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997 results on '"Li, Qiqi"'

1. Segment Discovery: Enhancing E-commerce Targeting

Author

Li, Qiqi, Singh, Roopali, Polpanumas, Charin, Fiez, Tanner, Kumar, Namita, and Chakrabarti, Shreya

Subjects
Computer Science - Machine Learning, Computer Science - Information Retrieval
Abstract

Modern e-commerce services frequently target customers with incentives or interventions to engage them in their products such as games, shopping, video streaming, etc. This customer engagement increases acquisition of more customers and retention of existing ones, leading to more business for the company while improving customer experience. Often, customers are either randomly targeted or targeted based on the propensity of desirable behavior. However, such policies can be suboptimal as they do not target the set of customers who would benefit the most from the intervention and they may also not take account of any constraints. In this paper, we propose a policy framework based on uplift modeling and constrained optimization that identifies customers to target for a use-case specific intervention so as to maximize the value to the business, while taking account of any given constraints. We demonstrate improvement over state-of-the-art targeting approaches using two large-scale experimental studies and a production implementation., Comment: Accepted at the CONSEQUENCES'24 workshop, co-located with ACM RecSys'24

Published
2024

2. Probabilistic Spatiotemporal Modeling of Day-Ahead Wind Power Generation with Input-Warped Gaussian Processes

Author

Li, Qiqi and Ludkovski, Mike

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control, Physics - Atmospheric and Oceanic Physics, Physics - Data Analysis, Statistics and Probability, Statistics - Applications
Abstract

We design a Gaussian Process (GP) spatiotemporal model to capture features of day-ahead wind power forecasts. We work with hourly-scale day-ahead forecasts across hundreds of wind farm locations, with the main aim of constructing a fully probabilistic joint model across space and hours of the day. To this end, we design a separable space-time kernel, implementing both temporal and spatial input warping to capture the non-stationarity in the covariance of wind power. We conduct synthetic experiments to validate our choice of the spatial kernel and to demonstrate the effectiveness of warping in addressing nonstationarity. The second half of the paper is devoted to a detailed case study using a realistic, fully calibrated dataset representing wind farms in the ERCOT region of Texas., Comment: 29 pages, 12 figures

Published
2024

5. Determination of six human milk oligosaccharides in dairy product by precolumn derivatization high performance liquid chromatography with fluorescence detection

Author

LI Qiqi, HUANG Jiale, MU Shuyi, LI Qishan, and HUANG Jingjun

Subjects
precolumn derivatization, human milk oligosaccharides, dairy product, 2-aminobenzamide, Food processing and manufacture, TP368-456
Abstract

Objective: To astablish a strong popularity, high sensitivity of six human milk oligosaccharides in dairy product by precolumn derivatization high performance liquid chromatography-fluorescence method. Methods: Aquatic product sample was precipitated with glacial acetic acid, filtration by using the filter paper. It was derived by 2-aminobenzamide solution. The extracts were centrifuged and filtered by using the organic filter membrane and analyzed by using a AdvanceBio Glycan Map column to be separated and detected，employing acetonitrile-50 mmol/L ammonium formate solution （pH 4.4） as the mobile phase for gradient elution. Results: Under the experimental conditions, six human milk oligosaccharides in dairy product were linear in the range of 1.00～400.0 mg/L, and the correlation coefficients were all over 0.999. The detection limit of the method was 4.1～10.9 mg/kg, the limits of quantification were 12.3～32.7 mg/kg, the mean recoveries rate was ranged from 71.3% to 90.2% with the intra-day precisions （n=6） in the range of 1.0%～6.3% and inter-day precisions （n=6） less than 10.0%. Conclusion: This method can meet the requirements of the daily detection of six human milk oligosaccharides due to its simplicity and sensitives.

Published
2024
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38. Physics Identity of Chinese Students before and after Gaokao: The Effect of High-Stake Testing

Author

Wang, Jianlan, Li, Qiqi, and Luo, Ying

Abstract

High-stakes testing (HST) is critical in the educational system of East-Asian countries like China, and the ongoing standards-based reform in many Western countries like the USA. HST has been criticized for corrupting an educational system through means like disengaging students from authentic learning. It seems that students' affective learning would be promoted if HST were abandoned. In this study, we used the mixed-method approach to investigate this hypothetical question by comparing the physics identity of high-school students in an HST-oriented educational context and college students who have transferred from an HST-oriented context to an almost HST-free one in China. We administered a physics identity survey with 108 students from two physics classes in a key high school and 233 undergraduate physics majors in a top-tier university who were separated by Gaokao as the most life-shaping test in China. We also interviewed sample participants about their physics learning. The data show the freshmen in college had a significantly higher average physics identity than the high-school students did. In the undergraduate physics program, the average physics identity had a deteriorating pattern from the freshmen, sophomores, to juniors. From high school to college, the students' intention of pursuing physics as a major or career was similarly low. Differently, the students' perception of lecturing changed, so did their self-discipline. Together, the findings suggested that simply discarding HST would not promote students' affective learning. An alternative was needed to replace the extrinsic pressure from HST, which could be either students' intrinsic engine of learning or another rewarding or coercive system.

Published
2022
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41. Image Deconvolution with Deep Image and Kernel Priors

Author

Wang, Zhunxuan, Wang, Zipei, Li, Qiqi, and Bilen, Hakan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Image deconvolution is the process of recovering convolutional degraded images, which is always a hard inverse problem because of its mathematically ill-posed property. On the success of the recently proposed deep image prior (DIP), we build an image deconvolution model with deep image and kernel priors (DIKP). DIP is a learning-free representation which uses neural net structures to express image prior information, and it showed great success in many energy-based models, e.g. denoising, super-resolution, inpainting. Instead, our DIKP model uses such priors in image deconvolution to model not only images but also kernels, combining the ideas of traditional learning-free deconvolution methods with neural nets. In this paper, we show that DIKP improve the performance of learning-free image deconvolution, and we experimentally demonstrate this on the standard benchmark of six standard test images in terms of PSNR and visual effects., Comment: In Proceedings of the 2019 IEEE International Conference on Computer Vision Workshops (ICCVW)

Published
2019
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