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Zurich, Switzerland

Credit Suisse Group is a Switzerland-based multinational financial services holding company, headquartered in Zürich, that operates the Credit Suisse Bank and other financial services investments. The company is organized as a stock corporation with four divisions: Investment Banking, Private Banking, Asset Management, and a Shared Services Group that provides marketing and support to the other three divisions.Credit Suisse was founded by Alfred Escher in 1856 under the name Schweizerische Kreditanstalt in order to fund the development of Switzerland's rail system. It issued loans that helped create Switzerland's electrical grid and the European rail system. It also helped develop the country's currency system and funded entrepreneurship. In the 1900s Credit Suisse began shifting to retail banking in response to the elevation of the middle-class and the growing popularity of savings accounts. Credit Suisse partnered with First Boston in 1978. After a large failed loan put First Boston under financial stress, Credit Suisse bought a controlling share of the bank in 1988. In the 1990s, Credit Suisse acquired the Winterthur Group, Swiss Volksbank, Swiss American Securities Inc. and Bank Leu among others. In the year 2000, it added the U.S. investment firm Donaldson, Lufkin & Jenrette.The company restructured itself in 2002, 2004 and 2006. It was one of the least affected banks during the global financial crisis, but afterwards began shrinking its investment business, executing layoffs and cutting costs. During the period between 2008 and 2012, Germany, Brazil, and the United States began a series of investigations into the use of Credit Suisse accounts for tax evasion. In May 2014, the company pleaded guilty to decades of conspiring to help US citizens "hide their wealth" in order to avoid taxes, and agreed to pay $2.6 billion in fines.In 2014, Credit Suisse had 888.2 USD Bn of assets under management according to the Scorpio Partnership . Wikipedia.

Agency: Cordis | Branch: FP7 | Program: CP | Phase: ICT-2013.6.2 | Award Amount: 4.31M | Year: 2013

The GreenDataNet project aims at designing and validating a new, system-level optimisation solution allowing urban data centres to radically improve their energy and environmental performance. The objective is to develop a set of beyond state-of-the-art technologies that will allow urban data centres to reach 80% of renewable power and decrease their average Power Usage Effectiveness (PUE) from 1.6-2.0 today to less than 1.3. GreenDataNet will enable energy monitoring and optimisation of IT, power, cooling and storage at three levels: servers and racks, individual data centres, and networks of data centres. To further reduce the need for grid power, GreenDataNet will also work on the integration of local photovoltaic energy in combination with an innovative, large-scale storage solution that will facilitate the connection of data centres to smart grids. Within this project, second-life electric vehicle Li-ion batteries will be investigated as a more advantageous solution for data centres to become actual smart grid nodes.The whole solution will be implemented as an open-source platform to allow third parties to provide additional optimisation modules and ensure the long-term sustainability of the project. Three demonstration sites will be utilised to test and validate the GreenDataNet concept: a data centre from Credit Suisse in Switzerland, a data centre from CEA in France that includes a large photovoltaic area and a smart grid test platform, and a pilot site in the Netherlands that is being used by a Dutch consortium working on Green IT technologies. In addition, research on heat reuse vs. free cooling will be conducted in a new data centre built by ICTroom in Belgium. Performance indicators that go beyond PUE will be experimented in the project and will support the work of the consortium in standardisation bodies like CEN/CENELEC/ETSI. Based on the project outcome, GreenDataNet will release guidelines to help make data centres more sustainable in the future.

Capriotti L.,Credit Suisse
Journal of Computational Finance | Year: 2011

We show how algorithmic differentiation can be used to efficiently implement the pathwise derivative method for the calculation of option sensitivities using Monte Carlo simulations. The main practical difficulty of the pathwise derivative method is that it requires the differentiation of the payout function. For the type of structured options for which Monte Carlo simulations are usually employed, these derivatives are typically cumbersome to calculate analytically, and too time consuming to evaluate with standard finite-difference approaches. In this paper we address this problem and show how algorithmic differentiation can be employed to calculate these derivatives very efficiently and with machine-precision accuracy. We illustrate the basic workings of this computational technique by means of simple examples, and we demonstrate with several numerical tests how the pathwise derivative method combined with algorithmic differentiation – especially in the adjoint mode – can provide speed-ups of several orders of magnitude with respect to standard methods. © 2011, Incisive Media Ltd. All rights reserved. Source

Freiesleben J.,Credit Suisse
International Journal of Production Economics | Year: 2010

As the concept of Six Sigma has been the dominant theme in the recent wave of quality initiatives, a body of related theoretical work slowly starts to evolve. Some contributions discuss the economic implications of better production quality and therewith provide the rationale for companies to strive for Six Sigma defect levels. Opposed to this, the complementary concept of design quality has received little recent attention. Previous contributions in the economics, operations or marketing literatures equate design quality to product differentiation and analyse economic implications of different product categories. However, this discussion is not providing insights into the economic effects of deviating from design optimality in the choice of a product's design. Such analysis would have great relevance for companies as they aim at addressing customer needs in the most precise way possible to avoid failure in the market. In this paper, we therefore discuss the economic effects of design quality using a novel focus on deviations from optimum designs. We find that deviations from both optimal feature composition and optimal production technology likely result in losses similar to those commonly attributed to poor production quality. One important implication of our approach is that higher design quality must not, as commonly assumed, be connected to higher production costs, but might in fact reduce the cost level. With our contribution, we aim at providing a rationale for investments in design quality improvement, a complement to the economic analysis of production quality and an inspiration for future empirical studies. © 2009 Elsevier B.V. All rights reserved. Source

A development and testing environment with reduced database storage requirements that uses synthetic data based on anonymized real data, which allows the use of sensitive data for testing while protecting such data as required by privacy laws, secrecy laws and company policies.

A method is dedicated to synchronizing master (HM) and slave clocks (HE) of a packet-switched network comprising at least two equipments (E

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