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Donald Trump Jr.'s Private DC Club Has Mysterious Ties to an Ex-Cop With a Controversial Past
Donald Trump Jr.'s Private DC Club Has Mysterious Ties to an Ex-Cop With a Controversial Past The Executive Branch has a reported membership list that includes Trumpworld elites like David Sacks. A WIRED review of corporate filings reveals an under-the-radar player: a notorious former DC police officer. When the Executive Branch soft-launched in Washington, DC, last spring, the private club's initial buzz centered on its starry roster of backers and founding members. The president's eldest son, Donald Trump Jr., is one of the club's several co-owners, according to previous reporting. Founding members reportedly include Trump administration AI czar David Sacks and his podcast cohost Chamath Palihapitiya, as well as crypto bigwigs Tyler and Cameron Winklevoss.
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The tiny tuxedo cat who became a naval hero
A 17-year-old British sailor saved Simon from the Hong Kong docks when he was likely a year old. Breakthroughs, discoveries, and DIY tips sent six days a week. One day in March of 1948, George Hickinbottom, a British sailor, was walking around the docks of Stonecutters Island in Hong Kong. When the 17-year-old spotted a small black-and-white tuxedo cat, barely out of kittenhood, he decided to smuggle the hungry, scrawny animal aboard his ship, the HMS . Hickinbottom didn't get in trouble.
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Supplementary Material for CrossGNN: Confronting Noisy Multivariate Time Series Via Cross Interaction Refinement Anonymous Author(s) Affiliation Address email Appendix 1
Correlation mechanism to capture cross-time dependency for forecasting. Besides, the dimension of the channel is set to 16 based on efficiency considerations. Weather, and the look-back window size is set as 96. Proposition 2. The time and space complexity for the Cross-variable GNN is Frequency enhanced decomposed transformer for long-term series forecasting.
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CrossGNN: Confronting Noisy Multivariate Time Series Via Cross Interaction Refinement
To address the above issues, we propose CrossGNN, a linear complexity GNN model to refine the cross-scale and cross-variable interaction for MTS. To deal with the unexpected noise in time dimension, an adaptive multi-scale identifier (AMSI) is leveraged to construct multi-scale time series with reduced noise.
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