Storage Fit Learning with Feature Evolvable Streams

Feature evolvable learning has been widely studied in recent years where old features will vanish and new features will emerge when learning with streams. Conventional methods usually assume that a label will be revealed after prediction at each time step. However, in practice, this assumption may not hold whereas no label will be given at most time steps. A good solution is to leverage the technique of manifold regularization to utilize the previous similar data to assist the refinement of the online model. Nevertheless, this approach needs to store all previous data which is impossible in learning with streams that arrive sequentially in large volume. Thus we need a buffer to store part of them. Considering that different devices may have different storage budgets, the learning approaches should be flexible subject to the storage budget limit. In this paper, we propose a new setting: Storage-Fit Feature-Evolvable streaming Learning (SF$^2$EL) which incorporates the issue of rarely-provided labels into feature evolution. Our framework is able to fit its behavior to different storage budgets when learning with feature evolvable streams with unlabeled data. Besides, both theoretical and empirical results validate that our approach can preserve the merit of the original feature evolvable learning i.e., can always track the best baseline and thus perform well at any time step

Nonlocal memory assisted entanglement distribution in optical fibers

Successful implementation of several quantum information and communication protocols require distributing entangled pairs of quantum bits in reliable manner. While there exists a substantial amount of recent theoretical and experimental activities dealing with non-Markovian quantum dynamics, experimental application and verification of the usefulness of memory-effects for quantum information tasks is still missing. We combine these two aspects and show experimentally that a recently introduced concept of nonlocal memory effects allows to protect and distribute polarization entangled pairs of photons in efficient manner within polarization-maintaining (PM) optical fibers. The introduced scheme is based on correlating the environments, i.e. frequencies of the polarization entangled photons, before their physical distribution. When comparing to the case without nonlocal memory effects, we demonstrate at least 12-fold improvement in the channel, or fiber length, for preserving the highly-entangled initial polarization states of photons against dephasing

Linking nutrient strategies with plant size along a grazing gradient: Evidence from Leymus chinensis in a natural pasture

AbstractStudying the changes in nutrient use strategies induced by grazing can provide insight into the process of grassland degradation and is important for improving grassland quality and enhancing ecosystem function. Dominant species in meadow steppe can optimize their use of limiting resources; however, the regulation of nutrient use strategies across grazing gradients is not fully understood. Therefore, in this study, we report an in situ study in which the impact of grazing rates on nutrient use strategies of Leymus chinensis, the dominant plant species in eastern Eurasian temperate steppes, was investigated. We conducted a large randomized controlled experiment (conducted continuously for five years in grassland plots in a natural pasture in Hailar, eastern Mongolia Plateau, China) to assess the effects of grazing rate treatments (0.00, 0.23, 0.34, 0.46, 0.69, and 0.92 adult cattle unit (AU) ha−1) on L. chinensis along a grazing gradient and employed a random sampling approach to compare the accumulation, allocation, and stoichiometry of C, N, and P in leaves and stems. Our findings demonstrated the follows: (i) The height of L. chinensis decreased with an increase in the grazing gradient, and the concentrations of C, N, and P significantly increased; (ii) the accumulation of C, N, and P per individual was negatively correlated with the concentration of aboveground tissues, suggesting that there was a tradeoff in L. chinensis between nutrient accumulation and concentration at the individual scale; (iii) the leaf-to-stem ratio of C, N, and P accumulation increased with grazing intensity, indicating a tradeoff in nutrient allocation and plant size at the individual plant level; and (iv) grazing rates were negatively correlated with the ratios of C:N and C:P in the stem; however, these ratios in leaves significantly increased with grazing intensity. Our findings suggest that L. chinensis in meadow steppe adapts to grazing disturbance through tradeoffs between plant size and nutrient use strategies. Moreover, our results imply that grazing produces a compensatory effect on nutrient use efficiency between the stems and leaves of L. chinensis