Discrete Task Research Articles

Psychometric properties of the Pediatric Evaluation of Disability Inventory - Patient Reported Outcome: A cognitively accessible measure of functional performance.

To evaluate the structural validity, internal reliability, and measurement invariance of the Pediatric Evaluation of Disability Inventory - Patient Reported Outcome (PEDI-PRO), a measure of functional performance of discrete tasks required to participate in everyday life situations important for adulthood. This was a cross-sectional study with 306 young people aged 14 to 22 years (mean 18 years 10 months, SD 2 years 5 months) with developmental disabilities (43.1% autism spectrum disorder only, 23.9% intellectual disability, 17.6% other disability, 11.4% both autism spectrum disorder and intellectual disability, 3.9% missing) completed the PEDI-PRO. Following COnsensus-based Standards for selection of health Measurement INstruments (COSMIN) criteria, we conducted a confirmatory factor analysis, applied a Rasch rating-scale model, examined Cronbach's alpha, Rasch person reliability and separation coefficients, and differential item functioning (DIF). Structural validity was good for the daily activities and mobility domains, and acceptable for the social/cognitive domain. The 3-point Likert response scale functioned as intended. All domains demonstrated acceptable internal consistency on all criteria. One or two items in each domain demonstrated DIF, but the impact on all domain scores was less than 1.0 threshold. The cognitively accessible design and innovative conceptual measurement framework probably contributed to these promising findings. The PEDI-PRO addresses a gap in high-quality patient-reported outcome measures that assess priority outcomes for young people with developmental disabilities.

Transforming Public Health Data Management in IoMT Networks Based on Innovative Offloading Scheme

Healthcare platform monitoring IoT-oriented technologies constitute the idea of Internet of Medical Things (IoMT) in public health and medical services. The amount and quality of created data have a substantial influence on data management and privacy compute offloading solutions are unable to keep up with the growing needs of the health industry, especially when fast and dependable communication was needed. The study suggests a unique approach called Mutated Barnacles Mating Optimization (MBMO) for assisting the data management issues in IoMT systems. The suggested MBMO framework successfully addresses problems that are common in the medical industry by using a data offloading technique. To overcome the issues of discrete tasks and resource allocation, guarantees the needs of dependable and efficient communication. We implemented Java software. The evaluation of the performance step encompasses several measures, such as energy consumption (J/ms) and Time delay (ms)model to assess the efficiency of the suggested forecasting algorithm. We performed an assessment of comparison with other established approaches results indicate that the suggested model produces superior results for assisting the data management issues in IoMT systems.

A semi-automatic method for block-structured hexahedral meshing of aortic dissections.

The article presents a semi-automatic approach to generating structured hexahedral meshes of patient-specific aortas ailed by aortic dissection. The condition manifests itself as a formation of two blood flow channels in the aorta, as a result of a tear in the inner layers of the aortic wall. Subsequently, the morphology of the aorta is greatly impacted, making the task of domain discretization highly challenging. The meshing algorithm presented herein is automatic for the individual lumina, whereas the tears require user interaction. Starting from an input (triangle) surface mesh, we construct an implicit surface representation as well as a topological skeleton, which provides a basis for the generation of a block-structure. Thereafter, the mesh generation is performed via transfinite maps. The meshes are structured and fully hexahedral, exhibit good quality and reliably match the original surface. As they are generated with computational fluid dynamics in mind, a fluid flow simulation is performed to verify their usefulness. Moreover, since the approach is based on valid block-structures, the meshes can be made very coarse (around 1000 elements for an entire aortic dissection domain), and thus promote using solvers based on the geometric multigrid method, which is typically reliant on the presence of a hierarchy of coarser meshes.

Efficient computation by molecular competition networks

Most biomolecular systems exhibit computation abilities, which are often achieved through complex networks such as signal transduction networks. Particularly, molecular competition in these networks can introduce crosstalk and serve as a hidden layer for cellular information processing. Despite the increasing evidence of competition contributing to efficient cellular computation, how this occurs and the extent of computational capacity it confers remain elusive. In this study, we introduced a mathematical model for molecular competition networks (MCNs) and employed a machine learning-based optimization method to explore their computational capacity. Our findings revealed that MCNs, when compared to their noncompetitive counterparts, demonstrate superior performance in both discrete decision-making and analog computation tasks. Furthermore, we highlighted the nonnegligible role of weak interactions and limited amounts of resources and examined how biological constraints influence the computational capacity of MCNs. The study suggested the potential of MCNs as efficient computational structures and provided new insights into cellular information processing. Published by the American Physical Society 2024

Complex behavior from intrinsic motivation to occupy future action-state path space

Most theories of behavior posit that agents tend to maximize some form of reward or utility. However, animals very often move with curiosity and seem to be motivated in a reward-free manner. Here we abandon the idea of reward maximization and propose that the goal of behavior is maximizing occupancy of future paths of actions and states. According to this maximum occupancy principle, rewards are the means to occupy path space, not the goal per se; goal-directedness simply emerges as rational ways of searching for resources so that movement, understood amply, never ends. We find that action-state path entropy is the only measure consistent with additivity and other intuitive properties of expected future action-state path occupancy. We provide analytical expressions that relate the optimal policy and state-value function and prove convergence of our value iteration algorithm. Using discrete and continuous state tasks, including a high-dimensional controller, we show that complex behaviors such as “dancing”, hide-and-seek, and a basic form of altruistic behavior naturally result from the intrinsic motivation to occupy path space. All in all, we present a theory of behavior that generates both variability and goal-directedness in the absence of reward maximization.

A Direct Comparison Between Discrete Choice With Duration and Composite Time Trade-Off Methods: Do They Produce Similar Results?

ObjectivesDiscrete choice experiments including a duration attribute (DCEd) represent a promising candidate method for valuing health-related quality-of-life instruments. However, it has not been established that DCEd can produce similar results as composite time trade-off (cTTO) or EuroQol Valuation Technology (EQ-VT) valuations of the EQ-5D-5L instrument. This study provides a direct comparison between cTTO and EQ-VT, and DCEd valuation methods. MethodsAn EQ-VT study was conducted in Trinidad and Tobago to value the EQ-5D-5L. 1079 respondents each completed 10 cTTO tasks and 12 discrete choice experiments tasks without a duration attribute. A separate sample of 970 respondents each completed 18 split-triplet DCEd tasks. Several regression models were applied to the EQ-VT data, and the DCEd data were analyzed using mixed logit models with an exponential discount rate. The estimated values were compared using scatterplots and Bland-Altman plots. ResultsThe ordering of dimensions was identical in level 5 for cTTO/EQ-VT and DCEd models, with pain/discomfort being the most important dimension and usual activities being least important. cTTO/EQ-VT models produced a value for state 55555 ranging between −0.52 and −0.69, whereas this was −0.543 for the nonlinear mixed logit model for the DCEd data. Scatterplots and Bland-Altman plots suggested excellent agreement between cTTO/EQ-VT and DCEd-based estimates. ConclusionsCTTO/EQ-VT and DCEd valuations produce similar results when correcting DCEd for nonlinear time preferences. The ordering of importance of the dimensions and scale are identical, suggesting that the 2 methods measure the same construct and produce similar results.

Human-like Learning in Temporally Structured Environments

Natural environments have correlations at a wide range of timescales. Human cognition is tuned to this temporal structure, as seen by power laws of learning and memory, and by spacing effects whereby the intervals between repeated training data affect how long knowledge is retained. Machine learning is instead dominated by batch iid training or else relatively simple nonstationarity assumptions such as random walks or discrete task sequences. The main contributions of our work are: (1) We develop a Bayesian model formalizing the brain's inductive bias for temporal structure and show our model accounts for key features of human learning and memory. (2) We translate the model into a new gradient-based optimization technique for neural networks that endows them with human-like temporal inductive bias and improves their performance in realistic nonstationary tasks. Our technical approach is founded on Bayesian inference over 1/f noise, a statistical signature of many natural environments with long-range, power law correlations. We derive a new closed-form solution to this problem by treating the state of the environment as a sum of processes on different timescales and applying an extended Kalman filter to learn all timescales jointly. We then derive a variational approximation of this model for training neural networks, which can be used as a drop-in replacement for standard optimizers in arbitrary architectures. Our optimizer decomposes each weight in the network as a sum of subweights with different learning and decay rates and tracks their joint uncertainty. Thus knowledge becomes distributed across timescales, enabling rapid adaptation to task changes while retaining long-term knowledge and avoiding catastrophic interference. Simulations show improved performance in environments with realistic multiscale nonstationarity. Finally, we present simulations showing our model gives essentially parameter-free fits of learning, forgetting, and spacing effects in human data. We then explore the analogue of human spacing effects in a deep net trained in a structured environment where tasks recur at different rates and compare the model's behavioral properties to those of people.

Controlling estimation error in reinforcement learning via Reinforced Operation

In Value-based and Actor-Critic reinforcement learning (RL) methods, both inaccuracy and instability of value estimation will detrimentally affect their performance. Some typical RL methods, such as Maxmin Q-learning and QMD3, are plagued by the underestimation problem while failing to trade off the estimation bias and variance jointly. We propose the Reinforced Operation (RO) to address these shortcomings, which selects the closest median among multiple Q-function. RO is applicable to any model-free RL method. Theoretically, we introduce the Mean Square Error (MSE) to jointly analyze the estimation bias and variance of value estimation methods. We also demonstrate the superiority of RO in MSE reduction and give an upper bound for the estimation bias of value estimation methods with arbitrary distribution to guide the calculation of the estimation bias of value estimation methods. Based on RO, we propose the variants of Q-learning and TD3, Reinforced Q-learning (RQ) and Reinforced Delayed Deep Deterministic policy gradient (RD3), respectively, to tackle different tasks. We empirically demonstrate that our method can reduce estimation error and achieve superior performance on discrete and continuous benchmark tasks.

American Dental Education Association Compendium Entrustable Professional Activities Workgroup report.

Entrustable professional activities (EPAs) are discrete clinical tasks that can be evaluated to help define readiness for independent practice in the health professions and are intended to increase trust in the dental graduate. EPAs provide a framework that bridges competencies to clinical practice. This report describes the work of the American Dental Education Association (ADEA) Compendium EPA Workgroup to develop a list of EPAs for dental education and supportive resources, including specifications and a glossary. Preliminary work including literature and resource review, mapping of existing competencies, and review of other health professions' EPAs informed the development of our EPAs list. Workgroup members achieved consensus using a modified Delphi process. A Qualtrics survey using a validated rubric for the assessment of EPAs as described in peer-reviewed literature was used. Dental educators, including academic deans, were surveyed for feedback on the content and format of the EPAs. Based on findings in the literature analysis of existing EPAs and competencies in health professions, a list of EPAs was developed along with a description of specifications. The EPA workgroup (nine members from multiple institutions) used the Delphi process in receiving feedback from various experts. A list of 11 core EPAs was vetted by dental educators including academic deans (n=∼23), and the process of development was reviewed by EPAs experts outside dental education. A glossary was developed to align language. These EPAs define the scope of dental practice. This report represents Phase 1 of the EPA framework development and vetting process. Future directions will include a broader vetting of the EPA list, faculty development, and national standardized technology that support this work to optimize implementation.

Auditory feedback decreases timing variability for discontinuous and continuous motor tasks in autistic adults.

Autistic individuals demonstrate greater variability and timing error in their motor performance than neurotypical individuals, likely due at least in part to atypical cerebellar characteristics and connectivity. These motor difficulties may differentially affect discrete as opposed to continuous movements in autistic individuals. Augmented auditory feedback has the potential to aid motor timing and variability due to intact auditory-motor pathways in autism and high sensitivity in autistic individuals to auditory stimuli. This experiment investigated whether there were differences in timing accuracy and variability in autistic adults as a function of task (discontinuous vs. continuous movements) and condition (augmented auditory feedback vs. no auditory feedback) in a synchronization-continuation paradigm. Ten autistic young adults aged 17-27 years of age completed the within-subjects study that involved drawing circles at 800 milliseconds intervals on a touch screen. In the discontinuous task, participants traced a series of discrete circles and paused at the top of each circle for at least 60 milliseconds. In the continuous task, participants traced the circles without pausing. Participants traced circles in either a non-auditory condition, or an auditory condition in which they heard a tone each time that they completed a circle drawing. Participants had significantly better timing accuracy on the continuous timing task as opposed to the discontinuous task. Timing consistency was significantly higher for tasks performed with auditory feedback. This research reveals that motor difficulties in autistic individuals affect discrete timing tasks more than continuous tasks, and provides evidence that augmented auditory feedback may be able to mitigate some of the timing variability present in autistic persons' movements. These results provide support for future investigation on the use of music-based therapies involving auditory feedback to address motor dysfunction in autistic individuals.

Action Candidate Driven Clipped Double Q-Learning for Discrete and Continuous Action Tasks.

Double Q-learning is a popular reinforcement learning algorithm in Markov decision process (MDP) problems. Clipped double Q-learning, as an effective variant of double Q-learning, employs the clipped double estimator to approximate the maximum expected action value. Due to the underestimation bias of the clipped double estimator, the performance of clipped Double Q-learning may be degraded in some stochastic environments. In this article, in order to reduce the underestimation bias, we propose an action candidate-based clipped double estimator (AC-CDE) for Double Q-learning. Specifically, we first select a set of elite action candidates with high action values from one set of estimators. Then, among these candidates, we choose the highest valued action from the other set of estimators. Finally, we use the maximum value in the second set of estimators to clip the action value of the chosen action in the first set of estimators and the clipped value is used for approximating the maximum expected action value. Theoretically, the underestimation bias in our clipped Double Q-learning decays monotonically as the number of action candidates decreases. Moreover, the number of action candidates controls the tradeoff between the overestimation and underestimation biases. In addition, we also extend our clipped Double Q-learning to continuous action tasks via approximating the elite continuous action candidates. We empirically verify that our algorithm can more accurately estimate the maximum expected action value on some toy environments and yield good performance on several benchmark problems. Code is available at https://github.com/Jiang-HB/ac_CDQ.

Reclaiming Logic Modeling for Evaluation: A Theory of Action Framework

Logic modeling, the process that explicates how programs are constructed and theorized to bring about change, is considered to be standard evaluation practice. However, logic modeling is often experienced as a transactional, jargon-laden, discrete task undertaken to produce a document to comply with the expectations of an external entity, the consequences of which have minimal or even negative influence on the quality of program evaluation. This article presents the Logic Modeling Theory of Action Framework (LMTAF) which elucidates needs, resources, and central activities of logic modeling, and describes its potential evaluation-related benefits. The LMTAF situates evaluators as the primary intended users of logic modeling, and logic modeling as a fundamental element of each stage of a program evaluation life cycle. We aim to reassert the value of logic modeling for evaluation and provide evaluation practitioners a useful touchstone for reflective practice and future action.

Dissecting muscle synergies in the task space

The muscle synergy is a guiding concept in motor control research that relies on the general notion of muscles ‘working together’ towards task performance. However, although the synergy concept has provided valuable insights into motor coordination, muscle interactions have not been fully characterised with respect to task performance. Here, we address this research gap by proposing a novel perspective to the muscle synergy that assigns specific functional roles to muscle couplings by characterising their task-relevance. Our novel perspective provides nuance to the muscle synergy concept, demonstrating how muscular interactions can ‘work together’ in different ways: (1) irrespective of the task at hand but also (2) redundantly or (3) complementarily towards common task-goals. To establish this perspective, we leverage information- and network-theory and dimensionality reduction methods to include discrete and continuous task parameters directly during muscle synergy extraction. Specifically, we introduce co-information as a measure of the task-relevance of muscle interactions and use it to categorise such interactions as task-irrelevant (present across tasks), redundant (shared task information), or synergistic (different task information). To demonstrate these types of interactions in real data, we firstly apply the framework in a simple way, revealing its added functional and physiological relevance with respect to current approaches. We then apply the framework to large-scale datasets and extract generalizable and scale-invariant representations consisting of subnetworks of synchronised muscle couplings and distinct temporal patterns. The representations effectively capture the functional interplay between task end-goals and biomechanical affordances and the concurrent processing of functionally similar and complementary task information. The proposed framework unifies the capabilities of current approaches in capturing distinct motor features while providing novel insights and research opportunities through a nuanced perspective to the muscle synergy.

Dissecting muscle synergies in the task space.

The muscle synergy is a guiding concept in motor control research that relies on the general notion of muscles 'working together' towards task performance. However, although the synergy concept has provided valuable insights into motor coordination, muscle interactions have not been fully characterised with respect to task performance. Here, we address this research gap by proposing a novel perspective to the muscle synergy that assigns specific functional roles to muscle couplings by characterising their task-relevance. Our novel perspective provides nuance to the muscle synergy concept, demonstrating how muscular interactions can 'work together' in different ways: (1) irrespective of the task at hand but also (2) redundantly or (3) complementarily towards common task-goals. To establish this perspective, we leverage information- and network-theory and dimensionality reduction methods to include discrete and continuous task parameters directly during muscle synergy extraction. Specifically, we introduce co-information as a measure of the task-relevance of muscle interactions and use it to categorise such interactions as task-irrelevant (present across tasks), redundant (shared task information), or synergistic (different task information). To demonstrate these types of interactions in real data, we firstly apply the framework in a simple way, revealing its added functional and physiological relevance with respect to current approaches. We then apply the framework to large-scale datasets and extract generalizable and scale-invariant representations consisting of subnetworks of synchronised muscle couplings and distinct temporal patterns. The representations effectively capture the functional interplay between task end-goals and biomechanical affordances and the concurrent processing of functionally similar and complementary task information. The proposed framework unifies the capabilities of current approaches in capturing distinct motor features while providing novel insights and research opportunities through a nuanced perspective to the muscle synergy.

Harmonization of the Legal Framework for Online Arbitration

Developed to resolve disputes worldwide in a digital environment, the program works only online. Unlike other court software that provides an online interface for discrete tasks, such as filling out documents online, and conducting online hearings, users of Alternative Dispute Resolution (ADR) do not go to court for traditional requests. The program is not a technological platform designed for making court decisions, but a tool to assist the court in resolving the disputes or cases of the applicants. Dispute resolution in a digital environment allows parties to resolve disputes confidentially, quickly, at their own discretion, and impartially, and to ensure worldwide enforcement of the decision. In this regard, special attention is paid to the wide introduction of information technologies in this field.

Auditory Versus Visual Interruptions: A Skeptical Perspective on Auditory Preemption and Suggestions for Advancing Theory

ABSTRACT Auditory preemption applies to the case where a person is engaged in an ongoing visual task and intermittently is required to engage in an additional discrete task. Some research has suggested that an interruption from an auditory discrete task is more damaging to the ongoing visual task than an equivalent interruption from a visual discrete task – a phenomenon that has been described formally as Auditory Preemption Theory. In this critical comment, I review the definition and history of the notion of auditory preemption and survey the most cited literature that has purported to show auditory preemption. I argue that the foundational evidence of auditory preemption has been characterized by shortcomings that weaken or preclude the conclusion that auditory preemption occurred. I argue that there is little evidence that the effect described by Auditory Preemption Theory is a modality effect – findings across laboratory studies appear approximately consistent with what would be expected due to sampling error if no auditory preemption effect exists. Plausible alternate candidates to explain phenomena that have been attributed to auditory preemption include startle responses, cognitive demands associated with speech generation in communication tasks, and possibly social demands associated with human communications. Suggestions from the open science movement to improve methods and theory are applied to the problem of building theories of interruption modalities. These suggestions include methodological reforms and making more explicit auxiliary assumptions about theories.

Exploring health preference heterogeneity in the UK: Using the online elicitation of personal utility functions approach to construct EQ-5D-5L value functions on societal, group and individual level.

A new method has recently been developed for valuing health states, called 'Online elicitation of Personal Utility Functions' (OPUF). In contrast to established methods, such as time trade-off or discrete choice experiments, OPUF does not require hundreds of respondents, but allows estimating utility functions for small groups and even at the individual level. In this study, we used OPUF to elicit EQ-5D-5L health state preferences from a (not representative) sample of the UK general population, and then compared utility functions on the societal-, group-, and individual level. A demo version of the survey is available at: https://eq5d5l.me. Data from 874 respondents were included in the analysis. For each respondent, we constructed a personal EQ-5D-5L value set. These personal value sets predicted respondents' choices in three hold-out discrete choice tasks with an accuracy of 78%. Overall, preferences varied greatly between individuals. However, PERMANOVA analysis showed that demographic characteristics explained only a small proportion of the variability between subgroups. While OPUF is still under development, it has important strengths: it can be used to construct value sets for patient reported outcome instruments such as EQ-5D-5L, while also allowing examination of underlying preferences in an unprecedented level of detail. In the future, OPUF could be used to complement existing methods, allowing valuation studies in smaller samples, and providing more detailed insights into the heterogeneity of preferences across subgroups.

Determining Optimal Breakpoints in Urban Power Networks with Genetic Algorithm

The article deals with the issues of optimizing the operating modes of 6-10 kV distribution networks constituted by the large-sized complicated meshed systems. In real time, such networks operate with breaking the circuit to eliminate equalizing currents. The goal of optimizing the networks’ operating modes is to minimize the active power loss. Determining the optimal breakpoints is a complicated discrete task, for which the method of genetic algorithm becomes the most suitable solution

Development of a Cancer Rehabilitation Dashboard to Collect Data on Physical Function in Cancer Patients and Survivors.

The aim of the study is to describe the development of a cancer rehabilitation dashboard that collects data on physical function for cancer survivors in a cancer institute. This project was conducted at the Miami Cancer Institute. The cancer rehabilitation dashboard was developed by a team of physicians, biostatistician, and medical informatics teams to record, report and track the physical function of cancer survivors. A multimodal approach to the measurement of physical function was used and included the Patient-Reported Outcome Measurement Information System-Physical Function short form, Patient-Reported Outcome Measurement Information System-Fatigue short form, Timed Up and Go Test, Sit-to-Stand Test in 30-sec test, four-stage balance test, and grip strength. To develop this system, a Cerner Power Form was developed based on the physical function data. To display the data, a dedicated flowsheet was developed and placed within the Oncology Viewpoint in Cerner Millennium. Thus, from inside any patient record, the flowsheet could easily be accessed by providers without leaving normal clinician workflows. Using native functionality, the data can also be shown in graphical format to facilitate dialog with patients and oncology teams. All patient data from the Cerner Power Form discrete task assays were integrated into an existing Oncology Data Warehouse for all patients. The data elements in the Cerner Power Form were identified in the electronic medical record system, loaded into the Oncology Data Warehouse, and related to the other source systems to develop reports and data visualizations such as the cancer rehabilitation dashboard. The cancer rehabilitation dashboard allows visualization of numerous parameters of physical function in cancer survivors evaluated and treated and their change over time. Rendered in Tableau, the cancer rehabilitation dashboard acts as a centralized, interactive data source to analyze and connect clinicians to near real-time data. The cancer rehabilitation dashboard was successfully developed and implemented into a cancer rehabilitation practice in a cancer institute and used to collect and track physical function data for cancer survivors receiving treatment and cancer survivors. This information has been used to direct the treatment plan and educate individual patients about the impact of the cancer and its treatment on physical function as well as oncology teams in a cancer institute. The cancer rehabilitation dashboard provides an insight into the physical function of cancer survivors receiving treatment and cancer survivors using both self-reported and objective metrics. It can be customized to suit the interests of clinicians and researchers wishing to improve the quality of life of this population.

Variational Structured Attention Networks for Deep Visual Representation Learning.

Convolutional neural networks have enabled major progresses in addressing pixel-level prediction tasks such as semantic segmentation, depth estimation, surface normal prediction and so on, benefiting from their powerful capabilities in visual representation learning. Typically, state of the art models integrate attention mechanisms for improved deep feature representations. Recently, some works have demonstrated the significance of learning and combining both spatial- and channel-wise attentions for deep feature refinement. In this paper, we aim at effectively boosting previous approaches and propose a unified deep framework to jointly learn both spatial attention maps and channel attention vectors in a principled manner so as to structure the resulting attention tensors and model interactions between these two types of attentions. Specifically, we integrate the estimation and the interaction of the attentions within a probabilistic representation learning framework, leading to VarIational STructured Attention networks (VISTA-Net). We implement the inference rules within the neural network, thus allowing for end-to-end learning of the probabilistic and the CNN front-end parameters. As demonstrated by our extensive empirical evaluation on six large-scale datasets for dense visual prediction, VISTA-Net outperforms the state-of-the-art in multiple continuous and discrete prediction tasks, thus confirming the benefit of the proposed approach in joint structured spatial-channel attention estimation for deep representation learning. The code is available at https://github.com/ygjwd12345/VISTA-Net.