Tootfinder

The Average Patient Fallacy
Alaleh Azhir, Shawn N. Murphy, Hossein Estiri
https://arxiv.org/abs/2509.26474 https://arxiv.org/pdf/2509.26474

The Average Patient Fallacy
Machine learning in medicine is typically optimized for population averages. This frequency weighted training privileges common presentations and marginalizes rare yet clinically critical cases, a bias we call the average patient fallacy. In mixture models, gradients from rare cases are suppressed by prevalence, creating a direct conflict with precision medicine. Clinical vignettes in oncology, cardiology, and ophthalmology show how this yields missed rare responders, delayed recognition of aty…

ChatGPT is useful for getting the average of the information in its weights on a given topic
Grok (Expert) is useful for filtering search engine slop into a summary with relevant, sourced, checkable web links
Neither of these necessarily produce The Truth, but in this example, Grok generates significantly more reliable output when precision matters. As time goes on and the volume of hallucinated nonsense on the Web mounts higher, Grok's ability to attribute its assertions t…

On the Design of Capacity-Achieving Distributions for Discrete-Time Poisson Channel with Low-Precision ADCs
Qianqian Li, Lintao Li, Lixiang Liu, Lei Yang, Caihong Gong, Hua Li, Shiya Hao, Xiaoming Dai
https://arxiv.org/abs/2509.17483

On the Design of Capacity-Achieving Distributions for Discrete-Time Poisson Channel with Low-Precision ADCs
This paper investigates the design of the capacity-achieving input distribution for the discrete-time Poisson channel (DTPC) under dark current effects with low-precision analog-to-digital converters (ADCs). This study introduces an efficient optimization algorithm that integrates the Newton-Raphson and Blahut-Arimoto (BA) methods to determine the capacity-achieving input distribution and the corresponding amplitudes of input mass points for the DTPC, subject to both peak and average power cons…