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		<title>Graph-Powered AI Model Spots Hidden Faults in Sensor Networks Before Disaster Strikes</title>
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		<pubDate>Tue, 06 Oct 2026 16:26:42 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Anomaly Detection]]></category>
		<category><![CDATA[ConvLSTM]]></category>
		<category><![CDATA[cyber-physical systems]]></category>
		<category><![CDATA[generative adversarial network]]></category>
		<category><![CDATA[graph convolutional network]]></category>
		<category><![CDATA[industrial control systems]]></category>
		<category><![CDATA[Internet of Things]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[multivariate time series]]></category>
		<category><![CDATA[SMAP]]></category>
		<category><![CDATA[SWaT]]></category>
		<category><![CDATA[Transformer]]></category>
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					<description><![CDATA[Researchers at the University of Tabriz have developed GCAT, a graph-convolutional adversarial transformer that detects anomalies in multivariate sensor data without labeled examples, achieving top F1-scores on NASA spacecraft benchmarks.]]></description>
		
		
		
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