How the brain represents the reward value of fat in the mouth.

The palatability and pleasantness of the sensory properties of foods drive food selection and intake and may contribute to overeating and obesity. Oral fat texture can make food palatable and pleasant. To analyze its neural basis, we correlated humans’ subjective reports of the pleasantness of the texture and flavor of a high- and low-fat food with a vanilla or strawberry flavor, with neural activations measured with functional magnetic resonance imaging. Activity in the midorbitofrontal and anterior cingulate cortex was correlated with the pleasantness of oral fat texture and in nearby locations with the pleasantness of flavor. The pregenual cingulate cortex showed a supralinear response to the combination of high fat and pleasant, sweet flavor, implicating it in the convergence of fat texture and flavor to produce a representation of highly pleasant stimuli. The subjective reports of oral fattiness were correlated with activations in the midorbitofrontal cortex and ventral striatum. The lateral hypothalamus and amygdala were more strongly activated by high- versus low-fat stimuli. This discovery of which brain regions track the subjective hedonic experience of fat texture will help to unravel possible differences in the neural responses in obese versus lean people to oral fat, a driver of food intake

Estimation and uncertainty of reversible Markov models

Reversibility is a key concept in Markov models and Master-equation models of molecular kinetics. The analysis and interpretation of the transition matrix encoding the kinetic properties of the model relies heavily on the reversibility property. The estimation of a reversible transition matrix from simulation data is therefore crucial to the successful application of the previously developed theory. In this work we discuss methods for the maximum likelihood estimation of transition matrices from finite simulation data and present a new algorithm for the estimation if reversibility with respect to a given stationary vector is desired. We also develop new methods for the Bayesian posterior inference of reversible transition matrices with and without given stationary vector taking into account the need for a suitable prior distribution preserving the meta- stable features of the observed process during posterior inference. All algorithms here are implemented in the PyEMMA software - http://pyemma.org - as of version 2.0

A direct measurement of tomographic lensing power spectra from CFHTLenS

We measure the weak gravitational lensing shear power spectra and their cross-power in two photometric redshift bins from the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS). The measurements are performed directly in multipole space in terms of adjustable band powers. For the extraction of the band powers from the data we have implemented and extended a quadratic estimator, a maximum likelihood method that allows us to readily take into account irregular survey geometries, masks, and varying sampling densities. We find the 68 per cent credible intervals in the $\sigma_8$-$\Omega_{\rm m}$-plane to be marginally consistent with results from $Planck$ for a simple five parameter $\Lambda$CDM model. For the projected parameter $S_8 \equiv \sigma_8(\Omega_{\rm m}/0.3)^{0.5}$ we obtain a best-fitting value of $S_8 = 0.768_{-0.039}^{+0.045}$. This constraint is consistent with results from other CFHTLenS studies as well as the Dark Energy Survey. Our most conservative model, including modifications to the power spectrum due to baryon feedback and marginalization over photometric redshift errors, yields an upper limit on the total mass of three degenerate massive neutrinos of $\Sigma m_\nu < 4.53 \, {\rm eV}$ at 95 per cent credibility, while a Bayesian model comparison does not favour any model extension beyond a simple five parameter $\Lambda$CDM model. Combining the shear likelihood with $Planck$ breaks the $\sigma_8$-$\Omega_{\rm m}$-degeneracy and yields $\sigma_8=0.818 \pm 0.013$ and $\Omega_{\rm m} = 0.300 \pm 0.011$ which is fully consistent with results from $Planck$ alone.Comment: 19 pages, 12 figures, 7 tables. Accepted for publication in MNRAS. Minor corrections and updates with respect to previous versio

EXPLORING THE IMPACT OF READABILITY OF PRIVACY POLICIES ON USERS’ TRUST

Empirical studies have repeatedly pointed out that the readability of a privacy policy is a potential source of trust of online users. Nevertheless, many online companies still keep the readability of their privacy policies at a low level. This could possibly coincide with a low compliance of their privacy policies with the guidelines of fair information practices and thus with users’ privacy expectations. Against this background, this study seeks to clarify the role of perceived and actual readability of us-er-friendly and -unfriendly privacy policies in shaping user’s trust in a mobile service provider. Tested for two different mobile service scenarios that differ in the sensitivity of user data (educational enter-tainment app vs. health app), our hypotheses are verified based on the responses of 539 online users. Our findings reveal that in the case of a user-unfriendly data-handling policy, the effect of actual readability of a privacy policy outweighs the effect of its perceived readability in forming users’ trust. At the same time, for a user-friendly privacy policy, only perceived readability plays a significant role in promoting users’ trust in the provider of an educational entertainment app. In a sensitive healthcare context, however, perceived and actual readability of privacy policies are almost equally important

Readability of Privacy Policies of Healthcare Websites

Health-related personal information is very privacy-sensitive. Online privacy policies inform Website users about the ways their personal information is gathered, processed and stored. In the light of increasing privacy concerns, privacy policies seem to be an important mechanism for increasing customer loyalty. However, in practice, consumers only rarely read privacy policies, possibly due to the common assumption that policies are hard to read. By designing and implementing an automated extraction and readability analysis toolset, we present the first study that provides empirical evidence on readability of over 5,000 privacy policies of health websites and over 1,000 privacy policies of top e-commerce sites. Our results confirm the difficulty of reading current privacy policies. We further show that health websites\u27 policies are more readable than top e-commerce ones, but policies of non-commercial health websites are worse readable than commercial ones. Our study also provides a solid policy text corpus for further research