与当前的AI或机器人系统相比，人类轻松地在环境中导航，使数据收集诸如Trivial之类的任务。但是，人类发现很难模拟隐藏在数据中的复杂关系。 AI系统，尤其是深度学习（DL）算法，令人印象深刻地捕获了这些复杂的关系。共生耦合人类和计算机的优势可以同时最大程度地减少所需的数据并构建复杂的输入到输出映射模型。本文通过展示新型的人机相互作用框架来使用最小的数据执行故障诊断来实现这种耦合。收集用于诊断复杂系统故障的数据是困难且耗时的。最小化所需数据将增加数据驱动模型在诊断故障时的实用性。该框架为人类用户提供指令，以收集数据，以减轻用于训练和测试故障诊断模型的数据之间的差异。该框架由三个组成部分组成：（1）用于开发培训数据集的数据收集的增强学习算法，（2）用于诊断故障的深度学习算法，以及（3）手持式数据收集数据收集的手持式现实应用程序，用于测试数据。所提出的框架提供了超过100 \％的精度，并在一个新的数据集上召回了每个故障条件的一个实例。此外，还进行了一项可用性研究，以评估手持式增强现实应用程序的用户体验，并且所有用户都能够遵循提供的步骤。

预后有助于实地系统或产品的寿命。量化该系统的当前健康状况使预后能够增强操作员的决策以保护系统的健康状况。由于（a）未知的身体关系和/（b）数据中的不规则性远远超出了问题的启动，因此很难为系统创建预后。传统上，三种不同的建模范例已被用来开发预后模型：基于物理学（PBM），数据驱动（DDM）和混合模型。最近，结合了基于PBM和DDM的方法并减轻其局限性的混合建模方法在预后域中获得了吸引力。在本文中，概述了基于模糊逻辑和生成对抗网络（GAN）的概念的组合概念的一种新型混合建模方法。基于Fuzzygan的方法将基于物理的模型嵌入模糊含义的聚集中。该技术将学习方法的输出限制为现实解决方案。轴承问题的结果表明，在模糊逻辑模型中添加基于物理的聚集的功效，以提高GAN对健康建模的能力并提供更准确的系统预后。

图像到图像转换是最近使用生成对冲网络（GaN）将图像从一个域转换为另一个域的趋势。现有的GaN模型仅利用转换的输入和输出方式执行培训。在本文中，我们执行GaN模型的语义注射训练。具体而言，我们用原始输入和输出方式训练，并注入几个时代，用于从输入到语义地图的翻译。让我们将原始培训称为输入图像转换为目标域的培训。原始训练中的语义训练注射改善了训练的GaN模型的泛化能力。此外，它还以更好的方式在生成的图像中以更好的方式保留分类信息。语义地图仅在训练时间使用，并且在测试时间不需要。通过在城市景观和RGB-NIR立体数据集上使用最先进的GaN模型进行实验。与原始训练相比，在注入语义训练后，我们遵守SSIM，FID和KID等方面的提高性能。

Cashews are grown by over 3 million smallholders in more than 40 countries worldwide as a principal source of income. As the third largest cashew producer in Africa, Benin has nearly 200,000 smallholder cashew growers contributing 15% of the country's national export earnings. However, a lack of information on where and how cashew trees grow across the country hinders decision-making that could support increased cashew production and poverty alleviation. By leveraging 2.4-m Planet Basemaps and 0.5-m aerial imagery, newly developed deep learning algorithms, and large-scale ground truth datasets, we successfully produced the first national map of cashew in Benin and characterized the expansion of cashew plantations between 2015 and 2021. In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations, which can fully capture texture information from discriminative time steps during a growing season. We further developed a Clustering Augmented Self-supervised Temporal Classification (CASTC) model to distinguish high-density versus low-density cashew plantations by automatic feature extraction and optimized clustering. Results show that the STCA model has an overall accuracy of 80% and the CASTC model achieved an overall accuracy of 77.9%. We found that the cashew area in Benin has doubled from 2015 to 2021 with 60% of new plantation development coming from cropland or fallow land, while encroachment of cashew plantations into protected areas has increased by 70%. Only half of cashew plantations were high-density in 2021, suggesting high potential for intensification. Our study illustrates the power of combining high-resolution remote sensing imagery and state-of-the-art deep learning algorithms to better understand tree crops in the heterogeneous smallholder landscape.

Attention mechanisms form a core component of several successful deep learning architectures, and are based on one key idea: ''The output depends only on a small (but unknown) segment of the input.'' In several practical applications like image captioning and language translation, this is mostly true. In trained models with an attention mechanism, the outputs of an intermediate module that encodes the segment of input responsible for the output is often used as a way to peek into the `reasoning` of the network. We make such a notion more precise for a variant of the classification problem that we term selective dependence classification (SDC) when used with attention model architectures. Under such a setting, we demonstrate various error modes where an attention model can be accurate but fail to be interpretable, and show that such models do occur as a result of training. We illustrate various situations that can accentuate and mitigate this behaviour. Finally, we use our objective definition of interpretability for SDC tasks to evaluate a few attention model learning algorithms designed to encourage sparsity and demonstrate that these algorithms help improve interpretability.

Artificial Intelligence (AI) has become commonplace to solve routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload on radiologists is steadily increasing. We project that the gap between the number of imaging exams and the number of expert radiologist readers required to cover this increase will continue to expand, consequently introducing a demand for AI-based tools that improve the efficiency with which radiologists can comfortably interpret these exams. AI has been shown to improve efficiency in medical-image generation, processing, and interpretation, and a variety of such AI models have been developed across research labs worldwide. However, very few of these, if any, find their way into routine clinical use, a discrepancy that reflects the divide between AI research and successful AI translation. To address the barrier to clinical deployment, we have formed MONAI Consortium, an open-source community which is building standards for AI deployment in healthcare institutions, and developing tools and infrastructure to facilitate their implementation. This report represents several years of weekly discussions and hands-on problem solving experience by groups of industry experts and clinicians in the MONAI Consortium. We identify barriers between AI-model development in research labs and subsequent clinical deployment and propose solutions. Our report provides guidance on processes which take an imaging AI model from development to clinical implementation in a healthcare institution. We discuss various AI integration points in a clinical Radiology workflow. We also present a taxonomy of Radiology AI use-cases. Through this report, we intend to educate the stakeholders in healthcare and AI (AI researchers, radiologists, imaging informaticists, and regulators) about cross-disciplinary challenges and possible solutions.

Radiance Fields (RF) are popular to represent casually-captured scenes for new view generation and have been used for applications beyond it. Understanding and manipulating scenes represented as RFs have to naturally follow to facilitate mixed reality on personal spaces. Semantic segmentation of objects in the 3D scene is an important step for that. Prior segmentation efforts using feature distillation show promise but don't scale to complex objects with diverse appearance. We present a framework to interactively segment objects with fine structure. Nearest neighbor feature matching identifies high-confidence regions of the objects using distilled features. Bilateral filtering in a joint spatio-semantic space grows the region to recover accurate segmentation. We show state-of-the-art results of segmenting objects from RFs and compositing them to another scene, changing appearance, etc., moving closer to rich scene manipulation and understanding. Project Page: https://rahul-goel.github.io/isrf/

Reduced system dependability and higher maintenance costs may be the consequence of poor electric power quality, which can disturb normal equipment performance, speed up aging, and even cause outright failures. This study implements and tests a prototype of an Online Sequential Extreme Learning Machine (OS-ELM) classifier based on wavelets for detecting power quality problems under transient conditions. In order to create the classifier, the OSELM-network model and the discrete wavelet transform (DWT) method are combined. First, discrete wavelet transform (DWT) multi-resolution analysis (MRA) was used to extract characteristics of the distorted signal at various resolutions. The OSELM then sorts the retrieved data by transient duration and energy features to determine the kind of disturbance. The suggested approach requires less memory space and processing time since it can minimize a large quantity of the distorted signal's characteristics without changing the signal's original quality. Several types of transient events were used to demonstrate the classifier's ability to detect and categorize various types of power disturbances, including sags, swells, momentary interruptions, oscillatory transients, harmonics, notches, spikes, flickers, sag swell, sag mi, sag harm, swell trans, sag spike, and swell spike.

Arbitrary Style Transfer is a technique used to produce a new image from two images: a content image, and a style image. The newly produced image is unseen and is generated from the algorithm itself. Balancing the structure and style components has been the major challenge that other state-of-the-art algorithms have tried to solve. Despite all the efforts, it's still a major challenge to apply the artistic style that was originally created on top of the structure of the content image while maintaining consistency. In this work, we solved these problems by using a Deep Learning approach using Convolutional Neural Networks. Our implementation will first extract foreground from the background using the pre-trained Detectron 2 model from the content image, and then apply the Arbitrary Style Transfer technique that is used in SANet. Once we have the two styled images, we will stitch the two chunks of images after the process of style transfer for the complete end piece.

Machine learning (ML) algorithms are remarkably good at approximating complex non-linear relationships. Most ML training processes, however, are designed to deliver ML tools with good average performance, but do not offer any guarantees about their worst-case estimation error. For safety-critical systems such as power systems, this places a major barrier for their adoption. So far, approaches could determine the worst-case violations of only trained ML algorithms. To the best of our knowledge, this is the first paper to introduce a neural network training procedure designed to achieve both a good average performance and minimum worst-case violations. Using the Optimal Power Flow (OPF) problem as a guiding application, our approach (i) introduces a framework that reduces the worst-case generation constraint violations during training, incorporating them as a differentiable optimization layer; and (ii) presents a neural network sequential learning architecture to significantly accelerate it. We demonstrate the proposed architecture on four different test systems ranging from 39 buses to 162 buses, for both AC-OPF and DC-OPF applications.