车载传感器的车载系统正在增强连接。这使信息共享能够实现对环境的更全面的理解。但是，通过公共蜂窝网络的同行通信带来了多个网络障碍以解决，需要网络系统来中继通信并连接无法直接连接的各方。 Web实时通信（WEBRTC）是跨车辆流媒体流媒体的良好候选者，因为它可以使延迟通信较低，同时将标准协议带到安全握手中，发现公共IP和横向网络地址转换（NAT）系统。但是，在基础架构中的端到端服务质量（QOS）适应，在该基础架构中，传输和接收是通过继电器解耦的，需要一种机制来有效地使视频流适应网络容量。为此，本文通过利用实时运输控制协议（RTCP）指标（例如带宽和往返时间）来调查解决分辨率，帧和比特率更改的机制。该解决方案旨在确保接收机上系统及时获得相关信息。在实际的5G测试台中分析了应用不同方法适应方法时对端到端吞吐量效率和反应时间的影响。

由于需要快速原型制作和广泛的测试，模拟在自主驾驶中的作用变得越来越重要。基于物理的模拟使用涉及多个利益和优势，以合理的成本消除了对原型，驱动因素和脆弱道路使用者的风险。但是，有两个主要局限性。首先，众所周知的现实差距是指现实与模拟之间的差异，这阻止了模拟自主驾驶体验实现有效的现实性能。其次，缺乏有关真实代理商的行为的经验知识，包括备用驾驶员或乘客以及其他道路使用者，例如车辆，行人或骑自行车的人。代理仿真通常是根据实际数据进行确定性，随机概率或生成的预编程的，但它不代表与特定模拟方案相互作用的真实试剂的行为。在本文中，我们提出了一个初步框架，以实现真实试剂与模拟环境（包括自动驾驶汽车）之间的实时互动，并从多个视图中从模拟传感器数据中生成合成序列，这些视图可用于培训依赖行为模型的预测系统。我们的方法将沉浸式的虚拟现实和人类运动捕获系统与Carla模拟器进行自主驾驶。我们描述了提出的硬件和软件体系结构，并讨论所谓的行为差距或存在。我们提出了支持这种方法的潜力并讨论未来步骤的初步但有希望的结果。

Content moderation is the process of screening and monitoring user-generated content online. It plays a crucial role in stopping content resulting from unacceptable behaviors such as hate speech, harassment, violence against specific groups, terrorism, racism, xenophobia, homophobia, or misogyny, to mention some few, in Online Social Platforms. These platforms make use of a plethora of tools to detect and manage malicious information; however, malicious actors also improve their skills, developing strategies to surpass these barriers and continuing to spread misleading information. Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems. In response to this recent ongoing issue, this paper presents an innovative approach to address this linguistic trend in social networks through the simulation of different content evasion techniques and a multilingual Transformer model for content evasion detection. In this way, we share with the rest of the scientific community a multilingual public tool, named "pyleetspeak" to generate/simulate in a customizable way the phenomenon of content evasion through automatic word camouflage and a multilingual Named-Entity Recognition (NER) Transformer-based model tuned for its recognition and detection. The multilingual NER model is evaluated in different textual scenarios, detecting different types and mixtures of camouflage techniques, achieving an overall weighted F1 score of 0.8795. This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content on social networks, making the fight against information disorders more effective.

Social insects such as ants communicate via pheromones which allows them to coordinate their activity and solve complex tasks as a swarm, e.g. foraging for food. This behaviour was shaped through evolutionary processes. In computational models, self-coordination in swarms has been implemented using probabilistic or action rules to shape the decision of each agent and the collective behaviour. However, manual tuned decision rules may limit the behaviour of the swarm. In this work we investigate the emergence of self-coordination and communication in evolved swarms without defining any rule. We evolve a swarm of agents representing an ant colony. We use a genetic algorithm to optimize a spiking neural network (SNN) which serves as an artificial brain to control the behaviour of each agent. The goal of the colony is to find optimal ways to forage for food in the shortest amount of time. In the evolutionary phase, the ants are able to learn to collaborate by depositing pheromone near food piles and near the nest to guide its cohorts. The pheromone usage is not encoded into the network; instead, this behaviour is established through the optimization procedure. We observe that pheromone-based communication enables the ants to perform better in comparison to colonies where communication did not emerge. We assess the foraging performance by comparing the SNN based model to a rule based system. Our results show that the SNN based model can complete the foraging task more efficiently in a shorter time. Our approach illustrates that even in the absence of pre-defined rules, self coordination via pheromone emerges as a result of the network optimization. This work serves as a proof of concept for the possibility of creating complex applications utilizing SNNs as underlying architectures for multi-agent interactions where communication and self-coordination is desired.

This project explores the feasibility of remote patient monitoring based on the analysis of 3D movements captured with smartwatches. We base our analysis on the Kinematic Theory of Rapid Human Movement. We have validated our research in a real case scenario for stroke rehabilitation at the Guttmann Institute5 (neurorehabilitation hospital), showing promising results. Our work could have a great impact in remote healthcare applications, improving the medical efficiency and reducing the healthcare costs. Future steps include more clinical validation, developing multi-modal analysis architectures (analysing data from sensors, images, audio, etc.), and exploring the application of our technology to monitor other neurodegenerative diseases.

Federated learning (FL) has been proposed as a privacy-preserving approach in distributed machine learning. A federated learning architecture consists of a central server and a number of clients that have access to private, potentially sensitive data. Clients are able to keep their data in their local machines and only share their locally trained model's parameters with a central server that manages the collaborative learning process. FL has delivered promising results in real-life scenarios, such as healthcare, energy, and finance. However, when the number of participating clients is large, the overhead of managing the clients slows down the learning. Thus, client selection has been introduced as a strategy to limit the number of communicating parties at every step of the process. Since the early na\"{i}ve random selection of clients, several client selection methods have been proposed in the literature. Unfortunately, given that this is an emergent field, there is a lack of a taxonomy of client selection methods, making it hard to compare approaches. In this paper, we propose a taxonomy of client selection in Federated Learning that enables us to shed light on current progress in the field and identify potential areas of future research in this promising area of machine learning.

3D Flash LiDAR是传统扫描激光雷达系统的替代方法，有望在紧凑的外形尺寸中进行精确的深度成像，并且没有运动部件，例如自动驾驶汽车，机器人技术和增强现实（AR）等应用。通常在图像传感器格式中使用单光子，直接飞行时间（DTOF）接收器实施，设备的操作可能会受到需要在室外场景中处理和压缩的大量光子事件的阻碍以及对较大数组的可扩展性。我们在这里提出了一个64x32像素（256x128 spad）DTOF成像器，该成像器通过将像素与嵌入式直方图使用像素一起克服这些局限性，该直方直方图锁定并跟踪返回信号。这大大降低了输出数据帧的大小，可在10 kfps范围内或100 kfps的最大帧速率进行直接深度读数。该传感器可选择性地读数检测表面或传感运动的像素，从而减少功耗和片外处理要求。我们演示了传感器在中端激光雷达中的应用。

我们研究不同损失功能对医学图像病变细分的影响。尽管在处理自然图像时，跨凝结（CE）损失是最受欢迎的选择，但对于生物医学图像分割，由于其处理不平衡的情况，软骰子损失通常是首选的。另一方面，这两个功能的组合也已成功地应用于此类任务中。一个较少研究的问题是在存在分布（OOD）数据的情况下所有这些损失的概括能力。这是指在测试时间出现的样本，这些样本是从与训练图像不同的分布中得出的。在我们的情况下，我们将模型训练在始终包含病变的图像上，但是在测试时间我们也有无病变样品。我们通过全面的实验对内窥镜图像和糖尿病脚图像的溃疡分割进行了全面的实验，分析了不同损失函数对分布性能的最小化对分布性能的影响。我们的发现令人惊讶：在处理OOD数据时，CE-DICE损失组合在分割分配图像中表现出色，这使我们建议通过这种问题采用CE损失，因为它的稳健性和能够概括为OOD样品。可以在\ url {https://github.com/agaldran/lesion_losses_ood}找到与我们实验相关的代码。

社会互动网络是建立文明的基材。通常，我们与我们喜欢的人建立新的纽带，或者认为通过第三方的干预，我们的关系损害了。尽管它们的重要性和这些过程对我们的生活产生的巨大影响，但对它们的定量科学理解仍处于起步阶段，这主要是由于很难收集大量的社交网络数据集，包括个人属性。在这项工作中，我们对13所学校的真实社交网络进行了彻底的研究，其中3,000多名学生和60,000名宣布正面关系和负面关系，包括对所有学生的个人特征的测试。我们引入了一个度量标准 - “三合会影响”，该指标衡量了最近的邻居在其接触关系中的影响。我们使用神经网络来预测关系，并根据他们的个人属性或三合会的影响来提取两个学生是朋友或敌人的可能性。或者，我们可以使用网络结构的高维嵌入来预测关系。值得注意的是，三合会影响（一个简单的一维度量）在预测两个学生之间的关系方面达到了最高的准确性。我们假设从神经网络中提取的概率 - 三合会影响的功能和学生的个性 - 控制真实社交网络的演变，为这些系统的定量研究开辟了新的途径。

生成对抗网络（GAN）是强大的机器学习模型，能够生成具有高分辨率的所需现象的完全合成样本。尽管他们成功了，但GAN的训练过程非常不稳定，通常有必要对网络实施几种附属启发式方法，以达到模型的可接受收敛。在本文中，我们介绍了一种新颖的方法来分析生成对抗网络培训的收敛性和稳定性。为此，我们建议分解对手Min-Max游戏的目标功能，将定期gan定义为傅立叶系列。通过研究连续交替梯度下降算法的截短傅里叶序列的动力学，我们能够近似实际流量并确定GAN收敛的主要特征。通过研究$ 2 $ - 参数gan的旨在产生未知指数分布的训练流，从经验上证实了这种方法。作为副产品，我们表明gan中的融合轨道是周期性轨道的小扰动，因此纳什均值是螺旋吸引子。从理论上讲，这证明了在甘斯中观察到的缓慢和不稳定的训练。