Recently, the rapid development of deepfake technology attracted strong attention from the community. Some previous work on deepfake detection achieved good results in the frequency domain, which inspires us to combin...
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Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent *** is well known that single 2D CT slices lack spatial-temporal characteristi...
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Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent *** is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT ***,3D CT blocks necessitate significantly higher hardware resources during the learning ***,efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD *** this paper,we propose a ULD network with the enhanced temporal correlation for this purpose,named *** designed TCE module is applied to enrich the discriminate feature representation of multiple sequential CT ***,we employ multi-scale feature maps to facilitate the localization and detection of lesions in various *** experiments are conducted on the DeepLesion benchmark demonstrate that thismethod achieves 66.84%and 78.18%for FS@0.5 and FS@1.0,respectively,outperforming compared state-of-the-art methods.
Despite recent advances in lane detection methods,scenarios with limited-or no-visual-clue of lanes due to factors such as lighting conditions and occlusion remain challenging and crucial for automated ***,current lan...
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Despite recent advances in lane detection methods,scenarios with limited-or no-visual-clue of lanes due to factors such as lighting conditions and occlusion remain challenging and crucial for automated ***,current lane representations require complex post-processing and struggle with specific *** by the DETR architecture,we propose LDTR,a transformer-based model to address these *** are modeled with a novel anchorchain,regarding a lane as a whole from the beginning,which enables LDTR to handle special lanes *** enhance lane instance perception,LDTR incorporates a novel multi-referenced deformable attention module to distribute attention around the ***,LDTR incorporates two line IoU algorithms to improve convergence efficiency and employs a Gaussian heatmap auxiliary branch to enhance model representation capability during *** evaluate lane detection models,we rely on Fr´echet distance,parameterized F1-score,and additional synthetic *** results demonstrate that LDTR achieves state-of-the-art performance on well-known datasets.
Hybrid Power-line/Visible-light Communication(HPVC)network has been one of the most promising Cooperative Communication(CC)technologies for constructing Smart Home due to its superior communication reliability and har...
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Hybrid Power-line/Visible-light Communication(HPVC)network has been one of the most promising Cooperative Communication(CC)technologies for constructing Smart Home due to its superior communication reliability and hardware *** research on HPVC networks focuses on the performance analysis and optimization of the Physical(PHY)layer,where the Power Line Communication(PLC)component only serves as the backbone to provide power to light Emitting Diode(LED)*** designing a Media Access Control(MAC)protocol remains a great challenge because it allows both PLC and Visible Light Communication(VLC)components to operate data transmission,i.e.,to achieve a true HPVC network *** solve this problem,we propose a new HPC network MAC protocol(HPVC MAC)based on Carrier Sense Multiple Access/Collision Avoidance(CSMA/CA)by combining IEEE 802.15.7 and IEEE 1901 ***,we add an Additional Assistance(AA)layer to provide the channel selection strategies for sensor stations,so that they can complete data transmission on the selected channel via the specified CSMA/CA mechanism,*** on this,we give a detailed working principle of the HPVC MAC,followed by the construction of a joint analytical model for mathematicalmathematical validation of the HPVC *** the modeling process,the impacts of PHY layer settings(including channel fading types and additive noise feature),CSMA/CA mechanisms of 802.15.7 and 1901,and practical configurations(such as traffic rate,transit buffer size)are comprehensively taken into ***,we prove the proposed analytical model has the ***,through extensive simulations,we characterize the HPVC MAC performance under different system parameters and verify the correctness of the corresponding analytical model with an average error rate of 4.62%between the simulation and analytical results.
Edge is the key information in the process of image smoothing. Some edges, especially the weak edges, are difficult to maintain, which result in the local area being over-smoothed. For the protection of weak edges, we...
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Edge is the key information in the process of image smoothing. Some edges, especially the weak edges, are difficult to maintain, which result in the local area being over-smoothed. For the protection of weak edges, we propose an image smoothing algorithm based on global sparse structure and parameter adaptation. The algorithm decomposes the image into high frequency and low frequency part based on global sparse structure. The low frequency part contains less texture information which is relatively easy to smoothen. The high frequency part is more sensitive to edge information so it is more suitable for the selection of smoothing parameters. To reduce the computational complexity and improve the effect, we propose a bicubic polynomial fitting method to fit all the sample values into a surface. Finally, we use Alternating Direction Method of Multipliers (ADMM) to unify the whole algorithm and obtain the smoothed results by iterative optimization. Compared with traditional methods and deep learning methods, as well as the application tasks of edge extraction, image abstraction, pseudo-boundary removal, and image enhancement, it shows that our algorithm can preserve the local weak edge of the image more effectively, and the visual effect of smoothed results is better.
The integration of deep learning with conventional structured light center extraction techniques improves the accuracy of extracting structural gold centers. The method is divided into three steps. The initial step in...
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This paper considers a platform-led e-commerce supply chain consisted of a supplier and an online *** supplier distributes products through the online platform operated with reselling or agency *** online platform inv...
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This paper considers a platform-led e-commerce supply chain consisted of a supplier and an online *** supplier distributes products through the online platform operated with reselling or agency *** online platform invests in blockchain technology and the supplier shares partial investment *** paper formulates four platform Stackelberg models to analyze the optimal pricing and information traceability level without and with cost-sharing mechanism under two platform *** the model comparison,the impact of the cost-sharing mechanism and the choice of the platform mode are *** suggests that the cost-sharing mechanism is effective in improving two members profits under two platform *** cost-sharing mechanism,the supplier benefits from the agency platform mode while the online platform benefits from the reselling platform mode,and thereby the win-win outcome cannot be achieved between two *** cost-sharing mechanism,the supplier benefits from the agency platform mode while the online platform benefits from the reselling(agency)platform mode with a low(high)blockchain investment efficiency,and thereby the win-win outcome can be achieved between two members through the agency platform mode under a high blockchain investment *** paper further extends to the case that the online platform operates with hybrid mode,and derive the insights into the value of the agency platform mode in achieving the win-win outcome through the cost-sharing mechanism.
To establish semantic associations between images and texts, existing Image-Text Retrieval (ITR) methods primarily focus on fixed-scale fragments, which only identify explicit semantic categories. Consequently, semant...
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Huntington’s disease (HD) is a debilitating neurodegenerative disorder caused by an abnormal expansion of CAG repeats (Cytosine, Adenine, Guanine) in the huntingtin gene (HTT). This mutation leads to the production o...
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Huntington’s disease (HD) is a debilitating neurodegenerative disorder caused by an abnormal expansion of CAG repeats (Cytosine, Adenine, Guanine) in the huntingtin gene (HTT). This mutation leads to the production of a mutant huntingtin protein, resulting in neuronal dysfunction and cell death. Current treatments primarily focus on symptomatic relief and do not address the underlying genetic cause. This review explores spliceosome-mediated RNA trans-splicing (SMaRT) therapy as an innovative and potential approach for HD treatment. SMaRT leverages the cell’s natural splicing machinery to correct mutant mRNA, thereby reducing toxic protein levels while restoring functional protein production. We compare SMaRT with other gene therapy strategies, such as antisense oligonucleotides, RNA interference, and CRISPR-based systems, highlighting SMaRT’s dual-action mechanism and its potential advantages in clinical applications. Additionally, we discuss the challenges and future directions for SMaRT therapy, emphasizing the need for further research to optimize its efficacy and safety. This review aims to provide a comprehensive overview of current and emerging therapies for HD, with a focus on the innovative potential of SMaRT.
There are many kinds of linear canonical transform(LCT)-based Wigner distributions(WDs),which are very effective in detecting noisy linear frequency-modulated(LFM) signals. Among WDs in LCT domains, the instantaneous ...
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There are many kinds of linear canonical transform(LCT)-based Wigner distributions(WDs),which are very effective in detecting noisy linear frequency-modulated(LFM) signals. Among WDs in LCT domains, the instantaneous cross-correlation function type of Wigner distribution(ICFWD) attracts much attention from scholars, because it achieves not only low computational complexity but also good detection ***, the existing LCT free parameters selection strategy, namely a solution of the expectation-based output signal-to-noise ratio(SNR) optimization model, is not unique. In this paper, by introducing the variance-based output SNR optimization model, a multiobjective optimization model is established. Then the existence and uniqueness of the optimal parameters of ICFWD are investigated. The solution of the multiobjective optimization model with respect to one-component LFM signal added with zero-mean stationary circular Gaussian noise is derived. A comparison of the unique parameters selection strategy and the previous one is carried out. The theoretical results are also verified by numerical simulations.
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