Canadian Space Agency Space Station Freedom utilization plans

Under the terms of the NASA/CSA Memorandum of Understanding, Canada will contribute the Mobile Servicing System and be entitled to use 3 percent of all Space Station utilization resources and user accommodations over the 30 year life of the Station. Equally importantly Canada, like NASA, can begin to exploit these benefits as soon as the Man-Tended Capability (MTC) phase begins, in early 1997. Canada has been preparing its scientific community to fully utilize the Space Station for the past five years; most specifically by encouraging, and providing funding, in the area of Materials Science and Applications, and in the area of Space Life Sciences. The goal has been to develop potential applications and an experienced and proficient Canadian community able to effectively utilize microgravity environment facilities such as Space Station Freedom. In addition, CSA is currently supporting four facilities; a Laser Test System, a Large Motion Isolation Mount, a Canadian Float Zone Furnace, and a Canadian Protein Crystallization Apparatus. In late April of this year CSA sent out a Solicitation of Interest (SOI) to potential Canadian user from universities, industry, and government. The intent of the SOI was to determine who was interested, and the type of payloads which the community at large intended to propose. The SOI will be followed by the release of an Announcement of Opportunity (AO) following governmental approval of the Long Term Space plan later this year, or early next year. Responses to the AO will be evaluated and prioritized in a fair and impartial payload selection process, within the guidelines set by our international partners and the Canadian Government. Payload selection is relatively simple compared to the development and qualification process. An end-to-end user support program is therefore also being defined. Much of this support will be provided at the new headquarters currently being built in St. Hubert, Quebec. It is recognized that utilizing the Space Station could be expensive for users; costing in many cases millions of dollars to get a payload from conception to retrieval. It is also recognized that some of the potential users cannot or will not invest a lot of money or effort into Space Station utilization, unless there is a perceived significant commercial potential. How best to fund Space Station payloads is under study. Space Station Freedom will provide the first opportunity for Canada to conduct experiments in a long-duration microgravity environment. CSA have been developing and funding potential users for some time, and considerable interest has been shown by the response to our SOI earlier this year. Canada can be one of the two earliest users for the Space Station, along with NASA. We hope to take full advantage of this opportunity

Locally adaptive smoothing with Markov random fields and shrinkage priors

We present a locally adaptive nonparametric curve fitting method that operates within a fully Bayesian framework. This method uses shrinkage priors to induce sparsity in order-k differences in the latent trend function, providing a combination of local adaptation and global control. Using a scale mixture of normals representation of shrinkage priors, we make explicit connections between our method and kth order Gaussian Markov random field smoothing. We call the resulting processes shrinkage prior Markov random fields (SPMRFs). We use Hamiltonian Monte Carlo to approximate the posterior distribution of model parameters because this method provides superior performance in the presence of the high dimensionality and strong parameter correlations exhibited by our models. We compare the performance of three prior formulations using simulated data and find the horseshoe prior provides the best compromise between bias and precision. We apply SPMRF models to two benchmark data examples frequently used to test nonparametric methods. We find that this method is flexible enough to accommodate a variety of data generating models and offers the adaptive properties and computational tractability to make it a useful addition to the Bayesian nonparametric toolbox.Comment: 38 pages, to appear in Bayesian Analysi

Stroke secondary prevention: everyone's business

Stroke secondary prevention is everyone’s business and requires cohesive working across the multiprofessional team and beyond [...

The influence of a six-week, high-intensity games intervention on the pulmonary oxygen uptake kinetics in prepubertal obese and normal-weight children

Background: The pulmonary oxygen uptake ( O2) response is deleteriously influenced by obesity in pre-pubertal children, as evidenced by a slower phase II response. To date, no studies have investigated the ability of an exercise intervention to ameliorate this. Objectives: To investigate the influence of a six week, high-intensity games orientated intervention on the O2 kinetic response of pre-pubertal obese (OB) and normal-weight (NW) children during heavy intensity exercise. Methods: Thirteen NW and fifteen OB children participated in a twice-weekly exercise intervention involving repeated bouts of 6-minutes of high-intensity, games-orientated exercises followed by 2 minutes of recovery. Sixteen NW and 11 OB children served as a control group. At baseline and post-intervention, each participant completed a graded-exercise test to volitional exhaustion and constant work rate heavy intensity exercise.Results: Post intervention, OB children demonstrated a reduced phase II τ (Pre: 30±8 cf. Post: 24±7 s), MRT (Pre: 50±10 cf. Post: 38±9 s) and phase II amplitude (Pre: 1.51±0.30 cf. Post: 1.34±0.27 l∙min-1). No changes were evident in the NW children. Conclusions: The present findings demonstrate that a six-week, high-intensity intervention can have a significant positive impact on the dynamic O2 response of obese pre-pubertal children

RELIABILITY OF A TRUNK MOUNTED ACCELEROMETER WHEN DETERMINING GAIT PARAMETERS IN PEOPLE WITH STROKE

Wearable sensors and accelerometers can objectively and reliably assess gait parameters in both healthy individuals and stroke patients. The purpose of this study was to determine whether a wireless tri-axial accelerometer is reliable when measuring spatio-temporal gait parameters in patients with stroke. Thirty-one chronic stroke patients (age: 59.5±13.6 years; time since stroke: 28.1±17.8 months) completed three repeated walks along a 10m flat walkway whilst wearing a trunk mounted accelerometer (BTS G-Walk) secured around the waist of the participant over the L5 vertebrae. Outcome measures included cadence, speed, stride length, %stride length/height, gait cycle duration, step length, stance and swing phase duration, single and double support duration for both symptomatic and asymptomatic lower limbs where relevant. Reliability was assessed via intraclass correlation coefficient (ICC), standard error of the mean (SEM) and smallest detectable change (SDC) values. ICCs were \u3e 0.75 for all parameters, excluding step length on the asymptomatic side (ICC = 0.70). SEM and the SDC were marginally larger for the symptomatic limb than the asymptomatic limb for gait cycle duration and step length, but smaller for all other outcomes. The study showed that the BTS G-Walk is a reliable tool for measuring spatio-temporal parameters in patients with stroke. Physiotherapists and clinicians often prefer detailed information on gait ability. As advanced technologies could help with specific goals relating to gait performance, such devices could be reliably implemented as an alternative to the gold standard in clinical and community settings to monitor patients outside of a lab-based environment

Understanding the experiences of engaging in a community-based, physical-activity focused secondary stroke prevention program

Research has evidenced that regular exercise can provide physical and physiological benefits for people living with stroke. Our study aims to explore the experiences of people living with stroke when participating in a community physical activity programme. This programme was created to offer targeted physical activity and education interventions following the discharge of patients from the healthcare pathway. This qualitative study involved semi-structured interviews with 16 participants living with stroke who were recruited from individuals who had engaged with the activity programme. A reflexive thematic analysis was conducted on the data, and four overarching themes were developed: (i) Feelings of appreciation, (ii) Interactions with other patients, (iii) Positive contributions of trained instructors, and iv) Personal progress. Generally, participants reported very positive perceptions of the exercise programme, and were very grateful for the opportunity that the exercise classes provided. We hope that these findings will offer practical suggestions for healthcare providers who might develop similar activity programmes for clinical populations

Data Dashboards for the Massachusetts Working Cities

The Mosakowski Institute is the Federal Reserve Bank of Boston’s Research Partner for the bank’s “Working Cities Challenge” program for mid-sized cities in Massachusetts. The Institute recently prepared “data dashboards” for twenty Massachusetts cities, compiling information about such subjects as demographics, income, employment, educational attainment, and health

General and Fine-Grained Video Understanding using Machine Learning & Standardised Neural Network Architectures

Recently, the broad adoption of the internet coupled with connected smart devices has seen a significant increase in the production, collection, and sharing of data. One of the biggest technical challenges in this new information age is how to effectively use machines to process and extract useful information from this data. Interpreting video data is of particular importance for many applications including surveillance, cataloguing, and robotics, however it is also particularly difficult due to video’s natural sparseness - for lots of data there is small amounts of useful information. This thesis examines and extends a number of Machine Learning models in a number of video understanding problem domains including captioning, detection and classification. Captioning videos with human like sentences can be considered a good indication of how well a machine can interpret and distill the contents of a video. Captioning generally requires knowledge of the scene, objects, actions, relationships and temporal dynamics. Current approaches break this problem into three stages with most works focusing on visual feature filtering techniques for supporting a caption generation module. Current approaches however still struggle to associate ideas described in captions with their visual components in the video. We find that captioning models tend to generate shorter more succinct captions, with overfitted training models performing significantly better than human annotators on the current evaluation metrics. After taking a closer look at the model and human generated captions we highlight that the main challenge for captioning models is to correctly identify and generate specific nouns and verbs, particularly rare concepts. With this in mind we experimentally analyse a handful of different concept grounding techniques, showing some to be promising in increasing captioning performance, particularly when concepts are identified correctly by the grounding mechanism. To strengthen visual interpretations, recent captioning approaches utilise object detections to attain more salient and detailed visual information. Currently, these detections are generated by an image based detector processing only a single video frame, however it’s desirable to capture the temporal dynamics of objects across an entire video. We take an efficient image object detection framework, and carry out an extensive exploration into the effects of a number of network modifications towards improving the model’s ability to perform on video data. We find a number of promising directions which improve upon the single frame baseline. Furthermore, to increase concept coverage for object detection in video we combine datasets from both the image and video domains. We then perform an in-depth analysis on the coverage of the combined detection dataset with the concepts found in captions from video captioning datasets. While the bulk of this thesis centres around general video understanding - random videos from the internet - it’s also useful to determine the performance of these Machine Learning techniques on a more fine-grained problem. We therefore introduce a new Tennis dataset, which includes broadcast video for five tennis matches with detailed annotations for match events and commentary style captions. We evaluate a number of modern Machine Learning techniques for performing shot classification, as a stand-alone and a precursor process for commentary generation, finding that current models are similarly effective for this fine-grained problem.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202