Deep Learning-Based Big Data Analytics Model Based on Teaching Reforms in Three-Dimensional Composition

With the development of online education and big data analysis, new teaching models and methods have emerged. The integration of online and offline teaching modes based on big data analysis has become an effective way to promote teaching reform and practice in the field of three-dimensional composition. It is important to incorporate teaching reform into the teaching of three-dimensional composition to improve the quality of education and better prepare students for their future careers. This paper evaluated the contribution of teaching reform to the improvement of student performance. This paper designed a Deep Learning (DL) big data analytics model for data clustering and classification. The student performance is monitored for both online teaching and offline teaching classes. The collected data is clustered with the directional clustering process for the computation of feature space. With the estimated feature space value Hidden Markov Model (HMM) is implemented for the estimation of statistical data derived from the feature spaces. The extracted data were applied over the RESENT- 50 model for the classification of students’ performance. The data analysis with DL model stated that student performance in offline teaching is more significant than offline teaching in 3-dimensional aspects

gravity waves, na lidar

Vertical energy transports due to dissipating gravity waves in the mesopause region (85–100 km) are analyzed using over 400 h of observational data obtained from a narrow-band sodium wind-temperature lidar located at Andes Lidar Observatory (ALO), Cerro Pachón (30.25°S, 70.73°W), Chile. Sensible heat flux is directly estimated using measured temperature and vertical wind; energy flux is estimated from the vertical wavenumber and frequency spectra of temperature perturbations; and enthalpy flux is derived based on its relationship with sensible heat and energy fluxes. Sensible heat flux is mostly downward throughout the region. Enthalpy flux exhibits an annual oscillation with maximum downward transport in July above 90 km. The dominant feature of energy flux is the exponential decrease from 10-2 to 10-4W m-2 with the altitude increases from 85 to 100 km and is larger during austral winter. The annual mean thermal diffusivity inferred from enthalpy flux decreases from 303m2 s-1 at 85 km to minimum 221m2 s-1 at 90 km then increases to 350m2 s-1 at 99 km. Results also show that shorter period gravity waves tend to dissipate at higher altitudes and generate more heat transport. The averaged vertical group velocities for high, medium, and low frequency waves are 4.15 m s-1, 1.15 m s-1, and 0.70 m s-1, respectively. Gravity wave heat transport brings significant cooling in the mesopause region at an average cooling rate of 6.7 ± 1.1 K per da

First Na Lidar Measurements of Turbulence Heat Flux, Thermal Diffusivity, and Energy Dissipation Rate in the Mesopause Region

Turbulence is ubiquitous in the mesopause region, where the atmospheric stability is low and wave breaking is frequent. Measuring turbulence is challenging in this region and is traditionally done by rocket soundings and radars. In this work, we show for the first time that the modern Na wind/temperature lidar located at Andes Lidar Observatory in Cerro Pachón, Chile, is able to directly measure the turbulence perturbations in temperature and vertical wind between 85 and 100 km. Using 150 h of lidar observations, we derived the frequency (ω) and vertical wave number (m) spectra for both gravity wave and turbulence, which follow the power law with slopes consistent with theoretical models. The eddy heat flux generally decreases with altitude from about −0.5 Km s−1 at 85 km to −0.1 Km s−1 at 100 km, with a local maximum of −0.6 Km s−1 at 93 km. The derived mean turbulence thermal diffusivity and energy dissipation rate are 43 m2 s−1 and 37 mW kg−1, respectively. The mean net cooling resulted from the heat transport and energy dissipation is −4.9 ± 1.5 K d−1, comparable to that due to gravity wave transport at −7.9 ± 1.9 K d−1. Turbulence key parameters show consistency with turbulence theories

First Measurement of Horizontal Wind and Temperature in the Lower Thermosphere (105–140 km) with a Na Lidar at Andes Lidar Observatory

We report the first measurement of nighttime atmospheric temperature and horizontal wind profiles in the lower thermosphere up to 140 km with the Na lidar at Andes Lidar Observatory in Cerro Pachón, Chile (30.25°S, 70.74°W), when enhanced thermospheric Na was observed. Temperature and horizontal wind were derived up to 140 km using various resolutions, with the lowest resolution of about 2.7 hr and 15 km above 130 km. Thus, the measurements span 60 km in vertical, more than double the traditional 25 km. On the night of 17 April 2015, the horizontal wind magnitude in the thermosphere exceeds 150 ms−1, consistent with past rocket measurements. The meridional wind shows a clear transition from the diurnal-tide-dominant mesopause to the semidiurnal-tide-dominant lower thermosphere. A lidar with a 100 times the power aperture product will be able to measure wind and temperature above 160 km and cover longer time span, providing key measurements for the study of atmosphere-space interactions in this region

A Joint Statistical and Dynamical Assessment of Atmospheric Response to North Pacific Oceanic Variability in CCSM3

ABSTRACT Atmospheric response to North Pacific oceanic variability is assessed in Community Climate System Model, version 3 (CCSM3) using two statistical methods and one dynamical method. All methods identify an equivalent barotropic low response to a warmer sea surface temperature (SST) anomaly in the Kuroshio Extension region (KOE) during early-midwinter. While all three methods capture the major features of the response, the generalized equilibrium feedback assessment method (GEFA) isolates the impact of KOE SST from a complex context, and thus makes itself an excellent choice for similar practice

Which Channel to Ask My Question? Personalized Customer Service Request Stream Routing using Deep Reinforcement Learning

Customer services are critical to all companies, as they may directly connect to the brand reputation. Due to a great number of customers, e-commerce companies often employ multiple communication channels to answer customers' questions, for example, chatbot and hotline. On one hand, each channel has limited capacity to respond to customers' requests, on the other hand, customers have different preferences over these channels. The current production systems are mainly built based on business rules, which merely considers tradeoffs between resources and customers' satisfaction. To achieve the optimal tradeoff between resources and customers' satisfaction, we propose a new framework based on deep reinforcement learning, which directly takes both resources and user model into account. In addition to the framework, we also propose a new deep-reinforcement-learning based routing method-double dueling deep Q-learning with prioritized experience replay (PER-DoDDQN). We evaluate our proposed framework and method using both synthetic and a real customer service log data from a large financial technology company. We show that our proposed deep-reinforcement-learning based framework is superior to the existing production system. Moreover, we also show our proposed PER-DoDDQN is better than all other deep Q-learning variants in practice, which provides a more optimal routing plan. These observations suggest that our proposed method can seek the trade-off where both channel resources and customers' satisfaction are optimal.Comment: 13 pages, 7 figure

Observation and Modeling of Gravity Wave Propagation through Reflection and Critical Layers above Andes Lidar Observatory at Cerro Pachón, Chile

A complex gravity wave event was observed from 04:30 to 08:10 UTC on 16 January 2015 by a narrow-band sodium lidar and an all-sky airglow imager located at Andes Lidar Observatory (ALO) in Cerro Pachón (30.25∘S, 70.73∘W), Chile. The gravity wave packet had a period of 18–35 min and a horizontal wavelength of about 40–50 km. Strong enhancements of the vertical wind perturbation, exceeding10 m s−1, were found at ∼90 km and ∼103 km, consistent with nearly evanescent wave behavior near a reflection layer. A reduction in vertical wavelength was found as the phase speed approached the background wind speed near ∼93 km. A distinct three-layered structure was observed in the lidar data due to refraction of the wave packet. A fully nonlinear model was used to simulate this event, which successfully reproduced the amplitudes and layered structure seen in observations. The model results provide dynamical insight, suggesting that a double reflection occurring at two separate heights caused the large vertical wind amplitudes, while the three-layered structure in the temperature perturbation was a result of relatively stable regions at those altitudes. The event provides a clear perspective on the filtering processes to which short-period, small-scale gravity waves are subject in mesosphere and lower thermosphere

Learning to Compose Representations of Different Encoder Layers towards Improving Compositional Generalization

Recent studies have shown that sequence-to-sequence (seq2seq) models struggle with compositional generalization (CG), i.e., the ability to systematically generalize to unseen compositions of seen components. There is mounting evidence that one of the reasons hindering CG is the representation of the encoder uppermost layer is entangled, i.e., the syntactic and semantic representations of sequences are entangled. However, we consider that the previously identified representation entanglement problem is not comprehensive enough. Additionally, we hypothesize that the source keys and values representations passing into different decoder layers are also entangled. Starting from this intuition, we propose \textsc{CompoSition} (\textbf{Compo}se \textbf{S}yntactic and Semant\textbf{i}c Representa\textbf{tion}s), an extension to seq2seq models which learns to compose representations of different encoder layers dynamically for different tasks, since recent studies reveal that the bottom layers of the Transformer encoder contain more syntactic information and the top ones contain more semantic information. Specifically, we introduce a \textit{composed layer} between the encoder and decoder to compose different encoder layers' representations to generate specific keys and values passing into different decoder layers. \textsc{CompoSition} achieves competitive results on two comprehensive and realistic benchmarks, which empirically demonstrates the effectiveness of our proposal. Codes are available at~\url{https://github.com/thinkaboutzero/COMPOSITION}.Comment: Accepted by Findings of EMNLP 202

Isolation and Characterization of 89K Pathogenicity Island-Positive ST-7 Strains of Streptococcus suis Serotype 2 from Healthy Pigs, Northeast China

Streptococcus suis is a swine pathogen which can also cause severe infection, such as meningitis, and streptococcal-like toxic shock syndrome (STSS), in humans. In China, most of the S. suis infections in humans were reported in the southern areas with warm and humid climates, but little attention had been paid to the northern areas. Data presented here showed that the virulent serotypes 1, 2, 7, and 9 of S. suis could be steadily isolated from the healthy pigs in the pig farms in all the three provinces of Northeast China. Notably, a majority of the serotype 2 isolates belonged to the 89K pathogenicity island-positive ST-7 clone that had historically caused the human STSS outbreaks in the Sichuan and Jiangsu provinces of China, although the human STSS case caused by S. suis had never been reported in northern areas of China. Data presented here indicated that the survey of S. suis should be expanded to or reinforced in the northern areas of China

The expression patterns and correlations of claudin-6, methy-CpG binding protein 2, DNA methyltransferase 1, histone deacetylase 1, acetyl-histone H3 and acetyl-histone H4 and their clinicopathological significance in breast invasive ductal carcinomas

<p>Abstract</p> <p>Background</p> <p>Claudin-6 is a candidate tumor suppressor gene in breast cancer, and has been shown to be regulated by DNA methylation and histone modification in breast cancer lines. However, the expression of claudin-6 in breast invasive ductal carcinomas and correlation with clinical behavior or expression of other markers is unclear. We considered that the expression pattern of claudin-6 might be related to the expression of DNA methylation associated proteins (methyl-CpG binding protein 2 (MeCP2) and DNA methyltransferase 1 (DNMT1)) and histone modification associated proteins (histone deacetylase 1 (HDAC1), acetyl-histone H3 (H3Ac) and acetyl- histone H4 (H4Ac)).</p> <p>Methods</p> <p>We have investigated the expression of claudin-6, MeCP2, HDAC1, H3Ac and H4Ac in 100 breast invasive ductal carcinoma tissues and 22 mammary gland fibroadenoma tissues using immunohistochemistry.</p> <p>Results</p> <p>Claudin-6 protein expression was reduced in breast invasive ductal carcinomas (<it>P </it>< 0.001). In contrast, expression of MeCP2 (<it>P </it>< 0.001), DNMT1 (<it>P </it>= 0.001), HDAC1 (<it>P </it>< 0.001) and H3Ac (<it>P </it>= 0.004) expressions was increased. Claudin-6 expression was inversely correlated with lymph node metastasis (<it>P </it>= 0.021). Increased expression of HDAC1 was correlated with histological grade (<it>P </it>< 0.001), age (<it>P </it>= 0.004), clinical stage (<it>P </it>= 0.007) and lymph node metastasis (<it>P </it>= 0.001). H3Ac expression was associated with tumor size (<it>P </it>= 0.044) and clinical stage of cancers (<it>P </it>= 0.034). MeCP2, DNMT1 and H4Ac expression levels did not correlate with any of the tested clinicopathological parameters (<it>P </it>> 0.05). We identified a positive correlation between MeCP2 protein expression and H3Ac and H4Ac protein expression.</p> <p>Conclusions</p> <p>Our results show that claudin-6 protein is significantly down-regulated in breast invasive ductal carcinomas and is an important correlate with lymphatic metastasis, but claudin-6 down-regulation was not correlated with upregulation of the methylation associated proteins (MeCP2, DNMT1) or histone modification associated proteins (HDAC1, H3Ac, H4Ac). Interestingly, the expression of MeCP2 was positively correlated with the expression of H3Ac and H3Ac protein expression was positively correlated with the expression of H4Ac in breast invasive ductal carcinoma</p> <p>Virtual slides</p> <p>The virtual slide(s) for this article can be found here: <url>http://www.diagnosticpathology.diagnomx.eu/vs/4549669866581452</url></p