Anti-Japanese Sentiment among Chinese University Students: The Influence of Contemporary Nationalist Propaganda

This study looks at the sources of anti-Japanese sentiment in today’s China. Using original survey data collected in June 2014 from 1,458 students at three elite universities in Beijing, we quantitatively investigate which factors are associated with stronger anti-Japanese sentiment among elite university students. In particular, we examine the link between the Chinese Communist Party (CCP)’s nationalist propaganda (especially patriotic education) and university students’ anti-Japanese sentiment. We find that nationalist propaganda does indeed have a significant effect on negative sentiment towards Japan. Reliance on state-sanctioned textbooks for information about Japan, visiting museums and memorials or watching television programmes and movies relating to the War of Resistance against Japan are all associated with higher levels of anti-Japanese sentiment. The findings suggest the effectiveness of nationalist propaganda in promoting anti-Japanese sentiment. We also find that alternative sources of information, especially personal contact with Japan, can mitigate anti-Japanese sentiment. Thus, visiting Japan and knowing Japanese people in person can potentially offset some of the influences of nationalist propaganda

Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction

Click-Through Rate (CTR) prediction is one of the most important machine learning tasks in recommender systems, driving personalized experience for billions of consumers. Neural architecture search (NAS), as an emerging field, has demonstrated its capabilities in discovering powerful neural network architectures, which motivates us to explore its potential for CTR predictions. Due to 1) diverse unstructured feature interactions, 2) heterogeneous feature space, and 3) high data volume and intrinsic data randomness, it is challenging to construct, search, and compare different architectures effectively for recommendation models. To address these challenges, we propose an automated interaction architecture discovering framework for CTR prediction named AutoCTR. Via modularizing simple yet representative interactions as virtual building blocks and wiring them into a space of direct acyclic graphs, AutoCTR performs evolutionary architecture exploration with learning-to-rank guidance at the architecture level and achieves acceleration using low-fidelity model. Empirical analysis demonstrates the effectiveness of AutoCTR on different datasets comparing to human-crafted architectures. The discovered architecture also enjoys generalizability and transferability among different datasets

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Visual Tracking and Recognition of the Human Hand

83 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2005.Through quantitative and visual experimental results, we demonstrate the effectiveness of our approach and point out its limitations.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD