Objective Of Task Research Articles

Behavioral trajectories of aging prairie voles (Microtus ochrogaster): Adapting behavior to social context wanes with advanced age

Several studies using mice have examined the effects of aging on cognitive tasks, as well as sensory and motor functions. However, few studies have examined the influence of aging on social behavior. Prairie voles (Microtus ochrogaster) are a socially monogamous and biparental rodent that live in small family groups and are now among the most popular rodent models for studies examining social behavior. Although the social behavioral trajectories of early-life development in prairie voles have been well-studied, how social behavior may change throughout adulthood remains unknown. Here we examined behavior in virgin male and female prairie voles in four different age groups: postnatal day (PND) 60-80, 140-160, 220-240, and 300-320. All animals underwent testing in a novel object task, a dominance test, a resident-intruder test, and several iterations of social approach and social interaction tests with varying types of social stimuli (i.e., novel same-sex conspecific, novel opposite-sex conspecific, familiar same-sex sibling/cagemate, small group of novel same-sex conspecifics). We found that age influenced neophobia and dominance, but not social approach behavior. Further, we found that young adult, but not older adult, prairie voles adapt prosocial and aggressive behavior relative to social context, and that selective aggression occurs in relation to age even in the absence of a pair bond. Our results suggest that prairie voles calibrate social phenotype in a context-dependent manner in young adulthood and stop adjusting behavior to social context in advanced age, demonstrating that social behavior is plastic not only throughout early development, but also well into adulthood. Together, this study provides insight into age-related changes in social behavior in prairie voles and shows that prairie voles may be a viable model for studying the cognitive and physiological benefits of social relationships and social engagement in advanced age.

Prediction of anemia using facial images and deep learning technology in the emergency department.

According to the WHO, anemia is a highly prevalent disease, especially for patients in the emergency department. The pathophysiological mechanism by which anemia can affect facial characteristics, such as membrane pallor, has been proven to detect anemia with the help of deep learning technology. The quick prediction method for the patient in the emergency department is important to screen the anemic state and judge the necessity of blood transfusion treatment. We trained a deep learning system to predict anemia using videos of 316 patients. All the videos were taken with the same portable pad in the ambient environment of the emergency department. The video extraction and face recognition methods were used to highlight the facial area for analysis. Accuracy and area under the curve were used to assess the performance of the machine learning system at the image level and the patient level. Three tasks were applied for performance evaluation. The objective of Task 1 was to predict patients' anemic states [hemoglobin (Hb) <13 g/dl in men and Hb <12 g/dl in women]. The accuracy of the image level was 82.37%, the area under the curve (AUC) of the image level was 0.84, the accuracy of the patient level was 84.02%, the sensitivity of the patient level was 92.59%, and the specificity of the patient level was 69.23%. The objective of Task 2 was to predict mild anemia (Hb <9 g/dl). The accuracy of the image level was 68.37%, the AUC of the image level was 0.69, the accuracy of the patient level was 70.58%, the sensitivity was 73.52%, and the specificity was 67.64%. The aim of task 3 was to predict severe anemia (Hb <7 g/dl). The accuracy of the image level was 74.01%, the AUC of the image level was 0.82, the accuracy of the patient level was 68.42%, the sensitivity was 61.53%, and the specificity was 83.33%. The machine learning system could quickly and accurately predict the anemia of patients in the emergency department and aid in the treatment decision for urgent blood transfusion. It offers great clinical value and practical significance in expediting diagnosis, improving medical resource allocation, and providing appropriate treatment in the future.

Columnar Lesions in Barrel Cortex Persistently Degrade Object Location Discrimination Performance.

Primary sensory cortices display functional topography, suggesting that even small cortical volumes may underpin perception of specific stimuli. Traditional loss-of-function approaches have a relatively large radius of effect (>1 mm), and few studies track recovery following loss-of-function perturbations. Consequently, the behavioral necessity of smaller cortical volumes remains unclear. In the mouse primary vibrissal somatosensory cortex (vS1), "barrels" with a radius of ∼150 μm receive input predominantly from a single whisker, partitioning vS1 into a topographic map of well defined columns. Here, we train animals implanted with a cranial window over vS1 to perform single-whisker perceptual tasks. We then use high-power laser exposure centered on the barrel representing the spared whisker to produce lesions with a typical volume of one to two barrels. These columnar-scale lesions impair performance in an object location discrimination task for multiple days without disrupting vibrissal kinematics. Animals with degraded location discrimination performance can immediately perform a whisker touch detection task with high accuracy. Animals trained de novo on both simple and complex whisker touch detection tasks showed no permanent behavioral deficits following columnar-scale lesions. Thus, columnar-scale lesions permanently degrade performance in object location discrimination tasks.

An Intelligent Recognition Method for Low-Grade Fault Based on Attention Mechanism and Encoder–Decoder Network Structure

Low-grade faults play an important role in controlling oil and gas accumulations, but their fault throw is small and difficult to identify. Traditional low-grade fault recognition methods are time-consuming and inaccurate. Therefore, this study proposes a combination of a simulated low-grade fault sample set and a self-constructed convolutional neural network to recognize low-grade faults. We used Wu’s method to generate 500 pairs of low-grade fault samples to provide the data for deep learning. By combining the attention mechanism with UNet, an SE-UNet with efficient allocation of limited attention resources was constructed, which can select the features that are more critical to the current task objective from ample feature information, thus improving the expression ability of the network. The network model is applied to real data, and the results show that the SE-UNet model has better generalization ability and can better recognize low-grade and more continuous faults. Compared with the original UNet model, the SE-UNet model is more accurate and has more advantages in recognizing low-grade faults.

Entorhinal-Hippocampal Circuit Integrity Is Related to Mnemonic Discrimination and Amyloid-β Pathology in Older Adults.

Mnemonic discrimination, a cognitive process that relies on hippocampal pattern separation, is one of the first memory domains to decline in aging and preclinical Alzheimer's disease. We tested whether functional connectivity (FC) within the entorhinal-hippocampal circuit, measured with high-resolution resting state fMRI, is associated with mnemonic discrimination and amyloid-β (Aβ) pathology in a sample of 64 cognitively normal human older adults (mean age, 71.3 ± 6.4 years; 67% female). FC was measured between entorhinal-hippocampal circuit nodes with known anatomical connectivity, as well as within cortical memory networks. Aβ pathology was measured with 18F-florbetapir-PET, and neurodegeneration was assessed with subregional volume from structural MRI. Participants performed both object and spatial versions of a mnemonic discrimination task outside of the scanner and were classified into low-performing and high-performing groups on each task using a median split. Low object mnemonic discrimination performance was specifically associated with increased FC between anterolateral entorhinal cortex (alEC) and dentate gyrus (DG)/CA3, supporting the importance of this connection to object memory. This hyperconnectivity between alEC and DG/CA3 was related to Aβ pathology and decreased entorhinal cortex volume. In contrast, spatial mnemonic discrimination was not associated with altered FC. Aβ was further associated with dysfunction within hippocampal subfields, particularly with decreased FC between CA1 and subiculum as well as reduced volume in these regions. Our findings suggest that Aβ may indirectly lead to memory impairment through entorhinal-hippocampal circuit dysfunction and neurodegeneration and provide a mechanism for increased vulnerability of object mnemonic discrimination.SIGNIFICANCE STATEMENT Mnemonic discrimination is a critical episodic memory process that is performed in the dentate gyrus (DG) and CA3 subfield of the hippocampus, relying on input from entorhinal cortex. Mnemonic discrimination is particularly vulnerable to decline in older adults; however, the mechanisms behind this vulnerability are still unknown. We demonstrate that object mnemonic discrimination impairment is related to hyperconnectivity between the anterolateral entorhinal cortex and DG/CA3. This hyperconnectivity was associated with amyloid-β pathology and neurodegeneration in entorhinal cortex, suggesting aberrantly increased network activity is a pathological process. Our findings provide a mechanistic explanation of the vulnerability of object compared to spatial mnemonic discrimination in older adults and has translational implications for choice of outcome measures in clinical trials for Alzheimer's disease.

Multi objective task scheduling algorithm in cloud computing using grey wolf optimization

Multi objective task scheduling algorithm in cloud computing using grey wolf optimization

A binocular perception deficit characterizes prey pursuit in developing mice

Integration of binocular information at the cellular level has long been studied in the mouse model to uncover the fundamental developmental mechanisms underlying mammalian vision. However, we lack an understanding of the corresponding ontogeny of visual behavior in mice that relies on binocular integration. To address this major outstanding question, we quantified the natural visually guided behavior of postnatal day 21 (P21) and adult mice using a live prey capture assay and a computerized-spontaneous perception of objects task (C-SPOT). We found a robust and specific binocular visual field processing deficit in P21 mice as compared to adults that corresponded to a selective increase in c-Fos expression in the anterior superior colliculus (SC) of the juveniles after C-SPOT. These data link a specific binocular perception deficit in developing mice to activity changes in the SC.

Man or machine? Comparing the difficulty of human translation versus neural machine translation post-editing

ABSTRACT This study aims to compare neural machine translation (NMT) post-editing and human translation in terms of task difficulty while considering source text (ST) complexity and machine translation (MT) quality levels, two factors that have been rarely examined in previous comparison studies. Data were obtained from 60 trainee translators concerning the perceived and objective difficulties of post-editing and human translation tasks and the participants’ performance. It was found that (1) the difficulty of the NMT post-editing task, compared to human translation, was significantly influenced by both NMT quality and ST complexity; the difficulty of the post-editing task was significantly lower than that of the human translation task only in the case of high-quality NMT paired with complex ST, while the results were mixed for other interactions between NMT quality and ST complexity levels; (2) no strong correlations were found between the participants’ perceived difficulty and the measurements of objective difficulty and task performance for both post-editing and human translation tasks. Practical and research implications were discussed.

A Honey Badger Optimization for Minimizing the Pollutant Environmental Emissions-Based Economic Dispatch Model Integrating Combined Heat and Power Units

Traditionally, the Economic Dispatch Model (EDM) integrating Combined Heat and Power (CHP) units aims to reduce fuel costs by managing power-only, CHP, and heat-only units. Today, reducing pollutant emissions to the environment is of paramount concern. This research presents a novel honey badger optimization algorithm (HBOA) for EDM-integrated CHP units. HBOA is a novel meta-heuristic search strategy inspired by the honey badger’s sophisticated hunting behavior. In HBOA, the dynamic searching activity of the honey badger, which includes digging and honing, is separated into exploration and exploitation phases. In addition, several modern meta-heuristic optimization algorithms are employed, which are the African Vultures Algorithm (AVO), Dwarf Mongoose Optimization Algorithm (DMOA), Coot Optimization Algorithm (COA), and Beluga Whale Optimization Algorithm (BWOA). These algorithms are applied in a comparative manner considering the seven-unit test system. Various loading levels are considered with different power and heat loading. Four cases are investigated for each loading level, which differ based on the objective task and the consideration of power losses. Moreover, considering the pollutant emissions minimization objective, the proposed HBOA achieves reductions, without loss considerations, of 75.32%, 26.053%, and 87.233% for the three loading levels, respectively, compared to the initial case. Moreover, considering minimizing pollutant emissions, the suggested HBOA achieves decreases of 75.32%, 26.053%, and 87.233%, relative to the baseline scenario, for the three loading levels, respectively. Similarly, it performs reductions of 73.841%, 26.155%, and 92.595%, respectively, for the three loading levels compared to the baseline situation when power losses are considered. Consequently, the recommended HBOA surpasses the AVO, DMOA, COA, and BWOA when the purpose is to minimize fuel expenditures. In addition, the proposed HBOA significantly reduces pollutant emissions compared to the baseline scenario.

A FAIR Digital Object Lab Software Stack

A FAIR Digital Object Lab Software Stack

A critical role for long-term potentiation mechanisms in the maintenance of object recognition memory in perirhinal cortex revealed by the infusion of zeta inhibitory pseudosubstrate.

Object recognition, the ability to discriminate between a novel and a familiar stimulus, is critically dependent upon the perirhinal cortex. Neural response reductions upon repetition of a stimulus, have been hypothesized to be the mechanism within perirhinal cortex that supports recognition memory function. Thus, investigations into the mechanisms of long-term depression (LTD) in perirhinal cortex has provided insight into the mechanism of object recognition memory formation, but the contribution of long-term potentiation (LTP) to object recognition memory formation has been less studied. Inhibition of atypical PKC activity by Zeta Inhibitory Pseudosubstrate (ZIP) impairs the maintenance of LTP but not LTD, thus here infusion of ZIP into the perirhinal cortex allowed us to investigate the contribution of LTP-like mechanisms to object recognition memory maintenance. Infusion of ZIP into the perirhinal cortex of rats 24 h after the sample phase impaired performance in an object recognition but not an object location task, in contrast infusion of ZIP into the hippocampus impaired performance in an object location but not an object recognition task. The impairment in object recognition by ZIP was prevented by administration of the peptide GluA23y, which blocks the endocytosis of GluA2 containing AMPA receptors. Finally, performance in a perceptual oddity task, which requires perirhinal cortex function, was not disrupted by ZIP. Together these results demonstrate the importance of LTP-like mechanisms to the maintenance of object recognition memory in the perirhinal cortex.

Iterative Learning-Based Robotic Controller With Prescribed Human–Robot Interaction Force

In this article, an iterative-learning-based robotic controller is developed, which aims at providing a prescribed assistance or resistance force to the human user. In the proposed controller, the characteristic parameter of the human upper limb movement is first learned by the robot using the measurable interaction force, a recursive least square (RLS)-based estimator, and the Adam optimization method. Then, the desired trajectory of the robot can be obtained, tracking which the robot can supply the human’s upper limb with a prescribed interaction force. Using this controller, the robot automatically adjusts its reference trajectory to embrace the differences between different human users with diverse degrees of upper limb movement characteristics. By designing a performance index in the form of interaction force integral, potential adverse effects caused by the time-related uncertainty during the learning process can be addressed. The experimental results demonstrate the effectiveness of the proposed method in supplying the prescribed interaction force to the human user. Note to Practitioners—This article concentrates on developing a novel control technique to make the robot supply a prescribed interaction force to the human user in the presence of time-related uncertainties. The proposed control method is applicable to various scenarios of the human–robot interaction, e.g., it can be used for rehabilitation robots to provide assistive or resistive force to stroke patients or for exoskeleton robots to provide assistive force to human users for completing heavy-load tasks. Moreover, the desired interaction force can be tailored for different human users according to their needs and different task objectives. Consequently, the proposed controller can serve diverse users and has a promising perspective in automation.

End-to-End Design of Bespoke, Dexterous Snake-Like Surgical Robots: A Case Study With the RAVEN II

Keyhole surgery requires highly dexterous snake-like robotic arms capable of bending around anatomical obstacles to access clinical targets that diverge from the direct port-of-access. Design optimization for these robots under patient-specific anatomical constraints is still lacking, particularly concerning the critical metric of dexterity. In this article, we propose an end-to-end design and production workflow for patient-specific surgical manipulators, assessing dexterity using orientability constrained by task space obstacles. In our work, parametric evolutionary optimization maximizes dexterity in patient-specific task spaces for challenging knee arthroscopy operations. We implement our framework in the design of SnakeRaven—a 3-D printed tool to be attached to the RAVEN II surgical robot in a phantom study for knee arthroscopy. The solution achieved more than three times the dexterity of a state-of-the-art rigid instrument and more than twice the dexterity of a volume-based approach for the same task. We further assemble and validate this design by teleoperating the robot to reach the desired clinical targets in a phantom. We also investigate the changes in the design morphology to changes in the task objectives and found an advantage in task specialization. We also observe guidelines for achieving a dexterous design produced by our algorithms.

Throwing Strategy in a Dual-Motor-Task of Aiming at the Bullseye While Walking in Virtual Reality

Humans perform movement tasks using motor and cognitive skills. The optimum outcome of the task is dependent on how the motor and cognitive skills are combined. When a dual-task is performed, this combination becomes even more important as the user has to devote limited available attention to the competing tasks. In this study, a ball throwing task to a specified location was performed in a virtual environment while the participant walked on a treadmill performing a dual-motor-task. Fifteen healthy participants took part in the study. The task paradigm involved three conditions-warm-up treadmill walking, throwing a ball to the target while standing, and throwing a ball to the target while walking on a treadmill. The throwing strategy was investigated with quantifiable task-space performance and scoring in the game. The correlation between the task-space performance and the cognitive tests was evaluated in the single and the dual-motor-task. Results demonstrate that during the throwing activity in the single and the dual-motor tasks, the choice of orienting the throwing plane in space was pre-determined prior to releasing the ball in a projectile motion and was interconnected with the motor ability. However, in dual-tasking, once the throwing plane was selected, the variation of hit point localization in the target plane was correlated with the cognitive ability. The task was executed with an allocation of motor and cognitive abilities to control specific performance variables while dual-tasking. This strategy potentially helped participants to reduce the load on attentional capacity during dual-tasking. This study showcases an optimized utilization of skill interaction to achieve the overall task objective. The paradigm designed in this work can be utilized to improve specific motor and cognitive skills by performing only one dual-motor-task.

Annotating Motion Primitives for Simplifying Action Search in Reinforcement Learning

Reinforcement learning in large-scale environments is challenging due to the many possible actions that can be taken in specific situations. We have previously developed a means of constraining, and hence speeding up, the search process through the use of motion primitives; motion primitives are sequences of pre-specified actions taken across a state series. As a byproduct of this work, we have found that if the motion primitives’ motions and actions are labeled, then the search can be sped up further. Since motion primitives may initially lack such details, we propose a theoretically viewpoint-insensitive and speed-insensitive means of automatically annotating the underlying motions and actions. We do this through a differential-geometric, spatio-temporal kinematics descriptor, which analyzes how the poses of entities in two motion sequences change over time. We use this descriptor in conjunction with a weighted-nearest-neighbor classifier to label the primitives using a limited set of training examples. In our experiments, we achieve high motion and action annotation rates for human-action-derived primitives with as few as one training sample. We also demonstrate that reinforcement learning using accurately labeled trajectories leads to high-performing policies more quickly than standard reinforcement learning techniques. This is partly because motion primitives encode prior domain knowledge and preempt the need to re-discover that knowledge during training. It is also because agents can leverage the labels to systematically ignore action classes that do not facilitate task objectives, thereby reducing the action space.

Anandamide and 2-arachidonoylglycerol differentially modulate autistic-like traits in a genetic model of autism based on FMR1 deletion in rats.

Autism spectrum disorder (ASD) has a multifactorial etiology. Major efforts are underway to understand the neurobiological bases of ASD and to develop efficacious treatment strategies. Recently, the use of cannabinoid compounds in children with neurodevelopmental disorders including ASD has received increasing attention. Beyond anecdotal reports of efficacy, however, there is limited current evidence supporting such an intervention and the clinical studies currently available have intrinsic limitations that make the interpretation of the findings challenging. Furthermore, as the mechanisms underlying the beneficial effects of cannabinoid compounds in neurodevelopmental disorders are still largely unknown, the use of drugs targeting the endocannabinoid system remains controversial. Here, we studied the role of endocannabinoid neurotransmission in the autistic-like traits displayed by the recently validated Fmr1-Δexon 8 rat model of autism. Fmr1-Δexon 8 rats showed reduced anandamide levels in the hippocampus and increased 2-arachidonoylglycerol (2-AG) content in the amygdala. Systemic and intra-hippocampal potentiation of anandamide tone through administration of the anandamide hydrolysis inhibitor URB597 ameliorated the cognitive deficits displayed by Fmr1-Δexon 8 rats along development, as assessed through the novel object and social discrimination tasks. Moreover, blockade of amygdalar 2-AG signaling through intra-amygdala administration of the CB1 receptor antagonist SR141716A prevented the altered sociability displayed by Fmr1-Δexon 8 rats. These findings demonstrate that anandamide and 2-AG differentially modulate specific autistic-like traits in Fmr1-Δexon 8 rats in a brain region-specific manner, suggesting that fine changes in endocannabinoid mechanisms contribute to ASD-related behavioral phenotypes.

Towards personalized persuasive dialogue generation for adversarial task oriented dialogue setting

In recent years, task-oriented virtual assistants have gained huge popularity and demand in both research and industry communities. The primary aim of a task-oriented dialogue agent is to assist end-users in accomplishing a task successfully and satisfactorily. Existing virtual agents have acquired proficiency in assisting users in solving simple tasks such as restaurant bookings. However, they operate under the deterministic presumption that end-users will have a servable task objective, which makes them inadequate under adversarial situations such as goal unavailability. On the other hand, human agents accomplish users’ tasks even in many goal unavailability scenarios by persuading them towards a similar goal to the user’s proposed task. Motivated by the limitation, the current work proposes and builds a novel transformer-based context-aware personalized persuasive virtual assistant (CoPersUasive VA), which also serves end-users in task unavailability situations. The proposed CoPersUasive VA recognizes goal conflicts through user sentiment and identifies an appropriate persuasion strategy using ongoing dialogue context and user personality. Depending on users’ proposed goals, it finds a similar servable goal and persuades them with the identified persuasion strategy. The obtained experimental results and detailed post-analysis firmly establish that the proposed model effectively enhances the capability of task-oriented virtual assistants to deal with the task failures caused by goal unavailability. The obtained findings also suggest the crucial role of dialogue context in identifying an appropriate and appealing persuasion strategy. The proposed CoPersUasive model could easily be adapted to any other domain by fine-tuning the model on an underlying task. Furthermore, we developed a personalized persuasive multi-intent conversational dialogue corpus annotated with intent, slot, sentiment, and dialogue act for electronic domain.22The dataset and code will be made available to accelerate research towards the development of persuasive virtual sales assistants.

Evaluation of children's cognitive load in processing and storage of their spatial working memory.

Working memory performance affects children’s learning. This study examined objective (task performance), subjective (self-report), and physiological (pupil dilation) cognitive load (CL) while children completed a spatial working memory complex span task. Frist, 80 Taiwanese 11-year-olds (40 boys) who participated in Experiment 1 confirmed the suitability of the materials. Then, 72 Taiwanese 11-year-olds (35 boys) were assigned to high and low complexity groups to participate in Experiment 2 to test the study hypothesis. Children had to recall at the end of a dual-task list and answer two questions regarding the difficulty and mental effort involved in processing and storage. Their pupil diameters were recorded using an eye-tracker. Two-way mixed ANOVA found that the processing requirements and memory load reduced storage and aggravated the subjective CL of storage; the subjective CL of processing was higher under highly complex conditions. Stepwise regression analysis indicated that subjective CL of processing predicted memory performance in low CL conditions, and physiological CL of processing predicted it in high CL conditions.

Psychiatrically relevant signatures of domain-general decision-making and metacognition in the general population

Human behaviours are guided by how confident we feel in our abilities. When confidence does not reflect objective performance, this can impact critical adaptive functions and impair life quality. Distorted decision-making and confidence have been associated with mental health problems. Here, utilising advances in computational and transdiagnostic psychiatry, we sought to map relationships between psychopathology and both decision-making and confidence in the general population across two online studies (N’s = 344 and 473, respectively). The results revealed dissociable decision-making and confidence signatures related to distinct symptom dimensions. A dimension characterised by compulsivity and intrusive thoughts was found to be associated with reduced objective accuracy but, paradoxically, increased absolute confidence, whereas a dimension characterized by anxiety and depression was associated with systematically low confidence in the absence of impairments in objective accuracy. These relationships replicated across both studies and distinct cognitive domains (perception and general knowledge), suggesting that they are reliable and domain general. Additionally, whereas Big-5 personality traits also predicted objective task performance, only symptom dimensions related to subjective confidence. Domain-general signatures of decision-making and metacognition characterise distinct psychological dispositions and psychopathology in the general population and implicate confidence as a central component of mental health.

Multi objective task scheduling based on hybrid metaheuristic algorithm for cloud environment

Cloud computing is gaining a huge popularity for on-demand services on a pay-per-use basis. However, single data centre is restricted in offering the services, as it does not have unlimited resource capacity mostly in the peak demand time. Generally, the count of Virtual Machines (VM) is more in public cloud; still, the security is not ensured. In contrast, the VMs are limited in private cloud with high security. So, the consideration of security levels in task scheduling is remains to be more critical for secured processing. This works intends to afford the optimization strategies for optimal task scheduling with multi-objective constraints in cloud environment. Accordingly, the proposed optimal task allocation framework considers the objectives such as execution time, risk probability, and task priority. For this, a new hybrid optimization algorithm known as Clan Updated Seagull Optimization (CUSO) algorithm is introduced in this work, which is the conceptual blending of Elephant Herding Optimization (EHO) and Seagull Optimization Algorithm (SOA). Finally, the performance of proposed work is evaluated over other conventional models with respect to certain performance measures.