With the continuous scaling of advanced integrated circuits, thermal management has emerged as a critical design challenge due to increasing power densities and structural complexity. To ensure system reliability, especially during the sign-off stage, thermal simulation tools must provide both high spatial resolution and computational efficiency. In this work, we present ThPA, a high-performance thermal simulation framework based on finite-difference discretization and an efficient fine-grained iterative solver. ThPA leverages an aggregation-based algebraic multigrid (AgAMG) preconditioned conjugate gradient (PCG) solver, which incorporates a novel double pairing aggregation strategy to reduce AgAMG setup overhead and accelerates convergence using Krylov-subspacebased multigrid cycles as the preconditioner. Experimental results show that ThPA achieves a speedup of up to $83 \times$ over commercial solvers, while maintaining a mean absolute temperature difference below $0.08^{\circ} \mathrm{C}$ and a root-mean-square temperature difference under $0.11^{\circ} \mathrm{C}$. These results validate the effectiveness of ThPA as a fast and accurate simulator for advanced IC design.

Prior work has examined how users judge their smartphone use, typically focusing on either usage duration or intention. How these two factors jointly shape such evaluations remains unclear. We conducted a two-week study with 104 participants, who reviewed their screenshots and provided labels of both usage intention and evaluation of time use. Across 73,000 sessions (6.1M screenshots), the relationship between duration and evaluation was initially linear but then bounded: positive evaluations declined and negative ones rose with longer phone use duration but both eventually stabilized, most often judged neutral. Trajectories varied by intention. Entertainment mirrored the overall trend; functional use continually lost positive evaluations, whereas information-seeking became increasingly positive during the first half hour before later declining; messaging-based connections slowly lost positive evaluations, while social media–based connections declined more quickly; finally, “no specific intention” unfolded in phases—from short positive use to regret-prone mid-length episodes to neutral long sessions.

To help users manage the overwhelming influx of smartphone notifications, this study explores how large language models (LLMs) can be leveraged to generate notification summaries. We developed an Android application that integrates ChatGPT to summarize notifications and conducted an in-the-wild deployment to examine how users guided the model. To further understand user expectations for LLM-generated summaries, we interviewed 20 participants following a week-long engagement with the app. Our findings reveal five main strategies that users employed in their prompts for generating summaries. Additionally, interviewees expected summaries to prioritize three types of notifications, preferred three levels of information disclosure influenced by content anticipation and perceived criticality, and used three different approaches to synthesizing notifications based on their interrelationships. Finally, interviewees envisioned notification summarization functioning like a virtual assistant, desiring capabilities beyond simple information condensation, including support for task and information management, revisiting archived content, and tracking activities for reflection.

Research indicates that smartphone users often speculate about notifications upon sensing their arrival, aiding their decision to attend to them. This speculation, however, relies on the presence of sufficient clues to associate with the notification, which are not always available. To address this challenge, through an experience sampling study, we investigated the effectiveness of delivering user-assigned alerts in influencing users' speculation accuracy, attendance effectiveness, and perceived disturbance. Our findings suggest that while user-assigned alerts enhanced the accuracy of speculation and improved participants' decisions to attend to notifications, the increased notification awareness sometimes led participants to view their decision to ignore notifications as less favorable. Moreover, we found that sporadic alert delivery disrupted the association between the alert and the notification, leading to no reduction in perceived disturbance nor improvement in speculation accuracy. In assigning alerts to notifications, participants considered five strategies: familiarity, distinctiveness, disturbance, emotional resonance, and dimension representation.

People nowadays can use multiple devices to interact with notifications, whether via noticing, glancing, reading, or acting upon them. Prior research has focused on actual usage or on device preferences. However, users’ ideal experience of cross-device notification-interaction might differ from their current practices (due to situational limitations) and/or across the four notification-interaction stages. We therefore conducted an experience-sampling method study with multi-device users to investigate these gaps and the influence of device context. Our results reveal that nearly half of the time, the non-phone devices the participants had ranked as their top preferences for notification-interaction were not actually used, due to the devices’ context. Beyond device context, the participants’ choices of devices for notification-interaction were heavily determined by 1) their preferences that particular notification-interaction stages to take place (or not) on particular devices; and 2) the device on which they had undertaken the former stage.

Most issue tracking systems for open source software (OSS) development include features for community members to embed visual contents, such as images and videos, to enhance the discussion. Although playing an important role, there is little knowledge on the characteristics of the visual contents to support their use. To address this gap, we conducted an empirical study on the Jupyter Notebook project. We found that more than a quarter of the issues in the Jupyter Notebook project included visual contents. Additionally, issues that had visual contents in the comments but not in the issue posts tended to have a longer discussion and to involve a larger number of discussants. In our qualitative analysis, we identified eight types of visual contents in our sample, with about 60% including screenshots or mockups of the main product. We also found that visual contents served diverse purposes, touching both problem and solution spaces of the issues. Our effort serves as an important step towards a comprehensive understanding of the characteristics of visual contents in OSS issue discussions. Our results provided several design implications for issue tracking systems to better facilitate the use of visual contents.