MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning

Driving safely requires multiple capabilities from human and intelligent agents, such as the generalizability to unseen environments, the safety awareness of the surrounding traffic, and the decision-making in complex multi-agent settings. Despite the great success of Reinforcement Learning (RL), most of the RL research works investigate each capability separately due to the lack of integrated environments. In this work, we develop a new driving simulation platform called MetaDrive to support the research of generalizable reinforcement learning algorithms for machine autonomy. MetaDrive is highly compositional, which can generate an infinite number of diverse driving scenarios from both the procedural generation and the real data importing. Based on MetaDrive, we construct a variety of RL tasks and baselines in both single-agent and multi-agent settings, including benchmarking generalizability across unseen scenes, safe exploration, and learning multi-agent traffic. The generalization experiments conducted on both procedurally generated scenarios and real-world scenarios show that increasing the diversity and the size of the training set leads to the improvement of the generalizability of the RL agents. We further evaluate various safe reinforcement learning and multi-agent reinforcement learning algorithms in MetaDrive environments and provide the benchmarks. Source code, documentation, and demo video are available at https://metadriverse.github.io/metadrive . More research projects based on MetaDrive simulator are listed at https://metadriverse.github.ioComment: Source code, documentation, and demo video are available at https://metadriverse.github.io/metadrive . More research projects based on MetaDrive simulator are listed at https://metadriverse.github.i

Cloning and Functional Analysis of the MADS-box CiMADS9 Gene from Carya illinoinensis

AbstractA MADS-box gene, CiMADS9, was cloned from the male flowers of Carya illinoinensis by rapid amplification of cDNA ends. The gene was 1 077bp with a 768bp open reading frame encoding 255 amino acids. Multiple sequence comparisons revealed that CiMADS9 is a typical MIKC-type MADS-box gene with a MADS-box domain and a K semi-conserved region. Phylogenetic analysis indicated that CiMADS9 belongs to the AGL15 group of the MADS-box gene family. Quantitative reverse transcription polymerase chain reaction analysis indicated that the expression levels in reproductive organs (i.e., flowers and young fruits) were considerably higher than in vegetative tissues (i.e., leaves and branches). The highest expression levels were observed in male flowers. An overexpression vector for CiMADS9 was constructed and the gene was inserted into the Arabidopsis thaliana genome. CiMADS9 expression was confirmed in all transgenic lines. Compared with wild-type plants, transgenic A. thaliana plants overexpressing CiMADS9 exhibited delayed flowering and an increased number of leaves

Transcriptome analysis reveals salt-stress-regulated biological processes and key pathways in roots of cotton (Gossypium hirsutum L.)

AbstractHigh salinity is one of the main factors limiting cotton growth and productivity. The genes that regulate salt stress in TM-1 upland cotton were monitored using microarray and real-time PCR (RT-PCR) with samples taken from roots. Microarray analysis showed that 1503 probe sets were up-regulated and 1490 probe sets were down-regulated in plants exposed for 3h to 100mM NaCl, and RT-PCR analysis validated 42 relevant/related genes. The distribution of enriched gene ontology terms showed such important processes as the response to water stress and pathways of hormone metabolism and signal transduction were induced by the NaCl treatment. Some key regulatory gene families involved in abiotic and biotic sources of stress such as WRKY, ERF, and JAZ were differentially expressed. Our transcriptome analysis might provide some useful insights into salt-mediated signal transduction pathways in cotton and offer a number of candidate genes as potential markers of tolerance to salt stress

Gene Expression Profiles Deciphering Rice Phenotypic Variation between Nipponbare (Japonica) and 93-11 (Indica) during Oxidative Stress

Rice is a very important food staple that feeds more than half the world's population. Two major Asian cultivated rice (Oryza sativa L.) subspecies, japonica and indica, show significant phenotypic variation in their stress responses. However, the molecular mechanisms underlying this phenotypic variation are still largely unknown. A common link among different stresses is that they produce an oxidative burst and result in an increase of reactive oxygen species (ROS). In this study, methyl viologen (MV) as a ROS agent was applied to investigate the rice oxidative stress response. We observed that 93-11 (indica) seedlings exhibited leaf senescence with severe lesions under MV treatment compared to Nipponbare (japonica). Whole-genome microarray experiments were conducted, and 1,062 probe sets were identified with gene expression level polymorphisms between the two rice cultivars in addition to differential expression under MV treatment, which were assigned as Core Intersectional Probesets (CIPs). These CIPs were analyzed by gene ontology (GO) and highlighted with enrichment GO terms related to toxin and oxidative stress responses as well as other responses. These GO term-enriched genes of the CIPs include glutathine S-transferases (GSTs), P450, plant defense genes, and secondary metabolism related genes such as chalcone synthase (CHS). Further insertion/deletion (InDel) and regulatory element analyses for these identified CIPs suggested that there may be some eQTL hotspots related to oxidative stress in the rice genome, such as GST genes encoded on chromosome 10. In addition, we identified a group of marker genes individuating the japonica and indica subspecies. In summary, we developed a new strategy combining biological experiments and data mining to study the possible molecular mechanism of phenotypic variation during oxidative stress between Nipponbare and 93-11. This study will aid in the analysis of the molecular basis of quantitative traits

Disrupted Asymmetry of Inter- and Intra-Hemispheric Functional Connectivity at Rest in Medication-Free Obsessive-Compulsive Disorder

Disrupted functional asymmetry of cerebral hemispheres may be altered in patients with obsessive-compulsive disorder (OCD). However, little is known about whether anomalous brain asymmetries originate from inter- and/or intra-hemispheric functional connectivity (FC) at rest in OCD. In this study, resting-state functional magnetic resonance imaging was applied to 40 medication-free patients with OCD and 38 gender-, age-, and education-matched healthy controls (HCs). Data were analyzed using the parameter of asymmetry (PAS) and support vector machine methods. Patients with OCD showed significantly increased PAS in the left posterior cingulate cortex, left precentral gyrus/postcentral gyrus, and right inferior occipital gyrus and decreased PAS in the left dorsolateral prefrontal cortex (DLPFC), bilateral middle cingulate cortex (MCC), left inferior parietal lobule, and left cerebellum Crus I. A negative correlation was found between decreased PAS in the left DLPFC and Yale–Brown Obsessive-compulsive Scale compulsive behavior scores in the patients. Furthermore, decreased PAS in the bilateral MCC could be used to distinguish OCD from HCs with a sensitivity of 87.50%, an accuracy of 88.46%, and a specificity of 89.47%. These results highlighted the contribution of disrupted asymmetry of intra-hemispheric FC within and outside the cortico-striato-thalamocortical circuits at rest in the pathophysiology of OCD, and reduced intra-hemispheric FC in the bilateral MCC may serve as a potential biomarker to classify individuals with OCD from HCs

Online Bayesian Data Fusion in Environment Monitoring Sensor Networks

Assuring reliable data collection in environment monitoring sensor network is a major design challenge. This paper gives a novel Bayesian model to reliably monitor physical phenomenon. We briefly review the errors on the data transfer channel between the sensor quantifying the physical phenomenon and the fusion node, and a discrete K -ary input and K -ary output channel is presented to model the data transfer channel, where K is the number of quantification levels at the sensor. Then, discrete time series models are used to estimate the mean value of the physical phenomenon, and the estimation error is modeled as a Gaussian process. Finally, based on the transition probability of the proposed data transfer channel and the probability of the estimated value transited to specific quantification levels, the level with the maximum posterior probability is decided to be the current value of the physical phenomenon. Evaluations based on real sensor data show that significant gain can be achieved by the proposed algorithms in environment monitoring sensor networks compared with channel-unaware algorithms

Effect of Carbon Content on Friction and Wear Properties of Copper Matrix Composites at High Speed Current-Carrying

The copper matrix composites were prepared by spark plasma sintering (SPS). The current-carrying friction and wear tests were carried out on a self-made HST-100 high-speed current-carrying friction and wear tester, and the effect of the graphite content on the current-carrying friction and wear properties of the composite material was studied. The results show that with an increase in graphite content, the average friction coefficient and wear rate of the two materials decreased significantly, the fluctuation amplitude of the friction coefficient was also significantly reduced, and the average friction coefficient of copper-coated graphite composite with graphite content of 10 wt.% was 0.100; when the graphite content was the same and more than 5.0 wt.%, the average friction coefficient and wear rate of copper–graphite composites were slightly higher than copper–copper-coated graphite composites; the current-carrying efficiency and current-carrying stability of the copper matrix composite were obviously higher than that of copper material; there was a mechanical wear area and arc erosion area on the wear surface of the composites, with the increase in graphite content, the adherence and the tear of the mechanical wear area weakened, the rolling, plastic deformation increased, and the surface roughness decreased obviously. The surface roughness of the wear surface of copper–copper-coated graphite composites with graphite content of 10 wt.% was 3.17 μm. The forms of arc erosion included melting and splashing, and were mainly distributed in the friction exit area

Sustainable shape memory polymers based on epoxidized natural rubber cured by zinc ferulate via oxa-Michael reaction

Although various shape memory polymers (SMPs) or diverse applications have been widely reported, the SMPs based on rubbers have been rarely realized due to the low triggering temperature of rubbers. In another aspect, the SMPs based on sustainable substances are highly desired for the growing shortage in fossil resources. In the present study, we accordingly developed the sustainable SMPs with tunable triggering temperature, based on natural rubber (NR) and ferulic acid (FA) as the raw materials. Specifically, the SMPs are based on a crosslinked network of epoxidized natural rubber (ENR) crosslinked by in situ formed zinc ferulate (ZDF) via oxa-Michael reaction. The excellent shape memory effect (SME) is found in these SMPs, as evidenced by the high fixity/recovery ratio and the tunable triggering temperature. With the incorporation of natural halloysite nanotubes (HNTs), the stress and recovery rate of the SMPs are found to be tunable, which widens the application of this kind of SMPs. The combination of adoption of sustainable raw materials, and the excellent and tunable SME makes these SMPs potentially useful in many applications, such as various actuators and heat-shrinkable package materials