17538498
Reasoning about Bayesian Network Classifiers
Bayesian network classifiers are used in many fields, and one common class of classifiers are naive Bayes classifiers. In this paper, we introduce an approach for reasoning about Bayesian network classifiers in which we explicitly convert them into Ordered Decision Diagrams (ODDS), which are then used to reason about the properties of these classifiers. Specifically, we present an algorithm for converting any naive Bayes classifier into an ODD, and we show theoretically and experimentally that this algorithm can give us an ODD that is tractable in size cvcn given an intractable number of instances. Since ODDS are tractable representations of classifiers, our algorithm allows us to efficiently test the equivalence of two naive Bayes classifiers and characterize discrepancies between them. We also show a number of additional results including a count of distinct classifiers that can be induced by changing some CPT in a naive Bayes classifier, and the range of allowable changes to a CPT which keeps the current classifier unchanged.
5672538
When do Numbers Really Matter?
Common wisdom has it that small distinctions in the probabilities quantifying a belief network do not matter much for the results of probabilistic queries. Yet, one can develop realistic scenarios under which small variations in network probabilities can lead to significant changes in computed queries. A pending theoretical question is then to analytically characterize parameter changes that do or do not matter. In this paper, we study the sensitivity of probabilistic queries to changes in network parameters and prove some tight bounds on the impact that such parameters can have on queries. Our analytical results pinpoint some interesting situations under which parameter changes do or do not matter. These results are important for knowledge engineers as they help them identify influential network parameters. They also help explain some of the previous experimental results and observations with regards to network robustness against parameter changes.
17611435
Extra unit-speed machines are almost as powerful as speedy machines for competitive flow time scheduling
We study online scheduling of jobs to minimize the flow time and stretch on parallel machines. We consider algorithms that are given extra resources so as to compensate the lack of future information. Recent results show that a modest increase in machine speed can provide very competitive performance; in particular, using O(1) times faster machines, the algorithm SRPT (shortest remaining processing time) is 1-competitive for both flow time [23] and stretch [12], and HDF (highest density first) is O(1)-competitive for weighted flow time [6]. Using extra unit-speed machines instead of faster machines is more challenging. This paper gives a non-trivial relationship between the extra-speed and extra-machine analysis. It shows that competitive results via faster machines can be transformed to similar results via extra machines, and hence giving the first algorithms that, using O(1) times unit-speed machines, are 1-competitive for flow time and stretch and O(1)-competitive for weighted flow time, respectively.
11373384
Joint Call Admission Control for Next Generation Wireless Network
Next generation wireless network (NGWN) will be heterogeneous in nature where different radio access technologies (RATs) such as UMTS, WiMax, WLAN, etc, coexist. This coexistence necessitates joint radio resource management (JRRM). This paper focuses on joint call admission control (JCAC) for NGWN. Factors that determine a user's preference for a particular RAT are discussed. We propose a JCAC algorithm which considers the user's preference in making admission decision. A specific case where the user prefers to be served by the RAT which has the least service cost is modeled and evaluated using Markov decision process. The results obtained shows that overall service cost in heterogeneous network can be significantly reduced by using the proposed joint call admission control
27492259
Generalized Filtered Back-Projection Reconstruction in Breast Tomosynthesis
2159703
High Speed Channel Coding Architectures for the Uncoordinated OR Channel
Though it promises high bandwidths, the optical medium is not popular in local area networks. This is because current optical networks do not offer the ease of use and setup that an uncoordinated multiple access network such as Ethernet offers. By careful design and implementation of high speed channel coding architectures, we show that it is possible for optical networks to exhibit these desirable properties while maintaining high optical transmission rates. This paper presents an interleaver-division multiple access (IDMA) architecture implemented with a rate 1/20, 64-state Viterbi decoder and a word-based interleaver. These structures allowed us to achieve optical data rates of 2Gbps in FPGA implementation and 5.4Gbps for 0.18mum ASIC implementation. The techniques presented can be adapted for other similar architectures
34112863
Compressed Indexes for Approximate String Matching
We revisit the problem of indexing a string S[1..n] to support searching all substrings in S that match a given pattern P[1..m] with at most k errors. Previous solutions either require an index of size exponential in k or need Ω(mk) time for searching. Motivated by the indexing of DNA sequences, we investigate space efficient indexes that occupy only O(n) space. For k = 1, we give an index to support matching in O(m + occ + logn loglogn) time. The previously best solution achieving this time complexity requires an index of size O(n logn). This new index can be used to improve existing indexes for k ≥2 errors. Among others, it can support matching with k=2 errors in O(m logn loglogn + occ) time.