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Researcher profile

Chang Jin

Chang Jin contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computation and LanguageArtificial Intelligenceastro-ph.IMastro-ph.SRCryptography and Securityhep-exMachine LearningMultiagent Systemsquant-ph

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Published work

4 published item(s)

preprint2026arXiv

Probing negative differential resistance in silicon with a P-I-N diode-integrated T center ensemble

Solid-state defect quantum systems are exquisite probes of their local charge environment. Nonlinear dynamical electric fields in solids are challenging to characterize directly, conventionally limited to coarse macroscopic methods which fail to capture subtle effects in the material. Here, through transient optical spectroscopy on an embedded T center ensemble, we realize the in-situ observation of a silicon PIN-diode phase transition to a regime of self-sustained carrier oscillatory dynamics characteristic of negative differential resistance. Manifest in both the ensemble electroluminescence and photoluminescence, we find a temperature and field-dependent phase space for persistent undamped amplitude oscillations indicative of a collective ensemble response to the field dynamics. These findings shed new light on the cryogenic behavior of silicon, provide fundamental insight into the physics of the T center for improved quantum device performance, and open a promising new direction for defect-based local quantum sensing in semiconductor devices.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

SkillSafetyBench: Evaluating Agent Safety under Skill-Facing Attack Surfaces

Reusable skills are becoming a common interface for extending large language model agents, packaging procedural guidance with access to files, tools, memory, and execution environments. However, this modularity introduces attack surfaces that are largely missed by existing safety evaluations: even when the user request is benign, task-relevant skill materials or local artifacts can steer an agent toward unsafe actions. We present SkillSafetyBench, a runnable benchmark for evaluating such skill-mediated safety failures. SkillSafetyBench includes 155 adversarial cases across 47 tasks, 6 risk domains, and 30 safety categories, each evaluated with a case-specific rule-based verifier. Experiments with multiple CLI agents and model backends show that localized non-user attacks can consistently induce unsafe behavior, with distinct failure patterns across domains, attack methods, and scaffold-model pairings. Our findings suggest that agent safety depends not only on model-level alignment, but also on how agents interpret skills, trust workflow context, and act through executable environments.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

AdMix: A Mixed Sample Data Augmentation Method for Neural Machine Translation

In Neural Machine Translation (NMT), data augmentation methods such as back-translation have proven their effectiveness in improving translation performance. In this paper, we propose a novel data augmentation approach for NMT, which is independent of any additional training data. Our approach, AdMix, consists of two parts: 1) introduce faint discrete noise (word replacement, word dropping, word swapping) into the original sentence pairs to form augmented samples; 2) generate new synthetic training data by softly mixing the augmented samples with their original samples in training corpus. Experiments on three translation datasets of different scales show that AdMix achieves signifi cant improvements (1.0 to 2.7 BLEU points) over strong Transformer baseline. When combined with other data augmentation techniques (e.g., back-translation), our approach can obtain further improvements.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Design and verification of the HXI collimator onboard the ASO-S mission

A space-borne hard X-ray collimator, comprising 91 pairs of grids, has been developed for the Hard X-ray Imager (HXI). The HXI is one of the three scientific instruments onboard the first Chinese solar mission: the Advanced Space-based Solar Observatory (ASO-S). The HXI collimator (HXI-C) is a spatial modulation X-ray telescope designed to observe hard X-rays emitted by energetic electrons in solar flares. This paper presents the detailed design of the HXI-C for the qualification model that will be inherited by the flight model. Series tests on the HXI-C qualification model are reported to verify the ability of the HXI-C to survive the launch and to operate normally in on-orbit environments. Furthermore, results of the X-ray beam test for the HXI-C are presented to indirectly identify the working performance of the HXI-C.

0 citations0 reviewsNo code linkedOpen access