50 research outputs found
Decoding Social Influence and the Wisdom of the Crowd in Financial Trading Network
In this paper, we study roles of social mechanisms in a financial system. Our data come from a novel on-line foreign exchange trading brokerage for individual investors, which also allows investors to form social network ties between each other and copy others' trades. From the dataset, we analyze the dynamics of this connected social influence systems in decision making processes. We discover that generally social trades outperform individual trades, but the social reputation of the top traders is not completely determined by their performance due to social feedback even when users are betting their own money. We also find that social influence plays a significant role in users' trades, especially decisions during periods of uncertainty. We report evidences suggesting that the dynamics of social influence contribute to market overreaction
From Microbes to Methane: AI-Based Predictive Modeling of Feed Additive Efficacy in Dairy Cows
In an era of increasing pressure to achieve sustainable agriculture, the
optimization of livestock feed for enhancing yield and minimizing environmental
impact is a paramount objective. This study presents a pioneering approach
towards this goal, using rumen microbiome data to predict the efficacy of feed
additives in dairy cattle.
We collected an extensive dataset that includes methane emissions from 2,190
Holstein cows distributed across 34 distinct sites. The cows were divided into
control and experimental groups in a double-blind, unbiased manner, accounting
for variables such as age, days in lactation, and average milk yield. The
experimental groups were administered one of four leading commercial feed
additives: Agolin, Kexxtone, Allimax, and Relyon. Methane emissions were
measured individually both before the administration of additives and over a
subsequent 12-week period. To develop our predictive model for additive
efficacy, rumen microbiome samples were collected from 510 cows from the same
herds prior to the study's onset. These samples underwent deep metagenomic
shotgun sequencing, yielding an average of 15.7 million reads per sample.
Utilizing innovative artificial intelligence techniques we successfully
estimated the efficacy of these feed additives across different farms. The
model's robustness was further confirmed through validation with independent
cohorts, affirming its generalizability and reliability.
Our results underscore the transformative capability of using targeted feed
additive strategies to both optimize dairy yield and milk composition, and to
significantly reduce methane emissions. Specifically, our predictive model
demonstrates a scenario where its application could guide the assignment of
additives to farms where they are most effective. In doing so, we could achieve
an average potential reduction of over 27\% in overall emissions.Comment: 51 pages, 24 figures, 11 tables, 93 reference
Static and expanding grid coverage with ant robots: Complexity results
AbstractIn this paper we study the strengths and limitations of collaborative teams of simple agents. In particular, we discuss the efficient use of āant robotsā for covering a connected region on the Z2 grid, whose area is unknown in advance, and which expands at a given rate, where n is the initial size of the connected region. We show that regardless of the algorithm used, and the robotsā hardware and software specifications, the minimal number of robots required in order for such a coverage to be possible is Ī©(n). In addition, we show that when the region expands at a sufficiently slow rate, a team of Ī(n) robots could cover it in at most O(n2lnn) time. This completion time can even be achieved by myopic robots, with no ability to directly communicate with each other, and where each robot is equipped with a memory of size O(1) bits w.r.t. the size of the region (therefore, the robots cannot maintain maps of the terrain, nor plan complete paths). Regarding the coverage of non-expanding regions in the grid, we improve the current best known result of O(n2) by demonstrating an algorithm that guarantees such a coverage with completion time of O(1kn1.5+n) in the worst case, and faster for shapes of perimeter length which is shorter than O(n)