Iterative Search Strategy Research Articles

Iterative search strategy with selective bi-directional prediction for low complexity multiview video coding

The multiview video coding (MVC) extension of H.264/AVC is the emerging standard for compression of impressive 3D and free-viewpoint video. The coding structure in MVC adopts motion and disparity estimation to exploit temporal and inter-view dependencies in MVC. It results in a considerable increase in encoding complexity. Most of the computational burden comes from uni-directional and bi-directional prediction. In this paper, an iterative search strategy is designed to speed up the uni-directional prediction in MVC. It can work with an adaptive search range adjustment through a confidence measure of a loop constraint to obtain both motion and disparity vectors jointly. Furthermore, a selective bi-directional prediction algorithm is proposed to enhance the coding performance by analyzing the statistical characteristics of bi-directional prediction in MVC. Experimental results demonstrate that, by using the proposed fast search, the temporal and inter-view redundancies of multiview video can be eliminated sufficiently with low complexity.

Minimum Zone Evaluation of Flatness Error Using an Adaptive Iterative Strategy for Coordinate Measuring Machines Data

In this paper, an adaptive and iterative neighborhood search strategy is proposed to precisely evaluate the flatness error. Firstly, an initial datum plane produced by least squares method (LSM) is calculated. And then, a group of candidate planes are generated by adjusting the direction angles of the LSM datum plane. The flatness errors conform to the minimum zone condition for each candidate datum plane that can be worked out. The one with minimum flatness error will be selected as the new start plane for the next search. The new search area is the zone between the new and old datum planes, with which the new search step is computed until the terminal condition is met. Numerical examples are given to validate the proposed strategy. The results show that the proposed method could get the precise value of the flatness error from the data of coordinate measuring machines and other equipment.

Swiftly Computing Center Strings

BackgroundThe center string (or closest string) problem is a classic computer science problem with important applications in computational biology. Given k input strings and a distance threshold d, we search for a string within Hamming distance at most d to each input string. This problem is NP complete.ResultsIn this paper, we focus on exact methods for the problem that are also swift in application. We first introduce data reduction techniques that allow us to infer that certain instances have no solution, or that a center string must satisfy certain conditions. We describe how to use this information to speed up two previously published search tree algorithms. Then, we describe a novel iterative search strategy that is effecient in practice, where some of our reduction techniques can also be applied. Finally, we present results of an evaluation study for two different data sets from a biological application.ConclusionsWe find that the running time for computing the optimal center string is dominated by the subroutine calls for d = dopt -1 and d = dopt. Our data reduction is very effective for both, either rejecting unsolvable instances or solving trivial positions. We find that this speeds up computations considerably.

Multiple Fundamental Frequency Estimation by Modeling Spectral Peaks and Non-Peak Regions

This paper presents a maximum-likelihood approach to multiple fundamental frequency (F0) estimation for a mixture of harmonic sound sources, where the power spectrum of a time frame is the observation and the F0s are the parameters to be estimated. When defining the likelihood model, the proposed method models both spectral peaks and non-peak regions (frequencies further than a musical quarter tone from all observed peaks). It is shown that the peak likelihood and the non-peak region likelihood act as a complementary pair. The former helps find F0s that have harmonics that explain peaks, while the latter helps avoid F0s that have harmonics in non-peak regions. Parameters of these models are learned from monophonic and polyphonic training data. This paper proposes an iterative greedy search strategy to estimate F0s one by one, to avoid the combinatorial problem of concurrent F0 estimation. It also proposes a polyphony estimation method to terminate the iterative process. Finally, this paper proposes a postprocessing method to refine polyphony and F0 estimates using neighboring frames. This paper also analyzes the relative contributions of different components of the proposed method. It is shown that the refinement component eliminates many inconsistent estimation errors. Evaluations are done on ten recorded four-part J. S. Bach chorales. Results show that the proposed method shows superior F0 estimation and polyphony estimation compared to two state-of-the-art algorithms.

A metasynthesis of midwives’ experience of hospital practice in publicly funded settings: compliance, resistance and authenticity

Worldwide, increasing percentages of women are giving birth in centralized hospitals in the belief that this maximizes safety for themselves and their babies. In parallel, there is international recognition that the number of birth interventions used in the routine care of labouring women is rising. This is fuelling concern about iatrogenesis, and, particularly, maternal and infant morbidity and mortality. It also has an adverse impact on the economics of health care. National and international policy characterizes midwives as the guardians of normal childbirth. This guardianship appears to be failing. The objective of this metasynthesis is to explore midwives' perceptions of hospital midwifery with a focus on labour ward practice to examine professional discourses around midwifery work in the current modernist, risk averse and consumerist childbirth context. Based on an iterative search strategy, 14 studies were selected for the metasynthesis. Three overarching themes were identified: 'power and control'; 'compliance with cultural norms'; and 'attempting to normalize birth'. Most midwives aimed to provide what they characterized as 'real midwifery' but this intention was often overwhelmed with heavy workloads and the normative pressure to provide equitable care to all women. This raises questions of authenticity, both in terms of midwives living out their beliefs, and in terms of acknowledgement of the power to resist. The theoretical insights generated by the metasynthesis could have resonance for other professional and occupational groups who wish to offer autonomous individualized services in an increasingly risk-averse target driven global society.

Identification and Gene Expression Analysis of a Taxonomically Restricted Cysteine-Rich Protein Family in Reef-Building Corals

The amount of genomic sequence information continues to grow at an exponential rate, while the identification and characterization of genes without known homologs remains a major challenge. For non-model organisms with limited resources for manipulative studies, high-throughput transcriptomic data combined with bioinformatics methods provide a powerful approach to obtain initial insights into the function of unknown genes. In this study, we report the identification and characterization of a novel family of putatively secreted, small, cysteine-rich proteins herein named Small Cysteine-Rich Proteins (SCRiPs). Their discovery in expressed sequence tag (EST) libraries from the coral Montastraea faveolata required the performance of an iterative search strategy based on BLAST and Hidden-Markov-Model algorithms. While a discernible homolog could neither be identified in the genome of the sea anemone Nematostella vectensis, nor in a large EST dataset from the symbiotic sea anemone Aiptasia pallida, we identified SCRiP sequences in multiple scleractinian coral species. Therefore, we postulate that this gene family is an example of lineage-specific gene expansion in reef-building corals. Previously published gene expression microarray data suggest that a sub-group of SCRiPs is highly responsive to thermal stress. Furthermore, data from microarray experiments investigating developmental gene expression in the coral Acropora millepora suggest that different SCRiPs may play distinct roles in the development of corals. The function of these proteins remains to be elucidated, but our results from in silico, transcriptomic, and phylogenetic analyses provide initial insights into the evolution of SCRiPs, a novel, taxonomically restricted gene family that may be responsible for a lineage-specific trait in scleractinian corals.

Mirador: A Simple Fast Search Interface for Global Remote Sensing Data Sets

A major challenge for remote sensing researchers is searching and acquiring relevant data files for their research projects based on content, space, and time constraints. Several structured query (SQ) and hierarchical navigation (HN) search interfaces have been developed to satisfy this requirement. However, the popularity of free-text (FT) search in the general domain led the Goddard Earth Sciences Data and Information Services Center to develop an FT search interface named Mirador that supports space-time queries, including a gazetteer and geophysical event gazetteer. In order to compensate for a slightly reduced search precision relative to SQ and HN methods, Mirador uses several search optimizations to return results quickly, enabling iterative search strategies.

ML Symbol Detection Based on the Shortest Path Algorithm for MIMO Systems

This paper presents a new maximum likelihood (ML) symbol detection algorithm for multiple-input multiple-output (MIMO) systems. To achieve the ML performance with low complexity, we search the integer points corresponding to symbol vectors in increasing order of the distance from the unconstrained least-squares solution. For each integer point, we test if it is the ML solution, and continue the integer point search until one of searched points is determined to be the ML solution. We present an efficient iterative search strategy, which is based on the shortest path algorithm for a graph. The simulation results show that the proposed algorithm has the lower complexity compared to the sphere decoding for channel matrices having low condition numbers. For further complexity reduction, we propose to use scaling, lattice-reduction, and regularization techniques. By applying these techniques, the computational complexity of proposed algorithm is reduced significantly when the channel matrix has a high condition number.

Targeted Analysis of Protein Termini

We describe a targeted analysis of protein isoforms by selective enrichment and identification of in vivo acetylated protein N-termini and protein C-termini. Our method allows the characterization of these protein termini regardless of their annotation in protein databases and requires no chemical derivatization. Using an iterative database search strategy that takes account of the enrichment protocol, 263 IPI annotated and 87 unpredicted acetylated N-termini were identified in the crude membrane fraction of human embryonic carcinoma cells. The N-acetylated peptides conform to the reported criteria for in vivo modification. In addition, 168 IPI annotated and 193 unpredicted C-termini were identified. Additionally, and for the first time, we also report on in vivo N-terminal propionylation. The significant number of unknown protein N- and C-termini suggests a high degree of novel transcription independent of annotated gene boundaries and/or specific protein processing. Biological relevance of several of these unpredicted protein termini could be curated from the literature, adding further weight to the argument to go beyond routine database search strategies. Our method will improve the correct annotation of genes and proteins in databases.

What is the empirical evidence that hospitals with higher-risk adjusted mortality rates provide poorer quality care? A systematic review of the literature.

BackgroundDespite increasing interest and publication of risk-adjusted hospital mortality rates, the relationship with underlying quality of care remains unclear. We undertook a systematic review to ascertain the extent to which variations in risk-adjusted mortality rates were associated with differences in quality of care.MethodsWe identified studies in which risk-adjusted mortality and quality of care had been reported in more than one hospital. We adopted an iterative search strategy using three databases – Medline, HealthSTAR and CINAHL from 1966, 1975 and 1982 respectively. We identified potentially relevant studies on the basis of the title or abstract. We obtained these papers and included those which met our inclusion criteria.ResultsFrom an initial yield of 6,456 papers, 36 studies met the inclusion criteria. Several of these studies considered more than one process-versus-risk-adjusted mortality relationship. In total we found 51 such relationships in a widen range of clinical conditions using a variety of methods. A positive correlation between better quality of care and risk-adjusted mortality was found in under half the relationships (26/51 51%) but the remainder showed no correlation (16/51 31%) or a paradoxical correlation (9/51 18%).ConclusionThe general notion that hospitals with higher risk-adjusted mortality have poorer quality of care is neither consistent nor reliable.

Cut-and-solve

Branch-and-bound and branch-and-cut use search trees to identify optimal solutions to combinatorial optimization problems. In this paper, we introduce an iterative search strategy which we refer to...

Cut-and-solve: An iterative search strategy for combinatorial optimization problems

Branch-and-bound and branch-and-cut use search trees to identify optimal solutions to combinatorial optimization problems. In this paper, we introduce an iterative search strategy which we refer to as cut-and-solve and prove optimality and termination for this method. This search is different from traditional tree search as there is no branching. At each node in the search path, a relaxed problem and a sparse problem are solved and a constraint is added to the relaxed problem. The sparse problems provide incumbent solutions. When the constraining of the relaxed problem becomes tight enough, its solution value becomes no better than the incumbent solution value. At this point, the incumbent solution is declared to be optimal. This strategy is easily adapted to be an anytime algorithm as an incumbent solution is found at the root node and continuously updated during the search. Cut-and-solve enjoys two favorable properties. Since there is no branching, there are no “wrong” subtrees in which the search may get lost. Furthermore, its memory requirement is negligible. For these reasons, it has potential for problems that are difficult to solve using depth-first or best-first search tree methods. In this paper, we demonstrate the cut-and-solve strategy by implementing a generic version of it for the Asymmetric Traveling Salesman Problem (ATSP). Our unoptimized implementation outperformed state-of-the-art solvers for five out of seven real-world problem classes of the ATSP. For four of these classes, cut-and-solve was able to solve larger (sometimes substantially larger) problems. Our code is available at our websites.

Dynamic Spectrum Quality Assessment and Iterative Computational Analysis of Shotgun Proteomic Data

In mass spectrometry-based proteomics, frequently hundreds of thousands of MS/MS spectra are collected in a single experiment. Of these, a relatively small fraction is confidently assigned to peptide sequences, whereas the majority of the spectra are not further analyzed. Spectra are not assigned to peptides for diverse reasons. These include deficiencies of the scoring schemes implemented in the database search tools, sequence variations (e.g. single nucleotide polymorphisms) or omissions in the database searched, post-translational or chemical modifications of the peptide analyzed, or the observation of sequences that are not anticipated from the genomic sequence (e.g. splice forms, somatic rearrangement, and processed proteins). To increase the amount of information that can be extracted from proteomic MS/MS datasets we developed a robust method that detects high quality spectra within the fraction of spectra unassigned by conventional sequence database searching and computes a quality score for each spectrum. We also demonstrate that iterative search strategies applied to such detected unassigned high quality spectra significantly increase the number of spectra that can be assigned from datasets and that biologically interesting new insights can be gained from existing data.

Comparison of Normal and Breast Cancer Cell Lines Using Proteome, Genome, and Interactome Data

Normal and cancer cell line proteomes were profiled using high throughput mass spectrometry techniques. Application of protein-level and peptide-level sample fractionation combined with LC-MS/MS analysis enabled identification of 2235 unmodified proteins representing a broad range of functional and compartmental classes. An iterative multistep search strategy was used to identify post-translational modifications, revealing several proteins that are preferentially modified in cancer cells. Information regarding both unmodified and modified protein forms was combined with publicly available gene expression and protein-protein interaction data. The resulting integrated dataset revealed several functionally related proteins that are differentially regulated between normal and cancer cell lines.

Reducing the Computation Time of Nonlinear Problems by an Adaptive Linear System Tolerance

Within the finite-element framework, nonlinear magnetic problems are often solved by an iterative line search strategy. The efforts to achieve convergence concentrate on the selection of an adequate relaxation factor. The line search is performed along a direction obtained by solving a system of linear equations. However, it is not required to compute this intermediate solution with a high accuracy, to ensure convergence. This paper shows how the accuracy of the solver can be modified at each nonlinear iteration, in order to reduce the overall computation time.

Sequence-based detection of distantly related proteins with the same fold.

Because proteins that have diverged beyond significant sequence similarity still retain the three-dimensional (3D) fold of their ancestor (Chothia and Lesk, 1986; Rost, 1997), the recognition of structural similarity between proteins provides powerful clues to ancestry. In fact, a large number of distant homology relationships were identified only after the structures of the proteins had been solved (Murzin, 1998). However, structures are being determined only for a small fraction of the proteins. There is a pressing need for improvement in the performance of sequence-based methods for the detection of proteins with the same fold but scant sequence similarity. Here, we examine how to achieve this goal by combining three kinds of information from a protein sequence. First, it has long been recognized that the use of multiplyaligned sequences from a protein family improves the sensitivity of homology detection. This idea is used by many recent computational procedures that exploit evolutionary information to uncover subtle sequence similarity. Examples of such procedures include sequence profiles (Gribskov et al., 1987), consensus templates or motifs (Taylor, 1986; Bairoch, 1991; Tatusov et al., 1994; Yi and Lander, 1994), positionspecific scoring matrices (PSSMs) (Henikoff and Henikoff, 1997), profile hidden Markov models (Eddy, 1998), and intermediate sequence methods (Holm and Sander, 1997; Neuwald et al., 1997; Park et al., 1997). PSI-BLAST (Altschul et al., 1997), one of the most widely used of these procedures, employs an iterative profile search strategy that combines the advantages of both PSSM and intermediate sequence methods. This program has been used effectively by several groups to assign 3D folds to predicted genome products (Teichmann et al., 1999). Second, proteins having the same fold also by definition have very similar secondary structures. In the light of the improved accuracy of secondary structure prediction (Rost and Sander, 1993), several groups have attempted to use sequencederived predictions to improve the sensitivity of fold recognition (Fischer and Eisenberg, 1996; Russel et al., 1996; Di Francesco et al., 1997; Rice and Eisenberg, 1997; Rost et al., 1997). These methods usually represent each protein in a template library by a one-dimensional (1D) string of symbols (profiles) each representing a distinctive 3D structural state, and then use dynamic programming (Needleman and Wunsch, 1970) to align the predicted structural profiles of the query

SCHEDULING OPEN SHOPS TO MINIMIZE TOTAL WEIGHTED TARDINESS

ABSTRACT It is well known that most of the open shop scheduling problems are NP-hard. Currently, the open shop scheduling research has been focusing on minimizing makespan. In this study, we shall examine the total weighted tardiness open shop scheduling problem, which is also NP-hard. We present a heuristic solution method, based on iterative search strategy, for solving large-scaled problems. Experimental results indicate that the proposed heuristic method significantly outperforms other methods in literature. Also, we present an exact solution method, based on branch-and-bound, for solving small-scaled problems.

SAVVYSEARCH: A Metasearch Engine That Learns Which Search Engines to Query

Search engines are among the most successful applications on the web today. So many search engines have been created that it is difficult for users to know where they are, how to use them, and what topics they best address. Metasearch engines reduce the user burden by dispatching queries to multiple search engines in parallel. The SAVVYSEARCH metasearch engine is designed to efficiently query other search engines by carefully selecting those search engines likely to return useful results and responding to fluctuating load demands on the web. SAVVYSEARCH learns to identify which search engines are most appropriate for particular queries, reasons about resource demands, and represents an iterative parallel search strategy as a simple plan.

Changing Representations During Search: A Comparative Study of Delta Coding

Delta coding is an iterative genetic search strategy that dynamically changes the representation of the search space in an attempt to exploit different problem representations. Delta coding sustains search by reinitializing the population at each iteration of search. This helps to avoid the asymptotic performance typically observed in genetic search as the population becomes more homogeneous. Here, the optimization ability of delta coding is empirically compared against CHC, ESGA, GENITOR, and random mutation hill-climbing (RMHC) on a suite of well-known test functions with and without Gray coding. Issues concerning the effects of Gray coding on these test functions are addressed.

Optimal production of secreted protein in fed‐batch reactors

AbstractThe optimal control policy for the maximization of the secreted heterologous protein in a fed‐batch bioreactor was obtained using SEY2102 as a model host yeast and SUC2‐s2 as a model secretory protein. A dynamic model for host cell growth, gene expression, and the secretion of expressed polypeptides was formulated. The optimal system trajectory contains multiple singular arcs that are distinct from one another. Optimal control requires transitions between these singular arcs. Optimal transitions between multiple singular arcs with bounded controls are uniquely located in observance with the Minimum Principle of Pontryagin. An iterative numerical search strategy for determining the optimal control showed successful convergence properties.