Minimalist AdaBoost for blemish identification in potatoes

We present a multi-class solution based on minimalist Ad- aBoost for identifying blemishes present in visual images of potatoes. Using training examples we use Real AdaBoost to rst reduce the fea- ture set by selecting ve features for each class, then train binary clas- siers for each class, classifying each testing example according to the binary classier with the highest certainty. Against hand-drawn ground truth data we achieve a pixel match of 83% accuracy in white potatoes and 82% in red potatoes. For the task of identifying which blemishes are present in each potato within typical industry dened criteria (10% coverage) we achieve accuracy rates of 93% and 94%, respectively

Spectroscopy of reflection-asymmetric nuclei with relativistic energy density functionals

Quadrupole and octupole deformation energy surfaces, low-energy excitation spectra and transition rates in fourteen isotopic chains: Xe, Ba, Ce, Nd, Sm, Gd, Rn, Ra, Th, U, Pu, Cm, Cf, and Fm, are systematically analyzed using a theoretical framework based on a quadrupole-octupole collective Hamiltonian (QOCH), with parameters determined by constrained reflection-asymmetric and axially-symmetric relativistic mean-field calculations. The microscopic QOCH model based on the PC-PK1 energy density functional and $\delta$-interaction pairing is shown to accurately describe the empirical trend of low-energy quadrupole and octupole collective states, and predicted spectroscopic properties are consistent with recent microscopic calculations based on both relativistic and non-relativistic energy density functionals. Low-energy negative-parity bands, average octupole deformations, and transition rates show evidence for octupole collectivity in both mass regions, for which a microscopic mechanism is discussed in terms of evolution of single-nucleon orbitals with deformation.Comment: 36 pages, 21 figures, Accepted for Publication in Physical Review

Nanoladder cantilevers made from diamond and silicon

We present a "nanoladder" geometry that minimizes the mechanical dissipation of ultrasensitive cantilevers. A nanoladder cantilever consists of a lithographically patterned scaffold of rails and rungs with feature size $\sim$ 100 nm. Compared to a rectangular beam of the same dimensions, the mass and spring constant of a nanoladder are each reduced by roughly two orders of magnitude. We demonstrate a low force noise of $158 (+62)(-42)\,$zN and $190 (+42)(-33)\,$zN in a one-Hz bandwidth for devices made from silicon and diamond, respectively, measured at temperatures between 100--150 mK. As opposed to bottom-up mechanical resonators like nanowires or nanotubes, nanoladder cantilevers can be batch-fabricated using standard lithography, which is a critical factor for applications in scanning force microscopy

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

Collaborative rating allocation

This paper studies the collaborative rating allocation problem, in which each user has limited ratings on all items. These users are termed "energy limited". Different from existing methods which treat each rating independently, we investigate the geometric properties of a user's rating vector, and design a matrix completion method on the simplex. In this method, a user's rating vector is estimated by the combination of user profiles as basis points on the simplex. Instead of using Euclidean metric, a non-linear pull-back distance measurement from the sphere is adopted since it can depict the geometric constraints on each user's rating vector. The resulting objective function is then efficiently optimized by a Riemannian conjugate gradient method on the simplex. Experiments on real-world data sets demonstrate our model's competitiveness versus other collaborative rating prediction methods