Publications

Full-Scale Information Diffusion Prediction With Reinforced Recurrent Networks
Cheng Yang
Hao Wang
Jian Tang
Chuan Shi
Maosong Sun
Ganqu Cui
Zhiyuan Liu
Information diffusion prediction is an important task, which studies how information items spread among users. With the success of deep lear… (see more)ning techniques, recurrent neural networks (RNNs) have shown their powerful capability in modeling information diffusion as sequential data. However, previous works focused on either microscopic diffusion prediction, which aims at guessing who will be the next influenced user at what time, or macroscopic diffusion prediction, which estimates the total numbers of influenced users during the diffusion process. To the best of our knowledge, few attempts have been made to suggest a unified model for both microscopic and macroscopic scales. In this article, we propose a novel full-scale diffusion prediction model based on reinforcement learning (RL). RL incorporates the macroscopic diffusion size information into the RNN-based microscopic diffusion model by addressing the nondifferentiable problem. We also employ an effective structural context extraction strategy to utilize the underlying social graph information. Experimental results show that our proposed model outperforms state-of-the-art baseline models on both microscopic and macroscopic diffusion predictions on three real-world datasets.
2021-09-01
IEEE Transactions on Neural Networks and Learning Systems (published)
doi.org
Full-Scale Information Diffusion Prediction With Reinforced Recurrent Networks
Cheng Yang
Hao Wang
Jian Tang
Chuan Shi
Maosong Sun
Ganqu Cui
Zhiyuan Liu
Information diffusion prediction is an important task, which studies how information items spread among users. With the success of deep lear… (see more)ning techniques, recurrent neural networks (RNNs) have shown their powerful capability in modeling information diffusion as sequential data. However, previous works focused on either microscopic diffusion prediction, which aims at guessing who will be the next influenced user at what time, or macroscopic diffusion prediction, which estimates the total numbers of influenced users during the diffusion process. To the best of our knowledge, few attempts have been made to suggest a unified model for both microscopic and macroscopic scales. In this article, we propose a novel full-scale diffusion prediction model based on reinforcement learning (RL). RL incorporates the macroscopic diffusion size information into the RNN-based microscopic diffusion model by addressing the nondifferentiable problem. We also employ an effective structural context extraction strategy to utilize the underlying social graph information. Experimental results show that our proposed model outperforms state-of-the-art baseline models on both microscopic and macroscopic diffusion predictions on three real-world datasets.
2021-09-01
IEEE Transactions on Neural Networks and Learning Systems (published)
doi.org
Promoting and Optimizing the Use of 3D-Printed Objects in Spontaneous Recognition Memory Tasks in Rodents: A Method for Improving Rigor and Reproducibility
Mehreen Inayat
Arely Cruz-Sanchez
Hayley H. A. Thorpe
Jude A. Frie
Blake Richards
Jibran Y. Khokhar
Maithe Arruda-Carvalho
2021-09-01
eNeuro (published)
doi.org
A Data Mining Analysis of Cross-Regional Study of Apparel Consumption
Osmud Rahman
Benjamin Fung
2021-08-28
Comprehensible Science (published)
doi.org
Successive-Cancellation Decoding of Reed-Muller Codes With Fast Hadamard Transform
Nghia Doan
Seyyed Ali Hashemi
Warren Gross
A novel permuted fast successive-cancellation list decoding algorithm with fast Hadamard transform (FHT-FSCL) is presented. The proposed dec… (see more)oder initializes
2021-08-28
ArXiv (preprint)
doi.org
arxiv.org
Approximate Bayesian Optimisation for Neural Networks
Nadhir Hassen
Irina Rish
2021-08-27
ArXiv (preprint)
arxiv.org
arxiv.org
Extracting Weighted Automata for Approximate Minimization in Language Modelling
Clara Lacroce
Prakash Panangaden
Guillaume Rabusseau
2021-08-25
Proceedings of the Fifteenth International Conference on Grammatical Inference (published)
proceedings.mlr.press
arxiv.org
HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images
Saverio Vadacchino
Raghav Mehta
Nazanin Mohammadi Sepahvand
Brennan Nichyporuk
James J. Clark
Tal Arbel
Segmentation of enhancing tumours or lesions from MRI is important for detecting new disease activity in many clinical contexts. However, ac… (see more)curate segmentation requires the inclusion of medical images (e.g., T1 post-contrast MRI) acquired after injecting patients with a contrast agent (e.g., Gadolinium), a process no longer thought to be safe. Although a number of modality-agnostic segmentation networks have been developed over the past few years, they have been met with limited success in the context of enhancing pathology segmentation. In this work, we present HAD-Net, a novel offline adversarial knowledge distillation (KD) technique, whereby a pre-trained teacher segmentation network, with access to all MRI sequences, teaches a student network, via hierarchical adversarial training, to better overcome the large domain shift presented when crucial images are absent during inference. In particular, we apply HAD-Net to the challenging task of enhancing tumour segmentation when access to post-contrast imaging is not available. The proposed network is trained and tested on the BraTS 2019 brain tumour segmentation challenge dataset, where it achieves performance improvements in the ranges of 16% - 26% over (a) recent modality-agnostic segmentation methods (U-HeMIS, U-HVED), (b) KD-Net adapted to this problem, (c) the pre-trained student network and (d) a non-hierarchical version of the network (AD-Net), in terms of Dice scores for enhancing tumour (ET). The network also shows improvements in tumour core (TC) Dice scores. Finally, the network outperforms both the baseline student network and AD-Net in terms of uncertainty quantification for enhancing tumour segmentation based on the BraTS 2019 uncertainty challenge metrics. Our code is publicly available at: https://github.com/SaverioVad/HAD_Net
2021-08-25
Proceedings of the Fourth Conference on Medical Imaging with Deep Learning (published)
proceedings.mlr.press
openreview.net
Monitoring non-pharmaceutical public health interventions during the COVID-19 pandemic
Yannan Shen
Guido Powell
Iris Ganser
Qulu Zheng
Chris Grundy
Anya Okhmatovskaia
David Buckeridge
2021-08-24
Scientific Data (published)
doi.org
Generating community measures of food purchasing activities using store-level electronic grocery transaction records: an ecological study in Montreal, Canada
Hiroshi Mamiya
Alexandra M. Schmidt
Erica E.M. Moodie
Yu Ma
David Buckeridge
2021-08-23
Public Health Nutrition (published)
doi.org
Magnetoencephalography resting-state correlates of executive and language components of verbal fluency
Victor Oswald
Younes Zerouali
Aubrée Boulet-Craig
M. Krajinovic
Caroline Laverdière
D. Sinnett
Pierre W. Jolicoeur
Sarah Lippé
Karim Jerbi
Philippe Robaey
2021-08-20
Scientific Reports (published)
doi.org
Lacking social support is associated with structural divergences in hippocampus–default network co-variation patterns
Chris Zajner
Nathan Spreng
Danilo Bzdok
Elaborate social interaction is a pivotal asset of the human species. The complexity of people’s social lives may constitute the dominatin… (see more)g factor in the vibrancy of many individuals’ environment. The neural substrates linked to social cognition thus appear especially susceptible when people endure periods of social isolation: here, we zoom in on the systematic inter-relationships between two such neural substrates, the allocortical hippocampus (HC) and the neocortical default network (DN). Previous human social neuroscience studies have focused on the DN, while HC subfields have been studied in most detail in rodents and monkeys. To bring into contact these two separate research streams, we directly quantified how DN subregions are coherently co-expressed with specific HC subfields in the context of social isolation. A two-pronged decomposition of structural brain scans from ∼40,000 UK Biobank participants linked lack of social support to mostly lateral subregions in the DN patterns. This lateral DN association co-occurred with HC patterns that implicated especially subiculum, presubiculum, CA2, CA3, and dentate gyrus. Overall, the subregion divergences within spatially overlapping signatures of HC-DN co-variation followed a clear segregation divide into the left and right brain hemispheres. Separable regimes of structural HC-DN co-variation also showed distinct associations with the genetic predisposition for lacking social support at the population level.
2021-08-19
bioRxiv (preprint)
doi.org