Deep Metric Learning (DML) learns a non-linear semantic embedding from input data that brings similar pairs together while keeping dissimilar data away from each other. To this end, many different methods are proposed in the last decade with promising results in various applications. The success of a DML algorithm greatly depends on its loss function. However, no loss function is perfect, and it deals only with some aspects of an optimal similarity embedding. Besides, the generalizability of the DML on unseen categories during the test stage is an important matter that is not considered by existing loss functions. To address these challenges, we propose novel approaches to combine different losses built on top of a shared deep feature extractor. The proposed ensemble of losses enforces the deep model to extract features that are consistent with all losses. Since the selected losses are diverse and each emphasizes different aspects of an optimal semantic embedding, our effective combining methods yield a considerable improvement over any individual loss and generalize well on unseen categories. Here, there is no limitation in choosing loss functions, and our methods can work with any set of existing ones. Besides, they can optimize each loss function as well as its weight in an end-to-end paradigm with no need to adjust any hyper-parameter. We evaluate our methods on some popular datasets from the machine vision domain in conventional Zero-Shot-Learning (ZSL) settings. The results are very encouraging and show that our methods outperform all baseline losses by a large margin in all datasets.

The ability to convert reciprocating, i.e., alternating, actuation into rotary motion using linkages is hindered fundamentally by their poor torque transmission capability around kinematic singularity configurations. Here, we harness the elastic potential energy of a linear spring attached to the coupler link of four-bar mechanisms to manipulate force transmission around the kinematic singularities. We developed a theoretical model to explore the parameter space for proper force transmission in slider-crank and rocker-crank four-bar kinematics. Finally, we verified the proposed model and methodology by building and testing a macro-scale prototype of a slider-crank mechanism. We expect this approach to enable the development of small-scale rotary engines and robotic devices with closed kinematic chains dealing with serial kinematic singularities, such as linkages and parallel manipulators.

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.

Synergetic use of sensors for soil moisture retrieval is attracting considerable interest due to the different advantages of different sensors. Active, passive, and optic data integration could be a comprehensive solution for exploiting the advantages of different sensors aimed at preparing soil moisture maps. Typically, pixel-based methods are used for multi-sensor fusion. Since, different applications need different scales of soil moisture maps, pixel-based approaches are limited for this purpose. Object-based image analysis employing an image object instead of a pixel could help us to meet this need. This paper proposes a segment-based image fusion framework to evaluate the possibility of preparing a multi-scale soil moisture map through integrated Sentinel-1, Sentinel-2, and Soil Moisture Active Passive (SMAP) data. The results confirmed that the proposed methodology was able to improve soil moisture estimation in different scales up to 20% better compared to pixel-based fusion approach.

乳腺癌是一种常见且致命的疾病，但是早期诊断时通常可以治愈。尽管大多数国家都有大规模筛查计划，但就乳腺癌筛查的单一全球公认政策尚无共识。疾病的复杂性；筛查方法的可用性有限，例如乳房X线摄影，磁共振成像（MRI）和超声筛选；公共卫生政策都将筛查政策制定。资源可用性问题需要设计符合预算的政策，该问题可以作为约束的部分可观察到的马尔可夫决策过程（CPOMDP）建模。在这项研究中，我们提出了一个多目标CPOMDP模型，用于乳腺癌筛查两个目标：最大程度地减少因乳腺癌而死亡的终生风险，并最大程度地调整了质量调整后的寿命。此外，我们考虑了扩展的动作空间，该空间允许筛查乳房X线摄影超出筛查方法。每个动作都对质量调整后的终身年份和终身风险以及独特的成本都有独特的影响。我们的结果揭示了针对不同预算水平的平均和高风险患者的最佳解决方案的帕累托前沿，决策者可以将其用于实践制定政策。

听诊器录制的胸部声音为新生儿的偏远有氧呼吸健康监测提供了机会。然而，可靠的监控需要高质量的心脏和肺部声音。本文介绍了新生胸部声音分离的新型非负基质分子（NMF）和非负矩阵协同分解（NMCF）方法。为了评估这些方法并与现有的单源分离方法进行比较，产生人工混合物数据集，包括心脏，肺和噪音。然后计算用于这些人造混合物的信噪比。这些方法也在现实世界嘈杂的新生儿胸部声音上进行测试，并根据生命符号估计误差评估，并在我们以前的作品中发达1-5的信号质量得分。此外，评估所有方法的计算成本，以确定实时处理的适用性。总的来说，所提出的NMF和NMCF方法都以2.7db到11.6db的下一个最佳现有方法而言，对于人工数据集，0.40至1.12的现实数据集的信号质量改进。发现10S记录的声音分离的中值处理时间为NMCF和NMF的342ms为28.3。由于稳定且稳健的性能，我们认为我们的提出方法可用于在真实的环境中弃绝新生儿心脏和肺部。提出和现有方法的代码可以在：https://github.com/egrooby-monash/heart-and-lung-sound-eparation。

基于深度学习的（DL）申请越来越受欢迎，并以前所未有的步伐推动。虽然正在进行许多研究工作以增强深度神经网络（DNN） - DL应用的核心 - 云和边缘系统中这些应用的实际部署挑战，它们对应用程序的可用性的影响并未充分调查。特别是，部署不同虚拟化平台的影响由云和边缘提供的DL应用程序的可用性（在端到端（E2E）推理时间）仍然是一个打开的问题。重要的是，资源弹性（通过放大），CPU固定和处理器类型（CPU VS GPU）配置已经显示在虚拟化开销上有影响力。因此，本研究的目标是研究这些潜在决定的部署选项对E2E性能的影响，从而实现了DL应用程序的可用性。为此，我们在改变处理器配置时，我们测量四种流行的执行平台（即，裸机，虚拟机（VM），容器和容器中的裸机，虚拟机（VM），容器和容器）的影响（扩展，CPU固定）和处理器类型。本研究揭示了一系列有趣的，有时是反向直观的发现，可以用作云解决方案架构师的最佳实践，以便在各种系统中有效地部署DL应用程序。值得注意的发现是，解决方案架构师必须了解DL应用特征，特别是它们的预处理和后处理要求，能够最佳选择和配置执行平台，确定使用GPU，并决定有效扩展范围。

非常希望知道模型的预测是多么不确定，特别是对于复杂的模型和难以理解的模型，如深度学习。虽然在扩散加权MRI中使用深度学习方法，但事先作品没有解决模型不确定性的问题。在这里，我们提出了一种深入的学习方法来估计扩散张量并计算估计不确定性。数据相关的不确定性由网络直接计算，并通过损耗衰减学习。使用Monte Carlo辍学来计算模型不确定性。我们还提出了一种评估预测不确定性的质量的新方法。我们将新方法与标准最小二乘张量估计和基于引导的不确定性计算技术进行比较。我们的实验表明，当测量数量小时，深度学习方法更准确，并且其不确定性预测比标准方法更好地校准。我们表明，新方法计算的估计不确定性可以突出显示模型的偏置，检测域移位，并反映测量中的噪声强度。我们的研究表明了基于深度学习的扩散MRI分析中建模预测不确定性的重要性和实际价值。

Convolutional Neural Networks (CNNs) have shown to be powerful medical image segmentation models. In this study, we address some of the main unresolved issues regarding these models. Specifically, training of these models on small medical image datasets is still challenging, with many studies promoting techniques such as transfer learning. Moreover, these models are infamous for producing over-confident predictions and for failing silently when presented with out-of-distribution (OOD) data at test time. In this paper, we advocate for multi-task learning, i.e., training a single model on several different datasets, spanning several different organs of interest and different imaging modalities. We show that not only a single CNN learns to automatically recognize the context and accurately segment the organ of interest in each context, but also that such a joint model often has more accurate and better-calibrated predictions than dedicated models trained separately on each dataset. Our experiments show that multi-task learning can outperform transfer learning in medical image segmentation tasks. For detecting OOD data, we propose a method based on spectral analysis of CNN feature maps. We show that different datasets, representing different imaging modalities and/or different organs of interest, have distinct spectral signatures, which can be used to identify whether or not a test image is similar to the images used to train a model. We show that this approach is far more accurate than OOD detection based on prediction uncertainty. The methods proposed in this paper contribute significantly to improving the accuracy and reliability of CNN-based medical image segmentation models.