我们考虑了在透明的蜂窝车辆到所有物品（C-V2X）系统中的联合渠道分配和电力分配的问题，其中多个车辆到网络（V2N）上行链路共享与多个车辆到车辆的时频资源（ v2v）排，使连接和自动驾驶汽车的团体可以紧密地一起旅行。由于在车辆环境中使用高用户移动性的性质，依赖全球渠道信息的传统集中优化方法在具有大量用户的C-V2X系统中可能不可行。利用多机构增强学习（RL）方法，我们提出了分布式资源分配（RA）算法来克服这一挑战。具体而言，我们将RA问题建模为多代理系统。仅基于本地渠道信息，每个排领导者充当代理，共同相互交互，因此选择了子频段和功率水平的最佳组合来传输其信号。为此，我们利用双重Q学习算法在同时最大化V2N链接的总和率的目标下共同训练代理，并满足所需延迟限制的每个V2V链接的数据包输送概率。仿真结果表明，与众所周知的详尽搜索算法相比，我们提出的基于RL的算法提供了紧密的性能。

成功的人工智能系统通常需要大量标记的数据来从文档图像中提取信息。在本文中，我们研究了改善人工智能系统在理解文档图像中的性能的问题，尤其是在培训数据受到限制的情况下。我们通过使用加强学习提出一种新颖的填充方法来解决问题。我们的方法将信息提取模型视为策略网络，并使用策略梯度培训来更新模型，以最大程度地提高补充传统跨凝结损失的综合奖励功能。我们使用标签和专家反馈在四个数据集上进行的实验表明，我们的填充机制始终提高最先进的信息提取器的性能，尤其是在小型培训数据制度中。

我们分析了通过从源到目标任务转移学习训练的深度学习模型的新泛化界限。我们的边界利用一个称为多数预测器准确性的数量，可以从数据中有效地计算出来。我们表明我们的理论在实践中很有用，因为这意味着大多数预测指标的准确性可以用作可转移性度量，这一事实也通过我们的实验验证。

合成孔径雷达（SAR）数据中的异常值（异常值）的存在以及统计图像模型中的错误指定可能导致推断不准确。为了避免此类问题，提出了基于强大的估计过程的瑞利回归模型，作为模拟此类数据的更现实的方法。本文旨在获得瑞利回归模型参数估计量与异常值的存在。提出的方法考虑了加权最大似然法，并使用模拟和测量的SAR图像提交了数值实验。使用蒙特卡洛模拟来评估有限信号长度中提出的可靠估计器性能，对离群值的敏感性和分解点。例如，非稳定估计器显示相对偏置值$ 65 $ - 折叠比损坏信号中强大方法提供的结果大。在灵敏度分析和分解点方面，强大的方案在两种措施的平均绝对值中分别降低了约96美元\％$和$ 10 \％$，以同情非稳定估计器。此外，使用两个SAR数据集比较了所提出的强稳定方案的地面类型和异常检测结果与文献中的竞争方法。

自我监督学习（SSL）利用基础数据结构来生成培训深网络的监督信号。这种方法提供了一种实用的解决方案，可用于学习多重免疫荧光大脑图像，其中数据通常比人类专家注释更丰富。基于对比度学习和图像重建的SSL算法表现出令人印象深刻的性能。不幸的是，这些方法是在自然图像而不是生物医学图像上设计和验证的。最近的一些作品已应用SSL来分析细胞图像。然而，这些作品均未研究SSL对多重免疫荧光脑图像的研究。这些作品还没有为采用特定的SSL方法提供明确的理论理由。在这些局限性的激励下，我们的论文介绍了从信息理论观点开发的一种自我监督的双损坏自适应掩盖自动编码器（DAMA）算法。 Dama的目标函数通过最大程度地降低像素级重建和特征级回归中的条件熵来最大化相互信息。此外，Dama还引入了一种新型的自适应掩码采样策略，以最大程度地提高相互信息并有效地学习脑细胞数据上下文信息。我们首次在多重免疫荧光脑图像上提供了SSL算法的广泛比较。我们的结果表明，Dama优于细胞分类和分割任务的其他SSL方法。 Dama还可以在Imagenet-1k上实现竞争精确度。 Dama的源代​​码可在https://github.com/hula-ai/dama上公开获得

In this paper we explore the task of modeling (semi) structured object sequences; in particular we focus our attention on the problem of developing a structure-aware input representation for such sequences. In such sequences, we assume that each structured object is represented by a set of key-value pairs which encode the attributes of the structured object. Given a universe of keys, a sequence of structured objects can then be viewed as an evolution of the values for each key, over time. We encode and construct a sequential representation using the values for a particular key (Temporal Value Modeling - TVM) and then self-attend over the set of key-conditioned value sequences to a create a representation of the structured object sequence (Key Aggregation - KA). We pre-train and fine-tune the two components independently and present an innovative training schedule that interleaves the training of both modules with shared attention heads. We find that this iterative two part-training results in better performance than a unified network with hierarchical encoding as well as over, other methods that use a {\em record-view} representation of the sequence \cite{de2021transformers4rec} or a simple {\em flattened} representation of the sequence. We conduct experiments using real-world data to demonstrate the advantage of interleaving TVM-KA on multiple tasks and detailed ablation studies motivating our modeling choices. We find that our approach performs better than flattening sequence objects and also allows us to operate on significantly larger sequences than existing methods.

Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases. Technological developments to increase the speed of acquisition often results in systems with a narrower spectral bandwidth, and hence a lower axial resolution. Traditionally, image-processing-based techniques have been utilized to reconstruct subsampled OCT data and more recently, deep-learning-based methods have been explored. In this study, we simulate reduced axial scan (A-scan) resolution by Gaussian windowing in the spectral domain and investigate the use of a learning-based approach for image feature reconstruction. In anticipation of the reduced resolution that accompanies wide-field OCT systems, we build upon super-resolution techniques to explore methods to better aid clinicians in their decision-making to improve patient outcomes, by reconstructing lost features using a pixel-to-pixel approach with an altered super-resolution generative adversarial network (SRGAN) architecture.

Modern deep neural networks have achieved superhuman performance in tasks from image classification to game play. Surprisingly, these various complex systems with massive amounts of parameters exhibit the same remarkable structural properties in their last-layer features and classifiers across canonical datasets. This phenomenon is known as "Neural Collapse," and it was discovered empirically by Papyan et al. \cite{Papyan20}. Recent papers have theoretically shown the global solutions to the training network problem under a simplified "unconstrained feature model" exhibiting this phenomenon. We take a step further and prove the Neural Collapse occurrence for deep linear network for the popular mean squared error (MSE) and cross entropy (CE) loss. Furthermore, we extend our research to imbalanced data for MSE loss and present the first geometric analysis for Neural Collapse under this setting.

Real-life tools for decision-making in many critical domains are based on ranking results. With the increasing awareness of algorithmic fairness, recent works have presented measures for fairness in ranking. Many of those definitions consider the representation of different ``protected groups'', in the top-$k$ ranked items, for any reasonable $k$. Given the protected groups, confirming algorithmic fairness is a simple task. However, the groups' definitions may be unknown in advance. In this paper, we study the problem of detecting groups with biased representation in the top-$k$ ranked items, eliminating the need to pre-define protected groups. The number of such groups possible can be exponential, making the problem hard. We propose efficient search algorithms for two different fairness measures: global representation bounds, and proportional representation. Then we propose a method to explain the bias in the representations of groups utilizing the notion of Shapley values. We conclude with an experimental study, showing the scalability of our approach and demonstrating the usefulness of the proposed algorithms.

The previous fine-grained datasets mainly focus on classification and are often captured in a controlled setup, with the camera focusing on the objects. We introduce the first Fine-Grained Vehicle Detection (FGVD) dataset in the wild, captured from a moving camera mounted on a car. It contains 5502 scene images with 210 unique fine-grained labels of multiple vehicle types organized in a three-level hierarchy. While previous classification datasets also include makes for different kinds of cars, the FGVD dataset introduces new class labels for categorizing two-wheelers, autorickshaws, and trucks. The FGVD dataset is challenging as it has vehicles in complex traffic scenarios with intra-class and inter-class variations in types, scale, pose, occlusion, and lighting conditions. The current object detectors like yolov5 and faster RCNN perform poorly on our dataset due to a lack of hierarchical modeling. Along with providing baseline results for existing object detectors on FGVD Dataset, we also present the results of a combination of an existing detector and the recent Hierarchical Residual Network (HRN) classifier for the FGVD task. Finally, we show that FGVD vehicle images are the most challenging to classify among the fine-grained datasets.