Learning Rich Features for Image Manipulation Detection

Image manipulation detection is different from traditional semantic object detection because it pays more attention to tampering artifacts than to image content, which suggests that richer features need to be learned. We propose a two-stream Faster R-CNN network and train it endto- end to detect the tampered regions given a manipulated image. One of the two streams is an RGB stream whose purpose is to extract features from the RGB image input to find tampering artifacts like strong contrast difference, unnatural tampered boundaries, and so on. The other is a noise stream that leverages the noise features extracted from a steganalysis rich model filter layer to discover the noise inconsistency between authentic and tampered regions. We then fuse features from the two streams through a bilinear pooling layer to further incorporate spatial co-occurrence of these two modalities. Experiments on four standard image manipulation datasets demonstrate that our two-stream framework outperforms each individual stream, and also achieves state-of-the-art performance compared to alternative methods with robustness to resizing and compression.Comment: CVPR 2018 Camera Read

An evaluation of outpatient satisfaction based on the national standard questionnaire: a satisfaction survey conducted in a tertiary hospital in Shenyang, China

BackgroundPatient satisfaction survey serves as a pivotal tool in evaluating the quality of healthcare services. China’s nationwide standard patient satisfaction measurement tool was introduced in 2019. This study aimed to assess the model fit of the national standard outpatient satisfaction questionnaire in a tertiary hospital and evaluate the outpatient satisfaction levels using this tool.MethodA cross-sectional survey using the national outpatient satisfaction questionnaire was conducted via message links to all hospital outpatients who registered between April and July 2022. The data collected underwent descriptive analysis, comparative analysis, and confirmatory factor analysis (CFA).ResultsA total of 6,012 valid responses were received and analyzed during this period, with 52.9% of the participants being women. The confirmatory factor analysis (CFA) model showed a good fit and identified doctor communication as having a positive effect and environmental factors as having a negative effect on outpatients’ satisfaction, with standardized regression weights of 0.46 and 0.42, respectively. Despite the remarkably high satisfaction levels, patients’ recommendation for using the services of this hospital surpassed the overall evaluation and total satisfaction scores.ConclusionA disparity was identified between the expectations and real experiences of outpatients, leading to some extent of dissatisfaction. To enhance satisfaction levels, the hospital should improve the communication skills of all clinical staff, simplify the environment layout for first-time visitors, and manage patient overloads

Differential Liquidity Provision in Uniswap v3 and Implications for Contract Design

Decentralized exchanges (DEXs) provide a means for users to trade pairs of assets on-chain without the need of a trusted third party to effectuate a trade. Amongst these, constant function market maker (CFMM) DEXs such as Uniswap handle the most volume of trades between ERC-20 tokens. With the introduction of Uniswap v3, liquidity providers are given the option to differentially allocate liquidity to be used for trades that occur within specific price intervals. In this paper, we formalize the profit and loss that liquidity providers can earn when providing specific liquidity positions to a contract. With this in hand, we are able to compute optimal liquidity allocations for liquidity providers who hold beliefs over how prices evolve over time. Ultimately, we use this tool to shed light on the design question regarding how v3 contracts should partition price space for permissible liquidity allocations. Our results show that a richer space of potential partitions can simultaneously benefit both liquidity providers and traders.Comment: 48 pages, 13 figure