523 skills found · Page 5 of 18
TheCodeTraveler / UITestSampleAppA sample app to demonstrate how to create Xamarin UITests using the Page Object architecture, Backdoor Methods and App Links (aka Deep Linking)
MJfadeaway / DASDAS: A deep adaptive sampling method for solving high-dimensional partial differential equations
ClintJang / Sample Swift Wkwebview Javascript Bridge And Schemewkwebview로 javascirpt bridge 방식과 url을 scheme 하는 방식을 셈플링했습니다. (I have tried to sample the method of javascirpt bridge and url scheme with wkwebview.)
AlbertMillan / Adversarial Training PytorchImplementation of adversarial training under fast-gradient sign method (FGSM), projected gradient descent (PGD) and CW using Wide-ResNet-28-10 on cifar-10. Sample code is re-usable despite changing the model or dataset.
SteiMi / DenseweightThe imbalanced regression method DenseWeight produces sample weights for data points in regression tasks so that there is a higher emphasis on ML model performance for rare (and often extreme) data points in comparison to common data points. This repository provides a Python package with which one can easily use DenseWeight.
jeremybytes / Using TaskSample project to show how to consume async methods / methods that return Task
JuliaDynamics / StreamSampling.jlSampling methods for data streams
yancy-zh / CameraAutoFocus本项目提供37种不同的图像卷积算子，用来评估图像的锐度、清晰度，并给出了比较不同算子和算法的方法，该方法可高效、准确的找到合适的图像清晰度评估算法，来适用于不同应用、不同类型和内容的图片，并根据最佳清晰度确定相机镜头的调焦角度。 This project provides a fast tool as well as the source code to find the best metric to adjust the camera focus that can be used in your camera based application. Not only does it decribe the advantages of each method and their comparison in evaluation the lens of your products' camera hardware, but also the sample results, validation patterns, images of the patterns under continous changing lens rotation angles are provided and visualized. The evaluation contains characterization of the lenses with respect to Focus Of View, image quality, and deformations caused due to misaligned lenses or lens characteristics. Meanwhile, different camera autofocus algorithms are compared and assessed for the suitability of the specific patterns and purposes.
catrujilla / PyDHMA Python library to numerically recover the complex wavefield information of samples from Digital Holographic Microscopy (DHM) recordings. Phase compensation, phase-shifting methods, and numerical propagators for different configurations and types of recordings in DHM are provided.
idanstei / Superiorized Photo Acoustic Non NEgative Reconstruction For Clinical Photoacoustic ImagingPhotoacoustic (PA) imaging can revolutionize medical ultrasound by augmenting it with molecular information. However, clinical translation of PA imaging remains a challenge due to the limited viewing angles and imaging depth. Described here is a new robust algorithm called Superiorized Photo-Acoustic Non-NEgative Reconstruction (SPANNER), designed to reconstruct PA images in real-time and to address these limitations. The method utilizes precise forward modeling of the PA propagation and reception of signals while accounting for the effects of acoustic absorption, element size, shape, and sensitivity, as well as the transducer's impulse response and directivity pattern. A fast superiorized conjugate gradient algorithm is used for inversion. SPANNER is compared to three reconstruction algorithms: delay-and-sum (DAS), universal back-projection (UBP), and model-based reconstruction (MBR). All four algorithms are applied to both simulations and experimental data acquired from tissue-mimicking phantoms, ex vivo tissue samples, and in vivo imaging of the prostates in patients. Simulations and phantom experiments highlight the ability of SPANNER to improve contrast to background ratio by up to 20 dB compared to all other algorithms, as well as a 3-fold increase in axial resolution compared to DAS and UBP. Applying SPANNER on contrast-enhanced PA images acquired from prostate cancer patients yielded a statistically significant difference before and after contrast agent administration, while the other three image reconstruction methods did not, thus highlighting SPANNER's performance in differentiating intrinsic from extrinsic PA signals and its ability to quantify PA signals from the contrast agent more accurately.
EEA-sensors / Sequential Parallelization ExamplesThis is a collection of code samples aimed at illustrating temporal parallelization methods for sequential data.
nkantas / LTCC Advanced Computational Methods In StatisticsAdvanced LTCC course in StatisticsThis course will provide an overview of Monte Carlo methods when used for problems in Statistics. After an introduction to simulation, its purpose and challenges, we will cover in more detail Importance Sampling, Markov Chain Monte Carlo and Sequential Monte Carlo. Whilst the main focus will be on the methodology and its relevance to applications, we will often mention relevant theoretical results and their importance for problems in practice.
JeroenMols / MethodCountExampleSample project to shrink huge libraries and reduce their method count.
joerglang / IDesign VirtualTradeMeA sample solution showing the project layout of a Method based design. Sample helps in understanding namespace and folder structure guidelines as propesed by IDesign.
tdunning / Feature ExtractionSample techniques for a variety of feature extraction methods
ShiguiLi / EVODiff[NeurIPS 2025🔥:] EVODiff is an inference-time refinement method for diffusion models that improves sampling efficiency and generative fidelity by systematically reducing conditional entropy, without relying on reference trajectories.
keihirano / Modern SamplingCourse Materials for AEA Short Course on Modern Sampling Methods
fritzsedlazeck / SVCollectorMethod to optimally select samples for validation and resequencing
shreyansh26 / LLM SamplingA collection of various LLM sampling methods implemented in pure Pytorch
indieweb / Indieauth Client PhpSample implementation and helper methods for an IndieAuth client.
