3,789 research outputs found
Low-energy behavior of spin-liquid electron spectral functions
We calculate the electron spectral function for a spin-liquid with a spinon
Fermi surface and a Dirac spin-liquid. Calculations are based upon the
slave-rotor mean-field theory. We consider the effect of gauge fluctuations
using a simple model and find the behavior is not strongly modified. The
results, distinct from conventional Mott insulator or band theory predictions,
suggest that measuring the spectral function e.g. via ARPES could help in the
experimental verification and characterization of spin liquids.Comment: 7 pages, 7 figure
The Sample Complexity of Search over Multiple Populations
This paper studies the sample complexity of searching over multiple
populations. We consider a large number of populations, each corresponding to
either distribution P0 or P1. The goal of the search problem studied here is to
find one population corresponding to distribution P1 with as few samples as
possible. The main contribution is to quantify the number of samples needed to
correctly find one such population. We consider two general approaches:
non-adaptive sampling methods, which sample each population a predetermined
number of times until a population following P1 is found, and adaptive sampling
methods, which employ sequential sampling schemes for each population. We first
derive a lower bound on the number of samples required by any sampling scheme.
We then consider an adaptive procedure consisting of a series of sequential
probability ratio tests, and show it comes within a constant factor of the
lower bound. We give explicit expressions for this constant when samples of the
populations follow Gaussian and Bernoulli distributions. An alternative
adaptive scheme is discussed which does not require full knowledge of P1, and
comes within a constant factor of the optimal scheme. For comparison, a lower
bound on the sampling requirements of any non-adaptive scheme is presented.Comment: To appear, IEEE Transactions on Information Theor
Development of a redox-free mitsunobu reaction exploiting phosphine oxides as precursors to dioxyphosphoranes
The development of the first redox-free protocol for the Mitsunobu reaction is described. This has been achieved by exploiting triphenylphosphine oxide – the unwanted by-product in the conventional Mitsunobu reaction – as the precursor to the active P(V) coupling reagent. Multinuclear NMR studies are consistent with hydroxyl activation via an alkoxyphosphonium salt
Reduced Order Model for Chemical Kinetics: A case study with Primordial Chemical Network
Chemical kinetics plays an important role in governing the thermal evolution
in reactive flows problems. The possible interactions between chemical species
increase drastically with the number of species considered in the system.
Various ways have been proposed before to simplify chemical networks with an
aim to reduce the computational complexity of the chemical network. These
techniques oftentimes require domain-knowledge experts to handcraftedly
identify important reaction pathways and possible simplifications. Here, we
propose a combination of autoencoder and neural ordinary differential equation
to model the temporal evolution of chemical kinetics in a reduced subspace. We
demonstrated that our model has achieved a close-to 10-fold speed-up compared
to commonly used astro-chemistry solver for a 9-species primordial network,
while maintaining 1 percent accuracy across a wide-range of density and
temperature.Comment: 10 pages, 8 figures, accepted to the ICML 2022 Machine Learning for
Astrophysics worksho
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
The rising popularity of intelligent mobile devices and the daunting
computational cost of deep learning-based models call for efficient and
accurate on-device inference schemes. We propose a quantization scheme that
allows inference to be carried out using integer-only arithmetic, which can be
implemented more efficiently than floating point inference on commonly
available integer-only hardware. We also co-design a training procedure to
preserve end-to-end model accuracy post quantization. As a result, the proposed
quantization scheme improves the tradeoff between accuracy and on-device
latency. The improvements are significant even on MobileNets, a model family
known for run-time efficiency, and are demonstrated in ImageNet classification
and COCO detection on popular CPUs.Comment: 14 pages, 12 figure
Automatic morphological trait characterization for corn plants via 3D holographic reconstruction
Plant breeding is an extremely important route to genetic improvements that can increase yield and plant adaptability. Genetic improvement requires careful measurement of plant phenotypes or plant trait characteristics, but phenotype measurement is a tedious and error-prone task for humans to perform. High-throughput phenotyping aims to eliminate the problems of manual phenotype measurement. In this paper, we propose and demonstrate the efficacy of an automatic corn plant phenotyping system based on 3D holographic reconstruction. Point cloud image data were acquired from a time-of-flight 3D camera, which was integrated with a plant rotating table to form a screening station. Our method has five main steps: point cloud data filtering and merging, stem segmentation, leaf segmentation, phenotypic data extraction, and 3D holographic visualization. In an experimental study with five corn plants at their early growth stage (V3), we obtained promising results with accurate 3D holographic reconstruction. The average measurement error rate for stem major axis, stem minor axis, stem height, leaf area, leaf length and leaf angle were at 7.92%, 15.20%, 7.45%, 21.89%, 10.25% and 11.09%, respectively. The most challenging trait to measure was leaf area due to partial occlusions and rolling of some leaves. In future work, we plan to extend and evaluate the usability of the system in an industrial plant breeding setting
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