Astronomical cross-matching is a basic method for aggregating the observational data of different wavelengths. By data aggregation, the properties of astronomical objects can be understood comprehensively. Aiming at d...Astronomical cross-matching is a basic method for aggregating the observational data of different wavelengths. By data aggregation, the properties of astronomical objects can be understood comprehensively. Aiming at decreasing the time consumed on I/O operations, several improved methods are introduced, including a processing flow based on the boundary growing model, which can reduce the database query operations; a concept of the biggest growing block and its determination which can improve the performance of task partition and resolve data-sparse problem; and a fast bitwise algorithm to compute the index numbers of the neighboring blocks, which is a significant efficiency guarantee. Experiments show that the methods can effectively speed up cross-matching on both sparse datasets and high-density datasets.展开更多
We introduce an algorithm to solve the block-edge problem taking advantage of the two different sky splitting functions： HTM and HEALPix. We make the cross-match with the two functions, and then we obtain the union s...We introduce an algorithm to solve the block-edge problem taking advantage of the two different sky splitting functions： HTM and HEALPix. We make the cross-match with the two functions, and then we obtain the union set of the two different sets. We use the ThreadPool technique to speed up the cross-match. In this way improved accuracy can be obtained on the cross-match. Our experiments show that this algorithm has a remarkable performance superiority compared with the previous ones and can be applied to the cross-match between large-scale catalogs. We give some ideas about solving the many-for-one situation occurred in the cross-match.展开更多
We advance here a novel methodology for robust intelligent biometric information management with inferences and predictions made using randomness and complexity concepts. Intelligence refers to learning, adap- tation,...We advance here a novel methodology for robust intelligent biometric information management with inferences and predictions made using randomness and complexity concepts. Intelligence refers to learning, adap- tation, and functionality, and robustness refers to the ability to handle incomplete and/or corrupt adversarial information, on one side, and image and or device variability, on the other side. The proposed methodology is model-free and non-parametric. It draws support from discriminative methods using likelihood ratios to link at the conceptual level biometrics and forensics. It further links, at the modeling and implementation level, the Bayesian framework, statistical learning theory (SLT) using transduction and semi-supervised lea- rning, and Information Theory (IY) using mutual information. The key concepts supporting the proposed methodology are a) local estimation to facilitate learning and prediction using both labeled and unlabeled data;b) similarity metrics using regularity of patterns, randomness deficiency, and Kolmogorov complexity (similar to MDL) using strangeness/typicality and ranking p-values;and c) the Cover – Hart theorem on the asymptotical performance of k-nearest neighbors approaching the optimal Bayes error. Several topics on biometric inference and prediction related to 1) multi-level and multi-layer data fusion including quality and multi-modal biometrics;2) score normalization and revision theory;3) face selection and tracking;and 4) identity management, are described here using an integrated approach that includes transduction and boosting for ranking and sequential fusion/aggregation, respectively, on one side, and active learning and change/ outlier/intrusion detection realized using information gain and martingale, respectively, on the other side. The methodology proposed can be mapped to additional types of information beyond biometrics.展开更多
基金Supported by National Natural Science Foundation of China (No.10978016)Natural Science Foundation of Tianjin (No. 08JCZDJC19700)Key Technologies Research and Development Program of Tianjin (No.09ZCKFGX00400)
文摘Astronomical cross-matching is a basic method for aggregating the observational data of different wavelengths. By data aggregation, the properties of astronomical objects can be understood comprehensively. Aiming at decreasing the time consumed on I/O operations, several improved methods are introduced, including a processing flow based on the boundary growing model, which can reduce the database query operations; a concept of the biggest growing block and its determination which can improve the performance of task partition and resolve data-sparse problem; and a fast bitwise algorithm to compute the index numbers of the neighboring blocks, which is a significant efficiency guarantee. Experiments show that the methods can effectively speed up cross-matching on both sparse datasets and high-density datasets.
基金supported by the National Natural Science Foundation of China(Grant Nos.10973021,11078013 and 11233004)
文摘We introduce an algorithm to solve the block-edge problem taking advantage of the two different sky splitting functions： HTM and HEALPix. We make the cross-match with the two functions, and then we obtain the union set of the two different sets. We use the ThreadPool technique to speed up the cross-match. In this way improved accuracy can be obtained on the cross-match. Our experiments show that this algorithm has a remarkable performance superiority compared with the previous ones and can be applied to the cross-match between large-scale catalogs. We give some ideas about solving the many-for-one situation occurred in the cross-match.
文摘We advance here a novel methodology for robust intelligent biometric information management with inferences and predictions made using randomness and complexity concepts. Intelligence refers to learning, adap- tation, and functionality, and robustness refers to the ability to handle incomplete and/or corrupt adversarial information, on one side, and image and or device variability, on the other side. The proposed methodology is model-free and non-parametric. It draws support from discriminative methods using likelihood ratios to link at the conceptual level biometrics and forensics. It further links, at the modeling and implementation level, the Bayesian framework, statistical learning theory (SLT) using transduction and semi-supervised lea- rning, and Information Theory (IY) using mutual information. The key concepts supporting the proposed methodology are a) local estimation to facilitate learning and prediction using both labeled and unlabeled data;b) similarity metrics using regularity of patterns, randomness deficiency, and Kolmogorov complexity (similar to MDL) using strangeness/typicality and ranking p-values;and c) the Cover – Hart theorem on the asymptotical performance of k-nearest neighbors approaching the optimal Bayes error. Several topics on biometric inference and prediction related to 1) multi-level and multi-layer data fusion including quality and multi-modal biometrics;2) score normalization and revision theory;3) face selection and tracking;and 4) identity management, are described here using an integrated approach that includes transduction and boosting for ranking and sequential fusion/aggregation, respectively, on one side, and active learning and change/ outlier/intrusion detection realized using information gain and martingale, respectively, on the other side. The methodology proposed can be mapped to additional types of information beyond biometrics.
