Observation and management of shallow anterior chamber after glaucoma surgery

AIM: To analyze the cause and management of shallow anterior chamber after glaucoma surgery.<p>METHODS: The cause and management of shallow anterior chamber after glaucoma surgery on 298 cases(462 eyes)were analyzed retrospectively.<p>RESULTS: In 298 cases(462 eyes), 99 eyes(21.4%)had shallow anterior chamber. In 358 eyes treated with trabeculectomy, 77 eyes(21.5%)had shallow anterior chamber. In 85 eyes treated with trabeculectomy+MMC(mitomycin C), 20 eyes(23.5%)had shallow anterior chamber. In 19 eyes treated with trabeculectomy combined with cataract phacoemulsification and intraocular lens implantation, 2 eyes(10.53%)had shallow anterior chamber. Shallow anterior chamber appeared at 1 day to 5 days postoperatively. Forty-two eyes(42.4%)were with excessive filtering, 6 eyes(6.1%)with malignant glaucoma, 29 eyes(29.3%)with choroidal detachment, 2 eyes(2.0%)with malignant glaucoma complicated by choroidal detachment. Of 99 eyes with shallow anterior chamber, anterior chamber of 79 eyes recovered treated by nonsurgical methods, 20 eyes treated by operation.<p>CONCLUSION: The common cause of shallow anterior chamber after glaucoma surgery was preoperative high intraocular pressure, inflammation, excessive filtering, conjunctival flap flushing and choroidal detachment. Most cases can be managed with nonsurgical methods. Surgical interference should be taken if necessary

Understanding Hidden Memories of Recurrent Neural Networks

Recurrent neural networks (RNNs) have been successfully applied to various natural language processing (NLP) tasks and achieved better results than conventional methods. However, the lack of understanding of the mechanisms behind their effectiveness limits further improvements on their architectures. In this paper, we present a visual analytics method for understanding and comparing RNN models for NLP tasks. We propose a technique to explain the function of individual hidden state units based on their expected response to input texts. We then co-cluster hidden state units and words based on the expected response and visualize co-clustering results as memory chips and word clouds to provide more structured knowledge on RNNs' hidden states. We also propose a glyph-based sequence visualization based on aggregate information to analyze the behavior of an RNN's hidden state at the sentence-level. The usability and effectiveness of our method are demonstrated through case studies and reviews from domain experts.Comment: Published at IEEE Conference on Visual Analytics Science and Technology (IEEE VAST 2017

Noise-Robust Fine-Tuning of Pretrained Language Models via External Guidance

Adopting a two-stage paradigm of pretraining followed by fine-tuning, Pretrained Language Models (PLMs) have achieved substantial advancements in the field of natural language processing. However, in real-world scenarios, data labels are often noisy due to the complex annotation process, making it essential to develop strategies for fine-tuning PLMs with such noisy labels. To this end, we introduce an innovative approach for fine-tuning PLMs using noisy labels, which incorporates the guidance of Large Language Models (LLMs) like ChatGPT. This guidance assists in accurately distinguishing between clean and noisy samples and provides supplementary information beyond the noisy labels, thereby boosting the learning process during fine-tuning PLMs. Extensive experiments on synthetic and real-world noisy datasets further demonstrate the superior advantages of our framework over the state-of-the-art baselines.Comment: EMNLP Findings 202

Contrastive Meta-Learning for Few-shot Node Classification

Few-shot node classification, which aims to predict labels for nodes on graphs with only limited labeled nodes as references, is of great significance in real-world graph mining tasks. Particularly, in this paper, we refer to the task of classifying nodes in classes with a few labeled nodes as the few-shot node classification problem. To tackle such a label shortage issue, existing works generally leverage the meta-learning framework, which utilizes a number of episodes to extract transferable knowledge from classes with abundant labeled nodes and generalizes the knowledge to other classes with limited labeled nodes. In essence, the primary aim of few-shot node classification is to learn node embeddings that are generalizable across different classes. To accomplish this, the GNN encoder must be able to distinguish node embeddings between different classes, while also aligning embeddings for nodes in the same class. Thus, in this work, we propose to consider both the intra-class and inter-class generalizability of the model. We create a novel contrastive meta-learning framework on graphs, named COSMIC, with two key designs. First, we propose to enhance the intra-class generalizability by involving a contrastive two-step optimization in each episode to explicitly align node embeddings in the same classes. Second, we strengthen the inter-class generalizability by generating hard node classes via a novel similarity-sensitive mix-up strategy. Extensive experiments on few-shot node classification datasets verify the superiority of our framework over state-of-the-art baselines. Our code is provided at https://github.com/SongW-SW/COSMIC.Comment: SIGKDD 202