Reading, Writing and Power

This article’s goal is to discuss the relationship between reading, writing and power. (Unfortunately, space forbids more than an introduction to this topic.) We live in a postmodern society where some people doubt whether truth is knowable. I will show that the power of the written word is great enough so that receptive readers can overcome the barriers against understanding it as the author meant it, and therefore reading can teach the truth. Obviously, this statement will be qualified at a later point. While this article will center on power, it is inseparable from the other traits of literature.The biblical term “Word” often means both the written and the spoken word. Much, if not all, I say will apply to the word spoken as well as the word written. I will begin by listing a few biblical texts that show the relationship between the Word and power

The Expressive Power of Word Embeddings

We seek to better understand the difference in quality of the several publicly released embeddings. We propose several tasks that help to distinguish the characteristics of different embeddings. Our evaluation of sentiment polarity and synonym/antonym relations shows that embeddings are able to capture surprisingly nuanced semantics even in the absence of sentence structure. Moreover, benchmarking the embeddings shows great variance in quality and characteristics of the semantics captured by the tested embeddings. Finally, we show the impact of varying the number of dimensions and the resolution of each dimension on the effective useful features captured by the embedding space. Our contributions highlight the importance of embeddings for NLP tasks and the effect of their quality on the final results.Comment: submitted to ICML 2013, Deep Learning for Audio, Speech and Language Processing Workshop. 8 pages, 8 figure

Transition Property for α\alpha-Power Free Languages with α≥2\alpha\geq 2 and k≥3k\geq 3 Letters

In 1985, Restivo and Salemi presented a list of five problems concerning power free languages. Problem $4$ states: Given $\alpha$-power-free words $u$ and $v$, decide whether there is a transition from $u$ to $v$. Problem $5$ states: Given $\alpha$-power-free words $u$ and $v$, find a transition word $w$, if it exists. Let $\Sigma_k$ denote an alphabet with $k$ letters. Let $L_{k,\alpha}$ denote the $\alpha$-power free language over the alphabet $\Sigma_k$, where $\alpha$ is a rational number or a rational "number with $+$". If $\alpha$ is a "number with $+$" then suppose $k\geq 3$ and $\alpha\geq 2$. If $\alpha$ is "only" a number then suppose $k=3$ and $\alpha>2$ or $k>3$ and $\alpha\geq 2$. We show that: If $u\in L_{k,\alpha}$ is a right extendable word in $L_{k,\alpha}$ and $v\in L_{k,\alpha}$ is a left extendable word in $L_{k,\alpha}$ then there is a (transition) word $w$ such that $uwv\in L_{k,\alpha}$. We also show a construction of the word $w$

Low-power Programmable Processor for Fast Fourier Transform Based on Transport Triggered Architecture

This paper describes a low-power processor tailored for fast Fourier transform computations where transport triggering template is exploited. The processor is software-programmable while retaining an energy-efficiency comparable to existing fixed-function implementations. The power savings are achieved by compressing the computation kernel into one instruction word. The word is stored in an instruction loop buffer, which is more power-efficient than regular instruction memory storage. The processor supports all power-of-two FFT sizes from 64 to 16384 and given 1 mJ of energy, it can compute 20916 transforms of size 1024.Comment: 5 pages, 4 figures, 1 table, ICASSP 2019 conferenc

From Pronoun to Identity: Tracing the History of the Word Otaku

Words are vessels of power; the power to convey meaning. Words can give shape to the identity of a group, and the same word can stereotype one for generations. This research paper is about one such word – ‘Otaku’. The aim of this research paper is to trace the history of the term ‘otaku’, and understand how it evolved from a second person pronoun to a term that identifies an entire subcultur

Fascism: A Review of Its History and Its Present Cultural Reality in the Americas

The Italians may have given us the word “fascismo,” but whether we use that word or the Spanish ”falangismo” or the German “National Socialism” (Naziism) we are talking about a form of social organization which has a complex history. Indeed, many persons wrongly believe that fascism as a political system first achieved state power in Italy in the 1920s. However, fascism in modern times first achieved independent (sovereign) power in the Americas -- in the Argentina of Juan Manuel de Rosas (1830s) and in the Confederate States of America (1860-1865)

Producing power-law distributions and damping word frequencies with two-stage language models

Standard statistical models of language fail to capture one of the most striking properties of natural languages: the power-law distribution in the frequencies of word tokens. We present a framework for developing statisticalmodels that can generically produce power laws, breaking generativemodels into two stages. The first stage, the generator, can be any standard probabilistic model, while the second stage, the adaptor, transforms the word frequencies of this model to provide a closer match to natural language. We show that two commonly used Bayesian models, the Dirichlet-multinomial model and the Dirichlet process, can be viewed as special cases of our framework. We discuss two stochastic processes-the Chinese restaurant process and its two-parameter generalization based on the Pitman-Yor process-that can be used as adaptors in our framework to produce power-law distributions over word frequencies. We show that these adaptors justify common estimation procedures based on logarithmic or inverse-power transformations of empirical frequencies. In addition, taking the Pitman-Yor Chinese restaurant process as an adaptor justifies the appearance of type frequencies in formal analyses of natural language and improves the performance of a model for unsupervised learning of morphology.48 page(s