A study of heat transfer and inlet flow behavior in molten polymer systems

Two separate studies have been done in the present work: one to study the entrance effects in flow in molten polymer systems and the other to investigate the heat transfer to thermally softened flowing polymer. The equipment for the entrance effect was designed to study the isothermal flow behavior of a polymer melt in the entrance region. The main object of this investigation was to find the entrance length for the melt to relax and to separate the pressure drop into viscous and elastic effect. Due to the failure in developing a proper pressure measuring device the experiment was not conclusive. In addition to the entrance flow study the heat transfer to thermally softened flowing polymer was studied: A depression in temperature in the central region of the tube and a peak in temperature near the wall when heating the flowing polymer was observed. When cooling the polymer melt the effect of viscous dissipation was greatly reduced due to the alteration of the velocity profile heat transfer to the cold wall. In general aspects the results of heat transfer study were in good agreement with the previous theoretical and experimental works

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

Classical dynamics near the triple collision in a three-body Coulomb problem

We investigate the classical motion of three charged particles with both attractive and repulsive interaction.The triple collision is a main source of chaos in such three body Coulomb problems.By employing the McGehee scaling technique, we analyse here for the first time in detail the three-body dynamics near the triple collision in 3 degrees of freedom.We reveal surprisingly simple dynamical patterns in large parts of the chaotic phase space. The underlying degree of order in the form of approximate Markov partitions may help in understanding the global structures observed in quantum spectra of two-electron atoms.Comment: 4 pages, 3 figure