Seismic hazards of the Iberian Peninsula - evaluation with Kernel functions

The seismic hazard of the Iberian Peninsula is analysed using a nonparametric methodology based on statistical kernel functions; the activity rate is derived from the catalogue data, both its spatial dependence (without a seismogenetic zonation) and its magnitude dependence (without using Gutenberg–Richter's law). The catalogue is that of the Instituto Geográfico Nacional, supplemented with other catalogues around the periphery; the quantification of events has been homogenised and spatially or temporally interrelated events have been suppressed to assume a Poisson process. The activity rate is determined by the kernel function, the bandwidth and the effective periods. The resulting rate is compared with that produced using Gutenberg–Richter statistics and a zoned approach. Three attenuation laws have been employed, one for deep sources and two for shallower events, depending on whether their magnitude was above or below 5. The results are presented as seismic hazard maps for different spectral frequencies and for return periods of 475 and 2475 yr, which allows constructing uniform hazard spectra.

A bivariate return period based on copulas for hydrologic dam design: accounting for reservoir routing in risk estimation

A multivariate analysis on flood variables is needed to design some hydraulic structures like dams, as the complexity of the routing process in a reservoir requires a representation of the full hydrograph. In this work, a bivariate copula model was used to obtain the bivariate joint distribution of flood peak and volume, in order to know the probability of occurrence of a given inflow hydrograph. However, the risk of dam overtopping is given by the maximum water elevation reached during the routing process, which depends on the hydrograph variables, the reservoir volume and the spillway crest length. Consequently, an additional bivariate return period, the so-called routed return period, was defined in terms of risk of dam overtopping based on this maximum water elevation obtained after routing the inflow hydrographs. The theoretical return periods, which give the probability of occurrence of a hydrograph prior to accounting for the reservoir routing, were compared with the routed return period, as in both cases hydrographs with the same probability will draw a curve in the peak-volume space. The procedure was applied to the case study of the Santillana reservoir in Spain. Different reservoir volumes and spillway lengths were considered to investigate the influence of the dam and reservoir characteristics on the results. The methodology improves the estimation of the Design Flood Hydrograph and can be applied to assess the risk of dam overtopping

Seismic hazard of the Iberian Peninsula: evaluation with Kernel functions

The seismic hazard of the Iberian Peninsula is analysed using a nonparametric methodology based on statistical kernel functions; the activity rate is derived from the catalogue data, both its spatial dependence (without a seismogenic zonation) and its magnitude dependence (without using Gutenberg–Richter's relationship). The catalogue is that of the Instituto Geográfico Nacional, supplemented with other catalogues around the periphery; the quantification of events has been homogenised and spatially or temporally interrelated events have been suppressed to assume a Poisson process. The activity rate is determined by the kernel function, the bandwidth and the effective periods. The resulting rate is compared with that produced using Gutenberg–Richter statistics and a zoned approach. Three attenuation relationships have been employed, one for deep sources and two for shallower events, depending on whether their magnitude was above or below 5. The results are presented as seismic hazard maps for different spectral frequencies and for return periods of 475 and 2475 yr, which allows constructing uniform hazard spectra

Power flow based monetary flow method for electricity transmission and wheeling pricing

This paper proposes a transmission and wheeling pricing method based on the monetary flow tracing along power flow paths: the monetary flow-monetary path method. Active and reactive power flows are converted into monetary flows by using nodal prices. The method introduces a uniform measurement for transmission service usages by active and reactive powers. Because monetary flows are related to the nodal prices, the impacts of generators and loads on operation constraints and the interactive impacts between active and reactive powers can be considered. Total transmission service cost is separated into more practical line-related costs and system-wide cost, and can be flexibly distributed between generators and loads. The method is able to reconcile transmission service cost fairly and to optimize transmission system operation and development. The case study on the IEEE 30 bus test system shows that the proposed pricing method is effective in creating economic signals towards the efficient use and operation of the transmission system. (c) 2005 Elsevier B.V. All rights reserved.

Calendar effects and the pricing of risk:the UK evidence

For some time there has been a puzzle surrounding the seasonal behaviour of stock returns. This paper demonstrates that there is an asymmetric relationship between risk and return across the different months of the year. The paper finds that systematic risk is only priced during the months of January, April and July. Variance risk and firm size are priced during several months of the year including January. An analysis of the relative behaviour of size based securities reveals that firm capitalization makes a valuable contribution to the magnitude of risk premiums.

Angel grants employees their wish – for time off in return for high performance:new breed of incentives based on what staff really want from their employer

Purpose – Describes a new breed of HR strategies that encourage employee involvement and commitment as part of high-performance working (HPW). Design/methodology/approach – Focuses on managing employee attitudes and skills through careful attention to leadership, reward and job-design policies. Highlights the differences between people's formal employment contracts and their less formal “psychological contracts”, and emphasizes the importance of the latter. Provides a case study of UK recruitment consultancy Angel Services Group Ltd, which allows staff who meet their daily targets to go home an hour early. Findings – Urges companies to have processes in place to understand the needs of individual employees. This can be done through leadership policies that require all supervisors and managers not only to manage their staff but also to know them as people. Practical implications – Emphasizes that organizations need to see HPW initiatives as part of the normal way of managing people, and not as “flavour of the month”. Originality/value – Outlines a wide range of initiatives that could help organizations to gain their employees' commitment.

Predicting class II MHC-Peptide binding:a kernel based approach using similarity scores

Background - Modelling the interaction between potentially antigenic peptides and Major Histocompatibility Complex (MHC) molecules is a key step in identifying potential T-cell epitopes. For Class II MHC alleles, the binding groove is open at both ends, causing ambiguity in the positional alignment between the groove and peptide, as well as creating uncertainty as to what parts of the peptide interact with the MHC. Moreover, the antigenic peptides have variable lengths, making naive modelling methods difficult to apply. This paper introduces a kernel method that can handle variable length peptides effectively by quantifying similarities between peptide sequences and integrating these into the kernel. Results - The kernel approach presented here shows increased prediction accuracy with a significantly higher number of true positives and negatives on multiple MHC class II alleles, when testing data sets from MHCPEP [1], MCHBN [2], and MHCBench [3]. Evaluation by cross validation, when segregating binders and non-binders, produced an average of 0.824 AROC for the MHCBench data sets (up from 0.756), and an average of 0.96 AROC for multiple alleles of the MHCPEP database. Conclusion - The method improves performance over existing state-of-the-art methods of MHC class II peptide binding predictions by using a custom, knowledge-based representation of peptides. Similarity scores, in contrast to a fixed-length, pocket-specific representation of amino acids, provide a flexible and powerful way of modelling MHC binding, and can easily be applied to other dynamic sequence problems.

Optimising market share and profit margin:SMDP-based tariff pricing under the smart grid paradigm

Smart grid technologies have given rise to a liberalised and decentralised electricity market, enabling energy providers and retailers to have a better understanding of the demand side and its response to pricing signals. This paper puts forward a reinforcement-learning-powered tool aiding an electricity retailer to define the tariff prices it offers, in a bid to optimise its retail strategy. In a competitive market, an energy retailer aims to simultaneously increase the number of contracted customers and its profit margin. We have abstracted the problem of deciding on a tariff price as faced by a retailer, as a semi-Markov decision problem (SMDP). A hierarchical reinforcement learning approach, MaxQ value function decomposition, is applied to solve the SMDP through interactions with the market. To evaluate our trading strategy, we developed a retailer agent (termed AstonTAC) that uses the proposed SMDP framework to act in an open multi-agent simulation environment, the Power Trading Agent Competition (Power TAC). An evaluation and analysis of the 2013 Power TAC finals show that AstonTAC successfully selects sell prices that attract as many customers as necessary to maximise the profit margin. Moreover, during the competition, AstonTAC was the only retailer agent performing well across all retail market settings.

Integrity-based kernel malware detection

Kernel-level malware is one of the most dangerous threats to the security of users on the Internet, so there is an urgent need for its detection. The most popular detection approach is misuse-based detection. However, it cannot catch up with today's advanced malware that increasingly apply polymorphism and obfuscation. In this thesis, we present our integrity-based detection for kernel-level malware, which does not rely on the specific features of malware. ^ We have developed an integrity analysis system that can derive and monitor integrity properties for commodity operating systems kernels. In our system, we focus on two classes of integrity properties: data invariants and integrity of Kernel Queue (KQ) requests. ^ We adopt static analysis for data invariant detection and overcome several technical challenges: field-sensitivity, array-sensitivity, and pointer analysis. We identify data invariants that are critical to system runtime integrity from Linux kernel 2.4.32 and Windows Research Kernel (WRK) with very low false positive rate and very low false negative rate. We then develop an Invariant Monitor to guard these data invariants against real-world malware. In our experiment, we are able to use Invariant Monitor to detect ten real-world Linux rootkits and nine real-world Windows malware and one synthetic Windows malware. ^ We leverage static and dynamic analysis of kernel and device drivers to learn the legitimate KQ requests. Based on the learned KQ requests, we build KQguard to protect KQs. At runtime, KQguard rejects all the unknown KQ requests that cannot be validated. We apply KQguard on WRK and Linux kernel, and extensive experimental evaluation shows that KQguard is efficient (up to 5.6% overhead) and effective (capable of achieving zero false positives against representative benign workloads after appropriate training and very low false negatives against 125 real-world malware and nine synthetic attacks). ^ In our system, Invariant Monitor and KQguard cooperate together to protect data invariants and KQs in the target kernel. By monitoring these integrity properties, we can detect malware by its violation of these integrity properties during execution.^

Integrity-Based Kernel Malware Detection

Kernel-level malware is one of the most dangerous threats to the security of users on the Internet, so there is an urgent need for its detection. The most popular detection approach is misuse-based detection. However, it cannot catch up with today's advanced malware that increasingly apply polymorphism and obfuscation. In this thesis, we present our integrity-based detection for kernel-level malware, which does not rely on the specific features of malware. We have developed an integrity analysis system that can derive and monitor integrity properties for commodity operating systems kernels. In our system, we focus on two classes of integrity properties: data invariants and integrity of Kernel Queue (KQ) requests. We adopt static analysis for data invariant detection and overcome several technical challenges: field-sensitivity, array-sensitivity, and pointer analysis. We identify data invariants that are critical to system runtime integrity from Linux kernel 2.4.32 and Windows Research Kernel (WRK) with very low false positive rate and very low false negative rate. We then develop an Invariant Monitor to guard these data invariants against real-world malware. In our experiment, we are able to use Invariant Monitor to detect ten real-world Linux rootkits and nine real-world Windows malware and one synthetic Windows malware. We leverage static and dynamic analysis of kernel and device drivers to learn the legitimate KQ requests. Based on the learned KQ requests, we build KQguard to protect KQs. At runtime, KQguard rejects all the unknown KQ requests that cannot be validated. We apply KQguard on WRK and Linux kernel, and extensive experimental evaluation shows that KQguard is efficient (up to 5.6% overhead) and effective (capable of achieving zero false positives against representative benign workloads after appropriate training and very low false negatives against 125 real-world malware and nine synthetic attacks). In our system, Invariant Monitor and KQguard cooperate together to protect data invariants and KQs in the target kernel. By monitoring these integrity properties, we can detect malware by its violation of these integrity properties during execution.

Usage-Based versus Measure-Based Unit Pricing: Is There a Better Index of Value?

In many product categories, unit prices facilitate price comparisons across brands and package sizes; this enables consumers to identify those products that provide the greatest value. However in other product categories, unit prices may be confusing. This is because there are two types of unit pricing, measure-based and usage-based. Measure-based unit prices are what the name implies; price is expressed in cents or dollars per unit of measure (e.g. ounce). Usage-based unit prices, on the other hand, are expressed in terms of cents or dollars per use (e.g., wash load or serving). The results of this study show that in two different product categories (i.e., laundry detergent and dry breakfast cereal), measure-based unit prices reduced consumers’ ability to identify higher value products, but when a usage-based unit price was provided, their ability to identify product value was increased. When provided with both a measure-based and a usage-based unit price, respondents did not perform as well as when they were provided only a usage-based unit price, additional evidence that the measure-based unit price hindered consumers’ comparisons. Finally, the presence of two potential moderators, education about the meaning of the two measures and having to rank order the options in the choice set in terms of value before choosing, did not eliminate these effects.

Three Essays in Empirical Asset Pricing

The financial crisis of 2007-2008 led to extraordinary government intervention in firms and markets. The scope and depth of government action rivaled that of the Great Depression. Many traded markets experienced dramatic declines in liquidity leading to the existence of conditions normally assumed to be promptly removed via the actions of profit seeking arbitrageurs. These extreme events motivate the three essays in this work. The first essay seeks and fails to find evidence of investor behavior consistent with the broad 'Too Big To Fail' policies enacted during the crisis by government agents. Only in limited circumstances, where government guarantees such as deposit insurance or U.S. Treasury lending lines already existed, did investors impart a premium to the debt security prices of firms under stress. The second essay introduces the Inflation Indexed Swap Basis (IIS Basis) in examining the large differences between cash and derivative markets based upon future U.S. inflation as measured by the Consumer Price Index (CPI). It reports the consistent positive value of this measure as well as the very large positive values it reached in the fourth quarter of 2008 after Lehman Brothers went bankrupt. It concludes that the IIS Basis continues to exist due to limitations in market liquidity and hedging alternatives. The third essay explores the methodology of performing debt based event studies utilizing credit default swaps (CDS). It provides practical implementation advice to researchers to address limited source data and/or small target firm sample size.