Cassini ISS mutual event astrometry of the mid-sized Saturnian satellites 2005-2012

Reproduced with permission from Astronomy & Astrophysics, © ES

Optimal path planning for nonholonomic robotics systems via parametric optimisation

Abstract. Motivated by the path planning problem for robotic systems this paper considers nonholonomic path planning on the Euclidean group of motions SE(n) which describes a rigid bodies path in n-dimensional Euclidean space. The problem is formulated as a constrained optimal kinematic control problem where the cost function to be minimised is a quadratic function of translational and angular velocity inputs. An application of the Maximum Principle of optimal control leads to a set of Hamiltonian vector field that define the necessary conditions for optimality and consequently the optimal velocity history of the trajectory. It is illustrated that the systems are always integrable when n = 2 and in some cases when n = 3. However, if they are not integrable in the most general form of the cost function they can be rendered integrable by considering special cases. This implies that it is possible to reduce the kinematic system to a class of curves defined analytically. If the optimal motions can be expressed analytically in closed form then the path planning problem is reduced to one of parameter optimisation where the parameters are optimised to match prescribed boundary conditions.This reduction procedure is illustrated for a simple wheeled robot with a sliding constraint and a conventional slender underwater vehicle whose velocity in the lateral directions are constrained due to viscous damping

A visual category filter for Google images

We extend the constellation model to include heterogeneous parts which may represent either the appearance or the geometry of a region of the object. The pans and their spatial configuration are learnt simultaneously and automatically, without supervision, from cluttered images. We describe how this model can be employed for ranking the output of an image search engine when searching for object categories. It is shown that visual consistencies in the output images can be identified, and then used to rank the images according to their closeness to the visual object category. Although the proportion of good images may be small, the algorithm is designed to be robust and is capable of learning in either a totally unsupervised manner, or with a very limited amount of supervision. We demonstrate the method on image sets returned by Google's image search for a number of object categories including bottles, camels, cars, horses, tigers and zebras

Re-ranking for Writer Identification and Writer Retrieval

Automatic writer identification is a common problem in document analysis. State-of-the-art methods typically focus on the feature extraction step with traditional or deep-learning-based techniques. In retrieval problems, re-ranking is a commonly used technique to improve the results. Re-ranking refines an initial ranking result by using the knowledge contained in the ranked result, e. g., by exploiting nearest neighbor relations. To the best of our knowledge, re-ranking has not been used for writer identification/retrieval. A possible reason might be that publicly available benchmark datasets contain only few samples per writer which makes a re-ranking less promising. We show that a re-ranking step based on k-reciprocal nearest neighbor relationships is advantageous for writer identification, even if only a few samples per writer are available. We use these reciprocal relationships in two ways: encode them into new vectors, as originally proposed, or integrate them in terms of query-expansion. We show that both techniques outperform the baseline results in terms of mAP on three writer identification datasets

On the segmentation and classification of hand radiographs

This research is part of a wider project to build predictive models of bone age using hand radiograph images. We examine ways of finding the outline of a hand from an X-ray as the first stage in segmenting the image into constituent bones. We assess a variety of algorithms including contouring, which has not previously been used in this context. We introduce a novel ensemble algorithm for combining outlines using two voting schemes, a likelihood ratio test and dynamic time warping (DTW). Our goal is to minimize the human intervention required, hence we investigate alternative ways of training a classifier to determine whether an outline is in fact correct or not. We evaluate outlining and classification on a set of 1370 images. We conclude that ensembling with DTW improves performance of all outlining algorithms, that the contouring algorithm used with the DTW ensemble performs the best of those assessed, and that the most effective classifier of hand outlines assessed is a random forest applied to outlines transformed into principal components

Seeing Tree Structure from Vibration

Humans recognize object structure from both their appearance and motion; often, motion helps to resolve ambiguities in object structure that arise when we observe object appearance only. There are particular scenarios, however, where neither appearance nor spatial-temporal motion signals are informative: occluding twigs may look connected and have almost identical movements, though they belong to different, possibly disconnected branches. We propose to tackle this problem through spectrum analysis of motion signals, because vibrations of disconnected branches, though visually similar, often have distinctive natural frequencies. We propose a novel formulation of tree structure based on a physics-based link model, and validate its effectiveness by theoretical analysis, numerical simulation, and empirical experiments. With this formulation, we use nonparametric Bayesian inference to reconstruct tree structure from both spectral vibration signals and appearance cues. Our model performs well in recognizing hierarchical tree structure from real-world videos of trees and vessels.Comment: ECCV 2018. The first two authors contributed equally to this work. Project page: http://tree.csail.mit.edu

Exercise and insulin resistance in PCOS: muscle insulin signalling and fibrosis

OBJECTIVE:Mechanisms of insulin resistance in polycystic ovary syndrome (PCOS) remain ill-defined, contributing to sub-optimal therapies. Recognising skeletal muscle plays a key role in glucose homeostasis we investigated early insulin signalling, its association with aberrant transforming growth factor β (TGFβ) regulated tissue fibrosis. We also explored the impact of aerobic exercise on these molecular pathways. METHODS:A secondary analysis from a cross-sectional study was undertaken in women with (n=30) or without (n=29) PCOS across lean and overweight BMIs. A subset of participants with (n=8) or without (n=8) PCOS who were overweight completed 12-weeks of aerobic exercise training. Muscle was sampled before and 30 min into a euglycaemic-hyperinsulinaemic clamp pre- and post-training. RESULTS:We found reduced signalling in PCOS of mechanistic target of rapamycin (mTOR). Exercise training augmented but did not completely rescue this signalling defect in women with PCOS. Genes in the TGFβ signalling network were upregulated in skeletal muscle in the overweight women with PCOS but were unresponsive to exercise training except for genes encoding LOX, collagen 1 and 3. CONCLUSIONS:We provide new insights into defects in early insulin signalling, tissue fibrosis, and hyperandrogenism in PCOS-specific insulin resistance in lean and overweight women. PCOS-specific insulin-signalling defects were isolated to mTOR, while gene expression implicated TGFβ ligand regulating a fibrosis in the PCOS-obesity synergy in insulin resistance and altered responses to exercise. Interestingly, there was little evidence for hyperandrogenism as a mechanism for insulin resistance

Validation of Continuous Glucose Monitoring in Children and Adolescents With Cystic Fibrosis: A prospective cohort study

OBJECTIVE: To validate continuous glucose monitoring (CGM) in children and adolescents with cystic fibrosis. RESEARCH DESIGN AND METHODS: Paired oral glucose tolerance tests (OGTTs) and CGM monitoring was undertaken in 102 children and adolescents with cystic fibrosis (age 9.5-19.0 years) at baseline (CGM1) and after 12 months (CGM2). CGM validity was assessed by reliability, reproducibility, and repeatability. RESULTS: CGM was reliable with a Bland-Altman agreement between CGM and OGTT of 0.81 mmol/l (95% CI for bias +/- 2.90 mmol/l) and good correlation between the two (r = 0.74-0.9; P < 0.01). CGM was reproducible with no significant differences in the coefficient of variation of the CGM assessment between visits and repeatable with a mean difference between CGM1 and CGM2 of 0.09 mmol/l (95% CI for difference +/- 0.46 mmol/l) and a discriminant ratio of 13.0 and 15.1, respectively. CONCLUSIONS: In this cohort of children and adolescents with cystic fibrosis, CGM performed on two occasions over a 12-month period was reliable, reproducible, and repeatable

A distributed collaborative platform for personal health profiles in patient-driven health social network

Health social networks (HSNs) have become an integral part of healthcare to augment the ability of people to communicate, collaborate, and share information in the healthcare domain despite obstacles of geography and time. Doctors disseminate relevant medical updates in these platforms and patients take into account opinions of strangers when making medical decisions. This paper introduces our efforts to develop a core platform called Distributed Platform for Health Profiles (DPHP) that enables individuals or groups to control their personal health profiles. DPHP stores user's personal health profiles in a non-proprietary manner which will enable healthcare providers and pharmaceutical companies to reuse these profiles in parallel in order to maximize the effort where users benefit from each usage for their personal health profiles. DPHP also facilitates the selection of appropriate data aggregators and assessing their offered datasets in an autonomous way. Experimental results were described to demonstrate the proposed search model in DPHP. Multiple advantages might arise when healthcare providers utilize DPHP to collect data for various data analysis techniques in order to improve the clinical diagnosis and the efficiency measurement for some medications in treating certain diseases

How brains make decisions

This chapter, dedicated to the memory of Mino Freund, summarizes the Quantum Decision Theory (QDT) that we have developed in a series of publications since 2008. We formulate a general mathematical scheme of how decisions are taken, using the point of view of psychological and cognitive sciences, without touching physiological aspects. The basic principles of how intelligence acts are discussed. The human brain processes involved in decisions are argued to be principally different from straightforward computer operations. The difference lies in the conscious-subconscious duality of the decision making process and the role of emotions that compete with utility optimization. The most general approach for characterizing the process of decision making, taking into account the conscious-subconscious duality, uses the framework of functional analysis in Hilbert spaces, similarly to that used in the quantum theory of measurements. This does not imply that the brain is a quantum system, but just allows for the simplest and most general extension of classical decision theory. The resulting theory of quantum decision making, based on the rules of quantum measurements, solves all paradoxes of classical decision making, allowing for quantitative predictions that are in excellent agreement with experiments. Finally, we provide a novel application by comparing the predictions of QDT with experiments on the prisoner dilemma game. The developed theory can serve as a guide for creating artificial intelligence acting by quantum rules.Comment: Latex file, 20 pages, 3 figure