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Journal articles on the topic 'Semi-distribution'

Author: Grafiati
Published: 25 May 2024

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1

Bouzar, Nadjib. "The semi-Sibuya distribution." Annals of the Institute of Statistical Mathematics 60, no. 2 (February 23, 2007): 459–64. http://dx.doi.org/10.1007/s10463-007-0118-4.

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2

Chen, Lingli, Qin Li, Chengdong Liu, Yu Peng, and Fang Yu. "Efficient mediated semi-quantum key distribution." Physica A: Statistical Mechanics and its Applications 582 (November 2021): 126265. http://dx.doi.org/10.1016/j.physa.2021.126265.

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3

Mathew, Thomas, and K. Jayakumar. "Bivariate Semi-Logistic Distribution and Processes." Journal of Statistical Research of Iran 3, no. 2 (March 1, 2007): 159–76. http://dx.doi.org/10.18869/acadpub.jsri.3.2.159.

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4

Chen, Yanbei, Xiatian Zhu, Wei Li, and Shaogang Gong. "Semi-Supervised Learning under Class Distribution Mismatch." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3569–76. http://dx.doi.org/10.1609/aaai.v34i04.5763.

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Semi-supervised learning (SSL) aims to avoid the need for collecting prohibitively expensive labelled training data. Whilst demonstrating impressive performance boost, existing SSL methods artificially assume that small labelled data and large unlabelled data are drawn from the same class distribution. In a more realistic scenario with class distribution mismatch between the two sets, they often suffer severe performance degradation due to error propagation introduced by irrelevant unlabelled samples. Our work addresses this under-studied and realistic SSL problem by a novel algorithm named Uncertainty-Aware Self-Distillation (UASD). Specifically, UASD produces soft targets that avoid catastrophic error propagation, and empower learning effectively from unconstrained unlabelled data with out-of-distribution (OOD) samples. This is based on joint Self-Distillation and OOD filtering in a unified formulation. Without bells and whistles, UASD significantly outperforms six state-of-the-art methods in more realistic SSL under class distribution mismatch on three popular image classification datasets: CIFAR10, CIFAR100, and TinyImageNet.
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5

Hallin, Marc, and Bas J. M. Werker. "Semi-parametric efficiency, distribution-freeness and invariance." Bernoulli 9, no. 1 (February 2003): 137–65. http://dx.doi.org/10.3150/bj/1068129013.

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6

Adrian, Donald Dean, Vijay P. Singh, and Zhi-Qiang Deng. "Diffusion-Based Semi-Infinite Fourier Probability Distribution." Journal of Hydrologic Engineering 7, no. 2 (March 2002): 154–67. http://dx.doi.org/10.1061/(asce)1084-0699(2002)7:2(154).

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7

Tsai, Chia-Wei, Chun-Wei Yang, and Narn-Yih Lee. "Lightweight mediated semi-quantum key distribution protocol." Modern Physics Letters A 34, no. 34 (November 5, 2019): 1950281. http://dx.doi.org/10.1142/s021773231950281x.

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Classical users can share a secret key with a quantum user by using a semi-quantum key distribution (SQKD) protocol. Allowing two classical users to share a secret key is the objective of the mediated semi-quantum key distribution (MSQKD) protocol. However, the existing MSQKD protocols need a quantum user to assist two classical users in distributing the secret keys, and these protocols require that the classical users be equipped with a Trojan horse photon detector. This reduces the practicability of the MSQKD protocols. Therefore, in this study we propose a lightweight MSQKD, in which the two participants and third party are classical users. Due to the usage of the one-way transmission strategy, the proposed lightweight MSQKD protocol is free from quantum Trojan horse attack. The proposed MSQKD is more practical than the existing MSQKD protocols.
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8

Kuttykrishnan, A. P., and K. Jayakumar. "Bivariate semi α-Laplace distribution and processes." Statistical Papers 49, no. 2 (August 3, 2006): 303–13. http://dx.doi.org/10.1007/s00362-006-0014-7.

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9

Ivanov, Roman V. "The Semi-Hyperbolic Distribution and Its Applications." Stats 6, no. 4 (October 21, 2023): 1126–46. http://dx.doi.org/10.3390/stats6040071.

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This paper studies a subclass of the class of generalized hyperbolic distribution called the semi-hyperbolic distribution. We obtain analytical expressions for the cumulative distribution function and, specifically, their first and second lower partial moments. Using the received formulas, we compute the value at risk, the expected shortfall, and the semivariance in the semi-hyperbolic model of the financial market. The formulas depend on the values of generalized hypergeometric functions and modified Bessel functions of the second kind. The research illustrates the possibility of analysis of generalized hyperbolic models using the same methodology as is employed for the well-established variance-gamma model.
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10

Massa, Francesco, Preeti Yadav, Amir Moqanaki, Walter O. Krawec, Paulo Mateus, Nikola Paunković, André Souto, and Philip Walther. "Experimental Semi-quantum Key Distribution With Classical Users." Quantum 6 (September 22, 2022): 819. http://dx.doi.org/10.22331/q-2022-09-22-819.

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Quantum key distribution, which allows two distant parties to share an unconditionally secure cryptographic key, promises to play an important role in the future of communication. For this reason such technique has attracted many theoretical and experimental efforts, thus becoming one of the most prominent quantum technologies of the last decades. The security of the key relies on quantum mechanics and therefore requires the users to be capable of performing quantum operations, such as state preparation or measurements in multiple bases. A natural question is whether and to what extent these requirements can be relaxed and the quantum capabilities of the users reduced. Here we demonstrate a novel quantum key distribution scheme, where users are fully classical. In our protocol, the quantum operations are performed by an untrusted third party acting as a server, which gives the users access to a superimposed single photon, and the key exchange is achieved via interaction-free measurements on the shared state. We also provide a full security proof of the protocol by computing the secret key rate in the realistic scenario of finite-resources, as well as practical experimental conditions of imperfect photon source and detectors. Our approach deepens the understanding of the fundamental principles underlying quantum key distribution and, at the same time, opens up new interesting possibilities for quantum cryptography networks
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11

Qian, Hangwei, Sinno Jialin Pan, and Chunyan Miao. "Distribution-Based Semi-Supervised Learning for Activity Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7699–706. http://dx.doi.org/10.1609/aaai.v33i01.33017699.

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Supervised learning methods have been widely applied to activity recognition. The prevalent success of existing methods, however, has two crucial prerequisites: proper feature extraction and sufficient labeled training data. The former is important to differentiate activities, while the latter is crucial to build a precise learning model. These two prerequisites have become bottlenecks to make existing methods more practical. Most existing feature extraction methods highly depend on domain knowledge, while labeled data requires intensive human annotation effort. Therefore, in this paper, we propose a novel method, named Distribution-based Semi-Supervised Learning, to tackle the aforementioned limitations. The proposed method is capable of automatically extracting powerful features with no domain knowledge required, meanwhile, alleviating the heavy annotation effort through semi-supervised learning. Specifically, we treat data stream of sensor readings received in a period as a distribution, and map all training distributions, including labeled and unlabeled, into a reproducing kernel Hilbert space (RKHS) using the kernel mean embedding technique. The RKHS is further altered by exploiting the underlying geometry structure of the unlabeled distributions. Finally, in the altered RKHS, a classifier is trained with the labeled distributions. We conduct extensive experiments on three public datasets to verify the effectiveness of our method compared with state-of-the-art baselines.
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12

张, 腾., 铭. 黎, and 海. 金. "Optimal margin distribution machine for semi-supervised clustering." SCIENTIA SINICA Informationis 52, no. 1 (January 1, 2022): 86. http://dx.doi.org/10.1360/ssi-2021-0187.

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13

Liu, Xinyuan, Jihua Zhu, Qinghai Zheng, Zhiqiang Tian, and Zhongyu Li. "Semi-Supervised Label Distribution Learning with Co-regularization." Neurocomputing 491 (June 2022): 353–64. http://dx.doi.org/10.1016/j.neucom.2022.03.041.

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14

Fajmonová, Zuzana, David Zelený, Vít Syrovátka, Grzegorz Vončina, and Michal Hájek. "Distribution of habitat specialists in semi-natural grasslands." Journal of Vegetation Science 24, no. 4 (November 7, 2012): 616–27. http://dx.doi.org/10.1111/jvs.12005.

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15

Glasser, Wolfgang G., Vipul Davé, and Charles E. Frazier. "Molecular Weight Distribution of (Semi-) Commercial Lignin Derivatives." Journal of Wood Chemistry and Technology 13, no. 4 (December 1993): 545–59. http://dx.doi.org/10.1080/02773819308020533.

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16

Cortés, Lina M., Andrés Mora-Valencia, and Javier Perote. "Measuring firm size distribution with semi-nonparametric densities." Physica A: Statistical Mechanics and its Applications 485 (November 2017): 35–47. http://dx.doi.org/10.1016/j.physa.2017.05.019.

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17

Krawec, Walter O. "Restricted attacks on semi-quantum key distribution protocols." Quantum Information Processing 13, no. 11 (August 8, 2014): 2417–36. http://dx.doi.org/10.1007/s11128-014-0802-2.

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18

Mayer, Christoph, Matthieu Paul, and Radu Timofte. "Adversarial feature distribution alignment for semi-supervised learning." Computer Vision and Image Understanding 202 (January 2021): 103109. http://dx.doi.org/10.1016/j.cviu.2020.103109.

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19

Averbakh, Igor, and Mehmet Baysan. "Batching and delivery in semi-online distribution systems." Discrete Applied Mathematics 161, no. 1-2 (January 2013): 28–42. http://dx.doi.org/10.1016/j.dam.2012.08.003.

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20

Zhu, Kong-Ni, Nan-Run Zhou, Yun-Qian Wang, and Xiao-Jun Wen. "Semi-Quantum Key Distribution Protocols with GHZ States." International Journal of Theoretical Physics 57, no. 12 (September 1, 2018): 3621–31. http://dx.doi.org/10.1007/s10773-018-3875-3.

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21

Creel, Michael, and John Loomis. "Semi-nonparametric Distribution-Free Dichotomous Choice Contingent Valuation." Journal of Environmental Economics and Management 32, no. 3 (March 1997): 341–58. http://dx.doi.org/10.1006/jeem.1997.0972.

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22

Lin, Po‐Hua, Chia‐Wei Tsai, and Tzonelih Hwang. "Mediated Semi‐Quantum Key Distribution Using Single Photons." Annalen der Physik 531, no. 8 (April 18, 2019): 1800347. http://dx.doi.org/10.1002/andp.201800347.

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23

Carriere, Jacques. "A semi-parametric estimator of a risk distribution." Insurance: Mathematics and Economics 13, no. 1 (September 1993): 75–81. http://dx.doi.org/10.1016/0167-6687(93)90537-y.

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24

Martin, R. Douglas, and Shengyu Zhang. "Expected Shortfall Semi-Scale T-Distribution M-Estimator." Journal of Mathematical Finance 13, no. 04 (2023): 483–500. http://dx.doi.org/10.4236/jmf.2023.134029.

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25

Azócar, Guillermo F., Alexander Brenning, and Xavier Bodin. "Permafrost distribution modelling in the semi-arid Chilean Andes." Cryosphere 11, no. 2 (April 6, 2017): 877–90. http://dx.doi.org/10.5194/tc-11-877-2017.

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Abstract. Mountain permafrost and rock glaciers in the dry Andes are of growing interest due to the increase in mining industry and infrastructure development in this remote area. Empirical models of mountain permafrost distribution based on rock glacier activity status and temperature data have been established as a tool for regional-scale assessments of its distribution; this kind of model approach has never been applied for a large portion of the Andes. In the present study, this methodology is applied to map permafrost favourability throughout the semi-arid Andes of central Chile (29–32° S), excluding areas of exposed bedrock. After spatially modelling of the mean annual air temperature distribution from scarce temperature records (116 station years) using a linear mixed-effects model, a generalized additive model was built to model the activity status of 3524 rock glaciers. A permafrost favourability index (PFI) was obtained by adjusting model predictions for conceptual differences between permafrost and rock glacier distribution. The results indicate that the model has an acceptable performance (median AUROC: 0.76). Conditions highly favourable to permafrost presence (PFI ≥ 0.75) are predicted for 1051 km2 of mountain terrain, or 2.7 % of the total area of the watersheds studied. Favourable conditions are expected to occur in 2636 km2, or 6.8 % of the area. Substantial portions of the Elqui and Huasco watersheds are considered to be favourable for permafrost presence (11.8 % each), while in the Limarí and Choapa watersheds permafrost is expected to be mostly limited to specific sub-watersheds. In the future, local ground-truth observations will be required to confirm permafrost presence in favourable areas and to monitor permafrost evolution under the influence of climate change.
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26

Csébfalvi, Anikó. "Displacement based moment distribution method for semi-rigid frames." Pollack Periodica 2, no. 3 (December 2007): 109–25. http://dx.doi.org/10.1556/pollack.2.2007.3.10.

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27

Jia, Xiuyi, Tao Wen, Weiping Ding, Huaxiong Li, and Weiwei Li. "Semi-supervised label distribution learning via projection graph embedding." Information Sciences 581 (December 2021): 840–55. http://dx.doi.org/10.1016/j.ins.2021.10.009.

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28

Xu Yue-Sheng, Yang Xin-Rong, Wang Hai-Yun, Tang Lei, Liu Cai-Chi, Wei Xin, and Qin Dao-Zhi. "Micro-distribution of carbon in semi-insulating gallium arsenide." Acta Physica Sinica 54, no. 4 (2005): 1904. http://dx.doi.org/10.7498/aps.54.1904.

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29

Wang, Ting, Dongning Zhao, Zhiwei Sun, and Weixin Xie. "Semi-quantum Key Distribution Protocol Based on Bell States." International Journal of Security and Its Applications 10, no. 12 (December 31, 2016): 97–110. http://dx.doi.org/10.14257/ijsia.2016.10.12.09.

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30

Wang, Yijun, Yingce Xia, Li Zhao, Jiang Bian, Tao Qin, Enhong Chen, and Tie-Yan Liu. "Semi-Supervised Neural Machine Translation via Marginal Distribution Estimation." IEEE/ACM Transactions on Audio, Speech, and Language Processing 27, no. 10 (October 2019): 1564–76. http://dx.doi.org/10.1109/taslp.2019.2921423.

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31

Harlamov, B. P. "Final Distribution of a Diffusion Process: Semi-Markov Approach." Theory of Probability & Its Applications 60, no. 3 (January 2016): 444–59. http://dx.doi.org/10.1137/s0040585x97t987764.

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32

Yu, Kun-Fei, Chun-Wei Yang, Ci-Hong Liao, and Tzonelih Hwang. "Authenticated semi-quantum key distribution protocol using Bell states." Quantum Information Processing 13, no. 6 (March 18, 2014): 1457–65. http://dx.doi.org/10.1007/s11128-014-0740-z.

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33

Alekseev, M., V. Yu Alexakhin, Yu Alexandrov, G. D. Alexeev, A. Amoroso, A. Arbuzov, B. Badełek, et al. "The polarised valence quark distribution from semi-inclusive DIS." Physics Letters B 660, no. 5 (March 2008): 458–65. http://dx.doi.org/10.1016/j.physletb.2007.12.056.

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34

LIMNIOS, N., B. OUHBI, and A. SADEK. "Empirical Estimator of Stationary Distribution for Semi-Markov Processes." Communications in Statistics - Theory and Methods 34, no. 4 (April 2005): 987–95. http://dx.doi.org/10.1081/sta-200054441.

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35

Biane, Philippe. "On the free convolution with a semi-circular distribution." Indiana University Mathematics Journal 46, no. 3 (1997): 0. http://dx.doi.org/10.1512/iumj.1997.46.1467.

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36

Qin, Anyong, Zhuolin Tan, Xingli Tan, Yongji Wu, Cheng Jing, and Yuan Yan Tang. "Distribution preserving-based deep semi-NMF for data representation." Neurocomputing 524 (March 2023): 69–83. http://dx.doi.org/10.1016/j.neucom.2022.12.046.

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37

Farkas, Walter. "Eigenvalue distribution of some fractal semi-elliptic differential operators." Mathematische Zeitschrift 236, no. 2 (February 2001): 291–320. http://dx.doi.org/10.1007/pl00004832.

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38

Tsai, Chih-Lun, and Tzonelih Hwang. "Semi-quantum Key Distribution Robust Against Combined Collective Noise." International Journal of Theoretical Physics 57, no. 11 (August 10, 2018): 3410–18. http://dx.doi.org/10.1007/s10773-018-3854-8.

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39

Liu, Zhi-Rou, and Tzonelih Hwang. "Mediated Semi-Quantum Key Distribution Without Invoking Quantum Measurement." Annalen der Physik 530, no. 4 (January 29, 2018): 1700206. http://dx.doi.org/10.1002/andp.201700206.

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40

Carl, Wilfried, and Yannick Rouault. "On the length distribution of semi-flexible linear micelles." Macromolecular Theory and Simulations 7, no. 5 (September 1, 1998): 497–500. http://dx.doi.org/10.1002/(sici)1521-3919(19980901)7:5<497::aid-mats497>3.0.co;2-b.

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41

Wang, Tao, Zhongzheng Huang, Jiawei Wu, Yuanzheng Cai, and Zuoyong Li. "Semi-Supervised Medical Image Segmentation with Co-Distribution Alignment." Bioengineering 10, no. 7 (July 21, 2023): 869. http://dx.doi.org/10.3390/bioengineering10070869.

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Medical image segmentation has made significant progress when a large amount of labeled data are available. However, annotating medical image segmentation datasets is expensive due to the requirement of professional skills. Additionally, classes are often unevenly distributed in medical images, which severely affects the classification performance on minority classes. To address these problems, this paper proposes Co-Distribution Alignment (Co-DA) for semi-supervised medical image segmentation. Specifically, Co-DA aligns marginal predictions on unlabeled data to marginal predictions on labeled data in a class-wise manner with two differently initialized models before using the pseudo-labels generated by one model to supervise the other. Besides, we design an over-expectation cross-entropy loss for filtering the unlabeled pixels to reduce noise in their pseudo-labels. Quantitative and qualitative experiments on three public datasets demonstrate that the proposed approach outperforms existing state-of-the-art semi-supervised medical image segmentation methods on both the 2D CaDIS dataset and the 3D LGE-MRI and ACDC datasets, achieving an mIoU of 0.8515 with only 24% labeled data on CaDIS, and a Dice score of 0.8824 and 0.8773 with only 20% data on LGE-MRI and ACDC, respectively.
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42

Lavda, Frantzeska, and Alexandros Kalousis. "Semi-Supervised Variational Autoencoders for Out-of-Distribution Generation." Entropy 25, no. 12 (December 14, 2023): 1659. http://dx.doi.org/10.3390/e25121659.

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Humans are able to quickly adapt to new situations, learn effectively with limited data, and create unique combinations of basic concepts. In contrast, generalizing out-of-distribution (OOD) data and achieving combinatorial generalizations are fundamental challenges for machine learning models. Moreover, obtaining high-quality labeled examples can be very time-consuming and expensive, particularly when specialized skills are required for labeling. To address these issues, we propose BtVAE, a method that utilizes conditional VAE models to achieve combinatorial generalization in certain scenarios and consequently to generate out-of-distribution (OOD) data in a semi-supervised manner. Unlike previous approaches that use new factors of variation during testing, our method uses only existing attributes from the training data but in ways that were not seen during training (e.g., small objects of a specific shape during training and large objects of the same shape during testing).
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43

Raskin, Lev, Iaroslav Sviatkin, Yuriy Ivanchikhin, and Roman Korsun. "SEMI-MARKOV RELIABILITY MODELS." Advanced Information Systems 6, no. 1 (April 6, 2022): 70–74. http://dx.doi.org/10.20998/2522-9052.2022.1.12.

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Traditional technologies for reliability analysis of semi-Markov systems are limited to obtaining a stationary state probability distribution. However, when solving practical control problems in such systems, the study of transient processes is of considerable interest. This implies the subject of research - the analysis of the laws of distribution of the system states probabilities. The goal of the work is to obtain the desired distribution at any time. The complexity of the problem solving is determined by the need to obtain a result for arbitrary distribution laws of the duration of the system's stay in each state before leaving. An easy-to-implement method for the analysis of semi-Markov reliability models has been suggested. The method is based on the possibility of approximating probability-theoretic descriptions of failure and recovery flows in the system using the Erlang distribution laws of the proper order. The developed computational scheme uses the most important property of Erlang flows, which are formed as a result of sieving the simplest Poisson flow. In this case, the semi-Markov model is reduced to the Markov one, which radically simplifies the analysis of real systems.
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44

Wang, Hung-Wen, Chia-Wei Tsai, Jason Lin, and Chun-Wei Yang. "Authenticated Semi-Quantum Key Distribution Protocol Based on W States." Sensors 22, no. 13 (July 2, 2022): 4998. http://dx.doi.org/10.3390/s22134998.

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In 2019, Wen et al. proposed authenticated semi-quantum key distribution (ASQKD) for identity and message using the teleportation of W states and GHZ-like states without pre-shared keys. However, the ASQKD protocol presents a vital issue in the teleportation of W states owing to its inappropriate design. Bob recovers the teleported W states without obtaining the position of the corresponding photons and then returns the recovered photons back to Alice. Hence, the teleportation of W states in Wen et al.’s ASQKD protocol was malfunctioning. Moreover, Wen et al.’s ASQKD protocol requires quantum memory, which strongly disobeys the definition of semi-quantum proposed by Boyer et al. Therefore, in this study, we discover the flaws of Wen et al.’s ASQKD protocol and propose an authenticated semi-quantum key distribution protocol. When compared to Wen et al.’s ASQKD protocol, the proposed ASQKD protocol has the following advantages: legal semi-quantum environment (i.e., does not require quantum memory), reduced quantum hardware requirement (i.e., based only on W states), does not involve classical cryptography (i.e., the hash function), and provided 1.6 times higher qubit efficiency.
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45

Tian, Yuan, Jian Li, Kai-Guo Yuan, Chao-Yang Li, Heng-Ji Li, and Xiu-Bo Chen. "An efficient semi-quantum key distribution protocol based on EPR and single-particle hybridization." quantum Information and Computation 21, no. 7&8 (June 2021): 563–76. http://dx.doi.org/10.26421/qic21.7-8-3.

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Quantum key distribution cannot satisfy some users without quantum capability, so semi-quantum key distribution emerges as the times required. Semi-quantum key distribution protocol is described as Alice has quantum ability to prepare and measure qubits with an arbitrary basis, while Bob only measures qubits with the computational basis or reflects qubits to Alice. However, most existing semi-quantum key distribution protocols have been performed with low eavesdropping detection probability. In this paper, we present an innovative semi-quantum key distribution protocol with high efficiency based on EPR and single-particle hybridization, in which the specific contents of {\scriptsize CTRL} or {\scriptsize SIFT} operations have been newly defined. Then, the security analysis indicates the proposed protocol is asymptotically secure with more high eavesdropping detection probability against individual eavesdropping attacks. Moreover, the efficiency analysis shows that the presented protocol is more efficient than similar literatures.
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46

Silva, Mário, Ricardo Faleiro, Paulo Mateus, and Emmanuel Zambrini Cruzeiro. "A coherence-witnessing game and applications to semi-device-independent quantum key distribution." Quantum 7 (August 22, 2023): 1090. http://dx.doi.org/10.22331/q-2023-08-22-1090.

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Semi-device-independent quantum key distribution aims to achieve a balance between the highest level of security, device independence, and experimental feasibility. Semi-quantum key distribution presents an intriguing approach that seeks to minimize users&apos; reliance on quantum operations while maintaining security, thus enabling the development of simplified and hardware fault-tolerant quantum protocols. In this work, we introduce a coherence-based, semi-device-independent, semi-quantum key distribution protocol built upon a noise-robust version of a coherence equality game that witnesses various types of coherence. Security is proven in the bounded quantum storage model, requiring users to implement only classical operations, specifically fixed-basis detections.
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47

Xu Zhaohui, 徐朝辉, and 耿文波 Geng Wenbo. "Study on the Photon Distribution Properties in Semi Infinite Tissue." APPLIED LASER 32, no. 1 (2012): 70–73. http://dx.doi.org/10.3788/al20123201.070.

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48

José Pacifico, Maria, and Fan Yang. "Hitting times distribution and extreme value laws for semi-flows." Discrete & Continuous Dynamical Systems - A 37, no. 11 (2017): 5861–81. http://dx.doi.org/10.3934/dcds.2017255.

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49

KURODA, Kohei, Yoshifuru SAITO, and Kiyoshi HORII. "Visualization of High Frequency Current Distribution By Semi-analytical Method." Journal of the Visualization Society of Japan 27, Supplement1 (2007): 211–12. http://dx.doi.org/10.3154/jvs.27.supplement1_211.

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50

Qu, Wen Ying, Fan Zhang, Jiao Jiao Wang, Xiao Gang Hu, and Qiang Zhu. "Effect of Slurry Temperature Distribution on Semi-Solid Die Casting." Solid State Phenomena 256 (September 2016): 107–12. http://dx.doi.org/10.4028/www.scientific.net/ssp.256.107.

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Abstract:
Semi-solid alloy slurries with different temperature distributions have diverse flow patterns of the slurries during die casting filling process. This different flow patterns can lead to various degrees of front separation of the slurry metal from the die cavity during die filling process. This separation can result in air entrapment, which is one of the origins for gas porosities and blisters occurred during followed heat treatment. Therefore, in this paper, the effects of slurry temperature distribution on filling patterns during die casting process were investigated. Based on partial filling experiments, positive and negative gradient temperature distribution, together with two homogeneous conditions 575°C, 579°C were compared by computer simulation. The results indicate that the positive gradient temperature condition of 357.0 slurry is more suitable for the semi-solid die casting of the connector, and 7 °C temperature gradient in slurry is appropriate for good filling.
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