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Thematische Bibliographien / Slay The Spire / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Slay The Spire“

Autor: Grafiati

Veröffentlicht am 30. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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1

Movchan, B. V. "TRANSLATING ONYMS IN ENGLISH VIDEO GAMES: A CASE STUDY BASED ON UKRAINIAN LOCALIZATION OF ‘SLAY THE SPIRE’." "Scientific notes of V. I. Vernadsky Taurida National University", Series: "Philology. Journalism" 1, no. 1 (2024): 242–48. http://dx.doi.org/10.32782/2710-4656/2024.1.1/41.

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2

Denitto, M., M. Bicego, A. Farinelli, and M. A. T. Figueiredo. "Spike and slab biclustering." Pattern Recognition 72 (December 2017): 186–95. http://dx.doi.org/10.1016/j.patcog.2017.07.021.

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3

Ročková, Veronika, and Edward I. George. "The Spike-and-Slab LASSO." Journal of the American Statistical Association 113, no. 521 (2018): 431–44. http://dx.doi.org/10.1080/01621459.2016.1260469.

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4

Louzada, Francisco, Taciana KO Shimizu, and Adriano K. Suzuki. "The Spike-and-Slab Lasso regression modeling with compositional covariates: An application on Brazilian children malnutrition data." Statistical Methods in Medical Research 29, no. 5 (2019): 1434–46. http://dx.doi.org/10.1177/0962280219863817.

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There are considerable challenges in analyzing large-scale compositional data. In this paper, we introduce the Spike-and-Slab Lasso linear regression in the presence of compositional covariates for parameter estimation and variable selection. We consider the well-known isometric log-ratio (ilr) coordinates to avoid misleading statistical inference. The separable and non-separable (adaptative) Spike-and-Slab Lasso penalties are compared to verify the advantages of each approach. The proposed method is illustrated on simulated and on real Brazilian child malnutrition data.

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5

Ishwaran, Hemant, and J. Sunil Rao. "Consistency of spike and slab regression." Statistics & Probability Letters 81, no. 12 (2011): 1920–28. http://dx.doi.org/10.1016/j.spl.2011.08.005.

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6

Liu, Tian, Yongfu Chen, Zhiyong Jin, Kai Li, Zhenting Wang, and Jiongzhi Zheng. "Spare Pose Graph Decomposition and Optimization for SLAM." MATEC Web of Conferences 256 (2019): 05003. http://dx.doi.org/10.1051/matecconf/201925605003.

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The graph optimization has become the mainstream technology to solve the problems of SLAM (simultaneous localization and mapping). The pose graph in the graph based SLAM is consisted with a series of nodes and edges that connect the adjacent or related poses. With the widespread use of mobile robots, the scale of pose graph has rapidly increased. Therefore, optimizing a large-scale pose graph is the bottleneck of application of graph based SLAM. In this paper, we propose an optimization method basing on the decomposition of pose graph, of which we have noticed the sparsity. With the extraction

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7

Cui, Kai, and Wenshan Cui. "Spike-and-Slab Dirichlet Process Mixture Models." Open Journal of Statistics 02, no. 05 (2012): 512–18. http://dx.doi.org/10.4236/ojs.2012.25066.

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8

Ročková, Veronika, and Edward I. George. "Negotiating multicollinearity with spike-and-slab priors." METRON 72, no. 2 (2014): 217–29. http://dx.doi.org/10.1007/s40300-014-0047-y.

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9

Ding, Xinghao, Zengyuan Mi, Yue Huang, and Wenbo Jin. "Robust RVM based on spike-slab prior." Journal of Electronics (China) 29, no. 6 (2012): 593–97. http://dx.doi.org/10.1007/s11767-012-0873-0.

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10

Wei, Weichen, Mohammadali Ghafarian, Bijan Shirinzadeh, Ammar Al-Jodah, and Rohan Nowell. "Posture and Map Restoration in SLAM Using Trajectory Information." Processes 10, no. 8 (2022): 1433. http://dx.doi.org/10.3390/pr10081433.

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SLAM algorithms generally use the last system posture to estimate its current posture. Errors in the previous estimations can build up and cause significant drift accumulation. This accumulation of error leads to the bias of choosing accuracy over robustness. On the contrary, sensors like GPS do not accumulate errors. But the noise distribution in the readings makes it difficult to apply in high-frequency SLAM systems. This paper presents an approach which uses the advantage of both tightly-coupled SLAM systems and highly robust absolute positioning systems to improve the robustness and accura

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11

Castillo, Ismaël, and Botond Szabó. "Spike and slab empirical Bayes sparse credible sets." Bernoulli 26, no. 1 (2020): 127–58. http://dx.doi.org/10.3150/19-bej1119.

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12

Xiaojun, Bi, and Wang Haibo. "Contractive Slab and Spike Convolutional Deep Boltzmann Machine." Neurocomputing 290 (May 2018): 208–28. http://dx.doi.org/10.1016/j.neucom.2018.02.048.

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13

Wang, Haibo, and Xiaojun Bi. "Contractive Slab and Spike Convolutional Deep Belief Network." Neural Processing Letters 49, no. 3 (2018): 1697–722. http://dx.doi.org/10.1007/s11063-018-9897-2.

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14

Castillo, Ismaël, and Étienne Roquain. "On spike and slab empirical Bayes multiple testing." Annals of Statistics 48, no. 5 (2020): 2548–74. http://dx.doi.org/10.1214/19-aos1897.

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15

Liu, Y., F. Y. Li, M. Zeng, M. Chen, and Z. M. Sheng. "Ultra-intense attosecond pulses emitted from laser wakefields in non-uniform plasmas." Laser and Particle Beams 31, no. 2 (2013): 233–38. http://dx.doi.org/10.1017/s0263034613000220.

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AbstractA scheme of generating ultra-intense attosecond pulses in ultra-relativistic laser interaction with under-dense plasmas is proposed. The attosecond pulse emission is caused by an oscillating transverse current sheet formed by an electron density spike composed of trapped electrons in the laser wakefield and the residual transverse momentum of electrons left behind the laser pulse when its front is strongly modulated. As soon as the attosecond pulse emerges, it tends to feed back to further enhance the transverse electron momentum and the transverse current. Consequently, the attosecond

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16

Zhang, Qi, Yihui Zhang, and Yemao Xia. "Bayesian Feature Extraction for Two-Part Latent Variable Model with Polytomous Manifestations." Mathematics 12, no. 5 (2024): 783. http://dx.doi.org/10.3390/math12050783.

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Semi-continuous data are very common in social sciences and economics. In this paper, a Bayesian variable selection procedure is developed to assess the influence of observed and/or unobserved exogenous factors on semi-continuous data. Our formulation is based on a two-part latent variable model with polytomous responses. We consider two schemes for the penalties of regression coefficients and factor loadings: a Bayesian spike and slab bimodal prior and a Bayesian lasso prior. Within the Bayesian framework, we implement a Markov chain Monte Carlo sampling method to conduct posterior inference.

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17

齐, 琪. "The Spike and Slab Lasso Logistic Regression Model Based on Prediction Correction Algorithm." Advances in Applied Mathematics 12, no. 01 (2023): 292–307. http://dx.doi.org/10.12677/aam.2023.121032.

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18

Qiao, Weizheng, and Xiaojun Bi. "Learning Hierarchical Representations with Spike-and-Slab Inception Network." Sensors 21, no. 19 (2021): 6382. http://dx.doi.org/10.3390/s21196382.

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Recently, deep convolutional neural networks (CNN) with inception modules have attracted much attention due to their excellent performances on diverse domains. Nevertheless, the basic CNN can only capture a univariate feature, which is essentially linear. It leads to a weak ability in feature expression, further resulting in insufficient feature mining. In view of this issue, researchers incessantly deepened the network, bringing parameter redundancy and model over-fitting. Hence, whether we can employ this efficient deep neural network architecture to improve CNN and enhance the capacity of i

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19

Ishwaran, Hemant, and J. Sunil Rao. "Spike and Slab Gene Selection for Multigroup Microarray Data." Journal of the American Statistical Association 100, no. 471 (2005): 764–80. http://dx.doi.org/10.1198/016214505000000051.

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20

Ishwaran, Hemant, and J. Sunil Rao. "Spike and slab variable selection: Frequentist and Bayesian strategies." Annals of Statistics 33, no. 2 (2005): 730–73. http://dx.doi.org/10.1214/009053604000001147.

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21

Castillo, Ismaël, and Romain Mismer. "Empirical Bayes analysis of spike and slab posterior distributions." Electronic Journal of Statistics 12, no. 2 (2018): 3953–4001. http://dx.doi.org/10.1214/18-ejs1494.

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22

Rockova, Veronika, and Kenichiro McAlinn. "Dynamic Variable Selection with Spike-and-Slab Process Priors." Bayesian Analysis 16, no. 1 (2021): 233–69. http://dx.doi.org/10.1214/20-ba1199.

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23

Leach, Justin M., Lloyd J. Edwards, Rajesh Kana, Kristina Visscher, Nengjun Yi, and Inmaculada Aban. "The spike-and-slab elastic net as a classification tool in Alzheimer’s disease." PLOS ONE 17, no. 2 (2022): e0262367. http://dx.doi.org/10.1371/journal.pone.0262367.

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Alzheimer’s disease (AD) is the leading cause of dementia and has received considerable research attention, including using neuroimaging biomarkers to classify patients and/or predict disease progression. Generalized linear models, e.g., logistic regression, can be used as classifiers, but since the spatial measurements are correlated and often outnumber subjects, penalized and/or Bayesian models will be identifiable, while classical models often will not. Many useful models, e.g., the elastic net and spike-and-slab lasso, perform automatic variable selection, which removes extraneous predicto

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24

Zhang, Juanjuan, Weixian Wang, Mingming Yang, and Maozai Tian. "Variational Bayesian Variable Selection in Logistic Regression Based on Spike-and-Slab Lasso." Mathematics 13, no. 13 (2025): 2205. https://doi.org/10.3390/math13132205.

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Logistic regression is often used to solve classification problems. This article combines the advantages of Bayesian methods and spike-and-slab Lasso to select variables in high-dimensional logistic regression. The method of introducing a new hidden variable or approximating the lower bound is used to solve the problem of logistic functions without conjugate priors. The Laplace distribution in spike-and-slab Lasso is expressed as a hierarchical form of normal distribution and exponential distribution, so that all parameters in the model are posterior distributions that are easy to deal with. C

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25

Hai Nguyen, Trong, Hoai Nhan Nguyen, Hung Kim Khanh Pham, and Quoc Phuong Pham. "A METHOD FOR TRAJECTORY TRACKING FOR DIFFERENTIAL DRIVE TYPE OF AUTOMATIC GUIDED VEHICLE." JOURNAL OF TECHNOLOGY & INNOVATION 1, no. 2 (2020): 51–53. http://dx.doi.org/10.26480/jtin.02.2021.51.53.

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This paper proposes trajectory tracking algorithm for differential drive type of Automatic Guided Vehicle (AGV) using backstepping control and simultaneous localization and mapping (SLAM). To guarantee the tracking errors go to zero, backstepping control method is proposed. By choosing appropriate Lyapunov function based on its kinematic modeling, system stability is guaranteed and a control law can be obtained. For its positioning, simultaneous localization and mapping (SLAM) algorithm is employed. The landmarks are detected using spike algorithm. The AGV position can be estimated using Kalma

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26

Zink, Andrea. "Sprachliche und andere Grenzfälle – Identitätskritik in Stevan Sremac’Pop Ćira i pop Spira." Zeitschrift für Slawistik 57, no. 2 (2012): 204–17. http://dx.doi.org/10.1524/slaw.2012.0013.

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27

Ishwaran, Hemant, Udaya,B Kogalur, and J. ,Sunil Rao. "spikeslab: Prediction and Variable Selection Using Spike and Slab Regression." R Journal 2, no. 2 (2010): 68. http://dx.doi.org/10.32614/rj-2010-018.

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28

Goodfellow, Ian J., Aaron Courville, and Yoshua Bengio. "Scaling Up Spike-and-Slab Models for Unsupervised Feature Learning." IEEE Transactions on Pattern Analysis and Machine Intelligence 35, no. 8 (2013): 1902–14. http://dx.doi.org/10.1109/tpami.2012.273.

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29

Antonelli, Joseph, Giovanni Parmigiani, and Francesca Dominici. "High-Dimensional Confounding Adjustment Using Continuous Spike and Slab Priors." Bayesian Analysis 14, no. 3 (2019): 805–28. http://dx.doi.org/10.1214/18-ba1131.

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30

Shelton, Jacquelyn A., Abdul-Saboor Sheikh, Jörg Bornschein, Philip Sterne, and Jörg Lücke. "Nonlinear Spike-And-Slab Sparse Coding for Interpretable Image Encoding." PLOS ONE 10, no. 5 (2015): e0124088. http://dx.doi.org/10.1371/journal.pone.0124088.

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31

Lu, Xiaoqiang, Yuan Yuan, and Pingkun Yan. "Sparse coding for image denoising using spike and slab prior." Neurocomputing 106 (April 2013): 12–20. http://dx.doi.org/10.1016/j.neucom.2012.09.014.

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32

Zhang, Nan, Shifei Ding, Jian Zhang, and Xingyu Zhao. "Robust spike-and-slab deep Boltzmann machines for face denoising." Neural Computing and Applications 32, no. 7 (2018): 2815–27. http://dx.doi.org/10.1007/s00521-018-3866-6.

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33

Shelton, Jacquelyn A., Abdul-Saboor Sheikh, Jörg Bornschein, Philip Sterne, and Jörg Lücke. "Nonlinear spike-and-slab sparse coding for interpretable image encoding." PLOS ONE 10 (June 5, 2015): 1–25. https://doi.org/10.1371/journal.pone.0124088.

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34

Skak, Mette. "Vendepunktet i 1920: Ruslands blodige borgerkrig og Polens mirakuløse sejr." Udenrigs, no. 1 (March 1, 2020): 98–103. https://doi.org/10.7146/udenrigs.i1.133537.

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Vi kaster også et blik tilbage på 1920, da Den Røde Hær endeligt fik overtaget mod De Hvide, og hvor Polen lidt overraskende slap fri af Ruslands greb. I Rusland har Putin netop lagt an til at forlænge sit styre – i en eller anden form – også efter hans præsident- periode slutter i 2024, men selv så spirer håbet og modstanden.

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35

Yi, Jieyi, and Niansheng Tang. "Variational Bayesian Inference in High-Dimensional Linear Mixed Models." Mathematics 10, no. 3 (2022): 463. http://dx.doi.org/10.3390/math10030463.

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In high-dimensional regression models, the Bayesian lasso with the Gaussian spike and slab priors is widely adopted to select variables and estimate unknown parameters. However, it involves large matrix computations in a standard Gibbs sampler. To solve this issue, the Skinny Gibbs sampler is employed to draw observations required for Bayesian variable selection. However, when the sample size is much smaller than the number of variables, the computation is rather time-consuming. As an alternative to the Skinny Gibbs sampler, we develop a variational Bayesian approach to simultaneously select v

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36

Koch, Brandon, David M. Vock, Julian Wolfson, and Laura Boehm Vock. "Variable selection and estimation in causal inference using Bayesian spike and slab priors." Statistical Methods in Medical Research 29, no. 9 (2020): 2445–69. http://dx.doi.org/10.1177/0962280219898497.

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Unbiased estimation of causal effects with observational data requires adjustment for confounding variables that are related to both the outcome and treatment assignment. Standard variable selection techniques aim to maximize predictive ability of the outcome model, but they ignore covariate associations with treatment and may not adjust for important confounders weakly associated to outcome. We propose a novel method for estimating causal effects that simultaneously considers models for both outcome and treatment, which we call the bilevel spike and slab causal estimator (BSSCE). By using a B

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37

Lei, Huxu, Chaowen Tan, Gangqiang Fan, Dejun Huang, Xiaoming Ding, and Jie Dang. "The Crystallization Behavior of TiO2-CaO-SiO2-Al2O3-MgO Pentabasic Slag with a Basicity of 1.1–1.4." Crystals 11, no. 6 (2021): 583. http://dx.doi.org/10.3390/cryst11060583.

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The utilization of titanium-containing blast furnace slag has been an unsolved problem for a long time. Failure to make effective use of the slag, which is caused by a high TiO2 content within it, not only results in a waste of resources, especially titanium, but also increases environmental risk. The key to address the problem is the enrichment and extraction of TiO2 from the slag first. Therefore, in order to study the enrichment of titanium, the crystallization behavior of TiO2-CaO-SiO2-Al2O3-MgO pentabasic slag, the main compositions of titanium-containing blast furnace slag, within the ba

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38

POOJA, DIVYA, SHIV PRASAD, BHUPINDER SINGH, et al. "Effect of bioaugmented Linz-Donawitz slag and biochar on physiological and yield attributes of wheat (Triticum aestivum)." Indian Journal of Agricultural Sciences 94, no. 1 (2024): 021–25. http://dx.doi.org/10.56093/ijas.v94i1.140177.

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Industrial wastes and agricultural by-products are increasingly used in crop production as supplements along with fertilizers. An experiment was conducted during the winter (rabi) seasons of 2021 and 2022 at the research farm of the ICAR-Indian Agricultural Research Institute, New Delhi to determine the individual and combined effects of bioaugmented Linz-Donawitz (LD) slag and biochar on physiological and yield attributes of wheat (Triticum aestivum L.) variety HD 2967. Bioaugmented products with cow-dung, LD slag and biochar in different combinations were prepared in laboratory scale and app

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39

Canale, A., A. Lijoi, B. Nipoti, and I. Prünster. "On the Pitman–Yor process with spike and slab base measure." Biometrika 104, no. 3 (2017): 681–97. http://dx.doi.org/10.1093/biomet/asx041.

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Summary For the most popular discrete nonparametric models, beyond the Dirichlet process, the prior guess at the shape of the data-generating distribution, also known as the base measure, is assumed to be diffuse. Such a specification greatly simplifies the derivation of analytical results, allowing for a straightforward implementation of Bayesian nonparametric inferential procedures. However, in several applied problems the available prior information leads naturally to the incorporation of an atom into the base measure, and then the Dirichlet process is essentially the only tractable choice

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40

Hernández-Lobato, José Miguel, Daniel Hernández-Lobato, and Alberto Suárez. "Expectation propagation in linear regression models with spike-and-slab priors." Machine Learning 99, no. 3 (2014): 437–87. http://dx.doi.org/10.1007/s10994-014-5475-7.

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41

Du, Sizhen, Guojie Song, Lei Han, and Haikun Hong. "Temporal Causal Inference with Time Lag." Neural Computation 30, no. 1 (2018): 271–91. http://dx.doi.org/10.1162/neco_a_01028.

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Accurate causal inference among time series helps to better understand the interactive scheme behind the temporal variables. For time series analysis, an unavoidable issue is the existence of time lag among different temporal variables. That is, past evidence would take some time to cause a future effect instead of an immediate response. To model this process, existing approaches commonly adopt a prefixed time window to define the lag. However, in many real-world applications, this parameter may vary among different time series, and it is hard to be predefined with a fixed value. In this lette

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42

Dey, Tanujit. "A Bimodal Spike and Slab Model for Variable Selection and Model Exploration." Journal of Data Science 10, no. 3 (2021): 363–83. http://dx.doi.org/10.6339/jds.201207_10(3).0002.

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43

Ročková, Veronika. "Bayesian estimation of sparse signals with a continuous spike-and-slab prior." Annals of Statistics 46, no. 1 (2018): 401–37. http://dx.doi.org/10.1214/17-aos1554.

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44

Deshpande, Sameer K., Veronika Ročková, and Edward I. George. "Simultaneous Variable and Covariance Selection With the Multivariate Spike-and-Slab LASSO." Journal of Computational and Graphical Statistics 28, no. 4 (2019): 921–31. http://dx.doi.org/10.1080/10618600.2019.1593179.

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45

Scheipl, Fabian, Ludwig Fahrmeir, and Thomas Kneib. "Spike-and-Slab Priors for Function Selection in Structured Additive Regression Models." Journal of the American Statistical Association 107, no. 500 (2012): 1518–32. http://dx.doi.org/10.1080/01621459.2012.737742.

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46

Yen, Tso-Jung. "A majorization–minimization approach to variable selection using spike and slab priors." Annals of Statistics 39, no. 3 (2011): 1748–75. http://dx.doi.org/10.1214/11-aos884.

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47

Zohrul Kabir, A. B. M., and S. H. A. Farrash. "Simulation of an integrated age replacement and spare provisioning policy using SLAM." Reliability Engineering & System Safety 52, no. 2 (1996): 129–38. http://dx.doi.org/10.1016/0951-8320(96)00013-0.

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48

Ben-Yosef, Erez, Lisa Tauxe, Thomas E. Levy, Ron Shaar, Hagai Ron, and Mohammad Najjar. "Geomagnetic intensity spike recorded in high resolution slag deposit in Southern Jordan." Earth and Planetary Science Letters 287, no. 3-4 (2009): 529–39. http://dx.doi.org/10.1016/j.epsl.2009.09.001.

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49

van den Bergh, Don, Julia M. Haaf, Alexander Ly, Jeffrey N. Rouder, and Eric-Jan Wagenmakers. "A Cautionary Note on Estimating Effect Size." Advances in Methods and Practices in Psychological Science 4, no. 1 (2021): 251524592199203. http://dx.doi.org/10.1177/2515245921992035.

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An increasingly popular approach to statistical inference is to focus on the estimation of effect size. Yet this approach is implicitly based on the assumption that there is an effect while ignoring the null hypothesis that the effect is absent. We demonstrate how this common null-hypothesis neglect may result in effect size estimates that are overly optimistic. As an alternative to the current approach, a spike-and-slab model explicitly incorporates the plausibility of the null hypothesis into the estimation process. We illustrate the implications of this approach and provide an empirical exa

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50

Sjukovs, Jegor. "Ansvar og kærlighed som våben." Udenrigs, no. 1 (March 1, 2020): 94–97. https://doi.org/10.7146/udenrigs.i1.133536.

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Vi kaster også et blik tilbage på 1920, da Den Røde Hær endeligt fik overtaget mod De Hvide, og hvor Polen lidt overraskende slap fri af Ruslands greb. I Rusland har Putin netop lagt an til at forlænge sit styre – i en eller anden form – også efter hans præsidentperiode slutter i 2024, men selv så spirer håbet og modstanden. Vi har oversat den 21-årige YouTube-aktivist Jegor Sjukovs brandtale i retten, hvor han var anklaget for at opfordre til ekstremisme. Det er stærke sager – både talen og resten af dette nummer af Udenrigs.

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