Anisotropic random resistor networks: a model for piezoresistive response of thick-film resistors

A number of evidences suggests that thick-film resistors are close to a metal-insulator transition and that tunneling processes between metallic grains are the main source of resistance. We consider as a minimal model for description of transport properties in thick-film resistors a percolative resistor network, with conducting elements governed by tunneling. For both oriented and randomly oriented networks, we show that the piezoresistive response to an applied strain is model dependent when the system is far away from the percolation thresold, while in the critical region it acquires universal properties. In particular close to the metal-insulator transition, the piezoresistive anisotropy show a power law behavior. Within this region, there exists a simple and universal relation between the conductance and the piezoresistive anisotropy, which could be experimentally tested by common cantilever bar measurements of thick-film resistors.Comment: 7 pages, 2 eps figure

Correcting the polarization effect in low frequency Dielectric Spectroscopy

We demonstrate a simple and robust methodology for measuring and analyzing the polarization impedance appearing at interface between electrodes and ionic solutions, in the frequency range from 1 to $10^6$ Hz. The method assumes no particular behavior of the electrode polarization impedance and it only makes use of the fact that the polarization effect dies out with frequency. The method allows a direct and un-biased measurement of the polarization impedance, whose behavior with the applied voltages and ionic concentration is methodically investigated. Furthermore, based on the previous findings, we propose a protocol for correcting the polarization effect in low frequency Dielectric Spectroscopy measurements of colloids. This could potentially lead to the quantitative resolution of the $\alpha$-dispersion regime of live cells in suspension

Cashing in on spinners: Revenue estimates of wild Dolphin-Swim tourism in the Hawaiian Islands

Wild dolphin-swim tourism has grown in specific locations where Hawaiian spinner dolphins (Stenella longirostris) have known resting habitat. The increased growth in dolphin-swim businesses has created an industry in Hawaii that earns an estimated $102 million (USD) annually in 2013. Semi-structured interviews with business owners, market research, and boat-based observations provide a platform for estimating revenue generated from dolphin tourism in two popular locations, Waianae, Oahu and Kailua-Kona, Hawaii Island. A revenue analysis of dolphin-swim tourism is presented using a peak season and utilization rate model. These predictions offer an accountability exercise based on a series of assumptions regarding wild dolphin-swim demand and an annual estimate of the number of viewing participants and revenue earned. The results show that dolphin viewing companies are making a larger profit than dolphin-swim businesses by approximately$19 million (USD) per year, however, both avenues are generating large earnings. Sizable differences between businesses in Kona and Waianae are discussed. The average lifetime revenue generated by a dolphin in 2013 is estimated at $3,364,316 (USD) for Waianae and$1,608,882 (USD) for Kona, and is presented as a first step in scenario analysis for policy makers looking to implement management in the bays where tourism occurs. This study offers the first revenue estimates of spinner dolphin tourism in Hawaii, which can provide context for further discussion on the impact and economic role of the dolphin-swim industry in the state

Fluctuation-induced first-order phase transition in Dzyaloshinskii-Moriya helimagnets

Two centuries of research on phase transitions have repeatedly highlighted the importance of critical fluctuations that abound in the vicinity of a critical point. They are at the origin of scaling laws obeyed by thermodynamic observables close to second-order phase transitions resulting in the concept of universality classes, that is of paramount importance for the study of organizational principles of matter. Strikingly, in case such soft fluctuations are too abundant they may alter the nature of the phase transition profoundly; the system might evade the critical state altogether by undergoing a discontinuous first-order transition into the ordered phase. Fluctuation-induced first-order transitions have been discussed broadly and are germane for superconductors, liquid crystals, or phase transitions in the early universe, but clear experimental confirmations remain scarce. Our results from neutron scattering and thermodynamics on the model Dzyaloshinskii-Moriya (DM) helimagnet (HM) MnSi show that such a fluctuation-induced first-order transition is realized between its paramagnetic and HM state with remarkable agreement between experiment and a theory put forward by Brazovskii. While our study clarifies the nature of the HM phase transition in MnSi that has puzzled scientists for several decades, more importantly, our conclusions entirely based on symmetry arguments are also relevant for other DM-HMs with only weak cubic magnetic anisotropies. This is in particular noteworthy in light of a wide range of recent discoveries that show that DM helimagnetism is at the heart of problems such as topological magnetic order, multiferroics, and spintronics.Comment: 19 pages, 9 figures, 2 table

Uso de modelos de simulação sócio-bio-econômico integrado como ferramenta para o desenvolvimento agrário na região sudoeste do Rio Grande Sul.

Suporte tecnológico tem sido oferecido aos produtores com a finalidade de aumentar a eficiência produtiva e fornecer subsídios para as suas tomadas de decisões; entretanto, os métodos tradicionais de pesquisa e extensão estão sendo cada vez mais questionados, principalmente quanto ao custo e tempo necessário para oferecer soluções aos problemas enfrentados pelos produtores.bitstream/item/109814/1/USO-DE-MODELOS-DE-SIMULACAO.pd

Lockin to Weak Ferromagnetism in TbNi2B2C and ErNi2B2C

This article describes a model in which ferromagnetism necessarily accompanies a spin-density-wave lockin transition in the borocarbide structure provided the commensurate phase wave vector satisfies Q = (m/n)a* with m even and n odd. The results account for the magnetic properties of TbNi2B2C, and are also possibly relevant also for those of ErNi2B2C.Comment: 4 page

Enhancement of the electronic contribution to the low temperature specific heat of Fe/Cr magnetic multilayer

We measured the low temperature specific heat of a sputtered $(Fe_{23\AA}/Cr_{12\AA})_{33}$ magnetic multilayer, as well as separate $1000\AA$ thick Fe and Cr films. Magnetoresistance and magnetization measurements on the multilayer demonstrated antiparallel coupling between the Fe layers. Using microcalorimeters made in our group, we measured the specific heat for $4<T<30 K$ and in magnetic fields up to $8 T$ for the multilayer. The low temperature electronic specific heat coefficient of the multilayer in the temperature range $4<T<14 K$ is $\gamma_{ML}=8.4 mJ/K^{2}g-at$. This is significantly larger than that measured for the Fe or Cr films (5.4 and $3.5 mJ/K^{2}mol$ respectively). No magnetic field dependence of $\gamma_{ML}$ was observed up to $8 T$. These results can be explained by a softening of the phonon modes observed in the same data and the presence of an Fe-Cr alloy phase at the interfaces.Comment: 20 pages, 5 figure

ROC curves in cost space

The final publication is available at Springer via http://dx.doi.org/10.1007/s10994-013-5328-9ROC curves and cost curves are two popular ways of visualising classifier performance, finding appropriate thresholds according to the operating condition, and deriving useful aggregated measures such as the area under the ROC curve (AUC) or the area under the optimal cost curve. In this paper we present new findings and connections between ROC space and cost space. In particular, we show that ROC curves can be transferred to cost space by means of a very natural threshold choice method, which sets the decision threshold such that the proportion of positive predictions equals the operating condition. We call these new curves rate-driven curves, and we demonstrate that the expected loss as measured by the area under these curves is linearly related to AUC. We show that the rate-driven curves are the genuine equivalent of ROC curves in cost space, establishing a point-point rather than a point-line correspondence. Furthermore, a decomposition of the rate-driven curves is introduced which separates the loss due to the threshold choice method from the ranking loss (Kendall τ distance). We also derive the corresponding curve to the ROC convex hull in cost space; this curve is different from the lower envelope of the cost lines, as the latter assumes only optimal thresholds are chosen.We would like to thank the anonymous referees for their helpful comments. This work was supported by the MEC/MINECO projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02, GVA project PROMETEO/2008/051, the COST-European Cooperation in the field of Scientific and Technical Research IC0801 AT, and the REFRAME project granted by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA), and funded by the Engineering and Physical Sciences Research Council in the UK and the Ministerio de Economia y Competitividad in Spain.Hernández Orallo, J.; Flach ., P.; Ferri Ramírez, C. (2013). ROC curves in cost space. Machine Learning. 93(1):71-91. https://doi.org/10.1007/s10994-013-5328-9S7191931Adams, N., & Hand, D. (1999). Comparing classifiers when the misallocation costs are uncertain. Pattern Recognition, 32(7), 1139–1147.Chang, J., & Yap, C. (1986). A polynomial solution for the potato-peeling problem. Discrete & Computational Geometry, 1(1), 155–182.Drummond, C., & Holte, R. (2000). Explicitly representing expected cost: an alternative to ROC representation. In Knowl. discovery & data mining (pp. 198–207).Drummond, C., & Holte, R. (2006). Cost curves: an improved method for visualizing classifier performance. Machine Learning, 65, 95–130.Elkan, C. (2001). The foundations of cost-sensitive learning. In B. Nebel (Ed.), Proc. of the 17th intl. conf. on artificial intelligence (IJCAI-01) (pp. 973–978).Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.Fawcett, T., & Niculescu-Mizil, A. (2007). PAV and the ROC convex hull. Machine Learning, 68(1), 97–106.Flach, P. (2003). The geometry of ROC space: understanding machine learning metrics through ROC isometrics. In Machine learning, proceedings of the twentieth international conference (ICML 2003) (pp. 194–201).Flach, P., Hernández-Orallo, J., & Ferri, C. (2011). A coherent interpretation of AUC as a measure of aggregated classification performance. In Proc. of the 28th intl. conference on machine learning, ICML2011.Frank, A., & Asuncion, A. (2010). UCI machine learning repository. http://archive.ics.uci.edu/ml .Hand, D. (2009). Measuring classifier performance: a coherent alternative to the area under the ROC curve. Machine Learning, 77(1), 103–123.Hernández-Orallo, J., Flach, P., & Ferri, C. (2011). Brier curves: a new cost-based visualisation of classifier performance. In Proceedings of the 28th international conference on machine learning, ICML2011.Hernández-Orallo, J., Flach, P., & Ferri, C. (2012). A unified view of performance metrics: translating threshold choice into expected classification loss. Journal of Machine Learning Research, 13, 2813–2869.Kendall, M. G. (1938). A new measure of rank correlation. Biometrika, 30(1/2), 81–93. doi: 10.2307/2332226 .Swets, J., Dawes, R., & Monahan, J. (2000). Better decisions through science. Scientific American, 283(4), 82–87