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1. MVGCN: Multi-View Graph Convolutional Neural Network for Surface Defect Identification Using Three-Dimensional Point Cloud

   https://doi.org/10.1115/1.4056005  

   Wang, Yinan; Sun, Wenbo; Jin, Jionghua; Kong, Zhenyu; Yue, Xiaowei (March 2023, Journal of Manufacturing Science and Engineering)

   Abstract Surface defect identification is a crucial task in many manufacturing systems, including automotive, aircraft, steel rolling, and precast concrete. Although image-based surface defect identification methods have been proposed, these methods usually have two limitations: images may lose partial information, such as depths of surface defects, and their precision is vulnerable to many factors, such as the inspection angle, light, color, noise, etc. Given that a three-dimensional (3D) point cloud can precisely represent the multidimensional structure of surface defects, we aim to detect and classify surface defects using a 3D point cloud. This has two major challenges: (i) the defects are often sparsely distributed over the surface, which makes their features prone to be hidden by the normal surface and (ii) different permutations and transformations of 3D point cloud may represent the same surface, so the proposed model needs to be permutation and transformation invariant. In this paper, a two-step surface defect identification approach is developed to investigate the defects’ patterns in 3D point cloud data. The proposed approach consists of an unsupervised method for defect detection and a multi-view deep learning model for defect classification, which can keep track of the features from both defective and non-defective regions. We prove that the proposed approach is invariant to different permutations and transformations. Two case studies are conducted for defect identification on the surfaces of synthetic aircraft fuselage and the real precast concrete specimen, respectively. The results show that our approach receives the best defect detection and classification accuracy compared with other benchmark methods. 

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2. Intermediates and mechanism in iron-catalyzed C–H methylation with trimethylaluminum

   https://doi.org/10.1039/D1CC05607G  

   Bhatia, Shilpa; DeMuth, Joshua C.; Neidig, Michael L. (November 2021, Chemical Communications)

   A mechanistic study is performed on the reaction method for iron-catalyzed C–H methylation with AlMe 3 reagent, previously proposed to involve cyclometalated iron( iii ) intermediates and an iron( iii )/( i ) reaction cycle. Detailed spectroscopic studies ( 57 Fe Mössbauer, EPR) during catalysis and in stoichiometric reactions identify iron( ii ) complexes, including cyclometalated iron( ii ) intermediates, as the major iron species formed in situ under catalytic reaction conditions. Reaction studies identify a cyclometalated iron( ii )-methyl species as the key intermediate leading to C–H methylated product upon reaction with oxidant, consistent with a previously proposed iron( ii )/iron( iii )/iron( i ) reaction manifold for C–H arylation. 

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3. Forward Invariance in Neural Network Controlled Systems

   https://doi.org/10.1109/LCSYS.2023.3341980  

   Harapanahalli, Akash; Jafarpour, Saber; Coogan, Samuel (January 2023, IEEE Control Systems Letters)

   We present a framework based on interval analysis and monotone systems theory to certify and search for forward invariant sets in nonlinear systems with neural network controllers. The framework (i) constructs localized first-order inclusion functions for the closed-loop system using Jacobian bounds and existing neural network verification tools; (ii) builds a dynamical embedding system where its evaluation along a single trajectory directly corre- sponds with a nested family of hyper-rectangles provably converging to an attractive set of the original system; (iii) utilizes linear transformations to build families of nested paralleletopes with the same properties. The framework is automated in Python using our interval analysis tool- box npinterval, in conjunction with the symbolic arith- metic toolbox sympy, demonstrated on an 8-dimensional leader-follower system. 

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4. Probabilistic Sparse Subspace Clustering Using Delayed Association.

   Jaberi, M. (January 2018, Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR))

   Discovering and clustering subspaces in high-dimensional data is a fundamental problem of machine learning with a wide range of applications in data mining, computer vision, and pattern recognition. Earlier methods divided the problem into two separate stages of finding the similarity matrix and finding clusters. Similar to some recent works, we integrate these two steps using a joint optimization approach. We make the following contributions: (i) we estimate the reliability of the cluster assignment for each point before assigning a point to a subspace. We group the data points into two groups of “certain” and “uncertain”, with the assignment of latter group delayed until their subspace association certainty improves. (ii) We demonstrate that delayed association is better suited for clustering subspaces that have ambiguities, i.e. when subspaces intersect or data are contaminated with outliers/noise. (iii) We demonstrate experimentally that such delayed probabilistic association leads to a more accurate self-representation and final clusters. The proposed method has higher accuracy both for points that exclusively lie in one subspace, and those that are on the intersection of subspaces. (iv) We show that delayed association leads to huge reduction of computational cost, since it allows for incremental spectral clustering 

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5. SSMD: a semi-supervised approach for a robust cell type identification and deconvolution of mouse transcriptomics data

   https://doi.org/10.1093/bib/bbaa307  

   Lu, Xiaoyu; Tu, Szu-Wei; Chang, Wennan; Wan, Changlin; Wang, Jiashi; Zang, Yong; Ramdas, Baskar; Kapur, Reuben; Lu, Xiongbin; Cao, Sha; et al (July 2021, Briefings in Bioinformatics)

   Abstract Deconvolution of mouse transcriptomic data is challenged by the fact that mouse models carry various genetic and physiological perturbations, making it questionable to assume fixed cell types and cell type marker genes for different data set scenarios. We developed a Semi-Supervised Mouse data Deconvolution (SSMD) method to study the mouse tissue microenvironment. SSMD is featured by (i) a novel nonparametric method to discover data set-specific cell type signature genes; (ii) a community detection approach for fixing cell types and their marker genes; (iii) a constrained matrix decomposition method to solve cell type relative proportions that is robust to diverse experimental platforms. In summary, SSMD addressed several key challenges in the deconvolution of mouse tissue data, including: (i) varied cell types and marker genes caused by highly divergent genotypic and phenotypic conditions of mouse experiment; (ii) diverse experimental platforms of mouse transcriptomics data; (iii) small sample size and limited training data source and (iv) capable to estimate the proportion of 35 cell types in blood, inflammatory, central nervous or hematopoietic systems. In silico and experimental validation of SSMD demonstrated its high sensitivity and accuracy in identifying (sub) cell types and predicting cell proportions comparing with state-of-the-arts methods. A user-friendly R package and a web server of SSMD are released via https://github.com/xiaoyulu95/SSMD. 

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