Similarity-based prediction methods utilize degradation trend analysis based on degradation indicators (DIs). These methods are gaining prominence in industrial predictive maintenance because they effectively address prognostics for machines with unknown failure mechanisms. However, current studies often neglect the discrepancies in degradation trends when constructing DIs from multi-sensor data and lack automatic normalization of operating regimes during feature fusion. In this study, a feature fusion methodology based on a signal-to-noise ratio metric that leverages slow feature analysis (SFA) is proposed. This customized metric utilizes SFA to quantify degradation trend discrepancies of constructed DIs, while automatically filtering out the effects of multiple operating regimes during feature fusion. The effectiveness and superiority of the proposed method are demonstrated using publicly available aero-engine and rolling bearing datasets.

Multivariate Time Series (MTS) forecasting is an essential problem in many fields. Accurate forecasting results can effectively help in making decisions. To date, many MTS forecasting methods have been proposed and widely applied. However, these methods assume that the predicted value of a single variable is affected by all other variables, ignoring the causal relationship among variables. To address the above issue, we propose a novel end-to-end deep learning model, termed graph neural network with neural Granger causality, namely CauGNN, in this paper. To characterize the causal information among variables, we introduce the neural Granger causality graph in our model. Each variable is regarded as a graph node, and each edge represents the casual relationship between variables. In addition, convolutional neural network filters with different perception scales are used for time series feature extraction, to generate the feature of each node. Finally, the graph neural network is adopted to tackle the forecasting problem of the graph structure generated by the MTS. Three benchmark datasets from the real world are used to evaluate the proposed CauGNN, and comprehensive experiments show that the proposed method achieves state-of-the-art results in the MTS forecasting task.

In recent years, multivariate time-series classification (MTSC) has attracted considerable attention owing to the advancement of sensing technology. Existing deep-learning-based MTSC techniques, which mostly rely on convolutional or recurrent neural networks, focus primarily on the temporal dependency of a single time series. Based on this, complex pairwise dependencies among multivariate variables can be better described using advanced graph methods, where each variable is regarded as a node in the graph, and their dependencies are regarded as edges. Furthermore, current spatial–temporal modeling (e.g., graph classification) methodologies based on graph neural networks (GNNs) are inherently flat and cannot hierarchically aggregate node information. To address these limitations, we propose a novel graph-pooling-based framework, MTPool, to obtain an expressive global representation of MTS. We first convert MTS slices into graphs using the interactions of variables via a graph structure learning module and obtain the spatial–temporal graph node features via a temporal convolutional module. To obtain global graph-level representation, we design an “encoder-decoder”-based variational graph pooling module to create adaptive centroids for cluster assignments. Then, we combine GNNs and our proposed variational graph pooling layers for joint graph representation learning and graph coarsening, after which the graph is progressively coarsened to one node. Finally, a differentiable classifier uses this coarsened representation to obtain the final predicted class. Experiments on ten benchmark datasets showed that MTPool outperforms state-of-the-art strategies in the MTSC task.

Multivariate time series forecasting, which analyzes historical time series to predict future trends, can effectively help decision-making. Complex relations among variables in MTS, including static, dynamic, predictable, and latent relations, have made it possible to mining more features of MTS. Modeling complex relations are not only essential in characterizing latent dependency as well as modeling temporal dependence but also brings great challenges in the MTS forecasting task. However, existing methods mainly focus on modeling certain relations among MTS variables. In this paper, we propose a novel end-to-end deep learning model, termed Multivariate Time Series Forecasting via Heterogeneous Graph Neural Networks (MTHetGNN). To characterize complex relations among variables, a relation embedding module is designed in MTHetGNN, where each variable is regarded as a graph node, and each type of edge represents a specific static or dynamic relationship. Meanwhile, a temporal embedding module is introduced for time series features extraction, where involving convolutional neural network (CNN) filters with different perception scales. Finally, a heterogeneous graph embedding module is adopted to handle the complex structural information generated by the two modules. Three benchmark datasets from the real world are used to evaluate the proposed MTHetGNN. The comprehensive experiments show that MTHetGNN achieves state-of-the-art results in the MTS forecasting task.

Graph similarity computation aims to predict a similarity score between one pair of graphs to facilitate downstream applications, such as finding the most similar chemical compounds similar to a query compound or Fewshot 3D Action Recognition. Recently, some graph similarity computation models based on neural networks have been proposed, which are either based on graph-level interaction or node-level comparison. However, when the number of nodes in the graph increases, it will inevitably bring about reduced representation ability or high computation cost. Motivated by this observation, we propose a graph partitioning and graph neural network-based model, called PSimGNN, to effectively resolve this issue. Specifically, each of the input graphs is partitioned into a set of subgraphs to extract the local structural features directly. Next, a novel graph neural network with an attention mechanism is designed to map each subgraph into an embedding vector. Some of these subgraph pairs are automatically selected for node-level comparison to supplement the subgraph-level embedding with fine-grained information. Finally, coarse-grained interaction information among subgraphs and fine-grained comparison information among nodes in different subgraphs are integrated to predict the final similarity score. Experimental results on graph datasets with different graph sizes demonstrate that PSimGNN outperforms state-of-the-art methods in graph similarity computation tasks using approximate Graph Edit Distance (GED) as the graph similarity metric.

Multivariate time series (MTS) forecasting has been widely used in various fields. Reasonable prediction results can help people make decisions, avoid risks, and increase profits. Generally, time series have two characteristics, namely long-term trends and short-term fluctuations. For example, stock prices will rise in the long term, but may fall slightly in the short term. These two characteristics are often relatively independent of each other. Existing forecasting methods usually do not distinguish between the two characteristics and thus cannot fully extract inherent attributes of the time series. In this paper, we propose a framework that can capture the long-term trends and short-term fluctuations of time series in parallel in order to enhance MTS forecasting performance. We formulate our model as a multi-task learning objective, the goal of which is to make predictions of long-term trends and shortterm fluctuations as close as possible to the ground truth. Experiments on three real-world datasets show that the proposed method uses more supervision information and can more accurately capture the changing trends of time series, thereby improving the prediction performance.

We aim at solving the problem of predicting people's ideology, or political tendency. We estimate it by using Twitter data, and formalize it as a classification problem. Ideology-detection has long been a challenging yet important problem. Certain groups, such as the policy makers, rely on it to make wise decisions. Back in the old days when labor-intensive survey-studies were needed to collect public opinions, analyzing ordinary citizens' political tendencies was uneasy. The rise of social medias, such as Twitter, has enabled us to gather ordinary citizen's data easily. However, the incompleteness of the labels and the features in social network datasets is tricky, not to mention the enormous data size and the heterogeneousity. The data differ dramatically from many commonly-used datasets, thus brings unique challenges. In our work, first we built our own datasets from Twitter. Next, we proposed TIMME, a multi-task multi-relational embedding model, that works efficiently on sparsely-labeled heterogeneous real-world dataset. It could also handle the incompleteness of the input features. Experimental results showed that TIMME is overall better than the state-of-the-art models for ideology detection on Twitter. Our findings include: links can lead to good classification outcomes without text; conservative voice is under-represented on Twitter; follow is the most important relation to predict ideology; retweet and mention enhance a higher chance of like, etc. Last but not least, TIMME could be extended to other datasets and tasks in theory.

A unique triple-shelled architecture with nonspherical shape has been successfully synthesized via a controllable multistep approach in case of polycrystalline SnO2 nanoboxes. The morphological characterizations demonstrate that SnO2 triple-shelled hollow boxes (THBs) are hierarchically constructed by numerous SnO2 primary nanoparticles. The formation mechanism of these SnO2 THBs is discussed based on a localized inside-out Ostwald ripening process. When SnO2 THBs are used as sensing material for toluene detection, the improved sensing performance can be attributed to the rational microstructures for fast transfer of gas molecules. (C) 2020 Elsevier B.V. All rights reserved.

The ability to compute similarity scores between graphs based on metrics such as Graph Edit Distance (GED) is important in many real-world applications. Computing exact GED values is typically an NP-hard problem and traditional algorithms usually achieve an unsatisfactory trade-off between accuracy and efficiency. Recently, Graph Neural Networks (GNNs) provide a data-driven solution for this task, which is more efficient while maintaining prediction accuracy in small graph (around 10 nodes per graph) similarity computation. Existing GNN-based methods, which either respectively embeds two graphs (lack of low-level cross-graph interactions) or deploy cross-graph interactions for whole graph pairs (redundant and time-consuming), are still not able to achieve competitive results when the number of nodes in graphs increases. In this paper, we focus on similarity computation for large-scale graphs and propose the "embedding-coarsening-matching" framework CoSimGNN, which first embeds and coarsens large graphs with adaptive pooling operation and then deploys fine-grained interactions on the coarsened graphs for final similarity scores. Furthermore, we create several synthetic datasets which provide new benchmarks for graph similarity computation. Detailed experiments on both synthetic and real-world datasets have been conducted and CoSimGNN achieves the best performance while the inference time is at most 1/3 of that of previous state-of-the-art.

Multivariate time series forecasting is widely used in various fields. Reasonable prediction results can assist people in planning and decision-making, generate benefits and avoid risks. Normally, there are two characteristics of time series, that is, long-term trend and short-term fluctuation. For example, stock prices will have a long-term upward trend with the market, but there may be a small decline in the short term. These two characteristics are often relatively independent of each other. However, the existing prediction methods often do not distinguish between them, which reduces the accuracy of the prediction model. In this paper, a MTS forecasting framework that can capture the long-term trends and short-term fluctuations of time series in parallel is proposed. This method uses the original time series and its first difference to characterize long-term trends and short-term fluctuations. Three prediction sub-networks are constructed to predict long-term trends, short-term fluctuations and the final value to be predicted. In the overall optimization goal, the idea of multi-task learning is used for reference, which is to make the prediction results of long-term trends and short-term fluctuations as close to the real values as possible while requiring to approximate the values to be predicted. In this way, the proposed method uses more supervision information and can more accurately capture the changing trend of the time series, thereby improving the forecasting performance.

Multivariate time series (MTS) forecasting is an important problem in many fields. Accurate forecasting results can effectively help decision-making and reduce sub-jectivity. To date, many MTS forecasting methods have been proposed and widely applied. However, these methods assume that the value to be predicted of a single variable is related to all other variables, which makes it difficult to select the true key variable in high-dimensional situations. To address the above issue, a novel end-to-end deep learning model, termed transfer entropy graph neural network (TEGNN) is proposed in this paper. For accurate variable selection, the transfer entropy (TE) graph is introduced to characterize the causal information among variables, in which each variable is regarded as a graph node. In addition, convolutional neural network (CNN) filters with different perception scales are used for time series feature extraction. What is more, graph neural network (GNN) is adopted to tackle the embedding and forecasting problem of graph structure composed of MTS. MTS data collected from the real world are used to evaluate the prediction performance of TEGNN. Our comprehensive experiments demon-strate that the proposed TEGNN consistently outperforms state-of-the-art MTS forecasting baselines.

The current state-of-the-art object recognition algorithms, deep convolutional neural networks (DCNNs), are inspired by the architecture of the mammalian visual system, and are capable of human-level performance on many tasks. However, even these algorithms make errors. As they are trained for object recognition tasks, it has been shown that DCNNs develop hidden representations that resemble those observed in the mammalian visual system. Moreover, DCNNs trained on object recognition tasks are currently among the best models we have of the mammalian visual system. This led us to hypothesize that teaching DCNNs to achieve even more brain-like representations could improve their performance. To test this, we trained DCNNs on a composite task, wherein networks were trained to: a) classify images of objects; while b) having intermediate representations that resemble those observed in neural recordings from monkey visual cortex. Compared with DCNNs trained purely for object categorization, DCNNs trained on the composite task had better object recognition performance and are more robust to label corruption. Interestingly, we also found that neural data was not required, but randomized data with the same statistics as neural data also boosted performance. Our results outline a new way to train object recognition networks, using strategies in which the brain - or at least the statistical properties of its activation patterns - serves as a teacher signal for training DCNNs.

In this work, we focus on large graph similarity computation problem and propose a novel embedding-coarsening-matching learning framework, which outperforms state-of-the-art methods in this task and has significant improvement in time efficiency. Graph similarity computation for metrics such as Graph Edit Distance (GED) is typically NP-hard, and existing heuristics-based algorithms usually achieves a unsatisfactory trade-off between accuracy and efficiency. Recently the development of deep learning techniques provides a promising solution for this problem by a data-driven approach which trains a network to encode graphs to their own feature vectors and computes similarity based on feature vectors. These deep-learning methods can be classified to two categories, embedding models and matching models. Embedding models such as GCN-Mean and GCN-Max, which directly map graphs to respective feature vectors, run faster but the performance is usually poor due to the lack of interactions across graphs. Matching models such as GMN, whose encoding process involves interaction across the two graphs, are more accurate but interaction between whole graphs brings a significant increase in time consumption (at least quadratic time complexity over number of nodes). Inspired by large biological molecular identification where the whole molecular is first mapped to functional groups and then identified based on these functional groups, our embedding-coarsening-matching learning framework first embeds and coarsens large graphs to coarsened graphs with denser local topology and then matching mechanism is deployed on the coarsened graphs for the final similarity scores. Detailed experiments have been conducted and the results demonstrate the efficiency and effectiveness of our proposed framework.

The current state-of-the-art object recognition algorithms, deep convolutional neural networks (DCNNs), are inspired by the architecture of the mammalian visual system [8], and capable of human-level performance on many tasks [15]. However, even these algorithms make errors. As DCNNs improve at object recognition tasks, they develop representations in their hidden layers that become more similar to those observed in the mammalian brains [24]. This led us to hypothesize that teaching DCNNs to achieve even more brain-like representations could improve their performance. To test this, we trained DCNNs on a composite task, wherein networks were trained to: a) classify images of objects; while b) having intermediate representations that resemble those observed in neural recordings from monkey visual cortex. Compared with DCNNs trained purely for object categorization, DCNNs trained on the composite task had better object recognition performance. Our results outline a new way to regularize object recognition networks, using transfer learning strategies in which the brain serves as a teacher for training DCNNs.

Vibration signal represents the health state of equipment operation. Abnormal detection of vibration signal can discover equipment faults in time, facilitate equipment maintenance and avoid safety accident. In view of the obvious difference in frequency components of vibration signal under different operation conditions, a clustering method based on frequency components and Cosine Distance is presented to establish more accurate models for samples under different operation conditions. For time sequence vibration signal with high dimension, time domain and frequency domain features are extracted to represent signal characteristics, and isolated forest model is used to detect abnormal data. The results show that, compared with the supervised fault diagnosis method, this method does not need fault sample data during modeling, and thus ensures detection accuracy while avoiding destructive experiments.

The lack of a simple operable method for longitudinal dynamic self-assembly of particles in a microchannel is one of the main problems in applying this technology to a wide range of researches, such as biomedical engineering, material science, and computation. Herein, a viscoelasticity-induced trapping microfluidic system for flowing particles is proposed to increase the maneuverability of longitudinal dynamic self-assembly of particles and achieve real-time control of the interparticle spacings and the frequency of particles passing through the outlet. Two kinds of functional microstructures and a side-channel were designed to preprocessing the randomly distributed particles to make particles no aggregation and evenly distributed and realize real-time control of the particle volume concentration. Randomly distributed particles could be focused into a line and become equally spaced in the center axis of a straight microchannel under the balance of the elastic force and the viscoelasticity-induced effective repulsive force. Besides, a finite element method model is established to analyze the processes of particles flowing in each functional microstructure. Therefore, a step forward in this microfluidic technology can provide significant promotion for a wide range of researches.

Monodisperse droplets are important in drug screening, and cell-based and biochemical research. However, conventional methods for creating droplets, such as co-flow, T-junction and flow-focusing, have poor monodispersity because of fluctuations in the flow rate. Because step emulsification is based on the principle of Laplace pressure, it is insensitive to the flow rate and yields a constant and high monodispersity. In the present study, we combine electrowetting and step emulsification to reduce the negative influence of flow-rate fluctuations and to prepare highly monodisperse droplets. We demonstrate that the flow rate and voltage applied to the droplets can independently influence the droplet size. This method has great potential in chip-based bioanalysis and cell-based studies where highly monodisperse droplets are needed.

The key problem which needs to be solved during the commercialization process of the organic light emitting diode (OLED) lighting technology is how to improve the efficiency and stability of optoelectronic materials. In order to solute this issue, we explore the regulation of Ag nanoparticles (NPs) on the photoluminescence (PL) characteristics of an efficient organic optoelectronic polymer poly[2-methoxy-5-(2-ethylhexyloxy)-1,4-phenylene-vinylene] (MEH-PPV). Because of the coupling between the localized surface plasmons from the Ag NPs and the PL emission of the MEH-PPV, the Ag NPs could effectively enhance the PL intensity, modulate the spectral line shape and shorten the lifetime of interchain species. The temperature-dependent PL measurement exhibits that with the introduction of Ag NPs, the thermal exchange process between the sample and the surrounding environment becomes more effective, and the activation energy decreases from 448 to 318 meV. The excitation intensity (EI)-dependent PL measurement demonstrates that the thermal accumulation effect and the nonlinearity of El-dependent PL enhancement are both suppressed by the Ag NPs. The results of this study will provide experimental guidance to the design and fabrication of OLED devices with high efficiency and stability. (C) 2018 Elsevier B.V. All rights reserved.

Based on a homemade extrusion platform designed for the fabrication of thermoplastic parts with micro capillaries,a novel plastic micro tube with 24 capillaries was first proposed all over the world (Micro Capillary Tube,MCT).These 24 capillaries are orderly located in the circular wall of a tube with a larger hollow channel.The effects of three processing parameters,i.e.injection air pressure,draw ratio and air gap distance,on the quality of the parts were investigated in the extrusion process with conditions of injection air pressures:0.21～0.36 kPa,air gap distance:35～110 mm and draw ratio:2.4～14.1.The results indicated that larger draw ratio could result in a smaller outer and capillary diameter,and there was a negative correlation between the sizes of MCT and draw ratio.However,the draw ratio did not show any influence on the dimensional proportions of cross section of MCT,and a similar trend could be observed in the air gap distance.In contrast,the injection air gap severely damaged the dimensional proportions and changed the shape and size of micro capillary,but it did not change the external size.

This work proposes a novel method for the semi-quantitative determination of hydrogen peroxide (H2O2) in routine and on-site applications.