Rethinking agency in language and society

The notion of agency is typically understood as stemming from the goals and desires of human actors. This is an assumption that has been taken on board in the study of language in society as well. In this article, I point out the problems with this assumption as well as another: the tendency to downplay if not dismiss the roles of non-human entities. I argue that these points about agency carry serious implications for the study of language in society. It is undeniable that various technological advancements ranging from relatively simple computer programs to highly developed artificial intelligence (AI) are increasingly involved in our use of language for communication. These are cases where the human element is increasingly distant from the use of language for communicative purposes. They pose conceptual challenges for the study of language in society and require a willingness to rethink the nature of agency.

ANALYTIC SPREAD AND INTEGRAL CLOSURE OF INTEGRALLY DECOMPOSABLE MODULES

We relate the analytic spread of a module expressed as the direct sum of two submodules with the analytic spread of its components. We also study a class of submodules whose integral closure can be expressed in terms of the integral closure of its row ideals, and therefore can be obtained by means of a simple computer algebra procedure. In particular, we analyze a class of modules, not necessarily of maximal rank, whose integral closure is determined by the family of Newton polyhedra of their row ideals.

Simulating Systems Thinking under Bounded Rationality

Brian Arthur’s El Farol bar model of bounded rationality provides a simple computer model of decision making in a complex, dynamic, and self-organized environment. Can systems thinking provide a viable alternative strategy to traditional methods for dealing with these types of problems? Nine different agents, designed from both traditional and systems perspectives, compete in fifteen variants of the El Farol environment and their performance in 4 categories—Winner, Top Performers, Competitive, and Vulnerable—is compared. We show that systems thinking is a competitive strategy that is, at least, on par with traditional strategies and may be less vulnerable to elimination or ruin. However, there are two consequential elements that emerge. First, all strategies have some environments where they succeed and others where they fail. Second, as the population of practitioners adopts these adaptive, systems-based strategies, the environment exhibits new behaviors with a new set of unintended consequences.

Simple Computer Vision Algorithm Production using OpenCV for “Virtual Ecosystem” project.

The main purposes of this research is to produce aComputer Vision (CV) algorithm using various library in pythonprogramming language (mainly OpenCV, Numpy, and ZBar) toautomate extraction and process information from an analogdrawing into a digital image to be used in the “Virtual Ecosystem”project. The Computer Vision step will include detecting datawithin the analog drawing using QR-code, determine drawingarea, replace white background with certain threshold withtransparency, and finally save the digital image following requiredratio.

A simple computer vision algorithm as a clinical aid for the pathologist

Histological analysis for cancer detection or stratification is performed by observing and examining a small portion of a biopsied tissue under a microscope. Nevertheless, to assign clinical meaning to the findings, the analysis and interpretation of an experienced Pathologist is always necessary. Using high-resolution images, these experts visually examine the sample looking for specific characteristics on the cell shapes and tissue distributions, so they could decide whether tissue regions are cancerous, and establish the malignancy level of it. However, with the increasing demand for work for those pathologists and the importance of accuracy on diagnostics, multiple attempts to simplify their work have been performed. Current Imaging technologies allow novel horizons in the automatized selection of some of the characteristics that indicate malignancy in a biopsy. In this work, we propose a simple computer vision algorithm that can be implemented as a screening method for focusing in histological areas with higher risk of malignancy saving time to the pathologist and helping to perform a more standardized work, an easy observation with the potential to become in an aid to daily clinical work.