Love Sip

Love Sip is an animated graduate thesis film. The entire film, including the credits, is 4 minutes 58 seconds long. The premier of the film occurred in December 2015 in the School of Film and Animation’s end of semester screenings at Rochester Institute of Technology. The story takes place in a vending machine. The main characters are a bottle of Soda and a bottle of Juice. They fall in love and try to escape from the vending machine. Their journey is challenging, action packed, and with the promise that their love for each will survive. Love Sip is a 3D animation that combines 2D with 3D software, including Adobe Photoshop, Autodesk Maya, Pixologic Zbrush, Adobe After Effects and Adobe Premiere. The final output format is 720P HD with a high-quality stereophonic track. This thesis was inspired by process, feelings, challenges, and breakthroughs in my production, and progressive learning and understanding of animation

Unitary and non-unitary N=2N=2 minimal models

The unitary $N = 2$ superconformal minimal models have a long history in string theory and mathematical physics, while their non-unitary (and logarithmic) cousins have recently attracted interest from mathematicians. Here, we give an efficient and uniform analysis of all these models as an application of a type of Schur-Weyl duality, as it pertains to the well-known Kazama-Suzuki coset construction. The results include straightforward classifications of the irreducible modules, branching rules, (super)characters and (Grothendieck) fusion rules.Comment: 32 page

Can pro-environmental behavior increase farmers’ income?—Evidence from arable land quality protection practices in China

In China, agricultural non-point source pollution is one of the key factors limiting farmers’ income growth, and pro-environmental behavior can address agricultural surface pollution. Based on field survey data from 591 farmers in Xinjiang, China, this study empirically estimates the impact of pro-environmental behavior on farmers’ income growth. The results show that pro-environmental behavior plays a significant positive role in increasing farmers’ income, and the positive effect continues in the long run. Specifically, pro-environmental behavior can optimize the allocation of agricultural production factors, thus resulting in farmers’ income growth. The mechanism analysis shows that pro-environmental behavior affects farmers’ income growth by promoting the increase in the size of arable land and farmers’ willingness to transfer their land in the future. These findings indicate that a sound reward–punishment system for pro-environmental behavior should be established; training on pro-environmental behavior should be strengthened, and a mechanism for linking the benefits of pro-environmental behavior among stakeholders should be constructed

Measuring, Interpreting, and Improving Fairness of Algorithms using Causal Inference and Randomized Experiments

Algorithm fairness has become a central problem for the broad adoption of artificial intelligence. Although the past decade has witnessed an explosion of excellent work studying algorithm biases, achieving fairness in real-world AI production systems has remained a challenging task. Most existing works fail to excel in practical applications since either they have conflicting measurement techniques and/ or heavy assumptions, or require code-access of the production models, whereas real systems demand an easy-to-implement measurement framework and a systematic way to correct the detected sources of bias. In this paper, we leverage recent advances in causal inference and interpretable machine learning to present an algorithm-agnostic framework (MIIF) to Measure, Interpret, and Improve the Fairness of an algorithmic decision. We measure the algorithm bias using randomized experiments, which enables the simultaneous measurement of disparate treatment, disparate impact, and economic value. Furthermore, using modern interpretability techniques, we develop an explainable machine learning model which accurately interprets and distills the beliefs of a blackbox algorithm. Altogether, these techniques create a simple and powerful toolset for studying algorithm fairness, especially for understanding the cost of fairness in practical applications like e-commerce and targeted advertising, where industry A/B testing is already abundant

Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search

On most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes. In this way, an ad will not be retrieved even if queries are related when the advertiser does not bid on corresponding keywords. Moreover, most ad retrieval approaches regard rewriting and ad-selecting as two separated tasks, and focus on boosting relevance between search queries and ads. Recently, in e-commerce sponsored search more and more personalized information has been introduced, such as user profiles, long-time and real-time clicks. Personalized information makes ad retrieval able to employ more elements (e.g. real-time clicks) as search signals and retrieval keys, however it makes ad retrieval more difficult to measure ads retrieved through different signals. To address these problems, we propose a novel ad retrieval framework beyond keywords and relevance in e-commerce sponsored search. Firstly, we employ historical ad click data to initialize a hierarchical network representing signals, keys and ads, in which personalized information is introduced. Then we train a model on top of the hierarchical network by learning the weights of edges. Finally we select the best edges according to the model, boosting RPM/CTR. Experimental results on our e-commerce platform demonstrate that our ad retrieval framework achieves good performance