Intergenerational co-design often assumes a Western democratic ideal of flattened hierarchies. Engaging with pluriversal design perspectives, this paper challenges these universalist assumptions by investigating extrafamilial co-design in a non-Western context shaped by Confucian values. We explore how unrelated older adults and teenagers negotiate authority within high-power-distance environments, using Virtual Reality as a "social disruptor" to redistribute expertise. We propose a taxonomy of four interaction modes-Unilateral Transmission, Benevolent Authority, Material Authority, and Synthesized Co-Creation-to describe how power is fluidly negotiated. Our findings reveal that design tools function as "political shields," allowing youth to assert agency while maintaining cultural harmony. We contribute a micro-analysis of power-asymmetric collaboration and provide workshop strategies designed for functional reciprocity. The rationale for these strategies is to navigate, rather than dismantle, rigid social structures, providing actionable insights for practitioners working in diverse, culturally situated design environments.

AI is reshaping workplace dynamics as people increasingly delegate tasks to intelligent assistants. Yet how AI delegates are perceived compared to human delegates—and how their performance and their received feedback shape perceptions—remains unclear. We conducted a 2×2×2 between-subject experiment where participants delegated a scheduling task to either a human or an AI agent, varying their competence (high vs. low) and valence of received feedback (positive vs. negative) toward their performance. Participants generally had higher trust in human assistants; yet a striking asymmetry emerged: when an AI assistant received negative feedback, participants felt the criticism as more self-directed—an “AI Phantom Limb” effect—whereas positive feedback transferred less. This asymmetry did not appear with human delegates. These findings highlight broader design implications, suggesting that AI delegation might blur the boundary between self and other. We also discuss how these findings extend theories of delegation and responsibility attribution to AI.

The era of the pandemic has brought about significant changes in human life and societal operations, particularly in vibrant dining spaces, which now exhibit extreme differences. The prohibition of physical contact, avoidance of communal dining, installation of partitions, and restrictions on entry to exhibition venues have led to gradual changes in emotional interactions, culinary cultures, and modes of sharing and communication among people. Experts foresee a world that will continuously combat infectious diseases, and in the face of such a reality, innovative approaches are necessary to address the relationships between individuals and life, culture, and art, potentially fostering new modes of value.

In this paper, we present a visualization system for users to study multivariate time series data. They first identify trends or anomalies from a global view and then examine details in a local view. Specifically, we train a neural network to project high-dimensional data to a two dimensional (2D) planar space while retaining global data distances. By aligning the 2D points with a predefined color map, high-dimensional data can be represented by colors. Because perceptual color differentiation may fail to reflect data distance, we optimize perceptual color differentiation on each map region by deformation. The region with large perceptual color differentiation will expand, whereas the region with small differentiation will shrink. Since colors do not occupy any space in visualization, we convey the overview of multivariate time series data by a calendar view. Cells in the view are color-coded to represent multivariate data at different time spans. Users can observe color changes over time to identify events of interest. Afterward, they study details of an event by examining parallel coordinate plots. Cells in the calendar view and the parallel coordinate plots are dynamically linked for users to obtain insights that are barely noticeable in large datasets. The experiment results, comparisons, conducted case studies, and the user study indicate that our visualization system is feasible and effective.

MOTIVATION: ChIP-Seq is the standard method to identify genome-wide DNA-binding sites for transcription factors (TFs) and histone modifications. There is a growing need to analyze experiments with biological replicates, especially for epigenomic experiments where variation among biological samples can be substantial. However, tools that can perform group comparisons are currently lacking. RESULTS: We present a peak-calling prioritization pipeline (PePr) for identifying consistent or differential binding sites in ChIP-Seq experiments with biological replicates. PePr models read counts across the genome among biological samples with a negative binomial distribution and uses a local variance estimation method, ranking consistent or differential binding sites more favorably than sites with greater variability. We compared PePr with commonly used and recently proposed approaches on eight TF datasets and show that PePr uniquely identifies consistent regions with enriched read counts, high motif occurrence rate and known characteristics of TF binding based on visual inspection. For histone modification data with broadly enriched regions, PePr identified differential regions that are consistent within groups and outperformed other methods in scaling False Discovery Rate (FDR) analysis. AVAILABILITY AND IMPLEMENTATION: http://code.google.com/p/pepr-chip-seq/.

The iLamp system is a sensor-enhanced desk lamp with surface-tracking capability based on received light intensity. It consists of two components: lamp and bookmark. The bookmark is a ZigBee-enabled sensor node that can report its sensed light intensity to the lamp with a user-friendly interface and two-way communication capability. The lamp can use its LEDs to locate user's reading surface to which the bookmark is attached, move toward the surface, and further tune its luminous intensity to meet user's preference. We develop the geometrical model for surface tracking. iLamp demonstrates a new centimeter-level location-tracking system using light intensity alone without other extra media or devices.