Publications

Invasion of Ukraine Discourse on TikTok Dataset
Benjamin D. Steel
Sara J. Parker
Derek Ruths
We present a dataset of videos and comments from the social media platform TikTok, centred around the invasion of Ukraine in 2022, an event … (voir plus)that launched TikTok into the geopolitical arena. The discourse around the invasion exposed myriad political behaviours and dynamics that are unexplored on this platform. To this end we provide a mass scale language and interaction dataset for further research into these processes. An initial investigation of language and social interaction dynamics are explored in this paper. The dataset and the library used to collect it are open sourced to the public.
2022-12-31
arXiv.org (prépublication)
doi.org
Invited commentary on Stoehr J et al: The personal impact of involvement in international global health outreach: A national survey of former operation smile student volunteers.
Dan Poenaru
2022-12-31
Journal of Pediatric Surgery (publié)
doi.org
Iorl: Inductive-Offline-Reinforcement-Learning for Traffic Signal Control Warmstarting
François-Xavier Devailly
Denis Larocque
Laurent Charlin
2022-12-31
Social Science Research Network (publié)
doi.org
ivadomed: A Medical Imaging Deep Learning Toolbox
Charley Gros
Andréanne Lemay
Olivier Vincent
Lucas Rouhier
Anthime Bucquet
Julien Cohen-Adad
Julien Cohen-Adad
Julien Cohen-Adad
ivadomed is an open-source Python package for designing, end-to-end training, and evaluating deep learning models applied to medical imaging… (voir plus) data. The package includes APIs, command-line tools, documentation, and tutorials. ivadomed also includes pre-trained models such as spinal tumor segmentation and vertebral labeling. Original features of ivadomed include a data loader that can parse image metadata (e.g., acquisition parameters, image contrast, resolution) and subject metadata (e.g., pathology, age, sex) for custom data splitting or extra information during training and evaluation. Any dataset following the Brain Imaging Data Structure (BIDS) convention will be compatible with ivadomed without the need to manually organize the data, which is typically a tedious task. Beyond the traditional deep learning methods, ivadomed features cutting-edge architectures, such as FiLM and HeMis, as well as various uncertainty estimation methods (aleatoric and epistemic), and losses adapted to imbalanced classes and non-binary predictions. Each step is conveniently configurable via a single file. At the same time, the code is highly modular to allow addition/modification of an architecture or pre/post-processing steps. Example applications of ivadomed include MRI object detection, segmentation, and labeling of anatomical and pathological structures. Overall, ivadomed enables easy and quick exploration of the latest advances in deep learning for medical imaging applications. ivadomed's main project page is available at https://ivadomed.org.
2022-12-31
Zenodo (publié)
doi.org
arxiv.org
Learning Syntactic Monoids from Samples by extending known Algorithms for learning State Machines
Simon Dieck
Sicco Verwer
François Coste
Faissal Ouardi
Guillaume Rabusseau
For the inference of regular languages, most current methods learn a version of deterministic finite automata. Syntactic monoids are an alte… (voir plus)rnative representation of regular languages, which have some advantages over automata. For example, traces can be parsed starting from any index and the star-freeness of the language they represent can be checked in polynomial time. But, to date, there existed no passive learning algorithm for syntactic monoids. In this paper, we prove that known state-merging algorithms for learning deterministic finite automata can be instrumented to learn syntactic monoids instead, by using as the input a special structure proposed in this paper: the interfix-graph. Further, we introduce a method to encode frequencies on the interfix-graph, such that models can also be learned from only positive traces. We implemented this structure and performed experiments with both traditional data and data containing only positive traces. As such this work answers basic theoretical and experimental questions regarding a novel passive learning algorithm for syntactic monoids.
2022-12-31
International Conference on Graphics and Interaction (publié)
dblp.uni-trier.de
List-GRAND: A Practical Way to Achieve Maximum Likelihood Decoding
Syed Mohsin Abbas
Marwan Jalaleddine
Warren J. Gross
Guessing random additive noise decoding (GRAND) is a recently proposed universal maximum likelihood (ML) decoder for short-length and high-r… (voir plus)ate linear block codes. Soft-GRAND (SGRAND) is a prominent soft-input GRAND variant, outperforming the other GRAND variants in decoding performance; nevertheless, SGRAND is not suitable for parallel hardware implementation. Ordered Reliability Bits-GRAND (ORBGRAND) is another soft-input GRAND variant that is suitable for parallel hardware implementation; however, it has lower decoding performance than SGRAND. In this article, we propose List-GRAND (LGRAND), a technique for enhancing the decoding performance of ORBGRAND to match the ML decoding performance of SGRAND. Numerical simulation results show that LGRAND enhances ORBGRAND’s decoding performance by 0.5–0.75 dB for channel codes of various classes at a target frame error rate (FER) of 10−7. For linear block codes of length 127/128 and different code rates, LGRAND’s VLSI implementation can achieve an average information throughput of 47.27–51.36 Gb/s. In comparison to ORBGRAND’s VLSI implementation, the proposed LGRAND hardware has a 4.84% area overhead.
2022-12-31
IEEE Transactions on Very Large Scale Integration (VLSI) Systems (publié)
doi.org
arxiv.org
Lower Bounds for Active Automata Learning.
Loes Kruger
Bharat Garhewal
François Coste
Frits W. Vaandrager
Faissal Ouardi
Guillaume Rabusseau
2022-12-31
ICGI (publié)
dblp.uni-trier.de
Maintenance Cost of Software Ecosystem Updates
Solomon Berhe
M. Maynard
Foutse Khomh
2022-12-31
ANT/EDI40 (publié)
doi.org
MARSY: a multitask deep-learning framework for prediction of drug combination synergy scores
Mohamed Reda El Khili
Safyan Aman Memon
Amin Emad
Combination therapies have emerged as a treatment strategy for cancers to reduce the probability of drug resistance and to improve outcomes.… (voir plus) Large databases curating the results of many drug screening studies on preclinical cancer cell lines have been developed, capturing the synergistic and antagonistic effects of combination of drugs in different cell lines. However, due to the high cost of drug screening experiments and the sheer size of possible drug combinations, these databases are quite sparse. This necessitates the development of transductive computational models to accurately impute these missing values. Here, we developed MARSY, a deep-learning multitask model that incorporates information on the gene expression profile of cancer cell lines, as well as the differential expression signature induced by each drug to predict drug-pair synergy scores. By utilizing two encoders to capture the interplay between the drug pairs, as well as the drug pairs and cell lines, and by adding auxiliary tasks in the predictor, MARSY learns latent embeddings that improve the prediction performance compared to state-of-the-art and traditional machine-learning models. Using MARSY, we then predicted the synergy scores of 133 722 new drug-pair cell line combinations, which we have made available to the community as part of this study. Moreover, we validated various insights obtained from these novel predictions using independent studies, confirming the ability of MARSY in making accurate novel predictions. An implementation of the algorithms in Python and cleaned input datasets are provided in https://github.com/Emad-COMBINE-lab/MARSY.
2022-12-31
Bioinformatics (publié)
doi.org
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning
Maziar Gomrokchi
Susan Amin
Hossein Aboutalebi
Alexander Wong
Doina Precup
While significant research advances have been made in the field of deep reinforcement learning, there have been no concrete adversarial atta… (voir plus)ck strategies in literature tailored for studying the vulnerability of deep reinforcement learning algorithms to membership inference attacks. In such attacking systems, the adversary targets the set of collected input data on which the deep reinforcement learning algorithm has been trained. To address this gap, we propose an adversarial attack framework designed for testing the vulnerability of a state-of-the-art deep reinforcement learning algorithm to a membership inference attack. In particular, we design a series of experiments to investigate the impact of temporal correlation, which naturally exists in reinforcement learning training data, on the probability of information leakage. Moreover, we compare the performance of \emph{collective} and \emph{individual} membership attacks against the deep reinforcement learning algorithm. Experimental results show that the proposed adversarial attack framework is surprisingly effective at inferring data with an accuracy exceeding
2022-12-31
IEEE Access (publié)
doi.org
arxiv.org
Motor cortex latent dynamics 1 encode arm movement direction and 2 urgency independently 3
Andrea Colins Rodriguez
Matthew G Perich
Lee Miller
Mark D. Humphries
10 The fluid movement of an arm is controlled by multiple parameters that can be set 11 independently. Recent studies argue that arm moveme… (voir plus)nts are generated by the collective 12 dynamics of neurons in motor cortex. But how these collective dynamics simultaneously encode 13 and control multiple parameters of movement is an open question. Using a task where monkeys 14 made sequential, varied arm movements, we show that the direction and urgency of arm 15 movements are simultaneously encoded in the low-dimensional trajectories of population 16 activity: each movement’s direction by a fixed, looped neural trajectory and its urgency by how 17 quickly that trajectory was traversed. Network models showed this latent coding is potentially 18 advantageous as it allows the direction and urgency of arm movement to be independently 19 controlled. Our results suggest how low-dimensional neural dynamics can define multiple 20 parameters of goal-directed movement simultaneously. 21
2022-12-31
(publié)
www.semanticscholar.org
Multi-view manifold learning of human brain-state trajectories.
Erica L. Busch
Jessie Huang
Andrew Benz
Tom Wallenstein
Guillaume Lajoie
Guy Wolf
Smita Krishnaswamy
Nicholas B. Turk-Browne
The complexity of the human brain gives the illusion that brain activity is intrinsically high-dimensional. Nonlinear dimensionality-reducti… (voir plus)on methods such as uniform manifold approximation and t-distributed stochastic neighbor embedding have been used for high-throughput biomedical data. However, they have not been used extensively for brain activity data such as those from functional magnetic resonance imaging (fMRI), primarily due to their inability to maintain dynamic structure. Here we introduce a nonlinear manifold learning method for time-series data—including those from fMRI—called temporal potential of heat-diffusion for affinity-based transition embedding (T-PHATE). In addition to recovering a low-dimensional intrinsic manifold geometry from time-series data, T-PHATE exploits the data’s autocorrelative structure to faithfully denoise and unveil dynamic trajectories. We empirically validate T-PHATE on three fMRI datasets, showing that it greatly improves data visualization, classification, and segmentation of the data relative to several other state-of-the-art dimensionality-reduction benchmarks. These improvements suggest many potential applications of T-PHATE to other high-dimensional datasets of temporally diffuse processes.
2022-12-31
Nature Computational Science (publié)
doi.org