11,126 research outputs found
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
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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
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