Browsing by Subject "context"

Now showing 1 - 4 of 4
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    Central Indiana STEM Talent Expansion Program: Student and Faculty Interventions
    (IEEE, 2015-08) Hundley, Stephen P.; Feldhaus, Charles R.; Watt, Jeffrey X.; Marrs, Kathleen A.; Gavrin, Andy; Mzumara, Howard; Department of Technology and Leadership Communication, School of Engineering and Technology
    Funded by 5-year, $2M grant from the National Science Foundation, the Central Indiana STEM Talent Expansion Program (CI-STEP) at Indiana University-Purdue University Indianapolis (IUPUI) is creating a pipeline of students and a campus culture change to increase the number of undergraduates obtaining Science, Technology, Engineering, and Mathematics (STEM) degrees. CI-STEP addresses initiatives needed for transforming the undergraduate STEM experience by propagating, expanding, and creating new evidence-based educational innovations in undergraduate STEM education at IUPUI.
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    E-CLoG: Counting edge-centric local graphlets
    (IEEE, 2017-12) Dave, Vachik S.; Ahmed, Nesreen K.; Al Hasan, Mohammad; Computer and Information Science, School of Science
    In recent years, graphlet counting has emerged as an important task in topological graph analysis. However, the existing works on graphlet counting obtain the graphlet counts for the entire network as a whole. These works capture the key graphical patterns that prevail in a given network but they fail to meet the demand of the majority of real-life graph related prediction tasks such as link prediction, edge/node classification, etc., which require to build features for an edge (or a vertex) of a network. To meet the demand for such applications, efficient algorithms are needed for counting local graphlets within the context of an edge (or a vertex). In this work, we propose an efficient method, titled E-CLOG, for counting all 3,4 and 5 size local graphlets with the context of a given edge for its all different edge orbits. We also provide a shared-memory, multi-core implementation of E-CLOG, which makes it even more scalable for very large real-world networks. In particular, We obtain strong scaling on a variety of graphs (14x-20x on 36 cores). We provide extensive experimental results to demonstrate the efficiency and effectiveness of the proposed method. For instance, we show that E-CLOG is faster than existing work by multiple order of magnitudes; for the Wordnet graph E-CLOG counts all 3,4 and 5-size local graphlets in 1.5 hours using a single thread and in only a few minutes using the parallel implementation, whereas the baseline method does not finish in more than 4 days. We also show that local graphlet counts around an edge are much better features for link prediction than well-known topological features; our experiments show that the former enjoys between 10% to 45% of improvement in the AUC value for predicting future links in three real-life social and collaboration networks.
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    Exploration of a new tool for assessing emotional inferencing after traumatic brain injury
    (Taylor and Francis, 2015-05) Zupan, Barbra; Neumann, Dawn; Babbage, Duncan R.; Willer, Barry; Department of Physical Medicine and Rehabilitation, IU School of Medicine
    bjective: To explore validity of an assessment tool under development—the Emotional Inferencing from Stories Test (EIST). This measure is being designed to assess the ability of people with traumatic brain injury (TBI) to make inferences about the emotional state of others solely from contextual cues. Methods and procedures: Study 1: 25 stories were presented to 40 healthy young adults. From this data, two versions of the EIST (EIST-1; EIST-2) were created. Study 2: Each version was administered to a group of participants with moderate-to-severe TBI—EIST 1 group: 77 participants; EIST-2 group: 126 participants. Participants also completed a facial affect recognition (DANVA2-AF) test. Participants with facial affect recognition impairment returned 2 weeks later and were re-administered both tests. Main outcomes: Participants with TBI scored significantly lower than the healthy group mean for EIST-1, F(1,114) = 68.49, p < 0.001, and EIST-2, F(1,163) = 177.39, p < 0.001. EIST scores in the EIST-2 group were significantly lower than the EIST-1 group, t = 4.47, p < 0.001. DANVA2-AF scores significantly correlated with EIST scores, EIST-1: r = 0.50, p < 0.001; EIST-2: r = 0.31, p < 0.001. Test–re-test reliability scores for the EIST were adequate. Conclusions: Both versions of the EIST were found to be sensitive to deficits in emotional inferencing. After further development, the EIST may provide clinicians valuable information for intervention planning.
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    The Mutual Impact of Global Strategy and Organizational Learning: Current Themes and Future Directions
    (Wiley, 2015-05) Hotho, Jasper; Easterby-Smith, Mark; Lyles, Marjorie A.; School of Business
    Despite the interest in issues of knowing and learning in the global strategy field, there has been limited mutual engagement and interaction between the fields of global strategy and organizational learning. The purpose of our article is to reflect on and articulate how the mutual exchange of ideas between these fields can be encouraged. To this end, we first conduct a review of the intersection of the fields of global strategy and organizational learning. We then present two recommendations regarding how the interaction between the two fields can be enhanced. Our first recommendation is for global strategy research to adopt a broader notion of organizational learning. Our second recommendation is for global strategy research to capitalize on its attention to context in order to inform and enhance organizational learning theory. We discuss the use of context in a number of common research designs and highlight how the scope for theoretical contributions back to organizational learning varies with the research design that is adopted.