Graph convolutional network model with a feature compensation module and dual-channel second-order pooling module for multimodal emotion recognition in conversation

Traffic flow prediction plays an important role in intelligent transportation systems and is of great significance in the applications of traffic control and urban planning. Due to the complexity of road traffic flow data, traffic flow prediction has been one of the challenging tasks to fully exploit the spatiotemporal characteristics of roads to improve prediction accuracy. In this study, a combined flow direction level traffic flow prediction graph convolutional network (GCN) and long short-term memory (LSTM) model based on spatiotemporal characteristics is proposed. First, a GCN model is employed to capture the topological structure of the data graph and extract the spatial features of road networks. Additionally, due to the capability to handle long-term dependencies, the long-term memory is used to predict the time series of traffic flow and extract the time features. The proposed model is evaluated using real-world data, which are obtained from the intersection of Liuquan Road and Zhongrun Avenue in the Zibo High-Tech Zone of China. The results show that the developed combined GCN-LSTM flow direction level traffic flow prediction model can perform better than the single models of the LSTM model and GCN model, and the combined ARIMA-LSTM model in traffic flow has a strong spatiotemporal correlation.

ABSTRACTLarge‐area, high‐resolution organic light‐emitting diode (OLED) displays require high‐performance oxide thin‐film transistors (TFTs) with excellent mobility and stability, as well as little threshold voltage shift, especially for G8.5+ OLED production lines. Our previous work demonstrated rare‐earth doped oxides are ideal channel layer materials for TFTs due to their superior mobility and stability. However, scaling up TFT production from pilot lines to mass manufacturing poses significant challenges, largely because of the complexity of multi‐element composite materials and intricate processes. In this study, an end‐to‐end graph convolutional network (GCN) model is employed to optimize TFT production processes. By encoding process parameters as graph nodes and features, the GCN effectively predicts critical drain current–gate voltage (I–V) characteristics, enabling precise calculation of electron mobility, subthreshold slope, and threshold voltage. The model achieves a mean absolute percentage error of 19.9% on validation data. Based on the optimized parameters, a TFT device with properties including a mobility of 37.8 cm2/Vs, a threshold voltage of 0.9 V, and a subthreshold slope of 0.62 V/dec was successfully produced. These results highlight the potential of GCN‐based models to address the complexities of TFT mass production, providing a powerful tool for process optimization and performance enhancement.

Graph convolutional network – Long short term memory neural network- multi layer perceptron- Gaussian progress regression model: A new deep learning model for predicting ozone concertation

Chronic obstructive pulmonary disease (COPD) is a common disease with high morbidity and mortality, where early detection benefits the population. However, the early diagnosis rate of COPD is low due to the absence or slight early symptoms. In this paper, a novel method based on graph convolution network (GCN) for early detection of COPD is proposed, which uses small and weakly labeled chest computed tomography image data from the publicly available Danish Lung Cancer Screening Trial database. The key idea is to construct a graph using regions of interest randomly selected from the segmented lung parenchyma and then input it into the GCN model for COPD detection. In this way, the model can not only extract the feature information of each region of interest but also the topological structure information between regions of interest, that is, graph structure information. The proposed GCN model achieves an acceptable performance with an accuracy of 0.77 and an area under a curve of 0.81, which is higher than the previous studies on the same dataset. GCN model also outperforms several state-of-the-art methods trained at the same time. As far as we know, it is also the first time using the GCN model on this dataset for COPD detection.Graphical abstract

Multi-modal graph context extraction and consensus-aware learning for emotion recognition in conversation

Open public places, such as pedestrian streets, parks, and squares, are vulnerable when the pedestrians thronged into the sidewalks. The crowd count changes dynamically over time with various external factors, such as surroundings, weekends, and peak hours, so it is essential to predict the accurate and timely crowd count. To address this issue, this study introduces graph convolutional network (GCN), a network-based model, to predict the crowd flow in a walking street. Compared with other grid-based methods, the model is capable of directly processing road network graphs. Experiments show the GCN model and its extension STGCN consistently and significantly outperform other five baseline models, namely HA, ARIMA, SVM, CNN and LSTM, in terms of RMSE, MAE and R2. Considering the computation efficiency, the standard GCN model was selected to predict the crowd. The results showed that the model obtains superior performances with higher prediction precision on weekends and peak hours, of which R2 are above 0.9, indicating the GCN model can capture the pedestrian features in the road network effectively, especially during the periods with massive crowds. The results will provide practical references for city managers to alleviate road congestion and help pedestrians make smarter planning and save travel time.

Skeleton-based action recognition aims to project skeleton sequences to action categories, where skeleton sequences are derived from multiple forms of pre-detected points. Compared with earlier methods that focus on exploring single-form skeletons via Graph Convolutional Networks (GCNs), existing methods tend to improve GCNs by leveraging multi-form skeletons due to their complementary cues. However, these methods (either adapting structure of GCNs or model ensemble) require the co-existence of all forms of skeletons during both training and inference stages, while a typical situation in real life is the existence of only partial forms for inference. To tackle this issue, we present Adaptive Cross-Form Learning (ACFL), which empowers well-designed GCNs to generate complementary representation from single-form skeletons without changing model capacity. Specifically, each GCN model in ACFL not only learns action representation from the single-form skeletons, but also adaptively mimics useful representations derived from other forms of skeletons. In this way, each GCN can learn how to strengthen what has been learned, thus exploiting model potential and facilitating action recognition as well. Extensive experiments conducted on three challenging benchmarks, i.e., NTU-RGB+D 120, NTU-RGB+D 60 and UAV-Human, demonstrate the effectiveness and generalizability of the proposed method. Specifically, the ACFL significantly improves various GCN models (i.e., CTR-GCN, MS-G3D, and Shift-GCN), achieving a new record for skeleton-based action recognition.

Exploring the impact of trip patterns on spatially aggregated crashes using floating vehicle trajectory data and graph Convolutional Networks

Applications of deep learning to electronic design automation (EDA) have recently begun to emerge, although they have mainly been limited to processing of regular structured data such as images. However, many EDA problems require processing irregular structures, and it can be non-trivial to manually extract important features in such cases. In this paper, a high performance graph convolutional network (GCN) model is proposed for the purpose of processing irregular graph representations of logic circuits. A GCN classifier is firstly trained to predict observation point candidates in a netlist. The GCN classifier is then used as part of an iterative process to propose observation point insertion based on the classification results. Experimental results show the proposed GCN model has superior accuracy to classical machine learning models on difficult-to-observation nodes prediction. Compared with commercial testability analysis tools, the proposed observation point insertion flow achieves similar fault coverage with an 11% reduction in observation points and a 6% reduction in test pattern count.

Ligand-based prediction of anti-bacterial compounds: Overcoming class imbalance in molecular data.

Multimodal Emotion Recognition in Conversation MERC holds significant importance in Natural Language Processing due to its broad range of applications. However, existing methods still face challenges in addressing the highly imbalanced class problem and extracting robust representations for complex conversational scenarios. which leads to a decrease in the generalization ability of the model and an inability to effectively recognize minority emotion classes. To address these challenges, a cause-based supervised contrastive learning framework with adversarial sample-label CaSCLA is proposed in this paper. Specifically, we employ a modality balancing technique to fuse the multimodal features, which are then fed into a novel causal-aware network to effectively capture the underlying causal relationships within dialogues. Besides, a supervised contrastive learning with adversarial sample-label method is proposed to alleviate the class imbalance problem by learning label representations and optimizing the similarity between sample features and label embeddings. Furthermore, CaSCLA applies an adversarial samples training strategy, constructing additional positive sample-label pairs to enhance the diversity of the data and increase the robustness of the model. Extensive experiments on the IEMOCAP and MELD benchmark datasets demonstrate that CaSCLA achieves competitive performance.

While neural networks (NNs) have achieved great results in various intelligent tasks like image classification and speech recognition, real-world scenarios have more applications beyond just NN processing like graph convolutional network (GCN) and deep-learning recommendation model (DLRM), which typically consist of sparse gathering (SpG) and sparse algebra (SpA). Their large application size leads to substantial data movement. Although the fusion of digital computing-in-memory (CIM) and multichip-module (MCM) can reduce data movement efficiently and scale out CIM’s capacity in a high-yield solution, the MCM-CIM system raises new challenges for beyond-NN acceleration: SpG involves repeated off-chip DRAM access, interchiplet access, and redundant reduction operations; SpA suffers from inter-CIM workload imbalance and intra-CIM under-utilization. Thus, we design TensorCIM as the CIM processor chiplet with three corresponding features: 1) the redundancy-eliminated gathering manager (REGM) dynamically maintains frequently accessed features and reduction results in the CIM to eliminate redundant accesses and reductions; 2) the equal operation-based CIM initializer (EOCI) calculates effective multiply-accumulation (MAC) operations and initializes CIM macros with a balanced inter-CIM workload at the subarray level; and 3) the input-lookahead CIM (ILA-CIM) architecture looks ahead at future inputs to fully utilize CIM logic. The fabricated MCM-CIM system consumes only 3.7 nJ/Gather for the GCN model, achieving 8.3-TFLOPS/W algebra efficiency at FP32.

Accurate short-term photovoltaic (PV) power forecasting is of great significance for the safe and stable operation of power system. Spatial information from neighboring PV sites contributes to improving forecasting performance. However, most of the current methods considering the spatial information of neighboring sites indiscriminately use all sites data for modeling, which will lead to information redundancy, resulting in low forecasting accuracy. Therefore, this paper proposes a short-term solar power forecasting method based on optimal graph structure considering surrounding spatio-temporal correlations. Firstly, the neighboring sites data is analyzed from the perspective of geographical and weather factors to select typical PV sites. Secondly, based on the complex network theory, a new index is proposed to evaluate the connectivity of the graph structure, which improves the predictive ability of the Graph Convolutional Network (GCN) model. Finally, considering numerical weather prediction (NWP) data, a hierarchical directed graph structure is constructed to indicate the unidirectional relationship between input samples, which is used as the input of GCN model to mine the spatio-temporal correlation around the targeted site. Through carrying out the case study, the proposed method shows excellent performance in improving accuracy of power forecasting compared with other benchmark methods.

The cross-modal retrieval task has raised continuous attention in recent years with the increasing scale of multi-modal data, which has broad application prospects including multimedia data management and intelligent search engine. Most existing methods mainly project data of different modalities into a common representation space where label information is often exploited to distinguish samples from different semantic categories. However, they typically treat each label as an independent individual and ignore the underlying semantic structure of labels. In this paper, we propose an end-to-end adaptive label-aware graph convolutional network (ALGCN) by designing both the instance representation learning branch and the label representation learning branch, which can obtain modality-invariant and discriminative representations for cross-modal retrieval. Firstly, we construct an instance representation learning branch to transform instances of different modalities into a common representation space. Secondly, we adopt Graph Convolutional Network (GCN) to learn inter-dependent classifiers in the label representation learning branch. In addition, a novel adaptive correlation matrix is proposed to efficiently explore and preserve the semantic structure of labels in a data-driven manner. Together with a robust self-supervision loss for GCN, the GCN model can be supervised to learn an effective and robust correlation matrix for feature propagation. Comprehensive experimental results on three benchmark datasets, NUS-WIDE, MIRFlickr and MS-COCO, demonstrate the superiority of ALGCN, compared with the state-of-the-art methods in cross-modal retrieval.

Non-coding RNA (ncRNA) are RNA sequences which don't code for a gene but instead carry important biological functions. The task of ncRNA classification consists in classifying a given ncRNA sequence into its family. While it has been shown that the graph structure of an ncRNA sequence folding is of great importance for the prediction of its family, current methods make use of machine learning classifiers on hand-crafted graph features. We improve on the state-of-the-art for this task with a graph convolutional network model which achieves an accuracy of 85.73% and an F1-score of 85.61% over 13 classes. Moreover, our model learns in an end-to-end fashion from the raw RNA graphs and removes the need for expensive feature extraction. To the best of our knowledge, this also represents the first successful application of graph convolutional networks to RNA folding data.