16 research outputs found
Ensemble neuronal responses in a large-scale realistic model of the cerebellar cortex
No full textRealistic simulation of central networks remains a challenge due to the complexity of internal connectivity and cellular mechanisms involved. We have recently built a realistic model of the cerebellar granular layer..
Quantum computation with programmable connections between gates
No full textA new model of quantum computation is considered, in which the connections between gates are programmed by the state of a quantum register. This new model of computation is shown to be more powerful than the usual quantum computation, e.g. in achieving the programmability of permutations of N different unitary channels with 1 use instead of N uses per channel. For this task, a new elemental resource is needed, the quantum switch, which can be programmed to switch the order of two channels with a single use of each one
Facing femininities : women in the National Portrait Gallery, 1856-1899.
No full textSIGLEAvailable from British Library Document Supply Centre-DSC:DXN029234 / BLDSC - British Library Document Supply CentreGBUnited Kingdo
„Rzeź niewiniątek” Piera Francesca Mazzucchellego ze zbiorów Museo Diocesano w Mediolanie jako wyraz inspiracji dziełem Guida Reniego
No full textThe following article is the first publication in Polish, in which one of the most important Lombardian Baroque artists, so far almost completely unknown in Poland, has been introduced in a closer approach. The starting point for analysing his work became the painting of Slaughter of the Innocents (ca. 1616) from the collection of Museo Diocesano in Milan, untypical as far as its composition is concerned, as it has no precedence in Italian art of early Baroque, with distinctive horizontal partitions intermingled with clearly accentuated diagonals. Mazzucchelli applied it also in his other painting of Abduction of Helen (at present in the collection of Colnaghi Gallery in New York) dated at ca. 1613, however the author does not agree with the date and moves it towards ca. 1620. Another painting discussed in the article, being an artistic exercise for the mentioned above works, is Mazzucchelli’s much earlier canvas with Jacob’s Struggle with the Angel (ca. 1610, Museo Diocesano in Milan). In its composition there reappears a horizontal dominant, which organises around it the inner structure of the painting. The author seeks the inspiration for this untypical composition of Slaughter of the Innocents from Milan in Guido Reni’s painting of the same subject (1611, Pinacoteca Nazionale in Bologna), where also appears a strong horizontal accent in the motive of clasping hands. Despite its stylistic differences, the iconographic analogies and applying a similar compositional type may indicate Mazzucchelli’s closer contacts with Bologna milieu
Direct recognition of crystal structures via three-dimensional convolutional neural networks with high accuracy and tolerance to random displacements and missing atoms
No full textComputational methods and machine learning algorithms for automatic information extraction are crucial to enable data-driven materials science. These approaches are changing materials characterization and analytics, which often require a user-specified threshold to e.g. detect structure or symmetries in structures with defects. Here, we present a machine learning-based approach that directly works on the original periodic arrangements of atoms based on a three-dimensional convolutional neural network without any transformation of descriptors. Our approach shows a high classification accuracy and tolerance to the presence of random displacements and missing atoms. Experimentally, we successfully reconstruct the ordered L12 precipitates extracted from atom probe tomography data, consistent with segmentation based on isocomposition surfaces. The convolutional layers are essential for the simultaneous identification of compositional and structural information, which also give rise to its high tolerance. Our work advances machine learning-based crystal structure identification for incomplete crystal structural data
3D deep learning for enhanced atom probe tomography analysis of nanoscale microstructures
No full textQuantitative analysis of microstructural features on the nanoscale, including
precipitates, local chemical orderings (LCOs) or structural defects (e.g.
stacking faults) plays a pivotal role in understanding the mechanical and
physical responses of engineering materials. Atom probe tomography (APT), known
for its exceptional combination of chemical sensitivity and sub-nanometer
resolution, primarily identifies microstructures through compositional
segregations. However, this fails when there is no significant segregation, as
can be the case for LCOs and stacking faults. Here, we introduce a 3D deep
learning approach, AtomNet, designed to process APT point cloud data at the
single-atom level for nanoscale microstructure extraction, simultaneously
considering compositional and structural information. AtomNet is showcased in
segmenting L12-type nanoprecipitates from the matrix in an AlLiMg alloy,
irrespective of crystallographic orientations, which outperforms previous
methods. AtomNet also allows for 3D imaging of L10-type LCOs in an AuCu alloy,
a challenging task for conventional analysis due to their small size and subtle
compositional differences. Finally, we demonstrate the use of AtomNet for
revealing 2D stacking faults in a Co-based superalloy, without any defected
training data, expanding the capabilities of APT for automated exploration of
hidden microstructures. AtomNet pushes the boundaries of APT analysis, and
holds promise in establishing precise quantitative microstructure-property
relationships across a diverse range of metallic materials
Convolutional neural network-assisted recognition of nanoscale L1<sub>2</sub> ordered structures in face-centred cubic alloys
No full textNanoscale L12-type ordered structures are widely used in face-centered cubic (FCC) alloys to exploit their hardening capacity and thereby improve mechanical properties. These fine-scale particles are typically fully coherent with matrix with the same atomic configuration disregarding chemical species, which makes them challenging to be characterized. Spatial distribution maps (SDMs) are used to probe local order by interrogating the three-dimensional (3D) distribution of atoms within reconstructed atom probe tomography (APT) data. However, it is almost impossible to manually analyze the complete point cloud (gt;10 million) in search for the partial crystallographic information retained within the data. Here, we proposed an intelligent L12-ordered structure recognition method based on convolutional neural networks (CNNs). The SDMs of a simulated L12-ordered structure and the FCC matrix were firstly generated. These simulated images combined with a small amount of experimental data were used to train a CNN-based L12-ordered structure recognition model. Finally, the approach was successfully applied to reveal the 3D distribution of L12–type δ′–Al3(LiMg) nanoparticles with an average radius of 2.54 nm in a FCC Al-Li-Mg system. The minimum radius of detectable nanodomain is even down to 5 Å. The proposed CNN-APT method is promising to be extended to recognize other nanoscale ordered structures and even more-challenging short-range ordered phenomena in the near future. © 2021, The Author(s)
Machine learning-enabled tomographic imaging of chemical short-range atomic ordering (Adv. Mater. 44/2024)
No full textAuthor Correction: Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA
No full text3D deep learning for enhanced atom probe tomography analysis of nanoscale microstructures
No full textQuantitative analysis of microstructural features on the nanoscale, including precipitates, local chemical orderings (LCOs) or structural defects (e.g. stacking faults) plays a pivotal role in understanding the mechanical and physical responses of engineering materials. Atom probe tomography (APT), known for its exceptional combination of chemical sensitivity and sub-nanometer resolution, primarily identifies microstructures through compositional segregations. However, this fails when there is no significant segregation, as can be the case for LCOs and stacking faults. Here, we introduce a 3D deep learning approach, AtomNet, designed to process APT point cloud data at the single-atom level for nanoscale microstructure extraction, simultaneously considering compositional and structural information. AtomNet is showcased in segmenting L12-type nanoprecipitates from the matrix in an AlLiMg alloy, irrespective of crystallographic orientations, which outperforms previous methods. AtomNet also allows for 3D imaging of L10-type LCOs in an AuCu alloy, a challenging task for conventional analysis due to their small size and subtle compositional differences. Finally, we demonstrate the use of AtomNet for revealing 2D stacking faults in a Co-based superalloy, without any stacking-faults-relevant samples in the training dataset, expanding the capabilities for automated exploration of hidden microstructures in APT data. AtomNet can thus recognize challenging microstructures, including nanoprecipitates with diameters above 2 nm, LCOs with diameters of about 1–2 nm without obvious compositional segregation, and even unforeseen planar defects by analyzing atom-atom environments. AtomNet pushes the boundaries of APT analysis, and holds promise in establishing precise quantitative microstructure-property relationships across a diverse range of metallic materials.LPD
