AI & ML
Optimal concentration and gradient sensing by perfect monitors
arXiv stat.TH0 views
arXiv:2608.08816v2 Announce Type: replace-cross Abstract: Cells often estimate chemical concentrations from only a few diffusing molecules, whose stochastic motion limits sensing precision. We consider a perfect monitoring instrument that records the instantaneous molecular density throughout a finite region without perturbing the concentration field or distinguishing molecular identities. The standard Berg-Purcell estimator weights all positions uniformly. Here we derive the optimal spatial weighting within a large class of unbiased estimators for both concentration and gradient sensing. Variance minimization maps to an electrostatic problem. Surprisingly, although the instrument monitors the entire volume, the optimal estimator assigns all weight to its boundary, reducing the uncertainty in both concentration and gradient sensing. We extend the results to arbitrary geometries and spatial dimensions.
Source:https://arxiv.org/abs/2608.08816PreviousCorrelation of divergency: c_delta. Being different in a similar way or notNextJaxMARL-BC: Multi-Agent Reinforcement Learning for Macroeconomic Modelling, in Seconds
Related posts
Hybrid retrieval with reciprocal rank fusion: notes from a RAG prototype
0dev.to
It is in co-creating, together. As humans do.
0dev.to
C64 Basic Dungeon Crawler: Stairs, Levels and Levelling Up (Part 11)
0Hacker News
Anthropic's internal AI use, at retail prices, now exceeds its employee pay
0Hacker News
Launch HN: Momentic (YC W24) – Agents that catch bugs by using your app
0Hacker News

Behavioral Design Unpacked: Nudges, Funnels & Persuasion | UX Unpacked #3
0dev.to UX