Time series motifs have been in the literature for about fifteen years, but have only recently begun to receive significant attention in the research community. This is perhaps due to the growing realization that they implicitly offer solutions to a host of time series problems, including rule discovery, anomaly detection, density estimation, semantic segmentation, etc. Recent work has improved the scalability to the point where exact motifs can be computed on datasets with up to a million data points in tenable time. However, in some domains, for example seismology, there is an insatiable need to address even larger datasets. In this work we show that a combination of a novel algorithm and a high-performance GPU allows us to significantly improve the scalability of motif discovery. We demonstrate the scalability of our ideas by finding the full set of exact motifs on a dataset with one hundred million subsequences, by far the largest dataset ever mined for time series motifs. Furthermore, we demonstrate that our algorithm can produce actionable insights in seismology and other domains.
In 2002, Thorsen et al. integrated thousands of micromechanical valves on a single microfluidic chip and demonstrated that the control of the fluidic networks can be simplified through multiplexors [1]. This enabled realization of highly parallel and automated fluidic processes with substantial sample economy advantage. Moreover, the fabrication of these devices by multilayer soft lithography was easy and reliable hence contributed to the power of the technology; microfluidic large scale integration (mLSI). Since then, mLSI has found use in wide variety of applications in biology and chemistry. In the meantime, efforts to improve the technology have been ongoing. These efforts mostly focus on; novel materials, components, micromechanical valve actuation methods, and chip architectures for mLSI. In this review, these technological advances are discussed and, recent examples of the mLSI applications are summarized. Recent advancements in microfluidic technologies have created new opportunities in the fields of biology and chemistry through miniaturization and automation of common processes, including mixing, metering, trapping, reaction, filtering, and separation, among others. Microfluidic large scale integration (mLSI) enables the fabrication of microfluidic chips containing hundreds or more of these functions using well-established lithography techniques, similar in principle to electronic LSI in the semiconductor industry [1]. Small feature dimensions, abundant spatial parallelism, and control over fluid flow provide mLSI technology with advantages such as high throughput, precise metering, automation and low reagent consumption. The implications of mLSI technology can especially be seen in recent advances in single cell, genomic, and protein analysis.
Since the inception of digital microfluidics, the synthesis problems of scheduling, placement and routing have been performed offline (before runtime) due to their algorithmic complexity. However, with the increasing maturity of digital microfluidic research, online synthesis is becoming a realistic possibility that can bring new benefits in the areas of dynamic scheduling, control-flow, fault-tolerance and live-feedback. This paper contributes to the digital microfluidic synthesis process by introducing a fast, novel path-based scheduling algorithm that produces better schedules than list scheduler for assays with high fan-out; path scheduler computes schedules in milliseconds, making it suitable for both offline and online synthesis.
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