Ponder: A Language for Specifying Security and Management Policies for Distributed Systems

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Ponder: Realising enterprise viewpoint concepts

This paper introduces the Ponder language for speciing distributed object enterprise concepts. Ponder, is a declarative language, which permits the specification of policies in terms of obligations, permissions and prohibitions and provides the means for defining roles, relationships and their configurations in nested communities. Ponder provides a concrete representation of most of the concepts of the Enterprise Viewpoint. The design of the language incorporates lessons drawn from several years of research on policy for security and distributed systems management as well as policy conflict analysis. The various language constructs are presented through a scenario for the operation, administration and maintenance of a mobile telecommunication network

Review of Protocols Used in Ultrasound Thrombolysis

Objectives: This paper focuses on the review of protocols used in thrombolysis studies with ultrasound. Materials and methods: Data from peer-review articles were acquired. Results: The protocols of several published reports are summarized in 3 tables (in vitro, in vivo, and clinical), providing detailed information concerning clot model, thrombolytic drug, treatment mode, sonication parameters, evaluation method, thrombolysis outcome, side effects, and conclusions. Conclusions: The aim of this review was to give an overview of the different protocols used so far in the field of sonothrombolysis and investigate the impact of several aspects involved on sonothrombolysis outcome

Bayesian Optimisation for Safe Navigation under Localisation Uncertainty

In outdoor environments, mobile robots are required to navigate through terrain with varying characteristics, some of which might significantly affect the integrity of the platform. Ideally, the robot should be able to identify areas that are safe for navigation based on its own percepts about the environment while avoiding damage to itself. Bayesian optimisation (BO) has been successfully applied to the task of learning a model of terrain traversability while guiding the robot through more traversable areas. An issue, however, is that localisation uncertainty can end up guiding the robot to unsafe areas and distort the model being learnt. In this paper, we address this problem and present a novel method that allows BO to consider localisation uncertainty by applying a Gaussian process model for uncertain inputs as a prior. We evaluate the proposed method in simulation and in experiments with a real robot navigating over rough terrain and compare it against standard BO methods.Comment: To appear in the proceedings of the 18th International Symposium on Robotics Research (ISRR 2017

Variational auto-encoded deep Gaussian processes

We develop a scalable deep non-parametric generative model by augmenting deep Gaussian processes with a recognition model. Inference is performed in a novel scalable variational framework where the variational posterior distributions are reparametrized through a multilayer perceptron. The key aspect of this reformulation is that it prevents the proliferation of variational parameters which otherwise grow linearly in proportion to the sample size. We derive a new formulation of the variational lower bound that allows us to distribute most of the computation in a way that enables to handle datasets of the size of mainstream deep learning tasks. We show the efficacy of the method on a variety of challenges including deep unsupervised learning and deep Bayesian optimization

Multiple Hamiltonian structure of Bogoyavlensky-Toda lattices

This paper is mainly a review of the multi--Hamiltonian nature of Toda and generalized Toda lattices corresponding to the classical simple Lie groups but it includes also some new results. The areas investigated include master symmetries, recursion operators, higher Poisson brackets, invariants and group symmetries for the systems. In addition to the positive hierarchy we also consider the negative hierarchy which is crucial in establishing the bi--Hamiltonian structure for each particular simple Lie group. Finally, we include some results on point and Noether symmetries and an interesting connection with the exponents of simple Lie groups. The case of exceptional simple Lie groups is still an open problem.Comment: 65 pages, 67 reference

An integrated probabilistic framework for robot perception, learning and memory

Learning and perception from multiple sensory modalities are crucial processes for the development of intelligent systems capable of interacting with humans. We present an integrated probabilistic framework for perception, learning and memory in robotics. The core component of our framework is a computational Synthetic Autobiographical Memory model which uses Gaussian Processes as a foundation and mimics the functionalities of human memory. Our memory model, that operates via a principled Bayesian probabilistic framework, is capable of receiving and integrating data flows from multiple sensory modalities, which are combined to improve perception and understanding of the surrounding environment. To validate the model, we implemented our framework in the iCub humanoid robotic, which was able to learn and recognise human faces, arm movements and touch gestures through interaction with people. Results demonstrate the flexibility of our method to successfully integrate multiple sensory inputs, for accurate learning and recognition. Thus, our integrated probabilistic framework offers a promising core technology for robust intelligent systems, which are able to perceive, learn and interact with people and their environments

MRI monitoring of lesions created at temperature below the boiling point and of lesions created above the boiling point using high intensity focused ultrasound

Magnetic Resonance Imaging (MRI) was utilized to monitor lesions created at temperature below the boiling point and lesions created at temperature above the boiling point using High Intensity Focused Ultrasound (HIFU) in freshly excised kidney, liver and brain and in vivo rabbit kidney and brain. T2-weighted fast spin echo (FSE) was proven as an excellent MRI sequence that can detect lesions with temperature above the boiling point in kidney. This advantage is attributed to the significant difference in signal intensity between the cavity and the thermal lesion. In liver the MRI sequence of Proton Density is recommended to detect lesions above boiling. In brain T1-W FSE was the optimum pulse sequence to detect lesions of either type. In order to monitor the temperature elevation during a HIFU exposure, T1-weighted fast spoiled gradient (FSPGR) was used. The shape of the focal temperature distribution was uniform with the absence of boiling, whereas with an exposure affected by boiling, the temperature distribution could be of irregular shape, demonstrating the drastic effects taking place during boiling. In order to confirm that boiling occurred, the temperature was estimated using the widely used method of Proton Resonance Frequency (PRF) shift

Computation of an MRI brain atlas from a population of Parkinson’s disease patients

Abstract Parkinson’s Disease (PD) is a degenerative disorder of the brain. This study presents an MRI-based brain atlas of PD to characterize associated alterations for diagnostic and interventional purposes. The atlas standardizes primarily the implicated subcortical regions such as the globus pallidus (GP), substantia nigra (SN), subthalamic nucleus (STN), caudate nucleus (CN), thalamus (TH), putamen (PUT), and red nucleus (RN). The data were 3.0 T MRI brain images from 16 PD patients and 10 matched controls. The images used were T1-weighted ( T 1 w ), T2-weighted ( T 2 w ) images, and Susceptibility Weighted Images (SWI). The T1w images were the reference for the inter-subject non-rigid registration available from 3DSlicer. Anatomic labeling was achieved with BrainSuite and regions were refined with the level sets segmentation of ITK-Snap. The subcortical centers were analyzed for their volume and signal intensity. Comparison with an age-matched control group unravels a significant PD-related T1w signal loss in the striatum (CN and PUT) centers, but approximately a constant volume. The results in this study improve MRI based PD localization and can lead to the development of novel biomarkers

Semantic-based policy engineering for autonomic systems

This paper presents some important directions in the use of ontology-based semantics in achieving the vision of Autonomic Communications. We examine the requirements of Autonomic Communication with a focus on the demanding needs of ubiquitous computing environments, with an emphasis on the requirements shared with Autonomic Computing. We observe that ontologies provide a strong mechanism for addressing the heterogeneity in user task requirements, managed resources, services and context. We then present two complimentary approaches that exploit ontology-based knowledge in support of autonomic communications: service-oriented models for policy engineering and dynamic semantic queries using content-based networks. The paper concludes with a discussion of the major research challenges such approaches raise