UI Update — Datazar

Probabilities for Bivariate Normal Distribution (pbv)Computes probabilities of the bivariate normal distribution in a vectorized R function (Drezner & Wesolowsky, 1990, <doi:10.1080/009496590088112 …

Critical Line Algorithm (CLA) The critical line method developed by the Nobel Prize winner H. Markowitz is a classical technique for the construction of a minimum-variance frontier within the paradigm of ‘the expected return-risk’ (mean-variance) and finding minimum portfolios. Considerable interest has recently been attracted to the development of a fast algorithm for the construction of the minimum-variance frontier. In some works, such algorithms have been used to find statistically stable optimal portfoli.o An Open-Source Implementation of the Critical-Line Algorithm for Portfolio Optimization The Constrained Critical Line Algorithm The Critical Line Method Applying Markowitz’s Critical Line Algorithm …

The post discusses highlights of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018).

This week’s newsletter brings you the most in demands skills in data science, explains how machines understand our language, explains classification using a dataset-based approach (and pictures), looks at the myths and realities of quantum machine learning, discusses text classification model comparison and selection, and lists the top 13 Python deep learning libraries. There’s still time to vote in our latest poll! Features

At: DePaul University Location: Chicago, ILWeb: www.depaul.eduPosition: Two tenure-track/tenured positions in Data Science/Computer Science

Kinetic Compressive Sensing (KCS) Parametric images provide insight into the spatial distribution of physiological parameters, but they are often extremely noisy, due to low SNR of tomographic data. Direct estimation from projections allows accurate noise modeling, improving the results of post-reconstruction fitting. We propose a method, which we name kinetic compressive sensing (KCS), based on a hierarchical Bayesian model and on a novel reconstruction algorithm, that encodes sparsity of kinetic parameters. Parametric maps are reconstructed by maximizing the joint probability, with an Iterated Conditional Modes (ICM) approach, alternating the optimization of activity time series (OS-MAP-OSL), and kinetic parameters (MAP-LM). We evaluated the proposed algorithm on a simulated dynamic phantom: a bias/variance study confirmed how direct estimates can improve the quality of parametric maps over a post-reconstruction fitting, and showed how the novel sparsity prior can further reduce their variance, without affecting bias. Real FDG PET human brain data (Siemens mMR, 40min) images were also processed. Results enforced how the proposed KCS-regularized direct method can produce spatially coherent images and parametric maps, with lower spatial noise and better tissue contrast. A GPU-based open source implementation of the algorithm is provided. …

DAPPER: Scaling Dynamic Author Persona Topic Model to Billion Word Corpora

Open source has always been a part of the data science landscape (as we explored here in 2015 and here in 2017). And while every tool in Technoslavia has some free offerings, we’re taking this chance to highlight the dominating force of open source in the ecosystem.

“One clear conclusion of this work is that, although the DL technology has achieved very promising results, there is still a significant need for further research into and development in how to easily and efficiently build high-quality production-ready DL systems. Traditional SE has high-quality tools and practices for reviewing, writing test, and debugging code. However, they are rarely sufficient for building production-ready systems containing DL components. If the SE community, together with the DL community, could make an effort in finding solutions to these challenges, the power of the DL technology could not only be made available to researchers and large technology companies, but also to the vast majority of companies around the world.” Anders Arpteg, Björn Brinne, Luka Crnkovic-Friis, Jan Bosch ( 29 October 2018 )