On the Sample Complexity of Multichannel Frequency Estimation via Convex Optimization

The use of multichannel data in line spectral estimation (or frequency estimation) is common for improving the estimation accuracy in array processing, structural health monitoring, wireless communications, and more. Recently proposed atomic norm methods have attracted considerable attention due to their provable superiority in accuracy, flexibility and robustness compared with conventional approaches. In this paper, we analyze atomic norm minimization for multichannel frequency estimation from noiseless compressive data, showing that the sample size per channel that ensures exact estimation decreases with the increase of the number of channels under mild conditions. In particular, given $L$ channels, order $K\left(\log K\right) \left(1+\frac{1}{L}\log N\right)$ samples per channel, selected randomly from $N$ equispaced samples, suffice to ensure with high probability exact estimation of $K$ frequencies that are normalized and mutually separated by at least $\frac{4}{N}$. Numerical results are provided corroborating our analysis.Comment: 14 pages, double column, to appear in IEEE Trans. Information Theor

Women’s Participation in Adult Education in Rural Areas of China: An Exploratory Study

This is an exploratory case study of adult education for women in a specific western rural locality in China, Qianjiang, based on interviews with four women. China has established a lifelong learning system and prioritized education in the process of social development. Adult education accounts for a significant part of the lifelong learning system and programs such as “Lifelong Learning Week” and “One Hundred Lifelong Learning Models” have been established. However, regardless of the achievements of gender equality in access to education reached in China in general, women in rural areas are still hindered by many factors for actively and effectively participating in the process of adult education and lifelong learning approaches according to the literature review. The following two main questions were examined: 1) In what ways factors including educational policies, health problems, traditional mindset, and economic situations, block women’s participation in education in rural areas of China? 2) How can distance learning and technology innovations benefit women’s education in rural areas? The results indicate that despite the different purposes of adult education, the purpose of developing economy is gradually becoming the most important and only goal of adult education. Surprisingly, unlike what was found in the literature review, the educational opportunities for women in rural areas in Qianjiang do not seem to be limited by the traditional understanding of gender roles, as shown in the literature review. Since information technologies such as laptops and smart phones are becoming more and more popular in rural China, well developed distance education programs may widen and facilitate women’s access to adult education in rural areas

Precision Target Guide Strategy for Applying SERS into Environmental Monitoring

Surface enhanced Raman spectroscopy (SERS) is a promising analytical technique that exhibits various applications in trace detection and identification. When it is applied into environmental monitoring, we should concern several key points to improve detection sensitivity and selectivity for the detection in complex matrix. In this tutorial review, we mainly focus on the strategies for improving the use of SERS into environmental application. The strategies are summarized for enhancing the ability of the substrate to selectively capture specific targets, and for achieving separation and concentration of the analytes from the matrix and the assembly structures for multiple phase detection. We have also introduced several newly developed detection systems using portable instruments and miniaturized devices that are more suitable for infield applications. In addition, we discuss the present challenges that hide it from wide real application and give the outlook for the future development in applying SERS in environmental monitoring

Selling the Data Product: Pricing Strategies and Welfare Implications

This paper examines the pricing and welfare implications of data as a factor of production with a stylized economic model. We introduce a generalized framework that specifies two types of data: 1) public data pricing, which maximizes social welfare, and 2) commercial data pricing, which maximizes the profit. The model reveals two takeaways: first, two prices may converge in the data economy. It is due to that data come from citizens and may be used to create value back to them. Therefore, a profit- seeking data seller might find it optimal to extend the user base, which is in line with the interest of the welfare maximizer. Second, the pricing gap between optimal prices does not change monotonically with the improvement of data quality. These findings shed new light on the current and future of data product operations, particularly in the understudied public sectors

LRBmat: A Novel Gut Microbial Interaction and Individual Heterogeneity Inference Method for Colorectal Cancer

Many diseases are considered to be closely related to the changes in the gut microbial community, including colorectal cancer (CRC), which is one of the most common cancers in the world. The diagnostic classification and etiological analysis of CRC are two critical issues worthy of attention. Many methods adopt gut microbiota to solve it, but few of them simultaneously take into account the complex interactions and individual heterogeneity of gut microbiota, which are two common and important issues in genetics and intestinal microbiology, especially in high-dimensional cases. In this paper, a novel method with a Binary matrix based on Logistic Regression (LRBmat) is proposed to deal with the above problem. The binary matrix can directly weakened or avoided the influence of heterogeneity, and also contain the information about gut microbial interactions with any order. Moreover, LRBmat has a powerful generalization, it can combine with any machine learning method and enhance them. The real data analysis on CRC validates the proposed method, which has the best classification performance compared with the state-of-the-art. Furthermore, the association rules extracted from the binary matrix of the real data align well with the biological properties and existing literatures, which are helpful for the etiological analysis of CRC. The source codes for LRBmat are available at https://github.com/tsnm1/LRBmat

T-Lymphocyte Responses to Intestinally Absorbed Antigens Can Contribute to Adipose Tissue Inflammation and Glucose Intolerance during High Fat Feeding

BACKGROUND: Obesity is associated with inflammation of visceral adipose tissues, which increases the risk for insulin resistance. Animal models suggest that T-lymphocyte infiltration is an important early step, although it is unclear why these cells are attracted. We have recently demonstrated that dietary triglycerides, major components of high fat diets, promote intestinal absorption of a protein antigen (ovalbumin, "OVA"). The antigen was partly transported on chylomicrons, which are prominently cleared in adipose tissues. We hypothesized that intestinally absorbed gut antigens may cause T-lymphocyte associated inflammation in adipose tissue. METHODOLOGY/PRINCIPAL FINDINGS: Triglyceride absorption promoted intestinal absorption of OVA into adipose tissue, in a chylomicron-dependent manner. Absorption tended to be higher in mesenteric than subcutaneous adipose tissue, and was lowest in gonadal tissue. OVA immunoreactivity was detected in stromal vascular cells, including endothelial cells. In OVA-sensitized mice, OVA feeding caused marked accumulation of CD3+ and osteopontin+ cells in mesenteric adipose tissue. The accumulating T-lymphocytes were mainly CD4+. As expected, high-fat (60% kCal) diets promoted mesenteric adipose tissue inflammation compared to low-fat diets (10% Kcal), as reflected by increased expression of osteopontin and interferon-gamma. Immune responses to dietary OVA further increased diet-induced osteopontin and interferon-gamma expression in mesenteric adipose. Inflammatory gene expression in subcutaneous tissue did not respond significantly to OVA or dietary fat content. Lastly, whereas OVA responses did not significantly affect bodyweight or adiposity, they significantly impaired glucose tolerance. CONCLUSIONS/SIGNIFICANCE: Our results suggest that loss or lack of immunological tolerance to intestinally absorbed T-lymphocyte antigens can contribute to mesenteric adipose tissue inflammation and defective glucose metabolism during high-fat dieting

Stock Volatility Prediction Based on Transformer Model Using Mixed-Frequency Data

With the increasing volume of high-frequency data in the information age, both challenges and opportunities arise in the prediction of stock volatility. On one hand, the outcome of prediction using tradition method combining stock technical and macroeconomic indicators still leaves room for improvement; on the other hand, macroeconomic indicators and peoples' search record on those search engines affecting their interested topics will intuitively have an impact on the stock volatility. For the convenience of assessment of the influence of these indicators, macroeconomic indicators and stock technical indicators are then grouped into objective factors, while Baidu search indices implying people's interested topics are defined as subjective factors. To align different frequency data, we introduce GARCH-MIDAS model. After mixing all the above data, we then feed them into Transformer model as part of the training data. Our experiments show that this model outperforms the baselines in terms of mean square error. The adaption of both types of data under Transformer model significantly reduces the mean square error from 1.00 to 0.86.Comment: Accepted by the 7th APWeb-WAIM International Joint Conference on Web and Big Data. (APWeb 2023