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"doi": "10.1109/BigData47090.2019.9005693",
"title": "Discovering Partial Periodic Spatial Patterns in Spatiotemporal Databases",
"normalizedTitle": "Discovering Partial Periodic Spatial Patterns in Spatiotemporal Databases",
"abstract": "Finding partial periodic patterns in very large databases is a challenging problem of great importance in many real-world applications. Most previous work focused on finding these patterns in temporal (or transactional) databases and did not recognize the spatial characteristics of items. In this paper, we propose a more flexible model of partial periodic spatial pattern that may be present in spatiotemporal database. Three constraints, maximum inter-arrival time(maxIAT), minimum period-support(minPS) and maximum distance(maxDist), have been employed to determine the interestingness of a pattern in a spatiotemporal database. The maxIAT controls the maximum duration in which a pattern must reappear to consider its occurrence as periodic within the data. The minPS controls the minimum number of periodic occurrences of a pattern within the data. The maxDist controls the maximum distance between the items in a pattern. All patterns satisfying these three constraints are returned. An efficient algorithm, called SpatioTemporal-Equivalence CLAss Transformation (ST-ECLAT), has also been described to discover all partial periodic spatial patterns in a spatiotemporal database. This algorithm employs a novel smart depth-first search technique to discover desired patterns effectively. Experimental results demonstrate that the proposed algorithm is efficient. We also present a case study in which we apply our model to find useful information in the air pollution database.",
"abstracts": [
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"content": "Finding partial periodic patterns in very large databases is a challenging problem of great importance in many real-world applications. Most previous work focused on finding these patterns in temporal (or transactional) databases and did not recognize the spatial characteristics of items. In this paper, we propose a more flexible model of partial periodic spatial pattern that may be present in spatiotemporal database. Three constraints, maximum inter-arrival time(maxIAT), minimum period-support(minPS) and maximum distance(maxDist), have been employed to determine the interestingness of a pattern in a spatiotemporal database. The maxIAT controls the maximum duration in which a pattern must reappear to consider its occurrence as periodic within the data. The minPS controls the minimum number of periodic occurrences of a pattern within the data. The maxDist controls the maximum distance between the items in a pattern. All patterns satisfying these three constraints are returned. An efficient algorithm, called SpatioTemporal-Equivalence CLAss Transformation (ST-ECLAT), has also been described to discover all partial periodic spatial patterns in a spatiotemporal database. This algorithm employs a novel smart depth-first search technique to discover desired patterns effectively. Experimental results demonstrate that the proposed algorithm is efficient. We also present a case study in which we apply our model to find useful information in the air pollution database.",
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"affiliation": "National Institute of Information and Communications Technology,Tokyo,Japan",
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"affiliation": "National Institute of Information and Communications Technology,Tokyo,Japan",
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"abstract": "Many scientific prediction problems have spatiotemporal data- and modeling-related challenges in handling complex variations in space and time using only sparse and unevenly distributed observations. This paper presents a novel deep learning architecture, Deep learning predictions for LocATion-dependent Time-sEries data (DeepLATTE), that explicitly incorporates theories of spatial statistics into neural networks to addresses these challenges. In addition to a feature selection module and a spatiotemporal learning module, DeepLATTE contains an autocorrelation-guided semi-supervised learning strategy to enforce both local autocorrelation patterns and global autocorrelation trends of the predictions in the learned spatiotemporal embedding space to be consistent with the observed data, overcoming the limitation of sparse and unevenly distributed observations. During the training process, both supervised and semi-supervised losses guide the updates of the entire network to: 1) prevent overfitting, 2) refine feature selection, 3) learn useful spatiotemporal representations, and 4) improve overall prediction. We conduct a demonstration of DeepLATTE using publicly available data for an important public health topic, air quality prediction, in a well-studied, complex physical environment - Los Angeles. The experiment demonstrates that the proposed approach provides accurate fine-spatial-scale air quality predictions and reveals the critical environmental factors affecting the results.",
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"title": "Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network",
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"abstract": "Recently, Graph Neural Networks (GNNs) have proven their effectiveness for recommender systems. Existing studies have applied GNNs to capture collaborative relations in the data. However, in real-world scenarios, the relations in a recommendation graph can be of various kinds. For example, two movies may be associated either by the same genre or by the same director/actor. If we use a single graph to elaborate all these relations, the graph can be too complex to process. To address this issue, we bring the idea of pre-training to process the complex graph step by step. Based on the idea of divide-and-conquer, we separate the large graph into three sub-graphs: user graph, item graph, and user-item interaction graph. Then the user and item embeddings are pre-trained from user and item graphs, respectively. To conduct pre-training, we construct the multi-relational user graph and item graph, respectively, based on their attributes.In this paper, we propose a novel Reinforced Attentive Multi-relational Graph Neural Network (RAM-GNN) to pre-train user and item embeddings on the user and item graph prior to the recommendation step. Specifically, we design a relation-level attention layer to learn the importance of different relations. Next, a Reinforced Neighbor Sampler (RNS) is applied to search the optimal filtering threshold for sampling top-k similar neighbors in the graph, which avoids the over-smoothing issue. We initialize the recommendation model with the pre-trained user/item embeddings. Finally, an aggregation-based GNN model is utilized to learn from the collaborative relations in the user-item interaction graph and provide recommendations. Our experiments demonstrate that RAM-GNN outperforms other state-of-the-art graph-based recommendation models and multi-relational graph neural networks.",
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"content": "Recently, Graph Neural Networks (GNNs) have proven their effectiveness for recommender systems. Existing studies have applied GNNs to capture collaborative relations in the data. However, in real-world scenarios, the relations in a recommendation graph can be of various kinds. For example, two movies may be associated either by the same genre or by the same director/actor. If we use a single graph to elaborate all these relations, the graph can be too complex to process. To address this issue, we bring the idea of pre-training to process the complex graph step by step. Based on the idea of divide-and-conquer, we separate the large graph into three sub-graphs: user graph, item graph, and user-item interaction graph. Then the user and item embeddings are pre-trained from user and item graphs, respectively. To conduct pre-training, we construct the multi-relational user graph and item graph, respectively, based on their attributes.In this paper, we propose a novel Reinforced Attentive Multi-relational Graph Neural Network (RAM-GNN) to pre-train user and item embeddings on the user and item graph prior to the recommendation step. Specifically, we design a relation-level attention layer to learn the importance of different relations. Next, a Reinforced Neighbor Sampler (RNS) is applied to search the optimal filtering threshold for sampling top-k similar neighbors in the graph, which avoids the over-smoothing issue. We initialize the recommendation model with the pre-trained user/item embeddings. Finally, an aggregation-based GNN model is utilized to learn from the collaborative relations in the user-item interaction graph and provide recommendations. Our experiments demonstrate that RAM-GNN outperforms other state-of-the-art graph-based recommendation models and multi-relational graph neural networks.",
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"normalizedAbstract": "Recently, Graph Neural Networks (GNNs) have proven their effectiveness for recommender systems. Existing studies have applied GNNs to capture collaborative relations in the data. However, in real-world scenarios, the relations in a recommendation graph can be of various kinds. For example, two movies may be associated either by the same genre or by the same director/actor. If we use a single graph to elaborate all these relations, the graph can be too complex to process. To address this issue, we bring the idea of pre-training to process the complex graph step by step. Based on the idea of divide-and-conquer, we separate the large graph into three sub-graphs: user graph, item graph, and user-item interaction graph. Then the user and item embeddings are pre-trained from user and item graphs, respectively. To conduct pre-training, we construct the multi-relational user graph and item graph, respectively, based on their attributes.In this paper, we propose a novel Reinforced Attentive Multi-relational Graph Neural Network (RAM-GNN) to pre-train user and item embeddings on the user and item graph prior to the recommendation step. Specifically, we design a relation-level attention layer to learn the importance of different relations. Next, a Reinforced Neighbor Sampler (RNS) is applied to search the optimal filtering threshold for sampling top-k similar neighbors in the graph, which avoids the over-smoothing issue. We initialize the recommendation model with the pre-trained user/item embeddings. Finally, an aggregation-based GNN model is utilized to learn from the collaborative relations in the user-item interaction graph and provide recommendations. Our experiments demonstrate that RAM-GNN outperforms other state-of-the-art graph-based recommendation models and multi-relational graph neural networks.",
"fno": "09671830",
"keywords": [
"Graph Theory",
"Learning Artificial Intelligence",
"Neural Nets",
"Recommender Systems",
"Recommendation Step",
"Relation Level Attention Layer",
"Different Relations",
"Collaborative Relations",
"User Item Interaction Graph",
"State Of The Art Graph Based Recommendation Models",
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"Graph Neural Networks",
"Recommendation Graph",
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"Complex Graph Step",
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"Item Graph",
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"Novel Reinforced Attentive Multirelational Graph Neural Network",
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"Filtering",
"Conferences",
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"Big Data",
"Motion Pictures",
"Graph Neural Networks",
"Recommender System",
"Graph Neural Network",
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],
"authors": [
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"affiliation": "University of Illinois at Chicago,Chicago,IL,USA",
"fullName": "Xiaohan Li",
"givenName": "Xiaohan",
"surname": "Li",
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{
"affiliation": "University of Illinois at Chicago,Chicago,IL,USA",
"fullName": "Zhiwei Liu",
"givenName": "Zhiwei",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Walmart Global Tech,San Francisco Bay Area,CA,USA",
"fullName": "Stephen Guo",
"givenName": "Stephen",
"surname": "Guo",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Illinois at Chicago,Chicago,IL,USA",
"fullName": "Zheng Liu",
"givenName": "Zheng",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Beihang University,Beijing,China",
"fullName": "Hao Peng",
"givenName": "Hao",
"surname": "Peng",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "University of Illinois at Chicago,Chicago,IL,USA",
"fullName": "Philip S. Yu",
"givenName": "Philip S.",
"surname": "Yu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Walmart Global Tech,San Francisco Bay Area,CA,USA",
"fullName": "Kannan Achan",
"givenName": "Kannan",
"surname": "Achan",
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"abstract": "With the great success of deep learning in various domains, graph neural networks (GNNs) also become a dominant approach to graph classification. By the help of a global readout operation that simply aggregates all node (or node-cluster) representations, existing GNN classifiers obtain a graph-level representation of an input graph and predict its class label using the representation. However, such global aggregation does not consider the structural information of each node, which results in information loss on the global structure. In this work, we propose structural semantic readout (SSRead) to summarize the node representations at the position-level, which allows to model the position-specific weight parameters for classification as well as to effectively capture the graph semantic relevant to the global structure. Given an input graph, SSRead aims to identify structurally-meaningful positions by using the semantic alignment between its nodes and structural prototypes, which encode the prototypical features of each position. The structural prototypes are optimized to minimize the alignment cost for all training graphs, while the other GNN parameters are trained to predict the class labels. Our experimental results demonstrate that SSRead significantly improves the classification performance and interpretability of GNN classifiers while being compatible with a variety of aggregation functions, GNN architectures, and learning frameworks.",
"abstracts": [
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"content": "With the great success of deep learning in various domains, graph neural networks (GNNs) also become a dominant approach to graph classification. By the help of a global readout operation that simply aggregates all node (or node-cluster) representations, existing GNN classifiers obtain a graph-level representation of an input graph and predict its class label using the representation. However, such global aggregation does not consider the structural information of each node, which results in information loss on the global structure. In this work, we propose structural semantic readout (SSRead) to summarize the node representations at the position-level, which allows to model the position-specific weight parameters for classification as well as to effectively capture the graph semantic relevant to the global structure. Given an input graph, SSRead aims to identify structurally-meaningful positions by using the semantic alignment between its nodes and structural prototypes, which encode the prototypical features of each position. The structural prototypes are optimized to minimize the alignment cost for all training graphs, while the other GNN parameters are trained to predict the class labels. Our experimental results demonstrate that SSRead significantly improves the classification performance and interpretability of GNN classifiers while being compatible with a variety of aggregation functions, GNN architectures, and learning frameworks.",
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"normalizedAbstract": "With the great success of deep learning in various domains, graph neural networks (GNNs) also become a dominant approach to graph classification. By the help of a global readout operation that simply aggregates all node (or node-cluster) representations, existing GNN classifiers obtain a graph-level representation of an input graph and predict its class label using the representation. However, such global aggregation does not consider the structural information of each node, which results in information loss on the global structure. In this work, we propose structural semantic readout (SSRead) to summarize the node representations at the position-level, which allows to model the position-specific weight parameters for classification as well as to effectively capture the graph semantic relevant to the global structure. Given an input graph, SSRead aims to identify structurally-meaningful positions by using the semantic alignment between its nodes and structural prototypes, which encode the prototypical features of each position. The structural prototypes are optimized to minimize the alignment cost for all training graphs, while the other GNN parameters are trained to predict the class labels. Our experimental results demonstrate that SSRead significantly improves the classification performance and interpretability of GNN classifiers while being compatible with a variety of aggregation functions, GNN architectures, and learning frameworks.",
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"affiliation": "University of Illinois at Urbana-Champaign (UIUC),Urbana,IL,United States",
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"affiliation": "Pohang University of Science and Technology (POSTECH),Pohang,Republic of Korea",
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"abstract": "In this paper, we study a novel meta aggregation scheme towards binarizing graph neural networks (GNNs). We begin by developing a vanilla 1-bit GNN framework that binarizes both the GNN parameters and the graph features. Despite the lightweight architecture, we observed that this vanilla framework suffered from insufficient discriminative power in distinguishing graph topologies, leading to a dramatic drop in performance. This discovery motivates us to devise meta aggregators to improve the expressive power of vanilla binarized GNNs, of which the aggregation schemes can be adaptively changed in a learnable manner based on the binarized features. Towards this end, we propose two dedicated forms of meta neighborhood aggregators, an exclusive meta aggregator termed as Greedy Gumbel Neighborhood Aggregator (GNA), and a diffused meta aggregator termed as Adaptable Hybrid Neighborhood Aggregator (ANA). GNA learns to exclusively pick one single optimal aggregator from a pool of candidates, while ANA learns a hybrid aggregation behavior to simultaneously retain the benefits of several individual aggregators. Furthermore, the proposed meta aggregators may readily serve as a generic plugin module into existing full-precision GNNs. Experiments across various domains demonstrate that the proposed method yields results superior to the state of the art.",
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"content": "In this paper, we study a novel meta aggregation scheme towards binarizing graph neural networks (GNNs). We begin by developing a vanilla 1-bit GNN framework that binarizes both the GNN parameters and the graph features. Despite the lightweight architecture, we observed that this vanilla framework suffered from insufficient discriminative power in distinguishing graph topologies, leading to a dramatic drop in performance. This discovery motivates us to devise meta aggregators to improve the expressive power of vanilla binarized GNNs, of which the aggregation schemes can be adaptively changed in a learnable manner based on the binarized features. Towards this end, we propose two dedicated forms of meta neighborhood aggregators, an exclusive meta aggregator termed as Greedy Gumbel Neighborhood Aggregator (GNA), and a diffused meta aggregator termed as Adaptable Hybrid Neighborhood Aggregator (ANA). GNA learns to exclusively pick one single optimal aggregator from a pool of candidates, while ANA learns a hybrid aggregation behavior to simultaneously retain the benefits of several individual aggregators. Furthermore, the proposed meta aggregators may readily serve as a generic plugin module into existing full-precision GNNs. Experiments across various domains demonstrate that the proposed method yields results superior to the state of the art.",
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"abstract": "Line art colorization is expensive and challenging to automate. A GAN approach is proposed, called Tag2Pix, of line art colorization which takes as input a grayscale line art and color tag information and produces a quality colored image. First, we present the Tag2Pix line art colorization dataset. A generator network is proposed which consists of convolutional layers to transform the input line art, a pre-trained semantic extraction network, and an encoder for input color information. The discriminator is based on an auxiliary classifier GAN to classify the tag information as well as genuineness. In addition, we propose a novel network structure called SECat, which makes the generator properly colorize even small features such as eyes, and also suggest a novel two-step training method where the generator and discriminator first learn the notion of object and shape and then, based on the learned notion, learn colorization, such as where and how to place which color. We present both quantitative and qualitative evaluations which prove the effectiveness of the proposed method.",
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"title": "Can I teach a robot to replicate a line art",
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"abstract": "Line art is arguably one of the fundamental and versatile modes of expression. We propose a pipeline for a robot to look at a grayscale line art and redraw it. The key novel elements of our pipeline are: a) we propose a novel task of mimicking line drawings, b) to solve the pipeline we modify the Quick-draw dataset and obtain supervised training for converting a line drawing into a series of strokes c) we propose a multi-stage segmentation and graph interpretation pipeline for solving the problem. The resultant method has also been deployed on a CNC plotter as well as a robotic arm. We have trained several variations of the proposed methods and evaluate these on a dataset obtained from Quick-draw. Through the best methods we observe an accuracy of around 98% for this task, which is a significant improvement over the baseline architecture we adapted from. This therefore allows for deployment of the method on robots for replicating line art in a reliable manner. We also show that while the rule-based vectorization methods do suffice for simple drawings, it fails for more complicated sketches, unlike our method which generalizes well to more complicated distributions.",
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"title": "Line Art Correlation Matching Feature Transfer Network for Automatic Animation Colorization",
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"abstract": "Automatic animation line art colorization is a challenging computer vision problem, since the information of the line art is highly sparse and abstracted and there exists a strict requirement for the color and style consistency between frames. Recently, a lot of Generative Adversarial Network (GAN) based image-to-image translation methods for single line art colorization have emerged. They can generate perceptually appealing results conditioned on line art images. However, these methods can not be adopted for the purpose of animation colorization because there is a lack of consideration of the in-between frame consistency. Existing methods simply input the previous colored frame as a reference to color the next line art, which will mislead the colorization due to the spatial misalignment of the previous colored frame and the next line art especially at positions where apparent changes happen. To address these challenges, we design a kind of correlation matching feature transfer model (called CMFT) to align the colored reference feature in a learnable way and integrate the model into an U-Net based generator in a coarse-to-fine manner This enables the generator to transfer the layer-wise synchronized features from the deep semantic code to the content progressively. Extension evaluation shows that CMFT model can effectively improve the in-between consistency and the quality of colored frames especially when the motion is intense and diverse.",
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"content": "Automatic animation line art colorization is a challenging computer vision problem, since the information of the line art is highly sparse and abstracted and there exists a strict requirement for the color and style consistency between frames. Recently, a lot of Generative Adversarial Network (GAN) based image-to-image translation methods for single line art colorization have emerged. They can generate perceptually appealing results conditioned on line art images. However, these methods can not be adopted for the purpose of animation colorization because there is a lack of consideration of the in-between frame consistency. Existing methods simply input the previous colored frame as a reference to color the next line art, which will mislead the colorization due to the spatial misalignment of the previous colored frame and the next line art especially at positions where apparent changes happen. To address these challenges, we design a kind of correlation matching feature transfer model (called CMFT) to align the colored reference feature in a learnable way and integrate the model into an U-Net based generator in a coarse-to-fine manner This enables the generator to transfer the layer-wise synchronized features from the deep semantic code to the content progressively. Extension evaluation shows that CMFT model can effectively improve the in-between consistency and the quality of colored frames especially when the motion is intense and diverse.",
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"abstract": "Line art plays a fundamental role in illustration and design, and allows for iteratively polishing designs. However, as they lack color, they can have issues in conveying final designs. In this work, we propose an interactive colorization approach based on a conditional generative adversarial network that takes both the line art and color hints as inputs to produce a high-quality colorized image. Our approach is based on a U-net architecture with a multi-discriminator framework. We propose a Concatenation and Spatial Attention module that is able to generate more consistent and higher quality of line art colorization from user given hints. We evaluate on a large-scale illustration dataset and comparison with existing approaches corroborate the effectiveness of our approach.",
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"content": "Line art plays a fundamental role in illustration and design, and allows for iteratively polishing designs. However, as they lack color, they can have issues in conveying final designs. In this work, we propose an interactive colorization approach based on a conditional generative adversarial network that takes both the line art and color hints as inputs to produce a high-quality colorized image. Our approach is based on a U-net architecture with a multi-discriminator framework. We propose a Concatenation and Spatial Attention module that is able to generate more consistent and higher quality of line art colorization from user given hints. We evaluate on a large-scale illustration dataset and comparison with existing approaches corroborate the effectiveness of our approach.",
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"normalizedAbstract": "Line art plays a fundamental role in illustration and design, and allows for iteratively polishing designs. However, as they lack color, they can have issues in conveying final designs. In this work, we propose an interactive colorization approach based on a conditional generative adversarial network that takes both the line art and color hints as inputs to produce a high-quality colorized image. Our approach is based on a U-net architecture with a multi-discriminator framework. We propose a Concatenation and Spatial Attention module that is able to generate more consistent and higher quality of line art colorization from user given hints. We evaluate on a large-scale illustration dataset and comparison with existing approaches corroborate the effectiveness of our approach.",
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"abstract": "Augmented reality (AR) environment allows user or multi-user to interact with 2D and 3D data. AR simply can provide a collaborative interactive AR environment for urban simulation, where users can interact naturally and intuitively. AR collaboration approach can be effectively used to develop face to face interfaces. This is because AR provides seamless interaction between real and virtual environments, the ability to enhance reality, the presence of spatial cues for face-to-face and remote collaboration, support of a tangible interface metaphor, the ability to transition smoothly between reality and virtuality. The fusion between real and virtual world, existed in AR environment, achieves higher interactivity as a key features of collaborative AR. Collaborative AR approach allows multi-user to simultaneously share a real world surrounding them and a virtual world. The features of collaboration in AR environment will be identified. The key for the proposed technique of precise registration between both worlds and multi-user are emphasized for the collaborations using AR environment. Common problems in AR environment and issues in collaborative AR will be discussed. This paper will give an overview for collaborative AR framework employed in urban simulation and the multi-user interaction on how to share these virtual spaces with other users in collaboration. The work will also cover numerous systems in different cases of collaborative AR environments for multiuser interaction.",
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"content": "Augmented reality (AR) environment allows user or multi-user to interact with 2D and 3D data. AR simply can provide a collaborative interactive AR environment for urban simulation, where users can interact naturally and intuitively. AR collaboration approach can be effectively used to develop face to face interfaces. This is because AR provides seamless interaction between real and virtual environments, the ability to enhance reality, the presence of spatial cues for face-to-face and remote collaboration, support of a tangible interface metaphor, the ability to transition smoothly between reality and virtuality. The fusion between real and virtual world, existed in AR environment, achieves higher interactivity as a key features of collaborative AR. Collaborative AR approach allows multi-user to simultaneously share a real world surrounding them and a virtual world. The features of collaboration in AR environment will be identified. The key for the proposed technique of precise registration between both worlds and multi-user are emphasized for the collaborations using AR environment. Common problems in AR environment and issues in collaborative AR will be discussed. This paper will give an overview for collaborative AR framework employed in urban simulation and the multi-user interaction on how to share these virtual spaces with other users in collaboration. The work will also cover numerous systems in different cases of collaborative AR environments for multiuser interaction.",
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"abstract": "Estimating the shape of transparent and refractive objects is one of the few open problems in 3D reconstruction. Under the assumption that the rays refract only twice when traveling through the object, we present the first approach to simultaneously reconstructing the 3D positions and normals of the object's surface at both refraction locations. Our acquisition setup requires only two cameras and one monitor, which serves as the light source. After acquiring the ray-ray correspondences between each camera and the monitor, we solve an optimization function which enforces a new position-normal consistency constraint. That is, the 3D positions of surface points shall agree with the normals required to refract the rays under Snell's law. Experimental results using both synthetic and real data demonstrate the robustness and accuracy of the proposed approach.",
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"abstract": "We present a novel technique, termed attached shadow coding, for estimating surface normals from shadows when the reflectance and lighting conditions are unknown. Our key idea is encoding surface points via attached shadows observed under different light source directions and then estimating surface normals on the basis of the similarity of the attached shadow codes. Because shadows do not rely on reflectance properties, our method is applicable to surfaces with various complex reflectances such as anisotropic and composite materials. Moreover, our method is robust against noise because it takes advantage of the combination of weak constraints imposed by a number of light sources. We theoretically show that the distance between the codes at two surface points is equal to the angle between the corresponding surface normals under the assumption of uniform lighting and a convex object. Our method embeds high-dimensional codes into a 3D surface normal space so that the inter-code distances are preserved. Furthermore, we extend the method in order to alleviate the effects of nonuniform lighting and cast shadows. Experimental results demonstrate the effectiveness of our method.",
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"content": "We present a novel technique, termed attached shadow coding, for estimating surface normals from shadows when the reflectance and lighting conditions are unknown. Our key idea is encoding surface points via attached shadows observed under different light source directions and then estimating surface normals on the basis of the similarity of the attached shadow codes. Because shadows do not rely on reflectance properties, our method is applicable to surfaces with various complex reflectances such as anisotropic and composite materials. Moreover, our method is robust against noise because it takes advantage of the combination of weak constraints imposed by a number of light sources. We theoretically show that the distance between the codes at two surface points is equal to the angle between the corresponding surface normals under the assumption of uniform lighting and a convex object. Our method embeds high-dimensional codes into a 3D surface normal space so that the inter-code distances are preserved. Furthermore, we extend the method in order to alleviate the effects of nonuniform lighting and cast shadows. Experimental results demonstrate the effectiveness of our method.",
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"affiliation": "The University of Tokyo, Japan",
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"abstract": "To visualize the volume data acquired from computation or sampling, it is necessary to estimate normals at the points corresponding to object surfaces. Volume data does not holds the geometric information for the surface comprising points, so it is necessary to calculate normals using local information at each point. The existing normal estimation methods have some problems of estimating incorrect normals at discontinuous, aliased or noisy points. Yagel et al solved some of these problems using their context-sensitive method. However this method requires too much processing time and it loses some information on detailed parts of the object surfaces. This paper proposes the surface-characteristic-sensitive normal estimation method which applies different operators according to characteristics of each surface for the normal calculation. This method has the same advantages of the context-sensitive method, and also some other advantages such as the less processing time and the reduction of the information loss on detailed parts.",
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"content": "To visualize the volume data acquired from computation or sampling, it is necessary to estimate normals at the points corresponding to object surfaces. Volume data does not holds the geometric information for the surface comprising points, so it is necessary to calculate normals using local information at each point. The existing normal estimation methods have some problems of estimating incorrect normals at discontinuous, aliased or noisy points. Yagel et al solved some of these problems using their context-sensitive method. However this method requires too much processing time and it loses some information on detailed parts of the object surfaces. This paper proposes the surface-characteristic-sensitive normal estimation method which applies different operators according to characteristics of each surface for the normal calculation. This method has the same advantages of the context-sensitive method, and also some other advantages such as the less processing time and the reduction of the information loss on detailed parts.",
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"fno": "71870159",
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"affiliation": "Seoul National University",
"fullName": "Byeong Seok Shin",
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"affiliation": "Seoul National University",
"fullName": "Yeong Gil Shin",
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"title": "VENet: Voting Enhancement Network for 3D Object Detection",
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"abstract": "Hough voting, as has been demonstrated in VoteNet, is effective for 3D object detection, where voting is a key step. In this paper, we propose a novel VoteNet-based 3D detector with vote enhancement to improve the detection accuracy in cluttered indoor scenes. It addresses the limitations of current voting schemes, i.e., votes from neighboring objects and background have significant negative impacts. Before voting, we replace the classic MLP with the proposed Attentive MLP (AMLP) in the backbone network to get better feature description of seed points. During voting, we design a new vote attraction loss (VALoss) to enforce vote centers to locate closely and compactly to the corresponding object centers. After voting, we then devise a vote weighting module to integrate the foreground/background prediction into the vote aggregation process to enhance the capability of the original VoteNet to handle noise from background voting. The three proposed strategies all contribute to more effective voting and improved performance, resulting in a novel 3D object detector, termed VENet. Experiments show that our method outperforms state-of-the-art methods on benchmark datasets. Ablation studies demonstrate the effectiveness of the proposed components.",
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"content": "Hough voting, as has been demonstrated in VoteNet, is effective for 3D object detection, where voting is a key step. In this paper, we propose a novel VoteNet-based 3D detector with vote enhancement to improve the detection accuracy in cluttered indoor scenes. It addresses the limitations of current voting schemes, i.e., votes from neighboring objects and background have significant negative impacts. Before voting, we replace the classic MLP with the proposed Attentive MLP (AMLP) in the backbone network to get better feature description of seed points. During voting, we design a new vote attraction loss (VALoss) to enforce vote centers to locate closely and compactly to the corresponding object centers. After voting, we then devise a vote weighting module to integrate the foreground/background prediction into the vote aggregation process to enhance the capability of the original VoteNet to handle noise from background voting. The three proposed strategies all contribute to more effective voting and improved performance, resulting in a novel 3D object detector, termed VENet. Experiments show that our method outperforms state-of-the-art methods on benchmark datasets. Ablation studies demonstrate the effectiveness of the proposed components.",
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"authors": [
{
"affiliation": "Nanjing University of Aeronautics and Astronautics",
"fullName": "Qian Xie",
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"abstract": "Choosing an appropriate cancer treatment is potentially the most important task in the treatment of a cancer patient. If it were possible to identify the best option for a patient (or at minimum to remove options that will not help the patient), then the general prognosis of the patient improves. However, this task becomes much more subtle due to characteristics such as high dimensionality found in many gene expression datasets. In this study, we seek to identify classifiers and feature selection techniques best suited for predicting a breast cancer patient's response to a cancer treatment. In order to determine this, we have collected a group of five high-dimensional breast cancer patient response datasets and use a group of four classifiers, and three feature selection techniques along with four feature subset sizes. Our results show that 5-Nearest Neighbor classifier and Signal-to-Noise feature selection technique are the most frequently top performing techniques. Statistical analysis confirms that these techniques are the top performing techniques. Thus, we recommend the use of 5-Nearest Neighbor and Signal-to-Noise for breast cancer patient response data. To our knowledge, this is the first study that focuses on the classification process on patient response data for breast cancer.",
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"abstract": "More than 90% of malignant tumors in the head and neck are squamous carcinomas. These patients are with an average survival rate of about 5 years. However, some of the head and neck cancer(HNC) patients had the poor survival rate because of development of second primary tumors. In this study, the sequencing was performed using the Illumina system and Sanger sequencing was used to validate all identified mutations. We analyzed primary and second primary tumors in HNC and identified 23 mutant verification only in second primary tumors; 32 mutant verification only in primary tumors; 38 mutant verification in both of them. This mutant verification only in second primary tumors might be the cause of the second primary oral cancer.",
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"content": "More than 90% of malignant tumors in the head and neck are squamous carcinomas. These patients are with an average survival rate of about 5 years. However, some of the head and neck cancer(HNC) patients had the poor survival rate because of development of second primary tumors. In this study, the sequencing was performed using the Illumina system and Sanger sequencing was used to validate all identified mutations. We analyzed primary and second primary tumors in HNC and identified 23 mutant verification only in second primary tumors; 32 mutant verification only in primary tumors; 38 mutant verification in both of them. This mutant verification only in second primary tumors might be the cause of the second primary oral cancer.",
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"abstract": "This study addresses the computational burden often encountered when analyzing gene-gene interactions in relation to time-to-event data, such as patient survival time or time-to-cancer relapse. The goal is to develop a method called Efficient Survival MDR (ES-MDR) that handles survival data by using Martingale Residuals to replace the survival outcome and uses the computationally efficient Quantitative MDR (QMDR) to identify significant interaction models. To demonstrate the strength of ES-MDR, two simulations are designed to evaluate the testing score’s null distribution and to study the success rate of the method. Additionally, ES-MDR is applied on real data with 14,935cases and 12,787 controls of European descent from the OncoArray Consortium that examined the relationship between genetic variants and lung cancer susceptibility. Martingale Residuals, which replace onset age of lung cancer, is treated as the survival outcome, cases are considered event at diagnosis age, and controls are considered censored at interview age. Froman exhaustive search over all one-way and two-way interaction models, we identified a strong association with chr17_41196821_INDEL_T_Dfrom BRCA1 gene and exm1568790_Afrom CBR1 gene as the top SNP-SNP interaction with lung cancer susceptibility at age-of-onset.",
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"abstract": "Lung cancer is the leading cause of cancer death. More than 236,740 new cases of lung cancer patients are expected in 2022, with an estimation of more than 130,180 deaths. Improving the survival rates or the patient's quality of life is partially covered by a common element: treatments. Cancer treatments are well known for the toxic outcomes and secondary effects on the patients. These toxicities cause different health problems that impact the patient's quality of life. Reducing toxicities without a decline on the positive survival effect is an important goal that aims to be pursued from the clinical perspective. On the other hand, clinical guidelines include general knowl-edge about cancer treatment recommendations to assist clinicians. Although they provide treatment recommendations based on cancer disease aspects and individual patient features, a statistical analysis taking into account treatment outcomes is not provided here. Therefore, the comparison between clinical guidelines with treatment patterns found in clinical data, would allow to validate the patterns found, as well as discovering alternative treatment patterns. In this work, we have analyzed a dataset containing lung cancer patients information including patients' data, prescribed treatments and outcomes obtained. Using a Subgroup Discovery method we identify patterns based on cancer stage while relying on treatment outcomes. Results are compared with clinical guide-lines and analyzed based on statistical and medical relevance using Subgroup Discovery metrics.",
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"title": "Categorization of Contact Events as Intended or Unintended using Pre-Contact Kinematic Features",
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"content": "Contact events are essential to the execution of manipulation tasks in virtual reality because they mark the different phases of an interaction, e.g. initial hand-object or tool-object contact. Generally, the nature of contact events can be distinguished in two categories: intended and unintended. Whereas intended contacts typically signalize the successful execution of a task, unintended contacts are the result of bad motor planning or execution, or are caused by accidental movements.Here we explore the use of kinematic features to distinguish intended and unintended contacts during an object placing task executed in virtual reality. A handheld object was placed over a horizontal surface with a vertical planar movement. The experimental setup enabled us to generate unintended contacts by triggering unexpected interruptions during the placing movement.Experimental results show significant differences between distributions of the kinematic features across intended and unintended contacts. These results indicate that the proposed kinematic features enable a robust distinction between intended and unintended contacts independent of substantial variations of movement properties (amplitude, duration, velocity).",
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"id": "proceedings/vrw/2020/6532/0/09090523",
"title": "Pre-Contact Kinematic Features for the Categorization of Contact Events as Intended or unintended",
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"abstract": "Microvessels are frequent targets for research into tissue development and disease progression. These complex and subtle differences between networks are currently difficult to visualize, making sample comparisons subjective and difficult to quantify. These challenges are due to the structure of microvascular networks, which are sparse but space-filling. This results in a complex and interconnected mesh that is difficult to represent and impractical to interpret using conventional visualization techniques. We develop a bi-modal visualization framework, leveraging graph-based and geometry-based techniques to achieve interactive visualization of microvascular networks. This framework allows researchers to objectively interpret the complex and subtle variations that arise when comparing microvascular networks.",
"abstracts": [
{
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"content": "Microvessels are frequent targets for research into tissue development and disease progression. These complex and subtle differences between networks are currently difficult to visualize, making sample comparisons subjective and difficult to quantify. These challenges are due to the structure of microvascular networks, which are sparse but space-filling. This results in a complex and interconnected mesh that is difficult to represent and impractical to interpret using conventional visualization techniques. We develop a bi-modal visualization framework, leveraging graph-based and geometry-based techniques to achieve interactive visualization of microvascular networks. This framework allows researchers to objectively interpret the complex and subtle variations that arise when comparing microvascular networks.",
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"authors": [
{
"affiliation": "University of Houston",
"fullName": "Pavel Govyadinov",
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{
"affiliation": "University of Houston",
"fullName": "Tasha Womack",
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{
"affiliation": "University of Houston",
"fullName": "Jason Eriksen",
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"affiliation": "University of Houston",
"fullName": "David Mayerich",
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{
"affiliation": "University of Houston",
"fullName": "Guoning Chen",
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"abstract": "This work investigates smoothing, fairing, or, more generally, filtering of mesh geometry. The approach transfers the ideas of optimal (Wiener) filtering to the setting of meshes. It extends fairing approaches that use only first order neighborhoods and allows to assume arbitrary local spectral properties of the mesh geometry. The definition of the local autocorrelation allows the design of filters for smoothing as well as for special effects in shape modeling.",
"abstracts": [
{
"abstractType": "Regular",
"content": "This work investigates smoothing, fairing, or, more generally, filtering of mesh geometry. The approach transfers the ideas of optimal (Wiener) filtering to the setting of meshes. It extends fairing approaches that use only first order neighborhoods and allows to assume arbitrary local spectral properties of the mesh geometry. The definition of the local autocorrelation allows the design of filters for smoothing as well as for special effects in shape modeling.",
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"abstract": "Modeling and detection of seasonality in time series is essential for accurate analysis, prediction and anomaly detection. Examples of seasonal effects at different scales abound: the increase in consumer product sales during the holiday season recurs yearly, and similarly household electricity usage has daily, weekly and yearly cycles. The period in real-world time series, however, may be obfuscated by noise and missing values arising in data acquisition. How can one learn the natural periodicity from incomplete multivariate time series? We propose a robust framework for multivariate period detection, called LAPIS. It encodes incomplete and noisy data as a sparse summary via a Ramanujan periodic dictionary. LAPIS can accurately detect a mixture of multiple periods in the same time series even when 70% of the observations are missing. A key innovation of our framework is that it exploits shared periods across individual time series even when they are not correlated or in-phase. Beyond detecting periods, LAPIS enables improvements in downstream applications such as forecasting, missing value imputation and clustering. At the same time our approach scales to large real-world data executing within seconds on datasets of length up to half a million time points.",
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"content": "Modeling and detection of seasonality in time series is essential for accurate analysis, prediction and anomaly detection. Examples of seasonal effects at different scales abound: the increase in consumer product sales during the holiday season recurs yearly, and similarly household electricity usage has daily, weekly and yearly cycles. The period in real-world time series, however, may be obfuscated by noise and missing values arising in data acquisition. How can one learn the natural periodicity from incomplete multivariate time series? We propose a robust framework for multivariate period detection, called LAPIS. It encodes incomplete and noisy data as a sparse summary via a Ramanujan periodic dictionary. LAPIS can accurately detect a mixture of multiple periods in the same time series even when 70% of the observations are missing. A key innovation of our framework is that it exploits shared periods across individual time series even when they are not correlated or in-phase. Beyond detecting periods, LAPIS enables improvements in downstream applications such as forecasting, missing value imputation and clustering. At the same time our approach scales to large real-world data executing within seconds on datasets of length up to half a million time points.",
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"abstract": "There are many techniques that reconstruct continuous 3D surfaces from scattered point data coming from laser range scanners. One of the most commonly used representations are point set surfaces (PSS) defined as the set of stationary points of a moving least squares (MLS) projection operator. One interesting property of the MLS projection is to automatically filter out high frequency noise, that is usually present in raw data due to scanning errors. Unfortunately, the MLS projection also smoothes out any high frequency feature, such as creases or corners, that may be present in the scanned geometry, and does not offer any possibility to distinguish between such feature and noise. The main contribution of this paper, is to present an alternative projection operator for surface reconstruction, based on the enriched reproducing kernel particle approximation (ERKPA), which allows the reconstruction process to account for high frequency features, by letting the user explicitly tag the corresponding areas of the scanned geometry.",
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"abstract": "Segmentation of 3D human body (HB) scan is a very challenging problem in applications exploiting human scan data. To tackle this problem, this paper proposes a topological approach based on Discrete Reeb Graph (DRG) which is an extension of the classical Reeb Graph to unorganized cloud of 3D points. The essence of the approach is detecting critical nodes in the DRG thus permitting the extraction of branches that represent the body parts. Because the human body shape representation is built upon global topological features that are preserved so long as the whole structure of the human body does not change, our approach is quite robust against noise, holes, irregular sampling, moderate reference change and posture variation. Experimental results performed on real scan data demonstrate the validity of our method.",
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"abstract": "Creating 3D geographical simulations have been useful to many applications of geological information. Generally, typical CG simulations cannot properly and accurately represent the multilayer geographical data, due to the lack of ability to display the relations or connections between each soil layer. Also, there are errors in the position of soil layers, and classification of soil layers which does not correspond to the actual data. This research therefore presents a 3D hydrogeological data which can display the complex internal structure of soil layer classification, and can calculate required geological information, such as the position of soil layers, the cross section of soil layers and height contours, accurately in correspondence to the actual data. Calculation to create the simulation begins by specifying the position of each soil layer, and then creating a surface for each soil layer by estimating from the received depth inputs, to calculate the cross section of each soil layer at critical heights calculated based on Morse theory. After that, we create the Reeb graph to create the surface and the complete internal structure of the geological information, which can be displayed in three-dimensions, with the ability to display soil layer cross sections and the soil layer structure, and it can also be used to represent and store the geographical information systematically for efficient retrieval.",
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"content": "Creating 3D geographical simulations have been useful to many applications of geological information. Generally, typical CG simulations cannot properly and accurately represent the multilayer geographical data, due to the lack of ability to display the relations or connections between each soil layer. Also, there are errors in the position of soil layers, and classification of soil layers which does not correspond to the actual data. This research therefore presents a 3D hydrogeological data which can display the complex internal structure of soil layer classification, and can calculate required geological information, such as the position of soil layers, the cross section of soil layers and height contours, accurately in correspondence to the actual data. Calculation to create the simulation begins by specifying the position of each soil layer, and then creating a surface for each soil layer by estimating from the received depth inputs, to calculate the cross section of each soil layer at critical heights calculated based on Morse theory. After that, we create the Reeb graph to create the surface and the complete internal structure of the geological information, which can be displayed in three-dimensions, with the ability to display soil layer cross sections and the soil layer structure, and it can also be used to represent and store the geographical information systematically for efficient retrieval.",
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