A Novel Vector-Field-Based Motion Planning Algorithm for 3D Nonholonomic Robots

This paper focuses on the motion planning for mobile robots in 3D, which are modelled by 6-DOF rigid body systems with nonholonomic kinematics constraints. We not only specify the target position, but also bring in the requirement of the heading direction at the terminal time, which gives rise to a new and more challenging 3D motion planning problem. The proposed planning algorithm involves a novel velocity vector field (VF) over the workspace, and by following the VF, the robot can be navigated to the destination with the specified heading direction. In order to circumvent potential collisions with obstacles and other robots, a composite VF is designed by composing the navigation VF and an additional VF tangential to the boundary of the dangerous area. Moreover, we propose a priority-based algorithm to deal with the motion coupling issue among multiple robots. Finally, numerical simulations are conducted to verify the theoretical results

Data-Free Quantization with Accurate Activation Clipping and Adaptive Batch Normalization

Data-free quantization is a task that compresses the neural network to low bit-width without access to original training data. Most existing data-free quantization methods cause severe performance degradation due to inaccurate activation clipping range and quantization error, especially for low bit-width. In this paper, we present a simple yet effective data-free quantization method with accurate activation clipping and adaptive batch normalization. Accurate activation clipping (AAC) improves the model accuracy by exploiting accurate activation information from the full-precision model. Adaptive batch normalization firstly proposes to address the quantization error from distribution changes by updating the batch normalization layer adaptively. Extensive experiments demonstrate that the proposed data-free quantization method can yield surprisingly performance, achieving 64.33% top-1 accuracy of ResNet18 on ImageNet dataset, with 3.7% absolute improvement outperforming the existing state-of-the-art methods.Comment: submitted to ICML202

An evaluation of wayfinding abilities in adolescent and young adult males with autism spectrum disorder

Background Wayfinding refers to traveling from place to place in the environment. Despite some research headway, it remains unclear whether individuals with Autism Spectrum Disorder (ASD) show strengths, weaknesses, or similarities in wayfinding compared with ability-matched typically developing (TD) controls. Method The current study tested 24 individuals with ASD, 24 mental-ability (MA) matched TD (MA-TD) controls, and 24 chronological-age (CA) matched TD (CA-TD) controls. Participants completed a route learning task and a survey learning task, both programmed in virtual environments, and a perspective taking task. Their parents completed questionnaires assessing their children’s everyday wayfinding activities and competence. Results Overall, CA-TD controls performed better than both the ASD group and the MA-TD group in both wayfinding tasks and the perspective taking task. Individuals with ASD performed similarly to the MA- TD controls on wayfinding performance except for backtracking routes. Perspective taking presented an area of deficit for people with ASD and it predicted individual differences in route learning and survey learning. Parents’ reports did not predict their children’s wayfinding performance. Two mini meta-analyses, including previous studies and the current study, showed a significant deficit in route learning, but not in survey learning for the ASD group relative to MA-TD controls. Conclusions Although participants with ASD showed impairments in wayfinding relative to CA-TD controls, the impairment is not specific to their ASD, but rather due to their mental age. Nevertheless, route reversal in route learning may present unique difficulty for people with ASD beyond the effects of mental age

Thermoplastic materials aging under various stresses

The most popular cable insulation material used is XLPE due to its excellent electrical and thermal properties. However, it does not lend itself to ease of recycling. As a result of an increase in concern worldwide regarding environmental protection, it is the objective of this work to investigate whether a thermoplastic material could be used to replace XLPE for cable insulation. Among thermoplastic materials, HDPE is regarded as one with the most similar properties as XLPE. Although it is clear that the performance of polymeric material changes with different stresses, especially polymer nanocomposites aging process under AC electric field stresses, there are also not many publications on how a superimposed AC voltage would affect the insulation’s performance in HVDC power systems. This paper reports the dielectric properties of HDPE under thermo-electrical stresses. DC stress with and without a superimposed AC stress were applied in the experiments undertaken. The degradation of materials with change in frequencies are summarized and discussed

Aging behaviour of polypropylene under various voltage stresses

Practical HVDC systems have superimposed AC or transients and the synergistic effect of these factors on polymer aging would be of interest. Although it is clear that partial discharges under AC stress will gradually degrade the insulation behavior of a polymeric material, there are not many publications detailing the effect of superimposed AC voltages on polymer performance in a HVDC power system. Assuming polypropylene (PP) is suitable for use as electrical insulation, this paper considers the behavior of PP under various voltage ratios and temperatures. Factors which cause the degradation of PP will be summarized and explained. To simulate the working condition, electro-thermal aging equipment will be used. Fourier transform infrared spectroscopy - attenuated total reflection (FTIR-ATR) measurement and dielectric spectroscopy measurement will be carried out before and after aging

Parrot Captions Teach CLIP to Spot Text

Despite CLIP being the foundation model in numerous vision-language applications, the CLIP suffers from a severe text spotting bias. Such bias causes CLIP models to `Parrot' the visual text embedded within images while disregarding the authentic visual semantics. We uncover that in the most popular image-text dataset LAION-2B, the captions also densely parrot (spell) the text embedded in images. Our analysis shows that around 50% of images are embedded with visual text content, and around 30% of captions words are in these embedded visual content. Based on such observation, we thoroughly inspect the different released versions of CLIP models and verify that the visual text is the dominant factor in measuring the LAION-style image-text similarity for these models. To examine whether these parrot captions shape the text spotting bias, we train a series of CLIP models with LAION subsets curated by different parrot-caption-oriented criteria. We show that training with parrot captions easily shapes such bias but harms the expected visual-language representation learning in CLIP models. This suggests that it is urgent to revisit either the design of CLIP-like models or the existing image-text dataset curation pipeline built on CLIP score filtering.Comment: project page: https://linyq17.github.io/CLIP-Parrot-Bias/. Add more analysis and ablation studies. Update Figure 3 with a more precise metri

Transcriptional suppression of breast cancer resistance protein (BCRP) by wild-type p53 through the NF-κB pathway in MCF-7 cells

AbstractBreast cancer resistance protein (BCRP) has been shown to confer multidrug resistance, but the mechanisms of its regulation are poorly understood. Here, we investigate the effects of wild-type and mutant p53, and nuclear factor kappa-B (NF-κB) (p50) on BCRP promoter activity in MCF-7 cells. Our results demonstrated that wild-type p53 markedly suppressed BCRP activity and enhanced the chemosensitivity of cells to mitoxantrone, whereas mutant p53 had little inhibitory effect. After inhibition of NF-κB, similar results were obtained. Following knockdown of endogenous p53, BCRP and p50 expressions were increased, and the chemosensitivity of the cells to mitoxantrone was decreased. We conclude that wild-type p53 acts as a negative regulator of BCRP gene transcription

SEPT: Towards Scalable and Efficient Visual Pre-Training

Recently, the self-supervised pre-training paradigm has shown great potential in leveraging large-scale unlabeled data to improve downstream task performance. However, increasing the scale of unlabeled pre-training data in real-world scenarios requires prohibitive computational costs and faces the challenge of uncurated samples. To address these issues, we build a task-specific self-supervised pre-training framework from a data selection perspective based on a simple hypothesis that pre-training on the unlabeled samples with similar distribution to the target task can bring substantial performance gains. Buttressed by the hypothesis, we propose the first yet novel framework for Scalable and Efficient visual Pre-Training (SEPT) by introducing a retrieval pipeline for data selection. SEPT first leverage a self-supervised pre-trained model to extract the features of the entire unlabeled dataset for retrieval pipeline initialization. Then, for a specific target task, SEPT retrievals the most similar samples from the unlabeled dataset based on feature similarity for each target instance for pre-training. Finally, SEPT pre-trains the target model with the selected unlabeled samples in a self-supervised manner for target data finetuning. By decoupling the scale of pre-training and available upstream data for a target task, SEPT achieves high scalability of the upstream dataset and high efficiency of pre-training, resulting in high model architecture flexibility. Results on various downstream tasks demonstrate that SEPT can achieve competitive or even better performance compared with ImageNet pre-training while reducing the size of training samples by one magnitude without resorting to any extra annotations.Comment: Accepted by AAAI 202

Assessment of HDPE aged under DC voltage combined with AC harmonic stresses of various frequencies

One of the challenges on the increasing reliance on isolated renewable generation sources is the transmission of power from these sources to centers of power demand. One possible approach is the use of high voltage direct current (HVDC) transmission. The power electronic converters are key components in HVDC transmission system. The converters produce the intended DC voltage for transmission but there may also be AC harmonics superimposed. The superimposed harmonics on the HVDC may have synergistic effects and may lead to further degradation in the cable insulation. Previous research has shown that partial discharge was the main cause of degradation in polymeric insulation under AC stress. However, few publications have demonstrated the effect of combined stress on cable insulation degradation. Additionally, the most popular cable insulation material, cross-linked polyethylene (XLPE), cannot be recycled. Alternative materials which can be recycled have been proposed and one such solution could be thermoplastic materials. In this study, HDPE was investigated as a reference material for thermoplastics and their potential use as insulation in HVDC cables. In this paper the effect of frequency on HDPE degradation under superimposed stresses was studied using the following approaches; equivalent phase resolved partial discharge (PRPD) plots, fourier transform infrared spectroscopy - attenuated total reflection (FTIR-ATR) and dielectric spectroscopy (DS) measurements were carried out. The results show that during aging and with a frequency increase, the voltage of PD events increased which in turn created more polar molecule groups on the surface. The amount of polar molecule groups was found to affect ε' and tanδ, with both increasing when more polar molecules were created. The results show that applying a higher AC frequency enhances polymer degradatio

Contextualizing Interpersonal Data Sharing in Smart Homes

A key feature of smart home devices is monitoring the environment and recording data. These devices provide security via motion-detection video alerts, cost-savings via thermostat usage history, and peace of mind via functions like auto-locking doors or water leak detectors. At the same time, the sharing of this information in interpersonal relationships---though necessary---is currently accomplished on an all-or-nothing basis. This can easily lead to oversharing in a multi-user environment. Although prior work has studied people\u27s perceptions of information sharing with vendors or ISPs, the sharing of household data among users who interact personally is less well understood. Interpersonal situations make data sharing much more context-based and, thus, more complicated. In this paper, we use themes from the theory of contextual integrity in an online survey (n=1,992) to study how people perceive data sharing with others in smart homes and inform future designs and research. Our results show that data recipients in a smart home can be reduced to three major groups, and data types matter more than device types. We also found that the types of access control desired by users can vary from scenario to scenario. Depending on whom they are sharing data with and about what data, participants expressed varying levels of comfort when presented with different types of access control (e.g., explicit approval versus time-limited access). Taken together, this provides strong evidence that a more dynamic access control system is needed, and we can design it in a more usable way