Factor Graphs for Heterogeneous Bayesian Decentralized Data Fusion

This paper explores the use of factor graphs as an inference and analysis tool for Bayesian peer-to-peer decentralized data fusion. We propose a framework by which agents can each use local factor graphs to represent relevant partitions of a complex global joint probability distribution, thus allowing them to avoid reasoning over the entirety of a more complex model and saving communication as well as computation cost. This allows heterogeneous multi-robot systems to cooperate on a variety of real world, task oriented missions, where scalability and modularity are key. To develop the initial theory and analyze the limits of this approach, we focus our attention on static linear Gaussian systems in tree-structured networks and use Channel Filters (also represented by factor graphs) to explicitly track common information. We discuss how this representation can be used to describe various multi-robot applications and to design and analyze new heterogeneous data fusion algorithms. We validate our method in simulations of a multi-agent multi-target tracking and cooperative multi-agent mapping problems, and discuss the computation and communication gains of this approach.Comment: 8 pages, 6 figures, 1 table, submitted to the 24th International Conference on Information Fusio

Heterogeneous Bayesian Decentralized Data Fusion: An Empirical Study

In multi-robot applications, inference over large state spaces can often be divided into smaller overlapping sub-problems that can then be collaboratively solved in parallel over `separate' subsets of states. To this end, the factor graph decentralized data fusion (FG-DDF) framework was developed to analyze and exploit conditional independence in heterogeneous Bayesian decentralized fusion problems, in which robots update and fuse pdfs over different locally overlapping random states. This allows robots to efficiently use smaller probabilistic models and sparse message passing to accurately and scalably fuse relevant local parts of a larger global joint state pdf, while accounting for data dependencies between robots. Whereas prior work required limiting assumptions about network connectivity and model linearity, this paper relaxes these to empirically explore the applicability and robustness of FG-DDF in more general settings. We develop a new heterogeneous fusion rule which generalizes the homogeneous covariance intersection algorithm, and test it in multi-robot tracking and localization scenarios with non-linear motion/observation models under communication dropout. Simulation and linear hardware experiments show that, in practice, the FG-DDF continues to provide consistent filtered estimates under these more practical operating conditions, while reducing computation and communication costs by more than 95%, thus enabling the design of scalable real-world multi-robot systems.Comment: 7 pages, 2 figures, submitted to IEEE Conference on Robotics and Automation (ICRA 2023

Exploiting Structure for Optimal Multi-Agent Bayesian Decentralized Estimation

A key challenge in Bayesian decentralized data fusion is the `rumor propagation' or `double counting' phenomenon, where previously sent data circulates back to its sender. It is often addressed by approximate methods like covariance intersection (CI) which takes a weighted average of the estimates to compute the bound. The problem is that this bound is not tight, i.e. the estimate is often over-conservative. In this paper, we show that by exploiting the probabilistic independence structure in multi-agent decentralized fusion problems a tighter bound can be found using (i) an expansion to the CI algorithm that uses multiple (non-monolithic) weighting factors instead of one (monolithic) factor in the original CI and (ii) a general optimization scheme that is able to compute optimal bounds and fully exploit an arbitrary dependency structure. We compare our methods and show that on a simple problem, they converge to the same solution. We then test our new non-monolithic CI algorithm on a large-scale target tracking simulation and show that it achieves a tighter bound and a more accurate estimate compared to the original monolithic CI.Comment: 4 pages, 4 figures. presented at the Inference and Decision Making for Autonomous Vehicles (IDMAV) RSS 2023 worksho

The Natural Cytotoxicity Receptor 1 Contribution to Early Clearance of Streptococcus pneumoniae and to Natural Killer-Macrophage Cross Talk

Natural killer (NK) cells serve as a crucial first line of defense against tumors, viral and bacterial infections. We studied the involvement of a principal activating natural killer cell receptor, natural cytotoxicity receptor 1 (NCR1), in the innate immune response to S. pneumoniae infection. Our results demonstrate that the presence of the NCR1 receptor is imperative for the early clearance of S. pneumoniae. We tied the ends in vivo by showing that deficiency in NCR1 resulted in reduced lung NK cell activation and lung IFNγ production at the early stages of S. pneumoniae infection. NCR1 did not mediate direct recognition of S. pneumoniae. Therefore, we studied the involvement of lung macrophages and dendritic cells (DC) as the mediators of NK-expressed NCR1 involvement in response to S. pneumoniae. In vitro, wild type BM-derived macrophages and DC expressed ligands to NCR1 and co-incubation of S. pneumoniae-infected macrophages/DC with NCR1-deficient NK cells resulted in significantly lesser IFNγ levels compared to NCR1-expressing NK cells. In vivo, ablation of lung macrophages and DC was detrimental to the early clearance of S. pneumoniae. NCR1-expressing mice had more potent alveolar macrophages as compared to NCR1-deficient mice. This result correlated with the higher fraction of NCR1-ligandhigh lung macrophages, in NCR1-expressing mice, that had better phagocytic activity compared to NCR1-liganddull macrophages. Overall, our results point to the essential contribution of NK-expressed NCR1 in early response to S. pneumoniae infection and to NCR1-mediated interaction of NK and S. pneumoniae infected-macrophages and -DC

Diffusion MRI of Structural Brain Plasticity Induced by a Learning and Memory Task

Background: Activity-induced structural remodeling of dendritic spines and glial cells was recently proposed as an important factor in neuroplasticity and suggested to accompany the induction of long-term potentiation (LTP). Although T1 and diffusion MRI have been used to study structural changes resulting from long-term training, the cellular basis of the findings obtained and their relationship to neuroplasticity are poorly understood. Methodology/Principal Finding: Here we used diffusion tensor imaging (DTI) to examine the microstructural manifestations of neuroplasticity in rats that performed a spatial navigation task. We found that DTI can be used to define the selective localization of neuroplasticity induced by different tasks and that this process is age-dependent in cingulate cortex and corpus callosum and age-independent in the dentate gyrus. Conclusion/Significance: We relate the observed DTI changes to the structural plasticity that occurs in astrocytes and discuss the potential of MRI for probing structural neuroplasticity and hence indirectly localizing LTP

Exact and Approximate Heterogeneous Bayesian Decentralized Data Fusion

In Bayesian peer-to-peer decentralized data fusion for static and dynamic systems, the underlying estimated or communicated distributions are frequently assumed to be homogeneous between agents. This requires each agent to process and communicate the full global joint distribution, and thus leads to high computation and communication costs irrespective of relevancy to specific local objectives. This work considers a family of heterogeneous decentralized fusion problems, where we consider the set of problems in which either the communicated or the estimated distributions describe different, but overlapping, states of interest that are subsets of a larger full global joint state. We exploit the conditional independence structure of such problems and provide a rigorous derivation for a family of exact and approximate heterogeneous conditionally factorized channel filter methods. We further extend existing methods for approximate conservative filtering and decentralized fusion in heterogeneous dynamic problems. Numerical examples show more than 99.5% potential communication reduction for heterogeneous channel filter fusion, and a multi-target tracking simulation shows that these methods provide consistent estimates.Comment: 13 pages, 6 figures, 2 tables, submitted to IEEE Transactions on Robotics (T-RO

Simulation Tool Coupling Nonlinear Electrophoresis and Reaction Kinetics for Design and Optimization of Biosensors

We present the development, formulation, validation, and demonstration of a fast, generic, and open source simulation tool, which integrates nonlinear electromigration with multispecies nonequilibrium kinetic reactions. The code is particularly useful for the design and optimization of new electrophoresis-based bioanlaytical assays, in which electrophoretic transport, separation, or focusing control analyte spatial concentration and subsequent reactions. By decoupling the kinetics solver from the electric field solver, we demonstrate an order of magnitude improvement in total simulation time for a series of 100 reaction simulations using a shared background electric field. The code can efficiently handle complex electrophoretic setups coupling sharp electric field gradients with bulk reactions, surface reactions, and competing reactions. For example, we demonstrate the use of the code for investigating accelerated reactions using isotachophoresis (ITP), revealing new regimes of operation which in turn enable significant improvement of the signal-to-noise ratio of ITP-based genotypic assays. The user can define arbitrary initial conditions and reaction rules, and we believe it will be a valuable tool for the design of novel bioanalytical assays. We will offer the code as open source, and it will be available for free download at http://microfluidics.technion.ac.il

Several siblings with Cystic Fibrosis as a risk factor for poor outcome

SummaryBackgroundOccurrence of Cystic Fibrosis (CF) in more than one member in a family is not uncommon. The aim of our study was to assess the influence of multiple siblings with CF on disease expression and outcome.MethodsStudy group consisted of 2-siblings (2-sibs, n = 42) or 3/4 siblings (3/4-sibs, n = 22) with CF in one family. Each sibling was matched by age, mutation, and gender to a single CF patient.Results3/4-sibs subgroup compared to singles showed a lower mean FEV1 with a faster decline rate (58.4 ± 27.5 vs. 72.7 ± 25.4 and −5 ± 6.4 vs. −1.7 ± 2.8 %predicted decline/year respectively, p < .05), more airway colonization by Pseudomonas aeruginosa and Mycobacterium abscessus (15 (68%) vs. 8 (36%) and 7 (32%) vs. 4 (18%), respectively, p < .05) and more lung transplants (5 (23%) vs. 2 (9%), respectively, p < .02). Last mean FEV1 within 3/4-sibs was significantly lower for the youngest sib (p < .05).ConclusionsThree or more CF patients in one family may be a risk factor for more severe disease and poor prognosis. In our view this reflects the burden of disease on the patients and families