Energy-Efficient Task Offloading for Semantic-Aware Networks

The limited computation capacity of user equipments restricts the local implementation of computation-intense applications. Edge computing, especially the edge intelligence system enables local users to offload the computation tasks to the edge servers for reducing the computational energy consumption of user equipments and fast task execution. However, the limited bandwidth of upstream channels may increase the task transmission latency and affect the computation offloading performance. To overcome the challenge of the limited resource of wireless communications, we adopt a semantic-aware task offloading system, where the semantic information of tasks are extracted and offloaded to the edge servers. Furthermore, a proximal policy optimization based multi-agent reinforcement learning algorithm (MAPPO) is proposed to coordinate the resource of wireless communications and the computation, so that the resource management can be performed distributedly and the computational complexity of the online algorithm can be reduced.Comment: Have been accepted by IEEE ICC 202

Meta Federated Reinforcement Learning for Distributed Resource Allocation

In cellular networks, resource allocation is usually performed in a centralized way, which brings huge computation complexity to the base station (BS) and high transmission overhead. This paper explores a distributed resource allocation method that aims to maximize energy efficiency (EE) while ensuring the quality of service (QoS) for users. Specifically, in order to address wireless channel conditions, we propose a robust meta federated reinforcement learning (\textit{MFRL}) framework that allows local users to optimize transmit power and assign channels using locally trained neural network models, so as to offload computational burden from the cloud server to the local users, reducing transmission overhead associated with local channel state information. The BS performs the meta learning procedure to initialize a general global model, enabling rapid adaptation to different environments with improved EE performance. The federated learning technique, based on decentralized reinforcement learning, promotes collaboration and mutual benefits among users. Analysis and numerical results demonstrate that the proposed \textit{MFRL} framework accelerates the reinforcement learning process, decreases transmission overhead, and offloads computation, while outperforming the conventional decentralized reinforcement learning algorithm in terms of convergence speed and EE performance across various scenarios.Comment: Submitted to TW

Fetal Brain Tissue Annotation and Segmentation Challenge Results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript submitte

A ‘Third Culture’ in Economics? An Essay on Smith, Confucius and the Rise of China

China's rise drives a growing impact of China on economics. So far, this mainly works via the force of example, but there is also an emerging role of Chinese thinking in economics. This paper raises the question how far Chinese perspectives can affect certain foundational principles in economics, such as the assumptions on individualism and self-interest allegedly originating in Adam Smith. I embark on sketching a 'third culture' in economics, employing a notion from cross-cultural communication theory, which starts out from the observation that the Chinese model was already influential during the European enlightenment, especially on physiocracy, suggesting a particular conceptualization of the relation between good government and a liberal market economy. I relate this observation with the current revisionist view on China's economic history which has revealed the strong role of markets in the context of informal institutions, and thereby explains the strong performance of the Chinese economy in pre-industrial times. I sketch the cultural legacy of this pattern for traditional Chinese conceptions of social interaction and behavior, which are still strong in rural society until today. These different strands of argument are woven together in a comparison between Confucian thinking and Adam Smith, especially with regard to the 'Theory of Moral Sentiments', which ends up in identifying a number of conceptual family resemblances between the two. I conclude with sketching a 'third culture' in economics in which moral aspects of economic action loom large, as well as contextualized thinking in economic policies

Distinguish between Stochastic and Chaotic Signals by a Local Structure-Based Entropy

As a measure of complexity, information entropy is frequently used to categorize time series, such as machinery failure diagnostics, biological signal identification, etc., and is thought of as a characteristic of dynamic systems. Many entropies, however, are ineffective for multivariate scenarios due to correlations. In this paper, we propose a local structure entropy (LSE) based on the idea of a recurrence network. Given certain tolerance and scales, LSE values can distinguish multivariate chaotic sequences between stochastic signals. Three financial market indices are used to evaluate the proposed LSE. The results show that the LSEFSTE100 and LSES&P500 are higher than LSESZI, which indicates that the European and American stock markets are more sophisticated than the Chinese stock market. Additionally, using decision trees as the classifiers, LSE is employed to detect bearing faults. LSE performs higher on recognition accuracy when compared to permutation entropy

Quantitative Analysis of Compatibility and Dispersibility in Nanocellulose-Reinforced Composites: Hansen Solubility and Raman Mapping

International audienceConsidering its high specific modulus, nanocellulose, including rigid cellulose nanocrystals (CNCs) and semiflexible cellulose nanofibrils (CNFs), is widely used as a nano-reinforcing filler for polymeric-based composites, which is regarded as the most promising application of these biomass nanoparticles. The quantitative evaluation of the compatibility and dispersion/aggregation state of nanocellulose in polymeric matrices is a critical issue, as it conditions the efficient stress transfer from the matrix to the filler and effective mechanical reinforcement effect. This study reports a comprehensive set of theories and methods to directly evaluate the compatibility and dispersibility of CNCs and CNFs in four polymer matrices with different polarities, where the compatibility was assessing by Hansen solubility and dispersibility by Raman mapping and cluster analysis. Triple-bond modification on the surface of nanocellulose is a promising approach for accurate recognition in composites, exhibiting the individual signal located in the Raman-silent regions of various polymeric matrices. Based on the discussion of the quantitative dispersion factor, a multiscale percolation model is proposed to better predict the mechanical properties of nanocellulose-reinforced composites based on Raman mapping results, in order to update traditional percolation models

Material Flow and Mechanical Properties of a Non-Keyhole Friction Stir Welded Aluminum Alloy with Improved Sleeve Bottom Geometry

Non-keyhole friction stir welding (N-KFSW) is a technique that can fabricate a welding joint without a keyhole through a one-time welding process. The Al–Mg–Si alloy was chosen as a research object, and the N-KFSW technique was investigated by numerical and experimental methods. Firstly, the sleeve bottoms of the N-KFSW welding tool system were innovatively optimized in this study. The optimal sleeve bottom with an 80° angle between the spiral groove and the sleeve inner side wall allowed avoiding the incomplete root penetration defect at the bottom of the stir zone (SZ), which was verified by numerical results and the C-shaped line height. Then, using a 3 mm-thick aluminum alloy as the experimental material, the material flow and joint formation characteristics and mechanical properties at 110, 150 and 190 mm/min welding speeds were studied and compared. The results showed that the SZ presented a drum shape due to the action of the clamping ring and the threads on the side wall of the sleeve. The SZ width decreased from 7.17 to 6.91 mm due to the decreased heat input. From 70 to 210 mm/min welding speed, the maximum tensile strength of the joint was 250 MPa at 190 mm/min, and the joint with relatively higher strength fractured at the heat-affected zone

Material Flow and Mechanical Properties of a Non-Keyhole Friction Stir Welded Aluminum Alloy with Improved Sleeve Bottom Geometry

Non-keyhole friction stir welding (N-KFSW) is a technique that can fabricate a welding joint without a keyhole through a one-time welding process. The Al–Mg–Si alloy was chosen as a research object, and the N-KFSW technique was investigated by numerical and experimental methods. Firstly, the sleeve bottoms of the N-KFSW welding tool system were innovatively optimized in this study. The optimal sleeve bottom with an 80° angle between the spiral groove and the sleeve inner side wall allowed avoiding the incomplete root penetration defect at the bottom of the stir zone (SZ), which was verified by numerical results and the C-shaped line height. Then, using a 3 mm-thick aluminum alloy as the experimental material, the material flow and joint formation characteristics and mechanical properties at 110, 150 and 190 mm/min welding speeds were studied and compared. The results showed that the SZ presented a drum shape due to the action of the clamping ring and the threads on the side wall of the sleeve. The SZ width decreased from 7.17 to 6.91 mm due to the decreased heat input. From 70 to 210 mm/min welding speed, the maximum tensile strength of the joint was 250 MPa at 190 mm/min, and the joint with relatively higher strength fractured at the heat-affected zone