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Deep Learning for System Trace Restoration [article]

Ilia Sucholutsky, Apurva Narayan, Matthias Schonlau, Sebastian Fischmeister
2019 arXiv   pre-print
traces for understanding the behavior of complex systems.  ...  As a result, this method can be used for data restoration even with streamed data.  ...  The use of LSTMs for restoration of lossy system traces has not been explored.  ... 
arXiv:1904.05411v1 fatcat:izqdmopefjb6de5rauv5finq5u

Pay attention and you won't lose it: a deep learning approach to sequence imputation

Ilia Sucholutsky, Apurva Narayan, Matthias Schonlau, Sebastian Fischmeister
2019 PeerJ Computer Science  
While a number of deep learning algorithms solve end-stage problems of prediction and classification, very few aim to solve the intermediate problems of data pre-processing, cleaning, and restoration.  ...  Long Short-Term Memory (LSTM) networks have previously been proposed as a solution for data restoration, but they suffer from a major bottleneck: a large number of sequential operations.  ...  BACKGROUND Sequence modelling with deep learning The most popular deep learning architecture for sequence modelling is Recurrent Neural Networks (RNNs), a type of neural network with an internal feedback  ... 
doi:10.7717/peerj-cs.210 pmid:33816863 pmcid:PMC7924680 fatcat:hkpkz7frtjb2pmpfypj4ngp36y

Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images [article]

Lequan Yu, Zhicheng Zhang, Xiaomeng Li, Lei Xing
2020 arXiv   pre-print
a good prior image to guide sinogram learning, and further design a novel residual sinogram learning strategy to effectively utilize the prior image information for better sinogram completion.  ...  We formulate our framework as a sinogram completion problem and train a neural network (SinoNet) to restore the metal-affected projections.  ...  The previous deep-learning-based methods usually formulate the MAR as an image restoration problem.  ... 
arXiv:2009.07469v1 fatcat:w4w3jiwiv5hpbj2dwagjbanwuy

Wavelet Scattering and Neural Networks for Railhead Defect Identification

Yang Jin
2021 Materials  
WSNs are functionally equivalent to deep convolutional neural networks while containing no parameters, thus suitable for non-intensive datasets. NNs can restore location and size information.  ...  Accurate and automatic railhead inspection is crucial for the operational safety of railway systems.  ...  In deep learning, NNs behave as fully connected layers for extracting representative characteristics [34] or decoders for restoring the original information [35] .  ... 
doi:10.3390/ma14081957 pmid:33919718 fatcat:llavono7srdkddavoedpbp3zcm

On-Demand Learning for Deep Image Restoration [article]

Ruohan Gao, Kristen Grauman
2017 arXiv   pre-print
Then, we propose an on-demand learning algorithm for training image restoration models with deep convolutional neural networks.  ...  While machine learning approaches to image restoration offer great promise, current methods risk training models fixated on performing well only for image corruption of a particular level of difficulty  ...  Related Work Deep Learning in Low-Level Vision: Deep learning for image restoration is on the rise. Vincent et al.  ... 
arXiv:1612.01380v3 fatcat:cxun3g7ovfacbagkickxlrdcaa

Restoration of Natural Images using Iterative Global and Local Adaptive Learning Scheme

Chiluka Ramesh
2020 International Journal of Advanced Trends in Computer Science and Engineering  
In this paper, we opted for various noised images such as Gaussian noised images, CCD and CMOS noised images for the restoration process.  ...  Purposefully we are unifying Sparse learning approach with neural networks and SVD to obtain a better-restored image from the effect of noises.  ...  RELATED WORK There have been a few endeavours to deal with the denoising issue by deep neural systems [21] .  ... 
doi:10.30534/ijatcse/2020/20912020 fatcat:ylmoozyubbarxiw75cagve3taq

Restoring Chaos Using Deep Reinforcement Learning [article]

Sumit Vashishtha, Siddhartha Verma
2019 arXiv   pre-print
We demonstrate that deep Reinforcement Learning (RL) is able to restore chaos in a transiently-chaotic regime of the Lorenz system of equations.  ...  Our results demonstrate the utility of using deep RL for controlling the occurrence of catastrophes and extreme-events in non-linear dynamical systems.  ...  To conclude, we have demonstrated the utility of deep reinforcement learning in restoring chaos for a transiently-chaotic system.  ... 
arXiv:1912.00947v1 fatcat:uxuvew6j3bc4bj7urknt4wykka

Artificial intelligence: The future of prosthodontics

Arnab Pradhan, Sanjoy Karmakar, Jayanta Bhattacharyya, Samiran Das, Soumitra Ghosh, Sourav Maji
2022 Zenodo  
It has shown the potential for providing a reliable diagnostic tool for tooth shade selection, automated restoration design, mapping the tooth preparation finishing line, but they are still in development  ...  Various aspects of technology are transforming dentistry for the better, Artificial Intelligence (AI) is one of them.  ...  Implant systems are often detected using deep learning-based object detection from panoramic radiographic images.  ... 
doi:10.5281/zenodo.6437556 fatcat:hcx2744kr5fcjmglfvqgyxp4pe

Detection of preventable fetal distress during labor from scanned cardiotocogram tracings using deep learning [article]

Martin G. Frasch, Shadrian B. Strong, David Nilosek, Joshua Leaverton, Barry S. Schifrin
2021 arXiv   pre-print
Using a unique archive of EFM collected over 50 years of practice in conjunction with adverse outcomes, we present a deep learning framework for training and detection of incipient or past fetal injury  ...  This framework is suited for automating an early warning and decision support system for maintaining fetal well-being during the stresses of labor.  ...  Deep Learning Pipeline We present a method for automated extraction of features in FHR and uterine contractions (UC) which are outlined in the above section.  ... 
arXiv:2106.00628v2 fatcat:o3fvq7aiy5fw7cwlvzqkah3qkm

The difference between using a device or investing in a philosophy

2018 British Dental Journal  
In addition to using AI for ceph tracing reports, CT Dent has spent a considerable amount of time developing and testing its own machine learning artificial intelligence.  ...  learning.  ... 
doi:10.1038/sj.bdj.2018.1070 pmid:30468169 fatcat:xtk3fbsvnvf77mjao77xwk4sei

90 second working time and 10 second cure

2018 British Dental Journal  
In addition to using AI for ceph tracing reports, CT Dent has spent a considerable amount of time developing and testing its own machine learning artificial intelligence.  ...  learning.  ... 
doi:10.1038/sj.bdj.2018.1069 pmid:30468206 fatcat:5m2lrsi36bblzjna2addintexu

Deep Contextual Bandits for Fast Neighbor-Aided Initial Access in mmWave Cell-Free Networks [article]

Insaf Ismath, Samad Ali, Nandana Rajatheva, Matti Latva-aho
2021 arXiv   pre-print
In this paper, a novel deep contextual bandit (DCB) learning method is proposed to provide instant IA using information from the neighboring active APs.  ...  Simulations are carried out with realistic channel models generated using the Wireless Insight ray-tracing tool.  ...  The performance of this neighbor-aided IA system is evaluated using realistic scenarios generated using a ray-tracing tool.  ... 
arXiv:2103.09694v1 fatcat:tgsqbw662ndmxo5shgeb4b5vg4

Detecting web attacks with end-to-end deep learning

Yao Pan, Fangzhou Sun, Zhongwei Teng, Jules White, Douglas C. Schmidt, Jacob Staples, Lee Krause
2019 Journal of Internet Services and Applications  
Second, we describe how RSMT trains a stacked denoising autoencoder to encode and reconstruct the call graph for end-to-end deep learning, where a low-dimensional representation of the raw features with  ...  obtain for production web applications.  ...  Acknowledgements We would like to thank the anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions.  ... 
doi:10.1186/s13174-019-0115-x fatcat:lcxeli4jybbvdcblrwwcnr6a6u

Deep-learning on-chip DSLM enabling video-rate volumetric imaging of neural activities in moving biological specimens [article]

Xiaopeng Chen, Junyu Ping, Yixuan Sun, Chengqiang Yi, Sijian Liu, Zhefeng Gong, Peng Fei
2021 bioRxiv   pre-print
Here we report that through combing a microfluidic chip-enabled digital scanning light-sheet illumination strategy with deep-learning based image restoration, we can realize isotropic 3D imaging of crawling  ...  The authors would like to thank Zhaofei Wang, Tingting Zhu, Jinrun Zhou for their helpful comments and discussion.  ...  (d) Deep-learning image restoration procedure. (1) De-net training. (2) Low-SNR raw images input. (3) High-SNR images output (De-net Output). (4) Lateral blurring using system PSF. (5) Iso-net training  ... 
doi:10.1101/2021.05.31.446320 fatcat:fmbck5gvh5divboavipptzoari

Beyond the Limitation of Pulse Width in Optical Time-domain Reflectometry [article]

Hao Wu, Ming Tang
2022 arXiv   pre-print
Optical time-domain reflectometry (OTDR) is the basis for distributed time-domain optical fiber sensing techniques.  ...  However, at the same time, the signal-to-noise ratio of the system is degraded, and higher speed equipment is required.  ...  The ODNet is trained for 800 epochs with a batch size of 64 and a learning rate of 0.0003.  ... 
arXiv:2203.09461v1 fatcat:jso3s6cyxfdelhrehu426fzjsy
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