Polymer particle size constitutes a crucial characteristic of product quality
in polymerization. Raman spectroscopy is an established and reliable process
analytical technology for in-line concentration monitoring. Recent approaches
and some theoretical considerations show a correlation between Raman signals
and particle sizes but do not determine polymer size from Raman spectroscopic
measurements accurately and reliably. With this in mind, we propose three
alternative machine learning workflows to perform this task, all involving
diffusion maps, a nonlinear manifold learning technique for dimensionality
reduction: (i) directly from diffusion maps, (ii) alternating diffusion maps,
and (iii) conformal autoencoder neural networks. We apply the workflows to a
data set of Raman spectra with associated size measured via dynamic light
scattering of 47 microgel (cross-linked polymer) samples in a diameter range of
208nm to 483 nm. The conformal autoencoders substantially outperform
state-of-the-art methods and results for the first time in a promising
prediction of polymer size from Raman spectra.

通过扩散映射方法和依托于拉曼光谱的数据集，建立了三个机器学习流程来准确预测从拉曼光谱中得出的聚合物颗粒大小，其中包括直接扩散映射、交替扩散映射和一种名为对等自编码器神经网络的方法。对比现有技术方法，这些自编码器在预测聚合物大小方面取得了有希望的首次突破。

非线性流形学习确定微胶体尺寸的拉曼光谱

Nonlinear Manifold Learning Determines Microgel Size from Raman  Spectroscopy

Intelligent human inputs are required both in the training and operation of
AI systems, and within the governance of blockchain systems and decentralized
autonomous organizations (DAOs). This paper presents a formal definition of
Human Intelligence Primitives (HIPs), and describes the design and
implementation of an Ethereum protocol for their on-chain collection, modeling,
and integration in machine learning workflows.

该论文提出了人类智能基元 (HIPs) 的正式定义，并描述了用于在以太坊协议上收集、建模和集成 HIPs 于机器学习工作流程中的设计和实现。

基于区块链的 “人在环路” 人工智能协议

A Blockchain Protocol for Human-in-the-Loop AI

Excessive reuse of test data has become commonplace in today's machine
learning workflows. Popular benchmarks, competitions, industrial scale tuning,
among other applications, all involve test data reuse beyond guidance by
statistical confidence bounds. Nonetheless, recent replication studies give
evidence that popular benchmarks continue to support progress despite years of
extensive reuse. We proffer a new explanation for the apparent longevity of
test data: Many proposed models are similar in their predictions and we prove
that this similarity mitigates overfitting. Specifically, we show empirically
that models proposed for the ImageNet ILSVRC benchmark agree in their
predictions well beyond what we can conclude from their accuracy levels alone.
Likewise, models created by large scale hyperparameter search enjoy high levels
of similarity. Motivated by these empirical observations, we give a
non-asymptotic generalization bound that takes similarity into account, leading
to meaningful confidence bounds in practical settings.

本文证明了在大尺度图像分类基准测试中，最新的提出的模型虽然其准确性相异，但其预测相似性很高，这一相似性缓解了过拟合问题。作者还根据这一相似性给出了一种新的泛化界限的数学模型，能够更好地应用于机器学习中测试数据的重复使用问题。