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18 results for “robust optimization”
An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization
<p>This is the data and source code used in the paper below:</p> <p>Sibghat Ullah, Hao Wang, Stefan Menzel, Bernhard Sendhoff and Thomas Bäck, “An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization”, in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 6-9 December 2019, doi: 10.1109/SSCI44817.2019.9002805</p> <p>This research investigates the potential of using meta-modeling techniques in the context of robust optimization namely optimization under uncertainty/noise. A systematic empirical comparison is performed for evaluating and comparing different meta-modeling techniques for robust optimization. The experimental setup includes three noise levels, six meta-modeling algorithms, and six benchmark problems from the continuous optimization domain, each for three different dimensionalities. Two robustness definitions: robust regularization and robust composition, are used in the experiments. The meta-modeling techniques are evaluated and compared with respect to the modeling accuracy and the optimal function values. The results clearly show that Kriging, Support Vector Machine and Polynomial regression perform excellently as they achieve high accuracy and the optimal point on the model landscape is close to the true optimum of test functions in most cases.</p>
Figure 6. (a1), (a2), (a3), (a4), (a5), (a6), (a7) and (a8) watermarked image is degraded respectively through JPEG2000 compression, JPEG compression, median filtering, adding Salt&Pepper noise, rotating, center cropping, surrounding cropping and scaling. (b1), (b2), (b3), (b4), (b5), (b6), (b7) and (b8) The corresponding extracted watermarks.-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>This paper has described a scheme for digital watermarking of still images based on discrete<br> wavelet transform. In the proposed method, the embedded logo watermark can be extracted without<br> access to the original image. It has been confirmed that the proposed watermarking method is able<br> to extract the embedded logo watermark from the watermarked images that have degraded through<br> compression, filtering, cropping and scaling. Although this algorithm is not robust against rotation,<br> it can completely extract the watermark from watermarked images that lose about 35% of their<br> areas by cropping attack.</p>
Figure 1. (a) Original watermark (b) extracted watermarks after compression(c) merged watermark-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>Therefore, each bit of the logo watermark is stored in one coefficient of a sub-block to keep<br> the capacity of watermarking fixed.<br> When a region of the watermarked image is destroyed; the whole watermark can be<br> extracted using other regions of the watermarked image by merging extracted watermarks. Figure 1<br> shows result of merging logo watermarks that were extracted from a compressed (with JPEG2000<br> algorithm) watermarked image.</p>
Figure 4. (a) The original "Hookah" image (b) Watermarked "Hookah" with Q=35 (c) The original "Baby" image (d) Watermarked "Baby" with Q=35-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>A set of distortions is applied to the watermarked image and the watermark is extracted from<br> the distorted image. We used bit correct rate (BCR) to evaluate our proposed algorithm and it is<br> calculated from the following equation [6].</p>
Figure 2. LL2 sub-band is divided into sub-block-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>In the following experiments, two gray-level images with size of 512 by 512, “Baby” and<br> “Hookah” are the test images. The binary image “IAU” with size of 32 by 32 is used in our<br> simulations as a watermark. Figure 3 shows the watermark. In the experiments Haar wavelet filter<br> was used for discrete wavelet transform. The level of wavelet decomposition (n) and the number of<br> sub-blocks (K) were also assumed to be 2 and 16 respectively.<br> The proposed watermarking algorithm is evaluated from the point view of embedded<br> watermark transparency and robustness; the result of each is shown in next two sections.</p>
Benchmark Instances for Robust Combinatorial Optimization with Budgeted Uncertainty
<p>We provide test instances for robust combinatorial optimization with budget uncertainty in the objective function.<br> The set contains nominal problems from the MIPLIB 2017 that have been converted into robust problems and instances of the robust knapsack problem. Both problem sets have been described and used for benchmarking in the paper "A Branch & Bound Algorithm for Robust Binary Optimization with Budget Uncertainty", published in Mathematical Programming Computation by Christina Büsing, Timo Gersing and Arie Koster.<br> Furthermore, we provide instances for robust weighted matching on bipartite graphs and robust weighted independent set. The latter are based on graphs for the clique problem of the second DIMACS implementation challenge (1993). Both problem sets have been described and used for benchmarking in the paper "Recycling Inequalities for Robust Combinatorial Optimization with Budget Uncertainty", presented at IPCO 2023 by the same authors.</p> <p> </p> <p>Paper "A Branch & Bound Algorithm for Robust Binary Optimization with Budget Uncertainty": <a href="https://doi.org/10.1007/s12532-022-00232-2"> https://doi.org/10.1007/s12532-022-00232-2</a><br> Paper "Recycling Inequalities for Robust Combinatorial Optimization with Budget Uncertainty": <a href="https://doi.org/10.1007/978-3-031-32726-1_5">https://doi.org/10.1007/978-3-031-32726-1_5</a><br> For algorithms solving these problems see: <a href="https://doi.org/10.5281/zenodo.7463371">https://doi.org/10.5281/zenodo.7463371</a></p>
Robust evidence for bats as reservoir hosts is lacking in most African virus studies – a review and call to optimize sampling and conserve bats
<p><span>Africa experiences frequent emerging disease outbreaks among humans, with bats often proposed as zoonotic pathogen hosts. We comprehensively reviewed virus-bat findings from papers published between 1978 and 2020 to evaluate the evidence that African bats are reservoirs and/or bridging hosts for viruses that cause human disease. We present data from 162 papers (of 1322) with original findings on (1) numbers and species of bats sampled across bat families and the continent, (2) how bats were selected for study inclusion, (3) if bats were terminally sampled, (4) what types of ecological data, if any, were recorded, and (5) which viruses were detected and with what methodology. We propose a scheme for evaluating presumed virus-host relationships by evidence type and quality, using the contrasting available evidence for </span><em>Orthoebolavirus</em> (formerly <em>Ebolavirus</em>) versus <em>Orthomarburgvirus</em> (formerly <em>Marburgvirus</em>) as an example. We review the wording in abstracts and discussions of all 162 papers, identifying key framing terms, how these refer to findings, and how they might contribute to people's beliefs about bats. We discuss the impact of scientific research communication on public perception and emphasize the need for strategies that minimize human-bat conflict and support bat conservation. Finally, we make recommendations for best practices that will improve virological study metadata.</p>
Code and data for Nguyen Le et al. "Robust optimal control of interacting multi-qubit systems for quantum sensing"
<p>This is the code and simulation data for the paper "Robust optimal control of interacting multi-qubit systems for quantum sensing" by Nguyen Le et al.</p>
Supplemental Data for "Adaptive Container Service: a New Paradigm for Robust and Optimized Bioinformatics Workflow Deployment in the Cloud."
<p>All supplemental data for "Adaptive Container Service: a New Paradigm for Robust and Optimized Bioinformatics Workflow Deployment in the Cloud."<br><br>Abstract:<br>We propose Adaptive Container Service (ACS), a new paradigm for deploying bioinformatics workflows in cloud computing environments. By encapsulating the entire workflow within a single virtual container, combined with automatic workflow checkpointing and dynamic migration to appropriately scaled containers, ACS-based deployment demonstrates several key advantages over alternative strategies: it enables optimal resource provision to any workflow that comprise of multiple applications with diverse computing needs; it provides protection against application-agnostic out-of-memory (OOM) errors or spot instance interruptions; and it reduces efforts required for workflow development, optimization, and management because it runs workflows with minimal or no code modifications. Proof-of-concept experiments show that ACS avoided both under- and over-provisioning in monolithic single-container deployment. Despite being deployed as a single container, it achieved comparable resource utilization efficiency as optimized Nextflow-managed, multi-modular workflows. Analysis of over 18,000 workflow runs demonstrated that ACS can effectively reduce workflow failures by two-thirds. These findings suggest that ACS frees developers from navigating the complexity of deploying robust workflows and rightsizing compute resources in the cloud, leading to significant reduction in workflow development time and savings in cloud computing costs.<br><br>Contains the following directories:<br>Fig2-bbtools: running metrics for BBTools<br>Fig3-rna-seq: running metrics for RNA-Seq<br>Fig4-Job_records: meta data and running metrics of 18,000+ jobs</p>
Robust evidence for bats as reservoir hosts is lacking in most African virus studies – a review and call to optimize sampling and conserve bats
Open the record for dataset details and reuse information.
OPTIM-TUNE: In-flight automatic optimal tuning of UAV controllers for robust operation
<p>Tuning results, interplay between gains and allowable radius of a cylindric force constraint. Use PS-Tricks in LaTeX to obtain the final graphical results. </p>
Near-optimal robust bilevel linear instances
<p>This repository contains the bilevel instances used in the paper Near-Optimal Robust Bilevel Optimization (https://arxiv.org/abs/1908.04040). They come in four groups, small, medium, large and mips instances, with different numbers of variables and constraints as described in the paper.</p> <p>The MIPS instances are constructed from the MIPS/RANDOM instances from https://coral.ise.lehigh.edu/data-sets/bilevel-instances/</p> <p>The /data folder contains instances in JLD format, the DataReader folder contains a Julia project for reading the data.</p>
Generating Optimal Robust Continuous Piecewise Linear Regression with Outliers Through Combinatorial Benders Decomposition - Data Sets
<p>Data Sets for the Paper: "Generating Optimal Robust Continuous Piecewise Linear Regression with Outliers Through Combinatorial Benders Decomposition".</p>
Preconditioners for robust optimal control problems under uncertainty - numerical tests
<p>Codes and data of the numerical experiments described in the manuscript "Preconditioners for optimal control problems under uncertainty".</p>
Optimal network sizes for most robust Turing patterns
<p>Dataset for Figures 3 and 4.</p>
RNA-seq datasets for "A neuron-optimized CRISPR/dCas9 activation system for robust and specific gene regulation"
GEO Series GSE117961. Rattus norvegicus. 9 samples. Type: Expression profiling by high throughput sequencing.
Optimized Method for Robust Transcriptome Profiling of Minute Tissues Using Laser Capture Microdissection and Low-Input RNA-Seq
GEO Series GSE95257. Mus musculus. 7 samples. Type: Expression profiling by high throughput sequencing.
Robustness testing and optimization of an adverse outcome pathway on cholestatic liver injury
GEO Series GSE152494. Homo sapiens; Mus musculus. 36 samples. Type: Expression profiling by array.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.