Effects of anion and cation doping on the thermoelectric properties of n-type PbS

The PbCl_xS_1-x and Pb_1-xBi_xS (x? =?0–0.05) bulks were fabricated with a facile method of hydrothermal synthesis and microwave sintering, and the effect of anionic and cationic donors on the thermoelectric performance of PbS was investigated. Although Cl^? and Bi³⁺ both effectively improved the thermoelectric properties of n-type PbS, more excellent thermoelectric performance was obtained from Cl^? doped samples because of higher electrical property and lower thermal conductivity at higher temperature (T? >?600?K). The thermoelectric figure of merit (ZT) reaches 1.04 for PbCl_0.015S_0.985 at 800?K and increases with temperature increasing without sign of saturation, which is probably the highest value ever reported for single-phase polycrystalline n-type PbS. The results also indicate that the hydrothermal synthesis and microwave sintering can realize anion doping as well as cation doping for n-type PbS at low cost, and PbS should be a robust alternative for PbTe thermoelectric materials.     

Image and Feature Space Based Domain Adaptation for Vehicle Detection

The application of deep learning in the field of object detection has experienced much progress. However, due to the domain shift problem, applying an off-the-shelf detector to another domain leads to a significant performance drop. A large number of ground truth labels are required when using another domain to train models, demanding a large amount of human and financial resources. In order to avoid excessive resource requirements and performance drop caused by domain shift, this paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our approach improves the cross-domain vehicle detection model from image space and feature space. We employ objectives of the generative adversarial network and cycle consistency loss for image style transfer in image space. For feature space, we align feature distributions between the source domain and the target domain to improve the detection accuracy. Experiments are carried out using the method with two different datasets, proving that this technique effectively improves the accuracy of vehicle detection in the target domain.     

A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train a translation evaluation system to select better candidate words for generating translations. In the experiments, we conducted extensive experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual corpora of NiuTrans are used in our experiments. The experiment results prove that the proposed methods can effectively improve the English to Chinese translation quality.     

Compressive properties of pristine and SiC-Te-added MgB2 powders,green compacts and spark-plasma-sintered bulks

Pristine and (SiC+Te)-added MgB₂ powders, green and spark plasma sintered (SPS) compacts were investigated from the viewpoint of quasi-static and dynamic (Split-Hopkinson Pressure Bar, SHPB) compressive mechanical properties The amount of the additive (SiC+Te) was selected to be the optimum one for maximization of the superconducting functional parameters. Pristine and added MgB₂ show very similar compressive parameters (tan δ, fracture strength, Vickers hardness, others) and fragment size in the SHPB test. However, for the bulk SPSed samples the ratio of intergranular to transgranular fracturing changes, the first one being stronger in the added sample. This is reflected in the quasi-static K_IC that is higher for the added sample. Despite this result, sintered samples are brittle and have roughly similar fragmentation behavior as for brittle engineering ceramics. In the fragmentation process, the composite nature of our samples should be considered with a special focus on MgB₂ blocks (colonies) that show the major contribution to fracturing. The Glenn-Chudnovsky model of fracturing under dynamic load provides the closest values to our experimental fragment size data.