Context-Patch Face Hallucination Based on Thresholding Locality-Constrained Representation and Reproducing Learning

Face hallucination is a technique that reconstruct high-resolution (HR) faces from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR face pairs. Most state-of-the-arts leverage position-patch prior knowledge of human face to estimate the optimal representation coefficients for each image patch. However, they focus only the position information and usually ignore the context information of image patch. In addition, when they are confronted with misalignment or the Small Sample Size (SSS) problem, the hallucination performance is very poor. To this end, this study incorporates the contextual information of image patch and proposes a powerful and efficient context-patch based face hallucination approach, namely Thresholding Locality-constrained Representation and Reproducing learning (TLcR-RL). Under the context-patch based framework, we advance a thresholding based representation method to enhance the reconstruction accuracy and reduce the computational complexity. To further improve the performance of the proposed algorithm, we propose a promotion strategy called reproducing learning. By adding the estimated HR face to the training set, which can simulates the case that the HR version of the input LR face is present in the training set, thus iteratively enhancing the final hallucination result. Experiments demonstrate that the proposed TLcR-RL method achieves a substantial increase in the hallucinated results, both subjectively and objectively. Additionally, the proposed framework is more robust to face misalignment and the SSS problem, and its hallucinated HR face is still very good when the LR test face is from the real-world. The MATLAB source code is available at https://github.com/junjun-jiang/TLcR-RL

The Impact of IT spending and Vendor Interaction on Innovation Deployment in Hospitals

This paper examines the impact of financial investment and vendor interactions on the availability of electronic health record (EHR) systems to healthcare workers and patients, which in turn impacts technology usage and hospital performance. We used hospital‐level data from the American Hospital Association (AHA) IT Supplemental Survey, the AHA Annual Survey Database, and the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey to test the EHR systems deployment model. The results show that both financial investment and vendor interactions significantly influence the availability of the HER in hospitals. Especially, collaboration with dominant vendors can lead to the high availability of the system and better performance

LLM for Patient-Trial Matching: Privacy-Aware Data Augmentation Towards Better Performance and Generalizability

The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care. However, current approaches face challenges such as data standardization, ethical considerations, and a lack of interoperability between Electronic Health Records (EHRs) and clinical trial criteria. In this paper, we explore the potential of large language models (LLMs) to address these challenges by leveraging their advanced natural language generation capabilities to improve compatibility between EHRs and clinical trial descriptions. We propose an innovative privacy-aware data augmentation approach for LLM-based patient-trial matching (LLM-PTM), which balances the benefits of LLMs while ensuring the security and confidentiality of sensitive patient data. Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%. Additionally, we present case studies to further illustrate the effectiveness of our approach and provide a deeper understanding of its underlying principles

“Ask Everyone?” Understanding How Social Q&A Feedback Quality Influences Consumers\u27 Purchase Intentions

Social question & answer (Q&A) feedback is a novel form of electronic word-of-mouth that allows customers to ask questions and share opinions with peer customers. Based on the stimulus-organism-response framework, this paper proposes a model to describe how social Q&A feedback quality affects consumers\u27 willingness to purchase by influencing their perceived risk, perceived usefulness, and use intention. We focused on the social Q&A feature named Ask Everyone in Taobao and collected 153 valid responses through an online survey. Canonical correlation analysis was used to identify the association between feedback characteristics and feedback quality. Then, PLS-SEM was conducted to test the proposed research model. Results show that feedback quality negatively associated with perceived risk, but had a positive impact on perceived usefulness, use intention, and purchase intention. Findings of this research has both theoretical and practical implications for facilitating social Q&A design in e-commerce platforms