味道是遵循社会趋势和行为的风味行业的焦点。新调味剂和分子的研究和开发在该领域至关重要。另一方面，自然风味的发展在现代社会中起着至关重要的作用。鉴于此，目前的工作提出了一个基于科学机器学习的新颖框架，以在风味工程和行业中解决新的问题。因此，这项工作带来了一种创新的方法来设计新的自然风味分子。评估了有关合成可及性，原子数以及与天然或伪天然产物的相似性的分子。

科学机器学习（SCIML）是对几个不同应用领域的兴趣越来越多的领域。在优化上下文中，基于SCIML的工具使得能够开发更有效的优化方法。但是，必须谨慎评估和执行实施优化的SCIML工具。这项工作提出了稳健性测试的推论，该测试通过表明其结果尊重通用近似值定理，从而确保了基于多物理的基于SCIML的优化的鲁棒性。该测试应用于一种新方法的框架，该方法在一系列基准测试中进行了评估，以说明其一致性。此外，将提出的方法论结果与可行优化的可行区域进行了比较，这需要更高的计算工作。因此，这项工作为保证在多目标优化中应用SCIML工具的稳健性测试提供了比存在的替代方案要低的计算努力。

本文研究了红外（IR）成像在乳房疾病检测中的潜在贡献。它比较了使用一些算法检测恶性乳房状况（例如支持向量机（SVM））在应用于公共数据时的一致性的结果。此外，为了利用实际IR成像的能力作为临床试验的补充，并使用高分辨率IR成像促进研究，我们认为使用了由自信训练的乳房医生修订的公共数据库是必不可少的。在我们的工作中，只有静态获取协议才被考虑。我们使用了来自Pro Engenharia（Proeng）公共数据库的LO2 IR单乳房图像（54个正常和48个发现）。这些图像是从联邦De Pernambuco大学（UFPE）大学医院收集的。我们采用了作者提出的相同功能，该功能使用顺序最小优化（SMO）分类器，获得了最佳结果，并获得了61.7％的准确性，而Youden指数为0.24。

这项工作提出了使用遗传算法（GA）在追踪和识别使用计算机断层扫描（CT）图像的人心包轮廓的过程中。我们假设心包的每个切片都可以通过椭圆建模，椭圆形需要最佳地确定其参数。最佳椭圆将是紧随心包轮廓的紧密椭圆形，因此，将人心脏的心外膜和纵隔脂肪适当地分开。追踪和自动识别心包轮廓辅助药物的医学诊断。通常，由于所需的努力，此过程是手动完成或根本不完成的。此外，检测心包可能会改善先前提出的自动化方法，这些方法将与人心脏相关的两种类型的脂肪分开。这些脂肪的量化提供了重要的健康风险标记信息，因为它们与某些心血管病理的发展有关。最后，我们得出的结论是，GA在可行数量的处理时间内提供了令人满意的解决方案。

Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains.

This paper proposes a novel observer-based controller for Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV) designed to directly receive measurements from a Vision-Aided Inertial Navigation System (VA-INS) and produce the required thrust and rotational torque inputs. The VA-INS is composed of a vision unit (monocular or stereo camera) and a typical low-cost 6-axis Inertial Measurement Unit (IMU) equipped with an accelerometer and a gyroscope. A major benefit of this approach is its applicability for environments where the Global Positioning System (GPS) is inaccessible. The proposed VTOL-UAV observer utilizes IMU and feature measurements to accurately estimate attitude (orientation), gyroscope bias, position, and linear velocity. Ability to use VA-INS measurements directly makes the proposed observer design more computationally efficient as it obviates the need for attitude and position reconstruction. Once the motion components are estimated, the observer-based controller is used to control the VTOL-UAV attitude, angular velocity, position, and linear velocity guiding the vehicle along the desired trajectory in six degrees of freedom (6 DoF). The closed-loop estimation and the control errors of the observer-based controller are proven to be exponentially stable starting from almost any initial condition. To achieve global and unique VTOL-UAV representation in 6 DoF, the proposed approach is posed on the Lie Group and the design in unit-quaternion is presented. Although the proposed approach is described in a continuous form, the discrete version is provided and tested. Keywords: Vision-aided inertial navigation system, unmanned aerial vehicle, vertical take-off and landing, stochastic, noise, Robotics, control systems, air mobility, observer-based controller algorithm, landmark measurement, exponential stability.

Model estimates obtained from traditional subspace identification methods may be subject to significant variance. This elevated variance is aggravated in the cases of large models or of a limited sample size. Common solutions to reduce the effect of variance are regularized estimators, shrinkage estimators and Bayesian estimation. In the current work we investigate the latter two solutions, which have not yet been applied to subspace identification. Our experimental results show that our proposed estimators may reduce the estimation risk up to $40\%$ of that of traditional subspace methods.

Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple degrees of freedom via user-generated signals remains challenging. To address this issue, various machine learning controllers have been developed to better predict movement intent. As these controllers become more intelligent and take on more autonomy in the system, the traditional approach of representing the human-machine interface as a human controlling a tool becomes limiting. One possible approach to improve the understanding of these interfaces is to model them as collaborative, multi-agent systems through the lens of joint action. The field of joint action has been commonly applied to two human partners who are trying to work jointly together to achieve a task, such as singing or moving a table together, by effecting coordinated change in their shared environment. In this work, we compare different prosthesis controllers (proportional electromyography with sequential switching, pattern recognition, and adaptive switching) in terms of how they present the hallmarks of joint action. The results of the comparison lead to a new perspective for understanding how existing myoelectric systems relate to each other, along with recommendations for how to improve these systems by increasing the collaborative communication between each partner.

Stress has a great effect on people's lives that can not be understated. While it can be good, since it helps humans to adapt to new and different situations, it can also be harmful when not dealt with properly, leading to chronic stress. The objective of this paper is developing a stress monitoring solution, that can be used in real life, while being able to tackle this challenge in a positive way. The SMILE data set was provided to team Anxolotl, and all it was needed was to develop a robust model. We developed a supervised learning model for classification in Python, presenting the final result of 64.1% in accuracy and a f1-score of 54.96%. The resulting solution stood the robustness test, presenting low variation between runs, which was a major point for it's possible integration in the Anxolotl app in the future.

A "heart attack" or myocardial infarction (MI), occurs when an artery supplying blood to the heart is abruptly occluded. The "gold standard" method for imaging MI is Cardiovascular Magnetic Resonance Imaging (MRI), with intravenously administered gadolinium-based contrast (late gadolinium enhancement). However, no "gold standard" fully automated method for the quantification of MI exists. In this work, we propose an end-to-end fully automatic system (MyI-Net) for the detection and quantification of MI in MRI images. This has the potential to reduce the uncertainty due to the technical variability across labs and inherent problems of the data and labels. Our system consists of four processing stages designed to maintain the flow of information across scales. First, features from raw MRI images are generated using feature extractors built on ResNet and MoblieNet architectures. This is followed by the Atrous Spatial Pyramid Pooling (ASPP) to produce spatial information at different scales to preserve more image context. High-level features from ASPP and initial low-level features are concatenated at the third stage and then passed to the fourth stage where spatial information is recovered via up-sampling to produce final image segmentation output into: i) background, ii) heart muscle, iii) blood and iv) scar areas. New models were compared with state-of-art models and manual quantification. Our models showed favorable performance in global segmentation and scar tissue detection relative to state-of-the-art work, including a four-fold better performance in matching scar pixels to contours produced by clinicians.