Universal fluctuations in growth dynamics of economic systems

The growth of business firms is an example of a system of complex interacting units that resembles complex interacting systems in nature such as earthquakes. Remarkably, work in econophysics has provided evidence that the statistical properties of the growth of business firms follow the same sorts of power laws that characterize physical systems near their critical points. Given how economies change over time, whether these statistical properties are persistent, robust, and universal like those of physical systems remains an open question. Here, we show that the scaling properties of firm growth previously demonstrated for publicly-traded U.S. manufacturing firms from 1974 to 1993 apply to the same sorts of firms from 1993 to 2015, to firms in other broad sectors (such as materials), and to firms in new sectors (such as Internet services). We measure virtually the same scaling exponent for manufacturing for the 1993 to 2015 period as for the 1974 to 1993 period and virtually the same scaling exponent for other sectors as for manufacturing. Furthermore, we show that fluctuations of the growth rate for new industries self-organize into a power law over relatively short time scales.Comment: 15 pages, 7 figure

Piece of Cake: It’s Just a Small Structure Replacement Right?

Successfully delivering a project, task, or even a group decision requires effective collaboration skills, and the great thing about skills is that they can be learned. With collaboration, you can effectively lead a team toward decision making and deliver results. In this session, we will analyze different methods of collaboration to help you build this skill and make you a more persuasive and compelling project manager

Anti-TB and Antibacterial Activities of Natural Products Extracts

Samples of numerous plant species were received from the southwestern part of the USA from Richard Spjut, and plant samples were collected here in Illinois. All were extracted with typical solvents, giving crude residues, some of which were subjected to counter-current or flash chromatographic methods. Some of the crude extracts and chromatographic fractions had anti-tuberculosis and/or antibacterial activity. In a general way, bioactive natural products are dealt with very well by Liang & Fang, 2006. More specifically, the southwestern part of the United States has a large variety of indigenous plants, many of which have not been investigated for their medicinal potential, and only very few have had their extracts separated into the individual compounds they may contain. But some information is available for Native American herbal uses (Moerman, 2003)

Not so different after all: The same oscillatory processes support different types of attention

n/

Natural Language Video Processing (Machine Learning-based Identification, Search, Extraction)

The systems and methods described herein provide for a natural-language to generative video process using language-classification and knowledge graph identifiers to find frame segments within a codex of videos that depict the scene described. The mechanics necessary for converting speech to meaningful entities that can be matched to non-text images/videos serve as an underlying basis for generative video

Graph Contrastive Learning for Materials

Recent work has shown the potential of graph neural networks to efficiently predict material properties, enabling high-throughput screening of materials. Training these models, however, often requires large quantities of labelled data, obtained via costly methods such as ab initio calculations or experimental evaluation. By leveraging a series of material-specific transformations, we introduce CrystalCLR, a framework for constrastive learning of representations with crystal graph neural networks. With the addition of a novel loss function, our framework is able to learn representations competitive with engineered fingerprinting methods. We also demonstrate that via model finetuning, contrastive pretraining can improve the performance of graph neural networks for prediction of material properties and significantly outperform traditional ML models that use engineered fingerprints. Lastly, we observe that CrystalCLR produces material representations that form clusters by compound class.Comment: 7 pages, 3 figures, NeurIPS 2022 AI for Accelerated Materials Design Worksho

Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models

The energy requirements of current natural language processing models continue to grow at a rapid, unsustainable pace. Recent works highlighting this problem conclude there is an urgent need for methods that reduce the energy needs of NLP and machine learning more broadly. In this article, we investigate techniques that can be used to reduce the energy consumption of common NLP applications. In particular, we focus on techniques to measure energy usage and different hardware and datacenter-oriented settings that can be tuned to reduce energy consumption for training and inference for language models. We characterize the impact of these settings on metrics such as computational performance and energy consumption through experiments conducted on a high performance computing system as well as popular cloud computing platforms. These techniques can lead to significant reduction in energy consumption when training language models or their use for inference. For example, power-capping, which limits the maximum power a GPU can consume, can enable a 15\% decrease in energy usage with marginal increase in overall computation time when training a transformer-based language model

Leader Development and Emotional Competence: Authentic Leadership, Self-Awareness, and Personal Integrity

Can leadership be taught? (Doh, 2003) The dramatic growth of leadership academies, institutes, and programs from political science to business and psychology to reserve officer training corps for U.S. military services makes this a compelling question. We say yes and then: How can it be taught? And how are leaders developed? Ours is a developmental theory for teaching leaders and suggests that emotional competence is at the heart of developing authentic leaders with personal integrity