DeepStory: Video Story QA by Deep Embedded Memory Networks

Question-answering (QA) on video contents is a significant challenge for achieving human-level intelligence as it involves both vision and language in real-world settings. Here we demonstrate the possibility of an AI agent performing video story QA by learning from a large amount of cartoon videos. We develop a video-story learning model, i.e. Deep Embedded Memory Networks (DEMN), to reconstruct stories from a joint scene-dialogue video stream using a latent embedding space of observed data. The video stories are stored in a long-term memory component. For a given question, an LSTM-based attention model uses the long-term memory to recall the best question-story-answer triplet by focusing on specific words containing key information. We trained the DEMN on a novel QA dataset of children's cartoon video series, Pororo. The dataset contains 16,066 scene-dialogue pairs of 20.5-hour videos, 27,328 fine-grained sentences for scene description, and 8,913 story-related QA pairs. Our experimental results show that the DEMN outperforms other QA models. This is mainly due to 1) the reconstruction of video stories in a scene-dialogue combined form that utilize the latent embedding and 2) attention. DEMN also achieved state-of-the-art results on the MovieQA benchmark.Comment: 7 pages, accepted for IJCAI 201

Candida Arthritis after Arthroscopic Arthroplasty in a Patient without Predisposing Factors

Because candidiasis is usually associated with immunosuppression, candida arthritis in an immunocompetent patient is rare. The symptoms of candidiasis are similar to bacterial infections, tuberculosis, and autoimmune diseases. In our patient with no predisposing factors, candida arthritis was initially excluded because the probability of occurrence was low. The patient had no leukocytosis, the acid-fast bacteria (AFB) stain was negative, and the autoimmune antibody screen was negative. After Candida parapsilosis was cultured in the synovial fluid, the patient was treated with amphotericin B (0.7 mg/kg/day) and oral fluconazole (400 mg/day). The treatment was successful and there were no side effects of the medications

Evaluation of Cell Toxicity and Surface Properties of Surface Modified Ti and Ti Alloys

This study was performed to investigate the surface properties and cell toxicity of the anodized and hydrothermally treated Ti, Ti-6Al-4V alloy and Ti-6Al-7Nb alloy. Bioactivity was evaluated from the surface activation layers formed on the surface of specimens in simulated body fluid (SBF) for a period of 30 days. Cell toxicity was evaluated based on the optical density of the survival cell. The porous oxide films were formed on all of the specimens by anodic oxidation. The oxide films of Ti were composed of strong anatase peaks without rutile peaks, Ti-6Al-4V alloy was composed of weak anatase peaks and weak rutile peaks and Ti-6Al-7Nb alloy was composed of strong anatase peaks and weak rutile peaks. The oxide films of all of the specimens exhibited an increase in the intensity of anatase peaks after hydrothermal treatment. The surface activation layers were formed only on the oxide films of Ti-6Al-7Nb alloy, also this alloy presented a significantly higher optical density than Ti and Ti-6Al-4V alloy on the MTT assay for cell toxicity evaluation

Size distributions of atmospheric particulate matter and associated trace metals in the multi-industrial city of Ulsan, Korea

Particulate matter (PM) was collected using micro-orifice uniform deposit impactors from a residential (RES) site and an industrial (IND) site in Ulsan, South Korea, in September-October 2014. The PM samples were measured based on their size distributions (11 stages), ranging from 0.06 ??m to over 18.0 ??m. Nine trace metals (As, Se, Cr, V, Cd, Pb, Ba, Sb, and Zn) associated with PM were analyzed. The PM samples exhibited weak bimodal distributions irrespective of sampling sites and events, and the mean concentrations of total PM (TPM) measured at the IND site (56.7 ??g/m3) was higher than that measured at the RES site (38.2 ??g/m3). The IND site also showed higher levels of nine trace metals, reflecting the influence of industrial activities and traffic emissions. At both sites, four trace metals (Ba, Zn, V, and Cr) contributed to over 80% of the total concentrations in TPM. The modality of individual trace metals was not strong except for Zn; however, the nine trace metals in PM2.5 and PM10 accounted for approximately 50% and 90% of the total concentrations in TPM, respectively. This result indicates that the size distributions of PM and trace metals are important to understand how respirable PM affects public health

GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning

Despite the remarkable progress in the development of predictive models for healthcare, applying these algorithms on a large scale has been challenging. Algorithms trained on a particular task, based on specific data formats available in a set of medical records, tend to not generalize well to other tasks or databases in which the data fields may differ. To address this challenge, we propose General Healthcare Predictive Framework (GenHPF), which is applicable to any EHR with minimal preprocessing for multiple prediction tasks. GenHPF resolves heterogeneity in medical codes and schemas by converting EHRs into a hierarchical textual representation while incorporating as many features as possible. To evaluate the efficacy of GenHPF, we conduct multi-task learning experiments with single-source and multi-source settings, on three publicly available EHR datasets with different schemas for 12 clinically meaningful prediction tasks. Our framework significantly outperforms baseline models that utilize domain knowledge in multi-source learning, improving average AUROC by 1.2%P in pooled learning and 2.6%P in transfer learning while also showing comparable results when trained on a single EHR dataset. Furthermore, we demonstrate that self-supervised pretraining using multi-source datasets is effective when combined with GenHPF, resulting in a 0.6%P AUROC improvement compared to models without pretraining. By eliminating the need for preprocessing and feature engineering, we believe that this work offers a solid framework for multi-task and multi-source learning that can be leveraged to speed up the scaling and usage of predictive algorithms in healthcare.Comment: Accepted by IEEE Journal of Biomedical and Health Informatic

A Case of Lambert-Eaton Myasthenic Syndrome Associated with Atypical Bronchopulmonary Carcinoid Tumor

The Lambert-Eaton myasthenic syndrome (LEMS) is typically recognized as a paraneoplastic syndrome associated with a small cell lung carcinoma (SCLC), whereas LEMS with other neuroendocrine lung tumors, including carcinoids or large cell lung carcinoma, are highly unusual. Here, we report a rare case of LEMS with atypical bronchopulmonary carcinoid tumor: A 65-yr-old man presented with progressive leg weakness and a diagnosis of LEMS was made by serial repetitive nerve stimulation test. Chest CT revealed a lung nodule with enlargement of paratracheal lymph nodes, and surgically resected lesion showed pathological features of atypical carcinoid tumor. We concluded that LEMS could be associated with rare pulmonary neuroendocrine tumor other than SCLC, which necessitates pathologic confirmation followed by aggressive treatment for optimal management in these rare cases