What does the abbreviation MRC stand for? Meaning: medical reserve corps.
ThesemeasuresystemandmethodshadthepracticalmeaningtoresearchesforE-OimagingsystemMRCmodelanditsmeasurements. 这些测试系统和方法对于光电成像系统MRC模型及其测量方法研究具有实际意义。 www.ceps.com.tw 5. Theperformanceofcooperativecommunicationsystemswithopportunisticrelayingandmaximal-ratio-combining(MRC) wasanalyzed. ...
Forward-Looking Statements This news release contains forward-looking statements within the meaning of Section 27A of the Securities Act and Section 21E of the Exchange Act indicated by the word “expect” and similar expressions. These statements, including those abou...
9 RegisterLog in Sign up with one click: Facebook Twitter Google Share on Facebook AcronymDefinition KMRCKochi Metro Rail Corporation(Kochi, India) KMRCKnowledge Media Research Center KMRCKnowledge Management Research Center(various universities) ...
Public Utility Holding CompanyNeither the Company nor any Subsidiary is an "investment company" or a company "controlled" by an "investment company" within the meaning of the Investment Company Act of 1940, as amended, or a "public utility holding company" within the meaning of the Public Util...
This news release contains forward-looking statements within the meaning of Section 27A of the Securities Act and Section 21E of the Exchange Act. Words such as “will,”“expect,”“expected,”“intend,”“believes,” "on-track," “well positioned,”“look forward,”“guidan...
This announcement contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933 and Section 21E of the Securities Exchange Act of 1934, including, without limitation, expectations regarding Comscore’s advertising suite of produc...
Generating curvilinear mask shapes in OPC instead of pure rectangular shapes is becoming more and more realistic as a method for improving wafer lithography performance. The main benefit of using curvilinear shapes is an improved process window, meaning that the wafer image is less sensitive to dose...
This linear layer is specific to the given source node, meaning that each source node has its own linear layer. This is what the model looks like: class GetLinearLayer(tf.keras.Model): def __init__(self, num_layers): super().__init__() self.num_layers = num_layers self.layer_wei...
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