A compact hybrid patch coupler operating at 3.5 GHz for fifth generation applications is presented. By introducing cross-slots with T-shaped heads on the edges, the proposed hybrid patch coupler’s overall size is red...
A compact hybrid patch coupler operating at 3.5 GHz for fifth generation applications is presented. By introducing cross-slots with T-shaped heads on the edges, the proposed hybrid patch coupler’s overall size is reduced significantly. The proposed coupler offers a size reduction of 79% compared to the overall size occupied by a conventional reference design. Simulation results show a successful fit of the miniature coupler such that the reflection coefficient and the isolation values are −44.27 dB and −24.35 dB at 3.5 GHz. The insertion coefficient and coupling coefficient values are −3.18 dB and −3.88 dB for the same resonance frequency, with 30% fractional bandwidth (FBW) and 89° phase difference, showing that the cross-slot patch coupler is capable of diagonally coupling the input signal to its outputs at 3.5 GHz.
This paper is designed to provide a comprehensive overview of the latest developments in fault tolerance methods for cloud computing. Maintaining high availability and reliability of cloud environments requires fault ...
详细信息
ISBN:
(数字)9798331540173
ISBN:
(纸本)9798331540180
This paper is designed to provide a comprehensive overview of the latest developments in fault tolerance methods for cloud computing. Maintaining high availability and reliability of cloud environments requires fault tolerance. This paper explores fault tolerance in the context of cloud computing and discusses recent challenges and innovations in the field. Moreover, it examines the ongoing research efforts to improve fault-tolerance architectures. At the end of the paper, the paper presents system-level metrics that are relevant to fault tolerance.
Vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction. In this paper, we build FIRE, a feedback-refinement dataset, consisting of 1.1M multi-t...
详细信息
The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive require...
详细信息
The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive requirements,especially in some infrastructure-limited areas or some emergency ***,the multi-UAVassisted MEC network remains largely *** this paper,the dynamic trajectory optimization and computation offloading are studied in a multi-UAVassisted MEC system where multiple UAVs fly over a target area with different trajectories to serve ground *** considering the dynamic channel condition and random task arrival and jointly optimizing UAVs'trajectories,user association,and subchannel assignment,the average long-term sum of the user energy consumption minimization problem is *** address the problem involving both discrete and continuous variables,a hybrid decision deep reinforcement learning(DRL)-based intelligent energyefficient resource allocation and trajectory optimization algorithm is proposed,named HDRT algorithm,where deep Q network(DQN)and deep deterministic policy gradient(DDPG)are invoked to process discrete and continuous variables,*** results show that the proposed HDRT algorithm converges fast and outperforms other benchmarks in the aspect of user energy consumption and latency.
High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation system, is a long-standing but still challenging task for industrial and academic communities. The lack of inte...
详细信息
As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting ...
As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions.
Discrimination against different user groups has received growing attention in the recommendation field. To address this problem, existing works typically remove sensitive attributes that may cause discrimination thro...
Discrimination against different user groups has received growing attention in the recommendation field. To address this problem, existing works typically remove sensitive attributes that may cause discrimination through adversary learning to achieve fair recommendations. However, these approaches leverage all available interactions for learning user representations and overlook the fact that different interactions have varying relevance to users’ sensitive attributes. Ignoring this issue may weaken the effectiveness of adversary learning in removing sensitive attributes. To tackle this challenge, we propose a novel model called GS-FairRec, which distinguishes between user interactions to achieve better removal of sensitive attributes. The model consists of three modules: graph sampling-based representation learning, pseudo-user representation learning, and adversarial learning. Firstly, the graph sampling-based representation learning module removes some irrelevant neighbors from a user-item bipartite graph and employs a graph convolutional network (GCN) to learn user/item representations. Next, items that are relevant to a user’s sensitive information but do not match their preferences are defined as the user’s pseudo-interest items, which are leveraged to learn the pseudo-user representation. In the adversarial learning module, the user’s two kinds of representations are fused for adversarial learning to remove sensitive information. Additionally, we design a new metric to measure the model’s ability to remove sensitive attributes based on how a generated recommendation list discloses the user’s sensitive attributes. Finally, we conduct experiments on two real-world datasets, and our results demonstrate the superiority of our proposed model in fairness tasks.
Language-queried target sound extraction (TSE) aims to extract specific sounds from mixtures based on language queries. Traditional fully-supervised training schemes require extensively annotated parallel audio-text d...
详细信息
The mining of software community structure is of great significance in identifying software design pattern, software maintenance, software security and optimizing software structure. To improve the accuracy of descrip...
详细信息
Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output ...
详细信息
ISBN:
(数字)9798331527471
ISBN:
(纸本)9798331527488
Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.
暂无评论