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In 2018, the position paper 'Imaging the adult with congenital heart disease: a multimodality imaging approach' was published. The paper highlights, in the first part, the different imaging modalities applied in adult congenital heart disease patients. In the second part, these modalities are discussed more detailed for moderate to complex anatomical defects. Because of the length of the paper, simple lesions were not touched on. However, imaging modalities to use for simple shunt lesions are still poorly known. One is looking for structured recommendations on which they can rely when dealing with an (undiscovered) shunt lesion. This information is lacking for the initial diagnostic process, during repair and at follow-up. Therefore, this paper will focus on atrial septal defect, ventricular septal defect, and persistent arterial duct. Pre-, intra-, and post-procedural imaging techniques will be systematically discussed. This position paper will offer algorithms that might help at a glance. The document is prepared for general cardiologists, trainees, medical students, imagers/technicians to select the most appropriate imaging modality and to detect the requested information for each specific lesion. It might serve as reference to which researchers could refer when setting up a (imaging) study. [ABSTRACT FROM AUTHOR]

A multirepeat in a string is a substring (factor) that appears a predefined number of times. A multirepeat is maximal if it cannot be extended either to the right or to the left and produce a multirepeat. In this paper, we present algorithms for two different versions of the problem of finding maximal multirepeats in a set of strings. In the case of arbitrary gaps, we propose an algorithm with O(σN2n + α) time complexity. When the gap is bounded in a small range c, we propose an algorithm with O((c2 + σ2)mN2n log(Nn) + α) time complexity. Here, N is the number of strings, n the mean length of each string, m the multiplicity of the multirepeat and α the number of reported occurrences. Our results extend previous work by considering sets of strings as well as by generalizing pairs to multirepeats. [ABSTRACT FROM AUTHOR]

Background. In April 2008, experts reviewed updates on sexually transmitted disease (STD) prevention and treatment in preparation for the revision of the Centers for Disease Control and Prevention (CDC) STD Treatment Guidelines. This included a review of cervical cancer screening in the STD clinical setting. Methods. Key questions were identified with assistance from an expert panel. Reviews of the literature were conducted using the PubMed computerized database and shared with the panel. Updated information was incorporated in the 2010 CDC STD Treatment Guidelines. Results. We recommend that STD clinics offering cervical screening services screen and treat women according to guidelines by the American College of Obstetrics and Gynecology, the American Cancer Society, the US Preventive Services Task Force, and the American Society for Colposcopists and Cervical Pathologists. New to the 2010 guidelines are higher age for initiating cervical screening (age ≥21 years) and less frequent intervals of screening (at least every 3 years). New recommendations include new technologies, such as liquid-based cytology and high-risk human papillomavirus (HPV) DNA tests. Liquid-based technologies are not recommended over conventional testing. HPV DNA tests are recommended as adjunct tests and with new indications for use in cervical screening and management. Stronger recommendations were issued for STD clinics offering cervical screening services to have protocols in place for follow-up of test results and referral (eg, colposcopy). Conclusions. Important additions to the 2010 STD Treatment Guidelines include information on updated algorithms for screening and management of women and recommendations for use of liquid-based cytology and high-risk HPV testing. [ABSTRACT FROM AUTHOR]

The Worldvolume Hybrid Monte Carlo method (WV-HMC method) is a reliable and versatile algorithm towards solving the sign problem. Like the tempered Lefschetz thimble method, this method removes the ergodicity problem inherent in algorithms based on Lefschetz thimbles. In addition to this advantage, the WV-HMC method significantly reduces the computational cost because one need not compute the Jacobian of deformation in generating configurations. A crucial step in this method is the RATTLE algorithm, where the Newton method is used at each molecular dynamics step to project a transported configuration onto a submanifold (worldvolume) in the complex space. In this paper, we simplify the RATTLE algorithm by employing a simplified Newton method (the fixed-point method) along with iterative solvers for orthogonal decompositions of vectors, and show that this algorithm further reduces the computational cost. We also apply this algorithm to the HMC algorithm for the generalized thimble method (GT-HMC method). We perform a numerical test for the convergence of the simplified RATTLE algorithm, and show that the convergence depends on the system size only weakly. The application of this simplified algorithm to various models will be reported in subsequent papers. [ABSTRACT FROM AUTHOR]

Machine learning (ML) and prediction modelling have become increasingly influential in healthcare, providing critical insights and supporting clinical decisions, particularly in the age of big data. This paper serves as an introductory guide for health researchers and readers interested in prediction modelling and explores how these technologies support clinical decisions, particularly with big data, and covers all aspects of the development, assessment and reporting of a model using ML. The paper starts with the importance of prediction modelling for precision medicine. It outlines different types of prediction and machine learning approaches, including supervised, unsupervised and semi-supervised learning, and provides an overview of popular algorithms for various outcomes and settings. It also introduces key theoretical ML concepts. The importance of data quality, preprocessing and unbiased model performance evaluation is highlighted. Concepts of apparent, internal and external validation will be introduced along with metrics for discrimination and calibration for different types of outcomes. Additionally, the paper addresses model interpretation, fairness and implementation in clinical practice. Finally, the paper provides recommendations for reporting and identifies common pitfalls in prediction modelling and machine learning. The aim of the paper is to help readers understand and critically evaluate research papers that present ML models and to serve as a first guide for developing, assessing and implementing their own. [ABSTRACT FROM AUTHOR]

The Marine Predators Algorithm (MPA) is a swarm intelligence algorithm developed based on the foraging behavior of the ocean's predators. This algorithm has drawbacks including, insufficient population diversity, leading to trapping in local optima and poor convergence. To mitigate these drawbacks, this paper introduces an enhanced MPA based on Adaptive Sampling with Maximin Distance Criterion (AM) and the horizontal and vertical crossover operators – i.e. Adaptive Crossover-based MPA (AC-MPA). The AM approach is used to generate diverse and well-distributed candidate solutions. Whereas the horizontal and vertical crossover operators maintain the population diversity during the search process. The performance of AC-MPA was tested using 51 benchmark functions from CEC2017, CEC2020, and CEC2022, with varying degrees of dimensionality, and the findings are compared with those of its basic version, variants, and numerous well-established metaheuristics. Additionally, 11 engineering optimization problems were utilized to verify the capabilities of the AC-MPA in handling real-world optimization problems. The findings clearly show that AC-MPA performs well in terms of its solution accuracy, convergence, and robustness. Furthermore, the proposed algorithm demonstrates considerable advantages in solving engineering problems, proving its effectiveness and adaptability. [ABSTRACT FROM AUTHOR]

In optimization, metaheuristic algorithms have received extensive attention and research due to their excellent performance. The slime mould algorithm (SMA) is a newly proposed metaheuristic algorithm. It has the characteristics of fewer parameters and strong optimization ability. However, with the increasing difficulty of optimization problems, SMA has some shortcomings in complex problems. For example, the main concerns are low convergence accuracy and prematurely falling into local optimal solutions. To overcome these problems, this paper has developed a variant of SMA called CCSMA. It is an improved SMA based on horizontal crossover (HC) and covariance matrix adaptive evolutionary strategy (CMAES). First, HC can enhance the exploitation of the algorithm by crossing the information between different individuals to promote communication within the population. Finally, CMAES facilitates algorithm exploration and exploitation to reach a balanced state by dynamically adjusting the size of the search range. This benefits the algorithm by allowing it to go beyond the local space to explore other solutions with better quality. To verify the superiority of the proposed algorithm, we select some new original and improved algorithms as competitors. CCSMA is compared with these competitors in 40 benchmark functions of IEEE CEC2017 and CEC2020. The results demonstrate that our work outperforms the competitors in terms of optimization accuracy and jumping out of the local space. In addition, CCSMA is applied to tackle three typical engineering optimization problems. These three problems include multiple disk clutch brake design, pressure vessel design, and speed reducer design. The results showed that CCSMA achieved the lowest optimization cost. This also proves that it is an effective tool for solving realistic optimization problems. [ABSTRACT FROM AUTHOR]

This paper investigates the Generalized Traveling Salesman Problem (GTSP), which is an extension of the well-known Traveling Salesman Problem (TSP), and it searches for an optimal tour in a clustered graph, such that every cluster is visited exactly once. In this paper, we describe a novel Memetic Algorithm (MA) for solving efficiently the GTSP. Our proposed MA has at its core a genetic algorithm (GA), completed by a Chromosome Enhancement Procedure (CEP), which is based on a TSP solver and the Shortest Path (SP) algorithm and for improving the convergence characteristics of the GA, a Local Search (LS) operation is applied for the best chromosomes in each generation. We tested our algorithm on a set of well-known instances from the literature and the achieved results prove that our novel memetic algorithm is highly competitive against the existing solution approaches from the specialized literature. [ABSTRACT FROM AUTHOR]

Discusses a study on theoretical population genetics. Information on the algorithms used in the study; Background on Melanesian gene trees; Example of an explicit state of a genealogy.

Concerns raised by Okazaki & Ueda (2022) on the paper by Sambridge et al. (2022) are addressed. Two issues are discussed and some new numerical results presented. The first concerns whether the properties of the Wasserstein time-series misfit introduced in our earlier paper will translate to model space non-uniqueness in a seismic waveform inversion setting. It is argued that this is unlikely, given the special conditions, which must exist between all observed/predicted seismic waveform pairs for non-uniqueness to result. The second issue discussed is the efficacy of using the Sliced Wasserstein algorithm of Bonneel et al. (2015) as an alternate to the marginal Wasserstein algorithm, as proposed by Okazaki & Ueda (2022). It is argued that for optimization-based waveform fitting, the Sliced Wasserstein algorithm is a viable alternate provided care is taken to ensure that conditions arise which do invalidate analytical derivative expressions of the resulting Wasserstein misfit. In practice, this would likely mean recasting the 2D Optimal Transport problem posed in our earlier paper onto unstructured grids. [ABSTRACT FROM AUTHOR]

Lucky imaging is a high-angular-resolution astronomical image reconstruction technique which can effectively reduce or remove the effects of atmospheric turbulence. There have been successful real-time lucky imaging algorithms and corresponding implementations in the spatial domain. Although the results of lucky imaging algorithms in the frequency domain are better than in the spatial domain, until now there has been no corresponding real-time algorithm and implementation due to its complexity. This paper proposes a real-time lucky imaging algorithm based on Fourier transform that features real-time processing and dynamic updating and display. The algorithm is implemented on a small- or medium-scale field programmable gate array (FPGA) development board. In this system, a two-dimensional fast Fourier transform of the image is carried out by means of dimension reduction. In order to achieve real-time lucky imaging in the frequency domain, the sum of the squares of the real and imaginary parts are used to replace the amplitude for data selection, and the method of image grouping is used to speed up the data selection. Compared with other real-time lucky imaging algorithms in the spatial domain, this algorithm shows great advantages in image quality. Moreover, the system can carry out real-time lucky imaging processing in the frequency domain for more than 10000 frames of an original image of 512 × 512 pixels continuously, and dynamic display with an image of 128 × 128 pixels. The experimental results illustrate the validity of the proposed algorithm and the feasibility of the proposed implementation techniques for the FPGA-based algorithm. [ABSTRACT FROM AUTHOR]

Parameter servers (PSs) placement is one of the most important factors for global model training on distributed deep learning. This paper formulates a novel problem for placement strategy of PSs in the dynamic available storage capacity, with the objective of minimizing the training time of the distributed deep learning under the constraints of storage capacity and the number of local PSs. Then, we provide the proof for the NP-hardness of the proposed problem. The whole training epochs are divided into two parts, i.e. the first epoch and the other epochs. For the first epoch, an approximation algorithm and a rounding algorithm are proposed in this paper, to solve the proposed problem. For the other epochs, an adjustment algorithm is proposed, by continuously adjusting the decisions for placement strategy of PSs to decrease the training time of the global model. Simulation results show that the proposed approximation algorithm and rounding algorithm perform better than existing works for all cases, in terms of the training time of global model. Meanwhile, the training time of global model for the proposed approximation algorithm is very close to that for optimal solution generated by the brute-force approach for all cases. Besides, the integrated algorithm outperforms the existing works when the available storage capacity varies during the training. [ABSTRACT FROM AUTHOR]

Summary Computational cell-type deconvolution is an important analytic technique for modeling the compositional heterogeneity of bulk gene expression data. A conceptually new Bayesian approach to this problem, BayesPrism, has recently been proposed and has subsequently been shown to be superior in accuracy and robustness against model misspecifications by independent studies; however, given that BayesPrism relies on Gibbs sampling, it is orders of magnitude more computationally expensive than standard approaches. Here, we introduce the InstaPrism package which re-implements BayesPrism in a derandomized framework by replacing the time-consuming Gibbs sampling step with a fixed-point algorithm. We demonstrate that the new algorithm is effectively equivalent to BayesPrism while providing a considerable speed and memory advantage. Furthermore, the InstaPrism package is equipped with a precompiled, curated set of references tailored for a variety of cancer types, streamlining the deconvolution process. Availability and implementation The package InstaPrism is freely available at: https://github.com/humengying0907/InstaPrism. The source code and evaluation pipeline used in this paper can be found at: https://github.com/humengying0907/InstaPrismSourceCode. [ABSTRACT FROM AUTHOR]

More accurate inversion of source fault geometry and slip parameters under the constraint of the Bayesian algorithm has become a research hotspot in the field of geodetic inversion in recent years. In nonlinear inversion, the determination of the weight ratio of the joint inversion of multisource data is more complicated. In this context, this paper proposes a simple and easily generalized weighting method for inversion of source fault parameters by joint geodetic multisource data under the Bayesian framework. This method determines the relative weight ratio of multisource data by root mean square error (RMSE) value and can be extended to other nonlinear search algorithms. To verify the validity of the method in this paper, this paper first sets up four sets of simulated seismic experiment schemes. The inversion results show that the joint inversion weighting method proposed in this paper has a significant decrease in the large residual value compared with the equal weight joint inversion and the single data source joint inversion method. The east–west deformation RMSE is 0.1458 mm, the north–south deformation RMSE is 0.2119 mm and the vertical deformation RMSE is 0.2756 mm. The RMSEs of the three directions are lower than those of other schemes, indicating that the proposed method is suitable for the joint inversion of source parameters under Bayesian algorithm. To further verify the applicability of the proposed method in complex earthquakes, the source parameters of the Maduo earthquake were inverted using the method of this paper. The focal depth of the inversion results in this paper is closer to the focal depth released by the GCMT agency. In terms of strike angle and dip angle, the joint inversion in this paper is also more inclined to the GCMT results. The joint inversion results generally conform to the characteristics of left-lateral strike-slip, which shows the adaptability of this method in complex earthquakes. [ABSTRACT FROM AUTHOR]

Emails are often used to illegal cybercrime today, so it is important to verify the identity of the email author. This paper proposes a general model for solving the problem of anonymous email author attribution, which can be used in email authorship identification and email authorship verification. The first situation is to find the author of an anonymous email among the many suspected targets. Another situation is to verify if an email was written by the sender. This paper extracts features from the email header and email body and analyzes the writing style and other behaviors of email authors. The behaviors of email authors are extracted through a statistical algorithm from email headers. Moreover, the author's writing style in the email body is extracted by a sequence-to-sequence bidirectional long short-term memory (BiLSTM) algorithm. This model combines multiple factors to solve the problem of anonymous email author attribution. The experiments proved that the accuracy and other indicators of proposed model are better than other methods. In email authorship verification experiment, our average accuracy, average recall and average F1-score reached 89.9%. In email authorship identification experiment, our model's accuracy rate is 98.9% for 10 authors, 92.9% for 25 authors and 89.5% for 50 authors. [ABSTRACT FROM AUTHOR]

We study consensus in a protocol capturing in a simplified manner the major features of the majority of Proof of Stake blockchains. A committee is formed; one member proposes a block; and the others can check its validity and vote for it. Blocks with a majority of votes are produced. When an invalid block is produced, the stakes of the members who voted for it are "slashed." Profit-maximizing members interact with adversaries seeking to disrupt consensus. When slashing is limited, free-riding and moral-hazard lead to invalid blocks in equilibrium. We propose a protocol modification producing only valid blocks in equilibrium. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

In mathematics, any form of probabilistic proof obtained through the application of a probabilistic method is not considered as a legitimate way of gaining mathematical knowledge. In a series of papers, Don Fallis has defended the thesis that there are no epistemic reasons justifying mathematicians' rejection of probabilistic proofs. This paper identifies such an epistemic reason. More specifically, it is argued here that if one adopts a conception of mathematical knowledge in which an epistemic subject can know a mathematical proposition based solely on a probabilistic proof, one is then forced to admit that such an epistemic subject can know several lottery propositions based solely on probabilistic evidence. Insofar as knowledge of lottery propositions on the basis of probabilistic evidence alone is denied by the vast majority of epistemologists, it is concluded that this constitutes an epistemic reason for rejecting probabilistic proofs as a means of acquiring mathematical knowledge. [ABSTRACT FROM AUTHOR]

Recent advancements in single-cell sequencing technologies have generated extensive omics data in various modalities and revolutionized cell research, especially in the single-cell RNA and ATAC data. The joint analysis across scRNA-seq data and scATAC-seq data has paved the way to comprehending the cellular heterogeneity and complex cellular regulatory networks. Multi-omics integration is gaining attention as an important step in joint analysis, and the number of computational tools in this field is growing rapidly. In this paper, we benchmarked 12 multi-omics integration methods on three integration tasks via qualitative visualization and quantitative metrics, considering six main aspects that matter in multi-omics data analysis. Overall, we found that different methods have their own advantages on different aspects, while some methods outperformed other methods in most aspects. We therefore provided guidelines for selecting appropriate methods for specific scenarios and tasks to help obtain meaningful insights from multi-omics data integration. [ABSTRACT FROM AUTHOR]

Decentralized storage of data is one of the typical applications in the blockchain network. However, most of the existing works neglected the storage balancing problem in the blockchain network, which has an immediate impact on the availability and stability of the network. Therefore, this paper proposes a storage balancing problem for non-local data storage in the blockchain network and proves that the problem is non-deterministic polynomial (NP)-hard. The criterion of the storage balance is established by a balanced coefficient in the proposed scheme. A heuristic matching algorithm (HMA), a genetic algorithm (GA) and a tabu search algorithm (TSA) are customized to solve the problem of imbalanced storage formalized in this paper. Compared with our previous algorithm fast matching algorithm (FMA), experimental results demonstrate that HMA achieves better performance in terms of accuracy, computation overhead and storage overhead. Specifically, the computation overhead of HMA is lower than that of FMA by 84.45% on average, whereas the storage overhead of HMA is lower than that of FMA by 32.26% on average. By using the initial solution of HMA, TSA achieves the highest accuracy among GA, TSA and moth-flame optimization (MFO). Meanwhile, by using the initial solution of FMA, TSA achieves the highest accuracy among GA, TSA and MFO. [ABSTRACT FROM AUTHOR]

In this paper, we discuss exact and parameterized algorithms for the problem of finding a read-once refutation (ROR) in an unsatisfiable Horn constraint system (HCS). Recall that a linear constraint system |$\mathbf {A \cdot x \ge b}$| is said to be an HCS if each entry in |$\textbf {A}$| belongs to the set |$\{0,1,-1\}$| and at most one entry in each row of |$\textbf {A}$| is positive. In this paper, we examine the importance of constraints in which more variables have negative coefficients than positive coefficients. In particular, we study the impact of the proportion of these 'net-negative' constraints has on the difficulty of finding RORs. There exist several algorithms for checking whether an HCS is feasible. To the best of our knowledge, these algorithms are not certifying, i.e. they do not provide a certificate of infeasibility. Our work is concerned with providing a specialized class of certificates called 'read-once refutations'. In an ROR, each constraint defining the HCS may be used at most once in the derivation of a refutation. The problem of checking if an HCS has an ROR has been shown to be NP-hard. We analyse the HCS ROR problem from three different algorithmic perspectives, viz. parameterized algorithms, exact exponential algorithms and approximation algorithms. In particular, we show that the HCS ROR problem is fixed-parameter tractable (FPT) with respect to the number of constraints in the system that have more variables with negative coefficient than variables with positive coefficient. Additionally, we show that the HCS ROR problem becomes easy when this parameter is both small and large. We also derive an algorithm that runs in time |$O(1.66^{m})$|⁠ , where |$m$| is the number of constraints in the HCS. On the lower-bound side, we derive a lower bound on the algorithmic resources needed for this problem using the Exponential Time Hypothesis. We also establish that the HCS ROR problem does not have a polynomial kernel when the number of constraints with three or more variables in a refutation is used as a parameter. Finally, we show that the problem of approximating the length of the shortest ROR in an HCS is NPO PB-complete 1. [ABSTRACT FROM AUTHOR]

A self-stabilizing distributed algorithm is guaranteed eventually to reach and stay at a legitimate configuration regardless of the initial configuration of a distributed system. In this paper, we propose the generalized dominating set problem, which is a generalization of the dominating set and |$k$| -redundant dominating set problems. In the generalized dominating set we propose in this paper, each node |$P_{i}$| is given its set of domination wish sets, and a generalized dominating set is a set of nodes such that each node is contained in the set or has a wish set in which all its members are in the set. We propose a self-stabilizing distributed algorithm for finding a minimal generalized dominating set in an arbitrary network under the unfair distributed daemon. The proposed algorithm converges in |$O(n^{3}m)$| steps and |$O(n)$| rounds, where |$n$| (resp. |$m$|⁠) is the number of nodes (resp. edges). Furthermore, it has the safe convergence property with safe convergence time in |$O(1)$| rounds. The space complexity of the proposed algorithm is |$O(\Delta \log n)$| bits per node, where |$\Delta $| is the maximum degree of nodes. [ABSTRACT FROM AUTHOR]

Objective: As the number of radiology artificial intelligence (AI) papers increases, there are new challenges for reviewing the AI literature as well as differences to be aware of, for those familiar with the clinical radiology literature. We aim to introduce a tool to aid in this process. Methods: In evidence‐based practise (EBP), you must Ask, Search, Appraise, Apply and Evaluate to come to an evidence‐based decision. The bottom‐up evidence‐based radiology (EBR) method allows for a systematic way of choosing the correct radiological investigation or treatment. Just as the population intervention comparison outcome (PICO) method is an established means of asking an answerable question; herein, we introduce the data algorithm training output (DATO) method to complement PICO by considering Data, Algorithm, Training and Output in the use of AI to answer the question. Results: We illustrate the DATO method with a worked example concerning bone age assessment from skeletal radiographs. After a systematic search, 17 bone age estimation papers (5 of which externally validated their results) were appraised. The paper with the best DATO metrics found that an ensemble model combining uncorrelated, high performing simple models should achieve error rates comparable to human performance. Conclusion: Considering DATO in the application of EBR to AI is a simple systematic approach to this potentially daunting subject. Advances in knowledge: The growth of AI in radiology means that radiologists and related professionals now need to be able to review not only clinical radiological literature but also research using AI methods. Considering Data, Algorithm, Training and Output in the application of EBR to AI is a simple systematic approach to this potentially daunting subject. [ABSTRACT FROM AUTHOR]

The issue of how a semantics should deal with self-attacking arguments was always a subject of debate among argumentation scholars. A consensus exists for extension-based semantics because those arguments are always rejected (as soon as the semantics in question respects conflict-freeness). In case of gradual semantics, the question is more complex, since other criteria are taken into account. In this paper, we check the impact of those arguments by using a principle-based approach. Principles like self-contradiction and strong self-contradiction prescribe how to deal with self-attacking arguments. We show that they are incompatible with the well-known equivalence principle (which is satisfied by almost all the existing gradual semantics), as well as with some other principles (e.g. counting). This incompatibility was not studied until now and the class of semantics satisfying self-contradiction is under-explored. In the present paper, we explore that class of semantics. We show links and incompatibilities between several principles. We define a new general oriented argumentation semantics that satisfies (strong) self-contradiction and a maximal number of compatible principles. We introduce an iterative algorithm to calculate our semantics and prove that it always converges. We also provide a characterization of our semantics. Finally, we experimentally show that our semantics is computationally efficient. [ABSTRACT FROM AUTHOR]

A number of text compression algorithms have been proposed in the past decades, which have been very effective and usually operate on conventional character/word-based approaches. A novel data compression perspective of data mining is explored in this research and the paper focuses on novel frequent sequence/pattern mining approach to text compression. This work attempts to make use of longer-range correlations between words in languages for achieving better text compression. We propose a novel and efficient method by making the compression of any word-level text in a universal manner for corpora across domains referred as Universal Huffman Tree-based encoding. The major contribution of this work is in terms of avoidance of code table communication to the decoder. Simulation results over benchmark datasets indicate that Universal Huffman encoding employing frequent sequence mining (achieves [20%, 89%] improvement in compression in reduced time. The paper also contributes a usable interface for Data compression that employs the proposed frequent sequent mining-based data compression algorithm. The interface supports features such as feedback, consistency, usability, navigation, visual appeal, performance and accessibility on par with existing compression softwares. The work results in an intelligent data compression software employing knowledge engineering perspective. [ABSTRACT FROM AUTHOR]

Proofs are a key feature of modern propositional and first-order theorem provers. Proofs generated by such tools serve as explanations for unsatisfiability of statements. However, these explanations are complicated by proofs which are not necessarily as concise as possible. There are a wide variety of compression techniques for propositional resolution proofs but fewer compression techniques for first-order resolution proofs generated by automated theorem provers. This paper describes an approach to compressing first-order logic proofs based on lifting proof compression ideas used in propositional logic to first-order logic. The first approach lifted from propositional logic delays resolution with unit clauses , which are clauses that have a single literal. The second approach is partial regularization , which removes an inference |$\eta $| when it is redundant in the sense that its pivot literal already occurs as the pivot of another inference in every path from |$\eta $| to the root of the proof. This paper describes the generalization of the algorithms LowerUnits and RecyclePivotsWithIntersection (Fontaine et al.. Compression of propositional resolution proofs via partial regularization. In Automated Deduction—CADE-23—23rd International Conference on Automated Deduction, Wroclaw, Poland, July 31–August 5, 2011 , p. 237--251. Springer, 2011) from propositional logic to first-order logic. The generalized algorithms compresses resolution proofs containing resolution and factoring inferences with unification. An empirical evaluation of these approaches is included. [ABSTRACT FROM AUTHOR]

In the past few years, much importance and attention have been attached to completely independent spanning trees (CISTs). Many results, such as edge-disjoint Hamilton cycles, traceability, number of spanning trees, structural properties, topological indices, etc. have been obtained on line graphs, and researchers have applied the line graphs of some interconnection networks such as generalized hypercubes, augmented cubes, crossed cubes, etc. into data center networks, such as SWCube, AQLCube, BCDC, etc. At the meanwhile, few results of CISTs are reported on the line graphs. In this paper, we establish the relation of edge-disjoint spanning trees in an interconnection network |$G$| ' with its line graph |$G$| by proposing a general algorithm for the first time. By this method, more CISTs can be obtained comparing with results in the literature. Then, the decrease of diameter is discussed and simulation experiments are shown on the line graphs of hypercubes. [ABSTRACT FROM AUTHOR]

This paper presents new evidence on two key developments in worldwide anti-trust in the last decade: (i) a downturn in the number of cartels detected by competition authorities and (ii) exponential growth in cases of monopolisation/abuse of dominance. Big Tech firms have been, undoubtedly, the main focus of the latter but almost totally absent in the former. These two developments offer perspectives on the description of Monopoly Capitalism as set out by Keith Cowling 40 years ago. Superficially at least, this seems to deny the prediction of ever-widening collusion, but, on the other hand, it resonates with the prediction of increasingly unassailable dominant firms. We suggest that the two trends can best be understood by the emergence of the Big Tech giants who have established dominance by exceeding tipping points in many markets. In turn, this leads to an alternative form of collusion— ‘mutual forbearance’ in which firms back away from aggressive competition in the other giants’ areas of strength. Given this dominance, they do not need collusion— put simply, no sizeable rivals are remaining with whom they need to collude. [ABSTRACT FROM AUTHOR]

In our previous paper [ 5 ], we introduced a hyperbolic jigsaw construction and constructed infinitely many noncommensurable, nonuniform, non-arithmetic lattices of |$\textrm{PSL}(2,{\mathbb R})$| with cusp set |$\mathbb{Q} \cup \{\infty \}$| (called pseudomodular groups by Long and Reid [ 4 ]), thus answering a question posed by Long and Reid. In this paper, we continue with our study of these jigsaw groups exploring questions of arithmeticity, pseudomodularity, and also related pseudo-Euclidean and continued fraction algorithms arising from these groups. We also answer another question of Long and Reid [ 4 ] by demonstrating a recursive formula for the tessellation of the hyperbolic plane arising from Weierstrass groups, which generalizes the well-known "Farey addition" used to generate the Farey tessellation. [ABSTRACT FROM AUTHOR]

Motivation Understanding causal effects is a fundamental goal of science and underpins our ability to make accurate predictions in unseen settings and conditions. While direct experimentation is the gold standard for measuring and validating causal effects, the field of causal graph theory offers a tantalizing alternative: extracting causal insights from observational data. Theoretical analysis has shown that this is indeed possible, given a large dataset and if certain conditions are met. However, biological datasets, frequently, do not meet such requirements but evaluation of causal discovery algorithms is typically performed on synthetic datasets, which they meet all requirements. Thus, real-life datasets are needed, in which the causal truth is reasonably known. In this work we first construct such a large-scale real-life dataset and then we perform on it a comprehensive benchmarking of various causal discovery methods. Results We find that the PC algorithm is particularly accurate at estimating causal structure, including the causal direction which is critical for biological applicability. However, PC does only produces cause-effect directionality, but not estimates of causal effects. We propose PC-NOTEARS (PCnt), a hybrid solution, which includes the PC output as an additional constraint inside the NOTEARS optimization. This approach combines PC algorithm's strengths in graph structure prediction with the NOTEARS continuous optimization to estimate causal effects accurately. PCnt achieved best aggregate performance across all structural and effect size metrics. Availability and implementation https://github.com/zhu-yh1/PC-NOTEARS. [ABSTRACT FROM AUTHOR]

Multilayer networks are used to represent the interdependence between the relational data of individuals interacting with each other via different types of relationships. To study the information-theoretic phase transitions in detecting the presence of planted partition among the nodes of a multilayer network with additional nodewise covariate information and diverging average degree, Ma and Nandy (2023, IEEE Trans. Inf. Theory , 69, 3203–3239) introduced Multi-Layer Contextual Stochastic Block Model. In this paper, we consider the problem of detecting planted partitions in the Multi-Layer Contextual Stochastic Block Model, when the average node degrees for each network are greater than |$1$|⁠. We establish the sharp phase transition threshold for detecting the planted bi-partition. Above the phase-transition threshold testing the presence of a bi-partition is possible, whereas below the threshold no procedure to identify the planted bi-partition can perform better than random guessing. We further establish that the derived detection threshold coincides with the threshold for weak recovery of the partition and provides a quasi-polynomial time algorithm to estimate it. [ABSTRACT FROM AUTHOR]

The field of quantum computing (QC) is expanding, with efforts being made to apply it to areas previously covered by classical algorithms and methods. Bioinformatics is one such domain that is developing in terms of QC. This article offers a broad mapping review of methods and algorithms of QC in bioinformatics, marking the first of its kind. It presents an overview of the domain and aids researchers in identifying further research directions in the early stages of this field of knowledge. The work presented here shows the current state-of-the-art solutions, focuses on general future directions, and highlights the limitations of current methods. The gathered data includes a comprehensive list of identified methods along with descriptions, classifications, and elaborations of their advantages and disadvantages. Results are presented not just in a descriptive table but also in an aggregated and visual format. [ABSTRACT FROM AUTHOR]

In this technological era, human activity recognition (HAR) plays a significant role in several applications like surveillance, health services, Internet of Things, etc. Recent advancements in deep learning and video summarization have motivated us to integrate these techniques for HAR. This paper introduces a computationally efficient HAR technique based on a deep learning framework, which works well in realistic and multi-view environments. Deep convolutional neural networks (DCNNs) normally suffer from different constraints, including data size dependencies, computational complexity, overfitting, training challenges and vanishing gradients. Additionally, with the use of advanced mobile vision devices, the demand for computationally efficient HAR algorithms with the requirement of limited computational resources is high. To address these issues, we used integration of DCNN with video summarization using keyframes. The proposed technique offers a solution that enhances performance with efficient resource utilization. For this, first, we designed a lightweight and computationally efficient deep learning architecture based on the concept of identity skip connections (features reusability), which preserves the gradient loss attenuation and can handle the enormous complexity of activity classes. Subsequently, we employed an efficient keyframe extraction technique to minimize redundancy and succinctly encapsulate the entire video content in a lesser number of frames. To evaluate the efficacy of the proposed method, we performed the experimentation on several publicly available datasets. The performance of the proposed method is measured in terms of evaluation parameters Precision, Recall, F-Measure and Classification Accuracy. The experimental results demonstrated the superiority of the presented algorithm over other existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the increasing strain on today's healthcare resources, there is a growing demand for pre-diagnosis testing. In response, researchers have suggested diverse machine learning models for disease prediction, among which logistic regression stands out as one of the most effective models. Its objective is to enhance the accuracy and efficiency of pre-diagnosis testing, thereby alleviating the burden on healthcare resources. However, when multiple medical institutions collaborate to train models, the untrusted cloud server may pose a risk of private data leakage, enabling participants to steal data from one another. Existing privacy-preserving methods often suffer from drawbacks such as high communication costs, long training times and lack of security proofs. Therefore, it is imperative to jointly train an excellent model collaboratively and uphold data privacy. In this paper, we develop a highly optimized two-party logistic regression algorithm based on CKKS scheme. The algorithm optimizes ciphertext operations by employing ciphertext segmentation and minimizing the multiplication depth, resulting in time savings. Furthermore, it utilizes least squares to approximate sigmoid functions within specific intervals that cannot be handled by homomorphic encryption. Finally, the proposed algorithm is evaluated on a breast cancer dataset, and simulation experiments demonstrate that the model's prediction accuracy, after machine learning training, exceeds 96% for two-sided encrypted data. [ABSTRACT FROM AUTHOR]

Two algorithms, both based around multiplication, one defined by Andrew Donald Booth in 1950 and the other defined by Anatoly Alexeevitch Karatsuba in 1960 can be applied to other types of operations. We know from recent results that to perform some algebraic transformations it is more efficient to calculate an inverse effect of a smaller magnitude together with an original action with a higher rank, than the initial operation, reaching the final element with less transitions. In this paper, we present an addition algorithm on |$\mathbb {Z}/m\mathbb {Z}$| of big-integer numbers based on these concepts, using an alternative to traditional hardware implementation for binary addition based on FULL-ADDER cells, allowing the reduction of space complexity compared with other techniques, such as carry-lookahead, letting us calculate a modular addition in an optimal order complexity of |$\mathcal {O}(n)$| without adding more complexity due to reduction operations. [ABSTRACT FROM AUTHOR]

Functional magnetic resonance imaging (fMRI) is a noninvasive and in-vivo imaging technique essential for measuring brain activity. Functional connectivity is used to study associations between brain regions, either while study subjects perform tasks or during periods of rest. In this paper, we propose a rigorous definition of task-evoked functional connectivity at the population level (ptFC). Importantly, our proposed ptFC is interpretable in the context of task-fMRI studies. An algorithm for estimating the ptFC is provided. We present the performance of the proposed algorithm compared to existing functional connectivity frameworks using simulations. Lastly, we apply the proposed algorithm to estimate the ptFC in a motor-task study from the Human Connectome Project. [ABSTRACT FROM AUTHOR]

The grouping of nodes into subsets that are relatively densely interconnected and separable from the rest of the network is a property often displayed in many complex real-world networks; this feature is known as a community structure. There is a growing demand for algorithms that can find partitions that resemble the community structure of a given network as closely as possible. However, most popular algorithms for community detection in graphs have one serious drawback, namely, they are heuristic-based and in many cases are unable to find a near-optimal solution. Moreover, their results are volatile, impacting the replicability of their results. In this paper, we investigate if the performance of greedy algorithms might be improved by initialising such algorithms with some carefully chosen partition of nodes, namely a partition obtained by embedding the nodes into real numbers space and then running a clustering algorithm on this latent representation. We believe that embedding will filter unwanted noise while retaining the proximity of nodes belonging to the same community or will learn more complex and elusive relations between nodes. Then, clustering algorithms run on this embedding will create a stable partitioning that will reduce the uncertainty in the initial phases of the community detection algorithms. The experiments show that the proposed procedure significantly improves the results over baseline community detection algorithms, namely Louvain and Leiden. It also reduces the inherent volatility of such algorithms. The impact depends on the given graph's properties, especially the strength of the community structure and degree distribution. The largest boost in performance is given in the cases when networks are 'noisier', that is, when the community structure is less pronounced and there are many connections between communities. Furthermore, the design and parametrization of the procedure depend on the network's topology, not on the size of the network itself. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the leaderless and the leader–follower coherence of tree-symmetric networks. Firstly, the analytical expressions for the product and the sum of the reciprocals of all nonzero Laplacian eigenvalues of tree-symmetric networks are computed. Secondly, comparing the leaderless and leader–follower coherence, the existence of leader nodes and network parameters have a great impact on the coherence in the noisy environment. Finally, the Laplacian eigenvalues of tree-symmetric networks were utilized to analyze other properties of the network. These research results have certain theoretical significance for the promotion of distributed system and block chain technology. [ABSTRACT FROM AUTHOR]

Burn injuries are severe problems for human. Accurate segmentation for burn wounds in patient surface can improve the calculation precision of %TBSA (total burn surface area), which is helpful in determining treatment plan. Recently, deep learning methods have been used to automatically segment wounds. However, owing to the difficulty of collecting relevant images as training data, those methods cannot often achieve fine segmentation. A burn image-generating framework is proposed in this paper to generate burn image datasets with annotations automatically. Those datasets can be used to increase segmentation accuracy and save the time of annotating. This paper brings forward an advanced burn image generation framework called Burn-GAN. The framework consists of four parts: Generating burn wounds based on the mainstream Style-GAN network; Fusing wounds with human skins by Color Adjusted Seamless Cloning (CASC); Simulating real burn scene in three-dimensional space; Acquiring annotated dataset through three-dimensional and local burn coordinates transformation. Using this framework, a large variety of burn image datasets can be obtained. Finally, standard metrics like precision, Pixel Accuracy (PA) and Dice Coefficient (DC) were utilized to assess the framework. With nonsaturating loss with R2 regularization (NSLR2) and CASC, the segmentation network gains the best results. The framework achieved precision at 90.75%, PA at 96.88% and improved the DC from 84.5 to 89.3%. A burn data-generating framework have been built to improve the segmentation network, which can automatically segment burn images with higher accuracy and less time than traditional methods. [ABSTRACT FROM AUTHOR]

Counter-cryptanalysis uses cryptanalytic techniques to detect cryptanalytic attacks. It was introduced by Stevens with a collision detection algorithm that detects whether a message is one of a colliding message pair constructed using a collision attack. Later, Stevens and Shumow improved the collision detection against SHA-1 by using unavoidable conditions. However, there are no results improving collision detection against MD5 due to its weak diffusion properties. In this paper, an improved collision detection algorithm against MD5 is proposed by using the 14-bit sufficient condition combinations. This leads to the dividing the 223 classes into four sets. Each element, belonging to the first two sets, holds the same sufficient condition combination. Our new algorithm can classify 126 classes efficiently. The runtime is 28.6% of the previous collision detection method. [ABSTRACT FROM AUTHOR]

This article presents a list of forthcoming papers on logic and related topics. The list includes "Ackermann's Implication for Typefree Logic," by K. Robering, "Multi-agent Only Knowing," by J. Y. Halpern and G. Lakemeyer, "Preferential Logics are X-logics," by L. Forget, V. Risch and P. Siegel, "PSPACE Reasoning for Graded Modal Logics," by S. Tobies.

Given up to |$d$| positive items in a large population of |$n$| items (⁠|$d \ll n$|⁠), the goal of threshold group testing is to efficiently identify the positives via tests, where a test on a subset of items is positive if the subset contains at least |$u$| positive items, negative if it contains up to |$\ell $| positive items and arbitrary (either positive or negative) otherwise. The parameter |$g = u - \ell - 1$| is called the gap. In non-adaptive strategies, all tests are fixed in advance and can be represented as a measurement matrix, in which each row and column represent a test and an item, respectively. In this paper, we consider non-adaptive threshold group testing with consecutive positives in which the items are linearly ordered and the positives are consecutive in that order. We show that by designing deterministic and strongly explicit measurement matrices, |$\lceil \log _{2}{\lceil \frac {n}{d} \rceil } \rceil + 2d + 3$| (respectively, |$\lceil \log _{2}{\lceil \frac {n}{d} \rceil } \rceil + 3d$|⁠) tests suffice to identify the positives in |$O \left (\log _{2}{\frac {n}{d}} + d \right)$| time when |$g = 0$| (respectively, |$g> 0$|⁠). The results significantly improve the state-of-the-art scheme that needs |$15 \lceil \log _{2}{\lceil \frac {n}{d} \rceil } \rceil + 4d + 71$| tests to identify the positives in |$O \left (\frac {n}{d} \log _{2}{\frac {n}{d}} + ud^{2} \right)$| time, and whose associated measurement matrices are random and (non-strongly) explicit. [ABSTRACT FROM AUTHOR]

This paper proposes a differential evolution algorithm with improved crossover operation (ICRDE) to deal with combined heat and power dynamic economic dispatch (CHPDED) problems with wind power. First, the improved crossover operation is used to maintain the population diversity by using original individuals, first mutated individuals, and second mutated individuals. Second, the scaling factor and weighted factor are incorporated into the mutation operation to improve the convergence efficiency of the algorithm. Third, adaptive control parameters are introduced to balance local exploitation and global exploration. Moreover, after being updated by the mutation and crossover operation of ICRDE at each generation, the solutions of ICRDE will be further amended using a constraint handling method, which improves the chance of acquiring feasible solutions. Experimental results demonstrate that ICRDE has strong global optimization ability and surpasses the compared algorithms for the CEC2017 benchmark functions, the combined heat and power economic dispatch problems, and the CHPDED problem with and without wind power. [ABSTRACT FROM AUTHOR]

In recent years, the sine cosine algorithm (SCA) has become one of the popular swarm intelligence algorithms due to its simple and convenient structure. However,the standard SCA tends to fall into the local optimum when solving complex multimodal tasks, leading to unsatisfactory results. Therefore, this study presents the SCA with communication and quality enhancement, called CCEQSCA. The proposed algorithm includes two enhancement strategies: the communication and collaboration strategy (CC) and the quality enhancement strategy (EQ). In the proposed algorithm, CC strengthens the connection of SCA populations by guiding the search agents closer to the range of optimal solutions. EQ improves the quality of candidate solutions to enhance the exploitation of the algorithm. Furthermore, EQ can explore potential candidate solutions in other scopes, thus strengthening the ability of the algorithm to prevent trapping in the local optimum. To verify the capability of CCEQSCA, 30 functions from the IEEE CEC2017 are analyzed. The proposed algorithm is compared with 5 advanced original algorithms and 10 advanced variants. The outcomes indicate that it is dominant over other comparison algorithms in global optimization tasks. The work in this paper is also utilized to tackle three typical engineering design problems with excellent optimization capabilities. It has been experimentally demonstrated that CCEQSCA works as an effective tool to tackle real issues with constraints and complex search space. [ABSTRACT FROM AUTHOR]

Cognitive Radio network (CRN) depends on opportunistic spectrum access and spectrum sensing for improving the wireless networks' spectrum efficiency. Since throughput maximization can result in high-energy consumption, the spectrum sensing technique should address the energy-throughput trade-off. The spectrum sensing time has to be determined by the considering the residual battery energies of each secondary user (SU). The primary user (PU) interference degrades the throughput of the entire network. Hence, the transmit power level should be determined by considering the PU interference and SU battery energy. This paper proposes the multi-objective brain storm optimization (MBSO) algorithm for handling energy-throughput trade-off in CRN. In this work, the sensing time is adaptively determined based on the residual battery energy of SUs, and the transmit power is determined based on the energy level of the PU signal and the residual battery energy of the SUs. A multi-objective optimization problem is formulated in order to maximize the throughput and minimize the packet error rate (PER) and is solved by applying the MBSO algorithm. Experimental results show that MBSO attains improved throughput, higher residual energy with lesser PER. The spectrum sensing performance is enhanced with higher probability of detection. [ABSTRACT FROM AUTHOR]

In this paper, we consider methods for generating draws of a binary random variable whose expectation conditional on covariates follows a logistic regression model with known covariate coefficients. We examine approximations for finding a "balancing intercept," that is, a value for the intercept of the logistic model that leads to a desired marginal expectation for the binary random variable. We show that a recently proposed analytical approximation can produce inaccurate results, especially when targeting more extreme marginal expectations or when the linear predictor of the regression model has high variance. We then formulate the balancing intercept as a solution to an integral equation, implement a numerical approximation for solving the equation based on Monte Carlo methods, and show that the approximation works well in practice. Our approach to the basic problem of the balancing intercept provides an example of a broadly applicable strategy for formulating and solving problems that arise in the design of simulation studies used to evaluate or teach epidemiologic methods. [ABSTRACT FROM AUTHOR]

The horizontal-to-vertical spectral ratio (HVSR) of ambient noise is commonly used to infer a site's resonance frequency (⁠|${f_{0,site}}$|⁠). HVSR calculations are performed most commonly using the Fourier amplitude spectrum obtained from a single merged horizontal component (e.g. the geometric mean component) from a three-component sensor. However, the use of a single merged horizontal component implicitly relies on the assumptions of azimuthally isotropic seismic noise and 1-D surface and subsurface conditions. These assumptions may not be justified at many sites, leading to azimuthal variability in HVSR measurements that cannot be accounted for using a single merged component. This paper proposes a new statistical method to account for azimuthal variability in the peak frequency of HVSR curves (⁠|${f_{0,HVSR}}$|⁠). The method uses rotated horizontal components at evenly distributed azimuthal intervals to investigate and quantify azimuthal variability. To ensure unbiased statistics for |${f_{0,HVSR}}$| are obtained, a frequency-domain window-rejection algorithm is applied at each azimuth to automatically remove contaminated time windows in which the |${f_{0,HVSR}}$| values are statistical outliers relative to those obtained from the majority of windows at that azimuth. Then, a weighting scheme is used to account for different numbers of accepted time windows at each azimuth. The new method is applied to a data set of 114 HVSR measurements with significant azimuthal variability in |${f_{0,HVSR}}$|⁠ , and is shown to reliably account for this variability. The methodology is also extended to the estimation of a complete lognormal-median HVSR curve that accounts for azimuthal variability. To encourage the adoption of this statistical approach to accounting for azimuthal variability in single-station HVSR measurements, the methods presented in this paper have been incorporated into hvsrpy , an open-source Python package for HVSR processing. [ABSTRACT FROM AUTHOR]

Modern data centers provide multiple parallel paths for end-to-end communications. Recent studies have been done on how to allocate rational paths for data flows to increase the throughput of data center networks. A centralized load balancing algorithm can improve the rationality of the path selection by using path bandwidth information. However, to ensure the accuracy of the information, current centralized load balancing algorithms monitor all the link bandwidth information in the path to determine the path bandwidth. Due to the excessive link bandwidth information monitored by the controller, however, much time is consumed, which is unacceptable for modern data centers. This paper proposes an algorithm called hidden Markov Model-based Load Balancing (HMMLB). HMMLB utilizes the hidden Markov Model (HMM) to select paths for data flows with fewer monitored links, less time cost, and approximate the same network throughput rate as a traditional centralized load balancing algorithm. To generate HMMLB, this research first turns the problem of path selection into an HMM problem. Secondly, deploying traditional centralized load balancing algorithms in the data center topology to collect training data. Finally, training the HMM with the collected data. Through simulation experiments, this paper verifies HMMLB's effectiveness. [ABSTRACT FROM AUTHOR]

This paper reports the experience of using the PAELLA algorithm as a helper tool in robust regression instead of as originally intended for outlier identification and removal. This novel usage of the algorithm takes advantage of the occurrence vector calculated by the algorithm in order to strengthen the effect of the more reliable samples and lessen the impact of those that otherwise would be considered outliers. Following that aim, a series of experiments is conducted in order to learn how to better use the information contained in the occurrence vector. Using a contrively difficult artificial data set, a reference predictive model is fit using the whole raw dataset. The second experiment reports the results of fitting a similar predictive model but discarding the samples marked as outliers by PAELLA. The third experiment uses the occurrence vector provided by PAELLA in order to classify the observations in multiple bins and fit every possible model changing which bins are considered for fitting and which are discarded in that particular model. The fourth experiment introduces a sampling process before fitting in which the occurrence vector represents the likelihood of being considered in the training data set. The fifth experiment considers the sampling process as an internal step to be performed interleaved between the training epochs. The last experiment compares our approach using weighted neural networks to a state of the art method. [ABSTRACT FROM AUTHOR]

The digital revolution provides many opportunities for researchers to develop and evolve data collection methods. A key process in qualitative research data collection is the transcription of interviews, focus groups or fieldwork notes. Transcription is the process of converting audio, video or notes into accessible written format for qualitative data analysis. Transcribing can be time intensive, costly and laborious with decisions and methods of transcribing requiring transparency. The development of intelligent speech recognition technology can change how qualitative data is transcribed. This methods paper describes audio data transcribing, current challenges, opportunities and implications in using intelligent speech recognition technology for transcribing. An application of this methodology is presented. [ABSTRACT FROM AUTHOR]