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Yiyang Zhang

Yiyang Zhang appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Machine LearningComputer Visioncond-mat.mes-hallcond-mat.mtrl-sciArtificial Intelligenceastro-ph.HEComputation and Languagegr-qchep-phhep-thphysics.atm-clusphysics.optics

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Published work

9 published item(s)

preprint2026arXiv

On an approach to canonicalizing elliptic Feynman integrals

We present generic expressions for the integrands of canonical bases under maximal cut in elliptic Feynman integral families with multiple kinematic scales. Such integrals frequently arise in phenomenologically relevant scattering processes. The derivation of our results starts from the Legendre normal form of elliptic curves, where the geometric properties of the curves are simple and explicit, and further kinematic singularities are presented as marked points. The simplicity of the normal form allows a straightforward construction of canonical bases with an arbitrary number of marked points. They can then be mapped into any univariate elliptic integral families via an appropriate Möbius transformation, leading to universal expressions for the integrands. As a demonstration, we discuss the application of our method to several concrete examples, including two new integral families whose canonical bases were not available in the literature. In several examples, we derive canonical bases for the full integral families without any cuts, demonstrating the simplicity of the sub-sector dependence of our canonical bases.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Ultrasound Vision-Language Alignment via Contrastive Learning

Ultrasound foundation models have achieved strong performance on structured prediction tasks but remain exclusively vision-based, limiting zero-shot and few-shot transfer to novel tasks where task-specific annotation is scarce. We address this gap with EchoCare-CLIP, a CLIP-style dual-encoder contrastive framework that aligns ultrasound images with clinical text in a shared embedding space. We curate a multi-organ corpus of over 16K image-text pairs spanning breast, liver, lung, and thyroid, with over 78% of captions derived from expert-annotated reports, and complement the remainder with a three-tier template-based and LLM-based caption generation pipeline. We evaluate model configurations spanning two text encoder families (CLIP, BioClinicalBERT) and two caption strategies (template-based, LLM-generated) against OpenAI CLIP and BiomedCLIP baselines. Our trained models consistently improve cross-modal alignment over baselines, with the best configuration achieving a paired alignment score of 0.682. However, stronger alignment does not guarantee better downstream performance: CLIP-based variants with partial fine-tuning achieve the strongest zero-shot classification on external held-out datasets (0.709 on BUSI; 0.626 on AULI), while full end-to-end fine-tuning degrades transfer due to overfitting. On linear probing and few-shot adaptation, model rankings are dataset-dependent, reflecting a trade-off between domain adaptation and representational generalizability. We further show that template-based captions match or outperform LLM-generated captions, suggesting lexical diversity is not a proxy for caption quality. Taken together, our results demonstrate that ultrasound vision-language alignment is achievable from public data alone, but robust clinical transfer requires careful balancing of domain adaptation, encoder capacity, and caption supervision quality.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Differentiate ChatGPT-generated and Human-written Medical Texts

Background: Large language models such as ChatGPT are capable of generating grammatically perfect and human-like text content, and a large number of ChatGPT-generated texts have appeared on the Internet. However, medical texts such as clinical notes and diagnoses require rigorous validation, and erroneous medical content generated by ChatGPT could potentially lead to disinformation that poses significant harm to healthcare and the general public. Objective: This research is among the first studies on responsible and ethical AIGC (Artificial Intelligence Generated Content) in medicine. We focus on analyzing the differences between medical texts written by human experts and generated by ChatGPT, and designing machine learning workflows to effectively detect and differentiate medical texts generated by ChatGPT. Methods: We first construct a suite of datasets containing medical texts written by human experts and generated by ChatGPT. In the next step, we analyze the linguistic features of these two types of content and uncover differences in vocabulary, part-of-speech, dependency, sentiment, perplexity, etc. Finally, we design and implement machine learning methods to detect medical text generated by ChatGPT. Results: Medical texts written by humans are more concrete, more diverse, and typically contain more useful information, while medical texts generated by ChatGPT pay more attention to fluency and logic, and usually express general terminologies rather than effective information specific to the context of the problem. A BERT-based model can effectively detect medical texts generated by ChatGPT, and the F1 exceeds 95%.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation

In unsupervised domain adaptation (UDA), classifiers for the target domain are trained with massive true-label data from the source domain and unlabeled data from the target domain. However, it may be difficult to collect fully-true-label data in a source domain given a limited budget. To mitigate this problem, we consider a novel problem setting where the classifier for the target domain has to be trained with complementary-label data from the source domain and unlabeled data from the target domain named budget-friendly UDA (BFUDA). The key benefit is that it is much less costly to collect complementary-label source data (required by BFUDA) than collecting the true-label source data (required by ordinary UDA). To this end, the complementary label adversarial network (CLARINET) is proposed to solve the BFUDA problem. CLARINET maintains two deep networks simultaneously, where one focuses on classifying complementary-label source data and the other takes care of the source-to-target distributional adaptation. Experiments show that CLARINET significantly outperforms a series of competent baselines.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Learning from a Complementary-label Source Domain: Theory and Algorithms

In unsupervised domain adaptation (UDA), a classifier for the target domain is trained with massive true-label data from the source domain and unlabeled data from the target domain. However, collecting fully-true-label data in the source domain is high-cost and sometimes impossible. Compared to the true labels, a complementary label specifies a class that a pattern does not belong to, hence collecting complementary labels would be less laborious than collecting true labels. Thus, in this paper, we propose a novel setting that the source domain is composed of complementary-label data, and a theoretical bound for it is first proved. We consider two cases of this setting, one is that the source domain only contains complementary-label data (completely complementary unsupervised domain adaptation, CC-UDA), and the other is that the source domain has plenty of complementary-label data and a small amount of true-label data (partly complementary unsupervised domain adaptation, PC-UDA). To this end, a complementary label adversarial network} (CLARINET) is proposed to solve CC-UDA and PC-UDA problems. CLARINET maintains two deep networks simultaneously, where one focuses on classifying complementary-label source data and the other takes care of source-to-target distributional adaptation. Experiments show that CLARINET significantly outperforms a series of competent baselines on handwritten-digits-recognition and objects-recognition tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Can static regular black holes form from gravitational collapse?

Starting from the Oppenheimer-Snyder model, we know how in classical general relativity the gravitational collapse of matter form a black hole with a central spacetime singularity. It is widely believed that the singularity must be removed by quantum gravity effects. Some static quantum-inspired singularity-free black hole solutions have been proposed in the literature, but when one considers simple examples of gravitational collapse the classical singularity is replaced by a bounce, after which the collapsing matter expands for ever. We may expect 3 possible explanations: $i)$ the static regular black hole solutions are not physical, in the sense that they cannot be realized in Nature, $ii)$ the final product of the collapse is not unique, but it depends on the initial conditions, or $iii)$ boundary effects play an important role and our simple models miss important physics. In the latter case, after proper adjustment, the bouncing solution would approach the static one. We argue that the "correct answer" may be related to the appearance of a ghost state in de Sitter spacetimes with super Planckian mass. Our black holes have indeed a de Sitter core and the ghost would make these configurations unstable. Therefore we believe that these black hole static solutions represent the transient phase of a gravitational collapse, but never survive as asymptotic states.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Absorption-Ablation-Excitation Mechanisms of Laser-Cluster Interactions in a Nanoaerosol System

The absorption-ablation-excitation mechanism in laser-cluster interactions is investigated by measuring Rayleigh scattering of aerosol clusters along with atomic emission from phase-selective laser-induced breakdown spectroscopy (PS-LIBS). As the excitation laser intensity is increased beyond 0.16GW/cm2, the scattering cross-section of TiO_2 clusters begins to decrease, concurrent with the onset of atomic emission of Ti, indicating a scattering-to-ablation transition and the formation of nanoplasmas. To better clarify the process, time-resolved measurements of scattering signals are examined for different excitation laser intensities. For increasing laser intensities, the cross-sections of clusters decrease during a single pulse, evincing the shorter ablation delay time and larger ratios of ablation clusters. Assessment of the electron energy distribution during the ablation process is conducted by non-dimensionalizing the Fokker-Planck equation, with analogous Strouhal Sl_E, Peclet Pe_E, and Damkohler Da_E numbers defined to characterize the laser-induced aerothermochemical environment. For conditions of Sl_E>>1, Pe_E>>1, and Da_E<<1, the electrons are excited to the conduction band by two-photon absorption, then relax to bottom of the conduction band by collisional electron energy loss to the lattice, and finally serve as the energy transfer media between laser field and lattice. The relation between delay time and excitation intensity is well predicted by this simplified model with quasi-steady assumption.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Approaching the Intrinsic Bandgap in Suspended High-Mobility Graphene Nanoribbons

We report electrical transport measurements on a suspended ultra-low-disorder graphene nanoribbon(GNR) with nearly atomically smooth edges that reveal a high mobility exceeding 3000 cm2 V-1 s-1 and an intrinsic band gap. The experimentally derived bandgap is in quantitative agreement with the results of our electronic-structure calculations on chiral GNRs with comparable width taking into account the electron-electron interactions, indicating that the origin of the bandgap in non-armchair GNRs is partially due to the magnetic zigzag edges.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Room-Temperature High On/Off Ratio in Suspended Graphene Nanoribbon Field Effect Transistors

We have fabricated suspended few layer (1-3 layers) graphene nanoribbon field effect transistors from unzipped multiwall carbon nanotubes. Electrical transport measurements show that current-annealing effectively removes the impurities on the suspended graphene nanoribbons, uncovering the intrinsic ambipolar transfer characteristic of graphene. Further increasing the annealing current creates a narrow constriction in the ribbon, leading to the formation of a large band-gap and subsequent high on/off ratio (which can exceed 104). Such fabricated devices are thermally and mechanically stable: repeated thermal cycling has little effect on their electrical properties. This work shows for the first time that ambipolar field effect characteristics and high on/off ratios at room temperature can be achieved in relatively wide graphene nanoribbon (15 nm ~50 nm) by controlled current annealing.

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