Jointly Modeling Embedding and Translation to Bridge Video and Language

Automatically describing video content with natural language is a fundamental challenge of multimedia. Recurrent Neural Networks (RNN), which models sequence dynamics, has attracted increasing attention on visual interpretation. However, most existing approaches generate a word locally with given previous words and the visual content, while the relationship between sentence semantics and visual content is not holistically exploited. As a result, the generated sentences may be contextually correct but the semantics (e.g., subjects, verbs or objects) are not true. This paper presents a novel unified framework, named Long Short-Term Memory with visual-semantic Embedding (LSTM-E), which can simultaneously explore the learning of LSTM and visual-semantic embedding. The former aims to locally maximize the probability of generating the next word given previous words and visual content, while the latter is to create a visual-semantic embedding space for enforcing the relationship between the semantics of the entire sentence and visual content. Our proposed LSTM-E consists of three components: a 2-D and/or 3-D deep convolutional neural networks for learning powerful video representation, a deep RNN for generating sentences, and a joint embedding model for exploring the relationships between visual content and sentence semantics. The experiments on YouTube2Text dataset show that our proposed LSTM-E achieves to-date the best reported performance in generating natural sentences: 45.3% and 31.0% in terms of BLEU@4 and METEOR, respectively. We also demonstrate that LSTM-E is superior in predicting Subject-Verb-Object (SVO) triplets to several state-of-the-art techniques

First-line single agent treatment with gefitinib in patients with advanced non-small-cell lung cancer

<p>Abstract</p> <p>Background</p> <p>Lung cancer is a malignant carcinoma which has the highest morbidity and mortality in Chinese population. Gefitinib, a tyrosine kinase (TK) inhibitor of epidermal growth factor receptor (EGFR), displays anti-tumor activity. The present data regarding first-line treatment with single agent gefitinib against non-small-cell lung cancer (NSCLC) in Chinese population are not sufficient.</p> <p>Purpose</p> <p>To assess the efficacy and toxicity of gefitinib in Chinese patients with advanced non-small-cell lung cancer (NSCLC), a study of single agent treatment with gefitinib in Chinese patients was conducted.</p> <p>Methods</p> <p>45 patients with advanced NSCLC were treated with gefitinib (250 mg daily) until the disease progression or intolerable toxicity.</p> <p>Results</p> <p>Among the 45 patients, 15 patients achieved partial response (PR), 17 patients experienced stable disease (SD), and 13 patients developed progression disease (PD). None of the patients achieved complete response (CR). The tumor response rate and disease control rate was 33% and 71.1%, respectively. Symptom remission rate was 72.5%, and median remission time was 8 days. Median overall survival and median progression-free survival was 15.3 months and 6.0 months, respectively. The main induced toxicities by gefitinib were skin rash and diarrhea (53.3% and 33.3%, respectively). The minor induced toxicities included dehydration and pruritus of skin (26.7% and 22.2%, respectively). In addition, hepatic toxicity and oral ulceration occurred in few patients (6.7% and 4.4%2, respectively).</p> <p>Conclusions</p> <p>Single agent treatment with gefitinib is effective and well tolerated in Chinese patients with advanced NSCLC.</p