Let AI Entertain You: Increasing User Engagement with Generative AI and Rejection Sampling

While generative AI excels in content generation, it does not always increase user engagement. This can be attributed to two main factors. First, generative AI generates content without incorporating explicit or implicit feedback about user interactions. Even if the generated content seems to be more informative or well-written, it does not necessarily lead to an increase in user activities, such as clicks. Second, there is a concern with the quality of the content generative AI produces, which often lacks the distinctiveness and authenticity that human-created content possesses. These two factors can lead to content that fails to meet specific needs and preferences of users, ultimately reducing its potential to be engaging. This paper presents a generic framework of how to improve user engagement with generative AI by leveraging user feedback. Our solutions employ rejection sampling, a technique used in reinforcement learning, to boost engagement metrics. We leveraged the framework in the context of email notification subject lines generation for an online social network, and achieved significant engagement metric lift including +1% Session and +0.4% Weekly Active Users. We believe our work offers a universal framework that enhances user engagement with generative AI, particularly when standard generative AI reaches its limits in terms of enhancing content to be more captivating. To the best of our knowledge, this represents an early milestone in the industry's successful use of generative AI to enhance user engagement

Large Language Models for Social Networks: Applications, Challenges, and Solutions

Large Language Models (LLMs) are transforming the way people generate, explore, and engage with content. We study how we can develop LLM applications for online social networks. Despite LLMs' successes in other domains, it is challenging to develop LLM-based products for social networks for numerous reasons, and it has been relatively under-reported in the research community. We categorize LLM applications for social networks into three categories. First is knowledge tasks where users want to find new knowledge and information, such as search and question-answering. Second is entertainment tasks where users want to consume interesting content, such as getting entertaining notification content. Third is foundational tasks that need to be done to moderate and operate the social networks, such as content annotation and LLM monitoring. For each task, we share the challenges we found, solutions we developed, and lessons we learned. To the best of our knowledge, this is the first comprehensive paper about developing LLM applications for social networks

Liver immune microenvironment and metastasis from colorectal cancer‐pathogenesis and therapeutic perspectives

A drastic difference exists between the 5‐year survival rates of colorectal cancer patients with localized cancer and distal organ metastasis. The liver is the most favorable organ for cancer metastases from the colorectum. Beyond the liver‐colon anatomic relationship, emerging evidence highlights the impact of liver immune microenvironment on colorectal liver metastasis. Prior to cancer cell dissemination, hepatocytes secrete multiple factors to recruit or activate immune cells and stromal cells in the liver to form a favorable premetastatic niche. The liver‐resident cells including Kupffer cells, hepatic stellate cells, and liver‐sinusoidal endothelial cells are co‐opted by the recruited cells, such as myeloid‐derived suppressor cells and tumor‐associated macrophages, to establish an immunosuppressive liver microenvironment suitable for tumor cell colonization and outgrowth. Current treatments including radical surgery, systemic therapy, and localized therapy have only achieved good clinical outcomes in a minority of colorectal cancer patients with liver metastasis, which is further hampered by high recurrence rate. Better understanding of the mechanisms governing the metastasis‐prone liver immune microenvironment should open new immuno‐oncology avenues for liver metastasis intervention

Human ACE2‑Functionalized Gold “Virus‑Trap” Nanostructures for Accurate Capture of SARS‑CoV‑2 and Single‑Virus SERS Detection

The current COVID-19 pandemic urges the extremely sensitive and prompt detection of SARS-CoV-2 virus. Here, we present a Human Angiotensin-converting-enzyme 2 (ACE2)-functionalized gold “virus traps” nanostructure as an extremely sensitive SERS biosensor, to selectively capture and rapidly detect S-protein expressed coronavirus, such as the current SARS-CoV-2 in the contaminated water, down to the single-virus level. Such a SERS sensor features extraordinary 106- fold virus enrichment originating from high-affinity of ACE2 with S protein as well as “virus-traps” composed of oblique gold nanoneedles, and 109- fold enhancement of Raman signals originating from multicomponent SERS effects. Furthermore, the identification standard of virus signals is established by machine-learning and identification techniques, resulting in an especially low detection limit of 80 copies mL− 1 for the simulated contaminated water by SARS-CoV-2 virus with complex circumstance as short as 5 min, which is of great significance for achieving real-time monitoring and early warning of coronavirus. Moreover, here-developed method can be used to establish the identification standard for future unknown coronavirus, and immediately enable extremely sensitive and rapid detection of novel virus

Spatiotemporal Variation and Hotspot Detection of the Avian Influenza A(H7N9) Virus in China, 2013–2017

This study aims to describe the spatial and temporal characteristics of human infections with H7N9 virus in China using data from 19 February 2013 to 30 September 2017 extracted from Centre for Health Protection of the Department of Health (CHP) and electronic databases managed by China’s Center for Disease Control (CDC) and provincial CDCs synthetically using the Geographic Information System (GIS) software ArcMap™ 10.2 and SaTScan. Based on the multiple analyses of the A(H7N9) epidemics, there was a strong seasonal pattern in A(H7N9) virus infection, with high activity in the first quarter of the year, especially in January, February, and April, and a gradual dying out in the third quarter. Spatial distribution analysis indicated that Eastern China contained the most severely affected areas, such as Zhejiang Province, and the distribution shifted from coastline areas to more inland areas over time. In addition, the cases exhibited local spatial aggregation, with high-risk areas most found in the southeast coastal regions of China. Shanghai, Jiangsu, Zhejiang, and Guangdong were the high-risk epidemic areas, which should arouse the attention of local governments. A strong cluster from 9 April 2017 to 24 June 2017 was also identified in Northern China, and there were many secondary clusters in Eastern and Southern China, especially in Zhejiang, Fujian, Jiangsu, and Guangdong Provinces. Our results suggested that the spatial-temporal clustering of H7N9 in China is fundamentally different, and is expected to contribute to accumulating knowledge on the changing temporal patterns and spatial dissemination during the fifth epidemic and provide data to enable adequate preparation against the next epidemic

Petrophysical facies characteristics and classification evaluation of Dongying Formation of Nanpu No. 4 structure

Strong diagenesis and reservoir heterogeneity as well as complex pore structure in the late stage of No. 4 structure in Nanpu sag make assessment of the petrophysical characteristics of its high-quality reservoir and evaluation of its effectiveness difficult. To address this, the sedimentary, diagenetic, and pore structure characteristics of the reservoir were comprehensively studied using core thin section analysis, scanning electron microscopy, X-ray diffraction, capillary analysis, and logging and oil tests. The results showed that the sedimentary facies of the Ed2 and Ed3 of the study area were mainly braided river delta types, and the sedimentary microfacies mainly developed in an underwater distributary channel, interdistributary bay, and mouth bar. Diagenetic facies can be divided into four types based on diagenesis and mineral types: weak dissolution facies, clay mineral filling facies, carbonate cementation facies, and compacted dense facies. The pore structure facies can be divided into four types based on the reservoir physical properties and mercury injection, I: macropore coarse throat type, II: macropore medium throat type, III: mesopore thin throat type, and IV: micropore throat type. Based on the superimposed cluster analysis of sedimentary, diagenesis, and pore structure, the reservoir petrophysical facies can be divided into PF1-PF4, and the corresponding quantitative classification and evaluation criteria can be established. PF1 is an advantageous reservoir with high oil, gas, and water productivity; PF2 is an oil-bearing reservoir with average productivity; PF3 is a poor reservoir with low productivity after reservoir reconstruction; and PF4 is an invalid reservoir. The quantitative classification and evaluation criteria of petrophysical facies are established by logging response rules, which provide technical support and a solid theoretical basis for the evaluation of reservoir effectiveness, superior reservoir prediction, and subsequent ongoing development in the study area