Weakly-supervised Caricature Face Parsing through Domain Adaptation

A caricature is an artistic form of a person's picture in which certain striking characteristics are abstracted or exaggerated in order to create a humor or sarcasm effect. For numerous caricature related applications such as attribute recognition and caricature editing, face parsing is an essential pre-processing step that provides a complete facial structure understanding. However, current state-of-the-art face parsing methods require large amounts of labeled data on the pixel-level and such process for caricature is tedious and labor-intensive. For real photos, there are numerous labeled datasets for face parsing. Thus, we formulate caricature face parsing as a domain adaptation problem, where real photos play the role of the source domain, adapting to the target caricatures. Specifically, we first leverage a spatial transformer based network to enable shape domain shifts. A feed-forward style transfer network is then utilized to capture texture-level domain gaps. With these two steps, we synthesize face caricatures from real photos, and thus we can use parsing ground truths of the original photos to learn the parsing model. Experimental results on the synthetic and real caricatures demonstrate the effectiveness of the proposed domain adaptation algorithm. Code is available at: https://github.com/ZJULearning/CariFaceParsing .Comment: Accepted in ICIP 2019, code and model are available at https://github.com/ZJULearning/CariFaceParsin

FedCPC: An Effective Federated Contrastive Learning Method for Privacy Preserving Early-Stage Alzheimer's Speech Detection

The early-stage Alzheimer's disease (AD) detection has been considered an important field of medical studies. Like traditional machine learning methods, speech-based automatic detection also suffers from data privacy risks because the data of specific patients are exclusive to each medical institution. A common practice is to use federated learning to protect the patients' data privacy. However, its distributed learning process also causes performance reduction. To alleviate this problem while protecting user privacy, we propose a federated contrastive pre-training (FedCPC) performed before federated training for AD speech detection, which can learn a better representation from raw data and enables different clients to share data in the pre-training and training stages. Experimental results demonstrate that the proposed methods can achieve satisfactory performance while preserving data privacy.Comment: accepted in IEEE-ASRU202

LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation

As personalized recommendation systems become vital in the age of information overload, traditional methods relying solely on historical user interactions often fail to fully capture the multifaceted nature of human interests. To enable more human-centric modeling of user preferences, this work proposes a novel explainable recommendation framework, i.e., LLMHG, synergizing the reasoning capabilities of large language models (LLMs) and the structural advantages of hypergraph neural networks. By effectively profiling and interpreting the nuances of individual user interests, our framework pioneers enhancements to recommendation systems with increased explainability. We validate that explicitly accounting for the intricacies of human preferences allows our human-centric and explainable LLMHG approach to consistently outperform conventional models across diverse real-world datasets. The proposed plug-and-play enhancement framework delivers immediate gains in recommendation performance while offering a pathway to apply advanced LLMs for better capturing the complexity of human interests across machine learning applications.Comment: 14 pages, 5 figure

Internet-Based HIV Self-Testing Among Men Who Have Sex With Men Through Pre-exposure Prophylaxis: 3-Month Prospective Cohort Analysis From China.

BACKGROUND: Routine HIV testing accompanied with pre-exposure prophylaxis (PrEP) requires innovative support in a real-world setting. OBJECTIVE: This study aimed to determine the usage of HIV self-testing (HIVST) kits and their secondary distribution to partners among men who have sex with men (MSM) in China, who use PrEP, in an observational study between 2018 and 2019. METHODS: In 4 major cities in China, we prospectively followed-up MSM from the China Real-world oral PrEP demonstration study, which provides daily or on-demand PrEP for 12 months, to assess the usage and secondary distribution of HIVST on quarterly follow-ups. Half of the PrEP users were randomized to receive 2 HIVSTs per month in addition to quarterly facility-based HIV testing. We evaluated the feasibility of providing HIVST to PrEP users. RESULTS: We recruited 939 MSM and randomized 471 to receive HIVST, among whom 235 (49.9%) were daily and 236 (50.1%) were on-demand PrEP users. At baseline, the median age was 29 years, 390 (82.0%) men had at least college-level education, and 119 (25.3%) had never undergone facility-based HIV testing before. Three months after PrEP initiation, 341 (74.5%) men had used the HIVST provided to them and found it very easy to use. Among them, 180 of 341 (52.8%) men had distributed the HIVST kits it to other MSM, and 132 (51.6%) among the 256 men who returned HIVST results reported that used it with their sexual partners at the onset of intercourse. Participants on daily PrEP were more likely to use HIVST (adjusted hazard ratio=1.3, 95% CI 1.0-1.6) and distribute HIVST kits (adjusted hazard ratio=1.3, 95% CI 1.1-1.7) than those using on-demand PrEP. CONCLUSIONS: MSM who used PrEP had a high rate of usage and secondary distribution of HIVST kits, especially among those on daily PrEP, which suggested high feasibility and necessity for HIVST after PrEP initiation. Assuming that fourth-generation HIVST kits are available, HIVST may be able to replace facility-based HIV testing to a certain extent. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR1800020374; https://www.chictr.org.cn/showprojen.aspx?proj=32481. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2019-036231