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"abstract": "AC LED driving does not use typical DC-DC converter-based driving but uses semiconductor switches and a linear regulator to activate a number of LEDs proportional to the input voltage at any given time. This allows bulky, expensive magnetics to be eliminated from the system. This paper presents a flexible simulation of a common AC LED system to find areas of significant power loss which will allow reduced development time and increased performance of future versions of an AC LED system. Simulations performed on several systems show that the common theme of linear regulator as the dominant loss of the system. Results further indicate that as the number of switches is increased, the loss of the MOSFET could be reduced significantly. For example, with three stacks binary switching, MOSFET loss is 29% of input power while with five switches the loss reduces to less than 1%.",
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"abstract": "Matrix multiplication is used in a variety of applications. It requires a lot of computation time especially for large-scale matrices. Parallel processing is a good choice for matrix multiplication operation. To overcome the efficiencies of existing algorithms for parallel matrix multiplication, a matrix multiplication processing scheme based on vector linear combination (VLC) was presented. The VLC scheme splits the matrix multiplication procedure into two steps. The first step obtains the weighted vectors by scalar multiplication. The second step gets the final result through a linear combination of the weighted vectors with identical row numbers. We present parallel matrix multiplication implementations using MapReduce (MR) based on VLC scheme and explain in detail the MR job. The map method receives the matrix input and generates intermediate (key, value) pairs according to the VLC scheme requirement. The reduce method conducts the scalar multiplication and vectors summation. In the end, the reduce method outputs the result in the way of row vector. Then performance theoretical analysis and experiment result comparing with other algorithms are proposed. Algorithm presented in this paper needs less computation time than other algorithms. Finally, we conclude the paper and propose future works.",
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"content": "Matrix multiplication is used in a variety of applications. It requires a lot of computation time especially for large-scale matrices. Parallel processing is a good choice for matrix multiplication operation. To overcome the efficiencies of existing algorithms for parallel matrix multiplication, a matrix multiplication processing scheme based on vector linear combination (VLC) was presented. The VLC scheme splits the matrix multiplication procedure into two steps. The first step obtains the weighted vectors by scalar multiplication. The second step gets the final result through a linear combination of the weighted vectors with identical row numbers. We present parallel matrix multiplication implementations using MapReduce (MR) based on VLC scheme and explain in detail the MR job. The map method receives the matrix input and generates intermediate (key, value) pairs according to the VLC scheme requirement. The reduce method conducts the scalar multiplication and vectors summation. In the end, the reduce method outputs the result in the way of row vector. Then performance theoretical analysis and experiment result comparing with other algorithms are proposed. Algorithm presented in this paper needs less computation time than other algorithms. Finally, we conclude the paper and propose future works.",
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"normalizedAbstract": "Matrix multiplication is used in a variety of applications. It requires a lot of computation time especially for large-scale matrices. Parallel processing is a good choice for matrix multiplication operation. To overcome the efficiencies of existing algorithms for parallel matrix multiplication, a matrix multiplication processing scheme based on vector linear combination (VLC) was presented. The VLC scheme splits the matrix multiplication procedure into two steps. The first step obtains the weighted vectors by scalar multiplication. The second step gets the final result through a linear combination of the weighted vectors with identical row numbers. We present parallel matrix multiplication implementations using MapReduce (MR) based on VLC scheme and explain in detail the MR job. The map method receives the matrix input and generates intermediate (key, value) pairs according to the VLC scheme requirement. The reduce method conducts the scalar multiplication and vectors summation. In the end, the reduce method outputs the result in the way of row vector. Then performance theoretical analysis and experiment result comparing with other algorithms are proposed. Algorithm presented in this paper needs less computation time than other algorithms. Finally, we conclude the paper and propose future works.",
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"abstract": "Sparse matrix vector multiplication (SpMV) is an important kernel in many areas of scientific computing, especially as a building block for iterative linear system solvers. We study how loss less nonzero compression can be used to overcome memory bandwidth limitations in FPGA-based SpMV implementations. We introduce a dictionary-based compression algorithm which reduces redundant nonzero values to improve memory bandwidth without reducing computation efficiency by making use of spare FPGA resources. We show how a sparse matrix in the CSR format can be converted to the proposed storage format on the CPU and that average compression ratios of 1.14 - 1.40 and up to 2.65 times can be achieved, over CSR, for relevant matrices in our benchmarks.",
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"abstract": "Solving of large sparse matrix linear equations is always the research focus of scientific and engineering calculation field. With the sparsity and symmetry characteristics of coefficient matrix, Compressed Sparse Row (CSR) is adopted in the storage of large sparse matrix linear equations. Under the condition of CSR, Symmetrica Successive Over Relaxations-Preconditioned Conjugate Gradient method (SSOR-PCG) is employed in the solution of large sparse matrix linear equations.",
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"abstract": "The finite element method (FEM) is used in a variety of numerical simulations to solve partial differential equations (PDEs) in solving a large linear system of equations at one of the computational phases. The cost of solving a large linear system with a linear solver often overwhelms with other computational phases in FEM analysis. For this reason, graphics processing units (GPUs) are widely adopted in iterative linear solvers such as generalized minimum residual (GMRES) to speed up the analysis. Nevertheless, there are two major drawbacks in iterative linear solvers in GPU. First, the system matrix does not fit in GPU memory as the scale of simulation increases. In such cases, we may face a abnormal slowdown of the GPU due to data transfer between the main memory and GPU memory. Second, the sparse matrix-vector product (SpMV) in the solvers is a hotspot because SpMV requires a lot of indirect memory access for non-zero elements of the sparse matrix. To solve these problems, a compression method for the sparse matrix storage format is needed as a reduction method for both required memory space and memory access. In this paper, we propose an improved compression method for conventional sparse matrix storage formats such as compressed sparse row (CSR) and ELLPACK (ELL). Assuming that part of the column indexes in those formats is consecutive and such part can be described with its minimum and maximum values. Such consecutive indexes will be compressed into two integers. Using this idea, the partial sum of the product for that part can be calculated without having to load those consecutive indexes from memory. Thereby, this compression method reduces the memory usage and memory access. In our experiments, our compression method could also reduce the memory usage 8 out of 10 matrices in CSR and ELL. In particular, the memory reduction ratio of the pwtk matrix is up to 26.6% in CSR. Furthermore, our compression method reduced the execution time of SpMV compared with CSR on various matrix.",
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"content": "The finite element method (FEM) is used in a variety of numerical simulations to solve partial differential equations (PDEs) in solving a large linear system of equations at one of the computational phases. The cost of solving a large linear system with a linear solver often overwhelms with other computational phases in FEM analysis. For this reason, graphics processing units (GPUs) are widely adopted in iterative linear solvers such as generalized minimum residual (GMRES) to speed up the analysis. Nevertheless, there are two major drawbacks in iterative linear solvers in GPU. First, the system matrix does not fit in GPU memory as the scale of simulation increases. In such cases, we may face a abnormal slowdown of the GPU due to data transfer between the main memory and GPU memory. Second, the sparse matrix-vector product (SpMV) in the solvers is a hotspot because SpMV requires a lot of indirect memory access for non-zero elements of the sparse matrix. To solve these problems, a compression method for the sparse matrix storage format is needed as a reduction method for both required memory space and memory access. In this paper, we propose an improved compression method for conventional sparse matrix storage formats such as compressed sparse row (CSR) and ELLPACK (ELL). Assuming that part of the column indexes in those formats is consecutive and such part can be described with its minimum and maximum values. Such consecutive indexes will be compressed into two integers. Using this idea, the partial sum of the product for that part can be calculated without having to load those consecutive indexes from memory. Thereby, this compression method reduces the memory usage and memory access. In our experiments, our compression method could also reduce the memory usage 8 out of 10 matrices in CSR and ELL. In particular, the memory reduction ratio of the pwtk matrix is up to 26.6% in CSR. Furthermore, our compression method reduced the execution time of SpMV compared with CSR on various matrix.",
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"abstract": "Multidimensional data summarization is a fundamental mechanism to accelerate the computation of machine learning (ML) models. On the other hand, relational DBMSs can scale beyond main memory limits, they can evaluate SQL queries in parallel and they hide complex internal system details. Heeding this motivation, we present a wide spectrum of alternative SQL queries to compute a summarization matrix that significantly accelerates the computation of many ML models in a data science language (e.g. Python). We consider two fundamental storage layouts: horizontal and vertical. Our proposed SQL queries lead to diverse query plans, which in turn yield highly different processing times. We identify storage layout (row vs column) and relational join optimization as two key performance factors. After careful analysis and bechmarking, we recommend two SQL queries that can work across DBMSs. We show UDFs, an extensibility mechanism, despite being faster, they have many disadvantages compared to plain SQL queries (not portable, system-dependent limitations, main memory, manual optimization required). An extensive experimental evaluation shows the pros and cons of our proposed SQL-based solution. Columnar storage provides an order of magnitude performance improvement over row storage. Moreover, SQL queries can match UDF performance on sparse matrices. We show that by exploiting the summarization matrix in Python, the computation of two popular statistical models (Linear Regression and PCA), is much faster than popular Python libraries (on a single machine) and also faster than Apache Spark (in parallel, in-memory solution for big data clusters). We also show our SQL-based solution exhibits linear speedup in parallel processing. In short, the DBMS can act as a backend linear algebra kernel.",
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"abstract": "In this paper, a random rough subspace based neural network ensemble method is proposed for insurance fraud detection. In this method, rough set reduction is firstly employed to generate a set of reductions which can keep the consistency of data information. Secondly, the reductions are randomly selected to construct a subset of reductions. Thirdly, each of the selected reductions is used to train a neural network classifier based on the insurance data. Finally, the trained neural network classifiers are combined using ensemble strategies. For validation, a real automobile insurance case is used to test the effectiveness and efficiency of our proposed method with two popular evaluation criteria including the percentage correctly classified (PCC) and the receive operating characteristic (ROC) curve. The experimental results show that our proposed model outperforms single classifier and other models used in comparison. The findings of this study reseal that the random rough subspace based neural network ensemble method can provide a faster and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection.",
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"content": "In this paper, a random rough subspace based neural network ensemble method is proposed for insurance fraud detection. In this method, rough set reduction is firstly employed to generate a set of reductions which can keep the consistency of data information. Secondly, the reductions are randomly selected to construct a subset of reductions. Thirdly, each of the selected reductions is used to train a neural network classifier based on the insurance data. Finally, the trained neural network classifiers are combined using ensemble strategies. For validation, a real automobile insurance case is used to test the effectiveness and efficiency of our proposed method with two popular evaluation criteria including the percentage correctly classified (PCC) and the receive operating characteristic (ROC) curve. The experimental results show that our proposed model outperforms single classifier and other models used in comparison. The findings of this study reseal that the random rough subspace based neural network ensemble method can provide a faster and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection.",
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"abstract": "Among the current medical insurance fraud detection methods, fraudulent nodes that can be detected mainly depend on empirical rules to determine medical insurance fraud patterns or a large amount of fraud data for machine learning. However, since the increasing concealment of fraud, rules and models designed based on empirical rules cannot cope with the rapidly changing fraud patterns. At the same time, the labels of fraudulent samples in medical insurance data are small in magnitude and unevenly distributed, making it challenging to support active mining. In addition, if the classical meta-path or meta-structure is used to model the medical insurance heterogeneous information network, patient information will be lost when connecting multi-order paths such as patient-physician-disease and bring ambiguous information that does not match the actual medical insurance data. In order to solve the above problems, this paper proposes a node similarity-based search method for medical insurance heterogeneous information network. On the one hand, the method uses GraphSAGE to learn the global low-dimensional representation of patient nodes and recalls nodes relevant to the query node as search candidate sets. On the other hand, by defining weighted meta-path and weighted meta-structure, the method solves the problem of ambiguity in the representation of heterogeneous information network. Based on weighted meta-path and weighted meta-structure, new algorithms W-Pathsim and W-Strucsim are proposed to calculate the similarity of nodes in heterogeneous information network. Finally, our method uses a multi-layer perceptron to return the nodes list that is highly similar to the query node in the candidate set to assist in medical insurance review. Experiments show that our method is better than the compared baseline methods.",
"abstracts": [
{
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"content": "Among the current medical insurance fraud detection methods, fraudulent nodes that can be detected mainly depend on empirical rules to determine medical insurance fraud patterns or a large amount of fraud data for machine learning. However, since the increasing concealment of fraud, rules and models designed based on empirical rules cannot cope with the rapidly changing fraud patterns. At the same time, the labels of fraudulent samples in medical insurance data are small in magnitude and unevenly distributed, making it challenging to support active mining. In addition, if the classical meta-path or meta-structure is used to model the medical insurance heterogeneous information network, patient information will be lost when connecting multi-order paths such as patient-physician-disease and bring ambiguous information that does not match the actual medical insurance data. In order to solve the above problems, this paper proposes a node similarity-based search method for medical insurance heterogeneous information network. On the one hand, the method uses GraphSAGE to learn the global low-dimensional representation of patient nodes and recalls nodes relevant to the query node as search candidate sets. On the other hand, by defining weighted meta-path and weighted meta-structure, the method solves the problem of ambiguity in the representation of heterogeneous information network. Based on weighted meta-path and weighted meta-structure, new algorithms W-Pathsim and W-Strucsim are proposed to calculate the similarity of nodes in heterogeneous information network. Finally, our method uses a multi-layer perceptron to return the nodes list that is highly similar to the query node in the candidate set to assist in medical insurance review. Experiments show that our method is better than the compared baseline methods.",
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"title": "Hierarchical Multi-Modal Fusion on Dynamic Heterogeneous Graph for Health Insurance Fraud Detection",
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"abstract": "In this paper, we devote to aggregating longitudinal and multi-modal information from heterogeneous neighbors to obtain an accurate node embedding on the dynamic heterogeneous graph. Recently, Heterogeneous Graph Neural Networks (GNNs) have attracted extensive attention in fraud detection. However, when faced with longitudinal and multi-modal data such as health insurance records, existing GNN-based fraud detectors always discard the multi-modal information (e.g., medication and treatment) of heterogeneous neighbors and ignore the inconsistent claimer behavior in the longitudinal records. To fully utilize the information, we represent the records in the form of a dynamic heterogeneous graph, and propose Hierarchical Multi-modal Fusion Graph Neural Network (HMF-GNN) which learns not only topological information, but also embeddings of longitudinal and multi-modal entities to improve the performance of fraud detection. Experimental results on two real-world health insurance datasets demonstrate that HMF-GNN outperforms state-of-the-art graph embedding methods and GNN-based fraud detectors.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In this paper, we devote to aggregating longitudinal and multi-modal information from heterogeneous neighbors to obtain an accurate node embedding on the dynamic heterogeneous graph. Recently, Heterogeneous Graph Neural Networks (GNNs) have attracted extensive attention in fraud detection. However, when faced with longitudinal and multi-modal data such as health insurance records, existing GNN-based fraud detectors always discard the multi-modal information (e.g., medication and treatment) of heterogeneous neighbors and ignore the inconsistent claimer behavior in the longitudinal records. To fully utilize the information, we represent the records in the form of a dynamic heterogeneous graph, and propose Hierarchical Multi-modal Fusion Graph Neural Network (HMF-GNN) which learns not only topological information, but also embeddings of longitudinal and multi-modal entities to improve the performance of fraud detection. Experimental results on two real-world health insurance datasets demonstrate that HMF-GNN outperforms state-of-the-art graph embedding methods and GNN-based fraud detectors.",
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"normalizedAbstract": "In this paper, we devote to aggregating longitudinal and multi-modal information from heterogeneous neighbors to obtain an accurate node embedding on the dynamic heterogeneous graph. Recently, Heterogeneous Graph Neural Networks (GNNs) have attracted extensive attention in fraud detection. However, when faced with longitudinal and multi-modal data such as health insurance records, existing GNN-based fraud detectors always discard the multi-modal information (e.g., medication and treatment) of heterogeneous neighbors and ignore the inconsistent claimer behavior in the longitudinal records. To fully utilize the information, we represent the records in the form of a dynamic heterogeneous graph, and propose Hierarchical Multi-modal Fusion Graph Neural Network (HMF-GNN) which learns not only topological information, but also embeddings of longitudinal and multi-modal entities to improve the performance of fraud detection. Experimental results on two real-world health insurance datasets demonstrate that HMF-GNN outperforms state-of-the-art graph embedding methods and GNN-based fraud detectors.",
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"authors": [
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"affiliation": "Xiamen University,Department of Automation,Xiamen,China",
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"affiliation": "College of Computer and Information Engineering, Xiamen University of Technology,Xiamen,China",
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"abstract": "After years of development, the traditional decision tree algorithm implementations represented by LightGBM has been very mature and widely used in various classification problems. However, when applying LightGBM to detecting fraud in health insurance data, we find that the performance of LightGBM is not ideal due to the large imbalance between the number of fraud examples and normal examples. To solve this problem, we propose a simple and effective LightGBM-based hard example mining algorithm (LHEM) for detecting health insurance fraud. Our motivation is to detect the large number of simple examples and the small number of hard examples in the dataset. Selecting these hard examples and discarding simple examples can balance the ratio of fraud and normal examples, thus improving the performance of the original model. We use the health insurance data collected in Zhejiang Jinhua to test our new method, and prove that the performance of our new method is better than LightGBM.",
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"abstract": "To solve the problems of low contrast and blurred details in low light images, a brand-new low light image enhancement algorithm combined with sky region segmentation is proposed for color images in this paper. In the proposed algorithm, the image enhancement is processed in HSV space to achieve color consistency. An adaptive global enhancement method is adopted in brightness enhancement. In addition, a simple nonlinear transformation is applied on the saturation component combined with sky region segmentation. Experimental results verify the effectiveness of the proposed algorithm, and reveal the proposed algorithm could improve the brightness, the contrast and details of low light images, while maintaining the color consistency to make the images more in line with the visual effects of human eyes.",
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"abstract": "The segmentation of sky images into regions of cloud and clear sky allows atmospheric scientists to determine the fraction of cloud cover and the distribution of cloud without resorting to subjective estimates by a human observer. This is a challenging problem because cloud boundaries and cirroform cloud regions are often semi-transparent and indistinct. In this study, we propose a lightweight, unsupervised methodology to identify cloud regions in ground-based hemispherical sky images. Our method offers a fast and adaptive approach without the necessity of fixed thresholds by utilizing K-means clustering on transformed pixel values. We present the results of our method for two data sets and compare them with three different methods in the literature.",
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"abstract": "Inclement weather, haze, and fog severely decrease the performance of outdoor imaging systems. Due to a large range of the depth-of-field, most image dehazing or enhancement methods suffer from color distortions and halo artifacts when applied to real-world hazy outdoor scenes, especially those with the sky. To effectively recover details in both distant and nearby regions as well as to preserve color fidelity of the sky, in this study, we propose a novel image defogging and enhancement approach based on a replaceable plug-in segmentation module and region-adaptive processing. First, regions of the grayish sky, pure white objects, and other parts are separated. Second, a luminance-inverted multi-scale Retinex with color restoration (MSRCR) and region-ratio-based adaptive Gamma correction are applied to non-grayish and non-white areas. Finally, the enhanced regions are stitched seamlessly by using a mean-filtered region mask. Extensive experiments show that the proposed approach not only outperforms several state-of-the-art defogging methods in terms of both visibility and color fidelity, but also provides enhanced outputs with fewer artifacts and halos, particularly in sky regions.",
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"title": "Augmented Reality based Surgical Navigation for Percutaneous Endoscopic Transforaminal Discectomy",
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"abstract": "Fluoroscopic guidance is a critical step for the puncture procedure in percutaneous endoscopic transforaminal discectomy (PETD). In this paper, we propose an AR surgical navigation system for PETD based on multi-modality imaging information, which contain fluoroscopy, optical tracking and depth camera. We also present a self-adaptive calibration and transformation method between 6-DOF optical tracking device and depth camera, which are in different coordinate systems. With substantially reduced frequency of fluoroscopy imaging, the system can accurately track and superimpose the virtual puncture needle on fluoroscopy images in real-time.",
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"title": "Predictive Modeling of Wildfires in the United States",
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"abstract": "Based on the social exchange theory, this research uses partner power as the moderating variable to explore the relationship between the trust, commitment, and mutual dependencies among organizations. This research proposes an integrated conceptual framework and conducts empirical research on procurement personnel of medical institutions. There are 321 valid questionnaires. The research results show that: 1. The higher the medical institution's awareness of social exchange characteristics, it will have a higher attitude of trust and commitment to its suppliers, and deepen its dependence on suppliers. 2. The relationship among trust, commitment and dependency. After the medical institutions trust their suppliers, they must first establish and maintain the commitment of long-term relationship, and then the trust to the supplier will generate dependency through commitment. 3. In the buying and selling relationship, social exchange characteristics often show a positive relationship with the partner dependency. Once the medical institution feels that the partner has high power, the higher the communication and partner reputation, the medical institution will instead try to reduce the dependency on partners and get rid of the disadvantage of being controlled. 4. In the case of high partner power, the power of the supplier can affect the decision or behavior of the medical institution, and high power dependency will also make social exchange characteristics (anticipated benefits, communication, partner reputation) have an inhibitory effect on dependency.",
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"content": "Based on the social exchange theory, this research uses partner power as the moderating variable to explore the relationship between the trust, commitment, and mutual dependencies among organizations. This research proposes an integrated conceptual framework and conducts empirical research on procurement personnel of medical institutions. There are 321 valid questionnaires. The research results show that: 1. The higher the medical institution's awareness of social exchange characteristics, it will have a higher attitude of trust and commitment to its suppliers, and deepen its dependence on suppliers. 2. The relationship among trust, commitment and dependency. After the medical institutions trust their suppliers, they must first establish and maintain the commitment of long-term relationship, and then the trust to the supplier will generate dependency through commitment. 3. In the buying and selling relationship, social exchange characteristics often show a positive relationship with the partner dependency. Once the medical institution feels that the partner has high power, the higher the communication and partner reputation, the medical institution will instead try to reduce the dependency on partners and get rid of the disadvantage of being controlled. 4. In the case of high partner power, the power of the supplier can affect the decision or behavior of the medical institution, and high power dependency will also make social exchange characteristics (anticipated benefits, communication, partner reputation) have an inhibitory effect on dependency.",
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"abstract": "Due to recent climate changes, we have seen more frequent and severe wildfires in the United States. Predicting wildfires is critical for natural disaster prevention and mitigation. Advances in technologies in data processing and communication enabled us to access remote sensing data. With the remote sensing data, valuable spatiotemporal statistical models can be created and used for resource management practices. This paper proposes a distributed learning framework that shares local data collected in ten locations in the western USA throughout the local agents. The local agents aim to predict wildfire grid maps one, two, three, and four weeks in advance while online processing the remote sensing data stream. The proposed model has distinct features that address the characteristic need in prediction evaluations, including dynamic online estimation and time-series modeling. Local fire event triggers are not isolated between locations, and there are confounding factors when local data is analyzed due to incomplete state observations. Compared to existing approaches that do not account for incomplete state observation within wildfire time-series data, on average, we can achieve higher prediction performance.",
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"abstract": "This research utilizes wildfire records between 1911 and 2015 to train various models to predict fire size through using temperature, wind, humidity, and precipitation as features. Our results show 1) Decision Tree based Classifier outperforms both linear and ridge regression 2) Government entities can leverage our methodology to manage wildfires more efficiently, effectively, and decreasing monetary damages.",
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"abstract": "Despite the advent in rendering, editing and preprocessing methods of 3D meshes, their real-time execution remains still infeasible for large-scale meshes. To ease and accelerate such processes, mesh simplification methods have been introduced with the aim to reduce the mesh resolution while preserving its appearance. In this work we attempt to tackle the novel task of learnable and differentiable mesh simplification. Compared to traditional simplification approaches that collapse edges in a greedy iterative manner, we propose a fast and scalable method that simplifies a given mesh in one-pass. The proposed method unfolds in three steps. Initially, a subset of the input vertices is sampled using a sophisticated extension of random sampling. Then, we train a sparse attention network to propose candidate triangles based on the edge connectivity of the sampled vertices. Finally, a classification network estimates the probability that a candidate triangle will be included in the final mesh. The fast, lightweight and differentiable properties of the proposed method makes it possible to be plugged in every learnable pipeline without introducing a significant overhead. We evaluate both the sampled vertices and the generated triangles under several appearance error measures and compare its performance against several state-of-the-art baselines. Furthermore, we showcase that the running performance can be up to 10× faster than traditional methods.",
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"abstract": "Individuals who have upper limb movement problems include people with cerebral palsy (CP) and stroke victims. Both these conditions lead to difficulties in daily activities such as reaching, grasping etc. Virtual reality (VR), which could provide a repetitive multimodal task-oriented rehabilitation environment for patients to undertake self-training in safety, is considered to be a suitable tool for medical health rehabilitation. Using electromyography (EMG) biofeedback in rehabilitation could provide patients with opportunities to improve the ability by assessing their muscle activity response and learning self-control of movement during specific training tasks. This paper presents a study on developing EMG as an important interactive tool in a VR based system for hand rotation and grasping motion rehabilitation. The input interface includes an EMG system and a real-time magnetic motion tracking system, and the output interface is a PC monitor. The developed EMG biofeedback based VR system enables the user to interact with virtual objects in real-time with multiform feedback. Ten healthy subjects participated in the preliminary task evaluation test, and the results suggest that the specified skills have improved during training. The beneficial effects of the developed system indicate the potential values for further clinical application.",
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"abstract": "3D morphable models are widely used for the shape representation of an object class in computer vision and graphics applications. In this work, we focus on deep 3D morphable models that directly apply deep learning on 3D mesh data with a hierarchical structure to capture information at multiple scales. While great efforts have been made to design the convolution operator, how to best aggregate vertex features across hierarchical levels deserves further attention. In contrast to resorting to mesh decimation, we propose an attention based module to learn mapping matrices for better feature aggregation across hierarchical levels. Specifically, the mapping matrices are generated by a compatibility function of the keys and queries. The keys and queries are trainable variables, learned by optimizing the target objective, and shared by all data samples of the same object class. Our proposed module can be used as a train-only drop-in replacement for the feature aggregation in existing architectures for both downsampling and upsampling. Our experiments show that through the end-to-end training of the mapping matrices, we achieve state-of-the-art results on a variety of 3D shape datasets in comparison to existing morphable models.",
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"abstract": "Multilevel representations and mesh reduction techniques have been used for accelerating the processing and the rendering of large datasets representing scalar- or vector-valued functions defined on complex 2D or 3D meshes. We present a method based on finite element approximations which combines these two approaches in a new and unique way that is conceptually simple and theoretically sound. The main idea is to consider mesh reduction as an approximation problem in appropriate finite element spaces. Starting with a very coarse triangulation of the functional domain, a hierarchy of highly non-uniform tetrahedral (or triangular in 2D) meshes is generated adaptively by local refinement. This process is driven by controlling the local error of the piecewise linear finite element approximation of the function on each mesh element. A reliable and efficient computation of the global approximation error and a multilevel preconditioned conjugate gradient solver are the key components of the implementation. In order to analyze the properties and advantages of the adaptively generated tetrahedral meshes, we implemented two volume visualization algorithms: an iso-surface extractor and a ray-caster. Both algorithms, while conceptually simple, show significant speedups over conventional methods delivering comparable rendering quality from adaptively compressed datasets.",
"abstracts": [
{
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"content": "Multilevel representations and mesh reduction techniques have been used for accelerating the processing and the rendering of large datasets representing scalar- or vector-valued functions defined on complex 2D or 3D meshes. We present a method based on finite element approximations which combines these two approaches in a new and unique way that is conceptually simple and theoretically sound. The main idea is to consider mesh reduction as an approximation problem in appropriate finite element spaces. Starting with a very coarse triangulation of the functional domain, a hierarchy of highly non-uniform tetrahedral (or triangular in 2D) meshes is generated adaptively by local refinement. This process is driven by controlling the local error of the piecewise linear finite element approximation of the function on each mesh element. A reliable and efficient computation of the global approximation error and a multilevel preconditioned conjugate gradient solver are the key components of the implementation. In order to analyze the properties and advantages of the adaptively generated tetrahedral meshes, we implemented two volume visualization algorithms: an iso-surface extractor and a ray-caster. Both algorithms, while conceptually simple, show significant speedups over conventional methods delivering comparable rendering quality from adaptively compressed datasets.",
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"Mesh Generation Multilevel Finite Element Method Adaptive Mesh Optimization Volume Data Visualization Multilevel Representations Mesh Reduction Techniques Adaptively Compressed Datasets Large Datasets Scalar Valued Functions Vector Valued Functions Global Approximation Error Coarse Triangulation Highly Nonuniform Tetrahedral Meshes Highly Nonuniform Triangular Meshes Adaptive Mesh Generation Local Refinement Local Error Control Piecewise Linear Finite Element Approximation Multilevel Preconditioned Conjugate Gradient Solver Iso Surface Extractor Ray Caster Speedup Rendering Quality"
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"abstract": "The problem of how to model a given planar curve by local splines defined by a number of knot points which is much smaller than the number of points defining the curve is addressed. To solve the problem an optimization technique is applied that minimizes an error norm, which reflects the discrepancy of the splines to the original curve. The error norm is defined as the total squared distance of the sample points from the local spline model. The objective function for the optimization process is the above error norm plus a term which ensures convergence to the correct solution. The objective function is minimized with respect to a set of independent variables, which are the locations of the knot points defining the local spline model. The initial locations of the knot points are selected heuristically. Experimental results show that the method converges to a solution that compares favorably with previous techniques.<>",
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"content": "The problem of how to model a given planar curve by local splines defined by a number of knot points which is much smaller than the number of points defining the curve is addressed. To solve the problem an optimization technique is applied that minimizes an error norm, which reflects the discrepancy of the splines to the original curve. The error norm is defined as the total squared distance of the sample points from the local spline model. The objective function for the optimization process is the above error norm plus a term which ensures convergence to the correct solution. The objective function is minimized with respect to a set of independent variables, which are the locations of the knot points defining the local spline model. The initial locations of the knot points are selected heuristically. Experimental results show that the method converges to a solution that compares favorably with previous techniques.<>",
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"title": "2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS)",
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"abstract": "In this paper, we show how one may (efficiently) construct two types of extremal combinatorial objects whose existence was previously conjectural. •Panchromatic Graphs: For fixed <tex>Z_$k\\in \\mathbb{N}$_Z</tex>, a <tex>Z_$k$_Z</tex>-panchromatic graph is, roughly speaking, a balanced bipartite graph with one partition class equipartitioned into <tex>Z_$k$_Z</tex> colour classes in which the common neighbourhoods of panchromatic <tex>Z_$k$_Z</tex>-sets of vertices are much larger than those of <tex>Z_$k$_Z</tex>-sets that repeat a colour. The question of their existence was raised by Karthik and Manurangsi [Combinatorica 2020]. •Threshold Graphs: For fixed <tex>Z_$k\\in \\mathbb{N}$_Z</tex>, a <tex>Z_$k$_Z</tex>-threshold graph is, roughly speaking, a balanced bipartite graph in which the common neighbourhoods of <tex>Z_$k$_Z</tex>-sets of vertices on one side are much larger than those of (<tex>Z_$k+1$_Z</tex>)-sets. The question of their existence was raised by Lin [JACM 2018]. Concretely, we provide probability distributions over graphs from which we can efficiently sample these objects in near linear time. These probability distributions are defined via varieties cut out by (carefully chosen) random polynomials, and the analysis of these constructions relies on machinery from algebraic geometry (such as the Lang-Weil estimate, for example). The technical tools developed to accomplish this might be of independent interest. As applications of our constructions, we show the following conditional time lower bounds on the parameterized set intersection problem where, given a collection of <tex>Z_$n$_Z</tex> sets over universe [<tex>Z_$n$_Z</tex>] and a parameter <tex>Z_$k$_Z</tex>, the goal is to find <tex>Z_$k$_Z</tex> sets with the largest intersection. •Assuming ETH, for any computable function <tex>Z_$F:\\mathbb{N}\\rightarrow \\mathbb{N}$_Z</tex>, no <tex>Z_$n^{o(k)}$_Z</tex>-time algorithm can approximate the parameterized set intersection problem up to factor <tex>Z_$F(k)$_Z</tex>. This improves considerably on the previously best-known result under ETH due to Lin [JACM 2018], who ruled out any <tex>Z_$n^{o(\\sqrt{k})}$_Z</tex> time approximation algorithm for this problem. •Assuming SETH, for every <tex>Z_$\\varepsilon > 0$_Z</tex> and any computable function <tex>Z_$F:\\mathbb{N} \\rightarrow \\mathbb{N}$_Z</tex>, no <tex>Z_$n^{k-\\varepsilon}$_Z</tex>-time algorithm can approximate the parameterized set intersection problem up to factor <tex>Z_$F(k)$_Z</tex>. No result of comparable strength was previously known under SETH, even for solving this problem exactly. Both these time lower bounds are obtained by composing panchromatic graphs with instances of the coloured variant of the parameterized set intersection problem (for which tight lower bounds were previously known).",
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"content": "In this paper, we show how one may (efficiently) construct two types of extremal combinatorial objects whose existence was previously conjectural. •Panchromatic Graphs: For fixed <tex>$k\\in \\mathbb{N}$</tex>, a <tex>$k$</tex>-panchromatic graph is, roughly speaking, a balanced bipartite graph with one partition class equipartitioned into <tex>$k$</tex> colour classes in which the common neighbourhoods of panchromatic <tex>$k$</tex>-sets of vertices are much larger than those of <tex>$k$</tex>-sets that repeat a colour. The question of their existence was raised by Karthik and Manurangsi [Combinatorica 2020]. •Threshold Graphs: For fixed <tex>$k\\in \\mathbb{N}$</tex>, a <tex>$k$</tex>-threshold graph is, roughly speaking, a balanced bipartite graph in which the common neighbourhoods of <tex>$k$</tex>-sets of vertices on one side are much larger than those of (<tex>$k+1$</tex>)-sets. The question of their existence was raised by Lin [JACM 2018]. Concretely, we provide probability distributions over graphs from which we can efficiently sample these objects in near linear time. These probability distributions are defined via varieties cut out by (carefully chosen) random polynomials, and the analysis of these constructions relies on machinery from algebraic geometry (such as the Lang-Weil estimate, for example). The technical tools developed to accomplish this might be of independent interest. As applications of our constructions, we show the following conditional time lower bounds on the parameterized set intersection problem where, given a collection of <tex>$n$</tex> sets over universe [<tex>$n$</tex>] and a parameter <tex>$k$</tex>, the goal is to find <tex>$k$</tex> sets with the largest intersection. •Assuming ETH, for any computable function <tex>$F:\\mathbb{N}\\rightarrow \\mathbb{N}$</tex>, no <tex>$n^{o(k)}$</tex>-time algorithm can approximate the parameterized set intersection problem up to factor <tex>$F(k)$</tex>. This improves considerably on the previously best-known result under ETH due to Lin [JACM 2018], who ruled out any <tex>$n^{o(\\sqrt{k})}$</tex> time approximation algorithm for this problem. •Assuming SETH, for every <tex>$\\varepsilon > 0$</tex> and any computable function <tex>$F:\\mathbb{N} \\rightarrow \\mathbb{N}$</tex>, no <tex>$n^{k-\\varepsilon}$</tex>-time algorithm can approximate the parameterized set intersection problem up to factor <tex>$F(k)$</tex>. No result of comparable strength was previously known under SETH, even for solving this problem exactly. Both these time lower bounds are obtained by composing panchromatic graphs with instances of the coloured variant of the parameterized set intersection problem (for which tight lower bounds were previously known).",
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"normalizedAbstract": "In this paper, we show how one may (efficiently) construct two types of extremal combinatorial objects whose existence was previously conjectural. •Panchromatic Graphs: For fixed -, a --panchromatic graph is, roughly speaking, a balanced bipartite graph with one partition class equipartitioned into - colour classes in which the common neighbourhoods of panchromatic --sets of vertices are much larger than those of --sets that repeat a colour. The question of their existence was raised by Karthik and Manurangsi [Combinatorica 2020]. •Threshold Graphs: For fixed -, a --threshold graph is, roughly speaking, a balanced bipartite graph in which the common neighbourhoods of --sets of vertices on one side are much larger than those of (-)-sets. The question of their existence was raised by Lin [JACM 2018]. Concretely, we provide probability distributions over graphs from which we can efficiently sample these objects in near linear time. These probability distributions are defined via varieties cut out by (carefully chosen) random polynomials, and the analysis of these constructions relies on machinery from algebraic geometry (such as the Lang-Weil estimate, for example). The technical tools developed to accomplish this might be of independent interest. As applications of our constructions, we show the following conditional time lower bounds on the parameterized set intersection problem where, given a collection of - sets over universe [-] and a parameter -, the goal is to find - sets with the largest intersection. •Assuming ETH, for any computable function -, no --time algorithm can approximate the parameterized set intersection problem up to factor -. This improves considerably on the previously best-known result under ETH due to Lin [JACM 2018], who ruled out any - time approximation algorithm for this problem. •Assuming SETH, for every - and any computable function -, no --time algorithm can approximate the parameterized set intersection problem up to factor -. No result of comparable strength was previously known under SETH, even for solving this problem exactly. Both these time lower bounds are obtained by composing panchromatic graphs with instances of the coloured variant of the parameterized set intersection problem (for which tight lower bounds were previously known).",
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"Computable Function",
"N Sup O K Sup Time Approximation Algorithm",
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"Approximation Algorithms",
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"Bipartite Graph",
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"Hardness Of Approximation",
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"affiliation": "Rutgers University,Department of Mathematics,Piscataway,USA",
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"abstract": "We present Face Swapping GAN (FSGAN) for face swapping and reenactment. Unlike previous work, FSGAN is subject agnostic and can be applied to pairs of faces without requiring training on those faces. To this end, we describe a number of technical contributions. We derive a novel recurrent neural network (RNN)-based approach for face reenactment which adjusts for both pose and expression variations and can be applied to a single image or a video sequence. For video sequences, we introduce continuous interpolation of the face views based on reenactment, Delaunay Triangulation, and barycentric coordinates. Occluded face regions are handled by a face completion network. Finally, we use a face blending network for seamless blending of the two faces while preserving target skin color and lighting conditions. This network uses a novel Poisson blending loss which combines Poisson optimization with perceptual loss. We compare our approach to existing state-of-the-art systems and show our results to be both qualitatively and quantitatively superior.",
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"content": "We present Face Swapping GAN (FSGAN) for face swapping and reenactment. Unlike previous work, FSGAN is subject agnostic and can be applied to pairs of faces without requiring training on those faces. To this end, we describe a number of technical contributions. We derive a novel recurrent neural network (RNN)-based approach for face reenactment which adjusts for both pose and expression variations and can be applied to a single image or a video sequence. For video sequences, we introduce continuous interpolation of the face views based on reenactment, Delaunay Triangulation, and barycentric coordinates. Occluded face regions are handled by a face completion network. Finally, we use a face blending network for seamless blending of the two faces while preserving target skin color and lighting conditions. This network uses a novel Poisson blending loss which combines Poisson optimization with perceptual loss. We compare our approach to existing state-of-the-art systems and show our results to be both qualitatively and quantitatively superior.",
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"abstract": "Harmonizing the style of all the furniture placed within a constrained space/scene has been regarded as one of the most important tasks in interior design. Most previous style analysis works measure the style similarity or compatibility of the objects based on predefined geometric features extracted from 3D models. However, “style” is a high-level semantic concept, which is difficult to be described explicitly by handcrafted geometric features. Deep neural network has been claimed to have more powerful ability to mimic the perception of human visual cortex. Therefore, in this work we utilize Triplet Convolutional Neural Network (Triplet CNN) to analyze style compatibility between 3D furniture models of different classes (e.g., a table and a lamp). It should be noted that analyzing the style compatibility between two or more furniture of different classes is quite difficult, as the given furniture may have distinctive structures or geometric elements. We conducted experiments based on a collected dataset containing 420 textured 3D furniture models. A group of raters were recruited from Amazon Mechanical Turk (AMT) to evaluate the comparative suitability of paired models within the dataset. The experimental results reveal that the proposed furniture style compatibility method based on deep learning is better than the state-of-the-art method and can be used for furniture recommendation.",
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