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To effectively address the challenges of large motions, complex backgrounds and large occlusions in videos, we introduce an end-to-end method for video frame interpolation based on recurrent residual convolution and depthwise over-parameterized convolution in this paper. Specifically, we devise a U-Net architecture utilizing recurrent residual convolution to enhance the quality of interpolated frame. First, the recurrent residual U-Net feature extractor is employed to extract features from input frames, yielding the kernel for each pixel. Subsequently, an adaptive collaboration of flows is utilized to warp the input frames, which are then fed into the frame synthesis network to generate initial interpolated frames. Finally, the proposed network incorporates depthwise over-parameterized convolution to further enhance the quality of interpolated frame. Experimental results on various datasets demonstrate the superiority of our method over state-of-the-art techniques in both objective and subjective evaluations. [ABSTRACT FROM AUTHOR]

Calibration of agent‐based models (ABMs) is a major challenge due to the complex nature of the systems being modeled, the heterogeneous nature of geographical regions, the varying effects of model inputs on the outputs, and computational intensity. Nevertheless, ABMs need to be carefully tuned to achieve the desirable goal of simulating spatiotemporal phenomena of interest, and a well‐calibrated model is expected to achieve an improved understanding of the phenomena. To address some of the above challenges, this article proposes an integrated framework of global sensitivity analysis (GSA) and calibration, called GSA‐CAL. Specifically, variance‐based GSA is applied to identify input parameters with less influence on differences between simulated outputs and observations. By dropping these less influential input parameters in the calibration process, this research reduces the computational intensity of calibration. Since GSA requires many simulation runs, due to ABMs' stochasticity, we leverage the high‐performance computing power provided by the advanced cyberinfrastructure. A spatially explicit ABM of influenza transmission is used as the case study to demonstrate the utility of the framework. Leveraging GSA, we were able to exclude less influential parameters in the model calibration process and demonstrate the importance of revising local settings for an epidemic pattern in an outbreak. [ABSTRACT FROM AUTHOR]

Understanding the characteristics of the rapidly evolving geospatial software ecosystem in the United States is critical to enable convergence research and education that are dependent on geospatial data and software. This paper describes a survey approach to better understand geospatial use cases, software and tools, and limitations encountered while using and developing geospatial software. The survey was broadcast through a variety of geospatial-related academic mailing lists and listservs. We report both quantitative responses and qualitative insights. As 42% of respondents indicated that they viewed their work as limited by inadequacies in geospatial software, ample room for improvement exists. In general, respondents expressed concerns about steep learning curves and insufficient time for mastering geospatial software, and often limited access to high-performance computing resources. If adequate efforts were taken to resolve software limitations, respondents believed they would be able to better handle big data, cover broader study areas, integrate more types of data, and pursue new research. Insights gained from this survey play an important role in supporting the conceptualization of a national geospatial software institute in the United States with the aim to drastically advance the geospatial software ecosystem to enable broad and significant research and education advances. [ABSTRACT FROM AUTHOR]

Summary: Scholars worldwide leverage science gateways/virtual research environments (VREs) for a wide variety of research and education endeavors spanning diverse scientific fields. Evaluating the value of a given science gateway/VRE to its constituent community is critical in obtaining the financial and human resources necessary to sustain operations and increase adoption in the user community. In this article, we feature a variety of exemplar science gateways/VREs and detail how they define impact in terms of, for example, their purpose, operation principles, and size of user base. Further, the exemplars recognize that their science gateways/VREs will continuously evolve with technological advancements and standards in cloud computing platforms, web service architectures, data management tools and cybersecurity. Correspondingly, we present a number of technology advances that could be incorporated in next‐generation science gateways/VREs to enhance their scope and scale of their operations for greater success/impact. The exemplars are selected from owners of science gateways in the Science Gateways Community Institute (SGCI) clientele in the United States, and from the owners of VREs in the International Virtual Research Environment Interest Group (VRE‐IG) of the Research Data Alliance. Thus, community‐driven best practices and technology advances are compiled from diverse expert groups with an international perspective to envisage futuristic science gateway/VRE innovations. [ABSTRACT FROM AUTHOR]

North American bat species have been undergoing extreme population declines due to the White‐Nose Syndrome (WNS) epidemic caused by the spread of its pathogen, Pseudogymnoascus destructans. Existing models that represent the spread of the disease are limited in their scalability for use in management decisions or lacked the sophistication necessary to capture the complexity of WNS spread. Grounded in the theory of geoexpression, we exploit the latent structure of geographical process concurrency by implementing our modeling software using a concurrent Entity Component System architecture. We demonstrate our model's computational tractability for millions of individual bats. This work is significant because it lays the foundation for the use of advanced cyberinfrastructure and cyberGIS to address challenges in geographical wildlife epidemiology that can be understood using dynamic geographical Individual‐Based Models. [ABSTRACT FROM AUTHOR]

Maintaining educational resources and training materials as timely, current, and aligned with the needs of students, practitioners, and other users of geospatial technologies is a persistent challenge. This is particularly problematic within CyberGIS, a subfield of Geographic Information Science and Technology (GIS&T) that involves high‐performance computing and advanced cyberinfrastructure to address computation‐ and data‐intensive problems. In this study, we analyzed and compared content from two open educational resources: (1) a popular online web resource that regularly covers CyberGIS‐related topics (GIS Stack Exchange) and (2) existing and proposed content in the GIS&T Body of Knowledge. While current curricula may build a student's conceptual understanding of CyberGIS, there is a noticeable lack of resources for practical implementation of CyberGIS tools. The results highlight discrepancies between the attention and frequency of CyberGIS topics according to a popular online help resource and the CyberGIS academic community. [ABSTRACT FROM AUTHOR]

Abstract Backgrounds The fungus Trichoderma reesei is an important workhorse for expression of homologous or heterologous genes, and the inducible cbh1 promoter is generally used. However, constitutive expression is more preferable in some cases than inducible expression that leads to production of unwanted cellulase components. In this work, constitutive promoters of T. reesei were screened and successfully used for high level homologous expression of xylanase II. Results The transcriptional profiles of 13 key genes that participate in glucose metabolism in T. reesei were analyzed by quantitative real-time reverse-transcription polymerase chain reaction (RT-qPCR). The results indicated that the mRNA levels of pdc (encoding pyruvate decarboxylase) and eno (encoding enolase) genes were much higher than other genes under high glucose conditions. Recombinant T. reesei strains that homologously expressed xylanase II were constructed by using the promoters of the pdc and eno genes, and they respectively produced 9266 IU/ml and 8866 IU/ml of xylanase activities in the cultivation supernatant in a medium with high glucose concentration. The productivities of xylanase II were 1.61 g/L (with the pdc promoter) and 1.52 g/L (with the eno promoter), approximately accounted for 83% and 82% of the total protein secreted by T. reesei, respectively. Conclusions This work demonstrates the screening of constitutive promoters by using RT-qPCR in T. reesei, and has obtained the highest expression of recombinant xylanase II to date by using these promoters.