1.
Buroni, Giovanni; Bontempi, Gianluca; Determe, Karl
A tutorial on network-wide multi-horizon traffic forecasting with deep learning Journal Article
In: CEUR Workshop Proceedings, 2841 , 2021, (Language of publication: en).
@article{info:hdl:2013/322517,
title = {A tutorial on network-wide multi-horizon traffic forecasting with deep learning},
author = {Giovanni Buroni and Gianluca Bontempi and Karl Determe},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/322517/3/EasyChair-Preprint-4944.pdf},
year = {2021},
date = {2021-01-01},
journal = {CEUR Workshop Proceedings},
volume = {2841},
abstract = {Traffic flow forecasting is fundamental to today's Intelligent Transportation Systems (ITS). It involves the task of learning traffic complex dynamics in order to predict future conditions. This is particularly challenging when it comes to predict the traffic status for multiple horizons into the future and at the same time for the entire transportation network. In this context deep learning models have recently shown promising results. This models can inherently capture the non-linear space-temporal correlations (ST) in traffic by taking advantage of the huge volume of data available. In this study the authors present a LSTM encoder-decoder for multi-horizon traffic flow predictions. We adopted a direct approach in which the model simultaneously predict traffic conditions for the entire Belgian motorway transport network at each time step. The results clearly show the superiority of this model when compared with other deep learning models. In the workshop, conference attendees will learn how to process and visualize mobility data, obtain optimal features for traffic flow forecasting, build a LSTM encoder-decoder and perform predictions in an online manner.},
note = {Language of publication: en},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Traffic flow forecasting is fundamental to today's Intelligent Transportation Systems (ITS). It involves the task of learning traffic complex dynamics in order to predict future conditions. This is particularly challenging when it comes to predict the traffic status for multiple horizons into the future and at the same time for the entire transportation network. In this context deep learning models have recently shown promising results. This models can inherently capture the non-linear space-temporal correlations (ST) in traffic by taking advantage of the huge volume of data available. In this study the authors present a LSTM encoder-decoder for multi-horizon traffic flow predictions. We adopted a direct approach in which the model simultaneously predict traffic conditions for the entire Belgian motorway transport network at each time step. The results clearly show the superiority of this model when compared with other deep learning models. In the workshop, conference attendees will learn how to process and visualize mobility data, obtain optimal features for traffic flow forecasting, build a LSTM encoder-decoder and perform predictions in an online manner.
2.
Bontempi, Gianluca; Chavarriaga, Ricardo; eD Canck, Hans; Girardi, Emanuela; Hoos, Holger H; Kilbane-Dawe, Iarla; Ball, Tonio; Nowe, Ann; Sousa, Jose; Bacciu, Davide; Aldinucci, Marco; eD Domenico, Manlio; Saffiotti, Alessandro; Maratea, Marco
The CLAIRE COVID-19 initiative: approach, experiences and recommendations Journal Article
In: Ethics and information technology, 2021, (DOI: 10.1007/s10676-020-09567-7).
@article{info:hdl:2013/321123,
title = {The CLAIRE COVID-19 initiative: approach, experiences and recommendations},
author = {Gianluca Bontempi and Ricardo Chavarriaga and Hans eD Canck and Emanuela Girardi and Holger H Hoos and Iarla Kilbane-Dawe and Tonio Ball and Ann Nowe and Jose Sousa and Davide Bacciu and Marco Aldinucci and Manlio eD Domenico and Alessandro Saffiotti and Marco Maratea},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/321123/3/Bontempi2021_Article_TheCLAIRECOVID-19InitiativeApp-2.pdf},
year = {2021},
date = {2021-01-01},
journal = {Ethics and information technology},
abstract = {A volunteer effort by Artificial Intelligence (AI) researchers has shown it can deliver significant research outcomes rapidly to help tackle COVID-19. Within two months, CLAIRE's self-organising volunteers delivered the World's first comprehensive curated repository of COVID-19-related datasets useful for drug-repurposing, drafted review papers on the role CT/X-ray scan analysis and robotics could play, and progressed research in other areas. Given the pace required and nature of voluntary efforts, the teams faced a number of challenges. These offer insights in how better to prepare for future volunteer scientific efforts and large scale, data-dependent AI collaborations in general. We offer seven recommendations on how to best leverage such efforts and collaborations in the context of managing future crises.},
note = {DOI: 10.1007/s10676-020-09567-7},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A volunteer effort by Artificial Intelligence (AI) researchers has shown it can deliver significant research outcomes rapidly to help tackle COVID-19. Within two months, CLAIRE's self-organising volunteers delivered the World's first comprehensive curated repository of COVID-19-related datasets useful for drug-repurposing, drafted review papers on the role CT/X-ray scan analysis and robotics could play, and progressed research in other areas. Given the pace required and nature of voluntary efforts, the teams faced a number of challenges. These offer insights in how better to prepare for future volunteer scientific efforts and large scale, data-dependent AI collaborations in general. We offer seven recommendations on how to best leverage such efforts and collaborations in the context of managing future crises.
3.
Bontempi, Gianluca
Statistical foundations of machine learning Book
2021, (Language of publication: en).
@book{info:hdl:2013/325210,
title = {Statistical foundations of machine learning},
author = {Gianluca Bontempi},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/325210/3/syl.pdf},
year = {2021},
date = {2021-01-01},
note = {Language of publication: en},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
4.
Guida, Sibilla Di; Han, The Anh T A H; Kirchsteiger, Georg; Lenaerts, Tom; Zisis, Ioannis
Repeated interaction and its impact on cooperation and surplus allocation---an experimental analysis Journal Article
In: Games, 12 (1), 2021, (DOI: 10.3390/g12010025).
@article{info:hdl:2013/321049,
title = {Repeated interaction and its impact on cooperation and surplus allocation---an experimental analysis},
author = {Sibilla Di Guida and The Anh T A H Han and Georg Kirchsteiger and Tom Lenaerts and Ioannis Zisis},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/321049/1/doi_304693.pdf},
year = {2021},
date = {2021-01-01},
journal = {Games},
volume = {12},
number = {1},
abstract = {This paper investigates how the possibility of affecting group composition combined with the possibility of repeated interaction impacts cooperation within groups and surplus distribution. We developed and tested experimentally a Surplus Allocation Game where cooperation of four agents is needed to produce surplus, but only two have the power to allocate it among the group members. Three matching procedures (corresponding to three separate experimental treatments) were used to test the impact of the variables of interest. A total of 400 subjects participated in our research, which was computer-based and conducted in a laboratory. Our results show that allowing for repeated interaction with the same partners leads to a self-selection of agents into groups with different life spans, whose duration is correlated with the behavior of both distributors and receivers. While behavior at the group level is diverse for surplus allocation and amount of cooperation, aggregate behavior is instead similar when repeated interaction is allowed or not allowed. We developed a behavioral model that captures the dynamics observed in the experimental data and sheds light into the rationales that drive the agents' individual behavior, suggesting that the most generous distributors are those acting for fear of rejection, not for true generosity, while the groups lasting the longest are those composed by this type of distributors and ``undemanding'' receivers.},
note = {DOI: 10.3390/g12010025},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper investigates how the possibility of affecting group composition combined with the possibility of repeated interaction impacts cooperation within groups and surplus distribution. We developed and tested experimentally a Surplus Allocation Game where cooperation of four agents is needed to produce surplus, but only two have the power to allocate it among the group members. Three matching procedures (corresponding to three separate experimental treatments) were used to test the impact of the variables of interest. A total of 400 subjects participated in our research, which was computer-based and conducted in a laboratory. Our results show that allowing for repeated interaction with the same partners leads to a self-selection of agents into groups with different life spans, whose duration is correlated with the behavior of both distributors and receivers. While behavior at the group level is diverse for surplus allocation and amount of cooperation, aggregate behavior is instead similar when repeated interaction is allowed or not allowed. We developed a behavioral model that captures the dynamics observed in the experimental data and sheds light into the rationales that drive the agents' individual behavior, suggesting that the most generous distributors are those acting for fear of rejection, not for true generosity, while the groups lasting the longest are those composed by this type of distributors and ``undemanding'' receivers.
5.
P`ere, Nathaniel Vincent Mon; Lenaerts, Tom; d S dos Pacheco, Jorge Manuel Santos J M; Dingli, David
Multistage feedback-driven compartmental dynamics of hematopoiesis Journal Article
In: iScience, 24 (4), 2021, (DOI: 10.1016/j.isci.2021.102326).
@article{info:hdl:2013/322462,
title = {Multistage feedback-driven compartmental dynamics of hematopoiesis},
author = {Nathaniel Vincent Mon P{`e}re and Tom Lenaerts and Jorge Manuel Santos J M d S dos Pacheco and David Dingli},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/322462/1/doi_306106.pdf},
year = {2021},
date = {2021-01-01},
journal = {iScience},
volume = {24},
number = {4},
abstract = {Human hematopoiesis is surprisingly resilient to disruptions, providing suitable responses to severe bleeding, long-lasting immune activation, and even bone marrow transplants. Still, many blood disorders exist which push the system past its natural plasticity, resulting in abnormalities in the circulating blood. While proper treatment of such diseases can benefit from understanding the underlying cell dynamics, these are non-trivial to predict due to the hematopoietic system's hierarchical nature and complex feedback networks. To characterize the dynamics following different types of perturbations, we investigate a model representing hematopoiesis as a sequence of compartments covering all maturation stages---from stem to mature cells---where feedback regulates cell production to ongoing necessities. We find that a stable response to perturbations requires the simultaneous adaptation of cell differentiation and self-renewal rates, and show that under conditions of continuous disruption---as found in chronic hemolytic states---compartment cell numbers evolve to novel stable states.},
note = {DOI: 10.1016/j.isci.2021.102326},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Human hematopoiesis is surprisingly resilient to disruptions, providing suitable responses to severe bleeding, long-lasting immune activation, and even bone marrow transplants. Still, many blood disorders exist which push the system past its natural plasticity, resulting in abnormalities in the circulating blood. While proper treatment of such diseases can benefit from understanding the underlying cell dynamics, these are non-trivial to predict due to the hematopoietic system's hierarchical nature and complex feedback networks. To characterize the dynamics following different types of perturbations, we investigate a model representing hematopoiesis as a sequence of compartments covering all maturation stages---from stem to mature cells---where feedback regulates cell production to ongoing necessities. We find that a stable response to perturbations requires the simultaneous adaptation of cell differentiation and self-renewal rates, and show that under conditions of continuous disruption---as found in chronic hemolytic states---compartment cell numbers evolve to novel stable states.
6.
Rivi`ere, Quentin; Xiao, Qiying; Gutsch, Annelie; Defrance, Matthieu; Webb, A A R; Verbruggen, Nathalie
Mg deficiency interacts with the circadian clock and phytochromes pathways in Arabidopsis Journal Article
In: Annals of Applied Biology, 178 (2), pp. 387-399, 2021, (DOI: 10.1111/aab.12659).
@article{info:hdl:2013/322914,
title = {Mg deficiency interacts with the circadian clock and phytochromes pathways in Arabidopsis},
author = {Quentin Rivi{`e}re and Qiying Xiao and Annelie Gutsch and Matthieu Defrance and A A R Webb and Nathalie Verbruggen},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/322914/3/MgDeficiencyInPlantsLightClock.pdf},
year = {2021},
date = {2021-01-01},
journal = {Annals of Applied Biology},
volume = {178},
number = {2},
pages = {387-399},
note = {DOI: 10.1111/aab.12659},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
7.
Ciortan, Madalina; Defrance, Matthieu
Contrastive self-supervised clustering of scRNA-seq data. Journal Article
In: BMC bioinformatics, 22 (1), pp. 280, 2021, (DOI: 10.1186/s12859-021-04210-8).
@article{info:hdl:2013/325214,
title = {Contrastive self-supervised clustering of scRNA-seq data.},
author = {Madalina Ciortan and Matthieu Defrance},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/325214/1/doi_308858.pdf},
year = {2021},
date = {2021-01-01},
journal = {BMC bioinformatics},
volume = {22},
number = {1},
pages = {280},
abstract = {Single-cell RNA sequencing (scRNA-seq) has emerged has a main strategy to study transcriptional activity at the cellular level. Clustering analysis is routinely performed on scRNA-seq data to explore, recognize or discover underlying cell identities. The high dimensionality of scRNA-seq data and its significant sparsity accentuated by frequent dropout events, introducing false zero count observations, make the clustering analysis computationally challenging. Even though multiple scRNA-seq clustering techniques have been proposed, there is no consensus on the best performing approach. On a parallel research track, self-supervised contrastive learning recently achieved state-of-the-art results on images clustering and, subsequently, image classification.},
note = {DOI: 10.1186/s12859-021-04210-8},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Single-cell RNA sequencing (scRNA-seq) has emerged has a main strategy to study transcriptional activity at the cellular level. Clustering analysis is routinely performed on scRNA-seq data to explore, recognize or discover underlying cell identities. The high dimensionality of scRNA-seq data and its significant sparsity accentuated by frequent dropout events, introducing false zero count observations, make the clustering analysis computationally challenging. Even though multiple scRNA-seq clustering techniques have been proposed, there is no consensus on the best performing approach. On a parallel research track, self-supervised contrastive learning recently achieved state-of-the-art results on images clustering and, subsequently, image classification.
8.
Pollaris, Arnaud; Bontempi, Gianluca
Latent Causation: An algorithm for pairs of correlated latent variables in Linear Non-Gaussian Structural Equation Modeling Miscellaneous
2020, (Conference: BNAIC/BENELEARN (19 & 20 November 2020: Leiden (online))).
@misc{info:hdl:2013/314680,
title = {Latent Causation: An algorithm for pairs of correlated latent variables in Linear Non-Gaussian Structural Equation Modeling},
author = {Arnaud Pollaris and Gianluca Bontempi},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/314680/3/BNAICBENELEARN_2020_Final_paper.pdf},
year = {2020},
date = {2020-01-01},
abstract = {This paper addresses the problem of inferring causation in a pair of linearly correlated continuous latent variables. We first discuss the limitations of the Direction Dependance Analysis (DDA) approach and then introduce the Latent Causation (LC). Five variants (in terms of dependency statistic) of the LC algorithm are assessed with ROC curves, then we consider the case of a latent confounder (uniform or chi-square distributed). While the distribution and the correlations of the latent confounder influence the accuracy, experimental results show the robustness of the method using bootstrapped p-values. Implications and limits of the experimental results are then discussed together with future directions.},
note = {Conference: BNAIC/BENELEARN (19 & 20 November 2020: Leiden (online))},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
This paper addresses the problem of inferring causation in a pair of linearly correlated continuous latent variables. We first discuss the limitations of the Direction Dependance Analysis (DDA) approach and then introduce the Latent Causation (LC). Five variants (in terms of dependency statistic) of the LC algorithm are assessed with ROC curves, then we consider the case of a latent confounder (uniform or chi-square distributed). While the distribution and the correlations of the latent confounder influence the accuracy, experimental results show the robustness of the method using bootstrapped p-values. Implications and limits of the experimental results are then discussed together with future directions.
9.
Simar, Cédric; Petieau, Mathieu; Cebolla, Ana Maria; Leroy, Axelle; Bontempi, Gianluca; Chéron, Guy
EEG-based brain-computer interface for alpha speed control of a small robot using the MUSE headband Inproceedings
In: 2020, (DOI: 10.1109/IJCNN48605.2020.9207486).
@inproceedings{info:hdl:2013/315071,
title = {EEG-based brain-computer interface for alpha speed control of a small robot using the MUSE headband},
author = {Cédric Simar and Mathieu Petieau and Ana Maria Cebolla and Axelle Leroy and Gianluca Bontempi and Guy Chéron},
url = {http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/315071},
year = {2020},
date = {2020-01-01},
abstract = {Non-invasive BMI applications are increasingly used in different contexts ranging from industrial, clinical and gaming. After having tested the difference between a classical EEG recorder with electroconductive gel (ANT system) and the MUSE EEG headband, we studied the BCI performances of the later during the control of a small robot. We demonstrated that the participants were able to successfully control the robot using an online brain-computer interface based on the signal power in different frequency bands (delta, theta and alpha) characterizing the eyes-opened and relaxed eyes-closed states. Additionally, we performed a correlation analysis which demonstrated that the BCI commands were more related to a delta or theta power decrease for the determination of the classifier output probability and to the alpha power increase for the speed control of the robot.},
note = {DOI: 10.1109/IJCNN48605.2020.9207486},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Non-invasive BMI applications are increasingly used in different contexts ranging from industrial, clinical and gaming. After having tested the difference between a classical EEG recorder with electroconductive gel (ANT system) and the MUSE EEG headband, we studied the BCI performances of the later during the control of a small robot. We demonstrated that the participants were able to successfully control the robot using an online brain-computer interface based on the signal power in different frequency bands (delta, theta and alpha) characterizing the eyes-opened and relaxed eyes-closed states. Additionally, we performed a correlation analysis which demonstrated that the BCI commands were more related to a delta or theta power decrease for the determination of the classifier output probability and to the alpha power increase for the speed control of the robot.
10.
Caro, Fabrizio De; Stefani, Jacopo De; Bontempi, Gianluca; Vaccaro, Alfredo A; Villacci, Domenico D
Robust Assessment of Short-Term Wind Power Forecasting Models on Multiple Time Horizons Journal Article
In: Technology and Economics of Smart Grids and Sustainable Energy, 5 (1), 2020, (DOI: 10.1007/s40866-020-00090-8).
@article{info:hdl:2013/314435,
title = {Robust Assessment of Short-Term Wind Power Forecasting Models on Multiple Time Horizons},
author = {Fabrizio De Caro and Jacopo De Stefani and Gianluca Bontempi and Alfredo A Vaccaro and Domenico D Villacci},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/314435/1/doi_298079.pdf},
year = {2020},
date = {2020-01-01},
journal = {Technology and Economics of Smart Grids and Sustainable Energy},
volume = {5},
number = {1},
abstract = {The massive penetration of renewable power generation in modern power grids is an effective way to reduce the impact of energy production on global warming. Unfortunately, the wind power generation may affect the regular operation of electrical systems, due to the stochastic and intermittent nature of the wind. For this reason, reducing the uncertainty about the wind evolution, e.g. by using short-term wind power forecasting methodologies, is a priority for system operators and wind producers to implement low-carbon power grids. Unfortunately, though the complexity of this task implies the comparison of several alternative forecasting methodologies and dimensionality reduction techniques, a general and robust procedure of model assessment still lacks in literature. In this paper the authors propose a robust methodology, based on extensive statistical analysis and resampling routines, to supply the most effective wind power forecasting method by testing a vast ensemble of methodologies over multiple time-scales and on a real case study. Experimental results on real data collected in an Italian wind farm show the potential of ensemble approaches integrating both statistical and machine learning methods.},
note = {DOI: 10.1007/s40866-020-00090-8},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The massive penetration of renewable power generation in modern power grids is an effective way to reduce the impact of energy production on global warming. Unfortunately, the wind power generation may affect the regular operation of electrical systems, due to the stochastic and intermittent nature of the wind. For this reason, reducing the uncertainty about the wind evolution, e.g. by using short-term wind power forecasting methodologies, is a priority for system operators and wind producers to implement low-carbon power grids. Unfortunately, though the complexity of this task implies the comparison of several alternative forecasting methodologies and dimensionality reduction techniques, a general and robust procedure of model assessment still lacks in literature. In this paper the authors propose a robust methodology, based on extensive statistical analysis and resampling routines, to supply the most effective wind power forecasting method by testing a vast ensemble of methodologies over multiple time-scales and on a real case study. Experimental results on real data collected in an Italian wind farm show the potential of ensemble approaches integrating both statistical and machine learning methods.
11.
Simar, Cédric; Cebolla, Ana Maria; Chartier, Ga"elle; Petieau, Mathieu; Bontempi, Gianluca; Berthoz, Alain; Cheron, Guy
Hyperscanning EEG and Classification Based on Riemannian Geometry for Festive and Violent Mental State Discrimination Journal Article
In: Frontiers in Neuroscience, 14 , 2020, (DOI: 10.3389/fnins.2020.588357).
@article{info:hdl:2013/317394,
title = {Hyperscanning EEG and Classification Based on Riemannian Geometry for Festive and Violent Mental State Discrimination},
author = {Cédric Simar and Ana Maria Cebolla and Ga{"e}lle Chartier and Mathieu Petieau and Gianluca Bontempi and Alain Berthoz and Guy Cheron},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/317394/3/pdf},
year = {2020},
date = {2020-01-01},
journal = {Frontiers in Neuroscience},
volume = {14},
abstract = {Interactions between two brains constitute the essence of social communication. Daily movements are commonly executed during social interactions and are determined by different mental states that may express different positive or negative behavioral intent. In this context, the effective recognition of festive or violent intent before the action execution remains crucial for survival. Here, we hypothesize that the EEG signals contain the distinctive features characterizing movement intent already expressed before movement execution and that such distinctive information can be identified by state-of-the-art classification algorithms based on Riemannian geometry. We demonstrated for the first time that a classifier based on covariance matrices and Riemannian geometry can effectively discriminate between neutral, festive, and violent mental states only on the basis of non-invasive EEG signals in both the actor and observer participants. These results pave the way for new electrophysiological discrimination of mental states based on non-invasive EEG recordings and cutting-edge machine learning techniques.},
note = {DOI: 10.3389/fnins.2020.588357},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Interactions between two brains constitute the essence of social communication. Daily movements are commonly executed during social interactions and are determined by different mental states that may express different positive or negative behavioral intent. In this context, the effective recognition of festive or violent intent before the action execution remains crucial for survival. Here, we hypothesize that the EEG signals contain the distinctive features characterizing movement intent already expressed before movement execution and that such distinctive information can be identified by state-of-the-art classification algorithms based on Riemannian geometry. We demonstrated for the first time that a classifier based on covariance matrices and Riemannian geometry can effectively discriminate between neutral, festive, and violent mental states only on the basis of non-invasive EEG signals in both the actor and observer participants. These results pave the way for new electrophysiological discrimination of mental states based on non-invasive EEG recordings and cutting-edge machine learning techniques.
12.
Colaprico, Antonio; Olsen, Catharina; Bailey, Matthew; Odom, Gabriel G J; Terkelsen, Thilde; Silva, Tiago Chedraoui; Olsen, André Vidas; Cantini, Laura; Zinovyev, Andrey; Barillot, Emmanuel; Noushmehr, Houtan; Bertoli, Gloria; Castiglioni, Isabella; Cava, Claudia; Bontempi, Gianluca; Chen, Xi Steven; Papaleo, Elena
Interpreting pathways to discover cancer driver genes with Moonlight Journal Article
In: Nature communications, 11 (1), 2020, (DOI: 10.1038/s41467-019-13803-0).
@article{info:hdl:2013/301750,
title = {Interpreting pathways to discover cancer driver genes with Moonlight},
author = {Antonio Colaprico and Catharina Olsen and Matthew Bailey and Gabriel G J Odom and Thilde Terkelsen and Tiago Chedraoui Silva and André Vidas Olsen and Laura Cantini and Andrey Zinovyev and Emmanuel Barillot and Houtan Noushmehr and Gloria Bertoli and Isabella Castiglioni and Claudia Cava and Gianluca Bontempi and Xi Steven Chen and Elena Papaleo},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/301750/1/doi_285394.pdf},
year = {2020},
date = {2020-01-01},
journal = {Nature communications},
volume = {11},
number = {1},
abstract = {Cancer driver gene alterations influence cancer development, occurring in oncogenes, tumor suppressors, and dual role genes. Discovering dual role cancer genes is difficult because of their elusive context-dependent behavior. We define oncogenic mediators as genes controlling biological processes. With them, we classify cancer driver genes, unveiling their roles in cancer mechanisms. To this end, we present Moonlight, a tool that incorporates multiple -omics data to identify critical cancer driver genes. With Moonlight, we analyze 8000+ tumor samples from 18 cancer types, discovering 3310 oncogenic mediators, 151 having dual roles. By incorporating additional data (amplification, mutation, DNA methylation, chromatin accessibility), we reveal 1000+ cancer driver genes, corroborating known molecular mechanisms. Additionally, we confirm critical cancer driver genes by analysing cell-line datasets. We discover inactivation of tumor suppressors in intron regions and that tissue type and subtype indicate dual role status. These findings help explain tumor heterogeneity and could guide therapeutic decisions.},
note = {DOI: 10.1038/s41467-019-13803-0},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Cancer driver gene alterations influence cancer development, occurring in oncogenes, tumor suppressors, and dual role genes. Discovering dual role cancer genes is difficult because of their elusive context-dependent behavior. We define oncogenic mediators as genes controlling biological processes. With them, we classify cancer driver genes, unveiling their roles in cancer mechanisms. To this end, we present Moonlight, a tool that incorporates multiple -omics data to identify critical cancer driver genes. With Moonlight, we analyze 8000+ tumor samples from 18 cancer types, discovering 3310 oncogenic mediators, 151 having dual roles. By incorporating additional data (amplification, mutation, DNA methylation, chromatin accessibility), we reveal 1000+ cancer driver genes, corroborating known molecular mechanisms. Additionally, we confirm critical cancer driver genes by analysing cell-line datasets. We discover inactivation of tumor suppressors in intron regions and that tissue type and subtype indicate dual role status. These findings help explain tumor heterogeneity and could guide therapeutic decisions.
13.
Duerinckx, Sarah; Jacquemin, Valérie; Drunat, Séverine; Vial, Yoann; Passemard, Sandrine; Perazzolo, Camille; Massart, Annick; Soblet, Julie; Racapé, Judith; Desmyter, Laurence; Badoer, Cindy; Papadimitriou, Sofia; Borgne, Yann-A"el Le; Lefort, Anne; Libert, Frédérick; Maertelaer, Viviane De; Rooman, Marianne; Costagliola, Sabine; Verloes, Alain; Lenaerts, Tom; Pirson, Isabelle; Abramowicz, Marc
Digenic inheritance of human primary microcephaly delineates centrosomal and non centrosomal pathways. Journal Article
In: Human mutation, 41 (2), pp. 512-524, 2020, (DOI: 10.1002/humu.23948).
@article{info:hdl:2013/296188,
title = {Digenic inheritance of human primary microcephaly delineates centrosomal and non centrosomal pathways.},
author = {Sarah Duerinckx and Valérie Jacquemin and Séverine Drunat and Yoann Vial and Sandrine Passemard and Camille Perazzolo and Annick Massart and Julie Soblet and Judith Racapé and Laurence Desmyter and Cindy Badoer and Sofia Papadimitriou and Yann-A{"e}l Le Borgne and Anne Lefort and Frédérick Libert and Viviane De Maertelaer and Marianne Rooman and Sabine Costagliola and Alain Verloes and Tom Lenaerts and Isabelle Pirson and Marc Abramowicz},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/296188/4/Supp_Mat.pdf},
year = {2020},
date = {2020-01-01},
journal = {Human mutation},
volume = {41},
number = {2},
pages = {512-524},
abstract = {Primary Microcephaly (PM) is characterized by a small head since birth and is vastly heterogeneous both genetically and phenotypically. While most cases are monogenic, genetic interactions between Aspm and Wdr62 have recently been described in a mouse model of PM. Here, we used two complementary, holistic in vivo approaches: high throughput DNA sequencing of multiple PM genes in human PM patients, and genome-edited zebrafish modeling for digenic inheritance of PM. Exomes of PM patients showed a significant burden of variants in 75 PM genes, that persisted after removing monogenic causes of PM (e.g., biallelic pathogenic variants in CEP152). This observation was replicated in an independent cohort of PM patients, where a PM gene panel showed in addition that the burden was carried by six centrosomal genes. Allelic frequencies were consistent with digenic inheritance. In zebrafish, non-centrosomal gene casc5 -/- produced a severe PM phenotype, that was not modified by centrosomal genes aspm or wdr62 invalidation. A digenic, quadriallelic PM phenotype was produced by aspm and wdr62. Our observations provide strong evidence for digenic inheritance of human PM, involving centrosomal genes. Absence of genetic interaction between casc5 and aspm or wdr62 further delineates centrosomal and non-centrosomal pathways in PM. This article is protected by copyright. All rights reserved.},
note = {DOI: 10.1002/humu.23948},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Primary Microcephaly (PM) is characterized by a small head since birth and is vastly heterogeneous both genetically and phenotypically. While most cases are monogenic, genetic interactions between Aspm and Wdr62 have recently been described in a mouse model of PM. Here, we used two complementary, holistic in vivo approaches: high throughput DNA sequencing of multiple PM genes in human PM patients, and genome-edited zebrafish modeling for digenic inheritance of PM. Exomes of PM patients showed a significant burden of variants in 75 PM genes, that persisted after removing monogenic causes of PM (e.g., biallelic pathogenic variants in CEP152). This observation was replicated in an independent cohort of PM patients, where a PM gene panel showed in addition that the burden was carried by six centrosomal genes. Allelic frequencies were consistent with digenic inheritance. In zebrafish, non-centrosomal gene casc5 -/- produced a severe PM phenotype, that was not modified by centrosomal genes aspm or wdr62 invalidation. A digenic, quadriallelic PM phenotype was produced by aspm and wdr62. Our observations provide strong evidence for digenic inheritance of human PM, involving centrosomal genes. Absence of genetic interaction between casc5 and aspm or wdr62 further delineates centrosomal and non-centrosomal pathways in PM. This article is protected by copyright. All rights reserved.
14.
Rocha, Luis Mateus; Singh, Vikramjit; Esch, Markus; Lenaerts, Tom; Liljeros, Fredrik; Thorson, Anna
Dynamic contact networks of patients and MRSA spread in hospitals Journal Article
In: Scientific reports, 10 (1), 2020, (DOI: 10.1038/s41598-020-66270-9).
@article{info:hdl:2013/308993,
title = {Dynamic contact networks of patients and MRSA spread in hospitals},
author = {Luis Mateus Rocha and Vikramjit Singh and Markus Esch and Tom Lenaerts and Fredrik Liljeros and Anna Thorson},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/308993/1/doi_292637.pdf},
year = {2020},
date = {2020-01-01},
journal = {Scientific reports},
volume = {10},
number = {1},
abstract = {Methicillin-resistant Staphylococcus aureus (MRSA) is a difficult-to-treat infection. Increasing efforts have been taken to mitigate the epidemics and to avoid potential outbreaks in low endemic settings. Understanding the population dynamics of MRSA is essential to identify the causal mechanisms driving the epidemics and to generalise conclusions to different contexts. Previous studies neglected the temporal structure of contacts between patients and assumed homogeneous behaviour. We developed a high-resolution data-driven contact network model of interactions between 743,182 patients in 485 hospitals during 3,059 days to reproduce the exact contact sequences of the hospital population. Our model captures the exact spatial and temporal human contact behaviour and the dynamics of referrals within and between wards and hospitals at a large scale, revealing highly heterogeneous contact and mobility patterns of individual patients. A simulation exercise of epidemic spread shows that heterogeneous contacts cause the emergence of super-spreader patients, slower than exponential polynomial growth of the prevalence, and fast epidemic spread between wards and hospitals. In our simulated scenarios, screening upon hospital admittance is potentially more effective than reducing infection probability to reduce the final outbreak size. Our findings are useful to understand not only MRSA spread but also other hospital-acquired infections.},
note = {DOI: 10.1038/s41598-020-66270-9},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Methicillin-resistant Staphylococcus aureus (MRSA) is a difficult-to-treat infection. Increasing efforts have been taken to mitigate the epidemics and to avoid potential outbreaks in low endemic settings. Understanding the population dynamics of MRSA is essential to identify the causal mechanisms driving the epidemics and to generalise conclusions to different contexts. Previous studies neglected the temporal structure of contacts between patients and assumed homogeneous behaviour. We developed a high-resolution data-driven contact network model of interactions between 743,182 patients in 485 hospitals during 3,059 days to reproduce the exact contact sequences of the hospital population. Our model captures the exact spatial and temporal human contact behaviour and the dynamics of referrals within and between wards and hospitals at a large scale, revealing highly heterogeneous contact and mobility patterns of individual patients. A simulation exercise of epidemic spread shows that heterogeneous contacts cause the emergence of super-spreader patients, slower than exponential polynomial growth of the prevalence, and fast epidemic spread between wards and hospitals. In our simulated scenarios, screening upon hospital admittance is potentially more effective than reducing infection probability to reduce the final outbreak size. Our findings are useful to understand not only MRSA spread but also other hospital-acquired infections.
15.
Anciaux, Ma"elle; Demetter, Pieter; Wind, Roland De; Galdon, Maria Gomez; Velde, Sylvie Vande; Lens, Gaspard; Craciun, Ligia; Deleruelle, Amélie; Larsimont, Denis; Lenaerts, Tom; Sclafani, Francesco; Deleporte, Amélie; Donckier, Vincent; Hendlisz, Alain; Vandeputte, Caroline
Infiltrative tumour growth pattern correlates with poor outcome in oesophageal cancer. Journal Article
In: BMJ open gastroenterology, 7 (1), 2020, (DOI: 10.1136/bmjgast-2020-000431).
@article{info:hdl:2013/312667,
title = {Infiltrative tumour growth pattern correlates with poor outcome in oesophageal cancer.},
author = {Ma{"e}lle Anciaux and Pieter Demetter and Roland De Wind and Maria Gomez Galdon and Sylvie Vande Velde and Gaspard Lens and Ligia Craciun and Amélie Deleruelle and Denis Larsimont and Tom Lenaerts and Francesco Sclafani and Amélie Deleporte and Vincent Donckier and Alain Hendlisz and Caroline Vandeputte},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/312667/1/doi_296311.pdf},
year = {2020},
date = {2020-01-01},
journal = {BMJ open gastroenterology},
volume = {7},
number = {1},
abstract = {Oesophageal cancer (OEC) is an aggressive disease with a poor survival rate. Prognostic markers are thus urgently needed. Due to the demonstrated prognostic value of histopathological growth pattern (HGP) in other cancers, we performed a retrospective assessment of HGP in patients suffering from invasive OEC.},
note = {DOI: 10.1136/bmjgast-2020-000431},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Oesophageal cancer (OEC) is an aggressive disease with a poor survival rate. Prognostic markers are thus urgently needed. Due to the demonstrated prognostic value of histopathological growth pattern (HGP) in other cancers, we performed a retrospective assessment of HGP in patients suffering from invasive OEC.
16.
Gruji'c, Jelena; Lenaerts, Tom
Do people imitate when making decisions? Evidence from a spatial Prisoner's Dilemma experiment: Do people imitate when making decisions Journal Article
In: Royal Society open science, 7 (7), 2020, (DOI: 10.1098/rsos.200618).
@article{info:hdl:2013/313051,
title = {Do people imitate when making decisions? Evidence from a spatial Prisoner's Dilemma experiment: Do people imitate when making decisions},
author = {Jelena Gruji{'c} and Tom Lenaerts},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/313051/3/rsos.200618.pdf},
year = {2020},
date = {2020-01-01},
journal = {Royal Society open science},
volume = {7},
number = {7},
abstract = {How do people decide which action to take? This question is best answered using Game Theory, which has proposed a series of decision-making mechanisms that people potentially use. In network simulations, wherein games are repeated and pay-off differences can be observed, those mechanisms often rely on imitation of successful behaviour. Surprisingly, little to no evidence has been provided about whether people actually imitate more successful opponents when altering their actions in that context. By comparing two experimental treatments wherein participants play the iterated Prisoner's Dilemma game in a lattice, we aim to answer whether more successful actions are imitated. While in the first treatment, participants have the possibility to use pay-off differences in making their decision, the second treatment hinders such imitation as no information about the gains is provided. If imitation of the more successful plays a role then there should be a difference in how players switch from cooperation to defection between both treatments. Although, cooperation and pay-off levels do not appear to be significantly different between both treatments, detailed analysis shows that there are behavioural differences: when confronted with a more successful co-player, the focal player will imitate her behaviour as the switching is related to the experienced pay-off inequality.},
note = {DOI: 10.1098/rsos.200618},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
How do people decide which action to take? This question is best answered using Game Theory, which has proposed a series of decision-making mechanisms that people potentially use. In network simulations, wherein games are repeated and pay-off differences can be observed, those mechanisms often rely on imitation of successful behaviour. Surprisingly, little to no evidence has been provided about whether people actually imitate more successful opponents when altering their actions in that context. By comparing two experimental treatments wherein participants play the iterated Prisoner's Dilemma game in a lattice, we aim to answer whether more successful actions are imitated. While in the first treatment, participants have the possibility to use pay-off differences in making their decision, the second treatment hinders such imitation as no information about the gains is provided. If imitation of the more successful plays a role then there should be a difference in how players switch from cooperation to defection between both treatments. Although, cooperation and pay-off levels do not appear to be significantly different between both treatments, detailed analysis shows that there are behavioural differences: when confronted with a more successful co-player, the focal player will imitate her behaviour as the switching is related to the experienced pay-off inequality.
17.
Domingos, Elias Fernández; Gruji'c, Jelena; Burguillo, Juan J C; Kirchsteiger, Georg; Santos, Francisco C; Lenaerts, Tom
Timing Uncertainty in Collective Risk Dilemmas Encourages Group Reciprocation and Polarization Journal Article
In: iScience, 23 (12), 2020, (DOI: 10.1016/j.isci.2020.101752).
@article{info:hdl:2013/315240,
title = {Timing Uncertainty in Collective Risk Dilemmas Encourages Group Reciprocation and Polarization},
author = {Elias Fernández Domingos and Jelena Gruji{'c} and Juan J C Burguillo and Georg Kirchsteiger and Francisco C Santos and Tom Lenaerts},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/315240/1/doi_298884.pdf},
year = {2020},
date = {2020-01-01},
journal = {iScience},
volume = {23},
number = {12},
abstract = {Social dilemmas are often shaped by actions involving uncertain returns only achievable in the future, such as climate action or voluntary vaccination. In this context, uncertainty may produce non-trivial effects. Here, we assess experimentally --- through a collective risk dilemma --- the effect of timing uncertainty, i.e. how uncertainty about when a target needs to be reached affects the participants' behaviors. We show that timing uncertainty prompts not only early generosity but also polarized outcomes, where participants' total contributions are distributed unevenly. Furthermore, analyzing participants' behavior under timing uncertainty reveals an increase in reciprocal strategies. A data-driven game-theoretical model captures the self-organizing dynamics underpinning these behavioral patterns. Timing uncertainty thus casts a shadow on the future that leads participants to respond early, whereas reciprocal strategies appear to be important for group success. Yet, the same uncertainty also leads to inequity and polarization, requiring the inclusion of new incentives handling these societal issues.},
note = {DOI: 10.1016/j.isci.2020.101752},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Social dilemmas are often shaped by actions involving uncertain returns only achievable in the future, such as climate action or voluntary vaccination. In this context, uncertainty may produce non-trivial effects. Here, we assess experimentally --- through a collective risk dilemma --- the effect of timing uncertainty, i.e. how uncertainty about when a target needs to be reached affects the participants' behaviors. We show that timing uncertainty prompts not only early generosity but also polarized outcomes, where participants' total contributions are distributed unevenly. Furthermore, analyzing participants' behavior under timing uncertainty reveals an increase in reciprocal strategies. A data-driven game-theoretical model captures the self-organizing dynamics underpinning these behavioral patterns. Timing uncertainty thus casts a shadow on the future that leads participants to respond early, whereas reciprocal strategies appear to be important for group success. Yet, the same uncertainty also leads to inequity and polarization, requiring the inclusion of new incentives handling these societal issues.
18.
Abels, Axel; Lenaerts, Tom; Trianni, Vito; Nowe, Ann
How Expert Confidence Can Improve Collective Decision-Making in Contextual Multi-Armed Bandit Problems Journal Article
In: Lecture notes in computer science, 12496 LNAI , pp. 125-138, 2020, (DOI: 10.1007/978-3-030-63007-2_10).
@article{info:hdl:2013/316598,
title = {How Expert Confidence Can Improve Collective Decision-Making in Contextual Multi-Armed Bandit Problems},
author = {Axel Abels and Tom Lenaerts and Vito Trianni and Ann Nowe},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/316598/4/howexpertconfidence.pdf},
year = {2020},
date = {2020-01-01},
journal = {Lecture notes in computer science},
volume = {12496 LNAI},
pages = {125-138},
abstract = {In collective decision-making (CDM) a group of experts with a shared set of values and a common goal must combine their knowledge to make a collectively optimal decision. Whereas existing research on CDM primarily focuses on making binary decisions, we focus here on CDM applied to solving contextual multi-armed bandit (CMAB) problems, where the goal is to exploit contextual information to select the best arm among a set. To address the limiting assumptions of prior work, we introduce confidence estimates and propose a novel approach to deciding with expert advice which can take advantage of these estimates. We further show that, when confidence estimates are imperfect, the proposed approach is more robust than the classical confidence-weighted majority vote.},
note = {DOI: 10.1007/978-3-030-63007-2_10},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In collective decision-making (CDM) a group of experts with a shared set of values and a common goal must combine their knowledge to make a collectively optimal decision. Whereas existing research on CDM primarily focuses on making binary decisions, we focus here on CDM applied to solving contextual multi-armed bandit (CMAB) problems, where the goal is to exploit contextual information to select the best arm among a set. To address the limiting assumptions of prior work, we introduce confidence estimates and propose a novel approach to deciding with expert advice which can take advantage of these estimates. We further show that, when confidence estimates are imperfect, the proposed approach is more robust than the classical confidence-weighted majority vote.
19.
Abels, Axel; Lenaerts, Tom; Trianni, Vito; Nowe, Ann
Collective Decision-Making as a Contextual Multi-armed Bandit Problem Journal Article
In: Lecture notes in computer science, 12496 LNAI , pp. 113-124, 2020, (DOI: 10.1007/978-3-030-63007-2_9).
@article{info:hdl:2013/316605,
title = {Collective Decision-Making as a Contextual Multi-armed Bandit Problem},
author = {Axel Abels and Tom Lenaerts and Vito Trianni and Ann Nowe},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/316605/5/colascdm.pdf},
year = {2020},
date = {2020-01-01},
journal = {Lecture notes in computer science},
volume = {12496 LNAI},
pages = {113-124},
abstract = {Collective decision-making (CDM) processes -- wherein the knowledge of a group of individuals with a common goal must be combined to make optimal decisions -- can be formalized within the framework of the deciding with expert advice setting. Traditional approaches to tackle this problem focus on finding appropriate weights for the individuals in the group. In contrast, we propose here meta-CMAB, a meta approach that learns a mapping from expert advice to expected outcomes. In summary, our work reveals that, when trying to make the best choice in a problem with multiple alternatives, meta-CMAB assures that the collective knowledge of experts leads to the best outcome without the need for accurate confidence estimates.},
note = {DOI: 10.1007/978-3-030-63007-2_9},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Collective decision-making (CDM) processes -- wherein the knowledge of a group of individuals with a common goal must be combined to make optimal decisions -- can be formalized within the framework of the deciding with expert advice setting. Traditional approaches to tackle this problem focus on finding appropriate weights for the individuals in the group. In contrast, we propose here meta-CMAB, a meta approach that learns a mapping from expert advice to expected outcomes. In summary, our work reveals that, when trying to make the best choice in a problem with multiple alternatives, meta-CMAB assures that the collective knowledge of experts leads to the best outcome without the need for accurate confidence estimates.
20.
Han, The Anh T A H; Pereira, Lu'is Marcelo; Santos, Francisco C; Lenaerts, Tom
To regulate or not: A social dynamics analysis of an idealised ai race Journal Article
In: The journal of artificial intelligence research, 69 , pp. 881-921, 2020, (DOI: 10.1613/JAIR.1.12225).
@article{info:hdl:2013/319033,
title = {To regulate or not: A social dynamics analysis of an idealised ai race},
author = {The Anh T A H Han and Lu{'i}s Marcelo Pereira and Francisco C Santos and Tom Lenaerts},
url = {https://dipot.ulb.ac.be/dspace/bitstream/2013/319033/17/1907-12393.pdf},
year = {2020},
date = {2020-01-01},
journal = {The journal of artificial intelligence research},
volume = {69},
pages = {881-921},
abstract = {Rapid technological advancements in Artificial Intelligence (AI), as well as the growing deployment of intelligent technologies in new application domains, have generated serious anxiety and a fear of missing out among different stake-holders, fostering a racing narrative. Whether real or not, the belief in such a race for domain supremacy through AI, can make it real simply from its consequences, as put forward by the Thomas theorem. These consequences may be negative, as racing for technological supremacy creates a complex ecology of choices that could push stake-holders to underestimate or even ignore ethical and safety procedures. As a consequence, different actors are urging to consider both the normative and social impact of these technological advancements, contemplating the use of the precautionary principle in AI innovation and research. Yet, given the breadth and depth of AI and its advances, it is difficult to assess which technology needs regulation and when. As there is no easy access to data describing this alleged AI race, theoretical models are necessary to understand its potential dynamics, allowing for the identification of when procedures need to be put in place to favour outcomes beneficial for all. We show that, next to the risks of setbacks and being reprimanded for unsafe behaviour, the time-scale in which domain supremacy can be achieved plays a crucial role. When this can be achieved in a short term, those who completely ignore the safety precautions are bound to win the race but at a cost to society, apparently requiring regulatory actions. Our analysis reveals that imposing regulations for all risk and timing conditions may not have the anticipated effect as only for specific conditions a dilemma arises between what is individually preferred and globally beneficial. Similar observations can be made for the long-term development case. Yet different from the short-term situation, conditions can be identified that require the promotion of risk-taking as opposed to compliance with safety regulations in order to improve social welfare. These results remain robust both when two or several actors are involved in the race and when collective rather than individual setbacks are produced by risk-taking behaviour. When defining codes of conduct and regulatory policies for applications of AI, a clear understanding of the time-scale of the race is thus required, as this may induce important non-trivial effects.},
note = {DOI: 10.1613/JAIR.1.12225},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Rapid technological advancements in Artificial Intelligence (AI), as well as the growing deployment of intelligent technologies in new application domains, have generated serious anxiety and a fear of missing out among different stake-holders, fostering a racing narrative. Whether real or not, the belief in such a race for domain supremacy through AI, can make it real simply from its consequences, as put forward by the Thomas theorem. These consequences may be negative, as racing for technological supremacy creates a complex ecology of choices that could push stake-holders to underestimate or even ignore ethical and safety procedures. As a consequence, different actors are urging to consider both the normative and social impact of these technological advancements, contemplating the use of the precautionary principle in AI innovation and research. Yet, given the breadth and depth of AI and its advances, it is difficult to assess which technology needs regulation and when. As there is no easy access to data describing this alleged AI race, theoretical models are necessary to understand its potential dynamics, allowing for the identification of when procedures need to be put in place to favour outcomes beneficial for all. We show that, next to the risks of setbacks and being reprimanded for unsafe behaviour, the time-scale in which domain supremacy can be achieved plays a crucial role. When this can be achieved in a short term, those who completely ignore the safety precautions are bound to win the race but at a cost to society, apparently requiring regulatory actions. Our analysis reveals that imposing regulations for all risk and timing conditions may not have the anticipated effect as only for specific conditions a dilemma arises between what is individually preferred and globally beneficial. Similar observations can be made for the long-term development case. Yet different from the short-term situation, conditions can be identified that require the promotion of risk-taking as opposed to compliance with safety regulations in order to improve social welfare. These results remain robust both when two or several actors are involved in the race and when collective rather than individual setbacks are produced by risk-taking behaviour. When defining codes of conduct and regulatory policies for applications of AI, a clear understanding of the time-scale of the race is thus required, as this may induce important non-trivial effects.