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Persistent memory (PMEM) allows direct access to fast storage at byte granularity. Previously, processor caches backed by persistent memory were not persistent, complicating the design of persistent applications and reducing their performance. A new generation of systems with flush-on-fail semantics effectively offer persistent caches, offering the potential for much simpler, faster PMEM programming models. This work proposes Whole Process Persistence (WPP), a new programming model for systems with persistent caches. In the WPP model, all process state is made persistent. On restart after power failure, this state is reloaded and execution resumes in an application-defined interrupt handler. We also describe the Zhuque runtime, which transparently provides WPP by interposing on the C bindings for system calls in userspace. It requires little or no programmer effort to run applications on Zhuque. Our measurements show that Zhuque outperforms state of the art PMEM libraries, demonstrating mean speedups across all benchmarks of 5.24x over PMDK, 3.01x over Mnemosyne, 5.43x over Atlas, and 4.11x over Clobber-NVM. More important, unlike existing systems, Zhuque places no restrictions on how applications implement concurrency, allowing us to run a newer version of Memcached on Zhuque and gain more than 7.5x throughput over the fastest existing persistent implementations. 

NMR chemical shifts provide a sensitive probe of protein structure and dynamics. Prediction of shifts, and therefore interpretation of shifts, particularly for the frequently measured amidic 15N sites, remains a tall challenge. We demonstrate that protein 15N chemical shift prediction from QM/MM predictions can be improved if conformational variation is included via MD sampling, focusing on the antibiotic target, E. coli Dihydrofolate reductase (DHFR). Variations of up to 25 ppm in predicted 15N chemical shifts are observed over the trajectory. For solution shifts the average of fluctuations on the low picosecond timescale results in a superior prediction to a single optimal conformation. For low temperature solid state measurements, the histogram of predicted shifts for locally minimized snapshots with specific solvent arrangements sampled from the trajectory explains the heterogeneous linewidths; in other words, the conformations and associated solvent are ‘frozen out’ at low temperatures and result in inhomogeneously broadened NMR peaks. We identified conformational degrees of freedom that contribute to chemical shift variation. Backbone torsion angles show high amplitude fluctuations during the trajectory on the low picosecond timescale. For a number of residues, including I60, ψ varies by up to 60º within a conformational basin during the MD simulations, despite the fact that I60 (and other sites studied) are in a secondary structure element and remain well folded during the trajectory. Fluctuations in ψ appear to be compensated by other degrees of freedom in the protein, including φ of the succeeding residue, resulting in “rocking” of the amide plane with changes in hydrogen bonding interactions. Good agreement for both room temperature and low temperature NMR spectra provides strong support for the specific approach to conformational averaging of computed chemical shifts. 

While low temperature NMR holds great promise for the analysis of unstable samples and for sensitizing NMR detection, spectral broadening in frozen protein samples is a common experimental challenge. One hypothesis explaining the additional linewidth is that a variety of conformations are in rapid equilibrium at room temperature and become frozen, creating an inhomogeneous distribution at cryogenic temperatures. Here we investigate conformational heterogeneity by measuring the backbone torsion angle (Ψ) in E. coli DHFR at 105K. Motivated by the particularly broad N chemical shift distribution in this and other examples, we modified an established NCCN Ψ experiment to correlate the chemical shift of Ni+1 to Ψi. With selective 15N and 13C enrichment of Ile, only the unique I60-I61 pair was expected to be detected in 13C’-15N correlation spectrum. For this unique amide we detected three different conformation basins based on dispersed chemical shifts. Backbone torsion angles Ψ were determined for each basin 114 ± 7 for the major peak, and 150 ± 8 and 164 ± 16° for the minor peak as contrasted with 118 for the X-ray crystal structure (and 118-130 for various previously reported structures). These studies support the hypothesis that inhomogeneous distributions of protein backbone torsion angles contribute to the lineshape broadening in low temperature NMR spectra. 

Knowledge Distillation (KD) (Hinton et al., 2015) is one of the most effective approaches for deploying large-scale pre-trained language models in low-latency environments by transferring the knowledge contained in the largescale models to smaller student models. Previous KD approaches use the soft labels and intermediate activations generated by the teacher to transfer knowledge to the student model parameters alone. In this paper, we show that having access to non-parametric memory in the form of a knowledge base with the teacher’s soft labels and predictions can further enhance student capacity and improve generalization. To enable the student to retrieve from the knowledge base effectively, we propose a new Retrieval-augmented KD framework with a loss function that aligns the relational knowledge in teacher and student embedding spaces. We show through extensive experiments that our retrieval mechanism can achieve state-of-the-art performance for taskspecific knowledge distillation on the GLUE benchmark (Wang et al., 2018a). 

Dimerized valence bond solids appear naturally in spin-1/2 systems on bipartite lattices, with the geometric frustrations playing a key role both in their stability and the eventual `melting' due to quantum fluctuations. Here, we ask the question of the stability of such dimerized solids in spin-1 systems, taking the anisotropic square lattice with bilinear and biquadratic spin-spin interactions as a paradigmatic model. The lattice can be viewed as a set of coupled spin-1 chains, which in the limit of vanishing inter-chain coupling are known to possess a stable dimer phase. We study this model using the density matrix renormalization group (DMRG) and infinite projected entangled-pair states (iPEPS) techniques, supplemented by the analytical mean-field and linear flavor wave theory calculations. While the latter predicts the dimer phase to remain stable up to a reasonably large interchain-to-intrachain coupling ratio r≲0.6, the DMRG and iPEPS find that the dimer solid melts for much weaker interchain coupling not exceeding r≲0.15. We find the transition into a magnetically ordered state to be first order, manifested by a hysteresis and order parameter jump, precluding the deconfined quantum critical scenario. The apparent lack of stability of dimerized phases in 2D spin-1 systems is indicative of strong quantum fluctuations that melt the dimer solid.