CM-CASL: Comparison-based Performance Modeling of Software Systems via Collaborative Active and Semisupervised Learning

Configuration tuning for large software systems is generally challenging due to the complex configuration space and expensive performance evaluation. Most existing approaches follow a two-phase process, first learning a regression-based performance prediction model on available samples and then searching for the configurations with satisfactory performance using the learned model. Such regression-based models often suffer from the scarcity of samples due to the enormous time and resources required to run a large software system with a specific configuration. Moreover, previous studies have shown that even a highly accurate regression-based model may fail to discern the relative merit between two configurations, whereas performance comparison is actually one fundamental strategy for configuration tuning. To address these issues, this paper proposes CM-CASL, a Comparison-based performance Modeling approach for software systems via Collaborative Active and Semisupervised Learning. CM-CASL learns a classification model that compares the performance of two given configurations, and enhances the samples through a collaborative labeling process by both human experts and classifiers using an integration of active and semisupervised learning. Experimental results demonstrate that CM-CASL outperforms two state-of-the-art performance modeling approaches in terms of both classification accuracy and rank accuracy, and thus provides a better performance model for the subsequent work of configuration tuning

A Unified BEV Model for Joint Learning of 3D Local Features and Overlap Estimation

Pairwise point cloud registration is a critical task for many applications, which heavily depends on finding correct correspondences from the two point clouds. However, the low overlap between input point clouds causes the registration to fail easily, leading to mistaken overlapping and mismatched correspondences, especially in scenes where non-overlapping regions contain similar structures. In this paper, we present a unified bird's-eye view (BEV) model for jointly learning of 3D local features and overlap estimation to fulfill pairwise registration and loop closure. Feature description is performed by a sparse UNet-like network based on BEV representation, and 3D keypoints are extracted by a detection head for 2D locations, and a regression head for heights. For overlap detection, a cross-attention module is applied for interacting contextual information of input point clouds, followed by a classification head to estimate the overlapping region. We evaluate our unified model extensively on the KITTI dataset and Apollo-SouthBay dataset. The experiments demonstrate that our method significantly outperforms existing methods on overlap estimation, especially in scenes with small overlaps. It also achieves top registration performance on both datasets in terms of translation and rotation errors.Comment: 8 pages. Accepted by ICRA-202

Shape effect of glyco-nanoparticles on macrophage cellular uptake and immune response

The shells of various poly(dl-lactide)-b-poly(acrylic acid) (PDLLA-b-PAA) spherical micelles and poly(l-lactide)-b-poly(acrylic acid) (PLLA-b-PAA) cylindrical micelles were functionalized with mannose to yield glyco-nanoparticles (GNPs) with different shapes and dimensions. All of these GNPs were shown to have good biocompatibility (up to 1 mg/mL). Cellular uptake experiments using RAW 264.7 have shown that the spherical GNPs were internalized to a much greater extent than the cylindrical GNPs and such a phenomenon was attributed to their different endocytosis pathways. It was demonstrated that spherical GNPs were internalized based on clathrin- and caveolin-mediated endocytosis while cylindrical GNPs mainly depended on clathrin-mediated endocytosis. We also found that longer cylindrical GNPs (Ln × Wn = 215 × 47 nm) can induce an inflammatory response (specifically interleukin 6) more efficiently than shorter cylindrical GNPs (Ln × Wn = 99 × 50 nm) and spherical GNPs (Dn = 46 nm)

3D-SeqMOS: A Novel Sequential 3D Moving Object Segmentation in Autonomous Driving

For the SLAM system in robotics and autonomous driving, the accuracy of front-end odometry and back-end loop-closure detection determine the whole intelligent system performance. But the LiDAR-SLAM could be disturbed by current scene moving objects, resulting in drift errors and even loop-closure failure. Thus, the ability to detect and segment moving objects is essential for high-precision positioning and building a consistent map. In this paper, we address the problem of moving object segmentation from 3D LiDAR scans to improve the odometry and loop-closure accuracy of SLAM. We propose a novel 3D Sequential Moving-Object-Segmentation (3D-SeqMOS) method that can accurately segment the scene into moving and static objects, such as moving and static cars. Different from the existing projected-image method, we process the raw 3D point cloud and build a 3D convolution neural network for MOS task. In addition, to make full use of the spatio-temporal information of point cloud, we propose a point cloud residual mechanism using the spatial features of current scan and the temporal features of previous residual scans. Besides, we build a complete SLAM framework to verify the effectiveness and accuracy of 3D-SeqMOS. Experiments on SemanticKITTI dataset show that our proposed 3D-SeqMOS method can effectively detect moving objects and improve the accuracy of LiDAR odometry and loop-closure detection. The test results show our 3D-SeqMOS outperforms the state-of-the-art method by 12.4%. We extend the proposed method to the SemanticKITTI: Moving Object Segmentation competition and achieve the 2nd in the leaderboard, showing its effectiveness

Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient

Recently, backpropagation through time inspired learning algorithms are widely introduced into SNNs to improve the performance, which brings the possibility to attack the models accurately given Spatio-temporal gradient maps. We propose two approaches to address the challenges of gradient input incompatibility and gradient vanishing. Specifically, we design a gradient to spike converter to convert continuous gradients to ternary ones compatible with spike inputs. Then, we design a gradient trigger to construct ternary gradients that can randomly flip the spike inputs with a controllable turnover rate, when meeting all zero gradients. Putting these methods together, we build an adversarial attack methodology for SNNs trained by supervised algorithms. Moreover, we analyze the influence of the training loss function and the firing threshold of the penultimate layer, which indicates a "trap" region under the cross-entropy loss that can be escaped by threshold tuning. Extensive experiments are conducted to validate the effectiveness of our solution. Besides the quantitative analysis of the influence factors, we evidence that SNNs are more robust against adversarial attack than ANNs. This work can help reveal what happens in SNN attack and might stimulate more research on the security of SNN models and neuromorphic devices

Efficient Scene Text Detection with Textual Attention Tower

Scene text detection has received attention for years and achieved an impressive performance across various benchmarks. In this work, we propose an efficient and accurate approach to detect multioriented text in scene images. The proposed feature fusion mechanism allows us to use a shallower network to reduce the computational complexity. A self-attention mechanism is adopted to suppress false positive detections. Experiments on public benchmarks including ICDAR 2013, ICDAR 2015 and MSRA-TD500 show that our proposed approach can achieve better or comparable performances with fewer parameters and less computational cost.Comment: Accepted by ICASSP 202