140 research outputs found
Time as a supervisor: temporal regularity and auditory object learning
Get PDFSensory systems appear to learn to transform incoming sensory information into perceptual representations, or "objects," that can inform and guide behavior with minimal explicit supervision. Here, we propose that the auditory system can achieve this goal by using time as a supervisor, i.e., by learning features of a stimulus that are temporally regular. We will show that this procedure generates a feature space sufficient to support fundamental computations of auditory perception. In detail, we consider the problem of discriminating between instances of a prototypical class of natural auditory objects, i.e., rhesus macaque vocalizations. We test discrimination in two ethologically relevant tasks: discrimination in a cluttered acoustic background and generalization to discriminate between novel exemplars. We show that an algorithm that learns these temporally regular features affords better or equivalent discrimination and generalization than conventional feature-selection algorithms, i.e., principal component analysis and independent component analysis. Our findings suggest that the slow temporal features of auditory stimuli may be sufficient for parsing auditory scenes and that the auditory brain could utilize these slowly changing temporal features
Dynamis of Healing
No full textThis book explores how traces of the energies and dynamics of Orthodox Christian theology and anthropology may be observed in the clinical work of depth psychology. Looking to theology to express its own religious truths and to psychology to see whether these truth claims show up in healing modalities, the author creatively engages both disciplines in order to highlight the possibilities for healing contained therein. Dynamis of Healing elucidates how theology and psychology are by no means fundamentally at odds with each other, but rather can work together in a beautiful and powerful synergia to address both the deepest needs and deepest desires of the human person for healing and flourishing.
Pia Sophia Chaudhari holds a doctorate in theology from the department of Psychiatry & Religion at Union Theological Seminary in New York. She is a founding co-chair of the Analytical Psychology and Orthodox Christianity Consultation (APOCC)
Analysis of Chinese patents associated with incremental clustering algorithms: A review / Archana Chaudhari
Get PDFWith the advent of Internet-of-Things (IoT) and overall Information-Technology world, an enormous amount of data is getting generated dynamically and in real-time mode, in almost all domains of research and application systems. Such huge data has embedded patterns and hidden information to extract and learn. This learning is incremental in nature for all involved entities and users, as the data is growing exponentially in real-time. To achieve learning from such dynamic data sources, incremental clustering algorithms are used mandatorily. This mandate has given rise to increased patents related to incremental clustering concept, which is primarily a significant part of Machine Learning field. In this paper, we contribute to the in-progress discussion on the use of intellectual property resources, particularly patents related to machine learning, incremental clustering, incremental learning with a special focus to country China. Due consideration of the prior art search, the author found that China the country of registration of the application extensively contributes to the intellectual property related to incremental clustering domain hence felt the need to undertake this detailed patent analysis about this topic. We hope all readers, research scholars will be benefited with the latest research presented in this paper pertaining to various patents in the advanced areas of computer engineering
THEORETICAL DEVELOPMENT OF RICE TRANSPLANTING MACHINE
No full textAs in India, the plantation cost is increasing day by day. As such time the efficiency of production is decreases. One of the reason behind this is the cost of labourship, availability of labours and the expenses during the farming. So my project is basically on the modification on such processes. For that I have designed the mechanical rice-transplanter machine which will be replacement of manual plantation process. My study is based on theoretical development of mechanical rice-transplanter and the basic design on the CAD-CAM software. For the design I have taken some consideration and designed a mechanical rice-planter. In the design padded wheel, gear drive and planting finger plays important role. As per the working of the rice-transplanter I have worked on some calculation area and find that it will be approx 95% or more than that efficient than the manual planting process for the samearea of planting. The design will be little complex due to the relative driving between the padded wheel and spur gear. Design involved the selection of padded wheel, spur gear assembly, belt drive and base design
Accelerating Hadoop Map-Reduce for small/intermediate data sizes using the Comet coordination framework:
No full textMapReduce has been emerging as a popular programming paradigm for data intensive computing in clustered environments. MapReduce as a framework for solving embarrassingly parallel problems has been extensively used on large clusters. These frameworks support ease of computation for petabytes of data mostly through the use of a distributed file system example the Google File System – used by the proprietary ‘Google Map-Reduce’.
In the "Map", the master node takes the input, divides it into smaller sub-problems, and distributes those to worker nodes. The worker node processes that smaller problem, and passes the answer back to its master node. In the "Reduce", the master node then takes the answers of the sub-problems and combines them to get the final output after reduces. The advantage of MapReduce is that, it allows for distributed processing of the map and reduction operations, assuming each operation is independent of the other, all can be executed in parallel.
We found that file writes and reads to the distributed file system, have an overhead especially for smaller data sizes of the order of few tens of GB’s. Our solution provides the MapReduce framework built over Comet framework utilizing TCP sockets for communication and coordination and uses in-memory operations for data whenever possible. The objective of this thesis is to
(1) understand the behaviors and limitations of MapReduce in the case of small-moderate datasets
(2) develop coordination and interaction framework to complement MapReduce-Hadoop to address these shortcomings
(3) demonstrate and evaluate using a real world application
In this thesis we use Comet and its services to build a MapReduce infrastructure that address the above requirements - specifically enable pull based scheduling of Map tasks as well as stream based coordination and data exchange. The framework is based on the master-worker concept. Comet is a decentralized (peer-to-peer) computational infrastructure that supports applications having high computational requirement.
Our System’s interfaces are similar to the Hadoop MapReduce framework, to make applications built on Hadoop easily portable to Comet-based framework. The details of the implementation and evaluation of an actual pharmaceutical problem, with its results have been described. We found that out solution can be used to accelerate the computations of medium sized data by delaying or avoiding the use of distributed file reads and writes.M.S.Includes bibliographical references (p. 58-59)by Shivangi Chaudhar
A Distributed Event Stream Processing Framework for Materialized Views over Heterogeneous Data Sources
No full textCharacterization and bio-chemical synthesis of Gd3+rare metal Complex with benzoxazole derivative
No full textAbstract: The grouping of some rare metal ions with an significant 2-(1,3-benzoxazole -2-yl - sulfanyl )-N-phenyl acetamide (BSPA) ligand to form coordination compounds is an important area of present research. Less explored biologically important 2-(1,3-benzoxazole -2-yl - sulfanyl )-N-phenyl acetamide ligand is allowed to react with solution of some rare metal perchlorates and attempt has been made to synthesize solid 2-(1,3-benzoxazole -2-yl- sulfanyl )-N-phenyl acetamide complexes. These 2-(1,3-benzoxazole-2-yl-sulfanyl )-N-phenyl acetamide complex are subjected to U.V Visible Spectroscopy, IR Spectroscopy, TGA Analysis, elemental analysis of these complex has been evaluated by standard methods and attempts have been made to correlate structural characteristics with properties of these 2-(1,3-benzoxazole -2-yl - sulfanyl )-N-phenyl acetamide complex.
Keywords: Spectroscopic analysis, characterization, 2-(1,3-Benzoxazole-2-yl-sulfanyl)-N-phenyl acetamide(BSPA) complex.
Title: Characterization and bio-chemical synthesis of Gd3+rare metal Complex with benzoxazole derivative
Author: Dr. Haresh R.Patel, Dr. H. D. Chaudhari
International Journal of Life Sciences Research
ISSN 2348-313X (Print), ISSN 2348-3148 (online)
Vol. 10, Issue 4, October 2022 - December 2022
Page No: 27-34
Research Publish Journals
Website: www.researchpublish.com
Published Date: 03-November-2022
DOI: https://doi.org/10.5281/zenodo.7276919
Paper Download Link (Source)
https://www.researchpublish.com/papers/characterization-and-bio-chemical-synthesis-of-gd3rare-metal-complex-with-benzoxazole-derivativeInternational Journal of Life Sciences Research, ISSN 2348-313X (Print), ISSN 2348-3148 (online), Research Publish Journals, Website: www.researchpublish.co
Maintaining Materialized Views over Loosely-Coupled Distributed Heterogeneous Data Sources
No full textSteel Surface Defect Detection Using Glcm, Gabor Wavelet, Hog, And Random Forest Classifier
No full textIn the current context of market opening, quality control is essential in the field of steel production where quality is combined with the reduction in manufacturing costs. This control can be described as a set of systems deployed to verify and maintain the desired level of quality. In the manufacturing processes of steel products, great importance is allocated to the surface condition and the possibilities of its inspection, production in progress. Simple visual inspection is unable to follow the product which is generally in motion, and even with a reduced speed of the process, the inspection of the surface can only be carried out as a sampling, which is not exhaustive. Inspection at the end of the process, for its part, could not be the ideal solution, since it will only allow the history of the process to be traced, and information on its trends. Therefore, defects in the final product, which are not detected and corrected, lead to the downgrading of products and incur additional costs.Automatic detection and recognition of surface faults in the metallurgical industry are objectives for which new technologies are being implemented, to obtain greater quality control and competitive advantages in production. To this end, a machine learning-based system is presented in this paper for the inspection of steel surface defects using various feature extraction techniques; Gray-Level Co-Occurrence Matrix (GLCM), Gabor Wavelet, and Histogram of Oriented Gradients (HOG). The classification of extracted features is accomplished by Random Forest Classifier
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