供应链平台（SCP）为下游行业提供了许多原材料。与传统的电子商务平台相比，由于用户兴趣有限，SCP中的数据更为稀疏。为了解决数据稀疏问题，可以应用跨域建议（CDR），从而通过源域信息提高目标域的建议性能。但是，将CDR应用于SCP，直接忽略了SCP中商品的层次结构，从而降低了建议性能。为了利用此功能，在本文中，我们以餐饮平台为例，并提出了图形跨域推荐模型GRES。该模型首先构造了树状图，以表示菜肴和成分不同节点的层次结构，然后应用我们提出的Tree2Vec方法将GCN和BERT模型组合到嵌入图中以嵌入图表以获取建议。商业数据集上的实验结果表明，GRES在供应链平台的跨域建议中明显优于最先进的方法。

图表神经网络（GNN）基于故障诊断（FD）近年来收到了越来越多的关注，因为来自来自多个应用域的数据可以有利地表示为图。实际上，与传统的FD方法相比，这种特殊的代表性表格导致了卓越的性能。在本次审查中，给出了GNN，对故障诊断领域的潜在应用以及未来观点的简单介绍。首先，通过专注于它们的数据表示，即时间序列，图像和图形，回顾基于神经网络的FD方法。其次，引入了GNN的基本原则和主要架构，注意了图形卷积网络，图注意网络，图形样本和聚合，图形自动编码器和空间 - 时间图卷积网络。第三，通过详细实验验证基于GNN的最相关的故障诊断方法，结论是基于GNN的方法可以实现良好的故障诊断性能。最后，提供了讨论和未来的挑战。

上下文：堆栈溢出对于寻求编程问题答案的软件开发人员非常有帮助。先前的研究表明，越来越多的问题质量低，因此从潜在的答案者那里获得了更少的关注。 Gao等。提出了一个基于LSTM的模型（即BilstM-CC），以自动从代码片段中生成问题标题，以提高问题质量。但是，只有在问题主体中使用代码段无法为标题生成提供足够的信息，而LSTMS无法捕获令牌之间的远程依赖性。目的：本文提出了基于深度学习的新型模型CCBERT，旨在通过充分利用整个问题主体的双模式信息来增强问题标题生成的性能。方法：CCBERT遵循编码器范式范式，并使用Codebert将问题主体编码为隐藏的表示形式，堆叠的变压器解码器以生成预测的代币，以及附加的复制注意层来完善输出分布。编码器和解码器都执行多头自我注意操作，以更好地捕获远程依赖性。本文构建了一个数据集，该数据集包含大约200,000个高质量问题，该数据从Stack Overflow正式发布的数据中滤除，以验证CCBERT模型的有效性。结果：CCBERT优于数据集上的所有基线模型。对仅代码和低资源数据集进行的实验表明，CCBERT的优势性能较小。人类评估还显示了CCBERT关于可读性和相关标准的出色表现。

难以通过二进制面具手动准确标记含糊不清的和复杂形状的目标。在医学图像分割中突出显示二元掩模下面的弱点，其中模糊是普遍的。在多个注释的情况下，通过二元面具对临床医生达成共识更具挑战性。此外，这些不确定的区域与病变结构有关，可能含有有利于诊断的解剖信息。然而，目前关于不确定性的研究主要关注模型培训和数据标签的不确定性。他们都没有调查病变本身的模糊性质的影响。通过图像消光，透过图像消光，将Alpha Matte作为软片介绍，代表医学场景中不确定的区域，并因此提出了一种新的不确定性量化方法来填补填补差距病变结构的不确定性研究。在这项工作中，我们在多任务框架中引入了一种新的架构，以在多任务框架中生成二进制掩模和alpha掩饰，这优于所有最先进的消光算法。建议的不确定性地图能够突出模糊地区和我们提出的新型多任务损失加权策略可以进一步提高性能并证明其具体的益处。为了充分评估我们提出的方法的有效性，我们首先用alpha哑布标记了三个医疗数据集，以解决医学场景中可用消光数据集的短缺，并证明alpha遮罩是一种比定性的二进制掩模更有效的标签方法和量化方面。

本文介绍了寻求信息（是）任务，概念和算法的信息重新分类。拟议的分类系统提供了新的维度，以研究寻求任务和方法的信息。新尺寸包括搜索迭代，搜索目标类型和程序的数量，以实现这些目标。寻求任务的信息沿着这些尺寸呼叫合适的计算解决方案的差异。然后，该文章评论了符合每个新类别的机器学习解决方案。该论文结束了对系统的评估活动进行了审查。

Learning efficient and interpretable policies has been a challenging task in reinforcement learning (RL), particularly in the visual RL setting with complex scenes. While neural networks have achieved competitive performance, the resulting policies are often over-parameterized black boxes that are difficult to interpret and deploy efficiently. More recent symbolic RL frameworks have shown that high-level domain-specific programming logic can be designed to handle both policy learning and symbolic planning. However, these approaches rely on coded primitives with little feature learning, and when applied to high-dimensional visual scenes, they can suffer from scalability issues and perform poorly when images have complex object interactions. To address these challenges, we propose \textit{Differentiable Symbolic Expression Search} (DiffSES), a novel symbolic learning approach that discovers discrete symbolic policies using partially differentiable optimization. By using object-level abstractions instead of raw pixel-level inputs, DiffSES is able to leverage the simplicity and scalability advantages of symbolic expressions, while also incorporating the strengths of neural networks for feature learning and optimization. Our experiments demonstrate that DiffSES is able to generate symbolic policies that are simpler and more and scalable than state-of-the-art symbolic RL methods, with a reduced amount of symbolic prior knowledge.

Recent advances in neural rendering imply a future of widespread visual data distributions through sharing NeRF model weights. However, while common visual data (images and videos) have standard approaches to embed ownership or copyright information explicitly or subtly, the problem remains unexplored for the emerging NeRF format. We present StegaNeRF, a method for steganographic information embedding in NeRF renderings. We design an optimization framework allowing accurate hidden information extractions from images rendered by NeRF, while preserving its original visual quality. We perform experimental evaluations of our method under several potential deployment scenarios, and we further discuss the insights discovered through our analysis. StegaNeRF signifies an initial exploration into the novel problem of instilling customizable, imperceptible, and recoverable information to NeRF renderings, with minimal impact to rendered images. Project page: https://xggnet.github.io/StegaNeRF/.

Virtual reality and augmented reality (XR) bring increasing demand for 3D content. However, creating high-quality 3D content requires tedious work that a human expert must do. In this work, we study the challenging task of lifting a single image to a 3D object and, for the first time, demonstrate the ability to generate a plausible 3D object with 360{\deg} views that correspond well with the given reference image. By conditioning on the reference image, our model can fulfill the everlasting curiosity for synthesizing novel views of objects from images. Our technique sheds light on a promising direction of easing the workflows for 3D artists and XR designers. We propose a novel framework, dubbed NeuralLift-360, that utilizes a depth-aware neural radiance representation (NeRF) and learns to craft the scene guided by denoising diffusion models. By introducing a ranking loss, our NeuralLift-360 can be guided with rough depth estimation in the wild. We also adopt a CLIP-guided sampling strategy for the diffusion prior to provide coherent guidance. Extensive experiments demonstrate that our NeuralLift-360 significantly outperforms existing state-of-the-art baselines. Project page: https://vita-group.github.io/NeuralLift-360/

The ubiquity of camera-embedded devices and the advances in deep learning have stimulated various intelligent mobile video applications. These applications often demand on-device processing of video streams to deliver real-time, high-quality services for privacy and robustness concerns. However, the performance of these applications is constrained by the raw video streams, which tend to be taken with small-aperture cameras of ubiquitous mobile platforms in dim light. Despite extensive low-light video enhancement solutions, they are unfit for deployment to mobile devices due to their complex models and and ignorance of system dynamics like energy budgets. In this paper, we propose AdaEnlight, an energy-aware low-light video stream enhancement system on mobile devices. It achieves real-time video enhancement with competitive visual quality while allowing runtime behavior adaptation to the platform-imposed dynamic energy budgets. We report extensive experiments on diverse datasets, scenarios, and platforms and demonstrate the superiority of AdaEnlight compared with state-of-the-art low-light image and video enhancement solutions.

Arbitrary style transfer (AST) transfers arbitrary artistic styles onto content images. Despite the recent rapid progress, existing AST methods are either incapable or too slow to run at ultra-resolutions (e.g., 4K) with limited resources, which heavily hinders their further applications. In this paper, we tackle this dilemma by learning a straightforward and lightweight model, dubbed MicroAST. The key insight is to completely abandon the use of cumbersome pre-trained Deep Convolutional Neural Networks (e.g., VGG) at inference. Instead, we design two micro encoders (content and style encoders) and one micro decoder for style transfer. The content encoder aims at extracting the main structure of the content image. The style encoder, coupled with a modulator, encodes the style image into learnable dual-modulation signals that modulate both intermediate features and convolutional filters of the decoder, thus injecting more sophisticated and flexible style signals to guide the stylizations. In addition, to boost the ability of the style encoder to extract more distinct and representative style signals, we also introduce a new style signal contrastive loss in our model. Compared to the state of the art, our MicroAST not only produces visually superior results but also is 5-73 times smaller and 6-18 times faster, for the first time enabling super-fast (about 0.5 seconds) AST at 4K ultra-resolutions. Code is available at https://github.com/EndyWon/MicroAST.