arXiv subjects

Hsiao-Yuan Chen

Publications and source records attributed to Hsiao-Yuan Chen.

3 recordsLinked to original sources

arXiv2026

Roadmap to Quantum Aesthetics

Quantum mechanics occupies a central position in contemporary science while remaining largely inaccessible to direct sensory experience. This paper proposes a roadmap to quantum aesthetics that examines how quantum concepts become aesthetic phenomena through artistic mediation rather than direct representation. Two complementary and orthogonal approaches are articulated. The first, a pioneering top-down approach, employs text-prompt-based generative AI to probe quantum aesthetics as a collective cultural construct embedded in large-scale training data. By systematically modulating the linguistic weight of the term "quantum," generative models are used as experimental environments to reveal how quantum imaginaries circulate within contemporary visual culture. The second, a bottom-up approach, derives aesthetic form directly from quantum-mechanical structures through the visualization of quantum-generated data, exemplified here by hydrogen atomic orbitals calculated from the Schrödinger equation. These approaches are framed not as competing methods but as intersecting paths within a navigable field of artistic research. They position quantum aesthetics as an emergent field of artistic research shaped by cultural imagination, computational mediation, and physical law, opening new directions for artistic practice and pedagogy at the intersection of art, data, artificial intelligence and quantum science.

physics.pop-ph↗

arXiv2023

iDML: Incentivized Decentralized Machine Learning

With the rising emergence of decentralized and opportunistic approaches to machine learning, end devices are increasingly tasked with training deep learning models on-devices using crowd-sourced data that they collect themselves. These approaches are desirable from a resource consumption perspective and also from a privacy preservation perspective. When the devices benefit directly from the trained models, the incentives are implicit - contributing devices' resources are incentivized by the availability of the higher-accuracy model that results from collaboration. However, explicit incentive mechanisms must be provided when end-user devices are asked to contribute their resources (e.g., computation, communication, and data) to a task performed primarily for the benefit of others, e.g., training a model for a task that a neighbor device needs but the device owner is uninterested in. In this project, we propose a novel blockchain-based incentive mechanism for completely decentralized and opportunistic learning architectures. We leverage a smart contract not only for providing explicit incentives to end devices to participate in decentralized learning but also to create a fully decentralized mechanism to inspect and reflect on the behavior of the learning architecture.

cs.LG↗

arXiv2021

Personal Data Gentrification

We live in an era in which the most valued services are not paid for in money, but in personal data. Every day, service providers collect the personal information of billions of individuals, information that sustain their infrastructure by marketing profiles labeled with this information to personal data consumers, such as advertisers. Not all uses of this personal data are for marketing; data consumers can also include, for instance, public health authorities tracking pandemics. In either case, individuals have undergone a process of Personal Data Gentrification, as data ownership has shifted from individuals to service providers and data consumers, as if the data is worth nothing to the individuals; these new owners then harness the data to obtain large profits. Current privacy-enhancing technologies are beginning to allow individuals to control and share less information. However, not sharing individuals' personal information at all could lead to Personal Data Blight, in which the potential of personal data in applications that benefit all of society remains forever latent. In this paper, we propose Personal Data Enfranchisement as a middle ground, empowering individuals to control the sharing of their personal information to shift the business flows of personal information. Based on these insights, we propose a model to gradually and incrementally make a shift from our current situation towards one of Personal Data Enfranchisement. Finally, we present a roadmap and some challenges towards achieving this bold vision.

cs.CY↗