Goal-Space Planning with Subgoal Models

This paper investigates a new approach to model-based reinforcement learning using background planning: mixing (approximate) dynamic programming updates and model-free updates, similar to the Dyna architecture. Background planning with learned models is often worse than model-free alternatives, such as Double DQN, even though the former uses significantly more memory and computation. The fundamental problem is that learned models can be inaccurate and often generate invalid states, especially when iterated many steps. In this paper, we avoid this limitation by constraining background planning to a set of (abstract) subgoals and learning only local, subgoal-conditioned models. This goal-space planning (GSP) approach is more computationally efficient, naturally incorporates temporal abstraction for faster long-horizon planning and avoids learning the transition dynamics entirely. We show that our GSP algorithm can learn significantly faster than a Double DQN baseline in a variety of situations

Near-miss event detection at railway level crossings

Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU$116 million annually. To better understand the causal factors of these accidents, a video analytics application is being developed to automatically detect near-miss incidents using forward facing videos from trains. As near-miss events occur more frequently than collisions, by detecting these occurrences there will be more safety data available for analysis. The application that is being developed will improve the objectivity of near-miss reporting by providing quantitative data about the position of vehicles at level crossings through the automatic analysis of video footage. In this paper we present a novel method for detecting near-miss occurrences at railway level crossings from video data of trains. Our system detects and localizes vehicles at railway level crossings. It also detects the position of railways to calculate the distance of the detected vehicles to the railway centerline. The system logs the information about the position of the vehicles and railway centerline into a database for further analysis by the safety data recording and analysis system, to determine whether or not the event is a near-miss. We present preliminary results of our system on a dataset of videos taken from a train that passed through 14 railway level crossings. We demonstrate the robustness of our system by showing the results of our system on day and night videos

Video analytics for the detection of near-miss incidents at railway level crossings and signal passed at danger events

Railway collisions remain a significant safety and financial concern for the Australian railway industry. Collecting data about events which could potentially lead to collisions helps to better understand the causal factors of railway collisions. In this thesis, we introduced Artificial Intelligence and Computer Vision algorithms which use cameras installed on trains to automatically detect Near-miss incidents at railway level crossings, and Signal Passed at Danger (SPAD) events. A SPAD is an event when a train passes a red signal without authority due to technical or human errors. Our experimental results demonstrate that it is possible to reliably detect these events

Video analytics for the detection of near-miss incidents on approach to railway level crossings

Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU$116 million annually. To better understand causal factors that contribute to these accidents, the Cooperative Research Centre for Rail Innovation is running a project entitled Baseline Level Crossing Video. The project aims to improve the recording of level crossing safety data by developing an intelligent system capable of detecting near-miss incidents and capturing quantitative data around these incidents. To detect near-miss events at railway level crossings a video analytics module is being developed to analyse video footage obtained from forward-facing cameras installed on trains. This paper presents a vision base approach for the detection of these near-miss events. The video analytics module is comprised of object detectors and a rail detection algorithm, allowing the distance between a detected object and the rail to be determined. An existing publicly available Histograms of Oriented Gradients (HOG) based object detector algorithm is used to detect various types of vehicles in each video frame. As vehicles are usually seen from a sideway view from the cabin’s perspective, the results of the vehicle detector are verified using an algorithm that can detect the wheels of each detected vehicle. Rail detection is facilitated using a projective transformation of the video, such that the forward-facing view becomes a bird’s eye view. Line Segment Detector is employed as the feature extractor and a sliding window approach is developed to track a pair of rails. Localisation of the vehicles is done by projecting the results of the vehicle and rail detectors on the ground plane allowing the distance between the vehicle and rail to be calculated. The resultant vehicle positions and distance are logged to a database for further analysis. We present preliminary results regarding the performance of a prototype video analytics module on a data set of videos containing more than 30 different railway level crossings. The video data is captured from a journey of a train that has passed through these level crossings

The effectiveness of computed tomography scans versus magnetic resonance imaging for decision making in patients with low back pain and radicular leg pain

BACKGROUND: Low back pain (LBP) and radicular leg pain (RLP) are among the most common types of pain in human beings. Although magnetic resonance imaging (MRI) is very sensitive for diagnosis of discopathy, some factors, such as overestimation of pathology, expensiveness, unavailability, and using it for patients with cardiac pacemaker or metal foreign bodies, limit the utility. The present study is designed to evaluate the efficacy of computed tomography scan (CTS) in patients with disc herniation in each level of lumbar spine versus MRI findings at the same level. 
 METHODS: In a prospective trial, 100 consecutive patients with LBP and RLP and signs and symptoms of discopathy referred to our private clinic from September 2004 to April 2005 were studied. CTS and MRI and their data were compared level by level; i.e. CTS of the patients analyzed according to clinical signs and symptoms and compared with MRI at the same level in axial view.
 RESULTS: Thirty-two patients had clinically S1 root signs and symptoms, in all of them CTS and MRI showed disc herniation at L5/S1 level in axial view. For L5/S1 level, positive predictive value (PPV) of CTS was 100%. In upper lumbar region, CTS findings were less reliable than MRI. CTS showed the pathology at 14.2% of upper lumbar, 27.2% at L3/L4 and 46.3% at L4/L5. In nine cases with more than one level involved, CTS confirmed the diagnosis in 11.1% of the cases.
 CONCLUSIONS: MRI is the gold standard for diagnosis of lumbar disc herniation, but CTS is sensitive in 100% for L5/S1, 68% for L4/L5, 60% for L3/L4, 0% for upper lumbar discopathies and finally 78% for multilevel involvement. Therefore, the higher the level of disc herniation is, the lower the sensitivity of CTS.
 KEY WORDS: Computed tomography scan, magnetic resonance imaging, low back pain, radicular leg pain

Future of Architectural Research: Proceedings of the Architectural Research Centers Consortium Conference

Research in architectural design and the built environment is diversifying and reaching new directions. Technological changes, such as new materials, design representations, and construction techniques have accelerated the need to advance knowledge across the design disciplines. Today, research is more important than ever and is becoming an integral component in the design practices. The theme of ARCC 2015 Conference –the FUTURE of Architectural Research – is intended to help define the future directions of architectural research and practice.The conference explored interdisciplinary approaches that address advanced materials, building technologies, environmental and energy concerns, computational design, automation in construction and design delivery methods.The research papers included in this book address the above stated themes and topics, and provide an excellent overview of cutting-edge architectural research and results. For example, several research papers relate to extremely low-energy and net-zero energy buildings, building-scale and community resiliency, new materials and building technologies, and advanced computational design, prototyping, and fabrication methods present some of the emerging issues. Other research papers focus on specific building types (e.g., commercial, residential, healthcare, learning environments), and discuss the intricacies and design methods that must be taken into account during the design process of these specific building typologies. Social and behavioral research papers discuss occupants’ psychological and physiological well-being and identify important considerations and factors that influence operational side of the built environment. Papers that focus on research and education in academia present some of the emerging methods for integrating research and pedagogical modes in architectural design, and discuss variety of case studies.It is our intention to increase discernibility of current architectural research, both practice-led and from the academy, and begin to form collaborative alliances that will result in wider implementation of research results, improved design practices and outcomes, increased funding for architectural research, and significant impacts on the society and built environment

Association between Outcome of severe traumatic brain injury and demographic, clinical, injury-related variables of patients

Background: Traumatic brain injury (TBI) is a main health problem among communities. There exists a variety of effective factors on the outcome of patients with TBI. We describe the demographic, clinical, and injury related variables of the patients with severe TBI, and determine the predictors of outcome. Materials and Methods: We did this cross-sectional study on all 267 adult patients with severe TBI admitted to three trauma centers of Isfahan University of Medical Sciences (IUMS) from March 20, 2014 to March 19, 2015. Data were extracted from patients' profiles. We considered the patients' outcome as discharged and died. We analyzed the collected data using descriptive (frequency, mean, and standard deviation) and analytical (independent t-test, Mann–Whitney U-test, Kruskal–Wallis test and logistic regression) statistics in Statistical Package for the Social Sciences (SPSS) 16.0. We considered p < 0.05 as the significance level. Results: The mean (SD) age of patients was 43.86 (18.40) years. The majority of the population was men (87.27%). Road traffic accidents (RTAs) were the most common mechanism of trauma (79.40%). The mean (SD) of Glasgow coma scale (GCS) was 6.03 (3.11). In 50.19% of the patients, the pupillary reflex was absent. One hundred and twenty-four patients (46.44%) died before discharge. We found age, gender, GCS, pupillary reflex, hypernatremia, and increased intracranial pressure (IICP) as the predictors of death in severe TBI. Conclusions: In this study, the mortality rate of patients with severe TBI was high. In addition, some factors were determined as the significant predictors of outcome. The findings can assist in planning to enhance the quality of care and reduce the mortality rate in the patients with severe TBI

Curvilinear steel elements in load-bearing structures of high-rise building spatial frames

The application of curvilinear elements in load-bearing metal structures of high-rise buildings supposes ensuring of their bearing capacity and serviceability. There may exist a great variety of shapes and orientations of such structural elements. In particular, it may be various flat curves of an open or closed oval profile such as circular or parabolic arch or ellipse. The considered approach implies creating vast internal volumes without loss in the load-bearing capacity of the frame. The basic concept makes possible a wide variety of layout and design solutions. The presence of free internal spaces of large volume in "skyscraper" type buildings contributes to resolving a great number of problems, including those of communicative nature. The calculation results confirm the basic assumptions