We propose a decentralized control algorithm for a minimalistic robotic swarm with limited capabilities such that the desired global behavior emerges. We consider the problem of searching for and encapsulating various targets present in the environment while avoiding collisions with both static and dynamic obstacles. The novelty of this work is the guaranteed generation of desired complex swarm behavior with constrained individual robots which have no memory, no localization, and no knowledge of the exact relative locations of their neighbors. Moreover, we analyze how the emergent behavior changes with different parameters of the task, noise in the sensor reading, and asynchronous execution.

创建复杂机器人行为的一种典型方法是组成原子控制器或技能，以使所产生的行为满足高级任务；但是，当无法使用一组技能完成任务时，很难知道如何修改技能以使任务成为可能。我们提出了一种将符号维修与身体可行性检查和实现相结合的方法，以自动修改现有技能，以便机器人可以执行以前不可行的任务。我们在线性时间逻辑（LTL）公式中编码机器人技能，以捕获安全性任务的安全限制和目标。此外，我们的编码捕获了完整的技能执行，而不是先前的工作，而在执行技能之前和之后只有世界状态才被考虑。我们的维修算法提出了符号修改，然后尝试通过修改受符号修复的LTL约束的原始技能来物理实施建议。如果技能不可能，我们会自动为符号维修提供其他约束。我们用巴克斯特和一个清晰的jack狼展示了我们的方法。

Models trained from real-world data tend to imitate and amplify social biases. Although there are many methods suggested to mitigate biases, they require a preliminary information on the types of biases that should be mitigated (e.g., gender or racial bias) and the social groups associated with each data sample. In this work, we propose a debiasing method that operates without any prior knowledge of the demographics in the dataset, detecting biased examples based on an auxiliary model that predicts the main model's success and down-weights them during the training process. Results on racial and gender bias demonstrate that it is possible to mitigate social biases without having to use a costly demographic annotation process.

Estimation of the T2 distribution from multi-echo T2-Weighted MRI (T2W) data can provide insight into the microscopic content of tissue using macroscopic imaging. This information can be used as a biomarker for several pathologies, such as tumor characterization, osteoarthritis, and neurodegenerative diseases. Recently, deep neural network (DNN) based methods were proposed for T2 distribution estimation from MRI data. However, these methods are highly sensitive to distribution shifts such as variations in the echo-times (TE) used during acquisition. Therefore, DNN-based methods cannot be utilized in large-scale multi-institutional trials with heterogeneous acquisition protocols. We present P2T2, a new physically-primed DNN approach for T2 distribution estimation that is robust to different acquisition parameters while maintaining state-of-the-art estimation accuracy. Our P2T2 model encodes the forward model of the signal decay by taking as input the TE acquisition array, in addition to the MRI signal, and provides an estimate of the corresponding T2 distribution as its output. Our P2T2 model has improved the robustness against distribution shifts in the acquisition process by more than 50% compared to the previously proposed DNN model. When tested without any distribution shifts, our model achieved about the same accuracy. Finally, when applied to real human MRI data, our P2T2 model produced the most detailed Myelin-Water fraction maps compared to both the MIML model and classical approaches. Our proposed physically-primed approach improved the generalization capacity of DNN models for T2 distribution estimation and their robustness against distribution shifts compared to previous approaches without compromising the accuracy.

Dynamic neural networks (DyNNs) have become viable techniques to enable intelligence on resource-constrained edge devices while maintaining computational efficiency. In many cases, the implementation of DyNNs can be sub-optimal due to its underlying backbone architecture being developed at the design stage independent of both: (i) the dynamic computing features, e.g. early exiting, and (ii) the resource efficiency features of the underlying hardware, e.g., dynamic voltage and frequency scaling (DVFS). Addressing this, we present HADAS, a novel Hardware-Aware Dynamic Neural Architecture Search framework that realizes DyNN architectures whose backbone, early exiting features, and DVFS settings have been jointly optimized to maximize performance and resource efficiency. Our experiments using the CIFAR-100 dataset and a diverse set of edge computing platforms have seen HADAS dynamic models achieve up to 57% energy efficiency gains compared to the conventional dynamic ones while maintaining the desired level of accuracy scores. Our code is available at https://github.com/HalimaBouzidi/HADAS

由于长期没有事件，处理动态数据时，陈旧问题是一个众所周知的问题。由于仅当节点参与事件时才更新节点的内存，因此其内存变为陈旧。通常，它是指缺乏社会帐户的时间停用等事件。为了克服内存的陈旧问题问题，除节点内存外，还来自节点邻居内存的信息。受此启发的启发，我们设计了一个更新的嵌入模块，该模块除节点邻居外还插入最相似的节点。我们的方法获得了与TGN相似的结果，并略有改进。这可能表明在微调我们的超参数后，尤其是时间阈值并使用可学习的相似度度量后，可能会有所改善。

来自磁共振成像（MRI）数据的自动脑肿瘤分割在评估治疗和个性化治疗分层的肿瘤反应中起重要作用.Manual分割是乏味的，主观的脑肿瘤细分算法有可能提供目标并且快速肿瘤分割。但是，这种算法的培训需要大量数据集，这些数据集并不总是可用的。数据增强技术可以减少对大型数据集的需求。然而，当前方法主要是参数，并且可能导致次优的性能。我们引入了两个非参数化的脑肿瘤分割的数据增强方法：混合结构正则化（MSR）和Shuffle像素噪声（SPN）.we评估了MSR和SPN增强对大脑肿瘤分割（BRATS）2018挑战数据集的附加值与编码器 - 解码器NNU-NNU-NNU-NET架构作为分割算法。从MSR和SPN改善NNU-NET分段与参数高斯噪声增强相比的准确性。当分别将MSR与肿瘤核心和全肿瘤实验的非参数增强分别增加了80％至82％和p值= 0.0022,00028。所提出的MSR和SPN增强有可能在其他任务中提高神经网络性能。