Use of Machine Learning Models of the ”Transformers” Type in the Construction of Services in a Gamified Web app.

The purpose of this document is to describe the use of a natural language processing model in the multiplatform system ”Gamivity” by means of a sentence similarity algorithm to offer a personalized experience module based on the conceptual relationship between questions. For the selection process, certain criteria were chosen that will allow several pre-trained models under the “Transformers” architecture for evaluation, later. These criteria were the language with which the model was altered; Python was the programming language used for the implementation. Regarding the evaluation phase of the selected models, the ”Sentence Transformers” library of the Python programming language was used. In addition, a work environment analogous to the module present in the ”Gamivity” system was built, in which the development platform ”Google Colab” was used to test these models. The criteria for choosing the candidate model were based on its effectiveness in relation to questions as well as the computational cost involved while performing the operations in the said model Based on the applied methodology, the model that yielded the best results was ”paraphrase-multilingual- MiniLM-L12-v2,” modified with a large corpus of text in Spanish and 50 other languages, which showed a degree of precision. When it comes to conceptually relating the questions provided it was found to be optimal, having relatively low computational cost when performing these operations. Keywords: sentence transformers, sentence similarity, relate questions, personalized learning. Resumen El presente documento, tiene como propósito el de describir la utilización de un modelo de procesamiento de lenguaje natural en el sistema multiplataforma “Gamivity”, por medio de un algoritmo de similitud de oraciones para ofrecer un módulo de experiencia personalizada a partir de la relación conceptual entre preguntas. Para el proceso de selección, se establecieron ciertos criterios que permitieron elegir varios modelos pre entrenados bajo la arquitectura “Transformers” para su posterior evaluación. Dichos criterios, fueron el idioma con el que fue entrenado el modelo, así como que el lenguaje de programación utilizado para la implementación fuese Python. En lo que concierne a la fase de evaluación de los modelos seleccionados, se hizo uso de la biblioteca “Sentence Transformers” del lenguaje de programación Python, además se construyó un entorno de trabajo análogo al módulo presente en el sistema “Gamivity”, en la plataforma de desarrollo “Google Colab” para poner a prueba dichos modelos, los criterios para la elección del modelo candidato, se resumen en la eficacia a la hora de relacionar preguntas, así como el coste computacional a la hora de realizar las operaciones involucradas en dicho proceso. A partir de la metodología aplicada, el modelo que mejor resultados generó fue “paraphrase-multilingual-MiniLM L12-v2”, entrenado con un gran corpus de texto en español, así como de otros 50 idiomas, el cual mostró un grado de precisión óptimo a la hora de relacionar conceptualmente las preguntas proporcionadas, así como su relativo bajo coste computacional a la hora de efectuar dichas operaciones. Palabras Clave: sentence transformers, sentence similarity, relacionar preguntas, aprendizaje personalizado

On the Use of Finite-Size Scaling to Measure Spin-Glass Exponents

Finite-size scaling (FSS) is a standard technique for measuring scaling exponents in spin glasses. Here we present a critique of this approach, emphasizing the need for all length scales to be large compared to microscopic scales. In particular we show that the replacement, in FSS analyses, of the correlation length by its asymptotic scaling form can lead to apparently good scaling collapses with the wrong values of the scaling exponents.Comment: RevTeX, 5 page

Effect of surfactants during drop formation in a microfluidic channel: a combined experimental and computational fluid dynamics approach

The effect of surfactants on the flow characteristics during rapid drop formation in a microchannel is investigated using high-speed imaging, micro-particle image velocimetry and numerical simulations; the latter are performed using a three- dimensional multiphase solver that accounts for the transport of soluble surfactants in the bulk and at the interface. Drops are generated in a flow-focusing microchannel, using silicone oil ( 4.6 mPa s) as the continuous phase and a 52 % w/w glycerol solution as the dispersed phase. A non-ionic surfactant (Triton X-100) is dissolved in the dispersed phase at concentrations below and above the critical micelle concentration. Good agreement is found between experimental and numerical data for the drop size, drop formation time and circulation patterns. The results reveal strong circulation patterns in the forming drop in the absence of surfactants, whose intensity decreases with increasing surfactant concentration. The surfactant concentration profiles in the bulk and at the interface are shown for all stages of drop formation. The surfactant interfacial concentration is large at the front and the back of the forming drop, while the neck region is almost surfactant free. Marangoni stresses develop away from the neck, contributing to changes in the velocity profile inside the drop

The importance of Icelandic ice sheet growth and retreat on mantle CO2 flux

Climate cycles may significantly affect the eruptive behavior of terrestrial volcanoes due to pressure changes caused by glacial loading, which raises the possibility that climate change may modulate CO2 degassing via volcanism. In Iceland, magmatism is likely to have been influenced by glacial activity. To explore if deglaciation therefore impacted CO2 flux we coupled a model of glacial loading over the last ∼120 ka to melt generation and transport. We find that a nuanced relationship exists between magmatism and glacial activity. Enhanced CO2 degassing happened prior to the main phase of late‐Pleistocene deglaciation, and it is sensitive to the duration of the growth of the ice sheet entering into the LGM, as well as the rate of ice loss. Ice sheet growth depresses melting in the upper mantle, creating a delayed pulse of CO2 out‐gassing as the magmatic system recovers from the effects of loading

Psychological interventions for psychogenic non epileptic seizures (pnes)

Las Crisis No Epilépticas Psicógenas (CNEP) representan un desafío clínico, debido a la complejidadde sus manifestaciones, etiología y comorbilidad. El objetivo de este artículo es presentarlos tratamientos psicológicos disponibles para abordar y tratar las CNEP. Se ha llevado a cabo unabúsqueda bibliográfica en las bases de datos Medline, Latindex, Redalyc, Cochrane Collaboration,EBSCO y Lilacs. Dentro de los abordajes diseñados para el tratamiento de las CNEP, se destacantres modelos de intervención psicoterapéutica que han obtenido resultados favorables en la puestaa prueba de sus intervenciones: dos modelos cognitivo conductuales y el tratamiento aumentadode Terapia Interpersonal. Se discuten estos hallazgos en función de las limitaciones, tantometodológicas como provenientes de los distintos estudios clínicos encontrados.Psychogenic Nonepileptic Seizures (PNES) pose a clinical challenge because of the diagnostic difficulty due to the complexity of their manifestations, etiology and comorbility. The aim of this paper is to outline the available psychological treatments developed to deal with PNES. To fulfill this purpose a literature search was carried out across the databases Medline, Latindex, Redalyc, Cochrane Collaboration EBSCO and Lilacs. Within the designs intended to deal with PNES, three models of psychotherapeutic approach stand out, due to the positive outcome measurements of their interventions: two cognitive-behavioral based interventions and the augmented model of Interpersonal Therapy. These findings are analyzed taking into account their methodological and clinical limitations.Fil: Korman, Guido Pablo. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sarudiansky, Mercedes. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Lanzillotti, Alejandra Inés. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Areco Pico, Maria Marta. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Tenreyro, Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: Scévola, Maria Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: Kochen, Sara Silvia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: D`Alessio, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentin

GenMAPP 2: New features and resources for pathway analysis

BACKGROUND: Microarray technologies have evolved rapidly, enabling biologists to quantify genome-wide levels of gene expression, alternative splicing, and sequence variations for a variety of species. Analyzing and displaying these data present a significant challenge. Pathway-based approaches for analyzing microarray data have proven useful for presenting data and for generating testable hypotheses. RESULTS: To address the growing needs of the microarray community we have released version 2 of Gene Map Annotator and Pathway Profiler (GenMAPP), a new GenMAPP database schema, and integrated resources for pathway analysis. We have redesigned the GenMAPP database to support multiple gene annotations and species as well as custom species database creation for a potentially unlimited number of species. We have expanded our pathway resources by utilizing homology information to translate pathway content between species and extending existing pathways with data derived from conserved protein interactions and coexpression. We have implemented a new mode of data visualization to support analysis of complex data, including time-course, single nucleotide polymorphism (SNP), and splicing. GenMAPP version 2 also offers innovative ways to display and share data by incorporating HTML export of analyses for entire sets of pathways as organized web pages. CONCLUSION: GenMAPP version 2 provides a means to rapidly interrogate complex experimental data for pathway-level changes in a diverse range of organisms

Free volume and permeability of mixed matrix membranes made from a Terbutil-M-terphenyl polyamide and a porous polymer network

Producción CientíficaA set of thermally rearranged mixed matrix membranes (TR-MMMs) was manufactured and tested for gas separation. These membranes were obtained through the thermal treatment of a precursor MMM with a microporous polymer network and an o-hydroxypolyamide,(HPA) created through a reaction of 2,2-bis(3-amino-4-hydroxyphenyl)-hexafluoropropane (APAF) and 5′-terbutil-m-terfenilo-3,3″-dicarboxylic acid dichloride (tBTmCl). This HPA was blended with different percentages of a porous polymer network (PPN) filler, which produced gas separation MMMs with enhanced gas permeability but with decreased selectivity. The thermal treatment of these MMMs gave membranes with excellent gas separation properties that did not show the selectivity decreasing trend. It was observed that the use of the PPN load brought about a small decrease in the initial mass losses, which were lower for increasing PPN loads. Regarding the glass transition temperature, it was observed that the use of the filler translated to a slightly lower Tg value. When these MMMs and TR-MMMs were compared with the analogous materials created from the isomeric 5′-terbutil-m-terfenilo-4,4″-dicarboxylic acid dichloride (tBTpCl), the permeability was lower for that of tBTmCl, compared with the one from tBTpCl, although selectivity was quite similar. This fact could be attributed to a lower rigidity as roughly confirmed by the segmental length of the polymer chain as studied by WAXS. A model for FFV calculation was proposed and its predictions compared with those evaluated from density measurements assuming a matrix-filler interaction or ideal independence. It turns out that permeability as a function of FFV for TR-MMMs follows an interaction trend, while those not thermally treated follow the non-interaction trend until relatively high PPN loads were reached.Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación - (projects PID2019- 109403RB-C21/AEI/10.13039/501100011033 and PID2019-109403RB-C22/AEI/10.13039/501100011033)Junta de Castilla y León, Unión Europea y Fondo Europeo de Desarrollo Regional (FEDER) - (project CLU2017-09, UIC082