Gotowa bibliografia na temat „Moduli randomization”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Spis treści
Zobacz listy aktualnych artykułów, książek, rozpraw, streszczeń i innych źródeł naukowych na temat „Moduli randomization”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Artykuły w czasopismach na temat "Moduli randomization"
Csorgő, M., Z. Y. Lin i Q. M. Shao. "Randomization Moduli of Continuity for ℓ2-Norm Squared Ornstein-Uhlenbeck Processes". Canadian Journal of Mathematics 45, nr 2 (1.04.1993): 269–83. http://dx.doi.org/10.4153/cjm-1993-013-3.
Pełny tekst źródłaStankiewicz, Anna, i Sławomir Juściński. "How to Make the Stress Relaxation Experiment for Polymers More Informative". Polymers 15, nr 23 (2.12.2023): 4605. http://dx.doi.org/10.3390/polym15234605.
Pełny tekst źródłaSchwamb, Megan E., Jeremy Kubica, Mario Jurić, Drew Oldag, Maxine West, Melissa DeLucchi i Matthew J. Holman. "Controlling Randomization in Astronomy Simulations". Research Notes of the AAS 8, nr 1 (19.01.2024): 25. http://dx.doi.org/10.3847/2515-5172/ad1f6b.
Pełny tekst źródłaSun, Shi-Hai, i Lin-Mei Liang. "Experimental demonstration of an active phase randomization and monitor module for quantum key distribution". Applied Physics Letters 101, nr 7 (13.08.2012): 071107. http://dx.doi.org/10.1063/1.4746402.
Pełny tekst źródłaWu, Jiayao, Chen He, Jiahui Xie, Xiaopeng Liu i Minghui Zhang. "Twin-Field Quantum Digital Signature with Fully Discrete Phase Randomization". Entropy 24, nr 6 (18.06.2022): 839. http://dx.doi.org/10.3390/e24060839.
Pełny tekst źródłaZhao, Yizhou, i Hua Sun. "Expand-and-Randomize: An Algebraic Approach to Secure Computation". Entropy 23, nr 11 (4.11.2021): 1461. http://dx.doi.org/10.3390/e23111461.
Pełny tekst źródłaSteffen, Alana D., Larisa A. Burke, Heather A. Pauls, Marie L. Suarez, Yingwei Yao, William H. Kobak, Miho Takayama i in. "Double-blinding of an acupuncture randomized controlled trial optimized with clinical translational science award resources". Clinical Trials 17, nr 5 (10.07.2020): 545–51. http://dx.doi.org/10.1177/1740774520934910.
Pełny tekst źródłaDidier, Gilles, Alberto Valdeolivas i Anaïs Baudot. "Identifying communities from multiplex biological networks by randomized optimization of modularity". F1000Research 7 (10.07.2018): 1042. http://dx.doi.org/10.12688/f1000research.15486.1.
Pełny tekst źródłaDidier, Gilles, Alberto Valdeolivas i Anaïs Baudot. "Identifying communities from multiplex biological networks by randomized optimization of modularity". F1000Research 7 (22.11.2018): 1042. http://dx.doi.org/10.12688/f1000research.15486.2.
Pełny tekst źródłaBaharsyah, Baharudin Adi, Endang Dian Setioningsih, Sari Luthfiyah i Wahyu Caesarendra. "Analyzing the Relationship between Dialysate Flow Rate Stability and Hemodialysis Machine Efficiency". Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics 5, nr 2 (30.05.2023): 86–91. http://dx.doi.org/10.35882/ijeeemi.v5i2.276.
Pełny tekst źródłaRozprawy doktorskie na temat "Moduli randomization"
Courtois, Jérôme. "Leak study of cryptosystem implementations in randomized RNS arithmetic". Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS290.
Pełny tekst źródłaWe will speak of strong analysis for an analysis which makes it possible to find the key to a cryptographic system. We define a weak analysis in the case where candidate keys are eliminated. The goal of this thesis is to understand the behavior of the random of Hamming distances produced by an ECC (Elliptic Curve for Cryptography) cryptographic system when using a RNS (Residue Number System) representation with the random moduli method. Chapter 2 introduces the different concepts for understanding this document. He brieflyintroducesthemodularmultiplicationalgorithm(MontgomeryalgorithmforRNS) which inspired the method of random moduli. Then it describes the algorithm which generatestheHammingdistancesequencesnecessaryforouranalysis. Thenitshowswhat level of resistance brings the method of random moduli against different classic attacks like DPA (Diferrential Power Analysis), CPA (Correlation Power Analysis), DPA of the second order and MIA (Mutual Information Analysis). We provide an understanding of the distribution of Hamming distances considered to be random variables. Following this, we add the Gaussian hypothesis on Hamming distances. We use MLE (Maximum Likelihood Estimator) and a strong analysis as to make Template Attacks to have a fine understanding of the level of random brought by the method of random moduli. The last Chapter 4 begins by briefly introducing the algorithmic choices which have been made to solve the problems of inversion of covariance matrices (symmetric definite positive) of Section 2.5 and the analysis of strong relationships between Hamming in Section 3.2. We use here Graphics Processing Unit (GPU) tools on a very large number of small size matrices. We talk about Batch Computing. The LDLt method presented at the beginning of this chapter proved to be insufficient to completely solve the problem of conditioned MLE presented in Section 3.4. We present work on the improvement of a diagonalization code of a tridiagonal matrix using the principle of Divide & Conquer developed by Lokmane Abbas-Turki and Stéphane Graillat. We present a generalization of this code, optimizations in computation time and an improvement of the accuracy of computations in simple precision for matrices of size lower than 32
Nadeem, Muhammad Hassan. "Linux Kernel Module Continuous Address Space Re-Randomization". Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/104685.
Pełny tekst źródłaMaster of Science
Address space layout randomization (ASLR) is a computer security technique used to prevent attacks that exploit memory disclosure and corruption vulnerabilities. ASLR works by randomly arranging the locations of key areas of a process such as the stack, heap, shared libraries and base address of the executable in the address space. This prevents an attacker from jumping to vulnerable code in memory and thus making it hard to launch control flow hijacking and code reuse attacks. ASLR makes it impossible for the attacker to leverage return-oriented programming (ROP) by pre-computing the location of code gadgets. Unfortunately, ASLR can be defeated by using memory disclosure vulnerabilities to unravel static randomization in an attack known as Just-In-Time ROP (JIT-ROP) attack. There exist techniques that extend the idea of ASLR by continually re-randomizing the program at run-time. With re-randomization, any leaked memory location is quickly obsoleted by rapidly and continuously rearranging memory. If the period of re-randomization is kept shorter than the time it takes for an attacker to create and launch their attack, then JIT-ROP attacks can be prevented. Unfortunately, there exists no continuous re-randomization implementation for the Linux kernel. To make matters worse, the ASLR implementation for the Linux kernel (KASLR) is limited. Specifically, for x86-64 CPUs, due to architectural restrictions, the Linux kernel is loaded in a narrow 1GB region of the memory. Likewise, all the kernel modules are loaded within the 1GB range of the kernel image. Due to this relatively low entropy, the Linux kernel is vulnerable to brute-force ROP attacks. In this thesis, we make two major contributions. First, we add support for position-independent kernel modules to Linux so that the modules can be placed anywhere in the 64-bit virtual address space and at any distance apart from each other. Second, we enable continuous KASLR re-randomization for Linux kernel modules by leveraging the position-independent model. Both contributions increase the entropy and reduce the chance of successful ROP attacks. Since prior art tackles only user-space programs, we also solve a number of challenges unique to the kernel code. We demonstrate the mechanism and the generality of our proposed re-randomization technique using several different, widely used device drivers, compiled as re-randomizable modules. Our experimental evaluation shows that the overhead of position-independent code is very low. Likewise, the cost of re-randomization is also small even at very high re-randomization frequencies.
Morris, David Dry. "Randomization analysis of experimental designs under non standard conditions". Diss., Virginia Polytechnic Institute and State University, 1987. http://hdl.handle.net/10919/53649.
Pełny tekst źródłaPh. D.
MARTELOTTE, MARCELA COHEN. "USING LINEAR MIXED MODELS ON DATA FROM EXPERIMENTS WITH RESTRICTION IN RANDOMIZATION". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2010. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=16422@1.
Pełny tekst źródłaEsta dissertação trata da aplicação de modelos lineares mistos em dados provenientes de experimentos com restrição na aleatorização. O experimento utilizado neste trabalho teve como finalidade verificar quais eram os fatores de controle do processo de laminação a frio que mais afetavam a espessura do material utilizado na fabricação das latas para bebidas carbonatadas. A partir do experimento, foram obtidos dados para modelar a média e a variância da espessura do material. O objetivo da modelagem era identificar quais fatores faziam com que a espessura média atingisse o valor desejado (0,248 mm). Além disso, era necessário identificar qual a combinação dos níveis desses fatores que produzia a variância mínima na espessura do material. Houve replicações neste experimento, mas estas não foram executadas de forma aleatória, e, além disso, os níveis dos fatores utilizados não foram reinicializados, nas rodadas do experimento. Devido a estas restrições, foram utilizados modelos mistos para o ajuste da média, e da variância, da espessura, uma vez que com tais modelos é possível trabalhar na presença de dados auto-correlacionados e heterocedásticos. Os modelos mostraram uma boa adequação aos dados, indicando que para situações onde existe restrição na aleatorização, a utilização de modelos mistos se mostra apropriada.
This dissertation presents an application of linear mixed models on data from an experiment with restriction in randomization. The experiment used in this study was aimed to verify which were the controlling factors, in the cold-rolling process, that most affected the thickness of the material used in the carbonated beverages market segment. From the experiment, data were obtained to model the mean and variance of the thickness of the material. The goal of modeling was to identify which factors were significant for the thickness reaches the desired value (0.248 mm). Furthermore, it was necessary to identify which combination of levels, of these factors, produced the minimum variance in the thickness of the material. There were replications of this experiment, but these were not performed randomly. In addition, the levels of factors used were not restarted during the trials. Due to these limitations, mixed models were used to adjust the mean and the variance of the thickness. The models showed a good fit to the data, indicating that for situations where there is restriction on randomization, the use of mixed models is suitable.
Hossain, Mohammad Zakir. "A small-sample randomization-based approach to semi-parametric estimation and misspecification in generalized linear mixed models". Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/24641.
Pełny tekst źródłaDi, Pace Brian S. "Site- and Location-Adjusted Approaches to Adaptive Allocation Clinical Trial Designs". VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/5706.
Pełny tekst źródłaJamal, Aygul. "A parallel iterative solver for large sparse linear systems enhanced with randomization and GPU accelerator, and its resilience to soft errors". Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS269/document.
Pełny tekst źródłaIn this PhD thesis, we address three challenges faced by linear algebra solvers in the perspective of future exascale systems: accelerating convergence using innovative techniques at the algorithm level, taking advantage of GPU (Graphics Processing Units) accelerators to enhance the performance of computations on hybrid CPU/GPU systems, evaluating the impact of errors in the context of an increasing level of parallelism in supercomputers. We are interested in studying methods that enable us to accelerate convergence and execution time of iterative solvers for large sparse linear systems. The solver specifically considered in this work is the parallel Algebraic Recursive Multilevel Solver (pARMS), which is a distributed-memory parallel solver based on Krylov subspace methods.First we integrate a randomization technique referred to as Random Butterfly Transformations (RBT) that has been successfully applied to remove the cost of pivoting in the solution of dense linear systems. Our objective is to apply this method in the ARMS preconditioner to solve more efficiently the last Schur complement system in the application of the recursive multilevel process in pARMS. The experimental results show an improvement of the convergence and the accuracy. Due to memory concerns for some test problems, we also propose to use a sparse variant of RBT followed by a sparse direct solver (SuperLU), resulting in an improvement of the execution time.Then we explain how a non intrusive approach can be applied to implement GPU computing into the pARMS solver, more especially for the local preconditioning phase that represents a significant part of the time to compute the solution. We compare the CPU-only and hybrid CPU/GPU variant of the solver on several test problems coming from physical applications. The performance results of the hybrid CPU/GPU solver using the ARMS preconditioning combined with RBT, or the ILU(0) preconditioning, show a performance gain of up to 30% on the test problems considered in our experiments.Finally we study the effect of soft fault errors on the convergence of the commonly used flexible GMRES (FGMRES) algorithm which is also used to solve the preconditioned system in pARMS. The test problem in our experiments is an elliptical PDE problem on a regular grid. We consider two types of preconditioners: an incomplete LU factorization with dual threshold (ILUT), and the ARMS preconditioner combined with RBT randomization. We consider two soft fault error modeling approaches where we perturb the matrix-vector multiplication and the application of the preconditioner, and we compare their potential impact on the convergence of the solver
Pokhilko, Victoria V. "Statistical Designs for Network A/B Testing". VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/6101.
Pełny tekst źródłaHan, Baoguang. "Statistical analysis of clinical trial data using Monte Carlo methods". Thesis, 2014. http://hdl.handle.net/1805/4650.
Pełny tekst źródłaIn medical research, data analysis often requires complex statistical methods where no closed-form solutions are available. Under such circumstances, Monte Carlo (MC) methods have found many applications. In this dissertation, we proposed several novel statistical models where MC methods are utilized. For the first part, we focused on semicompeting risks data in which a non-terminal event was subject to dependent censoring by a terminal event. Based on an illness-death multistate survival model, we proposed flexible random effects models. Further, we extended our model to the setting of joint modeling where both semicompeting risks data and repeated marker data are simultaneously analyzed. Since the proposed methods involve high-dimensional integrations, Bayesian Monte Carlo Markov Chain (MCMC) methods were utilized for estimation. The use of Bayesian methods also facilitates the prediction of individual patient outcomes. The proposed methods were demonstrated in both simulation and case studies. For the second part, we focused on re-randomization test, which is a nonparametric method that makes inferences solely based on the randomization procedure used in clinical trials. With this type of inference, Monte Carlo method is often used for generating null distributions on the treatment difference. However, an issue was recently discovered when subjects in a clinical trial were randomized with unbalanced treatment allocation to two treatments according to the minimization algorithm, a randomization procedure frequently used in practice. The null distribution of the re-randomization test statistics was found not to be centered at zero, which comprised power of the test. In this dissertation, we investigated the property of the re-randomization test and proposed a weighted re-randomization method to overcome this issue. The proposed method was demonstrated through extensive simulation studies.
Książki na temat "Moduli randomization"
Nelson, Trisalyn. Using conditional spatial randomization to identify insect infestation hot spots. Victoria, B.C: Pacific Forestry Centre, 2007.
Znajdź pełny tekst źródłaBianconi, Ginestra. Multilayer Network Models. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0010.
Pełny tekst źródłaPuranam, Phanish. Methodologies for Microstructures. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199672363.003.0009.
Pełny tekst źródłaCzęści książek na temat "Moduli randomization"
Ahlswede, Rudolf. "Identification Without Randomization". W Identification and Other Probabilistic Models, 83–101. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65072-8_4.
Pełny tekst źródłaPopkov, Yuri S., Alexey Yu Popkov, Yuri A. Dubnov i Alexander Yu Mazurov. "Randomized Parametric Models". W Entropy Randomization in Machine Learning, 113–56. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003306566-4.
Pełny tekst źródłaPopkov, Yuri S., Alexey Yu Popkov, Yuri A. Dubnov i Alexander Yu Mazurov. "Data Sources and Models". W Entropy Randomization in Machine Learning, 17–64. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003306566-2.
Pełny tekst źródłaPopkov, Yuri S., Alexey Yu Popkov, Yuri A. Dubnov i Alexander Yu Mazurov. "Entropy-Robust Estimation Procedures for Randomized Models and Measurement Noises". W Entropy Randomization in Machine Learning, 157–82. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003306566-5.
Pełny tekst źródłaMcArdle, John J., i John R. Nesselroade. "Notes on the inclusion of randomization in longitudinal studies." W Longitudinal data analysis using structural equation models., 323–27. Washington: American Psychological Association, 2014. http://dx.doi.org/10.1037/14440-030.
Pełny tekst źródłaDvir, Zeev, i Guangda Hu. "Matching-Vector Families and LDCs over Large Modulo". W Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, 513–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40328-6_36.
Pełny tekst źródłaFriedrich, Tobias, i Lionel Levine. "Fast Simulation of Large-Scale Growth Models". W Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, 555–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22935-0_47.
Pełny tekst źródłaLindemann, Christoph. "Employing The Randomization Technique for Solving Stochastic Petri Net Models". W Informatik-Fachberichte, 306–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76934-4_21.
Pełny tekst źródłaMathews, Ky L., i José Crossa. "Experimental Design for Plant Improvement". W Wheat Improvement, 215–35. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90673-3_13.
Pełny tekst źródłavon Eckardstein, Arnold. "High Density Lipoproteins: Is There a Comeback as a Therapeutic Target?" W Prevention and Treatment of Atherosclerosis, 157–200. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/164_2021_536.
Pełny tekst źródłaStreszczenia konferencji na temat "Moduli randomization"
Tobin, Josh, Lukas Biewald, Rocky Duan, Marcin Andrychowicz, Ankur Handa, Vikash Kumar, Bob McGrew i in. "Domain Randomization and Generative Models for Robotic Grasping". W 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. http://dx.doi.org/10.1109/iros.2018.8593933.
Pełny tekst źródłaShamsuddin, Abdul Fathaah, Abhijith P, Krupasankari Ragunathan, Deepak Raja Sekar P. M i Praveen Sankaran. "Domain Randomization on Deep Learning Models for Image Dehazing". W 2021 National Conference on Communications (NCC). IEEE, 2021. http://dx.doi.org/10.1109/ncc52529.2021.9530031.
Pełny tekst źródłaDeiab, Ibrahim M., i Mohamed A. Elbestawi. "Tribological Aspects of Workpiece/Fixture Contact in Machining Processes". W ASME 2002 International Mechanical Engineering Congress and Exposition. ASMEDC, 2002. http://dx.doi.org/10.1115/imece2002-39092.
Pełny tekst źródłaFeklisov, Egor, Mihail Zinderenko i Vladimir Frolov. "Procedural interior generation for artificial intelligence training and computer graphics". W International Conference "Computing for Physics and Technology - CPT2020". Bryansk State Technical University, 2020. http://dx.doi.org/10.30987/conferencearticle_5fce2771c14fa7.77481925.
Pełny tekst źródłaSanzharov, Vadim, Vladimir Frolov i Alexey Voloboy. "Variable photorealistic image synthesis for training dataset generation". W International Conference "Computing for Physics and Technology - CPT2020". Bryansk State Technical University, 2020. http://dx.doi.org/10.30987/conferencearticle_5fce27723872e5.04814843.
Pełny tekst źródłaSINGH, NAND KISHORE, KAZI ZAHIR UDDIN, RATNESHWAR JHA i BEHRAD KOOHBOR. "ANALYZING MICRO-MACRO TRANSITIONAL LENGTH SCALE IN UNIDIRECTIONAL COMPOSITES". W Thirty-sixth Technical Conference. Destech Publications, Inc., 2021. http://dx.doi.org/10.12783/asc36/35927.
Pełny tekst źródłaZhang, Wentai, Quan Chen, Can Koz, Liuyue Xie, Amit Regmi, Soji Yamakawa, Tomotake Furuhata, Kenji Shimada i Levent Burak Kara. "Data Augmentation of Engineering Drawings for Data-Driven Component Segmentation". W ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-91043.
Pełny tekst źródłaTsai, Cheng-Han (Lance), i Jen-Yuan (James) Chang. "A New Approach to Enhance Artificial Intelligence for Robot Picking System Using Auto Picking Point Annotation". W ASME 2021 30th Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/isps2021-65218.
Pełny tekst źródłaMurthy, Raghavendra, Marc P. Mignolet i Aly El-Shafei. "Nonparametric Stochastic Modeling of Uncertainty in Rotordynamics". W ASME Turbo Expo 2009: Power for Land, Sea, and Air. ASMEDC, 2009. http://dx.doi.org/10.1115/gt2009-59700.
Pełny tekst źródłaPecci, Filippo, Ivan Stoianov i Avi Ostfeld. "Optimal Design-for-Control of Water Distribution Networks via Convex Relaxation". W 2nd WDSA/CCWI Joint Conference. València: Editorial Universitat Politècnica de València, 2022. http://dx.doi.org/10.4995/wdsa-ccwi2022.2022.14267.
Pełny tekst źródłaRaporty organizacyjne na temat "Moduli randomization"
Hauer, Klaus, Ilona Dutzi, Christian Werner, Jürgen M. Bauer i Phoebe Ullrich. Implementation of intervention programs specifically tailored for patients with CI in early rehabilitation during acute hospitalization: a scoping review protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, październik 2022. http://dx.doi.org/10.37766/inplasy2022.10.0067.
Pełny tekst źródłaReimer, David, Astrid Olsen, Bent Sortkær i Rie Thomsen. Reducing inequality in access to Higher Education in Denmark: Technical report for Nextstep 1.0 intervention and data collection. Aarhus University, styczeń 2024. http://dx.doi.org/10.7146/aul.511.
Pełny tekst źródła