The Dynamics of the Age Structure, Dependency, and Consumption

We examine the dynamic interaction of the population age structure, economic dependency, and fertility, paying particular attention to the role of intergenerational transfers. In the short run, a reduction in fertility produces a %u201Cdemographic dividend%u201D that allows for higher consumption. In the long run, however, higher old-age dependency can more than offset this effect. To analyze these dynamics we develop a highly tractable continuous-time overlapping generations model in which population is divided into three groups (young, working age, and old) and transitions between groups take place in a probabilistic fashion. We show that most highly developed countries have fertility below the rate that maximizes steady state consumption. Further, the dependency-minimizing response to increased longevity is to raise fertility. In the face of the high taxes required to support transfers to a growing aged population, we demonstrate that the actual response of fertility will likely be exactly the opposite, leading to increased population aging.

Multi-view Temporal Ensemble for Classification of Non-Stationary Signals

In the classification of non-stationary time series data such as sounds, it is often tedious and expensive to get a training set that is representative of the target concept. To alleviate this problem, the proposed method treats the outputs of a number of deep learning sub-models as the views of the same target concept that can be linearly combined according to their complementarity. It is proposed that the view’s complementarity be the contribution of the view to the global view, chosen in this work to be the Laplacian eigenmap of the combined data. Complementarity is computed by alternate optimization, a process that involves the cost function of the Laplacian eigenmap and the weights of the linear combination. By blending the views in this way, a more complete view of the underlying phenomenon can be made available to the final classifier. Better generalization is obtained, as the consensus between the views reduces the variance while the increase in the discriminatory information reduces the bias. Data experiment with artificial views of environment sounds formed by deep learning structures of different configurations shows that the proposed method can improve the classification performance

An Approach For Analysis Of Human Interaction With Worker Assistance Systems Based On Eye Tracking And Motion Capturing

Human behavior in production systems influences productivity, product quality, work safety and overall process performance. To guide human behavior, digital worker assistance systems can be used to support cognitive decision tasks and sensory perception tasks. In doing so, the design of the assistance systems affects user experience and work results. To optimize and develop human-centric productions systems, data on human behavior and interaction with manufacturing equipment must be collected and analyzed. This analysis is expected to yield benefits regarding process monitoring, quality assurance, user experience and ergonomics. In addition, the results could be used for training purposes to monitor skill improvements. This paper presents a framework for data acquisition and analysis of human interaction with digital worker assistance systems. In addition to the overall system architecture, the individual development steps are discussed. An eye tracking device and a motion capturing camera are used for data collection and provide live information about human behavior in conjunction with a digital worker assistance system. The data is stored in a database and analyzed by custom analysis algorithms. The results are displayed in a dashboard application and show that the presented framework with eye tracking and motion capturing is suitable for the analysis of human interaction with worker assistance systems

Mapping and explaining the productivity of Pinus radiata in New Zealand

Mapping Pinus radiata productivity for New Zealand not only provides useful information for forest owners, industry stakeholders and policy managers, but also enables current and future plantations to be visualised, quantified, and planned. Using an extensive set of permanent sample plots, split into fitting (n = 1,146) and validation (n = 618) datasets, models of P. radiata 300 Index (an index of volume mean annual increment) and Site Index (an index of height growth) were developed using a regression kriging technique. Spatial predictions were accurate and accounted for 61% and 70% of the variance for 300 Index and Site Index, respectively. Productivity predicted from these surfaces for the entire plantation estate averaged 27.4 m³ ha⁻¹ yr⁻¹ for the 300 Index and 30.4 m for Site Index. Surfaces showed wide regional variation in this productivity, which was attributable mainly to variation in air temperature and root-zone water storage from site to site

Periosteum: biology, regulation, and response to osteoporosis therapies

Periosteum contains osteogenic cells that regulate the outer shape of bone and work in coordination with inner cortical endosteum to regulate cortical thickness and the size and position of a bone in space. Induction of periosteal expansion, especially at sites such as the lumbar spine and femoral neck, reduces fracture risk by modifying bone dimensions to increase bone strength. The cell and molecular mechanisms that selectively and specifically activate periosteal expansion, as well as the mechanisms by which osteoporosis drugs regulate periosteum, remain poorly understood. We speculate that an alternate strategy to protect human bones from fracture may be through targeting of the periosteum, either using current or novel agents. In this review, we highlight current concepts of periosteal cell biology, including their apparent differences from endosteal osteogenic cells, discuss the limited data regarding how the periosteal surface is regulated by currently approved osteoporosis drugs, and suggest one potential means through which targeting periosteum may be achieved. Improving our understanding of mechanisms controlling periosteal expansion will likely provide insights necessary to enhance current and develop novel interventions to further reduce the risk of osteoporotic fractures

Signal processing and analytics of multimodal biosignals

Ph. D. ThesisBiosignals have been extensively studied by researchers for applications in diagnosis, therapy, and monitoring. As these signals are complex, they have to be crafted as features for machine learning to work. This begs the question of how to extract features that are relevant and yet invariant to uncontrolled extraneous factors. In the last decade or so, deep learning has been used to extract features from the raw signals automatically. Furthermore, with the proliferation of sensors, more raw signals are now available, making it possible to use multi-view learning to improve on the predictive performance of deep learning. The purpose of this work is to develop an effective deep learning model of the biosignals and make use of the multi-view information in the sequential data. This thesis describes two proposed methods, namely: (1) The use of a deep temporal convolution network to provide the temporal context of the signals to the deeper layers of a deep belief net. (2) The use of multi-view spectral embedding to blend the complementary data in an ensemble. This work uses several annotated biosignal data sets that are available in the open domain. They are non-stationary, noisy and non-linear signals. Using these signals in their raw form without feature engineering will yield poor results with the traditional machine learning techniques. By passing abstractions that are more useful through the deep belief net and blending the complementary data in an ensemble, there will be improvement in performance in terms of accuracy and variance, as shown by the results of 10-fold validations.Nanyang Polytechni

Mapping the productivity of radiata pine

Forest owners, investors and policy makers all want to know the spread and productivity of New Zealand’s current and future radiata plantation. David Palmer, a geo-spatial analyst at Scion, has combined advanced statistical techniques with mapping technology to predict radiata 300 Index and Site Index for any location in New Zealand. The 300 Index is an index of volume mean annual increment, and the Site Index is for height and growth. The map of Site Index and 300 Index was built using growth measurement data from trees in 1,146 radiata pine permanent sample plots, planted between 1975 and 2003. The data was combined with a number of climate, land use, terrain and environmental variables to predict forest productivity under a range of conditions

Machine Learning for Optical Network Security Monitoring: A Practical Perspective

In order to accomplish cost-efficient management of complex optical communication networks, operators are seeking automation of network diagnosis and management by means of Machine Learning (ML). To support these objectives, new functions are needed to enable cognitive, autonomous management of optical network security. This paper focuses on the challenges related to the performance of ML-based approaches for detectionand localization of optical-layer attacks, and to their integration with standard Network Management Systems (NMSs). We propose a framework for cognitive security diagnostics that comprises an attack detection module with Supervised Learning (SL), Semi-Supervised Learning (SSL) and Unsupervised Learning (UL) approaches, and an attack localization module that deduces the location of a harmful connection and/or a breached link. The influence of false positives and false negatives is addressed by a newly proposed Window-based Attack Detection (WAD) approach. We provide practical implementation\ua0guidelines for the integration of the framework into the NMS and evaluate its performance in an experimental network testbed subjected to attacks, resulting with the largest optical-layer security experimental dataset reported to date