复杂的伤口通常会面临部分或完全损失皮肤厚度，从而通过次要意图愈合。它们可以是急性或慢性的，可以发现感染，缺血和组织坏死以及与全身性疾病的关联。全球研究机构报告了无数案件，最终涉及严重的公共卫生问题，因为它们涉及人力资源（例如医师和医疗保健专业人员），并对生活质量产生负面影响。本文提出了一个新的数据库，用于自动将复杂伤口自动分类为五个类别，即非缠绕区域，肉芽，纤维蛋白样组织和干性坏死，血肿。这些图像包括由压力，血管溃疡，糖尿病，燃烧和手术干预后的并发症引起的复杂伤口的不同情况。该数据集（称为ComplexWoundDB）是独一无二的，因为它可以从野外获得的27美元图像中的像素级分类，即在患者的房屋中收集图像，并由四名卫生专业人员标记。用不同的机器学习技术进行的进一步实验证明了解决计算机辅助复杂伤口组织分类问题的挑战。手稿阐明了该地区未来的方向，在文献中广泛使用的其他数据库中进行了详细比较。

Identifying anomalies has become one of the primary strategies towards security and protection procedures in computer networks. In this context, machine learning-based methods emerge as an elegant solution to identify such scenarios and learn irrelevant information so that a reduction in the identification time and possible gain in accuracy can be obtained. This paper proposes a novel feature selection approach called Finite Element Machines for Feature Selection (FEMa-FS), which uses the framework of finite elements to identify the most relevant information from a given dataset. Although FEMa-FS can be applied to any application domain, it has been evaluated in the context of anomaly detection in computer networks. The outcomes over two datasets showed promising results.

To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8,403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was done using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (r,{\theta}) domain was applied to raw IVOCT images to ensure that microvessels appear at all possible angles. Pre-processing methods included guidewire/shadow detection, lumen segmentation, pixel shifting, and noise reduction. DeepLab v3+ was used to segment microvessel candidates. A bounding box on each candidate was classified as either microvessel or non-microvessel using a shallow convolutional neural network. For better classification, we used data augmentation (i.e., angle rotation) on bounding boxes with a microvessel during network training. Data augmentation and pre-processing steps improved microvessel segmentation performance significantly, yielding a method with Dice of 0.71+/-0.10 and pixel-wise sensitivity/specificity of 87.7+/-6.6%/99.8+/-0.1%. The network for classifying microvessels from candidates performed exceptionally well, with sensitivity of 99.5+/-0.3%, specificity of 98.8+/-1.0%, and accuracy of 99.1+/-0.5%. The classification step eliminated the majority of residual false positives, and the Dice coefficient increased from 0.71 to 0.73. In addition, our method produced 698 image frames with microvessels present, compared to 730 from manual analysis, representing a 4.4% difference. When compared to the manual method, the automated method improved microvessel continuity, implying improved segmentation performance. The method will be useful for research purposes as well as potential future treatment planning.

TMIC是一种应用程序发明家扩展，用于部署ML模型，以在教育环境中使用Google Tochable Machine开发的图像分类。 Google Thotable Machine是一种直观的视觉工具，可为开发用于图像分类的ML模型提供面向工作流的支持。针对使用Google Tochable Machine开发的模型的使用，扩展TMIC可以作为App Inventor的一部分，以tensorflow.js为tensorflow.js导出的受过训练的模型，这是最受欢迎的基于块的编程环境之一，用于教学计算计算K-12。该扩展名是使用基于扩展图片的App Inventor扩展框架创建的，可在BSD 3许可下获得。它可用于在K-12中，在高等教育的入门课程中或有兴趣创建具有图像分类的智能应用程序的任何人。扩展TMIC是由Initiative Computa \ c {C} \ 〜Ao Na Escola的信息学和统计系的圣卡塔纳纳大学/巴西大学提供的研究工作的一部分，旨在在K-中引入AI教育。 12。

本文档描述了基于深度学习的点云几何编解码器和基于深度学习的点云关节几何和颜色编解码器，并提交给2022年1月发出的JPEG PLENO点云编码的建议。拟议的编解码器是基于最新的。基于深度学习的PC几何编码的发展，并提供了呼吁提案的一些关键功能。拟议的几何编解码器提供了一种压缩效率，可超过MPEG G-PCC标准和胜过MPEG的效率，或者与V-PCC Intra Intra Interra Interra Intra标准的竞争力均超过了jpeg呼叫提案测试集；但是，由于需要克服的质量饱和效应，关节几何和颜色编解码器不会发生同样的情况。

我们从一组稀疏的光谱时间序列中构建了一个物理参数化的概率自动编码器（PAE），以学习IA型超新星（SNE IA）的内在多样性。 PAE是一个两阶段的生成模型，由自动编码器（AE）组成，该模型在使用归一化流（NF）训练后概率地解释。我们证明，PAE学习了一个低维的潜在空间，该空间可捕获人口内存在的非线性特征范围，并且可以直接从数据直接从数据中准确地对整个波长和观察时间进行精确模拟SNE IA的光谱演化。通过引入相关性惩罚项和多阶段训练设置以及我们的物理参数化网络，我们表明可以在训练期间分离内在和外在的可变性模式，从而消除了需要进行额外标准化的其他模型。然后，我们在SNE IA的许多下游任务中使用PAE进行越来越精确的宇宙学分析，包括自动检测SN Outliers，与数据分布一致的样本的产生以及在存在噪音和不完整数据的情况下解决逆问题限制宇宙距离测量。我们发现，与以前的研究相一致的最佳固有模型参数数量似乎是三个，并表明我们可以用$ 0.091 \ pm 0.010 $ mag标准化SNE IA的测试样本，该样本对应于$ 0.074 \ pm。 0.010 $ mag如果删除了特殊的速度贡献。训练有素的模型和代码在\ href {https://github.com/georgestein/supaernova} {github.com/georgestein/supaernova}上发布

Thin-cap fibroatheroma (TCFA) and plaque rupture have been recognized as the most frequent risk factor for thrombosis and acute coronary syndrome. Intravascular optical coherence tomography (IVOCT) can identify TCFA and assess cap thickness, which provides an opportunity to assess plaque vulnerability. We developed an automated method that can detect lipidous plaque and assess fibrous cap thickness in IVOCT images. This study analyzed a total of 4,360 IVOCT image frames of 77 lesions among 41 patients. To improve segmentation performance, preprocessing included lumen segmentation, pixel-shifting, and noise filtering on the raw polar (r, theta) IVOCT images. We used the DeepLab-v3 plus deep learning model to classify lipidous plaque pixels. After lipid detection, we automatically detected the outer border of the fibrous cap using a special dynamic programming algorithm and assessed the cap thickness. Our method provided excellent discriminability of lipid plaque with a sensitivity of 85.8% and A-line Dice coefficient of 0.837. By comparing lipid angle measurements between two analysts following editing of our automated software, we found good agreement by Bland-Altman analysis (difference 6.7+/-17 degree; mean 196 degree). Our method accurately detected the fibrous cap from the detected lipid plaque. Automated analysis required a significant modification for only 5.5% frames. Furthermore, our method showed a good agreement of fibrous cap thickness between two analysts with Bland-Altman analysis (4.2+/-14.6 micron; mean 175 micron), indicating little bias between users and good reproducibility of the measurement. We developed a fully automated method for fibrous cap quantification in IVOCT images, resulting in good agreement with determinations by analysts. The method has great potential to enable highly automated, repeatable, and comprehensive evaluations of TCFAs.

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

A Digital Twin (DT) is a simulation of a physical system that provides information to make decisions that add economic, social or commercial value. The behaviour of a physical system changes over time, a DT must therefore be continually updated with data from the physical systems to reflect its changing behaviour. For resource-constrained systems, updating a DT is non-trivial because of challenges such as on-board learning and the off-board data transfer. This paper presents a framework for updating data-driven DTs of resource-constrained systems geared towards system health monitoring. The proposed solution consists of: (1) an on-board system running a light-weight DT allowing the prioritisation and parsimonious transfer of data generated by the physical system; and (2) off-board robust updating of the DT and detection of anomalous behaviours. Two case studies are considered using a production gas turbine engine system to demonstrate the digital representation accuracy for real-world, time-varying physical systems.

We consider infinite horizon Markov decision processes (MDPs) with fast-slow structure, meaning that certain parts of the state space move "fast" (and in a sense, are more influential) while other parts transition more "slowly." Such structure is common in real-world problems where sequential decisions need to be made at high frequencies, yet information that varies at a slower timescale also influences the optimal policy. Examples include: (1) service allocation for a multi-class queue with (slowly varying) stochastic costs, (2) a restless multi-armed bandit with an environmental state, and (3) energy demand response, where both day-ahead and real-time prices play a role in the firm's revenue. Models that fully capture these problems often result in MDPs with large state spaces and large effective time horizons (due to frequent decisions), rendering them computationally intractable. We propose an approximate dynamic programming algorithmic framework based on the idea of "freezing" the slow states, solving a set of simpler finite-horizon MDPs (the lower-level MDPs), and applying value iteration (VI) to an auxiliary MDP that transitions on a slower timescale (the upper-level MDP). We also extend the technique to a function approximation setting, where a feature-based linear architecture is used. On the theoretical side, we analyze the regret incurred by each variant of our frozen-state approach. Finally, we give empirical evidence that the frozen-state approach generates effective policies using just a fraction of the computational cost, while illustrating that simply omitting slow states from the decision modeling is often not a viable heuristic.