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KDnuggets News, November 30: What is Chebychev’s Theorem and How Does it Apply to Data Science? • Linux for Data Science Cheatsheet - KDnuggets

What is Chebychev's Theorem and How Does it Apply to Data Science? • Linux for Data Science Cheatsheet • The Complete Data Engineering Study Roadmap • 10 Amazing Machine Learning Visualizations You Should Know in 2023 • 7 SQL Concepts Needed for Data Science

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Siemens Taps Omniverse Replicator for Synthetic Data Generation

The company aims to accelerate its AI model development times from taking “months” to “days”.

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Basketball study automates patterns of play to compare teams' performance

New analysis of elite women's basketball automatically pinpoints a team's chances of high or low scoring plays despite the ball's trajectory looking the same, in research developed by QUT data scientists.

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Telegram shares data of users accused of copyright violation following court order • TechCrunch

The compliance is a remarkable illustration of the data Telegram stores on its users and can be made to disclose by authorities.

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Augmenting interictal mapping with neurovascular coupling biomarkers by structured factorization of epileptic EEG and fMRI data | Papers With Code

1 code implementation. EEG-correlated fMRI analysis is widely used to detect regional blood oxygen level dependent fluctuations that are significantly synchronized to interictal epileptic discharges, which can provide evidence for localizing the ictal onset zone. However, such an asymmetrical, mass-univariate approach cannot capture the inherent, higher order structure in the EEG data, nor multivariate relations in the fMRI data, and it is nontrivial to accurately handle varying neurovascular coupling over

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Uncertainty quantification in imaging and automatic horizon tracking: a Bayesian deep-prior based approach | Papers With Code

1 code implementation in PyTorch. In inverse problems, uncertainty quantification (UQ) deals with a probabilistic description of the solution nonuniqueness and data noise sensitivity. Setting seismic imaging into a Bayesian framework allows for a principled way of studying uncertainty by solving for the model posterior distribution. Imaging, however, typically constitutes only the first stage of a sequential workflow, and UQ becomes even more relevant when applied to subsequent tasks that are highly sensit

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Model Optimization | Papers With Code

To Optimize already existing models in Training/Inferencing tasks.

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Low-rank representation of head impact kinematics: A data-driven emulator | Papers With Code

Head motion induced by impacts has been deemed as one of the most important measures in brain injury prediction, given that the majority of brain injury metrics use head kinematics as input. Recently, researchers have focused on using fast approaches, such as machine learning, to approximate brain deformation in real-time for early brain injury diagnosis. However, those requires large number of kinematic measurements, and therefore data augmentation is required given the limited on-field measured data avai

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On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs | Papers With Code

Physics informed neural networks (PINNs) are deep learning based techniques for solving partial differential equations (PDEs) encounted in computational science and engineering. Guided by data and physical laws, PINNs find a neural network that approximates the solution to a system of PDEs. Such a neural network is obtained by minimizing a loss function in which any prior knowledge of PDEs and data are encoded. Despite its remarkable empirical success in one, two or three dimensional problems, there is lit