57,260 research outputs found

    Biochemical prevention and treatment of viral infections – A new paradigm in medicine for infectious diseases

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    For two centuries, vaccination has been the dominating approach to develop prophylaxis against viral infections through immunological prevention. However, vaccines are not always possible to make, are ineffective for many viral infections, and also carry certain risk for a small, yet significant portion of the population. In the recent years, FDA's approval and subsequent market acceptance of Synagis, a monoclonal antibody indicated for prevention and treatment of respiratory syncytial virus (RSV) has heralded a new era for viral infection prevention and treatment. This emerging paradigm, herein designated "Biochemical Prevention and Treatment", currently involves two aspects: (1) preventing viral entry via passive transfer of specific protein-based anti-viral molecules or host cell receptor blockers; (2) inhibiting viral amplification by targeting the viral mRNA with anti-sense DNA, ribozyme, or RNA interference (RNAi). This article summarizes the current status of this field

    Unsupervised Domain Adaptation on Reading Comprehension

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    Reading comprehension (RC) has been studied in a variety of datasets with the boosted performance brought by deep neural networks. However, the generalization capability of these models across different domains remains unclear. To alleviate this issue, we are going to investigate unsupervised domain adaptation on RC, wherein a model is trained on labeled source domain and to be applied to the target domain with only unlabeled samples. We first show that even with the powerful BERT contextual representation, the performance is still unsatisfactory when the model trained on one dataset is directly applied to another target dataset. To solve this, we provide a novel conditional adversarial self-training method (CASe). Specifically, our approach leverages a BERT model fine-tuned on the source dataset along with the confidence filtering to generate reliable pseudo-labeled samples in the target domain for self-training. On the other hand, it further reduces domain distribution discrepancy through conditional adversarial learning across domains. Extensive experiments show our approach achieves comparable accuracy to supervised models on multiple large-scale benchmark datasets.Comment: 8 pages, 6 figures, 5 tables, Accepted by AAAI 202

    Robust quantum repeater with atomic ensembles and single-photon sources

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    We present a quantum repeater protocol using atomic ensembles, linear optics and single-photon sources. Two local 'polarization' entangled states of atomic ensembles uu and dd are generated by absorbing a single photon emitted by an on-demand single-photon sources, based on which high-fidelity local entanglement between four ensembles can be established efficiently through Bell-state measurement. Entanglement in basic links and entanglement connection between links are carried out by the use of two-photon interference. In addition to being robust against phase fluctuations in the quantum channels, this scheme may speed up quantum communication with higher fidelity by about 2 orders of magnitude for 1280 km compared with the partial read (PR) protocol (Sangouard {\it et al.}, Phys. Rev. A {\bf77}, 062301 (2008)) which may generate entanglement most quickly among the previous schemes with the same ingredients.Comment: 5 pages 4 figure
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