Because the small CACHE size of computers, the scanning speed of DFA based multi-pattern string-matching algorithms slows down rapidly especially when the number of patterns is very large. For solving such problems, w...Because the small CACHE size of computers, the scanning speed of DFA based multi-pattern string-matching algorithms slows down rapidly especially when the number of patterns is very large. For solving such problems, we cut down the scanning time of those algorithms (i.e. DFA based) by rearranging the states table and shrinking the DFA alphabet size. Both the methods can decrease the probability of large-scale random memory accessing and increase the probability of continuously memory accessing. Then the hitting rate of the CACHE is increased and the searching time of on the DFA is reduced. Shrinking the alphabet size of the DFA also reduces the storage complication. The AC++algorithm, by optimizing the Aho-Corasick (i.e. AC) algorithm using such methods, proves the theoretical analysis. And the experimentation results show that the scanning time of AC++and the storage occupied is better than that of AC in most cases and the result is much attractive when the number of patterns is very large. Because DFA is a widely used base algorithm in may string matching algorithms, such as DAWG, SBOM etc., the optimizing method discussed is significant in practice.展开更多
In this paper, a discriminative structured dictionary learning algorithm is presented. To enhance the dictionary's discriminative power, the reconstruction error, classification error and inhomogeneous representat...In this paper, a discriminative structured dictionary learning algorithm is presented. To enhance the dictionary's discriminative power, the reconstruction error, classification error and inhomogeneous representation error are integrated into the objective function. The proposed approach learns a single structured dictionary and a linear classifier jointly. The learned dictionary encourages the samples from the same class to have similar sparse codes, and the samples from different classes to have dissimilar sparse codes. The solution to the objective function is achieved by employing a feature-sign search algorithm and Lagrange dual method. Experimental results on three public databases demonstrate that the proposed approach outperforms several recently proposed dictionary learning techniques for classification.展开更多
In this study, the target bioactive compounds （e.g., alkaloids, flavonoids, saponins and other polar compounds） in Vietnamese Caricapapaya leaves were extracted. The cytotoxic activities of the papaya leaves extract...In this study, the target bioactive compounds （e.g., alkaloids, flavonoids, saponins and other polar compounds） in Vietnamese Caricapapaya leaves were extracted. The cytotoxic activities of the papaya leaves extracts on the selected tumor cell lines, such as lung cancer cell line LU-1, carcinoma cell line KB, breast cancer cells MCF7 and leukemia cell line HL-60, were examined. Preliminary findings showed a high inhibitive activity of papaya leave extracts against the four tested tumor cell lines at the concentration of 100 μg/mL. Out of the bioactive compounds in papaya leaves extract, alkaloids showed the highest inhibitive activity （105.95% on MCF7 and 91.86% on LU-1）, followed by polar compounds （62.88% on LU-1 and 21.80% on KB）, and saponins （59.74% on MCF7 and 25.25% on LU-1）. Flavonoids has the lowest inhibitive activity on cell lines （e.g., 45.51% on MCF7 and 20.32% on LU-1）. Taken together, the results suggest that alkaloids have a relatively high inhibitive activity on the selected tumor cell lines and their stimulated concentration at 50% （IC50） values for on MCF7 and KB were 24.67 μg/mL and 33.56 μg/mL, respectively. However, the result pointed out the immunostimulatory ability of only polar compounds and saponins which could stimulate the growth of in vitro lymphocytes but not flavonoids and alkaloids. The SC50 （stimulated concentration at 50%） values of polar compounds and saponins were 287.87μg/mL and 192.99 μg/mL, respectively.展开更多
文摘Because the small CACHE size of computers, the scanning speed of DFA based multi-pattern string-matching algorithms slows down rapidly especially when the number of patterns is very large. For solving such problems, we cut down the scanning time of those algorithms (i.e. DFA based) by rearranging the states table and shrinking the DFA alphabet size. Both the methods can decrease the probability of large-scale random memory accessing and increase the probability of continuously memory accessing. Then the hitting rate of the CACHE is increased and the searching time of on the DFA is reduced. Shrinking the alphabet size of the DFA also reduces the storage complication. The AC++algorithm, by optimizing the Aho-Corasick (i.e. AC) algorithm using such methods, proves the theoretical analysis. And the experimentation results show that the scanning time of AC++and the storage occupied is better than that of AC in most cases and the result is much attractive when the number of patterns is very large. Because DFA is a widely used base algorithm in may string matching algorithms, such as DAWG, SBOM etc., the optimizing method discussed is significant in practice.
基金Supported by the National Natural Science Foundation of China(No.61379014)
文摘In this paper, a discriminative structured dictionary learning algorithm is presented. To enhance the dictionary's discriminative power, the reconstruction error, classification error and inhomogeneous representation error are integrated into the objective function. The proposed approach learns a single structured dictionary and a linear classifier jointly. The learned dictionary encourages the samples from the same class to have similar sparse codes, and the samples from different classes to have dissimilar sparse codes. The solution to the objective function is achieved by employing a feature-sign search algorithm and Lagrange dual method. Experimental results on three public databases demonstrate that the proposed approach outperforms several recently proposed dictionary learning techniques for classification.
文摘In this study, the target bioactive compounds （e.g., alkaloids, flavonoids, saponins and other polar compounds） in Vietnamese Caricapapaya leaves were extracted. The cytotoxic activities of the papaya leaves extracts on the selected tumor cell lines, such as lung cancer cell line LU-1, carcinoma cell line KB, breast cancer cells MCF7 and leukemia cell line HL-60, were examined. Preliminary findings showed a high inhibitive activity of papaya leave extracts against the four tested tumor cell lines at the concentration of 100 μg/mL. Out of the bioactive compounds in papaya leaves extract, alkaloids showed the highest inhibitive activity （105.95% on MCF7 and 91.86% on LU-1）, followed by polar compounds （62.88% on LU-1 and 21.80% on KB）, and saponins （59.74% on MCF7 and 25.25% on LU-1）. Flavonoids has the lowest inhibitive activity on cell lines （e.g., 45.51% on MCF7 and 20.32% on LU-1）. Taken together, the results suggest that alkaloids have a relatively high inhibitive activity on the selected tumor cell lines and their stimulated concentration at 50% （IC50） values for on MCF7 and KB were 24.67 μg/mL and 33.56 μg/mL, respectively. However, the result pointed out the immunostimulatory ability of only polar compounds and saponins which could stimulate the growth of in vitro lymphocytes but not flavonoids and alkaloids. The SC50 （stimulated concentration at 50%） values of polar compounds and saponins were 287.87μg/mL and 192.99 μg/mL, respectively.
