Vehicle-to-Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non-line-of-sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, we proposed a Context-Aware Target Classification (CA-TC) module coupled with a hybrid learning-based predictive modeling technique for CVS systems. The CA-TC consists of two modules: A Context-Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA-TC, making them more robust and reliable. The CAM leverages vehicles path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real-world data, we learn a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior. We combine offline training and online model updates with on-the-fly forecasting to account for new possible driver behaviors. Finally, our framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems.

为连接和自动化车辆（CAVS）开发安全性和效率应用需要大量的测试和评估。在关键和危险情况下对这些系统运行的需求使他们的评估负担非常昂贵，可能危险且耗时。作为替代方案，研究人员试图使用仿真平台研究和评估其算法和设计。建模驾驶员或人类操作员在骑士或其他与他们相互作用的车辆中的行为是此类模拟的主要挑战之一。虽然为人类行为开发完美的模型是一项具有挑战性的任务和一个开放的问题，但我们展示了用于驾驶员行为的模拟器中当前模型的显着增强。在本文中，我们为混合运输系统提供了一个模拟平台，其中包括人类驱动和自动化车辆。此外，我们分解了人类驾驶任务，并提供了模拟大规模交通情况的模块化方法，从而可以彻底研究自动化和主动的安全系统。通过互连模块的这种表示形式提供了一个可以调节的人解剖系统，以代表不同类别的驱动程序。此外，我们分析了一个大型驾驶数据集以提取表达参数，以最好地描述不同的驾驶特性。最后，我们在模拟器中重新创建了类似密集的交通情况，并对各种人类特异性和系统特异性因素进行了彻底的分析，研究了它们对交通网络性能和安全性的影响。

配子等合作驾驶系统，依靠沟通和信息交换，为每个特工创造情境感知。因此，控制部件的设计和性能与通信部件性能紧密耦合。车辆之间的信息流可以显着影响排的动态。因此，排列的性能和稳定性不仅取决于车辆的控制器，还取决于信息流拓扑（IFT）。 IFT可能导致某些排特性的限制，即稳定性和可扩展性。蜂窝载体 - 一切（C-V2X）已成为支持连接和自动化车辆应用的主要通信技术之一。由于数据包丢失，无线通道会创建随机链路中断和网络拓扑的变化。在本文中，我们使用一阶马尔可夫模型模拟车辆之间的通信链路，以捕获每个链路的普遍时间相关性。这些模型通过在系统设计阶段期间的通信链路更好地近似来实现性能评估。我们的方法是使用实​​验中的数据来使用马尔可夫链的分组间隙（IPG）和连续IPG状态的过渡概率矩阵来模拟分组间隙（IPG）。使用基于各种不同车辆密度和通信率的经验数据来源的模型从高保真模拟中收集训练数据。利用IPG模型，我们分析了一家车辆的平均方形稳定性，标准共识协议调整了理想的通信，并比较不同情景的性能下降。

合作驾驶依赖于车辆之间的沟通来造成情境感知。合作驾驶的一种应用是合作自适应巡航控制（CACC），其旨在提高公路运输安全性和能力。基于模型的通信（MBC）是一种新的范例，具有灵活的内容结构，用于广播联合车辆驱动程序预测行为模型。车辆复杂的动态和多样化的驾驶行为为建模过程增加了复杂性。高斯过程（GP）是一种完全数据驱动和非参数贝叶斯建模方法，可用作MBC的建模组件。通过为车辆产生本地GPS并将其超参数作为模型作为模型作为模型来向相邻车辆广播的知识来传播关于不确定性的知识。在该研究中，GP用于模拟每个车辆的速度轨迹，这允许车辆在通信损耗和/或低速率通信期间访问其前车辆的未来行为。此外，为了克服车辆排中的安全问题，考虑了每辆车的两种操作模式;免费下面和紧急制动。本文介绍了离散混合随机模型预测控制，该模型采用了系统模式以及GP模型捕获的不确定性。该拟议的控制设计方法找到了最佳的车速轨迹，其目的是实现具有小型车间隙的安全和有效的车辆，同时降低车辆对频繁通信的依赖性。模拟研究表明，考虑到具有低利率间歇性通信的上述通信范例的提出控制器的功效。

培训可以在各种城市和公路情景中自主推动的智能代理在过去几十年中是机器人学会的热门话题。然而，在道路拓扑和邻近车辆定位方面的驾驶环境的多样性使得这个问题非常具有挑战性。不言而喻，虽然自动驾驶的场景特定的驾驶政策是有前途的，并且可以提高运输安全性和效率，但它们显然不是一个通用的可扩展解决方案。相反，我们寻求决策计划和驾驶策略，可以概括为新颖和看不见的环境。在这项工作中，我们利用了人类司机学习其周围环境的抽象表达的关键思想，这在各种驾驶场景和环境中相当类似。通过这些陈述，人类司机能够快速适应新颖的环境和在看不见的条件下驱动。正式地，通过强制信息瓶颈，我们提取一个潜在的表示，最小化\ extentit {距离} - 我们介绍的量化，以便在驱动场景之间介绍不同驾驶配置之间的相似性。然后采用这种潜在的空间作为Q学习模块的输入，以学习更广泛的驾驶策略。我们的实验表明，使用这种潜在的表示可以将崩溃的数量减少到大约一半。

Accurate determination of a small molecule candidate (ligand) binding pose in its target protein pocket is important for computer-aided drug discovery. Typical rigid-body docking methods ignore the pocket flexibility of protein, while the more accurate pose generation using molecular dynamics is hindered by slow protein dynamics. We develop a tiered tensor transform (3T) algorithm to rapidly generate diverse protein-ligand complex conformations for both pose and affinity estimation in drug screening, requiring neither machine learning training nor lengthy dynamics computation, while maintaining both coarse-grain-like coordinated protein dynamics and atomistic-level details of the complex pocket. The 3T conformation structures we generate are closer to experimental co-crystal structures than those generated by docking software, and more importantly achieve significantly higher accuracy in active ligand classification than traditional ensemble docking using hundreds of experimental protein conformations. 3T structure transformation is decoupled from the system physics, making future usage in other computational scientific domains possible.

Variational autoencoders model high-dimensional data by positing low-dimensional latent variables that are mapped through a flexible distribution parametrized by a neural network. Unfortunately, variational autoencoders often suffer from posterior collapse: the posterior of the latent variables is equal to its prior, rendering the variational autoencoder useless as a means to produce meaningful representations. Existing approaches to posterior collapse often attribute it to the use of neural networks or optimization issues due to variational approximation. In this paper, we consider posterior collapse as a problem of latent variable non-identifiability. We prove that the posterior collapses if and only if the latent variables are non-identifiable in the generative model. This fact implies that posterior collapse is not a phenomenon specific to the use of flexible distributions or approximate inference. Rather, it can occur in classical probabilistic models even with exact inference, which we also demonstrate. Based on these results, we propose a class of latent-identifiable variational autoencoders, deep generative models which enforce identifiability without sacrificing flexibility. This model class resolves the problem of latent variable non-identifiability by leveraging bijective Brenier maps and parameterizing them with input convex neural networks, without special variational inference objectives or optimization tricks. Across synthetic and real datasets, latent-identifiable variational autoencoders outperform existing methods in mitigating posterior collapse and providing meaningful representations of the data.

Differentiable Architecture Search (DARTS) has attracted considerable attention as a gradient-based Neural Architecture Search (NAS) method. Since the introduction of DARTS, there has been little work done on adapting the action space based on state-of-art architecture design principles for CNNs. In this work, we aim to address this gap by incrementally augmenting the DARTS search space with micro-design changes inspired by ConvNeXt and studying the trade-off between accuracy, evaluation layer count, and computational cost. To this end, we introduce the Pseudo-Inverted Bottleneck conv block intending to reduce the computational footprint of the inverted bottleneck block proposed in ConvNeXt. Our proposed architecture is much less sensitive to evaluation layer count and outperforms a DARTS network with similar size significantly, at layer counts as small as 2. Furthermore, with less layers, not only does it achieve higher accuracy with lower GMACs and parameter count, GradCAM comparisons show that our network is able to better detect distinctive features of target objects compared to DARTS.

Deep learning techniques with neural networks have been used effectively in computational fluid dynamics (CFD) to obtain solutions to nonlinear differential equations. This paper presents a physics-informed neural network (PINN) approach to solve the Blasius function. This method eliminates the process of changing the non-linear differential equation to an initial value problem. Also, it tackles the convergence issue arising in the conventional series solution. It is seen that this method produces results that are at par with the numerical and conventional methods. The solution is extended to the negative axis to show that PINNs capture the singularity of the function at $\eta=-5.69$

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.