Movie101: A New Movie Understanding Benchmark

To help the visually impaired enjoy movies, automatic movie narrating systems are expected to narrate accurate, coherent, and role-aware plots when there are no speaking lines of actors. Existing works benchmark this challenge as a normal video captioning task via some simplifications, such as removing role names and evaluating narrations with ngram-based metrics, which makes it difficult for automatic systems to meet the needs of real application scenarios. To narrow this gap, we construct a large-scale Chinese movie benchmark, named Movie101. Closer to real scenarios, the Movie Clip Narrating (MCN) task in our benchmark asks models to generate role-aware narration paragraphs for complete movie clips where no actors are speaking. External knowledge, such as role information and movie genres, is also provided for better movie understanding. Besides, we propose a new metric called Movie Narration Score (MNScore) for movie narrating evaluation, which achieves the best correlation with human evaluation. Our benchmark also supports the Temporal Narration Grounding (TNG) task to investigate clip localization given text descriptions. For both two tasks, our proposed methods well leverage external knowledge and outperform carefully designed baselines. The dataset and codes are released at https://github.com/yuezih/Movie101.Comment: Accepted to ACL 202

Highly polarized multiwavelength er-doped fibre laser using all fibre lyot filter

A multiwavelength Er-doped fibre ring laser system using all fibre Lyot filter and highly nonlinear fibre was experimentally demonstrated, with degree of polarization up to 99.9% and 50 channels output

Estimating ammonia emissions from cropland in China based on the establishment of agro-region-specific models

ACKNOWLEDGMENTS This work was financially supported by Natural Science Foundation of China under a grant numbers 41877546 and U1612441, and a BBSRC-Newton Funded project (BB/N013484/1). This work also contributes to the activities of Top-notch Academic Programs Project of Jiangsu Higher Education Institution of China (PPZY2015A061), and Program for Student Innovation through Research and Training (1913A22).Peer reviewedPostprin

ICESsuHN105, a Novel Multiple Antibiotic Resistant ICE in Streptococcus suis Serotype 5 Strain HN105

Streptococcussuis serotype 5, an emerging zoonosis bacterial pathogen, has been isolated from infections in both pigs and humans. In this study, we sequenced the first complete genome of a virulent, multidrug-resistant SS5 strain HN105. The strain HN105 displayed enhanced pathogenicity in zebrafish and BABL/c mouse infection models. Comparative genome analysis identified a novel 80K integrative conjugative element (ICE), ICESsuHN105, as required for the multidrug resistance phenotype. Six corresponding antibiotic resistance genes in this ICE were identified, namely tet (O), tet (M), erm (two copies), aph, and spc. Phylogenetic analysis classified the element as a homolog of the ICESa2603 family, containing the typical family backbone and insertion DNA. DNA hybrids mediated by natural transformation between HN105 and ZY05719 verified the antibiotic resistant genes of ICESsuHN105 that could be transferred successfully, while they were dispersedly inserted with a single gene in different genomic locations of ZY05719(HN105) transformants. To further identify the horizontal transfer of ICESsuHN105 as a whole mobile genetic element, a circular intermediate form of ICESsuHN105 was detected by PCR. However, the effective conjugation using serotype 2 S. suis as recipients was not observed in current assays in vitro. Further studies confirmed the presence of the complete lantibiotic locus encoded in ICESsuHN105 that effectively inhibits the growth of other streptococci. In summary, this study demonstrated the presence of antibiotic resistance genes in ICE that are able to transfer between different clinical isolates and adapt to a broader range of Streptococcus serotype or species