Publications

Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior
Ayush K Chakravarthy
Trang M. Nguyen
Anirudh Goyal
Yoshua Bengio
Michael Curtis Mozer
2023-05-30
ArXiv (prépublication)
doi.org
arxiv.org
A graphical user interface for calculating the arterial input function during dynamic positron emission tomography
Y. Daoud
Liam Carroll
S. Enger
Purpose. Dynamic positron emission tomography (dPET) requires the acquisition of the arterial input function (AIF), conventionally obtained … (voir plus)via invasive arterial blood sampling. To obtain the AIF non-invasively, our group developed and combined two novel solutions consisting of (1) a detector, placed on a patient’s wrist during the PET scans to measure the radiation leaving the wrist and (2) a Geant4-based Monte Carlo simulation software. The simulations require patient-specific wrist geometry. The aim of this study was to develop a graphical user interface (GUI) allowing the user to import 2D ultrasound scans of a patient’s wrist, and measure the wrist features needed to calculate the AIF. Methods. The GUI elements were implemented using Qt5 and VTK-8.2.0. The user imports a patient’s wrist ultrasound scans, measures the radial artery and veins’ surface and depth to model a wrist phantom, then specifies the radioactive source used during the dPET scan. The phantom, the source, and the number of decay events are imported into the Geant4-based Monte Carlo software to run a simulation. In this study, 100 million decays of 18F and 68Ga were simulated in a wrist phantom designed based on an ultrasound scan. The detector’s efficiency was calculated and the results were analyzed using a clinical data processing algorithm developed in a previous study. Results. The detector’s total efficiency decreased by 3.5% for 18F and by 51.7% for 68Ga when using a phantom based on ultrasound scans compared to a generic wrist phantom. Similarly, the data processing algorithm’s accuracy decreased when using the patient-specific phantom, giving errors greater than 1.0% for both radioisotopes. Conclusions. This toolkit enables the user to run Geant4-based Monte Carlo simulations for dPET detector development applications using a patient-specific wrist phantom. Leading to a more precise simulation of the developed detector during dPET and the calculation of a personalized AIF.
2023-05-29
Physics in Medicine & Biology (publié)
doi.org
The neuroconnectionist research programme
Adrien Doerig
R. Sommers
Katja Seeliger
Blake Aaron Richards
J. Ismael
Grace W. Lindsay
Konrad Paul Kording
Talia Konkle
M. Gerven
Nikolaus Kriegeskorte
Tim Kietzmann
2023-05-29
Nature Reviews Neuroscience (publié)
doi.org
arxiv.org
Motor Cortex Latent Dynamics Encode Spatial and Temporal Arm Movement Parameters Independently
Andrea Colins Rodriguez
Matt G. Perich
Lee E. Miller
Mark D. Humphries
The fluid movement of an arm requires multiple spatiotemporal parameters to be set independently. Recent studies have argued that arm moveme… (voir plus)nts are generated by the collective dynamics of neurons in motor cortex. An untested prediction of this hypothesis is that independent parameters of movement must map to independent components of the neural dynamics. Using a task where three male monkeys made a sequence of reaching movements to randomly placed targets, we show that the spatial and temporal parameters of arm movements are independently encoded in the low-dimensional trajectories of population activity in motor cortex: each movement’s direction corresponds to a fixed neural trajectory through neural state space and its speed to how quickly that trajectory is traversed. Recurrent neural network models show that this coding allows independent control over the spatial and temporal parameters of movement by separate network parameters. Our results support a key prediction of the dynamical systems view of motor cortex, and also argue that not all parameters of movement are defined by different trajectories of population activity.
2023-05-25
BioRxiv (prépublication)
doi.org
Testing Feedforward Neural Networks Training Programs
Houssem Ben Braiek
Foutse Khomh
2023-05-25
ACM Transactions on Software Engineering and Methodology (publié)
doi.org
arxiv.org
Model evaluation for extreme risks
Toby Shevlane
Sebastian Farquhar
Ben Garfinkel
Mary Phuong
Jess Whittlestone
Jade Leung
Daniel Kokotajlo
Nahema A. Marchal
Markus Anderljung
Noam Kolt
Lewis Ho
Divya Siddarth
Shahar Avin
W. Hawkins
Been Kim
Iason Gabriel
Vijay Bolina
Jack Clark
Yoshua Bengio
Paul F. Christiano … (voir 1 de plus)
Allan Dafoe
2023-05-23
ArXiv (prépublication)
doi.org
arxiv.org
De novo motor learning creates structure in neural activity space that shapes adaptation
Joanna C. Chang
Matthew G Perich
Lee Miller
Juan A. Gallego
Claudia Clopath
2023-05-23
bioRxiv (prépublication)
doi.org
Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models
Setareh Dabiri
Vasileios Lioutas
Berend Zwartsenberg
Yunpeng Liu
Matthew Niedoba
Xiaoxuan Liang
Dylan Green
Justice Sefas
Jonathan Wilder Lavington
Frank N. Wood
Adam Ścibior
When training object detection models on synthetic data, it is important to make the distribution of synthetic data as close as possible to … (voir plus)the distribution of real data. We investigate specifically the impact of object placement distribution, keeping all other aspects of synthetic data fixed. Our experiment, training a 3D vehicle detection model in CARLA and testing on KITTI, demonstrates a substantial improvement resulting from improving the object placement distribution.
2023-05-23
ArXiv (prépublication)
doi.org
arxiv.org
Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling
Qidong Yang
Alex Hernández-García
Paula Harder
Venkatesh Ramesh
Prasanna Sattegeri
D. Szwarcman
Campbell Watson
David Rolnick
Climate simulations are essential in guiding our understanding of climate change and responding to its effects. However, it is computational… (voir plus)ly expensive to resolve complex climate processes at high spatial resolution. As one way to speed up climate simulations, neural networks have been used to downscale climate variables from fast-running low-resolution simulations, but high-resolution training data are often unobtainable or scarce, greatly limiting accuracy. In this work, we propose a downscaling method based on the Fourier neural operator. It trains with data of a small upsampling factor and then can zero-shot downscale its input to arbitrary unseen high resolution. Evaluated both on ERA5 climate model data and on the Navier-Stokes equation solution data, our downscaling model significantly outperforms state-of-the-art convolutional and generative adversarial downscaling models, both in standard single-resolution downscaling and in zero-shot generalization to higher upsampling factors. Furthermore, we show that our method also outperforms state-of-the-art data-driven partial differential equation solvers on Navier-Stokes equations. Overall, our work bridges the gap between simulation of a physical process and interpolation of low-resolution output, showing that it is possible to combine both approaches and significantly improve upon each other.
2023-05-22
ArXiv (prépublication)
doi.org
arxiv.org
Identifying Critical Neurons in ANN Architectures using Mixed Integer Programming
Mostafa Elaraby
Guy Wolf
Margarida Carvalho
2023-05-22
Integration of Constraint Programming, Artificial Intelligence, and Operations Research (publié)
doi.org
arxiv.org
Data Imputation with an Autoencoder and MAGIC
Devin Eddington
Andres Felipe Duque Correa
Guy Wolf
Kevin R. Moon
Missing data is a common problem in many applications. Imputing missing values is a challenging task, as the imputations need to be accurate… (voir plus) and robust to avoid introducing bias in downstream analysis. In this paper, we propose an ensemble method that combines the strengths of a manifold learning-based imputation method called MAGIC and an autoencoder deep learning model. We call our method Deep MAGIC. Deep MAGIC is trained on a linear combination of the mean squared error of the original data and the mean squared error of the MAGIC-imputed data. Experimental results on three benchmark datasets show that Deep MAGIC outperforms several state-of-the-art imputation methods, demonstrating its effectiveness and robustness in handling large amounts of missing data.
2023-05-20
SampTA/2023/Conference (publié)
doi.org
openreview.net
Graph Fourier MMD for Signals on Graphs
Samuel Leone
Aarthi Venkat
Guillaume Huguet
Alexander Tong
Guy Wolf
Smita Krishnaswamy
While numerous methods have been proposed for computing distances between probability distributions in Euclidean space, relatively little at… (voir plus)tention has been given to computing such distances for distributions on graphs. However, there has been a marked increase in data that either lies on graph (such as protein interaction networks) or can be modeled as a graph (single cell data), particularly in the biomedical sciences. Thus, it becomes important to find ways to compare signals defined on such graphs. Here, we propose Graph Fourier MMD (GFMMD), a novel distance between distributions and signals on graphs. GFMMD is defined via an optimal witness function that is both smooth on the graph and maximizes difference in expectation between the pair of distributions on the graph. We find an analytical solution to this optimization problem as well as an embedding of distributions that results from this method. We also prove several properties of this method including scale invariance and applicability to disconnected graphs. We showcase it on graph benchmark datasets as well on single cell RNA-sequencing data analysis. In the latter, we use the GFMMD-based gene embeddings to find meaningful gene clusters. We also propose a novel type of score for gene selection called "gene localization score" which helps select genes for cellular state space characterization.
2023-05-20
SampTA/2023/Conference (publié)
doi.org
openreview.net