太阳能的间歇性质挑战了光伏（PV）在电网中的大规模集成。使用深度学习的基于天空图像的太阳预测已被认为是预测短期波动的一种有希望的方法。但是，对于基于图像的太阳预测，几乎没有公开可用的标准化基准数据集，这限制了不同预测模型的比较和预测方法的探索。为了填补这些空白，我们介绍了Skipp'd-天空图像和光伏发电数据集。该数据集包含三年（2017-2019）的质量控制下采样的天空图像和PV发电数据，这些数据可用于使用深度学习的短期太阳能预测。此外，为了支持研究的灵活性，我们还提供了高分辨率，高频天空图像和PV发电数据以及并发的Sky录像。我们还包括一个包含数据处理脚本和基线模型实现的代码库，以供研究人员重现我们以前的工作并加速其在太阳预测中的研究。

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.

The rapid growth of machine translation (MT) systems has necessitated comprehensive studies to meta-evaluate evaluation metrics being used, which enables a better selection of metrics that best reflect MT quality. Unfortunately, most of the research focuses on high-resource languages, mainly English, the observations for which may not always apply to other languages. Indian languages, having over a billion speakers, are linguistically different from English, and to date, there has not been a systematic study of evaluating MT systems from English into Indian languages. In this paper, we fill this gap by creating an MQM dataset consisting of 7000 fine-grained annotations, spanning 5 Indian languages and 7 MT systems, and use it to establish correlations between annotator scores and scores obtained using existing automatic metrics. Our results show that pre-trained metrics, such as COMET, have the highest correlations with annotator scores. Additionally, we find that the metrics do not adequately capture fluency-based errors in Indian languages, and there is a need to develop metrics focused on Indian languages. We hope that our dataset and analysis will help promote further research in this area.

Chatbots, or bots for short, are multi-modal collaborative assistants that can help people complete useful tasks. Usually, when chatbots are referenced in connection with elections, they often draw negative reactions due to the fear of mis-information and hacking. Instead, in this paper, we explore how chatbots may be used to promote voter participation in vulnerable segments of society like senior citizens and first-time voters. In particular, we build a system that amplifies official information while personalizing it to users' unique needs transparently. We discuss its design, build prototypes with frequently asked questions (FAQ) election information for two US states that are low on an ease-of-voting scale, and report on its initial evaluation in a focus group. Our approach can be a win-win for voters, election agencies trying to fulfill their mandate and democracy at large.

Answering complex questions over textual resources remains a challenging problem$\unicode{x2013}$especially when interpreting the fine-grained relationships among multiple entities that occur within a natural-language question or clue. Curated knowledge bases (KBs), such as YAGO, DBpedia, Freebase and Wikidata, have been widely used in this context and gained great acceptance for question-answering (QA) applications in the past decade. While current KBs offer a concise representation of structured knowledge, they lack the variety of formulations and semantic nuances as well as the context of information provided by the natural-language sources. With BigText-QA, we aim to develop an integrated QA system which is able to answer questions based on a more redundant form of a knowledge graph (KG) that organizes both structured and unstructured (i.e., "hybrid") knowledge in a unified graphical representation. BigText-QA thereby is able to combine the best of both worlds$\unicode{x2013}$a canonical set of named entities, mapped to a structured background KB (such as YAGO or Wikidata), as well as an open set of textual clauses providing highly diversified relational paraphrases with rich context information.

Automation in farming processes is a growing field of research in both academia and industries. A considerable amount of work has been put into this field to develop systems robust enough for farming. Terrace farming, in particular, provides a varying set of challenges, including robust stair climbing methods and stable navigation in unstructured terrains. We propose the design of a novel autonomous terrace farming robot, Aarohi, that can effectively climb steep terraces of considerable heights and execute several farming operations. The design optimisation strategy for the overall mechanical structure is elucidated. Further, the embedded and software architecture along with fail-safe strategies are presented for a working prototype. Algorithms for autonomous traversal over the terrace steps using the scissor lift mechanism and performing various farming operations have also been discussed. The adaptability of the design to specific operational requirements and modular farm tools allow Aarohi to be customised for a wide variety of use cases.

Verifying the input-output relationships of a neural network so as to achieve some desired performance specification is a difficult, yet important, problem due to the growing ubiquity of neural nets in many engineering applications. We use ideas from probability theory in the frequency domain to provide probabilistic verification guarantees for ReLU neural networks. Specifically, we interpret a (deep) feedforward neural network as a discrete dynamical system over a finite horizon that shapes distributions of initial states, and use characteristic functions to propagate the distribution of the input data through the network. Using the inverse Fourier transform, we obtain the corresponding cumulative distribution function of the output set, which can be used to check if the network is performing as expected given any random point from the input set. The proposed approach does not require distributions to have well-defined moments or moment generating functions. We demonstrate our proposed approach on two examples, and compare its performance to related approaches.

We propose a principled way to define Gaussian process priors on various sets of unweighted graphs: directed or undirected, with or without loops. We endow each of these sets with a geometric structure, inducing the notions of closeness and symmetries, by turning them into a vertex set of an appropriate metagraph. Building on this, we describe the class of priors that respect this structure and are analogous to the Euclidean isotropic processes, like squared exponential or Mat\'ern. We propose an efficient computational technique for the ostensibly intractable problem of evaluating these priors' kernels, making such Gaussian processes usable within the usual toolboxes and downstream applications. We go further to consider sets of equivalence classes of unweighted graphs and define the appropriate versions of priors thereon. We prove a hardness result, showing that in this case, exact kernel computation cannot be performed efficiently. However, we propose a simple Monte Carlo approximation for handling moderately sized cases. Inspired by applications in chemistry, we illustrate the proposed techniques on a real molecular property prediction task in the small data regime.

围绕深度学习算法的长期挑战是解开和了解它们如何做出决定。可解释的人工智能（XAI）提供了方法，以解释算法的内部功能及其决策背后的原因，这些方式以人类用户的解释和可理解的方式提供了解释。 。到目前为止，已经开发了许多XAI方法，并且对这些策略进行比较分析似乎是为了辨别它们与临床预测模型的相关性。为此，我们首先实施了两个使用结构化表格和时间序列生理数据的创伤性脑损伤（TBI）（TBI）的预测模型。使用六种不同的解释技术来描述本地和全球水平的预测模型。然后，我们对每种策略的优点和缺点进行了批判性分析，突出了对使用这些方法感兴趣的研究人员的影响。根据几种XAI特征，例如可理解性，忠诚度和稳定性，将实施的方法相互比较。我们的发现表明，Shap是最稳定的，其保真度最高，但缺乏可理解性。另一方面，锚是最可理解的方法，但仅适用于表格数据而不是时间序列数据。

最近已经提出了压缩的随机梯度下降（SGD）算法，以解决分布式和分散的优化问题（例如在联合机器学习中出现的问题）中的通信瓶颈。现有的压缩SGD算法假定使用非自适应的阶梯尺寸（恒定或减小）来提供理论收敛保证。通常，在实践中对数据集和学习算法进行微调，以提供良好的经验性能。在许多学习方案中，这种微调可能是不切实际的，因此，使用自适应阶梯尺寸研究压缩SGD是很感兴趣的。由SGD在未压缩环境中有效训练神经网络的自适应阶梯尺寸方法的先前工作的激励，我们为压缩SGD开发了一种自适应阶梯尺寸方法。特别是，我们在压缩SGD中引入了一种缩放技术，我们用来在插值条件下为凸 - 平滑和强凸 - 平滑目标建立订单 - 最佳收敛速率，并在强烈的增长下为健康）状况。我们还通过仿真示例显示，如果没有这种缩放，算法就无法收敛。我们介绍了现实世界数据集的深神经网络的实验结果，并将我们提出的算法的性能与先前提出的文献压缩SGD方法进行比较，并在Resnet-18，Resnet-34和Densenet架构上的CIFAR-100架构上的性能提高了和CIFAR-10数据集的各种压缩级别。