High Runtime Research Articles

The epitope space of the human proteome

In the post-genome era, there is a great need for protein-specific affinity reagents to explore the human proteome. Antibodies are suitable as reagents, but generation of antibodies with low cross-reactivity to other human proteins requires careful selection of antigens. Here we show the results from a proteome-wide effort to map linear epitopes based on uniqueness relative to the entire human proteome. The analysis was based on a sliding window sequence similarity search using short windows (8, 10, and 12 amino acid residues). A comparison of exact string matching (Hamming distance) and a heuristic method (BLAST) was performed, showing that the heuristic method combined with a grid strategy allows for whole proteome analysis with high accuracy and feasible run times. The analysis shows that it is possible to find unique antigens for a majority of the human proteins, with relatively strict rules involving low sequence identity of the possible linear epitopes. The implications for human antibody-based proteomics efforts are discussed.

Designing multiple degenerate primers via consecutive pairwise alignments

BackgroundDifferent algorithms have been proposed to solve various versions of degenerate primer design problem. For one of the most general cases, multiple degenerate primer design problem, very few algorithms exist, none of them satisfying the criterion of designing low number of primers that cover high number of sequences. Besides, the present algorithms require high computation capacity and running time.ResultsPAMPS, the method presented in this work, usually results in a 30% reduction in the number of degenerate primers required to cover all sequences, compared to the previous algorithms. In addition, PAMPS runs up to 3500 times faster.ConclusionDue to small running time, using PAMPS allows designing degenerate primers for huge numbers of sequences. In addition, it results in fewer primers which reduces the synthesis costs and improves the amplification sensitivity.

Simulation and Optimization of Diode and Insulated Gate Bipolar Transistor Interaction in a Chopper Cell Using MATLAB and Simulink

Recently, a simulation method for power electronic devices has emerged, which has high accuracy and short run times based on a Fourier model of the device physics. This paper describes the use of the Fourier models for diodes and insulated gate bipolar transistors (IGBTs) and implementation in MATLAB and Simulink in a formal optimization strategy. In particular, this paper investigates coupled circuit, diode, and IGBT behavior. Conclusions are drawn concerning device loading and circuit design, particularly the role of stray inductance.

The quantification of erlotinib (OSI-774) and OSI-420 in human plasma by liquid chromatography–tandem mass spectrometry

An accurate and precise method was developed using HPLC-MS/MS to quantify erlotinib (OSI-774) and its O-desmethyl metabolite, OSI-420, in plasma. The advantages of this method include the use of a small sample volume, liquid–liquid extraction with high extraction efficiency and short chromatographic run times. The analytes were extracted from 100 μL plasma volume using hexane:ethyl acetate after midazolam was added to the sample for internal standardization. The compounds were separated on a Phenomenex C-18 Luna analytical column with acetonitrile:5 mM ammonium acetate as the mobile phase. All compounds were monitored by tandem mass spectrometry with electrospray positive ionization. The intra-day accuracy and precision (% coefficient of variation, % CV) estimates for erlotinib at 10 ng/mL were 90% and 9%, respectively. The intra-day accuracy and precision estimates for OSI-420 at 5 ng/mL were 80% and 4%, respectively. This method was used to quantify erlotinib and OSI-420 in plasma of patients ( n = 21) administered 150 mg erlotinib per day for non-small cell lung cancer.

Modeling and Controlling Parallel Tasks in Droplet-Based Microfluidic Systems

This paper presents general hardware-independent models and algorithms to automate the operation of droplet-based microfluidic systems. In these systems, discrete liquid volumes of typically less than 1 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$muhboxl$</tex> are transported across a planar array by dielectrophoretic or electrowetting effects for biochemical analysis. Unlike in systems based on continuous flow through channels, valves, and pumps, the droplet paths can be reconfigured on demand and even in real time. Algorithms that generate efficient sequences of control signals for moving one or many droplets from start to goal positions, subject to constraints such as specific features and obstacles on the array surface or limitations in the control circuitry, are developed. In addition, an approach toward automatic mapping of a biochemical analysis task onto a DMFS is investigated. Achieving optimality in these algorithms can be prohibitive for large-scale configurations because of the high asymptotic complexity of coordinating multiple moving droplets. Instead, these algorithms achieve a compromise between high runtime efficiency and a more limited nonglobal optimality in the generated control sequences.

Colombo: Lightweight middleware for service-oriented computing

Colombo is a lightweight platform for developing, deploying, and executing service-oriented applications. It provides optimized, native runtime support for the service-oriented-computing model, as opposed to the approach of layering service-oriented applications on a legacy runtime. This approach allows Colombo to provide high runtime performance, a small footprint, and simplified application development and deployment models. The Colombo runtime natively supports the full Web Services (WS) stack, providing transactional, reliable, and secure interactions among services. It defines a multilanguage service programming model that supports, among others, JavaTM and Business Process Execution Language for Web Services (BPEL4WS) service composition, and offers a deployment and discovery model fully based on declarative service descriptions (Web Service Description Language [WSDL] and WS-Policy). In this paper we describe these and other aspects of the architecture, design principles, and capabilities of the Colombo platform.

Static and adaptive distributed data replication using genetic algorithms

Fast dissemination and access of information in large distributed systems, such as the Internet, has become a norm of our daily life. However, undesired long delays experienced by end-users, especially during the peak hours, continue to be a common problem. Replicating some of the objects at multiple sites is one possible solution in decreasing network traffic. The decision of what to replicate where, requires solving a constraint optimization problem which is NP-complete in general. Such problems are known to stretch the capacity of a Genetic Algorithm (GA) to its limits. Nevertheless, we propose a GA to solve the problem when the read/write demands remain static and experimentally prove the superior solution quality obtained compared to an intuitive greedy method. Unfortunately, the static GA approach involves high running time and may not be useful when read/write demands continuously change, as is the case with breaking news. To tackle such case we propose a hybrid GA that takes as input the current replica distribution and computes a new one using knowledge about the network attributes and the changes occurred. Keeping in view more pragmatic scenarios in today's distributed information environments, we evaluate these algorithms with respect to the storage capacity constraint of each site as well as variations in the popularity of objects, and also examine the trade-off between running time and solution quality.

Protecting C programs from attacks via invalid pointer dereferences

Writes via unchecked pointer dereferences rank high among vulnerabilities most often exploited by malicious code. The most common attacks use an unchecked string copy to cause a buffer overrun, thereby overwriting the return address in the function's activation record. Then, when the function "returns", control is actually transferred to the attacker's code. Other attacks may overwrite function pointers, setjmp buffers, system-call arguments, or simply corrupt data to cause a denial of service.A number of techniques have been proposed to address such attacks. Some are limited to protecting the return address only; others are more general, but have undesirable properties such as having a high runtime overhead, requiring manual changes to the source code, or forcing programmers to give up control of data representations and memory management.This paper describes the design and implementation of a security tool for C programs that addresses all these issues: it has a low runtime overhead, does not require source code modification by the programmer, does not report false positives, and provides protection against a wide range of attacks via bad pointer dereferences, including but not limited to buffer overruns and attempts to access previously freed memory. The tool uses static analysis to identify potentially dangerous pointer dereferences, and memory locations that are legitimate targets of these pointers. Dynamic checks are then inserted; if at runtime the target of an unsafe dereference is not in the legitimate set, a potential security violation is reported, and the program is halted.

Efficient strategies for the simulation of railway wheel forming

Railway wheels and tyres are manufactured by a multi-stage hot-forming process. This process comprises upsetting, forging, punching, ring rolling and coining operations. The majority of these operations can readily be simulated using available commercial packages. However, the existence of the ring-rolling stage means that existing technology represents only a partial solution to this problem. The ring-rolling process carries a high run time penalty due to lack of constraint and the large number of increments required to complete a simulation. This paper considers the issues and difficulties associated with the finite element simulation of such a process. In particular, factors associated with run time and usability are addressed and the effectiveness of alternative solution methods and remeshing schemes are considered. It is seen that the poor conditioned nature of metal-forming simulation problems severely limits the usefulness of many iterative solution methods. It is found, however, that although the ring-rolling process remains a significant problem in terms of run time, the commercial viability of such activities can be enhanced by the use of solution schemes foundeds on adaptive preconditioning.

Practical methods for speeding-up the pairwise nearest neighbor method

The pairwise nearest neighbor (PNN) method is a simple and well-known method for codebook generation in vector quantization. In its exact form, it provides a good-quality codebook but at the cost of high run time. A fast exact algorithm was recently introduced to implement the PNN an order of magnitude faster than the original O(N 3 K) time algo- rithm. The run time, however, is still lower bounded by O(N 2 K), and therefore, additional speed-ups may be required in applications where time is an important factor. We consider two practical methods to reduce the amount of work caused by the distance calculations. Through experi- ments, we show that the run time can be reduced to 10 to 15% that of the original method for data sets in color quantization and in spatial vector quantization. © 2001 Society of Photo-Optical Instrumentation Engineers.

Randomised Local Search Algorithm for the Clustering Problem

We consider clustering as a combinatorial optimisation problem. Local search provides a simple and effective approach to many other combinatorial optimisation problems. It is therefore surprising how seldom it has been applied to the clustering problem. Instead, the best clustering results have been obtained by more complex techniques such as tabu search and genetic algorithms at the cost of high run time. We introduce a new randomised local search algorithm for the clustering problem. The algorithm is easy to implement, sufficiently fast, and competitive with the best clustering methods. The ease of implementation makes it possible to tailor the algorithm for various clustering applications with different distance metrics and evaluation criteria.

Genetic algorithm with deterministic crossover for vector quantization

Genetic algorithm (GA) provides high quality codebooks for vector quantization (VQ) at the cost of high running time. The crossover method is the most important choice of the algorithm. We introduce a new deterministic crossover method based on the pairwise nearest neighbor method. We show that high quality codebooks can be obtained within a few minutes instead of several hours as required by the previous GA-based methods. The method outperforms all comparative codebook generation methods in quality for the tested training sets.

A New Generation of Biological Aerated Filters

ABSTRACTBiological filters with fixed submerged beds are normally used for secondary or tertiary treatment and are recognized for their good performance. These processes are designed to meet, simultaneously, strict requirements of quality and operation. The achievement of the discharge consent requires the use of fine media which features a large support surface for the growth of biomass. On the other hand, the achievement of long filter run times is intrinsically related to the use of coarse media. Nowadays, all biological filters have mono‐media, which offers a compromise between these two conflicting criteria.The B2A process is a newly developed compact filter which ensures high filtration performance and long filter run times, even when used for crude sewage. The technology has been in use since 1992 for nitrification/ denitrification using an applied load of 0.7 kg/m3. d to obtain an effluent quality of 20 mg/l total nitrogen. Further tests have proved its efficiency in the removal of carbonaceous pollution.The B2A process is the first compact filter capable of treating screened crude sewage yet producing effluent qualities which are equal to that from conventional biological filters receiving settled sewage.

Improved Methods for Approximating Node Weighted Steiner Trees and Connected Dominating Sets

In this paper we study the Steiner tree problem in graphs for the case when vertices as well as edges have weights associated with them. A greedy approximation algorithm based on “spider decompositions” was developed by Klein and Ravi for this problem. This algorithm provides a worst case approximation ratio of 2 ln k, wherekis the number of terminals. However, the best known lower bound on the approximation ratio is (1−o(1)) ln k, assuming thatNP⊈DTIME[nO(log log n)], by a reduction from set cover. We show that for the unweighted case we can obtain an approximation factor of ln k. For the weighted case we develop a new decomposition theorem and generalize the notion of “spiders” to “branch-spiders” that are used to design a new algorithm with a worst case approximation factor of 1.5 ln k. We then generalize the method to yield an approximation factor of (1.35+ε) ln k, for any constantε>0. These algorithms, although polynomial, are not very practical due to their high running time, since we need to repeatedly find many minimum weight matchings in each iteration. We also develop a simple greedy algorithm that is practical and has a worst case approximation factor of 1.6103 ln k. The techniques developed for this algorithm imply a method of approximating node weighted network design problems defined by 0–1 proper functions as well. These new ideas also lead to improved approximation guarantees for the problem of finding a minimum node weighted connected dominating set. The previous best approximation guarantee for this problem was 3 ln nby Guha and Khuller. By a direct application of the methods developed in this paper we are able to develop an algorithm with an approximation factor of (1.35+ε) ln nfor any fixedε>0.

High Performance Java Compilation and Runtime Issues

High Performance Java Compilation and Runtime Issues

Dynamic motion planning in low obstacle density environments

A fundamental task for an autonomous robot is to plan its own motions. Exact approaches to the solution of this motion planning problem suffer from high worst-case running times. The weak and realistic low obstacle density (L.O.D.) assumption results in linear complexity in the number of obstacles of the free space (Van der Stappen et al., 1997). In this paper we address the dynamic version of the motion planning problem in which a robot moves among polygonal obstacles which move along polylines. The obstacles are assumed to move along constant complexity polylines, and to respect the low density property at any given time. We will show that in this situation a cell decomposition of the free space of size O( n 2 α( n) log 2 n) can be computed in O( n 2 α( n) log 2 n) time. The dynamic motion planning problem is then solved in O( n 2 α( n) log 3 n) time. We also show that these results are close to optimal.

Efficient Scheduling of Arbitrary Task Graphs to Multiprocessors Using a Parallel Genetic Algorithm

Given a parallel program represented by a task graph, the objective of a scheduling algorithm is to minimize the overall execution time of the program by properly assigning the nodes of the graph to the processors. This multiprocessor scheduling problem is NP-complete even with simplifying assumptions and becomes more complex under relaxed assumptions such as arbitrary precedence constraints, and arbitrary task execution and communication times. The present literature on this topic is a large repertoire of heuristics that produce good solutions in a reasonable amount of time. These heuristics, however, have restricted applicability in a practical environment because they have a number of fundamental problems including high time complexity, lack of scalability, and no performance guarantee with respect to optimal solutions. Recently, genetic algorithms (GAs) have been widely reckoned as a useful vehicle for obtaining high quality or even optimal solutions for a broad range of combinatorial optimization problems. While a few GAs for scheduling have already been suggested, in this paper we propose a novel GA-based algorithm with an objective to simultaneously meet the goals of high performance, scalability, and fast running time. The proposed parallel genetic scheduling (PGS) algorithm itself is a parallel algorithm which generates high quality solutions in a short time. By encoding the scheduling list as a chromosome, the PGS algorithm can potentially generate an optimal scheduling list which in turn leads to an optimal schedule. The major strength of the PGS algorithm lies in its two efficient genetic operators: the order crossover and mutation. These operators effectively combine the building-blocks of good scheduling lists to construct better lists. The proposed algorithm is evaluated through a robust comparison with two heuristics best known in terms of performance and time complexity. It outperforms both heuristics while taking considerably less running time. When evaluated with random task graphs for which optimal solutions are known, the PGS algorithm generates optimal solutions for more than half of the test cases and close-to-optimal for the other half.

Determining the Order of Processor Transactions in Statically Scheduled Multiprocessors

This paper addresses embedded multiprocessor implementation of iterative, real-time applications, such as digital signal and image processing, that are specified as dataflow graphs. Scheduling dataflow graphs on multiple processors involves assigning tasks to processors (processor assignment), ordering the execution of tasks within each processor (task ordering), and determining when each task must commence execution. We consider three scheduling strategies: fully-static, self-timed and ordered transactions, all of which perform the assignment and ordering steps at compile time. Run time costs are small for the fully-static strategy; however it is not robust with respect to changes or uncertainty in task execution times. The self-timed approach is tolerant of variations in task execution times, but pays the penalty of high run time costs, because processors need to explicitly synchronize whenever they communicate. The ordered transactions approach lies between the fully-static and self-timed strategies; in this approach the order in which processors communicate is determined at compile time and enforced at run time. The ordered transactions strategy retains some of the flexibility of self-timed schedules and at the same time has lower run time costs than the self-timed approach. In this paper we determine an order of processor transactions that is nearly optimal given information about task execution times at compile time, and for a given processor assignment and task ordering. The criterion for optimality is the average throughput achieved by the schedule. Our main result is that it is possible to choose a transaction order such that the resulting ordered transactions schedule incurs no performance penalty compared to the more flexible self-timed strategy, even when the higher run time costs implied by the self-timed strategy are ignored.

Full or-parallemism and restricted And-parallelism in BTM

BTM is a new And/Or parallel execution model for logic programs which exploits both full Or-parallelism and restricted And-parallelism. The advantages of high parallelism and low run time cost make BTJ, an experimental execution system of BTM implemented on a nonshared-memory multiprocessor system, achieve significant speedup for both And-parallel and Or-parallel logic programs.

Bivariate and trivariate normal integrals in affine space with applications in structural system reliability

Abstract Several methods for computing structural system reliability are reviewed. A method using bivariate and trivariate normal integrals in affine space with very high accuracy and little computer running time for the determination of structural system reliability is presented in this paper. The integration formulae of joint probabilities in orthogonal space are transformed into the bivariate and trivariate normal integrals in affine space constructed by the safety margin equations. Through transformations, the bivariate and trivariate normal integrals are turned into a combination of several one-dimensional integrals. The Gauss-Hermite numerical method is introduced to evaluate joint probabilities with an accuracy almost as high as that of the analytical solution and with much less computer time than that used by the methods published. The theory and illustrative examples demonstrate that the present method is practical and satisfactory in evaluating structural system reliability. It is also direct in physical concept and easy to implement.