In reference[1],the author proved the differentiable pseudoinvex functions must be semi-strictly prequasi-invex functions under certain condition.In this paper,two new types of generalized convex function is introduced,which are called strictly quasi α-preinvex functions and semistrictly quasi α-preinvex functions.Their relations are studied and a sufficient condition about semi-strictly quasi α-preinvex functions is presented.
To obtain a high robust of speech recognition for noisy conditions, a new pre-processing stage based on wavelet thresholding algorithm is proposed in this paper. The purpose of using the DWT is to benefit from its localization property in the time and frequency domains. Compromise function is proposed compared with hard and soft thresholding function. A new thresholding value, Neyman-Pearson criterion is proposed compared with the commonly used Sqtwolog, Rigrsure, minimaxi criterion. MSE and SNR are given to evaluate the improvement of noisy speech recognition performance. The result shows that the Neyman-Pearson criterion can get a better performance especially at adverse conditions.
In this paper, a robust methodology is proposed for planning multistage distribution networks. The scenario set is used to describe the uncertainty of DGs and loads in which the quantity and type are simultaneously considered. Furthermore, controllable load is considered apart from traditional load. Based on the constructed scenario, high-voltage/medium-voltage (HV/MV), feeder route, tie-line and capacitor are optimally planned in order to satisfy different level of DG penetration and load growth rate. An objective function is constituted, composed of the investment cost, operation cost and reliability cost. The investment, operation and reliability are maintained within their standard bounds as constraints. The discrete particle swarm optimization (PSO) method is employed in this paper for optimizing the planning problem. The results highlight the effectiveness of this method for solving the topology planning problem of distribution network.
We report our work on the Alibaba Cloud Quantum Development Platform (AC-QDP). The capability of AC-QDP's computational engine was already reported in \cite{CZH+18, ZHN+19}.In this follow-up article, we demonstrate with figures how AC-QDP helps in testing large-scale quantum algorithms (currently within the QAOA framework). We give new benchmark results on regular graphs. AC-QDP's QAOA framework can simulate thousands of qubits for up to $4$ layers. Then we discuss two interesting use cases we have implemented on the platform: 1. Optimal QAOA sequences for small-cycle free graphs; 2. Graph structure discovery.
Background: Real-world data (RWD) privacy is an increasingly complex topic within the scope of personalized medicine, as it implicates several sources of data. Objective: To assess how privacy-related experiences, when adjusted for age and education level, may shape adult research participants’ willingness to share various sources of real-world data with researchers. Methods: An electronic survey was conducted in April 2021 among adults (≥18 years of age) registered in ResearchMatch, a national health research registry. Descriptive analyses were conducted to assess survey participant demographics. Logistic regression was conducted to assess the association between participants’ five distinct privacy-related experiences and their willingness to share each of the 19 data sources with researchers, adjusting for education level and age range. Results: A total of 598 ResearchMatch adults were contacted and 402 completed the survey. Most respondents were over the age of 51 years (49% total) and held a master’s or bachelor’s degree (63% total). Over half of participants (54%) had their account accessed by someone without their permission. Almost half of participants (49%) reported the privacy of their personal information being violated. Analyses showed that, when adjusted for age range and education level, participants whose reputations were negatively affected as a result of information posted online were more likely to share electronic medical record data (OR = 2.074, 95% CI: 0.986–4.364) and genetic data (OR = 2.302, 95% CI: 0.894–5.93) versus those without this experience. Among participants who had an unpleasant experience as a result of giving out information online, those with some college/associates/trade school compared to those with a doctoral or other terminal degree were significantly more willing to share genetic data (OR = 1.064, 95% CI: 0.396–2.857). Across all privacy-related experiences, participants aged 18 to 30 were significantly more likely than those over 60 years to share music streaming data, ridesharing history data, and voting history data. Additionally, across all privacy-related experiences, those with a high school education were significantly more likely than those with a doctorate or other terminal degree to share credit card statement data. Conclusions: This study offers the first insights into how privacy-related experiences, adjusted for age range and education level, may shape ResearchMatch participants’ willingness to share several sources of real-world data sources with precision medicine researchers. Future work should further explore these insights.
Knowledge Graph can describe the concepts in the objective world and the relationships between these concepts in a structured way, and identify, discover and infer the relationships between things and concepts. It has been developed in the field of medical and health care. In this paper, the method of natural language processing has been used to build chronic disease knowledge graph, such as named entity recognition, relationship extraction. This method is beneficial to forecast analysis of chronic disease, network monitoring, basic education, etc. The research of this paper can greatly help medical experts in the treatment of chronic disease treatment, and assist primary clinicians with making more scientific decision, and can help Patients with chronic diseases to improve medical efficiency. In the end, it also has practical significance for clinical scientific research of chronic disease.
On the basis of a cost-effective embedded system, this paper gives a preceding vehicle distance measuring method with monocular vision techniques for highway driver assistance system. The road scenes are acquired with a monocular camera calibrated beforehand. The features of the road lanes are extracted and recognized, and then the external parameters of the camera are calibrated by three mutually non-coincident parallel lines constructed by road lanes which have same vanishing point and different slopes on imaging plane. To compensate the error of the distance estimation caused by pitch angle, the data regression modeling method is applied, which is based on calibrating some fixed distances to image pixel samples under different pitch angles accurately. Distance of the preceding vehicle is compensated dynamically by the data regression modeling. A statistical base of 200 video highway road images in the daytime is tested in our experiments. The experimental results show that the distance estimation error ratio is lower than 5%.
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