QoS-Aware Resource Management for Multi-phase Serverless Workflows with Aquatope

Multi-stage serverless applications, i.e., workflows with many computation and I/O stages, are becoming increasingly representative of FaaS platforms. Despite their advantages in terms of fine-grained scalability and modular development, these applications are subject to suboptimal performance, resource inefficiency, and high costs to a larger degree than previous simple serverless functions. We present Aquatope, a QoS-and-uncertainty-aware resource scheduler for end-to-end serverless workflows that takes into account the inherent uncertainty present in FaaS platforms, and improves performance predictability and resource efficiency. Aquatope uses a set of scalable and validated Bayesian models to create pre-warmed containers ahead of function invocations, and to allocate appropriate resources at function granularity to meet a complex workflow's end-to-end QoS, while minimizing resource cost. Across a diverse set of analytics and interactive multi-stage serverless workloads, Aquatope significantly outperforms prior systems, reducing QoS violations by 5x, and cost by 34% on average and up to 52% compared to other QoS-meeting methods

Grasp Stability Assessment Through Attention-Guided Cross-Modality Fusion and Transfer Learning

Extensive research has been conducted on assessing grasp stability, a crucial prerequisite for achieving optimal grasping strategies, including the minimum force grasping policy. However, existing works employ basic feature-level fusion techniques to combine visual and tactile modalities, resulting in the inadequate utilization of complementary information and the inability to model interactions between unimodal features. This work proposes an attention-guided cross-modality fusion architecture to comprehensively integrate visual and tactile features. This model mainly comprises convolutional neural networks (CNNs), self-attention, and cross-attention mechanisms. In addition, most existing methods collect datasets from real-world systems, which is time-consuming and high-cost, and the datasets collected are comparatively limited in size. This work establishes a robotic grasping system through physics simulation to collect a multimodal dataset. To address the sim-to-real transfer gap, we propose a migration strategy encompassing domain randomization and domain adaptation techniques. The experimental results demonstrate that the proposed fusion framework achieves markedly enhanced prediction performance (approximately 10%) compared to other baselines. Moreover, our findings suggest that the trained model can be reliably transferred to real robotic systems, indicating its potential to address real-world challenges.Comment: Accepted by IROS 202

Large Eddy Simulation of Microphysics and Influencing Factors in Shallow Convective Clouds

A flight of shallow convective clouds during the SCMS95 (Small Cumulus Microphysics Study 1995) observation project is simulated by the large eddy simulation (LES) version of the Weather Research and Forecasting Model (WRF-LES) with spectral bin microphysics (SBM). This study focuses on relative dispersion of cloud droplet size distributions, since its influencing factors are still unclear. After validation of the simulation by aircraft observations, the factors affecting relative dispersion are analyzed. It is found that the relationships between relative dispersion and vertical velocity, and between relative dispersion and adiabatic fraction are both negative. Furthermore, the negative relationships are relatively weak near the cloud base, strengthen with the increasing height first and then weaken again, which is related to the interplays among activation, condensation and evaporation for different vertical velocity and entrainment conditions. The results will be helpful to improve parameterizations related to relative dispersion (e.g., autoconversion and effective radius) in large-scale models

Modification of Frictional Properties of Hydrogel Surface via Laser Ablated Topographical Micro-Textures

Hydrogels and biological cartilage tissues are highly similar in structure and composition due to their unique characteristics such as high-water content and low friction coefficients. The introduction of hydrogel cartilage can effectively reduce the friction coefficient and wear coefficient of the original bone joint and the implanted metal bone joint (generally titanium alloy or stainless steel), which is considered as a perfect replacement material for artificial articular cartilage. How to accurately regulate the local tribological characteristics of hydrogel artificial cartilage according to patient weight and bone shape is one of the important challenges in the current clinical application field of medical hydrogels. In this study, the mechanism by which micro-pits improve the surface friction properties was studied. Ultraviolet lasers were used to efficiently construct micro-pits with different shapes on a polyvinyl alcohol hydrogel in one step. It was shown that by using such a maskless laser processing, the performance of each part of the artificial cartilage can be customized flexibly and effectively. We envision that the approach demonstrated in this article will provide an important idea for the development of a high-performance, continuous and accurate method for controlling surface friction properties of artificial cartilage

Detection of Micro-Defects on Irregular Reflective Surfaces Based on Improved Faster R-CNN

The detection of defects on irregular surfaces with specular reflection characteristics is an important part of the production process of sanitary equipment. Currently, defect detection algorithms for most irregular surfaces rely on the handcrafted extraction of shallow features, and the ability to recognize these defects is limited. To improve the detection accuracy of micro-defects on irregular surfaces in an industrial environment, we propose an improved Faster R-CNN model. Considering the variety of defect shapes and sizes, we selected the K-Means algorithm to generate the aspect ratio of the anchor box according to the size of the ground truth, and the feature matrices are fused with different receptive fields to improve the detection performance of the model. The experimental results show that the recognition accuracy of the improved model is 94.6% on a collected ceramic dataset. Compared with SVM (Support Vector Machine) and other deep learning-based models, the proposed model has better detection performance and robustness to illumination, which proves the practicability and effectiveness of the proposed method

Three-dimensional design custom-made uncemented stem for revision of cemented distal femoral endoprosthesis due to aseptic loosening

Abstract Background Revision of cemented distal femoral replacement (DFR) due to aseptic loosening is challenging because of the resultant femoral bone loss. This paper aims to examine the outcomes of three-dimensional (3D) design custom-made uncemented stems for revision. Methods Between January 2014 and December 2020, 17 patients received 3D design uncemented stems for revision of loosed cemented DFR. The femoral bone loss was classified into four Grades, and four types of uncemented stems were designed correspondingly. The revision stems were custom-made for each patient by measuring the diameter of the medullary cavity and the anterior curvature of the femur. Results The patient counts with their corresponding Grades of femoral bone loss were as follows: Grade I, 8 patients; Grade II, 5 patients; Grade III, 3 patients; and Grade IV, 1 patient. During the mean follow-up of 80 months, no revision failure was detected. The postoperative radiographic showed that the stem matched the femoral anterior curvature well. The femoral bone defect was completely filled by the 3D design stem in 10 of the 17 cases postoperatively. In the remaining cases, the persistent peri-stem defect was filled or partially restored during the follow-up. Conclusion 3D design custom-made uncemented stem created precise, stable, and durable fixation and provided satisfactory clinical outcomes, which seems to be a viable method for cemented DFR revision

Experimental Investigation on the Mechanical Performance of Steel-ECC Composite Girders with Corrugated Webs under Negative Moment

In order to improve the cracking performance in the negative moment region of composite continuous girder bridges with corrugated webs, engineered cementitious composite (ECC) is used instead of conventional normal concrete (NC). Web and concrete types are used as the main research parameters in experiments. The test results indicate that steel-ECC specimens have a higher flexural load capacity and stiffness than steel-NC specimens. The cracks of steel-ECC specimens are characterised by small width and dense distribution. Nonlinear finite element models are established and verified by experimental results. The simulated load–displacement curves are similar to the experimental ones, and the models have a high degree of accuracy. The ECC slab strength, thickness and width are used as parameters for the investigation to analyse the effect of the ECC slab on the flexural bearing capacity of composite girders. Compared with the results of calculations according to the code, the bearing capacity obtained from the parametric analysis is higher. It suggests that the contribution of the ECC slab needs to be considered when calculating the bearing capacity of the steel-ECC composite girder with corrugated webs