Alchemy can't be science--common sense tells us as much. But perhaps common sense is not the best measure of what science is, or was. In this book, Bruce Moran looks past contemporary assumptions and prejudices to determine what alchemists were actually doing in the context of early modern science. Examining the ways alchemy and chemistry were studied and practiced between 1400 and 1700, he shows how these approaches influenced their respective practitioners' ideas about nature and shaped their inquiries into the workings of the natural world. His work sets up a dialogue between what historians have usually presented as separate spheres; here we see how alchemists and early chemists exchanged ideas and methods and in fact shared a territory between their two disciplines. Distilling Knowledge suggests that scientific revolution may wear a different appearance in different cultural contexts. The metaphor of the Scientific Revolution, Moran argues, can be expanded to make sense of alchemy and other so-called pseudo-sciences--by including a new framework in which "process can count as an object, in which making leads to learning, and in which the messiness of conflict leads to discernment." Seen on its own terms, alchemy can stand within the bounds of demonstrative science.

Reacting to the perception that the break, early on in the scientific revolution, between alchemy and chemistry was clean and abrupt, Moran literately and engagingly recaps what was actually a slow process. Far from being the superstitious amalgam it is now considered, alchemy was genuine science before and during the scientific revolution. The distinctive alchemical procedure--distillation--became the fundamental method of analytical chemistry, and the alchemical goal of transmuting "base metals" into gold and silver led to the understanding of compounds and elements. What alchemy very gradually but finally lost in giving way to chemistry was its spiritual or religious aspect, the linkages it discerned between purely physical and psychological properties. Drawing saliently from the most influential alchemical and scientific texts of the medieval to modern epoch (especially the turbulent and eventful seventeenth century), Moran fashions a model short history of science volume

Written specifically for use in the educational program of the production division of Seagram Distillers Corporation, this volume provides a fundamental explanation of the physical and chemical processes involved in the operation of a grain alcohol distillery.

Distillation: Fundamentals and Principles — winner of the 2015 PROSE Award in Chemistry & Physics — is a single source of authoritative information on all aspects of the theory and practice of modern distillation, suitable for advanced students and professionals working in a laboratory, industrial plants, or a managerial capacity. It addresses the most important and current research on industrial distillation, including all steps in process design (feasibility study, modeling, and experimental validation), together with operation and control aspects. This volume features an extra focus on the conceptual design of distillation. - Winner of the 2015 PROSE Award in Chemistry & Physics from the Association of American Publishers - Practical information on the newest development written by recognized experts - Coverage of a huge range of laboratory and industrial distillation approaches - Extensive references for each chapter facilitates further study

The multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022. The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions. The volumes are organized in topical sections as follows: Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; . Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability; Part VI: Time series; financial machine learning; applications; applications: transportation; demo track.

The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023. The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows: Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering. Part II: ​Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning. Part III: ​Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning. Part IV: ​Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning. Part V: ​Robustness; Time Series; Transfer and Multitask Learning. Part VI: ​Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval. ​Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.