The Principle of Strain Reconstruction Tomography: Determination of Quench Strain Distribution from Diffraction Measurements

Evaluation of residual elastic strain within the bulk of engineering components or natural objects is a challenging task, since in general it requires mapping a six-component tensor quantity in three dimensions. A further challenge concerns the interpretation of finite resolution data in a way that is commensurate and non-contradictory with respect to continuum deformation models. A practical solution for this problem, if it is ever to be found, must include efficient measurement interpretation and data reduction techniques. In the present note we describe the principle of strain tomography by high energy X-ray diffraction, i.e. of reconstruction of the higher dimensional distribution of strain within an object from reduced dimension measurements; and illustrate the application of this principle to a simple case of reconstruction of an axisymmetric residual strain state induced in a cylindrical sample by quenching. The underlying principle of the analysis method presented in this paper can be readily generalised to more complex situations.Comment: 10 pages, 6 figure

The Principle of Strain Reconstruction Tomography: Determination of Quench Strain Distribution from Diffraction Measurements

Evaluation of residual elastic strain within the bulk of engineering components or natural objects is a challenging task, since in general it requires mapping a six-component tensor quantity in three dimensions. A further challenge concerns the interpretation of finite resolution data in a way that is commensurate and non-contradictory with respect to continuum deformation models. A practical solution for this problem, if it is ever to be found, must include efficient measurement interpretation and data reduction techniques. In the present note we describe the principle of strain tomography by high energy X-ray diffraction, i.e. of reconstruction of the higher dimensional distribution of strain within an object from reduced dimension measurements; and illustrate the application of this principle to a simple case of reconstruction of an axisymmetric residual strain state induced in a cylindrical sample by quenching. The underlying principle of the analysis method presented in this paper can be readily generalised to more complex situations.Comment: 10 pages, 6 figure

The Principle of Strain Reconstruction Tomography: Determination of Quench Strain Distribution from Diffraction Measurements

Evaluation of residual elastic strain within the bulk of engineering components or natural objects is a challenging task, since in general it requires mapping a six-component tensor quantity in three dimensions. A further challenge concerns the interpretation of finite resolution data in a way that is commensurate and non-contradictory with respect to continuum deformation models. A practical solution for this problem, if it is ever to be found, must include efficient measurement interpretation and data reduction techniques. In the present note we describe the principle of strain tomography by high energy X-ray diffraction, i.e. of reconstruction of the higher dimensional distribution of strain within an object from reduced dimension measurements; and illustrate the application of this principle to a simple case of reconstruction of an axisymmetric residual strain state induced in a cylindrical sample by quenching. The underlying principle of the analysis method presented in this paper can be readily generalised to more complex situations.Comment: 10 pages, 6 figure

In Situ Malignant Transformation and Progenitor-Mediated Cell Budding: Two Different Pathways for Breast Ductal and Lobular Tumor Invasion

The human breast lobular and ductal structures and the derived tumors from these structures differ substantial in their morphology, microenvironment, biological presentation, functions, and clinical prognosis. Based on these differences, we have proposed that pre-invasive lobular tumors may progress to invasive lesions through “in situ malignant transformation”, in which the entire myoepithelial cell layer within a given lobule or lobular clusters undergoes extensive degeneration and disruptions, which allows the entire epithelial cell population associated with these myoepithelial cell layers directly invade the stroma or vascular structures. In contrast, pre-invasive ductal tumors may invade the stroma or vascular structures through “progenitor-mediated cell budding”, in which focal myoepithelial cell degeneration-induced aberrant leukocyte infiltration causes focal disruptions in the tumor capsules, which selectively favor monoclonal proliferation of the overlying tumor stem cells or a biologically more aggressive cell clone. Our current study attempted to provide more direct morphological and immunohistochemical data that are consistent with our hypotheses

L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning

The ubiquity of camera-enabled devices has led to large amounts of unlabeled image data being produced at the edge. The integration of self-supervised learning (SSL) and federated learning (FL) into one coherent system can potentially offer data privacy guarantees while also advancing the quality and robustness of the learned visual representations without needing to move data around. However, client bias and divergence during FL aggregation caused by data heterogeneity limits the performance of learned visual representations on downstream tasks. In this paper, we propose a new aggregation strategy termed Layer-wise Divergence Aware Weight Aggregation (L-DAWA) to mitigate the influence of client bias and divergence during FL aggregation. The proposed method aggregates weights at the layer-level according to the measure of angular divergence between the clients' model and the global model. Extensive experiments with cross-silo and cross-device settings on CIFAR-10/100 and Tiny ImageNet datasets demonstrate that our methods are effective and obtain new SOTA performance on both contrastive and non-contrastive SSL approaches

Preparation and evaluation of PEGylated phospholipid membrane coated layered double hydroxide nanoparticles

AbstractThe aim of the present study was to develop layered double hydroxide (LDH) nanoparticles coated with PEGylated phospholipid membrane. By comparing the size distribution and zeta potential, the weight ratio of LDH to lipid materials which constitute the outside membrane was identified as 2:1. Transmission electron microscopy photographs confirmed the core-shell structure of PEGylated phospholipid membrane coated LDH (PEG-PLDH) nanoparticles, and cell cytotoxicity assay showed their good cell viability on Hela and BALB/C-3T3 cells over the concentration range from 0.5 to 50 μg/mL

FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients

Federated learning (FL) is a distributed learning paradigm that facilitates collaborative training of a shared global model across devices while keeping data localized. The deployment of FL in numerous real-world applications faces delays, primarily due to the prevalent reliance on supervised tasks. Generating detailed labels at edge devices, if feasible, is demanding, given resource constraints and the imperative for continuous data updates. In addressing these challenges, solutions such as federated semi-supervised learning (FSSL), which relies on unlabeled clients' data and a limited amount of labeled data on the server, become pivotal. In this paper, we propose FedAnchor, an innovative FSSL method that introduces a unique double-head structure, called anchor head, paired with the classification head trained exclusively on labeled anchor data on the server. The anchor head is empowered with a newly designed label contrastive loss based on the cosine similarity metric. Our approach mitigates the confirmation bias and overfitting issues associated with pseudo-labeling techniques based on high-confidence model prediction samples. Extensive experiments on CIFAR10/100 and SVHN datasets demonstrate that our method outperforms the state-of-the-art method by a significant margin in terms of convergence rate and model accuracy