Leveraging Vision-Language Foundation Models to Reveal Hidden Image-Attribute Relationships in Medical Imaging
Amar Kumar
Anita Kriz
B. Pertzov
Bridging biodiversity and ecosystem services through useful plant species
Nina Obiar
Isaac Eckert
Janelle Baker
Daniel Moerman
Genetic Analysis of Polyunsaturated Fatty Acids Biosynthesis Pathway Determines Four Distinct Thraustochytrid Types
Sou‐Yu Cheng
Yi‐Jing Chen
Hsin‐Yang Chang
Ming‐Der Huang
ABSTRACT Thraustochytrids, diverse marine unicellular protists encompassing over 10 recognised genera, are renowned for synthesising polyuns… (voir plus)aturated fatty acids (PUFAs), with content and composition varying substantially across genera. While PUFAs are known to be produced via PUFA synthase (PUFA‐S) and/or elongase/desaturase (ELO/DES) pathways, the distinctions in genes involved remain unexplored. This study analysed PUFA biosynthetic genes in 19 thraustochytrid strains across six genera, categorising them into four types. Type I exclusively utilises the ELO/DES pathway, Type II employs both PUFA‐S and complete ELO/DES pathways, while Types III and IV primarily rely on PUFA‐S, with Type III lacking the canonical Δ9 desaturase and Type IV missing most desaturase and elongase enzymes. Notably, the Δ9 desaturase and ATP‐citrate lyase (ACLY) are exclusive to Types I and II, while β‐carotene hydroxylase (CrtZ) is absent in these types. ACLY absence suggests alternative acetyl‐CoA supply pathways in Types III and IV, whereas CrtZ absence implies either a lack of specific xanthophylls or alternative biosynthetic pathways in Types I and II. Synteny analysis revealed conserved genomic organisation of PUFA biosynthetic genes, indicating a shared evolutionary trajectory. This study provides insights into the genetic diversity underlying PUFA biosynthesis in thraustochytrids, while proposing putative evolutionary pathways for the four lineages.
Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments
Luke Rowe
Roger Girgis
Anthony Gosselin
Felix Heide
Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments
Luke Rowe
Roger Girgis
Anthony Gosselin
Felix Heide
Conditional Diffusion Models are Medical Image Classifiers that Provide Explainability and Uncertainty for Free
Gian Mario Favero
Parham Saremi
Emily Kaczmarek
Brennan Nichyporuk
debug-gym: A Text-Based Environment for Interactive Debugging
Xingdi Yuan
Morgane M Moss
Charbel Feghali
Chinmay Singh
Darya Moldavskaya
Drew MacPhee
Lucas Caccia
Matheus Pereira
Minseon Kim
Marc-Alexandre Côté
debug-gym: A Text-Based Environment for Interactive Debugging
Xingdi Yuan
Morgane M Moss
Charbel Feghali
Chinmay Singh
Darya Moldavskaya
Drew MacPhee
Lucas Caccia
Matheus Pereira
Minseon Kim
Marc-Alexandre Côté
How do language models learn facts? Dynamics, curricula and hallucinations
Nicolas Zucchet
Jorg Bornschein
Stephanie Chan
Andrew Lampinen
Soham De
How do language models learn facts? Dynamics, curricula and hallucinations
Nicolas Zucchet
Jorg Bornschein
Stephanie Chan
Andrew Lampinen
Soham De
PRISM: High-Resolution&Precise Counterfactual Medical Image Generation using Language-guided Stable Diffusion
Amar Kumar
Anita Kriz
Mohammad Havaei
Developing reliable and generalizable deep learning systems for medical imaging faces significant obstacles due to spurious correlations, da… (voir plus)ta imbalances, and limited text annotations in datasets. Addressing these challenges requires architectures robust to the unique complexities posed by medical imaging data. The rapid advancements in vision-language foundation models within the natural image domain prompt the question of how they can be adapted for medical imaging tasks. In this work, we present PRISM, a framework that leverages foundation models to generate high-resolution, language-guided medical image counterfactuals using Stable Diffusion. Our approach demonstrates unprecedented precision in selectively modifying spurious correlations (the medical devices) and disease features, enabling the removal and addition of specific attributes while preserving other image characteristics. Through extensive evaluation, we show how PRISM advances counterfactual generation and enables the development of more robust downstream classifiers for clinically deployable solutions. To facilitate broader adoption and research, we make our code publicly available at https://github.com/Amarkr1/PRISM.
StarFlow: Generating Structured Workflow Outputs From Sketch Images
Patrice Bechard
Chao Wang
Amirhossein Abaskohi
Juan A. Rodriguez
David Vazquez
Spandana Gella
Sai Rajeswar
Perouz Taslakian