scholarly journals All-or-none visual categorization in the human brain

2019 ◽  
Author(s):  
Talia L. Retter ◽  
Fang Jiang ◽  
Michael A. Webster ◽  
Bruno Rossion
Keyword(s):  
Human Brain ◽  
Visual Presentation ◽  
Neural Responses ◽  
Behavioral Measures ◽  
Visual Categorization ◽  
Visual Events ◽  
Face Categorization ◽  
Wide Range ◽  
The Face ◽  
Spatio Temporal

AbstractWhether visual categorization, i.e., specific responses to a certain class of visual events across a wide range of exemplars, is graded or all-or-none in the human brain is largely unknown. We address this issue with an original frequency-sweep paradigm probing the evolution of responses between the minimum and optimal presentation times required to elicit both neural and behavioral face categorization responses. In a first experiment, widely variable natural images of nonface objects are progressively swept from 120 to 3 Hz (8.33 to 333 ms duration) in rapid serial visual presentation sequences; variable face exemplars appear every 1 s, enabling an implicit frequency-tagged face-categorization electroencephalographic (EEG) response at 1 Hz. In a second experiment, faces appear non-periodically throughout such sequences at fixed presentation rates, while participants explicitly categorize faces. Face-categorization activity emerges with stimulus durations as brief as 17 ms for both neural and behavioral measures (17 – 83 ms across individual participants neurally; 33 ms at the group level). The face-categorization response amplitude increases until 83 ms stimulus duration (12 Hz), implying graded categorization responses. However, a strong correlation with behavioral accuracy suggests instead that dilution from missed categorizations, rather than a decreased response to each face stimulus, may be responsible. This is supported in the second experiment by the absence of neural responses to behaviorally uncategorized faces, and equivalent amplitudes of isolated neural responses to only behaviorally categorized faces across presentation rates, consistent with the otherwise stable spatio-temporal signatures of face-categorization responses in both experiments. Overall, these observations provide original evidence that visual categorization of faces, while being widely variable across human observers, occurs in an all-or-none fashion in the human brain.

scholarly journals Kinematic Analysis of Lower Limb Joint Asymmetry during Gait in People with Multiple Sclerosis

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 598
Author(s):  
Massimiliano Pau ◽  
Bruno Leban ◽  
Michela Deidda ◽  
Federica Putzolu ◽  
Micaela Porta ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Lower Limb ◽  
Sagittal Plane ◽  
Cross Sectional Study ◽  
Camera Motion ◽  
Cross Sectional ◽  
Temporal Parameters ◽  
Wide Range ◽  
Edss Score ◽  
Spatio Temporal

The majority of people with Multiple Sclerosis (pwMS), report lower limb motor dysfunctions, which may relevantly affect postural control, gait and a wide range of activities of daily living. While it is quite common to observe a different impact of the disease on the two limbs (i.e., one of them is more affected), less clear are the effects of such asymmetry on gait performance. The present retrospective cross-sectional study aimed to characterize the magnitude of interlimb asymmetry in pwMS, particularly as regards the joint kinematics, using parameters derived from angle-angle diagrams. To this end, we analyzed gait patterns of 101 pwMS (55 women, 46 men, mean age 46.3, average Expanded Disability Status Scale (EDSS) score 3.5, range 1–6.5) and 81 unaffected individuals age- and sex-matched who underwent 3D computerized gait analysis carried out using an eight-camera motion capture system. Spatio-temporal parameters and kinematics in the sagittal plane at hip, knee and ankle joints were considered for the analysis. The angular trends of left and right sides were processed to build synchronized angle–angle diagrams (cyclograms) for each joint, and symmetry was assessed by computing several geometrical features such as area, orientation and Trend Symmetry. Based on cyclogram orientation and Trend Symmetry, the results show that pwMS exhibit significantly greater asymmetry in all three joints with respect to unaffected individuals. In particular, orientation values were as follows: 5.1 of pwMS vs. 1.6 of unaffected individuals at hip joint, 7.0 vs. 1.5 at knee and 6.4 vs. 3.0 at ankle (p < 0.001 in all cases), while for Trend Symmetry we obtained at hip 1.7 of pwMS vs. 0.3 of unaffected individuals, 4.2 vs. 0.5 at knee and 8.5 vs. 1.5 at ankle (p < 0.001 in all cases). Moreover, the same parameters were sensitive enough to discriminate individuals of different disability levels. With few exceptions, all the calculated symmetry parameters were found significantly correlated with the main spatio-temporal parameters of gait and the EDSS score. In particular, large correlations were detected between Trend Symmetry and gait speed (with rho values in the range of –0.58 to –0.63 depending on the considered joint, p < 0.001) and between Trend Symmetry and EDSS score (rho = 0.62 to 0.69, p < 0.001). Such results suggest not only that MS is associated with significantly marked interlimb asymmetry during gait but also that such asymmetry worsens as the disease progresses and that it has a relevant impact on gait performances.

ExpanDrogram: Dynamic Visualization of Big Data Segmentation over Time

2021 ◽  
Vol 13 (2) ◽  
pp. 1-27
Author(s):  
A. Khalemsky ◽  
R. Gelbard
Keyword(s):  
Big Data ◽  
Visual Presentation ◽  
Trend Detection ◽  
Dynamic Visualization ◽  
Segmentation Process ◽  
Key Points ◽  
Wide Range ◽  
History Of ◽  
The Individual ◽  
Over Time

In dynamic and big data environments the visualization of a segmentation process over time often does not enable the user to simultaneously track entire pieces. The key points are sometimes incomparable, and the user is limited to a static visual presentation of a certain point. The proposed visualization concept, called ExpanDrogram, is designed to support dynamic classifiers that run in a big data environment subject to changes in data characteristics. It offers a wide range of features that seek to maximize the customization of a segmentation problem. The main goal of the ExpanDrogram visualization is to improve comprehensiveness by combining both the individual and segment levels, illustrating the dynamics of the segmentation process over time, providing “version control” that enables the user to observe the history of changes, and more. The method is illustrated using different datasets, with which we demonstrate multiple segmentation parameters, as well as multiple display layers, to highlight points such as new trend detection, outlier detection, tracking changes in original segments, and zoom in/out for more/less detail. The datasets vary in size from a small one to one of more than 12 million records.

Forest path condition monitoring based on crowd-based trajectory data analysis

2020 ◽  
pp. 1-18
Author(s):  
Francisco Arcas-Tunez ◽  
Fernando Terroso-Saenz
Keyword(s):  
Information Acquisition ◽  
Road Network ◽  
Road Networks ◽  
Trajectory Data ◽  
Country Road ◽  
Wide Range ◽  
Sport Activities ◽  
Path Condition ◽  
Spatio Temporal ◽  
The City

The development of Road Information Acquisition Systems (RIASs) based on the Mobile Crowdsensing (MCS) paradigm has been widely studied for the last years. In that sense, most of the existing MCS-based RIASs focus on urban road networks and assume a car-based scenario. However, there exist a scarcity of approaches that pay attention to rural and country road networks. In that sense, forest paths are used for a wide range of recreational and sport activities by many different people and they can be also affected by different problems or obstacles blocking them. As a result, this work introduces SAMARITAN, a framework for rural-road network monitoring based on MCS. SAMARITAN analyzes the spatio-temporal trajectories from cyclists extracted from the fitness application Strava so as to uncover potential obstacles in a target road network. The framework has been evaluated in a real-world network of forest paths in the city of Cieza (Spain) showing quite promising results.

scholarly journals Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Naeem Ratyal ◽  
Imtiaz Ahmad Taj ◽  
Muhammad Sajid ◽  
Anzar Mahmood ◽  
Sohail Razzaq ◽  
...  
Keyword(s):  
Face Recognition ◽  
Superior Performance ◽  
Support Vector ◽  
Complex Nature ◽  
Alignment Algorithm ◽  
3D Face Recognition ◽  
3D Face ◽  
Norm Minimization ◽  
Wide Range ◽  
The Face

Face recognition aims to establish the identity of a person based on facial characteristics and is a challenging problem due to complex nature of the facial manifold. A wide range of face recognition applications are based on classification techniques and a class label is assigned to the test image that belongs to the unknown class. In this paper, a pose invariant deeply learned multiview 3D face recognition approach is proposed and aims to address two problems: face alignment and face recognition through identification and verification setups. The proposed alignment algorithm is capable of handling frontal as well as profile face images. It employs a nose tip heuristic based pose learning approach to estimate acquisition pose of the face followed by coarse to fine nose tip alignment using L2 norm minimization. The whole face is then aligned through transformation using knowledge learned from nose tip alignment. Inspired by the intrinsic facial symmetry of the Left Half Face (LHF) and Right Half Face (RHF), Deeply learned (d) Multi-View Average Half Face (d-MVAHF) features are employed for face identification using deep convolutional neural network (dCNN). For face verification d-MVAHF-Support Vector Machine (d-MVAHF-SVM) approach is employed. The performance of the proposed methodology is demonstrated through extensive experiments performed on four databases: GavabDB, Bosphorus, UMB-DB, and FRGC v2.0. The results show that the proposed approach yields superior performance as compared to existing state-of-the-art methods.

Kinetic images of neuronal activity of the human brain based on the spatio-temporal MNLS inverse: A theoretical study

1995 ◽  
Vol 7 (3) ◽  
pp. 193-200 ◽  
Author(s):  
Jia -Zhu Wang ◽  
Samuel J. Williamson ◽  
Lloyd Kaufman
Keyword(s):  
Human Brain ◽  
Neuronal Activity ◽  
Theoretical Study ◽  
Spatio Temporal

Lymphoedema after head and neck cancer treatment: an overview for clinical practice

2021 ◽  
Vol 26 (Sup4) ◽  
pp. S24-S29
Author(s):  
Claire Jeans ◽  
Bena Brown ◽  
Elizabeth C Ward ◽  
Anne E Vertigan
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Lymphatic System ◽  
Review Article ◽  
Psychological Issues ◽  
Wide Range ◽  
The Face ◽  
Protein Rich ◽  
The Impact

Lymphoedema is a disorder of the lymphatic system that presents as an atypical swelling and accumulation of protein-rich fluid within the interstitial spaces. Head and neck lymphoedema (HNL) is highly prevalent in patients who have been treated for head and neck cancer (HNC) and may manifest externally on the face and neck; internally within the oral cavity, pharynx or larynx; or as a combination of both. HNL is known to contribute to a wide range of physical, functional and psychological issues, and presents several unique challenges in terms of its management. This review article provides an overview of HNL for clinicians and aims to improve awareness of this condition and the impact it has on patients.

Predicting Human Mobility with Reinforcement-Learning-Based Long-Term Periodicity Modeling

2021 ◽  
Vol 12 (6) ◽  
pp. 1-23
Author(s):  
Shuo Tao ◽  
Jingang Jiang ◽  
Defu Lian ◽  
Kai Zheng ◽  
Enhong Chen
Keyword(s):  
Reinforcement Learning ◽  
Human Mobility ◽  
Recurrent Network ◽  
Mobility Prediction ◽  
Learning Framework ◽  
Temporal Features ◽  
Wide Range ◽  
Spatio Temporal ◽  
Historical Trajectory

Mobility prediction plays an important role in a wide range of location-based applications and services. However, there are three problems in the existing literature: (1) explicit high-order interactions of spatio-temporal features are not systemically modeled; (2) most existing algorithms place attention mechanisms on top of recurrent network, so they can not allow for full parallelism and are inferior to self-attention for capturing long-range dependence; (3) most literature does not make good use of long-term historical information and do not effectively model the long-term periodicity of users. To this end, we propose MoveNet and RLMoveNet. MoveNet is a self-attention-based sequential model, predicting each user’s next destination based on her most recent visits and historical trajectory. MoveNet first introduces a cross-based learning framework for modeling feature interactions. With self-attention on both the most recent visits and historical trajectory, MoveNet can use an attention mechanism to capture the user’s long-term regularity in a more efficient way. Based on MoveNet, to model long-term periodicity more effectively, we add the reinforcement learning layer and named RLMoveNet. RLMoveNet regards the human mobility prediction as a reinforcement learning problem, using the reinforcement learning layer as the regularization part to drive the model to pay attention to the behavior with periodic actions, which can help us make the algorithm more effective. We evaluate both of them with three real-world mobility datasets. MoveNet outperforms the state-of-the-art mobility predictor by around 10% in terms of accuracy, and simultaneously achieves faster convergence and over 4x training speedup. Moreover, RLMoveNet achieves higher prediction accuracy than MoveNet, which proves that modeling periodicity explicitly from the perspective of reinforcement learning is more effective.

scholarly journals Neural Responses in Parietal and Occipital Areas in Response to Visual Events Are Modulated by Prior Multisensory Stimuli

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e84331 ◽  
Author(s):  
Hamish Innes-Brown ◽  
Ayla Barutchu ◽  
David P. Crewther
Keyword(s):  
Neural Responses ◽  
Visual Events
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