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The Kolmogorov Spectrum and Its Oceanic Cousins: A Review

O. M. Phillips
1991 Proceedings of the Royal Society A  
The most convincing verification of the theory came from observations by Grant, Stewart and Moilliet under conditions that clearly satisfy the basic premises of the theory, but subsequent measurements  ...  It has long been known that the existence of a ks region in the sp isotropy and it is indicated that the success of Kolmogorov scaling in collapsing measured spectra in the dissipation range, does not  ...  Measurements were made in the leading wave and in the trailing waves and include conditions with a wide range of L0/rj = (e0/vN2)*; they were grouped into classes 1, with L0/ t j between 4100 and 2400;  ... 
doi:10.1098/rspa.1991.0084 fatcat:47gjwpuylfg53pa2glnxofira4

Plan in Maude Specifying an Active Network Programming Language

Mark-Oliver Stehr, Carolyn L. Talcott
2004 Electronical Notes in Theoretical Computer Science  
augmented with the CINNI explicit substitution calculus; and (2) the wide-spectrum approach to formal modeling supported by Maude.  ...  We also illustrate the wide-spectrum approach to formal modeling supported by Maude: executing PLAN programs; analyzing PLAN programs using search and model-checking; proving properties of particular PLAN  ...  We would like to thank the members of the Switchware team, especially Carl A.  ... 
doi:10.1016/s1571-0661(05)82538-1 fatcat:26taoxwaljbrdnl3yseug45tn4

Simulation photoelectric parameters of vertical junction solar cells

2021 International Journal of Advanced Trends in Computer Science and Engineering  
Many properties of the solar cells are being studied extensively. In this study, the basic photoelectric parameters of vertical junction solar cell were modeled.  ...  The main focus was on the comparison of the photoelectric parameters of a vertical junction solar cell consisting of 3 elements with the photoelectric parameters of a vertical solar cell consisting of  ...  ACKNOWLEDGEMENT The authors would like to thank the staff of the "Renewable Energy Sources" Research Laboratory of Andijan State University for their close assistance in writing this scientific article  ... 
doi:10.30534/ijatcse/2021/131022021 fatcat:meebiegpcvdyxlyxzqvkgisapm

YANG/NETCONF ROADM: Evolving Open DWDM Toward SDN Applications

Jan Kundrat, Josef Vojtech, Pavel Skoda, Rudolf Vohnout, Jan Radil, Ondrej Havlis
2018 Journal of Lightwave Technology  
The presented YANG model offers access to all functional components of a modern flexgrid ROADM and enables SDN applications to access and manipulate the media layer with no required external validation  ...  Dynamic provisioning of AWs, in general spectrum services, is achieved via Reconfigurable Optical Add Drop Multiplexers (ROADMs) operating with flexible spectrum allocation.  ...  ACKNOWLEDGEMENT The authors would like to thank M. Hůla, M. Altmann, R. Velc, R. Krejčí, and M. Vaško for their contribution towards this article.  ... 
doi:10.1109/jlt.2018.2822268 fatcat:ky7boedbgjdkzok5ht4jfnsrwm

Reconstructing the Hubble diagram of gamma-ray bursts using deep learning

Li Tang, Hai-Nan Lin, Xin Li, Liang Liu
2021 Monthly notices of the Royal Astronomical Society  
The trained network is used to calibrate the distance of 174 GRBs based on the Combo-relation.  ...  We calibrate the distance and reconstruct the Hubble diagram of gamma-ray bursts (GRBs) using deep learning.  ...  ACKNOWLEDGEMENTS This work has been supported by the National Natural Science Fund of China Grant Nos. 11603005, 11775038 and 11947406.  ... 
doi:10.1093/mnras/stab2932 fatcat:jnxhcotfurfqldu5b3fsdujtpe

DT-SV: A Transformer-based Time-domain Approach for Speaker Verification [article]

Nan Zhang, Jianzong Wang, Zhenhou Hong, Chendong Zhao, Xiaoyang Qu, Jing Xiao
2022 arXiv   pre-print
of different Transformer layers.  ...  Speaker verification (SV) aims to determine whether the speaker's identity of a test utterance is the same as the reference speech.  ...  For the diffluence loss L D , the Kullback-Leibler divergence, which is widely applied in the field of machine learning and deep learning, is utilized as a representation of distance: L D = 1 LT L l=1  ... 
arXiv:2205.13249v1 fatcat:37szwpr3xzc67gieeuaqnfk3nm

Wide and Deep Neural Networks Achieve Optimality for Classification [article]

Adityanarayanan Radhakrishnan, Mikhail Belkin, Caroline Uhler
2022 arXiv   pre-print
More generally, we create a taxonomy of infinitely wide and deep networks and show that these models implement one of three well-known classifiers depending on the activation function used: (1) 1-nearest  ...  neighbor (model predictions are given by the label of the nearest training example); (2) majority vote (model predictions are given by the label of the class with greatest representation in the training  ...  This involves analyzing infinitely wide and deep networks via the limiting NTK kernel given by K (L) as the number of hidden layers L → ∞.  ... 
arXiv:2204.14126v1 fatcat:ixmdpqwdsnh3xhxfumpfwblnxm

Wave optics propagation code for multiconjugate adaptive optics

Brent L. Ellerbroek, Gregory Cochran, Robert K. Tyson, Domenico Bonaccini, Michael C. Roggemann
2002 Adaptive Optics Systems and Technology II  
This code was more specifically developed to assess the impact of diffraction effects and a variety of implementation error sources upon the performance of the Gemini-South MCAO system.  ...  Several possibilities for parallelizing the code for faster execution and the modeling of extremely large telescopes (ELT's) are discussed.  ...  Setting L0 = oo yields the usual Kolmogorov spectrum.  ... 
doi:10.1117/12.454784 fatcat:jovsiapkivg27j7tcbw3bw3txi

An introduction to partial evaluation

Neil D. Jones
1996 ACM Computing Surveys  
Partial evaluation provides a unifying paradigm for a broad spectrum of work in program optimization, compiling, interpretation and the generation of automatic program generators [Bjørner et al. 1987;  ...  Much partial evaluation work to date has concerned automatic compiler generation from an interpretive definition of a programming language, but it also has important applications to scientific computing  ...  Special thanks are due to Carsten Gomard, Peter Sestoft and others in the TOPPS group at Copenhagen, Jacques Cohen, Robert Glu ¨ck, John Launchbury, Patrick O'Keefe, Carolyn Talcott, Dan Weise, and the  ... 
doi:10.1145/243439.243447 fatcat:zkzmmser2vh2tkjk2tqzajy3zu

Organizing the Aggregate: Languages for Spatial Computing [article]

Jacob Beal, Stefan Dulman, Kyle Usbeck, Mirko Viroli, Nikolaus Correll
2012 arXiv   pre-print
As the number of computing devices embedded into engineered systems continues to rise, there is a widening gap between the needs of the user to control aggregates of devices and the complex technology  ...  A large number of spatial computing domain specific languages (DSLs) have emerged across diverse domains, from biology and reconfigurable computing, to sensor networks and agent-based systems.  ...  Communications of the ACM, 43 (5) Jozwiak, L., Nedjah, N., and Figueroa, M. (2010) . Modern development methods and tools for embedded reconfigurable systems: A survey.  ... 
arXiv:1202.5509v2 fatcat:bmgan7ig5fghddait43avj43qy

Revisiting matrix-based inversion of scanning mobility particle sizer (SMPS) and humidified tandem differential mobility analyzer (HTDMA) data

Markus D. Petters
2021 Atmospheric Measurement Techniques  
Previously reported occasional failure to converge to a valid solution is reduced by switching from the L-curve method to generalized cross-validation as the metric to search for the optimal regularization  ...  The speed of inversion is improved by a factor of ~200, now requiring between 2 and 5 ms per SMPS scan when using 120 size bins.  ...  However, the hygroscopicity of the dominant mode sion of a single spectrum are 5 and 2 ms for L0 x0 B[0,∞] and decreases with decreasing diameter. The fraction of cases L2 B[0,∞] , respectively.  ... 
doi:10.5194/amt-14-7909-2021 fatcat:h4ui3z4dnva57npbctn6aqccim

Supervised Brain Network Learning based on Deep Recurrent Neural Networks

Shijie Zhao, Yan Cui, Linwei Huang, Li Xie, Yaowu Chen, Junwei Han, Lei Guo, Shu Zhang, Tianming Liu, Jinglei Lv
2020 IEEE Access  
Specifically, this hybrid framework first takes advantage of the great capacity of deep recurrent neural networks (DRNN) in modeling sequential data to learn the diverse regressors from real tfMRI data  ...  Extensive experiment results on different tfMRI datasets from Human connectome project (HCP) demonstrated the superiority of the proposed framework.  ...  We define a DRNN with L layers and n i neurons per layer.  ... 
doi:10.1109/access.2020.2984948 fatcat:vtqhryb6uncfzcucadfrlko56i

Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications

Han Cai, Ji Lin, Yujun Lin, Zhijian Liu, Haotian Tang, Hanrui Wang, Ligeng Zhu, Song Han
2022 ACM Transactions on Design Automation of Electronic Systems  
Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial intelligence (AI), including computer vision, natural language processing, and speech recognition.  ...  To reduce the large design cost of these manual solutions, we discuss the AutoML framework for each of them, such as neural architecture search (NAS) and automated pruning and quantization.  ...  HAWQ allows for the automatic selection of the relative quantization precision of each layer, based on the layer's Hessian spectrum.  ... 
doi:10.1145/3486618 fatcat:h6xwv2slo5eklift2fl24usine

Entanglement spectra of non-chiral topological (2+1)-dimensional phases with strong time-reversal breaking, Li-Haldane state counting, and PEPS [article]

Mark J. Arildsen, Norbert Schuch, Andreas W. W. Ludwig
2022 arXiv   pre-print
The state countings of the ES are consistent with our expectation: specifically, the ES contain representations of global SU(3) symmetry from the tensor products of the (lowest-lying) multiplet of primary  ...  states of a "high-velocity" chiral SU(3)_1 CFT with the full content of a "low-velocity" chiral SU(3)_1 CFT sector, a non-chiral structure beyond that observable in the trivial sector of the ES.  ...  + ∞ n=−∞ L−n e 2πinx/N , (5.11) and HR = vR 2π N 0 T (x)dx = 2πv R N L0 − c 24 (5.12) PR = 2π N L0 , (5.13) in contrast to the H L and P L of Eq. (5.6).  ... 
arXiv:2207.03246v1 fatcat:lsonfzkofvg5hcmmxvniggod4i

CMOS Fixed Pattern Noise Removal Based on Low Rank Sparse Variational Method

Tao Zhang, Xinyang Li, Jianfeng Li, Zhi Xu
2020 Applied Sciences  
It combines not only the continuity of the image itself, but also the structural and statistical characteristics of the stripes.  ...  At the same time, the low frequency information of the image is combined to achieve adaptive adjustment of some parameters, which simplifies the process of parameter adjustment, to a certain extent.  ...  (a) Original image; (b) WAFT; (c) UTV; (d) ASSTV; (e) VSNR; (f) SILR; (g) sparse; (h) LRSUTV. Figure 15 . 15 The power spectrum of the image in the solar active region.  ... 
doi:10.3390/app10113694 fatcat:32gfssdikzfotkok7p6yty6kti
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