Publications

Symptom network analysis of the sleep disorders diagnostic criteria based on the clinical text of the ICSD‐3
Christophe Gauld
Régis Lopez
Charles Morin
Pierre A. GEOFFROY
Julien Maquet
Pierre Desvergnes
Aileen McGonigal
Yves Dauvilliers
Pierre Philip
Guillaume Dumas
Jean‐Arthur Micoulaud‐Franchi
2021-07-15
Journal of Sleep Research (published)
doi.org
Design of Hesitation Gestures for Nonverbal Human-Robot Negotiation of Conflicts
AJung Moon
Maneezhay Hashmi
H. F. Machiel Van Der Loos
Elizabeth A. Croft
Aude Billard
When the question of who should get access to a communal resource first is uncertain, people often negotiate via nonverbal communication to … (see more)resolve the conflict. What should a robot be programmed to do when such conflicts arise in Human-Robot Interaction? The answer to this question varies depending on the context of the situation. Learning from how humans use hesitation gestures to negotiate a solution in such conflict situations, we present a human-inspired design of nonverbal hesitation gestures that can be used for Human-Robot Negotiation. We extracted characteristic features of such negotiative hesitations humans use, and subsequently designed a trajectory generator (Negotiative Hesitation Generator) that can re-create the features in robot responses to conflicts. Our human-subjects experiment demonstrates the efficacy of the designed robot behaviour against non-negotiative stopping behaviour of a robot. With positive results from our human-robot interaction experiment, we provide a validated trajectory generator with which one can explore the dynamics of human-robot nonverbal negotiation of resource conflicts.
2021-07-10
ACM Transactions on Human-Robot Interaction (published)
doi.org
Random Forest-Based Diffusion Information Geometry for Supervised Visualization and Data Exploration
Jake S. Rhodes
Adele Cutler
Guy Wolf
Kevin R. Moon
Most dimensionality reduction techniques to date are unsupervised; they do not take class labels into account (e.g., PCA, MDS, t-SNE, Isomap… (see more)). Such methods require large amounts of data and are often sensitive to noise that may obfuscate important patterns in the data. Various attempts at supervised dimensionality reduction methods that take into account auxiliary annotations (e.g., class labels) have been successfully implemented with goals of increased classification accuracy or improved data visualization. In this paper, we describe a novel supervised visualization technique based on random forest proximities and a diffusion-based information geometry. We show, both qualitatively and quantitatively, the advantages of our approach in retaining local and global structure in data, while demonstrating the spatial relevance of features important for the supervised task. Importantly, our approach is robust to noise and parameter tuning, thus making it simple to use while producing reliable visualizations for data exploration.
2021-07-10
Symposium on Software Performance (published)
doi.org
TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion
Jiapeng Wu
Yishi Xu
Yingxue Zhang
Chen Ma
Mark J. Coates
Jackie CK Cheung
Reasoning in a temporal knowledge graph (TKG) is a critical task for information retrieval and semantic search. It is particularly challengi… (see more)ng when the TKG is updated frequently. The model has to adapt to changes in the TKG for efficient training and inference while preserving its performance on historical knowledge. Recent work approaches TKG completion (TKGC) by augmenting the encoder-decoder framework with a time-aware encoding function. However, naively fine-tuning the model at every time step using these methods does not address the problems of 1) catastrophic forgetting, 2) the model's inability to identify the change of facts (e.g., the change of the political affiliation and end of a marriage), and 3) the lack of training efficiency. To address these challenges, we present the Time-aware Incremental Embedding (TIE) framework, which combines TKG representation learning, experience replay, and temporal regularization. We introduce a set of metrics that characterizes the intransigence of the model and propose a constraint that associates the deleted facts with negative labels. Experimental results on Wikidata12k and YAGO11k datasets demonstrate that the proposed TIE framework reduces training time by about ten times and improves on the proposed metrics compared to vanilla full-batch training. It comes without a significant loss in performance for any traditional measures. Extensive ablation studies reveal performance trade-offs among different evaluation metrics, which is essential for decision-making around real-world TKG applications.
2021-07-10
Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (published)
doi.org
arxiv.org
Tracking white and grey matter degeneration along the spinal cord axis in degenerative cervical myelopathy
Kevin Vallotton
Gergely David
Markus Hupp
Nikolai Pfender
Julien Cohen-Adad
Michael Fehlings
Rebecca S. Samson
Claudia A. M. Gandini Wheeler-Kingshott
Armin Curt
Patrick Freund
Maryam Seif
Tissue-specific neurodegeneration revealed by quantitative MRI, already apparent across the spinal cord in mild-moderate DCM prior to the on… (see more)set of severe clinical impairments. WM microstructural changes are particularly sensitive to remote pathologically and clinically eloquent changes in DCM.
2021-07-08
Journal of Neurotrauma (published)
doi.org
A Novel and Dedicated Machine Learning Model for Malware Classification
Miles Q. Li
Benjamin C. M. Fung
Philippe Charland
Steven H. H. Ding
: Malicious executables are comprised of functions that can be represented in assembly code. In the assembly code mining literature, many so… (see more)ftware reverse engineering tools have been created to disassemble executables, search function clones, and find vulnerabilities, among others. The development of a machine learning-based malware classification model that can simultaneously achieve excellent classification performance and provide insightful interpretation for the classification results remains to be a hot research topic. In this paper, we propose a novel and dedicated machine learning model for the research problem of malware classification. Our proposed model generates assembly code function clusters based on function representation learning and provides excellent interpretability for the classification results. It does not require a large or balanced dataset to train which meets the situation of real-life scenarios. Experiments show that our proposed approach outperforms previous state-of-the-art malware classification models and provides meaningful interpretation of classification results.
2021-07-05
Proceedings of the 16th International Conference on Software Technologies (published)
doi.org
Parallel and recurrent cascade models as a unifying force for understanding sub-cellular computation
Emerson F. Harkin
Peter R. Shen
Anish Goel
Blake A. Richards
Richard Naud
Neurons are very complicated computational devices, incorporating numerous non-linear processes, particularly in their dendrites. Biophysica… (see more)l models capture these processes directly by explicitly modelling physiological variables, such as ion channels, current flow, membrane capacitance, etc. However, another option for capturing the complexities of real neural computation is to use cascade models, which treat individual neurons as a cascade of linear and non-linear operations, akin to a multi-layer artificial neural network. Recent research has shown that cascade models can capture single-cell computation well, but there are still a number of sub-cellular, regenerative dendritic phenomena that they cannot capture, such as the interaction between sodium, calcium, and NMDA spikes in different compartments. Here, we propose that it is possible to capture these additional phenomena using parallel, recurrent cascade models, wherein an individual neuron is modelled as a cascade of parallel linear and non-linear operations that can be connected recurrently, akin to a multi-layer, recurrent, artificial neural network. Given their tractable mathematical structure, we show that neuron models expressed in terms of parallel recurrent cascades can themselves be integrated into multi-layered artificial neural networks and trained to perform complex tasks. We go on to discuss potential implications and uses of these models for artificial intelligence. Overall, we argue that parallel, recurrent cascade models provide an important, unifying tool for capturing single-cell computation and exploring the algorithmic implications of physiological phenomena.
2021-07-04
Neuroscience (published)
doi.org
The default mode network in cognition: a topographical perspective
Jonathan Smallwood
Boris C Bernhardt
Robert Leech
Danilo Bzdok
Elizabeth Jefferies
Daniel S. Margulies
2021-07-04
Nature Reviews Neuroscience (published)
doi.org
Fixed-Points for Quantitative Equational Logics
Radu Mardare
Prakash Panangaden
Gordon Plotkin
We develop a fixed-point extension of quantitative equational logic and give semantics in one-bounded complete quantitative algebras. Unlike… (see more) previous related work about fixed-points in metric spaces, we are working with the notion of approximate equality rather than exact equality. The result is a novel theory of fixed points which can not only provide solutions to the traditional fixed-point equations but we can also define the rate of convergence to the fixed point. We show that such a theory is the quantitative analogue of a Conway theory and also of an iteration theory; and it reflects the metric coinduction principle. We study the Bellman equation for a Markov decision process as an illustrative example.
2021-07-01
2021 36th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS) (published)
doi.org
arxiv.org
Universal Semantics for the Stochastic λ-Calculus
Pedro H. Azevedo de Amorim
Dexter Kozen
Radu Mardare
Prakash Panangaden
Michael Roberts
We define sound and adequate denotational and operational semantics for the stochastic lambda calculus. These two semantic approaches build … (see more)on previous work that used an explicit source of randomness to reason about higher-order probabilistic programs.
2021-07-01
2021 36th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS) (published)
doi.org
arxiv.org
A Brief Study on the Effects of Training Generative Dialogue Models with a Semantic loss
Prasanna Parthasarathi
Mohamed Abdelsalam
Joelle Pineau
Sarath Chandar
Neural models trained for next utterance generation in dialogue task learn to mimic the n-gram sequences in the training set with training o… (see more)bjectives like negative log-likelihood (NLL) or cross-entropy. Such commonly used training objectives do not foster generating alternate responses to a context. But, the effects of minimizing an alternate training objective that fosters a model to generate alternate response and score it on semantic similarity has not been well studied. We hypothesize that a language generation model can improve on its diversity by learning to generate alternate text during training and minimizing a semantic loss as an auxiliary objective. We explore this idea on two different sized data sets on the task of next utterance generation in goal oriented dialogues. We make two observations (1) minimizing a semantic objective improved diversity in responses in the smaller data set (Frames) but only as-good-as minimizing the NLL in the larger data set (MultiWoZ) (2) large language model embeddings can be more useful as a semantic loss objective than as initialization for token embeddings.
2021-06-30
Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue (published)
doi.org
arxiv.org
Directional Graph Networks: Anisotropic Aggregation in Graph Neural Networks via Directional Vector Fields
Dominique Beaini
Saro Passaro
Vincent Létourneau
William L. Hamilton
Gabriele Corso
Pietro Lio
The lack of anisotropic kernels in graph neural networks (GNNs) strongly limits their expressiveness, contributing to well-known issues such… (see more) as over-smoothing. To overcome this limitation, we propose the first globally consistent anisotropic kernels for GNNs, allowing for graph convolutions that are defined according to topologicaly-derived directional flows. First, by defining a vector field in the graph, we develop a method of applying directional derivatives and smoothing by projecting node-specific messages into the field. Then, we propose the use of the Laplacian eigenvectors as such vector field. We show that the method generalizes CNNs on an
2021-06-30
Proceedings of the 38th International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press