Meerkat 3d Model [WORK] Download
Jade Jinkins
Our bet is this: FMs will lower the barrier to entry for working with unstructured data, and technical teams will increasingly interact with these models to build new tools, surface insights, and deploy software.
A standalone Meerkat application for fine-grained error analysis of image classification models. Combining traditional dataframe and new FM-powered components can help us get a deeper understanding of the errors made by an image classifier.
In the Content Browser, open the Content > Assets > meerkat > Blueprints folder and locate the amlMeerkat_BP asset.
Decisions regarding immigration and emigration are crucial to understanding group dynamics in social animals, but dispersal is rarely treated in models of optimal behavior. We developed a model of evolutionarily stable dispersal and eviction strategies for a cooperative mammal, the meerkat Suricata suricatta. Using rank and group size as state variables, we determined state-specific probabilities that subordinate females would disperse and contrasted these with probabilities of eviction by the dominant female, based on the long-term fitness consequences of these behaviors but incorporating the potential for error. We examined whether long-term fitness considerations explain group size regulation in meerkats; whether long-term fitness considerations can lead to conflict between dominant and subordinate female group members; and under what circumstances those conflicts were likely to lead to stability, dispersal, or eviction. Our results indicated that long-term fitness considerations can explain group size regulation in meerkats. Group size distributions expected from predicted dispersal and eviction strategies matched empirical distributions most closely when emigrant survival was approximately that determined from the field study. Long-term fitness considerations may lead to conflicts between dominant and subordinate female meerkats, and eviction is the most likely result of these conflicts. Our model is computationally intensive but provides a general framework for incorporating future changes in the size of multimember cooperative breeding groups.
N2 - In the context of radio galaxy classification, most state-of-the-art neural network algorithms have been focused on single survey data. The question of whether these trained algorithms have cross-survey identification ability or can be adapted to develop classification networks for future surveys is still unclear. One possible solution to address this issue is transfer learning, which re-uses elements of existing machine learning models for different applications. Here we present radio galaxy classification based on a 13-layer Deep Convolutional Neural Network (DCNN) using transfer learning methods between different radio surveys. We find that our machine learning models trained from a random initialization achieve accuracies comparable to those found elsewhere in the literature. When using transfer learning methods, we find that inheriting model weights pre-trained on FIRST images can boost model performance when re-training on lower resolution NVSS data, but that inheriting pre-trained model weights from NVSS and re-training on FIRST data impairs the performance of the classifier. We consider the implication of these results in the context of future radio surveys planned for next-generation radio telescopes such as ASKAP, MeerKAT, and SKA1-MID.
AB - In the context of radio galaxy classification, most state-of-the-art neural network algorithms have been focused on single survey data. The question of whether these trained algorithms have cross-survey identification ability or can be adapted to develop classification networks for future surveys is still unclear. One possible solution to address this issue is transfer learning, which re-uses elements of existing machine learning models for different applications. Here we present radio galaxy classification based on a 13-layer Deep Convolutional Neural Network (DCNN) using transfer learning methods between different radio surveys. We find that our machine learning models trained from a random initialization achieve accuracies comparable to those found elsewhere in the literature. When using transfer learning methods, we find that inheriting model weights pre-trained on FIRST images can boost model performance when re-training on lower resolution NVSS data, but that inheriting pre-trained model weights from NVSS and re-training on FIRST data impairs the performance of the classifier. We consider the implication of these results in the context of future radio surveys planned for next-generation radio telescopes such as ASKAP, MeerKAT, and SKA1-MID.
In this paper, we approach the problem of defacement detection from a different angle: we use computer vision techniques to recognize if a website was defaced, similarly to how a human analyst decides if a website was defaced when viewing it in a web browser. We introduce MEERKAT, a defacement detection system that requires no prior knowledge about the website\u2019s content or its structure, but only its URL. Upon detection of a defacement, the system notifies the website operator that his website is defaced, who can then take appropriate action. To detect defacements, MEERKAT automatically learns high-level features from screenshots of defaced websites by combining recent advances in machine learning, like stacked autoencoders and deep neural networks, with techniques from computer vision. These features are then used to create models that allow for the detection of newly-defaced websites.