Plankton functional group models – An assessment

This Discussant’s Report provides a summary of the discussions that followed presentation of the approaches and ideas described in Thingstad et al. (this volume). The discussions, which addressed aspects of conceptual understanding and parameterization that are relevant to development of ecosystem models capable of emergent behavior at a range of scales, the benefits of functional group modeling, and some of the limitations of this approach, provide insights that are relevant to setting directions for future research efforts. One important point emerging from the discussions was that reconciling the requirements of simplicity versus complexity with the desire to obtain predictive capability is an important area where biogeochemical and ecosystem models can be improved

Dynamics of strong and radiative decays of Ds-mesons in the hadrogenesis conjecture

The positive parity scalar D$_{s0}^*$(2317) and axial-vector D$_{s1}^*$(2460) charmed strange mesons are generated by coupled-channel dynamics through the s-wave scattering of Goldstone bosons off the pseudoscalar and vector D(D$_s$)-meson ground states. The specific masses of these states are obtained as a consequence of the attraction arising from the Weinberg-Tomozawa interaction in the chiral Lagrangian. Chiral corrections to order Q$_\chi^2$ are calculated and found to be small. The D$_{s0}^*$(2317) and D$_{s1}^*$(2460) mesons decay either strongly into the isospin-violating $\pi^0$D$_s$ and $\pi^0$D$_s^*$ channels or electromagnetically. We show that the $\pi^0$-$\eta$ and (K$^0$D$^+$-K$^+$D$^0$) mixings act constructively to generate strong widths of the order of 140 keV and emphasize the sensitivity of this value to the $KD$ component of the states. The one-loop contribution to the radiative decay amplitudes of scalar and axial-vector states is calculated using the electromagnetic Lagrangian to chiral order Q$_\chi^2$. We show the importance of taking into account processes involving light vector mesons explicitly in the dynamics of electromagnetic decays. The radiative width are sensitive to both $\eta D_s$ and $KD$ components, hence providing information complementary to the strong widths on the positive parity $D_s$-meson structure.Comment: 4 pages, Invited Contribution to QNP09, Beijing, September 21-26, 200

Nach dem Quotenfall: (K)ein Grund zur Beunruhigung für das Textil- und Bekleidungsgewerbe?

Zum 1. Januar 2005 sind für die Mitglieder der World Trade Organisation (WTO) alle Quoten, die bisher den Handel mit textilen Produkten eingeschränkt haben, weggefallen. Für das deutschen Textil- und Bekleidungsgewerbe wird dies aber wahrscheinlich keine direkten, gravierend negativen Auswirkungen haben, da sich dort bereits in den letzten Jahrzehnten ein tief greifender Strukturwandel vollzogen hat und arbeitsintensive Prozesse abgebaut und ins Ausland verlagert wurden.Textilindustrie, Bekleidungsindustrie, WTO-Regeln, Wettbewerb, Außenhandelsbeschränkung, Standort, Deutschland, Welt

Asymptotic Conditional Distribution of Exceedance Counts: Fragility Index with Different Margins

Let $\bm X=(X_1,...,X_d)$ be a random vector, whose components are not necessarily independent nor are they required to have identical distribution functions $F_1,...,F_d$. Denote by $N_s$ the number of exceedances among $X_1,...,X_d$ above a high threshold $s$. The fragility index, defined by $FI=\lim_{s\nearrow}E(N_s\mid N_s>0)$ if this limit exists, measures the asymptotic stability of the stochastic system $\bm X$ as the threshold increases. The system is called stable if $FI=1$ and fragile otherwise. In this paper we show that the asymptotic conditional distribution of exceedance counts (ACDEC) $p_k=\lim_{s\nearrow}P(N_s=k\mid N_s>0)$, $1\le k\le d$, exists, if the copula of $\bm X$ is in the domain of attraction of a multivariate extreme value distribution, and if $\lim_{s\nearrow}(1-F_i(s))/(1-F_\kappa(s))=\gamma_i\in[0,\infty)$ exists for $1\le i\le d$ and some $\kappa\in{1,...,d}$. This enables the computation of the FI corresponding to $\bm X$ and of the extended FI as well as of the asymptotic distribution of the exceedance cluster length also in that case, where the components of $\bm X$ are not identically distributed

A hybrid generative/discriminative framework to train a semantic parser from an un-annotated corpus

We propose a hybrid generative/discriminative framework for semantic parsing which combines the hidden vector state (HVS) model and the hidden Markov support vector machines (HMSVMs). The HVS model is an extension of the basic discrete Markov model in which context is encoded as a stack-oriented state vector. The HM-SVMs combine the advantages of the hidden Markov models and the support vector machines. By employing a modified K-means clustering method, a small set of most representative sentences can be automatically selected from an un-annotated corpus. These sentences together with their abstract annotations are used to train an HVS model which could be subsequently applied on the whole corpus to generate semantic parsing results. The most confident semantic parsing results are selected to generate a fully-annotated corpus which is used to train the HM-SVMs. The proposed framework has been tested on the DARPA Communicator Data. Experimental results show that an improvement over the baseline HVS parser has been observed using the hybrid framework. When compared with the HM-SVMs trained from the fully annotated corpus, the hybrid framework gave a comparable performance with only a small set of lightly annotated sentences

Fast and reliable online learning to rank for information retrieval

The amount of digital data we produce every day far surpasses our ability to process this data, and finding useful information in this constant flow of data has become one of the major challenges of the 21st century. Search engines are one way of accessing large data collections. Their algorithms have evolved far beyond simply matching search queries to sets of documents. Today’s most sophisticated search engines combine hundreds of relevance signals to provide the best possible results for each searcher. Current approaches for tuning the parameters of search engines can be highly effective. However, they typically require considerable expertise and manual effort. They rely on supervised learning to rank, meaning that they learn from manually annotated examples of relevant documents for given queries. Obtaining large quantities of sufficiently accurate manual annotations is becoming increasingly difficult, especially for personalized search, access to sensitive data, or search in settings that change over time. In this thesis, I develop new online learning to rank techniques, based on insights from reinforcement learning. In contrast to supervised approaches, these methods allow search engines to learn directly from users’ interactions. User interactions can typically be observed easily and cheaply, and reflect the preferences of real users. Interpreting user interactions and learning from them is challenging, because they can be biased and noisy. The contributions of this thesis include a novel interleaved comparison method, called probabilistic interleave, that allows unbiased comparisons of search engine result rankings, and methods for learning quickly and effectively from the resulting relative feedback. The obtained analytical and experimental results show how search engines can effectively learn from user interactions. In the future, these and similar techniques can open up new ways for gaining useful information from ever larger amounts of data