The invisible power of fairness. How machine learning shapes democracy

Many machine learning systems make extensive use of large amounts of data regarding human behaviors. Several researchers have found various discriminatory practices related to the use of human-related machine learning systems, for example in the field of criminal justice, credit scoring and advertising. Fair machine learning is therefore emerging as a new field of study to mitigate biases that are inadvertently incorporated into algorithms. Data scientists and computer engineers are making various efforts to provide definitions of fairness. In this paper, we provide an overview of the most widespread definitions of fairness in the field of machine learning, arguing that the ideas highlighting each formalization are closely related to different ideas of justice and to different interpretations of democracy embedded in our culture. This work intends to analyze the definitions of fairness that have been proposed to date to interpret the underlying criteria and to relate them to different ideas of democracy.Comment: 12 pages, 1 figure, preprint version, submitted to The 32nd Canadian Conference on Artificial Intelligence that will take place in Kingston, Ontario, May 28 to May 31, 201

From infinite to two dimensions through the functional renormalization group

We present a novel scheme for an unbiased and non-perturbative treatment of strongly correlated fermions. The proposed approach combines two of the most successful many-body methods, i.e., the dynamical mean field theory (DMFT) and the functional renormalization group (fRG). Physically, this allows for a systematic inclusion of non-local correlations via the flow equations of the fRG, after the local correlations are taken into account non-perturbatively by the DMFT. To demonstrate the feasibility of the approach, we present numerical results for the two-dimensional Hubbard model at half-filling.Comment: 5 pages, 4 figure

Using Frost-damaged Soybeans in Livestock Rations

Soybeans are routinely grown in the upper Midwest as a cash crop. However, late planting coupled with an early freeze can result in frost-damaged or “green beans.” Even after processing, the resulting soybean meal and soy oil are still green due to high chlorophyll concentrations. Since the consumer is reluctant to purchase green soy oil, frost-damaged soybeans (FDS) are of little use to the processing industry and often are docked at local elevators. However, when done properly, FDS can be marketed effectively through livestock. Frost-damaged soybeans, green beans, and immature soybeans are all synonymous terms and will be denoted by FDS in the rest of this article. Raw refers to non-heat treated soybeans

Rubidium spacecraft atomic timing system Final report

Rubidium 87 atomic time and frequency reference system for manned space fligh

Dynamical Mean-Field Theory for Molecular Electronics: Electronic Structure and Transport Properties

We present an approach for calculating the electronic structure and transport properties of nanoscopic conductors that takes into account the dynamical correlations of strongly interacting d- or f-electrons by combining density functional theory calculations with the dynamical mean-field theory. While the density functional calculation yields a static mean-field description of the weakly interacting electrons, the dynamical mean-field theory explicitly takes into account the dynamical correlations of the strongly interacting d- or f-electrons of transition metal atoms. As an example we calculate the electronic structure and conductance of Ni nanocontacts between Cu electrodes. We find that the dynamical correlations of the Ni 3d-electrons give rise to quasi-particle resonances at the Fermi-level in the spectral density. The quasi-particle resonances in turn lead to Fano lineshapes in the conductance characteristics of the nanocontacts similar to those measured in recent experiments of magnetic nanocontacts.Comment: replaced with revised version; 11 pages; 9 figure

Optimal temperature overshoot profile found by limiting global sea level rise as a lower-cost climate target

The global temperature targets of limiting surface warming to below 2.0°C or even to 1.5°C have been widely accepted through the Paris Agreement. However, limiting surface warming has previously been proven insufficient to control sea level rise (SLR). Here, we explore a sea level target that is closer to coastal planning and associated adaptation measures than a temperature target. We find that a sea level target provides an optimal temperature overshoot profile through a physical constraint of SLR. The allowable temperature overshoot leads to lower mitigation costs and more effective long-term sea level stabilization compared to a temperature target leading to the same SLR by 2200. With the same mitigation cost as the temperature target, a SLR target could bring surface warming back to the targeted temperatures within this century, lead to a reduction of surface warming of the next century, and reduce and slow down SLR in the centuries thereafter

Complete Experimental Structure Determination of the p(3x2)pg Phase of Glycine on Cu{110}

We present a quantitative low energy electron diffraction (LEED) surface-crystallograpic study of the complete adsorption geometry of glycine adsorbed on Cu{110} in the ordered p(3×2) phase. The glycine molecules form bonds to the surface through the N atoms of the amino group and the two O atoms of the de-protonated carboxylate group, each with separate Cu atoms such that every Cu atom in the first layer is involved in a bond. Laterally, N atoms are nearest to the atop site (displacement 0.41 Å). The O atoms are asymmetrically displaced from the atop site by 0.54 Å and 1.18 Å with two very different O-Cu bond lengths of 1.93 Å and 2.18 Å. The atom positions of the upper-most Cu layers show small relaxations within 0.07 Å of the bulk-truncated surface geometry. The unit cell of the adsorbate layer consists of two glycine molecules, which are related by a glide-line symmetry operation. This study clearly shows that a significant coverage of adsorbate structures without this glide-line symmetry must be rejected, both on the grounds of the energy dependence of the spot intensities (LEED-IV curves) and of systematic absences in the LEED pattern

Clues on the evolution of the Carina dwarf spheroidal galaxy from the color distribution of its red giant stars

The thin red giant branch (RGB) of the Carina dwarf spheroidal galaxy appears at first sight quite puzzling and seemingly in contrast with the presence of several distinct bursts of star formation. In this Letter, we provide a measurement of the color spread of red giant stars in Carina based on new BVI wide-field observations, and model the width of the RGB by means of synthetic color-magnitude diagrams. The measured color spread, Sigma{V-I}=0.021 +/- 0.005, is quite naturally accounted for by the star-formation history of the galaxy. The thin RGB appears to be essentially related to the limited age range of its dominant stellar populations, with no need for a metallicity dispersion at a given age. This result is relatively robust with respect to changes in the assumed age-metallicity relation, as long as the mean metallicity over the galaxy lifetime matches the observed value ([Fe/H] = -1.91 +/- 0.12 after correction for the age effects). This analysis of photometric data also sets some constraints on the chemical evolution of Carina by indicating that the chemical abundance of the interstellar medium in Carina remained low throughout each episode of star formation even though these episodes occurred over many Gyr.Comment: 4 pages, 3 figures, accepted for publication in the Astrophysical Journal Letter

The Invisible Power of Fairness. How Machine Learning Shapes Democracy

Many machine learning systems make extensive use of large amounts of data regarding human behaviors. Several researchers have found various discriminatory practices related to the use of human-related machine learning systems, for example in the field of criminal justice, credit scoring and advertising. Fair machine learning is therefore emerging as a new field of study to mitigate biases that are inadvertently incorporated into algorithms. Data scientists and computer engineers are making various efforts to provide definitions of fairness. In this paper, we provide an overview of the most widespread definitions of fairness in the field of machine learning, arguing that the ideas highlighting each formalization are closely related to different ideas of justice and to different interpretations of democracy embedded in our culture. This work intends to analyze the definitions of fairness that have been proposed to date to interpret the underlying criteria and to relate them to different ideas of democracy