On the asymptotic normality of kernel estimators of the long run covariance of functional time series

We consider the asymptotic normality in L2 of kernel estimators of the long run covariance of stationary functional time series. Our results are established assuming a weakly dependent Bernoulli shift structure for the underlying observations, which contains most stationary functional time series models, under mild conditions. As a corollary, we obtain joint asymptotics for functional principal components computed from empirical long run covariance operators, showing that they have the favorable property of being asymptotically independent

Achieving 10Gbps Line-rate Key-value Stores with FPGAs

Distributed in-memory key-value stores such as memcached have become a critical middleware application within current web infrastructure. However, typical x86-based systems yield limited performance scalability and high power consumption as their architecture with its optimization for single thread performance is not wellmatched towards the memory-intensive and parallel nature of this application. In this paper we present the design of a novel memcached architecture implemented on Field Programmable Gate Arrays (FPGAs) which is the first in literature to achieve 10Gbps line rate processing for all packet sizes. By transformation of the functionality into a dataflow architecture, the implementation can not only provide significant speed-up but also operate at a lower power consumption than any x86. More specifically, with our prototype we have measured an increase of up to a factor of 36x in requests per second per Watt that can be serviced in comparison to the best published numbers for regular servers with optimized software. Additionally, we show that through the tight integration of network interface, memory and compute, round trip latency can be reduced down to below 4.5 microseconds

New observations in neuroscience using superresolution microscopy

© 2018 the authors. Superresolution microscopy (SM) techniques are among the revolutionary methods for molecular and cellular observations in the 21st century. SM techniques overcome optical limitations, and several new observations using SM lead us to expect these techniques to have a large impact on neuroscience in the near future. Several types of SM have been developed, including structured illumination microscopy (SIM), stimulated emission depletion microscopy (STED), and photoactivated localization microscopy (PALM)/stochastic optical reconstruction microscopy (STORM), each with special features. In this Minisymposium, experts in these different types of SM discuss the new structural and functional information about specific important molecules in neuroscience that has been gained with SM. Using these techniques, we have revealed novel mechanisms of endocytosis in nerve growth, fusion pore dynamics, and described quantitative new properties of excitatory and inhibitory synapses. Additional powerful techniques, including single molecule-guided Bayesian localization SM (SIMBA) and expansion microscopy (ExM), alone or combined with super-resolution observation, are also introduced in this session

Electrochemical synthesis of hydrogen peroxide from water and oxygen

H2O2 is important in large-scale industrial processes and smaller on-site activities. The present industrial route to H2O2 involves hydrogenation of an anthraquinone and O2 oxidation of the resulting dihydroanthraquinone — a costly method and one that is impractical for routine on-site use. Electrosynthesis of H2O2 is cost-effective and applicable on both large and small scales. This Review describes methods to design and assess electrode materials for H2O2 electrosynthesis. H2O2 can be prepared by oxidizing H2O at efficient anodic catalysts such as those based on BiVO4. Alternatively, H2O2 forms by partially reducing O2 at cathodes featuring either noble metal alloys or doped carbon. In addition to the catalyst materials used, one must also consider the form and geometry of the electrodes and the type of reactor in order to strike a balance between properties such as mass transport and electroactive area, both of which substantially affect both the selectivity and rate of reaction. Research into catalyst materials and reactor designs is arguably quite mature, such that the future of H2O2 electrosynthesis will instead depend on the design of complete and efficient electrosynthesis systems, in which the complementary properties of the catalysts and the reactor lead to optimal selectivity and overall yield