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, (H$_2$O)$_{40}$-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 $\delta m\equiv \Delta m_s-\Delta m_{\bar s}$ where $\Delta m_{s(\bar s)}$ 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