From research to farm : ex ante evaluation of strategic deworming in pig finishing

This paper upgrades generic and partial information from parasitological research for farm-specific decision support, using two methods from managerial sciences: partial budgeting and frontier analysis. The analysis focuses on strategic deworming in pig finishing and assesses both effects on economic performance and nutrient efficiency. The application of partial budgeting and frontier analysis is based on a production-theoretical system analysis which is necessary to integrate parasitological research results to assess aggregate economic and environmental impacts. Results show that both statistically significant and insignificant parasitological research results have to be taken into account. Partial budgeting and frontier analysis appear to be complementary methods: partial budgeting yields more discriminatory and communicative results, while frontier methods provide additional diagnostics through exploring optimization possibilities and economic-environmental trade-offs. Strategic deworming results in a win-win effect on economic and environmental performances. Gross margin increases with 3 to 12 € per average present finisher per year, depending on the cyclic pig price conditions. The impact on the nutrient balance ranges from +0.2 to –0.5 kg nitrogen per average present finisher per year. The observed efficiency improvements are mainly technical and further economic and environmental optimizations can be achieved through input re-allocation. A user-friendly spreadsheet is provided to translate the generic experimental information to farm-specific conditions

Ultra-precise measurement of optical frequency ratios

We developed a novel technique for frequency measurement and synthesis, based on the operation of a femtosecond comb generator as transfer oscillator. The technique can be used to measure frequency ratios of any optical signals throughout the visible and near-infrared part of the spectrum. Relative uncertainties of $10^{-18}$ for averaging times of 100 s are possible. Using a Nd:YAG laser in combination with a nonlinear crystal we measured the frequency ratio of the second harmonic $\nu_{SH}$ at 532 nm to the fundamental $\nu_0$ at 1064 nm, $\nu_{SH}/\nu_0 = 2.000 000 000 000 000 001 \times (1 \pm 7 \times 10^{-19})$.Comment: 4 pages, 4 figure

Accuracy of Sampling Quantum Phase Space in Photon Counting Experiment

We study the accuracy of determining the phase space quasidistribution of a single quantized light mode by a photon counting experiment. We derive an exact analytical formula for the error of the experimental outcome. This result provides an estimation for the experimental parameters, such as the number of events, required to determine the quasidistribution with assumed precision. Our analysis also shows that it is in general not possible to compensate the imperfectness of the photodetector in a numerical processing of the experimental data. The discussion is illustrated with Monte Carlo simulations of the photon counting experiment for the coherent state, the one photon Fock state, and the Schroedinger cat state.Comment: 11 pages REVTeX, 5 figures, uses multicol, epsfig, and pstricks. Submitted to Special Issue of Journal of Modern Optics on Quantum State Preparation and Measuremen

An efficient method to include equality constraints in branch current distribution system state estimation

Distribution system state estimation is a fundamental tool for the management and control functions envisaged for future distribution grids. The design of accurate and efficient algorithms is essential to provide estimates compliant with the needed accuracy requirements and to allow the real-time operation of the different applications. To achieve such requirements, peculiarities of the distribution systems have to be duly taken into account. Branch current-based estimators are an efficient solution for performing state estimation in radial or weakly meshed networks. In this paper, a simple technique, which exploits the particular formulation of the branch current estimators, is proposed to deal with zero injection and mesh constraints. Tests performed on an unbalanced IEEE 123-bus network show the capability of the proposed method to further improve efficiency performance of branch current estimators

Psychometrics in Practice at RCEC

A broad range of topics is dealt with in this volume: from combining the psychometric generalizability and item response theories to the ideas for an integrated formative use of data-driven decision making, assessment for learning and diagnostic testing. A number of chapters pay attention to computerized (adaptive) and classification testing. Other chapters treat the quality of testing in a general sense, but for topics like maintaining standards or the testing of writing ability, the quality of testing is dealt with more specifically.\ud All authors are connected to RCEC as researchers. They present one of their current research topics and provide some insight into the focus of RCEC. The selection of the topics and the editing intends that the book should be of special interest to educational researchers, psychometricians and practitioners in educational assessment

Power Spectrum Analysis of Far-IR Background Fluctuations in 160 Micron Maps From the Multiband Imaging Photometer for Spitzer

We describe data reduction and analysis of fluctuations in the cosmic far-IR background (CFIB) in observations with the Multiband Imaging Photometer for Spitzer (MIPS) instrument 160 micron detectors. We analyzed observations of an 8.5 square degree region in the Lockman Hole, part of the largest low-cirrus mapping observation with this instrument. We measured the power spectrum of the CFIB in these observations by fitting a power law to the IR cirrus component, the dominant foreground contaminant, and subtracting this cirrus signal. The CFIB power spectrum in the range 0.2 arc min^{-1} <k< 0.5 arc min^{-1} is consistent with previous measurements of a relatively flat component. However, we find a large power excess at low k, which falls steeply to the flat component in the range 0.03 arc min^{-1} <k< 0.1 arc min^{-1}. This low-k power spectrum excess is consistent with predictions of a source clustering "signature". This is the first report of such a detection in the far-IR.Comment: This is the version of the paper accepted by A&A, which includes various changes and new material. The superior-quality PDF with integrated figures may be downloaded at http://www-astro.lbl.gov/~bruce/spitzerpaper1/cfibaa_pub.pdf 15 pages, figures integrated with text. This paper supersedes astro-ph/050416

SensEmbed: Learning sense embeddings for word and relational similarity

Word embeddings have recently gained considerable popularity for modeling words in different Natural Language Processing (NLP) tasks including semantic similarity measurement. However, notwithstanding their success, word embeddings are by their very nature unable to capture polysemy, as different meanings of a word are conflated into a single representation. In addition, their learning process usually relies on massive corpora only, preventing them from taking advantage of structured knowledge. We address both issues by proposing a multifaceted approach that transforms word embeddings to the sense level and leverages knowledge from a large semantic network for effective semantic similarity measurement. We evaluate our approach on word similarity and relational similarity frameworks, reporting state-of-the-art performance on multiple datasets

Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach

This paper proposes a probabilistic approach for the detection and the tracking of particles in fluorescent time-lapse imaging. In the presence of a very noised and poor-quality data, particles and trajectories can be characterized by an a contrario model, that estimates the probability of observing the structures of interest in random data. This approach, first introduced in the modeling of human visual perception and then successfully applied in many image processing tasks, leads to algorithms that neither require a previous learning stage, nor a tedious parameter tuning and are very robust to noise. Comparative evaluations against a well-established baseline show that the proposed approach outperforms the state of the art.Comment: Published in Journal of Machine Vision and Application