Reviewerly/exHarmony huggingface dataset:
Multilayer sparse LSM = deep neural network

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Multilayer sparse LSM = deep neural networkAuthors: Zhaolun LiuGerard SchusterZhaolun LiuKing Abdullah University of Science and Technology (KAUST)Search for more papers by this author and Gerard SchusterKing Abdullah University of Science and Technology (KAUST)Search for more papers by this authorhttps://doi.org/10.1190/segam2019-3215033.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractWe recast the multilayered sparse inversion problem as a multilayered neural network problem. Unlike standard least squares migration (LSM) which finds the optimal reflectivity image, neural network least squares migration (NNLSM) finds both the optimal reflectivity image and the quasi-migration Green’s functions. These quasi-migration Green’s functions are also denoted as the convolutional filters in a convolutional neural network and are similar to migration Green’s functions. The advantage of NNLSM over standard LSM is that its computational cost is significantly less and it can be used for denoising migration images. Its disadvantage is that the NNLSM reflectivity image is only an approximation to the actual reflectivity distribution.Presentation Date: Tuesday, September 17, 2019Session Start Time: 1:50 PMPresentation Time: 1:50 PMLocation: 221DPresentation Type: OralKeywords: machine learning, least-squares migration, sparsePermalink: https://doi.org/10.1190/segam2019-3215033.1FiguresReferencesRelatedDetailsCited byMigration Deconvolution via Deep Learning21 April 2021 | Pure and Applied Geophysics, Vol. 178, No. 5Convolutional sparse coding for noise attenuation in seismic dataZhaolun Liu and Kai Lu4 January 2021 | GEOPHYSICS, Vol. 86, No. 1Deep convolutional neural network and sparse least-squares migrationZhaolun Liu, Yuqing Chen, and Gerard Schuster13 June 2020 | GEOPHYSICS, Vol. 85, No. 4 SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Aug 2019 CITATION INFORMATION Zhaolun Liu and Gerard Schuster, (2019), "Multilayer sparse LSM = deep neural network," SEG Technical Program Expanded Abstracts : 2323-2327. https://doi.org/10.1190/segam2019-3215033.1 Plain-Language Summary Keywordsmachine learningleast-squares migrationsparsePDF DownloadLoading ...
Shared invariance control for constraint satisfaction in multi-robot systems

In systems involving multiple intelligent agents, e.g. multi-robot systems, the satisfaction of environmental, inter-agent, and task constraints is essential to ensure safe and successful task execution. This requires a constraint enforcing control scheme, which is able to allocate and distribute the required evasive control actions adequately among the agents, ideally according to the role of the agents or the importance of the executed tasks. In this work, we propose a shared invariance control scheme in combination with a suitable agent prioritization to control multiple agents safely and reliably. Based on the projection of the constraints into the input spaces of the individual agents using input–output linearization, shared invariance control determines constraint enforcing control inputs and facilitates implementation in a distributed manner. In order to allow for shared evasive actions, the control approach introduces weighting factors derived from a two-stage prioritization scheme, which allots the weights according to a variety of factors such as a fixed task priority, the number of constraints affecting each agent or a manipulability measure. The proposed control scheme is proven to guarantee constraint satisfaction. The approach is illustrated in simulations and an experimental evaluation on a dual-arm robotic platform.
Networked Radar Systems for Cooperative Tracking of UAVs

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Auction based Rewards Distribution Method in Pool Mining

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Construction of Artificial Intelligence-Based Interactive Oral English Teaching Platform based on application problems of present intelligent products

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QUINCE

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Optimising Team Sport Training Plans With Grammatical Evolution

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A Multi-Factor Approach for Selection of Developers to Fix Bugs in a Program

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Content-Based Medical Visual Information Retrieval

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Analyzing the Impact of Inter-smell Relations on Software Maintainability

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Visual recognition in the wild by sampling deep similarity functions

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Cutting Blade Measurement of an Ultrasonic Cutting Machine Using Multi-Step Detection

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Who owns your voice?

We examine the ethical questions surrounding voice donation for speech synthesis technology, including questions of voice ownership, identity and unintended consequences. This is examined specifically in the context of non-professional volunteer voice donors in small communities. We propose a multi-step informed consent process that more fully engages with TTS voice donors.
On the Cryptanalysis of S-DES Using Binary Cuckoo Search Algorithm

The cryptanalysis of Simplified Data Encryption Standard (S-DES) is an NP-Hard combinatorial problem. The goal of this paper is twofold. First, we study the cryptanalysis of S-DES via a metaheuristic algorithm named Binary Cuckoo Search Algorithm, which mimics the obligate brood parasitic behavior of some cuckoo species and resembles the Lévy flight behavior of certain birds. Second, we compare the efficiency of Cuckoo Search Algorithm with that of previous research works using Genetic Algorithm and Memetic Algorithm in regard to cryptanalysis of S-DES. Extensive tests and experimentations show that the proposed Cuckoo Search-based algorithm outperforms the previous works based on both genetic and memetic algorithms.
Facial Landmark Detection using Gaussian Guided Regression Network

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Towards Replication in Computational Cognitive Modeling: a Machine Learning Perspective

The suggestions proposed by Lee et al. to improve cognitive modeling practices have significant parallels to the current best practices for improving reproducibility in the field of Machine Learning. In the current commentary on “robust modeling in cognitive science”, we highlight the practices that overlap and discuss how similar proposals have produced novel ongoing challenges, including cultural change towards open science, the scalability and interpretability of required practices, and the downstream effects of having robust practices that are fully transparent. Through this, we hope to inform future practices in computational modeling work with a broader scope.
Using Trust in Collaborative Filtering for Recommendations

Recommender systems are increasingly being used in e-commerce websites to solve the problem of finding right kind of information. Collaborative filtering is considered as most promising method for recommendation because it recommends items based on common interests of users. Trust Aware Recommender Systems (TARS) is an enhancement of traditional recommendation systems to improve recommendation quality which uses trusted users for recommending an item to an active user. From literature, it is proven that including all trusted users in recommendation process reduces its performance so this research work performs a filtration process on users for reduction of trusted neighborhood of an active user. The main idea of this research work is to keep only those users in trusted neighborhood whose rating behavior is similar to an active user. Subspace clustering method is used for filtration process. The proposed algorithm uses both implicit and explicit trust for trust value calculation. The results demonstrates that the proposed algorithm improves results in terms of Mean Absolute Error and Coverage as compared to other conventional methods.
Deep Learning Neural Architecture in Emotion Recognition for Romanian Language

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Decision Support for Mission-Centric Cyber Defence

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Structural Analysis of Combined Time and Energy Optimal Trajectory Planning with Quadratic Drag

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