A concave pairwise fusion approach to subgroup analysis

An important step in developing individualized treatment strategies is to correctly identify subgroups of a heterogeneous population, so that specific treatment can be given to each subgroup. In this paper, we consider the situation with samples drawn from a population consisting of subgroups with different means, along with certain covariates. We propose a penalized approach for subgroup analysis based on a regression model, in which heterogeneity is driven by unobserved latent factors and thus can be represented by using subject-specific intercepts. We apply concave penalty functions to pairwise differences of the intercepts. This procedure automatically divides the observations into subgroups. We develop an alternating direction method of multipliers algorithm with concave penalties to implement the proposed approach and demonstrate its convergence. We also establish the theoretical properties of our proposed estimator and determine the order requirement of the minimal difference of signals between groups in order to recover them. These results provide a sound basis for making statistical inference in subgroup analysis. Our proposed method is further illustrated by simulation studies and analysis of the Cleveland heart disease dataset

Multilink and AUV-Assisted Energy-Efficient Underwater Emergency Communications

Recent development in wireless communications has provided many reliable solutions to emergency response issues, especially in scenarios with dysfunctional or congested base stations. Prior studies on underwater emergency communications, however, remain under-studied, which poses a need for combining the merits of different underwater communication links (UCLs) and the manipulability of unmanned vehicles. To realize energy-efficient underwater emergency communications, we develop a novel underwater emergency communication network (UECN) assisted by multiple links, including underwater light, acoustic, and radio frequency links, and autonomous underwater vehicles (AUVs) for collecting and transmitting underwater emergency data. First, we determine the optimal emergency response mode for an underwater sensor node (USN) using greedy search and reinforcement learning (RL), so that isolated USNs (I-USNs) can be identified. Second, according to the distribution of I-USNs, we dispatch AUVs to assist I-USNs in data transmission, i.e., jointly optimizing the locations and controls of AUVs to minimize the time for data collection and underwater movement. Finally, an adaptive clustering-based multi-objective evolutionary algorithm is proposed to jointly optimize the number of AUVs and the transmit power of I-USNs, subject to a given set of constraints on transmit power, signal-to-interference-plus-noise ratios (SINRs), outage probabilities, and energy, which achieves the best tradeoff between the maximum emergency response time (ERT) and the total energy consumption (EC). Simulation results indicate that our proposed approach outperforms benchmark schemes in terms of energy efficiency (EE), contributing to underwater emergency communications.Comment: 15 page

SleepEGAN: A GAN-enhanced Ensemble Deep Learning Model for Imbalanced Classification of Sleep Stages

Deep neural networks have played an important role in automatic sleep stage classification because of their strong representation and in-model feature transformation abilities. However, class imbalance and individual heterogeneity which typically exist in raw EEG signals of sleep data can significantly affect the classification performance of any machine learning algorithms. To solve these two problems, this paper develops a generative adversarial network (GAN)-powered ensemble deep learning model, named SleepEGAN, for the imbalanced classification of sleep stages. To alleviate class imbalance, we propose a new GAN (called EGAN) architecture adapted to the features of EEG signals for data augmentation. The generated samples for the minority classes are used in the training process. In addition, we design a cost-free ensemble learning strategy to reduce the model estimation variance caused by the heterogeneity between the validation and test sets, so as to enhance the accuracy and robustness of prediction performance. We show that the proposed method can improve classification accuracy compared to several existing state-of-the-art methods using three public sleep datasets.Comment: 20 pages, 6 figure

Male Clients of Male Sex Workers in China: An Ignored High-Risk Population.

BackgroundThere is a high prevalence of HIV/syphilis among male sex workers, but no formal study has ever been conducted focusing on male clients of male sex workers (MCM). A detailed investigation was thus called for, to determine the burden and sociobehavioral determinants of HIV and syphilis among these MCM in China.MethodsAs part of a multicenter cross-sectional study, using respondent-driven and snowball sampling, 2958 consenting adult men who have sex with men (MSM) were recruited, interviewed, and tested for HIV and syphilis between 2008 and 2009. The distributions of sociodemographic characteristics, risk behaviors, and HIV/syphilis prevalence were determined and compared between MCM and other MSM.ResultsAmong recruited MSM, 5.0% (n = 148) were MCM. HIV prevalences for MCM and other MSM were 7.4% and 7.7%, whereas 18.9% and 14.0% were positive for syphilis, respectively. Condomless anal intercourse (CAI) was reported by 59.5% of MCM and 48.2% of MSM. Multiple logistic regression revealed that compared with other MSM, MCM were more likely to have less education [for ≤ elementary level, adjusted odds ratio (aOR) = 3.13, 95% confidence interval (95% CI): 1.42 to 6.90], higher income (for >500 US Dollars per month, aOR = 2.97, 95% CI: 1.53 to 5.77), more often found partners at parks/restrooms (aOR = 4.01, 95% CI: 2.34 to 6.85), reported CAI (aOR = 1.49, 95% CI: 1.05 to 2.10), reported a larger sexual network (for ≥ 10, aOR = 2.70, 95% CI: 1.44 to 5.07), and higher odds of syphilis (aOR = 1.54, 95% CI: 1.00 to 2.38).ConclusionsThe greater frequency of risk behaviors and high prevalence of HIV and syphilis indicated that HIV/syphilis prevention programs in China need to pay special attention to MCM as a distinct subgroup, which was completely ignored until date

ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text Translation

Joint speech-language training is challenging due to the large demand for training data and GPU consumption, as well as the modality gap between speech and language. We present ComSL, a speech-language model built atop a composite architecture of public pretrained speech-only and language-only models and optimized data-efficiently for spoken language tasks. Particularly, we propose to incorporate cross-modality learning into transfer learning and conduct them simultaneously for downstream tasks in a multi-task learning manner. Our approach has demonstrated effectiveness in end-to-end speech-to-text translation tasks, achieving a new state-of-the-art average BLEU score of 31.5 on the multilingual speech to English text translation task for 21 languages, as measured on the public CoVoST2 evaluation set

Vitamin D Receptor Gene, Matrix Metalloproteinase 3 Polymorphisms and the Risk of Intervertebral Disc Degeneration Susceptibility: Meta-Analysis

Several studies have evaluated the association between vitamin D receptor, matrix metalloproteinase 3 (MMP-3) polymorphisms and the risk of intervertebral disc degeneration susceptibility. The findings were inconsistent. This meta-analysis aimed to systematically assess the association between vitamin D receptor, MMP-3 polymorphisms and the risk of intervertebral disc degeneration susceptibility. A search of various databases was done covering all papers published until December 31th, 2014. Eight, 4, 3 studies were finally included that addressed the risk of intervertebral disc degeneration susceptibility and vitamin D receptor FokI (rs2228570), ApaI (rs7975232), and MMP-3 (rs731236) polymorphisms, respectively. FokI (f vs. F: summary odds ratio [OR], 1.13; 95% confidence interval [CI], 0.76–1.69; ff vs. FF: OR, 1.02; 95% CI, 0.59–1.77; ff vs. Ff/FF: OR, 1.05; 95% CI, 0.70–1.58), ApaI (a vs. A: OR, 0.73; 95% CI, 0.45–1.19; aa vs. AA: OR, 0.53; 95% CI, 0.22–1.25 p=0.14; aa vs. AA/Aa: OR, 0.69; 95% CI, 0.53–0.89) in the vitamin D receptor gene and MMP3 polymorphisms (5A vs. 6A: OR, 1.92; 95% CI, 0.77–4.80; 5A5A vs. 6A6A: OR, 2.17; 95% CI, 0.75–6.24; 5A5A vs. 5A6A/6A6A: OR, 1.58; 95% CI, 0.72–3.44) were not obviously associated with risk of intervertebral disc degeneration susceptibility. FokI, ApaI polymorphisms in the vitamin D receptor gene and MMP-3 polymorphism are not obvious risk factors for intervertebral disc degeneration susceptibility

Gay mobile apps and the evolving virtual risk environment: a cross-sectional online survey among men who have sex with men in China

The expansion of gay sex-seeking application (gay app) use among men who have sex with men (MSM) may create new virtual risk environments that are associated with STI transmission. The goals of this study were to compare sexual behaviors between gay app users and non-users, and to describe sexual behaviors among gay app users in China

Who is ChatGPT? Benchmarking LLMs' Psychological Portrayal Using PsychoBench

Large Language Models (LLMs) have recently showcased their remarkable capacities, not only in natural language processing tasks but also across diverse domains such as clinical medicine, legal consultation, and education. LLMs become more than mere applications, evolving into assistants capable of addressing diverse user requests. This narrows the distinction between human beings and artificial intelligence agents, raising intriguing questions regarding the potential manifestation of personalities, temperaments, and emotions within LLMs. In this paper, we propose a framework, PsychoBench, for evaluating diverse psychological aspects of LLMs. Comprising thirteen scales commonly used in clinical psychology, PsychoBench further classifies these scales into four distinct categories: personality traits, interpersonal relationships, motivational tests, and emotional abilities. Our study examines five popular models, namely text-davinci-003, gpt-3.5-turbo, gpt-4, LLaMA-2-7b, and LLaMA-2-13b. Additionally, we employ a jailbreak approach to bypass the safety alignment protocols and test the intrinsic natures of LLMs. We have made PsychoBench openly accessible via https://github.com/CUHK-ARISE/PsychoBench.Comment: Accepted for ICLR 2024 Oral Presentation. 15 pages (main text) and 5 pages (appendix