Relevant bibliographies by topics / Maximum Worst Case Entropy Selector

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Academic literature on the topic 'Maximum Worst Case Entropy Selector'

Author: Grafiati
Published: 14 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Maximum Worst Case Entropy Selector"

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ADDABBO, T., M. ALIOTO, A. FORT, S. ROCCHI, and V. VIGNOLI. "A VARIABILITY-TOLERANT FEEDBACK TECHNIQUE FOR THROUGHPUT MAXIMIZATION OF TRBGs WITH PREDEFINED ENTROPY." Journal of Circuits, Systems and Computers 19, no. 04 (2010): 879–95. http://dx.doi.org/10.1142/s0218126610006505.

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In this paper a probabilistic feedback technique to maximize the throughput of a generic True Random Bit Generator (TRBG) circuit, under a given constraint on the entropy, is discussed. In the proposed solution, the throughput of the device is dynamically and adaptively varied by an on-line entropy detector, such to obtain, with an arbitrary confidence level, an entropy greater than a given worst-case value. The approach, which has a general validity, introduces a method for making maximum use of the TRBG random bit generation capabilities, maximizing the generation throughput while preserving
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Nonato, Raphael Basilio Pires. "Multi-level Uncertain Fatigue Analysis of a Truss under Incomplete Available Information." Frattura ed Integrità Strutturale 17, no. 66 (2023): 17–37. http://dx.doi.org/10.3221/igf-esis.66.02.

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We predict the fatigue life of a planar tubular truss when geometrical parameters, material properties, and live loads are non-deterministic. A multi-level calculation uncertainty quantification framework code was designed to aggregate the finite element method and fatigue-induced sequential failures. Due to the incompleteness of the aleatory-type inputs, the maximum entropy principle was applied. Two sensitivity analyses were performed to report the most influencing factors. In terms of variance, the results suggest that the slope of the curve crack growth rate × stress intensity factor range
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Lee, Doyup, Yeongjae Cheon, and Wook-Shin Han. "Regularizing Attention Networks for Anomaly Detection in Visual Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (2021): 1845–53. http://dx.doi.org/10.1609/aaai.v35i3.16279.

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For stability and reliability of real-world applications, the robustness of DNNs in unimodal tasks has been evaluated. However, few studies consider abnormal situations that a visual question answering (VQA) model might encounter at test time after deployment in the real-world. In this study, we evaluate the robustness of state-of-the-art VQA models to five different anomalies, including worst-case scenarios, the most frequent scenarios, and the current limitation of VQA models. Different from the results in unimodal tasks, the maximum confidence of answers in VQA models cannot detect anomalou
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Zhou, Xingzhi, Zhiliang Tian, Boyang Zhang, et al. "Test-Time Adaptation on Noisy Data via Model-Pruning-Based Filtering and Flatness-Aware Entropy Minimization." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 10852–60. https://doi.org/10.1609/aaai.v39i10.33179.

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Test-time adaptation (TTA) deals with domain shifts during inference by training models based on only unlabeled test samples. Test samples may include noisy samples, which degrade domain adaptation. Existing methods rely on the model's output prediction to detect and filter noisy samples, and further search for flat regions during optimization, which makes the optimization more robust on noisy samples. However, there are two issues: (1) the output prediction tends to be inaccurate due to domain shifts, weakening noisy-sample detection; (2) current approaches for searching flat regions focus on
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Condro, Aryo Adhi, Lilik Budi Prasetyo, Siti Badriyah Rushayati, I. Putu Santikayasa, and Entang Iskandar. "Predicting Hotspots and Prioritizing Protected Areas for Endangered Primate Species in Indonesia under Changing Climate." Biology 10, no. 2 (2021): 154. http://dx.doi.org/10.3390/biology10020154.

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Indonesia has a large number of primate diversity where a majority of the species are threatened. In addition, climate change is conservation issues that biodiversity may likely face in the future, particularly among primates. Thus, species-distribution modeling was useful for conservation planning. Herein, we present protected areas (PA) recommendations with high nature-conservation importance based on species-richness changes. We performed maximum entropy (Maxent) to retrieve species distribution of 51 primate species across Indonesia. We calculated species-richness change and range shifts t
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Saputra, Muhammad Hadi, Sutomo, Eko Pujiono, et al. "The Prediction of Locusta migratoria (Linnaeus, 1758) Outbreak under Climate Change Scenario in Indonesia." Media Konservasi 29, no. 4 (2024): 583. http://dx.doi.org/10.29244/medkon.29.4.583.

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Locusta migratoria (Linnaeus, 1758) is one of the locusts known as important pests of food crops. Outbreaks of this species can cause catastrophic damage to maize, paddy, and many other crops. A species distribution model was used to identify the probability of the locust's current and future potential distribution in the Indonesian archipelago. The study relied on the machine learning method Maximum Entropy (Maxent) Model to forecast the future spread of the species in the Indonesian archipelago and to find the climate variable that influenced the distribution of Locusta migratoria. The resul
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Diamond, Phil, Peter Kloeden, and Igor Vladimirov. "Mean anisotropy of homogeneous Gaussian random fields and anisotropic norms of linear translation-invariant operators on multidimensional integer lattices." Journal of Applied Mathematics and Stochastic Analysis 16, no. 3 (2003): 209–31. http://dx.doi.org/10.1155/s1048953303000169.

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Sensitivity of output of a linear operator to its input can be quantified in various ways. In Control Theory, the input is usually interpreted as disturbance and the output is to be minimized in some sense. In stochastic worst-case design settings, the disturbance is considered random with imprecisely known probability distribution. The prior set of probability measures can be chosen so as to quantify how far the disturbance deviates from the white-noise hypothesis of Linear Quadratic Gaussian control. Such deviation can be measured by the minimal Kullback-Leibler informational divergence from
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Nanda, Aditya, Puneet Singla, and M. Amin Karami. "Conjugate unscented transformation–based uncertainty analysis of energy harvesters." Journal of Intelligent Material Systems and Structures 29, no. 18 (2018): 3614–33. http://dx.doi.org/10.1177/1045389x18798945.

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This article presents a probabilistic approach to investigate the effect of parametric uncertainties on the mean power, tip deflection, and tip velocity of linear and nonlinear energy harvesting systems. Recently developed conjugate unscented transformation algorithm is used to compute the statistical moments of the output variables with multidimensional Gaussian uncertainty in parameters. The principle of maximum entropy is used to construct the probability density function of output variables from the knowledge of obtained statistical moments. The probability density functions for mean power
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Arabameri, Alireza, Thomas Blaschke, Biswajeet Pradhan, Hamid Reza Pourghasemi, John P. Tiefenbacher, and Dieu Tien Bui. "Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study." Sensors 20, no. 2 (2020): 335. http://dx.doi.org/10.3390/s20020335.

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Gully erosion is a problem; therefore, it must be predicted using highly accurate predictive models to avoid losses caused by gully development and to guarantee sustainable development. This research investigates the predictive performance of seven multiple-criteria decision-making (MCDM), statistical, and machine learning (ML)-based models and their ensembles for gully erosion susceptibility mapping (GESM). A case study of the Dasjard River watershed, Iran uses a database of 306 gully head cuts and 15 conditioning factors. The database was divided 70:30 to train and verify the models. Their p
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Safabakhshpachehkenari, Mohammadreza, Hideki Tsubomatsu, and Hideyuki Tonooka. "Japan’s Urban-Environmental Exposures: A Tripartite Analysis of City Shrinkage, SAR-Based Deep Learning Versus Forward Modeling in Inundation Mapping, and Future Flood Schemes." Urban Science 9, no. 3 (2025): 71. https://doi.org/10.3390/urbansci9030071.

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This study investigates how urban decline and intensifying flood hazards interact to threaten Japan’s urban environments, focusing on three main dimensions. First, a fine-scale analysis of spatial shrinkage was conducted using transition potential maps generated with a maximum entropy classifier. This approach enabled the identification of neighborhoods at high risk of future abandonment, revealing that peripheral districts, such as Hirakue-cho and Shimoirino-cho, are especially susceptible due to their distance from central amenities. Second, this study analyzed the 2019 Naka River flood indu
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Dissertations / Theses on the topic "Maximum Worst Case Entropy Selector"

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MORETTI, RICCARDO. "Digital Nonlinear Oscillators: A Novel Class of Circuits for the Design of Entropy Sources in Programmable Logic Devices." Doctoral thesis, Università di Siena, 2021. http://hdl.handle.net/11365/1144376.

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In recent years, cybersecurity is gaining more and more importance. Cryptography is used in numerous applications, such as authentication and encryption of data in communications, access control to restricted or protected areas, electronic payments. It is safe to assume that the presence of cryptographic systems in future technologies will become increasingly pervasive, leading to a greater demand for energy efficiency, hardware reliability, integration, portability, and security. However, this pervasiveness introduces new challenges: the implementation of conventional cryptographic standard
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