Studies of the ATLAS Muon Spectrometer with Test Beam and Simulated Physics Data

In the ATLAS detector, muon related measurements are achieved by a huge Muon Spectrometer installed at the outermost region of the detector. At the LHC energies, high-pT muons are expected to be measured with a momentum resolution of â¼ 10% at 1 TeV . The main detecting element of the Muon Spectrometer is the Monitored Drift Tube chamber. The reconstruction potential of a BIS type Monitored Drift Tube chamber, in a special setup at the H8 Testbeam experimental area at CERN, is investigated. Data from the BIS muon chamber with both muon and positron beams are taken and the reconstruction of track segments in the chamber is studied. The correlation of the precision coordinate of the reconstructed track segment with the calorimeter cluster barycentre is also studied. In the ATLAS detector, muons lose parts of their energy in the Calorimetric System before reaching the Muon Spectrometer. As the muon energy increases radiative effects start playing a significant role in the energy loss mechanism and increase the probability for a big muon energy loss, often called as catastrophic. The probability for catastrophic energy losses of 350 GeV muons when they pass through the ATLAS calorimeters is studied with H8 testbeam data. Information on catastrophic muon energy losses in the calorimeters and also on the isolation of muons can be retrieved by a direct measurement of the calorimeter response. For this reason, a precise energ y deposition measurement method in the calorimeters is developed. The method enriches the muon object at the AOD anal- ysis level with information on calorimeter isolation and the collection of calorimeter cells associated with the measurement. The method is applied on single muon and t¯t â 4â + X events. Finally, the Z boson pair production through the process pp â ZZ(â) â ââââ, where â = e or â = μ is of great interest for the LHC physics searches because it constitutes the irreducible background to the observation of the Higgs boson production through the 4 lepton decay. The observation expectations of pp â ZZ â 4â channel with 1 fbâ1 of data is investigated and analysis methods on simulated events of signal and t¯t and Zb¯b backgrounds processes are described

Pyramid: Enhancing Selectivity in Big Data Protection with Count Featurization

Protecting vast quantities of data poses a daunting challenge for the growing number of organizations that collect, stockpile, and monetize it. The ability to distinguish data that is actually needed from data collected "just in case" would help these organizations to limit the latter's exposure to attack. A natural approach might be to monitor data use and retain only the working-set of in-use data in accessible storage; unused data can be evicted to a highly protected store. However, many of today's big data applications rely on machine learning (ML) workloads that are periodically retrained by accessing, and thus exposing to attack, the entire data store. Training set minimization methods, such as count featurization, are often used to limit the data needed to train ML workloads to improve performance or scalability. We present Pyramid, a limited-exposure data management system that builds upon count featurization to enhance data protection. As such, Pyramid uniquely introduces both the idea and proof-of-concept for leveraging training set minimization methods to instill rigor and selectivity into big data management. We integrated Pyramid into Spark Velox, a framework for ML-based targeting and personalization. We evaluate it on three applications and show that Pyramid approaches state-of-the-art models while training on less than 1% of the raw data

Aggregate quasi rents and auditor independence : evidence from audit firm mergers in China

Using a sample of audit firm mergers in China\u27s audit market, this paper provides evidence on the way auditor independence can be improved following audit firm mergers as a result of a change in the aggregate quasi rents that are exposed to risk (i.e., the quasi rents at stake). This setting allows us to examine the relationship between auditor independence and the aggregate quasi rents at stake directly after controlling for the confounding effects of auditor competence, audit firm brand name, and the self-selection problem that may exist in previous studies. We hypothesize that auditors become more independent in the post-merger period only if the mergers increase the aggregate quasi rents at stake. Proxying audit quality by the frequency of modified audit opinions (MAOs) and using a \u27\u27difference-in-differences\u27\u27 research design, we conduct separate tests for two types of mergers under the institutional arrangements in China: one with an increase in the aggregate quasi rents at stake and the other with little change in these rents. Consistent with our hypothesis, we observe an improvement in auditor independence, but only for mergers that increase auditors\u27 aggregate quasi rents at stake. Moreover, the post-merger increase in the propensity for MAOs in this type of merger is positively associated with the magnitude of the change in the aggregate quasi rents at stake. Our empirical findings support the theory that auditor independence is a positive function of the aggregate quasi rents at stake

An intelligent information forwarder for healthcare big data systems with distributed wearable sensors

© 2016 IEEE. An increasing number of the elderly population wish to live an independent lifestyle, rather than rely on intrusive care programmes. A big data solution is presented using wearable sensors capable of carrying out continuous monitoring of the elderly, alerting the relevant caregivers when necessary and forwarding pertinent information to a big data system for analysis. A challenge for such a solution is the development of context-awareness through the multidimensional, dynamic and nonlinear sensor readings that have a weak correlation with observable human behaviours and health conditions. To address this challenge, a wearable sensor system with an intelligent data forwarder is discussed in this paper. The forwarder adopts a Hidden Markov Model for human behaviour recognition. Locality sensitive hashing is proposed as an efficient mechanism to learn sensor patterns. A prototype solution is implemented to monitor health conditions of dispersed users. It is shown that the intelligent forwarders can provide the remote sensors with context-awareness. They transmit only important information to the big data server for analytics when certain behaviours happen and avoid overwhelming communication and data storage. The system functions unobtrusively, whilst giving the users peace of mind in the knowledge that their safety is being monitored and analysed

Inherent Agency Conflict Built into the Auditor Remuneration Model

This paper provides a model for audit market interventions. The study asks whether interventions in the audit market result in excessive premiums at the cost of quality and independence. The model was tested based on a historical data sample of 1,927 companies’ fiscal year financial statements, observed for the period 2010–2013. The testing strategy combined statistical analysis of the market concentration and regression of abnormal results. The findings do not support, for the Polish market, the conclusion that the audit market is used as a leverage for consulting services. This paper discusses possibilities of systematic risk for policymakers as a result of the negative interaction between regulated and non-regulated markets