This work explores the ability of classical electronic structure methods to efficiently represent (compress) the information content of full configuration interaction (FCI) wave functions. We introduce a benchmark set of four hydrogen model systems of different dimensionalities and distinctive electronic structures: a 1D chain, a 1D ring, a 2D triangular lattice, and a 3D close-packed pyramid. To assess the ability of a computational method to produce accurate and compact wave functions, we introduce the accuracy volume, a metric that measures the number of variational parameters necessary to achieve a target energy error. Using this metric and the hydrogen models, we examine the performance of three classical deterministic methods: (i) selected configuration interaction (sCI) realized both via an a posteriori (ap-sCI) and variational selection of the most important determinants, (ii) an a posteriori singular value decomposition (SVD) of the FCI tensor (SVD-FCI), and (iii) the matrix product state representation obtained via the density matrix renormalization group (DMRG). We find that the DMRG generally gives the most efficient wave function representation for all systems, particularly in the 1D chain with a localized basis. For the 2D and 3D systems, all methods (except DMRG) perform best with a delocalized basis, and the efficiency of sCI and SVD-FCI is closer to that of DMRG. For larger analogs of the models, the DMRG consistently requires the fewest parameters but still scales exponentially in 2D and 3D systems, and the performance of SVD-FCI is essentially equivalent to that of ap-sCI.
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Obes Surg
January 2025
Ziekenhuis Groep Twente, Almelo, Netherlands.
Background: This study aimed to create a comprehensive Core Outcome Set (COS) for assessing the long-term outcome (≥ 5 years) after Metabolic Bariatric Surgery (MBS), through the use of the Delphi method.
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BMC Med Educ
January 2025
HAN University of Applied Sciences, Academy Allied Health Sciences, Nijmegen, The Netherlands.
Background: Educational innovation in health professional education is needed to keep up with rapidly changing healthcare systems and societal needs. This study evaluates the implementation of PACE, an innovative curriculum designed by the physiotherapy department of the HAN University of Applied Sciences in The Netherlands. The PACE concept features an integrated approach to learning and assessment based on pre-set learning outcomes, personalized learning goals, flexible learning routes, and programmatic assessment.
View Article and Find Full Text PDFSci Rep
January 2025
School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong, China.
Knowledge-aware recommendation systems often face challenges owing to sparse supervision signals and redundant entity relations, which can diminish the advantages of utilizing knowledge graphs for enhancing recommendation performance. To tackle these challenges, we propose a novel recommendation model named Dual-Intent-View Contrastive Learning network (DIVCL), inspired by recent advancements in contrastive and intent learning. DIVCL employs a dual-view representation learning approach using Graph Neural Networks (GNNs), consisting of two distinct views: a local view based on the user-item interaction graph and a global view based on the user-item-entity knowledge graph.
View Article and Find Full Text PDFJ Therm Biol
January 2025
School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Private Bag X3, Wits, 2050, South Africa. Electronic address:
Questionnaires exploring tourists' perceptions of ideal climatic conditions are argued to be a more suitable data source for the development of tourism climate indices than the utilization and integration of expert opinion and pre-established thresholds. This assumes that those tourist respondents can accurately quantify meteorological conditions at a given point in time, and effectively discriminate between meteorological thresholds of suitable and unsuitable conditions. For variables such as rainfall and sunshine hours, this assumption is fairly reasonable.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Dept. of Engineering, King's College London, London WC2R 2LS, U.K.
Permeability is a measure of the degree to which cells can transport molecules across biological barriers. Units of permeability are distance per unit time (typically cm/s), where accurate measurements are needed to define drug delivery in homeostasis and to model dysfunction occurring during disease. This perspective offers a set of community-led guidelines to benchmark permeability data across multidisciplinary approaches and different biological contexts.
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