RNN Language Model with Word Clustering and Class-based Output Layer

The recurrent neural network language model (RNNLM) has shown significant promise for statistical language modeling. In this work, a new class-based output layer method is introduced to further improve the RNNLM. In this method, word class information is incorporated into the output layer by utilizing the Brown clustering algorithm to estimate a class-based language model. Experimental results show that the new output layer with word clustering not only improves the convergence obviously but also reduces the perplexity and word error rate in large vocabulary continuous speech recognition

Application and research of wireless laser methane sensor in drainage pipeline monitoring

Laser methane sensor has been widely promoted and successfully applied in coal mines as a new and effective technology building on the approach of laser-based absorption detection. Compared with the traditional catalytic methane sensor, the laser methane sensor discussed offers the important advantages of a long calibration period, high detection precision, the absence of zero drift and low power consumption, all of which are significant advantages for use in coal mining applications. By compensating for the temperature and pressure of the gases present, the accuracy of the methane sensor is evident across a wide range of temperatures and pressures, making it suitable for gas detection, including methane, in pipelines as well. The wireless laser approach which is incorporated into the methane sensor allows wireless transmission and data uploading to a cloud server through NB-IoT. This tackles the problem in gas pipeline monitoring of the length of many pipelines and thus the wide distribution of the sensors, avoiding complicated wiring and thus high associated cost. Further, remote data management can then be achieved, all of which greatly improves the flexibility and security of the management of the pipeline and the data generated

Sparse coding-based spatiotemporal saliency for action recognition

© 2015 IEEE. In this paper, we address the problem of human action recognition by representing image sequences as a sparse collection of patch-level spatiotemporal events that are salient in both space and time domain. Our method uses a multi-scale volumetric representation of video and adaptively selects an optimal space-time scale under which the saliency of a patch is most significant. The input image sequences are first partitioned into non-overlapping patches. Then, each patch is represented by a vector of coefficients that can linearly reconstruct the patch from a learned dictionary of basis patches. We propose to measure the spatiotemporal saliency of patches using Shannon's self-information entropy, where a patch's saliency is determined by information variation in the contents of the patch's spatiotemporal neighborhood. Experimental results on two benchmark datasets demonstrate the effectiveness of our proposed method

Optimized Broadcast for Deep Learning Workloads on Dense-GPU InfiniBand Clusters: MPI or NCCL?

Dense Multi-GPU systems have recently gained a lot of attention in the HPC arena. Traditionally, MPI runtimes have been primarily designed for clusters with a large number of nodes. However, with the advent of MPI+CUDA applications and CUDA-Aware MPI runtimes like MVAPICH2 and OpenMPI, it has become important to address efficient communication schemes for such dense Multi-GPU nodes. This coupled with new application workloads brought forward by Deep Learning frameworks like Caffe and Microsoft CNTK pose additional design constraints due to very large message communication of GPU buffers during the training phase. In this context, special-purpose libraries like NVIDIA NCCL have been proposed for GPU-based collective communication on dense GPU systems. In this paper, we propose a pipelined chain (ring) design for the MPI_Bcast collective operation along with an enhanced collective tuning framework in MVAPICH2-GDR that enables efficient intra-/inter-node multi-GPU communication. We present an in-depth performance landscape for the proposed MPI_Bcast schemes along with a comparative analysis of NVIDIA NCCL Broadcast and NCCL-based MPI_Bcast. The proposed designs for MVAPICH2-GDR enable up to 14X and 16.6X improvement, compared to NCCL-based solutions, for intra- and inter-node broadcast latency, respectively. In addition, the proposed designs provide up to 7% improvement over NCCL-based solutions for data parallel training of the VGG network on 128 GPUs using Microsoft CNTK.Comment: 8 pages, 3 figure

Optimization of the accelerated curing process of concrete using a fibre Bragg grating-based control system and microwave technology

In this paper, an investigation into the suitability of using fibre Bragg gratings (FBGs) for monitoring the accelerated curing process of concrete in a microwave heating environment is presented. In this approach, the temperature data provided by the FBGs are used to regulate automatically the microwave power so that a pre-defined temperature profile is maintained to optimize the curing process, achieving early strength values comparable to those of conventional heat-curing techniques but with significantly reduced energy consumption. The immunity of the FBGs to interference from the microwave radiation used ensures stable readings in the targeted environment, unlike conventional electronic sensor probes

Feasibility of retrieving Arctic sea ice thickness from the Chinese HY-2B Ku-band radar altimeter

With the continuous development of the China ocean dynamic environment satellite series (Haiyang-2, HY-2), it is urgent to explore the potential application of HY-2B in Arctic sea ice thickness retrievals. In this study, we first derive the Arctic radar freeboard and sea ice thickness during two cycles (from October 2019 to April 2020 and from October 2020 to April 2021) using the HY-2B radar altimeter and compare the results with the Alfred Wegener Institute (AWI) CryoSat-2 (CS-2) products. We evaluate our HY-2B sea ice freeboard and thickness products using Operation IceBridge (OIB) airborne data and Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) products. Finally, we estimate the uncertainties in the HY-2B sea ice freeboard and sea ice thickness. Here, we derive the radar freeboard by calculating the difference between the relative elevation of the floe obtained by subtracting the mean sea surface (MSS) height and sea surface height anomaly (SSHA) determined by an average of the 15 lowest points method. The radar freeboard deviation between HY-2B and CS-2 is within 0.02 m, whereas the sea ice thickness deviation between HY-2B and CS-2 is within 0.2 m. The HY-2B radar freeboards are generally thicker than AWI CS-2, except in spring (March and April). A spring segment likely has more floe points than an early winter segment. We also find that the deviations in radar freeboard and sea ice thickness between HY-2B and CS-2 over multiyear ice (MYI) are larger than those over first-year ice (FYI). The correlation between HY-2B (CS-2) sea ice freeboard retrievals and OIB values is 0.77 (0.84), with a root mean square error (RMSE) of 0.13 (0.10) m and a mean absolute error (MAE) of 0.12 (0.081) m. The correlation between HY-2B (CS-2) sea ice thickness retrievals and OIB values is 0.65 (0.80), with an RMSE of 1.86 (1.00) m and an MAE of 1.72 (0.75) m. The HY-2B sea ice freeboard uncertainty values range from 0.021 to 0.027 m, while the uncertainties in the HY-2B sea ice thickness range from 0.61 to 0.74 m. The future work will include reprocessing the HY-2B L1 data with a dedicated sea ice retracker, and using the radar waveforms to directly identify leads to release products that are more reasonable and suitable for polar sea ice thickness retrieval.</p

PT-Symmetric Quantum Rabi Model

In this work, we explore the PT-symmetric quantum Rabi model (PTQRM), which describes a PT-symmetric qubit coupled to a quantized light field. By employing the adiabatic approximation (AA), we are able to solve this model analytically in the parameter regime of interest and analyze various physical aspects. We investigate the static and dynamic properties of the model, using both the AA and numerical diagonalization. Our analysis reveals a multitude of exceptional points (EPs) that are closely connected with the exactly solvable points in the Hermitian counterpart of the model. Intriguingly, these EPs vanish and revive depending on the light-matter coupling strength. Furthermore, we discuss the time evolution of physical observables under the non-Hermitian Hamiltonian. Our work extends the theory of PT-symmetry into the full quantum light-matter interaction regime and provides insights that can be readily enlarged to a broad class of quantum optical systems.Comment: 6+7 pages, 6+6 figure

Off center D−D^- centers in a quantum well in the presence of a perpendicular magnetic field: angular momentum transition and magnetic evaporation

We investigate the effect of the position of the donor in the quantum well on the energy spectrum and the oscillator strength of the D- system in the presence of a perpendicular magnetic field. As a function of the magnetic field we find that when the D- centers are placed sufficiently off-center they undergo singlet-triplet transitions which are similar to those found in many-electron parabolic quantum dots. The main difference is that the number of such transitions depends on the position of the donor and only a finite number of such singlet-triplet transitions are found as function of the strength of the magnetic field. For sufficiently large magnetic fields the two electron system becomes unbound. For the near center D- system no singlet-triplet and no unbinding of the D- is found with increasing magnetic field. A magnetic field vs. donor position phase diagram is presented that depends on the width of the quantum well.Comment: 16 pages, 17 figures. Accepted for publication in Phys. Rev.