Non-Linear Editor for Text-Based Screencast

Screencasts, where computer screen is broadcast to a large audience on the web, are becoming popular as an online educational tool. Among various types of screencast content, popular are the contents that involve text editing, including computer programming. There are emerging platforms that support such text-based screencasts by recording every character insertion/deletion from the creator and reconstructing its playback on the viewer's screen. However, these platforms lack rich support for creating and editing the screencast itself, mainly due to the difficulty of manipulating recorded text changes; the changes are tightly coupled in sequence, thus modifying arbitrary part of the sequence is not trivial. We present a non-linear editing tool for text-based screencasts. With the proposed selective history rewrite process, our editor allows users to substitute an arbitrary part of a text-based screencast while preserving overall consistency of the rest of the text-based screencast.Comment: To appear in Adjunct Proceedings of the 30th Annual ACM Symposium on User Interface Software & Technology (UIST 2017, Poster

Adversarial Sampling and Training for Semi-Supervised Information Retrieval

Ad-hoc retrieval models with implicit feedback often have problems, e.g., the imbalanced classes in the data set. Too few clicked documents may hurt generalization ability of the models, whereas too many non-clicked documents may harm effectiveness of the models and efficiency of training. In addition, recent neural network-based models are vulnerable to adversarial examples due to the linear nature in them. To solve the problems at the same time, we propose an adversarial sampling and training framework to learn ad-hoc retrieval models with implicit feedback. Our key idea is (i) to augment clicked examples by adversarial training for better generalization and (ii) to obtain very informational non-clicked examples by adversarial sampling and training. Experiments are performed on benchmark data sets for common ad-hoc retrieval tasks such as Web search, item recommendation, and question answering. Experimental results indicate that the proposed approaches significantly outperform strong baselines especially for high-ranked documents, and they outperform IRGAN in NDCG@5 using only 5% of labeled data for the Web search task.Comment: Published in WWW 201

Lyapunov Exponent and the Solid-Fluid Phase Transition

We study changes in the chaotic properties of a many-body system undergoing a solid-fluid phase transition. To do this, we compute the temperature dependence of the largest Lyapunov exponents $\lambda_{max}$ for both two- and three-dimensional periodic systems of $N$-particles for various densities. The particles interact through a soft-core potential. The two-dimensional system exhibits an apparent second-order phase transition as indicated by a $\lambda$-shaped peak in the specific heat. The first derivative of $\lambda_{max}$ with respect to the temperature shows a peak at the same temperature. The three-dimensional system shows jumps, in both system energy and $\lambda_{max}$, at the same temperature, suggesting a first-order phase transition. Relaxation phenomena in the phase-transition region are analyzed by using the local time averages.Comment: 16 pages, REVTeX, 10 eps figures, epsfig.st

Pressure effects on the superconducting thin film Ba1−x_{1-x}Kx_{x}Fe2_{2}As2_{2}

We report electrical resistivity measurements on a high-quality Ba$_{1-x}$K$_{x}$Fe$_{2}$As$_{2}$ thin film ($x=0.4$) under pressure. The superconducting transition temperature (=39.95 K) of the optimally-doped thin film shows a dome shape with pressure, reaching a maximal value 40.8 K at 11.8 kbar. The unusually high superconducting transition temperature and its anomalous pressure dependence are ascribed to a lattice mismatch between the LaAlO$_3$ substrate and the thin film. The local temperature exponent of the resistivity ($n=d\text{ln}\Delta\rho/d\text{ln}T$) shows a funnel shape around the optimal pressure, suggesting that fluctuations associated with the anomalous normal state are responsible for high-temperature superconductivity.Comment: To appear in Appl. Phys. Let