Columbia University

Topic models can describe topics with interpretable directions learned by sparse autoencoders instead of word lists, and an LLM-based topic judge prefers that representation on eight datasets, with the largest margins on short and abstract texts.

Topic models can describe topics with interpretable directions learned by sparse autoencoders instead of word lists, and an LLM-based topic judge prefers that representation on eight datasets, with the largest margins on short and abstract texts.

Using satellite observations of solar-induced fluorescence together with model estimates of water table depth and aridity, and applying causality-guided explainable machine learning, this study quantified the relative roles of groundwater and climatic aridity in shaping the spatial pattern of photosynthesis across the contiguous United States, finding that groundwater's relative importance equals 48% to 101% of aridity's effect on forest photosynthesis, 30% to 58% in savannahs and shrublands, 22% to 42% in grasslands, and 15% to 32% in croplands.

Using satellite observations of solar-induced fluorescence together with model estimates of water table depth and aridity, and applying causality-guided explainable machine learning, this study quantified the relative roles of groundwater and climatic aridity in shaping the spatial pattern of photosynthesis across the contiguous United States, finding that groundwater's relative importance equals 48% to 101% of aridity's effect on forest photosynthesis, 30% to 58% in savannahs and shrublands, 22% to 42% in grasslands, and 15% to 32% in croplands.

The authors present SHERLOCK, an interpretable deep generative framework that represents genetic and pharmacological perturbations as structured interventions on a latent baseline cellular state, learns correlated and sparse perturbation representations, enables counterfactual estimation of downstream transcriptional effects under explicit identifiability assumptions, quantifies condition-dependent responses, and compositionally models combinatorial perturbations; across genome-scale CRISPR, chemical, and spatial perturbation datasets, it recovers perturbation relationships concordant with known biological pathways and pharmacological properties, identifies condition-dependent responses, and predicts combinatorial perturbation effects.

The authors present SHERLOCK, an interpretable deep generative framework that represents genetic and pharmacological perturbations as structured interventions on a latent baseline cellular state, learns correlated and sparse perturbation representations, enables counterfactual estimation of downstream transcriptional effects under explicit identifiability assumptions, quantifies condition-dependent responses, and compositionally models combinatorial perturbations; across genome-scale CRISPR, chemical, and spatial perturbation datasets, it recovers perturbation relationships concordant with known biological pathways and pharmacological properties, identifies condition-dependent responses, and predicts combinatorial perturbation effects.

Using neural ordinary differential equations (NODEs) optimized simultaneously across 290 mass-ratio time series of ice crystals grown in a levitation diffusion chamber, the study learned the unknown functional form of the transfer coefficient G in the depositional ice growth model, then used symbolic regression (PySR) to derive a closed-form expression; the weakly-constrained NODE model performed best on 138 of 290 experiments (MSE loss 16495, versus 41396 for no surface kinetics and 40751 for Nelson and Baker 1996), yielding G = a0·Gc^a1·[a2 + a3/m]^-1 + a4 with an added ice-crystal-mass term, and this expression predicted ice supersaturation and ice water content better than the capacitance growth model and Nelson and Baker 1996 in independent AIDA Aerosol and Cloud Chamber experiments.

Using neural ordinary differential equations (NODEs) optimized simultaneously across 290 mass-ratio time series of ice crystals grown in a levitation diffusion chamber, the study learned the unknown functional form of the transfer coefficient G in the depositional ice growth model, then used symbolic regression (PySR) to derive a closed-form expression; the weakly-constrained NODE model performed best on 138 of 290 experiments (MSE loss 16495, versus 41396 for no surface kinetics and 40751 for Nelson and Baker 1996), yielding G = a0·Gc^a1·[a2 + a3/m]^-1 + a4 with an added ice-crystal-mass term, and this expression predicted ice supersaturation and ice water content better than the capacitance growth model and Nelson and Baker 1996 in independent AIDA Aerosol and Cloud Chamber experiments.