通过腿部机器人在具有挑战性的环境上进行本地导航的通用方法需要路径计划，路径跟随和运动，这通常需要机动控制策略，以准确跟踪指挥速度。但是，通过将导航问题分解为这些子任务，我们限制了机器人的功能，因为各个任务不考虑完整的解决方案空间。在这项工作中，我们建议通过深入强化学习来训练端到端政策来解决完整的问题。机器人不必在提供的时间内到达目标位置，而不是不断跟踪预算的路径。该任务的成功仅在情节结束时进行评估，这意味着该策略不需要尽快到达目标。可以免费选择其路径和运动步态。以这种方式培训政策可以打开更多可能的解决方案，这使机器人能够学习更多复杂的行为。我们比较我们的速度跟踪方法，并表明任务奖励的时间依赖性对于成功学习这些新行为至关重要。最后，我们证明了在真正的四足动物机器人上成功部署政策。机器人能够跨越具有挑战性的地形，这是以前无法实现的，同时使用更节能的步态并达到更高的成功率。

我们提出了一种基于学习的方法，以通过穿越城市环境的移动机器人来重建当地地形进行机车。使用板载摄像头和机器人轨迹的深度测量流，该算法估计机器人附近的地形。这些相机的原始测量值嘈杂，仅提供部分和遮挡的观察结果，在许多情况下，这些观察结果并未显示机器人所占据的地形。因此，我们提出了一个3D重建模型，该模型忠实地重建了场景，尽管嘈杂的测量和大量丢失的数据来自相机布置的盲点。该模型由点云上的4D完全卷积网络组成，该网络学习了几何先验，以从上下文中完成场景和自动回归反馈，以利用时空的一致性并使用过去的证据。该网络只能通过合成数据对网络进行训练，并且由于广泛的增强，它在现实世界中是强大的，如四足机器人（Anymal）验证中所示，Anymal，遍历具有挑战性的设置。我们使用有效的稀疏张量实现在机器人的机载低功率计算机上运行管道，并表明所提出的方法的表现优于经典地图表示。

在这项工作中，我们介绍并研究了一种培训设置，该培训设置通过在单个工作站GPU上使用大量并行性来实现现实世界机器人任务的快速政策。我们分析和讨论不同培训算法组件在大规模平行制度中对最终政策绩效和培训时间的影响。此外，我们还提供了一种新颖的游戏启发课程，非常适合与数千个模拟机器人并行培训。我们通过训练四足机器人Anymal在具有挑战性的地形上行走来评估该方法。平行方法允许在不到四分钟的时间内对平坦地形进行培训政策，而在二十分钟内，地形不平衡。与以前的工作相比，这代表了多个数量级的加速。最后，我们将政策转移到真实的机器人中以验证该方法。我们开放培训代码，以帮助加速学习的腿部运动领域的进一步研究。

Determining and predicting reservoir formation properties for newly drilled wells represents a significant challenge. One of the variations of these properties evaluation is well-interval similarity. Many methodologies for similarity learning exist: from rule-based approaches to deep neural networks. Recently, articles adopted, e.g. recurrent neural networks to build a similarity model as we deal with sequential data. Such an approach suffers from short-term memory, as it pays more attention to the end of a sequence. Neural network with Transformer architecture instead cast their attention over all sequences to make a decision. To make them more efficient in terms of computational time, we introduce a limited attention mechanism similar to Informer and Performer architectures. We conduct experiments on open datasets with more than 20 wells making our experiments reliable and suitable for industrial usage. The best results were obtained with our adaptation of the Informer variant of Transformer with ROC AUC 0.982. It outperforms classical approaches with ROC AUC 0.824, Recurrent neural networks with ROC AUC 0.934 and straightforward usage of Transformers with ROC AUC 0.961.

Markowitz mean-variance portfolios with sample mean and covariance as input parameters feature numerous issues in practice. They perform poorly out of sample due to estimation error, they experience extreme weights together with high sensitivity to change in input parameters. The heavy-tail characteristics of financial time series are in fact the cause for these erratic fluctuations of weights that consequently create substantial transaction costs. In robustifying the weights we present a toolbox for stabilizing costs and weights for global minimum Markowitz portfolios. Utilizing a projected gradient descent (PGD) technique, we avoid the estimation and inversion of the covariance operator as a whole and concentrate on robust estimation of the gradient descent increment. Using modern tools of robust statistics we construct a computationally efficient estimator with almost Gaussian properties based on median-of-means uniformly over weights. This robustified Markowitz approach is confirmed by empirical studies on equity markets. We demonstrate that robustified portfolios reach the lowest turnover compared to shrinkage-based and constrained portfolios while preserving or slightly improving out-of-sample performance.

Network Intrusion and Detection Systems (NIDS) are essential for malicious traffic and cyberattack detection in modern networks. Artificial intelligence-based NIDS are powerful tools that can learn complex data correlations for accurate attack prediction. Graph Neural Networks (GNNs) provide an opportunity to analyze network topology along with flow features which makes them particularly suitable for NIDS applications. However, successful application of such tool requires large amounts of carefully collected and labeled data for training and testing. In this paper we inspect different versions of ToN-IoT dataset and point out inconsistencies in some versions. We filter the full version of ToN-IoT and present a new version labeled ToN-IoT-R. To ensure generalization we propose a new standardized and compact set of flow features which are derived solely from NetFlowv5-compatible data. We separate numeric data and flags into different categories and propose a new dataset-agnostic normalization approach for numeric features. This allows us to preserve meaning of flow flags and we propose to conduct targeted analysis based on, for instance, network protocols. For flow classification we use E-GraphSage algorithm with modified node initialization technique that allows us to add node degree to node features. We achieve high classification accuracy on ToN-IoT-R and compare it with previously published results for ToN-IoT, NF-ToN-IoT, and NF-ToN-IoT-v2. We highlight the importance of careful data collection and labeling and appropriate data preprocessing choice and conclude that the proposed set of features is more applicable for real NIDS due to being less demanding to traffic monitoring equipment while preserving high flow classification accuracy.

Migraine is a high-prevalence and disabling neurological disorder. However, information migraine management in real-world settings could be limited to traditional health information sources. In this paper, we (i) verify that there is substantial migraine-related chatter available on social media (Twitter and Reddit), self-reported by migraine sufferers; (ii) develop a platform-independent text classification system for automatically detecting self-reported migraine-related posts, and (iii) conduct analyses of the self-reported posts to assess the utility of social media for studying this problem. We manually annotated 5750 Twitter posts and 302 Reddit posts. Our system achieved an F1 score of 0.90 on Twitter and 0.93 on Reddit. Analysis of information posted by our 'migraine cohort' revealed the presence of a plethora of relevant information about migraine therapies and patient sentiments associated with them. Our study forms the foundation for conducting an in-depth analysis of migraine-related information using social media data.

Triplet extraction aims to extract entities and their corresponding relations in unstructured text. Most existing methods train an extraction model on high-quality training data, and hence are incapable of extracting relations that were not observed during training. Generalizing the model to unseen relations typically requires fine-tuning on synthetic training data which is often noisy and unreliable. In this paper, we argue that reducing triplet extraction to a template filling task over a pre-trained language model can equip the model with zero-shot learning capabilities and enable it to leverage the implicit knowledge in the language model. Embodying these ideas, we propose a novel framework, ZETT (ZEro-shot Triplet extraction by Template infilling), that is based on end-to-end generative transformers. Our experiments show that without any data augmentation or pipeline systems, ZETT can outperform previous state-of-the-art models with 25% less parameters. We further show that ZETT is more robust in detecting entities and can be incorporated with automatically generated templates for relations.

Task-oriented dialogue (TOD) systems have been applied in a range of domains to support human users to achieve specific goals. Systems are typically constructed for a single domain or language and do not generalise well beyond this. Their extension to other languages in particular is restricted by the lack of available training data for many of the world's languages. To support work on Natural Language Understanding (NLU) in TOD across multiple languages and domains simultaneously, we constructed MULTI3NLU++, a multilingual, multi-intent, multi-domain dataset. MULTI3NLU++ extends the English-only NLU++ dataset to include manual translations into a range of high, medium and low resource languages (Spanish, Marathi, Turkish and Amharic), in two domains (banking and hotels). MULTI3NLU++ inherits the multi-intent property of NLU++, where an utterance may be labelled with multiple intents, providing a more realistic representation of a user's goals and aligning with the more complex tasks that commercial systems aim to model. We use MULTI3NLU++ to benchmark state-of-the-art multilingual language models as well as Machine Translation and Question Answering systems for the NLU task of intent detection for TOD systems in the multilingual setting. The results demonstrate the challenging nature of the dataset, particularly in the low-resource language setting.

Automatic machine translation (MT) metrics are widely used to distinguish the translation qualities of machine translation systems across relatively large test sets (system-level evaluation). However, it is unclear if automatic metrics are reliable at distinguishing good translations from bad translations at the sentence level (segment-level evaluation). In this paper, we investigate how useful MT metrics are at detecting the success of a machine translation component when placed in a larger platform with a downstream task. We evaluate the segment-level performance of the most widely used MT metrics (chrF, COMET, BERTScore, etc.) on three downstream cross-lingual tasks (dialogue state tracking, question answering, and semantic parsing). For each task, we only have access to a monolingual task-specific model. We calculate the correlation between the metric's ability to predict a good/bad translation with the success/failure on the final task for the Translate-Test setup. Our experiments demonstrate that all metrics exhibit negligible correlation with the extrinsic evaluation of the downstream outcomes. We also find that the scores provided by neural metrics are not interpretable mostly because of undefined ranges. Our analysis suggests that future MT metrics be designed to produce error labels rather than scores to facilitate extrinsic evaluation.