与人类沟通对AIS有挑战性，因为它需要对世界的共同理解，复杂的语义（例如，隐喻或类似物），并且在多码模态手势（例如，指向手指，或图中的箭头）。我们在基于图案的基础上的绘画和猜测的语境中调查了这些挑战，这对研究界构成了一种新的挑战。在ICONARY中，猜测者试图通过编写图标来识别抽屉绘制的短语，以及抽屉迭代地修改绘图以帮助猜测响应的猜测。这次来回经常使用规范场景，视觉隐喻或图标组成来表达具有挑战性的词语，使其成为AI中混合语言和视觉/象征性通信的理想测试。我们提出模型进行图标，并在人类球员之间的55,000多场比赛中培训。我们的型号是熟练的玩家，能够在语言模型中雇用世界知识，以便在训练期间与看不见的文字一起玩。精英人类球员优于我们的模型，特别是在绘图任务中，留下了未来研究的重要缺口。我们将数据集，代码和评估设置释放为对社区的挑战http://www.github.com/allenai/conary。

It is well established in neuroscience that color vision plays an essential part in the human visual perception system. Meanwhile, many novel designs for computer vision inspired by human vision have achieved success in a wide range of tasks and applications. Nonetheless, how color differences affect machine vision has not been well explored. Our work tries to bridge this gap between the human color vision aspect of visual recognition and that of the machine. To achieve this, we curate two datasets: CIFAR10-F and CIFAR100-F, which are based on the foreground colors of the popular CIFAR datasets. Together with CIFAR10-B and CIFAR100-B, the existing counterpart datasets with information on the background colors of CIFAR test sets, we assign each image based on its color contrast level per its foreground and background color labels and use this as a proxy to study how color contrast affects machine vision. We first conduct a proof-of-concept study, showing the effect of color difference and validate our datasets. Furthermore, on a broader level, an important characteristic of human vision is its robustness against ambient changes; therefore, drawing inspirations from ophthalmology and the robustness literature, we analogize contrast sensitivity from the human visual aspect to machine vision and complement the current robustness study using corrupted images with our CIFAR-CoCo datasets. In summary, motivated by neuroscience and equipped with the datasets we curate, we devise a new framework in two dimensions to perform extensive analyses on the effect of color contrast and corrupted images: (1) model architecture, (2) model size, to measure the perception ability of machine vision beyond total accuracy. We also explore how task complexity and data augmentation play a role in this setup. Our results call attention to new evaluation approaches for human-like machine perception.

Semantic communication (SemCom) and edge computing are two disruptive solutions to address emerging requirements of huge data communication, bandwidth efficiency and low latency data processing in Metaverse. However, edge computing resources are often provided by computing service providers and thus it is essential to design appealingly incentive mechanisms for the provision of limited resources. Deep learning (DL)- based auction has recently proposed as an incentive mechanism that maximizes the revenue while holding important economic properties, i.e., individual rationality and incentive compatibility. Therefore, in this work, we introduce the design of the DLbased auction for the computing resource allocation in SemComenabled Metaverse. First, we briefly introduce the fundamentals and challenges of Metaverse. Second, we present the preliminaries of SemCom and edge computing. Third, we review various incentive mechanisms for edge computing resource trading. Fourth, we present the design of the DL-based auction for edge resource allocation in SemCom-enabled Metaverse. Simulation results demonstrate that the DL-based auction improves the revenue while nearly satisfying the individual rationality and incentive compatibility constraints.

Edge-assisted vehicle-to-everything (V2X) motion planning is an emerging paradigm to achieve safe and efficient autonomous driving, since it leverages the global position information shared among multiple vehicles. However, due to the imperfect channel state information (CSI), the position information of vehicles may become outdated and inaccurate. Conventional methods ignoring the communication delays could severely jeopardize driving safety. To fill this gap, this paper proposes a robust V2X motion planning policy that adapts between competitive driving under a low communication delay and conservative driving under a high communication delay, and guarantees small communication delays at key waypoints via power control. This is achieved by integrating the vehicle mobility and communication delay models and solving a joint design of motion planning and power control problem via the block coordinate descent framework. Simulation results show that the proposed driving policy achieves the smallest collision ratio compared with other benchmark policies.

最近的研究提出了一系列针对深度任务模型的专业优化算法。通常声称这些多任务优化（MTO）方法产生的解决方案优于仅通过优化任务损失的加权平均值而获得的解决方案。在本文中，我们对各种语言和视觉任务进行大规模实验，以检查这些主张的经验有效性。我们表明，尽管这些算法的设计和计算复杂性增加了，但MTO方法并未产生超出传统优化方法可实现的性能的任何改进。我们强调了替代策略，这些策略始终如一地提高性能概况，并指出可能导致次优效果的常见训练陷阱。最后，我们概述了可靠地评估MTO算法的性能并讨论潜在解决方案的挑战。

可重新配置的智能表面（RIS）可以显着增强TERA-HERTZ大量多输入多输出（MIMO）通信系统的服务覆盖范围。但是，获得有限的飞行员和反馈信号开销的准确高维通道状态信息（CSI）具有挑战性，从而严重降低了常规空间分裂多次访问的性能。为了提高针对CSI缺陷的鲁棒性，本文提出了针对RIS辅助TERA-HERTZ多用户MIMO系统的基于深度学习的（DL）基于速率的多访问（RSMA）方案。具体而言，我们首先提出了基于DL的混合数据模型驱动的RSMA预编码方案，包括RIS的被动预编码以及模拟主动编码和基本站（BS）的RSMA数字活动预码。为了实现RIS的被动预码，我们提出了一个基于变压器的数据驱动的RIS反射网络（RRN）。至于BS的模拟主动编码，我们提出了一个基于匹配器的模拟预编码方案，因为BS和RIS采用了Los-Mimo天线阵列结构。至于BS的RSMA数字活动预码，我们提出了一个低复杂性近似加权的最小均方误差（AWMMSE）数字编码方案。此外，为了更好地编码性能以及较低的计算复杂性，模型驱动的深层展开的主动编码网络（DFAPN）也是通过将所提出的AWMMSE方案与DL相结合的。然后，为了在BS处获得准确的CSI，以实现提高光谱效率的RSMA预编码方案，我们提出了一个CSI采集网络（CAN），具有低飞行员和反馈信号开销，下行链接飞行员的传输，CSI在此处使用CSI的CSI反馈。 （UES）和BS处的CSI重建被建模为基于变压器的端到端神经网络。

集成感应和通信（ISAC）代表范式转移，以前竞争的无线传输是共同设计的，可通过共同使用硬件平台来提高光谱，能源和硬件效率来和谐地运行。但是，由于诸如褪色和堵塞之类的对抗性因素，ISAC无融合可能会遭受高感知不确定性的影响。本文提出了一个多点ISAC（MPISAC）系统，该系统通过利用多雷达数据冗余来融合来自多个ISAC设备的输出，以实现更高的感应性能。此外，我们建议通过功能选择模块有效地探索传感和通信之间的性能权衡，该功能选择模块可适应地确定ISAC设备的工作状态（即传感或通信）。我们方法的症结在于采用融合模型，该模型通过假设检验和最佳投票分析来预测融合精度。仿真结果表明，MPISAC优于各种基准方案，并表明所提出的方法可以有效地跨越ISAC系统中的权衡区域。

准确地估算主要山区盆地中的积雪对于水资源经理来说至关重要，以便做出影响当地和全球经济，野生动植物和公共政策的决策。目前，此估计需要多个配备LIDAR的飞机飞行或原位测量值，两者均昂贵，稀疏和对可访问区域有偏见。在本文中，我们证明了来自多个，公开可用的卫星和天气数据源的空间和时间信息的融合，可以估算关键山区的积雪。我们的多源模型的表现优于单源估计值5.0英寸RMSE，并且优于稀疏的原位测量值的估计值1.2英寸RMSE。

应用于设备的射频指纹〜（RFF）的深度学习（DL）由于其非凡的分类性能而引起了物理层认证的极大关注。传统的DL-RFF技术通过采用最大似然估计〜（MLE）训练，倾向于过度拟合培训数据集中嵌入的通道统计信息。这限制了他们的实际应用，因为收集足够的培训数据来捕获所有可能的无线渠道环境的特征是具有挑战性的。为了应对这一挑战，我们提出了一个DL表示的DL框架学习〜（DRL），该框架首先学会通过对抗学习将输入信号分解为相关的组件和设备 - iRretrelevant组件。然后，它通过在给定的培训数据集中洗牌以训练后续的RFF提取器来综合一组增强信号。所提出的框架中的隐式数据增强在RFF提取器上实施了正则化，以避免在不收集未知通道的其他数据的情况下，可能会过度拟合设备 - IRRELELERVENT的通道统计。实验验证了所提出的方法，称为DR-RFF，就不明复杂的传播环境的普遍性而言，均优于常规方法，例如，即使所有训练数据都在简单的直接线上收集，即使所有训练数据都收集到分散多径褪色通道，即使 - 见面〜（LOS）传播路径。

Realizing human-like perception is a challenge in open driving scenarios due to corner cases and visual occlusions. To gather knowledge of rare and occluded instances, federated learning assisted connected autonomous vehicle (FLCAV) has been proposed, which leverages vehicular networks to establish federated deep neural networks (DNNs) from distributed data captured by vehicles and road sensors. Without the need of data aggregation, FLCAV preserves privacy while reducing communication costs compared with conventional centralized learning. However, it is challenging to determine the network resources and road sensor placements for multi-stage training with multi-modal datasets in multi-variant scenarios. This article presents networking and training frameworks for FLCAV perception. Multi-layer graph resource allocation and vehicle-road contrastive sensor placement are proposed to address the network management and sensor deployment problems, respectively. We also develop CarlaFLCAV, a software platform that implements the above system and methods. Experimental results confirm the superiority of the proposed techniques compared with various benchmarks.