The manganese-catalyzed α-fluoroalkenylation of arenes via C-H activation and C-F cleavage has been described. This protocol provides a very useful method for the synthesis of monofluoroalkenes with predominant unconventional E-isomer selectivity which complements the existing strategies for the access to these molecular architectures. In addition, the selectivity of β-defluorination in the catalytic cycle not only determines the configurations of the products but also obviates the use of external oxidants, providing a good example in the exploitation of manganese-catalyzed redox-neutral C-H transformations.
We present FLASH (Fast LSH Algorithm for Similarity search accelerated with HPC), a similarity search system for ultra-high dimensional datasets on a single machine, that does not require similarity computations and is tailored for high-performance computing platforms. By leveraging a LSH style randomized indexing procedure and combining it with several principled techniques, such as reservoir sampling, recent advances in one-pass minwise hashing, and count based estimations, we reduce the computational and parallelization costs of similarity search, while retaining sound theoretical guarantees.We evaluate FLASH on several real, high-dimensional datasets from different domains, including text, malicious URL, click-through prediction, social networks, etc. Our experiments shed new light on the difficulties associated with datasets having several million dimensions. Current state-of-the-art implementations either fail on the presented scale or are orders of magnitude slower than FLASH. FLASH is capable of computing an approximate k-NN graph, from scratch, over the full webspam dataset (1.3 billion nonzeros) in less than 10 seconds. Computing a full k-NN graph in less than 10 seconds on the webspam dataset, using brute-force (n 2 D), will require at least 20 teraflops. We provide CPU and GPU implementations of FLASH for replicability of our results 1 .
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