Phase transition in the Higgs model of scalar dyons

In the present paper we investigate the phase transition "Coulomb--confinement" in the Higgs model of abelian scalar dyons -- particles having both, electric $e$ and magnetic $g$, charges. It is shown that by dual symmetry this theory is equivalent to scalar fields with the effective squared electric charge e^{*2}=e^2+g^2. But the Dirac relation distinguishes the electric and magnetic charges of dyons. The following phase transition couplings are obtained in the one--loop approximation: \alpha_{crit}=e^2_{crit}/4\pi\approx 0.19, \tilde\alpha_{crit}=g^2_{crit}/4\pi\approx 1.29 and \alpha^*_{crit}\approx 1.48.Comment: 16 pages, 2 figure

Modeling of secondary organic aerosol yields from laboratory chamber data

Laboratory chamber data serve as the basis for constraining models of secondary organic aerosol (SOA) formation. Current models fall into three categories: empirical two-product (Odum), product-specific, and volatility basis set. The product-specific and volatility basis set models are applied here to represent laboratory data on the ozonolysis of α-pinene under dry, dark, and low-NOx conditions in the presence of ammonium sulfate seed aerosol. Using five major identified products, the model is fit to the chamber data. From the optimal fitting, SOA oxygen-to-carbon (O/C) and hydrogen-to-carbon (H/C) ratios are modeled. The discrepancy between measured H/C ratios and those based on the oxidation products used in the model fitting suggests the potential importance of particle-phase reactions. Data fitting is also carried out using the volatility basis set, wherein oxidation products are parsed into volatility bins. The product-specific model is most likely hindered by lack of explicit inclusion of particle-phase accretion compounds. While prospects for identification of the majority of SOA products for major volatile organic compounds (VOCs) classes remain promising, for the near future empirical product or volatility basis set models remain the approaches of choice

Instability of three dimensional conformally dressed black hole

The three dimensional black hole solution of Einstein equations with negative cosmological constant coupled to a conformal scalar field is proved to be unstable against linear circularly symmetric perturbations.Comment: 5 pages, REVTe

Role of aldehyde chemistry and NO_x concentrations in secondary organic aerosol formation

Aldehydes are an important class of products from atmospheric oxidation of hydrocarbons. Isoprene (2-methyl-1,3-butadiene), the most abundantly emitted atmospheric non-methane hydrocarbon, produces a significant amount of secondary organic aerosol (SOA) via methacrolein (a C_4-unsaturated aldehyde) under urban high-NO_x conditions. Previously, we have identified peroxy methacryloyl nitrate (MPAN) as the important intermediate to isoprene and methacrolein SOA in this NO_x regime. Here we show that as a result of this chemistry, NO_2 enhances SOA formation from methacrolein and two other α, β-unsaturated aldehydes, specifically acrolein and crotonaldehyde, a NO_x effect on SOA formation previously unrecognized. Oligoesters of dihydroxycarboxylic acids and hydroxynitrooxycarboxylic acids are observed to increase with increasing NO_2/NO ratio, and previous characterizations are confirmed by both online and offline high-resolution mass spectrometry techniques. Molecular structure also determines the amount of SOA formation, as the SOA mass yields are the highest for aldehydes that are α, β-unsaturated and contain an additional methyl group on the α-carbon. Aerosol formation from 2-methyl-3-buten-2-ol (MBO232) is insignificant, even under high-NO_2 conditions, as PAN (peroxy acyl nitrate, RC(O)OONO_2) formation is structurally unfavorable. At atmospherically relevant NO_2/NO ratios (3–8), the SOA yields from isoprene high-NO_x photooxidation are 3 times greater than previously measured at lower NO_2/NO ratios. At sufficiently high NO_2 concentrations, in systems of α, β-unsaturated aldehydes, SOA formation from subsequent oxidation of products from acyl peroxyl radicals+NO_2 can exceed that from RO_2+HO_2 reactions under the same inorganic seed conditions, making RO_2+NO_2 an important channel for SOA formation

Buy, sell, or hold? A sense-making account of factors influencing trading decisions

We investigated the effects of news valence, the direction of trends in graphically presented price series, and the culture and personality of traders in a financial trading task. Participants were given 12 virtual shares of financial assets and asked to use price graphs and news items to maximize their returns by buying, selling, or holding each one. In making their decisions, they were influenced by properties of both news items and price series but they relied more on the former. Western participants had lower trading latencies and lower return dispersions than Eastern participants. Those with greater openness to experience had lower trading latencies. Participants bought more shares when they forecast that prices would rise but failed to sell more when they forecast that they would fall. These findings are all consistent with the view that people trading assets try to make sense of information by incorporating it within a coherent narrative

Bringing Order to Special Cases of Klee's Measure Problem

Klee's Measure Problem (KMP) asks for the volume of the union of n axis-aligned boxes in d-space. Omitting logarithmic factors, the best algorithm has runtime O*(n^{d/2}) [Overmars,Yap'91]. There are faster algorithms known for several special cases: Cube-KMP (where all boxes are cubes), Unitcube-KMP (where all boxes are cubes of equal side length), Hypervolume (where all boxes share a vertex), and k-Grounded (where the projection onto the first k dimensions is a Hypervolume instance). In this paper we bring some order to these special cases by providing reductions among them. In addition to the trivial inclusions, we establish Hypervolume as the easiest of these special cases, and show that the runtimes of Unitcube-KMP and Cube-KMP are polynomially related. More importantly, we show that any algorithm for one of the special cases with runtime T(n,d) implies an algorithm for the general case with runtime T(n,2d), yielding the first non-trivial relation between KMP and its special cases. This allows to transfer W[1]-hardness of KMP to all special cases, proving that no n^{o(d)} algorithm exists for any of the special cases under reasonable complexity theoretic assumptions. Furthermore, assuming that there is no improved algorithm for the general case of KMP (no algorithm with runtime O(n^{d/2 - eps})) this reduction shows that there is no algorithm with runtime O(n^{floor(d/2)/2 - eps}) for any of the special cases. Under the same assumption we show a tight lower bound for a recent algorithm for 2-Grounded [Yildiz,Suri'12].Comment: 17 page

Developing a Generic Predictive Computational Model using Semantic data Pre-Processing with Machine Learning Techniques and its application for Stock Market Prediction Purposes

In this paper, we present a Generic Predictive Computational Model (GPCM) and apply it by building a Use Case for the FTSE 100 index forecasting. This involves the mining of heterogeneous data based on semantic methods (ontology), graph-based methods (knowledge graphs, graph databases) and advanced Machine Learning methods. The main focus of our research is data pre-processing aimed at a more efficient selection of input features. The GPCM model pipeline’s cycles involve the propagation of the (initially raw) data to the Graph Database structured by an ontology and regular updates of the features’ weights in the Graph Database by the feedback loop from the Machine Learning Engine. The Graph Database queries output the most valuable features that, in turn, serve as the input for the Machine Learning-based prediction. The end-product of this process is fed back to the Graph Database to update the weights. We report on practical experiments evaluating the effectiveness of the GPCM application in forecasting the FTSE 100 index. The underlying dataset contains multiple parameters related to predicting time-series data, where Long Short-Term Memory (LSTM) is known to be one of the most efficient machine learning methods. The most challenging task here has been to overcome the known restrictions of LSTM, which is capable of analysing one input parameter only. We solved this problem by combining several parallel LSTMs, a Concatenation unit, which merges the LSTMs’ outputs (into a time-series matrix), and a Linear Regression Unit, which produces the final resul

Conditional preparation of states containing a definite number of photons

A technique for conditionally creating single- or multimode photon-number states is analyzed using Bayesian theory. We consider the heralded N-photon states created from the photons produced by an unseeded optical parametric amplifier when the heralding detector is the time-multiplexed photon-number-resolving detector recently demonstrated by Fitch, et al. [Phys. Rev. A 68, 043814 (2003).] and simultaneously by Achilles, et al. [Opt. Lett. 28, 2387 (2003).]. We find that even with significant loss in the heralding detector, fields with sub-Poissonian photon-number distributions can be created. We also show that heralded multimode fields created using this technique are more robust against detector loss than are single-mode fields.Comment: 6 pages, 6 figures, reference added, typos corrected, content update