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Larger Problems (larger + problem)
Selected AbstractsAid, Relief, and Containment: The First Asylum Country and BeyondINTERNATIONAL MIGRATION, Issue 5 2002B.S. Chimni A fundamental problem that confronts the European Union today is how it can maintain its commitment to the institution of asylum while checking irregular migration and the abuse of its asylum system. In order to explore a response to this dilemma the paper addresses the following questions: what role can relief and aid policies play in influencing migration patterns? What should be the appropriate approach to the granting of relief and aid to developing countries of first asylum? Should it be viewed as a part of the larger problem of development or be treated as a distinct issue? What kind of a relief/aid model will help refugees return to post,conflict societies and stop the conflict from reproducing itself? The paper examines two different approaches to address these questions: the alliance,containment approach and the distributive,developmental approach. It also looks at some empirical evidence, which reveals that at present it is a conservative alliance,containment approach that informs EU relief and aid practices. This approach, however, does not help achieve the stated objective of checking abuse of asylum and migration procedures while sustaining a commitment to a liberal asylum regime. The paper goes on to identify the gaps in EU policy and the lessons that can be drawn. It concludes by looking at different policy alternatives and suggesting the adoption of a reformist distributive,developmental model. The implementation of this model holds out the hope of reverting to a more liberal asylum regime while controlling irregular migration and "bogus" asylum seekers, for the reformist distributive developmental model takes a more long,term view of migration trends and also seeks to address the growing North,South divide. [source] Re-estimating the difficulty of closing the digital divideJOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, Issue 12 2008Jeffrey James While we now know the most important determinants of the digital divide, such as income, skills, and infrastructure, little has been written about how these variables relate to one another. Yet, it is on the basis of one's answer to this question that the difficulty of closing the divide ultimately depends. In this article, I have sought to challenge the (implicit) prevailing assumption in most of the digital-preparedness literature that variables can be perfectly substituted for one another and, hence, added together. In particular, and drawing on available evidence, I view the relationship between, say computers and computer skills, as being nearer the opposite extreme, of totally limited substitutability. On this basis, I suggest that the components of digital-preparedness indexes be multiplied rather than added. Using multiplication rather than addition in most current indexes of digital preparedness reveals a substantial understatement of the real difficulty in closing the digital divide and a different set of policies to deal with this larger problem. Such policies should include sharing arrangements and the use of intermediaries. [source] Factors affecting the performance of parallel mining of minimal unique itemsets on diverse architecturesCONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 9 2009D. J. Haglin Abstract Three parallel implementations of a divide-and-conquer search algorithm (called SUDA2) for finding minimal unique itemsets (MUIs) are compared in this paper. The identification of MUIs is used by national statistics agencies for statistical disclosure assessment. The first parallel implementation adapts SUDA2 to a symmetric multi-processor cluster using the message passing interface (MPI), which we call an MPI cluster; the second optimizes the code for the Cray MTA2 (a shared-memory, multi-threaded architecture) and the third uses a heterogeneous ,group' of workstations connected by LAN. Each implementation considers the parallel structure of SUDA2, and how the subsearch computation times and sequence of subsearches affect load balancing. All three approaches scale with the number of processors, enabling SUDA2 to handle larger problems than before. For example, the MPI implementation is able to achieve nearly two orders of magnitude improvement with 132 processors. Performance results are given for a number of data sets. Copyright © 2009 John Wiley & Sons, Ltd. [source] SOLVING DYNAMIC WILDLIFE RESOURCE OPTIMIZATION PROBLEMS USING REINFORCEMENT LEARNINGNATURAL RESOURCE MODELING, Issue 1 2005CHRISTOPHER J. FONNESBECK ABSTRACT. An important technical component of natural resource management, particularly in an adaptive management context, is optimization. This is used to select the most appropriate management strategy, given a model of the system and all relevant available information. For dynamic resource systems, dynamic programming has been the de facto standard for deriving optimal state-specific management strategies. Though effective for small-dimension problems, dynamic programming is incapable of providing solutions to larger problems, even with modern microcomputing technology. Reinforcement learning is an alternative, related procedure for deriving optimal management strategies, based on stochastic approximation. It is an iterative process that improves estimates of the value of state-specific actions based in interactions with a system, or model thereof. Applications of reinforcement learning in the field of artificial intelligence have illustrated its ability to yield near-optimal strategies for very complex model systems, highlighting the potential utility of this method for ecological and natural resource management problems, which tend to be of high dimension. I describe the concept of reinforcement learning and its approach of estimating optimal strategies by temporal difference learning. I then illustrate the application of this method using a simple, well-known case study of Anderson [1975], and compare the reinforcement learning results with those of dynamic programming. Though a globally-optimal strategy is not discovered, it performs very well relative to the dynamic programming strategy, based on simulated cumulative objective return. I suggest that reinforcement learning be applied to relatively complex problems where an approximate solution to a realistic model is preferable to an exact answer to an oversimplified model. [source] Comparing arithmetic and semantic fact retrieval: Effects of problem size and sentence constraint on event-related brain potentialsPSYCHOPHYSIOLOGY, Issue 6 2003Kerstin Jost Abstract Event-related potentials were recorded with 61 electrodes from 16 students who verified either the correctness of single-digit multiplication problems or the semantic congruency of sentences. Multiplication problems varied in size and sentence fragments in constraint. Both semantic and arithmetic incongruencies evoked a typical N400 with a clear parieto-central maximum. In addition, numerically larger problems (8×7), in comparison to smaller problems (3×2), evoked a negativity starting at about 360 ms whose maximum was located over the right temporal-parietal scalp. These results indicate that the arithmetic incongruency and the problem-size effect are functionally distinct. It is suggested that the arithmetic and the semantic incongruency effects are both functionally related to a context-dependent spread of activation in specialized associative networks, whereas the arithmetic problem-size effect is due to rechecking routines that go beyond basic fact retrieval. [source] | ||||||||||