Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach

Traffic speed is central to characterizing the fluidity of the road network. Many transportation applications rely on it, such as real-time navigation, dynamic route planning, and congestion management. Rapid advances in sensing and communication techniques make traffic speed detection easier than ever. However, due to sparse deployment of static sensors or low penetration of mobile sensors, speeds detected are incomplete and far from network-wide use. In addition, sensors are prone to error or missing data due to various kinds of reasons, speeds from these sensors can become highly noisy. These drawbacks call for effective techniques to recover credible estimates from the incomplete data. In this work, we first identify the issue as a spatiotemporal kriging problem and propose a Laplacian enhanced low-rank tensor completion (LETC) framework featuring both lowrankness and multi-dimensional correlations for large-scale traffic speed kriging under limited observations. To be specific, three types of speed correlation including temporal continuity, temporal periodicity, and spatial proximity are carefully chosen and simultaneously modeled by three different forms of graph Laplacian, named temporal graph Fourier transform, generalized temporal consistency regularization, and diffusion graph regularization. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging. By performing experiments on two public million-level traffic speed datasets, we finally draw the conclusion and find our proposed LETC achieves the state-of-the-art kriging performance even under low observation rates, while at the same time saving more than half computing time compared with baseline methods. Some insights into spatiotemporal traffic data modeling and kriging at the network level are provided as well

Nexus sine qua non: Essentially connected neural networks for spatial-temporal forecasting of multivariate time series

Modeling and forecasting multivariate time series not only facilitates the decision making of practitioners, but also deepens our scientific understanding of the underlying dynamical systems. Spatial-temporal graph neural networks (STGNNs) are emerged as powerful predictors and have become the de facto models for learning spatiotemporal representations in recent years. However, existing architectures of STGNNs tend to be complicated by stacking a series of fancy layers. The designed models could be either redundant or enigmatic, which pose great challenges on their complexity and scalability. Such concerns prompt us to re-examine the designs of modern STGNNs and identify core principles that contribute to a powerful and efficient neural predictor. Here we present a compact predictive model that is fully defined by a dense encoder-decoder and a message-passing layer, powered by node identifications, without any complex sequential modules, e.g., TCNs, RNNs, and Transformers. Empirical results demonstrate how a simple and elegant model with proper inductive basis can compare favorably w.r.t. the state of the art with elaborate designs, while being much more interpretable and computationally efficient for spatial-temporal forecasting problem. We hope our findings would open new horizons for future studies to revisit the design of more concise neural forecasting architectures

Towards better traffic volume estimation: Tackling both underdetermined and non-equilibrium problems via a correlation-adaptive graph convolution network

Traffic volume is an indispensable ingredient to provide fine-grained information for traffic management and control. However, due to limited deployment of traffic sensors, obtaining full-scale volume information is far from easy. Existing works on this topic primarily focus on improving the overall estimation accuracy of a particular method and ignore the underlying challenges of volume estimation, thereby having inferior performances on some critical tasks. This paper studies two key problems with regard to traffic volume estimation: (1) underdetermined traffic flows caused by undetected movements, and (2) non-equilibrium traffic flows arise from congestion propagation. Here we demonstrate a graph-based deep learning method that can offer a data-driven, model-free and correlation adaptive approach to tackle the above issues and perform accurate network-wide traffic volume estimation. Particularly, in order to quantify the dynamic and nonlinear relationships between traffic speed and volume for the estimation of underdetermined flows, a speed patternadaptive adjacent matrix based on graph attention is developed and integrated into the graph convolution process, to capture non-local correlations between sensors. To measure the impacts of non-equilibrium flows, a temporal masked and clipped attention combined with a gated temporal convolution layer is customized to capture time-asynchronous correlations between upstream and downstream sensors. We then evaluate our model on a real-world highway traffic volume dataset and compare it with several benchmark models. It is demonstrated that the proposed model achieves high estimation accuracy even under 20% sensor coverage rate and outperforms other baselines significantly, especially on underdetermined and non-equilibrium flow locations. Furthermore, comprehensive quantitative model analysis are also carried out to justify the model designs

Dynamic and casual association between green investment, clean energy and environmental sustainability using advance quantile A.R.D.L. framework

This study examines the dynamic and causal relationship between green investment (G.I.), clean energy (C.E.), economic growth, and environmental sustainability with the help of an innovative approach named as quantile autoregressive distributed lagged (Q.A.R.D.L.) model using quarterly data from Q1-1995 to Q4-2019 for China. Our preliminary findings confirm data non-normality and structural breaks in all data series. Therefore, we have applied Q.A.R.D.L. that efficiently deals with these issues. We have further applied the Granger-causality in quantiles to check the causal association among the variables of interest. The findings through Q.A.R.D.L. estimation confirm that the error correction parameter is statistically significant with expected negative sign across major quantiles. In the long run, the results confirm that both C.E., and G.I. are significant mitigants of environmental pollution, however their emissions mitigating effects varies across lower, middle, and higher emissions quantiles. Furthermore, the findings through Granger-causality test confirm the existence of two-way causality between G.I., C.E., and carbon emissions across all quantiles. These results offer valuable policy implications

Unveiling the nexus between corporate social responsibility, industrial integration, economic growth and financial constraints under the node of firms sustainable performance

This research investigates the impact of corporate social responsibility (CSR), industrial integration, and economic growth in realising financial constraints using firm’s level attributes of sustainable performance. In doing so, this study utilised annual data of 555 Chinese real estate firms from 2015 to 2019 and applied a spatial effect model (SEM) to integrate spatial effects. This study also used two-step Generalized Method of Moments (GMM) and twostage least square (2SLS) methods to deal with possible endogeneity. Manifestly, we have constructed a mathematical derivation framework based on linear algebra and offer easy computing Moran’s index. The preliminary results revealed that CSR, industrial integration, and economic growth reduce financial constraints of listed real estate companies in China. However, these effects are not persistent at different stages of development. The findings of Moran index describe that the early and growth stages of CSR instigate financial constraints while the mature stage of CSR produces inhibitory effects that reduce financial constraints. Notably, these effects also varied across different regions. This outcome offers valuable policy recommendations

The Cognitive Load of Observation Tasks in 3D Video is Lower Than That in 2D Video

We are exposed to more and more 3D videos, some for entertainment and some for scientific research. Some experiments using 3D video as a stimulus focus only on its visual effect. We studied the cognitive difference between 3D and 2D videos by analyzing EEG. This research adopts a 2 x 4 experimental design, including 2D and 3D versions of 4 video scenes. These four video scenes can be classified into two simple task scenes and two complex task scenes. The simple task scenario and the complex task scenario each contain a video with violent content changes and a calm video. Subjects need to watch eight videos. We recorded the EEG information of the subjects and analyzed the power of alpha and theta oscillations. On this basis, we calculated the cognitive load index (CLI), which can be used as an indicator of cognitive load. The results showed that 3D videos that required subjects to perform simple tasks brought higher cognitive load to most subjects. When the video contains complex tasks, the cognitive load of subjects does not show similar regularity. Specifically, only half of the people had higher cognitive load when watching the 3D version of the video than when watching the 2D version. In addition, the cognitive load level of subjects showed significant individual differencesComment: 7 pages, 18 figure

Detecting Energy Theft in Different Regions Based on Convolutional and Joint Distribution Adaptation

© 2023 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TIM.2023.3291769Electricity theft has been a major concern all over the world. There are great differences in electricity consumption among residents from different regions. However, existing supervised methods of machine learning are not in detecting electricity theft from different regions, while the development of transfer learning provides a new view for solving the problem. Hence, an electricity-theft detection method based on Convolutional and Joint Distribution Adaptation(CJDA) is proposed. In particular, the model consists of three components: convolutional component (Conv), Marginal Distribution Adaptation(MDA) and Conditional Distribution Adaptation(CDA). The convolutional component can efficiently extract the customer’s electricity characteristics. The Marginal Distribution Adaptation can match marginal probability distributions and solve the discrepancies of residents from different regions while Conditional Distribution Adaptation can reduce the difference of the conditional probability distributions and enhance the discrimination of features between energy thieves and normal residents. As a result, the model can find a matrix to adapt the electricity residents in different regions to achieve electricity theft detection. The experiments are conducted on electricity consumption data from the Irish Smart Energy Trial and State Grid Corporation of China and metrics including ACC, Recall, FPR, AUC and F1Score are used for evaluation. Compared with other methods including some machine learning methods such as DT, RF and XGBoost, some deep learning methods such as RNN, CNN and Wide & Deep CNN and some up-to-date methods such as BDA, WBDA, ROCKET and MiniROCKET, our proposed method has a better effect on identifying electricity theft from different regions.Peer reviewe

Bufalin Induces Lung Cancer Cell Apoptosis via the Inhibition of PI3K/Akt Pathway

Bufalin is a class of toxic steroids which could induce the differentiation and apoptosis of leukemia cells, and induce the apoptosis of gastric, colon and breast cancer cells. However, the anti-tumor effects of bufalin have not been demonstrated in lung cancer. In this study we used A549 human lung adenocarcinoma epithelial cell line as the experimental model to evaluate the potential of bufalin in lung cancer chemotherapy. A549 cells were treated with bufalin, then the proliferation was detected by MTT assay and apoptosis was detected by flow cytometry analysis and Giemsa staining. In addition, A549 cells were treated by Akt inhibitor LY294002 in combination with bufalin and the activation of Akt and Caspase-3 as well as the expression levels of Bax, Bcl-2 and livin were examined by Western blot analysis. The results showed that Bufalin inhibited the proliferation of A549 cells and induced the apoptosis of A549 cells in a dose and time dependent manner. Mechanistically, we found that bufalin inhibited the activation of Akt. Moreover, bufalin synergized with Akt inhibitor to induce the apoptosis of A549 cells and this was associated with the upregulation of Bax expression, the downregulation of Bcl-2 and livin expression, and the activation of Caspase-3. In conclusion, our findings demonstrate that bufalin induces lung cancer cell apoptosis via the inhibition of PI3K/Akt pathway and suggest that bufalin is a potential regimen for combined chemotherapy to overcome the resistance of lung cancer cells to chemotherapeutics induced apoptosis

High-fidelity Facial Avatar Reconstruction from Monocular Video with Generative Priors

High-fidelity facial avatar reconstruction from a monocular video is a significant research problem in computer graphics and computer vision. Recently, Neural Radiance Field (NeRF) has shown impressive novel view rendering results and has been considered for facial avatar reconstruction. However, the complex facial dynamics and missing 3D information in monocular videos raise significant challenges for faithful facial reconstruction. In this work, we propose a new method for NeRF-based facial avatar reconstruction that utilizes 3D-aware generative prior. Different from existing works that depend on a conditional deformation field for dynamic modeling, we propose to learn a personalized generative prior, which is formulated as a local and low dimensional subspace in the latent space of 3D-GAN. We propose an efficient method to construct the personalized generative prior based on a small set of facial images of a given individual. After learning, it allows for photo-realistic rendering with novel views and the face reenactment can be realized by performing navigation in the latent space. Our proposed method is applicable for different driven signals, including RGB images, 3DMM coefficients, and audios. Compared with existing works, we obtain superior novel view synthesis results and faithfully face reenactment performance.Comment: 8 pages, 7 figure