Asset Protection in Escorting using Multi-Robot Systems

Swarm robotics is a field dedicated to the study of the design and development of certain multi-robot systems. Often times, these groups prove to be more beneficial than a single complex robot as swarms typically provide a more robust and potentially more efficient solution. One such case is the task of escorting a specified target while addressing any potential threats discovered in the environment. In this work, a control algorithm for a high volume, decentralized, homogeneous robot swarm was developed based upon a technique commonly used to model incompressible fluids known as Smoothed Particle Hydrodynamics (SPH). This proposed solution to the asset protection problem was tested against a more commonly accepted method for robot navigation known as potential fields. An alternate algorithm was developed based on this technique and manipulated to perform the same basic duty of asset protection. Both algorithms were tested in simulation using ARGoS as an environment and Swarmanoid’s Footbots as robot models. Five experiments were run in order to examine the functionality of both of these algorithms in relation to formation control and the protection of a mobile asset from mobile threats. The results proved the proposed SPH based algorithm comparable to the potential fields based method while minimizing the escape window and having a slightly higher response rate to introduced threats. These results hint that the concept of using fluid models for control of high volume swarms should further be explored and seriously considered as a potential solution to the asset protection problem

DeepReGraph co-clusters temporal gene expression and cis-regulatory elements through heterogeneous graph representation learning

This work presents DeepReGraph, a novel method for co-clustering genes and cis-regulatory elements (CREs) into candidate regulatory networks. Gene expression data, as well as data from three CRE activity markers from a publicly available dataset of mouse fetal heart tissue, were used for DeepReGraph concept proofing. In this study we used open chromatin accessibility from ATAC-seq experiments, as well as H3K27ac and H3K27me3 histone marks as CREs activity markers. However, this method can be executed with other sets of markers. We modelled all data sources as a heterogeneous graph and adapted a state-of-the-art representation learning algorithm to produce a low-dimensional and easy-to-cluster embedding of genes and CREs. Deep graph auto-encoders and an adaptive-sparsity generative model are the algorithmic core of DeepReGraph. The main contribution of our work is the design of proper combination rules for the heterogeneous gene expression and CRE activity data and the computational encoding of well-known gene expression regulatory mechanisms into a suitable objective function for graph embedding. We showed that the co-clusters of genes and CREs in the final embedding shed light on developmental regulatory mechanisms in mouse fetal-heart tissue. Such clustering could not be achieved by using only gene expression data. Function enrichment analysis proves that the genes in the co-clusters are involved in distinct biological processes. The enriched transcription factor binding sites in CREs prioritize the candidate transcript factors which drive the temporal changes in gene expression. Consequently, we conclude that DeepReGraph could foster hypothesis-driven tissue development research from high-throughput expression and epigenomic data. Full source code and data are available on the DeepReGraph GitHub project

A numerical study of one-patch colloidal particles: from square-well to Janus

We perform numerical simulations of a simple model of one-patch colloidal particles to investigate: (i) the behavior of the gas-liquid phase diagram on moving from a spherical attractive potential to a Janus potential and (ii) the collective structure of a system of Janus particles. We show that, for the case where one of the two hemispheres is attractive and one is repulsive, the system organizes into a dispersion of orientational ordered micelles and vesicles and, at low $T$, the system can be approximated as a fluid of such clusters, interacting essentially via excluded volume. The stability of this cluster phase generates a very peculiar shape of the gas and liquid coexisting densities, with a gas coexistence density which increases on cooling, approaching the liquid coexistence density at very low $T$.Comment: 9 pages, 10 figures, Phys. Chem. Chem. Phys. in press (2010

Discovering structure without labels

The scarcity of labels combined with an abundance of data makes unsupervised learning more attractive than ever. Without annotations, inductive biases must guide the identification of the most salient structure in the data. This thesis contributes to two aspects of unsupervised learning: clustering and dimensionality reduction. The thesis falls into two parts. In the first part, we introduce Mod Shift, a clustering method for point data that uses a distance-based notion of attraction and repulsion to determine the number of clusters and the assignment of points to clusters. It iteratively moves points towards crisp clusters like Mean Shift but also has close ties to the Multicut problem via its loss function. As a result, it connects signed graph partitioning to clustering in Euclidean space. The second part treats dimensionality reduction and, in particular, the prominent neighbor embedding methods UMAP and t-SNE. We analyze the details of UMAP's implementation and find its actual loss function. It differs drastically from the one usually stated. This discrepancy allows us to explain some typical artifacts in UMAP plots, such as the dataset size-dependent tendency to produce overly crisp substructures. Contrary to existing belief, we find that UMAP's high-dimensional similarities are not critical to its success. Based on UMAP's actual loss, we describe its precise connection to the other state-of-the-art visualization method, t-SNE. The key insight is a new, exact relation between the contrastive loss functions negative sampling, employed by UMAP, and noise-contrastive estimation, which has been used to approximate t-SNE. As a result, we explain that UMAP embeddings appear more compact than t-SNE plots due to increased attraction between neighbors. Varying the attraction strength further, we obtain a spectrum of neighbor embedding methods, encompassing both UMAP- and t-SNE-like versions as special cases. Moving from more attraction to more repulsion shifts the focus of the embedding from continuous, global to more discrete and local structure of the data. Finally, we emphasize the link between contrastive neighbor embeddings and self-supervised contrastive learning. We show that different flavors of contrastive losses can work for both of them with few noise samples

Molekulardynamische Untersuchungen heterogener Keimbildung

Heterogeneous nucleation phenomena, in particular the condensation of vapors in presence of a substrate, are studied by molecular dynamics simulations. The simulations reported to this date have paid little attention to the description on the substrate. Here the dynamics of the vapor phase and the surface are simultaneously treated. Two cases are studied: the condensation of argon and the condensation of platinum on polyethylene films. The fundamental difference between both systems is the relative strength of the adsorbate-substrate interactions. The United Atom Method is used to represent the interactions of methyl groups within the polymer. The properties of polyethylene in the bulk phase such as the glass transition temperature, the density and the formation of gauche defects in the crystalline phase can be well described with this model. The interactions between argon atoms can be well represented by the Lennard Jones potential. The Embedded Atom Method is used to describe interactions between platinum atoms since many body effects, important in metals, can be incorporated with a computation requirement similar to pair potentials. Cross interactions between different types of atoms and groups are here approximated by the Lennard Jones potential with Lorentz-Berthelot combining parameters. The aim of this investigation is to describe the dynamics of heterogeneous nucleation and to establish the variables which control the growth and structure formation of clusters on the surface, the nucleation rates, and possible modifications of the substrate during condensation. For this purpose, different conditions of the saturation of the vapor phase and temperature of the substrate were simulated in each of the systems studied. Stationary nucleation rates in vapor phase and on the surface are obtained from cluster size statistics using the method of Yasuoka and Matsumoto. Different growth mechanisms were observed in for the simulated systems. Argon tends to condense on the surface as two-dimensional islands which finally coalesce as layers on the polymer surface. Consistent with this type of growth the condensation in the regime of low saturated and undersaturated vapors can be explained by a two- dimensional model within the frame of the classical nucleation theory. Platinum clusters condense as three-dimensional islands and partially wet the polymer surface. For the first time the embedding of metal atoms and metal clusters growth into a polymer substrate, as observed in experiments, is attained by large-scale molecular simulations. Depending on their sizes, the platinum clusters can diffuse into the polymer matrix even at temperatures lower than the glass transition of the polymer. The routines used for the simulation and analysis have been specially developed for the systems studied. Among them are NpT and NVT ensemble molecular dynamics simulations for the preparation and equilibration of thin polymer films, simulations of condensation of argon and platinum on polyethylene films. Furthermore routines developed for the analysis of simulation results include the calculation of a) radial distribution functions, torsion angle distributions and density profiles for the characterization of polymers, b) algorithms for the recognition of clusters in bulk and on a surface and c) routines for the visualization of the performed simulations

Characterization, modeling, and simulation of multiscale directed-assembly systems

Nanoscience is a rapidly developing field at the nexus of all physical sciences which holds the potential for mankind to gain a new level of control of matter over matter and energy altogether. Directed-assembly is an emerging field within nanoscience in which non-equilibrium system dynamics are controlled to produce scalable, arbitrarily complex and interconnected multi-layered structures with custom chemical, biologically or environmentally-responsive, electronic, or optical properties. We construct mathematical models and interpret data from direct-assembly experiments via application and augmentation of classical and contemporary physics, biology, and chemistry methods. Crystal growth, protein pathway mapping, LASER tweezers optical trapping, and colloid processing are areas of directed-assembly with established experimental techniques. We apply a custom set of characterization, modeling, and simulation techniques to experiments to each of these four areas. Many of these techniques can be applied across several experimental areas within directed-assembly and to systems featuring multiscale system dynamics in general. We pay special attention to mathematical methods for bridging models of system dynamics across scale regimes, as they are particularly applicable and relevant to directed-assembly. We employ massively parallel simulations, enabled by custom software, to establish underlying system dynamics and develop new device production methods

Modeling and simulation of multi-cellular systems using hybrid FEM/Agent-based approaches

Muchas de las propiedades biomecánicas de los organismos multicelulares surgen directamente de las interacciones entre células. Las células de los órganos y tejidos interactúan entre sí y con su entorno de diferentes formas. Debido a este hecho, es fundamental analizar cómo estas interacciones se traducen como propiedades mecánicas a nivel del tejido. Por ejemplo, las adhesiones entre células determinan la rigidez aparente de una capa epitelial. Las interacciones célula-matriz pueden además determinar la formación de muchas estructuras biológicas y su morfología. Estos sistemas multicelulares no se pueden considerar como estructuras estáticas ya que sufren constantes cambios causados por la proliferación, la reorganización o la migración celular. Por lo tanto, es necesario estudiar la dinámica de la célula y las interacciones individuales para comprender plenamente cómo funcionan los fenómenos a escalas superiores, desde el desarrollo de tejidos hasta el crecimiento de tumores.Recientemente, el uso de enfoques basados en agentes se ha vuelto muy popular para modelar sistemas multicelulares. Los modelos basados en agentes representan células como entidades individuales. Estos modelos son especialmente adecuados para estudiar fenómenos biofísicos que ocurren a nivel celular. Aquí las interacciones célula-célula se pueden simular directamente de forma mecanicista. Además, estos modelos capturan realmente bien las heterogeneidades presentes en las estructuras biológicas. Por otra parte, los modelos continuos se utilizan comúnmente en problemas de escalas mayores. A diferencia de los modelos basados en agentes, en estos no representan células como entidades individuales, sino que se definen leyes constitutivas para modelar procesos biológicos, físicos y químicos. Por lo tanto, las propiedades celulares se promedian usando parámetros macroscópicos, y estos modelos a menudo trabajan con la densidad celular en lugar de entidades celulares separadas. En cualquier caso, los modelos continuos presentan una buena escalabilidad y una excelente representación de fenómenos físicos particulares como el transporte masivo y las transmisiones de fuerza en medios continuos.En esta tesis, se exploran las posibilidades que los enfoques híbridos pueden ofrecer para desarrollar nuevos modelos de sistemas multicelulares. Se presentan dos modelos híbridos diferentes que combinan un modelo basado en agentes y un modelo continuo. Ambos enfoques tienen en común que el modelo continuo se resuelve utilizando el método de los elementos finitos. También se muestra, siguiendo este patrón de diseño, cómo resolver varias de las limitaciones intrínsecas de cada tipo individual de modelo.En primer lugar, se presenta un modelo híbrido para simular la mecánica epitelial monocapa. Este modelo se centra en el modelado de las interacciones mecánicas célula-célula y célula-sustrato, pero también en la topología y morfología de los tejidos. Con este enfoque se reproducen tejidos epiteliales proliferativos, movimientos celular colectivo y procesos de migración. El segundo modelo presentado en esta tesis se ha diseñado para simular agregados celulares en entornos tridimensionales. Se estudian las interacciones mecánicas entre células, pero este modelo se centra especialmente en analizar cómo afecta el transporte de oxígeno a las células en un proceso de agrupamiento en 3D.Finalmente, se comparan los resultados de ambos modelos con datos experimentales de otros autores y se discuten los beneficios de combinar diferentes tipos de modelos. Se demuestra que los enfoques híbridos que se proponen en este trabajo son capaces de simular una amplia variedad de sistemas multicelulares. De hecho, son particularmente útiles para estudiar cómo algunos fenómenos emergen de las interacciones celulares individuales a escalas biológicas más grandes.<br /

Colloids with perception-dependent motility: Dynamics and structure of rotating aggregates and directed swarms

In this thesis we focus on two-dimensional systems of colloids governed by Brownian dynamics that are able to sense their neighbors via a visual-type of perception, then they can switch their motility between passive and active depending on a given perception parameter. Our setup corresponds to experiments performed in Bechinger's lab in Konstanz University, where they have considered cases of quorum-sensing (isotropic perception) and visual-type of perception (anisotropic perception). Here we study the case when the perception is both anisotropic and also misaligned with respect to the self-propulsion orientation vector. The purpose of this thesis is to characterize the emergence of collective behaviors in this model, as well as the dynamics and structural changes of the system. We provide novel strategies where the interplay between perception and motility of the agents allows them to self-organize into rotating aggregates and directed swarms. Our study sheds light in the understanding of active automatons with adaptable collective states, and can be implemented for example in macroscopic swarms of robots, or microscopic colloids activated by light. In chapter 2 we introduce the ingredients necessary to perform particle-based numerical simulations, like the integration method, interaction forces, boundary conditions, and optimization techniques. We also briefly comment on the organization and design of the Brownian dynamics code we developed to obtain results shown in this thesis. In chapter 3, we consider systems of colloids with discontinuous motility and misaligned visual perception. We explain how this type of interaction generically leads to aggregation and rotation of cohesive structures. Then, we characterize the resulting dynamics for different system parameters. In chapter 4 we characterize different types of circular structures that emerge in this model, as a function of the perception threshold and misalignment angle. We also derive analytical expressions from conservation equations corresponding to a solid-body rotation of a continuum aggregate driven by activity at the interface. We find an agreement between theory and numerical results for the density, size, and angular velocity of the aggregates as a function of the system parameters. In chapter 5 we consider a binary mixture of particles with different misalignment angle. Under given conditions, we find the striking case where the system aggregates, self-sorts into species subdomains which counter-rotate leading to a self-propulsion of the overall system. We characterize this process by means of dynamic parameters and their averages in steady state. We find cases where the directed swarms can either dilute or remain robust, or where the aggregate is species homogeneous and its center of mass describes random motion. We also study the swarms shape and how it can change for varying misalignment angle. In chapter 6 we study cases when the mixture is non-equimolar. In this case the system self-organizes into swarms describing helical trajectories. We also show an example of an externally guided system, where we dynamically change the misalignment angle of the particles, leading to a swarm performing run-and-turn motion