Comparing Human and Neural Network Learning of Climate Categories

This study compares how humans and neural networks classify climate types. Human subjects were asked to classify climates from monthly temperature and precipitation patterns. To model their learning process, the same data were used to produce input vectors that trained a pattern associator neural network. Both human subjects and the neural network classified climates accurately after 10 rounds of supervised learning. The neural network successfully modeled the rate of human learning and the ability to learn specific climate categories. Moreover, the neural network weights used to classify climates correspond to distinct visual characteristics in temperature and precipitation. These results suggest that neural networks can model the formation of visual categories.     

Learning about learning: lessons from public engagement and deliberation on urban river restoration

This paper provides a new discussion of how people learn through deliberative processes, drawing upon empirical analysis of a novel public engagement process for urban river restoration. Such critical evaluation is rare and yet will be crucial to both theoretical development and learning about engagement practice, not least in a policy area subject to strong regulatory drivers for public participation. The analysis supports two important learning mechanisms – the use of 'gatekeepers' of knowledge, interests and values, and the privileging of narrative. It provides new evidence of instrumental and communicative learning about shared priorities and criteria for effective river restoration that evolved through the deliberative process and directly informed the restoration scheme. It is important to question whether and how such site or context-specific learning might inform other restoration schemes. Finally, the paper questions the often ignored issue of expert learning, not least the issue of the link between individual and organizational learning.     

Deriving rules from activity diary data: A learning algorithm and results of computer experiments

 Activity-based models consider travel as a derived demand from the activities households need to conduct in space and time. Over the last 15 years, computational or rule-based models of activity scheduling have gained increasing interest in time-geography and transportation research. This paper argues that a lack of techniques for deriving rules from empirical data hinders the further development of rule-based systems in this area. To overcome this problem, this paper develops and tests an algorithm for inductively deriving rules from activity-diary data. The decision table formalism is used to exhaustively represent the theoretically possible decision rules that individuals may use in sequencing a given set of activities. Actual activity patterns of individuals are supplied to the system as examples. In an incremental learning process, the system progressively improves on the selection of rules used for reproducing the examples. Computer experiments based on simulated data are performed to fine-tune rule selection and rule value update functions. The results suggest that the system is effective and fairly robust for parameter settings. It is concluded, therefore, that the proposed approach opens up possibilities to derive empirically tested rule-based models of activity scheduling. Follow-up research will be concerned with testing the system on empirical data. Received: 31 January 2001 / Accepted: 13 September 2001     

利用一种新的神经网络模型识别点状地图符号

着重讨论了用一种新的神经网络模型识别点状地图符号的过程，主要包括网络的结构特点和学习算法以及学习训练过程，并验证了用该网络进行点状地图符号识别的有效性。     

A minimalistic approach for evapotranspiration estimation using the Prophet model

ABSTRACT

This study aimed to evaluate the potential of the recently introduced Prophet model for estimating reference evapotranspiration (ETo). A comparative study was conducted for benchmarking the model results with support vector regression (SVR) and temperature-based empirical models (Thornthwaite and Hargreaves) in southern Japan. The performance of the Prophet, SVR and temperature-based empirical models was evaluated by Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R²). The results indicate that temperature-based Prophet and SVR models have greater accuracy than the empirical models. The Prophet model with sole input of relative humidity, sunshine hours or windspeed showed acceptable accuracy (NSE > 0.80; R² > 0.80), while SVR models with similar inputs showed greater errors. Accuracy improved with increasing number of input parameters, giving excellent performance (NSE > 0.95; R² > 0.95) with all input parameters. Hence, the Prophet model is a new promising approach for modelling ETo with limited input variables.     

星载合成孔径雷达海洋遥感与大数据

星载合成孔径雷达以其全天候、全天时、不受云雨影响的工作特性在空间对海观测中起到了重要作用，又以其高空间分辨率、多极化、多成像模式的特点展示了其在海洋动力要素反演和海洋多尺度动力过程研究中独特的魅力.起步于20世纪70年代末的星载合成孔径雷达技术，迎来了发展的"黄金时期"，大数据和机器学习又赋予了星载合成孔径雷达海洋遥感更强大的生命力.本文首先阐述了星载合成孔径雷达大数据的5"V"特性，进而以高分辨率海面风场反演、海洋内波中尺度动力过程观测两类典型案例，阐述了大数据、机器学习等现代信息科学技术与卫星海洋遥感结合，实现海洋环境参数高精度反演和海洋动力过程科学深层次认知的研究.最后，展望了星载合成孔径雷达海洋遥感与大数据的发展前景.     

Multiobjective analysis of chaotic dynamic systems with sparse learning machines

Sparse learning machines provide a viable framework for modeling chaotic time-series systems. A powerful state-space reconstruction methodology using both support vector machines (SVM) and relevance vector machines (RVM) within a multiobjective optimization framework is presented in this paper. The utility and practicality of the proposed approaches have been demonstrated on the time series of the Great Salt Lake (GSL) biweekly volumes from 1848 to 2004. A comparison of the two methods is made based on their predictive power and robustness. The reconstruction of the dynamics of the Great Salt Lake volume time series is attained using the most relevant feature subset of the training data. In this paper, efforts are also made to assess the uncertainty and robustness of the machines in learning and forecasting as a function of model structure, model parameters, and bootstrapping samples. The resulting model will normally have a structure, including parameterization, that suits the information content of the available data, and can be used to develop time series forecasts for multiple lead times ranging from two weeks to several months.     

Living and working in urban working class communities

Much has been written on the apparent urban renaissance in UK cities and the new lifestyle arrangements, working time patterns, economic activities and the more general reordering of work and life that appears to accompany it. Certainly, recent decades have witnessed a range of economic, social and cultural changes in the lives of those living and working in cities and surrounding suburbs. Much of the attention in this work has focused on those groups for whom the changes have appeared most profound: the high-income earners returning to live in the city - the gentrifiers - or those who suffer multiple deprivations as a result of economic restructuring. Seemingly absent from many accounts of urban change are those places where, at first glance, the effects of change have been less pronounced: low-income, working class neighbourhoods where most people continue to get by, albeit in the context of a harsher, and less secure political economic context. In light of this apparent silence, this paper draws on interviews from Wythenshawe in South Manchester, to examine how low-income mothers cope, live and labour, in a rapidly changing city, as they perform paid work at the same time as ensuring the social reproduction of the household.     

Neo-liberal ruptures: Local political entities and neighbourhood networks in El Alto, Bolivia

In October 2003, hundreds of thousands of Bolivians took to the streets demanding the resignation of President Gonzalo Sánchez de Lozada. After 20 years of neo-liberal policies - and the failures to improve the living conditions of the majority - the proposal to export natural gas via Chile was taken by the population as yet another step to sustain an unjust political order. Facing a direct challenge by the population the Sánchez de Lozada administration responded with indiscriminate military force. The result was 63 dead and over 300 wounded, which deepened and extended the social rage and eventually forced the resignation of the President. The neo-liberal project - promoted and defended by Sánchez de Lozada - collapsed. The city of El Alto was the epicentre of the challenges to the legitimacy of this political order. This article focuses on the role of local political entities and neighbourhood networks from El Alto in articulating political spaces that challenged the legitimacy of the institutional infrastructure and led to the October 2003 ruptures in the neo-liberal project. Furthermore, I make the case that the particular histories and memories (of “relocalized” miners and indigenous/peasants) that converged in and defined this city were pivotal in the organization of a “political subsoil” that surged to the surface during the October 2003 events.     

Negotiating changing livelihoods: The sampan dwellers of Tam Giang Lagoon, Vi?t Nam

Central Vi?t Nam is one of the most vulnerable areas in the country to natural disasters. In 1985 a major typhoon hit the Tam Giang Lagoon coastal area in the province of Th?a Tiên-Hu?, Central Vi?t Nam, with severe impacts on the sampan dwellers who lived there on boats and fished for their livelihoods. Since then, the government has attempted to resettle them on land in order to decrease their vulnerability to such events. Consequently, this process has changed the livelihood options as well as the social networks of the sampan dwellers. This study of the resettlement village of Th?y ?i?n analyses the social networks and different forms of social capital being utilised by the resettled sampan dwellers as part of their changing livelihoods and questions whether the social capital formed will indeed lead to a long term decrease in vulnerability. It is found that while bonding and linking social capital have been newly formed, the trust required for bridging social capital formation is still missing, and this in turn is hindering the possibilities of sustainable livelihood formation.