Journal articles: 'Distance vectors'

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Guo, Li, Guo Feng Liu, and Yu E. Bao. "A Study of FCM Clustering Algorithm Based on Interval Multiple Attribute Information." Applied Mechanics and Materials 444-445 (October 2013): 676–80. http://dx.doi.org/10.4028/www.scientific.net/amm.444-445.676.

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In multiple attribute clustering algorithms with uncertain interval numbers, most of the distances between the interval-valued vectors only consider the differences of each interval endpoint ignoring a lot of information. To solve this problem, according to the differences between corresponding points in each interval number, this paper gives a distance formula between interval-valued vectors, extends a FCM clustering algorithm based on interval multiple attribute information. Through an example, we prove the validity and rationality of the algorithm. Keywords: interval-valued vector; FCM clustering algorithm; distance measure; fuzzy partition

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Wu, Shen Hui, Sridhar Jandhyala, Yashwant K. Malaiya, and Anura P. Jayasumana. "Antirandom Testing: A Distance-Based Approach." VLSI Design 2008 (March 17, 2008): 1–9. http://dx.doi.org/10.1155/2008/165709.

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Random testing requires each test to be selected randomly regardless of the tests previously applied. This paper introduces the concept of antirandom testing where each test applied is chosen such that its total distance from all previous tests is maximum. This spans the test vector space to the maximum extent possible for a given number of vectors. An algorithm for generating antirandom tests is presented. Compared with traditional pseudorandom testing, antirandom testing is found to be very effective when a high-fault coverage needs to be achieved with a limited number of test vectors. The superiority of the new approach is even more significant for testing bridging faults.

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Yi, Jing, Xue Dong Li, Xiao Feng Li, and Hong Ling Gou. "Research on P2P Transmission Optimizing Strategy." Applied Mechanics and Materials 513-517 (February 2014): 1797–802. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1797.

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To reduce the congestion of trunk network caused by P2P transmission, a P2P transmission optimizing scheme is introduced in metropolitan area network (MAN) in this paper. In this scheme distance vectors such as Huffman Code are used to represent topology relations of nodes in MAN, and distance vectors of nodes which store nodes in topology server are built. According to the distance vectors, physical distances of each node are calculated, and most transmission flow is controlled on the edge of MAN, thus transmission optimizing is realized.

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Lei, Lei, and She Kun. "Speaker Recognition Using Wavelet Packet Entropy, I-Vector, and Cosine Distance Scoring." Journal of Electrical and Computer Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/1735698.

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Today, more and more people have benefited from the speaker recognition. However, the accuracy of speaker recognition often drops off rapidly because of the low-quality speech and noise. This paper proposed a new speaker recognition model based on wavelet packet entropy (WPE), i-vector, and cosine distance scoring (CDS). In the proposed model, WPE transforms the speeches into short-term spectrum feature vectors (short vectors) and resists the noise. I-vector is generated from those short vectors and characterizes speech to improve the recognition accuracy. CDS fast compares with the difference between two i-vectors to give out the recognition result. The proposed model is evaluated by TIMIT speech database. The results of the experiments show that the proposed model can obtain good performance in clear and noisy environment and be insensitive to the low-quality speech, but the time cost of the model is high. To reduce the time cost, the parallel computation is used.

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Drakakis, Konstantinos, Roderick Gow, and Scott Rickard. "Common distance vectors between Costas arrays." Advances in Mathematics of Communications 3, no. 1 (2009): 35–52. http://dx.doi.org/10.3934/amc.2009.3.35.

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OZTURK, OZGUR, and HAKAN FERHATOSMANOGLU. "VECTOR SPACE INDEXING FOR BIOSEQUENCE SIMILARITY SEARCHES." International Journal on Artificial Intelligence Tools 14, no. 05 (October 2005): 811–26. http://dx.doi.org/10.1142/s0218213005002405.

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We present a multi-dimensional indexing approach for fast sequence similarity search in DNA and protein databases. In particular, we propose effective transformations of subsequences into numerical vector domains and build efficient index structures on the transformed vectors. We then define distance functions in the transformed domain and examine properties of these functions. We experimentally compared their (a) approximation quality for k-Nearest Neighbor (k-NN) queries and both (b) pruning ability and (c) approximation quality for ε-range queries. Results for k-NN queries, which we present here, show that our proposed distances FD2 and WD2 (i.e. Frequency and Wavelet Distance functions for 2-grams) perform significantly better than the others. We then develop effective index structures, based on R-trees and scalar quantization, on top of transformed vectors and distance functions. Promising results from the experiments on real biosequence data sets are presented.

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Qian, Yan, Chun Guang Bai, Bai Quan Chen, and Gui Jin Mu. "Research on the Factors of Wind Direction and Distance in the Impact of Sand Source on its Neighborhood: An Example from Three Regions of Southern Xinjiang, China." Applied Mechanics and Materials 507 (January 2014): 845–50. http://dx.doi.org/10.4028/www.scientific.net/amm.507.845.

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Wind-blown sand is now one of the most serious environmental issues around the world. The properties of wind and the distance from sand source to the affected locations are two most important factors for assessing the effect of the sand source on its neighborhood. To acquire better assessment result, the methods of wind vector interpolation and the measurement of the distance from sand source to the affected location have been researched. The adopted interpolation procedure for wind vector includes three steps: 1) resolve the existing wind vectors, 2) interpolate based on the resolved wind vectors using inverse distance weighted interpolation, and 3) compose the interpolated wind vectors. This paper also presents a new measuring method and its theoretical basis to acquire the distance from sand source to the affected location. This paper claims that the distance along the wind direction, based on which the range of the damage can be acquired, is more valuable for the damage evaluation. The presented method has been applied to three regions of Southern Xinjiang, China.

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Lin, Jyh-Shyan, Jen-Chun Chang, Rong-Jaye Chen, and Torleiv Klove. "Distance-Preserving and Distance-Increasing Mappings From Ternary Vectors to Permutations." IEEE Transactions on Information Theory 54, no. 3 (March 2008): 1334–39. http://dx.doi.org/10.1109/tit.2007.915706.

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Matsui, Muneya, Thomas Mikosch, and Gennady Samorodnitsky. "Distance covariance for stochastic processes." Probability and Mathematical Statistics 37, no. 2 (May 14, 2018): 355–72. http://dx.doi.org/10.19195/0208-4147.37.2.9.

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DISTANCE COVARIANCE FOR STOCHASTIC PROCESSESThe distance covariance of two random vectors is a measure of their dependence. The empirical distance covariance and correlation can be used as statistical tools for testing whether two random vectors are independent. We propose an analog of the distance covariance for two stochastic processes defined on some interval. Their empirical analogs can be used to test the independence of two processes.

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Zhang, Peng, Xiaogang Wang, and Peter X. K. Song. "Clustering Categorical Data Based on Distance Vectors." Journal of the American Statistical Association 101, no. 473 (March 2006): 355–67. http://dx.doi.org/10.1198/016214505000000312.

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Zhang, Feng, Zhen Hua Xie, Jiang Tao Cheng, Gao Lun Cui, and Lin Li. "Combination Weighting Method Based on Generalized Mahalanobis Distance and Weighting Relative Entropy." Advanced Materials Research 998-999 (July 2014): 1674–77. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.1674.

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Aimed at combination weighting in multiple attribute decision making, a new approach for combining different weighting vectors is proposed. The proposed approach considers the randomicity of weights themselves and the consistency among weighting vectors, constructs a constrained weighted relative entropy model. Aimed at the disadvantage in the TOPSIS based on Euclidean distance, the TOPSIS based on Mahalanobis distance is adopted to solve the coefficients of optimal weight vector. Finally, an example is conducted and the results show the proposed approach is effective and is more reasonable than three other combination approaches.

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Xue, Jun, Yingqiang Li, Tianhao Mu, and Shifu Chen. "High intra-tumor heterogeneity observed in esophageal squamous cell carcinoma." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e16522-e16522. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e16522.

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e16522 Background: Intra-tumor heterogeneity (ITH) is a critical factor in the metastasis of esophageal squamous cell carcinoma (ESCC). Analysis of ITH facilitates better diagnosis and medication. Cosine distance similarity reflects the directional relationship of each vector to analyze ITH better. Methods: Whole Exome Sequencing (WES) data of 185 tumor regions and 39 normal esophageal tissues from 39 patients were collected from NCBI database. We found highly variable SNPs in the East Asian populations from ExAC, GenomAD and other databases, and calculated the cosine distance of the SNPs between each tumor sample with the corresponding paracancerous sample. Sliding windows were incrementally advanced along the genome. Results: All 39 samples exhibited spatial ITH. Significant long distances were observed in patients with metastatic lymph nodes, and the homologous samples of the patients were roughly identical. In contrast, the sliding windows of different samples presented a distinctly heterogeneous scene. Conclusions: The cosine distance is suitable for the similarity calculation of high-dimensional vectors. The similarity of different samples can be calculated by using mutation sites as vectors. The cosine distance can be used to present further information and graph composition, compared with digital information. In the future, different samples can be clustered based on cosine distance by recruiting more samples and machine learning.

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Wang, Rongcun, Rubing Huang, Yansheng Lu, and Binbin Qu. "Clustering Analysis of Function Call Sequence for Regression Test Case Reduction." International Journal of Software Engineering and Knowledge Engineering 24, no. 08 (October 2014): 1197–223. http://dx.doi.org/10.1142/s0218194014500387.

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Regression test case reduction aims at selecting a representative subset from the original test pool, while retaining the largest possible fault detection capability. Cluster analysis has been proposed and applied for selecting an effective test case subset in regression testing. It groups test cases into clusters based on the similarity of historical execution profiles. In previous studies, historical execution profiles are represented as binary or numeric function coverage vectors. The vector-based similarity approaches only consider which functions or statements are covered and the number of times they are executed. However, the vector-based approaches do not take the relations and sequential information between function calls into account. In this paper, we propose cluster analysis of function call sequences to attempt to improve the fault detection effectiveness of regression testing even further. A test is represented as a function call sequence that includes the relations and sequential information between function calls. The distance between function call sequences is measured not only by the Levenshtein distance but also the Euclidean distance. To assess the effectiveness of our approaches, we designed and conducted experimental studies on five subject programs. The experimental results indicate that our approaches are statistically superior to the approaches based on the similarity of vectors (i.e. binary vectors and numeric vectors), random and greedy function-coverage-based maximization test case reduction techniques in terms of fault detection effectiveness. With respective to the cost-effectiveness, cluster analysis of sequences measured using the Euclidean distance is more effective than using the Levenshtein distance.

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Anderson, Jeffrey A., Ella Harvey Bowman, and Wei-Shau Hu. "Retroviral Recombination Rates Do Not Increase Linearly with Marker Distance and Are Limited by the Size of the Recombining Subpopulation." Journal of Virology 72, no. 2 (February 1, 1998): 1195–202. http://dx.doi.org/10.1128/jvi.72.2.1195-1202.1998.

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ABSTRACT Recombination occurs at high frequencies in all examined retroviruses. The previously determined homologous recombination rate in one retroviral replication cycle is 4% for markers 1.0 kb apart in spleen necrosis virus (SNV). This has often been used to suggest that approximately 30 to 40% of the replication-competent viruses with 7- to 10-kb genomes undergo recombination. These estimates were based on the untested assumption that a linear relationship exists between recombination rates and marker distances. To delineate this relationship, we constructed three sets of murine leukemia virus (MLV)-based vectors containing the neomycin phosphotransferase gene (neo) and the hygromycin phosphotransferase B gene (hygro). Each set contained one vector with a functionalneo and an inactivated hygro and one vector with a functional hygro and an inactivated neo. The two inactivating mutations in the three sets of vectors were separated by 1.0, 1.9, and 7.1 kb. Recombination rates after one round of replication were 4.7, 7.4, and 8.2% with markers 1.0, 1.9, and 7.1 kb apart, respectively. Thus, the rate of homologous recombination with 1.0 kb of marker distance is similar in MLV and SNV. The recombination rate increases when the marker distance increases from 1.0 to 1.9 kb; however, the recombination rates with marker distances of 1.9 and 7.1 kb are not significantly different. These data refute the previous assumption that recombination is proportional to marker distance and define the maximum recombining population in retroviruses.

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Huang, Guan. "A Content Based Image Retrieval Model Using Feature Space Dividing." Applied Mechanics and Materials 596 (July 2014): 388–93. http://dx.doi.org/10.4028/www.scientific.net/amm.596.388.

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This paper introduces a model for content based image retrieval. The proposed model extracts image color, texture and shape as feature vectors; and then the image feature space is divided into a group of search zones; during the image searching phase, the fractional order distance is utilized to evaluate the similarity between images. As the query image vector only needs to compare with library image vectors located in the same search zone, the time cost is largely reduced. Further more the fractional order distance is utilized to improve the vector matching accuracy. The experimental results demonstrated that the proposed model provides more accurate retrieval results with less time cost compared with other methods.

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Koča, Jaroslav, Milan Kratochvíl, Luděk Matyska, and Vladimír Kvasnička. "Mathematical model of reorganization of valence electrons involving two atoms." Collection of Czechoslovak Chemical Communications 51, no. 12 (1986): 2637–55. http://dx.doi.org/10.1135/cccc19862637.

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The algebraic description of states of atomic vectors and their interconversions in a form of 9-dimensional vectors is suggested. The elaborated algorithms LENGTH and PATH calculate the distance between two states of an atomic vector and construct all possible shortest paths between them, respectively. Illustrative applications demonstrate the chemical impact and interpretation of the theory.

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Gómez-Gordillo, Sebastian, Stavros Akras, Denise R. Gonçalves, and Wolfgang Steffen. "Distance mapping applied to four well-known planetary nebulae and a nova shell." Monthly Notices of the Royal Astronomical Society 492, no. 3 (January 13, 2020): 4097–111. http://dx.doi.org/10.1093/mnras/staa060.

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ABSTRACT Accurate distance estimates of astrophysical objects such as planetary nebulae (PNe), and nova and supernova remnants, among others, allow us to constrain their physical characteristics, such as size, mass, luminosity, and age. An innovative technique based on the expansion parallax method, the so-called distance mapping technique (DMT), provides distance maps of expanding nebulae as well as an estimation of their distances. The DMT combines the tangential velocity vectors obtained from 3D morpho-kinematic models and the observed proper motion vectors to estimate the distance. We applied the DMT to four PNe (NGC 6702, NGC 6543, NGC 6302, and BD+30 3639) and one nova remnant (GK Persei) and derived new distances in good agreement with previous studies. New simple morpho-kinematic shape models were generated for NGC 6543, NGC 6302, and NGC 6702, whereas for BD+30 3639 and GK Persei published models were used. We demonstrate that the DMT is a useful tool to obtain distance values of PNe, in addition to revealing kinematically peculiar regions within the nebulae. Distances are also derived from the trigonometric Gaia parallaxes. The effect of the non-negligible parallax offset in the second Gaia data release is also discussed.

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Jiexian, Zeng, Liu Xiupeng, and Fei Yu. "Multiscale Distance Coherence Vector Algorithm for Content-Based Image Retrieval." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/615973.

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Multiscale distance coherence vector algorithm for content-based image retrieval (CBIR) is proposed due to the same descriptor with different shapes and the shortcomings of antinoise performance of the distance coherence vector algorithm. By this algorithm, the image contour curve is evolved by Gaussian function first, and then the distance coherence vector is, respectively, extracted from the contour of the original image and evolved images. Multiscale distance coherence vector was obtained by reasonable weight distribution of the distance coherence vectors of evolved images contour. This algorithm not only is invariable to translation, rotation, and scaling transformation but also has good performance of antinoise. The experiment results show us that the algorithm has a higher recall rate and precision rate for the retrieval of images polluted by noise.

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ABBAS, SYED RAHAT, and MUHAMMAD ARIF. "MODIFIED NEAREST NEIGHBOR METHOD FOR MULTISTEP AHEAD TIME SERIES FORECASTING." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 03 (May 2007): 463–81. http://dx.doi.org/10.1142/s0218001407005545.

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Multistep ahead time series forecasting has become an important activity in various fields of science and technology due to its usefulness in future events management. Nearest neighbor search is a pattern matching algorithm for forecasting, and the accuracy of the method considerably depends on the similarity of the pattern found in the database with the reference pattern. Original time series is embedded into optimal dimension. The optimal dimension is determined by using autocorrelation function plot. The last vector in the embedded matrix is taken as the reference vector and all the previous vectors as candidate vectors. In nearest neighbor algorithm, the reference vector is matched with all the candidate vectors in terms of Euclidean distance and the best matched pattern is used for forecasting. In this paper, we have proposed a hybrid distance measure to improve the search of the nearest neighbor. The proposed method is based on cross-correlation and Euclidean distance. The candidate patterns are shortlisted by using cross-correlation and then Euclidean distance is used to select the best matched pattern. Moreover, in multistep ahead forecasting, standard nearest neighbor method introduces a bias in the search which results in higher forecasting errors. We have modified the search methodology to remove the bias by ignoring the latest forecasted value during the search of the nearest neighbor in the subsequent iteration. The proposed algorithm is evaluated on two benchmark time series as well as two real life time series.

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Dahl, Geir. "Polytopes related to the l∞-distance between vectors." Operations Research Letters 22, no. 1 (February 1998): 49–54. http://dx.doi.org/10.1016/s0167-6377(97)00048-5.

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Jen-Chun Chang, Rong-Jaye Chen, T. Klove, and Shi-Chun Tsai. "Distance-preserving mappings from binary vectors to permutations." IEEE Transactions on Information Theory 49, no. 4 (April 2003): 1054–59. http://dx.doi.org/10.1109/tit.2003.809507.

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Jen-Chun Chang. "Distance-increasing mappings from binary vectors to permutations." IEEE Transactions on Information Theory 51, no. 1 (January 2005): 359–63. http://dx.doi.org/10.1109/tit.2004.839527.

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Smolenskii, E. A., A. N. Zefirov, L. K. Maslova, I. V. Chuvaeva, and N. S. Zefirov. "Reduced Distance Matrices and Vectors for Acyclic Compounds." Doklady Chemistry 395, no. 4-6 (April 2004): 63–67. http://dx.doi.org/10.1023/b:doch.0000025223.33689.6f.

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Böttcher, Björn, Martin Keller-Ressel, and René L. Schilling. "Distance multivariance: New dependence measures for random vectors." Annals of Statistics 47, no. 5 (October 2019): 2757–89. http://dx.doi.org/10.1214/18-aos1764.

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Rachkovskij, D. A. "Binary Vectors for Fast Distance and Similarity Estimation." Cybernetics and Systems Analysis 53, no. 1 (January 2017): 138–56. http://dx.doi.org/10.1007/s10559-017-9914-x.

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Murata, Hiroshi, Takashi Onoda, and Seiji Yamada. "Comparative Analysis of Relevance for SVM-Based Interactive Document Retrieval." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 2 (March 20, 2013): 149–56. http://dx.doi.org/10.20965/jaciii.2013.p0149.

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Support Vector Machines (SVMs) were applied to interactive document retrieval that uses active learning. In such a retrieval system, the degree of relevance is evaluated by using a signed distance from the optimal hyperplane. It is not clear, however, how the signed distance in SVMs has characteristics of vector space model. We therefore formulated the degree of relevance by using the signed distance in SVMs and comparatively analyzed it with a conventional Rocchio-based method. Although vector normalization has been utilized as preprocessing for document retrieval, few studies explained why vector normalization was effective. Based on our comparative analysis, we theoretically show the effectiveness of normalizing document vectors in SVM-based interactive document retrieval. We then propose a cosine kernel that is suitable for SVM-based interactive document retrieval. The effectiveness of the method was compared experimentally with conventional relevance feedback for Boolean, Term Frequency and Term Frequency-Inverse Document Frequency representations of document vectors. Experimental results for a Text REtrieval Conference data set showed that the cosine kernel is effective for all document representations, especially Term Frequency representation.

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Tseng, Wei-Kuo, and Hsuan-Shih Lee. "The Vector Function for Distance Travelled in Great Circle Navigation." Journal of Navigation 60, no. 1 (December 15, 2006): 158–64. http://dx.doi.org/10.1017/s0373463307214122.

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Traditionally, on a great circle, the latitude or longitude of a waypoint is found by inspection. In this paper, using an elementary knowledge of vector algebra including linear combination of vectors and vector basis, we provide an easy method for finding the equation of a great circle path as a parameterized curve. By use of this vector function of distance travelled, the latitude and longitude of waypoints can be found based on the distance from departure point along a great circle. The approach is intended to appeal to the navigator who is interested in the mathematics of navigation and who, nowadays, solves his navigation problems with a personal computer.

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Wang, Jian Guo, Liang Wu Cheng, Wen Xing Zhang, and Bo Qin. "A Modified Incremental Support Vector Machine for Regression." Applied Mechanics and Materials 135-136 (October 2011): 63–69. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.63.

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support vector machine (SVM) has been shown to exhibit superior predictive power compared to traditional approaches in many studies, such as mechanical equipment monitoring and diagnosis. However, SVM training is very costly in terms of time and memory consumption due to the enormous amounts of training data and the quadratic programming problem. In order to improve SVM training speed and accuracy, we propose a modified incremental support vector machine (MISVM) for regression problems in this paper. The main concepts are that using the distance from the margin vectors which violate the Karush-Kuhn-Tucker (KKT) condition to the final decision hyperplane to evaluate the importance of each margin vectors, and the margin vectors whose distance is below the specified value are preserved, the others are eliminated. Then the original SVs and the remaining margin vectors are used to train a new SVM. The proposed MISVM can not only eliminate the unimportant samples such as noise samples, but also preserved the important samples. The effectiveness of the proposed MISVMs is demonstrated with two UCI data sets. These experiments also show that the proposed MISVM is competitive with previously published methods.

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Godsil, C. D. "Eigenpolytopes of Distance Regular Graphs." Canadian Journal of Mathematics 50, no. 4 (August 1, 1998): 739–55. http://dx.doi.org/10.4153/cjm-1998-040-8.

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AbstractLet X be a graph with vertex set V and let A be its adjacency matrix. If E is the matrix representing orthogonal projection onto an eigenspace of A with dimension m, then E is positive semi-definite. Hence it is the Gram matrix of a set of |V| vectors in Rm. We call the convex hull of a such a set of vectors an eigenpolytope of X. The connection between the properties of this polytope and the graph is strongest when X is distance regular and, in this case, it is most natural to consider the eigenpolytope associated to the second largest eigenvalue of A. The main result of this paper is the characterisation of those distance regular graphs X for which the 1-skeleton of this eigenpolytope is isomorphic to X.

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Van Bon, John. "Affine distance-transitive graphs with quadratic forms." Mathematical Proceedings of the Cambridge Philosophical Society 112, no. 3 (November 1992): 507–17. http://dx.doi.org/10.1017/s0305004100071188.

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The classification of all finite primitive distance-transitive graphs is basically divided into two cases. In the one case, known as the almost simple case, we have an almost simple group acting primitively as a group of automorphisms on the graph. In the other case, known as the affine case, the vertices of the graph can be identified with the vectors of a finite-dimensional vector space over some finite field. In this case the automorphism group G of the graph Γ contains a normal p-subgroup N which is elementary Abelian and acts regularly on the set of vertices of Γ. Let G0 be the subgroup of G that stabilizes a vertex. Identifying the vertices of Γ with G0-cosets in G, one obtains a vector space V on which N acts as a group of translations, G0, stabilizes 0 and, as Γ is primitive, G0 acts irreducibly on V.

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Deng, C., K. R. Thomas, and M. R. Capecchi. "Location of crossovers during gene targeting with insertion and replacement vectors." Molecular and Cellular Biology 13, no. 4 (April 1993): 2134–40. http://dx.doi.org/10.1128/mcb.13.4.2134.

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Gene targeting was used to introduce nonselectable genetic changes into chromosomal loci in mouse embryo-derived stem cells. The nonselectable markers were linked to a selectable marker in both insertion- and replacement-type vectors, and the transfer of the two elements to the Hprt locus was assayed. When insertion vectors were used as substrates, the frequency of transfer was highly dependent upon the distance between the nonselectable marker and the double-strand break in the vector. A marker located close to the vector ends was frequently lost, suggesting that a double-strand gap repair activity is involved in vector integration. When replacement vectors were used, cotransfer of a selectable marker and a nonselectable marker 3 kb apart was over 50%, suggesting that recombination between vector and target often occurs near the ends of the vector. To illustrate the use of replacement vectors to transfer specific mutations to the genome, we describe targeting of the delta F508 mutation to the CFTR gene in mouse embryo-derived stem cells.

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Deng, C., K. R. Thomas, and M. R. Capecchi. "Location of crossovers during gene targeting with insertion and replacement vectors." Molecular and Cellular Biology 13, no. 4 (April 1993): 2134–40. http://dx.doi.org/10.1128/mcb.13.4.2134-2140.1993.

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Gene targeting was used to introduce nonselectable genetic changes into chromosomal loci in mouse embryo-derived stem cells. The nonselectable markers were linked to a selectable marker in both insertion- and replacement-type vectors, and the transfer of the two elements to the Hprt locus was assayed. When insertion vectors were used as substrates, the frequency of transfer was highly dependent upon the distance between the nonselectable marker and the double-strand break in the vector. A marker located close to the vector ends was frequently lost, suggesting that a double-strand gap repair activity is involved in vector integration. When replacement vectors were used, cotransfer of a selectable marker and a nonselectable marker 3 kb apart was over 50%, suggesting that recombination between vector and target often occurs near the ends of the vector. To illustrate the use of replacement vectors to transfer specific mutations to the genome, we describe targeting of the delta F508 mutation to the CFTR gene in mouse embryo-derived stem cells.

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Akras, Stavros, and Wolfgang Steffen. "Distance mapping technique and the 3-D structure of BD +30°3639." Proceedings of the International Astronomical Union 7, S283 (July 2011): 304–5. http://dx.doi.org/10.1017/s1743921312011143.

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AbstractWe present a new kinematic analysis technique called distance mapping. It uses the observed proper motion vectors and the 3-D velocity field to determine the distance for each vector. From this information we generate maps that can be use as a constraint to morpho-kinematic modeling with SHAPE. It is applied to BD +30°3639, using the internal proper motion measurements by Li et al. (2002). We determine its distance at 1.40 kpc ± 0.15 kpc.

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Kekre, Dr H. B., Dr Tanuja K. Sarode, and Jagruti K. Save. "An Efficient Method for Similarity Measure in Independent PCA based Classification." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 9, no. 3 (July 15, 2013): 1099–109. http://dx.doi.org/10.24297/ijct.v9i3.3335.

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The paper presents a new approach of finding nearest neighbor in image classification algorithm by proposing efficient method for similarity measure. Generally in supervised classification, after finding the feature vectors of training images and testing images, nearest neighbor classifier does the classification job. This classifier uses different distance measures such as Euclidean distance, Manhattan distance etc. to find the nearest training feature vector. This paper proposes to use Mean Squared Error (MSE) to find the nearness between two images. Initially Independent Principal Component Analysis (PCA),which we discussed in our earlier work, is applied to images of each class to generate Eigen coordinate system for that class. Then for the given test image, a set of feature vectors is generated. New images are reconstructed using each Eigen coordinate system and the corresponding test feature vector. Lowest MSE between the given test image and new reconstructed image indicates the corresponding class for that image. The experiments are conducted on COIL-100 database. The performance is also compared withÂ distance based nearest neighbor classifier. Results show that the proposed method achieves high accuracy even for small size of training set.

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Lin, Te-Tsung, Shi-Chun Tsai, and Hsin-Lung Wu. "Simple Distance-Preserving Mappings From Ternary Vectors to Permutations." IEEE Transactions on Information Theory 54, no. 7 (July 2008): 3251–56. http://dx.doi.org/10.1109/tit.2008.924716.

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Yu, Seung-Eun, and DaeEun Kim. "Landmark vectors with quantized distance information for homing navigation." Adaptive Behavior 19, no. 2 (March 11, 2011): 121–41. http://dx.doi.org/10.1177/1059712311398669.

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Goldberg, Maxim J., and Seonja Kim. "Some Remarks on Diffusion Distances." Journal of Applied Mathematics 2010 (2010): 1–17. http://dx.doi.org/10.1155/2010/464815.

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As a diffusion distance, we propose to use a metric (closely related to cosine similarity) which is defined as the distance between two -normalized vectors. We provide a mathematical explanation as to why the normalization makes diffusion distances more meaningful. Our proposal is in contrast to that made some years ago by R. Coifman which finds the distance between certain unit vectors. In the second part of the paper, we give two proofs that an extension of mean first passage time to mean first passage cost satisfies the triangle inequality; we do not assume that the underlying Markov matrix is diagonalizable. We conclude by exhibiting an interesting connection between the (normalized) mean first passage time and the discretized solution of a certain Dirichlet-Poisson problem and verify our result numerically for the simple case of the unit circle.

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Wichmann, Matthias C., Matt J. Alexander, Merel B. Soons, Stephen Galsworthy, Laura Dunne, Robert Gould, Christina Fairfax, Marc Niggemann, Rosie S. Hails, and James M. Bullock. "Human-mediated dispersal of seeds over long distances." Proceedings of the Royal Society B: Biological Sciences 276, no. 1656 (September 30, 2008): 523–32. http://dx.doi.org/10.1098/rspb.2008.1131.

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Human activities have fundamental impacts on the distribution of species through altered land use, but also directly by dispersal of propagules. Rare long-distance dispersal events have a disproportionate importance for the spread of species including invasions. While it is widely accepted that humans may act as vectors of long-distance dispersal, there are few studies that quantify this process. We studied in detail a mechanism of human-mediated dispersal (HMD). For two plant species we measured, over a wide range of distances, how many seeds are carried by humans on shoes. While over half of the seeds fell off within 5 m, seeds were regularly still attached to shoes after 5 km. Semi-mechanistic models were fitted, and these suggested that long-distance dispersal on shoes is facilitated by decreasing seed detachment probability with distance. Mechanistic modelling showed that the primary vector, wind, was less important as an agent of long-distance dispersal, dispersing seeds less than 250 m. Full dispersal kernels were derived by combining the models for primary dispersal by wind and secondary dispersal by humans. These suggest that walking humans can disperse seeds to very long distances, up to at least 10 km, and provide some of the first quantified dispersal kernels for HMD.

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Tan, Wen Xue, and Xi Ping Wang. "Research on Intelligent Diagnosing Model Based Similarity Distance." Advanced Materials Research 308-310 (August 2011): 432–35. http://dx.doi.org/10.4028/www.scientific.net/amr.308-310.432.

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In order to help human expert solve the problem of disease diagnosing, we analyze the comparability and relativity between pattern similarity distance and diagnosis as to the solution means, and pioneer the theoretical model of similarity-distance on the basis of certainty factors vectors and fuzzy membership factors vectors, and its corresponding data structure mode. Furthermore, the software hierarchy of model and recognition algorithm are designed. Experimentation statistics demonstrate that compared with the human expert, the novel model could obtain a satisfying accuracy rate of diagnosis over 85%, and reduce a rate of misdiagnosis effectively.

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Dagwal, V. J., D. D. Pawar, Y. S. Solanke, and H. R. Shaikh. "Tilted universe with big rip singularity in Lyra geometry." Modern Physics Letters A 35, no. 24 (June 23, 2020): 2050196. http://dx.doi.org/10.1142/s0217732320501965.

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We have examined tilted cosmological models by using conformally flat space-time with wet dark fluid in Lyra geometry. In order to solve the field equations we have considered a power law. In this paper we have discussed tilted universe with time-dependent displacement field vector, heat conduction vectors and also discussed big rip singularity. Some physical and geometrical properties are also investigated. We have also extended our work to investigate the consistency of the derived model with observational parameter from the point of astrophysical phenomenon such as look-back time-redshift, proper distance, luminosity distance, angular-diameter distance and distance modulus.

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Al Mahmud, Nahyan, and Shahfida Amjad Munni. "Qualitative Analysis of PLP in LSTM for Bangla Speech Recognition." International journal of Multimedia & Its Applications 12, no. 5 (October 30, 2020): 1–8. http://dx.doi.org/10.5121/ijma.2020.12501.

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The performance of various acoustic feature extraction methods has been compared in this work using Long Short-Term Memory (LSTM) neural network in a Bangla speech recognition system. The acoustic features are a series of vectors that represents the speech signals. They can be classified in either words or sub word units such as phonemes. In this work, at first linear predictive coding (LPC) is used as acoustic vector extraction technique. LPC has been chosen due to its widespread popularity. Then other vector extraction techniques like Mel frequency cepstral coefficients (MFCC) and perceptual linear prediction (PLP) have also been used. These two methods closely resemble the human auditory system. These feature vectors are then trained using the LSTM neural network. Then the obtained models of different phonemes are compared with different statistical tools namely Bhattacharyya Distance and Mahalanobis Distance to investigate the nature of those acoustic features.

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Yuan, He Jin, and Cui Ru Wang. "A Novel Human Action Recognition Algorithm Based on Edit Distance." Advanced Materials Research 186 (January 2011): 261–65. http://dx.doi.org/10.4028/www.scientific.net/amr.186.261.

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A novel human action recognition algorithm based on edit distance is proposed in this paper. In the method, the mesh feature of each image in human action sequence is firstly calculated; then the feature vectors are quantized through a rival penalized competitive neural network; and through this processing, the time-sequential image sequences are converted into symbolic sequences. For human action recognition, the observed action is firstly vector quantized with the former competitive neural network; then the normalized edit distances to the training samples are calculated and the action which best matches the observed sequence is chosen as the final category. The experiments on Weizmann dataset demonstrate that our method is effective for human action recognition. The average recognition accuracy can reach above 94%.

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Na, Li, Danu Widatama, Tony Mulia, and Benyamin Kusumoputro. "Circular Construction of Iris Feature Vectors for Human Iris Recognition Systems." Applied Mechanics and Materials 330 (June 2013): 991–95. http://dx.doi.org/10.4028/www.scientific.net/amm.330.991.

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In this paper, the authors propose a new method to construct feature vectors of human iris based on circular technique. Since an iris has a circular shape, the circular construction of the iris feature vector is expected to have higher ability to capture iris characteristics. Different from the conventional method which constructs iris feature vectors through left to right scanning process, the circular technique scans the pixel-intensity value of iris circularly. As the result, the length of the constructed feature vectors is dependent on the length of iris radius. In the experiments, various recognition methods of feature vectors were compared. The iris recognition results show that the proposed system with Statistical Euclidean Distance method gives the highest recognition accuracy with 84.5%.

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Putz, Jasmin, Eva M. Vorwagner, and Gernot Hoch. "Flight performance of Monochamus sartor and Monochamus sutor, potential vectors of the pine wood nematode." Forestry Journal 62, no. 4 (December 1, 2016): 195–201. http://dx.doi.org/10.1515/forj-2016-0024.

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Abstract Flight performance of Monochamus sartor and Monochamus sutor, two potential vectors of the pine wood nematode, Bursaphelenchus xylophilus was evaluated in laboratory flight mill tests. Beetles emerging from logs infested in the laboratory and incubated under outdoor conditions as well as field collected beetles were used. The maximum distance flown by M. sartor in a single flight was 3,136.7 m. Mean distances (per beetle) per flight ranged from 694.6 m in females to 872.5 m in males for M. sartor. In 75% of all individual flights M. sartor flew less than 1 km; only 3.7% flew distances longer than 2 km. The mean cumulative distance travelled by M. sartor beetles throughout their lifespan was 7.5 km. The smaller M. sutor beetles flew faster and longer distances. The maximum distance per flight was 5,556.5 m; mean distances ranged from 1,653.6 m in females to 1178.3 m in males. The number of available laboratory reared beetles was too low for quantification of lifetime flight capacity for M. sutor. The findings are compared to published data from Monochamus galloprovincialis recorded on the same type of flight mill as well as to field data from mark-release-recapture studies. The high flight capacity of Monochamus beetles illustrates the importance of considering dispersal of the vectors when planning control measures against the pine wood nematode.

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López-Iñesta, Emilia, Francisco Grimaldo, and Miguel Arevalillo-Herráez. "Learning Similarity Scores by Using a Family of Distance Functions in Multiple Feature Spaces." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 08 (May 9, 2017): 1750027. http://dx.doi.org/10.1142/s0218001417500276.

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There exist a large number of distance functions that allow one to measure similarity between feature vectors and thus can be used for ranking purposes. When multiple representations of the same object are available, distances in each representation space may be combined to produce a single similarity score. In this paper, we present a method to build such a similarity ranking out of a family of distance functions. Unlike other approaches that aim to select the best distance function for a particular context, we use several distances and combine them in a convenient way. To this end, we adopt a classical similarity learning approach and face the problem as a standard supervised machine learning task. As in most similarity learning settings, the training data are composed of a set of pairs of objects that have been labeled as similar/dissimilar. These are first used as an input to a transformation function that computes new feature vectors for each pair by using a family of distance functions in each of the available representation spaces. Then, this information is used to learn a classifier. The approach has been tested using three different repositories. Results show that the proposed method outperforms other alternative approaches in high-dimensional spaces and highlight the benefits of using multiple distances in each representation space.

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Dai, Cai, and Xiujuan Lei. "A Decomposition-Based Multiobjective Evolutionary Algorithm with Adaptive Weight Adjustment." Complexity 2018 (September 12, 2018): 1–20. http://dx.doi.org/10.1155/2018/1753071.

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Recently, decomposition-based multiobjective evolutionary algorithms have good performances in the field of multiobjective optimization problems (MOPs) and have been paid attention by many scholars. Generally, a MOP is decomposed into a number of subproblems through a set of weight vectors with good uniformly and aggregate functions. The main role of weight vectors is to ensure the diversity and convergence of obtained solutions. However, these algorithms with uniformity of weight vectors cannot obtain a set of solutions with good diversity on some MOPs with complex Pareto optimal fronts (PFs) (i.e., PFs with a sharp peak or low tail or discontinuous PFs). To deal with this problem, an improved decomposition-based multiobjective evolutionary algorithm with adaptive weight adjustment (IMOEA/DA) is proposed. Firstly, a new method based on uniform design and crowding distance is used to generate a set of weight vectors with good uniformly. Secondly, according to the distances of obtained nondominated solutions, an adaptive weight vector adjustment strategy is proposed to redistribute the weight vectors of subobjective spaces. Thirdly, a selection strategy is used to help each subobjective space to obtain a nondominated solution (if have). Comparing with six efficient state-of-the-art algorithms, for example, NSGAII, MOEA/D, MOEA/D-AWA, EMOSA, RVEA, and KnEA on some benchmark functions, the proposed algorithm is able to find a set of solutions with better diversity and convergence.

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DePolo, Nicholas J., Cataline E. Harkleroad, Mordechai Bodner, Andrew T. Watt, Carol G. Anderson, Judith S. Greengard, Krishna K. Murthy, Thomas W. Dubensky, and Douglas J. Jolly. "The Resistance of Retroviral Vectors Produced from Human Cells to Serum Inactivation In Vivo and In Vitro Is Primate Species Dependent." Journal of Virology 73, no. 8 (August 1, 1999): 6708–14. http://dx.doi.org/10.1128/jvi.73.8.6708-6714.1999.

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ABSTRACT The ability to deliver genes as therapeutics requires an understanding of the vector pharmacokinetics similar to that required for conventional drugs. A first question is the half-life of the vector in the bloodstream. Retroviral vectors produced in certain human cell lines differ from vectors produced in nonhuman cell lines in being substantially resistant to inactivation in vitro by human serum complement (F. L. Cosset, Y. Takeuchi, J. L. Battini, R. A. Weiss, and M. K. Collins, J. Virol. 69:7430–7436, 1995). Thus, use of human packaging cell lines (PCL) may produce vectors with longer half-lives, resulting in more-efficacious in vivo gene therapy. However, survival of human PCL-produced vectors in vivo following systemic administration has not been explored. In this investigation, the half-lives of retroviral vectors packaged by either canine D17 or human HT1080 PCL were measured in the bloodstreams of macaques and chimpanzees. Human PCL-produced vectors exhibited significantly higher concentrations of circulating biologically active vector at the earliest time points measured (>1,000-fold in chimpanzees), as well as substantially extended half-lives, compared to canine PCL-produced vectors. In addition, the circulation half-life of human PCL-produced vector was longer in chimpanzees than in macaques. This was consistent with in vitro findings which demonstrated that primate serum inactivation of vector produced from human PCL increased with increasing phylogenetic distance from humans. These results establish that in vivo retroviral vector half-life correlates with in vitro resistance to complement. Furthermore, these findings should influence the choice of animal models used to evaluate retroviral-vector-based therapies.

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Xu, Yun Jie. "Application of Fault Phenomenon Vector Distance Discriminance in Mechanical System Fault Diagnosis." Key Engineering Materials 467-469 (February 2011): 686–91. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.686.

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Aiming at the problem of diagnosis difficulty caused by too many factors of mechanical system, a kind of diagnosing method based on fault phenomenon was presented. The research on mechanical system fault phenomenon space arrived at conclusion that the emergency of each fault phenomenon subject to 0-1 distribution. Therefore, phenomenon vector corresponding to each fault formed cluster whose accumulation point is expectation of vector. After exclusion of abnormal vectors, the distance discrimination was used to fault diagnosis to establish expert system based on fault phenomenon vector. The confirmed result was return back to fault database so that the system achieve self-learning of real-time diagnosis experiences. Finally, the example on X-type hydraulic excavator proves that the diagnostic method has characteristics of good real-time, simple operation and high diagnostic accuracy.

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Wang, Qi, Yingying Feng, Xiangde Zhang, Yanrui Sun, and Xiaojun Lu. "IWKNN: An Effective Bluetooth Positioning Method Based on Isomap and WKNN." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/8765874.

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Recently, Bluetooth-based indoor positioning has become a hot research topic. However, the instability of Bluetooth RSSI (Received Signal Strength Indicator) promotes a huge challenge in localization accuracy. To improve the localization accuracy, this paper measures the distance of RSSI vectors on their low-dimensional manifold and proposes a novel positioning method IWKNN (Isomap-based Weighted K-Nearest Neighbor). The proposed method firstly uses Isomap to generate low-dimensional embedding for RSSI vectors. Then, the distance of two given RSSI vectors is measured by Euclidean distance of their low-dimensional embeddings. Finally, the position is calculated by WKNN. Experiment indicates that the proposed approach is more robust and accurate.

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Yuan, Yong-bin, Sheng Lan, Xu Yu, and Miao Yu. "Algorithm of Fuzzy Support Vector Machine based on a Piecewise Linear Fuzzy Weight Method." International Journal of Cognitive Informatics and Natural Intelligence 12, no. 2 (April 2018): 62–76. http://dx.doi.org/10.4018/ijcini.2018040105.

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This article describes how fuzzy support vector machines (FSVMs) function well with good anti-noise performance, which receives the attention of many experts. However, the traditional center-distance fuzzy weight assignment method assigns support vectors with a small value of a membership degree and this weakens the role of support vectors in classification. In this article, a piecewise linear fuzzy weight computing method is proposed, in which boundary samples are assigned with a larger value of membership degree and samples far from the mean vector are assigned a smaller value of membership degree. The proposed method has a good classification performance, because the influence of noise samples is weakened and meanwhile the support vectors are paid much more attention. The experiments on the UCI database and MNIST data set fully verify the effectiveness of the proposed algorithm.

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Journal articles on the topic 'Distance vectors'

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