Publications

Directed Scattering for Knowledge Graph-Based Cellular Signaling Analysis
Aarthi Venkat
Joyce Chew
Ferran Cardoso Rodriguez
Christopher J. Tape
Michael Perlmutter
Smita Krishnaswamy
Directed graphs are a natural model for many phenomena, in particular scientific knowledge graphs such as molecular interaction or chemical … (see more)reaction networks that define cellular signaling relationships. In these situations, source nodes typically have distinct biophysical properties from sinks. Due to their ordered and unidirectional relationships, many such networks also have hierarchical and multiscale structure. However, the majority of methods performing node- and edge-level tasks in machine learning do not take these properties into account, and thus have not been leveraged effectively for scientific tasks such as cellular signaling network inference. We propose a new framework called Directed Scattering Autoencoder (DSAE) which uses a directed version of a geometric scattering transform, combined with the non-linear dimensionality reduction properties of an autoencoder and the geometric properties of the hyperbolic space to learn latent hierarchies. We show this method outperforms numerous others on tasks such as embedding directed graphs and learning cellular signaling networks.
2024-04-14
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (published)
doi.org
arxiv.org
PathOCl: Path-Based Prompt Augmentation for OCL Generation with GPT-4
Seif Abukhalaf
Mohammad Hamdaqa
Foutse Khomh
The rapid progress of AI-powered programming assistants, such as GitHub Copilot, has facilitated the development of software applications. T… (see more)hese assistants rely on large language models (LLMs), which are foundation models (FMs) that support a wide range of tasks related to understanding and generating language. LLMs have demonstrated their ability to express UML model specifications using formal languages like the Object Constraint Language (OCL). However, the context size of the prompt is limited by the number of tokens an LLM can process. This limitation becomes significant as the size of UML class models increases. In this study, we intro-duce PathOCL, a novel path-based prompt augmentation technique designed to facilitate OCL generation. PathOCL addresses the limi-tations of LLMs, specifically their token processing limit and the challenges posed by large UML class models. PathOCL is based on the concept of chunking, which selectively augments the prompts with a subset of UML classes relevant to the English specification. Our findings demonstrate that PathOCL, compared to augmenting the complete UML class model (UML-Augmentation), generates a higher number of valid and correct OCL constraints using the GPT-4 model. Moreover, the average prompt size crafted using PathOCL significantly decreases when scaling the size of the UML class models.
2024-04-14
ArXiv (preprint)
doi.org
arxiv.org
Assessing Numerical Analysis Performance with the Practi Mobile App
Maria Cutumisu
Kristin Garn
Raymond J. Spiteri
2024-04-12
Education Sciences (published)
doi.org
Model-independent Approach of the JUNO 8B Solar Neutrino Program
Jun-Zhang Zhao
Bin Yue
Haoqi Lu
Yufeng Li
J. Ling
Zeyuan Yu
Angel Abusleme
Thomas Adam
Shakeel Ahmad
Rizwan Ahmed
Sebastiano Aiello
Muhammad Akram
Abid Aleem
Tsagkarakis Alexandros
Fengpeng An
Q. An
Giuseppe Andronico
Nikolay Anfimov
Vito Antonelli
Tatiana Antoshkina … (see 479 more)
Burin Asavapibhop
J. Andr'e
Didier Auguste
Weidong Bai
Nikita Balashov
Wander Baldini
Andrea Barresi
Davide Basilico
Eric Baussan
Marco Bellato
Antonio Bergnoli
Thilo Birkenfeld
Sylvie Blin
D. Blum
Simon Blyth
Anastasia Bolshakova
Mathieu Bongrand
Clément Bordereau
Dominique Breton
Augusto Brigatti
Riccardo Brugnera
Riccardo Bruno
Antonio Budano
Jose Busto
I. Butorov
Anatael Cabrera
Barbara Caccianiga
Hao Cai
Xiao Cai
Yanke Cai
Zucong Cai
Riccardo Callegari
Antonio Cammi
Agustin Campeny
C. Cao
Guofu Cao
Jun Cao
Rossella Caruso
C. Cerna
Chi Chan
Jinfan Chang
Yun Chang
Guoming Chen
Pingping Chen
Po-An Chen
Shaomin Chen
Xurong Chen
Yixue Chen
Yu Chen
Zhiyuan Chen
Zikang Chen
Jie Cheng
Yaping Cheng
Alexander Chepurnov
Alexey Chetverikov
Davide Chiesa
Pietro Chimenti
Artem Chukanov
Gérard Claverie
Catia Clementi
Barbara Clerbaux
Marta Colomer Molla
Selma Conforti Di Lorenzo
Daniele Corti
Flavio Dal Corso
Olivia Dalager
C. Taille
Z. Y. Deng
Ziyan Deng
Wilfried Depnering
Marco Diaz
Xuefeng Ding
Yayun Ding
Bayu Dirgantara
Sergey Dmitrievsky
Tadeas Dohnal
Dmitry Dolzhikov
Georgy Donchenko
Jianmeng Dong
Evgeny Doroshkevich
Marcos Dracos
Frédéric Druillole
Ran Du
S. X. Du
Stefano Dusini
Martin Dvorak
Timo Enqvist
H. Enzmann
Andrea Fabbri
Dongsheng Fan
Lei Fan
Jian Fang
Wen Fang
Marco Fargetta
Dmitry Fedoseev
Zheng-hao Fei
Li-Cheng Feng
Qichun Feng
R. Ford
Amélie Fournier
H. Gan
Feng Gao
Alberto Garfagnini
Arsenii Gavrikov
Marco Giammarchi
Nunzio Giudice
Maxim Gonchar
G. Gong
Hui Gong
Yuri Gornushkin
A. Gottel
Marco Grassi
Maxim Gromov
Vasily Gromov
M. H. Gu
Xiang Gu
Yunting Gu
M. Guan
Yuduo Guan
Nunzio Guardone
Cong Guo
Jingyuan Guo
Wanlei Guo
Xinheng Guo
Yuhang Guo
Paul Hackspacher
Caren Hagner
Ran Han
Yang Han
Miao He
W. He
Tobias Heinz
Patrick Hellmuth
Yue-kun Heng
Rafael Herrera
Yuenkeung Hor
Shaojing Hou
Yee Hsiung
Bei-Zhen Hu
Hang Hu
Jianrun Hu
Jun Hu
Shouyang Hu
Tao Hu
Yuxiang Hu
Zhuojun Hu
Guihong Huang
Hanxiong Huang
Kaixuan Huang
Wenhao Huang
Xinglong Huang
X. T. Huang
Yongbo Huang
Jiaqi Hui
L. Huo
Wenju Huo
Cédric Huss
Safeer Hussain
Ara Ioannisian
Roberto Isocrate
Beatrice Jelmini
Ignacio Jeria
Xiaolu Ji
Huihui Jia
Junji Jia
Siyu Jian
Di Jiang
Wei Jiang
Xiaoshan Jiang
Xiang Jing
Cécile Jollet
L. Kalousis
Philipp Kampmann
Li Kang
Rebin Karaparambil
Narine Kazarian
Amina Khatun
Khanchai Khosonthongkee
Denis Korablev
K. Kouzakov
Alexey Krasnoperov
Nikolay Kutovskiy
Pasi Kuusiniemi
Tobias Lachenmaier
Cecilia Landini
Sébastien Leblanc
Victor Lebrin
F. Lefèvre
R. Lei
Rupert Leitner
Jason Leung
Daozheng Li
Demin Li
Fei Li
Fule Li
Gaosong Li
Huiling Li
Mengzhao Li
Min Li
Nan Li
Qingjiang Li
Ruhui Li
Ruiting Lei
Shanfeng Li
Tao Li
Teng Li
Weidong Li
Wei-guo Li
Xiaomei Li
Xiao-Nan Li
Xinglong Li
Yi Li
Yichen Li
Zepeng Li
Zhaohan Li
Zhibing Li
Ziyuan Li
Zonghui Li
Hao Liang
Jiaming Yan
Ayut Limphirat
Gen Lin
Shengxin Lin
Tao Lin
Ivano Lippi
Haidong Liu
H. Liu
Hongbang Liu
Hongjuan Liu
Hongtao Liu
Hui Liu
Jianglai Liu
Jinchang Liu
Min Liu
Qian Liu
Qi Liu
Runxuan Liu
Shubin Liu
Shulin Liu
Xiaowei Liu
Xiwen Liu
Yang Liu
Yunzhe Liu
Alexey Lokhov
Paolo Lombardi
Claudio Lombardo
K. Loo
Chuan Lu
Jingbin Lu
Junguang Lu
Shuxiang Lu
Bayarto Lubsandorzhiev
Sultim Lubsandorzhiev
Livia Ludhova
Arslan Lukanov
Daibin Luo
Feijun Luo
Guang Luo
Shu Luo
Wu Luo
Xiaojie Luo
Vladimir Lyashuk
Biao Ma
Bing Ma
R. Q. Ma
Si Ma
Xiaoyan Ma
Xubo Ma
Jihane Maalmi
Jingyu Mai
Yury Malyshkin
Roberto Carlos Mandujano
Fabio Mantovani
Francesco Manzali
Xin Mao
Yajun Mao
S. Mari
F. Marini
Cristina Martellini
Gisèle Martin-chassard
Agnese Martini
Matthias Mayer
Davit Mayilyan
Ints Mednieks
Yu Meng
Anselmo Meregaglia
Emanuela Meroni
David J. Meyhofer
Mauro Mezzetto
Jonathan Andrew Miller
Lino Miramonti
Paolo Montini
Michele Montuschi
Axel Muller
M. Nastasi
D. Naumov
Elena Naumova
Diana Navas-Nicolas
Igor Nemchenok
Minh Thuan Nguyen Thi
Alexey Nikolaev
F. Ning
Zhe Ning
Hiroshi Nunokawa
Lothar Oberauer
Juan Pedro Ochoa-Ricoux
Alexander Olshevskiy
Domizia Orestano
Fausto Ortica
Rainer Othegraven
A. Paoloni
Sergio Parmeggiano
Y. P. Pei
Nicomede Pelliccia
Anguo Peng
Yu Peng
Yuefeng Peng
Z-R Peng
Frédéric Perrot
P. Petitjean
Fabrizio Petrucci
Oliver Pilarczyk
Luis Felipe Piñeres Rico
Artyom Popov
Pascal Poussot
Ezio Previtali
Fazhi Qi
M. Qi
Sen Qian
Xiangyang Qian
Zhen Qian
Hao-xue Qiao
Zhonghua Qin
S. Qiu
Gioacchino Ranucci
Neill Raper
A. Re
Henning Rebber
Abdel Rebii
Mariia Redchuk
Bin Ren
Jie Ren
Barbara Ricci
Mariam Rifai
Mathieu Roche
Narongkiat Rodphai
Aldo M. Romani
Bedřich Roskovec
Xianhui Ruan
Arseniy Rybnikov
Andrey Sadovsky
Paolo Saggese
Simone Sanfilippo
Anut Sangka
Utane Sawangwit
Julia Sawatzki
Michaela Schever
Cédric Schwab
Konstantin Schweizer
Alexandr Selyunin
Andrea Serafini
Giulio Settanta
M. Settimo
Zhuang Shao
V. Sharov
Arina Shaydurova
Jingyan Shi
Yanan Shi
Vitaly Shutov
Andrey Sidorenkov
Fedor Šimkovic
Chiara Sirignano
Jaruchit Siripak
Monica Sisti
Maciej Slupecki
Mikhail Smirnov
Oleg Smirnov
Thiago Sogo-Bezerra
Sergey Sokolov
Julanan Songwadhana
Boonrucksar Soonthornthum
Albert Sotnikov
Ondvrej vSr'amek
Warintorn Sreethawong
A. Stahl
Luca Stanco
Konstantin Stankevich
Duvsan Vstef'anik
Hans Steiger
Jochen Steinmann
Tobias Sterr
M. Stock
Virginia Strati
Alexander Studenikin
Jun Su
Shifeng Sun
Xilei Sun
Yongjie Sun Sun
Yongzhao Sun
Zhengyang Sun
Narumon Suwonjandee
Michal Szelezniak
Jian Tang
Qiang Tang
Quan Tang
Xiao Tang
Alexander Tietzsch
Igor Tkachev
Tomas Tmej
M. Torri
K. Treskov
Andrea Triossi
Giancarlo Troni
Wladyslaw Trzaska
Cristina Tuve
Nikita Ushakov
Vadim Vedin
Giuseppe Verde
Maxim Vialkov
Benoit Viaud
Cornelius Moritz Vollbrecht
C. Volpe
Katharina von Sturm
Vit Vorobel
Dmitriy Voronin
Lucia Votano
Pablo Walker
Caishen Wang
Chung-Hsiang Wang
En Wang
Guoli Wang
Jian Wang
Jun Wang
Lucinda W. Wang
Meifen Wang
Meng Wang
Ruiguang Wang
Siguang Wang
Wei Wang
Wenshuai Wang
Xi Wang
Xiangyue Wang
Yangfu Wang
Yaoguang Wang
Yi Xing Wang
Yifang Wang
Yuanqing Wang
Yuman Wang
Zhe Wang
Z. Wang
Zhimin Wang
Zongyi Wang
Apimook Watcharangkool
Wei Wei
Wenlu Wei
Yadong Wei
K. Wen
Kaile Wen
Christopher Wiebusch
S. Wong
Bjoern Wonsak
Diru Wu
Qun Wu
Zhi Wu
Michael Wurm
Jacques Wurtz
Christian Wysotzki
Yufei Xi
Dongqin Xia
Xiang Xiao
Xiaochuan Xie
Yu-guang Xie
Z. P. Xie
Zhao-Liang Xin
Z. Xing
Benda D. Xu
Chengze Xu
Donglian Xu
Fanrong Xu
The physics potential of detecting 8B solar neutrinos will be exploited at the Jiangmen Underground Neutrino Observatory (JUNO), in a model-… (see more)independent manner by using three distinct channels of the charged current (CC), neutral current (NC), and elastic scattering (ES) interactions. Due to the largest-ever mass of 13C nuclei in the liquid scintillator detectors and the expected low background level, 8B solar neutrinos are observable in the CC and NC interactions on 13C for the first time. By virtue of optimized event selections and muon veto strategies, backgrounds from the accidental coincidence, muon-induced isotopes, and external backgrounds can be greatly suppressed. Excellent signal-to-background ratios can be achieved in the CC, NC, and ES channels to guarantee the observation of the 8B solar neutrinos. From the sensitivity studies performed in this work, we show that JUNO, with 10 yr of data, can reach the 1σ precision levels of 5%, 8%, and 20% for the 8B neutrino flux, sin 2 θ 12 , and Δ m 21 2 , respectively. Probing the details of both solar physics and neutrino physics would be unique and helpful. In addition, when combined with the Sudbury Neutrino Observatory measurement, the world's best precision of 3% is expected for the measurement of the 8B neutrino flux.
2024-04-12
The Astrophysical Journal (published)
doi.org
arxiv.org
Association between arterial oxygen and mortality across critically ill patients with hematologic malignancies: results from an international collaborative network
Guillaume Dumas
Idunn S. Morris
Tamishta Hensman
Sean M. Bagshaw
Alexandre Demoule
Bruno Ferreyro
Achille Kouatchet
Virginie Lemiale
Djamel Mokart
Frédéric Pène
Sangeeta Mehta
Elie Azoulay
Laveena Munshi
Laurent Argaud
François Barbier
Dominique Benoit
Naike Bigé
Fabrice Bruneel
Emmanuel Canet
Yves Cohen … (see 30 more)
Michael Darmon
Didier Gruson
Kada Klouche
Loay Kontar
Alexandre Lautrette
Christine Lebert
Guillaume Louis
Julien Mayaux
Anne-Pascale Meert
Anne-Sophie Moreau
Martine Nyunga
Vincent Peigne
Pierre Perez
Jean Herlé Raphalen
Carole Schwebel
Jean-Marie Tonnelier
Florent Wallet
Lara Zafrani
Bram Rochwerg
Farah Shoukat
Dean Fergusson
Paul Heffernan
Margaret Herridge
Sheldon Magder
Mark Minden
Rakesh Patel
Salman Qureshi
Aaron Schimmer
Santhosh Thyagu
Han Ting Wang
2024-04-10
Intensive Care Medicine (published)
doi.org
Evaluating Interventional Reasoning Capabilities of Large Language Models
Tejas Kasetty
Divyat Mahajan
Gintare Karolina Dziugaite
Alexandre Drouin
Dhanya Sridhar
Numerous decision-making tasks require estimating causal effects under interventions on different parts of a system. As practitioners consid… (see more)er using large language models (LLMs) to automate decisions, studying their causal reasoning capabilities becomes crucial. A recent line of work evaluates LLMs ability to retrieve commonsense causal facts, but these evaluations do not sufficiently assess how LLMs reason about interventions. Motivated by the role that interventions play in causal inference, in this paper, we conduct empirical analyses to evaluate whether LLMs can accurately update their knowledge of a data-generating process in response to an intervention. We create benchmarks that span diverse causal graphs (e.g., confounding, mediation) and variable types, and enable a study of intervention-based reasoning. These benchmarks allow us to isolate the ability of LLMs to accurately predict changes resulting from their ability to memorize facts or find other shortcuts. We evaluate six LLMs on the benchmarks, finding that GPT models show promising accuracy at predicting the intervention effects.
2024-04-08
ArXiv (preprint)
doi.org
arxiv.org
Evaluating Interventional Reasoning Capabilities of Large Language Models
Tejas Kasetty
Divyat Mahajan
Gintare Karolina Dziugaite
Alexandre Drouin
Dhanya Sridhar
Numerous decision-making tasks require estimating causal effects under interventions on different parts of a system. As practitioners consid… (see more)er using large language models (LLMs) to automate decisions, studying their causal reasoning capabilities becomes crucial. A recent line of work evaluates LLMs ability to retrieve commonsense causal facts, but these evaluations do not sufficiently assess how LLMs reason about interventions. Motivated by the role that interventions play in causal inference, in this paper, we conduct empirical analyses to evaluate whether LLMs can accurately update their knowledge of a data-generating process in response to an intervention. We create benchmarks that span diverse causal graphs (e.g., confounding, mediation) and variable types, and enable a study of intervention-based reasoning. These benchmarks allow us to isolate the ability of LLMs to accurately predict changes resulting from their ability to memorize facts or find other shortcuts. We evaluate six LLMs on the benchmarks, finding that GPT models show promising accuracy at predicting the intervention effects.
2024-04-08
ArXiv (preprint)
doi.org
arxiv.org
Scope Ambiguities in Large Language Models
Gaurav Kamath
Sebastian Schuster
Sowmya Vajjala
Siva Reddy
Abstract Sentences containing multiple semantic operators with overlapping scope often create ambiguities in interpretation, known as scope … (see more)ambiguities. These ambiguities offer rich insights into the interaction between semantic structure and world knowledge in language processing. Despite this, there has been little research into how modern large language models treat them. In this paper, we investigate how different versions of certain autoregressive language models—GPT-2, GPT-3/3.5, Llama 2, and GPT-4—treat scope ambiguous sentences, and compare this with human judgments. We introduce novel datasets that contain a joint total of almost 1,000 unique scope-ambiguous sentences, containing interactions between a range of semantic operators, and annotated for human judgments. Using these datasets, we find evidence that several models (i) are sensitive to the meaning ambiguity in these sentences, in a way that patterns well with human judgments, and (ii) can successfully identify human-preferred readings at a high level of accuracy (over 90% in some cases).1
2024-04-05
ArXiv (preprint)
doi.org
arxiv.org
Enjeux juridiques propres au modèle émergent des patients accompagnateurs dans les milieux de soins au Québec
Léa Boutrouille
Catherine Régis
Marie-Pascale Pomey
2024-04-03
Journal du Droit de la Santé et de l’Assurance - Maladie (JDSAM) (published)
doi.org
Deployment of digital technologies in African cities: emerging issues and policy recommendations for local governments
Leandry Jieutsa
Irina Gbaguidi
Wijdane Nadifi
Shin (Alexandre) Koseki
2024-04-01
Data & Policy (published)
doi.org
Machine Learning Robustness: A Primer
Houssem Ben Braiek
Foutse Khomh
This chapter explores the foundational concept of robustness in Machine Learning (ML) and its integral role in establishing trustworthiness … (see more)in Artificial Intelligence (AI) systems. The discussion begins with a detailed definition of robustness, portraying it as the ability of ML models to maintain stable performance across varied and unexpected environmental conditions. ML robustness is dissected through several lenses: its complementarity with generalizability; its status as a requirement for trustworthy AI; its adversarial vs non-adversarial aspects; its quantitative metrics; and its indicators such as reproducibility and explainability. The chapter delves into the factors that impede robustness, such as data bias, model complexity, and the pitfalls of underspecified ML pipelines. It surveys key techniques for robustness assessment from a broad perspective, including adversarial attacks, encompassing both digital and physical realms. It covers non-adversarial data shifts and nuances of Deep Learning (DL) software testing methodologies. The discussion progresses to explore amelioration strategies for bolstering robustness, starting with data-centric approaches like debiasing and augmentation. Further examination includes a variety of model-centric methods such as transfer learning, adversarial training, and randomized smoothing. Lastly, post-training methods are discussed, including ensemble techniques, pruning, and model repairs, emerging as cost-effective strategies to make models more resilient against the unpredictable. This chapter underscores the ongoing challenges and limitations in estimating and achieving ML robustness by existing approaches. It offers insights and directions for future research on this crucial concept, as a prerequisite for trustworthy AI systems.
2024-04-01
ArXiv (preprint)
doi.org
arxiv.org
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models
Aleksandar Botev
Soham De
Samuel L. Smith
Anushan Fernando
George-Cristian Muraru
Ruba Haroun
Leonard Berrada
Razvan Pascanu
Pier Giuseppe Sessa
Robert Dadashi
L'eonard Hussenot
Johan Ferret
Sertan Girgin
Olivier Bachem
Alek Andreev
Kathleen Kenealy
Thomas Mesnard
Cassidy Hardin
Surya Bhupatiraju
Shreya Pathak … (see 43 more)
Laurent Sifre
Morgane Rivière
Mihir Kale
J Christopher Love
Juliette Love
Pouya Dehghani Tafti
Armand Joulin
Noah Fiedel
Evan Senter
Yutian Chen 0001
Srivatsan Srinivasan
Guillaume Desjardins
David Mark Budden
Arnaud Doucet
Sharad Mandyam Vikram
Adam Paszke
Trevor Gale
Sebastian Borgeaud
Charlie Chen
Andy Brock
Antonia Paterson
Jenny Brennan
Meg Risdal
Raj Gundluru
N. Devanathan
Paul Mooney
Nilay Chauhan
Phil Culliton
Luiz GUStavo Martins
Elisa Bandy
David W. Huntsperger
Glenn Cameron
Arthur Zucker
Tris Brian Warkentin
Ludovic Peran
Minh Giang
Zoubin Ghahramani
Clément Farabet
Koray Kavukcuoglu
Demis Hassabis
Raia Hadsell
Yee Whye Teh
Nando de Frietas
We introduce RecurrentGemma, a family of open language models which uses Google's novel Griffin architecture. Griffin combines linear recurr… (see more)ences with local attention to achieve excellent performance on language. It has a fixed-sized state, which reduces memory use and enables efficient inference on long sequences. We provide two sizes of models, containing 2B and 9B parameters, and provide pre-trained and instruction tuned variants for both. Our models achieve comparable performance to similarly-sized Gemma baselines despite being trained on fewer tokens.
2024-04-01
arXiv (published)
doi.org
arxiv.org