The Biological Basis of a Universal Constraint on Color Naming: Cone Contrasts and the Two-Way Categorization of Colors

Many studies have provided evidence for the existence of universal constraints on color categorization or naming in various languages, but the biological basis of these constraints is unknown. A recent study of the pattern of color categorization across numerous languages has suggested that these patterns tend to avoid straddling a region in color space at or near the border between the English composite categories of “warm” and “cool”. This fault line in color space represents a fundamental constraint on color naming. Here we report that the two-way categorization along the fault line is correlated with the sign of the L- versus M-cone contrast of a stimulus color. Moreover, we found that the sign of the L-M cone contrast also accounted for the two-way clustering of the spatially distributed neural responses in small regions of the macaque primary visual cortex, visualized with optical imaging. These small regions correspond to the hue maps, where our previous study found a spatially organized representation of stimulus hue. Altogether, these results establish a direct link between a universal constraint on color naming and the cone-specific information that is represented in the primate early visual system

Augmenting a colour lexicon

Languages differ markedly in the number of colour terms in their lexicons. The Himba, for example, a remote culture in Namibia, were reported in 2005 to have only a 5-colour term language. We re-examined their colour naming using a novel computer-based method drawing colours from across the gamut rather than only from the saturated shell of colour space that is the norm in cross-cultural colour research. Measuring confidence in communication, the Himba now have seven terms, or more properly categories, that are independent of other colour terms. Thus, we report the first augmentation of major terms, namely green and brown, to a colour lexicon in any language. A critical examination of supervised and unsupervised machine-learning approaches across the two datasets collected at different periods shows that perceptual mechanisms can, at most, only to some extent explain colour category formation and that cultural factors, such as linguistic similarity are the critical driving force for augmenting colour terms and effective colour communication

Mind and time: a local holism?

Let “Change” denote the movement in time of events from future to present to past. All versions of the A-theory of time consider Change (or a variation thereof) to be metaphysically real. Change being metaphysically real is, in terms of the A-theory, a primitive, mindindependent fact – something to be attributed just to the nature of time. But the B-theory of time considers Change to arise just with reference to an experiencing subject, being merely an apparent feature of an experiencing subject’s experience. In this thesis, I characterise this B-theoretic depiction of Change as Change obtaining relative to a “subjective temporal frame of reference”, this frame of reference being defined by the impermanent relations of futurity, presentness and pastness in which, in terms of the B-theory, events merely appear to stand to an experiencing subject. There are a number of important arguments which tell against the A-theorist’s account of mindindependent, metaphysically real Change. Whilst these arguments might not be unanswerable, many philosophers find them weighty and they do, I believe, serve to consolidate the B-theoretic position that Change arises just with reference to an experiencing subject. But this need not, I propose, mean that the B-theorist is right to claim that Change is invariably mere appearance and, as such, invariably of no metaphysical significance. Rather, I claim that, with reference to certain philosophically respectable accounts of experiencing subjects, Change being metaphysically significant is an essential prerequisite of an experiencing subject’s perceptual experience as such experience is characterised by these accounts. Equivalently, this is to claim that, with reference to these accounts of experiencing subjects, the posited subjective temporal frame of reference, and the relations of futurity, presentness and pastness which define it, are to be accorded metaphysical significance. With reference to other philosophically respectable accounts of experiencing subjects, however, this is not the case since, I claim, Change being metaphysically significant is not an essential prerequisite of perceptual experience as it is characterised by these other accounts. This therefore suggests that there is a connection between the topic of the experiencing subject, and the topic of Change. More generally, it indicates that the metaphysics of mind, and the metaphysics of time, are correlated. Indeed, my principal claim in this thesis is that mind and time are inter-defined, forming a local holism

Classifying Controversiality in Article Data

The paper presents a statistical analysis that explores methods for measuring con- troversy in online news articles collected from 23 RSS feeds. Several baseline datasets are used to re-evaluate previous work and determine the predictive quality of uni- grams for classifying controversial documents. This is achieved by comparing con-troversy and sentiment, exploring sentiment variance, and considering entropy and standard deviation as potential features. The paper tests whether there are more controversial words in negative sentiment than in positive sentiment as well as whether there are more non-controversial words in positive sentiment than in negative sen-timent. Unlike previous studies, we determine that words alone were not useful for detecting controversy as they did not provide enough context. Consequently, fur-ther analysis yields a more fruitful approach using features to detect controversy such as standard deviation and entropy. Results demonstrate that entropy and standard deviation provide greater discrimination quality compared to using posi-tive and negative sentiment to classify controversial documents. Since words alone are not enough, we perform a crowdsourcing experiment on titles to provide more context than words alone. Although the titles are more beneficial than the words, we go one step further and utilize the summaries of the articles, which provide even more context. These features, along with the improvements, provide a cleaner sep-aration of data for classifying controversial documents and may provide useful in- sight for the design of future classification models

Relational Passage of Time

This book defends a relational theory of the passage of time. The realist view of passage developed in this book differs from the robust, substantivalist position. According to relationism, passage is nothing over and above the succession of events, one thing coming after another. Causally related events are temporally arranged as they happen one after another along observers’ worldlines. There is no unique global passage but a multiplicity of local passages of time. After setting out this positive argument for relationism, the author deals with five common objections to it: (a) triviality of deflationary passage, (b) a-directionality of passage, (c) the impossibility of experiencing passage, (d) fictionalism about passage, and (e) the incompatibility of passage with perduring objects. Relational Passage of Time will appeal to scholars and advanced students working in philosophy of time, metaphysics, and philosophy of physics

COVIDHealth: A Benchmark Twitter Dataset and Machine Learning based Web Application for Classifying COVID-19 Discussions

The COVID-19 pandemic has had adverse effects on both physical and mental health. During this pandemic, numerous studies have focused on gaining insights into health-related perspectives from social media. In this study, our primary objective is to develop a machine learning-based web application for automatically classifying COVID-19-related discussions on social media. To achieve this, we label COVID-19-related Twitter data, provide benchmark classification results, and develop a web application. We collected data using the Twitter API and labeled a total of 6,667 tweets into five different classes: health risks, prevention, symptoms, transmission, and treatment. We extracted features using various feature extraction methods and applied them to seven different traditional machine learning algorithms, including Decision Tree, Random Forest, Stochastic Gradient Descent, Adaboost, K-Nearest Neighbour, Logistic Regression, and Linear SVC. Additionally, we used four deep learning algorithms: LSTM, CNN, RNN, and BERT, for classification. Overall, we achieved a maximum F1 score of 90.43% with the CNN algorithm in deep learning. The Linear SVC algorithm exhibited the highest F1 score at 86.13%, surpassing other traditional machine learning approaches. Our study not only contributes to the field of health-related data analysis but also provides a valuable resource in the form of a web-based tool for efficient data classification, which can aid in addressing public health challenges and increasing awareness during pandemics. We made the dataset and application publicly available, which can be downloaded from this link https://github.com/Bishal16/COVID19-Health-Related-Data-Classification-Website.Comment: 27 pages, 6 figure

Data-Driven Supervised Learning for Life Science Data

Life science data are often encoded in a non-standard way by means of alpha-numeric sequences, graph representations, numerical vectors of variable length, or other formats. Domain-specific or data-driven similarity measures like alignment functions have been employed with great success. The vast majority of more complex data analysis algorithms require fixed-length vectorial input data, asking for substantial preprocessing of life science data. Data-driven measures are widely ignored in favor of simple encodings. These preprocessing steps are not always easy to perform nor particularly effective, with a potential loss of information and interpretability. We present some strategies and concepts of how to employ data-driven similarity measures in the life science context and other complex biological systems. In particular, we show how to use data-driven similarity measures effectively in standard learning algorithms