No abstract

We demonstrate a facile route to synthesize silver chloride nanocubes and derivative nanomaterials. For the synthesis of silver chloride nanocubes, silver nitrate and hydrochloric acid were used as precursors in ethylene glycol, and poly (vinyl pyrrolidone) as a surfactant. Molar ratio of the two precursors greatly influenced the morphology and composition of the final products. As-synthesized silver chloride nanocubes showed size-dependent optical properties in the visible region of light, which is likely due to a small amount of silver clusters formed on the surface of silver chloride nanocubes. Moreover, we show for the first time that simple reduction of silver chloride nanocubes with different reducing reagents leads to the formation of delicate nanostructures such as cube-shaped silver-nanoparticle aggregates, and silver chloride nanocubes with truncated corners and with silver-nanograin decorated corners. Additionally, we quantitatively investigated for the first time the evolution of silver chloride nanocubes to silver chloride nanocubes decorated with silver nanoparticles upon exposure to e-beam. Our novel and facile synthesis of silver chloride related nanoparticles with delicately controlled morphologies could be an important basis for fabricating efficient photocatalysts and antibacterial materials.

We report the synthesis of KNbO(3) nanowires (NWs) with a monoclinic phase, a phase not observed in bulk KNbO(3) materials. The monoclinic NWs can be synthesized via a hydrothermal method using metallic Nb as a precursor. The NWs are metastable, and thermal treatment at ∼450 °C changed the monoclinic phase into the orthorhombic phase, which is the most stable phase of KNbO(3) at room temperature. Furthermore, we fabricated energy-harvesting nanogenerators by vertically aligning the NWs on SrTiO(3) substrates. The monoclinic NWs showed significantly better energy conversion characteristics than orthorhombic NWs. Moreover, the frequency-doubling efficiency of the monoclinic NWs was ∼3 times higher than that of orthorhombic NWs. This work may contribute to the synthesis of materials with new crystalline structures and hence improve the properties of the materials for various applications.

Internet-connected devices, especially mobile devices such as smartphones, have become widely accessible in the past decade. Interaction with such devices has evolved into frequent and short-duration usage, and this phenomenon has resulted in a pervasive popularity of casual games in the game sector. On the other hand, development of casual games has become easier than ever as a result of the advancement of development tools. With the resulting fierce competition, now both acquisition and retention of users are the prime concerns in the field. In this study, we focus on churn prediction of mobile and online casual games. While churn prediction and analysis can provide important insights and action cues on retention, its application using play log data has been primitive or very limited in the casual game area. Most of the existing methods cannot be applied to casual games because casual game players tend to churn very quickly and they do not pay periodic subscription fees. Therefore, we focus on the new players and formally define churn using observation period (OP) and churn prediction period (CP). Using the definition, we develop a standard churn analysis process for casual games. We cover essential topics such as pre-processing of raw data, feature engineering including feature analysis, churn prediction modeling using traditional machine learning algorithms (logistic regression, gradient boosting, and random forests) and two deep learning algorithms (CNN and LSTM), and sensitivity analysis for OP and CP. Play log data of three different casual games are considered by analyzing a total of 193,443 unique player records and 10,874,958 play log records. While the analysis results provide useful insights, the overall results indicate that a small number of well-chosen features used as performance metrics might be sufficient for making important action decisions and that OP and CP should be properly chosen depending on the analysis goal.

Data is one of the most important factors in machine learning. However, even if we have high-quality data, there is a situation in which access to the data is restricted. For example, access to the medical data from outside is strictly limited due to the privacy issues. In this case, we have to learn a model sequentially only with the data accessible in the corresponding stage. In this work, we propose a new method for preserving learned knowledge by modeling the high-level feature space and the output space to be mutually informative, and constraining feature vectors to lie in the modeled space during training. The proposed method is easy to implement as it can be applied by simply adding a reconstruction loss to an objective function. We evaluate the proposed method on CIFAR-10/100 and a chest X-ray dataset, and show benefits in terms of knowledge preservation compared to previous approaches.