arXiv subjects

Jingjing Sun

Publications and source records attributed to Jingjing Sun.

10 recordsLinked to original sources

arXiv2026

Understanding Workplace Relatedness Support among Healthcare Professionals: A Four-Layer Model and Implications for Technology Design

Healthcare professionals (HCPs) face increasing occupational stress and burnout. Supporting HCPs need for relatedness is fundamental to their psychological wellbeing and resilience. However, how technologies could support HCPs relatedness in the workplace remains less explored. This study incorporated semi-structured interviews (n = 15) and co-design workshops (n = 21) with HCPs working in the UK National Health Service (NHS), to explore their current practices and preferences for workplace relatedness support, and how technology could be utilized to benefit relatedness. Qualitative analysis yielded a four-layer model of HCPs relatedness need, which includes Informal Interactions, Camaraderie and Bond, Community and Organizational Care, and Shared Identity. Workshops generated eight design concepts (e.g., Playful Encounter, Collocated Action, and Memories and Stories) that operationalize the four relatedness need layers. We conclude by highlighting the theoretical relevance, practical design implications, and the necessity to strengthen relatedness support for HCPs in the era of digitalization and artificial intelligence.

cs.HC↗

arXiv2025

LikeBench: Evaluating Subjective Likability in LLMs for Personalization

A personalized LLM should remember user facts, apply them correctly, and adapt over time to provide responses that the user prefers. Existing LLM personalization benchmarks are largely centered on two axes: accurately recalling user information and accurately applying remembered information in downstream tasks. We argue that a third axis, likability, is both subjective and central to user experience, yet under-measured by current benchmarks. To measure likability holistically, we introduce LikeBench, a multi-session, dynamic evaluation framework that measures likability across multiple dimensions by how much an LLM can adapt over time to a user's preferences to provide more likable responses. In LikeBench, the LLMs engage in conversation with a simulated user and learn preferences only from the ongoing dialogue. As the interaction unfolds, models try to adapt to responses, and after each turn, they are evaluated for likability across seven dimensions by the same simulated user. To the best of our knowledge, we are the first to decompose likability into multiple diagnostic metrics: emotional adaptation, formality matching, knowledge adaptation, reference understanding, conversation length fit, humor fit, and callback, which makes it easier to pinpoint where a model falls short. To make the simulated user more realistic and discriminative, LikeBench uses fine-grained, psychologically grounded descriptive personas rather than the coarse high/low trait rating based personas used in prior work. Our benchmark shows that strong memory performance does not guarantee high likability: DeepSeek R1, with lower memory accuracy (86%, 17 facts/profile), outperformed Qwen3 by 28% on likability score despite Qwen3's higher memory accuracy (93%, 43 facts/profile). Even SOTA models like GPT-5 adapt well in short exchanges but show only limited robustness in longer, noisier interactions.

cs.LG↗

arXiv2025

Beyond Wellbeing Apps: Co-Designing Immersive, Embodied, and Collective Digital Wellbeing Interventions for Healthcare Professionals

Healthcare professionals (HCPs) face increasing levels of stress and burnout. Technological wellbeing interventions provide accessible and flexible support for HCPs. While most studies have focused on mobile- and web-based programs, alternative technologies like virtual reality (VR), augmented reality (AR), tangible interfaces, and embodied technologies are emerging as engaging and effective tools for wellbeing interventions. However, there is still a lack of research on how such technologies are perceived among HCPs. This study explored HCPs' perceptions and preferences for various types of wellbeing technologies, by conducting a 2-phase co-design study involving 26 HCPs in idea generation, concept evaluation, prototype testing, and design iteration. From our findings, HCPs highly valued the potential of technologies to support mental health with immersive, embodied, and collective experiences. Furthermore, we provided design recommendations for wellbeing technologies for HCPs that sustain user engagement by meeting their needs for autonomy, competence, and relatedness in the experiences.

cs.HC↗

arXiv2024

Inpatient Overflow Management with Proximal Policy Optimization

Problem Definition: Managing inpatient flow in large hospital systems is challenging due to the complexity of assigning randomly arriving patients -- either waiting for primary units or being overflowed to alternative units. Current practices rely on ad-hoc rules, while prior analytical approaches struggle with the intractably large state and action spaces inherent in patient-unit matching. Scalable decision support is needed to optimize overflow management while accounting for time-periodic fluctuations in patient flow. Methodology/Results: We develop a scalable decision-making framework using Proximal Policy Optimization (PPO) to optimize overflow decisions in a time-periodic, long-run average cost setting. To address the combinatorial complexity, we introduce atomic actions, which decompose multi-patient routing into sequential assignments. We further enhance computational efficiency through a partially-shared policy network designed to balance parameter sharing with time-specific policy adaptations, and a queueing-informed value function approximation to improve policy evaluation. Our method significantly reduces the need for extensive simulation data, a common limitation in reinforcement learning applications. Case studies on hospital systems with up to twenty patient classes and twenty wards demonstrate that our approach matches or outperforms existing benchmarks, including approximate dynamic programming, which is computationally infeasible beyond five wards. Managerial Implications: Our framework offers a scalable, efficient, and explainable solution for managing patient flow in complex hospital systems. More broadly, our results highlight that domain-aware adaptation is more critical to improving algorithm performance than fine-tuning neural network parameters when applying general-purpose algorithms to specific applications.

math.OC↗

arXiv2024

Understanding Human-AI Collaboration in Music Therapy Through Co-Design with Therapists

The rapid development of musical AI technologies has expanded the creative potential of various musical activities, ranging from music style transformation to music generation. However, little research has investigated how musical AIs can support music therapists, who urgently need new technology support. This study used a mixed method, including semi-structured interviews and a participatory design approach. By collaborating with music therapists, we explored design opportunities for musical AIs in music therapy. We presented the co-design outcomes involving the integration of musical AIs into a music therapy process, which was developed from a theoretical framework rooted in emotion-focused therapy. After that, we concluded the benefits and concerns surrounding music AIs from the perspective of music therapists. Based on our findings, we discussed the opportunities and design implications for applying musical AIs to music therapy. Our work offers valuable insights for developing human-AI collaborative music systems in therapy involving complex procedures and specific requirements.

cs.HC↗

arXiv2023

Side-by-Side vs Face-to-Face: Evaluating Colocated Collaboration via a Transparent Wall-sized Display

Traditional wall-sized displays mostly only support side-by-side co-located collaboration, while transparent displays naturally support face-to-face interaction. Many previous works assume transparent displays support collaboration. Yet it is unknown how exactly its afforded face-to-face interaction can support loose or close collaboration, especially compared to the side-by-side configuration offered by traditional large displays. In this paper, we used an established experimental task that operationalizes different collaboration coupling and layout locality, to compare pairs of participants collaborating side-by-side versus face-to-face in each collaborative situation. We compared quantitative measures and collected interview and observation data to further illustrate and explain our observed user behavior patterns. The results showed that the unique face-to-face collaboration brought by transparent display can result in more efficient task performance, different territorial behavior, and both positive and negative collaborative factors. Our findings provided empirical understanding about the collaborative experience supported by wall-sized transparent displays and shed light on its future design.

cs.HC↗

arXiv2022

The Sparse Solution to $\mathcal{KS}$-Tensor Complementarity Problems

In view of the KS-tensor complementarity problem, the sparse solution of this problem is studied. Due to the nonconvexity and noncontinuity of the l_0-norm, it is a NP hard problem to find the sparse solution of the KS-tensor complementarity problem. In order to solve this problem, we transform it into a polynomial programming problem with constraints. Then we use the sequential quadratic programming (SQP) algorithm to solve this transformed problem. Numerical results show that the SQP algorithm can find the sparse solutions of the KS-tensor complementarity problem effectively.

math.OC↗

arXiv2022

Stochastic absolute value equations

We propose a new kind of stochastic absolute value equations involving absolute values of variables. By utilizing an equivalence relation to stochastic bilinear program, we investigate the expected value formulation for the proposed stochastic absolute value equations. We also consider the expected residual minimization formulation for the proposed stochastic absolute value equations. Under mild assumptions, we give the existence conditions for the solution of the stochastic absolute value equations. The solution of the stochastic absolute value equations can be gotten by solving the discrete minimization problem. And we also propose a smoothing gradient method to solve the discrete minimization problem. Finally, the numerical results and some discussions are given.

math.OC↗

arXiv2021

A High-fidelity, Machine-learning Enhanced Queueing Network Simulation Model for Hospital Ultrasound Operations

We collaborate with a large teaching hospital in Shenzhen, China and build a high-fidelity simulation model for its ultrasound center to predict key performance metrics, including the distributions of queue length, waiting time and sojourn time, with high accuracy. The key challenge to build an accurate simulation model is to understanding the complicated patient routing at the ultrasound center. To address the issue, we propose a novel two-level routing component to the queueing network model. We apply machine learning tools to calibrate the key components of the queueing model from data with enhanced accuracy.

cs.LG↗

arXiv2017

Hydrogen bubble nucleation by self-clustering: Density Functional Theory and statistical models studies using tungsten as a model system

Low-energy hydrogen irradiation is known to induce bubble formation in tungsten, while its atomistic mechanisms remain little understood. Using first-principles calculations and statistical models, we studied the self-clustering behavior of hydrogen in tungsten. Unlike previous speculations that hydrogen self-clusters are energetically unstable owing to the general repulsion between two hydrogens, we demonstrated that hydrogen self-cluster becomes more favorable as the cluster size increases. We found that hydrogen atoms would form two-dimensional platelet-like structures along {100} planes. These hydrogen self-clustering behaviors can be quantitative understood by the competition between long-ranged elastic attraction and local electronic repulsion. Further statistical analysis showed that there exists a critical hydrogen concentration above which hydrogen self-clusters are thermodynamically stable and kinetically feasible. Based on this critical hydrogen concentration, the plasma loading conditions under which hydrogen self-clusters form were predicted. Our predictions showed excellent agreement with experimental results of hydrogen bubble formation in tungsten exposed to low-energy hydrogen irradiation. Finally, we proposed a possible mechanism for the hydrogen bubble nucleation via hydrogen self-clustering. This work provides mechanistic insights and quantitative models towards understanding of plasma-induced hydrogen bubble formation in plasma-facing tungsten.

cond-mat.mtrl-sci↗