Performance Of Searches Research Articles

SigAlign: an alignment algorithm guided by explicit similarity criteria.

In biological sequence alignment, prevailing heuristic aligners achieve high-throughput by several approximation techniques, but at the cost of sacrificing the clarity of output criteria and creating complex parameter spaces. To surmount these challenges, we introduce 'SigAlign', a novel alignment algorithm that employs two explicit cutoffs for the results: minimum length and maximum penalty per length, alongside three affine gap penalties. Comparative analyses of SigAlign against leading database search tools (BLASTn, MMseqs2) and read mappers (BWA-MEM, bowtie2, HISAT2, minimap2) highlight its performance in read mapping and database searches. Our research demonstrates that SigAlign not only provides high sensitivity with a non-heuristic approach, but also surpasses the throughput of existing heuristic aligners, particularly for high-accuracy reads or genomes with few repetitive regions. As an open-source library, SigAlign is poised to become a foundational component to provide a transparent and customizable alignment process to new analytical algorithms, toolsand pipelines in bioinformatics.

An empirical study of the limitations of minimum bounding boxes for defining the extent of geospatial resources: the use of DGGS and other alternatives for improving the performance of spatial searches

A Minimum Bounding Box (MBB) is a rectangle which bounds a geographic feature or dataset. It is commonly used in spatial information systems as a simplified way of describing the spatial extent of a resource. MBBs are typically indexed for searching and discovering resources relevant to a given geographic area of interest. However, this simplification leads to a loss of precision in the description of the extent and can affect the overall precision of the search results. We propose an alternative technique for describing the spatial extent based on the use of DGGS tiles. To measure the precision improvements offered by our method, we designed and implemented an empirical method for evaluating the average precision, and applied it to three different systems: one based on MBB, another on Convex Hull, and ours based on DGGS. The three methods were evaluated with the same test collection obtained from some of the main European geospatial data catalogues compliant with the INSPIRE directive. The results showed that our method outperformed the other two. Where the catalogue average precision of the MBB search scenarios is between 73% and 97%, the DGGS is between 96% and 99%. Additionally, we propose a realistic method of transitioning from the current technologies to the technology we are proposing, considering the current state of the spatial data infrastructures.

The Impact of PDDL+ Language Features on Planning Performance: An Empirical Analysis on a Real-world Case Study

PDDL+ is an expressive formalism that allows for the use of planning in complex real-world applications. It includes a number of features designed to improve the readability and conciseness of the resulting knowledge models, but that are commonly doubted to have detrimental impact on the performance of domain-independent searches and heuristics. In this paper we empirically assess the impact of such features in a challenging real-world case study.

Improving the performance of weak supervision searches using transfer and meta-learning

Weak supervision searches have in principle the advantages of both being able to train on experimental data and being able to learn distinctive signal properties. However, the practical applicability of such searches is limited by the fact that successfully training a neural network via weak supervision can require a large amount of signal. In this work, we seek to create neural networks that can learn from less experimental signal by using transfer and meta-learning. The general idea is to first train a neural network on simulations, thereby learning concepts that can be reused or becoming a more efficient learner. The neural network would then be trained on experimental data and should require less signal because of its previous training. We find that transfer and meta-learning can substantially improve the performance of weak supervision searches.

Phase Limitations of Multipliers at Harmonics

The absolute stability of a Lurye system with a monotone nonlinearity is guaranteed by the existence of a suitable O'Shea-Zames-Falb (OZF) multiplier. We develop a numerically tractable phase condition under which there can be no suitable OZF multiplier for the transfer function of a given continuous-time plant. We provide its graphical interpretation. The condition may be tested in a systematic manner and leads to significantly improved results compared with the condition in the literature from which it is derived. Our results are useful to evaluate the performance of numerical searches for OZF multipliers.

Performance Enhancement of Tree-based Friends-of-friends Galaxy Finder for High-resolution Simulations of Galaxy Formation

Abstract Cosmological simulations are useful tools for studying the evolution of galaxies, and it is critical to accurately identify galaxies and their halos from raw simulation data. The friends-of-friends (FoF) algorithm has been widely adopted for this purpose because of its simplicity and expandability to higher dimensions. However, it is cost-inefficient when applied to high-resolution simulations because standard FoF implementation leads to too many distance calculations in dense regions. We confirm this through our exercise of applying the six-dimensional (6D) FoF galaxy finder code, VELOCIraptor, on the NewHorizon simulation. The high particle resolution of NewHorizon (M star ∼ 104 M ⊙) allows a large central number density (106 kpc−3) for typical galaxies, resulting in a few days to weeks of galaxy searches for just one snapshot. Even worse, we observed a significant decrease in the FoF performance in the high-dimensional 6D searches: “the curse of dimensionality” problem. To overcome these issues, we have developed several implementations that can be readily applied to any tree-based FoF code. They include limiting visits to tree nodes, reordering the list of particles for searching neighbor particles, and altering the tree structure. Compared to the run with the original code, the new run with these implementations results in the identical galaxy detection with the ideal performance, O ( N log N ) , N being the number of particles in a galaxy—with a speed gain of a factor of 2700 in 3D or 12 in a 6D FoF search.

Hybrid genetic search for the CVRP: Open-source implementation and SWAP* neighborhood

The vehicle routing problem is one of the most studied combinatorial optimization topics, due to its practical importance and methodological interest. Yet, despite extensive methodological progress, many recent studies are hampered by the limited access to simple and efficient open-source solution methods. Given the sophistication of current algorithms, reimplementation is becoming a difficult and time-consuming exercise that requires extensive care for details to be truly successful. Against this background, we use the opportunity of this short paper to introduce a simple – open-source – implementation of the hybrid genetic search (HGS) specialized to the capacitated vehicle routing problem (CVRP). This state-of-the-art algorithm uses the same general methodology as Vidal et al. (2012) but also includes additional methodological improvements and lessons learned over the past decade of research. In particular, it includes an additional neighborhood called SWAP* which consists in exchanging two customers between different routes without an insertion in place. As highlighted in our study, an efficient exploration of SWAP* moves significantly contributes to the performance of local searches. Moreover, as observed in experimental comparisons with other recent approaches on the classical instances of Uchoa et al. (2017), HGS still stands as a leading metaheuristic regarding solution quality, convergence speed, and conceptual simplicity.

Exotic diboson Z' decays in the Umu nu SSM

Searches for new leptophobic resonances at high energy colliders usually target their decay modes into pairs of light quarks, top quarks, or standard model bosons. Additional decay modes may also be present, producing signatures to which current searches are not sensitive. We investigate the performance of generic searches that look for resonances decaying into two large-radius jets. As benchmark for our analysis we use a supersymmetric text {U}(1)' extension of the Standard Model, the so-called Umu nu SSM, where all the SM decay modes of the Z' boson take place, plus additional (cascade) decays into new scalars. The generic searches use a generic multi-pronged jet tagger and take advantage of the presence of b quarks in the large-radius jets, and are sensitive to all these Z' decay modes (except into light quarks) at once. For couplings that are well below current experimental constraints, these generic searches are sensitive at the 3sigma -4sigma level with Run 2 LHC data.

A Reinforcement Learning-Based Framework for Solving the IP Mapping Problem

In network-on-chip (NoC) designs, the intellectual property (IP) mapping problem is a critical issue and is usually solved by heuristic searches. However, heuristic searches suffer from the problem of easily falling into the local optimum. To tackle this problem, this article proposes a reinforcement learning-based framework (RLF), which enhances the performance of heuristic searches through the neural network-based probability model. Within this framework, first, a neural network-based probability model for IP mapping is built and trained by reinforcement learning instead of supervised learning to overcome the difficulty of obtaining a high-quality labeled training set. Second, based on the pretrained probability model, the model-based heuristic uses the probability model to generate the initial population and then employs heuristic searches to find the optimal solution. Two model-based heuristics, i.e., the message passing neural network-pointer network-based genetic algorithm (MPN-GA) and the message passing neural network-pointer network-based PSMAP (MPN-PSMAP), are proposed as specific instances. Simulation results show that the MPN-GA reduces the communication cost by an average of 9.32% than the genetic algorithm (GA). The MPN-PSMAP achieves an average reduction in the communication cost of 8.37% than the PSMAP. Finally, two extensions are given as examples to show the good extensibility of this framework.

Landscape-scaled strategies can outperform Lévy random searches

Information on the relevant global scales of the search space, even if partial, should conceivably enhance the performance of random searches. Here we show numerically and analytically that the paradigmatic uninformed optimal Lévy searches can be outperformed by informed multiple-scale random searches in one (1D) and two (2D) dimensions, even when the knowledge about the relevant landscape scales is incomplete. We show in the low-density nondestructive regime that the optimal efficiency of biexponential searches that incorporate all key scales of the 1D landscape of size L decays asymptotically as η_{opt}∼1/sqrt[L], overcoming the result η_{opt}∼1/(sqrt[L]lnL) of optimal Lévy searches. We further characterize the level of limited information the searcher can have on these scales. We obtain the phase diagram of bi- and triexponential searches in 1D and 2D. Remarkably, even for a certain degree of lack of information, partially informed searches can still outperform optimal Lévy searches. We discuss our results in connection with the foraging problem.

Searching PubMed to Retrieve Publications on the COVID-19 Pandemic: Comparative Analysis of Search Strings.

BackgroundSince it was declared a pandemic on March 11, 2020, COVID-19 has dominated headlines around the world and researchers have generated thousands of scientific articles about the disease. The fast speed of publication has challenged researchers and other stakeholders to keep up with the volume of published articles. To search the literature effectively, researchers use databases such as PubMed.ObjectiveThe aim of this study is to evaluate the performance of different searches for COVID-19 records in PubMed and to assess the complexity of searches required.MethodsWe tested PubMed searches for COVID-19 to identify which search string performed best according to standard metrics (sensitivity, precision, and F-score). We evaluated the performance of 8 different searches in PubMed during the first 10 weeks of the COVID-19 pandemic to investigate how complex a search string is needed. We also tested omitting hyphens and space characters as well as applying quotation marks.ResultsThe two most comprehensive search strings combining several free-text and indexed search terms performed best in terms of sensitivity (98.4%/98.7%) and F-score (96.5%/95.7%), but the single-term search COVID-19 performed best in terms of precision (95.3%) and well in terms of sensitivity (94.4%) and F-score (94.8%). The term Wuhan virus performed the worst: 7.7% for sensitivity, 78.1% for precision, and 14.0% for F-score. We found that deleting a hyphen or space character could omit a substantial number of records, especially when searching with SARS-CoV-2 as a single term.ConclusionsComprehensive search strings combining free-text and indexed search terms performed better than single-term searches in PubMed, but not by a large margin compared to the single term COVID-19. For everyday searches, certain single-term searches that are entered correctly are probably sufficient, whereas more comprehensive searches should be used for systematic reviews. Still, we suggest additional measures that the US National Library of Medicine could take to support all PubMed users in searching the COVID-19 literature.

Boolean redux: Performance of advanced versus simple boolean searches and implications for upper-level instruction

Building off an earlier study, which examined whether or not it was beneficial to teach Boolean logic to introductory students, the present study examines the efficacy of Boolean OR searching in more advanced search strategies, for example, capstone projects and graduate theses and dissertations. Results show that both simple and advanced Boolean searches yield relevant results. Based on relevance, there is no compelling evidence that either search is superior. To capture all the literature on a topic, however, it is important that upper-level students know the relevant databases for their discipline and perform multiple searches. Results can help inform whether teaching Boolean search skills to upper-division students in disciplinary contexts is time well spent.

Performance of detection dogs and visual searches for scat detection and discrimination amongst related species with identical diets

Ecology often faces the problem that many threatened species are highly elusive but also conflict-laden. Thus, proper monitoring data are inevitable for their conservation and management. Indirect monitoring through scats is frequently used for such species, but scats of related species or species with similar diet are often visually indistinguishable. Since genetic methods for species identification are time-consuming and cost-intensive, a verification of the target species beforehand would be extremely beneficial in reducing effort to the analysis of the target species only. Such species discrimination could be provided through species-specific scat detection dogs. Therefore, we evaluated the reliability of species-specific scat detection dogs for two mustelid species feeding on identical diets: the Eurasian otter (Lutra lutra) and the American mink (Neovison vison), both of which are conflict-laden and increasing their populations and distribution ranges in central Europe. Their scats resemble each other in morphology and odour, exacerbating the differentiation even for experts. To evaluate whether detection dogs can reliably discriminate between related species feeding on similar diets and if their use would be beneficial, we tested their abilities against those of humans. We first proved that scat characteristics are not statistically different between species. Likewise, visual species identification through people with different experience levels was only partly successful. Experts showed higher average accuracy (0.89) than non-experts (0.72 and below), but detection dogs (4 dogs) were able to discriminate otter and mink scats under laboratory conditions with an accuracy of 0.95. Moreover, otter scat detection dogs found up to four times more scat samples in the field, were twice as fast as human searchers and found an almost equal number of scats with different characteristics, while humans mostly found older and larger scats placed on hotspots. We conclude that using detection dogs for species identity will allow subsequent laboratory analyses to be species-specific and avoid spending time and money on laboratory work of the wrong species. It also provides more precise and unbiased information about the target species.

Imaging the search space: a nature-inspired metaheuristic extension

Humans have a long history of exploration throughout which they have devised many imaging technologies such as telescopes, radars and satellites to increase the level of effectiveness and success of their expeditions. This paper proposes the use of imaging concepts to support the search effort of metaheuristics that deploy expedition teams simulating among other things ants, birds and chromosomes to explore the search space of optimization problems. The research involves proposing and developing a set of experimental imaging techniques. Another purpose of the paper is to measure the effectiveness of those proposed imaging techniques on improving the performance of metaheuristic searches that start from initial populations. As a case study an extend to Particle Swarm Optimization metaheuristic algorithm has been performed by implementing and incorporating the proposed imaging techniques and benchmarking them on a platform for comparing continuous optimizers in a black box setting called COCO. The performance of the developed techniques has been evaluated against each other, and against the particle swarm optimization algorithm alone based on the criterion of how many function evaluations were required to reach the set of target values defined by COCO platform. The results show that the use of imaging could produce better results.

Maternal anxiety and internet-based food elimination in suspected food allergy.

Food allergy is associated with stress, anxiety, and impairment in quality of life in both children and families. This study aimed to assess the anxiety of mothers in the case of suspected food allergy in their children and the influence of Internet usage on their levels of anxiety and food elimination. Ninety-two patients aged 0-2years presented at a pediatric allergy department following either a physicians' presumptive diagnosis or family's suspicion of food allergy (Group 1) and 99 healthy controls (Group 2) were evaluated. The clinical diagnosis of food allergy was confirmed or excluded by oral food challenge (OFC). The validated Turkish version of Spielberger State-Trait Anxiety Inventory (STAI) was applied to the mothers of both groups on admission and to Group 1 3months after OFC. The state and trait anxiety (STAI-S and STAI-T) scores were significantly higher in Group 1 than Group 2 (P<0.001, z=5.410, and P=0.021, z=2.304, respectively). Seventy-nine mothers (85.9%) had been eliminating foods on admission. Thirty-seven (40.2%) mothers had eliminated foods on their own. The performance of food allergy-related Web searches was significantly higher among mothers who eliminated foods by themselves than those who did not (P=0.004, χ2 =8.391, df=1). The second STAI-S score was significantly lower among mothers whose children's food allergy diagnosis was excluded than confirmed (effect size=1.06 (95% CI: 0.56-1.57), P=0.001, z=3.442). It is important to make a correct food allergy diagnosis with a detailed allergy work-up, including OFC, to avoid anxiety and not cause unnecessary food elimination.

MiTPeptideDB: a proteogenomic resource for the discovery of novel peptides.

The principal lines of research in MS/MS based Proteomics have been directed toward the molecular characterization of the proteins including their biological functions and their implications in human diseases. Recent advances in this field have also allowed the first attempts to apply these techniques to the clinical practice. Nowadays, the main progress in Computational Proteomics is based on the integration of genomic, transcriptomic and proteomic experimental data, what is known as Proteogenomics. This methodology is being especially useful for the discovery of new clinical biomarkers, small open reading frames and microproteins, although their validation is still challenging. We detected novel peptides following a proteogenomic workflow based on the MiTranscriptome human assembly and shotgun experiments. The annotation approach generated three custom databases with the corresponding peptides of known and novel transcripts of both protein coding genes and non-coding genes. In addition, we used a peptide detectability filter to improve the computational performance of the proteomic searches, the statistical analysis and the robustness of the results. These innovative additional filters are specially relevant when noisy next generation sequencing experiments are used to generate the databases. This resource, MiTPeptideDB, was validated using 43 cell lines for which RNA-Seq experiments and shotgun experiments were available. MiTPeptideDB is available at http://bit.ly/MiTPeptideDB. Supplementary data are available at Bioinformatics online.

Endosymbiotic Evolutionary Algorithm for an Integrated Model of the Vehicle Routing and Truck Scheduling Problem with a Cross-Docking System

This paper presents a model which integrates inbound and outbound logistics with a crossdocking system. This model integrates the problem of routing inbound vehicles between suppliers and cross-docks and outbound vehicles between cross-docks and retailers, considering logistics costs and the products properties. This model aims to minimize the total cost by optimizing assignment of products to suppliers and retailers and operations of inbound and outbound vehicles. We developed an endosymbiotic evolutionary algorithm, which yields good performance in concurrent searches for the solutions of multiple subproblems and validate the performance using several numerical examples.

Feature Data Selection for Improving the Performance of Entity Similarity Searches in the Internet of Things

Sensors are used to sense the state information of physical entities in the Internet of Things (IoT). Thus, a large amount of dynamic real-time data is generated. The entity similarity search based on the quantitative dynamic sensor data is thus worth studying. To the best of our knowledge, there is no research on the entity similarity search based on feature data selection for the quantitative dynamic sensor data in the IoT. This paper proposes a selection mechanism for the entity main features (SMEF). The SMEF is a feature data selection method based on the quantitative dynamic sensor data. It uses the feature matrix to delete the irrelevant entity features, applies an improved relief algorithm (iRelief) to calculate the feature data relevance and proposes a three-component storage relation table of the entities, models, and features (TEMF) for the dynamic feature weights calculation. The experimental results show that the similarity search algorithm based on feature data selection can improve the average search accuracy by more than 10%, as well as increase the search speed and reduce the data transmission and storage costs.

A probabilistic molecular fingerprint for big data settings

BackgroundAmong the various molecular fingerprints available to describe small organic molecules, extended connectivity fingerprint, up to four bonds (ECFP4) performs best in benchmarking drug analog recovery studies as it encodes substructures with a high level of detail. Unfortunately, ECFP4 requires high dimensional representations (≥ 1024D) to perform well, resulting in ECFP4 nearest neighbor searches in very large databases such as GDB, PubChem or ZINC to perform very slowly due to the curse of dimensionality.ResultsHerein we report a new fingerprint, called MinHash fingerprint, up to six bonds (MHFP6), which encodes detailed substructures using the extended connectivity principle of ECFP in a fundamentally different manner, increasing the performance of exact nearest neighbor searches in benchmarking studies and enabling the application of locality sensitive hashing (LSH) approximate nearest neighbor search algorithms. To describe a molecule, MHFP6 extracts the SMILES of all circular substructures around each atom up to a diameter of six bonds and applies the MinHash method to the resulting set. MHFP6 outperforms ECFP4 in benchmarking analog recovery studies. By leveraging locality sensitive hashing, LSH approximate nearest neighbor search methods perform as well on unfolded MHFP6 as comparable methods do on folded ECFP4 fingerprints in terms of speed and relative recovery rate, while operating in very sparse and high-dimensional binary chemical space.ConclusionMHFP6 is a new molecular fingerprint, encoding circular substructures, which outperforms ECFP4 for analog searches while allowing the direct application of locality sensitive hashing algorithms. It should be well suited for the analysis of large databases. The source code for MHFP6 is available on GitHub (https://github.com/reymond-group/mhfp).

The bi-objective traveling purchaser problem with deliveries

In this work we introduce a variant of the well-known Traveling Purchaser Problem in which the purchased products must be delivered to a set of customers. The objective is to minimize the total cost (purchasing plus traveling costs) and the waiting time of the customers, simultaneously, while satisfying the total demand. This problem is called the bi-objective Traveling Purchaser Problem with Deliveries. In order to approximate Pareto fronts for this problem, a relinked variable neighborhood search is proposed and tested over a large set of artificial instances. Our results show that our algorithm is highly competitive compared to the ϵ-constraint method in small instances. On the other hand, experiments carried out over large instances show that our algorithm is able to find Pareto front approximations with more points in a shorter running time for uncapacitated instances than for capacitated ones. Also, computational results show that the performance of some local searches used in our algorithm depends on the characteristics of the instances, this underlines the importance of designing a metaheuristic based on multiple local searches.