Cooperative Local Caching under Heterogeneous File Preferences

Local caching is an effective scheme for leveraging the memory of the mobile terminal (MT) and short range communications to save the bandwidth usage and reduce the download delay in the cellular communication system. Specifically, the MTs first cache in their local memories in off-peak hours and then exchange the requested files with each other in the vicinity during peak hours. However, prior works largely overlook MTs' heterogeneity in file preferences and their selfish behaviours. In this paper, we practically categorize the MTs into different interest groups according to the MTs' preferences. Each group of MTs aims to increase the probability of successful file discovery from the neighbouring MTs (from the same or different groups). Hence, we define the groups' utilities as the probability of successfully discovering the file in the neighbouring MTs, which should be maximized by deciding the caching strategies of different groups. By modelling MTs' mobilities as homogeneous Poisson point processes (HPPPs), we analytically characterize MTs' utilities in closed-form. We first consider the fully cooperative case where a centralizer helps all groups to make caching decisions. We formulate the problem as a weighted-sum utility maximization problem, through which the maximum utility trade-offs of different groups are characterized. Next, we study two benchmark cases under selfish caching, namely, partial and no cooperation, with and without inter-group file sharing, respectively. The optimal caching distributions for these two cases are derived. Finally, numerical examples are presented to compare the utilities under different cases and show the effectiveness of the fully cooperative local caching compared to the two benchmark cases

ADVANCES IN COOPERATON TECHNIQUES FOR WIRELESS COMMUNICATION NETWORKS

Ph.DDOCTOR OF PHILOSOPH

Design of FPGA-Implemented Reed-Solomon Erasure Code (RS-EC) Decoders With Fault Detection and Location on User Memory

Reed–Solomon erasure codes (RS-ECs) are widely used in packet communication and storage systems to recover erasures. When the RS-EC decoder is implemented on a field-programmable gate array (FPGA) in a space platform, it will suffer single-event upsets (SEUs) that can cause failures. In this article, the reliability of an RS-EC decoder implemented on an FPGA when there are errors in the user memory is first studied. Then, a fault detection and location scheme is proposed based on partial reencoding for the faults in the user memory of the RS-EC decoder. Furthermore, check bits are added in the generator matrix to improve the fault location performance. The theoretical analysis shows that the scheme could detect most faults with small missing and false detection probability. Experimental results on a case study show that more than 90% of the faults on user memory could be tolerated by the decoder, and all the other faults can be detected by the fault detection scheme when the number of erasures is smaller than the correction capability of the code. Although false alarms exist (with probability smaller than 4%), they can be used to avoid fault accumulation. Finally, the fault location scheme could accurately locate all the faults. The theoretical estimates are very close to the experiment results, which verifies the correctness of the analysis done.This work was supported in part by the National Natural Science Foundation of China under Grant 61501321, in part by the China Postdoctoral Science Foundation and Luoyang Newvid Technology Company, Ltd., and in part by the ACHILLES Project PID2019-104207RB-I00 funded by the Spanish Ministry of Science and Innovation

MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response

Large Language Models (LLMs) have shown immense potential in multimodal applications, yet the convergence of textual and musical domains remains relatively unexplored. To address this gap, we present MusiLingo, a novel system for music caption generation and music-related query responses. MusiLingo employs a single projection layer to align music representations from the pre-trained frozen music audio model MERT with the frozen LLaMA language model, bridging the gap between music audio and textual contexts. We train it on an extensive music caption dataset and fine-tune it with instructional data. Due to the scarcity of high-quality music Q&A datasets, we created the MusicInstruct (MI) dataset from MusicCaps, tailored for open-ended music inquiries. Empirical evaluations demonstrate its competitive performance in generating music captions and composing music-related Q&A pairs. Our introduced dataset enables notable advancements beyond previous ones

Influence of lifestyle on suboptimal health: Insights from a national cross-sectional survey in China

Background: Suboptimal health status (SHS) is a non-clinical or pre-disease state between optimal/ideal health and disease. While its etiology remains unclear, lifestyle is considered one of the most important risk factors. We aimed to examine the effects of lifestyles on SHS through a nationwide survey in China. Methods: We conducted a cross-sectional survey in 148 cities across China between 20 June and 31 August 2022, on 30 505 participants from rural and urban communities gathered through stratified quota sampling. We measured SHS with the Short-Form Suboptimal Health Status Questionnaire (SHSQ-SF). We gathered information on participants\u27 lifestyles (ie, smoking, alcohol consumption, breakfast habits, weekly food delivery frequency, intermittent fasting, sleep duration and physical activities) through face-to-face interview. We determined the relationship between lifestyle and SHS logistic regression analysis by based on odds ratios (ORs) and 95% confidence intervals (CIs). Results: We included 22 897 participants (female: 13 056, male: 9841), 12 108 (52.88%) of whom reported exposure to SHS. After adjusting for demographic characteristics, individuals who currently smoked (OR = 1.165; 95% CI = 1.058-1.283) and those who drank alcohol (OR = 1.483; 95% CI = 1.377.1.596) were at a higher risk of SHS than those who have never done either. In a dose-response way, takeaway food consumption was associated with a higher risk of SHS, while increased frequency of breakfast and mild-intensity exercise conversely reduced said risk. Individuals with shorter sleep duration had a higher risk of SHS when compared to those who slept for more than seven hours per day. Conclusions: We observed a relatively high prevalence of SHS across China, highlighting the importance of lifestyle in health promotion. Specifically, adopting healthy dietary habits, engaging in regular physical activity, and ensuring high-quality sleep are key in preventing SHS. Registration: Chinese Clinical Trial Registry (ChiCTR2200061046)

Bibliometric and visual analysis of intraoperative hypotension from 2004 to 2022

BackgroundIntraoperative hypotension (IOH) is a common complication occurring in surgical practice. This study aims to comprehensively review the collaboration and impact of countries, institutions, authors, journals, keywords, and critical papers on intraoperative hypotension from the perspective of bibliometric, and to evaluate the evolution of knowledge structure clustering and identify research hotspots and emerging topics.MethodsArticles and reviews related to IOH published from 2004 to 2022 were retrieved from the Web of Science Core Collection. Bibliometric analyses and visualization were conducted on Excel, CiteSpace, VOSviewer, and Bibliometrix (R-Tool of R-Studio).ResultsA total of 1,784 articles and reviews were included from 2004 to 2022. The number of articles on IOH gradually increased in the past few years, and peaked in 2021. These publications were chiefly from 1,938 institutions in 40 countries, led by America and China in publications. Sessler Daniel I published the most papers and enjoyed the highest number of citations. Analysis of the journals with the most outputs showed that most journals concentrated on perioperative medicine and clinical anesthesiology. Delirium, acute kidney injury and vasoconstrictor agents are the current and developing research hotspots. The keywords “Acute kidney injury”, “postoperative complication”, “machine learning”, “risk factors” and “hemodynamic instability” may also become new trends and focuses of the near future research.ConclusionThis study uses bibliometrics and visualization methods to comprehensively review the research on intraoperative hypotension, which is helpful for scholars to better understand the dynamic evolution of IOH and provide directions for future research

LyricWhiz: Robust Multilingual Zero-shot Lyrics Transcription by Whispering to ChatGPT

We introduce LyricWhiz, a robust, multilingual, and zero-shot automatic lyrics transcription method achieving state-of-the-art performance on various lyrics transcription datasets, even in challenging genres such as rock and metal. Our novel, training-free approach utilizes Whisper, a weakly supervised robust speech recognition model, and GPT-4, today's most performant chat-based large language model. In the proposed method, Whisper functions as the "ear" by transcribing the audio, while GPT-4 serves as the "brain," acting as an annotator with a strong performance for contextualized output selection and correction. Our experiments show that LyricWhiz significantly reduces Word Error Rate compared to existing methods in English and can effectively transcribe lyrics across multiple languages. Furthermore, we use LyricWhiz to create the first publicly available, large-scale, multilingual lyrics transcription dataset with a CC-BY-NC-SA copyright license, based on MTG-Jamendo, and offer a human-annotated subset for noise level estimation and evaluation. We anticipate that our proposed method and dataset will advance the development of multilingual lyrics transcription, a challenging and emerging task.Comment: 9 pages, 2 figures, 5 tables, accepted by ISMIR 202

On the Effectiveness of Speech Self-supervised Learning for Music

Self-supervised learning (SSL) has shown promising results in various speech and natural language processing applications. However, its efficacy in music information retrieval (MIR) still remains largely unexplored. While previous SSL models pre-trained on music recordings may have been mostly closed-sourced, recent speech models such as wav2vec2.0 have shown promise in music modelling. Nevertheless, research exploring the effectiveness of applying speech SSL models to music recordings has been limited. We explore the music adaption of SSL with two distinctive speech-related models, data2vec1.0 and Hubert, and refer to them as music2vec and musicHuBERT, respectively. We train $12$ SSL models with 95M parameters under various pre-training configurations and systematically evaluate the MIR task performances with 13 different MIR tasks. Our findings suggest that training with music data can generally improve performance on MIR tasks, even when models are trained using paradigms designed for speech. However, we identify the limitations of such existing speech-oriented designs, especially in modelling polyphonic information. Based on the experimental results, empirical suggestions are also given for designing future musical SSL strategies and paradigms