Human-computer Interaction (HCI) is a rapidly evolving field. It has undergone many changes, and several current challenges deserve more attention from the community. Meta-research – the study of research practices ...
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This preprint makes the claim of having computed the 9th Dedekind Number. This was done by building an efficient FPGA Accelerator for the core operation of the process, and parallelizing it on the Noctua 2 Supercluste...
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In recent years, many deep learning-based methods have been proposed to tackle the problem of optical flow estimation and achieved promising results. However, they hardly consider that most videos are compressed and t...
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Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography(CT),or magnetic resonance imaging(MRI).An automated brain cancer classification using computer a...
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Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography(CT),or magnetic resonance imaging(MRI).An automated brain cancer classification using computer aided diagnosis(CAD)models can be designed to assist *** the recent advancement in computer vision(CV)and deep learning(DL)models,it is possible to automatically detect the tumor from images using a computer-aided *** study focuses on the design of automated Henry Gas Solubility Optimization with Fusion of Handcrafted and Deep Features(HGSO-FHDF)technique for brain cancer *** proposed HGSO-FHDF technique aims for detecting and classifying different stages of brain *** proposed HGSO-FHDF technique involves Gabor filtering(GF)technique for removing the noise and enhancing the quality of MRI *** addition,Tsallis entropy based image segmentation approach is applied to determine injured brain regions in the MRI ***,a fusion of handcrafted with deep features using Residual Network(ResNet)is utilized as feature ***,HGSO algorithm with kernel extreme learning machine(KELM)model was utilized for identifying the presence of brain *** examining the enhanced brain tumor classification performance,a comprehensive set of simulations take place on the BRATS 2015 dataset.
Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced *** recognition of lung cancer is essential to increase the survival rate of persons and it rem...
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Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced *** recognition of lung cancer is essential to increase the survival rate of persons and it remains a crucial problem in the healthcare *** aided diagnosis(CAD)models can be designed to effectually identify and classify the existence of lung cancer using medical *** recently developed deep learning(DL)models find a way for accurate lung nodule classification ***,this article presents a deer hunting optimization with deep convolutional neural network for lung cancer detection and classification(DHODCNNLCC)*** proposed DHODCNN-LCC technique initially undergoes pre-processing in two stages namely contrast enhancement and noise ***,the features extraction process on the pre-processed images takes place using the Nadam optimizer with RefineDet *** addition,denoising stacked autoencoder(DSAE)model is employed for lung nodule ***,the deer hunting optimization algorithm(DHOA)is utilized for optimal hyper parameter tuning of the DSAE model and thereby results in improved classification *** experimental validation of the DHODCNN-LCC technique was implemented against benchmark dataset and the outcomes are assessed under various *** experimental outcomes reported the superior outcomes of the DHODCNN-LCC technique over the recent approaches with respect to distinct measures.
This microteaching session is like Nifty Assignments for instruction. Instead of having the presenters just talk about their teaching, they will simulate how they would actually teach something. Covering a range of to...
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Recently, unsupervised image denoising methods learning from paired noisy samples have received increasing attention. These methods build on the idea that the mean of multiple noisy images of the same scene is the ide...
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Recently, unsupervised image denoising methods learning from paired noisy samples have received increasing attention. These methods build on the idea that the mean of multiple noisy images of the same scene is the ideal clean image. However, these methods ignore the effect of Aleatoric uncertainty in the noisy image (e.g., pixels deviating from the expected distribution). The presence of Aleatoric uncertainty causes degradation of the reconstructed target pixels, resulting in high uncertainty for these pixels (i.e., low confidence), which in turn leads to sub-optimal denoising results. To address this problem, we propose a novel uncertainty-aware unsupervised image denoising method named Uncer2Natural (U2N). It dynamically predicts the Aleatoric uncertainty for each noisy sample and produces satisfactory denoising results by reducing the effect of Aleatoric uncertainty. Extensive experimental results show that U2N outperforms state-of-the- art unsupervised image denoising methods in terms of both quantitative metrics and qualitative visual quality.
作者:
Ling, YuTan, WeiminYan, BoSchool of Computer Science
Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China
Survival analysis aims at modeling the relationship between covariates and event occurrence with some untracked (censored) samples. In implementation, existing methods model the survival distribution with strong assum...
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In network games, individuals interact strategically within network environments to maximize their utilities. However, obtaining network structures is challenging. In this work, we propose an unsupervised learning mod...
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In network games, individuals interact strategically within network environments to maximize their utilities. However, obtaining network structures is challenging. In this work, we propose an unsupervised learning model, called data-dependent gated-prior graph variational autoencoder (GPGVAE), that infers the underlying latent interaction type (strategic complement vs. substitute) among individuals and the latent network structure based on their observed actions. Specially, we propose a spectral graph neural network (GNN) based encoder to predict the interaction type and a data-dependent gated prior that models network structures conditioned on the interaction type. We further propose a Transformer based mixture of Bernoulli encoder of network structures and a GNN based decoder of game actions. We systematically study the Monte Carlo gradient estimation methods and effectively train our model in a stage-wise fashion. Extensive experiments across various synthetic and real-world network games demonstrate that our model achieves state-of-the-art performances in inferring network structures and well captures interaction types. Copyright 2024 by the author(s)
Recent neural talking radiance field methods have shown great success in photorealistic audio-driven talking face synthesis. In this paper, we propose the first novel interactive framework that utilizes human instruct...
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