Delocalized electron holes on oxygen in a battery cathode

Oxide ions in transition metal oxide cathodes can store charge at high voltage offering a route towards higher energy density batteries. However, upon charging these cathodes, the oxidized oxide ions condense to form molecular O2 trapped in the material. Consequently, the discharge voltage is much lower than charge, leading to undesirable voltage hysteresis. Here we capture the nature of the electron holes on O2− before O2 formation by exploiting the suppressed transition metal rearrangement in ribbon-ordered Na0.6[Li0.2Mn0.8]O2. We show that the electron holes formed are delocalized across the oxide ions coordinated to two Mn (O–Mn2) arranged in ribbons in the transition metal layers. Furthermore, we track these delocalized hole states as they gradually localize in the structure in the form of trapped molecular O2 over a period of days. Establishing the nature of hole states on oxide ions is important if truly reversible high-voltage O-redox cathodes are to be realized.</p

Delocalized electron holes on oxygen in a battery cathode

Oxide ions in transition metal oxide cathodes can store charge at high voltage offering a route towards higher energy density batteries. However, upon charging these cathodes, the oxidized oxide ions condense to form molecular O2 trapped in the material. Consequently, the discharge voltage is much lower than charge, leading to undesirable voltage hysteresis. Here we capture the nature of the electron holes on O2− before O2 formation by exploiting the suppressed transition metal rearrangement in ribbon-ordered Na0.6[Li0.2Mn0.8]O2. We show that the electron holes formed are delocalized across the oxide ions coordinated to two Mn (O–Mn2) arranged in ribbons in the transition metal layers. Furthermore, we track these delocalized hole states as they gradually localize in the structure in the form of trapped molecular O2 over a period of days. Establishing the nature of hole states on oxide ions is important if truly reversible high-voltage O-redox cathodes are to be realized.</p

Gravitating monopole and its black hole solution in Brans-Dicke Theory

We find a self-gravitating monopole and its black hole solution in Brans-Dicke theory. We mainly discuss the properties of these solutions in the Einstein frame and compare the solutions with those in general relativity.Comment: 20 pages,revtex,26 figure

Kinetic modelling of competition and depletion of shared miRNAs by competing endogenous RNAs

Non-conding RNAs play a key role in the post-transcriptional regulation of mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact with their target RNAs through protein-mediated, sequence-specific binding, giving rise to extended and highly heterogeneous miRNA-RNA interaction networks. Within such networks, competition to bind miRNAs can generate an effective positive coupling between their targets. Competing endogenous RNAs (ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk. Albeit potentially weak, ceRNA interactions can occur both dynamically, affecting e.g. the regulatory clock, and at stationarity, in which case ceRNA networks as a whole can be implicated in the composition of the cell's proteome. Many features of ceRNA interactions, including the conditions under which they become significant, can be unraveled by mathematical and in silico models. We review the understanding of the ceRNA effect obtained within such frameworks, focusing on the methods employed to quantify it, its role in the processing of gene expression noise, and how network topology can determine its reach.Comment: review article, 29 pages, 7 figure

UBVRI Light Curves of 44 Type Ia Supernovae

We present UBVRI photometry of 44 type-Ia supernovae (SN Ia) observed from 1997 to 2001 as part of a continuing monitoring campaign at the Fred Lawrence Whipple Observatory of the Harvard-Smithsonian Center for Astrophysics. The data set comprises 2190 observations and is the largest homogeneously observed and reduced sample of SN Ia to date, nearly doubling the number of well-observed, nearby SN Ia with published multicolor CCD light curves. The large sample of U-band photometry is a unique addition, with important connections to SN Ia observed at high redshift. The decline rate of SN Ia U-band light curves correlates well with the decline rate in other bands, as does the U-B color at maximum light. However, the U-band peak magnitudes show an increased dispersion relative to other bands even after accounting for extinction and decline rate, amounting to an additional ~40% intrinsic scatter compared to B-band.Comment: 84 authors, 71 pages, 51 tables, 10 figures. Accepted for publication in the Astronomical Journal. Version with high-res figures and electronic data at http://astron.berkeley.edu/~saurabh/cfa2snIa

A data compression and optimal galaxy weights scheme for Dark Energy Spectroscopic Instrument and weak lensing data sets

Combining different observational probes, such as galaxy clustering and weak lensing, is a promising technique for unveiling the physics of the Universe with upcoming dark energy experiments. The galaxy redshift sample from the Dark Energy Spectroscopic Instrument (DESI) will have a significant overlap with major ongoing imaging surveys specifically designed for weak lensing measurements: The Kilo-Degree Survey (KiDS), the Dark Energy Survey (DES), and the Hyper Suprime-Cam (HSC) survey. In this work, we analyse simulated redshift and lensing catalogues to establish a new strategy for combining high-quality cosmological imaging and spectroscopic data, in view of the first-year data assembly analysis of DESI. In a test case fitting for a reduced parameter set, we employ an optimal data compression scheme able to identify those aspects of the data that are most sensitive to cosmological information and amplify them with respect to other aspects of the data. We find this optimal compression approach is able to preserve all the information related to the growth of structures

Selecting cash management models from a multiobjective perspective

[EN] This paper addresses the problem of selecting cash management models under different operating conditions from a multiobjective perspective considering not only cost but also risk. A number of models have been proposed to optimize corporate cash management policies. The impact on model performance of different operating conditions becomes an important issue. Here, we provide a range of visual and quantitative tools imported from Receiver Operating Characteristic (ROC) analysis. More precisely, we show the utility of ROC analysis from a triple perspective as a tool for: (1) showing model performance; (2) choosingmodels; and (3) assessing the impact of operating conditions on model performance. We illustrate the selection of cash management models by means of a numerical example.Work partially funded by projects Collectiveware TIN2015-66863-C2-1-R (MINECO/FEDER) and 2014 SGR 118.Salas-Molina, F.; Rodríguez-Aguilar, JA.; Díaz-García, P. (2018). Selecting cash management models from a multiobjective perspective. Annals of Operations Research. 261(1-2):275-288. https://doi.org/10.1007/s10479-017-2634-9S2752882611-2Ballestero, E. (2007). Compromise programming: A utility-based linear-quadratic composite metric from the trade-off between achievement and balanced (non-corner) solutions. European Journal of Operational Research, 182(3), 1369–1382.Ballestero, E., & Romero, C. (1998). Multiple criteria decision making and its applications to economic problems. Berlin: Springer.Bi, J., & Bennett, K. P. (2003). Regression error characteristic curves. In Proceedings of the 20th international conference on machine learning (ICML-03), pp. 43–50.Bradley, A. P. (1997). The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145–1159.da Costa Moraes, M. B., Nagano, M. S., & Sobreiro, V. A. (2015). Stochastic cash flow management models: A literature review since the 1980s. In Decision models in engineering and management (pp. 11–28). New York: Springer.Doumpos, M., & Zopounidis, C. (2007). Model combination for credit risk assessment: A stacked generalization approach. Annals of Operations Research, 151(1), 289–306.Drummond, C., & Holte, R. C. (2000). Explicitly representing expected cost: An alternative to roc representation. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 98–207). New York: ACM.Drummond, C., & Holte, R. C. (2006). Cost curves: An improved method for visualizing classifier performance. Machine Learning, 65(1), 95–130.Elkan, C. (2001). The foundations of cost-sensitive learning. In International joint conference on artificial intelligence (Vol. 17, pp. 973–978). Lawrence Erlbaum associates Ltd.Fawcett, T. (2006). An introduction to roc analysis. Pattern Recognition Letters, 27(8), 861–874.Flach, P. A. (2003). The geometry of roc space: understanding machine learning metrics through roc isometrics. In Proceedings of the 20th international conference on machine learning (ICML-03), pp. 194–201.Garcia-Bernabeu, A., Benito, A., Bravo, M., & Pla-Santamaria, D. (2016). Photovoltaic power plants: a multicriteria approach to investment decisions and a case study in western spain. Annals of Operations Research, 245(1–2), 163–175.Glasserman, P. (2003). Monte Carlo methods in financial engineering (Vol. 53). New York: Springer.Gregory, G. (1976). Cash flow models: a review. Omega, 4(6), 643–656.Hernández-Orallo, J. (2013). Roc curves for regression. Pattern Recognition, 46(12), 3395–3411.Hernández-Orallo, J., Flach, P., & Ferri, C. (2013). Roc curves in cost space. Machine Learning, 93(1), 71–91.Hernández-Orallo, J., Lachiche, N., & Martınez-Usó, A. (2014). Predictive models for multidimensional data when the resolution context changes. In Workshop on learning over multiple contexts at ECML, volume 2014.Metz, C. E. (1978). Basic principles of roc analysis. In Seminars in nuclear medicine (Vol. 8, pp. 283–298). Amsterdam: Elsevier.Miettinen, K. (2012). Nonlinear multiobjective optimization (Vol. 12). Berlin: Springer.Ringuest, J. L. (2012). Multiobjective optimization: Behavioral and computational considerations. Berlin: Springer.Ross, S. A., Westerfield, R., & Jordan, B. D. (2002). Fundamentals of corporate finance (sixth ed.). New York: McGraw-Hill.Salas-Molina, F., Pla-Santamaria, D., & Rodriguez-Aguilar, J. A. (2016). A multi-objective approach to the cash management problem. Annals of Operations Research, pp. 1–15.Srinivasan, V., & Kim, Y. H. (1986). Deterministic cash flow management: State of the art and research directions. Omega, 14(2), 145–166.Steuer, R. E., Qi, Y., & Hirschberger, M. (2007). Suitable-portfolio investors, nondominated frontier sensitivity, and the effect of multiple objectives on standard portfolio selection. Annals of Operations Research, 152(1), 297–317.Stone, B. K. (1972). The use of forecasts and smoothing in control limit models for cash management. Financial Management, 1(1), 72.Torgo, L. (2005). Regression error characteristic surfaces. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining (pp. 697–702). ACM.Yu, P.-L. (1985). Multiple criteria decision making: concepts, techniques and extensions. New York: Plenum Press.Zeleny, M. (1982). Multiple criteria decision making. New York: McGraw-Hill

Heavy quarkonium: progress, puzzles, and opportunities

A golden age for heavy quarkonium physics dawned a decade ago, initiated by the confluence of exciting advances in quantum chromodynamics (QCD) and an explosion of related experimental activity. The early years of this period were chronicled in the Quarkonium Working Group (QWG) CERN Yellow Report (YR) in 2004, which presented a comprehensive review of the status of the field at that time and provided specific recommendations for further progress. However, the broad spectrum of subsequent breakthroughs, surprises, and continuing puzzles could only be partially anticipated. Since the release of the YR, the BESII program concluded only to give birth to BESIII; the $B$-factories and CLEO-c flourished; quarkonium production and polarization measurements at HERA and the Tevatron matured; and heavy-ion collisions at RHIC have opened a window on the deconfinement regime. All these experiments leave legacies of quality, precision, and unsolved mysteries for quarkonium physics, and therefore beg for continuing investigations. The plethora of newly-found quarkonium-like states unleashed a flood of theoretical investigations into new forms of matter such as quark-gluon hybrids, mesonic molecules, and tetraquarks. Measurements of the spectroscopy, decays, production, and in-medium behavior of c\bar{c}, b\bar{b}, and b\bar{c} bound states have been shown to validate some theoretical approaches to QCD and highlight lack of quantitative success for others. The intriguing details of quarkonium suppression in heavy-ion collisions that have emerged from RHIC have elevated the importance of separating hot- and cold-nuclear-matter effects in quark-gluon plasma studies. This review systematically addresses all these matters and concludes by prioritizing directions for ongoing and future efforts.Comment: 182 pages, 112 figures. Editors: N. Brambilla, S. Eidelman, B. K. Heltsley, R. Vogt. Section Coordinators: G. T. Bodwin, E. Eichten, A. D. Frawley, A. B. Meyer, R. E. Mitchell, V. Papadimitriou, P. Petreczky, A. A. Petrov, P. Robbe, A. Vair

Standardized Direct Observation Assessment Tool: Using a Training Video.

BACKGROUND: We developed a DVD training tool to educate physicians evaluating emergency residents on accurate Standardized Direct Observation Assessment Tool (SDOT) application. OBJECTIVE: Our goal was to assess whether this training video improved attendings\u27 and senior residents\u27 SDOT use. METHODS: Participants voluntarily completed SDOT evaluations based on a scripted test video. A DVD with positive and negative scenarios of proper SDOT use was viewed. It included education on appropriate recording of 26 behaviors. The test scenario was viewed again and follow-up SDOTs submitted. Performances by attendings and residents on the pre- and post-test SDOTs were compared. RESULTS: Twenty-six attendings and 26 senior residents participated. Prior SDOT experience was noted for 8 attendings and 11 residents. For 20 anchors, participants recorded observed behaviors with statistically significant difference on one each of the pretest (no. 20; p = 0.034) and post-test (no. 14; p = 0.041) SDOTs. On global competency assessments, pretest medical knowledge (p = 0.016) differed significantly between groups. The training intervention changed one anchor (no. 5; p = 0.035) and one global assessment (systems-based practice; p = 0.031) more negatively for residents. Recording SDOTs with exact agreement occurred 48.73% for attendings pretest and 54.41% post-test; resident scores were 45.86% and 49.55%, respectively. DVD exposure slightly raised attending scores (p = 0.289) and significantly lowered resident scores (p = 0.046). CONCLUSIONS: Exposure to an independently developed SDOT training video tended to raise attending scores, though without significance, while at the same time lowered senior resident scores statistically significantly. Emergency attendings\u27 and senior residents\u27 SDOT scoring rarely differed with significance; about half of anchor behaviors were recorded with exact agreement. This suggests senior residents, with appropriate education, may participate in SDOT assessment