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Measurements are shown to be processes designed to return figures: they are effective. This effectivity allows for a formalization as Turing machines, which can be described employing computation theory. Inspired in the halting problem we draw some limitations for measurement procedures: procedures that verify if a quantity is measured cannot work in every case.
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Algorithmic decision-making systems are increasingly used throughout the public and private sectors to make important decisions or assist humans in making these decisions with real social consequences. While there has been substantial research in recent years to build fair decision-making algorithms, there has been less research seeking to understand the factors that affect peoples perceptions of fairness in these systems, which we argue is also important for their broader acceptance. In this research, we conduct an online experiment to better understand perceptions of fairness, focusing on three sets of factors: algorithm outcomes, algorithm development and deployment procedures, and individual differences. We find that people rate the algorithm as more fair when the algorithm predicts in their favor, even surpassing the negative effects of describing algorithms that are very biased against particular demographic groups. We find that this effect is moderated by several variables, including participants education level, gender, and several aspects of the development procedure. Our findings suggest that systems that evaluate algorithmic fairness through users feedback must consider the possibility of outcome favorability bias.
In this paper, an architecture designed for electrical measurement of the quality factor of MEMS resonators is proposed. An estimation of the measurement performance is made using PSPICE simulations taking into account the components non-idealities. An error on the measured Q value of only several percent is achievable, at a small integration cost, for sufficiently high quality factor values (Q > 100).
Using Rayleigh surface acoustic waves (SAW), the Youngs modulus, the density and the thickness of polycrystalline Silicon-Germanium (SiGe) films deposited on silicon and SiO2 were measured, in excellent agreement with theory. The dispersion curve of the propagating SAW is calculated with a Boundary Element Method (BEM)-Model based on Greens functions. The propagating SAW is generated with a nanosecond laser in a narrowband scheme projecting stripes from a mask on the surface of the sample. For this purpose a glass mask and a liquid crystal display (LCD) mask are used. The slope of the SAW is then measured using a probe beam setup. From the wavelength of the mask and the frequency of the measured SAW, the dispersion curve is determined point by point. Fitting the BEM-Model to the measured nonlinear dispersion curve provides several physical parameters simultaneously. In the present work this is demonstrated for the Youngs modulus, the density and the thickness of SiGe films. The results from the narrowband scheme measurement are in excellent agreement with separated measurements of the thickness (profilometer), the density (balance) and the Youngs modulus (nanoindenter).
The possibility of improving on the usual multivariate normal confidence was first discussed in Stein (1962). Using the ideas of shrinkage, through Bayesian and empirical Bayesian arguments, domination results, both analytic and numerical, have been obtained. Here we trace some of the developments in confidence set estimation.
Verifying whether a procedure is observationally pure is useful in many software engineering scenarios. An observationally pure procedure always returns the same value for the same argument, and thus mimics a mathematical function. The problem is challenging when procedures use private mutable global variables, e.g., for memoization of frequently returned answers, and when they involve recursion. We present a novel verification approach for this problem. Our approach involves encoding the procedures code as a formula that is a disjunction of path constraints, with the recursive calls being replaced in the formula with references to a mathematical function symbol. Then, a theorem prover is invoked to check whether the formula that has been constructed agrees with the function symbol referred to above in terms of input-output behavior for all arguments. We evaluate our approach on a set of realistic examples, using the Boogie intermediate language and theorem prover. Our evaluation shows that the invariants are easy to construct manually, and that our approach is effective at verifying observationally pure procedures.
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