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.

Deep Ensemble Convolutional Neural Networks has become a methodology of choice for analyzing medical images with a diagnostic performance comparable to a physician, including the diagnosis of Diabetic Retinopathy. However, commonly used techniques are deterministic and are therefore unable to provide any estimate of predictive uncertainty. Quantifying model uncertainty is crucial for reducing the risk of misdiagnosis. A reliable architecture should be well-calibrated to avoid over-confident predictions. To address this, we propose a UATTA-ENS: Uncertainty-Aware Test-Time Augmented Ensemble Technique for 5 Class PIRC Diabetic Retinopathy Classification to produce reliable and well-calibrated predictions.

大多数机器学习模型在假设培训，测试和部署数据是独立的和相同分布的假设下运行（i.i.d.）。这种假设通常在自然设置中通常保持真实。通常，部署数据受各种类型的分布换档。模型性能的大小与数据集分发的这种转变成比例。因此，有必要评估模型的不确定性和稳健性，以分配转变，以便在真实数据上实现其预期绩效的现实估计。提供评估不确定性和模型的鲁棒性的现有方法缺乏，并且通常无法涂漆完整的图片。此外，到目前为止大多数分析主要专注于分类任务。在本文中，我们使用Shifts天气预报数据集提出了更多的始终回归任务的有洞察力度量。我们还提供了使用这些指标的基线方法的评估。

Experimental sciences have come to depend heavily on our ability to organize, interpret and analyze high-dimensional datasets produced from observations of a large number of variables governed by natural processes. Natural laws, conservation principles, and dynamical structure introduce intricate inter-dependencies among these observed variables, which in turn yield geometric structure, with fewer degrees of freedom, on the dataset. We show how fine-scale features of this structure in data can be extracted from \emph{discrete} approximations to quantum mechanical processes given by data-driven graph Laplacians and localized wavepackets. This data-driven quantization procedure leads to a novel, yet natural uncertainty principle for data analysis induced by limited data. We illustrate the new approach with algorithms and several applications to real-world data, including the learning of patterns and anomalies in social distancing and mobility behavior during the COVID-19 pandemic.

Large-scale diffusion neural networks represent a substantial milestone in text-to-image generation, but they remain poorly understood, lacking interpretability analyses. In this paper, we perform a text-image attribution analysis on Stable Diffusion, a recently open-sourced model. To produce pixel-level attribution maps, we upscale and aggregate cross-attention word-pixel scores in the denoising subnetwork, naming our method DAAM. We evaluate its correctness by testing its semantic segmentation ability on nouns, as well as its generalized attribution quality on all parts of speech, rated by humans. We then apply DAAM to study the role of syntax in the pixel space, characterizing head--dependent heat map interaction patterns for ten common dependency relations. Finally, we study several semantic phenomena using DAAM, with a focus on feature entanglement, where we find that cohyponyms worsen generation quality and descriptive adjectives attend too broadly. To our knowledge, we are the first to interpret large diffusion models from a visuolinguistic perspective, which enables future lines of research. Our code is at https://github.com/castorini/daam.

这项研究提供了一个新颖的框架，以根据开源数据估算全球城市的公共交通巴士的经济，环境和社会价值。电动巴士是替代柴油巴士以获得环境和社会利益的引人注目的候选人。但是，评估总线电气化价值的最先进模型的适用性受到限制，因为它们需要可能难以购买的总线运营数据的细粒和定制数据。我们的估值工具使用通用过境饲料规范，这是全球运输机构使用的标准数据格式，为制定优先级排序策略提供了高级指导，以使总线机队电气化。我们开发了物理知识的机器学习模型，以评估每种运输途径的能耗，碳排放，健康影响以及总拥有成本。我们通过对大波士顿和米兰大都会地区的公交线路进行案例研究来证明我们的工具的可扩展性。

为了扩大培训数据，研究人员通常希望合并两个或更多使用不同标签方案创建的数据集。本文考虑了两个数据集，这些数据集标记了不同标签方案下的词性词性（POS）标签，并利用一个数据集的监督标签，以帮助为另一个数据集生成标签。本文进一步讨论了这种方法的理论困难，并提出了一种新型的监督架构，该架构采用变压器来解决两个完全脱节数据集的问题。结果与最初的期望和探索探索不同，以使用与不同标签合并数据集的使用。

本文介绍了我们对SMM4H 2022共享任务的提交，内容涉及自我报告的亲密伴侣暴力在Twitter上（英语）。这项任务的目的是准确确定给定推文的内容是否证明了某人报告自己的亲密伴侣暴力经历。提交的系统是五个罗伯塔模型组成的合奏，每个模型各自在验证数据集上由各自的F1分数加权。该系统的性能比基线要好13％，并且是该共享任务的总体性能最佳系统。

全向视频中的光流估计面临两个重要问题：缺乏基准数据集以及调整基于视频的方法以适应全向性质的挑战。本文提出了第一个具有360度视野Flow360的感知上天然合成的全向基准数据集，其中有40个不同的视频和4,000个视频帧。我们在数据集和现有的光流数据集之间进行了全面的特征分析和比较，这些数据集表现出感知现实主义，独特性和多样性。为了适应全向性质，我们提出了一个新颖的暹罗表示学习框架（SLOF）。我们以对比度的方式训练我们的网络，并结合了对比度损失和光流损失的混合损失函数。广泛的实验验证了所提出的框架的有效性，并在最新方法中显示出40％的性能提高。我们的Flow360数据集和代码可在https://siamlof.github.io/上找到。

深度学习已被积极应用于预测时间序列，从而导致了大量新的自回归模型体系结构。然而，尽管基于时间指数的模型具有吸引人的属性，例如随着时间的推移是连续信号函数，导致表达平滑，但对它们的关注很少。实际上，尽管基于天真的深度指数模型比基于经典时间指数的模型的手动预定义函数表示表达得多，但由于缺乏电感偏见和时间序列的非平稳性，它们的预测不足以预测。在本文中，我们提出了DeepTime，这是一种基于深度指数的模型，该模型通过元学习公式训练，该公式克服了这些局限性，从而产生了有效而准确的预测模型。对现实世界数据集的广泛实验表明，我们的方法通过最先进的方法实现了竞争成果，并且高效。代码可从https://github.com/salesforce/deeptime获得。