Hawking radiation-quasinormal modes correspondence for large AdS black holes

It is well-known that the non-strictly thermal character of the Hawking radiation spectrum generates a natural correspondence between Hawking radiation and black hole quasinormal modes. This main issue has been analyzed in the framework of Schwarzschild black holes, Kerr black holes and nonextremal Reissner-Nordstrom black holes. In this paper, by introducing the effective temperature, we reanalysis the non-strictly thermal character of large AdS black holes. The results show that the effective mass corresponding to the effective temperature is approximatively the average one in any dimension. And the other effective quantities can also be obtained. Based on the known forms of frequency in quasinormal modes, we reanalysis the asymptotic frequencies of the large AdS black hole in three and five dimensions. Then we get the formulas of the Bekenstein-Hawking entropy and the horizon's area quantization with functions of the quantum "overtone" number $n$.Comment: 6 page

MM-PCQA: Multi-Modal Learning for No-reference Point Cloud Quality Assessment

The visual quality of point clouds has been greatly emphasized since the ever-increasing 3D vision applications are expected to provide cost-effective and high-quality experiences for users. Looking back on the development of point cloud quality assessment (PCQA) methods, the visual quality is usually evaluated by utilizing single-modal information, i.e., either extracted from the 2D projections or 3D point cloud. The 2D projections contain rich texture and semantic information but are highly dependent on viewpoints, while the 3D point clouds are more sensitive to geometry distortions and invariant to viewpoints. Therefore, to leverage the advantages of both point cloud and projected image modalities, we propose a novel no-reference point cloud quality assessment (NR-PCQA) metric in a multi-modal fashion. In specific, we split the point clouds into sub-models to represent local geometry distortions such as point shift and down-sampling. Then we render the point clouds into 2D image projections for texture feature extraction. To achieve the goals, the sub-models and projected images are encoded with point-based and image-based neural networks. Finally, symmetric cross-modal attention is employed to fuse multi-modal quality-aware information. Experimental results show that our approach outperforms all compared state-of-the-art methods and is far ahead of previous NR-PCQA methods, which highlights the effectiveness of the proposed method. The code is available at https://github.com/zzc-1998/MM-PCQA

Sliding-Mode-Observer-Based Position Estimation for Sensorless Control of the Planar Switched Reluctance Motor

This paper proposes a position estimation method for a planar switched reluctance motor (PSRM). In the method, a second-order sliding mode observer (SMO) is used to achieve sensorless control of a PSRM for the first time. A sensorless closed-loop control strategy based on the SMO without a position sensor for the PSRM is constructed. The SMO mainly consists of a flux linkage estimation, an adaptive current estimation, an observing error calculation, and a position estimation section. An adaptive current observer is applied in the current estimation section to minimize the error between the measured and estimated currents and to increase the accuracy of the position estimation. The flux linkage is estimated by the voltage equation of the PSRM, and the estimated flux linkage is then used to estimate the phase current in the adaptive current observer. To calculate the observing error of the SMO using the measured and estimated phase currents, the observing error of the thrust force is introduced to replace the immeasurable state error of the position and speed of the mover. The sliding surface is designed based on the error of the thrust force, and stability analysis is given. Once the sliding surface is reached, the mover position is then estimated accurately. Finally, the effectiveness of the proposed method for the PSRM is verified experimentally

1β,10α:4β,5α-Diep­oxy-7αH-germacran-6β-ol monohydrate

In the title compound, C15H26O3·H2O, a sesquiterpenoid mol­ecule with a germacrene backbone that contains two epoxide groups and one hydroxyl group. Inter­molecular O—H⋯O hydrogen bonds between the ep­oxy groups and solvent water mol­ecules give rise to an infinite three-dimensional supra­molecular structure

Subjective and Objective Quality Assessment for in-the-Wild Computer Graphics Images

Computer graphics images (CGIs) are artificially generated by means of computer programs and are widely perceived under various scenarios, such as games, streaming media, etc. In practical, the quality of CGIs consistently suffers from poor rendering during the production and inevitable compression artifacts during the transmission of multimedia applications. However, few works have been dedicated to dealing with the challenge of computer graphics images quality assessment (CGIQA). Most image quality assessment (IQA) metrics are developed for natural scene images (NSIs) and validated on the databases consisting of NSIs with synthetic distortions, which are not suitable for in-the-wild CGIs. To bridge the gap between evaluating the quality of NSIs and CGIs, we construct a large-scale in-the-wild CGIQA database consisting of 6,000 CGIs (CGIQA-6k) and carry out the subjective experiment in a well-controlled laboratory environment to obtain the accurate perceptual ratings of the CGIs. Then, we propose an effective deep learning-based no-reference (NR) IQA model by utilizing multi-stage feature fusion strategy and multi-stage channel attention mechanism. The major motivation of the proposed model is to make full use of inter-channel information from low-level to high-level since CGIs have apparent patterns as well as rich interactive semantic content. Experimental results show that the proposed method outperforms all other state-of-the-art NR IQA methods on the constructed CGIQA-6k database and other CGIQA-related databases. The database along with the code will be released to facilitate further research

Fasciolopsis buski (Digenea: Fasciolidae) from China and India may represent distinct taxa based on mitochondrial and nuclear ribosomal DNA sequences

Sequences of primers used to amplify fragments of Fasciolopsis buski mitochondrial genome. (DOCX 17 kb

Organic carbon amendments affect the chemodiversity of soil dissolved organic matter and its associations with soil microbial communities

The “4 per mil” initiative recognizes the pivotal role of soil in carbon re-sequestration. The need for evidence to substantiate the influence of agricultural practices on chemical nature of soil carbon and microbial biodiversity has become a priority. However, owing to the molecular complexity of soil dissolved organic matter (DOM), specific linkages to microbial biodiversity have eluded researchers. Here, we characterized the chemodiversity of soil DOM, assessed the variation of soil bacterial community composition (BCC) and identified specific linkages between DOM traits and BCC. Sustained organic carbon amendment significantly (P < 0.05) increased total organic matter reservoirs, resulted in higher chemodiversity of DOM and emergence of recalcitrant moieties (H/C < 1.5). In the meantime, sustained organic carbon amendment shaped the BCC to a more eutrophic state while long-term chemical fertilization directed the BCC towards an oligotrophic state. Meanwhile, higher connectivity and complexity were observed in organic carbon amendment by DOM-BCC network analysis, indicating that soil microbes tended to have more interaction with DOM molecules after organic matter inputs. These results highlight the potential for organic carbon amendments to not only build soil carbon stocks and increase their resilience but also mediate the functional state of soil bacterial communities