Human Activity Classification with Online Growing Neural Gas

Panzner M, Beyer O, Cimiano P. Human Activity Classification with Online Growing Neural Gas. In: Workshop on New Challenges in Neural Computation (NC2). 2013: 106-113.In this paper we present an online approach to human ac- tivity classification based on Online Growing Neural Gas (OGNG). In contrast to state-of-the-art approaches that perform training in an offline fashion, our approach is online in the sense that it circumvents the need to store any training examples, processing the data on the fly and in one pass. The approach is thus particularly suitable in life-long learning settings where never-ending streams of data arise. We propose an archi- tecture that consists of two layers, allowing the storage of human actions in a more memory efficient structure. While the first layer (feature map) dynamically clusters Space-Time Interest Points (STIP) and serves as basis for the creation of histogram-based signatures of human actions, the second layer (class map) builds a classification model that relies on these human action signatures. We present experimental results on the KTH activity dataset showing that our approach has comparable per- formance to a Support Vector Machine (SVM) while performing online and avoiding to store examples explicitly

Automated four-dimensional long term imaging enables single cell tracking within organotypic brain slices to study neurodevelopment and degeneration.

Current approaches for dynamic profiling of single cells rely on dissociated cultures, which lack important biological features existing in tissues. Organotypic slice cultures preserve aspects of structural and synaptic organisation within the brain and are amenable to microscopy, but established techniques are not well adapted for high throughput or longitudinal single cell analysis. Here we developed a custom-built, automated confocal imaging platform, with improved organotypic slice culture and maintenance. The approach enables fully automated image acquisition and four-dimensional tracking of morphological changes within individual cells in organotypic cultures from rodent and human primary tissues for at least 3 weeks. To validate this system, we analysed neurons expressing a disease-associated version of huntingtin (HTT586Q138-EGFP), and observed that they displayed hallmarks of Huntington's disease and died sooner than controls. By facilitating longitudinal single-cell analyses of neuronal physiology, our system bridges scales necessary to attain statistical power to detect developmental and disease phenotypes

Deep Learning with Limited Labels for Medical Imaging

Recent advancements in deep learning-based AI technologies provide an automatic tool to revolutionise medical image computing. Training a deep learning model requires a large amount of labelled data. Acquiring labels for medical images is extremely challenging due to the high cost in terms of both money and time, especially for the pixel-wise segmentation task of volumetric medical scans. However, obtaining unlabelled medical scans is relatively easier compared to acquiring labels for those images. This work addresses the pervasive issue of limited labels in training deep learning models for medical imaging. It begins by exploring different strategies of entropy regularisation in the joint training of labelled and unlabelled data to reduce the time and cost associated with manual labelling for medical image segmentation. Of particular interest are consistency regularisation and pseudo labelling. Specifically, this work proposes a well-calibrated semi-supervised segmentation framework that utilises consistency regularisation on different morphological feature perturbations, representing a significant step towards safer AI in medical imaging. Furthermore, it reformulates pseudo labelling in semi-supervised learning as an Expectation-Maximisation framework. Building upon this new formulation, the work explains the empirical successes of pseudo labelling and introduces a generalisation of the technique, accompanied by variational inference to learn its true posterior distribution. The applications of pseudo labelling in segmentation tasks are also presented. Lastly, this work explores unsupervised deep learning for parameter estimation of diffusion MRI signals, employing a hierarchical variational clustering framework and representation learning

Exploring YouTube Comments to Understand Public Sentiment on COVID-19 Vaccines through Deep Learning-based Sentiment Analysis

COVID-19 was first found in China in 2019. Since then, it has quickly spread around the world, which has led to a lot of news stories and social media posts about the pandemic. YouTube, a popular video-sharing website, has become a valuable source of information on COVID-19 and other topics. However, it can be difficult to extract useful insights from the vast array of user comments that accompany these videos. One potential method for understanding public sentiment is to use sentiment analysis, which involves classifying text as positive, negative, or neutral. In this study, the dataset of over 44,000 YouTube comments related to COVID-19 vaccines was used, which was filtered to a total of 16,073 comments for analysis. The data was cleaned and organised using NeatText and then processed using GloVe word embedding, a technique for establishing statistical relationships between words. Based on the experiment, the performances of three different types of deep learning techniques: recurrent neural networks (RNN), gated recurrent units (GRU) and long short-term memory (LSTM) are compared in accurately classifying the sentiment of the comments. The study found that the GRU had the highest accuracy of 80.19%, followed by the LSTM with 79.00% accuracy, and the RNN with 67.15% accuracy

Metabolic heterogeneity and cross-feeding within isogenic yeast populations captured by DILAC

Genetically identical cells are known to differ in many physiological parameters such as growth rate and drug tolerance. Metabolic specialization is believed to be a cause of such phenotypic heterogeneity, but detection of metabolically divergent subpopulations remains technically challenging. We developed a proteomics-based technology, termed differential isotope labelling by amino acids (DILAC), that can detect producer and consumer subpopulations of a particular amino acid within an isogenic cell population by monitoring peptides with multiple occurrences of the amino acid. We reveal that young, morphologically undifferentiated yeast colonies contain subpopulations of lysine producers and consumers that emerge due to nutrient gradients. Deconvoluting their proteomes using DILAC, we find evidence for in situ cross-feeding where rapidly growing cells ferment and provide the more slowly growing, respiring cells with ethanol. Finally, by combining DILAC with fluorescence-activated cell sorting, we show that the metabolic subpopulations diverge phenotypically, as exemplified by a different tolerance to the antifungal drug amphotericin B. Overall, DILAC captures previously unnoticed metabolic heterogeneity and provides experimental evidence for the role of metabolic specialization and cross-feeding interactions as a source of phenotypic heterogeneity in isogenic cell populations

URLs can facilitate machine learning classification of news stories across languages and contexts

Comparative scholars studying political news content at scale face the challenge of addressing multiple languages. While many train individual supervised machine learning classifiers for each language, this is a costly and time-consuming process. We propose that instead of rely-ing on thematic labels generated by manual coding, researchers can use ‘distant’ labels created by cues in article URLs. Sections reflected in URLs (e.g., nytimes.com/politics/) can therefore help create training material for supervised machine learning classifiers. Using cues provided by news media organizations, such an approach allows for efficient political news identification at scale while facilitating imple-mentation across languages. Using a dataset of approximately 870,000 URLs of news-related content from four countries (Italy, Germany, Netherlands, and Poland), we test this method by providing a comparison to ‘classical’ supervised machine learning and a multilingual BERT model, across four news topics. Our results suggest that the use of URL section cues to distantly annotate texts provides a cheap and easy-to-implement way of classifying large volumes of news texts that can save researchers many valuable resources without having to sacrifice quality