Long Short-Term Memory Projection Recurrent Neural Network Architectures for Piano’s Continuous Note Recognition

Long Short-Term Memory (LSTM) is a kind of Recurrent Neural Networks (RNN) relating to time series, which has achieved good performance in speech recogniton and image recognition. Long Short-Term Memory Projection (LSTMP) is a variant of LSTM to further optimize speed and performance of LSTM by adding a projection layer. As LSTM and LSTMP have performed well in pattern recognition, in this paper, we combine them with Connectionist Temporal Classification (CTC) to study piano’s continuous note recognition for robotics. Based on the Beijing Forestry University music library, we conduct experiments to show recognition rates and numbers of iterations of LSTM with a single layer, LSTMP with a single layer, and Deep LSTM (DLSTM, LSTM with multilayers). As a result, the single layer LSTMP proves performing much better than the single layer LSTM in both time and the recognition rate; that is, LSTMP has fewer parameters and therefore reduces the training time, and, moreover, benefiting from the projection layer, LSTMP has better performance, too. The best recognition rate of LSTMP is 99.8%. As for DLSTM, the recognition rate can reach 100% because of the effectiveness of the deep structure, but compared with the single layer LSTMP, DLSTM needs more training time

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

An I/O efficient model checking algorithm for large-scale systems

Model checking is a powerful approach for the formal verification of hardware and software systems. However, this approach suffers from the state space explosion problem, which limits its application to large-scale systems due to space shortage. To overcome this drawback, one of the most effective solutions is to use external memory algorithms. In this paper, we propose an I/O efficient model checking algorithm for large-scale systems. To lower I/O complexity and improve time efficiency, we combine three new techniques: 1) a linear hash-sorting technique; 2) a cached duplicate detection technique; and 3) a dynamic path management technique. We show that the new algorithm has a lower I/O complexity than state-of-the-art I/O efficient model checking algorithms, including detect accepting cycle, maximal accepting predecessors, and iterative-deepening depth-first search. In addition, the experiments show that our algorithm obviously outperforms these three algorithms on the selected representative benchmarks in terms of performance

Overexpression of a maize sulfite oxidase gene in tobacco enhances tolerance to sulfite stress via sulfite oxidation and CAT-mediated H2O2 scavenging.

Sulfite oxidase (SO) plays an important role in sulfite metabolism. To date, the molecular mechanisms of sulfite metabolism in plants are largely unknown. Previously, a full-length cDNA of the putative sulfite oxidase gene from maize (ZmSO) was cloned, and its response to SO(2)/sulfite stress at the transcriptional level was characterized. In this study, the recombinant ZmSO protein was purified from E. coli. It exhibited sulfite-dependent activity and had strong affinity for the substrate sulfite. Over-expression (OE) of ZmSO in tobacco plants enhanced their tolerance to sulfite stress. The plants showed much less damage, less sulfite accumulation, but greater amounts of sulfate. This suggests that tolerance of transgenic plants to sulfite was enhanced by increasing SO expression levels. Interestingly, H(2)O(2) accumulation levels by histochemical detection and quantitative determination in the OE plants were much less than those in the wild-type upon sulfite stress. Furthermore, reductions of catalase levels detected in the OE lines were considerably less than in the wild-type plants. This indicates that SO may play an important role in protecting CAT from inhibition by excess sulfite. Collectively, these data demonstrate that transgenic tobacco plants over-expressing ZmSO enhance tolerance to excess sulfite through sulfite oxidation and catalase-mediated hydrogen peroxide scavenging. This is the first SO gene from monocots to be functionally characterized

The effect of depressant sesbania gum on the flotation of a talc-containing scheelite ore

Sodium silicate (SS) has been widely used as a depressant for the scheelite flotation. However, low selectivity and large amount of SS remains a tough problem. In this work, the effect of depressant sesbania gum (SGM) on the flotation of a talc-containing scheelite ore was investigated through pure mineral flotation and run-of-mine ore flotation tests. The flotation tests showed that the selective separation between scheelite and talc was achieved using a new reagent schedule, i.e., a lower dosage of SGM (80 mg/L) and a new collector of sulfonated oleic acid (SNaOL, 40 mol/L) at pH 9.0. A concentrate with WO3 grade of 52.0% and recovery of 78.4% was achieved by the run-of-mine ore batch flotation test. In addition, the mechanism of the selective separation was investigated by zeta-potential measurements, FTIR spectra and XPS analyses, which demonstrated that SNaOL absorbed more strongly and selectively on the “scheelite + SGM” surface than on the “talc + SGM” surface, while SGM could more selectively interact on the talc surface than on the scheelite surface. SGM absorbed on the talc surface by chemical adsorption between the Mg/Si atom of the talc surface and OH group in SGM molecule. Keywords: Scheelite, Talc, Sesbania gum, Sulfonated oleic acid, Adsorption mechanis

Energy Dissipation Enhanced by Multiple Hinges in Bridge Piers with CFST Y-Shaped Fuses

Concrete-filled steel tubular Y-shaped (CFST-Y) piers are good candidates for meeting the structural and aesthetic requirements of bridges. By using the theoretical and nonlinear static (pushover) analyses, the seismic performances of three types of CFST-Y piers were evaluated at different seismic hazard levels. The theoretical formulas were first proposed to estimate the lateral stiffnesses for piers with different pier–deck connections. Then, the structural ductility with the development of plastic hinges in piers was investigated based on the pushover analyses. The results demonstrate that the structural dimensions, deck mass, shear limit, and stiffness of bearings can remarkably affect the formation of hinges and thereby lead to different energy dissipation patterns to achieve the expected performance in piers. The findings suggest an economic design strategy of piers, using CFST-Y members as energy dissipation fuses with multiple hinges, to achieve low-level seismic performance cost-effectively