Everting, soft growing vine robots benefit from reduced friction with their environment, which allows them to navigate challenging terrain. Vine robots can use air pouches attached to their sides for lateral steering. However, when all pouches are serially connected, the whole robot can only perform one constant curvature in free space. It must contact the environment to navigate through obstacles along paths with multiple turns. This work presents a multi-segment vine robot that can navigate complex paths without interacting with its environment. This is achieved by a new steering method that selectively actuates each single pouch at the tip, providing high degrees of freedom with few control inputs. A small magnetic valve connects each pouch to a pressure supply line. A motorized tip mount uses an interlocking mechanism and motorized rollers on the outer material of the vine robot. As each valve passes through the tip mount, a permanent magnet inside the tip mount opens the valve so the corresponding pouch is connected to the pressure supply line at the same moment. Novel cylindrical pneumatic artificial muscles (cPAMs) are integrated into the vine robot and inflate to a cylindrical shape for improved bending characteristics compared to other state-of-the art vine robots. The motorized tip mount controls a continuous eversion speed and enables controlled retraction. A final prototype was able to repeatably grow into different shapes and hold these shapes. We predict the path using a model that assumes a piecewise constant curvature along the outside of the multi-segment vine robot. The proposed multi-segment steering method can be extended to other soft continuum robot designs.

Credit scoring models are the primary instrument used by financial institutions to manage credit risk. The scarcity of research on behavioral scoring is due to the difficult data access. Financial institutions have to maintain the privacy and security of borrowers' information refrain them from collaborating in research initiatives. In this work, we present a methodology that allows us to evaluate the performance of models trained with synthetic data when they are applied to real-world data. Our results show that synthetic data quality is increasingly poor when the number of attributes increases. However, creditworthiness assessment models trained with synthetic data show a reduction of 3\% of AUC and 6\% of KS when compared with models trained with real data. These results have a significant impact since they encourage credit risk investigation from synthetic data, making it possible to maintain borrowers' privacy and to address problems that until now have been hampered by the availability of information.

For many years, Evolutionary Algorithms (EAs) have been applied to improve Neural Networks (NNs) architectures. They have been used for solving different problems, such as training the networks (adjusting the weights), designing network topology, optimizing global parameters, and selecting features. Here, we provide a systematic brief survey about applications of the EAs on the specific domain of the recurrent NNs named Reservoir Computing (RC). At the beginning of the 2000s, the RC paradigm appeared as a good option for employing recurrent NNs without dealing with the inconveniences of the training algorithms. RC models use a nonlinear dynamic system, with fixed recurrent neural network named the \textit{reservoir}, and learning process is restricted to adjusting a linear parametric function. %so the performance of learning is fast and precise. However, an RC model has several hyper-parameters, therefore EAs are helpful tools to figure out optimal RC architectures. We provide an overview of the results on the area, discuss novel advances, and we present our vision regarding the new trends and still open questions.

Histopathology imaging is crucial for the diagnosis and treatment of skin diseases. For this reason, computer-assisted approaches have gained popularity and shown promising results in tasks such as segmentation and classification of skin disorders. However, collecting essential data and sufficiently high-quality annotations is a challenge. This work describes a pipeline that uses suspected melanoma samples that have been characterized using Multi-Epitope-Ligand Cartography (MELC). This cellular-level tissue characterisation is then represented as a graph and used to train a graph neural network. This imaging technology, combined with the methodology proposed in this work, achieves a classification accuracy of 87%, outperforming existing approaches by 10%.

In this work, we propose a framework relying solely on chat-based customer support (CS) interactions for predicting the recommendation decision of individual users. For our case study, we analyzed a total number of 16.4k users and 48.7k customer support conversations within the financial vertical of a large e-commerce company in Latin America. Consequently, our main contributions and objectives are to use Natural Language Processing (NLP) to assess and predict the recommendation behavior where, in addition to using static sentiment analysis, we exploit the predictive power of each user's sentiment dynamics. Our results show that, with respective feature interpretability, it is possible to predict the likelihood of a user to recommend a product or service, based solely on the message-wise sentiment evolution of their CS conversations in a fully automated way.

在本文中，我们介绍Bayesldm，这是一个用于贝叶斯纵向数据建模的系统，该系统由高级建模语言组成，具有针对复杂的多变量时间序列数据建模的特定功能，并与编译器相结合，可以生成优化的概率程序代码，以在指定模型中执行指定的推理。 Bayesldm支持贝叶斯网络模型的建模，其特定关注动态贝叶斯网络（DBN）的高效，声明性规范。 Bayesldm编译器将模型规范与可用数据和输出代码相结合，用于执行贝叶斯推断，以同时处理丢失的数据，同时处理未知模型参数。这些功能有可能通过抽象产生计算有效的概率推断代码的过程来显着加速域中的迭代建模工作流，这些迭代建模工作流程涉及复杂纵向数据的分析。我们描述了Bayesldm系统组件，评估表示和推理优化的效率，并提供了该系统在分析异质和部分观察到的移动健康数据的应用示例。

基于内核的测试提供了一个简单而有效的框架，该框架使用繁殖内核希尔伯特空间的理论设计非参数测试程序。在本文中，我们提出了新的理论工具，可用于在几种数据方案以及许多不同的测试问题中研究基于内核测试的渐近行为。与当前的方法不同，我们的方法避免使用冗长的$ u $和$ v $统计信息扩展并限制定理，该定理通常出现在文献中，并直接与希尔伯特空格上的随机功能合作。因此，我们的框架会导致对内核测试的简单明了的分析，只需要轻度的规律条件。此外，我们表明，通常可以通过证明我们方法所需的规律条件既足够又需要进行必要的规律条件来改进我们的分析。为了说明我们的方法的有效性，我们为有条件的独立性测试问题提供了一项新的内核测试，以及针对已知的基于内核测试的新分析。

创建能够证明终身学习的人工智能（AI）系统是一个基本挑战，并且已经提出了许多方法和指标来分析算法属性。但是，对于现有的终身学习指标，算法贡献被任务和场景结构混淆。为了减轻此问题，我们引入了一种算法 - 敏捷的可解释的替代模型方法，以估计终身学习算法的潜在特性。我们验证通过合成数据实验估算这些特性的方法。为了验证替代模型的结构，我们分析了来自流行的终身学习方法和基准的真实绩效数据，这些基线适用于终身分类和终身强化学习。

语义细分任务的目的是在像素级别上进行密集分类。深层模型在解决这项任务方面表现出进展。但是，这些方法的剩余问题是空间精度的丧失，通常是在分段对象的边界上产生的。我们提出的模型通过为特征表示形式提供内部结构来解决此问题，同时提取支持前者的全局表示。为了适应内部结构，在训练过程中，我们预测数据中的高斯混合模型，该模型与跳过连接和解码阶段合并，有助于避免换动态偏见。此外，我们的结果表明，我们可以通过提供集群行为并将其组合来通过提供学习表征（全球和本地）来改善语义细分。最后，我们提出的结果证明了我们在城市景观和合成数据集方面的进步。

在本文中，我们提出了一种多个内核测试程序，以推断几个因素（例如不同的治疗组，性别，病史）及其相互作用同时引起了人们的兴趣。我们的方法能够处理复杂的数据，并且当假设诸如相称性不能合理时，可以看作是无所不在的COX模型的替代方法。我们的方法结合了来自生存分析，机器学习和多次测试的众所周知的概念：加权的对数秩检验，内核方法和多个对比度测试。这样，可以检测到超出经典比例危害设置以外的复杂危险替代方案。此外，通过充分利用单个测试程序的依赖性结构以避免功率损失来进行多个比较。总的来说，这为阶乘生存设计提供了灵活而强大的程序，其理论有效性通过Martingale论证和$ v $统计的理论证明。我们在广泛的仿真研究中评估了方法的性能，并通过真实的数据分析对其进行了说明。