137 research outputs found

    Differentiable Visual Computing: Challenges and Opportunities

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    Classical algorithms typically contain domain-specific insights. This makes them often more robust, interpretable, and efficient. On the other hand, deep-learning models must learn domain-specific insight from scratch from a large amount of data using gradient-based optimization techniques. To have the best of both worlds, we should make classical visual computing algorithms differentiable to enable gradient-based optimization. Computing derivatives of classical visual computing algorithms is challenging: there can be discontinuities, and the computation pattern is often irregular compared to high-arithmetic intensity neural networks. In this article, we discuss the benefits and challenges of combining classical visual computing algorithms and modern data-driven methods, with particular emphasis to my thesis, which took one of the first steps toward addressing these challenges

    Abstracts of National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020

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    This book presents the abstracts of the papers presented to the Online National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020 (RDMPMC-2020) held on 26th and 27th August 2020 organized by the Department of Metallurgical and Materials Science in Association with the Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, India. Conference Title: National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020Conference Acronym: RDMPMC-2020Conference Date: 26–27 August 2020Conference Location: Online (Virtual Mode)Conference Organizer: Department of Metallurgical and Materials Engineering, National Institute of Technology JamshedpurCo-organizer: Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, IndiaConference Sponsor: TEQIP-

    Robust and fully automated segmentation of mandible from CT scans

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    Mandible bone segmentation from computed tomography (CT) scans is challenging due to mandible's structural irregularities, complex shape patterns, and lack of contrast in joints. Furthermore, connections of teeth to mandible and mandible to remaining parts of the skull make it extremely difficult to identify mandible boundary automatically. This study addresses these challenges by proposing a novel framework where we define the segmentation as two complementary tasks: recognition and delineation. For recognition, we use random forest regression to localize mandible in 3D. For delineation, we propose to use 3D gradient-based fuzzy connectedness (FC) image segmentation algorithm, operating on the recognized mandible sub-volume. Despite heavy CT artifacts and dental fillings, consisting half of the CT image data in our experiments, we have achieved highly accurate detection and delineation results. Specifically, detection accuracy more than 96% (measured by union of intersection (UoI)), the delineation accuracy of 91% (measured by dice similarity coefficient), and less than 1 mm in shape mismatch (Hausdorff Distance) were found.Comment: 4 pages, 5 figures, IEEE International Symposium on Biomedical Imaging (ISBI) 201

    Mycobacterium tuberculosis EsxL inhibits MHC-II expression by promoting hypermethylation in class-II transactivator loci in macrophages

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    Mycobacterium tuberculosis (Mtb) is known to modulate the host immune responses to facilitate its persistence inside the host cells. One of the key mechanisms includes repression of class-II transactivator (CIITA) and MHC-II expression in infected macrophages. However, the precise mechanism of CIITA and MHC-II down-regulation is not well studied. Mtb 6-kDa early secretory antigenic target (ESAT-6) is a known potent virulence and antigenic determinant. Mtb genome encodes 23 such ESAT-6 family proteins. We herein report that Mtb and M. bovis-BCG infection down-regulated the expression of CIITA/MHC-II by inducing hypermethylation in histone H3 Lysine 9 (H3K9me2/3). Further, we show that Mtb ESAT-6 family protein EsxL, encoded by Rv1198, is responsible for the down-regulation of CIITA/MHC-II by inducing H3K9me2/3. We further report that Mtb esxL induced the expression of nitric oxide synthetase (iNOS), NO production and p38-MAPK pathway, which in turn was responsible for the increased H3K9me2/3 in CIITA via up-regulation of euchromatic histone-lysine N-methyltransferase 2 (G9a). In contrast, inhibition of iNOS, p38-MAPK and G9a abrogated H3K9me2/3 resulting in increased CIITA expression. Chromatin immune precipitation assay confirmed that hypermethylation at the promoter IV (pIV) region of CIITA is mainly responsible for the CIITA down regulation and subsequently antigen presentation. We found that co-culture of macrophages infected with esxL expressing M. smegmatis and mouse spleenocytes led to down-regulation of IL-2, a key cytokine involved in T-cell proliferation. In summary, we show that Mtb esxL inhibits antigen presentation by enhancing H3K9me2/3 on CIITA promoter thereby repressing its expression through NO and p38-MAPK activation

    A theorem proving approach for automatically synthesizing visualizations of flow cytometry data

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    Abstract Background Polychromatic flow cytometry is a popular technique that has wide usage in the medical sciences, especially for studying phenotypic properties of cells. The high-dimensionality of data generated by flow cytometry usually makes it difficult to visualize. The naive solution of simply plotting two-dimensional graphs for every combination of observables becomes impractical as the number of dimensions increases. A natural solution is to project the data from the original high dimensional space to a lower dimensional space while approximately preserving the overall relationship between the data points. The expert can then easily visualize and analyze this low-dimensional embedding of the original dataset. Results This paper describes a new method, SANJAY, for visualizing high-dimensional flow cytometry datasets. This technique uses a decision procedure to automatically synthesize two-dimensional and three-dimensional projections of the original high-dimensional data while trying to minimize distortion. We compare SANJAY to the popular multidimensional scaling (MDS) approach for visualization of small data sets drawn from a representative set of benchmarks, and our experiments show that SANJAY produces distortions that are 1.44 to 4.15 times smaller than those caused due to MDS. Our experimental results show that SANJAY also outperforms the Random Projections technique in terms of the distortions in the projections. Conclusions We describe a new algorithmic technique that uses a symbolic decision procedure to automatically synthesize low-dimensional projections of flow cytometry data that typically have a high number of dimensions. Our algorithm is the first application, to our knowledge, of using automated theorem proving for automatically generating highly-accurate, low-dimensional visualizations of high-dimensional data

    Sanjay: Automatically Synthesizing Visualizations Of Flow Cytometry Data Using Decision Procedures

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    Polychromatic flow cytometry is a widely used technique for gathering and analyzing cellular data. The data generated is high-dimensional, and therefore notoriously difficult to visualize by a human expert. The traditional method of plotting every pair of observables of the original high-dimensional data leads to a combinatorial explosion in the number of visualizations. The usual solution is to project the data into a lower-dimensional space while approximately preserving key properties and relationships among data points. The lower dimensional data can then be easily analyzed with the help of specialized data visualization software

    Macro 64-Regions For Uniform Grids On Gpu

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    Uniform grids are a spatial subdivision acceleration structure well suited for ray tracing. They are known for their fast build times and ease of use, but suffer from slow traversals in the presence of empty space. To address this issue, we present macro 64-regions, a new GPU based approach for finding and storing empty volumes in an underlying uniform grid. This allows for fast traversals through regions that do not contain scene geometry. Further, unlike previous solutions to this problem, we do not store a hierarchical structure and therefore the traversal steps are simplified. Because macro 64-regions are dependent on an underlying grid, we also introduce an improvement in the grid construction process. Our method does not rely on sorting as previous methods do, but instead uses atomic operators to manage bookkeeping during the build. Using our proposed methods, we show a substantial improvement in build time, trace time, as well as an improvement in the consistency of rendering times for randomly generated views. © 2014 Springer-Verlag Berlin Heidelberg
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