arXiv subjects

Zheng Dai

Publications and source records attributed to Zheng Dai.

9 recordsLinked to original sources

arXiv2026

Higher odd-order nonlinear Hall effect in magnetic topological insulator Mn(Bi1-xSbx)2Te4

The nonlinear Hall effect is a new member of the Hall effect family, which attracts intense research interests, and it is closely related to the quantum geometry of quantum materials. The previous studies primarily concentrate on the second-order and third-order nonlinear Hall effect. However, the experimental study of higher-order nonlinear Hall effect is scarce at present. In this work, we report the observations of the higher odd-order (third-, fifth-, seventh-order) nonlinear Hall effect in magnetic topological insulator Mn(Bi1-xSbx)2Te4 thin flakes. The higher odd-order nonlinear Hall voltage exhibits a twofold angular dependence and exists only below the N\'eel temperature. It reaches its maximum near the charge neutral point and decays exponentially as the order of the nonlinear Hall effect increases. Furthermore, such higher odd-order nonlinear Hall effect is observed in both odd- and even-layer samples with comparable magnitudes. Theoretical analysis indicates that the higher odd-order nonlinear Hall effect responses may arise from the Berry curvature multipoles. Our work paves the way for the study of the higher-order nonlinear transport phenomena.

cond-mat.mes-hall↗

arXiv2026

Room-temperature third-order nonlinear anomalous Hall effect in ferromagnetic metal Fe3GaTe2

Berry curvature, as the imaginary component of quantum geometry, plays a crucial role in condensed matter physics. The spatial distribution of Berry curvature can be characterized by its dipole and multipole moments, which can induce the nonlinear anomalous Hall effect (NLAHE). To date, the NLAHE has been demonstrated in various materials, yet reports on room-temperature NLAHE are still limited. In this work, we report the observation of the third-order NLAHE in ferromagnetic metal Fe3GaTe2. The third-order NLAHE shows hysteretic behavior with the variation of magnetic field, where the coercive field is the same as that of the anomalous Hall effect, and the third-order NLAHE remains observable up to the Curie temperature (~350 K). The scaling analysis suggests that the third-order NLAHE may be attributed to the Berry curvature quadrupole. Our work not only provides an approach to study magnetic materials through nonlinear electric transports, but also opens up possibilities for the future development of room-temperature third-order nonlinear electronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Ablation Based Counterfactuals

Diffusion models are a class of generative models that generate high-quality samples, but at present it is difficult to characterize how they depend upon their training data. This difficulty raises scientific and regulatory questions, and is a consequence of the complexity of diffusion models and their sampling process. To analyze this dependence, we introduce Ablation Based Counterfactuals (ABC), a method of performing counterfactual analysis that relies on model ablation rather than model retraining. In our approach, we train independent components of a model on different but overlapping splits of a training set. These components are then combined into a single model, from which the causal influence of any training sample can be removed by ablating a combination of model components. We demonstrate how we can construct a model like this using an ensemble of diffusion models. We then use this model to study the limits of training data attribution by enumerating full counterfactual landscapes, and show that single source attributability diminishes with increasing training data size. Finally, we demonstrate the existence of unattributable samples.

cs.LG↗

arXiv2024

Observation of giant nonreciprocal charge transport from quantum Hall states in a topological insulator

Symmetry breaking in quantum materials is of great importance and can lead to nonreciprocal charge transport. Topological insulators provide a unique platform to study nonreciprocal charge transport due to their surface states, especially quantum Hall states under external magnetic field. Here, we report the observation of nonreciprocal charge transport mediated by quantum Hall states in devices composed of the intrinsic topological insulator Sn-Bi1.1Sb0.9Te2S, which is attributed to asymmetric scattering between quantum Hall states and Dirac surface states. A giant nonreciprocal coefficient of up to 2.26*10^5 A^-1 is found. Our work not only reveals the properties of nonreciprocal charge transport of quantum Hall states in topological insulators, but also paves the way for future electronic devices.

cond-mat.mes-hall↗

arXiv2023

Measuring the Success of Diffusion Models at Imitating Human Artists

Modern diffusion models have set the state-of-the-art in AI image generation. Their success is due, in part, to training on Internet-scale data which often includes copyrighted work. This prompts questions about the extent to which these models learn from, imitate, or copy the work of human artists. This work suggests that tying copyright liability to the capabilities of the model may be useful given the evolving ecosystem of generative models. Specifically, much of the legal analysis of copyright and generative systems focuses on the use of protected data for training. As a result, the connections between data, training, and the system are often obscured. In our approach, we consider simple image classification techniques to measure a model's ability to imitate specific artists. Specifically, we use Contrastive Language-Image Pretrained (CLIP) encoders to classify images in a zero-shot fashion. Our process first prompts a model to imitate a specific artist. Then, we test whether CLIP can be used to reclassify the artist (or the artist's work) from the imitation. If these tests match the imitation back to the original artist, this suggests the model can imitate that artist's expression. Our approach is simple and quantitative. Furthermore, it uses standard techniques and does not require additional training. We demonstrate our approach with an audit of Stable Diffusion's capacity to imitate 70 professional digital artists with copyrighted work online. When Stable Diffusion is prompted to imitate an artist from this set, we find that the artist can be identified from the imitation with an average accuracy of 81.0%. Finally, we also show that a sample of the artist's work can be matched to these imitation images with a high degree of statistical reliability. Overall, these results suggest that Stable Diffusion is broadly successful at imitating individual human artists.

cs.CV↗

arXiv2023

Training Data Attribution for Diffusion Models

Diffusion models have become increasingly popular for synthesizing high-quality samples based on training datasets. However, given the oftentimes enormous sizes of the training datasets, it is difficult to assess how training data impact the samples produced by a trained diffusion model. The difficulty of relating diffusion model inputs and outputs poses significant challenges to model explainability and training data attribution. Here we propose a novel solution that reveals how training data influence the output of diffusion models through the use of ensembles. In our approach individual models in an encoded ensemble are trained on carefully engineered splits of the overall training data to permit the identification of influential training examples. The resulting model ensembles enable efficient ablation of training data influence, allowing us to assess the impact of training data on model outputs. We demonstrate the viability of these ensembles as generative models and the validity of our approach to assessing influence.

stat.ML↗

arXiv2023

Constrained Submodular Optimization for Vaccine Design

Advances in machine learning have enabled the prediction of immune system responses to prophylactic and therapeutic vaccines. However, the engineering task of designing vaccines remains a challenge. In particular, the genetic variability of the human immune system makes it difficult to design peptide vaccines that provide widespread immunity in vaccinated populations. We introduce a framework for evaluating and designing peptide vaccines that uses probabilistic machine learning models, and demonstrate its ability to produce designs for a SARS-CoV-2 vaccine that outperform previous designs. We provide a theoretical analysis of the approximability, scalability, and complexity of our framework.

q-bio.QM↗

arXiv2022

Large Exchange Bias Effect and Coverage-Dependent Interfacial Coupling in CrI3/MnBi2Te4 van der Waals Heterostructures

Igniting interface magnetic ordering of magnetic topological insulators by building a van der Waals heterostructure can help to reveal novel quantum states and design functional devices. Here, we observe an interesting exchange bias effect, indicating successful interfacial magnetic coupling, in CrI3/MnBi2Te4 ferromagnetic insulator/antiferromagnetic topological insulator (FMI/AFM-TI) heterostructure devices. The devices originally exhibit a negative exchange bias field, which decays with increasing temperature and is unaffected by the back-gate voltage. When we change the device configuration to be half-covered by CrI3, the exchange bias becomes positive with a very large exchange bias field exceeding 300 mT. Such sensitive manipulation is explained by the competition between the FM and AFM coupling at the interface of CrI3 and MnBi2Te4, pointing to coverage-dependent interfacial magnetic interactions. Our work will facilitate the development of topological and antiferromagnetic devices.

cond-mat.mtrl-sci↗

arXiv2022

Image classifiers can not be made robust to small perturbations

The sensitivity of image classifiers to small perturbations in the input is often viewed as a defect of their construction. We demonstrate that this sensitivity is a fundamental property of classifiers. For any arbitrary classifier over the set of $n$-by-$n$ images, we show that for all but one class it is possible to change the classification of all but a tiny fraction of the images in that class with a perturbation of size $O(n^{1/\max{(p,1)}})$ when measured in any $p$-norm for $p \geq 0$. We then discuss how this phenomenon relates to human visual perception and the potential implications for the design considerations of computer vision systems.

cs.CV↗