MULTI-AGENT SYSTEMS: OBSERVABILITY, CLASSIFICATION AND CLUSTERING PREDICTION

This thesis studies three aspects of multi-agent systems, including clustering prediction, observability analysis and classification benchmarking. We introduce an auxiliary implicit sampling (AIS) algorithm for efficient Bayesian prediction of clusters, show that randomization enhances observability, and provide an optimality benchmark for time series classification (TSC) algorithms by the likelihood ratio test (LRT). Firstly, we present a Bayesian approach to predict the clustering of a multi-agent system from short-time partial observations. We characterize the clustering by the posterior of the clusters' sizes and centers, and introduce the AIS algorithm to sample the posterior. This algorithm leads to accurate predictions for the leading cluster, and overcomes the challenge of unobservability and high-dimensional sampling. Secondly, we propose a new observation strategy: we randomly select agents to be observed at each time. We prove that such randomization enhances observability. We provide an exact probability of being observable for linear systems. Toward this general result, we introduce two probabilistic extensions of the classical variational definition of observability: a likelihood-based definition and a Bayesian definition. Additionally, the Bayesian definition enables us to study observability of nonlinear systems. We show by numerical tests that randomization significantly improves the data assimilation of a nonlinear multi-agent system, particularly in its clustering prediction. Thirdly, we provide an optimality benchmark for TSC algorithms, which is more reliable than empirical benchmarks because the LRT is a theory-guaranteed optimal classifier. We test three scalable state-of-the-art TSC algorithms in distinguishing multi-agent systems and four more diffusions. These model-agnostic algorithms are suboptimal in classifying multi-agent systems, and are optimal or near optimal for the other simpler diffusions

Controlled release of paclitaxel from a self-assembling peptide hydrogel formed in situ and antitumor study in vitro

Background: A nanoscale injectable in situ-forming hydrogel drug delivery system was developed in this study. The system was based on a self-assembling peptide RADA16 solution, which can spontaneously form a hydrogel rapidly under physiological conditions. We used the RADA16 hydrogel for the controlled release of paclitaxel (PTX), a hydrophobic antitumor drug. Methods: The RADA16-PTX suspension was prepared simply by magnetic stirring, followed by atomic force microscopy, circular dichroism analysis, dynamic light scattering, rheological analysis, an in vitro release assay, and a cell viability test. Results: The results indicated that RADA16 and PTX can interact with each other and that the amphiphilic peptide was able to stabilize hydrophobic drugs in aqueous solution. The particle size of PTX was markedly decreased in the RADA16 solution compared with its size in water. The RADA16-PTX suspension could form a hydrogel in culture medium, and the elasticity of the hydrogel showed a positive correlation with peptide concentration. In vitro release measurements indicated that hydrogels with a higher peptide concentration had a longer half-release time. The RADA16-PTX hydrogel could effectively inhibit the growth of the breast cancer cell line, MDA-MB-435S, in vitro, and hydrogels with higher peptide concentrations were more effective at inhibiting tumor cell proliferation. The RADA16-PTX hydrogel was effective at controlling the release of PTX and inhibiting tumor cell growth in vitro. Conclusion: Self-assembling peptide hydrogels may work well as a system for drug delivery

CFLIT: Coexisting Federated Learning and Information Transfer

Future wireless networks are expected to support diverse mobile services, including artificial intelligence (AI) services and ubiquitous data transmissions. Federated learning (FL), as a revolutionary learning approach, enables collaborative AI model training across distributed mobile edge devices. By exploiting the superposition property of multiple-access channels, over-the-air computation allows concurrent model uploading from massive devices over the same radio resources, and thus significantly reduces the communication cost of FL. In this paper, we study the coexistence of over-the-air FL and traditional information transfer (IT) in a mobile edge network. We propose a coexisting federated learning and information transfer (CFLIT) communication framework, where the FL and IT devices share the wireless spectrum in an OFDM system. Under this framework, we aim to maximize the IT data rate and guarantee a given FL convergence performance by optimizing the long-term radio resource allocation. A key challenge that limits the spectrum efficiency of the coexisting system lies in the large overhead incurred by frequent communication between the server and edge devices for FL model aggregation. To address the challenge, we rigorously analyze the impact of the computation-to-communication ratio on the convergence of over-the-air FL in wireless fading channels. The analysis reveals the existence of an optimal computation-to-communication ratio that minimizes the amount of radio resources needed for over-the-air FL to converge to a given error tolerance. Based on the analysis, we propose a low-complexity online algorithm to jointly optimize the radio resource allocation for both the FL devices and IT devices. Extensive numerical simulations verify the superior performance of the proposed design for the coexistence of FL and IT devices in wireless cellular systems.Comment: The paper has been accepted for publication by IEEE Transactions on Wireless Communications (March 2023

A 10-b Fourth-Order Quadrature Bandpass Continuous-Time ΣΔ Modulator With 33-MHz Bandwidth for a Dual-Channel GNSS Receiver

This document is the Accepted Manuscript version of the following article: Junfeng Zhang, Yang Xu, Zehong Zhang, Yichuang Sun, Zhihua Wang, and Baoyong Chi, ‘A 10-b Fourth-Order Quadrature Bandpass Continuous-Time ΣΔ Modulator With 33-MHz Bandwidth for a Dual-Channel GNSS Receiver’, IEEE Transactions on Microwave Theory and Practice, Vol. 65 (4): 1303-1314, first published online 16 February 2017. The version of record is available online at DOI: 10.1109/TMTT.2017.266237, Published by IEEE. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A fourth-order quadrature bandpass continuous-time sigma-delta modulator for a dual-channel global navigation satellite system (GNSS) receiver is presented. With a bandwidth (BW) of 33 MHz, the modulator is able to digitalize the downconverted GNSS signals in two adjacent signal bands simultaneously, realizing dual-channel GNSS reception with one receiver channel instead of two independent receiver channels. To maintain the loop-stability of the high-order architecture, any extra loop phase shifting should be minimized. In the system architecture, a feedback and feedforward hybrid architecture is used to implement the fourth-order loop-filter, and a return-to-zero (RZ) feedback after the discrete-time differential operation is introduced into the input of the final integrator to realize the excess loop delay compensation, saving a spare summing amplifier. In the circuit implementation, power-efficient amplifiers with high-frequency active feedforward and antipole-splitting techniques are employed in the active RC integrators, and self-calibrated comparators are used to implement the low-power 3-b quantizers. These power saving techniques help achieve superior figure of merit for the presented modulator. With a sampling rate of 460 MHz, current-steering digital-analog converters are chosen to guarantee high conversion speed. Implemented in only 180-nm CMOS, the modulator achieves 62.1-dB peak signal to noise and distortion ratio, 64-dB dynamic range, and 59.3-dB image rejection ratio, with a BW of 33 MHz, and consumes 54.4 mW from a 1.8 V power supply.Peer reviewe

Kernelized Similarity Learning and Embedding for Dynamic Texture Synthesis

Dynamic texture (DT) exhibits statistical stationarity in the spatial domain and stochastic repetitiveness in the temporal dimension, indicating that different frames of DT possess a high similarity correlation that is critical prior knowledge. However, existing methods cannot effectively learn a promising synthesis model for high-dimensional DT from a small number of training data. In this paper, we propose a novel DT synthesis method, which makes full use of similarity prior knowledge to address this issue. Our method bases on the proposed kernel similarity embedding, which not only can mitigate the high-dimensionality and small sample issues, but also has the advantage of modeling nonlinear feature relationship. Specifically, we first raise two hypotheses that are essential for DT model to generate new frames using similarity correlation. Then, we integrate kernel learning and extreme learning machine into a unified synthesis model to learn kernel similarity embedding for representing DT. Extensive experiments on DT videos collected from the internet and two benchmark datasets, i.e., Gatech Graphcut Textures and Dyntex, demonstrate that the learned kernel similarity embedding can effectively exhibit the discriminative representation for DT. Accordingly, our method is capable of preserving the long-term temporal continuity of the synthesized DT sequences with excellent sustainability and generalization. Meanwhile, it effectively generates realistic DT videos with fast speed and low computation, compared with the state-of-the-art methods. The code and more synthesis videos are available at our project page https://shiming-chen.github.io/Similarity-page/Similarit.html.Comment: 13 pages, 12 figures, 2 table