Publications

Bayesian Spectral Graph Denoising with Smoothness Prior

Samuel Leone

Xingzhi Sun

Michael Perlmutter

Here we consider the problem of denoising features associated to complex data, modeled as signals on a graph, via a smoothness prior. This i… (see more)s motivated in part by settings such as single-cell RNA where the data is very high-dimensional, but its structure can be captured via an affinity graph. This allows us to utilize ideas from graph signal processing. In particular, we present algorithms for the cases where the signal is perturbed by Gaussian noise, dropout, and uniformly distributed noise. The signals are assumed to follow a prior distribution defined in the frequency domain which favors signals which are smooth across the edges of the graph. By pairing this prior distribution with our three models of noise generation, we propose Maximum A Posteriori (M.A.P.) estimates of the true signal in the presence of noisy data and provide algorithms for computing the M.A.P. Finally, we demonstrate the algorithms’ ability to effectively restore signals from white noise on image data and from severe dropout in single-cell RNA sequence data.

2024-03-12

Annual Conference on Information Sciences and Systems (published)

doi.org

arxiv.org

Online Bayesian optimization of vagus nerve stimulation

Lorenz Wernisch

Tristan Edwards

Antonin Berthon

Olivier Tessier-Larivière

Elvijs Sarkans

Myrta Stoukidi

Pascal Fortier-Poisson

Max Pinkney

Michael Thornton

Catherine Hanley

Susannah Lee

Joel Jennings

Ben Appleton

Phillip Garsed

Bret Patterson

Will Buttinger

Samuel Gonshaw

Matjaž Jakopec

Sudhakaran Shunmugam

Jorin Mamen … (see 4 more)

Aleksi Tukiainen

Guillaume Lajoie

Oliver Armitage

Emil Hewage

Objective. In bioelectronic medicine, neuromodulation therapies induce neural signals to the brain or organs, modifying their function. Stim… (see more)ulation devices capable of triggering exogenous neural signals using electrical waveforms require a complex and multi-dimensional parameter space to control such waveforms. Determining the best combination of parameters (waveform optimization or dosing) for treating a particular patient’s illness is therefore challenging. Comprehensive parameter searching for an optimal stimulation effect is often infeasible in a clinical setting due to the size of the parameter space. Restricting this space, however, may lead to suboptimal therapeutic results, reduced responder rates, and adverse effects. Approach. As an alternative to a full parameter search, we present a flexible machine learning, data acquisition, and processing framework for optimizing neural stimulation parameters, requiring as few steps as possible using Bayesian optimization. This optimization builds a model of the neural and physiological responses to stimulations, enabling it to optimize stimulation parameters and provide estimates of the accuracy of the response model. The vagus nerve (VN) innervates, among other thoracic and visceral organs, the heart, thus controlling heart rate (HR), making it an ideal candidate for demonstrating the effectiveness of our approach. Main results. The efficacy of our optimization approach was first evaluated on simulated neural responses, then applied to VN stimulation intraoperatively in porcine subjects. Optimization converged quickly on parameters achieving target HRs and optimizing neural B-fiber activations despite high intersubject variability. Significance. An optimized stimulation waveform was achieved in real time with far fewer stimulations than required by alternative optimization strategies, thus minimizing exposure to side effects. Uncertainty estimates helped avoiding stimulations outside a safe range. Our approach shows that a complex set of neural stimulation parameters can be optimized in real-time for a patient to achieve a personalized precision dosing.

2024-03-12

Journal of Neural Engineering (published)

doi.org

Assessing the Security of GitHub Copilot Generated Code - A Targeted Replication Study

Vahid Majdinasab

Michael Joshua Bishop

Shawn Rasheed

Arghavan Moradi Dakhel

Amjed Tahir

Foutse Khomh

2024-03-11

2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) (published)

doi.org

arxiv.org

Deep Learning Model Reuse in the HuggingFace Community: Challenges, Benefit and Trends

Mina Taraghi

Gianolli Dorcelus

Armstrong Foundjem

Florian Tambon

Foutse Khomh

The ubiquity of large-scale Pre-Trained Models (PTMs) is on the rise, sparking interest in model hubs, and dedicated platforms for hosting P… (see more)TMs. Despite this trend, a comprehensive exploration of the challenges that users encounter and how the community leverages PTMs remains lacking. To address this gap, we conducted an extensive mixed-methods empirical study by focusing on discussion forums and the model hub of HuggingFace, the largest public model hub. Based on our qualitative analysis, we present a taxonomy of the challenges and benefits associated with PTM reuse within this community. We then conduct a quantitative study to track model-type trends and model documentation evolution over time. Our findings highlight prevalent challenges such as limited guidance for beginner users, struggles with model output comprehensibility in training or inference, and a lack of model understanding. We also identified interesting trends among models where some models maintain high upload rates despite a decline in topics related to them. Additionally, we found that despite the introduction of model documentation tools, its quantity has not increased over time, leading to difficulties in model comprehension and selection among users. Our study sheds light on new challenges in reusing PTMs that were not reported before and we provide recommendations for various stakeholders involved in PTM reuse.

2024-03-11

2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) (published)

doi.org

arxiv.org

GAPS II: Development and Pilot Results of the Global Assessment in Pediatric Surgery, an Evidence-Based Pediatric Surgical Capacity Assessment Tool for Low-Resource Settings

Yasmine Yousef

Sarah Cairo

Etienne St-Louis

Laura F. Goodman

Doulia M. Hamad

Robert Baird

Emily R. Smith

Sherif Emil

Jean-Martin Laberge

Mohamed Abdelmalak

Zipporah Gathuy

Faye Evans

Maryam Ghavami Adel

Ki K. Bertille

Milind Chitnis

Leecarlo Millano

Peter Nthumba

Sergio d'Agostino

Bruno Cigliano

Luis Zea-Salazar … (see 4 more)

Emmanuel Ameh

Doruk Ozgediz

Elena Guadagno

Dan Poenaru

PURPOSE: Pediatric surgical care in low- and middle-income countries is often hindered by systemic gaps in healthcare resources, infrastruc… (see more)ture, training, and organisation. This study aims to develop and validate the Global Assessment of Pediatric Surgery (GAPS) to appraise pediatric surgical capacity and discriminate between levels of care across diverse healthcare settings. METHODS: The GAPS Version 1 was constructed through a synthesis of existing assessment tools and expert panel consultation. The resultant GAPS Version 2 underwent international pilot testing. Construct validation categorized institutions into providing Basic or Advanced Surgical Care. GAPS was further refined to Version 3 to include only questions with a > 75% response rate and those that significantly discriminated between Basic or Advanced Surgical settings. RESULTS: GAPS Version 1 included 139 items, which, after expert panel feedback, was expanded to 168 items in Version 2. Pilot testing, in 65 institutions yielded a high response rate. Of the 168 questions in GAPS Version 2, 64 significantly discriminated between Basic and Advanced Surgical Care. The refined GAPS Version 3 tool comprises 64 questions: Human Resources (9), Material Resources (39), Outcomes (3), Accessibility (3), and Education (10). CONCLUSION: The GAPS Version 3 tool presents a validated instrument for evaluating pediatric surgical capabilities in low-resource settings.

2024-03-11

Research Square (Research Square) (preprint)

doi.org

Refining GPT-3 Embeddings with a Siamese Structure for Technical Post Duplicate Detection

Xingfang Wu

Heng Li

Nobukazu Yoshioka

Hironori Washizaki

Foutse Khomh

2024-03-11

2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) (published)

doi.org

arxiv.org

Rethinking Machine Learning Benchmarks in the Context of Professional Codes of Conduct

Peter Henderson

Jieru Hu

Mona Diab

Joelle Pineau

2024-03-11

Proceedings of the Symposium on Computer Science and Law (published)

doi.org

Continuous iron spreading on carbon-shell composite nanotubes for electromagnetic wave absorption

Yuanyuan Zhang

Yining Li

Can Zhang

Zhenjie Guan

Liang Zhen

Jian-Tang Jiang

2024-03-10

Communications Materials (published)

doi.org

Improving and Generalizing Flow-Based Generative Models with Minibatch Optimal Transport

Yanlei Zhang

Continuous normalizing flows (CNFs) are an attractive generative modeling technique, but they have been held back by limitations in their si… (see more)mulation-based maximum likelihood training. We introduce the generalized \textit{conditional flow matching} (CFM) technique, a family of simulation-free training objectives for CNFs. CFM features a stable regression objective like that used to train the stochastic flow in diffusion models but enjoys the efficient inference of deterministic flow models. In contrast to both diffusion models and prior CNF training algorithms, CFM does not require the source distribution to be Gaussian or require evaluation of its density. A variant of our objective is optimal transport CFM (OT-CFM), which creates simpler flows that are more stable to train and lead to faster inference, as evaluated in our experiments. Furthermore, OT-CFM is the first method to compute dynamic OT in a simulation-free way. Training CNFs with CFM improves results on a variety of conditional and unconditional generation tasks, such as inferring single cell dynamics, unsupervised image translation, and Schrödinger bridge inference.

2024-03-10

TMLR (accepted)

openreview.net