Moving vehicle detection for automatic traffic monitoring

2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Directional independent component analysis with tensor representation

Author name used in this publication: David ZhangRefereed conference paper2007-2008 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Block independent component analysis for face recognition

Author name used in this publication: David ZhangBiometrics Research Centre, Department of ComputingRefereed conference paper2007-2008 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks

Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic

Atmospheric emissions from the deepwater Horizon spill constrain air-water partitioning, hydrocarbon fate, and leak rate

The fate of deepwater releases of gas and oil mixtures is initially determined by solubility and volatility of individual hydrocarbon species; these attributes determine partitioning between air and water. Quantifying this partitioning is necessary to constrain simulations of gas and oil transport, to predict marine bioavailability of different fractions of the gas-oil mixture, and to develop a comprehensive picture of the fate of leaked hydrocarbons in the marine environment. Analysis of airborne atmospheric data shows massive amounts (∼258,000 kg/day) of hydrocarbons evaporating promptly from the Deepwater Horizon spill; these data collected during two research flights constrain air-water partitioning, thus bioavailability and fate, of the leaked fluid. This analysis quantifies the fraction of surfacing hydrocarbons that dissolves in the water column (∼33% by mass), the fraction that does not dissolve, and the fraction that evaporates promptly after surfacing (∼14% by mass). We do not quantify the leaked fraction lacking a surface expression; therefore, calculation of atmospheric mass fluxes provides a lower limit to the total hydrocarbon leak rate of 32,600 to 47,700 barrels of fluid per day, depending on reservoir fluid composition information. This study demonstrates a new approach for rapid-response airborne assessment of future oil spills. Copyright 2011 by the American Geophysical Union

Simultaneous non-negative matrix factorization for multiple large scale gene expression datasets in toxicology

Non-negative matrix factorization is a useful tool for reducing the dimension of large datasets. This work considers simultaneous non-negative matrix factorization of multiple sources of data. In particular, we perform the first study that involves more than two datasets. We discuss the algorithmic issues required to convert the approach into a practical computational tool and apply the technique to new gene expression data quantifying the molecular changes in four tissue types due to different dosages of an experimental panPPAR agonist in mouse. This study is of interest in toxicology because, whilst PPARs form potential therapeutic targets for diabetes, it is known that they can induce serious side-effects. Our results show that the practical simultaneous non-negative matrix factorization developed here can add value to the data analysis. In particular, we find that factorizing the data as a single object allows us to distinguish between the four tissue types, but does not correctly reproduce the known dosage level groups. Applying our new approach, which treats the four tissue types as providing distinct, but related, datasets, we find that the dosage level groups are respected. The new algorithm then provides separate gene list orderings that can be studied for each tissue type, and compared with the ordering arising from the single factorization. We find that many of our conclusions can be corroborated with known biological behaviour, and others offer new insights into the toxicological effects. Overall, the algorithm shows promise for early detection of toxicity in the drug discovery process

An isolate of human immunodeficiency virus type 1 originally classified as subtype I represents a complex mosaic comprising three different group M subtypes (A, G, and I)

Full-length reference clones and sequences are currently available for eight human immunodeficiency virus type 1 (HIV-1) group M subtypes (A through H), but none have been reported for subtypes I and J, which have only been identified in a few individuals. Phylogenetic information for subtype I, in particular, is limited since only about 400 bp of env gene sequences have been determined for just two epidemiologically linked viruses infecting a couple who were heterosexual intravenous drug users from Cyprus. To characterize subtype I in greater detail, we employed long-range PCR to clone a full-length provirus (94CY032.3) from an isolate obtained from one of the individuals originally reported to be infected with this subtype. Phylogenetic analysis of C2-V3 env gene sequences confirmed that 94CY032.3 was closely related to sequences previously classified as subtype I. However, analysis of the remainder of its genome revealed various regions in which 94CY032.3 was significantly clustered with either subtype A or subtype G. Only sequences located in vpr and nef, as well as the middle portions of pol and env, formed independent lineages roughly equidistant from all other known subtypes. Since these latter regions most likely have a common origin, we classify them all as subtype I. These results thus indicate that the originally reported prototypic subtype I isolate 94CY032 represents a triple recombinant (A/G/I) with at least 11 points of recombination crossover. We also screened HIV-1 recombinants with regions of uncertain subtype assignment for the presence of subtype I sequences. This analysis revealed that two of the earliest mosaics from Africa, Z321B (A/G/?) and MAL (A/D/?), contain short segments of sequence which clustered closely with the subtype I domains of 94CY032.3. Since Z321 was isolated in 1976, subtype I as well as subtypes A and G must have existed in Central Africa prior to that date... (D'après résumé d'auteur

Perfluorooctanesulfonate (PFOS)-induced Sertoli cell injury through a disruption of F-actin and microtubule organization is mediated by Akt1/2

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