colmap.github.io的 10 大竞争对手

rerun.io

How chunks are framed and serialized in .rrd files

calib.io

We are often asked questions relating to the type of poses that need to be captured for realiable and robust camera calibration. While the answer to this is quite complex and depends on many factors, such as lens, setup, capture constraints, pattern budget, detector noise, etc., there are some fundamental mathematical

cs.cmu.edu

School of Computer Science - Carnegie Mellon University

arxiv.org

We perform a behavioral experiment of karma, a class of mechanisms for repeated resource allocation with attractive fairness and efficiency properties, in theory. Individuals in these mechanisms bid non-tradable credits that flow from resource consumers to yielders. Human subjects recruited on Amazon MTurk are repeatedly and randomly paired to bid karma according to time-varying individual urgency to acquire resources. Treatments varied in the urgency process (frequent moderate versus sporadic high urgency) and the richness of the bidding scheme (binary versus full range). Benchmarked against random allocation, karma achieves an (almost) Pareto improvement, despite subjects deviating from the theoretically optimal Nash bidding policy: maximum improvement is attained by subjects deviating by up to one karma bid unit on average, and positive improvement with deviations of up to 3-4 units. These findings hold across all treatments, among which no significant differences are found, with the exception of the sporadic high urgency process with binary bidding being (weakly) favorable. They offer behaviorally robust lower bounds for karma's expected performance in human populations and guide its future implementation.

github.com

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gmd.copernicus.org

Abstract. Spatial predictions of total organic carbon (TOC) concentrations and stocks are crucial for understanding marine sediments’ role as a significant carbon sink in the global carbon cycle. In this study, we present a geospatial prediction of global TOC concentrations and stocks on a 5 × 5 arcmin grid, using a novel neural network approach. We also provide and apply a new compilation of over 21 000 global TOC measurements and a new set of predictors, including features such as seafloor lithologies, benthic oxygen fluxes, and chlorophyll-a satellite data. Moreover, we compare different machine learning models based on their performance metrics and predictions and assess their strengths and limitations. For the dataset used, we find that the performance metrics of the models are comparable and that the neural network approach outperforms, on unseen data, methods such as k-nearest neighbours and random forests, which tend to overfit the training data. We provide estimates of mean TOC concentrations and stocks, both on continental shelves and in deep-sea settings across various marine regions and oceans. Our model suggests that the upper 10 cm of oceanic sediments harbour approximately 156 Pg of TOC stocks and have a mean TOC concentration of 0.61 %. Furthermore, we introduce a standardized methodology for quantifying predictive uncertainty using Monte Carlo dropout. The method was applied to our neural network model and underlying features to generate a map of information gain that measures the expected increase in model knowledge, achieved through additional sampling at specific locations, which is pivotal for sampling strategy planning.

transcribr.io

Download YouTube transcripts as clean Markdown files. Perfect for Obsidian, Notion, GitHub, and any Markdown-compatible tool. Timestamps and formatting preserved.

qualisys.com

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socialfinder.ai

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