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Stencil computations expose a large and complex space of equivalent implementations. These computations often rely on autotuning techniques, based on iterative compilation or machine learning (ML), to achieve high performance. Iterative compilation autotuning is a challenging and time-consuming task that may be unaffordable in many scenarios. Meanwhile, traditional ML autotuning approaches exploiting...
Many studies emphasize the importance of infant-directed speech: stronger articulated, higher-quality speech helps infants to better distinguish different speech sounds. This effect has been widely investigated in terms of the infant's perceptual capabilities, but few studies examined whether infant-directed speech has an effect on articulatory learning. In earlier studies, we developed a model that...
Diderot is a parallel domain-specific language forthe analysis and visualization of multidimensional scientific images, such as those produced by CT and MRI scanners. Diderot is designed to support algorithms that are based on differential tensor calculus and produces a higher-order mathematical model which allows direct manipulation of tensor fields. One of the main challenges of the Diderot implementation...
The rapid growth of 3D model resources for 3D printing has created an urgent need for 3D model retrieval systems. Benefiting from the evolution of hardware devices, visualized 3D models can be easily rendered using a tablet computer or handheld mobile device. In this paper, we present a novel 3D model retrieval method involving view-based features and deep learning. Because 2D images are highly distinguishable,...
Network pruning is an effective way to accelerate Convolutional Neural Networks (CNNs). In recent years, structured pruning methods are proposed in favor of unstructured methods as they have shown greater speedup in practical use. Existing structured methods does pruning along two main dimensions: 3D-filter wise, i.e., remove a 3D-fllter as a whole, and filter-shape wise, i.e., remove a same position...
Predictive autonomous vehicle guidance schemes can be configured to embed human driver-like decisions regarding multiple dynamic obstacle vehicle groups prevalent in public traffic, especially on highways. This paper proposes a vehicle grouping model and computation algorithm that facilitates dynamic grouping of surrounding object vehicles. This grouping serves to compute the time varying areas that...
Architectural models have always played an important role in computer graphics, virtual environments, and urban planning; due to the size, detail and complexity of such models. Creating detailed and realistic buildings needs time and extensive coding as well as significant domain expertise. Observing the content creation problem and the challenges of procedural modeling, we identified and developed...
Background Subtraction is the major important step in many image processing applications which can be applied in much of video surveillances. The major result of this method is accuracy as well as processing time. So we mainly focused on these two challenges. We parallelized the Two Layered CodeBook Model on Graphical Processing Unit (GPU) for increasing the processing speed and the accuracy of the...
We herein propose an evolutionary multi-agent system (EMAS for short) to build an ensemble of surrogates for prediction. In our EMAS, we employ six kinds of basic surrogates, including Gaussian process, Kriging model, polynomial response surface, radial basis function, radial basis function neural network, and support vector regression machine. We define each surrogate as one agent and co-evolve parameters...
Automated classification algorithms have been applied to breast cancer diagnosis in order to improve the diagnostic accuracy and turnover time. However, classification accuracy, sensitivity and specificity could still be improved further. Moreover, reducing computational cost is another challenge as the number of images to be analyzed is typically large. In this paper, a novel Pixel N-gram approach...
3D exploded views have been widely used for assembly instructions in many fields but have seldom been used in archaeology. For studying stone tools, relics are repeatedly assembled using indistinct traditional illustrations. We apply this powerful presentation technique on the stone tool models, and study algorithms for point cloud data. In addition to presenting principles for the restoration of...
We propose a new semi-automatic framework for tooth segmentation in Cone-Beam Computed Tomography (CBCT) combining shape priors based on a statistical shape model and graph cut optimization. Poor image quality and similarity between tooth and cortical bone intensities are overcome by strong constraints on the shape and on the targeted area. The segmentation quality was assessed on 64 tooth images...
We introduce a novel segmentation method for time-lapse image stacks of neurites based on the co-segmentation principle. Our method aggregates information from multiple stacks to improve the segmentation task, using a neurite model and a tree similarity term. The neurite model takes into account branching characteristics, such as local shape smoothness and continuity, while the tree similarity term...
We present a new method for cell segmentation which combines a marked point process model with a combinatorics-based method of finding global optima. The method employs an energy term that assesses possible segmentations by their fidelity to both local image information and a simple model of cell interaction, and we use a randomized iterative reweighting technique for its minimization. Our approach...
Medical image segmentation plays an important role in digital medical research, therapy planning, and computer aided diagnosis. However, the existence of noise and low contrast make automatic liver segmentation remains an open challenge. In this work we focus on a novel variational semi-automatic liver segmentation method. First, we used the signed distance functions (SDF) representing pattern shapes...
Based on the fact that coronary heart disease (CHD) is the leading cause of death among all cardiovascular abnormalities, clinicians are keen in early detection and continuous monitoring of arterial atherosclerosis. Conventional cardiac catheterization method yields 2D angiograms of cardiac vasculature with possibly missed abnormalities as well as its invasive nature involves risk to the patient....
The development of algorithms and models to be used for prediction of the reliability and health monitoring of components and sensors is of great importance in aerospace, automotive and power generation industry. For this purpose metamodels have been developed that are based on physical simulations and that are able to quantify the impact of uncertainties on system behavior. These surrogate metamodels...
All engineering disciplines are founded and rely on models, although they may differ on purposes and usages of modeling. Interdisciplinary domains such as Cyber Physical Systems (CPSs) seek approaches that incorporate different modeling needs and usages. Specifically, the Simulink modeling platform greatly appeals to CPS engineers due to its seamless support for simulation and code generation. In...
We present SimCoTest, a tool to generate small test suites with high fault revealing ability for Simulink/Stateflow controllers. SimCoTest uses meta-heuristic search to (1) maximize the likelihood of presence of specific failure patterns in output signals (failure-based test generation), and to (2) maximize diversity of output signal shapes (output diversity test generation). SimCoTest has been evaluated...
It is well-known that the NeQuick 2 model provides analytical expressions showing a relationship between topside and bottomside ionospheres. Its bottomside thickness parameter (B2bot) is a key parameter for studying topside electron density profile (EDP) and topside parameters, and its topside scale height (Hsc) is also used to identify the topside electron density profile. The B2bot computed using...
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