Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning

Spatio-temporal graph learning is a fundamental problem in the Web of Things era, which enables a plethora of Web applications such as smart cities, human mobility and climate analysis. Existing approaches tackle different learning tasks independently, tailoring their models to unique task characteristics. These methods, however, fall short of modeling intrinsic uncertainties in the spatio-temporal data. Meanwhile, their specialized designs limit their universality as general spatio-temporal learning solutions. In this paper, we propose to model the learning tasks in a unified perspective, viewing them as predictions based on conditional information with shared spatio-temporal patterns. Based on this proposal, we introduce Unified Spatio-Temporal Diffusion Models (USTD) to address the tasks uniformly within the uncertainty-aware diffusion framework. USTD is holistically designed, comprising a shared spatio-temporal encoder and attention-based denoising networks that are task-specific. The shared encoder, optimized by a pre-training strategy, effectively captures conditional spatio-temporal patterns. The denoising networks, utilizing both cross- and self-attention, integrate conditional dependencies and generate predictions. Opting for forecasting and kriging as downstream tasks, we design Gated Attention (SGA) and Temporal Gated Attention (TGA) for each task, with different emphases on the spatial and temporal dimensions, respectively. By combining the advantages of deterministic encoders and probabilistic diffusion models, USTD achieves state-of-the-art performances compared to deterministic and probabilistic baselines in both tasks, while also providing valuable uncertainty estimates

Graph Neural Processes for Spatio-Temporal Extrapolation

We study the task of spatio-temporal extrapolation that generates data at target locations from surrounding contexts in a graph. This task is crucial as sensors that collect data are sparsely deployed, resulting in a lack of fine-grained information due to high deployment and maintenance costs. Existing methods either use learning-based models like Neural Networks or statistical approaches like Gaussian Processes for this task. However, the former lacks uncertainty estimates and the latter fails to capture complex spatial and temporal correlations effectively. To address these issues, we propose Spatio-Temporal Graph Neural Processes (STGNP), a neural latent variable model which commands these capabilities simultaneously. Specifically, we first learn deterministic spatio-temporal representations by stacking layers of causal convolutions and cross-set graph neural networks. Then, we learn latent variables for target locations through vertical latent state transitions along layers and obtain extrapolations. Importantly during the transitions, we propose Graph Bayesian Aggregation (GBA), a Bayesian graph aggregator that aggregates contexts considering uncertainties in context data and graph structure. Extensive experiments show that STGNP has desirable properties such as uncertainty estimates and strong learning capabilities, and achieves state-of-the-art results by a clear margin.Comment: SIGKDD 202

Exploring the multiband gravitational wave background with a semi-analytic galaxy formation model

An enormous number of compact binary systems, spanning from stellar to supermassive levels, emit substantial gravitational waves during their final evolutionary stages, thereby creating a stochastic gravitational wave background (SGWB). We calculate the merger rates of stellar compact binaries and massive black hole binaries using a semi-analytic galaxy formation model -- Galaxy Assembly with Binary Evolution (GABE) in a unified and self-consistent approach, followed by an estimation of the multi-band SGWB contributed by those systems. We find that the amplitudes of the principal peaks of the SGWB energy density are within one order of magnitude $\Omega_{GW} \sim 10^{-9}- 10^{-8}$. This SGWB could easily be detected by the Square Kilometre Array (SKA), as well as planned interferometric detectors, such as the Einstein Telescope (ET) and the Laser Interferometer Space Antenna (LISA). The energy density of this background varies as $\Omega_{GW} \propto f^{2/3}$ in SKA band. The shape of the SGWB spectrum in the frequency range $\sim[10^{-4}$,$1]$Hz could allow the LISA to distinguish the black hole seed models. The amplitude of the SGWB from merging stellar binary black holes (BBHs) at $\sim 100$ Hz is approximately 10 and 100 times greater than those from merging binary neutron stars (BNSs) and neutron-star-black-hole (NSBH) mergers, respectively. Note that, since the cosmic star formation rate density predicted by GABE is somewhat lower than observational results by $\sim 0.2$ dex at z < $\sim 2$, the amplitude of the SGWB in the frequency range $\sim[1$, $10^{4}]$ Hz may be underestimated by a similar factor at most.Comment: 12 pages, 5 figure

TRPV1 Activation Attenuates High-Salt Diet-Induced Cardiac Hypertrophy and Fibrosis through PPAR- δ

High-salt diet-induced cardiac hypertrophy and fibrosis are associated with increased reactive oxygen species production. Transient receptor potential vanilloid type 1 (TRPV1), a specific receptor for capsaicin, exerts a protective role in cardiac remodeling that resulted from myocardial infarction, and peroxisome proliferation-activated receptors δ (PPAR-δ) play an important role in metabolic myocardium remodeling. However, it remains unknown whether activation of TRPV1 could alleviate cardiac hypertrophy and fibrosis and the effect of cross-talk between TRPV1 and PPAR-δ on suppressing high-salt diet-generated oxidative stress. In this study, high-salt diet-induced cardiac hypertrophy and fibrosis are characterized by significant enhancement of HW/BW%, LVEDD, and LVESD, decreased FS and EF, and increased collagen deposition. These alterations were associated with downregulation of PPAR-δ, UCP2 expression, upregulation of iNOS production, and increased oxidative/nitrotyrosine stress. These adverse effects of long-term high-salt diet were attenuated by chronic treatment with capsaicin. However, this effect of capsaicin was absent in TRPV1−/− mice on a high-salt diet. Our finding suggests that chronic dietary capsaicin consumption attenuates long-term high-salt diet-induced cardiac hypertrophy and fibrosis. This benefit effect is likely to be caused by TRPV1 mediated upregulation of PPAR-δ expression

Optimal real-time power dispatch of power grid with wind energy forecasting under extreme weather

With breakthroughs in the power electronics industry, the stability and rapid power regulation of wind power generation have been improved. Its power generation technology is becoming more and more mature. However, there are still weaknesses in the operation and control of power systems under the influence of extreme weather events, especially in real-time power dispatch. To optimally distribute the power of the regulation resources in a more stable manner, a wind energy forecasting-based power dispatch model with time-control intervals optimization is proposed. In this model, the outage of the wind energy under extreme weather is analyzed by an autoregressive integrated moving average model (ARIMA). Additionally, the other regulation resources are used to balance the corresponding wind power drop and power mismatch. Meanwhile, an algorithm names weighted mean of vectors (INFO) is employed to solve the real-time power dispatch and minimize the power deviation between the power command and real output. Lastly, the performance of the proposed optimal real-time power dispatch is executed in a simulation model with ten regulation resources. The simulation tests show that the combination of ARIMA and INFO can effectively improve the power control performance of the PD-WEF system

Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification

Blood pressure is a basic physiological parameter in the cardiovascular circulatory system. Long-term abnormal blood pressure will lead to various cardiovascular diseases, making the early detection and assessment of hypertension profoundly significant for the prevention and treatment of cardiovascular diseases. In this paper, we investigate whether or not deep learning can provide better results for hypertension risk stratification when compared to the classical signal processing and feature extraction methods. We tested a deep learning method for the classification and evaluation of hypertension using photoplethysmography (PPG) signals based on the continuous wavelet transform (using Morse) and pretrained convolutional neural network (using GoogLeNet). We collected 121 data recordings from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Database, each containing arterial blood pressure (ABP) and photoplethysmography (PPG) signals. The ABP signals were utilized to extract blood pressure category labels, and the PPG signals were used to train and test the model. According to the seventh report of the Joint National Committee, blood pressure levels are categorized as normotension (NT), prehypertension (PHT), and hypertension (HT). For the early diagnosis and assessment of HT, the timely detection of PHT and the accurate diagnosis of HT are significant. Therefore, three HT classification trials were set: NT vs. PHT, NT vs. HT, and (NT + PHT) vs. HT. The F-scores of these three classification trials were 80.52%, 92.55%, and 82.95%, respectively. The tested deep method achieved higher accuracy for hypertension risk stratification when compared to the classical signal processing and feature extraction method. Additionally, the method achieved comparable results to another approach that requires electrocardiogram and PPG signals.Applied Science, Faculty ofMedicine, Faculty ofOther UBCNon UBCElectrical and Computer Engineering, Department ofReviewedFacult

Intelligent optimal scheduling strategy of IES with considering the multiple flexible loads

As the energy system continues to evolve and advance, a relatively single energy system gradually turns to the comprehensive energy system. With the advancement of technology related to electric vehicle networks progresses, the flexible load in the power system has been significantly improved, and there will be more and more schedulable resources in the power system. On this basis, the paper mainly studies the integrated energy system including the power station of electric driven vehicles and a variety of flexible loads. First, an intelligent energy system model is built, including wind power, ES, cogeneration units, gas boilers, etc. Secondly, according to the flexible load response of electricity, heat and gas, the dynamic response of flexible loads such as electricity, heat and natural gas is incorporated into the optimal scheduling of the integrated energy system, and a model has been created for optimizing the integrated energy system with the goal of minimizing the system operational expenses. Finally, YALMIP is utilized to formulate the problem during modeling, while CPLEX is employed as the solver to find a solution. Through the example calculation, the system operation cost is reduced by 13.04%, the wind power consumption rate is increased by 8.65%, and the peak valley difference is reduced by 24.58%. This approach has been demonstrated to lower the overall operational expenses of the system, as well as assist in smoothing out peak and off-peak electricity demand, and increase the utilization of wind power