Mechanisms of femtosecond laser-induced refractive index modification of poly(methyl methacrylate)

The mechanisms of refractive index change in poly(methyl methacrylate) by frequency doubled femtosecond laser pulses are investigated. It is demonstrated that positive refractive index modificaton can be caused by a combination of depolymerization and crosslinking

Workforce planning and development in times of delivery system transformation

Background As implementation of the US Affordable Care Act (ACA) advances, many domestic health systems are considering major changes in how the healthcare workforce is organized. The purpose of this study is to explore the dynamic processes and interactions by which workforce planning and development (WFPD) is evolving in this new environment. Methods Informed by the theory of loosely coupled systems (LCS), we use a case study design to examine how workforce changes are being managed in Kaiser Permanente and Montefiore Health System. We conducted site visits with in-depth interviews with 8 to 10 stakeholders in each organization. Results Both systems demonstrate a concern for the impact of change on their workforce and have made commitments to avoid outsourcing and layoffs. Central workforce planning mechanisms have been replaced with strategies to integrate various stakeholders and units in alignment with strategic growth plans. Features of this new approach include early and continuous engagement of labor in innovation; the development of intermediary sense-making structures to garner resources, facilitate plans, and build consensus; and a whole system perspective, rather than a focus on single professions. We also identify seven principles underlying the WFPD processes in these two cases that can aid in development of a new and more adaptive workforce strategy in healthcare. Conclusions Since passage of the ACA, healthcare systems are becoming larger and more complex. Insights from these case studies suggest that while organizational history and structure determined different areas of emphasis, our results indicate that large-scale system transformations in healthcare can be managed in ways that enhance the skills and capacities of the workforce. Our findings merit attention, not just by healthcare administrators and union leaders, but by policymakers and scholars interested in making WFPD policies at a state and national level more responsive

Workforce Planning & Development in Times of Delivery System Transformation: The Stories of Kaiser Permanente and Montefiore Health System

As the implementation of the Affordable Care Act (ACA) advances, many health systems are taking bold measures to reorganize how they deliver care, and finding that in order to do so; they need to make major changes in how their healthcare workforces are organized.Understanding what workforce changes are occurring and how they are being managed is important not just for healthcare leaders, but for policymakers as well. Traditional methods of projecting provider shortages and justifying the allocation of public funding to expand various professional pipelines are giving way to the notion that there are many models of care delivery and that they have vastly different staffing configurations. Choices about staffing can have enormous implications for productivity, making assumptions about the demand for certain health professions a moving target.The authors focused on two very different health systems, Kaiser Permanente and Montefiore Health System, to better understand how diverse organizations are adapting to and planning for workforce changes in the post-ACA environment. They set out to examine not only how changes in healthcare delivery will alter the national demand for health workers, but also how individual organizations make choices about ways to reconfigure their workforce, and, ultimately, what kinds of local, state and federal policies will be most supportive of workforce transformations that advance both workers’ wellbeing and the value of their services

Analysis of Spatio-temporal Representations for Robust Footstep Recognition with Deep Residual Neural Networks

Human footsteps can provide a unique behavioural pattern for robust biometric systems. We propose spatio-temporal footstep representations from floor-only sensor data in advanced computational models for automatic biometric verification. Our models deliver an artificial intelligence capable of effectively differentiating the fine-grained variability of footsteps between legitimate users (clients) and impostor users of the biometric system. The methodology is validated in the largest to date footstep database, containing nearly 20,000 footstep signals from more than 120 users. The database is organized by considering a large cohort of impostors and a small set of clients to verify the reliability of biometric systems. We provide experimental results in 3 critical data-driven security scenarios, according to the amount of footstep data made available for model training: at airports security checkpoints (smallest training set), workspace environments (medium training set) and home environments (largest training set). We report state-of-the-art footstep recognition rates with an optimal equal false acceptance and false rejection rate of 0.7% (equal error rate), an improvement ratio of 371% from previous state-of-the-art. We perform a feature analysis of deep residual neural networks showing effective clustering of clients footstep data and provide insights of the feature learning process

Evaluation of supervised classification algorithms for human activity recognition with inertial sensors

The main aim of this work is to compare the performance of different algorithms for human activity recognition by extracting various statistical time domain and frequency domain features from the inertial sensor data. Our results show that Support Vector Machines with quadratic kernel classifier (accuracy: 93.5%) and Ensemble classifier with bagging and boosting (accuracy: 94.6%) outperforms other known activity classification algorithms. A parallel coordinate plot based on visualization of features is used to identify useful features or predictors for separating classes. This enabled exclusion of features that contribute least to classification accuracy in a multi-sensor system (five in our case), made the classifier lightweight in terms of number of useful features, training time and computational load and lends itself to real-time implementation

Analysis of spatio-temporal representations for robust footstep recognition with deep residual neural networks

IEEE: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.”Human footsteps can provide a unique behavioural pattern for robust biometric systems. We propose spatio-temporal footstep representations from floor-only sensor data in advanced computational models for automatic biometric verification. Our models deliver an artificial intelligence capable of effectively differentiating the fine-grained variability of footsteps between legitimate users (clients) and impostor users of the biometric system. The methodology is validated in the largest to date footstep database, containing nearly 20,000 footstep signals from more than 120 users. The database is organized by considering a large cohort of impostors and a small set of clients to verify the reliability of biometric systems. We provide experimental results in 3 critical data-driven security scenarios, according to the amount of footstep data made available for model training: at airports security checkpoints (smallest training set), workspace environments (medium training set) and home environments (largest training set). We report state-of-the-art footstep recognition rates with an optimal equal false acceptance and false rejection rate of 0.7% (equal error rate), an improvement ratio of 371% from previous state-of-the-art. We perform a feature analysis of deep residual neural networks showing effective clustering of client's footstep data and provide insights of the feature learning process.This work has been partially supported by Cognimetrics TEC2015-70627-R MINECO/FEDE

Design and implementation of a convolutional neural network on an edge computing smartphone for human activity recognition

Edge computing aims to integrate computing into everyday settings, enabling the system to be context-aware and private to the user. With the increasing success and popularity of deep learning methods, there is an increased demand to leverage these techniques in mobile and wearable computing scenarios. In this paper, we present an assessment of a deep human activity recognition system’s memory and execution time requirements, when implemented on a mid-range smartphone class hardware and the memory implications for embedded hardware. This paper presents the design of a convolutional neural network (CNN) in the context of human activity recognition scenario. Here, layers of CNN automate the feature learning and the influence of various hyper-parameters such as the number of filters and filter size on the performance of CNN. The proposed CNN showed increased robustness with better capability of detecting activities with temporal dependence compared to models using statistical machine learning techniques. The model obtained an accuracy of 96.4% in a five-class static and dynamic activity recognition scenario. We calculated the proposed model memory consumption and execution time requirements needed for using it on a mid-range smartphone. Per-channel quantization of weights and per-layer quantization of activation to 8-bits of precision post-training produces classification accuracy within 2% of floating-point networks for dense, convolutional neural network architecture. Almost all the size and execution time reduction in the optimized model was achieved due to weight quantization. We achieved more than four times reduction in model size when optimized to 8-bit, which ensured a feasible model capable of fast on-device inference