XGBoost是一种可扩展的树升压算法，已证明对许多实际兴趣的预测任务有效，特别是使用表格数据集。HyperParameter调整可以进一步提高预测性能，但与神经网络不同，大型数据集上许多模型的全批量培训可能是耗时的。由于（i）数据集大小与培训时间之间存在强大的线性关系，（ii）XGBoost模型满足排名假设，（iii）低保真模型可以发现有前途的封路数据配置，我们展示了统一的分支使得一个简单的快速基准，通过使用数据子集作为保真维度来加快大型XGBoost模型的调整。我们展示了该基线对大型表格数据集的效力，范围为15-70美元\ Mathrm {GB}美元。

贝叶斯优化（BO）是机器学习算法的封锁率优化（HPO）广泛流行的方法。在其核心，Bo迭代地评估有前途的配置，直到用户定义的预算（例如挂钟时间或迭代次数）耗尽。虽然在调整大量后的最终性能取决于提供的预算，但很难提前预先指定最佳价值。在这项工作中，我们为BO提出了一种有效而直观的终止标准，如果它足够接近全球Optima，则会自动停止程序。在广泛的实际HPO问题中，我们表明，与来自文献的现有基线相比，我们的终止标准实现了更好的测试性能，例如在改进概率下降到固定阈值以下时停止。我们还提供了证据表明，与我们的方法相比，这些基线对其自身的Quand参数的选择非常敏感。此外，我们发现在HPO的背景下可能会出现过度装备，这可以在文献中可以说是一个忽视的问题，并表明我们的终止标准减轻了小型和大型数据集的这种现象。

We are witnessing a widespread adoption of artificial intelligence in healthcare. However, most of the advancements in deep learning (DL) in this area consider only unimodal data, neglecting other modalities. Their multimodal interpretation necessary for supporting diagnosis, prognosis and treatment decisions. In this work we present a deep architecture, explainable by design, which jointly learns modality reconstructions and sample classifications using tabular and imaging data. The explanation of the decision taken is computed by applying a latent shift that, simulates a counterfactual prediction revealing the features of each modality that contribute the most to the decision and a quantitative score indicating the modality importance. We validate our approach in the context of COVID-19 pandemic using the AIforCOVID dataset, which contains multimodal data for the early identification of patients at risk of severe outcome. The results show that the proposed method provides meaningful explanations without degrading the classification performance.

Reliable and automated 3D plant shoot segmentation is a core prerequisite for the extraction of plant phenotypic traits at the organ level. Combining deep learning and point clouds can provide effective ways to address the challenge. However, fully supervised deep learning methods require datasets to be point-wise annotated, which is extremely expensive and time-consuming. In our work, we proposed a novel weakly supervised framework, Eff-3DPSeg, for 3D plant shoot segmentation. First, high-resolution point clouds of soybean were reconstructed using a low-cost photogrammetry system, and the Meshlab-based Plant Annotator was developed for plant point cloud annotation. Second, a weakly-supervised deep learning method was proposed for plant organ segmentation. The method contained: (1) Pretraining a self-supervised network using Viewpoint Bottleneck loss to learn meaningful intrinsic structure representation from the raw point clouds; (2) Fine-tuning the pre-trained model with about only 0.5% points being annotated to implement plant organ segmentation. After, three phenotypic traits (stem diameter, leaf width, and leaf length) were extracted. To test the generality of the proposed method, the public dataset Pheno4D was included in this study. Experimental results showed that the weakly-supervised network obtained similar segmentation performance compared with the fully-supervised setting. Our method achieved 95.1%, 96.6%, 95.8% and 92.2% in the Precision, Recall, F1-score, and mIoU for stem leaf segmentation and 53%, 62.8% and 70.3% in the AP, AP@25, and AP@50 for leaf instance segmentation. This study provides an effective way for characterizing 3D plant architecture, which will become useful for plant breeders to enhance selection processes.

This paper presents the development of a system able to estimate the 2D relative position of nodes in a wireless network, based on distance measurements between the nodes. The system uses ultra wide band ranging technology and the Bluetooth Low Energy protocol to acquire data. Furthermore, a nonlinear least squares problem is formulated and solved numerically for estimating the relative positions of the nodes. The localization performance of the system is validated by experimental tests, demonstrating the capability of measuring the relative position of a network comprised of 4 nodes with an accuracy of the order of 3 cm and an update rate of 10 Hz. This shows the feasibility of applying the proposed system for multi-robot cooperative localization and formation control scenarios.

Crypto-coins (also known as cryptocurrencies) are tradable digital assets. Notable examples include Bitcoin, Ether and Litecoin. Ownerships of cryptocoins are registered on distributed ledgers (i.e., blockchains). Secure encryption techniques guarantee the security of the transactions (transfers of coins across owners), registered into the ledger. Cryptocoins are exchanged for specific trading prices. While history has shown the extreme volatility of such trading prices across all different sets of crypto-assets, it remains unclear what and if there are tight relations between the trading prices of different cryptocoins. Major coin exchanges (i.e., Coinbase) provide trend correlation indicators to coin owners, suggesting possible acquisitions or sells. However, these correlations remain largely unvalidated. In this paper, we shed lights on the trend correlations across a large variety of cryptocoins, by investigating their coin-price correlation trends over a period of two years. Our experimental results suggest strong correlation patterns between main coins (Ethereum, Bitcoin) and alt-coins. We believe our study can support forecasting techniques for time-series modeling in the context of crypto-coins. We release our dataset and code to reproduce our analysis to the research community.

事实证明，联邦学习（FL）是利用分布式资源的最有希望的范式之一，使一组客户能够协作培训机器学习模型，同时保持数据分散。对该主题兴趣的爆炸性增长导致了几个核心方面的快速发展，例如沟通效率，处理非IID数据，隐私和安全能力。但是，假设客户的培训集被标记，大多数FL仅处理监督任务。为了利用分布式边缘设备上的巨大未标记数据，我们旨在通过解决分散设置中的异常检测问题来扩展FL范式到无监督任务。特别是，我们提出了一种新颖的方法，在这种方法中，通过预处理阶段，客户分组为社区，每个社区都具有相似的多数（即近距离）模式。随后，每个客户社区都以联合方式训练相同的异常检测模型（即自动编码器）。然后共享所得模型并用于检测加入相应联合过程的同一社区客户端内的异常情况。实验表明我们的方法是强大的，它可以检测到与理想分区一致的社区，在这种分区中，知道具有相同近距离模式的客户组。此外，性能要比客户专门培训模型在本地数据上训练，并且与理想社区分区的联合模型相当的性能要好得多。

在创建3D内容时，通常需要高度专业的技能来设计和生成对象和其他资产的模型。我们通过从多模式输入（包括2D草图，图像和文本）中检索高质量的3D资产来解决此问题。我们使用夹子，因为它为高级潜在特征提供了桥梁。我们使用这些功能来执行多模式融合，以解决影响常见数据驱动方法的缺乏艺术控制。我们的方法通过使用输入潜在的嵌入方式组合，可以通过3D资产数据库进行多模式条件特征驱动的检索。我们探讨了不同输入类型和加权方法的特征嵌入不同组合的影响。

联合学习（FL）为培训机器学习模型打开了新的观点，同时将个人数据保存在用户场所上。具体而言，在FL中，在用户设备上训练了模型，并且仅将模型更新（即梯度）发送到中央服务器以进行聚合目的。但是，近年来发表的一系列推理攻击泄漏了私人数据，这强调了需要设计有效的保护机制来激励FL的大规模采用。尽管存在缓解服务器端的这些攻击的解决方案，但几乎没有采取任何措施来保护用户免受客户端执行的攻击。在这种情况下，在客户端使用受信任的执行环境（TEE）是最建议的解决方案之一。但是，现有的框架（例如，Darknetz）需要静态地将机器学习模型的很大一部分放入T恤中，以有效防止复杂的攻击或攻击组合。我们提出了GradSec，该解决方案允许在静态或动态上仅在机器学习模型的TEE上进行保护，因此将TCB的大小和整体训练时间降低了30％和56％，相比之下 - 艺术竞争者。

这项工作描述了Push，这是一种原始的启发式，结合了可行性泵和转移。主要思想是通过适当的转移和其他圆形启发式方法来代替可行性泵的圆形阶段。该算法提出了不同的策略，具体取决于获得的部分舍入的性质。特别是，我们区分何时可行的部分解决方案，与潜在候选者不可行，而没有候选者不可行。我们使用阈值指示使用算法将变量的百分比，以及将其四舍五入到最近的整数中。最重要的是，我们的算法直接处理平等约束而无需复制行。我们在为2022的MIP竞赛中选择了算法的参数。最后，我们将我们的方法与其他开始启发式方法进行了比较，例如第一个800 MIPLIB2017实例在数量下订购的简单圆形，圆形，舍入和可行性泵非零件。