Swarming the SC’17 Student Cluster Competition

The Student Cluster Competition is a suite of challenges where teams of undergraduates design a computer cluster and then compete against each other through various benchmark applications. The present study will provide a select summary of the experiences of Team Swarm who represented the Georgia Institute of Technology at the SC’17 Student Cluster Competition. This report will first describe the competition and the members of Team Swarm. After this introduction, it focuses on three major aspects of the experience: the hardware and software architecture of the team’s computer cluster, the team’s system administration workflow and the team’s usage of cloud resources. Additionally, the appendix provides a brief description of the team members and their method of preparation.Undergraduat

Storyfier: Exploring Vocabulary Learning Support with Text Generation Models

Vocabulary learning support tools have widely exploited existing materials, e.g., stories or video clips, as contexts to help users memorize each target word. However, these tools could not provide a coherent context for any target words of learners' interests, and they seldom help practice word usage. In this paper, we work with teachers and students to iteratively develop Storyfier, which leverages text generation models to enable learners to read a generated story that covers any target words, conduct a story cloze test, and use these words to write a new story with adaptive AI assistance. Our within-subjects study (N=28) shows that learners generally favor the generated stories for connecting target words and writing assistance for easing their learning workload. However, in the read-cloze-write learning sessions, participants using Storyfier perform worse in recalling and using target words than learning with a baseline tool without our AI features. We discuss insights into supporting learning tasks with generative models.Comment: To appear at the 2023 ACM Symposium on User Interface Software and Technology (UIST); 16 pages (7 figures, 23 tables

Integration of Virtual Programming Lab in a process of teaching programming EduScrum based

Programming teaching is a key factor for technological evolution. The efficient way to learn to program is by programming and hard training and thus feedback is a crucial factor in the success and flow of the process. This work aims to analyse the potential use of VPL in the teaching process of programming in higher education. It also intends to verify whether, with VPL, it is possible to make students learning more effective and autonomous, with a reduction in the volume of assessment work by teachers. Experiments were carried out with the VPL, in the practical-laboratory classes of a curricular unit of initiation to programming in a higher education institution. The results supported by the responses to surveys, point to the validity of the model

What skills pay more? The changing demand and return to skills for professional workers

Technology is disrupting labor markets. We analyze the demand and reward for skills at occupation and state level across two time periods using job postings. First, we use principal components analysis to derive nine skills groups: ‘collaborative leader’, ‘interpersonal & organized’, ‘big data’, ‘cloud computing’, ‘programming’, ‘machine learning’, ‘research’, ‘math’ and ‘analytical’. Second, we comment on changes in the price and demand for skills over time. Third, we analyze non-linear returns to all skills groups and their interactions. We find that ‘collaborative leader’ skills become significant over time and that legacy data skills are replaced over time by innovative ones

The Cowl - v.59 - n.16 - Feb 2, 1995

The Cowl - student newspaper of Providence College. Volume 59, Number 16 - February 2, 1995. 20 pages

Metabolic and mechanical changes in ultra-endurance running races and the effects of a specific training on energy cost of running

The present thesis is divided into two parts. Part I: The objectives of the first part were to examine the factors affecting the ultra-endurance performance and in particular which aspects influence the cost of running (Cr). Consequently, we defined how the Cr and running mechanics changed during different types (i.e. level and uphill) of ultra-endurance races. Finally, we proposed a specific training protocol for improving the Cr in high-level ultra-marathoners. We assessed the Cr by measuring the oxygen consumption at one (or more) fixed speeds using a metabolic unit. Further, for the running mechanics measurement and the spring-mass model parameters computation we used video analysis. Other parameters such as maximal muscle power of the lower limbs (MMP), morphological properties of the gastrocnemius medialis and Achilles tendon stiffness were also measured. Our studies showed that the maximal oxygen uptake, the fraction of it maintained throughout the race and the Cr are the main physiological parameters affecting the ultra-endurance performance, both in level and uphill competitions. Moreover, low Cr values were related to high MMP, vertical stiffness (kvert), low foot print index (FPI), Achilles tendon stiffness and external work. These results indicate that MMP, kvert and FPI are important factors in determining ultra-endurance performance. Also, our studies reported that during ultra-endurance competitions athletes tend to change their running mechanics after a certain time (~4 hours) rather than after a certain distance covered. Then, by adding strength, explosive and power training to the usual endurance training it is possible to lower the cost of running leading to a better performance. From these conclusions we suggest new training protocol for the ultra-marathoners including strength, explosive and power training which maintain a correct and less expensive running technique during ultra-endurance events. Part II: The aim of the second part was to develop and validate a customized thermoplastic polyurethane insole shoe sensor for collecting data about the ground reaction forces (GRF), contact and aerial times. This prototype allowed us to collect vertical GRF and contact time by using piezoresistive force sensors (RFS). Our final model was composed by a rubber insole, five RFSs, an s-beam load cell, an acquisition device (NI myRIO) and a battery case. By using this device we can collect data on field, avoiding the restrictions imposed by the laboratory environmen

Bayesian nonparametric models for data exploration

Mención Internacional en el título de doctorMaking sense out of data is one of the biggest challenges of our time. With the emergence of technologies such as the Internet, sensor networks or deep genome sequencing, a true data explosion has been unleashed that affects all fields of science and our everyday life. Recent breakthroughs, such as self-driven cars or champion-level Go player programs, have demonstrated the potential benefits from exploiting data, mostly in well-defined supervised tasks. However, we have barely started to actually explore and truly understand data. In fact, data holds valuable information for answering most important questions for humanity: How does aging impact our physical capabilities? What are the underlying mechanisms of cancer? Which factors make countries wealthier than others? Most of these questions cannot be stated as well-defined supervised problems, and might benefit enormously from multidisciplinary research efforts involving easy-to-interpret models and rigorous data exploratory analyses. Efficient data exploration might lead to life-changing scientific discoveries, which can later be turned into a more impactful exploitation phase, to put forward more informed policy recommendations, decision-making systems, medical protocols or improved models for highly accurate predictions. This thesis proposes tailored Bayesian nonparametric (BNP) models to solve specific data exploratory tasks across different scientific areas including sport sciences, cancer research, and economics. We resort to BNP approaches to facilitate the discovery of unexpected hidden patterns within data. BNP models place a prior distribution over an infinite-dimensional parameter space, which makes them particularly useful in probabilistic models where the number of hidden parameters is unknown a priori. Under this prior distribution, the posterior distribution of the hidden parameters given the data will assign high probability mass to those configurations that best explain the observations. Hence, inference over the hidden variables can be performed using standard Bayesian inference techniques, therefore avoiding expensive model selection steps. This thesis is application-focused and highly multidisciplinary. More precisely, we propose an automatic grading system for sportive competitions to compare athletic performance regardless of age, gender and environmental aspects; we develop BNP models to perform genetic association and biomarker discovery in cancer research, either using genetic information and Electronic Health Records or clinical trial data; finally, we present a flexible infinite latent factor model of international trade data to understand the underlying economic structure of countries and their evolution over time.Uno de los principales desafíos de nuestro tiempo es encontrar sentido dentro de los datos. Con la aparición de tecnologías como Internet, redes de sensores, o métodos de secuenciación profunda del genoma, una verdadera explosión digital se ha visto desencadenada, afectando todos los campos científicos, así como nuestra vida diaria. Logros recientes como pueden ser los coches auto-dirigidos o programas que ganan a los seres humanos al milenario juego del Go, han demostrado con creces los posibles beneficios que podemos obtener de la explotación de datos, mayoritariamente en tareas supervisadas bien definidas. No obstante, apenas hemos empezado con la exploración de datos y su verdadero entendimiento. En verdad, los datos encierran información muy valiosa para responder a muchas de las preguntas más importantes para la humanidad: ¿Cómo afecta el envejecimiento a nuestras aptitudes físicas? ¿Cuáles son los mecanismos subyacentes del cáncer? ¿Qué factores explican la riqueza de ciertos países frente a otros? Si bien la mayoría de estas preguntas no pueden formularse como problemas supervisados bien definidos, éstas pueden ser abordadas mediante esfuerzos de investigación multidisciplinar que involucren modelos fáciles de interpretar y análisis exploratorios rigurosos. Explorar los datos de manera eficiente abre potencialmente la puerta a un sinnúmero de descubrimientos científicos en diversas áreas con impacto real en nuestras vidas, descubrimientos que a su vez pueden llevarnos a una mejor explotación de los datos, resultando en recomendaciones políticas adecuadas, sistemas precisos de toma de decisión, protocolos médicos optimizados o modelos con mejores capacidades predictivas. Esta tesis propone modelos Bayesianos no-paramétricos (BNP) adecuados para la resolución específica de tareas explorativas de los datos en diversos ámbitos científicos incluyendo ciencias del deporte, investigación contra el cáncer, o economía. Recurrimos a un planteamiento BNP para facilitar el descubrimiento de patrones ocultos inesperados subyacentes en los datos. Los modelos BNP definen una distribución a priori sobre un espacio de parámetros de dimensión infinita, lo cual los hace especialmente atractivos para enfoques probabilísticos donde el número de parámetros latentes es en principio desconocido. Bajo dicha distribución a priori, la distribución a posteriori de los parámetros ocultos dados los datos asignará mayor probabilidad a aquellas configuraciones que mejor explican las observaciones. De esta manera, la inferencia sobre el espacio de variables ocultas puede realizarse mediante técnicas estándar de inferencia Bayesiana, evitando el proceso de selección de modelos. Esta tesis se centra en el ámbito de las aplicaciones, y es de naturaleza multidisciplinar. En concreto, proponemos un sistema de gradación automática para comparar el rendimiento deportivo de atletas independientemente de su edad o género, así como de otros factores del entorno. Desarrollamos modelos BNP para descubrir asociaciones genéticas y biomarcadores dentro de la investigación contra el cáncer, ya sea contrastando información genética con la historia clínica electrónica de los pacientes, o utilizando datos de ensayos clínicos; finalmente, presentamos un modelo flexible de factores latentes infinito para datos de comercio internacional, con el objetivo de entender la estructura económica de los distintos países y su correspondiente evolución a lo largo del tiempo.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Joaquín Míguez Arenas.- Secretario: Daniel Hernández Lobato.- Vocal: Cédric Archambea

Captains at the STEM of Their Own Ship: An Examination of Underrepresented Minority Student Participation in a Self-Directed, ICT After-School Intervention

Recent studies have advocated for early adoption of Information and Communications Technology (ICT) in order to help a broader range of youth become creators rather than consumers of digital media, to open doors for opportunity in the lucrative technology sector, and to set them on a course for lifelong STEM/ICT learning. This study used data that was collected from a grant funded, multi-site, after-school program designed to help a group of students who are often underrepresented in ICT learn about computing through a unique instructional design for guiding students through the creation of mobile apps using a freely accessible block-based coding platform developed by MIT called App Inventor. The study employed a concurrent, triangulation mixed methods approach to data analysis. Data sources included participant-observer field notes, interviews, student artifacts, online surveys, and an assessment of outcomes related to a construct called computational thinking. The purpose of the intervention and this proposed study was to examine whether participants in the program learned coding and related concepts, developed an interest in STEM/ICT subject matter, and gained an optimistic view of their abilities related to 21st century computing skills. In addition, the researcher hoped to identify which aspects of the instructional design may have facilitated progress towards these goals