Community structure is an important factor in the behavior of real-world networks because it strongly affects the stability and thus the phase transition order of the spreading dynamics. We here propose a reversible s...
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This paper presents a novel approach to fast rendering realistic meta-balls in view space. Meta-ball rendering is widely used in computer graphics to represent liquid drops, and especially important to particle-based ...
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The nested parallel (a.k.a. fork-join) model is widely used for writing parallel programs. However, the two composition constructs, i.e. "k" (parallel) and "k" (serial), that comprise the nested-pa...
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ISBN:
(纸本)9781450342100
The nested parallel (a.k.a. fork-join) model is widely used for writing parallel programs. However, the two composition constructs, i.e. "k" (parallel) and "k" (serial), that comprise the nested-parallel model are insufficient in expressing "partial dependencies" in a program. We propose a new dataflow composition construct "" to express partial dependencies in algorithms in a processorand cache-oblivious way, thus extending the Nested Parallel (NP) model to the Nested Dataflow (ND) model. We redesign several divide-and-conquer algorithms ranging from dense linear algebra to dynamic-programming in the ND model and prove that they all have optimal span while retaining optimal cache complexity. We propose the design of runtime schedulers that map ND programs to multicore processors with multiple levels of possibly shared caches (i.e, Parallel Memory Hierarchies) and prove guarantees on their ability to balance nodes across processors and preserve locality. For this, we adapt space-bounded (SB) schedulers for the ND model. We show that our algorithms have increased "parallelizability" in the ND model, and that SB schedulers can use the extra parallelizability to achieve asymptotically optimal bounds on cache misses and running time on a greater number of processors than in the NP model. The running time for the algorithms in this paper is(Equation presented on a p-processor machine, where Q∗ is the parallel cache complexity of task t, Ci is the cost of cache miss at level-i cache which is of size Mi, and σ ∈ (0,1) is a constant.
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, mes...
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Cross-company defect prediction (CCDP) is a practical way that trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to target company. Unfortunately, larger i...
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This paper proposes a novel and efficient shape-based approach for hand dorsal vein recognition. A coarse-to-fine segmentation method is first introduced to precisely detect the boundaries of the vein areas. A general...
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ISBN:
(纸本)9781467399623
This paper proposes a novel and efficient shape-based approach for hand dorsal vein recognition. A coarse-to-fine segmentation method is first introduced to precisely detect the boundaries of the vein areas. A generalized graph model, namely Width Skeleton Model (WSM), is built then, which takes both the topology of the vein network and the width of the vessel into account, thereby achieving more comprehensive geometric representation and conveying more discriminative cues for identification. The models of different samples are further efficiently compared through a new matching scheme for similarity measurement, based on which the identity of the individual is finally decided. We evaluate the proposed approach on the NCUT database, and the rank-one recognition rate reaches 99.31%, which is superior to the state of the arts, clearly illustrating its competency.
Large graphs analytics has been an important aspect of many big data applications, such as web search, social networks and recommendation systems. Many research focuses on processing large scale graphs using distribut...
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In this paper, we propose a novel categorization framework to recognize tactile sequences based on two particular properties of the tactile data. For the first one, tactile sequences are spatio-temporal data which is ...
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ISBN:
(纸本)9781509006212
In this paper, we propose a novel categorization framework to recognize tactile sequences based on two particular properties of the tactile data. For the first one, tactile sequences are spatio-temporal data which is sequential and dynamic, depicting the process of grasping an object in different grasping stages;therefore, it is reasonable to discover the dynamical pattern by modeling tactile data as integral sequences rather than individual frames. For the second one, a tactile sequence contains various dynamical patterns in different stages of the grasping process;therefore, we decompose the whole sequence into multiple mini-sequences so as to enhance feature resolution. To address both properties in our framework, we take advantage of a Bag-of-system model using parameters of the Linear Dynamic system (LDS) as feature descriptors. Moreover, we employ the LDS with Symmetric Transition matrix (LDSST) rather than the original LDS as the building-block in order to obtain accurate codewords of the codebook of the Bag-of-system. The performance of our framework is evaluated on six real-world databases of three groups. Our experiments show that classification using LDSST is better than the original LDS, and the decomposition of tactile sequences does improve the accuracy of classification. The experiment results also show the superiority of our framework in comparison with other state-of-the-art sequence classifiers.
The manifold-ranking based method is widely used in semi-supervised learning, and its performance is closely related to the structure of the constructed graph. In this paper, we propose a novel graph structure named n...
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The accuracy of the Autonomous Underwater Vehicles (AUVs) navigation system determines whether they can safely operate and return. Traditional Dead-reckoning (DR) relies on the inertial sensors such as gyroscope and a...
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ISBN:
(纸本)9781450346375
The accuracy of the Autonomous Underwater Vehicles (AUVs) navigation system determines whether they can safely operate and return. Traditional Dead-reckoning (DR) relies on the inertial sensors such as gyroscope and accelerometer. A major challenge for DR navigation is from measurement error of the inertial sensors (gyroscope, accelerometer, etc.), especially when the AUV is near or at the ocean surface. The AUV's motion is affected by ocean waves, and its pitch angle changes rapidly with the waves. This rapid change and the measurement errors will cause great noise to the direction measured by gyroscopes, and then lead to a large error to the DR navigation. To address this problem, a novel DR method based on neural network (DR-N) is proposed to explore the time-varying relationship between acceleration measurement and orientation measurement, which leverages acoustic localization and neural network estimate timely pitch angle through the explored time-varying relationship. This method enables AUV's DR navigation with a single acceleration, without relying on both acceleration and gyroscope. Most importantly, we can improve the accuracy of AUV navigation through avoiding DR errors caused by gyroscope noise at the sea surface. Simulations show DR-N significantly improves navigation accuracy.
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