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 articleLuppi A, Mediano PAM, Rosas FE, et al., 2022,
A synergistic core for human brain evolution and cognition
, NATURE NEUROSCIENCE, Vol: 25, Pages: 771-+, ISSN: 1097-6256 -
Journal articleDmitrewski A, Molina-Solana M, Arcucci R, 2022,
CNTRLDA: A building energy management control system with real-time adjustments. Application to indoor temperature
, BUILDING AND ENVIRONMENT, Vol: 215, ISSN: 0360-1323- Author Web Link
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- Citations: 6
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Journal articleRosas FE, Mediano PAM, Luppi AI, et al., 2022,
Disentangling high-order mechanisms and high-order behaviours in complex systems
, NATURE PHYSICS, Vol: 18, Pages: 476-477, ISSN: 1745-2473- Author Web Link
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- Citations: 14
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Journal articleLuppi AI, Mediano PAM, Rosas FE, et al., 2022,
Whole-brain modelling identifies distinct but convergent paths to unconsciousness in anaesthesia and disorders of consciousness
, Communications Biology, Vol: 5, ISSN: 2399-3642The human brain entertains rich spatiotemporal dynamics, which are drastically reconfigured when consciousness is lost due to anaesthesia or disorders of consciousness (DOC). Here, we sought to identify the neurobiological mechanisms that explain how transient pharmacological intervention and chronic neuroanatomical injury can lead to common reconfigurations of neural activity. We developed and systematically perturbed a neurobiologically realistic model of whole-brain haemodynamic signals. By incorporating PET data about the cortical distribution of GABA receptors, our computational model reveals a key role of spatially-specific local inhibition for reproducing the functional MRI activity observed during anaesthesia with the GABA-ergic agent propofol. Additionally, incorporating diffusion MRI data obtained from DOC patients reveals that the dynamics that characterise loss of consciousness can also emerge from randomised neuroanatomical connectivity. Our results generalise between anaesthesia and DOC datasets, demonstrating how increased inhibition and connectome perturbation represent distinct neurobiological paths towards the characteristic activity of the unconscious brain.
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Journal articleScagliarini T, Marinazzo D, Guo Y, et al., 2022,
Quantifying high-order interdependencies on individual patterns via the local O-information: Theory and applications to music analysis
, Physical Review Research, Vol: 4, ISSN: 2643-1564High-order, beyond-pairwise interdependencies are at the core of biological, economic, and social complex systems, and their adequate analysis is paramount to understand, engineer, and control such systems. This paper presents a framework to measure high-order interdependence that disentangles their effect on each individual pattern exhibited by a multivariate system. The approach is centered on the local O-information, a new measure that assesses the balance between synergistic and redundant interdependencies at each pattern. To illustrate the potential of this framework, we present a detailed analysis of music scores from J. S. Bach, which reveals how high-order interdependence is deeply connected with highly nontrivial aspects of the musical discourse. Our results place the local O-information as a promising tool of wide applicability, which opens other perspectives for analyzing high-order relationships in the patterns exhibited by complex systems.
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Journal articleBuizza C, Casas CQ, Nadler P, et al., 2022,
Data Learning: Integrating Data Assimilation and Machine Learning
, JOURNAL OF COMPUTATIONAL SCIENCE, Vol: 58, ISSN: 1877-7503- Author Web Link
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- Citations: 18
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Journal articlePeill JM, Trinci KE, Kettner H, et al., 2022,
Validation of the psychological insight scale: a new scale to assess psychological insight following a psychedelic experience
, Journal of Psychopharmacology, Vol: 36, Pages: 31-45, ISSN: 0269-8811Introduction:As their name suggests, ‘psychedelic’ (mind-revealing) compounds are thought to catalyse processes of psychological insight; however, few satisfactory scales exist to sample this. This study sought to develop a new scale to measure psychological insight after a psychedelic experience: the Psychological Insight Scale (PIS).Methods:The PIS is a six- to seven-item questionnaire that enquires about psychological insight after a psychedelic experience (PIS-6) and accompanied behavioural changes (PIS item 7). In total, 886 participants took part in a study in which the PIS and other questionnaires were completed in a prospective fashion in relation to a planned psychedelic experience. For validation purposes, data from 279 participants were analysed from a non-specific ‘global psychedelic survey’ study.Results:Principal components analysis of PIS scores revealed a principal component explaining 73.57% of the variance, which displayed high internal consistency at multiple timepoints throughout the study (average Cronbach’s α = 0.94). Criterion validity was confirmed using the global psychedelic survey study, and convergent validity was confirmed via the Therapeutic-Realizations Scale. Furthermore, PIS scores significantly mediated the relationship between emotional breakthrough and long-term well-being.Conclusion:The PIS is complementary to current subjective measures used in psychedelic studies, most of which are completed in relation to the acute experience. Insight – as measured by the PIS – was found to be a key mediator of long-term psychological outcomes following a psychedelic experience. Future research may investigate how insight varies throughout a psychedelic process, its underlying neurobiology and how it impacts behaviour and mental health.
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Journal articleMediano PAM, Rosas FE, Farah JC, et al., 2022,
Integrated information as a common signature of dynamical and information-processing complexity
, Chaos: an interdisciplinary journal of nonlinear science, Vol: 32, Pages: 1-12, ISSN: 1054-1500The apparent dichotomy between information-processing and dynamical approaches to complexity science forces researchers to choose between two diverging sets of tools and explanations, creating conflict and often hindering scientific progress. Nonetheless, given the shared theoretical goals between both approaches, it is reasonable to conjecture the existence of underlying common signatures that capture interesting behavior in both dynamical and information-processing systems. Here, we argue that a pragmatic use of integrated information theory (IIT), originally conceived in theoretical neuroscience, can provide a potential unifying framework to study complexity in general multivariate systems. By leveraging metrics put forward by the integrated information decomposition framework, our results reveal that integrated information can effectively capture surprisingly heterogeneous signatures of complexity—including metastability and criticality in networks of coupled oscillators as well as distributed computation and emergent stable particles in cellular automata—without relying on idiosyncratic, ad hoc criteria. These results show how an agnostic use of IIT can provide important steps toward bridging the gap between informational and dynamical approaches to complex systems.Originally conceived within theoretical neuroscience, integrated information theory (IIT) has been rarely used in other fields—such as complex systems or non-linear dynamics—despite the great value it has to offer. In this article, we inspect the basics of IIT, dissociating it from its contentious claims about the nature of consciousness. Relieved of this philosophical burden, IIT presents itself as an appealing formal framework to study complexity in biological or artificial systems, applicable in a wide range of domains. To illustrate this, we present an exploration of integrated information in complex systems and relate it to other notions of complexity commonly used in sys
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Journal articleDekkers G, Rosas F, van Waterschoot T, et al., 2022,
Dynamic sensor activation and decision-level fusion in wireless acoustic sensor networks for classification of domestic activities
, Information Fusion, Vol: 77, Pages: 196-210, ISSN: 1566-2535For the past decades there has been a rising interest for wireless sensor networks to obtain information about an environment. One interesting modality is that of audio, as it is highly informative for numerous applications including speech recognition, urban scene classification, city monitoring, machine listening and classifying domestic activities. However, as they operate at prohibitively high energy consumption, commercialisation of battery-powered wireless acoustic sensor networks has been limited. To increase the network's lifetime, this paper explores the joint use of decision-level fusion and dynamic sensor activation. Hereby adopting a topology where processing – including feature extraction and classification – is performed on a dynamic set of sensor nodes that communicate classification outputs which are fused centrally. The main contribution of this paper is the comparison of decision-level fusion with different dynamic sensor activation strategies on the use case of automatically classifying domestic activities. Results indicate that using vector quantisation to encode the classification output, computed at each sensor node, can reduce the communication per classification output to 8 bit without loss of significant performance. As the cost for communication is reduced, local processing tends to dominate the overall energy budget. It is indicated that dynamic sensor activation, using a centralised approach, can reduce the average time a sensor node is active up to 20% by leveraging redundant information in the network. In terms of energy consumption, this resulted in an energy reduction of up to 80% as the cost for computation dominates the overall energy budget.
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Conference paperLever J, Arcucci R, Cai J, 2022,
Social Data Assimilation of Human Sensor Networks for Wildfires
, 15th ACM International Conference on Pervasive Technologies Related to Assistive Environments (PETRA), Publisher: ASSOC COMPUTING MACHINERY, Pages: 455-462 -
Conference paperArcucci R, Casas CQ, Joshi A, et al., 2022,
Merging Real Images with Physics Simulations via Data Assimilation
, 27th International European Conference on Parallel and Distributed Computing (Euro-Par), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 255-266, ISSN: 0302-9743 -
Conference paperCheng S, Quilodran-Casas C, Arcucci R, 2022,
Reduced Order Surrogate Modelling and Latent Assimilation for Dynamical Systems
, 22nd Annual International Conference on Computational Science (ICCS), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 31-44, ISSN: 0302-9743 -
Conference paperLever J, Arcucci R, 2022,
Towards Social Machine Learning for Natural Disasters
, 22nd Annual International Conference on Computational Science (ICCS), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 756-769, ISSN: 0302-9743 -
Journal articleVermeulen T, Reynders B, Rosas FE, et al., 2021,
Performance analysis of in-band collision detection for dense wireless networks
, Eurasip Journal on Wireless Communications and Networking, Vol: 2021, Pages: 1-23, ISSN: 1687-1472With the massive growth of wireless networks comes a bigger impact of collisions and interference, which has a negative effect on throughput and energy efficiency. To deal with this problem, we propose an in-band wireless collision and interference detection scheme based on full-duplex technology. To study its performance, we compare its throughput and energy efficiency with the performance of traditional half-duplex and symmetric in-band full-duplex transmissions. Our analysis considers a realistic protocol and overhead modeling, and a measurement-based self-interference model. Our results indicate that our proposed collision detection scheme can provide significant gains in terms of throughput and energy efficiency in large wireless networks. Moreover, when compared to half-duplex and symmetric full-duplex, our analysis shows that this scheme allows up to 45% more nodes in the network for the same energy consumption per bit. These results suggest that this could be an enabling technology towards efficient, dense wireless networks.
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Journal articleKettlun F, Rosas F, Oberli C, 2021,
A low-complexity channel training method for efficient SVD beamforming over MIMO channels
, Eurasip Journal on Wireless Communications and Networking, Vol: 2021, Pages: 1-22, ISSN: 1687-1472Singular value decomposition (SVD) beamforming is an attractive tool for reducing the energy consumption of data transmissions in wireless sensor networks whose nodes are equipped with multiple antennas. However, this method is often not practical due to two important shortcomings: it requires channel state information at the transmitter and the computation of the SVD of the channel matrix is generally too complex. To deal with these issues, we propose a method for establishing an SVD beamforming link without requiring feedback of actual channel or SVD coefficients to the transmitter. Concretely, our method takes advantage of channel reciprocity and a power iteration algorithm (PIA) for determining the precoding and decoding singular vectors from received preamble sequences. A low-complexity version that performs no iterations is proposed and shown to have a signal-to-noise-ratio (SNR) loss within 1 dB of the bit error rate of SVD beamforming with least squares channel estimates. The low-complexity method significantly outperforms maximum ratio combining diversity and Alamouti coding. We also show that the computational cost of the proposed PIA-based method is less than the one of using the Golub–Reinsch algorithm for obtaining the SVD. The number of computations of the low-complexity version is an order of magnitude smaller than with Golub–Reinsch. This difference grows further with antenna array size.
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