最近的开放式域问题的作品应答使用检索器模型引用外部知识库，可选地重新映射与单独的重新编制模型，并使用另一个读取器模型生成答案。尽管执行相关任务，但模型具有单独的参数，并且在训练期间略微耦合。在这项工作中，我们建议将猎犬和重新划分为依次应用于变压器架构内的硬注视机制，并将所产生的计算表示给读者送入。在这个奇异模型架构中，隐藏的表示从搬运者逐渐改进到Reranker到读者，这更有效地利用模型容量，并且当我们以端到端的方式训练时，还导致更好的梯度流动。我们还提出了一种预先训练的方法，以有效地培训这种架构。我们评估我们的自然问题和TriviaQA Open DataSets的模型以及固定参数预算，我们的模型优于以前的最先进模型1.0和0.7精确匹配分数。

Classically, the development of humanoid robots has been sequential and iterative. Such bottom-up design procedures rely heavily on intuition and are often biased by the designer's experience. Exploiting the non-linear coupled design space of robots is non-trivial and requires a systematic procedure for exploration. We adopt the top-down design strategy, the V-model, used in automotive and aerospace industries. Our co-design approach identifies non-intuitive designs from within the design space and obtains the maximum permissible range of the design variables as a solution space, to physically realise the obtained design. We show that by constructing the solution space, one can (1) decompose higher-level requirements onto sub-system-level requirements with tolerance, alleviating the "chicken-or-egg" problem during the design process, (2) decouple the robot's morphology from its controller, enabling greater design flexibility, (3) obtain independent sub-system level requirements, reducing the development time by parallelising the development process.

We present, Naamapadam, the largest publicly available Named Entity Recognition (NER) dataset for the 11 major Indian languages from two language families. In each language, it contains more than 400k sentences annotated with a total of at least 100k entities from three standard entity categories (Person, Location and Organization) for 9 out of the 11 languages. The training dataset has been automatically created from the Samanantar parallel corpus by projecting automatically tagged entities from an English sentence to the corresponding Indian language sentence. We also create manually annotated testsets for 8 languages containing approximately 1000 sentences per language. We demonstrate the utility of the obtained dataset on existing testsets and the Naamapadam-test data for 8 Indic languages. We also release IndicNER, a multilingual mBERT model fine-tuned on the Naamapadam training set. IndicNER achieves the best F1 on the Naamapadam-test set compared to an mBERT model fine-tuned on existing datasets. IndicNER achieves an F1 score of more than 80 for 7 out of 11 Indic languages. The dataset and models are available under open-source licenses at https://ai4bharat.iitm.ac.in/naamapadam.

Of late, insurance fraud detection has assumed immense significance owing to the huge financial & reputational losses fraud entails and the phenomenal success of the fraud detection techniques. Insurance is majorly divided into two categories: (i) Life and (ii) Non-life. Non-life insurance in turn includes health insurance and auto insurance among other things. In either of the categories, the fraud detection techniques should be designed in such a way that they capture as many fraudulent transactions as possible. Owing to the rarity of fraudulent transactions, in this paper, we propose a chaotic variational autoencoder (C-VAE to perform one-class classification (OCC) on genuine transactions. Here, we employed the logistic chaotic map to generate random noise in the latent space. The effectiveness of C-VAE is demonstrated on the health insurance fraud and auto insurance datasets. We considered vanilla Variational Auto Encoder (VAE) as the baseline. It is observed that C-VAE outperformed VAE in both datasets. C-VAE achieved a classification rate of 77.9% and 87.25% in health and automobile insurance datasets respectively. Further, the t-test conducted at 1% level of significance and 18 degrees of freedom infers that C-VAE is statistically significant than the VAE.

Abusive language is a concerning problem in online social media. Past research on detecting abusive language covers different platforms, languages, demographies, etc. However, models trained using these datasets do not perform well in cross-domain evaluation settings. To overcome this, a common strategy is to use a few samples from the target domain to train models to get better performance in that domain (cross-domain few-shot training). However, this might cause the models to overfit the artefacts of those samples. A compelling solution could be to guide the models toward rationales, i.e., spans of text that justify the text's label. This method has been found to improve model performance in the in-domain setting across various NLP tasks. In this paper, we propose RAFT (Rationale Adaptor for Few-shoT classification) for abusive language detection. We first build a multitask learning setup to jointly learn rationales, targets, and labels, and find a significant improvement of 6% macro F1 on the rationale detection task over training solely rationale classifiers. We introduce two rationale-integrated BERT-based architectures (the RAFT models) and evaluate our systems over five different abusive language datasets, finding that in the few-shot classification setting, RAFT-based models outperform baseline models by about 7% in macro F1 scores and perform competitively to models finetuned on other source domains. Furthermore, RAFT-based models outperform LIME/SHAP-based approaches in terms of plausibility and are close in performance in terms of faithfulness.

Federated learning (FL) on deep neural networks facilitates new applications at the edge, especially for wearable and Internet-of-Thing devices. Such devices capture a large and diverse amount of data, but they have memory, compute, power, and connectivity constraints which hinder their participation in FL. We propose Centaur, a multitier FL framework, enabling ultra-constrained devices to efficiently participate in FL on large neural nets. Centaur combines two major ideas: (i) a data selection scheme to choose a portion of samples that accelerates the learning, and (ii) a partition-based training algorithm that integrates both constrained and powerful devices owned by the same user. Evaluations, on four benchmark neural nets and three datasets, show that Centaur gains ~10% higher accuracy than local training on constrained devices with ~58% energy saving on average. Our experimental results also demonstrate the superior efficiency of Centaur when dealing with imbalanced data, client participation heterogeneity, and various network connection probabilities.

家庭中的移动操纵器可以为患有严重运动障碍的人提供越来越多的自治权，他们在没有照料者的帮助下通常无法完成日常生活（ADL）的活动。辅助移动操纵器的远距离运行可以使患有运动障碍的人能够独立执行自我保健和家庭任务，但是有限的运动功能会阻碍人们与机器人接触的能力。在这项工作中，我们介绍了一个独特的基于惯性的可穿戴辅助界面，该辅助界面嵌入了熟悉的头饰服装中，适用于具有严重运动障碍的人，可以通过移动操纵器进行远程处理和执行身体任务。我们评估了这种可穿戴的界面（n = 16）和有运动障碍的个体（n = 2），用于执行ADL和日常家庭任务。我们的结果表明，可穿戴界面使参与者能够完成错误率，高度可感知的易用性和低工作负载度量的身体任务。总体而言，这种基于惯性的可穿戴设备是一种新的辅助接口选项，可控制家庭中移动操纵器。

通过改变肌肉僵硬来适应符合性的能力对于人类灵巧的操纵技巧至关重要。在机器人电动机控制中纳入合规性对于执行具有人级敏捷性的现实力量相互作用任务至关重要。这项工作为合规机器人操作提供了一个深层的模型预测性变量阻抗控制器，该阻抗操纵结合了可变阻抗控制与模型预测控制（MPC）。使用最大化信息增益的勘探策略学习了机器人操纵器的广义笛卡尔阻抗模型。该模型在MPC框架内使用，以适应低级变量阻抗控制器的阻抗参数，以实现针对不同操纵任务的所需合规性行为，而无需进行任何重新培训或填充。使用Franka Emika Panda机器人操纵器在模拟和实际实验中运行的操作，使用Franka Emika Panda机器人操纵器评估深层模型预测性变量阻抗控制方法。将所提出的方法与无模型和基于模型的强化方法进行了比较，以可变阻抗控制，以进行任务和性能之间的可传递性。

归因引号的使用是新闻中信息传播的最直接，最少过滤的途径。因此，引用在新闻报道的概念，接收和分析中起着核心作用。由于报价比常规报告提供了更直接的窗口，因此对于记者和研究人员来说，它们是宝贵的资源。尽管大量的研究工作已致力于自动提取新闻的报价及其归因于演讲者的方法，但很少有当代来源的全面归因报价可供公众提供。在这里，我们提出了一个自适应网络界面，用于搜索QuoteBank，这是新闻中的大量报价集合，我们可以在https://quotebank.dlab.tools上提供。

由于看不见和新兴实体的频率，新闻中的命名实体链接（NEL）是一项具有挑战性的努力，因此需要使用无监督或零摄像的方法。但是，这种方法往往会带来警告，例如不整合新兴实体的合适知识库（例如Wikidata），缺乏可扩展性和不良的可解释性。在这里，我们考虑在Quotebank中的人歧义，这是新闻中大量的说话者归类的语言，并调查了NEL在网络规模的语料库中直观，轻巧且可扩展的启发式方法的适用性。我们表现最好的启发式歧义分别在Quotebank和Aida-Conll基准上分别占94％和63％。此外，提出的启发式方法与最先进的无监督和零摄像方法，本本系和MGenRE相比，从而成为无监督和零照片实体链接的强基础。