HARL: A Novel Hierachical Adversary Reinforcement Learning for Automoumous Intersection Management

As an emerging technology, Connected Autonomous Vehicles (CAVs) are believed to have the ability to move through intersections in a faster and safer manner, through effective Vehicle-to-Everything (V2X) communication and global observation. Autonomous intersection management is a key path to efficient crossing at intersections, which reduces unnecessary slowdowns and stops through adaptive decision process of each CAV, enabling fuller utilization of the intersection space. Distributed reinforcement learning (DRL) offers a flexible, end-to-end model for AIM, adapting for many intersection scenarios. While DRL is prone to collisions as the actions of multiple sides in the complicated interactions are sampled from a generic policy, restricting the application of DRL in realistic scenario. To address this, we propose a hierarchical RL framework where models at different levels vary in receptive scope, action step length, and feedback period of reward. The upper layer model accelerate CAVs to prevent them from being clashed, while the lower layer model adjust the trends from upper layer model to avoid the change of mobile state causing new conflicts. And the real action of CAV at each step is co-determined by the trends from both levels, forming a real-time balance in the adversarial process. The proposed model is proven effective in the experiment undertaken in a complicated intersection with 4 branches and 4 lanes each branch, and show better performance compared with baselines

Disentangled Contrastive Image Translation for Nighttime Surveillance

Nighttime surveillance suffers from degradation due to poor illumination and arduous human annotations. It is challengable and remains a security risk at night. Existing methods rely on multi-spectral images to perceive objects in the dark, which are troubled by low resolution and color absence. We argue that the ultimate solution for nighttime surveillance is night-to-day translation, or Night2Day, which aims to translate a surveillance scene from nighttime to the daytime while maintaining semantic consistency. To achieve this, this paper presents a Disentangled Contrastive (DiCo) learning method. Specifically, to address the poor and complex illumination in the nighttime scenes, we propose a learnable physical prior, i.e., the color invariant, which provides a stable perception of a highly dynamic night environment and can be incorporated into the learning pipeline of neural networks. Targeting the surveillance scenes, we develop a disentangled representation, which is an auxiliary pretext task that separates surveillance scenes into the foreground and background with contrastive learning. Such a strategy can extract the semantics without supervision and boost our model to achieve instance-aware translation. Finally, we incorporate all the modules above into generative adversarial networks and achieve high-fidelity translation. This paper also contributes a new surveillance dataset called NightSuR. It includes six scenes to support the study on nighttime surveillance. This dataset collects nighttime images with different properties of nighttime environments, such as flare and extreme darkness. Extensive experiments demonstrate that our method outperforms existing works significantly. The dataset and source code will be released on GitHub soon.Comment: Submitted to TI

Whole Genome Sequencing and Comparative Genomics Analyses of Pandoraea sp. XY-2, a New Species Capable of Biodegrade Tetracycline

Few bacteria are resistant to tetracycline and can even biodegrade tetracycline in the environment. In this study, we isolated a bacterium Pandoraea sp. XY-2, which could biodegrade 74% tetracycline at pH 7.0 and 30°C within 6 days. Thereafter, we determined the whole genome sequence of Pandoraea sp. XY-2 genome is a single circular chromosome of 5.06 Mb in size. Genomic annotation showed that two AA6 family members-encoding genes and nine glutathione S-transferase (GSTs)-encoding genes could be relevant to tetracycline biodegradation. In addition, the average nucleotide identities (ANI) analysis between the genomes of Pandoraea sp. XY-2 and other Pandoraea spp. revealed that Pandoraea sp. XY-2 belongs to a new species. Moreover, comparative genome analysis of 36 Pandoraea strains identified the pan and specific genes, numerous single nucleotide polymorphisms (SNPs), insertions, and deletion variations (InDels) and different syntenial relationships in the genome of Pandoraea sp. XY-2. Finally, the evolution and the origin analysis of genes related to tetracycline resistance revealed that the six tetA(48) genes and two specificgenes tetG and tetR in Pandoraea sp. XY-2 were acquired by horizontal gene transfer (HGT) events from sources related to Paraburkholderia, Burkholderia, Caballeronia, Salmonella, Vibrio, Proteobacteria, Pseudomonas, Acinetobacter, Flavimaricola, and some unidentified sources. As a new species, Pandoraea sp. XY-2 will be an excellent resource for the bioremediation of tetracycline-contaminated environment

Rechargeable Li/Cl2_2 battery down to -80 {\deg}C

Low temperature rechargeable batteries are important to life in cold climates, polar/deep-sea expeditions and space explorations. Here, we report ~ 3.5 - 4 V rechargeable lithium/chlorine (Li/Cl2) batteries operating down to -80 {\deg}C, employing Li metal negative electrode, a novel CO2 activated porous carbon (KJCO2) as the positive electrode, and a high ionic conductivity (~ 5 to 20 mS cm-1 from -80 {\deg}C to 25 {\deg}C) electrolyte comprised of 1 M aluminum chloride (AlCl3), 0.95 M lithium chloride (LiCl), and 0.05 M lithium bis(fluorosulfonyl)imide (LiFSI) in low melting point (-104.5 {\deg}C) thionyl chloride (SOCl2). Between room-temperature and -80 {\deg}C, the Li/Cl2 battery delivered up to ~ 30,000 - 4,500 mAh g-1 first discharge capacity and a 1,200 - 5,000 mAh g-1 reversible capacity (discharge voltages in ~ 3.5 to 3.1 V) over up to 130 charge-discharge cycles. Mass spectrometry and X-ray photoelectron spectroscopy (XPS) probed Cl2 trapped in the porous carbon upon LiCl electro-oxidation during charging. At lower temperature down to -80 {\deg}C, SCl2/S2Cl2 and Cl2 generated by electro-oxidation in the charging step were trapped in porous KJCO2 carbon, allowing for reversible reduction to afford a high discharge voltage plateau near ~ 4 V with up to ~ 1000 mAh g-1 capacity for SCl2/S2Cl2 reduction and up to ~ 4000 mAh g-1 capacity at ~ 3.1 V plateau for Cl2 reduction. Towards practical use, we made CR2032 Li/Cl2 battery cells to drive digital watches at -40 {\deg}C and light emitting diode at -80 {\deg}C, opening Li/Cl2 secondary batteries for ultra-cold conditions

High-resolution carbon isotope stratigraphy of the Lower and Middle Ordovician succession of the Yangtze Platform, China

Variation in the relative abundance of the stable carbon isotopes has been widely used to correlate Ordovician marine successions over the past two decades. To date, only a few of studies of Ordovician carbon chemostratigraphy have been conducted in South China. Most of the previous studies in this field have focused on specific time intervals and/or events in the Middle and Upper Ordovician. The Lower and Middle Ordovician of the Yangtze Platform is typically represented by a sedimentary succession dominated by carbonate rocks, which is ideal for studying the carbon chemostratigraphy. Three sections spanning the Nantsinkuan/Lunshan, Fenhsiang, Hunghuayuan, and Dawan/Zitai formations, corresponding to the TremadocianâDapingian in age, have been sampled for high-resolution δ13C chemostratigraphy. Our new δ13C data reveal five tie-points with the potential for global correlation: (1) a positive δ13C excursion in the lower Nantsinkuan Formation within the Tremadocian Rossodus manitouensis Zone; (2) an excursion with two peaks roughly within the late Tremadocian Paltodus âdeltiferâ Zone; (3) a positive δ13C shift in the lower Hunghuayuan Formation, within the early Floian Serratognathus diversus Zone; (4) a gradual positive δ13C shift in the late Floian, ranging from the uppermost S. diversus Zone to the basal Oepikodus evae Zone; (5) a minor negative shift in the lower Dawan/Zitai Formation, within the early Dapingian Baltoniodus triangularis Zone. These excursions are herein used for correlation of the Yangtze Platform strata with successions from South China, North China, the Argentine Precordillera, North America and Baltica. From a palaeogeographical perspective, the Gudongkou, Xiangshuidong and Daling sections represent depositional environments along an inner to outer ramp profile. The δ13C data from these sections show successively heavier (higher) δ13C values with increasing depositional depth, which can be interpreted as due to remineralization of organic carbon within the carbonate rocks formed in the shallow-water environment

Climate warming accelerates temporal scaling of grassland soil microbial biodiversity.

Determining the temporal scaling of biodiversity, typically described as species-time relationships (STRs), in the face of global climate change is a central issue in ecology because it is fundamental to biodiversity preservation and ecosystem management. However, whether and how climate change affects microbial STRs remains unclear, mainly due to the scarcity of long-term experimental data. Here, we examine the STRs and phylogenetic-time relationships (PTRs) of soil bacteria and fungi in a long-term multifactorial global change experiment with warming (+3 °C), half precipitation (-50%), double precipitation (+100%) and clipping (annual plant biomass removal). Soil bacteria and fungi all exhibited strong STRs and PTRs across the 12 experimental conditions. Strikingly, warming accelerated the bacterial and fungal STR and PTR exponents (that is, the w values), yielding significantly (P < 0.001) higher temporal scaling rates. While the STRs and PTRs were significantly shifted by altered precipitation, clipping and their combinations, warming played the predominant role. In addition, comparison with the previous literature revealed that soil bacteria and fungi had considerably higher overall temporal scaling rates (w = 0.39-0.64) than those of plants and animals (w = 0.21-0.38). Our results on warming-enhanced temporal scaling of microbial biodiversity suggest that the strategies of soil biodiversity preservation and ecosystem management may need to be adjusted in a warmer world

CQDFormer: Cyclic Quasi-Dynamic Transformers for Hourly Origin-Destination Estimation

Due to the inherent difficulty in direct observation of traffic demand (including generation, attraction, and assignment), the estimation of origin–destination (OD) poses a significant and intricate challenge in the realm of Intelligent Transportation Systems. As the state-of-the-art methods usually focus on a single traffic demand distribution, accurate estimation of OD in the face of diverse traffic demand and road structures remains a formidable task. To this end, this study proposes a novel model, Cyclic Quasi-Dynamic Transformers (CQDFormer), which leverages forward and backward neural networks for effective OD estimation and traffic assignment. The employment of quasi-dynamic assumption and self-attention mechanism enables CQDFormer to capture the diverse and non-linear characteristics inherent in traffic demand. We utilize calibrated simulations to generate traffic count-OD pairwise data. Additionally, we incorporate real prior matrices and traffic count data to mitigate the distributional shift between simulation and the reality. The proposed CQDFormer is examined using Simuation of Urban Mobility (SUMO), on a large-scale downtown area in Haikou, China, comprising 2328 roads and 1171 junctions. It is found that CQDFormer shows satisfied convergence performance, and achieves a reduction of RMSE by 46.98%, MAE by 45.40% and MAPE by 29.76%, in comparison to the state-of-the-art method with the best performance