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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]

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]

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]

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]

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]

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.

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]

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]

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]

The paper presents a novel phase-locked loop (PLL)-based resonant frequency tracking method for linear compressors with oscillatory motions. The proposed method achieves the phase-locked control between two signals by using PLL, which includes a multiplier, and a low-pass filter to obtain the phase signal difference. Consequently, based on the phase signal difference, the electrical frequency of the power supply can be adjusted through a PI (proportional-derivative) regulator such that the phase signal difference converges to 90° and the linear compressor works under the resonant frequency conditions. The analytical expression of the resonant frequency condition is proposed for the linear compressor. Then, the detailed design and procedure of the proposed method are presented. The designed method and the resonant frequency characteristics are verified by using the simulation experiment. The simulated results demonstrate that the proposed algorithm has faster response speed and higher convergence accuracy to step load change in comparison with the other commonly used algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology to assist geophysicists in determining the first-break event in a 3-D seismic data set using the well-known technique called dynamic time warping algorithm (DTW), which is usually used to find the optimal alignment between two time-series. We used the optimal path from the cost matrix to identify the first break in the seismogram using a few picks (seeds) made by an interpreter as a reference to perform this task. Furthermore, the data were pre-conditioned by the topographic and linear moveout to improve the method's accuracy. To demonstrate the technique's robustness, first, we applied the methodology in a synthetic seismic data. After demonstrating the efficiency of the algorithm, we applied the aforementioned methodology in the Polo-Miranga 3-D seismic cube located in the Recôncavo sedimentary basin, Bahia-Brazil, and in the seismic data acquired from the Blackfoot field in Alberta, Canada. The high-quality results showed consistency in determining the first break in all ranges of offsets, demonstrating an alternative way to accelerate this seismic processing step. Furthermore, we compared the results obtained by the proposed methodology with an algorithm based on comparing the short-time averages with long-time averages. Finally, we performed the static correction calculation to ensure that the time distortion resulting from the terrain and the low-velocity layer was mitigated in shoot gathers and in the stacked section. [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]

In diagnostic medicine, the true disease status of a patient is often represented on an ordinal scale—for example, cancer stage (0, I, II, III, or IV) or coronary artery disease severity measured using the Coronary Artery Disease Reporting and Data System (CAD-RADS) scale (none, minimal, mild, moderate, severe, or occluded). With advances in quantitation of diagnostic images and in artificial intelligence (AI), both supervised and unsupervised algorithms are being developed to help physicians correctly grade disease. Most of the diagnostic accuracy literature deals with binary disease status (disease present or absent); however, tests diagnosing ordinal-scaled diseases should not be reduced to a binary status just to simplify diagnostic accuracy testing. In this paper, we propose different characterizations of ordinal-scale accuracy for different clinical use scenarios, along with methods for comparing tests. In the simplest scenario, just the proportion of correct grades is considered; other scenarios address the magnitude and direction of misgrading; and at the other extreme, a weighted accuracy measure with weights based on the relative costs of different types of misgrading is presented. The various scenarios are illustrated using a coronary artery disease example where the accuracy of AI algorithms in providing patients with the correct CAD-RADS grade is assessed. [ABSTRACT FROM AUTHOR]

Harris hawks optimization (HHO) has been accepted as one of the well-established swarm-based methods in the community of optimization and machine learning that primarily works based on multiple dynamic features and various exploratory and exploitative traits. Compared with other optimization algorithms, it has been observed that HHO can obtain high-quality solutions for continuous and constrained complex and real-world problems. While there is a wide variety of strategies in the HHO for dealing with diverse situations, there are chances for sluggish performance, where the convergence rate can gradually slow with time, and the HHO may stay stuck in the current relatively better place and may be unable to explore other better areas. To mitigate this concern, this paper combines the Cauchy mutation mechanism into the HHO algorithm named CMHHO. This idea can boost performance and provide a promising optimizer for solving complex optimization problems. The Cauchy mutation mechanism can speed up the convergence of the solution and help HHO explore more promising regions compared to its basic release. On 30 IEEE CEC2017 benchmark functions, the study compared the proposed CMHHO with various conventional and advanced metaheuristics to validate its performance and quality of solutions. It has been found through experiments that the overall optimization performance of CMHHO is far superior to all competitors. The CMHHO method is applied to four engineering challenges to investigate the capabilities of the proposed algorithm in solving real-world problems, and experimental results show that the suggested algorithm is more successful than existing algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a solution to a long-standing problem concerning the joint distribution of allelic identity by descent between two individuals at two linked loci. Such distributions have important applications across various fields of genetics, and detailed formulas for selected relationships appear scattered throughout the literature. However, these results were obtained essentially by brute force, with no efficient method available for general pedigrees. The recursive algorithm described in this paper, and its implementation in R, allow efficient calculation of two-locus identity coefficients in any pedigree. As a result, many existing procedures and techniques may, for the first time, be applied to complex and inbred relationships. Two such applications are discussed, concerning the expected likelihood ratio in forensic kinship testing, and variances in realized relatedness. [ABSTRACT FROM AUTHOR]

Motivation: Data compression is crucial in effective handling of genomic data. Among several recently published algorithms, ERGC seems to be surprisingly good, easily beating all of the competitors. Results: We evaluated ERGC and the previously proposed algorithms GDC and iDoComp, which are the ones used in the original paper for comparison, on a wide data set including 12 assemblies of human genome (instead of only four of them in the original paper). ERGC wins only when one of the genomes (referential or target) contains mixed-cased letters (which is the case for only the two Korean genomes). In all other cases ERGC is on average an order of magnitude worse than GDC and iDoComp. [ABSTRACT FROM AUTHOR]

Even though numerous works focus on the few-shot learning issue by combining meta-learning, there are still limits to traditional graph classification problems. The antecedent algorithms directly extract features from the samples, and do not take into account the preference of the trained model to the previously "seen" targets. In order to overcome the aforementioned issues, an effective strategy with training an unbiased meta-learning algorithm was developed in this paper, which sorted out problems of target preference and few-shot under the meta-learning paradigm. First, the interactive attention extraction module as a supplement to feature extraction was employed, which improved the separability of feature vectors, reduced the preference of the model for a certain target, and remarkably improved the generalization ability of the model on the new task. Second, the graph neural network was used to fully mine the relationship between samples to constitute graph structures and complete image classification tasks at a node level, which greatly enhanced the accuracy of classification. A series of experimental studies were conducted to validate the proposed methodology, where the few-shot and semisupervised learning problem has been effectively solved. It also proved that our model has better accuracy than traditional classification methods on real-world datasets. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries retired from electric vehicles can provide considerable economic benefits when they are retired for secondary use. However, retired batteries after screening and restructuring still face the problem of inaccurate battery pack state-of-charge (SOC) estimation due to the existence of extreme inconsistency. To solve this problem, an adaptive fading unscented Kalman filtering (AFUKF) algorithm based on the cell difference model (CDM) is proposed in this paper for improving the accuracy of SOC estimation of retired lithium-ion battery packs. Firstly, an improved CDM based on a hypothetical Rint model is developed based on a second-order resistor/capacitor equivalent circuit model. Secondly, an AFUKF algorithm is developed to improve the adaptability and robustness of local state estimation against process modelling errors. Finally, characteristic data are obtained by conducting discharge tests on the screened retired lithium-ion batteries under specific operating conditions. The proposed method can improve the accuracy of SOC estimation of retired lithium-ion battery packs and provide a new idea for SOC estimation of retired lithium-ion battery packs, as shown by the simulated real experimental data. [ABSTRACT FROM AUTHOR]

Over the past decades, learning to rank (LTR) algorithms have been gradually applied to bioinformatics. Such methods have shown significant advantages in multiple research tasks in this field. Therefore, it is necessary to summarize and discuss the application of these algorithms so that these algorithms are convenient and contribute to bioinformatics. In this paper, the characteristics of LTR algorithms and their strengths over other types of algorithms are analyzed based on the application of multiple perspectives in bioinformatics. Finally, the paper further discusses the shortcomings of the LTR algorithms, the methods and means to better use the algorithms and some open problems that currently exist. [ABSTRACT FROM AUTHOR]

The sine cosine algorithm (SCA) is ametaheuristic algorithm proposed in recent years that does not resort to nature-relatedmetaphors but explores and exploits the search space with the help of two simple mathematical functions of sine and cosine. SCA has fewer parameters and a simple structure and is widely used in various fields. However, it tends to fall into local optimality because it does not have a well-balanced exploitation and exploration phase. Therefore, in this paper, a new, improved SCA algorithm (QCSCA) is proposed to improve the performance of the algorithm by introducing a quick move mechanism and a crisscross mechanism to SCA and adaptively improving one of the parameters. To verify the effectiveness of QCSCA, comparison experiments with some conventional metaheuristic algorithms, advanced metaheuristic algorithms, and SCA variants are conducted on IEEE CEC2017 and CEC2013. The experimental results show a significant improvement in the convergence speed and the ability to jump out of the local optimum of the QCSCA. The scalability of the algorithm is verified in the benchmark function. In addition, QCSCA is applied to 14 real-world datasets from the UCI machine learning database for selecting a subset of near-optimal features, and the experimental results show that QCSCA is still very competitive in feature selection (FS) compared to similar algorithms. Our experimental results and analysis show that QCSCA is an effective method for solving global optimization problems and FS problems. [ABSTRACT FROM AUTHOR]

Salp swarm algorithm (SSA) is a well-established population-based optimizer that exhibits strong exploration ability, but slow convergence and poor exploitation capability. In this paper, an endeavour is made to enhance the performance of the basic SSA. The new upgraded version of SSA named as 'adaptive strategy-based SSA (ABSSA) algorithm' is proposed in this paper. First, the exploratory scope and food source navigating commands of SSA are enriched using the inertiaweight and boosted global best-guided mechanism. Next, a novel velocity clamping strategy is designed to efficiently stabilize the balance between the exploration and exploitation operations. In addition, an adaptive conversion parameter tactic is designed to modify the position update equation to effectively intensify the local exploitation competency and solution accuracy. The effectiveness of the proposed ABSSA algorithm is verified by a series of problems, including 23 classical benchmark functions, 29 complex optimization problems fromCEC 2017, and five engineering design tasks. The experimental results show that the developed ABSSA approach performs significantly better than the standard SSA and other competitors. Moreover, ABSSA is implemented to handle path planning and obstacle avoidance (PPOA) tasks in autonomous mobile robots and compared with some swarm intelligent approach-based path planners. The experimental results indicate that the ABSSA-based PPOA method is a reliable path planning algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a model of the relationship between relative attributes, relative preferences, and market share. It shows how parameters in the model may be estimated, using survey data to illustrate the estimation procedure. Use of the model and data for a new product are also discussed. The eventual market share of solar energy for residential heating in Canada is predicted using the procedures developed. [ABSTRACT FROM AUTHOR]

Detecting the cosmic 21 cm signal from epoch of reionization has always been a difficult task. Although the Galactic foreground can be regarded as a smooth power-law spectrum, due to the chromaticity of the antenna, additional structure will be introduced into the global spectrum, making the polynomial fitting algorithm perform poorly. In this paper, we introduce an improved polynomial fitting algorithm – the Vari-Zeroth-Order Polynomial (VZOP) fitting and use it to fit the simulation data. This algorithm is developed for the upcoming low-frequency anechoic chamber experiment, yet it is a general method suitable for application in any single antenna-based global 21 cm signal experiment. VZOP defines a 24-h averaged beam model that brings information about the antenna beam into the polynomial model. Assuming that the beam can be measured, VZOP can successfully recover the 21 cm absorption feature, even if the beam is extremely frequency-dependent. In real observations, due to various systematics, the corrected measured beam contains residual errors that are not completely random. Assuming the errors are frequency-dependent, VZOP is capable of recovering the 21 cm absorption feature even when the error reaches 10 per cent. Even in the most extreme scenario where the errors are completely random, VZOP can at least give a fitting result that is not worse than the common polynomial fitting. In conclusion, the fitting effect of VZOP depends on the structure of the error and the accuracy of the beam measurement. [ABSTRACT FROM AUTHOR]

Calcium imaging data promises to transform the field of neuroscience by making it possible to record from large populations of neurons simultaneously. However, determining the exact moment in time at which a neuron spikes, from a calcium imaging data set, amounts to a non-trivial deconvolution problem which is of critical importance for downstream analyses. While a number of formulations have been proposed for this task in the recent literature, in this article, we focus on a formulation recently proposed in Jewell and Witten (2018. Exact spike train inference via $\ell_{0} $ optimization. The Annals of Applied Statistics12(4), 2457-2482) that can accurately estimate not just the spike rate, but also the specific times at which the neuron spikes. We develop a much faster algorithm that can be used to deconvolve a fluorescence trace of 100 000 timesteps in less than a second. Furthermore, we present a modification to this algorithm that precludes the possibility of a "negative spike". We demonstrate the performance of this algorithm for spike deconvolution on calcium imaging datasets that were recently released as part of the $\texttt{spikefinder}$ challenge (http://spikefinder.codeneuro.org/). The algorithm presented in this article was used in the Allen Institute for Brain Science's "platform paper" to decode neural activity from the Allen Brain Observatory; this is the main scientific paper in which their data resource is presented. Our $\texttt{C++}$ implementation, along with $\texttt{R}$ and $\texttt{python}$ wrappers, is publicly available. $\texttt{R}$ code is available on $\texttt{CRAN}$ and $\texttt{Github}$, and $\texttt{python}$ wrappers are available on $\texttt{Github}$; see https://github.com/jewellsean/FastLZeroSpikeInference. [ABSTRACT FROM AUTHOR]

In this data-driven era, where a large number of attributes are often publicly available, redundancy becomes a major problem, which leads to large storage and computational resource requirement. Feature selection is a method for reducing the dimensionality of the data by removing such redundant or misleading attributes. This leads to a selection of optimal feature subsets that can be used for further computation like the classification of data. Learning algorithms, when fitted on such optimal subsets of reduced dimensions, perform more efficiently and storing data also becomes easier. However, there exists a trade-off between the number of features selected and the accuracy obtained and the requirement for different tasks may vary. Thus, in this paper, a hybrid filter multi-objective evolutionary algorithm (HFMOEA) has been proposed based on the nondominated sorting genetic algorithm (NSGA-II) coupled with filter-based feature ranking methods for population initialization to obtain an optimal trade-off solution set to the problem. The two competing objectives for the algorithm are the minimization of the number of selected features and the maximization of the classification accuracy. The filter ranking methods used for population initialization help in faster convergence of the NSGA-II algorithm to the PF. The proposed HFMOEA method has been evaluated on 18 UCI datasets and 2 deep feature sets (features extracted from image datasets using deep learning models) to justify the viability of the approach with respect to the state-of-the-art. [ABSTRACT FROM AUTHOR]

The horizontal-to-vertical spectral ratio (HVSR) of ambient noise measurement is commonly used to estimate a site's resonance frequency (⁠|${f_0}$|⁠). For sites with a strong impedance contrast, the HVSR peak frequency (⁠|${f_{0,\mathrm{ HVSR}}}$|⁠) has been shown to be a good estimate of |${f_0}$|⁠. However, the random nature of ambient noise (both in time and space), in conjunction with variable environmental conditions and sensor coupling issues, can lead to uncertainty in |${f_{0,\mathrm{ HVSR}}}$| estimates. Hence, it is important to report |${f_{0,\mathrm{ HVSR}}}$| in a statistical manner (e.g. as a mean or median value with standard deviation). In this paper, we first discuss widely accepted procedures to process HVSR data and estimate the variance in |${f_{0,\mathrm{ HVSR}}}$|⁠. Then, we propose modifications to improve these procedures in two specific ways. First, we propose using a lognormal distribution to describe |${f_{0,\mathrm{ HVSR}}}$| rather than the more commonly used normal distribution. The use of a lognormal distribution for |${f_{0,\mathrm{ HVSR}}}$| has several advantages, including consistency with earthquake ground motion processing and allowing for a seamless transition between HVSR statistics in terms of both frequency and its reciprocal, period. Second, we introduce a new frequency-domain window-rejection algorithm to decrease variance and enhance data quality. Finally, we use examples of 114 high-variance HVSR measurements and 77 low-variance HVSR measurements collected at two case study sites to demonstrate the effectiveness of the new rejection algorithm and the proposed statistical approach. To encourage their adoption, and promote standardization, the rejection algorithm and lognormal statistics presented in this paper have been incorporated into hvsrpy , an open-source Python package for HVSR processing. [ABSTRACT FROM AUTHOR]

Good visualizations of computational fluid dynamics (CFD) results are key to making the best use of them. In many cases, CFD results involve multidimensional data embedded in 3D, and it can be difficult to recognize such complicated 3D shapes when they are projected onto 2D displays. A promising alternative approach uses a 3D printer to build real objects representing the CFD results. In this paper, we present a method of 3D printing the isosurfaces of CFD results to visualize them. However, there is a problem that the isosurfaces are not connected. Therefore, to prevent the structure from breaking up, we must find a way to connect them, and we achieve this by using beams to generate a support structure. We take a structural mechanics approach, using an algorithm to optimize the support structure such that the printed object does not break under gravity, enabling us to hold it safely for observation. [ABSTRACT FROM AUTHOR]

Semi-automated segmentation algorithms hold promise for improving extraction and identification of objects in images such as tumors in medical images of human tissue, counting plants or flowers for crop yield prediction or other tasks where object numbers and appearance vary from image to image. By blending markup from human annotators to algorithmic classifiers, the accuracy and reproducability of image segmentation can be raised to very high levels. At least, that is the promise of this approach, but the reality is less than clear. In this paper, we review the state-of-the-art in semi-automated image segmentation performance assessment and demonstrate it to be lacking the level of experimental rigour needed to ensure that claims about algorithm accuracy and reproducability can be considered valid. We follow this review with two experiments that vary the type of markup that annotators make on images, either points or strokes, in tightly controlled experimental conditions in order to investigate the effect that this one particular source of variation has on the accuracy of these types of systems. In both experiments, we found that accuracy substantially increases when participants use a stroke-based interaction. In light of these results, the validity of claims about algorithm performance are brought into sharp focus, and we reflect on the need for a far more control on variables for benchmarking the impact of annotators and their context on these types of systems. [ABSTRACT FROM AUTHOR]

This paper proposes a nested algorithm for truss topology optimization under plastic shakedown theory, which aims at maximizing the shakedown loading capacity. The nested algorithm consists of shakedown analysis and sensitivity-based optimization. Analytical sensitivity of shakedown load with respect to the design variables is derived in a concise form for the first time in conjunction with the adjoint variable scheme. A primal-dual shakedown algorithm is implemented for the determination of the shakedown load limit of structures and adjoint variables needed in the sensitivity analysis. The ground structure approach is applied to formulate the optimization problem for determining the optimized topology and cross-sections of the truss structure. Based on the analytical sensitivity above, the nested algorithm of truss structure topology optimization can be done efficiently by a gradient-based mathematical programming algorithm. Through independent shakedown analysis and optimization processes, the proposed approach facilitates the computation scale and efficiency for large-scale structure optimization design. Several numerical examples involving two- and three-dimensional truss illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In recent years, a range of novel and pseudonovel optimization algorithms has been proposed for solving engineering problems. Swarm intelligence optimization algorithms (SIAs) have become popular methods, and the whale optimization algorithm (WOA) is one of the highly discussed SIAs. However, regardless of novelty concerns about this method, the basic WOA is a weak method compared to top differential evolutions and particle swarm variants, and it suffers from the problem of poor initial population quality and slow convergence speed. Accordingly, in this paper, to increase the diversity of WOA versions and enhance the performance of WOA, a new WOA variant, named LXMWOA, is proposed, and based on the L'evy initialization strategy, the directional crossover mechanism, and the directional mutation mechanism. Specifically, the introduction of the L'evy initialization strategy allows initial populations to be dynamically distributed in the search space and enhances the global search capability of the WOA. Meanwhile, the directional crossover mechanism and the directional mutation mechanism can improve the local exploitation capability of the WOA. To evaluate its performance, using a series of functions and three models of engineering optimization problems, the LXMWOA was compared with a broad array of competitive optimizers. The experimental results demonstrate that the LXMWOA is significantly superior to its exploration and exploitation capability peers. Therefore, the proposed LXMWOA has great potential to be used for solving engineering problems. [ABSTRACT FROM AUTHOR]

Fault section location (FSL) plays a critical role in shortening blackout time and restoring power supply for distribution networks. This paper converts the FSL task into a binary optimization problem using the feeder terminal unit (FTU) information. The discrepancy between the reported overcurrent alarms and the expected overcurrent states of the FTUs is adopted as the objective function. It is a typical 0-1 combinatorial optimization problem with many local optima. An improved binary gaining-sharing knowledge-based algorithm (IBGSK) with mutation is proposed to effectively solve this challenging binary optimization problem. Since the original GSK cannot be applied in binary search space directly, and it is easy to get stuck in local optima, IBGSK encodes the individuals as binary vectors instead of real vectors. Moreover, an improved junior gaining and sharing phase and an improved senior gaining and sharing phase are designed to update individuals directly in binary search space. Furthermore, a binary mutation operator is presented and integrated into IBGSK to enhance its global search ability. The proposed algorithm is applied to two test systems, i.e. the IEEE 33-bus distribution network and the USA PG&E 69-bus distribution network. Simulation results indicate that IBGSK outperforms the other 12 advanced algorithms and the original GSK in solution quality, robustness, convergence speed, and statistics. It equilibrates the global search ability and the local search ability effectively. It can diagnose different fault scenarios with 100% and 99% success rates for these two test systems, respectively. Besides, the effect of mutation probability on IBGSK is also investigated, and the result suggests a moderate value. Overall, simulation results demonstrate that IBGSK shows highly promising potential for the FSL problem of distribution networks. [ABSTRACT FROM AUTHOR]

Researchers often face the problem of how to address missing data. Multiple imputation is a popular approach, with multiple imputation by chained equations (MICE) being among the most common and flexible methods for execution. MICE iteratively fits a predictive model for each variable with missing values, conditional on other variables in the data. In theory, any imputation model can be used to predict the missing values. However, if the predictive models are incorrectly specified, they may produce biased estimates of the imputed data, yielding inconsistent parameter estimates and invalid inference. Given the set of modeling choices that must be made in conducting multiple imputation, in this paper we propose a data-adaptive approach to model selection. Specifically, we adapt MICE to incorporate an ensemble algorithm, Super Learner, to predict the conditional mean for each missing value, and we also incorporate a local kernel-based estimate of variance. We present a set of simulations indicating that this approach produces final parameter estimates with lower bias and better coverage than other commonly used imputation methods. These results suggest that using a flexible machine learning imputation approach can be useful in settings where data are missing at random, especially when the relationships among the variables are complex. [ABSTRACT FROM AUTHOR]

Single-cell technologies provide us new ways to profile transcriptomic landscape, chromatin accessibility, spatial expression patterns in heterogeneous tissues at the resolution of single cell. With enormous generated single-cell datasets, a key analytic challenge is to integrate these datasets to gain biological insights into cellular compositions. Here, we developed a domain-adversarial and variational approximation, DAVAE, which can integrate multiple single-cell datasets across samples, technologies and modalities with a single strategy. Besides, DAVAE can also integrate paired data of ATAC profile and transcriptome profile that are simultaneously measured from a same cell. With a mini-batch stochastic gradient descent strategy, it is scalable for large-scale data and can be accelerated by GPUs. Results on seven real data integration applications demonstrated the effectiveness and scalability of DAVAE in batch-effect removing, transfer learning and cell-type predictions for multiple single-cell datasets across samples, technologies and modalities. Availability: DAVAE has been implemented in a toolkit package "scbean" in the pypi repository, and the source code can be also freely accessible at https://github.com/jhu99/scbean. All our data and source code for reproducing the results of this paper can be accessible at https://github.com/jhu99/davae_paper. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the accuracy of physical density prediction in single-energy CT (SECT) and dual-energy CT (DECT) by adapting a fully simulation-based method using a material-based forward projection algorithm (MBFPA). We used biological tissues referenced in ICRU Report 44 and tissue substitutes to prepare three different types of phantoms for calibrating the Hounsfield unit (HU)-to-density curves. Sinograms were first virtually generated by the MBFPA with four representative energy spectra (i.e. 80 kVp, 100 kVp, 120 kVp, and 6 MVp) and then reconstructed to form realistic CT images by adding statistical noise. The HU-to-density curves in each spectrum and their pairwise combinations were derived from the CT images. The accuracy of these curves was validated using the ICRP110 human phantoms. The relative mean square errors (RMSEs) of the physical density by the HU-to-density curves calibrated with kV SECT nearly presented no phantom size dependence. The kV–kV DECT calibrated curves were also comparable with those from the kV SECT. The phantom size effect became notable when the MV X-ray beams were employed for both SECT and DECT due to beam-hardening effects. The RMSEs were decreased using the biological tissue phantom. Simulation-based density prediction can be useful in the theoretical analysis of SECT and DECT calibrations. The results of this study indicated that the accuracy of SECT calibration is comparable with that of DECT using biological tissues. The size and shape of the calibration phantom could affect the accuracy, especially for MV CT calibrations. The present study is based on a full simulation environment, which accommodates various situations such as SECT, kV–kV DECT, and even kV–MV DECT. In this paper, we presented the advances pertaining to the accuracy of the physical density prediction when applied to SECT and DECT in the MV X-ray energy range. To the best of our knowledge, this study is the first to validate the physical density estimation both in SECT and DECT using human-type phantoms. [ABSTRACT FROM AUTHOR]

An increasing number of recent studies have suggested that doubly robust estimators with cross-fitting should be used when estimating causal effects with machine learning methods. However, not all existing programs that implement doubly robust estimators support machine learning methods and cross-fitting, or provide estimates on multiplicative scales. To address these needs, we developed AIPW , a software package implementing augmented inverse probability weighting (AIPW) estimation of average causal effects in R (R Foundation for Statistical Computing, Vienna, Austria). Key features of the AIPW package include cross-fitting and flexible covariate adjustment for observational studies and randomized controlled trials (RCTs). In this paper, we use a simulated RCT to illustrate implementation of the AIPW estimator. We also perform a simulation study to evaluate the performance of the AIPW package compared with other doubly robust implementations, including CausalGAM , npcausal , tmle , and tmle3. Our simulation showed that the AIPW package yields performance comparable to that of other programs. Furthermore, we also found that cross-fitting substantively decreases the bias and improves the confidence interval coverage for doubly robust estimators fitted with machine learning algorithms. Our findings suggest that the AIPW package can be a useful tool for estimating average causal effects with machine learning methods in RCTs and observational studies. [ABSTRACT FROM AUTHOR]

With the development of genome-wide association studies, how to gain information from a large scale of data has become an issue of common concern, since traditional methods are not fully developed to solve problems such as identifying loci-to-loci interactions (also known as epistasis). Previous epistatic studies mainly focused on local information with a single outcome (phenotype), while in this paper, we developed a two-stage global search algorithm, Greedy Equivalence Search with Local Modification (GESLM), to implement a global search of directed acyclic graph in order to identify genome-wide epistatic interactions with multiple outcome variables (phenotypes) in a case–control design. GESLM integrates the advantages of score-based methods and constraint-based methods to learn the phenotype-related Bayesian network and is powerful and robust to find the interaction structures that display both genetic associations with phenotypes and gene interactions. We compared GESLM with some common phenotype-related loci detecting methods in simulation studies. The results showed that our method improved the accuracy and efficiency compared with others, especially in an unbalanced case–control study. Besides, its application on the UK Biobank dataset suggested that our algorithm has great performance when handling genome-wide association data with more than one phenotype. [ABSTRACT FROM AUTHOR]

GCA is the most common large vessel vasculitis in the elderly population. In recent years, advanced imaging has changed the way GCA can be diagnosed in many locations. The GCA fast-track clinic approach combined with US examination allows prompt treatment and diagnosis with high certainty. Fast-track clinics have been shown to improve prognosis while being cost effective. However, all diagnostic modalities are highly operator dependent, and in many locations expertise in advanced imaging may not be available. In this paper, we review the current evidence on GCA diagnostics and propose a simple algorithm for diagnosing GCA for use by rheumatologists not working in specialist centres. [ABSTRACT FROM AUTHOR]

Approaches to seismic modelling using integral methods, notably Born–WKBJ (Wentzel–Kramers–Brillouin–Jeffreys), have seen an increase in applications in geophysical prospecting and near-surface exploration. Moreover, due to its linearity characteristic, which can fast and efficiently simulate full-waveform information, the Born–WKBJ based method performs well in seismic inversion. However, the Born approximation is the linearized wavefield simulation by Taylor expansion of the wavefield and omitting the high-order term, which cannot simulate the wavefield accurately, including the amplitude, phase and waveform information. For the seismic inversion, especially the amplitude variation with angle/offset (AVA/AVO) inversion, the amplitude is the most important element for estimating the parameters. In this paper, a Rytov–WKBJ approximation-based method, which can simulate the seismic amplitude information more accurately, is introduced to pre-stack seismic inversion. Besides, in order to improve the resolution of the inversion results, the ℓ1 − 2-norm regularized basis pursuit inversion is introduced to the inversion algorithm. Then, we demonstrate the superiority of the proposed method with the zero-offset and angle-dependent seismic data simulation. The model tests show that the proposed method performs better than the conventional method significantly both on amplitude and phase of the seismic data. Finally, the inversion tests of synthetic and field seismic data indicate that the proposed method can obtain more accurate results with a higher resolution. [ABSTRACT FROM AUTHOR]

Seismic phase detection, identification and first-onset picking are basic but essential routines to analyse earthquake data. As both the number of seismic stations, globally and regionally, and the number of experiments greatly increase due to ever greater availability of instrumentation, automated data processing becomes more and more essential. For example for modern seismic experiments involving 100s to even 1000s instruments, conventional human analyst-based identification and picking of seismic phases is becoming unfeasible, and the introduction of automatic algorithms mandatory. In this paper, we introduce DeepPhasePick, an automatic two-stage method that detects and picks P and S seismic phases from local earthquakes. The method is entirely based on highly optimized deep neural networks, consisting of a first stage that detects the phases using a convolutional neural network, and a second stage that uses two recurrent neural networks to pick both phases. Detection is performed on three-component seismograms. P - and S -picking is then conducted on the vertical and the two-horizontal components, respectively. Systematic hyperparameter optimization was applied to select the best model architectures and to define both the filter applied to pre-process the seismic data as well as the characteristics of the window sample used to feed the models. We trained DeepPhasePick using seismic records extracted from two sets of manually picked event waveforms originating from northern Chile (∼39 000 records for detection and ∼36 000 records for picking). In different tectonic regimes, DeepPhasePick demonstrated the ability to both detect P and S phases from local earthquakes with high accuracy, as well as predict P - and S -phase time onsets with an analyst level of precision. DeepPhasePick additionally computes onset uncertainties based on the Monte Carlo Dropout technique as an approximation of Bayesian inference. This information can then further feed an associator algorithm in an earthquake location procedure. [ABSTRACT FROM AUTHOR]

Describes the diagnostic role of a nomogram, a type of algorithm that accurately identifies non-organ-confined prostate cancer. Cost-effectiveness; Goal of replacing poorly predictive diagnostic procedures; Quantitative nuclear grade; Accuracy percentage.

Background in 2010, the European Working Group on Sarcopenia in Older People (EWGSOP) published a sarcopenia definition that aimed to foster advances in identifying and caring for people with sarcopenia. In early 2018, the Working Group met again (EWGSOP2) to update the original definition in order to reflect scientific and clinical evidence that has built over the last decade. This paper presents our updated findings. Objectives to increase consistency of research design, clinical diagnoses and ultimately, care for people with sarcopenia. Recommendations sarcopenia is a muscle disease (muscle failure) rooted in adverse muscle changes that accrue across a lifetime; sarcopenia is common among adults of older age but can also occur earlier in life. In this updated consensus paper on sarcopenia, EWGSOP2: (1) focuses on low muscle strength as a key characteristic of sarcopenia, uses detection of low muscle quantity and quality to confirm the sarcopenia diagnosis, and identifies poor physical performance as indicative of severe sarcopenia; (2) updates the clinical algorithm that can be used for sarcopenia case-finding, diagnosis and confirmation, and severity determination and (3) provides clear cut-off points for measurements of variables that identify and characterise sarcopenia. Conclusions EWGSOP2's updated recommendations aim to increase awareness of sarcopenia and its risk. With these new recommendations, EWGSOP2 calls for healthcare professionals who treat patients at risk for sarcopenia to take actions that will promote early detection and treatment. We also encourage more research in the field of sarcopenia in order to prevent or delay adverse health outcomes that incur a heavy burden for patients and healthcare systems. [ABSTRACT FROM AUTHOR]

In network and systems medicine, active module identification methods (AMIMs) are widely used for discovering candidate molecular disease mechanisms. To this end, AMIMs combine network analysis algorithms with molecular profiling data, most commonly, by projecting gene expression data onto generic protein–protein interaction (PPI) networks. Although active module identification has led to various novel insights into complex diseases, there is increasing awareness in the field that the combination of gene expression data and PPI network is problematic because up-to-date PPI networks have a very small diameter and are subject to both technical and literature bias. In this paper, we report the results of an extensive study where we analyzed for the first time whether widely used AMIMs really benefit from using PPI networks. Our results clearly show that, except for the recently proposed AMIM DOMINO, the tested AMIMs do not produce biologically more meaningful candidate disease modules on widely used PPI networks than on random networks with the same node degrees. AMIMs hence mainly learn from the node degrees and mostly fail to exploit the biological knowledge encoded in the edges of the PPI networks. This has far-reaching consequences for the field of active module identification. In particular, we suggest that novel algorithms are needed which overcome the degree bias of most existing AMIMs and/or work with customized, context-specific networks instead of generic PPI networks. [ABSTRACT FROM AUTHOR]

Motivation mRNA location corresponds to the location of protein translation and contributes to precise spatial and temporal management of the protein function. However, current assignment of subcellular localization of eukaryotic mRNA reveals important limitations: (1) turning multiple classifications into multiple dichotomies makes the training process tedious; (2) the majority of the models trained by classical algorithm are based on the extraction of single sequence information; (3) the existing state-of-the-art models have not reached an ideal level in terms of prediction and generalization ability. To achieve better assignment of subcellular localization of eukaryotic mRNA, a better and more comprehensive model must be developed. Results In this paper, SubLocEP is proposed as a two-layer integrated prediction model for accurate prediction of the location of sequence samples. Unlike the existing models based on limited features, SubLocEP comprehensively considers additional feature attributes and is combined with LightGBM to generated single feature classifiers. The initial integration model (single-layer model) is generated according to the categories of a feature. Subsequently, two single-layer integration models are weighted (sequence-based: physicochemical properties = 3:2) to produce the final two-layer model. The performance of SubLocEP on independent datasets is sufficient to indicate that SubLocEP is an accurate and stable prediction model with strong generalization ability. Additionally, an online tool has been developed that contains experimental data and can maximize the user convenience for estimation of subcellular localization of eukaryotic mRNA. [ABSTRACT FROM AUTHOR]

The reproducibility of research in bioinformatics refers to the notion that new methodologies/algorithms and scientific claims have to be published together with their data and source code, in a way that other researchers may verify the findings to further build more knowledge on them. The replication and corroboration of research results are key to the scientific process, and many journals are discussing the matter nowadays, taking concrete steps in this direction. In this journal itself, a recent opinion note has appeared highlighting the increasing importance of this topic in bioinformatics and computational biology, inviting the community to further discuss the matter. In agreement with that article, we would like to propose here another step into that direction with a tool that allows the automatic generation of a web interface, named web-demo, directly from source code in a simple and straightforward way. We believe this contribution can help make research not only reproducible but also more easily accessible. A web-demo associated to a published paper can accelerate an algorithm validation with real data, wide-spreading its use with just a few clicks. [ABSTRACT FROM AUTHOR]

Reduced-representation sequencing (RRS) provides cost-effective and time-saving genotyping platforms. Despite the outstanding advantage of RRS in throughput, the obtained genotype data usually contain a large number of errors. Several error correction methods employing the hidden Markov model (HMM) have been developed to overcome these issues. These methods assume that markers have a uniform error rate with no bias in the allele read ratio. However, bias does occur because of uneven amplification of genomic fragments and read mismapping. In this paper, we introduce an error correction tool, GBScleanR, which enables robust and precise error correction for noisy RRS-based genotype data by incorporating marker-specific error rates into the HMM. The results indicate that GBScleanR improves the accuracy by more than 25 percentage points at maximum compared to the existing tools in simulation data sets and achieves the most reliable genotype estimation in real data even with error-prone markers. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is not new, but recent years have seen a growing concern about the technology's political, economic and social impact, including debates about its governance. This paper describes how an analysis of the technology's governance should build on the understanding of AI as the creation of artificial agents, and that the challenge that governance seeks to address is best understood as one of material agency. It describes how relevant rules can be systematically analyzed across different applications by considering the fundamental properties of artificial agents. The paper concludes by describing how the framework can be applied for further governance studies, and as a means to bridge insights across social science and technical perspectives on AI. [ABSTRACT FROM AUTHOR]

High-fidelity Monte Carlo simulation of atmospheric turbulence phase screens is important for performance testing of astronomical adaptive optics systems. With a sparse spectrum model and an optimal sampling method, it is possible to generate an atmospheric turbulence phase screen with high fidelity. However, the phase screen generation speed is limited by the algorithm structure of this technique. A non-uniform fast Fourier transform technique is proposed in this paper to accelerate phase screen generation speed. This method is able to generate huge atmospheric turbulence phase screens with high fidelity and an acceptable time-cost enabling practical adaptive optics simulations of forthcoming Extremely Large Telescopes. [ABSTRACT FROM AUTHOR]