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Relevant bibliographies by topics / CP factorization

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Academic literature on the topic 'CP factorization'

Author: Grafiati

Published: 3 June 2025

Last updated: 20 July 2025

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Journal articles on the topic "CP factorization"

1

Lü, Gang, Jia-Qi Lei та Xin-Heng Guo. "CP Violation forB0→ρ0(ω)ρ0(ω)→π+π-π+π-in QCD Factorization". Advances in High Energy Physics 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/785648.

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In the QCD factorization (QCDF) approach we study the direct CP violation inB-0→ρ0(ω)ρ0(ω)→π+π-π+π-via theρ-ωmixing mechanism. We find that the CP violation can be enhanced by doubleρ-ωmixing when the masses of theπ+π-pairs are in the vicinity of theωresonance, and the maximum CP violation can reach 28%. We also compare the results from the naive factorization and the QCD factorization.

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Lihua, Gui, Zhao Xuyang, Zhao Qibin, and Cao Jianting. "Non-local Image Denoising by Using Bayesian Low-rank Tensor Factorization on High-order Patches." International Journal of Computer Science Issues 15, no. 5 (2018): 16–25. https://doi.org/10.5281/zenodo.1467648.

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Removing the noise from an image is vitally important in many real-world computer vision applications. One of the most effective method is block matching collaborative filtering, which employs low-rank approximation to the group of similar patches gathered by searching from the noisy image. However, the main drawback of this method is that the standard deviation of noises within the image is assumed to be known in advance, which is impossible for many real applications. In this paper, we propose a non-local filtering method by using the low-rank tensor decomposition method. For tensor decomposition, we choose CP model as the underlying low-rank approximation. Since we assume the noise variance is unknown and need to be learned from data itself, we employ the Bayesian CP factorization that can learn CP-rank as well as noise variance solely from the observed noisy tensor data, The experimental results on image and MRI denoising demonstrate the superiorities of our method in terms of flexibility and performance, as compared to other tensor-based denoising methods.

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Liu, Chang, Kun He, Ji Liu Zhou, and Yan Li Zhu. "Facial Expression Recognition Based on Orthogonal Nonnegative CP Factorization." Advanced Materials Research 143-144 (October 2010): 111–15. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.111.

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Facial Expression recognition based on Non-negative Matrix Factorization (NMF) requires the object images should be vectorized. The vectorization leads to information loss, since local structure of the images is lost. Moreover, NMF can not guarantee the uniqueness of the decomposition. In order to remedy these limitations, the facial expression image was considered as a high-order tensor, and an Orthogonal Non-negative CP Factorization algorithm (ONNCP) was proposed. With the orthogonal constrain, the low-dimensional presentations of samples were non-negative in ONNCP. The convergence characteristic of the algorithm was proved. The experiments indicate that, compared with other non-negative factorization algorithms, the algorithm proposed in the paper reduces the redundancy of the base image and has better recognition rate in facial expression recognition.

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Cheng, Hai-Yang, and Chun-Khiang Chua. "The B-CP Puzzles in QCD Factorization." Nuclear Physics B - Proceedings Supplements 207-208 (October 2010): 391–94. http://dx.doi.org/10.1016/j.nuclphysbps.2010.10.102.

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Huang, Xingfang, Shuang Xu, Chunxia Zhang, and Jiangshe Zhang. "Robust CP Tensor Factorization With Skew Noise." IEEE Signal Processing Letters 27 (2020): 785–89. http://dx.doi.org/10.1109/lsp.2020.2991581.

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Cui, GaoChao, Li Zhu, LiHua Gui, QiBin Zhao, JianHai Zhang, and JianTing Cao. "Multidimensional clinical data denoising via Bayesian CP factorization." Science China Technological Sciences 63, no. 2 (2019): 249–54. http://dx.doi.org/10.1007/s11431-018-9493-9.

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7

Safir, A. Salim. "CP violation in B decays within QCD factorization." European Physical Journal C 33, S1 (2004): s373—s375. http://dx.doi.org/10.1140/epjcd/s2004-03-1831-x.

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8

Wu, Yuankai, Huachun Tan, Yong Li, Jian Zhang, and Xiaoxuan Chen. "A Fused CP Factorization Method for Incomplete Tensors." IEEE Transactions on Neural Networks and Learning Systems 30, no. 3 (2019): 751–64. http://dx.doi.org/10.1109/tnnls.2018.2851612.

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9

Brandts, Jan, and Michal Křížek. "Factorization of cp-rank-3 completely positive matrices." Czechoslovak Mathematical Journal 66, no. 3 (2016): 955–70. http://dx.doi.org/10.1007/s10587-016-0303-9.

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10

Rajarama Bhat, B. V., and Hiroyuki Osaka. "A factorization property of positive maps on C*-algebras." International Journal of Quantum Information 18, no. 05 (2020): 2050019. http://dx.doi.org/10.1142/s0219749920500197.

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The purpose of this short paper is to clarify and present a general version of an interesting observation by [Piani and Mora, Phys. Rev. A 75 (2007) 012305], linking complete positivity of linear maps on matrix algebras to decomposability of their ampliations. Let [Formula: see text], [Formula: see text] be unital C*-algebras and let [Formula: see text] be positive linear maps from [Formula: see text] to [Formula: see text] [Formula: see text]. We obtain conditions under which any positive map [Formula: see text] from the minimal C*-tensor product [Formula: see text] to [Formula: see text], such that [Formula: see text], factorizes as [Formula: see text] for some positive map [Formula: see text]. In particular, we show that when [Formula: see text] are completely positive (CP) maps for some Hilbert spaces [Formula: see text] [Formula: see text], and [Formula: see text] is a pure CP map and [Formula: see text] is a CP map so that [Formula: see text] is also CP, then [Formula: see text] for some CP map [Formula: see text]. We show that a similar result holds in the context of positive linear maps when [Formula: see text] and [Formula: see text]. As an application, we extend IX Theorem of Ref. 4 (revisited recently by [Huber et al., Phys. Rev. Lett. 121 (2018) 200503]) to show that for any linear map [Formula: see text] from a unital C*-algebra [Formula: see text] to a C*-algebra [Formula: see text], if [Formula: see text] is decomposable for some [Formula: see text], where [Formula: see text] is the identity map on the algebra [Formula: see text] of [Formula: see text] matrices, then [Formula: see text] is CP.

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Book chapters on the topic "CP factorization"

1

Luo, Yue, Chunming Yang, Bo Li, Xujian Zhao, and Hui Zhang. "CP Tensor Factorization for Knowledge Graph Completion." In Knowledge Science, Engineering and Management. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10983-6_19.

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Huang, Rongping, Wenwu Gong, Jiaxin Lu, Zhejun Huang, and Lili Yang. "BACP: Bayesian Augmented CP Factorization for Traffic Data Imputation." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5618-6_10.

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Das, Malabika, Rajdeep Chakraborty, and Chandan Banerjee. "ID-Based Cryptography and Attribute-Based Cryptography." In Advances in Civil and Industrial Engineering. IGI Global, 2023. http://dx.doi.org/10.4018/979-8-3693-0044-2.ch019.

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Identity and attribute-based cryptosystems have now become the most interesting research area in cryptography. Id-based and attribute-based cryptography both are improved variants of public-key cryptography. The identity-based cryptography is based on the identity of the user, such as email address, IP address, or mobile number. On the other hand, in attribute-based cryptography users encrypt and decrypt the message based on their attributes. Both IBE and ABE are based on mathematical concepts like Integer factorization, Quadratic Residues, Discrete logarithm Problems, Diffie-Hellman problems, and Bilinear pairings. IBE allows users to encrypt and decrypt messages using their identities whereas ABE provides fine-grain access control, it allows organizations and individuals to access data based on specific attributes or properties. IBE and ABE have various applications in cloud computing, digital signatures, and secure data sharing in IoT. Most ABE schemes based on access structure can be categorized as Key-Policy ABE (KP-ABE) and ciphertext-based ABE (CP-ABE).

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Conference papers on the topic "CP factorization"

1

Cao, Jianting, Qibin Zhao, and Lihua Gui. "Tensor denoising using Bayesian CP factorization." In 2016 Sixth International Conference on Information Science and Technology (ICIST). IEEE, 2016. http://dx.doi.org/10.1109/icist.2016.7483384.

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Luo, Qiong, Zhi Han, Xiai Chen, et al. "Tensor RPCA by Bayesian CP Factorization with Complex Noise." In 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017. http://dx.doi.org/10.1109/iccv.2017.537.

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Cui, Gaochao, Lihua Gui, Qibin Zhao, Andrzej Cichocki, and Jianting Cao. "Bayesian CP factorization of incomplete tensor for EEG signal application." In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2016. http://dx.doi.org/10.1109/fuzz-ieee.2016.7737961.

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4

Wang, Rui, Yanan You, and Wenli Zhou. "Interferometric Phase Stack Data Filter Method via Bayesian CP Factorization." In IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020. http://dx.doi.org/10.1109/igarss39084.2020.9323383.

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Yang, Chaoqi, Cheng Qian, and Jimeng Sun. "GOCPT: Generalized Online Canonical Polyadic Tensor Factorization and Completion." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/326.

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Low-rank tensor factorization or completion is well-studied and applied in various online settings, such as online tensor factorization (where the temporal mode grows) and online tensor completion (where incomplete slices arrive gradually). However, in many real-world settings, tensors may have more complex evolving patterns: (i) one or more modes can grow; (ii) missing entries may be filled; (iii) existing tensor elements can change. Existing methods cannot support such complex scenarios. To fill the gap, this paper proposes a Generalized Online Canonical Polyadic (CP) Tensor factorization and completion framework (named GOCPT) for this general setting, where we maintain the CP structure of such dynamic tensors during the evolution. We show that existing online tensor factorization and completion setups can be unified under the GOCPT framework. Furthermore, we propose a variant, named GOCPTE, to deal with cases where historical tensor elements are unavailable (e.g., privacy protection), which achieves similar fitness as GOCPT but with much less computational cost. Experimental results demonstrate that our GOCPT can improve fitness by up to 2.8% on the JHU Covid data and 9.2% on a proprietary patient claim dataset over baselines. Our variant GOCPTE shows up to 1.2% and 5.5% fitness improvement on two datasets with about 20% speedup compared to the best model.

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GÓMEZ DUMM, D., та A. SZYNKMAN. "CP VIOLATION IN B → φK* DECAYS: AMPLITUDES, FACTORIZATION AND NEW PHYSICS". У Proceedings of the Fifth Latin American Symposium. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812773951_0037.

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7

Astrid, Marcella, Seung-Ik Lee, and Beom-Su Seo. "Rank selection of CP-decomposed convolutional layers with variational Bayesian matrix factorization." In 2018 20th International Conference on Advanced Communications Technology (ICACT). IEEE, 2018. http://dx.doi.org/10.23919/icact.2018.8323749.

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Astrid, Marcella, Seung-Ik Lee, and Beom-Su Seo. "Rank selection of CP-decomposed convolutional layers with variational Bayesian matrix factorization." In 2018 20th International Conference on Advanced Communications Technology (ICACT). IEEE, 2018. http://dx.doi.org/10.23919/icact.2018.8323750.

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9

Manikandan, S., R. Kumar, and C. V. Jawahar. "Tensorial factorization methods for manipulation of face videos." In IET International Conference on Visual Information Engineering (VIE 2006). IEE, 2006. http://dx.doi.org/10.1049/cp:20060577.

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10

Congying Han, A. Yong, Tiande Guo, Guangqi Shao, and Yang Hao. "Non-negative matrix factorization based on locally linear embedding." In 11th International Symposium on Operations Research and its Applications in Engineering, Technology and Management 2013 (ISORA 2013). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/cp.2013.2270.

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Reports on the topic "CP factorization"

1

Dahmes, Bryan. A Study of Factorization and a Measurement of CP Violation. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/922228.

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2

Neubert, Matthias. QCD Factorization for B {r_arrow} {pi}{pi} Decays: Strong Phases and CP Violation in the Heavy Quark Limit. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/10079.

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