Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

https://scholarlypublications.universiteitleiden.nl/access/item%3A2943283/view

Deep learning for visual understanding

Human activity recognition systems are developed as part of a framework to enable continuous monitoring of human behaviours in the area of ambient assisted living, sports injury detection, elderly care, rehabilitation, and entertainment and surveillance in smart home environments. The extraction of relevant features is the most challenging part of the mobile and wearable sensor-based human activity recognition pipeline. Feature extraction influences the algorithm performance and reduces computation time and complexity. However, current human activity recognition relies on handcrafted features that are incapable of handling complex activities especially with the current influx of multimodal and high dimensional sensor data. With the emergence of deep learning and increased computation powers, deep learning and artificial intelligence methods are being adopted for automatic feature learning in diverse areas like health, image classification, and recently, for feature extraction and classification of simple and complex human activity recognition in mobile and wearable sensors. Furthermore, the fusion of mobile or wearable sensors and deep learning methods for feature learning provide diversity, offers higher generalisation, and tackles challenging issues in human activity recognition. The focus of this review is to provide in-depth summaries of deep learning methods for mobile and wearable sensor-based human activity recognition. The review presents the methods, uniqueness, advantages and their limitations. We not only categorise the studies into generative, discriminative and hybrid methods but also highlight their important advantages. Furthermore, the review presents classification and evaluation procedures and discusses publicly available datasets for mobile sensor human activity recognition. Finally, we outline and explain some challenges to open research problems that require further research and improvements.

Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges

Machine learning on big data

Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art research techniques into different categories based on the underlying assumptions and approach adopted. Within each category we outline the basic anomaly detection technique, along with its variants and present key assumptions, to differentiate between normal and anomalous behavior. For each category, we present we also present the advantages and limitations and discuss the computational complexity of the techniques in real application domains. Finally, we outline open issues in research and challenges faced while adopting these techniques.

Deep Learning for Anomaly Detection: A Survey.

Autoencoder for words

This work presents a neighborhood preservation method to construct the latent manifold. This manifold preserves the relative Euclidean distances among neighboring data points. Its computation cost is close to the linear algorithm and its performance in preserving the local relationships is promising when we compared it with the methods, LLE and Isomap.

Manifold Construction by Local Neighborhood Preservation

This paper presents a novel technique to separate the pattern representation in each hidden layer to facilitate many classification tasks. This technique requires that all patterns in the same class will have near representions and the patterns in different classes will have distant representions. This requirement is applied to any two data patterns to train a selected hidden layer of the MLP or the RNN. The MLP can be trained layer by layer feedforwardly to accomplish resolved representations. The trained MLP can serve as a kind of kernel functions for categorizing multiple classes.

Resolving Hidden Representations

Dealing with large number of brain images in group analysis involves two kinds of analysis. One is to extract the information and relations in the population of brains, and the other is to combine the information of individual brain in the group. Linear or nonlinear dimension reduction algorithms are the main tools to perform the first analysis, which is to show the information of the population. The hidden relations in the distribution are therefore able to be visualized in low-dimensional and visible space. Image registration is the critical part of the second analysis, which is to integrate the information or statistics of individual brains. The statistics are registered to a template which is commonly the mean brain image of the population so that the statistics from different subjects can be compared in the same stereotaxic space. The process of registering images to the template is called normalization. The quality of registration decides the normalization and the interpretability of results. This work constructed ordered representations, from a set of brain images, as the multiple templates. The ordered representations are derived from self-organizing map. A novel method, transformation diversion, based on the ordered representations is proposed to improve the registration, which is a non-linear deformation, in a general manner. The discriminative low-dimensional representation of the population of Alzheimer disease and normal subjects are also shown. The set of ordered representations not only shows the population information but also improve the normalization process.

Construct adaptive template array for magnetic resonance images

We report the discovery of strong correlations between protein coding regions and the prediction errors when using the simple recurrent network to segment genome sequences. We are going to use SARS genome to demonstrate how we conduct training and derive corresponding results. The distribution of prediction error indicates how the underlying hidden regularity of the genome sequences and the results are consistent with the finding of biologists: predicated protein coding features of SARS genome. This implies that the simple recurrent network is capable of providing new features for further biological studies when applied on genome studies. The HA gene of influenza A subtype H1N1 is also analyzed in a similar way.

/pdf/segmentation-of-dna-using-simple-recurrent-neural-network-502alzuisr.pdf

Wei-Chen Cheng

Papers

Autoencoder for words

Manifold Construction by Local Neighborhood Preservation

Resolving Hidden Representations

Construct adaptive template array for magnetic resonance images

Segmentation of DNA using simple recurrent neural network