Knowledge representation and reasoning in law are essential to facilitate the automation of legal analysis and decision-making tasks. In this paper, we propose a new approach based on legal science, specifically legal taxonomy, for representing and reasoning with legal documents. Our approach interprets the regulations in legal documents as binary trees, which facilitates legal reasoning systems to make decisions and resolve logical contradictions. The advantages of this approach are twofold. First, legal reasoning can be performed on the basis of the binary tree representation of the regulations. Second, the binary tree representation of the regulations is more understandable than the existing sentence-based representations. We provide an example of how our approach can be used to interpret the regulations in a legal document.

Deep learning (DL) models of code have recently reported great progress for vulnerability detection. In some cases, DL-based models have outperformed static analysis tools. Although many great models have been proposed, we do not yet have a good understanding of these models. This limits the further advancement of model robustness, debugging, and deployment for the vulnerability detection. In this paper, we surveyed and reproduced 9 state-of-the-art (SOTA) deep learning models on 2 widely used vulnerability detection datasets: Devign and MSR. We investigated 6 research questions in three areas, namely model capabilities, training data, and model interpretation. We experimentally demonstrated the variability between different runs of a model and the low agreement among different models' outputs. We investigated models trained for specific types of vulnerabilities compared to a model that is trained on all the vulnerabilities at once. We explored the types of programs DL may consider "hard" to handle. We investigated the relations of training data sizes and training data composition with model performance. Finally, we studied model interpretations and analyzed important features that the models used to make predictions. We believe that our findings can help better understand model results, provide guidance on preparing training data, and improve the robustness of the models. All of our datasets, code, and results are available at https://figshare.com/s/284abfba67dba448fdc2.

We present hierarchical policy blending as optimal transport (HiPBOT). This hierarchical framework adapts the weights of low-level reactive expert policies, adding a look-ahead planning layer on the parameter space of a product of expert policies and agents. Our high-level planner realizes a policy blending via unbalanced optimal transport, consolidating the scaling of underlying Riemannian motion policies, effectively adjusting their Riemannian matrix, and deciding over the priorities between experts and agents, guaranteeing safety and task success. Our experimental results in a range of application scenarios from low-dimensional navigation to high-dimensional whole-body control showcase the efficacy and efficiency of HiPBOT, which outperforms state-of-the-art baselines that either perform probabilistic inference or define a tree structure of experts, paving the way for new applications of optimal transport to robot control. More material at https://sites.google.com/view/hipobot

Air pollution is an emerging problem that needs to be solved especially in developed and developing countries. In Vietnam, air pollution is also a concerning issue in big cities such as Hanoi and Ho Chi Minh cities where air pollution comes mostly from vehicles such as cars and motorbikes. In order to tackle the problem, the paper focuses on developing a solution that can estimate the emitted PM2.5 pollutants by counting the number of vehicles in the traffic. We first investigated among the recent object detection models and developed our own traffic surveillance system. The observed traffic density showed a similar trend to the measured PM2.5 with a certain lagging in time, suggesting a relation between traffic density and PM2.5. We further express this relationship with a mathematical model which can estimate the PM2.5 value based on the observed traffic density. The estimated result showed a great correlation with the measured PM2.5 plots in the urban area context.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

计算机系统的程序或功能中存在的软件漏洞是一个严重且至关重要的问题。通常，在由数百或数千个源代码语句组成的程序或功能中，只有很少的语句引起相应的漏洞。当前，在机器学习工具的协助下，专家在功能或程序级别上进行了脆弱性标签。将这种方法扩展到代码语句级别的成本更高和耗时，并且仍然是一个开放的问题。在本文中，我们提出了一种新颖的端到端深度学习方法，以识别与特定功能相关的脆弱性代码语句。受到现实世界中脆弱代码中观察到的特定结构的启发，我们首先利用相互信息来学习一组潜在变量，代表源代码语句与相应函数的漏洞的相关性。然后，我们提出了新颖的群集空间对比学习，以进一步改善与脆弱性相关的代码语句的强大选择过程。 200K+ C/C ++功能的实际数据集的实验结果表明，我们方法的优越性比其他最先进的基线相比。通常，我们的方法在无需监督的环境中在现实世界数据集上运行时，在Baselines上，VCP，VCA和TOP-10 ACC测量的较高性能在3 \％至14 \％之间。我们已发布的源代码样本可在\ href {https://github.com/vannguyennd/livuitcl} {https://github.com/vannguyennd/livuitcl。} {

时间序列异常检测在统计，经济学和计算机科学中进行了广泛的研究。多年来，使用基于深度学习的方法为时间序列异常检测提出了许多方法。这些方法中的许多方法都在基准数据集上显示了最先进的性能，给人一种错误的印象，即这些系统在许多实用和工业现实世界中都可以强大且可部署。在本文中，我们证明了最先进的异常检测方法的性能通过仅在传感器数据中添加小的对抗扰动来实质性地降解。我们使用不同的评分指标，例如预测错误，异常和分类评分，包括几个公共和私人数据集，从航空航天应用程序，服务器机器到发电厂的网络物理系统。在众所周知的对抗攻击中，来自快速梯度标志方法（FGSM）和预计梯度下降（PGD）方法，我们证明了最新的深神经网络（DNNS）和图形神经网络（GNNS）方法，这些方法声称这些方法是要对异常进行稳健，并且可能已集成在现实生活中，其性能下降到低至0％。据我们最好的理解，我们首次证明了针对对抗攻击的异常检测系统的脆弱性。这项研究的总体目标是提高对时间序列异常检测器的对抗性脆弱性的认识。

视力范围有限的自动驾驶机器人在避免多边形障碍的2D环境中找到了目标的途径。在发现环境图的过程中，机器人必须返回以前标记的某些位置，机器人遍历要返回的区域被定义为线段束的束序列。本文提出了一种新型算法，用于根据多次拍摄的方法找到沿线段束序列的大约最短路径。提出了该方法的三个因素，包括捆绑分区，共线条件和射击点的更新。然后，我们证明，如果共线条件成立，则确定问题的最短路径，否则，通过将方法的更新收敛到最短路径，获得的路径序列。该算法在Python中实现，一些数值示例表明，使用我们的方法的自主机器人的路径计划的运行时间比使用Li和Klette在Euclidean最短路径中使用Li和Klette的橡皮筋技术更快，Springer，53-89（2011年）（2011年） ）。

尽管智能摄像头停车系统概念已经存在数十年，但一些方法已经完全解决了该系统的可扩展性和可靠性。由于智能停车系统的基石是检测占用率的能力，因此传统方法使用分类主链从手动标记的网格中预测斑点。这是耗时的，失去了系统的可扩展性。此外，大多数方法都使用深度学习模型，使其并非无错误，也不可靠。因此，我们提出了一个端到端的智能相机停车系统，在该系统中，我们提供了一个称为OCPDET的对象检测器的自主检测占用。我们的探测器还提供了对比模块的有意义的信息：训练和空间知识，这些知识避免了推理过程中的错误检测。我们在现有的PKLOT数据集上基于OCPDET进行基准OCPDET，并与传统的分类解决方案相比达到竞争成果。我们还引入了一个额外的SNU-SPS数据集，其中我们从各种视图中估算了系统性能，并在停车任务中进行系统评估。我们数据集的结果表明，我们的系统对现实世界应用有望。

最近的人工智能（AI）算法已在各种医学分类任务上实现了放射科医生级的性能。但是，只有少数研究涉及CXR扫描异常发现的定位，这对于向放射学家解释图像级分类至关重要。我们在本文中介绍了一个名为Vindr-CXR的可解释的深度学习系统，该系统可以将CXR扫描分类为多种胸部疾病，同时将大多数类型的关键发现本地化在图像上。 Vindr-CXR接受了51,485次CXR扫描的培训，并通过放射科医生提供的边界盒注释进行了培训。它表现出与经验丰富的放射科医生相当的表现，可以在3,000张CXR扫描的回顾性验证集上对6种常见的胸部疾病进行分类，而在接收器操作特征曲线（AUROC）下的平均面积为0.967（95％置信区间[CI]：0.958---------0.958------- 0.975）。 VINDR-CXR在独立患者队列中也得到了外部验证，并显示出其稳健性。对于具有14种类型病变的本地化任务，我们的自由响应接收器操作特征（FROC）分析表明，VINDR-CXR以每扫描确定的1.0假阳性病变的速率达到80.2％的敏感性。还进行了一项前瞻性研究，以衡量VINDR-CXR在协助六名经验丰富的放射科医生方面的临床影响。结果表明，当用作诊断工具时，提出的系统显着改善了放射科医生本身之间的一致性，平均Fleiss的Kappa的同意增加了1.5％。我们还观察到，在放射科医生咨询了Vindr-CXR的建议之后，在平均Cohen的Kappa中，它们和系统之间的一致性显着增加了3.3％。