AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline

An open-source Mandarin speech corpus called AISHELL-1 is released. It is by far the largest corpus which is suitable for conducting the speech recognition research and building speech recognition systems for Mandarin. The recording procedure, including audio capturing devices and environments are presented in details. The preparation of the related resources, including transcriptions and lexicon are described. The corpus is released with a Kaldi recipe. Experimental results implies that the quality of audio recordings and transcriptions are promising.Comment: Oriental COCOSDA 201

L dwarfs detection from SDSS images using improved Faster R-CNN

We present a data-driven approach to automatically detect L dwarfs from Sloan Digital Sky Survey(SDSS) images using an improved Faster R-CNN framework based on deep learning. The established L dwarf automatic detection (LDAD) model distinguishes L dwarfs from other celestial objects and backgrounds in SDSS field images by learning the features of 387 SDSS images containing L dwarfs. Applying the LDAD model to the SDSS images containing 93 labeled L dwarfs in the test set, we successfully detected 83 known L dwarfs with a recall rate of 89.25% for known L dwarfs. Several techniques are implemented in the LDAD model to improve its detection performance for L dwarfs,including the deep residual network and the feature pyramid network. As a result, the LDAD model outperforms the model of the original Faster R-CNN, whose recall rate of known L dwarfs is 80.65% for the same test set. The LDAD model was applied to detect L dwarfs from a larger validation set including 843 labeled L dwarfs, resulting in a recall rate of 94.42% for known L dwarfs. The newly identified candidates include L dwarfs, late M and T dwarfs, which were estimated from color (i-z) and spectral type relation. The contamination rates for the test candidates and validation candidates are 8.60% and 9.27%, respectively. The detection results indicate that our model is effective to search for L dwarfs from astronomical images.Comment: 12 pages, 10 figures, accepted to be published in A

CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks

Dynamic graph data mining has gained popularity in recent years due to the rich information contained in dynamic graphs and their widespread use in the real world. Despite the advances in dynamic graph neural networks (DGNNs), the rich information and diverse downstream tasks have posed significant difficulties for the practical application of DGNNs in industrial scenarios. To this end, in this paper, we propose to address them by pre-training and present the Contrastive Pre-Training Method for Dynamic Graph Neural Networks (CPDG). CPDG tackles the challenges of pre-training for DGNNs, including generalization and long-short term modeling capability, through a flexible structural-temporal subgraph sampler along with structural-temporal contrastive pre-training schemes. Extensive experiments conducted on both large-scale research and industrial dynamic graph datasets show that CPDG outperforms existing methods in dynamic graph pre-training for various downstream tasks under three transfer settings.Comment: 12 pages, 6 figure

3D Imaging of Lithium Protrusions in Solid‐State Lithium Batteries using X‐Ray Computed Tomography

Solid‐state lithium batteries will revolutionize the lithium‐ion battery and energy storage applications if certain key challenges can be resolved. The formation of lithium‐protrusions (dendrites) that can cause catastrophic short‐circuiting is one of the main obstacles, and progresses by a mechanism that is not yet fully understood. By utilizing X‐ray computed tomography with nanoscale resolution, the 3D morphology of lithium protrusions inside short‐circuited solid electrolytes has been obtained for the first time. Distinguishable from adjacent voids, lithium protrusions partially filled cracks that tended to propagate intergranularly through the solid electrolyte, forming a large waved plane in the shape of the grain boundaries. Occasionally, the lithium protrusions bifurcate into flat planes in a transgranular mode. Within the cracks themselves, lithium protrusions are preferentially located in regions of relatively low curvature. The crack volume filled with lithium in two samples is 82.0% and 83.1%, even though they have distinctly different relative densities. Pre‐existing pores in the solid electrolyte, as a consequence of fabrication, can also be part‐filled with lithium, but do not have a significant influence on the crack path. The crack/lithium‐protrusion behavior qualitatively supports a model of propagation combining electrochemical and mechanical effects

Guanxinkang Decoction Exerts Its Antiatherosclerotic Effect Partly through Inhibiting the Endoplasmic Reticulum Stress

Purpose. To investigate the antiatherosclerotic effect of Guanxinkang (GXK) decoction on the apoptosis, mitochondrial membrane potential (MMP), and endoplasmic reticulum stress (ERS) of human umbilical vein endothelial cells (HUVEC) pretreated with homocysteinemia (HCY). Materials and Methods. HUVEC were randomly divided into 5 groups: (1) blank control group (control), (2) model control group (model), (3) GXK low dose group, (4) GXK medium dose group, and (5) GXK high dose group. For the three GXK groups, HCY was given to reach the concentration of 3.0 mmol/L after HUVEC had been incubated with rabbit serum containing GXK for two hours. At 3, 6, 12, and 24 h after HCY had been incubated with the cells, the HUVEC were collected for test of the apoptosis rate, MMP, and GRP78 protein (reflecting ERS). Results. In the model control group, the apoptosis rate and GRP 78 protein expression of HUVEC significantly increased (P<0.05), while MMP significantly decreased (P<0.05) compared with the blank control group. After GXK treatment of medium and high doses, the apoptosis rate and the GRP 78 protein expression significantly (P<0.05) decreased, while MMP significantly increased (P<0.05) in a time-dependent manner compared with the model control group. Conclusion. GXK can antagonize the injury of HUVEC caused by HCY and the antagonism effect increases with the concentration and treatment duration of GXK, with the possible mechanism of GXK antagonism being through inhibiting ERS caused by HCY

MonoOcc: Digging into Monocular Semantic Occupancy Prediction

Monocular Semantic Occupancy Prediction aims to infer the complete 3D geometry and semantic information of scenes from only 2D images. It has garnered significant attention, particularly due to its potential to enhance the 3D perception of autonomous vehicles. However, existing methods rely on a complex cascaded framework with relatively limited information to restore 3D scenes, including a dependency on supervision solely on the whole network's output, single-frame input, and the utilization of a small backbone. These challenges, in turn, hinder the optimization of the framework and yield inferior prediction results, particularly concerning smaller and long-tailed objects. To address these issues, we propose MonoOcc. In particular, we (i) improve the monocular occupancy prediction framework by proposing an auxiliary semantic loss as supervision to the shallow layers of the framework and an image-conditioned cross-attention module to refine voxel features with visual clues, and (ii) employ a distillation module that transfers temporal information and richer knowledge from a larger image backbone to the monocular semantic occupancy prediction framework with low cost of hardware. With these advantages, our method yields state-of-the-art performance on the camera-based SemanticKITTI Scene Completion benchmark. Codes and models can be accessed at https://github.com/ucaszyp/MonoOccComment: Accepted by ICRA 202

Determining the safety and effectiveness of Tai Chi: a critical overview of 210 systematic reviews of controlled clinical trials

Background - This overview summarizes the best available systematic review (SR) evidence on the health effects of Tai Chi. Methods - Nine databases (PubMed, Cochrane Library, EMBASE, Medline, Web of Science, China National Knowledge Infrastructure (CNKI), Chinese Scientific Journal Database (VIP), Sino-Med, and Wanfang Database) were searched for SRs of controlled clinical trials of Tai Chi interventions published between Jan 2010 and Dec 2020 in any language. Effect estimates were extracted from the most recent, comprehensive, highest-quality SR for each population, condition, and outcome. SR quality was appraised with AMSTAR 2 and overall certainty of effect estimates with the GRADE method. Results - Of the 210 included SRs, 193 only included randomized controlled trials, one only included non-randomized studies of interventions, and 16 included both. Common conditions were neurological (18.6%), falls/balance (14.7%), cardiovascular (14.7%), musculoskeletal (11.0%), cancer (7.1%), and diabetes mellitus (6.7%). Except for stroke, no evidence for disease prevention was found; however, multiple proxy-outcomes/risks factors were evaluated. One hundred and fourteen effect estimates were extracted from 37 SRs (2 high, 6 moderate, 18 low, and 11 critically low quality), representing 59,306 adults. Compared to active and/or inactive controls, 66 of the 114 effect estimates reported clinically important benefits from Tai Chi, 53 reported an equivalent or marginal benefit, and 6 an equivalent risk of adverse events. Eight of the 114 effect estimates (7.0%) were rated as high, 43 (37.7%) moderate, 36 (31.6%) low, and 27 (23.7%) very low certainty evidence due to concerns with risk of bias (92/114, 80.7%), imprecision (43/114, 37.7%), inconsistency (37/114, 32.5%), and publication bias (3/114, 2.6%). SR quality was often limited by the search strategies, language bias, inadequate consideration of clinical, methodological, and statistical heterogeneity, poor reporting standards, and/or no registered SR protocol. Conclusions - The findings suggest Tai Chi has multidimensional effects, including physical, psychological and quality of life benefits for a wide range of conditions, as well as multimorbidity. Clinically important benefits were most consistently reported for Parkinson’s disease, falls risk, knee osteoarthritis, low back pain, cerebrovascular, and cardiovascular diseases including hypertension. For most conditions, higher-quality SRs with rigorous primary studies are required

Co-Optimizing Battery Storage for Energy Arbitrage and Frequency Regulation in Real-Time Markets Using Deep Reinforcement Learning

Battery energy storage systems (BESSs) play a critical role in eliminating uncertainties associated with renewable energy generation, to maintain stability and improve flexibility of power networks. In this paper, a BESS is used to provide energy arbitrage (EA) and frequency regulation (FR) services simultaneously to maximize its total revenue within the physical constraints. The EA and FR actions are taken at different timescales. The multitimescale problem is formulated as two nested Markov decision process (MDP) submodels. The problem is a complex decision-making problem with enormous high-dimensional data and uncertainty (e.g., the price of the electricity). Therefore, a novel co-optimization scheme is proposed to handle the multitimescale problem, and also coordinate EA and FR services. A triplet deep deterministic policy gradient with exploration noise decay (TDD-ND) approach is used to obtain the optimal policy at each timescale. Simulations are conducted with real-time electricity prices and regulation signals data from the American PJM regulation market. The simulation results show that the proposed approach performs better than other studied policies in literature

Study on Task Offloading Algorithm for Internet of Vehicles on Highway Based on 5G MillimeterWave Communication

With the rapid development of the Internet of vehicles,the emerging new types of in-vehicle tasks put forward higher requirements for communication and computing capabilities.The development of satellite communication technology and the large-scale deployment of 5G millimeter-wave base stations provide safer and more reliable services for highway vehicle users.At the same time,mobile edge computing technology deploys mobile edge computing(MEC) servers with computing and storage capabi-lities around user terminals to provide computing services for on-board tasks while reducing transmission delays.Aiming at the problem of offloading decision-making and communication resource allocation of vehicle tasks in highway scenarios,the joint optimization problem of computing and communication resources is modeled as a 0-1 mixed integer linear programming problem.Firstly,the original optimization problem is decoupled into the resource block allocation sub-problem and the offloading decision sub-problem.Secondly,the sub-problems are solved by using the water injection algorithm and the particle swarm algorithm.Finally,the sub-problems are iteratively solved based on the heuristic algorithm to obtain the optimal resource block allocation scheme and offload decision vector.Simulation results show that the algorithm minimizes the average system delay while meeting the requirements of all on-board missions