The joint representations of knowledge graph have become an important approach to improve the quality of knowledge graph, which is beneficial to machine learning, data mining, and artificial intelligence applications. However, the previous work suffers severely from the noise in text when modeling the text information. To overcome this problem, this paper mines the high-quality reference sentences of the entities in the knowledge graph, to enhance the representation ability of the entities. A novel framework for joint representation learning of knowledge graphs and text information based on reference sentence noise-reduction is proposed, which embeds the entity, the relations, and the words into a unified vector space. The proposed framework consists of knowledge graph representation learning module, textual relation representation learning module, and textual entity representation learning module. Experiments on entity prediction, relation prediction, and triple classification tasks are conducted, results show that the proposed framework can significantly improve the performance of mining and fusing the text information. Especially, compared with the state-of-the-art method[15], the proposed framework improves the metric of H@10 by 5.08% and 3.93% in entity prediction task and relation prediction task, respectively, and improves the metric of accuracy by 5.08% in triple classification task.

The damage to the organic layer of aluminum (III) bis(2-methyl-8-quninolinato)-4-phenylphenolate (BAlq) film was investigated on the basis of the change in photoluminescence (PL) intensity. To suppress the bombardment of the substrate with high-energy particles such as γ-electrons and negative oxygen ions, we used a facing-target sputtering (FTS) system. A marked reduction, however, of the PL intensity of the organic layer was still observed upon the deposition of an indium tin oxide (ITO) film on the organic film. To reduce this reduction, we proposed the insertion of a sector-shaped metal shield near the target electrode, and we showed its effectiveness in reducing the damage. This reduction of the damage is thought to be caused by the elimination of γ-electrons incident to the organic film surface escaping from the target area near the substrate side. We confirmed that high-energy electron bombardment leads to a significant reduction of PL intensity of the organic layer. This indicates that high-energy electrons incident to the organic film surface play a key role in the damage of the organic layer during the sputtering process.

In this paper, we proposed a new high-rate oblique deposition method using two sputtering sources to obtain SiO2 films for a liquid crystal alignment layer. One sputtering source that operates in a metal mode supplies Si atoms to a substrate, and the other source that operates in an oxide mode supplies oxygen radicals to a substrate. To reduce the gas pressure of a deposition chamber and make the two sputtering sources operate in different modes, the sputtering sources were separated from the deposition chamber with stainless meshes, and Ar and oxygen gases were introduced separately through the two sputtering sources, i.e., Ar gas was introduced through the Si supply source and oxygen gas was introduced through the oxygen radical source. When Ar gas of 30 sccm and oxygen gas of 4 sccm were introduced into the system, the gas pressure of the deposition chamber was maintained below 1.7 mTorr and the films deposited at an incidence angle of more than 70 showed an elongated inclined columnar structure. Under this condition, a deposition rate of 30 nm/min was realized even at an incidence angle above 70, where most of the Si atoms incident to the substrate were supplied by the Si supply source and the oxygen radical source supplied oxygen radicals and promoted the oxidation of the film.

This paper presents a leakage-efficient instruction TLB (Translation Lookaside Buffer) design for embedded processors. The key observation is that when programs enter a physical page, the following instructions tend to be fetched from the same page for a rather long time. Thus, by employing a small storage component which holds the recent address-translation information, the TLB access frequency can be drastically decreased, and the instruction TLB can be turned into the low-leakage mode with the dual voltage supply technique. Based on such a design philosophy, three leakage control policies are proposed to maximize the leakage reduction efficiency. Evaluation results with eight MiBench programs show that the proposed design can reduce the leakage power of the instruction TLB by 50% on average, with only 0.01% performance degradation.

This paper presents a leakage efficient data TLB (Translation Look-aside Buffer) design for embedded processors. Due to the data locality in programs, data TLB references tend to hit only a small number of pages during short execution intervals. After dividing the overall execution time into smaller time slices, a leakage reduction mechanism is proposed to detect TLB entries which actually serve for virtual-to-physical address translations within each time slice. Thus, with the integration of the dual voltage supply technique, those TLB entries which are not used for address translations can be put into low leakage mode (with lower voltage supply) to save power. Evaluation results with eight MiBench programs show that the proposed design can reduce the leakage power of a data TLB by 37% on average, with performance degradation less than 0.01%.