Publications from our Researchers
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
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Journal articleCheng S, Chen J, Anastasiou C, et al., 2023,
Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models, JOURNAL OF SCIENTIFIC COMPUTING, Vol: 94, ISSN: 0885-7474
- Author Web Link
- Citations: 2
Journal articleHancock F, Cabral J, Luppi AI, et al., 2022,
Metastability, fractal scaling, and synergistic information processing: what phase relationships reveal about intrinsic brain activity, NeuroImage, Vol: 259, Pages: 1-16, ISSN: 1053-8119
Dynamic functional connectivity (dFC) in resting-state fMRI holds promise to deliver candidate biomarkers for clinical applications. However, the reliability and interpretability of dFC metrics remain contested. Despite a myriad of methodologies and resulting measures, few studies have combined metrics derived from different conceptualizations of brain functioning within the same analysis - perhaps missing an opportunity for improved interpretability. Using a complexity-science approach, we assessed the reliability and interrelationships of a battery of phase-based dFC metrics including tools originating from dynamical systems, stochastic processes, and information dynamics approaches. Our analysis revealed novel relationships between these metrics, which allowed us to build a predictive model for integrated information using metrics from dynamical systems and information theory. Furthermore, global metastability - a metric reflecting simultaneous tendencies for coupling and decoupling - was found to be the most representative and stable metric in brain parcellations that included cerebellar regions. Additionally, spatiotemporal patterns of phase-locking were found to change in a slow, non-random, continuous manner over time. Taken together, our findings show that the majority of characteristics of resting-state fMRI dynamics reflect an interrelated dynamical and informational complexity profile, which is unique to each acquisition. This finding challenges the interpretation of results from cross-sectional designs for brain neuromarker discovery, suggesting that individual life-trajectories may be more informative than sample means.
Journal articleWang Z, Chen J, Rosas FE, et al., 2022,
A hypergraph-based framework for personalized recommendations via user preference and dynamics clustering, Expert Systems with Applications, Vol: 204, Pages: 117552-117552, ISSN: 0957-4174
The ever-increasing number of users and items continuously imposes new challenges to existent clustering-based recommendation algorithms. To better simulate the interactions between users and items in the recommendation system, in this paper, we propose a collaborative filtering recommendation algorithm based on dynamics clustering and similarity measurement in hypergraphs (Hg-PDC). The main idea of Hg-PDC is to discover several interest communities by aggregating users with high attention, and make recommendations within each community, thereby improving the recommendation performance and reducing the time cost. Firstly, we introduce a hypergraph model to capture complex relations beyond pairwise relations, while preserving attention relations in the network. In addition, we construct a novel hypergraph model, which defines a user and his evaluated items to form a hyperedge. Secondly, an extended game dynamics clustering method is proposed for the constructed hypergraph to aggregate users with high attention into the same interest community. Here, we combine the payoff function in game theory with the traditional dynamics clustering method. Finally, we apply the dynamics clustering results and a new similarity measurement strategy with user preferences to recommend items for target users. The effectiveness of Hg-PDC is verified by experiments on six real datasets. Experimental results illustrate that our algorithm outperforms state-of-the-art algorithms in prediction errors and recommendation performance.
Journal articleVirgo N, Rosas FE, Biehl M, 2022,
Embracing sensorimotor history: Time-synchronous and time-unrolled Markov blankets in the free-energy principle., Behav Brain Sci, Vol: 45
The free-energy principle (FEP) builds on an assumption that sensor-motor loops exhibit Markov blankets in stationary state. We argue that there is rarely reason to assume a system's internal and external states are conditionally independent given the sensorimotor states, and often reason to assume otherwise. However, under mild assumptions internal and external states are conditionally independent given the sensorimotor history.
Journal articleGong H, Cheng S, Chen Z, et al., 2022,
An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics, ANNALS OF NUCLEAR ENERGY, Vol: 179, ISSN: 0306-4549
- Author Web Link
- Citations: 3
Journal articleZhuang Y, Cheng S, Kovalchuk N, et al., 2022,
Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device, Lab on a Chip: miniaturisation for chemistry, physics, biology, materials science and bioengineering, Vol: 22, Pages: 3187-3202, ISSN: 1473-0189
A major challenge in the field of microfluidics is to predict and control drop interactions. This work develops an image-based data-driven model to forecast drop dynamics based on experiments performed on a microfluidics device. Reduced-order modelling techniques are applied to compress the recorded images into low-dimensional spaces and alleviate the computational cost. Recurrent neural networks are then employed to build a surrogate model of drop interactions by learning the dynamics of compressed variables in the reduced-order space. The surrogate model is integrated with real-time observations using data assimilation. In this paper we developed an ensemble-based latent assimilation algorithm scheme which shows an improvement in terms of accuracy with respect to the previous approaches. This work demonstrates the possibility to create a reliable data-driven model enabling a high fidelity prediction of drop interactions in microfluidics device. The performance of the developed system is evaluated against experimental data (i.e., recorded videos), which are excluded from the training of the surrogate model. The developed scheme is general and can be applied to other dynamical systems.
Journal articleChagot L, Quilodran-Casas C, Kalli M, et al., 2022,
Surfactant-laden droplet size prediction in a flow-focusing microchannel: a data-driven approach, LAB ON A CHIP, Vol: 22, Pages: 3848-3859, ISSN: 1473-0197
Journal articleCheng S, Prentice IC, Huang Y, et al., 2022,
Data-driven surrogate model with latent data-assimilation: application to wildfire forecasting, Journal of Computational Physics, Vol: 464, ISSN: 0021-9991
The large and catastrophic wildfires have been increasing across the globe in the recent decade, highlighting the importance of simulating and forecasting fire dynamics in near real-time. This is extremely challenging due to the complexities of physical models and geographical features. Running physics-based simulations for large wildfire events in near real-time are computationally expensive, if not infeasible. In this work, we develop and test a novel data-model integration scheme for fire progression forecasting, that combines Reduced-order modelling, recurrent neural networks (Long-Short-Term Memory), data assimilation, and error covariance tuning. The Reduced-order modelling and the machine learning surrogate model ensure the efficiency of the proposed approach while the data assimilation enables the system to adjust the simulation with observations. We applied this algorithm to simulate and forecast three recent large wildfire events in California from 2017 to 2020. The deep-learning-based surrogate model runs around 1000 times faster than the Cellular Automata simulation which is used to generate training data-sets. The daily fire perimeters derived from satellite observation are used as observation data in Latent Assimilation to adjust the fire forecasting in near real-time. An error covariance tuning algorithm is also performed in the reduced space to estimate prior simulation and observation errors. The evolution of the averaged relative root mean square error (R-RMSE) shows that data assimilation and covariance tuning reduce the RMSE by about 50% and considerably improves the forecasting accuracy. As a first attempt at a reduced order wildfire spread forecasting, our exploratory work showed the potential of data-driven machine learning models to speed up fire forecasting for various applications.
Journal articleMediano PAM, Rosas FE, Bor D, et al., 2022,
The strength of weak integrated information theory, Trends in Cognitive Sciences, Vol: 26, Pages: 646-655, ISSN: 1364-6613
The integrated information theory of consciousness (IIT) is divisive: while some believe it provides an unprecedentedly powerful approach to address the ‘hard problem’, others dismiss it on grounds that it is untestable. We argue that the appeal and applicability of IIT can be greatly widened if we distinguish two flavours of the theory: strong IIT, which identifies consciousness with specific properties associated with maxima of integrated information; and weak IIT, which tests pragmatic hypotheses relating aspects of consciousness to broader measures of information dynamics. We review challenges for strong IIT, explain how existing empirical findings are well explained by weak IIT without needing to commit to the entirety of strong IIT, and discuss the outlook for both flavours of IIT.
Journal articleLever J, Arcucci R, 2022,
Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting, Journal of Computational Social Science, Vol: 5, Pages: 1427-1465, ISSN: 2432-2717
The intensity of wildfires and wildfire season length is increasing due to climate change, causing a greater threat to the local population. Much of this population are increasingly adopting social media, and sites like Twitter are increasingly being used as a real-time human-sensor network during natural disasters; detecting, tracking and documenting events. The human-sensor concept is currently largely omitted by wildfire models, representing a potential loss of information. By including Twitter data as a source in our models, we aim to help disaster managers make more informed, socially driven decisions, by detecting and monitoring online social media sentiment over the course of a wildfire event. This paper implements machine learning in a wildfire prediction model, using social media and geophysical data sources with Sentiment Analysis to predict wildfire characteristics with high accuracy. We also use wildfire-specific attributes to predict online social dynamics, as this has been shown to be indicative of localised disaster severity. This may be useful for disaster management teams in identifying areas of immediate danger. We combine geophysical satellite data from the Global Fire Atlas with social data provided by Twitter. We perform data collection and subsequent analysis & visualisation, and compare regional differences in online social sentiment expression. Following this, we compare and contrast different machine learning models for predicting wildfire attributes. We demonstrate social media is a predictor of wildfire activity, and present models which accurately model wildfire attributes. This work develops the concept of the human sensor in the context of wildfires, using users’ Tweets as noisy subjective sentimental accounts of current localised conditions. This work contributes to the development of more socially conscious wildfire models, by incorporating social media data into wildfire prediction and modelling.
Journal articleMediano PAM, Rosas FE, Luppi AI, et al., 2022,
Greater than the parts: a review of the information decomposition approach to causal emergence., Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 380, Pages: 20210246-20210246, ISSN: 1364-503X
Emergence is a profound subject that straddles many scientific disciplines, including the formation of galaxies and how consciousness arises from the collective activity of neurons. Despite the broad interest that exists on this concept, the study of emergence has suffered from a lack of formalisms that could be used to guide discussions and advance theories. Here, we summarize, elaborate on, and extend a recent formal theory of causal emergence based on information decomposition, which is quantifiable and amenable to empirical testing. This theory relates emergence with information about a system's temporal evolution that cannot be obtained from the parts of the system separately. This article provides an accessible but rigorous introduction to the framework, discussing the merits of the approach in various scenarios of interest. We also discuss several interpretation issues and potential misunderstandings, while highlighting the distinctive benefits of this formalism. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.
Journal articleCheng S, Jin Y, Harrison S, et al., 2022,
Parameter flexible wildfire prediction using machine learning techniques: forward and inverse modelling, Remote Sensing, Vol: 14, ISSN: 2072-4292
Parameter identification for wildfire forecasting models often relies on case-by-case tuning or posterior diagnosis/analysis, which can be computationally expensive due to the complexity of the forward prediction model. In this paper, we introduce an efficient parameter flexible fire prediction algorithm based on machine learning and reduced order modelling techniques. Using a training dataset generated by physics-based fire simulations, the method forecasts burned area at different time steps with a low computational cost. We then address the bottleneck of efficient parameter estimation by developing a novel inverse approach relying on data assimilation techniques (latent assimilation) in the reduced order space. The forward and the inverse modellings are tested on two recent large wildfire events in California. Satellite observations are used to validate the forward prediction approach and identify the model parameters. By combining these forward and inverse approaches, the system manages to integrate real-time observations for parameter adjustment, leading to more accurate future predictions.
Journal articleMcCulloch DE-W, Knudsen GM, Barrett FS, et al., 2022,
Psychedelic resting-state neuroimaging: a review and perspective on balancing replication and novel analyses, Neuroscience & Biobehavioral Reviews, Vol: 138, Pages: 104689-104689, ISSN: 0149-7634
Clinical research into serotonergic psychedelics is expanding rapidly, showing promising efficacy across myriad disorders. Resting-state functional magnetic resonance imaging (rs-fMRI) is a commonly used strategy to identify psychedelic-induced changes in neural pathways in clinical and healthy populations. Here we, a large group of psychedelic imaging researchers, review the 42 research articles published to date, based on the 17 unique studies evaluating psychedelic effects on rs-fMRI, focusing on methodological variation. Prominently, we observe that nearly all studies vary in data processing and analysis methodology, two datasets are the foundation of over half of the published literature, and there is lexical ambiguity in common outcome metric terminology. We offer guidelines for future studies that encourage coherence in the field. Psychedelic rs-fMRI will benefit from the development of novel methods that expand our understanding of the brain mechanisms mediating its intriguing effects; yet, this field is at a crossroads where we must also consider the critical importance of consistency and replicability to effectively converge on stable representations of the neural effects of psychedelics.
Conference paperLever J, Arcucci R, Cai J, 2022,
Social Data Assimilation of Human Sensor Networks for Wildfires, Pages: 455-462
We present an implementation of a human sensor network in the context of wildfires. A human sensor network can be thought of as a socially nuanced abstraction of a physical sensing model, where social media users are considered noisy remote sensors with variable reliability and location. This allows real-time social modelling of physical events. We apply this concept to data collected from Twitter & Reddit in the context of California wildfires, performing sentimental & topical analysis over the period of a wildfire season to extract themes, sentiments and discussions. We assimilate this social media data in a predictive model trained by machine learning approaches for time series. Both Long Short Term Memory (LSTM) & AutoRegressive Integrated Moving Average (ARIMA) models are employed. We assimilate the human sensor networks, to overcome the limitations & biases exhibited by individual social media platform demographics. We implement Optimal Interpolation and Ensemble Kalman Filter architectures on our models & data. Finally we compare and evaluate performance, and discuss how these implementations could benefit current wildfire models.
Book chapterLever J, Arcucci R, 2022,
Towards Social Machine Learning for Natural Disasters, Computational Science – ICCS 2022 22nd International Conference, London, UK, June 21–23, 2022, Proceedings, Part III, Publisher: Springer, Pages: 756-769, ISBN: 9783031087561
The four-volume set LNCS 13350, 13351, 13352, and 13353 constitutes the proceedings of the 22ndt International Conference on Computational Science, ICCS 2022, held in London, UK, in June 2022.* The total of 175 full papers and 78 short ...
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