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Neutron scattering study of spin dynamics in superconducting (Tl,Rb)2Fe4Se5

Physical Review B 2013年第10期87卷 100501(R)-100501(R)页

作者： Songxue Chi Feng Ye Wei Bao Minghu Fang H. D. Wang C. H. Dong A. T. Savici G. E. Granroth M. B. Stone R. S. Fishman Quantum Condensed Matter Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Department of Physics and Astronomy University of Kentucky Lexington Kentucky 40506 USA Department of Physics Renmin University of China Beijing 100872 China Department of Physics Zhejiang University Hangzhou 310027 China Neutron Data Analysis and Visualization Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Materials Science and Technology Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA

We observed in superconducting (Tl,Rb)2Fe4Se5 spin-wave branches that span an energy range from 6.5 to 209 meV. Spin dynamics are successfully described by a Heisenberg localized spin model whose dominant in-plane interactions include only the nearest (J1 and J1′) and next nearest neighbor (J2 and J2′) exchange terms within and between the tetramer spin blocks, respectively. These experimentally determined exchange constants would crucially constrain the theoretical viewpoints on magnetism and superconductivity in the Fe-based materials.

关键词： NEUTRON scattering SUPERCONDUCTORS HEISENBERG model MAGNETIC materials MAGNETISM

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Surface reconstruction with higher-order smoothness

arXiv

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arXiv 2017年

作者： Pan, Rongjiang Skala, Vaclav School of Computer Science and Technology Shandong University Jinan250100 China Centre of Computer Graphics and Data Visualization Department of Computer Science and Engineering University of West Bohemia Plzen Czech Republic

This work proposes a method to reconstruct surfaces with higher-order smoothness from noisy 3D measurements. The reconstructed surface is implicitly represented by the zero level-set of a continuous valued embedding function. The key idea is to find a function whose higher-order derivatives are regularized and whose gradient is best aligned with a vector field defined by the input point set. In contrast to methods based on the first-order variation of the function that are biased towards the constant functions and treat the extraction of the isosurface without aliasing artifacts as an afterthought, we impose higher-order smoothness directly on the embedding function. After solving a convex optimization problem with a multi-scale iterative scheme, a triangulated surface can be extracted using the marching cubes algorithm. We demonstrated the proposed method on several data sets obtained from raw laser-scanners and multi-view stereo approaches. Experimental results confirm that our approach allows us to reconstruct smooth surfaces from points in the presence of noise, outliers, large missing parts and very coarse orientation information. Copyright © 2017, The Authors. All rights reserved.

关键词： Convex optimization

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Topology exploration with hierarchical landscapes

Topology exploration with hierarchical landscapes

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Workshop at SIGGRAPH Asia 2012, WASA 2012

作者： Demir, Dogan Beketayev, Kenes Weber, Gunther H. Bremer, Peer-Timo Pascucci, Valerio Hamann, Bernd Scientific Computing and Imaging Institute School of Computing University of Utah United States Computational Research Divisition Lawrence Berkeley National Laboratory United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States Institute for Data Analysis and Visualization Computer Science Department University of California Davis CA United States

ISBN: (纸本)9781450318358

Topological landscapes have been proposed as a visual metaphor for contour trees that does not require an understanding of the theory involved in defining contour trees. The idea is to create a representative terrain with the same topological structure as a given contour tree. This representation exploits the natural human ability to interpret topography and results in an intuitive visualization of otherwise abstract information. However, topological landscapes still suffer from some of the same limitations as traditional contour tree visualization. Most notable is the fact that for complex functions landscapes can quickly become highly complex, exceeding the limits of human comprehension as well as the available computing resources. To address this challenge, we propose a new framework for the dynamic creation and visualization of hierarchical topological landscapes. Our system provides an interactive visualization of complex functions by utilizing a hierarchical decomposition of the contour tree as well as focus+context type interactions. For three dimensional data, we link the landscape display to flexible isosurface extraction in order to correlate terrain features with their corresponding three dimensional counterparts. We demonstrate the utility and versatility of our approach on a variety of both low and high dimensional data sets. © 2012 ACM.

关键词： visualization

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Correlative Tomography - Extraction of Reliable Information with Adequate Resolution from mm Scale Down to Sub-nm Scale

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Microscopy and Microanalysis 2014年第S3期20卷 838-839页

作者： Frank Mücklich Johannes Webel Hisham Aboulfadl N. Lindow H.-C. Hege Department Materials Science and Engineering Saarland University Saarbrücken Germany Material Engineering Center Saarland (MECS) Steinbeis Research Center Saarbrücken Germany Department of visualization and data analyses Zuse Institute Berlin Berlin Germany

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Maximizing adaptivity in hierarchical topological models

Maximizing adaptivity in hierarchical topological models

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International Conference on Shape Modeling and Applications

作者： P.-T. Bremer V. Pascucci B. Hamann Department of Computer Science University of Illinois Urbana-Champaign USA Lawrence Livermore National Laboratory Center for Applied Scientific Computing USA Institute for Data Analysis and Visualization Department of Computer Science University of California Davis USA

We present an approach to hierarchically encode the topology of functions over triangulated surfaces. Its Morse-Smale complex, a well known structure in computational topology, describes the topology of a function. Following concepts of Morse theory, a Morse-Smale complex (and therefore a function's topology) can be simplified by successively canceling pairs of critical points. We demonstrate how cancellations can be effectively encoded to produce a highly adaptive topology-based multi-resolution representation of a given function. Contrary to the approach, we avoid encoding the complete complex in a traditional mesh hierarchy. Instead, the information is split into a new structure we call a cancellation forest and a traditional dependency graph. The combination of this new structure with a traditional mesh hierarchy proofs to be significantly more flexible than the one previously reported. In particular, we can create hierarchies that are guaranteed to be of logarithmic height.

关键词： Topology data visualization data analysis Computer science Encoding Level set Shape Scientific computing Laboratories Transfer functions

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Analyzing the Impact of Security Measures on Wi-Fi Quality of Service

Analyzing the Impact of Security Measures on Wi-Fi Quality o...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Thomas J. Kopcho Mostafa M. Fouda Cameron J. Krome Anna T. Quach Israel G. Olaveson Department of Computer Science Idaho State University Pocatello ID Department of Electrical and Computer Engineering Idaho State University Pocatello ID AI Data & Visualization Idaho National Laboratory Idaho Falls ID

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

This study examines the relationship between Quality of Service (QoS) and security in Wi-Fi networks. Using a detailed dataset consisting of QoS metrics of file transfers under various security configurations, we analyze the impact of these settings on network performance. Our findings provide a quantification to accurately show that different encryption protocols, such as TKIP and CCMP, significantly affect QoS, with disabling security generally improving performance. The study provides a framework for optimizing security settings to enhance both security and QoS in future wireless networks.

关键词： Measurement Protocols Statistical analysis Wireless networks Quality of service Throughput Encryption Security Internet of Things Wireless fidelity

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The contour spectrum 97

The contour spectrum

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Proceedings of the 8th conference on visualization '97

作者： Chandrajit L. Bajaj Valerio Pascucci Daniel R. Schikore Shastra Lab & Center for Image Analysis and Data Visualization Department of Computer Sciences Purdue University West Lafayette IN and Department of Computer Science & TICAM University of Texas at Austin Austin TX P.O. Box 808 Mail Stop L-561 Livermore CA and Shastra Lab & Center for Image Analysis and Data Visualization Department of Computer Sciences Purdue University West Lafayette IN

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A Lightweight AI Model for Anomaly Detection in Wireless Networks

A Lightweight AI Model for Anomaly Detection in Wireless Net...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Thomas J. Kopcho Mostafa M. Fouda Cameron J. Krome Department of Computer Science Idaho State University Pocatello ID Department of Electrical and Computer Engineering Idaho State University Pocatello ID AI Data & Visualization Idaho National Laboratory Idaho Falls ID

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

Detecting network anomalies is critical for wireless network security and reliability. Traditional AI methods often require substantial computational resources, particularly when deployed on cloud servers, leading to increased network load and latency. In contrast, deploying AI models directly on end devices enables real-time data processing at the source, facilitating faster anomaly detection and mitigation. We propose a lightweight hierarchical AI model where initial inferences are made by a simple model and outputs confidence scores. Using a modified TOPSIS-based method, we determine an optimal confidence score threshold. Inferences below this threshold are forwarded to a more complex model for accurate analysis, reducing overall computational demands while maintaining high detection performance. Our approach is tested on a 5 G network dataset, demonstrating competitive performance compared to other anomaly detection models.

关键词： Computational modeling Wireless networks Real-time systems Servers Security Reliability Artificial intelligence Anomaly detection Load modeling Testing

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Understanding perspectives on sharing neutron data at oak ridge national laboratory

引用

data science Journal 2017年 16卷

作者： Donaldson, Devan Ray Martin, Shawn Proffen, Thomas Department of Information and Library Science School of Informatics and Computing Indiana University BloomingtonIN United States Neutron Data Analysis and Visualization Oak Ridge National Laboratory PO Box 2008 Oak RidgeTN United States

Even though the importance of sharing data is frequently discussed, data sharing appears to be limited to a few fields, and practices within those fields are not well understood. This study examines perspectives on sharing neutron data collected at Oak Ridge National Laboratory’s neutron sources. Operation at user facilities has traditionally focused on making data accessible to those who create them. The recent emphasis on open data is shifting the focus to ensure that the data produced are reusable by others. This mixed methods research study included a series of surveys and focus group interviews in which 13 data consumers, data managers, and data producers answered questions about their perspectives on sharing neutron data. data consumers reported interest in reusing neutron data for comparison/verification of results against their own measurements and testing new theories using existing data. They also stressed the importance of establishing context for data, including how data are produced, how samples are prepared, units of measurement, and how temperatures are determined. data managers expressed reservations about reusing others’ data because they were not always sure if they could trust whether the people responsible for interpreting data did so correctly. data producers described concerns about their data being misused, competing with other users, and over-reliance on data producers to understand data. We present the Consumers Managers Producers (CMP) Model for understanding the interplay of each group regarding data sharing. We conclude with policy and system recommendations and discuss directions for future research. © 2017 The Author(s).

关键词： Managers

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Audio spectrogram analysis in IoT paradigm for the classification of psychological-emotional characteristics

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International Journal of Information Technology (Singapore) 2024年 1-11页

作者： Kumar, Ankit Singh, Sushil Kumar Bhardwaj, Indu Singh, Prakash Kumar Khanna, Ashish Brahma, Biswajit Dr. APJ Abdul Kalam Technical University Lucknow India Department of Computer Engineering Marwadi University Rajkot India Department of Electronics and Communication Engineering Galgotias University U.P. Greater Noida India Rajkiya Engineering College U.P Mainpuri India Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology (GGSIPU) New Delhi India Senior Data and Visualization Engineer McKesson Corporation USA 32559 Lake Bridgeport St Fremont CA United States

Psychological activities have various dimensions in which they correlate with their respective behavior generated by the human body. Understanding the relationship of psychological events from external action units is one of the research subjects to explore various human behavior and their dependencies. The study of psychological analysis in the medical field is very time-consuming and costly. It requires constant monitoring of the patient for some time and various interrogation sessions to finalize the emotional severity of an individual. The challenges in exploring human emotions propel the requirement of computer vision techniques in this field. The proposed study explicitly evaluates the recognition of psychological-emotional activities with the help of an audio spectrogram of people fetched through an IoT (Internet of Things) device comprising a microphone to investigate its correlation with psychological events. The audio samples are collected in an asymmetric environment where the chances of the noise are random. Noise cancellation, low power consumption, and sensitivity controls are some of the prominent features of the microphone IoT that have been used to extract raw audio samples. The proposed system follows the extraction of features such as mel-frequency cepstral coefficients (MFCC), harmonic to noise rate (HNR), zero crossing rate (ZCR), and Generative Adversarial Networks (GAN) from the audio spectrogram. The study uses a deep learning-based model containing a convolutional neural network model to recognize and classify different psychological-emotional stages including happiness, anger, disgust, surprise, fear, and sadness from audio spectrogram features. The average accuracy of the classification model for the recognition of all emotions is found to be 99.42% in a maximum of 312 iterations. The model is found to be robust for various applications such as preventing suicidal cases, improving decision-making in the diagnosis of depression patients, im

关键词： Audio spectrogram Convolutional neural network (CNN) Emotion recognition Generative adversarial networks (GAN) Harmonic to noise rate (HNR) Mel-frequency cepstral coefficient (MFCC) Zero crossing rate (ZCR)

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