Operation of weaving partial Steiner triple systems

We introduce an operation of a kind of product which associates with a partial Steiner triple system another partial Steiner triple system, the starting one being a quotient of the result. We discuss relations of our product to some other product-like constructions and prove some preservation/non-preservation theorems. In particular, we show series of anti-Pasch Steiner triple systems which are obtained that way

Inference of single-cell phylogenies from lineage tracing data using Cassiopeia.

The pairing of CRISPR/Cas9-based gene editing with massively parallel single-cell readouts now enables large-scale lineage tracing. However, the rapid growth in complexity of data from these assays has outpaced our ability to accurately infer phylogenetic relationships. First, we introduce Cassiopeia-a suite of scalable maximum parsimony approaches for tree reconstruction. Second, we provide a simulation framework for evaluating algorithms and exploring lineage tracer design principles. Finally, we generate the most complex experimental lineage tracing dataset to date, 34,557 human cells continuously traced over 15 generations, and use it for benchmarking phylogenetic inference approaches. We show that Cassiopeia outperforms traditional methods by several metrics and under a wide variety of parameter regimes, and provide insight into the principles for the design of improved Cas9-enabled recorders. Together, these should broadly enable large-scale mammalian lineage tracing efforts. Cassiopeia and its benchmarking resources are publicly available at www.github.com/YosefLab/Cassiopeia

Self-Enforcing Access Control for Encrypted RDF

The amount of raw data exchanged via web protocols is steadily increasing. Although the Linked Data infrastructure could potentially be used to selectively share RDF data with different individuals or organisations, the primary focus remains on the unrestricted sharing of public data. In order to extend the Linked Data paradigm to cater for closed data, there is a need to augment the existing infrastructure with robust security mechanisms. At the most basic level both access control and encryption mechanisms are required. In this paper, we propose a flexible and dynamic mechanism for securely storing and efficiently querying RDF datasets. By employing an encryption strategy based on Functional Encryption (FE) in which controlled data access does not require a trusted mediator, but is instead enforced by the cryptographic approach itself, we allow for fine-grained access control over encrypted RDF data while at the same time reducing the administrative overhead associated with access control management

Hydrogen bonding in organic systems: a study using x-ray and neutron diffraction and database analyses.

This thesis covers three topics related to the field of crystal engineering. Three different approaches to improving the understanding of hydrogen bonding are covered; analysis of a family of related molecules, investigations of specific functional groups and a systematic, data-driven study of intramolecular hydrogen bonding patterns. Chapters 2 to 4 and chapter 11 cover the background theory to the different methods used to obtain the data discussed in the remainder of the thesis. X-ray and neutron diffraction techniques are discussed, along with sections describing the Cambridge Structural Database, which was used as a data source throughout this work, and a brief section on intermolecular forces. Crystal structure analyses of seventeen gem-alkynol molecules are given in chapters 5 to 10. The gem-alkynol functionality is particularly interesting for a study of intermolecular interactions as it is a combination of both a strong and weak hydrogen bonding group. The group of molecules was investigated with the aim of locating robust supramolecular motifs. The group is subdivided into sections containing molecules with similar structures and their packing patterns are discussed. The second experimental section, chapters 12 and 13, comprises statistical studies into the function of the azido and cyano functional groups as hydrogen bond acceptors. The technique used was to use the Cambridge Structural Database as a data source for the main analysis, then complement the results with simple theoretical calculations. The remaining chapter, 14, describes a systematic analysis of intermolecular hydrogen bonded motifs. A data-driven approach was designed which allows direct comparison of motifs by means of a probability ordered list

Learning Models over Relational Data using Sparse Tensors and Functional Dependencies

Integrated solutions for analytics over relational databases are of great practical importance as they avoid the costly repeated loop data scientists have to deal with on a daily basis: select features from data residing in relational databases using feature extraction queries involving joins, projections, and aggregations; export the training dataset defined by such queries; convert this dataset into the format of an external learning tool; and train the desired model using this tool. These integrated solutions are also a fertile ground of theoretically fundamental and challenging problems at the intersection of relational and statistical data models. This article introduces a unified framework for training and evaluating a class of statistical learning models over relational databases. This class includes ridge linear regression, polynomial regression, factorization machines, and principal component analysis. We show that, by synergizing key tools from database theory such as schema information, query structure, functional dependencies, recent advances in query evaluation algorithms, and from linear algebra such as tensor and matrix operations, one can formulate relational analytics problems and design efficient (query and data) structure-aware algorithms to solve them. This theoretical development informed the design and implementation of the AC/DC system for structure-aware learning. We benchmark the performance of AC/DC against R, MADlib, libFM, and TensorFlow. For typical retail forecasting and advertisement planning applications, AC/DC can learn polynomial regression models and factorization machines with at least the same accuracy as its competitors and up to three orders of magnitude faster than its competitors whenever they do not run out of memory, exceed 24-hour timeout, or encounter internal design limitations.Comment: 61 pages, 9 figures, 2 table

Information Architecture for a Chemical Modeling Knowledge Graph

Machine learning models for chemical property predictions are high dimension design challenges spanning multiple disciplines. Free and open-source software libraries have streamlined the model implementation process, but the design complexity remains. In order better navigate and understand the machine learning design space, model information needs to be organized and contextualized. In this work, instances of chemical property models and their associated parameters were stored in a Neo4j property graph database. Machine learning model instances were created with permutations of dataset, learning algorithm, molecular featurization, data scaling, data splitting, hyperparameters, and hyperparameter optimization techniques. The resulting graph contains over 83,000 nodes and 4 million edges and can be explored with interactive visualization software. The structure of the property graph is centered around models and molecules which enables efficient and intuitive inter- and intra-model evaluation. We use a curated lipophilicity dataset to demonstrate graph use cases. Difficult to predict molecules were identified across multiple models simultaneously. Powerful and expressive graph queries were implemented to identify molecular fragments that were both prevalent and associated with high lipophilicity prediction error