Introduction: Understanding and Studying Meditation

2021 ◽  
Author(s):  
Miguel Farias ◽  
David Brazier ◽  
Mansur Lalljee
Keyword(s):  
Mental Objects ◽  
Tree Image

The purpose of this chapter is twofold. First, it explores the different meanings of meditation and its varieties across Eastern and Western traditions, including the more recent therapeutic developments. Using a Meditation Tree image, specifically created for this volume, the chapter gives examples of practices from these traditions that rely on multiple techniques, such as concentration, recitation, breathing, singing, and visualization of physical or mental objects, among others. It highlights the richness of practices but equally of experiences and expected goals, which have led to debates and tensions among meditation experts and movements for over two thousand years. Second, the chapter summarizes the structure of this volume and the major achievements in the study of meditation, as well as current limitations and controversies.

Aristotle's Formal Identity of Intellect and Object: A Solution to the Problem of Modal Epistemology

2019 ◽  
Vol 1 (1) ◽  
pp. 84-107
Author(s):  
Robert C. Koons
Keyword(s):  
Human Mind ◽  
Modal Epistemology ◽  
De Anima ◽  
Object A ◽  
Formal Identity ◽  
Identity Thesis ◽  
The Mind ◽  
Mental Objects ◽  
Aristotelian Metaphysics

In De Anima Book III, Aristotle subscribed to a theory of formal identity between the human mind and the extra-mental objects of our understanding. This has been one of the most controversial features of Aristotelian metaphysics of the mind. I offer here a defense of the Formal Identity Thesis, based on specifically epistemological arguments about our knowledge of necessary or essential truths.

Hawthorne's Oak Tree Image

1960 ◽  
Vol 15 (2) ◽  
pp. 181-185
Author(s):  
Virginia O. Birdsall
Keyword(s):  
Oak Tree ◽  
Tree Image

scholarly journals A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5

2021 ◽  
Vol 13 (9) ◽  
pp. 1619
Author(s):  
Bin Yan ◽  
Pan Fan ◽  
Xiaoyan Lei ◽  
Zhijie Liu ◽  
Fuzeng Yang
Keyword(s):  
Real Time ◽  
Apple Tree ◽  
Detection Method ◽  
Recognition Algorithm ◽  
Medium Size ◽  
Feature Maps ◽  
Detection Algorithms ◽  
Fusion Mode ◽  
Visual Attention Mechanism ◽  
Tree Image

The apple target recognition algorithm is one of the core technologies of the apple picking robot. However, most of the existing apple detection algorithms cannot distinguish between the apples that are occluded by tree branches and occluded by other apples. The apples, grasping end-effector and mechanical picking arm of the robot are very likely to be damaged if the algorithm is directly applied to the picking robot. Based on this practical problem, in order to automatically recognize the graspable and ungraspable apples in an apple tree image, a light-weight apple targets detection method was proposed for picking robot using improved YOLOv5s. Firstly, BottleneckCSP module was improved designed to BottleneckCSP-2 module which was used to replace the BottleneckCSP module in backbone architecture of original YOLOv5s network. Secondly, SE module, which belonged to the visual attention mechanism network, was inserted to the proposed improved backbone network. Thirdly, the bonding fusion mode of feature maps, which were inputs to the target detection layer of medium size in the original YOLOv5s network, were improved. Finally, the initial anchor box size of the original network was improved. The experimental results indicated that the graspable apples, which were unoccluded or only occluded by tree leaves, and the ungraspable apples, which were occluded by tree branches or occluded by other fruits, could be identified effectively using the proposed improved network model in this study. Specifically, the recognition recall, precision, mAP and F1 were 91.48%, 83.83%, 86.75% and 87.49%, respectively. The average recognition time was 0.015 s per image. Contrasted with original YOLOv5s, YOLOv3, YOLOv4 and EfficientDet-D0 model, the mAP of the proposed improved YOLOv5s model increased by 5.05%, 14.95%, 4.74% and 6.75% respectively, the size of the model compressed by 9.29%, 94.6%, 94.8% and 15.3% respectively. The average recognition speeds per image of the proposed improved YOLOv5s model were 2.53, 1.13 and 3.53 times of EfficientDet-D0, YOLOv4 and YOLOv3 and model, respectively. The proposed method can provide technical support for the real-time accurate detection of multiple fruit targets for the apple picking robot.

Etymology and Semantic Spectrum of adhimukti and Related Terms in Buddhist Texts

2019 ◽  
Vol 36 (1) ◽  
pp. 3-29
Author(s):  
Giacomo Benedetti
Keyword(s):  
External Reality ◽  
Original Meaning ◽  
Meditative Practice ◽  
Stable Representation ◽  
The Common ◽  
The Mind ◽  
Mental Objects

The action noun adhimukti derives from the verb adhi-muc, not attested in Classical Sanskrit but in P?li. It is regularly used in the passive, with the original meaning ‘to be fastened to’, and then ‘to adhere’. This meaning is not used in a concrete sense, but in a metaphorical one, referred to mind and mental objects, so that adhimukti can be used to express inclination, faith in a doctrine, and also intentional and stable representation of an image or an idea in meditative practice, sometimes with the effect of transformation of external reality. The common feature appears to be adherence or the fixing of the mind on its object.

scholarly journals Fast Recognition System for Tree Images based on Caffe Platform and Deep Learning

2020 ◽  
Author(s):  
Zongchen Li ◽  
Wenzhuo Zhang ◽  
Guoxiong Zhou
Keyword(s):  
Deep Learning ◽  
Dual Task ◽  
Learning Algorithm ◽  
Recognition System ◽  
Support Vector ◽  
Similar Species ◽  
Graphical Interface ◽  
Fast Recognition ◽  
System Solution ◽  
Tree Image

Abstract Aiming at the difficult problem of complex extraction for tree image in the existing complex background, we took tree species as the research object and proposed a fast recognition system solution for tree image based on Caffe platform and deep learning. In the research of deep learning algorithm based on Caffe framework, the improved Dual-Task CNN model (DCNN) is applied to train the image extractor and classifier to accomplish the dual tasks of image cleaning and tree classification. In addition, when compared with the traditional classification methods represented by Support Vector Machine (SVM) and Single-Task CNN model, Dual-Task CNN model demonstrates its superiority in classification performance. Then, in order for further improvement to the recognition accuracy for similar species, Gabor kernel was introduced to extract the features of frequency domain for images in different scales and directions, so as to enhance the texture features of leaf images and improve the recognition effect. The improved model was tested on the data sets of similar species. As demonstrated by the results, the improved deep Gabor convolutional neural network (GCNN) is advantageous in tree recognition and similar tree classification when compared with the Dual-Task CNN classification method. Finally, the recognition results of trees can be displayed on the application graphical interface as well. In the application graphical interface designed based on Ubantu system, it is capable to perform such functions as quick reading of and search for picture files, snapshot, one-key recognition, one-key e

scholarly journals Use of theories and models in geometry education research: A critical review

2019 ◽  
Vol 24 (1) ◽  
pp. 43-54
Author(s):  
Shweta Sharma
Keyword(s):  
Education Research ◽  
Critical Review ◽  
Three Dimensional ◽  
Geometric Figure ◽  
Future Studies ◽  
Geometry Class ◽  
Mental Objects ◽  
Operational Capacity ◽  
Geometry Education ◽  
Figural Concepts

The aim of this article is to provide a critical review of the theories and the model used in the field of geometry education research. The article critically discusses van Hiele’s theory, Fischbein’s theory of figural concepts, Duval’ s theory of figural apprehension, the Spatial Operational Capacity (SOC) model by Wessels and van Niekerk, and the Sfard’s commognition theory. The van Hiele’s theory proposed a sequential order of development through which the learners construct their understanding of geometry concepts. Fischbein’ s theory of figural concepts suggested that a geometric figure is always comprised of a visible representation and a concept. Duval’s theory of figural apprehension underscored the heuristic value of a geometry figure for solving geometry problems. The SOC model by Wessels and van Niekerk emphasised the importance of instructional design incorporating a variety of physical and mental objects to work with to develop geometry concepts. Finally, the article discusses Sfard’s commognition theory that emphasises the communicative function of language in developing geometry concepts. There are two major concerns highlighted with respect to these theories and the model. Firstly, these theories and model emphasise the development of the two-dimensional geometry concepts, neglecting the development of the concepts of three-dimensional geometry. Secondly, these theories and the model fail to acknowledge the multilingual context of geometry class. The article aims to highlight the dearth of studies that explore the multilingual context of geometry class and calls for future studies in this direction.  

Action

2020 ◽  
pp. 15-26
Author(s):  
Stephen K. Reed
Keyword(s):  
Video Game ◽  
Educational Materials ◽  
Audio Feedback ◽  
Computer Screen ◽  
Virtual Objects ◽  
Computer Interfaces ◽  
Maria Montessori ◽  
Physical Objects ◽  
Mental Objects ◽  
Mental Actions

Actions can be either physical, virtual, or mental and act on either physical, virtual, or mental objects. For instance, Maria Montessori constructed educational materials that enabled students to learn by manipulation. The materials required physical actions on physical objects, such as combining beads to depict operations on numbers. Nintendo’s Wii video game supported physical actions on virtual objects. Gestures are actions that often apply to imaginary objects. Virtual actions involve manipulating computer consoles such as those used in robotic surgery to operate on physical objects. Virtual actions on virtual objects occur in many video games and instructional software. Virtual actions on mental objects occur in computer systems that use audio feedback to help the blind learn to navigate. Mental actions can be captured in brain–computer interfaces to control both physical robots and information on a computer screen. Mental actions on mental objects produce mental simulations. The increasing popularity of augmented reality will require more research on the pairing of physical, virtual, and mental actions and objects.

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