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Time-domain spectroscopy encompasses a wide range of techniques, such as Fourier-transform infrared, pump–probe, Fourier-transform Raman, and two-dimensional electronic spectroscopies. These methods enable various applications, such as molecule characterization, excited state dynamics studies, or spectral classification. Typically, these techniques rarely use sampling schemes that exploit the prior knowledge scientists typically have before the actual experiment. Indeed, not all sampling coordinates carry the same amount of information, and a careful selection of the sampling points may notably affect the resulting performance. In this work, we rationalize, with examples, the various advantages of using an optimal sampling scheme tailored to the specific experimental characteristics and/or expected results. We show that using a sampling scheme optimizing the Fisher information minimizes the variance of the desired parameters. This can greatly improve, for example, spectral classifications and multidimensional spectroscopy. We demonstrate how smart sampling may reduce the acquisition time of an experiment by one to two orders of magnitude, while still providing a similar level of information. [ABSTRACT FROM AUTHOR]

We present a novel approach for systematically exploring the conformational space of small molecules with multiple internal torsions. Identifying unique conformers through a systematic conformational search is important for obtaining accurate thermodynamic functions (e.g., free energy), encompassing contributions from the ensemble of all local minima. Traditional geometry optimizers focus on one structure at a time, lacking transferability from the local potential-energy surface (PES) around a specific minimum to optimize other conformers. In this work, we introduce a physics-driven meta-Gaussian processes (meta-GPs) method that not only enables efficient exploration of target PES for locating local minima but, critically, incorporates physical surrogates that can be applied universally across the optimization of all conformers of the same molecule. Meta-GPs construct surrogate PESs based on the optimization history of prior conformers, dynamically selecting the most suitable prior mean function (representing prior knowledge in Bayesian learning) as a function of the optimization progress. We systematically benchmarked the performance of multiple GP variants for brute-force conformational search of amino acids. Our findings highlight the superior performance of meta-GPs in terms of efficiency, comprehensiveness of conformer discovery, and the distribution of conformers compared to conventional non-surrogate optimizers and other non-meta-GPs. Furthermore, we demonstrate that by concurrently optimizing, training GPs on the fly, and learning PESs, meta-GPs exhibit the capacity to generate high-quality PESs in the torsional space without extensive training data. This represents a promising avenue for physics-based transfer learning via meta-GPs with adaptive priors in exploring torsional conformer space. [ABSTRACT FROM AUTHOR]

This paper presents a method to perform the real-time creation of models that are used to represent aspects of tracked objects in video frames. Object modeling is done during the task of tracking previously unseen selected objects, and both tracking and model creation are implemented using the WiSARD weightless neural network and occur in real time, starting from no prior knowledge. The main purpose of this work is to track an object through camera images and, simultaneously, create a model that describes the presented appearances along with the transitions between each learned aspect. To achieve this goal, an object tracker based on the ClusWiSARD weightless neural network model was used to determine the states that describe the observed objects. In this way, it is possible to obtain a system that capture knowledge about the visual structures of the learned objects, creating relationships between the possible appearances, and being able to transit over the model aspects in an appropriate way. Furthermore, the created models have visual representations that can be used to show the learned aspects and validate the state transitions, in addition to being able to visualize occluded parts of objects. [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]

In recent years, deep metric learning and its probabilistic extensions claimed state-of-the-art results in the face verification task. Despite improvements in face verification, probabilistic methods received little attention in the research community and practical applications. Previous metric learning benchmarks avoided comparison with probabilistic methods, and it is still unclear whether they generalize well to image retrieval tasks beyond faces. In this paper, we, for the first time, perform an in-depth analysis and unified comparison of known probabilistic methods in verification and retrieval tasks. We study different design choices and argue that many of them have limited impact, while only a few probabilistic methods surpass modern metric learning approaches. Combining the best ideas of earlier works, we propose a simple modification of the top-performing method, achieving new state-of-the-art results among probabilistic methods. Finally, we study confidence prediction and show that it correlates with data quality, but contains little information about prediction error probability. We thus provide a new confidence evaluation benchmark and establish a baseline for future confidence prediction research. PyTorch implementation is publicly released. [ABSTRACT FROM AUTHOR]

Hyperparameter optimization (HPO) methods alleviate the significant effort required to obtain hyperparameters that perform optimally on visual learning problems. Existing methods are computationally inefficient because they are task agnostic (i.e., they do not adapt to a given task). We present HyperSTAR (System for Task Aware Hyperparameter Recommendation), a task-aware HPO algorithm that improves HPO efficiency for a target dataset by using prior knowledge from previous hyperparameter searches to recommend effective hyperparameters conditioned on the target dataset. HyperSTAR ranks and recommends hyperparameters by predicting their performance on the target dataset. To do so, it learns a joint dataset-hyperparameter space in an end-to-end manner that enables its performance predictor to use previously found effective hyperparameters for other similar tasks. The hyperparameter recommendations of HyperSTAR combined with existing HPO techniques lead to a task-aware HPO system that reduces the time to find the optimal hyperparameters for the target learning problem. Our experiments on image classification, object detection, and model pruning validate that HyperSTAR reduces the evaluation of different hyperparameter configurations by about 50 % compared to existing methods and, when combined with Hyperband, uses only 25 % of the budget required by the vanilla Hyperband and Bayesian Optimized Hyperband to achieve the best performance. [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]

Energy is vital to international trade, social security, and financial markets. Crude oil, as a non-renewable resource, is affected by complex factors. To better capture this influence, we introduce stochastic differential equations (SDEs) to depict crude oil prices. This paper establishes time-dependent linear and space-dependent nonlinear SDEs respectively. Time-dependent linear SDEs are established by recovering the drift and diffusion terms based on point estimation and sliding window. Space-dependent nonlinear SDEs are established by sparse Bayesian learning. Empirical results show that time-dependent linear SDE can better describe the actual fluctuations of historical crude oil prices, and can predict more accurately compared with constant coefficient linear SDE, namely geometric Brownian motion. The space-dependent nonlinear SDE can achieve the effect of time-dependent linear SDEs on historical data, while the former is more accurate in predicting. In addition, the former gained an in-depth understanding of the intrinsic dynamics. In summary, the models proposed in this study provide powerful tools for better understanding and predicting crude oil prices, which make a positive impact on the stability of the global economy and energy markets. [ABSTRACT FROM AUTHOR]

For the derivative estimation, nonparametric regression with unimodal kernels performs well under independent errors, while it breaks down under correlated errors. In this paper, we propose the local polynomial regression based on bimodal kernels for the derivative estimation under correlated errors. Unlike the conventional local polynomial estimator, the proposed estimator does not require any prior knowledge about the correlation structure of errors. For the proposed estimator, we deduce the main theoretical results, including the asymptotic bias, asymptotic variance, and asymptotic normality. Based on the asymptotic mean integrated squared error, we also provide a data-driven bandwidth selection criterion. Subsequently, we compare three popular bimodal kernels from the robustness and efficiency. Simulation studies show that the heavy-tailed bimodal kernel is more robust and efficient than the other two bimodal kernels and two popular unimodal kernels, especially for high-frequency oscillation functions. Finally, two real data examples are presented to illustrate the feasibility of the proposed estimator. [ABSTRACT FROM AUTHOR]

In this paper, we devise a stochastic extragradient-like algorithm incorporated with inertial terms, which requires a single projection onto our feasible set and employs a stochastic approximation version of an Armijo-type line search scheme along a feasible direction, for solving pseudomonotone stochastic variational inequalities. In the algorithm, two different stepsize strategies are employed to update steplength sequences without using the prior knowledge of the Lipschitz constant of involved operators. In the process of the stochastic approximation, we iteratively reduce the variance of stochastic errors. The almost sure convergence, the complexity analysis, and rates are provided in a dimensional space under reasonable conditions. Finally, some numerical experiments with graphical illustrations are reported to demonstrate the applicability and the efficiency of our algorithm in comparison with some projection type methods in the literature. [ABSTRACT FROM AUTHOR]

Background: Outdoor Adventure Education (OAE) predominantly centers learning around individual goal setting and experiences and has not traditionally elevated Indigenous Knowledge Systems (IKS). Purpose: This research focuses on understanding how student learning and inquiry is affected through OAE that emphasizes the importance of IKS as a starting point for environmental and experiential data collection. Methodology/Approach: Over the course of two multi-day river trips on Idaho's Salmon River during the summer of 2023, 30 incoming university students encountered an Adventure Learning (AL) educational framework centered around IKS. Students used citizen science methods to collect environmental and experiential data to be shared digitally using GIS applications prior to oncampus learning. Web-based surveys will be sent to assess learning impacts. Results will be analyzed for changes in appreciation and awareness of IKS. Conclusions: We anticipate that the data will demonstrate an appreciation and awareness of other ways of knowing within OAE that decenters a Western epistemic ideal of goal driven knowledge production and elevates IKS as a recognized and respected approach to knowledge production. Implications: Incorporating a hybrid environment for incoming university students to address personal knowledge production in context to IKS will promote various decolonizing projects within higher education through shifting student inquiry. [ABSTRACT FROM AUTHOR]

In this article, we introduce a forward–backward splitting method with a new step size rule for finding a singularity point of an inclusion problem which is defined by means of a sum of a single-valued vector field and a multi-valued vector field on a Hadamard manifold. Using a Mann, viscosity and an inertial extrapolation method, we establish a convergence result without prior knowledge of the Lipschitz constant of the underlying operator. We present some applications of our result to variational inequality problem. Finally, we present some numerical examples to demonstrate the numerical behavior of our proposed method. The result discuss in this article extends and complements many related results in the literature. [ABSTRACT FROM AUTHOR]

We present a new limited environmental information path planning procedure (IEIPPP) that finds collision-free paths without prior knowledge of feasible paths or obstacle locations by analyzing an image set of the area of interest. IEIPPP uses COLMAP to process the image set and reconstruct a three-dimensional point cloud model of the environment. Then, mechanical selective rapidly exploring random tree star is used to find the required path on the point cloud model. Gravitation and repulsion are introduced to correct the positions of random nodes and reduce the collision probability, and an elastic potential energy calculation is introduced to balance the height difference between adjacent nodes and stabilize vertical fluctuation of the path. To reduce computational cost and running time, a target-based sampling strategy is used to enable selective sampling. We evaluate IEIPPP with different image datasets and show that it can identify a collision-free path without other sensor equipment. [ABSTRACT FROM AUTHOR]

The development of intelligent education has led to the emergence of knowledge tracing as a fundamental task in the learning process. Traditionally, the knowledge state of each student has been determined by assessing their performance in previous learning activities. In recent years, Deep Learning approaches have shown promising results in capturing complex representations of human learning activities. However, the interpretability of these models is often compromised due to the end-to-end training strategy they employ. To address this challenge, we draw inspiration from advancements in graph neural networks and propose a novel model called GELT (Graph Embeddings based Lite-Transformer). The purpose of this model is to uncover and understand the relationships between skills and questions. Additionally, we introduce an energy-saving attention mechanism for predicting knowledge states that is both simple and effective. This approach maintains high prediction accuracy while significantly reducing computational costs compared to conventional attention mechanisms. Extensive experimental results demonstrate the superior performance of our proposed model compared to other state-of-the-art baselines on three publicly available real-world datasets for knowledge tracking. [ABSTRACT FROM AUTHOR]

Zero-shot sketch-based image retrieval (ZS-SBIR) has attracted increasing attention in computer vision, with the aim of searching the natural images that match a given sketch under the setting of zero-shot learning. In this paper, we propose a novel ZS-SBIR method based on the specifically developed domain-aware dual attention (DADA) module and similarity loss, which utilizes prior knowledge more fully than previous work. The DADA module emphasizes the importance of different channels and spaces according to the prior knowledge of whether the input is a sketch or a natural image. To preserve more prior knowledge from the models pre-trained on ImageNet, a similarity loss function is designed to supervise inter-class similarity. Furthermore, the center loss is introduced into our model, which can bridge the representation gap between the sketch domain and the natural image domain. We perform experiments on the TU-Berlin extended dataset and the Sketchy extended dataset, and the corresponding results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Nowadays, the idea of active learning is gradually adopted to assist domain adaptation. However, due to the existence of domain shift, the traditional active learning methods originating from semi-supervised scenarios can not be directly applied to domain adaptation. To solve the problem, active domain adaptation is proposed as a new domain adaptation paradigm, which aims to improve the performance of the model by annotating a small amount of target domain samples. In this regard, we propose an active domain adaptation method named Boosting Active Domain Adaptation with Exploration of Samples (BADA), dividing Active DA into two related issues: sample selection and sample utilization. We design the instability selection criterion based on predictive consistency and the diversity selection criterion. For the remaining unlabeled samples, we design a self-training framework, which screens out reliable samples and unreliable samples through the sample screening mechanism similar to selection criteria. And we adopt respective loss functions for reliable samples and unreliable samples. Experiments show that BADA remarkably outperforms previous active learning methods and Active DA methods on several domain adaptation datasets. [ABSTRACT FROM AUTHOR]

This investigation aimed to expand upon prior knowledge concerning age-related disparities in the motor initiation phase. We operationalized a robust pipeline for identifying source activity, leveraging EEG sensor-level data. Our analytical framework involved a comparative assessment of source activity in both elderly and young adults across distinct laterality aspects of the motor task. Subsequently, we rendered these findings in precise anatomical coordinates and elucidated the underpinnings of the observed disparities. Remarkably, these disparities remained congruent with the contemporary body of knowledge within this domain. Nevertheless, our meticulous application of cluster-based statistical testing yielded no statistically significant distinctions when juxtaposing subjects from varying age groups, hand laterality classifications, and frequency range considerations. Furthermore, we undertook a meticulous examination of the maximal points within clusters demonstrating the most pronounced significance within the elderly and young adult cohorts during tasks involving the right hand. This refined approach unearthed a noteworthy correlation between source power within the theta frequency range and the subjects' age, corroborating existing reference studies and thereby shedding newfound light upon the latent neural mechanisms at play. [ABSTRACT FROM AUTHOR]

Practical acoustic propagation modeling is significantly affected by ocean dynamics, and then can be exploited in numerous oceanic applications, where "practical" refers to modeling acoustic propagation in simulations that mimic real-world ocean environments. Physics-based numerical models provide approximate solutions of wave equation and rely on accurate prior environmental knowledge while the environment of practical scenarios is largely unknown. In contrast, data-driven machine learning offers a promising avenue to estimate practical acoustic propagation by learning from dataset. However, collecting such a high-quality, noise-free, and dense dataset remains challenging. Under the practical hurdle posed by the above approaches, the emergence of physics-informed neural network (PINN) presents an alternative to integrate physics and data but with limited representation capacity. In this work, a framework, termed spatial domain decomposition-based physics-informed neural networks (SPINNs), is proposed to enhance the representation capacity in spatially dependent oceanic scenarios and effectively learn from incomplete and biased prior physics and noisy dataset. Experiments demonstrate SPINNs' advantages over PINN in practical acoustic propagation estimation. The learning capacity of SPINNs toward physics and dataset during training is further analyzed. This work holds promise for practical applications and future expansion. [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]

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]

Post-silicon validation is one of the most critical processes in modern semiconductor manufacturing. Specifically, correct and deep understanding in test cases of manufactured devices is key to enable post-silicon tuning and debugging. This analysis is typically performed by experienced human experts. However, with the fast development in semiconductor industry, test cases can contain hundreds of variables. The resulting high-dimensionality poses enormous challenges to experts. Thereby, some recent prior works have introduced data-driven variable selection algorithms to tackle these problems and achieved notable success. Nevertheless, for these methods, experts are not involved in training and inference phases, which may lead to bias and inaccuracy due to the lack of prior knowledge. Hence, this letter for the first time aims to design a novel conditional variable selection approach while keeping experts in the loop. In this way, we expect that our algorithm can be more efficiently and effectively trained to identify the most critical variables under certain expert knowledge. Extensive experiments on both synthetic and real-world datasets from industry have been conducted and shown the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Efficiently removing clouds from remote sensing imagery presents a significant challenge, yet it is crucial for a variety of applications. This paper introduces a novel sparse function, named the tri-fiber-wise sparse function, meticulously engineered for the targeted tasks of cloud detection and removal. This function is adept at capturing cloud characteristics across three dimensions, leveraging the sparsity of mode-1, -2, and -3 fibers simultaneously to achieve precise cloud detection. By incorporating the concept of tensor multi-rank, which describes the global correlation, we have developed a tri-fiber-wise sparse-based model that excels in both detecting and eliminating clouds from images. Furthermore, to ensure that the cloud-free information accurately matches the corresponding areas in the observed data, we have enhanced our model with an extended box-constraint strategy. The experiments showcase the notable success of the proposed method in cloud removal. This highlights its potential and utility in enhancing the accuracy of remote sensing imagery. [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]

Universal domain adaptation aims to transfer the knowledge of common classes from the source domain to the target domain without any prior knowledge on the label set, which requires distinguishing in the target domain the unknown samples from the known ones. Recent methods usually focused on categorizing a target sample into one of the source classes rather than distinguishing known and unknown samples, which ignores the inter-sample affinity between known and unknown samples, and may lead to suboptimal performance. Aiming at this issue, we propose a novel UniDA framework where such inter-sample affinity is exploited. Specifically, we introduce a knowability-based labeling scheme which can be divided into two steps: (1) Knowability-guided detection of known and unknown samples based on the intrinsic structure of the neighborhoods of samples, where we leverage the first singular vectors of the affinity matrix to obtain the knowability of every target sample. (2) Label refinement based on neighborhood consistency to relabel the target samples, where we refine the labels of each target sample based on its neighborhood consistency of predictions. Then, auxiliary losses based on the two steps are used to reduce the inter-sample affinity between the unknown and the known target samples. Finally, experiments on four public datasets demonstrate that our method significantly outperforms existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Antimicrobial/antibiotic de-escalation (ADE) is a key feature of antimicrobial stewardship programs (ASP) that relies mainly on individual panels for determining ADE events based on subjective ranking of antibiotics' spectrum activity. The lack of consensus among ASP experts leads to reproducibility issues in the measure of this clinical outcome, making difficult to assess its real impact on patient care. The S3 score (Simplified Spectrum Score) app was developed to allow an objective ranking of antibiotics. Ranking was achieved by developing a database harboring pairs of bacteria-antibiotics for which each molecule was assigned a score based on published and clinically validated data from a recognized international committee. S3 score shows a strong correlation relationship and substantial agreement to a clinically validated spectrum score, and its framework enables any person to use it for ADE detection without assuming prior knowledge or training. In addition, its design enables regular updates and sustainability. [ABSTRACT FROM AUTHOR]

The goal of blind image super-resolution (BISR) is to recover the corresponding high-resolution image from a given low-resolution image with unknown degradation. Prior related research has primarily focused effectively on utilizing the kernel as prior knowledge to recover the high-frequency components of image. However, they overlooked the function of structural prior information within the same image, which resulted in unsatisfactory recovery performance for textures with strong self-similarity. To address this issue, we propose a two stage blind super-resolution network that is based on kernel estimation strategy and is capable of integrating structural texture as prior knowledge. In the first stage, we utilize a dynamic kernel estimator to achieve degradation presentation embedding. Then, we propose a triple path attention groups consists of triple path attention blocks and a global feature fusion block to extract structural prior information to assist the recovery of details within images. The quantitative and qualitative results on standard benchmarks with various degradation settings, including Gaussian8 and DIV2KRK, validate that our proposed method outperforms the state-of-the-art methods in terms of fidelity and recovery of clear details. The relevant code is made available on this link as open source. [ABSTRACT FROM AUTHOR]

Background: Classification of binary data arises naturally in many clinical applications, such as patient risk stratification through ICD codes. One of the key practical challenges in data classification using machine learning is to avoid overfitting. Overfitting in supervised learning primarily occurs when a model learns random variations from noisy labels in training data rather than the underlying patterns. While traditional methods such as regularization and early stopping have demonstrated effectiveness in interpolation tasks, addressing overfitting in the classification of binary data, in which predictions always amount to extrapolation, demands extrapolation-enhanced strategies. One such approach is hybrid mechanistic/data-driven modeling, which integrates prior knowledge on input features into the learning process, enhancing the model's ability to extrapolate. Results: We present NoiseCut, a Python package for noise-tolerant classification of binary data by employing a hybrid modeling approach that leverages solutions of defined max-cut problems. In a comparative analysis conducted on synthetically generated binary datasets, NoiseCut exhibits better overfitting prevention compared to the early stopping technique employed by different supervised machine learning algorithms. The noise tolerance of NoiseCut stems from a dropout strategy that leverages prior knowledge of input features and is further enhanced by the integration of max-cut problems into the learning process. Conclusions: NoiseCut is a Python package for the implementation of hybrid modeling for the classification of binary data. It facilitates the integration of mechanistic knowledge on the input features into learning from data in a structured manner and proves to be a valuable classification tool when the available training data is noisy and/or limited in size. This advantage is especially prominent in medical and biomedical applications where data scarcity and noise are common challenges. The codebase, illustrations, and documentation for NoiseCut are accessible for download at https://pypi.org/project/noisecut/. The implementation detailed in this paper corresponds to the version 0.2.1 release of the software. [ABSTRACT FROM AUTHOR]

Prioritization or ranking of different cell types in a Single-cell RNA Sequencing (scRNA-Seq) framework can be performed in a variety of ways, some of these include: i) obtaining an indication of the proportion of cell types between the different conditions under study, ii) counting the number of differentially expressed genes (DEGs) between cell types and conditions in the experiment or, iii) prioritizing cell types based on prior knowledge about the conditions under study (i.e., a specific disease). These methods have drawbacks and limitations thus novel methods for improving cell ranking are required. Here we present a novel methodology that exploits prior knowledge in combination with expert-user information to accentuate cell types from a scRNA-seq analysis that yield the most biologically meaningful results with respect to a disease under study. Our methodology allows for ranking and prioritization of cell-types based on how well their expression profiles relate to the molecular mechanisms and drugs associated with a disease. Molecular mechanisms, as well as drugs, are incorporated as prior knowledge in a standardized, structured manner. Cell-types are then ranked/prioritized based on how well results from data-driven analysis of scRNA-seq data match the predefined prior knowledge. In additional cell-cell communication perturbations between disease and control networks are used to further prioritize/rank cell-types. Our methodology has substantial advantages to more traditional cell ranking techniques and provides an informative complementary methodology that utilizes prior knowledge in a rapid and automated manner, that has previously not been attempted by other studies. The current methodology is also implemented as an R package entitled Single Cell Ranking Analysis Toolkit (scRANK) and is available for download and installation via GitHub (https://#hub.com/aoulas/scRANK) Author summary: Single-cell RNA Sequencing (scRNA-Seq) provides an additive resolution down to the cellular level that was previously not available from traditional "Bulk" RNA sequencing experiments. However, it is often difficult to prioritize the specific cell-types which are primarily responsible for the cause of a disease. This work presents a novel methodology that utilizes predefined prior knowledge information to highlight informative cell types from a scRNA-seq analysis. Our methodology allows for ranking and prioritization of cell-types based on how well predefined prior knowledge, in the form of pathways and drugs, are mapped to the results obtained from a basic analysis of a scRNA-Seq dataset. Prior knowledge is incorporated in a standardized, structured manner, whereby a checklist is attained by querying the human disease database called MalaCards. This checklist comes in the form of pathways and drugs associated with the disease. The expert-user is prompted to "edit" the prior knowledge checklist by removing or adding terms from the list of predefined terms. Our methodology also utilizes cell-cell communication perturbations between disease and control networks to further prioritize/rank cell-types. We believe, this methodology may offer substantial advantages to existing cell prioritizing or ranking techniques and provide a rapid and automated approach that further compliments other methods of cell prioritization in a scRNA-Seq framework. [ABSTRACT FROM AUTHOR]

Effective transfer learning for molecular property prediction has shown considerable strength in addressing insufficient labeled molecules. Many existing methods either disregard the quantitative relationship between source and target properties, risking negative transfer, or require intensive training on target tasks. To quantify transferability concerning task-relatedness, we propose Principal Gradient-based Measurement (PGM) for transferring molecular property prediction ability. First, we design an optimization-free scheme to calculate a principal gradient for approximating the direction of model optimization on a molecular property prediction dataset. We have analyzed the close connection between the principal gradient and model optimization through mathematical proof. PGM measures the transferability as the distance between the principal gradient obtained from the source dataset and that derived from the target dataset. Then, we perform PGM on various molecular property prediction datasets to build a quantitative transferability map for source dataset selection. Finally, we evaluate PGM on multiple combinations of transfer learning tasks across 12 benchmark molecular property prediction datasets and demonstrate that it can serve as fast and effective guidance to improve the performance of a target task. This work contributes to more efficient discovery of drugs, materials, and catalysts by offering a task-relatedness quantification prior to transfer learning and understanding the relationship between chemical properties. Transfer learning is known to enhance molecular property prediction in limited data sets, however, negative transfer due to insufficient relatedness between source and target tasks continues to be a major challenge. Here, the authors develop a principal gradient-based measurement to evaluate the quantitative transferability from the source property to the target property before applying transfer learning, significantly improving the transfer learning performance. [ABSTRACT FROM AUTHOR]

Impulsive blind deconvolution (IBD) is a popular method to recover impulsive sources for bearing fault diagnosis. Its underpinnings are in the design of objective functions based on prior knowledge of impulsive sources and a transfer function to describe transmission path influences. However, popular objective functions cannot retain waveform impulsiveness and periodicity cyclostationarity simultaneously, and the single convolution operation of IBD methods is insufficient to describe transmission paths composed of multiple linear and nonlinear units. Inspired by the MaxPooling period modulation intensity (MPMI) and convolutional sparse learning (CSL), an adaptive multi-D-norm-driven sparse unfolding deconvolution network (AMD-SUDN) is proposed in this paper. The core strategy is that one target vector with simultaneous impulsiveness and cyclostationarity is constructed automatically through the MPMI; then, this vector is substituted into the multi D-norm to design objective functions. Moreover, an iterative soft threshold algorithm (ISTA) for the CSL model is derived, and its iterative steps are unfolded into one deconvolution network. The algorithm's performance and the hyperparameter configuration are investigated by a set of numerical simulations. Finally, the proposed AMD-SUDN is applied to detect the impulsive features of bearing faults. All comparative results verify that the proposed AMD-SUDN achieves a better deconvolution accuracy than state-of-the-art IBD methods. [ABSTRACT FROM AUTHOR]

Unpaired image-to-image translation (I2IT) involves establishing an effective mapping between the source and target domains to enable cross-domain image transformation. Previous contrastive learning methods inadequately accounted for the variations in features between two domains and the interrelatedness of elements within the features. Consequently, this can result in challenges encompassing model instability and the blurring of image edge features. To this end, we propose a multi-attention bidirectional contrastive learning method for unpaired I2IT, referred to as MabCUT. We design separate embedding blocks for each domain based on depthwise separable convolutions and train them simultaneously from both the source and target domains. Then we utilize a pixel-level multi-attention extractor to query images from embedding blocks in order to select feature blocks with crucial information, thus preserving essential features from the source domain. To enhance the feature representation capability of the model, we incorporate depthwise separable convolutions for the generator. We conducted comprehensive evaluations using three datasets, demonstrating that our approach enhances the quality of unpaired I2IT while avoiding the issue of mode collapse-related image blurring. [ABSTRACT FROM AUTHOR]

The use of Experience Sampling Methods (ESM) to assess students' experiences, motivation, and emotions by sending signals to students at random or fixed time points has grown due to recent technological advances. Such methods offer several advantages, such as capturing the construct in the moment (i.e., when the events are fresh on respondents' minds) or providing a better understanding of the temporal and dynamic nature of the construct, and are often considered to be more valid than retrospective self-reports. This article investigates the validity and reliability of a variant of the ESM, the DEBE (an acronym for difficult, easy, boring and engaging, and pronounced 'Debbie') feedback, which captures student-driven (as and when the student wants to report) momentary self-reports of cognitive-affective states during a lecture. The DEBE feedback is collected through four buttons on mobile phones/laptops used by students. We collected DEBE feedback from several video lectures (N = 722, 8 lectures) in different courses and examined the threats to validity and reliability. Our analysis revealed variables such as student motivation, learning strategies, academic performance, and prior knowledge did not affect the feedback-giving behavior. Monte Carlo simulations showed that for a class size of 50 to 120, on average, 30 students can provide representative and actionable feedback, and the feedback was tolerant up to 20% of the students giving erroneous or biased feedback. The article discusses in detail the aforementioned and other validity and reliability threats that need to be considered when working with such data. These findings, although specific to the DEBE feedback, are intended to supplement the momentary self-report literature, and the study is expected to provide a roadmap for establishing validity and reliability of such novel data types. [ABSTRACT FROM AUTHOR]

An online authentic learning environment (OnALE) is proposed in this study to facilitate students' learning of inferential statistics in a real-life context. The efficacy of the OnALE, in comparison to the conventional approach relative to the students' performance, was explored. Respondents from the experimental group were purposively selected to complete the Perception Questionnaire regarding the features of the OnALE. The Analysis of Covariance (ANCOVA) on the post-test scores using prior knowledge scores as covariate disclosed a significant variance in the post-test scores between control and experimental groups (F (1, 74) = 10.924, p < 0.05, partial η2 = 0.129), with the experimental group displaying a higher mean score. Outcomes from the Perception Questionnaire revealed that all respondents at least agreed that each authentic learning characteristic in the OnALE facilitated their learning. The highest and the lowest rated characteristics were Collaboration and Multiple Roles and Perspectives, respectively. The framework of the OnALE characteristics for varying levels of students' performance unveiled the combinations of authentic learning characteristics beneficial to students from different performing groups. This framework functions as a guideline for statistics educators and learning designers to provide an effective online learning environment. [ABSTRACT FROM AUTHOR]

The book "Corpus Linguistics for Writing Development" by Philip Durrant, published in 2023 by Routledge, offers advanced beginners in the fields of education, language, and linguistics selected corpus linguistic techniques for investigating the writing development of language learners. It provides programming skills in R without requiring prior knowledge of R. The book covers various aspects of writing development, such as vocabulary, grammar, and linguistic patterns, and demonstrates their practical implementation using corpus data and R code. It ends abruptly without a concluding chapter or exercises for further practice. Therefore, the book is only partially recommended for beginners without programming skills. [Extracted from the article]

Elselijn Kingma argues that Christopher Boorse's biostatistical theory (the BST) does not show how the reference classes it uses are objective and naturalistic. Recently, philosophers of medicine have attempted to rebut Kingma's concerns. I argue that these rebuttals are theoretically unconvincing, and that there are clear examples of physicians adjusting their reference classes according to their prior knowledge of health and disease. I focus on the use of age-adjusted reference classes to diagnose low bone mineral density in children. In addition to using the BST's age, sex, and species, physicians also choose to use other factors to define reference classes, such as pubertal status, bone age, body size, and muscle mass. I show that physicians calibrate the reference classes they use according to their prior knowledge of health and disease. Reference classes are also chosen for pragmatic reasons, such as to predict fragility fractures. [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]

How does ecological complexity influence decision making? To facilitate interpretation, laboratory studies often focus on decision tasks with limited options, but animals presumably face more variety in the wild. For example, sometimes species must choose between ephemeral and permanent options, as with choosing between mobile prey and stationary food. The optimal choice is to prioritize the ephemeral option, because it will disappear if not selected first, whereas the permanent option will always be available. In experimental tasks with just these two choices, capuchin monkeys (Sapajus (Cebus) apella) learn to maximize their rewards. However, in the wild, animals presumably face additional sets of choices, for instance two ephemeral or two permanent options, which may make it more difficult to learn the best way to maximize their payouts. Here we show that adding configurations during learning lowers the capuchins' preference for choosing the ephemeral option first. Because recent theoretical work suggests that this more complex version could be solved by grouping the elements through configural learning, half of our subjects underwent training proposed to aid in the configural learning process prior to experiencing the added complexity. This training did not improve the capuchins' ability to pick the ephemeral option first. We consider both what this means for capuchins' and other species' decision making in more complex environments and how we use experimental results to understand animals' cognition and behaviour. • Capuchins and cleaner fish can solve a dichotomous choice to maximize rewards. • Capuchins failed to do so when complexity was added, even after training. • Capuchins' decisions might differ in more complex natural environments. [ABSTRACT FROM AUTHOR]

Multi-frame image super-resolution reconstruction focuses on obtaining a high-resolution (HR) image from a low-resolution image. Since the super-resolution problem is ill-posed, it is frequent to use regularization techniques. However, the choice of these regularization terms is not always direct. Generally, every term should contain some prior knowledge over the image, that is why it plays an important role in increasing the quality of the restored HR image. In this paper, we propose a total variable-order variation based-regularization to provide a natural-looking and an effective reconstruction of the desired HR image. We also present some existence and uniqueness results of our proposed model. The alternating direction method of multipliers algorithm is employed to implement the numerical simulations. Experimental results reveal that the proposed approach can reconstruct and recover high-quality results visually and quantitatively. [ABSTRACT FROM AUTHOR]

3D shape processing is a fundamental computer application. Specifically, 3D mesh could provide a natural and detailed way for object representation. However, due to its non-uniform and irregular data structure, applying deep learning technologies to 3D mesh is difficult. Furthermore, previous deep learning approaches for 3D mesh mainly focus on local structural features and there is a loss of information. In this paper, to make better mesh shape awareness, a novel deep learning approach is proposed, which aims to full-use the information of mesh data and exploit comprehensive features for more accurate classification. To utilize self-attention mechanism and learn global features of mesh edges, we propose a novel attention-based structure with the edge attention module. Then, for local feature learning, our model aggregates edge features from adjacent edges. We refine the network by discarding pooling layers for efficiency. Thus, it captures comprehensive features from both local and global fields for better shape awareness. Moreover, we adopt spatial position encoding module based on spatial information of edges to enhance the model to better recognize edges and make full use of mesh data. We demonstrate effectiveness of our model in classification tasks with numerous experiments which show outperforming results on popular datasets. [ABSTRACT FROM AUTHOR]

Test-time adaptation (TTA) has been proven to effectively improve the adaptability of deep learning semantic segmentation models facing continuous changeable scenes. However, most of the existing TTA algorithms lack an explicit exploration of domain gaps, especially those based on visual domain prompts. To address these issues, this paper proposes a self-prompt strategy based on uncertainty, guiding the model to continuously focus on regions with high uncertainty (i.e., regions with a larger domain gap). Specifically, we still use the Mean-Teacher architecture with the predicted entropy from the teacher network serving as the input to the prompt module. The prompt module processes uncertain maps and guides the student network to focus on regions with higher entropy, enabling continuous adaptation to new scenes. This is a self-prompting strategy that requires no prior knowledge and is tested on widely used benchmarks. In terms of the average performance, our method outperformed the baseline algorithm in TTA and continual TTA settings of Cityscapes-to-ACDC by 3.3% and 3.9%, respectively. Our method also outperformed the baseline algorithm by 4.1% and 3.1% on the more difficult Cityscapes-to-(Foggy and Rainy) Cityscapes setting, which also surpasses six other current TTA methods. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Virtual reality (VR) teaching methods have potential to support medical students acquire increasing amounts of knowledge. EVENT (Easy VR EducatioN Tool) is an open educational resource software for immersive VR environments, which is designed for use without programming skills. In this work, EVENT was used in a medical student VR course on pancreatic cancer. METHODS: Medical students were invited to participate in the course. Before and after VR simulation, participants completed a multiple-choice knowledge assessment, with a maximum score of 10, and a VR experience questionnaire. The primary endpoint compared pre- and post-VR simulation test scores. Secondary endpoints included usability and factors that could affect learning growth and test results. RESULTS: Data from 117 of the 135 participating students was available for analysis. Student test scores improved by an average of 3.4 points (95% CI 3.1-3.7, P < 0.001) after VR course. The secondary endpoints of gender, age, prior knowledge regarding the medical subject, professional training completed in the medical field, video game play, three-dimensional imagination skills, or cyber-sickness had no major impact on test scores or final ranking (top or bottom 25%). The 27 students whose post-VR simulation test scores ranked in the top 25% had no prior experience with VR. The average System Usability Scale score was 86.1, which corresponds to an excellent outcome for user-friendliness. Questionnaire responses post-VR simulation show students (81.2% [95/117]) interest in more VR options in medical school. CONCLUSIONS: We present a freely available software that allows for the development of VR teaching lessons without programming skills. [ABSTRACT FROM AUTHOR]

Landmark detection under large pose with occlusion has been one of the challenging problems in the field of facial analysis. Recently, many works have predicted pose or occlusion together in the multi-task learning (MTL) paradigm, trying to tap into their dependencies and thus alleviate this issue. However, such implicit dependencies are weakly interpretable and inconsistent with the way humans exploit inter-task coupling relations, i.e., accommodating the induced explicit effects. This is one of the essentials that hinders their performance. To this end, in this paper, we propose a Cascaded Iterative Transformer (CIT) to jointly predict facial landmark, occlusion probability, and pose. The proposed CIT, besides implicitly mining task dependencies in a shared encoder, innovatively employs a cost-effective and portability-friendly strategy to pass the decoders' predictions as prior knowledge to human-like exploit the coupling-induced effects. Moreover, to the best of our knowledge, no dataset contains all these task annotations simultaneously, so we introduce a new dataset termed MERL-RAV-FLOP based on the MERL-RAV dataset. We conduct extensive experiments on several challenging datasets (300W-LP, AFLW2000-3D, BIWI, COFW, and MERL-RAV-FLOP) and achieve remarkable results. The code and dataset can be accessed in https://github.com/Iron-LYK/CIT. [ABSTRACT FROM AUTHOR]

This study was conducted among undergraduate students pursuing a Primary Education degree at the University of Zaragoza. Its primary aim was to enhance and elevate the quality of participation during theoretical sessions, thereby fostering meaningful learning that contributes to the improvement of academic performance among the participants. To achieve this objective, a quasi-experimental case study was meticulously crafted. This research design was structured to not only stimulate and augment participation but also to cultivate meaningful learning, ultimately enhancing students' academic achievements. The study employed a comprehensive approach to monitor participation, utilizing observation records to track engagement levels, and anecdotal records to delineate the progression of sessions and the quality of responses. Through meticulous analysis, it can be deduced that the integration of thinking routines as a pedagogical tool in expository sessions significantly enhances student engagement. These routines effectively activate students' prior knowledge, establishing meaningful connections with the subject matter at hand. Moreover, the incorporation of thinking routines has been observed to elevate the quality of student participation. By fostering reflective practices, aiding in the formulation of inquiries, promoting collaborative learning, and nurturing critical thinking skills, these routines play a pivotal role in enriching the educational experience and bolstering academic performance. [ABSTRACT FROM AUTHOR]

In a context defined by the internationalisation of educational policies and the supranational nature of school programmes, we highlight the desirability of promoting local strategies for teaching environmental sustainability in order to contribute to the formation of global citizenship in children. Based on the experience of a horticultural curricular project in a school in northern Spain, the aim of this article is to reflect on the need for socio-educational communities to transform the objectives of environmental education into tools with which children can co-responsibly build connections to modify or enrich their everyday concepts of caring for the planet. To do this, semiotic analysis of different official school documents is used as a key methodology. Our findings invite consideration of the fact that pedagogies designed to train children in global citizenship competencies should not be limited to the classroom or to reproducing the proposals of institutional documents. Rather, they should be based on the prior knowledge and experiences of all members of the community, above all, of the children. From this perspective, the promotion of empirical learning situations is essential for the acquisition of meaningful and appropriate environmental contents, in the sense that they allow children, as future global citizens, to recognise the ethical repercussions of their own actions and decisions. [ABSTRACT FROM AUTHOR]

The year 2024 marks one decade of scholarship in the new interdisciplinary field of Digital Death, concerning the study of death, dying and grief in the digital age. This paper addresses one key subfield of Digital Death Studies, here termed Digital Grief Studies, which centres on theory, research and design concerning grief in today's digitally saturated contexts. It argues that a classic grand pattern in scholarly treatments of grief—Grief Universalism—with a long, problematic history in Grief and Bereavement Studies, is reappearing in Digital Grief Studies. The Continuing Bonds theory of grief and its application in theory, research and design in Digital Grief Studies is used to demonstrate Grief Universalism in action in our field via hypothetical and fictional examples. This builds toward this paper's big aim: to illustrate what we as an emerging field stand to gain from positioning the established field of Grief and Bereavement Studies as a veritable goldmine of advances—as well as pitfalls, wrong turns, and recurrent problem patterns to be avoided—generated over a hundred years of scholarship concerning human grief. Harnessing this wealth of prior learning and leveraging it toward the furtherance of our field in the coming decade and beyond becomes more crucial as we repel the seemingly perennial magnetism of Grief Universalism, as we operate within an interdisciplinary field vulnerable to Universalism and as yet unaware of its perils, and amid contemporary digital cultures and environments that may preserve and reinforce universalist grief framings. [ABSTRACT FROM AUTHOR]

This paper discusses using ground-based imagery to determine the attitude of a flying projectile assuming prior knowledge of its external geometry. It presents a segmentation-based approach to follow the object and evaluates it quantitatively with simulated data and qualitatively with both simulated and real data. Two experimental cases are considered: One assumes reliable target distance measurement from an auxiliary range sensor, while the other assumes no range information. The results show that in the case of an unknown projectile–camera distance, with projectile dimensions of 1.378 m and 0.08 m in length and diameter, the estimated distance, in-plane location, and pitch angle accuracies are about 50 m, 0.15 m, and 6 degrees, respectively. Yaw angle estimation is ambiguous. In the second case, assuming that the projectile–camera distance is known resolves the ambiguity of yaw estimation, resulting in accuracies of about 0.15 m, 3 degrees, and 20 degrees for in-plane location, pitch, and yaw angles, respectively. These accuracies were normalized to a 1-km projectile–camera distance. [ABSTRACT FROM AUTHOR]