Environment identification based memory scheme for estimation of distribution algorithms in dynamic environments

Copyright @ Springer-Verlag 2010.In estimation of distribution algorithms (EDAs), the joint probability distribution of high-performance solutions is presented by a probability model. This means that the priority search areas of the solution space are characterized by the probability model. From this point of view, an environment identification-based memory management scheme (EI-MMS) is proposed to adapt binary-coded EDAs to solve dynamic optimization problems (DOPs). Within this scheme, the probability models that characterize the search space of the changing environment are stored and retrieved to adapt EDAs according to environmental changes. A diversity loss correction scheme and a boundary correction scheme are combined to counteract the diversity loss during the static evolutionary process of each environment. Experimental results show the validity of the EI-MMS and indicate that the EI-MMS can be applied to any binary-coded EDAs. In comparison with three state-of-the-art algorithms, the univariate marginal distribution algorithm (UMDA) using the EI-MMS performs better when solving three decomposable DOPs. In order to understand the EI-MMS more deeply, the sensitivity analysis of parameters is also carried out in this paper.This work was supported by the National Nature Science Foundation of China (NSFC) under Grant 60774064, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01

The anatomy of teleneurosurgery in China

With its huge population and vast territory, China faces a great challenge in providing modern advanced health care services to all parts of the country. The advances of information communication technologies (ICTs) and the advent of internet have revolutionised the means in the delivery of healthcare via telemedicine to remote and underserved populations, which to a certain extent has been very well exploited in China, especially where 70% peasants residing in the rural areas. This paper reviews the latest development in telemedicine infrastructure in China with the focus on the development of teleneurosurgery, drawing from the results gained from a 3-year networking project between Europe and China on telemedicine (TIME, 2005–2007) funded by European Commission under Asia ICT programme, with an aim to shape up envisages of future medical care in China. Comparison with its counterparts in Europe is also addressed

Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme

The controlled outflows from a reservoir or dam are highly dependent on the decisions made by the reservoir operators, instead of a natural hydrological process. Difference exists between the natural upstream inflows to reservoirs and the controlled outflows from reservoirs that supply the downstream users. With the decision maker's awareness of changing climate, reservoir management requires adaptable means to incorporate more information into decision making, such as water delivery requirement, environmental constraints, dry/wet conditions, etc. In this paper, a robust reservoir outflow simulation model is presented, which incorporates one of the well-developed data-mining models (Classification and Regression Tree) to predict the complicated human-controlled reservoir outflows and extract the reservoir operation patterns. A shuffled cross-validation approach is further implemented to improve CART's predictive performance. An application study of nine major reservoirs in California is carried out. Results produced by the enhanced CART, original CART, and random forest are compared with observation. The statistical measurements show that the enhanced CART and random forest overperform the CART control run in general, and the enhanced CART algorithm gives a better predictive performance over random forest in simulating the peak flows. The results also show that the proposed model is able to consistently and reasonably predict the expert release decisions. Experiments indicate that the release operation in the Oroville Lake is significantly dominated by SWP allocation amount and reservoirs with low elevation are more sensitive to inflow amount than others

COVID-CBR: a deep learning architecture featuring case-based reasoning for classification of COVID-19 from chest x-ray images

Background and Objectives: This study aims to assist rapid accurate diagnosis of COVID-19 based on chest x-ray (CXR) images to provide supplementary information, leading to screening program for early detection of COVID-19 based on CXR images by developing an interpretable, robust and performant AI system. Methods: A case-based reasoning approach built upon autoencoder deep learning architecture is applied to classify COVID-19 from other non-COVID-19 as well as normal subjects from chest x-ray images. The system integrates the interpretation and decision-making together by producing a set of profiles that in appearance resemble the training samples and hence explain the outcome of classifications. Three classes are studied, which are COVID-19 (n=250), other non-COVID-19 diseases (NCD) (n=384), including TB and ARDS, and normal (n=327). Results: This COVID-CBR system sustains the average sensitivity and specificity of 93.1±3.58% and 96.1±4.10% respectively for classification of these three classes. In comparison with the current state of the art, including COVID-Net, VGG-16 and other explainable AI systems, the developed COVID-CBR system appears to perform similar or better when classifying multi-class categories. Conclusion: This paper presents a case-based reasoning deep learning system for detection of COVID-19 from chest x-ray images. Comparison with several state of the art systems is conducted. Although the improvement tends to be marginal, especially for VGG-16, the novelty of this work manifests its interpretable feature building upon case-based reasoning, leading to revealing this viral insight and hence ascertaining more effective treatment and drugs while maintaining being transparent. Furthermore, different from several other current explainable networks that highlight key regions or the points of an input that activate the network, i.e. heat maps, this work is constructed upon whole training images, i.e. case-based, whereby each training image belongs to one of the case clusters

Characterization of deep sub-wavelength nanowells by imaging the photon state scattering spectra

Optical-matter interactions and photon scattering in a sub-wavelength space are of great interest in many applications, such as nanopore-based gene sequencing and molecule characterization. Previous studies show that spatial distribution features of the scattering photon states are highly sensitive to the dielectric and structural properties of the nanopore array and matter contained on or within them, as a result of the complex optical-matter interaction in a confined system. In this paper, we report a method for shape characterization of subwavelength nanowells using photon state spatial distribution spectra in the scattering near field. Far-field parametric images of the near-field optical scattering from sub-wavelength nanowell arrays on a SiN substrate were obtained experimentally. Finite-difference time-domain simulations were used to interpret the experimental results. The rich features of the parametric images originating from the interaction of the photons and the nanowells were analyzed to recover the size of the nanowells. Experiments on nanoholes modified with Shp2 proteins were also performed. Results show that the scattering distribution of modified nanoholes exhibits significant differences compared to empty nanoholes. This work highlights the potential of utilizing the photon status scattering of nanowells for molecular characterization or other virus detection applications