8,835 research outputs found
Configuration Management of Distributed Systems over Unreliable and Hostile Networks
Economic incentives of large criminal profits and the threat of legal consequences have pushed criminals to continuously improve their malware, especially command and control channels. This thesis applied concepts from successful malware command and control to explore the survivability and resilience of benign configuration management systems.
This work expands on existing stage models of malware life cycle to contribute a new model for identifying malware concepts applicable to benign configuration management. The Hidden Master architecture is a contribution to master-agent network communication. In the Hidden Master architecture, communication between master and agent is asynchronous and can operate trough intermediate nodes. This protects the master secret key, which gives full control of all computers participating in configuration management. Multiple improvements to idempotent configuration were proposed, including the definition of the minimal base resource dependency model, simplified resource revalidation and the use of imperative general purpose language for defining idempotent configuration.
Following the constructive research approach, the improvements to configuration management were designed into two prototypes. This allowed validation in laboratory testing, in two case studies and in expert interviews. In laboratory testing, the Hidden Master prototype was more resilient than leading configuration management tools in high load and low memory conditions, and against packet loss and corruption. Only the research prototype was adaptable to a network without stable topology due to the asynchronous nature of the Hidden Master architecture.
The main case study used the research prototype in a complex environment to deploy a multi-room, authenticated audiovisual system for a client of an organization deploying the configuration. The case studies indicated that imperative general purpose language can be used for idempotent configuration in real life, for defining new configurations in unexpected situations using the base resources, and abstracting those using standard language features; and that such a system seems easy to learn.
Potential business benefits were identified and evaluated using individual semistructured expert interviews. Respondents agreed that the models and the Hidden Master architecture could reduce costs and risks, improve developer productivity and allow faster time-to-market. Protection of master secret keys and the reduced need for incident response were seen as key drivers for improved security. Low-cost geographic scaling and leveraging file serving capabilities of commodity servers were seen to improve scaling and resiliency. Respondents identified jurisdictional legal limitations to encryption and requirements for cloud operator auditing as factors potentially limiting the full use of some concepts
The Active CryoCubeSat Technology: Active Thermal Control for Small Satellites
Modern CubeSats and Small Satellites have advanced in capability to tackle science and technology missions that would usually be reserved for more traditional, large satellites. However, this rapid growth in capability is only possible through the fast-to-production, low-cost, and advanced technology approach used by modern small satellite engineers. Advanced technologies in power generation, energy storage, and high-power density electronics have naturally led to a thermal bottleneck, where CubeSats and Small Satellites can generate more power than they can easily reject. The Active CryoCubeSat (ACCS) is an advanced active thermal control technology (ATC) for Small Satellites and CubeSats, which hopes to help solve this thermal problem. The ACCS technology is based on a two-stage design. An integrated miniature cryocooler forms the first stage, and a single-phase mechanically pumped fluid loop heat exchanger the second. The ACCS leverages advanced 3D manufacturing techniques to integrate the ATC directly into the satellite structure, which helps to improve the performance while simultaneously miniaturizing and simplifying the system. The ACCS system can easily be scaled to mission requirements and can control zonal temperature, bulk thermal rejection, and dynamic heat transfer within a satellite structure. The integrated cryocooler supports cryogenic science payloads such as advanced LWIR electro-optical detectors. The ACCS hopes to enable future advanced CubeSat and Small Satellite missions in earth science, heliophysics, and deep space operations. This dissertation will detail the design, development, and testing of the ACCS system technology
Machine Learning Applications in Advanced Additive Manufacturing: Process Modeling, Microstructure Analysis, and Defect Detection
Non-destructive evaluation (NDE) techniques are critical for assessing the integrity, health, and mechanical properties of materials manufactured from various methods. High fidelity NDE techniques are essential for quality control but often lead to massive data generation. Such a vast data load cannot be manually processed, this leads to a severe bottleneck for process engineers. Machine learning (ML) offers a solution to this problem by providing powerful and adaptable algorithms capable of learning patterns, identifying features, and finding hidden relationships in large sets of data. Various ML models are used in this work to improve predictions, improve measurements, detect anomalies, classify anomalies, segment images, determine material health, and directly model behavior. These neural network or ML models are implemented to perform these tasks by utilizing data gathered through various NDE techniques. Additive manufacturing enables the production of complex geometries and customized parts with reduced waste and lead times. The development of new material printing capability and techniques is necessary to expand its capabilities to produce high performance parts with unique properties and functionality. Contributions to advanced additive manufacturing are made via the application of customized machine learning algorithms in this work. The development of a novel grain image generation method was completed to improve grain and grain boundary image segmentation methods on microstructure images. Convolutional Neural Networks (CNNs) were also applied to datasets of Stainless Steel Powder to help identify, qualify, and classify the health of the powder prior to print application. A feasibility study of the implementation of Binder Jetting (BJT) is conducted on Martian and Lunar regolith using a simplistic binder in this work. The need for efficient techniques to process data gathered from NDE methods is crucial to enhance the accuracy, efficiency, and speed of the analysis of this data. This will lead to faster development and implementation of advanced manufacturing techniques
Random graph matching at Otter's threshold via counting chandeliers
We propose an efficient algorithm for graph matching based on similarity
scores constructed from counting a certain family of weighted trees rooted at
each vertex. For two Erd\H{o}s-R\'enyi graphs whose edges
are correlated through a latent vertex correspondence, we show that this
algorithm correctly matches all but a vanishing fraction of the vertices with
high probability, provided that and the edge correlation
coefficient satisfies , where is
Otter's tree-counting constant. Moreover, this almost exact matching can be
made exact under an extra condition that is information-theoretically
necessary. This is the first polynomial-time graph matching algorithm that
succeeds at an explicit constant correlation and applies to both sparse and
dense graphs. In comparison, previous methods either require or
are restricted to sparse graphs.
The crux of the algorithm is a carefully curated family of rooted trees
called chandeliers, which allows effective extraction of the graph correlation
from the counts of the same tree while suppressing the undesirable correlation
between those of different trees
Parallelization techniques for quantum simulation of fermionic systems
Mapping fermionic operators to qubit operators is an essential step for simulating fermionic systems on a quantum computer. We investigate how the choice of such a mapping interacts with the underlying qubit connectivity of the quantum processor to enable (or impede) parallelization of the resulting Hamiltonian-simulation algorithm. It is shown that this problem can be mapped to a path coloring problem on a graph constructed from the particular choice of encoding fermions onto qubits and the fermionic interactions onto paths. The basic version of this problem is called the weak coloring problem. Taking into account the fine-grained details of the mapping yields what is called the strong coloring problem, which leads to improved parallelization performance. A variety of illustrative analytical and numerical examples are presented to demonstrate the amount of improvement for both weak and strong coloring-based parallelizations. Our results are particularly important for implementation on near-term quantum processors where minimizing circuit depth is necessary for algorithmic feasibility
Modeling and design of heterogeneous hierarchical bioinspired spider web structures using generative deep learning and additive manufacturing
Spider webs are incredible biological structures, comprising thin but strong
silk filament and arranged into complex hierarchical architectures with
striking mechanical properties (e.g., lightweight but high strength, achieving
diverse mechanical responses). While simple 2D orb webs can easily be mimicked,
the modeling and synthesis of 3D-based web structures remain challenging,
partly due to the rich set of design features. Here we provide a detailed
analysis of the heterogenous graph structures of spider webs, and use deep
learning as a way to model and then synthesize artificial, bio-inspired 3D web
structures. The generative AI models are conditioned based on key geometric
parameters (including average edge length, number of nodes, average node
degree, and others). To identify graph construction principles, we use
inductive representation sampling of large experimentally determined spider web
graphs, to yield a dataset that is used to train three conditional generative
models: 1) An analog diffusion model inspired by nonequilibrium thermodynamics,
with sparse neighbor representation, 2) a discrete diffusion model with full
neighbor representation, and 3) an autoregressive transformer architecture with
full neighbor representation. All three models are scalable, produce complex,
de novo bio-inspired spider web mimics, and successfully construct graphs that
meet the design objectives. We further propose algorithm that assembles web
samples produced by the generative models into larger-scale structures based on
a series of geometric design targets, including helical and parametric shapes,
mimicking, and extending natural design principles towards integration with
diverging engineering objectives. Several webs are manufactured using 3D
printing and tested to assess mechanical properties
Efficient Distributed Decomposition and Routing Algorithms in Minor-Free Networks and Their Applications
In the LOCAL model, low-diameter decomposition is a useful tool in designing
algorithms, as it allows us to shift from the general graph setting to the
low-diameter graph setting, where brute-force information gathering can be done
efficiently. Recently, Chang and Su [PODC 2022] showed that any
high-conductance network excluding a fixed minor contains a high-degree vertex,
so the entire graph topology can be gathered to one vertex efficiently in the
CONGEST model using expander routing. Therefore, in networks excluding a fixed
minor, many problems that can be solved efficiently in LOCAL via low-diameter
decomposition can also be solved efficiently in CONGEST via expander
decomposition.
In this work, we show improved decomposition and routing algorithms for
networks excluding a fixed minor in the CONGEST model. Our algorithms cost
rounds deterministically. For bounded-degree
graphs, our algorithms finish in
rounds.
Our algorithms have a wide range of applications, including the following
results in CONGEST.
1. A -approximate maximum independent set in a network
excluding a fixed minor can be computed deterministically in
rounds, nearly matching the
lower bound of Lenzen and Wattenhofer [DISC
2008].
2. Property testing of any additive minor-closed property can be done
deterministically in rounds if is a constant or
rounds if the maximum degree
is a constant, nearly matching the lower
bound of Levi, Medina, and Ron [PODC 2018].Comment: To appear in PODC 202
Exactly soluble models in many-body physics
Almost all phenomena in the universe are described, at the fundamental level, by quantum manybody
models. In general, however, a complete understanding of large systems with many degrees of
freedom is impossible. While in general many-body quantum systems are intractable, there are
special cases for which there are techniques that allow for an exact solution.
Exactly soluble models are interesting because they are soluble; beyond this, they can be used to
gain intuition for further reaching many-body systems, including when they can be leveraged to help
with numerical approximations for general models. The work presented in this thesis considers
exactly soluble models of quantum many-body systems.
The first part of this thesis extends the family of many-body spin models for which we can find a freefermion
solution.
A solution method that was developed for a specific free-fermion model is generalized in such a way
that allows application to a broader class of many-body spin system than was previously known to be
free. Models which admit a solution via this method are characterized by a graph theory invariants: in
brief it is shown that a quantum spin system has an exact description via non-interacting fermions if
its frustration graph is claw-free and contains a simplicial clique.
The second part of this thesis gives an explicit example of how the usefulness of exactly soluble
models can extend beyond the solution itself. This chapter pertains to the calculation of the
topological entanglement entropy in topologically ordered loop-gas states. Topological entanglement
entropy gives an understanding of how correlations may extend throughout a system. In this chapter
the topological entanglement entropy of two- and three-dimensional loop-gas states is calculated in
the bulk and at the boundary. We obtain a closed form expression for the topological entanglement in
terms of the anyonic theory that the models support
Exponential Qubit Reduction in Optimization for Financial Transaction Settlement
We extend the qubit-efficient encoding presented in [Tan et al., Quantum 5,
454 (2021)] and apply it to instances of the financial transaction settlement
problem constructed from data provided by a regulated financial exchange. Our
methods are directly applicable to any QUBO problem with linear inequality
constraints. Our extension of previously proposed methods consists of a
simplification in varying the number of qubits used to encode correlations as
well as a new class of variational circuits which incorporate symmetries,
thereby reducing sampling overhead, improving numerical stability and
recovering the expression of the cost objective as a Hermitian observable. We
also propose optimality-preserving methods to reduce variance in real-world
data and substitute continuous slack variables. We benchmark our methods
against standard QAOA for problems consisting of 16 transactions and obtain
competitive results. Our newly proposed variational ansatz performs best
overall. We demonstrate tackling problems with 128 transactions on real quantum
hardware, exceeding previous results bounded by NISQ hardware by almost two
orders of magnitude.Comment: 16 pages, 8 figure
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