cuenca-Ecuador. Los maestros recibieron formación en diversos temas referentes a la educación de niños con altas capacidades, durante un módulo con una duración de 36 horas presenciales y 40 autónomas. Para evaluar las actitudes se utilizó el cuestionario “Opiniones sobre la educación de las personas con altas capacidades” de Gagné y Nadeau. Se evidenció que las actitudes mejoraron en los seis factores del cuestionario y en tres de estos existieron diferencias estadísticamente significativas. Esta mejoría evidente en las actitudes de maestros puede influir en las buenas prácticas educativas hacia estudiantes con altas capacidades, para aprovechar y despuntar su potencial. Se deja en manifiesto la necesidad de una formación docente pertinente y de calidad, que en la mayoría de escenarios formativos de Ecuador es un asunto pendiente.
Rehabilitación neuropsicológica en paciente con traumatismo craneoencefálico por daño cerebral sobrevenido. Estudio de caso.
Authors: Olarte María Del Carmen
Published: September 2019

El daño cerebral sobrevenido, causado por accidentes de tráfico y caídas como causas más frecuentes, producen lesiones extra e intercerebrales, focales o difusas, con alteración de la memoria en la mayoría de las etiologías, causan impacto en la vida del paciente, con olvidos de transcurso, distracción y bloqueos, que requieren estrategias y técnicas adecuadas en el proceso de rehabilitación después de la intervención clínica.Muestra poblacional: Mujer de 20 años, con accidente doméstico, diagnosticada conTraumatismo craneoencefálico (TCE) y tratamiento farmacológico, secuelas después de 2 años en amnesia retrograda, funciones ejecutivas y dificultades de atención y concentración, estrés ansiedad y problemas emocionales. El objetivo propuesto fue mejorar el funcionamiento de la paciente en sus ámbitos familiar, laboral y social, buscando ayudar en la restauración de las funciones alteradas.Metodología: Se realizaron 16 sesiones dos horas semanales grupales con adultos con deterioro cognitivo y 4 individuales. La valoración neuropsicológica Individual: Minimental- State Examination, Test Neuropsi, Batería de Evaluación del Lóbulo frontal, Medición rápida de Habilidad Intelectual, Barsit.Técnicas de Intervención: Relajación con imaginación temática; estrategias de enfoque restaurativo con base en actividades específicas; estrategias de enfoque compensatorio en autorregulación y metacognitivas uso de claves mnésicas, adquisición de nuevos aprendizajes; metas significativas, vitales y psicoeducación.Conclusiones: Las alteraciones en la memoria por demencia retrógrada, afectada por la incapacidad de recordar eventos ocurridos antes del TCE, mejoró visiblemente igual que la memoria semántica y episódica; restauración del equilibrio en las funciones ejecutivas en toma de decisiones, procesos de planificación, ejecución de metas y trabajo significativo. El trabajo grupal le permitió adquirir técnicas para el estrés y la ansiedad diaria igualmente el manejo de emociones.
Atribuciones causales en lectura en alumnos chilenos de educación básica. Análisis en función del sexo
Authors: Lagos San Martín Nelly , Granados Alós Lucía , Fernández-Sogorb Aitana , Aparisi Sierra David
Published: September 2019

La teoría de las atribuciones cobra importancia en tanto se relaciona con la motivación. Desde una perspectiva motivacional, las atribuciones que realizan los estudiantes se relacionan directamente con el autoconcepto, la autoestima y las metas de aprendizaje. El objetivo de este estudio fue establecer si existen diferencias entre las atribuciones causales en función del sexo. Para realizar el estudio se contó con 1702 alumnos de 20 centros de Ñuble en Chile, 905 niños (53,2%) y 797 niñas (46,8%) de edades entre 10 y 12 años (M = 10.06; DE= 1.05). La evaluación se realizó con la Sydney Attribution Scale (SAS) que mide dos áreas académicas sobre las que se realizan atribuciones (Lectura y Matemáticas), dos resultados hipotéticos (éxito y fracaso) y tres tipos de causas (capacidad, esfuerzo y causas externas). Los datos fueron analizados a partir del cálculo de diferencias de medias y pruebas p para determinar la presencia de diferencias estadísticas significativas. Los resultados mostraron que las niñas atribuyen más sus éxitos a la capacidad, al esfuerzo y a causas externas que los niños, así como también que los niños atribuyen más sus fracasos a la falta de esfuerzo. De los resultados se puede informar que existen diferencias estadísticamente significativas entre las atribuciones de niños y niñas. Siendo las niñas, quienes, por el tipo de atribuciones, peligran más en su autoestima y los niños más en sus expectativas.
Empatía en futuros maestros: diferencias por género
Authors: Vizoso-Gómez Carmen Mªz
Published: September 2019

La empatía representa la capacidad para entender los sentimientos de los demás y para ponerse en su lugar. Según la perspectiva multidimensional propuesta por Davis (1980, 1983), la empatía incluye cuatro factores: Toma de perspectiva, Fantasía, Preocupación empática y Malestar personal. Teniendo en cuenta que la empatía es una de las características más relevantes para el desempeño profesional en el ámbito educativo, es necesario evaluar esta capacidad en los futuros maestros para poder diseñar programas de formación adecuados. Por lo tanto, el principal objetivo de este trabajo es evaluar la empatía en los futuros maestros y analizar si existen diferencias por género en las puntuaciones de los factores que la definen. La muestra está compuesta por 162 estudiantes de la Universidad de León. Se empleó el Interpersonal Reactivity Index (Davis, 1980, 1983), en su versión adaptada al español. Los resultados indican que existen diferencias significativas por género entre los futuros maestros en cuanto a los cuatro factores que caracterizan la empatía. Concretamente, las mujeres presentan puntuaciones significativamente mayores en la toma de perspectiva, fantasía, preocupación empática y en malestar personal. Por lo tanto, se debería considerar estas diferencias a la hora de diseñar los programas formativos dirigidos al desarrollo de la empatía en los futuros maestros.
Infecciones de transmisión sexual en población joven ¿qué mantiene su exposición al riesgo?
Authors: Giménez-García Cristina , Nebot-García Juan , Bisquert-Bover Mar , Elipe-Miravet Marcel , Gil-Llario María Dolores
Published: September 2019

Las infecciones de transmisión sexual, suponen uno de los riesgos más prevalentes entre lapoblación joven. En España, su incidencia se ha incrementado en los últimos años, siendo uno los países más afectados de Europa. Si bien se han llevado a cabo distintos esfuerzos preventivos para revertir la situación, no han mostrado la eficacia esperada. Por eso, resulta necesario profundizar en el análisis de aquellos factores que podrían mantener las conductas de riesgo. Así pues, el objetivo del presente estudio es evaluar el papel de factores psicológicos en la prevención de infecciones de transmisión sexual entre población joven. Para ello, 984 jóvenes de entre 18 y 24 años (32,7% hombres y 67,3% mujeres), sexualmente activos, cumplimentaron un cuestionario online sobre estilos de vida con una sección específica sobre salud sexual. Tras los análisis se observa cómo, solamente,un 37,2%, usa el preservativo en el coito vaginal de modo sistemático y un 12,1% en el coito anal. Asimismo, se observa cómo un mayor uso del preservativo se relaciona con tener miedo a las infecciones de transmisión sexual (rho = ,098; p = ,002), pero también con una mayor edad de inicio en las prácticas sexuales (rho = ,075; p = ,021), menor frecuencia en el consumo de alcohol (rho = -,069; p = ,037), menores niveles de ansiedad (rho = -,094; p = ,003), una mayor satisfacción con la imagen corporal (rho = ,066; p = ,033) y un menor uso de pornografía (rho = -,107; p = ,001). En concreto, además del temor a las infecciones de transmisión sexual, el menor uso de pornografía y una menor ansiedad, también contribuirían a una mayor probabilidad de uso del preservativo (Chi2 = 29,18; p = ,001). Así pues, parece evidente la necesidad de incluir también estos otros elementos en los programas de prevención de riesgos sexuales, para mejorar su eficacia entre la población joven.
Análisis de clases latentes como técnica de identificación de tipologías
Authors: Ondé Pérez Daniel , Alvarado Izquierdo Jesús María
Published: September 2019

En Psicología es frecuente encontrar situaciones en las que se necesita realizar algún tipo de clasificación de personas en subgrupos o clases. Existen técnicas de análisis multivariado como el Análisis Clúster Jerárquico (HCA) que se utilizan habitualmente para este fin. Actualmente, existe un interés creciente por la técnica de Análisis de Clases Latentes (LCA), si bien es una técnica relativamente poco conocida y utilizada. Varios autores han destacado que el LCA presenta importantes ventajas respecto al HCA, en especial que el LCA permite obtener medidas de bondad de ajuste. El objetivo de este trabajo es presentar varias aplicaciones del LCA tanto a partir de un estudio de simulación como a partir de datos reales, y comparar el desempeño de esta técnica frente al HCA. Los resultados a partir de la simulación indican que el LCA tiene una elevada capacidad para detectar estructuras de clase. Los resultados del estudio a partir de datos reales muestran que las distintas clases o mixturas presentes en los datos pueden estar solapadas, lo que dificulta la agrupación de clases al aplicar LCA. El HCA puede ser una buena herramienta de análisis para el investigador aplicado, ya que puede orientar sobre el mejor modelo de LCA que se debería interpretar. En contextos de investigación en los que el modelo teórico no es claro, se recomienda utilizar ambas técnicas con el fin de buscar convergencia de resultados.
The genus Agave in agroforestry systems of Mexico
Authors: Torres-García Ignacio , Rendón-Sandoval Francisco Javier , Blancas José , Casas Alejandro , Moreno-Calles Ana Isabel
Published: September 2019

Background: The genus Agave L. is recognized for its wide distribution in Mexican ecosystems. Species have been described as multipurpose as part of agroforestry systems (AFS). There has not been a systematized, detailed analysis about its richness in AFS nor their ecological, economic, and cultural relevance.

Questions: What is the Agave richness in Mexican AFS? What is their ecological, agronomical, economic, and cultural relevance? What are the risks and perspectives for strengthening their role in AFS?

Species studied: 31 Agave species in Mexican AFS.

Study site and dates: AFS throughout the Mexican territory. January to August 2018.

Methods: Systematization of published information, scientific reports, repositories, and our fieldwork, was performed. The database “The genus Agave in AFS of Mexico” was created, containing information about Agave richness in AFS, ecological, economic, and cultural relevance, as well as the current and future perspectives of the AFS they are included in.

Results: We recorded 31 species with 22 uses that were part of AFS practices (hedgerows, boundaries and live fences), in home gardens, terraces, and agroforests in temperate, semiarid, and sub-humid regions, managed by 12 cultural groups. The main benefits of agaves are soil retention, infiltration of water, and satisfaction of socio-economic and cultural needs. The decrease of multipurpose species and functions was observed in relation with the commercialization of mezcal.

Conclusions: There has been ample recognition of AFS as settings for conservation and use of Agave, as well as the importance of the knowledge and management practices associated to agave species in AFS.
The Hintons’ legacy to the knowledge of the flora of Mexico
Authors: Hinton George S. , Villaseñor José Luis , Ortiz Enrique
Published: September 2019

Background : The Hinton family comprises three generations of plant collectors that have made an important contribution to the knowledge of the flora of Mexico. They have collected in 13 Mexican states and have provided material for the description of 8 genera and about 555 species new to science.
Question : What is the contribution of the collections of the Hintons to our knowledge of the flora of Mexico? Where was their main collecting effort and how many species have been recorded?
Species study : Vascular plants
Study site : Mexico
Method : Different national and foreign databases were consulted and a list of species collected by the Hintons was obtained. The records were reviewed to eliminate synonymy and a complete list of species and their distribution by states was compiled.
Results : A total of 28,947 records were obtained and 25,717 of them were identified to species level. In addition to the family's collection, the herbaria with the largest number of records are LL-TEX, K, IEB, and MEXU. The records come from about 2,752 different collecting localities throughout Mexico and account for 5,730 species, 1,573 genera, and 233 families.
Conclusions : Many of the specimens collected by the Hintons come from areas that are still  underexplored today. The contribution of the Hinton family to the knowledge of the flora of Mexico represents an important legacy and their collections constitute an enduring source of information for people interested in the flora of Mexico .
Parent Perspectives on Youth Sport Concussion Management
Authors: Daprano Corinne Mary , Davies Susan , Bernstein Elana
Published: September 2019

Concussions are one of the most discussed health concerns at all levels of sport. While much of this discussion focuses on football and the National Football League (NFL), there is growing awareness in the sport community that the risks of concussions extend to head injuries in all sports. Much remains unknown about youth concussions, particularly in terms of how parents feel about concussions and youth sport safety regulations. The purpose of this study was to examine parents’ perceptions and attitudes toward youth sport concussions. Results indicated that despite increased awareness and education there remain gaps in parental knowledge of concussions and the proper management of concussions.  These findings led to a set of recommendations regarding parental concussion prevention and management education.
Education Versus Athletics: What Will Division I Football and Basketball Players Choose?
Authors: Rudd Andy , Ridpath David
Published: September 2019

For many years the desire for money and winning in Division I athletics,  particularly in the sports of football (Football Bowl Subdivision) and men’s basketball, have encouraged colleges and universities to provide special admission for athletes with exceptional athletic ability, who in turn, often are less prepared to succeed academically. This has resulted in the widespread occurrence of unethical academic support practices (e.g., taking classes and writing papers for students and providing answers to exams) in order to maintain athletes’ eligibility and increase graduation rates to appease the public and to present the case that intercollegiate athletic are about education first. As one means of curbing academic misconduct, the authors recommend providing Division I football and basketball the option of playing their sport only without any academic eligibility requirement. Athletes who are struggling academically or lack academic commitment would no longer need to be bolstered by illegitimate academic support or less than accurate metrics. The present exploratory study sought to determine how many Division I football and basketball players would choose the option of playing their sport only versus playing and pursuing their degree under current National Collegiate Athletic Association Guidelines (NCAA) guidelines. The results showed that the majority would still choose to play and pursue their degree. Overall, the belief that one can play professionally did not diminish athletes’ desire to play and earn their degree.  However, there were also a percentage of athletes that believed they can play professionally and also desired to play their sport only.
Farklı iskeletsel maloklüzyon tiplerinde derin Spee eğrisinin dentoiskeletsel karakteristiği
Authors: AKBULUT Ayşe Selenge , KİM Kyung-a
Published: September 2019

Amaç: Dişsel faktörlerin derin Spee Eğrisi (SE) üzerindeki
etkisini incelemek ve farklı sagittal ve vertikal iskeletsel maloklüzyon
gruplarında SE karakteristiğini karşılaştırmaktır.

Gereç ve Yöntem: Bu çalışmaya SE 3 mm’den derin olan toplam 184 Koreli
hasta dahil edildi. Hastalar sagittal ve vertikal iskeletsel paternlerine göre
ayrı ayrı sınıflandırıldı. Alçı modeller ve lateral sefalometrik filmler
üzerinde Spee Eğrisi derinliği (SED), overjet, overbite, keserler arası açı,
alt keser, alt kanin ve alt molarların mandibular düzlem ve mandibular oklüzal
düzlem ile yaptıkları açı, alt keser ve kaninlerin mandibular düzlemden
uzaklıkları, kaninler arası genişlik, premolarlar arası genişlik, molarlar
arası genişlik ve ark uzunluğu parametreleri ölçüldü. Gruplar arasında
değişkenlerin karşılaştırılması Tukey post-hoc testi ile birlikte varyans
analizi ile yapıldı (α=0.05).

Bulgular: Derin SE, Sınıf 2’de keserlerin labioversiyonu, kanin ve
molarların mezial angulasyonu ile; Sınıf 3’te ise kaninler arası genişliğin
artması ve molarlar arası genişliğin artması, keserlerin linguoversiyonu, kanin
ve molarların distal angulasyonu ile oluşmuştu. Her grup sagittal iskeletsel
paterni doğrultusunda dişsel kompanzasyon gösterdi. Hipodiverjan grubunda derin
SE; overbite fazlalığı, keserlerin labioversiyonu, kanin ve molarların mezial
angulasyonu ile karakterizeydi. Optimum büyüme modeli ve hiperdiverjan
gruplarının her ikisi de keser ve kaninlerde ekstrüzyon göstermekle birlikte
hiperdiverjan grubu bunlara ilaveten keserlerde linguoversiyon göstermekteydi.

Sonuç: Farklı sagittal ve vertikal
iskeletsel maloklüzyon gruplarında transvers ark genişliği, keserlerin
inklinasyonu, kanin ve molarların angulasyonu, keser ve kaninlerin ekstrüzyon
miktarları gibi dişsel faktörler SE karakteristiğine etki edebilir.
Hızlı üst çene genişletmesi ve yüz maskesi kombine uygulamasının mandibular dentisyona etkisi
Authors: UZUNER Fatma Deniz , GÜLŞEN Şenol , KALE VARLIK Selin , TORTOP Tuba
Published: September 2019

Amaç: Bu
çalışmanın amacı, Hızlı
Üst Çene Genişletmesi (HÜÇG) ve Yüz maskesi (YM) kombine uygulamasının mandibular
dentisyona etkisini değerlendirmektir.

Gereç ve Yöntem: Bu retrospektif
çalışmada daimi dişlenme döneminde olan, ortalama iskelet yaşı 12 yıl 1 ay
olan, posterior çapraz kapanış ve maksiller retrognatinin eşlik ettiği
iskeletsel Sınıf 3 anomaliye (ANB<0) sahip olup tedavi protokolü gereği
yapıştırmalı tip Hyrax vidalı HÜÇG aygıtı ile Petit tip YM’nin kombine uygulandığı
25 bireye (10 kız, 15 erkek) ait tedavi başı ve sonu sefalometrik film ve
ortodontik modeller değerlendirildi. HÜÇG-YM kullanımı ortalama 8.7 aydı.
Ortodontik modellerin 3D görünümünde 3Shape Ortho Analyzer 2013-1 programıyla
alt çenede intermolar, interkanin genişlik, ark derinliği ve alt anterior bölge
kanin-kanin arası çapraşıklık miktarı Little düzensizlik indeksi (LDİ) metodu
kullanılarak ölçüldü. Lateral sefalometrik filmlerde alt keser eğimi (IMPA
açısı) ölçüldü. Verilerin istatistiksel olarak değerlendirilmesinde
eşleştirilmiş t-testi kullanıldı. İstatistiksel anlamlılık değeri p<0.01 olarak
kabul edildi.

Bulgular: Kombine uygulama
sonunda alt dental arkta intermolar genişlikte ortalama 1.38 mm anlamlı artış
bulundu (p<0.001). Buna karşılık, interkanin genişliğinde değişim saptanmadı.
Ark derinliğinde ise 1.45 mm anlamlı azalma (p<0.001) ve LDİ değerinde 1.46
mm anlamlı artış görüldü (p<0.01). Alt keser eğiminde (IMPA açısı)
istatistiksel olarak anlamlı değişim saptanmadı.

Sonuç: HÜÇG-YM kombine uygulaması sonunda
alt çenede oluşan intermolar genişlikteki artış ark boyunu arttırırken, buna
karşılık ark derinliğindeki azalma ark boyunu azaltmaktadır. Klinisyenler bu
kombine uygulamayla alt çenede kesici dişlerde çapraşıklık miktarının artabileceğini
göz önünde bulundurmalıdır.
Peripheral Blood Mononuclear Cells Preparation v1
Authors: Davis Coakley John
Published: September 2019

How to prepare PBMCs for Stimulation experiment
Ex vivo stimulation of peripheral blood mononuclear cells (PBMC) v1
Authors: Davis Coakley John
Published: September 2019

Stimulation of PBMCs to study TH1 and TH17 Lymphocytes. PBMCs were incubated in medium alone, or stimulated for 5 hours with functional mAbs specific for CD3 and CD28, 0.05 μg/ml lipopolysaccharide (LPS), or 50 ng/ml phorbol myristate acetate (PMA) and 1 μg/ml ionomycin. Wells intended for intracellular staining for IL-23, IL-12, IL-17A, and IFN-γ were stimulated in the presence of brefeldin A to prevent cytokine release from the cells. Antibody panels with 0.2% saponin were used for intracellular staining of IL12, IL23, IL17A and IFNγ. Antibody panels with Permeabilization Buffer (Miltenyi FoxP3 Staining Buffer Set) were made for intracellular staining of RORγ(t) and T-Bet.