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