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.

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.

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。

语言，视觉和多模式预审查的大量融合正在出现。在这项工作中，我们介绍了通用多模式基础模型BEIT-3，该模型BEIT-3，该模型在视觉和视觉任务上都实现了最新的转移性能。具体来说，我们从三个方面提出了大融合：骨干架构，预训练任务和模型扩展。我们介绍了多道路变压器进行通用建模，其中模块化体系结构可以实现深融合和模态特定的编码。基于共享的骨干，我们以统一的方式对图像（Imglish），文本（英语）和图像文本对（“平行句子”）进行蒙面的“语言”建模。实验结果表明，BEIT-3在对象检测（COCO），语义分割（ADE20K），图像分类（Imagenet），视觉推理（NLVR2），视觉询问答案（VQAV2），图像字幕上获得最先进的性能（可可）和跨模式检索（Flickr30k，可可）。

我们启动差异私有（DP）估计的研究，并访问少量公共数据。为了对D维高斯人进行私人估计，我们假设公共数据来自高斯人，该高斯与私人数据的基础高斯人的总变化距离可能消失了。我们表明，在纯或集中DP的约束下，D+1个公共数据样本足以从私人样本复杂性中删除对私人数据分布的范围参数的任何依赖性，而在没有公共数据的情况下，这是必不可少的。对于分离的高斯混合物，我们假设基本的公共和私人分布是相同的，我们考虑两个设置：（1）当给出独立于维度的公共数据时，可以根据多种方式改善私人样本复杂性混合组件的数量以及对分布范围参数的任何依赖性都可以在近似DP情况下去除； （2）当在维度上给出了一定数量的公共数据线性时，即使在集中的DP下，也可以独立于范围参数使私有样本复杂性使得可以对整体样本复杂性进行其他改进。

人工智能的最新趋势是将验证的模型用于语言和视觉任务，这些模型已经实现了非凡的表现，但也令人困惑。因此，以各种方式探索这些模型的能力对该领域至关重要。在本文中，我们探讨了模型的可靠性，在其中我们将可靠的模型定义为一个不仅可以实现强大的预测性能，而且在许多涉及不确定性（例如选择性预测，开放式设置识别）的决策任务上，在许多决策任务上表现出色，而且表现良好。强大的概括（例如，准确性和适当的评分规则，例如在分布数据集中和分发数据集上的对数可能性）和适应性（例如，主动学习，几乎没有射击不确定性）。我们设计了40个数据集的10种任务类型，以评估视觉和语言域上可靠性的不同方面。为了提高可靠性，我们分别开发了VIT-PLEX和T5-PLEX，分别针对视觉和语言方式扩展了大型模型。 PLEX极大地改善了跨可靠性任务的最先进，并简化了传统协议，因为它可以改善开箱即用的性能，并且不需要设计分数或为每个任务调整模型。我们演示了高达1B参数的模型尺寸的缩放效果，并预处理数据集大小最多4B示例。我们还展示了PLEX在具有挑战性的任务上的功能，包括零射门的开放式识别，主动学习和对话语言理解中的不确定性。

预审前的语言模型在自然语言处理的各个领域都取得了成功，包括阅读理解任务。但是，当将机器学习方法应用于新域时，标记的数据可能并不总是可用。为了解决这个问题，我们使用对源域数据进行预处理的监督，以降低特定于域的下游任务的样本复杂性。我们通过将任务转移与域适应性相结合以微调验证的模型，而没有目标任务中的数据来评估特定于领域的阅读理解任务的零射击性能。我们的方法在4个域中的3个域中的下游域特异性阅读理解任务上超过了域自适应预测。