Article
Computer Science, Interdisciplinary Applications
M. Busse, S. Ferstl, M. A. Kimm, L. Hehn, K. Steiger, S. Allner, M. Mueller, E. Drecoll, T. Buerkner, M. Dierolf, B. Gleich, W. Weichert, F. Pfeiffer
Summary: This article introduces a laboratory-based X-ray method for precise micromorphologic investigation of tissue samples in three dimensions, with the advantages of versatility in multi-scale investigations and nondestructive imaging.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Cell Biology
Simone Pelicci, Laura Furia, Pier Giuseppe Pelicci, Mario Faretta
Summary: The modern fluorescence microscope combines different technologies with varying performance levels. However, the best results are achieved by maximizing one parameter while compromising others, which limits the adoption of new optical microscopy tools in research labs.
Article
Engineering, Civil
Soomok Lee, Seung-Woo Seo
Summary: This paper proposes a multi-modal fusion-based localization framework that uses multiple map matching sources to achieve highly accurate and robust real-time localization. Experiments have shown that combining multiple map matching sources yields more reliable results compared to using a single map matching.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Biomedical
Fangyu Liu, Shizhong Yuan, Weimin Li, Qun Xu, Bin Sheng
Summary: In this paper, a patch-based deep multi-modal learning framework is proposed for brain disease diagnosis. The method integrates multimodal imaging features and jointly learns local patches to improve diagnostic accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Bo Cheng, Jia Zhu, Meimei Guo
Summary: In this paper, a Multi-modal Joint entity Alignment Framework (MultijAF) is proposed to effectively utilize the knowledge of various modalities for entity alignment. By learning embeddings of different modalities, using a multi-modal fusion network, and designing a Numerical Process Module (NPM), the framework achieves satisfactory alignment performance. Additionally, an unsupervised multi-modal EA method is introduced to reduce the cost of labeling data.
Article
Physics, Multidisciplinary
Alexey Yamilov, Sergey E. Skipetrov, Tyler W. Hughes, Momchil Minkov, Zongfu Yu, Hui Cao
Summary: Recent numerical calculations show that Anderson localization of light can be achieved in three dimensions with a random arrangement of metallic spheres, but not with dielectric ones. This finding challenges the existence of three-dimensional localization of light, which has remained elusive despite extensive studies over the past 40 years. The researchers conducted brute-force numerical simulations using advanced techniques and demonstrated the three-dimensional localization of vector electromagnetic waves in randomly assembled metallic spheres, highlighting the absence of localization in dielectric spheres.
Article
Engineering, Multidisciplinary
Peng-Fei Qu, Qi-Zhi Zhu, Li -Mao Zhang, Wei-Jian Li, Tao Ni, Tao You
Summary: This paper presents a fractional plastic framework to consider the plastic strain localization of rock-like materials. The framework utilizes a non-coaxial plastic flow controlled by the Riemann-Liouville (RL) fractional derivative, without the need for additional plastic potential. The ability of the fractional constitutive model to predict the mechanical behaviors of rock-like materials is evaluated using triaxial compression test data. Numerical examples are implemented to demonstrate the effectiveness of the proposed framework and the influence of the fractional order on strain localization.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Computer Science, Artificial Intelligence
Miguel Oliveira, Eurico Pedrosa, Andre Pinto de Aguiar, Daniela Ferreira Pinto Dias Rato, Filipe Neves dos Santos, Paulo Dias, Vitor Santos
Summary: This paper presents a novel calibration methodology for multi-sensor, multi-modal robotic systems. The approach formulates the calibration as an extended optimization problem and makes use of a topological representation to recalculate the sensor poses. This atomic transformations optimization method (ATOM) is applicable to different calibration problems and achieves comparable accuracy to state-of-the-art methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Baochen Xiong, Xiaoshan Yang, Fan Qi, Changsheng Xu
Summary: This paper addresses the problem of multimodal federated learning, which is difficult to solve using traditional FL methods due to modality discrepancy. To overcome this challenge, a unified framework is proposed that utilizes the co-attention mechanism to fuse complementary information from different modalities, and incorporates a personalization method based on MAML to adapt the final model for each client.
Article
Computer Science, Information Systems
Lei Gao, Ling Guan
Summary: With the rapid advancements in sensory and computing technology, the attention towards multi-modal data sources representing the same pattern or phenomenon has increased. This paper proposes a discriminative vectorial framework for multi-modal feature representation in knowledge discovery, utilizing multi-modal hashing and discriminative correlation maximization analysis. The proposed framework minimizes semantic similarity among different modalities and extracts intrinsic discriminative representations across multiple data sources, leading to improved results in various applications.
IEEE TRANSACTIONS ON MULTIMEDIA
(2022)
Article
Computer Science, Software Engineering
Zhitong Huang, Nanxuan Zhao, Jing Liao
Summary: This paper proposes a unified framework UniColor to support colorization in multiple modalities. The framework includes a two-stage colorization approach and a Transformer-based network to generate diverse and high-quality colorization results. Both qualitative and quantitative comparisons demonstrate the superiority of the proposed method. An interactive interface is also designed to showcase the effectiveness of the unified framework in practical usage.
ACM TRANSACTIONS ON GRAPHICS
(2022)
Article
Materials Science, Multidisciplinary
Christopher M. Magazzeni, Hazel M. Gardner, Inigo Howe, Phillip Gopon, John C. Waite, David Rugg, David E. J. Armstrong, Angus J. Wilkinson
Summary: The method presented allows for registration and correlation of property maps of materials, facilitating the study of micron-scale microstructural features and extraction of correlations between multiple features of interest.
JOURNAL OF MATERIALS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Alan Q. Wang, Evan M. Yu, Adrian V. Dalca, Mert R. Sabuncu
Summary: KeyMorph is a deep learning-based image registration framework that enhances robustness and interpretability by automatically detecting corresponding keypoints, incorporating symmetries, and handling image translations. This framework shows excellent performance in solving the registration problem of multi-modal brain MRI scans.
MEDICAL IMAGE ANALYSIS
(2023)
Article
Engineering, Electrical & Electronic
Shuyu Wang, Zhaojia Sun, Yuliang Zhao, Lei Zuo
Summary: By designing a highly stretchable hydrogel resistive sensor, multi-modal perception of soft fingers can be achieved with high sensitivity, low hysteresis, and high reliability. Additionally, studying the impact of the hydrogel sensor on the pneumatic actuator's dynamics provides insights into how the sensor's placement location influences perception.
SENSORS AND ACTUATORS A-PHYSICAL
(2021)
Article
Multidisciplinary Sciences
Zezhou Li, Zhiheng Xie, Yao Zhang, Xilong Mu, Jisheng Xie, Hai-Jing Yin, Ya-Wen Zhang, Colin Ophus, Jihan Zhou
Summary: Deciphering the three-dimensional atomic structure of solid-solid interfaces in core-shell nanomaterials is key to understanding their catalytical, optical, and electronic properties. In this study, the authors used atomic resolution electron tomography to investigate the atomic structures of palladium-platinum core-shell nanoparticles at the single-atom level. They found that the core-shell interface is atomically diffuse with an average thickness of 4.2 angstrom, regardless of the particle's morphology or crystallographic texture. The high concentration of Pd in the diffusive interface is related to the dissolution of free Pd atoms from the Pd seeds, as confirmed by cryogenic electron microscopy.
NATURE COMMUNICATIONS
(2023)
Article
Materials Science, Multidisciplinary
McLean P. Echlin, Andrew T. Polonsky, James Lamb, Remco Geurts, Steven J. Randolph, Aurelien Botman, Tresa M. Pollock
Summary: The combination of femtosecond laser with focused ion beam scanning electron microscope has led to the development of a new 3D imaging platform that can gather multimodal datasets at sub-pm voxel resolutions. This platform has been used to generate large 3D datasets for a variety of materials systems, providing vital information for materials research and design.
Article
Materials Science, Multidisciplinary
Xiaoxian Zhang, Jean-Charles Stinville, Tresa M. Pollock, Fionn P. E. Dunne
Summary: Based on investigation of fatigue crack nucleation at annealing twin boundaries (TBs) in polycrystal nickel-based superalloy Rene 88DT, it was found that elastic anisotropy plays a key role in driving local elastic constraint and slip activation, leading to TBs being preferential sites for crack nucleation. The crystallographic orientation of parent grain/twin pair also plays a crucial role in slip activation and fatigue crack nucleation, with the most damaging parent grain orientations identified.
JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS
(2021)
Article
Materials Science, Multidisciplinary
Jonathan M. Hestroffer, Marat Latypov, Jean-Charles Stinville, Marie-Agathe Charpagne, Valery Valle, Matthew P. Miller, Tresa M. Pollock, Irene J. Beyerlein
Summary: This study investigates the evolution of intragranular lattice rotations and slip activity in a high performance, polycrystalline face centered cubic material during monotonic and cyclic loading using a combination of in-situ high-resolution digital image correlation (HR-DIC), Heaviside-DIC method (H-DIC), and crystal plasticity finite element (CPFE). The results show that most grains develop intragranular lattice rotation gradients that span the grain, regardless of their size and lattice orientation. The analysis of slip lines reveals agreement in the active slip systems and changes in local slip activity across individual grains. The findings suggest that deforming grains are divided into sub-granular regions of uniform lattice rotation, most often associated with one or two active slip systems.
Article
Engineering, Mechanical
S. Hemery, J. C. Stinville
Summary: This study monitored the microstructurally small crack growth in Ti-6Al-4V and Ti-6Al-2Sn-4Zr-2Mo with equiaxed and bimodal microstructures. The influence of microstructure on the lifetime variability observed in Ti alloys was evaluated, and primary alpha grains, basal plane cracking, and misalignment across boundaries were identified as key features for high crack growth rates. Dwell periods were found to induce significant small crack acceleration.
INTERNATIONAL JOURNAL OF FATIGUE
(2022)
Article
Materials Science, Multidisciplinary
Wyatt A. Witzen, Andrew T. Polonsky, Paul F. Rottmann, Kira M. Pusch, McLean P. Echlin, Tresa M. Pollock, Irene J. Beyerlein
Summary: Additive manufacturing of high strength metallic materials produces unique microstructures and defects. This study characterizes the microstructure and defect boundaries of an additive manufactured tantalum (Ta) product using the combination of three-dimensional electron backscattered diffraction (EBSD) and crystallographic geometrically necessary dislocation (GND) theory. The results show that the microstructure of AM Ta is highly oriented along the build direction, but also contains large crystallographic orientation gradients. Analysis reveals the presence of highly misoriented subboundaries with large dislocation densities, forming a complex network throughout the microstructure. TEM measurements confirm the existence of a high dislocation density at the microscale and indicate a cell-like dislocation network structure.
JOURNAL OF MATERIALS SCIENCE
(2022)
Article
Materials Science, Multidisciplinary
J. C. Stinville, W. Ludwig, P. G. Callahan, M. P. Echlin, V. Valle, T. M. Pollock, H. Proudhon
Summary: This study enables imaging of bulk slip events within the 3D microstructure through the combined use of X-ray diffraction contrast tomography and topotomography. Correlative measurements were performed using various methods to validate the observation of slip events and significant differences were found between bulk and surface grains, highlighting the need for 3D observations to better understand deformation in polycrystalline materials.
MATERIALS CHARACTERIZATION
(2022)
Article
Chemistry, Physical
Maxwell Pinz, George Weber, Jean Charles Stinville, Tresa Pollock, Somnath Ghosh
Summary: This paper develops a probabilistic crack nucleation model for the Ni-based superalloy Rene 88DT under fatigue loading using a Bayesian inference approach. The underlying mechanisms driving crack nucleation are identified through a data-driven, machine learning approach. Experimental fatigue-loaded microstructures are characterized to correlate the grain morphology and crystallography to the crack nucleation sites. A multiscale model, incorporating experimental polycrystalline microstructures, is developed for fatigue simulations.
NPJ COMPUTATIONAL MATERIALS
(2022)
Article
Materials Science, Multidisciplinary
Andrew T. Polonsky, Narendran Raghavan, McLean P. Echlin, Michael M. Kirka, Ryan R. Dehoff, Tresa M. Pollock
Summary: Three-dimensional characterization is used to understand the processing-structure relationships in additively manufactured materials. In this study, electron beam melting is used to fabricate bulk samples of Inconel 718, and TriBeam tomography and thermal simulation software are used to analyze the microstructural development and predict grain morphologies. The research provides insight into controlling the as-printed microstructure and understanding the competing processes of grain nucleation and epitaxial growth.
MATERIALS CHARACTERIZATION
(2022)
Article
Multidisciplinary Sciences
J. C. Stinville, J. M. Hestroffer, M. A. Charpagne, A. T. Polonsky, M. P. Echlin, C. J. Torbet, V. Valle, K. E. Nygren, M. P. Miller, O. Klaas, A. Loghin, I. J. Beyerlein, T. M. Pollock
Summary: The development of high-fidelity mechanical property prediction models relies on large volumes of microstructural feature data. However, spatially correlated measurements of 3D microstructure and deformation fields have been rare. This study presents a unique multi-modal dataset that combines state-of-the-art experimental techniques for 3D tomography and high-resolution deformation field measurements.
Article
Materials Science, Multidisciplinary
Wyatt A. Witzen, McLean P. Echlin, Marie-Agathe Charpagne, Tresa M. Pollock, Irene J. Beyerlein
Summary: This study investigates the intragranular distributions of geometrically necessary dislocations (GNDs) in a polycrystalline tantalum sample under shock compression loading. Using TriBeam tomography, a highly resolved 3D map of the microstructure was obtained, allowing for the examination of grain boundaries, orientations, and voids. By combining the 3D characterization, GND formulation, and a sample with approximately 6000 grains, correlations between GND density per grain and grain characteristics were analyzed. The results show that GND density increases closer to the spall plane and that grains containing voids have high GND density concentrations in the intragranular region surrounding the void.
Article
Materials Science, Multidisciplinary
N. R. Brodnik, C. Muir, N. Tulshibagwale, J. Rossin, M. P. Echlin, C. M. Hamel, S. L. B. Kramer, T. M. Pollock, J. D. Kiser, C. Smith, S. H. Daly
Summary: Experimental solid mechanics is experiencing a crucial moment where the integration of machine learning (ML) approaches into the discovery process is rapidly increasing. The adoption of ML methods in mechanics originated from non-science and engineering applications, raising concerns about the reliability of the obtained physical results. To address this, it is necessary to incorporate physical principles into ML architectures, evaluate and compare them using benchmark datasets, and test their broad applicability. These principles allow for meaningful categorization, comparison, evaluation, and extension of ML models across various experimental and computational frameworks. Two different use cases, acoustic emission and resonant ultrasound spectroscopy, are examined to demonstrate the application of these principles and discussions are provided regarding the future prospects of trustworthy ML in experimental mechanics.
JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS
(2023)
Article
Chemistry, Physical
Devendra K. Jangid, Neal R. Brodnik, Michael G. Goebel, Amil Khan, SaiSidharth Majeti, McLean P. Echlin, Samantha H. Daly, Tresa M. Pollock, B. S. Manjunath
Summary: In computer vision, single-image super-resolution (SISR) has been extensively explored on optical images, but its application on images outside this domain, such as scientific experiment images, is not well investigated. This paper presents a broadly adaptable approach for applying state-of-art SISR networks to generate high-resolution EBSD images.
NPJ COMPUTATIONAL MATERIALS
(2022)
Article
Engineering, Manufacturing
Devendra K. Jangid, Neal R. Brodnik, Amil Khan, Michael G. Goebel, McLean P. Echlin, Tresa M. Pollock, Samantha H. Daly, B. S. Manjunath
Summary: This paper presents a GAN capable of producing realistic microstructure morphology features and demonstrates its capabilities on a dataset of crystalline titanium grain shapes. It also introduces an approach to train deep learning networks to understand material-specific descriptor features based on existing conceptual relationships.
INTEGRATING MATERIALS AND MANUFACTURING INNOVATION
(2022)
Article
Engineering, Manufacturing
Paul R. Dawson, Matthew P. Miller, Tresa M. Pollock, Joe Wendorf, Leah H. Mills, Jean Charles Stinville, Marie Agathe Charpagne, McLean P. Echlin
Summary: The MechMet software package is a new finite element tool for solving elasticity field equations in polycrystals and investigating microstructure-induced heterogeneity. It can compute various mechanical metrics and generate formatted output files for visualization using Paraview or VisIt.
INTEGRATING MATERIALS AND MANUFACTURING INNOVATION
(2021)
