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。

语义细分需要在处理大量数据时学习高级特征的方法。卷积神经网络（CNN）可以学习独特和适应性的特征，以实现这一目标。但是，由于遥感图像的大尺寸和高空间分辨率，这些网络无法有效地分析整个场景。最近，Deep Transformers证明了它们能够记录图像中不同对象之间的全局相互作用的能力。在本文中，我们提出了一个新的分割模型，该模型将卷积神经网络与变压器结合在一起，并表明这种局部和全局特征提取技术的混合物在遥感分割中提供了显着优势。此外，提出的模型包括两个融合层，这些融合层旨在有效地表示网络的多模式输入和输出。输入融合层提取物具有总结图像内容与高程图（DSM）之间关系的地图。输出融合层使用一种新型的多任务分割策略，其中使用特定于类的特征提取层和损耗函数来识别类标签。最后，使用快速制定的方法将所有不明的类标签转换为其最接近的邻居。我们的结果表明，与最新技术相比，提出的方法可以提高分割精度。

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

The acquisition of high-quality human annotations through crowdsourcing platforms like Amazon Mechanical Turk (MTurk) is more challenging than expected. The annotation quality might be affected by various aspects like annotation instructions, Human Intelligence Task (HIT) design, and wages paid to annotators, etc. To avoid potentially low-quality annotations which could mislead the evaluation of automatic summarization system outputs, we investigate the recruitment of high-quality MTurk workers via a three-step qualification pipeline. We show that we can successfully filter out bad workers before they carry out the evaluations and obtain high-quality annotations while optimizing the use of resources. This paper can serve as basis for the recruitment of qualified annotators in other challenging annotation tasks.

Achieving artificially intelligent-native wireless networks is necessary for the operation of future 6G applications such as the metaverse. Nonetheless, current communication schemes are, at heart, a mere reconstruction process that lacks reasoning. One key solution that enables evolving wireless communication to a human-like conversation is semantic communications. In this paper, a novel machine reasoning framework is proposed to pre-process and disentangle source data so as to make it semantic-ready. In particular, a novel contrastive learning framework is proposed, whereby instance and cluster discrimination are performed on the data. These two tasks enable increasing the cohesiveness between data points mapping to semantically similar content elements and disentangling data points of semantically different content elements. Subsequently, the semantic deep clusters formed are ranked according to their level of confidence. Deep semantic clusters of highest confidence are considered learnable, semantic-rich data, i.e., data that can be used to build a language in a semantic communications system. The least confident ones are considered, random, semantic-poor, and memorizable data that must be transmitted classically. Our simulation results showcase the superiority of our contrastive learning approach in terms of semantic impact and minimalism. In fact, the length of the semantic representation achieved is minimized by 57.22% compared to vanilla semantic communication systems, thus achieving minimalist semantic representations.

With the evolution of power systems as it is becoming more intelligent and interactive system while increasing in flexibility with a larger penetration of renewable energy sources, demand prediction on a short-term resolution will inevitably become more and more crucial in designing and managing the future grid, especially when it comes to an individual household level. Projecting the demand for electricity for a single energy user, as opposed to the aggregated power consumption of residential load on a wide scale, is difficult because of a considerable number of volatile and uncertain factors. This paper proposes a customized GRU (Gated Recurrent Unit) and Long Short-Term Memory (LSTM) architecture to address this challenging problem. LSTM and GRU are comparatively newer and among the most well-adopted deep learning approaches. The electricity consumption datasets were obtained from individual household smart meters. The comparison shows that the LSTM model performs better for home-level forecasting than alternative prediction techniques-GRU in this case. To compare the NN-based models with contrast to the conventional statistical technique-based model, ARIMA based model was also developed and benchmarked with LSTM and GRU model outcomes in this study to show the performance of the proposed model on the collected time series data.

Boundary conditions (BCs) are important groups of physics-enforced constraints that are necessary for solutions of Partial Differential Equations (PDEs) to satisfy at specific spatial locations. These constraints carry important physical meaning, and guarantee the existence and the uniqueness of the PDE solution. Current neural-network based approaches that aim to solve PDEs rely only on training data to help the model learn BCs implicitly. There is no guarantee of BC satisfaction by these models during evaluation. In this work, we propose Boundary enforcing Operator Network (BOON) that enables the BC satisfaction of neural operators by making structural changes to the operator kernel. We provide our refinement procedure, and demonstrate the satisfaction of physics-based BCs, e.g. Dirichlet, Neumann, and periodic by the solutions obtained by BOON. Numerical experiments based on multiple PDEs with a wide variety of applications indicate that the proposed approach ensures satisfaction of BCs, and leads to more accurate solutions over the entire domain. The proposed correction method exhibits a (2X-20X) improvement over a given operator model in relative $L^2$ error (0.000084 relative $L^2$ error for Burgers' equation).

Understanding how biological neural networks carry out learning using spike-based local plasticity mechanisms can lead to the development of powerful, energy-efficient, and adaptive neuromorphic processing systems. A large number of spike-based learning models have recently been proposed following different approaches. However, it is difficult to assess if and how they could be mapped onto neuromorphic hardware, and to compare their features and ease of implementation. To this end, in this survey, we provide a comprehensive overview of representative brain-inspired synaptic plasticity models and mixed-signal CMOS neuromorphic circuits within a unified framework. We review historical, bottom-up, and top-down approaches to modeling synaptic plasticity, and we identify computational primitives that can support low-latency and low-power hardware implementations of spike-based learning rules. We provide a common definition of a locality principle based on pre- and post-synaptic neuron information, which we propose as a fundamental requirement for physical implementations of synaptic plasticity. Based on this principle, we compare the properties of these models within the same framework, and describe the mixed-signal electronic circuits that implement their computing primitives, pointing out how these building blocks enable efficient on-chip and online learning in neuromorphic processing systems.

本文考虑通过模型量化提高联邦学习（FL）的无线通信和计算效率。在提出的Bitwidth FL方案中，Edge设备将其本地FL模型参数的量化版本训练并传输到协调服务器，从而将它们汇总为量化的全局模型并同步设备。目的是共同确定用于本地FL模型量化的位宽度以及每次迭代中参与FL训练的设备集。该问题被视为一个优化问题，其目标是在每卷工具采样预算和延迟要求下最大程度地减少量化FL的训练损失。为了得出解决方案，进行分析表征，以显示有限的无线资源和诱导的量化误差如何影响所提出的FL方法的性能。分析结果表明，两个连续迭代之间的FL训练损失的改善取决于设备的选择和量化方案以及所学模型固有的几个参数。给定基于线性回归的这些模型属性的估计值，可以证明FL训练过程可以描述为马尔可夫决策过程（MDP），然后提出了基于模型的增强学习（RL）方法来优化动作的方法选择迭代。与无模型RL相比，这种基于模型的RL方法利用FL训练过程的派生数学表征来发现有效的设备选择和量化方案，而无需强加其他设备通信开销。仿真结果表明，与模型无RL方法和标准FL方法相比，提出的FL算法可以减少29％和63％的收敛时间。

医疗图像分割有助于计算机辅助诊断，手术和治疗。数字化组织载玻片图像用于分析和分段腺，核和其他生物标志物，这些标志物进一步用于计算机辅助医疗应用中。为此，许多研究人员开发了不同的神经网络来对组织学图像进行分割，主要是这些网络基于编码器编码器体系结构，并且还利用了复杂的注意力模块或变压器。但是，这些网络不太准确地捕获相关的本地和全局特征，并在多个尺度下具有准确的边界检测，因此，我们提出了一个编码器折叠网络，快速注意模块和多损耗函数（二进制交叉熵（BCE）损失的组合） ，焦点损失和骰子损失）。我们在两个公开可用数据集上评估了我们提出的网络的概括能力，用于医疗图像分割Monuseg和Glas，并胜过最先进的网络，在Monuseg数据集上提高了1.99％的提高，而GLAS数据集则提高了7.15％。实施代码可在此链接上获得：https：//bit.ly/histoseg