Publications

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
Design and Application of Adaptive Sparse Deep Echo State Network
Cuili Yang
Sheng Yang
Jian Tang
Bing Li
The prediction of appliances energy consumption in building belongs to time series forecasting problem, which can be solved by echo state ne… (voir plus)twork (ESN). However, due to the randomly initialized inputs and reservoir, some redundant or irrelevant components are inevitably generated in original ESN. To solve this problem, the adaptive sparse deep echo state network (ASDESN) is proposed, in which the information is processed layer by layer. Firstly, the principal component analysis (PCA) layer is inserted to penalize the redundant projection transmitted between sub-reservoirs. Secondly, the coordinate descent based adaptive sparse learning method is proposed to generate the sparse output weights. Particularly, the designed adaptive threshold strategy is able to enlarge the sparsity of output weights as network depth increases. Moreover, the echo state property (ESP) of ASDESN is given to ensure its applications. The experiment results in both simulated benchmark and real appliances energy datasets illustrate that the proposed ASDESN outperforms other ESNs with higher prediction accuracy and stability.
2024-01-31
IEEE transactions on consumer electronics (publié)
doi.org
Heterogeneous ensemble prediction model of CO emission concentration in municipal solid waste incineration process using virtual data and real data hybrid-driven
Runyu Zhang
Jian Tang
Heng Xia
Jiakun Chen
Wen Yu
JunFei Qiao
2024-01-31
Journal of Cleaner Production (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
Neural semantic tagging for natural language-based search in building information models: Implications for practice
Mehrzad Shahinmoghadam
S Ebrahimi Kahou
Ali Motamedi
2024-01-31
Computers in industry (Print) (publié)
doi.org
Patient-Centered Surgical Care for Children in Low and Lower-Middle Income Countries (LMICs) - A Systematic Scoping Review of the Literature
Riya Sawhney
Kacylia Roy Proulx
Ayla Gerk
Elena Guadagno
Dan Poenaru
2024-01-31
Journal of Pediatric Surgery (publié)
doi.org
Properties and Styles of Software Technology Tutorials
Deeksha M. Arya
Jin L.C. Guo
Martin P. Robillard
A large number of tutorials for popular software development technologies are available online, and those about the same technology vary wid… (voir plus)ely in their presentation. We studied the design of tutorials in the software documentation landscape for five popular programming languages: Java, C#, Python, Javascript, and Typescript. We investigated the extent to which tutorial pages, i.e. resources, differ and report statistics of variations in resource properties. We developed a framework for characterizing resources based on their distinguishing attributes, i.e. properties that vary widely for the resource, relative to other resources. Additionally, we propose that a resource can be represented by its resource style, i.e. the combination of its distinguishing attributes. We discuss three techniques for characterizing resources based on our framework, to capture notable and relevant content and presentation properties of tutorial pages. We apply these techniques on a data set of 2551 resources to validate that our framework identifies valid and interpretable styles. We contribute this framework for reasoning about the design of resources in the online software documentation landscape.
2024-01-31
IEEE Transactions on Software Engineering (publié)
doi.org