Current Search Algorithms Research Articles

Testing strengths, limitations, and biases of current pulsar timing arrays’ detection analyses on realistic data

State-of-the-art searches for gravitational waves (GWs) in pulsar timing array (PTA) datasets model the signal as an isotropic, Gaussian, and stationary process described by a power law. In practice, none of these properties are expected to hold for an incoherent superposition of GWs generated by a cosmic ensemble of supermassive black hole binaries (SMBHBs). This stochastic signal is usually referred to as the GW background (GWB) and is expected to be the primary signal in the PTA band. We performed a systematic investigation of the performance of current search algorithms, using a simple power-law model to characterise GW signals in realistic datasets. We used, as the baseline dataset, synthetic realisations of timing residuals mimicking the European PTA (EPTA) second data release (DR2). Thus, we included in the dataset uneven time stamps, achromatic and chromatic red noise, and multi-frequency observations. We then injected timing residuals from an ideal isotropic, Gaussian, single power-law stochastic process and from a realistic population of SMBHBs, performing a methodical investigation of the recovered signal. We found that current search models are efficient at recovering the GW signal, but several biases can be identified due to the signal-template mismatch, which we identified via probability-probability (P–P) plots and quantified using Kolmogorov-Smirnov (KS) statistics. We discuss our findings in light of the signal observed in the EPTA DR2 and corroborate its consistency with a SMBHB origin.

EFFICIENT AND EXPRESSIVE SEARCH OVER ENCRYPTED DATA WITH MULTI FACTOR AUTHENTICATION SCHEME

More data owners are tempted to outsource their data to cloud services as a result of the development of cloud storage. Sensitive data should be encrypted before being outsourced due to privacy concerns. To assure data availability, a variety of searchable encryption techniques are used. However, the effectiveness of data consumers' queries is not given much consideration by the current search algorithms, particularly in the case of several owners. In this study, linguistic coefficients are used to create an encrypted search index. In particular, the encrypted search index utilizing Lagrange co-efficient makes use of secure inner-product calculation for both search and relevance assessment by taking into account a sizable amount of data in the cloud. A system architecture of preferred search over encrypted data in the cloud scene is built, and the requirements in terms of efficiency and privacy are specified. This enables the clo ud servers to do a secure search without knowing any sensitive data (e.g., keywords and trapdoors).For each data owner, the Lagrange coefficient is used to build indices that can support searches over numerous keyword fields and enable accu rate relevance computation in order to produce an efficient search.To protect user data privacy, a multifactor authenticati on technique might be utilised.After logging in to the system, the user will receive three different sorts of authentications, including an OTP utilizing a number, a captcha, and an OTP image. This picture will serve as a key. In order to accomplish secure search and relevance score calculation, the user's preference and the search query are expressed in vector form. Additionally, Preferred Search over Encrypted Data (PSED) is consistent with cloud computing's scalability and adaptability. Finally, a thorough security study verifies the security of our scheme, and a performance analysis highlights its effectiveness and efficiency. Keywords: Encrypted Data, Inner product calculation, Multifactor authentication, Data privacy

Show Me the Way! Bilevel Search for Synthesizing Programmatic Strategies

The synthesis of programmatic strategies requires one to search in large non-differentiable spaces of computer programs. Current search algorithms use self-play approaches to guide this search. The issue with these approaches is that the guiding function often provides a weak search signal. This is because self-play functions only measure how well a program performs against other programs. Thus, while small changes to a losing program might not transform it into a winning one, such changes might represent steps in the direction of a winning program. In this paper we introduce a bilevel search algorithm that searches concurrently in the space of programs and in a space of state features. Each iteration of the search in the space of features defines a set of target features that the search in the program space attempts to achieve (i.e., features one observes while following the strategy encoded in a program). We hypothesize the combination of a self-play function and a feature-based one provides a stronger search signal for synthesis. While both functions are used to guide the search in the program space, the self-play function is used to guide the search in the feature space, to allow for the selection of target features that are more likely to lead to winning programs. We evaluated our bilevel algorithm in MicroRTS, a real-time strategy game. Our results show that the bilevel search synthesizes stronger strategies than methods that search only in the program space. Also, the strategies our method synthesizes obtained the highest winning rate in a simulated tournament with several baseline agents, including the best agents from the two latest MicroRTS competitions.

Stable bulged G-quadruplexes in the human genome: identification, experimental validation and functionalization.

DNA sequence composition determines the topology and stability of G-quadruplexes (G4s). Bulged G-quadruplex structures (G4-Bs) are a subset of G4s characterized by 3D conformations with bulges. Current search algorithms fail to capture stable G4-B, making their genome-wide study infeasible. Here, we introduced a large family of computationally defined and experimentally verified potential G4-B forming sequences (pG4-BS). We found 478263 pG4-BS regions that do not overlap 'canonical' G4-forming sequences in the human genome and are preferentially localized in transcription regulatory regions including R-loops and open chromatin. Over 90% of protein-coding genes contain pG4-BS in their promoter or gene body. We observed generally higher pG4-BS content inR-loops and their flanks,longer genes that are associated with brain tissue, immune and developmental processes. Also, the presence of pG4-BS on both template and non-template strands in promoters is associated with oncogenesis, cardiovascular disease and stemness. Our G4-BS models predicted G4-forming ability in vitro with 91.5% accuracy. Analysis of G4-seq and CUT&Tag data strongly supports the existence of G4-BS conformations genome-wide. We reconstructed a novel G4-B 3D structure located in the E2F8 promoter. This study defines a large family of G4-like sequences, offering new insights into the essential biological functions and potential future therapeutic uses of G4-B.

Diversified-Intensified Current Search Algorithm and Its Application to Optimal State-Feedback Controller Design for Tractor Active Suspension System

Wydawnictwo SIGMA-NOT wydaje czasopisma fachowe informujące swoich czytelników o najnowszych osiągnięciach naukowych i nowoczesnych rozwiązaniach technicznych w Polsce i na świecie, popularyzuje problemy techniczne oraz poszerza wiedzę i kulturę techniczną.

Training of artificial neural networks with the multi-population based artifical bee colony algorithm

ABSTRACT Nowadays, artificial intelligence has gained recognition in every aspect of life. Artificial neural networks, one of the most efficient artificial intelligence techniques, is remarkably successful in computers’ acquisition of the learning and interpretation capabilities of humans and attainment of meaningful results. Whether artificial intelligence networks can yield meaningful results is directly related to how the network is trained. The traditional algorithms, which are used to train artificial intelligence networks, do not always yield successful results in complicated problems and real-life problems. Metaheuristic algorithms are efficient techniques developed in order to figure out time-consuming and challenging problems fast and as optimally as possible. This study makes use of the artificial bee colony algorithm, which has been widely used recently in solving many kinds of problems so as to train artificial neural networks efficiently. Within this study, different strategies are used on subpopulations, so that the algorithm can search without getting tangled with the local optima. And also same and different parameter settings are considered for each population to reflect different search behaviours. The proposed approach was analysed through applied results of different data sets. The results yielded that the used strategies can be promising alternatives to the current search algorithms.

Massive Ship Fault Data Retrieval Algorithm Supporting Complex Query in Cloud Computing

Lou, H., 2019. Massive ship fault data retrieval algorithm supporting complex query in cloud computing. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 1013–1018. Coconut Creek (Florida), ISSN 0749-0208.Aiming at the problems of the current retrieval algorithm for massive ship fault data retrieval in cloud computing, which does not support complex query, large retrieval cost and low precision, a massive ship fault data retrieval algorithm supporting complex query in cloud computing based on query performance prediction is proposed. Introducing the Lagrangian algorithm to preprocess the ship fault data information; combining the attribute values of the ship fault data and the context to calculate the similarity between different attributes, and identifying the same attribute according to the similarity; The attribute recognition result is queried by using multiple retrieval models, and the model complex query request is converted into a one-dimensional query key value, and the query key values obtained by the plurality of models are predicted, and the search result with the optimal prediction performance of the complex query mode is used as the final result. The experimental results show that the proposed algorithm has good retrieval accuracy in complex query mode, and it has less overhead than the current search algorithm.

An enhanced search algorithm for the charged fuel enrichment in equilibrium cycle analysis of REBUS-3

This paper presents an enhanced search algorithm for the charged fuel enrichment in equilibrium cycle analysis of REBUS-3. The current enrichment search algorithm of REBUS-3 takes a large number of iterations to yield a converged solution or even terminates without a converged solution when the user-specified initial guesses are far from the solution. To resolve the convergence problem and to reduce the computational time, an enhanced search algorithm was developed. The enhanced algorithm is based on the idea of minimizing the number of enrichment estimates by allowing drastic enrichment changes and by optimizing the current search algorithm of REBUS-3. Three equilibrium cycle problems with recycling, without recycling and of high discharge burnup were defined and a series of sensitivity analyses were performed with a wide range of user-specified initial guesses. Test results showed that the enhanced search algorithm is able to produce a converged solution regardless of the initial guesses. In addition, it was able to reduce the number of flux calculations by a factor of 2.9, 1.8, and 1.7 for equilibrium cycle problems with recycling, without recycling, and of high discharge burnup, respectively, compared to the current search algorithm.

A new weighted pathfinding algorithms to reduce the search time on grid maps

Artificial Intelligence (AI) techniques are utilized widely in the field of Expert Systems (ES) - as applied to robotics, video games self-driving vehicles and so on. Pathfinding algorithms are a class of heuristic algorithms based on AI techniques which are used in ES as decision making functions for the purpose of solving problems that would otherwise require human competence or expertise. ES fields that use pathfinding algorithms and operate in real-time face many challenges: for example time constraints, optimality and memory overhead for storing the paths which are found. For these algorithms to work, appropriate problem-specific maps must be constructed. In relation to this, the uniform-cost grid set-up is the most appropriate for ES applications. In this method, each node in a graph is represented as a tile, and the weight “between” tiles is set at a constant value, usually this is set to 1. In the state-of-the-art heuristic algorithms used with this data structure, multiplying the heuristic function by a weight greater than one is well-known technique. In this paper, we present three new techniques using various weights to accelerate heuristic search of grid maps. The first such technique is based on the iteration of a heuristic search algorithm associated with weight-set w. The second technique is based on the length between the start node and goal node, which is then associated with w. The last technique is based on the travel cost and is associated with a weight-set α. These techniques are applicable to a wide class of heuristic search algorithms. Therefore, we implement them, here, within the A*, the Bidirectional A* (Bi-A*) and Jump Point Search (JPS) algorithms; thus obtaining a family of new algorithms. Furthermore, it is seen that the use of these new algorithms results in significant improvements over current search algorithms. We evaluate them in path-planning benchmarks and show the amended JPS technique's greater stability, across weight values, over the other two techniques. However, it is also shown that this technique yields poor results in terms of cost solution.

Improving Gnutella Query Search Algorithm with Jumps

The measurement of a search algorithm for unstructured P2P network centres on the number of nodes not receiving their requested files (number of failures) and the number of hops per query. Most current search algorithms are unable to guarantee the success of the query. This study involves a comparison of the strengths and weaknesses of three algorithms of Gnutella P2P protocol, namely Flood, Random Walk, and Random Walk with Neighbours Table. Based on this comparison, a new query search method—referred to as Random Walk with Jumps—is proposed. The experiment proves that the proposed algorithm can obtain a better result with a small number of failures and a minimum number of hops.

Current Search: Performance Evaluation and Application to DC Motor Speed Control System Design

This paper proposes the current search (CS) metaheuristics conceptualized from the electric current flowing through electric networks for optimization problems with continuous design variables. The CS algorithm possesses two powerful strategies, exploration and exploitation, for searching the global optimum. Based on the stochastic process, the derivatives of the objective function is unnecessary for the proposed CS. To evaluate its performance, the CS is tested against several unconstrained optimization problems. The results obtained are compared to those obtained by the popular search techniques, i.e., the genetic algorithm (GA), the particle swarm optimization (PSO), and the adaptive tabu search (ATS). As results, the CS outperforms other algorithms and provides superior results. The CS is also applied to a constrained design of the optimum PID controller for the dc motor speed control system. From experimental results, the CS has been successfully applied to the speed control of the dc motor.

Elastase Digests: New Ammunition for Shotgun Membrane Proteomics

Despite many advances in membrane proteomics during the last decade the fundamental problem of accessing the transmembrane regions itself has only been addressed to some extent. The present study establishes a method for the nano-LC-based analysis of complex membrane proteomes on the basis of a methanolic porcine pancreatic elastase digest to increase transmembrane coverage. Halobacterium salinarium purple and Corynebacterium glutamicum membranes were successfully analyzed by using the new protocol. We demonstrated that elastase digests yield a large proportion of transmembrane peptides, facilitating membrane protein identification. The potential for characterization of a membrane protein through full sequence coverage using elastase is there but is restricted to the higher abundance protein components. Compatibility of the work flow with the two most common mass spectrometric ionization techniques, ESI and MALDI, was shown. Currently better results are obtained using ESI mainly because of the low response of MALDI for strictly neutral peptides. New findings concerning elastase specificity in complex protein mixtures reveal a new prospect beyond the application in shotgun experiments. Furthermore peptide mass fingerprinting with less specific enzymes might be done in the near future but requires an adaptation of current search algorithms to the new proteases.

Refined repetitive sequence searches utilizing a fast hash function and cross species information retrievals

BackgroundSearching for small tandem/disperse repetitive DNA sequences streamlines many biomedical research processes. For instance, whole genomic array analysis in yeast has revealed 22 PHO-regulated genes. The promoter regions of all but one of them contain at least one of the two core Pho4p binding sites, CACGTG and CACGTT. In humans, microsatellites play a role in a number of rare neurodegenerative diseases such as spinocerebellar ataxia type 1 (SCA1). SCA1 is a hereditary neurodegenerative disease caused by an expanded CAG repeat in the coding sequence of the gene. In bacterial pathogens, microsatellites are proposed to regulate expression of some virulence factors. For example, bacteria commonly generate intra-strain diversity through phase variation which is strongly associated with virulence determinants. A recent analysis of the complete sequences of the Helicobacter pylori strains 26695 and J99 has identified 46 putative phase-variable genes among the two genomes through their association with homopolymeric tracts and dinucleotide repeats. Life scientists are increasingly interested in studying the function of small sequences of DNA. However, current search algorithms often generate thousands of matches – most of which are irrelevant to the researcher.ResultsWe present our hash function as well as our search algorithm to locate small sequences of DNA within multiple genomes. Our system applies information retrieval algorithms to discover knowledge of cross-species conservation of repeat sequences. We discuss our incorporation of the Gene Ontology (GO) database into these algorithms. We conduct an exhaustive time analysis of our system for various repetitive sequence lengths. For instance, a search for eight bases of sequence within 3.224 GBases on 49 different chromosomes takes 1.147 seconds on average. To illustrate the relevance of the search results, we conduct a search with and without added annotation terms for the yeast Pho4p binding sites, CACGTG and CACGTT. Also, a cross-species search is presented to illustrate how potential hidden correlations in genomic data can be quickly discerned. The findings in one species are used as a catalyst to discover something new in another species. These experiments also demonstrate that our system performs well while searching multiple genomes – without the main memory constraints present in other systems.ConclusionWe present a time-efficient algorithm to locate small segments of DNA and concurrently to search the annotation data accompanying the sequence. Genome-wide searches for short sequences often return hundreds of hits. Our experiments show that subsequently searching the annotation data can refine and focus the results for the user. Our algorithms are also space-efficient in terms of main memory requirements. Source code is available upon request.

Deriving statistical models for predicting peptide tandem MS product ion intensities.

Improved search algorithms and scoring functions are required before the identification of peptide tandem MS data can be considered to be fully reliable and automatable. The development of models that can accurately predict product ion spectra from a peptide sequence would certainly help achieve this goal, but this firstly requires a better understanding of the process of fragmentation of peptides in the gas-phase. We summarize recent developments in this area and show that the prediction of product ion spectra is feasible and should improve the identification of peptide tandem MS data, especially for peptides that currently give low or insignificant scores with current search algorithms.