Publications

Software-Engineering Design Patterns for Machine Learning Applications
Hironori Washizaki
Foutse Khomh
Yann‐Gaël Guéhéneuc
Hironori Takeuchi
Naotake Natori
Takuo Doi
Satoshi Okuda
In this study, a multivocal literature review identified 15 software-engineering design patterns for machine learning applications. Findings… (voir plus) suggest that there are opportunities to increase the patterns’ adoption in practice by raising awareness of such patterns within the community.
2022-03-01
Computer (published)
doi.org
Software-Engineering Design Patterns for Machine Learning Applications
Hironori Washizaki
Foutse Khomh
Yann‐Gaël Guéhéneuc
Hironori Takeuchi
Naotake Natori
Takuo Doi
Satoshi Okuda
In this study, a multivocal literature review identified 15 software-engineering design patterns for machine learning applications. Findings… (voir plus) suggest that there are opportunities to increase the patterns’ adoption in practice by raising awareness of such patterns within the community.
2022-03-01
Computer (publié)
doi.org
The Role of Robotics in Achieving the United Nations Sustainable Development Goals - The Experts' Meeting at the 2021 IEEE/RSJ IROS Workshop [Industry Activities]
Vincent Mai
Bram Vanderborght
Tamás Haidegger
Alaa Khamis
Niraj Bhargava
Dominik B.O. Boesl
Katleen Gabriels
An Jacobs
AJung Moon
Robin Murphy
Yasushi Nakauchi
Edson Prestes
Bhavani Rao R.
Ricardo Vinuesa
Carl-Maria Mörch
The development and deployment of robotic technologies can have an important role in efforts to achieve the United Nations’ (UN) Sustainab… (voir plus)le Development Goals (SDGs)—with both enabling and inhibiting impacts. During a workshop at the 2021 IEEE/Robotics Society of Japan International Conference on Intelligent Robots and Systems (IROS 2021), experts from various disciplines analyzed the role of robotics in achieving the SDGs. This article provides a summary of the most important outcomes of the workshop. During the workshop panels, the variety of roles that robots can play in enabling the SDGs was underlined. The panelists discussed the challenges to the adoption of robots and to their deployment at their full potential. The probable undesirable effects of robots were also considered, and the panelists suggested approaches to correctly design SDG-relevant robotic solutions. Governance frameworks were also discussed, with respect to their contents as well as the challenges to build them. The role of military funding was briefly analyzed. Finally, several proposals for actions and policies were made. The contents of the workshop, including contributing papers and videos from the panelists, as well as additional information about future initiatives regarding robotics and the SDGs, are available at www.sustainablerobotics.org.
2022-03-01
IEEE Robotics Autom. Mag. (publié)
doi.org
The Role of Robotics in Achieving the United Nations Sustainable Development Goals - The Experts' Meeting at the 2021 IEEE/RSJ IROS Workshop [Industry Activities]
Vincent Mai
Bram Vanderborght
Tamás P. Haidegger
Alaa M. Khamis
Niraj Bhargava
Dominik B. O. Boesl
K. Gabriels
An Jacobs
AJung Moon
R. Murphy
Yasushi Nakauchi
Edson Prestes
Bhavani Rao R.
Ricardo Vinuesa
Carl-Maria Mörch
2022-03-01
IEEE Robotics Autom. Mag. (published)
doi.org
Combining Modular Skills in Multitask Learning
Edoardo Ponti
Alessandro Sordoni
Siva Reddy
2022-02-28
ArXiv (prépublication)
arxiv.org
arxiv.org
Multiscale PHATE identifies multimodal signatures of COVID-19
Manik Kuchroo
Je-chun Huang
Patrick Wong
Jean-Christophe Grenier
Dennis Shung
Alexander Tong
Carolina Lucas
Jon Klein
Daniel B. Burkhardt
Scott Gigante
Abhinav Godavarthi
Bastian Rieck
Benjamin Israelow
Michael Simonov
Tianyang Mao
Ji Eun Oh
Julio Silva
Takehiro Takahashi
Camila D. Odio
Arnau Casanovas-Massana … (voir 10 de plus)
John Fournier
Shelli Farhadian
Charles S. Dela Cruz
Albert I. Ko
Matthew Hirn
F. Perry Wilson
Julie Hussin
Guy Wolf
Akiko Iwasaki
Smita Krishnaswamy
2022-02-28
Nature Biotechnology (publié)
doi.org
Multiscale PHATE identifies multimodal signatures of COVID-19
Manik Kuchroo
Je-chun Huang
Patrick W. Wong
Jean-Christophe Grenier
Dennis L. Shung
Alexander Tong
C. Lucas
J. Klein
Daniel B. Burkhardt
Scott Gigante
Abhinav Godavarthi
Bastian Rieck
Benjamin Israelow
Michael Simonov
Tianyang Mao
Ji Eun Oh
Julio Silva
Takehiro Takahashi
C. Odio
Arnau Casanovas‐massana … (voir 10 de plus)
John Byrne Fournier
Shelli F. Farhadian
C. D. Dela Cruz
A. Ko
Matthew Hirn
F. Wilson
Julie Hussin
Guy Wolf
Akiko Iwasaki
Smita Krishnaswamy
2022-02-28
Nature Biotechnology (published)
doi.org
Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI
Joshua D. Durso-Finley
Jean-Pierre R. Falet
Brennan Nichyporuk
Douglas Arnold
Tal Arbel
Precision medicine for chronic diseases such as multiple sclerosis (MS) involves choosing a treatment which best balances efficacy and side … (voir plus)effects/preferences for individual patients. Making this choice as early as possible is important, as delays in finding an effective therapy can lead to irreversible disability accrual. To this end, we present the first deep neural network model for individualized treatment decisions from baseline magnetic resonance imaging (MRI) (with clinical information if available) for MS patients which (a) predicts future new and enlarging T2 weighted (NE-T2) lesion counts on follow-up MRI on multiple treatments and (b) estimates the conditional average treatment effect (CATE), as defined by the predicted future suppression of NE-T2 lesions, between different treatment options relative to placebo. Our model is validated on a proprietary federated dataset of 1817 multi-sequence MRIs acquired from MS patients during four multi-centre randomized clinical trials. Our framework achieves high average precision in the binarized regression of future NE-T2 lesions on five different treatments, identifies heterogeneous treatment effects, and provides a personalized treatment recommendation that accounts for treatment-associated risk (side effects, patient preference, administration difficulties).
2022-02-28
MIDL.io/2022/Conference (publié)
doi.org
openreview.net
Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering
Jing Zhang
Xiaokang Zhang
Jifan Yu
Jian Tang
Jie Tang
Cuiping Li
Hong Chen
Recent works on knowledge base question answering (KBQA) retrieve subgraphs for easier reasoning. The desired subgraph is crucial as a small… (voir plus) one may exclude the answer but a large one might introduce more noises. However, the existing retrieval is either heuristic or interwoven with the reasoning, causing reasoning on the partial subgraphs, which increases the reasoning bias when the intermediate supervision is missing. This paper proposes a trainable subgraph retriever (SR) decoupled from the subsequent reasoning process, which enables a plug-and-play framework to enhance any subgraph-oriented KBQA model. Extensive experiments demonstrate SR achieves significantly better retrieval and QA performance than existing retrieval methods. Via weakly supervised pre-training as well as the end-to-end fine-tuning, SR achieves new state-of-the-art performance when combined with NSM (He et al., 2021), a subgraph-oriented reasoner, for embedding-based KBQA methods. Codes and datasets are available online (https://github.com/RUCKBReasoning/SubgraphRetrievalKBQA)
2022-02-27
ArXiv (preprint)
doi.org
arxiv.org
Healthsheet: Development of a Transparency Artifact for Health Datasets
Negar Rostamzadeh
Diana Mincu
Subhrajit Roy
Andrew J Smart
Lauren Wilcox
Mahima Pushkarna
Jessica Schrouff
Razvan Amironesei
Nyalleng Moorosi
Katherine Heller
Machine learning (ML) approaches have demonstrated promising results in a wide range of healthcare applications. Data plays a crucial role i… (voir plus)n developing ML-based healthcare systems that directly affect people’s lives. Many of the ethical issues surrounding the use of ML in healthcare stem from structural inequalities underlying the way we collect, use, and handle data. Developing guidelines to improve documentation practices regarding the creation, use, and maintenance of ML healthcare datasets is therefore of critical importance. In this work, we introduce Healthsheet, a contextualized adaptation of the original datasheet questionnaire [22] for health-specific applications. Through a series of semi-structured interviews, we adapt the datasheets for healthcare data documentation. As part of the Healthsheet development process and to understand the obstacles researchers face in creating datasheets, we worked with three publicly-available healthcare datasets as our case studies, each with different types of structured data: Electronic health Records (EHR), clinical trial study data, and smartphone-based performance outcome measures. Our findings from the interviewee study and case studies show 1) that datasheets should be contextualized for healthcare, 2) that despite incentives to adopt accountability practices such as datasheets, there is a lack of consistency in the broader use of these practices 3) how the ML for health community views datasheets and particularly Healthsheets as diagnostic tool to surface the limitations and strength of datasets and 4) the relative importance of different fields in the datasheet to healthcare concerns.
2022-02-26
ArXiv (preprint)
doi.org
arxiv.org
More Than Meets the Eye: Art Engages the Social Brain
Janneke E. P. van Leeuwen
Jeroen Boomgaard
Danilo Bzdok
S. Crutch
J. Warren
Here we present the viewpoint that art essentially engages the social brain, by demonstrating how art processing maps onto the social brain … (voir plus)connectome—the most comprehensive diagram of the neural dynamics that regulate human social cognition to date. We start with a brief history of the rise of neuroaesthetics as the scientific study of art perception and appreciation, in relation to developments in contemporary art practice and theory during the same period. Building further on a growing awareness of the importance of social context in art production and appreciation, we then set out how art engages the social brain and outline candidate components of the “artistic brain connectome.” We explain how our functional model for art as a social brain phenomenon may operate when engaging with artworks. We call for closer collaborations between the burgeoning field of neuroaesthetics and arts professionals, cultural institutions and diverse audiences in order to fully delineate and contextualize this model. Complementary to the unquestionable value of art for art’s sake, we argue that its neural grounding in the social brain raises important practical implications for mental health, and the care of people living with dementia and other neurological conditions.
2022-02-25
Frontiers in Neuroscience (published)
doi.org
More Than Meets the Eye: Art Engages the Social Brain
Janneke E. P. van Leeuwen
Jeroen Boomgaard
Danilo Bzdok
Sebastian J. Crutch
Jason D. Warren
2022-02-25
Frontiers in Neuroscience (publié)
doi.org