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.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Multibeam forward-looking sonar (MFLS) plays an important role in underwater detection. There are several challenges to the research on underwater object detection with MFLS. Firstly, the research is lack of available dataset. Secondly, the sonar image, generally processed at pixel level and transformed to sector representation for the visual habits of human beings, is disadvantageous to the research in artificial intelligence (AI) areas. Towards these challenges, we present a novel dataset, the underwater acoustic target detection (UATD) dataset, consisting of over 9000 MFLS images captured using Tritech Gemini 1200ik sonar. Our dataset provides raw data of sonar images with annotation of 10 categories of target objects (cube, cylinder, tyres, etc). The data was collected from lake and shallow water. To verify the practicality of UATD, we apply the dataset to the state-of-the-art detectors and provide corresponding benchmarks for its accuracy and efficiency.

As an effective method to deliver external materials into biological cells, microinjection has been widely applied in the biomedical field. However, the cognition of cell mechanical property is still inadequate, which greatly limits the efficiency and success rate of injection. Thus, a new rate-dependent mechanical model based on membrane theory is proposed for the first time. In this model, an analytical equilibrium equation between the injection force and cell deformation is established by considering the speed effect of microinjection. Different from the traditional membrane-theory-based model, the elastic coefficient of the constitutive material in the proposed model is modified as a function of the injection velocity and acceleration, effectively simulating the influence of speeds on the mechanical responses and providing a more generalized and practical model. Using this model, other mechanical responses at different speeds can be also accurately predicted, including the distribution of membrane tension and stress and the deformed shape. To verify the validity of the model, numerical simulations and experiments are carried out. The results show that the proposed model can match the real mechanical responses well at different injection speeds.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

人重新识别是识别非重叠摄像机的个体的问题。尽管在重新识别问题中取得了显着进展，但由于同一人的外观变化以及其他外观相似的人，这仍然是一个具有挑战性的问题。一些先前的作品通过将正样本的特征与负面的特征分开来解决这些问题。但是，现有模型的性能在很大程度上取决于用于培训的样品的特征和统计数据。因此，我们提出了一个名为“采样独立鲁棒特征表示网络”（sirnet）的新型框架，该框架学习了从随机选择的样品中嵌入的分离特征。对精心设计的采样独立的最大差异损失引入了与集群同一人的模型样本。结果，所提出的框架可以使用学识渊博的功能产生额外的硬质量/积极因素，从而可以更好地辨别其他身份。大规模基准数据集的广泛实验结果验证了所提出的模型比以前的最新模型更有效。

图形上的神经扩散是一类新型的图形神经网络，最近引起了越来越多的关注。图形神经偏微分方程（PDE）的能力在解决图形神经网络（GNN）的常见障碍方面的能力，例如过度平滑和瓶颈的问题，但尚未对其对逆性攻击的稳健性。在这项工作中，我们探讨了图神经PDE的稳健性。我们从经验上证明，与其他GNN相比，图形神经PDE在本质上对拓扑扰动更为强大。我们通过利用在图形拓扑扰动下利用热半群的稳定性来提供对这一现象的见解。我们讨论了各种图扩散操作员，并将它们与现有的图神经PDE相关联。此外，我们提出了一个一般图形神经PDE框架，可以通过该框架来定义新的强大GNN。我们验证了新模型在多个基准数据集上实现了可比的最新性能。

联合学习（FL）已成为解决数据筒仓问题的实用解决方案，而不会损害用户隐私。它的一种变体垂直联合学习（VFL）最近引起了人们的关注，因为VFL与企业对利用更有价值的功能的需求相匹配，以构建更好的机器学习模型，同时保留用户隐私。当前在VFL中的工作集中于为特定VFL算法开发特定的保护或攻击机制。在这项工作中，我们提出了一个评估框架，该框架提出了隐私 - 私人评估问题。然后，我们将此框架作为指南，以全面评估针对三种广泛依据的VFL算法的大多数最先进的隐私攻击的广泛保护机制。这些评估可以帮助FL从业人员在特定要求下选择适当的保护机制。我们的评估结果表明：模型反转和大多数标签推理攻击可能会因现有保护机制而挫败；很难防止模型完成（MC）攻击，这需要更高级的MC靶向保护机制。根据我们的评估结果，我们为提高VFL系统的隐私保护能力提供具体建议。

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。

联合学习（FL）使参与方能够在不公开私人数据信息的情况下协作建立一个全球模型。必须采用适当的保护机制，以满足保留\ textit {privacy}并维护高模型\ textit {utility}的相反要求。此外，为了实现大规模的模型培训和部署，联合学习系统实现高\ textit {效率}是一项任务。我们提出了一个统一的联合学习框架，可以调和水平和垂直的联合学习。基于此框架，我们制定和量化了隐私泄漏，公用事业损失和降低效率之间的权衡，这使我们成为了联合学习系统的无午餐定理（NFL）定理。 NFL表示，期望FL算法同时在某些情况下同时提供出色的隐私，实用性和效率是不现实的。然后，我们分析了几种广泛补习的保护机制的隐私泄漏，效用损失和效率降低的下限，包括\ textit {Randomization}，\ textIt {同粒子加密}，\ textit {secretit {secret {sertial {sertion {sertion {compression} {Compression}。我们的分析可以作为选择保护参数以满足特定要求的指南。