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 articleTimmermann Slater CB, Kettner H, Letheby C, et al., 2021,
Psychedelics alter metaphysical beliefs
, Scientific Reports, Vol: 11, Pages: 1-12, ISSN: 2045-2322Can the use of psychedelic drugs induce lasting changes in metaphysical beliefs? While it is popularly believed that they can, this question has never been formally tested. Here we exploited a large sample derived from prospective online surveying to determine whether and how beliefs concerning the nature of reality, consciousness, and free-will, change after psychedelic use. Results revealed significant shifts away from ‘physicalist’ or ‘materialist’ views, and towards panpsychism and fatalism, post use. With the exception of fatalism, these changes endured for at least 6 months, and were positively correlated with the extent of past psychedelic-use and improved mental-health outcomes. Path modelling suggested that the belief-shifts were moderated by impressionability at baseline and mediated by perceived emotional synchrony with others during the psychedelic experience. The observed belief-shifts post-psychedelic-use were consolidated by data from an independent controlled clinical trial. Together, these findings imply that psychedelic-use may causally influence metaphysical beliefs—shifting them away from ‘hard materialism’. We discuss whether these apparent effects are contextually independent.
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Journal articleLuppi A, Mediano PAM, Rosas FE, et al., 2021,
What it is like to be a bit: an integrated information decomposition account of emergent mental phenomena
, Neuroscience of Consciousness, Vol: 7, ISSN: 2057-2107A central question in neuroscience concerns the relationship between consciousness and its physical substrate. Here, we argue that a richer characterization of consciousness can be obtained by viewing it as constituted of distinct information-theoretic elements. In other words, we propose a shift from quantification of consciousness—viewed as integrated information—to its decomposition. Through this approach, termed Integrated Information Decomposition (ΦID), we lay out a formal argument that whether the consciousness of a given system is an emergent phenomenon depends on its information-theoretic composition—providing a principled answer to the long-standing dispute on the relationship between consciousness and emergence. Furthermore, we show that two organisms may attain the same amount of integrated information, yet differ in their information-theoretic composition. Building on ΦID’s revised understanding of integrated information, termed ΦR, we also introduce the notion of ΦR-ing ratio to quantify how efficiently an entity uses information for conscious processing. A combination of ΦR and ΦR-ing ratio may provide an important way to compare the neural basis of different aspects of consciousness. Decomposition of consciousness enables us to identify qualitatively different ‘modes of consciousness’, establishing a common space for mapping the phenomenology of different conscious states. We outline both theoretical and empirical avenues to carry out such mapping between phenomenology and information-theoretic modes, starting from a central feature of everyday consciousness: selfhood. Overall, ΦID yields rich new ways to explore the relationship between information, consciousness, and its emergence from neural dynamics.
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Journal articleGan HM, Fernando S, Molina-Solana M, 2021,
Scalable object detection pipeline for traffic cameras: Application to Tfl JamCams
, Expert Systems with Applications, Vol: 182, Pages: 1-15, ISSN: 0957-4174With CCTV systems being installed in the transport infrastructures of many cities, there is an abundance of data to be extracted from the footage. This paper explores the application of the YOLOv3 object detection algorithm trained on the COCO dataset to the Transport for London’s (TfL) JamCam feed. The result, open-sourced and publicly available, is a series of easy to deploy Docker pipelines to create, store and serve (through a REST API) data on identified objects on that feed. The pipelines can be deployed to any Linux machine with an NVIDIA GPU to support accelerated computation. We studied how different confidence thresholds affect detections of relevant objects (cars, trucks and pedestrians) in London JamCam scenes. By running the system continuously for 3 weeks, we built a dataset of more than 2200 detection datapoints for each camera (̃6 datapoints an hour). We further visualised the detections on an animated geospatial map, showcasing their effectiveness in identifying traffic patterns typical of an urban city like London, portraying the variation on different object population levels throughout the day.
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Journal articleKuc J, Kettner H, Rosas F, et al., 2021,
Psychedelic experience dose-dependently modulated by cannabis: results of a prospective online survey
, Psychopharmacology, Vol: 239, Pages: 1425-1440, ISSN: 0033-3158Rationale.Classic psychedelics are currently being studied as novel treatments for a range of psychiatric disorders. However, research on how psychedelics interact with other psychoactive substances remains scarce.ObjectivesThe current study aimed to explore the subjective effects of psychedelics when used alongside cannabis.MethodsParticipants (n = 321) completed a set of online surveys at 2 time points: 7 days before, and 1 day after a planned experience with a serotonergic psychedelic. The collected data included demographics, environmental factors (so-called setting) and five validated questionnaires: Mystical Experience Questionnaire (MEQ), visual subscales of Altered States of Consciousness Questionnaire (ASC-Vis), Challenging Experience Questionnaire (CEQ), Ego Dissolution Inventory (EDI) and Emotional Breakthrough Inventory (EBI). Participants were grouped according to whether they had reported using no cannabis (n = 195) or low (n = 53), medium (n = 45) or high (n = 28) dose, directly concomitant with the psychedelic. Multivariate analysis of covariance (MANCOVA) and contrasts was used to analyse differences in subjective effects between groups while controlling for potential confounding contextual ‘setting’ variables.ResultsThe simultaneous use of cannabis together with classic serotonergic psychedelics was associated with more intense psychedelic experience across a range of measures: a linear relationship was found between dose and MEQ, ASC-Vis and EDI scores, while a quadratic relationship was found for CEQ scores. No relationship was found between the dose of cannabis and the EBI.ConclusionsResults imply a possible interaction between the cannabis and psychedelic on acute subjective experiences; however, design limitations hamper our ability to draw firm inferences on directions of causality and the clinical implications of any such interactions.
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Journal articleGatica M, Cofre R, Mediano PAM, et al., 2021,
High-order interdependencies in the aging brain
, Brain Connectivity, Vol: 11, Pages: 734-744, ISSN: 2158-0022Background: Brain interdependencies can be studied from either a structural/anatomical perspective (“structural connectivity”) or by considering statistical interdependencies (“functional connectivity” [FC]). Interestingly, while structural connectivity is by definition pairwise (white-matter fibers project from one region to another), FC is not. However, most FC analyses only focus on pairwise statistics and they neglect higher order interactions. A promising tool to study high-order interdependencies is the recently proposed O-Information, which can quantify the intrinsic statistical synergy and the redundancy in groups of three or more interacting variables.Methods: We analyzed functional magnetic resonance imaging (fMRI) data obtained at rest from 164 healthy subjects with ages ranging in 10 to 80 years and used O-Information to investigate how high-order statistical interdependencies are affected by age.Results: Older participants (from 60 to 80 years old) exhibited a higher predominance of redundant dependencies compared with younger participants, an effect that seems to be pervasive as it is evident for all orders of interaction. In addition, while there is strong heterogeneity across brain regions, we found a “redundancy core” constituted by the prefrontal and motor cortices in which redundancy was evident at all the interaction orders studied.Discussion: High-order interdependencies in fMRI data reveal a dominant redundancy in functions such as working memory, executive, and motor functions. Our methodology can be used for a broad range of applications, and the corresponding code is freely available.Impact statementPast research has showcased multiple changes to the brain's structural and functional properties caused by aging. Here we expand prior work through recent advancements in multivariate information theory, which provide richer and more theoretically principled analyses than existing alternatives. We show that the
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Journal articleMediano PAM, Rosas FE, Barrett AB, et al., 2021,
Decomposing spectral and phasic differences in nonlinear features between datasets
, Physical Review Letters, Vol: 127, Pages: 1-4, ISSN: 0031-9007When employing nonlinear methods to characterize complex systems, it is important to determine to what extent they are capturing genuine nonlinear phenomena that could not be assessed by simpler spectral methods. Specifically, we are concerned with the problem of quantifying spectral and phasic effects on an observed difference in a nonlinear feature between two systems (or two states of the same system). Here we derive, from a sequence of null models, a decomposition of the difference in an observable into spectral, phasic, and spectrum-phase interaction components. Our approach makes no assumptions about the structure of the data and adds nuance to a wide range of time series analyses.
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Journal articleRosas De Andraca FE, Morales P,
A generalisation of the maximum entropy principle for curved statistical manifolds
, Physical Review Research, ISSN: 2643-1564The maximum entropy principle (MEP) is one of the most prominent methods to investigate andmodel complex systems. Despite its popularity, the standard form of the MEP can only generateBoltzmann-Gibbs distributions, which are ill-suited for many scenarios of interest. As a principledapproach to extend the reach of the MEP, this paper revisits its foundations in information geometryand shows how the geometry of curved statistical manifolds naturally leads to a generalisation of theMEP based on the Rényi entropy. By establishing a bridge between non-Euclidean geometry andthe MEP, our proposal sets a solid foundation for the numerous applications of the Rényi entropy,and enables a range of novel methods for complex systems analysis.
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Conference paperRosas FE, Mediano PAM, Gastpar M, 2021,
Learning, compression, and leakage: Minimising classification error via meta-universal compression principles
, 2020 IEEE Information Theory Workshop (ITW), Publisher: IEEE, Pages: 1-5Learning and compression are driven by the common aim of identifying and exploiting statistical regularities in data, which opens the door for fertile collaboration between these areas. A promising group of compression techniques for learning scenarios is normalised maximum likelihood (NML) coding, which provides strong guarantees for compression of small datasets — in contrast with more popular estimators whose guarantees hold only in the asymptotic limit. Here we consider a NMLbased decision strategy for supervised classification problems, and show that it attains heuristic PAC learning when applied to a wide variety of models. Furthermore, we show that the misclassification rate of our method is upper bounded by the maximal leakage, a recently proposed metric to quantify the potential of data leakage in privacy-sensitive scenarios.
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Journal articleMedina-Mardones AM, Rosas FE, Rodríguez SE, et al., 2021,
Hyperharmonic analysis for the study of high-order information-theoretic signals
, Journal of Physics: Complexity, Vol: 2, Pages: 1-16, ISSN: 2632-072XNetwork representations often cannot fully account for the structural richness of complex systems spanning multiple levels of organisation. Recently proposed high-order information-theoretic signals are well-suited to capture synergistic phenomena that transcend pairwise interactions; however, the exponential-growth of their cardinality severely hinders their applicability. In this work, we combine methods from harmonic analysis and combinatorial topology to construct efficient representations of high-order information-theoretic signals. The core of our method is the diagonalisation of a discrete version of the Laplace–de Rham operator, that geometrically encodes structural properties of the system. We capitalise on these ideas by developing a complete workflow for the construction of hyperharmonic representations of high-order signals, which is applicable to a wide range of scenarios.
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Journal articleHuitzil I, Molina-Solana M, Gómez-Romero J, et al., 2021,
Minimalistic fuzzy ontology reasoning: An application to Building Information Modeling
, Applied Soft Computing, Vol: 103, Pages: 1-15, ISSN: 1568-4946This paper presents a minimalistic reasoning algorithm to solve imprecise instance retrieval in fuzzy ontologies with application to querying Building Information Models (BIMs)—a knowledge representation formalism used in the construction industry. Our proposal is based on a novel lossless reduction of fuzzy to crisp reasoning tasks, which can be processed by any Description Logics reasoner. We implemented the minimalistic reasoning algorithm and performed an empirical evaluation of its performance in several tasks: interoperation with classical reasoners (Hermit and TrOWL), initialization time (comparing TrOWL and a SPARQL engine), and use of different data structures (hash tables, databases, and programming interfaces). We show that our software can efficiently solve very expressive queries not available nowadays in regular or semantic BIMs tools.
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Journal articleTajnafoi G, Arcucci R, Mottet L, et al., 2021,
Variational Gaussian process for optimal sensor placement
, Applications of Mathematics, Vol: 66, Pages: 287-317, ISSN: 0373-6725Sensor placement is an optimisation problem that has recently gained great relevance. In order to achieve accurate online updates of a predictive model, sensors are used to provide observations. When sensor location is optimally selected, the predictive model can greatly reduce its internal errors. A greedy-selection algorithm is used for locating these optimal spatial locations from a numerical embedded space. A novel architecture for solving this big data problem is proposed, relying on a variational Gaussian process. The generalisation of the model is further improved via the preconditioning of its inputs: Masked Autoregressive Flows are implemented to learn nonlinear, invertible transformations of the conditionally modelled spatial features. Finally, a global optimisation strategy extending the Mutual Information-based optimisation and fine-tuning of the selected optimal location is proposed. The methodology is parallelised to speed up the computational time, making these tools very fast despite the high complexity associated with both spatial modelling and placement tasks. The model is applied to a real three-dimensional test case considering a room within the Clarence Centre building located in Elephant and Castle, London, UK.
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Journal articleWu P, Chang X, Yuan W, et al., 2021,
Fast data assimilation (FDA): Data assimilation by machine learning for faster optimize model state
, JOURNAL OF COMPUTATIONAL SCIENCE, Vol: 51, ISSN: 1877-7503- Author Web Link
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- Citations: 6
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Conference paperBonavita M, Arcucci R, Carrassi A, et al., 2021,
Machine Learning for Earth System Observation and Prediction
, Publisher: AMER METEOROLOGICAL SOC, Pages: E710-E716, ISSN: 0003-0007- Author Web Link
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- Citations: 17
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Journal articleKettner HS, Rosas F, Timmermann C, et al., 2021,
Psychedelic Communitas: intersubjective experience during psychedelic group sessions predicts enduring changes in psychological wellbeing and social connectedness
, Frontiers in Pharmacology, Vol: 12, ISSN: 1663-9812Background: Recent years have seen a resurgence of research on the potential of psychedelic substances to treat addictive and mood disorders. Historically and contemporarily, psychedelic studies have emphasized the importance of contextual elements ('set and setting') in modulating acute drug effects, and ultimately, influencing long-term outcomes. Nevertheless, current small-scale clinical and laboratory studies have tended to bypass a ubiquitous contextual feature of naturalistic psychedelic use: its social dimension. This study introduces and psychometrically validates an adapted Communitas Scale, assessing acute relational experiences of perceived togetherness and shared humanity, in order to investigate psychosocial mechanisms pertinent to psychedelic ceremonies and retreats.Methods: In this observational, web-based survey study, participants (N = 886) were measured across five successive time-points: 2 weeks before, hours before, and the day after a psychedelic ceremony; as well as the day after, and 4 weeks after leaving the ceremony location. Demographics, psychological traits and state variables were assessed pre-ceremony, in addition to changes in psychological wellbeing and social connectedness from before to after the retreat, as primary outcomes. Using correlational and multiple regression (path) analyses, predictive relationships between psychosocial 'set and setting' variables, communitas, and long-term outcomes were explored.Results: The adapted Communitas Scale demonstrated substantial internal consistency (Cronbach's alpha = 0.92) and construct validity in comparison with validated measures of intra-subjective (visual, mystical, challenging experiences questionnaires) and inter-subjective (perceived emotional synchrony, identity fusion) experiences. Furthermore, communitas during ceremony was significantly correlated with increases in psychological wellbeing (r = 0.22), social connectedness (r = 0.25), and other salient mental health outcomes. Path
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Journal articleCheng S, Pain CC, Guo Y-K, et al., 2021,
Real-time Updating of Dynamic Social Networks for COVID-19 Vaccination Strategies
<jats:title>Abstract</jats:title><jats:p>Vaccination strategy is crucial in fighting the COVID-19 pandemic. Since the supply is still limited in many countries, contact network-based interventions can be most powerful to set an efficient strategy by identifying high-risk individuals or communities. However, due to the high dimension, only partial and noisy network information can be available in practice, especially for dynamic systems where contact networks are highly time-variant. Furthermore, the numerous mutations of SARS-CoV-2 have a significant impact on the infectious probability, requiring real-time network updating algorithms. In this study, we propose a sequential network updating approach based on data assimilation techniques to combine different sources of temporal information. We then prioritise the individuals with high-degree or high-centrality, obtained from assimilated networks, for vaccination. The assimilation-based approach is compared with the standard method (based on partially observed networks) and a random selection strategy in terms of vaccination effectiveness in a SIR model. The numerical comparison is first carried out using real-world face-to-face dynamic networks collected in a high school, followed by sequential multi-layer networks generated relying on the Barabasi-Albert model emulating large-scale social networks with several communities.</jats:p>
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