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Matt Barker

Publications and source records attributed to Matt Barker.

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Disrupt Your Research Using Generative AI Powered ScienceSage

Large Language Models (LLM) are disrupting science and research in different subjects and industries. Here we report a minimum-viable-product (MVP) web application called $\textbf{ScienceSage}$. It leverages generative artificial intelligence (GenAI) to help researchers disrupt the speed, magnitude and scope of product innovation. $\textbf{ScienceSage}$ enables researchers to build, store, update and query a knowledge base (KB). A KB codifies user's knowledge/information of a given domain in both vector index and knowledge graph (KG) index for efficient information retrieval and query. The knowledge/information can be extracted from user's textual documents, images, videos, audios and/or the research reports generated based on a research question and the latest relevant information on internet. The same set of KBs interconnect three functions on $\textbf{ScienceSage}$: 'Generate Research Report', 'Chat With Your Documents' and 'Chat With Anything'. We share our learning to encourage discussion and improvement of GenAI's role in scientific research.

cs.IR

Motion Classification Based on Harmonic Micro-Doppler Signatures Using a Convolutional Neural Network

We demonstrate the classification of common motions of held objects using the harmonic micro-Doppler signatures scattered from harmonic radio-frequency tags. Harmonic tags capture incident signals and retransmit at harmonic frequencies, making them easier to distinguish from clutter. We characterize the motion of tagged handheld objects via the time-varying frequency shift of the harmonic signals (harmonic Doppler). With complex micromotions of held objects, the time-frequency response manifests complex micro-Doppler signatures that can be used to classify the motions. We developed narrow-band harmonic tags at 2.4/4.8 GHz that support frequency scalability for multi-tag operation, and a harmonic radar system to transmit a 2.4 GHz continuous-wave signal and receive the scattered 4.8 GHz harmonic signal. Experiments were conducted to mimic four common motions of held objects from 35 subjects in a cluttered indoor environment. A 7-layer convolutional neural network (CNN) multi-label classifier was developed and obtained a real time classification accuracy of 94.24%, with a response time of 2 seconds per sample with a data processing latency of less than 0.5 seconds.

eess.SP

A mean field game model of firm--level innovation

Knowledge spillovers occur when a firm researches a new technology and that technology is adapted or adopted by another firm, resulting in a social value of the technology that is larger than the initially predicted private value. As a result, firms systematically under--invest in research compared with the socially optimal investment strategy. Understanding the level of under--investment, as well as policies to correct it, is an area of active economic research. In this paper, we develop a new model of spillovers, taking inspiration from the available microeconomic data. We prove existence and uniqueness of solutions to the model, and we conduct some initial simulations to understand how indirect spillovers contribute to the productivity of a sector.

math.OC

Comparing the best reply strategy and mean field games: the stationary case

Mean field games (MFGs) and the best reply strategy (BRS) are two methods of describing competitive optimisation of systems of interacting agents. The latter can be interpreted as an approximation of the respective MFG system. In this paper we present a systematic analysis and comparison of the two approaches in the stationary case. We provide novel existence and uniqueness results for the stationary boundary value problems related to the MFG and BRS formulations, and we present an analytical and numerical comparison of the two paradigms in a variety of modelling situations.

math.OC

From mean field games to the best reply strategy in a stochastic framework

This paper builds on the work of Degond, Herty and Liu by considering N-player stochastic differential games. The control corresponding to a Nash equilibrium of such a game is approximated through model predictive control (MPC) techniques. In the case of a linear quadratic running-cost, considered here, the MPC method is shown to approximate the solution to the control problem by the best reply strategy (BRS) for the running cost. We then compare the MPC approach when taking the mean field limit with the popular mean field game (MFG) strategy. We find that our MPC approach reduces the two coupled PDEs to a single PDE, greatly increasing the simplicity and tractability of the original problem. We give two examples of applications of this approach to previous literature and conclude with future perspectives for this research.

math.OC