If a ML system uses gender information in credit scoring, then gender information is probably relevant for credit scoring. We all know that women, for example, are more risk averse than men on average and that there are more men with very low IQ's; and more men take part in dangerous activities than can maim them. All those things contribute to credit risk. I looked at some actuarial motorcycle accident data from a Swedish insurance company a couple of years ago, and the accident rate of young men (18-25 maybe) was something like 40 times higher than women in the same age interval. Of course, EU law requires us to offer the same rate to men and women, so we have to ignore this; and thus the women pay more than they should if things were fair.

The sudden release of magnetic energy on the Sun drives powerful solar flares, which are difficult to predict. Kusano et al. derived physics-based thresholds for the onset of large solar flares and show how they can be predicted from routine solar observations (see the Perspective by Veronig). They tested their method using observations of the Sun from 2008 to 2019. In most cases, the method correctly identifies which regions will produce large flares within the next 20 hours, although there are some false positives and false negatives. The method also provides the exact location where each flare will begin and limits on how powerful it will be. Accurate predictions of solar flares could improve forecasts of space weather conditions around Earth. Science , this issue p. [587][1]; see also p. [504][2] Solar flares are highly energetic events in the Sun’s corona that affect Earth’s space weather. The mechanism that drives the onset of solar flares is unknown, hampering efforts to forecast them, which mostly rely on empirical methods. We present the κ -scheme, a physics-based model to predict large solar flares through a critical condition of magnetohydrodynamic instability, triggered by magnetic reconnection. Analysis of the largest (X-class) flares from 2008 to 2019 (during solar cycle 24) shows that the κ -scheme predicts most imminent large solar flares, with a small number of exceptions for confined flares. We conclude that magnetic twist flux density, close to a magnetic polarity inversion line on the solar surface, determines when and where solar flares may occur and how large they can be. [1]: /lookup/doi/10.1126/science.aaz2511 [2]: /lookup/doi/10.1126/science.abb6150

Typically this requires casting the original deterministic physics-based model into a probabilistic framework where inputs or control variables (CVs) are treated as random variables with probability distributions derived from available experimental data, manufacturing constraints, design criteria, expert judgment, and/or other domain knowledge (e.g., see [1]). Running the physics-based model with CVs sampled according to these distributions yields corresponding realizations of the system response as characterized by quantities of interest (QoIs). Analysis of the uncertainty propagation from the CVs to the QoIs informs decision-making, e.g., it informs engineering decisions aimed at improving the quality and reliability of designed products and helps identify potential risks at early stages in the design and manufacturing process. Quantitatively assessing uncertainty propagation presents a fundamental challenge due to the computational cost of the underlying physics-based model. Even for a low number of CVs and QoIs, uncertainty quantification (UQ) for, e.g., accelerating the simulation-aided design of multiscale systems and data-centric engineering tasks more generally ([2]), requires a large number of repeated observations of QoIs to achieve a high degree of confidence in such an analysis. The sampling cost is further exacerbated in real-world applications where distributions on QoIs are typically non-Gaussian, skewed, and/or mutually correlated, and therefore need to be characterized by their full probability density function (PDF) rather than through summary statistics such as mean and variance. The computational cost of nonparametric methods to estimate these densities can become prohibitively high when using a fully-featured physics-based model to compute each sample. One approach to alleviate the computational burden is to derive a cheaper-to-compute surrogate for the physicsbased model's response enabling much faster generation of output data and thus overcoming computational bottlenecks.

Over the course of the last several years there was a strong interest in application of modern optimal control techniques to the field of character animation. This interest was fueled by introduction of efficient learning based algorithms for policy optimization, growth in computation power, and game engine improvements. It was shown that it is possible to generate natural looking control of a character by using two ingredients. First, the simulated agent must adhere to a motion capture dataset. And second, the character aims to track the control input from the user. The paper aims at closing the gap between the researchers and users by introducing an open source implementation of physics based character control in Unity framework that has a low entry barrier and a steep learning curve.

"It's not rocket science" may be a tired cliché, but that doesn't mean designing rockets is any less complicated. Time, cost and safety prohibit testing the stability of a test rocket using a physical build "trial and error" approach. But even computational simulations are extremely time consuming. A single analysis of an entire SpaceX Merlin rocket engine, for example, could take weeks, even months, for a supercomputer to provide satisfactory predictions. One group of researchers at The University of Texas at Austin is developing new "scientific machine learning" methods to address this challenge.

Computer scientists use causal inference for reasoning. In causal inference, researchers are interested in finding the relationship between two observable events. In this paper, we will explore the first step towards finding causality using probabilistic fuzzy logic (PFL). We will also show that PFL is more precise than Pearl’s causality model.

Ideas and formalisms from far-from-equilibrium thermodynamics are ported to the context of stochastic computational processes, via following and extending Tadaki's algorithmic thermodynamics. A Principle of Maximum Algorithmic Caliber is proposed, providing guidance as to what computational processes one should hypothesize if one is provided constraints to work within. It is conjectured that, under suitable assumptions, computational processes obeying algorithmic Markov conditions will maximize algorithmic caliber. It is proposed that in accordance with this, real-world cognitive systems may operate in substantial part by modeling their environments and choosing their actions to be (approximate and compactly represented) algorithmic Markov networks. These ideas are suggested as potential early steps toward a general theory of the operation of pragmatic generally intelligent systems.

This paper deals with the design of a sensing matrix along with a sparse recovery algorithm by utilizing the probability-based prior information for compressed sensing system. With the knowledge of the probability for each atom of the dictionary being used, a diagonal weighted matrix is obtained and then the sensing matrix is designed by minimizing a weighted function such that the Gram of the equivalent dictionary is as close to the Gram of dictionary as possible. An analytical solution for the corresponding sensing matrix is derived which leads to low computational complexity. We also exploit this prior information through the sparse recovery stage and propose a probability-driven orthogonal matching pursuit algorithm that improves the accuracy of the recovery. Simulations for synthetic data and application scenarios of surveillance video are carried out to compare the performance of the proposed methods with some existing algorithms. The results reveal that the proposed CS system outperforms existing CS systems.

This paper presents the design of our system, namely MTab, for Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2019). MTab combines the voting algorithm and the probability models to solve critical problems of the matching tasks.

We invite submissions to the Mathematical and Scientific Machine Learning (MSML 2020: http://msml-conf.org/), MSML 2020 conference is a newly established conference, with emphasis on promoting the study of mathematical theory and algorithms of machine learning, and applications of machine learning in more traditional scientific and engineering disciplines. This conference aims to bring together the communities of machine learning, applied mathematics, and computational science and engineering, to exchange ideas and progress in this fast growing field. Papers should be submitted by Sat Nov 30, 2019 20:00 PM UTC using the conference submission system at: https://cmt3.research.microsoft.com/MSML2020 After the initial review, the authors will have two weeks to submit their responses.