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Robust Ensembling Network for Unsupervised Domain Adaptation [article]

Han Sun, Lei Lin, Ningzhong Liu, Huiyu Zhou
<span title="2021-08-21">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Recently, in order to address the unsupervised domain adaptation (UDA) problem, extensive studies have been proposed to achieve transferrable models. Among them, the most prevalent method is adversarial domain adaptation, which can shorten the distance between the source domain and the target domain. Although adversarial learning is very effective, it still leads to the instability of the network and the drawbacks of confusing category information. In this paper, we propose a Robust Ensembling
more &raquo; ... etwork (REN) for UDA, which applies a robust time ensembling teacher network to learn global information for domain transfer. Specifically, REN mainly includes a teacher network and a student network, which performs standard domain adaptation training and updates weights of the teacher network. In addition, we also propose a dual-network conditional adversarial loss to improve the ability of the discriminator. Finally, for the purpose of improving the basic ability of the student network, we utilize the consistency constraint to balance the error between the student network and the teacher network. Extensive experimental results on several UDA datasets have demonstrated the effectiveness of our model by comparing with other state-of-the-art UDA algorithms.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.09473v1">arXiv:2108.09473v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/a3p5u2lqirdj7nlosd34uzt5xi">fatcat:a3p5u2lqirdj7nlosd34uzt5xi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210829115013/https://arxiv.org/pdf/2108.09473v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/39/ef/39efb108ee54a5504b44fc12ea7a799c6a1bba80.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.09473v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Localizing Occluders with Compositional Convolutional Networks [article]

Adam Kortylewski, Qing Liu, Huiyu Wang, Zhishuai Zhang, Alan Yuille
<span title="2019-11-18">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Compositional convolutional networks are generative compositional models of neural network features, that achieve state of the art results when classifying partially occluded objects, even when they have not been exposed to occluded objects during training. In this work, we study the performance of CompositionalNets at localizing occluders in images. We show that the original model is not able to localize occluders well. We propose to overcome this limitation by modeling the feature activations
more &raquo; ... as a mixture of von-Mises-Fisher distributions, which also allows for an end-to-end training of CompositionalNets. Our experimental results demonstrate that the proposed extensions increase the model's performance at localizing occluders as well as at classifying partially occluded objects.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1911.08571v1">arXiv:1911.08571v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/newhhzijfzh55afux3olpivyey">fatcat:newhhzijfzh55afux3olpivyey</a> </span>
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Rethinking Normalization and Elimination Singularity in Neural Networks [article]

Siyuan Qiao, Huiyu Wang, Chenxi Liu, Wei Shen, Alan Yuille
<span title="2019-11-21">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we study normalization methods for neural networks from the perspective of elimination singularity. Elimination singularities correspond to the points on the training trajectory where neurons become consistently deactivated. They cause degenerate manifolds in the loss landscape which will slow down training and harm model performances. We show that channel-based normalizations (e.g. Layer Normalization and Group Normalization) are unable to guarantee a far distance from
more &raquo; ... n singularities, in contrast with Batch Normalization which by design avoids models from getting too close to them. To address this issue, we propose BatchChannel Normalization (BCN), which uses batch knowledge to avoid the elimination singularities in the training of channel-normalized models. Unlike Batch Normalization, BCN is able to run in both large-batch and micro-batch training settings. The effectiveness of BCN is verified on many tasks, including image classification, object detection, instance segmentation, and semantic segmentation. The code is here: https://github.com/joe-siyuan-qiao/Batch-Channel-Normalization.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1911.09738v1">arXiv:1911.09738v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/px7eboxodng7lbaxfbpbrmkxru">fatcat:px7eboxodng7lbaxfbpbrmkxru</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200929141919/https://arxiv.org/pdf/1911.09738v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/0e/7b/0e7b80b85705872b241f2592bc62ccbe48de090f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1911.09738v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Structured Multi-task Learning for Molecular Property Prediction [article]

Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, Jian Tang
<span title="2022-02-22">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Multi-task learning for molecular property prediction is becoming increasingly important in drug discovery. However, in contrast to other domains, the performance of multi-task learning in drug discovery is still not satisfying as the number of labeled data for each task is too limited, which calls for additional data to complement the data scarcity. In this paper, we study multi-task learning for molecular property prediction in a novel setting, where a relation graph between tasks is
more &raquo; ... . We first construct a dataset including around 400 tasks as well as a task relation graph. Then to better utilize such relation graph, we propose a method called SGNN-EBM to systematically investigate the structured task modeling from two perspectives. (1) In the latent space, we model the task representations by applying a state graph neural network (SGNN) on the relation graph. (2) In the output space, we employ structured prediction with the energy-based model (EBM), which can be efficiently trained through noise-contrastive estimation (NCE) approach. Empirical results justify the effectiveness of SGNN-EBM. Code is available on https://github.com/chao1224/SGNN-EBM.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.04695v1">arXiv:2203.04695v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/26rvhnc73rgnhczv6q2svx26li">fatcat:26rvhnc73rgnhczv6q2svx26li</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220316130000/https://arxiv.org/pdf/2203.04695v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/85/fe/85fe3ac8f468c510bb975d2917b4150a4544b910.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.04695v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Low-Dose Bisphenol A Exposure: A Seemingly Instigating Carcinogenic Effect on Breast Cancer

Zhe Wang, Huiyu Liu, Sijin Liu
<span title="2016-11-21">2016</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ifj4v2fyuvegzgqkhtt4dthbna" style="color: black;">Advanced Science</a> </i> &nbsp;
Huiyu Liu: Dr. Huiyu Liu is currently a professor at Beijing University of Chemical Technology.  ...  Liu moved to the current position at the end of 2015.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/advs.201600248">doi:10.1002/advs.201600248</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28251049">pmid:28251049</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5323866/">pmcid:PMC5323866</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ewa27ripsffmta7dgn4qtrtxhy">fatcat:ewa27ripsffmta7dgn4qtrtxhy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201105123820/http://ir.rcees.ac.cn/bitstream/311016/39354/1/Low-Dose%20Bisphenol%20A%20Exposure%20A%20Seemingly%20Instigating%20Carcinogenic%20Effect%20on%20Breast%20Cancer.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2c/5d/2c5d7b4c89a5b23ad905fe1fb104e87727de2d0b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/advs.201600248"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> wiley.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5323866" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

A Dense Siamese U-Net trained with Edge Enhanced 3D IOU Loss for Image Co-segmentation [article]

Xi Liu, Xiabi Liu, Huiyu Li, Xiaopeng Gong
<span title="2021-08-17">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Image co-segmentation has attracted a lot of attentions in computer vision community. In this paper, we propose a new approach to image co-segmentation through introducing the dense connections into the decoder path of Siamese U-net and presenting a new edge enhanced 3D IOU loss measured over distance maps. Considering the rigorous mapping between the signed normalized distance map (SNDM) and the binary segmentation mask, we estimate the SNDMs directly from original images and use them to
more &raquo; ... ine the segmentation results. We apply the Siamese U-net for solving this problem and improve its effectiveness by densely connecting each layer with subsequent layers in the decoder path. Furthermore, a new learning loss is designed to measure the 3D intersection over union (IOU) between the generated SNDMs and the labeled SNDMs. The experimental results on commonly used datasets for image co-segmentation demonstrate the effectiveness of our presented dense structure and edge enhanced 3D IOU loss of SNDM. To our best knowledge, they lead to the state-of-the-art performance on the Internet and iCoseg datasets.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.07491v1">arXiv:2108.07491v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wvnvudb4irap7on2cjkcawwjle">fatcat:wvnvudb4irap7on2cjkcawwjle</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210829153929/https://arxiv.org/pdf/2108.07491v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/07/75/07755d919d4b6a8a84d7b73867061f5bdbc70d2e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.07491v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A New Three-stage Curriculum Learning Approach to Deep Network Based Liver Tumor Segmentation [article]

Huiyu Li, Xiabi Liu, Said Boumaraf, Weihua Liu, Xiaopeng Gong, Xiaohong Ma
<span title="2019-10-17">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Our source code of these preprocessing steps is provided at https://github.com/Huiyu-Li/Preprocess-of-CT-data. Parameter Settings.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.07895v1">arXiv:1910.07895v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rqmnvc3zgnhfzmmvtyye275uiy">fatcat:rqmnvc3zgnhfzmmvtyye275uiy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200925074013/https://arxiv.org/ftp/arxiv/papers/1910/1910.07895.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7b/b8/7bb804ab0a226fa1ecc6b9875f9da30d291d42aa.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.07895v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

An evidential fusion approach for gender profiling

Jianbing Ma, Weiru Liu, Paul Miller, Huiyu Zhou
<span title="">2016</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ozlq63ehnjeqxf6cuxxn27cqra" style="color: black;">Information Sciences</a> </i> &nbsp;
CCTV (Closed-Circuit TeleVision) systems are broadly deployed in the present world. To ensure in-time reaction for intelligent surveillance, it is a fundamental task for real-world applications to determine the gender of people of interest. However, normal video algorithms for gender profiling (usually face profiling) have three drawbacks. First, the profiling result is always uncertain. Second, the profiling result is not stable. The degree of certainty usually varies over time, sometimes even
more &raquo; ... to the extent that a male is classified as a female, and vice versa. Third, for a robust profiling result in cases that a person's face is not visible, other features, such as body shape, are required. These algorithms may provide different recognition results -at the very least, they will provide different degrees of certainties. To overcome these problems, in this paper, we introduce an Dempster-Shafer (DS) evidential approach that makes use of profiling results from multiple algorithms over a period of time, in particular, Denoeux's cautious rule is applied for fusing mass functions through time lines. Experiments show that this approach does provide better results than single profiling results and classic fusion results. Furthermore, it is found that if severe mis-classification has occurred at the beginning of the time line, the combination can yield undesirable results. To remedy this weakness, we further propose three extensions to the evidential approach proposed above incorporating notions of time-window, time-attenuation, and time-discounting, respectively. These extensions also applies Denoeux's rule along with time lines and take the DS approach as a special case. Experiments show that these three extensions do provide better results than their predecessor when mis-classifications occur.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.ins.2015.11.011">doi:10.1016/j.ins.2015.11.011</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kyvt3bd3xzfobfdxxegcjhr4fy">fatcat:kyvt3bd3xzfobfdxxegcjhr4fy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190427142213/https://pure.qub.ac.uk/ws/files/17844529/SCBFJournalIS.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c6/d3/c6d3e988d71572e5176e6769fdb7432a34a50a76.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.ins.2015.11.011"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Multi-scale Edge-based U-shape Network for Salient Object Detection [article]

Han Sun, Yetong Bian, Ningzhong Liu, Huiyu Zhou
<span title="2021-08-21">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Deep-learning based salient object detection methods achieve great improvements. However, there are still problems existing in the predictions, such as blurry boundary and inaccurate location, which is mainly caused by inadequate feature extraction and integration. In this paper, we propose a Multi-scale Edge-based U-shape Network (MEUN) to integrate various features at different scales to achieve better performance. To extract more useful information for boundary prediction, U-shape Edge
more &raquo; ... k modules are embedded in each decoder units. Besides, the additional down-sampling module alleviates the location inaccuracy. Experimental results on four benchmark datasets demonstrate the validity and reliability of the proposed method. Multi-scale Edge based U-shape Network also shows its superiority when compared with 15 state-of-the-art salient object detection methods.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.09408v1">arXiv:2108.09408v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/eqkp5l5bgrhjremfwquu5n5apy">fatcat:eqkp5l5bgrhjremfwquu5n5apy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210829112154/https://arxiv.org/pdf/2108.09408v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fe/26/fe26a830962d3f744c5341f1849f25ea1b3270cb.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.09408v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

MPI: Multi-receptive and Parallel Integration for Salient Object Detection [article]

Han Sun, Jun Cen, Ningzhong Liu, Dong Liang, Huiyu Zhou
<span title="2021-08-08">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Liu N et al. [19] proposes a novel saliency detection algorithm called PiCA-Net that can selectively focus on informative context locations for each pixel.  ...  -X., Liu, J.-J., Fan, D.-P., et al.: EGNet: Edge guidance network for salient object detection. In: the IEEE International Conference on Computer Vision, 2019, pp. 8779-8788. 5.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.03618v1">arXiv:2108.03618v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ek7dosr4vnckxdz45qmpe5r4iq">fatcat:ek7dosr4vnckxdz45qmpe5r4iq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210812153414/https://arxiv.org/ftp/arxiv/papers/2108/2108.03618.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/8c/9f/8c9fe8fce7936acea501ddbe9b5c592e6504d1b4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.03618v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Molecular Characterization of blaIMP–4-Carrying Enterobacterales in Henan Province of China

Wentian Liu, Huiyue Dong, Tingting Yan, Xuchun Liu, Jing Cheng, Congcong Liu, Songxuan Zhang, Xiang Feng, Luxin Liu, Zhenya Wang, Shangshang Qin
<span title="2021-02-17">2021</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/67anf6qgandy5adz4rakospiuq" style="color: black;">Frontiers in Microbiology</a> </i> &nbsp;
., 2014; Liu et al., 2015) .  ...  Our previous study together with recent findings from China revealed the dominance of NDM-type MBL among CRECL; whether IMP-4 is the second most common MBL in CRECL needs further study (Liu et al., 2015  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fmicb.2021.626160">doi:10.3389/fmicb.2021.626160</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33679645">pmid:33679645</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7925629/">pmcid:PMC7925629</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rebhgctg25apdoc4j4kfbthl4m">fatcat:rebhgctg25apdoc4j4kfbthl4m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210428173215/https://fjfsdata01prod.blob.core.windows.net/articles/files/626160/pubmed-zip/.versions/1/.package-entries/fmicb-12-626160/fmicb-12-626160.pdf?sv=2018-03-28&amp;sr=b&amp;sig=A3lGlRHvttu8Iyb8FojRzlEUAqkbrr5PiXEUccg4Rlg%3D&amp;se=2021-04-28T17%3A32%3A44Z&amp;sp=r&amp;rscd=attachment%3B%20filename%2A%3DUTF-8%27%27fmicb-12-626160.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/cb/66/cb66693b1072e09b24dfb56c2cbc990ff695c36a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fmicb.2021.626160"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> frontiersin.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7925629" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Gelatin microcapsules for enhanced microwave tumor hyperthermia

Qijun Du, Changhui Fu, Jian Tie, Tianlong Liu, Linlin Li, Xiangling Ren, Zhongbing Huang, Huiyu Liu, Fangqiong Tang, Li Li, Xianwei Meng
<span title="">2015</span> <i title="Royal Society of Chemistry (RSC)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ugccpkgktvgjxkzlcyxrfrfqeq" style="color: black;">Nanoscale</a> </i> &nbsp;
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1039/c4nr07104b">doi:10.1039/c4nr07104b</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/25613756">pmid:25613756</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5rlqlav4bra43b4mjydavv2lka">fatcat:5rlqlav4bra43b4mjydavv2lka</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170814093028/http://www.rsc.org/suppdata/nr/c4/c4nr07104b/c4nr07104b1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ae/2a/ae2a2b6c9e6cfc913e3a55adc1be1ffd231f72d7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1039/c4nr07104b"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Integrated GANs: Semi-Supervised SAR Target Recognition

Fei Gao, Qiuyang Liu, Jinping Sun, Amir Hussain, Huiyu Zhou
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
With the advantage of working in all weathers and all days, synthetic aperture radar (SAR) imaging systems have a great application value. As an efficient image generation and recognition model, generative adversarial networks (GANs) have been applied to SAR image analysis and achieved promising performance. However, the cost of labeling a large number of SAR images limits the performance of the developed approaches and aggravates the mode collapsing problem. This paper presents a novel
more &raquo; ... namely Integrated GANs (I-GAN), which consists of a conditional GANs, an unconditional GANs and a classifier, to achieve semi-supervised generation and recognition simultaneously. The unconditional GANs assist the conditional GANs to increase the diversity of the generated images. A co-training method for the conditional GANs and the classifier is proposed to enrich the training samples. Since our model is capable of representing training images with rich characteristics, the classifier can achieve better recognition accuracy. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset proves that our method achieves better results in accuracy when labeled samples are insufficient, compared against other state-of-the-art techniques. INDEX TERMS Synthetic aperture radar (SAR), generative adversarial networks (GANs), semi-supervised learning, generation, recognition.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2935167">doi:10.1109/access.2019.2935167</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/noita7lgubfqtia3lfen3gidma">fatcat:noita7lgubfqtia3lfen3gidma</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210429134355/https://ieeexplore.ieee.org/ielx7/6287639/8600701/08798625.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fb/1a/fb1a014ca37cadd96e0989ea1fff9e38e820b39c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2935167"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Malware Classification Based on Shallow Neural Network

Pin Yang, Huiyu Zhou, Yue Zhu, Liang Liu, Lei Zhang
<span title="2020-12-02">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hijy7jexkvcipg3tulqv73bck4" style="color: black;">Future Internet</a> </i> &nbsp;
Liu et al. [20] proposed using GCN and CNN to process the API call graph and calculate the similarity between samples for malicious code family clustering. Raff et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/fi12120219">doi:10.3390/fi12120219</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4vqn3gs25jaaxc7jy72e2nqtoy">fatcat:4vqn3gs25jaaxc7jy72e2nqtoy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201205163004/https://res.mdpi.com/d_attachment/futureinternet/futureinternet-12-00219/article_deploy/futureinternet-12-00219-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/de/58/de5868bf2063d0f6a401f78641567f8b065aaab2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/fi12120219"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Comprehensive analysis of peroxiredoxins expression profiles and prognostic values in breast cancer

Jie Mei, Leiyu Hao, Xiaorui Liu, Guangshun Sun, Rui Xu, Huiyu Wang, Chaoying Liu
<span title="2019-08-06">2019</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/mwbemhev3vagjfqqczhnr5d3vi" style="color: black;">Biomarker Research</a> </i> &nbsp;
The function of PRDX3 and PRDX4 in BrCa is largely ambiguous, Liu et al. reveals that downregulation of PRDX3 potentiates PP2-induced apoptosis in MCF-7 cells, which suggests the tumor suppressor role  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s40364-019-0168-9">doi:10.1186/s40364-019-0168-9</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31402980">pmid:31402980</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6683561/">pmcid:PMC6683561</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sacuuds5bzaghdo6ktydpk4oaa">fatcat:sacuuds5bzaghdo6ktydpk4oaa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200325011936/https://link.springer.com/content/pdf/10.1186%2Fs40364-019-0168-9.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/14/f4/14f43855ebe48700fb21d7a6a3b8e9da52d5ea2f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s40364-019-0168-9"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6683561" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>
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