Accurate localization method combining optimized hybrid neural networks for geomagnetic localization with multi-feature dead reckoning

Location-based services provide significant economic and social benefits. The ubiquity, low cost, and accessibility of geomagnetism are highly advantageous for localization. However, the existing geomagnetic localization methods suffer from location ambiguity. To address these issues, we propose a fusion localization algorithm based on particle swarm optimization. First, we construct a five-dimensional hybrid LSTM (5DHLSTM) neural network model, and the 5DHLSTM network structure parameters are optimized via particle swarm optimization (PSO) to achieve geomagnetic localization. The eight-dimensional BiLSTM (8DBiLSTM) algorithm is subsequently proposed for heading estimation in dead reckoning, which effectively improves the heading accuracy. Finally, fusion localization is achieved by combining geomagnetic localization with an improved pedestrian dead reckoning (IPDR) based on an extended Kalman filter (EKF). To validate the localization performance of the proposed PSO-5DHLSTM-IPDR method, several extended experiments using Xiaomi 10 and Hi Nova 9 are conducted in two different scenarios. The experimental results demonstrate that the proposed method improves localization accuracy and has good robustness and flexibilit

FGF22 deletion causes hidden hearing loss by affecting the function of inner hair cell ribbon synapses

Ribbon synapses are important structures in transmitting auditory signals from the inner hair cells (IHCs) to their corresponding spiral ganglion neurons (SGNs). Over the last few decades, deafness has been primarily attributed to the deterioration of cochlear hair cells rather than ribbon synapses. Hearing dysfunction that cannot be detected by the hearing threshold is defined as hidden hearing loss (HHL). The relationship between ribbon synapses and FGF22 deletion remains unknown. In this study, we used a 6-week-old FGF22 knockout mice model (Fgf22–/–) and mainly focused on alteration in ribbon synapses by applying the auditory brainstem response (ABR) test, the immunofluorescence staining, the patch-clamp recording, and quantitative real-time PCR. In Fgf22–/– mice, we found the decreased amplitude of ABR wave I, the reduced vesicles of ribbon synapses, and the decreased efficiency of exocytosis, which was suggested by a decrease in the capacitance change. Quantitative real-time PCR revealed that Fgf22–/– led to dysfunction in ribbon synapses by downregulating SNAP-25 and Gipc3 and upregulating MEF2D expression, which was important for the maintenance of ribbon synapses’ function. Our research concluded that FGF22 deletion caused HHL by affecting the function of IHC ribbon synapses and may offer a novel therapeutic target to meet an ever-growing demand for deafness treatment

oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation

With the rapid development of deep learning models and hardware support for dense computing, the deep learning workload characteristics changed significantly from a few hot spots on compute-intensive operations to a broad range of operations scattered across the models. Accelerating a few compute-intensive operations using the expert-tuned implementation of primitives does not fully exploit the performance potential of AI hardware. Various efforts have been made to compile a full deep neural network (DNN) graph. One of the biggest challenges is to achieve high-performance tensor compilation by generating expert level performance code for the dense compute-intensive operations and applying compilation optimization at the scope of DNN computation graph across multiple compute-intensive operations. We present oneDNN Graph Compiler, a tensor compiler that employs a hybrid approach of using techniques from both compiler optimization and expert-tuned kernels for high performance code generation of the deep neural network graph. oneDNN Graph Compiler addresses unique optimization challenges in the deep learning domain, such as low-precision computation, aggressive fusion of graph operations, optimization for static tensor shapes and memory layout, constant weight optimization, and memory buffer reuse. Experimental results demonstrate significant performance gains over existing tensor compiler and primitives library for performance-critical DNN computation graphs and end-to-end models on Intel Xeon Scalable Processors.Comment: 10 pages excluding reference, 9 figures, 1 tabl

Music Solfeggio Learning Platform Construction and Application

To better develop the music learning, this paper completes the design and realization of a music solfeggio teaching system by combining with practical teaching conditions of the music academy. Firstly, it elaborates the main functions needing to be possessed by solfeggio teaching system by starting from actual demands of the users, puts forward overall design scheme of the system, and gives detailed design to main function module and database of the system. Secondly, it analyzes and researches theoretical basis of the solfeggio teaching system design, and proposes the construction scheme of teaching knowledge point repository and question bank system, including solfeggio repository information setting and system paper constructing strategy. It is indicated by the system analysis results that: this platform design provides an effective learning and inspection means to the implementation of solfeggio teaching. Thus, it draws the conclusions that: learning system of this paper can directly serve for course learning of the students majoring in music, and it has important practical significance and application value in promoting development of the music education informationization

Music Solfeggio Learning Platform Construction and Application

To better develop the music learning, this paper completes the design and realization of a music solfeggio teaching system by combining with practical teaching conditions of the music academy. Firstly, it elaborates the main functions needing to be possessed by solfeggio teaching system by starting from actual demands of the users, puts forward overall design scheme of the system, and gives detailed design to main function module and database of the system. Secondly, it analyzes and researches theoretical basis of the solfeggio teaching system design, and proposes the construction scheme of teaching knowledge point repository and question bank system, including solfeggio repository information setting and system paper constructing strategy. It is indicated by the system analysis results that: this platform design provides an effective learning and inspection means to the implementation of solfeggio teaching. Thus, it draws the conclusions that: learning system of this paper can directly serve for course learning of the students majoring in music, and it has important practical significance and application value in promoting development of the music education informationization

Music Learning Based on Computer Software

In order to better develop and improve students’ music learning, the authors proposed the method of music learning based on computer software. It is still a new field to use computer music software to assist teaching. Hereby, we conducted an in-depth analysis on the computer-enabled music learning and the music learning status in secondary schools, obtaining the specific analytical data. Survey data shows that students have many cognitive problems in the current music classroom, and yet teachers have not found a reasonable countermeasure to them. Against this background, the introduction of computer music software to music learning is a new trial that can not only cultivate the students’ initiatives of music learning, but also enhance their abilities to learn music. Therefore, it is concluded that the computer software based music learning is of great significance to improving the current music learning modes and means

Radiation-induced lymphopenia and the survival of women with cervical cancer: a meta-analysis

The current systematic analysis and meta-analysis was aimed to evaluate the association between radiation-induced lymphopenia (RIL) and survival of women with cervical cancer (CC). PubMed, Embase, Web of Science, and Cochrane Library were searched for relevant cohort studies comparing survival between women with CC who developed versus not developed RIL after radiotherapy. We pooled the results using a random-effects model that incorporates heterogeneity. In the meta-analysis, 952 women with CC were included from eight cohort studies. Overall, 378 (39.7%) of them had RIL after radiotherapy. During a median follow-up duration of 41.8 months, pooled results showed that RIL was independently associated with poor overall survival (hazard ratio [HR]: 2.67, 95% confidence interval [CI]: 1.81 to 3.94, p < 0.001; I2 = 20%) and progression-free survival (HR: 2.17, 95% CI: 1.58 to 2.98, p < 0.001; I2 = 0%). Predefined subgroup analyses showed similar results in patients with grade 3-4 and grade 4 RIL, in patients with RIL diagnosed during or after the radiotherapy, and in studies with quality score of seven or eight points (p values for subgroup effect all < 0.05). In conclusion, women with RIL were associated with poor survival after radiotherapy for CC

Pseudo-Twin Neural Network of Full Multi-Layer Perceptron for Ultra-Short-Term Wind Power Forecasting

In recent wind power forecasting studies, deep neural networks have shown powerful performance in estimating future power from wind power data. In this paper, a pseudo-twin neural network model of full multi-layer perceptron is proposed for power forecasting in wind farms. In this model, the input wind power data are divided into physical attribute data and historical power data. These two types of input data are processed separately by the feature extraction module of the pseudo-twin structure to obtain physical attribute features and historical power features. To ensure comprehensive integration and establish a connection between the two types of extracted features, a feature mixing module is introduced to cross-mix the features. After mixing, a set of multi-layer perceptrons is used as a power regression module to forecast wind power. In this paper, simulation research is carried out based on measured data. The proposed model is compared with mainstream models such as CNN, RNN, LSTM, GRU, and hybrid neural network. The results show that the MAE and RMSE of the single-step forecasting of the proposed model are reduced by up to 21.88% and 16.85%, respectively. Additionally, the MAE and RMSE of the 1 h rolling forecasting (six steps ahead) are reduced by up to 31.58% and 40.92%, respectively