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1. Interactive Inverse Spatio-Temporal Crowd Motion Design

   https://doi.org/10.1145/3384382.3384528  

   Mathew, T.; Benes, B.; Aliaga, D. (May 2020, ACM Symposium on Interactive 3D Graphics)

   null (Ed.)

   We introduce a new inverse modeling method to interactively design crowd animations. Few works focus on providing succinct high-level and large-scale crowd motion modeling. Our methodology is to read in real or virtual agent trajectory data and automatically infer a set of parameterized crowd motion models. Then, components of the motion models can be mixed, matched, and altered enabling rapidly producing new crowd motions. Our results show novel animations using real-world data, using synthetic data, and imitating real-world scenarios. Moreover, by combining our method with our interactive crowd trajectory sketching tool, we can create complex spatio-temporal crowd animations in about a minute. 

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2. DiPS: Differentiable Policy for Sketching in Recommender Systems

   https://doi.org/10.1609/aaai.v36i6.20625  

   Ghosh, Aritra; Mitra, Saayan; Lan, Andrew (June 2022, Proceedings of the AAAI Conference on Artificial Intelligence)

   In sequential recommender system applications, it is important to develop models that can capture users' evolving interest over time to successfully recommend future items that they are likely to interact with. For users with long histories, typical models based on recurrent neural networks tend to forget important items in the distant past. Recent works have shown that storing a small sketch of past items can improve sequential recommendation tasks. However, these works all rely on static sketching policies, i.e., heuristics to select items to keep in the sketch, which are not necessarily optimal and cannot improve over time with more training data. In this paper, we propose a differentiable policy for sketching (DiPS), a framework that learns a data-driven sketching policy in an end-to-end manner together with the recommender system model to explicitly maximize recommendation quality in the future. We also propose an approximate estimator of the gradient for optimizing the sketching algorithm parameters that is computationally efficient. We verify the effectiveness of DiPS on real-world datasets under various practical settings and show that it requires up to 50% fewer sketch items to reach the same predictive quality than existing sketching policies. 

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3. DiPS: Differentiable Policy for Sketching in Recommender Systems

   Aritra Ghosh, Saayan Mitra (February 2022, AAAI Conference on Artificial Intelligence (AAAI))

   In sequential recommender system applications, it is important to develop models that can capture users’ evolving interest over time to successfully recommend future items that they are likely to interact with. For users with long histories, typical models based on recurrent neural networks tend to forget important items in the distant past. Recent works have shown that storing a small sketch of past items can improve sequential recommendation tasks. However, these works all rely on static sketching policies, i.e., heuristics to select items to keep in the sketch, which are not necessarily optimal and cannot improve over time with more training data. In this paper, we propose a differentiable policy for sketching (DiPS), a framework that learns a data-driven sketching policy in an end-to-end manner together with the recommender system model to explicitly maximize recommendation quality in the future. We also propose an approximate estimator of the gradient for optimizing the sketching algorithm parameters that is computationally efficient. We verify the effectiveness of DiPS on real-world datasets under various practical settings and show that it requires up to 50% fewer sketch items to reach the same predictive quality than existing sketching policies. 

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4. Investigating Priming Effects of Sketch Evaluation Instructions on Idea Generation Productivity

   https://doi.org/10.1115/detc2022-91313  

   Weaver, Morgan B.; Buck, Jacob; Merzdorf, Hillary; Dorozhkin, Denis; Douglas, Kerrie; Linsey, Julie (August 2022, Proceedings of the ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC-CIE2022)

   Abstract Engineering design involves intensive visual-spatial reasoning, and engineers depend upon external representation to develop concepts during idea generation. Previous research has not explored how our visual representation skills influence our idea generation effectiveness. A designer’s deficit in sketching skills could create a need for increased focus on the task of visual representation reducing cognitive resources available for the task at hand — generating concept. Further, this effect could be compounded if designers believed that their sketching skill would be evaluated or judged by their peers. This evaluation apprehension could cause additional mental workload distracting from the production of idea generation. The goal of this study is to investigate and better understand the relationship between designers’ sketching skills and idea generation abilities. In this paper, we present preliminary results of the relationship between independent measures of sketching skill and idea generation ability from an entry-level engineering design and graphics course. During data collection, task instructions were given in two ways to independent groups: one group was instructed upfront that sketching would be evaluated, while the second group was kept blind to the sketch evaluation. In this paper, we also examine the potential priming effects of sketch quality evaluation apprehension on idea generation productivity. The results show that sketching quality and idea quantity are largely independent, and that the priming effects of sketch evaluation instructions are small to negligible on idea generation productivity. 

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5. Solving Program Sketches with Large Integer Values

   https://doi.org/10.1145/3532849  

   Hu, Qinheping; Singh, Rishabh; D’Antoni, Loris (June 2022, ACM Transactions on Programming Languages and Systems)

   Program sketching is a program synthesis paradigm in which the programmer provides a partial program with holes and assertions. The goal of the synthesizer is to automatically find integer values for the holes so that the resulting program satisfies the assertions. The most popular sketching tool, Sketch , can efficiently solve complex program sketches but uses an integer encoding that often performs poorly if the sketched program manipulates large integer values. In this article, we propose a new solving technique that allows Sketch to handle large integer values while retaining its integer encoding. Our technique uses a result from number theory, the Chinese Remainder Theorem, to rewrite program sketches to only track the remainders of certain variable values with respect to several prime numbers. We prove that our transformation is sound and the encoding of the resulting programs are exponentially more succinct than existing Sketch encodings. We evaluate our technique on a variety of benchmarks manipulating large integer values. Our technique provides speedups against both existing Sketch solvers and can solve benchmarks that existing Sketch solvers cannot handle. 

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