Imaging diagnosis-computed tomography of traction bronchiectasis secondary to pulmonary fibrosis in a Patterdale Terrier

An 8-year-old, Patterdale terrier was referred for evaluation of tachypnoea, exercise intolerance, and weight loss. Computed tomographic images showed pneumomediastinum, diffuse pulmonary ground glass opacity, and marked dilatation of peripheral bronchi, but no evidence of thickened bronchial walls. The histopathologic diagnosis was diffuse pulmonary interstitial fibrosis, type II pneumocyte hyperplasia, and bronchiectasis. The lack of evidence of primary bronchitis supported a diagnosis of traction bronchiectasis. Traction bronchiectasis can occur as a sequela to pulmonary fibrosis in dogs. (C) 2016 American College of Veterinary Radiology

Time Protection: the Missing OS Abstraction

Timing channels enable data leakage that threatens the security of computer systems, from cloud platforms to smartphones and browsers executing untrusted third-party code. Preventing unauthorised information flow is a core duty of the operating system, however, present OSes are unable to prevent timing channels. We argue that OSes must provide time protection in addition to the established memory protection. We examine the requirements of time protection, present a design and its implementation in the seL4 microkernel, and evaluate its efficacy as well as performance overhead on Arm and x86 processors

Synthesizing Functional Reactive Programs

Functional Reactive Programming (FRP) is a paradigm that has simplified the construction of reactive programs. There are many libraries that implement incarnations of FRP, using abstractions such as Applicative, Monads, and Arrows. However, finding a good control flow, that correctly manages state and switches behaviors at the right times, still poses a major challenge to developers. An attractive alternative is specifying the behavior instead of programming it, as made possible by the recently developed logic: Temporal Stream Logic (TSL). However, it has not been explored so far how Control Flow Models (CFMs), as synthesized from TSL specifications, can be turned into executable code that is compatible with libraries building on FRP. We bridge this gap, by showing that CFMs are indeed a suitable formalism to be turned into Applicative, Monadic, and Arrowized FRP. We demonstrate the effectiveness of our translations on a real-world kitchen timer application, which we translate to a desktop application using the Arrowized FRP library Yampa, a web application using the Monadic threepenny-gui library, and to hardware using the Applicative hardware description language ClaSH.Comment: arXiv admin note: text overlap with arXiv:1712.0024

Assessment and Evaluation of Sand Control Methods for a North Sea Field

Imperial Users onl

Joining refractory/austenitic bimetal tubing Supplemental report

Joining bimetal tubing consisting of austenitic stainless steel with inner lining of niobium or tantalu

Learning Bayes-optimal dendritic opinion pooling

In functional network models, neurons are commonly conceptualized as linearly summing presynaptic inputs before applying a non-linear gain function to produce output activity. In contrast, synaptic coupling between neurons in the central nervous system is regulated by dynamic permeabilities of ion channels. So far, the computational role of these membrane conductances remains unclear and is often considered an artifact of the biological substrate. Here we demonstrate that conductance-based synaptic coupling allow neurons to represent, process and learn uncertainties. We suggest that membrane potentials and conductances on dendritic branches code opinions with associated reliabilities. The biophysics of the membrane combines these opinions by taking account their reliabilities, and the soma thus acts as a decision maker. We derive a gradient-based plasticity rule, allowing neurons to learn desired target distributions and weight synaptic inputs by their relative reliabilities. Our theory explains various experimental findings on the system and single-cell level related to multi-sensory integration, and makes testable predictions on dendritic integration and synaptic plasticity.Comment: 36 pages, 10 figures; Mihai A. Petrovici and Walter Senn share senior authorshi

Exact computation of the Maximum Entropy Potential of spiking neural networks models

Understanding how stimuli and synaptic connectivity in uence the statistics of spike patterns in neural networks is a central question in computational neuroscience. Maximum Entropy approach has been successfully used to characterize the statistical response of simultaneously recorded spiking neurons responding to stimuli. But, in spite of good performance in terms of prediction, the fitting parameters do not explain the underlying mechanistic causes of the observed correlations. On the other hand, mathematical models of spiking neurons (neuro-mimetic models) provide a probabilistic mapping between stimulus, network architecture and spike patterns in terms of conditional proba- bilities. In this paper we build an exact analytical mapping between neuro-mimetic and Maximum Entropy models.Comment: arXiv admin note: text overlap with arXiv:1309.587