Han Qiao

Nadia Mariyan Smith, Nils Bonfils, Han Qiao et al.

Permacomputing is a nascent concept and community of practice concerned with developing alternative computing systems grounded in principles of resilience, reuse, sufficiency, and ecological limits. However, research engaging with permacomputing remains in an early stage of development, raising concerns about whether permacomputing can move beyond reflective critique to become a meaningful alternative practice. Through a research-through-design case study, we documented our experience moving a personal website from a data centre in Texas to a self-hosted solar-powered server built from reclaimed electronics. Guided by permacomputing principles and relational aesthesis, we explore what it takes for permacomputing to reconfigure material and perceptual relations. Our findings reveal the frictions of moving away from a maximalist techno-aesthetic while attempting to re-use already existing technologies, potential ways to overcome these challenges through building a community of practice, and the transformative potential of visibilizing and visceralizing digital infrastructures to cultivate more responsible ways of relating to technology. This paper contributes to emerging research on permacomputing and its aesthetics by bringing it into dialogue with theories of non-place and relational aesthesis. Rather than functioning as a purely symbolic gesture, permacomputing practices can cultivate greater collective autonomy, agency, and responsibility in how communities engage and create meaning within digital infrastructures. In the context of socio-ecological crises and anti-colonial transformation, our research offers a situated approach to building and relating to computing technologies in the ashes of dominant technological paradigms.

Omid Poursaeed, Tianxing Jiang, Han Qiao et al.

Point clouds provide a compact and efficient representation of 3D shapes. While deep neural networks have achieved impressive results on point cloud learning tasks, they require massive amounts of manually labeled data, which can be costly and time-consuming to collect. In this paper, we leverage 3D self-supervision for learning downstream tasks on point clouds with fewer labels. A point cloud can be rotated in infinitely many ways, which provides a rich label-free source for self-supervision. We consider the auxiliary task of predicting rotations that in turn leads to useful features for other tasks such as shape classification and 3D keypoint prediction. Using experiments on ShapeNet and ModelNet, we demonstrate that our approach outperforms the state-of-the-art. Moreover, features learned by our model are complementary to other self-supervised methods and combining them leads to further performance improvement.