Based on considerable neurophysiological evidence, Roy (2012) proposed the theory that localist representation is widely used in the brain, starting from the lowest levels of processing. Grandmother cells are a special case of localist representation. In this article, I present the theory that grandmother cells are also widely used in the brain. To support the proposed theory, I present neurophysiological evidence and an analysis of the concept of grandmother cells. Konorski (1967) first predicted the existence of grandmother cells (he called them “gnostic” neurons) - single neurons that respond to complex stimuli such as faces, hands, expressions, objects, and so on. The term “grandmother cell” was introduced by Jerry Lettvin in 1969 (Barlow, 1995).

This study is an attempt to use group information collected on climate change from farmers in eastern Uttar Pradesh, India to address a key question related to climate change policy: How to encourage farmers to adapt to climate change? First, we investigate farmers’ perception of and adaptation to climate change using content analysis and group information. The findings are then compared with climatic and agriculture information collected through secondary sources. Results suggest that though farmers are aware of long-term changes in climatic factors (temperature and rainfall, for example), they are unable to identify these changes as climate change. Farmers are also aware of risks generated by climate variability and extreme climatic events. However, farmers are not taking concrete steps in dealing with perceived climatic changes, although we find out that farmers are changing their agricultural and farming practices. These included changing sowing and harvesting timing, cultivation of crops of short duration varieties, inter-cropping, changing cropping pattern, investment in irrigation, and agroforestry. Note that these changes may be considered as passive response or adaptation strategies to climate change. Perhaps farmers are implicitly taking initiatives to adapt climate change. Finally, the paper suggests some policy interventions to scale up adaptation to climate change in Indian agriculture.

With the advent of high-dimensional stored big data and streaming data, suddenly machine learning on a very large scale has become a critical need. Such machine learning should be extremely fast, should scale up easily with volume and dimension, should be able to learn from streaming data, should automatically perform dimension reduction for high-dimensional data, and should be deployable on hardware. Neural networks are well positioned to address these challenges of large scale machine learning. In this paper, we present a method that can effectively handle large scale, high-dimensional data. It is an online method that can be used for both streaming and large volumes of stored big data. It primarily uses Kohonen nets, although only a few selected neurons (nodes) from multiple Kohonen nets are actually retained in the end; we discard all Kohonen nets after training. We use Kohonen nets both for dimensionality reduction through feature selection and for building an ensemble of classifiers using single Kohonen neurons. The method is meant to exploit massive parallelism and should be easily deployable on hardware that implements Kohonen nets. Some initial computational results are presented.

The debate about representation in the brain and the nature of the cognitive system has been going on for decades now. This paper examines the neurophysiological evidence, primarily from single cell recordings, to get a better perspective on both the issues. After an initial review of some basic concepts, the paper reviews the data from single cell recordings – in cortical columns and of category-selective and multisensory neurons. In neuroscience, columns in the neocortex (cortical columns) are understood to be a basic functional/computational unit. The paper reviews the fundamental discoveries about the columnar organization and finds that it reveals a massively parallel search mechanism. This columnar organization could be the most extensive neurophysiological evidence for the widespread use of localist representation in the brain. The paper also reviews studies of category-selective cells. The evidence for category-selective cells reveals that localist representation is also used to encode complex abstract concepts at the highest levels of processing in the brain. A third major issue is the nature of the cognitive system in the brain and whether there is a form that is purely abstract and encoded by single cells. To provide evidence for a single-cell based purely abstract cognitive system, the paper reviews some of the findings related to multisensory cells. It appears that there is widespread usage of multisensory cells in the brain in the same areas where sensory processing takes place. Plus there is evidence for abstract modality invariant cells at higher levels of cortical processing. Overall, that reveals the existence of a purely abstract cognitive system in the brain. The paper also argues that since there is no evidence for dense distributed representation and since sparse representation is actually used to encode memories, there is actually no evidence for distributed representation in the brain. Overall, it appears that, at an abstract level, the brain is a massively parallel, distributed computing system that is symbolic. The paper also explains how grounded cognition and other theories of the brain are fully compatible with localist representation and a purely abstract cognitive system.

Increasing levels of financial inequality prompt questions about the relationship between income and well-being. Using a twins sample from the Survey of Midlife Development in the U. S. and controlling for personality as core self-evaluations (CSE), we found that men, but not women, had higher subjective financial well-being (SFWB) when they had higher incomes. This relationship was due to ‘unshared environmental’ factors rather than genes, suggesting that the effect of income on SFWB is driven by unique experiences among men. Further, for women and men, we found that CSE influenced income and SFWB, and that both genetic and environmental factors explained this relationship. Given the relatively small and male-specific relationship between income and SFWB, and the determination of both income and SFWB by personality, we propose that policy makers focus on malleable factors beyond merely income in order to increase SFWB, including financial education and building self-regulatory capacity.

Theory suggests that human behavior has implications for disease spread. We examine the hypothesis that individuals engage in voluntary defensive behavior during an epidemic. We estimate the number of passengers missing previously purchased flights as a function of concern for swine flu or A/H1N1 influenza using 1.7 million detailed flight records, Google Trends, and the World Health Organization's FluNet data. We estimate that concern over “swine flu,” as measured by Google Trends, accounted for 0.34% of missed flights during the epidemic. The Google Trends data correlates strongly with media attention, but poorly (at times negatively) with reported cases in FluNet. Passengers show no response to reported cases. Passengers skipping their purchased trips forwent at least $50 M in travel related benefits. Responding to actual cases would have cut this estimate in half. Thus, people appear to respond to an epidemic by voluntarily engaging in self-protection behavior, but this behavior may not be responsive to objective measures of risk. Clearer risk communication could substantially reduce epidemic costs. People undertaking costly risk reduction behavior, for example, forgoing nonrefundable flights, suggests they may also make less costly behavior adjustments to avoid infection. Accounting for defensive behaviors may be important for forecasting epidemics, but linking behavior with epidemics likely requires consideration of risk communication.

Reliable estimates of the impacts and costs of biological invasions are critical to developing credible management, trade and regulatory policies. Worldwide, forests and urban trees provide important ecosystem services as well as economic and social benefits, but are threatened by non-native insects. More than 450 non-native forest insects are established in the United States but estimates of broad-scale economic impacts associated with these species are largely unavailable. We developed a novel modeling approach that maximizes the use of available data, accounts for multiple sources of uncertainty, and provides cost estimates for three major feeding guilds of non-native forest insects. For each guild, we calculated the economic damages for five cost categories and we estimated the probability of future introductions of damaging pests. We found that costs are largely borne by homeowners and municipal governments. Wood- and phloem-boring insects are anticipated to cause the largest economic impacts by annually inducing nearly $1.7 billion in local government expenditures and approximately $830 million in lost residential property values. Given observations of new species, there is a 32% chance that another highly destructive borer species will invade the U.S. in the next 10 years. Our damage estimates provide a crucial but previously missing component of cost-benefit analyses to evaluate policies and management options intended to reduce species introductions. The modeling approach we developed is highly flexible and could be similarly employed to estimate damages in other countries or natural resource sectors.

A two-part presentation from the ASU Library and Knowledge Enterprise Research Data Management Office. Presented at the 2023 Rocky Mountain Advanced Computing Consortium (RMACC).

Session 1: Data management planning is an integral step in the research data life cycle. Large amounts of data and lengthy code accompanying supercomputing runs are no exception. Planning before analysis will benefit research and the researcher by providing a clear strategy for collecting, storing, analyzing, and sharing the data at the end of the research cycle. Supercomputing can require significant storage beyond scratch space, but researchers typically need to be informed of what tools are appropriate and available. Framed within the planning phase of the life cycle, this presentation presents ASU’s Storage Selector as a quick and easy tool to find the most appropriate storage resources provided by the university to help researchers choose a proper storage and management solution for their research data at the right time in their project. We will also explore the DMP Tool, developed by the California Digital Library, which provides a resource-rich platform for writing data management plans, including institutional-specific guidance, feedback request, and public plans that can be used as guides.

Session 2: This presentation overviews the ongoing working relationship between the ASU Library Open Science and Scholarly Communication division, Research Data Management Office, and Research Computing. We will explore these teams’ interdisciplinary relationships and interdependence as the institution increasingly supports open science practices and initiatives. We will include case studies regarding the decision-making process, data-sharing decisions, and opportunities and challenges when transferring research data from a high-performance computing environment to the ASU Research Data Repository. Finally, we will share lessons learned as we intentionally shepherd research data from active project management and storage to final publication and preservation.

(Preprint.) Today's college and university learning landscapes are dynamic and
characterized by increased student demand for highly flexible and self-paced online learning opportunities. Recent fiscal conditions in higher education make learning landscape development more challenging due to finite resources and competing priorities. Similarly, academic libraries are experiencing substantial budget and staff reductions. Despite these trends, academic libraries are in a strong position to contribute to surrounding learning landscapes by expanding student online learning opportunities and promoting the critical use of information. Evolving learning technologies available for free or at low cost provide higher education and libraries with the tools to respond to this fluid environment.