arXiv subjects

Han Meng

Publications and source records attributed to Han Meng.

At least 37 records · Page 2Linked to original sources

arXiv2025

What is Stigma Attributed to? A Theory-Grounded, Expert-Annotated Interview Corpus for Demystifying Mental-Health Stigma

Mental-health stigma remains a pervasive social problem that hampers treatment-seeking and recovery. Existing resources for training neural models to finely classify such stigma are limited, relying primarily on social-media or synthetic data without theoretical underpinnings. To remedy this gap, we present an expert-annotated, theory-informed corpus of human-chatbot interviews, comprising 4,141 snippets from 684 participants with documented socio-cultural backgrounds. Our experiments benchmark state-of-the-art neural models and empirically unpack the challenges of stigma detection. This dataset can facilitate research on computationally detecting, neutralizing, and counteracting mental-health stigma. Our corpus is openly available at https://github.com/HanMeng2004/Mental-Health-Stigma-Interview-Corpus.

cs.CL↗

arXiv2025

ALATDYN: a set of Anharmonic LATtice DYNamics codes to compute thermodynamic and thermal transport properties of crystalline solids

We introduce a lattice dynamics package which calculates elastic, thermodynamic and thermal transport properties of crystalline materials from data on their force and potential energy as a function of atomic positions. The data can come from density functional theory (DFT) calculations or classical molecular dynamics runs performed in a supercell. First, the model potential parameters, which are anharmonic force constants are extracted from the latter runs. Then, once the anharmonic model is defined, thermal conductivity and equilibrium properties at finite temperatures can be computed using lattice dynamics, Boltzmann transport theories, and a variational principle respectively. In addition, the software calculates the mechanical properties such as elastic tensor, Gruneisen parameters and the thermal expansion coefficient within the quasi-harmonic approximation (QHA). Phonons, elastic constants and thermodynamic properties results applied to the germanium crystal will be illustrated. Using the force constants as a force field, one may also perform molecular dynamics (MD) simulations in order to investigate the combined effects of anharmonicity and defect scattering beyond perturbation theory.

cond-mat.mtrl-sci↗

arXiv2025

Empowering Social Service with AI: Insights from a Participatory Design Study with Practitioners

In social service, administrative burdens and decision-making challenges often hinder practitioners from performing effective casework. Generative AI (GenAI) offers significant potential to streamline these tasks, yet exacerbates concerns about overreliance, algorithmic bias, and loss of identity within the profession. We explore these issues through a two-stage participatory design study. We conducted formative co-design workshops (\textit{n=27}) to create a prototype GenAI tool, followed by contextual inquiry sessions with practitioners (\textit{n=24}) using the tool with real case data. We reveal opportunities for AI integration in documentation, assessment, and worker supervision, while highlighting risks related to GenAI limitations, skill retention, and client safety. Drawing comparisons with GenAI tools in other fields, we discuss design and usage guidelines for such tools in social service practice.

cs.HC↗

arXiv2025

Deconstructing Depression Stigma: Integrating AI-driven Data Collection and Analysis with Causal Knowledge Graphs

Mental-illness stigma is a persistent social problem, hampering both treatment-seeking and recovery. Accordingly, there is a pressing need to understand it more clearly, but analyzing the relevant data is highly labor-intensive. Therefore, we designed a chatbot to engage participants in conversations; coded those conversations qualitatively with AI assistance; and, based on those coding results, built causal knowledge graphs to decode stigma. The results we obtained from 1,002 participants demonstrate that conversation with our chatbot can elicit rich information about people's attitudes toward depression, while our AI-assisted coding was strongly consistent with human-expert coding. Our novel approach combining large language models (LLMs) and causal knowledge graphs uncovered patterns in individual responses and illustrated the interrelationships of psychological constructs in the dataset as a whole. The paper also discusses these findings' implications for HCI researchers in developing digital interventions, decomposing human psychological constructs, and fostering inclusive attitudes.

cs.HC↗

arXiv2025

The Dark Side of AI Companionship: A Taxonomy of Harmful Algorithmic Behaviors in Human-AI Relationships

As conversational AI systems increasingly permeate the socio-emotional realms of human life, they bring both benefits and risks to individuals and society. Despite extensive research on detecting and categorizing harms in AI systems, less is known about the harms that arise from social interactions with AI chatbots. Through a mixed-methods analysis of 35,390 conversation excerpts shared on r/replika, an online community for users of the AI companion Replika, we identified six categories of harmful behaviors exhibited by the chatbot: relational transgression, verbal abuse and hate, self-inflicted harm, harassment and violence, mis/disinformation, and privacy violations. The AI contributes to these harms through four distinct roles: perpetrator, instigator, facilitator, and enabler. Our findings highlight the relational harms of AI chatbots and the danger of algorithmic compliance, enhancing the understanding of AI harms in socio-emotional interactions. We also provide suggestions for designing ethical and responsible AI systems that prioritize user safety and well-being.

cs.HC↗

arXiv2025

Population Aware Diffusion for Time Series Generation

Diffusion models have shown promising ability in generating high-quality time series (TS) data. Despite the initial success, existing works mostly focus on the authenticity of data at the individual level, but pay less attention to preserving the population-level properties on the entire dataset. Such population-level properties include value distributions for each dimension and distributions of certain functional dependencies (e.g., cross-correlation, CC) between different dimensions. For instance, when generating house energy consumption TS data, the value distributions of the outside temperature and the kitchen temperature should be preserved, as well as the distribution of CC between them. Preserving such TS population-level properties is critical in maintaining the statistical insights of the datasets, mitigating model bias, and augmenting downstream tasks like TS prediction. Yet, it is often overlooked by existing models. Hence, data generated by existing models often bear distribution shifts from the original data. We propose Population-aware Diffusion for Time Series (PaD-TS), a new TS generation model that better preserves the population-level properties. The key novelties of PaD-TS include 1) a new training method explicitly incorporating TS population-level property preservation, and 2) a new dual-channel encoder model architecture that better captures the TS data structure. Empirical results in major benchmark datasets show that PaD-TS can improve the average CC distribution shift score between real and synthetic data by 5.9x while maintaining a performance comparable to state-of-the-art models on individual-level authenticity.

cs.LG↗

arXiv2024

IFDID: Information Filter upon Diversity-Improved Decoding for Diversity-Faithfulness Tradeoff in NLG

Some Natural Language Generation (NLG) tasks require both faithfulness and diversity. The decoding strategy is intensively related to the quality of the generated text. Strategies such as beam search, greedy search, etc., perform with low diversity and high repetition. On the other hand, guided decoding, the solution towards diversity, may generate unfaithful expressions. To this end, this paper presents Information Filter upon Diversity-Improved Decoding (IFDID) to obtain the tradeoff between diversity and faithfulness. IFDID is a two-stage decoding strategy leveraging the proposed Enhance-Filter framework, which achieves the tradeoff by increasing the probabilities of some typical tokens being selected and subsequently filtering them by their information amount. To verify the effectiveness, we compare our method with other baselines on related CommonGEN, RocStories and AdGen benchmarks, which cover Chinese and English datasets. Our numerical experimental results and human evaluation outcomes verify the effectiveness of the proposed approach, as our approach achieves a 1.24 higher ROUGE score describing faithfulness as well as higher diversity represented by 62.5% higher upon Dist-2 than traditional approaches, demonstrating that IFDID is a novel SOTA decoding strategy for the tradeoff between diversity and faithfulness.

cs.CL↗

arXiv2023

Large Language Models in Medical Term Classification and Unexpected Misalignment Between Response and Reasoning

This study assesses the ability of state-of-the-art large language models (LLMs) including GPT-3.5, GPT-4, Falcon, and LLaMA 2 to identify patients with mild cognitive impairment (MCI) from discharge summaries and examines instances where the models' responses were misaligned with their reasoning. Utilizing the MIMIC-IV v2.2 database, we focused on a cohort aged 65 and older, verifying MCI diagnoses against ICD codes and expert evaluations. The data was partitioned into training, validation, and testing sets in a 7:2:1 ratio for model fine-tuning and evaluation, with an additional metastatic cancer dataset from MIMIC III used to further assess reasoning consistency. GPT-4 demonstrated superior interpretative capabilities, particularly in response to complex prompts, yet displayed notable response-reasoning inconsistencies. In contrast, open-source models like Falcon and LLaMA 2 achieved high accuracy but lacked explanatory reasoning, underscoring the necessity for further research to optimize both performance and interpretability. The study emphasizes the significance of prompt engineering and the need for further exploration into the unexpected reasoning-response misalignment observed in GPT-4. The results underscore the promise of incorporating LLMs into healthcare diagnostics, contingent upon methodological advancements to ensure accuracy and clinical coherence of AI-generated outputs, thereby improving the trustworthiness of LLMs for medical decision-making.

cs.CL↗

arXiv2022

Multi-faceted Graph Attention Network for Radar Target Recognition in Heterogeneous Radar Network

Radar target recognition (RTR), as a key technology of intelligent radar systems, has been well investigated. Accurate RTR at low signal-to-noise ratios (SNRs) still remains an open challenge. Most existing methods are based on a single radar or the homogeneous radar network, which do not fully exploit frequency-dimensional information. In this paper, a two-stream semantic feature fusion model, termed Multi-faceted Graph Attention Network (MF-GAT), is proposed to greatly improve the accuracy in the low SNR region of the heterogeneous radar network. By fusing the features extracted from the source domain and transform domain via a graph attention network model, the MF-GAT model distills higher-level semantic features before classification in a unified framework. Extensive experiments are presented to demonstrate that the proposed model can greatly improve the RTR performance at low SNRs.

eess.SP↗

arXiv2022

Spatio-Temporal-Frequency Graph Attention Convolutional Network for Aircraft Recognition Based on Heterogeneous Radar Network

This paper proposes a knowledge-and-data-driven graph neural network-based collaboration learning model for reliable aircraft recognition in a heterogeneous radar network. The aircraft recognizability analysis shows that: (1) the semantic feature of an aircraft is motion patterns driven by the kinetic characteristics, and (2) the grammatical features contained in the radar cross-section (RCS) signals present spatial-temporal-frequency (STF) diversity decided by both the electromagnetic radiation shape and motion pattern of the aircraft. Then a STF graph attention convolutional network (STFGACN) is developed to distill semantic features from the RCS signals received by the heterogeneous radar network. Extensive experiment results verify that the STFGACN outperforms the baseline methods in terms of detection accuracy, and ablation experiments are carried out to further show that the expansion of the information dimension can gain considerable benefits to perform robustly in the low signal-to-noise ratio region.

eess.SP↗

arXiv2020

Thermal conductivity of one-dimensional carbon-boron nitride van der Waals heterostructure: A molecular dynamics study

Investigating thermal transport in van der Waals heterostructure is of scientific interest and practical importance for their applications in a broad range. In this work, thermal conductivity of one-dimensional heterostructure consisting of carbon and boron nitride nanotubes is systematically investigated via molecular dynamics simulations. Thermal conductivity is found to have strong dependences on temperature, length and diameter. In addition, the axial strain and intensity of van der Waals interaction are demonstrated to be able to modulate thermal conductivity up to about 43% and 37%, respectively. Moreover, the dependence of thermal conductivity on the chirality of componential nanotubes is studied. These results are explained based on lattice dynamics insights. This work not only provides feasible strategies to modulate thermal conductivity, but also enhances the understanding of the fundamental physics of phonon transport in one-dimensional heterostructure.

cond-mat.mes-hall↗

arXiv2019

Thermal conductivity of molybdenum disulfide nanotube from molecular dynamics simulations

Single layer molybdenum disulfide (SLMoS2), a semiconductor possesses intrinsic bandgap and high electron mobility, has attracted great attention due to its unique electronic, optical, mechanical and thermal properties. Although thermal conductivity of SLMoS2 has been widely investigated recently, less studies focus on molybdenum disulfide nanotube (MoS2NT). Here, the comprehensive temperature, size and strain effect on thermal conductivity of MoS2NT are investigated. A chirality-dependent strain effect is identified in thermal conductivity of zigzag nanotube, in which the phonon group velocity can be significantly reduced by strain. Besides, results show that thermal conductivity has a ~T-1 and a ~L\b{eta} relation with temperature from 200 to 400 K and length from 10 to 320 nm, respectively. This work not only provides feasible strategies to modulate the thermal conductivity of MoS2NT, but also offers useful insights into the fundamental mechanisms that govern the thermal conductivity, which can be used for the thermal management of low dimensional materials in optical, electronic and thermoelectrical devices. Introduction.

cond-mat.mes-hall↗

arXiv2018

Superior thermal conductivity of poly (ethylene oxide) for solid-state electrolytes: a molecular dynamics study

Solid-state lithium-ion batteries (SSLIBs) are considered to be the new generation of devices for energy storage due to better performance and safety. Poly (ethylene oxide) (PEO) based material becomes one of the best candidate of solid electrolytes, while its thermal conductivity is crucial to heat dissipation inside batteries. In this work, we study the thermal conductivity of PEO by molecular dynamics simulation. By enhancing the structure order, thermal conductivity of aligned crystalline PEO is obtained as high as 60 W/m-K at room temperature, which is two orders higher than the value (0.37 W/m-K) of amorphous structure. Interestingly, thermal conductivity of ordered structure shows a significant stepwise negative temperature dependence, which is attributed to the temperature-induced morphology change. Our study offers useful insights into the fundamental mechanisms that govern the thermal conductivity of PEO but not hinder the ionic transport, which can be used for the thermal management and further optimization of high-performance SSLIBs.

physics.app-ph↗

arXiv2017

Generalized two-temperature model for coupled phonons

The design of graphene-based composite with high thermal conductivity requires a comprehensive understanding of phonon coupling in graphene. We extended the two-temperature model to coupled groups of phonon. The study give new physical quantities, the phonon-phonon coupling factor and length, to characterize the couplings quantitatively. Besides, our proposed coupling length has an obvious dependence on system size. Our studies can not only observe the nonequilibrium between different groups of phonon, but explain theoretically the thermal resistance inside graphene.

cond-mat.mes-hall↗

arXiv2016

The unexpected thermal conductivity from graphene disk, carbon nanocone to carbon nanotube

Graphene and single-wall carbon nanotube (SWCNT) have attracted great attention because of their ultra-high thermal conductivity. However, there are few works exploring the relations of their thermal conductivity quantitatively. The carbon nanocone (CNC) is a graded structure fall in between graphene disk (GD) and SWCNT. We perform non-equilibrium molecular dynamics (NEMD) simulation to study the thermal conductivity of CNC with different apex angles, and then compare them with that of GD and SWCNT. Our results show that, different from the homogeneous thermal conductivity in SWCNT, the CNC also has a natural graded thermal conductivity which is similar to the GD. Unexpectedly, the graded rate keeps almost the same when the apex angle decreases from 180° (GD) to 19°, but then suddenly declines to zero when the apex angle decreases from 19° to 0° (SWCNT). What is more interesting, the graded effect is not diminished when the interatomic force constant is weakened and mean free path is shorten. That is, besides nanoscale, the graded effect can be observed in macroscale graphene or CNC structures.

cond-mat.mes-hall↗

arXiv2016

Nano-Cross-Junction Effect on Phonon Transport in Silicon-Nanowire-Cages

Wave effects of phonons can give rise to controllability of heat conduction beyond that by particle scattering at surfaces and interfaces. In this work, we propose a new class of 3D nanostructure: a silicon-nanowire-cage (SiNWC) structure consisting of silicon nanowires (SiNWs) connected by nano-cross-junctions (NCJs). We perform equilibrium molecular dynamics (MD) simulations, and find an ultralow value of thermal conductivity of SiNWC, 0.173 Wm-1K-1, which is one order lower than that of SiNWs. By further modal analysis and atomistic Green's function calculations, we identify that the large reduction is due to significant phonon localization induced by the phonon local resonance and hybridization at the junction part in a wide range of phonon modes. This localization effect does not require the cage to be periodic, unlike the phononic crystals, and can be realized in structures that are easier to synthesize, for instance in a form of randomly oriented SiNWs network.

cond-mat.mes-hall↗

arXiv2016

Enhancing thermal conductivity of bulk polyethylene along two directions by paved crosswise laminate

Recently, some reports show that the ultra-low thermal conductivity of bulk polymers can be enhanced along one direction, which limits its applications. Here, we proposed paved crosswise laminate methods which can enhance the thermal conductivity of bulk polyethylene (PE) along two directions. We find that the thermal conductivity of paved crosswise polyethylene laminate (PPEL) reaches as high as 181 W/m-K along two in-plane directions, which is three orders of magnitude larger than bulk amorphous polyethylene and even more than two times larger than PE single chain (54 W/m-K). The analyses of mechanism indicated that PPEL is a much more crystal-like structure due to the inter-chain van der Waals interactions. Our study may provide guides on the design and fabrication of polymer structures with high thermal conductivity.

cond-mat.mtrl-sci↗

arXiv1998

Probing flavor changing interactions in photon-photon collisions

We examine the subprocess $γγ\to t\bar{c}+\bar{t}c$ at electron-positron colliders in the two-Higgs-doublet model with flavor-changing scalar couplings, where all the one-loop contribuions are considered, and the results are applicable to the whole mass range of the weakly coupled Higgs bosons. Because of the heavy top quark mass, this process is important in probing the flavor-changing top-charm-scalar vertex and could be detectable at the Next Linear Collider, if the values of the parameters are favorable. The results show that this process is more promising than the direct $e^{+} e^{-}$ process for discovering flavor changing scalar interactions.

hep-ph↗