知识图（KG）通常不完整，我们经常希望推断出现有的新事实。这可以被认为是二进制分类问题;我们的目标是预测新事实是真或假的。不幸的是，我们通常只有积极的例子（已知事实），但我们也需要负面的例子来训练分类器。要解决此问题，通常使用负面采样策略生成否定示例。但是，这可以产生可能降低性能的假否定，是计算昂贵的，并且不会产生校准的分类概率。在本文中，我们提出了一种培训程序，通过向损失函数添加新的正则化术语来消除对负面采样的需要。我们的两个关系嵌入模型（DISTMULT和简单）的结果显示了我们的提案的优点。

图形神经网络（GNNS）在提供图形结构时良好工作。但是，这种结构可能并不总是在现实世界应用中可用。该问题的一个解决方案是推断任务特定的潜在结构，然后将GNN应用于推断的图形。不幸的是，可能的图形结构的空间与节点的数量超级呈指数，因此任务特定的监督可能不足以学习结构和GNN参数。在这项工作中，我们提出了具有自我监督或拍打的邻接和GNN参数的同时学习，这是通过自我监督来推断图形结构的更多监督的方法。一个综合实验研究表明，缩小到具有数十万个节点的大图和胜过了几种模型，以便在已建立的基准上学习特定于任务的图形结构。

Designing a local planner to control tractor-trailer vehicles in forward and backward maneuvering is a challenging control problem in the research community of autonomous driving systems. Considering a critical situation in the stability of tractor-trailer systems, a practical and novel approach is presented to design a non-linear MPC(NMPC) local planner for tractor-trailer autonomous vehicles in both forward and backward maneuvering. The tractor velocity and steering angle are considered to be control variables. The proposed NMPC local planner is designed to handle jackknife situations, avoiding multiple static obstacles, and path following in both forward and backward maneuvering. The challenges mentioned above are converted into a constrained problem that can be handled simultaneously by the proposed NMPC local planner. The direct multiple shooting approach is used to convert the optimal control problem(OCP) into a non-linear programming problem(NLP) that IPOPT solvers can solve in CasADi. The controller performance is evaluated through different backup and forward maneuvering scenarios in the Gazebo simulation environment in real-time. It achieves asymptotic stability in avoiding static obstacles and accurate tracking performance while respecting path constraints. Finally, the proposed NMPC local planner is integrated with an open-source autonomous driving software stack called AutowareAi.

We introduce a new probabilistic temporal logic for the verification of Markov Decision Processes (MDP). Our logic is the first to include operators for causal reasoning, allowing us to express interventional and counterfactual queries. Given a path formula $\phi$, an interventional property is concerned with the satisfaction probability of $\phi$ if we apply a particular change $I$ to the MDP (e.g., switching to a different policy); a counterfactual allows us to compute, given an observed MDP path $\tau$, what the outcome of $\phi$ would have been had we applied $I$ in the past. For its ability to reason about different configurations of the MDP, our approach represents a departure from existing probabilistic temporal logics that can only reason about a fixed system configuration. From a syntactic viewpoint, we introduce a generalized counterfactual operator that subsumes both interventional and counterfactual probabilities as well as the traditional probabilistic operator found in e.g., PCTL. From a semantics viewpoint, our logic is interpreted over a structural causal model (SCM) translation of the MDP, which gives us a representation amenable to counterfactual reasoning. We provide a proof-of-concept evaluation of our logic on a reach-avoid task in a grid-world model.

Vision transformers (ViTs) are quickly becoming the de-facto architecture for computer vision, yet we understand very little about why they work and what they learn. While existing studies visually analyze the mechanisms of convolutional neural networks, an analogous exploration of ViTs remains challenging. In this paper, we first address the obstacles to performing visualizations on ViTs. Assisted by these solutions, we observe that neurons in ViTs trained with language model supervision (e.g., CLIP) are activated by semantic concepts rather than visual features. We also explore the underlying differences between ViTs and CNNs, and we find that transformers detect image background features, just like their convolutional counterparts, but their predictions depend far less on high-frequency information. On the other hand, both architecture types behave similarly in the way features progress from abstract patterns in early layers to concrete objects in late layers. In addition, we show that ViTs maintain spatial information in all layers except the final layer. In contrast to previous works, we show that the last layer most likely discards the spatial information and behaves as a learned global pooling operation. Finally, we conduct large-scale visualizations on a wide range of ViT variants, including DeiT, CoaT, ConViT, PiT, Swin, and Twin, to validate the effectiveness of our method.

Machine learning algorithms have revolutionized different fields, including natural language processing, computer vision, signal processing, and medical data processing. Despite the excellent capabilities of machine learning algorithms in various tasks and areas, the performance of these models mainly deteriorates when there is a shift in the test and training data distributions. This gap occurs due to the violation of the fundamental assumption that the training and test data are independent and identically distributed (i.i.d). In real-world scenarios where collecting data from all possible domains for training is costly and even impossible, the i.i.d assumption can hardly be satisfied. The problem is even more severe in the case of medical images and signals because it requires either expensive equipment or a meticulous experimentation setup to collect data, even for a single domain. Additionally, the decrease in performance may have severe consequences in the analysis of medical records. As a result of such problems, the ability to generalize and adapt under distribution shifts (domain generalization (DG) and domain adaptation (DA)) is essential for the analysis of medical data. This paper provides the first systematic review of DG and DA on functional brain signals to fill the gap of the absence of a comprehensive study in this era. We provide detailed explanations and categorizations of datasets, approaches, and architectures used in DG and DA on functional brain images. We further address the attention-worthy future tracks in this field.

我们通过实验验证一个实时机器学习框架，能够控制拉曼放大器的泵功率值以在二维（2D）中塑造信号功率演变：频率和光纤距离。在我们的设置中，优化了四个一阶反向传输泵的功率值，以实现所需的2D功率配置文件。泵功率优化框架包括一个卷积神经网络（CNN），然后是差分进化（DE）技术，在线应用于放大器设置，以自动实现目标2D功率配置文件。可实现的2D配置文件的结果表明，该框架能够确保获得的最大绝对误差（MAE）（<0.5 dB）与获得的目标2D配置文件之间。此外，该框架在多目标设计方案中进行了测试，该方案的目标是在跨度结束时达到固定增益水平的2D配置文件，共同在整个光纤长度上进行最小的光谱游览。在这种情况下，实验结果断言，对于目标扁平增益水平的2D轮廓，当设置在泵功率值中不受物理限制时，DE获得的最大增益偏差小于1 dB。模拟结果还证明，有足够的泵功率可用，可以实现更高的目标增益水平的更好的增益偏差（小于0.6 dB）。

本文为基于MPC的基于MPC模型的增强学习方法的计划模块提出了一个新的评分功能，以解决使用奖励功能得分轨迹的固有偏见。所提出的方法使用折现价值和折扣价值提高了现有基于MPC的MBRL方法的学习效率。该方法利用最佳轨迹来指导策略学习，并根据现实世界更新其状态行动价值函数，并增强板载数据。在选定的Mujoco健身环境中评估了所提出方法的学习效率，以及在学习的模拟机器人模型中学习运动技能。结果表明，所提出的方法在学习效率和平均奖励回报方面优于当前的最新算法。

标准扩散模型涉及图像变换 - 添加高斯噪声 - 以及逆转此降解的图像恢复操作员。我们观察到，扩散模型的生成行为并不是很大程度上取决于图像降解的选择，实际上，可以通过改变这种选择来构建整个生成模型家族。即使使用完全确定性的降解（例如，模糊，掩蔽等），培训和测试时间更新规则是基于扩散模型的培训和测试时间更新规则，可以轻松地概括为创建生成模型。这些完全确定的模型的成功使社区对扩散模型的理解质疑，这依赖于梯度Langevin动力学或变异推理中的噪声，并为反转任意过程的广义扩散模型铺平了道路。我们的代码可从https://github.com/arpitbansal297/cold-diffusion-models获得

使用增强现实（AR）用于导航目的，这表明在手术手术过程中协助医生有益。这些应用通常需要知道外科手术工具和患者的姿势，以提供外科医生在任务执行过程中可以使用的视觉信息。现有的医学级跟踪系统使用放置在手术室内的红外摄像头（OR）来识别感兴趣的对象附加并计算其姿势的复古反射标记。一些市售的AR头式显示器（HMD）使用类似的摄像头进行自定位，手动跟踪和估算对象的深度。这项工作提出了一个使用AR HMD的内置摄像机来准确跟踪复古反射标记的框架，例如在手术过程中使用的标记，而无需集成任何其他组件。该框架还能够同时跟踪多个工具。我们的结果表明，横向翻译的准确度为0.09 +-0.06毫米，可以实现标记的跟踪和检测，纵向翻译的0.42 +-0.32 mm，绕垂直轴旋转的0.80 +-0.39 ver。此外，为了展示所提出的框架的相关性，我们在手术程序的背景下评估了系统的性能。该用例旨在在骨科过程中复制K-Wire插入的场景。为了进行评估，为两名外科医生和一名生物医学研究人员提供了视觉导航，每次都进行了21次注射。该用例的结果提供了与基于AR的导航程序报告的相当精度。