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Graph entity dependencies (GEDs) are novel graph constraints, unifying keys and functional dependencies, for property graphs. They have been found useful in many real-world data quality and data management tasks, including fact checking on social media networks and entity resolution. In this paper, we study the discovery problem of GEDs -- finding a minimal cover of valid GEDs in a given graph data. We formalise the problem, and propose an effective and efficient approach to overcome major bottlenecks in GED discovery. In particular, we leverage existing graph partitioning algorithms to enable fast GED-scope discovery, and employ effective pruning strategies over the prohibitively large space of candidate dependencies. Furthermore, we define an interestingness measure for GEDs based on the minimum description length principle, to score and rank the mined cover set of GEDs. Finally, we demonstrate the scalability and effectiveness of our GED discovery approach through extensive experiments on real-world benchmark graph data sets; and present the usefulness of the discovered rules in different downstream data quality management applications.

Think College Reports present descriptive data in narrative or tabular form to provide timely information to researchers, practitioners, and policymakers for review and use. This report provides an overview of descriptive program and student-level data provided by Transition and Postsecondary Programs for Students with Intellectual Disabilities (TPSIDs) during the 2019-2020 academic year. Program data includes program characteristics, academic access, student supports, and integration of the program within the institute of higher education (IHE) during the fifth and final year of the 2015-2020 funding. Student data include student demographics, course enrollments, employment activities, and engagement in student life. This report also provides information on the strategic partnerships and financial sustainability of TPSID programs. Additionally, the report provides trends over time as well as descriptive data on the outcomes of students who exited TPSID programs in previous years. Finally, the report will highlight the impact of COVID-19 on student experiences and outcomes. [For the executive summary, see ED627033. For "Year Four Annual Report of the TPSID Model Demonstration Projects (2018-2019). Think College Reports," see ED611254.]

In mid-March 2020, the emergence of COVID-19 throughout the United States led to programmatic changes at most higher education institutions, including those involved in implementing model demonstration projects known as Transition and Postsecondary Programs for Students with Intellectual Disability (TPSID) projects. This Insight Brief describes how the COVID-19 pandemic impacted college students with intellectual disability (ID) enrolled in the colleges and universities offering TPSID programs.

Graph pattern matching is a fundamental operation for the analysis and exploration ofdata graphs. In thispaper, we presenta novel approachfor efficiently finding homomorphic matches for hybrid graph patterns, where each pattern edge may be mapped either to an edge or to a path in the input data, thus allowing for higher expressiveness and flexibility in query formulation. A key component of our approach is a lightweight index structure that leverages graph simulation to compactly encode the query answer search space. The index can be built on-the-fly during query execution and does not have to be persisted to disk. Using the index, we design a multi-way join algorithm to enumerate query solutions without generating any potentially exploding intermediate results. We demonstrate through extensive experiments that our approach can efficiently evaluate a wide range / broad spectrum of graph pattern queries and greatly outperforms existing approaches and recent graph query engines/systems.

We address the problem of summarizing embedded tree patterns extracted from large data trees. We do so by defining and mining closed and maximal embedded unordered tree patterns from a single large data tree. We design an embedded frequent pattern mining algorithm extended with a local closedness checking technique. This algorithm is called {\em closedEmbTM-prune} as it eagerly eliminates non-closed patterns. To mitigate the generation of intermediate patterns, we devise pattern search space pruning rules to proactively detect and prune branches in the pattern search space which do not correspond to closed patterns. The pruning rules are accommodated into the extended embedded pattern miner to produce a new algorithm, called {\em closedEmbTM-prune}, for mining all the closed and maximal embedded frequent patterns from large data trees. Our extensive experiments on synthetic and real large-tree datasets demonstrate that, on dense datasets, {\em closedEmbTM-prune} not only generates a complete closed and maximal pattern set which is substantially smaller than that generated by the embedded pattern miner, but also runs much faster with negligible overhead on pattern pruning.

The COVID-19 pandemic demands assimilation of all available biomedical knowledge to decode its mechanisms of pathogenicity and transmission. Despite the recent renaissance in unsupervised neural networks for decoding unstructured natural languages, a platform for the real-time synthesis of the exponentially growing biomedical literature and its comprehensive triangulation with deep omic insights is not available. Here, we present the nferX platform for dynamic inference from over 45 quadrillion possible conceptual associations extracted from unstructured biomedical text, and their triangulation with Single Cell RNA-sequencing based insights from over 25 tissues. Using this platform, we identify intersections between the pathologic manifestations of COVID-19 and the comprehensive expression profile of the SARS-CoV-2 receptor ACE2. We find that tongue keratinocytes and olfactory epithelial cells are likely under-appreciated targets of SARS-CoV-2 infection, correlating with reported loss of sense of taste and smell as early indicators of COVID-19 infection, including in otherwise asymptomatic patients. Airway club cells, ciliated cells and type II pneumocytes in the lung, and enterocytes of the gut also express ACE2. This study demonstrates how a holistic data science platform can leverage unprecedented quantities of structured and unstructured publicly available data to accelerate the generation of impactful biological insights and hypotheses., Comment: 5 figures

Between 2010 and 2020, the Office of Postsecondary Education in the U.S. Department of Education funded 52 model demonstration projects called Transition and Postsecondary Programs for Students with Intellectual Disabilities (TPSIDs). These grantees created or expanded inclusive higher education programs, enrolling 4,279 students with intellectual disability (ID) on 103 college or university campuses in 31 states. This Fast Fact provides an executive summary of the findings shared in the Annual Report of the Cohort 2 TPSID Model Demonstration Projects (Year Five 2019-2020). In mid-March of 2020, the emergence of COVID-19 throughout the United States led to changes in the implementation of services in most institutions of higher education, including those involved in TPSID projects. Throughout this summary, the authors reflect on the impact the pandemic had on students attending TPSIDs. [For "Annual Report of the Cohort 2 TPSID Model Demonstration Projects (Year 5, 2019-2020). Think College Reports," see ED627031.]