Tracking control for multi-agent consensus with an active leader and variable topology

In this paper, we consider the coordination control of a group of autonomous mobile agents with multiple leaders. Different interconnection topologies are investigated. At first, a necessary and sufficient condition is proved in the case of fixed interconnection topology. Then a sufficient condition is proposed when the interconnection topology is switched. With a simple first-order dynamics model by using the neighborhood rule, both results show that the group behavior of the agents will converge to the polytope formed by the leaders.Comment: 6 page

Efficient Bi-Level Optimization for Recommendation Denoising

The acquisition of explicit user feedback (e.g., ratings) in real-world recommender systems is often hindered by the need for active user involvement. To mitigate this issue, implicit feedback (e.g., clicks) generated during user browsing is exploited as a viable substitute. However, implicit feedback possesses a high degree of noise, which significantly undermines recommendation quality. While many methods have been proposed to address this issue by assigning varying weights to implicit feedback, two shortcomings persist: (1) the weight calculation in these methods is iteration-independent, without considering the influence of weights in previous iterations, and (2) the weight calculation often relies on prior knowledge, which may not always be readily available or universally applicable. To overcome these two limitations, we model recommendation denoising as a bi-level optimization problem. The inner optimization aims to derive an effective model for the recommendation, as well as guiding the weight determination, thereby eliminating the need for prior knowledge. The outer optimization leverages gradients of the inner optimization and adjusts the weights in a manner considering the impact of previous weights. To efficiently solve this bi-level optimization problem, we employ a weight generator to avoid the storage of weights and a one-step gradient-matching-based loss to significantly reduce computational time. The experimental results on three benchmark datasets demonstrate that our proposed approach outperforms both state-of-the-art general and denoising recommendation models. The code is available at https://github.com/CoderWZW/BOD.Comment: 11pages, 5 figures, 6 table

Berberine Improves Insulin Sensitivity by Inhibiting Fat Store and Adjusting Adipokines Profile in Human Preadipocytes and Metabolic Syndrome Patients

Berberine is known to inhibit the differentiation of 3T3-L1 cells in vitro, improve glycemic control, and attenuate dyslipidemia in clinical study. The aim of this study was to investigate the effects of berberine on preadipocytes isolated from human omental fat and in metabolic syndrome patients treated with berberine for 3 months. We have shown that treatment with 10 μM berberine resulted in a major inhibition of human preadipocyte differentiation and leptin and adiponectin secretion accompanied by downregulation of PPARγ2, C/EBPα, adiponectin, and leptin mRNA expression. After 3 months of treatment, metabolic syndrome patients showed decrease in their BMI (31.5 ± 3.6 versus 27.4 ± 2.4 kg/m2) and leptin levels (8.01 versus 5.12 μg/L), as well as leptin/adiponectin ratio and HOMA-IR. These results suggest that berberine improves insulin sensitivity by inhibiting fat store and adjusting adipokine profile in human preadipocytes and metabolic syndrome patients

Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations

Tripartite graph-based recommender systems markedly diverge from traditional models by recommending unique combinations such as user groups and item bundles. Despite their effectiveness, these systems exacerbate the longstanding cold-start problem in traditional recommender systems, because any number of user groups or item bundles can be formed among users or items. To address this issue, we introduce a Consistency and Discrepancy-based graph contrastive learning method for tripartite graph-based Recommendation. This approach leverages two novel meta-path-based metrics consistency and discrepancy to capture nuanced, implicit associations between the recommended objects and the recommendees. These metrics, indicative of high-order similarities, can be efficiently calculated with infinite graph convolutional networks layers under a multi-objective optimization framework, using the limit theory of GCN

What Really Matters for Graph Contrastive Learning-Based Recommendations? A Unified Learning Strategy

Graph contrastive learning (GCL) has become increasingly popular in recommendation due to its remarkable ability to reduce reliance on labels. Typically, GCL employs data augmentation methods, e.g., structure perturbation and representation perturbation, and CL loss to enhance performance. Recent studies have shown that structural perturbation plays a minor role in GCL, but there is still a lack of exploration on the representation perturbation. Therefore, we compare the two data perturbations in detail and reveal that both of them have a limited impact on performance. Simply combining recommendation loss and CL loss can produce comparable improvements. Besides, we identify a shared principle between the designs of recommendation loss and CL loss: both aim to optimize representation by increasing similarity between a target node and its positive samples while decreasing similarity with negative samples. Based on these findings, we propose Compact Graph Contrastive Learning (CGCL), a streamlined strategy that eliminates the data augmentation and deeply unifies recommendation loss and CL loss by elegantly incorporating their respective contributions. Leveraging the benefits of the unified strategy, we discover that our model can learn a concentrated mode length distribution of representation, which can enhance the ability to debias and thus improve the performance of the recommendation. This is a novel perspective on representation learning, and we also validate its rationality through rigorous experiments. Our comprehensive study on multiple benchmark datasets demonstrates that CGCL outperforms existing GCL methods. © 2023 IEEE

Alpha EEG Spectral Characteristics in the Parieto-Occipital lobe of Elderly Patients with Chronic Insomnia and Mild Cognitive Impairment

ObjectiveAnalysis of Alpha Spectral Characteristics in the Parieto-Occipital lobe of Elderly Individuals with Chronic Insomnia and Mild Cognitive Impairment （MCI）.MethodsEighty elderly individuals with chronic insomnia and mild cognitive impairment （MCI） were selected from the community in Fuzhou, Fujian Province, from June 2019 to December 2020. This included a healthy control group （HC） of30 individuals, a chronic insomnia group （CI） of 20 individuals, and a chronic insomnia with MCI group （CI-MCI） of 30 individuals. The Pittsburgh Sleep Quality Index （PSQI） was used to assess sleep quality, while the Fuzhou version of the Montreal Cognitive Assessment （MoCA） was employed to evaluate cognitive functions, including visuospatial execution, naming, attention, verbal fluency, abstraction, delayed recall, and orientation. Daytime resting-state electroencephalography （EEG） was collected using the Neuroscan synchronous EEG recording system, comparing the power values and trends of alpha waves （8–13 Hz） across channels in the occipital lobe. Additionally, we analyzed the changes in alpha spectral power in relation to cognitive functions.Results（1）PSQI scores：Compared to the HC group, the daytime functional impairment scores in the CI-MCI group were significantly higher, with a statistically significant difference （P <0. 05）. The total PSQI scores, sleep quality, sleep onset latency, total sleep time, sleep efficiency, and sleep disturbance scores were significantly higher in the CI group and the CI-MCI group, while the use of hypnotics score was significantly reduced, with statistically significant differences （P <0. 05）. （2）MoCA scores：Compared to the HC and CI groups, the CI-MCI group exhibited significantly lower total MoCA scores, as well as reductions in visuospatial execution, naming, attention, verbal fluency, delayed recall, and orientation, with statistically significant differences （P <0. 05）. （3）EEG spectral characteristics：Compared to the HC group, the CI-MCI group showed significantly increased alpha spectral power at sites P1, P6, POZ, PO4, and PO6 in the Parieto-Occipital lobe, with statistically significant differences （P <0. 05）. Additionally, the topographical maps of the alpha spectral power in the parietal and occipital lobes were notably stronger in the CI-MCI group compared to the HC group. （4）Correlation between changes in occipital alpha spectral power and cognitive function：The alpha spectral power at site P6 in the right parietal lobe （r =0. 444, P =0. 023）, at site POZ in the occipital lobe （r =0. 444, P =0. 023）, and at site PO4 in the right parietal lobe （r =0. 478, P =0. 014） all showed a positive correlation with visuospatial executive function.ConclusionThe changes in Parieto-Occipital lobe alpha spectral power are indicative of cognitive impairment in elderly individuals with chronic insomnia and mild cognitive impairment （MCI）. Moreover, Parieto-Occipital lobe alpha spectral power can modulate cognitive function in this population