EALink: An Efficient and Accurate Pre-trained Framework for Issue-Commit Link Recovery

Issue-commit links, as a type of software traceability links, play a vital role in various software development and maintenance tasks. However, they are typically deficient, as developers often forget or fail to create tags when making commits. Existing studies have deployed deep learning techniques, including pretrained models, to improve automatic issue-commit link recovery.Despite their promising performance, we argue that previous approaches have four main problems, hindering them from recovering links in large software projects. To overcome these problems, we propose an efficient and accurate pre-trained framework called EALink for issue-commit link recovery. EALink requires much fewer model parameters than existing pre-trained methods, bringing efficient training and recovery. Moreover, we design various techniques to improve the recovery accuracy of EALink. We construct a large-scale dataset and conduct extensive experiments to demonstrate the power of EALink. Results show that EALink outperforms the state-of-the-art methods by a large margin (15.23%-408.65%) on various evaluation metrics. Meanwhile, its training and inference overhead is orders of magnitude lower than existing methods.Comment: 13 pages, 6 figures, published to AS

Foundation Model Based Native AI Framework in 6G with Cloud-Edge-End Collaboration

Future wireless communication networks are in a position to move beyond data-centric, device-oriented connectivity and offer intelligent, immersive experiences based on task-oriented connections, especially in the context of the thriving development of pre-trained foundation models (PFM) and the evolving vision of 6G native artificial intelligence (AI). Therefore, redefining modes of collaboration between devices and servers and constructing native intelligence libraries become critically important in 6G. In this paper, we analyze the challenges of achieving 6G native AI from the perspectives of data, intelligence, and networks. Then, we propose a 6G native AI framework based on foundation models, provide a customization approach for intent-aware PFM, present a construction of a task-oriented AI toolkit, and outline a novel cloud-edge-end collaboration paradigm. As a practical use case, we apply this framework for orchestration, achieving the maximum sum rate within a wireless communication system, and presenting preliminary evaluation results. Finally, we outline research directions for achieving native AI in 6G.Comment: 8 pages, 4 figures, 1 tabl

UrbanGenoGAN: pioneering urban spatial planning using the synergistic integration of GAN, GA, and GIS

Introduction: Urban spatial planning is critical for the development of sustainable and livable cities. However, traditional planning methods often face challenges in handling complex planning scenarios and large-scale data.Methods: This paper introduces UrbanGenoGAN, a novel algorithm that integrates generative adversarial networks (GANs), genetic optimization algorithms (GOAs), and geographic information system (GIS) to address these challenges. Leveraging the generative power of GANs, the optimization capabilities of genetic algorithms, and the spatial analysis capabilities of GIS, UrbanGenoGAN is designed to generate optimized urban plans that cater to various urban planning challenges. Our methodology details the algorithm’s design and integration of its components, data collection and preprocessing, and the training and implementation processes.Results: Through rigorous evaluation metrics, comparative analysis with existing methodologies, and case studies, the proposed algorithm demonstrates significant improvement in urban planning outcomes. The research also explores the technical and practical considerations for implementing UrbanGenoGAN, including scalability, computational efficiency, data privacy, and ethical considerations.Discussion: The findings suggest that the integration of advanced machine learning and optimization techniques with spatial analysis offers a promising approach to enhancing decision-making in urban spatial planning. This work contributes to the growing field of AI applications in urban planning and paves the way for more efficient and sustainable urban development

Hybrid weakness and continuous flowering caused by compound expression of FTLs in Chrysanthemum morifolium × Leucanthemum paludosum intergeneric hybridization

Hybridization is an important evolutionary mechanism ubiquitous to plants. Previous studies have shown that hybrid polyploidization of cultivated chrysanthemum, ‘Zhongshanzigui’, and Leucanthemum paludosum exhibit spring-flowering traits. This study explores the function of the LpFTLs gene via the phenotype of A. thaliana after heterologous transformation of the LpFTLs gene, and analyzes the mechanism ofthe continuous flowering phenotype and heterosis of hybrid offspring. The results suggest that the flowering phenotype of hybrid offspring in spring may be related to the expression of the LpFTLs gene. Ectopic expression of Leucanthemum paludosumLpFTLs in Arabidopsis thaliana resulted in earlier flowering, indicating that the LpFTLs gene also affects the flowering time in L. paludosum. Compound expression of FTLs in C. morifolium × L. paludosum intergeneric hybridization directly leads to serious heterosis in the hybrid offspring. Moreover, continuous flowering appears to be accompanied by hybrid weakness under the balance of vegetative and reproductive growth. Therefore, in future studies on chrysanthemum breeding, a suitable balance point must be established to ensure the target flowering time under normal growth

Temporal Changes in Extracellular Vesicle Hemostatic Protein Composition Predict Favourable Left Ventricular Remodeling after Acute Myocardial Infarction

The subset of plasma extracellular vesicles (EVs) that coprecipitate with low-density lipoprotein (LDL-EVs) carry coagulation and fibrinolysis pathway proteins as cargo. We investigated the association between LDL-EV hemostatic/fibrinolysis protein ratios and post-acute myocardial infarction (post-AMI) left ventricular (LV) remodeling which precedes heart failure. Protein concentrations of von Willebrand factor (VWF), SerpinC1 and plasminogen were determined in LDL-EVs extracted from plasma samples obtained at baseline (within 72 h post-AMI), 1 month and 6 months post-AMI from 198 patients. Patients were categorized as exhibiting adverse (n = 98) or reverse (n = 100) LV remodeling based on changes in LV end-systolic volume (increased or decreased ≥15) over a 6-month period. Multiple level longitudinal data analysis with structural equation (ML-SEM) model was used to assess predictive value for LV remodeling independent of baseline differences. At baseline, protein levels of VWF, SerpinC1 and plasminogen in LDL-EVs did not differ between patients with adverse versus reverse LV remodeling. At 1 month post-AMI, protein levels of VWF and SerpinC1 decreased whilst plasminogen increased in patients with adverse LV remodeling. In contrast, VWF and plasminogen decreased whilst SerpinC1 remained unchanged in patients with reverse LV remodeling. Overall, compared with patients with adverse LV remodeling, higher levels of SerpinC1 and VWF but lower levels of plasminogen resulted in higher ratios of VWF:Plasminogen and SerpinC1:Plasminogen at both 1 month and 6 months post-AMI in patients with reverse LV remodeling. More importantly, ratios VWF:Plasminogen (AUC = 0.674) and SerpinC1:Plasminogen (AUC = 0.712) displayed markedly better prognostic power than NT-proBNP (AUC = 0.384), troponin-I (AUC = 0.467) or troponin-T (AUC = 0.389) (p \u3c 0.001) to predict reverse LV remodeling post-AMI. Temporal changes in the ratios of coagulation to fibrinolysis pathway proteins in LDL-EVs outperform current standard plasma biomarkers in predicting post-AMI reverse LV remodeling. Our findings may provide clinical cues to uncover the cellular mechanisms underpinning post-AMI reverse LV remodeling

The late Eocene rise of SE Tibet formed an Asian ‘Mediterranean’ climate

Southeastern (SE) Tibet forms the transition zone between the high interior Tibetan Plateau and the lowlands of southwest China. So understanding the elevation history of SE Tibet, a biodiversity hotspot, enlightens our understanding of the interactions between tectonics, monsoon dynamics and biodiversity. Here we reconstruct the uplift history of the Markam Basin, SE Tibet, during the middle−late Eocene based on U − Pb dating, plant fossil assemblages, and stable and clumped isotope analyses. Our results suggest that the floor of the Markam Basin was at an elevation of 2.6 ± 0.9 km between 42 Ma and 39 Ma, where the mean annual air temperature (MAAT) was 13.2 ± 2.4 °C. The basin then rose rapidly to 3.8 (+0.6/−0.8) km before 36 Ma. Integrated with existing paleoelevation data, we propose that the high plateau boundary (∼3.0 km) of SE Tibet formed during the late Eocene. Numerical climate modeling with realistic paleo-landscapes shows that with the rise of SE Tibet, a Mediterranean-like climate developed in the region characterized by bi-modal precipitation with two wet seasons in boreal spring and autumn. The high topographic relief of SE Tibet, coupled with this distinctive Mediterranean-like climate system, helped develop the high biodiversity of the Hengduan Mountains

A distinctive Eocene Asian monsoon and modern biodiversity resulted from the rise of eastern Tibet

The uplift of eastern Tibet, Asian monsoon development and the evolution of globally significant Asian biodiversity are all linked, but in obscure ways. Sedimentology, geochronology, clumped isotope thermometry, and fossil leaf-derived numerical climate data from the Relu Basin, eastern Tibet, show at ∼50–45 Ma the basin was a hot (mean annual air temperature, MAAT, ∼27 °C) dry desert at low-elevation of 0.6 ± 0.6 km. Rapid basin rise to 2.0 ± 0.9 km at 45–42 Ma and to 2.9 ± 0.9 km at 42–40 Ma, with MAATs of ∼20 and ∼16 °C, respectively, accompanied seasonally varying increased annual precipitation to >1500 mm. From ∼39 to 34 Ma, the basin attained 3.5 ± 1.0 km, near its present-day elevation (∼3.7 km), and MAAT cooled to ∼6 °C. Numerically-modelled Asian monsoon strength increased significantly when this Eocene uplift of eastern Tibet was incorporated. The simulation/proxy congruence points to a distinctive Eocene Asian monsoon, quite unlike that seen today, in that it featured bimodal precipitation and a winter-wet regime, and this enhanced biodiversity modernisation across eastern Asia. The Paleogene biodiversity of Asia evolved under a continually modifying monsoon influence, with the modern Asian monsoon system being unique to the present and a product of a long gradual development in the context of an ever-changing Earth system