SALSA: a tool to estimate the stray light contamination for low-Earth orbit observatories

Stray light contamination reduces considerably the precision of photometric of faint stars for low altitude spaceborne observatories. When measuring faint objects, the necessity of coping with stray light contamination arises in order to avoid systematic impacts on low signal-to-noise images. Stray light contamination can be represented by a flat offset in CCD data. Mitigation techniques begin by a comprehensive study during the design phase, followed by the use of target pointing optimisation and post-processing methods. We present a code that aims at simulating the stray-light contamination in low-Earth orbit coming from reflexion of solar light by the Earth. StrAy Light SimulAtor (SALSA) is a tool intended to be used at an early stage as a tool to evaluate the effective visible region in the sky and, therefore to optimise the observation sequence. SALSA can compute Earth stray light contamination for significant periods of time allowing missionwide parameters to be optimised (e.g. impose constraints on the point source transmission function (PST) and/or on the altitude of the satellite). It can also be used to study the behaviour of the stray light at different seasons or latitudes. Given the position of the satellite with respect to the Earth and the Sun, SALSA computes the stray light at the entrance of the telescope following a geometrical technique. After characterising the illuminated region of the Earth, the portion of illuminated Earth that affects the satellite is calculated. Then, the flux of reflected solar photons is evaluated at the entrance of the telescope. Using the PST of the instrument, the final stray light contamination at the detector is calculated. The analysis tools include time series analysis of the contamination, evaluation of the sky coverage and an objects visibility predictor. Effects of the South Atlantic Anomaly and of any shutdown periods of the instrument can be added. Several designs or mission concepts can be easily tested and compared. The code is not thought as a stand-alone mission designer. Its mandatory inputs are a time series describing the trajectory of the satellite and the characteristics of the instrument. This software suite has been applied to the design and analysis of CHEOPS (CHaracterizing ExOPlanet Satellite). This mission requires very high precision photometry to detect very shallow transits of exoplanets. Different altitudes and characteristics of the detector have been studied in order to find the best parameters, that reduce the effect of contamination. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Magnetic anomaly map of the Weddell Sea Region, Antarctic (Scale 1:2 500 000)

This paper describes a 1 : 2 500 000 scale aeromagnetic anomaly map produced by the joint efforts of VNIIOkeangeologia, Polar Marine Geological Research Expedition (PMGRE) and the Alfred Wegener Institute for Polar and Marine Research (AWl) for the Weddell Sea region covering 1 850 000 km' of West Antarctica. Extensive regional magnetic survey flights with line-spacing of about 20 km and 5 km were carried out by the PMGRE between 1977 and 1989. In course of these investigations the PMGRE flew 9 surveys with flight-line spacing of 20 km and 6 surveys with flight-line spacing of 5 km mainly over the mountain areas of southern Palmer Land, western Dronning Maud Land, Coats Land and Pensacola Mountains, over the Ronne lee Shelf and the Filchner Ice Shelf and the central part of the Weddell Sea. More than 215 000 line-kilometers of total field aeromagnetic data have been acquired by using an Ilyushin Il-14 ski-equipped aircraft. Survey operations were centered on the field base stations Druzhnaya-1, -2, and -3, from which the majority of the Weddell Sea region network was completed. The composite map of the Weddell Sea region is prepared in colour, showing magnetic anomaly contours at intervals of 50-100 nT with supplemental contours at an interval of 25 nT in low gradient areas, on a polar stereographic projection. The compiled colour magnetic anomaly map of the Weddell Sea region demonstrates that features of large areal extent, such as geologic provinces, fold-belts, ancient eratonic fragments and other regional structural features can be readily delineated. The map allows a comparison of regional magnetic features with similar-scale geological structures on geological and geophysical maps. It also provides a database for the future production of the ''Digital Magnetic Anomaly Map of Antarctica'' in the framework of the Scientific Committee on Antarctic Research/International Association of Geomagnetism and Aeronomy (SCAR/IAGA) compilation.

The impact of unstable aids on consumption volatility in developing countries

In recent years, a large and expanding literature has examined the properties of developing economies with regard to the macroeconomic cycle.1 One such property that is characteristic of developing economies is large fluctuations in consumption. Meanwhile, aid for the low income countries is extremely volatile, and under certain circumstances, the volatile aid amplifies the consumption volatility. This document examines whether it is possible that the volatile aid yields high consumption volatility in African countries that constitute the majority of the low income countries. Our numerical analysis reveals that the strongly influential aid disbursements yield a considerably large fluctuation in consumption.

PUE attack detection in CWSNs using anomaly detection techniques

Cognitive wireless sensor network (CWSN) is a new paradigm, integrating cognitive features in traditional wireless sensor networks (WSNs) to mitigate important problems such as spectrum occupancy. Security in cognitive wireless sensor networks is an important problem since these kinds of networks manage critical applications and data. The specific constraints of WSN make the problem even more critical, and effective solutions have not yet been implemented. Primary user emulation (PUE) attack is the most studied specific attack deriving from new cognitive features. This work discusses a new approach, based on anomaly behavior detection and collaboration, to detect the primary user emulation attack in CWSN scenarios. Two non-parametric algorithms, suitable for low-resource networks like CWSNs, have been used in this work: the cumulative sum and data clustering algorithms. The comparison is based on some characteristics such as detection delay, learning time, scalability, resources, and scenario dependency. The algorithms have been tested using a cognitive simulator that provides important results in this area. Both algorithms have shown to be valid in order to detect PUE attacks, reaching a detection rate of 99% and less than 1% of false positives using collaboration.

Electoral Volatility, Political Sophistication, Trust and Efficacy

In this article we investigate voter volatility and analyze the causes and motives of switching vote intentions. We test two main sets of variables linked to volatility in literature; political sophistication and ‘political (dis)satisfaction’. Results show that voters with low levels of political efficacy tend to switch more often, both within a campaign and between elections. In the analysis we differentiate between campaign volatility and inter-election volatility and by doing so show that the dynamics of a campaign have a profound impact on volatility. The campaign period is when the lowly sophisticated switch their vote intention. Those with higher levels of interest in politics have switched their intention before the campaign has started. The data for this analysis are from the three wave PartiRep Belgian Election Study (2009).

Stochastic volatility and the goodness-of-fit of the Heston model

Recently, Drǎgulescu and Yakovenko proposed an analytical formula for computing the probability density function of stock log returns, based on the Heston model, which they tested empirically. Their research design inadvertently favourably biased the fit of the data to the Heston model, thus overstating their empirical results. Furthermore, Drǎgulescu and Yakovenko did not perform any goodness-of-fit statistical tests. This study employs a research design that facilitates statistical tests of the goodness-of-fit of the Heston model to empirical returns. Robustness checks are also performed. In brief, the Heston model outperformed the Gaussian model only at high frequencies and even so does not provide a statistically acceptable fit to the data. The Gaussian model performed (marginally) better at medium and low frequencies, at which points the extra parameters of the Heston model have adverse impacts on the test statistics. © 2005 Taylor & Francis Group Ltd.

Sticky credit spreads, macroeconomic activity and equity market volatility

We uncover high persistence in credit spread series that can obscure the relationship between the theoretical determinants of credit risk and observed credit spreads. We use a Markovswitching model, which also captures the stability (low frequency changes) of credit ratings, to show why credit spreads may continue to respond to past levels of credit risk, even though the state of the economy has changed. A bivariate model of credit spreads and either macroeconomic activity or equity market volatility detects large and significant correlations that are consistent with theory but have not been observed in previous studies. © 2010 Nova Science Publishers, Inc. All rights reserved.

Wage matters & globalization: South Florida's low-wage immigrant plant nursery workers and business protectionism in the age of neoliberalism

Ornamental plant production in the State of Florida is an anomaly with respect to current theories of globalization and particularly their explanation of the employment of low-wage, immigrant labor. Those theories dictate that unskilled jobs that do not need to be performed within highly developed countries are outsourced to where labor is cheaper and more flexible. However, the State of Florida remains an important site of ornamental plant production in the US amidst a global economic environment of outsourcing and transnational corporate expansion. This dissertation relies on 50 semi-structured interviews with insiders of the Florida plant nursery industry, focus groups, and participant observation to explain how US trade, labor, and migration policy-making at local levels are not removed from larger global processes taking place in the world since the 1970s. In Florida, elite market players of the plant nursery industry have been able to resist global trends in free trade, operating instead in a protected market. They have done this by appealing to scientific justifications and through arbitrary implementations of neoliberal ideology that keeps small and middle range business alive, while maintaining a seemingly endless supply of marginalized and exploited low-wage, immigrant workers.^

Electoral Volatility, Political Sophistication, Trust and Efficacy

In this article we investigate voter volatility and analyze the causes and motives of switching vote intentions. We test two main sets of variables linked to volatility in literature; political sophistication and ‘political (dis)satisfaction’. Results show that voters with low levels of political efficacy tend to switch more often, both within a campaign and between elections. In the analysis we differentiate between campaign volatility and inter-election volatility and by doing so show that the dynamics of a campaign have a profound impact on volatility. The campaign period is when the lowly sophisticated switch their vote intention. Those with higher levels of interest in politics have switched their intention before the campaign has started. The data for this analysis are from the three wave PartiRep Belgian Election Study (2009).

Feature Selection for Anomaly Detection Using Optical Emission Spectroscopy

To maintain the pace of development set by Moore's law, production processes in semiconductor manufacturing are becoming more and more complex. The development of efficient and interpretable anomaly detection systems is fundamental to keeping production costs low. As the dimension of process monitoring data can become extremely high anomaly detection systems are impacted by the curse of dimensionality, hence dimensionality reduction plays an important role. Classical dimensionality reduction approaches, such as Principal Component Analysis, generally involve transformations that seek to maximize the explained variance. In datasets with several clusters of correlated variables the contributions of isolated variables to explained variance may be insignificant, with the result that they may not be included in the reduced data representation. It is then not possible to detect an anomaly if it is only reflected in such isolated variables. In this paper we present a new dimensionality reduction technique that takes account of such isolated variables and demonstrate how it can be used to build an interpretable and robust anomaly detection system for Optical Emission Spectroscopy data.

Measuring, Modeling, and Forecasting Volatility and Correlations from High-Frequency Data

This dissertation contains four essays that all share a common purpose: developing new methodologies to exploit the potential of high-frequency data for the measurement, modeling and forecasting of financial assets volatility and correlations. The first two chapters provide useful tools for univariate applications while the last two chapters develop multivariate methodologies. In chapter 1, we introduce a new class of univariate volatility models named FloGARCH models. FloGARCH models provide a parsimonious joint model for low frequency returns and realized measures, and are sufficiently flexible to capture long memory as well as asymmetries related to leverage effects. We analyze the performances of the models in a realistic numerical study and on the basis of a data set composed of 65 equities. Using more than 10 years of high-frequency transactions, we document significant statistical gains related to the FloGARCH models in terms of in-sample fit, out-of-sample fit and forecasting accuracy compared to classical and Realized GARCH models. In chapter 2, using 12 years of high-frequency transactions for 55 U.S. stocks, we argue that combining low-frequency exogenous economic indicators with high-frequency financial data improves the ability of conditionally heteroskedastic models to forecast the volatility of returns, their full multi-step ahead conditional distribution and the multi-period Value-at-Risk. Using a refined version of the Realized LGARCH model allowing for time-varying intercept and implemented with realized kernels, we document that nominal corporate profits and term spreads have strong long-run predictive ability and generate accurate risk measures forecasts over long-horizon. The results are based on several loss functions and tests, including the Model Confidence Set. Chapter 3 is a joint work with David Veredas. We study the class of disentangled realized estimators for the integrated covariance matrix of Brownian semimartingales with finite activity jumps. These estimators separate correlations and volatilities. We analyze different combinations of quantile- and median-based realized volatilities, and four estimators of realized correlations with three synchronization schemes. Their finite sample properties are studied under four data generating processes, in presence, or not, of microstructure noise, and under synchronous and asynchronous trading. The main finding is that the pre-averaged version of disentangled estimators based on Gaussian ranks (for the correlations) and median deviations (for the volatilities) provide a precise, computationally efficient, and easy alternative to measure integrated covariances on the basis of noisy and asynchronous prices. Along these lines, a minimum variance portfolio application shows the superiority of this disentangled realized estimator in terms of numerous performance metrics. Chapter 4 is co-authored with Niels S. Hansen, Asger Lunde and Kasper V. Olesen, all affiliated with CREATES at Aarhus University. We propose to use the Realized Beta GARCH model to exploit the potential of high-frequency data in commodity markets. The model produces high quality forecasts of pairwise correlations between commodities which can be used to construct a composite covariance matrix. We evaluate the quality of this matrix in a portfolio context and compare it to models used in the industry. We demonstrate significant economic gains in a realistic setting including short selling constraints and transaction costs.

On the effect of Arabian Sea and Indian Ocean sea surface temperature anomaly on the monsoon rainfall of south-east of Iran

Southeast region of the country has hot and dry weather which causes to happen heavy rainfall in short time period of warm seasons and to occur river flooding. These precipitations are influenced by monsoon system of India ocean. In these thesis, It was tried to evaluate the relation between thermal anomaly of sea surface in India ocean and Arab sea which effects on southeast monsoon precipitations of Iran, For evaluation of this happening in southeast, data were collected from 7 synoptic observation stations of Bandar Abbas, Minab, Kerman , Bam, Chabahar, Iranshahr, Zahedan and 17 rain gauge stations during June to September of each year from 1980 to 2010. Rainy days were determine and then some information about synoptic circulation models, maps of average pressure of sea surface, geopotential height of 700hP surface, geopotential height of 500hP surface, temperature of 850 hPa surface, humidity of 700 hPa surface, vertical velocity of 700 hPa surface, vertical velocity of 500 hP and humidity of 2 meters height for 6 systems were extracted from NCEP/NCAR website for evaluation. By evaluation of these systems it was determined that the monsoon low pressure system tab brings needed humidity of these precipitations to this region from India ocean and Arab sea with a vast circulation. It is seen that warm air pool locates on Iran and cold air pool locates on west of India at 800 hPa surface. In a rainy day this warm air transfers to high latitudes and influences the temperature trough of southeast cold air pool of the country. In the middle surfaces of 700 and 500 hPa, the connection between low height system above India and low height system above the higher latitudes causes the low height system above India to be strength and developed. By evaluation of humidity at 2 meters height and 700 hPa surface we observe that humidity Increases in the southeast region. With penetrating of the low height system of India above the 700 and 500 hPa surfaces of southeast of Iran, the value of negative omega (Rising vertical velocity) is increased. In the second pace, it was shown the evaluation of how the correlation between sea surface temperature anomaly in India Ocean and Arab sea influences southeast monsoon precipitation of Iran. For this purpose the data of water surface temperature anomaly of Arab sea and India ocean, the data of precipitation anomaly of 7 synoptic stations , mentioned above, and correlation coefficient among the data of precipitation anomaly and water surface temperature anomaly of Arab Sea, east and west of India ocean were calculated. In conclusion it was shown that the maximum correlation coefficient of precipitation anomaly had belonged to India Ocean in June and no meaningful correlation was resulted in July among precipitation anomaly and sea surface temperature anomaly for three regions, which were evaluated.

Wage Matters & Globalization: South Florida’s Low-Wage Immigrant Plant Nursery Workers and Business Protectionism in the Age of Neoliberalism

Ornamental plant production in the State of Florida is an anomaly with respect to current theories of globalization and particularly their explanation of the employment of low-wage, immigrant labor. Those theories dictate that unskilled jobs that do not need to be performed within highly developed countries are outsourced to where labor is cheaper and more flexible. However, the State of Florida remains an important site of ornamental plant production in the US amidst a global economic environment of outsourcing and transnational corporate expansion. This dissertation relies on 50 semi-structured interviews with insiders of the Florida plant nursery industry, focus groups, and participant observation to explain how US trade, labor, and migration policy-making at local levels are not removed from larger global processes taking place in the world since the 1970s. In Florida, elite market players of the plant nursery industry have been able to resist global trends in free trade, operating instead in a protected market. They have done this by appealing to scientific justifications and through arbitrary implementations of neoliberal ideology that keeps small and middle range business alive, while maintaining a seemingly endless supply of marginalized and exploited low-wage, immigrant workers.

Intermittent Fasting Induces Hypothalamic Modifications Resulting In Low Feeding Efficiency, Low Body Mass And Overeating.

Intermittent fasting (IF) is an often-used intervention to decrease body mass. In male Sprague-Dawley rats, 24 hour cycles of IF result in light caloric restriction, reduced body mass gain, and significant decreases in the efficiency of energy conversion. Here, we study the metabolic effects of IF in order to uncover mechanisms involved in this lower energy conversion efficiency. After 3 weeks, IF animals displayed overeating during fed periods and lower body mass, accompanied by alterations in energy-related tissue mass. The lower efficiency of energy use was not due to uncoupling of muscle mitochondria. Enhanced lipid oxidation was observed during fasting days, whereas fed days were accompanied by higher metabolic rates. Furthermore, an increased expression of orexigenic neurotransmitters AGRP and NPY in the hypothalamus of IF animals was found, even on feeding days, which could explain the overeating pattern. Together, these effects provide a mechanistic explanation for the lower efficiency of energy conversion observed. Overall, we find that IF promotes changes in hypothalamic function that explain differences in body mass and caloric intake.

Low-density Lipoprotein Cholesterol And Radiotherapy-induced Carotid Atherosclerosis In Subjects With Head And Neck Cancer.

Radiotherapy (RT) is a risk factor for accelerated carotid artery atherosclerotic disease in subjects with head and neck cancer. However, the risk factors of RT-induced carotid artery remodeling are not established. This study aimed to investigate the effects of RT on carotid and popliteal arteries in subjects with head and neck cancer and to evaluate the relationship between baseline clinical and laboratory features and the progression of RT-induced atherosclerosis. Eleven men (age = 57.9 ± 6.2years) with head and neck cancer who underwent cervical bilateral irradiation were prospectively examined by clinical and laboratory analysis and by carotid and popliteal ultrasound before and after treatment (mean interval between the end of RT and the post-RT assessment = 181 ± 47 days). No studied subject used hypocholesterolemic medications. Significant increases in carotid intima-media thickness (IMT) (0.95 ± 0.08 vs. 0.87 ± 0.05 mm; p < 0.0001) and carotid IMT/diameter ratio (0.138 ± 0.013 vs. 0.129 ± 0.014; p = 0.001) were observed after RT, while no changes in popliteal structural features were detected. In addition, baseline low-density lipoprotein cholesterol levels showed a direct correlation with RT-induced carotid IMT change (r = 0.66; p = 0.027), while no other studied variable exhibited a significant relationship with carotid IMT change. These results indicate that RT-induced atherosclerosis is limited to the irradiated area and also suggest that it may be predicted by low-density lipoprotein cholesterol levels in subjects with head and neck cancer.