The original version of this Article omitted the following from the Acknowledgements:'J.D. and H. Zhang acknowledge initial funding for design of the meta-atoms provided by the National Science Foundation under aw...
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The original version of this Article omitted the following from the Acknowledgements:'J.D. and H. Zhang acknowledge initial funding for design of the meta-atoms provided by the National Science Foundation under award CMMI-1266251. Z.L. and H. Zheng contributed to the Device Fabrication section and were independently funded as visiting scholars by the National Natural Science Foundation of China under award 51772042 and the "111" project (No. B13042) led by Professor Huaiwu Zhang. Later work contained within the Device Modeling and Device Characterization sections and some revisions to the manuscript were funded under Defense Advanced Research Projects Agency Defense Sciences Office (DSO) Program: EXTREME Optics and Imaging (EXTREME) under Agreement No. HR00111720029. The authors also acknowledge fabrication facility support by the Harvard University center for Nanoscale Systems funded by the National Science Foundation under award 0335765. The views, opinions and/or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the department of Defense or the U.S. Government.' This has been corrected in both the PDF and HTML versions of the Article.
The origin of artificial intelligence is investigated, based on which the concepts of hybrid intelligence and parallel intelligence are presented. The paradigm shift in Intelligence indicates the ''new normal&...
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Repetitive processes are a class of 2D systems that can be used to model physical systems and also there are applications, such as iterative learning control, where using a repetitive processes setting for design has ...
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We present a scheme to generate an effective interaction Hamiltonian for systems that cannot interact directly and show how it can be used in scenarios such as non-local measurements and photon-photon interactions. &#...
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In this paper, we analyze the L 2 -gain of a class of switched linear systems under sampled-data state-feedback control. We consider switched linear systems whose switching signal is a regenerative process. Using the ...
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In this paper, we analyze the L 2 -gain of a class of switched linear systems under sampled-data state-feedback control. We consider switched linear systems whose switching signal is a regenerative process. Using the lifting approach and piecewise-constant approximations, we derive a sequence whose limit inferior upper-bounds the L 2 -gain of the closed-loop system. Each term of the sequence can be found by solving a linear matrix inequality. We illustrate the results by the L 2 -gain analysis of a linear system with a failure-prone controller.
Mott insulator plays a central role in strongly correlated physics, where the repulsive Coulomb interaction dominates over the electron kinetic energy and leads to insulating states with one electron occupying each un...
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Centrality is widely recognized as one of the most critical measures to provide insight in the structure and function of complex networks. While various centrality measures have been proposed for single-layer networks...
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Electroencephalography is a non-invasive technique for recording brain bioelectric activity, which has potential applications in various fields such as human-computer interaction and neuroscience. Among them, analysis...
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Electroencephalography is a non-invasive technique for recording brain bioelectric activity, which has potential applications in various fields such as human-computer interaction and neuroscience. Among them, analysis of the risk of schizophrenia using EEG data is a relatively new research topic. However, there are many difficulties in analyzing EEG data, including its complex composition, low amplitude as well as low signal-to-noise ratio. Some of the existing methods of analysis are based on feature extraction and machine learning to differentiate the phase of schizophrenia (First-episode schizophrenia, Healthy controls or Clinical high-risk) that samples belong to. However, medical research requires the use of machine learning not only to give more accurate classification results, but also to give the results that can be applied to pathological studies. The main purpose of this study is to obtain the weight values as the representation of influence of each frequency band on the classification of schizophrenia phases on the basis of a more effective classification method using the LES feature extraction, and then the weight values are processed and applied to improve the accuracy of machine learning classification. We propose a method called weight-voting to obtain the weights of sub-bands features by using results of classification for voting to fit the actual categories of EEG data, and using weights for reclassification. Through this method, we can first obtain the influence of each band in distinguishing three schizophrenia phases, and analyze the effect of band features on the risk of schizophrenia contributing to the study of psychopathology. In addition, the weights applied to the original classifier can achieve the upgrade of the classification effect, which contributes to the BCI-assisted system of diagnosis and treatment. Our results show that there is a high correlation between the change of weight of low gamma band and the difference between HC, CHR an
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