Real-time Short Video Recommendation on Mobile Devices

Short video applications have attracted billions of users in recent years, fulfilling their various needs with diverse content. Users usually watch short videos on many topics on mobile devices in a short period of time, and give explicit or implicit feedback very quickly to the short videos they watch. The recommender system needs to perceive users' preferences in real-time in order to satisfy their changing interests. Traditionally, recommender systems deployed at server side return a ranked list of videos for each request from client. Thus it cannot adjust the recommendation results according to the user's real-time feedback before the next request. Due to client-server transmitting latency, it is also unable to make immediate use of users' real-time feedback. However, as users continue to watch videos and feedback, the changing context leads the ranking of the server-side recommendation system inaccurate. In this paper, we propose to deploy a short video recommendation framework on mobile devices to solve these problems. Specifically, we design and deploy a tiny on-device ranking model to enable real-time re-ranking of server-side recommendation results. We improve its prediction accuracy by exploiting users' real-time feedback of watched videos and client-specific real-time features. With more accurate predictions, we further consider interactions among candidate videos, and propose a context-aware re-ranking method based on adaptive beam search. The framework has been deployed on Kuaishou, a billion-user scale short video application, and improved effective view, like and follow by 1.28%, 8.22% and 13.6% respectively.Comment: Accepted by CIKM 2022, 10 page

Early detection of cotton verticillium wilt based on root magnetic resonance images

Verticillium wilt (VW) is often referred to as the cancer of cotton and it has a detrimental effect on cotton yield and quality. Since the root system is the first to be infested, it is feasible to detect VW by root analysis in the early stages of the disease. In recent years, with the update of computing equipment and the emergence of large-scale high-quality data sets, deep learning has achieved remarkable results in computer vision tasks. However, in some specific areas, such as cotton root MRI image task processing, it will bring some challenges. For example, the data imbalance problem (there is a serious imbalance between the cotton root and the background in the segmentation task) makes it difficult for existing algorithms to segment the target. In this paper, we proposed two new methods to solve these problems. The effectiveness of the algorithms was verified by experimental results. The results showed that the new segmentation model improved the Dice and mIoU by 46% and 44% compared with the original model. And this model could segment MRI images of rapeseed root cross-sections well with good robustness and scalability. The new classification model improved the accuracy by 34.9% over the original model. The recall score and F1 score increased by 59% and 42%, respectively. The results of this paper indicate that MRI and deep learning have the potential for non-destructive early detection of VW diseases in cotton

Evolution of the Surface Structures on SrTiO3_3(110) Tuned by Ti or Sr Concentration

The surface structure of the SrTiO$_3$(110) polar surface is studied by scanning tunneling microscopy and X-ray photoelectron spectroscopy. Monophased reconstructions in (5$\times$1), (4$\times$1), (2$\times$8), and (6$\times$8) are obtained, respectively, and the evolution between these phases can be tuned reversibly by adjusting the Ar$^{+}$ sputtering dose or the amount of Sr/Ti evaporation. Upon annealing, the surface reaches the thermodynamic equilibrium that is determined by the surface metal concentration. The different electronic structures and absorption behaviors of the surface with different reconstructions are investigated.Comment: 10 pages, 14 figure

Visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves

Leaf nitrogen concentration (LNC) is a critical indicator of crop nutrient status. In this study, the feasibility of using visible and near-infrared spectroscopy combined with deep learning to estimate LNC in cotton leaves was explored. The samples were collected from cotton’s whole growth cycle, and the spectra were from different measurement environments. The random frog (RF), weighted partial least squares regression (WPLS), and saliency map were used for characteristic wavelength selection. Qualitative models (partial least squares discriminant analysis (PLS-DA), support vector machine for classification (SVC), convolutional neural network classification (CNNC) and quantitative models (partial least squares regression (PLSR), support vector machine for regression (SVR), convolutional neural network regression (CNNR)) were established based on the full spectra and characteristic wavelengths. Satisfactory results were obtained by models based on CNN. The classification accuracy of leaves in three different LNC ranges was up to 83.34%, and the root mean square error of prediction (RMSEP) of quantitative prediction models of cotton leaves was as low as 3.36. In addition, the identification of cotton leaves based on the predicted LNC also achieved good results. These results indicated that the nitrogen content of cotton leaves could be effectively detected by deep learning and visible and near-infrared spectroscopy, which has great potential for real-world application

Semisupervised Learning Based Disease-Symptom and Symptom-Therapeutic Substance Relation Extraction from Biomedical Literature

With the rapid growth of biomedical literature, a large amount of knowledge about diseases, symptoms, and therapeutic substances hidden in the literature can be used for drug discovery and disease therapy. In this paper, we present a method of constructing two models for extracting the relations between the disease and symptom and symptom and therapeutic substance from biomedical texts, respectively. The former judges whether a disease causes a certain physiological phenomenon while the latter determines whether a substance relieves or eliminates a certain physiological phenomenon. These two kinds of relations can be further utilized to extract the relations between disease and therapeutic substance. In our method, first two training sets for extracting the relations between the disease-symptom and symptom-therapeutic substance are manually annotated and then two semisupervised learning algorithms, that is, Co-Training and Tri-Training, are applied to utilize the unlabeled data to boost the relation extraction performance. Experimental results show that exploiting the unlabeled data with both Co-Training and Tri-Training algorithms can enhance the performance effectively

Two-step method for preparing calcium oxalate film on marble surface for stone protective

Calcium oxalate film was prepared by a novel two-step method on the surface of the marble substrate. The seed film was coated by a chemical reaction process, providing a good connection to the marble surface. Meanwhile, calcium oxalate solution was interwoven into the seed film to form a continuous network at room temperature. The x-ray diffraction (XRD) and scanning electron microscopy (SEM) analysis results indicated that the calcium oxalate film prepared by the two-step method showed a more intensive crystallinity degree and homogenous than that by the traditional oxalate treatment method (a scattered seed film). Subsequently, it was found such calcium oxalate film is feasible for preventing the marble substrate from chemical weathering. Furthermore, the change of the chromatic value, water absorption properties and adhesion strength of the marble substrates by the film is minimal. This method overcomes the limitations of traditional oxalate treatment process and has great potential for the protection of marble artifacts

DataSheet_1_Brassica juncea BRC1-1 induced by SD negatively regulates flowering by directly interacting with BjuFT and BjuFUL promoter.docx

Flowering is crucial for sexual reproductive success in angiosperms. The core regulatory factors, such as FT, FUL, and SOC1, are responsible for promoting flowering. BRANCHED 1 (BRC1) is a TCP transcription factor gene that plays an important role in the regulation of branching and flowering in diverse plant species. However, the functions of BjuBRC1 in Brassica juncea are largely unknown. In this study, four homologs of BjuBRC1 were identified and the mechanism by which BjuBRC1 may function in the regulation of flowering time was investigated. Amino acid sequence analysis showed that BjuBRC1 contained a conserved TCP domain with two nuclear localization signals. A subcellular localization assay verified the nuclear localization of BjuBRC1. Expression analysis revealed that BjuBRC1-1 was induced by short days and was expressed abundantly in the leaf, flower, and floral bud but not in the root and stem in B. juncea. Overexpression of BjuBRC1-1 in the Arabidopsis brc1 mutant showed that BjuBRC1-1 delayed flowering time. Bimolecular fluorescent complementary and luciferase complementation assays showed that four BjuBRC1 proteins could interact with BjuFT in vivo. Notably, BjuBRC1 proteins formed heterodimers in vivo that may impact on their function of negatively regulating flowering time. Yeast one-hybrid, dual-luciferase reporter, and luciferase activity assays showed that BjuBRC1-1 could directly bind to the promoter of BjuFUL, but not BjuFT or BjuSOC1, to repress its expression. These results were supported by the reduced expression of AtFUL in transgenic Arabidopsis overexpressing BjuBRC1-1. Taken together, the results indicate that BjuBRC1 genes likely have a conserved function in the negative regulation of flowering in B. juncea.</p

Optimizing Row Spacing Increases Stalk Lodging Resistance by Improving Light Distribution in Dense Maize Populations

Dense planting effectively increases maize yield while increasing stalk lodging risk. Appropriate row spacing can improve the maize population structure and stalk lodging resistance, but its physiological ecological mechanisms and interaction with planting density are unclear. Here, a two-year field experiment to determine the joint effects of row spacing and planting density on maize stem characteristics and the quantitative relationship of the light condition within a maize population with stalk lodging resistance indicated that the stalk mechanical strength showed a quadratic function relationship with photosynthetically active radiation (PAR), whereas the lodging rate showed an exponential function relationship with basal light transmittance (LT). Further, the basal LT was significantly positively correlated with basal internode thickness, dry weight per unit stem length (DWUL), mechanical and cortical tissue thickness, and lignin and cellulose contents. Increasing the planting density decreased the basal LT and PAR; correspondingly decreased the basal internode thickness, DWUL, mechanical and cortical tissue thickness, lignin and cellulose contents, and stalk mechanical strength; and increased the lodging rate, while increasing row spacing did the opposite. Thus, optimizing the row spacing enhanced the lodging resistance through LT and PAR improvement of the lower part of the population and further increased the grain yield by optimizing the yield components. The appropriate row spacing varied with the planting density. The proper strategy for high stalk lodging resistance and grain yielding under this experimental condition was 67,500 plants ha&minus;1 density with 60 + 60 cm equal row spacing

Optimizing Row Spacing Increases Stalk Lodging Resistance by Improving Light Distribution in Dense Maize Populations

Dense planting effectively increases maize yield while increasing stalk lodging risk. Appropriate row spacing can improve the maize population structure and stalk lodging resistance, but its physiological ecological mechanisms and interaction with planting density are unclear. Here, a two-year field experiment to determine the joint effects of row spacing and planting density on maize stem characteristics and the quantitative relationship of the light condition within a maize population with stalk lodging resistance indicated that the stalk mechanical strength showed a quadratic function relationship with photosynthetically active radiation (PAR), whereas the lodging rate showed an exponential function relationship with basal light transmittance (LT). Further, the basal LT was significantly positively correlated with basal internode thickness, dry weight per unit stem length (DWUL), mechanical and cortical tissue thickness, and lignin and cellulose contents. Increasing the planting density decreased the basal LT and PAR; correspondingly decreased the basal internode thickness, DWUL, mechanical and cortical tissue thickness, lignin and cellulose contents, and stalk mechanical strength; and increased the lodging rate, while increasing row spacing did the opposite. Thus, optimizing the row spacing enhanced the lodging resistance through LT and PAR improvement of the lower part of the population and further increased the grain yield by optimizing the yield components. The appropriate row spacing varied with the planting density. The proper strategy for high stalk lodging resistance and grain yielding under this experimental condition was 67,500 plants ha−1 density with 60 + 60 cm equal row spacing

Colorimetric PCR-Based microRNA Detection Method Based on Small Organic Dye and Single Enzyme

microRNAs (miRNAs) have been a class of promising disease diagnostic biomarkers and therapeutic targets for their important biological functions. However, because of the high homology, interference from precursors (pri-miRNA, pre-miRNA), as well as limitations in the current assay technologies, it poses high demand and challenge for a specific, efficient, and economic miRNA assay method. Here, we propose a new miRNA detection method based on a label-free probe and a small organic dye with sequence dependence, realizing the sequence-specific and colorimetric detection of target miRNA. What is pleasantly surprising, only one enzyme is enough to propel the whole miRNA assay process, greatly simplifying the reaction component and detection process. Together with PCR amplification for the high enough sensitivity a nd three checks for specificity control, a detection limit of 5 fM was obtained and even one mutation could be discriminated visually. Overall, the new method makes much progress in convenience and economy of PCR-based miRNA assay method so that miRNA assay is going to be more friendly and affordable