@article {14620,
	title = {Guest Editors{\textquoteright} Introduction to the Special Section on Algorithms in Bioinformatics},
	journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
	volume = {5},
	year = {2008},
	month = {2008/12//Oct},
	pages = {482 - 483},
	keywords = {Abstracts, Algorithm design and analysis, Bioinformatics, Biological system modeling, biology computing, Computational Biology, Computational modeling, Computer science, Genomics, sequences},
	isbn = {1545-5963},
	doi = {10.1109/TCBB.2008.116},
	author = {Giancarlo,Raffaele and Hannenhalli, Sridhar}
}
@conference {13357,
	title = {Approximate Data Collection in Sensor Networks using Probabilistic Models},
	booktitle = {Proceedings of the 22nd International Conference on Data Engineering, 2006. ICDE {\textquoteright}06},
	year = {2006},
	month = {2006/04/03/07},
	pages = {48 - 48},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Wireless sensor networks are proving to be useful in a variety of settings. A core challenge in these networks is to minimize energy consumption. Prior database research has proposed to achieve this by pushing data-reducing operators like aggregation and selection down into the network. This approach has proven unpopular with early adopters of sensor network technology, who typically want to extract complete "dumps" of the sensor readings, i.e., to run "SELECT *" queries. Unfortunately, because these queries do no data reduction, they consume significant energy in current sensornet query processors. In this paper we attack the "SELECT " problem for sensor networks. We propose a robust approximate technique called Ken that uses replicated dynamic probabilistic models to minimize communication from sensor nodes to the network{\textquoteright}s PC base station. In addition to data collection, we show that Ken is well suited to anomaly- and event-detection applications. A key challenge in this work is to intelligently exploit spatial correlations across sensor nodes without imposing undue sensor-to-sensor communication burdens to maintain the models. Using traces from two real-world sensor network deployments, we demonstrate that relatively simple models can provide significant communication (and hence energy) savings without undue sacrifice in result quality or frequency. Choosing optimally among even our simple models is NPhard, but our experiments show that a greedy heuristic performs nearly as well as an exhaustive algorithm.},
	keywords = {Batteries, Biological system modeling, Biosensors, data mining, Databases, Energy consumption, Intelligent networks, Intelligent sensors, Robustness, Wireless sensor networks},
	isbn = {0-7695-2570-9},
	doi = {10.1109/ICDE.2006.21},
	author = {Chu,D. and Deshpande, Amol and Hellerstein,J. M and Wei Hong}
}
@conference {14817,
	title = {Unobtrusiveness and efficiency in idle cycle stealing for PC grids},
	booktitle = {Parallel and Distributed Processing Symposium, 2004. Proceedings. 18th International},
	year = {2004},
	month = {2004/04/26/30},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Summary form only given. Studies have shown that for a significant fraction of the time desktop PCs and workstations are under-utilized. To exploit these idle resources, various desktop/workstation grid systems have been developed. The ultimate goal of such systems is to maximize efficiency of resource usage while maintaining low obtrusiveness to machine owners. To this end, we created a new fine-grain cycle stealing approach and conducted a performance comparison study against the traditional coarse-grain cycle stealing. We developed a prototype of fine-grain cycle stealing, the Linger-Longer system, on a Linux cluster. The experiments on a cluster of desktop Linux PCs with benchmark applications show that, overall, fine-grain cycle stealing can improve efficiency of idle cycle usage by increasing the guest job throughput by 50\% to 70\%, while limiting obtrusiveness with no more than 3\% of host job slowdown.},
	keywords = {Biological system modeling, Computational modeling, Computer science, Computer simulation, Contracts, desktop grid system, fine-grain cycle stealing approach, grid computing, idle cycle stealing, Linger-Longer system, Linux, Linux cluster, microcomputers, PC grids, Personal communication networks, Prototypes, resource allocation, Throughput, workstation clusters, workstation grid systems, Workstations},
	isbn = {0-7695-2132-0},
	doi = {10.1109/IPDPS.2004.1302987},
	author = {Ryu, K. D and Hollingsworth, Jeffrey K}
}
@conference {14529,
	title = {Modeling the effect of a nearby boundary on the HRTF},
	booktitle = {2001 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP {\textquoteright}01)},
	volume = {5},
	year = {2001},
	month = {2001///},
	pages = {3337-3340 vol.5 - 3337-3340 vol.5},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Understanding and simplified modeling of the head related transfer function (HRTF) holds the key to many applications in spatial audio. We develop an analytical solution to the problem of scattering of sound from a sphere in the vicinity of an infinite plane. Using this solution we study the influence of a nearby scattering rigid surface, on a spherical model for the HRTF},
	keywords = {Acoustic scattering, acoustic signal processing, acoustic wave reflection, acoustic wave scattering, architectural acoustics, audio signal processing, Biological system modeling, boundary effect modeling, Computer interfaces, Ear, Educational institutions, Frequency, Head related transfer function, HRTF, HUMANS, infinite plane, Laboratories, Nails, Raman scattering, rigid surface, room environment, sound pressure level, sound scattering, spatial audio, sphere, spherical model, Transfer functions, wall influence},
	isbn = {0-7803-7041-4},
	doi = {10.1109/ICASSP.2001.940373},
	author = {Gumerov, Nail A. and Duraiswami, Ramani}
}
