arXiv subjects

Hong Shen

Publications and source records attributed to Hong Shen.

At least 73 records · Page 4Linked to original sources

arXiv2025

Bridging Cultural Distance Between Models Default and Local Classroom Demands: How Global Teachers Adopt GenAI to Support Everyday Teaching Practices

Generative AI (GenAI) is rapidly entering K-12 classrooms, offering teachers new ways for teaching practices. Yet GenAI models are often trained on culturally uneven datasets, embedding a "default culture" that often misaligns with local classrooms. To understand how teachers navigate this gap, we defined the new concept Cultural Distance (the gap between GenAI's default cultural repertoire and the situated demands of teaching practice) and conducted in-depth interviews with 30 K-12 teachers, 10 each from South Africa, Taiwan, and the United States, who had integrated AI into their teaching practice. These teachers' experiences informed the development of our three-level cultural distance framework. This work contributes the concept and framework of cultural distance, six illustrative instances spanning in low, mid, high distance levels with teachers' experiences and strategies for addressing them. Empirically, we offer implications to help AI designers, policymakers, and educators create more equitable and culturally responsive GenAI tools for education.

cs.HC↗

arXiv2025

Dense Matter in Neutron Stars with eXTP

In this White Paper, we present the potential of the enhanced X-ray Timing and Polarimetry (eXTP) mission to constrain the equation of state of dense matter in neutron stars, exploring regimes not directly accessible to terrestrial experiments. By observing a diverse population of neutron stars - including isolated objects, X-ray bursters, and accreting systems - eXTP's unique combination of timing, spectroscopy, and polarimetry enables high-precision measurements of compactness, spin, surface temperature, polarimetric signals, and timing irregularity. These multifaceted observations, combined with advances in theoretical modeling, pave the way toward a comprehensive description of the properties and phases of dense matter from the crust to the core of neutron stars. Under development by an international Consortium led by the Institute of High Energy Physics of the Chinese Academy of Sciences, the eXTP mission is planned to be launched in early 2030.

astro-ph.HE↗

arXiv2025

POET: Supporting Prompting Creativity and Personalization with Automated Expansion of Text-to-Image Generation

State-of-the-art visual generative AI tools hold immense potential to assist users in the early ideation stages of creative tasks -- offering the ability to generate (rather than search for) novel and unprecedented (instead of existing) images of considerable quality that also adhere to boundless combinations of user specifications. However, many large-scale text-to-image systems are designed for broad applicability, yielding conventional output that may limit creative exploration. They also employ interaction methods that may be difficult for beginners. Given that creative end users often operate in diverse, context-specific ways that are often unpredictable, more variation and personalization are necessary. We introduce POET, a real-time interactive tool that (1) automatically discovers dimensions of homogeneity in text-to-image generative models, (2) expands these dimensions to diversify the output space of generated images, and (3) learns from user feedback to personalize expansions. An evaluation with 28 users spanning four creative task domains demonstrated POET's ability to generate results with higher perceived diversity and help users reach satisfaction in fewer prompts during creative tasks, thereby prompting them to deliberate and reflect more on a wider range of possible produced results during the co-creative process. Focusing on visual creativity, POET offers a first glimpse of how interaction techniques of future text-to-image generation tools may support and align with more pluralistic values and the needs of end users during the ideation stages of their work.

cs.CV↗

arXiv2025

The equation of state for neutron stars with speed of sound constraints via Bayesian inference

The parametrized equation of state (EOS) of neutron stars is investigated by Bayesian inference method with various constraints from both nuclear physics and modern astronomical observations. The expansion coefficients correspond to the properties of symmetric nuclear matter and the density dependence of the symmetry energy. The empirical values of the symmetry energy at subsaturation density and the density of crust-core phase transition are considered to limit the low-density behavior of EOS, i.e. $L_{\mathrm{sym}} $, while the speed of sound of neutron star matter and mass-radius observations of millisecond pulsars PSR J0030+0451 and PSR J0740+6620 are adopted to eliminate the high-order expansion coefficients, such as $Q_{\mathrm{sat}}$ and $Q_{\mathrm{sym}} $. Finally, our analysis reveals that the skewness coefficient $Q_{\mathrm{sat}}$ of the energy per nucleon in symmetric nuclear matter (SNM) exhibits the strongest correlation with the speed of sound, constrained to $Q_{\mathrm{sat}} = -69.50_{-31.93}^{+16.52} \, \mathrm{MeV}$, whose uncertainties are much smaller than those of the experiments of heavy-ion collisions. The symmetry energy parameters are determined as follows: slope $L_{\mathrm{sym}} = 34.32_{-11.85}^{+13.66} \, \mathrm{MeV}$, curvature $K_{\mathrm{sym}} = -58.45_{-89.46}^{+88.47} \, \mathrm{MeV}$, and skewness $Q_{\mathrm{sym}} = 302.28_{-231.89}^{+251.62} \, \mathrm{MeV}$. Additionally, the radii of canonical ($1.4 \, M_{\odot}$) and massive ($2.0 \, M_{\odot}$) neutron stars are predicted as $R_{1.4} = 11.85_{-0.15}^{+0.06} \, \text{km}$ and $R_{2.0} = 11.42_{-0.35}^{+0.23} \, \text{km}$, respectively, with a maximum mass of $M_{\mathrm{max}} = 2.12_{-0.05}^{+0.11} \, M_{\odot}$. The tidal deformability is $Λ_{1.4} = 303.57_{-45.22}^{+47.95}$ at $1.4 \, M_\odot$, which is consistent with the analysis of the GW170817 event.

nucl-th↗

arXiv2025

Radial oscillations of neutron stars within density-dependent relativistic-mean field model

The radial oscillations of neutron stars are studied using equations of state derived from density-dependent relativistic mean-field (DDRMF) models, which effectively describe the ground-state properties of finite nuclei. A novel numerical approach, the finite volume method (FVM), is employed to solve the eigenvalue problem associated with oscillation frequencies. Compared to conventional methods such as the finite difference method and shooting method, the FVM avoids the numerical instability encountered at high frequencies with an equation of state that includes a discontinuous adiabatic index and offers greater computational efficiency. The oscillation frequencies of high-order modes exhibit a similar trend of change. The radial displacements and pressure perturbations are largely influenced by the EOSs of crust region. {The frequency of the first excited state shows a strong linear relationship with both the slope and skewness parameters of the symmetry energy.} These findings suggest that the density dependence of the symmetry energy can be constrained through observations of neutron star radial oscillation frequencies.

nucl-th↗

arXiv2025

Nuclear Matter and Finite Nuclei: Relativistic Thomas-Fermi Approximation Versus Relativistic Mean-Field Approach

The Thomas-Fermi approximation is a powerful method that has been widely used to describe atomic structures, finite nuclei, and nonuniform matter in supernovae and neutron-star crusts. Nonuniform nuclear matter at subnuclear density is assumed to be composed of a lattice of heavy nuclei surrounded by dripped nucleons, and the Wigner-Seitz cell is commonly introduced to simplify the calculations. The self-consistent Thomas--Fermi approximation can be employed to study both a nucleus surrounded by nucleon gas in the Wigner-Seitz cell and an isolated nucleus in the nuclide chart. A detailed comparison is made between the self-consistent Thomas-Fermi approximation and the relativistic mean-field approach for the description of finite nuclei, based on the same nuclear interaction. These results are then examined using experimental data from the corresponding nuclei.

nucl-th↗

arXiv2025

Structuralist Approach to AI Literary Criticism: Leveraging Greimas Semiotic Square for Large Language Models

Large Language Models (LLMs) excel in understanding and generating text but struggle with providing professional literary criticism for works with profound thoughts and complex narratives. This paper proposes GLASS (Greimas Literary Analysis via Semiotic Square), a structured analytical framework based on Greimas Semiotic Square (GSS), to enhance LLMs' ability to conduct in-depth literary analysis. GLASS facilitates the rapid dissection of narrative structures and deep meanings in narrative works. We propose the first dataset for GSS-based literary criticism, featuring detailed analyses of 48 works. Then we propose quantitative metrics for GSS-based literary criticism using the LLM-as-a-judge paradigm. Our framework's results, compared with expert criticism across multiple works and LLMs, show high performance. Finally, we applied GLASS to 39 classic works, producing original and high-quality analyses that address existing research gaps. This research provides an AI-based tool for literary research and education, offering insights into the cognitive mechanisms underlying literary engagement.

cs.CL↗

arXiv2025

A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents

Due to its general-purpose nature, Generative AI is applied in an ever-growing set of domains and tasks, leading to an expanding set of risks of harm impacting people, communities, society, and the environment. These risks may arise due to failures during the design and development of the technology, as well as during its release, deployment, or downstream usages and appropriations of its outputs. In this paper, building on prior taxonomies of AI risks, harms, and failures, we construct a taxonomy specifically for Generative AI failures and map them to the harms they precipitate. Through a systematic analysis of 499 publicly reported incidents, we describe what harms are reported, how they arose, and who they impact. We report the prevalence of each type of harm, underlying failure mode, and harmed stakeholder, as well as their common co-occurrences. We find that most reported incidents are caused by use-related issues but bring harm to parties beyond the end user(s) of the Generative AI system at fault, and that the landscape of Generative AI harms is distinct from that of traditional AI. Our work offers actionable insights to policymakers, developers, and Generative AI users. In particular, we call for the prioritization of non-technical risk and harm mitigation strategies, including public disclosures and education and careful regulatory stances.

cs.CY↗

arXiv2025

REALM: A Dataset of Real-World LLM Use Cases

Large Language Models (LLMs), such as the GPT series, have driven significant industrial applications, leading to economic and societal transformations. However, a comprehensive understanding of their real-world applications remains limited. To address this, we introduce REALM, a dataset of over 94,000 LLM use cases collected from Reddit and news articles. REALM captures two key dimensions: the diverse applications of LLMs and the demographics of their users. It categorizes LLM applications and explores how users' occupations relate to the types of applications they use. By integrating real-world data, REALM offers insights into LLM adoption across different domains, providing a foundation for future research on their evolving societal roles.

cs.HC↗

arXiv2025

Rethinking Theory of Mind Benchmarks for LLMs: Towards A User-Centered Perspective

The last couple of years have witnessed emerging research that appropriates Theory-of-Mind (ToM) tasks designed for humans to benchmark LLM's ToM capabilities as an indication of LLM's social intelligence. However, this approach has a number of limitations. Drawing on existing psychology and AI literature, we summarize the theoretical, methodological, and evaluation limitations by pointing out that certain issues are inherently present in the original ToM tasks used to evaluate human's ToM, which continues to persist and exacerbated when appropriated to benchmark LLM's ToM. Taking a human-computer interaction (HCI) perspective, these limitations prompt us to rethink the definition and criteria of ToM in ToM benchmarks in a more dynamic, interactional approach that accounts for user preferences, needs, and experiences with LLMs in such evaluations. We conclude by outlining potential opportunities and challenges towards this direction.

cs.HC↗

arXiv2025

Effective Automation to Support the Human Infrastructure in AI Red Teaming

As artificial intelligence (AI) systems become increasingly embedded in critical societal functions, the need for robust red teaming methodologies continues to grow. In this forum piece, we examine emerging approaches to automating AI red teaming, with a particular focus on how the application of automated methods affects human-driven efforts. We discuss the role of labor in automated red teaming processes, the benefits and limitations of automation, and its broader implications for AI safety and labor practices. Drawing on existing frameworks and case studies, we argue for a balanced approach that combines human expertise with automated tools to strengthen AI risk assessment. Finally, we highlight key challenges in scaling automated red teaming, including considerations around worker proficiency, agency, and context-awareness.

cs.CY↗

arXiv2025

Effects of symmetry energy on the properties of hadron-quark mixed phase in hybrid stars

We study how the nuclear symmetry energy slope affects the properties of hadron-quark pasta phases and their sizes in massive hybrid stars ($> 2 M_{\odot}$). We utilize the relativistic mean-field model with a density-dependent isovector coupling constant fitted by varying values of the symmetry energy slope $L$ to describe the hadronic matter, while the quark matter is described by a modified MIT bag model with vector interactions. We construct the hadron-quark phase transition using the energy minimization (EM) method and compare with the Gibbs construction (GC) in the hybrid star. We find that the massive hybrid stars generally tend to have a hadron-quark pasta phase core rather than a pure quark core for most values of $L$, except in some special cases where they have a small pure quark core. Furthermore, we show that the mass and size of the pasta phase core are strongly influenced by the symmetry energy. The radius associated with the onset of pasta phases is also influenced by the symmetry energy. Additionally, we also evaluate the effects of the surface tension $σ$, the bag constant $B$, and the vector interaction $G_{V}$.

nucl-th↗

arXiv2025

Discovering Structural Hole Spanners in Dynamic Networks via Graph Neural Networks

Structural Hole (SH) theory states that the node which acts as a connecting link among otherwise disconnected communities gets positional advantages in the network. These nodes are called Structural Hole Spanners (SHS). SHSs have many applications, including viral marketing, information dissemination, community detection, etc. Numerous solutions are proposed to discover SHSs; however, most of the solutions are only applicable to static networks. Since real-world networks are dynamic networks; consequently, in this study, we aim to discover SHSs in dynamic networks. Discovering SHSs is an NP-hard problem, due to which, instead of discovering exact k SHSs, we adopt a greedy approach to discover top-k SHSs. Motivated from the success of Graph Neural Networks (GNNs) on various graph mining problems, we design a Graph Neural Network-based model, GNN-SHS, to discover SHSs in dynamic networks, aiming to reduce the computational cost while achieving high accuracy. We analyze the efficiency of the proposed model through exhaustive experiments, and our results show that the proposed GNN-SHS model is at least 31.8 times faster and, on an average 671.6 times faster than the comparative method, providing a considerable efficiency advantage.

cs.SI↗

arXiv2025

Effective Graph-Neural-Network based Models for Discovering Structural Hole Spanners in Large-Scale and Diverse Networks

A Structural Hole Spanner (SHS) is a set of nodes in a network that act as a bridge among different otherwise disconnected communities. Numerous solutions have been proposed to discover SHSs that generally require high run time on large-scale networks. Another challenge is discovering SHSs across different types of networks for which the traditional one-model-fit-all approach fails to capture the inter-graph difference, particularly in the case of diverse networks. Therefore, there is an urgent need of developing effective solutions for discovering SHSs in large-scale and diverse networks. Inspired by the recent advancement of graph neural network approaches on various graph problems, we propose graph neural network-based models to discover SHS nodes in large scale networks and diverse networks. We transform the problem into a learning problem and propose an efficient model GraphSHS, that exploits both the network structure and node features to discover SHS nodes in large scale networks, endeavouring to lessen the computational cost while maintaining high accuracy. To effectively discover SHSs across diverse networks, we propose another model Meta-GraphSHS based on meta-learning that learns generalizable knowledge from diverse training graphs (instead of directly learning the model) and utilizes the learned knowledge to create a customized model to identify SHSs in each new graph. We theoretically show that the depth of the proposed graph neural network model should be at least $Ω(\sqrt{n}/\log n)$ to accurately calculate the SHSs discovery problem. We evaluate the performance of the proposed models through extensive experiments on synthetic and real-world datasets. Our experimental results show that GraphSHS discovers SHSs with high accuracy and is at least 167.1 times faster than the comparative methods on large-scale real-world datasets.

cs.SI↗

arXiv2025

Maintenance of Structural Hole Spanners in Dynamic Networks

Structural Hole (SH) spanners are the set of users who bridge different groups of users and are vital in numerous applications. Despite their importance, existing work for identifying SH spanners focuses only on static networks. However, real-world networks are highly dynamic where the underlying structure of the network evolves continuously. Consequently, we study SH spanner problem for dynamic networks. We propose an efficient solution for updating SH spanners in dynamic networks. Our solution reuses the information obtained during the initial runs of the static algorithm and avoids the recomputations for the nodes unaffected by the updates. Experimental results show that the proposed solution achieves a minimum speedup of 3.24 over recomputation. To the best of our knowledge, this is the first attempt to address the problem of maintaining SH spanners in dynamic networks.

cs.SI↗

arXiv2025

Influence of effective nucleon mass on equation of state for supernova simulations and neutron stars

We investigate the influence of the effective nucleon mass on the equation of state (EOS), which is constructed for simulations of core-collapse supernovae and binary neutron star mergers, within the relativistic mean-field (RMF) framework. The study introduces a new RMF parameter set, TM1m, which is a modification of the TM1e model with an adjusted effective mass, maintaining the saturation properties of nuclear matter. The TM1m model, with a larger effective mass ratio ($M^{\ast}/M \sim 0.8$) compared to the TM1e model ($M^{\ast}/M \sim 0.63$), is employed to construct a new EOS table, EOS5. This EOS table is designed to offer insights into the influence of the effective nucleon mass on the EOS within a relativistic framework, particularly above the saturation density. The results of EOS5 are compared with those obtained from other models, including both relativistic and nonrelativistic approaches. The properties of cold neutron stars, calculated using the TM1m model, are compatible with the existence of a $2\ M_\odot$ pulsar and the latest constraints on the tidal deformability and radii of a canonical $1.4\ M_\odot$ neutron star, derived from astrophysical observations.

nucl-th↗

arXiv2025

User-Driven Value Alignment: Understanding Users' Perceptions and Strategies for Addressing Biased and Discriminatory Statements in AI Companions

Large language model-based AI companions are increasingly viewed by users as friends or romantic partners, leading to deep emotional bonds. However, they can generate biased, discriminatory, and harmful outputs. Recently, users are taking the initiative to address these harms and re-align AI companions. We introduce the concept of user-driven value alignment, where users actively identify, challenge, and attempt to correct AI outputs they perceive as harmful, aiming to guide the AI to better align with their values. We analyzed 77 social media posts about discriminatory AI statements and conducted semi-structured interviews with 20 experienced users. Our analysis revealed six common types of discriminatory statements perceived by users, how users make sense of those AI behaviors, and seven user-driven alignment strategies, such as gentle persuasion and anger expression. We discuss implications for supporting user-driven value alignment in future AI systems, where users and their communities have greater agency.

cs.HC↗

arXiv2025

Global AI Governance: Where the Challenge is the Solution- An Interdisciplinary, Multilateral, and Vertically Coordinated Approach

Current global AI governance frameworks struggle with fragmented disciplinary collaboration, ineffective multilateral coordination, and disconnects between policy design and grassroots implementation. This study, guided by Integration and Implementation Science (IIS) initiated a structured interdisciplinary dialogue at the UN Science Summit, convening legal, NGO, and HCI experts to tackle those challenges. Drawing on the common ground of the experts: dynamism, experimentation, inclusivity, and paradoxical governance, this study, through thematic analysis and interdisciplinary comparison analysis, identifies four core principles of global AI governance. Furthermore, we translate these abstract principles into concrete action plans leveraging the distinct yet complementary perspectives of each discipline. These principles and action plans are then integrated into a five-phase, time-sequential framework including foundation building, experimental verification, collaborative optimization, global adaptation, and continuous evolution phases. This multilevel framework offers a novel and concrete pathway toward establishing interdisciplinary, multilateral, and vertically coordinated AI governance, transforming global AI governance challenges into opportunities for political actions.

cs.CY↗