The Birth of Symbols in Design

2009 ◽  
Author(s):  
Amitabha Mukerjee ◽  
Madan Mohan Dabbeeru
Keyword(s):  
Learning System ◽  
Locally Linear Embedding ◽  
Intermediate Step ◽  
Functional Design ◽  
Image Schemas ◽  
Wide Range ◽  
Usage Patterns ◽  
Human Usage ◽  
Low Dimensional ◽  
Design Scenario

In the widespread endeavour to standardize a vocabulary for design, the semantics for the terms, especially at the detailed levels, are often defined based on the exigencies of the implementation. In human usage, each symbol has a wide range of associations, and any attempt at definition will miss many of these, resulting in brittleness. Human flexibility in symbol usage is possible because our symbols are learned from a vast experience of the world. Here we propose the very first steps towards a process by which CAD systems may acquire symbols is by learning usage patterns or image schemas grounded on experience. Subsequently, more abstract symbols may be derived based on these grounded symbols, which thereby retain the flexibility inherent in a learning system. In many design tasks, the “good designs” lie along regions that can be mapped to lower dimensional surfaces or manifolds, owing to latent interdependencies between the variables. These low-dimensional structures (sometimes called chunks) may constitute the intermediate step between the raw experience and the eventual symbol that arises after these patterns become stabilized through communication. In a multi-functional design scenario, we use a locally linear embedding (LLE) to discover these manifolds, which are compact descriptions for the space of “good designs”. We illustrate the approach with a simple 2-parameter latch-and-bolt design, and with a 8-parameter universal motor.

Using Symbol Emergence to Discover Multi-Lingual Translations in Design

2010 ◽  
Author(s):  
Amitabha Mukerjee ◽  
Madan Mohan Dabbeeru
Keyword(s):  
Performance Measures ◽  
Computational Design ◽  
Design Knowledge ◽  
Image Schemas ◽  
Design Object ◽  
Knowledge Based ◽  
Actual Design ◽  
Image Schema ◽  
Low Dimensional ◽  
Design Scenario

Incorporating design knowledge into computational design requires “symbols” — but this term as used in knowledge-based models of design is a formal term, defined only in terms of other symbols. For most humans, symbols are [term : meaning] pairs that emerge while interacting with real designs. However, both the term and its interpretation vary considerably across design groups, particularly in today’s international cooperative design scenario. For translating symbols in design, one needs to incorporate the design context, which is since the actual design object and its characteristics form the most relevant part of the context. In this work, we consider an embodied symbols approach towards translation, where models corresponding to symbol semantics are discovered based on functional norms in a given design context. The functions are available as performance measures on a given task, and lead to low-dimensional characterizations (called image schema) that reveal inter-relations in the input space that must hold for functional validity. Some of these image schemas eventually acquire language labels and become symbols. Since different designers differ in experience and in language their symbols differ somewhat. Here we consider how independent language agents may map these low-dimensional characterizations (called chunks) to units of languages based on human commentary produced in the same context. We demonstrate how this process may work for the simple domain of insertion tasks and fits, and learn both the image schemas and the language labels in two different languages, English and Telugu.

Geometry and Physics: Volume II

2018 ◽  
Keyword(s):  
Moduli Spaces ◽  
Mirror Symmetry ◽  
Geometric Analysis ◽  
Mathematical Physics ◽  
Poisson Geometry ◽  
Special Holonomy ◽  
Low Dimensional Topology ◽  
Generalized Complex Structures ◽  
Wide Range ◽  
Low Dimensional

These volumes contain the proceedings of the conference held at Aarhus, Oxford and Madrid in September 2016 to mark the seventieth birthday of Nigel Hitchin, one of the world’s foremost geometers and Savilian Professor of Geometry at Oxford. The proceedings contain twenty-nine articles, including three by Fields medallists (Donaldson, Mori and Yau). The articles cover a wide range of topics in geometry and mathematical physics, including the following: Riemannian geometry, geometric analysis, special holonomy, integrable systems, dynamical systems, generalized complex structures, symplectic and Poisson geometry, low-dimensional topology, algebraic geometry, moduli spaces, Higgs bundles, geometric Langlands programme, mirror symmetry and string theory. These volumes will be of interest to researchers and graduate students both in geometry and mathematical physics.

scholarly journals Bond-Graph model based stepper motor output characteristics simulation implemented in LabVIEW environment

2021 ◽  
Vol 7 (1.) ◽  
Author(s):  
Zsolt Molnár
Keyword(s):  
Block Diagram ◽  
Real Life ◽  
Graph Model ◽  
Bond Graph ◽  
Visual Programming ◽  
Stepper Motor ◽  
Intermediate Step ◽  
Virtual Device ◽  
Wide Range ◽  
Made In

In the industry, simulations are of great importance. They enable measurements to be made in different conditions about a virtual device, which are highly comparable to measurements made in a real life scenarios. Because of their wide range of usage in lower power drive systems, where precision and simplicity is a must, the subject of study is a permanent magnet stepper motor. For precise positioning purposes, it is essential to know the positioning behaviour of these devices. The model construction process involved an intermediate step, which consisted of creating the Bond-Graph of the motor based on pre-defined models available in the literature in this field. In the next step, the Bond-Graph model was converted to a block diagram of the motor. This permitted the direct implementation of the motor model in LabVIEW visual programming environment. The preliminary steps allows us to check and confirm the functionality and correctness of the model. This article covers in detail the model conversion and implementation steps of the simulation. At the end, the functionality of the simulation was tested.

scholarly journals Graph representation learning: a survey

2020 ◽  
Vol 9 ◽  
Author(s):  
Fenxiao Chen ◽  
Yun-Cheng Wang ◽  
Bin Wang ◽  
C.-C. Jay Kuo
Keyword(s):  
Graph Embedding ◽  
Large Data ◽  
Representation Learning ◽  
Graph Representation ◽  
Data Sets ◽  
Graph Data ◽  
Graph Properties ◽  
Wide Range ◽  
Regular Lattices ◽  
Low Dimensional

Abstract Research on graph representation learning has received great attention in recent years since most data in real-world applications come in the form of graphs. High-dimensional graph data are often in irregular forms. They are more difficult to analyze than image/video/audio data defined on regular lattices. Various graph embedding techniques have been developed to convert the raw graph data into a low-dimensional vector representation while preserving the intrinsic graph properties. In this review, we first explain the graph embedding task and its challenges. Next, we review a wide range of graph embedding techniques with insights. Then, we evaluate several stat-of-the-art methods against small and large data sets and compare their performance. Finally, potential applications and future directions are presented.

scholarly journals Inference of a Mesoscopic Population Model from Population Spike Trains

2020 ◽  
Vol 32 (8) ◽  
pp. 1448-1498 ◽  
Author(s):  
Alexandre René ◽  
André Longtin ◽  
Jakob H. Macke
Keyword(s):  
Population Model ◽  
Single Neuron ◽  
Simulated Data ◽  
Population Data ◽  
Model Parameters ◽  
Population Activity ◽  
Mcmc Sampling ◽  
Wide Range ◽  
Low Dimensional ◽  
Aggregate Population

Understanding how rich dynamics emerge in neural populations requires models exhibiting a wide range of behaviors while remaining interpretable in terms of connectivity and single-neuron dynamics. However, it has been challenging to fit such mechanistic spiking networks at the single-neuron scale to empirical population data. To close this gap, we propose to fit such data at a mesoscale, using a mechanistic but low-dimensional and, hence, statistically tractable model. The mesoscopic representation is obtained by approximating a population of neurons as multiple homogeneous pools of neurons and modeling the dynamics of the aggregate population activity within each pool. We derive the likelihood of both single-neuron and connectivity parameters given this activity, which can then be used to optimize parameters by gradient ascent on the log likelihood or perform Bayesian inference using Markov chain Monte Carlo (MCMC) sampling. We illustrate this approach using a model of generalized integrate-and-fire neurons for which mesoscopic dynamics have been previously derived and show that both single-neuron and connectivity parameters can be recovered from simulated data. In particular, our inference method extracts posterior correlations between model parameters, which define parameter subsets able to reproduce the data. We compute the Bayesian posterior for combinations of parameters using MCMC sampling and investigate how the approximations inherent in a mesoscopic population model affect the accuracy of the inferred single-neuron parameters.

scholarly journals Usage Patterns of Cosmetic and Personal Care Products among Female Population in Saudi Arabia: Important Factors for Exposure and Risk Assessment

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Heba Shaaban ◽  
Wejdan Alhajri
Keyword(s):  
Risk Assessment ◽  
Saudi Arabia ◽  
Adverse Events ◽  
Personal Care Products ◽  
Published Data ◽  
Personal Care ◽  
Female Population ◽  
Frequency Of Use ◽  
Wide Range ◽  
Usage Patterns

Reliable data regarding the usage patterns of personal care products (PCPs) are needed to determine the health risks posed by the ingredients of these products such as parabens, phthalates, and bisphenol A. There are no published data regarding the consumption patterns of PCPs in the Middle East in general and in Saudi Arabia in particular. To fill this gap, this study aimed to assess important factors such as the percentage of users and the frequency of use and co-use of twenty-three cosmetic and PCPs among the female population in Saudi Arabia. Additionally, this study aimed to assess the common cosmetic-related adverse events among the participants. The studied products included general hygiene, hair care, skin care, makeup, fragrances, and other products. The data were collected from 709 female participants of different age groups through a digital questionnaire. It was found that eighteen of the investigated products are consumed by at least 50% of the respondents. The frequency of use of PCPs varied over a wide range. Cosmetic-related adverse events were experienced by 16.1% of the participants. Use frequencies of many hygiene and makeup products were correlated with each other. This study provides, for the first time, baseline data on the usage patterns of a large number of widely consumed PCPs among female population pertaining to several sociodemographic strata. Such information is crucial for exposure and risk assessment and also needed for updating the current knowledge on usage of PCPs in Saudi Arabia.

scholarly journals Toward Social Media Content Recommendation Integrated with Data Science and Machine Learning Approach for E-Learners

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1798 ◽  
Author(s):  
Zeinab Shahbazi ◽  
Yung Cheol Byun
Keyword(s):  
Social Media ◽  
Computer Science ◽  
Data Science ◽  
Learning System ◽  
Local Context ◽  
Great Success ◽  
Learning Context ◽  
Wide Range ◽  
E Learning ◽  
Learning Software

Electronic Learning (e-learning) has made a great success and recently been estimated as a billion-dollar industry. The users of e-learning acquire knowledge of diversified content available in an application using innovative means. There is much e-learning software available—for example, LMS (Learning Management System) and Moodle. The functionalities of this software were reviewed and we recognized that learners have particular problems in getting relevant recommendations. For example, there might be essential discussions about a particular topic on social networks, such as Twitter, but that discussion is not linked up and recommended to the learners for getting the latest updates on technology-updated news related to their learning context. This has been set as the focus of the current project based on symmetry between user project specification. The developed project recommends relevant symmetric articles to e-learners from the social network of Twitter and the academic platform of DBLP. For recommendations, a Reinforcement learning model with optimization is employed, which utilizes the learners’ local context, learners’ profile available in the e-learning system, and the learners’ historical views. The recommendations by the system are relevant tweets, popular relevant Twitter users, and research papers from DBLP. For matching the local context, profile, and history with the tweet text, we recognized that terms in the e-learning system need to be expanded to cover a wide range of concepts. However, this diversification should not include such terms which are irrelevant. To expand terms of the local context, profile and history, the software used the dataset of Grow-bag, which builds concept graphs of large-scale Computer Science topics based on the co-occurrence scores of Computer Science terms. This application demonstrated the need and success of e-learning software that is linked with social media and sends recommendations for the content being learned by the e-Learners in the e-learning environment. However, the current application only focuses on the Computer Science domain. There is a need for generalizing such applications to other domains in the future.

Expression levels and codon usage patterns in nuclear genes of the filarial nematodeWucheraria bancroftiand the blood flukeSchistosoma haematobium

2016 ◽  
Vol 91 (1) ◽  
pp. 72-79 ◽  
Author(s):  
G.A. Mazumder ◽  
A. Uddin ◽  
S. Chakraborty
Keyword(s):  
Natural Selection ◽  
Codon Usage ◽  
Evolutionary Relationship ◽  
Nuclear Genes ◽  
Codon Usage Pattern ◽  
Minor Role ◽  
Usage Pattern ◽  
Genetic Characteristics ◽  
Wide Range ◽  
Usage Patterns

AbstractSynonymous codons are used with different frequencies, a phenomenon known as codon bias, which exists in many genomes and is mainly resolute by mutation and selection. To elucidate the genetic characteristics and evolutionary relationship ofWucheraria bancroftiandSchistosoma haematobiumwe examined the pattern of synonymous codon usage in nuclear genes of both the species. The mean overall GC contents ofW. bancroftiandS. haematobiumwere 43.41 and 36.37%, respectively, which suggests that genes in both the species were AT rich. The value of the High Effective Number of Codons in both species suggests that codon usage bias was weak. Both species had a wide range of P3 distribution in the neutrality plot, with a significant correlation between P12 and P3. The codons were closer to the axes in correspondence analysis, suggesting that mutation pressure influenced the codon usage pattern in these species. We have identified the more frequently used codons in these species, most codons ending with an A or T. The nucleotides A/T and C/G were not proportionally used at the third position of codons, which reveals that natural selection might influence the codon usage patterns. The regression equation of P12 on P3 suggests that natural selection might have played a major role, while mutational pressure played a minor role in codon usage pattern in both species. These results form the basis of exploring the evolutionary mechanisms and the heterologous expression of medically important proteins ofW. bancroftiandS. haematobium.

Self-Organizing Map Learning Nonlinearly Embedded Manifolds

2005 ◽  
Vol 4 (1) ◽  
pp. 22-31 ◽  
Author(s):  
Timo Similä
Keyword(s):  
Learning Algorithm ◽  
Image Data ◽  
High Dimensional ◽  
Locally Linear Embedding ◽  
Complex Data ◽  
Dimensional Manifold ◽  
Self Organizing Map ◽  
Training Strategy ◽  
Low Dimensional ◽  
Self Organizing

One of the main tasks in exploratory data analysis is to create an appropriate representation for complex data. In this paper, the problem of creating a representation for observations lying on a low-dimensional manifold embedded in high-dimensional coordinates is considered. We propose a modification of the Self-organizing map (SOM) algorithm that is able to learn the manifold structure in the high-dimensional observation coordinates. Any manifold learning algorithm may be incorporated to the proposed training strategy to guide the map onto the manifold surface instead of becoming trapped in local minima. In this paper, the Locally linear embedding algorithm is adopted. We use the proposed method successfully on several data sets with manifold geometry including an illustrative example of a surface as well as image data. We also show with other experiments that the advantage of the method over the basic SOM is restricted to this specific type of data.

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