We present nbodykit, an open-source, massively parallel Python toolkit for analyzing large-scale structure (LSS) data. Using Python bindings of the Message Passing Interface, we provide parallel ...implementations of many commonly used algorithms in LSS. nbodykit is both an interactive and scalable piece of scientific software, performing well in a supercomputing environment while still taking advantage of the interactive tools provided by the Python ecosystem. Existing functionality includes estimators of the power spectrum, two- and three-point correlation functions, a friends-of-friends grouping algorithm, mock catalog creation via the halo occupation distribution technique, and approximate N-body simulations via the FastPM scheme. The package also provides a set of distributed data containers, insulated from the algorithms themselves, that enables nbodykit to provide a unified treatment of both simulation and observational data sets. nbodykit can be easily deployed in a high-performance computing environment, overcoming some of the traditional difficulties of using Python on supercomputers. We provide performance benchmarks illustrating the scalability of the software. The modular, component-based approach of nbodykit allows researchers to easily build complex applications using its tools. The package is extensively documented at http://nbodykit.readthedocs.io, which also includes an interactive set of example recipes for new users to explore. As open-source software, we hope nbodykit provides a common framework for the community to use and develop in confronting the analysis challenges of future LSS surveys.
Estimating rates of COVID-19 infection and associated mortality is challenging due to uncertainties in case ascertainment. We perform a counterfactual time series analysis on overall mortality data ...from towns in Italy, comparing the population mortality in 2020 with previous years, to estimate mortality from COVID-19. We find that the number of COVID-19 deaths in Italy in 2020 until September 9 was 59,000-62,000, compared to the official number of 36,000. The proportion of the population that died was 0.29% in the most affected region, Lombardia, and 0.57% in the most affected province, Bergamo. Combining reported test positive rates from Italy with estimates of infection fatality rates from the Diamond Princess cruise ship, we estimate the infection rate as 29% (95% confidence interval 15-52%) in Lombardy, and 72% (95% confidence interval 36-100%) in Bergamo.
A survey of intrusion detection techniques in Cloud Modi, Chirag; Patel, Dhiren; Borisaniya, Bhavesh ...
Journal of network and computer applications,
January 2013, 2013, 2013-1-00, Letnik:
36, Številka:
1
Journal Article
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In this paper, we survey different intrusions affecting availability, confidentiality and integrity of Cloud resources and services. Proposals incorporating Intrusion Detection Systems (IDS) and ...Intrusion Prevention Systems (IPS) in Cloud are examined. We recommend IDS/IPS positioning in Cloud environment to achieve desired security in the next generation networks.
Cloud computing offers scalable on-demand services to consumers with greater flexibility and lesser infrastructure investment. Since Cloud services are delivered using classical network protocols and ...formats over the Internet, implicit vulnerabilities existing in these protocols as well as threats introduced by newer architectures raise many security and privacy concerns. In this paper, we survey the factors affecting Cloud computing adoption, vulnerabilities and attacks, and identify relevant solution directives to strengthen security and privacy in the Cloud environment.
Virtualization plays a vital role in the construction of cloud computing. However, various vulnerabilities are existing in current virtualization implementations, and thus there are various security ...challenges at virtualization layer. In this paper, we investigate different vulnerabilities and attacks at virtualization layer of cloud computing. We examine the proposals of cloud intrusion detection system (IDS) and intrusion detection and prevention system frameworks. We recommend the cloud IDS requirements and research scope to achieve desired level of security at virtualization layer of cloud computing.
We investigate the anisotropic clustering of the Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 12 sample, which consists of 1198 006 galaxies in the redshift range 0.2 < z < 0.75 and a ...sky coverage of 10 252 deg2. We analyse this data set in Fourier space, using the power-spectrum multipoles to measure redshift-space distortions simultaneously with the Alcock-Paczynski effect and the baryon acoustic oscillation scale. We include the power-spectrum monopole, quadrupole and hexadecapole in our analysis and compare our measurements with a perturbation-theory-based model, while properly accounting for the survey window function. To evaluate the reliability of our analysis pipeline, we participate in a mock challenge, which results in systematic uncertainties significantly smaller than the statistical uncertainties. While the high-redshift constraint on fs8 at zeff = 0.61 indicates a small (~1.4s) deviation from the prediction of the Planck ...CDM (... cold dark matter) model, the low-redshift constraint is in good agreement with Planck ...CDM. This paper is part of a set that analyses the final galaxy clustering data set from BOSS. The measurements and likelihoods presented here are combined with others in Alam et al. to produce the final cosmological constraints from BOSS. (ProQuest: ... denotes formulae/symbols omitted.)
The consumer's short term load forecasting plays an essential role in microgrid energy distribution. However, the load forecasting at consumer level is more challenging than at substation level due ...to high volatility and uncertainty in energy consumption. In literature, many machine learning-based forecasting models have been explored. However, there is a need of developing a robust and accurate model to handle highly inconsistent energy consumption. In this article, we propose a robust and accurate model for consumer's short term load forecasting, which uses feasible techniques such as random forest, support vector regressor, and long short term memory as base predictors to handle varying traits of energy consumption. For the final decision-making on forecasting result of these predictors, it assigns weights to each predictor dynamically as per the forecasting efficacy. The proposed model is tested on different consumer's varying traits of energy consumption. The experimental results show that the proposed model achieves forecasting error reduction by 3.46 and 2.53 in terms of average RMSE and MAE, respectively, in comparison with the existing models. It is robust and accurate even in presence of highly volatile and uncertain load patterns, and thus, it can better fit for microgrid energy management.
Dual Active Bridge (DAB) converter is well suited for off-board fast Electric Vehicle Charging Stations (EVCS) and in DC microgrid-connected EVCS applications. However, proper modification in DAB ...converter topology is crucial for fast charging, as the charging current is very high, and any oscillations would shorten the EV battery life. As per literature, DAB converter topology integrated with a series inductor at the output for EV battery charging reduces the output current ripple by 40%. However, as per our observation, potentially unstable dynamics are introduced in the system with the inclusion of an LC filter. This affects the controller stability; hence, these dynamics must be damped using active or passive damping. Passive damping is less efficient due to the considerable losses it produces, partly induced by low-frequency noise and partly from switching-frequency noise. Also, placing the damping at the selective resonant frequency is very complex. In comparison, active damping offers selective placement and loss reduction. This paper presents the DAB converter mathematical modeling using the generalized average model (GAM) and the average output current linearization model (AOCLM). The paper presents stability analysis and proposes a novel and robust approach to control the dynamics in the current and voltage control loops during EV charging, thereby reducing battery degradation. The charge flow to the battery is controlled using Constant Current Constant Voltage (CCCV) algorithm. The proposed scheme also eliminates the need for extra sensors by selectively attenuating the oscillations at the frequency of interest. The performance of the proposed model for EV fast charging application is verified using MATLAB/Simulink and experimentally validated in a real-time simulator OPAL-RT hardware platform.
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