无监督的图像传输可用于医疗应用内和模式间转移，其中大量配对训练数据不丰富。为了确保从输入到目标域的结构映射，现有的未配对医疗图像转移的方法通常基于周期矛盾，由于学习了反向映射，导致了其他计算资源和不稳定。本文介绍了一种新颖的单向域映射方法，在整个培训过程中不需要配对数据。通过采用GAN体系结构和基于贴片不变性的新颖发电机损失来确保合理的转移。更确切地说，对发电机的输出进行了评估和比较，并在不同的尺度上进行了比较，这使人们对高频细节以及隐式数据增强进行了越来越多的关注。这个新颖的术语还提供了通过对斑块残差建模输入依赖性量表图来预测不确定性的机会。提出的方法在三个著名的医疗数据库上进行了全面评估。这些数据集的卓越精度与未配对图像转移的四种不同的最新方法相比，这表明了这种方法对不确定性感知的医学图像翻译的巨大潜力。建议的框架的实施在此处发布：https：//github.com/anger-man/unsupervise-image-image-transfer-and-uq。

实时估计实际对象深度是各种自主系统任务（例如3D重建，场景理解和状况评估）的重要模块。在机器学习的最后十年中，将深度学习方法的广泛部署到计算机视觉任务中产生了成功，从而成功地从简单的RGB模式中实现了现实的深度综合。这些模型大多数基于配对的RGB深度数据和/或视频序列和立体声图像的可用性。到目前为止，缺乏序列，立体声数据和RGB深度对使深度估计成为完全无监督的单图像转移问题，到目前为止几乎没有探索过。这项研究以生成神经网络领域的最新进展为基础，以建立完全无监督的单发深度估计。使用Wasserstein-1距离（一种新型的感知重建项和手工制作的图像过滤器）实现并同时优化了两个用于RGB至深度和深度RGB传输的发电机。我们使用工业表面深度数据以及德克萨斯州3D面部识别数据库，人类肖像的Celebamask-HQ数据库和记录人体深度的超现实数据集来全面评估模型。对于每个评估数据集，与最先进的单图像转移方法相比，提出的方法显示出深度准确性的显着提高。

实时估计实际环境深度是各种自主系统任务（例如本地化，障碍检测和姿势估计）的重要模块。在机器学习的最后十年中，将深度学习方法的广泛部署到计算机视觉任务中，从简单的RGB模式中产生了成功的方法，以实现现实的深度综合。尽管这些模型中的大多数都基于配对的深度数据或视频序列和立体声图像的可用性，但缺乏以无监督方式面对单像深度综合的方法。因此，在这项研究中，将生成神经网络领域的最新进步杠杆化以完全无监督的单像深度综合。更确切地说，使用Wasserstein-1距离实现了两个用于RGB至深度和深度RGB传输的周期符合发电机，并同时优化。为了确保所提出的方法的合理性，我们将模型应用于自称的工业数据集以及著名的NYU DEPTH V2数据集，从而可以与现有方法进行比较。在这项研究中，观察到的成功表明，在现实世界应用中，不成对的单像深度估计的潜力很高。

用于量化大型内燃机的圆柱衬里的最先进的方法，需要拆卸和切割检查的衬里。接下来是基于实验室的高分辨率显微表面深度测量，该测量基于轴承载荷曲线（也称为Abbott-Firestone曲线），对磨损进行了定量评估。这种参考方法具有破坏性，耗时且昂贵。此处介绍的研究的目的是开发无损但可靠的方法来量化表面地形。提出了一个新型的机器学习框架，该框架允许预测代表衬里表面反射RGB图像的深度轮廓的轴承载荷曲线。这些图像可以使用简单的手持显微镜收集。涉及两个神经网络模块的联合深度学习方法也优化了表面粗糙度参数的预测质量。使用定制数据库对网络堆栈进行训练，该数据库包含422个完美对齐的深度轮廓和大型气体发动机衬里的反射图像对。观察到的方法的成功表明，其在服务过程中对发动机进行现场磨损评估的巨大潜力。

Deep-learning of artificial neural networks (ANNs) is creating highly functional tools that are, unfortunately, as hard to interpret as their natural counterparts. While it is possible to identify functional modules in natural brains using technologies such as fMRI, we do not have at our disposal similarly robust methods for artificial neural networks. Ideally, understanding which parts of an artificial neural network perform what function might help us to address a number of vexing problems in ANN research, such as catastrophic forgetting and overfitting. Furthermore, revealing a network's modularity could improve our trust in them by making these black boxes more transparent. Here we introduce a new information-theoretic concept that proves useful in understanding and analyzing a network's functional modularity: the relay information $I_R$. The relay information measures how much information groups of neurons that participate in a particular function (modules) relay from inputs to outputs. Combined with a greedy search algorithm, relay information can be used to {\em identify} computational modules in neural networks. We also show that the functionality of modules correlates with the amount of relay information they carry.

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.

Local patterns play an important role in statistical physics as well as in image processing. Two-dimensional ordinal patterns were studied by Ribeiro et al. who determined permutation entropy and complexity in order to classify paintings and images of liquid crystals. Here we find that the 2 by 2 patterns of neighboring pixels come in three types. The statistics of these types, expressed by two parameters, contains the relevant information to describe and distinguish textures. The parameters are most stable and informative for isotropic structures.

It is well known that conservative mechanical systems exhibit local oscillatory behaviours due to their elastic and gravitational potentials, which completely characterise these periodic motions together with the inertial properties of the system. The classification of these periodic behaviours and their geometric characterisation are in an on-going secular debate, which recently led to the so-called eigenmanifold theory. The eigenmanifold characterises nonlinear oscillations as a generalisation of linear eigenspaces. With the motivation of performing periodic tasks efficiently, we use tools coming from this theory to construct an optimization problem aimed at inducing desired closed-loop oscillations through a state feedback law. We solve the constructed optimization problem via gradient-descent methods involving neural networks. Extensive simulations show the validity of the approach.

Artificial intelligence(AI) systems based on deep neural networks (DNNs) and machine learning (ML) algorithms are increasingly used to solve critical problems in bioinformatics, biomedical informatics, and precision medicine. However, complex DNN or ML models that are unavoidably opaque and perceived as black-box methods, may not be able to explain why and how they make certain decisions. Such black-box models are difficult to comprehend not only for targeted users and decision-makers but also for AI developers. Besides, in sensitive areas like healthcare, explainability and accountability are not only desirable properties of AI but also legal requirements -- especially when AI may have significant impacts on human lives. Explainable artificial intelligence (XAI) is an emerging field that aims to mitigate the opaqueness of black-box models and make it possible to interpret how AI systems make their decisions with transparency. An interpretable ML model can explain how it makes predictions and which factors affect the model's outcomes. The majority of state-of-the-art interpretable ML methods have been developed in a domain-agnostic way and originate from computer vision, automated reasoning, or even statistics. Many of these methods cannot be directly applied to bioinformatics problems, without prior customization, extension, and domain adoption. In this paper, we discuss the importance of explainability with a focus on bioinformatics. We analyse and comprehensively overview of model-specific and model-agnostic interpretable ML methods and tools. Via several case studies covering bioimaging, cancer genomics, and biomedical text mining, we show how bioinformatics research could benefit from XAI methods and how they could help improve decision fairness.

Generic Object Tracking (GOT) is the problem of tracking target objects, specified by bounding boxes in the first frame of a video. While the task has received much attention in the last decades, researchers have almost exclusively focused on the single object setting. Multi-object GOT benefits from a wider applicability, rendering it more attractive in real-world applications. We attribute the lack of research interest into this problem to the absence of suitable benchmarks. In this work, we introduce a new large-scale GOT benchmark, LaGOT, containing multiple annotated target objects per sequence. Our benchmark allows researchers to tackle key remaining challenges in GOT, aiming to increase robustness and reduce computation through joint tracking of multiple objects simultaneously. Furthermore, we propose a Transformer-based GOT tracker TaMOS capable of joint processing of multiple objects through shared computation. TaMOs achieves a 4x faster run-time in case of 10 concurrent objects compared to tracking each object independently and outperforms existing single object trackers on our new benchmark. Finally, TaMOs achieves highly competitive results on single-object GOT datasets, setting a new state-of-the-art on TrackingNet with a success rate AUC of 84.4%. Our benchmark, code, and trained models will be made publicly available.