Publications

Min-Max Optimisation for Nonconvex-Nonconcave Functions Using a Random Zeroth-Order Extragradient Algorithm
Amir Ali Farzin
Yuen-Man Pun
Philipp Braun
Antoine Lesage-Landry
Youssef Diouane
Iman Shames
2024-12-31
Trans. Mach. Learn. Res. (publié)
doi.org
arxiv.org
Mixed-Integer Second-Order Cone Programming for Multi-period Scheduling of Flexible AC Transmission System Devices
Mohamad Charara
Martin De Montigny
Nivine Abou Daher
Hanane Dagdougui
Antoine Lesage-Landry
With the increasing energy demand and the growing integration of renewable sources of energy, power systems face operational challenges such… (voir plus) as overloads, losses, and stability concerns, particularly as networks operate near their capacity limits. Flexible alternating current transmission system (FACTS) devices are essential to ensure reliable grid operations and enable the efficient integration of renewable energy. This work introduces a mixed-integer second-order cone programming (MISOCP) model for the multi-period scheduling of key FACTS devices in electric transmission systems. The proposed model integrates four key control mechanisms: (i) on-load tap changers (OLTCs) for voltage regulation via discrete taps; (ii) static synchronous compensators (STATCOMs) and (iii) shunt reactors for reactive power compensation; and (iv) thyristor-controlled series capacitors (TCSCs) for adjustable impedance and flow control. The objective is to minimize active power losses using a limited number of control actions while meeting physical and operational constraints at all times throughout the defined time horizon. To ensure tractability, the model employs a second-order cone relaxation of the power flow. Device-specific constraints are handled via binary expansion and linearization: OLTCs and shunt reactors are modelled with discrete variables, STATCOMs through reactive power bounds, and TCSCs using a reformulation-linearization technique (RLT). A multi-period formulation captures the sequential nature of decision making, ensuring consistency across time steps. The model is evaluated on the IEEE 9-bus, 30-bus, and RTS96 test systems, demonstrating its ability to reduce losses, with potential applicability to larger-scale grids.
2024-12-31
arXiv (prépublication)
doi.org
arxiv.org
MLOps, LLMOps, FMOps, and Beyond
Chakkrit Kla Tantithamthavorn
Fabio Palomba
Foutse Khomh
Joselito Joey Chua
2024-12-31
IEEE Software (publié)
doi.org
Morphometric characteristics of tibial nerve and their relationship with age
Shahram Oveisgharan
Armand Collin
Jingyun Yang
Sue E. Leurgans
Veronique VanderHorst
David A. Bennett
Julien Cohen-Adad
Osvaldo Delbono
Aron S. Buchman
Peripheral nerve comprises a crucial component of the distributed motor/sensory system. However, there is a paucity of data on peripheral ne… (voir plus)rve morphology derived from large numbers of older adults. This study aimed to quantify the morphometric characteristics of myelinated nerve fibres of the tibial nerve obtained from deceased community-dwelling older adults and examine their association with age. The tibial nerves were obtained from consecutive autopsies of older adults without a history of diabetes who were participants of the Rush Memory and Aging Project, an ongoing longitudinal clinical-autopsy study. A nerve fascicle, obtained from a fixed popliteal segment of the tibial nerve, was separated from the blood vessels and adipose tissue for postmortem examination under an optical microscope. Morphometric characteristics of the myelinated nerve fibres were automatically segmented and quantified using our open-source software AxonDeepSeg. The participants (N = 140) had a mean age of 92.0 years (SD = 5.4) at death, and 72.1% (N = 101) were women. We examined 754 247 myelinated nerve fibres, with an average 5387 (SD = 3436) nerve fibres per participant. The average diameter of myelinated nerve fibres was 4.9 µm (SD = 3.1), axon diameter was 2.0 µm (SD = 1.4), myelin thickness was 1.4 µm (SD = 0.96) and the g-ratio (ratio of axon diameter to myelinated nerve fibre diameter) was 0.45 (SD = 0.17). The relationship between axon diameter and myelin thickness was nonlinear. Myelin was thicker in larger axons up to a diameter of 8 µm, beyond which myelin thickness plateaued. Older age at death was associated with smaller myelinated nerve fibres, smaller axons and thinner myelin. However, age at death was not correlated with myelinated nerve fibre density and was not associated with the average of g-ratio. The association between older age and smaller myelinated nerve fibres was largely attributable to a lower percentage of myelinated nerve fibres >8 µm. We conclude that the smaller tibial myelinated nerve fibres observed in older adults may reflect axonal atrophy rather than degeneration and regeneration of the myelinated nerve fibres. Further research is needed to investigate the pathologies and molecular mechanisms underlying these age-related morphometric changes and their clinical implications in older adults.
2024-12-31
Brain Communications (publié)
doi.org
Most German Speakers Ignore the Cue That Best Predicts Plural Class
Kate McCurdy
Timothy J. O'Donnell
Adam Lopez
Sharon Goldwater
Researchers generally assume that speakers use the linguistic information available to them. For instance, if one grammatical category robus… (voir plus)tly predicts another grammatical category, we expect speakers to reproduce this conditional relationship during language production. Here, we investigate this assumption for grammatical gender in German. Gender is the single cue which most strongly predicts the plural class of existing German nouns, but behavioral studies with novel nouns have found mixed results regarding the role of gender in plural generalization. Across three experiments, we examine how individual German speakers use grammatical gender when producing plural forms of novel nouns. We find that most speakers effectively ignore gender during plural class production, even under experimental manipulations that encourage them to attend to this cue. These results point toward an underexplored direction in cognitive science: accounting for the linguistic information that speakers do not use.
2024-12-31
Open Mind (publié)
doi.org
Multilingual Language Model Pretraining using Machine-translated Data
Jiayi Wang
Yao Lu
Maurice Weber
Max Ryabinin
David Ifeoluwa Adelani
Yihong Chen
Raphael Tang
Pontus Stenetorp
2024-12-31
EMNLP (publié)
doi.org
arxiv.org
Multimodal and Force-Matched Imitation Learning with a See-Through Visuotactile Sensor
Trevor Ablett
Oliver Limoyo
Adam Sigal
Affan Jilani
Jonathan Kelly
Kaleem Siddiqi
Francois Hogan
Gregory Dudek
Kinesthetic Teaching is a popular approach to collecting expert robotic demonstrations of contact-rich tasks for imitation learning (IL), bu… (voir plus)t it typically only measures motion, ignoring the force placed on the environment by the robot. Furthermore, contact-rich tasks require accurate sensing of both reaching and touching, which can be difficult to provide with conventional sensing modalities. We address these challenges with a See-Through-your-Skin (STS) visuotactile sensor, using the sensor both (i) as a measurement tool to improve kinesthetic teaching, and (ii) as a policy input in contact-rich door manipulation tasks. An STS sensor can be switched between visual and tactile modes by leveraging a semi-transparent surface and controllable lighting, allowing for both pre-contact visual sensing and during-contact tactile sensing with a single sensor. First, we propose tactile force matching, a methodology that enables a robot to match forces read during kinesthetic teaching using tactile signals. Second, we develop a policy that controls STS mode switching, allowing a policy to learn the appropriate moment to switch an STS from its visual to its tactile mode. Finally, we study multiple observation configurations to compare and contrast the value of visual and tactile data from an STS with visual data from a wrist-mounted eye-in-hand camera. With over 3,000 test episodes from real-world manipulation experiments, we find that the inclusion of force matching raises average policy success rates by 62.5%, STS mode switching by 30.3%, and STS data as a policy input by 42.5%. Our results highlight the utility of see-through tactile sensing for IL, both for data collection to allow force matching, and for policy execution to allow accurate task feedback.
2024-12-31
IEEE Transactions on Robotics (publié)
doi.org
arxiv.org
Multi-Robot Decentralized Collaborative SLAM in Planetary Analogue Environments: Dataset, Challenges, and Lessons Learned
Pierre-Yves Lajoie
Karthik Soma
Haechan Mark Bong
Alice Lemieux-Bourque
Rongge Zhang
Vivek Shankar Varadharajan
Giovanni Beltrame
Decentralized collaborative simultaneous localization and mapping (C-SLAM) is essential to enable multirobot missions in unknown environment… (voir plus)s without relying on preexisting localization and communication infrastructure. This technology is anticipated to play a key role in the exploration of the Moon, Mars, and other planets. In this article, we share insights and lessons learned from C-SLAM experiments involving three robots operating on a Mars analogue terrain and communicating over an ad hoc network. We examine the impact of limited and intermittent communication on C-SLAM performance, as well as the unique localization challenges posed by planetary-like environments. Additionally, we introduce a novel dataset collected during our experiments, which includes real-time peer-to-peer inter-robot throughput and latency measurements. This dataset aims to support future research on communication-constrained, decentralized multirobot operations.
2024-12-31
IEEE Transactions on Field Robotics (publié)
doi.org
arxiv.org
A Multi-Robot Exploration Planner for Space Applications
Vivek Shankar Vardharajan
Giovanni Beltrame
We propose a distributed multi-robot exploration planning method designed for complex, unconstrained environments featuring steep elevation … (voir plus)changes. The method employs a two-tiered approach: a local exploration planner that constructs a grid graph to maximize exploration gain and a global planner that maintains a sparse navigational graph to track visited locations and frontier information. The global graphs are periodically synchronized among robots within communication range to maintain an updated representation of the environment. Our approach integrates localization loop closure estimates to correct global graph drift. In simulation and field tests, the proposed method achieves 50% lower computational runtime compared to state-of-the-art methods while demonstrating superior exploration coverage. We evaluate its performance in two simulated subterranean environments and in field experiments at a Mars-analog terrain.
2024-12-31
IEEE Robotics and Automation Letters (publié)
doi.org
NeoBERT: A Next-Generation BERT
Lola Le Breton
Quentin Fournier
Mariam El Mezouar
John Xavier Morris
A. Chandar
Recent innovations in architecture, pre-training, and fine-tuning have led to the remarkable in-context learning and reasoning abilities of … (voir plus)large auto-regressive language models such as LLaMA and DeepSeek. In contrast, encoders like BERT and RoBERTa have not seen the same level of progress despite being foundational for many downstream NLP applications. To bridge this gap, we introduce NeoBERT, a next-generation encoder that redefines the capabilities of bidirectional models by integrating state-of-the-art advancements in architecture, modern data, and optimized pre-training methodologies. NeoBERT is designed for seamless adoption: it serves as a plug-and-play replacement for existing base models, relies on an optimal depth-to-width ratio, and leverages an extended context length of 4,096 tokens. Despite its compact 250M parameter footprint, it achieves state-of-the-art results on the massive MTEB benchmark, outperforming BERT large, RoBERTa large, NomicBERT, and ModernBERT under identical fine-tuning conditions. In addition, we rigorously evaluate the impact of each modification on GLUE and design a uniform fine-tuning and evaluation framework for MTEB. We release all code, data, checkpoints, and training scripts to accelerate research and real-world adoption.
2024-12-31
Trans. Mach. Learn. Res. (publié)
doi.org
openreview.net
Neural Incremental Dynamic Inversion Control of a Multirotor Robotic Airship
Ely Carneiro de Paiva
José Raul Azinheira
Rafael de Angelis Cordeiro
José Reginaldo H. Carvalho
Apolo Marton
Giovanni Beltrame
2024-12-31
International Journal of Intelligent Systems (publié)
doi.org
Nteasee: A mixed methods study of expert and general population perspectives on deploying AI for health in African countries
Mercy Nyamewaa Asiedu
Iskandar Haykel
Awa Dieng
K. Kauer
Tousif Ahmed
Florence Ofori
Charisma Chan
Stephen R. Pfohl
Negar Rostamzadeh
Katherine Heller
2024-12-31
FAccT (publié)
doi.org
arxiv.org