arXiv subjects

Yiyang Zhang

Publications and source records attributed to Yiyang Zhang.

52 records · Page 3Linked to original sources

arXiv2024

Robust field-free switching using large unconventional spin-orbit torque in an all-van der Waals heterostructure

The emerging all-van der Waals (vdW) magnetic heterostructure provides a new platform to control the magnetization by the electric field beyond the traditional spintronics devices. One promising strategy is using unconventional spin-orbit torque (SOT) exerted by the out-of-plane polarized spin current to enable deterministic magnetization switching and enhance the switching efficiency. However, in all-vdW heterostructures, large unconventional SOT remains elusive and the robustness of the field-free switching against external magnetic field hasn't been examined, which hinder further applications. Here we demonstrate the field-free switching in an all-vdW heterostructure combining a type-II Weyl semimetal TaIrTe4 and above-room-temperature ferromagnet Fe3GaTe2. The fully field-free switching can be achieved at 2.56 x 10^10 A per m2 at 300K and a large SOT effective field efficiency of the out-of-plane polarized spin current generated by TaIrTe4 is determined to be 0.37. Moreover, we find that the switching polarity cannot be changed until the external in-plane magnetic field reaches 252mT, indicating a robust switching against the magnetic field. The numerical simulation suggests the large unconventional SOT reduces the switching current density and enhances the robustness of the switching. Our work shows that all-vdW heterostructures are promising candidates for future highly efficient and stable SOT-based devices.

cond-mat.mes-hall↗

arXiv2024

Openstory++: A Large-scale Dataset and Benchmark for Instance-aware Open-domain Visual Storytelling

Recent image generation models excel at creating high-quality images from brief captions. However, they fail to maintain consistency of multiple instances across images when encountering lengthy contexts. This inconsistency is largely due to in existing training datasets the absence of granular instance feature labeling in existing training datasets. To tackle these issues, we introduce Openstory++, a large-scale dataset combining additional instance-level annotations with both images and text. Furthermore, we develop a training methodology that emphasizes entity-centric image-text generation, ensuring that the models learn to effectively interweave visual and textual information. Specifically, Openstory++ streamlines the process of keyframe extraction from open-domain videos, employing vision-language models to generate captions that are then polished by a large language model for narrative continuity. It surpasses previous datasets by offering a more expansive open-domain resource, which incorporates automated captioning, high-resolution imagery tailored for instance count, and extensive frame sequences for temporal consistency. Additionally, we present Cohere-Bench, a pioneering benchmark framework for evaluating the image generation tasks when long multimodal context is provided, including the ability to keep the background, style, instances in the given context coherent. Compared to existing benchmarks, our work fills critical gaps in multi-modal generation, propelling the development of models that can adeptly generate and interpret complex narratives in open-domain environments. Experiments conducted within Cohere-Bench confirm the superiority of Openstory++ in nurturing high-quality visual storytelling models, enhancing their ability to address open-domain generation tasks. More details can be found at https://openstorypp.github.io/

cs.CV↗

arXiv2024

All-voltage control of Giant Magnetoresistance

The aim of voltage control of magnetism is to reduce the power consumption of spintronic devices. For a spin valve, the magnetization directions of two ferromagnetic layers determine the giant magnetoresistance magnitude. However, achieving all-voltage manipulation of the magnetization directions between parallel and antiparallel states is a significant challenge. Here, we demonstrate that by utilizing two exchange-biased Co/IrMn bilayers with opposite pinning directions and with ferromagnetic coupling through the Ruderman-Kittel-Kasuya-Yosida interaction between two Co layers, the magnetization directions of the two ferromagnetic layers of a spin valve can be switched between parallel and antiparallel states through allvoltage-induced strain control. The all-voltage controlled giant magnetoresistance is repeatable and nonvolatile. The rotation of magnetizations in the two Co layers under voltages, from antiparallel to parallel states, occurs in opposite directions as revealed through simulations utilizing the Landau-Lifshitz-Gilbert equation. This work can provide valuable reference for the development of low-power all-voltage-controlled spintronic devices.

cond-mat.mes-hall↗

arXiv2024

Turbulence-induced oscillation on particle detachment from a wall

Particle resuspension is a ubiquitous phenomenon with pivotal relevance in numerous natural and industrial contexts. In this study, we present findings on the resuspension of individual micro-sized particles, captured through high-speed camera experiments. Our observations reveal a universal behavior whereby a particle undergoes oscillatory motion due to turbulent excitation prior to its detachment from the surface. This motion is characterized by dimensionless number Ad and S. The frequency of particles oscillation is analyzed and it shows the frequency of particle oscillations increased with decreasing particle size. We establish a new model that the particle is a linear oscillator driven by stochastic torque from turbulence. It is shown that the stochastic oscillation is the key mechanism for particle detachment from a wall within a certain range of friction velocities.

physics.flu-dyn↗

arXiv2023

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%.

cs.CL↗

arXiv2023

Mask-guided BERT for Few Shot Text Classification

Transformer-based language models have achieved significant success in various domains. However, the data-intensive nature of the transformer architecture requires much labeled data, which is challenging in low-resource scenarios (i.e., few-shot learning (FSL)). The main challenge of FSL is the difficulty of training robust models on small amounts of samples, which frequently leads to overfitting. Here we present Mask-BERT, a simple and modular framework to help BERT-based architectures tackle FSL. The proposed approach fundamentally differs from existing FSL strategies such as prompt tuning and meta-learning. The core idea is to selectively apply masks on text inputs and filter out irrelevant information, which guides the model to focus on discriminative tokens that influence prediction results. In addition, to make the text representations from different categories more separable and the text representations from the same category more compact, we introduce a contrastive learning loss function. Experimental results on public-domain benchmark datasets demonstrate the effectiveness of Mask-BERT.

cs.CL↗

arXiv2023

Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training

Modern supervised learning neural network models require a large amount of manually labeled data, which makes the construction of domain-specific knowledge graphs time-consuming and labor-intensive. In parallel, although there has been much research on named entity recognition and relation extraction based on distantly supervised learning, constructing a domain-specific knowledge graph from large collections of textual data without manual annotations is still an urgent problem to be solved. In response, we propose an integrated framework for adapting and re-learning knowledge graphs from one coarse domain (biomedical) to a finer-define domain (oncology). In this framework, we apply distant-supervision on cross-domain knowledge graph adaptation. Consequently, no manual data annotation is required to train the model. We introduce a novel iterative training strategy to facilitate the discovery of domain-specific named entities and triples. Experimental results indicate that the proposed framework can perform domain adaptation and construction of knowledge graph efficiently.

cs.AI↗

arXiv2021

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.

cs.LG↗

arXiv2020

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.

cs.LG↗

arXiv2019

Gravitational Waves from First-Order Phase Transition in a Simple Axion-Like Particle Model

We consider a gauge-singlet complex scalar field $Φ$ with a global $U(1)$ symmetry that is spontaneously broken at some high energy scale $f_a$. As a result, the angular part of the $Φ$-field becomes an axion-like particle (ALP). We show that if the $Φ$-field has a non-zero coupling $κ$ to the Standard Model Higgs boson, there exists a certain region in the $\left(f_a, κ\right)$ parameter space where the global $U(1)$ symmetry-breaking induces a strongly first order phase transition, thereby producing stochastic gravitational waves that are potentially observable in current and future gravitational-wave detectors. In particular, we find that future gravitational-wave experiments such as TianQin, BBO and Cosmic Explorer could probe a broad range of the energy scale $10^3 \, {\rm GeV} \lesssim f_a \lesssim 10^{8} \, {\rm GeV}$, independent of the ALP mass. Since all the ALP couplings to the Standard Model particles are proportional to inverse powers of the energy scale $f_a$ (up to model-dependent ${\cal O}(1)$ coefficients), the gravitational-wave detection prospects are largely complementary to the current laboratory, astrophysical and cosmological probes of the ALP scenarios.

hep-ph↗

arXiv2019

Magnetic field production at a first-order electroweak phase transition

We study the generation of magnetic field seeds during a first-order electroweak phase transition, by numerically evolving the classical equations of motion of the bosonic electroweak theory on the lattice. The onset of the transition is implemented by the random nucleation of bubbles with an arbitrarily oriented Higgs field in the broken phase. We find that about 10% of the latent heat is converted into magnetic energy, with most of the magnetic fields being generated in the last stage of the phase transition when the Higgs oscillates around the true vacuum. The energy spectrum of the magnetic field has a peak that shifts towards larger length scales as the phase transition unfolds. By the end of our runs the peak wavelength is of the order of the bubble percolation scale, or about a third of our lattice size.

hep-ph↗

arXiv2017

Vacuum Topology and the Electroweak Phase Transition

We investigate if the topology of pure gauge fields in the electroweak vacuum can play a role in classical dynamics at the electroweak phase transition. Our numerical analysis shows that magnetic fields are produced if the initial vacuum has non-trivial Chern-Simons number, and the fields are helical if the Chern-Simons number changes during the phase transition.

hep-th↗

arXiv2015

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.

gr-qc↗

arXiv2014

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.

physics.atm-clus↗

arXiv2011

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.

cond-mat.mes-hall↗

arXiv2011

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.

cond-mat.mes-hall↗