Graph Exploration Matters: Improving both individual-level and system-level diversity in WeChat Feed Recommender

There are roughly three stages in real industrial recommendation systems, candidates generation (retrieval), ranking and reranking. Individual-level diversity and system-level diversity are both important for industrial recommender systems. The former focus on each single user's experience, while the latter focus on the difference among users. Graph-based retrieval strategies are inevitably hijacked by heavy users and popular items, leading to the convergence of candidates for users and the lack of system-level diversity. Meanwhile, in the reranking phase, Determinantal Point Process (DPP) is deployed to increase individual-level diverisity. Heavily relying on the semantic information of items, DPP suffers from clickbait and inaccurate attributes. Besides, most studies only focus on one of the two levels of diversity, and ignore the mutual influence among different stages in real recommender systems. We argue that individual-level diversity and system-level diversity should be viewed as an integrated problem, and we provide an efficient and deployable solution for web-scale recommenders. Generally, we propose to employ the retrieval graph information in diversity-based reranking, by which to weaken the hidden similarity of items exposed to users, and consequently gain more graph explorations to improve the system-level diveristy. Besides, we argue that users' propensity for diversity changes over time in content feed recommendation. Therefore, with the explored graph, we also propose to capture the user's real-time personalized propensity to the diversity. We implement and deploy the combined system in WeChat App's Top Stories used by hundreds of millions of users. Offline simulations and online A/B tests show our solution can effectively improve both user engagement and system revenue

Fundamental issues, technology development and challenges of boiling heat transfer, critical heat flux and two-phase flow phenomena with nanofluids

This paper presents a comprehensive and critical review of studies on nucleate pool boiling heat transfer, flow boiling heat transfer, critical heat flux (CHF) and two-phase flow phenomena with nanofluids. First, general analysis of the available studies on the relevant topics is presented. Then, studies of physical properties of nanofluids are discussed. Next, boiling heat transfer, CHF phenomena and the relevant physical mechanisms are explored. Finally, future research needs have been identified according to the review and analysis. As the first priority, the physical properties of nanofluids have a significant effect on the boiling and CHF characteristics but the lack of the accurate knowledge of the physical properties has greatly limited the studies. Fundamentals of boiling heat transfer and CHF phenomena with Nanofluids have not yet been well understood. Flow regimes are important in understanding the boiling and CHF phenomena and should be focused on. Two phase pressure drops of nanofluids should also be studies. Furthermore, economic evaluation of the enhancement technology with nanofluid should be considered for the new heat transfer enhancement technology with nanofluids. Finally, applied research should be targeted to achieve an enabling practical heat transfer and CHF enhancement technology for engineering application with nanofluids

Flow patterns and flow pattern maps for adiabatic and diabatic gas liquid two phase flow in microchannels: fundamentals, mechanisms and applications

This paper mainly presents comprehensive review on the research regarding adiabatic and diabatic gas–liquid two-phase flow patterns, bubble growth, flow pattern transitions and flow pattern maps in microchannels over the past 15 years. First, criteria for distinction of macro- and micro-channels are discussed. Then, fundamentals of gas liquid two-phase flow patterns, flow pattern maps and techniques for two phase flow visualization and sensing are presented. Next, experimental studied of adiabatic and diabatic two phase flow patterns, bubble behaviour, flow pattern transitions and flow pattern maps in microchannels with plain and enhanced structures are reviewed. Finally, applications of flow patterns and flow pattern maps are discussed. Flow pattern based mechanistic heat transfer prediction methods are focused on and studies on unstable and transient two phase flow patterns and heat transfer in microscale channels are addressed. According to the review and analysis, recommendations on the future research needs have been given. Systematic and accurate experimental data on flow patterns, bubble growth, flow pattern transitions are still needed. In particular, there are lacks general flow pattern transition criteria. Therefore, effort should be made to develop generalized flow pattern transition criteria based on well documented experimental observation and data. Furthermore, studies of mechanistic and theoretical models for flow patterns, flow pattern transitions bubble growth in microchannels should be further conducted. As an important topic, unstable and transient gas liquid two phase flow patterns and heat transfer in microchannels should be systematically investigated as well in order to understand the flow pattern transition mechanisms in microchannels with plain and enhanced structures

Progress and Prospects for Research and Technology Development of Supercritical CO2 Thermal Conversion Systems for Power, Energy Storage, and Waste Heat Recovery

CO2 is an environmentally friendly heat transfer fluid and has many advantages in thermal energy and power systems due to its peculiar thermal transport and physical properties. Supercritical CO2 (S-CO2) thermal energy conversion systems are promising for innovative technology in domestic and industrial applications including heat pump, air-conditioning, power generation, renewable energy systems, energy storage, thermal management, waste heat recovery and others. Both S-CO2 and transcritical CO2 thermodynamic cycles have been extensively investigated in order to improve the efficiencies of thermal and power systems and achieve net zero carbon emissions. This paper focuses on the progress and prospects for current research and technology development of S-CO2 thermal energy conversion systems and their applications including power generation, energy storage and waste heat recovery. First, the CO2 thermal transport and physical properties and benefits using CO2 as a heat transfer fluid in thermal energy and power systems are discussed. Then, classification of CO2 thermodynamic systems is presented. Next, S-CO2 for power generation, energy storage and waste heat recovery systems are presented. Finally, research needs of subcritical and supercritical CO2 heat transfer, fluid flow and heat exchangers for the development of various thermal energy and power systems are discussed

Multi-Granularity Click Confidence Learning via Self-Distillation in Recommendation

Recommendation systems rely on historical clicks to learn user interests and provide appropriate items. However, current studies tend to treat clicks equally, which may ignore the assorted intensities of user interests in different clicks. In this paper, we aim to achieve multi-granularity Click confidence Learning via Self-Distillation in recommendation (CLSD). Due to the lack of supervised signals in click confidence, we first apply self-supervised learning to obtain click confidence scores via a global self-distillation method. After that, we define a local confidence function to adapt confidence scores at the user group level, since the confidence distributions can be varied among user groups. With the combination of multi-granularity confidence learning, we can distinguish the quality of clicks and model user interests more accurately without involving extra data and model structures. The significant improvements over different backbones on industrial offline and online experiments in a real-world recommender system prove the effectiveness of our model. Recently, CLSD has been deployed on a large-scale recommender system, affecting over 400 million users

Experimental study and modelling of average void fraction of gas-liquid two-phase flow in a helically coiled rectangular channel

Void fraction is an important parameter in designing and simulating the relevant gas-liquid two-phase flow equipment and systems. Although numerous experimental research and modelling of void fraction in straight circular channels have been conducted over the past decades, the experimental data and prediction methods for the average void fraction in helically coiled channels are limited and needed. Especially, there is no such information in helically coiled channels with rectangular cross section. Therefore, it is essential to advance the relevant knowledge through experiments and to develop the corresponding prediction methods in helically coiled rectangular channels. This paper presents experimental results of the average void fraction and new models for the void fraction in a horizontal helically coiled rectangular channel. First, experiments were conducted with air-water two-phase flow in the horizontal helically coiled rectangular channel at a wide range of test conditions: the liquid superficial velocity ranges from 0.11 to 2 m/s and the gas superficial velocity ranges from 0.18 to 16 m/s. The average void fractions were measured with a quick-closing valve (QCV) method. The measured void fraction ranges from 0.012 to 0.927 which cover four flow regimes including unsteady pulsating, bubbly, intermittent and annular flow observed with a high speed camera. Second, comparisons of the entire measured average void fraction data to 32 void fraction models and correlations were made. It shows a low accuracy of these models and correlations in predicting the experimental data for the void fraction smaller than 0.5 while the drift flux model of Dix (Woldesemayat and Ghajar, 2007) predicts 98.3% of the entire experimental data within ±10% for the void fraction larger than 0.5. Therefore, the Dix model is recommended for the void fraction larger than 0.5. Furthermore, the observed flow regimes in the coiled channels were compared to two mechanistic flow regime maps developed for horizontal straight circular tubes. The flow regime maps do not capture all flow regimes in the present study. Finally, the effects of the limiting affecting parameters on the void fraction models are analyzed according to the physical phenomena and mechanisms. Incorporating the main affecting parameters, new void fraction models have been proposed for the void fractions in the ranges of 0 < α ≤ 0.2 and 0.2 < α ≤ 0.5 respectively according to the slip flow model. Both models predict the experimental data reasonably well. Overall, the new proposed models and the recommended model predict 92.8% of the entire void fraction data within ±30%

Learning from All Sides: Diversified Positive Augmentation via Self-distillation in Recommendation

Personalized recommendation relies on user historical behaviors to provide user-interested items, and thus seriously struggles with the data sparsity issue. A powerful positive item augmentation is beneficial to address the sparsity issue, while few works could jointly consider both the accuracy and diversity of these augmented training labels. In this work, we propose a novel model-agnostic Diversified self-distillation guided positive augmentation (DivSPA) for accurate and diverse positive item augmentations. Specifically, DivSPA first conducts three types of retrieval strategies to collect high-quality and diverse positive item candidates according to users' overall interests, short-term intentions, and similar users. Next, a self-distillation module is conducted to double-check and rerank these candidates as the final positive augmentations. Extensive offline and online evaluations verify the effectiveness of our proposed DivSPA on both accuracy and diversity. DivSPA is simple and effective, which could be conveniently adapted to other base models and systems. Currently, DivSPA has been deployed on multiple widely-used real-world recommender systems

Correlated alterations in prostate basal cell layer and basement membrane

Our recent studies revealed that focal basal cell layer disruption (FBCLD) induced auto-immunoreactions represented a contributing factor for human prostate tumor progression and invasion. As the basement membrane surrounds and attaches to the basal cell layer, our current study assessed whether FBCLD would impact the physical integrity of the associated basement membrane. Paraffin sections from 25-human prostate tumors were subjected to double immunohistochemistry to simultaneously elucidate the basal cell layer and the basement membrane with corresponding biomarkers. The physical integrity of the basement membrane overlying FBCLD was examined to determine the extent of correlated alterations. Of a total of 89 FBCLD encountered, 76 (85 %) showed correlated alterations in the overlying basement membrane, which included distinct focal disruptions or fragmentations. In the remaining 13 (15%) FBCLD, the overlying basement membrane showed significant attenuation or reduction of the immunostaining intensity. The basement membrane in all or nearly all ducts or acini with p63 positive basal cells was substantially thicker and more uniform than that in ducts or acini without p63 positive basal cells, and also, a vast majority of the focal disruptions occurred near basal cells that lack p63 expression. These findings suggest that focal disruptions in the basal cell layer and alterations in the basement membrane are correlated events and that the physical and functional status of the basal cells could significantly impact the physical integrity of the overlying basement membrane. As the degradation of both the basal cell layer and the basement membrane is a pre-requisite for prostate tumor invasion or progression, ducts or acini with focally disrupted basal cell layer and basement membrane are likely at greater risk to develop invasive lesions. Thus, further elucidation of the specific molecules and mechanism associated with these events may lead to the development of a more effective alternative for repeat biopsy to monitor tumor progression and invasion