142 research outputs found
Planar Manipulation via Learning Regrasping
Regrasping is important for robots to reorient objects in planar manipulation
tasks. Different placements of objects can provide robots with alternative
grasp configurations, which are used in complex planar manipulation tasks that
require multiple pick-rotate-and-place steps due to the constraints of the
environment and robot kinematics. In this work, our goal is to generate diverse
placements of objects on the plane using deep neural networks. We propose a
pipeline with the stages of orientation generation, position refinement, and
placement discrimination to obtain accurate and diverse stable placements based
on the perception of point clouds. A large-scale dataset is created for
training, including simulated placements and contact information between
objects and the plane. The simulation results show that our pipeline
outperforms the start-of-the-art, achieving an accuracy rate of 90.4% and a
diversity rate of 81.3% in simulation on generated placements. Our pipeline is
also validated in real-robot experiments. With the generated placements,
sequential pick-rotate-and-place steps are calculated for the robot to reorient
objects to goal poses that are not reachable within one step. Videos and
dataset are available at https://sites.google.com/view/pmvlr2022/
A Tale of Two Cities: Data and Configuration Variances in Robust Deep Learning
Deep neural networks (DNNs), are widely used in many industries such as image
recognition, supply chain, medical diagnosis, and autonomous driving. However,
prior work has shown the high accuracy of a DNN model does not imply high
robustness (i.e., consistent performances on new and future datasets) because
the input data and external environment (e.g., software and model
configurations) for a deployed model are constantly changing. Hence, ensuring
the robustness of deep learning is not an option but a priority to enhance
business and consumer confidence. Previous studies mostly focus on the data
aspect of model variance. In this article, we systematically summarize DNN
robustness issues and formulate them in a holistic view through two important
aspects, i.e., data and software configuration variances in DNNs. We also
provide a predictive framework to generate representative variances
(counterexamples) by considering both data and configurations for robust
learning through the lens of search-based optimization
A Review on Security Issues and Solutions of the Internet of Drones
The Internet of Drones (IoD) has attracted increasing attention in recent years because of its portability and automation, and is being deployed in a wide range of fields (e.g., military, rescue and entertainment). Nevertheless, as a result of the inherently open nature of radio transmission paths in the IoD, data collected, generated or handled by drones is plagued by many security concerns. Since security and privacy are among the foremost challenges for the IoD, in this paper we conduct a comprehensive review on security issues and solutions for IoD security, discussing IoD-related security requirements and identifying the latest advancement in IoD security research. This review analyzes a host of important security technologies with emphases on authentication techniques and blockchain-powered schemes. Based on a detailed analysis, we present the challenges faced by current methodologies and recommend future IoD security research directions. This review shows that appropriate security measures are needed to address IoD security issues, and that newly designed security solutions should particularly consider the balance between the level of security and cost efficiency
Genome-wide analysis of Dongxiang wild rice (Oryza rufipogon Griff.) to investigate lost/acquired genes during rice domestication
This file reports the functional annotation of 99,092 DXWR transcripts from the NCBI NR database using the software blast2go. This file is in the tab delimited format and can be opened using the software Excel. (TXT 12649Â kb
Rosuvastatin Reduces Neuroinflammation in the Hemorrhagic Transformation After rt-PA Treatment in a Mouse Model of Experimental Stroke
Hemorrhagic transformation (HT) is a serious complication that stimulates inflammation during reperfusion therapy after acute ischemic stroke. Rosuvastatin, a 3-hydroxymethyl-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitor, might improve the outcome of HT by inhibiting neuroinflammation. This study aimed to explore the protective effects of rosuvastatin against HT after recombinant tissue plasminogen activator (rt-PA) treatment in mice with experimental stroke via the attenuation of inflammation. A total of one hundred sixty-nine male BALB/c mice were used in the experiment. HT was successfully established in 70 mice that were subjected to 3 h of middle cerebral artery occlusion (MCAO) followed by a 10 mg/kg rt-PA injection over 10 min and reperfusion for 24 h. The mice were then administered rosuvastatin (1 mg/kg, 5 mg/kg) or saline (vehicle). The brain water content and neurological deficits (wire hang and adhesive removal somatosensory tests) were assessed at 24 h after rt-PA reperfusion following MCAO surgery. The morphology, blood-brain barrier (BBB) permeability and number of astrocytes and microglia were assessed by immunohistochemistry, electron microscopy and western blotting at 24 h after rt-PA reperfusion following MCAO surgery. Rosuvastatin protected against impaired neurological function and reversed the BBB leakage observed in the HT group. The increased activation of astrocytes and microglia and secretion of inflammatory factors caused by HT damage were significantly attenuated by high-dose rosuvastatin treatment vs. normal-dose rosuvastatin treatment. Related inflammatory pathways, such as the nuclear factor kappa B (NF-κB) and mitogen-activated protein kinase (MAPK) pathways, were downregulated in the rosuvastatin-treated groups compared with the HT group. In conclusion, our results indicate that rosuvastatin is a promising therapeutic agent for HT after rt-PA reperfusion following MCAO surgery in mice, as it attenuates neuroinflammation. Additionally, high-dose rosuvastatin treatment could have a greater anti-inflammatory effect on HT than normal-dose rosuvastatin treatment
Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching
Correlation based stereo matching has achieved outstanding performance, which
pursues cost volume between two feature maps. Unfortunately, current methods
with a fixed model do not work uniformly well across various datasets, greatly
limiting their real-world applicability. To tackle this issue, this paper
proposes a new perspective to dynamically calculate correlation for robust
stereo matching. A novel Uncertainty Guided Adaptive Correlation (UGAC) module
is introduced to robustly adapt the same model for different scenarios.
Specifically, a variance-based uncertainty estimation is employed to adaptively
adjust the sampling area during warping operation. Additionally, we improve the
traditional non-parametric warping with learnable parameters, such that the
position-specific weights can be learned. We show that by empowering the
recurrent network with the UGAC module, stereo matching can be exploited more
robustly and effectively. Extensive experiments demonstrate that our method
achieves state-of-the-art performance over the ETH3D, KITTI, and Middlebury
datasets when employing the same fixed model over these datasets without any
retraining procedure. To target real-time applications, we further design a
lightweight model based on UGAC, which also outperforms other methods over
KITTI benchmarks with only 0.6 M parameters.Comment: Accepted by ICCV202
The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) is a powerful technique to facilitate
language model with proprietary and private data, where data privacy is a
pivotal concern. Whereas extensive research has demonstrated the privacy risks
of large language models (LLMs), the RAG technique could potentially reshape
the inherent behaviors of LLM generation, posing new privacy issues that are
currently under-explored. In this work, we conduct extensive empirical studies
with novel attack methods, which demonstrate the vulnerability of RAG systems
on leaking the private retrieval database. Despite the new risk brought by RAG
on the retrieval data, we further reveal that RAG can mitigate the leakage of
the LLMs' training data. Overall, we provide new insights in this paper for
privacy protection of retrieval-augmented LLMs, which benefit both LLMs and RAG
systems builders. Our code is available at
https://github.com/phycholosogy/RAG-privacy
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