Parameterized Construction of Program Representations for Sparse Dataflow Analyses

Data-flow analyses usually associate information with control flow regions. Informally, if these regions are too small, like a point between two consecutive statements, we call the analysis dense. On the other hand, if these regions include many such points, then we call it sparse. This paper presents a systematic method to build program representations that support sparse analyses. To pave the way to this framework we clarify the bibliography about well-known intermediate program representations. We show that our approach, up to parameter choice, subsumes many of these representations, such as the SSA, SSI and e-SSA forms. In particular, our algorithms are faster, simpler and more frugal than the previous techniques used to construct SSI - Static Single Information - form programs. We produce intermediate representations isomorphic to Choi et al.'s Sparse Evaluation Graphs (SEG) for the family of data-flow problems that can be partitioned per variables. However, contrary to SEGs, we can handle - sparsely - problems that are not in this family

Real-Time Heuristics and Metaheuristics for Static and Dynamic Weapon Target Assignments

The problem of targeting and engaging individual missiles (targets) with an arsenal of interceptors (weapons) is known as the weapon target assignment problem. This problem has been well-researched since the seminal work in 1958. There are two distinct categories of the weapon target assignment problem: static and dynamic. The static weapon target assignment problem considers a single instance in which a known number of incoming missiles is to be engaged with a finite number of interceptors. By contrast, the dynamic weapon target assignment problem considers either follow on engagement(s) should the first engagement(s) fail, a subsequent salvo of incoming missiles, or both. This research seeks to define and solve a realistic dynamic model. First, assignment heuristics and metaheuristics are developed to provide rapid near-optimal solutions to the static weapon target assignment. Next, a technique capable of determining how many of each interceptor type to reserve for a second salvo by means of approximate dynamic programming is developed. Lastly, a model that realistically considers erratic flight paths of incoming missiles and determines assignments and firing sequences of interceptors within a simulation to minimize the number of hits to a protected asset is developed. Additionally, the first contemporary survey of the weapon target assignment problem since 1985 is presented. Collectively, this work extends the research of missile defense into practical application more so than currently is found within the literature

Advancements in Compiler Design and Optimization Techniques

The modern period has seen advancements in compiler design, optimization technique, and software system efficiency. The influence of the most recent developments in compiler design and optimization techniques on program execution speed, memory utilization, and overall software quality is highlighted in this study. The design of the compiler is advanced by the efficient code that is now structured in research with high-speed performance without manual intervention. The influence of the most recent developments in compiler design and optimization techniques on program execution speed, memory utilization, and overall software quality is highlighted in this paper's thorough analysis

Resource Planning in Disaster Response - Decision Support Models and Methodologies

Managing the response to natural, man-made, and technical disasters is becoming increasingly important in the light of climate change, globalization, urbanization, and growing conflicts. Sudden onset disasters are typically characterized by high stakes, time pressure, and uncertain, conflicting or lacking information. Since the planning and management of response is a complex task, decision makers of aid organizations can thus benefit from decision support methods and tools. A key task is the joint allocation of rescue units and the scheduling of incidents under different conditions of collaboration. The authors present an approach to support decision makers who coordinate response units by (a) suggesting mathematical formulations of decision models, (b) providing heuristic solution procedures, and (c) evaluating the heuristics against both current best practice behavior and optimal solutions. The computational experiments show that, for the generated problem instances, (1) current best practice behavior can be improved substantially by our heuristics, (2) the gap between heuristic and optimal solutions is very narrow for instances without collaboration, and (3) the described heuristics are capable of providing solutions for all generated instances in less than a second on a state-of-the-art PC

Linking Policy Research and Practice in 'STIG Systems': Many Obstacles, but Some Ways Forward

This paper reflects on the relevance of systems thinking about the interdependent policy issues bearing on the dynamics of science, technology and innovation in their relationship to economic growth. Considering the approach that characterizes much of the current economics literatures treatment of technology and growth policies, we pose the critical question: what kind of systems paradigm is likely to prove particularly fruitful in that particular problem-domain: Evolutionary, neo-Schumpeterian, and complex system dynamics approaches are conceptually attractive and we analyze their respective virtues while also acknowledging their more serious problematic features. Those become visible quickly when trying connect systems-relevant research with practical policy-making in this field. Not content to have simply identified some significant obstructions in the path toward that goal, the paper also suggests some potentially feasible ways forward.Techonological Change, systems paradigm, STIG systems,