Controlling mode orientations and frequencies in levitated cavity optomechanics

Cavity optomechanics offers quantum cooling, quantum control and measurement of small mechanical oscillators. However the optical backactions that underpin quantum control can significantly disturb the oscillator modes: mechanical frequencies are shifted by the optical spring effect and light-matter hybridisation in strong coupling regimes; mechanical modes hybridise with each other via the cavity mode. This is even more pertinent in the field of levitated optomechanics, where optical trapping fully determines the mechanical modes and their frequencies. Here, using the coherent-scattering (CS) set-up that allowed quantum ground state cooling of a levitated nanoparticle, we show that -- when trapping away from a node of the cavity standing wave -- the CS field opposes optical spring shifts and mechanical mode hybridisation. At an optimal cancellation point, independent of most experimental parameters, we demonstrate experimentally that it is possible to strongly cavity cool and control the {\em unperturbed} modes. Suppression of the cavity-induced mode hybridisation in the $x-y$ plane is quantified by measuring the $S_{xy}(\omega)$ correlation spectra which are seen to always be anti-correlated except at the cancellation point where they become uncorrelated. The findings have implications for directional force sensing using CS set-ups

Learning relative features through adaptive pooling for image classification

© 2014 IEEE. Bag-of-Feature (BoF) representations and spatial constraints have been popular in image classification research. One of the most successful methods uses sparse coding and spatial pooling to build discriminative features. However, minimizing the reconstruction error by sparse coding only considers the similarity between the input and codebooks. In contrast, this paper describes a novel feature learning approach for image classification by considering the dissimilarity between inputs and prototype images, or what we called reference basis (RB). First, we learn the feature representation by max-margin criterion between the input and the RB. The learned hyperplane is stored as the relative feature. Second, we propose an adaptive pooling technique to assemble multiple relative features generated by different RBs under the SVM framework, where the classifier and the pooling weights are jointly learned. Experiments based on three challenging datasets: Caltech-101, Scene 15 and Willow-Actions, demonstrate the effectiveness and generality of our framework

miR-375 suppresses IGF1R expression and contributes to inhibition cell progression in laryngeal squamous cell carcinoma

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Sustaining supply chain relationships for co-operative success: the case of South Devon Organic Producers Co-operative (UK)

This is the final version. Available on open access from Wageningen Academic Publishers via the DOI in this recordCo-operatives play a vital role in supplying various goods and services in the UK, as well as in other parts of the world. In the past twenty years co-operatives have become important players in modern organic food supply chains, providing small-scale farmers with access to knowledge and markets, alongside opportunities to scale up their production. This teaching case is developed from qualitative interviews with current and former members and employees from the South Devon Organic Producers (SDOP) Co-operative, an award-winning organic vegetable grower co-operative based in South Devon (UK). The case is supplemented with interviews with key managerial personnel at the SDOP’s main stakeholder Riverford Organic Farms Limited. The case explores how the relationship between SDOP and Riverford is the key to understanding SDOP’s participation in the organic food chain.British Academ

PIM-1 as a Multifunctional Framework to Enable High-Performance Solid-State Lithium-Sulfur Batteries

Poly(ethylene oxide) (PEO) is a promising solid electrolyte material for solid-state lithium–sulfur (Li–S) batteries, but low intrinsic ionic conductivity, poor mechanical properties, and failure to hinder the polysulfide shuttle effect limits its application. Herein, a polymer of intrinsic microporosity (PIM) is synthesized and applied as an organic framework to comprehensively enhance the performance of PEO by forming a composite electrolyte (PEO-PIM). The unique structure of PIM-1 not only enhances the mechanical strength and hardness over the PEO electrolyte by an order of magnitude, increasing stability toward the metallic lithium anode but also increases its ionic conductivity by lowering the degree of crystallinity. Furthermore, the PIM-1 is shown to effectively trap lithium polysulfide species to mitigate against the detrimental polysulfide shuttle effect, as electrophilic 1,4-dicyanooxanthrene functional groups possess higher binding energy to polysulfides. Benefiting from these properties, the use of PEO-PIM composite electrolyte has achieved greatly improved rate performance, long-cycling stability, and excellent safety features for solid-state Li-S batteries. This methodology offers a new direction for the optimization of solid polymer electrolytes

Using Latent Class Analyses to Examine Health Disparities among Young Children in Socially Disadvantaged Families during the COVID-19 Pandemic

Rising income inequality is strongly linked to health disparities, particularly in regions where uneven distribution of wealth and income has long been a concern. Despite emerging evidence of COVID-19-related health inequalities for adults, limited evidence is available for children and their parents. This study aimed to explore subtypes of families of preschoolers living in the disadvantaged neighborhoods of Hong Kong based on patterns of family hardship and to compare their patterns of parenting behavior, lifestyle practices, and wellbeing during the COVID-19 pandemic. Data were collected from 1338 preschoolers and their parents during March to June 2020. Latent class analysis was performed based on 11 socioeconomic and disease indicators. Multivariate logistic regressions were used to examine associations between identified classes and variables of interest during the COVID-19 pandemic. Four classes of family hardship were identified. Class 1 (45.7%) had the lowest disease and financial burden. Class 2 (14.0%) had the highest financial burden. Class 3 (5.9%) had the highest disease burden. Class 4 (34.5%) had low family income but did not receive government welfare assistance. Class 1 (low hardship) had lower risks of child maltreatment and adjustment problems than Class 2 (poverty) and Class 3 (poor health). However, children in Class 1 (low hardship) had higher odds of suffering psychological aggression and poorer physical wellbeing than those in Class 4 (low income), even after adjusting for child age and gender. The findings emphasize the need to adopt flexible intervention strategies in the time of large disease outbreak to address diverse problems and concerns among socially disadvantaged families

Effect of hyperbaric oxygen on mesenchymal stem cells for lumbar fusion in vivo

<p>Abstract</p> <p>Background</p> <p>Hyperbaric oxygen (HBO) therapy has been proved in improving bone healing, but its effects on mesenchymal stem cells (MSCs) <it>in vivo </it>is not clear. The aims of this study are to clarify whether the HBO therapy has the same enhancing effect on MSCs with regard to bone formation and maturation and to ascertain whether the transplanted MSCs survive in the grafted area and contribute to new bone formation.</p> <p>Methods</p> <p>Twenty-three adult rabbits underwent posterolateral fusion at L4-L5 level. The animals were divided into three groups according to the material implanted and subsequent treatment: (1) Alginate carrier (n = 6); (2) Alginate-MSCs composite (n = 11); and (3) Alginate-MSCs composite with HBO therapy (n = 6). After 12 weeks, spine fusion was examined using radiographic examination, manual testing, and histological examination. Using a PKH fluorescence labeling system, whether the transplanted MSCs survived and contributed to new bone formation in the grafted area after HBO therapy was also examined.</p> <p>Results</p> <p>The bilateral fusion areas in each animal were evaluated independently. By radiographic examination and manual palpation, union for the Alginate, Alginate-MSCs, and Alginate-MSCs-HBO groups was 0 of 12, 10 of 22, and 6 of 12 respectively. The difference between the Alginate-MSCs and Alginate-MSCs-HBO groups was not significant (P = 0.7997). The fluorescence microscopy histological analysis indicated that the transplanted PKH67-labeled MSCs survived and partly contributed to new bone formation in the grafted area.</p> <p>Conclusions</p> <p>This study demonstrated that the preconditioned MSCs could survive and yield bone formation in the grafted area. HBO therapy did not enhance the osteogenic ability of MSCs and improve the success of spine fusion in the rabbit model. Although there was no significant effect of HBO therapy on MSCs for spine fusion, the study encourages us to research a more basic approach for determining the optimal oxygen tension and pressure that are required to maintain and enhance the osteogenic ability of preconditioned MSCs. Further controlled <it>in vivo </it>and <it>in vitro </it>studies are required for achieving a better understanding of the effect of HBO treatment on MSCs.</p

Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization

Adversarial self-play in two-player games has delivered impressive results when used with reinforcement learning algorithms that combine deep neural networks and tree search. Algorithms like AlphaZero and Expert Iteration learn tabula-rasa, producing highly informative training data on the fly. However, the self-play training strategy is not directly applicable to single-player games. Recently, several practically important combinatorial optimisation problems, such as the travelling salesman problem and the bin packing problem, have been reformulated as reinforcement learning problems, increasing the importance of enabling the benefits of self-play beyond two-player games. We present the Ranked Reward (R2) algorithm which accomplishes this by ranking the rewards obtained by a single agent over multiple games to create a relative performance metric. Results from applying the R2 algorithm to instances of a two-dimensional and three-dimensional bin packing problems show that it outperforms generic Monte Carlo tree search, heuristic algorithms and integer programming solvers. We also present an analysis of the ranked reward mechanism, in particular, the effects of problem instances with varying difficulty and different ranking thresholds