Abstract
Density functional calculation of electronic structures of materials is one of the most used techniques in theoretical solid state physics. These calculations retrieve single electron wavefunctions and their eigenenergies. The berry suite of programs amplifies the usefulness of DFT by ordering the eigenstates in analytic bands, allowing the differentiation of the wavefunctions in reciprocal space. It can then calculate Berry connections and curvatures and the second harmonic generation conductivity. The berry software is implemented for two dimensional materials and was tested in hBN and InSe. In the near future, more properties and functionalities are expected to be added.Program summary Program Title: berry CPC Library link to program files: https://doi .org /10 .17632 /mpbbksz2t7 .1 Developer's repository link: https://github .com /ricardoribeiro -2020 /berry Licensing provisions: MIT Programming language: Python3 Nature of problem: Differentiation of Bloch wavefunctions in reciprocal space, numerically obtained from a DFT software, applied to two dimensional materials. This enables the numeric calculation of material's properties such as Berry geometries and Second Harmonic conductivity. Solution method: Extracts Kohn-Sham functions from a DFT calculation, orders them by analytic bands using graph and AI methods and calculates the gradient of the wavefunctions along an electronic band. Additional comments including restrictions and unusual features: Applies only to two dimensional materials, and only imports Kohn-Sham functions from Quantum Espresso package.

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We propose Soteria, a system for distributed privacy-preserving Machine Learning (ML) that leverages Trusted Execution Environments (e.g. Intel SGX) to run code in isolated containers (enclaves). Unlike previous work, where all ML-related computation is performed at trusted enclaves, we introduce a hybrid scheme, combining computation done inside and outside these enclaves. The conducted experimental evaluation validates that our approach reduces the runtime of ML algorithms by up to 41%, when compared to previous related work. Our protocol is accompanied by a security proof, as well as a discussion regarding resilience against a wide spectrum of ML attacks.