Publications

LitLLM: A Toolkit for Scientific Literature Review
Shubham Agarwal
Issam Hadj Laradji
Laurent Charlin
Christopher Pal
Conducting literature reviews for scientific papers is essential for understanding research, its limitations, and building on existing work.… (voir plus) It is a tedious task which makes an automatic literature review generator appealing. Unfortunately, many existing works that generate such reviews using Large Language Models (LLMs) have significant limitations. They tend to hallucinate-generate non-actual information-and ignore the latest research they have not been trained on. To address these limitations, we propose a toolkit that operates on Retrieval Augmented Generation (RAG) principles, specialized prompting and instructing techniques with the help of LLMs. Our system first initiates a web search to retrieve relevant papers by summarizing user-provided abstracts into keywords using an off-the-shelf LLM. Authors can enhance the search by supplementing it with relevant papers or keywords, contributing to a tailored retrieval process. Second, the system re-ranks the retrieved papers based on the user-provided abstract. Finally, the related work section is generated based on the re-ranked results and the abstract. There is a substantial reduction in time and effort for literature review compared to traditional methods, establishing our toolkit as an efficient alternative. Our open-source toolkit is accessible at https://github.com/shubhamagarwal92/LitLLM and Huggingface space (https://huggingface.co/spaces/shubhamagarwal92/LitLLM) with the video demo at https://youtu.be/E2ggOZBAFw0.
2024-02-01
ArXiv (prépublication)
doi.org
arxiv.org
Mindfulness meditation styles differently modulate source-level MEG microstate dynamics and complexity
Antea D’Andrea
Pierpaolo Croce
Jordan O’Byrne
Karim Jerbi CoCo Lab
Annalisa Pascarella
Antonino Raffone
Vittorio Pizzella
Laura Marzetti
2024-02-01
Frontiers in Neuroscience (publié)
doi.org
Adaptation, Translation, and Validation of a Patient-Reported Experience Measure for Children and Young People for the Canadian Context
Zanib Nafees
Julia Ferreira
Elena Guadagno
Jo Wray
Agneta Anderzén-Carlsson
Dan Poenaru
2024-01-31
Journal of Pediatric Surgery (publié)
doi.org
Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning
Mohammad-Javad Darvishi-Bayazi
Mohammad Sajjad Ghaemi
Timothee LESORT
Md Rifat Arefin
Jocelyn Faubert
Irina Rish
Pathology diagnosis based on EEG signals and decoding brain activity holds immense importance in understanding neurological disorders. With … (voir plus)the advancement of artificial intelligence methods and machine learning techniques, the potential for accurate data-driven diagnoses and effective treatments has grown significantly. However, applying machine learning algorithms to real-world datasets presents diverse challenges at multiple levels. The scarcity of labelled data, especially in low regime scenarios with limited availability of real patient cohorts due to high costs of recruitment, underscores the vital deployment of scaling and transfer learning techniques. In this study, we explore a real-world pathology classification task to highlight the effectiveness of data and model scaling and cross-dataset knowledge transfer. As such, we observe varying performance improvements through data scaling, indicating the need for careful evaluation and labelling. Additionally, we identify the challenges of possible negative transfer and emphasize the significance of some key components to overcome distribution shifts and potential spurious correlations and achieve positive transfer. We see improvement in the performance of the target model on the target (NMT) datasets by using the knowledge from the source dataset (TUAB) when a low amount of labelled data was available. Our findings indicate a small and generic model (e.g. ShallowNet) performs well on a single dataset, however, a larger model (e.g. TCN) performs better on transfer and learning from a larger and diverse dataset.
2024-01-31
Computers in Biology and Medicine (publié)
doi.org
arxiv.org
Author Correction: BCG immunization induces CX3CR1hi effector memory T cells to provide cross-protection via IFN-γ-mediated trained immunity.
Kim A. Tran
Erwan Pernet
Mina Sadeghi
Jeffrey Downey
Julia Chronopoulos
Elizabeth Lapshina
Oscar Tsai
Eva Kaufmann
Jun Ding
Maziar Divangahi
2024-01-31
Nature Immunology (publié)
doi.org
Co-developing The Canadian MPS Registry: A longitudinal rare disease patient registry
John J. Mitchell
Michal Inbar-Feigenberg
Kim Angel
Pranesh Chakraborty
Monica Lamoureux
John Adams
Beth K. Potter
Sylvia Stockler-Ipsirolgu
Alison H. Howie
Alex Pace
Nancy J. Butcher
Cheryl Greenberg
Robin Hayeems
Anne-Marie Laberge
Jeff Round
Martin Offringa
Maryam Oskoui
Chelsea Ruth
Andreas Schulze
Kathy N. Speechley … (voir 4 de plus)
Kednapa Thavorn
Yannis Trakadis
Kumanan Wilson
Thierry Lacaze
2024-01-31
Molecular Genetics and Metabolism (publié)
doi.org
Family‐centred care interventions for children with chronic conditions: A scoping review
Andrea J. Chow
Ammar Saad
Zobaida Al‐Baldawi
Ryan Iverson
Becky Skidmore
Isabel Jordan
Nicole Pallone
Maureen Smith
Pranesh Chakraborty
Jamie Brehaut
Eyal Cohen
Sarah Dyack
Jane Gillis
Sharan Goobie
Cheryl Greenberg
Robin Hayeems
Brian Hutton
Michal Inbar-Feigenberg
Shailly Jain-Ghai
Sara Khangura … (voir 18 de plus)
Jennifer MacKenzie
John J. Mitchell
Zeinab Moazin
Stuart G. Nicholls
Amy Pender
Chitra Prasad
Andreas Schulze
Komudi Siriwardena
Rebecca N. Sparkes
Kathy N. Speechley
Sylvia Stockler
Monica Taljaard
Mari Teitelbaum
Yannis Trakadis
Clara Van Karnebeek
Jagdeep S. Walia
Kumanan Wilson
Beth K. Potter
2024-01-31
Health Expectations (publié)
doi.org
Improving Pediatric Trauma Education by Teaching Non-technical Skills: A Randomized Controlled Trial
Fabio Botelho
Ayla Gerk
Jason M. Harley
Dan Poenaru
2024-01-31
Journal of Pediatric Surgery (publié)
doi.org
Inter- and intra-year forest change detection and monitoring of aboveground biomass dynamics using Sentinel-2 and Landsat
Flavie Pelletier
Jeffrey A. Cardille
Michael A. Wulder
Joanne C. White
Txomin Hermosilla
2024-01-31
Remote Sensing of Environment (publié)
doi.org
Linking biodiversity, ecosystem function, and Nature’s contributions to people: a macroecological energy flux perspective
Ana Carolina Antunes
Emilio Berti
Ulrich Brose
Myriam R. Hirt
Dirk N. Karger
Louise M. J. O'Connor
Laura J. Pollock
Wilfried Thuiller
Benoit Gauzens
2024-01-31
Trends in Ecology & Evolution (publié)
doi.org
Machine Learning Informed Diagnosis for Congenital Heart Disease in Large Claims Data Source
Ariane J. Marelli
Chao Li
Aihua Liu
Hanh Nguyen
Harry Moroz
James M. Brophy
Liming Guo
David L. Buckeridge
Jian Tang
Archer Y. Yang
Yue Li
2024-01-31
JACC: Advances (publié)
doi.org
Model Collapse Demystified: The Case of Regression
Elvis Dopgima Dohmatob
Yunzhen Feng
Julia Kempe
In the era of proliferation of large language and image generation models, the phenomenon of "model collapse" refers to the situation whereb… (voir plus)y as a model is trained recursively on data generated from previous generations of itself over time, its performance degrades until the model eventually becomes completely useless, i.e the model collapses. In this work, we study this phenomenon in the setting of high-dimensional regression and obtain analytic formulae which quantitatively outline this phenomenon in a broad range of regimes. In the special case of polynomial decaying spectral and source conditions, we obtain modified scaling laws which exhibit new crossover phenomena from fast to slow rates. We also propose a simple strategy based on adaptive regularization to mitigate model collapse. Our theoretical results are validated with experiments.
2024-01-31
arXiv (publié)
doi.org
arxiv.org