Deep Short Text Classification with Knowledge Powered Attention

Short text classification is one of important tasks in Natural Language Processing (NLP). Unlike paragraphs or documents, short texts are more ambiguous since they have not enough contextual information, which poses a great challenge for classification. In this paper, we retrieve knowledge from external knowledge source to enhance the semantic representation of short texts. We take conceptual information as a kind of knowledge and incorporate it into deep neural networks. For the purpose of measuring the importance of knowledge, we introduce attention mechanisms and propose deep Short Text Classification with Knowledge powered Attention (STCKA). We utilize Concept towards Short Text (C- ST) attention and Concept towards Concept Set (C-CS) attention to acquire the weight of concepts from two aspects. And we classify a short text with the help of conceptual information. Unlike traditional approaches, our model acts like a human being who has intrinsic ability to make decisions based on observation (i.e., training data for machines) and pays more attention to important knowledge. We also conduct extensive experiments on four public datasets for different tasks. The experimental results and case studies show that our model outperforms the state-of-the-art methods, justifying the effectiveness of knowledge powered attention

2.5-D poroelastic wave modelling in double porosity media

To approximate seismic wave propagation in double porosity media, the 2.5-D governing equations of poroelastic waves are developed and numerically solved. The equations are obtained by taking a Fourier transform in the strike or medium-invariant direction over all of the field quantities in the 3-D governing equations. The new memory variables from the Zener model are suggested as a way to represent the sum of the convolution integrals for both the solid particle velocity and the macroscopic fluid flux in the governing equations. By application of the memory equations, the field quantities at every time step need not be stored. However, this approximation allows just two Zener relaxation times to represent the very complex double porosity and dual permeability attenuation mechanism, and thus reduce the difficulty. The 2.5-D governing equations are numerically solved by a time-splitting method for the non-stiff parts and an explicit fourth-order Runge-Kutta method for the time integration and a Fourier pseudospectral staggered-grid for handling the spatial derivative terms. The 2.5-D solution has the advantage of producing a 3-D wavefield (point source) for a 2-D model but is much more computationally efficient than the full 3-D solution. As an illustrative example, we firstly show the computed 2.5-D wavefields in a homogeneous single porosity model for which we reformulated an analytic solution. Results for a two-layer, water-saturated double porosity model and a laterally heterogeneous double porosity structure are also presente

Healthcare providers’ perception of faecal microbiota transplantation with clostridium difficile infection and inflammatory bowel disease: a quantitative systematic review

Background: Clostridioides difficile infection (CDI) and inflammatory bowel disease (IBD) are global gastroenterological diseases that cause considerable burden on human health, healthcare systems, and society. Faecal microbiota transplantation (FMT) is an effective treatment for recurrent Clostridioides Difficile Infection (rCDI) and a promising therapy for IBD. However, indication for FMT in IBD is still unofficial. Consequently, the National Institute for Health and Care Excellence (NICE) is seeking healthcare providers’ advice on whether to update FMT guidelines. Methods: A systematic review methodology was adopted for this study. Five databases (CINAHL, MEDLINE, Cochrane Library, Scopus, Web of Science) and grey literature were systematically searched for English language literature to 14 May 2021. The quality of the included studies was then appraised using the Institute for Public Health Sciences cross-sectional studies tool, after which the findings of the studies were narratively synthesised. Results: Thirteen cross-sectional studies with 4110 validated questionnaire responses were included. Narrative synthesis found that 39.43% of respondents were familiar with FMT (N = 3746, 95%CI = 37.87%–41%), 58.81% of respondents would recommend FMT to their patients (N = 1141, 95%CI = 55.95%–61.67%), 66.67% of respondents considered lack of clinical evidence was the greatest concern regarding FMT (N = 1941, 95%CI = 64.57%–68.77%), and 40.43% respondents would not implement FMT due to concerns about infection transmission (N = 1128, 95%CI = 37.57%–43.29%). Conclusion: Healthcare providers’ knowledge of FMT is relatively low and education is an effective strategy to improve it. As knowledge of FMT increases, willingness to recommend it also increases. Strengthening FMT clinical efficacy and reducing infection can enhance its public acceptance, application and popularity. However, further research is required to explore the donor screening procedure

M3PT: A Multi-Modal Model for POI Tagging

POI tagging aims to annotate a point of interest (POI) with some informative tags, which facilitates many services related to POIs, including search, recommendation, and so on. Most of the existing solutions neglect the significance of POI images and seldom fuse the textual and visual features of POIs, resulting in suboptimal tagging performance. In this paper, we propose a novel Multi-Modal Model for POI Tagging, namely M3PT, which achieves enhanced POI tagging through fusing the target POI's textual and visual features, and the precise matching between the multi-modal representations. Specifically, we first devise a domain-adaptive image encoder (DIE) to obtain the image embeddings aligned to their gold tags' semantics. Then, in M3PT's text-image fusion module (TIF), the textual and visual representations are fully fused into the POIs' content embeddings for the subsequent matching. In addition, we adopt a contrastive learning strategy to further bridge the gap between the representations of different modalities. To evaluate the tagging models' performance, we have constructed two high-quality POI tagging datasets from the real-world business scenario of Ali Fliggy. Upon the datasets, we conducted the extensive experiments to demonstrate our model's advantage over the baselines of uni-modality and multi-modality, and verify the effectiveness of important components in M3PT, including DIE, TIF and the contrastive learning strategy.Comment: Accepted by KDD 202

Genetic assessment of inbred chicken lines indicates genomic signatures of resistance to Marek\u27s disease

Background: Marek’s disease (MD) is a highly contagious pathogenic and oncogenic disease primarily affecting chickens. However, the mechanisms of genetic resistance for MD are complex and not fully understood. MD-resistant line 63 and MD-susceptible line 72 are two highly inbred progenitor lines of White Leghorn. Recombinant Congenic Strains (RCS) were developed from these two lines, which show varied susceptibility to MD. Results: We investigated genetic structure and genomic signatures across the genome, including the line 63 and line 72, six RCSs, and two reciprocally crossed flocks between the lines 63 and 72 (F1 63 × 72 and F1 72 × 63) using Affymetrix® Axiom® HD 600 K genotyping array. We observed 18 chickens from RCS lines were specifically clustered into resistance sub-groups distributed around line 63. Additionally, homozygosity analysis was employed to explore potential genetic components related to MD resistance, while runs of homozygosity (ROH) are regions of the genome where the identical haplotypes are inherited from each parent. We found several genes including SIK, SOX1, LIG4, SIK1 and TNFSF13B were contained in ROH region identified in resistant group (line 63 and RCS), and these genes have been reported that are contribute to immunology and survival. Based on FST based population differential analysis, we also identified important genes related to cell death and anti-apoptosis, including AKT1, API5, CDH13, CFDP and USP15, which could be involved in divergent selection during inbreeding process. Conclusions: Our findings offer valuable insights for understanding the genetic mechanism of resistance to MD and the identified genes could be considered as candidate biomarkers in further evaluation

Coefficient of variation method combined with XGboost ensemble model for wheat growth monitoring

IntroductionObtaining wheat growth information accurately and efficiently is the key to estimating yields and guiding agricultural development.MethodsThis paper takes the precision agriculture demonstration area of Jiaozuo Academy of Agriculture and Forestry in Henan Province as the research area to obtain data on wheat biomass, nitrogen content, chlorophyll content, and leaf area index. By using the coefficient of variation method, a Comprehensive Growth Monitoring Indicator (CGMI) was constructed to perform fractional derivative processing on drone spectral data, and correlation analysis was performed on the fractional derivative spectra with a single indicator and CGMI, respectively. Then, grey correlation analysis was carried out on differential spectral bands with high correlation, the grey correlation coefficients between differential spectral bands were calculated, and spectral bands with high correlation were screened and taken as input variables for the model. Next, ridge regression, random forest, and XGboost models were used to establish a wheat CGMI inversion model, and the coefficient of determination (R2) and root mean squared error (RMSE) were adopted for accuracy evaluation to optimize the wheat optimal growth inversion model.Results and discussionThe results of the study show that: using the data of wheat biomass, nitrogen content, chlorophyll content and leaf area index to construct the comprehensive growth monitoring indicators, the correlation between the wheat growth monitoring indicators and the spectra was calculated, and the results showed that the correlation between the comprehensive growth monitoring indicators and the single indicator correlation had different degrees of increase, and the growth rate could reach 82.22%. The correlation coefficient between the comprehensive growth monitoring indexes and the differential spectra reached 0.92 at the flowering stage, and compared with the correlation coefficient with the original spectra at the same period, the correlation coefficients increased to different degrees, which indicated that the differential processing of spectral data could effectively enhance the spectral correlation. The three models of Random Forest, Ridge Regression and XGBoost were used to construct the wheat growth inversion model with the best effect at the flowering stage, and the XGBoost model had the highest inversion accuracy when comparing in the same period, with the training and test sets reaching 0.904 and 0.870, and the RMSEs were 0.050 and 0.079, so that the XGBoost model can be used as an effective method of monitoring the growth of wheat. To sum up, this study demonstrates that the combination of constructing comprehensive growth monitoring indicators and differential processing spectra can effectively improve the accuracy of wheat growth monitoring, bringing new methods for precision agriculture management

Direct-Current Generator Based on Dynamic Water-Semiconductor Junction with Polarized Water as Moving Dielectric Medium

There is a rising prospective in harvesting energy from water droplets, as microscale energy is required for the distributed sensors in the interconnected human society. However, achieving a sustainable direct-current generating device from water flow is rarely reported, and the quantum polarization principle of the water molecular remains uncovered. Herein, we propose a dynamic water-semiconductor junction with moving water sandwiched between two semiconductors as a moving dielectric medium, which outputs a sustainable direct-current voltage of 0.3 V and current of 0.64 uA with low internal resistance of 390 kilohm. The sustainable direct-current electricity is originating from the dynamic water polarization process in water-semiconductor junction, in which water molecules are continuously polarized and depolarized driven by the mechanical force and Fermi level difference, during the movement of the water on silicon. We further demonstrated an encapsulated portable power-generating device with simple structure and continuous direct-current voltage, which exhibits its promising potential application in the field of wearable electronic generators

Diet-induced changes in bacterial communities in the jejunum and their associations with bile acids in Angus beef cattle

The small intestine, while serving as the main absorption organ, also possesses a unique bacterial environment and holds the critical function of conversion of primary bile acids. Bile acids are, in turn, able to regulate bacterial composition and promote the growth of bacteria that convert primary bile acids to secondary bile acids. However, in beef cattle, few studies have explored the influence of diets on jejunal bacterial communities and examined its relationships with bile acids. Here, we examined the impact of grain- and grass-based diets on jejunal and fecal bacterial communities’ composition and investigated possible association of bacterial features with bile acids. We demonstrated that the influences of diets on intestinal bacteria can be observed in young beef cattle after weaning. A significantly higher level of microbial diversity was documented in feces of grass-fed cattle comparing to grain-fed cattle. Top 20 important genera identified with random forest analysis on fecal bacterial community can be good candidates for microbial biomarkers. Moreover, the jejunal bacteria of adult Angus beef cattle exhibited significant differences in microbial composition and metabolic potential under different diets. Global balances and bacteria signatures predictive of bile acids were identified, indicative of the potential association of bacterial features with bile acids. The findings from this study provided novel insights into the relationships between jejunal bacteria and bile acids under different diets in Angus beef cattle. Our results should help us gain a better understanding of potential health benefits of grass-fed beef.https://doi.org/10.1186/s42523-020-00051-