Search

Word embeddings trained on natural corpora (e.g., newspaper collections, Wikipedia or the Web) excel in capturing thematic similarity (“topical relatedness”) on word pairs such as ‘coffee’ and ‘cup’ or ’bus’ and ‘road’. However, they are less successful on pairs showing taxonomic similarity, like ‘cup’ and ‘mug’ (near synonyms) or ‘bus’ and ‘train’ (types of public transport). Moreover, purely taxonomy-based embeddings (e.g. those trained on a random-walk of WordNet’s structure) outperform natural-corpus embeddings in taxonomic similarity but underperform them in thematic similarity. Previous work suggests that performance gains in both types of similarity can be achieved by enriching natural-corpus embeddings with taxonomic information from taxonomies like Word-Net. This taxonomic enrichment can be done by combining natural-corpus embeddings with taxonomic embeddings (e.g. those trained on a random-walk of WordNet’s structure). This paper conducts a deep analysis of this assumption and shows that both the size of the natural corpus and of the random-walk coverage of the WordNet structure play a crucial role in the performance of combined (enriched) vectors in both similarity tasks. Specifically, we show that embeddings trained on medium-sized natural corpora benefit the most from taxonomic enrichment whilst embeddings trained on large natural corpora only benefit from this enrichment when evaluated on taxonomic similarity tasks. The implication of this is that care has to be taken in controlling the size of the natural corpus and the size of the random-walk used to train vectors. In addition, we find that, whilst the WordNet structure is finite and it is possible to fully traverse it in a single pass, the repetition of well-connected WordNet concepts in extended random-walks effectively reinforces taxonomic relations in the learned embeddings.

Human–robot teaming (HrT) is being adopted in an increasing range of industries and work environments. Effective HrT relies on the success of complex and dynamic human–robot interaction. Although it may be optimal for robots to possess all the social and emotional skills to function as productive team members, certain cognitive capabilities can enable them to develop attitude-based competencies for optimizing teams. Despite the extensive research into the human–human team structure, the domain of HrT research remains relatively limited. In this sense, incorporating established human–human teaming (HhT) elements may prove practical. One key element is mutual performance monitoring (MPM), which involves the reciprocal observation and active anticipation of team members' actions within the team setting, fostering enhanced team coordination and communication. By adopting this concept, this study uses ML-based visual action recognition as a potential tool for developing an effective way to monitor the human component in HrT. This study utilizes a data modeling approach on an existing dataset, the "Industrial Human Action Recognition Dataset" (InHARD), curated specifically for human action recognition assembly tasks in industrial environments involving human–robot collaborations. This paper presents the results of this modeling approach in analyzing the dataset to implement a theoretical concept that can be a first step toward enabling the system to adapt dynamically. The outcomes emphasize the significance of implementing state-of-the-art team concepts by integrating modern technologies and assessing the possibility of advancing HrT in this direction. [ABSTRACT FROM AUTHOR]

Community integration (CI) is often regarded as the foundation of rehabilitation endeavors after stroke; nevertheless, few studies have investigated the relationship between inpatient rehabilitation (clinical and demographic) variables and long-term CI. To identify novel classes of patients having similar temporal patterns in CI and relate them to baseline features. Retrospective observational cohort study analyzing (n = 287) adult patients with stroke admitted to rehabilitation between 2003 and 2018, including baseline Functional Independence Measure (FIM) at discharge, follow-ups (m = 1264) of Community Integration Questionnaire (CIQ) between 2006 and 2022. Growth mixture models (GMMs) were fitted to identify CI trajectories, and baseline predictors were identified using multivariate logistic regression (reporting AUC) with 10-fold cross validation. Each patient was assessed at 2.7 (2.2–3.7), 4.4 (3.7–5.6), and 6.2 (5.4–7.4) years after injury, 66% had a fourth assessment at 7.9 (6.8–8.9) years. GMM identified three classes of trajectories: Lowest CI (n=105, 36.6%): The lowest mean total CIQ; highest proportion of dysphagia (47.6%) and aphasia (46.7%), oldest at injury, largest length of stay (LOS), largest time to admission, and lowest FIM. Highest CI (n=63, 21.9%): The highest mean total CIQ, youngest, shortest LOS, highest education (27% university) highest FIM, and Intermediate CI (n=119, 41.5%): Intermediate mean total CIQ and FIM scores. Age at injury OR: 0.89 (0.85–0.93), FIM OR: 1.04 (1.02–1.07), hypertension OR: 2.86 (1.25–6.87), LOS OR: 0.98 (0.97–0.99), and high education OR: 3.05 (1.22–7.65) predicted highest CI, and AUC was 0.84 (0.76–0.93). Novel clinical (e.g. hypertension) and demographic (e.g. education) variables characterized and predicted long-term CI trajectories. [ABSTRACT FROM AUTHOR]

Social media is a crucial communication tool (e.g., with 430 million monthly active users in online forums such as Reddit), being an objective of Natural Language Processing (NLP) techniques. One of them (word embeddings) is based on the quotation, "You shall know a word by the company it keeps," highlighting the importance of context in NLP. Meanwhile, "Context is everything in Emotion Research." Therefore, we aimed to train a model (W2V) for generating word associations (also known as embeddings) using a popular Coronavirus Reddit forum, validate them using public evidence and apply them to the discovery of context for specific emotions previously reported as related to psychological resilience. We used Pushshiftr, quanteda, broom, wordVectors, and superheat R packages. We collected all 374,421 posts submitted by 104,351 users to Reddit/Coronavirus forum between January 2020 and July 2021. W2V identified 64 terms representing the context for seven positive emotions (gratitude, compassion, love, relief, hope, calm, and admiration) and 52 terms for seven negative emotions (anger, loneliness, boredom, fear, anxiety, confusion, sadness) all from valid experienced situations. We clustered them visually, highlighting contextual similarity. Although trained on a "small" dataset, W2V can be used for context discovery to expand on concepts such as psychological resilience. [ABSTRACT FROM AUTHOR]

This article describes John Kelleher's experience in observing the creations of his preschool daughter. Both he and his wife are formally trained in the arts, and looked forward to guiding their daughter down an artistic path. In his mind, what makes a great artist usually involves a great deal of technical ability and commitment to a complex idea. While these are not the qualities of a toddler, the author was continually amazed by what his daughter was able to convey with the simplest strokes of a crayon. He observed that when students of art were formally trained, they were really giving up a bit of creativity for a type of visual clarity. The author's daughter had no interest in that visual clarity. She worked only in conceptual clarity. She had no concrete intent and no concrete audience. She was not burdened with technique or the physical ability to control her marks; instead, she concentrated on the subject and how to translate her vision onto paper. In thinking about how his daughter drew, he was reminded of a lesson he had given many times to college freshmen, many of whom had never taken an art class before, concerning visual efficiency and the way an artist can choose how to reveal his or her subject matter. This is often a balance between abstraction, narrative, and technical facility. His daughter brought this difference between ways of seeing and representing the world to life for him. She drew the way that she did because she did not have a grasp of our visual culture.

In order to mitigate early hypoglycemia in preterm infants, some clinicians have recently explored interventions such as delivery room commencement of dextrose infusions or delivery room administration of buccal dextrose gel. This review aimed to systematically investigate the literature regarding the provision of delivery room (prior to admission) parenteral glucose as a method to reduce the risk of initial hypoglycemia (measured at the time of NICU admission blood testing) in preterm infants. Using PRISMA guidelines a literature search (May 2022) was conducted using PubMed, Embase, Scopus, Cochrane Library, OpenGrey, and Prospero databases. The clinicaltrials.gov database was searched for possible completed/ongoing clinical trials. Studies that included moderate preterm ( ≤ 33+6 weeks) or younger birth gestations or very low birth weight (or smaller) infants, and that administered parenteral glucose in the delivery room were included. The literature was appraised via data extraction, narrative synthesis, and critical review of the study data. A total of five studies (published 2014–2022) were eligible for inclusion (three before-after "quasi-experimental" studies, one retrospective cohort study, and one case-control study). Most included studies used intravenous dextrose as the intervention. Individual study effects (odds ratios) favored the intervention in all included studies. It was felt that the low number of studies, the variability in study design, and the nonadjustment for confounding co-interventions (co-exposures) precluded a meta-analysis. Quality assessment of the studies revealed a spectrum of bias from low to high risk, however, most studies had moderate to high risk of bias, and their direction of bias favored the intervention. This extensive search and systematic appraisal of the literature indicates that there exists few studies (these are low grade and at moderate to high risk of bias) for the interventions of either intravenous or buccal dextrose given in the delivery room. It is not clear if these interventions impact on rates of early (NICU admission) hypoglycemia in these preterm infants. Obtaining intravenous access in the delivery room is not guaranteed and can be difficult in these small infants. Future research should consider various routes for commencing delivery room glucose in these preterm infants and should take the form of randomized controlled trials. [ABSTRACT FROM AUTHOR]

Agent-based models can be used to better understand the impacts of lifting restrictions or implementing interventions during a pandemic. However, agent-based models are computationally expensive, and running a model of a large population can result in a simulation taking too long to run for the model to be a useful analysis tool during a public health crisis. To reduce computing time and power while running a detailed agent-based model for the spread of COVID-19 in the Republic of Ireland, we introduce a scaling factor that equates 1 agent to 100 people in the population. We present the results from model validation and show that the scaling factor increases the variability in the model output, but the average model results are similar in scaled and un-scaled models of the same population, and the scaled model is able to accurately simulate the number of cases per day in Ireland during the autumn of 2020. We then test the usability of the model by using the model to explore the likely impacts of increasing community mixing when schools reopen after summer holidays. [ABSTRACT FROM AUTHOR]

Accurate early predictions of a patient's likely cognitive improvement as a result of a stroke rehabilitation programme can assist clinicians in assembling more effective therapeutic programs. In addition, sufficient levels of explainability, which can justify these predictions, are a crucial requirement, as reported by clinicians. This article presents a machine learning (ML) prediction model targeting cognitive improvement after therapy for stroke surviving patients. The prediction model relies on electronic health records from201 ischemic stroke surviving patients containing demographic information, cognitive assessments at admission from 24 different standardized neuropsychology tests (e.g., TMT, WAIS-III, Stroop, RAVLT, etc.), and therapy information collected during rehabilitation (72,002 entries collected between March 2007 and September 2019). The study population covered young-adult patients with a mean age of 49.51 years and only 4.47% above 65 years of age at the stroke event (no age filter applied). Twenty different classification algorithms (from Python's Scikit-learn library) are trained and evaluated, varying their hyper-parameters and the number of features received as input. Best-performing models reported Recall scores around 0.7 and F1 scores of 0.6, showing the model's ability to identify patients with poor cognitive improvement. The study includes a detailed feature importance report that helps interpret the model's inner decision workings and exposes the most influential factors in the cognitive improvement prediction. The study showed that certain therapy variables (e.g., the proportion of memory and orientation executed tasks) had an important influence on the final prediction of the cognitive improvement of patients at individual and population levels. This type of evidence can serve clinicians in adjusting the therapeutic settings (e.g., type and load of therapy activities) and selecting the one that maximizes cognitive improvement. [ABSTRACT FROM AUTHOR]

BACKGROUND: Stroke is a major worldwide cause of serious long-term disability. Most previous studies addressing functional independence included only inpatients with limited follow-up. OBJECTIVE: To identify novel classes of patients having similar temporal patterns in motor functional independence and relate them to baseline clinical features. METHODS: Retrospective observational cohort study, data were obtained for n = 428 adult patients with ischemic stroke admitted to rehabilitation (March 2005–March 2020), including baseline clinical features and follow-ups of motor Functional Independence Measure (mFIM) categorized as poor, fair or good. Growth mixture models (GMMs) were fitted to identify classes of patients with similar mFIM trajectories. RESULTS: GMM identified three classes of trajectories (1,664 mFIM assessments): C1 (11.2 %), 97.9% having poor admission mFIM, at 4.93 years 61.1% still poor, with the largest percentage of hypertension, neglect, dysphagia, diabetes and dyslipidemia of all three classes. C2 (23.1%), 99% had poor admission mFIM, 25% poor discharge mFIM, the largest percentage of aphasia and greatest mFIM gain, at 4.93 years only 6.2% still poor. C3 (65.7%) the youngest, lowest NIHSS, 37.7% poor admission mFIM, 73% good discharge mFIM, only 4.6% poor discharge mFIM, 90% good at 4.93 years. CONCLUSIONS: GMM identified novel motor functional classes characterized by baseline features. [ABSTRACT FROM AUTHOR]

Current state-of-the-art neural machine translation (NMT) architectures usually do not take document-level context into account. However, the document-level context of a source sentence to be translated could encode valuable information to guide the MT model to generate a better translation. In recent times, MT researchers have turned their focus to this line of MT research. As an example, hierarchical attention network (HAN) models use document-level context for translation prediction. In this work, we studied translations produced by the HAN-based MT systems. We examined how contextual information improves translation in document-level NMT. More specifically, we investigated why context-aware models such as HAN perform better than vanilla baseline NMT systems that do not take context into account. We considered Hindi-to-English, Spanish-to-English and Chinese-to-English for our investigation. We experimented with the formation of conditional context (i.e., neighbouring sentences) of the source sentences to be translated in HAN to predict their target translations. Interestingly, we observed that the quality of the target translations of specific source sentences highly relates to the context in which the source sentences appear. Based on their sensitivity to context, we classify our test set sentences into three categories, i.e., context-sensitive, context-insensitive and normal. We believe that this categorization may change the way in which context is utilized in document-level translation. [ABSTRACT FROM AUTHOR]

Age is one of the most important risk factors when it comes to stroke risk prediction. However, including age as a risk factor in a stroke prediction model can give rise to a number of difficulties. Age often dominates the risk score, and also not all risk factors contribute proportionally to stroke risk by age. In this study we investigate a number of common stroke risk factors, using Framingham heart study data from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center to determine if they appear to contribute proportionally by age to a stroke risk score. As we find evidence that there is some non-proportionality by age, we then create a set of logistic regression risk models that each predict the 5 year stroke risk for a different age group. The age group models are shown to be better calibrated when compared to a model for all ages that includes age as a risk factor. This suggests that to get better predictions for stroke risk it may be necessary to consider alternative methods for including age in stroke risk prediction models that account for the non-proportionality of the other risk factors as age changes. [ABSTRACT FROM AUTHOR]