Direct volume rendering(DVR)is a technique that emphasizes structures of interest(SOIs)within a volume visually,while simultaneously depicting adjacent regional information,e.g.,the spatial location of a structure con...
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Direct volume rendering(DVR)is a technique that emphasizes structures of interest(SOIs)within a volume visually,while simultaneously depicting adjacent regional information,e.g.,the spatial location of a structure concerning its *** DVR,transfer function(TF)plays a key role by enabling accurate identification of SOIs interactively as well as ensuring appropriate visibility of *** generation typically involves non-intuitive trial-and-error optimization of rendering parameters,which is time-consuming and *** at mitigating this manual process have led to approaches that make use of a knowledge database consisting of pre-designed TFs by domain *** these approaches,a user navigates the knowledge database to find the most suitable pre-designed TF for their input volume to visualize the *** these approaches potentially reduce the workload to generate the TFs,they,however,require manual TF navigation of the knowledge database,as well as the likely fine tuning of the selected TF to suit the *** this work,we propose a TF design approach,CBR-TF,where we introduce a new content-based retrieval(CBR)method to automatically navigate the knowledge *** of pre-designed TFs,our knowledge database contains volumes with SOI *** an input volume,our CBR-TF approach retrieves relevant volumes(with SOI labels)from the knowledge database;the retrieved labels are then used to generate and optimize TFs of the *** approach largely reduces manual TF navigation and fine *** our CBR-TF approach,we introduce a novel volumetric image feature which includes both a local primitive intensity profile along the SOIs and regional spatial semantics available from the co-planar images to the *** the regional spatial semantics,we adopt a convolutional neural network to obtain high-level image feature *** the intensity profile,we extend the dynamic time warping technique to address subtle alignment
The rapid development of deep learning provides great convenience for production and ***,the massive labels required for training models limits further ***-shot learning which can obtain a high-performance model by le...
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The rapid development of deep learning provides great convenience for production and ***,the massive labels required for training models limits further ***-shot learning which can obtain a high-performance model by learning few samples in new tasks,providing a solution for many scenarios that lack *** paper summarizes few-shot learning algorithms in recent years and proposes a ***,we introduce the few-shot learning task and its ***,according to different implementation strategies,few-shot learning methods in recent years are divided into five categories,including data augmentation-based methods,metric learning-based methods,parameter optimization-based methods,external memory-based methods,and other ***,We investigate the application of few-shot learning methods and summarize them from three directions,including computer vision,human-machine language interaction,and robot ***,we analyze the existing few-shot learning methods by comparing evaluation results on mini Image Net,and summarize the whole paper.
Subspace clustering has shown great potential in discovering the hidden low-dimensional subspace structures in high-dimensional data. However, most existing methods still face the problem of noise distortion and overl...
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Multi-access edge computing has become an effective paradigm to provide offloading services for computation-intensive and delay-sensitive tasks on vehicles. However, high mobility of vehicles usually incurs spatio-tem...
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Session-based recommendation is a popular research topic that aims to predict users’next possible interactive item by exploiting anonymous *** existing studies mainly focus on making predictions by considering users...
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Session-based recommendation is a popular research topic that aims to predict users’next possible interactive item by exploiting anonymous *** existing studies mainly focus on making predictions by considering users’single interactive *** recent efforts have been made to exploit multiple interactive behaviors,but they generally ignore the influences of different interactive behaviors and the noise in interactive *** address these problems,we propose a behavior-aware graph neural network for session-based ***,different interactive sequences are modeled as directed ***,the item representations are learned via graph neural ***,a sparse self-attention module is designed to remove the noise in behavior ***,the representations of different behavior sequences are aggregated with the gating mechanism to obtain the session *** results on two public datasets show that our proposed method outperforms all competitive *** source code is available at the website of GitHub.
The health care system encompasses the participation of individuals,groups,agencies,and resources that offer services to address the requirements of the person,community,and population in terms of *** to the rising de...
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The health care system encompasses the participation of individuals,groups,agencies,and resources that offer services to address the requirements of the person,community,and population in terms of *** to the rising debates on the healthcare systems in relation to diseases,treatments,interventions,medication,and clinical practice guidelines,the world is currently discussing the healthcare industry,technology perspectives,and healthcare *** gain a comprehensive understanding of the healthcare systems research paradigm,we offered a novel contextual topic modeling approach that links up the CombinedTM model with our healthcare Bert to discover the contextual topics in the domain of *** research work discovered 60 contextual topics among them fteen topics are the hottest which include smart medical monitoring systems,causes,and effects of stress and anxiety,and healthcare cost estimation and twelve topics are the ***,thirty-three topics are showing in-significant *** further investigated various clusters and correlations among the topics exploring inter-topic distance maps which add depth to the understanding of the research structure of this scientific *** current study enhances the prior topic modeling methodologies that examine the healthcare literature from a particular disciplinary *** further extends the existing topic modeling approaches that do not incorporate contextual information in the topic discovery process adding contextual information by creating sentence embedding vectors through transformers-based *** also utilized corpus tuning,the mean pooling technique,and the hugging face *** method gives a higher coherence score as compared to the state-of-the-art models(LSA,LDA,and Ber Topic).
As a frontier technology,holography has important research values in fields such as bio-micrographic imaging,light feld modulation and data ***,the real-time acquisition of 3D scenes and high-fidelity reconstruction t...
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As a frontier technology,holography has important research values in fields such as bio-micrographic imaging,light feld modulation and data ***,the real-time acquisition of 3D scenes and high-fidelity reconstruction technology has not yet made a breakthrough,which has seriously hindered the development of ***,a novel holographic camera is proposed to solve the above inherent problems *** proposed holographic camera consists of the acquisition end and the calculation *** the acquisition end of the holographic camera,specially configured liquid materials and liquid lens structure based on voice-coil motor-driving are used to produce the liquid camera,so that the liquid camera can quickly capture the focus stack of the real 3D scene within 15 *** the calculation end,a new structured focus stack network(FS-Net)is designed for hologram *** training the FS-Net with the focus stack renderer and learnable Zernike phase,it enables hologram calculation within 13 *** the first device to achieve real-time incoherent acquisition and high-fidelity holographic reconstruction of a real 3D scene,our proposed holographic camera breaks technical bottlenecks of difficulty in acquiring the real 3D scene,low quality of the holographic reconstructed image,and incorrect defocus *** experimental results demonstrate the effectiveness of our holographic camera in the acquisition of focal plane information and hologram calculation of the real 3D *** proposed holographic camera opens up a new way for the application of holography in fields such as 3D display,light field modulation,and 3D measurement.
Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least t...
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Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least two ***, the performance of FedRecs is compromised due to highly sparse on-device data for each client. Second, the system's robustness is undermined by the vulnerability to model poisoning attacks launched by malicious users. In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec. Unlike previous contrastive learning approaches in FedRecs that necessitate clients to share their private parameters, our CL4FedRec aligns with the basic FedRec learning protocol, ensuring compatibility with most existing FedRec implementations. We then evaluate the robustness of FedRecs equipped with CL4FedRec by subjecting it to several state-of-the-art model poisoning attacks. Surprisingly, our observations reveal that contrastive learning tends to exacerbate the vulnerability of FedRecs to these attacks. This is attributed to the enhanced embedding uniformity, making the polluted target item embedding easily proximate to popular items. Based on this insight, we propose an enhanced and robust version of CL4FedRec(rCL4FedRec) by introducing a regularizer to maintain the distance among item embeddings with different popularity levels. Extensive experiments conducted on four commonly used recommendation datasets demonstrate that rCL4FedRec significantly enhances both the model's performance and the robustness of FedRecs.
Dear Editor,This letter presents a new transfer learning framework for the deep multi-agent reinforcement learning(DMARL) to reduce the convergence difficulty and training time when applying DMARL to a new scenario [1...
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Dear Editor,This letter presents a new transfer learning framework for the deep multi-agent reinforcement learning(DMARL) to reduce the convergence difficulty and training time when applying DMARL to a new scenario [1], [2].
In general,physicians make a preliminary diagnosis based on patients’admission narratives and admission conditions,largely depending on their experiences and professional *** automatic and accurate tentative diagnosi...
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In general,physicians make a preliminary diagnosis based on patients’admission narratives and admission conditions,largely depending on their experiences and professional *** automatic and accurate tentative diagnosis based on clinical narratives would be of great importance to physicians,particularly in the shortage of medical *** its great value,little work has been conducted on this diagnosis ***,in this study,we propose a fusion model that integrates the semantic and symptom features contained in the clinical *** semantic features of the input text are initially captured by an attention-based Bidirectional Long Short-Term Memory(BiLSTM)*** symptom concepts,recognized from the input text,are then vectorized by using the term frequency-inverse document frequency method based on the relations between symptoms and ***,two fusion strategies are utilized to recommend the most potential candidate for the international classification of diseases *** training and evaluation are performed on a public clinical *** results show that both fusion strategies achieved a promising performance,in which the best performance obtained a top-3 accuracy of 0.7412.
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