22420647
Adaptive intra-frame quantization for very low bit rate video coding
This paper presents, for I-frames, a generic relationship among the quantization value, data bits, and their spatial complexity. This relationship provides a mechanism to select the quantization value for an I-frame (with different spatial complexity) at given bit budget before actually encoding it. Therefore it helps to adaptively distribute the bit budget among I-, P- and B-frames in a sequence. Experiments show that the adaptive intra frame quantization algorithm reduces the number of frame skippings significantly with comparable average PSNRs, and thus improves the overall subjective quality of the reconstructed video.
981043
Learning Event Durations from Event Descriptions
We have constructed a corpus of news articles in which events are annotated for estimated bounds on their duration. Here we describe a method for measuring inter-annotator agreement for these event duration distributions. We then show that machine learning techniques applied to this data yield coarse-grained event duration information, considerably outperforming a baseline and approaching human performance.
20260133
Temporal aggregates for Web services on the semantic Web
In this paper we describe how we encode our temporal aggregates ontology in OWL for Web services on the semantic Web. We also present one example to show how to use the ontology to represent temporal aggregates information.
8588212
Temporal Aggregates in OWL-Time
In this paper, we describe our approach for representing temporal aggregates in OWL-Time, and a systematic way of mapping iCalendar recurrence sets to OWL-Time temporal sequences. We present several examples of natural language expressions for temporal aggregates and how they would be represented in OWL-Time; some examples can be described in both iCalendar and OWL-Time, while others can only be represented in OWL-Time. We also illustrate how we can translate from a natural language sentence to our representation.
13046627
Adaptive frame skipping based on spatio-temporal complexity for low bit-rate video coding
Abstract Conventional rate control schemes focus on the adjustment of quantization values to retain a certain buffer level, and buffer adaptive frame skippings are used to avoid buffer overflow at low bit-rates. In this paper, an adaptive frame-skipping scheme is proposed to achieve the balance between spatial and temporal quality, and to maintain the buffer level. The occurrence of frame skipping is jointly dependent on the temporal and spatial complexity of the video, and on the fullness of the buffer. This helps to achieve a balanced spatial and temporal quality and to enhance the overall perceptual quality. Experimental results show that the new scheme is simple but very effective, with large average PSNR gains and consistently improved visual quality, and the improvement in perceptual quality is much significant than that in average PSNRs.
32927228
Infrared Face Recognition by Using Blood Perfusion Data
This paper presents a blood perfusion model of human faces based on thermodynamics and thermal physiology. The target is to convert the facial temperature data which are liable to ambient temperature into consistent blood perfusion data in order to improve the performance of infrared (IR) face recognition. Our large number of experiments has demonstrated that the blood perfusion data are less sensitive to ambient temperature if the human bodies are in steady state, and the real data testing demonstrated that the performance by means of blood perfusion data is significantly superior to that via temperature data in terms of recognition rate.
24069960
Study of Improved Hierarchy Genetic Algorithm Based on Adaptive Niches
Canonical genetic algorithms have the defects of pre-maturity and stagnation when applied in optimizing problems. In order to avoid the shortcomings, an adaptive niche hierarchy genetic algorithm (ANHGA) is proposed. The algorithm is based on the adaptive mutation operator and crossover operator to adjust the crossover rate and probability of mutation of each individual, whose mutation values are decided using individual gradient. This approach is applied in Percy and Shubert function optimization. Comparisons of niche genetic algorithm (NGA), hierarchy genetic algorithm (HGA) and ANHGA have been done by establishing a simulation model and the results of mathematics model and actual industrial model show that ANHGA is feasible and efficient in the design of multi-extremum.
12021051
COE: A General Approach for Efficient Genome-Wide Two-Locus Epistasis Test in Disease Association Study
The availability of high density single nucleotide polymorphisms (SNPs) data has made genome-wide association study computationally challenging. Two-locus epistasis (gene-gene interaction) detection has attracted great research interest as a promising method for genetic analysis of complex diseases. In this paper, we propose a general approach, COE, for efficient large scale gene-gene interaction analysis, which supports a wide range of tests. In particular, we show that many commonly used statistics are convex functions. From the observed values of the events in two-locus association test, we can develop an upper bound of the test value. Such an upper bound only depends on single-locus test and the genotype of the SNP-pair. We thus group and index SNP-pairs by their genotypes. This indexing structure can benefit the computation of all convex statistics. Utilizing the upper bound and the indexing structure, we can prune most of the SNP-pairs without compromising the optimality of the result. Our approach is especially efficient for large permutation test. Extensive experiments demonstrate that our approach provides orders of magnitude performance improvement over the brute force approach.
6961558
Minimizing a stochastic maximum-reliability path
We consider a stochastic network interdiction problem in which the goal is to detect an evader, who selects a maximum-reliability path. Subject to a resource constraint, the interdictor installs sensors on a subset of the network’s arcs to minimize the value of the evader’s maximum-reliability path, i.e., to maximize the detection probability. When this decision is made, the evader’s origin–destination pair is known to the interdictor only through a probability distribution. Our model is framed as a stochastic mixed-integer program and solved by an enhanced L-shaped decomposition method. Our primary enhancement is via a valid inequality, which we call a step inequality. In earlier work [Morton et al., IIE Trans 39 (2007), 3–14], we developed step inequalities for the special case in which the evader encounters at most one sensor on an origin–destination path. Here, we generalize the step inequality to the case where the evader encounters multiple sensors. In this more general setting, the step inequality is tightly coupled to the decomposition scheme. An efficient separation algorithm identifies violated step inequalities and strengthens the linear programming relaxation of the L-shaped method’s master program. We apply this solution procedure with further computational enhancements to a collection of test problems. © 2008 Wiley Periodicals, Inc. NETWORKS, Vol. 52(3), 111–119 2008
2768791
Finding representative set from massive data
In the information age, data is pervasive. In some applications, data explosion is a significant phenomenon. The massive data volume poses challenges to both human users and computers. In this project, we propose a new model for identifying representative set from a large database. A representative set is a special subset of the original dataset, which has three main characteristics: It is significantly smaller in size compared to the original dataset. It captures the most information from the original dataset compared to other subsets of the same size. It has low redundancy among the representatives it contains. We use information-theoretic measures such as mutual information and relative entropy to measure the representativeness of the representative set. We first design a greedy algorithm and then present a heuristic algorithm that delivers much better performance. We run experiments on two real datasets and evaluate the effectiveness of our representative set in terms of coverage and accuracy. The experiments show that our representative set attains expected characteristics and captures information more efficiently.
3020798
Statistical learning makes the hybridization of particle swarm and differential evolution more efficient—A novel hybrid optimizer
This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions, by combining the advantages of two powerful population-based metaheuristics—differential evolution (DE) and particle swarm optimization (PSO). In the hybrid denoted by DEPSO, each individual in one generation chooses its evolution method, DE or PSO, in a statistical learning way. The choice depends on the relative success ratio of the two methods in a previous learning period. The proposed DEPSO is compared with its PSO and DE parents, two advanced DE variants one of which is suggested by the originators of DE, two advanced PSO variants one of which is acknowledged as a recent standard by PSO community, and also a previous DEPSO. Benchmark tests demonstrate that the DEPSO is more competent for the global optimization of multimodal functions due to its high optimization quality.