We present a new neural sequence-tosequence model for extractive summarization called SWAP-NET (Sentences and Words from Alternating Pointer Networks). Extractive summaries comprising a salient subset of input sentences, often also contain important key words. Guided by this principle, we design SWAP-NET that models the interaction of key words and salient sentences using a new twolevel pointer network based architecture. SWAP-NET identifies both salient sentences and key words in an input document, and then combines them to form the extractive summary. Experiments on large scale benchmark corpora demonstrate the efficacy of SWAP-NET that outperforms state-of-the-art extractive summarizers.
We address the problem of one-shot unconstrained face recognition. This is addressed by using a deep attribute representation of faces. While face recognition has considered the use of attribute based representations, for one-shot face recognition, the methods proposed so far have been using different features that represent the limited example available. We postulate that by using an intermediate attribute representation, it is possible to outperform purely face based feature representation for one-shot recognition. We use two one-shot face recognition techniques based on exemplar SVM and one-shot similarity kernel to compare face based deep feature representations against deep attribute based representation. The evaluation on standard dataset of 'Labeled faces in the wild' suggests that deep attribute based representations can outperform deep feature based face representations for this problem of one-shot face recognition.
India is an agricultural country. Farmers are the life blood of nation. The agriculture sector in India is expected to generate better momentum in the next few years due to increased investments in agricultural infrastructure such as irrigation facilities, warehousing and cold storage. Factors such as reduced transaction costs and time, improved port gate management and better fiscal incentives would contribute to the sector's growth. Furthermore, the growing use of genetically modified crops will likely improve the yield for Indian farmers. But the current condition of Farmer is very pathetic. The Farmers should be introduced to the smart farming techniques because upon their well-being depends the welfare of the nation. The project supports various objectives such as Market Information, New Techniques regarding better farming, Governments Schemes, Resource Information, Latest Videos, Support Scientific Research, Loan Information. We have taken our initiative to provide betterment in this field with recent technology .It will help farmers to know agro information for leading to achieve success.
Multisystem inflammatory syndrome in children (MIS-C) is a rare but serious condition that can develop 4–6 weeks after a school age child becomes infected by SARS-CoV-2. To date, in the United States more than 8,862 cases of MIS-C have been identified and 72 deaths have occurred. This syndrome typically affects children between the ages of 5–13; 57% are Hispanic/Latino/Black/non-Hispanic, 61% of patients are males and 100% have either tested positive for SARS-CoV-2 or had direct contact with someone with COVID-19. Unfortunately, diagnosis of MIS-C is difficult, and delayed diagnosis can lead to cardiogenic shock, intensive care admission, and prolonged hospitalization. There is no validated biomarker for the rapid diagnosis of MIS-C. In this study, we used Grating-coupled Fluorescence Plasmonic (GCFP) microarray technology to develop biomarker signatures in pediatric salvia and serum samples from patients with MIS-C in the United States and Colombia. GCFP measures antibody-antigen interactions at individual regions of interest (ROIs) on a gold-coated diffraction grating sensor chip in a sandwich immunoassay to generate a fluorescent signal based on analyte presence within a sample. Using a microarray printer, we designed a first-generation biosensor chip with the capability of capturing 33 different analytes from 80 μL of sample (saliva or serum). Here, we show potential biomarker signatures in both saliva and serum samples in six patient cohorts. In saliva samples, we noted occasional analyte outliers on the chip within individual samples and were able to compare those samples to 16S RNA microbiome data. These comparisons indicate differences in relative abundance of oral pathogens within those patients. Microsphere Immunoassay (MIA) of immunoglobulin isotypes was also performed on serum samples and revealed MIS-C patients had several COVID antigen-specific immunoglobulins that were significantly higher than other cohorts, thus identifying potential new targets for the second-generation biosensor chip. MIA also identified additional biomarkers for our second-generation chip, verified biomarker signatures generated on the first-generation chip, and aided in second-generation chip optimization. Interestingly, MIS-C samples from the United States had a more diverse and robust signature than the Colombian samples, which was also illustrated in the MIA cytokine data. These observations identify new MIS-C biomarkers and biomarker signatures for each of the cohorts. Ultimately, these tools may represent a potential diagnostic tool for use in the rapid identification of MIS-C.
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