Task Performance Research Articles

Disease trend analysis platform accurately predicts the occurrence of cervical cancer under mixed diseases

Cervical cancer (CC) is one of the most common gynecological malignancies. Cytological screening, while being the most common and accurate method for detecting cervical cancer, is both time-consuming and costly. Predicting CC based on bioinformatics can assist in the rapid early screening of CC in clinical practice. Most recent CC prediction methods require a large amount of detection data or sequencing data and are not ideal for CC detection in complex disease samples. We developed the Disease trend analysis platform (Dtap), which can quickly predict the occurrence of diseases using only blood routine data. Blood routine data was collected from 1,292 cervical cancer patients, 4,860 patients with complex diseases, and 4,980 healthy individuals from various sources. The results show that the Dtap-based trend model maintained good and stable performance in the prediction task of multiple datasets as well as complex disease samples. Finally, we built DTAPCC (http://bioinfor.imu.edu.cn/dtapcc), a Dtap-based CC disease prediction platform, to help users quickly predict CC and visualize trend features.

Efficient Local Coherent Structure Learning via Self-Evolution Bipartite Graph.

Dimensionality reduction (DR) targets to learn low-dimensional representations for improving discriminability of data, which is essential for many downstream machine learning tasks, such as image classification, information clustering, etc. Non-Gaussian issue as a long-standing challenge brings many obstacles to the applications of DR methods that established on Gaussian assumption. The mainstream way to address above issue is to explore the local structure of data via graph learning technique, the methods based on which however suffer from a common weakness, that is, exploring locality through pairwise points causes the optimal graph and subspace are difficult to be found, degrades the performance of downstream tasks, and also increases the computation complexity. In this article, we first propose a novel self-evolution bipartite graph (SEBG) that uses anchor points as the landmark of subclasses, and learns anchor-based rather than pairwise relationships for improving the efficiency of locality exploration. In addition, we develop an efficient local coherent structure learning (ELCS) algorithm based on SEBG, which possesses the ability of updating the edges of graph in learned subspace automatically. Finally, we also provide a multivariable iterative optimization algorithm to solve proposed problem with strict theoretical proofs. Extensive experiments have verified the superiorities of the proposed method compared to related SOTA methods in terms of performance and efficiency on several real-world benchmarks and large-scale image datasets with deep features.

ZCHSound: Open-Source ZJU Paediatric Heart Sound Database With Congenital Heart Disease.

From 2020 to 2022, we collaborated with experienced cardiac surgeons from three general children's hospitals to collect heart sound signals from 1259 participants using electronic stethoscopes. To ensure the accuracy of the labels, the labels for all data were confirmed by two cardiac experts. To establish the baseline of ZCHsound, we extracted 84 features and used machine learning models to evaluate the performance of the classification task. The ZCHSound database was divided into two datasets: one is a high-quality, filtered clean heart sound dataset, and the other is a low-quality, noisy heart sound dataset. In the evaluation of the high-quality dataset, our random forest ensemble model achieved an F1 score of 90.3% in the classification task of normal and pathological heart sounds. This study has successfully established a large-scale, high-quality, rigorously standardized pediatric CHD sound database with precise disease diagnosis. This database not only provides important learning resources for clinical doctors in auscultation knowledge but also offers valuable data support for algorithm engineers in developing intelligent auscultation algorithms.

Hybrid fragment-SMILES tokenization for ADMET prediction in drug discovery

Background:Drug discovery and development is the extremely costly and time-consuming process of identifying new molecules that can interact with a biomarker target to interrupt the disease pathway of interest. In addition to binding the target, a drug candidate needs to satisfy multiple properties affecting absorption, distribution, metabolism, excretion, and toxicity (ADMET). Artificial intelligence approaches provide an opportunity to improve each step of the drug discovery and development process, in which the first question faced by us is how a molecule can be informatively represented such that the in-silico solutions are optimized.Results:This study introduces a novel hybrid SMILES-fragment tokenization method, coupled with two pre-training strategies, utilizing a Transformer-based model. We investigate the efficacy of hybrid tokenization in improving the performance of ADMET prediction tasks. Our approach leverages MTL-BERT, an encoder-only Transformer model that achieves state-of-the-art ADMET predictions, and contrasts the standard SMILES tokenization with our hybrid method across a spectrum of fragment library cutoffs.Conclusion:The findings reveal that while an excess of fragments can impede performance, using hybrid tokenization with high frequency fragments enhances results beyond the base SMILES tokenization. This advancement underscores the potential of integrating fragment- and character-level molecular features within the training of Transformer models for ADMET property prediction.

Institutional quarantine and dishonest behavior

Quarantine has been implemented worldwide to mitigate the spread of infectious diseases such as COVID-19. Although recent literature has outlined the mental and psychological costs of quarantine, its ethical costs are not fully understood. In two online experiments conducted during an institutional quarantine event (481 participants), we find that institutional quarantine leads to more dishonest behavior, including lying about the outcome of a random-draw task and overreporting one’s performance in a real-effort task to gain financial benefits. By directly manipulating individuals’ perceived psychological ownership, we provide suggestive evidence that psychological ownership may be one mechanism underlying the relationship between institutional quarantine and dishonest behavior. A complementary experiment (226 participants) suggests that anxiety and frustration may also serve as explanatory factors for this effect. We then discuss the implications of our findings, which may inspire approaches to mitigate the negative effects of institutional quarantine on honesty.

Measuring the velocity of spatio-temporal attention waves

A sequence of visual or auditory events may be perceived as a single continuing sequence or as two or more separate sequences occurring in parallel. The latter percept occurs when the perceived distance between events is large, and the timing is fast, and is referred to as “streaming.” Several researchers have previously argued that streaming indicates a velocity constraint on the movement of attention. To test this hypothesis the present experiment measured tradeoffs between distance and timing for the onset or loss of streaming in a rectangular pattern of displayed lights. Two linear tradeoffs were found, one corresponding to the loss of streaming when the light pattern was slowed down, and one corresponding to the onset of streaming when the light pattern was sped up. The slopes of these linear relations are interpreted as integer multiples of the velocity of spatio-temporal attention waves. A process model postulates that participants adjust the wavelength of their spatio-temporal attentional traveling wave to match the height of the displayed rectangle. Streaming is assumed to occur when peaks in the attentional traveling wave coincide with the onsets of lights at the top and bottom of the displayed rectangle. Additional supporting evidence for temporal and spatial attention waves is reviewed. This model may be useful for understanding some forms of attentional deficits as well as expert attentional skills arising in musical performance, sports, meditation, and other tasks.

NovPhy: A Physical Reasoning Benchmark for Open-world AI Systems

Due to the emergence of AI systems that interact with the physical environment, there is an increased interest in incorporating physical reasoning capabilities into those AI systems. But is it enough to only have physical reasoning capabilities to operate in a real physical environment? In the real world, we constantly face novel situations we have not encountered before. As humans, we are competent at successfully adapting to those situations. Similarly, an agent needs to have the ability to function under the impact of novelties in order to properly operate in an open-world physical environment. To facilitate the development of such AI systems, we propose a new benchmark, NovPhy, that requires an agent to reason about physical scenarios in the presence of novelties and take actions accordingly. The benchmark consists of tasks that require agents to detect and adapt to novelties in physical scenarios. To create tasks in the benchmark, we develop eight novelties representing a diverse novelty space and apply them to five commonly encountered scenarios in a physical environment, related to applying forces and motions such as rolling, falling, and sliding of objects. According to our benchmark design, we evaluate two capabilities of an agent: the performance on a novelty when it is applied to different physical scenarios and the performance on a physical scenario when different novelties are applied to it. We conduct a thorough evaluation with human players, learning agents, and heuristic agents. Our evaluation shows that humans' performance is far beyond the agents' performance. Some agents, even with good normal task performance, perform significantly worse when there is a novelty, and the agents that can adapt to novelties typically adapt slower than humans. We promote the development of intelligent agents capable of performing at the human level or above when operating in open-world physical environments. benchmark website: https://github.com/phy-q/novphy

A Coding Framework and Benchmark Towards Low-Bitrate Video Understanding.

Video compression is indispensable to most video analysis systems. Despite saving the transportation bandwidth, it also deteriorates downstream video understanding tasks, especially at low-bitrate settings. To systematically investigate this problem, we first thoroughly review the previous methods, revealing that three principles, i.e., task-decoupled, label-free, and data-emerged semantic prior, are critical to a machine-friendly coding framework but are not fully satisfied so far. In this paper, we propose a traditional-neural mixed coding framework that simultaneously fulfills all these principles, by taking advantage of both traditional codecs and neural networks (NNs). On one hand, the traditional codecs can efficiently encode the pixel signal of videos but may distort the semantic information. On the other hand, highly non-linear NNs are proficient in condensing video semantics into a compact representation. The framework is optimized by ensuring that a transportation-efficient semantic representation of the video is preserved w.r.t. the coding procedure, which is spontaneously learned from unlabeled data in a self-supervised manner. The videos collaboratively decoded from two streams (codec and NN) are of rich semantics, as well as visually photo-realistic, empirically boosting several mainstream downstream video analysis task performances without any post-adaptation procedure. Furthermore, by introducing the attention mechanism and adaptive modeling scheme, the video semantic modeling ability of our approach is further enhanced. Fianlly, we build a low-bitrate video understanding benchmark with three downstream tasks on eight datasets, demonstrating the notable superiority of our approach. All codes, data, and models will be open-sourced for facilitating future research.

When Follower Anxiety Expression Promotes Task Performance? A Self-Concordance Model Perspective

When Follower Anxiety Expression Promotes Task Performance? A Self-Concordance Model Perspective

Self-Supervised Medical Image Segmentation Using Deep Reinforced Adaptive Masking.

Self-supervised learning aims to learn transferable representations from unlabeled data for downstream tasks. Inspired by masked language modeling in natural language processing, masked image modeling (MIM) has achieved certain success in the field of computer vision, but its effectiveness in medical images remains unsatisfactory. This is mainly due to the high redundancy and small discriminative regions in medical images compared to natural images. Therefore, this paper proposes an adaptive hard masking (AHM) approach based on deep reinforcement learning to expand the application of MIM in medical images. Unlike predefined random masks, AHM uses an asynchronous advantage actor-critic (A3C) model to predict reconstruction loss for each patch, enabling the model to learn where masking is valuable. By optimizing the non-differentiable sampling process using reinforcement learning, AHM enhances the understanding of key regions, thereby improving downstream task performance. Experimental results on two medical image datasets demonstrate that AHM outperforms state-of-the-art methods. Additional experiments under various settings validate the effectiveness of AHM in constructing masked images.

Improving Human Sustainability at Work by Reducing Cognitive Load of Task Performance

Improving Human Sustainability at Work by Reducing Cognitive Load of Task Performance

The Influence of Task-Irrelevant Color Perception on Flanker Task Performance: Insights from Behavioral and ERP Data

Perception of color as a task-relevant stimulus can affect cognition and behavior in the flanker task; however, it remains unclear whether it has the same impact when it is a task-irrelevant stimulus dimension. To this end, we applied four-letter flanker tasks with or without colored (red/blue) to 23 healthy young adults, while recording the event-related potentials (ERPs) and behavioral performance. The flanker task included four kinds of color types: non-color letter (NC), all color letter (AC), flanker color letter (FC), and target color letter (TC), each flanker task included congruent and incongruent conditions. The behavioral data demonstrated the classic conflict effect across all color types of flanker tasks in both reaction times (RTs) and accuracy, the significant interaction and main effect of color type factors were only observed in accuracy. The ERP results showed significant interaction between conflict factor (congruent, incongruent) and color type (NC, AC, FC, and TC), and the color type factor enhanced the fronto-central P2 (180–200 ms), descended the fronto-centro-parietal N2b (260–320 ms), and increased the fronto-central P3b (360–520 ms). The fronto-central P2 and the fronto-central P3b were larger for TC than NC, AC, and FC in the congruent condition, while the fronto-central P3b was smaller for NC than AC, FC, and TC in the incongruent condition. Furthermore, the fronto-centro-parietal N2b was decreased successively in NC, AC, FC, and TC in both congruent and incongruent conditions. Overall, our findings suggested that the task-irrelevant stimuli dimension of color can capture some attentional resources and is affected by the location of color (target/flanker) and the type of task trial (congruent/incongruent) in the flanker task.

Emotional challenges in the retail industry Uncovering the role of emotional exhaustion in shop attendants' performance

Background: Within the scope of the organization, performance is an important topic to be researched because of the facts that are crucial for the future and sustainability of the organization. To salesperson task performance are considered the most important and influential aspect in assessing their performance. In addition, emotional labor is known to be a display rule that is often found in front-line workers such as salesperson. So this study seeks to examine the relationship between emotional labor and task performance, through the mediating role of emotional exhaustion. Method: This study collected data from 58 participants who work as salesperson in the retail industry. The three instruments used in this study were the Emotional Labor Scale (ELS), the Maslach Burnout Inventory (MBI), and the Individual Work Performance Scale (IWPS). Result: The results showed that emotional exhaustion did not mediate the relationship between emotional labor (surface acting and deep acting) and task performance. Conclusion: However, the use of surface acting strategy is able to make retail salesperson experience higher exhaustion due to significant and positive relations. Novelty/Originality in this article: This study offers a new perspective in understanding the complex dynamics between emotional labor, emotional exhaustion, and task performance among retail salespeople. This study provides important insights into how psychological factors may influence job effectiveness in an industry that relies heavily on customer interactions.

Using Augmentation-Based AI Tool at Work: A Daily Investigation of Learning-Based Benefit and Challenge

Augmentation-based artificial intelligence (AI) artifacts are increasingly being incorporated into the workplace. The coupling of employees and AI tools, given their complementary strengths, expands and expedites employees’ access to information and affords important learning opportunities. However, existing research has yet to fully understand the learning-based benefits and challenges for employees in augmentation. Integrating insights from AI augmentation literature and cognitive load theory, we conducted a daily diary study to understand employees’ experience using augmentation-based AI at work on a daily basis. We theorized and found that, on the one hand, frequent usage of augmentation-based AI during a workday was associated with greater knowledge gain and subsequently better task performance at the end of the workday. On the other hand, using augmentation-based AI frequently also led employees to experience information overload, which in turn impaired their performance and recovery at the end of the workday. In addition to elucidating the countervailing mechanisms, we identified employee openness to experience as a dispositional factor, and positive affect as a momentary state that shaped the effects of using augmentation-based AI over the workday. Our research has implications for understanding AI augmentation dynamics from a learning-based perspective, as well as AI’s impact on employees at large.

Getting value out of working memory through strategic prioritisation: Implications for storage and control.

Working memory is an active system responsible for the temporary maintenance and processing of information in the support of cognition and action. In keeping with this, a growing body of research has explored the close links between working memory and attention, and how these might be harnessed to impact performance and possibly improve working memory efficiency. This is theoretically and practically important, given that working memory is a central hub in complex cognition yet is extremely capacity- and resource-limited. We review work carried out over the last 10 years or so looking at how high "value" items in working memory can be strategically prioritised through selective attention, drawing principally from visual working memory paradigms with young adult participants, while also discussing how the core effects extend to different task domains and populations. A consistent set of core findings emerges, with improved memory for items that are allocated higher value but no change in overall task performance, and a recency advantage regardless of point allocation when items are encountered sequentially. Value-directed prioritisation is effortful, under top-down strategic control, and appears to vary with perceptual distraction and executive load. It is driven by processes operating during encoding, maintenance, and retrieval, though the extent to which these are influenced by different features of the task context remains to be mapped out. We discuss implications for working memory, attention, and strategic control, and note some possible future directions of travel for this promising line of research.

A Latent Trait Approach to the Measurement of Physical Fitness

ABSTRACT A latent trait model for the measurement of physical fitness is proposed. It links the performance in competitions or the laboratory to a latent trait. In contrast to usual approaches that simply consider the sum of scores obtained from quite differently scaled tasks like time to run 100 m and jumping distance to obtain a measure of fitness, it distinguishes between the trait to be measured and the performance in the tradition of latent trait models. The latent trait model that is used is able to account for continuous observations, which distinguishes it from the classical latent trait models for binary or categorical data typically used in the measurement of mental abilities. Tools for the investigation of the contribution of single tasks and the link between task performances with respect to the latent trait are proposed and illustrated exemplarily by using decathlon data.

The influence of reward and loss outcomes after free- and forced-tasks on voluntary task choice.

In four experiments, we investigated the impact of outcomes and processing mode (free versus forced) on subsequent voluntary task-switching behavior. Participants freely chose between two tasks or were forced to perform one, and the feedback they received randomly varied after correct performance (reward or no-reward; loss or no-loss). In general, we reasoned that the most recently applied task goal is usually the most valued one, leading people to prefer task repetitions over switches. However, the task values might be additionally biased by previous outcomes and the previous processing mode. Indeed, negatively reinforcing tasks with no-reward or losses generally resulted in more subsequent switches. Additionally, participants demonstrated a stronger attachment to free- compared to forced-tasks, as indicated by more switches when the previous task was forced, suggesting that people generally value free over forced-choice task goals. Moreover, the reward manipulation had a greater influence on switching behavior following free- compared to forced-tasks in Exp. 1 and Exp. 3, suggesting a stronger emphasis on evaluating rewarding outcomes associated with free-task choices. However, this inflationary effect on task choice seemed to be limited to reward and situations where task choice and performance more strongly overlap. Specifically, there was no evidence that switching behavior was differentially influenced after free-and forced-task as a function of losses (Exp. 2) or reward when task choice and task performance were separated (Exp. 4). Overall, the results provide new insights into how the valuation of task goals based on choice freedom and outcome feedback can influence voluntary task choices.

The Mediating Role of Perceived Investment in Employee Development in the Relationship between Transformational Leadership and Task Performance among First-Line Managers in the Banking Sector

The Mediating Role of Perceived Investment in Employee Development in the Relationship between Transformational Leadership and Task Performance among First-Line Managers in the Banking Sector

Circadian-informed lighting improves vigilance, sleep, and subjective sleepiness during simulated night shift work.

Shiftwork is associated with cognitive impairment and reduced sleep time and quality, largely due to circadian misalignment. This study tested if circadian-informed lighting could improve cognitive performance and sleep during simulated night shifts versus dim control lighting. Nineteen healthy participants (Mean±SD 29±10 years, 12 males, 7 females) were recruited to a laboratory study consisting of two counterbalanced 8-day lighting conditions (order randomized) 1-month apart: 1) control lighting condition- dim, blue-depleted and 2) circadian-informed lighting condition- blue-enriched and blue-depleted where appropriate. Participants underwent an adaptation night (22:00h - 07:00h), then four nights of simulated nightwork (cognitive testing battery of nine tasks, 00:00h - 08:00h) and sleep during the day (10:00h - 19:00h). Psychomotor vigilance task (PVT) lapses, Karolinska Sleepiness Scale (KSS) scores, and polysomnography-derived sleep outcomes were compared between conditions and across days using mixed models. Significant condition-by-day-by-time of task interaction effects were found for PVT lapses, median reaction time, and reaction speed, with ~50% fewer lapses by the end of simulated shiftwork with circadian-informed lighting versus control (mean±SD 7.4±5.0 vs. 15.6±6.1). KSS was lower around the nightshift midpoints on days 6 and 7 with circadian versus control lighting. Participants slept 52 minutes longer [95% CIs: 27.5, 76.5 mins] by Day 7 with circadian-informed versus control lighting, p<0.001. Effects were inconsistent on other performance tasks. Circadian-informed lighting improved sleep, sleepiness, and vigilance compared to control lighting. These findings support the potential for lighting interventions to improve sleep and vigilance in night shift workers chronically exposed to dim lighting.

Алгоритм количественной оценки кардиореспираторной синхронизации для характеристики функционального состояния и межсистемных взаимодействий у спортсменов

Introduction. The rhythm of the heart (heart rate, HR) is closely related to the rhythm of breathing. The phenomenon of respiratory sinus arrhythmia (an increase in heart rate during inhalation and a decrease during exhalation) is well known. Cardiorespiratory interactions and synchronization of these two signals are evaluated differently in the literature. The purpose of this work was to propose and test an approach to assess cardiorespiratory synchronization – the calculation of the cross-correlation coefficient between heart rate and respiration curve, for a more objective characterization of an athlete’s bodily states. Methods. A photoplethysmogram and respiration were recorded in 45 healthy athletes (18-25 years old) in three situations: rest, paced breathing at 6 times per minute (resonant frequency) and the performance of a sensorimotor task (tapping with the rhythmic metronome sounds and then reproducing a given rhythm from memory). For the HR and respiration curves, filtering and smoothing algorithms using the Savitsky-Goley method were applied sequentially, and then the cross-correlation coefficient between the two curves was calculated. The spectral parameters of heart rate variability (LF and HF) were also obtained, since during spontaneous breathing its contribution to the HR is reflected in HF band, and when breathing at a resonant frequency, a peak occurs at a frequency of 0.1 Hz in the LF band. Results. The cross-correlation coefficient, as well as the peak of HR spectral power at 0.1 Hz, increases significantly when breathing at a resonant frequency. The relationship between these indicators is most accurately described not by a linear, but by a logarithmic dependence. When performing a sensorimotor task, the so-called respiratory waves in the heart rhythm spectrum (HF) do not change from rest to tapping by a metronome and then to holding the rhythm by memory. At the same time, the cross-correlation coefficient demonstrates significant changes between these situations. In addition, a significant correlation was found between the change in the cross-correlation coefficient and the stability of rhythm maintenance (from memory): an increase in the cardiorespiratory synchronization leads to a decrease in motor rhythm stability. Conclusion. Assessment of cardiorespiratory synchronization due to the peculiarities of both signals (changes in heart rate and respiratory phases) requires preliminary preparation of data arrays: filtering and smoothing the stepped curve of heart rate dynamics. The calculation of the cross-correlation coefficient can be used to assess cardiorespiratory synchronization in real time and is likely to be applicable in short time fragments to assess emotional reactions and other short-term fluctuations in psychophysiological conditions, where the classical method of assessing changes in heart rhythm in the frequency or time domains is not applicable.