Control strategy of central pattern generator gait movement under condition of attention selection

As a typical rhythmic movement, human being＇s rhythmic gait movement can be generated by a central pattern generator （CPG） located in a spinal cord by self- oscillation. Some kinds of gait movements are caused by gait frequency and amplitude variances. As an important property of human being＇s motion vision, the attention selection mechanism plays a vital part in the regulation of gait movement. In this paper, the CPG model is amended under the condition of attention selection on the theoretical basis of Matsuoka neural oscillators. Regulation of attention selection signal for the CPG model parameters and structure is studied, which consequentially causes the frequency and amplitude changes of gait movement output. Further, the control strategy of the CPG model gait movement under the condition of attention selection is discussed, showing that the attention selection model can regulate the output model of CPG gait movement in three different ways. The realization of regulation on the gait movement frequency and amplitude shows a variety of regulation on the CPG gait movement made by attention selection and enriches the controllability of CPG gait movement, which demonstrates potential influence in engineering applications.

Deep neural network based on multi-level wavelet and attention for structured illumination microscopy

Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.

Auditory attention model based on Chirplet for cross-corpus speech emotion recognition

To solve the problem of mismatching features in an experimental database, which is a key technique in the field of cross-corpus speech emotion recognition, an auditory attention model based on Chirplet is proposed for feature extraction.First, in order to extract the spectra features, the auditory attention model is employed for variational emotion features detection. Then, the selective attention mechanism model is proposed to extract the salient gist features which showtheir relation to the expected performance in cross-corpus testing.Furthermore, the Chirplet time-frequency atoms are introduced to the model. By forming a complete atom database, the Chirplet can improve the spectrum feature extraction including the amount of information. Samples from multiple databases have the characteristics of multiple components. Hereby, the Chirplet expands the scale of the feature vector in the timefrequency domain. Experimental results show that, compared to the traditional feature model, the proposed feature extraction approach with the prototypical classifier has significant improvement in cross-corpus speech recognition. In addition, the proposed method has better robustness to the inconsistent sources of the training set and the testing set.

A selective attention-based contextual perception approach for a humanoid robot

A humanoid robot is always flooded by sensed information when sensing the environment, and it usually needs significant time to compute and process the sensed information. In this paper, a selective attention-based contextual perception approach was proposed for humanoid robots to sense the environment with high efficiency. First, the connotation of attention window （AW） is extended to make a more general and abstract definition of AW, and its four kinds of operations and state transformations are also discussed. Second, the attention control policies are described, which integrate intensionguided perceptual objects selection and distractor inhibition, and can deal with emergent issues. Distractor inhibition is used to filter unrelated information. Last, attention policies are viewed as the robot＇s perceptual modes, which can control and adjust the perception efficiency. The experimental results show that the presented approach can promote the perceptual efficiency significantly, and the perceptual cost can be effectively controlled through adopting different attention policies.

Using eye movements in the dot-probe paradigm to investigate attention bias in illness anxiety disorder

BACKGROUND Illness anxiety disorder(IAD)is a common,distressing,and debilitating condition with the key feature being a persistent conviction of the possibility of having one or more serious or progressive physical disorders.Because eye movements are guided by visual-spatial attention,eye-tracking technology is a comparatively direct,continuous measure of attention direction and speed when stimuli are oriented.Researchers have tried to identify selective visual attention biases by tracking eye movements within dot-probe paradigms because dot-probe paradigm can distinguish these attentional biases more clearly.AIM To examine the association between IAD and biased processing of illness-related information.METHODS A case-control study design was used to record eye movements of individuals with IAD and healthy controls while participants viewed a set of pictures from four categories(illness-related,socially threatening,positive,and neutral images).Biases in initial orienting were assessed from the location of the initial shift in gaze,and biases in the maintenance of attention were assessed from the duration of gaze that was initially fixated on the picture per image category.RESULTS The eye movement of the participants in the IAD group was characterized by an avoidance bias in initial orienting to illness-related pictures.There was no evidence of individuals with IAD spending significantly more time viewing illness-related images compared with other images.Patients with IAD had an attention bias at the early stage and overall attentional avoidance.In addition,this study found that patients with significant anxiety symptoms showed attention bias in the late stages of attention processing.CONCLUSION Illness-related information processing biases appear to be a robust feature of IAD and may have an important role in explaining the etiology and maintenance of the disorder.

CSMCCVA:Framework of cross-modal semantic mapping based on cognitive computing of visual and auditory sensations

Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search engine.This study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of cognitive system,and establishes a brain-like cross-modal semantic mapping framework based on cognitive computing of visual and auditory sensations.The mechanism of visual-auditory multisensory integration,selective attention in thalamo-cortical,emotional control in limbic system and the memory-enhancing in hippocampal were considered in the framework.Then,the algorithms of cross-modal semantic mapping were given.Experimental results show that the framework can be effectively applied to the cross-modal semantic mapping,and also provides an important significance for brain-like computing of non-von Neumann structure.

Neural mechanisms of top-down divided and selective spatial attention in visual and auditory perception

Top-down attention mechanisms require the selection of specificobjects or locations;however,the brain mechanism involved when attention is allocated across different modalities is not well understood.The aim of this study was to use functional magnetic resonance imaging to define the neural mechanisms underlyingdivided and selective spatial attention.A concurrent audiovisual stimulus was used,and subjects were prompted to focus on a visual,auditory and audiovisual stimulus in a Posner paradigm.Ourbehavioral results confirmed the better performance of selectiveattention compared to devided attention.We found differences in the activation level of the frontoparietal network,visual/auditorycortex,the putamen and the salience network under differentattention conditions.We further used Granger causality(GC)toexplore effective connectivity differences between tasks.Differences in GC connectivity between visual and auditory selective tasksreflected the visual dominance effect under spatial attention.In addition,our results supported the role of the putamen inredistributing attention and the functional separation of the saliencenetwork.In summary,we explored the audiovisual top-down allocation of attention and observed the differences in neuralmechanisms under endogenous attention modes,which revealedthe differences in cross-modal expression in visual and auditory attention under attentional modulation.

Changes of Effective Connectivity in the Alpha Band Characterize Differential Processing of Audiovisual Information in Cross-Modal Selective Attention

Cross-modal selective attention enhances the processing of sensory inputs that are most relevant to the task at hand.Such differential processing could be mediated by a swift network reconfiguration on the macroscopic level,but this remains a poorly understood process.To tackle this issue,we used a behavioral paradigm to introduce a shift of selective attention between the visual and auditory domains,and recorded scalp electroencephalographic signals from eight healthy participants.The changes in effective connectivity caused by the cross-modal attentional shift were delineated by analyzing spectral Granger Causality(GC),a metric of frequency-specific effective connectivity.Using data-driven methods of pattern-classification and feature-analysis,we found that a change in the a band(12 Hz-15 Hz) of GC is a stable feature across different individuals that can be used to decode the attentional shift.Specifically,auditory attention induces more pronounced information flow in the α band,especially from the parietal-occipital areas to the temporal-parietal areas,compared to the case of visual attention,reflecting a reconfiguration of interaction in the macroscopic brain network accompanying different processing.Our results support the role of α oscillation in organizing the information flow across spatially-separated brain areas and,thereby,mediating cross-modal selective attention.

A Selective Attention Guided Initiative Semantic Cognition Algorithm for Service Robot

With the development of artificial intelligence and robotics, the study on service robot has made a significant progress in recent years. Service robot is required to perceive users and environment in unstructured domestic environment. Based on the perception,service robot should be capable of understanding the situation and discover service task. So robot can assist humans for home service or health care more accurately and with initiative. Human can focus on the salient things from the mass observation information. Humans are capable of utilizing semantic knowledge to make some plans based on their understanding of the environment. Through intelligent space platform, we are trying to apply this process to service robot. A selective attention guided initiatively semantic cognition algorithm in intelligent space is proposed in this paper. It is specifically designed to provide robots with the cognition needed for performing service tasks. At first, an attention selection model is built based on saliency computing and key area. The area which is highly relevant to service task could be located and referred as focus of attention（FOA）. Second, a recognition algorithm for FOA is proposed based on a neural network. Some common objects and user behavior are recognized in this step. At last, a unified semantic knowledge base and corresponding reasoning engine is proposed using recognition result. Related experiments in a real life scenario demonstrated that our approach is able to mimic the recognition process in humans, make robots understand the environment and discover service task based on its own cognition. In this way, service robots can act smarter and achieve better service efficiency in their daily work.

Neuropsychological characteristics of selective attention in children with nonverbal learning disabilities

Background Children with nonverbal learning disabilities (NLD) usually manifest defective attention function This study sought to investigate the neuropsychological characteristics of selective attention, such as attention control, working memory, and attention persistence of the frontal lobe in children with NLD Methods Using the auditory detection test (ADT), Wisconsin card sorting test (WCST), and C WISC, 27 children with NLD and 33 normal children in the control group were tested, and the results of C WISC subtests were analyzed with factor analysis Results Compared with the control group, the correct response rate in the auditory detection test in the NLD group was much lower ( P <0 01), and the number of incorrect responses was much higher ( P <0 01); NLD children also scored lower in WCST categories achieved (CA) and perseverative errors (PE) ( P <0 05) Factor analysis showed that perceptual organization (PO) related to visual space and freedom from distractibility (FD) relating to attention persistence in the NLD group were obviously lower than in the control group ( P <0 01) Conclusions Children with NLD have attention control disorder and working memory disorder mainly in the frontal lobe We believe that the disorder is particularly prominent in the right frontal lobe

Guiding Attention by Cooperative Cues

A common assumption in visual attention is based on the rationale of ＂limited capacity of information processing＂. From this view point there is little consideration of how different information channels or modules are cooperating because cells in processing stages are forced to compete for the limited resource. To examine the mechanism behind the cooperative behavior of information channels, a computational model of selective attention is implemented based on two hypotheses. Unlike the traditional view of visual attention, the cooperative behavior is assumed to be a dynamic integration process between the bottom-up and top-down information. Furthermore, top-down information is assumed to provide a contextual cue during selection process and to guide the attentional allocation among many bottom-up candidates. The result from a series of simulation with still and video images showed some interesting properties that could not be explained by the competitive aspect of selective attention alone.

A Plan to Save the Species China hopes to draw more attention to endangered species by selecting regional representatives

Always plainly dressed and amiable, Ji Xianlin wouldn’t be given a second glance in a crowd. Yet looks can be deceiving. He was the country’s leading scholar of

An asymptotically optimal public parking lot location algorithm based on intuitive reasoning

In order to solve the problems of road traffic congestion and the increasing parking time caused by the imbalance of parking lot supply and demand,this paper proposes an asymptotically optimal public parking lot location algorithm based on intuitive reasoning to optimize the parking lot location problem.Guided by the idea of intuitive reasoning,we use walking distance as indicator to measure the variability among location data and build a combinatorial optimization model aimed at guiding search decisions in the solution space of complex problems to find optimal solutions.First,Selective Attention Mechanism(SAM)is introduced to reduce the search space by adaptively focusing on the important information in the features.Then,Quantum Annealing(QA)algorithm with quantum tunneling effect is used to jump out of the local extremum in the search space with high probability and further approach the global optimal solution.Experiments on the parking lot location dataset in Luohu District,Shenzhen,show that the proposed method has improved the accuracy and running speed of the solution,and the asymptotic optimality of the algorithm and its effectiveness in solving the public parking lot location problem are verified.