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Hugh Laurie proved he actually was House MD, till he apologised
There are two kinds of people in the English-speaking world. Those who haven’t watched House MD and those who know it’s never lupus. Those who think that The Pitt is a good show and those who know that if you could reason with religious people, there would be no religious people.
Diel metabolic plasticity of CAM photosynthesis in MD-2 pineapple (Ananas comosus) under contrasting tropical environments: biochemical patterns and agronomic implications.
Abstract The current understanding of Crassulacean Acid Metabolism (CAM), including semi-controlled studies in pineapple, does not fully explain outcomes observed under commercial field conditions. Although empirical agronomy confirms a strong climatic influence on growth and development, mechanistic explanations at the metabolic level particularly for photosynthate allocation remain scarce. This study evaluated how environmental variation affects diel CAM outputs and how such effects can be...
Speculative Sampling For Faster Molecular Dynamics
arXiv:2606.02455v1 Announce Type: cross Abstract: Molecular dynamics (MD) is a key tool for simulating the dynamical behavior of atomic systems. However, MD is inherently serial, which makes it difficult to increase single-system throughput with concurrent compute. To address this, we introduce Langevin Speculative Dynamics (LSD), a distributed and model-agnostic speculative sampler for accelerating MD without adding relative error.
Speculative Sampling For Faster Molecular Dynamics
arXiv:2606.02455v1 Announce Type: new Abstract: Molecular dynamics (MD) is a key tool for simulating the dynamical behavior of atomic systems. However, MD is inherently serial, which makes it difficult to increase single-system throughput with concurrent compute. To address this, we introduce Langevin Speculative Dynamics (LSD), a distributed and model-agnostic speculative sampler for accelerating MD without adding relative error.
Protein Language Model Embeddings Improve Generalization of Implicit Transfer Operators
Announce Type: replace Abstract: Molecular dynamics (MD) is a central computational tool in physics, chemistry, and biology, enabling quantitative prediction of experimental observables as expectations over high-dimensional molecular distributions such as Boltzmann distributions and transition densities. However, conventional MD is fundamentally limited by the high computational cost required to generate independent samples. Generative molecular dynamics (GenMD) has recently emerged as an...
Protein Language Model Embeddings Improve Generalization of Implicit Transfer Operators
Announce Type: replace-cross Abstract: Molecular dynamics (MD) is a central computational tool in physics, chemistry, and biology, enabling quantitative prediction of experimental observables as expectations over high-dimensional molecular distributions such as Boltzmann distributions and transition densities. However, conventional MD is fundamentally limited by the high computational cost required to generate independent samples. Generative molecular dynamics (GenMD) has recently emerged as...
From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
Announce Type: replace Abstract: Machine Learning Interatomic Potentials (MLIPs) sometimes fail to reproduce the physical smoothness of the quantum potential energy surface (PES), leading to erroneous behavior in downstream simulations that standard energy and force regression evaluations can miss. Existing evaluations, such as microcanonical molecular dynamics (MD), are computationally expensive and primarily probe near-equilibrium states. To improve evaluation metrics for MLIPs, we...
From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
Announce Type: replace-cross Abstract: Machine Learning Interatomic Potentials (MLIPs) sometimes fail to reproduce the physical smoothness of the quantum potential energy surface (PES), leading to erroneous behavior in downstream simulations that standard energy and force regression evaluations can miss. Existing evaluations, such as microcanonical molecular dynamics (MD), are computationally expensive and primarily probe near-equilibrium states. To improve evaluation metrics for MLIPs, we...
Group Entropies and Mirror Duality: A Class of Flexible Mirror Descent Updates for Machine Learning
arXiv:2603.08651v2 Announce Type: replace Abstract: We introduce a comprehensive theoretical and algorithmic framework that bridges formal group theory and group entropies with modern machine learning, paving the way for an infinite, flexible family of Mirror Descent (MD) optimization algorithms. Our approach exploits the rich structure of group entropies, which are generalized entropic functionals governed by group composition laws, encompassing and significantly extending all trace-form...
Consensus-based adaptive sampling and approximation for high-dimensional energy landscapes
Announce Type: replace Abstract: We present a consensus-based framework that unifies phase space exploration with posterior-residual-based adaptive sampling for surrogate construction in high-dimensional energy landscapes. Unlike standard approximation tasks where sampling points can be freely queried, physical systems with complex energy landscapes such as molecular dynamics (MD) do not have direct access to arbitrary sampling regions due to the physical constraints and energy barriers; the...