108 research outputs found
Enhance Diamond Coating Adhesion by Oriented Interlayer Microcracking
In this paper, we report a microcrack toughening mechanism for enhancing the adhesion of diamondcoating. The oriented microcracks were formed within the TiC interlayer to dissipate strain energy and accommodate deformation via the crack opening-closing mechanism, thus enhancing the coating/substrate interfacial toughness. The delamination of diamondcoating was effectively prevented when the parallel microcracks were confined within the interlayer and arrested at interfaces of coating/interlayer/substrate. Density functional theory calculations revealed that the highly anisotropicfracture strength of the TiC phase energetically favors crack initiation and propagation along (100) planes only, which are 54.7° away from the interface. These microcracks are constrained inside the interlayer by the two strong interfaces in the substrate/interlayer/coating system. The new microcrack toughening mechanism with these combined features has a wide application to enhance the adhesion of thin-film coatings
Proteus: Simulating the Performance of Distributed DNN Training
DNN models are becoming increasingly larger to achieve unprecedented
accuracy, and the accompanying increased computation and memory requirements
necessitate the employment of massive clusters and elaborate parallelization
strategies to accelerate DNN training. In order to better optimize the
performance and analyze the cost, it is indispensable to model the training
throughput of distributed DNN training. However, complex parallelization
strategies and the resulting complex runtime behaviors make it challenging to
construct an accurate performance model. In this paper, we present Proteus, the
first standalone simulator to model the performance of complex parallelization
strategies through simulation execution. Proteus first models complex
parallelization strategies with a unified representation named Strategy Tree.
Then, it compiles the strategy tree into a distributed execution graph and
simulates the complex runtime behaviors, comp-comm overlap and bandwidth
sharing, with a Hierarchical Topo-Aware Executor (HTAE). We finally evaluate
Proteus across a wide variety of DNNs on three hardware configurations.
Experimental results show that Proteus achieves average prediction
error and preserves order for training throughput of various parallelization
strategies. Compared to state-of-the-art approaches, Proteus reduces prediction
error by up to
A Modified Brain MR Image Segmentation and Bias Field Estimation Model Based on Local and Global Information
Because of the poor radio frequency coil uniformity and gradient-driven eddy currents, there is much noise and intensity inhomogeneity (bias) in brain magnetic resonance (MR) image, and it severely affects the segmentation accuracy. Better segmentation results are difficult to achieve by traditional methods; therefore, in this paper, a modified brain MR image segmentation and bias field estimation model based on local and global information is proposed. We first construct local constraints including image neighborhood information in Gaussian kernel mapping space, and then the complete regularization is established by introducing nonlocal spatial information of MR image. The weighting between local and global information is automatically adjusted according to image local information. At the same time, bias field information is coupled with the model, and it makes the model reduce noise interference but also can effectively estimate the bias field information. Experimental results demonstrate that the proposed algorithm has strong robustness to noise and bias field is well corrected
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification
Learning unbiased node representations for imbalanced samples in the graph
has become a more remarkable and important topic. For the graph, a significant
challenge is that the topological properties of the nodes (e.g., locations,
roles) are unbalanced (topology-imbalance), other than the number of training
labeled nodes (quantity-imbalance). Existing studies on topology-imbalance
focus on the location or the local neighborhood structure of nodes, ignoring
the global underlying hierarchical properties of the graph, i.e., hierarchy. In
the real-world scenario, the hierarchical structure of graph data reveals
important topological properties of graphs and is relevant to a wide range of
applications. We find that training labeled nodes with different hierarchical
properties have a significant impact on the node classification tasks and
confirm it in our experiments. It is well known that hyperbolic geometry has a
unique advantage in representing the hierarchical structure of graphs.
Therefore, we attempt to explore the hierarchy-imbalance issue for node
classification of graph neural networks with a novelty perspective of
hyperbolic geometry, including its characteristics and causes. Then, we propose
a novel hyperbolic geometric hierarchy-imbalance learning framework, named
HyperIMBA, to alleviate the hierarchy-imbalance issue caused by uneven
hierarchy-levels and cross-hierarchy connectivity patterns of labeled
nodes.Extensive experimental results demonstrate the superior effectiveness of
HyperIMBA for hierarchy-imbalance node classification tasks.Comment: Accepted by Web Conference (WWW) 202
Quantum Image Processing and Its Application to Edge Detection: Theory and Experiment
Processing of digital images is continuously gaining in volume and relevance,
with concomitant demands on data storage, transmission and processing power.
Encoding the image information in quantum-mechanical systems instead of
classical ones and replacing classical with quantum information processing may
alleviate some of these challenges. By encoding and processing the image
information in quantum-mechanical systems, we here demonstrate the framework of
quantum image processing, where a pure quantum state encodes the image
information: we encode the pixel values in the probability amplitudes and the
pixel positions in the computational basis states. Our quantum image
representation reduces the required number of qubits compared to existing
implementations, and we present image processing algorithms that provide
exponential speed-up over their classical counterparts. For the commonly used
task of detecting the edge of an image, we propose and implement a quantum
algorithm that completes the task with only one single-qubit operation,
independent of the size of the image. This demonstrates the potential of
quantum image processing for highly efficient image and video processing in the
big data era.Comment: 13 pages, including 9 figures and 5 appendixe
Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation
Social networks are considered to be heterogeneous graph neural networks
(HGNNs) with deep learning technological advances. HGNNs, compared to
homogeneous data, absorb various aspects of information about individuals in
the training stage. That means more information has been covered in the
learning result, especially sensitive information. However, the
privacy-preserving methods on homogeneous graphs only preserve the same type of
node attributes or relationships, which cannot effectively work on
heterogeneous graphs due to the complexity. To address this issue, we propose a
novel heterogeneous graph neural network privacy-preserving method based on a
differential privacy mechanism named HeteDP, which provides a double guarantee
on graph features and topology. In particular, we first define a new attack
scheme to reveal privacy leakage in the heterogeneous graphs. Specifically, we
design a two-stage pipeline framework, which includes the privacy-preserving
feature encoder and the heterogeneous link reconstructor with gradients
perturbation based on differential privacy to tolerate data diversity and
against the attack. To better control the noise and promote model performance,
we utilize a bi-level optimization pattern to allocate a suitable privacy
budget for the above two modules. Our experiments on four public benchmarks
show that the HeteDP method is equipped to resist heterogeneous graph privacy
leakage with admirable model generalization
Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization
Dynamic graph neural networks (DGNNs) are increasingly pervasive in
exploiting spatio-temporal patterns on dynamic graphs. However, existing works
fail to generalize under distribution shifts, which are common in real-world
scenarios. As the generation of dynamic graphs is heavily influenced by latent
environments, investigating their impacts on the out-of-distribution (OOD)
generalization is critical. However, it remains unexplored with the following
two major challenges: (1) How to properly model and infer the complex
environments on dynamic graphs with distribution shifts? (2) How to discover
invariant patterns given inferred spatio-temporal environments? To solve these
challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE)
framework for OOD generalization by modeling complex coupled environments and
exploiting spatio-temporal invariant patterns. Specifically, we first design
the environment-aware EA-DGNN to model environments by multi-channel
environments disentangling. Then, we propose an environment instantiation
mechanism for environment diversification with inferred distributions. Finally,
we discriminate spatio-temporal invariant patterns for out-of-distribution
prediction by the invariant pattern recognition mechanism and perform
fine-grained causal interventions node-wisely with a mixture of instantiated
environment samples. Experiments on real-world and synthetic dynamic graph
datasets demonstrate the superiority of our method against state-of-the-art
baselines under distribution shifts. To the best of our knowledge, we are the
first to study OOD generalization on dynamic graphs from the environment
learning perspective.Comment: Accepted by the 37th Conference on Neural Information Processing
Systems (NeurIPS 2023
Does Graph Distillation See Like Vision Dataset Counterpart?
Training on large-scale graphs has achieved remarkable results in graph
representation learning, but its cost and storage have attracted increasing
concerns. Existing graph condensation methods primarily focus on optimizing the
feature matrices of condensed graphs while overlooking the impact of the
structure information from the original graphs. To investigate the impact of
the structure information, we conduct analysis from the spectral domain and
empirically identify substantial Laplacian Energy Distribution (LED) shifts in
previous works. Such shifts lead to poor performance in cross-architecture
generalization and specific tasks, including anomaly detection and link
prediction. In this paper, we propose a novel Structure-broadcasting Graph
Dataset Distillation (SGDD) scheme for broadcasting the original structure
information to the generation of the synthetic one, which explicitly prevents
overlooking the original structure information. Theoretically, the synthetic
graphs by SGDD are expected to have smaller LED shifts than previous works,
leading to superior performance in both cross-architecture settings and
specific tasks. We validate the proposed SGDD across 9 datasets and achieve
state-of-the-art results on all of them: for example, on the YelpChi dataset,
our approach maintains 98.6% test accuracy of training on the original graph
dataset with 1,000 times saving on the scale of the graph. Moreover, we
empirically evaluate there exist 17.6% ~ 31.4% reductions in LED shift crossing
9 datasets. Extensive experiments and analysis verify the effectiveness and
necessity of the proposed designs. The code is available in the GitHub
repository: https://github.com/RingBDStack/SGDD.Comment: Accepted by NeurIPS 202
Basic characteristics of mine dust suppression foam and the quantitative evaluation method of its performance
The priority direction of mine dust control is to suppress dust generation and flying at the source. Foam, a gas-liquid two-phase medium, has some unique advantages of large dust covering area, strong adhesion ability and fast wetting of dust. It is an efficient way of dust suppression, especially for respiratory dust. However, there is limited research on the morphology and properties of dust suppression foam in the past, resulting in a certain blindness in the preparation and utilization of dust suppression foam. And there is a problem of a large amount of spray foam in exchange for higher dust suppression efficiency, which restricts the low-cost application of this technology in mines. Therefore, in this study, a theoretical derivation was combined with experimental research and quantitative analysis to study the process and law of dust suppression foam drain, the micromorphology of dust suppression foam, the performance influence mechanism and the quantitative evaluation method. These results show that the drain factor w is related to the height of the foam column and the liquid of foam in the foaming process. The higher height of the dust suppression foam and the greater liquid content, the value of the discharge factor w will be lower. The predicted value of w and the theoretical discharge curve calculated by the drainage model show a high degree of consistency with the experimental results, which verifies the accuracy of the theoretical model. The results show foam size distribution, average diameter with the concentration of foaming agent changes. At a low concentration (1%), the number of foam decreases and large particle size bubbles increase. The addition of low-concentration (<0.3%) polymer to the foaming agent has no obvious effect on the wetting angle of coal dust, but the contact angle increases when enlarging the concentration of polymer. In terms of foaming performance and foam stability performance, the production efficiency and stability of dust suppression foam can be improved after the addition of 0.1% polymer. Based on the analysis of the whole process from the generation of dust suppression foam to its action on the dust cutting source, the indicators for evaluating the foam performance are proposed, and the quantitative evaluation criteria and four grades of dust suppression foam performance are given
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