Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction

The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. Indeed, the effective prediction of city-wide parking availability can improve parking efficiency, help urban planning, and ultimately alleviate city congestion. However, it is a non-trivial task for predicting citywide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e.g., camera, ultrasonic sensor, and GPS). To this end, we propose Semi-supervised Hierarchical Recurrent Graph Neural Network (SHARE) for predicting city-wide parking availability. Specifically, we first propose a hierarchical graph convolution structure to model non-Euclidean spatial autocorrelation among parking lots. Along this line, a contextual graph convolution block and a soft clustering graph convolution block are respectively proposed to capture local and global spatial dependencies between parking lots. Additionally, we adopt a recurrent neural network to incorporate dynamic temporal dependencies of parking lots. Moreover, we propose a parking availability approximation module to estimate missing real-time parking availabilities from both spatial and temporal domain. Finally, experiments on two real-world datasets demonstrate the prediction performance of SHARE outperforms seven state-of-the-art baselines.Comment: 8 pages, 9 figures, AAAI-202

Block iliac bone grafting enhances osseous healing of alveolar reconstruction in older cleft patients : a radiological and histological evaluation

Older alveolar cleft patients (&12 years old) often have wide bone defect as well as teeth loss, resulting in poor osseous healing with conventional alveolar bone grafting (ABG). In this study, we investigated a surgical technique of block iliac bone grafting for the alveolar cleft reconstruction and evaluated the clinical and radiological outcomes of these cleft patients. Fifteen patients were included in this study. All cases received preoperative cone bean computed tomography (CBCT) scans for the alveolar cleft evaluation. Osseous outcomes of block iliac bone grafting were assessed at 1 week, 3- and 6-month postoperatively. Volume changes and bone resorption rates were calculated using the measurement modules of Simplant software. Bone samples from one patient undergoing dental implantation were assessed by micro-CT and histological examination. The morbidities of donor-site were analyzed by clinical examination and questionnaire survey. The average age of the case series was 18.53±2.50 years. The intraoral incision of thirteen cases healed well. However, two cases had oronasal fistula and graft exposure at 1-week postoperatively. The results of follow-up CBCT scans showed significant resistance to radiation on both sides of the bone graft, suggesting a good osseous healing and new bone formation. The mean residual bone volume was 1.68±0.26 cm3, 1.29±0.23 cm3 and 1.15±0.23 cm3 at 1-week, 3- and 6-month postoperatively. Correspondingly, the mean bone resorption rates in 3- and 6-month postoperative were 21.78±6.88% and 30.66±8.97%, respectively. From micro-CT and HE examinations, the block bone samples exhibited a cancellous structure in which mature bone trabecula and functional blood vessels appeared. The average scores of donor-site morbidities were drastically decreased at 3- and 6-month postoperatively compared with those at 1-week postoperatively. Our results demonstrated that block iliac bone grafting could achieve satisfying osseous outcomes in older alveolar cleft patients, and this technique provided favorable bony condition for further treatments, especially dental implantation

Quaternion-Based Graph Convolution Network for Recommendation

Graph Convolution Network (GCN) has been widely applied in recommender systems for its representation learning capability on user and item embeddings. However, GCN is vulnerable to noisy and incomplete graphs, which are common in real world, due to its recursive message propagation mechanism. In the literature, some work propose to remove the feature transformation during message propagation, but making it unable to effectively capture the graph structural features. Moreover, they model users and items in the Euclidean space, which has been demonstrated to have high distortion when modeling complex graphs, further degrading the capability to capture the graph structural features and leading to sub-optimal performance. To this end, in this paper, we propose a simple yet effective Quaternion-based Graph Convolution Network (QGCN) recommendation model. In the proposed model, we utilize the hyper-complex Quaternion space to learn user and item representations and feature transformation to improve both performance and robustness. Specifically, we first embed all users and items into the Quaternion space. Then, we introduce the quaternion embedding propagation layers with quaternion feature transformation to perform message propagation. Finally, we combine the embeddings generated at each layer with the mean pooling strategy to obtain the final embeddings for recommendation. Extensive experiments on three public benchmark datasets demonstrate that our proposed QGCN model outperforms baseline methods by a large margin.Comment: 13 pages, 7 figures, 6 tables. Submitted to ICDE 202

Personalized Federated Learning under Mixture of Distributions

The recent trend towards Personalized Federated Learning (PFL) has garnered significant attention as it allows for the training of models that are tailored to each client while maintaining data privacy. However, current PFL techniques primarily focus on modeling the conditional distribution heterogeneity (i.e. concept shift), which can result in suboptimal performance when the distribution of input data across clients diverges (i.e. covariate shift). Additionally, these techniques often lack the ability to adapt to unseen data, further limiting their effectiveness in real-world scenarios. To address these limitations, we propose a novel approach, FedGMM, which utilizes Gaussian mixture models (GMM) to effectively fit the input data distributions across diverse clients. The model parameters are estimated by maximum likelihood estimation utilizing a federated Expectation-Maximization algorithm, which is solved in closed form and does not assume gradient similarity. Furthermore, FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.Comment: International Conference on Machine Learning (ICML'23

Coordinated planning of multi-area multi-energy systems by a novel routing algorithm based on random scenarios

As an important Energy Internet technology, energy router (ER) can implement energy routing and information interconnection for the electrical grid within a certain geography range. However, multi-energy supply allocation between multi-energy routers (MERs) has to be resolved, which can connect isolated area networks and expand the scale of the energy Internet. In this paper, equipment capacity and network routing are planned for multi-area multi-energy system (MAMES), which integrates multiple MERs with a virtual multi-energy router (VMER). Firstly, based on the VMER, the concept of MAMES is proposed. Then, the correlation model of multi-energy networks in VMER is constructed, based on MER energy transmission conversion extension model and hybrid power flow model. And the novel routing algorithm is proposed to determine the energy candidate set of the integrated energy networks. During planning, the random scenario generation based on Latin hypercube sampling (LHS) and scenario reduction based on k-means clustering are applied. Finally, the simulation shows that the planning method based on the routing algorithm can effectively obtain the capacity of energy equipment inside MERs and the routing layout between MERs in the MAMES. Proposed routing algorithm and operation scenarios reduction greatly improve the efficiency of planning. Correlated planning helps improve energy efficiency, environment protection, and economic benefits of MAMES.</p

Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning

Crude oil is one of the most important types of energy and its prices have a great impact on the global economy. Therefore, forecasting crude oil prices accurately is an essential task for investors, governments, enterprises and even researchers. However, due to the extreme nonlinearity and nonstationarity of crude oil prices, it is a challenging task for the traditional methodologies of time series forecasting to handle it. To address this issue, in this paper, we propose a novel approach that incorporates ensemble empirical mode decomposition (EEMD), sparse Bayesian learning (SBL), and addition, namely EEMD-SBL-ADD, for forecasting crude oil prices, following the &ldquo;decomposition and ensemble&rdquo; framework that is widely used in time series analysis. Specifically, EEMD is first used to decompose the raw crude oil price data into components, including several intrinsic mode functions (IMFs) and one residue. Then, we apply SBL to build an individual forecasting model for each component. Finally, the individual forecasting results are aggregated as the final forecasting price by simple addition. To validate the performance of the proposed EEMD-SBL-ADD, we use the publicly-available West Texas Intermediate (WTI) and Brent crude oil spot prices as experimental data. The experimental results demonstrate that the EEMD-SBL-ADD outperforms some state-of-the-art forecasting methodologies in terms of several evaluation criteria such as the mean absolute percent error (MAPE), the root mean squared error (RMSE), the directional statistic (Dstat), the Diebold&ndash;Mariano (DM) test, the model confidence set (MCS) test and running time, indicating that the proposed EEMD-SBL-ADD is promising for forecasting crude oil prices

Distribution and Health Risk Assessment on Dietary Exposure of Polycyclic Aromatic Hydrocarbons in Vegetables in Nanjing, China

In a market basket study made in Nanjing, China, in which the most common consumed nine kinds of vegetables foodstuffs were sampled, the contents of 16 polycyclic aromatic hydrocarbons (PAHs) were analyzed using gas chromatography with mass spectrometer detector (GC-MS). The results showed that the total amount of 16 PAHs was within the range of 60.5~312 ng g−1 (wet weight). The ranking of total concentrations for different types of vegetables in decreasing order was leafy vegetable, fruit vegetable, and rhizome vegetable. Source analysis suggested that coal, oil, or other incomplete combustion of biomass mainly contributed to the concentration of PAHs. The margin of exposure (MOE) approach with age/gender group-specific daily dietary exposure level was used to estimate the carcinogenic risk. The calculated total mean MOE in the case of BaP and PAH4 (sum of BaA, CHR, BbF, and BaP) was 14960 and 7723, respectively, for local residents. In addition, the MOEs in PAH4 for some groups of both male and female were below the critical limit of 10 000 proposed by EFSA. Therefore, health effect owing to the consumption of vegetables on local residents needs high concern

Feature-level deeper self-attention network for sequential recommendation

Sequential recommendation, which aims to recommend next item that the user will likely interact in a near future, has become essential in various Internet applications. Existing methods usually consider the transition patterns between items, but ignore the transition patterns between features of items. We argue that only the item-level sequences cannot reveal the full sequential patterns, while explicit and implicit feature-level sequences can help extract the full sequential patterns. In this paper, we propose a novel method named Feature-level Deeper Self-Attention Network (FDSA) for sequential recommendation. Specifically, FDSA first integrates various heterogeneous features of items into feature sequences with different weights through a vanilla attention mechanism. After that, FDSA applies separated self-attention blocks on item-level sequences and feature-level sequences, respectively, to model item transition patterns and feature transition patterns. Then, we integrate the outputs of these two blocks to a fully-connected layer for next item recommendation. Finally, comprehensive experimental results demonstrate that considering the transition relationships between features can significantly improve the performance of sequential recommendation