Generic regularity of conservative solutions to Camassa-Holm type equations

This paper mainly proves the generic properties of the Camassa-Holm equation and the two-component Camassa-Holm equation by Thom's transversality Lemma. We reveal their differences in generic regularity and singular behavior

Antroquinonol Exerts Immunosuppressive Effect on CD8 +

Antroquinonol was investigated as antioxidant and inhibition of inflammatory responses. Our study was to evaluate its immunosuppressive effect on CD8+ T cells and protective effect on depigmentation. CD8+ T cells were treated with antroquinonol in vitro, and C57BL/6 mice were treated with antroquinonol with or without H2O2 in vivo for 50 consecutive days. We found antroquinonol could inhibit proliferation of CD8+ T cells and suppress the production of cytokines IL-2 and IFN-γ and T cell activation markers CD69 and CD137 in vitro. H2O2 treatment induced depigmentation and reduced hair follicle length, skin thickness, and tyrosinase expression in vivo. Whereas, antroquinonol obviously ameliorated depigmentation of mice skin and resisted the reduction of hair follicle length, skin thickness, and tyrosinase expression induced by H2O2. Antroquinonol decreased CD8+ T cell infiltration in mice skin, inhibited the production of IL-2 and IFN-γ, and decreased the expression of CXCL10 and CXCR3. Summarily, our data shows antroquinonol inhibits CD8+ T cell proliferation in vitro. It also reduces CD8+ T cell infiltration and proinflammatory cytokine secretion and suppresses the thinning of epidermal layer in vivo. Our findings suggest that antroquinonol exerts immunosuppressive effects on CD8+ T cell proliferation and activation to resist depigmentation induced by H2O2

Soil temperature prediction based on explainable artificial intelligence and LSTM

Soil temperature is a key parameter in many disciplines, and its research has important practical significance. In recent years, the prediction of soil temperature by deep learning has achieved good results. However, deep learning is difficult to popularize in practical use because of its opacity. This study aims to interpret and analyze the Long Short Term Memory Network (LSTM) model for global soil temperature prediction using SHapley Additive exPlanation (SHAP), Permutation Importance (PI) and Partial Dependence Plot (PDP). The results show that Temperature of air at 2 m above the surface of land has the greatest influence on the prediction of soil temperature, and its SHAP and PI characteristic values have significant seasonality. Meanwhile, radiation also has a certain influence on the prediction results. There was a significant positive correlation between the temperature of 2 m and the soil temperature. The explanatory insights provided in this paper enhance the transparency and confidence of the model, which promotes the applicability of soil temperature prediction models in relevant fields

Serving MoE Models on Resource-constrained Edge Devices via Dynamic Expert Swapping

Mixture of experts (MoE) is a popular technique in deep learning that improves model capacity with conditionally-activated parallel neural network modules (experts). However, serving MoE models in resource-constrained latency-critical edge scenarios is challenging due to the significantly increased model size and complexity. In this paper, we first analyze the behavior pattern of MoE models in continuous inference scenarios, which leads to three key observations about the expert activations, including temporal locality, exchangeability, and skippable computation. Based on these observations, we introduce PC-MoE, an inference framework for resource-constrained continuous MoE model serving. The core of PC-MoE is a new data structure, Parameter Committee, that intelligently maintains a subset of important experts in use to reduce resource consumption. The optimal configuration of Parameter Committee is found offline by a profiling-guided committee planner, and expert swapping and request handling at runtime are managed by an adaptive committee scheduler. To evaluate the effectiveness of PC-MoE, we conduct experiments using state-of-the-art MoE models on common computer vision and natural language processing tasks. The results demonstrate optimal trade-offs between resource consumption and model accuracy achieved by PC-MoE. For instance, on object detection tasks with the Swin-MoE model, our approach can reduce memory usage and latency by 42.34% and 18.63% with only 0.10% accuracy degradation

Physical-layer key distribution using synchronous complex dynamics of DBR semiconductor lasers

Common-signal-induced synchronization of semiconductor lasers with optical feedback inspired a promising physical key distribution with information-theoretic security and potential in high rate. A significant challenge is the requirement to shorten the synchronization recovery time for increasing key rate without sacrificing operation parameter space for security. Here, open-loop synchronization of wavelength-tunable multi-section distributed Bragg reflector (DBR) lasers is proposed as a solution for physical-layer key distribution. Experiments show that the synchronization is sensitive to two operation parameters, i.e., currents of grating section and phase section. Furthermore, fast wavelength-shift keying synchronization can be achieved by direct modulation on one of the two currents. The synchronization recovery time is shortened by one order of magnitude compared to close-loop synchronization. An experimental implementation is demonstrated with a final key rate of 5.98 Mbit/s over 160 km optical fiber distance. It is thus believed that fast-tunable multi-section semiconductor lasers opens a new avenue of high-rate physical-layer key distribution using laser synchronization.Comment: 13 pages, 5 figure

Contrastive Graph Pooling for Explainable Classification of Brain Networks

Functional magnetic resonance imaging (fMRI) is a commonly used technique to measure neural activation. Its application has been particularly important in identifying underlying neurodegenerative conditions such as Parkinson's, Alzheimer's, and Autism. Recent analysis of fMRI data models the brain as a graph and extracts features by graph neural networks (GNNs). However, the unique characteristics of fMRI data require a special design of GNN. Tailoring GNN to generate effective and domain-explainable features remains challenging. In this paper, we propose a contrastive dual-attention block and a differentiable graph pooling method called ContrastPool to better utilize GNN for brain networks, meeting fMRI-specific requirements. We apply our method to 5 resting-state fMRI brain network datasets of 3 diseases and demonstrate its superiority over state-of-the-art baselines. Our case study confirms that the patterns extracted by our method match the domain knowledge in neuroscience literature, and disclose direct and interesting insights. Our contributions underscore the potential of ContrastPool for advancing the understanding of brain networks and neurodegenerative conditions

Prevention of Dermal Abscess Formation Caused by Staphylococcus aureus Using Phage JD007 in Nude Mice

Aim: In this study, Staphylococcus phage JD007 bactericidal activity and induced immune responses during treatment were assessed in a dermal abscess model.Materials and Methods: Dermal abscesses in nude mice were established by injecting a clinical isolate of S. aureus SA325 isolated from the back under-dermal abscess of an in-patient.Results: Phage JD007 was able to inhibit the growth of S. aureus SA325 at MOI = 1 or 10, significantly preventing the formation of dermal abscesses. Moderate immune responses were observed in the prevention group through detection of cytokines.Conclusion: Phage JD007 inhibits the formation of dermal abscesses caused by a clinical S. aureus strain in nude mice without robust immune responses