可持续性需要提高能源效率，而最小的废物则需要提高能源效率。因此，未来的电力系统应提供高水平的灵活性IIN控制能源消耗。对于能源行业的决策者和专业人员而言，对未来能源需求/负载的精确预测非常重要。预测能源负载对能源提供者和客户变得更有优势，使他们能够建立有效的生产策略以满足需求。这项研究介绍了两个混合级联模型，以预测不同分辨率中的多步户家庭功耗。第一个模型将固定小波变换（SWT）集成为有效的信号预处理技术，卷积神经网络和长期短期记忆（LSTM）。第二种混合模型将SWT与名为Transformer的基于自我注意的神经网络结构相结合。使用时频分析方法（例如多步预测问题中的SWT）的主要限制是，它们需要顺序信号，在多步骤预测应用程序中有问题的信号重建问题。级联模型可以通过使用回收输出有效地解决此问题。实验结果表明，与现有的多步电消耗预测方法相比，提出的混合模型实现了出色的预测性能。结果将为更准确和可靠的家庭用电量预测铺平道路。

Function approximation has enabled remarkable advances in applying reinforcement learning (RL) techniques in environments with high-dimensional inputs, such as images, in an end-to-end fashion, mapping such inputs directly to low-level control. Nevertheless, these have proved vulnerable to small adversarial input perturbations. A number of approaches for improving or certifying robustness of end-to-end RL to adversarial perturbations have emerged as a result, focusing on cumulative reward. However, what is often at stake in adversarial scenarios is the violation of fundamental properties, such as safety, rather than the overall reward that combines safety with efficiency. Moreover, properties such as safety can only be defined with respect to true state, rather than the high-dimensional raw inputs to end-to-end policies. To disentangle nominal efficiency and adversarial safety, we situate RL in deterministic partially-observable Markov decision processes (POMDPs) with the goal of maximizing cumulative reward subject to safety constraints. We then propose a partially-supervised reinforcement learning (PSRL) framework that takes advantage of an additional assumption that the true state of the POMDP is known at training time. We present the first approach for certifying safety of PSRL policies under adversarial input perturbations, and two adversarial training approaches that make direct use of PSRL. Our experiments demonstrate both the efficacy of the proposed approach for certifying safety in adversarial environments, and the value of the PSRL framework coupled with adversarial training in improving certified safety while preserving high nominal reward and high-quality predictions of true state.

The Internet of Senses (IoS) holds the promise of flawless telepresence-style communication for all human `receptors' and therefore blurs the difference of virtual and real environments. We commence by highlighting the compelling use cases empowered by the IoS and also the key network requirements. We then elaborate on how the emerging semantic communications and Artificial Intelligence (AI)/Machine Learning (ML) paradigms along with 6G technologies may satisfy the requirements of IoS use cases. On one hand, semantic communications can be applied for extracting meaningful and significant information and hence efficiently exploit the resources and for harnessing a priori information at the receiver to satisfy IoS requirements. On the other hand, AI/ML facilitates frugal network resource management by making use of the enormous amount of data generated in IoS edge nodes and devices, as well as by optimizing the IoS performance via intelligent agents. However, the intelligent agents deployed at the edge are not completely aware of each others' decisions and the environments of each other, hence they operate in a partially rather than fully observable environment. Therefore, we present a case study of Partially Observable Markov Decision Processes (POMDP) for improving the User Equipment (UE) throughput and energy consumption, as they are imperative for IoS use cases, using Reinforcement Learning for astutely activating and deactivating the component carriers in carrier aggregation. Finally, we outline the challenges and open issues of IoS implementations and employing semantic communications, edge intelligence as well as learning under partial observability in the IoS context.

Automatic segmentation is essential for the brain tumor diagnosis, disease prognosis, and follow-up therapy of patients with gliomas. Still, accurate detection of gliomas and their sub-regions in multimodal MRI is very challenging due to the variety of scanners and imaging protocols. Over the last years, the BraTS Challenge has provided a large number of multi-institutional MRI scans as a benchmark for glioma segmentation algorithms. This paper describes our contribution to the BraTS 2022 Continuous Evaluation challenge. We propose a new ensemble of multiple deep learning frameworks namely, DeepSeg, nnU-Net, and DeepSCAN for automatic glioma boundaries detection in pre-operative MRI. It is worth noting that our ensemble models took first place in the final evaluation on the BraTS testing dataset with Dice scores of 0.9294, 0.8788, and 0.8803, and Hausdorf distance of 5.23, 13.54, and 12.05, for the whole tumor, tumor core, and enhancing tumor, respectively. Furthermore, the proposed ensemble method ranked first in the final ranking on another unseen test dataset, namely Sub-Saharan Africa dataset, achieving mean Dice scores of 0.9737, 0.9593, and 0.9022, and HD95 of 2.66, 1.72, 3.32 for the whole tumor, tumor core, and enhancing tumor, respectively. The docker image for the winning submission is publicly available at (https://hub.docker.com/r/razeineldin/camed22).

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.

In collective decision-making, designing algorithms that use only local information to effect swarm-level behaviour is a non-trivial problem. We used machine learning techniques to teach swarm members to map their local perceptions of the environment to an optimal action. A curriculum inspired by Machine Education approaches was designed to facilitate this learning process and teach the members the skills required for optimal performance in the collective perception problem. We extended upon previous approaches by creating a curriculum that taught agents resilience to malicious influence. The experimental results show that well-designed rules-based algorithms can produce effective agents. When performing opinion fusion, we implemented decentralised resilience by having agents dynamically weight received opinion. We found a non-significant difference between constant and dynamic weights, suggesting that momentum-based opinion fusion is perhaps already a resilience mechanism.

肺癌是癌症相关死亡率的主要原因。尽管新技术（例如图像分割）对于改善检测和较早诊断至关重要，但治疗该疾病仍然存在重大挑战。特别是，尽管治愈性分辨率增加，但许多术后患者仍会出现复发性病变。因此，非常需要预后工具，可以更准确地预测患者复发的风险。在本文中，我们探讨了卷积神经网络（CNN）在术前计算机断层扫描（CT）图像中存在的分割和复发风险预测。首先，随着医学图像分割的最新进展扩展，剩余的U-NET用于本地化和表征每个结节。然后，确定的肿瘤将传递给第二个CNN进行复发风险预测。该系统的最终结果是通过随机的森林分类器产生的，该分类器合成具有临床属性的第二个网络的预测。分割阶段使用LIDC-IDRI数据集，并获得70.3％的骰子得分。复发风险阶段使用了国家癌症研究所的NLST数据集，并获得了73.0％的AUC。我们提出的框架表明，首先，自动结节分割方法可以概括地为各种多任务系统提供管道，其次，深度学习和图像处理具有改善当前预后工具的潜力。据我们所知，这是第一个完全自动化的细分和复发风险预测系统。

对抗性持续学习对于持续学习问题有效，因为存在特征对齐过程，从而产生了对灾难性遗忘问题敏感性低的任务不变特征。然而，ACL方法施加了相当大的复杂性，因为它依赖于特定于任务的网络和歧视器。它还经历了一个迭代培训过程，该过程不适合在线（单周）持续学习问题。本文提出了一种可扩展的对抗性持续学习（比例）方法，提出了一个参数生成器，将共同特征转换为特定于任务的功能，并在对抗性游戏中进行单个歧视器，以诱导共同的特征。训练过程是在元学习时尚中使用三个损失功能组合进行的。缩放比例优于明显的基线，其准确性和执行时间都明显。

许多现实世界的分类问题的班级标签频率不平衡；一个被称为“阶级失衡”问题的著名问题。经典的分类算法往往会偏向多数级别，使分类器容易受到少数族裔类别的分类。尽管文献富含解决此问题的方法，但随着问题的维度的增加，许多方法没有扩展，并且运行它们的成本变得越来越高。在本文中，我们提出了端到端的深层生成分类器。我们提出了一个域构成自动编码器，以将潜在空间保留为发电机的先验，然后将其用于与其他两个深网，一个歧视器和一个分类器一起玩对抗游戏。对三个不同的多级不平衡问题进行了广泛的实验，并与最先进的方法进行了比较。实验结果证实了我们方法比流行算法在处理高维不平衡分类问题方面具有优势。我们的代码可在https://github.com/tanmdl/slppl-gan上找到。

当代群指标通常是孤立使用的，专注于在个人或集体层面上提取信息。这些很少集成以推断群，其个人成员及其整体集体动态的顶级操作图片。本文的主要贡献是将有关群体的一系列指标组织成本体论的信息标记集合，以从外部观察者的识别剂的角度来表征群体。我们的贡献显示了我们标题为\ emph {swarm Analytics}的新研究领域的基础，其主要关注的是设计和组织群体标记的集合，以了解，检测，识别，跟踪和学习特定的见解关于一个群体系统。我们介绍了我们设计的信息标记框架，为群研究提供了新的途径，尤其是针对异质和认知群，这些群可能需要更高级的能力来检测机构并分类代理的影响和反应。