Impact of observational uncertainties on universal scaling of MHD turbulence

Scaling exponents are the central quantitative prediction of theories of turbulence and in-situ satellite observations of the high Reynolds number solar wind flow have provided an extensive testbed of these. We propose a general, instrument independent method to estimate the uncertainty of velocity field fluctuations. We obtain the systematic shift that this uncertainty introduces into the observed spectral exponent. This shift is essential for the correct interpretation of observed scaling exponents. It is sufficient to explain the contradiction between spectral features of the Elsasser fields observed in the solar wind with both theoretical models and numerical simulations of Magnetohydrodynamic turbulence

Interpreting Magnetic Variance Anisotropy Measurements in the Solar Wind

The magnetic variance anisotropy ($\mathcal{A}_m$) of the solar wind has been used widely as a method to identify the nature of solar wind turbulent fluctuations; however, a thorough discussion of the meaning and interpretation of the $\mathcal{A}_m$ has not appeared in the literature. This paper explores the implications and limitations of using the $\mathcal{A}_m$ as a method for constraining the solar wind fluctuation mode composition and presents a more informative method for interpreting spacecraft data. The paper also compares predictions of the $\mathcal{A}_m$ from linear theory to nonlinear turbulence simulations and solar wind measurements. In both cases, linear theory compares well and suggests the solar wind for the interval studied is dominantly Alfv\'{e}nic in the inertial and dissipation ranges to scales $k \rho_i \simeq 5$.Comment: 15 pages, 10 figures, accepted for publication in The Astrophysical Journa

Dynamics of plasma blobs in a shear flow

The global dynamic of plasma blobs in a shear flow is investigated in a simple magnetized torus using the spatial Fourier harmonics (k-space) framework. Direct experimental evidence of a linear drift in k space of the density fluctuation energy synchronized with blob events is presented. During this drift, an increase of the fluctuation energy and a production of the kinetic energy associated with blobs are observed. The energy source of the blob is analyzed using an advection-dissipation-type equation that includes blob-flow exchange energy, linear drift in k space, nonlinear processes, and viscous dissipations. We show that blobs tap their energy from the dominant E B vertical background flow during the linear drift stage. The exchange of energy is unidirectional as there is no evidence that blobs return energy to the flow

Nanofriction behavior of cluster-assembled carbon films

We have characterized the frictional properties of nanostructured (ns) carbon films grown by Supersonic Cluster Beam Deposition (SCBD) via an Atomic Force-Friction Force Microscope (AFM-FFM). The experimental data are discussed on the basis of a modified Amonton's law for friction, stating a linear dependence of friction on load plus an adhesive offset accounting for a finite friction force in the limit of null total applied load. Molecular Dynamics simulations of the interaction of the AFM tip with the nanostructured carbon confirm the validity of the friction model used for this system. Experimental results show that the friction coefficient is not influenced by the nanostructure of the films nor by the relative humidity. On the other hand the adhesion coefficient depends on these parameters.Comment: 22 pages, 6 figures, RevTex

Paradoxes in Fair Computer-Aided Decision Making

Computer-aided decision making--where a human decision-maker is aided by a computational classifier in making a decision--is becoming increasingly prevalent. For instance, judges in at least nine states make use of algorithmic tools meant to determine "recidivism risk scores" for criminal defendants in sentencing, parole, or bail decisions. A subject of much recent debate is whether such algorithmic tools are "fair" in the sense that they do not discriminate against certain groups (e.g., races) of people. Our main result shows that for "non-trivial" computer-aided decision making, either the classifier must be discriminatory, or a rational decision-maker using the output of the classifier is forced to be discriminatory. We further provide a complete characterization of situations where fair computer-aided decision making is possible

Scale-dependent angle of alignment between velocity and magnetic field fluctuations in solar wind turbulence

Under certain conditions, freely decaying magnetohydrodynamic (MHD) turbulence evolves in such a way that velocity and magnetic field fluctuations delta v and delta B approach a state of alignment in which delta v proportional to delta B. This process is called dynamic alignment. Boldyrev has suggested that a similar kind of alignment process occurs as energy cascades from large to small scales through the inertial range in strong incompressible MHD turbulence. In this study, plasma and magnetic field data from the Wind spacecraft, data acquired in the ecliptic plane near 1 AU, are employed to investigate the angle theta(tau) between velocity and magnetic field fluctuations in the solar wind as a function of the time scale tau of the fluctuations and to look for the scaling relation similar to tau(1/4) predicted by Boldyrev. We find that the angle appears to scale like a power law at large inertial range scales, but then deviates from power law behavior at medium to small inertial range scales. We also find that small errors in the velocity vector measurements can lead to large errors in the angle measurements at small time scales. As a result, we cannot rule out the possibility that the observed deviations from power law behavior arise from errors in the velocity measurements. When we fit the data from 2 x 10(3) s to 2 x 10(4) s with a power law of the form proportional to tau(p), our best fit values for p are in the range 0.27-0.36

An example of traffic-accomodating application

The traffic generated by multimedia applications presents a great amount of burstiness, which can hardly be described by a static set of traffic parameters. The dynamic and efficient usage of the resources is one of the fundamental aspects of multimedia networks: the traffic specification should first reflect the real traffic demand, but optimise, at the same time, the resources requested. This paper presents an example of application able to accommodate its traffic to managing QoS dynamically. The paper is focused on the technique used to implement the Dynamic Reallocation Scheme (RVBR) taking into account problems deriving from delay during the reallocation phase

Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings. These systems function by learning from historical decisions, often taken by humans. In order to maximize the utility of these systems (or, classifiers), their training involves minimizing the errors (or, misclassifications) over the given historical data. However, it is quite possible that the optimally trained classifier makes decisions for people belonging to different social groups with different misclassification rates (e.g., misclassification rates for females are higher than for males), thereby placing these groups at an unfair disadvantage. To account for and avoid such unfairness, in this paper, we introduce a new notion of unfairness, disparate mistreatment, which is defined in terms of misclassification rates. We then propose intuitive measures of disparate mistreatment for decision boundary-based classifiers, which can be easily incorporated into their formulation as convex-concave constraints. Experiments on synthetic as well as real world datasets show that our methodology is effective at avoiding disparate mistreatment, often at a small cost in terms of accuracy.Comment: To appear in Proceedings of the 26th International World Wide Web Conference (WWW), 2017. Code available at: https://github.com/mbilalzafar/fair-classificatio