This study examined how higher performing middle schools in the Rio Grande Valley use data to drive instructional decisions. Three research questions guided this study: (a) to what extent do higher performing, Title-1, middle schools in the Rio Grande Valley utilize data to make schoolwide instructional decisions; (b) how does the principal support data use for instructional decision-making; and (c) what do teachers perceive to be the processes that have led to the current level of data use in instructional decision making? A mixed-methods multiple-case study included middle schools that were drawn from a list of higher performing schools according to Just for the Kids and the National Center for Educational Achievement. To be included in the study, schools had to be located in the Rio Grande Valley, Texas, specifically in the counties of Starr, Cameron or Hidalgo. Additionally, the schools needed to be designated a Title-1 school, according to federal criteria. Data for the study was collected using a survey, followed by one-on-one interviews. Descriptive analyses was then conducted using the survey data. The interview data was analyzed using first-level coding followed by the use of cross case analysis to determine themes common to all cases. The findings from this research revealed that data is used extensively in the schools studied; primarily to determine the instructional scope of what is taught. It was found that while data use was extensive, the source and purpose of data use was limited to that which was directly tied to the state-administered assessment (TAKS). The second major finding was that principals create the necessary conditions for data use that becomes an embedded practice, where teachers can take risks with their colleagues in reviewing and using data. This study concludes that more principals can lead their schools to greater levels of data use by creating the necessary conditions for change. At the same time, the findings suggest that there is a need for leaders at all levels to examine and mitigate the unintended consequences of data use that is derived from a single-source and for a single purpose—that is, performance on the state exam (TAKS). / text

In many applications, such as conversational agents, virtual reality, movies, and games, animated facial expressions of computer-generated (CG) characters are used to communicate, teach, or entertain. With an increased demand for CG characters, it is important to animate accurate, realistic facial expressions because human facial expressions communicate a wealth of information. However, realistically animating faces is challenging and time-consuming for two reasons. First, human observers are adept at detecting anomalies in realistic CG facial animations. Second, traditional animation techniques based on keyframing sometimes approximate the dynamics of facial expressions or require extensive artistic input while high-resolution performance capture techniques are cost prohibitive. In this thesis, we develop a framework to explore representations of two key facial expressions, blinks and smiles, and we show that data-driven models are needed to realistically animate these expressions. Our approach relies on utilizing high-resolution performance capture data to build models that can be used in traditional keyframing systems. First, we record large collections of high-resolution dynamic expressions through video and motion capture technology. Next, we build expression-specific models of the dynamic data properties of blinks and smiles. We explore variants of the model and assess whether viewers perceive the models as more natural than the simplified models present in the literature. In the first part of the thesis, we build a generative model of the characteristic dynamics of blinks: fast closing of the eyelids followed by a slow opening. Blinks have a characteristic profile with relatively little variation across instances or people. Our results demonstrate the need for an accurate model of eye blink dynamics rather than simple approximations, as viewers perceive the difference. In the second part of the thesis, we investigate how spatial and temporal linearities impact smile genuineness and build a model for genuine smiles. Our perceptual results indicate that a smile model needs to preserve temporal information. With this model, we synthesize perceptually genuine smiles that outperform traditional animation methods accompanied by plausible head motions. In the last part of the thesis, we investigate how blinks synchronize with the start and end of spontaneous smiles. Our analysis shows that eye blinks correlate with the end of the smile and occur before the lip corners stop moving downwards. We argue that the timing of blinks relative to smiles is useful in creating compelling facial expressions. Our work is directly applicable to current methods in animation. For example, we illustrate how our models can be used in the popular framework of blendshape animation to increase realism while keeping the system complexity low. Furthermore, our perceptual results can inform the design of realistic animation systems by highlighting common assumptions that over-simplify the dynamics of expressions.

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Telecom today has become an essence of life. Everywhere we see people using their smart phones for calling, checking email or accessing internet. To handle all these kinds of services without any intrusion is a very challenging task. This study deals with software testing which helps to ensure the quality of service to the end user. Software testing is an essential part in the software development process. Software development for telecom domain might not look as safety critical as of an airplane or nuclear reactor but it is arguably more complex. The main focus of this study is to provide automation to the unit testing of different types of radio connections that can be assigned to the end user based on the requested service and capacity of the 3G network. This research is sponsored by Ericsson to improve the testing of User Equipment Radio Connection Handling system that controls multiple possible radio connection configurations. This research attempts to identify and test all possible transitions between radio connection states. This will improve the existing manual state testing system, where changes in connection states cause dramatic impacts on test fixtures. As a solution, an automatic test case executor is proposed that generates possible transitions, which are later executed and verified automatically.

abstract: Healthcare operations have enjoyed reduced costs, improved patient safety, and innovation in healthcare policy over a huge variety of applications by tackling prob- lems via the creation and optimization of descriptive mathematical models to guide decision-making. Despite these accomplishments, models are stylized representations of real-world applications, reliant on accurate estimations from historical data to jus- tify their underlying assumptions. To protect against unreliable estimations which can adversely affect the decisions generated from applications dependent on fully- realized models, techniques that are robust against misspecications are utilized while still making use of incoming data for learning. Hence, new robust techniques are ap- plied that (1) allow for the decision-maker to express a spectrum of pessimism against model uncertainties while (2) still utilizing incoming data for learning. Two main ap- plications are investigated with respect to these goals, the first being a percentile optimization technique with respect to a multi-class queueing system for application in hospital Emergency Departments. The second studies the use of robust forecasting techniques in improving developing countries’ vaccine supply chains via (1) an inno- vative outside of cold chain policy and (2) a district-managed approach to inventory control. Both of these research application areas utilize data-driven approaches that feature learning and pessimism-controlled robustness. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2018

This study examined the impact of principals' data-driven decision-making practices on student achievement using the theoretical frame of Dervin's sense-making theory. This study is a quantitative cross-sectional research design where principals' perceptions about data were quantitatively captured at a single point in time. The participants for this study were 253 rural school principals currently serving in schools across Texas, and included both males and females across all ethnic groups, including white, African American, Hispanic, Asian, Native American and other. A developed survey instrument was administered to principals. The findings from the quantitative SEM analyses indicated that the Principal Uses Data to Improve Student Achievement latent variable (Factor 1) and the Principal and Staff Ability to Analyze Data to Improve Student Achievement latent variable (Factor 2) were significantly and positively associated with student achievement. Higher scores on these two latent variables were associated with better student achievement. There was no statistical association between the Principal Uses Data to Design Teacher Professional Development latent variable (Factor 3) and this target outcome. In total, the three latent variables accounted for 6% of the variance in student achievement (TAKS). When the campus level outcome was considered, no statistically significant associations between any of the latent variables and this outcome were evident. In total, the three latent variables accounted for less than 2% of the variance in campus level.

Data Week UPC 2021 - día 3 / Data WeeK UPC es un evento anual organizado por las Facultades de Negocios e Ingeniería, con el propósito de reunir a investigadores y expertos en la gestión empresarial para reflexionar acerca del papel de la Ciencia de Datos en la generación de valor en las organizaciones. Nueve expositores de distintas instituciones se unirán a las 4 fechas del Data Week 2021 este 23, 25, 26 y 27 de noviembre, para reflexionar acerca de los retos en el proceso de la transformación de datos para la toma de decisiones. No se pierdan la oportunidad de participar en este espacio en el que discutiremos las principales tendencias en cuanto a la aplicación de la ciencia de datos en la gestión empresarial. 7:00 PM EL CIENTÍFICO DE DATOS EN LAS ORGANIZACIONES DATA DRIVEN Ante la necesidad de manipular o manejar software, lenguajes especializados, capturar, procesar, analizar y representar grandes cantidades de datos, ¿cuáles son los perfiles requeridos para responder a esta nueva realidad? En esta charla abordaremos los perfiles relacionados con la ciencia de datos y su papel en el presente y el futuro de las organizaciones. 8:00 PM EL PAPEL DE LOS MODELOS PREDICTIVOS EN LA TOMA DE DECISIONES EMPRESARIALES Las organizaciones enfrentan muchos retos en el proceso de implementar modelos de machine learning y crear una cultura de data-driven. En esta charla se aborda el papel estratégico de los modelos predictivos para detectar riesgos en diferentes frentes. La aplicación de la ciencia en los datos permite un conocimiento cada vez mayor como base para la toma de decisiones empresariales."

This research focuses on the development of optimal sailing maneuvers for an AC75 foiling sailboat competing in the America's Cup. The America's Cup is the oldest, most prestigious, and technologically advanced sailboat racing competition in the world. Each iteration brings new and innovative sailboat designs which drastically improve sailing performance but increase complexity in the control of the sailboat system. This added complexity in the design and operation of the AC75 sailboat presents many challenges to the development of optimal sailing maneuvers. These challenges arise from the introduction of extra degrees of freedom and articulations in the boat such as the canting mechanisms (hydrofoils), which result in complex dynamical behaviors. The sailboat system is nonlinear, high-dimensional, and highly unstable. These complex characteristics require the development of high-order models, which are often intractable, or which introduce significant delays making them not well-suited for real-time control. The optimal maneuvers were achieved via the exploration of out-of-the-box solutions through data-driven controls and optimization. We used a high-fidelity sailboat simulator for the data generation process, and data-driven optimization schemes, such as Artificial Neural Networks (ANN), Extremum Seeking Control (ESC), and Jacobian Learning (JL) to optimize the sailing maneuvers. The optimizations were performed separately on various sailing maneuvers including close-hauled, tacking, and takeoff, as well as combinations of these maneuvers as performed during a race. The close-hauled and tacking maneuvers were optimized to achieve maximum Velocity Made Good (VMG) and minimum loss of VMG, respectively. The takeoff maneuver was optimized for maximum VMG and minimum time for the boat's transitions from displacement mode to foiling mode. The optimal solutions are subject to physical constraints and operational constraints enforced by the humans (sailors) in the loop. These maneuvers were developed for various heading angles (True Wind Angle (TWA)) and environmental conditions, such as True Wind Speed (TWS). Additionally, we performed an in-depth analysis of the optimal parameter settings obtained for close-hauled sailing to discern general trends in the parameter space. The trend of optimal parameters versus the wind direction provides a good understanding of the parameter space for varying sailing directions which can help guide the sailor's decisions during a race. The results show how optimization and controls can play a significant role in the development of advisory systems for complex human-operated systems. Lastly, the maneuvers developed in this search serve as performance benchmarks and provide insightful information about the underlying dynamics of the boat. / Mechanical Engineering

Asynchronous design is an alternative to the more widely used synchronous design which allows for the elimination of a global clock network and associated design issues such as clock skew. Uncle is a toolflow that provides automated assistance for transforming a synchronous system specified in Verilog RTL to an asynchronous system. With assistance from Uncle an asynchronous delay-insensitive microprocessor is implemented using NULL Convention Logic (NCL) and verified to function properly. An advantage of asynchronous design is that it can be data-driven. Data-driven design allows specific blocks of logic to only be active when they are needed. Data-driven design is implemented to bypass parts of the asynchronous microprocessor. These parts included the ALU and the peripheral hardware multiplier. This resulted in a reduction of total power consumed and an increase in speed. Overall, it was concluded that asynchronous design with Uncle was a viable alternative to synchronous design.

Water quality management (WQM) significantly affects water use and ecosystem health, which is helpful for achieving sustainability in environmental and economic aspects. However, the implementation of water quality management is still challenging in practice due to the uncertainty and nonlinearity existing in water systems, as well as the difficulty of the integration of simulation and optimization analyses. Therefore, effective optimization frameworks for handling nonlinearity, various uncertainties, and integrated complex water quality simulation models are highly desired. This dissertation tries to address such challenges by proposing new efficient hybrid inexact optimization models for water quality management under uncertainty through: i) developing an interval quadratic programming (IQP) model for handling both nonlinearity and uncertainty expressed as intervals for water quality management, and solving the developed model by three algorithms to compare and investigate the most effective and straightforward solution algorithm for IQP-WQM problems; ii) developing a simulation-based interval chance-constrained quadratic programming model, which is able to deal with nonlinearity and uncertainties with multiple formats, and implementing a real-world case study of phosphorus control in the central Grand River, Ontario, Canada; iii) proposing a data-driven interval credibility constrained quadratic programming model for water quality management by utilizing a data-driven surrogate model (i.e., inexact linear regression) to incorporate a complex water quality simulation model with the optimization framework to overcome challenges from the integrated simulation-optimization. The performance of the proposed frameworks/models was tested by different case studies and various mathematical techniques (e.g., sensitivity analysis). The results indicate the proposed models are capable of dealing with nonlinearity and various uncertainties, and significantly reducing the computational burden from simulation-optimization analysis. Coupling such efforts in developing efficient hybrid inexact optimization models for water quality management under uncertainty can provide useful tools to solve large-scale complex water quality management problems in a robust manner, and further provide reliable and effective decision supports for water quality planning and management. / Thesis / Doctor of Philosophy (PhD) / Water quality management plays a key role in facilitating environmental and economic sustainability. However, many challenges still exist in practical water quality management problems, such as various uncertainties and complexities, as well as complicated integrated simulation-optimization analysis. Therefore, the goal of this dissertation is to address such challenges by developing a set of efficient hybrid inexact optimization models for water quality management under uncertainty through: i) developing an interval quadratic programming model for water quality management, and investigating its effective and straightforward solution algorithms; ii) leveraging the power of data-driven modeling and proposing efficient data-driven optimization models based on hybrid inexact programming for water quality management. Robust and effective water quality planning schemes for large-scale water quality management problems can be obtained based on the proposed frameworks/models.