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Stock prediction aims to predict the future trends of a stock in order to help investors to make good investment decisions. Traditional solutions for stock prediction are based on time-series models. With the recent success of deep neural networks in modeling sequential data, deep learning has become a promising choice for stock prediction.However, most existing deep learning solutions are not optimized towards the target of investment, i.e., selecting the best stock with highest expected revenue. Specifically, they typically formulate stock prediction as a classification (to predict stock trend) or a regression problem (to predict stock price). More importantly, they largely treat the stocks as independent of each other. The valuable signal in the rich relations between stocks (or companies), such as two stocks are in the same sector and two companies have a supplier-customer relation, is not considered.In this work, we contribute a new deep learning solution, named Relational Stock Ranking (RSR), for stock prediction. Our RSR method advances existing solutions in two major aspects: 1) tailoring the deep learning models for stock ranking, and 2) capturing the stock relations in a time-sensitive manner. The key novelty of our work is the proposal of a new component in neural network modeling, named Temporal Graph Convolution, which jointly models the temporal evolution and relation network of stocks. To validate our method, we perform back-testing on the historical data of two stock markets, NYSE and NASDAQ. Extensive experiments demonstrate the superiority of our RSR method. It outperforms state-of-the-art stock prediction solutions achieving an average return ratio of 98% and 71% on NYSE and NASDAQ, respectively.

The formation of a two-dimensional electron gas (2-DEG) using SrTiO3 (STO)-based heterostructures provides promising opportunities in oxide electronics. We realized the formation of 2-DEG using several amorphous layers grown by the atomic layer deposition (ALD) technique at 300 °C which is a process compatible with mass production and thereby can provide the realization of potential applications. We found that the amorphous LaAlO3 (LAO) layer grown by the ALD process can generate 2-DEG (∼1 × 1013/cm2) with an electron mobility of 4–5 cm2/V·s. A much higher electron mobility was observed at lower temperatures. More remarkably, amorphous YAlO3 (YAO) and Al2O3 layers, which are not polar-perovskite-structured oxides, can create 2-DEG as well. 2-DEG was created by means of the important role of trimethylaluminum, Me3Al, as a reducing agent for STO during LAO and YAO ALD as well as the Al2O3 ALD process at 300 °C. The deposited oxide layer also plays an essential role as a catalyst that enables Me3Al to reduce the STO. The electrons were localized very near to the STO surface, and the source of carriers was explained based on the oxygen vacancies generated in the STO substrate.

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Recommender Systems have become a very useful tool for a large variety of domains. Researchers have been attempting to improve their algorithms in order to issue better predictions to the users. However, one of the current challenges in the area refers to how to properly evaluate the predictions generated by a recommender system. In the extent of offline evaluations, some traditional concepts of evaluation have been explored, such as accuracy, Root Mean Square Error and P@N for top-k recommendations. In recent years, more research have proposed some new concepts such as novelty, diversity and serendipity. These concepts have been addressed with the goal to satisfy the users' requirements. Numerous definitions and metrics have been proposed in previous work. On the absence of a specific summarization on evaluations of recommendation combining traditional metrics and recent progresses, this paper surveys and organizes the main research that present definitions about concepts and propose metrics or strategies to evaluate recommendations. In addition, this survey also settles the relationship between the concepts, categorizes them according to their objectives and suggests potential future topics on user satisfaction.

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Explainability and effectiveness are two key aspects for building recommender systems. Prior efforts mostly focus on incorporating side information to achieve better recommendation performance. However, these methods have some weaknesses: (1) prediction of neural network-based embedding methods are hard to explain and debug;(2) symbolic, graph-based approaches (e.g., meta path-based models) require manual efforts and domain knowledge to define patterns and rules, and ignore the item association types (e.g. substitutable and complementary). In this paper, we propose a novel joint learning framework to integrate induction of explainable rules from knowledge graph with construction of a rule-guided neural recommendation model. The framework encourages two modules to complement each other in generating effective and explainable recommendation: 1) inductive rules, mined from item-centric knowledge graphs, summarize common multi-hop relational patterns for inferring different item associations and provide human-readable explanation for model prediction; 2) recommendation module can be augmented by induced rules and thus have better generalization ability dealing with the cold-start issue. Extensive experiments 1 show that our proposed method has achieved significant improvements in item recommendation over baselines on real-world datasets. Our model demonstrates robust performance over "noisy" item knowledge graphs, generated by linking item names to related entities. ACM Reference Format: