arXiv subjects

Lin Nie

Publications and source records attributed to Lin Nie.

At least 19 recordsLinked to original sources

arXiv2026

Metric Aggregation Divergence: A Hidden Validity Threat in Agent-Based Policy Optimization and a Contractual Remedy

Metric aggregation divergence (MAD) is the silent inconsistency that arises when distinct pipeline stages in an agent-based model coupled with a multi-objective evolutionary algorithm (ABM+MOEA) independently re-implement how an outcome metric is extracted from simulation trajectories. Unlike deliberate analytical choices, MAD operates at the level of pipeline architecture: each stage is internally coherent, and the inconsistency becomes visible only when cross-stage outputs are compared. Code inspection of EpidemiOptim, a JAIR-published epidemic policy toolbox, reveals three structurally independent aggregation paths in peer-reviewed code. A faithful replication of this structure produces champion disagreement in 64.2% of independent runs (n=500, 95% CI: [59.9%, 68.3%]). In a 300-seed policy-flip experiment, divergent aggregation causes the optimizer to recommend the wrong champion in 83% of replications, with a mean welfare gap of 2.19 units and a Gini inequality gap of 0.050 units. In a follow-up inference audit, 3 of 249 flipped seeds cross the significance boundary itself. A complementary enterprise follow-up produces the predicted null under near-commensurable rankings (rho = 0.991), while a public upstream rerun of the Lake Problem DPS workflow shows that the archived published-path recommendation reaches joint-threshold success 0.401 whereas a shared contract-path rule reaches 0.552. We introduce the metric contract - a single shared callable enforced at dispatch time across all pipeline stages - as the remedy. Framed as standard engineering discipline applied to the cross-stage metric interface, the contract eliminates divergence by construction with approximately 3% runtime overhead.

cs.MA↗

arXiv2026

Two-component $\gamma$-Ray Structure from the CR Sources Within Dense Clouds

Recent observations have revealed that several cosmic ray (CR) sources themselves exhibit pronounced double power-law features in their radiation spectra. Combined with the phenomenon of two-component structure in the observed CR energy spectrum supported by multi-messenger data, this raises a fundamental question: can the two-component structure of the cosmic ray energy spectrum and the double power-law feature of the gamma-ray radiation energy spectrum from supernova remnants be understood within a unified picture? In this study, we propose a two-component model that incorporates the re-acceleration of background ``sea" CR particles by astrophysical sources to systematically explain the formation of double power-law spectra within those sources. Our model successfully reproduces the gamma-ray observations of multiple CR sources. The results support that double power-law structures may be a generic feature of Galactic CR sources within crushed clouds. This work offers a new theoretical perspective on the origin and propagation of cosmic rays, and its predictions may be further tested with future observations of a larger sample of CR sources.

astro-ph.HE↗

arXiv2026

A Multi-messenger Search for a Nearby Microquasar Contributor to the Cosmic Ray Knee

Recently, LHAASO has detected five microquasars with high confidence, which are associated with SS 433, V4641 Sgr, GRS 1915+105, MAXI J1820+070, and Cygnus X-1, respectively. Except for Cygnus X-1, the maximum energies of gamma-ray photons emitted from these sources all exceed 100 TeV, strongly suggesting that microquasars are capable of accelerating cosmic-ray particles to energies above the PeV range. This work investigates the origin of the cosmic-ray knee region based on gamma-ray observational data from the aforementioned sources, combined with cosmic-ray proton, helium, and all-particle energy spectra, as well as anisotropy observations. Calculations indicate that these known sources contribute negligibly to the cosmic-ray knee region. However, further joint analysis reveals that a single microquasar located in a region approximately on the 2.6 kiloparsec scale in the anti-Galactic center direction can reasonably reproduce the observed cosmic-ray proton, helium, and all-particle energy spectra, as well as anisotropy features detected near Earth. We propose that this region may host one or several unidentified microquasars or similar systems, whose accelerated cosmic rays could dominate the observational characteristics of the knee region.

astro-ph.HE↗

arXiv2026

Joint constraint on the propagation origin of the cosmic-ray spectral knee from energy spectrum and anisotropy observations

The origin mechanism of the cosmic-ray knee region remains an unresolved mystery, with acceleration, interaction, and propagation models drawing significant attention. The latest experimental observations of the PeV total spectrum, composition energy spectrum, and anisotropy-particularly the precise measurements of the proton spectrum by the LHAASO experiment-have provided crucial breakthroughs in uncovering its origin. Based on the latest LHAASO measurements of the proton energy spectrum, combined with cosmic-ray spectral and anisotropy data, this study proposes that the spectral index variation in the knee region arises from changes in the propagation coefficient. By introducing a knee position $\rm \mathcal{R}_{knee}$ and an index variation $\rm \delta_{knee}$, we construct a rigidity-dependent double-power-law diffusion model to reproduce the knee-region spectral structure. Through modifications to the diffusion coefficient, we successfully replicate the observed knee-region spectral structure in the LHAASO proton spectrum and calculate the corresponding anisotropy. Under current data and model dependencies, a joint analysis of the energy spectrum and anisotropy does not support the propagation origin model of the cosmic-ray knee at a 95\% confidence level. We hope that future LHAASO experiments will provide precise measurements of the energy spectra and anisotropies of various nuclei in the knee region, thereby offering a definitive test of the propagation model as the origin mechanism of the knee-region spectral structure.

astro-ph.HE↗

arXiv2026

PEARL: Prototype-Enhanced Alignment for Label-Efficient Representation Learning with Deployment-Driven Insights from Digital Governance Communication Systems

In many deployed systems, new text inputs are handled by retrieving similar past cases, for example when routing and responding to citizen messages in digital governance platforms. When these systems fail, the problem is often not the language model itself, but that the nearest neighbors in the embedding space correspond to the wrong cases. Modern machine learning systems increasingly rely on fixed, high-dimensional embeddings produced by large pretrained models and sentence encoders. In real-world deployments, labels are scarce, domains shift over time, and retraining the base encoder is expensive or infeasible. As a result, downstream performance depends heavily on embedding geometry. Yet raw embeddings are often poorly aligned with the local neighborhood structure required by nearest-neighbor retrieval, similarity search, and lightweight classifiers that operate directly on embeddings. We propose PEARL (Prototype-Enhanced Aligned Representation Learning), a label-efficient approach that uses limited supervision to softly align embeddings toward class prototypes. The method reshapes local neighborhood geometry while preserving dimensionality and avoiding aggressive projection or collapse. Its aim is to bridge the gap between purely unsupervised post-processing, which offers limited and inconsistent gains, and fully supervised projections that require substantial labeled data. We evaluate PEARL under controlled label regimes ranging from extreme label scarcity to higher-label settings. In the label-scarce condition, PEARL substantially improves local neighborhood quality, yielding 25.7% gains over raw embeddings and more than 21.1% gains relative to strong unsupervised post-processing, precisely in the regime where similarity-based systems are most brittle.

cs.LG↗

arXiv2025

Mock Observations for the CSST Mission: Multi-Channel Imager--Instrument Simulation

The Chinese Space Station Survey Telescope (CSST), a two-meter aperture astronomical space telescope under China's manned space program, is equipped with multiple back-end scientific instruments. As an astronomical precision measurement module of the CSST, the Multi-Channel Imager (MCI) can cover a wide wavelength range from ultraviolet to near-infrared with three-color simultaneous high-precision photometry and imaging, which meets the scientific requirements for various fields. The diverse scientific objectives of MCI require not only a robust airborne platform, advanced optical systems, and observing facilities but also comprehensive software support for scientific operations and research. To this end, it is essential to develop realistic observational simulation software to thoroughly evaluate the MCI data stream and provide calibration tools for future scientific investigations. The MCI instrument simulation software will serve as a foundation for the development of the MCI data processing pipeline and will facilitate improvements in both hardware and software, as well as in the observational operation strategy, in alignment with the mission's scientific goals. In conclusion, we present a comprehensive overview of the MCI instrument simulation and some corresponding performances of the MCI data processing pipeline.

astro-ph.IM↗

arXiv2025

Mock Observations for the CSST Mission: Main Surveys -- the Mock Catalogue

The Chinese Space Station Survey Telescope (CSST) is a flagship space mission, designed to carry out a large-area sky survey to explore the nature of dark matter and dark energy in the Universe. The onboard multi-band imaging and slitless spectroscopic modules will enable us to obtain photometric data for billions of galaxies and stars, as well as hundreds of millions of spectroscopic measurements, advancing various scientific analyses such as galaxy clustering and weak gravitational lensing. To support the image simulations for the main survey of the CSST mission, we present a mock catalogue of stars and galaxies. For stars, the mock catalogue is generated using either Galaxia or TRILEGAL, both of which provide a range of stellar properties to meet the requirements of CSST image simulations. For galaxies, we built a mock light-cone up to redshift z~3.5 from the cosmological Nbody simulation and populated the mock galaxy catalogue from the dark mater haloes using a semi-analytical galaxy formation model. We then performed a full-sky ray-tracing simulation of weak gravitational lensing to obtain lensing shear at the position of each galaxy in the light-cone. To support both multi-band imaging and slitless spectroscopic simulations, we computed the spectral energy distribution (SED) for each galaxy based on its star formation history using a supervised deep-learning model and determined the magnitudes in each band using the CSST throughputs. Finally, the properties of our mock galaxies include positions, redshifts, stellar masses, shapes, sizes, SEDs, lensing shears and magnifications. We have validated our mock catalogue against observational data and theoretical models, with results showing good overall agreement. The catalogue provides a flexible dataset for the development of CSST image processing and can support a wide range of cosmological analyses within the CSST mission.

astro-ph.IM↗

arXiv2025

Mock Observations for the CSST Mission: Multi-Channel Imager--The Cluster Field

The Multi-Channel Imager (MCI), one of the instruments aboard the China Survey Space Telescope (CSST), is designed to simultaneously observe the sky in three filters, covering wavelengths from the near-ultraviolet (NUV) to the near-infrared (NIR). With its large field of view ($7.5^{\prime}\times7.5^{\prime}$), MCI is particularly well-suited for observing galaxy clusters, providing a powerful tool for investigating galaxy evolution, dark matter and dark energy through gravitational lensing. Here we present a comprehensive simulation framework of a strong lensing cluster as observed by MCI, aiming to fully exploit its capabilities in capturing lensing features. The framework simulates a strong lensing cluster from the CosmoDC2 catalog, calculating the gravitational potential and performing ray-tracing to derive the true positions, shapes and light distribution of galaxies within the cluster field. Additionally, the simulation incorporates intra-cluster light (ICL) and spectral energy distributions (SEDs), enabling further strong lensing analyses, such as ICL seperation from galaxy light and mass reconstruction combining strong and weak lensing measurements. This framework provides a critical benchmark for testing the MCI data pipeline and maximizing its potential in galaxy cluster research.

astro-ph.IM↗

arXiv2025

Mock Observations for the CSST Mission: Main Surveys--An Overview of Framework and Simulation Suite

The Chinese Space Station Survey Telescope (CSST) is a flagship space-based observatory. Its main survey camera is designed to conduct high spatial resolution near-ultraviolet to near-infrared imaging and low-resolution spectroscopic surveys. To maximize the scientific output of CSST, we have developed a comprehensive, high-fidelity simulation pipeline for reproducing both imaging and spectroscopic observations. This paper presents an overview of the simulation framework, detailing its implementation and components. Built upon the GalSim package and incorporating the latest CSST instrumental specifications, our pipeline generates pixel-level mock observations that closely replicate the expected instrumental and observational conditions. The simulation suite integrates realistic astrophysical object catalogs, instrumental effects, point spread function (PSF) modeling, and observational noises to produce accurate synthetic data. We describe the key processing stages of the simulation, from constructing the input object catalogs to modeling the telescope optics and detector responses. Furthermore, we introduce the most recent release of simulated datasets, which provide a crucial testbed for data processing pipeline developments, calibration strategies, and scientific analyses, ensuring that CSST will meet its stringent requirements. Our pipeline serves as a vital tool for optimizing CSST main survey strategies and ensuring robust cosmological measurements.

astro-ph.IM↗

arXiv2025

HEAS: Hierarchical Evolutionary Agent-Based Simulation Framework for Multi-Objective Policy Search

HEAS is a Python framework that connects agent-based simulation, evolutionary search, and scenario-based evaluation in a single reproducible pipeline. It is designed for researchers who study systems where local interactions produce system-level outcomes-ecosystems, organizations, markets, or regulatory environments-and who need to search over candidate strategies and compare them across uncertain scenarios. HEAS combines three modules: a hierarchy runtime for composing simulations from reusable process layers, an evolutionary tuner for single- or multi-objective search backed by DEAP, and a game module for evaluating strategies across scenario ensembles. Its central design principle is the "metric contract": the same outcome function is shared by optimization, evaluation, and validation, so that different parts of an analysis cannot silently rank strategies by different quantities.

cs.MA↗

arXiv2025

Structural Equation-VAE: Disentangled Latent Representations for Tabular Data

Learning interpretable latent representations from tabular data remains a challenge in deep generative modeling. We introduce SE-VAE (Structural Equation-Variational Autoencoder), a novel architecture that embeds measurement structure directly into the design of a variational autoencoder. Inspired by structural equation modeling, SE-VAE aligns latent subspaces with known indicator groupings and introduces a global nuisance latent to isolate construct-specific confounding variation. This modular architecture enables disentanglement through design rather than through statistical regularizers alone. We evaluate SE-VAE on a suite of simulated tabular datasets and benchmark its performance against a series of leading baselines using standard disentanglement metrics. SE-VAE consistently outperforms alternatives in factor recovery, interpretability, and robustness to nuisance variation. Ablation results reveal that architectural structure, rather than regularization strength, is the key driver of performance. SE-VAE offers a principled framework for white-box generative modeling in scientific and social domains where latent constructs are theory-driven and measurement validity is essential.

cs.LG↗

arXiv2025

Eco-efficiency as a Catalyst for Citizen Co-production: Evidence from Chinese Cities

We examine whether higher eco-efficiency encourages local governments to co-produce environmental solutions with citizens. Using Chinese provincial data and advanced textual analysis, we find that high eco-efficiency strongly predicts more collaborative responses to environmental complaints. Causal inference suggests that crossing a threshold of eco-efficiency increases co-production probabilities by about 24 percentage points, indicating eco-efficiency's potential as a catalyst for participatory environmental governance.

econ.GN↗

arXiv2025

A Possible Common Physic Picture Reflected by the Gamma Ray Emission of the Galactic Center

Long-term observations of the Galactic center by Fermi and HESS have revealed a novel phenomenon: the high-energy gamma-ray spectrum from the Galactic center exhibits a double power-law structure. In this study, we propose a new explanation for this phenomenon. We suggest that the low-energy (GeV) power-law spectrum originates from interactions between trapped background ``sea" cosmic ray particles and the dense gaseous environment near the Galactic center. In contrast, the bubble-like structure in the high-energy (TeV) spectrum is produced by protons accelerated during active phases of the Galactic center, through the same physical process. Based on this framework, we first calculate the gamma-ray emission generated by cosmic ray protons accelerated in the Galactic center. Then, using a spatially-dependent cosmic ray propagation model, we compute the energy spectrum of background ``sea" cosmic ray protons and their associated diffuse gamma-ray emission in the Galactic center region. The results closely reproduce the observations from Fermi-LAT and HESS, suggesting that their long-term data support this picture: high-energy cosmic rays in the local region originate from nearby cosmic ray sources, while low-energy cosmic rays are a unified contribution from distant cosmic ray sources. We predict that some extended Galactic sources, which remain undetectable in the GeV energy range, may become observable in the TeV range. We hope that future observations will detect more such sources, allowing us to further test and validate our model.

astro-ph.HE↗

arXiv2025

An Ultra-Fast Image Simulation Technique with Spatially Variable Point Spread Functions

Simulated images are essential in algorithm development and instrument testing for optical telescopes. During real observations, images obtained by optical telescopes are affected by spatially variable point spread functions (PSFs), a crucial effect requiring accurate simulation. Traditional methods segment images into patches, convolve patches with individual PSFs, and reassemble them as a whole image. Although widely used, these approaches suffer from slow convolution processes and reduced image fidelity due to abrupt PSF transitions between different patches. This paper introduces a novel method for generating simulated images with spatial continuously varying PSFs. Our approach firstly decomposes original images into PSF bases derived with the principal component analysis method. The entire image is then convolved with these PSF bases to create image bases. Finally, we multiply the coefficients of image bases with these image bases for each pixels and add the multiplication results along each pixel to obtain the final simulated image. Our method could generate high-fidelity simulated images with spatially variable PSFs without boundary artifacts. The method proposed in this paper significantly improves the speed of astronomical image simulation, potentially advancing observational astronomy and instrumental development.

astro-ph.IM↗

arXiv2025

Enhancing Citizen-Government Communication with AI: Evaluating the Impact of AI-Assisted Interactions on Communication Quality and Satisfaction

This study integrates critical AI scholarship with relational communication theories to explain how AI language modifications shape the quality of government-citizen communication. Distinguishing between informational-cognitive quality (clarity, ease of response) and expressive-constitutive quality (politeness, respectfulness, feeling heard, trust, urgency, empathy), we hypothesize that AI yields uncontested benefits for the former but contested effects for the latter, potentially enhancing relational markers while muting authentic emotional cues. Using a vignette-based survey with 220 citizens and 214 civil servants in China, we assess perceptions across five interaction contexts: service requests, policy inquiries, complaints, suggestions, and emergencies. Results from paired t-tests and mixed-effects regressions support the claim that AI enhances both informational-cognitive and expressive-constitutive quality from the perspectives of citizens and civil servants, with significant improvements in clarity, politeness, satisfaction, trust, and empathy, but provide no consistent evidence of urgency or empathy signals. These findings suggest that concerns over algorithmic emotional flattening may be overstated or context-specific; they offer theoretical insights into AI-mediated public interactions and practical implications for fostering trust and efficiency in digital governance.

cs.CY↗

arXiv2024

X-ray Binaries: a potential dominant contributor to the cosmic ray spectral knee structure

``PeVatrons" refer to astrophysical sources capable of accelerating particles to energies $\sim$PeV and higher, potentially contributing to the cosmic ray spectrum in the knee region. Recently, HAWC and LHAASO have discovered a new type PeVatrons -- X-ray binaries, allowing us to investigate in greater depth of the contributions of these sources to cosmic rays around the knee region. There are hundreds of X-ray binaries in our galaxy observed, which are potential PeVatrons.In this work, we derive the radial distribution of X-ray binaries in the Galaxy. Then we use the DRAGON package to calculate energy spectrum, anisotropy of cosmic rays as well as the resulting diffuse gamma ray emissions, after considering them as factories of cosmic rays in the knee energy bands. Our findings show that the contributions from X-ray binary PeVatrons may be dominant. More X-ray binary PeVatrons can be observed by LHAASO and HAWC in the future, and will confirm the contribution of X-ray binaries to high energy cosmic rays.

astro-ph.HE↗

arXiv2024

Achieving Semantic Consistency: Contextualized Word Representations for Political Text Analysis

Accurately interpreting words is vital in political science text analysis; some tasks require assuming semantic stability, while others aim to trace semantic shifts. Traditional static embeddings, like Word2Vec effectively capture long-term semantic changes but often lack stability in short-term contexts due to embedding fluctuations caused by unbalanced training data. BERT, which features transformer-based architecture and contextual embeddings, offers greater semantic consistency, making it suitable for analyses in which stability is crucial. This study compares Word2Vec and BERT using 20 years of People's Daily articles to evaluate their performance in semantic representations across different timeframes. The results indicate that BERT outperforms Word2Vec in maintaining semantic stability and still recognizes subtle semantic variations. These findings support BERT's use in text analysis tasks that require stability, where semantic changes are not assumed, offering a more reliable foundation than static alternatives.

cs.CL↗

arXiv2024

UPdec-Webb: A Dataset for Coaddition of JWST NIRCam Images

We present the application of the image coaddition algorithm, Up-sampling and PSF Deconvolution Coaddition (UPDC), for stacking multiple exposure images captured by the James Webb Space Telescope (JWST) Near-Infrared Camera (NIRCam). By addressing the point spread function (PSF) effect, UPDC provides visually enhanced and sharper images. Furthermore, the anti-aliasing and super-resolution capabilities of UPDC make it easier to deblend sources overlapped on images, yielding a higher accuracy of aperture photometry. We apply this algorithm to the SMACS J0723 imaging data. Comparative analysis with the Drizzle algorithm demonstrates significant improvements in detecting faint sources, achieving accurate photometry, and effectively deblending (super-resolution) closely packed sources. {As a result, we have newly detected a pair of close binary stars that were previously unresolvable in the original exposures or the Drizzled image.} These improvements significantly benefit various scientific projects conducted by JWST. The resulting dataset, named "UPdec-Webb", can be accessible through the official website of the Chinese Virtual Observatory (ChinaVO).

astro-ph.IM↗