Multi-objective Optimization of Space-Air-Ground Integrated Network Slicing Relying on a Pair of Central and Distributed Learning Algorithms

As an attractive enabling technology for next-generation wireless communications, network slicing supports diverse customized services in the global space-air-ground integrated network (SAGIN) with diverse resource constraints. In this paper, we dynamically consider three typical classes of radio access network (RAN) slices, namely high-throughput slices, low-delay slices and wide-coverage slices, under the same underlying physical SAGIN. The throughput, the service delay and the coverage area of these three classes of RAN slices are jointly optimized in a non-scalar form by considering the distinct channel features and service advantages of the terrestrial, aerial and satellite components of SAGINs. A joint central and distributed multi-agent deep deterministic policy gradient (CDMADDPG) algorithm is proposed for solving the above problem to obtain the Pareto optimal solutions. The algorithm first determines the optimal virtual unmanned aerial vehicle (vUAV) positions and the inter-slice sub-channel and power sharing by relying on a centralized unit. Then it optimizes the intra-slice sub-channel and power allocation, and the virtual base station (vBS)/vUAV/virtual low earth orbit (vLEO) satellite deployment in support of three classes of slices by three separate distributed units. Simulation results verify that the proposed method approaches the Pareto-optimal exploitation of multiple RAN slices, and outperforms the benchmarkers.Comment: 19 pages, 14 figures, journa

Any-Size-Diffusion: Toward Efficient Text-Driven Synthesis for Any-Size HD Images

Stable diffusion, a generative model used in text-to-image synthesis, frequently encounters resolution-induced composition problems when generating images of varying sizes. This issue primarily stems from the model being trained on pairs of single-scale images and their corresponding text descriptions. Moreover, direct training on images of unlimited sizes is unfeasible, as it would require an immense number of text-image pairs and entail substantial computational expenses. To overcome these challenges, we propose a two-stage pipeline named Any-Size-Diffusion (ASD), designed to efficiently generate well-composed images of any size, while minimizing the need for high-memory GPU resources. Specifically, the initial stage, dubbed Any Ratio Adaptability Diffusion (ARAD), leverages a selected set of images with a restricted range of ratios to optimize the text-conditional diffusion model, thereby improving its ability to adjust composition to accommodate diverse image sizes. To support the creation of images at any desired size, we further introduce a technique called Fast Seamless Tiled Diffusion (FSTD) at the subsequent stage. This method allows for the rapid enlargement of the ASD output to any high-resolution size, avoiding seaming artifacts or memory overloads. Experimental results on the LAION-COCO and MM-CelebA-HQ benchmarks demonstrate that ASD can produce well-structured images of arbitrary sizes, cutting down the inference time by 2x compared to the traditional tiled algorithm

VDGCNeT: A novel network-wide Virtual Dynamic Graph Convolution Neural network and Transformer-based traffic prediction model

We address the problem of traffic prediction on large-scale road networks. We propose a novel deep learning model, Virtual Dynamic Graph Convolution Neural Network and Transformer with Gate and Attention mechanisms (VDGCNeT), to comprehensively extract complex, dynamic and hidden spatial dependencies of road networks for achieving high prediction accuracy. For this purpose, we advocate the use of a virtual dynamic road graph that captures the dynamic and hidden spatial dependencies of road segments in real road networks instead of purely relying on the physical road connectivity. We further design a novel framework based on Graph Convolution Neural Network (GCN) and Transformer to analyze dynamic and hidden spatial–temporal features. The gate mechanism is utilized for concatenating learned spatial and temporal features from Spatial and Temporal Transformers, respectively, while the Attention-based Similarity is used to update dynamic road graph. Two real-world traffic datasets from large-scale road networks with different properties are used for training and testing our model. We compare our VDGCNeT against nine other well-known models in the literature. Our results demonstrate that the proposed VDGCNeT is capable of achieving highly accurate predictions – on average 96.77% and 91.68% accuracy on PEMS-BAY and METR-LA datasets respectively. Overall, our VDGCNeT performs the best when compared against other existing models

Uplink Secure Receive Spatial Modulation Empowered by Intelligent Reflecting Surface

With the emergence of the fifth generation (5G) era, the development of the Internet of Things (IoT) network has been accelerated with a new impetus, making it imperative to strive for a more reliable and efficient network environment. To accomplish this, we introduce and investigate a novel proposal for the intelligent reflecting surface (IRS) enabled uplink secure receive spatial modulation (SM), named IRS-USRSM, to resolve the security issues arising from the open wireless transmission environment in the 5G IoT network. In the IRS-USRSM scheme, we assume that the passive eavesdropper is directly connected to the uplink user and occasionally connected to the IRS. To achieve enhanced secrecy with finite alphabet inputs, a joint transmitter perturbation and IRS reflection design for physical layer security is proposed to guarantee secure and reliable transmission of IRS-USRSM. Specifically, two categories of IRSbased random phase compensation strategies, namely, random perturbation compensation and random path synthesize, along with maximum likelihood detection and suboptimal detection are proposed to meet the variant design requirements between achieved performance and system cost. Furthermore, in order to evaluate the performance limits of the IRS-USRSM, the closedform results of average bit error probabilities and discrete-input continuous-output memoryless channel capacities are derived using the method of moment generating function. Simulation results are presented to verify the correctness of our theoretical analyses, as well as to demonstrate the efficiency and superiority of the proposed IRS-USRSM scheme

Sexual Knowledge, attitudes and behaviors among unmarried migrant female workers in China: a comparative analysis

<p>Abstract</p> <p>Background</p> <p>In recent years, many studies have focused on adolescent's sex-related issues in China. However, there have been few studies of unmarried migrant females' sexual knowledge, attitudes and behaviors, which is important for sexual health education and promotion.</p> <p>Methods</p> <p>A sample of 5156 unmarried migrant female workers was selected from three manufacturing factories, two located in Shenzhen and one in Guangzhou, China. Demographic data, sexual knowledge, attitudes and behaviors were assessed by self-administered questionnaires. Multivariate logistic regression analysis was conducted to examine the factors associated with premarital sexual intercourse.</p> <p>Results</p> <p>The average age of the unmarried female workers included in the sample was 20.2 years, and majority of them showed a low level of sex-related knowledge. Females from the west of China demonstrated a significant lower level of sex-related knowledge than those from the eastern or central provinces (<it>p </it>< 0.05). Approximately 13% of participants held a favorable attitude towards premarital sexual intercourse, and youths from the east/central were more likely to have favorable attitudes compared with those from the west (<it>p </it>< 0.05). About 17.0% of the unmarried female workers reported having engaged in premarital sexual intercourse, and females from the east/central were more likely to have experienced premarital sexual intercourse than those from the west (<it>p </it>< 0.05). Multivariate analysis revealed that age, education, current residential type, dating, sexual knowledge, attitudes, and pattern of communication were significantly associated with premarital sexual intercourse.</p> <p>Conclusion</p> <p>The unmarried migrant female workers lack sexual knowledge and a substantial proportion of them are engaged in premarital sexual behaviors. Interventions aimed at improving their sexual knowledge and related skills are needed.</p

Identification, Replication, and Fine-Mapping of Loci Associated with Adult Height in Individuals of African Ancestry

Adult height is a classic polygenic trait of high heritability (h2 ∼0.8). More than 180 single nucleotide polymorphisms (SNPs), identified mostly in populations of European descent, are associated with height. These variants convey modest effects and explain ∼10% of the variance in height. Discovery efforts in other populations, while limited, have revealed loci for height not previously implicated in individuals of European ancestry. Here, we performed a meta-analysis of genome-wide association (GWA) results for adult height in 20,427 individuals of African ancestry with replication in up to 16,436 African Americans. We found two novel height loci (Xp22-rs12393627, P = 3.4×10−12 and 2p14-rs4315565, P = 1.2×10−8). As a group, height associations discovered in European-ancestry samples replicate in individuals of African ancestry (P = 1.7×10−4 for overall replication). Fine-mapping of the European height loci in African-ancestry individuals showed an enrichment of SNPs that are associated with expression of nearby genes when compared to the index European height SNPs (P<0.01). Our results highlight the utility of genetic studies in non-European populations to understand the etiology of complex human diseases and traits

Type 2 Diabetes Variants Disrupt Function of SLC16A11 through Two Distinct Mechanisms

Type 2 diabetes (T2D) affects Latinos at twice the rate seen in populations of European descent. We recently identified a risk haplotype spanning SLC16A11 that explains ∼20% of the increased T2D prevalence in Mexico. Here, through genetic fine-mapping, we define a set of tightly linked variants likely to contain the causal allele(s). We show that variants on the T2D-associated haplotype have two distinct effects: (1) decreasing SLC16A11 expression in liver and (2) disrupting a key interaction with basigin, thereby reducing cell-surface localization. Both independent mechanisms reduce SLC16A11 function and suggest SLC16A11 is the causal gene at this locus. To gain insight into how SLC16A11 disruption impacts T2D risk, we demonstrate that SLC16A11 is a proton-coupled monocarboxylate transporter and that genetic perturbation of SLC16A11 induces changes in fatty acid and lipid metabolism that are associated with increased T2D risk. Our findings suggest that increasing SLC16A11 function could be therapeutically beneficial for T2D. Video Abstract [Figure presented] Keywords: type 2 diabetes (T2D); genetics; disease mechanism; SLC16A11; MCT11; solute carrier (SLC); monocarboxylates; fatty acid metabolism; lipid metabolism; precision medicin