Different from rough sets in Pawlak’s sense, which is a binary approximation operations based structure, in this paper, we propose a new rough equivalence relation based on triple approximation operations induced by ...
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Although bigdata has been one of most popular topics since last several years, how to effectively conduct bigdata analysis is a big challenge for every field. This paper tries to address some fundamental scientific ...
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With explosive growth in fingerprint databases, Automatic Fingerprint Identification System has become more challenging than ever. Consequently, it is necessary to develop a fast and exact fingerprint indexing to meet...
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With explosive growth in fingerprint databases, Automatic Fingerprint Identification System has become more challenging than ever. Consequently, it is necessary to develop a fast and exact fingerprint indexing to meet the efficiency and accuracy. In this paper, learning Compact Binary Minutia Cylinder Code (CBMCC) is proposed as an effective and discriminative feature representation and Multi-Index Hashing (MIH) is suitably adopted to accelerate the exact search in fingerprint indexing field for the first time. Firstly, we analyze Minutia Cylinder Code to find that it is strongly bit-correlated and awfully unbalanced. Accordingly, we propose an optimization model to learn CBMCC with the balanced independent property and the minimal binary quantization loss. Finally, MIH method further speeds up the exact search in Hamming space by building multiple hash tables on binary code substrings. The performance test shows that CBMCC is effective and discriminative as it has the maximum intra-bit variance while the minimum inter-bit correlation. Furthermore, numerous experiments on public databases demonstrate that CBMCC-MIH is quite outstanding for fingerprint indexing since it achieves an extremely small error rate with a fairly low penetration rate. (c) 2017 Elsevier B.V. All rights reserved.
In this paper the definition of domination is generalized to the case that the elements of the traffic matrices may have negative values. It is proved that D3 dominates D3 ＋ λ（D2 - D1） for any λ ≥0 if D1 dominat...
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In this paper the definition of domination is generalized to the case that the elements of the traffic matrices may have negative values. It is proved that D3 dominates D3 ＋ λ（D2 - D1） for any λ ≥0 if D1 dominates D2. Let u（D） be the set of all the traffic matrices that are dominated by the traffic matrix D. It is shown that u （ D∞） and u （D ∈） are isomorphic. Besides, similar results are obtained on multi-commodity flow problems. Fhrthermore, the results are the generalized to integral flows.
Stochastic gradient descent(SGD) is one of the most common optimization algorithms used in pattern recognition and machine *** algorithm and its variants are the preferred algorithm while optimizing parameters of deep...
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Stochastic gradient descent(SGD) is one of the most common optimization algorithms used in pattern recognition and machine *** algorithm and its variants are the preferred algorithm while optimizing parameters of deep neural network for their advantages of low storage space requirement and fast computation *** studies on convergence of these algorithms were based on some traditional assumptions in optimization ***,the deep neural network has its unique *** assumptions are inappropriate in the actual optimization process of this kind of *** this paper,we modify the assumptions to make them more consistent with the actual optimization process of deep neural *** on new assumptions,we studied the convergence and convergence rate of SGD and its two common variant *** addition,we carried out numerical experiments with LeNet-5,a common network framework,on the data set MNIST to verify the rationality of our assumptions.
There are various types of pyramid schemes that have inflicted or are inflicting losses on many people in the *** propose a pyramid scheme model which has the principal characters of many pyramid schemes that have app...
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There are various types of pyramid schemes that have inflicted or are inflicting losses on many people in the *** propose a pyramid scheme model which has the principal characters of many pyramid schemes that have appeared in recent years: promising high returns, rewarding the participants for recruiting the next generation of participants, and the organizer takes all of the money away when they find that the money from the new participants is not enough to pay the previous participants interest and *** assume that the pyramid scheme is carried out in the tree network, Erd?s–Réney(ER) random network, Strogatz–Watts(SW) small-world network, or Barabasi–Albert(BA) scale-free *** then give the analytical results of the generations that the pyramid scheme can last in these *** also use our model to analyze a pyramid scheme in the real world and we find that the connections between participants in the pyramid scheme may constitute a SW small-world network.
Web search query data are obtained to reflect social spots and serve as novel economic indicators. When faced with high-dimensional query data, selecting keywords that have plausible predictive ability and can reduce ...
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Web search query data are obtained to reflect social spots and serve as novel economic indicators. When faced with high-dimensional query data, selecting keywords that have plausible predictive ability and can reduce dimensionality is critical. This paper presents a new integrative method that combines Hurst Exponent （HE） and Time Difference Correlation （TDC） analysis to select keywords with powerful predictive ability. The method is called the HE-TDC screening method and requires keywords with predictive ability to satisfy two characteristics, namely, high correlation and fluctuation memorability similar to the predicting target series. An empirical study is employed to predict the volume of tourism visitors in the Jiuzhai Valley scenic area. The study shows that keywords selected using HE-TDC method produce a model with better robustness and predictive ability.
We propose a novel and efficient technique to extract individual fingerprints from a slap-image and identify them into their corresponding indices i.e. index, middle, ring or little finger of left/right hand. We pose ...
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Over the past decades, three-dimensional(3D) face recognition has developed rapidly due to its intrinsic invariance to pose and illumination changes. Yet despite this, deep learning is rarely used in 3D face recogniti...
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Over the past decades, three-dimensional(3D) face recognition has developed rapidly due to its intrinsic invariance to pose and illumination changes. Yet despite this, deep learning is rarely used in 3D face recognition due to lack of training data. Noting that the majority of the recognition processes are based on facial depth map instead of real 3D data, we focus on the generation of facial depth map to surmount the shortage of training data. In this paper, we present a novel paradigm, Facial Depth Descend, which generates facial depth map from existing 3D face data. The key to this generation paradigm is to recombine facial components from existing faces and thereafter generate brand new faces based on them. We then propose a learning framework RPC to generate recognition-friendly faces. First, it extracts three facial components (eyes, nose and mouth) from the 3D data of real faces. Then, with these components as input, a Relative Location Estimator (RLE) is used to predict the relative location among them so that they can be composed in a reasonable manner. Finally, the method feeds a Pix2Pix network with these composed facial components to extrapolate areas surrounding them and output a full facial depth map. We here enforce an identity preserving loss on the generation network to make the facial depth map more discriminative and favorable for recognition tasks. Since we can choose different identities' different components as input, RPC is theoretically able to generate a vast number of novel 2.5D faces. More specifically, RPC can generate an enlarged face dataset as large as N-3 identities, where N is the number of identities in the original one. Experiments show that, adding the generated depth maps to the training dataset can help improve the recognition rate in 2.5D face recognition. (C) 2021 Published by Elsevier B.V.
An Asian option is a special type of option contract which reduces the volatility inherent in the option because of the averaging feature, so it is one of the most actively exotic options traded in today's financi...
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An Asian option is a special type of option contract which reduces the volatility inherent in the option because of the averaging feature, so it is one of the most actively exotic options traded in today's financial derivative market. As an application of the uncertain process in the field of finance, the uncertain finance assumes that the asset price follows an uncertain differential equation. In this paper, Asian options are proposed in the uncertain financial market based on a mean-reverting stock model and their pricing formulas are derived. In addition, some numerical algorithms are designed to compute the prices of the Asian options on the basis of the pricing formulas.
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