Quantitative reducibility of Gevrey quasi-periodic cocycles and its applications

We establish a quantitative version of strong almost reducibility result for $\mathrm{sl}(2,\mathbb{R})$ quasi-periodic cocycle close to a constant in Gevrey class. We prove that, for the quasi-periodic Schr\"odinger operators with small Gevrey potentials, the length of spectral gaps decays sub-exponentially with respect to its labelling, the long range duality operator has pure point spectrum with sub-exponentially decaying eigenfunctions for almost all phases and the spectrum is an interval for discrete Schr\"odinger operator acting on $\mathbb{Z}^d$ with small separable potentials. All these results are based on a refined KAM scheme, and thus are perturbative.Comment: 27 page

Asymptotic Spectral Flow

In this paper we study the asymptotic behavior of the spectral flow of a one-parameter family $\{D_s\}$ of Dirac operators acting on the spinor bunldle $S$ twisted by a vector bundle $E$ of rank $k$, with the parameter $s\in [0,r]$ when $r$ gets sufficiently large. Our method uses the variation of eta invariant and local index theory technique. The key is a uniform estimate of the eta invariant $\bar{\eta}(D_r)$ which is established via local index theory technique and heat kernel estimate

TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

Unsupervised semantic segmentation aims to obtain high-level semantic representation on low-level visual features without manual annotations. Most existing methods are bottom-up approaches that try to group pixels into regions based on their visual cues or certain predefined rules. As a result, it is difficult for these bottom-up approaches to generate fine-grained semantic segmentation when coming to complicated scenes with multiple objects and some objects sharing similar visual appearance. In contrast, we propose the first top-down unsupervised semantic segmentation framework for fine-grained segmentation in extremely complicated scenarios. Specifically, we first obtain rich high-level structured semantic concept information from large-scale vision data in a self-supervised learning manner, and use such information as a prior to discover potential semantic categories presented in target datasets. Secondly, the discovered high-level semantic categories are mapped to low-level pixel features by calculating the class activate map (CAM) with respect to certain discovered semantic representation. Lastly, the obtained CAMs serve as pseudo labels to train the segmentation module and produce the final semantic segmentation. Experimental results on multiple semantic segmentation benchmarks show that our top-down unsupervised segmentation is robust to both object-centric and scene-centric datasets under different semantic granularity levels, and outperforms all the current state-of-the-art bottom-up methods. Our code is available at \url{https://github.com/damo-cv/TransFGU}.Comment: Accepted by ECCV 2022, Oral, open-source

Optimization of laccase production from Trametes versicolor by solid fermentation

The regulation of culture conditions, especially the optimization of substrate constituents, is crucial for laccase production by solid fermentation. To develop an inexpensive optimized substrate formulation to produce high-activity laccase, a uniform design formulation experiment was devised. The solid fermentation of Trametes versicolor was performed with natural aeration, natural substrate pH (about 6.5), environmental humidity of 60% and two different temperature stages (at 37 &deg;C for 3 days, and then at 30 &deg;C for the next 17 days). From the experiment, a regression equation for laccase activity, in the form of a second-degree polynomial model, was constructed using multivariate regression analysis and solved with unconstrained optimization programming. The optimized substrate formulation for laccase production was then calculated. Tween 80 was found to have a negative effect on laccase production in solid fermentation; the optimized solid substrate formulation was 10.8% glucose, 27.7% wheat bran, 9.0% (NH4)2SO4, and 52.5% water. In a scaled-up verification of solid fermentation at a 10 kg scale, laccase activity from T. versicolor in the optimized substrate formulation reached 110.9 IU/g of dry mass.<br /

Exploring the antibiotics innovation system and R&D policies in China: mission oriented innovation?

One possible response to the growing problem of Antimicrobial Resistance (AMR) in pathogenic infections is the development of new types of antibiotics. However, the pharmaceutical companies that have traditionally led such innovation face a lack of incentives at the present time due to high levels of market uncertainty and low expected returns. Mission oriented innovation with coordinated investment and market-shaping policies may offer an approach to accelerating antibiotic innovation. This paper aims to evaluate whether preCovid-19 Chinese policies concerning AMR can be seen as constituting a mission-oriented approach and whether these policies have influenced antibiotics innovation in China. It adopts a mixed method approach to deliver several insights. By using historical event analysis based on data collected from interviews, public and commercial databases as well as policy documents, the paper finds that China’s recent actions concerning AMR since 2008 comprise many elements of mission-oriented innovation policy. The National Action Plan to Contain AMR has provided a clear mission since 2016 to tackle the problem of AMR and provides the opportunity to coordinate and integrate these policies into a more coherent and evolving mission-oriented innovation approach. Analysis of relevant research grants and publications suggest that these policies (including the 2016 National Action Plan) have drawn the scientific community towards antibiotics research and provided more support to this area. Case studies following the development of new antibiotics are used to illustrate how the established elements of mission oriented innovation policy have or have not contributed to antibiotics innovation in China. Further research is required to more comprehensively analyse R&D investments, and to understand the effects of recent policies, especially after 2016

Increase in cotton yield through improved leaf physiological functioning under the soil condition of reduced chemical fertilization compensated by the enhanced organic liquid fertilization

IntroductionLow agricultural nutrient input efficiency remains a significant impediment for crop production globally. To address this issue in cotton agroecosystems, there is a need to develop sustainable crop nutrient management strategies to achieve high crop yields. We hypothesized that organic liquid fertilizer (OF) combined with reduced chemical fertilizer (CF) would enhance cotton yield by improving leaf functioning and soil properties. However, the underlying mechanism and its related process is poorly understood.MethodsThis study explored the effects of OF combined with reduced CF on cotton yield, physiology and soil properties. Treatments included a single application of CF (CF: N, P2O5 and K2O applied at 228, 131 and 95 kg ha−1) and combined applications of OF and CF (OF0.6−OF1.4) in the following ratios: OF0.6, OF+60% CF; OF0.8, OF+80% CF; OF1.0, OF+100% CF; OF1.2, OF+120% CF; OF1.4, OF+140% CF. Results and discussionThe result showed that compared with CF, OF0.8, OF1.0 and OF1.2 increased soil organic matter (SOM) content by 9.9%, 16.3% and 23.7%, respectively. Compared with CF, the OF0.6, OF0.8, OF1.0, and OF1.2 treatments increased leaf area (LA) by 10.6−26.1%, chlorophyll content (Chl content) by 6.8−39.6%, and the efficiency of photosystem II (PSII) light energy (Y(II)), electron transfer rate of PSII (ETR) and photochemical quenching (qP) by 3.6−26.3%, 4.7−15.3% and 4.3−9.8%, respectively. The OF0.8 treatment increased net photosynthetic rate (Pn), stomatal conductance (Gs) and transpiration rate (E) by 22.0%, 27.4% and 26.8%, respectively, resulting in higher seed cotton yield. The seed cotton yield and economic coefficient were positively correlated with Pn, E, Gs and Y(II) from the full boll stage to the boll opening stage. In summary, the OF0.8 treatment can maintain a high SOM content and photosynthetic performance with reduced chemical fertilizer input without sacrificing yield. The integration of OF+80% CF (OF0.8) is a promising nutrient management strategy for highly efficient cotton production under mulch drip irrigation systems