最近，自我监督的神经语言模型最近已应用于生物序列数据，进步的结构，功能和突变效应预测。一些蛋白质语言模型，包括MSA变压器和Alphafold的Evoformer，将进化相关蛋白的多个序列比对作为输入。 MSA Transformer的行专注的简单组合导致了最新的无监督结构接触预测。我们证明，MSA变压器柱浓度的简单和通用组合与MSA中序列之间的锤距距离密切相关。因此，基于MSA的语言模型编码详细的系统发育关系。我们进一步表明，这些模型可以将编码功能和结构约束的共同进化信号与反映历史意义的系统发育相关性分开。为了评估这一点，我们从POTTS模型中生成了在天然MSA训练的POTTS模型的合成MSA。我们发现，当使用MSA变压器与推断的POTTS模型时，无监督的接触预测对系统发育噪声的弹性更大。

Digital media have enabled the access to unprecedented literary knowledge. Authors, readers, and scholars are now able to discover and share an increasing amount of information about books and their authors. Notwithstanding, digital archives are still unbalanced: writers from non-Western countries are less represented, and such a condition leads to the perpetration of old forms of discrimination. In this paper, we present the Under-Represented Writers Knowledge Graph (URW-KG), a resource designed to explore and possibly amend this lack of representation by gathering and mapping information about works and authors from Wikidata and three other sources: Open Library, Goodreads, and Google Books. The experiments based on KG embeddings showed that the integrated information encoded in the graph allows scholars and users to be more easily exposed to non-Western literary works and authors with respect to Wikidata alone. This opens to the development of fairer and effective tools for author discovery and exploration.

The study proposes and tests a technique for automated emotion recognition through mouth detection via Convolutional Neural Networks (CNN), meant to be applied for supporting people with health disorders with communication skills issues (e.g. muscle wasting, stroke, autism, or, more simply, pain) in order to recognize emotions and generate real-time feedback, or data feeding supporting systems. The software system starts the computation identifying if a face is present on the acquired image, then it looks for the mouth location and extracts the corresponding features. Both tasks are carried out using Haar Feature-based Classifiers, which guarantee fast execution and promising performance. If our previous works focused on visual micro-expressions for personalized training on a single user, this strategy aims to train the system also on generalized faces data sets.

In this work, a re-design of the Moodledata module functionalities is presented to share learning objects between e-learning content platforms, e.g., Moodle and G-Lorep, in a linkable object format. The e-learning courses content of the Drupal-based Content Management System G-Lorep for academic learning is exchanged designing an object incorporating metadata to support the reuse and the classification in its context. In such an Artificial Intelligence environment, the exchange of Linkable Learning Objects can be used for dialogue between Learning Systems to obtain information, especially with the use of semantic or structural similarity measures to enhance the existent Taxonomy Assistant for advanced automated classification.

Emerging applications such as Deep Learning are often data-driven, thus traditional approaches based on auto-tuners are not performance effective across the wide range of inputs used in practice. In the present paper, we start an investigation of predictive models based on machine learning techniques in order to optimize Convolution Neural Networks (CNNs). As a use-case, we focus on the ARM Compute Library which provides three different implementations of the convolution operator at different numeric precision. Starting from a collation of benchmarks, we build and validate models learned by Decision Tree and naive Bayesian classifier. Preliminary experiments on Midgard-based ARM Mali GPU show that our predictive model outperforms all the convolution operators manually selected by the library.

In this paper, we propose the first-ever real benchmark thought for evaluating Neural Radiance Fields (NeRFs) and, in general, Neural Rendering (NR) frameworks. We design and implement an effective pipeline for scanning real objects in quantity and effortlessly. Our scan station is built with less than 500$ hardware budget and can collect roughly 4000 images of a scanned object in just 5 minutes. Such a platform is used to build ScanNeRF, a dataset characterized by several train/val/test splits aimed at benchmarking the performance of modern NeRF methods under different conditions. Accordingly, we evaluate three cutting-edge NeRF variants on it to highlight their strengths and weaknesses. The dataset is available on our project page, together with an online benchmark to foster the development of better and better NeRFs.

这项工作在拆分计算领域迈出了重大步骤，即如何拆分深神经网络以将其早期部分托管在嵌入式设备上，而其余则在服务器上。到目前为止，已经确定了潜在的分裂位置，以利用独特的建筑方面，即基于层尺寸。在此范式下，只有在执行分裂并重新训练整个管道后，才能评估分裂的疗效，从而对所有合理的分裂点在时间方面进行详尽的评估。在这里，我们表明，不仅层的结构确实很重要，而且其中包含的神经元的重要性也很重要。如果神经元相对于正确的班级决策，神经元很重要。因此，应在具有高密度的重要神经元的层后立即施加拆分，以保留流动的信息。根据这个想法，我们提出了可解释的拆分（i-split）：通过提供有关该分型在分类准确性方面的表现，事先对其有效实现的可靠性，以确定最合适的分裂点的过程。作为I-Split的另一个重大贡献，我们表明，多类分类问题的分裂点的最佳选择还取决于网络必须处理的特定类别。详尽的实验已在两个网络（VGG16和Resnet-50）以及三个数据集（Tiny-Imagenet-200，Notmnist和胸部X射线肺炎）上进行。源代码可在https://github.com/vips4/i-split上获得。

本文介绍了一个基于量子神经网络的深度学习系统，用于在平面上特定几何模式（两个摩尔分类问题）的点的二进制分类。我们认为，混合深度学习系统（经典 +量子）的使用不仅可以在计算加速度方面带来合理的好处，而且在理解基本现象和机制方面都可以带来好处。这将导致创建新的机器学习形式，以及量子计算世界中的强大发展。所选数据集基于2D二进制分类生成器，该生成器有助于测试特定算法的有效性；它是一组2D点，形成两个散布的半圆。它在二维表示空间中显示了两个分离的数据集：因此，功能是单个点的两个坐标，$ x_1 $和$ x_2 $。目的是产生一个量子深神经网络，其可识别和分类点的可训练参数数量最少。

尽管传记在语义网络中广泛传播，但自动提取传记事件的资源和方法受到限制。这种限制减少了结构化的，可读的传记信息的数量，尤其是关于属于代表性不足的人的人的数量。我们的工作通过为生活事件的语义注释提供一组准则来挑战这一限制。该准则旨在与现有的ISO语义注释标准可互操作：ISO-TIMEML（ISO-24617-1）和SEMAF（ISO-24617-4）。通过代表不足的作家的维基百科传记的注释任务，即非西方国家，移民或属于少数民族的作者，对准则进行了测试。 4个注释者注释了1,000个句子，平均通知者协议为0.825。由此产生的语料库被映射在Ontonotes上。这样的映射允许扩展我们的语料库，表明已经存在现有资源可以用于传记事件提取任务。

自治系统正在成为海洋部门内无处不在和获得势头。由于运输的电气化同时发生，自主海洋船只可以降低环境影响，降低成本并提高效率。虽然仍然需要密切的监控以确保安全，但最终目标是完全自主权。一个主要的里程碑是开发一个控制系统，这足以处理任何也稳健和可靠的天气和遇到。此外，控制系统必须遵守防止海上碰撞的国际法规，以便与人类水手进行成功互动。由于Colregs被编写为人类思想来解释，因此它们以暧昧的散文写成，因此不能获得机器可读或可核实。由于这些挑战和各种情况进行了解决，古典模型的方法证明了实现和计算沉重的复杂性。在机器学习（ML）内，深增强学习（DRL）对广泛的应用表现出了很大的潜力。 DRL的无模型和自学特性使其成为自治船只的有希望的候选人。在这项工作中，使用碰撞风险理论将Colregs的子集合在于基于DRL的路径和障碍物避免系统。由此产生的自主代理在训练场景中的训练场景，孤立的遇难情况和基于AIS的真实情景模拟中动态地插值。