arXiv subjects

Chen Xia

Publications and source records attributed to Chen Xia.

17 recordsLinked to original sources

arXiv2026

DH-VLM: Dual-Horizon Cooperative Latent Reasoning for Autonomous Driving

Large-scale language models for autonomous driving enable enhanced global understanding and long-horizon planning. However, when deployed in isolated vehicles, limited sensing range and occlusions restrict reliable decision-making, and the substantial computational and latency overhead makes on-board deployment impractical. Cooperative driving provides a potential solution by leveraging external agents for information exchange, but existing methods remain limited in semantic reasoning capability under practical constraints. To address these challenges, we propose DH-VLM, a dual-horizon cooperative latent reasoning framework that enables asymmetric semantic cooperation between the infrastructure and ego vehicle. The infrastructure aggregates multi-layer hidden states to form a global-reasoning horizon latent guidance, which is integrated into the ego model through an Infrastructure-Driven Latent Evolution mechanism for conditional latent refinement. This enables the ego vehicle to leverage long-range contextual understanding while preserving autonomous decision-making within its local planning horizon. Furthermore, we construct a cooperation-oriented question-answer (QA) dataset covering fundamental scene understanding and ego-personalized comprehension to support counterfactual and safety-aware reasoning. Extensive experiments demonstrate that DH-VLM achieves state-of-the-art planning performance, outperforming the previous state of the art by 14.6% in L2 error and 26.9% in collision rate. Compared with query-based end-to-end cooperative driving methods, our approach reduces the communication cost by 57.3% and GPU memory usage by 25.5%, while maintaining strong robustness against infrastructure guidance errors, providing a practical and robust paradigm for cooperative autonomous driving.

cs.RO↗

arXiv2026

Empirical Grounding Improves the Realism of LLM Agents Simulating Human Behavior During Disruptions

Large language model (LLM) agents offer a generative approach to simulating human behavior under conditions that may have few or no direct historical analogues, a common challenge in disaster and infrastructure-disruption planning. However, this generative capacity creates a validity problem: individually plausible agent reasoning may fail to reproduce empirical population behavior. We evaluate whether empirical grounding improves the statistical realism of LLM-agent simulations during disruptions. Specifically, we develop an empirically grounded LLM-agent framework that embeds demographic profiles from the American Community Survey, baseline routines from the American Time Use Survey, and urban spatial context into agent initialization, memory, decision prompts, and activity execution. An independent household survey conducted during the July 2024 Philadelphia heatwave is reserved as an external validation benchmark. Compared with an ungrounded LLM-agent baseline, the grounded model improved reconstruction of normal daily routines, increasing mean correlation with empirical activity profiles from 0.528 to 0.912 and reducing mean squared error from 0.066 to 0.008. Under heatwave conditions, the grounded model better reproduced survey-derived activity profiles, increasing mean correlation from 0.349 to 0.836 and reducing mean squared error from 0.098 to 0.012. The grounded model captured 46.4% of observed heatwave response amplitude, compared with 20.6% for the ungrounded baseline. These findings show that empirical grounding can make LLM agents more statistically credible simulators of population behavior while revealing remaining gaps in modeling human adaptation during disruptions.

cs.AI↗

arXiv2026

Dark Matter Attenuation inside the Earth: A Boltzmann Equation Approach

For strongly interacting or boosted dark matter, propagation through the Earth can involve sizable scattering and energy loss, reshaping the underground flux in energy, direction, and normalization. Scattered particles may still fall within the detector acceptance, so the detector-side signal depends on phase-space transport from the Earth's surface to the underground detector. In this work, we formulate this transport problem with the Boltzmann equation. Its integral solution organizes successive scattering effects as a deterministic expansion in scattering orders. We analyze the transport equation in flat-Earth and spherical-Earth geometries, and apply the method to Dirac dark matter with an isoscalar vector interaction. The iterative solution agrees well with the Monte Carlo spectrum.

hep-ph↗

arXiv2025

UniMM-V2X: MoE-Enhanced Multi-Level Fusion for End-to-End Cooperative Autonomous Driving

Autonomous driving holds transformative potential but remains fundamentally constrained by the limited perception and isolated decision-making with standalone intelligence. While recent multi-agent approaches introduce cooperation, they often focus merely on perception-level tasks, overlooking the alignment with downstream planning and control, or fall short in leveraging the full capacity of the recent emerging end-to-end autonomous driving. In this paper, we present UniMM-V2X, a novel end-to-end multi-agent framework that enables hierarchical cooperation across perception, prediction, and planning. At the core of our framework is a multi-level fusion strategy that unifies perception and prediction cooperation, allowing agents to share queries and reason cooperatively for consistent and safe decision-making. To adapt to diverse downstream tasks and further enhance the quality of multi-level fusion, we incorporate a Mixture-of-Experts (MoE) architecture to dynamically enhance the BEV representations. We further extend MoE into the decoder to better capture diverse motion patterns. Extensive experiments on the DAIR-V2X dataset demonstrate our approach achieves state-of-the-art (SOTA) performance with a 39.7% improvement in perception accuracy, a 7.2% reduction in prediction error, and a 33.2% improvement in planning performance compared with UniV2X, showcasing the strength of our MoE-enhanced multi-level cooperative paradigm.

cs.RO↗

arXiv2024

V2I-Calib++: A Multi-terminal Spatial Calibration Approach in Urban Intersections for Collaborative Perception

Urban intersections, dense with pedestrian and vehicular traffic and compounded by GPS signal obstructions from high-rise buildings, are among the most challenging areas in urban traffic systems. Traditional single-vehicle intelligence systems often perform poorly in such environments due to a lack of global traffic flow information and the ability to respond to unexpected events. Vehicle-to-Everything (V2X) technology, through real-time communication between vehicles (V2V) and vehicles to infrastructure (V2I), offers a robust solution. However, practical applications still face numerous challenges. Calibration among heterogeneous vehicle and infrastructure endpoints in multi-end LiDAR systems is crucial for ensuring the accuracy and consistency of perception system data. Most existing multi-end calibration methods rely on initial calibration values provided by positioning systems, but the instability of GPS signals due to high buildings in urban canyons poses severe challenges to these methods. To address this issue, this paper proposes a novel multi-end LiDAR system calibration method that does not require positioning priors to determine initial external parameters and meets real-time requirements. Our method introduces an innovative multi-end perception object association technique, utilizing a new Overall Distance metric (oDist) to measure the spatial association between perception objects, and effectively combines global consistency search algorithms with optimal transport theory. By this means, we can extract co-observed targets from object association results for further external parameter computation and optimization. Extensive comparative and ablation experiments conducted on the simulated dataset V2X-Sim and the real dataset DAIR-V2X confirm the effectiveness and efficiency of our method. The code for this method can be accessed at: https://github.com/MassimoQu/v2i-calib.

cs.RO↗

arXiv2024

Search for Cosmic-ray Boosted Sub-MeV Dark-Matter-Electron Scattering in PandaX-4T

We report the first search for the elastic scatterings between cosmic-ray boosted sub-MeV dark matter and electrons in the PandaX-4T liquid xenon experiment. Sub-MeV dark matter particles can be accelerated by scattering with electrons in the cosmic rays and produce detectable electron recoil signals in the detector. Using the commissioning data from PandaX-4T of 0.63~tonne$\cdot$year exposure, we set new constraints on DM-electron scattering cross sections for DM masses ranging from 10~eV/$c^2$ to 3~keV/$c^2$.

hep-ex↗

arXiv2024

Nuclear Production and Analytic Attenuation of Energetic MeV Solar Dark Matter

We propose a solar production mechanism of MeV dark matter to overcome the energy threshold in direct detection experiments. In particular, the proton and deuteron fussion to ${}^3 \mathrm{He}$ of the $pp$ chain that produces energetic neutrino and gamma photon with 5.5$\,$MeV of energy release can also produce a pair of dark matter particles. Besides, we establish an analytical formalism of using the Boltzmann equation to study the solar attenuation effect on the produced dark matter flux. The projected sensitivity is illustrated with Argon target at the DarkSide-LowMass experiment.

hep-ph↗

arXiv2024

Boosted Dark Matter From Centaurus A and Its Detection

Dark matter can be boosted by high energy particles in astrophysical environments through elastic scattering. We study the production of boosted dark matter via scattering with electrons in the relativistic jet of the closest active galactic nucleus, Centaurus A, and its detection in the Super-Kamiokande experiment. Since there are a huge number of electrons in the jet and dark matter is extremely dense around the supermassive black hole that powers the jet, the number of boosted dark matter is tremendously large. Compared to boosted dark matter from blazars, the dark matter flux from Centaurus A is enhanced due to the proximity of Centaurus A. The constraint on dark matter-electron scattering cross section set by Super-Kamiokande is more stringent, down to $\sim 10^{-36} \, \mathrm{cm}^2$ for $\mathrm{MeV}$ dark matter.

hep-ph↗

arXiv2023

Diurnal modulation of electron recoils from DM-nucleon scattering through the Migdal effect

Halo dark matter (DM) particles could lose energy due to the scattering off nuclei within the Earth before reaching the underground detectors of DM direct detection experiments. This Earth shielding effect can result in diurnal modulation of the DM-induced recoil event rates observed underground due to the self-rotation of the Earth. For electron recoil signals from DM-electron scatterings, the current experimental constraints are very stringent such that the diurnal modulation cannot be observed for halo DM. We propose a novel type of diurnal modulation effect: diurnal modulation in electron recoil signals induced by DM-nucleon scattering via the Migdal effect. We set so far the most stringent constraints on DM-nucleon scattering cross section via the Migdal effect for sub-GeV DM using the S2-only data of PandaX-II and PandaX-4T with improved simulations of the Earth shielding effect. Based on the updated constraints, we show that the Migdal effect induced diurnal modulation of electron events can still be significant in the low energy region, and can be probed by experiments such as PandaX-4T in the near future.

hep-ph↗

arXiv2023

Azimuthal asymmetry in cosmic-ray boosted dark matter flux

Light halo dark matter (DM) particles up-scattered by high-energy cosmic rays (referred to as CRDM) can be energetic and become detectable at conventional DM and neutrino experiments. We show that the CRDM flux has a novel and detectable morphological feature. Unlike most of the recently proposed boosted DM (BDM) models which predict azimuthally symmetric DM fluxes around the Galactic Center, the CRDM flux breaks the azimuthal symmetry significantly. Using cosmic-ray electron distribution in the whole Galaxy and optimized search region in the sky according to the morphology of the CRDM flux, we derive so far the most stringent constraints on the DM-electron scattering cross section from the Super-Kamiokande (SK) IV data, which improves the previous constraints from the SK-IV full-sky data by more than an order of magnitude. Based on the improved constraints, we predict that the azimuthal symmetry-breaking effect can be observed in the future Hyper-Kamiokande experiment at $\sim 3σ$ level.

hep-ph↗

arXiv2022

An activity-based spatial-temporal community electricity vulnerability assessment framework

The power system is among the most important critical infrastructures in urban cities and is getting increasingly essential in supporting people s daily activities. However, it is also susceptible to most natural disasters such as tsunamis, floods, or earthquakes. Electricity vulnerability, therefore, forms a crucial basis for community resilience. This paper aims to present an assessment framework of spatial-temporal electricity vulnerability to support the building of community resilience against power outages. The framework includes vulnerability indexes in terms of occupant demographics, occupant activity patterns, and urban building characteristics. To integrate factors in these aspects, we also proposed a process as activity simulation-mapping-evaluation-visualization to apply the framework and visualize results. This framework can help planners make an effective first-time response by identifying the most vulnerable areas when a massive power outage happens during natural disasters. It can also be integrated into community resilience analysis models and potentially contributes to effective disaster risk management

eess.SY↗

arXiv2022

Community Time-Activity Trajectory Modelling based on Markov Chain Simulation and Dirichlet Regression

Accurate modeling of human time-activity trajectory is essential to support community resilience and emergency response strategies such as daily energy planning and urban seismic vulnerability assessment. However, existing modeling of time-activity trajectory is only driven by socio-demographic information with identical activity trajectories shared among the same group of people and neglects the influence of the environment. To further improve human time-activity trajectory modeling, this paper constructs community time-activity trajectory and analyzes how social-demographic and built environment influence people s activity trajectory based on Markov Chains and Dirichlet Regression. We use the New York area as a case study and gather data from American Time Use Survey, Policy Map, and the New York City Energy & Water Performance Map to evaluate the proposed method. To validate the regression model, Box s M Test and T-test are performed with 80% data training the model and the left 20% as the test sample. The modeling results align well with the actual human behavior trajectories, demonstrating the effectiveness of the proposed method. It also shows that both social-demographic and built environment factors will significantly impact a community's time-activity trajectory. Specifically, 1) Diversity and median age both have a significant influence on the proportion of time people assign to education activity. 2) Transportation condition affects people s activity trajectory in the way that longer commute time decreases the proportion of biological activity (eg. sleeping and eating) and increases people s working time. 3) Residential density affects almost all activities with a significant p-value for all biological needs, household management, working, education, and personal preference.

eess.SY↗

arXiv2022

Dual Domain-Adversarial Learning for Audio-Visual Saliency Prediction

Both visual and auditory information are valuable to determine the salient regions in videos. Deep convolution neural networks (CNN) showcase strong capacity in coping with the audio-visual saliency prediction task. Due to various factors such as shooting scenes and weather, there often exists moderate distribution discrepancy between source training data and target testing data. The domain discrepancy induces to performance degradation on target testing data for CNN models. This paper makes an early attempt to tackle the unsupervised domain adaptation problem for audio-visual saliency prediction. We propose a dual domain-adversarial learning algorithm to mitigate the domain discrepancy between source and target data. First, a specific domain discrimination branch is built up for aligning the auditory feature distributions. Then, those auditory features are fused into the visual features through a cross-modal self-attention module. The other domain discrimination branch is devised to reduce the domain discrepancy of visual features and audio-visual correlations implied by the fused audio-visual features. Experiments on public benchmarks demonstrate that our method can relieve the performance degradation caused by domain discrepancy.

cs.CV↗

arXiv2022

Production and attenuation of cosmic-ray boosted dark matter

Light sub-GeV halo dark matter (DM) particles up-scattered by high-energy cosmic-rays (CRs) (referred to as CRDM) can be energetic and become detectable by conventional DM direct detection experiments. We perform a refined analysis on the exclusion bounds of the spin-independent DM-nucleon scattering cross section $σ_{χp}$ in this approach. For the exclusion lower bounds, we determine the parameter of the effective distance $D_\text{eff}$ for CRDM production using spatial-dependent CR fluxes and including the contributions from the major heavy CR nuclear species. We obtain $D_\text{eff}\simeq 9$ kpc for CRDM particles with kinetic energy above $\sim 1~\text{GeV}$, which pushes the corresponding exclusion lower bounds down to $σ_{χp} \sim 4\times 10^{-32}~\text{cm}^2$ for DM particle mass at MeV scale and below. For the exclusion upper bounds from Earth attenuation, previous estimations neglecting the nuclear form factor leaded to typical exclusion upper bounds of $σ_{χp}\sim\mathcal{O}(10^{-28})~\text{cm}^2$ from the XENON1T data. Using both the analytic and numerical approaches, we show that for CRDM particles, the presence of the nuclear form factor strongly suppresses the effect of Earth attenuation. Consequently, the cross section that can be excluded by the XENON1T data can be a few orders of magnitude higher, which closes the gap in the cross sections excluded by the XENON1T experiment and that by the astrophysical measurements such that for the cosmic microwave background (CMB), galactic gas cloud cooling, and structure formation, etc..

hep-ph↗

arXiv2021

Constraining light dark matter upscattered by ultrahigh-energy cosmic rays

Light halo dark matter (DM) particles upscattered by high-energy cosmic rays (CRs) can be energetic, and become detectable by conventional direct detection experiments. The current constraints derived from space-based direct CR measurements can reach $\mathcal{O}(10^{-31})\text{ cm}^{2}$ for a constant DM-nucleon scattering cross section. We show that if the CR energy spectrum follows a power law of type $\sim E^{-3}$, the derived constraints on the scattering cross section will be highly insensitive to DM particle mass. This suggests that ultrahigh-energy CRs (UHECRs) indirectly measured by ground-based detectors can be used to place constraints on ultralight DM particles, as $E^{-3}$ is a very good approximation of the UHECR energy spectrum up to energy $\sim10^{20}\text{ eV}$. Using the recent UHECR flux data, we show that the current constraints derived from space-based CR measurements can in principle be extended to ultralight DM particles far below eV scale.

hep-ph↗

arXiv2020

Unsupervised Morphological Paradigm Completion

We propose the task of unsupervised morphological paradigm completion. Given only raw text and a lemma list, the task consists of generating the morphological paradigms, i.e., all inflected forms, of the lemmas. From a natural language processing (NLP) perspective, this is a challenging unsupervised task, and high-performing systems have the potential to improve tools for low-resource languages or to assist linguistic annotators. From a cognitive science perspective, this can shed light on how children acquire morphological knowledge. We further introduce a system for the task, which generates morphological paradigms via the following steps: (i) EDIT TREE retrieval, (ii) additional lemma retrieval, (iii) paradigm size discovery, and (iv) inflection generation. We perform an evaluation on 14 typologically diverse languages. Our system outperforms trivial baselines with ease and, for some languages, even obtains a higher accuracy than minimally supervised systems.

cs.CL↗

arXiv2019

Visualizing Trends of Key Roles in News Articles

There are tons of news articles generated every day reflecting the activities of key roles such as people, organizations and political parties. Analyzing these key roles allows us to understand the trends in news. In this paper, we present a demonstration system that visualizes the trend of key roles in news articles based on natural language processing techniques. Specifically, we apply a semantic role labeler and the dynamic word embedding technique to understand relationships between key roles in the news across different time periods and visualize the trends of key role and news topics change over time.

cs.CL↗