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Since SSH’s standardization nearly 20 years ago, real-world requirements for a remote access protocol and our understanding of how to build secure cryptographic network protocols have both evolved significantly. In this work, we introduce Hop, a transport and remote access protocol designed to support today’s needs. Building on modern cryptographic advances, Hop reduces SSH protocol complexity and overhead while simultaneously addressing many of SSH’s shortcomings through a cryptographically-mediated delegation scheme, native host identification based on lessons from TLS and ACME, client authentication for modern enterprise environments, and support for client roaming and intermittent connectivity. We present concrete design requirements for a modern remote access protocol, describe our proposed protocol, and evaluate its performance. We hope that our work encourages discussion of what a modern remote access protocol should look like in the future. 

The future of the STEM workforce rests partly on the strength of the STEM teacher workforce to teach and nurture new generations of STEM graduates. However, the STEM teacher workforce is facing critical decline with the annual production dropping from about 31,000 a decade ago to around 20,000 in the last few years. This is concerning given the need for more STEM teachers to meet rising demands. Although production is decreasing, there are improvements in the diversity and qualifications of STEM teachers, including more female teachers and those with higher degrees in STEM fields. Investments in teacher salaries and financial support for STEM education can help address the shortage and improve the future STEM teacher workforce and STEM workforce. 

Learning the Hamiltonian underlying a quantum many-body system in thermal equilibrium is a fundamental task in quantum learning theory and experimental sciences. To learn the Gibbs state of local Hamiltonians at any inverse temperature β, the state-of-the-art provable algorithms fall short of the optimal sample and computational complexity, in sharp contrast with the locality and simplicity in the classical cases. In this work, we present a learning algorithm that learns each local term of a n-qubit D-dimensional Hamiltonian to an additive error ϵ with sample complexity $$\tilde{O}\left(\frac{e^{\mathrm{poly}(\beta)}}{\beta^2\epsilon^2}\right)\log(n)$$. The protocol uses parallelizable local quantum measurements that act within bounded regions of the lattice and near-linear-time classical post-processing. Thus, our complexity is near optimal with respect to n, ϵ and is polynomially tight with respect to β. We also give a learning algorithm for Hamiltonians with bounded interaction degree with sample and time complexities of similar scaling on n but worse on β, ϵ. At the heart of our algorithm is the interplay between locality, the Kubo-Martin-Schwinger condition, and the operator Fourier transform at arbitrary temperatures. 

During development and under normal physiological conditions, biological tissues are continuously subjected to substantial mechanical stresses. In response to large deformations, cells in a tissue must undergo multicellular rearrangements to maintain integrity and robustness. However, how these events are connected in time and space remains unknown. Here, using theoretical modeling, we study the mechanical plasticity of cell monolayers under large deformations. Our results suggest that the jamming-unjamming (solid-fluid) transition can vary significantly depending on the degree of deformation, implying that tissues are highly unconventional materials. We elucidate the origins of this behavior. We also demonstrate how large deformations are accommodated through a series of cellular rearrangements, similar to avalanches in non-living materials. We find that these ‘tissue avalanches’ are governed by stress redistribution and the spatial distribution of “soft” or vulnerable spots, which are more prone to undergo rearrangements. Finally, we propose a simple and experimentally accessible framework to infer tissue-level stress and predict avalanches based on static images.