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Summary: Hospital readmission prediction is defined as an evaluation task to model the historical medical data to predict whether patients will be readmitted after discharge. In the past few years, many feasible and effective prediction methods have been proposed, however, most of them neglect the imbalanced distribution of medical data, which causes great difficulties in modeling. Thus, we proposed a new hospital readmission prediction method, which utilizes hybrid‐sampling and self‐paced balance learning strategies to solve the class‐imbalance problem. To be specifically, we first employ an interference negative sample deletion strategy to reduce the probability of important majority class samples being deleted. Then, we design a hard positive sample generation strategy to generate more positive samples. Meanwhile, we also introduce a self‐paced balance factor during the oversampling process to improve the similarity between newly generated minority class samples and hard positive samples. Finally, we perform the experiments on six real‐world readmission datasets to indicate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Little research has examined specific instructional variables that influence the development and effectiveness of task‐analysis instruction. We conducted two experiments using text‐based task analyses to teach college students to create single‐subject reversal design graphs. In Experiment 1, we tested the effects of presenting antecedent and outcome stimuli on graphing performance (accuracy, yield, time to completion). Different groups of participants experienced graphing tutorials with descriptions and pictures of (a) responses; (b) antecedent stimuli and responses; (c) responses and outcomes of correct responses; and (d) antecedent stimuli, responses, and outcomes. In Experiment 2, we compared tutorials with and without pictures. Collectively, the results suggest that graphing accuracy was positively affected by task analyses that included pictures and descriptions of antecedent stimuli and that adding outcome stimuli further benefited graphing accuracy. These results suggest critical instructional elements that should be included in future task analyses of graphing or other complex behavior chains. [ABSTRACT FROM AUTHOR]

Computational models of the cardiovascular system are increasingly used for the diagnosis, treatment, and prevention of cardiovascular disease. Before being used for translational applications, the predictive abilities of these models need to be thoroughly demonstrated through verification, validation, and uncertainty quantification. When results depend on multiple uncertain inputs, sensitivity analysis is typically the first step required to separate relevant from unimportant inputs, and is key to determine an initial reduction on the problem dimensionality that will significantly affect the cost of all downstream analysis tasks. For computationally expensive models with numerous uncertain inputs, sample‐based sensitivity analysis may become impractical due to the substantial number of model evaluations it typically necessitates. To overcome this limitation, we consider recently proposed Multifidelity Monte Carlo estimators for Sobol' sensitivity indices, and demonstrate their applicability to an idealized model of the common carotid artery. Variance reduction is achieved combining a small number of three‐dimensional fluid–structure interaction simulations with affordable one‐ and zero‐dimensional reduced‐order models. These multifidelity Monte Carlo estimators are compared with traditional Monte Carlo and polynomial chaos expansion estimates. Specifically, we show consistent sensitivity ranks for both bi‐ (1D/0D) and tri‐fidelity (3D/1D/0D) estimators, and superior variance reduction compared to traditional single‐fidelity Monte Carlo estimators for the same computational budget. As the computational burden of Monte Carlo estimators for Sobol' indices is significantly affected by the problem dimensionality, polynomial chaos expansion is found to have lower computational cost for idealized models with smooth stochastic response. [ABSTRACT FROM AUTHOR]

Sentiment analysis is a fine‐grained analysis task that aims to identify the sentiment polarity of a specified sentence. Existing methods in Chinese sentiment analysis tasks only consider sentiment features from a single pole and scale and thus cannot fully exploit and utilise sentiment feature information, making their performance less than ideal. To resolve the problem, the authors propose a new method, GP‐FMLNet, that integrates both glyph and phonetic information and design a novel feature matrix learning process for phonetic features with which to model words that have the same pinyin information but different glyph information. Our method solves the problem of misspelling words influencing sentiment polarity prediction results. Specifically, the authors iteratively mine character, glyph, and pinyin features from the input comments sentences. Then, the authors use soft attention and matrix compound modules to model the phonetic features, which empowers their model to keep on zeroing in on the dynamic‐setting words in various positions and to dispense with the impacts of the deceptive‐setting ones. Experiments on six public datasets prove that the proposed model fully utilises the glyph and phonetic information and improves on the performance of existing Chinese sentiment analysis algorithms. [ABSTRACT FROM AUTHOR]

Background Objectives Methods Results and Conclusions Takeaways Scholars have confirmed the vital roles of self‐regulated learning (SRL) behaviours in predicting task performance, especially within non‐linear technology‐rich learning environments (TREs). However, few studies focused on the learning costs (e.g., study effort and time‐on‐task) related to SRL and the efficiency outcome of SRL (i.e., the relative relationship between learning costs and performance).This study examined the relationship between students' SRL behaviours and problem‐solving efficiency in the context of TREs.Eighty‐two medical students accomplished a diagnostic task in a computer‐simulated environment, and they were classified into the efficient or less efficient group according to diagnostic performance and time‐on‐task. Then we coded students' SRL behaviours from trace data and counted the frequency of each SRL behaviour. The recurrence quantification and lag sequential analyses were performed to extract the dynamic characteristics of SRL behaviours, including recurrent patterns and sequential transitions.Efficient students conducted more frequent Self‐reflection behaviours than the less efficient. For the recurrent patterns, efficient students tended to exhibit longer SRL behaviour sequences comprising a variety of different SRL behaviours (e.g., Task Analysis > Add Test > Add Hypotheses > Categorise Evidence) as well as longer sequences of repeated SRL behaviours (e.g., Add Test > Add Test > Add Test > Add Test). Moreover, efficient students exhibited more sequential transitions between different SRL behaviours than less efficient.Overall, this study revealed the effects of SRL on problem‐solving efficiency, which inspired researchers to incorporate problem‐solving efficiency as an evaluation criterion of SRL processes. [ABSTRACT FROM AUTHOR]

Goal setting is a component of many behavior‐change interventions, with the Specific, Measurable, Attainable, Relevant, Time‐Bound (SMART) goal‐setting method being a commonly used strategy. The current study used a multiple‐baseline across participants design to evaluate the efficacy of behavioral skills training (BST) to teach individuals to set health‐related SMART goals in response to multiple hypothetical health scenarios. Participant attempts at goal setting were scored according to a task analysis encompassing each major element of SMART goals. Goal‐setting performance noticeably improved following an average of 1.25 h of BST, and participants rated the intervention and outcomes favorably. [ABSTRACT FROM AUTHOR]

The employment interview is used to assess myriad constructs to inform personnel selection decisions. This article describes the first meta‐analytic review of the criterion‐related validity of interview‐based assessments of specific constructs (i.e., related to task and contextual performance). As such, this study explores the suitability of the interview for predicting specific dimensions of performance, and furthermore, if and how interviews should be designed to inform the assessment of distinct constructs. A comprehensive search process identified k = 37 studies comprising N = 30,646 participants (N = 4449 with the removal of one study). Results suggest that constructs related to task (ρ = .30) and contextual (ρ = .28) performance are assessed with similar levels of criterion‐related validity. Although interview evaluations of task and contextual performance constructs did not show discriminant validity within the interview itself, interview evaluations were more predictive of the targeted criterion construct than of alternative constructs. We further found evidence that evaluations of contextual performance constructs might particularly benefit from the adoption of more structured interview scoring procedures. However, we expect that new research on interview design factors may find additional moderating effects and we point to critical gaps in our current body of literature on employment interviews. These results illustrate how a construct‐specific approach to interview validity can spur new developments in the modeling, assessment, and selection of specific work performance constructs. [ABSTRACT FROM AUTHOR]

As one of the most important reasons of irreparable losses at work, human error is still a significant problem in spite of the rapid technological advancement and reduced role of humans in the systems set up and control. Therefore, with the aim of reducing human error, this study presents a new method to improve human reliability using the hybrid approach of SPAR‐H and DSM. For this purpose, the number of 53 tasks was specified for the terminal conductor using Hierarchical Task Analysis (HTA). Then, the error related to each task was calculated utilizing the Standardized Plant Analysis‐Risk Human (SPAR‐H) method, and based on the expert team's opinion. Next, the terminal conductor's tasks were entered into the DSM along with the error numbers and dependencies between tasks, and the most optimal arrangement of tasks was obtained by coding in MATLAB software. Finally, by using the SPAR‐H method, the error of the terminal conductor was calculated based on the new tasks arrangement and compared with the primary state. Based on the obtained results, the 30% increase in reliability shows the significant contribution of the optimal arrangement of tasks in reducing human error. The proposed method of this study helps managers to reviewing the task structure in high‐risk processes as an effective strategy to reduce human error. [ABSTRACT FROM AUTHOR]

Urbanization has amplified the importance of three‐dimensional structures in urban environments for a wide range of phenomena that are of significant interest to diverse stakeholders. With the growing availability of 3D urban data, numerous studies have focused on developing visual analysis techniques tailored to the unique characteristics of urban environments. However, incorporating the third dimension into visual analytics introduces additional challenges in designing effective visual tools to tackle urban data's diverse complexities. In this paper, we present a survey on visual analytics of 3D urban data. Our work characterizes published works along three main dimensions (why, what, and how), considering use cases, analysis tasks, data, visualizations, and interactions. We provide a fine‐grained categorization of published works from visualization journals and conferences, as well as from a myriad of urban domains, including urban planning, architecture, and engineering. By incorporating perspectives from both urban and visualization experts, we identify literature gaps, motivate visualization researchers to understand challenges and opportunities, and indicate future research directions. [ABSTRACT FROM AUTHOR]

Recent advances in deep convolutional neural networks have shown improved performance in face super‐resolution through joint training with other tasks such as face analysis and landmark prediction. However, these methods have certain limitations. One major limitation is the requirement for manual marking information on the dataset for multi‐task joint learning. This additional marking process increases the computational cost of the network model. Additionally, since prior information is often estimated from low‐quality faces, the obtained guidance information tends to be inaccurate. To address these challenges, a novel Decoder Structure Guided CNN‐Transformer Network (DCTNet) is introduced, which utilises the newly proposed Global‐Local Feature Extraction Unit (GLFEU) for effective embedding. Specifically, the proposed GLFEU is composed of an attention branch and a Transformer branch, to simultaneously restore global facial structure and local texture details. Additionally, a Multi‐Stage Feature Fusion Module is incorporated to fuse features from different network stages, further improving the quality of the restored face images. Compared with previous methods, DCTNet improves Peak Signal‐to‐Noise Ratio by 0.23 and 0.19 dB on the CelebA and Helen datasets, respectively. Experimental results demonstrate that the designed DCTNet offers a simple yet powerful solution to recover detailed facial structures from low‐quality images. [ABSTRACT FROM AUTHOR]

When people make decisions, they act in a way that is either automatic ("rote"), or more thoughtful ("reflective"). But do people notice when others are behaving in a rote way, and do they care? We examine the detection of rote behavior and its consequences in U.S. adults, focusing specifically on pedagogy and learning. We establish repetitiveness as a cue for rote behavior (Experiment 1), and find that rote people are seen as worse teachers (Experiment 2). We also find that the more a person's feedback seems similar across groups (indicating greater rote‐ness), the more negatively their teaching is evaluated (Experiment 3). A word‐embedding analysis of an open‐response task shows people naturally cluster rote and reflective teachers into different semantic categories (Experiment 4). We also show that repetitiveness can be decoupled from perceptions of rote‐ness given contextual explanation (Experiment 5). Finally, we establish two additional cues to rote behavior that can be tied to quality of teaching (Experiment 6). These results empirically show that people detect and care about scripted behaviors in pedagogy, and suggest an important extension to formal frameworks of social reasoning. [ABSTRACT FROM AUTHOR]

Three‐dimensional (3D) object detection, that is, localizing and classifying all critical objects in a 3D space, is essential for downstream construction scene analysis tasks. However, accurate instance segmentation, few 2D object segmentation and 3D object detection data sets, high‐quality feature representations for depth estimation, and limited 3D cues from a single red‐green‐blue (RGB) image pose significant challenges to 3D object detection and severely hinder its practical applications. In response to these challenges, an improved cascade‐based network with a transformer backbone and a boundary‐patch‐refinement method is proposed to build hierarchical features and refine object boundaries, resulting in better results in 2D object detection and instance segmentation. Furthermore, a novel self‐supervised monocular depth learning method is proposed to extract better feature representations for depth estimation from construction site video data with unknown camera parameters. Additionally, a pseudo‐LiDAR point cloud method and a 3D object detection method with a density‐based clustering algorithm are proposed to detect 3D objects in a construction scene without help from 3D labels, which will serve as a good foundation for other downstream 3D tasks. Finally, the proposed model is evaluated for object instance segmentation and depth estimation on the moving objects in construction sites (MOCS) and construction scene data sets. It brings a 9.16% gain in terms of mean average precision (mAP) for object detection and a 4.92% gain in mask mAP for object instance segmentation. The average order accuracy and relative mean error for depth estimation are improved by 0.94% and 60.56%, respectively. This study aims to overcome the challenges and limitations of 3D object detection and facilitate practical applications in construction scene analysis. [ABSTRACT FROM AUTHOR]

Fluorescence microscopy data describe protein localization patterns at single-cell resolution and have the potential to reveal whole-proteome functional information with remarkable precision. Yet, extracting biologically meaningful representations from cell micrographs remains a major challenge. Existing approaches often fail to learn robust and noise-invariant features or rely on supervised labels for accurate annotations. We developed PIFiA (Protein Image-based Functional Annotation), a self-supervised approach for protein functional annotation from single-cell imaging data. We imaged the global yeast ORF-GFP collection and applied PIFiA to generate protein feature profiles from single-cell images of fluorescently tagged proteins. We show that PIFiA outperforms existing approaches for molecular representation learning and describe a range of downstream analysis tasks to explore the information content of the feature profiles. Specifically, we cluster extracted features into a hierarchy of functional organization, study cell population heterogeneity, and develop techniques to distinguish multi-localizing proteins and identify functional modules. Finally, we confirm new PIFiA predictions using a colocalization assay, suggesting previously unappreciated biological roles for several proteins. Paired with a fully interactive website (https://thecellvision.org/pifia/), PIFiA is a resource for the quantitative analysis of protein organization within the cell. Synopsis: PIFiA is a self-supervised deep-learning approach for protein functional annotation from single-cell images. It generates feature profiles from images of the yeast ORF-GFP collection that can be used in downstream analyses. PIFiA features identify new functional groups of proteins within organelles and proteins with heterogeneous localizations. PIFiA features successfully predict protein–protein interactions and members of protein complexes. PIFiA outperforms previous methods on four different standards of protein function. Images and analysis are available at thecellvision.org/pifia. PIFiA is a self-supervised deep-learning approach for protein functional annotation from single-cell images. It generates feature profiles from images of the yeast ORF-GFP collection that can be used in downstream analyses. [ABSTRACT FROM AUTHOR]

Usability evaluation is a key element to ensure a positive user experience with any software and it is especially important in educational software tools where there are many different actors involved (lecturers, students, administrators, etc.). However, evaluating usability is not an easy task for nonexpert evaluators. To facilitate this evaluation task, this article presents a Methodology for Usability Testing (MUT) and a system (CALMUT) that assists nonexpert evaluators in the application of the methodology by automatizing the calculations and facilitating their interpretation. This can be very useful for learning and instructional designers but also to people involved in the decision of introducing or not a new educational software. To develop the proposal, a literature review of different usability metrics, methods, and systems was carried out first, followed by a selection and adaptation for novice usability evaluators. This article also presents a case study where lecturers tested the usability of an educational software following the proposal and shows that using MUT and CALMUT helps people without previous experience detect the main usability problems of educational systems before deciding whether to use them or not. [ABSTRACT FROM AUTHOR]

Heatwaves are known to arise from the interplay between large‐scale climate variability, synoptic weather patterns, and regional to local‐scale surface processes. Though recent research has made important progress for each individual contributing factor, ways to properly incorporate multiple or all of them in a unified analysis are still lacking. In this study, we consider a wide range of possible predictor variables from the ERA5 Reanalysis version 5 and ask how much information on heatwave occurrence in Europe can be learned from each of them. To simplify the problem, we first adapt the recently developed logistic principal component analysis to the task of compressing large binary heatwave fields to a small number of interpretable principal components. The relationships between heatwaves and various climate variables can then be learned by a neural network. Starting from the simple notion that the importance of a variable is given by its impact on the performance of our statistical model, we arrive naturally at the definition of Shapley values. Classic results of game theory show that this is the only fair way of distributing the overall success of a model among its inputs. We find a nonlinear model that explains 70% of reduced heatwave variability. The biggest individual contribution (27% of the 70%) comes from upper level geopotential; top‐level soil moisture is in second place (15%). Beyond this decomposition, Shapley interaction values enable us to quantify overlapping information and positive synergies between all pairs of predictors. [ABSTRACT FROM AUTHOR]

Facial beauty analysis is an important topic in human society. It may be used as a guidance for face beautification applications such as cosmetic surgery. Deep neural networks (DNNs) have recently been adopted for facial beauty analysis and have achieved remarkable performance. However, most existing DNN‐based models regard facial beauty analysis as a normal classification task. They ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty analysis. To be specific, landmarks of the whole face and facial organs are introduced to extract geometric features to make the decision. Inspired by this, we introduce a novel dual‐branch network for facial beauty analysis: one branch takes the Swin Transformer as the backbone to model the full face and global patterns, and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial parts. Additionally, the designed multi‐scale feature fusion module can further facilitate our network to learn complementary semantic information between the two branches. In model optimisation, we propose a hybrid loss function, where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric features. Experiments performed on the SCUT‐FBP5500 dataset and the SCUT‐FBP dataset demonstrate that our model outperforms the state‐of‐the‐art convolutional neural networks models, which proves the effectiveness of the proposed geometric regularisation and dual‐branch structure with the hybrid network. To the best of our knowledge, this is the first study to introduce a Vision Transformer into the facial beauty analysis task. [ABSTRACT FROM AUTHOR]

Fear of cancer recurrence (FCR) involves anxiety about the possible return or progression of the disease. It is common among people surviving cancer, covering a range of adaptive and maladaptive responses including clinical presentations of FCR, for which different psychological interventions have been developed, most within the cognitive‐behavioural paradigm. Recently, emotion‐focused therapy (EFT) has been proposed as an alternative and has been the subject of research focusing on the cancer population and cancer‐related issues, including FCR. In this study, we looked closely at a successful case from a larger exploratory study, carrying out a discovery‐phase task analysis aimed at identifying the main components of EFT–FCR. We found that this approach generally followed the usual structure of an EFT intervention, with four distinct phases. However, we identified some specific secondary processes (e.g., hypervigilance and catastrophising) and clarified the nature of the core pain in this presentation as existential (e.g., fear of dying). [ABSTRACT FROM AUTHOR]

The emergency department (ED) is at the forefront of the chemical, biological, radiological, and nuclear (CBRN) response. This study adopted a multilevel systems approach using the human factors/ergonomics (HFE) method of hierarchical task analysis (HTA) with document analyses of CBRN plans to represent work‐as‐Imagined. Work‐as‐imagined was compared with data from semistructured interviews using prompt cards for CBRN scenarios (n = 57) representing work‐as‐done. The aim was to provide methodological evidence for the use of HTA with an empirical synthesis of the ED in response to CBRN events. HTA was the preferred systems mapping tool because it aligns with a systems thinking approach, allows multiple‐level comparisons, highlights variability, and has an established usability track record. This study demonstrates the usability of HTA in the context of the ED responding to a CBRN event. The findings for core CBRN concepts included (1) liaise and communicate, (2) isolate and contain, and (3) personal protective equipment. [ABSTRACT FROM AUTHOR]

This report surveys advances in deep learning‐based modelling techniques that address four different 3D indoor scene analysis tasks, as well as synthesis of 3D indoor scenes. We describe different kinds of representations for indoor scenes, various indoor scene datasets available for research in the aforementioned areas, and discuss notable works employing machine learning models for such scene modelling tasks based on these representations. Specifically, we focus on the analysis and synthesis of 3D indoor scenes. With respect to analysis, we focus on four basic scene understanding tasks – 3D object detection, 3D scene segmentation, 3D scene reconstruction and 3D scene similarity. And for synthesis, we mainly discuss neural scene synthesis works, though also highlighting model‐driven methods that allow for human‐centric, progressive scene synthesis. We identify the challenges involved in modelling scenes for these tasks and the kind of machinery that needs to be developed to adapt to the data representation, and the task setting in general. For each of these tasks, we provide a comprehensive summary of the state‐of‐the‐art works across different axes such as the choice of data representation, backbone, evaluation metric, input, output and so on, providing an organized review of the literature. Towards the end, we discuss some interesting research directions that have the potential to make a direct impact on the way users interact and engage with these virtual scene models, making them an integral part of the metaverse. [ABSTRACT FROM AUTHOR]

Background: Prostate‐specific membrane antigen (PSMA) PET imaging represents a valuable source of information reflecting disease stage, response rate, and treatment optimization options, particularly with PSMA radioligand therapy. Quantification of radiopharmaceutical uptake in healthy organs from PSMA images has the potential to minimize toxicity by extrapolation of the radiation dose delivery towards personalization of therapy. However, segmentation and quantification of uptake in organs requires labor‐intensive organ delineations that are often not feasible in the clinic nor scalable for large clinical trials. Purpose: In this work we develop and test the PSMA Healthy organ segmentation network (PSMA‐Hornet), a fully‐automated deep neural net for simultaneous segmentation of 14 healthy organs representing the normal biodistribution of [18F]DCFPyL on PET/CT images. We also propose a modified U‐net architecture, a self‐supervised pre‐training method for PET/CT images, a multi‐target Dice loss, and multi‐target batch balancing to effectively train PSMA‐Hornet and similar networks. Methods: The study used manually‐segmented [18F]DCFPyL PET/CT images from 100 subjects, and 526 similar images without segmentations. The unsegmented images were used for self‐supervised model pretraining. For supervised training, Monte‐Carlo cross‐validation was used to evaluate the network performance, with 85 subjects in each trial reserved for model training, 5 for validation, and 10 for testing. Image segmentation and quantification metrics were evaluated on the test folds with respect to manual segmentations by a nuclear medicine physician, and compared to inter‐rater agreement. The model's segmentation performance was also evaluated on a separate set of 19 images with high tumor load. Results: With our best model, the lowest mean Dice coefficient on the test set was 0.826 for the sublingual gland, and the highest was 0.964 for liver. The highest mean error in tracer uptake quantification was 13.9% in the sublingual gland. Self‐supervised pretraining improved training convergence, train‐to‐test generalization, and segmentation quality. In addition, we found that a multi‐target network produced significantly higher segmentation accuracy than single‐organ networks. Conclusions: The developed network can be used to automatically obtain high‐quality organ segmentations for PSMA image analysis tasks. It can be used to reproducibly extract imaging data, and holds promise for clinical applications such as personalized radiation dosimetry and improved radioligand therapy. [ABSTRACT FROM AUTHOR]

The current study evaluated the effectiveness of video feedback (VF) to improve tumbling skills with three high‐school cheerleaders. Researchers evaluated the effects of VF using a multiple baseline across behaviors design. Results demonstrate that VF was effective in increasing the percentage of correct steps of the target skills for all three participants. This study contributes to the literature on the use of VF as a procedure to increase sports performance. [ABSTRACT FROM AUTHOR]

In unmanned aerial vehicle (UAV)‐assisted wireless sensor networks (WSNs), UAVs are employed to collect sensing data from each sensor node (SN). Because of limited battery capacity, shortening the time of data collection by the UAV is necessary. Notably, the task completion time is related to UAV trajectory and spectrum allocation. In this paper, joint trajectory design and spectrum allocation is studied to minimize the task completion time of UAVs while ensuring the target upload data amount for each SN. The cases that all SNs are located within the communication range of a UAV is explored first; the formulated problem is non‐convex and difficult to solve directly. Hence, an iterative algorithm based on block coordinate descent and successive convex approximation is proposed to decompose and transformed the original problem into two convex optimizations. Furthermore, the proposed algorithm is applied to the general case that all SNs are widely distributed by leveraging the spiral algorithm and traveling salesman problem technique. Simulation results show that the proposed algorithm can effectively reduce the task time of UAV compared to other benchmark algorithms. [ABSTRACT FROM AUTHOR]

Developmental Coordination Disorder (DCD) is a neurodevelopmental disorder of unknown etiology that affects one in 20 children. There is an indication that DCD has an underlying genetic component due to its high heritability. Therefore, we explored the use of a recombinant inbred family of mice known as the BXD panel to understand the genetic basis of complex traits (i.e., motor learning) through identification of quantitative trait loci (QTLs). The overall aim of this study was to utilize the QTL approach to evaluate the genome‐to‐phenome correlation in BXD strains of mice in order to better understand the human presentation of DCD. Results of this current study confirm differences in motor learning in selected BXD strains and strains with altered cerebellar volume. Five strains – BXD15, BXD27, BXD28, BXD75, and BXD86 – exhibited the most DCD‐like phenotype when compared with other BXD strains of interest. Results indicate that BXD15 and BXD75 struggled primarily with gross motor skills, BXD28 primarily had difficulties with fine motor skills, and BXD27 and BXD86 strains struggled with both fine and gross motor skills. The functional roles of genes within significant QTLs were assessed in relation to DCD‐like behavior. Only Rab3a (Ras‐related protein Rab‐3A) emerged as a high likelihood candidate gene for the horizontal ladder rung task. This gene is associated with brain and skeletal muscle development, but lacked nonsynonymous polymorphisms. This study along with Gill et al. (same issue) is the first studies to specifically examine the genetic linkage of DCD using BXD strains of mice. [ABSTRACT FROM AUTHOR]

A variety of neural networks have been presented to deal with issues in deep learning in the last decades. Despite the prominent success achieved by the neural network, it still lacks theoretical guidance to design an efficient neural network model, and verifying the performance of a model needs excessive resources. Previous research studies have demonstrated that many existing models can be regarded as different numerical discretizations of differential equations. This connection sheds light on designing an effective recurrent neural network (RNN) by resorting to numerical analysis. Simple RNN is regarded as a discretisation of the forward Euler scheme. Considering the limited solution accuracy of the forward Euler methods, a Taylor‐type discrete scheme is presented with lower truncation error and a Taylor‐type RNN (T‐RNN) is designed with its guidance. Extensive experiments are conducted to evaluate its performance on statistical language models and emotion analysis tasks. The noticeable gains obtained by T‐RNN present its superiority and the feasibility of designing the neural network model using numerical methods. [ABSTRACT FROM AUTHOR]

Natural language processing (NLP) is a field of study that focuses on data analysis on texts with certain methods. NLP includes tasks such as sentiment analysis, spam detection, entity linking, and question answering, to name a few. Entity linking is an NLP task that is used to map mentions specified in the text to the entities of a Knowledge Base. In this study, we analysed the efficacy of bi‐encoder entity linking models for multilingual biomedical texts. Using surface‐based, approximate nearest neighbour search and embedding approaches during the candidate generation phase, accuracy, and recall values were measured on language representation models such as BERT, SapBERT, BioBERT, and RoBERTa according to language and domain. The proposed entity linking framework was analysed on the BC5CDR and Cantemist datasets for English and Spanish, respectively. The framework achieved 76.75% accuracy for the BC5CDR and 60.19% for the Cantemist. In addition, the proposed framework was compared with previous studies. The results highlight the challenges that come with domain‐specific multilingual datasets. [ABSTRACT FROM AUTHOR]

Resampling‐based weather generators simulate new time series of weather variables by reordering the observed values such that the statistics of the simulated data are consistent with the observed ones. These generators are fully data‐driven, easy to implement, and capable of reproducing the dynamics among weather variables. However, although the simulated time series is new, the weather fields produced at arbitrary time steps are replicas of those found in observations, limiting the spatial variability of simulations and preventing the generation of extreme weather fields beyond the range of observed values. To address these limitations, we propose the integration of the Direct Sampling algorithm—a data‐driven method for producing simulations—into resampling‐based weather generators. By incorporating Direct Sampling as a post‐processing step on the outputs of the weather generator, we enhance the spatial variability of the generated weather fields and enable the generation of extreme weather fields. We introduce an approach for generating out‐of‐sample extreme weather fields using Direct Sampling. This method involves utilizing a set of control points in conjunction with Direct Sampling, where the values of these control points are informed by return period analysis. The proposed approach is validated using precipitation, temperature, and cloud cover weather fields in a region of northwest India. The experimental results confirm that Direct Sampling enhances the spatial variability of the weather fields and facilitates the generation of out‐of‐sample precipitation fields that accurately adhere to the spatial statistics provided by return precipitation level maps, as well as the observed precipitation weather field employed in the analysis. Plain Language Summary: Weather generators (WG) are tools for generating artificial weather data. Applications use WG outputs for several tasks, including risk, uncertainty, and climate change analysis. WGs based on "resampling " conforms to a type of WGs that is easy to implement, understand and produce data with properties resembling historical data. However, although those WGs generate new artificial time series, those series are sorted versions of historical weather fields (i.e., weather data values at the spatial domain) Furthermore, those WGs can't generate weather fields with out‐of‐sample data values, that is, extreme weather. In this work, we research the applicability of the Direct Sampling algorithm for creating variations of the simulated weather fields by the WG, and for generating artificial precipitation fields with extreme values. We found that Direct Sampling post‐processing of weather generator outputs is a simple approach to improve the variations of weather fields and for generating extreme precipitation fields conditioned on information provided by an extreme precipitation analysis. The methods exposed in our work show a way to improve the design of those WGs that can benefit several applications like the ones searching the generation of hypothetical extreme weather fields, or seeking better uncertainty quantification or estimates in risk analysis tasks. Key Points: Spatial variability improvement of weather generators based on resampling via the Direct Sampling algorithmDirect Sampling for extreme precipitation fields generation using control points and return periodsEmpirical validation using statistical and connectivity metrics on a data set with precipitation, temperature and cloud cover variables [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) in the wild is an active and challenging field of research. A system for automatic FER finds use in a wide range of applications related to advanced human–computer interaction (HCI), human–robot interaction (HRI), human behavioral analysis, gaming and entertainment, etc. Since their inception, convolutional neural networks (CNNs) have attained state‐of‐the‐art accuracy in the facial analysis task. However, recognizing facial expressions in the wild with high confidence running on a low‐cost embedded device remains challenging. To this end, this study presents an efficient dual‐channel ensembled deep CNN (DCE‐DCNN) for FER in the wild. Initially, two DCNNs, namely the DCNNG$$ {\mathrm{DCNN}}_G $$ and DCNNS$$ {\mathrm{DCNN}}_S $$, are trained separately on the grayscale and Scharr‐convolved vertical gradient facial images, respectively. The proposed network later integrates the two pre‐trained DCNNs to obtain the dual‐channel integrated DCNN (DCI‐DCNN). Finally, all three neural networks, namely the DCNNG$$ {\mathrm{DCNN}}_G $$, DCNNS$$ {\mathrm{DCNN}}_S $$, and DCI‐DCNN, are jointly fine‐tuned to get a single dual‐channel‐multi‐output model. The multi‐output model produces three prediction scores for the given input facial image. The prediction scores are thus fused using the max‐voting ensemble scheme to obtain the DCE‐DCNN with the final classification label. On the FER2013, RAF‐DB, NCAER‐S, AffectNet, and CKPlus benchmark FER datasets, the proposed DCE‐DCNN consistently outperforms the two individual DCNNs and numerous state‐of‐the‐art CNNs. Moreover, the network achieves competitive recognition accuracy on all four FER in the wild datasets with reduced memory storage size and parameters. The proposed DCE‐DCNN model with high throughput on resource‐limited embedded devices is suitable for applications that seek real‐time classification of facial expressions in the wild with high confidence. [ABSTRACT FROM AUTHOR]

A growing amount of research use pre‐trained language models to address few/zero‐shot text classification problems. Most of these studies neglect the semantic information hidden implicitly beneath the natural language names of class labels and develop a meta learner from the input texts solely. In this work, we demonstrate how label information can be utilized to extract enhanced feature representation of the input text from a Transformer‐based pre‐trained language model such as AraBERT. In addition, how this approach can improve performance when the data resources are scarce like in the Arabic language and the input text is short with little semantic information as is the case using tweets. The work also applies zero‐shot text classification to predict new classes with no training examples across different domains including sarcasm detection and sentiment analysis using the information in the last layer of a trained classifier in a transfer learning setting. Experiments show that our approach has a better performance for the few‐shot sentiment classification compared to baseline models and models trained without augmenting label information. Moreover, the zero‐shot implementation achieved an accuracy up to 0.874 in Arabic sarcasm detection from a model trained on a sentiment analysis task. [ABSTRACT FROM AUTHOR]

Motor atypicalities are common in autism spectrum disorder (ASD) and are often evident prior to classical ASD symptoms. Despite evidence of differences in neural processing during imitation in autistic individuals, research on the integrity and spatiotemporal dynamics of basic motor processing is surprisingly sparse. To address this need, we analysed electroencephalography (EEG) data recorded from a large sample of autistic (n = 84) and neurotypical (n = 84) children and adolescents while they performed an audiovisual speeded reaction time (RT) task. Analyses focused on RTs and response‐locked motor‐related electrical brain responses over frontoparietal scalp regions: the late Bereitschaftspotential, the motor potential and the reafferent potential. Evaluation of behavioural task performance indicated greater RT variability and lower hit rates in autistic participants compared to typically developing age‐matched neurotypical participants. Overall, the data revealed clear motor‐related neural responses in ASD, but with subtle differences relative to typically developing participants evident over fronto‐central and bilateral parietal scalp sites prior to response onset. Group differences were further parsed as a function of age (6–9, 9–12 and 12–15 years), sensory cue preceding the response (auditory, visual and bi‐sensory audiovisual) and RT quartile. Group differences in motor‐related processing were most prominent in the youngest group of children (age 6–9), with attenuated cortical responses observed for young autistic participants. Future investigations assessing the integrity of such motor processes in younger children, where larger differences may be present, are warranted. [ABSTRACT FROM AUTHOR]

Knowledge graph embedding models are used to learn low‐dimensional representations of entities and relations in knowledge graphs. In this paper, we propose Multi‐RAttE, an attention‐based learning method for multiple relational knowledge graph embedding representation, which divides the information transfer in the knowledge graph into cross‐relational information transfer and relation‐specific information transfer, and divides the embedding of knowledge graph entities into structural embedding and multi‐relational embedding for joint learning. To objectively analyse the performance of the Multi‐RAttE model, we select two typical datasets and different representative baseline models for experimental evaluation on several tasks such as link prediction, multi‐relation prediction and node classification. The experimental results show that the Multi‐RAttE model improves 8% over the state‐of‐the‐art model Composition‐based Multi‐Relational Graph Convolutional Networks (CompGCN) in terms of Hits@1 metric on the link prediction task on FB15k‐237 dataset; on the multi‐relation prediction task, the accuracy improves by 1.8% and 3.7% in the auc metric and F1 metric, respectively. The experimental results have proved that the Multi‐RAttE model can effectively perform the representation of multiple relations. [ABSTRACT FROM AUTHOR]

Engineered solutions are becoming more complex and multi‐disciplinary in nature. This evolution requires new techniques to enhance design and analysis tasks that incorporate data integration and interoperability across various engineering tool suites spanning multiple domains at different abstraction levels. Semantic web technologies (SWT) offer data integration and interoperability benefits as well as other opportunities to enhance reasoning across knowledge represented in multiple disparate models. This paper introduces the digital engineering framework for integration and interoperability (DEFII) for incorporating SWT into engineering design and analysis tasks. The framework includes three notional interfaces for interacting with ontology‐aligned data. It also introduces a novel model interface specification diagram (MISD) that provides a tool‐agnostic model representation enabled by SWT that exposes data stored for use by external users through standards‐based interfaces. Use of the framework results in a tool‐agnostic authoritative source of truth spanning the entire project, system, or mission. [ABSTRACT FROM AUTHOR]

This paper proposes a unified formal framework, Formal Framework For FLUID (F3FLUID), for the development of safety‐critical interactive systems. This framework is based on the Formal Language of User Interface Design (FLUID) pivot modeling language defined in the FORMEDICIS project, which enables high‐level system requirements for interactive systems to be specified in the FLUID language. This modeling language is specifically designed for handling concepts of safety‐critical interactive systems, including domain knowledge. A FLUID model is used as a source model for the generation of several target models in different modeling languages to support the formal verification methods, such as theorem proving and model checking. In this paper, we use the Event‐B modeling language for checking functional behaviors, user interactions, safety properties, and domain properties. A FLUID model is transformed into an Event‐B model, and then, the Rodin tool is used to check the internal consistency with respect to the given safety properties. We illustrate the operational semantics of the FLUID language, and the transformation strategy of FLUID models into Event‐B models, including the tool development. We use the ProB model checker to analyze the temporal properties and to animate the formalized specification. In addition, an interactive cooperative objects (ICOs) model is derived from the Event‐B model for animation, visualization and validation of dynamic behaviors, visual properties, and task analysis. Finally, an industrial case study, complying with the ARINC 661 standard, Multi‐Purpose Interactive Applications (MPIA), is used to illustrate the effectiveness of our F3FLUID framework for the development of safety‐critical interactive systems. [ABSTRACT FROM AUTHOR]

Agriculture provides food, raw materials, and employment opportunities for a significant percentage of the world's population. Climate, economic, political, social, and other conditions affect decision making in agricultural processes. In many cases, these conditions imply the loss of suitability of many areas for some traditional crops. In contrast, these areas can produce new crops by taking advantage of changing conditions. In this sense, having reliable tools and information for decision making is essential in adapting to new agricultural productivity scenarios. The above implies having sufficient and relevant data sources to reduce the uncertainty in the decision‐making processes. However, data by nature tend to be diverse in structure, storage formats, and access protocols. Data fusion tasks have been immersed in a multitude of applications and have been approached from different points of view when implementing a suitable solution. We propose a multi‐domain data fusion strategy to support data analysis tasks in agricultural contexts. We also describe all the data sources collected, which are the main input to the proposed strategy. The combined data sources were also evaluated through a preliminary exploratory analysis in a multi‐label learning approach. Finally, the data fusion strategy is explained through an example in agricultural crop production. [ABSTRACT FROM AUTHOR]

Estimating the depth of a construction scene from a single red‐green‐blue image is a crucial prerequisite for various applications, including work zone safety, localization, productivity analysis, activity recognition, and scene understanding. Recently, self‐supervised representation learning methods have made significant progress and demonstrated state‐of‐the‐art performance on monocular depth estimation. However, the two leading open challenges are the ambiguity of estimated depth up to an unknown scale and representation transferability for a downstream task, which severely hinders the practical deployment of self‐supervised methods. We propose a prior information‐based method, not depending on additional sensors, to recover the unknown scale in monocular vision and predict per‐pixel absolute depth. Moreover, a new learning paradigm for a self‐supervised monocular depth estimation model is constructed to transfer the pre‐trained self‐supervised model to other downstream construction scene analysis tasks. Meanwhile, we also propose a novel depth loss to enforce depth consistency when transferring to a new downstream task and two new metrics to measure transfer performance. Finally, we verify the effectiveness of scale recovery and representation transferability in isolation. The new learning paradigm with our new metrics and depth loss is expected to estimate the monocular depth of a construction scene without depth ground truth like light detection and ranging. Our models will serve as a good foundation for further construction scene analysis tasks. [ABSTRACT FROM AUTHOR]

Over the past years, an increasing number of publications in information visualization, especially within the field of visual analytics, have mentioned the term "embedding" when describing the computational approach. Within this context, embeddings are usually (relatively) low‐dimensional, distributed representations of various data types (such as texts or graphs), and since they have proven to be extremely useful for a variety of data analysis tasks across various disciplines and fields, they have become widely used. Existing visualization approaches aim to either support exploration and interpretation of the embedding space through visual representation and interaction, or aim to use embeddings as part of the computational pipeline for addressing downstream analytical tasks. To the best of our knowledge, this is the first survey that takes a detailed look at embedding methods through the lens of visual analytics, and the purpose of our survey article is to provide a systematic overview of the state of the art within the emerging field of embedding visualization. We design a categorization scheme for our approach, analyze the current research frontier based on peer‐reviewed publications, and discuss existing trends, challenges, and potential research directions for using embeddings in the context of visual analytics. Furthermore, we provide an interactive survey browser for the collected and categorized survey data, which currently includes 122 entries that appeared between 2007 and 2023. [ABSTRACT FROM AUTHOR]

Most real‐world networks are both dynamic and multivariate in nature, meaning that the network is associated with various attributes and both the network structure and attributes evolve over time. Visualizing dynamic multivariate networks is of great significance to the visualization community because of their wide applications across multiple domains. However, it remains challenging because the techniques should focus on representing the network structure, attributes and their evolution concurrently. Many real‐world network analysis tasks require the concurrent usage of the three aspects of the dynamic multivariate networks. In this paper, we analyze current techniques and present a taxonomy to classify the existing visualization techniques based on three aspects: temporal encoding, topology encoding, and attribute encoding. Finally, we survey application areas and evaluation methods; and discuss challenges for future research. [ABSTRACT FROM AUTHOR]

Deep neural network (DNN) surrogates of expensive physics simulations are enabling a rapid change in the way that common experimental design and analysis tasks are approached. Surrogate models allow simulations to be performed in parallel and separately from downstream tasks, thereby enabling analyses that would be impossible with the simulation in‐the‐loop; surrogates based on DNNs can effectively emulate diverse non‐scalar data of the types collected in fusion and laboratory‐astrophysics experiments. The challenge is in training the surrogate model, for which large ensembles of physics simulations must be run, preferably without wasting computational effort on uninteresting simulations. In this paper, we present an iterative sampling scheme that can preferentially propose simulations in interesting regions of parameter space without neglecting unexplored regions, allowing high‐quality and wide‐ranging surrogate models to be trained using 2–3 times fewer simulations compare to space‐filling designs. Our approach uses an explicit importance function defined on the simulation output space, balanced against a measure of simulation density which serves as a proxy for surrogate accuracy. It is easy to implement and can be tuned to find interesting simulations early in the study, allowing surrogates to be trained quickly and refined as new simulations become available; this represents an important step towards the routine generation of deep surrogate models quickly enough to be truly relevant to experimental work. [ABSTRACT FROM AUTHOR]

Design patterns are well‐known solutions to recurring design problems that are widely adopted in the software industry, either as formal means of communication or as a way to improve structural quality, enabling proper software extension. However, the adoption and correct instantiation of patterns is not a trivial task and requires substantial design experience. Some patterns are conceptually close or present similar design alternatives, leading novice developers to improper pattern selection, thereby reducing maintainability. Additionally, the mis‐instantiation of a GoF (Gang‐of‐Four) design pattern, leads to phenomena such as pattern grime or architecture decay. To alleviate this problem, in this work we propose an approach that can help software engineers to more easily and safely select the proper design pattern, for a given design problem. The approach relies on decision trees, which are constructed using domain knowledge, while options are conveyed to software engineers through an Eclipse Theia plugin. To assess the usefulness and the perceived benefits of the approach, as well as the usability of the tool support, we have conducted an industrial validation study, using various data collection methods, such as questionnaires, focus groups, and task analysis. The results of the study suggest that the proposed approach is promising, since it increases the probability of the proper pattern being selected, and various useful future work suggestions have been obtained by the practitioners. [ABSTRACT FROM AUTHOR]

Very often, in the course of uncertainty quantification tasks or data analysis, one has to deal with high‐dimensional random variables. Here the interest is mainly to compute characterizations like the entropy, the Kullback–Leibler divergence, more general f$$ f $$‐divergences, or other such characteristics based on the probability density. The density is often not available directly, and it is a computational challenge to just represent it in a numerically feasible fashion in case the dimension is even moderately large. It is an even stronger numerical challenge to then actually compute said characteristics in the high‐dimensional case. In this regard it is proposed to approximate the discretized density in a compressed form, in particular by a low‐rank tensor. This can alternatively be obtained from the corresponding probability characteristic function, or more general representations of the underlying random variable. The mentioned characterizations need point‐wise functions like the logarithm. This normally rather trivial task becomes computationally difficult when the density is approximated in a compressed resp. low‐rank tensor format, as the point values are not directly accessible. The computations become possible by considering the compressed data as an element of an associative, commutative algebra with an inner product, and using matrix algorithms to accomplish the mentioned tasks. The representation as a low‐rank element of a high order tensor space allows to reduce the computational complexity and storage cost from exponential in the dimension to almost linear. [ABSTRACT FROM AUTHOR]

Outlier or anomaly detection is an important task in data analysis. We discuss the problem from a geometrical perspective and provide a framework which exploits the metric structure of a data set. Our approach rests on the manifold assumption, that is, that the observed, nominally high‐dimensional data lie on a much lower dimensional manifold and that this intrinsic structure can be inferred with manifold learning methods. We show that exploiting this structure significantly improves the detection of outlying observations in high dimensional data. We also suggest a novel, mathematically precise and widely applicable distinction between distributional and structural outliers based on the geometry and topology of the data manifold that clarifies conceptual ambiguities prevalent throughout the literature. Our experiments focus on functional data as one class of structured high‐dimensional data, but the framework we propose is completely general and we include image and graph data applications. Our results show that the outlier structure of high‐dimensional and non‐tabular data can be detected and visualized using manifold learning methods and quantified using standard outlier scoring methods applied to the manifold embedding vectors. This article is categorized under:Technologies > Structure Discovery and ClusteringFundamental Concepts of Data and Knowledge > Data ConceptsTechnologies > Visualization [ABSTRACT FROM AUTHOR]

Objective: Designing practical decision support tools and other health care technology in health services research relies on a clear understanding of the cognitive processes that underlie the use of these tools. Unfortunately, methods to explore cognitive processes are rarely used in health services research. Thus, the objective of this manuscript is to introduce cognitive task analysis (CTA), a family of methods to study cognitive processes involved in completing a task, to a health services research audience. This methods article describes CTA procedures, proposes a framework for their use in health services research studies, and provides an example of its application in a pilot study. Data Sources and Study Setting: Observations and interviews of health care providers involved in discharge planning at six hospitals in the Veterans Health Administration. Study Design: Qualitative study of discharge planning using CTA. Data Collection/Extraction Methods: Data were collected from structured observations and semi‐structured interviews using the Critical Decision Method and analyzed using thematic analysis. Principal Findings: We developed an adaptation of CTA that could be used in a clinical environment to describe clinical decision‐making and other cognitive processes. The adapted CTA framework guides the user through four steps: (1) Planning, (2) Environmental Analysis, (3) Knowledge Elicitation, and (4) Analyses and Results. This adapted CTA framework provides an iterative and systematic approach to identifying and describing the knowledge, expertise, thought processes, procedures, actors, goals, and mental strategies that underlie completing a clinical task. Conclusions: A better understanding of the cognitive processes that underly clinical tasks is key to developing health care technology and decision‐support tools that will have a meaningful impact on processes of care and patient outcomes. Our adapted framework offers a more rigorous and detailed method for identifying task‐related cognitive processes in implementation studies and quality improvement. Our adaptation of this underutilized qualitative research method may be helpful to other researchers and inform future research in health services research. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the applicability of simultaneous methodologies to describe dynamic textural complexity. In a simultaneous task, temporal dominance of sensations (TDS) and temporal textural complexity (TTC) were performed in one session (n = 17). A further 18 participants performed TDS and TTC on separate occasions; data from the simultaneous and separate session were then compared. A further aim was to investigate the temporal nature of textural complexity—temporal drivers of textural complexity were examined, as well as ratings of textural complexity across time. Regression modeling and multivariate analyses indicated little difference in how the participants in the simultaneous and separate tasks performed; only minor differences in the use of TDS attributes were noted. Positive drivers included texture contrast and crumbly/particles, while smooth was a negative driver. Significant differences in TTC data separated into three time intervals were found; a trend of increasing textural complexity during mastication until a midway point followed by a decline emerged. Further research with larger samples is needed to support these findings. However, for the first time this work supports the use of simultaneous tasks in sensory evaluation and indicates the importance of temporality in direct ratings of complexity. Practical applications: Simultaneous tasks are not commonly used in sensory evaluation, this research suggests their applicability and value; ultimately they may save time and money in research and product development. Direct ratings of sensory complexity are often reported prior to or after descriptive temporal evaluation; this may underestimate true perceived complexity, indicating the importance of considering temporal measures. Sensory complexity is considered an important sensory construct given its arousal potential; thus, identification of drivers of complexity may inform product development. [ABSTRACT FROM AUTHOR]

Coreference resolution is a task in discourse analysis that links several headwords used in any document object. We suggest pointer networks‐based coreference resolution for Korean using multi‐task learning (MTL) with an attention mechanism for a hierarchical structure. As Korean is a head‐final language, the head can easily be found. Our model learns the distribution by referring to the same entity position and utilizes a pointer network to conduct coreference resolution depending on the input headword. As the input is a document, the input sequence is very long. Thus, the core idea is to learn the word‐ and sentence‐level distributions in parallel with MTL, while using a shared representation to address the long sequence problem. The suggested technique is used to generate word representations for Korean based on contextual information using pre‐trained language models for Korean. In the same experimental conditions, our model performed roughly 1.8% better on CoNLL F1 than previous research without hierarchical structure. [ABSTRACT FROM AUTHOR]

Performance for three independent living tasks were examined via task analyses. Given the relatively small sample size (n = 56) of individuals with intellectual and developmental disabilities, we simulated 1000 replications based upon observed results using Monte Carlo techniques. The results of the current study suggest that: (1) adaptive behavior was more related to task performance than IQ; and (2) after statistically controlling for adaptive behavior, task performance was influenced by the first step. Task performance appear to be over‐estimated with the inclusion of the first step. [ABSTRACT FROM AUTHOR]

Aims: This exploratory pilot study (N = 3 sessions, 793 speech units) used task analysis to refine an early model of the attachment task in attachment‐based family therapy (ABFT). The attachment task aims to repair long‐standing parent–adolescent relational ruptures that inhibit parents from being a resource for adolescents recovering from depression and/or suicidality. Methods: Video recordings of three attachment task sessions with strong adherence to the model were selected for intensive study. Two sessions were rated as successful (i.e., attachment was repaired) and one as unsuccessful (i.e., attachment repair did not occur). Sessions were then coded using Structural Analysis of Social Behavior (SASB). Results: The current SASB analysis provided empirical support for our previous clinical impression that the task involves three parts: (I) adolescent disclosure of attachment rupture, (II) parent disclosure, and (III) a more mutual conversation. SASB also provided insights into more subtle elements of the model. Conclusion: Successful attachment sessions were associated with high parent affiliation and autonomy—both while affirming the adolescent's story and when disclosing their own experience. Unsuccessful task attempts were associated with parental enmeshment and hostile belittling, blaming, and distancing. An enhanced model of ideal parent behaviors during the attachment task is offered, including space for parent disclosure—perhaps even apology—that is both warm and independent, leading to a more mutual parent–adolescent conversation. Clinically, the findings support the importance of the therapist focusing on the process and quality of parent–child interactions to facilitate attachment repair. [ABSTRACT FROM AUTHOR]

Human Factors methods play a key role in challenging assumptions, analyzing interactions, and informing decision‐making in complex sociotechnical systems and organizations that manage safety risks. Structured methodological approaches also have a role to play in better understanding properties of systems such as adaptation. Adaptation is increasingly recognized as being an important feature that supports the production of safety in complex sociotechnical systems. A safety management intervention, introduced to address a real risk in UK air traffic control but which resulted in unanticipated, maladaptive, and emergent effects, is analyzed using the Hierarchical Task Analysis, Systems Theoretic Accident Model and Processes, Functional Resonance Analysis Method, Human Factors Analysis and Classification System, Cognitive Work Analysis, Critical Decision Method, and Event Analysis of Systemic Teamwork Methods. The results from the application of each of the methods are presented and the different perspectives on adaptation that the methods provide are compared. A methodological framework is presented that has the potential to explore the factors of adaptation across the organizational hierarchy and assist safety practitioners in supporting decision makers in safety‐related organizations. [ABSTRACT FROM AUTHOR]

Deep multi‐model clustering is a challenging task for data analysis since it learns a universal semantic representation to find correct clusters from heterogeneous samples. However, most existing methods 1) lack an effective approach to getting a global representation of visual instances, which results in a huge semantic gap between visual and textual space. 2) hardly consider partial multi‐modal, where each instance is represented by only one modality. In reality, the pairing information for modalities is not available for all instances. To tackle the above issues, we propose a novel model called the Two‐Stage Partial Image‐Text Clustering (TPIT‐C) model. Firstly, we build an interpretable reasoning network to obtain the salient regions and semantic concepts of the scene in order to generate global semantic concepts. Secondly, we construct an adversarial learning module to align textual and visual instances into a unified space by virtue of cycle‐consistency. The experimental evaluations on public unpaired multi‐model datasets illustrated that the proposed method has better performance and the effectiveness of our algorithm in the partial image‐text clustering task. [ABSTRACT FROM AUTHOR]

Structurally, the book comprises 35 chapters plus an overview (chapter 1) and an epilogue (part VI, chapter 37) by the editors each of which has its own outstanding merit and objectives. The inspiration for reviewing this book primarily arises for a consuming interest in learning novel and innovative approaches in Language Testing and Assessment (LTA) and also came about through years of teaching and research experience in TESOL at the tertiary level, and observing language teachers in training and practicing language teachers who were seeking to learn more about effectiveness in LTA. Three chapters (12-14) are experience-based studies, and the next chapter (chapter 15) is a data-based study. [Extracted from the article]

Face detection is the basic step of many face analysis tasks. In practice, face detectors usually run on mobile devices with limited memory and computing resources. Therefore, it is important to keep face detectors lightweight. To this end, current methods usually focus on directly designing lightweight detectors. Nevertheless, it is not fully explored whether the resource consumption of these lightweight detectors can be further suppressed without too much sacrifice on accuracy. In this study, we propose to apply the network pruning method to the lightweight face detection network, to further reduce its parameters and floating point operations. To identify the channels of less importance, we perform network training with sparsity regularisation on channel scaling factors of each layer. Then, we remove the connections and corresponding weights with near‐zero scaling factors after sparsity training. We apply the proposed pruning pipeline to a state‐of‐the‐art face detection method, EagleEye, and get a shrunken EagleEye model, which has a reduced number of computing operations and parameters. The shrunken model achieves comparable accuracy as the unpruned model. By using the proposed method, the shrunken EagleEye achieves a 56.3% reduction of parameter size with almost no accuracy loss on the WiderFace dataset. [ABSTRACT FROM AUTHOR]