Periodic human activities with implicit workflows are common in manufacturing, sports, and daily life. While short-term periodic activities—characterized by simple structures and high-contrast patterns—have been widely studied, long-term periodic workflows with low-contrast patterns remain largely underexplored. To bridge this gap, we introduce the first benchmark comprising 580 multimodal human activity sequences featuring long-term periodic workflows. The benchmark supports three evaluation tasks aligned with real-world applications: unsupervised periodic workflow detection, task completion tracking, and procedural anomaly detection. We also propose a lightweight, training-free baseline for modeling diverse periodic workflow patterns. Experiments show that: (i) our benchmark presents significant challenges to both unsupervised periodic detection methods and zero-shot approaches based on powerful large language models (LLMs); (ii) our baseline outperforms competing methods by a substantial margin in all evaluation tasks; and (iii) in real-world applications, our baseline demonstrates deployment advantages on par with traditional supervised workflow detection approaches, eliminating the need for annotation and retraining. Our project page is https://sites.google.com/view/periodicworkflow.

Delirium is a syndrome characterized by acute and fluctuating change in attention, awareness, and cognition. Common in older adults, especially those with underlying medical conditions, delirium is associated with higher mortality rates, longer hospital stays, and increased healthcare costs. Early identification and management of delirium therefore becomes essential in order to prevent adverse outcomes, improve patient health, and reduce healthcare costs. As multiple predisposing (e.g., neurological disorders) and precipitating factors (e.g., medications) can be involved in the aetiology of delirium, detecting the syndrome early on can be challenging. In this work, we present an interpretable multimodal checklist that can aid clinicians in delirium detection. Specifically, we leverage causal decision trees to extract most relevant features for delirium detection which are then used in learning the predictive checklist. Experiments demonstrate the efficacy of the approach over existing methods in terms of both detection accuracy and interpretability.

A fundamental problem in drug discovery is to design molecules that bind to specific proteins. To tackle this problem using machine learning methods, here we propose a novel and effective framework, known as GraphBP, to generate 3D molecules that bind to given proteins by placing atoms of specific types and locations to the given binding site one by one. In particular, at each step, we first employ a 3D graph neural network to obtain geometry-aware and chemically informative representations from the intermediate contextual information. Such context includes the given binding site and atoms placed in the previous steps. Second, to preserve the desirable equivariance property, we select a local reference atom according to the designed auxiliary classifiers and then construct a local spherical coordinate system. Finally, to place a new atom, we generate its atom type and relative location w.r.t. the constructed local coordinate system via a flow model. We also consider generating the variables of interest sequentially to capture the underlying dependencies among them. Experiments demonstrate that our GraphBP is effective to generate 3D molecules with binding ability to target protein binding sites. Our implementation is available at https://github.com/divelab/GraphBP.

In recent years, artificial intelligence (AI) generated art has become very popular. From generating art works in the style of famous artists like Paul Cezanne and Claude Monet to simulating styles of art movements like Ukiyo-e, a variety of creative applications have been explored using AI. However, there has been very little focus concerning the ethical implications of AI generated art. Can AI model artists' styles without stereotyping them? Does AI do justice to the socio-cultural nuances of art movements? In this work, we take a first step towards analyzing these issues. Leveraging directed acyclic graphs to represent potential processes of art creation, we propose a simple metric to quantify confounding bias due to the lack of modeling the influence of art movements in learning artists' styles. As a case study, we consider the popular cycleGAN model and analyze confounding bias across various genres. The proposed metric is more effective than state-of-the-art outlier detection method in understanding the influence of art movements in artworks. We also highlight how the proposed metric can aid in determining authenticity of artworks. We hope our work triggers discussions related to ethical implications of AI generated art.

Restricted and repetitive behaviors (RRBs) are a core symptom and an early marker of autism. Current research and intervention for RRB heavily rely on professional experience and effort. Guided Play is a technology that uses instrumented games and toys as a platform to understand children's play behavior and facilitate behavioral intervention during play. This paper presents the design and implementation of a prototype based on the technology, as well as an evaluation on 6 children with autism. The results show that children with RRBs in physical world activities also exhibit similar patterns in a similar digital activity, and that digital coaching can reduce RRBs by expanding children's play skill repertoire and promoting symbolic play.

Ongoing student feedback on course content and assignments can be valuable for MOOC instructors in the absence of face-to-face-interaction. To collect ongoing feedback and scalably identify valuable suggestions, we built the MOOC Collaborative Assessment and Feedback Engine (M-CAFE). This mobile platform allows MOOC students to numerically assess the course, their own performance, and provide textual suggestions about how the course could be improved on a weekly basis. M-CAFE allows students to visualize how they compare with their peers and read and evaluate what others have suggested, providing peer-to-peer collaborative filtering. We evaluate M-CAFE based on data from two EdX MOOCs.