Although augmentations (e.g., perturbation of graph edges, image crops) boost the efficiency of Contrastive Learning (CL), feature level augmentation is another plausible, complementary yet not well researched strategy. Thus, we present a novel spectral feature argumentation for contrastive learning on graphs (and images). To this end, for each data view, we estimate a low-rank approximation per feature map and subtract that approximation from the map to obtain its complement. This is achieved by the proposed herein incomplete power iteration, a non-standard power iteration regime which enjoys two valuable byproducts (under mere one or two iterations): (i) it partially balances spectrum of the feature map, and (ii) it injects the noise into rebalanced singular values of the feature map (spectral augmentation). For two views, we align these rebalanced feature maps as such an improved alignment step can focus more on less dominant singular values of matrices of both views, whereas the spectral augmentation does not affect the spectral angle alignment (singular vectors are not perturbed). We derive the analytical form for: (i) the incomplete power iteration to capture its spectrum-balancing effect, and (ii) the variance of singular values augmented implicitly by the noise. We also show that the spectral augmentation improves the generalization bound. Experiments on graph/image datasets show that our spectral feature augmentation outperforms baselines, and is complementary with other augmentation strategies and compatible with various contrastive losses.

概念相关性估计（CRE）任务是确定两个给定的概念是否相关。尽管可以轻松适应此任务的语义文本相似性（STS）任务的现有方法，但CRE任务具有一些独特的属性，可以利用这些属性来扩大数据集以解决其数据稀缺问题。在本文中，我们构造了一个名为CycreteGraph（概念相关性估计图）的图，以利用CRE属性。对于从混凝土图中采样的新概念对，我们添加了一个额外的步骤，以基于简单但有效的质量阈值来滤除低质量的新概念对。我们将ConcreteGraph数据扩展应用于三个基于变压器的模型以显示其功效。详细的消融研究用于质量阈值进一步表明，即使有限的高质量数据也比大量未替代数据更有益。本文是第一个在数据集上使用的文章，而建议的具体图可以提高变压器的准确性超过2％。在CNSE和CNSS数据集上，所有三个变压器都借助ConcreteGraph，均可超越当前最先进的方法，概念交互图（CIG）。

我们提出了一种新型的动态约束不确定性加权损失，以实验处理平衡多个任务对ICML EXVO 2022挑战的贡献的问题。多任务旨在共同认识到声乐爆发中表达的情绪和人口特征。我们的策略结合了不确定性重量和平均动态重量的优势，通过用约束术语扩展权重以使学习过程更具解释。我们使用轻巧的多EXIT CNN体系结构来实施我们提出的损失方法。实验性H-均值得分（0.394）显示出比基线H均值得分的显着改善（0.335）。

图形神经网络（GNN）由于从图形结构数据中学习表示能力而引起了很多关注。尽管GNN在许多域中成功地应用了，但GNN的优化程度较低，并且在节点分类的性能很大程度上受到了长尾节点学位分布的影响。本文着重于通过归一化提高GNN的性能。详细说明，通过研究图中的节点度的长尾巴分布，我们提出了一种新颖的GNN归一化方法，该方法称为RESNORM（\ textbf {res}将长尾巴分布纳入正常分布，通过\ textbf {norm} alization）。 RESNOR的$比例$操作重塑节点标准偏差（NSTD）分布，以提高尾部节点的准确性（\ textit {i}。\ textit {e}。，低度节点）。我们提供了理论解释和经验证据，以理解上述$ scale $的机制。除了长期的分销问题外，过度光滑也是困扰社区的基本问题。为此，我们分析了标准偏移的行为，并证明了标准移位是重量矩阵上的预处理，从而增加了过度平滑的风险。考虑到过度光滑的问题，我们为Resnorm设计了一个$ Shift $操作，以低成本的方式模拟了特定于学位的参数策略。广泛的实验验证了重新分类对几个节点分类基准数据集的有效性。

图对比度学习（GCL）改善了图表的学习，从而导致SOTA在各种下游任务上。图扩大步骤是GCL的重要但几乎没有研究的步骤。在本文中，我们表明，通过图表增强获得的节点嵌入是高度偏差的，在某种程度上限制了从学习下游任务的学习区分特征的对比模型。隐藏功能（功能增强）。受到所谓矩阵草图的启发，我们提出了Costa，这是GCL的一种新颖的协变功能空间增强框架，该框架通过维护原始功能的``好草图''来生成增强功能。为了强调Costa的特征增强功能的优势，我们研究了一个保存记忆和计算的单视图设置（除了多视图ONE）。我们表明，与基于图的模型相比，带有Costa的功能增强功能可比较/更好。

多代理协作感知可以通过使代理商能够通过交流相互共享互补信息来显着升级感知表现。它不可避免地会导致感知表现与沟通带宽之间的基本权衡。为了解决这个瓶颈问题，我们提出了一个空间置信度图，该图反映了感知信息的空间异质性。它使代理只能在空间上共享稀疏而感知的关键信息，从而有助于沟通。基于这张新型的空间置信度图，我们提出了2Comm，即沟通有效的协作感知框架。其中2Comm具有两个不同的优势：i）它考虑了实用的压缩，并使用较少的沟通来通过专注于感知至关重要的领域来实现更高的感知表现； ii）它可以通过动态调整涉及通信的空间区域来处理不同的通信带宽。要评估2comm的位置，我们考虑了在现实世界和模拟方案中使用两种模式（相机/激光镜头）和两种代理类型（CAR/无人机）的3D对象检测：OPV2V，v2x-sim，dair-v2x和我们的原始的Coperception-uavs。其中2comm始终优于先前的方法；例如，它实现了超过$ 100,000 \ times $较低的通信量，并且在OPV2V上仍然优于脱颖而出和v2x-vit。我们的代码可在https://github.com/mediabrain-sjtu/where2comm上找到。

协作感知最近显示出具有对单一主体感知的感知能力的巨大潜力。现有的协作感知方法通常考虑理想的交流环境。但是，实际上，通信系统不可避免地遭受了延迟问题，从而导致潜在的性能降解和安全关键应用程序（例如自动驾驶）的高风险。从机器学习的角度来看，为了减轻不可避免的沟通潜伏期造成的效果，我们提出了第一个延迟感知的协作感知系统，该系统积极采用从多个代理到同一时间戳的异步感知特征，从而促进了协作的稳健性和有效性。为了实现此类特征级别的同步，我们提出了一个新型的延迟补偿模块，称为Syncnet，该模块利用特征注意的共生估计和时间调制技术。实验结果表明，在最新的协作感知数据集V2X-SIM上，我们的方法优于最先进的协作感知方法15.6％。

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which makes it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.

Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applications, these covariates are typically collected at different frequencies over a long time horizon. In this paper, we propose a deep spectral Q-learning algorithm, which integrates principal component analysis (PCA) with deep Q-learning to handle the mixed frequency data. In theory, we prove that the mean return under the estimated optimal policy converges to that under the optimal one and establish its rate of convergence. The usefulness of our proposal is further illustrated via simulations and an application to a diabetes dataset.