Generative Model Watermarking Based on Human Visual System

Intellectual property protection of deep neural networks is receiving attention from more and more researchers, and the latest research applies model watermarking to generative models for image processing. However, the existing watermarking methods designed for generative models do not take into account the effects of different channels of sample images on watermarking. As a result, the watermarking performance is still limited. To tackle this problem, in this paper, we first analyze the effects of embedding watermark information on different channels. Then, based on the characteristics of human visual system (HVS), we introduce two HVS-based generative model watermarking methods, which are realized in RGB color space and YUV color space respectively. In RGB color space, the watermark is embedded into the R and B channels based on the fact that HVS is more sensitive to G channel. In YUV color space, the watermark is embedded into the DCT domain of U and V channels based on the fact that HVS is more sensitive to brightness changes. Experimental results demonstrate the effectiveness of the proposed work, which improves the fidelity of the model to be protected and has good universality compared with previous methods.Comment: https://scholar.google.com/citations?user=IdiF7M0AAAAJ&hl=e

Awareness and preparedness level of medical workers for radiation and nuclear emergency response

Radiological science and nuclear technology have made great strides in the twenty-first century, with wide-ranging applications in various fields, including energy, medicine, and industry. However, those developments have been accompanied by the inherent risks of exposure to nuclear radiation, which is a source of concern owing to its potentially adverse effects on human health and safety and which is of particular relevance to medical personnel who may be exposed to certain cancers associated with low-dose radiation in their working environment. While medical radiation workers have seen a decrease in their occupational exposure since the 1950s thanks to improved measures for radiation protection, a concerning lack of understanding and awareness persists among medical professionals regarding these potential hazards and the required safety precautions. This issue is further compounded by insufficient capabilities in emergency response. This highlights the urgent need to strengthen radiation safety education and training to ensure the well-being of medical staff who play a critical role in radiological and nuclear emergencies. This review examines the health hazards of nuclear radiation to healthcare workers and the awareness and willingness and education of healthcare workers on radiation protection, calling for improved training programs and emergency response skills to mitigate the risks of radiation exposure in the occupational environment, providing a catalyst for future enhancement of radiation safety protocols and fostering of a culture of safety in the medical community

Integrating single-cell and bulk transcriptomic analyses to develop a cancer-associated fibroblast-derived biomarker for predicting prognosis and therapeutic response in breast cancer

BackgroundCancer-associated fibroblasts (CAFs) contribute to the progression and treatment of breast cancer (BRCA); however, risk signatures and molecular targets based on CAFs are limited. This study aims to identify novel CAF-related biomarkers to develop a risk signature for predicting the prognosis and therapeutic response of patients with BRCA.MethodsCAF-related genes (CAFRGs) and a risk signature based on these genes were comprehensively analyzed using publicly available bulk and single-cell transcriptomic datasets. Modular genes identified from bulk sequencing data were intersected with CAF marker genes identified from single-cell analysis to obtain reliable CAFRGs. Signature CAFRGs were screened via Cox regression and least absolute shrinkage and selection operator (LASSO) analyses. Multiple patient cohorts were used to validate the prognosis and therapeutic responsiveness of high-risk patients stratified based on the CAFRG-based signature. In addition, the relationship between the CAFRG-based signature and clinicopathological factors, tumor immune landscape, functional pathways, chemotherapy sensitivity and immunotherapy sensitivity was examined. External datasets were used and sample experiments were performed to examine the expression pattern of MFAP4, a key CAFRG, in BRCA.ResultsIntegrated analyses of single-cell and bulk transcriptomic data as well as prognostic screening revealed a total of 43 prognostic CAFRGs; of which, 14 genes (TLN2, SGCE, SDC1, SAV1, RUNX1, PDLIM4, OSMR, NT5E, MFAP4, IGFBP6, CTSO, COL12A1, CCDC8 and C1S) were identified as signature CAFRGs. The CAFRG-based risk signature exhibited favorable efficiency and accuracy in predicting survival outcomes and clinicopathological progression in multiple BRCA cohorts. Functional enrichment analysis suggested the involvement of the immune system, and the immune infiltration landscape significantly differed between the risk groups. Patients with high CAF-related risk scores (CAFRSs) exhibited tumor immunosuppression, enhanced cancer hallmarks and hyposensitivity to chemotherapy and immunotherapy. Five compounds were identified as promising therapeutic agents for high-CAFRS BRCA. External datasets and sample experiments validated the downregulation of MFAP4 and its strong correlation with CAFs in BRCA.ConclusionsA novel CAF-derived gene signature with favorable predictive performance was developed in this study. This signature may be used to assess prognosis and guide individualized treatment for patients with BRCA

The strategic response of banks to macroprudential policies: Evidence from mortgage stress tests in Canada

Following the crisis, macroprudential regulations targeting mortgage-market vulnerabilities were widely adopted, their success often depending on intermediaries' responses. We show that Canadian banks behaved strategically to limit the potency of recently implemented mortgage stress tests, requiring borrower qualification based on the mode of 5-year rates posted by the Big 6 banks rather than transaction rates. The government aimed to cool credit markets, but since many mortgages are government-insured, Big 6 interests were not aligned. Using DiD comparing changes in 5-year spreads with 3-year spreads, unaffected by the policy, we find rates were lowered encouraging continued borrowing, muting the tests' impact

Dynamic competition in negotiated price markets

This paper develops a framework for investigating dynamic competition in markets where price is negotiated between an individual customer and multiple firms repeatedly. Using contractlevel data for the Canadian mortgage market, we provide evidence of an "invest-then-harvest" pricing pattern: lenders offer relatively low interest rates to attract new borrowers and poach rivals' existing customers, and then at renewal charge interest rates which can be higher than what may be available through other lenders in the marketplace. We build a dynamic model of price negotiation with search and switching frictions to capture key market features. We estimate the model and use it to investigate (i) the effects of dynamic competition on borrowers' and banks' payoffs, (ii) the implications of dynamic versus static settings for mergerstudies, and (iii) the impacts from recent Canadian macroprudential policies

Study of parachute inflation process using fluid–structure interaction method

A direct numerical modeling method for parachute is proposed firstly, and a model for the star-shaped folded parachute with detailed structures is established. The simplified arbitrary Lagrangian–Eulerian fluid–structure interaction (SALE/FSI) method is used to simulate the inflation process of a folded parachute, and the flow field calculation is mainly based on operator splitting technique. By using this method, the dynamic variations of related parameters such as flow field and structure are obtained, and the load jump appearing at the end of initial inflation stage is captured. Numerical results including opening load, drag characteristics, swinging angle, etc. are well consistent with wind tunnel tests. In addition, this coupled method can get more space–time detailed information such as geometry shape, structure, motion, and flow field. Compared with previous inflation time method, this method is a completely theoretical analysis approach without relying on empirical coefficients, which can provide a reference for material selection, performance optimization during parachute design

Debt relief programs and money left on the table: Evidence from Canada's response to COVID-19

This paper analyzes the effectiveness of debt-relief programs targeting short-run household liquidity constraints implemented in Canada in response to the COVID-19 pandemic. These programs allowed individuals to push off mortgage and credit card payments and cut in half interest rates on credit card debt. Using credit bureau data, we document that, despite potential savings above $4 billion, enrollment was limited: 24% for mortgages and 7% for credit cards. By exploiting the richness of our data set, we provide evidence that close to 80% of individuals were unaware of the credit card relief program while others faced important fixed nonmonetary costs preventing uptake

Scene Classification With Recurrent Attention of VHR Remote Sensing Images

International audienceScene classification of remote sensing images has drawn great attention because of its wide applications. In this paper, with the guidance of the human visual system (HVS), we explore the attention mechanism and propose a novel end-to-end attention recurrent convolutional network (ARCNet) for scene classification. It can learn to focus selectively on some key regions or locations and just process them at high-level features, thereby discarding the noncritical information and promoting the classification performance. The contributions of this paper are threefold. First, we design a novel recurrent attention structure to squeeze high-level semantic and spatial features into several simplex vectors for the reduction of learning parameters. Second, an end-to-end network named ARCNet is proposed to adaptively select a series of attention regions and then to generate powerful predictions by learning to process them sequentially. Third, we construct a new data set named OPTIMAL-31, which contains more categories than popular data sets and gives researchers an extra platform to validate their algorithms. The experimental results demonstrate that our model makes great promotion in comparison with the state-of-the-art approaches

Housing affordability and parental income support

In many countries, the cost of housing has greatly outpaced income growth, leading to a housing affordability crisis. Leveraging Canadian loan-level data and quasi-experimental variation in payment-to-income constraints, we document an increasing reliance of first-time homebuyers on financial help from their parents, through mortgage co-signing. We show that parental support can effectively relax borrowing constraints-potentially to riskier borrowers

The role of intermediaries in selection markets: Evidence from mortgage lending

We study the role of intermediaries (brokers) in the Canadian mortgage market. In this market, consumers can search for quotes in one of two ways: on their own or via a broker. We provide descriptive evidence that borrowers who transact through brokers are different from those who do not. Broker-clients finance larger loans, are more leveraged, and are less creditworthy. After controlling for observable borrower characteristics that might explain these facts, we estimate a model of mortgage demand to disentangle two explanations for why borrowers wind up with these riskier mortgage products: (i) brokers steer borrowers towards products that are more profitable for them, and (ii) borrowers have (unobserved) preferences for riskier loans, i.e., selection on unobservables. We find that brokers steer borrowers to mortgages with longer amortization, while a borrower's own (unobservable) characteristics drive their decision for smaller down payments