Denoising enabled channel estimation for underwater acoustic communications: A sparsity-aware model-driven learning approach

It has always been difficult to achieve accurate information of the channel for underwater acoustic communications because of the severe underwater propagation conditions, including frequency-selective property, high relative mobility, long propagation latency, and intensive ambient noise, etc. To this end, a deep unfolding neural network based approach is proposed, in which multiple layers of the network mimic the iterations of the classical iterative sparse approximation algorithm to extract the inherent sparse features of the channel by exploiting deep learning, and a scheme based on the Sparsity-Aware DNN (SA-DNN) for UAC estimation is proposed to improve the estimation accuracy. Moreover, we propose a Denoising Sparsity-Aware DNN (DeSA-DNN) based enhanced method that integrates a denoising CNN module in the sparsity-aware deep network, so that the degradation brought by intensive ambient noise could be eliminated and the estimation accuracy can be further improved. Simulation results demonstrate that the performance of the proposed schemes is superior to the state-of-the-art compressed sensing based and iterative sparse recovery schems in the aspects of channel recovery precision, pilot overhead, and robustness, particularly under unideal circumstances of intensive ambient noise or inadequate measurement pilots

Optimizing and Fine-tuning Large Language Model for Urban Renewal

This study aims to innovatively explore adaptive applications of large language models (LLM) in urban renewal. It also aims to improve its performance and text generation quality for knowledge question-answering (QA) tasks. Based on the ChatGLM, we automatically generate QA datasets using urban renewal scientific literature corpora in a self-instruct manner and then conduct joint fine-tuning training on the model using the Prefix and LoRA fine-tuning methods to create an LLM for urban renewal. By guiding the LLM to automatically generate QA data based on prompt words and given text, it is possible to quickly obtain datasets in the urban renewal field and provide data support for the fine-tuning training of LLMs. The experimental results show that the joint fine-tuning training method proposed in this study can significantly improve the performance of LLM on the QA tasks. Compared with LoRA fine-tuning, the method improves the Bleu and Rouge metrics on the test by about 5%; compared with the model before fine-tuning, the method improves the Bleu and Rouge metrics by about 15%-20%. This study demonstrates the effectiveness and superiority of the joint fine-tuning method using Prefix and LoRA for ChatGLM in the urban renewal knowledge QA tasks. It provides a new approach for fine-tuning LLMs on urban renewal-related tasks.Comment: 11 pages, 2 figures, 2 tables, 41 reference

Grid-Based Essential Urban Land Use Classification: A Data and Model Driven Mapping Framework in Xiamen City

Accurate and timely mapping of essential urban land use categories (EULUC) is vital to understanding urban land use distribution, pattern, and composition. Recent advances in leveraging big open data and machine learning algorithms have demonstrated the possibility of large-scale mapping of EULUC in a new cost-effective way. However, they are still limited by the transferability of samples, models, and classification results across space, particularly across different cities. Given the heterogeneities of environmental and socioeconomic conditions among cities, in-depth studies of data and model adaptation towards city-specific EULUC mappings are highly required to support policy making, and urban renewal planning and management practices. In addition, the trending need for timely and detailed small land unit data processing with finer data granularity becomes increasingly important. We proposed a City Meta Unit (CMU) data model and classification framework driven by multisource data and artificial intelligence (AI) algorithms to address these challenges. The CMU Framework was innovatively applied to systematically set up a grid-based data model and classify urban land use with an improved AI algorithm by applying Moore neighborhood correlations. Specifically, we selected Xiamen, Fujian, in China, a coastal city, as the typical testbed to implement this proposed framework and apply an AI transfer learning technique for grid and parcel land-use study. Experimental results with our proposed CMU framework showed that the grid-based land use classification performance achieves overall accuracies of 81.17% and 76.55% for level I (major classes) and level II (minor classes), which is much higher than the parcel-based land use classification (overall accuracies of 72.37% for level I, and 68.99% for level II). We further investigated the relationship between training sample size and classification performance and quantified the contribution of different data sources to urban land use classifications. The CMU framework makes data collections and processing intelligent and efficient, with finer granularity, saving time and cost by using existing open social data. Incorporating the CMU framework with the proposed grid-based model is an effective and new approach for urban land use classification, which can be flexibly extended and applied to various cities

Integrated analysis of methylation profiles and transcriptome of Marek's disease virus-infected chicken spleens reveal hypomethylation of CD4 and HMGB1 genes might promote Marek's disease tumorigenesis

ABSTRACT: Marek's disease (MD) is a lymphoproliferative neoplastic disease caused by Marek's disease virus (MDV). Previous studies have showed that DNA methylation was involved in MD development, but systematic studies are still lacking. Herein, we performed whole genome bisulfite sequencing (WGBS) and RNA-seq in MDV-infected tumorous spleens (IN), noninfected spleens (NoIN), and survivor (SUR) spleens of chickens to identify the genes playing important roles in MD tumor transformation. We generated the first genome-wide DNA methylation profile of MDV-infected, noninfected, and survivor chickens. Combined the WGBS and RNA-Seq, we found that the expression of 25% differential expression genes (DEGs) were significantly correlated with methylation of CpG sites in their gene bodies or promoters. Further, we focused on the DEGs with differentially methylated regions (DMRs) on genes’ body and promoter, and it showed the expression of 60% DEGs were significantly correlated with methylation of CpG sites in DMRs. Finally, we identified 8 genes, including CD4, CTLA4, DTL, HMGB1, LGMN, NUP210, RAD52, and ZAP70, and their expression was negatively correlated with methylation of DMRs in their promoters in both IN vs. NoIN and IN vs. SUR. These 8 genes showed specifically high expression in IN groups and clustered in module turquoise analyzed by WGCNA. Out of 8 genes, CD4 and HMGB1 were drop in QTLs associated with MD resistance. Thus, we overexpressed the 2 genes to simulate their high expression in the IN group and found they significantly promoted MDCC-MSB-1 cell proliferation, which revealed they might play promoting roles in MD tumorigenesis in IN due to their high expression induced by hypomethylation