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"abstract": "There have been several proposals for transforming and querying XML structures, but very few of them offer a data logic approach. This article describes data structures and some primitives as a framework to efficiently manipulate XML well-formed documents. The implementation of the querying system, based on the proposed framework, is performed in the Prolog programming language. Results indicate that this approach allows the solution of both deductive and recursive queries from XML documents.",
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"abstract": "Finding appropriate permutations of rows and columns of a matrix may help users to see hidden patterns in datasets. This paper presents a set of binarization-based matrix reordering algorithms able to reveal some patterns in a quantitative data set. In these algorithms, matrix binarization converts a matrix into a set of binary ones, from which the algorithms calculate desired groups of similar rows and columns. PQR trees provide a linear order of rows and columns that obey these groups as much as possible. These algorithms use mean or median filter as smoothing techniques to minimize data noise in intermediate matrix permutation steps. They also use feature vectors or thresholding for defining binarization thresholds in intermediate steps. Our experiments with synthetic matrices revealed that our algorithms are competitive with other matrix reordering algorithms in terms of quality of reordering (Moore stress) and runtime. We observed that our set of algorithms is suitable to reveal Circumplex pattern with all tested noise ratios, and other data canonical patterns with low noise ratio.",
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"abstract": "Many sparse matrix computations can be speeded up if the matrix is first reordered. Reordering was originally developed for direct methods but it has recently become popular for improving the cache locality of parallel iterative solvers since reordering the matrix to reduce bandwidth and wave front can improve the locality of reference of sparse matrix-vector multiplication (SpMV), the key kernel in iterative solvers. In this paper, we present the first parallel implementations of two widely used reordering algorithms: Reverse Cut hill-McKee (RCM) and Sloan. On 16 cores of the Stampede supercomputer, our parallel RCM is 5.56 times faster on the average than a state-of-the-art sequential implementation of RCM in the HSL library. Sloan is significantly more constrained than RCM, but our parallel implementation achieves a speedup of 2.88X on the average over sequential HSL-Sloan. Reordering the matrix using our parallel RCM and then performing 100 SpMV iterations is twice as fast as using HSL-RCM and then performing the SpMV iterations, it is also 1.5 times faster than performing the SpMV iterations without reordering the matrix.",
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"content": "In order to efficiently exploit available parallelism, multicore processors must address contention for shared resources as cache hierarchy. This fact becomes even more important when irregular codes are executed on them, which is the case for sparse matrix ones. In this paper a technique for increasing locality of sparse matrix codes on multicore platforms is presented. The technique consists on reorganizing the data guided by a locality model which introduces the concept of windows of locality. The evaluation of the reordering technique has been performed on two different leading multicore platforms: Intel Core2Duo and Intel Xeon. Experimental results show important performance improvements when using our reordered matrices with respect to original ones. In particular, an average execution time reduction of about 30% is achieved considering different number of running threads. These results are due to an improved overall cache behavior. Likewise, a comparison of our proposal with some standard reordering techniques is included in the paper. Results point out that the reordering technique always outperforms standard algorithms and is effective for matrices with any structure.",
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"abstract": "This paper presents a novel framework to estimate parametric model- s for 3D clothed humans from partial point clouds. It is a challenging problem due to factors such as arbitrary human shape and pose, large variations in clothing details, and significant missing data. Existing methods mainly focus on estimating the parametric model of undressed bodies or reconstructing the non-parametric 3D shapes from point clouds. In this paper, we propose a hierarchical regression framework to learn the parametric model of detailed human shapes from partial point clouds of a single depth frame. Benefiting from the favorable ability of deep neural networks to model nonlinearity, the proposed framework cascades several successive regression networks to estimate the parameters of detailed 3D human body models in a coarse-to-fine manner. Specifically, the first global regression network extracts global deep features of point clouds to obtain an initial estimation of the undressed human model. Based on the initial estimation, the local regression network then refines the undressed human model by using the local features of neighborhood points of human joints. Finally, the clothing details are inferred as an additive displacement on the refined undressed model using the vertex-level regression network. The experimental results demonstrate that the proposed hierarchical regression approach can accurately predict detailed human shapes from partial point clouds and outperform prior works in the recovery accuracy of 3D human models.",
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"abstract": "Generative Adversarial Networks (GANs) have shown great capacity on image generation, in which a discriminative model guides the training of a generative model to construct images that resemble real images. Recently, GANs have been extended from generating images to generating sequences (e.g., poems, music and codes). Existing GANs on sequence generation mainly focus on general sequences, which are grammar-free. In many real-world applications, however, we need to generate sequences in a formal language with the constraint of its corresponding grammar. For example, to test the performance of a database, one may want to generate a collection of SQL queries, which are not only similar to the queries of real users, but also follow the SQL syntax of the target database. Generating such sequences is highly challenging because both the generator and discriminator of GANs need to consider the structure of the sequences and the given grammar in the formal language. To address these issues, we study the problem of syntax-aware sequence generation with GANs, in which a collection of real sequences and a set of pre-defined grammatical rules are given to both discriminator and generator. We propose a novel GAN framework, namely TreeGAN, to incorporate a given Context-Free Grammar (CFG) into the sequence generation process. In TreeGAN, the generator employs a recurrent neural network (RNN) to construct a parse tree. Each generated parse tree can then be translated to a valid sequence of the given grammar. The discriminator uses a tree-structured RNN to distinguish the generated trees from real trees. We show that TreeGAN can generate sequences for any CFG and its generation fully conforms with the given syntax. Experiments on synthetic and real data sets demonstrated that TreeGAN significantly improves the quality of the sequence generation in context-free languages.",
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"affiliation": "University of Pennsylvania,Penn Image Computing and Science Laboratory,Philadelphia,PA,USA,19104",
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"abstract": "To generate new images for a given category, most deep generative models require abundant training images from this category, which are often too expensive to acquire. To achieve the goal of generation based on only a few images, we propose matching-based Generative Adversarial Network (GAN) for few-shot generation, which includes a matching generator and a matching discriminator. Matching generator can match random vectors with a few conditional images from the same category and generate new images for this category based on the fused features. The matching discriminator extends conventional GAN discriminator by matching the feature of generated image with the fused feature of conditional images. Extensive experiments on three datasets demonstrate the effectiveness of our proposed method.",
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"content": "To generate new images for a given category, most deep generative models require abundant training images from this category, which are often too expensive to acquire. To achieve the goal of generation based on only a few images, we propose matching-based Generative Adversarial Network (GAN) for few-shot generation, which includes a matching generator and a matching discriminator. Matching generator can match random vectors with a few conditional images from the same category and generate new images for this category based on the fused features. The matching discriminator extends conventional GAN discriminator by matching the feature of generated image with the fused feature of conditional images. Extensive experiments on three datasets demonstrate the effectiveness of our proposed method.",
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"affiliation": "Shanghai Jiao Tong University,MoE Key Lab of Artificial Intelligence,Shanghai,China",
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"abstract": "We present a generative attribute controller (GAC), a novel functionality for generating or editing an image while intuitively controlling large variations of an attribute. This controller is based on a novel generative model called the conditional filtered generative adversarial network (CFGAN), which is an extension of the conventional conditional GAN (CGAN) that incorporates a filtering architecture into the generator input. Unlike the conventional CGAN, which represents an attribute directly using an observable variable (e.g., the binary indicator of attribute presence) so its controllability is restricted to attribute labeling (e.g., restricted to an ON or OFF control), the CFGAN has a filtering architecture that associates an attribute with a multi-dimensional latent variable, enabling latent variations of the attribute to be represented. We also define the filtering architecture and training scheme considering controllability, enabling the variations of the attribute to be intuitively controlled using typical controllers (radio buttons and slide bars). We evaluated our CFGAN on MNIST, CUB, and CelebA datasets and show that it enables large variations of an attribute to be not only represented but also intuitively controlled while retaining identity. We also show that the learned latent space has enough expressive power to conduct attribute transfer and attribute-based image retrieval.",
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"content": "We present a generative attribute controller (GAC), a novel functionality for generating or editing an image while intuitively controlling large variations of an attribute. This controller is based on a novel generative model called the conditional filtered generative adversarial network (CFGAN), which is an extension of the conventional conditional GAN (CGAN) that incorporates a filtering architecture into the generator input. Unlike the conventional CGAN, which represents an attribute directly using an observable variable (e.g., the binary indicator of attribute presence) so its controllability is restricted to attribute labeling (e.g., restricted to an ON or OFF control), the CFGAN has a filtering architecture that associates an attribute with a multi-dimensional latent variable, enabling latent variations of the attribute to be represented. We also define the filtering architecture and training scheme considering controllability, enabling the variations of the attribute to be intuitively controlled using typical controllers (radio buttons and slide bars). We evaluated our CFGAN on MNIST, CUB, and CelebA datasets and show that it enables large variations of an attribute to be not only represented but also intuitively controlled while retaining identity. We also show that the learned latent space has enough expressive power to conduct attribute transfer and attribute-based image retrieval.",
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"abstract": "Cancer-related medical expenses and labor loss cost annually $10,000 billion worldwide. Lung cancer-related deaths exceed 70,000 cases globally every year. Furthermore, 225,000 new cases were detected in the United States in 2016, and 4.3 million new cases in China in 2015. Statistically, most lung cancer related deaths were due to late stage detection. Like other types of cancer, early detection of lung cancer could be the best strategy to save lives. In this paper, we propose a novel neural-network based algorithm, which we refer to as entropy degradation method (EDM), to detect small cell lung cancer (SCLC) from computed tomography (CT) images. This research could facilitate early detection of lung cancers. The training data and testing data are high-resolution lung CT scans provided by the National Cancer Institute. We selected 12 lung CT scans from the library, 6 of which are for healthy lungs, and the remaining 6 are scans from patients with SCLC. We randomly take 5 scans from each group to train our model, and used the remaining two scans to test. Our algorithms achieves an accuracy of 77.8%.",
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"content": "Cancer-related medical expenses and labor loss cost annually $10,000 billion worldwide. Lung cancer-related deaths exceed 70,000 cases globally every year. Furthermore, 225,000 new cases were detected in the United States in 2016, and 4.3 million new cases in China in 2015. Statistically, most lung cancer related deaths were due to late stage detection. Like other types of cancer, early detection of lung cancer could be the best strategy to save lives. In this paper, we propose a novel neural-network based algorithm, which we refer to as entropy degradation method (EDM), to detect small cell lung cancer (SCLC) from computed tomography (CT) images. This research could facilitate early detection of lung cancers. The training data and testing data are high-resolution lung CT scans provided by the National Cancer Institute. We selected 12 lung CT scans from the library, 6 of which are for healthy lungs, and the remaining 6 are scans from patients with SCLC. We randomly take 5 scans from each group to train our model, and used the remaining two scans to test. Our algorithms achieves an accuracy of 77.8%.",
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"abstract": "We propose a statistical method for uncovering gene pathways that characterize cancer heterogeneity. To incorporate knowledge of the pathways into the model, we define a set of activities of pathways from microarray gene expression data based on the sparse probabilistic principal component analysis. A pathway activity logistic regression model is then formulated for cancer phenotype. To select pathway activities related to binary cancer phenotypes, we use the elastic net for the parameter estimation and derive a model selection criterion for selecting tuning parameters included in the model estimation. Our proposed method can also reverse-engineer gene networks based on the identified multiple pathways that enables us to discover novel gene-gene associations relating with the cancer phenotypes. We illustrate the whole process of the proposed method through the analysis of breast cancer gene expression data.",
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"abstract": "Tumor heterogeneity has been increasingly recognized as one of the potentially contributing factors in explaining drug resistance. In order to gain better understanding of heterogeneous cancer cell populations and different cells' responses to various drugs, we use fluorescent proteins to mark the cells and a live-cell dynamic imaging platform to collect cell-by-cell measurements. After addressing the issue of fluorescent reporter variance in a Bayesian inference framework, we decompose the different cell types in the mixture and calculate their proportions and counts over time responding to different drug treatments. Additionally, the drug response as characterized by the cell death rate was also computed for these cells, and their different sensitivity was demonstrated. Overall, this work represents an important advancement toward evaluating cancer heterogeneity and drug responses in heterogeneous cancer cell populations.",
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"abstract": "Corpuses of unstructured textual data, such as text messages between individuals, are often predictive of medical issues such as depression. The text data usually used in healthcare applications has high value and great variety, but is typically small in volume. Generating labeled unstructured text data is important to improve models by augmenting these small datasets, as well as to facilitate anonymization. While methods for labeled data generation exist, not all of them generalize well to small datasets. In this work, we thus perform a much needed systematic comparison of conditional text generation models that are promising for small datasets due to their unified architectures. We identify and implement a family of nine conditional sequence generative adversarial networks for text generation, which we collectively refer to as cSeqGAN models. These models are characterized along two orthogonal design dimensions: weighting strategies and feedback mechanisms. We conduct a comparative study evaluating the generation ability of the nine cSeqGAN models on three diverse text datasets with depression and sentiment labels. To assess the quality and realism of the generated text, we use standard machine learning metrics as well as human assessment via a user study. While the unconditioned models produced predictive text, the cSeqGAN models produced more realistic text. Our comparative study lays a solid foundation and provides important insights for further text generation research, particularly for the small datasets common within the healthcare domain.",
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"content": "Corpuses of unstructured textual data, such as text messages between individuals, are often predictive of medical issues such as depression. The text data usually used in healthcare applications has high value and great variety, but is typically small in volume. Generating labeled unstructured text data is important to improve models by augmenting these small datasets, as well as to facilitate anonymization. While methods for labeled data generation exist, not all of them generalize well to small datasets. In this work, we thus perform a much needed systematic comparison of conditional text generation models that are promising for small datasets due to their unified architectures. We identify and implement a family of nine conditional sequence generative adversarial networks for text generation, which we collectively refer to as cSeqGAN models. These models are characterized along two orthogonal design dimensions: weighting strategies and feedback mechanisms. We conduct a comparative study evaluating the generation ability of the nine cSeqGAN models on three diverse text datasets with depression and sentiment labels. To assess the quality and realism of the generated text, we use standard machine learning metrics as well as human assessment via a user study. While the unconditioned models produced predictive text, the cSeqGAN models produced more realistic text. Our comparative study lays a solid foundation and provides important insights for further text generation research, particularly for the small datasets common within the healthcare domain.",
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"abstract": "A visual summary of the controversial aspects of an item enables both customers and marketers to identify and address complaints and concerns about the item effectively. In this paper, we propose a novel visual analytics system, to visually explore when a controversy occurs and the causes behind the controversy via user-generated reviews with text and ratings in various domains, such as restaurants, home goods, and cultural products. Quantitative analysis of the ratings of an item is first applied to characterize the evolution of controversy over time. A novel aspect-extraction method based on hierarchical clustering is proposed to identify aspect-level reasons garnered from review texts that explain why a controversy occurs. Our system allows the user to interactively explore the time-evolving controversy trend, major aspects of reviews, and sentiment divergences of aspects to understand in depth the controversy in reviews. We evaluate the effectiveness of the proposed aspect-extraction method by means of accuracy of aspect identification, the usefulness of our system using three case studies in different domains, and a user study.",
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"abstract": "Reproducing the virtual world is the development direction of film technology and film art. However, the current technology is not ready to build a true \"comprehensive cinema\". However, the current technology is not ready to construct a truly \"total cinema\". The viewer of a 360 degree virtual reality movie can choose viewing directions freely when watching a movie. Therefore, traditional language technique in filmmaking for guiding viewers’ attention is less effective to a great extent. Viewers are no longer guided by directors or film editors in a passive way, but have almost complete freedom in choosing what they want to watch. This brings new challenges for story-telling. In Cinematic Virtual Reality (CVR), the viewing process is not decided by the filmmaker, instead viewers are guided to watch. Guiding viewers to catch the preset plots effectively and accurately within fixed time can ensure complete viewing experience. Based on eye tracking in CVR, this paper makes an experiment with different attention guiding methods, explores the differences of effects of these methods and attention patterns in watching VR movies, and provides new basis and references for narrative mode in VR movies from the perspective of eye tracking.",
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"abstract": "The atmospheric electric field potential gradient is studied during snowfall, and the intensity of snowflakes electrifying depending on weather conditions is estimated. Applying modern data mining methods and high-mountain monitoring, where the anthropogenic factors on the electric field variations is insignificant, enables the identification of the influence of snowfall and snowstorms on the electric field's daily variations. Numerical data of the electrification process in the atmosphere are obtained, and relationships between electric field values and snowfall intensity, wind speed and temperature are demonstrated. Modern neural network techniques for data mining are applied in this context.",
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"affiliation": "High-Mountain Geophysical Institute, Nalchik, Russia",
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"content": "Atmospheric sciences is the study of physical and chemical phenomena occurring within the Earth's atmosphere. The study entails understanding the state of the Earth's atmosphere, how it is changing over time and why. Understanding how various weather events develop and evolve is often conducted through retrospective analysis of past atmospheric events. Atmospheric scientists can then utilize tools to better predict potential hazards and provide earlier warnings for events that may impact life and property. Several atmospheric state variables can be measured to identify high-impact events, one of which is surface atmospheric pressure. Many weather events are characterized by variations in surface pressure from the mean pressure value (i.e., pressure-perturbations). Accordingly, there is significant interest in extracting and tracking pressure-perturbations both spatially and temporally to better understand the evolution of weather events. Here, we present a visualization and analysis environment that allows interactive exploration of pressure-perturbation data sets. Our system, for the first time, enables atmospheric scientists to interactively explore the spatiotemporal behaviors of pressure-perturbations for a range of values and provides support to leverage other conventional data sets such as radar imagery and wind observations. It also allows atmospheric scientists to evaluate model and parameter sensitivity, which is difficult if not impossible with conventional visualization tools in atmospheric sciences. Finally, we demonstrate the utility of our approach for retrospective analysis using different case studies of recorded severe weather events.",
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"affiliation": "SCI Institute, University of Utah, United States of America",
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"affiliation": "SCI Institute, University of Utah, United States of America",
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"affiliation": "SCI Institute, University of Utah, United States of America",
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"affiliation": "Department of Atmospheric Sciences, University of Utah, United States of America",
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"doi": "10.1109/CVPRW.2018.00142",
"title": "Scene Understanding Networks for Autonomous Driving Based on Around View Monitoring System",
"normalizedTitle": "Scene Understanding Networks for Autonomous Driving Based on Around View Monitoring System",
"abstract": "Modern driver assistance systems rely on a wide range of sensors (RADAR, LIDAR, ultrasound and cameras) for scene understanding and prediction. These sensors are typically used for detecting traffic participants and scene elements required for navigation. In this paper we argue that relying on camera based systems, specifically Around View Monitoring (AVM) system has great potential to achieve these goals in both parking and driving modes with decreased costs. The contributions of this paper are as follows: we present a new end-to-end solution for delimiting the safe drivable area for each frame by means of identifying the closest obstacle in each direction from the driving vehicle, we use this approach to calculate the distance to the nearest obstacles and we incorporate it into a unified end-to-end architecture capable of joint object detection, curb detection and safe drivable area detection. Furthermore, we describe the family of networks for both a high accuracy solution and a low complexity solution. We also introduce further augmentation of the base architecture with 3D object detection.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Modern driver assistance systems rely on a wide range of sensors (RADAR, LIDAR, ultrasound and cameras) for scene understanding and prediction. These sensors are typically used for detecting traffic participants and scene elements required for navigation. In this paper we argue that relying on camera based systems, specifically Around View Monitoring (AVM) system has great potential to achieve these goals in both parking and driving modes with decreased costs. The contributions of this paper are as follows: we present a new end-to-end solution for delimiting the safe drivable area for each frame by means of identifying the closest obstacle in each direction from the driving vehicle, we use this approach to calculate the distance to the nearest obstacles and we incorporate it into a unified end-to-end architecture capable of joint object detection, curb detection and safe drivable area detection. Furthermore, we describe the family of networks for both a high accuracy solution and a low complexity solution. We also introduce further augmentation of the base architecture with 3D object detection.",
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"normalizedAbstract": "Modern driver assistance systems rely on a wide range of sensors (RADAR, LIDAR, ultrasound and cameras) for scene understanding and prediction. These sensors are typically used for detecting traffic participants and scene elements required for navigation. In this paper we argue that relying on camera based systems, specifically Around View Monitoring (AVM) system has great potential to achieve these goals in both parking and driving modes with decreased costs. The contributions of this paper are as follows: we present a new end-to-end solution for delimiting the safe drivable area for each frame by means of identifying the closest obstacle in each direction from the driving vehicle, we use this approach to calculate the distance to the nearest obstacles and we incorporate it into a unified end-to-end architecture capable of joint object detection, curb detection and safe drivable area detection. Furthermore, we describe the family of networks for both a high accuracy solution and a low complexity solution. We also introduce further augmentation of the base architecture with 3D object detection.",
"fno": "610000b074",
"keywords": [
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"LIDAR",
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{
"affiliation": null,
"fullName": "JeongYeol Baek",
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"affiliation": null,
"fullName": "Ioana Veronica Chelu",
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"affiliation": null,
"fullName": "Livia Iordache",
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{
"affiliation": null,
"fullName": "Vlad Paunescu",
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{
"affiliation": null,
"fullName": "HyunJoo Ryu",
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"affiliation": null,
"fullName": "Alexandru Ghiuta",
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{
"affiliation": null,
"fullName": "Andrei Petreanu",
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"affiliation": null,
"fullName": "YunSung Soh",
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{
"affiliation": null,
"fullName": "Andrei Leica",
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"content": "An extreme severe convective weather process occurred in Qingdao urban district on 13 June 2018, accompanied by lightning, strong wind, short-term heavy rainfall, hail and tornado. The reanalysis data of ERA5 were used to analyze the weather process by using the method of potential vorticity and moist potential vorticity. The analysis revealed that the process was caused by the combined acting from the two systems of surface low pressure and upper-level cold vortex, resulting in the occurrence of local severe convective weather. The development mechanism was that, first, the upper-level dynamic tropopause collapsed, and the dry and cold air layer invaded downward from the upper, resulting in the strong baroclinic instability in the middle and high level; second, the large positive potential vorticity reached the middle troposphere and triggered the cyclonic circulation in the middle troposphere. In addition, it was more accurate for both the timing and the wetted regions by using the moist potential vorticity to characterize the stratification instability region. The formation and evolution of the positive center of MPV2 component were about 2~3 hours ahead of the development of severe convective weather.",
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"abstract": "A critical problem of Smooth Particle Hydrodynamics (SPH) methods is the numerical dissipation in viscosity computation. This leads to unrealistic results where high frequency details, like turbulence, are smoothed out. To address this issue, we introduce a viscosity-based vorticity correction scheme for SPH fluids, without complex time integration or limited time steps. In our method, the energy difference in viscosity computation is used to correct the vorticity field. Instead of solving Biot-Savart integrals, we adopt stream function, which is easier to solve and more efficient, to recover the velocity field from the vorticity difference. Our method can increase the existing vortex significantly and generate additional turbulence at potential position. Moreover, it is simple to implement and can be easily integrated with other SPH methods.",
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"content": "A critical problem of Smooth Particle Hydrodynamics (SPH) methods is the numerical dissipation in viscosity computation. This leads to unrealistic results where high frequency details, like turbulence, are smoothed out. To address this issue, we introduce a viscosity-based vorticity correction scheme for SPH fluids, without complex time integration or limited time steps. In our method, the energy difference in viscosity computation is used to correct the vorticity field. Instead of solving Biot-Savart integrals, we adopt stream function, which is easier to solve and more efficient, to recover the velocity field from the vorticity difference. Our method can increase the existing vortex significantly and generate additional turbulence at potential position. Moreover, it is simple to implement and can be easily integrated with other SPH methods.",
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"affiliation": "School of Computer and Communication Engineering, University of Science and Technology Beijing",
"fullName": "Sinuo Liu",
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"affiliation": "School of Computer and Communication Engineering, University of Science and Technology Beijing",
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"affiliation": "School of Computer and Communication Engineering, University of Science and Technology Beijing",
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"affiliation": "School of Computer and Communication Engineering, University of Science and Technology Beijing",
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"affiliation": "School of Computer and Communication Engineering, University of Science and Technology Beijing",
"fullName": "Yalan Zhang",
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"abstract": "We process information in memory and different people have different memory capacity. It is therefore important to understand possible impact of memory capacity when it comes to graph comprehension. In an attempt towards this direction, we conducted a user study investigating the impact of working memory capacity on graph reading task performance. Forty-six university students participated in the study performing a graph reading task with one hundred graph drawings of different complexity levels. Their working memory capacity and task performance (accuracy and time) were measured and recorded. The results of regression analyses indicated that working memory capacity was a significant predictor of performance accuracy, but not for response time. In this paper, we present the details of the study and discuss our findings and limitations of the study. Possible future research directions are also suggested.",
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