准确的术中诊断对于在脑肿瘤手术期间提供安全有效的护理至关重要。我们的护理标准诊断方法是时间，资源和劳动密集型，限制了获得最佳手术治疗的机会。为了解决这些局限性，我们提出了一种替代工作流程，该工作流程结合了刺激的拉曼组织学（SRH），一种快速的光学成像方法，以及对SRH图像的深层自动解释，用于术中脑肿瘤诊断和实时手术决策支持。在这里，我们介绍了OpenSRH，这是来自300多名脑肿瘤患者和1300多个独特全幻灯片光学图像的第一个公共数据集。 OPENSRH包含来自最常见的脑肿瘤诊断，完整的病理注释，整个幻灯片肿瘤分割，原始和加工的光学成像数据的数据，用于端到端模型的开发和验证。我们为使用弱（即患者级）诊断标签的基于补丁的整个幻灯片分类和推断提供了一个框架。最后，我们基准了两项计算机视觉任务：多类组织学脑肿瘤分类和基于斑块的对比表示学习。我们希望OpenSRH能够促进快速光学成像和基于ML的手术决策支持的临床翻译，以提高精密医学时代的癌症手术的获取，安全性和功效。数据集访问，代码和基准可在opensrh.mlins.org上找到。

背景：精确诊断颅底肿瘤对于提供个性化的手术治疗策略至关重要。由于肿瘤多样性和缺乏术中病理资源，术中诊断可能具有挑战性。目的：开发独立且平行的术中病理学工作流程，可以使用无标签的光学成像和人工智能提供快速准确的颅底肿瘤诊断。方法：我们使用了基于光纤激光，无标签，非消费性，高分辨率显微镜方法（$ <$ <$ <$ <$ 60秒，每1 $ \ times $ 1 mm $ $^\ text {2} $），称为刺激的拉曼组织学（SRH），以对颅底肿瘤患者的连续多中心队列进行成像。然后，使用三种表示学习策略：跨渗透性，自我监督的对比度学习和监督对比度学习，使用SRH图像来训练卷积神经网络（CNN）模型。我们训练有素的CNN模型在持有的多中心SRH数据集上进行了测试。结果：SRH能够成像良性和恶性颅底肿瘤的诊断特征。在三种表示策略中，有监督的对比度学习最有效地学习了每种颅底肿瘤类型的独特和诊断SRH图像特征。在我们的多中心测试集中，跨渗透性达到了91.5％的总体诊断准确性，自我监督的对比度学习为83.9％，并且有监督的对比度学习为96.6％。我们训练有素的模型能够鉴定出肿瘤正常的边缘，并检测整个SRH图像中微观肿瘤浸润的区域。结论：具有训练有素的人工智能模型的SRH可以对颅底肿瘤标本进行快速准确的术中分析，以告知手术决策。

The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.

6th Generation (6G) industrial wireless subnetworks are expected to replace wired connectivity for control operation in robots and production modules. Interference management techniques such as centralized power control can improve spectral efficiency in dense deployments of such subnetworks. However, existing solutions for centralized power control may require full channel state information (CSI) of all the desired and interfering links, which may be cumbersome and time-consuming to obtain in dense deployments. This paper presents a novel solution for centralized power control for industrial subnetworks based on Graph Neural Networks (GNNs). The proposed method only requires the subnetwork positioning information, usually known at the central controller, and the knowledge of the desired link channel gain during the execution phase. Simulation results show that our solution achieves similar spectral efficiency as the benchmark schemes requiring full CSI in runtime operations. Also, robustness to changes in the deployment density and environment characteristics with respect to the training phase is verified.

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.

Transfer learning is a popular technique for improving the performance of neural networks. However, existing methods are limited to transferring parameters between networks with same architectures. We present a method for transferring parameters between neural networks with different architectures. Our method, called DPIAT, uses dynamic programming to match blocks and layers between architectures and transfer parameters efficiently. Compared to existing parameter prediction and random initialization methods, it significantly improves training efficiency and validation accuracy. In experiments on ImageNet, our method improved validation accuracy by an average of 1.6 times after 50 epochs of training. DPIAT allows both researchers and neural architecture search systems to modify trained networks and reuse knowledge, avoiding the need for retraining from scratch. We also introduce a network architecture similarity measure, enabling users to choose the best source network without any training.

Biometrics is the science of identifying an individual based on their intrinsic anatomical or behavioural characteristics, such as fingerprints, face, iris, gait, and voice. Iris recognition is one of the most successful methods because it exploits the rich texture of the human iris, which is unique even for twins and does not degrade with age. Modern approaches to iris recognition utilize deep learning to segment the valid portion of the iris from the rest of the eye, so it can then be encoded, stored and compared. This paper aims to improve the accuracy of iris semantic segmentation systems by introducing a novel data augmentation technique. Our method can transform an iris image with a certain dilation level into any desired dilation level, thus augmenting the variability and number of training examples from a small dataset. The proposed method is fast and does not require training. The results indicate that our data augmentation method can improve segmentation accuracy up to 15% for images with high pupil dilation, which creates a more reliable iris recognition pipeline, even under extreme dilation.

The Elo algorithm, due to its simplicity, is widely used for rating in sports competitions as well as in other applications where the rating/ranking is a useful tool for predicting future results. However, despite its widespread use, a detailed understanding of the convergence properties of the Elo algorithm is still lacking. Aiming to fill this gap, this paper presents a comprehensive (stochastic) analysis of the Elo algorithm, considering round-robin (one-on-one) competitions. Specifically, analytical expressions are derived characterizing the behavior/evolution of the skills and of important performance metrics. Then, taking into account the relationship between the behavior of the algorithm and the step-size value, which is a hyperparameter that can be controlled, some design guidelines as well as discussions about the performance of the algorithm are provided. To illustrate the applicability of the theoretical findings, experimental results are shown, corroborating the very good match between analytical predictions and those obtained from the algorithm using real-world data (from the Italian SuperLega, Volleyball League).

Energy based control methods are at the core of modern robotic control algorithms. In this paper we present a general approach to virtual model/mechanism control, which is a powerful design tool to create energy based controllers. We present two novel virtual-mechanisms designed for robotic minimally invasive surgery, which control the position of a surgical instrument while passing through an incision. To these virtual mechanisms we apply the parameter tuning method of Larby and Forni 2022, which optimizes for local performance while ensuring global stability.

We prove that for $c>0$ a sufficiently small universal constant that a random set of $c d^2/\log^4(d)$ independent Gaussian random points in $\mathbb{R}^d$ lie on a common ellipsoid with high probability. This nearly establishes a conjecture of~\cite{SaundersonCPW12}, within logarithmic factors. The latter conjecture has attracted significant attention over the past decade, due to its connections to machine learning and sum-of-squares lower bounds for certain statistical problems.