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"abstract": "Raman Microscopy and imaging can be used for a wide range of applications, such as the chemical identification of particles and smallest contaminations, the analysis of the distribution of a multitude of different components in a complex mixture. In recent years, the applications of Raman Microscopy and imaging in the pharmaceutical world, including capsules, packaging, coatings, sample homogeneity analysis, chemical composition, and combinatorial chemistry, has increased and is projected to continue increasing. After an introduction to the basic principles of Raman Microscopy and imaging, the developments of these methods and the application of Raman Microscopy and imaging in Process Analytical Technologies (PAT), Active Pharmaceutical Ingredient determination (API), and drug discovery and development will be reviewed.",
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"content": "Raman Microscopy and imaging can be used for a wide range of applications, such as the chemical identification of particles and smallest contaminations, the analysis of the distribution of a multitude of different components in a complex mixture. In recent years, the applications of Raman Microscopy and imaging in the pharmaceutical world, including capsules, packaging, coatings, sample homogeneity analysis, chemical composition, and combinatorial chemistry, has increased and is projected to continue increasing. After an introduction to the basic principles of Raman Microscopy and imaging, the developments of these methods and the application of Raman Microscopy and imaging in Process Analytical Technologies (PAT), Active Pharmaceutical Ingredient determination (API), and drug discovery and development will be reviewed.",
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"fullName": "Zhen Tian",
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{
"proceeding": {
"id": "12OmNzVGcJp",
"title": "2011 IEEE International Conference on Bioinformatics and Biomedicine",
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"id": "12OmNz4BduY",
"doi": "10.1109/BIBM.2011.37",
"title": "Computer-Based Image Analysis of Liver Steatosis with Large-Scale Microscopy Imagery and Correlation with Magnetic Resonance Imaging Lipid Analysis",
"normalizedTitle": "Computer-Based Image Analysis of Liver Steatosis with Large-Scale Microscopy Imagery and Correlation with Magnetic Resonance Imaging Lipid Analysis",
"abstract": "Most pathology analyses and measurements are prevalently carried out by trained reviewers in both clinical and research settings. Therefore, the resulting outputs are inexorably biased by interpreters and degraded with poor reproducibility. In this paper, we propose a computerized image analysis paradigm enabling quantitative characterizations of steatosis areas in microscopy images of pediatric liver biopsies. With the same set of patients, we also acquired the lipid measurements from magnetic resonance imaging data analysis for correlation investigation. Our preliminary results suggest a high correlation between the steatosis areas quantized with microscopy images and the lipid percentages calculated from radiology imaging data. Additionally, we compared the per formance of the proposed analysis method with those of three certified pathologists and a popular commercial algorithm. The results suggest the superiority of our method to both human reviewers and the commercial method in terms of the steatosis lipid correlation strength. This demonstrates that the developed method is promising for generating quantitative and reliable analysis results to better support further liver disease study.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Most pathology analyses and measurements are prevalently carried out by trained reviewers in both clinical and research settings. Therefore, the resulting outputs are inexorably biased by interpreters and degraded with poor reproducibility. In this paper, we propose a computerized image analysis paradigm enabling quantitative characterizations of steatosis areas in microscopy images of pediatric liver biopsies. With the same set of patients, we also acquired the lipid measurements from magnetic resonance imaging data analysis for correlation investigation. Our preliminary results suggest a high correlation between the steatosis areas quantized with microscopy images and the lipid percentages calculated from radiology imaging data. Additionally, we compared the per formance of the proposed analysis method with those of three certified pathologists and a popular commercial algorithm. The results suggest the superiority of our method to both human reviewers and the commercial method in terms of the steatosis lipid correlation strength. This demonstrates that the developed method is promising for generating quantitative and reliable analysis results to better support further liver disease study.",
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"fno": "06120462",
"keywords": [
"Biomedical MRI",
"Data Analysis",
"Diseases",
"Liver",
"Medical Image Processing",
"Paediatrics",
"Computer Based Image Analysis",
"Liver Steatosis",
"Large Scale Microscopy Imagery",
"Magnetic Resonance Imaging Lipid Data Analysis",
"Pediatric Liver Biopsies",
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"Radiology",
"Image Segmentation",
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"fullName": "Jun Kong",
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"fullName": "Michael J. Lee",
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"fullName": "Joel H. Saltz",
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"abstract": "Summary form only given. Pathologists and cancer biologists rely on tissue and cellular analysis to study cancer expression, genetic profiles, and cellular morphology to understand the underlying basis for a disease and to grade the level of disease progression. Conventional analysis of tissue histology and sample cytology includes the steps of examination of the stained tissue or cell smear under a microscope, scoring the expression relative to the most highly expressing (densely stained) area on a predefined scale for normal, cancer, stromal regions based on the morphology of the tissue, estimating the percentage area of cancer tissue relative of normal and stroma, and multiplying the score by the percentage area of cancer region and converting to another predefined scale for statistical analyses. Most of this analysis is done manually or with limited tools to aid the scoring process. Over the last 5 years, automated and semi- automated microscope slide scanners have become available in the marketplace. These scanners rely on sophisticated microscopes and allow for the digitization of the entire sample at varying magnifications. This has led to the emergence of digital pathology and a growing amount of image data. Each sample digitized is typically of the order of 2.7 GB to 10 GB in size depending on the magnification of the digitizing system with an image size of 30,000 ? 30,000 pixels or larger. Further, current software and methods for automated scoring of tissue is very limited. This has led to an increased interest in identifying novel solutions to automated histology and cytology analysis. In order to achieve high computational accuracy with reasonable turnaround times, novel approaches from the data and resource management perspective are also required to address handling of image sizes outlined above. Two developments in computer industry make the current generation of scientists more likely to solve the performance challenges associated with the large image sizes. First, the emergence of multi-core processors allow for parallel processing within a single PC using of-the-shelf components. It is virtually impossible today to buy a PC that does not have at least two computational cores. Furthermore, all major manufacturers have announced processors containing four, eight and even sixteen cores for the next two years, providing an omnipresent potential for parallel processing on every desktop PC. Second, as of today, there is no reasonable size medical or research institution in the US not having a PC cluster in their computing arsenal. The next generation of digital pathology systems will require the ability to share and process data across disparate institutes. Hence, PC clusters would allow for parallelism by analyzing multiple images simultaneously. They would also offer an opportunity to speed up the analysis of a single image. However, exploiting the computational power of multi-core architectures and PC clusters requires modifications to existing, sequential image analysis codes and cautious evaluation of alternative and novel algorithms with respect to their potential for parallelism. This tutorial will provide an overview of the application domain and present an overview of the challenges. Specifically, opportunities for novel image analysis and pattern recognition algorithms that can leverage frameworks for shared and distributed parallel computing will be discussed along with examples from ongoing research in the labs of the instructors. Topics to be covered will include: 1) Overview of digital pathology 2) Applications and Challenges (Immunohistochemistry, H&E analysis, FISH, Alternate Image Modalities - Spectral Imaging, Histology, Cytology) 3) Architectural Developments (Multi-core, Networking, GPU processing, Storage) 4) Image Analysis Pipelines and Algorithms (Image Segmentation, Nuclei Detection, Morphometric Features, Karyometric Features, ...) 5) Performance implications (data management, exploiting multi-core processors, exploiting PC cluster) 6) Emerging trends and applications.",
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"content": "Summary form only given. Pathologists and cancer biologists rely on tissue and cellular analysis to study cancer expression, genetic profiles, and cellular morphology to understand the underlying basis for a disease and to grade the level of disease progression. Conventional analysis of tissue histology and sample cytology includes the steps of examination of the stained tissue or cell smear under a microscope, scoring the expression relative to the most highly expressing (densely stained) area on a predefined scale for normal, cancer, stromal regions based on the morphology of the tissue, estimating the percentage area of cancer tissue relative of normal and stroma, and multiplying the score by the percentage area of cancer region and converting to another predefined scale for statistical analyses. Most of this analysis is done manually or with limited tools to aid the scoring process. Over the last 5 years, automated and semi- automated microscope slide scanners have become available in the marketplace. These scanners rely on sophisticated microscopes and allow for the digitization of the entire sample at varying magnifications. This has led to the emergence of digital pathology and a growing amount of image data. Each sample digitized is typically of the order of 2.7 GB to 10 GB in size depending on the magnification of the digitizing system with an image size of 30,000 ? 30,000 pixels or larger. Further, current software and methods for automated scoring of tissue is very limited. This has led to an increased interest in identifying novel solutions to automated histology and cytology analysis. In order to achieve high computational accuracy with reasonable turnaround times, novel approaches from the data and resource management perspective are also required to address handling of image sizes outlined above. Two developments in computer industry make the current generation of scientists more likely to solve the performance challenges associated with the large image sizes. First, the emergence of multi-core processors allow for parallel processing within a single PC using of-the-shelf components. It is virtually impossible today to buy a PC that does not have at least two computational cores. Furthermore, all major manufacturers have announced processors containing four, eight and even sixteen cores for the next two years, providing an omnipresent potential for parallel processing on every desktop PC. Second, as of today, there is no reasonable size medical or research institution in the US not having a PC cluster in their computing arsenal. The next generation of digital pathology systems will require the ability to share and process data across disparate institutes. Hence, PC clusters would allow for parallelism by analyzing multiple images simultaneously. They would also offer an opportunity to speed up the analysis of a single image. However, exploiting the computational power of multi-core architectures and PC clusters requires modifications to existing, sequential image analysis codes and cautious evaluation of alternative and novel algorithms with respect to their potential for parallelism. This tutorial will provide an overview of the application domain and present an overview of the challenges. Specifically, opportunities for novel image analysis and pattern recognition algorithms that can leverage frameworks for shared and distributed parallel computing will be discussed along with examples from ongoing research in the labs of the instructors. Topics to be covered will include: 1) Overview of digital pathology 2) Applications and Challenges (Immunohistochemistry, H&E analysis, FISH, Alternate Image Modalities - Spectral Imaging, Histology, Cytology) 3) Architectural Developments (Multi-core, Networking, GPU processing, Storage) 4) Image Analysis Pipelines and Algorithms (Image Segmentation, Nuclei Detection, Morphometric Features, Karyometric Features, ...) 5) Performance implications (data management, exploiting multi-core processors, exploiting PC cluster) 6) Emerging trends and applications.",
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"normalizedAbstract": "Summary form only given. Pathologists and cancer biologists rely on tissue and cellular analysis to study cancer expression, genetic profiles, and cellular morphology to understand the underlying basis for a disease and to grade the level of disease progression. Conventional analysis of tissue histology and sample cytology includes the steps of examination of the stained tissue or cell smear under a microscope, scoring the expression relative to the most highly expressing (densely stained) area on a predefined scale for normal, cancer, stromal regions based on the morphology of the tissue, estimating the percentage area of cancer tissue relative of normal and stroma, and multiplying the score by the percentage area of cancer region and converting to another predefined scale for statistical analyses. Most of this analysis is done manually or with limited tools to aid the scoring process. Over the last 5 years, automated and semi- automated microscope slide scanners have become available in the marketplace. These scanners rely on sophisticated microscopes and allow for the digitization of the entire sample at varying magnifications. This has led to the emergence of digital pathology and a growing amount of image data. Each sample digitized is typically of the order of 2.7 GB to 10 GB in size depending on the magnification of the digitizing system with an image size of 30,000 ? 30,000 pixels or larger. Further, current software and methods for automated scoring of tissue is very limited. This has led to an increased interest in identifying novel solutions to automated histology and cytology analysis. In order to achieve high computational accuracy with reasonable turnaround times, novel approaches from the data and resource management perspective are also required to address handling of image sizes outlined above. Two developments in computer industry make the current generation of scientists more likely to solve the performance challenges associated with the large image sizes. First, the emergence of multi-core processors allow for parallel processing within a single PC using of-the-shelf components. It is virtually impossible today to buy a PC that does not have at least two computational cores. Furthermore, all major manufacturers have announced processors containing four, eight and even sixteen cores for the next two years, providing an omnipresent potential for parallel processing on every desktop PC. Second, as of today, there is no reasonable size medical or research institution in the US not having a PC cluster in their computing arsenal. The next generation of digital pathology systems will require the ability to share and process data across disparate institutes. Hence, PC clusters would allow for parallelism by analyzing multiple images simultaneously. They would also offer an opportunity to speed up the analysis of a single image. However, exploiting the computational power of multi-core architectures and PC clusters requires modifications to existing, sequential image analysis codes and cautious evaluation of alternative and novel algorithms with respect to their potential for parallelism. This tutorial will provide an overview of the application domain and present an overview of the challenges. Specifically, opportunities for novel image analysis and pattern recognition algorithms that can leverage frameworks for shared and distributed parallel computing will be discussed along with examples from ongoing research in the labs of the instructors. Topics to be covered will include: 1) Overview of digital pathology 2) Applications and Challenges (Immunohistochemistry, H&E analysis, FISH, Alternate Image Modalities - Spectral Imaging, Histology, Cytology) 3) Architectural Developments (Multi-core, Networking, GPU processing, Storage) 4) Image Analysis Pipelines and Algorithms (Image Segmentation, Nuclei Detection, Morphometric Features, Karyometric Features, ...) 5) Performance implications (data management, exploiting multi-core processors, exploiting PC cluster) 6) Emerging trends and applications.",
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"abstract": "Current analysis of tumor proliferation, the most salient breast cancer prognostic biomarker, is limited to subjective mitosis counting by pathologists in localized regions of tissue images. This study presents the first data-driven integrative approach to characterize the severity of tumor growth and spread on a categorical and molecular level, utilizing multiple biologically salient deep learning classifiers to develop a comprehensive prognostic model. Our approach achieves pathologist-level performance on three-class categorical tumor severity prediction. It additionally pioneers prediction of molecular expression data from a tissue image, obtaining a Spearman's rank correlation coefficient of 0.60 with ex vivo mean calculated RNA expression. Furthermore, our framework is applied to identify over two hundred unprecedented biomarkers critical to the accurate assessment of tumor proliferation, validating our proposed integrative pipeline as the first to holistically and objectively analyze histopathological images.",
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"affiliation": "Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong",
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"abstract": "From the simple measurement of tissue attributes in pathology workflow to designing an explainable diagnostic/prognostic AI tool, access to accurate semantic segmentation of tissue regions in histology images is a prerequisite. However, delineating different tissue regions manually is a laborious, time-consuming and costly task that requires expert knowledge. On the other hand, the state-of-the-art automatic deep learning models for semantic segmentation require lots of annotated training data and there are only a limited number of tissue region annotated images publicly available. To obviate this issue in computational pathology projects and collect large-scale region annotations efficiently, we propose an efficient interactive segmentation network that requires minimum input from the user to accurately annotate different tissue types in the histology image. The user is only required to draw a simple squiggle inside each region of interest so it will be used as the guiding signal for the model. To deal with the complex appearance and amorph geometry of different tissue regions we introduce several automatic and minimalistic guiding signal generation techniques that help the model to become robust against the variation in the user input. By experimenting on a dataset of breast cancer images, we show that not only does our proposed method speed up the interactive annotation process, it can also outperform the existing automatic and interactive region segmentation models.",
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"abstract": "This research designs and implements the Radial Basis Function (RBF) spatiotemporal interpolation method to assess the trend of daily PM2.5 concentration for the contiguous United States over the year of 2009, at both the census block group level and county level. This research also compares the performance of the RBF spatiotemporal interpolation with the Inverse Distance Weighting (IDW) spatiotemporal interpolation. Traditionally, when handling spatiotemporal interpolation, researchers tend to treat space and time separately and reduce the spatiotemporal interpolation problems to a sequence of snapshots of spatial interpolations. In this paper, PM2.5 data interpolation is conducted in the continuous space-time domain by integrating space and time simultaneously under the assumption that spatial and temporal dimensions are equally important when interpolating a continuous changing phenomenon in the space-time domain. The RBF-based spatiotemporal interpolation methods are evaluated by leave-one-out cross validation. More importantly, this study explores computational issues (computer processing speed) faced during implementation of spatiotemporal interpolation for huge data sets. Parallel programming techniques and an advanced data structure named k-d tree are adapted in this paper to address the computational challenges.",
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"abstract": "Event cameras are bio-inspired sensors that respond to brightness changes asynchronously and output in the form of event streams instead of frame-based images. They own outstanding advantages compared with traditional cameras: higher temporal resolution, higher dynamic range, and lower power consumption. However, the spatial resolution of existing event cameras is insufficient and challenging to be enhanced at the hardware level while maintaining the asynchronous philosophy of circuit design. Therefore, it is imperative to explore the algorithm of event stream super-resolution, which is a non-trivial task due to the sparsity and strong spatio-temporal correlation of the events from an event camera. In this paper, we propose an end-to-end framework based on spiking neural network for event stream super-resolution, which can generate high-resolution (HR) event stream from the input low-resolution (LR) event stream. A spatiotemporal constraint learning mechanism is proposed to learn the spatial and temporal distributions of the event stream simultaneously. We validate our method on four large-scale datasets and the results show that our method achieves state-of-the-art performance. The satisfying results on two downstream applications, i.e. object classification and image reconstruction, further demonstrate the usability of our method. To prove the application potential of our method, we deploy it on a mobile platform. The high-quality HR event stream generated by our real-time system demonstrates the effectiveness and efficiency of our method.",
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"content": "Event cameras are bio-inspired sensors that respond to brightness changes asynchronously and output in the form of event streams instead of frame-based images. They own outstanding advantages compared with traditional cameras: higher temporal resolution, higher dynamic range, and lower power consumption. However, the spatial resolution of existing event cameras is insufficient and challenging to be enhanced at the hardware level while maintaining the asynchronous philosophy of circuit design. Therefore, it is imperative to explore the algorithm of event stream super-resolution, which is a non-trivial task due to the sparsity and strong spatio-temporal correlation of the events from an event camera. In this paper, we propose an end-to-end framework based on spiking neural network for event stream super-resolution, which can generate high-resolution (HR) event stream from the input low-resolution (LR) event stream. A spatiotemporal constraint learning mechanism is proposed to learn the spatial and temporal distributions of the event stream simultaneously. We validate our method on four large-scale datasets and the results show that our method achieves state-of-the-art performance. The satisfying results on two downstream applications, i.e. object classification and image reconstruction, further demonstrate the usability of our method. To prove the application potential of our method, we deploy it on a mobile platform. The high-quality HR event stream generated by our real-time system demonstrates the effectiveness and efficiency of our method.",
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"title": "Video Super Resolution Based on Motion Compensation",
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"abstract": "An image super-resolution method and device provided by acquiring the captured video of the power equipment. The method according to the time sequence of the image frames in the video of the power equipment, extracts the appearance image of the power equipment, selects the reference image, obtains the target image, and obtains the motion vector of any pixel moving from the position coordinates in the target image to the position coordinates in the reference image. The reference images are input into the spatiotemporal sub-pixel convolution network, and then the two-dimensional super-resolution image is output, which improves the image clarity, and can image the moving power equipment with high resolution. In this paper We use time fusion strategy in the model of video SR. Our model constructs a motion compensation mechanism based on spatial transformation network. This mechanism can be trained jointly for video SR. The results obtained by our method are proved to be superior in peak signal to noise ratio (PSNR).",
"abstracts": [
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"content": "An image super-resolution method and device provided by acquiring the captured video of the power equipment. The method according to the time sequence of the image frames in the video of the power equipment, extracts the appearance image of the power equipment, selects the reference image, obtains the target image, and obtains the motion vector of any pixel moving from the position coordinates in the target image to the position coordinates in the reference image. The reference images are input into the spatiotemporal sub-pixel convolution network, and then the two-dimensional super-resolution image is output, which improves the image clarity, and can image the moving power equipment with high resolution. In this paper We use time fusion strategy in the model of video SR. Our model constructs a motion compensation mechanism based on spatial transformation network. This mechanism can be trained jointly for video SR. The results obtained by our method are proved to be superior in peak signal to noise ratio (PSNR).",
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"affiliation": "University of Chinese Academy of Sciences,Beijing,China",
"fullName": "Zheng Chang",
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"abstract": "Visual data upsampling has been an important research topic for improving the perceptual quality and benefiting various computer vision applications. In recent years, we have witnessed remarkable progresses brought by the re-naissance of deep learning techniques for video or image super-resolution. However, most existing methods focus on advancing super-resolution at either spatial or temporal direction, i.e, to increase the spatial resolution or the video frame rate. In this paper, we instead turn to discuss both directions jointly and tackle the spatiotemporal upsampling problem. Our method is based on an important observation that: even the direct cascade of prior research in spatial and temporal super-resolution can achieve the spatiotemporal upsampling, changing orders for combining them would lead to results with a complementary property. Thus, we propose a dual-stream fusion network to adaptively fuse the intermediate results produced by two spatiotemporal up-sampling streams, where the first stream applies the spatial super-resolution followed by the temporal super-resolution, while the second one is with the reverse order of cascade. Extensive experiments verify the efficacy of the proposed method against several baselines. Moreover, we investigate various spatial and temporal upsampling methods as the basis in our two-stream model and demonstrate the flexibility with wide applicability of the proposed framework.",
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"title": "3D visualization of unsteady 2D airplane wake vortices",
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"abstract": "Air flowing around the wing tips of an airplane forms horizontal tornado-like vortices that can be dangerous to following aircraft. The dynamics of such vortices, including ground and atmospheric effects, can be predicted by numerical simulation, allowing the safety and capacity of airports to be improved. We introduce three-dimensional techniques for visualizing time-dependent, two-dimensional wake vortex computations, and the hazard strength of such vortices near the ground. We describe a vortex core tracing algorithm and a local tiling method to visualize the vortex evolution. The tiling method converts time-dependent, two-dimensional vortex cores into three-dimensional vortex tubes. Finally, a novel approach is used to calculate the induced rolling moment on the following airplane at each grid point within a region near the vortex tubes and thus allows three-dimensional visualization of the hazard strength of the vortices.<>",
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"content": "Air flowing around the wing tips of an airplane forms horizontal tornado-like vortices that can be dangerous to following aircraft. The dynamics of such vortices, including ground and atmospheric effects, can be predicted by numerical simulation, allowing the safety and capacity of airports to be improved. We introduce three-dimensional techniques for visualizing time-dependent, two-dimensional wake vortex computations, and the hazard strength of such vortices near the ground. We describe a vortex core tracing algorithm and a local tiling method to visualize the vortex evolution. The tiling method converts time-dependent, two-dimensional vortex cores into three-dimensional vortex tubes. Finally, a novel approach is used to calculate the induced rolling moment on the following airplane at each grid point within a region near the vortex tubes and thus allows three-dimensional visualization of the hazard strength of the vortices.<>",
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"normalizedAbstract": "Air flowing around the wing tips of an airplane forms horizontal tornado-like vortices that can be dangerous to following aircraft. The dynamics of such vortices, including ground and atmospheric effects, can be predicted by numerical simulation, allowing the safety and capacity of airports to be improved. We introduce three-dimensional techniques for visualizing time-dependent, two-dimensional wake vortex computations, and the hazard strength of such vortices near the ground. We describe a vortex core tracing algorithm and a local tiling method to visualize the vortex evolution. The tiling method converts time-dependent, two-dimensional vortex cores into three-dimensional vortex tubes. Finally, a novel approach is used to calculate the induced rolling moment on the following airplane at each grid point within a region near the vortex tubes and thus allows three-dimensional visualization of the hazard strength of the vortices.",
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"abstract": "Edge bundling techniques provide a visual simplification of cluttered graph drawings or trail sets. While many bundling techniques exist, only few recent ones can handle large datasets and also allow selective bundling based on edge attributes. We present a new technique that improves on both above points, in terms of increasing both the scalability and computational speed of bundling, while keeping the quality of the results on par with state-of-the-art techniques. For this, we shift the bundling process from the image space to the spectral (frequency) space, thereby increasing computational speed. We address scalability by proposing a data streaming process that allows bundling of extremely large datasets with limited GPU memory. We demonstrate our technique on several real-world datasets and by comparing it with state-of-the-art bundling methods.",
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"abstract": "Graph pattern matching is a hot spot in the big data era, which is to find answer graphs matching a given query graph in a data graph of graph databases. “Matching” means two graphs satisfy some relation, such as isomorphism, simulation, bisimulation, etc. Since there are seldom algorithms for the subgraph bisimulation, our work commits to solve the graph pattern matching problem involving bisimulation relations through the model checking technology. We characterize query graphs by modal formulas. By model checking the formulas in the data graphs, the answer graphs bisimilar to the query graphs can be discovered. We add <sub>*</sub> to basic modal logic language resulting in ML + <sub>*</sub> language, and add r* to form ML + <sub>*</sub> formulas. Then a theorem which states that ML + <sub>*</sub> formulas characterize finite directed graphs modulo bisimulation is put forward. Furthermore, we list steps to find answer graphs bisimilar to a query graph.",
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"abstract": "Due to the complexity of graph query languages, the need for visual query interfaces that can reduce the burden of query formulation is fundamental to the spreading of graph data management tools to a wider community. Despite the significant progress towards building such query interfaces to simplify visual subgraph query formulation task, construction of current generation visual interfaces is not data-driven. That is, it does not exploit the underlying data graphs to automatically generate the contents of various panels in the interface. Such data-driven construction has several benefits such as superior support for subgraph query formulation and portability of the interface across different graph databases. In this demonstration, we present a novel data-driven visual subgraph query interface construction engine called DaVinci. Specifically, it automatically generates from the underlying database two key components of the visual interface to aid subgraph query formulation, namely canned patterns and node labels.",
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"proceeding": {
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"title": "Multimedia and Ubiquitous Engineering, International Conference on",
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"doi": "10.1109/MUE.2009.67",
"title": "A Directed Labeled Graph Frequent Pattern Mining Algorithm Based on Minimum Code",
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"abstract": "Most of existing frequent subgraph mining algorithms are used to deal with undirected unlabeled marked graph. A few of them aim at directed graph or labeled graph. But in the real world ,a lot of connections have direction and label,so directed labeled graph mining is more meaningful. Now we are analyzing a financial network which can be modeled by a directed weighted graph. We are interested in the patterns which are frequent.The graph pattern means a uniform expression of graphs which has different marked nodes but same structure. In our application we consider direction and weight as part of the pattern. It’s different from subgraph because subgraph mining consider the labels of nodes. This paper proposes a new algorithm mSpan for directed labeled graph frequent pattern mining. Based on FP-growth, the algorithm gets a minimum edge code and an abstract node code sequence to indentify a directed graph pattern uniquely through minimum extension. It can solve the graph pattern isomorphic problem and the redundant extension problem. The experiment shows that mSpan can mine all frequent directed, labeled graph patterns.",
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"abstract": "We present a method for spotting a subgraph in a graph repository. Subgraph spotting is a very interesting research problem for various application domains where the use of a relational data structure is mandatory. Our proposed method accomplishes subgraph spotting through graph embedding. We achieve automatic indexation of a graph repository during off-line learning phase, where we (i) break the graphs into 2-node sub graphs (a.k.a. cliques of order 2), which are primitive building-blocks of a graph, (ii) embed the 2-node sub graphs into feature vectors by employing our recently proposed explicit graph embedding technique, (iii) cluster the feature vectors in classes by employing a classic agglomerative clustering technique, (iv) build an index for the graph repository and (v) learn a Bayesian network classifier. The subgraph spotting is achieved during the on-line querying phase, where we (i) break the query graph into 2-node sub graphs, (ii) embed them into feature vectors, (iii) employ the Bayesian network classifier for classifying the query 2-node sub graphs and (iv) retrieve the respective graphs by looking-up in the index of the graph repository. The graphs containing all query 2-node sub graphs form the set of result graphs for the query. Finally, we employ the adjacency matrix of each result graph along with a score function, for spotting the query graph in it. The proposed subgraph spotting method is equally applicable to a wide range of domains, offering ease of query by example (QBE) and granularity of focused retrieval. Experimental results are presented for graphs generated from two repositories of electronic and architectural document images.",
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"content": "We present a method for spotting a subgraph in a graph repository. Subgraph spotting is a very interesting research problem for various application domains where the use of a relational data structure is mandatory. Our proposed method accomplishes subgraph spotting through graph embedding. We achieve automatic indexation of a graph repository during off-line learning phase, where we (i) break the graphs into 2-node sub graphs (a.k.a. cliques of order 2), which are primitive building-blocks of a graph, (ii) embed the 2-node sub graphs into feature vectors by employing our recently proposed explicit graph embedding technique, (iii) cluster the feature vectors in classes by employing a classic agglomerative clustering technique, (iv) build an index for the graph repository and (v) learn a Bayesian network classifier. The subgraph spotting is achieved during the on-line querying phase, where we (i) break the query graph into 2-node sub graphs, (ii) embed them into feature vectors, (iii) employ the Bayesian network classifier for classifying the query 2-node sub graphs and (iv) retrieve the respective graphs by looking-up in the index of the graph repository. The graphs containing all query 2-node sub graphs form the set of result graphs for the query. Finally, we employ the adjacency matrix of each result graph along with a score function, for spotting the query graph in it. The proposed subgraph spotting method is equally applicable to a wide range of domains, offering ease of query by example (QBE) and granularity of focused retrieval. Experimental results are presented for graphs generated from two repositories of electronic and architectural document images.",
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"abstract": "Graph neural network (GNN) is effective to model graphs for distributed representations of nodes and an entire graph. Recently, research on the expressive power of GNN attracted growing attention. A highly expressive GNN has the ability to generate discriminative graph representations. However, in the end-to-end training process for a certain graph learning task, an expressive GNN could generate graph representations overfitting the training data for the target task but losing information important for the model generalization, thus reducing the generalizability. In this paper, we propose Distribution Preserving GNN (DP-GNN), a GNN framework that can improve the generalizability of expressive GNN models by preserving several kinds of distribution information in graph representations and node representations. Besides the generalizability, by applying an expressive GNN backbone, DP-GNN can also have high expressive power. We evaluate the proposed DP-GNN framework on multiple benchmark datasets for graph classification tasks. The experimental results demonstrate that our model achieves state-of-the-art performances.",
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"content": "Graph neural network (GNN) is effective to model graphs for distributed representations of nodes and an entire graph. Recently, research on the expressive power of GNN attracted growing attention. A highly expressive GNN has the ability to generate discriminative graph representations. However, in the end-to-end training process for a certain graph learning task, an expressive GNN could generate graph representations overfitting the training data for the target task but losing information important for the model generalization, thus reducing the generalizability. In this paper, we propose Distribution Preserving GNN (DP-GNN), a GNN framework that can improve the generalizability of expressive GNN models by preserving several kinds of distribution information in graph representations and node representations. Besides the generalizability, by applying an expressive GNN backbone, DP-GNN can also have high expressive power. We evaluate the proposed DP-GNN framework on multiple benchmark datasets for graph classification tasks. The experimental results demonstrate that our model achieves state-of-the-art performances.",
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"content": "While Graph Neural Network (GNN) has shown superiority in learning node representations of homogeneous graphs, leveraging GNN on heterogeneous graphs remains a challenging problem. The dominating reason is that GNN learns node representations by aggregating neighbors' information regardless of node types. Some work is proposed to alleviate such issue by exploiting relations or meta-path to sample neighbors with distinct categories, then use attention mechanism to learn different importance for different categories. However, one limitation is that the learned representations for different types of nodes should own different feature spaces, while all the above work still project node representations into one feature space. Moreover, after exploring massive heterogeneous graphs, we identify a fact that multiple nodes with the same type always connect to a node with another type, which reveals the many-to-one schema, a.k.a. the hierarchical tree structure. But all the above work cannot preserve such tree structure, since the exact multi-hop path correlation from neighbors to the target node would be erased through aggregation. Therefore, to overcome the limitations of the literature, we propose T-GNN, a tree structure-aware graph neural network model for graph representation learning. Specifically, the proposed T-GNN consists of two modules: (1) the integrated hierarchical aggregation module and (2) the relational metric learning module. The integrated hierarchical aggregation module aims to preserve the tree structure by combining GNN with gated recurrent unit to integrate the hierarchical and sequential neighborhood information on the tree structure to node representations. The relational metric learning module aims to preserve the heterogeneity by embedding each type of nodes into a type-specific space with distinct distribution based on similarity metrics. In this way, our proposed T-GNN is capable of simultaneously preserving the heterogeneity and the tree structure inherent in heterogeneous graphs. Finally, we conduct extensive experiments to show the outstanding performance of T-GNN in tasks of node clustering and classification, inductive node clustering and classification, and link prediction.",
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"abstract": "We propose two recommendation algorithms for e-commerce with supply limits, a scenario that has not been intensively studied in the literature. One algorithm is a linear programming-based algorithm that uses historical data to approximate customer arrival patterns and generate shadow prices for inventories. The price of inventory can be introduced to existing recommendation algorithms to obtain adjusted rankings for recommendation. The other algorithm balances expected revenue and inventory consumption, and it uses a simple penalty function to reduce the chance of recommending low-inventory-level products. Both algorithms are suitable for online recommendation systems for grocery stores with both online and offline channels, and can incorporate the features of perishable products, which need to be sold within limited time. Both algorithms are tested in a simulation using estimated parameters from Freshippo, a supermarket owned by the Alibaba Group. The numerical results show that both algorithms can generate higher sales volume and higher revenue.",
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"affiliation": "Shanghai Jiao Tong University,Antai College of Economics and Management,Shanghai,China",
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"title": "Comparing Recommendation Algorithms in Session-based E-commerce Sites",
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"abstract": "Recommender systems have become widely used in various website applications. With the integration of deep learning and recommender systems, the classic Session-based Recommender System (SRS) appears, which can obtain implicit feedback from explicit interactions. Some scholars have pro-posed many effective recommendation algorithms to provide better recommendation service in SRS. In order to compare the performance of these session-based recommendation algorithms, we consider a simple E-commerce SRS scenario and choose four representative session-based recommendation algorithms in this paper. Then we do some evaluation experiments. The experimental results show that the combination of local preferences and global preferences will improve the recommendation performance significantly, and for GNNs and RNN s in session recommendation based on deep learning, we also conclude that the prediction effect of GNN s is slightly superior to RNN s on long sessions.",
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"content": "Recommender systems have become widely used in various website applications. With the integration of deep learning and recommender systems, the classic Session-based Recommender System (SRS) appears, which can obtain implicit feedback from explicit interactions. Some scholars have pro-posed many effective recommendation algorithms to provide better recommendation service in SRS. In order to compare the performance of these session-based recommendation algorithms, we consider a simple E-commerce SRS scenario and choose four representative session-based recommendation algorithms in this paper. Then we do some evaluation experiments. The experimental results show that the combination of local preferences and global preferences will improve the recommendation performance significantly, and for GNNs and RNN s in session recommendation based on deep learning, we also conclude that the prediction effect of GNN s is slightly superior to RNN s on long sessions.",
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"authors": [
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"fullName": "Mingtian Peng",
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"affiliation": "College of Computer Science Beijing Institute of Technology,Beijing,China",
"fullName": "Jiahe Zhang",
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"affiliation": "College of Computer Science Beijing Institute of Technology,Beijing,China",
"fullName": "Shilin Wen",
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"affiliation": "College of Computer Science Beijing Institute of Technology,Beijing,China",
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"abstract": "Abstraction is one of the most important strategies for dealing with the state space explosion problem in model checking. With an abstract model, the state space is largely reduced, however, a counterexample found in such a model that does not satisfy the desired property may not exist in the concrete model. Therefore, how to check whether a reported counterexample is spurious is a key problem in the abstraction-refinement loop. Particularly, there are often thousands of millions of states in systems of industrial scale, how to check spurious counterexamples in these systems practically is a significant problem. In this paper, by re-analyzing spurious counterexamples, a new formal definition of spurious path is given. Based on it, efficient algorithms for detecting spurious counterexamples are presented. By the new algorithms, when dealing with infinite counterexamples, the finite prefix to be analyzed will be polynomially shorter than the one dealt by the existing algorithm. Moreover, in practical terms, the new algorithms can naturally be parallelized that makes multi-core processors contributes more in spurious counterexample checking. In addition, by the new algorithms, the state resulting in a spurious path (false state) that is hidden shallower will be reported earlier. Hence, as long as a false state is detected, lots of iterations for detecting all the false states will be avoided. Experimental results show that the new algorithms perform well along with the growth of system scale.",
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"abstract": "Despite being one of the most reliable approaches for ensuring system correctness, model checking requires auxiliary tools to fully avail. In this work, we tackle the issue of its results being hard to interpret and present OERITTE, a tool for automatic visual counterexample explanation for function block diagrams. To learn what went wrong, the user can inspect a parse tree of the violated LTL formula and a table view of a counterexample, where important variables are highlighted. Then, on the function block diagram of the system under verification, they can receive a visualization of causality relationships between the calculated values of interest and intermediate results or inputs of the function block diagram. Thus, OERITTE serves to decrease formal model and specification debugging efforts along with making model checking more utilizable for complex industrial systems.",
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"abstract": "This paper tries to bring the problem of technical algorithmic bias to the attention of the high-dimensional data mining community. A system suffering from algorithmic bias results in systematic unfair treatment of certain users or data, with technical algorithmic bias arising specifically from technical constraints. We illustrate this problem, which so far has been neglected in high-dimensional data mining, for a real world music recommendation system. Due to a problem of measuring distances in high dimensional spaces, songs closer to the center of all data are recommended over and over again, while songs far from the center are not recommended at all. We show that these so-called hub songs do not carry a specific semantic meaning and that deleting them from the data base promotes other songs to hub songs being recommended disturbingly often as a consequence. We argue that it is the ethical responsibility of data mining researchers to care about the fairness of their algorithms in high-dimensional spaces.",
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"abstract": "Face morphing attacks can compromise Face Recognition System (FRS) by exploiting their vulnerability. Face Morphing Attack Detection (MAD) techniques have been developed in recent past to deter such attacks and mitigate risks from morphing attacks. MAD algorithms, as any other algorithms should treat the images of subjects from different ethnic origins in an equal manner and provide non-discriminatory results. While the promising MAD algorithms are tested for robustness, there is no study comprehensively bench-marking their behaviour against various ethnicities. In this paper, we study and present a comprehensive analysis of algorithmic fairness of the existing Single image-based Morph Attack Detection (S-MAD) algorithms. We attempt to better understand the influence of ethnic bias on MAD algorithms and to this extent, we study the performance of MAD algorithms on a newly created dataset consisting of four different ethnic groups. With Extensive experiments using six different S-MAD techniques, we first present benchmark of detection performance and then measure the quantitative value of the algorithmic fairness for each of them using Fairness Discrepancy Rate (FDR). The results indicate the lack of fairness on all six different SMAD methods when trained and tested on different ethnic groups suggesting the need for reliable MAD approaches to mitigate the algorithmic bias.",
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"content": "Face morphing attacks can compromise Face Recognition System (FRS) by exploiting their vulnerability. Face Morphing Attack Detection (MAD) techniques have been developed in recent past to deter such attacks and mitigate risks from morphing attacks. MAD algorithms, as any other algorithms should treat the images of subjects from different ethnic origins in an equal manner and provide non-discriminatory results. While the promising MAD algorithms are tested for robustness, there is no study comprehensively bench-marking their behaviour against various ethnicities. In this paper, we study and present a comprehensive analysis of algorithmic fairness of the existing Single image-based Morph Attack Detection (S-MAD) algorithms. We attempt to better understand the influence of ethnic bias on MAD algorithms and to this extent, we study the performance of MAD algorithms on a newly created dataset consisting of four different ethnic groups. With Extensive experiments using six different S-MAD techniques, we first present benchmark of detection performance and then measure the quantitative value of the algorithmic fairness for each of them using Fairness Discrepancy Rate (FDR). The results indicate the lack of fairness on all six different SMAD methods when trained and tested on different ethnic groups suggesting the need for reliable MAD approaches to mitigate the algorithmic bias.",
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"abstract": "Widespread use of GPS terminals has made it possible to collect geospatial movement data, and visualization is an effective method to understand such data. However, for large scale movement analysis, because the data set includes complex movements of individuals, it is difficult to understand using naive visualization methods. In order to solve this problem, we developed a visualization technique that can represent large scale movement data in an aggregate manner. This visualization technique has two representations: \"amoeba representation\" and \"amoeba colony representation.\" Amoeba representation represents the distance moved from a point in any direction with map scale in a geographical space, and amoeba colony representation represents movements over a wide geographical space.",
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"abstract": "Subjective Logic (SL), as one of the state-of-the-art belief models, has been proposed to model an opinion that explicitly deals with its uncertainty. SL offers a variety of operators to update opinions consisting of belief, disbelief, and uncertainty. However, SL operators lack scalability to derive opinions from a large-scale network data due to the sequential procedures of combining two opinions, instead of collective procedures dealing with multiple opinions concurrently. In addition, SL's performance in predicting unknown opinions has been validated only when the uncertainty mass is sufficiently low. To enhance scalability and prediction accuracy of unknown opinions in SL, we take a hybrid approach by combining SL with Probabilistic Soft Logic (PSL). PSL provides collective reasoning with high scalability based on relationships between opinions but does not deal with uncertainty. By taking the merits of both SL and PSL, we propose a probabilistic logic algorithm, called Collective Subjective Logic (CSL) that provides high scalability and high prediction accuracy while dealing with uncertain opinions. Our proposed CSL is generic to deal with uncertain opinions with both high scalability and high prediction accuracy of unknown opinions over a large-scale network dataset. Through the extensive simulation experiments, we validated the outperformance of CSL compared against SL and PSL in terms of prediction accuracy of unknown opinions and algorithmic complexity using Epinions and two road traffic datasets.",
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