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We present an unsupervised 3D shape co-segmentation method which learns a set of deformable part templates from a shape collection. To accommodate structural variations in the collection, our network composes each shape by a selected subset…

Computer Vision and Pattern Recognition · Computer Science 2024-04-29 Zhiqin Chen , Qimin Chen , Hang Zhou , Hao Zhang

Diffusion autoencoders (DAEs) are typically formulated as a noise prediction model and trained with a linear-$\beta$ noise schedule that spends much of its sampling steps at high noise levels. Because high noise levels are associated with…

Computer Vision and Pattern Recognition · Computer Science 2025-05-01 Pramook Khungurn , Sukit Seripanitkarn , Phonphrm Thawatdamrongkit , Supasorn Suwajanakorn

Metasurfaces have shown promising potentials in shaping optical wavefronts while remaining compact compared to bulky geometric optics devices. Design of meta-atoms, the fundamental building blocks of metasurfaces, relies on trial-and-error…

Understanding degradation in battery cathodes and other functional materials requires simultaneous knowledge of structural, chemical, and electronic changes in three dimensions (3D). Here, we present a simultaneous ADF-EDS-EELS tomography…

Rutile RuO$_2$ has attracted significant interest due to its putative unconventional electronic and magnetic properties and its proximity to superconductivity. However, the measurement and interpretation of its electronic structure has been…

We compute the form factors parametrizing radiative leptonic decays of heavy mesons B^+\to\gamma e^+\nu for photon energies much larger than \Lambda_{QCD}, where perturbative QCD methods for exclusive processes can be combined with the…

High Energy Physics - Phenomenology · Physics 2011-05-05 Gregory P. Korchemsky , Dan Pirjol , Tung-Mow Yan

Atomistic simulations of the experimental W L$_3$-edge extended X-ray absorption fine structure (EXAFS) of bcc tungsten at T = 300 K were performed using classical molecular dynamics (MD) and reverse Monte Carlo (RMC) methods. The MD-EXAFS…

Materials Science · Physics 2018-02-27 Inga Jonane , Andris Anspoks , Alexei Kuzmin

We present Standard Model predictions for the complete set of phenomenologically relevant electroweak precision pseudo-observables related to the Z-boson: the leptonic and bottom-quark effective weak mixing angles $\sin^2\theta_{\rm…

High Energy Physics - Phenomenology · Physics 2020-01-13 Ievgen Dubovyk , Ayres Freitas , Janusz Gluza , Tord Riemann , Johann Usovitsch

Machine learning in atomistic materials science has grown to become a powerful tool, with most approaches focusing on atomic arrangements, typically decomposed into local atomic environments. This approach, while well-suited for…

Materials Science · Physics 2025-03-12 Austin Zadoks , Antimo Marrazzo , Nicola Marzari

Note: The final version of this article was published in Computers and Geosciences, Volume 206, January 2026, 106038. DOI: 10.1016/j.cageo.2025.106038. Readers should refer to the published version for the most up-to-date content.…

Computational modelling of materials using machine learning, ML, and historical data has become integral to materials research. The efficiency of computational modelling is strongly affected by the choice of the numerical representation for…

Electron Backscattering Diffraction (EBSD) provides important information to discriminate phase transformation products in steels. This task is conventionally performed by an expert, who carries a high degree of subjectivity and requires…

We apply a new method "force enhanced atomic refinement" (FEAR) to create a computer model of amorphous silicon (a-Si), based upon the highly precise X-ray diffraction experiments of Laaziri et al. The logic underlying our calculation is to…

Materials Science · Physics 2016-12-28 Anup Pandey , Parthapratim Biswas , Bishal Bhattarai , D. A. Drabold

Density functional theory within the local or semilocal density approximations (DFT-LDA/GGA) has become a workhorse in electronic structure theory of solids, being extremely fast and reliable for energetics and structural properties, yet…

Two-dimensional materials are expected to play an important role in next-generation electronics and optoelectronic devices. Recently, twisted bilayer graphene and transition metal dichalcogenides have attracted significant attention due to…

Strongly correlated systems containing d/f-electrons present a challenge to conventional density functional theory (DFT), such as the widely used local density approximation (LDA) or generalized gradient approximation (GGA). In this work,…

Strongly Correlated Electrons · Physics 2024-01-19 Bei-Lei Liu , Yue-Chao Wang , Yu Liu , Hai-Feng Liu , Hai-Feng Song

In this work quantum metrology techniques are applied to the imaging of objects with a non-uniform refractive spatial profile. A sensible improvement on the classical accuracy is shown to be found when the "Twin Beam State" (TWB) is used.…

Optics · Physics 2020-09-21 Giuseppe Ortolano , Ivano Ruo Berchera , Enrico Predazzi

Various studies of Standard Model parameters involve measuring the properties of a coherent admixture of $D^0$ and $\overline{D}^0$ states. A typical example is the determination of the Unitarity Triangle angle $\gamma$ in the decays $B\to…

High Energy Physics - Phenomenology · Physics 2018-02-13 Anton Poluektov

We show that the information gained in spectroscopic experiments regarding the number and distribution of atomic environments can be used as a valuable constraint in the refinement of the atomic-scale structures of nanostructured or…

Materials Science · Physics 2015-05-14 Matthew J Cliffe , Martin T. Dove , D. A. Drabold , Andrew L. Goodwin

We present the first attempt to perform short glass fiber semantic segmentation from X-ray computed tomography volumetric datasets at medium (3.9 {\mu}m isotropic) and low (8.3 {\mu}m isotropic) resolution using deep learning architectures.…

Computer Vision and Pattern Recognition · Computer Science 2019-01-07 Tomasz Konopczyński , Danish Rathore , Jitendra Rathore , Thorben Kröger , Lei Zheng , Christoph S. Garbe , Simone Carmignato , Jürgen Hesser
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