2,513 research outputs found
Life Tables of Bactrocera cucurbitae (Coquillett) (Diptera: Tephritidae): with a Mathematical Invalidation for Applying the Jackknife Technique to the Net Reproductive Rate
Life table data for the melon fly, Bactrocera cucurbitae (Coquillett), reared on cucumber (Cucumis sativus L.) were collected under laboratory and simulated field conditions. Means and standard errors of life table parameters were estimated for two replicates using the jackknife technique. At 25ºC, the intrinsic rates of increase (_r_) found for the two replicates were 0.1354 and 0.1002 day-1, and the net reproductive rates (_R_~0~) were 206.3 and 66.0 offspring, respectively. When the cucumbers kept under simulated field conditions were covered with leaves, the _r_ and _R_~0~ for the two replicates were 0.0935 and 0.0909 day-1, 17.5 and 11.4 offspring, respectively. However, when similar cucumbers were left uncovered, the _r_ and _R_~0~ for the two replicates were 0.1043 and 0.0904 day-1, and 27.7 and 10.1 offspring, respectively. Our results revealed that considerable variability between replicates in both laboratory and field conditions is possible; this variability should be taken into consideration in data collection and application of life tables. Mathematical analysis has demonstrated that applying the jackknife technique results in unrealistic pseudo-_R_~0~ and overestimation of its variance. We suggest that the jackknife technique should not be used for the estimation of variability of _R_~0~
Alchemical and structural distribution based representation for improved QML
We introduce a representation of any atom in any chemical environment for the
generation of efficient quantum machine learning (QML) models of common
electronic ground-state properties. The representation is based on scaled
distribution functions explicitly accounting for elemental and structural
degrees of freedom. Resulting QML models afford very favorable learning curves
for properties of out-of-sample systems including organic molecules,
non-covalently bonded protein side-chains, (HO)-clusters, as well as
diverse crystals. The elemental components help to lower the learning curves,
and, through interpolation across the periodic table, even enable "alchemical
extrapolation" to covalent bonding between elements not part of training, as
evinced for single, double, and triple bonds among main-group elements
Understanding molecular representations in machine learning: The role of uniqueness and target similarity
The predictive accuracy of Machine Learning (ML) models of molecular
properties depends on the choice of the molecular representation. Based on the
postulates of quantum mechanics, we introduce a hierarchy of representations
which meet uniqueness and target similarity criteria. To systematically control
target similarity, we rely on interatomic many body expansions, as implemented
in universal force-fields, including Bonding, Angular, and higher order terms
(BA). Addition of higher order contributions systematically increases
similarity to the true potential energy and predictive accuracy of the
resulting ML models. We report numerical evidence for the performance of BAML
models trained on molecular properties pre-calculated at electron-correlated
and density functional theory level of theory for thousands of small organic
molecules. Properties studied include enthalpies and free energies of
atomization, heatcapacity, zero-point vibrational energies, dipole-moment,
polarizability, HOMO/LUMO energies and gap, ionization potential, electron
affinity, and electronic excitations. After training, BAML predicts energies or
electronic properties of out-of-sample molecules with unprecedented accuracy
and speed
Strange sea asymmetry in nucleons
We evaluate the medium effects in nucleon which can induce an asymmetry of
the strange sea. The short-distance effects determined by the weak interaction
can give rise to where is the medium-induced mass of strange quark by a few KeV at
most, but the long-distance effects by strong interaction could be sizable.Comment: 4 pages and no figures, Talk presented at the Third
Circum-Pan-Pacific Symposium on "High Energy Spin Physics", Oct. 8-13, 2001,
Beijing, Chin
Qubit measurement using a quantum point contact with a quantum Langevin equation approach
We employ a quantum Langevin equation approach to establish non-Markovian
dynamical equations, on a fully microscopic basis, to investigate the
measurement of the state of a coupled quantum dot qubit by a nearby quantum
point contact. The ensuing Bloch equations allow us to examine qubit relaxation
and decoherence induced by measurement, and also the noise spectrum of meter
output current with the help of a quantum regression theorem, at arbitrary
bias-voltage and temperature. Our analyses provide a clear resolution of a
recent debate concerning the occurrence of a quantum oscillation peak in the
noise spectrum.Comment: 5 pages, 3 figures, submitted, published version in Phys. Rev.
Employee training in small and medium-sized enterprises in Mongolia
In many developed and developing countries, small and medium-sized enterprises (SMEs) play a significantrole in their development. All researches show that globally, SMEs account for more than 90 percent of all business activities and earlier studies have established that SMEs have a critical role in contributing to a country’s economic growth[2] such as reducing poverty and unemployment,and creating employment opportunities. In addition, they contribute to reducing inequitable income distribution, enhancing competitiveness of enterprises, as well as promoting social stability. Therefore, the development of SMEs represents theprimary goal of every modern economy. There are many factors that influence the development of SMEs andone of the most important among them is the human resource management practice, especially employees’ training. Therefore, the main aim of our study is to examine the level of awareness of the training and development (T&D) policy among owners or managers of SMEs, their attitude and organisational support towards training and to analyse the training-related problems encountered by SMEs within the context of Mongolia
Ab initio machine learning in chemical compound space
Chemical compound space (CCS), the set of all theoretically conceivable
combinations of chemical elements and (meta-)stable geometries that make up
matter, is colossal. The first principles based virtual sampling of this space,
for example in search of novel molecules or materials which exhibit desirable
properties, is therefore prohibitive for all but the smallest sub-sets and
simplest properties. We review studies aimed at tackling this challenge using
modern machine learning techniques based on (i) synthetic data, typically
generated using quantum mechanics based methods, and (ii) model architectures
inspired by quantum mechanics. Such Quantum mechanics based Machine Learning
(QML) approaches combine the numerical efficiency of statistical surrogate
models with an {\em ab initio} view on matter. They rigorously reflect the
underlying physics in order to reach universality and transferability across
CCS. While state-of-the-art approximations to quantum problems impose severe
computational bottlenecks, recent QML based developments indicate the
possibility of substantial acceleration without sacrificing the predictive
power of quantum mechanics
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