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Abstract Background Prosthetic legs help individuals with an amputation regain locomotion. Recently, deep neural network (DNN)-based control methods, which take advantage of the end-to-end learning capability of the network, have been proposed. One prominent challenge for these learning-based approaches is obtaining data for the training, particularly for the training of a mid-level controller. In this study, we propose a method for generating synthetic gait patterns (vertical load and lower limb joint angles) using a generative adversarial network (GAN). This approach enables a mid-level controller to execute ambulation modes that are not included in the training datasets. Methods The conditional GAN is trained on benchmark datasets that contain the gait data of individuals without amputation; synthetic gait patterns are generated from the user input. Further, a DNN-based controller for the generation of impedance parameters is trained using the synthetic gait pattern and the corresponding synthetic stiffness and damping coefficients. Results The trained GAN generated synthetic gait patterns with a coefficient of determination of 0.97 and a structural similarity index of 0.94 relative to benchmark data that were not included in the training datasets. We trained a DNN-based controller using the GAN-generated synthetic gait patterns for level-ground walking, standing-to-sitting motion, and sitting-to-standing motion. Four individuals without amputation participated in bypass testing and demonstrated the ambulation modes. The model successfully generated control parameters for the knee and ankle based on thigh angle and vertical load. Conclusions This study demonstrates that synthetic gait patterns can be used to train DNN models for impedance control. We believe a conditional GAN trained on benchmark datasets can provide reliable gait data for ambulation modes that are not included in its training datasets. Thus, designing gait data using a conditional GAN could facilitate the efficient and effective training of controllers for prosthetic legs.

This paper delves into the pivotal role of water electrolysis (WE) in green hydrogen production, a process utilizing renewable energy sources through electrolysis. The term "green hydrogen" signifies its distinction from conventional "grey" or "brown" hydrogen produced from fossil fuels, emphasizing the importance of decarbonization in the hydrogen value chain. WE becomes a linchpin, balancing surplus green energy, stabilizing the grid, and addressing challenges in hard-to-abate sectors like long-haul transport and heavy industries. This paper navigates through electrolysis variants, technological challenges, and the crucial association between electrolytic hydrogen production and renewable energy sources (RESs). Energy consumption aspects are scrutinized, highlighting the need for optimization strategies to enhance efficiency. This paper systematically addresses electrolysis fundamentals, technologies, scaling issues, and the nexus with energy sources. It emphasizes the transformative potential of electrolytic hydrogen in the broader energy landscape, underscoring its role in shaping a sustainable future. Through a systematic analysis, this study bridges the gap between detailed technological insights and the larger energy system context, offering a holistic perspective. This paper concludes by summarizing key findings, showcasing the prospects, challenges, and opportunities associated with hydrogen production via water electrolysis for the energy transition. [ABSTRACT FROM AUTHOR]

Background: Prosthetic legs help individuals with an amputation regain locomotion. Recently, deep neural network (DNN)-based control methods, which take advantage of the end-to-end learning capability of the network, have been proposed. One prominent challenge for these learning-based approaches is obtaining data for the training, particularly for the training of a mid-level controller. In this study, we propose a method for generating synthetic gait patterns (vertical load and lower limb joint angles) using a generative adversarial network (GAN). This approach enables a mid-level controller to execute ambulation modes that are not included in the training datasets. Methods: The conditional GAN is trained on benchmark datasets that contain the gait data of individuals without amputation; synthetic gait patterns are generated from the user input. Further, a DNN-based controller for the generation of impedance parameters is trained using the synthetic gait pattern and the corresponding synthetic stiffness and damping coefficients. Results: The trained GAN generated synthetic gait patterns with a coefficient of determination of 0.97 and a structural similarity index of 0.94 relative to benchmark data that were not included in the training datasets. We trained a DNN-based controller using the GAN-generated synthetic gait patterns for level-ground walking, standing-to-sitting motion, and sitting-to-standing motion. Four individuals without amputation participated in bypass testing and demonstrated the ambulation modes. The model successfully generated control parameters for the knee and ankle based on thigh angle and vertical load. Conclusions: This study demonstrates that synthetic gait patterns can be used to train DNN models for impedance control. We believe a conditional GAN trained on benchmark datasets can provide reliable gait data for ambulation modes that are not included in its training datasets. Thus, designing gait data using a conditional GAN could facilitate the efficient and effective training of controllers for prosthetic legs. [ABSTRACT FROM AUTHOR]

Powered lower-limb assistive devices, such as prostheses and exoskeletons, are a promising option for helping mobility-impaired individuals regain functional gait. Gait phase prediction plays an important role in controlling these devices and evaluating whether the device generates a gait similar to that of individuals with intact limbs. This study proposes a gait phase prediction method based on a deep neural network (DNN). The long short-term memory (LSTM)-based model predicts a continuous gait phase from the 250 ms history of the vertical load, thigh angle, knee angle, and ankle angle, commonly available on powered lower-limb assistive devices. One unified model was trained using publicly available benchmark datasets containing intact limb gaits for level-ground walking (LGW) and ascending stairs (SA). A phase prediction error of 1.28% for all benchmark datasets was obtained. The model was subsequently applied to a state machine-controlled powered prosthetic leg dataset collected fromfour individuals with unilateral transfemoral amputation. The gait phase prediction results (a phase prediction error of 5.70%) indicate that the model trained on benchmark data can be used for a system not included in the training dataset with no post-processing, such as model adaptation. Furthermore, it provided information regarding evaluation of the controller: whether the prosthetic leg generated normal gait. In conclusion, the proposed gait phase predictionmodel will facilitate efficient gait prediction and evaluation of controllers for powered lower-limb assistive devices. [ABSTRACT FROM AUTHOR]

Powered lower-limb assistive devices, such as prostheses and exoskeletons, are a promising option for helping mobility-impaired individuals regain functional gait. Gait phase prediction plays an important role in controlling these devices and evaluating whether the device generates a gait similar to that of individuals with intact limbs. This study proposes a gait phase prediction method based on a deep neural network (DNN). The long short-term memory (LSTM)-based model predicts a continuous gait phase from the 250 ms history of the vertical load, thigh angle, knee angle, and ankle angle, commonly available on powered lower-limb assistive devices. One unified model was trained using publicly available benchmark datasets containing intact limb gaits for level-ground walking (LGW) and ascending stairs (SA). A phase prediction error of 1.28% for all benchmark datasets was obtained. The model was subsequently applied to a state machine-controlled powered prosthetic leg dataset collected fromfour individuals with unilateral transfemoral amputation. The gait phase prediction results (a phase prediction error of 5.70%) indicate that the model trained on benchmark data can be used for a system not included in the training dataset with no post-processing, such as model adaptation. Furthermore, it provided information regarding evaluation of the controller: whether the prosthetic leg generated normal gait. In conclusion, the proposed gait phase predictionmodel will facilitate effcient gait prediction and evaluation of controllers for powered lower-limb assistive devices. [ABSTRACT FROM AUTHOR]

The evaluation of clustering algorithms can involve running them on a variety of benchmark problems, and comparing their outputs to the reference, ground-truth groupings provided by experts. Unfortunately, many research papers and graduate theses consider only a small number of datasets. Also, the fact that there can be many equally valid ways to cluster a given problem set is rarely taken into account. In order to overcome these limitations, we have developed a framework whose aim is to introduce a consistent methodology for testing clustering algorithms. Furthermore, we have aggregated, polished, and standardised many clustering benchmark dataset collections referred to across the machine learning and data mining literature, and included new datasets of different dimensionalities, sizes, and cluster types. An interactive datasets explorer, the documentation of the Python API, a description of the ways to interact with the framework from other programming languages such as R or MATLAB, and other details are all provided at https://clustering-benchmarks.gagolewski.com.

The field of remote sensing has undergone a remarkable shift where vast amounts of imagery are now readily available to researchers. New technologies, such as uncrewed aircraft systems, make it possible for anyone with a moderate budget to gather their own remotely sensed data, and methodological innovations have added flexibility for processing and analyzing data. These changes create both the opportunity and need to reproduce, replicate, and compare remote sensing methods and results across spatial contexts, measurement systems, and computational infrastructures. Reproducing and replicating research is key to understanding the credibility of studies and extending recent advances into new discoveries. However, reproducibility and replicability (R&R) remain issues in remote sensing because many studies cannot be independently recreated and validated. Enhancing the R&R of remote sensing research will require significant time and effort by the research community. However, making remote sensing research reproducible and replicable does not need to be a burden. In this paper, we discuss R&R in the context of remote sensing and link the recent changes in the field to key barriers hindering R&R while discussing how researchers can overcome those barriers. We argue for the development of two research streams in the field: (1) the coordinated execution of organized sequences of forward-looking replications, and (2) the introduction of benchmark datasets that can be used to test the replicability of results and methods. [ABSTRACT FROM AUTHOR]

We present DALES Objects, a large-scale instance segmentation benchmark dataset for aerial lidar. DALES Objects contains close to half a billion hand-labeled points, including semantic and instance segmentation labels. DALES Objects is an extension of the DALES (Varney et al., 2020) dataset, adding additional intensity and instance segmentation annotation. This paper provides an overview of the data collection, preprocessing, hand-labeling strategy, and final data format. We propose relevant evaluation metrics and provide insights into potential challenges when evaluating this benchmark dataset. Finally, we provide information about how researchers can access the dataset for their use at go.udayton.edu/dales3d.

Data analysis is a critical part of quantitative proteomics studies in interpreting biological questions. Numerous computational tools for protein quantification, imputation and differential expression (DE) analysis were generated in the past decade and the search for optimal tools is still going on. Moreover, due to the rapid development of RNA sequencing (RNA-seq) technology, a vast number of DE analysis methods were created for that purpose. The applicability of these newly developed RNA-seq-oriented tools to proteomics data remains in doubt. In order to benchmark these analysis methods, a proteomics dataset consisting of proteins derived from humans, yeast and drosophila, in defined ratios, was generated in this study. Based on this dataset, DE analysis tools, including microarray- and RNA-seq-based ones, imputation algorithms and protein quantification methods were compared and benchmarked. Furthermore, applying these approaches to two public datasets showed that RNA-seq-based DE tools achieved higher accuracy (ACC) in identifying DEPs. This study provides useful guidelines for analyzing quantitative proteomics datasets. All the methods used in this study were integrated into the Perseus software, version 2.0.3.0, which is available at https://www.maxquant.org/perseus. [ABSTRACT FROM AUTHOR]

The accuracy of correlation energy recovered by coupled cluster single‐, double‐, and perturbative triple‐excitations, CCSD(T), has led to the method being considered the gold standard of computational chemistry. The application of CCSD(T) has been limited to medium‐sized molecular systems due to its steep scaling with molecular size. The recent development of alternative domain‐based local pair natural orbital coupled‐cluster method, DLPNO‐CCSD(T), has significantly broadened the range of chemical systems to which CCSD(T) level calculations can be applied. Condensed systems such as ionic liquids (ILs) have a large contribution from London dispersion forces of up to 150 kJ mol−1 in large‐scale clusters. Ionic liquids show appreciable charge transfer effects that result in the increased valence orbital delocalization over the entire ionic network, raising the question whether the application of methods based on localized orbitals is reliable for these semi‐Coulombic materials. Here the performance of DLPNO‐CCSD(T) is validated for the prediction of correlation interaction energies of two data sets incorporating single‐ion pairs of protic and aprotic ILs. DLPNO‐CCSD(T) produced results within chemical accuracy with tight parameter settings and a non‐iterative treatment of triple excitations. To achieve spectroscopic accuracy of 1 kJ mol−1, especially for hydrogen‐bonded ILs and those containing halides, the DLPNO settings had to be increased by two orders of magnitude and include the iterative treatment of triple excitations, resulting in a 2.5‐fold increase in computational cost. Two new sets of parameters are put forward to produce the performance of DLPNO‐CCSD(T) within chemical and spectroscopic accuracy. [ABSTRACT FROM AUTHOR]

With the gaining popularity and proliferation of building information modeling (BIM) techniques, a growing demand emerges for accurate, up-to-date and semantically-enriched digital representations of built environments. In this regard, current mobile indoor mapping systems like the Microsoft HoloLens or Matterport allow to efficiently acquire triangle meshes of indoor building environments. However, manually reconstructing digital models of building interiors on the basis of these triangle meshes is a cumbersome and time-consuming task. Consequently, in this work, we propose a fully automatic, voxel-based indoor reconstruction approach to derive semantically-enriched and geometrically completed indoor models in voxel representation from unstructured triangle meshes. The presented approach does not require room surfaces such as walls, ceilings or floors to be planar or aligned with the coordinate axes. Furthermore, it does not rely on a clear vertical subdivision in distinct floor levels and even allows for slanted floors such as ramps or stair flights. It thus can also be applied to challenging indoor environments featuring curved room surfaces and complex vertical room layouts. The proposed approach labels voxels as 'Ceiling', 'Floor', 'Wall', 'Wall Opening', 'Interior Object' and 'Empty Interior'. Room surfaces are geometrically completed in case of holes in the input triangle meshes caused by occlusion or incomplete mapping. Furthermore, the derived interior space is partitioned into rooms and connecting transition spaces. To demonstrate the performance of our approach, we conduct a thorough quantitative evaluation on four labeled benchmark datasets. To this aim, we present a novel and adequate, automatic evaluation method. The four datasets have been acquired with the Microsoft HoloLens and are available along with the manually modeled ground truth. We also release the code of our implementation of the voxel-based indoor reconstruction approach presented in this paper as well as the code for the automated evaluation against the ground truth data at https://github.com/huepat/voxir. [ABSTRACT FROM AUTHOR]

Materials discovery via machine learning has become an increasingly popular method due to its ability to rapidly predict materials properties in a time-efficient and low-cost manner. However, one limitation in this field is the lack of benchmark datasets, particularly those that encompass the size, tasks, material systems, and data modalities present in the materials informatics literature. This makes it difficult to identify optimal machine learning model choices including algorithm, model architecture, data splitting, and data featurization for a given task. Here, we attempt to address this lack of benchmark datasets by assembling a unique repository of 50 different datasets for materials properties. The data contains both experimental and computational data, data suited for regression as well as classification, sizes ranging from 12 to 6354 samples, and materials systems spanning the diversity of materials research. Data were extracted from 16 publications. In addition to cleaning the data where necessary, each dataset was split into train, validation, and test splits. For datasets with more than 100 values, train-val-test splits were created, either with a 5-fold or 10-fold cross-validation method, depending on what each respective paper did in their studies. Datasets with less than 100 values had train-test splits created using the Leave-One-Out cross-validation method. These benchmark data can serve as a basis for a more diverse benchmark dataset in the future to further improve their effectiveness in the comparison of machine learning models.

When recording neural activity from extracellular electrodes, both in vivo and in vitro, spike sorting is a required and very important processing step that allows for identification of single neurons' activity. Spike sorting is a complex algorithmic procedure, and in recent years many groups have attempted to tackle this problem, resulting in numerous methods and software packages. However, validation of spike sorting techniques is complicated. It is an inherently unsupervised problem and it is hard to find universal metrics to evaluate performance. Simultaneous recordings that combine extracellular and patch-clamp or juxtacellular techniques can provide ground-truth data to evaluate spike sorting methods. However, their utility is limited by the fact that only a few cells can be measured at the same time. Simulated ground-truth recordings can provide a powerful alternative mean to rank the performance of spike sorters. We present here MEArec, a Python-based software which permits flexible and fast simulation of extracellular recordings. MEArec allows users to generate extracellular signals on various customizable electrode designs and can replicate various problematic aspects for spike sorting, such as bursting, spatio-temporal overlapping events, and drifts. We expect MEArec will provide a common testbench for spike sorting development and evaluation, in which spike sorting developers can rapidly generate and evaluate the performance of their algorithms. [ABSTRACT FROM AUTHOR]

Streaming data are increasingly present in real-world applications such as sensor measurements, satellite data feed, stock market, and financial data. The main characteristics of these applications are the online arrival of data observations at high speed and the susceptibility to changes in the data distributions due to the dynamic nature of real environments. The data stream mining community still faces some primary challenges and difficulties related to the comparison and evaluation of new proposals, mainly due to the lack of publicly available high quality non-stationary real-world datasets. The comparison of stream algorithms proposed in the literature is not an easy task, as authors do not always follow the same recommendations, experimental evaluation procedures, datasets, and assumptions. In this paper, we mitigate problems related to the choice of datasets in the experimental evaluation of stream classifiers and drift detectors. To that end, we propose a new public data repository for benchmarking stream algorithms with real-world data. This repository contains the most popular datasets from literature and new datasets related to a highly relevant public health problem that involves the recognition of disease vector insects using optical sensors. The main advantage of these new datasets is the prior knowledge of their characteristics and patterns of changes to adequately evaluate new adaptive algorithms. We also present an in-depth discussion about the characteristics, reasons, and issues that lead to different types of changes in data distribution, as well as a critical review of common problems concerning the current benchmark datasets available in the literature. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new dataset for the resource-constrained multi-project scheduling problem and evaluate the performance of multi-project extensions of the single-project schedule generation schemes. This manuscript contributes to the existing research in three ways. First, we provide an overview of existing benchmark datasets and classify the multi-project literature based on the type of datasets that are used in these studies. Furthermore, we evaluate the existing summary measures that are used to classify instances and provide adaptations to the data generation procedure of Browning and Yassine (J Scheduling 13(2):143-161, 2010a). With this adapted generator we propose a new dataset that is complimentary to the existing ones. Second, we propose decoupled versions of the single-project scheduling schemes, building on insights from the existing literature. A computational experiment shows that the decoupled variants outperform the existing priority rule heuristics and that the best priority rules differ for the two objective functions under study. Furthermore, we analyse the effect of the different parameters on the performance of the heuristics. Third, we implement a genetic algorithm that incorporates specific multi-project operators and test it on all datasets. The experiment shows that the new datasets are challenging and provide opportunities for future research. [ABSTRACT FROM AUTHOR]

Spatio-temporal fusion (STF) aims at fusing (temporally dense) coarse resolution images and (temporally sparse) fine resolution images to generate image series with adequate temporal and spatial resolution. In the last decade, STF has drawn a lot of attention and many STF methods have been developed. However, to date the STF domain still lacks benchmark datasets, which is a pressing issue that needs to be addressed in order to foster the development of this field. In this review, we provide (for the first time in the literature) a robust benchmark STF dataset that includes three important characteristics: (1) diversity of regions, (2) long timespan, and (3) challenging scenarios. We also provide a survey of highly representative STF techniques, along with a detailed quantitative and qualitative comparison of their performance with our newly presented benchmark dataset. The proposed dataset is public and available online. [ABSTRACT FROM AUTHOR]

The validation process is done to ensure the performance of nuclear codes compared to experimental results and to determine the degree of reliability of codes output. The development of nuclear power plants (NPPs) designs and the need for safer power plants has led to the increasing diversity/extension of nuclear codes and, as a result, the validation process has become more complicated. In this study, single physics validation including neutronic, core thermal-hydraulics (CTH), system thermal-hydraulics (STH), fuel performance, and multi-physics validation especially coupled neutronic and thermal-hydraulics (CNTH) are discussed. An international collective effort to provide benchmark data and test facilities including core test facility (CTF), separate effect test facility (SETF), and integral test facility (ITF) accompanied with addressing challenges related to scaling issues has been made. However, validation processes may suffer from some challenges. Non-quantifiable parameters cannot be given by experimental measurements. Moreover, for measurable parameters, faulty sensors can lead to incorrect results. Neural network, especially generative deep learning, with the ability to learn the probability distribution of training data and to generate unknown patterns may be a good candidate for the challenges mentioned above, e.g., detecting faulty sensors values and estimating non-quantifiable parameters based on quantifiable ones. Therefore, the application of artificial intelligence (AI) in the validation process may lead to a reduction of the volume of experimental measurements /construction of test facilities which alone is valuable. [ABSTRACT FROM AUTHOR]

The present paper gives an overview of liquid metal research activities performed in recent years at the Karlsruhe Institute of Technology (KIT). The work is motivated by applications in liquid metal blankets for a DEMO fusion reactor where lead lithium (PbLi), which serves as a neutron multiplier and tritium breeder, interacts with the plasma-confining magnetic field as it flows in the blanket covering the inner walls of the reactor. Liquid metal magnetohydrodynamic (MHD) research at KIT supports blanket design activities through theoretical and experimental investigations. Predictive computational tools are developed and validated by empirical data obtained for fundamental problems, such as flows in non-uniform magnetic fields or magneto-convective heat transfer from submerged obstacles. In addition, technological developments like pressure drop reduction by insulating flow channel inserts are pursued both theoretically and experimentally. Two complementary experimental facilities (MEKKA and MaPLE) provide a unique and versatile platform for MHD investigations at fusion relevant parameters. Using NaK as a model fluid in MEKKA allows experiments to be conducted at high Hartmann numbers in large complex geometries, such as scaled blanket mock-ups of ITER test blanket modules. For magneto-convection and heat transfer studies, MaPLE is well suited since it enables experiments with the prototypical fluid PbLi in test sections inclined at various orientations with respect to gravity. Some recent results have been selected to illustrate the broad spectrum of MHD activities at KIT. [ABSTRACT FROM AUTHOR]

The real resistance that a ship must face when it is navigating in ice floes is the key factor for knowing the necessary power and the required engine size. The aim of this work is to provide valuable data to help other research in which numerical frameworks will be developed to study ship navigation in broken ice. In this work, we used paraffin wax as an alternative to obtain affordable solutions, avoiding the high cost of ice tests. The experiments were carried out in a traditional basin facility and they consisted of towing tank tests with a ship model using different concentrations of blocks simulated by the use of paraffin wax. Photogrammetry was used as technique to determine the initial position of the ice blocks, which is important as starting data in the current development of numerical simulation code for studying the features of ship resistance in drift ice. These data are available for some ice concentrations in attached files. In addition, a procedure for testing in traditional towing facilities is presented and discussed. The results of the resistance obtained in the experiments in the presence of simulated floes are presented for three concentrations and three model speeds. Some findings may be applicable to ice sailing, under given circumstances.

Recent years have seen a surge of interest in dialogue translation, which is a significant application task for machine translation (MT) technology. However, this has so far not been extensively explored due to its inherent characteristics including data limitation, discourse properties and personality traits. In this article, we give the first comprehensive review of dialogue MT, including well-defined problems (e.g., 4 perspectives), collected resources (e.g., 5 language pairs and 4 sub-domains), representative approaches (e.g., architecture, discourse phenomena and personality) and useful applications (e.g., hotel-booking chat system). After systematical investigation, we also build a state-of-the-art dialogue NMT system by leveraging a breadth of established approaches such as novel architectures, popular pre-training and advanced techniques. Encouragingly, we push the state-of-the-art performance up to 62.7 BLEU points on a commonly-used benchmark by using mBART pre-training. We hope that this survey paper could significantly promote the research in dialogue MT.

Without reliable software metrics threshold values, the efficient quality evaluation of software could not be done. In order to derive reliable thresholds, we have to address several challenges, which impact the final result. For instance, software metrics implementations vary in various software metrics tools, including varying threshold values that result from different threshold derivation approaches. In addition, the programming language is also another important aspect. In this paper, we present the results of an empirical study aimed at comparing systematically obtained threshold values for nine software metrics in four object-oriented programming languages (i.e., lava, C++, C#, and Python). We addressed challenges in the threshold derivation domain within introduced adjustments of the benchmark- based threshold derivation approach. The data set was selected in a uniform way, allowing derivation repeatability, while input values were collected using a single software metric tool, enabling the comparison of derived thresholds among the chosen object-oriented programming languages. Within the performed empirical study, the comparison reveals that threshold values differ between different programming languages. [ABSTRACT FROM AUTHOR]

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics., Competing Interests: Conflict of interest The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

The miRcomp-Shiny web application allows interactive performance assessments and comparisons of qPCR-based microRNA expression and quality estimation methods using a benchmark data set. This work is motivated by two distinct use cases: (1) selection of methodology and quality thresholds for use analyzing one's own data, and (2) comparison of novel expression estimation algorithms with currently-available methodology. The miRcomp-Shiny application is implemented in the R/Shiny language and can be installed on any operating system on which R can be installed. It is made freely available as part of the miRcomp package (version 1.3.3 and later) available through the Bioconductor project at: http://bioconductor.org/packages/miRcomp. The web application is hosted at https://laurenkemperman.shinyapps.io/mircomp/. A detailed description of how to use the web application is available at: http://lkemperm.github.io/miRcomp_shiny_app

In this paper, two novel approaches are proposed to improve the performance of online least squares support vector machine for classification problem. First, the parameters of support vector classifier model including kernel width parameter are simultaneously updated when a new sample arrives. In that model, kernel width parameter is a nonlinear term which cannot be estimated via least squares solution. Therefore, unscented Kalman filter is adopted to train all the parameters where Karush-Kuhn-Tucker conditions are satisfied. Second, a variable-size moving window, which is updated by an intelligent strategy, is proposed to construct the support vector set. Thus, the proposed model captures the dynamics of data quickly while precluding itself to become clumsy due to big amount of useless data. In addition, adaptive support vector set provides a lower computational load especially for the large data sets. Simultaneous training of the model parameters by unscented Kalman filter and intelligent update of support vector set provides a superior classification performance compared to the online support vector classification approaches in the literature. [ABSTRACT FROM AUTHOR]