3,839 research outputs found
Associated production of a neutral top-Higgs with a heavy-quark pair in the \gamma\gamma collisions at ILC
We have studied the associated production processes of a neutral top-Higgs in
the topcolor assisted technicolor model with a pair of heavy quarks in
\gamma\gamma collisions at the International Linear Collider (ILC). We find
that the cross section for t\bar{t}h_t in \gamma\gamma collisions is at the
level of a few fb with the c.m. energy \sqrt{s}=1000 GeV, which is consistent
with the results of the cross section of t\bar{t}H in the standard model and
the cross section of t\bar{t}h in the minimal supersymmetric standard modeland
the little Higgs models. It should be distinct that hundreds of to thousands of
h_t per year can be produced at the ILC, this process of \gamma\gamma \to
t\bar{t}h_t is really interesting in testing the standard model and searching
the signs of technicolor.Comment: 4 pages, 4 figures, some references are adde
The Next-Gen Crop Nutrient Stress Identification with High-Precision Sensing Technology in Digital Agriculture
Crop yields are facing significant losses from nutrient deficiencies. Over-fertilizing also has negative economic and environmental impacts. It is challenging to optimize fertilizing without an accurate diagnosis. Recently, plant phenotyping has demonstrated outstanding capabilities in estimating crop traits. As one of the leading technologies, LeafSpec, provides high-quality crop image data for improving phenotyping quality. In this study, novel algorithms are developed for LeafSpec to identify crop nutrient deficiencies more accurately. Combined with UAV system, this technology will bring growers a robust solution for fertilizing diagnosis and scientific crop management
Petuum: A New Platform for Distributed Machine Learning on Big Data
What is a systematic way to efficiently apply a wide spectrum of advanced ML
programs to industrial scale problems, using Big Models (up to 100s of billions
of parameters) on Big Data (up to terabytes or petabytes)? Modern
parallelization strategies employ fine-grained operations and scheduling beyond
the classic bulk-synchronous processing paradigm popularized by MapReduce, or
even specialized graph-based execution that relies on graph representations of
ML programs. The variety of approaches tends to pull systems and algorithms
design in different directions, and it remains difficult to find a universal
platform applicable to a wide range of ML programs at scale. We propose a
general-purpose framework that systematically addresses data- and
model-parallel challenges in large-scale ML, by observing that many ML programs
are fundamentally optimization-centric and admit error-tolerant,
iterative-convergent algorithmic solutions. This presents unique opportunities
for an integrative system design, such as bounded-error network synchronization
and dynamic scheduling based on ML program structure. We demonstrate the
efficacy of these system designs versus well-known implementations of modern ML
algorithms, allowing ML programs to run in much less time and at considerably
larger model sizes, even on modestly-sized compute clusters.Comment: 15 pages, 10 figures, final version in KDD 2015 under the same titl
Dichlorido{[2-(diphenylphosphino)phenyliminomethyl]ferrocene-κ2 N,P}platinum(II) dichloromethane hemisolvate
In the title compound, [FePt(C5H5)(C24H19NP)Cl2]·0.5CH2Cl2, the PtII atom adopts a distorted square-planar geometry defined by one P atom and one N atom from the bidentate [2-(diphenylphosphino)phenyliminomethyl]ferrocene ligand and two Cl atoms. Two disordered dichloromethane solvent molecules are each 0.25-occupied on a twofold rotation axis
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