Call a sequence of k Boolean variables or their negations a k-tuple. For a set V of n Boolean variables, let Tk（V） denote the set of all 2^kn^k possible k-tuples on V. Randomly generate a set C of k-tuples by includ...
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Call a sequence of k Boolean variables or their negations a k-tuple. For a set V of n Boolean variables, let Tk（V） denote the set of all 2^kn^k possible k-tuples on V. Randomly generate a set C of k-tuples by including every k-tuple in Tk（V） independently with probability p, and let Q be a given set of q ＂bad＂ tuple assignments. An instance I = （C, Q） is called satisfiable if there exists an assignment that does not set any of the k-tuples in C to a bad tupie assignment in Q. Suppose that θ, q 〉 0 are fixed and ε=ε（n） 〉 0 be such in 2 that ε Inn/ In Inn → ∞. Let k ≥ （1 ＋ θ） log2 n and let p0 = ln2/qn^k-1. We prove that
lim∞ P[I issatisfiable] ={1,p≤（1-ε）p0, 0,p≥（1＋ε）p0.
This paper proposes a tetrahedral data model for unstructured data management. The model defines the four components of unstructured data including: basic attributes, semantic characteristics, low-level features and r...
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This paper proposes a tetrahedral data model for unstructured data management. The model defines the four components of unstructured data including: basic attributes, semantic characteristics, low-level features and raw data on its four facets, and the relations between these components. The internal implementation structure of the model and the data query language are designed and briefly introduced. This model provides a unified, integrated and associated description for different kinds of unstructured data, and supports intelligent data services such as associated retrieval and data mining. An example is given to demonstrate how to use the model for describing and manipulating data from a sample video base.
Diffusion magnetic resonance imaging (dMRI) is a crucial non-invasive technique for exploring the microstructure of the living human brain. Traditional hand-crafted and model-based tissue microstructure reconstruction...
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ISBN:
(数字)9798331520526
ISBN:
(纸本)9798331520533
Diffusion magnetic resonance imaging (dMRI) is a crucial non-invasive technique for exploring the microstructure of the living human brain. Traditional hand-crafted and model-based tissue microstructure reconstruction methods often require extensive diffusion gradient sampling, which can be time-consuming and limits the clinical applicability of tissue microstructure information. Recent advances in deep learning have shown promise in microstructure estimation; however, accurately estimating tissue microstructure from clinically feasible dMRI scans remains challenging without appropriate constraints. This paper introduces a novel framework that achieves high-fidelity and rapid diffusion microstructure imaging by simultaneously leveraging anatomical information from macro-level priors and mutual information across parameters. This approach enhances time efficiency while maintaining accuracy in microstructure estimation. Experimental results demonstrate that our method outperforms four state-of-the-art techniques, achieving a peak signal-to-noise ratio (PSNR) of 30.51±0.58 and a structural similarity index measure (SSIM) of 0.97±0.004 in estimating parametric maps of multiple diffusion models. Notably, our method achieves a 15× acceleration compared to the dense sampling approach, which typically utilizes 270 diffusion gradients.
Trust brings a new method for building scalable and fine-grained access control mechanism in P2P systems. The quantificational expression of trust and the calculation of trust in a trust network are the basis of trust...
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Trust brings a new method for building scalable and fine-grained access control mechanism in P2P systems. The quantificational expression of trust and the calculation of trust in a trust network are the basis of trust degree based access control. In this paper, the properties of trust is analyzed by referring to the fruits from social science; the semantics of trust in the context of access control is described, and a trust degree based access control model named TDBAC is introduced. Basing on the properties and semantics of trust, a computational trust model which includes a multilevel comprehensive evaluation method for expressing direct trust and calculators for computing recommended trust is put forward. To compute trust in a trust network, an algorithm that transforms a trust network to a computable expression is given. The algorithm simplifies the computation process and is also flexible.
In this paper,we propose a representative model based algorithm to calculate maximal *** a formal theory Γ and a fact set Δ,the algorithm begins by accepting all models of refutation by facts of Γ with respect to ...
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In this paper,we propose a representative model based algorithm to calculate maximal *** a formal theory Γ and a fact set Δ,the algorithm begins by accepting all models of refutation by facts of Γ with respect to Δ and filters these models to obtain the models of *** to the completeness of R-calculus,the relevant maximal contraction is obtained *** order to improve the efficiency,we divide the models into different classes according to the assignments of atomic propositions and only select relevant representative models to verify the satisfiability of each *** algorithm is correct,and all maximal contractions of a given problem can be calculated by *** could make a selection according to their requirements.A benchmark algorithm is also *** show that the algorithm has a good performance on normal revision problems.
Although matrix multiplication plays an essential role in a wide range of applications,previous works only focus on optimizing dense or sparse matrix *** Sparse Approximate Matrix Multiply(SpAMM)is an algorithm to acc...
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Although matrix multiplication plays an essential role in a wide range of applications,previous works only focus on optimizing dense or sparse matrix *** Sparse Approximate Matrix Multiply(SpAMM)is an algorithm to accelerate the multiplication of decay matrices,the sparsity of which is between dense and sparse *** addition,large-scale decay matrix multiplication is performed in scientific applications to solve cutting-edge *** optimize large-scale decay matrix multiplication using SpAMM on supercomputers such as Sunway Taihulight,we present swSpAMM,an optimized SpAMM algorithm by adapting the computation characteristics to the architecture features of Sunway ***,we propose both intra-node and inter-node optimizations to accelerate swSpAMM for large-scale *** intra-node optimizations,we explore algorithm parallelization and block-major data layout that are tailored to better utilize the architecture advantage of Sunway *** inter-node optimizations,we propose a matrix organization strategy for better distributing sub-matrices across nodes and a dynamic scheduling strategy for improving load balance across *** compare swSpAMM with the existing GEMM library on a single node as well as large-scale matrix multiplication methods on multiple *** experiment results show that swSpAMM achieves a speedup up to 14.5×and 2.2×when compared to xMath library on a single node and 2D GEMM method on multiple nodes,respectively.
Currently, a large number of Web information on the Internet is presented in structured objects. Mining object information from Web is of great importance for Web data management. MDR algorithm is a fully automated da...
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Dependency parsing aims to identify relationships between words in one sentence. In this paper, we propose a novel graph-based end-to-end dependency parsing model, including POS tagger and Joint Bilinear Model (JBM). ...
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In data-driven science projects, researchers distributed in different institutions often wish to easily team up for data and computing resource sharing to address challenging scientific problems. Typical VO based auth...
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A first order inference system, named R-calculus, is defined to develop the specifications. This system intends to eliminate the laws which are not consistent with users' requirements. The R-calculus consists of t...
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A first order inference system, named R-calculus, is defined to develop the specifications. This system intends to eliminate the laws which are not consistent with users' requirements. The R-calculus consists of the structural rules, an axiom, a cut rule, and the rules for logical connectives. Some examples are given to demonstrate the usage of the R-calculus. Furthermore, the properties regarding reachability and completeness of the R-calculus are formally defined and proved.
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