Encouraging and astonishing developments have recently been achieved in image-based diagnostic *** medical care and imaging technology are becoming increasingly ***,the current diagnosis pattern of signal to image to ...
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Encouraging and astonishing developments have recently been achieved in image-based diagnostic *** medical care and imaging technology are becoming increasingly ***,the current diagnosis pattern of signal to image to knowledge inevitably leads to information distortion and noise introduction in the procedure of image reconstruction(from signal to image).Artificial intelligence(AI)technologies that can mine knowledge from vast amounts of data offer opportunities to disrupt established *** this prospective study,for the first time,we develop an AI-based signal-toknowledge diagnostic scheme for lung nodule classification directly from the computed tomography(CT)raw data(the signal).We find that the raw data achieves almost comparable performance with CT,indicating that it is possible to diagnose diseases without reconstructing ***,the incorporation of raw data through three common convolutional network structures greatly improves the performance of the CT models in all cohorts(with a gain ranging from 0.01 to 0.12),demonstrating that raw data contains diagnostic information that CT does not *** results break new ground and demonstrate the potential for direct signal-to-knowledge domain analysis.
Collaborative federated learning (CFL) enables device cooperation in training shared machine learning models without reliance on a parameter server. However, the absence of a parameter server also impacts vulnerabilit...
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ISBN:
(数字)9781728190549
ISBN:
(纸本)9781728190556
Collaborative federated learning (CFL) enables device cooperation in training shared machine learning models without reliance on a parameter server. However, the absence of a parameter server also impacts vulnerabilities associated with adversarial attacks, including privacy inference and Byzantine attacks. In this context, this paper introduces a novel CFL framework that enables each device to individually determine the subset of devices to transmit FL parameters to over the wireless network, based on its neighboring devices' location, current loss, and connection information, to achieve privacy protection and robust aggregation. This is formulated as an optimization problem whose goal is to minimize CFL training loss while satisfying the privacy preservation, robust aggregation, and transmission delay requirements. To solve this problem, a proximal policy optimization (PPO)-based reinforcement learning (RL) algorithm integrated with a graph neural network (GNN) is proposed. Compared to traditional algorithms that use global information with high computational complexity, the proposed GNN-RL method can be deployed on devices based on neighboring information with lower computational overhead. Simulation results show that the proposed algorithm can protect data privacy and increase identification accuracy by 15% compared to an algorithm in which devices are partially clustered for model aggregation.
The rapid advancement of artificial intelligence(AI) has significantly impacted photonics, creating a symbiotic relationship that accelerates the development and applications of both fields. From the perspective of AI...
The rapid advancement of artificial intelligence(AI) has significantly impacted photonics, creating a symbiotic relationship that accelerates the development and applications of both fields. From the perspective of AI aiding photonics, deep-learning methods and various intelligent algorithms have been developed for designing complex photonic structures, where traditional design approaches fall short. AI's capability to process and analyze large data sets has enabled the discovery of novel materials, such as for photovoltaics,leading to enhanced light absorption and efficiency. AI is also significantly transforming the field of optical imaging with improved performance. In addition, AI-driven techniques have revolutionized optical communication systems by optimizing signal processing and enhancing the bandwidth and reliability of data transmission. Conversely, the contribution of photonics to AI is equally profound. Photonic technologies offer unparalleled advantages in the development of AI hardware, providing solutions to overcome the bottlenecks of electronic systems. The implementation of photonic neural networks, leveraging the high speed and parallelism of optical computing, demonstrates significant improvements in the processing speed and energy efficiency of AI computations. Furthermore, advancements in optical sensors and imaging technologies not only enrich AI applications with high-quality data but also expand the capabilities of AI in fields such as autonomous vehicles and medical imaging. We provide comprehensive knowledge and a detailed analysis of the current state of the art, addressing both challenges and opportunities at the intersection of AI and photonics. The multifaceted interactions between AI and photonics will be explored, illustrating how AI has become an indispensable tool in the development of photonics and how photonics, in turn,facilitates advancements in AI. Through a collection of case studies and examples, we underscore the potential
Interpersonal relationship quality is pivotal in social and occupational contexts. Existing analysis of interpersonal relationships mostly rely on subjective self-reports, whereas objective quantification remains chal...
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In real-world scenarios, capturing scenes with excessive dynamic range often leads to the partial loss of highlight or dark area information due to irradiance variations and limitations in the capture capabilities of ...
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Intellectual property transactions have shown a strong growth momentum in recent years, but the patent transaction market has been plagued by the matching degree of consumers and sellers, resulting in frequent problem...
Intellectual property transactions have shown a strong growth momentum in recent years, but the patent transaction market has been plagued by the matching degree of consumers and sellers, resulting in frequent problems such as low patent transformation efficiency and poor transaction quality. This paper proposes a method of recommending patents to consumers by experts to improve the environment of patent transactions. Through the analysis of the past transaction information of the patent, the effective path information of the target is extracted. The graph neural network is used to describe the characteristics and semantics among experts, patents and consumers, and then capture the potential weight among them through the common attention mechanism, and then dynamically integrate them to predict the occurrence of recommendation behavior. The paper makes reasonable use of social information and expert information in the transaction, which significantly improves the rationality and accuracy of expert recommendation.
With the increasing amount of computation in high-performance computing, the scale of interconnection networks is becoming larger and larger. It is inevitable that processors or links in the network become faulty. The...
With the increasing amount of computation in high-performance computing, the scale of interconnection networks is becoming larger and larger. It is inevitable that processors or links in the network become faulty. Therefore, it is very important for a processor to understand the security status of itself and neighbors, and then adaptively and reliably communicate. However, the existing security information models consider faulty processors or faulty links separately, and do not consider the situation where both processors and links fail simultaneously. The Exchanged Hypercube is an excellent interconnection network structure which achieves a balance between network functionality and hardware overhead while preserving many excellent properties of the Hypercube. In this paper, we propose an adaptive fault-tolerant routing algorithm of the Exchanged Hypercube based on an improved local security information model, which considers the faulty processors and faulty links simultaneously. This algorithm always can select suitable processors for routing based on the local security information and unsafe coefficients of processors, which greatly reduces the impact of the faulty of networks. The adaptive fault-tolerant routing algorithm of the Exchanged Hypercube network is not only suitable for regular networks but also for heterogeneous networks since the Exchanged Hypercube is a semi regular network. Sufficient experimental results show that the algorithm proposed in this paper has a lower packet loss rate, a higher success rate, and a shorter average path length.
The field of pose estimation has a wide range of application prospects in various industries in the current era. With the continuous development of deep learning techniques, the effects in the field of human pose esti...
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As is well known, differential algebraic equations (DAEs), which are able to describe dynamic changes and underlying constraints, have been widely applied in engineering fields such as fluid dynamics, multi-body dynam...
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Thanks to the continuous development of deep learning and the updating of deep neural networks, the accuracy of various computer vision tasks continue improving. On the one hand, the accuracy of image recognition is s...
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