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In this paper, we introduce a novel dual inertial Tseng's extragradient method for solving variational inequality problems in real Hilbert spaces, particularly those involving pseudomonotone and Lipschitz continuous operators. Our secondary method incorporates variable step‐size, updated at each iteration based on some previous iterates. A notable advantage of these algorithms is their ability to operate without prior knowledge of Lipschitz‐type constants and without the need for any line‐search procedure. We establish the convergence theorem of the proposed algorithms under mild assumptions. To illustrate the numerical behavior of the algorithms and to make comparisons with other methods, we conduct several numerical experiments. The results of these evaluations are showcased and thoroughly examined to exemplify the practical significance and effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

We present a versatile and robust framework to render implicit surfaces defined by black‐box functions that only provide function value queries. We assume that the input function is locally Lipschitz continuous; however, we presume no prior knowledge of its Lipschitz constants. Our pre‐processing step generates a discrete acceleration structure, a Lipschitz field, that provides data to infer local and directional Lipschitz upper bounds. These bounds are used to compute safe step sizes along rays during rendering. The Lipschitz field is constructed by generating local polynomial approximations to the input function, then bounding the derivatives of the approximating polynomials. The accuracy of the approximation is controlled by the polynomial degree and the granularity of the spatial resolution used during fitting, which is independent from the resolution of the Lipschitz field. We demonstrate that our process can be implemented in a massively parallel way, enabling straightforward integration into interactive and real‐time modelling workflows. Since the construction only requires function value evaluations, the input surface may be represented either procedurally or as an arbitrarily filtered grid of function samples. We query the original implicit representation upon ray trace, as such, we preserve the geometric and topological details of the input as long as the Lipschitz field supplies conservative estimates. We demonstrate our method on both procedural and discrete implicit surfaces and compare its exact and approximate variants. [ABSTRACT FROM AUTHOR]

Integrative analysis has emerged as a prominent tool in biomedical research, offering a solution to the "small n$$ n $$ and large p$$ p $$" challenge. Leveraging the powerful capabilities of deep learning in extracting complex relationship between genes and diseases, our objective in this study is to incorporate deep learning into the framework of integrative analysis. Recognizing the redundancy within candidate features, we introduce a dedicated feature selection layer in the proposed integrative deep learning method. To further improve the performance of feature selection, the rich previous researches are utilized by an ensemble learning method to identify "prior information". This leads to the proposed prior assisted integrative deep learning (PANDA) method. We demonstrate the superiority of the PANDA method through a series of simulation studies, showing its clear advantages over competing approaches in both feature selection and outcome prediction. Finally, a skin cutaneous melanoma (SKCM) dataset is extensively analyzed by the PANDA method to show its practical application. [ABSTRACT FROM AUTHOR]

Atomic‐resolution scanning transmission electron microscopy (STEM) characterization requires precise tilting of the specimen to a high symmetric zone axis, which is usually processed in reciprocal space by following the diffraction patterns. However, for small‐sized nanocrystalline materials, their diffraction patterns are often too faint to guide the tilting process. Here, a simple and effective tilting method is developed based on the diffraction contrast change of the shadow image in the Ronchigram. The misorientation angle of the specimen can be calculated and tilted to the zone axis based on the position of the shadow image with lowest intensity. This method requires no prior knowledge of the sample and the maximum misorientation angle that can be corrected is >±6.9° with sub‐mrad accuracy. It operates in real space, without recording the diffraction patterns of the specimens, making it particularly effective for nanocrystalline materials. Combined with the scripting to control the microscope, the sample can be automatically tilted to the zone axis under low dose conditions (<0.17 e− Å−2 s−1), facilitating the imaging of beam sensitive materials such as zeolites or metal‐organic frameworks. This automated tilting method can significantly contribute to the atomic‐scale characterization of the nanocrystalline materials by STEM imaging. [ABSTRACT FROM AUTHOR]

Time‐lapse images carry out important information about dynamic changes in Earth's interior, which can be inferred using different full waveform inversion schemes. The estimation process is performed by manipulating more than one seismic dataset, associated with the baseline and monitors surveys. The time‐lapse variations can be so minute and localized that quantifying the uncertainties becomes fundamental to assessing the reliability of the results. The Bayesian formulation of the full waveform inversion problem naturally provides confidence levels in the solution, but evaluating the uncertainty of time‐lapse seismic inversion remains a challenge due to the ill‐posedness and high dimensionality of the problem. The Hamiltonian Monte Carlo can effectively sample over high‐dimensional distributions with affordable computational efforts. In this context, we explore the sequential approach in a Bayesian fashion for time‐lapse full waveform inversion using the Hamiltonian Monte Carlo method. The idea relies on integrating the baseline survey information as prior knowledge to the monitor estimation. We compare this methodology with a parallel scheme in perfect and a simple perturbed acquisition geometry scenario considering the Marmousi and a typical Brazilian pre‐salt velocity model. We also investigate the correlation effect between baseline and monitor samples on the propagated uncertainties. The results show that samples between different surveys are weakly correlated in the sequential case, while the parallel strategy provides time‐lapse images with lower dispersion. Our findings demonstrate that both methodologies are robust in providing uncertainties even in non‐repeatable scenarios. [ABSTRACT FROM AUTHOR]

Mastery‐based assignments typically provide students with multiple opportunities to improve their performance, but getting students to take advantage of these opportunities is difficult. We report on the implementation of a two‐part series of nudges designed to improve students' engagement with and performance on mastery‐based assignments in high‐enrollment, introductory undergraduate courses. Students received one or two email‐based nudges for each assignment, providing both decision information and decision assistance. Delivered via email by a digital coaching system that provides tailored support to students, we evaluated the effectiveness of viewing versus not viewing these personalized nudges across introductory courses in mathematics, computer science and engineering over five terms at our large, research‐intensive university. We used multilevel modelling with a sample of 30,693 assignment scores nested within 79 assignments and 5349 undergraduate students to explore the relationship between students' viewing the nudges and their scores while controlling for performance in other courses and students' baseline proclivities to read email from the coaching system. We find that viewing versus not viewing nudges is significantly related to improved outcomes. Viewing the first of the two‐part nudge series is associated with a 3% increase in scores on average. Importantly, we found a significant interaction with prior academic performance such that students with the lowest average academic performance across all courses are predicted to benefit the most—up to 9%—from nudging. Positive impacts are observed in most courses where nudges were implemented and are found to be most impactful for later versus earlier assignments within a term. We discuss the implications of these results for supporting students' decision‐making within mastery‐based assignment schemes and suggest future research for tailored nudges to support student success. Practitioner notesWhat is already known about this topic Nudges are known to be broadly effective, but heterogeneity is observed especially in education where the research base is relatively limited.Mastery‐based grading is an alternative approach to grading that, among other features, emphasizes learning through an explicit feedback cycle of struggle and revision without penalty.What this paper adds This study assesses the effectiveness of personalized nudges in the context of mastery‐based grading in undergraduate STEM courses.We examined the efficacy of viewing versus not viewing nudges in the real learning settings of three STEM courses enrolling more than 5000 students.We found the nudges to be effective overall, correlated with improvements of approximately 3% in students' assignment scores and most impactful for students with the lowest average academic performance.Implications for practice and/or policy Nudging by email or text message has a low overhead cost and yet may provide materially significant gains in students' assessment scores.Nudges can be effectively targeted to specific subsets of students and tailored based on students' prior and concurrent learning analytics data. [ABSTRACT FROM AUTHOR]

How do task experiences support proximal and longer‐term desires to reengage? Central to this question is the interplay between perceptions of task difficulty and the situational interest. This interplay is increasingly researched but not yet well understood. Micro‐analytic, latent studies are important tools for replicating past findings and extending our understanding in this area. This study aimed to extend our understanding of the interplay between readiness (prior knowledge and individual interest), a sequence of mathematics task experiences (situational interest and perceived difficulty) and reengagement beyond the set of tasks (i.e. optional follow‐up engagement). Participants (ages 20–49; n = 301) completed a series of maths tasks online, self‐reporting their task experiences after each task. Participants were offered an optional opportunity to follow‐up with the task content as a measure of their continued interest. Both readiness components assessed were important predictors of future situational interest (positive) and perceptions of task difficulty (negative). Persistent interconnections between the task experience components across time supported past evidence regarding the important role of situational interest and the challenges of perceived difficulty for sustained engagement. Modelling indicated that task success (β = 0.36), situational interest (β = 0.16) and perceptions of task difficulty (β = 0.21) can converge on choices to reengage with an object going forward. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to incorporate a deep learning prior with k‐space data fidelity for accelerating hyperpolarized carbon‐13 MRSI, demonstrated on synthetic cancer datasets. Methods: A two‐site exchange model, derived from the Bloch equation of MR signal evolution, was firstly used in simulating training and testing data, that is, synthetic phantom datasets. Five singular maps generated from each simulated dataset were used to train a deep learning prior, which was then employed with the fidelity term to reconstruct the undersampled MRI k‐space data. The proposed method was assessed on synthetic human brain tumor images (N = 33), prostate cancer images (N = 72), and mouse tumor images (N = 58) for three undersampling factors and 2.5% additive Gaussian noise. Furthermore, varied levels of Gaussian noise with SDs of 2.5%, 5%, and 10% were added on synthetic prostate cancer data, and corresponding reconstruction results were evaluated. Results: For quantitative evaluation, peak SNRs were approximately 32 dB, and the accuracy was generally improved for 5 to 8 dB compared with those from compressed sensing with L1‐norm regularization or total variation regularization. Reasonable normalized RMS error were obtained. Our method also worked robustly against noise, even on a data with noise SD of 10%. Conclusion: The proposed singular value decomposition + iterative deep learning model could be considered as a general framework that extended the application of deep learning MRI reconstruction to metabolic imaging. The morphology of tumors and metabolic images could be measured robustly in six times acceleration using our method. [ABSTRACT FROM AUTHOR]

Although teachers have opportunities to learn about many things through teaching experience, we know little about how they develop science subject matter knowledge in this setting. With both limited opportunities to learn science subject matter knowledge before becoming teachers and minimal science professional development available while working as a teacher, it is important to understand the extent to which elementary teachers develop science subject matter knowledge in their regular classroom practice and the factors that influence that development. In this longitudinal, mixed methods study we collected both quantitative and qualitative data before and following their final field experience, which was their first opportunity to have significant teaching experience. Findings suggest two important factors for subject matter knowledge development: time for considering science subject matter and a learning setting that values science. In contrast, indicators of learner capacity (i.e., prior knowledge) and time teaching the topics were not associated with teacher subject matter knowledge development. [ABSTRACT FROM AUTHOR]

Image denoising aims to remove noise from images and improve the quality of images. However, most image denoising methods heavily rely on pairwise training strategies and strict prior knowledge about image structure or noise distribution. While these methods exhibit significant results when handling known types of noise, their generalization performance diminishes when confronted with images containing unknown noise distributions. To address this issue, a two‐stage approach is introduced for enhancing the generalizability of image denoising. The proposed method does not rely on a large amount of paired data or prior knowledge of the noise type and level. Instead, it constructs a denoising pipeline with improved generalizability through an MLP‐based denoiser and generative diffusion prior. Specifically, in the first stage, an initial denoised image is predicted with a structure resembling that of the underlying clean image by introducing an MLP‐based U‐shaped denoising network aided by an implicit structural prior. In the second stage, the generalizability and quality of the denoiser are further enhanced by conditioning the result obtained from the previous stage on the pretrained denoising diffusion null‐space model. Extensive experimentation on multiple datasets demonstrates that this method exhibits better denoising performance and generalizability than other image denoising methods. [ABSTRACT FROM AUTHOR]

Police in England and Wales are invested with specific legal powers to detain a person—who is not under arrest—and search them or their vehicle for unlawful items. The exercise of this coercive power has long been a source of tension and mistrust, particularly among minoritised and other marginalised communities, and has been repeatedly implicated among the causes of serious public disorder. Although concerns about the misuse of stop and search have created a recurring cycle of crisis and reform—stretching back over more than four decades—the fundamental problem remains unchanged. How did this happen and what should now be done about it? These questions will be addressed by considering how the policy problem has been framed over time, reviewing evidence on the impact of stop and search and identifying policy challenges associated with regulating the powers, including lessons that can be learnt from previous attempts at reform. [ABSTRACT FROM AUTHOR]

X‐ray security checks aim to detect contraband in luggage; however, the detection accuracy is hindered by the overlapping and significant size differences of objects in X‐ray images. To address these challenges, the authors introduce a novel network model named Multi‐Scale Feature Attention (MSFA)‐DEtection TRansformer (DETR). Firstly, the pyramid feature extraction structure is embedded into the self‐attention module, referred to as the MSFA. Leveraging the MSFA module, MSFA‐DETR extracts multi‐scale feature information and amalgamates them into high‐level semantic features. Subsequently, these features are synergised through attention mechanisms to capture correlations between global information and multi‐scale features. MSFA significantly bolsters the model's robustness across different sizes, thereby enhancing detection accuracy. Simultaneously, A new initialisation method for object queries is proposed. The authors' foreground sequence extraction (FSE) module extracts key feature sequences from feature maps, serving as prior knowledge for object queries. FSE expedites the convergence of the DETR model and elevates detection accuracy. Extensive experimentation validates that this proposed model surpasses state‐of‐the‐art methods on the CLCXray and PIDray datasets. [ABSTRACT FROM AUTHOR]

Self‐derivation through integration is the process of integrating novel facts and producing new knowledge never directly taught. Knowledge integration has been studied with the presentation of two novel facts. However, in educational settings, individuals are required to integrate new information with prior knowledge learned days, months, or years earlier. Prior knowledge robustly predicts learning outcomes, but less is known about self‐derivation through the integration of new information with prior knowledge. Thus, in Study 1, we examined adults' (n = 25) memory integration of new facts with prior knowledge. The participants had 52% accuracy in self‐derivation. In Study 2 (n = 86), we examined whether reactivating prior knowledge before the novel fact presentation facilitated self‐derivation through integration with prior knowledge. Results indicated that performance was significantly higher for those whose prior knowledge was directly reactivated (55% accuracy) in comparison with the control group (42%). Pedagogical implications are discussed. We form new knowledge through direct instruction (reading a book) and also by connecting things we learn to create new knowledge not directly taught. We examined adults' derivation of new knowledge through the combination of novel information with the knowledge they already possess. Adults created new knowledge through integrating new information with existing knowledge and their performance improved when they read a reminder paragraph prior to learning the new information. [ABSTRACT FROM AUTHOR]

We present an algorithm for constructing efficient surrogate frequency‐domain models of (nonlinear) parametric dynamical systems in a non‐intrusive way. To capture the dependence of the underlying system on frequency and parameters, our proposed approach combines rational approximation and smooth interpolation. In the approximation effort, locally adaptive sparse grids are applied to effectively explore the parameter domain even if the number of parameters is modest or high. Adaptivity is also employed to build rational approximations that efficiently capture the frequency dependence of the problem. These two features enable our method to build surrogate models that achieve a user‐prescribed approximation accuracy, without wasting resources in "oversampling" the frequency and parameter domains. Thanks to its non‐intrusiveness, our proposed method, as opposed to projection‐based techniques for model order reduction, can be applied regardless of the complexity of the underlying physical model. Notably, our algorithm for adaptive sampling can be used even when prior knowledge of the problem structure is not available. To showcase the effectiveness of our approach, we apply it in the study of an aerodynamic bearing. Our method allows us to build surrogate models that adequately identify the bearing's behavior with respect to both design and operational parameters, while still achieving significant speedups. [ABSTRACT FROM AUTHOR]

This study aimed to investigate whether the presence (when using physical manipulatives [PMs]) or absence (when using virtual manipulatives [VMs]) of haptic sensory feedback (i.e., open‐ended haptic manipulation of physical materials with the use of the hands) during experimentation can impact preschoolers' conceptual understanding of concepts concerning three different subject domains (i.e., balance beam, sinking/floating, and springs). The participants were 132 preschoolers (5–6 years old), 44 per subject domain, who were equally divided into two conditions differing in the means of experimentation (PM or VM) they used. The data of this exploratory study were collected through clinical interviews and analyzed both qualitatively and quantitatively. The findings indicated that preschoolers' mean score improved in both conditions for each subject domain, (probably) as a result of their participation in the experimentation phase of the interviews, across all domains and conditions. No statistically significant difference in preschoolers' learning between the two conditions was found in the balance beam domain. In the sinking/floating domain, VM were found to be more conducive to preschoolers' learning than PM, whereas in the springs domain PM were found to have enhanced preschoolers' learning more than VM did. These findings have important implications for science teaching and learning in the early childhood years. First, we provide information on when PM or VM is conducive to kindergarteners' science learning. Second, we report on how prior embodied knowledge, established through haptic sensory input and related to the task at hand, affects learning through PM or VM experimentation. [ABSTRACT FROM AUTHOR]

Injection molding is a prevalent method for producing plastic components, yet determining the ideal process parameters has predominantly relied on heuristic approaches. In this research, a data‐driven injection molding process optimization framework is developed to simultaneously minimize warpage, cycle time, and clamping force. Employing multi‐objective Bayesian optimization (MBO), the framework is applied to a fan blade model for verification. Incorporating those objectives enables the selection of an injection molding machine with the proper maximum clamping force within acceptable tolerance and production time. Furthermore, non‐moldable regimes are considered in design space, which allows less prior knowledge for setting design space. The optimized results are compared with those obtained using NSGA‐III, a well‐established genetic algorithm‐based optimization technique, as a benchmark. The optimization frameworks produce a Pareto front for the three‐dimensional outputs, revealing distinct trade‐off relationships between cycle time and warpage, as well as clamping force and warpage. The MBO framework demonstrates a superior Pareto front compared to NSGA‐III when utilizing a limited data set, underscoring its benefits in scenarios where costly simulations or experiments are necessary. These findings are anticipated to contribute to the optimization of manufacturing processes, ultimately enhancing productivity in real‐world industries. [ABSTRACT FROM AUTHOR]

Purpose: To develop a novel deep learning‐based method inheriting the advantages of data distribution prior and end‐to‐end training for accelerating MRI. Methods: Langevin dynamics is used to formulate image reconstruction with data distribution before facilitate image reconstruction. The data distribution prior is learned implicitly through the end‐to‐end adversarial training to mitigate the hyper‐parameter selection and shorten the testing time compared to traditional probabilistic reconstruction. By seamlessly integrating the deep equilibrium model, the iteration of Langevin dynamics culminates in convergence to a fix‐point, ensuring the stability of the learned distribution. Results: The feasibility of the proposed method is evaluated on the brain and knee datasets. Retrospective results with uniform and random masks show that the proposed method demonstrates superior performance both quantitatively and qualitatively than the state‐of‐the‐art. Conclusion: The proposed method incorporating Langevin dynamics with end‐to‐end adversarial training facilitates efficient and robust reconstruction for MRI. Empirical evaluations conducted on brain and knee datasets compellingly demonstrate the superior performance of the proposed method in terms of artifact removing and detail preserving. [ABSTRACT FROM AUTHOR]

Deep images can provide rich spatial structure information, which can effectively exclude the interference of illumination and road texture in road scene segmentation and make better use of the prior knowledge of road area. This paper first proposes a new cross‐modal feature maintenance and encouragement network. It includes a quantization statistics module as well as a maintenance and encouragement module for effective fusion between multimodal data. Meanwhile, for the problem that if the road segmentation is performed directly using a segmentation network, there will be a lack of supervised guidance with clear physical meaningful information and poor interpretability of learning features, this paper proposes two road segmentation models based on prior knowledge of deep image: disparity information and surface normal vector information. Then, a two‐branch neural network is used to process the colour image and the processed depth image separately, to achieve the full utilization of the complementary features of the two modalities. The experimental results on the KITTI road dataset and Cityscapes dataset show that the method in this paper has good road segmentation performance and high computational efficiency. [ABSTRACT FROM AUTHOR]

A key issue in the transition to decarbonized energy sources with constant power sources is inaccurate current sharing and voltage quality. The main reason which causes these issues is the inevitable system impedance mismatches. This study addresses this issue by proposing an enhanced power‐sharing strategy that regulates adjustable resistance and compensates for voltage drops across the line impedance at the output of distributed generation converters. The control approach involves analyzing the mathematical relationship between system impedance mismatches and voltage drops in DC residential systems. Utilizing low‐computation proportional‐integral controllers, the secondary current sharing and load voltage controllers regulate converter outputs to compensate for mismatched resistance and voltage, ensuring proper current sharing and load voltage quality across various load conditions. Through the implementation of the proposed scheme incorporating adjustable resistance and voltage shifting terms, both the current‐sharing error and load voltage deviation are gradually reduced to negligible values, while maintaining stable performance in steady‐state conditions, without the need for prior knowledge of system parameters. Simulations and experimental studies are both conducted to demonstrate the effectiveness of the proposed control scheme. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This research employs the fuzzy‐set qualitative comparative analysis (fsQCA) method to investigate the configurations of multiple factors influencing scientific concept learning, including augmented reality (AR) technology, the concept map (CM) strategy and individual differences (eg, prior knowledge, experience and attitudes). A quasi‐experiment was conducted with 194 seventh‐grade students divided into four groups: AR and CM (N = 52), AR and non‐CM (N = 51), non‐AR and CM (N = 40), non‐AR and non‐CM (N = 51). These students participated in a science lesson on ‘The structure of peach blossom’. This study represents students' science learning outcomes by measuring their academic performance and cognitive load. The fsQCA results reveal that: (1) factors influencing students' academic performance and cognitive load are interdependent, and a single factor cannot constitute a necessary condition for learning outcomes; (2) multiple pathways can lead to the same learning outcome, challenging the notion of a singular best path derived from traditional analysis methods; (3) the configurations of good and poor learning outcomes exhibit asymmetry. For example, high prior knowledge exists in both configurations leading to good and poor learning outcomes, depending on how other conditions are combined. Practitioner notes What is already known about this topic Augmented reality proves to be a useful technological tool for improving science learning. The concept map can guide students to describe the relationships between concepts and make a connection between new knowledge and existing knowledge structures. Individual differences have been emphasized as essential external factors in controlling the effectiveness of learning. What this paper adds This study innovatively employed the fsQCA analysis method to reveal the complex phenomenon of the scientific concept learning process at a fine‐grained level. This study discussed how individual differences interact with AR and concept map strategy to influence scientific concept learning. Implications for practice and/or policy No single factor present or absent is necessary for learning outcomes, but the combinations of AR and concept map strategy always obtain satisfactory learning outcomes. There are multiple pathways to achieving good learning outcomes rather than a single optimal solution. The implementation of educational interventions should fully consider students' individual differences, such as prior knowledge, experience and attitudes. What is already known about this topic Augmented reality proves to be a useful technological tool for improving science learning. The concept map can guide students to describe the relationships between concepts and make a connection between new knowledge and existing knowledge structures. Individual differences have been emphasized as essential external factors in controlling the effectiveness of learning. What this paper adds This study innovatively employed the fsQCA analysis method to reveal the complex phenomenon of the scientific concept learning process at a fine‐grained level. This study discussed how individual differences interact with AR and concept map strategy to influence scientific concept learning. Implications for practice and/or policy No single factor present or absent is necessary for learning outcomes, but the combinations of AR and concept map strategy always obtain satisfactory learning outcomes. There are multiple pathways to achieving good learning outcomes rather than a single optimal solution. The implementation of educational interventions should fully consider students' individual differences, such as prior knowledge, experience and attitudes. [ABSTRACT FROM AUTHOR]

Phase‐field methods are attractive for simulating the mechanical failure of geometrically complex porous microstructures described by 2D/3D x‐ray μ$$ \mu $$CT images in subsurface (e.g., CO 2$$ {}_2 $$ storage) and manufacturing (e.g., Li‐ion battery) applications. They capture the nucleation, growth, and branching of fractures without prior knowledge of the propagation path or having to remesh the domain. Their drawback lies in the high computational cost for the typical domain sizes encountered in practice. We present a multiscale preconditioner that significantly accelerates the convergence of Krylov solvers in computing solutions of linear(ized) systems arising from the sequential discretization of the momentum and crack‐evolution equations in phase‐field methods. The preconditioner is an algebraic reformulation of a recent pore‐level multiscale method (PLMM) by the authors and consists of a global preconditioner MG$$ {\mathrm{M}}_{\mathrm{G}} $$ and a local smoother ML$$ {\mathrm{M}}_{\mathrm{L}} $$. Together, MG$$ {\mathrm{M}}_{\mathrm{G}} $$ and ML$$ {\mathrm{M}}_{\mathrm{L}} $$ attenuate low‐ and high‐frequency errors simultaneously. The proposed MG$$ {\mathrm{M}}_{\mathrm{G}} $$, used in the momentum equation only, is a simplification of a recent variant proposed by the authors that is much cheaper and easier to deploy in existing solvers. The smoother ML$$ {\mathrm{M}}_{\mathrm{L}} $$, used in both the momentum and crack‐evolution equations, is built such that it is compatible with MG$$ {\mathrm{M}}_{\mathrm{G}} $$ and more robust and efficient than black‐box smoothers like ILU(k$$ k $$). We test MG$$ {\mathrm{M}}_{\mathrm{G}} $$ and ML$$ {\mathrm{M}}_{\mathrm{L}} $$ systematically for static‐ and evolving‐crack problems on complex 2D/3D porous microstructures, and show that they outperform existing algebraic multigrid solvers. We also probe different strategies for updating MG$$ {\mathrm{M}}_{\mathrm{G}} $$ as cracks evolve and show the associated cost can be minimized if MG$$ {\mathrm{M}}_{\mathrm{G}} $$ is updated adaptively and infrequently. Both MG$$ {\mathrm{M}}_{\mathrm{G}} $$ and ML$$ {\mathrm{M}}_{\mathrm{L}} $$ are scalable on parallel machines and can be implemented non‐intrusively in existing codes. [ABSTRACT FROM AUTHOR]

This study evaluates the implementation of policies and practices related to prior learning assessment (PLA) for military training and experience in Colorado community colleges. Through conducting interviews and a document analysis, this case study aimed to provide policy and practice recommendations for states, systems, and institutions to consider for military enrollment and PLA. [ABSTRACT FROM AUTHOR]

The classical PID (proportional‐integral‐derivative) control is the most basic and widely used control algorithm in engineering systems by far. Whether PID control can effectively deal with coupled multiagent uncertain nonlinear dynamics is well worth investigating. In this article, a class of PID controlled multiagent stochastic systems with coupled uncertain dynamics is considered, where the control input is nonaffine. Each agent has its own regulation objective, and only has access to its own regulation error information for PID feedback. First, a PID parameter set will be constructed explicitly based on some prior knowledge about the nonlinear functions, such that the overall multiagent system can be stabilized globally in the mean square sense, as long as the PID parameters are chosen from the constructed set. Moreover, the regulation error will be proven to have an upper bound proportional to the noise intensity along the reference signal. Furthermore, it will be shown that the regulation error of each agent can be arbitrarily small by choosing the PID parameters suitably large. Finally, the theoretical results will be verified by simulations. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and natural language processing technologies have fuelled the growth of Pedagogical Conversational Agents (PCAs) with empathic conversational capabilities. However, no systematic literature review has explored the intersection between conversational agents, education and emotion. Therefore, this study aimed to outline the key aspects of designing, implementing and evaluating these agents. The data sources were empirical studies, including peer‐reviewed conference papers and journal articles, and the most recent publications, from the ACM Digital Library, IEEE Xplore, ProQuest, ScienceDirect, Scopus, SpringerLink, Taylor & Francis Online, Web of Science and Wiley Online Library. The remaining papers underwent a rigorous quality assessment. A filter study meeting the objective was based on keywords. Comparative analysis and synthesis of results were used to handle data and combine study outcomes. Out of 1162 search results, 13 studies were selected. The results indicate that agents promote dialogic learning, proficiency in knowledge domains, personalized feedback and empathic abilities as essential design principles. Most implementations employ a quantitative approach, and two variables are used for evaluation. Feedback types play a vital role in achieving positive results in learning performance and student perceptions. The main limitations and gaps are the time range for literature selection, the level of integration of the empathic field and the lack of a detailed development stage report. Moreover, future directions are the ethical implications of agents operating beyond scheduled learning times and the adoption of Responsible AI principles. In conclusion, this review provides a comprehensive framework of empathic PCAs, mostly in their evaluation. The systematic review registration number is osf.io/3xk6a.Practitioner notesWhat is already known about this topicEmotions play a pivotal role in shaping the interaction process, making it essential to consider them when designing methodological strategies or learning tools.Empathic Pedagogical Conversational Agents (PCAs) have emerged as a crucial approach for enhancing and personalizing the learning experience (24/7) for pupils and supporting human teachers in their teaching process.Despite the creation of numerous empathic PCAs, there is a scarcity of Systematic Literature Reviews (SLRs) on their application in the educational field, particularly concerning the integration of emotional abilities in combination with the competencies of each subject.What this paper addsIt offers new insights into the design principles underlying the integration of the empathic field.It reviews different approaches for incorporating students' prior knowledge in real time.It provides a comprehensive and up‐to‐date overview of the research designs used for implementation, including quantitative, qualitative and mixed methods.It examines the factors that influence the effectiveness of empathic PCA in teaching and learning.It evaluates the types of feedback that enhance the impact of the empathic field on learning outcomes.Implications for practice and/or policyIt is crucial to grasp the topics that this paper introduces in order to effectively integrate new learning tools into any context.Techno‐pedagogical designers seeking to gain insights into empathic PCAs will find immense value in this SLR, as it comprehensively covers each stage of the process.For future research endeavours, this study offers a wealth of ideas to draw upon, enabling researchers to address the challenges outlined and explore new avenues of investigation. [ABSTRACT FROM AUTHOR]

We present a method for generating structures in three‐dimensional space that appear to be impossible when viewed from specific perspectives. Previous approaches focus on helping users to edit specific structures and require users to have knowledge of structural positioning causing the impossibility. On the contrary, our system is designed to aid users without prior knowledge to explore a wide range of potentially impossible structures. The essence of our method lies in features we call visual bridges that confuse viewers regarding the depth of the resulting structure. We use these features as starting points and employ procedural modeling to systematically generate the result. We propose scoring functions for enforcing desirable spatial arrangement of the result and use Sequential Monte Carlo to sample outputs that score well under these functions. We also present a proof‐of‐concept user interface and demonstrate various results generated using our system. [ABSTRACT FROM AUTHOR]

This article discusses the importance of improving the quality of multiple-choice questions in assessments to measure current learning rather than prior learning. The use of multiple-choice questions is common in many assessment plans and has various benefits, such as evaluating large classes and being useful for adaptive learning platforms. The article provides guidelines for faculty to improve the quality of multiple-choice questions, including clarity, logical arrangement of answer choices, simplicity in language, and maintaining item independence. By implementing these improvements, instructors can identify gaps in student understanding and make improvements in pedagogy. The goal is to promote equitable evaluation practices and drive rapid and equitable improvements in pedagogy. [Extracted from the article]

Summary: Prior knowledge plays a crucial role in image completion. Although almost all of the existing image completion methods use prior knowledge to complete the image to be repaired from different perspectives, the learning and modeling of the prior knowledge is still a challenging problem. In order to address this issue, we propose a novel prior knowledge distillation framework (PKD‐Net) which could distill prior knowledge of structure and style from multiple semantic space and generates not only plausible content but also consistent style with surrounding image area. Our PKD‐Net replaces the skip connection in the vanilla U‐Net with a semantic shift attention module. The semantic shift attention module takes features from encoder layer and those from decoder layer as input pairs to output shifted features which take into account the long‐range dependency of encoder layer features and corresponding decoder layer features from the perspective of local structure and style. Semantic shift attention module models the global interdependencies in local spatial structures (patches centered at each position) and style (appearance texture) dimensions respectively, which could implement distillation of prior knowledge from two aspects: structure and style. Experiments on multiple datasets including faces (CelebA, CelebA‐HQ) and natural images (ImageNet, Places2, Paris Street View) demonstrate that our proposed approach generates higher quality completion results than existing ones. [ABSTRACT FROM AUTHOR]

Due to the ambiguity related to the lack of phase information, determining the physical parameters of multilayer thin films from measured neutron and X‐ray reflectivity curves is, on a fundamental level, an underdetermined inverse problem. This ambiguity poses limitations on standard neural networks, constraining the range and number of considered parameters in previous machine learning solutions. To overcome this challenge, a novel training procedure has been designed which incorporates dynamic prior boundaries for each physical parameter as additional inputs to the neural network. In this manner, the neural network can be trained simultaneously on all well‐posed subintervals of a larger parameter space in which the inverse problem is underdetermined. During inference, users can flexibly input their own prior knowledge about the physical system to constrain the neural network prediction to distinct target subintervals in the parameter space. The effectiveness of the method is demonstrated in various scenarios, including multilayer structures with a box model parameterization and a physics‐inspired special parameterization of the scattering length density profile for a multilayer structure. In contrast to previous methods, this approach scales favourably when increasing the complexity of the inverse problem, working properly even for a five‐layer multilayer model and a periodic multilayer model with up to 17 open parameters. [ABSTRACT FROM AUTHOR]

Cross‐domain object detection aims to generalize the distribution of features extracted by an object detector from an annotated domain to an unknown and unlabelled domain. Although one‐stage cross‐domain object detectors have significant advantages in deployment than two‐stage ones, they suffer from two problems. First, neglect of category features and inaccurate alignment between multiple category features would lead to decreased domain adaptation efficiency. Second, one‐stage detectors are more sensitive to imbalance of samples and negative samples severely affect the alignment process of domain adaptation. To overcome these two problems, an innovative category‐related attention domain adaptive method that refines discrimination for each category's feature has been proposed in this paper. In the proposed method, a group of domain discriminators is assigned to each category to refine the fine‐grained features between categories. The discriminators are trained via an adversarial discriminant framework to align the fine‐grained distributions cross different domains. A category attention alignment (CAA) module is proposed to navigate more attention to the foreground regions in instance‐level, which effectively alleviates the negative migration problem caused by the positive and negative sample imbalance of the one‐stage detector. Specifically, two sub‐modules in the CAA module are developed: a local CAA module and a global CAA module. These modules aim to optimize the domain offsets in both the local and global dimensions. In addition, a progressive global alignment module is designed to align image‐level features, offering prior knowledge of migration for the CAA module. The progressive global alignment module and CAA module collaboratively engage in benign competition with the backbone network across various levels. Extensive transferring experiments are conducted among cityscapes, foggy cityscapes, SIM10K, and KITTI. Experimental results show that the proposed method has much superior performance than other one‐stage cross‐domain detectors. [ABSTRACT FROM AUTHOR]

This work illustrates the use of normative models in a longitudinal neuroimaging study of children aged 6–17 years and demonstrates how such models can be used to make meaningful comparisons in longitudinal studies, even when individuals are scanned with different scanners across successive study waves. More specifically, we first estimated a large‐scale reference normative model using Hierarchical Bayesian Regression from N = 42,993 individuals across the lifespan and from dozens of sites. We then transfer these models to a longitudinal developmental cohort (N = 6285) with three measurement waves acquired on two different scanners that were unseen during estimation of the reference models. We show that the use of normative models provides individual deviation scores that are independent of scanner effects and efficiently accommodate inter‐site variations. Moreover, we provide empirical evidence to guide the optimization of sample size for the transfer of prior knowledge about the distribution of regional cortical thicknesses. We show that a transfer set containing as few as 25 samples per site can lead to good performance metrics on the test set. Finally, we demonstrate the clinical utility of this approach by showing that deviation scores obtained from the transferred normative models are able to detect and chart morphological heterogeneity in individuals born preterm. [ABSTRACT FROM AUTHOR]

Although children learn more when teachers gesture, it is not clear how gesture supports learning. Here, we sought to investigate the nature of the memory processes that underlie the observed benefits of gesture on lasting learning. We hypothesized that instruction with gesture might create memory representations that are particularly resistant to interference. We investigated this possibility in a classroom study with 402 second‐ and third‐grade children. Participants received classroom‐level instruction in mathematical equivalence using videos with or without accompanying gesture. After instruction, children solved problems that were either visually similar to the problems that were taught, and consistent with an operational interpretation of the equal sign (interference), or visually distinct from equivalence problems and without an equal sign (control) in order to assess the role of gesture in resisting interference after learning. Gesture facilitated learning, but the effects of gesture and interference varied depending on type of problem being solved and the strategies that children used to solve problems prior to instruction. Some children benefitted from gesture, while others did not. These findings have implications for understanding the mechanisms underlying the beneficial effect of gesture on mathematical learning, revealing that gesture does not work via a general mechanism like enhancing attention or engagement that would apply to children with all forms of prior knowledge. [ABSTRACT FROM AUTHOR]

This study presents a distributed adaptive sliding mode consensus protocol (DASMCP) to solve the consensus problem of the high‐order heterogeneous closed‐loop nonlinear uncertainty multi‐agent system without prior knowledge. The proposed adaptive gains adjustment mechanism can decrease the states and input chattering. In addition, the DASMCP is independent of prior knowledge of communication topology and error dynamics, and its finite time convergence bound can be preset. Moreover, two simulation examples are shown to verify the performance and the engineering application scenario. [ABSTRACT FROM AUTHOR]

Background: The diagnosis of prenatal placenta accreta spectrum (PAS) with magnetic resonance imaging (MRI) is highly dependent on radiologists' experience. A deep learning (DL) method using the prior knowledge that PAS‐related signs are generally found along the utero‐placental borderline (UPB) may help radiologists, especially those with less experience, to mitigate this issue. Purpose: To develop a DL tool for antenatal diagnosis of PAS using T2‐weighted MR images. Study Type: Retrospective. Subjects: Five hundred and forty pregnant women with clinically suspected PAS disorders from two institutions, divided into training (409), internal test (103), and external test (28) datasets. Field Strength/Sequence: Sagittal T2‐weighted fast spin echo sequence at 1.5 T and 3 T. Assessment: An nnU‐Net was trained for placenta segmentation. The UPB straightening approach was used to extract the utero‐placental boundary region. The UPB image was then fed into DenseNet‐PAS for PAS diagnosis. DenseNet‐PP learnt placental position information to improve the PAS diagnosis performance. Three radiologists with 8, 10, and 12 years of experience independently evaluated the images. Two radiologists marked the placenta tissue. Histopathological findings were the reference standard. Statistical Tests: Area under the curve (AUC) was used to evaluate the classification. Dice coefficient evaluated the segmentation between radiologists and the model performance. The Mann–Whitney U‐test or the chi‐squared test assessed the significance of differences. Decision curve analysis was used to determine clinical effectiveness. DeLong's test was used to compare AUCs. Results: Of the 540 patients, 170 had PAS disorders confirmed by histopathology. The DL model using UPB images and placental position yielded the highest AUC of 0.860 and 0.897 in internal test and external test cohorts, respectively, significantly exceeding the performance of three radiologists (internal test AUC, 0.737–0.770). Data Conclusion: By extracting the UPB image, this fully automatic DL pipeline achieved high accuracy and may assist radiologists in PAS diagnosis using MRI. Level of Evidence: 3 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

The sparse prior has been widely adopted to establish data models for numerous applications. In this context, most of them are based on one of three foundational paradigms: the conventional sparse representation, the convolutional sparse representation, and the multi‐layer convolutional sparse representation. When the data morphology has been adequately addressed, a sparse representation can be obtained by solving the sparse coding problem specified by the data model. This article presents a comprehensive overview of these three models and their corresponding sparse coding problems and demonstrates that they can be solved using convex and non‐convex optimization approaches. When the data morphology is not known or cannot be analyzed, it must be learned from training data, thereby formulating dictionary learning problems. This article addresses two different dictionary learning paradigms. In an unsupervised scenario, dictionary learning involves the alternating or joint resolution of sparse coding and dictionary updating. Another option is to create a recurrent neural network by unrolling algorithms designed to solve sparse coding problems. These networks can then be used in a supervised learning setting to facilitate the training of dictionaries via forward‐backward optimization. This article lists numerous applications in various domains and outlines several directions for future research related to the sparse prior. This article is categorized under:Statistical Learning and Exploratory Methods of the Data Sciences > Modeling MethodsStatistical and Graphical Methods of Data Analysis > Modeling Methods and AlgorithmsStatistical Models > Nonlinear Models [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping (SLAM) is a crucial technology for intelligent mobile robots to operate successfully in unknown environments. While many excellent SLAM systems have been developed in recent years, most assume that the environment is static, resulting in poor performance in dynamic environments. To address this limitation, we propose multiview stereo (MVS)‐SLAM (MVS‐SLAM), a real‐time semantic RGBD SLAM system with improved‐multiview geometry, built on the RGBD mode of ORB‐SLAM3. MVS‐SLAM tightly integrates semantic and geometric information to tackle the challenges posed by dynamic scenes. To meet the real‐time requirements, the semantic module leverages the latest and fastest object detection network, YOLOv7, to provide semantic prior knowledge for the geometric module. We introduce a novel geometric constraint method that capitalizes on depth images and semantic information to recover three‐dimensional (3D) feature points and initial camera pose. We use a 3D coordinate error threshold to identify dynamic points and remove them using the K‐means clustering algorithm. This approach effectively reduces the impact of dynamic points. We validate MVS‐SLAM using challenging dynamic sequences from the TUM data set, demonstrating that it significantly improves localization accuracy and system robustness in all types of dynamic environments. [ABSTRACT FROM AUTHOR]

This study investigated the effects of presenting domain information (basic information about the domain) either together with or instead of offering exploratory practice (an exploratory opportunity in a simulation‐based representation of the learning domain) prior to inquiry learning for facilitating students' hypothesis generation and subsequent inquiry processes and their knowledge acquisition. Secondary school students (n = 118) completed a simulation‐based inquiry task on force and motion. They were randomly assigned to one of four conditions: the D + E condition (n = 29), in which domain information and exploratory practice were available; the D condition (n = 30), in which only domain information was available; the E condition (n = 32), in which only exploratory practice was available; or the C condition (n = 27), with no support at all. Students' knowledge was measured with a pre‐ and posttest and a test on knowledge of variables. Inquiry processes were inferred from information students entered in a Hypothesis Scratchpad and an Observation tool, and from a final summary that they had to write. Results indicated that providing students with domain information alone helps to foster their knowledge of variables before generating hypotheses and leads to knowledge acquisition. The results also showed that the opportunity to explore before experimenting does not affect students' inquiry behavior or learning performance, even when combined with providing students with domain information. [ABSTRACT FROM AUTHOR]

Culturally responsive personalized learning (CRPL) emphasizes the importance of aligning personalized learning approaches with previous research on culturally responsive practices to consider social, cultural, and linguistic contexts for learning. In the present discussion, we briefly summarize two bodies of literature considered in defining and developing a framework for CRPL: technology‐enabled personalized learning and culturally relevant, responsive, and sustaining pedagogy. We then provide a definition and framework consisting of six key principles of CRPL, along with a brief discussion of theories and empirical evidence to support these principles. These six principles include agency, dynamic adaptation, connection to lived experiences, consideration of social movements, opportunities for collaboration, and shared power. These principles fall into three domains: fostering flexible student‐centered learning experiences, leveraging relevant content and practices, and supporting meaningful interactions within a community. Finally, we conclude with some implications of this framework for researchers, policymakers, and practitioners working to ensure that all students receive high‐quality learning opportunities that are both personalized and culturally responsive. Report Number: RR‐23‐09 [ABSTRACT FROM AUTHOR]

Many researchers use AI to improve the accuracy of early diagnostic techniques. However, as a result of the tumor's uneven shape, fuzzy borders and too few data, existing tumor segmentation methods do not propose accurate segmentation results. We innovative introduces the prior knowledge learned to filter the noise information and guide the final network to generate a more accurate segmentation model. First, we introduce a classification network with an attention block to highlight the potential location of the brain tumor and also obtain the rough diagnosis result as the prior knowledge. Second, we provide a novel image fusion network consisting of a transformer with cross attention to merge tumor localization information with brain MRI images. Third, we propose a novel multilayer transformer experience information fusion network to combine the classic U‐Net network to handle the guiding of prior knowledge. The higher performance of the suggested method is demonstrated by comparison with contemporary methods. [ABSTRACT FROM AUTHOR]

We used a 2 (prior knowledge: low vs. high) × 4 (instructional approach: unitary vs. unitary‐pictorial vs. equation vs. equation‐pictorial) ANOVA to examine the relationship between instructional approach, student prior knowledge, and personal belief of best practice for learning of the find‐whole percentage problems, which pose a challenge for many middle school students. The unit percentage concept is central to both the unitary approach and the unitary‐pictorial approach, where the latter has a diagram to scaffold the unit percentage concept. Both the equation approach and the equation‐pictorial approach, in contrast, are algebra approaches that integrate relevant information to form an equation, allowing learners to solve for an unknown (e.g., x). Furthermore, the equation‐pictorial approach relies on the proportional concept, scaffolded by a diagram to form an equation. A student's best practice, reflected by what is known as the 'actual – optimal bests dichotomy', details her belief in capability to perform task complexity (i.e., simple task vs. complex task). The concept of element interactivity within cognitive load theory framework predicts differential instructional efficiency: equation‐pictorial > equation approach > unitary‐pictorial > unitary. Our findings (N = 218 secondary students) showed that performance outcomes favored high prior knowledge students for complex problems and, to a lesser extent, practice problems and simple problems. Low prior knowledge students benefitted most from the equation‐pictorial approach, and they invested higher mental effort than high prior knowledge students across three approaches (unitary, unitary‐pictorial, equation) but not the equation‐pictorial approach. Importantly, cognitive load imposition, by proxy of students' mental effort, was unrelated to students' belief in optimal best. [ABSTRACT FROM AUTHOR]

The graph diameter D is correlated with the mean‐square radius of gyration Rg2 and is a useful measure to design and control the network architecture. The fraction d of segments located in the diameter chain, defined by d = D/ns, where ns is the total number of segments in the polymer, is investigated for various statistical network polymers. It is found that the relationship between d and the cycle rank r of the well‐developed statistical networks follows a master curve. As a convenient measure to define the well‐developed networks, the network maturity index (NMI) is proposed. The random crosslinked networks with NMI > 3 are considered well‐developed, but a larger value of NMI is required for the nonrandom, heterogeneous networks. With the help of prior knowledge of the relationship between Rg2 and D, the master curve for the relationship between the contraction factor g and r can also be determined. [ABSTRACT FROM AUTHOR]

Conceptual change is the process of developing a new understanding of an idea or related set of ideas and has been researched and theorized extensively in the last few decades. Although there is ongoing debate about how and why conceptual change occurs, all agree that individuals' prior knowledge plays a role, everyone engages differently in the process, and the context of the learning environment is influential. In this paper we build upon the work explored by Jimenez‐Martinez (this issue) on conceptual change in hydrogeology, and explore how the conceptual change theory of Vosniadou may facilitate understanding the learning process in hydrogeology. Vosniadou's theory is particularly applicable because it addresses the learning of ideas that combine abstract (GW flow) and visible (water flow) concepts. A pathway for exploring hydrogeology students' mental models (from naïve framework theory, to synthetic models, to scientific mental models) and identifying misconceptions specifically within hydrogeology using methods established by Vosniadou and colleagues is proposed as a means to address some of the challenges identified by Jimenez‐Martinez. [ABSTRACT FROM AUTHOR]

The volume and the velocity of data in social media are increasing and the social media has become a very useful environment to detect and track the real‐world events. However, to fulfill this, it is crucial to group‐related texts according to their topics and clustering takes an essential role at this point since we have no prior knowledge about the topics and their evolution in social media. In this survey, we review the current approaches and techniques proposed for short text stream clustering in recent years. The reviewed techniques are grouped according to their methodology and discussed in detail. Also, the datasets utilized to evaluate the performance of the proposed methods and the results are summarized together with the clustering quality measures used for these evaluations. Furthermore, current challenges about short‐text stream clustering are discussed. This article is categorized under:Data: Types and Structure > Streaming Data [ABSTRACT FROM AUTHOR]

Narrative texts have been advocated as tools to tackle science learning challenges, and there is even the proposal of a "narrative effect" on learning. We believe it is necessary to examine previous evidence on this effect, as well as to characterize the process of learning through science narrative texts more broadly. In this article, we offer a theoretical review drawing on three frameworks, namely on pedagogical aspects of text learning, linguistic features of texts, and cognitive aspects of text comprehension. Based on that, we analyzed two complementary questions. First, we reviewed 36 studies to ask if science narrative texts can benefit learning and memory outcomes at different educational levels (i.e., the "If" question). We found encouraging evidence for the use of science narrative texts at various educational levels, especially in delayed assessments and longer‐lasting interventions. Second, we gathered and linked ideas, hints, and evidence on how the process of learning with science narrative texts takes place, namely on conditions and underlying processes (i.e., the "How" question). There are many features from conditions (texts, learners, activities, wider context) and underlying processes (integration with prior knowledge, affective dispositions, and cognitive abilities) that can help to account for variability in outcomes; yet, ideas and evidence are not always tightly connected. We suggest that education and research should focus on specific narrative effects, that specify with what (texts), with whom (learners), when and where (activities and wider context) these effects occur, as well as "why" (underlying processes). We believe the proposed framing can help both make sense of previous evidence and inform future educational practices and research and provide some recommendations in this regard. [ABSTRACT FROM AUTHOR]

Summary: Load balancing is often a challenge in task‐parallel applications. The balancing problems are divided into static and dynamic. "Static" means that we have some prior knowledge about load information and perform balancing before execution, while "dynamic" must rely on partial information of the execution status to balance the load at runtime. Conventionally, work stealing is a practical approach used in almost all shared memory systems. In distributed memory systems, the communication overhead can make stealing tasks too late. To improve, people have proposed a reactive approach to relax communication in balancing load. The approach leaves one dedicated thread per process to monitor the queue status and offload tasks reactively from a slow to a fast process. However, reactive decisions might be mistaken in high imbalance cases. First, this article proposes a performance model to analyze reactive balancing behaviors and understand the bound leading to incorrect decisions. Second, we introduce a proactive approach to improve further balancing tasks at runtime. The approach exploits task‐based programming models with a dedicated thread as well, namely Tcomm$$ Tcomm $$. Nevertheless, the main idea is to force Tcomm$$ Tcomm $$ not only to monitor load; it will characterize tasks and train load prediction models by online learning. "Proactive" indicates offloading tasks before each execution phase proactively with an appropriate number of tasks at once to a potential victim (denoted by an underloaded/fast process). The experimental results confirm speedup improvements from 1.5×$$ 1.5\times $$ to 3.4×$$ 3.4\times $$ in important use cases compared to the previous solutions. Furthermore, this approach can support co‐scheduling tasks across multiple applications. [ABSTRACT FROM AUTHOR]

This paper addresses the integrated tracking and identification problem of a manoeuvring reentry target that performs intentional lateral manoeuvres to disrupt ground radars. Unlike previous approaches, prior knowledge of the lift‐induced drag is incorporated into a new manoeuvring model to describe the reentry target dynamics more explicitly. This model can account for the constraint between lift and drag, which is beneficial in ensuring the reliability of target state estimation. Noticing that the lift‐induced drag is an inherent characteristics of a reentry target that distinguishes the target's identity from others belonging to the same class, the integrated target tracking and identification problem is formulated within the framework of the multiple hypothesis testing about a set of manoeuvring models constructed by different prior knowledge. The proposed approach enables the authors to derive the optimal solution to the given problem in a mathematically rigorous manner. To cope with the real‐time implementation issue, a hypothesis merging strategy is also devised in view of maintaining the target identification performance. Simulation results demonstrate that the proposed scheme provides superior performance and reliability both in target tracking and identification compared to the existing method, despite imperfectness of prior knowledge. [ABSTRACT FROM AUTHOR]

An increasing percentage of students entering postsecondary institutions are working at least part‐time and balancing multiple identities, responsibilities, and demands on their time and resources. As student demographics change, postsecondary institutions must rise to meet these students where they are. Two effective ways to do that include Competency‐based Education (CBE) and Credit for Prior Learning (CPL), methods by which students are able to leverage what they have already learned outside of a traditional classroom. This article explores the benefits of these tools, particularly CPL, to both students and institutions, using evidence from research. Among these benefits are reduced time and expense, higher degree completion rates, and increased student confidence and self‐efficacy. This research also acknowledges equity considerations of CPL and CBE, including efforts to expand the reach of these options to more working learners, as well as how these tools help establish stronger connections between learning and work. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated that it is possible to combine machine learning with data assimilation to reconstruct the dynamics of a physical model partially and imperfectly observed. The surrogate model can be defined as an hybrid combination where a physical model based on prior knowledge is enhanced with a statistical model estimated by a neural network (NN). The training of the NN is typically done offline, once a large enough data set of model state estimates is available. By contrast, with online approaches the surrogate model is improved each time a new system state estimate is computed. Online approaches naturally fit the sequential framework encountered in geosciences where new observations become available with time. In a recent methodology paper, we have developed a new weak‐constraint 4D‐Var formulation which can be used to train a NN for online model error correction. In the present article, we develop a simplified version of that method, in the incremental 4D‐Var framework adopted by most operational weather centers. The simplified method is implemented in the European Center for Medium‐Range Weather Forecasts (ECMWF) Object‐Oriented Prediction System, with the help of a newly developed Fortran NN library, and tested with a two‐layer two‐dimensional quasi geostrophic model. The results confirm that online learning is effective and yields a more accurate model error correction than offline learning. Finally, the simplified method is compatible with future applications to state‐of‐the‐art models such as the ECMWF Integrated Forecasting System. Plain Language Summary: We have recently proposed a general framework for combining data assimilation (DA) and machine learning (ML) techniques to train a neural network for online model error correction. In the present article, we develop a simplified version of this online training method, compatible with future applications to more realistic models. Using numerical illustrations, we show that the new method is effective and yields a more accurate model error correction than the usual offline learning approach. The results show the potential of incorporating DA and ML tightly, and pave the way toward an application to the Integrated Forecasting System used for operational numerical weather prediction at the European Centre for Medium‐Range Weather Forecasts. Key Points: Variants of weak‐constraint 4D‐Var can be used to train neural networks for online model error correctionOnline learning yields a more accurate model error correction than offline learningThe new, simplified method, developed in the incremental 4D‐Var framework, can be easily applied in operational weather models [ABSTRACT FROM AUTHOR]

Background & Aims: Cognitive load theory assumes that the higher the learner's prior knowledge (i.e., the more expert the learner), the lower the intrinsic cognitive load (complexity) experienced for a given problem. While this is the case in many scenarios, there can be cases in which the converse is also true, resulting in more expert learners reporting higher intrinsic cognitive load than novices for the same problem. This can occur in relation to problems involving complex systems (e.g., ecological systems), for which novices' problem representations may underestimate problem complexity and therefore report lower intrinsic load than experts. This finding is borne out in the current paper. Samples, Methods & Results: In Study 1 with 118 participants from the Black Forest area in Germany, participants with higher levels of forestry and ecological expertise evaluated a problem relating to the restructuring of the Black Forest to adapt to climate change as more complex than did novices. In Study 2 (within‐subjects design, n = 66 primary‐school students), we conceptually replicated this finding in a domain more typical of cognitive load theory studies, mathematics. We found that higher prior knowledge also reduced the underestimation of the complexity of 'tricky', but frequently used, mathematics word problems. Conclusion: Our findings suggest that cognitive load theory's assumptions about intrinsic load and prior knowledge should be refined, as there seems to exist a sub‐set of problem‐solving tasks for which the traditional relationship between prior knowledge and reported ICL is reversed. [ABSTRACT FROM AUTHOR]

How can educators create space for students to practice making the worlds we are trying to collectively build? Inspired by genealogies that are grounded in and emerge from social movements, this paper uplifts the possibilities, tensions, and new questions that emerge when we take seriously the role of our classroom pedagogies. The authors offer a reflexive, methodological approach that pushes against the theory/practice divide and that stays with the importance of inhabiting theory through practice. They reflect on the role their classroom pedagogies in enacting their commitments to justice, organizing their offerings around the following themes: (1) Enter, or how their learnings from previous teaching experiences shape praxis through the pauses, recalibrations, and persistent questions they provoke; (2) how they Open and create space in their classrooms through rituals and routines, and toward what ends; and finally, (3) how they aim to Build with their students within and beyond the bounds of the classroom. The authors share the guiding questions that prompted these reflections, as well as the echoes across their offerings, inviting readers to reflect on their own teaching practice in community. [ABSTRACT FROM AUTHOR]

Summary: Sleep‐mediated memory benefits are modulated by several factors. Prior knowledge is assumed critical for consolidation during sleep, despite inconclusive empirical findings. Additionally, prior knowledge facilitates encoding, leading to differences in memory strength already before the retention filled with sleep. We tested whether increasing memory strength of unfamiliar learning material pre‐sleep can restore sleep‐mediated memory benefits in cases of low prior knowledge. One‐hundred and fifty‐four healthy young students learned translations of Dutch words. One group was German‐speaking, the other French‐speaking. As French is less similar to Dutch than German, we expected a lower prior knowledge in French participants. We manipulated memory strength during pre‐sleep encoding by varying the number of learning possibilities (one and two rounds for German‐speaking, two and three rounds for French‐speaking participants). When using the same learning paradigm for both groups (two rounds), lower prior knowledge modulated sleep‐mediated memory benefits: French‐speaking participants showed no advantage in memory after nighttime sleep compared with daytime wakefulness. In contrast, German‐speaking participants showed robust sleep‐mediated memory benefits. However, increasing memory strength before sleep restored sleep‐mediated memory benefits in French subjects to a level of German‐speaking participants. Conversely, reducing the training in German‐speaking participants reduced sleep‐mediated memory benefits. Our results show that prior knowledge and memory strength strongly modulate sleep‐associated memory benefits. However, in cases of low prior knowledge, sleep‐mediated memory benefits can be successfully restored by additional training. While prior knowledge might modulate encoding and consolidation processes more generally, its effect on sleep‐specific processes of memory retention might be less important than previously assumed. [ABSTRACT FROM AUTHOR]