CST Publications

CST's 50 Most Recent Publications

[1]
X. Aguilar, "Performance Monitoring, Analysis, and Real-Time Introspection on Large-Scale Parallel Systems," Doctoral thesis : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2020:1, 2020.
[2]
O. O. Özcan et al., "Differential Coding Strategies in Glutamatergic and GABAergic Neurons in the Medial Cerebellar Nucleus," Journal of Neuroscience, vol. 40, no. 1, pp. 159-170, 2020.
[3]
T. Lindeberg, "Provably scale-covariant continuous hierarchical networks based on scale-normalized differential expressions coupled in cascade," Journal of Mathematical Imaging and Vision, vol. 62, no. 1, pp. 120-148, 2020.
[4]
L. Finnveden, Y. Jansson and T. Lindeberg, "The problems with using STNs to align CNN feature maps," in Northern Lights Deep Learning Workshop 2020, Tromsø, Norway, 20-21 Jan 2020, 2020.
[5]
E. Brocke, "Method development for co-simulation of electrical-chemical systems in Neuroscience," Doctoral thesis : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2020:9, 2020.
[6]
T. Lindeberg, "Scale selection," in Computer Vision : A Reference Guide, 2nd ed. : Springer, 2020.
[7]
M. Mohagheghi Nejad, "Interaction of sensory and motor signals in the basal ganglia in health and disease," Doctoral thesis Stockholm : Kungliga Tekniska högskolan, TRITA-EECS-AVL, 2019:12, 2019.
[8]
C.-Y. Gao et al., "DCA for genome-wide epistasis analysis : the statistical genetics perspective," Physical Biology, vol. 16, no. 2, 2019.
[9]
W. Köpp and T. Weinkauf, "Temporal Treemaps: Static Visualization of Evolving Trees," IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 534-543, 2019.
[12]
M. Wiesenberger et al., "Reproducibility, accuracy and performance of the FELTOR code and library on parallel computer architectures," Computer Physics Communications, vol. 238, pp. 145-156, 2019.
[13]
K. T. Blackwell et al., "Molecular mechanisms underlying striatal synaptic plasticity : relevance chronic alcohol consumption and seeking," European Journal of Neuroscience, vol. 49, no. 6, pp. 768-783, 2019.
[14]
E. Aurell and F. Montana, "Thermal power of heat flow through a qubit," Physical review. E, vol. 99, no. 4, 2019.
[15]
G. H. Galal-Edeen et al., "HCI of Arabia : The challenges of HCI research in Egypt," interactions, vol. 26, no. 3, pp. 55-59, 2019.
[16]
L. Orellana et al., "Oncogenic mutations at the EGFR ectodomain structurally converge to remove a steric hindrance on a kinase-coupled cryptic epitope," Proceedings of the National Academy of Sciences of the United States of America, vol. 116, no. 20, pp. 10009-10018, 2019.
[17]
G. Hahn et al., "Portraits of communication in neuronal networks," Nature Reviews Neuroscience, vol. 20, no. 2, pp. 117-127, 2019.
[18]
S. Narasimhamurthy et al., "SAGE : Percipient Storage for Exascale Data Centric Computing," Parallel Computing, vol. 83, pp. 22-33, 2019.
[19]
[20]
T. Lindeberg, "Provably scale-covariant networks from oriented quasi quadrature measures in cascade," in Scale Space and Variational Methods in Computer Vision, 2019, pp. 328-340.
[21]
C. Simmendinger et al., "Interoperability strategies for GASPI and MPI in large-scale scientific applications," The international journal of high performance computing applications, vol. 33, no. 3, pp. 554-568, 2019.
[22]
F. Mirus et al., "Predicting vehicle behaviour using LSTMs and a vector power representation for spatial positions," in ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2019, pp. 113-118.
[23]
E. Wärnberg and A. Kumar, "Perturbing low dimensional activity manifolds in spiking neuronal networks," PloS Computational Biology, vol. 15, no. 5, 2019.
[24]
H. Zhou et al., "Embedded Kinetic Simulation of Ganymede's Magnetosphere : Improvements and Inferences," Journal of Geophysical Research - Space Physics, vol. 124, no. 7, pp. 5441-5460, 2019.
[25]
F. Mirus, B. Zorn and J. Conradt, "Short-term trajectory planning using reinforcement learning within a neuromorphic control architecture," in ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2019, pp. 649-654.
[26]
R. H. Martinez Mayorquin, A. Lansner and P. Herman, "Probabilistic associative learning suffices for learning the temporal structure of multiple sequences," PLoS ONE, vol. 14, no. 8, 2019.
[28]
R. Iakymchuk et al., "Hierarchical approach for deriving a reproducible unblocked LU factorization," The international journal of high performance computing applications, vol. 33, no. 5, pp. 791-803, 2019.
[29]
S. Aronsson et al., "Supporting after action review in simulator mission training : Co-creating visualization concepts for training of fast-jet fighter pilots," The Journal of Defence Modeling and Simulation : Applications, Methodology, Technology, vol. 16, no. 3, pp. 219-231, 2019.
[30]
L. Orellana et al., "eBDIMS server : protein transition pathways with ensemble analysis in 2D-motion spaces," Bioinformatics, vol. 35, no. 18, pp. 3505-3507, 2019.
[31]
I. B. Peng et al., "Analyzing the Suitability of Contemporary 3D-Stacked PIM Architectures for HPC Scientific Applications," in CF '19 - PROCEEDINGS OF THE 16TH ACM INTERNATIONAL CONFERENCE ON COMPUTING FRONTIERS, 2019, pp. 256-262.
[32]
X. Aguilar et al., "An On-Line Performance Introspection Framework for Task-Based Runtime Systems," in 19th International Conference on Computational Science, ICCS 2019, 2019, pp. 238-252.
[33]
K. Szalisznyó, D. N. Silverstein and J. Tóth, "Neural dynamics in co-morbid schizophrenia and OCD : A computational approach," Journal of Theoretical Biology, vol. 473, pp. 80-94, 2019.
[34]
S. Rivas Gomez et al., "Decoupled Strategy for Imbalanced Workloads in MapReduce Frameworks," in Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018, 2019, pp. 921-927.
[35]
Van D. Nguyen et al., "Direct Finite Element Simulation of the Turbulent Flow Past a Vertical Axis Wind Turbine," Renewable energy, vol. 135, pp. 238-247, 2019.
[37]
Van D. Nguyen et al., "Diffusion MRI simulation in thin-layer and thin-tube media using a discretization on manifolds," Journal of magnetic resonance, vol. 299, pp. 176-187, 2019.
[38]
S. Rivas-Gomez, "High-Performance I/O Programming Models for Exascale Computing," Doctoral thesis : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2019:77, 2019.
[39]
H. Saikia, F. Yang and C. Peters, "Priority driven Local Optimization for Crowd Simulation," in AAMAS '19 : PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS, 2019, pp. 2180-2182.
[40]
L. Ahmed, "Scalable Analysis of Large Datasets in Life Sciences," Doctoral thesis : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2019:69, 2019.
[41]
C. P. Sishtla et al., "Particle-in-Cell Simulations of Plasma Dynamics in Cometary Environment," in Journal of Physics : Conference Series, 2019.
[42]
J.-R. Li et al., "SpinDoctor : a Matlab toolbox for diffusion MRI simulation," NeuroImage, vol. 202, 2019.
[43]
S. Rivas-Gomez et al., "uMMAP-IO: User-level Memory-mapped I/O for HPC," in Proceedings of the 26th IEEE International Conference on High-Performance Computing, Data, and Analytics (HiPC'19),, 2019.
[44]
S. Rivas-Gomez et al., "Persistent Coarrays: Integrating MPI Storage Windows in Coarray Fortran," in Proceedings of the 26th European MPI Users' Group Meeting (EuroMPI 2019), 2019, pp. 1-8.
[45]
Van D. Nguyen, "High Performance Finite Element Methods with Application to Simulation of Vertical Axis Wind Turbines and Diffusion MRI," Doctoral thesis : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2019:76, 2019.
[46]
A. Petras et al., "A computational model of open-irrigated radiofrequency catheter ablation accounting for mechanical properties of the cardiac tissue," International Journal for Numerical Methods in Biomedical Engineering, 2019.
[47]
Van D. Nguyen et al., "Portable simulation framework for diffusion MRI," Journal of magnetic resonance, vol. 309, 2019.
[48]
A. Petras et al., "Tissue Drives Lesion : Computational Evidence of Interspecies Variability in Cardiac Radiofrequency Ablation," in FUNCTIONAL IMAGING AND MODELING OF THE HEART, FIMH 2019, 2019, pp. 139-146.
[49]
M. Filipović, "Characterisation of inputs and outputs of striatal medium spiny neurons in health and disease," Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2019:86, 2019.
[50]
F. Wendt et al., "Ocean Energy Systems Wave Energy Modelling Task : Modelling, Verification and Validation of Wave Energy Converters," Journal of Marine Science and Engineering, vol. 7, no. 11, 2019.
Full list in the KTH publications portal
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Belongs to: Computational Science and Technology
Last changed: Feb 17, 2020