Ukrainian President Volodymyr Zelensky has vowed to overhaul state-owned energy companies, after a major corruption scandal engulfed the country's energy sector. Around $100 million (£76m) has been embezzled, anti-graft investigators said, causing outrage in a country where Russian attacks have resulted in crippling power outages. Alongside a full audit of their financial activities, the management of these companies is to be renewed, Zelensky wrote in a post on X on Saturday. Energoatom, the state nuclear company at the heart of the scandal, will have a new supervisory board within a week, he added. Several of those implicated in the scandal have close links to the Ukrainian president.

We propose Cormorant, a rotationally covariant neural network architecture for learning the behavior and properties of complex many-body physical systems. We apply these networks to molecular systems with two goals: learning atomic potential energy surfaces for use in Molecular Dynamics simulations, and learning ground state properties of molecules calculated by Density Functional Theory. Some of the key features of our network are that (a) each neuron explicitly corresponds to a subset of atoms; (b) the activation of each neuron is covariant to rotations, ensuring that overall the network is fully rotationally invariant. Furthermore, the non-linearity in our network is based upon tensor products and the Clebsch-Gordan decomposition, allowing the network to operate entirely in Fourier space. Cormorant significantly outperforms competing algorithms in learning molecular Potential Energy Surfaces from conformational geometries in the MD-17 dataset, and is competitive with other methods at learning geometric, energetic, electronic, and thermodynamic properties of molecules on the GDB-9 dataset.

Molecular conformer generation is a fundamental task in computational chemistry. Several machine learning approaches have been developed, but none have outperformed state-of-the-art cheminformatics methods.

This is achieved via a novel structured parameterization and imposing structure regularization on the matrix basis.

Consequently, evaluating models on test splits that might have leaked into the training set is prone to misleading conclusions.

We illustrate the online optimization process of RCL in Figure 1. We set b = 10 and A = I for the cost function in Eqn. The testing process is almost instant and takes less than 1 second. It does not use robustification during online optimization. By Theorem 4.1, there is a trade-off (governed by ML predictions for those problem instances that are adversarial to ROBD.

We study a challenging form of Smoothed Online Convex Optimization, a.k.a.

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Janta Power secures $5.5 million in seed funding to expand vertical solar towers that produce 50% more energy than traditional flat panels while using one-third the land.