Yutong Wu

Yutong Wu, Arlei Silva

Mass peer-production environments are shaped by a complex interplay between decentralized coordination, platform design, and potential conflict over resources. While online infrastructures enable large-scale collaboration, they can also introduce coordination challenges and contested interactions among participants. The Reddit r/place experiment provides a unique socio-technical setting for studying these dynamics across three distinct editions (2017, 2022, and 2023). By allowing millions of participants to collaborate and compete as they update pixels on a finite shared digital canvas, the events generated fine-grained traces comprising hundreds of millions of actions taken over multiple days. In this paper, we examine how participation, collaboration, and conflict evolve within r/place. First, we conduct a longitudinal, cross-edition analysis of engagement and collaboration patterns across the 2017, 2022, and 2023 events. Second, to investigate collaborative activity beyond surviving final artifacts, we introduce a scalable graph-based dynamic clustering framework and apply it to the 2017 event to reconstruct coalition trajectories from behavioral interaction logs, enabling the analysis of both persistent and transient efforts. Our findings reveal recurring organizational patterns across editions: participation remains highly concentrated to few participants despite individual rate-limiting constraints, larger coalitions exhibit both greater coordination inefficiencies and lower median per-participant activity, while still success increasingly concentrates within large collaborative groups over the course of an event. Analysis of recovered coalition trajectories further shows that coalition outcomes are difficult to predict based on state characteristics during much of the event, highlighting the dynamic and contested nature of collaborative production in r/place.

Tananun Songdechakraiwut, Yutong Wu

The human brain is a complex system, and understanding its mechanisms has been a long-standing challenge in neuroscience. The study of the functional connectome, which maps the functional connections between different brain regions, has provided valuable insights through various advanced analysis techniques developed over the years. Similarly, neural networks, inspired by the brain's architecture, have achieved notable success in diverse applications but are often noted for their lack of interpretability. In this paper, we propose a novel approach that bridges neural networks and human brain functions by leveraging brain-inspired techniques. Our approach, grounded in the insights from the functional connectome, offers scalable ways to characterize topology of large neural networks using stable statistical and machine learning techniques. Our empirical analysis demonstrates its capability to enhance the interpretability of neural networks, providing a deeper understanding of their underlying mechanisms.

Chuanyang Jin, Yutong Wu, Jing Cao et al.

Theory of Mind (ToM), the ability to understand people's mental states, is an essential ingredient for developing machines with human-level social intelligence. Recent machine learning models, particularly large language models, seem to show some aspects of ToM understanding. However, existing ToM benchmarks use unimodal datasets - either video or text. Human ToM, on the other hand, is more than video or text understanding. People can flexibly reason about another person's mind based on conceptual representations (e.g., goals, beliefs, plans) extracted from any available data. To address this, we introduce a multimodal Theory of Mind question answering (MMToM-QA) benchmark. MMToM-QA comprehensively evaluates machine ToM both on multimodal data and on different kinds of unimodal data about a person's activity in a household environment. To engineer multimodal ToM capacity, we propose a novel method, BIP-ALM (Bayesian Inverse Planning Accelerated by Language Models). BIP-ALM extracts unified representations from multimodal data and utilizes language models for scalable Bayesian inverse planning. We conducted a systematic comparison of human performance, BIP-ALM, and state-of-the-art models, including GPT-4. The experiments demonstrate that large language models and large multimodal models still lack robust ToM capacity. BIP-ALM, on the other hand, shows promising results, by leveraging the power of both model-based mental inference and language models.

Cong Wang, Yutong Wu, Zhixun Su et al.

In the field of multimedia, single image deraining is a basic pre-processing work, which can greatly improve the visual effect of subsequent high-level tasks in rainy conditions. In this paper, we propose an effective algorithm, called JDNet, to solve the single image deraining problem and conduct the segmentation and detection task for applications. Specifically, considering the important information on multi-scale features, we propose a Scale-Aggregation module to learn the features with different scales. Simultaneously, Self-Attention module is introduced to match or outperform their convolutional counterparts, which allows the feature aggregation to adapt to each channel. Furthermore, to improve the basic convolutional feature transformation process of Convolutional Neural Networks (CNNs), Self-Calibrated convolution is applied to build long-range spatial and inter-channel dependencies around each spatial location that explicitly expand fields-of-view of each convolutional layer through internal communications and hence enriches the output features. By designing the Scale-Aggregation and Self-Attention modules with Self-Calibrated convolution skillfully, the proposed model has better deraining results both on real-world and synthetic datasets. Extensive experiments are conducted to demonstrate the superiority of our method compared with state-of-the-art methods. The source code will be available at \url{https://supercong94.wixsite.com/supercong94}.