Reviewerly/exHarmony huggingface dataset:
Inferring fault friction properties and background stress using fluid flow and dynamic rupture modeling, and machine learning techniques – Concept case study of the M4.8 Timpson (TX) earthquake

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Inferring fault friction properties and background stress using fluid flow and dynamic rupture modeling, and machine learning techniques – Concept case study of the M4.8 Timpson (TX) earthquakeAuthors: Dawid SzafranskiBenchun DuanDawid SzafranskiTexas A&M UniversitySearch for more papers by this author and Benchun DuanTexas A&M UniversitySearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3428135.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractPhysics-based simulations in studies of induced seismicity are very important in improving the understanding of the complicated processes and building more direct causal relations between human activity and earthquakes, but are often limited by our knowledge about medium and fault properties. In this study we alleviate that problem by combining results of fluid flow and dynamic rupture simulations, with supervised machine learning methods to invert for selected fault properties. Integration of stochastic and deterministic approaches saves a lot of computation time and provides solution with roots in the physical laws. Benchmarking against the reported magnitude of M4.8 Timpson (TX) earthquake, we provide additional constraints on fault frictional properties and background stress.Presentation Date: Tuesday, October 13, 2020Session Start Time: 9:20 AMPresentation Time: 9:45 AMLocation: Poster Station 4Presentation Type: PosterKeywords: induced seismicity, modeling, inversion, machine learningPermalink: https://doi.org/10.1190/segam2020-3428135.1FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Dawid Szafranski and Benchun Duan, (2020), "Inferring fault friction properties and background stress using fluid flow and dynamic rupture modeling, and machine learning techniques – Concept case study of the M4.8 Timpson (TX) earthquake," SEG Technical Program Expanded Abstracts : 1339-1343. https://doi.org/10.1190/segam2020-3428135.1 Plain-Language Summary Keywordsinduced seismicitymodelinginversionmachine learningPDF DownloadLoading ...
An ADMM Approach for Constructing Abnormal Subspace of Sparse PCA

Despite the popularity of principal component analysis (PCA) as an anomaly detection technique, the main shortage of PCA-based anomaly detection models is their interpretability. Constructing the abnormal subspace of PCA (i.e., the subspace spanned by the least significant principal components (PCs)), with sparse and orthogonal loading vectors provides a means of anomaly interpretation. However, solving all abnormal sparse PCs one by one through semi-definite programming is time consuming. In this paper, we derive an adapted projection deflation method for extracting least significant PCs and propose an alternating direction method of multipliers (ADMM) solution for constructing the sparse abnormal subspace. Our experiments on two real world datasets showed that the proposed ADMM solution achieved comparable detection accuracy and sparsity as the SDP solution and is 10 times more efficient, which makes it more suitable for application domains with higher dimensions.
Vehicle Re-Identification by Deep Feature Embedding and Approximate Nearest Neighbors

Disorganized urban growth has led cities to chaos, which has brought countless challenges for their development in several sectors, such as traffic organization, public safety, and transportation. Vehicular re-identification (ReID) technologies have become increasingly important in this context since these allow to produce insights capable of benefiting many areas. As recently, methods frequently resorted to Deep Learning and Convolutional Neural Networks (CNNs), especially in the design of loss functions capable of improving the learning capacity of CNNs. Couple and triplet loss techniques have gained prominence, but their effectiveness depends on the mining of samples to converge properly. In this paper, we investigate a new simple approach for vehicle ReID by combining sample mining strategy and approximate nearest neighbor (ANN) method to improve retrieval quality. By relying only on relatively low-dimensional deep features, we were able to obtain state-of-the-art performance on the VeRi-776 dataset in terms of mAP, HIT@1, and HIT@5 metrics, but using relatively simple CNN and ANN methods, which are feasible in CPU for real-time scenarios.
Intelligent Control System for Distribution Electrical Network with Stochastic Generation

The article is devoted to the control of distribution electrical networks (DEN) with stochastic generation which operate as the Internet of Energy (IoE). The concept of the energy Internet is considered. The analysis of approaches to DEN control is carried out. It is established that the most promising are decentralized and distributed control systems. The basis for developing the intelligent control system (ICS) of DEN with stochastic generation is an approach based on a distributed multi-agent control system (MACS). The main advantages of MACS are openness and scalability. A block diagram of a DEN operating on the IoE principle is presented. The set of agents has been developed for DEN ICS. For each agent, the functions performed have been described and General rules to coordinate actions with other agents have been developed.
Accelerated Filtering and in situ Verification for Energy-Optimized Genome Read Mapping

Whole genome sequencing (WGS) includes sequencing and assembly pipelines to extract biological genomes for new advances in healthcare, agriculture and environmental research. It produces small random sections of the genome, called reads, and then re-assembled by mapping those reads to a reference genome. This process called read mapping produces a large volume of data, which are disparately processed by compute- and memory-intensive filtering and verification algorithms. As such, the problem of energy-frugal read mapping has remained an open challenge. In this paper, we propose an accelerated read mapping methodology with combined filtering and verification, implemented on an FPGA platform. Core to our methodology is an algorithm based on q-gram lemma for filtration with Myers bit-vector for verification in tandem. Through in situ verification, the proposed implementation optimizes resource utilization between filtration and verification and introduces parallel pipelines in computation and storage processes. Our experimental analysis shows that this methodology gives up to 8.7× energy efficiency when implemented on the Zynq Ultrascale+ FPGA platform, compared with the state-of-the-art software and hardware approaches.
Opportunities and Implementation of Big Data Management in Academic Libraries

Data has changed the world in an unbelievable way and made an impact on our lifestyles at an exceptional rate. Big data is now the latest science of exploring and forecasting human-machine behavior dealing with a massive amount of associated data. The study is intended to understand the intensity and the competencies of librarians in implementing big data initiative project in academic libraries by the Government of Karnataka State. The study also tries to understand the application of big data in these libraries; 68 (87.17%) librarians completed the survey out of 78 respondents. The results of the study showed a strong association, that is, 72 (92.30%) respondents had the essential competencies and 58 (75.64%) librarians ability, intensity, readiness in implementing big data in academic libraries.
Intelligent Classification and Automatic Annotation of Violations based on Neural Network Language Model

To solve the problem of lack on automatic classification and annotation of a large number of violation cases in power supply enterprises, a Neural Network Language Model (NNLM) based on word vector is proposed in this paper. This model adopts word vector to represent text features, extracts essential features of text information by using neural network. Simulation studies are carried out on data collected from South China Grid to evaluate the performance of NNLM, the result of which is compared with Naive Bayes Classifier (NBC) and Logistic Regression (LR). The experimental results show that the classification accuracy of the NNLM is as high as 99%, which is much higher than classification accuracy of the NBC and LR. The NNLM effectively improves the accuracy rate of classification and the efficiency of annotation.
Capturing kinetic wave demonstrations for sound control

In musical acoustics, wave propagation, reflection, phase inversion, and boundary conditions can be hard to conceptualize. Physical kinetic wave demonstrations offer visible and tangible experiences of wave behavior and facilitate active learning. We implement such kinetic demonstrations, a long spring and a Shive machine, using contemporary fabrication techniques. Furthermore, we employ motion capture (MoCap) technology to transform these kinetic assemblies into audio controllers. Time-varying coordinates of Mo-Cap markers integrated into the assemblies are mapped to audio parameters, closing a multi-sensory loop where visual analogues of acoustic phenomena are in turn used to control digital audio. The project leads to a pedagogical practice where fabrication and sensing technologies are used to reconstitute demonstrations for the eye as controllers for the ear.
Top-Down Parsing Error Correction Applied to Part of Speech Tagging

Natural Language Processing (NLP) applications are growing in popularity and importance, all of these applications rely upon the basic steps of tokenization, stemming and Part-of-Speech tagging (POS), and while those steps already work really well on English texts, the same cannot be said for every language. This paper investigates the use of techniques developed to be used as error correction in compilers as a means to improve Part-of-Speech tagging, specifically in Brazilian Portuguese.
Resource Management for Processing Wide Area Data Streams on Supercomputers

Modern scientific instruments generate enormous amount of data. Typically, the data collected from the instruments are stored in one or more files that are then moved to a distant supercomputer for processing. The final results are sent back to the user. In order to make effective use of the time on expensive instruments, experimenters want to process the data as they are generated. They want to stream the data from instruments’ memory directly to a supercomputer’s memory for analysis. Since the compute nodes in a supercomputer are not connected directly to the wide area network, the data streams need to be passed through intermediate gateway nodes. As opposed to the best effort file transfers, data streaming applications require resources at a specific time for a specific period. In this paper, we present a system model for enabling data streaming through gateway nodes and an algorithm to efficiently allocate gateway node resources along with compute nodes. We evaluate the algorithm using real-world traces on the Chameleon Cloud. The results show that our system can schedule compute and gateway resources efficiently for streaming analysis.
Learning Single-view Object Reconstruction with Scaling Volume-View Supervision

Producing a 3D voxel from a single view by deep learning-based methods has garnered increasing attention. Several state-of-the-art works introduce the recurrent neural network(RNN) to fuse features and generate full volumetric occupancy. However, the inputs are unable to be fully exploited to improve the reconstruction due to long-term memory loss. And most of the works have considered using 3D supervision for the whole optimization to recover the full volume, but lack detailed silhouette supervision to refine the reconstruction process. To address these issues, an end-to-end object reconstruction network with scaling volume-view supervision is proposed. We introduce an auto-encoder 3D volume predicting network that takes a single arbitrary image as input and outputs a voxel occupancy grid. And a scaling volume-view supervision module, which uses up-sampling to zoom errors and increase penalties, is leveraged to improve both the global and local optimization. Extensive experimental analysis on ShapeNet dataset shows that our network has superior performance when the scaling volumeview supervision is involved and the deep residual module boosts the reconstruction performance and speeds up the optimization effectively.
Towards Quantifying Intrinsic Generalization of Deep ReLU Networks

Understanding the underlying mechanisms that enable the empirical successes of deep neural networks is essential for further improving their performance and explaining such networks. Towards this goal, a specific question is how to explain the "surprising" behavior of the same over-parametrized deep neural networks that can generalize well on real datasets and at the same time "memorize" training samples when the labels are randomized. In this paper, we demonstrate that deep ReLU networks generalize from training samples to new points via piece-wise linear interpolation. We provide a quantified analysis on the generalization ability of a deep ReLU network: Given a fixed point x and a fixed direction in the input space $\mathcal{S}$, there is always a segment such that any point on the segment will be classified the same as the fixed point x. We call this segment the generalization interval. We show that the generalization intervals of a ReLU network behave similarly along pairwise directions between samples of the same label in both real and random cases on the MNIST and CIFAR-10 datasets. This result suggests that the same interpolation mechanism is used in both cases. Additionally, for datasets using real labels, such networks provide a good approximation of the underlying manifold in the data, where the changes are much smaller along tangent directions than along normal directions. Our systematic experiments demonstrate for the first time that such deep neural networks generalize through the same interpolation and explain the differences between their performance on datasets with real and random labels.
FeedBucket

Standard development libraries for Virtual and Mixed Reality support haptic feedback through low-level parameters, which do not guide developers in creating effective interactions. In this paper, we report some preliminary results on a simplified structure for the creation, assignment and execution of haptic feedback for standard controllers with the optional feature of synchronizing an haptic pattern to an auditory feedback. In addition, we present the results of a preliminary test investigating the users' ability in recognizing variations in intensity and/or duration of the stimulus, especially when the two dimensions are combined for encoding information.
A reputation-enhanced model for trust-based collaborative filtering recommender system

The following topics are dealt with: learning (artificial intelligence); neural nets; convolutional neural nets; feature extraction; pattern classification; recurrent neural nets; image classification; natural language processing; text analysis; object detection.
Research on Air-Ground Joint Debugging System of Flight Inspection

Flight inspection is an important method and means for verifying and evaluating whether the air signals of civil aviation communications, navigation, and surveillance equipment of flight procedures, are intact and effective or not. It is directly related to civil aviation flight safety. Flight inspection uses high-performance aircraft equipped with special flight inspection equipment to complete. During the inspection, the ground technician adjusts the ground navigation equipment based on the data reported by the flight inspector. This paper proposes to develop a flight inspection air-ground joint debugging system. After the inspection equipment on the aircraft obtains the navigation equipment signal, the signal parameters are sent to the ground flight inspection joint debugging system in a data format through the ground-air data link. The system automatically judges whether the parameters are normal or not, otherwise communicate with the navigation equipment according to the software preset instructions and adjust the parameters of the ground navigation equipment, and finally complete the flight inspection adjustment of the navigation equipment signals.
Explicit Formulas for Calculating Fourier Coefficients of Three Variables Using Tomograms

The great role of Fourier coefficients in the digital processing of multidimensional signals is known. The classic cubic formulas for the calculation of Fourier coefficients use the values of an approximate function at individual points. Due to the advent of computerized tomograph, new information operators have emerged that do not directly use the function value at individual points in the integration area. These are projections-integrals from an approximate function along a given system of straight lines, tomograms-images of traces of an approximate function of three variables on given planes, etc. The classic Fourier coefficients calculation using projections is based on the formulas used for direct and inverse Radon conversion. For the 2D case, such studies were performed in classical computed tomography circuits, including the direct Fourier method Another approach to calculate 2D Fourier coefficients without using a direct and inverse Radon transform was proposed in 2000 by Oleg M. Lytvyn. This paper deals with the generalization of this method in the 3D case. It is a continuation of the research begun by the authors in 2019. This paper proposes explicit formulas for the limits of integration for the re-integrals arising in the proposed method. Thus, this paper is devoted to the further development of a new scientific direction in the theory of approximate Fourier coefficients of the function of three variables using new information operators.
Exploring propagation factors of social media moods for stock prices prediction

Weibo, the most widely-used social media in China, makes researchers highly regard its profound impact in public and gather moods for social computing and analysis, such as financial prediction. Most existing literatures concern excessively on text semantic or sentiment mining techniques, but neglect the procedure of moods dissemination and its factors. This paper proposes an integrated framework of social media moods mining, which creatively focuses on information transmission and propagating factors analysis, to predict stock prices more accurately. For the part of propagating factors on social media, several essential factors are distinguished in the dissemination process, such as emotional absorption of forwarding, influence of content and poster, user categories, release time, etc. to optimize the fitting effect of original model. And the count of forwarding also matters on predicting stock prices. Searching a given finance-related keyword, from Weibo we collected over 500,000 micro-blogs and their user information. Then we adopt the proposed integrated framework to predict stock price fluctuation, as well as the simple neural network method. Experiments demonstrate that the former outperformed the latter. The results also show that user categories and the count of forwarding differ on the lag phase of influence. And more, this paper studies the fitting effect of prediction models for different periods of the stock curve. The results indicate that the model works the best in the rising periods of stock prices curves, relatively well in the declining and the worst in the random fluctuating.
A Preliminary Study of Fusion ARTs with Adaptively Information Intensity Attenuation Controlling

Fusion ART is an enhanced version of Adaptive Resonance Theory (ART) which is derived from a biologically-plausible theory of human cognitive information processing. Due to its well-established ability of learning associative mappings across multimodal pattern channels in an online and incremental manner, fusion ART has been widely applied in many real world learning problems. In this paper, we take a Fusion Architecture for Learning, Cognition, and Navigation (FALCON) as the specification and essential backbone of fusion ART and introduce an intensity attenuation controller δ for adaptively adjusting the intensity of information captured from the environment, by taking inspiration from Broadbent-Treisman Filter-Attenuation's perceptual model of environmental attention. Particularly, we propose both an adaptive δ detection algorithm as well as a δ-based pruning algorithm to enhance the learning performance of FALCON while reduce the redundant memory storage incurred by the "detrimental δ". To verify the effectiveness and efficiency of our proposed method, comprehensive experimental studies are carried out on a classical minefield navigation task.
Mining Knowledge within Categories in Global and Local Fashion for Multi-Label Text Classification

Multi-label text classification (MLTC) is an important task in natural language processing, which assigns multiple labels to each text in the dataset. Typical method like Binary Relevance (BR) is arguably the most intuitive solution for the task. It works by decomposing the multi-label learning task into a number of independent binary learning tasks while ignoring the correlation between labels. Recently, neural network models attract much attention. Researchers view the MLTC task as a sequence generation problem. Although some new methods based on generative model (e.g. sequence-to-sequence), such as novel decoder structure and various attention mechanisms, can improve the performance. These methods still have some short-comings, such as unreasonable loss function, unclear ordering of target labels. To address these limitations, we propose a simple and effective novel model, which combines the merits of neural network and BRs methods. Our model also takes into account the categories and levels of labels. We decompose the MLTC problem to binary classification, together with global and local extractor to avoid the impact of label ordering and cumulative error. Experimental results show that our model achieves an improvement of 3.0% micro-F1 and a reduction of 6.0% hamming loss on AAPD dataset compared with the state-of-the-art work. And obtained good performance on RCV1-V2 dataset.