The emergence of pre-trained language models (PLMs) has shown great success in many Natural Language Processing (NLP) tasks including text classification. Due to the minimal to no feature engineering required when using these models, PLMs are becoming the de facto choice for any NLP task. However, for domain-specific corpora (e.g., financial, legal, and industrial), fine-tuning a pre-trained model for a specific task has shown to provide a performance improvement. In this paper, we compare the performance of four different PLMs on three public domain-free datasets and a real-world dataset containing domain-specific words, against a simple SVM linear classifier with TFIDF vectorized text. The experimental results on the four datasets show that using PLMs, even fine-tuned, do not provide significant gain over the linear SVM classifier. Hence, we recommend that for text classification tasks, traditional SVM along with careful feature engineering can pro-vide a cheaper and superior performance than PLMs.

Development of guidance, navigation and control frameworks/algorithms for swarms attracted significant attention in recent years. That being said, algorithms for planning swarm allocations/trajectories for engaging with enemy swarms is largely an understudied problem. Although small-scale scenarios can be addressed with tools from differential game theory, existing approaches fail to scale for large-scale multi-agent pursuit evasion (PE) scenarios. In this work, we propose a reinforcement learning (RL) based framework to decompose to large-scale swarm engagement problems into a number of independent multi-agent pursuit-evasion games. We simulate a variety of multi-agent PE scenarios, where finite time capture is guaranteed under certain conditions. The calculated PE statistics are provided as a reward signal to the high level allocation layer, which uses an RL algorithm to allocate controlled swarm units to eliminate enemy swarm units with maximum efficiency. We verify our approach in large-scale swarm-to-swarm engagement simulations.

在未知环境中存在动态障碍的情况下，避免碰撞是无人系统最关键的挑战之一。在本文中，我们提出了一种方法，该方法可以鉴定出椭圆形的障碍，以估计线性和角度障碍速度。我们提出的方法是基于任何对象的概念，可以由椭圆形表示。为了实现这一目标，我们提出了一种基于高斯混合模型，kyachiyan算法和改进算法的变异贝叶斯估计的方法。与现有的基于优化的方法不同，我们提出的方法不需要了解集群数量，并且可以实时操作。此外，我们定义一个基于椭圆形的特征向量以匹配两个及时的接近点帧。我们的方法可以应用于具有静态和动态障碍的任何环境，包括具有旋转障碍的环境。我们将算法与其他聚类方法进行比较，并表明当与轨迹计划器结合时，整体系统可以在存在动态障碍物的情况下有效地穿越未知环境。

多智能体增强学习（Marl）问题通常需要代理商之间的合作，以解决任务。集中化和权力下放是用于玛尔合作的两种方法。虽然由于部分可观测性和非间手性，但易于分散的方法易于收敛到次优解决方案，但涉及集中化的方法遭受可扩展性限制和懒惰的代理问题。集中式培训分散执行范式带出了这两种方法中最好的;然而，集中培训仍然具有可扩展性的上限，而不仅适用于获得的协调性能，而且还具有模型大小和培训时间。在这项工作中，我们采用分散执行范例的集中培训，并调查跨越可变数量的训练型模型的泛化和转移能力。通过特定的MARL问题中的可变数量的代理进行评估，然后对每个训练配置进行可变数量的代理进行贪婪评估来评估此容量。因此，我们分析了培训与评估的代理计数的每个组合的评估性能。我们对捕食者猎物和交通连接环境进行实验评估，并证明可以通过较少的药剂训练获得类似或更高的评估性能。我们得出结论，进行培训的最佳代理商可能与目标代理的数量不同，并且争论在大量代理中的转移可以是比在训练期间直接越来越多的药剂缩放更有效的解决方案。

One of the most efficient methods for model compression is hint distillation, where the student model is injected with information (hints) from several different layers of the teacher model. Although the selection of hint points can drastically alter the compression performance, conventional distillation approaches overlook this fact and use the same hint points as in the early studies. Therefore, we propose a clustering based hint selection methodology, where the layers of teacher model are clustered with respect to several metrics and the cluster centers are used as the hint points. Our method is applicable for any student network, once it is applied on a chosen teacher network. The proposed approach is validated in CIFAR-100 and ImageNet datasets, using various teacher-student pairs and numerous hint distillation methods. Our results show that hint points selected by our algorithm results in superior compression performance compared to state-of-the-art knowledge distillation algorithms on the same student models and datasets.