A fully integrated differential impulse radio transmitter by He, Jin, Zhang, Yue Ping, Lim, Wei Meng, Xiong, Yong-Zhong

“…This paper presents a fully integrated differential impulse radio transmitter for ultra-wideband (UWB) applications. …”
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Exploring farmers’ perspectives on agroforestry practices in Rangamati Hill tracts of Bangladesh by Setu Chakma, Mithun Kumar Ghosh, Md Mahbub Alam, Ajnabi Sultana, Eco Talukder, Netui Chakma

“…Agroforestry has been a vital component of the livelihoods of hill communities in Bangladesh, supplying households with food and energy, generating income, and helping to protect the environment. …”
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Employee Training and Development/ by Noe, Raymond A., author 323555

Speeding up deep neural network training with decoupled and analytic learning by Zhuang, Huiping

“…These lockings impose strong synchronism among modules (a consecutive stack of layers), rendering most modules idle during training. A fully decoupled learning method using delayed gradients (FDG) is first proposed which addresses all the three lockings. …”
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Eccentric compression performance of jointed precast concrete-encased concrete-filled steel tube columns with dry connections by Zhu, Gaoming, Tan, Kang Hai

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TFIDF meets deep document representation : a re-visit of co-training for text classification by Chen, Zhiwei

“…However, two sufficient and redundant views of an instance are often not available to fully facilitate co-training in the past. With the recent develop- ment of deep learning, we now have both traditional TFIDF representation and deep representation for documents. …”
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SleepVST: sleep staging from near-infrared video signals using pre-trained transformers by Carter, JF, Jorge, J, Gibson, O, Tarassenko, L

Strength and Conditioning for Team Sports : Sport-Specific Physical Preparation for High Performance / by Gamble, Paul, author 459233, Taylor & Francis Group (Online service) 645650

“…This new edition also includes an appendix that provides detailed examples of training programmes for a range of team sports. Fully illustrated throughout, it is essential reading for all serious students of strength and conditioning, and for any practitioner seeking to extend their professional practice.…”
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Repeatability, reproducibility, and observer variability of body composition assessment with single-slice lumbar MRI by Nowak, M, Núñez, L, Hill, CE, Davis, T, Cardiel, LF, Schooler, GR, Mermekli, A, Salvati, R, Thomaides Brears, H, Pansini, M, Robson, MD

Immune correlates of early clearance of Mycobacterium tuberculosis among tuberculosis household contacts in Indonesia by Setiabudiawan, TP, Apriani, L, Verrall, AJ, Utami, F, Schneider, M, Indrati, AR, Halim, PP, Kaplonek, P, Malca, H, Lee, JS, Moorlag, SJCFM, de Bree, LCJ, Mourits, VP, Joosten, LAB, Netea, MG, Alisjahbana, B, McNamara, RP, Alter, G, van Laarhoven, A, Ussher, JE, Sharples, K, Koeken, VACM, Hill, PC, van Crevel, R

Grand challenges in entomology: priorities for action in the coming decades by Luke, Sarah H., Roy, Helen E., Thomas, Chris D., Tilley, Luke A. N., Ward, Simon, Watt, Allan, Carnaghi, Manuela, Jaworski, Coline C., Tercel, Maximillian P. T. G., Woodrow, Charlie, Aown, Susmita, Banfield-Zanin, Jennifer A., Barnsley, Sarah L., Berger, Iris, Brown, Mark J. F., Bull, James C., Campbell, Heather, Carter, Ruth A. B., Charalambous, Magda, Cole, Lorna J., Ebejer, Martin J., Farrow, Rachel A., Fartyal, Rajendra S., Grace, Miriam, Highet, Fiona, Hill, Jane K., Hood, Amelia S. C., Kent, Eleanor S., Krell, Frank-Thorsten, Leather, Simon R., Leybourne, Daniel J., Littlewood, Nick A., Lyons, Ashley, Matthews, Graham, Mc Namara, Louise, Menéndez, Rosa, Merrett, Peter, Mohammed, Sajidha, Murchie, Archie K., Noble, Michael, Paiva, Maria-Rosa, Pannell, Michael J., Phon, Chooi-Khim, Port, Gordon, Powell, Charlotte, Rosell, Stewart, Sconce, Francisca, Shortall, Chris R., Slade, Eleanor M., Sutherland, Jamie P., Weir, Jamie C., Williams, Christopher D., Zielonka, Natalia B., Dicks, Lynn V.

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Multi-path region mining for weakly supervised 3D semantic segmentation on point clouds by Wei, Jiacheng, Lin, Guosheng, Yap, Kim-Hui, Hung, Tzu-Yi, Xie, Lihua

“…Then, we use the point-level pseudo label to train a point cloud segmentation network in a fully supervised manner. …”
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Complex-image-based sparse sar imaging and its equivalence by Bi, Hui, Bi, Guoan, Zhang, Bingchen, Hong, Wen

“…It is found that if the input MF-recovered SAR complex image is obtained via fully sampled raw data, the proposed method can achieve an identical high-resolution image to that obtained by the azimuth-range decouple algorithm. …”
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Deep learning for object detection and image segmentation by Tan, Yan Hwa

“…In the proposed approach, we segmented and labelled multiple perspective of a view using a convolutional neural network. A large amount of training data is required to train a deep neural network for segmentation. …”
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Reliability analysis and data driven modelling of railway component failure by Wang, Jinlong

“…The developed feature extraction method guaranteed a larger amount of available training data to improve the machine learning accuracy. …”
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Scene text detection using neural network by Tan, Yi Hong

“…The second is to use supervised pre-training followed by domain-specific finetuning to coaching giant convolutional neural networks once the tagged training information is lean [1]. we tend to train a replacement model supported a dataset collected by ourselves from google web site. …”
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ACDC: online unsupervised cross-domain adaptation by de Carvalho, Marcus, Pratama, Mahardhika, Zhang, Jie, Yee, Edward Yapp Kien

“…We consider the problem of online unsupervised cross-domain adaptation, where two independent but related data streams with different feature spaces – a fully labeled source stream and an unlabeled target stream – are learned together. …”
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Optimizing AR PAM image enhancement: learning & model based approaches with GANs & deep CNNs by Liu, Chenyang

“…This study addresses the challenge of improving image quality in acoustic resolution photoacoustic imaging.Introducing acoustic resolution (AR) and optical resolution ( OR images to train a deep learning network architecture MultiResU Net which is a Fully Connected U shaped Convolutional Network (U Net) that incorporates multiple residual blocks ) enhances the quality of AR PAM images. …”
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Image segmentation with minimal human supervision by Shin, G

“…</p> <br> <p>In the first part, we demonstrate that considering a model's uncertainty in predictions can significantly reduce the number of annotations required to train a segmenter that performs nearly as well as a fully-supervised one. …”

An analytic end-to-end collaborative learning algorithm by Li, Sitan, Cheah, Chien Chern

“…The proposed End-to-End algorithm trains multiple two-layer fully connected networks concurrently and collaborative learning can be used to further combine their strengths to improve accuracy. …”
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