本文的目的是比较医学声学任务中不同可学习的前端。已经实施了一个框架，以将人类的呼吸道声音和心跳分为两类，即健康或受病理影响。在获得两个合适的数据集后，我们开始使用两个可学习的前端（叶子和nnaudio）对声音进行分类，以及一个不可学习的基线前端，即mel-Filterbanks。然后，计算出的功能将被馈送到两种不同的CNN模型中，即VGG16和EfficityNet。前端根据参数，计算资源和有效性的数量进行了仔细的基准测试。这项工作表明了神经音频分类系统中可学习前端的整合如何提高性能，尤其是在医学声学领域。但是，此类框架的使用使所需的数据数量更大。因此，如果可用于培训的数据量足够大以帮助特征学习过程，则它们很有用。

Relation extraction (RE) is a sub-discipline of information extraction (IE) which focuses on the prediction of a relational predicate from a natural-language input unit (such as a sentence, a clause, or even a short paragraph consisting of multiple sentences and/or clauses). Together with named-entity recognition (NER) and disambiguation (NED), RE forms the basis for many advanced IE tasks such as knowledge-base (KB) population and verification. In this work, we explore how recent approaches for open information extraction (OpenIE) may help to improve the task of RE by encoding structured information about the sentences' principal units, such as subjects, objects, verbal phrases, and adverbials, into various forms of vectorized (and hence unstructured) representations of the sentences. Our main conjecture is that the decomposition of long and possibly convoluted sentences into multiple smaller clauses via OpenIE even helps to fine-tune context-sensitive language models such as BERT (and its plethora of variants) for RE. Our experiments over two annotated corpora, KnowledgeNet and FewRel, demonstrate the improved accuracy of our enriched models compared to existing RE approaches. Our best results reach 92% and 71% of F1 score for KnowledgeNet and FewRel, respectively, proving the effectiveness of our approach on competitive benchmarks.

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.

我们考虑为移动机器人构建视觉异常检测系统的问题。标准异常检测模型是使用仅由非异常数据组成的大型数据集训练的。但是，在机器人技术应用中，通常可以使用（可能很少）的异常示例。我们解决了利用这些数据以通过与Real-NVP损失共同使辅助外离群损失损失共同使实际NVP异常检测模型的性能提高性能的问题。我们在新的数据集（作为补充材料）上进行定量实验，该数据集在室内巡逻方案中设计为异常检测。在不连接测试集中，我们的方法优于替代方案，并表明即使少数异常框架也可以实现重大的性能改进。

dadapy是用于分析和表征高维数据歧管的Python软件包。它提供了估计固有维度和概率密度的方法，用于执行基于密度的聚类和比较不同的距离指标。我们回顾包装的主要功能，并在玩具案例和现实世界中的使用中举例说明其使用情况。dadapy可在开源Apache 2.0许可下自由使用。

对不合作空间居民对象的姿势估计是近距离操作中自治的关键资产。在这种情况下，单眼相机是一个有价值的解决方案，因为它们的系统要求低。但是，关联的图像处理算法要么在实时实现实时实现的计算上太昂贵，要么准确地计算不足。在本文中，我们提出了利用神经网络体系结构的姿势估计软件，可以将其缩放到不同的准确性延迟权衡。我们设计的管道与Edge Tensor处理单元兼容，以显示低功率机学习加速器能够在空间中实现人工智能开发。在基准航天器姿势估计数据集以及有意开发的Cosmo Photorealistic数据集上测试了神经网络，该数据集在各种随机姿势和可触摸的太阳能电池板方向上描绘了Cosmo-Skymed卫星。我们体系结构的最轻版本可在两个数据集上达到最新的精度，但在网络复杂性的一小部分中，每秒以每秒7.7帧的速度在珊瑚开发板中以2.2W的速度运行。

电报是全球最常用的即时消息传递应用之一。其成功之所以在于提供高隐私保护和社交网络，如频道 - 虚拟房间，其中只有管理员可以发布和广播到所有订户的消息。然而，这些相同的功能促成了边界活动的出现，并且与在线社交网络一样常见，假账户的沉重存在。通过引入频道的验证和诈骗标记，电报开始解决这些问题。不幸的是，问题远未解决。在这项工作中，我们通过收集35,382个不同的渠道和超过130,000,000消息来进行大规模分析电报。我们研究电报标记为验证或骗局的渠道，突出显示类比和差异。然后，我们转到未标记的频道。在这里，我们发现一些臭名昭着的活动也存在于虚拟网络的隐私保存服务，例如梳理，共享非法成人和版权保护内容。此外，我们还确定并分析了另外两种类型的渠道：克隆和假货。克隆是发布另一个频道确切内容的频道，以获得订阅者和促进服务。相反，假货是试图冒充名人或知名服务的渠道。即使是最先进的用户甚至很难确定。要自动检测假频道，我们提出了一种机器学习模型，可以以86％的准确性识别它们。最后，我们研究了Sabmyk，这是一种阴谋理论，即利用假货和克隆在达到超过1000万用户的平台上迅速传播。

Computational units in artificial neural networks follow a simplified model of biological neurons. In the biological model, the output signal of a neuron runs down the axon, splits following the many branches at its end, and passes identically to all the downward neurons of the network. Each of the downward neurons will use their copy of this signal as one of many inputs dendrites, integrate them all and fire an output, if above some threshold. In the artificial neural network, this translates to the fact that the nonlinear filtering of the signal is performed in the upward neuron, meaning that in practice the same activation is shared between all the downward neurons that use that signal as their input. Dendrites thus play a passive role. We propose a slightly more complex model for the biological neuron, where dendrites play an active role: the activation in the output of the upward neuron becomes optional, and instead the signals going through each dendrite undergo independent nonlinear filterings, before the linear combination. We implement this new model into a ReLU computational unit and discuss its biological plausibility. We compare this new computational unit with the standard one and describe it from a geometrical point of view. We provide a Keras implementation of this unit into fully connected and convolutional layers and estimate their FLOPs and weights change. We then use these layers in ResNet architectures on CIFAR-10, CIFAR-100, Imagenette, and Imagewoof, obtaining performance improvements over standard ResNets up to 1.73%. Finally, we prove a universal representation theorem for continuous functions on compact sets and show that this new unit has more representational power than its standard counterpart.

Many challenging reinforcement learning (RL) problems require designing a distribution of tasks that can be applied to train effective policies. This distribution of tasks can be specified by the curriculum. A curriculum is meant to improve the results of learning and accelerate it. We introduce Success Induced Task Prioritization (SITP), a framework for automatic curriculum learning, where a task sequence is created based on the success rate of each task. In this setting, each task is an algorithmically created environment instance with a unique configuration. The algorithm selects the order of tasks that provide the fastest learning for agents. The probability of selecting any of the tasks for the next stage of learning is determined by evaluating its performance score in previous stages. Experiments were carried out in the Partially Observable Grid Environment for Multiple Agents (POGEMA) and Procgen benchmark. We demonstrate that SITP matches or surpasses the results of other curriculum design methods. Our method can be implemented with handful of minor modifications to any standard RL framework and provides useful prioritization with minimal computational overhead.

Fruit is a key crop in worldwide agriculture feeding millions of people. The standard supply chain of fruit products involves quality checks to guarantee freshness, taste, and, most of all, safety. An important factor that determines fruit quality is its stage of ripening. This is usually manually classified by experts in the field, which makes it a labor-intensive and error-prone process. Thus, there is an arising need for automation in the process of fruit ripeness classification. Many automatic methods have been proposed that employ a variety of feature descriptors for the food item to be graded. Machine learning and deep learning techniques dominate the top-performing methods. Furthermore, deep learning can operate on raw data and thus relieve the users from having to compute complex engineered features, which are often crop-specific. In this survey, we review the latest methods proposed in the literature to automatize fruit ripeness classification, highlighting the most common feature descriptors they operate on.