Search Efficient Binary Network Embedding

Traditional network embedding primarily focuses on learning a dense vector representation for each node, which encodes network structure and/or node content information, such that off-the-shelf machine learning algorithms can be easily applied to the vector-format node representations for network analysis. However, the learned dense vector representations are inefficient for large-scale similarity search, which requires to find the nearest neighbor measured by Euclidean distance in a continuous vector space. In this paper, we propose a search efficient binary network embedding algorithm called BinaryNE to learn a sparse binary code for each node, by simultaneously modeling node context relations and node attribute relations through a three-layer neural network. BinaryNE learns binary node representations efficiently through a stochastic gradient descent based online learning algorithm. The learned binary encoding not only reduces memory usage to represent each node, but also allows fast bit-wise comparisons to support much quicker network node search compared to Euclidean distance or other distance measures. Our experiments and comparisons show that BinaryNE not only delivers more than 23 times faster search speed, but also provides comparable or better search quality than traditional continuous vector based network embedding methods

Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data

Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-scale condensed graph as its substitution, has immediate benefits for various graph learning tasks. However, existing graph condensation methods rely on the joint optimization of nodes and structures in the condensed graph, and overlook critical issues in effectiveness and generalization ability. In this paper, we advocate a new Structure-Free Graph Condensation paradigm, named SFGC, to distill a large-scale graph into a small-scale graph node set without explicit graph structures, i.e., graph-free data. Our idea is to implicitly encode topology structure information into the node attributes in the synthesized graph-free data, whose topology is reduced to an identity matrix. Specifically, SFGC contains two collaborative components: (1) a training trajectory meta-matching scheme for effectively synthesizing small-scale graph-free data; (2) a graph neural feature score metric for dynamically evaluating the quality of the condensed data. Through training trajectory meta-matching, SFGC aligns the long-term GNN learning behaviors between the large-scale graph and the condensed small-scale graph-free data, ensuring comprehensive and compact transfer of informative knowledge to the graph-free data. Afterward, the underlying condensed graph-free data would be dynamically evaluated with the graph neural feature score, which is a closed-form metric for ensuring the excellent expressiveness of the condensed graph-free data. Extensive experiments verify the superiority of SFGC across different condensation ratios.Comment: Accepted by NeurIPS 202

Research on deep hole drilling vibration suppression based on magnetorheological fluid damper

Based on the working principle of magnetorheological fluid damping, in this paper, a set of squeezing mode Magneto-rheological Fluid (MRF) dampers is designed for drilling vibration suppression in deep hole machines. Elaborate analysis of the correlativity between the dynamic morphology trajectory of the machined hole surface, the vibration of the drilling tool-shaft, and the theoretical derivation of the damping force, is put forward in accordance with the Bingham model and Euler-Bernoulli beam Equation. Simultaneously, the contrast analysis of the vibration suppression effect is carried out through the drilling experiments with and without an MRF damper. In addition, a series of measurements on the vibration characteristics of the drilling shaft, the drilling tool and the guide surface wear patterns, and the machine hole surface are analyzed, respectively. Both the drilling experiments and theory studies have revealed that the strength of the magnetic field changed with the drill shaft at different levels of vibration. The MRF damper could suppress the vibration with nonlinear characteristics initiatively and instantaneously, by variable damping, which can eventually improve the surface roughness. In addition, according to the phenomenon of tool tipping, the breakage of the guide bars and the machine hole surface deduces the condition of the vibration effect objectively

Association between Non-High-Density-Lipoprotein-Cholesterol Levels and the Prevalence of Asymptomatic Intracranial Arterial Stenosis

Objective: The aim of this study was to assess the association between non-high-density-lipoprotein-cholesterol (non-HDL-C) and the prevalence of asymptomatic intracranial arterial stenosis (ICAS). Methods and Results: The Asymptomatic Polyvascular Abnormalities Community (APAC) study is a prospective cohort study based on the Kailuan district (China) population. A total of 5351 eligible subjects, aged ≥40, and without history of stroke or myocardial infarction, were enrolled in this study. Transcranial Doppler Ultrasonography (TCD) was performed on all enrolled subjects for the evaluation of ICAS presence. Out of 5351 patients, 698 subjects showed evidence of ICAS (prevalence of 13.04%). Multivariate analysis showed that non-HDL-C is an independent indicator for the presence of ICAS (OR = 1.15, 95%CI: 1.08 – 1.23), but with a gender difference (P for interaction<0.01): in men, non-HDL-C is an independent indicator for ICAS (multivariate-adjusted OR = 1.28, 95%CI: 1.18–1.39), but not in women (multivariate-adjusted OR = 1.03, 95%CI: 0.93–1.14). Subjects were divided into five subgroups based non-HDL-C levels and these levels correlated linearly with the prevalence of ICAS (P for trend <0.01). Compared with the first quintile, multivariate-adjusted OR (95%CI) of the second, third, fourth and fifth quintiles were: 1.05 (0.71–1.56), 1.33 (0.91–1.95), 1.83 (1.27–2.63), 2.48 (1.72–3.57), respectively. Conclusion: Non-HDL-C is an independent predictor of ICAS prevalence in men but not in women, suggesting that non-HDL-C levels could be used as a surveillance factor in the primary prevention of ischemic stroke, especially in men

Dynamic reconfiguration of distribution network considering the uncertainty of distributed generation and loads

This study presents a new methodology to perform the distribution network dynamic reconfiguration (DNDR), taking into consideration the stochastic variations of loads and distributed generation (DG) of power. To solve the heavy computational burden that exists in traditional algorithms of the DNDR, this study first establishes the nodal sensitivity models to calculate the nodal variations caused by nodal power variations. Then, the DNDR is executed utilizing a co-evolutionary algorithm with the goal of loss minimization. The stochastic power flow calculations (PFCs) based on the nodal sensitivity are performed in the DNDR to handle the power fluctuations of the DGs and loads. Finally, the modified IEEE 33-bus test system and a practical distribution system are used for simulations. The simulation results validate the quickness and effectiveness of the proposed DNDR method

Ideal Cardiovascular Health Metrics on the Prevalence of Asymptomatic Intracranial Artery Stenosis: A Cross-Sectional Study

Background and Purpose Intracranial Artery Stenosis (ICAS) is one of the most common causes of ischemic stroke in Asia. Previous studies have shown the number of ideal cardiovascular health (CVH) metrics was associated with lower risk of stroke. This study aimed to investigate the relationship between ideal CVH metrics and prevalence of ICAS. Methods: A random sample of 5,412 participants (selected from Kailuan Study as a reference population) aged 40 years or older (40.10% women), free of stroke, transient ischemic attack, and coronary disease, were enrolled in the Asymptomatic Polyvascular Abnormalities Community study from 2010 to 2011. We collected information on the seven CVH metrics (including smoking, body mass index, dietary intake, physical activity, blood pressure, total cholesterol and fasting blood glucose); and assessed ICAS by transcranial Doppler. The relationship between the ideal CVH metrics and prevalence of ICAS was analyzed using the multivariate logistic regression. Results: After adjusting for age, sex, and other potential confounders, the adjusted odds ratios(95% confidence interval) for ICAS were 0.76(0.58–0.99), 0.55(0.43–0.72), 0.49(0.37–0.65), 0.43(0.31–0.61), and 0.36(0.22–0.62), respectively, for those having 2, 3, 4, 5, and 6–7 ideal CVH metrics compared with those having 0–1 ideal metric(p-trend<0.0001). Similar inverse associations were observed in different age and gender groups (all p-trends<0.05). Conclusion: We found a clear gradient relationship between the number of ideal CVH metrics and lower prevalence of ICAS in a Chinese population, which supports the importance of ideal health behaviors and factors in the prevention of ICAS

Non-High-Density Lipoprotein Cholesterol on the Risks of Stroke: A Result from the Kailuan Study

Aims To prospectively explore the association between non-high-density lipoprotein cholesterol (non-HDLC) and the risks of stroke and its subtypes. Methods: A total of 95,916 participants (18-98 years old; 76,354 men and 19,562 women) from a Chinese urban community who were free of myocardial infarction and stroke at baseline time point (2006-2007) were eligible and enrolled in the study. The serum non-HDLC levels of participants were determined by subtracting the high-density lipoprotein cholesterol (HDLC) from total serum cholesterol. The primary outcome was the first occurrence of stroke, which was diagnosed according to the World Health Organization criteria and classified into three subtypes: ischemic stroke, intracerebral hemorrhage, or subarachnoid hemorrhage. The Cox proportional hazards models were used to estimate risk of stroke and its subtypes. Results: During the four-year follow-up, we identified 1614 stroke events (1,156 ischemic, 416 intracerebral hemorrhagic and 42 subarachnoid hemorrhagic). Statistical analyses showed that hazard ratios (HR) (95% Confidence Interval: CI) of serum Non-HDLC level for total and subtypes of stroke were: 1.08 (1.03-1.12) (total), 1.10 (1.05-1.16) (ischemic), 1.03 (0.96-1.10) (intracerebral hemorrhage) and 0.83 (0.66-1.05) (subarachnoid hemorrhage). HR for non-HDLC refers to the increase per each 20 mg/dl. For total and ischemic stroke, the risks were significantly higher in the fourth and fifth quintiles of non-HDLC concentrations compared to the first quintile after adjusting the confounding factors (total stroke: 4th quintile HR=1.33 (1.12-1.59); 5th quintile HR = 1.36 (1.15-1.62); ischemic stroke: 4th quintile HR =1.34 (1.09-1.66); 5th quintile HR = 1.53 (1.24-1.88)). Conclusions: Our data suggest that serum non-HDLC level is an independent risk factor for total and ischemic stroke, and that higher serum non-HDLC concentrations are associated with increased risks for total stroke and ischemic stroke, but not for intracerebral and subarachnoid hemorrhage