变形金刚是最先进的深度学习模型，由堆叠的注意力和点亮的完全连接的层组成，用于处理顺序数据。变压器不仅普遍存在于自然语言处理（NLP），而且最近，他们启发了一股新的计算机视觉（CV）应用研究。在这项工作中，应用视觉变压器（VIT）以预测二维介绍模型模拟的状态变量。我们的实验表明，当使用来自对应于各种边界条件和温度的鉴定模型的少量微生物图像时，vit utem最先进的卷积神经网络（CNN）。这项工作开辟了应用vit的其他模拟的可能性，并提高了有趣的研究方向，了解Lepence Maps如何了解不同现象的基础物理学。

相分离在相关电子材料的新功能的出现中起着核心作用。混合相位的结构强烈依赖于非平衡相位分离动态，这迄今为止尚未系统地研究，特别是在理论方面。借助现代机器学习方法，我们展示了Falicov-Kimball模型的第一型大型动力学蒙特卡罗模拟，这是规范强烈相关的电子系统之一。我们发现一个不寻常的相位分离场景，其中域粗化在两个不同的尺度同时发生：棋盘簇的生长在较小的长度尺度和超级集群的扩展，这是相同标志的棋盘集群的聚合，更大规模。我们表明超级集群的出现是由于子分子对称的隐藏动态破裂。被阻止棋盘图案和超集群的生长被示出由相关诱导的自捕集机制产生。类似于本工作中报告的玻璃状行为可能是用于其他相关电子系统的通用。

近年来，手性磁铁吸引了大量的研究兴趣，因为它们支持了各种拓扑缺陷，例如天空和bimerons，并通过多种技术允许其观察和操纵。它们在Spintronics领域也具有广泛的应用，尤其是在开发用于存储存储设备的新技术方面。但是，这些实验和理论研究中产生的大量数据需要足够的工具，其中机器学习至关重要。我们使用卷积神经网络（CNN）来识别手性磁铁热力学阶段中的相关特征，包括（抗）天际，bimeron，以及螺旋和铁磁状态。我们使用灵活的多标签分类框架，该框架可以正确分类，其中混合了不同的特征和相位。然后，我们训练CNN从晶格蒙特卡洛模拟的中间状态的快照中预测最终状态的特征。训练有素的模型允许在编队过程中可靠地识别不同阶段。因此，CNN可以显着加快3D材料的大规模模拟，这些模拟迄今为止一直是定量研究的瓶颈。此外，这种方法可以应用于手性磁体的现实世界图像中混合状态和新兴特征的识别。

We present a neural flow wavefunction, Gauge-Fermion FlowNet, and use it to simulate 2+1D lattice compact quantum electrodynamics with finite density dynamical fermions. The gauge field is represented by a neural network which parameterizes a discretized flow-based transformation of the amplitude while the fermionic sign structure is represented by a neural net backflow. This approach directly represents the $U(1)$ degree of freedom without any truncation, obeys Guass's law by construction, samples autoregressively avoiding any equilibration time, and variationally simulates Gauge-Fermion systems with sign problems accurately. In this model, we investigate confinement and string breaking phenomena in different fermion density and hopping regimes. We study the phase transition from the charge crystal phase to the vacuum phase at zero density, and observe the phase seperation and the net charge penetration blocking effect under magnetic interaction at finite density. In addition, we investigate a magnetic phase transition due to the competition effect between the kinetic energy of fermions and the magnetic energy of the gauge field. With our method, we further note potential differences on the order of the phase transitions between a continuous $U(1)$ system and one with finite truncation. Our state-of-the-art neural network approach opens up new possibilities to study different gauge theories coupled to dynamical matter in higher dimensions.

The potential for complex systems to exhibit tipping points in which an equilibrium state undergoes a sudden and often irreversible shift is well established, but prediction of these events using standard forecast modeling techniques is quite difficult. This has led to the development of an alternative suite of methods that seek to identify signatures of critical phenomena in data, which are expected to occur in advance of many classes of dynamical bifurcation. Crucially, the manifestations of these critical phenomena are generic across a variety of systems, meaning that data-intensive deep learning methods can be trained on (abundant) synthetic data and plausibly prove effective when transferred to (more limited) empirical data sets. This paper provides a proof of concept for this approach as applied to lattice phase transitions: a deep neural network trained exclusively on 2D Ising model phase transitions is tested on a number of real and simulated climate systems with considerable success. Its accuracy frequently surpasses that of conventional statistical indicators, with performance shown to be consistently improved by the inclusion of spatial indicators. Tools such as this may offer valuable insight into climate tipping events, as remote sensing measurements provide increasingly abundant data on complex geospatially-resolved Earth systems.

机器学习最近被出现为研究复杂现象的有希望的方法，其特征是丰富的数据集。特别地，以数据为中心的方法为手动检查可能错过的实验数据集中自动发现结构的可能性。在这里，我们介绍可解释的无监督监督的混合机学习方法，混合相关卷积神经网络（Hybrid-CCNN），并将其应用于使用基于Rydberg Atom阵列的可编程量子模拟器产生的实验数据。具体地，我们应用Hybrid-CCNN以通过可编程相互作用分析在方形格子上的新量子阶段。初始无监督的维度降低和聚类阶段首先揭示了五个不同的量子相位区域。在第二个监督阶段，我们通过培训完全解释的CCNN来细化这些相界并通过训练每个阶段提取相关的相关性。在条纹相中的每个相捕获量子波动中专门识别的特征空间加权和相关的相关性并鉴定两个先前未检测到的相，菱形和边界有序相位。这些观察结果表明，具有机器学习的可编程量子模拟器的组合可用作有关相关量子态的详细探索的强大工具。

深度学习方法已被证明可以有效地表示量子多体系统的地面波函数。现有方法由于其图像样结构而使用卷积神经网络（CNN）进行方格。对于非方格晶格，现有方法使用图形神经网络（GNN），其中未精确捕获结构信息，从而需要其他手工制作的Sublattice编码。在这项工作中，我们提出了晶格卷积，其中使用一组建议的操作将非方格晶格转换为类似网格的增强晶格，可以在上进行定期卷积。根据提议的晶格卷积，我们设计了使用自我门控和注意机制的晶格卷积网络（LCN）。实验结果表明，我们的方法在PAR上的性能或比Spin 1/2 $ J_1 $ - $ J_2 $ HEISENBERG模型在Square，Honeycomb，Triangular和Kagome Lattices上的现有方法更好，而无需使用手工制作的编码。

虽然卷积神经网络（CNNS）在许多愿景任务中显示出显着的结果，但它们仍然是通过简单但具有挑战性的视觉推理问题所紧张的。在计算机视觉中最近的变压器网络成功的启发，在本文中，我们介绍了经常性视觉变压器（RVIT）模型。由于经常性连接和空间注意在推理任务中的影响，该网络实现了来自SVRT数据集的同样不同视觉推理问题的竞争结果。空间和深度尺寸中的重量共享正规化模型，允许它使用较少的自由参数学习，仅使用28K培训样本。全面的消融研究证实了混合CNN +变压器架构的重要性和反馈连接的作用，其迭代地细化内部表示直到获得稳定的预测。最后，本研究可以更深入地了解对求解视觉抽象推理任务的注意力和经常性联系的作用。

机器学习，特别是深度学习方法在许多模式识别和数据处理问题，游戏玩法中都优于人类的能力，现在在科学发现中也起着越来越重要的作用。机器学习在分子科学中的关键应用是通过使用密度函数理论，耦合群或其他量子化学方法获得的电子schr \“ odinger方程的Ab-Initio溶液中的势能表面或力场。我们回顾了一种最新和互补的方法：使用机器学习来辅助从第一原理中直接解决量子化学问题。具体来说，我们专注于使用神经网络ANSATZ功能的量子蒙特卡洛（QMC）方法，以解决电子SCHR \ “ Odinger方程在第一和第二量化中，计算场和激发态，并概括多个核构型。与现有的量子化学方法相比，这些新的深QMC方法具有以相对适度的计算成本生成高度准确的Schr \“ Odinger方程的溶液。

彩票假说上的基础工作提出了令人兴奋的推论：在一个任务的背景下找到的获胜门票可以转移到类似的任务中，甚至可能在不同的架构中。这引起了广泛的兴趣，但是缺乏研究这种普遍性的方法。我们利用重新规范化群体理论是一种从理论物理学的有力工具来满足这一需求。我们发现，迭代幅度修剪是用于发现获胜门票的主要算法，是一种重新归一化组方案，可以看作是诱导参数空间中的流动。我们证明，带有可转让获胜门票的Resnet-50模型具有具有共同特性的流量，正如该理论所期望的那样。对BERT模型进行了类似的观察结果，并有证据表明其流量接近固定点。此外，我们利用我们的框架来研究跨重新结构架构转移的获胜门票，观察到较小的模型具有比较大模型更均匀的属性的流动，从而使它们之间的转移变得复杂。

Machine learning has emerged recently as a powerful tool for predicting properties of quantum many-body systems. For many ground states of gapped Hamiltonians, generative models can learn from measurements of a single quantum state to reconstruct the state accurately enough to predict local observables. Alternatively, kernel methods can predict local observables by learning from measurements on different but related states. In this work, we combine the benefits of both approaches and propose the use of conditional generative models to simultaneously represent a family of states, by learning shared structures of different quantum states from measurements. The trained model allows us to predict arbitrary local properties of ground states, even for states not present in the training data, and without necessitating further training for new observables. We numerically validate our approach (with simulations of up to 45 qubits) for two quantum many-body problems, 2D random Heisenberg models and Rydberg atom systems.

Despite great progress in simulating multiphysics problems using the numerical discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate noisy data into existing algorithms, mesh generation remains complex, and high-dimensional problems governed by parameterized PDEs cannot be tackled. Moreover, solving inverse problems with hidden physics is often prohibitively expensive and requires different formulations and elaborate computer codes. Machine learning has emerged as a promising alternative, but training deep neural networks requires big data, not always available for scientific problems. Instead, such networks can be trained from additional information obtained by enforcing the physical laws (for example, at random points in the continuous space-time domain). Such physics-informed learning integrates (noisy) data and mathematical models, and implements them through neural networks or other kernel-based regression networks. Moreover, it may be possible to design specialized network architectures that automatically satisfy some of the physical invariants for better accuracy, faster training and improved generalization. Here, we review some of the prevailing trends in embedding physics into machine learning, present some of the current capabilities and limitations and discuss diverse applications of physics-informed learning both for forward and inverse problems, including discovering hidden physics and tackling high-dimensional problems.

To ensure proper knowledge representation of the kitchen environment, it is vital for kitchen robots to recognize the states of the food items that are being cooked. Although the domain of object detection and recognition has been extensively studied, the task of object state classification has remained relatively unexplored. The high intra-class similarity of ingredients during different states of cooking makes the task even more challenging. Researchers have proposed adopting Deep Learning based strategies in recent times, however, they are yet to achieve high performance. In this study, we utilized the self-attention mechanism of the Vision Transformer (ViT) architecture for the Cooking State Recognition task. The proposed approach encapsulates the globally salient features from images, while also exploiting the weights learned from a larger dataset. This global attention allows the model to withstand the similarities between samples of different cooking objects, while the employment of transfer learning helps to overcome the lack of inductive bias by utilizing pretrained weights. To improve recognition accuracy, several augmentation techniques have been employed as well. Evaluation of our proposed framework on the `Cooking State Recognition Challenge Dataset' has achieved an accuracy of 94.3%, which significantly outperforms the state-of-the-art.

我们开发了一种多尺度方法，以从实验或模拟中观察到的物理字段或配置的数据集估算高维概率分布。通过这种方式，我们可以估计能量功能（或哈密顿量），并有效地在从统计物理学到宇宙学的各个领域中生成多体系统的新样本。我们的方法 - 小波条件重新归一化组（WC-RG） - 按比例进行估算，以估算由粗粒磁场来调节的“快速自由度”的条件概率的模型。这些概率分布是由与比例相互作用相关的能量函数建模的，并以正交小波为基础表示。 WC-RG将微观能量函数分解为各个尺度上的相互作用能量之和，并可以通过从粗尺度到细度来有效地生成新样品。近相变，它避免了直接估计和采样算法的“临界减速”。理论上通过结合RG和小波理论的结果来解释这一点，并为高斯和$ \ varphi^4 $字段理论进行数值验证。我们表明，多尺度WC-RG基于能量的模型比局部电位模型更通用，并且可以在所有长度尺度上捕获复杂的多体相互作用系统的物理。这是针对反映宇宙学中暗物质分布的弱透镜镜头的，其中包括与长尾概率分布的长距离相互作用。 WC-RG在非平衡系统中具有大量的潜在应用，其中未知基础分布{\ it先验}。最后，我们讨论了WC-RG和深层网络体系结构之间的联系。

本文通过研究阶段转换的$ Q $State Potts模型，通过许多无监督的机器学习技术，即主成分分析（PCA），$ K $ - 梅尔集群，统一歧管近似和投影（UMAP），和拓扑数据分析（TDA）。即使在所有情况下，我们都能够检索正确的临界温度$ t_c（q）$，以$ q = 3,4 $和5 $，结果表明，作为UMAP和TDA的非线性方法依赖于有限尺寸效果，同时仍然能够区分第一和二阶相转换。该研究可以被认为是在研究相转变的调查中使用不同无监督的机器学习算法的基准。

We apply the Hierarchical Autoregressive Neural (HAN) network sampling algorithm to the two-dimensional $Q$-state Potts model and perform simulations around the phase transition at $Q=12$. We quantify the performance of the approach in the vicinity of the first-order phase transition and compare it with that of the Wolff cluster algorithm. We find a significant improvement as far as the statistical uncertainty is concerned at a similar numerical effort. In order to efficiently train large neural networks we introduce the technique of pre-training. It allows to train some neural networks using smaller system sizes and then employing them as starting configurations for larger system sizes. This is possible due to the recursive construction of our hierarchical approach. Our results serve as a demonstration of the performance of the hierarchical approach for systems exhibiting bimodal distributions. Additionally, we provide estimates of the free energy and entropy in the vicinity of the phase transition with statistical uncertainties of the order of $10^{-7}$ for the former and $10^{-3}$ for the latter based on a statistics of $10^6$ configurations.

量子点（QDS）阵列是一个有前途的候选系统，实现可扩展的耦合码头系统，并用作量子计算机的基本构建块。在这种半导体量子系统中，设备现在具有数十个，必须仔细地将系统仔细设置为单电子制度并实现良好的Qubit操作性能。必要点位置的映射和栅极电压的电荷提出了一个具有挑战性的经典控制问题。随着QD Qubits越来越多的QD Qubits，相关参数空间的增加充分以使启发式控制不可行。近年来，有一个相当大的努力自动化与机器学习（ML）技术相结合的基于脚本的算法。在这一讨论中，我们概述了QD器件控制自动化进展的全面概述，特别强调了在二维电子气体中形成的基于硅和GaAs的QD。将基于物理的型号与现代数值优化和ML相结合，证明在屈服高效，可扩展的控制方面已经证明非常有效。通过计算机科学和ML的理论，计算和实验努力的进一步整合，在推进半导体和量子计算平台方面具有巨大的潜力。

我们研究限制的Boltzmann机器（RBM）提取的特征当它在各种温度下旋转模型的自旋配置时训练。使用训练的RBM，我们获得了自旋配置的迭代重建（RBM流量）的流程，并在某些情况下发现流程接近阶段转换点$ T = T_C $ IN ISING模型。由于在重建配置中强调提取的特征，因此在这种固定点处的配置应该除了提取的特征之外。然后，我们研究了固定点对各种参数的依赖性，并猜测RBM流程的固定点处于相位过渡点的状态。我们还通过分析训练RBM的重量矩阵来提供猜想的支持证据。

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP.

我们研究了重整化组（RG）和深神经网络之间的类比，其中随后的神经元层类似于沿RG的连续步骤。特别地，我们通过在抽取RG下明确计算在DIMIMATION RG下的一个和二维insing模型中的相对熵或kullback-leibler发散，以及作为深度的函数的前馈神经网络中的相对熵或kullback-leibler发散。我们观察到单调增加到参数依赖性渐近值的定性相同的行为。在量子场理论方面，单调增加证实了相对熵和C定理之间的连接。对于神经网络，渐近行为可能对机器学习中的各种信息最大化方法以及解开紧凑性和概括性具有影响。此外，虽然我们考虑的二维误操作模型和随机神经网络都表现出非差异临界点，但是对任何系统的相位结构的相对熵看起来不敏感。从这个意义上讲，需要更精细的探针以充分阐明这些模型中的信息流。