arXiv subjects

Zhang Chao

Publications and source records attributed to Zhang Chao.

5 recordsLinked to original sources

arXiv2026

Ekedahl-Oort strata under natural embeddings of orthogonal and unitary Shimura varieties

In this paper, we study the behavior of Ekedahl-Oort strata under natural embeddings between the good reductions modulo $p$ of GSpin Shimura varieties and Rapoport-Smithling-Zhang unitary Shimura varieties, a prototypical setting for the construction of special cycles in the Kudla program. In each case, we determine the EO stratum containing the image of a given EO stratum under the embedding. We also compute discrete invariants of these Shimura varieties, including their $p$-ranks and $a$-numbers; in the GSpin case, these are obtained via the Kuga-Satake embedding.

math.NT↗

arXiv2026

ShipTraj-R1: Reinforcing Ship Trajectory Prediction in Large Language Models via Group Relative Policy Optimization

Recent advancements in reinforcement fine-tuning have significantly improved the reasoning ability of large language models (LLMs). In particular, methods such as group relative policy optimization (GRPO) have demonstrated strong capabilities across various fields. However, applying LLMs to ship trajectory prediction remains largely unexplored. In this paper, we propose ShipTraj-R1, a novel LLM-based framework that reformulates ship trajectory prediction as a text-to-text generation problem. (1) We design a dynamic prompt containing trajectory information about conflicting ships to guide the model to achieve adaptive chain-of-thought (CoT) reasoning. (2) We introduce a comprehensive rule-based reward mechanism to incentivize the reasoning format and prediction accuracy of the model. (3) Our ShipTraj-R1 is reinforced through the GRPO mechanism guided by domain-specific prompts and rewards, and utilizes the Qwen3 as the model backbone. Extensive experimental results on two complex and real-world maritime datasets show that the proposed ShipTraj-R1 achieves the least error compared with state-of-the-art deep learning and LLM-based baselines.

cs.AI↗

arXiv2024

Simultaneous Verification of Genuine Multipartite Nonlocality and Full Network Nonlocality

Genuine multipartite nonlocality and nonlocality arising in networks composed of several independent sources have been separately investigated. While some genuinely entangled states cannot be verified by violating a single Bell-type inequality, a quantum network consisting of different sources allows for the certification of the non-classicality of all sources. In this paper, we propose the first method to verify both types of nonlocality simultaneously in a single experiment. We consider a quantum network comprising a bipartite source and a tripartite source. We demonstrate that there are quantum correlations cannot be simulated if the tripartite source distributes biseparable systems while the bipartite source distributes even stronger-than-quantum systems. These correlations can be used to verify both the genuine multipartite nonlocality of generalized Greenberger-Horne-Zeilinger states and the full network nonlocality that is stronger than all the existing results. Experimentally, we observe both types of nonlocality in a high fidelity photonic quantum network by violating a single network Bell inequality.

quant-ph↗

arXiv2020

Star Formation Occurs in Dense Gas, but What Does "Dense" Mean?

We report results of a project to map HCN and HCO+ J = 1-0 emission toward a sample of molecular clouds in the inner Galaxy, all containing dense clumps that are actively engaged in star formation. We compare these two molecular line tracers with millimeter continuum emission and extinction, as inferred from 13CO, as tracers of dense gas in molecular clouds. The fraction of the line luminosity from each tracer that comes from the dense gas, as measured by AV > 8 mag, varies substantially from cloud to cloud. In all cases, a substantial fraction (in most cases, the majority) of the total luminosity arises in gas below the AV > 8 mag threshold and outside the region of strong mm continuum emission. Measurements of the luminosity of HCN toward other galaxies will likely be dominated by such gas at lower surface density. Substantial, even dominant, contributions to the total line luminosity can arise in gas with densities typical of the cloud as a whole (densities about 100 per cubic cm). Defining the dense clump from the HCN or HCO+ emission itself, similarly to previous studies, leads to a wide range of clump properties, with some being considerably larger and less dense than in previous studies. HCN and HCO+ have similar ability to trace dense gas for the clouds in this sample. For the two clouds with low virial parameters, the 13CO is definitely a worse tracer of the dense gas, but for the other four, it is equally good (or bad) at tracing dense gas.

astro-ph.GA↗

arXiv2011

I Know Where You are and What You are Sharing: Exploiting P2P Communications to Invade Users' Privacy

In this paper, we show how to exploit real-time communication applications to determine the IP address of a targeted user. We focus our study on Skype, although other real-time communication applications may have similar privacy issues. We first design a scheme that calls an identified targeted user inconspicuously to find his IP address, which can be done even if he is behind a NAT. By calling the user periodically, we can then observe the mobility of the user. We show how to scale the scheme to observe the mobility patterns of tens of thousands of users. We also consider the linkability threat, in which the identified user is linked to his Internet usage. We illustrate this threat by combining Skype and BitTorrent to show that it is possible to determine the file-sharing usage of identified users. We devise a scheme based on the identification field of the IP datagrams to verify with high accuracy whether the identified user is participating in specific torrents. We conclude that any Internet user can leverage Skype, and potentially other real-time communication systems, to observe the mobility and file-sharing usage of tens of millions of identified users.

cs.NI↗