91,943 research outputs found
A machine learning framework for quantifying chemical segregation and microstructural features in atom probe tomography data
Atom probe tomography (APT) is ideally suited to characterize and understand
the interplay of chemical segregation and microstructure in modern
multicomponent materials. Yet, the quantitative analysis typically relies on
human expertise to define regions of interest. We introduce a computationally
efficient, multistage machine learning strategy to identify chemically distinct
domains in a semi automated way, and subsequently quantify their geometric and
compositional characteristics. In our algorithmic pipeline, we first coarse
grain the APT data into voxels, collect the composition statistics, and
decompose it via clustering in composition space. The composition
classification then enables the real space segmentation via a density based
clustering algorithm, thus revealing the microstructure at voxel resolution.
Our approach is demonstrated for a Sm(Co,Fe)ZrCu alloy. The alloy exhibits two
precipitate phases with a plate-like, but intertwined morphology. The primary
segmentation is further refined to disentangle these geometrically complex
precipitates into individual plate like parts by an unsupervised approach based
on principle component analysis, or a U-Net based semantic segmentation trained
on the former. Following the chemical and geometric analysis, detailed chemical
distribution and segregation effects relative to the predominant plate-like
geometry can be readily mapped without resorting to the initial voxelization
Learning Segmentation Masks with the Independence Prior
An instance with a bad mask might make a composite image that uses it look
fake. This encourages us to learn segmentation by generating realistic
composite images. To achieve this, we propose a novel framework that exploits a
new proposed prior called the independence prior based on Generative
Adversarial Networks (GANs). The generator produces an image with multiple
category-specific instance providers, a layout module and a composition module.
Firstly, each provider independently outputs a category-specific instance image
with a soft mask. Then the provided instances' poses are corrected by the
layout module. Lastly, the composition module combines these instances into a
final image. Training with adversarial loss and penalty for mask area, each
provider learns a mask that is as small as possible but enough to cover a
complete category-specific instance. Weakly supervised semantic segmentation
methods widely use grouping cues modeling the association between image parts,
which are either artificially designed or learned with costly segmentation
labels or only modeled on local pairs. Unlike them, our method automatically
models the dependence between any parts and learns instance segmentation. We
apply our framework in two cases: (1) Foreground segmentation on
category-specific images with box-level annotation. (2) Unsupervised learning
of instance appearances and masks with only one image of homogeneous object
cluster (HOC). We get appealing results in both tasks, which shows the
independence prior is useful for instance segmentation and it is possible to
unsupervisedly learn instance masks with only one image.Comment: 7+5 pages, 13 figures, Accepted to AAAI 201
Fast and Accurate Neural Word Segmentation for Chinese
Neural models with minimal feature engineering have achieved competitive
performance against traditional methods for the task of Chinese word
segmentation. However, both training and working procedures of the current
neural models are computationally inefficient. This paper presents a greedy
neural word segmenter with balanced word and character embedding inputs to
alleviate the existing drawbacks. Our segmenter is truly end-to-end, capable of
performing segmentation much faster and even more accurate than
state-of-the-art neural models on Chinese benchmark datasets.Comment: To appear in ACL201
A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging
The immune system is critical in modulating cancer progression, but knowledge of immune composition, phenotype, and interactions with tumor is limited. We used multiplexed ion beam imaging by time-of-flight (MIBI-TOF) to simultaneously quantify in situ expression of 36 proteins covering identity, function, and immune regulation at sub-cellular resolution in 41 triple-negative breast cancer patients. Multi-step processing, including deep-learning-based segmentation, revealed variability in the composition of tumor-immune populations across individuals, reconciled by overall immune infiltration and enriched co-occurrence of immune subpopulations and checkpoint expression. Spatial enrichment analysis showed immune mixed and compartmentalized tumors, coinciding with expression of PD1, PD-L1, and IDO in a cell-type- and location-specific manner. Ordered immune structures along the tumor-immune border were associated with compartmentalization and linked to survival. These data demonstrate organization in the tumor-immune microenvironment that is structured in cellular composition, spatial arrangement, and regulatory-protein expression and provide a framework to apply multiplexed imaging to immune oncology
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