染色揭示了抽吸物的微结构，同时创建组织病理学幻灯片。染色变异被定义为源和目标之间的色差差异，是由于染色过程中的特征变化引起的，导致分布变化和目标的性能差。染色归一化的目的是将目标的色谱分布与源的色谱分布相匹配。然而，染色归一化会导致潜在的形态变形，从而导致错误的诊断。我们提出了Fusion，这是一种通过在无监督的测试时间方案中调整模型来促进污渍适应的新方法，从而消除了目标末端进行重大标记的必要性。 Fusion通过更改目标的批准统一统计数据，并使用加权因子将其与源统计融合在一起。根据加权因子，该算法减少到两个极端之一。尽管缺乏培训或监督，但融合超过了分类和密集预测（细分）的现有等效算法，如两个公共数据集上的全面实验所证明的那样。

Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications is high. Today, attempts to assess models' clinical knowledge typically rely on automated evaluations on limited benchmarks. There is no standard to evaluate model predictions and reasoning across a breadth of tasks. To address this, we present MultiMedQA, a benchmark combining six existing open question answering datasets spanning professional medical exams, research, and consumer queries; and HealthSearchQA, a new free-response dataset of medical questions searched online. We propose a framework for human evaluation of model answers along multiple axes including factuality, precision, possible harm, and bias. In addition, we evaluate PaLM (a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US Medical License Exam questions), surpassing prior state-of-the-art by over 17%. However, human evaluation reveals key gaps in Flan-PaLM responses. To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, recall of knowledge, and medical reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal important limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLM models for clinical applications.

This paper presents a novel federated reinforcement learning (Fed-RL) methodology to enhance the cyber resiliency of networked microgrids. We formulate a resilient reinforcement learning (RL) training setup which (a) generates episodic trajectories injecting adversarial actions at primary control reference signals of the grid forming (GFM) inverters and (b) trains the RL agents (or controllers) to alleviate the impact of the injected adversaries. To circumvent data-sharing issues and concerns for proprietary privacy in multi-party-owned networked grids, we bring in the aspects of federated machine learning and propose a novel Fed-RL algorithm to train the RL agents. To this end, the conventional horizontal Fed-RL approaches using decoupled independent environments fail to capture the coupled dynamics in a networked microgrid, which leads us to propose a multi-agent vertically federated variation of actor-critic algorithms, namely federated soft actor-critic (FedSAC) algorithm. We created a customized simulation setup encapsulating microgrid dynamics in the GridLAB-D/HELICS co-simulation platform compatible with the OpenAI Gym interface for training RL agents. Finally, the proposed methodology is validated with numerical examples of modified IEEE 123-bus benchmark test systems consisting of three coupled microgrids.

Empirical studies suggest that machine learning models trained with empirical risk minimization (ERM) often rely on attributes that may be spuriously correlated with the class labels. Such models typically lead to poor performance during inference for data lacking such correlations. In this work, we explicitly consider a situation where potential spurious correlations are present in the majority of training data. In contrast with existing approaches, which use the ERM model outputs to detect the samples without spurious correlations, and either heuristically upweighting or upsampling those samples; we propose the logit correction (LC) loss, a simple yet effective improvement on the softmax cross-entropy loss, to correct the sample logit. We demonstrate that minimizing the LC loss is equivalent to maximizing the group-balanced accuracy, so the proposed LC could mitigate the negative impacts of spurious correlations. Our extensive experimental results further reveal that the proposed LC loss outperforms the SoTA solutions on multiple popular benchmarks by a large margin, an average 5.5% absolute improvement, without access to spurious attribute labels. LC is also competitive with oracle methods that make use of the attribute labels. Code is available at https://github.com/shengliu66/LC.

Contrails, short for condensation trails, are line-shaped ice clouds produced by aircraft engine exhaust when they fly through cold and humid air. They generate a greenhouse effect by absorbing or directing back to Earth approximately 33% of emitted outgoing longwave radiation. They account for over half of the climate change resulting from aviation activities. Avoiding contrails and adjusting flight routes could be an inexpensive and effective way to reduce their impact. An accurate, automated, and reliable detection algorithm is required to develop and evaluate contrail avoidance strategies. Advancement in contrail detection has been severely limited due to several factors, primarily due to a lack of quality-labeled data. Recently, proposed a large human-labeled Landsat-8 contrails dataset. Each contrail is carefully labeled with various inputs in various scenes of Landsat-8 satellite imagery. In this work, we benchmark several popular segmentation models with combinations of different loss functions and encoder backbones. This work is the first to apply state-of-the-art segmentation techniques to detect contrails in low-orbit satellite imagery. Our work can also be used as an open benchmark for contrail segmentation and is publicly available.

End-to-end multilingual ASR has become more appealing because of several reasons such as simplifying the training and deployment process and positive performance transfer from high-resource to low-resource languages. However, scaling up the number of languages, total hours, and number of unique tokens is not a trivial task. This paper explores large-scale multilingual ASR models on 70 languages. We inspect two architectures: (1) Shared embedding and output and (2) Multiple embedding and output model. In the shared model experiments, we show the importance of tokenization strategy across different languages. Later, we use our optimal tokenization strategy to train multiple embedding and output model to further improve our result. Our multilingual ASR achieves 13.9%-15.6% average WER relative improvement compared to monolingual models. We show that our multilingual ASR generalizes well on an unseen dataset and domain, achieving 9.5% and 7.5% WER on Multilingual Librispeech (MLS) with zero-shot and finetuning, respectively.

Impact craters are formed due to continuous impacts on the surface of planetary bodies. Most recent deep learning-based crater detection methods treat craters as circular shapes, and less attention is paid to extracting the exact shapes of craters. Extracting precise shapes of the craters can be helpful for many advanced analyses, such as crater formation. This paper proposes a combination of unsupervised non-deep learning and semi-supervised deep learning approach to accurately extract shapes of the craters and detect missing craters from the existing catalog. In unsupervised non-deep learning, we have proposed an adaptive rim extraction algorithm to extract craters' shapes. In this adaptive rim extraction algorithm, we utilized the elevation profiles of DEMs and applied morphological operation on DEM-derived slopes to extract craters' shapes. The extracted shapes of the craters are used in semi-supervised deep learning to get the locations, size, and refined shapes. Further, the extracted shapes of the craters are utilized to improve the estimate of the craters' diameter, depth, and other morphological factors. The craters' shape, estimated diameter, and depth with other morphological factors will be publicly available.

Synthetic datasets are often used to pretrain end-to-end optical flow networks, due to the lack of a large amount of labeled, real-scene data. But major drops in accuracy occur when moving from synthetic to real scenes. How do we better transfer the knowledge learned from synthetic to real domains? To this end, we propose CLIP-FLow, a semi-supervised iterative pseudo-labeling framework to transfer the pretraining knowledge to the target real domain. We leverage large-scale, unlabeled real data to facilitate transfer learning with the supervision of iteratively updated pseudo-ground truth labels, bridging the domain gap between the synthetic and the real. In addition, we propose a contrastive flow loss on reference features and the warped features by pseudo ground truth flows, to further boost the accurate matching and dampen the mismatching due to motion, occlusion, or noisy pseudo labels. We adopt RAFT as the backbone and obtain an F1-all error of 4.11%, i.e. a 19% error reduction from RAFT (5.10%) and ranking 2$^{nd}$ place at submission on the KITTI 2015 benchmark. Our framework can also be extended to other models, e.g. CRAFT, reducing the F1-all error from 4.79% to 4.66% on KITTI 2015 benchmark.

来自多个RGB摄像机的无标记人类运动捕获（MOCAP）是一个广泛研究的问题。现有方法要么需要校准相机，要么相对于静态摄像头校准它们，该摄像头是MOCAP系统的参考框架。每个捕获会话都必须先验完成校准步骤，这是一个乏味的过程，并且每当有意或意外移动相机时，都需要重新校准。在本文中，我们提出了一种MOCAP方法，该方法使用了多个静态和移动的外部未校准的RGB摄像机。我们方法的关键组成部分如下。首先，由于相机和受试者可以自由移动，因此我们选择接地平面作为常见参考，以代表身体和相机运动，与代表摄像机坐标中身体的现有方法不同。其次，我们了解相对于接地平面的短人类运动序列（$ \ sim $ 1SEC）的概率分布，并利用它在摄像机和人类运动之间消除歧义。第三，我们将此分布用作一种新型的多阶段优化方法的运动，以适合SMPL人体模型，并且摄像机在图像上的人体关键点构成。最后，我们证明我们的方法可以在从航空摄像机到智能手机的各种数据集上使用。与使用静态摄像头的单眼人类MOCAP任务相比，它还提供了更准确的结果。我们的代码可在https://github.com/robot-ception-group/smartmocap上进行研究。

多限制攀岩机器人的运动计划必须考虑机器人的姿势，联合扭矩，以及它如何使用接触力与环境相互作用。本文着重于使用非传统运动来探索不可预测的环境（例如火星洞穴）的机器人运动计划。我们的机器人概念Reachbot使用可扩展和可伸缩的动臂作为四肢，在攀爬时实现了大型可伸缩度工作区。每个可扩展的动臂都由旨在抓住岩石表面的微生物抓地力封顶。 Reachbot利用其大型工作空间来绕过障碍物，裂缝和挑战地形。我们的计划方法必须具有多功能性，以适应可变的地形特征和鲁棒性，以减轻用刺抓握随机性质的风险。在本文中，我们引入了一种图形遍历算法，以根据适用于握把的可用地形特征选择一个离散的grasps序列。该离散的计划是由一个解耦运动计划者互补的，该计划者使用基于抽样的计划和顺序凸面编程的组合来考虑身体运动和最终效应器运动的交替阶段，以优化单个阶段。我们使用运动规划师在模拟的2D洞穴环境中计划轨迹，至少有95％的成功概率，并在基线轨迹上表现出改善的鲁棒性。最后，我们通过对2D平面原型进行实验来验证运动计划算法。