Towards a Multi-Subject Analysis of Neural Connectivity

Directed acyclic graphs (DAGs) and associated probability models are widely used to model neural connectivity and communication channels. In many experiments, data are collected from multiple subjects whose connectivities may differ but are likely to share many features. In such circumstances it is natural to leverage similarity between subjects to improve statistical efficiency. The first exact algorithm for estimation of multiple related DAGs was recently proposed by Oates et al. 2014; in this letter we present examples and discuss implications of the methodology as applied to the analysis of fMRI data from a multi-subject experiment. Elicitation of tuning parameters requires care and we illustrate how this may proceed retrospectively based on technical replicate data. In addition to joint learning of subject-specific connectivity, we allow for heterogeneous collections of subjects and simultaneously estimate relationships between the subjects themselves. This letter aims to highlight the potential for exact estimation in the multi-subject setting.Comment: to appear in Neural Computation 27:1-2

Deep Learning-Based Part Labeling of Tree Components in Point Cloud Data

Point cloud data analysis plays a crucial role in forest management, remote sensing, and wildfire monitoring and mitigation, necessitating robust computer algorithms and pipelines for segmentation and labeling of tree components. This thesis presents a novel pipeline that employs deep learning models, such as the Point-Voxel Transformer (PVT), and synthetic tree point clouds for automatic tree part-segmentation. The pipeline leverages the expertise of environmental artists to enhance the quality and diversity of training data and investigates alternative subsampling methods to optimize model performance. Furthermore, we evaluate various label propagation techniques to improve the labeling of synthetic tree point clouds. By comparing different community detection methods and graph connectivity inference techniques, we demonstrate that K-NN connectivity inference and carefully selected community detection methods significantly enhance labeling accuracy, efficiency, and coverage. The proposed methods hold the potential to improve the quality of forest management and monitoring applications, enable better assessment of wildfire hazards, and facilitate advancements in remote sensing and forestry fields

Deep Learning-Based Part Labeling of Tree Components in Point Cloud Data

Point cloud data analysis plays a crucial role in forest management, remote sensing, and wildfire monitoring and mitigation, necessitating robust computer algorithms and pipelines for segmentation and labeling of tree components. This thesis presents a novel pipeline that employs deep learning models, such as the Point-Voxel Transformer (PVT), and synthetic tree point clouds for automatic tree part-segmentation. The pipeline leverages the expertise of environmental artists to enhance the quality and diversity of training data and investigates alternative subsampling methods to optimize model performance. Furthermore, we evaluate various label propagation techniques to improve the labeling of synthetic tree point clouds. By comparing different community detection methods and graph connectivity inference techniques, we demonstrate that K-NN connectivity inference and carefully selected community detection methods significantly enhance labeling accuracy, efficiency, and coverage. The proposed methods hold the potential to improve the quality of forest management and monitoring applications, enable better assessment of wildfire hazards, and facilitate advancements in remote sensing and forestry fields

Supervised estimation of Granger-based causality between time series

Brain effective connectivity aims to detect causal interactions between distinct brain units and it is typically studied through the analysis of direct measurements of the neural activity, e.g., magneto/electroencephalography (M/EEG) signals. The literature on methods for causal inference is vast. It includes model-based methods in which a generative model of the data is assumed and model-free methods that directly infer causality from the probability distribution of the underlying stochastic process. Here, we firstly focus on the model-based methods developed from the Granger criterion of causality, which assumes the autoregressive model of the data. Secondly, we introduce a new perspective, that looks at the problem in a way that is typical of the machine learning literature. Then, we formulate the problem of causality detection as a supervised learning task, by proposing a classification-based approach. A classifier is trained to identify causal interactions between time series for the chosen model and by means of a proposed feature space. In this paper, we are interested in comparing this classification-based approach with the standard Geweke measure of causality in the time domain, through simulation study. Thus, we customized our approach to the case of a MAR model and designed a feature space which contains causality measures based on the idea of precedence and predictability in time. Two variations of the supervised method are proposed and compared to a standard Granger causal analysis method. The results of the simulations show that the supervised method outperforms the standard approach, in particular it is more robust to noise. As evidence of the efficacy of the proposed method, we report the details of our submission to the causality detection competition of Biomag2014, where the proposed method reached the 2nd place. Moreover, as empirical application, we applied the supervised approach on a dataset of neural recordings of rats obtaining an important reduction in the false positive rate

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure