G-VAE: A Continuously Variable Rate Deep Image Compression Framework

Rate adaption of deep image compression in a single model will become one of the decisive factors competing with the classical image compression codecs. However, until now, there is no perfect solution that neither increases the computation nor affects the compression performance. In this paper, we propose a novel image compression framework G-VAE (Gained Variational Autoencoder), which could achieve continuously variable rate in a single model. Unlike the previous solutions that encode progressively or change the internal unit of the network, G-VAE only adds a pair of gain units at the output of encoder and the input of decoder. It is so concise that G-VAE could be applied to almost all the image compression methods and achieve continuously variable rate with negligible additional parameters and computation. We also propose a new deep image compression framework, which outperforms all the published results on Kodak datasets in PSNR and MS-SSIM metrics. Experimental results show that adding a pair of gain units will not affect the performance of the basic models while endowing them with continuously variable rate

A Research on Community-Based Livestock of Qinghai-Tibet Plateau

Qinghai-Tibet Plateau locates in Southwestern China, covering the whole area of Tibet Autonomous Region, Qinghai Province, Southern part of Gansu Province, Northwestern part of Sichuan Province and Northwestern part of Yunnan Province, with an area of around 139.08 million hectares of natural grassland, accounting for 39% of the total area of natural grassland in China. It is also the largest natural ecozones in China and one of the least disturbed regions by human activities, with its air, water sources, soil, grassland, wildlife in their pristine state. Qinghai-Tibet Plateau is the native home for Tibetan people. Grassland animal husbandry is the foundation of the economy of QTP and the main source of livelihood for local nomadic people. During the long term of concerted evolution with the nature, Tibetan people living on Qinghai-Tibet Plateau have formed a uniquely holistic grassland ecological culture that is compatible with their production system and the ecosystem. The majority of Tibetan people observe Tibetan Buddhism. Their respect for nature and their belief in that all sentient beings are equal take deep root in their traditional culture. Their harmonious co-existence with nature exemplifies the eco-civilization ideas and provides a solid cultural foundation for both ecology conservation and featured animal husbandry development. On Qinghai-Tibet Plateau, national policies and initiatives such as dual contract of livestock and forage, natural grassland vegetation recovery, returning grazing land to grassland, grassland ecosystem subsidy and rewarding mechanism have been implemented, playing an important role in promoting grassland ecosystem conservation and grassland animal husbandry development. However, since grassland animal husbandry is a complex system involving grassland, farm animal, environment, society, economy, culture, etc, there are still many outstanding problems to be solved

Statistical Origin of Constituent-Quark Scaling in the QGP hadronization

Nonextensive statistics in a Blast-Wave model (TBW) is implemented to describe the identified hadron production in relativistic p+p and nucleus-nucleus collisions. Incorporating the core and corona components within the TBW formalism allows us to describe simultaneously some of the major observations in hadronic observables at the Relativistic Heavy-Ion Collider (RHIC): the Number of Constituent Quark Scaling (NCQ), the large radial and elliptic flow, the effect of gluon saturation and the suppression of hadron production at high transverse momentum (pT) due to jet quenching. In this formalism, the NCQ scaling at RHIC appears as a consequence of non-equilibrium process. Our study also provides concise reference distributions with a least chi2 fit of the available experimental data for future experiments and models.Comment: 4 pages, 3 figures; added two tables, explained a little bit more on TBW_p

Capn2 Correlates With insulin Resistance States in Pcos as Evidenced By Multi-Dataset analysis

OBJECTIVE: IR emerges as a feature in the pathophysiology of PCOS, precipitating ovulatory anomalies and endometrial dysfunctions that contribute to the infertility challenges characteristic of this condition. Despite its clinical significance, a consensus on the precise mechanisms by which IR exacerbates PCOS is still lacking. This study aims to harness bioinformatics tools to unearth key IR-associated genes in PCOS patients, providing a platform for future therapeutic research and potential intervention strategies. METHODS: We retrieved 4 datasets detailing PCOS from the GEO, and sourced IRGs from the MSigDB. We applied WGCNA to identify gene modules linked to insulin resistance, utilizing IR scores as a phenotypic marker. Gene refinement was executed through the LASSO, SVM, and Boruta feature selection algorithms. qPCR was carried out on selected samples to confirm findings. We predicted both miRNA and lncRNA targets using the ENCORI database, which facilitated the construction of a ceRNA network. Lastly, a drug-target network was derived from the CTD. RESULTS: Thirteen genes related to insulin resistance in PCOS were identified via WGCNA analysis. LASSO, SVM, and Boruta algorithms further isolated CAPN2 as a notably upregulated gene, corroborated by biological verification. The ceRNA network involving lncRNA XIST and hsa-miR-433-3p indicated a possible regulatory link with CAPN2, supported by ENCORI database. Drug prediction analysis uncovered seven pharmacological agents, most being significant regulators of the endocrine system, as potential candidates for addressing insulin resistance in PCOS. CONCLUSIONS: This study highlights the pivotal role of CAPN2 in insulin resistance within the context of PCOS, emphasizing its importance as both a critical biomarker and a potential therapeutic target. By identifying CAPN2, our research contributes to the expanding evidence surrounding the CAPN family, particularly CAPN10, in insulin resistance studies beyond PCOS. This work enriches our understanding of the mechanisms underlying insulin resistance, offering insights that bridge gaps in the current scientific landscape

Investigating Zcs(3985)Z_{cs}(3985) and Zcs(4000)Z_{cs}(4000) exotic states in Λb→Zcs−p\Lambda_b\to Z^-_{cs}p decays

We study the $Z_{cs}(3985)$ and $Z_{cs}(4000)$ exotic states in the decays of $\Lambda_b$ baryons through a molecular scenario. In the final state interaction, the $\Lambda_b\to \Lambda_c D_s^{(*)-}$ decays are followed by the $\Lambda_c D_s^{(*)-}$ to $Z^-_{cs}p$ rescatterings via exchange of a $D^{(*)}$ meson. We predict a branching fraction of $(3.1^{+1.4}_{-2.6})\times 10^{-4}$ for $\Lambda_b\to Z^-_{cs}p$, which can be measured in the $\Lambda_b\to J/\psi K^{(*)-}p$ decay. This study provides insights into the nature of exotic hadrons and their production mechanisms, and guides future experimental searches for the $Z_{cs}(3985)$ and $Z_{cs}(4000)$.Comment: 11 pages, 3 figure

Laboratory Evaluation of a Novel Self-Healable Polymer Gel for CO2 Leakage Remediation during CO2 Storage and CO2 Flooding

For CO2 storage in subsurface reservoirs, one of the most crucial requirements is the ability to remediate the leakage caused by the natural fractures or newly generated fractures due to the increasing pore pressure associated with CO2 injection. For CO2 Enhanced Oil Recovery (EOR), high conductivity features such as fractures and void space conduits can severely restrict the CO2 sweep efficiency. Polymer gels have been developed to plug the leakage and improve the sweep efficiency. This work evaluated a CO2 resistant branched self-healable preformed particle gel (CO2-BRPPG) for CO2 plugging purpose. This novel CO2-BRPPG can reform a mechanical robust adhesive bulk gel after being placed in the reservoir and efficiently seal fractures. In this work, the swelling kinetics, self-healing behavior, thermal stability, CO2 stability, rheology, adhesion property and plugging performance of this novel CO2-BRPPG were studied in the laboratory. Results showed that this CO2-BRPPG has good self-healing abilities, and the self-healed bulk gel has excellent mechanical and adhesion strength. Gel with a swelling ratio of ten has an elastic modulus of over 2000 Pa, and the adhesion strength to sandstone is 1.16 psi. The CO2-BRPPG has good CO2 phase stability at 65 °C, and no dehydration was observed after 60 days of exposure to 2900 psi CO2 at 65 °C. Core flooding test proved that the swelled particles could reform a bulk gel after being placed in the fractures, and the reformed bulky gel has excellent CO2 plugging efficiency. The supercritical CO2 breakthrough pressure gradient was 265 psi/feet (5.48 MPa/m). This work could offer the experimental basis for the field application of this CO2-BRPPG in CO2 storage and CO2 enhanced oil recovery

Data-driven offline learning approach for excavating control of cutter suction dredgers

Cutter suction dredgers (CSDs) play a very important role in the construction of ports, waterways and navigational channels. Currently, most of CSDs are mainly manipulated by human operators, and a large amount of instrument data needs to be monitored in real time in case of unforeseen accidents. In order to reduce the heavy workload of the operators, we propose a data-driven offline learning approach, named Preprocessing-Prediction-Learning Control (PPLC), for obtaining the optimal control policy of the excavating operation of CSDs. The proposed framework consists of three modules, i.e., a data preprocessing module, a dynamics prediction module realized by a Convolutional Neural Network (CNN), and a deep reinforcement learning based control module. The first module is responsible for filtering out irrelevant variables through correlation analysis and dimensionality reduction of raw data. The second module works as a state transition function that provides the dynamics prediction of the excavating operation of a CSD. To realize the learning control, the third module employs the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to control the swing speed during the excavating operation. The simulation results show that the proposed framework can provide an effective and reliable solution to the automated excavating control of a CSD

Searching for Black Hole Candidates by LAMOST and ASAS-SN

Most dynamically confirmed stellar-mass black holes (BHs) and their candidates were originally selected from X-ray outbursts. In the present work, we search for BH candidates in the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) survey using the spectra along with photometry from the All Sky Automated Survey for SuperNovae (ASAS-SN), where the orbital period of the binary may be revealed by the periodic light curve, such as the ellipsoidal modulation type. Our sample consists of nine binaries, where each source contains a giant star with large radial velocity variation (ΔV_R ≳ 70 km s^(-1)) and periods known from light curves. We focus on the nine sources with long periods (T_(ph) > 5 days) and evaluate the mass M_2 of the optically invisible companion. Since the observed ΔV_R from only a few repeating spectroscopic observations is a lower limit of the real amplitude, the real mass M_2 can be significantly higher than the current evaluation. It is likely an efficient method to place constraints on M 2 by combining ΔV_R from LAMOST and T_(ph) from ASAS-SN, particularly by the ongoing LAMOST Medium Resolution Survey