Machine learning algorithms can be used to detect Alzheimer disease with RMI-images. One of the challenges of these algorithms is to clearly extract image features that show small variants of brain cells changes, whic...
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This work proposes a distributed estimation and control approach in which a team of aerial agents equipped with radio jamming devices collaborate in order to intercept and concurrently track-and-jam a malicious target...
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Automated analysis of breast cancer (BC) histopathology images is a challenging task due to the high resolution, multiple magnifications, color variations, the presence of image artifacts, and morphological variabilit...
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Graph neural networks (GNNs) have gained increasing popularity, while usually suffering from unaffordable computations for real-world large-scale applications. Hence, pruning GNNs is of great need but largely unexplor...
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Graph neural networks (GNNs) have gained increasing popularity, while usually suffering from unaffordable computations for real-world large-scale applications. Hence, pruning GNNs is of great need but largely unexplored. The recent work Unified GNN Sparsification (UGS) studies lottery ticket learning for GNNs, aiming to find a subset of model parameters and graph structures that can best maintain the GNN performance. However, it is tailed for the transductive setting, failing to generalize to unseen graphs, which are common in inductive tasks like graph classification. In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity. To prune the input graphs, we design a predictive model to generate importance scores for each edge based on the input. To prune the model parameters, it views the weight’s magnitude as their importance scores. Then we design an iterative co-pruning strategy to trim the graph edges and GNN weights based on their importance scores. Although it might be strikingly simple, ICPG surpasses the existing pruning method and can be universally applicable in both inductive and transductive learning settings. On 10 graph-classification and two node-classification benchmarks, ICPG achieves the same performance level with 14.26%–43.12% sparsity for graphs and 48.80%–91.41% sparsity for the GNN model.
Most blockchain systems currently adopt resource-consuming protocols to achieve consensus between miners;for example,the Proof-of-Work(PoW)and Practical Byzantine Fault Tolerant(PBFT)schemes,which have a high consumpt...
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Most blockchain systems currently adopt resource-consuming protocols to achieve consensus between miners;for example,the Proof-of-Work(PoW)and Practical Byzantine Fault Tolerant(PBFT)schemes,which have a high consumption of computing/communication resources and usually require reliable communications with bounded ***,these protocols may be unsuitable for Internet of Things(IoT)networks because the IoT devices are usually lightweight,battery-operated,and deployed in an unreliable wireless ***,this paper studies an efficient consensus protocol for blockchain in IoT networks via reinforcement ***,the consensus protocol in this work is designed on the basis of the Proof-of-Communication(PoC)scheme directly in a single-hop wireless network with unreliable communications.A distributed MultiAgent Reinforcement Learning(MARL)algorithm is proposed to improve the efficiency and fairness of consensus for miners in the blockchain *** this algorithm,each agent uses a matrix to depict the efficiency and fairness of the recent consensus and tunes its actions and rewards carefully in an actor-critic framework to seek effective *** results from the simulation show that the fairness of consensus in the proposed algorithm is guaranteed,and the efficiency nearly reaches a centralized optimal solution.
Digital image has been used in various fields as an essential carrier. Many color images have been constantly produced since their more realistic description, which takes up much storage space and network bandwidth. T...
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Face normalization is a critical technique for improving the robustness and generalizability of face recognition systems by reducing intra-personal variations arising from expressions, poses, occlusions, illuminations...
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Unmanned Aerial Vehicles (UAVs) have extensive applications such as logistics transportation and aerial photography. However, UAVs are sensitive to winds. Traditional control methods, such as proportional- integral-de...
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Unmanned Aerial Vehicles (UAVs) have extensive applications such as logistics transportation and aerial photography. However, UAVs are sensitive to winds. Traditional control methods, such as proportional- integral-derivative controllers, generally fail to work well when the strength and direction of winds are changing frequently. In this work deep reinforcement learning algorithms are combined with a domain randomization method to learn robust wind-resistant hovering policies. A novel reward function is designed to guide learning. This reward function uses a constant reward to maintain a continuous flight of a UAV as well as a weight of the horizontal distance error to ensure the stability of the UAV at altitude. A five-dimensional representation of actions instead of the traditional four dimensions is designed to strengthen the coordination of wings of a UAV. We theoretically explain the rationality of our reward function based on the theories of Q-learning and reward shaping. Experiments in the simulation and real-world application both illustrate the effectiveness of our method. To the best of our knowledge, it is the first paper to use reinforcement learning and domain randomization to explore the problem of robust wind-resistant hovering control of quadrotor UAVs, providing a new way for the study of wind-resistant hovering and flying of UAVs. IEEE
Offset quadrature amplitude modulation-based filter bank multicarrier (FBMC-OQAM) method is a promising technology for future wireless communication systems. It offers several advantages over traditional orthogonal fr...
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