A Neural Model for Biological Movement Recognition: A Neurophysiologically Plausible Theory

2004 ◽  
pp. 443-470 ◽  
Author(s):  
Martin A. Giese
Keyword(s):  
Neural Model ◽  
Biological Movement ◽  
Movement Recognition ◽  
Plausible Theory

scholarly journals Development of Biological Movement Recognition by Interaction between Active Basis Model and Fuzzy Optical Flow Division

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Bardia Yousefi ◽  
Chu Kiong Loo
Keyword(s):  
Optical Flow ◽  
Human Action Recognition ◽  
General Characteristic ◽  
Human Action ◽  
Biological Movement ◽  
Movement Recognition ◽  
Proposed Model ◽  
The Brain ◽  
Flow Division ◽  
Form Information

Following the study on computational neuroscience through functional magnetic resonance imaging claimed that human action recognition in the brain of mammalian pursues two separated streams, that is, dorsal and ventral streams. It follows up by two pathways in the bioinspired model, which are specialized for motion and form information analysis (Giese and Poggio 2003). Active basis model is used to form information which is different from orientations and scales of Gabor wavelets to form a dictionary regarding object recognition (human). Also biologically movement optic-flow patterns utilized. As motion information guides share sketch algorithm in form pathway for adjustment plus it helps to prevent wrong recognition. A synergetic neural network is utilized to generate prototype templates, representing general characteristic form of every class. Having predefined templates, classifying performs based on multitemplate matching. As every human action has one action prototype, there are some overlapping and consistency among these templates. Using fuzzy optical flow division scoring can prevent motivation for misrecognition. We successfully apply proposed model on the human action video obtained from KTH human action database. Proposed approach follows the interaction between dorsal and ventral processing streams in the original model of the biological movement recognition. The attained results indicate promising outcome and improvement in robustness using proposed approach.

Stochastic stability of a neural model for binocular rivalry

2014 ◽  
Vol 1 ◽  
pp. 739-742
Author(s):  
Tetsuya Shimokawa ◽  
Kenji Leibnitz ◽  
Ferdinand Peper
Keyword(s):  
Binocular Rivalry ◽  
Stochastic Stability ◽  
Neural Model

Oil Saturation Log Prediction Using Neural Network in New Steamflood Area

2020 ◽  
Author(s):  
A. Syahputra
Keyword(s):  
Neural Network ◽  
Water Saturation ◽  
Prediction Method ◽  
Neural Model ◽  
Training Model ◽  
Acquisition Time ◽  
Oil Saturation ◽  
Full Field ◽  
Remaining Oil ◽  
Observation Wells

Surveillance is very important in managing a steamflood project. On the current surveillance plan, Temperature and steam ID logs are acquired on observation wells at least every year while CO log (oil saturation log or SO log) every 3 years. Based on those surveillance logs, a dynamic full field reservoir model is updated quarterly. Typically, a high depletion rate happens in a new steamflood area as a function of drainage activities and steamflood injection. Due to different acquisition time, there is a possibility of misalignment or information gaps between remaining oil maps (ie: net pay, average oil saturation or hydrocarbon pore thickness map) with steam chest map, for example a case of high remaining oil on high steam saturation interval. The methodology that is used to predict oil saturation log is neural network. In this neural network method, open hole observation wells logs (static reservoir log) such as vshale, porosity, water saturation effective, and pay non pay interval), dynamic reservoir logs as temperature, steam saturation, oil saturation, and acquisition time are used as input. A study case of a new steamflood area with 16 patterns of single reservoir target used 6 active observation wells and 15 complete logs sets (temperature, steam ID, and CO log), 19 incomplete logs sets (only temperature and steam ID) since 2014 to 2019. Those data were divided as follows ~80% of completed log set data for neural network training model and ~20% of completed log set data for testing the model. As the result of neural model testing, R2 is score 0.86 with RMS 5% oil saturation. In this testing step, oil saturation log prediction is compared to actual data. Only minor data that shows different oil saturation value and overall shape of oil saturation logs are match. This neural network model is then used for oil saturation log prediction in 19 incomplete log set. The oil saturation log prediction method can fill the gap of data to better describe the depletion process in a new steamflood area. This method also helps to align steam map and remaining oil to support reservoir management in a steamflood project.

Toward a Neural Model of the Openness-Psychoticism Dimension: Functional Connectivity in the Default and Frontoparietal Control Networks

2019 ◽  
Author(s):  
Scott D. Blain ◽  
Rachael Grazioplene ◽  
Yizhou Ma ◽  
Colin G. DeYoung
Keyword(s):  
Functional Connectivity ◽  
Structural Equation ◽  
Psychotic Disorders ◽  
Neural Model ◽  
Equation Modeling ◽  
Control Networks ◽  
Psychosis Proneness ◽  
Human Connectome Project ◽  
Intrinsic Connectivity Networks ◽  
Personality Psychopathology

Psychosis proneness has been linked to heightened Openness to Experience and to cognitive deficits. Openness and psychotic disorders are associated with the default and frontoparietal networks, and the latter network is also robustly associated with intelligence. We tested the hypothesis that functional connectivity of the default and frontoparietal networks is a neural correlate of the openness-psychoticism dimension. Participants in the Human Connectome Project (N = 1003) completed measures of psychoticism, openness, and intelligence. Resting state functional magnetic resonance imaging was used to identify intrinsic connectivity networks. Structural equation modeling revealed relations among personality, intelligence, and network coherence. Psychoticism, openness, and especially their shared variance, were related positively to default network coherence and negatively to frontoparietal coherence. These associations remained after controlling for intelligence. Intelligence was positively related to frontoparietal coherence. Research suggests psychoticism and openness are linked in part through their association with connectivity in networks involving experiential simulation and cognitive control. We propose a model of psychosis risk that highlights roles of the default and frontoparietal networks. Findings echo research on functional connectivity in psychosis patients, suggesting shared mechanisms across the personality-psychopathology continuum.

Hybrid Deep Neural Model for Duplicate Question Detection in Transliterated Bi-lingual Data

2019 ◽  
Vol 12 ◽  
Author(s):  
Seema Rani ◽  
Avadhesh Kumar ◽  
Naresh Kumar
Keyword(s):  
Question Answering ◽  
Neural Model ◽  
Manhattan Distance ◽  
Semantic Matching ◽  
English Only ◽  
Detection Model ◽  
Proposed Model ◽  
Reported Study ◽  
Social Media Platforms ◽  
Efficient Information

Background: Duplicate content often corrupts the filtering mechanism in online question answering. Moreover, as users are usually more comfortable conversing in their native language questions, transliteration adds to the challenges in detecting duplicate questions. This compromises with the response time and increases the answer overload. Thus, it has now become crucial to build clever, intelligent and semantic filters which semantically match linguistically disparate questions. Objective: Most of the research on duplicate question detection has been done on mono-lingual, majorly English Q&A platforms. The aim is to build a model which extends the cognitive capabilities of machines to interpret, comprehend and learn features for semantic matching in transliterated bi-lingual Hinglish (Hindi + English) data acquired from different Q&A platforms. Method: In the proposed DQDHinglish (Duplicate Question Detection) Model, firstly language transformation (transliteration & translation) is done to convert the bi-lingual transliterated question into a mono-lingual English only text. Next a hybrid of Siamese neural network containing two identical Long-term-Short-memory (LSTM) models and Multi-layer perceptron network is proposed to detect semantically similar question pairs. Manhattan distance function is used as the similarity measure. Result: A dataset was prepared by scrapping 100 question pairs from various social media platforms, such as Quora and TripAdvisor. The performance of the proposed model on the basis of accuracy and F-score. The proposed DQDHinglish achieves a validation accuracy of 82.40%. Conclusion: A deep neural model was introduced to find semantic match between English question and a Hinglish (Hindi + English) question such that similar intent questions can be combined to enable fast and efficient information processing and delivery. A dataset was created and the proposed model was evaluated on the basis of performance accuracy. To the best of our knowledge, this work is the first reported study on transliterated Hinglish semantic question matching.

Events and framing

2018 ◽  
Author(s):  
Paul M. Pietroski
Keyword(s):  
Framing Effects ◽  
Theory Of Meaning ◽  
Adverbial Modification ◽  
Plausible Theory

Examples of adverbial modification, as in ‘Alvin chased Theodore gleefully yesterday’, were supposed to illustrate virtues of the Davidsonian conjecture reviewed in chapter four. But it is argued that such examples provide further evidence against the conjecture. A theory of meaning, for an expression-generating procedure that children can acquire, is concerned with how expressions like ‘Alvin chased Theodore’ are understood (not how the expressions are related to the events that occurred). A theory of truth is concerned with how expressions are related to the events that occurred (not how the expressions are understood). If one forces a plausible theory of meaning to also serve as a theory of truth, then ordinary action reports present deep metaphysical puzzles, as opposed to mundane “framing effects” that reveal the representational format that humans use to understand linguistic expressions.

Mother Culture, Meet Mother Nature

2018 ◽  
Author(s):  
Luc Faucher ◽  
Pierre Poirier
Keyword(s):  
Evolutionary Algorithms ◽  
Cultural Change ◽  
Evolutionary Information ◽  
Multiple Forms ◽  
Human Immune System ◽  
Units Of Selection ◽  
Pluralistic Approach ◽  
Human Immune ◽  
Plausible Theory ◽  
Different Time Scales

Research on the adaptive characteristics of the human immune system reveals that evolutionary algorithms are not strictly matters of replication. And research in genomics suggests that there is no a single source of evolutionary information that carries the same content in every environment. A plausible theory of cultural evolution must acknowledge the possibility that multiple selective algorithms are operating at different time-scales, on different units of selection, with different logical structures; but it must explain how different selective processes are interfaced to yield culturally stable phenomena. This paper advances an empirically plausible approach to memetics that recognizes a wider variety of evolutionary algorithms; and it advances a pluralistic approach to cultural change. Finally, it shows that multiple forms of processing, operating at different timescales, on different units of selection, collectively sustain the human capacity to form and use certain types of representations.

scholarly journals Quality Evaluation of Potato Tubers Using Neural Image Analysis Method

Agriculture ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 112 ◽  
Author(s):  
Andrzej Przybylak ◽  
Radosław Kozłowski ◽  
Ewa Osuch ◽  
Andrzej Osuch ◽  
Piotr Rybacki ◽  
...  
Keyword(s):  
Image Analysis ◽  
Neural Model ◽  
Assessment Method ◽  
Analysis Method ◽  
Potato Tubers ◽  
Specialized Knowledge ◽  
Analysis Techniques ◽  
Quality Assessment Method ◽  
Sorting Process ◽  
Image Analysis Techniques

This paper describes the research aimed at developing an effective quality assessment method for potato tubers using neural image analysis techniques. Nowadays, the methods used to identify damage and diseases are time-consuming, require specialized knowledge, and often rely on subjective judgment. This study showed the use of the developed neural model as a tool supporting the evaluation of potato tubers during the sorting process in the storage room.

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