A novel 1.5 '' quadruple antenna for tri-band GPS applications

A new global positioning system (GPS) antenna is proposed to cover the three GPS bands (L1, L2, and L5, namely 1575, 1227, and 1176 MHz) with the L5 band to be added after 2006. The developed antenna size is only 1.5" x 1.5" in aperture corresponding to lambda/7 x lambda/7 (lambda = free space wavelength) and lambda/13 thick. Quadrature feeding is employed to ensure right-hand circular polarized (RHCP) radiation. The final miniature antenna exhibits a gain greater than 2 dBi, and to our knowledge this is the smallest such size for circular polarized (CP) operation covering all three bands. Detailed parametric simulations leading to the best design along with measurements for the constructed antenna are presented

Transcriptome and Comparative Gene Expression Analysis of Sogatella furcifera (Horváth) in Response to Southern Rice Black-Streaked Dwarf Virus

BACKGROUND: The white backed planthopper (WBPH), Sogatella furcifera (Horváth), causes great damage to many crops by direct feeding or transmitting plant viruses. Southern rice black-streaked dwarf virus (SRBSDV), transmitted by WBPH, has become a great threat to rice production in East Asia. METHODOLOGY/PRINCIPAL FINDINGS: By de novo transcriptome assembling and massive parallel pyrosequencing, we constructed two transcriptomes of WBPH and profiled the alternation of gene expression in response to SRBSDV infection in transcriptional level. Over 25 million reads of high-quality DNA sequences and 81388 different unigenes were generated using Illumina technology from both viruliferous and non-viruliferous WBPH. WBPH has a very similar gene ontological distribution to other two closely related rice planthoppers, Nilaparvata lugens and Laodelphax striatellus. 7291 microsatellite loci were also predicted which could be useful for further evolutionary analysis. Furthermore, comparative analysis of the two transcriptomes generated from viruliferous and non-viruliferous WBPH provided a list of candidate transcripts that potentially were elicited as a response to viral infection. Pathway analyses of a subset of these transcripts indicated that SRBSDV infection may perturb primary metabolism and the ubiquitin-proteasome pathways. In addition, 5.5% (181 out of 3315) of the genes in cell cytoskeleton organization pathway showed obvious changes. Our data also demonstrated that SRBSDV infection activated the immunity regulatory systems of WBPH, such as RNA interference, autophagy and antimicrobial peptide production. CONCLUSIONS/SIGNIFICANCE: We employed massively parallel pyrosequencing to collect ESTs from viruliferous and non-viruliferous samples of WBPH. 81388 different unigenes have been obtained. We for the first time described the direct effects of a Reoviridae family plant virus on global gene expression profiles of its insect vector using high-throughput sequencing. Our study will provide a road map for future investigations of the fascinating interactions between Reoviridae viruses and their insect vectors, and provide new strategies for crop protection

Rethink left-behind experience: new categories and its relationship with aggression

Left-behind experience refers to the experience of children staying behind in their hometown under the care of only one parent or their relatives while one or both of their parents leave to work in other places. College students with left-behind experience showed higher aggression levels. To further explore the relationship between left-behind experience and aggression, the current study categorized left-behind experience using latent class analysis and explored its relationship with aggression. One thousand twenty-eight Chinese college students with left-behind experience were recruited, and their aggression levels were assessed. The results showed that there were four categories of left-behind experience: “starting from preschool, frequent contact” (35.5%), “less than 10 years in duration, limited contact” (27.0%), “starting from preschool, over 10 years in duration, limited contact” (10.9%), and “starting from school age, frequent contact” (26.6%). Overall, college students who reported frequent contact with their parents during the left-behind period showed lower levels of aggression than others did. Females were less aggressive than males in the “starting from preschool, frequent contact” left-behind situation, while males were less aggressive than females in the “starting from school age, frequent contact” situation. These findings indicate that frequent contact with leaving parents contributes to decreasing aggression of college students with left-behind experience. Meanwhile, gender is an important factor in this relationship

Cross-subject dual-domain fusion network with task-related and task-discriminant component analysis enhancing one-shot SSVEP classification

This study addresses the significant challenge of developing efficient decoding algorithms for classifying steady-state visual evoked potentials (SSVEPs) in scenarios characterized by extreme scarcity of calibration data, where only one calibration is available for each stimulus target. To tackle this problem, we introduce a novel cross-subject dual-domain fusion network (CSDuDoFN) incorporating task-related and task-discriminant component analysis (TRCA and TDCA) for one-shot SSVEP classification. The CSDuDoFN framework is designed to comprehensively transfer information from source subjects, while TRCA and TDCA are employed to exploit the single available calibration of the target subject. Specifically, we develop multi-reference least-squares transformation (MLST) to map data from both source subjects and the target subject into the domain of sine-cosine templates, thereby mitigating inter-individual variability and benefiting transfer learning. Subsequently, the transformed data in the sine-cosine templates domain and the original domain data are separately utilized to train a convolutional neural network (CNN) model, with the adequate fusion of their feature maps occurring at distinct network layers. To further capitalize on the calibration of the target subject, source aliasing matrix estimation (SAME) data augmentation is incorporated into the training process of the ensemble TRCA (eTRCA) and TDCA models. Ultimately, the outputs of the CSDuDoFN, eTRCA, and TDCA are combined for SSVEP classification. The effectiveness of our proposed approach is comprehensively evaluated on three publicly available SSVEP datasets, achieving the best performance on two datasets and competitive performance on one. This underscores the potential for integrating brain-computer interface (BCI) into daily life.Comment: 10 pages,6 figures, and 3 table

Continual Task Allocation in Meta-Policy Network via Sparse Prompting

How to train a generalizable meta-policy by continually learning a sequence of tasks? It is a natural human skill yet challenging to achieve by current reinforcement learning: the agent is expected to quickly adapt to new tasks (plasticity) meanwhile retaining the common knowledge from previous tasks (stability). We address it by "Continual Task Allocation via Sparse Prompting (CoTASP)", which learns over-complete dictionaries to produce sparse masks as prompts extracting a sub-network for each task from a meta-policy network. By optimizing the sub-network and prompts alternatively, CoTASP updates the meta-policy via training a task-specific policy. The dictionary is then updated to align the optimized prompts with tasks' embedding, thereby capturing their semantic correlations. Hence, relevant tasks share more neurons in the meta-policy network via similar prompts while cross-task interference causing forgetting is effectively restrained. Given a trained meta-policy with updated dictionaries, new task adaptation reduces to highly efficient sparse prompting and sub-network finetuning. In experiments, CoTASP achieves a promising plasticity-stability trade-off without storing or replaying any past tasks' experiences and outperforms existing continual and multi-task RL methods on all seen tasks, forgetting reduction, and generalization to unseen tasks.Comment: Accepted by ICML 202

Deep Metric Learning Assisted by Intra-variance in A Semi-supervised View of Learning

Deep metric learning aims to construct an embedding space where samples of the same class are close to each other, while samples of different classes are far away from each other. Most existing deep metric learning methods attempt to maximize the difference of inter-class features. And semantic related information is obtained by increasing the distance between samples of different classes in the embedding space. However, compressing all positive samples together while creating large margins between different classes unconsciously destroys the local structure between similar samples. Ignoring the intra-class variance contained in the local structure between similar samples, the embedding space obtained from training receives lower generalizability over unseen classes, which would lead to the network overfitting the training set and crashing on the test set. To address these considerations, this paper designs a self-supervised generative assisted ranking framework that provides a semi-supervised view of intra-class variance learning scheme for typical supervised deep metric learning. Specifically, this paper performs sample synthesis with different intensities and diversity for samples satisfying certain conditions to simulate the complex transformation of intra-class samples. And an intra-class ranking loss function is designed using the idea of self-supervised learning to constrain the network to maintain the intra-class distribution during the training process to capture the subtle intra-class variance. With this approach, a more realistic embedding space can be obtained in which global and local structures of samples are well preserved, thus enhancing the effectiveness of downstream tasks. Extensive experiments on four benchmarks have shown that this approach surpasses state-of-the-art method