Robotic harvesting research has seen significant achievements in the past decade, with breakthroughs being made in machine vision, robot manipulation, autonomous navigation and mapping. However, the missing capability of obstacle handling during the grasping process has severely reduced harvest success rate and limited the overall performance of robotic harvesting. This work focuses on leaf interference caused slip detection and handling, where solutions to robotic grasping in an unstructured environment are proposed. Through analysis of the motion and force of fruit grasping under leaf interference, the connection between object slip caused by leaf interference and inadequate harvest performance is identified for the first time in the literature. A learning-based perception and manipulation method is proposed to detect slip that causes problematic grasps of objects, allowing the robot to implement timely reaction. Our results indicate that the proposed algorithm detects grasp slip with an accuracy of 94%. The proposed sensing-based manipulation demonstrated great potential in robotic fruit harvesting, and could be extended to other pick-place applications.
A large number of recent studies on adversarial attack have verified that a Deep Neural Network (DNN) model designed for nonsequential recognition (NSR) tasks (e.g., classification, detection and segmentation) can be easily fooled by adversarial examples. However, only a few researches pay attention to the adversarial attack on sequential recognition (SR). They either apply the attack methods proposed for NSR to SR by neglecting the sequential dependencies, or focus on attacking specific SR models without considering the generality. In this paper, we study the adversarial attack on the general and popular DNN structure of CNN+RNN, i.e., the combination of convolutional neural network (CNN) and recurrent neural network (RNN), which has been widely used in various SR tasks. We take the scene text recognition (STR) and image captioning (IC) as case study, and derive the objective function for attacking the CNN+RNN based models with targeted and untargeted attack modes, and then developed an optimization-based algorithm to learn adversarial perturbations from the derived gradients of each character (or word) in sequence by incorporating the sequential dependencies. Extensive experiments show that our proposed method can effective fool several state-of-the-arts including four STR models and two IC models with higher successful rate and less time consumption, comparing to three latest attack methods. CCS CONCEPTS • Information systems → Multimedia information systems; • Security and privacy → Software and application security.
BackgroundRegional anesthesia has anti-proliferative and pro-apoptotic effects in various cancers. Therefore, the purpose of this study was to investigate the effects of ropivacaine on the proliferation, migration, invasion, and apoptosis of glioma cells in vitro.MethodsUnder ropivacaine stimulation conditions, proliferation, apoptosis, migration, and invasion of glioma cells were determined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazol-3-ium bromide (MTT), flow cytometry, and transwell assays, respectively. Western blot assay was employed to measure the protein expression levels in glioma cells. The expression levels of small nucleolar RNA host gene 16 (SNHG16) and miR-424-5p were assessed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The interaction relationship between SNHG16 and miR-424-5p was predicted and confirmed using a bioinformatics database and dual-luciferase reporter, RNA immunoprecipitation (RIP) and RNA pull-down assays.ResultsAfter treatment with ropivacaine, proliferation, migration, and invasion were repressed while apoptosis was enhanced in glioma cells in a dose-depended manner. In addition, ropivacaine impeded SNHG16 expression in glioma cells. Importantly, overexpression of SNHG16 abolished the ropivacaine-induced effects on glioma cells. Analogously, knockdown of miR-424-5p counteracted the function of ropivacaine in glioma cells. We also found that SNHG16 bound to miR-424-5p and negatively regulated miR-424-5p expression in glioma cells. The rescue experiments indicated that ropivacaine might regulate glioma progression by targeting the SNHG16/miR-424-5p axis.ConclusionOur findings revealed the anti-tumor effects of ropivacaine in glioma by targeting the SNHG16/miR-424-5p axis. These data might extend the understanding of regulatory mechanisms by which ropivacaine could suppress glioma development.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.