Premium Payback Period Model and its Application in Stock Investment

A simple and effective investment strategy has always been the pursuit of both academicians and practitioners. This thesis introduces for the first time the concept of Premium Payback Period (PPP), the time required to earn back the premium paid for an asset. PPP is a powerful stock valuation model, which takes into account the company’s current accounting information and future earning ability. In the stock market, a stock’s PPP can be computed from its PB and ROE. In the real economy, a company’s PPP can be observed from the date of establishment to IPO. As a rule of thumb, stocks with PPP 9.5 years are overvalued, where the threshold PPP is obtained from observation in real economy. PPP is proved to be an effective investment strategy in terms of stock selection as well as market timing. A pilot empirical study in Chapter 2 shows that a portfolio of stocks with PPP lower than 5 years can achieve excess return. In Chapter 3, I attempt to demonstrate the power of PPP model in selecting undervalued stocks. The size effect and PPP effect are incorporated in one framework and investigates both bull and bear market conditions. Investment recommendation is to invest in firms with small size and low PPP. When the indicators conflict, PPP criterion is the priority in the bear market and size criterion is the priority in the bull market. In Chapter 4, I endeavor to extend the application of PPP model to market timing. Both Treynor and Mazuy Model and Henriksson and Merton Model confirm the poor market timing performance of 10 Chinese equity-type funds. PPP model can help improve market timing significantly by adjusting position in stock market in line with PPP value of stock index. This research thus provides strong evidence that the PPP model performs as an effective investment strategy by selecting undervalued stocks and entering or exiting the stock market at appropriate timing

Positive solutions for a system of nnth-order nonlinear boundary value problems

In this paper, we investigate the existence, multiplicity and uniqueness of positive solutions for the following system of $n$th-order nonlinear boundary value problems $$\begin{cases} u^{(n)}(t)+f(t,u(t),v(t))=0,0<t<1,\\v^{(n)}(t)+g(t,u(t),v(t))=0, 0<t<1,\\ u(0)=u'(0)=\ldots=u^{(n-2)}(0)=u(1)=0,\\ v(0)= v'(0)=\ldots=v^{(n-2)}(0)=v(1)=0. \end{cases}$$ Based on a priori estimates achieved by using Jensen's integral inequality, we use fixed point index theory to establish our main results. Our assumptions on the nonlinearities are mostly formulated in terms of spectral radii of associated linear integral operators. In addition, concave and convex functions are utilized to characterize coupling behaviors of $f$ and $g$, so that we can treat the three cases: the first with both superlinear, the second with both sublinear, and the last with one superlinear and the other sublinear

Straggler Mitigation and Latency Optimization in Blockchain-based Hierarchical Federated Learning

Cloud-edge-device hierarchical federated learning (HFL) has been recently proposed to achieve communication-efficient and privacy-preserving distributed learning. However, there exist several critical challenges, such as the single point of failure and potential stragglers in both edge servers and local devices. To resolve these issues, we propose a decentralized and straggler-tolerant blockchain-based HFL (BHFL) framework. Specifically, a Raft-based consortium blockchain is deployed on edge servers to provide a distributed and trusted computing environment for global model aggregation in BHFL. To mitigate the influence of stragglers on learning, we propose a novel aggregation method, HieAvg, which utilizes the historical weights of stragglers to estimate the missing submissions. Furthermore, we optimize the overall latency of BHFL by jointly considering the constraints of global model convergence and blockchain consensus delay. Theoretical analysis and experimental evaluation show that our proposed BHFL based on HieAvg can converge in the presence of stragglers, which performs better than the traditional methods even when the loss function is non-convex and the data on local devices are non-independent and identically distributed (non-IID)

SAM-PARSER: Fine-tuning SAM Efficiently by Parameter Space Reconstruction

Segment Anything Model (SAM) has received remarkable attention as it offers a powerful and versatile solution for object segmentation in images. However, fine-tuning SAM for downstream segmentation tasks under different scenarios remains a challenge, as the varied characteristics of different scenarios naturally requires diverse model parameter spaces. Most existing fine-tuning methods attempt to bridge the gaps among different scenarios by introducing a set of new parameters to modify SAM's original parameter space. Unlike these works, in this paper, we propose fine-tuning SAM efficiently by parameter space reconstruction (SAM-PARSER), which introduce nearly zero trainable parameters during fine-tuning. In SAM-PARSER, we assume that SAM's original parameter space is relatively complete, so that its bases are able to reconstruct the parameter space of a new scenario. We obtain the bases by matrix decomposition, and fine-tuning the coefficients to reconstruct the parameter space tailored to the new scenario by an optimal linear combination of the bases. Experimental results show that SAM-PARSER exhibits superior segmentation performance across various scenarios, while reducing the number of trainable parameters by $\approx 290$ times compared with current parameter-efficient fine-tuning methods

Treatment of benign prostatic hyperplasia with finasteride: Evidence from a meta-analysis

Purpose: To clarify the usefulness and safety of finasteride in the treatment of patients with benign prostatic hyperplasia (BPH) compared to placebo group or controls.Methods: In a meta-analysis, PubMed and Web of Science were searched to include relevant studies. The results were combined with a random effect model. Publication bias was evaluated using Egger regression asymmetry test.Results: Fourteen publications involving 17,364 patients were included in the study. Pooled results indicated that International Prostate Symptom Score (IPSS) in the finasteride group was lower [weighted mean difference (WMD) = -0.77, 95% CI= -0.97 to -0.57] compared to the placebo group. The usefulness of finasteride was higher in total prostate volume (TPV) [WMD= 0.13, 95%CI= 0.00 to 0.26] but lower in serum DHT [WMD= -1.18, 95%CI= -1.51 to -0.86] when compared to the placebo group. Drug-related adverse event was higher in the finasteride treatment group when compared to placebo group [summary RR= 1.95, 95%CI= 1.31-2.90].Conclusion: Finasteride could improve the symptom score (IPSS and TPV) and reduce serum DHT. However, the potential adverse events, especially the drug-related adverse events in Finasteride treatment should be attention.Keywords: Benign prostatic hyperplasia, Finasteride, Meta-analysi

Wildfire Smoke Detection with Cross Contrast Patch Embedding

The Transformer-based deep networks have increasingly shown significant advantages over CNNs. Some existing work has applied it in the field of wildfire recognition or detection. However, we observed that the vanilla Transformer is not friendly for extracting smoke features. Because low-level information such as color, transparency and texture is very important for smoke recognition, and transformer pays more attention to the semantic relevance between middle- or high-level features, and is not sensitive to the subtle changes of low-level features along the space. To solve this problem, we propose the Cross Contrast Patch Embedding(CCPE) module based on the Swin Transformer, which uses the multi-scales spatial frequency contrast information in both vertical and horizontal directions to improve the discrimination of the network on the underlying details. The fuzzy boundary of smoke makes the positive and negative label assignment for instances in a dilemma, which is another challenge for wildfires detection. To solve this problem, a Separable Negative Sampling Mechanism(SNSM) is proposed. By using two different negative instance sampling strategies on positive images and negative images respectively, the problem of supervision signal confusion caused by label diversity in the process of network training is alleviated. This paper also releases the RealFire Test, the largest real wildfire test set so far, to evaluate the proposed method and promote future research. It contains 50,535 images from 3,649 video clips. The proposed method has been extensively tested and evaluated on RealFire Test dataset, and has a significant performance improvement compared with the baseline detection models