Edge-Aware Mirror Network for Camouflaged Object Detection

Existing edge-aware camouflaged object detection (COD) methods normally output the edge prediction in the early stage. However, edges are important and fundamental factors in the following segmentation task. Due to the high visual similarity between camouflaged targets and the surroundings, edge prior predicted in early stage usually introduces erroneous foreground-background and contaminates features for segmentation. To tackle this problem, we propose a novel Edge-aware Mirror Network (EAMNet), which models edge detection and camouflaged object segmentation as a cross refinement process. More specifically, EAMNet has a two-branch architecture, where a segmentation-induced edge aggregation module and an edge-induced integrity aggregation module are designed to cross-guide the segmentation branch and edge detection branch. A guided-residual channel attention module which leverages the residual connection and gated convolution finally better extracts structural details from low-level features. Quantitative and qualitative experiment results show that EAMNet outperforms existing cutting-edge baselines on three widely used COD datasets. Codes are available at https://github.com/sdy1999/EAMNet.Comment: ICME2023 pape

Analysis and Forecast of Railway Freight Volume based on Prophet-Deep AR Model

The research on railway freight volume forecast is of great significance to the allocation of railway freight transport resources, the formulation of transport plans and the organization of railway freight transport. This study, by fully mining the railway freight ticket data information, put forward the precise forecast model of railway freight volume under different types of freight. Firstly, the railway freight ticket data are cleaned, regulated and integrated, and the time series of the daily number of railway freight trains are constructed, get the trend, periodicity and holiday data of railway traffic data, and set the parameters of Chinese holidays and rest days according to the demand characteristics of different categories. Secondly, the forecasting result of the Prophet is taken as a cooperative parameter. DeepAR is used to forecast, and a combined model of the Prophet-DeepAR is constructed. Finally, the combined model was validated with Shanghai Railway Bureau data from January 1, 2015 to December 31, 2018 for the food and tobacco category, and with Prophet-DeepAR, LSTM, Wavelet, BILSTM, and Prophet-LSTM, prophet-wavelet and Prophet-Bilstm are used to compare the prediction results. The results show that the Prophet-DeepAR model can extract the multi-dimensional periodicity of freight traffic and mine the trend information of freight traffic, and get the prediction result with high precision. It has better accuracy than other models

Federated Learning over a Wireless Network: Distributed User Selection through Random Access

User selection has become crucial for decreasing the communication costs of federated learning (FL) over wireless networks. However, centralized user selection causes additional system complexity. This study proposes a network intrinsic approach of distributed user selection that leverages the radio resource competition mechanism in random access. Taking the carrier sensing multiple access (CSMA) mechanism as an example of random access, we manipulate the contention window (CW) size to prioritize certain users for obtaining radio resources in each round of training. Training data bias is used as a target scenario for FL with user selection. Prioritization is based on the distance between the newly trained local model and the global model of the previous round. To avoid excessive contribution by certain users, a counting mechanism is used to ensure fairness. Simulations with various datasets demonstrate that this method can rapidly achieve convergence similar to that of the centralized user selection approach

Photonic Floquet skin-topological effect

Non-Hermitian skin effect and photonic topological edge states are of great interest in non-Hermitian physics and optics. However, the interplay between them is largly unexplored. Here, we propose and demonstrate experimentally the non-Hermitian skin effect that constructed from the nonreciprocal flow of Floquet topological edge states, which can be dubbed 'Floquet skin-topological effect'. We first show the non-Hermitian skin effect can be induced by pure loss when the one-dimensional (1D) system is periodically driven. Next, based on a two-dimensional (2D) Floquet topological photonic lattice with structured loss, we investigate the interaction between the non-Hermiticity and the topological edge states. We observe that all the one-way edge states are imposed onto specific corners, featuring both the non-Hermitian skin effect and topological edge states. Furthermore, a topological switch for the skin-topological effect is presented by utilizing the gap-closing mechanism. Our experiment paves the way of realizing non-Hermitian topological effects in nonlinear and quantum regimes

Relationships among gut microbes, the interleukin family, and hypertension: a mediation Mendelian randomization study

PurposeObservational studies have increasingly recognized the influence of gut microbes on blood pressure modulation. Despite these findings, a direct causal link between gut flora and hypertension remains unestablished due to inherent confounders and the challenges of reverse causality in observational research. In this study, we sought to elucidate the causal relationship between specific gut flora and hypertension and its intermediary mediators.MethodsWe employed a two-sample Mendelian randomization (MR) and mediation MR analysis, analyzing 211 species of gut bacteria, with a focus on the interleukin family as potential mediators and hypertension as the primary outcome. The central methodological technique was inverse variance-weighted estimation, supplemented by various other estimators.ResultsOur findings revealed that two bacterial species positively correlated with hypertension risk, while five exhibited a negative association. Further validation was conducted using sensitivity analyses. Notably, our mediation MR results suggest interleukin-1 receptor type 2 (IL-1R2) as a mediator for the effect of the genus Clostridium innocuum group on hypertension, accounting for a mediation proportion of 14.07% [mediation effect: (b = 0.0007, 95%CI: 0.0002–0.0011); proportion mediation = 14.07% (4.26–23.40%)].ConclusionOur research confirms a genetic causal relationship between specific gut microbes and hypertension, emphasizing the potential mediating role of interleukin-1 receptor type 2 (IL-1R2) and offering insights for clinical hypertension interventions