A Decentralized Processing Schema for Efficient and Robust Real-time Multi-GNSS Satellite Clock Estimation

Real-time multi-GNSS precise point positioning (PPP) requires the support of high-rate satellite clock corrections. Due to the large number of ambiguity parameters, it is difficult to update clocks at high frequency in real-time for a large reference network. With the increasing number of satellites of multi-GNSS constellations and the number of stations, real-time high-rate clock estimation becomes a big challenge. In this contribution, we propose a decentralized clock estimation (DECE) strategy, in which both undifferenced (UD) and epoch-differenced (ED) mode are implemented but run separately in different computers, and their output clocks are combined in another process to generate a unique product. While redundant UD and/or ED processing lines can be run in offsite computers to improve the robustness, processing lines for different networks can also be included to improve the clock quality. The new strategy is realized based on the Position and Navigation Data Analyst (PANDA) software package and is experimentally validated with about 110 real-time stations for clock estimation by comparison of the estimated clocks and the PPP performance applying estimated clocks. The results of the real-time PPP experiment using 12 global stations show that with the greatly improved computational efficiency, 3.14 cm in horizontal and 5.51 cm in vertical can be achieved using the estimated DECE clock

Underpricing of Initial Public Offering (IPO) in China

This paper studies the issue of initial public offerings (IPO) underpricing on Chinese A-share market using the sample of 838 IPO shares from 2002 to 2014 excluding 2013. The research reviews some classic western theories and hypotheses as well as studies carried by Chinese scholars which shows the difference of IPO underpricing between Chinese stock market and other stock markets. Based on these literature, a multiple regression model is constructed to test the influential factors of IPO underpricing in Chinese stock market. From the regression result, 3 main reason of the high IPO underpricing level in China can be concluded. The first cause is the problem of informational asymmetry which is owing to the immature IPO regulation, therefore the disclosure of information is not efficient. The second factor is the irrational investment behaviour in Chinese market. It confirms the phenomenon of severe speculation in secondary market. Thirdly, the result of variables that used to represent the special features of Chinese stock market shows that the high inequity of demand and supply in Chinese stock market still exists after the reform of IPO system and it is a main reason of IPO underpricing in China

A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning

Federated learning (FL) enables multiple clients to collaboratively learn a shared model without sharing their individual data. Concerns about utility, privacy, and training efficiency in FL have garnered significant research attention. Differential privacy has emerged as a prevalent technique in FL, safeguarding the privacy of individual user data while impacting utility and training efficiency. Within Differential Privacy Federated Learning (DPFL), previous studies have primarily focused on the utility-privacy trade-off, neglecting training efficiency, which is crucial for timely completion. Moreover, differential privacy achieves privacy by introducing controlled randomness (noise) on selected clients in each communication round. Previous work has mainly examined the impact of noise level ($\sigma$) and communication rounds ($T$) on the privacy-utility dynamic, overlooking other influential factors like the sample ratio ($q$, the proportion of selected clients). This paper systematically formulates an efficiency-constrained utility-privacy bi-objective optimization problem in DPFL, focusing on $\sigma$, $T$, and $q$. We provide a comprehensive theoretical analysis, yielding analytical solutions for the Pareto front. Extensive empirical experiments verify the validity and efficacy of our analysis, offering valuable guidance for low-cost parameter design in DPFL

A Cost-Effectiveness Analysis of Comprehensive Smoking-Cessation Interventions Based on the Community and Hospital Collaboration

BackgroundThe prevalence of cigarette smoking in China is high and the utilization of smoking cessation clinics is very low. Multicomponent smoking cessation interventions involving community and hospital collaboration have the potential to increase the smoking cessation rate. However, the cost-effectiveness of this intervention model is unknown.MethodsWe conducted a smoking cessation intervention trial in 19 community health service centers in Beijing, China. A cost-effectiveness analysis was performed from a societal perspective to compare three strategies of smoking cessation: no intervention (NI), pharmacological intervention (PI), and comprehensive intervention (CI) (PI plus online health promotion). A Markov model, with a time horizon of 20 years, was used to simulate the natural progression of estimated 10,000 male smokers. A cross-sectional survey was conducted to obtain data on costs and quality-adjusted life years (QALYs) by using the five-level EuroQol-5-dimension (EQ-5D-5L) questionnaire. Probabilistic sensitivity analysis was performed to explore parameters of uncertainty in the model.ResultsA total of 680 participants were included in this study, including 283 in the PI group and 397 in the CI group. After 6 months of follow-up, the smoking cessation rate reached 30.0% in the CI group and 21.2% in the PI group. Using the Markov model, compared with the NI group, the intervention strategies of the PI group and the CI group were found to be cost-effective, with an incremental cost-effectiveness ratio (ICER) of $535.62/QALY and$366.19/QALY, respectively. The probabilistic sensitivity analysis indicated that the CI strategy was always the most cost-effective intervention.ConclusionCI for smoking cessation, based in hospital and community in China, is more cost-effective than PI alone. Therefore, this smoking cessation model should be considered to be implemented in healthcare settings

The IFN-γ-related long non-coding RNA signature predicts prognosis and indicates immune microenvironment infiltration in uterine corpus endometrial carcinoma

BackgroundOne of the most common diseases that have a negative impact on women’s health is endometrial carcinoma (EC). Advanced endometrial cancer has a dismal prognosis and lacks solid prognostic indicators. IFN-γ is a key cytokine in the inflammatory response, and it has also been suggested that it has a role in the tumor microenvironment. The significance of IFN-γ-related genes and long non-coding RNAs in endometrial cancer, however, is unknown.MethodsThe Cancer Genome Atlas (TCGA) database was used to download RNA-seq data from endometrial cancer tissues and normal controls. Genes associated with IFN-γ were retrieved from the gene set enrichment analysis (GSEA) website. Co-expression analysis was performed to find lncRNAs linked to IFN-γ gene. The researchers employed weighted co-expression network analysis (WGCNA) to find lncRNAs that were strongly linked to survival. The prognostic signature was created using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. The training cohort, validation cohort, and entire cohort of endometrial cancer patients were then split into high-risk and low-risk categories. To investigate variations across different risk groups, we used survival analysis, enrichment analysis, and immune microenvironment analysis. The platform for analysis is R software (version X64 3.6.1).ResultsBased on the transcript expression of IFN-γ-related lncRNAs, two distinct subgroups of EC from TCGA cohort were formed, each with different outcomes. Ten IFN-γ-related lncRNAs were used to build a predictive signature using Cox regression analysis and the LASSO regression, including CFAP58, LINC02014, UNQ6494, AC006369.1, NRAV, BMPR1B-DT, AC068134.2, AP002840.2, GS1-594A7.3, and OLMALINC. The high-risk group had a considerably worse outcome (p < 0.05). In the immunological microenvironment, there were also substantial disparities across different risk categories.ConclusionOur findings give a reference for endometrial cancer prognostic type and immunological status assessment, as well as prospective molecular markers for the disease

Multi-dimensional multiplexing optical secret sharing framework with cascaded liquid crystal holograms

Secret sharing is a promising technology for information encryption by splitting the secret information into different shares. However, the traditional scheme suffers from information leakage in decryption process since the amount of available information channels is limited. Herein, we propose and demonstrate an optical secret sharing framework based on the multi-dimensional multiplexing liquid crystal (LC) holograms. The LC holograms are used as spatially separated shares to carry secret images. The polarization of the incident light and the distance between different shares are served as secret keys, which can significantly improve the information security and capacity. Besides, the decryption condition is also restricted by the applied external voltage due to the variant diffraction efficiency, which further increases the information security. In implementation, an artificial neural network (ANN) model is developed to carefully design the phase distribution of each LC hologram. With the advantage of high security, high capacity and simple configuration, our optical secret sharing framework has great potentials in optical encryption and dynamic holographic display

Underpricing of Initial Public Offering (IPO) in China

This paper studies the issue of initial public offerings (IPO) underpricing on Chinese A-share market using the sample of 838 IPO shares from 2002 to 2014 excluding 2013. The research reviews some classic western theories and hypotheses as well as studies carried by Chinese scholars which shows the difference of IPO underpricing between Chinese stock market and other stock markets. Based on these literature, a multiple regression model is constructed to test the influential factors of IPO underpricing in Chinese stock market. From the regression result, 3 main reason of the high IPO underpricing level in China can be concluded. The first cause is the problem of informational asymmetry which is owing to the immature IPO regulation, therefore the disclosure of information is not efficient. The second factor is the irrational investment behaviour in Chinese market. It confirms the phenomenon of severe speculation in secondary market. Thirdly, the result of variables that used to represent the special features of Chinese stock market shows that the high inequity of demand and supply in Chinese stock market still exists after the reform of IPO system and it is a main reason of IPO underpricing in China

A Time Traveler’s Note on Proper Names and Definite Descriptions

This essay aims to coherently introduce a four-dimensional view adapting to the three-spatial-plus-one- temporal-dimensions (3+1) physical world. To orient the discussions, the essay presents several central claims. First, the only description a proper name abbreviates is that of being called, yet a proper name is capable of bringing up the entire object from its birth to its end. Second, there is a crucial difference in the behaviors of proper names and definite descriptions. Third, a co-knowing state may be decisive in exchanging information about the physical world. Lastly, one way to consider the truth value of propositions containing fictional characters is to consider such propositions as about a summarized or entailed property of the physically stored coding texts. On the other hand, fictional worlds typically are well-established four-dimensional simulations