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.

Weakly-supervised learning (WSL) has been proposed to alleviate the conflict between data annotation cost and model performance through employing sparsely-grained (i.e., point-, box-, scribble-wise) supervision and has shown promising performance, particularly in the image segmentation field. However, it is still a very challenging problem due to the limited supervision, especially when only a small number of labeled samples are available. Additionally, almost all existing WSL segmentation methods are designed for star-convex structures which are very different from curvilinear structures such as vessels and nerves. In this paper, we propose a novel sparsely annotated segmentation framework for curvilinear structures, named YoloCurvSeg, based on image synthesis. A background generator delivers image backgrounds that closely match real distributions through inpainting dilated skeletons. The extracted backgrounds are then combined with randomly emulated curves generated by a Space Colonization Algorithm-based foreground generator and through a multilayer patch-wise contrastive learning synthesizer. In this way, a synthetic dataset with both images and curve segmentation labels is obtained, at the cost of only one or a few noisy skeleton annotations. Finally, a segmenter is trained with the generated dataset and possibly an unlabeled dataset. The proposed YoloCurvSeg is evaluated on four publicly available datasets (OCTA500, CORN, DRIVE and CHASEDB1) and the results show that YoloCurvSeg outperforms state-of-the-art WSL segmentation methods by large margins. With only one noisy skeleton annotation (respectively 0.14%, 0.02%, 1.4%, and 0.65% of the full annotation), YoloCurvSeg achieves more than 97% of the fully-supervised performance on each dataset. Code and datasets will be released at https://github.com/llmir/YoloCurvSeg.

Recent advances in neural rendering imply a future of widespread visual data distributions through sharing NeRF model weights. However, while common visual data (images and videos) have standard approaches to embed ownership or copyright information explicitly or subtly, the problem remains unexplored for the emerging NeRF format. We present StegaNeRF, a method for steganographic information embedding in NeRF renderings. We design an optimization framework allowing accurate hidden information extractions from images rendered by NeRF, while preserving its original visual quality. We perform experimental evaluations of our method under several potential deployment scenarios, and we further discuss the insights discovered through our analysis. StegaNeRF signifies an initial exploration into the novel problem of instilling customizable, imperceptible, and recoverable information to NeRF renderings, with minimal impact to rendered images. Project page: https://xggnet.github.io/StegaNeRF/.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis. Transformer reformats the image into separate patches and realize global communication via the self-attention mechanism. However, positional information between patches is hard to preserve in such 1D sequences, and loss of it can lead to sub-optimal performance when dealing with large amounts of heterogeneous tissues of various sizes in 3D medical image segmentation. Additionally, current methods are not robust and efficient for heavy-duty medical segmentation tasks such as predicting a large number of tissue classes or modeling globally inter-connected tissues structures. Inspired by the nested hierarchical structures in vision transformer, we proposed a novel 3D medical image segmentation method (UNesT), employing a simplified and faster-converging transformer encoder design that achieves local communication among spatially adjacent patch sequences by aggregating them hierarchically. We extensively validate our method on multiple challenging datasets, consisting anatomies of 133 structures in brain, 14 organs in abdomen, 4 hierarchical components in kidney, and inter-connected kidney tumors). We show that UNesT consistently achieves state-of-the-art performance and evaluate its generalizability and data efficiency. Particularly, the model achieves whole brain segmentation task complete ROI with 133 tissue classes in single network, outperforms prior state-of-the-art method SLANT27 ensembled with 27 network tiles, our model performance increases the mean DSC score of the publicly available Colin and CANDI dataset from 0.7264 to 0.7444 and from 0.6968 to 0.7025, respectively.

如今，渴望数据的深神经网络（DNNS）的创建者搜索互联网训练饲料，使用户几乎无法控制或了解何时将其数据用于模型培训。为了使用户能够抵消不需要的数据使用，我们设计，实施和评估一个实用系统，该系统使用户能够检测其数据是否用于培训DNN模型。我们展示了用户如何创建我们称为同位素的特殊数据点，该数据点在培训期间将“伪造功能”引入DNN中。仅查询访问训练的模型，并且对模型培训过程不了解或对数据标签的控制，用户可以应用统计假设测试来检测模型是否通过对用户的培训进行培训来了解与其同位素相关的虚假特征数据。这有效地将DNNS对记忆和虚假相关性的脆弱性变成了数据出处的工具。我们的结果证实了在多种设置中的功效，检测并区分了数百种具有高精度的同位素。我们进一步表明，我们的系统在公共ML-AS-AS-Service平台和较大的模型（例如ImageNet）上工作，可以使用物理对象代替数字标记，并且通常对几种自适应对策保持坚固。

由于生成对抗网络（GAN）的突破，3D可控制的肖像合成已大大提高。但是，用精确的3D控制操纵现有的面部图像仍然具有挑战性。虽然连接gan倒置和3D感知，但噪声到图像是一种直接的解决方案，但它效率低下，可能导致编辑质量明显下降。为了填补这一空白，我们提出了3D-FM GAN，这是一个专门为3D可控制的面部操作设计的新型有条件GAN框架，并且在端到端学习阶段后不需要任何调整。通过小心地编码输入面图像和3D编辑的基于物理的渲染，我们的图像生成器提供了高质量，具有身份的3D控制面部操纵。为了有效地学习这种新颖的框架，我们制定了两种基本的训练策略和一种新颖的乘法共同调制体系结构，可在天真的方案上显着改善。通过广泛的评估，我们表明我们的方法在各种任务上的表现优于先前的艺术，具有更好的编辑性，更强的身份保存和更高的照片真实性。此外，我们在大型姿势编辑和室外图像上展示了设计更好的概括性。

基于最大元素间间距（IES）约束（MISC）标准，提出了一种新型的稀疏阵列（SA）结构。与传统的MISC阵列相比，所提出的SA配置称为改进的MISC（IMISC），显着提高了均匀的自由度（UDOF）并减少了相互耦合。特别是，IMISC阵列由六个均匀的线性阵列（ULA）组成，可以由IES集确定。IES集受两个参数的约束，即最大IE和传感器数。也得出了IMISC阵列的UDOF，并且也分析了IMISC阵列的重量函数。拟议的IMISC阵列在对现有SAS的UDOF方面具有很大的优势，而它们的相互耦合保持低水平。进行模拟以证明IMISC阵列的优势。

缺乏对深度学习系统的洞察力阻碍了他们的系统设计。在科学和工程学中，建模是一种用于了解内部过程不透明的复杂系统的方法。建模用更简单的代理代替复杂的系统，该系统更适合解释。从中汲取灵感，我们使用高斯流程为神经网络构建了一类代理模型。我们没有从神经网络的某些限制案例中得出内核，而是从经验上从神经网络的自然主义行为中学习了高斯过程的内核。我们首先通过两项案例研究评估我们的方法，灵感来自先前对神经网络行为的理论研究，在这些案例研究中，我们捕获了学习低频的神经网络偏好，并确定了深层神经网络中的病理行为。在进一步的实践案例研究中，我们使用学识渊博的内核来预测神经网络的泛化特性。

紧急车辆（EMV）在应对城市地区的医疗紧急情况和火灾爆发等时间关键电话方面起着至关重要的作用。现有的EMV调度方法通常会根据历史流量数据数据和设计流量信号相应地优化路线；但是，我们仍然缺乏一种系统的方法来解决EMV路由和流量信号控制之间的耦合。在本文中，我们提出了EMVLIGHT，这是一个分散的加固学习（RL）框架，用于联合动态EMV路由和交通信号的先发制人。我们采用具有政策共享和空间折现因子的多代理优势行为者 - 批评方法。该框架通过多级RL代理的创新设计和新型的基于压力的奖励功能来解决EMV导航和交通信号控制之间的耦合。拟议的方法使EMVLIGHT能够学习网络级的合作交通信号相阶段阶段策略，这些策略不仅减少EMV旅行时间，而且还缩短了非EMV的旅行时间。基于仿真的实验表明，EMVLIGHT可使EMV旅行时间减少$ 42.6 \％$，以及与现有方法相比，$ 23.5 \％$短的平均旅行时间。