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Towards Fair Deep Clustering With Multi-State Protected Variables [article]

Bokun Wang, Ian Davidson
<span title="2019-01-29">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Correspondence to: Bokun Wang <bbwang@ucdavis.edu>. http://fairml.how/tutorial/index.html/ 2 http://approximatelycorrect.com/2016/11/07/thefoundations-of-algorithmic-bias/  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.10053v1">arXiv:1901.10053v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dpvhr2uy5nb4lpuhrv5cu77yne">fatcat:dpvhr2uy5nb4lpuhrv5cu77yne</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200930172936/https://arxiv.org/pdf/1901.10053v1.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/3a/81/3a81f485ef485fa66ffb2c5fd8c35cd6313647ea.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.10053v1" 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>

High-Speed Femto-Joule per Bit Silicon-Conductive Oxide Nanocavity Modulator [article]

Erwen Li, Bokun Zhou, Yunfei Bo, Alan X. Wang
<span title="2020-03-03">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Wang are School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331 USA (email: wang@engr.orst.edu).  ...  High-Speed Femto-Joule per Bit Silicon-Conductive Oxide Nanocavity Modulator Erwen Li, Bokun Zhou, Yunfei Bo, and Alan X.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.00983v1">arXiv:2004.00983v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wsy4lijghbf3ndq7hlkmtuklai">fatcat:wsy4lijghbf3ndq7hlkmtuklai</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200407012108/https://arxiv.org/ftp/arxiv/papers/2004/2004.00983.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] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.00983v1" 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>

Knowledge Distillation of Grassmann Manifold Network for Remote Sensing Scene Classification

Ling Tian, Zhichao Wang, Bokun He, Chu He, Dingwen Wang, Deshi Li
<span title="2021-11-11">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a> </i> &nbsp;
Due to device limitations, small networks are necessary for some real-world scenarios, such as satellites and micro-robots. Therefore, the development of a network with both good performance and small size is an important area of research. Deep networks can learn well from large amounts of data, while manifold networks have outstanding feature representation at small sizes. In this paper, we propose an approach that exploits the advantages of deep networks and shallow Grassmannian manifold
more &raquo; ... rks. Inspired by knowledge distillation, we use the information learned from convolutional neural networks to guide the training of the manifold networks. Our approach leads to a reduction in model size, which addresses the problem of deploying deep learning on resource-limited embedded devices. Finally, a series of experiments were conducted on four remote sensing scene classification datasets. The method in this paper improved the classification accuracy by 2.31% and 1.73% on the UC Merced Land Use and SIRIWHU datasets, respectively, and the experimental results demonstrate the effectiveness of our approach.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/rs13224537">doi:10.3390/rs13224537</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dzl273zgebblxc75iqfzj4bogu">fatcat:dzl273zgebblxc75iqfzj4bogu</a> </span>
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IntSGD: Adaptive Floatless Compression of Stochastic Gradients [article]

Konstantin Mishchenko and Bokun Wang and Dmitry Kovalev and Peter Richtárik
<span title="2022-03-20">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
., Wang, Y.-X., Azizzadenesheli, K., and Anandkumar, A. SignSGD: Compressed optimisation for non-convex problems. In Dy, J. and Krause, A.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2102.08374v2">arXiv:2102.08374v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4fj5jjriirc5vehj5t3grwit5a">fatcat:4fj5jjriirc5vehj5t3grwit5a</a> </span>
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Adversarial Cross-Modal Retrieval

Bokun Wang, Yang Yang, Xing Xu, Alan Hanjalic, Heng Tao Shen
<span title="">2017</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lahlxihmo5fhzpexw7rundu24u" style="color: black;">Proceedings of the 2017 ACM on Multimedia Conference - MM &#39;17</a> </i> &nbsp;
Cross-modal retrieval aims to enable flexible retrieval experience across different modalities (e.g., texts vs. images). The core of crossmodal retrieval research is to learn a common subspace where the items of different modalities can be directly compared to each other. In this paper, we present a novel Adversarial Cross-Modal Retrieval (ACMR) method, which seeks an effective common subspace based on adversarial learning. Adversarial learning is implemented as an interplay between two
more &raquo; ... s. The first process, a feature projector, tries to generate a modality-invariant representation in the common subspace and to confuse the other process, modality classifier, which tries to discriminate between different modalities based on the generated representation. We further impose triplet constraints on the feature projector in order to minimize the gap among the representations of all items from different modalities with same semantic labels, while maximizing the distances among semantically different images and texts. Through the joint exploitation of the above, the underlying cross-modal semantic structure of multimedia data is better preserved when this data is projected into the common subspace. Comprehensive experimental results on four widely used benchmark datasets show that the proposed ACMR method is superior in learning effective subspace representation and that it significantly outperforms the state-of-the-art cross-modal retrieval methods. CCS CONCEPTS · Information systems → Multimedia and multimodal retrieval;
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3123266.3123326">doi:10.1145/3123266.3123326</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/mm/WangYXHS17.html">dblp:conf/mm/WangYXHS17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7pbuzhkqsbbt7bmhyj45qazyea">fatcat:7pbuzhkqsbbt7bmhyj45qazyea</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190218140140/https://static.aminer.org/pdf/20170130/pdfs/mm/lgw2b9yepeubislhfgv6q4xuicnkacoo.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/61/06/61060bea27a3410260988540b627ccc5ba131822.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3123266.3123326"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Optimal Algorithms for Stochastic Multi-Level Compositional Optimization [article]

Wei Jiang, Bokun Wang, Yibo Wang, Lijun Zhang, Tianbao Yang
<span title="2022-03-11">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Related Work This section briefly reviews related work on stochastic two-level and multi-level compositional optimization problems. 2.1 Two-Level Compositional Optimization Wang et al. [2017a] first  ...  In a subsequent work [Wang et al., 2017b] , the accelerated stochastic compositional proximal gradient (ASC-PG) is proposed to improve the complexity to O 1/ 4.5 , O 1/ 2 , and O (1/ ) for non-convex,  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.07530v3">arXiv:2202.07530v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kx7tl5to4raftjabaimrbzfjum">fatcat:kx7tl5to4raftjabaimrbzfjum</a> </span>
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Fully Convolutional Networks and a Manifold Graph Embedding-Based Algorithm for PolSAR Image Classification

Chu He, Bokun He, Mingxia Tu, Yan Wang, Tao Qu, Dingwen Wang, Mingsheng Liao
<span title="2020-05-05">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a> </i> &nbsp;
According to Wang et al. [31] , the whole scene has been divided into 11 species: rapeseed, grass, forest, peas, lucerne, wheat, beets, bare soil, stem beans, water and potatoes.  ...  Wang combined the scattering entropy and co-polarization ratio for the initial classification and then used the Wishart distribution for the iterations, which made the terrain more separable [5] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/rs12091467">doi:10.3390/rs12091467</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rn64olu635hfdaoj3awjvw545a">fatcat:rn64olu635hfdaoj3awjvw545a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200508042714/https://res.mdpi.com/d_attachment/remotesensing/remotesensing-12-01467/article_deploy/remotesensing-12-01467-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/78/7a/787a2879fae748dbc75e03620b9c1685067444d6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/rs12091467"> <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>

Performance and Hydration Mechanism of Modified Tabia with Composite-Activated Coal Gangue

Yanbing Zhao, Caiqian Yang, Songlin Cheng, Zhiren Wu, Bokun Wang
<span title="2022-01-21">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3mrc5eqrxbd25gqpgrtad576x4" style="color: black;">Crystals</a> </i> &nbsp;
The feasibility of modified tabia (MT) with composite-activated coal gangue (CACG) as the subgrade material of low-grade highways was experimentally investigated. A composite activation method was employed to improve the pozzolanic activity of coal gangue. The effect of CACG content on the mechanical properties of MT was investigated through a series of experiments. It was found that the pozzolanic reactivity of coal gangue was remarkably enhanced by the composite activation method. Compared
more &raquo; ... h traditional tabia (TT), the unconfined compressive strength, splitting strength, and flexural tensile strength of the MT with 50% of CACG content increased by 5.03 times, 9.71 times, and 1.50 times, respectively. The impermeability of specimens with CACG significantly improved. Furthermore, the mass loss rate of MT was less than 2.83%, while it reached up to 34.20% in TT after being conditioned to 40 freeze–thaw cycles. Finally, the microstructure change and hydration mechanism of MT are discussed and revealed.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/cryst12020150">doi:10.3390/cryst12020150</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/a7sl6nn6hre6ldctktkubjscxe">fatcat:a7sl6nn6hre6ldctktkubjscxe</a> </span>
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A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning

Yida Zhu, Haiyong Luo, Qu Wang, Fang Zhao, Bokun Ning, Qixue Ke, Chen Zhang
<span title="2019-02-14">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
Wang et al. [30] applied a machine learning algorithm to classify the neighboring GSM station's signal in different environments and identify the users' current context by signal recognition.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s19040786">doi:10.3390/s19040786</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/m4l7kopuyrfttf3mxejcxtnfvi">fatcat:m4l7kopuyrfttf3mxejcxtnfvi</a> </span>
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Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications [article]

Bokun Wang, Tianbao Yang
<span title="2022-05-17">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Wang et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.12396v3">arXiv:2202.12396v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/m5bcbxpfzzb4pddz5k5asmnjsm">fatcat:m5bcbxpfzzb4pddz5k5asmnjsm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220622234428/https://arxiv.org/pdf/2202.12396v3.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/87/12/87121a0f23030139ef71b42470f6d0408da16ea8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.12396v3" 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>

Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold [article]

Bokun Wang, Shiqian Ma, Lingzhou Xue
<span title="2022-03-21">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
., sparse PCA with streaming data, seem to be very limited (Yang and Xu, 2015; Wang and Lu, 2016) .  ...  Popular methods include ProxSGD (Rosasco et al., 2014) , ProxSVRG Zhang, 2014), ProxSARAH (Pham et al., 2019) and ProxSpiderBoost (Wang et al., 2019) .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.01209v2">arXiv:2005.01209v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bzr3vabgljhhtlumwjgemvzftq">fatcat:bzr3vabgljhhtlumwjgemvzftq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220521195916/https://arxiv.org/pdf/2005.01209v2.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/66/50/66508a022305246bc7a160b0ec9693de1adfe418.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.01209v2" 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>

Relationship Prior and Adaptive Knowledge Mimic Based Compressed Deep Network for Aerial Scene Classification

Chu He, Bokun He, Xiaohuo Yin, Wenwei Wang, Mingsheng Liao
<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;
BOKUN HE received the M.Sc. degree from the School of Electronic Information, Wuhan University, China, in 2019. Her research interests include image processing and computer vision.  ...  first category of methods is based on low-level features for describing the spectra, textures, The associate editor coordinating the review of this manuscript and approving it for publication was Dong Wang  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2932229">doi:10.1109/access.2019.2932229</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lwf4g4lsxngpnezlja5ci6lioe">fatcat:lwf4g4lsxngpnezlja5ci6lioe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210718030142/https://ieeexplore.ieee.org/ielx7/6287639/8600701/08782458.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/01/11/0111e8639a4f8435d7f007406296a1b2b0f93ca2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2932229"> <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>

Disruption of Interneuron Neurogenesis in Premature Newborns and Reversal with Estrogen Treatment

Mahima Tibrewal, Bokun Cheng, Preeti Dohare, Furong Hu, Rana Mehdizadeh, Ping Wang, Deyou Zheng, Zoltan Ungvari, Praveen Ballabh
<span title="2017-12-15">2017</span> <i title="Society for Neuroscience"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s7bticdwizdmhll4taefg57jde" style="color: black;">Journal of Neuroscience</a> </i> &nbsp;
Many Preterm-born children suffer from neurobehavioral disorders. Premature birth terminates the hypoxic in utero environment and supply of maternal hormones. As the production of interneurons continues until the end of pregnancy, we hypothesized that premature birth would disrupt interneuron production and that restoration of the hypoxic milieu or estrogen treatment might reverse interneuron generation. To test these hypotheses, we compared interneuronal progenitors in the medial ganglionic
more &raquo; ... nences (MGEs), lateral ganglionic eminences (LGEs), and caudal ganglionic eminences (CGEs) between preterm-born [born on embryonic day (E) 29; examined on postnatal day (D) 3 and D7] and term-born (born on E32; examined on D0 and D4) rabbits at equivalent postconceptional ages. We found that both total and cycling Nkx2.1 ϩ , Dlx2 ϩ , and Sox2 ϩ cells were more abundant in the MGEs of preterm rabbits at D3 compared with term rabbits at D0, but not in D7 preterm relative to D4 term pups. Total Nkx2.1 ϩ progenitors were also more numerous in the LGEs of preterm pups at D3 compared with term rabbits at D0. Dlx2 ϩ cells in CGEs were comparable between preterm and term pups. Simulation of hypoxia by dimethyloxalylglycine treatment did not affect the number of interneuronal progenitors. However, estrogen treatment reduced the density of total and proliferating Nkx2.1 ϩ and Dlx2 ϩ cells in the MGEs and enhanced Ascl1 transcription factor. Estrogen treatment also reduced Ki67, c-Myc, and phosphorylation of retinoblastoma protein, suggesting inhibition of the G1-to-S phase transition. Hence, preterm birth disrupts interneuron neurogenesis in the MGE and estrogen treatment reverses interneuron neurogenesis in preterm newborns by cell-cycle inhibition and elevation of Ascl1. We speculate that estrogen replacement might partially restore neurogenesis in human premature infants. Prematurity results in developmental delays and neurobehavioral disorders, which might be ascribed to disturbances in the development of cortical interneurons. Here, we show that preterm birth disrupts interneuron neurogenesis in the medial ganglionic eminence (MGE) and, more importantly, that estrogen treatment reverses this perturbation in the population of interneuron progenitors in the MGE. The estrogen seems to restore neurogenesis by inhibiting the cell cycle and elevating Ascl1 expression. As preterm birth causes plasma estrogen level to drop 100-fold, the estrogen replacement in preterm infants is physiological. We speculate that estrogen replacement might ameliorate disruption in production of interneurons in human premature infants.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1523/jneurosci.1875-17.2017">doi:10.1523/jneurosci.1875-17.2017</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29246927">pmid:29246927</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5792473/">pmcid:PMC5792473</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pxfljvjz2rbdfaypm3nemmt43u">fatcat:pxfljvjz2rbdfaypm3nemmt43u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200206100236/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC5792473&amp;blobtype=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/63/7b/637b069024214a4843bc66f2581c34b9d2c99373.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1523/jneurosci.1875-17.2017"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5792473" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Diagnosis Value of the Serum Amyloid A Test in Neonatal Sepsis: A Meta-Analysis

Haining Yuan, Jie Huang, Bokun Lv, Wenying Yan, Guang Hu, Jian Wang, Bairong Shen
<span title="">2013</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/icbhosh775h7bgzgot6avm3cua" style="color: black;">BioMed Research International</a> </i> &nbsp;
Neonatal sepsis (NS), a common disorder for humans, is recognized as a leading global public health challenge. This meta-analysis was performed to assess the accuracy of the serum amyloid A (SAA) test for diagnosing NS. The studies that evaluated the SAA test as a diagnotic marker were searched in Pubmed, EMBASE, the Cochrane Library, and Google Network between January 1996 and June 2013. A total of nine studies including 823 neonates were included in our meta-analysis. Quality of each study
more &raquo; ... evaluated by the quality assessment of diagnostic accuracy studies tool (QUADAS). The SAA test showed moderate accuracy in the diagnosis of NS both at the first suspicion of sepsis and 8–96 h after the sepsis onset, both withQ*=0.91, which is similar to the PCT and CRP tests for the diagnosis of NS in the same period. Heterogeneity between studies was also explained by cut-off point, SAA assay, and age of included neonates. On the basis of our meta-analysis, therefore, SAA could be promising and meaningful in the diagnosis of NS.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2013/520294">doi:10.1155/2013/520294</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/23984377">pmid:23984377</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3747616/">pmcid:PMC3747616</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hnaumngzrfa7jouo4vy4s6ygda">fatcat:hnaumngzrfa7jouo4vy4s6ygda</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190304075240/http://pdfs.semanticscholar.org/ba2c/f549a8bb671923b4e416c2548f91651d9aed.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/ba/2c/ba2cf549a8bb671923b4e416c2548f91651d9aed.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2013/520294"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3747616" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Evaluation of a Single-Channel EEG-Based Sleep Staging Algorithm

Shanguang Zhao, Fangfang Long, Xin Wei, Xiaoli Ni, Hui Wang, Bokun Wei
<span title="2022-03-01">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vyslcn4ljzdq3jes5w7fln3qyu" style="color: black;">International Journal of Environmental Research and Public Health</a> </i> &nbsp;
Sleep staging is the basis of sleep assessment and plays a crucial role in the early diagnosis and intervention of sleep disorders. Manual sleep staging by a specialist is time-consuming and is influenced by subjective factors. Moreover, some automatic sleep staging algorithms are complex and inaccurate. The paper proposes a single-channel EEG-based sleep staging method that provides reliable technical support for diagnosing sleep problems. In this study, 59 features were extracted from three
more &raquo; ... pects: time domain, frequency domain, and nonlinear indexes based on single-channel EEG data. Support vector machine, neural network, decision tree, and random forest classifier were used to classify sleep stages automatically. The results reveal that the random forest classifier has the best sleep staging performance among the four algorithms. The recognition rate of the Wake phase was the highest, at 92.13%, and that of the N1 phase was the lowest, at 73.46%, with an average accuracy of 83.61%. The embedded method was adopted for feature filtering. The results of sleep staging of the 11-dimensional features after filtering show that the random forest model achieved 83.51% staging accuracy under the condition of reduced feature dimensions, and the coincidence rate with the use of all features for sleep staging was 94.85%. Our study confirms the robustness of the random forest model in sleep staging, which also represents a high classification accuracy with appropriate classifier algorithms, even using single-channel EEG data. This study provides a new direction for the portability of clinical EEG monitoring.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/ijerph19052845">doi:10.3390/ijerph19052845</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35270548">pmid:35270548</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8910622/">pmcid:PMC8910622</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cqvraie64zetnhsbqggqurz3mu">fatcat:cqvraie64zetnhsbqggqurz3mu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220505141134/https://mdpi-res.com/d_attachment/ijerph/ijerph-19-02845/article_deploy/ijerph-19-02845.pdf?version=1646124028" 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/89/0e89c0d3a0b36da8d23b5263f8d535fbd6172fad.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/ijerph19052845"> <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> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8910622" 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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