11,161 research outputs found
Seed vigor, aging, and osmopriming affect anion and sugar leakage during imbition of maize (Zea mays L.) caryopses
Conductivity was significantly increased by aging and decreased by osmopriming of maize (Zea mays L.) caryopses. Chloride, phosphate, and sulfate were the main anions that leaked out of maize seeds; their leakage was closely related to conductivity, increased by aging, and decreased by osmopriming. The anion leakage of isolated embryos correlated closely to seed vigor and was more sensitive to aging and priming than that of the whole seed. Anion leakage may be a more sensitive measure for seed vigor than bulk conductivity readings. Aging did not increase the sugar leakage of whole seeds but significantly increased the sugar leakage of isolated embryos. Sugar leakage was not closely related to total soluble sugar content of seeds. While priming decreased seed conductivity, the decreased anion and sugar leakage of the primed seeds was mainly caused by the washing effect during priming. The total anions or sugars left in the polyethylene glycol (PEG) solution after priming and in the conductivity solution of the primed seeds was almost the same as in the conductivity solution of the unprimed seeds alone
Semiparametric Discrete Choice Models for Bundles
We propose two approaches to estimate semiparametric discrete choice models
for bundles. Our first approach is a kernel-weighted rank estimator based on a
matching-based identification strategy. We establish its complete asymptotic
properties and prove the validity of the nonparametric bootstrap for inference.
We then introduce a new multi-index least absolute deviations (LAD) estimator
as an alternative, of which the main advantage is its capacity to estimate
preference parameters on both alternative- and agent-specific regressors. Both
methods can account for arbitrary correlation in disturbances across choices,
with the former also allowing for interpersonal heteroskedasticity. We also
demonstrate that the identification strategy underlying these procedures can be
extended naturally to panel data settings, producing an analogous localized
maximum score estimator and a LAD estimator for estimating bundle choice models
with fixed effects. We derive the limiting distribution of the former and
verify the validity of the numerical bootstrap as an inference tool. All our
proposed methods can be applied to general multi-index models. Monte Carlo
experiments show that they perform well in finite samples
Senior Recital: Mark T. Moen, Violin; Chenqui Ouyang, Piano; November 18, 2020
Kemp Recital HallNovember 18, 2020Friday Evening6:00 p.m
A REEVALUATION OF THE GROWTH DECLINE IN PINE IN GEORGIA, AND IN GEORGIA-ALABAMA COMBINED
Using an improved testing procedure based on bootstrap and weighted jack-knife confidence intervals with the same model as used in Bechtold et al. (1991) and Ruark et al. (1991), analysis in this paper generally confirm the results of a significant decrease in growth rate in pine in Georgia and Alabama for 1972 - 1982 (5th cycle) relative to 1961 - 1972 (4th cycle) discussed in these papers
Existence and Stability of Symmetric Periodic Simultaneous Binary Collision Orbits in the Planar Pairwise Symmetric Four-Body Problem
We extend our previous analytic existence of a symmetric periodic
simultaneous binary collision orbit in a regularized fully symmetric equal mass
four-body problem to the analytic existence of a symmetric periodic
simultaneous binary collision orbit in a regularized planar pairwise symmetric
equal mass four-body problem. We then use a continuation method to numerically
find symmetric periodic simultaneous binary collision orbits in a regularized
planar pairwise symmetric 1, m, 1, m four-body problem for between 0 and 1.
Numerical estimates of the the characteristic multipliers show that these
periodic orbits are linearly stability when , and are
linearly unstable when .Comment: 6 figure
Design of a low-noise aeroacoustic wind tunnel facility at Brunel University
This paper represents the design principle of a quiet, low turbulence and moderately high speed aeroacoustic wind tunnel which was recently commissioned at Brunel University. A new hemi-anechoic chamber was purposely built to facilitate aeroacoustic measurements. The wind tunnel can achieve a maximum speed of about 80 ms-1. The turbulence intensity of the free jet in the potential core is between 0.1–0.2%. The noise characteristic of the aeroacoustic wind tunnel was validated by three case studies. All of which can demonstrate a very low background noise produced by the bare jet in comparison to the noise radiated from the cylinder rod/flat plate/airfoil in the air stream.The constructions of the aeroacoustic wind tunnel and the hemi-anechoic chamber are financially supported by the School of Engineering and Design at Brunel University
Deep Regionlets for Object Detection
In this paper, we propose a novel object detection framework named "Deep
Regionlets" by establishing a bridge between deep neural networks and
conventional detection schema for accurate generic object detection. Motivated
by the abilities of regionlets for modeling object deformation and multiple
aspect ratios, we incorporate regionlets into an end-to-end trainable deep
learning framework. The deep regionlets framework consists of a region
selection network and a deep regionlet learning module. Specifically, given a
detection bounding box proposal, the region selection network provides guidance
on where to select regions to learn the features from. The regionlet learning
module focuses on local feature selection and transformation to alleviate local
variations. To this end, we first realize non-rectangular region selection
within the detection framework to accommodate variations in object appearance.
Moreover, we design a "gating network" within the regionlet leaning module to
enable soft regionlet selection and pooling. The Deep Regionlets framework is
trained end-to-end without additional efforts. We perform ablation studies and
conduct extensive experiments on the PASCAL VOC and Microsoft COCO datasets.
The proposed framework outperforms state-of-the-art algorithms, such as
RetinaNet and Mask R-CNN, even without additional segmentation labels.Comment: Accepted to ECCV 201
Compilation by stochastic Hamiltonian sparsification
Simulation of quantum chemistry is expected to be a principal application of
quantum computing. In quantum simulation, a complicated Hamiltonian describing
the dynamics of a quantum system is decomposed into its constituent terms,
where the effect of each term during time-evolution is individually computed.
For many physical systems, the Hamiltonian has a large number of terms,
constraining the scalability of established simulation methods. To address this
limitation we introduce a new scheme that approximates the actual Hamiltonian
with a sparser Hamiltonian containing fewer terms. By stochastically
sparsifying weaker Hamiltonian terms, we benefit from a quadratic suppression
of errors relative to deterministic approaches. Relying on optimality
conditions from convex optimisation theory, we derive an appropriate
probability distribution for the weaker Hamiltonian terms, and compare its
error bounds with other probability ansatzes for some electronic structure
Hamiltonians. Tuning the sparsity of our approximate Hamiltonians allows our
scheme to interpolate between two recent random compilers: qDRIFT and
randomized first order Trotter. Our scheme is thus an algorithm that combines
the strengths of randomised Trotterisation with the efficiency of qDRIFT, and
for intermediate gate budgets, outperforms both of these prior methods.Comment: 17 pages, 1 figure, 1 algorith
Tight bounds on the simultaneous estimation of incompatible parameters
The estimation of multiple parameters in quantum metrology is important for a vast array of applications in quantum information processing. However, the unattainability of fundamental precision bounds for incompatible observables has greatly diminished the applicability of estimation theory in many practical implementations. The Holevo Cramer-Rao bound (HCRB) provides the most fundamental, simultaneously attainable bound for multi-parameter estimation problems. A general closed form for the HCRB is not known given that it requires a complex optimisation over multiple variables. In this work, we show that the HCRB can be solved analytically for two parameters. For more parameters, we generate a lower bound to the HCRB. Our work greatly reduces the complexity of determining the HCRB to solving a set of linear equations. We apply our formalism to magnetic field sensing. Our results provide fundamental insight and make significant progress towards the estimation of multiple incompatible observables
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