JDsearch: A Personalized Product Search Dataset with Real Queries and Full Interactions

Recently, personalized product search attracts great attention and many models have been proposed. To evaluate the effectiveness of these models, previous studies mainly utilize the simulated Amazon recommendation dataset, which contains automatically generated queries and excludes cold users and tail products. We argue that evaluating with such a dataset may yield unreliable results and conclusions, and deviate from real user satisfaction. To overcome these problems, in this paper, we release a personalized product search dataset comprised of real user queries and diverse user-product interaction types (clicking, adding to cart, following, and purchasing) collected from JD.com, a popular Chinese online shopping platform. More specifically, we sample about 170,000 active users on a specific date, then record all their interacted products and issued queries in one year, without removing any tail users and products. This finally results in roughly 12,000,000 products, 9,400,000 real searches, and 26,000,000 user-product interactions. We study the characteristics of this dataset from various perspectives and evaluate representative personalization models to verify its feasibility. The dataset can be publicly accessed at Github: https://github.com/rucliujn/JDsearch.Comment: Accepted to SIGIR 202

Diurnal Cycle Model of Lake Ice Surface Albedo : A Case Study of Wuliangsuhai Lake

Ice surface albedo is an important factor in various optical remote sensing technologies used to determine the distribution of snow or melt water on the ice, and to judge the formation or melting of lake ice in winter, especially in cold and arid areas. In this study, field measurements were conducted at Wuliangsuhai Lake, a typical lake in the semi-arid cold area of China, to investigate the diurnal variation of the ice surface albedo. Observations showed that the diurnal variations of the ice surface albedo exhibit bimodal characteristics with peaks occurring after sunrise and before sunset. The curve of ice surface albedo with time is affected by weather conditions. The first peak occurs later on cloudy days compared with sunny days, whereas the second peak appears earlier on cloudy days. Four probability density distribution functions—Laplace, Gauss, Gumbel, and Cauchy—were combined linearly to model the daily variation of the lake ice albedo on a sunny day. The simulations of diurnal variation in the albedo during the period from sunrise to sunset with a solar altitude angle higher than 5° indicate that the Laplace combination is the optimal statistical model. The Laplace combination can not only describe the bimodal characteristic of the diurnal albedo cycle when the solar altitude angle is higher than 5°, but also reflect the U-shaped distribution of the diurnal albedo as the solar altitude angle exceeds 15°. The scale of the model is about half the length of the day, and the position of the two peaks is closely related to the moment of sunrise, which reflects the asymmetry of the two peaks of the ice surface albedo. This study provides a basis for the development of parameterization schemes of diurnal variation of lake ice albedo in semi-arid cold regions

Integrating audio and visual modalities for multimodal personality trait recognition via hybrid deep learning

Recently, personality trait recognition, which aims to identify people’s first impression behavior data and analyze people’s psychological characteristics, has been an interesting and active topic in psychology, affective neuroscience and artificial intelligence. To effectively take advantage of spatio-temporal cues in audio-visual modalities, this paper proposes a new method of multimodal personality trait recognition integrating audio-visual modalities based on a hybrid deep learning framework, which is comprised of convolutional neural networks (CNN), bi-directional long short-term memory network (Bi-LSTM), and the Transformer network. In particular, a pre-trained deep audio CNN model is used to learn high-level segment-level audio features. A pre-trained deep face CNN model is leveraged to separately learn high-level frame-level global scene features and local face features from each frame in dynamic video sequences. Then, these extracted deep audio-visual features are fed into a Bi-LSTM and a Transformer network to individually capture long-term temporal dependency, thereby producing the final global audio and visual features for downstream tasks. Finally, a linear regression method is employed to conduct the single audio-based and visual-based personality trait recognition tasks, followed by a decision-level fusion strategy used for producing the final Big-Five personality scores and interview scores. Experimental results on the public ChaLearn First Impression-V2 personality dataset show the effectiveness of our method, outperforming other used methods

乌梁素海湖冰晴天反照率日变化特征的统计模型比较和分析

Based on the features of diurnal variation of ice surface albedo in the Lake Ulansuhai, the relationship between solar elevation angle and geographic coordinates and Julian days, together with normalized time, to express the diurnal variations of the ice albedo. Linear combinations of four probability density distribution functions with exponential forms, including Laplace, Gauss, Gumbel, and Cauchy equations, were used to simulate the diurnal variations in observed ice albedo from sunrise to sunset with solar elevation angle more than 5°. The results reveals that the Laplace combined statistical model is the best fit to the observations. It can not only clearly show the double-peak distribution in the diurnal variation in ice surface albedo when the elevation angle is greater than 5°, but also express the U-type distribution between the two peaks as the elevation angle is more than 15°. This model has advantages of simple form in expression and clear physical meaning. The length parameter is close to half day, and the peak location is associated with the time of sunrise, and the asymmetry of the two peaks can be also shown. It provides a solid foundation for the parameterization schemes on the diurnal variations in lake ice surface albedo in different regions. © 2020 by Journal of Lake Sciences.Peer reviewe

Ion−π interactions for constructing organic luminescent materials

Abstract Organic luminescent materials are one of the research hotspots worldwide. The luminescent efficiency of these materials can be tuned by introducing noncovalent interactions. Ion−π interactions, mainly including anion−π interactions and cation−π interactions, are relatively new but powerful noncovalent interactions with extensive applications in supramolecular chemistry. The use of ion−π interactions to fabricate highly luminescent materials is initiated since they were introduced to develop aggregation‐induced emission luminogens in 2017. Then, this research field is undergoing through challenging but very prospective developments. In this minireview, we start with a brief introduction of ion−π interactions and then make a summary of the reports on organic fluorescence based on ion−π interactions and room temperature phosphorescence induced by ion−π interactions. The applications of organic luminescent materials based on ion−π interactions in bioimaging, imaging‐guided anticancer and antibacterial treatment are also discussed. The challenges and prospects of using anion−π and cation−π interactions for constructing organic luminogens are presented in the final part. It is expected that this minireview will provide some guidelines for fabricating novel organic luminescent materials and further extend the application potential of ion−π interactions

Comparative Analysis of China Surface Air Temperature Series for the Past 100 Years

Temperature change plays a crucial role in global change sciences. In the past several decades, comprehensive findings have been achieved on temperature change in China for the past 100 years. Several time series have been created to illustrate the averaged surface air temperature for the country. The correlations of these series range from 0.73 to 0.97. It is also achieved in better data quality, wider spatial data coverage, improved homogeneity of time series, and enhanced reliability of findings. The results show an annual mean temperature increase by 0.78±0.27°C per 100 years in China for the period 1906–2005. After prolonging the period till 2007, it is found that 2007 is rated as the warmest year in the past 100 years. Although all the series, except one, reflect temperature changes in the eastern part of China before the 1930s, they represent the general temperature change in most parts of the country after the 1930s. Tang, G., Y. Ding, S. Wang, et al., 2010: Comparative analysis of China surface air temperature series for the past 100 years. Adv. Clim. Change Res., 1, doi: 10.3724/SP.J.1248.2010.00011

Hyperspectral Images Classification With Gabor Filtering and Convolutional Neural Network

Recently, the capability of deep learning-based approaches, especially deep convolutional neural networks (CNNs), has been investigated for hyperspectral remote sensing feature extraction (FE) and classification. Due to the large number of learnable parameters in convolutional filters, lots of training samples are needed in deep CNNs to avoid the overfitting problem. On the other hand, Gabor filtering can effectively extract spatial information including edges and textures, which may reduce the FE burden of the CNNs. In this letter, in order to make the most of deep CNN and Gabor filtering, a new strategy, which combines Gabor filters with convolutional filters, is proposed for hyperspectral image classification to mitigate the problem of overfitting. The obtained results reveal that the proposed model provides competitive results in terms of classification accuracy, especially when only a limited number of training samples are available