Existing works for extracting navigation objects from webpages focus on navigation menus, so as to reveal the information architecture of the site. However, web 2.0 sites such as social networks, e-commerce portals etc. are making the understanding of the content structure in a web site increasingly di cult. Dynamic and personalized elements such as top stories, recommended list in a webpage are vital to the understanding of the dynamic nature of web 2.0 sites. To be er understand the content structure in web 2.0 sites, in this paper we propose a new extraction method for navigation objects in a webpage. Our method will extract not only the static navigation menus, but also the dynamic and personalized page-speci c navigation lists. Since the navigation objects in a webpage naturally come in blocks, we rst cluster hyperlinks into di erent blocks by exploiting spatial locations of hyperlinks, the hierarchical structure of the DOM-tree and the hyperlink density.en we identify navigation objects from those blocks using the SVM classi er with novel features such as anchor text lengths etc. Experiments on real-world data sets with webpages from various domains and styles veri ed the e ectiveness of our method.

Wav2vec 2.0 is a state-of-the-art speech recognition model which maps speech audio waveforms into latent representations. The largest version of wav2vec 2.0 contains 317 million parameters. Hence, the inference latency of wav2vec 2.0 will be a bottleneck in production, leading to high costs and a significant environmental footprint. To improve wav2vec's applicability to a production setting, we explore multiple model compression methods borrowed from the domain of large language models. Using a teacher-student approach, we distilled the knowledge from the original wav2vec 2.0 model into a student model, which is 2 times faster, 4.8 times smaller than the original model. More importantly, the student model is 2 times more energy efficient than the original model in terms of CO 2 emission. This increase in performance is accomplished with only a 7% degradation in word error rate (WER). Our quantized model is 3.6 times smaller than the original model, with only a 0.1% degradation in WER. To the best of our knowledge, this is the first work that compresses wav2vec 2.0.

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Background : Angong Niuhuang Pill (ANP) is one of the most famous drugs to treat stroke in China, but there is no definite treatment period in drug instruction. In this study, we used middle cerebral artery occlusion (MCAO) model to evaluate its therapeutic effects of different treatment periods and studied its toxic effect in rats. Methods : Protective effect of ANP was observed in the cerebral ischemia-reperfusion model in rats; ANP (270 mg/kg) three different treatment period included 1 day, 4 days and 7 days. The observation period was 30 days. Therapeutic effect was evaluated by detecting neurological function, cerebral infraction volume, brain histology and cytokines. Three dose including 550, 1640, 4910 mg/kg were studied in toxicology study. The administration period was 30 days. Toxic effect was evaluated by detecting appearance, behavior, excrement character, food-intake, body weight, hematological parameters and biomarkers such as TBA, GSTα, Cystatin C, clusterin, GSH, S-100B and MBP. Results : Seven days treatment period of ANP had better effect than 1 day and 4 days treatment periods in rat MCAO model from neurological function scores, the volume of cerebral infarction, brain histology and the serum content of IL-1β, TNF-α and NO; the brain content of IL-1β and NO. The results of 30 days multiple dose toxicology study showed no animal death in all groups; in ANP 4910 mg/kg group, the kidney and liver coefficient increased about 10%, the body weight grew more slowly, the TBA increased slightly. There was no abnormal change in histology. These all recovered after drug withdraw for 8 weeks. Conclusion: Seven days treatment period of ANP had more protective effect than 1 day and 4 days treatment periods in ischemic stroke rat. No observed adverse effect level (NOAEL) of ANP was 1640 mg/kg; the safety margin of ANP was 270-1640 mg/kg. These data provided reference to modify drug instruction.