Deep learning geodemographics with autoencoders and geographic convolution

We present two approaches to creating geodemographic classifications using deep neural networks. Both deep neural networks are based on autoencoders, which allow automating dimensionality reduction before clustering. The second approach also introduces the idea of geographic convolution in neural networks, which aims to mirror in the geographical domain the approach of graphical convolution, which has revolutionised image processes in the past decade. To test our approaches, we created a geodemographic classification based on the United Kingdom Census 2011 for the county of Leicestershire and compared it to the official 2011 Output Area Classification. Our results show that the two deep neural networks are successful in creating classifications which are statistically similar to the official classification and demonstrate high cluster homogeneity.</p

On stability of recurrent neural networks : an approach from volterra integro-differential equations

The uniform asymptotic stability of recurrent neural networks (RNNs) with distributed delay is analyzed by comparing RNNs to linear Volterra integro-differential systems under Lipschitz continuity of activation functions. The stability criteria obtained have unified and extended many existing results on RNNs

Stability analysis of recurrent neural networks : a Volterra integro-differential equation approach

The stability of a special class of nonlinear Volterra integro-differential systems are analyzed by comparing them to linear Volterra integro-differential systems. The results are used to determine the stability properties of recurrent neural networks with distributed delays, including constant discrete delays as a special case. The obtained stability criteria have unified and extended many existing results on recurrent neural networks

Learning Digital Geographies through a Graph-Based Semi-supervised Approach

As social media have become an integral part of many people’s everyday life, there has been an increasing interest in exploring how the content shared through those online platforms comes to contribute to the collaborative creation of places in physical space. Indeed, the distinction between online and physical spaces and activities is rapidly degrading. However, exploring those digital geographies is a complex task, due to the quantity and variety of data. In this paper, we introduce a semi-supervised, deep neural network approach to classify geo-located social media posts based on their text content, media content, and geographic location, using a limited set of pre-defined categories. Our approach combines a stacked multi-modal autoencoder neural network to create joint representations of text and images, and graph convolution neural network for semi-supervised classification. The results presented in this paper show that our approach performs the classification of social media content with higher accuracy than a traditional Support Vector Machine model. Thus, the presented approach has the potential to develop in a powerful tool to complement content analysis in the study of digital geographies

Discrete-time analogs for a class of continuous-time recurrent neural networks

This paper is concerned with the problem of local and global asymptotic stability for a class of discrete-time recurrent neural networks, which provide discrete-time analogs to their continuous-time counterparts, i.e., continuous-time recurrent neural networks with distributed delay. Some stability criteria, which include some existing results as their special cases, are derived. A discussion about the dynamical consistence of discrete-time neural networks versus their continuous-time counterparts is provided. An unconventional finite difference method is proposed and an example is also given to show the effectiveness of the method

The trend premium around the world: Evidence from the stock market

This paper studies the predictive power of the trend strategy in the international stock market. Using data from 49 markets, we find that a trend signal exploiting the short-, intermediate-, and long-term price information can predict stock returns cross-sectionally in the international market. The significance of the trend strategy is associated with market-level characteristics such as macroeconomic conditions, culture, and the information environment. The trend premium is more pronounced in markets with a more advanced macroeconomic status, a higher level of information uncertainty and individualism, and better accessibility to foreign investors. Nevertheless, the trend strategy only outperforms the momentum strategy in a relatively short horizon

Parallel co-simulation of heavy-haul train braking dynamics with strong nonlinearities

A new wave of research interests on train braking dynamics has been raised which requires more accurate and faster train braking dynamics simulations. Simulations with fully nonlinear fluid dynamic air brake models and nonlinear friction draft gear models are more accurate, however these are not yet available. This paper developed a model that has combined both of these aspects. Simulations were achieved by using a parallel co-simulation method which requires minimum working loads to merge legacy codes. A case study was carried out to simulate a heavy haul train braking commencing with a minimum service brake and then followed by a full-service brake before the minimum service brake was fully executed. Such transient simulations were not able to be accurately conducted by using empirical air brake models, but only by using fluid dynamic air brake models. Influences of co-simulation synchronization frequencies on computing time and results of the simulations were also investigated. Simulation results have recommended a synchronization frequency of 100 Hz. The corresponding simulation was 43% faster than real-time for a 152-vehicle train

IPSadas: Identity-privacy-aware Secure and Anonymous Data Aggregation Scheme

Intelligent systems are technologically advanced machines that can sense and respond to the surrounding environment. They have been widely used in medicine, military, transportation, automation, and other fields. However, when these systems deal with their environments, problems such as leakage of identities may occur. The adversary can damage the system communication and attack important nodes. To handle resource-constrained wireless sensor network environments, we propose a secure and anonymous data aggregation scheme. First, based on the bilinear mapping operation and onion routing concepts, we propose a key negotiation and secure information transmission scheme, which conducts confidential transmission and anonymous forwarding of messages in data aggregation. Second, an aggregation routing scheme based on link direction and residual energy is proposed to pledge messages that can arrive the base station without passing through many nodes, which saves network resources to a certain extent. Third, on the basis of the first two contributions, we propose an identity-privacy-aware secure and anonymous data aggregation scheme that protects the identity's privacy. This scheme can conceal the real identity of important nodes and protect the anonymity of messages and link relationships. In addition, an anonymous identity update and synchronization scheme is also proposed to ensure the reliability and security of communication. Meanwhile, our performance evaluations and simulations show that the proposed framework is more effective than several standard schemes with respect to the ability against various attacks, security, and overhead

Supplemental material for Infections in hospitalized lupus nephritis patients: characteristics, risk factors, and outcomes

<p>Supplemental material for Infections in hospitalized lupus nephritis patients: characteristics, risk factors, and outcomes by P Liu, H Z Tan, H Li, C C Lim and J C J Choo in Lupus</p

Editorial on Special Issue: “Applications of Intelligent and Fuzzy Theory in Data Science”

Editorial on Special Issue: “Applications of Intelligent and Fuzzy Theory in Data Science