arXiv subjects

Yu-Ting Cheng

Publications and source records attributed to Yu-Ting Cheng.

9 recordsLinked to original sources

arXiv2026

Generative Horcrux: Designing AI Carriers for Afterlife Selves

As generative AI technologies rapidly advance, AI agents are gaining the ability not only to collect data and perform tasks but also to respond to environments and evolve over time. This shift opens new possibilities for reimagining digital legacy - raising critical questions about how we remember, commemorate, and interact with the traces of the deceased. The forms of these AI agents are particularly important, as they act as vessels for digital legacies - much like urns for ashes. We will ask: What kinds of devices or representations would we want to store our digital selves or legacies in? How do we envision future generations interacting with these forms? The question is not only about the function of these agents or the object's role as a storage vessel but also the meaning it carries, the memories it preserves, and its connection to the extended notion of our "Generative Horcrux." This three-hour in-person workshop invites design practitioners and researchers from diverse backgrounds to explore the emerging landscape of generative AI agent-based digital legacy. This workshop uses fiction and hands on prototyping to explore how AI agents might reconfigure memory, identity, and posthumous presence in future sociotechnical worlds. We anticipate that this session will foster interdisciplinary dialogue and contribute conceptually and methodologically to HCI, design research, and AI ethics.

cs.HC↗

arXiv2025

Tunable frequency conversion and comb generation with a superconducting artificial atom

We investigate the power spectral density emitted by a superconducting artificial atom coupled to the end of a semi-infinite transmission line and driven by two continuous radio-frequency fields. In this setup, we observe the generation of multiple frequency peaks and the formation of frequency combs with equal detuning between those peaks. The frequency peaks originate from wave mixing of the drive fields, mediated by the artificial atom, highlighting the potential of this system as both a frequency converter and a frequency-comb generator. We demonstrate precise control and tunability in generating these frequency features, aligning well with theoretical predictions, across a relatively wide frequency range (tens of MHz, exceeding the linewidth of the artificial atom). The extensive and simple tunability of this frequency converter and comb generator, combined with its small physical footprint, makes it promising for quantum optics on chips and other applications in quantum technology.

quant-ph↗

arXiv2023

Liquid Droplet as Adaptive Material while Levitating via Coupling between Plasma and Kelvin Force

Fascinating in art and science, the ability to float is also captivating and relevant in practical applications, such as Penning and ion traps that are fundamental to quantum computing. In this work, we first reproduce the classic water bridge by glycerol and, as it breaks down due to thermal agitation, observe that a lump of glycerol with mass~2.5 g can float and exhibit near-periodic oscillations. Through experiments, finite element analysis, and simulations, we discover that the stability of the floating droplet is made possible by the interaction between three mechanisms: Deformation, Plasma, and Kelvin force. Note that glycerol cluster (GC) falls in the class of adaptive materials that can change their properties or behavior in response to varying environmental conditions, i.e., stimuli-responsive. Furthermore, the stimuli, modified by the deformation of GC, collaborate with it to create this unique simple, yet stable, floating system. Backed up by simulations, this process, operated by only a single pair of electrodes, holds the potential to develop a simple yet powerful railgun.

cond-mat.soft↗

arXiv2023

Microwave amplification via interfering multi-photon processes in a half-waveguide quantum electrodynamics system

We investigate the amplification of a microwave probe signal by a superconducting artificial atom, a transmon, strongly coupled to the end of a one-dimensional semi-infinite transmission line. The end of the transmission line acts as a mirror for microwave fields. Due to the weak anharmonicity of the artificial atom, a strong pump field creates multi-photon excitations among the dressed states. Transitions between these dressed states, Rabi sidebands, give rise to either amplification or attenuation of the weak probe. We obtain a maximum amplitude amplification of about 18 %, higher than in any previous experiment with a single artificial atom, due to constructive interference between Rabi sidebands. We also characterize the noise properties of the system by measuring the spectrum of spontaneous emission.

quant-ph↗

arXiv2022

Seeing through Things: Exploring the Design Space of Privacy-Aware Data-Enabled Objects

Increasing amounts of sensor-augmented research objects have been used in design research. We call these objects Data-Enabled Objects, which can be integrated into daily activities capturing data about people's detailed whereabouts, behaviours and routines. These objects provide data perspectives on everyday life for contextual design research. However, data-enabled objects are still computational devices with limited privacy awareness and nuanced data sharing. To better design data-enabled objects, we explore privacy design spaces by inviting 18 teams of undergraduate design students to re-design the same type of sensor-enabled home research camera. We developed the Connected Peekaboo Toolkit (CPT) to support the design teams in designing, building, and directly deploying their prototypes in real home studies. We conducted Thematic Analysis to analyse their outcomes which led us to interpret that privacy is not just an obstacle but can be a driver by unfolding an exploration of possible design spaces for data-enabled objects.

cs.HC↗

arXiv2021

Thing Constellation Visualizer: Exploring Emergent Relationships of Everyday Objects

Designing future IoT ecosystems requires new approaches and perspectives to understand everyday practices. While researchers recognize the importance of understanding social aspects of everyday objects, limited studies have explored the possibilities of combining data-driven patterns with human interpretations to investigate emergent relationships among objects. This work presents Thing Constellation Visualizer (thingCV), a novel interactive tool for visualizing the social network of objects based on their co-occurrence as computed from a large collection of photos. ThingCV enables perspective-changing design explorations over the network of objects with scalable links. Two exploratory workshops were conducted to investigate how designers navigate and make sense of a network of objects through thingCV. The results of eight participants showed that designers were actively engaged in identifying interesting objects and their associated clusters of related objects. The designers projected social qualities onto the identified objects and their communities. Furthermore, the designers changed their perspectives to revisit familiar contexts and to generate new insights through the exploration process. This work contributes a novel approach to combining data-driven models with designerly interpretations of thing constellation towards More-Than Human-Centred Design of IoT ecosystems.

cs.HC↗

arXiv2020

Evidence for spontaneous arrangement of two-way flow in water bridge via particle image velocimetry

By revisiting the century-old problem of water bridge, we demonstrate that it is in fact dynamic and comprises of two coaxial water currents that carry different charges and flow in opposite directions. This spontaneous separation is triggered by the different stages to construct the water bridge. Initially, a flow is facilitated by the cone jet that is powered by H+ and flows out of the positive-electrode beaker. An opposing cone-jet from negative beaker is established later and forced to take the outer route. This spontaneous arrangement of two-way flow is revealed by using fluorescein and carbon powder as tracers, and the Particle Image Velocimetry, These two opposing flows are found to carry non-equal flux that results in a net transport of water to the negative beaker. We manage to estimate the flow speed and cross-sectional area of these co-axial flows as a function of time and applied voltage. Note that the water on the outer layer functions as a millimeter tube that confines and interacts strongly with the flow inside. This provides a first natural and yet counter example to the recently reported near-frictionless flow in an equally miniatureized soft wall made from ferrofluid.

cond-mat.soft↗

arXiv2019

Human-AI Co-Learning for Data-Driven AI

Human and AI are increasingly interacting and collaborating to accomplish various complex tasks in the context of diverse application domains (e.g., healthcare, transportation, and creative design). Two dynamic, learning entities (AI and human) have distinct mental model, expertise, and ability; such fundamental difference/mismatch offers opportunities for bringing new perspectives to achieve better results. However, this mismatch can cause unexpected failure and result in serious consequences. While recent research has paid much attention to enhancing interpretability or explainability to allow machine to explain how it makes a decision for supporting humans, this research argues that there is urging the need for both human and AI should develop specific, corresponding ability to interact and collaborate with each other to form a human-AI team to accomplish superior results. This research introduces a conceptual framework called "Co-Learning," in which people can learn with/from and grow with AI partners over time. We characterize three key concepts of co-learning: "mutual understanding," "mutual benefits," and "mutual growth" for facilitating human-AI collaboration on complex problem solving. We will present proof-of-concepts to investigate whether and how our approach can help human-AI team to understand and benefit each other, and ultimately improve productivity and creativity on creative problem domains. The insights will contribute to the design of Human-AI collaboration.

cs.HC↗

arXiv2019

Analyses of celestial pole offsets with VLBI, LLR, and optical observations

This work aims to explore the possibilities of determining the long-period part of the precession-nutation of the Earth with techniques other than very long baseline interferometry (VLBI). Lunar laser ranging (LLR) is chosen for its relatively high accuracy and long period. Results of previous studies could be updated using the latest data with generally higher quality, which would also add ten years to the total time span. Historical optical data are also analyzed for their rather long time-coverage to determine whether it is possible to improve the current Earth precession-nutation model.

physics.geo-ph↗