Integrated photonic programmable random matrix generator with minimal active components by Kevin Zelaya, Mostafa Honari-Latifpour, Mohammad-Ali Miri

“…In quantum information processing, random unitary operations are essential to boson sampling algorithms for multiphoton states in linear photonic circuits. …”
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Differentially Private Image Classification by Learning Priors from Random Processes by Xinyu Tang, Ashwinee Panda, Vikash Sehwag, Prateek Mittal

“… In privacy-preserving machine learning, differentially private stochastic gradient descent (DP-SGD) performs worse than SGD due to per-sample gradient clipping and noise addition. A recent focus in private learning research is improving the performance of DP-SGD on private data by incorporating priors that are learned on real-world public data. …”
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An Algorithm for the Conditional Distribution of Independent Binomial Random Variables Given the Sum by Kelly Ayres, Steven E. Rigdon

“…We want the distribution of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>[</mo><msub><mi>X</mi><mn>1</mn></msub><mo>,</mo><mo>…</mo><mo>,</mo><msub><mi>X</mi><mi>k</mi></msub><mo>]</mo></mrow></semantics></math></inline-formula> conditioned on <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>X</mi><mn>1</mn></msub><mo>+</mo><mo>⋯</mo><mo>+</mo><msub><mi>X</mi><mi>k</mi></msub><mo>=</mo><mi>n</mi></mrow></semantics></math></inline-formula>. We propose both a random walk MH algorithm and an independence sampling MH algorithm for simulating from this conditional distribution. …”
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Using Random Forest to Improve the Downscaling of Global Livestock Census Data. by Gaëlle Nicolas, Timothy P Robinson, G R William Wint, Giulia Conchedda, Giuseppina Cinardi, Marius Gilbert

“…Two main factors were evaluated, based on sample census datasets of cattle in Africa and chickens in Asia. …”
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RELIABILITY ANALYSIS OF OUTPUT SHAFT OF CUTTING EDGE UNDER RANDOM LOAD by ZHAO LiJuan, JIN YuJi, HUANG Kai

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Fragility Analysis of Masonry Structures Subjected to Random Sequential Ground Motions by Jialiu Pan, Lei Cao, Yongqun Zhang, Jiaxu Shen

“…Following this, 21 sets of random sequential ground motions are generated. Using the Incremental Dynamic Analysis (IDA) method, 1260 nonlinear response samples of the structure under varying sequential ground motions are obtained, providing an insight into the fragility of the masonry structure subjected to these ground motions.…”
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Detection of Graduation Potential in Prospective Students using the Random Forest Algorithm by Puguh Hasta Gunawan, Irving Vitra Paputungan

“…The model was developed using the Random Forest algorithm with parameters set to max_depth = 15 and random_state = 42. …”
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Hybrid Random Feature Selection and Recurrent Neural Network for Diabetes Prediction by Oyebayo Ridwan Olaniran, Aliu Omotayo Sikiru, Jeza Allohibi, Abdulmajeed Atiah Alharbi, Nada MohammedSaeed Alharbi

“…The method first trains multiple LSTM/BiLSTM base models on dynamically sampled feature subsets to promote diversity, followed by a meta-learner that integrates predictions into a final robust output. …”
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Energy consumption forecasting and thermal insulator selection with random forest regression by Mohammed Fellah, Salma Ouhaibi, Naoual Belouaggadia, Khalifa Mansouri

“…The model used in this study is Random Forest (RF), which belongs to the family of ensemble learning models.The data used in this study come from numerical simulations carried out with Matlab and consist of 1400 samples, derived from the analysis of 35 thermal insulators distributed across 20 climate zones. …”
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On the Maximum Probability of Full Rank of Random Matrices over Finite Fields by Marija Delić, Jelena Ivetić

“…In this paper, we study the probability of full rank for a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>K</mi><mo>×</mo><mi>N</mi></mrow></semantics></math></inline-formula> random matrix over the finite field <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi mathvariant="double-struck">F</mi><mi>q</mi></msub></semantics></math></inline-formula>, where <i>q</i> is a prime power, under the assumption that the rows of the matrix are sampled independently from a probability distribution <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="script">P</mi></semantics></math></inline-formula> over <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msubsup><mi mathvariant="double-struck">F</mi><mi>q</mi><mi>N</mi></msubsup></semantics></math></inline-formula>. …”
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Implementasi Algoritma Random Forest Untuk Menentukan Penerima Bantuan Raskin by Ilham Kurniawan, Duwi Cahya Putri Buani, Abdussomad Abdussomad, Widya Apriliah, Rizal Amegia Saputra

“…Accuracy is measured by correctly and incorrectly grouped samples. The results obtained show that the random forest classification algorithm has precision, recall, f-measure values with a value of 97%, an accuracy value of 97.26% and an ROC value of 0.970, better than other classification algorithms, namely the difference of 5.11% with the support vector classification algorithm. machine and 8.87% with naive bayes classification algorithm. …”
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Cathepsins and neurological diseases: a Mendelian randomization study by Haitao Sun, Qingqing Tang, Xue Yan, Wanying Xie, Yueshan Xu, Weimin Zhang

“…To address this, we utilized a two-sample Mendelian randomization (MR) approach to assess the potential causal effect of cathepsins on the development of neurological diseases.MethodsThis study conducted a two-sample two-way MR study using pooled data from published genome-wide association studies to evaluate the relationship between 10 cathepsins (B, D, E, F, G, H, L2, O, S, and Z) and 7 neurological diseases, which included ischemic stroke, cerebral hemorrhage, Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, amyotrophic lateral sclerosis, and epilepsy. …”
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Identification of druggable genes for schizophrenia based on Mendelian randomization by LIAN Kun, CHEN Yilan, ZHANG Lei, YE Fan, GAO Yunyong, YANG Wei

“…Methods Druggable genes were obtained from eQTLGen database and integrated with genome-wide association studies (GWAS) data of SCZ. Two-sample Mendelian randomization (MR) and colocalization analysis were used to calculate the association between expression quantitative trait loci (eQTL) of druggable genes and the presence of common pathogenic variants in SCZ. …”
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Obesity and Multiple Sclerosis: A Mendelian Randomization Study. by Lauren E Mokry, Stephanie Ross, Nicholas J Timpson, Stephen Sawcer, George Davey Smith, J Brent Richards

“…Therefore, we elected to perform Mendelian randomization (MR) analyses to evaluate whether genetically increased BMI is associated with an increased risk of MS.…”
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Kidney damage on fertility and pregnancy: A Mendelian randomization. by Jin Ren, Qiuyan Huang, Xiaowei Lie, Xingli Tong, Qi Yao, Ge Zhou

“…Hence, this study aimed to investigate the causal effect of kidney damage on fertility and pregnancy using Mendelian randomization (MR).<h4>Methods</h4>We first used two-sample MR to examine the effects of kidney damage on fertility and pregnancy. …”
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Research on test strategy for randomness based on deep learning by Dongyu CHEN, Hua CHEN, Limin FAN, Yifang FU, Jian WANG

“…In order to achieve better test performance, researches on the randomness test strategies based on deep learning were conducted, including the batch average strategy proposed by EUROCRYPT 2021 and the selection strategy for data unit size.By introducing the randomness statistical test model based on deep learning methods, the statistical distribution and test power expression of two test strategies were theoretically derived, and it was pointed out that: (i) the batch average strategy could amplify the prediction accuracy of the model, but it was prone to an increase in the probability of the second type of error in statistics, instead reducing the statistical test power; (ii) the smaller data units of the deep model generally obtained the more powerful statistical tests.Based on the above understanding, a new bit-level deep learning model was proposed for randomness statistical tests, which gained the advantage of prediction with 80 times fewer parameters and 50% samples, compared with the previous work on linear congruent generator (LCG) algorithm, and achieved significant prediction advantages with 10～20 times fewer parameters by extending the model to apply to 5～7 rounds of Speck, compared with the model proposed by Gohr.…”
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Research on test strategy for randomness based on deep learning by Dongyu CHEN, Hua CHEN, Limin FAN, Yifang FU, Jian WANG

“…In order to achieve better test performance, researches on the randomness test strategies based on deep learning were conducted, including the batch average strategy proposed by EUROCRYPT 2021 and the selection strategy for data unit size.By introducing the randomness statistical test model based on deep learning methods, the statistical distribution and test power expression of two test strategies were theoretically derived, and it was pointed out that: (i) the batch average strategy could amplify the prediction accuracy of the model, but it was prone to an increase in the probability of the second type of error in statistics, instead reducing the statistical test power; (ii) the smaller data units of the deep model generally obtained the more powerful statistical tests.Based on the above understanding, a new bit-level deep learning model was proposed for randomness statistical tests, which gained the advantage of prediction with 80 times fewer parameters and 50% samples, compared with the previous work on linear congruent generator (LCG) algorithm, and achieved significant prediction advantages with 10～20 times fewer parameters by extending the model to apply to 5～7 rounds of Speck, compared with the model proposed by Gohr.…”
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Optimizing Circuit Reusing and its Application in Randomized Benchmarking by Zhuo Chen, Guoding Liu, Xiongfeng Ma

“…Quantum learning tasks often leverage randomly sampled quantum circuits to characterize unknown systems. …”
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TWO-POINT BOUNDARY PROBLEM SOLUTION BY NON-GRADIENT RANDOM SEARCH METHOD by V. A. Malkin

“…The initial conditions of conjugate set of equations are determined by use of non-gradient random search method.The non-gradient random search method based on application of stochastic procedures for range of problems solving, including the deterministic ones. …”
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Penerapan Feature Engineering dan Hyperparameter Tuning untuk Meningkatkan Akurasi Model Random Forest pada Klasifikasi Risiko Kredit by Nadea Putri Nur Fauzi, Siti Khomsah, Aditya Dwi Putra Wicaksono

“…Metodologi penelitian yang digunakan adalah Sample, Explore, Modify, Models, dan Assess (SEMMA). …”
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