Publications

Towards Adaptive Cybersecurity for Green IoT

Talal Halabi

Martine Bellaiche

Benjamin Fung

The Internet of Things (IoT) paradigm has led to an explosion in the number of IoT devices and an exponential rise in carbon footprint incur… (see more)red by overburdened IoT networks and pervasive cloud/edge communications. Hence, there is a growing interest in industry and academia to enable the efficient use of computing infrastructures by optimizing the management of data center and IoT resources (hardware, software, network, and data) and reducing operational costs to slash greenhouse gas emissions and create healthy environments. Cybersecurity has also been considered in such efforts as a contributor to these environmental issues. Nonetheless, most green security approaches focus on designing low-overhead encryption schemes and do not emphasize energy-efficient security from architectural and deployment viewpoints. This paper sheds light on the emerging paradigm of adaptive cybersecurity as one of the research directions to support sustainable computing in green IoT. It presents three potential research directions and their associated methods for designing and deploying adaptive security in green computing and resource-constrained IoT environments to save on energy consumption. Such efforts will transform the development of data-driven IoT security solutions to be greener and more environment-friendly.

2022-11-24

2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) (published)

Age differences in functional brain networks associated with loneliness and empathy

Laetitia Mwilambwe-Tshilobo

Roni Setton

Danilo Bzdok

Gary R. Turner

R. Nathan Spreng

Abstract Loneliness is associated with differences in resting-state functional connectivity (RSFC) within and between large-scale networks i… (see more)n early- and middle-aged adult cohorts. However, age-related changes in associations between sociality and brain function into late adulthood are not well understood. Here, we examined age differences in the association between two dimensions of sociality—loneliness and empathic responding—and RSFC of the cerebral cortex. Self-report measures of loneliness and empathy were inversely related across the entire sample of younger (mean age = 22.6y, n = 128) and older (mean age = 69.0y, n = 92) adults. Using multivariate analyses of multi-echo fMRI RSFC, we identified distinct functional connectivity patterns for individual and age group differences associated with loneliness and empathic responding. Loneliness in young and empathy in both age groups was related to greater visual network integration with association networks (e.g., default, fronto-parietal control). In contrast, loneliness was positively related to within- and between-network integration of association networks for older adults. These results extend our previous findings in early- and middle-aged cohorts, demonstrating that brain systems associated with loneliness, as well as empathy, differ in older age. Further, the findings suggest that these two aspects of social experience engage different neurocognitive processes across human life-span development.

2022-11-23

Network Neuroscience (published)

Momentum Extragradient is Optimal for Games with Cross-Shaped Spectrum

Junhyung Lyle Kim

Gauthier Gidel

Anastasios Kyrillidis

Fabian Pedregosa

Google Research

The extragradient method has recently gained a lot of attention, due to its convergence behavior on smooth games. In games, the eigenvalues … (see more)of the Jacobian of the vector field are distributed on the complex plane, exhibiting more convoluted dynamics compared to minimization. In this work, we take a polynomial-based analysis of the extragradient with momentum for optimizing games with \emph{cross-shaped} spectrum on the complex plane. We show two results: first, the extragradient with momentum exhibits three different modes of convergence based on the hyperparameter setup: when the eigenvalues are distributed

2022-11-23

NeurIPS.cc/2022/Workshop/OPT (poster)

GraphCG: Unsupervised Discovery of Steerable Factors in Graphs

Shengchao Liu

Chengpeng Wang

Weili Nie

Hanchen Wang

Jiarui Lu

Bolei Zhou

Jian Tang

Deep generative models have been extensively explored recently, especially for the graph data such as molecular graphs and point clouds. Yet… (see more), much less investigation has been carried out on understanding the learned latent space of deep graph generative models. Such understandings can open up a unified perspective and provide guidelines for essential tasks like controllable generation. In this paper, we first examine the representation space of the recent deep generative model trained for graph data, observing that the learned representation space is not perfectly disentangled. Based on this observation, we then propose an unsupervised method called GraphCG, which is model-agnostic and task-agnostic for discovering steerable factors in graph data. Specifically, GraphCG learns the semantic-rich directions via maximizing the corresponding mutual information, where the edited graph along the same direction will possess certain steerable factors. We conduct experiments on two types of graph data, molecular graphs and point clouds. Both the quantitative and qualitative results show the effectiveness of GraphCG for discovering steerable factors. The code will be public in the near future.

2022-11-22

NeurIPS.cc/2022/Workshop/GLFrontiers (published)

SMPL-IK: Learned Morphology-Aware Inverse Kinematics for AI Driven Artistic Workflows

Vikram Voleti

Boris Oreshkin

Florent Bocquelet

Félix Harvey

Louis-Simon Ménard

Chris Pal

2022-11-22

SIGGRAPH Asia 2022 Technical Communications (published)

Assessing the potential for virtualizable care in the pediatric emergency department.

Esli Osmanlliu

Brett Burstein

Robyn Tamblyn

David Buckeridge

INTRODUCTION There is increasing interest for patient-to-provider telemedicine in pediatric acute care. The suitability of telemedicine (vir… (see more)tualizability) for visits in this setting has not been formally assessed. We estimated the proportion of in-person pediatric emergency department (PED) visits that were potentially virtualizable, and identified factors associated with virtualizable care. METHODS This was a retrospective analysis of in-person visits at the PED of a Canadian tertiary pediatric hospital (02/2018-12/2019). Three definitions of virtualizable care were developed: (1) a definition based on "resource use" classifying visits as virtualizable if they resulted in a home discharge, no diagnostic testing, and no return visit within 72 h; (2) a "diagnostic definition" based on primary ED diagnosis; and (3) a stringent "combined definition" by which visits were classified as virtualizable if they met both the resource use and diagnostic definitions. Multivariable logistic regression was used to identify factors associated with telemedicine suitability. RESULTS There were 130,535 eligible visits from 80,727 individual patients during the study period. Using the most stringent combined definition of telemedicine suitability, 37.9% (95% confidence interval (CI) 37.6%-38.2%) of in-person visits were virtualizable. Overnight visits (adjusted odds ratio (aOR) 1.16-1.37), non-Canadian citizenship (aOR 1.10-1.18), ethnocultural vulnerability (aOR 1.14-1.22), and a consultation for head trauma (aOR 3.50-4.60) were associated with higher telemedicine suitability across definitions. DISCUSSION There is a high potential for patient-to-provider telemedicine in the PED setting. Local patient and visit-level characteristics must be considered in the design of safe and inclusive telemedicine models for pediatric acute care.

2022-11-20

Journal of Telemedicine and Telecare (published)

Learning from uncertain concepts via test time interventions

Ivaxi Sheth

Aamer Abdul Rahman

Laya Rafiee Sevyeri

Mohammad Havaei

Samira Ebrahimi Kahou

With neural networks applied to safety-critical applications, it has become increasingly important to understand the defining features of de… (see more)cision-making. Therefore, the need to uncover the black boxes to rational representational space of these neural networks is apparent. Concept bottleneck model (CBM) encourages interpretability by predicting human-understandable concepts. They predict concepts from input images and then labels from concepts. Test time intervention, a salient feature of CBM, allows for human-model interactions. However, these interactions are prone to information leakage and can often be ineffective inappropriate communication with humans. We propose a novel uncertainty based strategy, \emph{SIUL: Single Interventional Uncertainty Learning} to select the interventions. Additionally, we empirically test the robustness of CBM and the effect of SIUL interventions under adversarial attack and distributional shift. Using SIUL, we observe that the interventions suggested lead to meaningful corrections along with mitigation of concept leakage. Extensive experiments on three vision datasets along with a histopathology dataset validate the effectiveness of our interventional learning.

2022-11-20

NeurIPS.cc/2022/Workshop/TSRML (accepted)

Striving for data-model efficiency: Identifying data externalities on group performance

Esther Rolf

Ben Packer

Alex Beutel

Fernando Diaz

2022-11-20

NeurIPS.cc/2022/Workshop/TSRML (accepted)

GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction

Dominic Masters

Josef Dean

Kerstin Klaeser

Zhiyi Li

Samuel Maddrell-Mander

Adam Sanders

Hatem Helal

Deniz Beker

Ladislav Rampášek

Dominique Beaini

2022-11-18

ArXiv (preprint)

APP: Anytime Progressive Pruning

Diganta Misra

Bharat Runwal

Tianlong Chen

Zhangyang Wang

Irina Rish

With the latest advances in deep learning, several methods have been investigated for optimal learning settings in scenarios where the data … (see more)stream is continuous over time. However, training sparse networks in such settings has often been overlooked. In this paper, we explore the problem of training a neural network with a target sparsity in a particular case of online learning: the anytime learning at macroscale paradigm (ALMA). We propose a novel way of progressive pruning, referred to as \textit{Anytime Progressive Pruning} (APP); the proposed approach significantly outperforms the baseline dense and Anytime OSP models across multiple architectures and datasets under short, moderate, and long-sequence training. Our method, for example, shows an improvement in accuracy of

2022-11-17

ACML.org/2022/Workshop/CLL (published)

Clinically Plausible Pathology-Anatomy Disentanglement in Patient Brain MRI with Structured Variational Priors

Anjun Hu

Jean-Pierre R. Falet

Brennan Nichyporuk

Changjian Shui

Douglas Arnold

Sotirios A. Tsaftaris

Tal Arbel

We propose a hierarchically structured variational inference model for accurately disentangling observable evidence of disease (e.g. brain l… (see more)esions or atrophy) from subject-specific anatomy in brain MRIs. With flexible, partially autoregressive priors, our model (1) addresses the subtle and fine-grained dependencies that typically exist between anatomical and pathological generating factors of an MRI to ensure the clinical validity of generated samples; (2) preserves and disentangles finer pathological details pertaining to a patient's disease state. Additionally, we experiment with an alternative training configuration where we provide supervision to a subset of latent units. It is shown that (1) a partially supervised latent space achieves a higher degree of disentanglement between evidence of disease and subject-specific anatomy; (2) when the prior is formulated with an autoregressive structure, knowledge from the supervision can propagate to the unsupervised latent units, resulting in more informative latent representations capable of modelling anatomy-pathology interdependencies.

2022-11-15

ArXiv (preprint)