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。
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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。
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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。
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Quadruped robots are currently used in industrial robotics as mechanical aid to automate several routine tasks. However, presently, the usage of such a robot in a domestic setting is still very much a part of the research. This paper discusses the understanding and virtual simulation of such a robot capable of detecting and understanding human emotions, generating its gait, and responding via sounds and expression on a screen. To this end, we use a combination of reinforcement learning and software engineering concepts to simulate a quadruped robot that can understand emotions, navigate through various terrains and detect sound sources, and respond to emotions using audio-visual feedback. This paper aims to establish the framework of simulating a quadruped robot that is emotionally intelligent and can primarily respond to audio-visual stimuli using motor or audio response. The emotion detection from the speech was not as performant as ERANNs or Zeta Policy learning, still managing an accuracy of 63.5%. The video emotion detection system produced results that are almost at par with the state of the art, with an accuracy of 99.66%. Due to its "on-policy" learning process, the PPO algorithm was extremely rapid to learn, allowing the simulated dog to demonstrate a remarkably seamless gait across the different cadences and variations. This enabled the quadruped robot to respond to generated stimuli, allowing us to conclude that it functions as predicted and satisfies the aim of this work.
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Searching long egocentric videos with natural language queries (NLQ) has compelling applications in augmented reality and robotics, where a fluid index into everything that a person (agent) has seen before could augment human memory and surface relevant information on demand. However, the structured nature of the learning problem (free-form text query inputs, localized video temporal window outputs) and its needle-in-a-haystack nature makes it both technically challenging and expensive to supervise. We introduce Narrations-as-Queries (NaQ), a data augmentation strategy that transforms standard video-text narrations into training data for a video query localization model. Validating our idea on the Ego4D benchmark, we find it has tremendous impact in practice. NaQ improves multiple top models by substantial margins (even doubling their accuracy), and yields the very best results to date on the Ego4D NLQ challenge, soundly outperforming all challenge winners in the CVPR and ECCV 2022 competitions and topping the current public leaderboard. Beyond achieving the state-of-the-art for NLQ, we also demonstrate unique properties of our approach such as gains on long-tail object queries, and the ability to perform zero-shot and few-shot NLQ.
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Machine Translation (MT) system generally aims at automatic representation of source language into target language retaining the originality of context using various Natural Language Processing (NLP) techniques. Among various NLP methods, Statistical Machine Translation(SMT). SMT uses probabilistic and statistical techniques to analyze information and conversion. This paper canvasses about the development of bilingual SMT models for translating English to fifteen low-resource Indian Languages (ILs) and vice versa. At the outset, all 15 languages are briefed with a short description related to our experimental need. Further, a detailed analysis of Samanantar and OPUS dataset for model building, along with standard benchmark dataset (Flores-200) for fine-tuning and testing, is done as a part of our experiment. Different preprocessing approaches are proposed in this paper to handle the noise of the dataset. To create the system, MOSES open-source SMT toolkit is explored. Distance reordering is utilized with the aim to understand the rules of grammar and context-dependent adjustments through a phrase reordering categorization framework. In our experiment, the quality of the translation is evaluated using standard metrics such as BLEU, METEOR, and RIBES
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We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.
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Cashews are grown by over 3 million smallholders in more than 40 countries worldwide as a principal source of income. As the third largest cashew producer in Africa, Benin has nearly 200,000 smallholder cashew growers contributing 15% of the country's national export earnings. However, a lack of information on where and how cashew trees grow across the country hinders decision-making that could support increased cashew production and poverty alleviation. By leveraging 2.4-m Planet Basemaps and 0.5-m aerial imagery, newly developed deep learning algorithms, and large-scale ground truth datasets, we successfully produced the first national map of cashew in Benin and characterized the expansion of cashew plantations between 2015 and 2021. In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations, which can fully capture texture information from discriminative time steps during a growing season. We further developed a Clustering Augmented Self-supervised Temporal Classification (CASTC) model to distinguish high-density versus low-density cashew plantations by automatic feature extraction and optimized clustering. Results show that the STCA model has an overall accuracy of 80% and the CASTC model achieved an overall accuracy of 77.9%. We found that the cashew area in Benin has doubled from 2015 to 2021 with 60% of new plantation development coming from cropland or fallow land, while encroachment of cashew plantations into protected areas has increased by 70%. Only half of cashew plantations were high-density in 2021, suggesting high potential for intensification. Our study illustrates the power of combining high-resolution remote sensing imagery and state-of-the-art deep learning algorithms to better understand tree crops in the heterogeneous smallholder landscape.
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We propose an ensemble approach to predict the labels in linear programming word problems. The entity identification and the meaning representation are two types of tasks to be solved in the NL4Opt competition. We propose the ensembleCRF method to identify the named entities for the first task. We found that single models didn't improve for the given task in our analysis. A set of prediction models predict the entities. The generated results are combined to form a consensus result in the ensembleCRF method. We present an ensemble text generator to produce the representation sentences for the second task. We thought of dividing the problem into multiple small tasks due to the overflow in the output. A single model generates different representations based on the prompt. All the generated text is combined to form an ensemble and produce a mathematical meaning of a linear programming problem.
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The previous fine-grained datasets mainly focus on classification and are often captured in a controlled setup, with the camera focusing on the objects. We introduce the first Fine-Grained Vehicle Detection (FGVD) dataset in the wild, captured from a moving camera mounted on a car. It contains 5502 scene images with 210 unique fine-grained labels of multiple vehicle types organized in a three-level hierarchy. While previous classification datasets also include makes for different kinds of cars, the FGVD dataset introduces new class labels for categorizing two-wheelers, autorickshaws, and trucks. The FGVD dataset is challenging as it has vehicles in complex traffic scenarios with intra-class and inter-class variations in types, scale, pose, occlusion, and lighting conditions. The current object detectors like yolov5 and faster RCNN perform poorly on our dataset due to a lack of hierarchical modeling. Along with providing baseline results for existing object detectors on FGVD Dataset, we also present the results of a combination of an existing detector and the recent Hierarchical Residual Network (HRN) classifier for the FGVD task. Finally, we show that FGVD vehicle images are the most challenging to classify among the fine-grained datasets.
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