64 research outputs found
Lane Change Strategy for Autonomous Vehicle
Recently, people’s demand for smart vehicles continues to improve. As the core of smart driving, driverless vehicle becomes the most concerned technology. Lane change, the most common behavior in driverless situation, greatly affect the road efficiency. Fast and safe lane change operations have very practical significance in reducing traffic accidents. This paper uses driverless vehicle as research object, and the pathing planning and pathing tracking for lane change situation are studied. An efficient path planning method and trajectory tracking controller are designed and simulated. The main content contains the three following aspects: (1) A set of comprehensive lane change strategy is designed for different working conditions. Then path planning for lane change is researched based on mass point model and an efficient path planning method based on polynomial is proposed and optimized. (2) Kinematic model and 3 DOFs dynamic model of driverless vehicle based on magic tire model are established using SIMULINK. Several simulation and test are done to verify the rationality of the model. (3) The trajectory -tracking control system based on PID controller is designed. Then run simulation based on the model established and according to the results, the trajectory -tracking control system can track the lane-changing path accurately and analysis is made. Key word: Driverless vehicle, Lane change, Path planning, Trajectory tracking contro
Exploring Security Commits in Python
Python has become the most popular programming language as it is friendly to
work with for beginners. However, a recent study has found that most security
issues in Python have not been indexed by CVE and may only be fixed by 'silent'
security commits, which pose a threat to software security and hinder the
security fixes to downstream software. It is critical to identify the hidden
security commits; however, the existing datasets and methods are insufficient
for security commit detection in Python, due to the limited data variety,
non-comprehensive code semantics, and uninterpretable learned features. In this
paper, we construct the first security commit dataset in Python, namely
PySecDB, which consists of three subsets including a base dataset, a pilot
dataset, and an augmented dataset. The base dataset contains the security
commits associated with CVE records provided by MITRE. To increase the variety
of security commits, we build the pilot dataset from GitHub by filtering
keywords within the commit messages. Since not all commits provide commit
messages, we further construct the augmented dataset by understanding the
semantics of code changes. To build the augmented dataset, we propose a new
graph representation named CommitCPG and a multi-attributed graph learning
model named SCOPY to identify the security commit candidates through both
sequential and structural code semantics. The evaluation shows our proposed
algorithms can improve the data collection efficiency by up to 40 percentage
points. After manual verification by three security experts, PySecDB consists
of 1,258 security commits and 2,791 non-security commits. Furthermore, we
conduct an extensive case study on PySecDB and discover four common security
fix patterns that cover over 85% of security commits in Python, providing
insight into secure software maintenance, vulnerability detection, and
automated program repair.Comment: Accepted to 2023 IEEE International Conference on Software
Maintenance and Evolution (ICSME
Gray Matter Atrophy in Parkinson’s Disease and the Parkinsonian Variant of Multiple System Atrophy: A Combined ROI- and Voxel-Based Morphometric Study
OBJECTIVES: Parkinson’s disease (PD) and the parkinsonian variant of multiple system atrophy (MSA-P) are distinct neurodegenerative disorders that share similar clinical features of parkinsonism. The morphological alterations of these diseases have yet to be understood. The purpose of this study was to evaluate gray matter atrophy in PD and MSA-P using regions of interest (ROI)-based measurements and voxel-based morphometry (VBM). METHODS: We studied 41 patients with PD, 20 patients with MSA-P, and 39 controls matched for age, sex, and handedness using an improved T1-weighted sequence that eased gray matter segmentation. The gray matter volumes were measured using ROI and VBM. RESULTS: ROI volumetric measurements showed significantly reduced bilateral putamen volumes in MSA-P patients compared with those in PD patients and controls (po0.05), and the volumes of the bilateral caudate nucleus were significantly reduced in both MSA-P and PD patients compared with those in the controls (po0.05). VBM analysis revealed multifocal cortical and subcortical atrophy in both MSA-P and PD patients, and the volumes of the cerebellum and temporal lobes were remarkably reduced in MSA-P patients compared with the volumes in PD patients (po0.05). CONCLUSIONS: Both PD and MSA-P are associated with gray matter atrophy, which mainly involves the bilateral putamen, caudate nucleus, cerebellum, and temporal lobes. ROI and VBM can be used to identify these morphological alterations, and VBM is more sensitive and repeatable and less time-consuming, which may have potential diagnostic value
Nearly quantized conductance plateau of vortex zero mode in an iron-based superconductor
Majorana zero-modes (MZMs) are spatially-localized zero-energy fractional
quasiparticles with non-Abelian braiding statistics that hold a great promise
for topological quantum computing. Due to its particle-antiparticle
equivalence, an MZM exhibits robust resonant Andreev reflection and 2e2/h
quantized conductance at low temperature. By utilizing variable-tunnel-coupled
scanning tunneling spectroscopy, we study tunneling conductance of vortex bound
states on FeTe0.55Se0.45 superconductors. We report observations of conductance
plateaus as a function of tunnel coupling for zero-energy vortex bound states
with values close to or even reaching the 2e2/h quantum conductance. In
contrast, no such plateau behaviors were observed on either finite energy
Caroli-de Genne-Matricon bound states or in the continuum of electronic states
outside the superconducting gap. This unique behavior of the zero-mode
conductance reaching a plateau strongly supports the existence of MZMs in this
iron-based superconductor, which serves as a promising single-material platform
for Majorana braiding at a relatively high temperature
Efficient and ultra-stable perovskite light-emitting diodes
Perovskite light-emitting diodes (PeLEDs) have emerged as a strong contender
for next-generation display and information technologies. However, similar to
perovskite solar cells, the poor operational stability remains the main
obstacle toward commercial applications. Here we demonstrate ultra-stable and
efficient PeLEDs with extraordinary operational lifetimes (T50) of 1.0x10^4 h,
2.8x10^4 h, 5.4x10^5 h, and 1.9x10^6 h at initial radiance (or current
densities) of 3.7 W/sr/m2 (~5 mA/cm2), 2.1 W/sr/m2 (~3.2 mA/cm2), 0.42 W/sr/m2
(~1.1 mA/cm2), and 0.21 W/sr/m2 (~0.7 mA/cm2) respectively, and external
quantum efficiencies of up to 22.8%. Key to this breakthrough is the
introduction of a dipolar molecular stabilizer, which serves two critical roles
simultaneously. First, it prevents the detrimental transformation and
decomposition of the alpha-phase FAPbI3 perovskite, by inhibiting the formation
of lead and iodide intermediates. Secondly, hysteresis-free device operation
and microscopic luminescence imaging experiments reveal substantially
suppressed ion migration in the emissive perovskite. The record-long PeLED
lifespans are encouraging, as they now satisfy the stability requirement for
commercial organic LEDs (OLEDs). These results remove the critical concern that
halide perovskite devices may be intrinsically unstable, paving the path toward
industrial applications.Comment: This is a preprint of the paper prior to peer review. New and updated
results may be available in the final version from the publishe
Deep Weighted Averaging Classifiers
Recent advances in deep learning have achieved impressive gains in
classification accuracy on a variety of types of data, including images and
text. Despite these gains, however, concerns have been raised about the
calibration, robustness, and interpretability of these models. In this paper we
propose a simple way to modify any conventional deep architecture to
automatically provide more transparent explanations for classification
decisions, as well as an intuitive notion of the credibility of each
prediction. Specifically, we draw on ideas from nonparametric kernel
regression, and propose to predict labels based on a weighted sum of training
instances, where the weights are determined by distance in a learned
instance-embedding space. Working within the framework of conformal methods, we
propose a new measure of nonconformity suggested by our model, and
experimentally validate the accompanying theoretical expectations,
demonstrating improved transparency, controlled error rates, and robustness to
out-of-domain data, without compromising on accuracy or calibration.Comment: 13 pages, 8 figures, 5 tables, added DOI and updated to meet ACM
formatting requirements, In Proceedings of FAT* (2019
Polydatin ameliorates lipid and glucose metabolism in type 2 diabetes mellitus by downregulating proprotein convertase subtilisin/kexin type 9 (PCSK9)
Robust estimation of bacterial cell count from optical density
Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data
Maleic anhydride-modified xylanase and its application to the clarification of fruits juices
At presently, the catalytic activity of xylanase is sub-optimal, and the required reaction conditions are harsh. To improve its catalytic activity and stability, xylanase (XY) was chemically modified with maleic anhydride (MA). The enzymatic properties of this maleic anhydride-modified xylanase (MA-XY) were then evaluated and analyzed spectroscopically. The results showed that the thermal stability, use of organic solvents, storage stability and the pH range of 3.0 to 9.0 for MA-XY were better than that for XY alone. The kinetic parameters of the enzyme (Km values) decreased from 40.63 to 30.23Â mg/mL. Spectroscopic analysis showed that XY had been modified by the acylation reaction to become a tertiary structure. An assay based on clarifying fruit juices showed that the clarification capacity and reducing sugar content using MA-XY increased compared with those using XY. Overall, this study provides a theoretical basis for improving the application of XY in the food industry
Intelligent Radar Jamming Recognition in Open Set Environment Based on Deep Learning Networks
Jamming recognition is an essential step in radar detection and anti-jamming in the complex electromagnetic environment. When radars detect an unknown type of jamming that does not occur in the training set, the existing radar jamming recognition algorithms fail to correctly recognize it. However, these algorithms can only recognize this type of jamming as one that already exists in our jamming library. To address this issue, we present two models for radar jamming open set recognition (OSR) that can accurately classify known jamming and distinguish unknown jamming in the case of small samples. The OSR model based on the confidence score can distinguish known jamming from unknown jamming by assessing the reliability of the sample output probability distribution and setting thresholds. Meanwhile, the OSR model based on OpenMax can output the probability of jamming belonging to not only all known classes but also unknown classes. Experimental results show that the two OSR models exhibit high recognition accuracy for known and unknown jamming and play a vital role in sensing complex jamming environments
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