Action Information Research Articles

Chronic alcohol exposure alters action control via hyperactive premotor corticostriatal activity

Alcohol use disorder (AUD) alters decision-making control over actions, but disruptions to the responsible neural circuit mechanisms are unclear. Premotor corticostriatal circuits are implicated in balancing goal-directed and habitual control over actions and show disruption in disorders with compulsive, inflexible behaviors, including AUD. However, whether there is a causal link between disrupted premotor activity and altered action control is unknown. Here, we find that mice chronically exposed to alcohol (chronic intermittent ethanol [CIE]) showed impaired ability to use recent action information to guide subsequent actions. Prior CIE exposure resulted in aberrant increases in the calcium activity of premotor cortex (M2) neurons that project to the dorsal medial striatum (M2-DMS) during action control. Chemogenetic reduction of this CIE-induced hyperactivity in M2-DMS neurons rescued goal-directed action control. This suggests a direct, causal relationship between chronic alcohol disruption to premotor circuits and decision-making strategy and provides mechanistic support for targeting activity of human premotor regions as a potential treatment in AUD.

New and Noteworthy Records of Angiosperms from Pacific Biogeographical Region at Department of Chocó in Colombia, Including Four New Records for the Country

Six new or noteworthy records of Angiosperms from Colombia are presented based in herbarium specimens from the Chocó department in Colombia. Four are first records for the country, one is the first record for the Chocó department and one is the most recent record after 28 years of an endemic species to the Chocó Biogeographical region in Colombia. Cynometra dwyerii (Fabaceae), Pouteria fossicola (Sapotaceae) and Tachigali panamensis (Fabaceae) were heretofore known as restricted to Central America; Mosquitoxylum jamaicense (Anacardiaceae) was previously known from Central America, Caribbean and Ecuador, but no record for Colombia had been found; Casearia thamnia (Salicaceae) was recorded from Colombia through a specimen of the year 1916 from the Bolivar department, here its first record from the Chocó department is presented; Cremastosperma chococola (Annonaceae) is a endemic species to the biogeographic Chocó in Colombia, only known through three specimens and not collected since the year 1990. Information about protologue, type specimens, distinctive morphology, current geographical distribution, common names, and uses are provided for each record, besides notes about the relevance of the novel records are here presented. A geographical distribution map for these species in Colombia is presented, as well as digital plates composed by photographs of herbarium vouchers. Continue inventoring the biodiversity in the biogeographic Chocó as one of the global biodiversity hotspots is imperative. These records presented here provide useful information for future biological research and conservation actions for their respective species or their habitats.

A Q-Learning-Based Routing Approach for Energy Efficient Information Transmission in Wireless Sensor Network

Nowadays, wireless sensor networks have played an important role in many applications. In these applications, a large number of wireless sensors are deployed in an environment to collect information and form network to transmit collected information to the base station or sink. Because wireless sensors have to work for a long period of time without maintenance, the energy consumption of wireless sensors has a great impact on the lifetime of wireless sensor network. To reduce and balance the energy consumption of wireless sensors, many information transmission routing approaches have been proposed. However, most of them do not consider all energy consumption factors of wireless sensors. To this end, an innovative information transmission routing approach based on Q-learning is proposed in this paper, which enables wireless sensors to adaptively select suitable neighboring sensors to achieve energy efficient information transmission in a decentralized manner. Based on the proposed approach, a wireless sensor first collects state and action information of its neighboring sensors, which includes information transmission direction and distance, the remaining energy, the energy consumption for information transmission and information transmission action. Then, all this information is used to update the Q-values of neighboring sensors, so as to enable the wireless sensor to select suitable neighboring sensor to transmit information according to Q-values. From simulation experiments, it can be seen that the proposed approach enables wireless sensors to reduce and balance the energy consumption of wireless sensors and extend the lifetime of the entire wireless sensor network.

Application of action and posture feature information matching algorithm in college aerobics

With the increasingly rich recreational activities of college students, diversified learning needs and complex physical education resources bring challenges to college physical education. In order to optimize the teaching effect of calisthenics in colleges and universities, this paper proposes a matching method of posture features based on dynamic time warping. Firstly, the dynamic time warping algorithm is introduced, and then the matching model of posture features of calisthenics is constructed on this basis. Finally, the application effect of the model is tested and analyzed. The results show that the model can capture the video frame accurately, and its matching accuracy reaches 94.8%, which greatly improves the accuracy of aerobics action recognition. Good posture matching effect is conducive to teachers to obtain a clear learning situation of students, and provide a reference for adjusting the teaching progress and teaching methods of calisthenics. Under the teaching mode of this model, the average professional score of the students in calisthenics reaches 85 points, which is 25 points higher than that under the convolutional neural network model. It also proves the validity and feasibility of this method in the course of calisthenics in colleges and universities, which is beneficial to enhance the physical quality of college students and enrich the content of calisthenics teaching.

Detection method of limb movement in competitive sports training based on deep learning

Human posture detection is easily affected by the external environment, resulting in blurred results of limb feature extraction. In order to improve the accuracy and speed of human motion detection, this paper proposes a deep learning-based motion detection method in competitive sports training. The double parallel convolution network algorithm in the depth learning algorithm is used to process the collected action information, extract the body action features, and greatly reduce the operation scale; With the help of the theory of motion mechanics, the mechanical parameters in the motion process are calculated to eliminate outliers and reduce feature dimensions; With the help of motion inertial sensors and joint degrees of freedom, the limb motion detection results are obtained. The experimental results show that the average recognition rate of the method for different motion actions is 99.5%, and the average detection time is 148 ms, with good application performance.

No need to integrate action information during coarse semantic processing of man-made tools.

Action representation of man-made tools consists of two subtypes: structural action representation concerning how to grasp an object, and functional action representation concerning the skilled use of an object. Compared to structural action representation, functional action representation plays the dominant role in fine-grained (i.e., basic level) object recognition. However, it remains unclear whether the two types of action representation are involved differently in the coarse semantic processing in which the object is recognized at a superordinate level (i.e., living/non-living). Here we conducted three experiments using the priming paradigm, in which video clips displaying structural and functional action hand gestures were used as prime stimuli and grayscale photos of man-made tools were used as target stimuli. Participants recognized the target objects at the basic level in Experiment 1 (i.e., naming task) and at the superordinate level in Experiments 2 and 3 (i.e., categorization task). We observed a significant priming effect for functional action prime-target pairs only in the naming task. In contrast, no priming effect was found in either the naming or the categorization task for the structural action prime-target pairs (Experiment 2), even when the categorization task was preceded by a preliminary action imitation of the prime gestures (Experiment 3). Our results suggest that only functional action information is retrieved during fine-grained object processing. In contrast, coarse semantic processing does not require the integration of either structural or functional action information.

Not only perception but also grasping actions can obey Weber’s law

Weber’s law, the principle that the uncertainty of perceptual estimates increases proportionally with object size, is regularly violated when considering the uncertainty of the grip aperture during grasping movements. The origins of this perception-action dissociation are debated and are attributed to various reasons, including different coding of visual size information for perception and action, biomechanical factors, the use of positional information to guide grasping, or, sensorimotor calibration. Here, we contrasted these accounts and compared perceptual and grasping uncertainties by asking people to indicate the visually perceived center of differently sized objects (Perception condition) or to grasp and lift the same objects with the requirement to achieve a balanced lift (Action condition). We found that the variability (uncertainty) of contact positions increased as a function of object size in both perception and action. The adherence of the Action condition to Weber’s law and the consequent absence of a perception-action dissociation contradict the predictions based on different coding of visual size information and sensorimotor calibration. These findings provide clear evidence that human perceptual and visuomotor systems rely on the same visual information and suggest that the previously reported violations of Weber’s law in grasping movements should be attributed to other factors.

Wear‐free indoor fall detection based on RFID and deep residual networks

SummarySince falls of the elderly can easily cause serious health problems in daily life, fall detection has received the attention of researchers. Traditionally, wearable sensors have been used to detect whether a person has fallen. However, wearable sensors may bring inconvenience to users' activities and affect user experience. In this paper, a fall detection approach based on RFID is proposed. In the proposed approach, non‐contact passive tags are used to construct an array of tags. Fall detection will be performed without the user wearing the device. The RSSI and phase data are collected when the reader queries the tags. Furthermore, an action segmentation algorithm is designed to quickly extract human action information based on the short‐term variance change of the phase signal. Subsequently, a deep residual network is built to classify fall movements and daily movements. Experiments show that the system can handle differences among users and locations and has an excellent performance in terms of recognition accuracy and efficiency, with an average accuracy rate of 96.77%.

A syntactic method for robot imitation learning of complex sequence task

PurposeAiming at the problems of weak generalization of robot imitation learning methods and higher accuracy requirements of low-level detectors, this study aims to propose an imitation learning method based on structural grammar.Design/methodology/approachThe paper proposes a hybrid training model based on artificial immune algorithm and the Baum–Welch algorithm to extract the action information of the demonstration activity to form the {action-object} sequence and extract the symbol description of the scene to form the symbol primitives sequence. Then, probabilistic context-free grammar is used to characterize and manipulate these sequences to form a grammar space. Minimum description length criteria are used to evaluate the quality of the grammar in the grammar space, and the improved beam search algorithm is used to find the optimal grammar.FindingsIt is found that the obtained general structure can parse the symbol primitive sequence containing noise and obtain the correct sequence, thereby guiding the robot to perform more complex and higher-order demonstration tasks.Practical implicationsUsing this strategy, the robot completes the fourth-order Hanoi tower task has been verified.Originality/valueAn imitation learning method for robots based on structural grammar is first proposed. The experimental results show that the method has strong generalization ability and good anti-interference performance.

Skeleton-based Human Action Recognition via Large-kernel Attention Graph Convolutional Network.

The skeleton-based human action recognition has broad application prospects in the field of virtual reality, as skeleton data is more resistant to data noise such as background interference and camera angle changes. Notably, recent works treat the human skeleton as a non-grid representation, e.g., skeleton graph, then learns the spatio-temporal pattern via graph convolution operators. Still, the stacked graph convolution plays a marginal role in modeling long-range dependences that may contain crucial action semantic cues. In this work, we introduce a skeleton large kernel attention operator (SLKA), which can enlarge the receptive field and improve channel adaptability without increasing too much computational burden. Then a spatiotemporal SLKA module (ST-SLKA) is integrated, which can aggregate long-range spatial features and learn long-distance temporal correlations. Further, we have designed a novel skeleton-based action recognition network architecture called the spatiotemporal large-kernel attention graph convolution network (LKA-GCN). In addition, large-movement frames may carry significant action information. This work proposes a joint movement modeling strategy (JMM) to focus on valuable temporal interactions. Ultimately, on the NTU-RGBD 60, NTU-RGBD 120 and Kinetics-Skeleton 400 action datasets, the performance of our LKA-GCN has achieved a state-of-the-art level.

The Effect of Leverage, Market Value, and Profitability on Share Prices

This study analyzes the effect of leverage, market value, and profitability on share prices. This study used a sample of food and beverage industry companies on the Indonesia Stock Exchange (IDX) for the 2019-2021 period. The sampling technique in this study was purposive sampling using 12 companies. The method used in this study is quantitative and uses financial statements data. The research model is multiple linear regression using the SPSS version 25 application. The results of this study are partial. Namely, the DER, PBV, PER, and ROA variables do not affect stock prices. Simultaneously all variables, namely DER, PBV, PER, and ROA, do not affect stock prices. This research is expected not to pay attention to the amount of profit the company alone without knowing the profit capability to generate cash for the company because it is only an illustration of the company's performance in the short term. In addition, investors also need to pay attention to the performance of the company's organizational governance tools to obtain better corporate action information in making investment decisions

Comparative Efficacy of East Asian Herbal Formulae Containing Astragali Radix-Cinnamomi Ramulus Herb-Pair against Diabetic Peripheral Neuropathy and Mechanism Prediction: A Bayesian Network Meta-Analysis Integrated with Network Pharmacology.

The Astragali Radix-Cinnamomi Ramulus herb-pair (ACP) has been widely used in the treatment of diabetic peripheral neuropathy (DPN) as part of East Asian herbal medicine (EAHM). Eligible randomized controlled trials (RCTs) were identified by searching 10 databases. The outcomes investigated were response rate, sensory nerve conduction velocity (SNCV), and motor nerve conduction velocity (MNCV) in four regions of the body. The compounds in the ACP and their targets of action, disease targets, common targets, and other relevant information were filtered using network pharmacology. Forty-eight RCTs, with 4308 participants, and 16 different interventions were identified. Significant differences were observed in the response rate, MNCV, and SNCV, as all EAHM interventions were superior to conventional medicine or lifestyle modification. The EAHM formula containing the ACP ranked highest in more than half of the assessed outcomes. Furthermore, major compounds, such as quercetin, kaempferol, isorhamnetin, formononetin, and beta-sitosterol, were found to suppress the symptoms of DPN. The results of this study suggest that EAHM may increase therapeutic efficacy in DPN management, and EAHM formulations containing the ACP may be more suitable for improving treatment response rates to NCV and DPN therapy.

Event-Triggered Deep Reinforcement Learning Using Parallel Control: A Case Study in Autonomous Driving

This paper utilizes parallel control to investigate the problem of event-triggered deep reinforcement learning and develops an event-triggered deep Q-network (ETDQN) for decision-making of autonomous driving, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without training an explicit triggering condition</i> . Based on the framework of parallel control, the developed ETDQN incorporates information of actions into the feedback and constructs a dynamic control policy. First, in the realization of the dynamic control policy, we integrate the current state and the previous action to construct the augmented state as well as the augmented Markov decision process. Meanwhile, it is shown theoretically that the goal of the developed dynamic control policy is to learn the variation rate of the action. The augmented state contains information of the current state and the previous action, which enables the developed ETDQN to directly design the immediate reward considering communication loss. Then, based on dueling double deep Q-network (dueling DDQN), we establish the augmented action-value, value, and advantage functions to directly learn the optimal event-triggered decision-making policy of autonomous driving without an explicit triggering condition. It is worth noticing that the developed ETDQN applies to various deep Q-networks (DQNs). Empirical results demonstrate that, in event-triggered control, the developed ETDQN outperforms dueling DDQN and reduces communication loss effectively. Source code of this paper is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lujingweihh/Event-triggered-deep-Q-network</uri> https://github.com/lujingweihh/Event-triggered-deep-Q-network.

You Have to Send the Right Message: Examining the Influence of Protective Action Guidance on Message Perception Outcomes across Prior Hazard Warning Experience to Three Hazards

Abstract A long-term goal for warning-message designers is to determine the most effective type of message that can instruct individuals to act quickly and prevent loss of life and/or injury when faced with an imminent threat. One likely way to increase an individual’s behavioral intent to act when they are faced with risk information is to provide protective action information or guidance. This study investigated participant perceptions (understanding, believing, personalizing, deciding, milling, self-efficacy, and response efficacy) in response to the National Weather Service’s experimental product Twitter messages for three hazard types (tornado, snow squall, and dust storm), with each message varying by inclusion and presentation of protective action information placed in the tweet text and the visual graphic. We also examine the role of prior hazard warning experience on message perception outcomes. To examine the effects, the experiment used a between-subjects design in which participants were randomly assigned to one hazard type and received one of four warning messages. Participants then took a post-test measuring message perceptions, efficacy levels, prior hazard warning experience, and demographics. The results showed that, for each hazard and prior hazard experience level, messages with protective action guidance in both the text and graphic increase their understanding, belief, ability to decide, self-efficacy, and response efficacy. These results reinforce the idea that well-designed messages that include protective action guidance work well regardless of hazard type or hazard warning experience. Significance Statement Preventing injury and/or loss of life during a hazardous event is a prime concern for disaster communicators. The study provides insights to practitioners on how to effectively communicate protective actions to audiences with varying familiarity with the hazard through Twitter posts. We experimented with tweet message design and content for three hazards: tornado, snow squall, and dust storm, to find that posts that include protective action guidance in both the text and image increase participant perceptions that they could perform the suggested protective actions, regardless of hazard type or hazard warning experience. Given our findings, practitioners should consider including protective action guidance in message text and graphic to warn members of the public with varied prior warning experience.

The Alien to Cyprus Entomofauna (ACE) database: a review of the current status of alien insects (Arthropoda, Insecta) including an updated species checklist, discussion on impacts and recommendations for informing management

Alien insects represent one of the most species rich groups of organisms introduced to Europe, with some responsible for adverse social-economic, human-health, biodiversity and ecosystem impacts. The impacts of invasive alien species, especially on island ecosystems, have been a hot topic of research worldwide. Cyprus is a Mediterranean island at the biogeographic crossroads of Asia, Africa and Europe. This study presents the database of the alien insects of the island of Cyprus as a whole, created through an extensive review including grey literature and online sources. The Alien to Cyprus Entomofauna (ACE) triples the known number of alien insects and adds supplemental information to existing species. Data concerning a total of 349 alien insects are presented alongside an updated checklist and recommendations for informing management. The status of alien insects on the island, their origin, trophic guilds, establishment, pathways of introduction and impacts are discussed. Developing an alien species inventory for the island is challenging due to its geographic position and the increasing movement of people and goods leading to new species introductions. This publication constitutes an important first step towards providing information for effective actions to tackle invasive alien insects on Cyprus. The checklist and accompanying information can underpin understanding of the status and trends of alien species including providing information for risk assessments. ACE will continue to be maintained and updated as new records for Cyprus are made.

Action knowledge for video captioning with graph neural networks

Many existing video captioning methods capture action information in the video by exploiting features extracted from an action recognition model. However, directly using the action features without object-specific representation may not well capture the object interactions. Consequently, the generated captions may not be accurate enough in describing the action and the object in the scenes. To address this issue, we propose to incorporate the action features as the edge features in a graph neural network where the nodes represent objects, thereby capturing a finer visual representation of object-action-object relationships. Previous graph-based video captioning methods commonly relied on a pretrained object detection model to create the node representations. The object detection model, however, may miss detecting some important objects. To alleviate this problem, we further introduce a grid-based node representation where the nodes are represented by the features extracted from grids of video frames. Using this representation, the important objects in the scenes are captured more thoroughly. To avoid adding any complexity during inference, the knowledge of the proposed graph is transferred to another neural network via knowledge distillation. Our proposed method achieved state-of-the-art results on two popular video captioning datasets, i.e., MSVD and MSR-VTT, on all metrics. The code of our proposed method is available at https://github.com/Sejong-VLI/V2T-Action-Graph-JKSUCIS-2023.

Wi-NN: Human Gesture Recognition System Based on Weighted KNN

Gesture recognition, the basis of human–computer interaction (HCI), is a significant component for the development of smart home, VR, and senior care management. Most gesture recognition methods still depend on sensors worn by the user or video-based gestures for recognition, can be used for fine-grained gesture recognition. our paper implements a gesture recognition method that is independent of environment and gesture drawing direction, and it achieves gesture recognition classification by using small sample data. Wi-NN, proposed in this study, does not require the user to wear additional device. In this case, channel state information (CSI) extracted from Wi-Fi signal is used to capture the action information of the human body via CSI. After pre-processing to reduce the interference of environmental noise as much as possible, clear action information is extracted using the feature extraction method based on time domain to obtain the gesture action feature data. The gathered data are integrated with the weighted k-nearest neighbor (KNN) classification recognizer for classification task. The experiment outcomes revealed that the accuracy scores of the same gesture for different users and different gestures for the same user under the same environment were 93.1% and 89.6%, respectively. The experiments in different environments also achieved good recognition results, and by comparing with other experimental methods, the experiments in this paper have better recognition results. Evidently, good classification results were generated after the original data were processed and incorporated into the weighted KNN.

Correlation of apolipoprotein A-I with T cell subsets and interferon-ү in coronary artery disease.

The association of Apolipoprotein A-I (APOAI) with T cell subsets and interferon-ү (IFN-γ) in patients with coronary artery disease (CAD) has been not reported. Thus, this study aimed to investigate the association of APOAI with T cell subsets and IFN-γ in CAD. This study included a total of 107 patients with CAD including acute coronary syndrome and chronic coronary syndrome. T cell subsets, and CD3-CD56+ natural killer cells were quantified by flow cytometric analysis. The serum concentrations of IFN-ү were measured by enzyme-linked immunosorbent assay. Lipid profiles, C-reactive protein (CRP), and fibrinogen were measured in the clinical laboratory. Clinical data was obtained duration hospitalization. The CD4+ T cells were higher in patients of the low-APOAI group (<median: 1.2 mmol/L) than in patients of the high-APOAI group(≥median: 1.2 mmol/L) (p < .05). The CD8+ T cells were lower in patients of the low APOAI group than in patients of the high-APOAI group (p < .05). APOAI was inversely associated with CD4+ T cells, IFN-γ, and was positively associated with CD8+ T cells (p < .05). No correlation was observed between CD3 + CD56+ cells, regulatory T cells (Tregs), and CD3-CD56+ natural killer cells and APOAI (p > .05). The high-density lipoprotein cholesterol (HDL-C) was also inversely associated with CD4+ T cells (p < .05), and positively associated with CD8+ T cells (p < .05). Lastly, APOA1 and HDL-C did not correlated with fibrinogen and CRP (p > .05). The present study demonstrated the correlation of APOAI with T cell subsets and IFN-γ in CAD. These results provided novel information for the regulatory action between APOAI and T cell subsets and inflammatory immunity in CAD.

Working memory is supported by learning to represent items as actions.

Working memory is typically described as a set of processes that allow for the maintenance and manipulation of information for proximal actions, yet the "action" portion of this construct is commonly overlooked. In contrast, neuroscience-informed theories of working memory have emphasized the hierarchical nature of memory representations, including both goals and sensory representations. These two representational domains are combined for the service of actions. Here, we tested whether, as it is commonly measured (i.e., with computer-based stimuli and button-based responses), working memory involved the planning of motor actions (i.e., specific button presses). Next, we examined the role of motor plan learning in successful working memory performance. Results showed that visual working memory performance was disrupted by unpredictable motor mappings, indicating a role for motor planning in working memory. Further, predictable motor mappings were in fact learned over the course of the experiment, thereby causing the measure of working memory to be partially a measure of participants' ability to learn arbitrary associations between visual stimuli and motor responses. Such learning was not highly specific to certain mappings; in sequences of short tasks, participants improved in their abilities to learn to represent items as actions in working memory. We discuss implications for working memory theories in light of hierarchical structure learning and ecological validity.

A Two-stream Convolutional Neural Network-based Pornography Recognition Method

The main approach taken to identify pornographic video content is achieved by performing pornography detection on the video content. By extracting features from video key frames and using some common neural network models to recognize the extracted key frame images, a certain accuracy rate can be obtained. However, another key information of video recognition, action information, is ignored, which leads to misclassification of some indistinguishable videos such as sumo wrestling and boxing. A dual-stream convolutional neural network-based pornographic video recognition method is proposed to address this problem. The experimental results show that the dual-stream convolutional neural network effectively improves the recognition rate of indistinguishable pornographic videos.