Runtime Improvements Research Articles

New batch construction heuristics to optimise the performance of order picking systems

Two new batch construction heuristics called K-means Batching (KMB) and Self-organisation Map Batching (SOMB) are developed and verified by simulation experiments. Both KMB and SOMB have a preferment of superior performance in total travel distance and average picking vehicle utility, and even a conspicuous improvement in total CPU running time. Besides, this paper investigates the overall performance of order picking systems integrating storage assignment, order batching and picker routing to find the optimal policy combinations under different order types. The sensitivity analysis is performed to distinguish the relative importance of the various strategies to enhance the performance of operations management.

Classifying short gene expression time-courses with Bayesian estimation of piecewise constant functions

Analyzing short time-courses is a frequent and relevant problem in molecular biology, as, for example, 90% of gene expression time-course experiments span at most nine time-points. The biological or clinical questions addressed are elucidating gene regulation by identification of co-expressed genes, predicting response to treatment in clinical, trial-like settings or classifying novel toxic compounds based on similarity of gene expression time-courses to those of known toxic compounds. The latter problem is characterized by irregular and infrequent sample times and a total lack of prior assumptions about the incoming query, which comes in stark contrast to clinical settings and requires to implicitly perform a local, gapped alignment of time series. The current state-of-the-art method (SCOW) uses a variant of dynamic time warping and models time series as higher order polynomials (splines). We suggest to model time-courses monitoring response to toxins by piecewise constant functions, which are modeled as left-right Hidden Markov Models. A Bayesian approach to parameter estimation and inference helps to cope with the short, but highly multivariate time-courses. We improve prediction accuracy by 7% and 4%, respectively, when classifying toxicology and stress response data. We also reduce running times by at least a factor of 140; note that reasonable running times are crucial when classifying response to toxins. In conclusion, we have demonstrated that appropriate reduction of model complexity can result in substantial improvements both in classification performance and running time. A Python package implementing the methods described is freely available under the GPL from http://bioinformatics.rutgers.edu/Software/MVQueries/.

Fast and sensitive determination of aluminium with RP-HPLC using an ultra-short monolithic column

A fast and sensitive reversed-phase high-performance liquid-chromatographic method for determination of aluminium in aqueous samples has been developed. The fluorescent aluminium-lumogallion complex (λex 505 nm, λem 574 nm), was formed with a pre-column reagent and then separated on a Chromolith® RP-18e Guard column using a two-tiered, stepped gradient program, with matrix elimination (5/95 (v/v) methanol/water), followed by elution (90/10 (v/v) methanol/water). This method achieved a run time of 2.5 min without compromising sensitivity (limit of detection = 5.6 × 10−10 M, limit of quantification = 7.2 × 10−7 M), precision (3.5% at 3.71 × 10−6 M) or accuracy (recovery = 97.5% ± 3.2%, n = 5, P = 0.95). This represents an improvement in run time by >50% compared to the fastest previously published method [Lee et al., Clin. Chem., 1996, 42, 1405–1411]. This method uses MES buffer (2-(N-morpholino)ethanesulfonic acid), which compared to other buffers is more easily purified, does not complex with aluminium, and can be used in lower concentrations. This method was applied to analysis of: deionised water for column comparison; seawater for matrix interference effects; and tea-infusion for calibration and recovery studies.

Learning Genetic Regulatory Network Connectivity from Time Series Data

Recent experimental advances facilitate the collection of time series data that indicate which genes in a cell are expressed. This information can be used to understand the genetic regulatory network that generates the data. Typically, Bayesian analysis approaches are applied which neglect the time series nature of the experimental data, have difficulty in determining the direction of causality, and do not perform well on networks with tight feedback. To address these problems, this paper presents a method to learn genetic network connectivity which exploits the time series nature of experimental data to achieve better causal predictions. This method first breaks up the data into bins. Next, it determines an initial set of potential influence vectors for each gene based upon the probability of the gene's expression increasing in the next time step. These vectors are then combined to form new vectors with better scores. Finally, these influence vectors are competed against each other to determine the final influence vector for each gene. The result is a directed graph representation of the genetic network's repression and activation connections. Results are reported for several synthetic networks with tight feedback showing significant improvements in recall and runtime over Yu's dynamic Bayesian approach. Promising preliminary results are also reported for an analysis of experimental data for genes involved in the yeast cell cycle.

Analyzing transit service reliability using detailed data from automatic vehicular locator systems

AbstractThe widespread adoption of automated vehicle location (AVL) systems and automatic passenger counters (APCs) in the transit industry has opened new venues in operations and system monitoring. In 2005, Metro Transit, Minnesota, implemented AVL system and partially implemented APC technologies. To date there has been little effort to employ the collected data in evaluating transit performance. This research uses such data to assess performance issues along a cross‐town route in the Metro Transit system. We generate a series of visual and analytical analyses to predict run time, schedule adherence and reliability of the transit route at two scales: the time point segment and the route level to demonstrate ways of identifying causes of decline in reliability levels. The analytical models show that while headways are maintained, schedule revisions are needed to improve run time and schedule adherence. Finally, the analysis suggests that many scheduled stops along this route are underutilized and recommends stop consolidation as a tool to decrease variability of service through concentrating passenger demand along a fewer number of stops. Copyright © 2010 John Wiley & Sons, Ltd.

A Tractable Class of Algorithms for Reliable Routing in Stochastic Networks

The goal of this article is to provide the theoretical basis for enabling tractable solutions to the “arriving on time” problem and enabling its use in real-time mobile phone applications. Optimal routing in transportation networks with highly varying traffic conditions is a challenging problem due to the stochastic nature of travel-times on links of the network. The definition of optimality criteria and the design of solution methods must account for the random nature of the travel-time on each link. Most common routing algorithms consider the expected value of link travel-time as a sufficient statistic for the problem and produce least expected travel-time paths without consideration of travel-time variability. However, in numerous practical settings the reliability of the route is also an important decision factor. In this article, we consider the following optimality criterion: maximizing the probability of arriving on time at a destination given a departure time and a time budget. We present an efficient algorithm for finding an optimal routing policy with a well bounded computational complexity, improving on an existing solution that takes an unbounded number of iterations to converge to the optimal solution. A routing policy is an adaptive algorithm that determines the optimal solution based on en route travel-times and therefore provides better reliability guarantees than an a-priori solution. Novel speed-up techniques to efficiently compute the adaptive optimal strategy and methods to prune the search space of the problem are also investigated. Finally, an extension of this algorithm which allows for both timevarying traffic conditions and spatio-temporal correlations of link travel-time distributions is presented. The dramatic runtime improvements provided by the algorithm are demonstrated for practical scenarios in California.

Variation and defect tolerance for diode-based nano crossbars

Bottom-up self-assembly nanofabrication process yields nanodevices with significantly more variations compared to the conventional top-down lithography used in CMOS fabrication. This is in addition to an increased defect density expected for self-assembled nanodevices. Therefore, it is one of the major design challenges to tolerate variation and defects in emerging nano architectures. In this paper, we present different solutions for variation tolerant logic mapping for molecular (diode-based) crossbar array nano architectures using Simulated Annealing as well as a heuristic algorithm. Experimental results and comparisons with exhaustive search and defect-unaware mapping shows the effectiveness of the proposed methods in variation and defect tolerance as well as run time improvement.

Verified Distance Computation Between Convex Hulls of Octrees Using Interval Optimization Techniques

AbstractDistance computation is an important task in many application areas, including biomechanics, robot systems and computer games. Depending on the intended use, requirements differ. For example, in a medical context, a verified result may be of interest. In this paper we will demonstrate the use of an interval optimization algorithm for computing a verified enclosure of the minimum distance between the convex hulls of two octrees. We will also discuss runtime improvements. (© 2010 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Yield-Driven Near-Threshold SRAM Design

Voltage scaling is desirable in static RAM (SRAM) to reduce energy consumption. However, commercial SRAM is susceptible to functional failures when VDD is scaled down. Although several published SRAM designs scale VDD to 200-300 mV, these designs do not sufficiently consider SRAM robustness, limiting them to small arrays because of yield constraints, and may not correctly target the minimum energy operation point. We examine the effects on area and energy for the differential 6T and 8T bit cells as VDD is scaled down, and the bit cells are either sized and doped, or assisted appropriately to maintain the same yield as with full VDD. SRAM robustness is calculated using importance sampling, resulting in a seven-order run-time improvement over Monte Carlo sampling. Scaling 6T and 8T SRAM VDD down to 500 mV and scaling 8T SRAM to 300 mV results in a 50% and 83% dynamic energy reduction, respectively, with no reduction in robustness and low area overhead, but increased leakage per bit. Using this information, we calculate the supply voltage for a minimum total energy operation (VMIN) based on activity factor and find that it is significantly higher for SRAM than for logic.

Automated Design Debugging With Maximum Satisfiability

As contemporary very large scale integration designs grow in complexity, design debugging has rapidly established itself as one of the largest bottlenecks in the design cycle today. Automated debug solutions such as those based on Boolean satisfiability (SAT) enable engineers to reduce the debug effort by localizing possible error sources in the design. Unfortunately, adaptation of these techniques to industrial designs is still limited by the performance and capacity of the underlying engines. This paper presents a novel formulation of the debugging problem using MaxSAT to improve the performance and applicability of automated debuggers. Our technique not only identifies errors in the design but also indicates when the bug is excited in the error trace. MaxSAT allows for a simpler formulation of the debugging problem, reducing the problem size by 80% compared to a conventional SAT-based technique. Empirical results demonstrate the effectiveness of the proposed formulation as run-time improvements of 4.5 × are observed on average. This paper introduces two performance improvements to further reduce the time required to find all error sources within the design by an order of magnitude.

Parallel probabilistic model checking on general purpose graphics processors

We present algorithms for parallel probabilistic model checking on general purpose graphic processing units (GPGPUs). Our improvements target the numerical components of the traditional sequential algorithms. In particular, we capitalize on the fact that in most of them operations like matrix–vector multiplication and solving systems of linear equations are the main complexity bottlenecks. Since linear algebraic operations can be implemented very efficiently on GPGPUs, the new parallel algorithms show considerable runtime improvements compared to their counterparts on standard architectures. We implemented our parallel algorithms on top of the probabilistic model checker PRISM. The prototype implementation was evaluated on several case studies in which we observed significant speedup over the standard CPU implementation of the tool.

Misty Mountain clustering: application to fast unsupervised flow cytometry gating

BackgroundThere are many important clustering questions in computational biology for which no satisfactory method exists. Automated clustering algorithms, when applied to large, multidimensional datasets, such as flow cytometry data, prove unsatisfactory in terms of speed, problems with local minima or cluster shape bias. Model-based approaches are restricted by the assumptions of the fitting functions. Furthermore, model based clustering requires serial clustering for all cluster numbers within a user defined interval. The final cluster number is then selected by various criteria. These supervised serial clustering methods are time consuming and frequently different criteria result in different optimal cluster numbers. Various unsupervised heuristic approaches that have been developed such as affinity propagation are too expensive to be applied to datasets on the order of 106 points that are often generated by high throughput experiments.ResultsTo circumvent these limitations, we developed a new, unsupervised density contour clustering algorithm, called Misty Mountain, that is based on percolation theory and that efficiently analyzes large data sets. The approach can be envisioned as a progressive top-down removal of clouds covering a data histogram relief map to identify clusters by the appearance of statistically distinct peaks and ridges. This is a parallel clustering method that finds every cluster after analyzing only once the cross sections of the histogram. The overall run time for the composite steps of the algorithm increases linearly by the number of data points. The clustering of 106 data points in 2D data space takes place within about 15 seconds on a standard laptop PC. Comparison of the performance of this algorithm with other state of the art automated flow cytometry gating methods indicate that Misty Mountain provides substantial improvements in both run time and in the accuracy of cluster assignment.ConclusionsMisty Mountain is fast, unbiased for cluster shape, identifies stable clusters and is robust to noise. It provides a useful, general solution for multidimensional clustering problems. We demonstrate its suitability for automated gating of flow cytometry data.

Efficient motif finding algorithms for large-alphabet inputs

BackgroundWe consider the problem of identifying motifs, recurring or conserved patterns, in the biological sequence data sets. To solve this task, we present a new deterministic algorithm for finding patterns that are embedded as exact or inexact instances in all or most of the input strings.ResultsThe proposed algorithm (1) improves search efficiency compared to existing algorithms, and (2) scales well with the size of alphabet. On a synthetic planted DNA motif finding problem our algorithm is over 10× more efficient than MITRA, PMSPrune, and RISOTTO for long motifs. Improvements are orders of magnitude higher in the same setting with large alphabets. On benchmark TF-binding site problems (FNP, CRP, LexA) we observed reduction in running time of over 12×, with high detection accuracy. The algorithm was also successful in rapidly identifying protein motifs in Lipocalin, Zinc metallopeptidase, and supersecondary structure motifs for Cadherin and Immunoglobin families.ConclusionsOur algorithm reduces computational complexity of the current motif finding algorithms and demonstrate strong running time improvements over existing exact algorithms, especially in important and difficult cases of large-alphabet sequences.

Computing Equivalent Transformations for Combinatorial Optimization by Branch-and-Bound Search

Branch-and-Bound search is a basic algorithm for solving combinatorial optimization problems. Here we introduce a new lower-bounding methodology that can be incorporated into any branch-and-bound solver, and demonstraint its use on the MaxSAT constraint optimization problem. The approach is to adapt a “minimum-height equivalent transformation” framework that was first developed in the context of computer vision. We present efficient algorithms to realize this framework within the MaxSAT domain, and demonstrate their feasibility by implementing them within the state-of-the-art maxsatz solver. We evaluate the solver on test sets from the 2009 MaxSAT competition; we observe a basic performance tradeoff whereby the (quadratic) time cost of computing the transformations may or may not be worthwhile in exchange for better bounds and more frequent pruning. For specific test sets, the trade-off does result in significant improvement in both prunings and overall run-time.

Efficient Uplink Modeling for Dynamic System-Level Simulations of Cellular and Mobile Networks

A novel theoretical framework for uplink simulations is proposed. It allows investigations which have to cover a very long (real-) time and which at the same time require a certain level of accuracy in terms of radio resource management, quality of service, and mobility. This is of particular importance for simulations of self-organizing networks. For this purpose, conventional system level simulators are not suitable due to slow simulation speeds far beyond real-time. Simpler, snapshot-based tools are lacking the aforementioned accuracy. The runtime improvements are achieved by deriving abstract theoretical models for the MAC layer behavior. The focus in this work is long term evolution, and the most important uplink effects such as fluctuating interference, power control, power limitation, adaptive transmission bandwidth, and control channel limitations are considered. Limitations of the abstract models will be discussed as well. Exemplary results are given at the end to demonstrate the capability of the derived framework.

Inverse center location problems

Abstract We investigate the inverse 1-center location problem on trees and outline combinatorial algorithms with time complexity O ( n 2 ) in case that the topology of the tree does not change. In the uniform cost model an improved running time of O ( n log n ) can be obtained. If topology changes occur, the complexity increases by a factor bounded by n . This improves earlier results of Yang and Zhang.

Energy Optimization for Many-Core Platforms: Communication and PVT Aware Voltage-Island Formation and Voltage Selection Algorithm

In this paper, we propose a novel approach to voltage-island formation, for the energy optimization of many-core architectures, which mitigates the impact of process, voltage, and temperature (PVT) variations. The islands are created by balancing their shape constraints imposed by intra and inter-island communication with the desire to limit the spatial extent of each island to minimize PVT impact. In addition, to reduce the number of voltage levels in the design, we propose an efficient voltage selection approach that provides near optimal results, for a set of 33 examined cases, with more than a ten times speedup compared to the best-known previous methods. This run-time improvement is important, especially for large many-core platforms. Finally, we present an evaluation platform considering pre-fabrication and post-fabrication PVT scenarios where multiple applications with hundreds to thousands of tasks are mapped onto many-core platforms with hundreds to thousands of cores to evaluate the proposed techniques. Results show that the average energy savings for 33 test cases using the proposed methods are 37% compared to 16% obtained using previous methods.

Preference-based learning to rank

This paper presents an efficient preference-based ranking algorithm running in two stages. In the first stage, the algorithm learns a preference function defined over pairs, as in a standard binary classification problem. In the second stage, it makes use of that preference function to produce an accurate ranking, thereby reducing the learning problem of ranking to binary classification. This reduction is based on the familiar QuickSort and guarantees an expected pairwise misranking loss of at most twice that of the binary classifier derived in the first stage. Furthermore, in the important special case of bipartite ranking, the factor of two in loss is reduced to one. This improved bound also applies to the regret achieved by our ranking and that of the binary classifier obtained. Our algorithm is randomized, but we prove a lower bound for any deterministic reduction of ranking to binary classification showing that randomization is necessary to achieve our guarantees. This, and a recent result by Balcan et al., who show a regret bound of two for a deterministic algorithm in the bipartite case, suggest a trade-off between achieving low regret and determinism in this context. Our reduction also admits an improved running time guarantee with respect to that deterministic algorithm. In particular, the number of calls to the preference function in the reduction is improved from Ω(n 2) to O(nlog?n). In addition, when the top k ranked elements only are required (k?n), as in many applications in information extraction or search engine design, the time complexity of our algorithm can be further reduced to O(klog?k+n). Our algorithm is thus practical for realistic applications where the number of points to rank exceeds several thousand.

A FPTAS for computing a symmetric Leontief competitive economy equilibrium

In this paper, we consider a linear complementarity problem (LCP) arisen from the Nash and Arrow–Debreu competitive economy equilibria where the LCP coefficient matrix is symmetric. We prove that the decision problem, to decide whether or not there exists a complementary solution, is NP-complete. Under certain conditions, an LCP solution is guaranteed to exist and we present a fully polynomial-time approximation scheme (FPTAS) for approximating a complementary solution, although the LCP solution set can be non-convex or non-connected. Our method is based on approximating a quadratic social utility optimization problem (QP) and showing that a certain KKT point of the QP problem is an LCP solution. Then, we further show that such a KKT point can be approximated with a new improved running time complexity $${{O}((\frac{n^4}{\epsilon})\log\log(\frac{1}{\epsilon}))}$$ arithmetic operation in accuracy $${\epsilon \in (0,1)}$$. We also report preliminary computational results which show that the method is highly effective. Applications in competitive market model problems with other utility functions are also presented, including global trading and dynamic spectrum management problems.

Coenzyme Q10 Supplementation Decreases Oxidative Stress and Improves Physical Performance in Young Swimmers: A Pilot Study

Background: The purpose of this study was to evaluate the effects of short term (12 day) Coenzyme Q10 (CoQ10) supplementation on blood oxidative stress biomarkers and physical performance in young swimmers. Methods: Sixteen young swimmers (7 males and 9 females, aged 15.13±0.96 years) who were involved in a sport hero project at Chiang Mai province participated as subjects during this 22 day study. Regular training for swimmers was carried out for the first 9 days. Supplementation with 300mg CoQ10 (Soft gel; Swanson Ultra, USA) was then administered daily for 12 days. Blood samples were collected in EDTA-anticoagulant tubes before (days 1 and 9 of the control period) and after the 12 day supplementation period (day 22). Plasma was separated and used for the determination of malondialdehyde (MDA), nitric oxide (NOx), protein hydroperoxide (PrOOH), total antioxidant capacity (TAC), and CoQ10; whereas reduced glutathione (GSH) was measured in erythrocytes. Exhaustive exercise time was evaluated before (days 1 and 9) and after-supplementation (day 22) on a mechanical treadmill using a modified Bruce protocol. Swimming speeds for both 100 and 800 meters were also recorded. Repeated measurement and Bonferroni correction were used for statistical analysis (p = 0.05). Results: Over the course of the 9 day control period before supplementation, all parameters (MDA, NOx, PrOOH, CoQ10, GSH, TAC, treadmill exhaustion time, and swimming time for either 100 or 800 meters) did not differ (p>0.05). After CoQ10 supplementation, the levels of plasma MDA, NOx, and PrOOH were significantly decreased when compared to the pre-supplement period (p 0.05). Conclusion: Twelve days of CoQ10 supplementation reduces oxidative stress, improves running time until exhaustion, as well as short distance swim sprint time, within a sample of young swimmers.