Financial derivative valuation is the key of the adoption of the International Financial Reporting Standards (IFRS), which are based on fair value accounting. When the derivatives do not have an active market, the inputs and methods for estimating their fair value will be more subjective and, the derivative valuation will be less reliable. The goal of this research is to design a derivative valuation service to aid the accounting professionals to meet the reliability requirement of the IFRS which, in the derivative setting, requires derivative valuation be objective and free from errors. First, we incorporate the various valuation models and their risk factors into the service as the basis for providing an objective valuation result. Second, we provide a user interface to simplified management of parameters and provisions of term sheets of derivatives to avoid users’ errors in parsing the term sheets. Third, when the users hold a large number of derivatives, the derivative valuation to meet real-time constraints in financial reporting. We thus developed the service in parallel computing environment to reduce computational time of the valuation process. Empirical results of derivative valuation are also presented.

Asian barrier options are barrier options whose trigger is based on an average underlying price. They provide the advantages of both Asian options and barrier options. This paper introduces the first quadratic-time lattice algorithm to
price European-style Asian barrier options. It is by far the most efficient lattice algorithm with convergence guarantees.
The algorithm relies on the Lagrange multipliers to optimally distribute the number of states for each node of the multinomial lattice. We also show experiment results to demonstrate effectiveness and efficiency of our algorithm by comparing with
Monte Carlo simulations.

While solid-state drives are excellent alternatives to hard disks in mobile devices, a number of performance and reliability issues need to be addressed. In this work, we design an efficient flash management scheme for the performance improvement of low-cost MLC flash memory devices. Specifically, we design an efficient flash management scheme for multi-chipped flash memory devices with cache support, and develop a two-level address translation mechanism with an adaptive caching policy. We evaluated the approach on real workloads. The results demonstrate that it can improve the performance of multi-chipped solid-state drives through logical-to-physical mappings and concurrent accesses to flash chips.

Since 2005, the next-generation sequencing technologies dramatically accelerate the throughput of DNA sequencing in a much faster rate than the growth rate of computer speed as predicted by the “Moore’s Law.” The number of reads per genome has also increased dramatically due to the high-throughput sequencing technology. It is a problem even to load and run these sequencing data in memory. There is an urgent need for de novo assemblers to efficiently handle the huge amount of sequencing data using scalable commodity servers in the clouds.

In this paper, we present CloudBrush, a parallel algorithm that runs on the MapReduce framework of cloud computing for de novo assembly of high-throughput sequencing data. The algorithm uses Myers’s bi-directed string graphs as its basis and consists of two main stages: graph construction and graph simplification. First, a vertex is defined for each non-redundant sequence read. We present a prefix-and-extend algorithm to identify overlaps between a pair of reads and to reduce transitive edges. The graph is further simplified by using conventional operations including linear path compression, dead-end tip removal and bubble removal. We also present a new operation, similar neighbour detection and edge adjustment, abbreviated as SNEE, to detect and simplify braid structure in the string graph. Besides, we also prune edges from one side of a node if at least one of these edges is not similar with the others. Note that, after doing so, all paths in a remaining connected subgraph corresponds to similar subsequences of the underlying genome. We then traverse each connected subgraph to find a long path supported by a sufficient amount of reads to represent the subgraph.

Preliminary results show that the CloudBrush assembler, compared with Contrail and Edena on the sequencing data of E. coli genomes, may yield longer contigs.

Social network applications are becoming increasingly popular on mobile devices. A mobile presence service is an essential component of a social network application because it maintains each mobile user's presence information, such as the current status (online/offline), GPS location and network address, and also updates the user's online friends with the information continually. If presence updates occur frequently, the enormous number of messages distributed by presence servers may lead to a scalability problem in a large-scale mobile presence service. To address the problem, we propose an efficient and scalable server architecture, called PresenceCloud, which enables mobile presence services to support large-scale social network applications. PresenceCloud organizes presence servers into a quorum-based server-to-server architecture for efficient presence searching. It also leverages a directed search algorithm and a one-hop caching strategy to achieve small constant search latency. We analyze the performance of PresenceCloud in terms of the search cost and search satisfaction level. The search cost is defined as the total number of messages generated by the presence server when a user arrives; and search satisfaction level is defined as the time it takes to search for the arriving user's friend list. The results of simulations demonstrate that PresenceCloud achieves performance gains in the search cost without compromising search satisfaction.

We give a sound and complete procedure for fence insertion for concurrent finite-state programs running under the classical TSO memory model. This model allows "write to read" relaxation corresponding to the addition of an unbounded store buffer between each processor and the main memory. We introduce a novel machine model, called the Single-Buffer (SB) semantics, and show that the reachability problem for a program under TSO can be reduced to the reachability problem under SB. We present a simple and effective backward reachability analysis algorithm for the latter, and propose a counter-example guided fence insertion procedure. The procedure is augmented by a placement constraint, that allows the user to choose the places inside the program where fences may be inserted. For a given placement constraint, the method infers automatically all minimal sets of fences that ensure correctness of the program. We have implemented a prototype and run it successfully on all standard benchmarks, together with several challenging examples that are beyond the applicability of existing methods.

This paper proposes a weakly-supervised approach for extracting instances of se-mantic classes. This method constructs simple wrappers automatically based on specified seed instances and uses a com-pression model to assess the contextual ev-idence of its extraction. By adopting this compression model, our approach can bet-ter avoid erroneous extractions in a noisy corpus such as the Web. The empiri-cal results show that our system performs quite consistently even when operating on a noisy text with a lot of possibly irrelevant documents.

Penaeus Genome Database (PAGE) is a genome database with integrated analysis tools which is Penaeus genome oriented for over 200,000 Expressed Sequence Tags (ESTs). In PAGE, we provide sequences and tentative functional annotations for each assemble contigs and ESTs. Users can conduct search easily in keywords or sequences to those ESTs and contigs in specific or across species.