CARTOGRAPHER: A tool for string diagrammatic reasoning

We introduce cartographer, a tool for editing and rewriting string diagrams of symmetric monoidal categories. Our approach is principled: the layout exploits the isomorphism between string diagrams and certain cospans of hypergraphs; the implementation of rewriting is based on the soundness and completeness of convex double-pushout rewriting for string diagram rewriting

Completeness of Nominal PROPs

We introduce nominal string diagrams as string diagrams internal in the category of nominal sets. This leads us to define nominal PROPs and nominal monoidal theories. We show that the categories of ordinary PROPs and nominal PROPs are equivalent. This equivalence is then extended to symmetric monoidal theories and nominal monoidal theories, which allows us to transfer completeness results between ordinary and nominal calculi for string diagrams

Annual Report 2017-2018

LETTER FROM THE DEAN I am pleased to share with you the College of Computing and Digital Media’s (CDM) 2017-18 annual report, highlighting the many achievements across our community. It was a big year. We began offering five new programs (two bachelor’s, two master’s, and one PhD) across our three schools, in addition to several new certificate programs through our Institute for Professional Development. We built new, cutting-edge spaces to support these and other programs— most notably a 4,500 square-foot makerspace, a robotics and medical engineering lab, an augmented and virtual reality lab, and plans for a cyber-physical systems project lab. Our faculty continued to pursue their research and creative agendas, offering collaborative opportunities with students and partners. CDM students and alumni were celebrated for their many achievements— everything from leading the winning teams at the U.S. Cyber Challenge and Campus 1871 to showcasing their games at juried festivals and winning national screenwriting competitions. We encouraged greater research and teaching collaboration, both between our own schools and with units outside CDM. Design and Computing faculty are working together on an NSA grant for smart home devices that considers both software and interface/design, as well as a new grant-funded game lab. One Project Bluelight film team collaborated with The Theatre School and the School of Music while CDM and College of Science and Health faculty joined forces to research the links between traumatic brain injury, domestic violence, and deep games. It has been exciting and inspiring to witness the accomplishments of our innovative and dedicated community. We are proud to provide the space and resources for them to do their exceptional work. David MillerDean, College of Computing and Digital Mediahttps://via.library.depaul.edu/cdmannual/1001/thumbnail.jp

Completeness of Nominal PROPs

We introduce nominal string diagrams as string diagrams internal in the category of nominal sets. This leads us to define nominal PROPs and nominal monoidal theories. We show that the categories of ordinary PROPs and nominal PROPs are equivalent. This equivalence is then extended to symmetric monoidal theories and nominal monoidal theories, which allows us to transfer completeness results between ordinary and nominal calculi for string diagrams

Tools and Algorithms for the Construction and Analysis of Systems

This book is Open Access under a CC BY licence. The LNCS 11427 and 11428 proceedings set constitutes the proceedings of the 25th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2019, which took place in Prague, Czech Republic, in April 2019, held as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019. The total of 42 full and 8 short tool demo papers presented in these volumes was carefully reviewed and selected from 164 submissions. The papers are organized in topical sections as follows: Part I: SAT and SMT, SAT solving and theorem proving; verification and analysis; model checking; tool demo; and machine learning. Part II: concurrent and distributed systems; monitoring and runtime verification; hybrid and stochastic systems; synthesis; symbolic verification; and safety and fault-tolerant systems

Provenance Management for Collaborative Data Science Workflows

Collaborative data science activities are becoming pervasive in a variety of communities, and are often conducted in teams, with people of different expertise performing back-and-forth modeling and analysis on time-evolving datasets. Current data science systems mainly focus on specific steps in the process such as training machine learning models, scaling to large data volumes, or serving the data or the models, while the issues of end-to-end data science lifecycle management are largely ignored. Such issues include, for example, tracking provenance and derivation history of models, identifying data processing pipelines and keeping track of their evolution, analyzing unexpected behaviors and monitoring the project health, and providing the ability to reason about specific analysis results. We address these challenges by ingesting, managing, and analyzing rich provenance information generated during data science projects, and using it to enable users to easily publish, share, and discover data analytics projects. We first describe the design of our unified provenance and metadata management system, called ProvDB. We adopt a schema-later approach and use a flexible graph-based provenance representation model that combines the core concepts in version control and provenance management. We describe several ingestion mechanisms for this provenance model and show how heterogeneous data analysis environments can be served with natural extensions to this framework. We also describe a set of novel features of the system including graph queries for retrospective provenance, fileviews for data transformations, introspective queries for debugging, and continuous monitoring queries for anomaly detection. We then illustrate how to support deep learning modeling lifecycle via the extensibility mechanism in ProvDB. We describe techniques to compactly store and efficiently query the rich set of data artifacts generated during deep learning modeling lifecycle. We also describe a high-level domain specific language that helps raise the abstraction level during model exploration and enumeration and accelerate the modeling process. Lastly, we propose graph query operators and develop efficient evaluation techniques to address the verbose and evolving nature of such provenance graphs. First, we introduce a graph segmentation operator, which queries the provenance of a collection of user-given vertices (e.g., versioned files, author names) via flexible boundary criteria. Second, we propose a graph summarization operator to aggregate the results of multiple segmentation operations, and allow multi-resolution interaction with the aggregation result to understand similar and abnormal behaviors in those segments