Counterpoint: Representing Forged Concepts as Emergent Variables Using Composite-Based Structural Equation Modeling

Yu, X., Zaza, S., Schuberth, F., & Henseler, J. (2021). Counterpoint: Representing Forged Concepts as Emergent Variables Using Composite-Based Structural Equation Modeling. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 52(SI), 114-130. https://doi.org/10.1145/3505639.3505647Studying and modeling theoretical concepts is a cornerstone activity in information systems (IS) research. Researchers have been familiar with one type of theoretical concept, namely behavioral concepts, which are assumed to exist in nature and measured by a set of observable variables. In this paper, we present a second type of theoretical concept, namely forged concepts, which are designed and assumed to emerge within their environment. While behavioral concepts are classically operationalized as latent variables, forged concepts are better specified as emergent variables. Additionally, we propose composite-based structural equation modeling (SEM) as a subtype of SEM that is eminently suitable to analyze models containing emergent variables. We shed light on the composite-based SEM steps: model specification, model identification, model estimation, and model assessment. Then, we present an illustrative example from the domain of IS research to demonstrate these four steps and show how modeling with emergent variables proceeds.authorsversionpublishe

Construct validity, longitudinal measurement invariance, incremental validity and predictive validity of the Original Grit Scale in Chinese young adults

Although many studies have attempted to validate grit scales because of the construct’s popularity, most have considered the shorter rather than the longer Original Grit Scale (Grit-O). We examined the Grit-O’s construct validity, longitudinal measurement invariance, incremental validity for academic performance, and longitudinal predictive validity for subjective well-being among young Chinese. We used a cross-sectional sample of 3,322 college students and a longitudinal sample of 1,884 college students, tested twice over 10 months. The first-order factor model fit the data better than other models and showed partial configural and metric measurement invariance over time. Grit and its two facets longitudinally predicted subjective well-being (i.e., life satisfaction, happiness, positive affect, negative affect, and depression) but had negligible incremental validity for two semesters’ grades after controlling for conscientiousness. So, while the Grit-O could be a useful construct for young adults, its predictive value overlaps with a better-established construct, conscientiousness

12-h clock regulation of genetic information flow by XBP1s

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Pan, Y., Ballance, H., Meng, H., Gonzalez, N., Kim, S., Abdurehman, L., York, B., Chen, X., Schnytzer, Y., Levy, O., Dacso, C. C., McClung, C. A., O'Malley, B. W., Liu, S., & Zhu, B. 12-h clock regulation of genetic information flow by XBP1s. Plos Biology, 18(1), (2020): e3000580, doi:10.1371/journal.pbio.3000580.Our group recently characterized a cell-autonomous mammalian 12-h clock independent from the circadian clock, but its function and mechanism of regulation remain poorly understood. Here, we show that in mouse liver, transcriptional regulation significantly contributes to the establishment of 12-h rhythms of mRNA expression in a manner dependent on Spliced Form of X-box Binding Protein 1 (XBP1s). Mechanistically, the motif stringency of XBP1s promoter binding sites dictates XBP1s’s ability to drive 12-h rhythms of nascent mRNA transcription at dawn and dusk, which are enriched for basal transcription regulation, mRNA processing and export, ribosome biogenesis, translation initiation, and protein processing/sorting in the Endoplasmic Reticulum (ER)-Golgi in a temporal order consistent with the progressive molecular processing sequence described by the central dogma information flow (CEDIF). We further identified GA-binding proteins (GABPs) as putative novel transcriptional regulators driving 12-h rhythms of gene expression with more diverse phases. These 12-h rhythms of gene expression are cell autonomous and evolutionarily conserved in marine animals possessing a circatidal clock. Our results demonstrate an evolutionarily conserved, intricate network of transcriptional control of the mammalian 12-h clock that mediates diverse biological pathways. We speculate that the 12-h clock is coopted to accommodate elevated gene expression and processing in mammals at the two rush hours, with the particular genes processed at each rush hour regulated by the circadian and/or tissue-specific pathways.This study was supported by the American Diabetes Association junior faculty development award 1-18-JDF-025 to B.Z., by funding from National Institute of Health HD07879 and 1P01DK113954 to B.W.O, by funding from National Science Foundation award 1703170 to C.C.D. and B.Z., and by funding from Brockman Foundation to C.C.D and B.W.O. This work was further supported by the UPMC Genome Center with funding from UPMC’s Immunotherapy and Transplant Center. This research was supported in part by the University of Pittsburgh Center for Research Computing through the resources provided. Research reported in this publication was further supported by the National Institute of Diabetes And Digestive And Kidney Diseases of the National Institutes of Health under award number P30DK120531 to Pittsburgh Liver Research Center, in which both S.L. and B.Z. are members. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

SMAUG: End-to-End Full-Stack Simulation Infrastructure for Deep Learning Workloads

In recent years, there has been tremendous advances in hardware acceleration of deep neural networks. However, most of the research has focused on optimizing accelerator microarchitecture for higher performance and energy efficiency on a per-layer basis. We find that for overall single-batch inference latency, the accelerator may only make up 25-40%, with the rest spent on data movement and in the deep learning software framework. Thus far, it has been very difficult to study end-to-end DNN performance during early stage design (before RTL is available) because there are no existing DNN frameworks that support end-to-end simulation with easy custom hardware accelerator integration. To address this gap in research infrastructure, we present SMAUG, the first DNN framework that is purpose-built for simulation of end-to-end deep learning applications. SMAUG offers researchers a wide range of capabilities for evaluating DNN workloads, from diverse network topologies to easy accelerator modeling and SoC integration. To demonstrate the power and value of SMAUG, we present case studies that show how we can optimize overall performance and energy efficiency for up to 1.8-5x speedup over a baseline system, without changing any part of the accelerator microarchitecture, as well as show how SMAUG can tune an SoC for a camera-powered deep learning pipeline.Comment: 14 pages, 20 figure