Sun Yat-sen University

About: Sun Yat-sen University is a education organization based out in Guangzhou, Guangdong, China. It is known for research contribution in the topics: Population & Cancer. The organization has 115149 authors who have published 113763 publications receiving 2286465 citations. The organization is also known as: Zhongshan University & SYSU.

CrowdBC: A Blockchain-Based Decentralized Framework for Crowdsourcing

Abstract: Crowdsourcing systems which utilize the human intelligence to solve complex tasks have gained considerable interest and adoption in recent years. However, the majority of existing crowdsourcing systems rely on central servers, which are subject to the weaknesses of traditional trust-based model, such as single point of failure. They are also vulnerable to distributed denial of service (DDoS) and Sybil attacks due to malicious users involvement. In addition, high service fees from the crowdsourcing platform may hinder the development of crowdsourcing. How to address these potential issues has both research and substantial value. In this paper, we conceptualize a blockchain-based decentralized framework for crowdsourcing named CrowdBC, in which a requester's task can be solved by a crowd of workers without relying on any third trusted institution, users’ privacy can be guaranteed and only low transaction fees are required. In particular, we introduce the architecture of our proposed framework, based on which we give a concrete scheme. We further implement a software prototype on Ethereum public test network with real-world dataset. Experiment results show the feasibility, usability, and scalability of our proposed crowdsourcing system.

Jointly learning heterogeneous features for RGB-D activity recognition

Abstract: In this paper, we focus on heterogeneous features learning for RGB-D activity recognition. We find that features from different channels (RGB, depth) could share some similar hidden structures, and then propose a joint learning model to simultaneously explore the shared and feature-specific components as an instance of heterogeneous multi-task learning. The proposed model formed in a unified framework is capable of: 1) jointly mining a set of subspaces with the same dimensionality to exploit latent shared features across different feature channels, 2) meanwhile, quantifying the shared and feature-specific components of features in the subspaces, and 3) transferring feature-specific intermediate transforms (i-transforms) for learning fusion of heterogeneous features across datasets. To efficiently train the joint model, a three-step iterative optimization algorithm is proposed, followed by a simple inference model. Extensive experimental results on four activity datasets have demonstrated the efficacy of the proposed method. A new RGB-D activity dataset focusing on human-object interaction is further contributed, which presents more challenges for RGB-D activity benchmarking.

Recent progress in photosensitizers for overcoming the challenges of photodynamic therapy: from molecular design to application.

Abstract: Photodynamic therapy (PDT), a therapeutic mode involving light triggering, has been recognized as an attractive oncotherapy treatment. However, nonnegligible challenges remain for its further clinical use, including finite tumor suppression, poor tumor targeting, and limited therapeutic depth. The photosensitizer (PS), being the most important element of PDT, plays a decisive role in PDT treatment. This review summarizes recent progress made in the development of PSs for overcoming the above challenges. This progress has included PSs developed to display enhanced tolerance of the tumor microenvironment, improved tumor-specific selectivity, and feasibility of use in deep tissue. Based on their molecular photophysical properties and design directions, the PSs are classified by parent structures, which are discussed in detail from the molecular design to application. Finally, a brief summary of current strategies for designing PSs and future perspectives are also presented. We expect the information provided in this review to spur the further design of PSs and the clinical development of PDT-mediated cancer treatments.