Irregular Tasks Research Articles

Self-control enhances vigilance performance in temporally irregular tasks: an fNIRS frontoparietal investigation.

The present study investigated whether trait self-control impacted operators' behavior and associated neural resource strategies during a temporally irregular vigilance task. Functional near-infrared spectroscopy (fNIRS) readings of oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (HbR) from 29 participants were recorded fromthe prefrontal and parietal cortices. Self-control was associated with better perceptual sensitivity (A') in the task with the irregular event schedule. A left-lateralized effect of HbO2 was found for temporal irregularity within the dorsomedial prefrontal cortex, in accordance with functional transcranial doppler (fTCD) studies. Self-control increased HbR (decreasing activation) at right superior parietal lobule (rSPL; supporting vigilance utilization) and right inferior parietal lobule (rIPL; supporting resource reallocation). However, only rSPL was associated with the vigilance decrement-where decreases in activation led to better perceptual sensitivity in the temporally irregular task. Additionally, short stress-state measures suggest decreases in task engagement in individuals with higher self-control in the irregular task. The authors suggest a trait-state-brain-behavior relationship for self-control during difficult vigilance tasks. Implications for the study include steps toward rectifying the resource utilization vs. allocation debate in vigilance-as well as validating HbO2 and HbR as effective constructs for predicting operators' mental resources through fNIRS.

Smartwatch-Based Interventions for People With Dementia: User-Centered Design Approach.

Assistive technologies can help people living with dementia maintain their everyday activities. Nevertheless, there is a gap between the potential and use of these materials. Involving future users may help close this gap, but the impact on people with dementia is unclear. We aimed to determine if user-centered development of smartwatch-based interventions together with people with dementia is feasible. In addition, we evaluated the extent to which user feedback is plausible and therefore helpful for technological improvements. We examined the interactions between smartwatches and people with dementia or people with mild cognitive impairment. All participants were prompted to complete 2 tasks (drinking water and a specific cognitive task). Prompts were triggered using a smartphone as a remote control and were repeated up to 3 times if participants failed to complete a task. Overall, 50% (20/40) of the participants received regular prompts, and 50% (20/40) received intensive audiovisual prompts to perform everyday tasks. Participants' reactions were observed remotely via cameras. User feedback was captured via questionnaires, which included topics like usability, design, usefulness, and concerns. The internal consistency of the subscales was calculated. Plausibility was also checked using qualitative approaches. Participants noted their preferences for particular functions and improvements. Patients struggled with rating using the Likert scale; therefore, we assisted them with completing the questionnaire. Usability (mean 78 out of 100, SD 15.22) and usefulness (mean 9 out of 12) were rated high. The smartwatch design was appealing to most participants (31/40, 76%). Only a few participants (6/40, 15%) were concerned about using the watch. Better usability was associated with better cognition. The observed success and self-rated task comprehension were in agreement for most participants (32/40, 80%). In different qualitative analyses, participants' responses were, in most cases, plausible. Only 8% (3/40) of the participants were completely unaware of their irregular task performance. People with dementia can have positive experiences with smartwatches. Most people with dementia provided valuable information. Developing assistive technologies together with people with dementia can help to prioritize the future development of functional and nonfunctional features.

The wages of irregular tasks: workers’ compensation benefits and occupational misclassification

The wages of irregular tasks: workers’ compensation benefits and occupational misclassification

Multi-AUV cooperative search method based on dynamic optimal coverage

This paper introduces a multi-AUV target search method founded on dynamic optimization coverage, aiming to plan paths that achieve complete coverage without backtracking and with reduced travel costs for participating AUVs in cooperative search missions. The study comprises three key components: 1) Environment Modeling:We present a mixed-type grid approach, capable of accurately modeling concave–convex polygons. 2) Area Allocation:A dynamic cooperative partition strategy based on K-means+ is employed to facilitate optimal area allocation for AUVs. 3) Path Planning: The paper introduces a search path planning algorithm called the Optimal Allocation Minimum Spanning Tree (OA-MST). OA-MST generates globally smooth and non-backtracking scanning paths within irregular task areas using an adaptive spanning tree. Moreover, the study includes multiple simulation experiments to validate the method’s efficacy. OA-MST is compared with the lawnmower strategy and four advanced Minimum Spanning Tree (MST) generation methods under identical area allocation conditions. The results demonstrate that the proposed collaborative search method significantly enhances path smoothness and reduces path costs across diverse scenarios. In addition, sea trials are conducted to verify the feasibility and practicality of implementing the proposed methods in engineering applications.

Time-Based and Event-Based Prospective Memory in Adults with Autism Spectrum Disorder: A Virtual Week Investigation.

Autism spectrum disorder (ASD) is a complex developmental condition including persistent challenges with social communication, restricted interests, and repetitive behavior. Though prospective memory failures are commonly observed in ASD population it has been less studied among adults with ASD. Prospective memory (PM) refers to the execution of delayed intentions. There are contradictory findings of regular and irregular prospective memory task among autistic adults. The present study is an attempt to investigate prospective memory performance among adults with autism spectrum disorder using the Virtual Week board game. Virtual Week (Rendell & Craik, 2000) (3-day Version) is a computerized board game in which participants after rolling a die move token clockwise around the board. Each round of the board represents one virtual "day." Adults between 16 and 25 years of age who were diagnosed with ASD (N = 23) were compared with non-ASD (N = 26) adults. Analyses of variance were used to analyze the data. Results revealed that autistic adults as compared to normal adults showed poorer performance on time-based task than on event-based task. A significant difference was evident between regular and irregular prospective memory tasks across both tasks among autistic adults. Results also revealed that ASD difficulties are associated with the prospective component of the irregular task. Prospective memory failures are widely observed in ASD group, and have important implications for functional independence. The findings of this study provide insight into prospective memory challenges that adults with autism spectrum disorder face in a daily basis.

What does a Peer Support Worker do in a Forensic Mental Health Clinic for Addicted Offenders?

IntroductionPeer support work can be an effective way to support patients and their participation in psychiatric treatment. Unlike in general psychiatry there is less experience with peer support work in forensic mental health inpatient settings. Characteristics different from general psychiatry, e.g., regarding patient structure and background, might lead to different tasks of peer support workers and subjects of conversation.ObjectivesWe aim to present an overview of tasks and conversation topics of a peer support worker in a forensic mental health setting for addicted offenders during an 12 month period. We address tasks on regular and irregular basis and the most frequent conversation topics.MethodsWe used the anonymized work documentation about weekly working activities and conversation notes of the peer support worker to extract information. Extracted data were thematically analyzed and clustered into themes for tasks and conversation topics.ResultsResults reveal several recurring and routine tasks, like joining ward rounds and changes of shift or leading a recovery patient group along with one-to-one conversations with patients according to their request. These topics were expanded by irregular tasks like group discussions for special occasions, e.g., after incidents. During one-on-one conversations, patients addressed topics, e.g., about their substance use history, thoughts and issues about their therapy or ways to achieve their goals in the future.ConclusionsPeer support is a well-accepted offer that can contain various different tasks in groups and in one-on-one settings as well. Although the peer support worker is seated on one ward, there are many requests from other wards, too. There also exists a broad range of conversation topics, some might be also present in general psychiatric wards and others that might be more unique to forensic settings. The broad range of tasks and acceptance of peer support make it necessary to provide corresponding resources like peer support staff and payment.Disclosure of InterestNone Declared

Corticospinal Control of a Challenging Ankle Task in Incomplete Spinal Cord Injury.

After incomplete spinal cord injury (iSCI), the control of lower extremity movements may be affected by impairments in descending corticospinal tract function. Previous iSCI studies demonstrated relatively well-preserved movement control during simple alternating dorsiflections and plantar flexions albeit with severely reduced motor strength and range of motion. This task, however, required comparably limited fine motor control, impeding the sensitivity to assess the modulatory capacity of corticospinal control. Therefore, we introduced a more challenging ankle motor task necessitating complex and dynamic feedback-based movement adjustments to modulate corticospinal drive. Nineteen individuals with iSCI and 22 control subjects performed two different ankle movement tasks: (1) a regular, auditory-guided ankle movement task at a constant frequency as baseline assessment and (2) an irregular, visually guided ankle movement task following a pre-defined trajectory as a more challenging motor task. Both tasks were performed separately and in a randomized order. Electromyography (EMG) and kinematic data were recorded. The EMG frequency characteristics were investigated using wavelet transformations. Control participants exhibited a shift of relative EMG intensity from higher (>100 Hz) to lower frequencies (20-60 Hz) comparing the regular with the irregular movement task. There is evidence that EMG activity within these lower frequencies comprise information on corticospinal drive. The EMG frequency shift was less pronounced for the less impaired leg and absent for the more impaired leg of individuals with iSCI. The precision error during the irregular task was significantly higher for individuals with iSCI (more impaired leg: 12.34 ± 11.14%; less impaired leg: 6.93 ± 2.74%) compared with control participants (4.10 ± 0.84%). These results, along with the walking performance, correlated well with the delta frequency shift between the regular and irregular movement task in the 38 Hz band (corticospinal drive frequency) in the iSCI group, suggesting that task performance is related to the capacity to modulate corticospinal control. The irregular movement task holds promise as a tool for revealing further insights into corticospinal control of single-joint movements. It may serve as a surrogate marker for the assessment of modulatory capacity and the integrity of corticospinal control in individuals with iSCI early after injury and throughout rehabilitation.

Performance analysis of task-based multi-frontal sparse linear solvers: Structure matters

Efficiently exploiting computational resources in heterogeneous platforms is a real challenge which has motivated the adoption of the task-based programming paradigm where resource usage is dynamic and adaptive. Unfortunately, classical performance visualization techniques used in routine performance analysis often fail to provide any insight in this new context, especially when the application structure is irregular. In this paper, we propose several performance visualization techniques and modeling strategies motivated by the analysis of task-based multifrontal sparse linear solvers whose structure is particularly complex. We show that by building on both a performance model of irregular tasks and on structure of the application (in particular the elimination tree), we can detect and highlight anomalies and understand resource utilization from the application point-of-view in a very insightful way. We validate these novel performance analysis techniques with the QR_mumps sparse parallel solver by describing a series of case studies where we identify and address non trivial performance issues thanks to our visualization methodology.

Age-Related Differences in Intentional Forgetting of Prospective Memory.

This study investigated age-related differences in intentional forgetting (IF) of prospective memory (memory for actions to be performed in the future) in young (19-30 years) and late-midlife adults (LMA; 57-75 years). Prospective memory (PM) performance was examined by using the Virtual Week (VW) Task. An IF procedure was embedded into the VW task and the participants were instructed to forget some of the PM tasks that they were to remember and execute later on a virtual day. The study compared performances of the young and the LMA participants in the context of event- or time-based regular and irregular tasks. The results confirmed previous findings in showing that LMA participants exhibited worse PM than younger participants in lab-based tasks. In addition, although PM and IF performances separately have been shown to be affected by cognitive aging, larger age-related differences were not found in PM performance under IF conditions.

Multiqueue Scheduling of Heterogeneous Tasks With Bounded Response Time in Hybrid Green IaaS Clouds

Cost-effective task scheduling is an important operation in green infrastructure-as-a-service clouds (GICs) as the energy consumed by users’ tasks is drastic. The irregular task arrival forces private GIC to adopt hybrid clouds to outsource some tasks to dynamic and reliable virtual machines (VMs) of public external clouds. However, temporal differences in revenue, electricity prices, wind and solar energy, and VM running prices of public external clouds make it difficult to dispatch all tasks in a cost-effective way while satisfying users’ specified response time constraints. Unlike existing methods, we propose a multiqueue scheduling (MQS) method that investigates such temporal differences in hybrid GICs (HGICs). Specially, this work first gives mathematical relations between rejected tasks and service rates of servers in private GIC. In each iteration of MQS, this paper formulates a profit maximization problem for HGIC and solves it by a novel meta-heuristic optimization method by combing simulated annealing, particle swarm optimization, and genetic algorithm. Trace-driven experiments based on real-life data demonstrate that profit and throughput of MQS are larger than typical task scheduling algorithms while meeting tasks’ response time constraints.

Stream parallelism with ordered data constraints on multi-core systems

It is often a challenge to keep input/output tasks/results in order for parallel computations over data streams, particularly when stateless task operators are replicated to increase parallelism when there are irregular tasks. Maintaining input/output order requires additional coding effort and may significantly impact the application’s actual throughput. Thus, we propose a new implementation technique designed to be easily integrated with any of the existing C++ parallel programming frameworks that support stream parallelism. In this paper, it is first implemented and studied using SPar, our high-level domain-specific language for stream parallelism. We discuss the results of a set of experiments with real-world applications revealing how significant performance improvements may be achieved when our proposed solution is integrated within SPar, especially for data compression applications. Also, we show the results of experiments performed after integrating our solution within FastFlow and TBB, revealing no significant overheads.

Learning From the Past: Uncovering Design Process Models Using an Enriched Process Mining

Design documents and design project footprints accumulated by corporate information technology systems have increasingly become valuable sources of evidence for design information and knowledge management. Identification and extraction of such embedded information and knowledge into a clear and usable format will greatly accelerate continuous learning from past design efforts for competitive product innovation and efficient design process management in future design projects. Most of the existing design information extraction systems focus on either organizing design documents for efficient retrieval or extracting relevant product information for product optimization. Different from traditional systems, this paper proposes a methodology of learning and extracting useful knowledge using past design project documents from design process perspective based on process mining techniques. Particularly different from conventional techniques that deal with timestamps or event logs only, a new process mining approach that is able to directly process textual data is proposed at the first stage of the proposed methodology. The outcome is a hierarchical process model that reveals the actual design process hidden behind a large amount of design documents and enables the connection of various design information from different perspectives. At the second stage, the discovered process model is analyzed to extract multifaceted knowledge patterns by applying a number of statistical analysis methods. The outcomes range from task dependency study from workflow analysis, identification of irregular task execution from performance analysis, cooperation pattern discovery from social net analysis to evaluation of personal contribution based on role analysis. Relying on the knowledge patterns extracted, lessons and best practices can be uncovered which offer great support to decision makers in managing any future design initiatives. The proposed methodology was tested using an email dataset from a university-hosted multiyear multidisciplinary design project.

HPC‐GAP: engineering a 21st‐century high‐performance computer algebra system

SummarySymbolic computation has underpinned a number of key advances in Mathematics and Computer Science. Applications are typically large and potentially highly parallel, making them good candidates for parallel execution at a variety of scales from multi‐core to high‐performance computing systems. However, much existing work on parallel computing is based around numeric rather than symbolic computations. In particular, symbolic computing presents particular problems in terms of varying granularity and irregular task sizes that do not match conventional approaches to parallelisation. It also presents problems in terms of the structure of the algorithms and data. This paper describes a new implementation of the free open‐source GAP computational algebra system that places parallelism at the heart of the design, dealing with the key scalability and cross‐platform portability problems. We provide three system layers that deal with the three most important classes of hardware: individual shared memory multi‐core nodes, mid‐scale distributed clusters of (multi‐core) nodes and full‐blown high‐performance computing systems, comprising large‐scale tightly connected networks of multi‐core nodes. This requires us to develop new cross‐layer programming abstractions in the form of new domain‐specific skeletons that allow us to seamlessly target different hardware levels. Our results show that, using our approach, we can achieve good scalability and speedups for two realistic exemplars, on high‐performance systems comprising up to 32000 cores, as well as on ubiquitous multi‐core systems and distributed clusters. The work reported here paves the way towards full‐scale exploitation of symbolic computation by high‐performance computing systems, and we demonstrate the potential with two major case studies. © 2016 The Authors. Concurrency and Computation: Practice and Experience Published by John Wiley & Sons Ltd.

Two-Level Task Scheduling for Irregular Applications on GPU Platform

With a data parallel design, GPUs depend on uniform work distribution to expose their full potential. Therefore, irregular applications suffer from serious performance degradation as it is highly challenging to schedule irregular tasks on a GPU: It requires understandings of GPU architecture and irregular applications to devise a scheduling most suitable in this context, not to mention error-prone concurrent programming. This paper proposes a two-level scheduling to distribute irregular tasks and enable resource sharing on GPUs, by managing tasks and threads hierarchically. Meanwhile, we manage to group cache friendly tasks for more data reuse in L1 cache. We further extend our scheduling to handle nested irregularities. Besides, we devise a programming framework to facilitate the task scheduling for application programmers. The experimental results show that our approach effectively improves performance of six irregular applications on a typical platform, yielding a harmonic-mean speedup of $$2.1\times $$2.1× at a small schedule cost, and does not burden programmers with lots of work.

A Parallel Sparse Direct Solver via Hierarchical DAG Scheduling

We present a parallel sparse direct solver for multicore architectures based on Directed Acyclic Graph (DAG) scheduling. Recently, DAG scheduling has become popular in advanced Dense Linear Algebra libraries due to its efficient asynchronous parallel execution of tasks. However, its application to sparse matrix problems is more challenging as it has to deal with an enormous number of highly irregular tasks. This typically results in substantial scheduling overhead both in time and space, which causes overall parallel performance to be suboptimal. We describe a parallel solver based on two-level task parallelism: tasks are first generated from a parallel tree traversal on the assembly tree; next, those tasks are further refined by using algorithms - by - blocks to gain fine-grained parallelism. The resulting fine-grained tasks are asynchronously executed after their dependencies are analyzed. Our approach is distinct from others in that we adopt two-level task scheduling to mirror the two-level parallelism. As a result, we reduce scheduling overhead, and increase efficiency and flexibility. The proposed parallel sparse direct solver is evaluated for the particular problems arising from the hp -Finite Element Method where conventional sparse direct solvers do not scale well.

CUIRRE: An open-source library for load balancing and characterizing irregular applications on GPUs

While Graphics Processing Units (GPUs) show high performance for problems with regular structures, they do not perform well for irregular tasks due to the mismatches between irregular problem structures and SIMD-like GPU architectures. In this paper, we introduce a new library, CUIRRE, for improving performance of irregular applications on GPUs. CUIRRE reduces the load imbalance of GPU threads resulting from irregular loop structures. In addition, CUIRRE can characterize irregular applications for their irregularity, thread granularity and GPU utilization. We employ this library to characterize and optimize both synthetic and real-world applications. The experimental results show that a 1.63× on average and up to 2.76× performance improvement can be achieved with the centralized task pool approach in the library at a 4.57% average overhead with static loading ratios. To avoid the cost of exhaustive searches of loading ratios, an adaptive loading ratio method is proposed to derive appropriate loading ratios for different inputs automatically at runtime. Our task pool approach outperforms other load balancing schemes such as the task stealing method and the persistent threads method. The CUIRRE library can easily be applied on many other irregular problems.

A Virtual Week study of prospective memory function in autism spectrum disorders

Prospective memory (PM) refers to the implementation of delayed intentions, a cognitive ability that plays a critical role in daily life because of its involvement in goal-directed behavior and consequently the development and maintenance of independence. Emerging evidence indicates that PM may be disrupted in autism spectrum disorders (ASDs), potentially contributing to the functional difficulties that characterize this group. However, the degree, nature, and specificity of ASD-related impairment remains poorly understood. In the current study, children between 8 and 12years of age who were diagnosed with ASDs (n=30) were compared with typically developing children (n=30) on a child-appropriate version of the Virtual Week board game. This measure provides an opportunity to investigate the different sorts of PM failures that occur. The ASD group showed significant PM impairment on measures of time-based (but not event-based) prospective remembering. However, only a subtle difference emerged between regular and irregular PM tasks, and group differences were consistent across these tasks. Because regular and irregular tasks differentially load retrospective memory, these data imply that the PM difficulties seen in ASDs may primarily reflect a monitoring deficit and not an encoding and memory storage deficit. PM performance was poorer under conditions of high ongoing task absorption, but the magnitude of this effect did not vary as a function of group. In both groups, time-based (but not event-based) PM difficulties were associated with functional outcomes in daily life, but only an inconsistent association with executive control emerged.

An investigation of prospective memory functions in people with traumatic brain injury using Virtual Week

Prospective memory (PM) refers to memory for future intentions and is critically linked to independent living. Previous laboratory research has shown that people who have sustained a traumatic brain injury (TBI) have difficulties with PM, but few of these have used measures of PM that closely represent the types of PM activities that occur in everyday life. One measure that incorporates more ecologically valid tasks, and which also allows systematic investigation of different PM task parameters (regular, irregular, and event and time based), is Virtual Week. Consequently, in the present study, Virtual Week was administered to participants with TBI (n = 18) and demographically matched controls (n = 18). Consistent with considerable prior literature, the results indicated that people with TBI had significant difficulties executing PM tasks, with these deficits more pronounced for time-based than for event-based tasks. These data point to there being a relatively global PM deficit in people with TBI. Of particular interest was the finding that the magnitude of TBI impairment was consistent across regular and irregular tasks. Because the key distinction between these tasks is that they place low and high demands on retrospective memory, respectively, these data suggest that failures of retrospective memory are not the major cause of TBI-related impairment in PM. The implications of these results for the assessment and rehabilitation of PM impairment in people with TBI are discussed.

Prospective memory in Parkinson disease during a virtual week: Effects of both prospective and retrospective demands.

This study investigated the effect of Parkinson's disease (PD) on event-based prospective memory tasks with varying demand on (1) the amount of strategic attentional monitoring required for intention retrieval (prospective component), and (2) the retrospective memory processes required to remember the contents of the intention or the entire constellation of prospective memory tasks. Twenty-four older adults with PD and 28 healthy older adults performed the computerized Virtual Week task, a multi-intention prospective memory paradigm that simulates everyday prospective memory tasks. The Virtual Week included regular (low retrospective memory demand) and irregular (high retrospective memory demand) prospective memory tasks with cues that were focal (low strategic monitoring demand) or less focal (high strategic monitoring demand) to the ongoing activity. For the regular prospective memory tasks, PD participants were impaired when the prospective memory cues were less focal. For the irregular prospective memory tasks, PD participants were impaired regardless of prospective memory cue type. PD participants also had impaired retrospective memory for irregular tasks, which was associated with worse prospective memory for these tasks during the Virtual Week. When retrospective memory demands are minimized, prospective memory in PD can be supported by cues that reduce the executive control demands of intention retrieval. However, PD-related deficits in self-initiated encoding or planning processes have strong negative effects on the performance of prospective memory tasks, with increased retrospective memory demand.

The Implementation and Optimization of Irregular Application Task Models Based on the Cell BE Processor

The task parallelization is proposed in the OpenMP3.0 specification, which aims to resolve the irregular parallel computing problems. This paper presents the novel irregular application task model on Cell BE processors, which could reduce the difficulty of irregular applied parallel programming. In this Model, the two kinds of optimization techniques for maximum task numbers and maximum recursive layer are realized to avoid producing a large amount of fine-grained tasks and improve effectively the program performance of parallel execution. The experimental results show that the speedup of the typical irregular application is increased to 5.3 in a single Cell processor with six SPEs. Keyword: Heterogeneous Multi-Core, Irregular, Task Parallel, Breadth-First