Dual task

Knowledge tracing (KT) refers to predicting learners' performance in the future according to their historical responses, which has become an essential task in intelligent tutoring systems. Most deep learning-based methods usually model the learners' knowledge states via recurrent neural networks (RNNs) or attention mechanisms. Recently emerging graph neural networks (GNNs) assist the KT model to capture the relationships such as question-skill and question-learner. However, non-pairwise and complex higher-order information among responses is ignored...

BACKGROUND: The laterality of motor symptoms is considered a key feature of Parkinson's disease (PD). Here, we investigated whether gait and turning asymmetry coincided with symptom laterality as determined by the MDS-UPRDS part III and whether it was increased compared to healthy controls (HC). METHODS: We analyzed the asymmetry of gait and turning with and without a cognitive dual task (DT) using motion capture systems and wearable sensors in 97 PD patients mostly from Hoehn & Yahr stage II and III and 36 age-matched HC...

Treatment effect estimation (TEE) is the task of determining the impact of various treatments on patient outcomes. Current TEE methods fall short due to reliance on limited labeled data and challenges posed by sparse and high-dimensional observational patient data. To address the challenges, we introduce a novel pre-training and fine-tuning framework, KG-TREAT, which synergizes large-scale observational patient data with biomedical knowledge graphs (KGs) to enhance TEE. Unlike previous approaches, KG-TREAT constructs dual-focus KGs and integrates a deep bi-level attention synergy method for in-depth information fusion, enabling distinct encoding of treatment-covariate and outcome-covariate relationships...

INTRODUCTION: With aging, dual task (DT) ability declines and is more cognitively demanding than single tasks. Rapidly declining DT performance is regarded as a predictor of neurodegenerative disease. Task training and non-invasive transcranial electrical stimulation (tES) are methods applied to optimize the DT ability of the elderly. METHODS: A systematic search was carried out in the PUBMED, TDCS (transcranial direct current stimulation) databases, as well as Web of Science, and a qualitative analysis was conducted in 56 included studies...

BACKGROUND: Liver lesions mainly occur inside the liver parenchyma, which are difficult to locate and have complicated relationships with essential vessels. Thus, preoperative planning is crucial for the resection of liver lesions. Accurate segmentation of the hepatic and portal veins (PVs) on computed tomography (CT) images is of great importance for preoperative planning. However, manually labeling the mask of vessels is laborious and time-consuming, and the labeling results of different clinicians are prone to inconsistencies...

It is becoming harder to tell rumors from non-rumors as social media becomes a key news source, which invites malicious manipulation that could do harm to the public's health or cause financial loss. When faced with situations when the session structure of comment sections is deliberately disrupted, traditional models do not handle them adequately. In order to do this, we provide a novel rumor detection architecture that combines dual comparison learning, adversarial training, and attention filters. We suggest the attention filter module to achieve the filtering of some dangerous comments as well as the filtering of some useless comments, allowing the nodes to enter the GAT graph neural network with greater structural information...

Feature pyramids are widely adopted in visual detection models for capturing multiscale features of objects. However, the utilization of feature pyramids in practical object detection tasks is prone to complex background interference, resulting in suboptimal capture of discriminative multiscale foreground semantic features. In this article, a foreground capture feature pyramid network (FCFPN) for multiscale object detection is proposed, to address the problem of inadequate feature learning in complex backgrounds...

Federated recommender systems (FRSs), with their improved privacy-preserving advantages to jointly train recommendation models from numerous devices while keeping user data distributed, have been widely explored in modern recommender systems (RSs). However, conventional FRSs require transmitting the entire model between the server and clients, which brings a huge carbon footprint for cost-conscious cross-device learning tasks. While several efforts have been dedicated to improving the efficiency of FRSs, it's suboptimal to treat the whole model as the objective of compact design...

INTRODUCTION: There is growing evidence suggesting that dual-task training benefits people with Parkinson's disease (PD) on both physical and cognitive outcomes. However, there is no known data regarding professionals' educational needs and barriers to its implementation. This study aimed to explore the barriers and educational needs of healthcare and exercise professionals to integrate dual-task training into their practice with people with PD. METHODS: We conducted a study based on a web survey...

Several studies have shown that parafoveal processing is essential in reading development. In this study, we explore the effect of transposing and substituting inner and outer letters in a flanker lexical decision task administered to 78 children and 65 adults. The results show a significant interaction between the Group factor and the Flanker factor, suggesting differences in the effects of flankers for children and adults. In the case of adults, transposed and substituted letters generated benefit of the same magnitude in comparison to the unrelated condition, but of lesser magnitude than the Identity condition...

INTRODUCTION: To determine if demographic variables and measures of cognitive function, functional mobility, self-reported balance self-efficacy, and self-reported physical activity can predict gait speed variability during single-task walking (STgscv), during cognitive-motor dual-tasking (DTgscv), and dual-task effect on gait speed variability (DTEgscv) in older adults. METHODS: In 62 older adults, demographics were recorded and cognitive function (including the Montreal Cognitive Assessment, MoCA), functional mobility, balance self-efficacy (Activity-Specific Balance Confidence Scale, ABC), and self-reported physical activity (Physical Activity Scale for the Elderly, PASE) were assessed...

Semi-supervised learning has made significant progress in medical image segmentation. However, existing methods primarily utilize information from a single dimensionality, resulting in sub-optimal performance on challenging magnetic resonance imaging (MRI) data with multiple segmentation objects and anisotropic resolution. To address this issue, we present a Hybrid Dual Mean-Teacher (HD-Teacher) model with hybrid, semi-supervised, and multi-task learning to achieve effective semi-supervised segmentation. HD-Teacher employs a 2D and a 3D mean-teacher network to produce segmentation labels and signed distance fields from the hybrid information captured in both dimensionalities...

INTRODUCTION: With increasing life expectancy of older adult population, maintaining independence and well-being in later years is of paramount importance. This study aims to investigate the impact of three distinct interventions: cognitive training, resistance training and a combination of both, compared with an inactive control group, on cognitive performance, mobility and quality of life in adults aged ≥65 years. METHODS AND ANALYSIS: This trial will investigate healthy older adults aged ≥65 years living independently without cognitive impairments...

BACKGROUND: In the pursuit of finer Brain-Computer Interface commands, research focus has shifted towards classifying EEG signals for multiple tasks. While single-joint multitasking motor imagery provides support, distinguishing between EEG signals from the same joint remains challenging due to their similar brain spatial distribution. NEW METHOD: We designed experiments involving three motor imagery tasks-wrist extension, wrist flexion, and wrist abduction-with six participants...

PURPOSE: Specular reflections in endoscopic images not only disturb visual perception but also hamper computer vision algorithm performance. However, the intricate nature and variability of these reflections, coupled with a lack of relevant datasets, pose ongoing challenges for removal. METHODS: We present EndoSRR, a robust method for eliminating specular reflections in endoscopic images. EndoSRR comprises two stages: reflection detection and reflection region inpainting...

Regularized multi-task learning (RMTL) has shown good performance in tackling multi-task binary problems. Although RMTL can be used to handle multi-class problems based on "one-versus-one" and "one-versus-rest" techniques, the information of the samples is not fully utilized and the class imbalance problem occurs. Motivated by the regularization technique in RMTL, we propose an original multi-task multi-class model termed MTKSVCR based on "one-versus-one-versus-rest" strategy to achieve better testing accuracy...

Alzheimer's Disease is the most prevalent neurodegenerative disease, and is a leading cause of disability among the elderly. Eye movement behaviour demonstrates potential as a non-invasive biomarker for Alzheimer's Disease, with changes detectable at an early stage after initial onset. This paper introduces a new publicly available dataset: EM-COGLOAD (available at https://osf.io/zjtdq/, DOI: 10.17605/OSF.IO/ZJTDQ). A dual-task paradigm was used to create effects of declined cognitive performance in 75 healthy adults as they carried out visual tracking tasks...

The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of various subsequent machine learning tasks. However, current dual-channel graph convolutional neural networks are limited by the number of convolution layers, which hinders the performance improvement of the models. Graph convolutional neural networks superimpose multi-layer graph convolution operations, which would occur in smoothing phenomena, resulting in performance decreasing as the increasing number of graph convolutional layers...

The chlorine radical is a strong HAT (Hydrogen Atom Transfer) agent that is very useful for the functionalization of C(sp3)-H bonds. Albeit highly attractive, its generation from the poorly oxidizable chloride ion mediated by an excited photoredox catalyst is a difficult task. We now report that 8Rf8-4CzIPN, an electron-deficient fluorous derivative of the benchmark 4CzIPN photoredox catalyst belonging to the donor-acceptor carbazole-cyanoarene family, is not only a better photooxidant than 4CzIPN, but also becomes an excellent host for the chloride ion...

Anastomosis is a common and critical part of reconstructive procedures within gastrointestinal, urologic, and gynecologic surgery. The use of autonomous surgical robots such as the smart tissue autonomous robot (STAR) system demonstrates an improved efficiency and consistency of the laparoscopic small bowel anastomosis over the current da Vinci surgical system. However, the STAR workflow requires auxiliary manual monitoring during the suturing procedure to avoid missed or wrong stitches. To eliminate this monitoring task from the operators, we integrated an optical coherence tomography (OCT) fiber sensor with the suture tool and developed an automatic tissue classification algorithm for detecting missed or wrong stitches in real time...