Geospatial Information Systems are used by researchers and Humanitarian Assistance and Disaster Response (HADR) practitioners to support a wide variety of important applications. However, collaboration between these actors is difficult due to the heterogeneous nature of geospatial data modalities (e.g., multi-spectral images of various resolutions, timeseries, weather data) and diversity of tasks (e.g., regression of human activity indicators or detecting forest fires). In this work, we present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias, pre-trained on large amounts of unlabelled earth observation data in a self-supervised manner. We envision how such a model may facilitate cooperation between members of the community. We show preliminary results on the first step of the roadmap, where we instantiate an architecture that can process a wide variety of geospatial data modalities and demonstrate that it can achieve competitive performance with domain-specific architectures on tasks relating to the U.N.'s Sustainable Development Goals.

深度神经网络在数据流是I.I.D的规范环境中的预测和分类任务上表现良好，标记的数据很丰富，并且类标签平衡。随着分配变化的挑战，包括非平稳或不平衡数据流。解决了这一挑战的一种强大方法是在大量未标记的数据上对大型编码器进行自我监督的预处理，然后进行特定于任务的调整。鉴于一项新任务，更新这些编码器的权重是具有挑战性的，因为需要微调大量权重，因此，他们忘记了有关先前任务的信息。在目前的工作中，我们提出了一个模型体系结构来解决此问题，以一个离散的瓶颈为基础，其中包含成对的单独和可学习的（键，价值）代码。在此设置中，我们遵循编码；通过离散瓶颈处理表示形式；和解码范式，其中输入被馈送到预处理的编码器中，编码器的输出用于选择最近的键，并将相应的值馈送到解码器以求解当前任务。该模型只能在推理过程中获取和重复使用有限数量的这些（密钥，值）对，从而启用本地化和上下文依赖的模型更新。从理论上讲，我们研究了所提出的模型最小化分布的影响的能力，并表明与（键，值）配对的这种离散瓶颈降低了假设类别的复杂性。我们经验验证了提出的方法在各种基准数据集的挑战性分配转移方案下的好处，并表明所提出的模型将共同的脆弱性降低到非i.i.d。与其他各种基线相比，非平稳培训分布。

Neural networks are known to be a class of highly expressive functions able to fit even random inputoutput mappings with 100% accuracy. In this work we present properties of neural networks that complement this aspect of expressivity. By using tools from Fourier analysis, we highlight a learning bias of deep networks towards low frequency functions -i.e. functions that vary globally without local fluctuations -which manifests itself as a frequency-dependent learning speed. Intuitively, this property is in line with the observation that over-parameterized networks prioritize learning simple patterns that generalize across data samples. We also investigate the role of the shape of the data manifold by presenting empirical and theoretical evidence that, somewhat counter-intuitively, learning higher frequencies gets easier with increasing manifold complexity.

Temporal action segmentation tags action labels for every frame in an input untrimmed video containing multiple actions in a sequence. For the task of temporal action segmentation, we propose an encoder-decoder-style architecture named C2F-TCN featuring a "coarse-to-fine" ensemble of decoder outputs. The C2F-TCN framework is enhanced with a novel model agnostic temporal feature augmentation strategy formed by the computationally inexpensive strategy of the stochastic max-pooling of segments. It produces more accurate and well-calibrated supervised results on three benchmark action segmentation datasets. We show that the architecture is flexible for both supervised and representation learning. In line with this, we present a novel unsupervised way to learn frame-wise representation from C2F-TCN. Our unsupervised learning approach hinges on the clustering capabilities of the input features and the formation of multi-resolution features from the decoder's implicit structure. Further, we provide the first semi-supervised temporal action segmentation results by merging representation learning with conventional supervised learning. Our semi-supervised learning scheme, called ``Iterative-Contrastive-Classify (ICC)'', progressively improves in performance with more labeled data. The ICC semi-supervised learning in C2F-TCN, with 40% labeled videos, performs similar to fully supervised counterparts.

Learning linear temporal logic (LTL) formulas from examples labeled as positive or negative has found applications in inferring descriptions of system behavior. We summarize two methods to learn LTL formulas from examples in two different problem settings. The first method assumes noise in the labeling of the examples. For that, they define the problem of inferring an LTL formula that must be consistent with most but not all of the examples. The second method considers the other problem of inferring meaningful LTL formulas in the case where only positive examples are given. Hence, the first method addresses the robustness to noise, and the second method addresses the balance between conciseness and specificity (i.e., language minimality) of the inferred formula. The summarized methods propose different algorithms to solve the aforementioned problems, as well as to infer other descriptions of temporal properties, such as signal temporal logic or deterministic finite automata.

To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n{\deg}831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper.

我们考虑使用人解剖模型来解释黑盒系统的时间行为的问题。为此，根据最近的研究趋势，我们依靠确定性有限自动机（DFAS）和线性时间逻辑（LTL）公式的基本但可解释的模型。与学习DFA和LTL公式的大多数现有作品相反，我们仅依靠积极的例子。我们的动机是，通常很难从黑盒系统中观察到负面例子。为了仅从积极的示例中学习有意义的模型，我们设计了依赖于模型作为正规化器的简洁性和语言最小性的算法。为此，我们的算法采用了两种方法：一种符号和反例引导。尽管符号方法利用语言最小值作为约束满意度问题的有效编码，但反例引入的人依靠生成合适的负面示例来修剪搜索。两种方法都为我们提供了有效的算法，并在学习模型上具有理论保证。为了评估我们的算法的有效性，我们在合成数据上评估了所有算法。

环境微生物（EMS）的使用通过监测和分解污染物提供了高效，低成本和无害的环境污染补救措施。这取决于如何正确分段和确定EMS。为了增强透明，嘈杂且对比度较低的弱可见EM图像的分割，在本研究中提出了成对深度学习功能网络（PDLF-NET）。 PDLFS的使用使网络通过将每个图像的成对深度学习特征与基本模型Segnet的不同块相连，从而使网络更加关注前景（EMS）。利用shi和tomas描述符，我们在贴片上提取每个图像的深度特征，这些图像使用VGG-16模型以每个描述符为中心。然后，为了学习描述符之间的中间特征，基于Delaunay三角定理进行功能的配对以形成成对的深度学习特征。在该实验中，PDLF-NET可实现89.24％，63.20％，77.27％，35.15％，89.72％，91.44％和89.30％的出色分割结果，分别为IOU，DICE，DICE，VOE，灵敏度，精确性和特定性，精确性和特定性，精确性和特定性，精确性和特定性。

在这个大数据时代，当前一代很难从在线平台中包含的大量数据中找到正确的数据。在这种情况下，需要一个信息过滤系统，可以帮助他们找到所需的信息。近年来，出现了一个称为推荐系统的研究领域。推荐人变得重要，因为他们拥有许多现实生活应用。本文回顾了推荐系统在电子商务，电子商务，电子资源，电子政务，电子学习和电子生活中的不同技术和发展。通过分析有关该主题的最新工作，我们将能够详细概述当前的发展，并确定建议系统中的现有困难。最终结果为从业者和研究人员提供了对建议系统及其应用的必要指导和见解。

在时间动作细分中，时间戳监督只需要每个视频序列的少数标记帧。对于未标记的框架，以前的作品依靠分配硬标签，并且在微妙的违反注释假设的情况下，性能迅速崩溃。我们提出了一种基于新型的期望最大化方法（EM）方法，该方法利用了未标记框架的标签不确定性，并且足够强大以适应可能的注释误差。有了准确的时间戳注释，我们提出的方法会产生SOTA结果，甚至超过了几个指标和数据集中完全监督的设置。当应用于缺少动作段的时间戳注释时，我们的方法呈现出稳定的性能。为了进一步测试我们的配方稳健性，我们介绍了Skip-Tag监督的新挑战性注释设置。此设置会放松约束，并需要对视频中任何固定数量的随机帧进行注释，从而使其比时间戳监督更灵活，同时保持竞争力。