As the number of distributed services (or microservices) of cloud-native applications grows, resource management becomes a challenging task. These applications tend to be user-facing and latency-sensitive, and our goal is to continuously minimize the amount of CPU resources allocated while still satisfying the application latency SLO. Although previous efforts have proposed simple heuristics and sophisticated ML-based techniques, we believe that a practical resource manager should accurately scale CPU resources for diverse applications, with minimum human efforts and operation overheads. To this end, we ask: can we systematically break resource management down to subproblems solvable by practical policies? Based on the notion of CPU-throttle-based performance target, we decouple the mechanisms of SLO feedback and resource control, and implement a two-level framework -- Autothrottle. It combines a lightweight learned controller at the global level, and agile per-microservice controllers at the local level. We evaluate Autothrottle on three microservice applications, with both short-term and 21-day production workload traces. Empirical results show Autothrottle's superior CPU core savings up to 26.21% over the best-performing baselines across applications, while maintaining the latency SLO.

文本摘要模型通常经过培训，以产生满足人类质量要求的摘要。但是，现有的摘要文本评估指标只是摘要质量的粗略代理，与人类评分和抑制摘要多样性的相关性低。为了解决这些问题，我们提出了SummScore，这是基于CrossCoder的摘要质量评估的综合指标。首先，通过采用原始的苏格拉外测量模式并比较原始文本的语义，SummScore摆脱了抑制摘要多样性的抑制。借助文本匹配的预训练交叉编码器，SummScore可以有效地捕获摘要语义之间的细微差异。其次，为了提高全面性和解释性，SummScore由四个细粒子模型组成，它们分别测量连贯性，一致性，流利性和相关性。我们使用半监督的多轮训练来提高模型在极有限的注释数据上的性能。广泛的实验表明，与人类评分相关的上述四个维度中，SummScore在上述四个维度中的现有评估指标显着优于现有的评估指标。我们还为16个主流摘要模型提供了SummScore的质量评估结果，以供以后研究。

文档检索使用户能够准确，快速找到所需的文档。为了满足检索效率的要求，普遍的深神经方法采用了基于表示的匹配范式，该范式通过离线预先存储文档表示节省了在线匹配时间。但是，上述范式会消耗庞大的本地存储空间，尤其是将文档存储为单词元素表示时。为了解决这个问题，我们提出了TGTR，这是一种基于主题的文本表示模型，用于文档检索。遵循基于表示的匹配范式，TGTR将文档表示脱机存储以确保检索效率，而通过使用新颖的主题格式表示，而不是传统的单词元素，则大大降低了存储要求。实验结果表明，与单词粒度的基线相比，TGTR在检索准确性方面始终在TREC CAR和MS MARCO上竞争，但其所需的存储空间的少于1/10。此外，TGTR绝大多数在检索准确性方面超过了全球粒度的基线。

具有最小延迟的人工神经网络的决策对于诸如导航，跟踪和实时机器动作系统之类的许多应用来说是至关重要的。这要求机器学习硬件以高吞吐量处理多维数据。不幸的是，处理卷积操作是数据分类任务的主要计算工具，遵循有挑战性的运行时间复杂性缩放法。然而，在傅立叶光学显示器 - 光处理器中同心地实现卷积定理，使得不迭代的O（1）运行时复杂度以超过1,000×1,000大矩阵的数据输入。在此方法之后，这里我们展示了具有傅里叶卷积神经网络（FCNN）加速器的数据流多核图像批处理。我们将大规模矩阵的图像批量处理显示为傅立叶域中的数字光处理模块执行的被动的2000万点产品乘法。另外，我们通过利用多种时空衍射令并进一步并行化该光学FCNN系统，从而实现了最先进的FCNN加速器的98倍的产量改进。综合讨论与系统能力边缘工作相关的实际挑战突出了傅立叶域和决议缩放法律的串扰问题。通过利用展示技术中的大规模平行性加速卷积带来了基于VAN Neuman的机器学习加速度。

在本文中，我们通过利用全新监督学习来推进面部表情识别（FER）的表现。本领域技术的当前状态通常旨在通过具有有限数量的样本的培训模型来识别受控环境中的面部表达。为了增强学习模型的各种场景的稳健性，我们建议通过利用标记的样本以及大量未标记的数据来执行全能监督学习。特别是，我们首先使用MS-CeleB-1M作为面部池，其中包括大约5,822k未标记的面部图像。然后，采用基于少量标记样品的原始模型来通过进行基于特征的相似性比较来选择具有高度自信心的样本。我们发现以这种全局监督方式构建的新数据集可以显着提高学习的FER模型的泛化能力，并因此提高了性能。然而，随着使用更多的训练样本，需要更多的计算资源和培训时间，在许多情况下通常不能实惠。为了减轻计算资源的要求，我们进一步采用了数据集蒸馏策略，以将目标任务相关知识从新的开采样本中蒸馏，并将其压缩成一组非常小的图像。这种蒸馏的数据集能够提高FER的性能，额外的额外计算成本。我们在五个流行的基准和新构造的数据集中执行广泛的实验，其中可以使用所提出的框架在各种设置下实现一致的收益。我们希望这项工作作为一个坚实的基线，并帮助缓解FER的未来研究。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.