arXiv:2602.16880v1 Announce Type: new Abstract: Uniform interpolation is a strong form of interpolation providing an interpretation of propositional quantifiers within a propositional logic. Pitts’ seminal work establishes this property for intuitionistic propositional logic relying on a sequent calculus in which na”ive backward proof-search terminates. This constructive approach has been adapted to a wide range of logics, including intuitionistic modal logics. Surprisingly, no intuitionistic modal logic with independent box and diamond has yet been shown to satisfy uniform interpolation. […]

Ver mais

Multi-source precipitation products (MSPs) from satellite retrievals and reanalysis are widely used for hydroclimatic monitoring, yet spatially heterogeneous biases and limited skill for extremes still constrain their hydrologic utility. Here we develop a dual-stage TransUNet-based multi-source precipitation merging framework (DDL-MSPMF) that integrates six MSPs with four ERA5 near-surface physical predictors. A first-stage classifier estimates daily precipitation occurrence probability, and a second-stage regressor fuses the classifier outputs together with all predictors to estimate daily precipitation amount at 0.25 degree […]

Ver mais

Table of Links Abstract and 1. Introduction Related Works 2.1 Traditional Index Selection Approaches 2.2 RL-based Index Selection Approaches Index Selection Problem Methodology 4.1 Formulation of the DRL Problem 4.2 Instance-Aware Deep Reinforcement Learning for Efficient Index Selection System Framework of IA2 5.1 Preprocessing Phase 5.2 RL Training and Application Phase Experiments 6.1 Experimental Setting 6.2 Experimental Results 6.3 End-to-End Performance Comparison 6.4 Key Insights Conclusion and Future Work, and References 5 System Framework of IA2 As shown […]

Ver mais

arXiv:2602.12368v1 Announce Type: new Abstract: This work introduces the Nirenberg Neural Network: a numerical approach to the Nirenberg problem of prescribing Gaussian curvature on $S^2$ for metrics that are pointwise conformal to the round metric. Our mesh-free physics-informed neural network (PINN) approach directly parametrises the conformal factor globally and is trained with a geometry-aware loss enforcing the curvature equation. Additional consistency checks were performed via the Gauss-Bonnet theorem, and spherical-harmonic expansions were fit to the learnt models to […]

Ver mais

[Editor’s Note: This class was a part of the 2019 DFL Jane Street Fellowship.] Firstly, a huge thank-you to the participants in the study group that led to this guide, for their enthusiastic participation, interesting perspectives and insights, and useful feedback and contributions: Scott Cameron, Jean Michel Sarr, Suvarna Kadam, James Allingham, Bharati Srinivasan, Lood van Niekerk, and Witold Szejgis. Thank you too to the Depth First Learning team for bringing me on board, and especially to Avital […]

Ver mais

arXiv:2506.12371v4 Announce Type: replace-cross Abstract: Identifying and measuring biases associated with sensitive attributes is a crucial consideration in healthcare to prevent treatment disparities. One prominent issue is inaccurate pulse oximeter readings, which tend to overestimate oxygen saturation for dark-skinned patients and misrepresent supplemental oxygen needs. Most existing research has revealed statistical disparities linking device measurement errors to patient outcomes in intensive care units (ICUs) without causal formalization. This study causally investigates how racial discrepancies in oximetry measurements affect […]

Ver mais

Mitchell Hashimoto: My AI Adoption Journey Some really good and unconventional tips in here for getting to a place with coding agents where they demonstrably improve your workflow and productivity. I particularly liked: Reproduce your own work – when learning to use coding agents Mitchell went through a period of doing the work manually, then recreating the same solution using agents as an exercise: I literally did the work twice. I’d do the work manually, and then I’d […]

Ver mais