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The coexistence of high-temperature intrinsic ferromagnetic ordering, large magnetic anisotropy, along with novel mechanical properties such as ferroelasticity and flexibility, in experimental feasible two-dimensional (2D) crystals is greatly appealing for nanoscale spintronics. However, the progress in identifying such materials is limited. Here, by first-principles calculations, we report the findings of an extraordinary combination of the above qualities for the first time in a new 2D exfoliated FeSi nanosheet in the P4/nmm space group. Due to the strong anion-mediated superexchange interaction, the monolayer FeSi (ML-FeSi) exhibits a Curie temperature Tc as high as 830 K, surpassing the current experimental record (344 K for ML-Cr3Te4). Furthermore, including FeSi, such isostructural lattices all demonstrate exceptional softness, as evidenced by their ultra-low in-plane stiffness. Remarkably, the transition metal atom and square-shaped crystal form work together to give this family of ML materials unique properties that can transition from Ising-like 2D ferromagnets in FeSi, MnP, MnAs, CrP, FeI, and VAs to 2D-XY ones in CrAs, VP, and multiferroic MnGe and TiTe. Overall, our work highlights such 2D lattices as promising candidates in emerging multifunctional device applications and nontrivial topological spintronics., Comment: 16 pages, 5 figures

Protein complex formation is a central problem in biology, being involved in most of the cell's processes, and essential for applications, e.g. drug design or protein engineering. We tackle rigid body protein-protein docking, i.e., computationally predicting the 3D structure of a protein-protein complex from the individual unbound structures, assuming no conformational change within the proteins happens during binding. We design a novel pairwise-independent SE(3)-equivariant graph matching network to predict the rotation and translation to place one of the proteins at the right docked position relative to the second protein. We mathematically guarantee a basic principle: the predicted complex is always identical regardless of the initial locations and orientations of the two structures. Our model, named EquiDock, approximates the binding pockets and predicts the docking poses using keypoint matching and alignment, achieved through optimal transport and a differentiable Kabsch algorithm. Empirically, we achieve significant running time improvements and often outperform existing docking software despite not relying on heavy candidate sampling, structure refinement, or templates.

Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience. We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data. To meet this need, we are building MedPerf, an open framework for benchmarking machine learning in the medical domain. MedPerf will enable federated evaluation in which models are securely distributed to different facilities for evaluation, thereby empowering healthcare organizations to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status, and our roadmap. We call for researchers and organizations to join us in creating the MedPerf open benchmarking platform.

Recent advancements in ultra-low-power machine learning (TinyML) hardware promises to unlock an entirely new class of smart applications. However, continued progress is limited by the lack of a widely accepted benchmark for these systems. Benchmarking allows us to measure and thereby systematically compare, evaluate, and improve the performance of systems and is therefore fundamental to a field reaching maturity. In this position paper, we present the current landscape of TinyML and discuss the challenges and direction towards developing a fair and useful hardware benchmark for TinyML workloads. Furthermore, we present our four benchmarks and discuss our selection methodology. Our viewpoints reflect the collective thoughts of the TinyMLPerf working group that is comprised of over 30 organizations., Comment: 6 pages, 1 figure, 3 tables

Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But ML training presents three unique benchmarking challenges absent from other domains: optimizations that improve training throughput can increase the time to solution, training is stochastic and time to solution exhibits high variance, and software and hardware systems are so diverse that fair benchmarking with the same binary, code, and even hyperparameters is difficult. We therefore present MLPerf, an ML benchmark that overcomes these challenges. Our analysis quantitatively evaluates MLPerf's efficacy at driving performance and scalability improvements across two rounds of results from multiple vendors., Comment: MLSys 2020

Most data intensive applications often access only a few fields of the objects they are operating on. Since NVM provides fast, byte-addressable access to durable memory, it is possible to access various fields of an object stored in NVM directly without incurring any serialization and deserialization cost. This paper proposes a novel tiered object storage model that modifies a data structure such that only a chosen subset of fields of the data structure are stored in NVM, while the remaining fields are stored in a cheaper (and a traditional) storage layer such as HDDs/SSDs. We introduce a novel linear-programming based optimization framework for deciding the field placement. Our proof of concept demonstrates that a tiered object storage model improves the execution time of standard operations by up to 50\% by avoiding the cost of serialization/deserialization and by reducing the memory footprint of operations.

Poplar is one of the best fast-growing trees in the world, widely used for windbreak and wood product. Although architecture of poplar has direct impact on its applications, it has not been descried in previous poplar models, probably because of the difficulties raised by measurement, data processing and parameterization. In this paper, the functional-structural model GreenLab is calibrated by using poplar data of 3, 4, 5, 6 years old. The data was acquired by simplifying measurement. The architecture was also simplified by classifying the branches into several types (physiological age) using clustering analysis, which decrease the number of parameters. By multi-fitting the sampled data of each tree, the model parameters were identified and the plant architectures at different tree ages were simulated.