Descent into darkness : the destruction of the Roman Catholic Church in Russia, 1917-1923 / by 413341 Zatko, James J.

Clinical Outcome of Non-descent Vaginal Hysterectomy Versus Abdominal Hysterectomy by Kenusha Devi Tiwari, Ganesh Dangal, Aruna Karki, Hema Kumari Pradhan, Ranjana Shrestha, Kabin Bhattachan, Sonu Bharati

“…Keywords: Hysterectomy; non-descent vaginal hysterectomy; vault infection; wound infection …”
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Effects of rotor separation on the axial descent performance of dual-rotor configurations by Marcel Veismann, Christopher Dougherty, Morteza Gharib

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Powered Descent Guidance via First-Order Optimization With Expansive Projection by Jiwoo Choi, Jong-Han Kim

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A Geometric Interpretation of Stochastic Gradient Descent Using Diffusion Metrics by Rita Fioresi, Pratik Chaudhari, Stefano Soatto

Subjects: “…stochastic gradient descent…”
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Merge and Labeling as Descent with Modification of Categorization: A Neo-Lennebergian Approach by Koji Hoshi

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Using the Stochastic Gradient Descent Optimization Algorithm on Estimating of Reactivity Ratios by Iosif Sorin Fazakas-Anca, Arina Modrea, Sorin Vlase

Subjects: “…gradient descent…”
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The causal effect of serum micronutrients on malignant kidney neoplasm in European descent by Pengfei Qiao, Pengfei Qiao, Zhentao Tian, Zhentao Tian

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Analysis of Homozygous-by-Descent (HBD) Segments for Purebred and Crossbred Pigs in Russia by Siroj Bakoev, Anatoly Kolosov, Faridun Bakoev, Olga Kostyunina, Nekruz Bakoev, Timofey Romanets, Olga Koshkina, Lyubov Getmantseva

Subjects: “…homozygous-by-descent (HBD)…”
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A simplified convergence theory for Byzantine resilient stochastic gradient descent by Lindon Roberts, Edward Smyth

“…In the presence of one or more malicious servers sending incorrect information (a Byzantine adversary), standard algorithms for model training such as stochastic gradient descent (SGD) fail to converge. In this paper, we present a simplified convergence theory for the generic Byzantine Resilient SGD method originally proposed by Blanchard et al. (2017) [3]. …”
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Efficient neural codes naturally emerge through gradient descent learning by Ari S. Benjamin, Ling-Qi Zhang, Cheng Qiu, Alan A. Stocker, Konrad P. Kording

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Self-Calibration of UAV Thermal Imagery Using Gradient Descent Algorithm by Radosław Szostak, Mirosław Zimnoch, Przemysław Wachniew, Alina Jasek-Kamińska

“…It consists of the following steps: (i) devignetting using the single image vignette correction algorithm, (ii) georeferencing using image metadata, scale-invariant feature transform (SIFT) stitching, and gradient descent optimisation, and (iii) inter-image temperature consistency optimisation by minimisation of bias between overlapping thermal images using gradient descent optimisation. …”
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Stabilization of stochastic functional differential systems by steepest descent feedback controls by Xuetao Yang, Quanxin Zhu

“…Furthermore, the novel Lyapunov functionals and the induced steepest descent feedback controls are constructed to obtain such stability conditions that are weaker than the classical ones. …”
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Belief-Rule-Base Inference Method Based on Gradient Descent With Momentum by Yu Guan, Yanggeng Fu, Longjiang Chen, Genggeng Liu, Lan Sun

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Clustered Federated Learning Based on Momentum Gradient Descent for Heterogeneous Data by Xiaoyi Zhao, Ping Xie, Ling Xing, Gaoyuan Zhang, Huahong Ma

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The American Dream: Is Immigration Associated with Life Satisfaction for Latinos of Mexican Descent? by Vickie M. Mays, Rosina Becerra, Susan D. Cochran

“…The Latino population is one of the largest immigrant groups in the United States, with the majority being of Mexican descent. Whether immigrating to the US is positive for the well-being of Mexican immigrants and future generations is an important question. …”
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Incremental Nonnegative Tucker Decomposition with Block-Coordinate Descent and Recursive Approaches by Rafał Zdunek, Krzysztof Fonał

“…We propose two computational approaches for updating the factors in the incremental model: one is based on the recursive update model, and the other uses the concept of the block Kaczmarz method that belongs to coordinate descent methods. The experimental results performed on various datasets and streaming data demonstrate high efficiently of both algorithmic approaches, with respect to the baseline NTD methods.…”
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Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization by Wafa Njima, Rafik Zayani, Iness Ahriz, Michel Terre, Ridha Bouallegue

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Iterative quantum algorithm for combinatorial optimization based on quantum gradient descent by Xin Yi, Jia-Cheng Huo, Yong-Pan Gao, Ling Fan, Ru Zhang, Cong Cao

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Normalized stochastic gradient descent learning of general complex‐valued models by T. Paireder, C. Motz, M. Huemer

“…Abstract The stochastic gradient descent (SGD) method is one of the most prominent first‐order iterative optimisation algorithms, enabling linear adaptive filters as well as general nonlinear learning schemes. …”
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