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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., Comment: 11 pages, 7 figures, appendix included

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.

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., Comment: Accepted to the 1 st Workshop on Generative AI and Law

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., Comment: 14 pages, 6 figures

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., Comment: 24 pages, 9 figures

Ultracold spinor atoms in the weak and strong interaction regime have received extensive investigations, while the behavior in the intermediate regime is less understood. We numerically investigate ultracold spinor atomic ensembles of finite size in the intermediate interaction regime, and reveal the evolution of the eigenstates from the strong to the intermediate regime. In the strong interaction regime, it has been well known that the low-lying eigenenergy spectrum presents the well-gaped multi-manifold structure, and the energy gaps protect the categorization of the eigenstates. In the intermediate interaction regime, it is found that the categorization of the eigenstates is preserved, and the eigenenergy spectrum become quasi-continuum, with different manifolds becoming overlapped. The overlapping induces both direct and avoided crossings between close-lying manifolds, which is determined by the combined symmetries of the eigenstates involved in the crossing. A modified t-J model is derived to describe the low-lying eigenstates in the intermediate regime, which can capture the formation and crossings of the manifolds. State preparation through the avoided crossings is also investigated., Comment: 8 pages,6 figures

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.

High-Curie-temperature ferromagnets are promising candidates for designing new spintronic devices. Here we have successfully synthesized a single-crystal sample of the itinerant ferromagnet Mn$ _{5}$Ge$_{3}$ used flux method and its critical properties were investigated by means of bulk dc-magnetization at the boundary between the ferromagnetic (FM) and paramagnetic (PM) phase. Critical exponents $ \beta=0.336 \pm 0.001 $ with a critical temperature $ T_{c}=300.29 \pm 0.01 $ K and $ \gamma=1.193 \pm 0.003 $ with $ T_{c} = 300.15 \pm 0.05 $ K are obtained by the modified Arrott plot, whereas $ \delta = 4.61 \pm 0.03 $ is deduced by a critical isotherm analysis at $ T_{c} = 300 $ K. The self-consistency and reliability of these critical exponents are verified by the Widom scaling law and the scaling equations. Further analysis reveals that the spin coupling in Mn$ _{5}$Ge$_{3}$ exhibits three-dimensional Ising-like behavior. The magnetic exchange is found to decay as $ J(r)\approx r^{-4.855} $ and the spin interactions are extended beyond the nearest neighbors, which may be related to different set of Mn--Mn interactions with unequal magnitude of exchange strengths. Additionally, the existence of noncollinear spin configurations in Mn$ _{5} $Ge$ _{3} $ results in a small deviation of obtained critical exponents from those for standard 3D-Ising model., Comment: 8 pages, 6 figures. arXiv admin note: substantial text overlap with arXiv:1803.05905 by other authors

We perform a detailed analysis of the magnetotransport and de Haas-van Alphen (dHvA) oscillations in crystal PdGa which is predicted to be a typical chiral Fermion semimetal from CoSi family holding a large Chern number. The unsaturated quadratic magnetoresistance (MR) and nonlinear Hall resistivity indicate that PdGa is a multi-band system without electron-hole compensation. Angle-dependent resistivity in PdGa shows weak anisotropy with twofold or threefold symmetry when the magnetic field rotates within the (1$\bar{1}$0) or (111) plane perpendicular to the current. Nine or three frequencies are extracted after the fast Fourier-transform analysis (FFT) of the dHvA oscillations with B//[001] or B//[011], respectively, which is confirmed to be consistent with the Fermi surfaces (FSs) obtained from first-principles calculations with spin-orbit coupling (SOC) considered., Comment: 8 pages, 7 figures

We present a study on the magnetization, anomalous Hall effect (AHE) and novel longitudinal resistivity in layered antiferromagnet Co$_{0.29}$TaS$_{2}$. Of particular interests in Co$_{0.29}$TaS$_{2}$ are abundant magnetic transitions, which show that the magnetic structures are tuned by temperature or magnetic field. With decreasing temperature, Co$_{0.29}$TaS$_{2}$ undergoes two transitions at T$_{t1}\sim$ 38.3 K and T$_{t2}\sim$ 24.3 K. Once the magnetic field is applied, another transition T$_{t3}\sim$ 34.3 K appears between 0.3 T and 5 T. At 2 K, an obvious ferromagnetic hysteresis loop within H$_{t1}\sim\pm$ 6.9 T is observed, which decreases with increasing temperature and eventually disappears at T$_{t2}$. Besides, Co$_{0.29}$TaS$_{2}$ displays step-like behavior as another magnetic transition around H$_{t2}\sim\pm$ 4 T, which exists until $\sim$ T$_{t1}$. These characteristic temperatures and magnetic fields mark complex magnetic phase transitions in Co$_{0.29}$TaS$_{2}$, which are also evidenced in transport results. Large AHE dominates in the Hall resistivity with the conspicuous value of R$_{s}$/R$_{0}\sim 10^{5}$, considering that the tiny net magnetization (0.0094$\mu_{B}$/Co) alone would not lead to this value, thus the contribution of Berry curvature is necessary. The longitudinal resistivity illustrates a prominent irreversible behavior within H$_{t1}$. The abrupt change at H$_{t2}$ below T$_{t1}$, corresponding to the step-like magnetic transitions, is also observed. Synergy between the magnetism and topological properties, both playing a crucial role, may be the key factor of large AHE in antiferromagnet, which also offers a new perspective in magnetic topological materials with the platform of Co$_{0.29}$TaS$_{2}$., Comment: 5 pages,4 figures

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., Comment: 8 pages, 2 figures