Types Of Modalities Research Articles

Transitivity and Modality in Joe Biden's Justificatory Speeches about the US Withdrawal from Afghanistan: A Critical Discourse Analysis

The purpose of this paper is to critically examine transitivity and modality of the US President Joe Biden's speeches in which he declares the US military's departure from Afghanistan. The stud tries to answer the following questions: 1.What is the dominant type of transitivity process and modality that is used in Biden's speech in order to justify his decision to withdraw and justify the American military's withdrawal? 2.How do transitivity processes and modality reveal the ideological construction in Biden's speeches to justify the American military's withdrawal?. The study concluded that the material process the most frequently and dominantly in his speeches, with a percentage of 35% and a frequency of 63 times, while the verbal process is employed the least, with a percentage of 6% and a frequency of 12 times. Modality of prediction is most frequently, 29 times with a percentage of 59%, whereas the modality of possibility and obligation is used twice with a percentage of 4%.It is concluded that material process is used most frequently whereas the verbal process is used least frequently to enforce the idea that action, not words, is what he is known for. It is also concluded that the modality of prediction most frequently to impose the idea that US is comprehending very well the political and military events.

Resting-state fMRI seizure onset localization meta-analysis: comparing rs-fMRI to other modalities including surgical outcomes

ObjectiveResting-state functional MRI (rs-fMRI) may localize the seizure onset zone (SOZ) for epilepsy surgery, when compared to intracranial EEG and surgical outcomes, per a prior meta-analysis. Our goals were to further characterize this agreement, by broadening the queried rs-fMRI analysis subtypes, comparative modalities, and same-modality comparisons, hypothesizing SOZ-signal strength may overcome this heterogeneity.MethodsPubMed, Embase, Scopus, Web of Science, and Google Scholar between April 2010 and April 2020 via PRISMA guidelines for SOZ-to-established-modalities were screened. Odd ratios measured agreement between SOZ and other modalities. Fixed- and random-effects analyses evaluated heterogeneity of odd ratios, with the former evaluating differences in agreement across modalities and same-modality studies.ResultsIn total, 9,550 of 14,384 were non-duplicative articles and 25 met inclusion criteria. Comparative modalities were EEG 7, surgical outcome 6, intracranial EEG 5, anatomical MRI 4, EEG-fMRI 2, and magnetoencephalography 1. Independent component analysis 9 and seed-based analysis 8 were top rs-fMRI methods. Study-level odds ratio heterogeneity in both the fixed- and random-effects analysis was significant (p < 0.001). Marked cross-modality and same-modality systematic differences in agreement between rs-fMRI and the comparator were present (p = 0.005 and p = 0.002), respectively, with surgical outcomes having higher agreement than EEG (p = 0.002) and iEEG (p = 0.007). The estimated population mean sensitivity and specificity were 0.91 and 0.09, with predicted values across studies ranging from 0.44 to 0.96 and 0.02 to 0.67, respectively.SignificanceWe evaluated centrality and heterogeneity in SOZ agreement between rs-fMRI and comparative modalities using a wider variety of rs-fMRI analyzing subtypes and comparative modalities, compared to prior. Strong evidence for between-study differences in the agreement odds ratio was shown by both the fixed- and the random-effects analyses, attributed to rs-fMRI analysis variability. Agreement with rs-fMRI differed by modality type, with surgical outcomes having higher agreement than EEG and iEEG. Overall, sensitivity was high, but specificity was low, which may be attributed in part to differences between other modalities.

The internationalization of scientist stereotypes through new media: Thai students’ perceptions of scientists from graphic interchange formats (GIFs)

ABSTRACT Background Students from Eastern cultures have historically viewed scientists differently than those from Western cultures, largely due to the high value of science in Eastern educational systems, reinforced by Eastern values and maintained by geographic distance from the Occident. With the proliferation of new media, Western (stereotypical) portrayals of scientists are now reaching a younger, more diverse, and global audience, outside of a formal education context (via social media, for example). Purpose The present study explored Eastern culture students’ perceptions of scientists in one specific type of new media modality, Graphic Interchange Formats (GIFs). Sample This study analyzed 97 adolescent aged Thai students’ (42 secondary and 55 tertiary level) perceptions of scientists as portrayed in four different GIFs from the high/low warmth and high/low competence dimensions of the pancultural Stereotype Content Model framework. Design and Methods Upon viewing each GIF through an online survey, participants narratively described their thoughts of the scientist/s therein. Responses were analyzed using directed content analysis to identify and categorize dimensions of high/low warmth and competence. Results Findings suggest that Thai students perceived scientists similarly to the High-Competence and Low-Warmth stereotype. Female scientists were perceived as equally competent (yet warmer than) their male counterparts. Conclusions The present study provides practical suggestions for enhancing warmth in portrayals of scientists in media, such as friendly imagery of scientists engaging in prosocial activities (e.g. friendly appearance, collaboration, etc.). Educators and new media creators can challenge and correct damaging stereotypical representations of scientists by understanding the role of digital media in shaping students’ perceptions of scientists around the world.

Feasibility, usability and engagement of a tangible interface for upper limb rehabilitation after stroke.

Here we present a study that explores the feasibility, user engagement, and usability of an interactive system that involves the use of tangible objects with various shapes and properties to interact with virtual tasks designed for upper limb rehabilitation following a stroke. We evaluated five different types of interaction modalities, including three grasps modalities (power grasp, lateral grasp and tripod grasp) and two strength modalities. These were tested using five basic game-like tasks with a total of 20 stroke patients. High levels of enjoyment were reported, and the system overall was considered feasible. The usability mean score (63.3) was acceptable for a system still in development. Enabling the use of tangibles during serious games in rehabilitation settings can enhance the transfer of learning to real-world situations.

A Comparative Study of Learning Modalities and Academic Performance in a Private Senior High School in Southern Philippines

This quantitative study aimed to identify the level of academic performance among senior high school students in terms of synchronous, asynchronous, and modular learning modalities. Furthermore, the study aimed to determine if there is a significant difference in academic performance as analyzed according to types of learning modalities. Descriptive-comparative was utilized to answer the research questions with 362 respondents under the Science, Technology, Engineering, and Mathematics strand. The findings showed that students under synchronous got a higher mean with outstanding descriptive equivalent, and students under asynchronous and modular got a very satisfactory descriptive equivalent. The result implied that students who chose synchronous classes gained higher grades than those who chose the other two learning modalities. This further indicates that synchronous classes have effectively delivered lessons and led to students’ higher academic performance. Modular to Asynchronous and Modular to Synchronous have .000 significant differences, and Asynchronous to Synchronous has 0.006. This result implied a significant difference among the types of learning modalities. Regarding participant’s personal information, sex, and parental support, they had no significant moderating effects on their academic performance as analyzed according to types of learning modalities. In contrast, school support had significant moderating effects on academic performance as analyzed according to types of learning modalities. Hence, this study recommended that the school continue offering online classes and strategize plans for modular students for a better educational outcome.

Large language models as an academic resource for radiologists stepping into artificial intelligence research.

Radiologists increasingly use artificial intelligence (AI) to enhance diagnostic accuracy and optimize workflows. However, many lack the technical skills to effectively apply machine learning (ML) and deep learning (DL) algorithms, limiting the accessibility of these methods to radiology researchers who could otherwise benefit from them. Large language models (LLMs), such as GPT-4o, may serve as virtual advisors, offering tailored algorithm recommendations for specific research needs. This study evaluates GPT-4o's effectiveness as a recommender system to enhance radiologists' understanding and implementation of AI in research. GPT-4o was used to recommend ML and DL algorithms based on specific details provided by researchers, including dataset characteristics, modality types, data sizes, and research objectives. The model acted as a virtual advisor, guiding researchers in selecting the most appropriate models for their studies. The study systematically evaluated GPT-4o's recommendations for clarity, task alignment, model diversity, and baseline selection. Responses were graded to assess the model's ability to meet the needs of radiology researchers. GPT-4o effectively recommended appropriate ML and DL algorithms for various radiology tasks, including segmentation, classification, and regression in medical imaging. The model suggested a diverse range of established and innovative algorithms, such as U-Net, Random Forest, Attention U-Net, and EfficientNet, aligning well with accepted practices. GPT-4o shows promise as a valuable tool for radiologists and early career researchers by providing clear and relevant AI and ML algorithm recommendations. Its ability to bridge the knowledge gap in AI implementation could democratize access to advanced technologies, fostering innovation and improving radiology research quality. Further studies should explore integrating LLMs into routine workflows and their role in ongoing professional development.

Prevalence and clinical aspects of os trigonum: a meta-analysis.

Os trigonum (OT) is one of the most common accessory ossicles in the foot and ankle region. It is believed that OT was first described by Rosenmuller in 1804, but von Bardeleben is the one who coined the nomenclature for this anatomical variation in 1883. The literature indicates that OT is extremely variable in prevalence, with reported rates ranging from 1.7% to 32.5%. In clinical practice, OT is a considerable factor that may predispose patients to posterior ankle impingement syndrome (PAIS). The aim of this study was to provide comprehensive data on the prevalence and anatomical characteristics of OT. We conducted a comprehensive search of the main electronic databases to find the relevant research. Information concerning geographical origin, prevalence, gender distribution, imaging modality, and morphometry of OT was extracted. The PRISMA guidelines were strictly followed throughout the study. The reliability of the included studies was assessed using the AQUA tool. A total of 41 studies (n = 36,612 feet) were included in this meta-analysis. This meta-analysis showed that OT was present in 9.0% (95% CI: 7.4-10.8) of the feet, and was present bilaterally in 32.7% (95% CI: 23.3-43.7) of cases. Regarding the types of imaging modality used in the evaluation of OT by the included studies, prevalence assessed by X-rays was 8.2% (95% CI: 6.8-9.9), MRI was 24.2% (95% CI: 14.6-37.3), CT was 21.0% (95% CI: 12.8-32.5), and cadaveric dissection was 5.0% (95% CI: 3.4-7.4). OT was most commonly found in East Asia (11.0%, 95% CI: 7.1-16.8). Two studies provided enough data to calculate the odds ratio linking PAIS with OT. The analysis showed that patients with PAIS are almost 16 times more likely to have OT compared to those without PAIS (OR = 15.98, 95% CI = 0.255-1002.8). OT is a very common accessory ossicle and is present in nearly one in 10 feet. The highest prevalence is noted in the East Asian population. MRI and CT show the highest prevalence of OT. It is important to keep in mind the possible presence of OT when a patient is diagnosed with PAIS.

Internally consistent and fully unbiased multimodal MRI brain template construction from UK Biobank: Oxford-MM

Abstract Anatomical magnetic resonance imaging (MRI) templates of the brain are essential to group-level analyses and image processing pipelines, as they provide a reference space for spatial normalisation. While it has become common for studies to acquire multimodal MRI data, many templates are still limited to one type of modality, usually either scalar or tensor based. Aligning each modality in isolation does not take full advantage of the available complementary information, such as strong contrast between tissue types in structural images, or axonal organisation in the white matter in diffusion tensor images. Most existing strategies for multimodal template construction either do not use all modalities of interest to inform the template construction process, or do not use them in a unified framework. Here, we present multimodal, cross-sectional templates constructed from UK Biobank data: the Oxford-MultiModal-1 (OMM-1) template and age-dependent templates for each year of life between 45 and 81 years. All templates are fully unbiased to represent the average shape of the populations they were constructed from, and internally consistent through jointly informing the template construction process with T1-weighted (T1), T2-weighted fluid-attenuated inversion recovery (T2-FLAIR), and diffusion tensor imaging (DTI) data. The OMM-1 template was constructed with a multiresolution, iterative approach using 240 individuals in the 50–55-year age range. The age-dependent templates were estimated using a Gaussian process, which describes the change in average brain shape with age in 37,330 individuals. All templates show excellent contrast and alignment within and between modalities. The global brain shape and size are not preconditioned on existing templates, although maximal possible compatibility with MNI-152 space was maintained through rigid alignment. We showed benefits in registration accuracy across two datasets (UK Biobank and HCP), when using the OMM-1 as the template compared with FSL’s MNI-152 template, and found that the use of age-dependent templates further improved accuracy to a small but detectable extent. All templates are publicly available and can be used as a new reference space for uni- or multimodal spatial alignment.

The Association Between Telehealth Use During Buprenorphine Treatment for Opioid Use Disorder and Clinical Outcomes: A Retrospective Cohort Study.

Background: Patients with opioid use disorder (OUD) represent a high-risk population due to increased rates of adverse health outcomes and death. To evaluate whether telehealth utilization during OUD treatment compared with in-person encounters alone was associated with emergency department (ED) utilization, inpatient admissions, and mortality within three years of initiating buprenorphine. Methods: We conducted a retrospective cohort study within the Veterans Health Administration among Veterans treated for OUD between 2012 and -2022. The primary exposure was modality of care, characterized as telehealth encounters (with or without an in-person visit) compared with in-person visits only. Outcomes included an ED visit, inpatient admission, or mortality within three years of the index buprenorphine prescription. We measured the association between each type of treatment modality and outcomes through Cox proportional hazards regression modeling, adjusting for demographic and clinical covariates and confounders. Findings: Of the 57,021 Veterans diagnosed with OUD and who initiated buprenorphine, 38,072 Veterans met study eligibility criteria. The majority of Veterans were male, non-Hispanic White, 25-44 years of age, and lived in urban areas. Approximately 60% of this entire cohort experienced at least one ED visit, 40% experienced an inpatient admission, and 8% died during follow-up. Telehealth use compared with in-person visits only was associated with reduced ED visits (adjusted hazard ratio [aHR] 0.81; 95% confidence interval [CI] 0.77-0.85), inpatient admissions (aHR: 0.71; 95% CI: 0.67-0.76), and mortality (aHR: 0.80; 95% CI: 0.67-0.94). Conclusions: Telehealth may help overcome barriers to in-person care. During buprenorphine treatment for OUD, telehealth as a point of contact with providers and the health care system may reduce more adverse health outcomes, potentially through improving treatment retention. Qualitative studies may help shed light on the mechanisms through which telehealth directly impacts clinical outcomes.

A Novel Taxonomy for Navigating and Classifying Synthetic Data in Healthcare Applications.

Data-driven technologies have improved the efficiency, reliability and effectiveness of healthcare services, but come with an increasing demand for data, which is challenging due to privacy-related constraints on sharing data in healthcare contexts. Synthetic data has recently gained popularity as potential solution, but in the flurry of current research it can be hard to oversee its potential. This paper proposes a novel taxonomy of synthetic data in healthcare to navigate the landscape in terms of three main varieties. Data Proportion comprises different ratios of synthetic data in a dataset and associated pros and cons. Data Modality refers to the different data formats amenable to synthesis and format-specific challenges. Data Transformation concerns improving specific aspects of a dataset like its utility or privacy with synthetic data. Our taxonomy aims to help researchers in the healthcare domain interested in synthetic data to grasp what types of datasets, data modalities, and transformations are possible with synthetic data, and where the challenges and overlaps between the varieties lie.

Non-linear communication via graded modal session types

Session types provide guarantees about concurrent behaviour and can be understood through their correspondence with linear logic, with propositions as sessions and proofs as processes. However, a strictly linear setting is somewhat limiting since there exist various useful patterns of communication that rely on non-linear behaviours. For example, shared channels provide a way to repeatedly spawn a process with binary communication along a fresh linear session-typed channel. Non-linearity can be introduced in a controlled way in programming through the general concept of graded modal types, which are a framework encompassing various kinds of coeffect typing (describing how computations make demands on their context). This paper shows how graded modal types can be leveraged alongside session types to enable various well-known non-linear concurrency behaviours to be re-introduced in a precise manner in a type system with a linear basis. The ideas here are demonstrated using Granule, a functional programming language with linear, indexed, and graded modal types. We then define a core calculus capturing the requisite type features and our new graded modal session-typed primitives. We give an operational model and establish key properties following from the typing.

Quantifying tumor specificity using Bayesian probabilistic modeling for drug and immunotherapeutic target discovery

In diseases such as cancer, the design of new therapeutic strategies requires extensive, costly, and unfortunately sometimes deadly testing to reveal life threatening off-target effects. We hypothesized that the disease specificity of targets can be systematically learned for all genes by jointly evaluating complementary molecular measurements of healthy tissues using a hierarchical Bayesian modeling approach. Our method, BayesTS, integrates protein and gene expression evidence and includes tunable parameters to moderate tissue essentiality. Applied to all protein coding genes, BayesTS outperforms alternative strategies to define therapeutic targets and nominates previously unknown targets while allowing for incorporation of new types of modalities. To expand target repertoires, we show that extension of BayesTS to splicing antigens and combinatorial target pairs results in more specific targets for therapy. We expect that BayesTS will facilitate improved target prioritization for oncology drug development, ultimately leading to the discovery of more effective and safer treatments.

Head and neck cancer treatment delays in 2021: Estimating distributional effects by site, surgery, and p16-positivity

BackgroundFollowing decades of policies increasing access to high-quality cancer treatment, the COVID-19 pandemic upended the U.S. healthcare system. The pandemic's disruption likely affected an often-overlooked dimension of quality cancer treatment: timely initiation. Timely treatment initiation is especially critical for head and neck cancer (HNC). We aimed to assess how the treatment interval (diagnosis to treatment initiation) changed in 2021 for different types of HNCs and treatment modalities. MethodologyWe analyzed Surveillance, Epidemiological, End Results (SEER) case data for years 2007–2021. Using ICD site codes, cancers were restricted to oropharynx, oral cavity, other pharynx, larynx. Oropharynx cancers were stratified by Human Papillomavirus (HPV) subtype based on p16-positivity codes. The outcome of interest was a variable measuring the number of days from diagnosis to treatment initiation. Cases were stratified by site and whether they received surgery as first course of treatment. To overcome validity threats from skewed treatment interval data and unobserved heterogeneity, we constructed an unconditional quantile regression model to estimate the effect of treatment in 2021 across the distribution of the treatment interval. Results155,273 patients in SEER initiated HNC treatment between 2007 and 2021. The median treatment interval was 29 days (Interquartile Range = 2–48). Among patients not receiving surgery, 2021 was associated with delayed treatment for all sites except oral cavity. For patients receiving surgery, 2021 was only associated with delayed treatment for p16+ Oropharynx cancer. DiscussionHNC patients overall, but HPV+ Oropharynx cancer patients especially, experienced treatment delays in 2021. These delays, and their consequences, warrant policymaking attention.

Robust Multimodal Representation under Uncertain Missing Modalities

Multimodal representation learning has gained significant attention across various fields, yet it faces challenges when dealing with missing modalities in real-world applications. Existing solutions are confined to specific scenarios, such as single-modality missing or missing modalities in test cases, thereby restricting their applicability. To address a more general scenario of uncertain missing modalities in both training and testing phases, we propose Robust Multimodal Representation under Uncertain Missing Modalities (RMRU). This framework projects each modality's representation into a shared subspace, enabling the reconstruction of any missing modalities within a unified model. We propose an interaction refinement module that utilizes cross-modal attention to enhance these reconstructions, particularly beneficial in scenarios with limited complete modality data. Furthermore, we introduce an iterative training strategy that alternately trains different modules to effectively utilize both complete and incomplete modality data. Experimental results on four benchmark datasets demonstrate the superiority of RMRU over existing baselines, particularly in scenarios with a high rate of missing modalities. Remarkably, our proposed RMRU can be broadly applied to diverse scenarios, regardless of modality types and quantities.

17 Caregiver experiences with enteral feeding transitions

Abstract Background Children with medical complexity (CMC) are an expanding group within paediatrics due to advances in medical care, often including a dependence on medical technology. Issues such as oromotor swallowing dysfunction and feeding difficulties are common in this population. As a result, many CMC require the use of enteral tubes for feeding. While evidence does support improved nutritional status with the use of enteral feeding tubes, there is limited literature discussing how these interventions impact other patient centered outcomes, such as quality of life for the child, changes in parental caregiving, and logistical implications for the family. A comprehensive understanding of the positive and negative outcomes associated with selection and transition between different feeding tube options is needed. Objectives To describe and explore the caregiver and patient experience with different feeding tubes, with a focus on the experience of transitioning between different enteral feeding modalities. Design/Methods We conducted semi-structured interviews with 16 participants, including both caregivers and two patients. Reflexive thematic analysis was utilized to identify preliminary themes from interviews. Themes were further refined by the study team. Results The themes identified through interviews included: A) the pros and cons with each type of enteral feeding modality, B) the emotional impacts on patients and caregivers, C) logistical impacts on day-to-day life for families, and D) the experience of transitioning between feeding tubes. Globally, participants spoke positively about their transition from nasogastric feeding to either gastrostomy or gastrojejunal feeding tubes. Caregivers also felt that they were integral to the decision-making process, though some felt in hindsight the transition process was too slow. Conclusion This study highlights both the complexity and heterogeneity of the caregiver experience with different feeding tubes. It found that most families consistently viewed their transitions with enteral feeding tubes positively, as this was seen as a significant milestone in helping their child both nutritionally and with their quality of life. Finally, further research could be done to explore experiences with gastrojejunal and jejunal feeding, as this patient population is not well elucidated within the literature.

Modality deep-learning frameworks for fake news detection on social networks: a systematic literature review

Fake news on social networks is a challenging problem due to the rapid dissemination and volume of information, as well as the ease of creating and sharing content anonymously. Fake news stories are problematic not only for the credibility of online journalism, but also due to their detrimental real-world consequences. The primary research objective of this study is: What are the recent state-of-the-art modalities based on deep learning to detect fake news in social networks. This paper presents a systematic literature review of deep learning-based fake news detection models in social networks. The methodology followed a rigorous approach, including predefined criteria for study selection of deep learning modalities. This study focuses on the types of deep learning modalities; unimodal (refers to the use of a single model for analysis or modeling purposes) and multimodal models (refers to the integration of multiple models). The results of this review reveal the strengths and weaknesses of modalities approaches, as well as the limitations of low-resource languages datasets. Furthermore, it provides insights into future directions for deep learning models and different fact checking techniques. At the end of this study, we discuss the problem of fake news detection in the era of large language models in terms of advantages, drawbacks, and challenges.

Modality in Climate Change-Related Discourse by King Charles III

The article presents a corpus-based study that aims at establishing how modality is used in climate change-related discourse by King Charles III of the United Kingdom of Great Britain and Northern Ireland (the UK). The study involved a corpus of speeches on the topic of climate change delivered by King Charles III from 2005 to 2023. The corpus was analysed in the computer software AntConc (Anthony) in order to compute the frequency of the occurrence of the central modal verbs. Thereafter, they were analysed qualitatively to establish the types of modality associated with them. The analysis revealed that climate change-related discourse by King Charles III was marked by a high frequency of the occurrence of the modal verbs will and can. Their frequency, pragmatic roles, and association with the different types of modality are further discussed in the article.

Sensitivity by Parametricity

The work of Fuzz has pioneered the use of functional programming languages where types allow reasoning about the sensitivity of programs. Fuzz and subsequent work (e.g., DFuzz and Duet) use advanced technical devices like linear types, modal types, and partial evaluation. These features usually require the design of a new programming language from scratch—a significant task on its own! While these features are part of the classical toolbox of programming languages, they are often unfamiliar to non-experts in this field. Fortunately, recent studies (e.g., Solo) have shown that linear and complex types in general, are not strictly needed for the task of determining programs’ sensitivity since this can be achieved by annotating base types with static sensitivity information. In this work, we take a different approach. We propose to enrich base types with information about the metric relation between values, and we present the novel idea of applying parametricity to derive direct proofs for the sensitivity of functions. A direct consequence of our result is that calculating and proving the sensitivity of functions is reduced to simply type-checking in a programming language with support for polymorphism and type-level naturals. We formalize our main result in a calculus, prove its soundness, and implement a software library in the programming language Haskell–where we reason about the sensitivity of canonical examples. We show that the simplicity of our approach allows us to exploit the type inference of the host language to support a limited form of sensitivity inference. Furthermore, we extend the language with a privacy monad to showcase how our library can be used in practical scenarios such as the implementation of differentially private programs, where the privacy guarantees depend on the sensitivity of user-defined functions. Our library, called Spar, is implemented in less than 500 lines of code.

A Modal Type Theory of Expected Cost in Higher-Order Probabilistic Programs

The design of online learning algorithms typically aims to optimise the incurred loss or cost , e.g., the number of classification mistakes made by the algorithm. The goal of this paper is to build a type-theoretic framework to prove that a certain algorithm achieves its stated bound on the cost. Online learning algorithms often rely on randomness, their loss functions are often defined as expectations, precise bounds are often non-polynomial (e.g., logarithmic) and proofs of optimality often rely on potential-based arguments. Accordingly, we present pλ-amor, a type-theoretic graded modal framework for analysing (expected) costs of higher-order probabilistic programs with recursion. pλ-amor is an effect-based framework which uses graded modal types to represent potentials, cost and probability at the type level. It extends prior work (λ-amor) on cost analysis for deterministic programs. We prove pλ-amor sound relative to a Kripke step-indexed model which relates potentials with probabilistic coupling. We use pλ-amor to prove cost bounds of several examples from the online machine learning literature. Finally, we describe an extension of pλ-amor with a graded comonad and describe the relationship between the different modalities.

The influence of assistance in home-based exercise programmes for individuals with intellectual disabilities.

Regular physical activity (PA) decreases the risk of comorbidities associated with a sedentary lifestyle in individuals with intellectual disabilities (IDs). They also may experience additional barriers that may prevent PA, including access, proper instruction and support. At-home PA programming is a feasible alternative to long-term adherence. This study aimed to determine (1) how an at-home DVD programme affects PA adherence and (2) the extent to which caregiver support influences PA. Thirty-one adolescent or adult-aged participants with mild or moderate ID were randomised into a DVD or control group exercise intervention. Participants were given autonomy to choose what type of PA modality from the intervention they would like to participate in. Caregiver support during the PA was also measured. The two-way repeated-measures MANOVA demonstrated that the intervention DVD group was statistically significant, with a large effect size in PA minutes (P=0.014, ηP2=0.236) and rate of perceived exertion (P=0.002, ηP2=0.342) compared with the control group. The findings demonstrated that using an adapted DVD increased PA minutes and the rate of perceived exertion without high levels of caregiver support.