Maize-Alfalfa Intercropping Promote Ecosystem Services Than Fertilized Single Crops

Phosphorus is a non-renewable source of fertilization, which will challenge the future of food production and cropland sustainability worldwide. Crop diversity is known to promote food production, yet its capacity to alleviate the dependence of multiple ecosystem services on non-renewable fertilization remains virtually unknown. Here, we conducted a field experiment to quantify the contribution of maize-alfalfa intercropping to support multiple ecosystem services under contrasting levels of phosphorus fertilization. We showed that unfertilized intercropping systems can support larger levels of multiple ecosystem services such as soil microbial habitat, plant-soil mutualism, nutrient cycling, and soil carbon storage compared with phosphorus-fertilized single crops. Intercropping also helped to reduce important tradeoffs in productivity and soil biodiversity compared with fertilized single crops. Together, our results provide evidence that intercropping systems are efficient in maintaining multiple ecosystem services and can help alleviate our global dependence on non-renewable fertilization

Large magnetothermal conductivity of HoMnO_3 single crystals and its relation to the magnetic-field induced transitions of magnetic structure

We study the low-temperature heat transport of HoMnO_3 single crystals to probe the magnetic structures and their transitions induced by magnetic field. It is found that the low-T thermal conductivity (\kappa) shows very strong magnetic-field dependence, with the strongest suppression of nearly 90% and the biggest increase of 20 times of \kappa compared to its zero-field value. In particular, some ``dip"-like features show up in \kappa(H) isotherms for field along both the ab plane and the c axis. These behaviors are found to shed new light on the complex H-T phase diagram and the field-induced re-orientations of Mn^{3+} and Ho^{3+} spin structures. The results also demonstrate a significant spin-phonon coupling in this multiferroic compound.Comment: 5 pages, 4 figures, accepted for publication in Phys. Rev.

Online multi-modal robust non-negative dictionary learning for visual tracking

© 2015 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality