气候变化已成为最大的全球性问题之一，越来越多地损害地球的居住地。最近的发展如加利福尼亚州和加拿大的非凡热浪，以及德国的毁灭性洪水指向气候变化在极端天气不断增长的频率下的作用。在过去的五十年中，天气和气候的数值模型已经看到了巨大的改善，但仍有严格的限制仍有待克服。空间和时间本地化预测是需要一个小时，以便有效适应措施，以尽量减少生命和财产丧失。基于人工智能的方法正在展示有希望的导致改进预测，但仍然受到必要硬件和软件所需的可用性来处理地球地球的规模所需的软硬件和软件的限制。量子计算是一种新兴范式，在几个领域中发现了潜在的适用性。在这种意见作品中，我们认为为量子计算机设计的人工智能算法的新发展 - 也称为量子人工智能（QAI） - 可以提供进一步进一步的气候变化科学所需的关键突破。预计天气和气候预测的改善将级联到众多社会福利。

尽管只有几个兴趣类的示例，但很少有声音事件检测是检测声音事件的任务。该框架在生物声学中特别有用，在生物声学中，通常需要注释很长的录音，但是专家注释时间是有限的。本文概述了Dcase 2022 Challenge中包含的第二次发射生物声音事件检测任务的第二版。介绍了任务目标，数据集和基准的详细描述，以及所获得的主要结果以及提交系统的特征。该任务收到了15个不同团队的提交，其中13个得分高于基线。最高的F-评分在评估集中为60％，这对去年的版本有了巨大的进步。高度表现的方法利用了原型网络，转导学习，并解决了所有目标类别的事件长度。此外，通过分析每个子集的结果，我们可以确定系统面临的主要困难，并得出结论，很少有展示的生物声音事件检测仍然是一个开放的挑战。

Covid-19疫苗是我们最好的赌注，用于减轻大流行的持续冲击。但是，疫苗也预计将是有限的资源。最佳分配策略，特别是在具有访问不公平的国家和热点的时间分离，可能是停留疾病传播的有效方式。我们通过提出一种新的管道VACSIM来实现这个问题，将深度加强学习模型延装到用于优化Covid-19疫苗的分布的上下文的匪徒方法中。虽然加强学习模型建议了更好的行动和奖励，但上下文匪徒允许在现实世界场景中每天到日常实施的在线修改。我们评估此框架，防止与印度五个不同状态的Covid-19案例发生比例分配疫苗的天真分配方法（Assam，Delhi，Jharkhand，Maharashtra和Nagaland），并展示高达9039潜力的潜在感染，并增加了显着增加在通过VacSim方法的45天内限制差异的疗效。我们的型号和平台对印度所有国家和潜在的全球范围内都是可扩张的。我们还提出了新的评估策略，包括标准的基于区间模型的预测和对我们模型的因果关系评估。由于所有模型都携带可能需要在各种情况下进行测试的假设，因此我们开源我们的模型Vackim并贡献了与Openai健身房兼容的新型加固学习环境，以使其在全球的现实世界应用中可扩展。 （http://vacsim.tavlab.iiitd.edu.in：8000/）。

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.

Object movement identification is one of the most researched problems in the field of computer vision. In this task, we try to classify a pixel as foreground or background. Even though numerous traditional machine learning and deep learning methods already exist for this problem, the two major issues with most of them are the need for large amounts of ground truth data and their inferior performance on unseen videos. Since every pixel of every frame has to be labeled, acquiring large amounts of data for these techniques gets rather expensive. Recently, Zhao et al. [1] proposed one of a kind Arithmetic Distribution Neural Network (ADNN) for universal background subtraction which utilizes probability information from the histogram of temporal pixels and achieves promising results. Building onto this work, we developed an intelligent video surveillance system that uses ADNN architecture for motion detection, trims the video with parts only containing motion, and performs anomaly detection on the trimmed video.

The machine translation mechanism translates texts automatically between different natural languages, and Neural Machine Translation (NMT) has gained attention for its rational context analysis and fluent translation accuracy. However, processing low-resource languages that lack relevant training attributes like supervised data is a current challenge for Natural Language Processing (NLP). We incorporated a technique known Active Learning with the NMT toolkit Joey NMT to reach sufficient accuracy and robust predictions of low-resource language translation. With active learning, a semi-supervised machine learning strategy, the training algorithm determines which unlabeled data would be the most beneficial for obtaining labels using selected query techniques. We implemented two model-driven acquisition functions for selecting the samples to be validated. This work uses transformer-based NMT systems; baseline model (BM), fully trained model (FTM) , active learning least confidence based model (ALLCM), and active learning margin sampling based model (ALMSM) when translating English to Hindi. The Bilingual Evaluation Understudy (BLEU) metric has been used to evaluate system results. The BLEU scores of BM, FTM, ALLCM and ALMSM systems are 16.26, 22.56 , 24.54, and 24.20, respectively. The findings in this paper demonstrate that active learning techniques helps the model to converge early and improve the overall quality of the translation system.

We study the problem of planning under model uncertainty in an online meta-reinforcement learning (RL) setting where an agent is presented with a sequence of related tasks with limited interactions per task. The agent can use its experience in each task and across tasks to estimate both the transition model and the distribution over tasks. We propose an algorithm to meta-learn the underlying structure across tasks, utilize it to plan in each task, and upper-bound the regret of the planning loss. Our bound suggests that the average regret over tasks decreases as the number of tasks increases and as the tasks are more similar. In the classical single-task setting, it is known that the planning horizon should depend on the estimated model's accuracy, that is, on the number of samples within task. We generalize this finding to meta-RL and study this dependence of planning horizons on the number of tasks. Based on our theoretical findings, we derive heuristics for selecting slowly increasing discount factors, and we validate its significance empirically.

As language models have grown in parameters and layers, it has become much harder to train and infer with them on single GPUs. This is severely restricting the availability of large language models such as GPT-3, BERT-Large, and many others. A common technique to solve this problem is pruning the network architecture by removing transformer heads, fully-connected weights, and other modules. The main challenge is to discern the important parameters from the less important ones. Our goal is to find strong metrics for identifying such parameters. We thus propose two strategies: Cam-Cut based on the GradCAM interpretations, and Smooth-Cut based on the SmoothGrad, for calculating the importance scores. Through this work, we show that our scoring functions are able to assign more relevant task-based scores to the network parameters, and thus both our pruning approaches significantly outperform the standard weight and gradient-based strategies, especially at higher compression ratios in BERT-based models. We also analyze our pruning masks and find them to be significantly different from the ones obtained using standard metrics.

Neoplasms (NPs) and neurological diseases and disorders (NDDs) are amongst the major classes of diseases underlying deaths of a disproportionate number of people worldwide. To determine if there exist some distinctive features in the local wiring patterns of protein interactions emerging at the onset of a disease belonging to either of these two classes, we examined 112 and 175 protein interaction networks belonging to NPs and NDDs, respectively. Orbit usage profiles (OUPs) for each of these networks were enumerated by investigating the networks' local topology. 56 non-redundant OUPs (nrOUPs) were derived and used as network features for classification between these two disease classes. Four machine learning classifiers, namely, k-nearest neighbour (KNN), support vector machine (SVM), deep neural network (DNN), random forest (RF) were trained on these data. DNN obtained the greatest average AUPRC (0.988) among these classifiers. DNNs developed on node2vec and the proposed nrOUPs embeddings were compared using 5-fold cross validation on the basis of average values of the six of performance measures, viz., AUPRC, Accuracy, Sensitivity, Specificity, Precision and MCC. It was found that nrOUPs based classifier performed better in all of these six performance measures.

Customers are rapidly turning to social media for customer support. While brand agents on these platforms are motivated and well-intentioned to help and engage with customers, their efforts are often ignored if their initial response to the customer does not match a specific tone, style, or topic the customer is aiming to receive. The length of a conversation can reflect the effort and quality of the initial response made by a brand toward collaborating and helping consumers, even when the overall sentiment of the conversation might not be very positive. Thus, through this study, we aim to bridge this critical gap in the existing literature by analyzing language's content and stylistic aspects such as expressed empathy, psycho-linguistic features, dialogue tags, and metrics for quantifying personalization of the utterances that can influence the engagement of an interaction. This paper demonstrates that we can predict engagement using initial customer and brand posts.