We consider problems involving how to schedule broadcasts in a pulled-based data-dissemination service, such as the DirecPC system, where data requested by the clients is delivered via broadcast. In particular, we consider the case where all the data items are of equal size and preemption is not allowed. We give an o ine O(1)-speed O(1)-approximation algorithm for the problem of minimizing the average response time. We provide worst-case analysis, under various objective functions, of the online algorithms that have appeared in the literature, namely, Most Requests First, First Come First Served, and Longest Wait First.

We show that for every probabilistic FIN-type learner with success ratio greater than~,

In this study, we analyzed temporal gene expression patterns in human peripheral blood mononuclear cells (PBMCs) infected with the Francisella tularensis live vaccine strain from 1 to 24 h utilizing a whole human Affymetrix gene chip. We found that a considerable number of induced genes had similar expression patterns and functions as reported previously for gene expression profiling in patients with ulceroglandular tularemia. Among the six uniquely regulated genes reported for tularemia patients as being part of the alarm signal gene cluster, five, namely caspase 1, PSME2, TAP-1, GBP1, and GCH1, were induced in vitro. We also detected four out of the seven potential biomarkers reported in tularemia patients, namely TNFAIP6 at 4 h and STAT1, TNFSF10, and SECTM1 at 16 and 24 h. These observations underscore the value of using microarray expression profiling as an in vitro tool to identify potential biomarkers for human infection and disease. Our results indicate the potential involvement of several host pathways/processes in Francisella infection, notably those involved in calcium, zinc ion binding, PPAR signaling, and lipid metabolism, which further refines the current knowledge of F. tularensis infection and its effects on the human host. Ultimately, this study provides support for utilizing in vitro microarray gene expression profiling in human PBMCs to identify biomarkers of infection and predict in vivo immune responses to infectious agents.

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SUMMARYWe consider problems involving how to schedule broadcasts in a pulled-based data-dissemination service, such as the DirecPC system, where data requested by the clients is delivered via broadcast. In particular, we consider the case where all the data items are of equal size and preemption is not allowed. We give an o ine O(1)-speed O(1)-approximation algorithm for the problem of minimizing the average response time. We provide worst-case analysis, under various objective functions, of the online algorithms that have appeared in the literature, namely, Most Requests First, First Come First Served, and Longest Wait First.

Consider a scenario in which an algorithmic machine, M, is being fed the graph of a computable function f. M is said to finitely identify f just in case after inspecting a finite portion of the graph of f it emits its first conjecture which is a program for f , and it never abandons this conjecture thereafter. A team of machines is a multiset of such machines. A team is said to be successful just in case each member of some nonempty subset, of predetermined size, of the team is successful. The ratio of the number of machines required to be successful to the size of the team is referred to as the success ratio of the team. The present paper investigates the finite identification of computable functions by teams of learning machines. The results presented complete the picture for teams with success ratio 1 2 and greater. It is shown that at success ratio 1 2 , introducing redundancy in the team can result in increased learning power. In particular it is established that larger collections of functions can be learned by employing teams of 4 machines and requiring at least 2 to be successful than by employing teams of 2 machines and requiring at least 1 to be successful. Surprisingly, it is also shown that introducing further redundancy at success ratio 1 2 does not yield any extra learning power. In particular, it is shown that the collections of functions that can be finitely identified by a team of 2m machines requiring at least m to be successful is the same as: • the collections of functions that can be finitely identified by a team of 4 machines requiring at least 2 to be successful, if m is even, and • the collections of functions that can be identified by a team of 2 machines requiring at least 1 to be successful, if m is odd. These latter results require development of sophisticated simulation techniques.

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