Case study the poultry industry in Colombia

"As developing countries open their economies further to trade, their food industries are striving to raise safety and quality standards in order to compete in new markets. Such is the case with the Colombian poultry industry... Critical questions face the Colombian poultry industry: Is it ready to compete with foreign poultry producers on price, quality, and safety? Can industry efforts to produce better quality products assure an increased share of domestic and regional markets? This brief reviews the private initiatives undertaken by the Colombian poultry industry to assure food safety in light of these questions." from TextFood safety ,food security ,Public health ,

τ\tau-Flavour Violation at the LHC

We study the conditions required for $\chi_2 \to \chi + \tau^\pm \mu^\mp$ decays to yield observable tau flavour violation at the LHC, for cosmologically interesting values of the neutralino relic density. These condition can be achieved in the framework of a SU(5) model with a see-saw mechanism that allows a possible coexistence of a LHC signal a low prediction for radiative LFV decays.Comment: 7 pages, 5 figures, Prepared for the proceedings of the workshop: "LC09: $e^+ e^-$ Physics at the TeV Scale and the Dark Matter Connection", 21-24 September 2009, Perugia, Ital

Sketch-based Influence Maximization and Computation: Scaling up with Guarantees

Propagation of contagion through networks is a fundamental process. It is used to model the spread of information, influence, or a viral infection. Diffusion patterns can be specified by a probabilistic model, such as Independent Cascade (IC), or captured by a set of representative traces. Basic computational problems in the study of diffusion are influence queries (determining the potency of a specified seed set of nodes) and Influence Maximization (identifying the most influential seed set of a given size). Answering each influence query involves many edge traversals, and does not scale when there are many queries on very large graphs. The gold standard for Influence Maximization is the greedy algorithm, which iteratively adds to the seed set a node maximizing the marginal gain in influence. Greedy has a guaranteed approximation ratio of at least (1-1/e) and actually produces a sequence of nodes, with each prefix having approximation guarantee with respect to the same-size optimum. Since Greedy does not scale well beyond a few million edges, for larger inputs one must currently use either heuristics or alternative algorithms designed for a pre-specified small seed set size. We develop a novel sketch-based design for influence computation. Our greedy Sketch-based Influence Maximization (SKIM) algorithm scales to graphs with billions of edges, with one to two orders of magnitude speedup over the best greedy methods. It still has a guaranteed approximation ratio, and in practice its quality nearly matches that of exact greedy. We also present influence oracles, which use linear-time preprocessing to generate a small sketch for each node, allowing the influence of any seed set to be quickly answered from the sketches of its nodes.Comment: 10 pages, 5 figures. Appeared at the 23rd Conference on Information and Knowledge Management (CIKM 2014) in Shanghai, Chin

Collective resonances in plasmonic crystals: Size matters

Periodic arrays of metallic nanoparticles may sustain Surface Lattice Resonances (SLRs), which are collective resonances associated with the diffractive coupling of Localized Surface Plasmon Resonances (LSPRs). By investigating a series of arrays with varying number of particles, we traced the evolution of SLRs to its origins. Polarization resolved extinction spectra of arrays formed by a few nanoparticles were measured, and found to be in very good agreement with calculations based on a coupled dipole model. Finite size effects on the optical properties of the arrays are observed, and our results provide insight into the characteristic length scales for collective plasmonic effects: for arrays smaller than 5 x 5 particles, the Q-factors of SLRs are lower than those of LSPRs; for arrays larger than 20 x 20 particles, the Q-factors of SLRs saturate at a much larger value than those of LSPRs; in between, the Q-factors of SLRs are an increasing function of the number of particles in the array.Comment: 4 figure

Stability of Influence Maximization

The present article serves as an erratum to our paper of the same title, which was presented and published in the KDD 2014 conference. In that article, we claimed falsely that the objective function defined in Section 1.4 is non-monotone submodular. We are deeply indebted to Debmalya Mandal, Jean Pouget-Abadie and Yaron Singer for bringing to our attention a counter-example to that claim. Subsequent to becoming aware of the counter-example, we have shown that the objective function is in fact NP-hard to approximate to within a factor of $O(n^{1-\epsilon})$ for any $\epsilon > 0$. In an attempt to fix the record, the present article combines the problem motivation, models, and experimental results sections from the original incorrect article with the new hardness result. We would like readers to only cite and use this version (which will remain an unpublished note) instead of the incorrect conference version.Comment: Erratum of Paper "Stability of Influence Maximization" which was presented and published in the KDD1