A spatio-temporal model to reveal oscillator phenotypes in molecular clocks: Parameter estimation elucidates circadian gene transcription dynamics in single-cells.

We propose a stochastic distributed delay model together with a Markov random field prior and a measurement model for bioluminescence-reporting to analyse spatio-temporal gene expression in intact networks of cells. The model describes the oscillating time evolution of molecular mRNA counts through a negative transcriptional-translational feedback loop encoded in a chemical Langevin equation with a probabilistic delay distribution. The model is extended spatially by means of a multiplicative random effects model with a first order Markov random field prior distribution. Our methodology effectively separates intrinsic molecular noise, measurement noise, and extrinsic noise and phenotypic variation driving cell heterogeneity, while being amenable to parameter identification and inference. Based on the single-cell model we propose a novel computational stability analysis that allows us to infer two key characteristics, namely the robustness of the oscillations, i.e. whether the reaction network exhibits sustained or damped oscillations, and the profile of the regulation, i.e. whether the inhibition occurs over time in a more distributed versus a more direct manner, which affects the cells' ability to phase-shift to new schedules. We show how insight into the spatio-temporal characteristics of the circadian feedback loop in the suprachiasmatic nucleus (SCN) can be gained by applying the methodology to bioluminescence-reported expression of the circadian core clock gene Cry1 across mouse SCN tissue. We find that while (almost) all SCN neurons exhibit robust cell-autonomous oscillations, the parameters that are associated with the regulatory transcription profile give rise to a spatial division of the tissue between the central region whose oscillations are resilient to perturbation in the sense that they maintain a high degree of synchronicity, and the dorsal region which appears to phase shift in a more diversified way as a response to large perturbations and thus could be more amenable to entrainment

Design and Performance Analysis of Genetic Algorithms for Topology Control Problems

In this dissertation, we present a bio-inspired decentralized topology control mechanism, called force-based genetic algorithm (FGA), where a genetic algorithm (GA) is run by each autonomous mobile node to achieve a uniform spread of mobile nodes and to provide a fully connected network over an unknown area. We present a formal analysis of FGA in terms of convergence speed, uniformity at area coverage, and Lyapunov stability theorem. This dissertation emphasizes the use of mobile nodes to achieve a uniform distribution over an unknown terrain without a priori information and a central control unit. In contrast, each mobile node running our FGA has to make its own movement direction and speed decisions based on local neighborhood information, such as obstacles and the number of neighbors, without a centralized control unit or global knowledge. We have implemented simulation software in Java and developed four different testbeds to study the effectiveness of different GA-based topology control frameworks for network performance metrics including node density, speed, and the number of generations that GAs run. The stochastic behavior of FGA, like all GA-based approaches, makes it difficult to analyze its convergence speed. We built metrically transitive homogeneous and inhomogeneous Markov chain models to analyze the convergence of our FGA with respect to the communication ranges of mobile nodes and the total number of nodes in the system. The Dobrushin contraction coefficient of ergodicity is used for measuring convergence speed for homogeneous and inhomogeneous Markov chain models of our FGA. Furthermore, convergence characteristic analysis helps us to choose the nearoptimal values for communication range, the number of mobile nodes, and the mean node degree before sending autonomous mobile nodes to any mission. Our analytical and experimental results show that our FGA delivers promising results for uniform mobile node distribution over unknown terrains. Since our FGA adapts to local environment rapidly and does not require global network knowledge, it can be used as a real-time topology controller for commercial and military applications

Illuminating meaningful diversity in complex feature spaces through adaptive grid-based genetic algorithms

In many fields there exist problems for which multiple solutions of suitably high performance may be found across distinct regions of the search space. Optimisation of the search towards including these distinct solutions is important not only to understanding these spaces but also to avoiding local optima. This is the goal of a type of genetic algorithms called illumination algorithms. In Chapter 2, we demonstrate the use of an illumination algorithm in the exploration of networks sharing only a given set of structural features (valid networks). This method produces a population of valid networks that are more diverse than those produced using state of the art methods, however, it was found to be too inefficient to be usable in real-world problems. Additionally, setting an appropriate resolution of the search requires some amount of prior knowledge of the space of solutions. Addressing this problem is the focus of Chapter 3, in which we develop three extensions to the method: a) an exact method of mutation whereby only valid networks are explored, b) an adaptive mechanism for setting the resolution of the search, c) a principle for tuning mutations parameters to the search’ s resolution. We show that with these additions our method is able to increase the diversity of solutions found in significantly fewer iterations. Finally, in Chapter 4 we expand our method for use in more general problem spaces. We benchmark it against the state of the art. In all tested landscapes, we show that our method is able to identify more meaningful niches in the spaces in the same number of iterations. We conclude by highlighting the limits of our framework and discuss further directions

Complex and Adaptive Dynamical Systems: A Primer

An thorough introduction is given at an introductory level to the field of quantitative complex system science, with special emphasis on emergence in dynamical systems based on network topologies. Subjects treated include graph theory and small-world networks, a generic introduction to the concepts of dynamical system theory, random Boolean networks, cellular automata and self-organized criticality, the statistical modeling of Darwinian evolution, synchronization phenomena and an introduction to the theory of cognitive systems. It inludes chapter on Graph Theory and Small-World Networks, Chaos, Bifurcations and Diffusion, Complexity and Information Theory, Random Boolean Networks, Cellular Automata and Self-Organized Criticality, Darwinian evolution, Hypercycles and Game Theory, Synchronization Phenomena and Elements of Cognitive System Theory.Comment: unformatted version of the textbook; published in Springer, Complexity Series (2008, second edition 2010

Small-World Brain Networks Revisited.

It is nearly 20 years since the concept of a small-world network was first quantitatively defined, by a combination of high clustering and short path length; and about 10 years since this metric of complex network topology began to be widely applied to analysis of neuroimaging and other neuroscience data as part of the rapid growth of the new field of connectomics. Here, we review briefly the foundational concepts of graph theoretical estimation and generation of small-world networks. We take stock of some of the key developments in the field in the past decade and we consider in some detail the implications of recent studies using high-resolution tract-tracing methods to map the anatomical networks of the macaque and the mouse. In doing so, we draw attention to the important methodological distinction between topological analysis of binary or unweighted graphs, which have provided a popular but simple approach to brain network analysis in the past, and the topology of weighted graphs, which retain more biologically relevant information and are more appropriate to the increasingly sophisticated data on brain connectivity emerging from contemporary tract-tracing and other imaging studies. We conclude by highlighting some possible future trends in the further development of weighted small-worldness as part of a deeper and broader understanding of the topology and the functional value of the strong and weak links between areas of mammalian cortex.DSB acknowledges support from the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, the Army Research Laboratory and the Army Research Office through contract numbers W911NF-10-2-0022 and W911NF-14-1-0679, the National Institute of Health (2-R01-DC-009209-11, 1R01HD086888-01, R01-MH107235, R01-MH107703, and R21-M MH-106799), the Office of Naval Research, and the National Science Foundation (BCS-1441502, CAREER PHY-1554488, and BCS-1631550).This is the final version of the article. It first appeared from Sage at http://dx.doi.org/10.1177/1073858416667720

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Network Science for IoT

The research work presented in this thesis is based on the concept and defintion of network that can spread in several and different real world contexts. Indeed, we can refer to a network in a telecommunications sense considering a collection of transmitters, receivers, and communication channels that send or are used to send information to one another. However, as a matter of fact, in nature there are other several examples of networks: the human brain is one of them. The relationship between the actors in Hollywood can be studied in terms of network as well, a generic social community can be compared to a network, eco-systems are networks of species. The recent Network Science aims at studying all these systems using a set of common mathematical methods. In the following of the thesis, we will focus on some of well known telecommunications networks issues using standard telecommunications procedures to address them, with relevant reference to video flow transmissions and management of electric vehicles networks. At the same time, different models aiming at reach the same goals in contexts that may differ from a telecommunications setup can be used. In more details, we will evaluate queueing systems, jamming problems, groups recognition in networks, and mobile computing using game theoretic approaches. It is worth noting that this aspect can be also seen in a reverse order. Indeed, we will discuss how standard telecommunications analysis can be used to investigate on problems not directly related to a telecommunications background. In particular, one of our future purposes is to investigate on the brain connectivity that is raising significant interest in the recent scientific society

Architecting a One-to-many Traffic-Aware and Secure Millimeter-Wave Wireless Network-in-Package Interconnect for Multichip Systems

With the aggressive scaling of device geometries, the yield of complex Multi Core Single Chip(MCSC) systems with many cores will decrease due to the higher probability of manufacturing defects especially, in dies with a large area. Disintegration of large System-on-Chips(SoCs) into smaller chips called chiplets has shown to improve the yield and cost of complex systems. Therefore, platform-based computing modules such as embedded systems and micro-servers have already adopted Multi Core Multi Chip (MCMC) architectures overMCSC architectures. Due to the scaling of memory intensive parallel applications in such systems, data is more likely to be shared among various cores residing in different chips resulting in a significant increase in chip-to-chip traffic, especially one-to-many traffic. This one-to-many traffic is originated mainly to maintain cache-coherence between many cores residing in multiple chips. Besides, one-to-many traffics are also exploited by many parallel programming models, system-level synchronization mechanisms, and control signals. How-ever, state-of-the-art Network-on-Chip (NoC)-based wired interconnection architectures do not provide enough support as they handle such one-to-many traffic as multiple unicast trafficusing a multi-hop MCMC communication fabric. As a result, even a small portion of such one-to-many traffic can significantly reduce system performance as traditional NoC-basedinterconnect cannot mask the high latency and energy consumption caused by chip-to-chipwired I/Os. Moreover, with the increase in memory intensive applications and scaling of MCMC systems, traditional NoC-based wired interconnects fail to provide a scalable inter-connection solution required to support the increased cache-coherence and synchronization generated one-to-many traffic in future MCMC-based High-Performance Computing (HPC) nodes. Therefore, these computation and memory intensive MCMC systems need an energy-efficient, low latency, and scalable one-to-many (broadcast/multicast) traffic-aware interconnection infrastructure to ensure high-performance. Research in recent years has shown that Wireless Network-in-Package (WiNiP) architectures with CMOS compatible Millimeter-Wave (mm-wave) transceivers can provide a scalable, low latency, and energy-efficient interconnect solution for on and off-chip communication. In this dissertation, a one-to-many traffic-aware WiNiP interconnection architecture with a starvation-free hybrid Medium Access Control (MAC), an asymmetric topology, and a novel flow control has been proposed. The different components of the proposed architecture are individually one-to-many traffic-aware and as a system, they collaborate with each other to provide required support for one-to-many traffic communication in a MCMC environment. It has been shown that such interconnection architecture can reduce energy consumption and average packet latency by 46.96% and 47.08% respectively for MCMC systems. Despite providing performance enhancements, wireless channel, being an unguided medium, is vulnerable to various security attacks such as jamming induced Denial-of-Service (DoS), eavesdropping, and spoofing. Further, to minimize the time-to-market and design costs, modern SoCs often use Third Party IPs (3PIPs) from untrusted organizations. An adversary either at the foundry or at the 3PIP design house can introduce a malicious circuitry, to jeopardize an SoC. Such malicious circuitry is known as a Hardware Trojan (HT). An HTplanted in the WiNiP from a vulnerable design or manufacturing process can compromise a Wireless Interface (WI) to enable illegitimate transmission through the infected WI resulting in a potential DoS attack for other WIs in the MCMC system. Moreover, HTs can be used for various other malicious purposes, including battery exhaustion, functionality subversion, and information leakage. This information when leaked to a malicious external attackercan reveals important information regarding the application suites running on the system, thereby compromising the user profile. To address persistent jamming-based DoS attack in WiNiP, in this dissertation, a secure WiNiP interconnection architecture for MCMC systems has been proposed that re-uses the one-to-many traffic-aware MAC and existing Design for Testability (DFT) hardware along with Machine Learning (ML) approach. Furthermore, a novel Simulated Annealing (SA)-based routing obfuscation mechanism was also proposed toprotect against an HT-assisted novel traffic analysis attack. Simulation results show that,the ML classifiers can achieve an accuracy of 99.87% for DoS attack detection while SA-basedrouting obfuscation could reduce application detection accuracy to only 15% for HT-assistedtraffic analysis attack and hence, secure the WiNiP fabric from age-old and emerging attacks

Research summary, January 1989 - June 1990

The Research Institute for Advanced Computer Science (RIACS) was established at NASA ARC in June of 1983. RIACS is privately operated by the Universities Space Research Association (USRA), a consortium of 62 universities with graduate programs in the aerospace sciences, under a Cooperative Agreement with NASA. RIACS serves as the representative of the USRA universities at ARC. This document reports our activities and accomplishments for the period 1 Jan. 1989 - 30 Jun. 1990. The following topics are covered: learning systems, networked systems, and parallel systems