Despite recent advances in cancer treatments that prolong patients' lives, treatment-induced cardiotoxicity (i.e., the various heart damages caused by cancer treatments) emerges as one major side effect. The clinical decision-making process of cardiotoxicity is challenging, as early symptoms may happen in non-clinical settings and are too subtle to be noticed until life-threatening events occur at a later stage; clinicians already have a high workload focusing on the cancer treatment, no additional effort to spare on the cardiotoxicity side effect. Our project starts with a participatory design study with 11 clinicians to understand their decision-making practices and their feedback on an initial design of an AI-based decision-support system. Based on their feedback, we then propose a multimodal AI system, CardioAI, that can integrate wearables data and voice assistant data to model a patient's cardiotoxicity risk to support clinicians' decision-making. We conclude our paper with a small-scale heuristic evaluation with four experts and the discussion of future design considerations.

., the six-organ dysfunction assessment of SOFA in Figure 1) play a vital role in sepsis identification within clinicians' workflow, providing evidence-based risk assessments essential for sepsis diagnosis. However, artificial intelligence (AI) sepsis prediction models typically generate a single sepsis risk score without incorporating clinical calculators for assessing organ dysfunctions, making the models less convincing and transparent to clinicians. To bridge the gap, we propose to mimic clinicians' workflow with a novel framework SepsisCalc to integrate clinical calculators into the predictive model, yielding a clinically transparent and precise model for utilization in clinical settings. Practically, clinical calculators usually combine information from multiple component variables in Electronic Health Records (EHR), and might not be applicable when the variables are (partially) missing. We mitigate this issue by representing EHRs as temporal graphs and integrating a learning module to dynamically add the accurately estimated calculator to the graphs. Experimental results on real-world datasets show that the proposed model outperforms state-of-the-art methods on sepsis prediction tasks. Moreover, we developed a system to identify organ dysfunctions and potential sepsis risks, providing a human-AI interaction tool for deployment, which can help clinicians understand the prediction outputs and prepare timely interventions for the corresponding dysfunctions, paving the way for actionable clinical decision-making support for early intervention.

Literacy—the ability to read, write, and comprehend text—is an important topic addressed by UNESCO. Despite global efforts to promote adult literacy education, rural areas with limited resources still lag behind. As livestreaming has gained popularity in China, many streamers leveraged its accessibility and affordability to reach low-literate adults. To gain a better understanding of the practices and challenges faced by adult literacy education through livestreaming, we conducted a mixed-methods study involving a 7-day observation of livestreaming sessions and an interview study with twelve streamers and ten viewers. We discovered streamers’ altruistic motives and unique interactive approaches. Viewers perceived livestreaming as a more engaging, community-supportive method than traditional approaches. We also identified both shared and unique challenges for streamers and viewers that limit its efficacy as a learning tool. Finally, we recognized opportunities to enhance educational equity, emphasizing design implications for advancing adult literacy education and promoting diversity in livestreaming.

Chinese traditional opera (Xiqu) performers often experience skin problems due to the long-term use of heavy-metal-laden face paints. To explore the current skincare challenges encountered by Xiqu performers, we conducted an online survey (N=136) and semi-structured interviews (N=15) as a formative study. We found that incomplete makeup removal is the leading cause of human-induced skin problems, especially the difficulty in removing eye makeup. Therefore, we proposed EyeVis, a prototype that can visualize the residual eye makeup and record the time make-up was worn by Xiqu performers. We conducted a 7-day deployment study (N=12) to evaluate EyeVis. Results indicate that EyeVis helps to increase Xiqu performers’ awareness about removing makeup, as well as boosting their confidence and security in skincare. Overall, this work also provides implications for studying the work of people who wear makeup on a daily basis, and helps to promote and preserve the intangible cultural heritage of practitioners.

Rehabilitation therapies are widely employed to assist people with motor impairments in regaining control over their affected body parts. Nevertheless, factors such as fatigue and low self-efficacy can hinder patient compliance during extensive rehabilitation processes. Utilizing hand redirection in virtual reality (VR) enables patients to accomplish seemingly more challenging tasks, thereby bolstering their motivation and confidence. While previous research has investigated user experience and hand redirection among able-bodied people, its effects on motor-impaired people remain unexplored. In this paper, we present a VR rehabilitation application that harnesses hand redirection. Through a user study and semi-structured interviews, we examine the impact of hand redirection on the rehabilitation experiences of people with motor impairments and its potential to enhance their motivation for upper limb rehabilitation. Our findings suggest that patients are not sensitive to hand movement inconsistency, and the majority express interest in incorporating hand redirection into future long-term VR rehabilitation programs.

Trees play a crucial role in promoting green, ecological, and low-carbon cities, with street trees being essential for urban roadways. Understanding the 3-D structure and biological characteristics of these trees requires accurate separation of wood and leaf. Mobile laser scanning (MLS) technology, known for its high efficiency and resolution, offers significant advantages. MLS data, however, often contain missing or overlapping areas due to occlusions and scanning geometry, complicating precise urban tree modeling. To address these challenges, this article introduces a coarse-to-fine approach for distinguishing wood from leaves in urban street trees. The proposed method begins with a hierarchical workflow that integrates the density-based spatial clustering of applications with noise (DBSCAN) algorithm to identify individual tree nodes. These nodes form the basis for constructing a graph structure for each tree. By leveraging prior knowledge of branch tilt angles, we enhance the shortest path algorithm, facilitating the extraction of features like shortest path frequency and length. This initial step completes a coarse differentiation between wood and leaves. To further refine accuracy, the identified wood and leaf points undergo analysis to extract multiscale geometric features. Integrating these features with the random forest (RF) algorithm results in a more precise separation of wood and leaf points. Our method demonstrates promising segmentation capabilities in MLS-captured roadside trees. Compared to four state-of-the-art methods for wood and leaf separation, our approach shows superior accuracy and efficiency, particularly in accurately identifying trunk points and minor branch points, as well as classifying the outer canopy layer.

Virtual reality (VR) and associated technologies have rapidly grew, creating new opportunities for improving mental health. In order to provide an immersive and concentrated meditation experience, this paper offers the idea of VR-assisted meditation, which integrates the advantages of VR technology with mindfulness techniques. The suggested technique, which is known as LOOP Meditation, is mainly aimed toward novice meditators and places a strong emphasis on the value of movement and breath awareness when meditating. Existing VR experiences and meditation applications sometimes ignore the value of including physical movement, which can improve mindful body sensations and maintain interest. By creating a software that incorporates body movement and breath sensing into virtual reality surroundings, LOOP Meditation addresses this gap. The LOOP Meditation design and implementation are examined in this paper along with its possible advantages and consequences for people looking to develop their meditation practice. A pilot research comparing the efficacy of this strategy to conventional meditation techniques is offered. The study’s findings add to the expanding body of knowledge on VR-assisted meditation and demonstrate how it may have a positive effect on mental health.

Style information, such as tone, mood, and genre of artworks, is important for museum visitors to appreciate them better. However, such information can be challenging for non-art specialists to comprehend in the short period that they view artworks. The sense of smell is instrumental for humans to assist their image memory, color, emotion, and shape association. However, it is rarely used in the appreciation of artworks. Taking Western landscape painting as an example, this research explores the following research questions (RQs): 1) How does the intervention of the sense of smell improve the acquisition of style information in paintings? 2) How does the intervention of the sense of smell enhance the immersion in painting appreciation? To answer RQs, we first recruited seven art specialists to participate in a co-design workshop to design a prototype of the virtual museum with olfactory intervention. We then conducted an experiment with 12 non-specialists who viewed several paintings in the VR museum while being exposed to olfactory stimuli that were designed to be correlated with the style information of the paintings. We found potential effects of smell stimuli on enhancing the perception of style information for non-art specialists. Moreover, we found that olfactory intervention has both positive and negative impacts on immersiveness. Finally, we provide design implications for future virtual museum design with olfactory stimuli.

Molecular image retrieval is one of the crucial steps in automatic mining and utilization of biochemistry-related literatures, which is also a relatively open and challenging task in cross fields of biochemistry and artificial intelligence. The challenges come from two aspects: 1) there is a lack of open datasets and evaluation criteria for molecular image retrieval. 2) Common retrieval methods always ignore that molecular image retrieval has cross-modal information of both images and SMILES texts. To address the first challenge, we firstly construct a new molecular image retrieval benchmark, named MIR, including 130770 molecular images, labeled structural similarity, and reasonable evaluation metrics. Faced with the second challenge, we propose an effective cross-modal pre-training framework for molecular image retrieval following CLIP. Experimental results reflect the effectiveness of our proposed benchmark MIR and cross-modal pre-training framework.