Experimental investigation of the non-Markovian dynamics of classical and quantum correlations

We experimentally investigate the dynamics of classical and quantum correlations of a Bell diagonal state in a non-Markovian dephasing environment. The sudden transition from classical to quantum decoherence regime is observed during the dynamics of such kind of Bell diagonal state. Due to the refocusing effect of the overall relative phase, the quantum correlation revives from near zero and then decays again in the subsequent evolution. However, the non-Markovian effect is too weak to revive the classical correlation, which remains constant in the same evolution range. With the implementation of an optical $\sigma_{x}$ operation, the sudden transition from quantum to classical revival regime is obtained and correlation echoes are formed. Our method can be used to control the revival time of correlations, which would be important in quantum memory.Comment: extended revision, accepted for publication in Physical Review

Lensing reconstruction from the cosmic microwave background polarization with machine learning

The lensing effect of the cosmic microwave background (CMB) is a powerful tool for our study of the distribution of matter in the universe. Currently, the quadratic estimator (EQ) method, which is widely used to reconstruct lensing potential, has been known to be sub-optimal for the low-noise levels polarization data from next-generation CMB experiments. To improve the performance of the reconstruction, other methods, such as the maximum likelihood estimator and machine learning algorithms are developed. In this work, we present a deep convolutional neural network model named the Residual Dense Local Feature U-net (RDLFUnet) for reconstructing the CMB lensing convergence field. By simulating lensed CMB data with different noise levels to train and test network models, we find that for noise levels less than $5\mu$K-arcmin, RDLFUnet can recover the input gravitational potential with a higher signal-to-noise ratio than the previous deep learning and the traditional QE methods at almost the entire observation scales.Comment: 12 pages, 8 figures, accepted by Ap

Distributed Task-Oriented Communication Networks with Multimodal Semantic Relay and Edge Intelligence

In this article, we present a novel framework, named distributed task-oriented communication networks (DTCN), based on recent advances in multimodal semantic transmission and edge intelligence. In DTCN, the multimodal knowledge of semantic relays and the adaptive adjustment capability of edge intelligence can be integrated to improve task performance. Specifically, we propose the key techniques in the framework, such as semantic alignment and complement, a semantic relay scheme for deep joint source-channel relay coding, and collaborative device-server optimization and inference. Furthermore, a multimodal classification task is used as an example to demonstrate the benefits of the proposed DTCN over existing methods. Numerical results validate that DTCN can significantly improve the accuracy of classification tasks, even in harsh communication scenarios (e.g., low signal-to-noise regime), thanks to multimodal semantic relay and edge intelligence.Comment: 7 pages, 5 figures, 1 table, accepted by IEEE Communications Magazin

VersaT2I: Improving Text-to-Image Models with Versatile Reward

Recent text-to-image (T2I) models have benefited from large-scale and high-quality data, demonstrating impressive performance. However, these T2I models still struggle to produce images that are aesthetically pleasing, geometrically accurate, faithful to text, and of good low-level quality. We present VersaT2I, a versatile training framework that can boost the performance with multiple rewards of any T2I model. We decompose the quality of the image into several aspects such as aesthetics, text-image alignment, geometry, low-level quality, etc. Then, for every quality aspect, we select high-quality images in this aspect generated by the model as the training set to finetune the T2I model using the Low-Rank Adaptation (LoRA). Furthermore, we introduce a gating function to combine multiple quality aspects, which can avoid conflicts between different quality aspects. Our method is easy to extend and does not require any manual annotation, reinforcement learning, or model architecture changes. Extensive experiments demonstrate that VersaT2I outperforms the baseline methods across various quality criteria

Mathematical modeling of simultaneous carbon-nitrogen-sulfur removal from industrial wastewater

A mathematical model of carbon, nitrogen and sulfur removal (C-N-S) from industrial wastewater was constructed considering the interactions of sulfate-reducing bacteria (SRB), sulfide-oxidizing bacteria (SOB), nitrate-reducing bacteria (NRB), facultative bacteria (FB), and methane producing archaea (MPA). For the kinetic network, the bioconversion of C-N by heterotrophic denitrifiers (NO\ua0→\ua0NO\ua0→\ua0N), and that of C-S by SRB (SO\ua0→\ua0S) and SOB (S\ua0→\ua0S) was proposed and calibrated based on batch experimental data. The model closely predicted the profiles of nitrate, nitrite, sulfate, sulfide, lactate, acetate, methane and oxygen under both anaerobic and micro-aerobic conditions. The best-fit kinetic parameters had small 95% confidence regions with mean values approximately at the center. The model was further validated using independent data sets generated under different operating conditions. This work was the first successful mathematical modeling of simultaneous C-N-S removal from industrial wastewater and more importantly, the proposed model was proven feasible to simulate other relevant processes, such as sulfate-reducing, sulfide-oxidizing process (SR-SO) and denitrifying sulfide removal (DSR) process. The model developed is expected to enhance our ability to predict the treatment of carbon-nitrogen-sulfur contaminated industrial wastewater

Erratum: A multi-objective optimization-based layer-by-layer blade-coating approach for organic solar cells: Rational control of vertical stratification for high performance (Energy and Environmental Science (2019) 12 (3118-3132) DOI: 10.1039/C9EE02295C)

The Acknowledgements section should have included the following sentence: "This work was performed in part on the SAXS/ WAXS beamline at the Australian Synchrotron, part of ANSTO". The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers

A multi-objective optimization-based layer-by-layer blade-coating approach for organic solar cells:Rational control of vertical stratification for high performance

A major breakthrough in organic solar cells (OSCs) in the last thirty years was the development of the bulk heterojunction (BHJ) solution processing strategy, which effectively provided a nanoscale phase-separated morphology, aiding in the separation of Coulombically bound excitons and facilitating charge transport and extraction. Compared with the application of the layer-by-layer (LbL) approach proposed in the same period, the BHJ spin-coating technology shows overwhelming advantages for evaluating the performance of photovoltaic materials and achieving more-efficient photoelectric conversion. Thus, in this study, we have further compared the BHJ and LbL processing strategies via the doctor-blade coating technology because it is a roll-to-roll compatible high-throughput thin film fabrication route. We systematically evaluated multiple target parameters, including morphological characteristics, optical simulation, physical kinetics, device efficiency, and blend stability issues. It is worth emphasizing that our findings disprove the old stereotypes such as the BHJ processing method is superior to the LbL technology for the preparation of high-performance OSCs and the LbL approach requires an orthogonal solvent and donor/acceptor materials with special solubility. Our studies demonstrate that the LbL blade-coating approach is a promising strategy to effectively reduce the efficiency-stability gap of OSCs and even a superior alternative to the BHJ method in commercial applications

RNA Sequencing of Formalin-Fixed, Paraffin-Embedded Specimens for Gene Expression Quantification and Data Mining

Background. Proper rRNA depletion is crucial for the successful utilization of FFPE specimens when studying gene expression. We performed a study to evaluate two major rRNA depletion methods: Ribo-Zero and RNase H. RNAs extracted from 4 samples were treated with the two rRNA depletion methods in duplicate and sequenced (N=16). We evaluated their reducibility, ability to detect RNA, and ability to molecularly subtype these triple negative breast cancer specimens. Results. Both rRNA depletion methods produced consistent data between the technical replicates. We found that the RNase H method produced higher quality RNAseq data as compared to the Ribo-Zero method. In addition, we evaluated the RNAseq data generated from the FFPE tissue samples for noncoding RNA, including lncRNA, enhancer/super enhancer RNA, and single nucleotide variation (SNV). We found that the RNase H is more suitable for detecting high-quality, noncoding RNAs as compared to the Ribo-Zero and provided more consistent molecular subtype identification between replicates. Unfortunately, neither method produced reliable SNV data. Conclusions. In conclusion, for FFPE specimens, the RNase H rRNA depletion method performed better than the Ribo-Zero. Neither method generates data sufficient for SNV detection

Neuroprotective effect of apocynin nitrone in oxygen glucose deprivation-treated SH-SY5Y cells and rats with ischemic stroke

Purpose: To investigate the neuroprotective potential of apocynin nitrone (AN-1), a nitrone analogue of apocynin, in rat brain tissue as a novel candidate for ischemic stroke treatment.Methods: In vitro neuroprotection of AN-1 was studied in SH-SY5Y cells treated with oxygen glucose deprivation (OGD). Cell viability was measured using 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2Htetrazolium bromide (MTT) assay, and intracellular reactive oxygen species (ROS) level was investigated using flow cytometry. The protection of AN-1 in cerebral ischemia-reperfusion (I/R) rats was evaluated by cerebral infarct area and neurological deficit score. Oxidative stress of the ischemic hemisphere was assessed by malondialdehyde (MDA), glutathione (GSH) and superoxide dismutase (SOD) levels.Results: In OGD-treated SH-SY5Y cells, AN-1 reduced cell death and ROS level. In I/R rats, AN-1 exerted potential protection against neurological deficit by reducing infarction area, decreasing neurological deficit score and relieving oxidative stress. AN-1 exhibited stronger action than its parent compound apocynin in vitro, but the two had similar effects in vivo. In addition, AN-1 demonstrated efficacy close to or higher than the positive reference Edaravone® both in vitro and in vivo.Furthermore, AN-1 showed lower toxicity than apocynin in vitro.Conclusion: The results suggest that AN-1 may be a potential neuroprotective agent for the treatment of ischemic stroke in human.Keywords: Apocynin nitrone, Cerebral ischemia-reperfusion injury, Neuroprotection, Reactive oxygen species, Oxidative stres