Rethinking Individual Global Max in Cooperative Multi-Agent Reinforcement Learning

In cooperative multi-agent reinforcement learning, centralized training and decentralized execution (CTDE) has achieved remarkable success. Individual Global Max (IGM) decomposition, which is an important element of CTDE, measures the consistency between local and joint policies. The majority of IGM-based research focuses on how to establish this consistent relationship, but little attention has been paid to examining IGM's potential flaws. In this work, we reveal that the IGM condition is a lossy decomposition, and the error of lossy decomposition will accumulated in hypernetwork-based methods. To address the above issue, we propose to adopt an imitation learning strategy to separate the lossy decomposition from Bellman iterations, thereby avoiding error accumulation. The proposed strategy is theoretically proved and empirically verified on the StarCraft Multi-Agent Challenge benchmark problem with zero sight view. The results also confirm that the proposed method outperforms state-of-the-art IGM-based approaches.Comment: Accept at NeurIPS 202

PRIS: Practical robust invertible network for image steganography

Image steganography is a technique of hiding secret information inside another image, so that the secret is not visible to human eyes and can be recovered when needed. Most of the existing image steganography methods have low hiding robustness when the container images affected by distortion. Such as Gaussian noise and lossy compression. This paper proposed PRIS to improve the robustness of image steganography, it based on invertible neural networks, and put two enhance modules before and after the extraction process with a 3-step training strategy. Moreover, rounding error is considered which is always ignored by existing methods, but actually it is unavoidable in practical. A gradient approximation function (GAF) is also proposed to overcome the undifferentiable issue of rounding distortion. Experimental results show that our PRIS outperforms the state-of-the-art robust image steganography method in both robustness and practicability. Codes are available at https://github.com/yanghangAI/PRIS, demonstration of our model in practical at http://yanghang.site/hide/

Automatic 2-D/3-D Vessel Enhancement in Multiple Modality Images Using a Weighted Symmetry Filter

Automated detection of vascular structures is of great importance in understanding the mechanism, diagnosis and treatment of many vascular pathologies. However, automatic vascular detection continues to be an open issue because of difficulties posed by multiple factors such as poor contrast, inhomogeneous backgrounds, anatomical variations, and the presence of noise during image acquisition. In this paper, we propose a novel 2D/3D symmetry filter to tackle these challenging issues for enhancing vessels from different imaging modalities. The proposed filter not only considers local phase features by using a quadrature filter to distinguish between lines and edges, but also uses the weighted geometric mean of the blurred and shifted responses of the quadrature filter, which allows more tolerance of vessels with irregular appearance. As a result, this filter shows a strong response to the vascular features under typical imaging conditions. Results based on 8 publicly available datasets (six 2D datasets, one 3D dataset and one 3D synthetic dataset) demonstrate its superior performance to other state-ofthe- art methods

GP-NAS-ensemble: a model for NAS Performance Prediction

It is of great significance to estimate the performance of a given model architecture without training in the application of Neural Architecture Search (NAS) as it may take a lot of time to evaluate the performance of an architecture. In this paper, a novel NAS framework called GP-NAS-ensemble is proposed to predict the performance of a neural network architecture with a small training dataset. We make several improvements on the GP-NAS model to make it share the advantage of ensemble learning methods. Our method ranks second in the CVPR2022 second lightweight NAS challenge performance prediction track

A compactness based saliency approach for leakages detection in fluorescein angiogram

This study has developed a novel saliency detection method based on compactness feature for detecting three common types of leakage in retinal fluorescein angiogram: large focal, punctate focal, and vessel segment leakage. Leakage from retinal vessels occurs in a wide range of retinal diseases, such as diabetic maculopathy and paediatric malarial retinopathy. The proposed framework consists of three major steps: saliency detection, saliency refinement and leakage detection. First, the Retinex theory is adapted to address the illumination inhomogeneity problem. Then two saliency cues, intensity and compactness, are proposed for the estimation of the saliency map of each individual superpixel at each level. The saliency maps at different levels over the same cues are fused using an averaging operator. Finally, the leaking sites can be detected by masking the vessel and optic disc regions. The effectiveness of this framework has been evaluated by applying it to different types of leakage images with cerebral malaria. The sensitivity in detecting large focal, punctate focal and vessel segment leakage is 98.1, 88.2 and 82.7 %, respectively, when compared to a reference standard of manual annotations by expert human observers. The developed framework will become a new powerful tool for studying retinal conditions involving retinal leakage

Joint time‐slot and power allocation algorithm for data and energy integrated networks supporting internet of things (IoT)

IoT is an essential enabler for smart cities and smart society. However, its deployment at large scale faces a big challenge: battery replacement as most IoT devices are battery‐powered or even battery‐less. In a hostile environment, it is infeasible to replace batteries. Radio frequency (RF)‐enable wireless energy transfer (WET) is a promising technology to solve this problem. Since RF is also used for wireless data communication, a data and energy integrated network (DEIN) is the way forward. Based on the DEIN technology, a time allocation model is designed in this paper to manage the RF energy and uplink data transmission in different time slots. In the IoT scenario, the DEIN's primary service is to collect environmental information such as temperature, humidity, and luminance. Therefore, the uplink data transmission of the battery‐powered/battery‐less IoT nodes deserves more attention. To increase the uplink data transmission in case of consuming less energy in the DEIN system, we propose a joint time slot and power allocation algorithm to minimize the system's consumed energy for transmitting per bit of uplink data. It aims to maximize the efficiency of the DEIN system's energy utilization, which helps to achieve an energy‐efficient DEIN

Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving

In recent years there have been remarkable advancements in autonomous driving. While autonomous vehicles demonstrate high performance in closed-set conditions, they encounter difficulties when confronted with unexpected situations. At the same time, world models emerged in the field of model-based reinforcement learning as a way to enable agents to predict the future depending on potential actions. This led to outstanding results in sparse reward and complex control tasks. This work provides an overview of how world models can be leveraged to perform anomaly detection in the domain of autonomous driving. We provide a characterization of world models and relate individual components to previous works in anomaly detection to facilitate further research in the field.Comment: Accepted for publication at SSCI 202

Alternate erosion and deposition in the Yangtze Estuary and its future change

The morphological changing trend of the Yangtze Estuary, the largest estuary of Asia, has become a focus of research in recent years. Based on a long series of topographic data from 1950 to 2015, this paper studied the erosion-deposition pattern of the entire Yangtze Estuary. An alternation between erosion and deposition was found during the past 65 years, which was in correspondence to the alternation between flood and dry periods identified by multi-year average duration days of high-level water flow (defined as discharge ≥ 60,000 m3/s, namely, D≥60,000) from the Yangtze River Basin. A quantitative relationship was further developed between the erosional/depositional rate of the Yangtze Estuary and the interpreting variables of yearly water discharge, D≥60,000 and yearly river sediment load, with contributing rates of 1%, 59% and 40%, respectively. Mechanism behind the alternate erosion and deposition pattern was analyzed by examining residual water surface slope and the corresponding capacity of sediment transport in flood and dry periods. In flood periods, a larger discharge results in steeper slope of residual water level which permits a greater capacity of sediment transport. Therefore, more bed materials can be washed to the sea, leading to erosion of the estuary. In contrast, flatter slope of residual water level occurs in dry periods, and deposition dominates the estuarine area due to the decreased capacity of sediment transport and the increased backwater effect of flood-tide. Coastal dynamics and estuarine engineering projects alter the local morphological changes, but slightly affect the total erosional/depositional rate of the whole estuarine region. Heavy sedimentation within the Yangtze Estuary after the impoundment of the Three Gorges Dam can be attributed to the reduced occurrence frequency of flood years due to water regulation by the dam, and largely (at least 36%–52%) sourced from the sea. Deposition is still possible to occur in the Yangtze Estuary in the future, because the multi-year average D≥60,000 is unlikely to exceed the critical value of 14 days/yr which corresponds to the future equilibrium state of the Yangtze Estuary, under the water regulation of the large cascade dams in the upper Yangtze. Nevertheless, the mean depositional rate will not surpass the peak value of the past years, since the total sediment load entering the Yangtze Estuary has presented a decreasing trend