Real-Time Marker Localization Learning for GelStereo Tactile Sensing

Visuotactile sensing technology is becoming more popular in tactile sensing, but the effectiveness of the existing marker detection localization methods remains to be further explored. Instead of contour-based blob detection, this paper presents a learning-based marker localization network for GelStereo visuotactile sensing called Marknet. Specifically, the Marknet presents a grid regression architecture to incorporate the distribution of the GelStereo markers. Furthermore, a marker rationality evaluator (MRE) is modelled to screen suitable prediction results. The experimental results show that the Marknet combined with MRE achieves 93.90% precision for irregular markers in contact areas, which outperforms the traditional contour-based blob detection method by a large margin of 42.32%. Meanwhile, the proposed learning-based marker localization method can achieve better real-time performance beyond the blob detection interface provided by the OpenCV library through GPU acceleration, which we believe will lead to considerable perceptual sensitivity gains in various robotic manipulation tasks

Efficient Unbalanced Quorum PSI from Homomorphic Encryption

Multiparty private set intersection (mPSI) protocol is capable of finding the intersection of multiple sets securely without revealing any other information. However, its limitation lies in processing only those elements present in every participant\u27s set, which proves inadequate in scenarios where certain elements are common to several, but not all, sets. In this paper, we introduce an innovative variant of the mPSI protocol named unbalanced quorum PSI to fill in the gaps of the mPSI protocol. Unlike the previous quorum-PSI proposals which detect elements present in at least $k$ out of $n$ equal sets, our protocol is particularly tailored for unbalanced cases where the size of the receiver\u27s set is much smaller than the size of the senders\u27 sets. Our work achieves logarithmic communication complexity in the semi-honest setting, significantly surpassing previous work in efficiency. The benchmarks show that it takes 22.7 seconds in WAN and 14.7 seconds in LAN for online computation, and only 87.8 MB of total communication to intersect 5535 elements across 15 sets, each containing $2^{24}$ elements. Compared to prior work, this is roughly an 87$\times$ reduction in communication and a 31$\times$ reduction in online time. Our protocols can be easily extended to the larger set with $2^{28}$ elements which is nearly impractical for prior work

Real-time moving object classification with automatic scene division

ABSTRACT We address the problem of moving object classification. Our aim is to classify moving objects of traffic scene videos into pedestrians, bicycles and vehicles. Instead of supervised learning and manual labeling of large training samples, our classifiers are initialized and refined online automatically. With efficient features extracted and organized, the approach can be real-time and achieve high classification accuracy. Once the view or scene changes detected, the algorithm can automatically refine the classifiers and adapt them to new environments. Experimental results demonstrate the effectiveness and robustness of the proposed approach

Experimental Study on Stress and Strain Characteristics of Solidified Clay under Seawater Condition

This paper presents the results of a laboratory study on the stress-strain relationship of solidified clay formed in seawater corrosion condition. An automatic triaxial apparatus was used and the axial stress and strain was monitored continuously. The dry density was 1.0g/cm3, the cement contents were 4, 6, 8 and 10% by weight of dry soil particles, and the curing time was 28, 60 and 90 days respectively. Test results indicate that the stress strain relationship of cemented clay was affected by soil density, cement content and curing period. A behaviour of strain hardening to strain softening occurred with the increase of cement content. Strong structure will form in cemented clay when the admixture content is 10% or more. The increase in strength of the solidified foundation is resulted from the increase in internal friction angle and cohesive force. The cohesive force increases obviously with the increase of the cement content and the curing age, but the change of internal friction angle is not pronounced after reaching a certain value

RNA Sequencing of Formalin-Fixed, Paraffin-Embedded Specimens for Gene Expression Quantification and Data Mining

Background. Proper rRNA depletion is crucial for the successful utilization of FFPE specimens when studying gene expression. We performed a study to evaluate two major rRNA depletion methods: Ribo-Zero and RNase H. RNAs extracted from 4 samples were treated with the two rRNA depletion methods in duplicate and sequenced (N=16). We evaluated their reducibility, ability to detect RNA, and ability to molecularly subtype these triple negative breast cancer specimens. Results. Both rRNA depletion methods produced consistent data between the technical replicates. We found that the RNase H method produced higher quality RNAseq data as compared to the Ribo-Zero method. In addition, we evaluated the RNAseq data generated from the FFPE tissue samples for noncoding RNA, including lncRNA, enhancer/super enhancer RNA, and single nucleotide variation (SNV). We found that the RNase H is more suitable for detecting high-quality, noncoding RNAs as compared to the Ribo-Zero and provided more consistent molecular subtype identification between replicates. Unfortunately, neither method produced reliable SNV data. Conclusions. In conclusion, for FFPE specimens, the RNase H rRNA depletion method performed better than the Ribo-Zero. Neither method generates data sufficient for SNV detection

Causality of immune cells on primary sclerosing cholangitis: a bidirectional two-sample Mendelian randomization study

BackgroundObservational studies have indicated that immune dysregulation in primary sclerosing cholangitis (PSC) primarily involves intestinal-derived immune cells. However, the causal relationship between peripheral blood immune cells and PSC remains insufficiently understood.MethodsA bidirectional two-sample Mendelian randomization (MR) analysis was implemented to determine the causal effect between PBC and 731 immune cells. All datasets were extracted from a publicly available genetic database. The standard inverse variance weighted (IVW) method was selected as the main method for the causality analysis. Cochran’s Q statistics and MR-Egger intercept were performed to evaluate heterogeneity and pleiotropy.ResultsIn forward MR analysis, the expression ratios of CD11c on CD62L+ myeloid DC (OR = 1.136, 95% CI = 1.032–1.250, p = 0.009) and CD62L-myeloid DC AC (OR = 1.267, 95% CI = 1.086–1.477, p = 0.003) were correlated with a higher risk of PSC. Each one standard deviation increase of CD28 on resting regulatory T cells (Treg) (OR = 0.724, 95% CI = 0.630–0.833, p < 0.001) and CD3 on secreting Treg (OR = 0.893, 95% CI = 0.823–0.969, p = 0.007) negatively associated with the risk of PSC. In reverse MR analysis, PSC was identified with a genetic causal effect on EM CD8+ T cell AC, CD8+ T cell AC, CD28− CD127− CD25++ CD8+ T cell AC, CD28− CD25++ CD8+ T cell AC, CD28− CD8+ T cell/CD8+ T cell, CD28− CD8+ T cell AC, and CD45 RA− CD28− CD8+ T cell AC.ConclusionOur study indicated the evidence of causal effects between PSC and immune cells, which may provide a potential foundation for future diagnosis and treatment of PSC

Ontology based autonomous robot task processing framework

IntroductionIn recent years, the perceptual capabilities of robots have been significantly enhanced. However, the task execution of the robots still lacks adaptive capabilities in unstructured and dynamic environments.MethodsIn this paper, we propose an ontology based autonomous robot task processing framework (ARTProF), to improve the robot's adaptability within unstructured and dynamic environments. ARTProF unifies ontological knowledge representation, reasoning, and autonomous task planning and execution into a single framework. The interface between the knowledge base and neural network-based object detection is first introduced in ARTProF to improve the robot's perception capabilities. A knowledge-driven manipulation operator based on Robot Operating System (ROS) is then designed to facilitate the interaction between the knowledge base and the robot's primitive actions. Additionally, an operation similarity model is proposed to endow the robot with the ability to generalize to novel objects. Finally, a dynamic task planning algorithm, leveraging ontological knowledge, equips the robot with adaptability to execute tasks in unstructured and dynamic environments.ResultsExperimental results on real-world scenarios and simulations demonstrate the effectiveness and efficiency of the proposed ARTProF framework.DiscussionIn future work, we will focus on refining the ARTProF framework by integrating neurosymbolic inference