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Researchers have extensively explored indoor localization in recent years. Robotic applications often require precise positioning, which is difficult when access to a GPS is limited or compromised. Many of the existing approaches require either prior knowledge of the local environment and fixed landmarks or complex and expensive hardware to achieve the necessary degree of accuracy. In this article, we examine the use of the HTC virtual reality hardware along with our novel approach, VIVEPOSE, to perform indoor robotic localization. We then compare the accuracy of our proposed indoor localization system to current approaches that use traditional odometry sensor-based localization. Finally, we present and demonstrate a leader-follower approach using VIVEPOSE and show how the HTC Vive tracker can be successful for indoor localization for a robotics application.

Background Gut-directed hypnosis (GDH) is a complimentary therapy for Inflammatory Bowel Disease (IBD), that can be learnt by patients to practice self-hypnosis. GDH in IBD has augmented remission and improved inflammation. GDH has a history of successful use for Irritable Bowel Syndrome (IBS). In IBD it may also improve IBS-like symptoms in remission and recovery from surgery. GDH is suitable for youth and adult IBD patients. In hypnosis, a relaxed state is inducted then suggestions to subconscious mind processes are made. In IBD, the mechanism of action of GDH is unknown but may influence the disease stress response. Aims Aims are the development of a GDH self-hypnosis protocol for IBD, with appropriate target symptoms. Patients first learn to practice with a clinician, then as complimentary psychotherapy for remission augmentation, IBS-like symptoms, and surgery recovery. Methods GDH is practiced first with a clinician, and then by patients as self-hypnosis (table 1). Patients receive psycho-education on GDH for IBD. Next, appropriate treatment goals are made, based on target symptoms. Relaxation techniques induce patient to a deeply relaxed state. Therapeutic suggestions specific to patient goals are given: verbal suggestions, visualizations, and post-hypnotic suggestions. Suggestions can focus on having a healthy digestive system, inflammation and symptoms reduction, and achievement and sustainment of remission. Patients emerge from hypnosis, are debriefed, and encouraged to practice ongoing self-hypnosis. Results In IBD, GDH self-hypnosis can be learnt from clinicians and practiced by patients as a complimentary therapy. Patients’ achievement and sustainment of remission, with clinical markers of inflammation can be monitored. Patients can monitor subjective improvement of IBS-like symptoms and post surgery, recovery progress can be monitored. Conclusions GDH has a history of use for IBS. In IBD, it has been shown to modulate remission, and may improve IBS-like symptoms, and in surgery recovery. The mechanism of action of GDH in IBD may influence the disease stress response. Clinicians trained in GDH are limited currently. Patients may learn GDH self-hypnosis as a complimentary psychotherapy. Funding Agencies None

A great number of computer vision publications have focused on distinguishing between human action recognition and classification rather than the intensity of actions performed. Indexing the intensity which determines the performance of human actions is a challenging task due to the uncertainty and information deficiency that exists in the video inputs. To remedy this uncertainty, in this paper we coupled fuzzy logic rules with the neural-based action recognition model to rate the intensity of a human action as intense or mild. In our approach, we used a Spatio-Temporal LSTM to generate the weights of the fuzzy-logic model, and then demonstrate through experiments that indexing of the action intensity is possible. We analyzed the integrated model by applying it to videos of human actions with different action intensities and were able to achieve an accuracy of 89.16% on our intensity indexing generated dataset. The integrated model demonstrates the ability of a neuro-fuzzy inference module to effectively estimate the intensity index of human actions., Comment: 1 pages, 6 figures, 2 algorithms. Published at IEEE Access

Formation control has been a popular research topic in recent years. This is primarily due to the the use of formation control to coordinate the movement of a swarm of robots. In this paper, with the primary objective of guaranteeing image overlap, a novel bird flocking inspired formation control algorithm was developed to control a swarm of unmanned aerial vehicles (UAVs). Our approach uses only stereo cameras, global positioning system (GPS), and inertial measurement units (IMUs), and removes the need of feature or pattern matching. By utilizing stereo cameras, virtual tracking points can be calculated. The convergence of these tracking points are chosen as the primary formation control goal because if the virtual tracking points are converged, it is guaranteed that the field of view of the cameras of the various UAVs always have some overlap. This allows for different types of stitching methods to be used to form a larger image with no gaps, depending on the configuration of tracking points. Five different control algorithms were developed: direct method, proportional integral derivative, bang-bang, short window model predictive, and long window model predictive. The formation controller was tested in three simulation environments to verify the robustness and the effectiveness of our algorithm.

To meet new needs and to respond to changes in the energy market, Hydro-Quebec Trans Energie (HQT) under-´ takes an important research project, named PRIAD, to improve existing tools for asset management and modelling system. The aim of this project is to assess the impact of the unavailability of equipment, such as breakers, sectionalizers, physical buses, power transformers, etc., on the performance of the Hydro-Quebec (HQ) network. Power system node-breaker (PSNB) models are of common use in operation, while bus-branch models are usually used in planning studies. PRIAD relies on a transmission system reliability simulator named PRISME. PRISME is HQ’s first planning application that requires a PSNB model. To this end, we have developed an algorithm to convert the traditional bus-branch network model to a detailed node-breaker representation. To generate the node-breaker model (PSS/E format), we used the state estimator file in IEEE format, the Energy Management System (EMS) network connectivity model (CIM format), and the state of switches from the Supervisory Control and Data Acquisition System (SCADA) file.

Cloud computing is now a global standard computing topology and has been widely studied for many years. Less frequently researched is the use of cloud and edge computing to optimize the performance of a system as a whole. One important aspect of cloud and edge computing is managing the placement of the applications in the network system so as to minimize each application’s runtime, given the resources of system’s devices and the capabilities of the system’s network. The properties of containerized applications now make this possible. The process of containerization creates a lightweight, mobile, packaged application for each of the algorithms in a system. These applications can then be deployed easily and quickly on any layer of the cloud and edge computing architectures. In this research, a fuzzy placement control system is designed to place applications on the cloud-edge model. As verified by simulation, the fuzzy placement control system proposed reduces the total runtime of the applications by placing applications in an efficient location.

Hydro-Quebec TransEnergie (HQT) has been employing predictive modelling methods to manage its assets for over a decade. To meet new needs and to respond to changes in the energy market, HQT undertakes an important research project in order to improve existing tools for asset management and modelling system. In this paper, we present a quick review of the global asset management model at HQT. Furthermore, we introduce a Contingency Analysis (CA) approach which will be integrated in the reliability simulator module of the HQT global asset management model. To this end, we augment the traditional bus-branch data model to provide a detailed node-breaker representation that contains detailed nodes, breakers and other switching devices. Then, we analyze the impact of the equipment unavailability on the network behaviour using a detailed CA approach. This contingency analysis approach not only informs the asset management engineers in case of violations, but also suggests several remedial actions to eliminate the violation.

The Internet of Things has enabled a wide variety of new applications not possible only a short time ago. Sensing data found at the Edge of the network, close to the environment where people are found, is a critical component with many modern applications. Often restrictions at the device level or on the available bandwidth limit the ability to capture all locally available data required for processing and analysis. In this research, we present a novel method for extracting sound from video data where no original sound was present. Our novel method of sound synthesis first uses the image features output from a Convolutional Neural Network (CNN) to determine class prediction weights using an advanced Long Short Term Memory (LSTM) network. A Generative Adversarial Network (GAN) is then used to generate the representative sound of the predicted class for the input video sample. By combining the output of many Auto Sound Generators in a System of Systems framework, we show that new applications emerge that were never before possible.

Computer Vision is a valuable tool that can be used to enhance the components of many working systems. In particular, many applications can be improved by incorporating instance segmentation into their designs to help better process visual information in the surrounding environment. However, instance segmentation algorithms have traditionally been too slow to be used by any real-time systems that could benefit from using them. This includes examples such as self-driving vehicles or autonomous drones. In this work we provide an overview of the shortcomings for current instance segmentation algorithms, introduce an ongoing effort to create a new one that achieves ultrafast speeds without sacrificing competitive accuracy, and explain the advantages of employing an ultra-fast real-time version of one as a component in different systems.

The appeal of low cost computing and on demand scaling of cloud technologies has resulted in the migration of many software applications to the cloud. This increased reliance on the cloud translates to a direct dependence on the cloud data centers, which form the modern cloud. These data centers are complex buildings composed of many system of systems that interact to host the end applications. Detecting anomalous events in this system of systems and then identifying their root cause in a timely manner is a demanding task. In this paper, we present a graphical model to encapsulate the cloud data center system of systems and share a method for reducing the search space for root cause analysis.

In movie productions, the Foley Artist is responsible for creating an overlay soundtrack that helps the movie come alive for the audience. This requires the artist to first identify the sounds that will enhance the experience for the listener thereby reinforcing the Directors's intention for a given scene. In this paper, we present AutoFoley, a fully-automated deep learning tool that can be used to synthesize a representative audio track for videos. AutoFoley can be used in the applications where there is either no corresponding audio file associated with the video or in cases where there is a need to identify critical scenarios and provide a synthesized, reinforced soundtrack. An important performance criterion of the synthesized soundtrack is to be time-synchronized with the input video, which provides for a realistic and believable portrayal of the synthesized sound. Unlike existing sound prediction and generation architectures, our algorithm is capable of precise recognition of actions as well as inter-frame relations in fast moving video clips by incorporating an interpolation technique and Temporal Relationship Networks (TRN). We employ a robust multi-scale Recurrent Neural Network (RNN) associated with a Convolutional Neural Network (CNN) for a better understanding of the intricate input-to-output associations over time. To evaluate AutoFoley, we create and introduce a large scale audio-video dataset containing a variety of sounds frequently used as Foley effects in movies. Our experiments show that the synthesized sounds are realistically portrayed with accurate temporal synchronization of the associated visual inputs. Human qualitative testing of AutoFoley show over 73% of the test subjects considered the generated soundtrack as original, which is a noteworthy improvement in cross-modal research in sound synthesis., Comment: 14 pages, 14 figures

Background IBD patients have a heightened risk for mental health illness, but general psychotherapy has shown mixed results. This psychotherapy protocol specialized for IBD patients uses recommended mental health therapies to treat specific chronic mental health illness. Therapy focuses on practicing CBT, clinical hypnosis, and emotion regulation healthy coping skills to self-manage chronic mental health symptoms. Standard therapies for acute mental illness, e.g. anxiety, depression, and suicidality, should be used as appropriate. Aims Aims of psychotherapy in IBD include improvement of mental health, symptoms management, quality of life (QOL), and adherence to medical treatments. Cognitive behavioural therapy (CBT) changes maladaptive coping behaviours and thinking. Clinical hypnosis improves somatic symptoms, e.g. chronic pain, nausea, and cramping. Emotion regulation improves ability to process stressful emotions, and has been shown to be affected by chronic diseases, including moderate to severe Crohn’s disease. Methods Patients learn to self-manage chronic mental health symptoms with healthy coping skills. CBT allows patients to identify maladaptive coping behaviours and thinking. Healthy coping behaviours and thinking patterns are chosen, supported, and adapted to. Clinical hypnosis uses relaxation techniques, e.g. progressive muscle relaxation, visualizations, and positive suggestions as a complimentary therapy for somatic symptoms. Emotion regulation develops tolerance and healthy processing of stressors. Results Therapy effectiveness is evaluated by improvements in patients’ mental health, symptoms management, QOL, and adherence to medical treatments. Mental health improvement, QOL, and emotion regulation are monitored by patient self-report, e.g. questionnaires. Maladaptive coping behaviours and thinking changed using CBT are individual to each patient. Somatic symptoms improved using clinical hypnosis are determined by monitoring or patient self-report. Adherence to medical treatments, as appropriate, is monitored. Conclusions This IBD psychotherapy protocol uses gut-directed CBT, clinical hypnosis, and emotion regulation therapies, designed to improve mental health, symptoms management, QOL, and adherence to medical treatments. Healthy coping skills treat maladaptive coping behaviours and thinking, somatic symptoms, and emotion dysregulation. Chronic mental health illness associated with IBD, is treated using this specialized psychotherapy. Funding Agencies None

Alzheimer's disease (AD) is categorized as a progressive loss of cognitive functions such as impaired short-term memory and spatial cognition. In addition to memory difficulties, people with Alzheimer's experience challenges with visual perception and even recognizing loved ones. This study attempts to develop a platform to support patients who suffer from face perception impairment with an assistive intelligence device. Until recently, the development of wearable IoT devices has been hindered by the lack of their computational power. To achieve this goal, we develop a wearable assistive IoT with complex embedded artificial perception using deep learning for Alzheimer's patients. The experiments show the capability of the proposed system in identifying known faces in a near real-time manner.

The area of Formation Control has been extensively explored by researchers in recent years. The popularity of Formation Control to researchers is primarily do to its use to coordinate the movement of a swarm of robots. However, there are multiple algorithms that can coordinate the control of robotic swarm. In this paper, a novel leader-follower formation control algorithm was developed to control a formation of unmanned aerial vehicles (UAVs) using only stereo camera, Global Positioning Systems (GPS) and Inertial Measurement Units (IMUs), without the need of feature or pattern matching. Stereo cameras are chosen for formation control purposes because they allow the camera to guarantee that the field of view of the cameras of the various UAVs always have some overlap. Assuring there are no gaps in the field of view of the UAVs is important for missions such as search and rescue. Two different control algorithms were developed: direct method, and PID control. The results were tested in a simulation environments consisting of non-erroneous non-linear dynamics to verify its robustness. A novel formation controller is successfully developed which uses GPS, IMU and stereo camera input, without the need for feature or pattern matching and the computed results verify the effectiveness of our developed algorithm.

Cloud computing infrastructures have become the de-facto platform for data driven machine learning applications. However, these centralized models of computing are unqualified for dispersed high-volume real-time edge data intensive applications such as real time object detection, where video streams may be captured at multiple geographical locations. While many recent advancements in object detection have been made using Convolutional Neural Networks, these performance improvements only focus on a single, contiguous object detection model. In this paper, we propose a distributed Edge-Cloud R-CNN pipeline. By splitting the object detection pipeline into components and dynamically distributing these components in the cloud, we can achieve optimal performance to enable real time object detection. As a proof of concept, we evaluate the performance of the proposed system on a distributed computing platform including cloud servers and edge-embedded devices for real-time object detection on live video streams.

The use of Blockchain in the Internet of Things (IoT) promises to provide an avenue of decentralized, fault-resistant management and data immutability. The wide range of research on blockchain, focused on how to improve upon its drawbacks of latency and resource consumption, demonstrates that the blockchain design has flexibility. Almost every aspect of blockchain can be tailored to fit the requirements of a desired application. However, the wide range of options in configuring blockchains presents challenges in the adoption of blockchains to secure IoT. In this paper, we present the opportunities and challenges of implementing blockchains and present a use case of integrating blockchain into an IoT framework for securing sensor data acquisition.

Deep neural networks (DNNs) are currently among the most commonly used machine learning methods in content understanding such as computer vision and natural language understanding (NLU). One of the best characteristics of these methods is their modular design – the ability to change the connectivity patterns of layers, try different activation functions, inject different statistical approaches such as normalization and dropout in the network, and many other actions – in every aspect of deep learning networks. While the majority of deep learning applications simply use cross-entropy, L 1 , and L 2 losses, subjective loss function can actually result in impressive performance improvement. In addition, architecting the last layer of DNNs – referred to as the prediction layer – according to the needs of the application increases the discriminative power of the DNNs. This paper aims to investigate how particular choices of loss functions and prediction layer architecture affect deep neural networks and their learning dynamics, as well as the robustness of various effects. Furthermore a real-life application to measure customer loyalty called Deep Net Promoter Score (DeepNPS) from online product reviews is also proposed. The results are promising for learning more latent features and matching the customer feedback with the NPS score.

Virtual reality (VR) has become a very popular gaming platform in recent years, but has not been used as much in the research domain. Several technologies used to create virtual environments can also be used to assist in research. For example, recent developments in VR allow for easier integration of humans in the loop for control and manipulation of complex, multi-modal system of systems. In this paper we show some of the methods in which a virtually emulated system can be integrated into an artificial intelligence (AI) based system of systems. We demonstrate how this integration allows the reseacher to more easily understand and analyse the inter-system dynamics, and to improve performance using post-simulation visualization, real-time simulation interaction, hardware emulation, and physical object tracking. The results of using virtual reality as a simulation environment shows the usefulness of the tool to system of systems researchers.

Modern applications of Unmanned Aerial Vehicles are increasingly attracting the attention of traditional safety and reliability fields. There exist many standard approaches for determining UAV fault detection. However, there doesn't exist a method that is not only model independent but also has the ability to detect faults which have not been predefined for the UAV system. In this research we present two supervised machine learning algorithms implementing Logistic Regression and Linear Discriminant Analysis of Algorithms, respectively, to predict UAV faults. The data which has been used for these approaches comes from discrete-sampled, de-noised analog signals based on the voltage and current inputs belonging to four actuators of the UAV drones. In addition, we demonstrate that by using a five-fold cross validation process to generate different types of training and test datasets, the optimized model can be selected. We verify our results through an analysis describing the accuracies of our proposed model.

Background: Glioblastoma Multiforme (GBM) has well documented systemic and local immunosuppressive mechanisms to escape immune surveillance and grow. GBM tumor cells as well as the microglia within it have a high incidence of PD-L1 surface expression which makes it more susceptible to anti-PD-L1 antagonism and ADCC through avelumab therapy. Methods: This is a single center, phase 2, open label, add-on, single dose study of 156 weeks duration in patients receiving standard therapy for newly diagnosed GBM. In total 30 patients will be entered into the study within 3 weeks of finishing their last day of combined radiotherapy/temozolomide. The following are the results of the first interim analysis completed when the first eight patients completed 52 weeks or an end of study visit. Results: 24 patients have so far started therapy. There as been no unexpected treatment emergent adverse event (TEAE). Two patients transiently withheld therapy because of immune related TEAE’s and none permanently. The objective response rate at week 52 for the first eight patients was 50% with 2 (25%) having a complete response and 1 (12.5%) a partial response. Conclusions: These preliminary results suggest that the addition of avelumab to standard therapy in patients with GBM is safe. Efficacy trends look promising.

Cutting edge research today requires researchers to perform computationally intensive calculations and/or create models and simulations using large sums of data in order to reach research-backed conclusions. As datasets, models, and calculations increase in size and scope they present a computational and analytical challenge to the researcher. Advances in cloud computing and the emergence of big data analytic tools are ideal to aid the researcher in tackling this challenge. Although researchers have been using cloud-based software services to propel their research, many institutions have not considered harnessing the Infrastructure as a Service model. The reluctance to adopt Infrastructure as a Service in academia can be attributed to many researchers lacking the high degree of technical experience needed to design, procure, and manage custom cloud-based infrastructure. In this paper, we propose a comprehensive solution consisting of a fully independent cloud automation framework and a modular data analytics platform which will allow researchers to create and utilize domain specific cloud solutions irrespective of their technical knowledge, reducing the overall effort and time required to complete research.

Modern mobile devices are resource limited. We can utilize cloud computing to ensure optimum utilization of mobile device resources. Energy consumption and device latency can be reduced significantly by computation offloading. To ensure optimum utilization of mobile device resources and environment resources such as bandwidth and latency, computation offloading must be done in a strategic way. We have extended our work on a previous paper by proposing a dynamic algorithm based on machine learning which considers network topologies and device resources and makes a decision to offload computation to the cloud. The algorithm adopts with changes in environment and device parameters at run-time. We have designed a framework that resides both in the device, and in the cloud. The device framework monitors device and network parameters and based on user activity decides whether or not to offload computation to the cloud. When the device and network parameters crosses a certain threshold then it sends data to the cloud server using python sockets. The cloud framework receives the data and runs the app in the cloud. After executing the app in the cloud, the framework retrieves the output and sends it back to the local device. The device framework then gives the data to the app and the app completes its execution. We have performed a simulation of the system in a cloud server, and showed that energy can be saved by computation offloading. We also present a financial estimation to calculate the cost of computation offloading.

Pedestrian recognition is a key problem for a number of application domains namely autonomous driving, search and rescue, surveillance and robotics. Real-time pedestrian recognition entails determining if a pedestrian is in an image frame. State-of-art pedestrian detection convolution neural networks(CNN) such as Fast R-CNN depend on computationally expensive region detection algorithms to hypothesize pedestrian locations. This paper presents a simple, fast and very accurate approach by cascading fast regional detection and deep convolution networks. Convolution networks have been shown to excel at image classification. However, convolution networks are notoriously slow at inference time. In this work, we introduce a fast regional detection cascaded with deep convolution networks that enables real-time pedestrian detection that could be used to alert a driver if a pedestrian is on the roadway. The classification CNN has given an accuracy of 95.7%, with a processing rate of about 15 frames per second on a low performance system without a graphical processing unit (GPU).

The cyberspace dimension adds to the four dimensions traditionally associated with United States defense. Threats to cyberspace connote real threats to U.S. security interests. The nation's electric grid is one of the nation's critical infrastructures that is subject to cyber threats. The authors explore what institutional efforts have been taken to address these threats and conclude that a practice of information sharing would help normalize cyberwar toward a more traditional deterrence. In the twenty-first century, bits and bytes are as threatening as bullets and bombs… - William J. Lynn 1

In this paper, a novel centralized robotic swarm of heterogeneous unmanned vehicles consisting of autonomous surface vehicles and micro-aerial vehicles is presented. The swarm robots operate in an outdoor environment and are equipped with cameras and Global Positioning Systems (GPS). Manipulations of the swarm demonstrate how aspects of individual robotic platforms can be controlled cooperatively to accomplish a group task in an efficient manner. We demonstrate the use of air-based robots to build a map of important features of the local environment, such as the locations of targets. The map is then sent to a cloud-based cluster on a remote network. The cloud performs clustering algorithms using the map to calculate optimal clusters of the targets. The cloud then performs an auctioning algorithm to assign the clusters to the surface-based robots based on several factors such as relative position and capacities. The surface-robots then travel to their assigned clusters to complete the allocated tasks. Lastly, we present the results of simulating our cooperative swarm in both software and hardware, demonstrating the effectiveness of our proposed algorithm.

While all cloud based platforms possess security vulnerabilities, the additional security challenges with container systems stem from the sharing of Host OS among independent containers. If a malicious application was to break into the root of container Daemon, it could gain root access into the host kernel thereby compromising the entire system. It could create Denial-Of-Service attack for other user applications, rejecting service to other applications. In this paper, we propose a quantum network security framework for the cloud. We devise a means by which quantum particles, denoted entangled bell pairs, are routed to network nodes. This enables teleportation of quantum information between source and destination only when root privileges are required by an application. The secure quantum channel works on a use-once only policy, so the key data cannot be easily copied, regenerated or spoofed without detection. A network framework for multiple pre-staged channels is devised and we illustrate that policy for network routing of entangle particles formulated as a multi-tenant teleportation network, capable of disseminating key data to servers hosting Docker container applications. The framework can achieve provably high levels of security and is capable of integration into a cloud data center for securing applications using Docker Containers. We also describe quantum network layer protocols for cloud container security that leverage the unique properties of quantum entanglement. To resolve security concerns, this layer would control access between application and container daemon, thereby facilitating restricted communication with proper authentication.

High performance computing (HPC) means the aggregation of computational power to increase the ability of processing large problems in science, engineering, and business. HPC on the cloud allows performing on demand HPC tasks by high performance clusters in a cloud environment. The connection structure of the nodes in HPC clusters should provide fast internode communication. It is important that scalability is preserved as well. This paper proposes a hypercube topology for connecting the nodes in an HPC cluster that facilitates fast communications between nodes. In addition, the proposed hypercube topology provides the ability to scale, which is needed for high performance computing on the cloud.

Automated, efficient software deployment is essential for today's modern cloud hosting providers. With advances in cloud technology, on demand cloud services offered by public providers are becoming increasingly powerful, anchoring the ecosystem of cloud services. Cloud infrastructure services are appealing in part because they enable customers to acquire and release infrastructure resources on demand for applications in response to load surges. This paper addresses the challenge of building an effective multi-cloud application deployment controller as a customer add-on outside of the cloud utility service itself. Such external controllers must function within the constraints of the cloud providers' APIs. In this paper, we describe the different steps necessary to deploy applications using such external controller. Then with a set of candidates for such external controllers, we use the proposed taxonomy to survey several management tools such as Chef, SaltStack, and Ansible for application automation on cloud computing services based on the defined model. We use the taxonomy and survey results not only to identify similarities and differences of the architectural approaches of cloud computing, but also to identify areas requiring further research.

The growing diversity of energy sources and an emerging and clearly established set of national security threats to the grid make its safekeeping a national imperative. Nationalizing the transmission grid cures the capital planning paralysis that plagues the new technology and infrastructure investments required to keep pace with the increasing demand and the evolving threats to the electrical grid.

A novel linear-dendritic block copolymer has been synthesized and evaluated for targeted delivery. The use of the dendron as the micellar exterior block in this architecture allows the presentation of a relatively small quantity of ligands in clusters for enhanced targeting, thus maintaining a long circulation time of these “patchy” micelles. The polypeptide linear hydrophobic block drives formation of micelles that carry core-loaded drugs, and their unique design gives them extremely high stability in vivo. We have found that these systems lead to extended time periods of increased accumulation in the tumor (up to 5 days) compared with nontargeted vehicles. We also demonstrate a fourfold increase in efficacy of paclitaxel when delivered in the targeted nanoparticle systems, while significantly decreasing in vivo toxicity of the chemotherapy treatment., National Institute for Biomedical Imaging and Bioengineering (U.S.), National Cancer Institute (U.S.) (R01EB008082-01A2)

Introduction: With a Canadian aging population, the prevalence of mild traumatic brain injury (mTBI) among elderly is increasing and the age criterion of the Canadian CT head rule (CCHR) is challenged by many emergency physicians. We evaluated if increasing the age criterion of the CCHR would maintain its validity. Methods: We conducted an historical cohort study using the medical charts of all patients 65 years old or more who consulted at a Level One Trauma Centre emergency department (ED) for a mTBI between 2010 and 2014. The main outcome measures were clinically important brain injury (CIBI) on Computed Tomography (CT) and the presence of the CCHR criteria. The clinical and radiological data collection was standardized. Univariate analysis was performed to measure the predictive capacities of modified age cut-offs at 70 and 75 years old. Results: Out of the 104 confirmed mTBI in this study, 32 (30,8%) had CIBI on CT scan. Sensitivity and specificity [C.I. 95%] of the CCHR were 100% [89.1 - 100] and 0% [0.0 5.0] for an age criterion of 65 years old and above; 100% [89.1 - 100] and 4,2% [0.9 11.7] for a modified criterion of 70 years old; 100% [89.1 - 100] and 13,9% [6.9 24.1] for 75 years old. Furthermore, for an age criterion of 80 and 85 years old, sensitivity was respectively 90,6% [75.0 98.0] and 75,0% [56.6 88.5]. Conclusion: In our cohort, increasing the age criterion of the CCHR for minor head injury to 75 years old would benefit ED by further reducing CT scans without missing CIBI. A larger prospective study is indicated to confirm the proposed modification.

Mobile devices have limited resources in terms of power and bandwidth. Cloud computing offers a way to reduce the power consumption of mobile devices by offloading computation to the cloud. However, offloading computation means an increase in communication energy consumption. The trade-off between energy and network characteristics (bandwidth/latency) in a mobile device is very important. Therefore computation offloading must be done strategically. The optimum utilization of the available mobile device resources needs to be assured. In this paper, we propose an intelligent and dynamic algorithm to offload computation to the cloud. We focus on offloading computation based upon the communication topology, device energy and user inputs. We analyze the cost of offloading computation for different user inputs. Based on the inputs, we decide whether to offload the application to the cloud or not. We have simulated our algorithm in MATLAB®, and compared our result to previous approaches. We have found out that our algorithm saves more time, compared to a previous approach, and also reduces device energy usage by moving energy hungry processes to the cloud.

This paper uses State Estimation (SE) methods to validate transmission line parameters with Phasor Measurement Units (PMUs) at its ends. This exercise was needed prior to the commissioning of a new network voltage control strategy. Equations for the specific Hydro-Quebec's measurements scheme are developed. Good behaviour of the algorithm is proven with simulations. The method is applied on a real line with real measurements. A bad data measurement is identified and eliminated. The system under study is then declared coherent.

Prior work has shown that Visual SLAM (VSLAM) algorithms can successfully be used for realtime processing on local robots. As the data processing requirements increase, due to image size or robot velocity constraints, local processing may no longer be practical. Offloading the VSLAM processing to systems running in a cloud deployment of Robot Operating System (ROS) is proposed as a method for managing increasing processing constraints. The traditional bottleneck with VSLAM performing feature identification and matching across a large database. In this paper, we present a system and algorithms to reduce computational time and storage requirements for feature identification and matching components of VSLAM by offloading the processing to a cloud comprised of a cluster of compute nodes. We compare this new approach to our prior approach where only the local resources of the robot were used, and examine the increase in throughput made possible with this new processing architecture.

Errors in power system topology are difficult to handle with classical methods based on state estimation. This paper proposes a pre-processing method for detecting and identifying topology errors and bad measurements before a state estimator solution. Two algorithms are described. These are used at the substation level or at the substation voltage level. The first algorithm is more suited to systems (substations) where no switch flow measurements are available. Therefore, it does not require any additional measurements other than the ones currently used by the state estimator. The topology error detection uses a bus-branch model. The model relies on the connectivity information and on the impedance values of the non-zero impedance branches. The second algorithm is more suited to systems (substations) where some switch flow measurements are available. Even if these are not required by the algorithm, using them add redundancy and provides a more reliable solution. The topology error detection uses a bus section—switch device model. The model uses only branch connectivity. The first algorithm provides better results when no switch flow measurements are available. The second one is superior when some switch flow measurements are available. These algorithms complement each other and they can be use together to cover a much broader scope of the topology error detection and identification problem.

Resume Objectifs Etudier les caracteristiques des patients faisant une demande d’acces au dossier medical, les motifs de ces demandes d’acces, la satisfaction des demandeurs. Methodes Etude prospective de toutes les demandes d’acces au dossier medical dans 2 etablissements volontaires. Recueil de donnees aupres de l’etablissement lors de la demande d’acces puis aupres du demandeur apres l’acces au dossier medical. Resultats Au total, 94 demandes d’acces au dossier medical ont ete incluses. Le delai d’obtention du dossier etait superieur a celui preconise par la loi dans plus de 3 cas sur 4. La plupart des demandes etaient liees soit a la continuite des soins, soit a un defaut d’information du patient lors de la prise en charge dans l’etablissement, soit a une exigence d’un tiers. La modalite de mise a disposition du dossier etait dans plus de 90 % des cas l’envoi par courrier. Plus d’un tiers des demandeurs etaient mecontents de leur experience d’acces au dossier medical. Les difficultes principales concernaient le delai d’obtention trop long, la complexite de la procedure, des frais d’acces juges trop eleves, l’absence de choix concernant les differentes modalites d’acces, le manque d’exhaustivite des documents transmis et la difficulte de comprehension des informations en l’absence d’un accompagnement medical. Conclusion Des actions d’amelioration doivent etre mises en place pour diminuer le nombre de demandes d’acces au dossier medical et ameliorer la satisfaction des demandeurs. En lien avec l’arrete du 5 mars 2004, des axes d’amelioration a adapter a chaque etablissement de sante sont proposes.

This paper proposes a coupled sliding-surface method for the design of trajectory control of a flexible-link robot. First, a sliding surface, coupling the joint velocity with the link bending moment at the joint, is defined based on the energy dynamics of the flexible link. Then a new trajectory–tracking control scheme is designed based on the coupled sliding surface, and extended to an adaptive scheme to cope with parametric uncertainties, where the Lyapunov stability theorem is used as a mathematical design tool. The proposed control is a collocated control designed based on a distributed-parameter dynamic model and hence is free from the so-called spillover instability. Using only the joint actuator, the proposed control guarantees stability throughout the entire trajectory control and asymptotic stability at desired goal positions. The proposed control is a PID control for the rigid dynamics and a proportional control for the flexible dynamics, with feed-forward and dynamics compensation. As a result,...

Amazon recently estimated that the cost of energy for its datacenters reached 42% of the total cost of operation. Our previous research proposed an algorithm to predict how much cloud workload is expected at a specific time. This allows physical servers determined not to be needed to be placed in a low-power sleep state to save energy. If more system capacity is required, servers in a sleep state are transitioned back to an active state. In this paper, we extend our prior research by presenting both a stochastic model for state change as well as a new approach to determining the sampling frequency for performing the prediction of the expected capacity. The first result we show is that this allows the optimal prediction time horizon to be chosen. We next present a dynamic prediction quantization method to determine the optimal number of prediction calculation intervals. Both of these new algorithms allow us to predict future load within required Service Level Agreements while minimizing the number of prediction calculations. This effectively optimizes our ability to predict while minimizing the detrimental effect of additional calculations on our cloud resources. Finally, we test this model by simulating the stochastic time horizon and dynamic quantization algorithms and compare the results with three competing methods. We show that our model provides up to a 20% reduction in the number of calculations required while maintaining the given Service Level Agreement.

A key challenge for any large-scale computation today, whether in “big data” or in handling large-scale web services, has to do with the management of data. In the big data context, the arbitrary separation of storage and computation increases latency and decreases performance. ZeroVM is a lightweight container-based virtualization platform that provides deterministic process execution and isolation. The philosophy behind ZeroVM is to virtualize applications then move the application to the data. This provides the ability to transform or process data in situ, rather than moving data to where the application is located. With the ability to move and execute application next to data, ZeroVM changes the conventional wisdom on infrastructure centric commuting models and enables even more data centric computing models to be used for Big-Data Analytics. The ZeroVM distributed processing framework proposed in this paper presents new opportunities for processing, storing and using data, particularly in big data analytics.

State estimation problems for distribution networks differ from classical state estimation ones. The limited number of measurements makes the problem mathematically underdetermined without the addition of pseudo-measurements. Issues associated with the connection of distributed resources and control efficiency have led to the requirement for greater observability of power networks, but this has to be balanced against the cost of additional measurement and telemetry, including possible redundancy. The impact of estimating accurately the system state is examined in the context of transversal voltage control.

The urban world in which many of us live does not lend itself to the metric of Euclidean geometry. Assuming that the avenues are perpendicular to the streets in a city, the distance from “fifth and fifty-first” to “seventh and thirty-fourth” is not the familiar Euclidean distance found by applying the Pythagorean theorem. The distance must instead be measured in blocks from fifth to seventh avenues and then from fifty-first to thirty-fourth streets. This taxicab metric, one of several me tries used in mathematics (Eisenberg and Khabbaz 1992), is practical for many applications and helps students pursue interesting investigations while deepening their understanding of familiar topics.

Resume Nous avons etudie le devenir de 87 purpuras thrombopeniques idiopathiques de l'enfant de 1973 a 1995. Population et methode. Ces 87 observations (57 garcons et 32 filles) representent la totalite des enfants hospitalises pour purpura thrombopenique en 23 ans dans un meme service de pediatrie non specialise en hematologie. Nous avons recherche, en 1996, des informations sur l'etat de sante actuel de ces patients par interrogatoire ecrit ou telephonique du medecin traitant ou du patient lui-meme. Resultats. Nous avons observe deux hemorragies cerebrales, l'une, initiale, a entraine le deces et l'autre, apres plusieurs annees de thrombopenie, a ete suivie d'une guerison sans sequelle. A long terme, nous avons obtenu des nouvelles de 63 patients sur 87 (72,4 %). Tous sont indemnes de signes cliniques, mais 11 d'entre eux n'etaient pas gueris en 1996. Aucun de ces 63 patients n'a developpe de maladie auto-immune. Deux enfants de la serie sont decedes, l'un d'hemorragie cerebrale et l'autre d'un accident de la voie publique, le purpura thrombopenique etant gueri. Conclusion. Nous avons obtenu des nouvelles de plus de 72 % des patients hospitalises pour purpura thrombopenique idiopathique avec un recul prolonge. Malgre deux hemorragies cerebrales, dont l'une a entraine le deces, il s'agit, dans la grande majorite des cas, d'une maladie benigne qui ne doit pas entrainer de decisions therapeutiques trop lourdes.

The Jerusalem artichoke (Helianthus tuberosus L.) represents an interesting source of carbohydrate because of its high fructose content (75 % of total carbohydrates). An experiment on storage of two varieties of the Jerusalem artichoke tubers ('Kharkov' and 'Violet de Rennes') showed that the quality of tubers was preserved during the first 7 weeks of cold storage. Beyond this period, the loss in total carbohydrate content was evaluated at 0.19% for the 'Kharkov' and 0.26% for the 'Violet de Rennes' variety of the fresh matter/week. Between the seventh and the thirteenth week of cold storage the tubers lost about 16.7 % and 19.1 %, respectively, of their initial carbohydrates. From this study we came to the conclusion that storage at 4°C for the two varieties ('Kharkov' and 'Violet de Rennes') beyond 7 weeks would cause a decrease of general reserves in tubers.

Radio-induced sarcoma is known to occur several years following bone irradiation especially when this treatment is combined to high dose chemotherapy regimens prior to allogeneic haematopoietic stem cell transplantation (HSCT) in very young children. However, little is known about the stimulus of aggressive bony surgery in the development of these tumours.We report the case of a young girl in whom dental extraction was rapidly followed by the occurrence of a localized tumour 11 years after allogeneic haematopoietic stem cell transplantation using total body irradiation (TBI) for a haemophagocytic lymphophistiocytosis (HLH).This tumour involved tooth socket and all the right side of the mandible and was diagnosed as an osteogenic osteosarcoma of the zygomatic bone.This tumour had the characteristics of a radio-induced sarcoma. Thanks to the very short time between the dental extraction and the occurrence of the osteosarcoma at the same location, we discuss the role of the dental extraction as a trigger of osteosarcoma development.

Cloud computing is evolving into the default operational framework running modern data centers. Efficient data center operation is concerned with the total amount of energy consumed as well as assuring adequate resources are available to process all of the incoming work requests. Existing research has demonstrated several algorithms that can be used to determine the optimal number of resources required to service these requests. However, a key issue not addressed in these algorithms is determining the frequency of recalculating the number of required resources. Changing the required resources at a rate slower than the optimal update frequency results in lower energy efficiency due to the over allocation of resources. Changing the resources at a rate higher than the optimal frequency results in insufficient time for systems to change state, which results in SLA violations. In this paper, a stochastic optimization model is presented that determines the optimal update frequency for changing the states of the nodes of the cloud as well as determining the proper frequency for recalculating the maximum expected load, which improves the determination of the optimum number of resources required, therefore maximizes energy efficiency and minimizes SLA violations.