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Journal articles on the topic 'Binary vector'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Arai, Kenichi, and Hiroyuki Okazaki. "N-Dimensional Binary Vector Spaces." Formalized Mathematics 21, no. 2 (2013): 75–81. http://dx.doi.org/10.2478/forma-2013-0008.

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Summary The binary set {0, 1} together with modulo-2 addition and multiplication is called a binary field, which is denoted by F2. The binary field F2 is defined in [1]. A vector space over F2 is called a binary vector space. The set of all binary vectors of length n forms an n-dimensional vector space Vn over F2. Binary fields and n-dimensional binary vector spaces play an important role in practical computer science, for example, coding theory [15] and cryptology. In cryptology, binary fields and n-dimensional binary vector spaces are very important in proving the security of cryptographic systems [13]. In this article we define the n-dimensional binary vector space Vn. Moreover, we formalize some facts about the n-dimensional binary vector space Vn.

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2

Fei, Sheng-wei. "Fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm for feature selection and parameter optimization." Advances in Mechanical Engineering 9, no. 1 (2017): 168781401668529. http://dx.doi.org/10.1177/1687814016685294.

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In this article, fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm is proposed, and the improved binary bat algorithm is used to select the appropriate features and kernel parameter of relevance vector machine. In the improved binary bat algorithm, the new velocities updating method of the bats is presented in order to ensure the decreasing of the probabilities of changing their position vectors’ elements when the position vectors’ elements of the bats are equal to the current best location’s element, and the increasing of the probabilities of changing their position vectors’ elements when the position vectors’ elements of the bats are unequal to the current best location’s element, which are helpful to strengthen the optimization ability of binary bat algorithm. The traditional relevance vector machine trained by the training samples with the unreduced features can be used to compare with the proposed improved binary bat algorithm–relevance vector machine method. The experimental results indicate that improved binary bat algorithm–relevance vector machine has a stronger fault diagnosis ability of bearing than the traditional relevance vector machine trained by the training samples with the unreduced features, and fault diagnosis of bearing based on improved binary bat algorithm–relevance vector machine is feasible.

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3

Ku, Ning-Yun, Shun-Chieh Chang, and Sha-Hwa Hwang. "Binary Search Vector Quantization." AASRI Procedia 8 (2014): 112–17. http://dx.doi.org/10.1016/j.aasri.2014.08.019.

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4

ZHANG, LI, WEI-DA ZHOU, TIAN-TIAN SU, and LI-CHENG JIAO. "DECISION TREE SUPPORT VECTOR MACHINE." International Journal on Artificial Intelligence Tools 16, no. 01 (2007): 1–15. http://dx.doi.org/10.1142/s0218213007003163.

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A new multi-class classifier, decision tree SVM (DTSVM) which is a binary decision tree with a very simple structure is presented in this paper. In DTSVM, a problem of multi-class classification is decomposed into a series of ones of binary classification. Here, the binary decision tree is generated by using kernel clustering algorithm, and each non-leaf node represents one binary classification problem. By compared with the other multi-class classification methods based on the binary classification SVMs, the scale and the complexity of DTSVM are less, smaller number of support vectors are needed, and has faster test speed. The final simulation results confirm the feasibility and the validity of DTSVM.

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Tissier, Julien, Christophe Gravier, and Amaury Habrard. "Near-Lossless Binarization of Word Embeddings." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7104–11. http://dx.doi.org/10.1609/aaai.v33i01.33017104.

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Word embeddings are commonly used as a starting point in many NLP models to achieve state-of-the-art performances. However, with a large vocabulary and many dimensions, these floating-point representations are expensive both in terms of memory and calculations which makes them unsuitable for use on low-resource devices. The method proposed in this paper transforms real-valued embeddings into binary embeddings while preserving semantic information, requiring only 128 or 256 bits for each vector. This leads to a small memory footprint and fast vector operations. The model is based on an autoencoder architecture, which also allows to reconstruct original vectors from the binary ones. Experimental results on semantic similarity, text classification and sentiment analysis tasks show that the binarization of word embeddings only leads to a loss of ∼2% in accuracy while vector size is reduced by 97%. Furthermore, a top-k benchmark demonstrates that using these binary vectors is 30 times faster than using real-valued vectors.

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Bodmann, Bernhard, My Le, Letty Reza, Matthew Tobin, and Mark Tomforde. "Frame theory for binary vector spaces." Involve, a Journal of Mathematics 2, no. 5 (2010): 589–602. http://dx.doi.org/10.2140/involve.2009.2.589.

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7

Zhou, Changhe, Liren Liu, and Zhijiang Wang. "Binary-encoded vector–matrix multiplication architecture." Optics Letters 17, no. 24 (1992): 1800. http://dx.doi.org/10.1364/ol.17.001800.

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8

Cha, Sung-Hyuk, Charles Tappert, and Sungsoo Yoon. "Enhancing Binary Feature Vector Similarity Measures." Journal of Pattern Recognition Research 1, no. 1 (2006): 63–77. http://dx.doi.org/10.13176/11.20.

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9

Brouwer, A. E. "An inequality in binary vector spaces." Discrete Mathematics 59, no. 3 (1986): 315–17. http://dx.doi.org/10.1016/0012-365x(86)90177-9.

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10

Upadhyaya, Narayana M., Brian Surin, Kerrie Ramm, et al. "Agrobacterium-mediated transformation of Australian rice cultivars Jarrah and Amaroo using modified promoters and selectable markers." Functional Plant Biology 27, no. 3 (2000): 201. http://dx.doi.org/10.1071/pp99078.

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We report the first successfulAgrobacterium-mediated transformation of Australian elite rice cultivars, Jarrah and Amaroo, using binary vectors with our improved promoters and selectable markers. Calli derived from mature embryos were used as target tissues. The binary vectors contained hph(encoding hygromycin resistance) or bar (encoding herbicide resistance) as the selectable marker gene and uidA (gus) or sgfpS65T as the reporter gene driven by different promoters. Use of Agrobacterium strain AGL1 carrying derivatives of an improved binary vector pWBVec8, wherein the CaMV35S driven hph gene is interrupted by the castor bean catalase 1 intron, produced a 4- fold higher number of independent transgenic lines compared to that produced with the use of strain EHA101 car-rying the binary vector pIG121-Hm wherein the CaMV35S driven hph is intronless. The Ubiquitin promoter produced 30-fold higher &szlig;-glucuronidase (GUS) activity (derivatives of binary vector pWBVec8) in transgenic plants than the CaMV35S promoter (pIG121-Hm). The two modified SCSV promoters produced GUS activity com-parable to that produced by the Ubiquitin promoter. Progeny analysis (R1) for hygromycin resistance and GUS activ-ity with selected lines showed both Mendelian and non-Mendelian segregation. Lines showing very high levels of GUS activity in T0 showed a reduced level of GUS activity in their T1 progeny, while lines with moderate levels of GUS activity showed increased levels in T1 progeny. Stable heritable green fluorescent protein (GFP) expression was also observed in few transgenic plants produced with the binary vector pTO134 which had the CaMV35S promoter-driven selectable marker gene bar and a modified CaMV35S promoter-driven reporter gene sgfpS65T.

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11

Journal, Baghdad Science. "An algorithm for binary codebook design based on the average bitmap replacement error (ABPRE)." Baghdad Science Journal 8, no. 2 (2011): 684–88. http://dx.doi.org/10.21123/bsj.8.2.684-688.

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In this paper, an algorithm for binary codebook design has been used in vector quantization technique, which is used to improve the acceptability of the absolute moment block truncation coding (AMBTC) method. Vector quantization (VQ) method is used to compress the bitmap (the output proposed from the first method (AMBTC)). In this paper, the binary codebook can be engender for many images depending on randomly chosen to the code vectors from a set of binary images vectors, and this codebook is then used to compress all bitmaps of these images. The chosen of the bitmap of image in order to compress it by using this codebook based on the criterion of the average bitmap replacement error (ABPRE). This paper is suitable to reduce bit rates (increase compression ratios) with little reduction of performance (PSNR).

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12

Nguyen, Duc Manh, and Sunghwan Kim. "A novel construction for quantum stabilizer codes based on binary formalism." International Journal of Modern Physics B 34, no. 08 (2020): 2050059. http://dx.doi.org/10.1142/s0217979220500599.

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In this research, we propose a novel construction of quantum stabilizer code based on a binary formalism. First, from any binary vector of even length, we generate the parity-check matrix of the quantum code from a set composed of elements from this vector and its relations by shifts via subtraction and addition. We prove that the proposed matrices satisfy the condition constraint for the construction of quantum codes. Finally, we consider some constraint vectors which give us quantum stabilizer codes with various dimensions and a large minimum distance with code length from six to twelve digits.

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Bai, Xue Liang, Dan Wang, Ning Ning Liu, et al. "Construction of Two Vectors for a Bypass of Monocotyledon Plants Photorespiration." Advanced Materials Research 340 (September 2011): 351–56. http://dx.doi.org/10.4028/www.scientific.net/amr.340.351.

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In order to modify the photorespiration of monocotyledonous crops, we aimed to construct vectors that will be used to introduce a bypass to the native photorespiration pathway. Firstly, we cloned the encoding sequences of glyoxylate carboligase (GCL) and tartronic semialdehyde reductase (TSR) fromE. coli, glycolate dehydrogenase (GDH) fromArabidopsis thalianaand chloroplast transit peptide (cTP) from rice. Then we constructed a universal vector pEXP harboring the encoding sequence of cTP for targeting a protein into chloroplast. By insertion of these three encoding sequences into the universal vector pEXP, we obtained the expression cassettes for GCL, TSR and GDH, respectively. Finally, we inserted the cassettes for GCL and TSR in tandem into the binary vector pCAMBIA 1301, and for GDH into another binary vector, pPGN, to obtain our plant expression vectors pCAMBIA 1301-TG and pPGN-GDH, respectively. These two expression vectors possess different selection resistance and can be used to transform monocots together, to introduce the bypass pathway of photorespiration. By this way, the transgenic plants can recycle glycolate, the by-product of photosynthesis in C3plants, within the chloroplast, simultaneously, save energy and avoid the loss of ammonia, which will contribute to improved growth.

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14

Zhang, Daokun, Jie Yin, Xingquan Zhu, and Chengqi Zhang. "Search Efficient Binary Network Embedding." ACM Transactions on Knowledge Discovery from Data 15, no. 4 (2021): 1–27. http://dx.doi.org/10.1145/3436892.

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Traditional network embedding primarily focuses on learning a continuous vector representation for each node, preserving network structure and/or node content information, such that off-the-shelf machine learning algorithms can be easily applied to the vector-format node representations for network analysis. However, the learned continuous vector representations are inefficient for large-scale similarity search, which often involves finding nearest neighbors measured by distance or similarity in a continuous vector space. In this article, we propose a search efficient binary network embedding algorithm called BinaryNE to learn a binary code for each node, by simultaneously modeling node context relations and node attribute relations through a three-layer neural network. BinaryNE learns binary node representations using a stochastic gradient descent-based online learning algorithm. The learned binary encoding not only reduces memory usage to represent each node, but also allows fast bit-wise comparisons to support faster node similarity search than using Euclidean or other distance measures. Extensive experiments and comparisons demonstrate that BinaryNE not only delivers more than 25 times faster search speed, but also provides comparable or better search quality than traditional continuous vector based network embedding methods. The binary codes learned by BinaryNE also render competitive performance on node classification and node clustering tasks. The source code of the BinaryNE algorithm is available at https://github.com/daokunzhang/BinaryNE.

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15

LACOLLE, B., N. SZAFRAN, and P. VALENTIN. "GEOMETRIC MODELLING AND ALGORITHMS FOR BINARY MIXTURES." International Journal of Computational Geometry & Applications 04, no. 03 (1994): 243–60. http://dx.doi.org/10.1142/s021819599400015x.

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We present some computational methods in a particular case of mixing and separation theory, as an application of classical results in the field of computational geometry. Our aim is the production of some given mixtures by mixing parts of basic products. In the geometrical approach we use, products or mixtures are characterized by vectors and the mixing process by vector sums, in the vector space of physico-chemical species. The feasibility of a mixture is viewed as a point-inclusion in the convex set of mixtures which is a zonotope associated with basic mixtures. This paper is concerned with binary mixtures characterized by two species. The geometrical approach leads to plane geometry problems and gives complete solutions to the optimal fabrication of a mixture, as well as to the fabrication of a sequence of several mixtures. Using the framework of computational geometry, we present efficient algorithms for solving the main problems related to the management of binary mixtures.

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16

Ghosh, Anjan K. "Binary vector multiplications with nonlinear optical interfaces." Applied Optics 26, no. 16 (1987): 3195. http://dx.doi.org/10.1364/ao.26.003195.

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17

Fränti, Pasi, and Timo Kaukoranta. "Binary vector quantizer design using soft centroids." Signal Processing: Image Communication 14, no. 9 (1999): 677–81. http://dx.doi.org/10.1016/s0923-5965(98)00037-x.

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18

Fletcher, Roger, and Gaetano Zanghirati. "Binary separation and training support vector machines." Acta Numerica 19 (May 2010): 121–58. http://dx.doi.org/10.1017/s0962492910000024.

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We introduce basic ideas of binary separation by a linear hyperplane, which is a technique exploited in the support vector machine (SVM) concept. This is a decision-making tool for pattern recognition and related problems. We describe a fundamental standard problem (SP) and show how this is used in most existing research to develop a dual-based algorithm for its solution. This algorithm is shown to be deficient in certain aspects, and we develop a new primal-based SQP-like algorithm, which has some interesting features. Most practical SVM problems are not adequately handled by a linear hyperplane. We describe the nonlinear SVM technique, which enables a nonlinear separating surface to be computed, and we propose a new primal algorithm based on the use of low-rank Cholesky factors.It may be, however, that exact separation is not desirable due to the presence of uncertain or mislabelled data. Dealing with this situation is the main challenge in developing suitable algorithms. Existing dual-based algorithms use the idea of L1 penalties, which has merit. We suggest how penalties can be incorporated into a primal-based algorithm. Another aspect of practical SVM problems is often the huge size of the data set, which poses severe challenges both for software package development and for control of ill-conditioning. We illustrate some of these issues with numerical experiments on a range of problems.

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19

Pan, V. "Binary segmentation for matrix and vector operations." Computers & Mathematics with Applications 25, no. 3 (1993): 69–71. http://dx.doi.org/10.1016/0898-1221(93)90144-k.

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20

Rachkovskij, D. A. "Vector Data Transformation Using Random Binary Matrices." Cybernetics and Systems Analysis 50, no. 6 (2014): 960–68. http://dx.doi.org/10.1007/s10559-014-9687-4.

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21

Lipecki, Z. "On binary-type approximations in vector lattices." Archiv der Mathematik 62, no. 6 (1994): 545–53. http://dx.doi.org/10.1007/bf01193743.

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22

Yu, Bao Min. "A Feasible Algorithm for Designing Biorthogonal Binary Finitely Supported Multiwavelet Functions." Advanced Materials Research 225-226 (April 2011): 1092–95. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.1092.

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Wavelet analysis has been a powerful tool for exploring and solving many complicated problems in natural science and engineering computation. In this paper, the notion of vector-valued multiresolution analysis is introduced and the definition of the biorthogonal vector-valued bivariate wavelet functions is given. The existence of biorthogonal vector-valued binary wavelet functions associated with a pair of biorthogonal vector-valued finitely supported binary scaling functions is investigated. An algorithm for constructing a class of biorthogonal vector-valued finitely supported binary wavelet functions is presented by virtue of multiresolution analysis and matrix theory.

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23

Ren, Bo Yan, Zheng Qin, and Feng Fei Zhao. "Optional Feature Vector Generation for Linear Value Function Approximation with Binary Features." Advanced Materials Research 756-759 (September 2013): 3967–71. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3967.

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Linear value function approximation with binary features is important in the research of Reinforcement Learning (RL). When updating the value function, it is necessary to generate a feature vector which contains the features that should be updated. In high dimensional domains, the generation process will take lot more time, which reduces the performance of algorithm a lot. Hence, this paper introduces Optional Feature Vector Generation (OFVG) algorithm as an improved method to generate feature vectors that can be combined with any online, value-based RL method that uses and expands binary features. This paper shows empirically that OFVG performs well in high dimensional domains.

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24

Lu, Hui-Juan, Xue-Rong Zhou, Zhu-Xun Gong, and Narayana M. Upadhyaya. "Generation of selectable marker-free transgenic rice using double right-border (DRB) binary vectors." Functional Plant Biology 28, no. 3 (2001): 241. http://dx.doi.org/10.1071/pp00129.

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Currently employed transformation systems require selectable marker genes encoding antibiotic or herbicide resistance, along with the gene of interest (GOI), to select transformed cells from among a large population of untransformed cells. The continued presence of these selectable markers, especially in food crops such as rice (Oryza sativa L.), is of increasing public concern. Techniques based on DNA recombination and Agrobacterium-mediated co-transformation with two binary vectors in a single or two different Agrobacterium strains, or with super-binary vectors carrying two sets of T-DNA border sequences (twin T-DNA vectors), have been employed by researchers to produce selectable marker-free (SMF) transgenic progeny. We have developed a double right-border (DRB) binary vector carrying two copies of T-DNA right-border (RB) sequences flanking a selectable marker gene, followed by a GOI and one copy of the left border sequence. Two types of T-DNA inserts, one initiated from the first RB containing both the selectable gene and the GOI, and the other from the second RB containing only the GOI, were expected to be produced and integrated into the genome. In the subsequent generation, these inserts could segregate away from each other, allowing the selection of the progeny with only the GOI. We tested this vector using two selectable marker genes and successfully obtained progeny plants in which the second selectable marker gene segregated away from the first. Using the DRB binary vector system, we recovered SMF transgenic lines containing a rice ragged stunt virus (RRSV)-derived synthetic resistance gene in the rice cultivars Jarrah and Xiu Shui. Approximately 36–64% of the primary transformants of these cultivars yielded SMF progeny. Among SMF Jarrah transgenic progeny <50% of plants contained the RRSV transgene. Thus, we have developed an efficient vector for producing SMF plants that allows straightforward cloning of any GOIs in comparison with the published ‘twin T-DNA’ vectors.

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25

Wang, Sheng, Haiying Xing, Chenlei Hua, Hui-Shan Guo, and Jie Zhang. "An Improved Single-Step Cloning Strategy Simplifies the Agrobacterium tumefaciens-Mediated Transformation (ATMT)-Based Gene-Disruption Method for Verticillium dahliae." Phytopathology® 106, no. 6 (2016): 645–52. http://dx.doi.org/10.1094/phyto-10-15-0280-r.

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The soilborne fungal pathogen Verticillium dahliae infects a broad range of plant species to cause severe diseases. The availability of Verticillium genome sequences has provided opportunities for large-scale investigations of individual gene function in Verticillium strains using Agrobacterium tumefaciens-mediated transformation (ATMT)-based gene-disruption strategies. Traditional ATMT vectors require multiple cloning steps and elaborate characterization procedures to achieve successful gene replacement; thus, these vectors are not suitable for high-throughput ATMT-based gene deletion. Several advancements have been made that either involve simplification of the steps required for gene-deletion vector construction or increase the efficiency of the technique for rapid recombinant characterization. However, an ATMT binary vector that is both simple and efficient is still lacking. Here, we generated a USER-ATMT dual-selection (DS) binary vector, which combines both the advantages of the USER single-step cloning technique and the efficiency of the herpes simplex virus thymidine kinase negative-selection marker. Highly efficient deletion of three different genes in V. dahliae using the USER-ATMT-DS vector enabled verification that this newly-generated vector not only facilitates the cloning process but also simplifies the subsequent identification of fungal homologous recombinants. The results suggest that the USER-ATMT-DS vector is applicable for efficient gene deletion and suitable for large-scale gene deletion in V. dahliae.

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Xu, Qingyang. "Minimal Structural ART Neural Network and Fault Diagnosis Application of Gas Turbine." Open Mechanical Engineering Journal 10, no. 1 (2016): 13–22. http://dx.doi.org/10.2174/1874155x01610010013.

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Adaptive Resonance Theory (ART) model is a special neural network based on unsupervised learning which simulates the cognitive process of human. However, ART1 can be only used for binary input, and ART2 can be used for binary and analog vectors which have complex structures and complicated calculations. In order to improve the real-time performance of the network, a minimal structural ART is proposed which combines the merits of the two models by subsuming the bottom-up and top-down weight. The vector similarity test is used instead of vigilance test. Therefore, this algorithm has a simple structure like ART1 and good performance as ART2 which can be used for both binary and analog vector classification, and it has a high efficiency. Finally, a gas turbine fault diagnosis experiment exhibits the validity of the new network.

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27

Guenther, Nick, and Matthias Schonlau. "Support Vector Machines." Stata Journal: Promoting communications on statistics and Stata 16, no. 4 (2016): 917–37. http://dx.doi.org/10.1177/1536867x1601600407.

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Support vector machines are statistical- and machine-learning techniques with the primary goal of prediction. They can be applied to continuous, binary, and categorical outcomes analogous to Gaussian, logistic, and multinomial regression. We introduce a new command for this purpose, svmachines. This package is a thin wrapper for the widely deployed libsvm (Chang and Lin, 2011, ACM Transactions on Intelligent Systems and Technology 2(3): Article 27). We illustrate svmachines with two examples.

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28

Tambayong, Chrisanty Y., Jullia Titaley, and Rinancy Tumilaar. "EKSISTENSI RUANG VEKTOR ATAS LAPANGAN TERHADAP MODUL BEBAS." JURNAL ILMIAH SAINS 19, no. 1 (2019): 37. http://dx.doi.org/10.35799/jis.19.1.2019.22536.

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EKSISTENSI RUANG VEKTOR ATAS LAPANGANTERHADAP MODUL BEBAS ABSTRAKModul merupakan perluasan dari ruang vektor. Suatu Ruang vektor atas lapangan merupakan suatu himpunan vektor – vektor dengan dua operasi yaitu penjumlahan dan perkalian dengan skalar. Sebarang ruang vektor atas lapangan dapat dipandang sebagai modul atas ring Tetapi tidak semua modul dapat dinyatakan sebagai ruang vektor atas lapangan. Tujuan penelitian ini adalah untuk memberikan counter example bahwa tidak semua modul merupakan ruang vektor atas lapangan. Dari hasil penelitian dapat disimpulkan bahwa setiap modul yang memiliki basis merupakan ruag vektor atas lapangan adalah modul bebas.Kata kunci : Ruang vektor, modul, dan modul bebas THE EXISTENCE OF VECTOR SPACE OVER A FIELD OF FREE MODULE ABSTRACTModule is an extension of a vector space. A vector space over a field is a set of vectors with two binary operation. The binary operation is addition and scalar multiplication. A vector space is a module over a field, many of basic concepts that we defined for vector space can also be defined module. But it’s not all module vector space over a field. The purpose of this tesis is for give counter example that it’s not all module vector space over a field. From the result of this research show that a module have bases is vector space over a field is free module.Keywords : vector space, module, free module

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29

Wang, Dong-li, Jian-guo Zheng, and Yan Zhou. "Binary tree of posterior probability support vector machines." Journal of Zhejiang University SCIENCE C 12, no. 2 (2011): 83–87. http://dx.doi.org/10.1631/jzus.c1000022.

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30

Guo, Zhenhua, David Zhang, Lei Zhang, and Wangmeng Zuo. "Palmprint verification using binary orientation co-occurrence vector." Pattern Recognition Letters 30, no. 13 (2009): 1219–27. http://dx.doi.org/10.1016/j.patrec.2009.05.010.

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31

Frisch, David A., Larry W. Harris-Haller, Nathaniel T. Yokubaitis, Terry L. Thomas, Susan H. Hardin, and Timothy C. Hall. "Complete sequence of the binary vector Bin 19." Plant Molecular Biology 27, no. 2 (1995): 405–9. http://dx.doi.org/10.1007/bf00020193.

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32

Kim, Sung Ryul, Sichul Lee, Hong Gyu Kang, Jong Seong Jeon, Kyung Me Kim, and Gynheung An. "A complete sequence of the pGA1611 binary vector." Journal of Plant Biology 46, no. 3 (2003): 211–14. http://dx.doi.org/10.1007/bf03030451.

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33

Kim, Jun Yeup, Seung-Min Park, Kwang-Eun Ko, and Kwee-Bo Sim. "Optimal EEG Channel Selection by Genetic Algorithm and Binary PSO based on a Support Vector Machine." Journal of Institute of Control, Robotics and Systems 19, no. 6 (2013): 527–33. http://dx.doi.org/10.5302/j.icros.2013.13.1864.

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34

Luo, Jian, Shu-Cherng Fang, Zhibin Deng, and Xiaoling Guo. "Soft Quadratic Surface Support Vector Machine for Binary Classification." Asia-Pacific Journal of Operational Research 33, no. 06 (2016): 1650046. http://dx.doi.org/10.1142/s0217595916500469.

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In this paper, a kernel-free soft quadratic surface support vector machine model is proposed for binary classification directly using a quadratic function for separation. Properties (including the solvability, uniqueness and support vector representation of the optimal solution) of the proposed model are derived. Results of computational experiments on some artificial and real-world classifying data sets indicate that the proposed soft quadratic surface support vector machine model may outperform Dagher’s quadratic model and other soft support vector machine models with a Quadratic or Gaussian kernel in terms of the classification accuracy and robustness.

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35

CHEN, RONG-CHANG, TUNG-SHOU CHEN, and CHIH-CHIANG LIN. "A NEW BINARY SUPPORT VECTOR SYSTEM FOR INCREASING DETECTION RATE OF CREDIT CARD FRAUD." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 02 (2006): 227–39. http://dx.doi.org/10.1142/s0218001406004624.

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Recently, a new personalized model has been developed to prevent credit card fraud. This model is promising; however, there remains some problems. Existing approaches cannot identify well credit card frauds from few data with skewed distributions. This paper proposes to address the problem using a binary support vector system (BSVS). The proposed BSVS is based on the support vectors in the support vector machines (SVM) and the genetic algorithm (GA) is employed to select support vectors. To obtain a high true negative rate, self-organizing mapping (SOM) is first employed to estimate the distribution model of the input data. Then BSVS is used to best train the data according to the input data distribution to obtain a high detection rate. Experimental results show that the proposed BSVS is effective especially for predicting a high true negative rate.

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36

Beardon, A. F., and N. Lord. "Repeated vector products." Mathematical Gazette 104, no. 561 (2020): 460–68. http://dx.doi.org/10.1017/mag.2020.100.

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In [1] the second author observed that it is possible to have a binary operation * on a set X with the property that two different arrangements of brackets in a given combination x1 * … * xn of elements of X yield the same outcome for all choices of the xj. For example, for the operation of subtraction on the set of real numbers, we have $$\left[ {a - \left( {b - c} \right)} \right] - d = a - \left[ {b - \left( {c - d} \right)} \right]$$ for all real numbers a, b, c and d. The author then asked whether or not a similar example might hold for an n-fold vector product on three-dimensional Euclidean space3. We shall show here that no such example can exist; thus two different arrangements of brackets in a repeated vector product will, for some vectors, yield different answers.

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37

Mehmood, Faisal, and Fu-Gui Shi. "M-Hazy Vector Spaces over M-Hazy Field." Mathematics 9, no. 10 (2021): 1118. http://dx.doi.org/10.3390/math9101118.

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The generalization of binary operation in the classical algebra to fuzzy binary operation is an important development in the field of fuzzy algebra. The paper proposes a new generalization of vector spaces over field, which is called M-hazy vector spaces over M-hazy field. Some fundamental properties of M-hazy field, M-hazy vector spaces, and M-hazy subspaces are studied, and some important results are also proved. Furthermore, the linear transformation of M-hazy vector spaces is studied and their important results are also proved. Finally, it is shown that M-fuzzifying convex spaces are induced by an M-hazy subspace of M-hazy vector space.

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38

Steinbach, Bernd, and Christian Posthoff. "Set-based SAT-solving." Facta universitatis - series: Electronics and Energetics 20, no. 3 (2007): 395–414. http://dx.doi.org/10.2298/fuee0703395s.

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The 3-SAT problem is one of the most important and interesting NP-complete problems with many applications in different areas. In several previous papers we showed the use of ternary vectors and set-theoretic considerations as well as binary codings and bit-parallel vector operations in order to solve this problem. Lists of orthogonal ternary vectors have been the main data structure, the intersection of ternary vectors (representing sets of binary solution candidates) was the most important set theoretic operation. The parallelism of the solution process has been established on the register level, i.e. related to the existing hardware, by using a binary coding of the ternary vectors, and it was also possible to transfer the solution process to a hierarchy of processors working in parallel. This paper will show further improvements of the existing algorithms which are easy to understand and result in very efficient algorithms and implementations. Some examples will be presented that will show the recent results.

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BERGERON, ANNE, and SYLVIE HAMEL. "VECTOR ALGORITHMS FOR APPROXIMATE STRING MATCHING." International Journal of Foundations of Computer Science 13, no. 01 (2002): 53–65. http://dx.doi.org/10.1142/s0129054102000947.

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Vector algorithms allow the computation of an output vector r = r1 r2 ⋯ rm given an input vector e = e1 e2 ⋯ em in a bounded number of operations, independent of m the length of the vectors. The allowable operations are usually restricted to bit-wise operations available in processors, including shifts and binary addition with carry. These restrictions imple that the existence of a vector algorithm for a particular problem opens the way to extremely fast implementations, using the inherent parallelism of bit-wise operations. This paper presents general results on the existence and construction of vertor algorithms, with a particular focus on problems arising from computational biology. We show that efficient vector algorithms exist for the problem of approximate string matching with arbitrary weighted distances, generalizing a previous result by G. Myers. We also characterize a class of automata for which vector algorithms can be automatically derived from the transition table of the automata.

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40

Nezuka, Tomohiro, Takafumi Fujita, Makoto Ikeda, and Kunihiro Asada. "A Binary Image Sensor for Motion Detection." Journal of Robotics and Mechatronics 12, no. 5 (2000): 508–14. http://dx.doi.org/10.20965/jrm.2000.p0508.

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This Paper proposes a binary image sensor for motion detection. The sensor detects motion vectors using block-matching method. Each pixel of the image sensor has a shift register. Exclusive OR(XOR) circuits in each pixel compare the shifted image of the previous frame and the image of the current frame. The outputs of the XOR circuits are converted to current value. The current-sum circuits calculate the current-sum of pixel outputs. The current-sum of a matching block represents the sum of absolute difference. The motion vector is obtained by routine of shifting image of the previous frame and comparing the sum of absolute difference. The chip was fabricated using 1.2um 2-Metal 2-Poly-Si CMOS technology and composed of 32 × 32 pixel array and peripheral circuits in a 7.3mm × 7.3mm die.

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41

Zhang, Rui, Tong Bo Liu, and Ming Wen Zheng. "An New Fuzzy Support Vector Machine for Binary Classification." Advanced Materials Research 433-440 (January 2012): 2856–61. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2856.

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In this paper, we proposed a new fuzzy support vector machine(called L2–FSVM here), which error part of object is L2–norm.Meanwhile we introduce a new method of generating fuzzy memberships so as to reduce to effects of outliers. The experimental results demonstrate that the L2-FSVM method provides improved ability to reduce to effects of outliers in comparison with traditional SVMs and FSVMs, and claim that L2–FSVM is the best way to solve the binary classiﬁcation in the three methods stated above.

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42

Dor, Avner, Eitan Greenshtein, and Ephraim Korach. "Optimal and myopic search in a binary random vector." Journal of Applied Probability 35, no. 02 (1998): 463–72. http://dx.doi.org/10.1017/s0021900200015084.

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Let X = (X 1, …, X n ) be a random binary vector, with a known joint distribution P. It is necessary to inspect the coordinates sequentially in order to determine if X i = 0 for every i, i = 1, …, n. We find bounds for the ratio of the expected number of coordinates inspected using optimal and greedy searching policies.

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43

Nagel, R., A. Elliott, A. Masel, R. G. Birch, and J. M. Manners. "Electroporation of binary Ti plasmid vector intoAgrobacterium tumefaciensandAgrobacterium rhizogenes." FEMS Microbiology Letters 67, no. 3 (1990): 325–28. http://dx.doi.org/10.1111/j.1574-6968.1990.tb04041.x.

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44

Baram, Y. "Memorizing binary vector sequences by a sparsely encoded network." IEEE Transactions on Neural Networks 5, no. 6 (1994): 974–81. http://dx.doi.org/10.1109/72.329695.

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45

Ehlen, Stephan. "Vector valued theta functions associated with binary quadratic forms." Forum Mathematicum 28, no. 5 (2016): 893–908. http://dx.doi.org/10.1515/forum-2015-0034.

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AbstractWe study the space of vector valued theta functions for the Weil representation of a positive definite even lattice of rank two with fundamental discriminant. We work out the relation of this space to the corresponding scalar valued theta functions of weight one and determine an orthogonal basis with respect to the Petersson inner product. Moreover, we give an explicit formula for the Petersson norms of the elements of this basis.

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46

Xiaolin Wu. "Optimal binary vector quantization via enumeration of covering codes." IEEE Transactions on Information Theory 43, no. 2 (1997): 638–45. http://dx.doi.org/10.1109/18.556119.

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Ebrahimpour, Zeinab, Wanggen Wan, Arash Sioofy Khoojine, and Li Hou. "Twin Hyper-Ellipsoidal Support Vector Machine for Binary Classification." IEEE Access 8 (2020): 87341–53. http://dx.doi.org/10.1109/access.2020.2990611.

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48

Nacir, Diana López, and Federico R. Urban. "Vector fuzzy dark matter, fifth forces, and binary pulsars." Journal of Cosmology and Astroparticle Physics 2018, no. 10 (2018): 044. http://dx.doi.org/10.1088/1475-7516/2018/10/044.

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Yan, L. J., and S. H. Hwang. "Vector quantisation based on a quasi-binary search algorithm." IET Image Processing 5, no. 1 (2011): 49. http://dx.doi.org/10.1049/iet-ipr.2010.0120.

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50

Dor, Avner, Eitan Greenshtein, and Ephraim Korach. "Optimal and myopic search in a binary random vector." Journal of Applied Probability 35, no. 2 (1998): 463–72. http://dx.doi.org/10.1239/jap/1032192861.

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Let X = (X1, …, Xn) be a random binary vector, with a known joint distribution P. It is necessary to inspect the coordinates sequentially in order to determine if Xi = 0 for every i, i = 1, …, n. We find bounds for the ratio of the expected number of coordinates inspected using optimal and greedy searching policies.

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