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Journal articleYao Q, Evans T, Chen B, et al., 2021,
Higher-order temporal network effects through triplet evolution
, Scientific Reports, Vol: 11, Pages: 1-17, ISSN: 2045-2322We study the evolution of networks through ‘triplets’ — three-node graphlets. We develop a method to compute a transitionmatrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions inthe evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only.The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstratethat non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore,this also reveals that different patterns of higher-order interaction are involved in different real-world situations.To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate ouralgorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods.Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as wefind our method, along with two other methods based on non-local interactions, give the best overall performance. Theresults also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understandand predict the evolution of different real-world systems.
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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 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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Conference paperRosas De Andraca FE, Azari M, Arani A, 2020,
Mobile cellular-connected UAVs: reinforcement learning for sky limits
, IEEE Globecom Workshops 2020, Publisher: IEEE, Pages: 1-6A cellular-connected unmanned aerial vehicle (UAV) faces several key challenges concerning connectivity and energy efficiency. Through a learning-based strategy, we propose a general novel multi-armed bandit (MAB) algorithm to reduce disconnectivity time, handover rate, and energy consumption of UAV by taking into account its time of task completion. By formulating the problem as a function of UAV's velocity, we show how each of these performance indicators (PIs) is improved by adopting a proper range of corresponding learning parameter, e.g. 50% reduction in HO rate as compared to a blind strategy. However, results reveal that the optimal combination of the learning parameters depends critically on any specific application and the weights of PIs on the final objective function.
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Journal articleSzigeti B, Kartner L, Blemings A, et al., 2021,
Self-blinding citizen science to explore psychedelic microdosing
, eLife, Vol: 10, Pages: 1-26, ISSN: 2050-084XMicrodosing is the practice of regularly using low doses of psychedelic drugs. Anecdotal reports suggest that microdosing enhances well-being and cognition; however, such accounts are potentially biased by the placebo effect. This study used a ‘self-blinding’ citizen science initiative, where participants were given online instructions on how to incorporate placebo control into their microdosing routine without clinical supervision. The study was completed by 191 participants, making it the largest placebo-controlled trial on psychedelics to-date. All psychological outcomes improved significantly from baseline to after the 4 weeks long dose period for the microdose group; however, the placebo group also improved and no significant between-groups differences were observed. Acute (emotional state, drug intensity, mood, energy, and creativity) and post-acute (anxiety) scales showed small, but significant microdose vs. placebo differences; however, these results can be explained by participants breaking blind. The findings suggest that anecdotal benefits of microdosing can be explained by the placebo effect.
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Journal articleTurkheimer FE, Rosas FE, Dipasquale O, et al., 2021,
A complex systems perspective on neuroimaging studies of behavior and its disorders
, The Neuroscientist: reviews at the interface of basic and clinical neurosciences, Vol: 28, Pages: 382-399, ISSN: 1073-8584The study of complex systems deals with emergent behavior that arises as a result of nonlinear spatiotemporal interactions between a large number of components both within the system, as well as between the system and its environment. There is a strong case to be made that neural systems as well as their emergent behavior and disorders can be studied within the framework of complexity science. In particular, the field of neuroimaging has begun to apply both theoretical and experimental procedures originating in complexity science—usually in parallel with traditional methodologies. Here, we illustrate the basic properties that characterize complex systems and evaluate how they relate to what we have learned about brain structure and function from neuroimaging experiments. We then argue in favor of adopting a complex systems-based methodology in the study of neuroimaging, alongside appropriate experimental paradigms, and with minimal influences from noncomplex system approaches. Our exposition includes a review of the fundamental mathematical concepts, combined with practical examples and a compilation of results from the literature.
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Journal articleSahasranaman A, Jensen HJ, 2021,
Spread of COVID-19 in urban neighbourhoods and slums of the developing world
, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 18, ISSN: 1742-5689- Author Web Link
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Journal articleSibani P, Boettcher S, Jensen HJ, 2021,
Record dynamics of evolving metastable systems: theory and applications
, European Physical Journal B: Condensed Matter and Complex Systems, Vol: 94, Pages: 1-23, ISSN: 1434-6028Record Dynamics (RD) deals with complex systems evolving through a sequence of metastable stages. These are macroscopically distinguishable and appear stationary, except for the sudden and rapid changes, called quakes, which induce the transitions from one stage to the next. This phenomenology is well known in physics as “physical aging”, but from the vantage point of RD, the evolution of a class of systems of physical, biological, and cultural origin is rooted in a hierarchically structured configuration space and can, therefore, be analyzed by similar statistical tools. This colloquium paper strives to present in a coherent fashion methods and ideas that have gradually evolved over time. To this end, it first describes the differences and similarities between RD and two widespread paradigms of complex dynamics, Self-Organized Criticality and Continuous Time Random Walks. It then outlines the Poissonian nature of records events in white noise time-series, and connects it to the statistics of quakes in metastable hierarchical systems, arguing that the relaxation effects of quakes can generally be described by power laws unrelated to criticality. Several different applications of RD have been developed over the years. Some of these are described, showing the basic RD hypothesis and how the log-time homogeneity of quake dynamics, can be empirically verified in a given context. The discussion summarizes the paper and briefly mentions applications not discussed in detail. Finally, the outlook points to possible improvements and to new areas of research where RD could be of use.
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Conference paperRosas De Andraca FE, Mediano P, Biehl M, et al., 2020,
Causal Blankets: Theory and Algorithmic Framework
, ECML/PKDD 2020
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