The electrification of shared mobility has become popular across the globe. Many cities have their new shared e-mobility systems deployed, with continuously expanding coverage from central areas to the city edges. A key challenge in the operation of these systems is fleet rebalancing, i.e., how EVs should be repositioned to better satisfy future demand. This is particularly challenging in the context of expanding systems, because i) the range of the EVs is limited while charging time is typically long, which constrain the viable rebalancing operations; and ii) the EV stations in the system are dynamically changing, i.e., the legitimate targets for rebalancing operations can vary over time. We tackle these challenges by first investigating rich sets of data collected from a real-world shared e-mobility system for one year, analyzing the operation model, usage patterns and expansion dynamics of this new mobility mode. With the learned knowledge we design a high-fidelity simulator, which is able to abstract key operation details of EV sharing at fine granularity. Then we model the rebalancing task for shared e-mobility systems under continuous expansion as a Multi-Agent Reinforcement Learning (MARL) problem, which directly takes the range and charging properties of the EVs into account. We further propose a novel policy optimization approach with action cascading, which is able to cope with the expansion dynamics and solve the formulated MARL. We evaluate the proposed approach extensively, and experimental results show that our approach outperforms the state-of-the-art, offering significant performance gain in both satisfied demand and net revenue.

提出了一种称为Trust-Region Boosting（TRBOOST）的通用梯度提升机，用于执行监督的机器学习任务。现有的梯度提升机（GBM）已经在许多问题上取得了最先进的结果。但是，在性能和一般性之间保持平衡存在一些困难。一阶算法适用于比二阶算法更多的一般损失函数。虽然表演通常不如后者那么好。TRBOOST基于信任区域算法将GBMS概括为适合任意损失功能，同时保持良好的性能作为二阶算法。进行了几项数值实验，以确认TRBOOST可以获得竞争成果，同时为收敛提供额外的好处。

在本文中，我们提出了与IEEE计算机协会在CVPR 2022上同时与IEEE计算机协会研讨会同时举行的多手术检测挑战。我们的多手术检测挑战旨在检测自动图像操作，包括但不限于图像编辑，图像合成，图像合成，图像，图像，图像，图像合成，图像，图像编辑一代，图像Photoshop等。我们的挑战吸引了来自世界各地的674支团队，约有2000个有效的结果提交数量。我们邀请了前十支球队为挑战提供解决方案，其中三支球队在大结局中获得了奖项。在本文中，我们介绍了前三名团队的解决方案，以增强图像伪造检测领域的研究工作。

表问题回答（TQA）是一项重要但不足的任务。大多数现有的QA数据集都采用非结构化文本格式，只有很少的数据集使用表作为上下文。据我们所知，在生物医学领域中，没有任何TQA数据集存在经常用于提供信息的生物医学领域。在本文中，我们首先使用22个模板和关于鉴别诊断的生物医学教科书中的上下文来回答数据集Biotabqa的桌子问题。 Biotabqa不仅可以用来教授模型如何从表中回答问题，还可以评估模型如何推广到看不见的问题，这是生物医学应用的重要情况。为了实现概括评估，我们将模板分为17个培训和5个跨任务评估。然后，我们使用BioTABQA上的单个和多任务学习开发两个基准。此外，我们探索教学学习，这是一种显示出令人印象深刻的概括性能的技术。实验结果表明，我们的指导调整模型在各种评估设置中平均比单一和多任务基准平均比单一和多任务基准，更重要的是，更重要的是，指令调整的模型在交叉任务上的基准比5％ 。

共享的电子移动服务已被广泛测试和在全球城市中驾驶，并且已经编织成现代城市规划的结构。本文研究了这些系统中的实用而重要的问题：如何在空间和时间跨空间和时间部署和管理其基础架构，以便在可持续的盈利能力的同时对用户无处不在。然而，在现实世界的系统中，评估不同部署策略的性能，然后找到最佳计划是非常昂贵的，因为它通常是不可行的，可以对试用和错误进行许多迭代。我们通过设计高保真仿真环境来解决这一目标，该环境摘要在细粒度下共享电子移动系统的关键操作细节，并使用从现实世界中收集的数据进行校准。这使我们能够尝试任意部署计划来学习在实际在实际系统中实施任何内容之前的特定上下文。特别是，我们提出了一种新的多代理神经检索方法，其中我们设计了一个分层控制器以产生暂定部署计划。然后使用多模拟范例，即并行评估的生成的部署计划进行测试，其中结果用于用深增强学习训练控制器。通过这种闭环，控制器可以被引导以在将来的迭代中产生更好的部署计划的概率。在我们的仿真环境中，已经广泛评估了所提出的方法，实验结果表明它优于基于基于基于基于的基于基于基于的启发式的服务覆盖范围和净收入的方法。

在开放的书本回答（OBQA）任务中，从分散注意力的信息中选择相关段落和句子对于推理问题的答案至关重要。 HOTPOTQA数据集旨在教授和评估系统以进行段落排名和句子选择。许多现有框架使用单独的模型分别选择相关段落和句子。这样的系统不仅在模型的参数方面具有很高的复杂性，而且还无法将训练这两个任务训练在一起的优势，因为一项任务可能对另一个任务有益。在这项工作中，我们提出了一个简单而有效的框架，可以通过共同排名段落和选择句子来解决这些限制。此外，我们提出一致性和相似性约束，以促进段落排名和句子选择之间的相关性和相互作用。实验表明，我们的框架可以与以前的系统实现竞争性结果，并就相关句子的确切匹配而优于28 \％在HOTPOTQA数据集上。

我们提出了一种深层签名/对数符号FBSDE算法，以求解具有状态和路径依赖性特征的前回向随机微分方程（FBSDE）。通过将深度签名/对数签名转换纳入复发性神经网络（RNN）模型，我们的算法缩短了训练时间，提高了准确性，并扩展了与现有文献中方法相比的时间范围。此外，我们的算法可以应用于涉及高频数据，模型歧义和随机游戏等广泛的应用程序和路径依赖的选项定价，这些定价与抛物线偏差方程（PDES）以及路径依赖性依赖性链接有关PDE（PPDE）。最后，我们还得出了深度签名/对数签名FBSDE算法的收敛分析。

Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole object, yet ignore the co-occurrence confounder of the object and context (e.g., fish and water), which makes the model inspection hard to distinguish object boundaries. Besides, the use of CAM also brings a dilemma problem that the classification and localization always suffer from a performance gap and can not reach their highest accuracy simultaneously. In this paper, we propose a casual knowledge distillation method, dubbed KD-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention (CI), which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the de-biased object feature, we additionally propose a multi-teacher causal distillation framework to balance the absorption of classification knowledge and localization knowledge during model training. Extensive experiments on several benchmarks demonstrate the effectiveness of KD-CI-CAM in learning clear object boundaries from confounding contexts and addressing the dilemma problem between classification and localization performance.

In this paper, a semantic communication framework for image transmission is developed. In the investigated framework, a set of servers cooperatively transmit images to a set of users utilizing semantic communication techniques. To evaluate the performance of studied semantic communication system, a multimodal metric is proposed to measure the correlation between the extracted semantic information and the original image. To meet the ISS requirement of each user, each server must jointly determine the semantic information to be transmitted and the resource blocks (RBs) used for semantic information transmission. We formulate this problem as an optimization problem aiming to minimize each server's transmission latency while reaching the ISS requirement. To solve this problem, a value decomposition based entropy-maximized multi-agent reinforcement learning (RL) is proposed, which enables servers to coordinate for training and execute RB allocation in a distributed manner to approach to a globally optimal performance with less training iterations. Compared to traditional multi-agent RL, the proposed RL improves the valuable action exploration of servers and the probability of finding a globally optimal RB allocation policy based on local observation. Simulation results show that the proposed algorithm can reduce the transmission delay by up to 16.1% compared to traditional multi-agent RL.

New architecture GPUs like A100 are now equipped with multi-instance GPU (MIG) technology, which allows the GPU to be partitioned into multiple small, isolated instances. This technology provides more flexibility for users to support both deep learning training and inference workloads, but efficiently utilizing it can still be challenging. The vision of this paper is to provide a more comprehensive and practical benchmark study for MIG in order to eliminate the need for tedious manual benchmarking and tuning efforts. To achieve this vision, the paper presents MIGPerf, an open-source tool that streamlines the benchmark study for MIG. Using MIGPerf, the authors conduct a series of experiments, including deep learning training and inference characterization on MIG, GPU sharing characterization, and framework compatibility with MIG. The results of these experiments provide new insights and guidance for users to effectively employ MIG, and lay the foundation for further research on the orchestration of hybrid training and inference workloads on MIGs. The code and results are released on https://github.com/MLSysOps/MIGProfiler. This work is still in progress and more results will be published soon.