Information Theory-Guided Heuristic Progressive Multi-View Coding

Multi-view representation learning aims to capture comprehensive information from multiple views of a shared context. Recent works intuitively apply contrastive learning to different views in a pairwise manner, which is still scalable: view-specific noise is not filtered in learning view-shared representations; the fake negative pairs, where the negative terms are actually within the same class as the positive, and the real negative pairs are coequally treated; evenly measuring the similarities between terms might interfere with optimization. Importantly, few works study the theoretical framework of generalized self-supervised multi-view learning, especially for more than two views. To this end, we rethink the existing multi-view learning paradigm from the perspective of information theory and then propose a novel information theoretical framework for generalized multi-view learning. Guided by it, we build a multi-view coding method with a three-tier progressive architecture, namely Information theory-guided hierarchical Progressive Multi-view Coding (IPMC). In the distribution-tier, IPMC aligns the distribution between views to reduce view-specific noise. In the set-tier, IPMC constructs self-adjusted contrasting pools, which are adaptively modified by a view filter. Lastly, in the instance-tier, we adopt a designed unified loss to learn representations and reduce the gradient interference. Theoretically and empirically, we demonstrate the superiority of IPMC over state-of-the-art methods.Comment: This paper is accepted by the jourcal of Neural Networks (Elsevier) by 2023. A revised manuscript of arXiv:2109.0234

Theoretical Analysis of Random Scattering Induced by Microlensing

Theoretical investigations into the deflection angle caused by microlenses offer a direct path to uncovering principles of the cosmological microlensing effect. This work specifically concentrates on the the probability density function (PDF) of the light deflection angle induced by microlenses. We have made several significant improvements to the widely used formula from Katz et al. First, we update the coefficient from 3.05 to 1.454, resulting in a better fit between the theoretical PDF and our simulation results. Second, we developed an elegant fitting formula for the PDF that can replace its integral representation within a certain accuracy, which is numerically divergent unless arbitrary upper limits are chosen. Third, to facilitate further theoretical work in this area, we have identified a more suitable Gaussian approximation for the fitting formula.Comment: 15 pages, 6 figures, accepted for publication in Research in Astronomy and Astrophysic

Ecological Protection Alone Is Not Enough to Conserve Ecosystem Carbon Storage: Evidence from Guangdong, China

The increase in atmospheric CO2 caused by land use and land cover change (LUCC) is one of the drivers of the global climate. As one of the most typical high-urbanization areas, the ecological conflicts occurring in Guangdong Province warrant urgent attention. A growing body of evidence suggests LUCC could guide the future ecosystem carbon storage, but most LUCC simulations are simply based on model results without full consistency with the actual situation. Fully combined with the territorial spatial planning project and based on the land use pattern in 2010 and 2020, we have used the Markov and Patch-generating Land Use Simulation (PLUS) model to simulate the future four land use scenarios: the Business as Usual (BU), Ecological Protection (EP), Farmland Protection (FP), and Economic Development (ED) scenario, and the ecosystem carbon storage was assessed by the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. The results show that the built-up area experience further expansion in all scenarios, the largest scale happened in ED and the smallest in FP. Besides, the forest area in the EP scenario is the largest, while the land use pattern developed based on the previous circumstances in the BU scenario. Furthermore, the carbon storage plunged from 1619.21 Tg C in 2010 to 1606.60 Tg C in 2020, with a total decrease of 12.61 Tg C. Urban expansion caused 79.83% of total carbon losses, of which 31.56% came from farmland. In 2030, the carbon storage dropped in all scenarios, and their storage amount has a relationship of FP \u3e BU \u3e EP \u3e ED. To better resolve the ecological problems and conserve ecosystem carbon storage, not only ecological protection but also the protection of the land near the city such as farmland protection strategies must be considered

Rapid Diagnosis by Microfluidic Techniques

Pathogenic bacteria in an aqueous or airborne environments usually cause infectious diseases in hospital or among the general public. One critical step in the successful treatment of the pathogen-caused infections is rapid diagnosis by identifying the causative microorganisms, which helps to provide early warning of the diseases. However, current standard identification based on cell culture and traditional molecular biotechniques often depends on costly or time-consuming detection methods and equipments, which are not suitable for point-of-care tests. Microfluidic-based technique has recently drawn lots of attention, due to the advantage that it has the potential of providing a faster, more sensitive, and higher-throughput identification of causative pathogens in an automatic manner by integrating micropumps and valves to control the liquid accurately inside the chips. In this chapter, microfluidic techniques for serodiagnosis of amebiasis, allergy, and rapid analysis of airborne bacteria are described. The microfluidic chips that integrate microcolumns, protein microarray, or a staggered herringbone mixer structure with sample to answer capability have been introduced and shown to be powerful in rapid diagnosis especially in medical fields

Insights from Niche Markets: Explainable and Predictive Values of Consumption Tendency on Credit Risks

The rapid development of FinTech drives the growing popularity of digital payment transactions. This phenomenon, especially given the increasing number of offline and online transactions being recorded in a real-time manner, offers great opportunities for financial service platforms to track consumers’ consumption tendencies and dynamically monitor and evaluate their creditworthiness. In our recent research, we first theorized the value of category-level consumption tendency based on the self-regulatory theory and employed econometric methods to empirically test the relationship between category-level consumption tendency and credit behavior. Then, we proposed a Deep Hierarchical Partial Attention-based Model (DHPAM) to predict credit default risk with full employment of product category features. We provided strong experimental evidence to show that the proposed DHPAM outperforms the state-of-the-art machine learning models. This paper, based on theories, empirical analyses, and a prediction model, offers comprehensive and practical guidance on the optimal utilization of consumption information in credit risk management

Plasma lensing interpretation of FRB 20201124A bursts at the end of September 2021

When the radio photons propagate through a non-uniform electron density volume, the plasma lensing effect can induce an extreme magnification to the observed flux at certain frequencies. Because the plasma lens acts as a diverging lens, it can extremely suppress the observed flux when aligned with source. These two properties can theoretically cause a highly magnified Fast Radio Burst (FRB) to faint or even disappear for a period of time. In this paper, we interpret that the significant increase in burst counts followed by a sudden quenching in FRB 20201124A in September 2021 can be attributed to plasma lensing. Based on the one-dimensional Gaussian lens model, we search for double main-peak structures in spectra just before its extinction on September 29, 2021. After the de-dispersion and de-scintillation procedures, we find eight bursts with double main-peaks at stable positions. There are three parameters in our modelling, the height and width of the one-dimension Gaussian lens and its distance to the source. We reformulate them as a combined parameter $\mathrm{P}_0 \propto \left ( \frac{a}{\mathrm{AU}}\right )\sqrt{\frac{\mathrm{kpc}}{D_{\mathrm{LS}}} \frac{\mathrm{pc}\;\mathrm{cm}^{-3}}{N_0} }$. The frequency spectra can give an accurate estimation of $\mathrm{P}_0$ corresponding to $\left ( \frac{a}{\mathrm{AU}}\right )\sqrt{\frac{\mathrm{kpc}}{D_{\mathrm{LS}}} \frac{\mathrm{pc}\;\mathrm{cm}^{-3}}{N_0} } \approx 28.118$, while the time of arrival only give a relatively loose constraint on $a^2/D_{\mathrm{LS}}$. Comparing with the observation dynamic spectra, we suggest that for a plasma lens in host galaxy, e.g., $D_{\mathrm{LS}}\approx 1\mathrm{kpc}$, the width of lens can not be larger than $40\mathrm{AU}$. At last, we estimate the relative transverse motion velocity between the lens and source, $v\approx98\left(\frac{a}{\mathrm{AU}}\right)\mathrm{km/s}$.Comment: 9 pages, 12 figures. Comments are welcom

Analysis of Demographic Characteristics and Psychological Factors of Opioid Addicts in Zhengzhou Area

Objective: To explore the demographic characteristics and psychological factors of patients with opioid addiction. Methods: A random number method was used to select 200 opioid-addicted patients admitted to the 7th People’s Hospital of Zhengzhou from January 2019 to February 2020. Demographic characteristics and psychosocial factors were analyzed. Result: The proportion of opioid addicts aged 21 ~ 30 was the highest; the proportion of men was significantly higher; the proportion of people who is between jobs/unemployed is the highest; the proportion of junior middle school was the highest, and the proportion of unmarried was relatively high; the proportion of opioid addicts with ignorance/curiosity for the cause of first addiction was the highest; the use of suction is snorting, accounting for the highest proportion. According to the analysis of relevant social and psychological factors, the proportion of single parent family group is the highest, the proportion of parent tension is the highest, and the proportion of bad life coping style is relatively high. At the same time, dependent psychology occupies the highest proportion in psychological factors of relapse patients. Conclusion: By analyzing the demographic characteristics of opioid addicts and the psychosocial factors of their addiction, we can strengthen prevention and management for specific groups to reduce the new addition and relapse of opioid addicts

Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation

Generative adversarial networks (GANs) have achieved remarkable progress in the natural image field. However, when applying GANs in the remote sensing (RS) image generation task, an extraordinary phenomenon is observed: the GAN model is more sensitive to the size of training data for RS image generation than for natural image generation. In other words, the generation quality of RS images will change significantly with the number of training categories or samples per category. In this paper, we first analyze this phenomenon from two kinds of toy experiments and conclude that the amount of feature information contained in the GAN model decreases with reduced training data. Then we establish a structural causal model (SCM) of the data generation process and interpret the generated data as the counterfactuals. Based on this SCM, we theoretically prove that the quality of generated images is positively correlated with the amount of feature information. This provides insights for enriching the feature information learned by the GAN model during training. Consequently, we propose two innovative adjustment schemes, namely Uniformity Regularization (UR) and Entropy Regularization (ER), to increase the information learned by the GAN model at the distributional and sample levels, respectively. We theoretically and empirically demonstrate the effectiveness and versatility of our methods. Extensive experiments on three RS datasets and two natural datasets show that our methods outperform the well-established models on RS image generation tasks. The source code is available at https://github.com/rootSue/Causal-RSGAN