Content Recognition and Context Modeling for Document Analysis and Retrieval

The nature and scope of available documents are changing significantly in many areas of document analysis and retrieval as complex, heterogeneous collections become accessible to virtually everyone via the web. The increasing level of diversity presents a great challenge for document image content categorization, indexing, and retrieval. Meanwhile, the processing of documents with unconstrained layouts and complex formatting often requires effective leveraging of broad contextual knowledge. In this dissertation, we first present a novel approach for document image content categorization, using a lexicon of shape features. Each lexical word corresponds to a scale and rotation invariant local shape feature that is generic enough to be detected repeatably and is segmentation free. A concise, structurally indexed shape lexicon is learned by clustering and partitioning feature types through graph cuts. Our idea finds successful application in several challenging tasks, including content recognition of diverse web images and language identification on documents composed of mixed machine printed text and handwriting. Second, we address two fundamental problems in signature-based document image retrieval. Facing continually increasing volumes of documents, detecting and recognizing unique, evidentiary visual entities (\eg, signatures and logos) provides a practical and reliable supplement to the OCR recognition of printed text. We propose a novel multi-scale framework to detect and segment signatures jointly from document images, based on the structural saliency under a signature production model. We formulate the problem of signature retrieval in the unconstrained setting of geometry-invariant deformable shape matching and demonstrate state-of-the-art performance in signature matching and verification. Third, we present a model-based approach for extracting relevant named entities from unstructured documents. In a wide range of applications that require structured information from diverse, unstructured document images, processing OCR text does not give satisfactory results due to the absence of linguistic context. Our approach enables learning of inference rules collectively based on contextual information from both page layout and text features. Finally, we demonstrate the importance of mining general web user behavior data for improving document ranking and other web search experience. The context of web user activities reveals their preferences and intents, and we emphasize the analysis of individual user sessions for creating aggregate models. We introduce a novel algorithm for estimating web page and web site importance, and discuss its theoretical foundation based on an intentional surfer model. We demonstrate that our approach significantly improves large-scale document retrieval performance

CELL-BASED AND BIOCHEMICAL SCREENS FOR SMALL-MOLECULE INHIBITORS OF DYNEIN AND OF HSP70

Dyneins are protein motor complexes that generate force towards the minus ends of microtubules, and cytoplasmic dynein plays a variety of important roles in cell. A small library of synthetic chemicals based on the nature product purealin was first examined for inhibition of cytoplasmic dynein heavy chain and cell growth. The compounds showed effective antiproliferative activity against a mouse leukemia cell line, but selective activities against a small panel of human carcinoma cell lines. Purealin and some of its analogues showed concentration dependent inhibitory effects against the microtubule-stimulated ATPase activity of both bovine cytoplasmic dynein heavy chain as well as the recombinant motor domain of human cytoplasmic dynein in an uncompetitive pattern, indicating that they do not compete with the binding of ATP. Purealin also weakly inhibited p53 nuclear accumulation after DNA damage. Treatment of cells with small interfering RNAs of cytoplasmic dynein heavy chain were also used as a positive control in this assay, although the lifetime of the protein turned out to be too long for the siRNA approach to be useful in a screening protocol.A strategy was built to screen for dynein inhibitors based on a GFP-GR nuclear translocation assay by using a mouse mammary adenocarcinoma cell line (3617.4) stably expressing the fluorescent protein. A small library of synthetic compounds was screened for inhibition of hormone-stimulated GFP-GR nuclear translocation. Several compounds was found to elicit the desired phenotype, and these compounds inhibited the ATPase activity of cytoplasmic dynein heavy chain motor domain without competing for the hydrolyzable ATP-binding site. Biochemical specificity tests showed that the compounds did not compete for GR binding nor inhibit the ATPase activities of Hsp70, Hsp90 or myosin. Libraries of compounds designed to be Hsp70-perturbing agents were also evaluated. Previous data showed that the Hsp70 chaperone class is induced in certain breast cancer cells and that antisense-mediated knockdown of Hsp70 triggers apoptosis, indicating that Hsp70s represent a new target for breast cancer therapy. A small molecule inhibitor of Hsp70 co-chaperone interaction (MAL3-101), as well as several analogues, showed antiproliferative activity against SK-BR3 breast cancer cells

Design of a novel α-shaped flue gas route flame incinerator for the treatment of municipal waste materials.

In order to improve the combustion characteristics of municipal waste materials and reduce excess pollutants generated during the incineration process, this study develops a novel waste incinerator with an α-shaped flue gas route. This has been achieved through the application of momentum vector synthesis theory in order to modify the secondary air structure in a conventional incinerator, resulting in enhanced combustion efficiency of the incinerator. Computational fluid dynamics (CFD)-based cold-state test results demonstrate that, with appropriate modifications to the design of the incinerator, the flue gas propagates through a longer α-shaped route rather than conventional L-shaped route. Hot state tests have been carried out on a full-scale 750 tons/day waste incinerator. Test rests show that the temperature of the flue gas increases by 138% under the front arch when secondary air supply is being incorporated into the design of the incinerator, resulting in better combustion of the municipal waste materials, lower emissions and higher thermal efficiency of the incinerator. The results obtained in this study confirm the rationality and feasibility of momentum flow rate method for better design of waste incinerators

Fluid-structure interaction modeling on a 3D ray-strengthened caudal fin

In this paper, we present a numerical model capable of solving the fluid-structure interaction problems involved in the dynamics of skeleton-reinforced fish fins. In this model, the fluid dynamics is simulated by solving the Navier-Stokes equations using a finite-volume method based on an overset, multi-block structured grid system. The bony rays embedded in the fin are modeled as nonlinear Euler-Bernoulli beams. To demonstrate the capability of this model, we numerically investigate the effect of various ray stiffness distributions on the deformation and propulsion performance of a 3D caudal fin. Our numerical results show that with specific ray stiffness distributions, certain caudal fin deformation patterns observed in real fish (e.g. the cupping deformation) can be reproduced through passive structural deformations. Among the four different stiffness distributions (uniform, cupping, W-shape and heterocercal) considered here, we find that the cupping distribution requires the least power expenditure. The uniform distribution, on the other hand, performs the best in terms of thrust generation and efficiency. The uniform stiffness distribution, per se, also leads to 'cupping' deformation patterns with relatively smaller phase differences between various rays. The present model paves the way for future work on dynamics of skeleton-reinforced membranes

Combustion characterization of hybrid methane-hydrogen gas in domestic swirl stoves.

Combustion of hybrid natural gas (methane) and hydrogen mixture in domestic swirl stoves has been characterized using hot-state experiments and numerical analysis. The detailed combustion mechanism of methane and hydrogen (GRI-Mech 3.0) has been simplified to obtain reduced number of chemical reactions involved (82% reduction). The novel simplified combustion mechanism developed has been used to obtain combustion characteristics of hybrid methane-hydrogen mixture. The difference between the calculations from the detailed and the simplified mechanisms has been found to be < 1%. A numerical model, based on the simplified combustion model, is developed, rigorously tested and validated against hot-state tests. The results depict that the maximum difference in combustion zone's average temperature is < 13%. The investigations have then been extended to hybrid methane-hydrogen mixtures with varying volume fraction of hydrogen. The results show that for a mixture containing 15% hydrogen, the release of CO due to combustion reduces by 25%, while the combustion zone's average temperature reduces by 6.7%. The numerical results and hot-state tests both confirm that the temperature remains stable when hybrid methane-hydrogen mixture is used in domestic swirl gas stoves, demonstrating its effectiveness in cooking processes

Theoretical and experimental investigations on the combustion characteristics of three components mixed municipal solid waste.

The combustion characteristics of Mixed Municipal Solid Waste (MMSW) play a vital role in dictating the efficiency of the incineration process. At present, few studies on combustion characteristics of three components MMSW and the establishment of corresponding comprehensive kinetic model of single component waste have been reported. In the present study, based on the law of mass action and Badzioch's relation, the mathematical expressions for describing the TG (Thermogravimetric) curves and the DT (Differential thermal) curves of single component MMSW are derived. A comprehensive kinetic model for the combustion characteristics of single component MMSW is developed and the appropriateness of the model is confirmed by the experimental results. The calculated TG curves closely agree with the experimental curves; the maximum deviation between the experimental and calculated curves is within 5%. Based on the principle of mixture experiments, the co-combustion characteristics of MMSW composed of food bag, disposable chopstick and cotton cloth are studied by using TGA (Thermogravimetric Analysis) and DTA (Differential Thermal Analysis). It has been found that the activation energy of three components MMSW is lower than that of single component. Finally, based on multiple regression analysis for the design of mixture experiments and the corresponding data, an empirical formula for calculating activation energy of three components MMSW is obtained. The experimental and calculated values match closely; the maximum deviations between them is within 7%. The empirical formula provides a robust way to calculate activation energy of three components MMSW