Publications

The distribution, ecology and predicted habitat use of the Critically Endangered angelshark (Squatina squatina) in coastal waters of Wales and the central Irish Sea

Joanna Barker

Jake Davies

Monika Goralczyk

Surshti Patel

John O'Connor

Jim Evans

Jackson Wesley Evans

Rowland Sharp

Matthew Gollock

Fenella R. Wood

Frank N. Wood

James Rosindell

Charlie Bartlett

Brett J. Garner

Dafydd Jones

D. J. Jones

Declan Quigley

Ben Wray

Billy Wray

Abstract The angelshark (Squatina squatina) has the northernmost range of any angel shark species, but there is limited information on its d… (voir plus)istribution, habitat use and ecology at higher latitudes. To address this, Angel Shark Project: Wales gathered 2231 S. squatina records and 142 anecdotal resources from fishers, coastal communities and archives. These spanned the coastal waters of Wales and the central Irish Sea and were dated from 1812 to 2020, with 97.62% of records within 11.1 km (6 nm) of the coast. Commercial, recreational and charter boat fishers provided the majority of S. squatina records (97.18%), with significantly more sightings from three decades (1970s, 1980s and 1990s) and in the months of September, June, August and July (in descending order). The coastal area between Bardsey Island and Strumble Head had the most S. squatina records (n = 1279), with notable concentrations also found in Carmarthen Bay, Conwy Bay and the Outer Severn Estuary. Species distribution models (SDM) identified four environmental variables that had significant influence on S. squatina distribution, depth, chlorophyll‐a concentration, sea surface temperature (SST) and salinity, and these varied between the quarters (Q) of the year. SDM model outputs predicted a larger congruous area of suitable habitat in Q3 (3176 km2) compared to Q2 (2051 km2), with suitability along the three glacial moraines (Sarn Badrig, Sarn‐y‐Bwch and Sarn Cynfelyn) strongly presented. Comparison of modelled environmental variables at the location of S. squatina records for each Q identified reductions in depth and salinity, and increases in chlorophyll‐a and SST when comparing Q2 or Q3 with Q1 or Q4. This shift may suggest S. squatina are making seasonal movements to shallow coastal waters in Q2 and Q3. This is supported by 23 anecdotal resources and may be driven by reproductive behaviour, as there were 85 records of S. squatina individuals ≤60 cm in the dataset, inferred as recently born or juvenile life‐history stages. The results have helped fill significant evidence gaps identified in the Wales Angelshark Action Plan and immediate next research steps are suggested.

2022-07-05

Journal of Fish Biology (publié)

doi.org

From Precision Medicine to Precision Convergence for Multilevel Resilience—The Aging Brain and Its Social Isolation

Laurette Dubé

Patricia P. Silveira

Daiva E. Nielsen

Spencer Moore

Catherine Paquet

J. Miguel Cisneros-Franco

Gina Kemp

Bärbel Knauper

Yu Ma

Mehmood Khan

Gillian Bartlett-Esquilant

Alan C. Evans

Lesley K. Fellows

Jorge L. Armony

R. Nathan Spreng

Jian-Yun Nie

Shawn T. Brown

Georg Northoff

Danilo Bzdok

2022-07-04

Frontiers in Public Health (publié)

doi.org

Incentivized Security-Aware Computation Offloading for Large-Scale Internet of Things Applications

Talal Halabi

Adel Abusitta

Glaucio H.S. Carvalho

Benjamin C. M. Fung

2022-07-04

2022 7th International Conference on Smart and Sustainable Technologies (SpliTech) (publié)

doi.org

Approaching automated applicator digitization from a new angle: Using sagittal images to improve deep learning accuracy and robustness in high-dose-rate prostate brachytherapy

Luca Leon Weishaupt

Hisham Kamal Sayed

Ximeng Mao

Richard Choo

Bradley J. Stish

Shirin A. Enger

Christopher Deufel

2022-06-30

Brachytherapy (publié)

doi.org

Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge

Ian Porada

Alessandro Sordoni

Jackie CK Cheung

2022-06-30

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (publié)

doi.org

arxiv.org

Exploring the roles of artificial intelligence in surgical education: A scoping review

Elif Bilgic

Andrew Gorgy

Alison Yang

Michelle Cwintal

Hamed Ranjbar

Kalin Kahla

Dheeksha Reddy

Kexin Li

Helin Ozturk

Eric Zimmermann

Andrea Quaiattini

S. A. Rahimi

Dan Poenaru

Jason M. Harley

2022-06-30

The American Journal of Surgery (publié)

doi.org

Fast Heterogeneous Task Mapping for Reducing Edge DNN Latency

M. L. Kornelsen

S. H. Mozafari

J. J. Clark

B. H. Meyer

W. J. Gross

To meet DNN inference latency constraints on resource-constrained edge devices, we employ heterogeneous computing, utilizing multiple proces… (voir plus)sing elements (e.g. CPU + GPU) accelerate inference. This leads to the challenge of efficiently mapping DNN operations to heterogeneous processing elements. For this task, we introduce a novel genetic algorithm (GA) optimizer. Through intelligent initialization and a customized mutation operation, we are able to evaluate 20x fewer generations while finding superior configurations compared with a baseline GA. Using our mapping optimizer, we find device placement configurations that achieve 15%, 24%, and 31% inference speed-up for BERT, SqueezeBERT, and InceptionV3,respectively.

2022-06-30

IEEE International Conference on Application-Specific Systems, Architectures, and Processors (publié)

doi.org

IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control

FranÃ§ois-Xavier Devailly

Denis Larocque

Laurent Charlin

Scaling adaptive traffic signal control involves dealing with combinatorial state and action spaces. Multi-agent reinforcement learning atte… (voir plus)mpts to address this challenge by distributing control to specialized agents. However, specialization hinders generalization and transferability, and the computational graphs underlying neural-network architectures—dominating in the multi-agent setting—do not offer the flexibility to handle an arbitrary number of entities which changes both between road networks, and over time as vehicles traverse the network. We introduce Inductive Graph Reinforcement Learning (IG-RL) based on graph-convolutional networks which adapts to the structure of any road network, to learn detailed representations of traffic signal controllers and their surroundings. Our decentralized approach enables learning of a transferable-adaptive-traffic-signal-control policy. After being trained on an arbitrary set of road networks, our model can generalize to new road networks and traffic distributions, with no additional training and a constant number of parameters, enabling greater scalability compared to prior methods. Furthermore, our approach can exploit the granularity of available data by capturing the (dynamic) demand at both the lane level and the vehicle level. The proposed method is tested on both road networks and traffic settings never experienced during training. We compare IG-RL to multi-agent reinforcement learning and domain-specific baselines. In both synthetic road networks and in a larger experiment involving the control of the 3,971 traffic signals of Manhattan, we show that different instantiations of IG-RL outperform baselines.

2022-06-30

IEEE Transactions on Intelligent Transportation Systems (publié)

doi.org

arxiv.org

Leishmania parasites exchange drug-resistance genes through extracellular vesicles

Noélie Douanne

George Dong

Atia Amin

Lorena Bernardo

Mathieu Blanchette

David Langlais

Martin Olivier