Notice and Consent in a World of Big Data

Nowadays individuals are often presented with long and complex privacy notices routinely written by lawyers for lawyers, and are then requested to either ‘consent’ or abandon the use of the desired service. The over-use of notice and consent presents increasing challenges in an age of ‘Big Data’. These phenomena are receiving attention particularly in the context of the current review of the OECD Privacy Guidelines. In 2012 Microsoft sponsored an initiative designed to engage leading regulators, industry executives, public interest advocates, and academic experts in frank discussions about the role of individual control and notice and consent in data protection today, and alternative models for providing better protection for both information privacy and valuable data flows in the emerging world of Big Data and cloud computing

25 jaar moord in Nederland: Een trendanalyse van geslacht en leeftijd van slachtoffers van moord.

Deze studie beschrijft de trend in slachtofferschap van moord in Nederland in deperiode 1992-2016. Hierbij is gebruik gemaakt van de Dutch Homicide Monitor. Debevindingen laten zien dat het moordcijfer sinds de jaren negentig aan het dalen is.Deze daling is het grootst onder mannelijke en vrouwelijke slachtoffers in de leeftijd van20 tot en met 39 jaar. Dit onderzoek benadrukt het belang om de discussie rondom dedalende moordtrend te verschuiven naar een verdiepende analyse van geslacht en leeftijdbij slachtoffers.Security and Global Affair

Forecasting in the light of Big Data

Predicting the future state of a system has always been a natural motivation for science and practical applications. Such a topic, beyond its obvious technical and societal relevance, is also interesting from a conceptual point of view. This owes to the fact that forecasting lends itself to two equally radical, yet opposite methodologies. A reductionist one, based on the first principles, and the naive inductivist one, based only on data. This latter view has recently gained some attention in response to the availability of unprecedented amounts of data and increasingly sophisticated algorithmic analytic techniques. The purpose of this note is to assess critically the role of big data in reshaping the key aspects of forecasting and in particular the claim that bigger data leads to better predictions. Drawing on the representative example of weather forecasts we argue that this is not generally the case. We conclude by suggesting that a clever and context-dependent compromise between modelling and quantitative analysis stands out as the best forecasting strategy, as anticipated nearly a century ago by Richardson and von Neumann

Learning and Matching Multi-View Descriptors for Registration of Point Clouds

Critical to the registration of point clouds is the establishment of a set of accurate correspondences between points in 3D space. The correspondence problem is generally addressed by the design of discriminative 3D local descriptors on the one hand, and the development of robust matching strategies on the other hand. In this work, we first propose a multi-view local descriptor, which is learned from the images of multiple views, for the description of 3D keypoints. Then, we develop a robust matching approach, aiming at rejecting outlier matches based on the efficient inference via belief propagation on the defined graphical model. We have demonstrated the boost of our approaches to registration on the public scanning and multi-view stereo datasets. The superior performance has been verified by the intensive comparisons against a variety of descriptors and matching methods

Electronic Structure and Valence Band Spectra of Bi4Ti3O12

The x-ray photoelectron valence band spectrum and x-ray emission valence-band spectra (Ti K _beta_5, Ti L_alpha, O K_alpha) of Bi4Ti3O12 are presented (analyzed in the common energy scale) and interpreted on the basis of a band-structure calculation for an idealized I4/mmm structure of this material.Comment: 6 pages + 7 PostScript figures, RevTex3.0, to be published in Phys.Rev.B52 (Oct.95). Figures also available via anonymous ftp at ftp://ftp.physik.uni-osnabrueck.de/pub/apostnik/BiTiO

Datatrust: Or, the political quest for numerical evidence and the epistemologies of Big Data

Recently, there has been renewed interest in so-called evidence-based policy making. Enticed by the grand promises of Big Data, public officials seem increasingly inclined to experiment with more data-driven forms of governance. But while the rise of Big Data and related consequences has been a major issue of concern across different disciplines, attempts to develop a better understanding of the phenomenon's historical foundations have been rare. This short commentary addresses this gap by situating the current push for numerical evidence within a broader socio-political context, demonstrating how the epistemological claims of Big Data science intersect with specific forms of trust, truth, and objectivity. We conclude by arguing that regulators' faith in numbers can be attributed to a distinct political culture, a representative democracy undermined by pervasive public distrust and uncertainty

Direct proteomic and high-resolution microscopy biopsy analysis identifies distinct ventricular fates in severe aortic stenosis

The incidence of aortic valve stenosis (AS), the most common reason for aortic valve replacement (AVR), increases with population ageing. While untreated AS is associated with high mortality, different hemodynamic subtypes range from normal left-ventricular function to severe heart failure. However, the molecular nature underlying four different AS subclasses, suggesting vastly different myocardial fates, is unknown. Here, we used direct proteomic analysis of small left-ventricular biopsies to identify unique protein expression profiles and subtype-specific AS mechanisms. Left-ventricular endomyocardial biopsies were harvested from patients during transcatheter AVR, and inclusion criteria were based on echocardiographic diagnosis of severe AS and guideline-defined AS-subtype classification: 1) normal ejection fraction (EF)/high-gradient; 2) low EF/high-gradient; 3) low EF/low-gradient; and 4) paradoxical low-flow/low-gradient AS. Samples from non-failing donor hearts served as control. We analyzed 25 individual left-ventricular biopsies by data-independent acquisition mass spectrometry (DIA-MS), and 26 biopsies by histomorphology and cardiomyocytes by STimulated Emission Depletion (STED) superresolution microscopy. Notably, DIA-MS reliably detected 2273 proteins throughout each individual left-ventricular biopsy, of which 160 proteins showed significant abundance changes between AS-subtype and non-failing samples including the cardiac ryanodine receptor (RyR2). Hierarchical clustering segregated unique proteotypes that identified three hemodynamic AS-subtypes. Additionally, distinct proteotypes were linked with AS-subtype specific differences in cardiomyocyte hypertrophy. Furthermore, superresolution microscopy of immunolabeled biopsy sections showed subcellular RyR2-cluster fragmentation and disruption of the functionally important association with transverse tubules, which occurred specifically in patients with systolic dysfunction and may hence contribute to depressed left-ventricular function in AS

Big data: Finders keepers, losers weepers?

This article argues that big data’s entrepreneurial potential is based not only on new technological developments that allow for the extraction of non-trivial, new insights out of existing data, but also on an ethical judgment that often remains implicit: namely the ethical judgment that those companies that generate these new insights can legitimately appropriate (the fruits of) these insights. As a result, the business model of big data companies is essentially founded on a libertarian-inspired ‘finders, keepers’ ethic. The article argues, next, that this presupposed ‘finder, keepers’ ethic is far from unproblematic and relies itself on multiple unconvincing assumptions. This leads to the conclusion that the conduct of companies working with big data might lack ethical justification

Electronic structure of the MO oxides (M=Mg, Ca, Ti, V) in the GW approximation

The quasiparticle band structures of nonmagnetic monoxides, MO (M=Mg, Ca, Ti, and V), are calculated by the GW approximation. The band gap and the width of occupied oxygen 2p states in insulating MgO and CaO agree with experimental observation. In metallic TiO and VO, conduction bands originated from metal 3d states become narrower. Then the partial densities of transition metal e_g and t_2g states show an enhanced dip between the two. The effects of static screening and dynamical correlation are discussed in detail in comparison with the results of the Hartree-Fock approximation and the static Coulomb hole plus screened exchange approximation. The d-d Coulomb interaction is shown to be very much reduced by on-site and off-site d-electron screening in TiO and VO. The dielectric function and the energy loss spectrum are also presented and discussed in detail.Comment: 10 pages, 5 figure