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Journal articles on the topic 'Hidden group'

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Published: 9 March 2023
Last updated: 10 March 2023

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1

Berger, Ruth I. "Hidden Group Structure." Mathematics Magazine 78, no. 1 (February 1, 2005): 45. http://dx.doi.org/10.2307/3219272.

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2

Berger, Ruth I. "Hidden Group Structure." Mathematics Magazine 78, no. 1 (February 2005): 45–48. http://dx.doi.org/10.1080/0025570x.2005.11953299.

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3

Bhatia, Manjot, Sunil Kumar Muttoo, and M. P. S. Bhatia. "Secure Group Communication with Hidden Group Key." Information Security Journal: A Global Perspective 22, no. 1 (January 2, 2013): 21–34. http://dx.doi.org/10.1080/19393555.2013.780272.

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4

Luo, Heng, Ruimin Shen, Changyong Niu, and Carsten Ullrich. "Sparse Group Restricted Boltzmann Machines." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 429–34. http://dx.doi.org/10.1609/aaai.v25i1.7923.

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Since learning in Boltzmann machines is typically quite slow, there is a need to restrict connections within hidden layers. However, theresulting states of hidden units exhibit statistical dependencies. Based on this observation, we propose using l1/l2 regularization upon the activation probabilities of hidden units in restricted Boltzmann machines to capture the local dependencies among hidden units. This regularization not only encourages hidden units of many groups to be inactive given observed data but also makes hidden units within a group compete with each other for modeling observed data. Thus, the l1/l2 regularization on RBMs yields sparsity at both the group and the hidden unit levels. We call RBMs trained with the regularizer sparse group RBMs (SGRBMs). The proposed SGRBMs are appliedto model patches of natural images, handwritten digits and OCR English letters. Then to emphasize that SGRBMs can learn more discriminative features we applied SGRBMs to pretrain deep networks for classification tasks. Furthermore, we illustrate the regularizer can also be applied to deep Boltzmann machines, which lead to sparse group deep Boltzmann machines. When adapted to the MNIST data set, a two-layer sparse group Boltzmann machine achieves an error rate of 0.84%, which is, to our knowledge, the best published result on the permutation-invariant version of the MNIST task.
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5

Friedl, Katalin, Miklos Santha, Frédéric Magniez, and Pranab Sen. "Quantum Testers for Hidden Group Properties." Fundamenta Informaticae 91, no. 2 (2009): 325–40. http://dx.doi.org/10.3233/fi-2009-0046.

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6

Shulman, Lawrence. "The Hidden Group in the Classroom." Journal of Teaching in Social Work 1, no. 2 (October 13, 1987): 3–31. http://dx.doi.org/10.1300/j067v01n02_02.

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7

CHEN, N. "Hidden semi-group structure in crystals." Current Opinion in Solid State and Materials Science 10, no. 1 (February 2006): 15–18. http://dx.doi.org/10.1016/j.cossms.2006.02.016.

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8

Jones-Berry, Stephanie. "Military families are a ‘hidden homeless group’." Nursing Standard 31, no. 31 (March 29, 2017): 10. http://dx.doi.org/10.7748/ns.31.31.10.s8.

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9

Grimes, Seamus. "The Sydney Irish: A Hidden Ethnic Group." Irish Geography 21, no. 2 (July 1988): 69–77. http://dx.doi.org/10.1080/00750778809478792.

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10

Maciejovsky, Boris, and David V. Budescu. "Too Much Trust in Group Decisions: Uncovering Hidden Profiles by Groups and Markets." Organization Science 31, no. 6 (November 2020): 1497–514. http://dx.doi.org/10.1287/orsc.2020.1363.

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A crucial challenge for organizations is to pool and aggregate information effectively. Traditionally, organizations have relied on committees and teams, but recently many organizations have explored the use of information markets. In this paper, the authors compared groups and markets in their ability to pool and aggregate information in a hidden-profiles task. In Study 1, groups outperformed markets when there were no conflicts of interest among participants, whereas markets outperformed groups when conflicts of interest were present. Also, participants had more trust in groups to uncover hidden profiles than in markets. Study 2 generalized these findings to a simple prediction task, confirming that people had more trust in groups than in markets. These results were not qualified by conflicts of interest. Drawing on experienced forecasters from Good Judgment Open, Study 3 found that familiarity and experience with markets increased the endorsement and use of markets relative to traditional committees.
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11

Kaplan, Robert E. "Some Hidden Elements of Control in Group Facilitation." Small Group Behavior 16, no. 4 (November 1985): 462–76. http://dx.doi.org/10.1177/104649648501600403.

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12

Wu, A. C. T. "Hidden quantum group structure in Chern-Simons theory." Journal of Physics A: Mathematical and General 26, no. 18 (September 21, 1993): L941—L944. http://dx.doi.org/10.1088/0305-4470/26/18/010.

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13

Stern, A., and P. Vitale. "Hidden quantum group symmetry in the chiral model." Nuclear Physics B 496, no. 3 (July 1997): 571–89. http://dx.doi.org/10.1016/s0550-3213(97)00242-3.

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14

Bimonte, G., R. Musto, A. Stern, and P. Vitale. "Hidden quantum group structure in Einstein's general relativity." Nuclear Physics B 525, no. 1-2 (August 1998): 483–503. http://dx.doi.org/10.1016/s0550-3213(98)00280-6.

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15

Ebbes, Peter, Rajdeep Grewal, and Wayne S. DeSarbo. "Modeling strategic group dynamics: A hidden Markov approach." Quantitative Marketing and Economics 8, no. 2 (March 17, 2010): 241–74. http://dx.doi.org/10.1007/s11129-010-9081-0.

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16

Cheng, Yichen, James Y. Dai, and Charles Kooperberg. "Group association test using a hidden Markov model." Biostatistics 17, no. 2 (April 1, 2016): 221–34. http://dx.doi.org/10.1093/biostatistics/kxv035.

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Summary In the genomic era, group association tests are of great interest. Due to the overwhelming number of individual genomic features, the power of testing for association of a single genomic feature at a time is often very small, as are the effect sizes for most features. Many methods have been proposed to test association of a trait with a group of features within a functional unit as a whole, e.g. all SNPs in a gene, yet few of these methods account for the fact that generally a substantial proportion of the features are not associated with the trait. In this paper, we propose to model the association for each feature in the group as a mixture of features with no association and features with non-zero associations to explicitly account for the possibility that a fraction of features may not be associated with the trait while other features in the group are. The feature-level associations are first estimated by generalized linear models; the sequence of these estimated associations is then modeled by a hidden Markov chain. To test for global association, we develop a modified likelihood ratio test based on a log-likelihood function that ignores higher order dependency plus a penalty term. We derive the asymptotic distribution of the likelihood ratio test under the null hypothesis. Furthermore, we obtain the posterior probability of association for each feature, which provides evidence of feature-level association and is useful for potential follow-up studies. In simulations and data application, we show that our proposed method performs well when compared with existing group association tests especially when there are only few features associated with the outcome.
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17

Chang, Huilan, Hong-Bin Chen, Hung-Lin Fu, and Chie-Huai Shi. "Reconstruction of hidden graphs and threshold group testing." Journal of Combinatorial Optimization 22, no. 2 (January 30, 2010): 270–81. http://dx.doi.org/10.1007/s10878-010-9291-0.

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18

Ivanyos, Gabor. "Finding hidden Borel subgroups of the general linear group." Quantum Information and Computation 12, no. 7&8 (July 2012): 661–69. http://dx.doi.org/10.26421/qic12.7-8-10.

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We present a quantum algorithm for solving the hidden subgroup problem in the general linear group over a finite field where the hidden subgroup is promised to be a conjugate of the group of the invertible lower triangular matrices. The complexity of the algorithm is polynomial when size of the base field is not much smaller than the degree.
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19

Maclaury, Susan. "“The Hidden Group”: The Role of Group Dynamics in Teaching Health Education." Journal of Health Education 31, no. 3 (June 2000): 156–60. http://dx.doi.org/10.1080/10556699.2000.10604673.

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20

Bonneau, P., M. Flato, M. Gerstenhaber, and G. Pinczon. "The hidden group structure of quantum groups: Strong duality, rigidity and preferred deformations." Communications in Mathematical Physics 161, no. 1 (March 1994): 125–56. http://dx.doi.org/10.1007/bf02099415.

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21

Gharibi, Mirmojtaba. "Reduction from non-injective hidden shift problem to injective hidden shift problem." Quantum Information and Computation 13, no. 3&4 (March 2013): 221–30. http://dx.doi.org/10.26421/qic13.3-4-4.

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We introduce a simple tool that can be used to reduce non-injective instances of the hidden shift problem over arbitrary group to injective instances over the same group. In particular, we show that the average-case non-injective hidden shift problem admit this reduction. We show similar results for (non-injective) hidden shift problem for bent functions. We generalize the notion of influence and show how it relates to applicability of this tool for doing reductions. In particular, these results can be used to simplify the main results by Gavinsky, Roetteler, and Roland about the hidden shift problem for the Boolean-valued functions and bent functions, and also to generalize their results to non-Boolean domains (thereby answering an open question that they pose).
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22

Salt, Neil, and Sandra J. Robertson. "A Hidden Client Group? Communication Impairment in Hospice Patients." International Journal of Language & Communication Disorders 33, S1 (January 1998): 96–101. http://dx.doi.org/10.3109/13682829809179404.

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23

EL KINANI, E. H. "HIDDEN QUANTUM GROUP SYMMETRY IN 2-D FRACTIONAL SUPERSYMMETRY." Modern Physics Letters A 15, no. 35 (November 20, 2000): 2139–43. http://dx.doi.org/10.1142/s0217732300002711.

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In this letter, we discuss how some quantum algebraic structures can be realized in terms of 2-D fractional supersymmetry generators. In particular, we show that there is a kind of quantum w∞-symmetry which induces the quantum enveloping U t( sl (2)) algebra.
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24

Davanzo, Hernán, and Silvia González. "An Analytic Group Psychotherapy Session: Interpretations and Hidden Texts." International Journal of Group Psychotherapy 48, no. 3 (July 1998): 347–61. http://dx.doi.org/10.1080/00207284.1998.11491549.

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25

Rajagopal, G., KK Singh, AC Anand, KM Rai, and J. Jayaram. "Ex-Servicemen Medical Aid Group (ESMAG) : The Hidden Force." Medical Journal Armed Forces India 64, no. 1 (January 2008): 61–64. http://dx.doi.org/10.1016/s0377-1237(08)80151-7.

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26

Kuznetsov, L., and George M. Zaslavsky. "Hidden renormalization group for the near-separatrix Hamiltonian dynamics." Physics Reports 288, no. 1-6 (September 1997): 457–85. http://dx.doi.org/10.1016/s0370-1573(97)00037-9.

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27

Hamilton, Janis R. "Kidd blood group system: outwardly simple with hidden complexity." ISBT Science Series 14, no. 1 (September 27, 2018): 3–8. http://dx.doi.org/10.1111/voxs.12458.

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28

Kang, Kai, Xinyuan Song, X. Joan Hu, and Hongtu Zhu. "Bayesian adaptive group lasso with semiparametric hidden Markov models." Statistics in Medicine 38, no. 9 (November 28, 2018): 1634–50. http://dx.doi.org/10.1002/sim.8051.

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29

Moldovyan, D., R. Fahrutdinov, A. Mirin, and A. Kostina. "Digital Signature Scheme with Hidden Group Possessing Two-Dimensional Cyclicity." Proceedings of Telecommunication Universities 7, no. 2 (June 30, 2021): 85–93. http://dx.doi.org/10.31854/1813-324x-2021-7-2-85-93.

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A method is proposed for constructing digital signature schemes based on the hidden discrete logarithm problem, which meet ageneral criterion of post-quantum resistance. The method provides a relatively small size of the public key and signature. Based on the method, a practical digital signature scheme has been developed, in which the exponentiation operation in a hidden group with two-dimensional cyclicity is the basic cryptographic primitive. The algebraic support of a cryptoscheme is a four-dimensional finite non-commutative algebra with associative multiplication operation. By specifying algebra using abasis vector multiplication table with half of empty cells, the performance of signature generation and authentication procedures is improved. A public key is a triple of four-dimensional vectors calculated as images of elements of a hidden group which are mapped using two types of masking operations: 1) mutually commutative with the exponentiation operation and 2) not having this property.
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30

FENNER, STEPHEN, and YONG ZHANG. "ON THE COMPLEXITY OF THE HIDDEN SUBGROUP PROBLEM." International Journal of Foundations of Computer Science 24, no. 08 (December 2013): 1221–34. http://dx.doi.org/10.1142/s0129054113500305.

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We study the computational complexity of the HIDDEN SUBGROUP problem, a well-studied problem in quantum computing. First we show that several proposed generalizations or variants of this problem, including HIDDEN COSET, HIDDEN SHIFT, and ORBIT COSET, are all equivalent or reducible to HIDDEN SUBGROUP. Then we study the relationship between the decision version and search version of HIDDEN SUBGROUP over various group classes. We show that the two versions are polynomial-time equivalent over permutation groups, and over dihedral groups given the order of the group is smooth. Finally, we give nonadaptive program checkers for HIDDEN SUBGROUP and its decision version.
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31

Collins, Regina, and Nancy Steffen-Fluhr. "Hidden patterns." Equality, Diversity and Inclusion: An International Journal 38, no. 2 (March 11, 2019): 265–82. http://dx.doi.org/10.1108/edi-09-2017-0183.

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PurposeThe purpose of this paper is to describe how one group of ADVANCE Project researchers investigated faculty co-authorship networks to identify relationships between women’s positions in these networks, their research productivity and their advancement at the university – and to make those relationships transparent.Design/methodology/approachMultiple methods for capturing faculty network data were evaluated, including collecting self-reported data and mining bibliometric data from various web-based sources. Faculty co-authorship networks were subsequently analyzed using several methodologies including social network analysis (SNA), network visualizations and the Kaplan–Meier product limit estimator.FindingsResults suggest that co-authorship provides an important way for faculty to signal the value of their work, meaning that co-authoring with many others may be beneficial to productivity and promotion. However, patterns of homophily indicate that male faculty tend to collaborate more with other men, reducing signaling opportunities for women. Visualizing these networks can assist faculty in finding and connecting with new collaborators and can provide administrators with unique views of the interactions within their organizations. Finally, Kaplan–Meier survival studies showed longitudinal differences in the retention and advancement of faculty based on gender.Originality/valueTogether, these findings begin to shed light on subtle differences that, over time, may account for the significant gender disparities at STEM institutions, patterns which should be investigated and addressed by administrators. Lessons learned, as well as the novel use of SNA and Kaplan–Meier in investigating gender differences in STEM faculty, provide important findings for other researchers seeking to conduct similar studies at their own institutions.
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32

Zeng-Wei Hong, Zeng-Wei Hong, Wei-Wei Shen Zeng-Wei Hong, 金凱儀 Wei-Wei Shen, and Yen-Lin Chen Kai-Yi Chin. "The Impact of a Hidden Object Game on English Vocabulary Learning and Motivation." 網際網路技術學刊 23, no. 1 (January 2022): 073–79. http://dx.doi.org/10.53106/160792642022012301007.

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<p>This paper investigates the impact of the classroom uses of a digital hidden object game in which students should match visual objects with their English vocabulary equivalents. The essential features of this game are similar to conventional classroom uses of pictures to help learn new English words, so this study investigates whether playing a hidden object game fosters English vocabulary learning and learning motivation. The 57 subjects studying at a junior high school (i.e. seventh-graders) in Taiwan were carefully assigned to form an experimental (N=28) and control group (N=29) and the only teaching variable between the two groups was controlled by the treatment with or without a hidden object game in class. It is revealed that compared with control group, the students in the hidden object game group significantly outperformed regarding vocabulary recognition. However, even though the experimental group also showed relatively higher learning motivation, there was no significant difference between two modes.</p> <p>&nbsp;</p>
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33

Alemu, Habtamu, Wei Wu, and Junhong Zhao. "Feedforward Neural Networks with a Hidden Layer Regularization Method." Symmetry 10, no. 10 (October 19, 2018): 525. http://dx.doi.org/10.3390/sym10100525.

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In this paper, we propose a group Lasso regularization term as a hidden layer regularization method for feedforward neural networks. Adding a group Lasso regularization term into the standard error function as a hidden layer regularization term is a fruitful approach to eliminate the redundant or unnecessary hidden layer neurons from the feedforward neural network structure. As a comparison, a popular Lasso regularization method is introduced into standard error function of the network. Our novel hidden layer regularization method can force a group of outgoing weights to become smaller during the training process and can eventually be removed after the training process. This means it can simplify the neural network structure and it minimizes the computational cost. Numerical simulations are provided by using K-fold cross-validation method with K = 5 to avoid overtraining and to select the best learning parameters. The numerical results show that our proposed hidden layer regularization method prunes more redundant hidden layer neurons consistently for each benchmark dataset without loss of accuracy. In contrast, the existing Lasso regularization method prunes only the redundant weights of the network, but it cannot prune any redundant hidden layer neurons.
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34

Barlow, Julie H., Lesley A. Cullen, Sonia Davis, and Bethan Williams. "The hidden benefit of group education for people with arthritis." British Journal of Therapy and Rehabilitation 4, no. 1 (January 1997): 38–41. http://dx.doi.org/10.12968/bjtr.1997.4.1.14527.

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35

Thomas, Veronica L., Robert D. Jewell, and Jennifer Wiggins Johnson. "Hidden consumption behaviour: an alternative response to social group influence." European Journal of Marketing 49, no. 3/4 (April 13, 2015): 512–31. http://dx.doi.org/10.1108/ejm-06-2013-0336.

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Purpose – This paper aims to examine how conflicting brand preferences between a social group and an individual may lead the individual to hide their consumption. Specifically, the authors examine the conditions under which hiding behaviour is most likely to occur and the impact of susceptibility to interpersonal influence on the decision to hide. Design/methodology/approach – Two experiments were conducted using a combination of student and adult samples. Analysis of variance and regression analyses were used to test the hypotheses. Findings – Findings suggest that individuals are most likely to hide their consumption behaviour when group sanctions for non-conformity are severe, but the likelihood of being caught is low. Further, individual differences in susceptibility to interpersonal influence are found to affect individuals’ decisions to hide their consumption behaviour. Research limitations/implications – By identifying hidden consumption behaviour as a possible response to preference conflict, this research contributes to the literature on social influence and extends our understanding of how consumers behave when influenced by social group pressure. Originality/value – The present work establishes hiding behaviour (a concept which has yet to be thoroughly explored in the literature) as an alternative yet viable response to preference conflict.
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36

Hallgren, Sean, Alexander Russell, and Amnon Ta-Shma. "The Hidden Subgroup Problem and Quantum Computation Using Group Representations." SIAM Journal on Computing 32, no. 4 (January 2003): 916–34. http://dx.doi.org/10.1137/s009753970139450x.

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37

Lu, Li, Y. Connie Yuan, and Poppy Lauretta McLeod. "Twenty-Five Years of Hidden Profiles in Group Decision Making." Personality and Social Psychology Review 16, no. 1 (September 6, 2011): 54–75. http://dx.doi.org/10.1177/1088868311417243.

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38

Frank, Alejandro, François Leyvraz, and Kurt Bernardo Wolf. "Hidden symmetry and potential group of the Maxwell fish‐eye." Journal of Mathematical Physics 31, no. 12 (December 1990): 2757–68. http://dx.doi.org/10.1063/1.528979.

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39

Aldaya, Victor, José Navarro-Salas, and Miguel Navarro. "Dynamics on the Virasoro group, 2D gravity and hidden symmetries." Physics Letters B 260, no. 3-4 (May 1991): 311–16. http://dx.doi.org/10.1016/0370-2693(91)91617-5.

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40

Shoaf, Lisa M., and Michael G. Shoaf. "The Hidden Advantages of Focus Group Interviews in Educational Research." Journal of School Public Relations 27, no. 3 (July 1, 2006): 342–54. http://dx.doi.org/10.3138/jspr.27.3.342.

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41

Stevens, Kay. "Interaction: The Hidden Key to Success in Group Piano Teaching." International Journal of Music Education os-13, no. 1 (May 1989): 3–10. http://dx.doi.org/10.1177/025576148901300101.

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42

Abraham-Shrauner, B. "Hidden symmetries and nonlocal group generators for ordinary differential equations." IMA Journal of Applied Mathematics 56, no. 2 (April 1, 1996): 235–52. http://dx.doi.org/10.1093/imamat/56.2.235.

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43

Abraham-Shrauner, B. "Hidden symmetries and nonlocal group generators for ordinary differential equations." IMA Journal of Applied Mathematics 56, no. 3 (June 1, 1996): 235–52. http://dx.doi.org/10.1093/imamat/56.3.235.

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44

Basu Mallick, B., and A. Kundu. "Hidden quantum group structure in a relativistic quantum integrable model." Physics Letters B 287, no. 1-3 (August 1992): 149–53. http://dx.doi.org/10.1016/0370-2693(92)91891-c.

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45

Tausczik, Yla, and Xiaoyun Huang. "The Impact of Group Size on the Discovery of Hidden Profiles in Online Discussion Groups." ACM Transactions on Social Computing 2, no. 3 (December 17, 2019): 1–25. http://dx.doi.org/10.1145/3359758.

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46

Battegay, Raymond. "Apparent and Hidden Changes in Group Members According to the Different Phases of Group Psychotherapy." International Journal of Group Psychotherapy 39, no. 3 (July 1989): 337–53. http://dx.doi.org/10.1080/00207284.1989.11491173.

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47

Bao, Yuan, Zhaobin Liu, Zhongxuan Luo, and Sibo Yang. "Smooth Group L1/2 Regularization for Pruning Convolutional Neural Networks." Symmetry 14, no. 1 (January 13, 2022): 154. http://dx.doi.org/10.3390/sym14010154.

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In this paper, a novel smooth group L1/2 (SGL1/2) regularization method is proposed for pruning hidden nodes of the fully connected layer in convolution neural networks. Usually, the selection of nodes and weights is based on experience, and the convolution filter is symmetric in the convolution neural network. The main contribution of SGL1/2 is to try to approximate the weights to 0 at the group level. Therefore, we will be able to prune the hidden node if the corresponding weights are all close to 0. Furthermore, the feasibility analysis of this new method is carried out under some reasonable assumptions due to the smooth function. The numerical results demonstrate the superiority of the SGL1/2 method with respect to sparsity, without damaging the classification performance.
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48

Ke, Hai-Feng, Cheng-Bo Lu, Xiao-Bo Li, Gao-Yan Zhang, Ying Mei, and Xue-Wen Shen. "An Incremental Optimal Weight Learning Machine of Single-Layer Neural Networks." Scientific Programming 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/3732120.

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An optimal weight learning machine with growth of hidden nodes and incremental learning (OWLM-GHNIL) is given by adding random hidden nodes to single hidden layer feedforward networks (SLFNs) one by one or group by group. During the growth of the networks, input weights and output weights are updated incrementally, which can implement conventional optimal weight learning machine (OWLM) efficiently. The simulation results and statistical tests also demonstrate that the OWLM-GHNIL has better generalization performance than other incremental type algorithms.
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49

MARDOYAN, L. G., A. N. SISSAKIAN, and V. M. TER-ANTONYAN. "HIDDEN SYMMETRY OF THE YANG–COULOMB MONOPOLE." Modern Physics Letters A 14, no. 19 (June 21, 1999): 1303–7. http://dx.doi.org/10.1142/s0217732399001395.

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Bound system composed of the Yang monopole coupled to a particle of the isospin by the SU(2) and Coulomb interaction is considered. The generalized Runge–Lenz vector and the SO(6) group of hidden symmetry are established. It is also shown that the group of hidden symmetry makes it possible to calculate the spectrum of system by a pure algebraic method.
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50

Moore, C., and A. Russell. "For distinguishing conjugate Hidden subgroups, the pretty good measurement is as good as it gets." Quantum Information and Computation 7, no. 8 (November 2007): 752–65. http://dx.doi.org/10.26421/qic7.8-5.

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Recently Bacon, Childs and van Dam showed that the ``pretty good measurement'' (PGM) is optimal for the Hidden Subgroup Problem on the dihedral group $D_n$ in the case where the hidden subgroup is chosen uniformly from the $n$ involutions. We show that, for any group and any subgroup $H$, the PGM is the optimal one-register experiment in the case where the hidden subgroup is a uniformly random conjugate of $H$. We go on to show that when $H$ forms a Gel'fand pair with its parent group, the PGM is the optimal measurement for any number of registers. In both cases we bound the probability that the optimal measurement succeeds. This generalizes the case of the dihedral group, and includes a number of other examples of interest.
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