Modelling the cohort effect in CBD models using a piecewise linear approach

This paper discusses a new pattern of mortality model which is built on the form and knowledge of the two-factor mortality model named after its designers Cairns, Blake and Dowd (2006). This model – the CBD model – is widely used and has been extended by the authors in a number of ways, including by the use of a cohort effect. In this paper, we propose a range of new parsimonious approaches to model the cohort effect. Instead of adding a cohort factor to an age-period model we model the effect by building discontinuities into the pattern of rates within each year. The fit of the resulting models is close to that available from the best of the CBD derivatives

A Piecewise Linear Cohort Extension to the Cairns-Blake-Dowd Model

Age-Period-Cohort (“APC”) models have been criticised on a number of grounds. One area of concern is in relation to projecting future cohorts. However, we would argue that such projection is unnecessary in some key cases, such as for closed defined benefit pension schemes. More fundamental issues relate to the fit itself. APC models typically use at least one parameter for each cohort, in addition to those used for parameters age and period. This leads to a large number of parameters which are not necessarily independent. However, the model we propose here uses a potentially far smaller number of parameters that essentially describe times where a new type of cohort emerges. This is similar to the trend-change models of mortality improvement discussed by as described by Sweeting (2011), Coelho and Nunes (2011), and van Berkum et al (2014). Because this cohort approach identifies a small number of changes in cohort rather than imposing a new cohort parameter for each year of birth, this reduces the risk of interdependence

Similarity Learning via Kernel Preserving Embedding

Data similarity is a key concept in many data-driven applications. Many algorithms are sensitive to similarity measures. To tackle this fundamental problem, automatically learning of similarity information from data via self-expression has been developed and successfully applied in various models, such as low-rank representation, sparse subspace learning, semi-supervised learning. However, it just tries to reconstruct the original data and some valuable information, e.g., the manifold structure, is largely ignored. In this paper, we argue that it is beneficial to preserve the overall relations when we extract similarity information. Specifically, we propose a novel similarity learning framework by minimizing the reconstruction error of kernel matrices, rather than the reconstruction error of original data adopted by existing work. Taking the clustering task as an example to evaluate our method, we observe considerable improvements compared to other state-of-the-art methods. More importantly, our proposed framework is very general and provides a novel and fundamental building block for many other similarity-based tasks. Besides, our proposed kernel preserving opens up a large number of possibilities to embed high-dimensional data into low-dimensional space.Comment: Published in AAAI 201

Research on Influence Factors of the Internet Financial Product Consumption Based on Innovation Diffusion Theory

This article takes the personal characteristic as a point of penetration,through a literature review, put forwards three antecedents factors,that are personal innovation, product cognition and perceived risk, focusing on the relationship among the there factors,the conceptual model was tested by structural equation model .The findings are that all of the above three aspects influence the choice of the Internet financial products. They also mutual influence between the three, personal innovation has a positive impact on product cognition and gives a negative impact on perceived risk, at the same time, product cognition affects the perceived risk negatively

Physical Activity, Screen Time, and Emotional Well-Being during the 2019 Novel Coronavirus Outbreak in China

We aimed to evaluate the effects of the COVID-19 lock down on lifestyle in China during the initial stage of the pandemic. A questionnaire was distributed to Chinese adults living in 31 provinces of China via the internet using a snowball sampling strategy. Information on 7-day physical activity recall, screen time, and emotional state were collected between January 24 and February 2, 2020. ANOVA, χ² test, and Spearman's correlation coefficients were used for statistical analysis. 12,107 participants aged 18-80 years were included. During the initial phase of the COVID-19 outbreak, nearly 60% of Chinese adults had inadequate physical activity (95% CI 56.6%-58.3%), which was more than twice the global prevalence (27.5%, 25.0%-32.2%). Their mean screen time was more than 4 hours per day while staying at home (261.3 ± 189.8 min per day), and the longest screen time was found in young adults (305.6 ± 217.5 min per day). We found a positive and significant correlation between provincial proportions of confirmed COVID-19 cases and negative affect scores (r = 0.501, p = 0.004). Individuals with vigorous physical activity appeared to have a better emotional state and less screen time than those with light physical activity. During this nationwide lockdown, more than half of Chinese adults temporarily adopted a sedentary lifestyle with insufficient physical activity, more screen time, and poor emotional state, which may carry considerable health risks. Promotion of home-based self-exercise can potentially help improve health and wellness.This study was funded by the National Key Technology R&D Program of China (2019YFF0301600),and the National Natural Science Foundation of China (11775059 and 31900845)

The Evaluation of Toxicity Induced by Psoraleae Fructus in Rats Using Untargeted Metabonomic Method Based on UPLC-Q-TOF/MS

Psoraleae Fructus is the dry and mature fruit of leguminous plant Psoralea corylifolia L., with the activity of warming kidney and enhancing yang, warming spleen, and other effects. However, large doses can cause liver and kidney toxicity. Therefore, it is necessary to evaluate the toxicity of Psoraleae Fructus systematically. Although traditional biochemical indicators and pathological tests have been used to evaluate the safety of drug, these methods lack sensitivity and specificity, so a fast and sensitive analytical method is urgently needed. In this study, an ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) method was used to analyze the metabolic profiles of rat plasma. The changes of metabolites in plasma samples were detected by partial least squares-discriminant analysis (PLS-DA). Compared with the control group, after 7 days of administration, the pathological sections showed liver and kidney toxicity, and the metabolic trend was changed. Finally, 13 potential biomarkers related to the toxicity of Psoraleae Fructus were screened. The metabolic pathways involved were glycerol phospholipids metabolism, amino acid metabolism, energy metabolism, and so forth. The discovery of these biomarkers laid a foundation for better explaining the hepatotoxicity and nephrotoxicity of Psoraleae Fructus and provided a guarantee for its safety evaluation

Flashlight: Scalable Link Prediction with Effective Decoders

Link prediction (LP) has been recognized as an important task in graph learning with its broad practical applications. A typical application of LP is to retrieve the top scoring neighbors for a given source node, such as the friend recommendation. These services desire the high inference scalability to find the top scoring neighbors from many candidate nodes at low latencies. There are two popular decoders that the recent LP models mainly use to compute the edge scores from node embeddings: the HadamardMLP and Dot Product decoders. After theoretical and empirical analysis, we find that the HadamardMLP decoders are generally more effective for LP. However, HadamardMLP lacks the scalability for retrieving top scoring neighbors on large graphs, since to the best of our knowledge, there does not exist an algorithm to retrieve the top scoring neighbors for HadamardMLP decoders in sublinear complexity. To make HadamardMLP scalable, we propose the Flashlight algorithm to accelerate the top scoring neighbor retrievals for HadamardMLP: a sublinear algorithm that progressively applies approximate maximum inner product search (MIPS) techniques with adaptively adjusted query embeddings. Empirical results show that Flashlight improves the inference speed of LP by more than 100 times on the large OGBL-CITATION2 dataset without sacrificing effectiveness. Our work paves the way for large-scale LP applications with the effective HadamardMLP decoders by greatly accelerating their inference