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Researcher profile

An Wang

An Wang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computation and LanguageArtificial Intelligencecond-mat.str-elcond-mat.supr-conMachine LearningApplicationsComputer Visioncond-mat.mtrl-sciCryptography and Securitymath.CVMultiagent SystemsNeural and Evolutionary Computingphysics.opticsquant-ph

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Published work

11 published item(s)

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
preprint2026arXiv

The Kähler submanifolds between the ball bundles and the complex Euclidean space

In this paper, we provide a sufficient condition on the non-existence of the common Kähler submanifolds between the complex Euclidean space and the ball bundles of some Hermitian vector bundles over Kähler manifolds. Then we get the non-existence theorems on several classes of ball bundles whose base spaces are Hermitian symmetric spaces or the complete Kähler-Einstein manifolds.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Evaluating the Performance of Low-Cost PM2.5 Sensors in Mobile Settings

Low-cost sensors (LCS) for measuring air pollution are increasingly being deployed in mobile applications but questions concerning the quality of the measurements remain unanswered. For example, what is the best way to correct LCS data in a mobile setting? Which factors most significantly contribute to differences between mobile LCS data and higher-quality instruments? Can data from LCS be used to identify hotspots and generate generalizable pollutant concentration maps? To help address these questions we deployed low-cost PM2.5 sensors (Alphasense OPC-N3) and a research-grade instrument (TSI DustTrak) in a mobile laboratory in Boston, MA, USA. We first collocated these instruments with stationary PM2.5 reference monitors at nearby regulatory sites. Next, using the reference measurements, we developed different models to correct the OPC-N3 and DustTrak measurements, and then transferred the corrections to the mobile setting. We observed that more complex correction models appeared to perform better than simpler models in the stationary setting; however, when transferred to the mobile setting, corrected OPC-N3 measurements agreed less well with corrected DustTrak data. In general, corrections developed using minute-level collocation measurements transferred better to the mobile setting than corrections developed using hourly-averaged data. Mobile laboratory speed, OPC-N3 orientation relative to the direction of travel, date, hour-of-the-day, and road class together explain a small but significant amount of variation between corrected OPC-N3 and DustTrak measurements during the mobile deployment. Persistent hotspots identified by the OPC-N3s agreed with those identified by the DustTrak. Similarly, maps of PM2.5 distribution produced from the mobile corrected OPC-N3 and DustTrak measurements agreed well.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Family of Lanthanide Noncentrosymmetric Superconductors La$_4$$TX$ ($T$ = Ru, Rh, Ir; $X$ = Al, In)

We report the discovery of superconductivity in a series of noncentrosymmetric compounds La$_4$$TX$ ($T$ = Ru, Rh, Ir; $X$ = Al, In), which have a cubic crystal structure with space group $F\bar{4}3m$. La$_4$RuAl, La$_4$RhAl, La$_4$IrAl, La$_4$RuIn and La$_4$IrIn exhibit bulk superconducting transitions with critical temperatures $T_c$ of 1.77 K, 3.05 K, 1.54 K, 0.58 K and 0.93 K, respectively. The specific heat of the La$_4$$T$Al compounds are consistent with an $s$-wave model with a fully open superconducting gap. In all cases, the upper critical fields are well described by the Werthamer-Helfand-Hohenberg model, and the values are well below the Pauli limit, indicating that orbital limiting is the dominant pair-breaking mechanism. Density functional theory (DFT) calculations reveal that the degree of band splitting by the antisymmetric spin-orbit coupling (ASOC) shows considerable variation between the different compounds. This indicates that the strength of the ASOC is highly tunable across this series of superconductors, suggesting that these are good candidates for examining the relationship between the ASOC and superconducting properties in noncentrosymmetric superconductors.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A general scheme of differential imaging employing weak measurement

We propose and experimentally realize a general scheme of differential imaging employing the idea of weak measurement. We show that the weak coupling between the system of interest and a two-level ancilla can introduce a two-beam circuit after an arbitrary pre-selection of the ancilla. By choosing the post-selection orthogonal to the pre-selection measurement, an effective imaging platform based on differential operations is shown achieved. Experimental results on both the Sagnac interferometer and ultra-thin Wollaston prism demonstrate that our imaging scheme successfully yields the boundary information of complex geometric configurations.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Rethinking Surgical Instrument Segmentation: A Background Image Can Be All You Need

Data diversity and volume are crucial to the success of training deep learning models, while in the medical imaging field, the difficulty and cost of data collection and annotation are especially huge. Specifically in robotic surgery, data scarcity and imbalance have heavily affected the model accuracy and limited the design and deployment of deep learning-based surgical applications such as surgical instrument segmentation. Considering this, we rethink the surgical instrument segmentation task and propose a one-to-many data generation solution that gets rid of the complicated and expensive process of data collection and annotation from robotic surgery. In our method, we only utilize a single surgical background tissue image and a few open-source instrument images as the seed images and apply multiple augmentations and blending techniques to synthesize amounts of image variations. In addition, we also introduce the chained augmentation mixing during training to further enhance the data diversities. The proposed approach is evaluated on the real datasets of the EndoVis-2018 and EndoVis-2017 surgical scene segmentation. Our empirical analysis suggests that without the high cost of data collection and annotation, we can achieve decent surgical instrument segmentation performance. Moreover, we also observe that our method can deal with novel instrument prediction in the deployment domain. We hope our inspiring results will encourage researchers to emphasize data-centric methods to overcome demanding deep learning limitations besides data shortage, such as class imbalance, domain adaptation, and incremental learning. Our code is available at https://github.com/lofrienger/Single_SurgicalScene_For_Segmentation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Semi-Supervised Formality Style Transfer with Consistency Training

Formality style transfer (FST) is a task that involves paraphrasing an informal sentence into a formal one without altering its meaning. To address the data-scarcity problem of existing parallel datasets, previous studies tend to adopt a cycle-reconstruction scheme to utilize additional unlabeled data, where the FST model mainly benefits from target-side unlabeled sentences. In this work, we propose a simple yet effective semi-supervised framework to better utilize source-side unlabeled sentences based on consistency training. Specifically, our approach augments pseudo-parallel data obtained from a source-side informal sentence by enforcing the model to generate similar outputs for its perturbed version. Moreover, we empirically examined the effects of various data perturbation methods and propose effective data filtering strategies to improve our framework. Experimental results on the GYAFC benchmark demonstrate that our approach can achieve state-of-the-art results, even with less than 40% of the parallel data.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Spin-triplet superconductivity in Weyl nodal-line semimetals

Topological semimetals are three dimensional materials with symmetry-protected massless bulk excitations. As a special case, Weyl nodal-line semimetals are realized in materials either having no inversion or broken time-reversal symmetry and feature bulk nodal lines. The 111-family of materials, LaNiSi, LaPtSi and LaPtGe (all lacking inversion symmetry), belong to this class. Here, by combining muon-spin rotation and relaxation with thermodynamic measurements, we find that these materials exhibit a fully-gapped superconducting ground state, while spontaneously breaking time-reversal symmetry at the superconducting transition. Since time-reversal symmetry is essential for protecting the normal-state topology, its breaking upon entering the superconducting state should remarkably result in a topological phase transition. By developing a minimal model for the normal-state band structure and assuming a purely spin-triplet pairing, we show that the superconducting properties across the family can be described accurately. Our results demonstrate that the 111-family reported here provides an ideal test-bed for investigating the rich interplay between the exotic properties of Weyl nodal-line fermions and unconventional superconductivity.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Towards Effective Multi-Task Interaction for Entity-Relation Extraction: A Unified Framework with Selection Recurrent Network

Entity-relation extraction aims to jointly solve named entity recognition (NER) and relation extraction (RE). Recent approaches use either one-way sequential information propagation in a pipeline manner or two-way implicit interaction with a shared encoder. However, they still suffer from poor information interaction due to the gap between the different task forms of NER and RE, raising a controversial question whether RE is really beneficial to NER. Motivated by this, we propose a novel and unified cascade framework that combines the advantages of both sequential information propagation and implicit interaction. Meanwhile, it eliminates the gap between the two tasks by reformulating entity-relation extraction as unified span-extraction tasks. Specifically, we propose a selection recurrent network as a shared encoder to encode task-specific independent and shared representations and design two sequential information propagation strategies to realize the sequential information flow between NER and RE. Extensive experiments demonstrate that our approaches can achieve state-of-the-art results on two common benchmarks, ACE05 and SciERC, and effectively model the multi-task interaction, which realizes significant mutual benefits of NER and RE.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

On the Performance of Generative Adversarial Network (GAN) Variants: A Clinical Data Study

Generative Adversarial Network (GAN) is a useful type of Neural Networks in various types of applications including generative models and feature extraction. Various types of GANs are being researched with different insights, resulting in a diverse family of GANs with a better performance in each generation. This review focuses on various GANs categorized by their common traits.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Strange metal behavior in a pure ferromagnetic Kondo lattice

The strange metal phases found to develop in a wide range of materials near a quantum critical point (QCP), have posed a long-standing mystery. The frequent association of strange metals with unconventional superconductivity and antiferromagnetic QCPs has led to a belief that they are highly entangled quantum states. Ferromagnets, by contrast are regarded as an unlikely setting for strange metals, for they are weakly entangled and their QCPs are often interrupted by competing phases or first order phase transitions. Here, we provide compelling evidence that the stoichiometric heavy fermion ferromagnet CeRh$_6$Ge$_4$ becomes a strange metal at a pressure-induced QCP: specific heat and resistivity measurements demonstrate that the FM transition is continuously suppressed to zero temperature revealing a strange metal phase. We argue that strong magnetic anisotropy plays a key role in this process,injecting entanglement, in the form of triplet resonating valence bonds (tRVBs) into the ordered ferromagnet. We show that the singular transformation from tRVBs into Kondo singlets that occurs at the QCP causes a jump in the Fermi surface volume: a key driver of strange metallic behavior. Our results open up a new direction for research into FM quantum criticality, while also establishing an important new setting for the strange metal problem. Most importantly, strange metallic behavior at a FM quantum critical point suggests that it is quantum entanglement rather than the destruction of antiferromagnetism that is the common driver of the many varied examples of strange metallic behavior.

0 citations0 reviewsNo code linkedOpen access