601 research outputs found
Weakly supervised segment annotation via expectation kernel density estimation
Since the labelling for the positive images/videos is ambiguous in weakly
supervised segment annotation, negative mining based methods that only use the
intra-class information emerge. In these methods, negative instances are
utilized to penalize unknown instances to rank their likelihood of being an
object, which can be considered as a voting in terms of similarity. However,
these methods 1) ignore the information contained in positive bags, 2) only
rank the likelihood but cannot generate an explicit decision function. In this
paper, we propose a voting scheme involving not only the definite negative
instances but also the ambiguous positive instances to make use of the extra
useful information in the weakly labelled positive bags. In the scheme, each
instance votes for its label with a magnitude arising from the similarity, and
the ambiguous positive instances are assigned soft labels that are iteratively
updated during the voting. It overcomes the limitations of voting using only
the negative bags. We also propose an expectation kernel density estimation
(eKDE) algorithm to gain further insight into the voting mechanism.
Experimental results demonstrate the superiority of our scheme beyond the
baselines.Comment: 9 pages, 2 figure
Load/displacement and energy absorption performances and improvements of structural members under tensile and compressive loading conditions.
The research programs detailed in this thesis focus on the load/displacement and energy absorption performances and improvements of structural members under tensile and compressive loading conditions. A theoretical model for the prediction of energy absorption capabilities of aluminum foam filled braided stainless steel tubes under tensile loading conditions has been developed based upon the unit cell concept. Comparisons between the energy absorption predictions of the analytical model and experimental observations were found to be in good agreement for assembly lengths of approximately 400 mm. Experimental investigations were also completed for energy absorbers which function under axial compressive loading conditions. The crush characteristics and energy absorption capacity of AA6061-T6 extrusions with centrally located through-hole discontinuities were investigated and analyzed. Three different types of geometrical discontinuities, namely, circular, slotted and elliptical holes were fabricated into AA6061-T6 extrusions which had a length of 200 mm, nominal side width of 38.1 mm and wall thickness of 3.15 mm. (Abstract shortened by UMI.)Dept. of Mechanical, Automotive, and Materials Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .C447. Source: Masters Abstracts International, Volume: 44-03, page: 1485. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005
Finding conserved patterns in biological sequences, networks and genomes
Biological patterns are widely used for identifying biologically interesting regions
within macromolecules, classifying biological objects, predicting functions and studying
evolution. Good pattern finding algorithms will help biologists to formulate and
validate hypotheses in an attempt to obtain important insights into the complex
mechanisms of living things.
In this dissertation, we aim to improve and develop algorithms for five biological
pattern finding problems. For the multiple sequence alignment problem, we propose
an alternative formulation in which a final alignment is obtained by preserving pairwise
alignments specified by edges of a given tree. In contrast with traditional NPhard
formulations, our preserving alignment formulation can be solved in polynomial
time without using a heuristic, while having very good accuracy.
For the path matching problem, we take advantage of the linearity of the query
path to reduce the problem to finding a longest weighted path in a directed acyclic
graph. We can find k paths with top scores in a network from the query path in
polynomial time. As many biological pathways are not linear, our graph matching
approach allows a non-linear graph query to be given. Our graph matching formulation
overcomes the common weakness of previous approaches that there is no
guarantee on the quality of the results.
For the gene cluster finding problem, we investigate a formulation based on constraining the overall size of a cluster and develop statistical significance estimates that
allow direct comparisons of clusters of different sizes. We explore both a restricted
version which requires that orthologous genes are strictly ordered within each cluster,
and the unrestricted problem that allows paralogous genes within a genome and clusters
that may not appear in every genome. We solve the first problem in polynomial
time and develop practical exact algorithms for the second one.
In the gene cluster querying problem, based on a querying strategy, we propose
an efficient approach for investigating clustering of related genes across multiple
genomes for a given gene cluster. By analyzing gene clustering in 400 bacterial
genomes, we show that our algorithm is efficient enough to study gene clusters across
hundreds of genomes
Predicting protein folding pathways at the mesoscopic level based on native interactions between secondary structure elements
<p>Abstract</p> <p>Background</p> <p>Since experimental determination of protein folding pathways remains difficult, computational techniques are often used to simulate protein folding. Most current techniques to predict protein folding pathways are computationally intensive and are suitable only for small proteins.</p> <p>Results</p> <p>By assuming that the native structure of a protein is known and representing each intermediate conformation as a collection of fully folded structures in which each of them contains a set of interacting secondary structure elements, we show that it is possible to significantly reduce the conformation space while still being able to predict the most energetically favorable folding pathway of large proteins with hundreds of residues at the mesoscopic level, including the pig muscle phosphoglycerate kinase with 416 residues. The model is detailed enough to distinguish between different folding pathways of structurally very similar proteins, including the streptococcal protein G and the peptostreptococcal protein L. The model is also able to recognize the differences between the folding pathways of protein G and its two structurally similar variants NuG1 and NuG2, which are even harder to distinguish. We show that this strategy can produce accurate predictions on many other proteins with experimentally determined intermediate folding states.</p> <p>Conclusion</p> <p>Our technique is efficient enough to predict folding pathways for both large and small proteins at the mesoscopic level. Such a strategy is often the only feasible choice for large proteins. A software program implementing this strategy (SSFold) is available at <url>http://faculty.cs.tamu.edu/shsze/ssfold</url>.</p
Automatic Side-Scan Sonar Image Enhancement in Curvelet Transform Domain
We propose a novel automatic side-scan sonar image enhancement algorithm based on curvelet transform. The proposed algorithm uses the curvelet transform to construct a multichannel enhancement structure based on human visual system (HVS) and adopts a new adaptive nonlinear mapping scheme to modify the curvelet transform coefficients in each channel independently and automatically. Firstly, the noisy and low-contrast sonar image is decomposed into a low frequency channel and a series of high frequency channels by using curvelet transform. Secondly, a new nonlinear mapping scheme, which coincides with the logarithmic nonlinear enhancement characteristic of the HVS perception, is designed without any parameter tuning to adjust the curvelet transform coefficients in each channel. Finally, the enhanced image can be reconstructed with the modified coefficients via inverse curvelet transform. The enhancement is achieved by amplifying subtle features, improving contrast, and eliminating noise simultaneously. Experiment results show that the proposed algorithm produces better enhanced results than state-of-the-art algorithms
Urbanisation and health in China.
China has seen the largest human migration in history, and the country's rapid urbanisation has important consequences for public health. A provincial analysis of its urbanisation trends shows shifting and accelerating rural-to-urban migration across the country and accompanying rapid increases in city size and population. The growing disease burden in urban areas attributable to nutrition and lifestyle choices is a major public health challenge, as are troubling disparities in health-care access, vaccination coverage, and accidents and injuries in China's rural-to-urban migrant population. Urban environmental quality, including air and water pollution, contributes to disease both in urban and in rural areas, and traffic-related accidents pose a major public health threat as the country becomes increasingly motorised. To address the health challenges and maximise the benefits that accompany this rapid urbanisation, innovative health policies focused on the needs of migrants and research that could close knowledge gaps on urban population exposures are needed
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