MOTIVATION: Biomedical Named Entity Recognition (BioNER) aims to identify biomedical domain-specific entities (e.g. gene, chemical and disease) from unstructured texts. Despite deep learning-based methods for BioNER achieving satisfactory results, there is still much room for improvement. Firstly, most existing methods use independent sentences as training units and ignore inter-sentence context, which usually leads to the labeling inconsistency problem. Secondly, previous document-level BioNER works have approved that the inter-sentence information is essential, but what information should be regarded as context remains ambiguous. Moreover, there are still few pre-training-based BioNER models that have introduced inter-sentence information. Hence, we propose a cache-based inter-sentence model called BioNER-Cache to alleviate the aforementioned problems. RESULTS: We propose a simple but effective dynamic caching module to capture inter-sentence information for BioNER. Specifically, the cache stores recent hidden representations constrained by predefined caching rules. And the model uses a query-and-read mechanism to retrieve similar historical records from the cache as the local context. Then, an attention-based gated network is adopted to generate context-related features with BioBERT. To dynamically update the cache, we design a scoring function and implement a multi-task approach to jointly train our model. We build a comprehensive benchmark on four biomedical datasets to evaluate the model performance fairly. Finally, extensive experiments clearly validate the superiority of our proposed BioNER-Cache compared with various state-of-the-art intra-sentence and inter-sentence baselines. AVAILABILITYAND IMPLEMENTATION: Code will be available at https://github.com/zgzjdx/BioNER-Cache. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Style transfer studies can be categorized into two types-artistic and photorealistic. The high-speed transfer has been well-studied for artistic styles but remains challenging for photorealistic styles. To guarantee semantic accuracy and style faithfulness, prior photorealistic style transfer techniques often rely on intensive feature matching, hierarchical stylization, and complex auxiliary smoothing. Such high design complexity severely limits the space of transfer speed improvement. In this paper, we propose to accelerate the transfer through a single-level stylization without complex auxiliary smoothing. We design a two-stage "stylization and re-stylization" training pipeline to enhance style faithfulness. The stylization/re-stylization stage consists of two core steps: feature aggregation and redistribution. A new type of layers, Feature Aggregation (FA) layers, is proposed to gradually aggregate multi-scale style features into content features at each spatial location. A Spatially coherent Content-style Preserving (SCP) loss at feature map level is then used to preserve semantic accuracy. The SCP loss provides effective guidance on redistributing the aggregated features between locations to enforce spatial coherence of style-sensitive content semantic. Experimental results show that compared to previous competitive methods, our method reduces at least 72% run time while achieving better image synthesis quality based on both subjective and objective evaluation metrics. Ablation studies validate the major contribution of our proposed SCP loss and re-stylization to the quality of our synthesized images.

Recent research has made great progress in realizing neural style transfer of images, which denotes transforming an image to a desired style. Many users start to use their mobile phones to record their daily life, and then edit and share the captured images and videos with other users. However, directly applying existing style transfer approaches on videos, i.e., transferring the style of a video frame by frame, requires an extremely large amount of computation resources. It is still technically unaffordable to perform style transfer of videos on mobile phones. To address this challenge, we propose MVStylizer, an efficient edge-assisted photorealistic video style transfer system for mobile phones. Instead of performing stylization frame by frame, only key frames in the original video are processed by a pre-trained deep neural network (DNN) on edge servers, while the rest of stylized intermediate frames are generated by our designed optical-flow-based frame interpolation algorithm on mobile phones. A meta-smoothing module is also proposed to simultaneously upscale a stylized frame to arbitrary resolution and remove style transfer related distortions in these upscaled frames. In addition, for the sake of continuously enhancing the performance of the DNN model on the edge server, we adopt a federated learning scheme to keep retraining each DNN model on the edge server with collected data from mobile clients and syncing with a global DNN model on the cloud server. Such a scheme effectively leverages the diversity of collected data from various mobile clients and efficiently improves the system performance. Our experiments demonstrate that MVStylizer can generate stylized videos with an even better visual quality compared to the state-of-the-art method while achieving 75.5× speedup for 1920×1080 videos.

This paper presents an adaptable service assurance system that can be easily integrated into an operation support system for the telecommunication business. It adopts technologies like CORBA, Java, and workflow management. It also adopts off-the-shelf products to shorten the development cycle.