Modular vehicles have become an area of academic interest in the field of multi-agent systems. Modularity allows vehicles to connect and disconnect with each other mid-transit which provides a balance between efficiency and flexibility when solving complex and large scale tasks in urban or aerial transportation. This paper details a generalized scheme to route multiple modular agents on a graph to a predetermined set of target nodes. The objective is to visit all target nodes while incurring minimum resource expenditure. Agents that are joined together will incur the equivalent cost of a single agent, which is motivated by the logistical benefits of traffic reduction and increased fuel efficiency. To solve this problem, we introduce a heuristic algorithm that seeks to balance the optimality of the path that an agent takes and the cost benefit of joining agents. Our approach models the agents and targets as point charges, where the agents take the path of highest attractive force from its target node and neighboring agents. We validate our approach by simulating multiple modular agents along real-world transportation routes in the road network of Champaign-Urbana, Illinois, USA. For two vehicles, it performed equally compared to an existing modular-agent routing algorithm. Three agents were then routed using our method and the performance was benchmarked against non-modular agents using a simple shortest path policy where it performs better than the non-modular implementation 81 percent of the time. Moreover, we show that the proposed algorithm operates faster than existing routing methods for modular agents.

This paper provides a unique approach with AI algorithms to predict emerging stock markets volatility. Traditionally, stock volatility is derived from historical volatility,Monte Carlo simulation and implied volatility as well. In this paper, the writer designs a consolidated model with back-propagation neural network and genetic algorithm to predict future volatility of emerging stock markets and found that the results are quite accurate with low errors.

Abstract--This article focuses on training work carried out in artificial intelligence (AI) at the National Center for Supercomputing Applications (NCSA) at the University of Illinois Urbana-Champaign via a research experience for undergraduates (REU) program named FoDOMMaT. It also describes why we are interested in AI, and concludes by discussing what we've learned from running this program and its predecessor over six years. The National Research is an important part of the university's Center for Supercomputing Applications (NCSA) at the work: "At Illinois, our focus on research shapes our University of Illinois Urbana-Champaign is such an identity, permeates our classrooms and fuels our outreach. As a public, land-grant university, security, etc.) have been brought together for over we have the responsibility to create new knowledge 35 years to work on asking and solving some of the and new ideas and translate these into better ways of world's most challenging research questions. This is a preprint; Please cite the final paper: D. S. Most of NCSA's resources are research grants for specific Bottom-up successes can lead to changes Machine Learning Models and Tools. V. Kindratenko is in strategy, while strategic investments can support PI, D. S.

A computer built using tens of thousands of living brain cells can recognise simple patterns of light and electricity. It could eventually be incorporated into a robot that also uses living muscle tissues. Artificially intelligent algorithms inspired by the brain called neural networks have been used for everything from chatbots to searching for new laws of physics.

Intel and the National Science Foundation (NSF), joint funders of the Machine Learning for Wireless Networking Systems (MLWiNS) program, today announced recipients of awards for research projects into ultra-dense wireless systems that deliver the throughput, latency and reliability requirements of future applications – including distributed machine learning computations over wireless edge networks. Institutions: University of Illinois Urbana-Champaign and University of Washington Project Leads: Pramod Viswanath (University of Illinois Urbana-Champaign) and Sewoong Oh (University of Washington) Project Description: This project will use deep learning applications in the physical layer of communications systems, which will enable researchers to: 1) study the operation of new neural-network based, nonlinear channel codes through jointly trained encoders and decoders, 2) integrate information-theory, which can reduce the number of parameters to be learned and improve the training efficiency of communication systems, to create non-linear codes in feedback channels, and 3) design a family of non-linear neural codes for interference networks.

How can the advantages of deep learning be brought to the emerging world of embedded IoT devices? The authors discuss several core challenges in embedded and mobile deep learning, as well as recent solutions demonstrating the feasibility of building IoT applications that are powered by effective, efficient, and reliable deep learning models.