从分区的输入空间中生成不安全的子要求，以支持验证引导的测试案例以正式验证黑盒模型，这对研究人员来说是一个具有挑战性的问题。搜索空间的大小使详尽的搜索在计算上是不切实际的。本文调查了一种元热疗法方法，以在分区的输入空间中搜索不安全的候选子要求。我们提出了一种负选择算法（NSA），用于识别给定安全性质内候选人的不安全区域。 NSA算法的元效力能力使得在验证这些区域的一部分时估算庞大的不安全区域成为可能。我们利用分区空间的并行执行来生产安全区域。基于安全区域的先验知识的NSA用于识别候选不安全区域，然后使用Marabou框架来验证NSA结果。我们的初步实验和评估表明，该程序在用Marabou框架验证的高精度验证时发现候选人不安全的子裁定。

在本文中，我们为表面缺陷检测任务呈现了一种完整的局部二进制模式（RCLBP）框架。我们的方法使用非本地（NL）表示滤波器的组合具有小波阈值和完成的本地二进制模式（CLBP）以提取燃料特征，该特征被馈入到用于表面缺陷检测的分类器中。本文结合了三个组分：建立基于非局部（NL）的去噪技术，该方法具有小波阈值的滤波器，以在保留纹理和边缘的同时代替噪声图像​​。其次，使用CLBP技术提取辨别特征。最后，鉴别特征被馈送到分类器中以构建检测模型并评估所提出的框架的性能。使用来自东北大学（NEU）的真实世界钢结构缺陷数据库来评估缺陷检测模型的性能。实验结果表明，所提出的方法RCLBP是噪声稳健，可在类内和阶级变化的不同条件下应用表面缺陷检测，并且具有照明变化。

标准卷积神经网络（CNN）设计很少专注于明确捕获各种功能以增强网络性能的重要性。相反，大多数现有方法遵循增加或调整网络深度和宽度的间接方法，这在许多情况下显着提高了计算成本。受生物视觉系统的启发，我们提出了一种多样化和自适应的卷积网络（DA $ ^ {2} $ - net），它使任何前锋CNN能够明确地捕获不同的功能，并自适应地选择并强调最具信息性的功能有效地提高网络的性能。 DA $ ^ {2} $ - NET会引入可忽略不计的计算开销，它旨在与任何CNN架构轻松集成。我们广泛地评估了基准数据集的DA $ ^ {2} $ - 网上，包括CNN架构的CNN100，SVHN和Imagenet，包括CNN100。实验结果显示DA $ ^ {2} $ - NET提供了具有非常最小的计算开销的显着性能改进。

This paper considers a combination of actuation tendons and measurement strings to achieve accurate shape sensing and direct kinematics of continuum robots. Assuming general string routing, a methodical Lie group formulation for the shape sensing of these robots is presented. The shape kinematics is expressed using arc-length-dependent curvature distributions parameterized by modal functions, and the Magnus expansion for Lie group integration is used to express the shape as a product of exponentials. The tendon and string length kinematic constraints are solved for the modal coefficients and the configuration space and body Jacobian are derived. The noise amplification index for the shape reconstruction problem is defined and used for optimizing the string/tendon routing paths, and a planar simulation study shows the minimal number of strings/tendons needed for accurate shape reconstruction. A torsionally stiff continuum segment is used for experimental evaluation, demonstrating mean (maximal) end-effector absolute position error of less than 2% (5%) of total length. Finally, a simulation study of a torsionally compliant segment demonstrates the approach for general deflections and string routings. We believe that the methods of this paper can benefit the design process, sensing and control of continuum and soft robots.

Light guide plates are essential optical components widely used in a diverse range of applications ranging from medical lighting fixtures to back-lit TV displays. In this work, we introduce a fully-integrated, high-throughput, high-performance deep learning-driven workflow for light guide plate surface visual quality inspection (VQI) tailored for real-world manufacturing environments. To enable automated VQI on the edge computing within the fully-integrated VQI system, a highly compact deep anti-aliased attention condenser neural network (which we name LightDefectNet) tailored specifically for light guide plate surface defect detection in resource-constrained scenarios was created via machine-driven design exploration with computational and "best-practices" constraints as well as L_1 paired classification discrepancy loss. Experiments show that LightDetectNet achieves a detection accuracy of ~98.2% on the LGPSDD benchmark while having just 770K parameters (~33X and ~6.9X lower than ResNet-50 and EfficientNet-B0, respectively) and ~93M FLOPs (~88X and ~8.4X lower than ResNet-50 and EfficientNet-B0, respectively) and ~8.8X faster inference speed than EfficientNet-B0 on an embedded ARM processor. As such, the proposed deep learning-driven workflow, integrated with the aforementioned LightDefectNet neural network, is highly suited for high-throughput, high-performance light plate surface VQI within real-world manufacturing environments.

Prescriptive process monitoring methods seek to improve the performance of a process by selectively triggering interventions at runtime (e.g., offering a discount to a customer) to increase the probability of a desired case outcome (e.g., a customer making a purchase). The backbone of a prescriptive process monitoring method is an intervention policy, which determines for which cases and when an intervention should be executed. Existing methods in this field rely on predictive models to define intervention policies; specifically, they consider policies that trigger an intervention when the estimated probability of a negative outcome exceeds a threshold. However, the probabilities computed by a predictive model may come with a high level of uncertainty (low confidence), leading to unnecessary interventions and, thus, wasted effort. This waste is particularly problematic when the resources available to execute interventions are limited. To tackle this shortcoming, this paper proposes an approach to extend existing prescriptive process monitoring methods with so-called conformal predictions, i.e., predictions with confidence guarantees. An empirical evaluation using real-life public datasets shows that conformal predictions enhance the net gain of prescriptive process monitoring methods under limited resources.

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.

In this paper, we elaborate on the design and discuss the results of a multi-agent simulation that we have developed using the PSI cognitive architecture. We demonstrate that imbuing agents with intrinsic needs for group affiliation, certainty and competence will lead to the emergence of social behavior among agents. This behavior expresses itself in altruism toward in-group agents and adversarial tendencies toward out-group agents. Our simulation also shows how parameterization can have dramatic effects on agent behavior. Introducing an out-group bias, for example, not only made agents behave aggressively toward members of the other group, but it also increased in-group cohesion. Similarly, environmental and situational factors facilitated the emergence of outliers: agents from adversarial groups becoming close friends. Overall, this simulation showcases the power of psychological frameworks, in general, and the PSI paradigm, in particular, to bring about human-like behavioral patterns in an emergent fashion.

我们研究数据近似和优化中的关键工具之一：低分配颜色。正式地，给定有限集系统$（x，\ nathcal s）$，两颜色的$ \ chi的\ emph {vrionpancy}：x \ to \ to \ to \ { - 1,1 \} $定义为$ \ max_ {s \ in \ Mathcal s} | {\ chi（s）} | $，其中$ \ chi（s）= \ sum \ limits_ {x \ in s} \ chi（x）$。我们提出了一种随机算法，对于任何$ d> 0 $和$（x，\ mathcal s）$，带有双重粉碎功能$ \ pi^*（k）= o（k^d）$，返回带有预期的着色差异$ o \ left（{\ sqrt {| x |^{1-1/d} \ log | \ mathcal s |}}}} \ right）$（此绑定是紧密的）时间$ \ tilde o \ left（{{ | \ Mathcal S | \ CDOT | X |^{1/d}+| X |^{2+1/d}}} \ right）$，在$ o \ left的先前最佳时间（| \ Mathcal）改进s | \ cdot | x |^3 \ right）$至少为$ | x |^{2-1/d} $时，当$ | \ | \ Mathcal S | \ geq | x | $。该设置包括许多几何类别，有界双VC维度的家庭等。直接的结果，我们获得了一种改进的算法来构建子分数大小的$ \ varepsilon $ approximations。我们的方法使用原始偶重新升高，通过对随机更新的权重进行了改进的分析，并通过匹配度的匹配数低 - 计算几何形状的基本结构。特别是，我们获得了相同的$ | x |^{2-1/d} $ factor factor factor factor facter intherting the Match of crotsing number $ o \ left的施工时间（{| x |^{1-1/d} } \ right）$，这是自1980年代以来的第一个改进。所提出的算法非常简单，这使得首次有可能具有近乎最佳差异的颜色，并且在高于$ 2 $的尺寸的抽象和几何套装系统中，对于抽象和几何设置系统的近似近似值。

以安全为导向的研究思想和应用的开发需要精细的车辆轨迹数据，这些数据不仅具有很高的精度，而且还捕获了大量关键安全事件。本文介绍了Citysim数据集，该数据集的设计核心目的是促进基于安全的研究和应用。 Citysim的车辆轨迹从在12个不同位置录制的1140分钟的无人机视频中提取。它涵盖了各种道路几何形状，包括高速公路基本段，编织段，高速公路合并/偏离段，信号交叉点，停止对照的交叉点以及没有符号/信号控制的交叉点。通过五步操作生成CitySim轨迹，以确保轨迹精度。此外，数据集提供了车辆旋转的边界框信息，该信息被证明可以改善安全评估。与其他基于视频的轨迹数据集相比，CitySim数据集的严重性更高，包括切入，合并和分歧事件，其严重性更高。此外，CitySim通过提供相关资产（如记录位置的3D基本地图和信号时间）来促进对数字双胞胎应用的研究。这些功能为安全研究和应用程序提供了更全面的条件，例如自动驾驶汽车安全和基于位置的安全分析。该数据集可在https://github.com/ozheng1993/ucf-sst-citysim-dataset上在线获得。