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The accurate determination of correlation energy is a challenging task in many-electron quantum chemistry calculations, especially for metals. A recent work proposes an efficient scheme to speed up the calculation of correlation energy, reducing the computational time by up to two orders of magnitude.
A new study proposes a full-scale model of the entorhinal cortex–dentate gyrus–CA3 network, providing a conceptual overview of the computational properties of this brain network, to show that it is an efficient pattern separator.
A human mobility model that takes into account social interaction and long-term memory mechanisms is proposed, shedding more light onto the interplay between human movements and urban growth.
Compressing scientific data is essential to save on storage space, but doing so effectively while ensuring that the conclusions from the data are not affected remains a challenging task. A recent paper proposes a new method to identify numerical noise from floating-point atmospheric data, which can lead to a more effective compression.
Quantum defects in two-dimensional materials offer promises for the next-generation quantum information technology. However, the rational design of these defects faces challenges, and thus, requires the development of advanced theoretical and computational models.
Mobile-phone data reveal a cognitive strategy in human navigation and motivate the development of a new route planning model, with potential implications for traffic forecasting and transportation planning.
Finding a parameter that can accurately identify the order–disorder phase transition, especially for complex physical systems with high-dimensional configurational space, is a challenging task. Recent work proposes a machine learning approach to effectively tackle this challenge.
An efficient parallelization technique for tensor network contraction, developed by a careful balance between memory requirement and computational time, speeds up classical simulation of quantum computers.
A new study uses longitudinal mobility data to identify how individuals behave at different stages of the COVID pandemic, elucidating benefits and challenges of using this type of data for decision-making by epidemiologists and policy-makers.
A framework called Detect is proposed to detect subtle effects of brain disorders, making it possible to delineate anomalous brain connections within specific individuals.
Recent work introduces a powerful new web tool that enables a faster and statistically more reliable data mining of transcriptomics and metatranscriptomics for inflammatory bowel disease (IBD) research.
Modeling of the multiscale dynamics of new bone formation in tissue scaffolds is still challenging due to the computational complexity in solving the mechanics–material–biology interactions. Recent work proposes a machine learning approach to address this challenge.
A model for Drosophila embryonic development is presented by integrating several types of experimental data spanning over several layers of space and time.
Making sense of single-cell data requires various computational efforts such as clustering, visualization and gene regulatory network inference, often addressed by different methods. DeepSEM provides an all-in-one solution.
A graph-neural-network-based framework is proposed for the refinement of protein structure models, substantially improving the efficacy and efficiency of refining protein models when compared with the state-of-the-art approaches.
Detection of molecular quantitative trait loci (QTL) facilitates mechanistic insights into disease-associated genetic variants. A new study describes BaseQTL, which exploits allele-specific expression to map molecular QTL from sequencing reads even without paired genotype data.
A study based on effective dimension shows that a quantum neural network can have increased capability and trainability as compared to its classical counterpart.
