scholarly journals Optimal Diffusion Learning over Networks - Part I: Single-Task Algorithms

Author(s):  
Ricardo Merched
Keyword(s):  
2019 ◽  
Vol 62 (7) ◽  
pp. 2099-2117 ◽  
Author(s):  
Jason A. Whitfield ◽  
Zoe Kriegel ◽  
Adam M. Fullenkamp ◽  
Daryush D. Mehta

Purpose Prior investigations suggest that simultaneous performance of more than 1 motor-oriented task may exacerbate speech motor deficits in individuals with Parkinson disease (PD). The purpose of the current investigation was to examine the extent to which performing a low-demand manual task affected the connected speech in individuals with and without PD. Method Individuals with PD and neurologically healthy controls performed speech tasks (reading and extemporaneous speech tasks) and an oscillatory manual task (a counterclockwise circle-drawing task) in isolation (single-task condition) and concurrently (dual-task condition). Results Relative to speech task performance, no changes in speech acoustics were observed for either group when the low-demand motor task was performed with the concurrent reading tasks. Speakers with PD exhibited a significant decrease in pause duration between the single-task (speech only) and dual-task conditions for the extemporaneous speech task, whereas control participants did not exhibit changes in any speech production variable between the single- and dual-task conditions. Conclusions Overall, there were little to no changes in speech production when a low-demand oscillatory motor task was performed with concurrent reading. For the extemporaneous task, however, individuals with PD exhibited significant changes when the speech and manual tasks were performed concurrently, a pattern that was not observed for control speakers. Supplemental Material https://doi.org/10.23641/asha.8637008


2020 ◽  
Vol 15 (4) ◽  
pp. 487-500
Author(s):  
Thaer S. Manaseer ◽  
Jackie L. Whittaker ◽  
Codi Isaac ◽  
Kathryn Schneider ◽  
Mary Roduta Roberts ◽  
...  

2018 ◽  
Vol 23 (1) ◽  
pp. 56-65
Author(s):  
Moehamad Adi Rochmat

Revised NIOSH Lifting Equation (RNLE) merupakan sebuah aplikasi yang dibuat oleh National Institute for Occupational Safety and Health (NIOSH) yang merupakan sebuah institusi di Amerika yang mengembangkan perangkat penilaian dalam bidang keselamatan dan kesehatan kerja. Salah satu aplikasi yang dikembangkan dinamakan Revised NIOSH Lifting Equation Single Task yang digunakan untuk menguji aktifitas pemindahan barang tanpa perpindahan posisi kaki. Aplikasi yang dibangun akan memberikan penilaian terhadap sistem kerja yang dilakukan oleh seorang pekerja. Salah satu hasil perhitungan dari aplikasi tersebut adalah nilai Lifting Index (LI) yang menyatakan tingkat resiko pekerjaan. Aplikasi RNLE dikembangkan dengan dasar program Microsoft Office Excel. Rumus perhitungan untuk mendapatkan nilai LI dan parameter yang diperlukan disediakan oleh NIOSH dan dapat dipelajari. Penelitian ini mengembangkan rekomendasi perbaikan sistem kerja pada aplikasi RNLE dengan menggunakan program Microsoft Office Excel. Rekomendasi perbaikan yang diutamakan adalah posisi awal benda dan posisi akhir yang sebaiknya diatur sedemikian sehingga mengoptimalkan kemampuan pekerja. Cara optimalisasi yang bisa dilakukan dengan merubah salah satu nilai parameter masukkan tanpa merubah nilai yang lainnya.


Gerontology ◽  
2021 ◽  
pp. 1-10
Author(s):  
He Zhou ◽  
Catherine Park ◽  
Mohammad Shahbazi ◽  
Michele K. York ◽  
Mark E. Kunik ◽  
...  

<b><i>Background:</i></b> Cognitive frailty (CF), defined as the simultaneous presence of cognitive impairment and physical frailty, is a clinical symptom in early-stage dementia with promise in assessing the risk of dementia. The purpose of this study was to use wearables to determine the most sensitive digital gait biomarkers to identify CF. <b><i>Methods:</i></b> Of 121 older adults (age = 78.9 ± 8.2 years, body mass index = 26.6 ± 5.5 kg/m<sup>2</sup>) who were evaluated with a comprehensive neurological exam and the Fried frailty criteria, 41 participants (34%) were identified with CF and 80 participants (66%) were identified without CF. Gait performance of participants was assessed under single task (walking without cognitive distraction) and dual task (walking while counting backward from a random number) using a validated wearable platform. Participants walked at habitual speed over a distance of 10 m. A validated algorithm was used to determine steady-state walking. Gait parameters of interest include steady-state gait speed, stride length, gait cycle time, double support, and gait unsteadiness. In addition, speed and stride length were normalized by height. <b><i>Results:</i></b> Our results suggest that compared to the group without CF, the CF group had deteriorated gait performances in both single-task and dual-task walking (Cohen’s effect size <i>d</i> = 0.42–0.97, <i>p</i> &#x3c; 0.050). The largest effect size was observed in normalized dual-task gait speed (<i>d</i> = 0.97, <i>p</i> &#x3c; 0.001). The use of dual-task gait speed improved the area under the curve (AUC) to distinguish CF cases to 0.76 from 0.73 observed for the single-task gait speed. Adding both single-task and dual-task gait speeds did not noticeably change AUC. However, when additional gait parameters such as gait unsteadiness, stride length, and double support were included in the model, AUC was improved to 0.87. <b><i>Conclusions:</i></b> This study suggests that gait performances measured by wearable sensors are potential digital biomarkers of CF among older adults. Dual-task gait and other detailed gait metrics provide value for identifying CF above gait speed alone. Future studies need to examine the potential benefits of gait performances for early diagnosis of CF and/or tracking its severity over time.


Metals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 870
Author(s):  
Robby Neven ◽  
Toon Goedemé

Automating sheet steel visual inspection can improve quality and reduce costs during its production. While many manufacturers still rely on manual or traditional inspection methods, deep learning-based approaches have proven their efficiency. In this paper, we go beyond the state-of-the-art in this domain by proposing a multi-task model that performs both pixel-based defect segmentation and severity estimation of the defects in one two-branch network. Additionally, we show how incorporation of the production process parameters improves the model’s performance. After manually constructing a real-life industrial dataset, we first implemented and trained two single-task models performing the defect segmentation and severity estimation tasks separately. Next, we compared this to a multi-task model that simultaneously performs the two tasks at hand. By combining the tasks into one model, both segmentation tasks improved by 2.5% and 3% mIoU, respectively. In the next step, we extended the multi-task model using sensor fusion with process parameters. We demonstrate that the incorporation of the process parameters resulted in a further mIoU increase of 6.8% and 2.9% for the defect segmentation and severity estimation tasks, respectively.


2021 ◽  
pp. 1-20
Author(s):  
Tianqi Wang ◽  
Yin Hong ◽  
Quanyi Wang ◽  
Rongfeng Su ◽  
Manwa Lawrence Ng ◽  
...  

Background: Previous studies explored the use of noninvasive biomarkers of speech and language for the detection of mild cognitive impairment (MCI). Yet, most of them employed single task which might not have adequately captured all aspects of their cognitive functions. Objective: The present study aimed to achieve the state-of-the-art accuracy in detecting individuals with MCI using multiple spoken tasks and uncover task-specific contributions with a tentative interpretation of features. Methods: Fifty patients clinically diagnosed with MCI and 60 healthy controls completed three spoken tasks (picture description, semantic fluency, and sentence repetition), from which multidimensional features were extracted to train machine learning classifiers. With a late-fusion configuration, predictions from multiple tasks were combined and correlated with the participants’ cognitive ability assessed using the Montreal Cognitive Assessment (MoCA). Statistical analyses on pre-defined features were carried out to explore their association with the diagnosis. Results: The late-fusion configuration could effectively boost the final classification result (SVM: F1 = 0.95; RF: F1 = 0.96; LR: F1 = 0.93), outperforming each individual task classifier. Besides, the probability estimates of MCI were strongly correlated with the MoCA scores (SVM: –0.74; RF: –0.71; LR: –0.72). Conclusion: Each single task tapped more dominantly to distinct cognitive processes and have specific contributions to the prediction of MCI. Specifically, picture description task characterized communications at the discourse level, while semantic fluency task was more specific to the controlled lexical retrieval processes. With greater demands on working memory load, sentence repetition task uncovered memory deficits through modified speech patterns in the reproduced sentences.


Author(s):  
Lasse Pelzer ◽  
Christoph Naefgen ◽  
Robert Gaschler ◽  
Hilde Haider

AbstractDual-task costs might result from confusions on the task-set level as both tasks are not represented as distinct task-sets, but rather being integrated into a single task-set. This suggests that events in the two tasks are stored and retrieved together as an integrated memory episode. In a series of three experiments, we tested for such integrated task processing and whether it can be modulated by regularities between the stimuli of the two tasks (across-task contingencies) or by sequential regularities within one of the tasks (within-task contingencies). Building on the experimental approach of feature binding in action control, we tested whether the participants in a dual-tasking experiment will show partial-repetition costs: they should be slower when only the stimulus in one of the two tasks is repeated from Trial n − 1 to Trial n than when the stimuli in both tasks repeat. In all three experiments, the participants processed a visual-manual and an auditory-vocal tone-discrimination task which were always presented concurrently. In Experiment 1, we show that retrieval of Trial n − 1 episodes is stable across practice if the stimulus material is drawn randomly. Across-task contingencies (Experiment 2) and sequential regularities within a task (Experiment 3) can compete with n − 1-based retrieval leading to a reduction of partial-repetition costs with practice. Overall the results suggest that participants do not separate the processing of the two tasks, yet, within-task contingencies might reduce integrated task processing.


Author(s):  
Georgianna Lin ◽  
Malcolm Haynes ◽  
Sarthak Srinivas ◽  
Pramod Kotipalli ◽  
Thad Starner

Where should a HWD be placed in a user's visual field? We present two studies that compare comfort, preference, task efficiency and accuracy for various HWD positions. The first study offsets a 9.2° horizontal field-of-view (FOV) display temporally (toward the ear) from 0° to 30° in 10° steps. 30° proves too uncomfortable while 10° is the most preferred position for a simple button-pushing game, corroborating results from previous single-task reading experiments. The second experiment uses a Magic Leap One to compare 10° x 10° FOV interfaces centered at line-of-sight, temporally offset 15° (center-right), inferiorly offset 15° (bottom-center), and offset in both directions (bottom-right) for an order picking task. The bottom-right position proved worst in terms of accuracy and several subjective metrics when compared to the line-of-sight position.


2021 ◽  
Vol 36 (6) ◽  
pp. 1048-1048
Author(s):  
Daliah Ross ◽  
Mark E Wagshul ◽  
Meltem Izzetoglu ◽  
Roee Holtzer

Abstract Objective Greater intraindividual variability (IIV) in behavioral and cognitive performance is a risk factor for adverse outcomes but research concerning IIV in neural signal is scarce. Using functional near-infrared spectroscopy (fNIRS), we showed that IIV in oxygenated hemoglobin (HbO2) levels in the prefrontal cortex increased from single task (Single-Task-Walk–STW; Single-Task-Alpha–STA) to Dual-Task-Walk (DTW) conditions in older adults. Herein, we predicted that, consistent with the neural inefficiency hypothesis, reduced cortical thickness would be associated with greater increases in IIV in fNIRS-derived HbO2 from single tasks to DTW when adjusting for behavioral performance. Method Participants were right-handed older adults without dementia recruited from the community (N = 55; M(SD) age = 74.84(4.97); %female = 49.1). Neuroimaging included fNIRS for HbO2 levels in the prefrontal cortex during tasks and MRI for cortical thickness. IIV was operationalized using the SD of fNIRS-derived HbO2 observations assessed during a 30-s interval in each experimental condition. Results Moderation analyses, assessed through linear mixed effects models, revealed that in several frontal (p &lt; 0.02), parietal (p &lt; 0.02), temporal (p &lt; 0.01), and occipital (p &lt; 0.01) regions, thinner cortex was associated with greater increases in HbO2 IIV from the single tasks to DTW. Conclusion Reduced cortical thickness was associated with inefficient increases in IIV in fNIRS-derived HbO2 from single tasks to dual-task walking. Worse IIV in gait performance under DTW predicts adverse mobility outcomes. Reduced cortical thickness and worse IIV of fNIRS-derived HbO2 during DTW are possible brain mechanisms that explain the risk of developing mobility impairments in aging and disease populations.


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