Evaluating machine learning-driven intrusion detection systems in IoT: Performance and energy consumption
Saeid Jamshidi
Kawser Wazed Nafi
Amin Nikanjam
Foutse Khomh
2025-06-01
Computers & Industrial Engineering (published)
doi.org
arxiv.org
GNN-based Decentralized Perception in Multirobot Systems for Predicting Worker Actions
Ali Imran
Giovanni Beltrame
David St-Onge
In industrial environments, predicting human actions is essential for ensuring safe and effective collaboration between humans and robots. T… (see more)his paper introduces a perception framework that enables mobile robots to understand and share information about human actions in a decentralized way. The framework first allows each robot to build a spatial graph representing its surroundings, which it then shares with other robots. This shared spatial data is combined with temporal information to track human behavior over time. A swarm-inspired decision-making process is used to ensure all robots agree on a unified interpretation of the human's actions. Results show that adding more robots and incorporating longer time sequences improve prediction accuracy. Additionally, the consensus mechanism increases system resilience, making the multi-robot setup more reliable in dynamic industrial settings.
2025-06-01
IEEE Robotics and Automation Letters (published)
doi.org
arxiv.org
STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent
Yewon Lee
Philip Huang
Yizhou Huang
Krishna Murthy
Andrew Zou Li
Fabian Damken
Eric Heiden
Kevin A. Smith
Derek Nowrouzezahrai
Fabio Ramos
Florian Shkurti
Carnegie-mellon University
M. I. O. Technology
Technische Universitat Darmstadt
Nvidia
M. University
University of Sydney
Planning for many manipulation tasks, such as using tools or assembling parts, often requires both symbolic and geometric reasoning. Task an… (see more)d Motion Planning (TAMP) algorithms typically solve these problems by conducting a tree search over high-level task sequences while checking for kinematic and dynamic feasibility. While performant, most existing algorithms are highly inefficient as their time complexity grows exponentially with the number of possible actions and objects. Additionally, they only find a single solution to problems in which many feasible plans may exist. To address these limitations, we propose a novel algorithm called Stein Task and Motion Planning (STAMP) that leverages parallelization and differentiable simulation to efficiently search for multiple diverse plans. STAMP relaxes discrete-and-continuous TAMP problems into continuous optimization problems that can be solved using variational inference. Our algorithm builds upon Stein Variational Gradient Descent, a gradient-based variational inference algorithm, and parallelized differentiable physics simulators on the GPU to efficiently obtain gradients for inference. Further, we employ imitation learning to introduce action abstractions that reduce the inference problem to lower dimensions. We demonstrate our method on two TAMP problems and empirically show that STAMP is able to: 1) produce multiple diverse plans in parallel; and 2) search for plans more efficiently compared to existing TAMP baselines.
2025-06-01
IEEE Robotics and Automation Letters (published)
doi.org
openreview.net
Persistent signs of poisoning after massive drug ingestion: move the ultrasound probe to the stomach.
N. Lautrou-cabasson
H. Pirollet
C. Lombois
Guillaume Dumas
2025-05-15
Intensive Care Medicine (published)
doi.org
Plasticity as the Mirror of Empowerment
David Abel
Michael Bowling
Andre Barreto
Will Dabney
Shi Dong
Steven Hansen
Anna Harutyunyan
Khimya Khetarpal
Clare Lyle
Razvan Pascanu
Georgios Piliouras
Doina Precup
Jonathan Richens
Mark Rowland
Tom Schaul
Satinder Singh
2025-05-15
ArXiv (preprint)
arxiv.org
arxiv.org
Language Agents Mirror Human Causal Reasoning Biases. How Can We Help Them Think Like Scientists?
Anthony GX-Chen
Dongyan Lin
Mandana Samiei
Doina Precup
Blake Richards
Rob Fergus
Kenneth Marino
2025-05-14
ArXiv (preprint)
arxiv.org
arxiv.org
The CASTOR mission
Patrick Côté
Tyrone E. Woods
John B. Hutchings
Jason D. Rhodes
Rubén Sánchez-Janssen
Alan D. Scott
John Pazder
Melissa Amenouche
Michael Balogh
Simon Blouin
Alain Cournoyer
Maria R. Drout
Nick Kuzmin
Katherine J. Mack
Laura Ferrarese
Wesley C. Fraser
Sarah C. Gallagher
Frédéric Grandmont
Daryl Haggard
Paul Harrison … (see 160 more)
Vincent Hénault-Brunet
J. J. Kavelaars
Viraja Khatu
Joel C. Roediger
Jason Rowe
Marcin Sawicki
Jesper Skottfelt
Matt Taylor
Ludo van Waerbeke
Laurie Amen
Dhananjhay Bansal
Martin Bergeron
Toby Brown
Greg Burley
Hum Chand
Isaac Cheng
Ryan Cloutier
Nolan Dickson
Oleg Djazovski
Ivana Damjanov
James Doherty
Kyle Finner
Macarena García Del Valle Espinosa
Jennifer Glover
Ana I. Gómez de Castro
Or Graur
Tim Hardy
Michelle Kao
Denis Leahy
Deborah Lokhorst
Alex I. Malz
Allison Man
Madeline A. Marshall
Sean McGee
Ryan McKenzie
Kai Michaud
Surhud S. More
David Morris
Patrick W. Morris
Thibaud Moutard
Wasi Naqvi
Matt Nicholl
Gaël Noirot
M. S. Oey
Cyrielle Opitom
Samir Salim
Bryan R. Scott
Charles A. Shapiro
Daniel Stern
Annapurni Subramaniam
David Thilke
Ivan Wevers
Dmitri Vorobiev
L. Y. Aaron Yung
Frédéric Zamkotsian
Suzanne Aigrain
Anahita Alavi
Martin Barstow
Peter Bartosik
Hadleigh Bluhm
Jo Bovy
Peter Cameron
Raymond G. Carlberg
Jessie L. Christiansen
Yuyang Chen
Paul Crowther
Kristen Dage
Aaron L. Dotter
Patrick Dufour
Jean Dupuis
Ben Dryer
Angaraj Duara
Gwendolyn M. Eadie
Marielle R. Eduardo
Vincente Estrada-Carpenter
Sébastien Fabbro
Andreas Faisst
Nicole M. Ford
Morgan Fraser
Boris T. Gaensicke
Shashkiran Ganesh
Poshak Gandhi
Melissa L. Graham
Rebecca Hamel
Martin Hellmich
John Hennessy
Kaitlyn Hessel
Jeremy Heyl
Catherine Heymans
Yashar Hezaveh
Renée Hložek
Michael E. Hoenk
Andrew Holland
Eric Huff
Ian Hutchinson
Ikuru Iwata
April D. Jewell
Doug Johnstone
Maia Jones
Todd Jones
Dustin Lang
Jon Lapington
Justin Larivière
Cameron Lawlor-Forsyth
Denis Laurin
Charles Lee
Ronan Legin
Ting S. Li
Sungsoon Lim
Bethany Ludwig
Matt Kozun
Vivek M.
Robert Mann
Alan W. McConnachie
Evan McDonough
Stanimir Metchev
David R. Miller
Takashi Moriya
Cameron Morgan
Julio Navarro
Yaël Nazé
Shouleh Nikzad
Vivek Oad
Nathalie Ouellette
Emily K. Pass
Will J. Percival
Laurence Perreault Levasseur
Joe Postma
Nayyer Raza
Gordon T. Richards
Harvey Richer
Carmelle Robert
Erik Rosolowsky
John J. Ruan
Sarah Rugheimer
Samar Safi-Harb
Kanak Saha
Vicky Scowcroft
Federico Sestito
Himanshu Sharma
James Sikora
Gregory R. Sivakoff
Thirupathi Sivarani
Patrick Smith
Warren Soh
Robert Sorba
Smitha Subramanian
Hossen Teimoorinia
Harry I. Teplitz
Shaylin Thadani
Shavon Thadani
Aaron Tohuvavohu
Kim A. Venn
Nicholas Vieira
Jeremy J. Webb
Paul Wiegert
Ryan Wierckx
Yanqin Wu
Jade Yeung
Sukyoung K. Yi
2025-05-14
Journal of Astronomical Telescopes, Instruments, and Systems (published)
doi.org
The CASTOR mission
Patrick Côté
T. Woods
John Hutchings
J. Rhodes
R. Sánchez-Janssen
Alan D. Scott
J. Pazder
Melissa Amenouche
Michael Balogh
Simon Blouin
Alain Cournoyer
M. Drout
Nick Kuzmin
Katherine J. Mack
Laura Ferrarese
Wesley C. Fraser
Sarah C. Gallagher
Frederic J. Grandmont
Daryl Haggard
Paul Harrison … (see 160 more)
Vincent Hénault-Brunet
J. Kavelaars
V. Khatu
J. Roediger
J. Rowe
Marcin Sawicki
Jesper Skottfelt
Matt Taylor
Ludo van Waerbeke
Laurie Amen
Dhananjhay Bansal
Martin Bergeron
Toby Brown
Greg Burley
Hum Chand
Isaac Cheng
Ryan Cloutier
N. Dickson
Oleg Djazovski
Ivana Damjanov
James Doherty
K. Finner
Macarena García Del Valle Espinosa
Jennifer Glover
A. I. Gómez de Castro
Or Graur
Tim Hardy
Michelle Kao
D A Leahy
Deborah Lokhorst
A. I. Malz
Allison Man
Madeline A. Marshall
Sean McGee
Ryan McKenzie
Kai Michaud
Surhud S. More
David Morris
Patrick W. Morris
T. Moutard
Wasi Naqvi
Matthew Nicholl
G. Noirot
M. S. Oey
C. Opitom
Samir Salim
Bryan R. Scott
Charles Shapiro
Daniel Stern
A. Subramaniam
David Thilke
I. Wevers
Dmitri Vorobiev
L. Y. Aaron Yung
Frédéric Zamkotsian
S. Aigrain
A. Alavi
Martin Barstow
Peter Bartosik
Hadleigh Bluhm
J. Bovy
Peter Cameron
R. Carlberg
Jessie L. Christiansen
Yuyang Chen
Paul Crowther
Kristen Dage
Aaron Dotter
Patrick Dufour
Jean Dupuis
B. Dryer
A. Duara
Gwendolyn M. Eadie
Marielle R. Eduardo
V. Estrada-Carpenter
Sébastien Fabbro
A. Faisst
N. M. Ford
Morgan Fraser
Boris T. Gaensicke
Shashkiran Ganesh
Poshak Gandhi
Melissa L. Graham
Rebecca Hamel
Martin Hellmich
John J. Hennessy
Kaitlyn Hessel
J. Heyl
Catherine Heymans
Yashar Hezaveh
Renée Hložek
Michael Hoenk
Andrew Holland
Eric Huff
Ian Hutchinson
Ikuru Iwata
April D. Jewell
Doug Johnstone
Maia Jones
Todd Jones
D. Lang
J. Lapington
Justin Larivière
C. Lawlor-Forsyth
Denis Laurin
Charles Lee
Ronan Legin
Ting S. Li
Sungsoon Lim
Bethany Ludwig
Matt Kozun
V. M
Robert Mann
Alan McConnachie
Evan McDonough
S. Metchev
David R. Miller
Takashi Moriya
Cameron Morgan
Julio F. Navarro
Y. Nazé
Shouleh Nikzad
Vivek Oad
N. N.-Q. Ouellette
E. Pass
Will J. Percival
Laurence Perreault-Levasseur
Joe Postma
Nayyer Raza
G. T. Richards
Harvey Richer
Carmelle Robert
Erik Rosolowsky
J. Ruan
Sarah Rugheimer
S. Safi-Harb
Kanak Saha
Vicky Scowcroft
F. Sestito
Himanshu Sharma
James Sikora
G. Sivakoff
T. S. Sivarani
Patrick Smith
Warren Soh
R. Sorba
S. Subramanian
Hossen Teimoorinia
H. Teplitz
Shaylin Thadani
Shavon Thadani
Aaron Tohuvavohu
K. Venn
Nicholas Vieira
Jeremy J. Webb
P. Wiegert
Ryan Wierckx
Yanqin Wu
Jade Yeung
Sukyoung K. Yi
2025-05-14
Journal of Astronomical Telescopes Instruments and Systems (published)
doi.org
An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration
Hiroki Naganuma
Ryuichiro Hataya
Kotaro Yoshida
Ioannis Mitliagkas
2025-05-13
TMLR (accepted)
openreview.net
openreview.net
Influence of scanning plane on Human Spinal Cord functional Magnetic Resonance echo planar imaging
Marta Moraschi
Silvia Tommasin
Laura Maugeri
Mauro DiNuzzo
Julien Cohen-Adad
Marco Masullo
Fabio Mangini
Lorenzo Giovannelli
Daniele Mascali
Tommaso Gili
Valerio Pisani
Ugo Nocentini
Federico Giove
Michela Fratini
BACKGROUND: Functional Magnetic Resonance Imaging (fMRI) is based on the Blood Oxygenation Level Dependent contrast and has been exploited f… (see more)or the indirect study of the neuronal activity within both the brain and the spinal cord. However, the interpretation of spinal cord fMRI (scfMRI) is still controversial and its diffusion is rather limited because of technical limitations. Overcoming these limitations would have a beneficial effect for the assessment and follow-up of spinal injuries and neurodegenerative diseases. PURPOSE: This study was aimed at systematically verify whether sagittal scanning in scfMRI using EPI readout is a viable alternative to the more common axial scanning, and at optimizing a pipeline for EPI-based scfMRI data analysis, based on Spinal Cord Toolbox (SCT). METHODS: Forty-five healthy subjects underwent MRI acquisition in a Philips Achieva 3T MRI scanner. T2*-weighted fMRI data were acquired using a GE-EPI sequence along sagittal and axial planes during an isometric motor task. Differences on benchmarks were assessed via paired two-sample t-test at p=0.05. RESULTS: We investigated the impact of the acquisition strategy by means of various metrics such as Temporal Signal to Noise Ratio (tSNR), Dice Coefficient to assess geometric distortions, Reproducibility and Sensitivity. tSNR was higher in axial than in sagittal scans, as well as reproducibility within the whole cord mask (t=7.4, p0.01) and within the GM mask (t=4.2, p0.01). The other benchmarks, associated with distortion and functional response, showed no differenc
2025-05-12
PLOS One (published)
doi.org
arxiv.org
Learning Penalty for Optimal Partitioning via Automatic Feature Extraction
Tung L. Nguyen
Toby Dylan Hocking
2025-05-12
ArXiv (preprint)
arxiv.org
arxiv.org
Ctrl-V: Higher Fidelity Autonomous Vehicle Video Generation with Bounding-Box Controlled Object Motion
Ge Ya Luo
Zhi Hao Luo
Anthony Gosselin
Alexia Jolicoeur-Martineau
Chris Pal
2025-05-10
TMLR (accepted)
openreview.net
openreview.net