computational algorithms play a crucial role in shaping the aesthetic impact of animated visual effects (VFX). This abstract explores the intersection of technical algorithms and artistic expression in the realm of VF...
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computational algorithms play a crucial role in shaping the aesthetic impact of animated visual effects (VFX). This abstract explores the intersection of technical algorithms and artistic expression in the realm of VFX, focusing on their collaborative role in modern animation. The integration of computational algorithms enhances the realism and creativity of animated VFX. Advanced rendering techniques such as ray tracing and global illumination algorithms simulate light behavior with high fidelity, achieving photorealistic effects that captivate audiences. Procedural algorithms generate complex geometries and textures efficiently, facilitating the creation of intricate and visually appealing animations. Beyond technical fidelity, algorithms also enable novel artistic expressions. Generative algorithms, for instance, allow animators to explore unconventional designs and styles, pushing the boundaries of visual storytelling. algorithms in simulation contribute to the lifelike movement of characters and elements, enhancing emotional engagement and narrative impact. The synergy between computational algorithms and artistic vision in animated VFX underscores their transformative impact on contemporary animation. This abstract highlights the essential role of algorithms in fostering innovation and creativity, advancing the field of animated visual effects into new realms of aesthetic possibility.
computational algorithms and automated decision making systems that include them offer potential to improve public policy and organizations. But computational algorithms based on biased data encode those biases into a...
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computational algorithms and automated decision making systems that include them offer potential to improve public policy and organizations. But computational algorithms based on biased data encode those biases into algorithms, models and their outputs. Systemic racism is institutionalized bias with respect to race, ethnicity and related attributes. Such bias is located in data that encode the results and outputs of decisions that have been discriminatory, in procedures and processes that may intentionally or unintentionally disadvantage people based on race, and in policies that may discriminate by race. computational algorithms may exacerbate systemic racism if they are not designed, developed, and used-that is, enacted-with attention to identifying and remedying bias specific to race. Advancing social equity in digital governance requires systematic, ongoing efforts to assure that automated decision making systems, and their enactment in complex public organizational arrangements, are free from bias.
CpG islands are generally known as the epigenetic regulatory regions in accordance with histone modifications, methylation, and promoter activity. There is a significant need for the exact mapping of DNA methylation i...
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CpG islands are generally known as the epigenetic regulatory regions in accordance with histone modifications, methylation, and promoter activity. There is a significant need for the exact mapping of DNA methylation in CpG islands to understand the diverse biological functions. However, the precise identification of CpG islands from the whole genome through experimental and computational approaches is still challenging. Numerous computational methods are being developed to detect the CpG-enriched regions, effectively, to reduce the time and cost of the experiments. Here, we review some of the latest computational CpG detection methods that utilize clustering, patterns and physical-distance like parameters for CpG island detection. The comparative analyses of the methods relying on different principles and parameters allow prioritizing the algorithms for specific CpG associated datasets to achieve higher accuracy and sensitivity. A number of computational tools based on the window, Hidden Markov Model, density and distance-/length-based algorithms are being applied on human or mammalian genomes for accurate CpG detection. Comparative analyses of CpG island detection algorithms facilitate to prefer the method according to the target genome and required parameters to attain higher accuracy, specificity, and performance. There is still a need for efficient computational CpG detection methods with lower false-positive results. This review provides a better understanding about the principles of tools that will assist to prioritize and develop the algorithms for accurate CpG islands detection.
Background: Pathology report defects refer to errors in the pathology reports, such as transcription/voice recognition errors and incorrect nondiagnostic information. Examples of the latter include incorrect gender, i...
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This talk will review new technologies and trends in artificial intelligence (AI) from algorithmic, data modeling, and applications perspective. Subjects include algorithms and technologies being developed from specif...
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This talk will review new technologies and trends in artificial intelligence (AI) from algorithmic, data modeling, and applications perspective. Subjects include algorithms and technologies being developed from specific AI to general AI, explainable AI, small sample learning and zero-shot learning, non-deep-structure learning, and computational brain science. Also, design of reliable and robust AI algorithms and its applications will be discussed.
Layered queueing networks (LQNs) are an extension of ordinary queueing networks useful to model simultaneous resource possession and stochastic call graphs in distributed systems. Existing computational algorithms for...
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Layered queueing networks (LQNs) are an extension of ordinary queueing networks useful to model simultaneous resource possession and stochastic call graphs in distributed systems. Existing computational algorithms for LQNs have primarily focused on mean-value analysis. However, other solution paradigms, such as normalizing constant analysis and mean-field approximation, can improve the computation of LQN mean and transient performance metrics, state probabilities, and response time distributions. Motivated by this observation, we propose the first LQN meta-solver, called LN, that allows for the dynamic selection of the performance analysis paradigm to be iteratively applied to the submodels arising from layer decomposition. We report experimentswhere this added flexibility helps us to reduce the LQN solution errors. We also demonstrate that the meta-solver approach eases the integration of LQNs with other formalisms, such as caching models, enabling the analysis of more general classes of layered stochastic networks. Additionally, to support the accurate evaluation of the LQN submodels, we develop novel algorithms for homogeneous queueing networks consisting of an infinite server node and a set of identical queueing stations. In particular, we propose an exact method of moment algorithms, integration techniques for normalizing constants, and a fast non-iterative mean-value analysis technique.
This research article introduces a hybrid optimization algorithm, referred to as Grey Wolf Optimizer-Teaching Learning Based Optimization (GWO-TLBO), which extends the Grey Wolf Optimizer (GWO) by integrating it with ...
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This research article introduces a hybrid optimization algorithm, referred to as Grey Wolf Optimizer-Teaching Learning Based Optimization (GWO-TLBO), which extends the Grey Wolf Optimizer (GWO) by integrating it with Teaching-Learning-Based Optimization (TLBO). The benefit of GWO is that it explores potential solutions in a way similar to how grey wolves hunt, but the challenge with this approach comes during fine-tuning, where the algorithm settles too early on suboptimal results. This weakness can be compensated by integrating TLBO method into the algorithm to improve its search power of solutions as in teaches students how to learn and teachers are knowledge felicitator. GWO-TLBO algorithm was applied for several benchmark optimization problems to evaluate its effectiveness in simple to complex scenarios. It is also faster, more accurate and reliable when compare to other existing optimization algorithms. This novel approach achieves a balance between exploration and exploitation, demonstrating adaptability in identifying new solutions but also quickly zoom in on (near) global optima: this renders it a reliable choice for challenging optimization problems according to the analysis and results.
Accurate fire risk prediction is crucial to mitigate the significant threats of wildfires to ecosystems, human life, and property. This article reviews various computational algorithms for predicting the risk of wildf...
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Accurate fire risk prediction is crucial to mitigate the significant threats of wildfires to ecosystems, human life, and property. This article reviews various computational algorithms for predicting the risk of wildfires, highlighting their methodologies, related work, and findings. Key algorithms include Logistic Regression, Random Forest, Artificial Neural Networks, and Deep Learning models such as Convolutional Neural Networks and Long Short-Term Memory Neural Networks. The review synthesizes contributions from numerous studies to provide a comprehensive overview of the state of the art in predicting the risk of wildfires. Advances in computational technologies and the availability of large volumes of data have enabled the development of more accurate algorithms to predict and assess fire risk more accurately. Wildfires exert catastrophic effects on natural ecosystems, air quality, and human health, and the increasing frequency and intensity of these fires, intensified by climate change, underscores the need for more accurate predictive models to mitigate their impacts.
The primary objective of this paper is to develop a new method for root-finding by combining forward and finite-difference techniques in order to provide an efficient, derivative-free algorithm with a lower processing...
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The primary objective of this paper is to develop a new method for root-finding by combining forward and finite-difference techniques in order to provide an efficient, derivative-free algorithm with a lower processing cost per iteration. This will be accomplished by combining forward and finite-difference techniques. We also detail the convergence criterion that was devised for the root-finding approach, and we show that the method that was recommended is quintic-order convergent. We addressed a few engineering issues in order to illustrate the validity and application of the developed root-finding algorithm. The quantitative results justified the constructed root-finding algorithm's robust performance in comparison to other quintic-order methods that can be found in the literature. For the graphical analysis, we make use of the newly discovered method to plot some novel polynomiographs that are attractive to the eye, and then we evaluate these new plots in relation to previously established quintic-order root-finding strategies. The graphic analysis demonstrates that the newly created method for root-finding has better convergence with the larger area than the other comparable methods do.
Monte Carlo sampling methods are the standard procedure for approximating complicated integrals of multidimensional posterior distributions in Bayesian inference. In this work, we focus on the class of layered adaptiv...
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Monte Carlo sampling methods are the standard procedure for approximating complicated integrals of multidimensional posterior distributions in Bayesian inference. In this work, we focus on the class of layered adaptive importance sampling algorithms, which is a fam-ily of adaptive importance samplers where Markov chain Monte Carlo algorithms are em-ployed to drive an underlying multiple importance sampling scheme. The modular nature of the layered adaptive importance sampling scheme allows for different possible imple-mentations, yielding a variety of different performances and computational costs. In this work, we propose different enhancements of the classical layered adaptive importance sampling setting in order to increase the efficiency and reduce the computational cost, of both upper and lower layers. The different variants address computational challenges arising in real-world applications, for instance with highly concentrated posterior distri-butions. Furthermore, we introduce different strategies for designing cheaper schemes, for instance, recycling samples generated in the upper layer and using them in the final esti-mators in the lower layer. Different numerical experiments show the benefits of the pro-posed schemes, comparing with benchmark methods presented in the literature, and in several challenging scenarios. (c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://***/licenses/by-nc-nd/4.0/ )
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