在广泛的应用中存在针刺问题，包括罕见疾病预测，生态资源管理，欺诈检测和材料特性优化。当相对于数据集大小的最佳条件存在极端不平衡时，就会出现针中的问题。例如，在开放式材料项目数据库中，在146K总材料中，只有0.82％的泊松比为负。但是，当前的最新优化算法并未设计出能够找到这些具有挑战性的多维针中问题的解决方案，从而导致与全球最佳或pige孔变为当地最低限度的缓慢收敛。在本文中，我们提出了一种基于缩放记忆的初始化算法，标题为Zombi，该算法构建了常规的贝叶斯优化原则，以在更少的时间和更少的实验中快速有效地优化针中的针刺问题，并通过解决常见的融合和常见的融合和较少的实验。鸽子问题。 Zombi从先前表现最佳的评估实验中积极提取知识，以在采样搜索范围内迭代放大到全局最佳的“针”，然后预留出低表现的历史实验的记忆，以加速计算时间。我们验证了该算法在两种现实世界中的5维针中的性能上的性能：发现辅助泊松比的发现和发现高热电图的优点材料的发现。与传统的贝叶斯优化相比，Zombi算法显示了400倍的计算时间加速度，并有效地发现了100个以下实验的材料，高达3倍的材料比当前最新算法发现的材料高度优化。

Transfer learning increasingly becomes an important tool in handling data scarcity often encountered in machine learning. In the application of high-throughput thickness as a downstream process of the high-throughput optimization of optoelectronic thin films with autonomous workflows, data scarcity occurs especially for new materials. To achieve high-throughput thickness characterization, we propose a machine learning model called thicknessML that predicts thickness from UV-Vis spectrophotometry input and an overarching transfer learning workflow. We demonstrate the transfer learning workflow from generic source domain of generic band-gapped materials to specific target domain of perovskite materials, where the target domain data only come from limited number (18) of refractive indices from literature. The target domain can be easily extended to other material classes with a few literature data. Defining thickness prediction accuracy to be within-10% deviation, thicknessML achieves 92.2% (with a deviation of 3.6%) accuracy with transfer learning compared to 81.8% (with a deviation of 3.6%) 11.7% without (lower mean and larger standard deviation). Experimental validation on six deposited perovskite films also corroborates the efficacy of the proposed workflow by yielding a 10.5% mean absolute percentage error (MAPE).

Perovskite Photovoltaics（PV）在过去十年方面取得了快速发展，方便小区实验室规模设备的电力转换效率;然而，成功的商业化仍然需要进一步发展低成本，可扩展和高通量的制造技术。开发新的制造技术的关键挑战之一是高维参数空间，并且可以使用机器学习（ML）来加速Perovskite PV缩放。在这里，我们介绍了一个ML引导框架，用于制造过程优化的顺序学习。我们在环境条件下将我们的方法应用于快速喷雾等离子体处理（RSPP）技术，用于钙钛矿薄膜。通过有限的筛选100条件工艺条件进行实验预算，我们证明了最佳设备的效率提高至18.5％，我们还通过实验发现了10个独特的条件，以生产超过17％效率的顶级设备，这是5比伪随机拉丁超立方体采样更高的成功率。我们的模型由三种创新启用：（a）通过将数据从现有的实验数据作为软限制将数据纳入实验过程之间的灵活知识转移; （b）在选择下一个实验时纳入主观人类观察和ML见解; （c）首先使用贝叶斯优化定位兴趣区域的自适应策略，然后对高效设备进行本地勘探。此外，在虚拟基准测试中，我们的框架在传统的实验方法（例如，一个可变的AT-AT-AT-A-A-Time采样）上，我们的框架更快地实现了有限的实验预算。

实现一般逆设计可以通过用户定义的属性极大地加速对新材料的发现。然而，最先进的生成模型往往限于特定的组成或晶体结构。这里，我们提出了一种能够一般逆设计的框架（不限于给定的一组元件或晶体结构），其具有在实际和往复空间中编码晶体的广义可逆表示，以及来自变分的属性结构潜空间autoencoder（vae）。在三种设计情况下，该框架通过用户定义的形成能量，带隙，热电（TE）功率因数和组合产生142个新晶体。在训练数据库中缺席的这些生成的晶体通过第一原理计算验证。成功率（验证的第一原理验证的目标圆形晶体/数量的设计晶体）范围为7.1％和38.9％。这些结果表示利用生成模型朝着性质驱动的一般逆设计的重要步骤，尽管在与实验合成结合时仍然存在实际挑战。

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

This paper presents a practical global optimization algorithm for the K-center clustering problem, which aims to select K samples as the cluster centers to minimize the maximum within-cluster distance. This algorithm is based on a reduced-space branch and bound scheme and guarantees convergence to the global optimum in a finite number of steps by only branching on the regions of centers. To improve efficiency, we have designed a two-stage decomposable lower bound, the solution of which can be derived in a closed form. In addition, we also propose several acceleration techniques to narrow down the region of centers, including bounds tightening, sample reduction, and parallelization. Extensive studies on synthetic and real-world datasets have demonstrated that our algorithm can solve the K-center problems to global optimal within 4 hours for ten million samples in the serial mode and one billion samples in the parallel mode. Moreover, compared with the state-of-the-art heuristic methods, the global optimum obtained by our algorithm can averagely reduce the objective function by 25.8% on all the synthetic and real-world datasets.

Score-based diffusion models have captured widespread attention and funded fast progress of recent vision generative tasks. In this paper, we focus on diffusion model backbone which has been much neglected before. We systematically explore vision Transformers as diffusion learners for various generative tasks. With our improvements the performance of vanilla ViT-based backbone (IU-ViT) is boosted to be on par with traditional U-Net-based methods. We further provide a hypothesis on the implication of disentangling the generative backbone as an encoder-decoder structure and show proof-of-concept experiments verifying the effectiveness of a stronger encoder for generative tasks with ASymmetriC ENcoder Decoder (ASCEND). Our improvements achieve competitive results on CIFAR-10, CelebA, LSUN, CUB Bird and large-resolution text-to-image tasks. To the best of our knowledge, we are the first to successfully train a single diffusion model on text-to-image task beyond 64x64 resolution. We hope this will motivate people to rethink the modeling choices and the training pipelines for diffusion-based generative models.

Deep learning-based methods have achieved significant performance for image defogging. However, existing methods are mainly developed for land scenes and perform poorly when dealing with overwater foggy images, since overwater scenes typically contain large expanses of sky and water. In this work, we propose a Prior map Guided CycleGAN (PG-CycleGAN) for defogging of images with overwater scenes. To promote the recovery of the objects on water in the image, two loss functions are exploited for the network where a prior map is designed to invert the dark channel and the min-max normalization is used to suppress the sky and emphasize objects. However, due to the unpaired training set, the network may learn an under-constrained domain mapping from foggy to fog-free image, leading to artifacts and loss of details. Thus, we propose an intuitive Upscaling Inception Module (UIM) and a Long-range Residual Coarse-to-fine framework (LRC) to mitigate this issue. Extensive experiments on qualitative and quantitative comparisons demonstrate that the proposed method outperforms the state-of-the-art supervised, semi-supervised, and unsupervised defogging approaches.

Conversational recommender systems (CRSs) often utilize external knowledge graphs (KGs) to introduce rich semantic information and recommend relevant items through natural language dialogues. However, original KGs employed in existing CRSs are often incomplete and sparse, which limits the reasoning capability in recommendation. Moreover, only few of existing studies exploit the dialogue context to dynamically refine knowledge from KGs for better recommendation. To address the above issues, we propose the Variational Reasoning over Incomplete KGs Conversational Recommender (VRICR). Our key idea is to incorporate the large dialogue corpus naturally accompanied with CRSs to enhance the incomplete KGs; and perform dynamic knowledge reasoning conditioned on the dialogue context. Specifically, we denote the dialogue-specific subgraphs of KGs as latent variables with categorical priors for adaptive knowledge graphs refactor. We propose a variational Bayesian method to approximate posterior distributions over dialogue-specific subgraphs, which not only leverages the dialogue corpus for restructuring missing entity relations but also dynamically selects knowledge based on the dialogue context. Finally, we infuse the dialogue-specific subgraphs to decode the recommendation and responses. We conduct experiments on two benchmark CRSs datasets. Experimental results confirm the effectiveness of our proposed method.

Automatic image colorization is a particularly challenging problem. Due to the high illness of the problem and multi-modal uncertainty, directly training a deep neural network usually leads to incorrect semantic colors and low color richness. Existing transformer-based methods can deliver better results but highly depend on hand-crafted dataset-level empirical distribution priors. In this work, we propose DDColor, a new end-to-end method with dual decoders, for image colorization. More specifically, we design a multi-scale image decoder and a transformer-based color decoder. The former manages to restore the spatial resolution of the image, while the latter establishes the correlation between semantic representations and color queries via cross-attention. The two decoders incorporate to learn semantic-aware color embedding by leveraging the multi-scale visual features. With the help of these two decoders, our method succeeds in producing semantically consistent and visually plausible colorization results without any additional priors. In addition, a simple but effective colorfulness loss is introduced to further improve the color richness of generated results. Our extensive experiments demonstrate that the proposed DDColor achieves significantly superior performance to existing state-of-the-art works both quantitatively and qualitatively. Codes will be made publicly available.