Publications

Tree semantic segmentation from aerial image time series

Earth's forests play an important role in the fight against climate change, and are in turn negatively affected by it. Effective monitoring … (voir plus)of different tree species is essential to understanding and improving the health and biodiversity of forests. In this work, we address the challenge of tree species identification by performing semantic segmentation of trees using an aerial image dataset spanning over a year. We compare models trained on single images versus those trained on time series to assess the impact of tree phenology on segmentation performances. We also introduce a simple convolutional block for extracting spatio-temporal features from image time series, enabling the use of popular pretrained backbones and methods. We leverage the hierarchical structure of tree species taxonomy by incorporating a custom loss function that refines predictions at three levels: species, genus, and higher-level taxa. Our findings demonstrate the superiority of our methodology in exploiting the time series modality and confirm that enriching labels using taxonomic information improves the semantic segmentation performance.

2025-07-28

Environmental Data Science (publié)

doi.org

arxiv.org

A neural manifold view of the brain

Matthew G. Perich

Devika Narain

Juan Álvaro Gallego

2025-07-27

Nature Neuroscience (publié)

doi.org

QComp: A QSAR-Based Imputation Framework for Drug Discovery.

Bingjia Yang

Yunsie Chung

Archer Y. Yang

Bo Yuan

Tianchi Chen

Xiang Yu

In drug discovery, in vitro and in vivo experiments generate biochemical activity data that are crucial for evaluating the efficacy and toxi… (voir plus)city of compounds. These data sets are massive, sparse, and ever-evolving. Quantitative structure-activity relationship (QSAR) models, which predict biochemical activities from compound structures, face challenges in integrating the evolving experimental data agilely as studies progress. We developed QSAR-Complete (QComp), an imputation framework, to address these challenges. While QSAR models are updated at a slow pace through extensive retraining on enlarging data sets, QComp leverages existing QSAR models to immediately exploit new experimental data and improves the imputation of missing data. We demonstrate that the improvement is robust and substantial for imputing in vivo assays with only in vitro experimental data. Additionally, QComp assists in finding the optimal sequence of experiments by quantifying the reduction in statistical uncertainty for specific end points, aiding in rational decision-making throughout the drug discovery process.

2025-07-27

Journal of Chemical Information and Modeling (publié)

doi.org

Semantic change in adults is not primarily a generational phenomenon

Gaurav Kamath

Michelle Yang

Siva Reddy

Morgan Sonderegger

Dallas Card

A central question in the study of language change is whether or not such change is generational. If a language changes over time generation… (voir plus)-by-generation, the process looks as follows: New generations of speakers introduce innovations, while older speakers conserve their usage patterns, and the language changes as new generations replace older ones. At the opposite extreme, language change could be a zeitgeist phenomenon, in which changes are universally adopted by speakers simultaneously, regardless of age or generational cohort. This paper asks this question in the context of word meaning change. We analyze meaning change in over 100 words across more than 7.9 million U.S. congressional speeches, to observe whether, when a word sense rises or falls in prominence, adult speakers from different generations uniformly adopt it, or those from older generations conserve their prior usage. Using language model-based word sense induction methods, we identify different senses of each word, and then model the prevalence of each of these word senses as a function of time and speaker age. We find that most words show a small but statistically significant effect of speaker age; across almost 140 y of Congress, older speakers typically take longer than younger speakers to follow changes in word usage, but nevertheless do so within a few years. Our findings indicate that despite minor age-based differences, word meaning change among mature speakers is likely not a generational process, but rather a zeitgeist process, in which older adult speakers can readily adopt new word usage patterns.

2025-07-27

Proceedings of the National Academy of Sciences (publié)

doi.org

A systematic review of risk stratification for pediatric appendicitis

Mahshid Mortazavi

Alexandra Dimmer

Elena Guadagno

Dan Poenaru

Sherif Emil

2025-07-26

Pediatric surgery international (Print) (publié)

doi.org

Leveraging Structure Between Environments: Phylogenetic Regularization Incentivizes Disentangled Representations

Jason Hartford

Recently, learning invariant predictors across varying environments has been shown to improve the generalization of supervised learning meth… (voir plus)ods. This line of investigation holds great potential for application to biological problem settings, where data is often naturally heterogeneous. Biological samples often originate from different distributions, or environments. However, in biological contexts, the standard "invariant prediction" setting may not completely fit: the optimal predictor may in fact vary across biological environments. There also exists strong domain knowledge about the relationships between environments, such as the evolutionary history of a set of species, or the differentiation process of cell types. Most work on generic invariant predictors have not assumed the existence of structured relationships between environments. However, this prior knowledge about environments themselves has already been shown to improve prediction through a particular form of regularization applied when learning a set of predictors. In this work, we empirically evaluate whether a regularization strategy that exploits environment-based prior information can be used to learn representations that better disentangle causal factors that generate observed data. We find evidence that these methods do in fact improve the disentanglement of latent embeddings. We also show a setting where these methods can leverage phylogenetic information to estimate the number of latent causal features.

2025-07-25

Transactions on Machine Learning Research (accepté)

doi.org

openreview.net

Uncovering Hidden Factions through Text-Network Representations: Unsupervised Public Opinion Mapping of Iran on Twitter in the 2022 Unrest

Sahar Omidi Shayegan

Jean-François Godbout

Reihaneh Rabbany

Ideological mapping on social media is typically framed as a supervised classification task that depends on stable party systems and abundan… (voir plus)t annotated data. These assumptions fail in contexts with weak political institutionalization, such as Iran. We recast ideology detection as a fully unsupervised mapping problem and introduce a text-network representation system, uncovering latent ideological factions on Persian Twitter during the 2022 Mahsa Amini protests. Using hundreds of millions of Persian tweets, we learn joint text–network embeddings by fine-tuning ParsBERT with a combined masked-language-modeling and contrastive objective and by passing the embeddings through a Graph Attention Network trained for link prediction on time-batched subgraphs. The pipeline integrates semantic and structural signals without observing labels. Density-based clustering reveals eight ideological blocs whose spatial relations mirror known political alliances. Alignment with 883 expert-labeled accounts yields 53% accuracy. This label-free framework scales to label-scarce contexts, offering new leverage for studying political debates online.

2025-07-25

colmweb.org/COLM/2025/Workshop/NLPOR (publié)

openreview.net

What Can Grokking Teach Us About Learning Under Nonstationarity?

Clare Lyle

Gharda Sokar

Razvan Pascanu

Andr'as Gyorgy

In continual learning problems, it is often necessary to overwrite components of a neural network's learned representation in response to ch… (voir plus)anges in the data stream; however, neural networks often exhibit \primacy bias, whereby early training data hinders the network's ability to generalize on later tasks. While feature-learning dynamics of nonstationary learning problems are not well studied, the emergence of feature-learning dynamics is known to drive the phenomenon of grokking, wherein neural networks initially memorize their training data and only later exhibit perfect generalization. This work conjectures that the same feature-learning dynamics which facilitate generalization in grokking also underlie the ability to overwrite previous learned features as well, and methods which accelerate grokking by facilitating feature-learning dynamics are promising candidates for addressing primacy bias in non-stationary learning problems. We then propose a straightforward method to induce feature-learning dynamics as needed throughout training by increasing the effective learning rate, i.e. the ratio between parameter and update norms. We show that this approach both facilitates feature-learning and improves generalization in a variety of settings, including grokking, warm-starting neural network training, and reinforcement learning tasks.

2025-07-25

ArXiv (prépublication)

doi.org

arxiv.org

Comparative genomics of
<i>Pseudomonas paraeruginosa</i>

Maxime Déraspe

Lori L. Burrows

Romé Voulhoux

Daniela Centrón

J. Corbeil

Paul H Roy

2025-07-24

Journal of Bacteriology (publié)

doi.org

Comparative genomics of Pseudomonas paraeruginosa

Maxime Déraspe

Lori L. Burrows

Romé Voulhoux

Daniela Centrón

J. Corbeil

Paul H Roy

ABSTRACT The PA7-clade (or group 3) of Pseudomonas aeruginosa is now recognized as a distinct species, Pseudomonas paraeruginosa. We report … (voir plus)here the genomic sequences of six new strains of P. paraeruginosa: Zw26 (the first complete genome of a cystic fibrosis isolate of P. paraeruginosa), draft genomes of four burn and wound strains from Argentina very closely related to PA7, and of Pa5196, the strain in which arabinosylation of type IV pili was documented. We compared the genomes of 82 strains of P. paraeruginosa and confirmed that the species is divided into two sub-clades. Core genomes are very similar, while most differences are found in “regions of genomic plasticity” (RGPs). Several genomic deletions were identified, and most are common to the CR1 sub-clade that includes Zw26 and Pa5196. All strains lack the type 3 secretion system (T3SS) and instead use an alternative virulence strategy involving an exolysin, a characteristic shared with group 5 P. aeruginosa. All strains tend to be multiresistant like PA7, with a significant proportion of carbapenem-resistant strains, either oprD mutants or carrying carbapenemase genes. Although P. paraeruginosa is still relatively rare, it has a worldwide distribution. Its multiresistance and its alternative virulence strategy need to be considered in future therapeutic development. IMPORTANCE Pseudomonas aeruginosa is an important opportunistic pathogen causing respiratory infections, notably in cystic fibrosis, and burn and wound infections. Our study reports six new genomes of Pseudomonas paraeruginosa, a new species recently reported as distinct from P. aeruginosa. The number of sequenced genomes of P. paraeruginosa is only about 1% that of P. aeruginosa. We compare the genomic content of nearly all strains of P. paraeruginosa in GenBank, highlighting the differences in core and accessory genomes, antimicrobial resistance genes, and virulence factors. This novel species is very similar in environmental spectrum to P. aeruginosa but is notably resistant to last-line antibiotics and uses an alternative virulence strategy based on exolysin—this strategy being shared with some P. aeruginosa outliers.

2025-07-24

Journal of Bacteriology (publié)

doi.org

A Generative Approach to LLM Harmfulness Mitigation with Red Flag Tokens

2025-07-24

SoLaR @ Conference on Language Modeling (poster)

doi.org