Finding diverse solutions has become important in many combinatorial search domains, including Automated Planning, Path Planning and Constraint Programming. Much of the work in these directions has however focussed on coming up with appropriate diversity metrics and compiling those metrics in to the solvers/planners. Most approaches use linear-time greedy algorithms for exploring the state space of solution combinations for generating a diverse set of solutions, limiting not only their completeness but also their effectiveness within a time bound. In this paper, we take a combinatorial search perspective on generating diverse solutions. We present a generic bi-level optimization framework for finding cost-sensitive diverse solutions. We propose complete methods under this framework, which guarantee finding a set of cost sensitive diverse solutions satisficing the given criteria whenever there exists such a set. We identify various aspects that affect the performance of these exhaustive algorithms and propose techniques to improve them. Experimental results show the efficacy of the proposed framework compared to an existing greedy approach.
Given the growing importance of large-scale graph analytics, there is a need to improve the performance of graph analysis frameworks without compromising on productivity. GraphMat is our solution to bridge this gap between a user-friendly graph analytics framework and native, hand-optimized code. GraphMat functions by taking vertex programs and mapping them to high performance sparse matrix operations in the backend. We thus get the productivity benefits of a vertex programming framework without sacrificing performance. GraphMat is a single-node multicore graph framework written in C++ which has enabled us to write a diverse set of graph algorithms with the same effort compared to other vertex programming frameworks. GraphMat performs 1.1-7X faster than high performance frameworks such as GraphLab, CombBLAS and Galois. GraphMat also matches the performance of MapGraph, a GPU-based graph framework, despite running on a CPU platform with significantly lower compute and bandwidth resources. It achieves better multicore scalability (13-15X on 24 cores) than other frameworks and is 1.2X off native, hand-optimized code on a variety of graph algorithms. Since GraphMat performance depends mainly on a few scalable and well-understood sparse matrix operations, GraphMat can naturally benefit from the trend of increasing parallelism in future hardware.
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The Center for Nonproliferation Studies (CNS) strives to combat the spread of weapons of mass destruction (WMD) by disseminating timely information and analysis and training the next generation of nonproliferation specialists. CNS at the Monterey Institute of International Studies is the largest nongovernmental organization in the United States devoted exclusively to research and training on nonproliferation issues.Dr. William Potter established the Center in 1989 with a handful of Institute students. Today, CNS has a fulltime staff of more than 65 specialists and over 75 graduate student research assistants located in offices in Monterey, California, Washington, DC and Almaty, Kazakhstan. CNS is organized into five research programs: the Chemical and Biological Weapons Nonproliferation Program, the East Asia Nonproliferation Program, the International Organizations and Nonproliferation Program, the Newly Independent States Nonproliferation Program, and the WMD Terrorism Research Program (WMDTRP). Each program supports the Centerʹs mission by training graduate students, building a worldwide community of nonproliferation experts, publishing both on-line and print resources on all aspects of WMD, providing background material to the media, and producing analysis for use by educational institutions, government, and the general public.The WMD Terrorism Research Program conducts work on the use or potential use of chemical, biological, radiological and nuclear (CBRN) weapons by non-state actors. The Program focuses on the motivational aspects of terrorism in the WMD context, bringing together terrorism scholars from the social sciences (history and political science) and technical experts from the sciences (microbiology, medicine, chemistry, and physics) to approach the WMD terrorism problem in an interdisciplinary fashion. Project Research Staff Project OverviewCertain types of infrastructure -critical infrastructure (CI) -play vital roles in underpinning our economy, security and way of life. These complex and often interconnected systems have become so ubiquitous and essential to day-to-day life that they are easily taken for granted. Often it is only when the important services provided by such infrastructure are interrupted -when we lose easy access to electricity, health care, telecommunications, transportation or water, for example -that we are conscious of our great dependence on these networks and of the vulnerabilities that stem from such dependence.Unfortunately, it must be assumed that many terrorists are all too aware that CI facilities pose high-value targets that, if successfully attacked, have the potential to dramatically disrupt the normal rhythm of society, cause public fear and intimidation, and generate significant publicity. Indeed, revelations emerging at the time of this writing about al Qa'ida's efforts to prepare for possible attacks on major financial facilities in New York, New Jersey, and the District of Columbia remind us just how real and immediate such threats to CI may be. Simply b...
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