Weak Signal Detection Based on Adaptive Cascaded Bistable Stochastic Resonance System

AbstractStochastic resonance system is an effective method to extract weak signal, however, system output is directly influenced by system parameters. Aiming to this, a method about weak periodic signal extraction was developed based on adaptive stochastic resonance. Firstly cascaded stochastic resonance system was established in order to achieve better low-pass filtering effect. And then, variance of zero point distance was chosen as measurement index of cascade system. It's able to overcome the shortage that traditional adaptive stochastic resonance system needs to know the signal frequency beforehand. Also, it could obtain optimum system parameters adaptively. Basing on these parameters, input signal will be handled, and optimum output could be obtained. Furthermore, different periodic signal have been recognized, and finally the validity of the method is verified through simulation experiments

Comparative analysis of phosphoproteomic in the intestine of Sepia lycidas under different salinity environments

Cuttlefish are sensitive to the breeding environment, and the low-salinity environment significantly impacts their growth and immunity. So far, it is difficult to breed this species artificially. This study was conducted in Sepia lycidas. And the aim was to investigate the differences in protein phosphorylation in the intestine of S. lycidas under different salinity conditions. Firstly, 999 phosphoproteins (specific peptide ≥ 1), 1928 phosphopeptides, and 2727 phosphorylation sites were identified. Among them were 284 down-regulated expression phosphorylation sites (corresponding to 115 phosphoproteins) and 674 up-regulated expression phosphorylation sites (corresponding to 408 phosphoproteins) in the intestine under a low salinity environment compared with that under a natural salinity environment. Next, GO analysis found that more phosphoproteins corresponding to differentially expressed phosphorylation sites were related to anatomical structure development, multicellular organism development, regulation of the cellular process, etc. The molecular functions of these proteins mainly contain protein binding, transferase activity, catalytic activity, and heterocyclic compound binding. And they are mainly involved in the cellular components of intracellular anatomical structure, organelle, and cytoplasm. KEGG enrichment analysis of the differential phosphoproteins suggested that many significantly enriched pathways were related to the phosphatidylinositol signaling system, cell junction (adherens junction and tight junction), and inositol phosphate metabolism. Finally, changes in environmental salinity can affect the intestinal structure, metabolism, and immune homeostasis of S. lycidas

Laser Intensity Noise Suppression for Preparing Audio-Frequency 795 nm Squeezed Vacuum State of Light at Rubidium D1 Line

Laser intensity noise suppression has essential effects on preparation and characterization of the audio-frequency squeezed vacuum state of light based on a sub-threshold optical parametric oscillator (OPO).We have implemented two feedback loops by using relevant acousto-optical modulators (AOM) to stabilize the intensity of 795-nm near infrared (NIR) fundamental laser and 397.5-nm ultraviolet (UV) laser generated by cavity-enhanced frequency doubling.Typical peak-to-peak laser intensity fluctuation with a bandwidth of $\sim10$ kHz in a half hour has been improved from $\pm7.45$$\%$ to $\pm0.06$$\%$ for 795-nm NIR laser beam, and from $\pm9.04$$\%$ to $\pm0.05$$\%$ for 397.5-nm UV laser beam, respectively. The squeezing level of the squeezed vacuum state at 795 nm prepared by the sub-threshold OPO with a PPKTP crystal has been improved from -3.3 to -4.0 dB around 3$\sim$9 kHz of audio analysis frequency range.Comment: 5 pages, 4 figure

Graph Analysis in Decentralized Online Social Networks with Fine-Grained Privacy Protection

Graph analysts cannot directly obtain the global structure in decentralized social networks, and analyzing such a network requires collecting local views of the social graph from individual users. Since the edges between users may reveal sensitive social interactions in the local view, applying differential privacy in the data collection process is often desirable, which provides strong and rigorous privacy guarantees. In practical decentralized social graphs, different edges have different privacy requirements due to the distinct sensitivity levels. However, the existing differentially private analysis of social graphs provide the same protection for all edges. To address this issue, this work proposes a fine-grained privacy notion as well as novel algorithms for private graph analysis. We first design a fine-grained relationship differential privacy (FGR-DP) notion for social graph analysis, which enforces different protections for the edges with distinct privacy requirements. Then, we design algorithms for triangle counting and k-stars counting, respectively, which can accurately estimate subgraph counts given fine-grained protection for social edges. We also analyze upper bounds on the estimation error, including k-stars and triangle counts, and show their superior performance compared with the state-of-the-arts. Finally, we perform extensive experiments on two real social graph datasets and demonstrate that the proposed mechanisms satisfying FGR-DP have better utility than the state-of-the-art mechanisms due to the finer-grained protection

SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation

Diffusion models based on permutation-equivariant networks can learn permutation-invariant distributions for graph data. However, in comparison to their non-invariant counterparts, we have found that these invariant models encounter greater learning challenges since 1) their effective target distributions exhibit more modes; 2) their optimal one-step denoising scores are the score functions of Gaussian mixtures with more components. Motivated by this analysis, we propose a non-invariant diffusion model, called $\textit{SwinGNN}$, which employs an efficient edge-to-edge 2-WL message passing network and utilizes shifted window based self-attention inspired by SwinTransformers. Further, through systematic ablations, we identify several critical training and sampling techniques that significantly improve the sample quality of graph generation. At last, we introduce a simple post-processing trick, $\textit{i.e.}$, randomly permuting the generated graphs, which provably converts any graph generative model to a permutation-invariant one. Extensive experiments on synthetic and real-world protein and molecule datasets show that our SwinGNN achieves state-of-the-art performances. Our code is released at https://github.com/qiyan98/SwinGNN

Reaching the last mile: best practices in leveraging the power of ICTs to communicate climate services to farmers at scale

This report reviews key ICTs for Development (ICT4D) Programs, Innovations and Information Exchange Platforms which are experimented within South Asia to explore the use and scale-ability of these innovative approaches to other parts of Africa and the developing world. Learning from the pioneering experiences of pilot projects across India and Africa in ICT development, we assess the potential ICTs offer to not only communicate climate information and related advisory services but also to build capacity and increase the resilience of rural smallholders. It is our hope that such South-South learning can pave the way for improved cross-regional experience sharing to tackle common challenges in reaching ‘the last mile’ with salient rural extension services, including climate information services

Towards Automatic Boundary Detection for Human-AI Hybrid Essay in Education

Human-AI collaborative writing has been greatly facilitated with the help of modern large language models (LLM), e.g., ChatGPT. While admitting the convenience brought by technology advancement, educators also have concerns that students might leverage LLM to partially complete their writing assignment and pass off the human-AI hybrid text as their original work. Driven by such concerns, in this study, we investigated the automatic detection of Human-AI hybrid text in education, where we formalized the hybrid text detection as a boundary detection problem, i.e., identifying the transition points between human-written content and AI-generated content. We constructed a hybrid essay dataset by partially removing sentences from the original student-written essays and then instructing ChatGPT to fill in for the incomplete essays. Then we proposed a two-step detection approach where we (1) Separated AI-generated content from human-written content during the embedding learning process; and (2) Calculated the distances between every two adjacent prototypes (a prototype is the mean of a set of consecutive sentences from the hybrid text in the embedding space) and assumed that the boundaries exist between the two prototypes that have the furthest distance from each other. Through extensive experiments, we summarized the following main findings: (1) The proposed approach consistently outperformed the baseline methods across different experiment settings; (2) The embedding learning process (i.e., step 1) can significantly boost the performance of the proposed approach; (3) When detecting boundaries for single-boundary hybrid essays, the performance of the proposed approach could be enhanced by adopting a relatively large prototype size, leading to a $22$\% improvement (against the second-best baseline method) in the in-domain setting and an $18$\% improvement in the out-of-domain setting.Comment: 9 pages including references, 2 figure

Community-Aware Efficient Graph Contrastive Learning via Personalized Self-Training

In recent years, graph contrastive learning (GCL) has emerged as one of the optimal solutions for various supervised tasks at the node level. However, for unsupervised and structure-related tasks such as community detection, current GCL algorithms face difficulties in acquiring the necessary community-level information, resulting in poor performance. In addition, general contrastive learning algorithms improve the performance of downstream tasks by increasing the number of negative samples, which leads to severe class collision and unfairness of community detection. To address above issues, we propose a novel Community-aware Efficient Graph Contrastive Learning Framework (CEGCL) to jointly learn community partition and node representations in an end-to-end manner. Specifically, we first design a personalized self-training (PeST) strategy for unsupervised scenarios, which enables our model to capture precise community-level personalized information in a graph. With the benefit of the PeST, we alleviate class collision and unfairness without sacrificing the overall model performance. Furthermore, the aligned graph clustering (AlGC) is employed to obtain the community partition. In this module, we align the clustering space of our downstream task with that in PeST to achieve more consistent node embeddings. Finally, we demonstrate the effectiveness of our model for community detection both theoretically and experimentally. Extensive experimental results also show that our CEGCL exhibits state-of-the-art performance on three benchmark datasets with different scales.Comment: 12 pages, 7 figure