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Kondo screening in a charge-insulating spinon metal [dataset] [Download]
Title: Kondo screening in a charge-insulating spinon metal [dataset] Contributors: Creator: Gomilšek, Matjaž (Jožef Stefan Institute, Jamova c. 39, SI-1000 Ljubljana, Slovenia & Durham University, South Rd, Durham DH1 3LE, United Kingdom)
Data curator: Gomilšek, Matjaž (Jožef Stefan Institute, Jamova c. 39, SI-1000 Ljubljana, Slovenia & Durham University, South Rd, Durham DH1 3LE, United Kingdom)
Creator: Žitko, Rok (Jožef Stefan Institute, Jamova c. 39, SI-1000 Ljubljana, Slovenia & Faculty of Mathematics and Physics, University of Ljubljana, Jadranska c. 19, SI-1000 Ljubljana, Slovenia)
Creator: Klanjšek, Martin (Jožef Stefan Institute, Jamova c. 39, SI-1000 Ljubljana, Slovenia)
Creator: Pregelj, Martin (Jožef Stefan Institute, Jamova c. 39, SI-1000 Ljubljana, Slovenia)
Creator: Baines, Christopher (Laboratory for Muon Spin Spectroscopy, Paul Scherrer Institute, CH-5232 Villigen PSI, Switzerland)
Creator: Li, Yuesheng (Experimental Physics VI, Center for Electronic Correlations and Magnetism, University of Augsburg, 86159 Augsburg, Germany)
Creator: Zhang, Qingming (National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China & School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China)
Creator: Zorko, Andrej (Jožef Stefan Institute, Jamova c. 39, SI-1000 Ljubljana, Slovenia)
Contact person: Zorko, Andrej (Jožef Stefan Institute, Jamova c. 39, SI-1000 Ljubljana, Slovenia)
Description: Keywords: Kondo effect, Spin liquid, Non-Fermi liquid, Gauge field, Geometric frustration, Magnetism, Impurity, Percolation, Muon-spin relaxation, Nuclear magnetic resonance, Numerical renormalization group Date Uploaded: 17 April 2019 -
Optimisation of the coherence transfer delay in PSYCOSY experiments [dataset] [Download]
Title: Optimisation of the coherence transfer delay in PSYCOSY experiments [dataset] Contributors: Creator: Kenwright, Alan (Durham University, UK)
Description: Keywords: NMR Data Date Uploaded: 4 April 2019 -
A configurational force driven cracking particle method for modelling crack propagation in 2D [dataset] [Download]
Title: A configurational force driven cracking particle method for modelling crack propagation in 2D [dataset] Contributors: Creator: Weilong, Ai (Durham University, UK)
Creator: Bird, Robert (Durham University, UK)
Creator: Coombs, William M. (Durham University, UK)
Creator: Augarde, Charles E. (Durham University, UK)
Description: Keywords: configurational force, cracking particle method, meshless, crack propagation Date Uploaded: 3 April 2019 -
Data management plans (DMPs and maDMPs) [other] [Download]
Title: Data management plans (DMPs and maDMPs) [other] Contributors: Creator: Syrotiuk, Nicholas (Durham University)
Description: Slides available on Prezi.com until we pay to download them in a proprietary file format: https://prezi.com/p/udmgch_km-mb
Keywords: Data management plans, Machine-actionable data management plans, DMPs, maDMPs Date Uploaded: 2 April 2019 -
Local magnetism, magnetic order and spin freezing in the 'nonmetallic metal' FeCrAs [dataset] [Download]
Title: Local magnetism, magnetic order and spin freezing in the 'nonmetallic metal' FeCrAs [dataset] Contributors: Creator: Lancaster, T (Durham University, UK)
Creator: Huddart, B M (Durham University, UK)
Creator: Birch, M T (Durham University, UK)
Creator: Pratt, F L (STFC-ISIS Facility, UK)
Creator: Porter, D G (Diamond Light Source, UK)
Creator: Clark, S J (Durham University, UK)
Creator: Julian, S R (University of Toronto, Canada)
Creator: Hatton, P D (Durham University, UK)
Creator: Blundell, S J (University of Oxford, UK)
Creator: Wu, W (University of Toronto, Canada)
Description: Keywords: muon-spin relaxation, non-Fermi liquid, density functional theory, non-collinear magnetic order, magnetic x-ray scattering Date Uploaded: 27 March 2019 -
Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach [Neural Network Weights] [Download]
Title: Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach [Neural Network Weights] Contributors: Creator: Atapour-Abarghouei, Amir (Durham University, UK)
Contact person: Atapour-Abarghouei, Amir (Durham University, UK)
Editor: Atapour-Abarghouei, Amir (Durham University, UK)
Contact person: Breckon, Toby P. (Durham University, UK)
Editor: Breckon, Toby P. (Durham University, UK)
Description: The data file has been created using PyTorch ( https://pytorch.org
) and contains the weights of a generative model trained to perform monocular depth estimation and semantic segmentation. The file is needed as part of the pipeline for the project at https://github.com/atapour/temporal-depth-segmentation
Instructions on how the file can be used and how the code is run can also be found at https://github.com/atapour/temporal-depth-segmentation
Keywords: Monocular Depth Estimation, Semantic Segmentation, Temporal Consistency, Computer Vision Date Uploaded: 15 March 2019 -
Temporally Consistent Depth Enabled by a Multi-Task Approach [Neural Network Checkpoint]
Title: Temporally Consistent Depth Enabled by a Multi-Task Approach [Neural Network Checkpoint] Contributors: Creator: Amir Atapour-Abarghouei (Durham University, UK)
Contact person: Amir Atapour-Abarghouei (Durham University, UK)
Editor: Amir Atapour-Abarghouei (Durham University, UK)
Contact person: Toby P. Breckon (Durham University, UK)
Editor: Toby P. Breckon (Durham University, UK)
Description: Keywords: Semantic segmentation, Temporal consistency, Monocular depth estimation, Scene understanding, Computer vision Date Uploaded: -
Domain wall encoding of integer variables for quantum annealing and QAOA [dataset] [Download]
Title: Domain wall encoding of integer variables for quantum annealing and QAOA [dataset] Contributors: Creator: Chancellor, Nicholas (Durham University)
Contact person: Chancellor, Nicholas (Durham University)
Data collector: Chancellor, Nicholas (Durham University)
Data curator: Chancellor, Nicholas (Durham University)
Editor: Chancellor, Nicholas (Durham University)
Description: Keywords: quantum computing, quantum annealing Date Uploaded: 11 March 2019 -
Coupling Molecular Dynamics and Deep Learning to Mine Protein Conformational Space [data and software] [Download]
Title: Coupling Molecular Dynamics and Deep Learning to Mine Protein Conformational Space [data and software] Contributors: Creator: Degiacomi, Matteo (Durham University, UK)
Contact person: Degiacomi, Matteo (Durham University, UK)
Data collector: Degiacomi, Matteo (Durham University, UK)
Data curator: Degiacomi, Matteo (Durham University, UK)
Editor: Degiacomi, Matteo (Durham University, UK)
Description: This work features generative neural networks (autoencoders) trained on protein structures produced by molecular simulations. Autoencoders are used to obtain new, plausible conformations complementing and extending pre-existing ones, usable in a protein-protein docking scenario. Keywords: molecular modelling, deep learning, molecular dynamics, proteins Date Uploaded: 11 March 2019 -
The magnetic field, temperature, strain and angular dependence of the critical current density for Nb-Ti [dataset] [Download]
Title: The magnetic field, temperature, strain and angular dependence of the critical current density for Nb-Ti [dataset] Contributors: Creator: Chislett-McDonald, S. B. L. (Superconductivity Group, Centre for Materials Physics, Department of Physics, Durham University, DH1 3LE, UK)
Editor: Kovari, M. (Culham Centre for Fusion Energy, Culham Science Centre, Abingdon, OX14 3EB, UK)
Editor: Surrey, E. (Culham Centre for Fusion Energy, Culham Science Centre, Abingdon, OX14 3EB, UK)
Editor: Hampshire, D. P. (Superconductivity Group, Centre for Materials Physics, Department of Physics, Durham University, DH1 3LE, UK)
Editor: Tsui, Y. (Superconductivity Group, Centre for Materials Physics, Department of Physics, Durham University, DH1 3LE, UK)
Description: Keywords: Multifilamentary superconductors, Type II superconductors, Niobium-Titanium Date Uploaded: 8 March 2019