SafeWeb: A Middleware for Securing Ruby-Based Web Applications

Web applications in many domains such as healthcare and finance must process sensitive data, while complying with legal policies regarding the release of different classes of data to different parties. Currently, software bugs may lead to irreversible disclosure of confidential data in multi-tier web applications. An open challenge is how developers can guarantee these web applications only ever release sensitive data to authorised users without costly, recurring security audits. Our solution is to provide a trusted middleware that acts as a “safety net” to event-based enterprise web applications by preventing harmful data disclosure before it happens. We describe the design and implementation of SafeWeb, a Ruby-based middleware that associates data with security labels and transparently tracks their propagation at different granularities across a multi-tier web architecture with storage and complex event processing. For efficiency, maintainability and ease-of-use, SafeWeb exploits the dynamic features of the Ruby programming language to achieve label propagation and data flow enforcement. We evaluate SafeWeb by reporting our experience of implementing a web-based cancer treatment application and deploying it as part of the UK National Health Service (NHS)

Logarithmic Corrections to Rotating Extremal Black Hole Entropy in Four and Five Dimensions

We compute logarithmic corrections to the entropy of rotating extremal black holes using quantum entropy function i.e. Euclidean quantum gravity approach. Our analysis includes five dimensional supersymmetric BMPV black holes in type IIB string theory on T^5 and K3 x S^1 as well as in the five dimensional CHL models, and also non-supersymmetric extremal Kerr black hole and slowly rotating extremal Kerr-Newmann black holes in four dimensions. For BMPV black holes our results are in perfect agreement with the microscopic results derived from string theory. In particular we reproduce correctly the dependence of the logarithmic corrections on the number of U(1) gauge fields in the theory, and on the angular momentum carried by the black hole in different scaling limits. We also explain the shortcomings of the Cardy limit in explaining the logarithmic corrections in the limit in which the (super)gravity description of these black holes becomes a valid approximation. For non-supersymmetric extremal black holes, e.g. for the extremal Kerr black hole in four dimensions, our result provides a stringent testing ground for any microscopic explanation of the black hole entropy, e.g. Kerr/CFT correspondence.Comment: LaTeX file, 50 pages; v2: added extensive discussion on the relation between boundary condition and choice of ensemble, modified analysis for slowly rotating black holes, all results remain unchanged, typos corrected; v3: minor additions and correction

Beneficial effect of 24-month bilateral subthalamic stimulation on quality of sleep in Parkinson's disease

BACKGROUND Subthalamic nucleus (STN) deep brain stimulation (DBS) improves quality of life (QoL), motor, and sleep symptoms in Parkinson’s disease (PD). However, the long-term effects of STN-DBS on sleep and its relationship with QoL outcome are unclear. METHODS In this prospective, observational, multicenter study including 73 PD patients undergoing bilateral STN-DBS, we examined PDSleep Scale (PDSS), PDQuestionnaire-8 (PDQ-8), Scales for Outcomes in PD-motor examination, -activities of daily living, and -complications (SCOPA-A, -B, -C), and levodopa-equivalent daily dose (LEDD) preoperatively, at 5 and 24 months follow-up. Longitudinal changes were analyzed with Friedman-tests or repeated-measures ANOVA, when parametric tests were applicable, and Bonferroni-correction for multiple comparisons. Post-hoc, visits were compared with Wilcoxon signed-rank/t-tests. The magnitude of clinical responses was investigated using effect size. RESULTS Significant beneficial effects of STN-DBS were observed for PDSS, PDQ-8, SCOPA-A, -B, and -C. All outcomes improved significantly at 5 months with subsequent decrements in gains at 24 months follow-up which were significant for PDSS, PDQ-8, and SCOPA-B. Comparing baseline and 24 months follow-up, we observed significant improvements of PDSS (small effect), SCOPA-A (moderate effect), -C, and LEDD (large effects). PDSS and PDQ-8 improvements correlated significantly at 5 and 24 months follow-up. CONCLUSIONS In this multicenter study with a 24 months follow-up, we report significant sustained improvements after bilateral STN-DBS using a PD-specific sleep scale and a significant relationship between sleep and QoL improvements. This highlights the importance of sleep in holistic assessments of DBS outcomes

Non-local rheology in dense granular flows -- Revisiting the concept of fluidity

Granular materials belong to the class of amorphous athermal systems, like foams, emulsion or suspension they can resist shear like a solid, but flow like a liquid under a sufficiently large applied shear stress. They exhibit a dynamical phase transition between static and flowing states, as for phase transitions of thermodynamic systems, this rigidity transition exhibits a diverging length scales quantifying the degree of cooperatively. Several experiments have shown that the rheology of granular materials and emulsion is non-local, namely that the stress at a given location does not depend only on the shear rate at this location but also on the degree of mobility in the surrounding region. Several constitutive relations have recently been proposed and tested successfully against numerical and experimental results. Here we use discrete elements simulation of 2D shear flows to shed light on the dynamical mechanism underlying non-locality in dense granular flows

Robust and private Bayesian inference

We examine the robustness and privacy of Bayesian inference, under assumptions on the prior, and with no modifications to the Bayesian framework. First, we generalise the concept of differential privacy to arbitrary dataset distances, outcome spaces and distribution families. We then prove bounds on the robustness of the posterior, introduce a posterior sampling mechanism, show that it is differentially private and provide finite sample bounds for distinguishability-based privacy under a strong adversarial model. Finally, we give examples satisfying our assumptions