Vortex-line condensation in three dimensions: A physical mechanism for bosonic topological insulators

Bosonic topological insulators (BTI) in three dimensions are symmetry-protected topological phases (SPT) protected by time-reversal and boson number conservation {symmetries}. BTI in three dimensions were first proposed and classified by the group cohomology theory which suggests two distinct root states, each carrying a $\mathbb{Z}_2$ index. Soon after, surface anomalous topological orders were proposed to identify different root states of BTI, which even leads to a new BTI root state beyond the group cohomology classification. In this paper, we propose a universal physical mechanism via \textit{vortex-line condensation} {from} a 3d superfluid to achieve all {three} root states. It naturally produces bulk topological quantum field theory (TQFT) description for each root state. Topologically ordered states on the surface are \textit{rigorously} derived by placing TQFT on an open manifold, which allows us to explicitly demonstrate the bulk-boundary correspondence. Finally, we generalize the mechanism to $Z_N$ symmetries and discuss potential SPT phases beyond the group cohomology classification.Comment: ReVTeX 4.1 (published version

Building real-time embedded applications on QduinoMC: a web-connected 3D printer case study

Single Board Computers (SBCs) are now emerging with multiple cores, ADCs, GPIOs, PWM channels, integrated graphics, and several serial bus interfaces. The low power consumption, small form factor and I/O interface capabilities of SBCs with sensors and actuators makes them ideal in embedded and real-time applications. However, most SBCs run non-realtime operating systems based on Linux and Windows, and do not provide a user-friendly API for application development. This paper presents QduinoMC, a multicore extension to the popular Arduino programming environment, which runs on the Quest real-time operating system. QduinoMC is an extension of our earlier single-core, real-time, multithreaded Qduino API. We show the utility of QduinoMC by applying it to a specific application: a web-connected 3D printer. This differs from existing 3D printers, which run relatively simple firmware and lack operating system support to spool multiple jobs, or interoperate with other devices (e.g., in a print farm). We show how QduinoMC empowers devices with the capabilities to run new services without impacting their timing guarantees. While it is possible to modify existing operating systems to provide suitable timing guarantees, the effort to do so is cumbersome and does not provide the ease of programming afforded by QduinoMC.http://www.cs.bu.edu/fac/richwest/papers/rtas_2017.pdfAccepted manuscrip

A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series

This paper proposes a novel fault diagnosis approach based on generative adversarial networks (GAN) for imbalanced industrial time series where normal samples are much larger than failure cases. We combine a well-designed feature extractor with GAN to help train the whole network. Aimed at obtaining data distribution and hidden pattern in both original distinguishing features and latent space, the encoder-decoder-encoder three-sub-network is employed in GAN, based on Deep Convolution Generative Adversarial Networks (DCGAN) but without Tanh activation layer and only trained on normal samples. In order to verify the validity and feasibility of our approach, we test it on rolling bearing data from Case Western Reserve University and further verify it on data collected from our laboratory. The results show that our proposed approach can achieve excellent performance in detecting faulty by outputting much larger evaluation scores

MARACAS: a real-time multicore VCPU scheduling framework

This paper describes a multicore scheduling and load-balancing framework called MARACAS, to address shared cache and memory bus contention. It builds upon prior work centered around the concept of virtual CPU (VCPU) scheduling. Threads are associated with VCPUs that have periodically replenished time budgets. VCPUs are guaranteed to receive their periodic budgets even if they are migrated between cores. A load balancing algorithm ensures VCPUs are mapped to cores to fairly distribute surplus CPU cycles, after ensuring VCPU timing guarantees. MARACAS uses surplus cycles to throttle the execution of threads running on specific cores when memory contention exceeds a certain threshold. This enables threads on other cores to make better progress without interference from co-runners. Our scheduling framework features a novel memory-aware scheduling approach that uses performance counters to derive an average memory request latency. We show that latency-based memory throttling is more effective than rate-based memory access control in reducing bus contention. MARACAS also supports cache-aware scheduling and migration using page recoloring to improve performance isolation amongst VCPUs. Experiments show how MARACAS reduces multicore resource contention, leading to improved task progress.http://www.cs.bu.edu/fac/richwest/papers/rtss_2016.pdfAccepted manuscrip

Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are often limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct health index. In this paper, a novel online data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RUL of bearings. More concretely, the raw vibrations of training bearings are first processed using the Hilbert-Huang transform (HHT) and a novel nonlinear degradation indicator is constructed as the label for learning. The CNN is then employed to identify the hidden pattern between the extracted degradation indicator and the vibration of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings' RULs are predicted by using a $\epsilon$-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by transferring to bearings undergoing different operating conditions

Tropospheric O3 modeling study: Contributions of anthropogenic and biogenic sources to O3-CO and O3-CH2O correlations

Tropospheric O3 and CO are major pollutants in the troposphere. Strong correlation between O3 and CO was observed during the DISCOVER-AQ aircraft experiment in July 2011 over the Washington-Baltimore area. The observed correlation does not vary significantly with time or altitude in the boundary layer. The observations are simulated well by a regional chemical transport model. We analyze the model results to understand the factors contributing to the observed O3-CO regression slope, which has been used in past studies to estimate the anthropogenic O3 production amount. We trace separately four different CO sources: primary anthropogenic emissions, oxidation of anthropogenic VOCs, oxidation of biogenic isoprene, and transport from the lateral and upper model boundaries. Modeling analysis suggests that the contribution from biogenic isoprene oxidation to the observed O3-CO regression slope is as large as that from primary anthropogenic CO emissions. As a result of decrease of anthropogenic primary CO emissions during the past decades, biogenic CO from oxidation of isoprene is increasingly important. Consequently, observed and simulated O3-CO regression slopes can no longer be used directly with an anthropogenic CO emission inventory to quantify anthropogenic O3 production over the United States. The consistent enhancement of O3 relative to CO observed in the boundary layer, as indicated by the O3-CO regression slope, provides a useful constraint on model photochemistry and emissions. As an extension, we analyze the scenario of O3-CO regression slopes in the entire United States and China regions. The O3-CO regression slope ~ 0.3 is simulated over the eastern outflow regions over the ocean. Over the eastern inland regions of both countries, the O3-CO regression slope is lower than that over the outflow region, reflecting in part continuous O3 production in the outflow region. The simulation result shows that the proportion of contribution from biogenic isoprene to the regressed O3-CO slopes various depending on the corresponding local emission scenario. While biogenic isoprene oxidation makes a comparable contribution as anthropogenic emissions in the eastern US, the latter dominates over eastern China. Over the western inland regions of both countries, the O3-CO regression slope can be higher than the eastern inland regions due to transport from lateral and upper boundaries. The observations of O3-CO regression slope provide the means to understand the relative importance of anthropogenic and biogenic emissions on O3 as well as transport. In addition to O3-CO, strong correlations and consistent linear regression slopes of O3-CH2O and CO-CH2O were also observed during the DISCOVER-AQ aircraft experiment in July 2011 over the Washington-Baltimore area. Same as CO, we also analyze the model results to understand the factors contributing to the observed O3-CH2O regression slope by tracing separately three different CH2O sources: primary secondary anthropogenic sources, biogenic isoprene oxidation, and transport from model boundaries. Results show biogenic isoprene oxidation makes the largest contribution to the regression slope of O3-CH2O across much of the eastern United States, providing a good indicator for O3 enhanced by biogenic VOCs. In contrast, the regression slope of O3-CO is controlled by both anthropogenic and biogenic emissions. Therefore, the CO-CH2O linear relationship can be applied to track the contributions to surface O3 by anthropogenic and biogenic factors. Making use of these linear dependences, we build a fast-response ozone estimator using near surface CH2O and CO concentrations as inputs. We examine the quality of this O3 estimator by increasing or decreasing anthropogenic emissions by up to 50%. The estimated O3 distribution is in reasonably good agreement with the full-model simulations (R2 >0.77 in the range of -30% to +50% of anthropogenic emissions). The analysis provides the basis for using high-quality geostationary satellites with UV, thermal infrared, or near infrared instruments for observing CH2O and CO to improve surface O3 distribution monitoring. The estimation model also provides 6 observation-derived regional metrics to evaluate and improve full-fledged 3-D air quality models. The NASA DISCOVER-AQ airborne campaigns were also carried out around the Houston and Denver metropolitan areas in the summers of 2013 and 2014, respectively. Using the 2011 national emissions inventory (NEI), a regional chemical transport model (REAM) is applied to analyze the aircraft observations. We find that major model discrepancies are driven by large underestimates of alkane emissions in both regions. Modeling analysis suggests increases of alkane emissions by a factor of 15 in the Houston Ship Channel, where ship-transport, ship-unloading, storage, domestic transportation of oil take place, and by a factor of 5 in the regions of oil and gas exploration of Denver. The large increase of alkane emissions has drastically different effects on O3 concentrations depending on the strength of biogenic emissions. A useful metric to diagnose the effects of alkane emissions on photochemistry is the least-squares regression slope of O3 to CH2O, which increases by 30% and 80% in Houston and Denver, respectively, due to the increases of alkane emissions, leading to good agreement between model simulations and aircraft observations. Our finding implies that alkane emissions from oil and gas related sources may be substantially underestimated by the NEI, leading to corresponding underestimates of anthropogenic contributions to O3 particularly over the western United States where biogenic VOC emissions are low. In regions like Denver, reducing alkane emissions is urgently required to control summertime O3 concentrations.Ph.D