Classification of ledger accounts for creameries

In presenting this Classification of Ledger Accounts for Creameries it is the aim of the Bureau of Markets to emphasize the importance of the use of a definite and logical classification of accounts for keeping the financial records of any business and to describe in detail a classification which can be used advantageously by creameries. The use of such a classification is not only a great aid to the bookkeeper in the performance of routine duties, but its consistent use also insures a uniform method of presenting the financial information from year to year regardless of changes in the personnel. The use of these uniform methods by an industry as a whole makes possible the exchange of data regarding business operations, which is of untold value as a guide to efficient operation

Privacy-Preserving Gaussian Process Regression -- A Modular Approach to the Application of Homomorphic Encryption

Much of machine learning relies on the use of large amounts of data to train models to make predictions. When this data comes from multiple sources, for example when evaluation of data against a machine learning model is offered as a service, there can be privacy issues and legal concerns over the sharing of data. Fully homomorphic encryption (FHE) allows data to be computed on whilst encrypted, which can provide a solution to the problem of data privacy. However, FHE is both slow and restrictive, so existing algorithms must be manipulated to make them work efficiently under the FHE paradigm. Some commonly used machine learning algorithms, such as Gaussian process regression, are poorly suited to FHE and cannot be manipulated to work both efficiently and accurately. In this paper, we show that a modular approach, which applies FHE to only the sensitive steps of a workflow that need protection, allows one party to make predictions on their data using a Gaussian process regression model built from another party's data, without either party gaining access to the other's data, in a way which is both accurate and efficient. This construction is, to our knowledge, the first example of an effectively encrypted Gaussian process

Rearrangement of the Keratin Cytoskeleton after Combined Treatment with Microtubule and Microfilament Inhibitors

In addition to containing microtubule and microfilament systems, vertebrate epithelial cells contain an elaborate keratin intermediate-filament cytoskeleton. Little is known about its structural organization or function. Using indirect immunofluorescence microscopy with an antikeratin antiserum probe, we found that destabilization of microtubules and microfilaments with cytostatic drugs induces significant alterations in the cytoskeletal organizationof keratin filaments in HeLa and fetal mouse epidermal cells. Keratin filament organizationwas observed to undergo a rapid (1-2 h) transition from a uniform distribution to an open lattice of keratin fibers stabilized by membrane-associated focal centers . Since addition of any one drug alone did not elicit significant organizational change in the keratin cytoskeleton,we suggest that microfilaments and microtubules have a combined role in maintaining the arrangement of keratin in these cells. Vimentin filaments, the only other intermediate sized filaments found in HeLa cells, did not co-distribute with keratin in untreated or drug treated cells. These findings offer a new way to approach the study of the dynamics and functional roles of the keratin cytoskeleton in epithelial cells

Adversarially Learned Anomaly Detection on CMS open data: re-discovering the top quark

We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the problem of detecting new physics processes in proton–proton collisions at the Large Hadron Collider. Anomaly detection based on ALAD matches performances reached by Variational Autoencoders, with a substantial improvement in some cases. Training the ALAD algorithm on 4.4 fb⁻¹ of 8 TeV CMS Open Data, we show how a data-driven anomaly detection and characterization would work in real life, re-discovering the top quark by identifying the main features of the tt̄ experimental signature at the LHC