Publications

JUNO sensitivity to invisible decay modes of neutrons
Juno Collaboration Angel Abusleme
Angel Abusleme
Thomas Adam
Kai Adamowicz
Shakeel Ahmad
Rizwan Ahmed
Sebastiano Aiello
Fengpeng An
Qi An
Giuseppe Andronico
Nikolay Anfimov
Vito Antonelli
Tatiana Antoshkina
João Pedro Athayde Marcondes de André
Didier Auguste
Weidong Bai
Nikita Balashov
Wander Baldini
Andrea Barresi
Davide Basilico … (voir 648 de plus)
Eric Baussan
Marco Bellato
Marco Beretta
Antonio Bergnoli
Daniel Bick
Lukas Bieger
Svetlana Biktemerova
Thilo Birkenfeld
Iwan Blake
Simon Blyth
Anastasia Bolshakova
Mathieu Bongrand
Dominique Breton
Augusto Brigatti
Riccardo Brugnera
Riccardo Bruno
Antonio Budano
Jose Busto
Anatael Cabrera
Barbara Caccianiga
Hao Cai
Xiao Cai
Yanke Cai
Zucong Cai
Zhiyan Cai
Stéphane Callier
Steven Calvez
Antonio Cammi
Agustin Campeny
Chuanya Cao
Guofu Cao
Jun Cao
Rossella Caruso
Cédric Cerna
Vanessa Cerrone
Jinfan Chang
Yun Chang
Auttakit Chatrabhuti
Chao Chen
Guoming Chen
Pingping Chen
Shaomin Chen
Xin Chen
Yiming Chen
Yixue Chen
Yu Chen
Zelin Chen
Zhangming Chen
Zhiyuan Chen
Zikang Chen
Jie Cheng
Yaping Cheng
Yu Chin Cheng
Yuanyuan Zhang
Alexander Chepurnov
Alexey Chetverikov
Davide Chiesa
Pietro Chimenti
Yen-Ting Chin
Po-Lin Chou
Ziliang Chu
Artem Chukanov
Gérard Claverie
Catia Clementi
Barbara Clerbaux
Marta Colomer Molla
Selma Conforti Di Lorenzo
Alberto Coppi
Daniele Corti
Simon Csakli
Chenyang Cui
Flavio Dal Corso
Olivia Dalager
Jaydeep Datta
Christophe De La Taille
C. Taille
Zhi Deng
Ziyan Deng
Xiaoyu Ding
Xuefeng Ding
Yayun Ding
Bayu Dirgantara
Carsten Dittrich
Sergey Dmitrievsky
Tadeas Dohnal
Dmitry Dolzhikov
Georgy Donchenko
Jianmeng Dong
Evgeny Doroshkevich
Wei Dou
Marcos Dracos
Frédéric Druillole
Ran Du
S. X. Du
Yujie Duan
Katherine Dugas
Stefano Dusini
Hongyue Duyang
Jessica Eck
Timo Enqvist
Andrea Fabbri
Ulrike Fahrendholz
Lei Fan
Jian Fang
W. X. Fang
Dmitry Fedoseev
Haiping Peng
Li-Cheng Feng
Qichun Feng
Federico Ferraro
Amélie Fournier
Fritsch Fritsch
Haonan Gan
Feng Gao
Alberto Garfagnini
Arsenii Gavrikov
Marco Giammarchi
Nunzio Giudice
Maxim Gonchar
G. Gong
Hui Gong
Guanghua Gong
Yuri Gornushkin
Marco Grassi
Maxim Gromov
Vasily Gromov
Minghao Gu
Xiang Zhou
Xiaofei Gu
Yunting Gu
Mengyun Guan
Yu Gu
Yuduo Guan
Nunzio Guardone
Rosa Maria Guizzetti
Cong Guo
Wanlei Guo
Caren Hagner
Hechong Han
Ran Han
Yang Han
Jinhong He
Miao He
Wei He
Xinhai He
Tobias Heinz
Patrick Hellmuth
Rafael Herrera
Yuenkeung Hor
Shaojing Hou
Yee Hsiung
Bei-Zhen Hu
Hang Hu
Jun Hu
Peng Hu
Shouyang Hu
T. Hu
Yuxiang Hu
Zhuojun Hu
Guihong Huang
Hanxiong Huang
Jinhao Huang
Jun-Hao Huang
Xin Huang
Kaixuan Huang
Shengheng Huang
Wenhao Huang
X. T. Huang
Yongbo Huang
Jiaqi Hui
Lei Huo
Wenju Huo
Cédric Huss
Safeer Hussain
Leonard Imbert
Ara Ioannisian
Roberto Isocrate
Arshak Jafar
Beatrice Jelmini
Ignacio Jeria
Xiaolu Ji
Huihui Jia
Junji Jia
Siyu Jian
Cailian Jiang
Di Jiang
Guangzheng Jiang
Wei Jiang
Xiaoshan Jiang
Xiaozhao Jiang
Yixuan Jiang
Xiang Jing
Cécile Jollet
Li Kang
Rebin Karaparabil
Narine Kazarian
Ali Khan
Amina Khatun
Khanchai Khosonthongkee
Denis Korablev
Konstantin Kouzakov
Alexey Krasnoperov
Sergey Kuleshov
Sindhujha Kumaran
Nikolay Kutovskiy
Loïc Labit
Tobias Lachenmaier
Haojing Lai
Cecilia Landini
Sébastien Leblanc
Frederic Lefevre
Rupert Leitner
Jason Leung
Demin Li
Yi Wang
Fule Li
Fei Li
Gaosong Li
Hongjian Li
Huang Li
Jiajun Li
Min Li
Nan Li
Qingjiang Li
Ruhui Li
Rui Li
Shanfeng Li
Shuo Li
Tao Li
Teng Li
Weidong Li
Weiguo Li
Xiaomei Li
Xiao-Nan Li
Xinglong Li
Yi Li
Yichen Li
Yufeng Li
Zhaohan Li
Zhibing Li
Ziyuan Li
Zonghai Li
An-An Liang
Hao Liang
Jiaming Yan
Yilin Liao
Jiajun Liao
Y. P. Liao
Ayut Limphirat
Guey-Lin Lin
Yuzhong Liao
Shengxin Lin
Tao Lin
Jiajie Ling
Xin Ling
Ivano Lippi
Caimei Liu
Yang Liu
Fengcheng Liu
Haidong Liu
Haotian Liu
Hongbang Liu
Hongjuan Liu
Hongtao Liu
Hongyang Liu
Jianglai Liu
Jiaxi Liu
Jinchang Liu
Min Liu
Qian Liu
Qin Liu
Runxuan Liu
Sheng Liu
Shubin Liu
Shulin Liu
Xiaowei Liu
Xiwen Liu
Xuewei Liu
Yankai Liu
Zhen Liu
Lorenzo Loi
Alexey Lokhov
Paolo Lombardi
Claudio Lombardo
Kai Loo
Chuan Lu
Haoqi Lu
Jingbin Lu
Junguang Lu
Meishu Lu
Peizhi Lu
Shuxian Du
Xianguo Lu
Bayarto Lubsandorzhiev
Sultim Lubsandorzhiev
Livia Ludhova
Arslan Lukanov
Feng Luo
Guang Luo
Fengjiao Luo
Jianyi Luo
Shu Luo
Wuming Luo
Xiaojie Luo
Vladimir Lyashuk
Biao Ma
Bing Ma
Qiumei Ma
Bangzheng Ma
Si Ma
Xiaoyan Ma
Xubo Ma
Jihane Maalmi
Jingyu Mai
Marco Malabarba
Yury Malyshkin
Roberto Carlos Mandujano
Fabio Mantovani
Xin Mao
Yajun Mao
S. Mari
Filippo Marini
Stefano M. Mari
Agnese Martini
Matthias Mayer
Davit Mayilyan
Ints Mednieks
Yu Meng
Anita Meraviglia
Anselmo Meregaglia
Emanuela Meroni
Lino Miramonti
Nikhil Mohan
Michele Montuschi
Cristobal Morales Reveco
Massimiliano Nastasi
Dmitry V. Naumov
Elena Naumova
Diana Navas-Nicolas
Igor Nemchenok
Minh Thuan Nguyen Thi
Alexey Nikolaev
Feipeng Ning
Zhe Ning
Hiroshi Nunokawa
Lothar Oberauer
Juan Pedro Ochoa-Ricoux
Alexander Olshevskiy
Domizia Orestano
Fausto Ortica
Rainer Othegraven
Alessandro Paoloni
George Parker
Sergio Parmeggiano
Achilleas Patsias
Y. P. Pei
Luca Pelicci
Yatian Pei
Anguo Peng
Zhaoyuan Peng
Elisa Percalli
Willy Perrin
Frédéric Perrot
P. Petitjean
Fabrizio Petrucci
Pierre-Alexandre Petitjean
Oliver Pilarczyk
Luis Felipe Piñeres Rico
Artyom Popov
Pascal Poussot
Ezio Previtali
Fazhi Qi
M. Qi
Xiaohui Qi
Sen Qian
Xiangyang Qian
Zhen Qian
Hao Qiao
Xiaohui Qian
Zhonghua Qin
Shoukang Qiu
Manhao Qu
Z. Qu
Gioacchino Ranucci
A. Re
Zhenning Qu
Abdel Rebii
Mariia Redchuk
Alessandra Re
Gioele Reina
Bin Ren
Jie Ren
Yuhan Ren
Barbara Ricci
Komkrit Rientong
Mariam Rifai
Mathieu Roche
Narongkiat Rodphai
Aldo Romani
Bedřich Roskovec
Xichao Ruan
Arseniy Rybnikov
Andrey Sadovsky
Paolo Saggese
Deshan Sandanayake
Anut Sangka
Giuseppe Sava
Utane Sawangwit
Michaela Schever
Cédric Schwab
Konstantin Schweizer
Alexandr Selyunin
Andrea Serafini
Mariangela Settimo
Junyu Shao
Vladislav Sharov
Hexi Shi
Jingyan Shi
Yanan Shi
Vitaly Shutov
Andrey Sidorenkov
Fedor Šimkovic
Apeksha Singhal
Chiara Sirignano
Jaruchit Siripak
Monica Sisti
Mikhail Smirnov
Oleg Smirnov
Sergey Sokolov
Julanan Songwadhana
Boonrucksar Soonthornthum
Albert Sotnikov
Warintorn Sreethawong
Achim Stahl
Luca Stanco
Konstantin Stankevich
Hans Steiger
Jochen Steinmann
Tobias Sterr
M. Stock
Virginia Strati
Matthias Raphael Stock
Michail Strizh
Alexander Studenikin
Aoqi Su
Jun Su
G. X. Sun
Shifeng Sun
Xilei Sun
Yongjie Sun
Guangbao Sun
Yongzhao Sun
Zhengyang Sun
Narumon Suwonjandee
Akira Takenaka
Xiaohan Tan
Jian Tang
Jing-Yu Tang
Qiang Tang
Quan Tang
Jingzhe Tang
Xiao Tang
Vidhya Thara Hariharan
Igor Tkachev
Tomas Tmej
M. Torri
Andrea Triossi
Wladyslaw Trzaska
Marco Danilo Claudio Torri
Y. Tung
Cristina Tuve
Nikita Ushakov
Vadim Vedin
Yu-Chen Tung
Carlo Venettacci
Giuseppe Verde
Maxim Vialkov
Benoit Viaud
Cornelius Moritz Vollbrecht
Katharina von Sturm
Vit Vorobel
Dmitriy Voronin
Lucia Votano
Pablo Walker
Caishen Wang
Chung-Hsiang Wang
En Wang
Guoli Wang
Yuekun Heng
Jian Wang
Jun Wang
Li Wang
Lucinda W. Wang
Meng Wang
Mingyuan Wang
Qianchuan Wang
Lu Wang
Ruiguang Wang
Sibo Wang
Siguang Wang
Wei Wang
Wenshuai Wang
Xi Wang
Xiangyue Wang
Yangfu Wang
Yaoguang Wang
Yifang Wang
Yong Wang
Yuyi Wang
Zhe Wang
Z. Wang
Zhimin Wang
Apimook Watcharangkool
Wei Wei
Wenlu Wei
Yadong Wei
Yuehuan Wei
Liangjian Wen
Jun Weng
Christopher Wiebusch
Rosmarie Wirth
Chengxin Wu
Diru Wu
Qun Wu
Yinhui Wu
Yiyang Wu
Zhi Wu
Michael Wurm
Jacques Wurtz
Christian Wysotzki
Yufei Xi
Dongmei Xia
Shishen Xian
Ziqian Xiang
Fei Xiao
Xiang Xiao
Xiaochuan Xie
Yijun Xie
Yuguang Xie
Zhao Xin
Zhizhong Xing
Benda Xu
Cheng Xu
Donglian Xu
Fanrong Xu
Hangkun Xu
Jiayang Xu
Jilei Xu
Jing Xu
Jinghuan Xu
Meihang Xu
Xunjie Xu
Yin Xu
Yu Xu
Baojun Yan
Qiyu Yan
Taylor Yan
Xiongbo Yan
Yupeng Yan
Changgen Yang
Chengfeng Yang
Fengfan Yang
Jie Yang
Lei Yang
Pengfei Yang
Xiaoyu Yang
Yifan Yang
Yixiang Yang
Zekun Yang
Haifeng Yao
Jiaxuan Ye
Mei Ye
Ziping Ye
Frédéric Yermia
Zhengyun You
Boxiang Yu
Chiye Yu
Chunxu Yu
Guojun Yu
Hongzhao Yu
Miao Yu
Xianghui Yu
Zeyuan Yu
Zezhong Yu
Cenxi Yuan
Chengzhuo Yuan
Zhenxiong Yuan
Baobiao Yue
Noman Zafar
Kirill Zamogilnyi
Vitalii Zavadskyi
Fanrui Zeng
Shan Zeng
Tingxuan Zeng
Yuda Zeng
Liang Zhan
Aiqiang Zhang
Bin Zhang
Binting Zhang
Feiyang Zhang
Hangchang Zhang
Haosen Zhang
Honghao Zhang
Jiawen Zhang
Jie Zhang
Jingbo Zhang
Jinnan Zhang
Junwei Zhang
Lei Zhang
Ping Zhang
Qingmin Zhang
Shiqi Zhang
Shu Zhang
Shuihan Zhang
Siyuan Zhang
Tao Zhang
Xiaomei Zhang
Xin Zhang
Xuantong Zhang
Yibing Zhang
Yinhong Zhang
Yiyu Zhang
Yongpeng Zhang
Yu Zhang
Yumei Zhang
Zhenyu Zhang
Zhijian Zhang
Jie Zhao
Rong Zhao
Runze Zhao
Shujun Zhao
Tianhao Zhao
Hua Zheng
Yangheng Zheng
Jing Zhou
Li Zhou
Nan Zhou
Shun Zhou
Tong Zhou
Xing Zhou
Jingsen Zhu
Kangfu Zhu
Kejun Zhu
Zhihang Zhu
Bo Zhuang
Honglin Zhuang
Liang Zong
Jiaheng Zou
2025-01-03
The European Physical Journal C (publié)
doi.org
arxiv.org
A hierarchical Bayesian brain parcellation framework for fusion of functional imaging datasets
Da Zhi
Ladan Shahshahani
Caroline Nettekoven
Ana Lúısa Pinho
Danilo Bzdok
Jörn Diedrichsen
2025-01-01
Imaging Neuroscience (publié)
doi.org
Access Inequality in LEO Satellite Networks: A Case Study of High-Latitude Coverage in Northern Québec
Mohammed Almekhlafi
Antoine Lesage-Landry
Gunes Karabulut Kurt
Low Earth orbit (LEO) satellite networks play a crucial role in bridging the digital divide, particularly in remote and high-latitude region… (voir plus)s. However, access inequality remains a significant challenge, limiting broadband connectivity for communities in northern areas compared to mid-latitude urban regions. This study reviews recent advancements in non-terrestrial networks (NTNs). We conduct a detailed analysis of coverage disparities in LEO satellite networks considering LEO networks, namely Starlink, Telesat-like, Kuiper-like, and OneWeb, with a specific focus on Québec, Canada versus urban centers in New York City, USA. Our findings highlight a significant disparity in the number of visible satellites resulting in increased transmission delays and reduced network reliability in high-latitude regions. Additionally, we observe that higher elevation angles, more accessible in mid-latitude regions especially for Starlink and Kuiper, contribute to superior signal quality and transmission rates. To mitigate this gap, we propose an inter-constellation/orbit roaming mechanism that enables ground users to be served by different LEO constellations—leveraging OneWeb's and Telesat's strong polar coverage along with the high satellite density of Starlink and Kuiper at mid-latitudes. Jointly, terrestrial network (TN) expansion can enhance signal quality and transmission efficiency, particularly in underserved areas where NTNs act as edge computing and backhaul infrastructures. Additionally, the associated challenges—such as roaming handovers, and radio resource and network slicing management are discussed in detail, where designing a unified management and control entity to ensure seamless interoperability is not a trivial task. Furthermore, we envision wireless power transfer through either relay-based (ground-to-satellite-to-ground) or direct (satellite-to-ground) power beaming as a sustainable approach to energize TN components in remote regions. These strategies collectively support the scalability and resilience of NTNs in bridging the global access inequality.
2024-12-31
IEEE Open Journal of Vehicular Technology (publié)
doi.org
Adaptive Local Training in Federated Learning.
Donald Shenaj
Eugene Belilovsky
Pietro Zanuttigh
In federated learning multiple clients collaboratively train a global machine learning model by exchanging their locally trained model weigh… (voir plus)ts instead of raw data. In the standard setting, every client trains its local model for the same number of epochs. We introduce ALT (Adaptive Local Training), a simple yet effective feedback mechanism that can be introduced on top of any federated learning scheme at the client side to limit unnecessary and degrading computations. ALT dynamically adjusts the number of training epochs for each client based on the similarity between the local representation and the global one, ensuring that well-aligned clients can train longer without experiencing client drift while in case of too large drifts the training is stopped earlier. We evaluated ALT on federated partitions of the CIFAR-10 and Tiny-ImageNet datasets, demonstrating its effectiveness in improving both model convergence speed and accuracy. The code is available at https://github.com/LTTM/ALT.
2024-12-31
European Signal Processing Conference (publié)
doi.org
openreview.net
Advocacy for Children With Surgical Diseases in Nigeria: National Policy Status, Gaps, and Solutions
Justina O. Seyi-Olajide
Ayla Gerk
Elena Guadagno
Adesoji Ademuyiwa
Emmanuel A. Ameh
Dan Poenaru
2024-12-31
Journal of Pediatric Surgery (publié)
doi.org
Anticancer Monotherapy and Polytherapy Drug Response Prediction Using Deep Learning: Guidelines and Best Practices
Amin Emad
David Earl Hostallero
2024-12-31
Methods in Molecular Biology (inconnu)
doi.org
Attention as a Hypernetwork
Simon Schug
Seijin Kobayashi
Yassir Akram
João Sacramento
Razvan Pascanu
Transformers can under some circumstances generalize to novel problem instances whose constituent parts might have been encountered during t… (voir plus)raining, but whose compositions have not. What mechanisms underlie this ability for compositional generalization? By reformulating multi-head attention as a hypernetwork, we reveal that a composable, low-dimensional latent code specifies key-query specific operations. We find empirically that this latent code is predictive of the subtasks the network performs on unseen task compositions, revealing that latent codes acquired during training are reused to solve unseen problem instances. To further examine the hypothesis that the intrinsic hypernetwork of multi-head attention supports compositional generalization, we ablate whether making the hypernetwork-generated linear value network nonlinear strengthens compositionality. We find that this modification improves compositional generalization on abstract reasoning tasks. In particular, we introduce a symbolic version of the Raven's Progressive Matrices human intelligence test, which gives us precise control over the problem compositions encountered during training and evaluation. We demonstrate on this task how scaling model size and data enables compositional generalization in transformers and gives rise to a functionally structured latent space.
2024-12-31
ICLR (publié)
doi.org
arxiv.org
Audio Prototypical Network For Controllable Music Recommendation
Fırat Öncel
Emiliano Penaloza
Haolun Wu
Shubham Gupta
Mirco Ravanelli
Laurent Charlin
Cem Subakan
Traditional recommendation systems represent user preferences in dense representations obtained through black-box encoder models. While thes… (voir plus)e models often provide strong recommendation performance, they lack interpretability for users, leaving users unable to understand or control the system's modeling of their preferences. This limitation is especially challenging in music recommendation, where user preferences are highly personal and often evolve based on nuanced qualities like mood, genre, tempo, or instrumentation. In this paper, we propose an audio prototypical network for controllable music recommendation. This network expresses user preferences in terms of prototypes representative of semantically meaningful features pertaining to musical qualities. We show that the model obtains competitive recommendation performance compared to popular baseline models while also providing interpretable and controllable user profiles.
2024-12-31
MLSP (publié)
doi.org
openreview.net
Automated UML Visualization of Software Ecosystems: Tracking Versions, Dependencies, and Security Updates
Vanessa Kan
M. P. Lnu
Solomon Berhe
C. El Kari
Marc Maynard
Foutse Khomh
2024-12-31
ANT/EDI40 (publié)
doi.org
Automatic segmentation of spinal cord lesions in MS: A robust tool for axial T2-weighted MRI scans
Enamundram Naga Karthik
Julian McGinnis
Ricarda Wurm
Sebastian Ruehling
Robert Graf
Jan Valosek
Pierre-Louis Benveniste
Markus Lauerer
Jason Talbott
Rohit Bakshi
Shahamat Tauhid
Timothy Shepherd
Achim Berthele
Claus Zimmer
Bernhard Hemmer
Daniel Rueckert
Benedikt Wiestler
Jan S. Kirschke
Julien Cohen-Adad
Mark Mühlau
Deep learning models have achieved remarkable success in segmenting brain white matter lesions in multiple sclerosis (MS), becoming integral… (voir plus) to both research and clinical workflows. While brain lesions have gained significant attention in MS research, the involvement of spinal cord lesions in MS is relatively understudied. This is largely owed to the variability in spinal cord magnetic resonance imaging (MRI) acquisition protocols, high individual anatomical differences, the complex morphology and size of spinal cord lesions - and lastly, the scarcity of labeled datasets required to develop robust segmentation tools. As a result, automatic segmentation of spinal cord MS lesions remains a significant challenge. Although some segmentation tools exist for spinal cord lesions, most have been developed using sagittal T2-weighted (T2w) sequences primarily focusing on cervical spines. With the growing importance of spinal cord imaging in MS, axial T2w scans are becoming increasingly relevant due to their superior sensitivity in detecting lesions compared to sagittal acquisition protocols. However, most existing segmentation methods struggle to effectively generalize to axial sequences due to differences in image characteristics caused by the highly anisotropic spinal cord scans. To address these challenges, we developed a robust, open-source lesion segmentation tool tailored specifically for axial T2w scans covering the whole spinal cord. We investigated key factors influencing lesion segmentation, including the impact of stitching together individually acquired spinal regions, straightening the spinal cord, and comparing the effectiveness of 2D and 3D convolutional neural networks (CNNs). Drawing on these insights, we trained a multi-center model using an extensive dataset of 582 MS patients, resulting in a dataset comprising an entirety of 2,167 scans. We empirically evaluated the model’s segmentation performance across various spinal segments for lesions with varying sizes. Our model significantly outperforms the current state-of-the-art methods, providing consistent segmentation across cervical, thoracic and lumbar regions. To support the broader research community, we integrate our model into the widely-used Spinal Cord Toolbox (v7.0 and above), making it accessible via the command sct_deepseg lesion_ms_axial_t2 -i <path-to-image.nii.gz>.
2024-12-31
Imaging Neuroscience (publié)
doi.org
Balancing Profit and Fairness in Risk-Based Pricing Markets
Jesse Thibodeau
Hadi Nekoei
Afaf Taïk
Janarthanan Rajendran
Golnoosh Farnadi
Dynamic, risk-based pricing can systematically exclude vulnerable consumer groups from essential resources such as health insurance and cons… (voir plus)umer credit. We show that a regulator can realign private incentives with social objectives through a learned, interpretable tax schedule. First, we provide a formal proposition that bounding each firm's \emph{local} demographic gap implicitly bounds the \emph{global} opt-out disparity, motivating firm-level penalties. Building on this insight we introduce \texttt{MarketSim} -- an open-source, scalable simulator of heterogeneous consumers and profit-maximizing firms -- and train a reinforcement learning (RL) social planner (SP) that selects a bracketed fairness-tax while remaining close to a simple linear prior via an
2024-12-31
arXiv (prépublication)
doi.org
arxiv.org
A Biodiversity Observation Network to support conservation action and mainstream knowledge in Canada
Andrew Gonzalez
Mary I. O'Connor
Amanda E. Bates
Kyle Bobiwash
A. Cole Burton
Paul van Dam-Bates
Isaac Eckert
Dominique Gravel
C. Julián Idrobo
Laura Pollock
Andrew D.F. Simon
Margaret A. Slein
Péter Sólymos
Brian M. Starzomski
Jennifer Sunday
Eden Tekwa
Canada has begun an ambitious project to build an observing system to monitor the changing state of its biodiversity and ecosystems. A Canad… (voir plus)a-wide Biodiversity Observation Network (CAN BON) can support the measurement, mapping, and modelling of biodiversity change—the losses and gains in the diversity of plant, animal, and microbial life—and ecosystem services. This initiative responds to eight challenges presently constraining Canada's capacity to deliver timely and robust knowledge to achieve its biodiversity goals. CAN BON is conceived as a network connecting diverse organizations to support sustained biodiversity monitoring by collaboration among universities, museums, governments, industries, NGOs, community groups, and Indigenous organizations. This inclusive network will “mobilize monitoring data” to (1) combine observation and computing infrastructures and traditional knowledge to track and understand biodiversity losses and gains across the country; and (2) link the accumulated data and knowledge to models to inform the detection and attribution of biodiversity change needed to support biodiversity policy with forecasts from local to national levels. We expect that CAN BON will foster the mainstreaming of biodiversity data and knowledge into other sectors of the economy and society, and thereby support the technical and social innovation in Canada's transition to a nature-positive future.
2024-12-31
Formal Aspects of Component Software (publié)
doi.org