@article {12464,
	title = {Video Pr{\'e}cis: Highlighting Diverse Aspects of Videos},
	journal = {IEEE Transactions on Multimedia},
	volume = {12},
	year = {2010},
	month = {2010/12//},
	pages = {853 - 868},
	abstract = {Summarizing long unconstrained videos is gaining importance in surveillance, web-based video browsing, and video-archival applications. Summarizing a video requires one to identify key aspects that contain the essence of the video. In this paper, we propose an approach that optimizes two criteria that a video summary should embody. The first criterion, {\textquotedblleft}coverage,{\textquotedblright} requires that the summary be able to represent the original video well. The second criterion, {\textquotedblleft}diversity,{\textquotedblright} requires that the elements of the summary be as distinct from each other as possible. Given a user-specified summary length, we propose a cost function to measure the quality of a summary. The problem of generating a précis is then reduced to a combinatorial optimization problem of minimizing the proposed cost function. We propose an efficient method to solve the optimization problem. We demonstrate through experiments (on KTH data, unconstrained skating video, a surveillance video, and a YouTube home video) that optimizing the proposed criterion results in meaningful video summaries over a wide range of scenarios. Summaries thus generated are then evaluated using both quantitative measures and user studies.},
	keywords = {$K$-means, CAMERAS, combinatorial mathematics, combinatorial optimization, Cost function, data compression, Exemplar selection, Image segmentation, Internet, Iron, Length measurement, multimedia systems, Ncut, optimisation, Optimization methods, original video, Permission, shot segmentation, Surveillance, user specified summary length, video précis, Video sharing, video signal processing, Video summarization},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2010.2058795},
	author = {Shroff, N. and Turaga,P. and Chellapa, Rama}
}
@conference {15269,
	title = {A cooperative combinatorial Particle Swarm Optimization algorithm for side-chain packing},
	booktitle = {IEEE Swarm Intelligence Symposium, 2009. SIS {\textquoteright}09},
	year = {2009},
	month = {2009/04/30/March},
	pages = {22 - 29},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Particle Swarm Optimization (PSO) is a well-known, competitive technique for numerical optimization with real-parameter representation. This paper introduces CCPSO, a new Cooperative Particle Swarm Optimization algorithm for combinatorial problems. The cooperative strategy is achieved by splitting the candidate solution vector into components, where each component is optimized by a particle. Particles move throughout a continuous space, their movements based on the influences exerted by static particles that then get feedback based on the fitness of the candidate solution. Here, the application of this technique to side-chain packing (a proteomics optimization problem) is investigated. To verify the efficiency of the proposed CCPSO algorithm, we test our algorithm on three side-chain packing problems and compare our results with the provably optimal result. Computational results show that the proposed algorithm is very competitive, obtaining a conformation with an energy value within 1\% of the provably optimal solution in many proteins.},
	keywords = {Algorithm design and analysis, Amino acids, combinatorial mathematics, cooperative combinatorial particle swarm optimization algorithm, Design optimization, Encoding, Feedback, numerical optimization, Optimization methods, particle swarm optimisation, Particle swarm optimization, Partitioning algorithms, Proteins, proteomics, proteomics optimization, Robustness, side-chain packing},
	isbn = {978-1-4244-2762-8},
	doi = {10.1109/SIS.2009.4937840},
	author = {Lapizco-Encinas,G. and Kingsford, Carl and Reggia, James A.}
}
@conference {14621,
	title = {Hardness of flip-cut problems from optical mapping [DNA molecules application]},
	booktitle = {Compression and Complexity of Sequences 1997. Proceedings},
	year = {1997},
	month = {1997/06/11/13},
	pages = {275 - 284},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Optical mapping is a new technology for constructing restriction maps. Associated computational problems include aligning multiple partial restriction maps into a single {\textquotedblleft}consensus{\textquotedblright} restriction map, and determining the correct orientation of each molecule, which was formalized as the exclusive binary flip cut (EBFC) problem by Muthukrishnan and Parida (see Proc. of the First ACM Conference on Computational Molecular Biology (RECOMB), Santa Fe, p.209-19, 1997). Here, the authors prove that the EBFC problem, as well as a number of its variants, are NP-complete. They also identify another problem formalized as binary shift cut (BSC) problem motivated by the fact that there might be missing fragments at the beginnings and/or the ends of the molecules, and prove it to be NP-complete. Therefore, they do not have efficient, that is, polynomial time solutions unless P=NP},
	keywords = {binary shift cut, Biochemistry, Bioinformatics, biological techniques, Biology, biomedical optical imaging, combinatorial mathematics, combinatorial problem, computational complexity, computational problems, DNA, DNA molecule, exclusive binary flip cut, flip-cut problem hardness, Genetic engineering, Genomics, MATHEMATICS, molecular biology, molecular biophysics, molecule orientation, multiple partial restriction maps, NP-complete problem, optical mapping, polynomial time solutions, Polynomials, sequences},
	isbn = {0-8186-8132-2},
	doi = {10.1109/SEQUEN.1997.666922},
	author = {Dancik,V. and Hannenhalli, Sridhar and Muthukrishnan,S.}
}
