Publications

Optimising Electric Vehicle Charging Station Placement using Advanced Discrete Choice Models
Steven Lamontagne
Margarida Carvalho
Emma Frejinger
Bernard Gendron
Miguel F. Anjos
Ribal Atallah
D'epartement d'informatique et de recherche op'erationnelle
U. Montr'eal
S. O. Mathematics
U. Edinburgh
Institut de Recherche d'Hydro-Qu'ebec
We present a new model for finding the optimal placement of electric vehicle charging stations across a multi-period time frame so as to max… (voir plus)imise electric vehicle adoption. Via the use of advanced discrete choice models and user classes, this work allows for a granular modelling of user attributes and their preferences in regard to charging station characteristics. Instead of embedding an analytical probability model in the formulation, we adopt a simulation approach and pre-compute error terms for each option available to users for a given number of scenarios. This results in a bilevel optimisation model that is, however, intractable for all but the simplest instances. Using the pre-computed error terms to calculate the users covered by each charging station allows for a maximum covering model, for which solutions can be found more efficiently than for the bilevel formulation. The maximum covering formulation remains intractable in some instances, so we propose rolling horizon, greedy, and GRASP heuristics to obtain good quality solutions more efficiently. Extensive computational results are provided, which compare the maximum covering formulation with the current state-of-the-art, both for exact solutions and the heuristic methods. Keywords: Electric vehicle charging stations, facility location, integer programming, discrete choice models, maximum covering
2023-08-31
INFORMS Journal on Computing (publié)
doi.org
arxiv.org
PP02  Presentation Time: 4:39 PM
Maryam Rahbaran
Jonathan Kalinowski
James Man Git Tsui
Joseph DeCunha
Kevin Croce
Brian Bergmark
Philip Devlin
S. Enger
2023-08-31
Brachytherapy (publié)
doi.org
Saturday, June 24, 20238:30 AM - 9:30 AMMSOR01 Presentation Time: 8:30 AM
Mélodie Cyr
N. Chabaytah
Joud Babik
Behnaz Behmand
S. Enger
2023-08-31
Brachytherapy (publié)
doi.org
Uncovering and leveraging the return of voluntary motor programs after paralysis using a bi-cortical neuroprosthesis
Maude Duguay
Marco Bonizzato
Hugo Delivet-Mongrain
Nicolas Fortier-Lebel
Marina Martinez
2023-08-31
Progress in Neurobiology (publié)
doi.org
Prospective evaluation of bleeding risk among thrombocytopenic patients admitted in intensive care unit.
Geoffroy HARIRI
Vincent Belossi
Louis Pérol
Louai Missri
Paul GABARRE
Vincent BONNY
Tomas URBINA
Jean-Luc Baudel
Bertrand GUIDET
Jérémie JOFFRE
Eric Maury
Guillaume Dumas
Hafid AIT-OUFELLA
2023-08-30
Journal of critical care (publié)
doi.org
Beyond the ML Model: Applying Safety Engineering Frameworks to Text-to-Image Development
Shalaleh Rismani
Renee Shelby
Andrew J Smart
Renelito Delos Santos
AJung Moon
Negar Rostamzadeh
Identifying potential social and ethical risks in emerging machine learning (ML) models and their applications remains challenging. In this … (voir plus)work, we applied two well-established safety engineering frameworks (FMEA, STPA) to a case study involving text-to-image models at three stages of the ML product development pipeline: data processing, integration of a T2I model with other models, and use. Results of our analysis demonstrate the safety frameworks – both of which are not designed explicitly examine social and ethical risks – can uncover failure and hazards that pose social and ethical risks. We discovered a broad range of failures and hazards (i.e., functional, social, and ethical) by analyzing interactions (i.e., between different ML models in the product, between the ML product and user, and between development teams) and processes (i.e., preparation of training data or workflows for using an ML service/product). Our findings underscore the value and importance of examining beyond an ML model in examining social and ethical risks, especially when we have minimal information about an ML model.
2023-08-28
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (publié)
doi.org
arxiv.org
Policy composition in reinforcement learning via multi-objective policy optimization
Shruti Mishra
Ankit Anand
Jordan Hoffmann
Nicolas Heess
Martin A. Riedmiller
Abbas Abdolmaleki
Doina Precup
We enable reinforcement learning agents to learn successful behavior policies by utilizing relevant pre-existing teacher policies. The teach… (voir plus)er policies are introduced as objectives, in addition to the task objective, in a multi-objective policy optimization setting. Using the Multi-Objective Maximum a Posteriori Policy Optimization algorithm (Abdolmaleki et al. 2020), we show that teacher policies can help speed up learning, particularly in the absence of shaping rewards. In two domains with continuous observation and action spaces, our agents successfully compose teacher policies in sequence and in parallel, and are also able to further extend the policies of the teachers in order to solve the task. Depending on the specified combination of task and teacher(s), teacher(s) may naturally act to limit the final performance of an agent. The extent to which agents are required to adhere to teacher policies are determined by hyperparameters which determine both the effect of teachers on learning speed and the eventual performance of the agent on the task. In the humanoid domain (Tassa et al. 2018), we also equip agents with the ability to control the selection of teachers. With this ability, agents are able to meaningfully compose from the teacher policies to achieve a superior task reward on the walk task than in cases without access to the teacher policies. We show the resemblance of composed task policies with the corresponding teacher policies through videos.
2023-08-28
ArXiv (prépublication)
doi.org
arxiv.org
Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction
Renee Shelby
Shalaleh Rismani
Kathryn Henne
AJung Moon
Negar Rostamzadeh
Paul Nicholas
N'Mah Yilla-Akbari
Jess Gallegos
Andrew J Smart
Emilio Garcia
Gurleen Virk
2023-08-28
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (publié)
doi.org
arxiv.org
What does it mean to be a responsible AI practitioner: An ontology of roles and skills
Shalaleh Rismani
AJung Moon
With the growing need to regulate AI systems across a wide variety of application domains, a new set of occupations has emerged in the indus… (voir plus)try. The so-called responsible Artificial Intelligence (AI) practitioners or AI ethicists are generally tasked with interpreting and operationalizing best practices for ethical and safe design of AI systems. Due to the nascent nature of these roles, however, it is unclear to future employers and aspiring AI ethicists what specific function these roles serve and what skills are necessary to serve the functions. Without clarity on these, we cannot train future AI ethicists with meaningful learning objectives. In this work, we examine what responsible AI practitioners do in the industry and what skills they employ on the job. We propose an ontology of existing roles alongside skills and competencies that serve each role. We created this ontology by examining the job postings for such roles over a two-year period (2020-2022) and conducting expert interviews with fourteen individuals who currently hold such a role in the industry. Our ontology contributes to business leaders looking to build responsible AI teams and provides educators with a set of competencies that an AI ethics curriculum can prioritize.
2023-08-28
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (publié)
doi.org
arxiv.org
Beyond performance: the role of task demand, effort, and individual differences in ab initio pilots
Mohammad-Javad Darvishi-Bayazi
Andrew Law
Sergio Mejia Romero
Sion Jennings
Irina Rish
Jocelyn Faubert
Aviation safety depends on the skill and expertise of pilots to meet the task demands of flying an aircraft in an effective and efficient ma… (voir plus)nner. During flight training, students may respond differently to imposed task demands based on individual differences in capacity, physiological arousal, and effort. To ensure that pilots achieve a common desired level of expertise, training programs should account for individual differences to optimize pilot performance. This study investigates the relationship between task performance and physiological correlates of effort in ab initio pilots. Twenty-four participants conducted a flight simulator task with three difficulty levels and were asked to rate their perceived demand and effort using the NASA TLX. We recorded heart rate, EEG brain activity, and pupil size to assess changes in the participants’ mental and physiological states across different task demands. We found that, despite group-level correlations between performance error and physiological responses, individual differences in physiological responses to task demands reflected different levels of participant effort and task efficiency. These findings suggest that physiological monitoring of student pilots might provide beneficial insights to flight instructors to optimize pilot training at the individual level.
2023-08-27
Scientific Reports (publié)
doi.org
From Assistive Devices to Manufacturing Cobot Swarms
Monica Li
Bruno Belzile
Ali Imran
Lionel Birglen
Giovanni Beltrame
David St-Onge
This paper provides an overview of the latest trends in robotics research and development, with a particular focus on applications in manufa… (voir plus)cturing and industrial settings. We highlight recent advances in robot design, including cutting-edge collaborative robot mechanics and advanced safety features, as well as exciting developments in perception and human-swarm interaction. By examining recent contributions from Kinova, a leading robotics company, we illustrate the differences between industry and academia in their approaches to developing innovative robotic systems and technologies that enhance productivity and safety in the workplace. Ultimately, this paper demonstrates the tremendous potential of robotics to revolutionize manufacturing and industrial operations, and underscores the crucial role of companies like Kinova in driving this transformation forward.
2023-08-27
IEEE International Symposium on Robot and Human Interactive Communication (publié)
doi.org
Global fMRI signal topography differs systematically across the lifespan
Jason S. Nomi
Danilo Bzdok
Jingwei Li
Taylor Bolt
Catie Chang
Salome Kornfeld
Zachary T. Goodman
B.T. Thomas Yeo
R. Nathan Spreng
Lucina Q. Uddin
The global signal (GS) in resting-state fMRI, known to contain artifacts and non-neuronal physiological signals, also contains important neu… (voir plus)ral information related to individual state and trait characteristics. Here we show distinct linear and curvilinear lifespan patterns of GS topography in a cross-sectional lifespan sample, demonstrating its importance for consideration in studies of development and aging. Subcortical brain regions such as the thalamus and putamen show linear associations with the GS across the lifespan. The thalamus has stronger coupling in older-age individuals compared with younger-aged individuals, while the putamen has stronger coupling in younger individuals compared with older individuals. The subcortical nucleus basalis shows a u-shaped pattern similar to cortical regions within the lateral frontoparietal network and dorsal attention network, where coupling with the GS is stronger at early and old age, with weaker coupling in middle age. This differentiation in coupling strength between subcortical and cortical brain activity across the lifespan supports a dual-layer model of GS composition, where subcortical aspects of the GS are differentiated from cortical aspects of the GS. We find that these subcortical-cortical contributions to the GS depend strongly on the lifespan stage of individuals. Our findings demonstrate how neurobiological information within the GS differs across development and highlight the need to carefully consider whether or not to remove this signal when investigating age-related functional differences in the brain.
2023-08-27
bioRxiv (prépublication)
doi.org