基于超宽带（UWB）范围的多机器人系统中相对定位的系统最近已成为GNSS贬低环境的强大解决方案。可伸缩性仍然是主要挑战之一，尤其是在临时部署中。最近的解决方案包括系统中不同机器人或节点的主动和被动定位模式的动态分配。随着较大规模的系统的分布越来越多，关键的研究问题出现在此类本地化系统的安全性和可信度领域。本文研究了协作决策过程与分布式分类帐技术的潜在整合。具体而言，我们研究了一种方法，用于在区块链中智能合约中运行UWB角色分配算法的方法。在以前的作品中，我们分别研究了ROS2与HyperLeDger织物区块链的集成，并引入了一种用于基于UWB的本地化的新算法。在本文中，我们通过（i）运行实验扩展了这些工作移动机器人。这使我们能够通过增强的身份和数据访问管理在安全且可信赖的过程中提供相同的功能。我们的结果表明，UWB角色分配对六个自动移动机器人的连续变化空间形成的有效性，同时证明对添加不影响本地化过程的区块链层的潜伏期和计算资源的影响很小。

近年来，多机器人系统已受到行业和学术界的越来越多的关注。除了需要对相对本地化的准确和强大的估计，对系统的安全性和信任对于实现更广泛的采用至关重要。在本文中，我们提出了一个使用HyperLeDger Fabric在工业应用中进行多机器人协作的框架。我们依靠区块链身份来进行地面和空中机器人的相互作用，并使用智能合约进行协作决策。使用超宽带（UWB）本地化进行自动导航和机器人协作，这扩展了我们以前在基于面料的车队管理方面的工作。我们专注于使用地面机器人和空中机器人检查仓库般的环境，并存储有关区块链中发现的对象的信息。我们衡量添加区块链层，分析交易延迟的影响，并将与区块链相关过程的资源利用与已经运行的数据处理模块进行比较。

语音助手等对话用户界面非常受欢迎。然而，它们被设计为默认情况下是单语的，缺乏对双语对话体验的支持或敏感性。在此挑衅论文中，我们强调了双语用户VA互动中面临的语言生产挑战。我们认为，通过促进双语互动中看到的现象，例如代码转换，我们可以为双语用户提供更具包容性和改进的用户体验。我们还通过支持多种语言识别，并对语音输出中代码转换的偏好敏感，探索可以实现这一目标的方法。

The recent emergence of new algorithms for permuting models into functionally equivalent regions of the solution space has shed some light on the complexity of error surfaces, and some promising properties like mode connectivity. However, finding the right permutation is challenging, and current optimization techniques are not differentiable, which makes it difficult to integrate into a gradient-based optimization, and often leads to sub-optimal solutions. In this paper, we propose a Sinkhorn re-basin network with the ability to obtain the transportation plan that better suits a given objective. Unlike the current state-of-art, our method is differentiable and, therefore, easy to adapt to any task within the deep learning domain. Furthermore, we show the advantage of our re-basin method by proposing a new cost function that allows performing incremental learning by exploiting the linear mode connectivity property. The benefit of our method is compared against similar approaches from the literature, under several conditions for both optimal transport finding and linear mode connectivity. The effectiveness of our continual learning method based on re-basin is also shown for several common benchmark datasets, providing experimental results that are competitive with state-of-art results from the literature.

Deep Reinforcement Learning is emerging as a promising approach for the continuous control task of robotic arm movement. However, the challenges of learning robust and versatile control capabilities are still far from being resolved for real-world applications, mainly because of two common issues of this learning paradigm: the exploration strategy and the slow learning speed, sometimes known as "the curse of dimensionality". This work aims at exploring and assessing the advantages of the application of Quantum Computing to one of the state-of-art Reinforcement Learning techniques for continuous control - namely Soft Actor-Critic. Specifically, the performance of a Variational Quantum Soft Actor-Critic on the movement of a virtual robotic arm has been investigated by means of digital simulations of quantum circuits. A quantum advantage over the classical algorithm has been found in terms of a significant decrease in the amount of required parameters for satisfactory model training, paving the way for further promising developments.

Surrogate models are necessary to optimize meaningful quantities in physical dynamics as their recursive numerical resolutions are often prohibitively expensive. It is mainly the case for fluid dynamics and the resolution of Navier-Stokes equations. However, despite the fast-growing field of data-driven models for physical systems, reference datasets representing real-world phenomena are lacking. In this work, we develop AirfRANS, a dataset for studying the two-dimensional incompressible steady-state Reynolds-Averaged Navier-Stokes equations over airfoils at a subsonic regime and for different angles of attacks. We also introduce metrics on the stress forces at the surface of geometries and visualization of boundary layers to assess the capabilities of models to accurately predict the meaningful information of the problem. Finally, we propose deep learning baselines on four machine learning tasks to study AirfRANS under different constraints for generalization considerations: big and scarce data regime, Reynolds number, and angle of attack extrapolation.

Most benchmarks for studying surgical interventions focus on a specific challenge instead of leveraging the intrinsic complementarity among different tasks. In this work, we present a new experimental framework towards holistic surgical scene understanding. First, we introduce the Phase, Step, Instrument, and Atomic Visual Action recognition (PSI-AVA) Dataset. PSI-AVA includes annotations for both long-term (Phase and Step recognition) and short-term reasoning (Instrument detection and novel Atomic Action recognition) in robot-assisted radical prostatectomy videos. Second, we present Transformers for Action, Phase, Instrument, and steps Recognition (TAPIR) as a strong baseline for surgical scene understanding. TAPIR leverages our dataset's multi-level annotations as it benefits from the learned representation on the instrument detection task to improve its classification capacity. Our experimental results in both PSI-AVA and other publicly available databases demonstrate the adequacy of our framework to spur future research on holistic surgical scene understanding.

This paper describes the ESPnet Unsupervised ASR Open-source Toolkit (EURO), an end-to-end open-source toolkit for unsupervised automatic speech recognition (UASR). EURO adopts the state-of-the-art UASR learning method introduced by the Wav2vec-U, originally implemented at FAIRSEQ, which leverages self-supervised speech representations and adversarial training. In addition to wav2vec2, EURO extends the functionality and promotes reproducibility for UASR tasks by integrating S3PRL and k2, resulting in flexible frontends from 27 self-supervised models and various graph-based decoding strategies. EURO is implemented in ESPnet and follows its unified pipeline to provide UASR recipes with a complete setup. This improves the pipeline's efficiency and allows EURO to be easily applied to existing datasets in ESPnet. Extensive experiments on three mainstream self-supervised models demonstrate the toolkit's effectiveness and achieve state-of-the-art UASR performance on TIMIT and LibriSpeech datasets. EURO will be publicly available at https://github.com/espnet/espnet, aiming to promote this exciting and emerging research area based on UASR through open-source activity.

Spoken language understanding (SLU) is a task aiming to extract high-level semantics from spoken utterances. Previous works have investigated the use of speech self-supervised models and textual pre-trained models, which have shown reasonable improvements to various SLU tasks. However, because of the mismatched modalities between speech signals and text tokens, previous methods usually need complex designs of the frameworks. This work proposes a simple yet efficient unsupervised paradigm that connects speech and textual pre-trained models, resulting in an unsupervised speech-to-semantic pre-trained model for various tasks in SLU. To be specific, we propose to use unsupervised automatic speech recognition (ASR) as a connector that bridges different modalities used in speech and textual pre-trained models. Our experiments show that unsupervised ASR itself can improve the representations from speech self-supervised models. More importantly, it is shown as an efficient connector between speech and textual pre-trained models, improving the performances of five different SLU tasks. Notably, on spoken question answering, we reach the state-of-the-art result over the challenging NMSQA benchmark.

在智能的建筑管理中，了解房间的人数及其位置对于更好地控制其照明，通风和供暖，并以降低的成本和改善的舒适度很重要。这通常是通过使用安装在房间天花板上的紧凑型嵌入式设备并集成低分辨率红外摄像机的人员来实现的，从而掩盖了每个人的身份。但是，为了准确检测，最新的深度学习模型仍然需要使用大量注释的图像数据集进行监督培训。在本文中，我们研究了适用于基于低分辨率红外图像的人检测的具有成本效益的方法。结果表明，对于此类图像，我们可以减少监督和计算的量，同时仍然达到高水平的检测准确性。从需要图像中每个人的边界框注释的单杆探测器，到仅依靠不包含人的未标记图像的自动编码器，可以在注释成本方面节省大量，以及计算较低的模型费用。我们在具有低分辨率红外图像的两个具有挑战性的顶级数据集上验证了这些实验发现。