Dynamic critical exponents of Swendsen-Wang and Wolff algorithms by nonequilibrium relaxation

With a nonequilibrium relaxation method, we calculate the dynamic critical exponent z of the two-dimensional Ising model for the Swendsen-Wang and Wolff algorithms. We examine dynamic relaxation processes following a quench from a disordered or an ordered initial state to the critical temperature T_c, and measure the exponential relaxation time of the system energy. For the Swendsen-Wang algorithm with an ordered or a disordered initial state, and for the Wolff algorithm with an ordered initial state, the exponential relaxation time fits well to a logarithmic size dependence up to a lattice size L=8192. For the Wolff algorithm with a disordered initial state, we obtain an effective dynamic exponent z_exp=1.19(2) up to L=2048. For comparison, we also compute the effective dynamic exponents through the integrated correlation times. In addition, an exact result of the Swendsen-Wang dynamic spectrum of a one-dimension Ising chain is derived.Comment: 13 pages, 6 figure

Methods for molecular characterization of dissolved organic matter in the alpine water environment: an overview

The alpine area has become a sensitive indicator and amplifier of global climate change and human activities because of its unique geographical and climatic conditions. Being an essential part of biochemical cycling, dissolved organic matter (DOM) could effectively help understand the process, structure, and function of alpine aquatic ecosystems. Due to the low content and sampling difficulties, the analysis of DOM in alpine water demands high sensitivity with low sample volume, which has not been comprehensively reviewed. This review summarizes the DOM sampling, pretreatment, and analysis methods involving the characterization of concentration, spectroscopy, and molecular structure. Overall, conventional parameters are the basis of advanced characterization methods. Spectroscopic tests can reveal the optical properties of DOM in response to lights from ultraviolet to infrared wavelengths, to distinguish the chemical composition. Molecular structure characterizations can provide microscopic information such as functional groups, element ratios, and molecular weights. The combination of multiple methods can depict DOM composition from multiple perspectives. In sum, optimized sampling and pretreatment, high-sensitivity molecular characterization, and method integration are crucial for effectively analyzing DOM components in alpine waters. These perspectives help to standardize the DOM characterization process and to understand the correlation between DOM composition and its properties, as well as the migration and transformation of DOM

DynamicLight: Dynamically Tuning Traffic Signal Duration with DRL

Deep reinforcement learning (DRL) is becoming increasingly popular in implementing traffic signal control (TSC). However, most existing DRL methods employ fixed control strategies, making traffic signal phase duration less flexible. Additionally, the trend of using more complex DRL models makes real-life deployment more challenging. To address these two challenges, we firstly propose a two-stage DRL framework, named DynamicLight, which uses Max Queue-Length to select the proper phase and employs a deep Q-learning network to determine the duration of the corresponding phase. Based on the design of DynamicLight, we also introduce two variants: (1) DynamicLight-Lite, which addresses the first challenge by using only 19 parameters to achieve dynamic phase duration settings; and (2) DynamicLight-Cycle, which tackles the second challenge by actuating a set of phases in a fixed cyclical order to implement flexible phase duration in the respective cyclical phase structure. Numerical experiments are conducted using both real-world and synthetic datasets, covering four most commonly adopted traffic signal intersections in real life. Experimental results show that: (1) DynamicLight can learn satisfactorily on determining the phase duration and achieve a new state-of-the-art, with improvement up to 6% compared to the baselines in terms of adjusted average travel time; (2) DynamicLight-Lite matches or outperforms most baseline methods with only 19 parameters; and (3) DynamicLight-Cycle demonstrates high performance for current TSC systems without remarkable modification in an actual deployment. Our code is released at Github.Comment: 9 pages, 5figure

The Multimodal Information based Speech Processing (MISP) 2022 Challenge: Audio-Visual Diarization and Recognition

The Multi-modal Information based Speech Processing (MISP) challenge aims to extend the application of signal processing technology in specific scenarios by promoting the research into wake-up words, speaker diarization, speech recognition, and other technologies. The MISP2022 challenge has two tracks: 1) audio-visual speaker diarization (AVSD), aiming to solve ``who spoken when'' using both audio and visual data; 2) a novel audio-visual diarization and recognition (AVDR) task that focuses on addressing ``who spoken what when'' with audio-visual speaker diarization results. Both tracks focus on the Chinese language, and use far-field audio and video in real home-tv scenarios: 2-6 people communicating each other with TV noise in the background. This paper introduces the dataset, track settings, and baselines of the MISP2022 challenge. Our analyses of experiments and examples indicate the good performance of AVDR baseline system, and the potential difficulties in this challenge due to, e.g., the far-field video quality, the presence of TV noise in the background, and the indistinguishable speakers.Comment: 5 pages, 4 figures, to be published in ICASSP202

Soil diazotrophic abundance, diversity, and community assembly mechanisms significantly differ between glacier riparian wetlands and their adjacent alpine meadows

Global warming can trigger dramatic glacier area shrinkage and change the flux of glacial runoff, leading to the expansion and subsequent retreat of riparian wetlands. This elicits the interconversion of riparian wetlands and their adjacent ecosystems (e.g., alpine meadows), probably significantly impacting ecosystem nitrogen input by changing soil diazotrophic communities. However, the soil diazotrophic community differences between glacial riparian wetlands and their adjacent ecosystems remain largely unexplored. Here, soils were collected from riparian wetlands and their adjacent alpine meadows at six locations from glacier foreland to lake mouth along a typical Tibetan glacial river in the Namtso watershed. The abundance and diversity of soil diazotrophs were determined by real-time PCR and amplicon sequencing based on nifH gene. The soil diazotrophic community assembly mechanisms were analyzed via iCAMP, a recently developed null model-based method. The results showed that compared with the riparian wetlands, the abundance and diversity of the diazotrophs in the alpine meadow soils significantly decreased. The soil diazotrophic community profiles also significantly differed between the riparian wetlands and alpine meadows. For example, compared with the alpine meadows, the relative abundance of chemoheterotrophic and sulfate-respiration diazotrophs was significantly higher in the riparian wetland soils. In contrast, the diazotrophs related to ureolysis, photoautotrophy, and denitrification were significantly enriched in the alpine meadow soils. The iCAMP analysis showed that the assembly of soil diazotrophic community was mainly controlled by drift and dispersal limitation. Compared with the riparian wetlands, the assembly of the alpine meadow soil diazotrophic community was more affected by dispersal limitation and homogeneous selection. These findings suggest that the conversion of riparian wetlands and alpine meadows can significantly alter soil diazotrophic community and probably the ecosystem nitrogen input mechanisms, highlighting the enormous effects of climate change on alpine ecosystems

Unimodal productivity-biodiversity relationship along the gradient of multidimensional resources across Chinese grasslands

Resources can affect plant productivity and biodiversity simultaneously and thus are key drivers of their relationships in addition to plant-plant interactions. However, most previous studies only focused on a single resource while neglecting the nature of resource multidimensionality. Here we integrated four essential resources for plant growth into a single metric of resource diversity (RD) to investigate its effects on the productivity-biodiversity relationship (PBR) across Chinese grasslands. Results showed that habitats differing in RD have different PBRs − positive in low-resource habitats, but neutral in medium- and high-resource ones—while collectively, a weak positive PBR was observed. However, when excluding direct effects of RD on productivity and biodiversity, PBR in high-resource habitats became negative, which leads to a unimodal instead of a positive PBR along the RD gradient. By integrating resource effects and changing plant-plant interactions into a unified framework with the RD gradient, our work contributes to uncovering underlying mechanisms for inconsistent PBRs at large scales

Protosappanin A protects against atherosclerosis via anti- hyperlipidemia, anti-inflammation and NF-κB signaling pathway in hyperlipidemic rabbits

Objective(s): Protosappanin A (PrA) is an effective and major ingredient of Caesalpinia sappan L. The current study was aimed to explore the effect of PrA on atherosclerosis (AS). Materials and Methods: Firstly, the experimental model of AS was established in rabbits by two-month feeding of high fat diet. Then, the rabbits were randomly divided into five groups and treated with continuous high lipid diet (model control), high lipid diet containing rosuvastatin (positive control), 5 mg/kg PrA (low dose) or 25 mg/kg PrA (high dose). Results: Our results showed that PrA markedly alleviated AS as indicated by hematoxylin/eosin (HE) staining. PrA also reduced hyperlipidemia (as demonstrated by the serum levels of total blood cholesterol (TC), triglyceride (TG), low-density lipoprotein (LDL) and high-density lipoprotein (HDL)) in a time and dose-dependent manner, and decreased inflammation (as indicated by the serum levels of matrix metalloproteinase-9 [MMP-9], interleukin-6 [IL-6] and tumor necrosis factor-α [TNF-α]). Moreover, PrA significantly inactivated nuclear factor kappa B (NF-κB) signaling as indicated by nuclear NF-κB p65 protein expression, as well as the mRNA expression and serum levels of downstream genes, interferon-γ (IFN-γ) and interferon-gamma-inducible protein 10 (IP10). Conclusion: This study proved that PrA might protect against atherosclerosis via anti-hyperlipidemia, anti-inflammation and NF-κB signaling pathways in hyperlipidemic rabbits