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1. DIMA: a depthwise CNN in-memory accelerator

   https://doi.org/10.1145/3240765.3240799  

   Angizi, Shaahin; He, Zhezhi; Fan, Deliang (November 2018, International Conference on Computer-Aided Design)

   In this work, we first propose a deep depthwise Convolutional Neural Network (CNN) structure, called Add-Net, which uses binarized depthwise separable convolution to replace conventional spatial-convolution. In Add-Net, the computationally expensive convolution operations (i.e. Multiplication and Accumulation) are converted into hardware-friendly Addition operations. We meticulously investigate and analyze the Add-Net's performance (i.e. accuracy, parameter size and computational cost) in object recognition application compared to traditional baseline CNN using the most popular large scale ImageNet dataset. Accordingly, we propose a Depthwise CNN In-Memory Accelerator (DIMA) based on SOT-MRAM computational sub-arrays to efficiently accelerate Add-Net within non-volatile MRAM. Our device-to-architecture co-simulation results show that, with almost the same inference accuracy to the baseline CNN on different data-sets, DIMA can obtain ~1.4× better energy-efficiency and 15.7× speedup compared to ASICs, and, ~1.6× better energy-efficiency and 5.6× speedup over the best processing-in-DRAM accelerators. 

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2. Excited-state effects on magnetic properties of U( iii ) and U( iv ) pyrazolylborate complexes

   https://doi.org/10.1039/C9CC04800F  

   Higgins, Robert F.; Tatebe, Caleb J.; Bart, Suzanne C.; Shores, Matthew P. (August 2019, Chemical Communications)

   For a family of uranium pyrazolylborate complexes, we observe correlations between excited-state mixing and slow relaxation of magnetization for U( iii ) complexes, and U⋯B distances in U( iv ) complexes. 

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3. Differentially private estimation of U statistics

   Kamalika Chaudhuri, Po-Ling Loh (January 2024, Proceedings of Machine Learning Research)

   Under Review for COLT 2024 (Ed.)

   In this paper, we consider the problem of private estimation of U statistics. U statistics are widely used estimators that naturally arise in a broad class of problems, from nonparametric signed rank tests to subgraph counts in random networks. They are simply averages of an appropriate kernel applied to all subsets of a given size (also known as the degree) of sample size n. However, despite the recent outpouring of interest in private mean estimation, private algorithms for more general U statistics have received little attention. We propose a framework where, for a broad class of U statistics, one can use existing tools in private mean estimation to obtain confidence intervals where the private error does not overwhelm the irreducible error resulting from the variance of the U statistics. However, in specific cases that arise when the U statistics degenerate or have vanishing moments, the private error may be of a larger order than the non-private error. To remedy this, we propose a new thresholding-based approach that uses Hajek projections to re-weight different subsets. As we show, this leads to more accurate inference in certain settings. 

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4. Anionic G•U pairs in bacterial ribosomal rRNAs

   https://doi.org/10.1261/rna.079583.123  

   Westhof, Eric; Watson, Zoe L; Zirbel, Craig L.; Cate, Jamie H.D. (April 2023, RNA)

   Wobble GU pairs (or G•U) occur frequently within double-stranded RNA helices interspersed between standard G=C and A-U Watson-Crick pairs. Another type of G•U pair interacting via their Watson-Crick edges has been observed in the A site of ribosome structures between a modified U34 in the tRNA anticodon triplet and G+3 in the mRNA. In such pairs the electronic structure of the U is changed with a negative charge on N3(U), resulting in two H-bonds between N1(G)…O4(U) and N2(G)…N3(U). Here, we report that such pairs occur in other highly conserved positions in ribosomal RNAs of bacteria in the absence of U modification. An anionic cis Watson-Crick G•G pair is also observed and well conserved in the small subunit. These pairs are observed in tightly folded regions. 

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5. On Differentially Private U Statistics

   Chaudhuri, Kamalika; Loh, Po-Ling; Pandey, Shourya; Sarkar, Purnamrita (July 2024, https://doi.org/10.48550/arXiv.2407.04945)

   We consider the problem of privately estimating a parameter 𝔼[h(X1,…,Xk)], where X1, X2, …, Xk are i.i.d. data from some distribution and h is a permutation-invariant function. Without privacy constraints, standard estimators are U-statistics, which commonly arise in a wide range of problems, including nonparametric signed rank tests, symmetry testing, uniformity testing, and subgraph counts in random networks, and can be shown to be minimum variance unbiased estimators under mild conditions. Despite the recent outpouring of interest in private mean estimation, privatizing U-statistics has received little attention. While existing private mean estimation algorithms can be applied to obtain confidence intervals, we show that they can lead to suboptimal private error, e.g., constant-factor inflation in the leading term, or even Θ(1/n) rather than O(1/n²) in degenerate settings. To remedy this, we propose a new thresholding-based approach using local Hájek projections to reweight different subsets of the data. This leads to nearly optimal private error for non-degenerate U-statistics and a strong indication of near-optimality for degenerate U-statistics. 

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