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  4. Recently, the choice of ligand and geometric control of mononuclear complexes, which can affect the relaxation pathways and blocking temperature, have received wide attention in the field of single-ion magnets (SIMs). To find out the influence of the coordination environment on SIMs, two four-coordinate mononuclear Co( ii ) complexes [NEt 4 ][Co(PPh 3 )X 3 ] (X = Cl − , 1; Br − , 2) have been synthesized and studied by X-ray single crystallography, magnetic measurements, high-frequency and -field EPR (HF-EPR) spectroscopy and theoretical calculations. Both complexes are in a cubic space group Pa 3̄ (No. 205), containing a slightly distorted tetrahedral moiety with crystallographically imposed C 3 v symmetry through the [Co(PPh 3 )X 3 ] − anion. The direct-current (dc) magnetic data and HF-EPR spectroscopy indicated the anisotropic S = 3/2 spin ground states of the Co( ii ) ions with the easy-plane anisotropy for 1 and 2. Ab initio calculations were performed to confirm the positive magnetic anisotropies of 1 and 2. Frequency- and temperature-dependent alternating-current (ac) magnetic susceptibility measurements revealed slow magnetic relaxation for 1 and 2 at an applied dc field. Finally, the magnetic properties of 1 and 2 were compared to those ofmore »other Co( ii ) complexes with a [CoAB 3 ] moiety.« less
    Free, publicly-accessible full text available May 17, 2023
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  8. To process real-world datasets, modern data-parallel systems often require extremely large amounts of memory, which are both costly and energy inefficient. Emerging non-volatile memory (NVM) technologies offer high capacity compared to DRAM and low energy compared to SSDs. Hence, NVMs have the potential to fundamentally change the dichotomy between DRAM and durable storage in Big Data processing. However, most Big Data applications are written in managed languages and executed on top of a managed runtime that already performs various dimensions of memory management. Supporting hybrid physical memories adds a new dimension, creating unique challenges in data replacement. This article proposes Panthera, a semantics-aware, fully automated memory management technique for Big Data processing over hybrid memories. Panthera analyzes user programs on a Big Data system to infer their coarse-grained access patterns, which are then passed to the Panthera runtime for efficient data placement and migration. For Big Data applications, the coarse-grained data division information is accurate enough to guide the GC for data layout, which hardly incurs overhead in data monitoring and moving. We implemented Panthera in OpenJDK and Apache Spark. Based on Big Data applications’ memory access pattern, we also implemented a new profiling-guided optimization strategy, which is transparent tomore »applications. With this optimization, our extensive evaluation demonstrates that Panthera reduces energy by 32–53% at less than 1% time overhead on average. To show Panthera’s applicability, we extend it to QuickCached, a pure Java implementation of Memcached. Our evaluation results show that Panthera reduces energy by 28.7% at 5.2% time overhead on average.« less
    Free, publicly-accessible full text available November 30, 2022
  9. From the perspective of expressive power, this work compares multi-layer Graph Neural Networks (GNNs) with a simplified alternative thatwe call Graph-Augmented Multi-Layer Perceptrons (GA-MLPs), which first augments node features with certain multi-hop operators on the graph and then applies an MLP in a node-wise fashion. From the perspective of graph isomorphism testing,we showboth theoretically and numerically that GA-MLPs with suitable operators can distinguish almost all non-isomorphic graphs, just like the Weifeiler-Lehman (WL) test. However, by viewing them as node-level functions and examining the equivalence classes they induce on rooted graphs, we prove a separation in expressive power between GA-MLPs and GNNs that grows exponentially in depth. In particular, unlike GNNs, GA-MLPs are unable to count the number of attributed walks. We also demonstrate via community detection experiments that GA-MLPs can be limited by their choice of operator family, as compared to GNNs with higher flexibility in learning.