合作的感知在将车辆的感知范围扩展到超出其视线之外至关重要。然而，在有限的通信资源下交换原始感官数据是不可行的。为了实现有效的合作感知，车辆需要解决以下基本问题：需要共享哪些感官数据？，在哪个分辨率？，以及哪个车辆？为了回答这个问题，在本文中，提出了一种新颖的框架来允许加强学习（RL）基于车辆关联，资源块（RB）分配和通过利用基于四叉的点的协作感知消息（CPM）的内容选择云压缩机制。此外，引入了联合的RL方法，以便在跨车辆上加速训练过程。仿真结果表明，RL代理能够有效地学习车辆关联，RB分配和消息内容选择，同时在接收的感官信息方面最大化车辆的满足。结果还表明，与非联邦方法相比，联邦RL改善了培训过程，可以在与非联邦方法相同的时间内实现更好的政策。

血管内操作中的自主机器人有可能安全可靠地浏览循环系统，同时降低对人体错误的敏感性。但是，训练机器人的过程涉及许多挑战，例如由于机器学习算法的效率低下而导致的长期培训持续时间以及导管与血管内幻影之间的相互作用引起的安全问题。物理模拟器已在血管内手术的背景下使用，但通常用于员工培训，通常不符合自主插管目标。此外，大多数当前的模拟器都是封闭消息，它阻碍了安全可靠的自主系统的协作开发。在这项工作中，我们介绍了Cathsim，Cathsim是一种开源模拟环境，可加快用于自主内血管内导航的机器学习算法的开发。我们首先使用最先进的血管内机器人模拟高保真导管和主动脉。然后，我们在模拟环境中提供了导管和主动脉之间实时力传感的能力。我们通过使用两种流行的强化学习算法，近端策略优化（PPO）和软参与者（SAC）在两个主要动脉内执行两个不同的导管插入任务来验证我们的模拟器。实验结果表明，使用我们的开源模拟器，我们可以成功训练增强型学习剂以执行不同的自主插管任务。

使用本机LUT作为独立培训推理运营商的FPGA特定的DNN架构已被证明实现了有利的区域准确性和能量准确性权衡。该领域的第一个工作Lutnet，对标准DNN基准测试表现出最先进的性能。在本文中，我们提出了学习的基于LUT的拓扑结构的优化，从而导致更高效率的设计，而不是通过直接使用现成的手工设计的网络。本类架构的现有实现需要手动规范的每拉特的输入数，K。选择合适的k先验是具有挑战性的，并且在甚至高粒度下这样做，例如，如此。每个层，是一种耗时和错误的过程，可以留下FPGA的空间灵活性欠缺。此外，先验工作请参阅随机连接的LUT输入，不保证网络拓扑的良好选择。为了解决这些问题，我们提出了逻辑收缩，一种细粒度的网格剪枝方法，使K将自动学习，用于针对FPGA推理的神经网络中的每一个LUT。通过删除确定为低于重要性的LUT输入，我们的方法会增加所得加速器的效率。我们的GPU友好的LUT输入拆卸解决方案能够在培训期间加工大型拓扑，可忽略不计的放缓。通过逻辑收缩，我们可以分别更好地完成CNV网络的最佳Lutnet实现的区域和能源效率，分别将CIFAR-10分别达到1.54倍和1.31倍，同时匹配其精度。该实现也达到2.71倍的区域效率同样准确，严重修剪的BNN。在具有双重净架构的Imagenet上，逻辑收缩的就业导致综合后面积减少2.67倍VS Lutnet，允许以前在今天最大的FPGA上实现的实施。

Designing experiments often requires balancing between learning about the true treatment effects and earning from allocating more samples to the superior treatment. While optimal algorithms for the Multi-Armed Bandit Problem (MABP) provide allocation policies that optimally balance learning and earning, they tend to be computationally expensive. The Gittins Index (GI) is a solution to the MABP that can simultaneously attain optimality and computationally efficiency goals, and it has been recently used in experiments with Bernoulli and Gaussian rewards. For the first time, we present a modification of the GI rule that can be used in experiments with exponentially-distributed rewards. We report its performance in simulated 2- armed and 3-armed experiments. Compared to traditional non-adaptive designs, our novel GI modified design shows operating characteristics comparable in learning (e.g. statistical power) but substantially better in earning (e.g. direct benefits). This illustrates the potential that designs using a GI approach to allocate participants have to improve participant benefits, increase efficiencies, and reduce experimental costs in adaptive multi-armed experiments with exponential rewards.

Modelling and forecasting real-life human behaviour using online social media is an active endeavour of interest in politics, government, academia, and industry. Since its creation in 2006, Twitter has been proposed as a potential laboratory that could be used to gauge and predict social behaviour. During the last decade, the user base of Twitter has been growing and becoming more representative of the general population. Here we analyse this user base in the context of the 2021 Mexican Legislative Election. To do so, we use a dataset of 15 million election-related tweets in the six months preceding election day. We explore different election models that assign political preference to either the ruling parties or the opposition. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods. These results demonstrate that analysis of public online data can outperform conventional polling methods, and that political analysis and general forecasting would likely benefit from incorporating such data in the immediate future. Moreover, the same Twitter dataset with geographical attributes is positively correlated with results from official census data on population and internet usage in Mexico. These findings suggest that we have reached a period in time when online activity, appropriately curated, can provide an accurate representation of offline behaviour.

Existing federated classification algorithms typically assume the local annotations at every client cover the same set of classes. In this paper, we aim to lift such an assumption and focus on a more general yet practical non-IID setting where every client can work on non-identical and even disjoint sets of classes (i.e., client-exclusive classes), and the clients have a common goal which is to build a global classification model to identify the union of these classes. Such heterogeneity in client class sets poses a new challenge: how to ensure different clients are operating in the same latent space so as to avoid the drift after aggregation? We observe that the classes can be described in natural languages (i.e., class names) and these names are typically safe to share with all parties. Thus, we formulate the classification problem as a matching process between data representations and class representations and break the classification model into a data encoder and a label encoder. We leverage the natural-language class names as the common ground to anchor the class representations in the label encoder. In each iteration, the label encoder updates the class representations and regulates the data representations through matching. We further use the updated class representations at each round to annotate data samples for locally-unaware classes according to similarity and distill knowledge to local models. Extensive experiments on four real-world datasets show that the proposed method can outperform various classical and state-of-the-art federated learning methods designed for learning with non-IID data.

This is paper for the smooth function approximation by neural networks (NN). Mathematical or physical functions can be replaced by NN models through regression. In this study, we get NNs that generate highly accurate and highly smooth function, which only comprised of a few weight parameters, through discussing a few topics about regression. First, we reinterpret inside of NNs for regression; consequently, we propose a new activation function--integrated sigmoid linear unit (ISLU). Then special charateristics of metadata for regression, which is different from other data like image or sound, is discussed for improving the performance of neural networks. Finally, the one of a simple hierarchical NN that generate models substituting mathematical function is presented, and the new batch concept ``meta-batch" which improves the performance of NN several times more is introduced. The new activation function, meta-batch method, features of numerical data, meta-augmentation with metaparameters, and a structure of NN generating a compact multi-layer perceptron(MLP) are essential in this study.

The existing methods for video anomaly detection mostly utilize videos containing identifiable facial and appearance-based features. The use of videos with identifiable faces raises privacy concerns, especially when used in a hospital or community-based setting. Appearance-based features can also be sensitive to pixel-based noise, straining the anomaly detection methods to model the changes in the background and making it difficult to focus on the actions of humans in the foreground. Structural information in the form of skeletons describing the human motion in the videos is privacy-protecting and can overcome some of the problems posed by appearance-based features. In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. We present a novel taxonomy of algorithms based on the various learning approaches. We conclude that skeleton-based approaches for anomaly detection can be a plausible privacy-protecting alternative for video anomaly detection. Lastly, we identify major open research questions and provide guidelines to address them.

The Government of Kerala had increased the frequency of supply of free food kits owing to the pandemic, however, these items were static and not indicative of the personal preferences of the consumers. This paper conducts a comparative analysis of various clustering techniques on a scaled-down version of a real-world dataset obtained through a conjoint analysis-based survey. Clustering carried out by centroid-based methods such as k means is analyzed and the results are plotted along with SVD, and finally, a conclusion is reached as to which among the two is better. Once the clusters have been formulated, commodities are also decided upon for each cluster. Also, clustering is further enhanced by reassignment, based on a specific cluster loss threshold. Thus, the most efficacious clustering technique for designing a food kit tailored to the needs of individuals is finally obtained.

Machine learning-based segmentation in medical imaging is widely used in clinical applications from diagnostics to radiotherapy treatment planning. Segmented medical images with ground truth are useful for investigating the properties of different segmentation performance metrics to inform metric selection. Regular geometrical shapes are often used to synthesize segmentation errors and illustrate properties of performance metrics, but they lack the complexity of anatomical variations in real images. In this study, we present a tool to emulate segmentations by adjusting the reference (truth) masks of anatomical objects extracted from real medical images. Our tool is designed to modify the defined truth contours and emulate different types of segmentation errors with a set of user-configurable parameters. We defined the ground truth objects from 230 patient images in the Glioma Image Segmentation for Radiotherapy (GLIS-RT) database. For each object, we used our segmentation synthesis tool to synthesize 10 versions of segmentation (i.e., 10 simulated segmentors or algorithms), where each version has a pre-defined combination of segmentation errors. We then applied 20 performance metrics to evaluate all synthetic segmentations. We demonstrated the properties of these metrics, including their ability to capture specific types of segmentation errors. By analyzing the intrinsic properties of these metrics and categorizing the segmentation errors, we are working toward the goal of developing a decision-tree tool for assisting in the selection of segmentation performance metrics.