Algorithmic Complexity for Short Binary Strings Applied to Psychology: A Primer

Since human randomness production has been studied and widely used to assess executive functions (especially inhibition), many measures have been suggested to assess the degree to which a sequence is random-like. However, each of them focuses on one feature of randomness, leading authors to have to use multiple measures. Here we describe and advocate for the use of the accepted universal measure for randomness based on algorithmic complexity, by means of a novel previously presented technique using the the definition of algorithmic probability. A re-analysis of the classical Radio Zenith data in the light of the proposed measure and methodology is provided as a study case of an application.Comment: To appear in Behavior Research Method

Measurement of the Transverse Beam Spin Asymmetry in Elastic Electron Proton Scattering and the Inelastic Contribution to the Imaginary Part of the Two-Photon Exchange Amplitude

We report on a measurement of the asymmetry in the scattering of transversely polarized electrons off unpolarized protons, A$_\perp$, at two Q$^2$ values of \qsquaredaveragedlow (GeV/c)$^2$ and \qsquaredaveragedhighII (GeV/c)$^2$ and a scattering angle of $30^\circ < \theta_e < 40^\circ$. The measured transverse asymmetries are A$_{\perp}$(Q$^2$ = \qsquaredaveragedlow (GeV/c)$^2$) = (\experimentalasymmetry alulowcorr $\pm$ \statisticalerrorlow$_{\rm stat}$ $\pm$ \combinedsyspolerrorlowalucor$_{\rm sys}$) $\times$ 10$^{-6}$ and A$_{\perp}$(Q$^2$ = \qsquaredaveragedhighII (GeV/c)$^2$) = (\experimentalasymme tryaluhighcorr $\pm$ \statisticalerrorhigh$_{\rm stat}$ $\pm$ \combinedsyspolerrorhighalucor$_{\rm sys}$) $\times$ 10$^{-6}$. The first errors denotes the statistical error and the second the systematic uncertainties. A$_\perp$ arises from the imaginary part of the two-photon exchange amplitude and is zero in the one-photon exchange approximation. From comparison with theoretical estimates of A$_\perp$ we conclude that $\pi$N-intermediate states give a substantial contribution to the imaginary part of the two-photon amplitude. The contribution from the ground state proton to the imaginary part of the two-photon exchange can be neglected. There is no obvious reason why this should be different for the real part of the two-photon amplitude, which enters into the radiative corrections for the Rosenbluth separation measurements of the electric form factor of the proton.Comment: 4 figures, submitted to PRL on Oct.

Number preferences in lotteries

We explore people's preferences for numbers in large proprietary data sets from two different lottery games. We find that choice is far from uniform, and exhibits some familiar and some new tendencies and biases. Players favor personally meaningful and situationally available numbers, and are attracted towards numbers in the center of the choice form. Frequent players avoid winning numbers from recent draws, whereas infrequent players chase these. Combinations of numbers are formed with an eye for aesthetics, and players tend to spread their numbers relatively evenly across the possible range

Interpretation of Quasi-static C-v Characteristics of Mosos Capacitors On Soi Substrates

The fabrication of two-terminal MOSOS capacitors incorporating SOI substrates is described. Results of quasi-static C-V measurements are presented for the first time and compared to existing theoretical models. The suitability of the technique to assess rapidly the quality of an SOI MOS fabrication process is finally discussed

A methodology to involve domain experts and machine learning techniques in the design of human-centered algorithms

Part 2: MethodologicalInternational audienceMachine learning techniques are increasingly applied in Decision Support Systems. The selection processes underlying a conclusion often become black-boxed. Thus, the decision flow is not always comprehensible by developers or end users. It is unclear what the priorities are and whether all of the relevant information is used. In order to achieve human interpretability of the created algorithms, it is recommended to include domain experts in the modelling phase. Their knowledge is elicited through a combination of machine learning and social science techniques. The idea is not new, but it remains a challenge to extract and apply the experts’ experience without overburdening them. The current paper describes a methodology set to unravel, define and categorize the implicit and explicit domain knowledge in a less intense way by making use of co-creation to design human-centered algorithms, when little data is available. The methodology is applied to a case in the health domain, targeting a rheumatology triage problem. The domain knowledge is obtained through dialogue, by alternating workshops and data science exercises