246 research outputs found
Higher-order multi-scale deep Ritz method for multi-scale problems of authentic composite materials
The direct deep learning simulation for multi-scale problems remains a
challenging issue. In this work, a novel higher-order multi-scale deep Ritz
method (HOMS-DRM) is developed for thermal transfer equation of authentic
composite materials with highly oscillatory and discontinuous coefficients. In
this novel HOMS-DRM, higher-order multi-scale analysis and modeling are first
employed to overcome limitations of prohibitive computation and Frequency
Principle when direct deep learning simulation. Then, improved deep Ritz method
are designed to high-accuracy and mesh-free simulation for macroscopic
homogenized equation without multi-scale property and microscopic lower-order
and higher-order cell problems with highly discontinuous coefficients.
Moreover, the theoretical convergence of the proposed HOMS-DRM is rigorously
demonstrated under appropriate assumptions. Finally, extensive numerical
experiments are presented to show the computational accuracy of the proposed
HOMS-DRM. This study offers a robust and high-accuracy multi-scale deep
learning framework that enables the effective simulation and analysis of
multi-scale problems of authentic composite materials
Hyponormal Toeplitz Operators on the Dirichlet Spaces
We completely characterize the hyponormality of bounded Toeplitz operators with Sobolev symbols on the Dirichlet space and the harmonic Dirichlet space
In vitro corrosion of Mg–1.21Li–1.12Ca–1Y alloy
AbstractThe influence of the microstructure on mechanical properties and corrosion behavior of the Mg–1.21Li–1.12Ca–1Y alloy was investigated using OM, SEM, XRD, EPMA, EDS, tensile tests and corrosion measurements. The results demonstrated that the microstructure of the Mg–1.21Li–1.12Ca–1Y alloy was characterized by α-Mg substrate and intermetallic compounds Mg2Ca and Mg24Y5. Most of the fine Mg2Ca particles for the as-cast alloy were distributed along the grain boundaries, while for the as-extruded along the extrusion direction. The Mg24Y5 particles with a larger size than the Mg2Ca particles were positioned inside the grains. The mechanical properties of Mg–1.21Li–1.12Ca–1Y alloy were improved by the grain refinement and dispersion strengthening. Corrosion pits initiated at the α-Mg matrix neighboring the Mg2Ca particles and subsequently the alloy exhibited general corrosion and filiform corrosion as the corrosion product layer of Mg(OH)2 and MgCO3 became compact and thick
A Graph-based Relevance Matching Model for Ad-hoc Retrieval
To retrieve more relevant, appropriate and useful documents given a query,
finding clues about that query through the text is crucial. Recent deep
learning models regard the task as a term-level matching problem, which seeks
exact or similar query patterns in the document. However, we argue that they
are inherently based on local interactions and do not generalise to ubiquitous,
non-consecutive contextual relationships. In this work, we propose a novel
relevance matching model based on graph neural networks to leverage the
document-level word relationships for ad-hoc retrieval. In addition to the
local interactions, we explicitly incorporate all contexts of a term through
the graph-of-word text format. Matching patterns can be revealed accordingly to
provide a more accurate relevance score. Our approach significantly outperforms
strong baselines on two ad-hoc benchmarks. We also experimentally compare our
model with BERT and show our advantages on long documents.Comment: To appear at AAAI 202
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Scaling up contrastive language-image pretraining (CLIP) is critical for
empowering both vision and multimodal models. We present EVA-CLIP-18B, the
largest and most powerful open-source CLIP model to date, with 18-billion
parameters. With only 6-billion training samples seen, EVA-CLIP-18B achieves an
exceptional 80.7% zero-shot top-1 accuracy averaged across 27 widely recognized
image classification benchmarks, outperforming its forerunner EVA-CLIP
(5-billion parameters) and other open-source CLIP models by a large margin.
Remarkably, we observe a consistent performance improvement with the model size
scaling of EVA-CLIP, despite maintaining a constant training dataset of
2-billion image-text pairs from LAION-2B and COYO-700M. This dataset is openly
available and much smaller than the in-house datasets (e.g., DFN-5B, WebLI-10B)
employed in other state-of-the-art CLIP models. EVA-CLIP-18B demonstrates the
potential of EVA-style weak-to-strong visual model scaling. With our model
weights made publicly available, we hope to facilitate future research in
vision and multimodal foundation models
Evaluating Modules in Graph Contrastive Learning
The recent emergence of contrastive learning approaches facilitates the
research on graph representation learning (GRL), introducing graph contrastive
learning (GCL) into the literature. These methods contrast semantically similar
and dissimilar sample pairs to encode the semantics into node or graph
embeddings. However, most existing works only performed model-level evaluation,
and did not explore the combination space of modules for more comprehensive and
systematic studies. For effective module-level evaluation, we propose a
framework that decomposes GCL models into four modules: (1) a sampler to
generate anchor, positive and negative data samples (nodes or graphs); (2) an
encoder and a readout function to get sample embeddings; (3) a discriminator to
score each sample pair (anchor-positive and anchor-negative); and (4) an
estimator to define the loss function. Based on this framework, we conduct
controlled experiments over a wide range of architectural designs and
hyperparameter settings on node and graph classification tasks. Specifically,
we manage to quantify the impact of a single module, investigate the
interaction between modules, and compare the overall performance with current
model architectures. Our key findings include a set of module-level guidelines
for GCL, e.g., simple samplers from LINE and DeepWalk are strong and robust; an
MLP encoder associated with Sum readout could achieve competitive performance
on graph classification. Finally, we release our implementations and results as
OpenGCL, a modularized toolkit that allows convenient reproduction, standard
model and module evaluation, and easy extension
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