Global hybrids from the semiclassical atom theory satisfying the local density linear response

We propose global hybrid approximations of the exchange-correlation (XC) energy functional which reproduce well the modified fourth-order gradient expansion of the exchange energy in the semiclassical limit of many-electron neutral atoms and recover the full local density approximation (LDA) linear response. These XC functionals represent the hybrid versions of the APBE functional [Phys. Rev. Lett. 106, 186406, (2011)] yet employing an additional correlation functional which uses the localization concept of the correlation energy density to improve the compatibility with the Hartree-Fock exchange as well as the coupling-constant-resolved XC potential energy. Broad energetical and structural testings, including thermochemistry and geometry, transition metal complexes, non-covalent interactions, gold clusters and small gold-molecule interfaces, as well as an analysis of the hybrid parameters, show that our construction is quite robust. In particular, our testing shows that the resulting hybrid, including 20\% of Hartree-Fock exchange and named hAPBE, performs remarkably well for a broad palette of systems and properties, being generally better than popular hybrids (PBE0 and B3LYP). Semi-empirical dispersion corrections are also provided.Comment: 12 pages, 4 figure

The State of Altmetrics: A Tenth Anniversary Celebration

Altmetric’s mission is to help others understand the influence of research online.We collate what people are saying about published research in sources such as the mainstream media, policy documents, social networks, blogs, and other scholarly and non-scholarly forums to provide a more robust picture of the influence and reach of scholarly work. Altmetric works with some of the biggest publishers, funders, businesses and institutions around the world to deliver this data in an accessible and reliable format. Contents Altmetrics, Ten Years Later, Euan Adie (Altmetric (founder) & Overton) Reflections on Altmetrics, Gemma Derrick (University of Lancaster), Fereshteh Didegah (Karolinska Institutet & Simon Fraser University), Paul Groth (University of Amsterdam), Cameron Neylon (Curtin University), Jason Priem (Our Research), Shenmeng Xu (University of North Carolina at Chapel Hill), Zohreh Zahedi (Leiden University) Worldwide Awareness and Use of Altmetrics, Yin-Leng Theng (Nanyang Technological University) Leveraging Machine Learning on Altmetrics Big Data, Saeed-Ul Hassan (Information Technology University), Naif R. Aljohani (King Abdulaziz University), Timothy D. Bowman (Wayne State University) Altmetrics as Social-Spatial Sensors, Vanash M. Patel (West Hertfordshire Hospitals NHS Trust), Robin Haunschild (Max Planck Institute for Solid State Research), Lutz Bornmann (Administrative Headquarters of the Max Planck Society) Altmetric’s Fable of the Hare and the Tortoise, Mike Taylor (Digital Science) The Future of Altmetrics: A Community Vision, Liesa Ross (Altmetric), Stacy Konkiel (Altmetric

Modern computing: Vision and challenges

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

Social media metrics for new research evaluation

This chapter approaches, both from a theoretical and practical perspective, the most important principles and conceptual frameworks that can be considered in the application of social media metrics for scientific evaluation. We propose conceptually valid uses for social media metrics in research evaluation. The chapter discusses frameworks and uses of these metrics as well as principles and recommendations for the consideration and application of current (and potentially new) metrics in research evaluation.Comment: Forthcoming in Glanzel, W., Moed, H.F., Schmoch U., Thelwall, M. (2018). Springer Handbook of Science and Technology Indicators. Springe

Learning Sequences: Their Existence, Effect, and Evolution

Much is known about the importance of learning and some of the distinct learning processes that organizations use (e.g., trial-and-error learning, vicarious learning, experimental learning, and improvisational learning). Yet surprisingly little is known about whether these processes combine over time in ordered ways, because most research on learning explores one particular process. Using theory elaboration and theory-building methods and data on the accumulated country entries of entrepreneurial firms, we address this gap. Our core contribution is an emergent theoretical framework that develops the concept of learning sequences. We find that learning sequences exist and are influenced by initial conditions. We also find that learning sequences evolve in fundamentally distinct ways over time and with repeated use. Finally, data show how different learning sequences differentially affect both shorter- and longer-term performance, suggesting that it matters which learning processes are used and when. Overall, our findings on learning sequences have important implications for learning theory, international entrepreneurship, and the growing literature on process management

Energy densities in the strong-interaction limit of density functional theory

We discuss energy densities in the strong-interaction limit of density functional theory, deriving an exact expression within the definition (gauge) of the electrostatic potential of the exchange-correlation hole. Exact results for small atoms and small model quantum dots are compared with available approximations defined in the same gauge. The idea of a local interpolation along the adiabatic connection is discussed, comparing the energy densities of the Kohn-Sham, the physical, and the strong-interacting systems. We also use our results to analyze the local version of the Lieb-Oxford bound, widely used in the construction of approximate exchange-correlation functionals.Comment: 12 page