Der vollständige conditor, schweitzerbäcker und destillateur.

Vol. 1 has added t.-p.: Le refraicheur, oder ausführliche und deutliche Unterweisung zur Verfertigung allerlei Arten gefrornen und andrer Erfrischungen ... Von Christian Gustav Haupt ... Von neuem und mit Zusätzen hrsg. von Franz Xaver Czerdelinczkj ...

Jahrbuch des Vereins der Spiritus-Fabrikanten in Deutschland, des Vereins der Stärke-Interessenten in Deutschland und der Brennerei-Berufsgenossenschaft.

Mode of access: Internet.

The Corn distillery stated to the consideration of the landed interest of England.

Signatures: [A]⁴ B-I⁴ [K]1.

An inquiry into the policy and justice of the prohibition of the use of grain in the distilleries ...

Mode of access: Internet.

Half-a-century of successful trade : being a sketch of the rise and development of the business of W & A Gilbey, 1857-1907 /

Includes index.

The experienced English housekeeper : for the use and ease of ladies, housekeepers, cooks, &c., written purely from practice : dedicated to the Hon. Lady Elizabeth Warburton, whom the author lately served as housekeeper : consisting of several hundred original receipts, most of which never appeared in print /

Includes recipes for wine, mead, and other liquors; illustrations of kitchen stoves using coal; illustrations of layout of dishes for multi-course meal. Sample recipes: To bake herrings, To make cream pancakes, To make a blanc-mange of isinglass, To make cowslip wine.

The experienced English housekeeper : for the use and ease of ladies, housekeepers, cooks, &c. : written purely from practice, dedicated to the Hon. Lady Elizabeth Warburton, whom the author lately served as housekeeper : consisting of several hundred original receipts, most of which never appeared in print ...

Includes index.

British Manufacturing Industries. Edited by G.P. Bevan.

Imperfect; wanting the volume entitled, "Paper, by Prof. Archer, Printing, by Joseph Hatton, etc."

International brewers' journal.

Mode of access: Internet.

Industries agricoles de fermentation, distillerie agricole et industrielle, alcools, eaux-de-vie de fruits, rhums,

Mode of access: Internet.

Incremental learning for interactive e-mail filtering

In this article, we propose a framework, namely, Prediction-Learning-Distillation (PLD) for interactive document classification and distilling misclassified documents. Whenever a user points out misclassified documents, the PLD learns from the mistakes and identifies the same mistakes from all other classified documents. The PLD then enforces this learning for future classifications. If the classifier fails to accept relevant documents or reject irrelevant documents on certain categories, then PLD will assign those documents as new positive/negative training instances. The classifier can then strengthen its weakness by learning from these new training instances. Our experiments’ results have demonstrated that the proposed algorithm can learn from user-identified misclassified documents, and then distil the rest successfully.

To what extent has research conducted by the GaWC Research Network aided our understanding of large EU law firm geography?

This thesis explores whether a specific group of large EU law firms exhibited multiple common behaviours regarding their EU geographies between 1998 and 2009. These potentially common behaviours included their preferences for trading in certain EU locations, their usage of law firm alliances, and the specific reasons why they opened or closed EU branch offices. If my hypothesis is confirmed, this may indicate that certain aspects of large law firm geography are predictable – a finding potentially of interest to various stakeholders globally, including legal regulators, academics and law firms. In testing my hypothesis, I have drawn on research conducted by the Globalization and World Cities (GaWC) Research Network to assist me. Between 1999 and 2010, the GaWC published seven research papers exploring the geographies of large US and UK law firms. Several of the GaWC’s observations arising from these studies were evidence-based; others were speculative – including a novel approach for explaining legal practice branch office change, not adopted in research conducted previously or subsequently. By distilling the GaWC’s key observations these papers into a series of “sub-hypotheses”, I been able to test whether the geographical behaviours of my novel cohort of large EU law firms reflect those suggested by the GaWC. The more the GaWC’s suggested behaviours are observed among my cohort, the more my hypothesis will be supported. In conducting this exercise, I will additionally evaluate the extent to which the GaWC’s research has aided our understanding of large EU law firm geography. Ultimately, my findings broadly support most of the GaWC’s observations, notwithstanding our cohort differences and the speculative nature of several of the GaWC’s propositions. My investigation has also allowed me to refine several of the GaWC’s observations regarding commonly-observable large law firm geographical behaviours, while also addressing a key omission from the group’s research output.

Saccharomyces cerevisiae in the production of whisk(e)y

Whisky is a major global distilled spirit beverage. Whiskies are produced from cereal starches that are saccharified, fermented and distilled prior to spirit maturation. The strain of Saccharomyces cerevisiae employed in whisky fermentations is crucially important not only in terms of ethanol yields, but also for production of minor yeast metabolites which collectively contribute to development of spirit flavour and aroma characteristics. Distillers must therefore pay very careful attention to the strain of yeast exploited to ensure consistency of fermentation performance and spirit congener profiles. In the Scotch whisky industry, initiatives to address sustainability issues facing the industry (for example, reduced energy and water usage) have resulted in a growing awareness regarding criteria for selecting new distilling yeasts with improved efficiency. For example, there is now a desire for Scotch whisky distilling yeasts to perform under more challenging conditions such as high gravity wort fermentations. This article highlights the important roles of S. cerevisiae strains in whisky production and describes key fermentation performance attributes sought in distiller's yeast, such as high alcohol yields, stress tolerance and desirable congener profiles. We hope that the information herein will be useful for whisky producers and yeast suppliers in selecting new distilling strains of S. cerevisiae, and for the scientific community to stimulate further research in this area.

Flexible Computing Systems For AI Acceleration At The Extreme Edge Of The IoT

Embedding intelligence in extreme edge devices allows distilling raw data acquired from sensors into actionable information, directly on IoT end-nodes. This computing paradigm, in which end-nodes no longer depend entirely on the Cloud, offers undeniable benefits, driving a large research area (TinyML) to deploy leading Machine Learning (ML) algorithms on micro-controller class of devices. To fit the limited memory storage capability of these tiny platforms, full-precision Deep Neural Networks (DNNs) are compressed by representing their data down to byte and sub-byte formats, in the integer domain. However, the current generation of micro-controller systems can barely cope with the computing requirements of QNNs. This thesis tackles the challenge from many perspectives, presenting solutions both at software and hardware levels, exploiting parallelism, heterogeneity and software programmability to guarantee high flexibility and high energy-performance proportionality. The first contribution, PULP-NN, is an optimized software computing library for QNN inference on parallel ultra-low-power (PULP) clusters of RISC-V processors, showing one order of magnitude improvements in performance and energy efficiency, compared to current State-of-the-Art (SoA) STM32 micro-controller systems (MCUs) based on ARM Cortex-M cores. The second contribution is XpulpNN, a set of RISC-V domain specific instruction set architecture (ISA) extensions to deal with sub-byte integer arithmetic computation. The solution, including the ISA extensions and the micro-architecture to support them, achieves energy efficiency comparable with dedicated DNN accelerators and surpasses the efficiency of SoA ARM Cortex-M based MCUs, such as the low-end STM32M4 and the high-end STM32H7 devices, by up to three orders of magnitude. To overcome the Von Neumann bottleneck while guaranteeing the highest flexibility, the final contribution integrates an Analog In-Memory Computing accelerator into the PULP cluster, creating a fully programmable heterogeneous fabric that demonstrates end-to-end inference capabilities of SoA MobileNetV2 models, showing two orders of magnitude performance improvements over current SoA analog/digital solutions.

Concepts and methods for efficient decentralized learning in federated settings

Deep Neural Networks (DNNs) have revolutionized a wide range of applications beyond traditional machine learning and artificial intelligence fields, e.g., computer vision, healthcare, natural language processing and others. At the same time, edge devices have become central in our society, generating an unprecedented amount of data which could be used to train data-hungry models such as DNNs. However, the potentially sensitive or confidential nature of gathered data poses privacy concerns when storing and processing them in centralized locations. To this purpose, decentralized learning decouples model training from the need of directly accessing raw data, by alternating on-device training and periodic communications. The ability of distilling knowledge from decentralized data, however, comes at the cost of facing more challenging learning settings, such as coping with heterogeneous hardware and network connectivity, statistical diversity of data, and ensuring verifiable privacy guarantees. This Thesis proposes an extensive overview of decentralized learning literature, including a novel taxonomy and a detailed description of the most relevant system-level contributions in the related literature for privacy, communication efficiency, data and system heterogeneity, and poisoning defense. Next, this Thesis presents the design of an original solution to tackle communication efficiency and system heterogeneity, and empirically evaluates it on federated settings. For communication efficiency, an original method, specifically designed for Convolutional Neural Networks, is also described and evaluated against the state-of-the-art. Furthermore, this Thesis provides an in-depth review of recently proposed methods to tackle the performance degradation introduced by data heterogeneity, followed by empirical evaluations on challenging data distributions, highlighting strengths and possible weaknesses of the considered solutions. Finally, this Thesis presents a novel perspective on the usage of Knowledge Distillation as a mean for optimizing decentralized learning systems in settings characterized by data heterogeneity or system heterogeneity. Our vision on relevant future research directions close the manuscript.