Quantitative analysis of Matthew effect and sparsity problem of recommender systems

Recommender systems have received great commercial success. Recommendation has been used widely in areas such as e-commerce, online music FM, online news portal, etc. However, several problems related to input data structure pose serious challenge to recommender system performance. Two of these problems are Matthew effect and sparsity problem. Matthew effect heavily skews recommender system output towards popular items. Data sparsity problem directly affects the coverage of recommendation result. Collaborative filtering is a simple benchmark ubiquitously adopted in the industry as the baseline for recommender system design. Understanding the underlying mechanism of collaborative filtering is crucial for further optimization. In this paper, we do a thorough quantitative analysis on Matthew effect and sparsity problem in the particular context setting of collaborative filtering. We compare the underlying mechanism of user-based and item-based collaborative filtering and give insight to industrial recommender system builders

Effects of (Small) Permanent Charge and Channel Geometry on Ionic Flows via Classical Poisson--Nernst--Planck Models

In this work, we examine effects of permanent charges on ionic flows through ion channels via a quasi-one-dimensional classical Poisson--Nernst--Planck (PNP) model. The geometry of the three-dimensional channel is presented in this model to a certain extent, which is crucial for the study in this paper. Two ion species, one positively charged and one negatively charged, are considered with a simple profile of permanent charges: zeros at the two end regions and a constant $Q_0$ over the middle region. The classical PNP model can be viewed as a boundary value problem (BVP) of a singularly perturbed system. The singular orbit of the BVP depends on $Q_0$ in a regular way. Assuming $|Q_0|$ is small, a regular perturbation analysis is carried out for the singular orbit. Our analysis indicates that effects of permanent charges depend on a rich interplay between boundary conditions and the channel geometry. Furthermore, interesting common features are revealed: for $Q_0=0$, only an average quantity of the channel geometry plays a role; however, for $Q_0\neq 0$, details of the channel geometry matter; in particular, to optimize effects of a permanent charge, the channel should have a short and narrow neck within which the permanent charge is confined. The latter is consistent with structures of typical ion channels

The plasma membrane lipid rafts/caveolae-mediated PACAP signalling in PC12 cells

Ph.DDOCTOR OF PHILOSOPH

SESS: A Self-Supervised and Syntax-Based Method for Sentiment Classification

PACLIC 23 / City University of Hong Kong / 3-5 December 200

Synergistic Multiscale Detail Refinement via Intrinsic Supervision for Underwater Image Enhancement

Visually restoring underwater scenes primarily involves mitigating interference from underwater media. Existing methods ignore the inherent scale-related characteristics in underwater scenes. Therefore, we present the synergistic multi-scale detail refinement via intrinsic supervision (SMDR-IS) for enhancing underwater scene details, which contain multi-stages. The low-degradation stage from the original images furnishes the original stage with multi-scale details, achieved through feature propagation using the Adaptive Selective Intrinsic Supervised Feature (ASISF) module. By using intrinsic supervision, the ASISF module can precisely control and guide feature transmission across multi-degradation stages, enhancing multi-scale detail refinement and minimizing the interference from irrelevant information in the low-degradation stage. In multi-degradation encoder-decoder framework of SMDR-IS, we introduce the Bifocal Intrinsic-Context Attention Module (BICA). Based on the intrinsic supervision principles, BICA efficiently exploits multi-scale scene information in images. BICA directs higher-resolution spaces by tapping into the insights of lower-resolution ones, underscoring the pivotal role of spatial contextual relationships in underwater image restoration. Throughout training, the inclusion of a multi-degradation loss function can enhance the network, allowing it to adeptly extract information across diverse scales. When benchmarked against state-of-the-art methods, SMDR-IS consistently showcases superior performance. The code is publicly available at: https://github.com/zhoujingchun03/SMDR-IS

An analysis of the costs of energy saving and CO 2 mitigation in rural households in China

Households may imperfectly implement energy saving measures. This study identifies two factors resulting in imperfect use of energy-saving technology by households. Households often continue to use old technologies alongside new ones, and the energy-saving technologies have shorter actual lifetimes than their designed lifetimes. These two factors are considered when computing marginal energy conservation cost and marginal CO₂ abatement cost using data collected from a survey of rural households in three provinces in China. The results show that there are cost reduction for most space heating technologies, and their marginal abatement cost under full implementation ranges from −60 to 15 USD/t-CO₂, while the marginal abatement cost of cooking technologies ranges from 12 to 85 USD/t-CO₂. The marginal abatement costs of the majority of technologies increased after accounting for the two implementation factors. The marginal abatement cost in the imperfect implementation scenario is higher, with a range of −1 to 15 USD/t-CO₂ for space heating, and 18 to 165 USD/t-CO₂ for cooking. Assuming implementation factors are constant until 2035, annually achievable CO₂ abatement by 2035 is estimated to be 57, 11, and 10 Mt-CO₂/y in Hebei, Guizhou, and Guangxi Provinces.The authors gratefully acknowledge the financial support of the China Ministry of Science and Technology in the national 973 program: Equity and justice in climate change and regional development (funding code: A.02.12.00301). This research was partly funded by the General Research Fund of the Hong Kong Research Grants Council (14619315