模拟湍流的模拟，尤其是在大气中云的边缘，是一项固有的挑战。迄今为止，执行此类实验的最佳计算方法是直接数值模拟（DNS）。 DNS涉及在三维空间中的离散网格盒上解决流体流的非线性部分微分方程，也称为Navier-Stokes方程。这是一个有价值的范式，它指导了数值天气预测模型来计算降雨形成。但是，对于天气预报社区的实用实用程序，不能为DNS执行DNS。在这里，我们介绍了DeepClouds.ai，这是一个3D-UNET，该Unet模拟了上升的云DNS实验的输出。通过将内部3D立方体映射到完整的3D立方体，从DNS离散化的网格模拟的输出中映射到完整的3D立方体来解决DNS中域大小的问题。我们的方法有效地捕获了湍流动力学，而无需解决复杂的动力核心。基线表明，基于深度学习的仿真与通过各种得分指标衡量的基于部分差异方程的模型相媲美。该框架可用于通过在大气中的大物理领域进行模拟来进一步进一步发展湍流和云流的科学。通过高级参数化方案改善天气预测，这将导致社会福利。

基于深度学习（DL）的降尺度已成为地球科学中的流行工具。越来越多的DL方法被采用来降低降水量的降水量数据，并在局部（〜几公里甚至更小）的尺度上产生更准确和可靠的估计值。尽管有几项研究采用了降水的动力学或统计缩减，但准确性受地面真理的可用性受到限制。衡量此类方法准确性的一个关键挑战是将缩小的数据与点尺度观测值进行比较，这些观察值通常在如此小的尺度上是无法使用的。在这项工作中，我们进行了基于DL的缩减，以估计印度气象部（IMD）的当地降水数据，该数据是通过近似从车站位置到网格点的价值而创建的。为了测试不同DL方法的疗效，我们采用了四种不同的缩小方法并评估其性能。所考虑的方法是（i）深度统计缩小（DEEPSD），增强卷积长期记忆（ConvlstM），完全卷积网络（U-NET）和超分辨率生成对抗网络（SR-GAN）。 SR-GAN中使用的自定义VGG网络是在这项工作中使用沉淀数据开发的。结果表明，SR-GAN是降水数据缩减的最佳方法。 IMD站的降水值验证了缩小的数据。这种DL方法为统计缩减提供了有希望的替代方法。

降水控制地球气候，其日常时空波动具有重大的社会经济影响。通过改善温度和压力等各种物理领域的预测来衡量数值天气预测（NWP）的进步；然而，降水预测中存在很大的偏见。我们通过深度学习来增强著名的NWP模型CFSV2的输出，以创建一个混合模型，该模型在1日，2天和3天的交货时间内改善了短期全局降水量。为了混合使用，我们通过使用修改的DLWP-CS体系结构来解决全局数据的球形，从而将所有字段转换为立方体投影。动态模型沉淀和表面温度输出被喂入改良的DLWP-CS（UNET），以预测地面真相降水。虽然CFSV2的平均偏差为土地+5至+7毫米/天，但多元深度学习模型将其降低到-1至+1 mm/天。卡特里娜飓风在2005年，伊万飓风，2010年的中国洪水，2005年的印度洪水和2008年的缅甸风暴纳尔吉斯（Myanmar Storm Nargis）用于确认混合动力学深度学习模型的技能大大提高。 CFSV2通常在空间模式中显示中度至大偏置，并在短期时间尺度上高估了沉淀。拟议的深度学习增强了NWP模型可以解决这些偏见，并大大改善了预测降水的空间模式和幅度。与CFSV2相比，深度学习增强了CFSV2在重要的土地区域的平均偏差为1天铅1天。时空深度学习系统开辟了途径，以进一步提高全球短期降水预测的精度和准确性。

该调查侧重于地球系统科学中的当前问题，其中可以应用机器学习算法。它概述了以前的工作，在地球科学部，印度政府的持续工作，以及ML算法的未来应用到一些重要的地球科学问题。我们提供了与本次调查的比较的比较，这是与机器学习相关的多维地区的思想地图，以及地球系统科学（ESS）中机器学习的Gartner的炒作周期。我们主要关注地球科学的关键组成部分，包括大气，海洋，地震学和生物圈，以及覆盖AI / ML应用程序统计侦查和预测问题。

The rise in data has led to the need for dimension reduction techniques, especially in the area of non-scalar variables, including time series, natural language processing, and computer vision. In this paper, we specifically investigate dimension reduction for time series through functional data analysis. Current methods for dimension reduction in functional data are functional principal component analysis and functional autoencoders, which are limited to linear mappings or scalar representations for the time series, which is inefficient. In real data applications, the nature of the data is much more complex. We propose a non-linear function-on-function approach, which consists of a functional encoder and a functional decoder, that uses continuous hidden layers consisting of continuous neurons to learn the structure inherent in functional data, which addresses the aforementioned concerns in the existing approaches. Our approach gives a low dimension latent representation by reducing the number of functional features as well as the timepoints at which the functions are observed. The effectiveness of the proposed model is demonstrated through multiple simulations and real data examples.

Differentiable rendering aims to compute the derivative of the image rendering function with respect to the rendering parameters. This paper presents a novel algorithm for 6-DoF pose estimation through gradient-based optimization using a differentiable rendering pipeline. We emphasize two key contributions: (1) instead of solving the conventional 2D to 3D correspondence problem and computing reprojection errors, images (rendered using the 3D model) are compared only in the 2D feature space via sparse 2D feature correspondences. (2) Instead of an analytical image formation model, we compute an approximate local gradient of the rendering process through online learning. The learning data consists of image features extracted from multi-viewpoint renders at small perturbations in the pose neighborhood. The gradients are propagated through the rendering pipeline for the 6-DoF pose estimation using nonlinear least squares. This gradient-based optimization regresses directly upon the pose parameters by aligning the 3D model to reproduce a reference image shape. Using representative experiments, we demonstrate the application of our approach to pose estimation in proximity operations.

Differentiable Search Indices (DSIs) encode a corpus of documents in the parameters of a model and use the same model to map queries directly to relevant document identifiers. Despite the strong performance of DSI models, deploying them in situations where the corpus changes over time is computationally expensive because reindexing the corpus requires re-training the model. In this work, we introduce DSI++, a continual learning challenge for DSI to incrementally index new documents while being able to answer queries related to both previously and newly indexed documents. Across different model scales and document identifier representations, we show that continual indexing of new documents leads to considerable forgetting of previously indexed documents. We also hypothesize and verify that the model experiences forgetting events during training, leading to unstable learning. To mitigate these issues, we investigate two approaches. The first focuses on modifying the training dynamics. Flatter minima implicitly alleviate forgetting, so we optimize for flatter loss basins and show that the model stably memorizes more documents (+12\%). Next, we introduce a generative memory to sample pseudo-queries for documents and supplement them during continual indexing to prevent forgetting for the retrieval task. Extensive experiments on novel continual indexing benchmarks based on Natural Questions (NQ) and MS MARCO demonstrate that our proposed solution mitigates forgetting by a significant margin. Concretely, it improves the average Hits@10 by $+21.1\%$ over competitive baselines for NQ and requires $6$ times fewer model updates compared to re-training the DSI model for incrementally indexing five corpora in a sequence.

Computational notebooks, such as Jupyter notebooks, are interactive computing environments that are ubiquitous among data scientists to perform data wrangling and analytic tasks. To measure the performance of AI pair programmers that automatically synthesize programs for those tasks given natural language (NL) intents from users, we build ARCADE, a benchmark of 1082 code generation problems using the pandas data analysis framework in data science notebooks. ARCADE features multiple rounds of NL-to-code problems from the same notebook. It requires a model to understand rich multi-modal contexts, such as existing notebook cells and their execution states as well as previous turns of interaction. To establish a strong baseline on this challenging task, we develop PaChiNCo, a 62B code language model (LM) for Python computational notebooks, which significantly outperforms public code LMs. Finally, we explore few-shot prompting strategies to elicit better code with step-by-step decomposition and NL explanation, showing the potential to improve the diversity and explainability of model predictions.

Harvesting question-answer (QA) pairs from customer service chatlog in the wild is an efficient way to enrich the knowledge base for customer service chatbots in the cold start or continuous integration scenarios. Prior work attempts to obtain 1-to-1 QA pairs from growing customer service chatlog, which fails to integrate the incomplete utterances from the dialog context for composite QA retrieval. In this paper, we propose N-to-N QA extraction task in which the derived questions and corresponding answers might be separated across different utterances. We introduce a suite of generative/discriminative tagging based methods with end-to-end and two-stage variants that perform well on 5 customer service datasets and for the first time setup a benchmark for N-to-N DialogQAE with utterance and session level evaluation metrics. With a deep dive into extracted QA pairs, we find that the relations between and inside the QA pairs can be indicators to analyze the dialogue structure, e.g. information seeking, clarification, barge-in and elaboration. We also show that the proposed models can adapt to different domains and languages, and reduce the labor cost of knowledge accumulation in the real-world product dialogue platform.

By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance. While this capability has been demonstrated in other fields such as computer vision, natural language processing or speech recognition, it remains to be shown in robotics, where the generalization capabilities of the models are particularly critical due to the difficulty of collecting real-world robotic data. We argue that one of the keys to the success of such general robotic models lies with open-ended task-agnostic training, combined with high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks. The project's website and videos can be found at robotics-transformer.github.io