Computer modeling, complex biological systems, and their simplifications

1980 ◽  
Vol 239 (1) ◽  
pp. R1-R6
Author(s):  
D. Garfinkel
Keyword(s):  
Biological System ◽  
Short Term Memory ◽  
Human Mind ◽  
Social Processes ◽  
Short Term ◽  
Term Memory ◽  
Amount Of Information ◽  
The Social ◽  
Complex Nonlinear Systems ◽  
The Mind

The amount of information defining a biological system, as specified in its genome, is vastly larger than the amount of information the human mind can handle simultaneously in its short-term memory (7 +/- 2 items at most). In such a situation the mind tends to simplify, linearize, and consider only a few of many variables that may be involved. This may be limiting when an experimenter interprets his own experiments without help from theory or modeling as is common in biology. The Michaelis-Menten model, which is very useful although not necessarily valid, and its linearizations are described as an example of this. The social processes and obligations involved in simplifying complex situations are discussed. Computer simulation provides a method for investigating complex nonlinear systems that does not require excessive simplification of the biological system being studied and whose economics are becoming steadily more favorable.

scholarly journals The Mind and Brain of Short-Term Memory

2008 ◽  
Vol 59 (1) ◽  
pp. 193-224 ◽  
Author(s):  
John Jonides ◽  
Richard L. Lewis ◽  
Derek Evan Nee ◽  
Cindy A. Lustig ◽  
Marc G. Berman ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Mind And Brain ◽  
The Mind

Where Are They Going? Predicting Human Behaviors in Crowded Scenes

2021 ◽  
Vol 17 (4) ◽  
pp. 1-19
Author(s):  
Bo Zhang ◽  
Rui Zhang ◽  
Niccolo Bisagno ◽  
Nicola Conci ◽  
Francesco G. B. De Natale ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Behavior Prediction ◽  
Short Term ◽  
Memory Module ◽  
Term Memory ◽  
The Social ◽  
Spatial Configurations ◽  
Long Short Term Memory ◽  
Crowd Behaviors ◽  
Crowded Scenes

In this article, we propose a framework for crowd behavior prediction in complicated scenarios. The fundamental framework is designed using the standard encoder-decoder scheme, which is built upon the long short-term memory module to capture the temporal evolution of crowd behaviors. To model interactions among humans and environments, we embed both the social and the physical attention mechanisms into the long short-term memory. The social attention component can model the interactions among different pedestrians, whereas the physical attention component helps to understand the spatial configurations of the scene. Since pedestrians’ behaviors demonstrate multi-modal properties, we use the generative model to produce multiple acceptable future paths. The proposed framework not only predicts an individual’s trajectory accurately but also forecasts the ongoing group behaviors by leveraging on the coherent filtering approach. Experiments are carried out on the standard crowd benchmarks (namely, the ETH, the UCY, the CUHK crowd, and the CrowdFlow datasets), which demonstrate that the proposed framework is effective in forecasting crowd behaviors in complex scenarios.

scholarly journals Acoustics and Resonance in Poetry: The Psychological Reality of Rhyme in Baudelaire’s “Les Chats”

2021 ◽  
Vol 8 (1) ◽  
pp. 7-39
Author(s):  
Reuven Tsur
Keyword(s):  
Eye Movement ◽  
Short Term Memory ◽  
Human Mind ◽  
Auditory Information ◽  
Cognitive Structures ◽  
Syntactic Complexity ◽  
Short Term ◽  
Term Memory ◽  
Psychological Reality ◽  
Visual Phenomenon

This article uses the term “psychological reality” in this sense: the extent to which the constructs of linguistic theory can be taken to have a basis in the human mind, i.e., to somehow be reflected in human cognitive structures. This article explores the human cognitive structures in which the constructs of phonetic theory may be reflected. The last section is a critique of the psychological reality of sound patterns in Baudelaire’s “Les Chats”, as discussed in three earlier articles. In physical terms, it defines “resonant” as “tending to reinforce or prolong sounds, especially by synchronous vibration”. In phonetic terms it defines “resonant” as “where intense precategorical auditory information lingers in short-term memory”. The effect of rhyme in poetry is carried by similar overtones vibrating in the rhyme fellows, resonating like similar overtones on the piano. In either case, we do not compare overtones item by item, just hear their synchronous vibration. I contrast this conception to three approaches: one that points out similar sounds of “internal rhymes”, irrespective of whether they may be contained within the span of short-term memory (i.e., whether they may have psychological relit); one that claims that syntactic complexity may cancel the psychological reality of “internal rhymes” (whereas I claim that it merely backgrounds rhyme); and one that found through an eye-tracking experiment that readers fixate longer on verse-final rhymes than on other words, assuming regressive eye-movement (I claim that rhyme is an acoustic not visual phenomenon; and that there is a tendency to indicate discontinuation by prolonging the last sounds in ordinary speech and blank verse too, as well as in music — where no rhyme is involved).

scholarly journals Parallelisms in Arabic

2014 ◽  
Vol 2 (11) ◽  
pp. 68-87
Author(s):  
Hisham Monassar
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Modern Standard Arabic ◽  
Term Memory ◽  
Standard Arabic ◽  
Context Dependent ◽  
The Mind ◽  
Classical Arabic ◽  
Modern Standard ◽  
Opinion Writing

Parallelism in Arabic is investigated through data from three Arabic varieties: Modern Standard Arabic (MSA), Classical Arabic (CA), and (Yemeni) Adeni Arabic (AA). Parallelism in Arabic is examined at different linguistic levels: morphological and lexical, syntactic, and textual. Parallelism seems to be inherent and is more likely in writings that aim to convince or restate theses and topics. However, the occurrence of parallelisms is genre-specific, purpose-oriented, and situation/context-dependent. It is predictable in sermons, public speeches/addresses, and opinion writing. Apparently, parallelism, particularly beyond reduplication and lexical level, triggers resonance in the mind of the listener/reader, retaining the respective information in short term memory and thus marking it for emphasis.

scholarly journals Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection

2020 ◽  
Vol 14 (2) ◽  
pp. 169
Author(s):  
Auliya Rahman Isnain ◽  
Agus Sihabuddin ◽  
Yohanes Suyanto
Keyword(s):  
Social Media ◽  
Hate Speech ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Speech Detection ◽  
Feature Extraction Method ◽  
The Social ◽  
Long Short Term Memory ◽  
Hidden Layer

Currently, the discussion about hate speech in Indonesia is warm, primarily through social media. Hate speech is communication that disparages a person or group based on characteristics such as (race, ethnicity, gender, citizenship, religion and organization). Twitter is one of the social media that someone uses to express their feelings and opinions through tweets, including tweets that contain expressions of hatred because Twitter has a significant influence on the success or destruction of one's image.This study aims to detect hate speech or not hate Indonesian speech tweets by using the Bidirectional Long Short Term Memory method and the word2vec feature extraction method with Continuous bag-of-word (CBOW) architecture. For testing the BiLSTM purpose with the calculation of the value of accuracy, precision, recall, and F-measure.The use of word2vec and the Bidirectional Long Short Term Memory method with CBOW architecture, with epoch 10, learning rate 0.001 and the number of neurons 200 on the hidden layer, produce an accuracy rate of 94.66%, with each precision value of 99.08%, recall 93, 74% and F-measure 96.29%. In contrast, the Bidirectional Long Short Term Memory with three layers has an accuracy of 96.93%. The addition of one layer to BiLSTM increased by 2.27%.

scholarly journals What’s in a chunk? Chunking and data compression in verbal short-term memory

2018 ◽  
Author(s):  
Dennis Graham Norris ◽  
Kristjan Kalm
Keyword(s):  
Data Compression ◽  
Verbal Memory ◽  
Short Term Memory ◽  
Memory Capacity ◽  
Information Capacity ◽  
Long Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Amount Of Information

Short-term verbal memory is improved when words in the input can be chunked into larger units. Miller (1956) suggested that the capacity of verbal short-term memory is determined by the number of chunks that can be stored in memory, and not by the number of items or the amount of information. But how does the improvement due to chunking come about? Is memory really determined by the number of chunks? One possibility is that chunking is a form of data compression. Chunking allows more information to be stored in the available capacity. An alternative is that chunking operates primarily by redintegration. Chunks exist only in long-term memory, and enable items in the input which correspond to chunks to be reconstructed more reliably from a degraded trace. We review the data favoring each of these views and discuss the implications of treating chunking as data compression. Contrary to Miller, we suggest that memory capacity is primarily determined by the amount of information that can be stored. However, given the limitations on the representations that can be stored in verbal short-term memory, chunking can sometimes allow the information capacity of short-term memory to be exploited more efficiently.

scholarly journals Predicting Geolocation of Tweets: Using Combination of CNN and BiLSTM

2021 ◽  
Author(s):  
Rhea Mahajan ◽  
Vibhakar Mansotra
Keyword(s):  
Neural Network ◽  
Social Behavior ◽  
Social Networking ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
The Social ◽  
Median Error ◽  
Long Short Term Memory ◽  
The City

AbstractTwitter is one of the most popular micro-blogging and social networking platforms where users post their opinions, preferences, activities, thoughts, views, etc., in form of tweets within the limit of 280 characters. In order to study and analyse the social behavior and activities of a user across a region, it becomes necessary to identify the location of the tweet. This paper aims to predict geolocation of real-time tweets at the city level collected for a period of 30 days by using a combination of convolutional neural network and a bidirectional long short-term memory by extracting features within the tweets and features associated with the tweets. We have also compared our results with previous baseline models and the findings of our experiment show a significant improvement over baselines methods achieving an accuracy of 92.6 with a median error of 22.4 km at city level prediction.

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