Social Movements, Public Policy, and Democratic Consolidation in Latin America

This work studies how different social mobilisation processes have influenced policy processes in Latin America (2000-2003) and vice versa. Studying these interrelations includes three issues of empirical and theoretical importance. First, it explores under what conditions an investment project or policy initiative that is strongly supported by a democratically elected government on the basis of economic and technical arguments may trigger the emergence of a social movement; and under what conditions a social movement may successfully preclude the implementation of such project or policy initiative. Second, this work explores if these social movements have actually compensated for the absence of channels of participation and representation that work to influence the institutional policy process. Third and final, it studies if the influence and impact of these social movements have contributed to improve the design and implementation of public policies in the medium term and to promote the democratic consolidation in the region. Although the work is based on evidence from many countries in the region, there are mainly two case studies presented with more detail: the 'Water War' in Cochabamba, Bolivia (2001-2002) and the conflict triggered by the project to build a new airport in Mexico City (2001-2002). The 'Gas War' of Bolivia (2003) is also explored with less detail.

Storage and Querying of Large Persistent Arrays

The scientic and analytical applications today are increasingly becoming data in- tensive. Many such applications deal with data that is multidimensional in nature. Traditionally, relational database systems have been used by many data intensive application, and relational paradigm has proved to be both natural and ecient. However, for multidimensional data, when the number of dimensions becomes large, relational databases are inecient both in terms of storage and query response time. In this thesis, we explore linearised storage, and indexed and skiplist based retrieval on persistent arrays. The application programs are provided with a logical view of multidimensional array. The techniques have been implemented in a home-grown database management system called MuBase

On Design and Applications of Practical Concurrent Data Structures

The proliferation of multicore processors is having an enormous impact on software design and development. In order to exploit parallelism available in multicores, there is a need to design and implement abstractions that programmers can use for general purpose applications development. A common abstraction for coordinated access to memory is a concurrent data structure. Concurrent data structures are challenging to design and implement as they are required to be correct, scalable, and practical under various application constraints. In this thesis, we contribute to the design of efficient concurrent data structures, propose new design techniques and improvements to existing implementations. Additionally, we explore the utilization of concurrent data structures in demanding application contexts such as data stream processing.In the first part of the thesis, we focus on data structures that are difficult to parallelize due to inherent sequential bottlenecks. We present a lock-free vector design that efficiently addresses synchronization bottlenecks by utilizing the combining technique. Typical combining techniques are blocking. Our design introduces combining without sacrificing non-blocking progress guarantees. We extend the vector to present a concurrent lock-free unbounded binary heap that implements a priority queue with mutable priorities.In the second part of the thesis, we shift our focus to concurrent search data structures. In order to offer strong progress guarantee, typical implementations of non-blocking search data structures employ a "helping" mechanism. However, helping may result in performance degradation. We propose help-optimality, which expresses optimization in amortized step complexity of concurrent operations. To describe the concept, we revisit the lock-free designs of a linked-list and a binary search tree and present improved algorithms. We design the algorithms without using any language/platform specific constructs; we do not use bit-stealing or runtime type introspection of objects. Thus, our algorithms are portable. We further delve into multi-dimensional data and similarity search. We present the first lock-free multi-dimensional data structure and linearizable nearest neighbor search algorithm. Our algorithm for nearest neighbor search is generic and can be adapted to other data structures.In the last part of the thesis, we explore the utilization of concurrent data structures for deterministic stream processing. We propose solutions to two challenges prevalent in data stream processing: (1) efficient processing on cloud as well as edge devices and (2) deterministic data-parallel processing at high-throughput and low-latency. As a first step, we present a methodology for customization of streaming aggregation on low-power multicore embedded platforms. Then we introduce Viper, a communication module that can be integrated into stream processing engines for the coordination of threads analyzing data in parallel

Health Information Exchange Use in Primary Care

Indiana University-Purdue University Indianapolis (IUPUI)The United States has invested over $40 billion in digitizing the health care system, yet the anticipated gains in improved care coordination, quality, and cost savings remain largely unrealized. This is due in part to limited interoperability and low rates of health information exchange (HIE) use, which can support care coordination and improve provider decision-making. Primary care providers are central to the US health care delivery system and frequently function as care coordinators, yet capability and HIE use gaps among these providers limit the potential of these digital systems to achieve their intended goals. I study HIE use in the context of primary care to examine 1) factors associated with provider HIE use, 2) the extent and nature of team-based HIE use, and 3) differences in HIE system use patterns across discrete groups of system users. First, I use a national sample of primary care providers to analyze market and practice factors related to HIE use for patient referrals. Overall, I find that only 43% of primary care provider referrals used HIE. Furthermore, I find substantial variation in HIE use rates across electronic health record (EHR) vendors. Second, I use HIE system log data to understand the breadth and depth of HIE use among teams, a care model underpinning primary care delivery reform efforts. I find that although use of HIE systems remains low, in primary care settings it overwhelmingly takes place in a manner consistent with team-based care workflows. Furthermore, team-based use does not differ in breadth from single provider HIE use, but illustrates less depth before and after visits. Third, I apply cluster analysis to 16 HIE use measures representing 7 use attributes, and identify 5 discrete user groups. I then compare two of these user groups and find user-level variation in volume and efficiency of use, both of which have implications for HIE system design and usability improvements. Ultimately, these findings help to inform how HIE use can be increased and improved in primary care, moving the US health care system closer to realizing the coordination, quality, and cost savings made possible by a digitized delivery system

AllScale API

Effectively implementing scientific algorithms in distributed memory parallel applications is a difficult task for domain scientists, as evident by the large number of domain-specific languages and libraries available today attempting to facilitate the process. However, they usually provide a closed set of parallel patterns and are not open for extension without vast modifications to the underlying system. In this work, we present the AllScale API, a programming interface for developing distributed memory parallel applications with the ease of shared memory programming models. The AllScale API is closed for a modification but open for an extension, allowing new user-defined parallel patterns and data structures to be implemented based on existing core primitives and therefore fully supported in the AllScale framework. Focusing on high-level functionality directly offered to application developers, we present the design advantages of such an API design, detail some of its specifications and evaluate it using three real-world use cases. Our results show that AllScale decreases the complexity of implementing scientific applications for distributed memory while attaining comparable or higher performance compared to MPI reference implementations

Numerical Tools for the Control of the Unsteady Heating of an Airfoil

International audienceThis paper concerns the real time control of the boundary layer on an aircraft wing. This new approach consists in heating the surface in an unsteady regime using electrically resistant strips embedded in the wing skin. The control of the boundary layer's separation and transition point will provide a reduction in friction drag, and hence a reduction in fuel consumption. This new method consists in applying the required thermal power in the different strips in order to ensure the desired temperatures on the aircraft wing. We also have to determine the optimum size of these strips (length, width and distance between two strips). This implies finding the best mathematical model corresponding to the physics enabling us to facilitate the calculation for any type of material used for the wings. Secondly, the heating being unsteady, and, as during a flight the flow conditions or the ambient temperatures vary, the thermal power needed changes and must be chosen as fast as possible in order to ensure optimal operating conditions

An integrated hardware/software design methodology for signal processing systems

This paper presents a new methodology for design and implementation of signal processing systems on system-on-chip (SoC) platforms. The methodology is centered on the use of lightweight application programming interfaces for applying principles of dataflow design at different layers of abstraction. The development processes integrated in our approach are software implementation, hardware implementation, hardware-software co-design, and optimized application mapping. The proposed methodology facilitates development and integration of signal processing hardware and software modules that involve heterogeneous programming languages and platforms. As a demonstration of the proposed design framework, we present a dataflow-based deep neural network (DNN) implementation for vehicle classification that is streamlined for real-time operation on embedded SoC devices. Using the proposed methodology, we apply and integrate a variety of dataflow graph optimizations that are important for efficient mapping of the DNN system into a resource constrained implementation that involves cooperating multicore CPUs and field-programmable gate array subsystems. Through experiments, we demonstrate the flexibility and effectiveness with which different design transformations can be applied and integrated across multiple scales of the targeted computing system