高度动态的移动ad-hoc网络（MANET）仍然是开发和部署强大，高效和可扩展的路由协议的最具挑战性环境之一。在本文中，我们提出了DeepCQ +路由协议，以一种新颖的方式将新兴的多代理深度增强学习（Madrl）技术集成到现有的基于Q学习的路由协议及其变体中，并在各种拓扑结构中实现了持续更高的性能和移动配置。在保持基于Q学习的路由协议的整体协议结构的同时，DeepCQ +通过精心设计的Madrl代理替换静态配置的参数化阈值和手写规则，使得不需要这些参数的配置。广泛的模拟表明，与其基于Q学习的对应物相比，DeptCQ +产生的端到端吞吐量显着增加了端到端延迟（跳数）的明显劣化。在定性方面，也许更重要的是，Deepcq +在许多情况下维持了非常相似的性能提升，即在网络尺寸，移动条件和交通动态方面没有接受过培训。据我们所知，这是Madrl框架的第一次成功应用MANET路由问题，即使在训练有素的场景范围之外的环境中，即使在训练范围之外的环境中也能够高度的可扩展性和鲁棒性。这意味着我们的基于Marl的DeepCQ +设计解决方案显着提高了基于Q学习的CQ +基线方法的性能，以进行比较，并提高其实用性和解释性，因为现实世界的MANET环境可能会在训练范围的MANET场景之外变化。讨论了进一步提高性能和可扩展性的增益的额外技术。

Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have in turn led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgency to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. In this paper, we present our work on integrating multiple data sources in SmokeyNet, a deep learning model using spatio-temporal information to detect smoke from wildland fires. Camera image data is integrated with weather sensor measurements and processed by SmokeyNet to create a multimodal wildland fire smoke detection system. We present our results comparing performance in terms of both accuracy and time-to-detection for multimodal data vs. a single data source. With a time-to-detection of only a few minutes, SmokeyNet can serve as an automated early notification system, providing a useful tool in the fight against destructive wildfires.

Thanks to the development of 2D keypoint detectors, monocular 3D human pose estimation (HPE) via 2D-to-3D uplifting approaches have achieved remarkable improvements. Still, monocular 3D HPE is a challenging problem due to the inherent depth ambiguities and occlusions. To handle this problem, many previous works exploit temporal information to mitigate such difficulties. However, there are many real-world applications where frame sequences are not accessible. This paper focuses on reconstructing a 3D pose from a single 2D keypoint detection. Rather than exploiting temporal information, we alleviate the depth ambiguity by generating multiple 3D pose candidates which can be mapped to an identical 2D keypoint. We build a novel diffusion-based framework to effectively sample diverse 3D poses from an off-the-shelf 2D detector. By considering the correlation between human joints by replacing the conventional denoising U-Net with graph convolutional network, our approach accomplishes further performance improvements. We evaluate our method on the widely adopted Human3.6M and HumanEva-I datasets. Comprehensive experiments are conducted to prove the efficacy of the proposed method, and they confirm that our model outperforms state-of-the-art multi-hypothesis 3D HPE methods.

While witnessing the noisy intermediate-scale quantum (NISQ) era and beyond, quantum federated learning (QFL) has recently become an emerging field of study. In QFL, each quantum computer or device locally trains its quantum neural network (QNN) with trainable gates, and communicates only these gate parameters over classical channels, without costly quantum communications. Towards enabling QFL under various channel conditions, in this article we develop a depth-controllable architecture of entangled slimmable quantum neural networks (eSQNNs), and propose an entangled slimmable QFL (eSQFL) that communicates the superposition-coded parameters of eS-QNNs. Compared to the existing depth-fixed QNNs, training the depth-controllable eSQNN architecture is more challenging due to high entanglement entropy and inter-depth interference, which are mitigated by introducing entanglement controlled universal (CU) gates and an inplace fidelity distillation (IPFD) regularizer penalizing inter-depth quantum state differences, respectively. Furthermore, we optimize the superposition coding power allocation by deriving and minimizing the convergence bound of eSQFL. In an image classification task, extensive simulations corroborate the effectiveness of eSQFL in terms of prediction accuracy, fidelity, and entropy compared to Vanilla QFL as well as under different channel conditions and various data distributions.

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.

本文介绍了一个分散的多代理轨迹计划（MATP）算法，该算法保证在有限的沟通范围内在障碍物丰富的环境中生成安全，无僵硬的轨迹。所提出的算法利用基于网格的多代理路径计划（MAPP）算法进行僵局，我们引入了子目标优化方法，使代理会收敛到从MAPP生成的无僵局生成的路点。此外，提出的算法通过采用线性安全走廊（LSC）来确保优化问题和避免碰撞的可行性。我们验证所提出的算法不会在随机森林和密集的迷宫中造成僵局，而不论沟通范围如何，并且在飞行时间和距离方面的表现都优于我们以前的工作。我们通过使用十个四肢的硬件演示来验证提出的算法。

最近的预训练的语言模型（PLM）通过学习语言特征和上下文化的句子表示，在许多自然语言处理任务上取得了巨大成功。由于未清楚地识别出在PLM的堆叠层中捕获的属性，因此通常首选嵌入最后一层的直接方法，而不是从PLM中得出句子表示。本文介绍了基于注意力的合并策略，该策略使该模型能够保留每一层中捕获的图层信号，并学习下游任务的消化语言特征。对比度学习目标可以使层面上的注意力汇集到无监督和监督的举止。它导致预先训练嵌入的各向异性空间并更均匀。我们评估我们的模型关于标准语义文本相似性（STS）和语义搜索任务。结果，我们的方法改善了基础对比度的BERT_BASE和变体的性能。

符号检测是现代通信系统中的一个基本且具有挑战性的问题，例如多源多输入多输出（MIMO）设置。迭代软干扰取消（SIC）是该任务的最新方法，最近动机的数据驱动的神经网络模型，例如深度，可以处理未知的非线性通道。但是，这些神经网络模型需要在应用之前对网络进行全面的时间量培训，因此在实践中不容易适合高度动态的渠道。我们介绍了一个在线培训框架，该框架可以迅速适应频道中的任何更改。我们提出的框架将最近的深层发展方法与新兴的生成对抗网络（GAN）统一，以捕获频道中的任何变化，并快速调整网络以维持模型的最佳性能。我们证明，我们的框架在高度动态的通道上显着优于最近的神经网络模型，甚至超过了我们实验中静态通道上的神经网络模型。

具有提高可传递性的对抗性攻击 - 在已知模型上精心制作的对抗性示例的能力也欺骗了未知模型 - 由于其实用性，最近受到了很多关注。然而，现有的可转移攻击以确定性的方式制作扰动，并且常常无法完全探索损失表面，从而陷入了贫穷的当地最佳最佳效果，并且遭受了低传递性的折磨。为了解决这个问题，我们提出了细心多样性攻击（ADA），该攻击以随机方式破坏了不同的显着特征以提高可转移性。首先，我们将图像注意力扰动到破坏不同模型共享的通用特征。然后，为了有效避免局部优势差，我们以随机方式破坏了这些功能，并更加详尽地探索可转移扰动的搜索空间。更具体地说，我们使用发电机来产生对抗性扰动，每个扰动都根据输入潜在代码而以不同的方式打扰。广泛的实验评估证明了我们方法的有效性，优于最先进方法的可转移性。代码可在https://github.com/wkim97/ada上找到。

已知视觉问题答案（VQA）的任务受到VQA模型的问题的困扰，从而利用数据集中的偏见来做出最终预测。已经提出了许多先前基于合奏的偏数方法，其中有目的地训练了一个额外的模型以帮助训练强大的目标模型。但是，这些方法从训练数据的标签统计数据或直接从单局分支中计算出模型的偏差。相反，在这项工作中，为了更好地了解目标VQA模型的偏见，我们提出了一种生成方法来训练偏差模型\ emph {直接来自目标模型}，称为GenB。特别是，GENB采用生成网络来通过对抗目标和知识蒸馏的结合来学习偏见。然后，我们将目标模型以GENB作为偏置模型为单位，并通过广泛的实验显示了我们方法对包括VQA CP2，VQA-CP1，VQA-CP1，GQA-OOD和VQA-CE在内的各种VQA偏置数据集的影响。