351 research outputs found
COORDINATION OF LEADER-FOLLOWER MULTI-AGENT SYSTEM WITH TIME-VARYING OBJECTIVE FUNCTION
This thesis aims to introduce a new framework for the distributed control of multi-agent systems with adjustable swarm control objectives. Our goal is twofold: 1) to provide an overview to how time-varying objectives in the control of autonomous systems may be applied to the distributed control of multi-agent systems with variable autonomy level, and 2) to introduce a framework to incorporate the proposed concept to fundamental swarm behaviors such as aggregation and leader tracking. Leader-follower multi-agent systems are considered in this study, and a general form of time-dependent artificial potential function is proposed to describe the varying objectives of the system in the case of complete information exchange. Using Lyapunov methods, the stability and boundedness of the agents\u27 trajectories under single order and higher order dynamics are analyzed. Illustrative numerical simulations are presented to demonstrate the validity of our results. Then, we extend these results for multi-agent systems with limited information exchange and switching communication topology. The first steps of the realization of an experimental framework have been made with the ultimate goal of verifying the simulation results in practice
Adapt and Diffuse: Sample-adaptive Reconstruction via Latent Diffusion Models
Inverse problems arise in a multitude of applications, where the goal is to
recover a clean signal from noisy and possibly (non)linear observations. The
difficulty of a reconstruction problem depends on multiple factors, such as the
structure of the ground truth signal, the severity of the degradation, the
implicit bias of the reconstruction model and the complex interactions between
the above factors. This results in natural sample-by-sample variation in the
difficulty of a reconstruction task, which is often overlooked by contemporary
techniques. Recently, diffusion-based inverse problem solvers have established
new state-of-the-art in various reconstruction tasks. However, they have the
drawback of being computationally prohibitive. Our key observation in this
paper is that most existing solvers lack the ability to adapt their compute
power to the difficulty of the reconstruction task, resulting in long inference
times, subpar performance and wasteful resource allocation. We propose a novel
method that we call severity encoding, to estimate the degradation severity of
noisy, degraded signals in the latent space of an autoencoder. We show that the
estimated severity has strong correlation with the true corruption level and
can give useful hints at the difficulty of reconstruction problems on a
sample-by-sample basis. Furthermore, we propose a reconstruction method based
on latent diffusion models that leverages the predicted degradation severities
to fine-tune the reverse diffusion sampling trajectory and thus achieve
sample-adaptive inference times. We utilize latent diffusion posterior sampling
to maintain data consistency with observations. We perform experiments on both
linear and nonlinear inverse problems and demonstrate that our technique
achieves performance comparable to state-of-the-art diffusion-based techniques,
with significant improvements in computational efficiency.Comment: 14 pages, 6 figures, preliminary versio
Questionable and Unquestionable in Quantum Mechanics
We derive the basic postulates of quantum physics from a few very simple
operational assumptions based exclusively on the relative frequencies of
observable events (measurement operations and measurement outcomes). We isolate
a notion which can be identified with the system's own state, in the sense that
it characterizes the system's probabilistic behavior against all possible
measurement operations. We investigate some important features of the possible
states of the system. All those investigations remain within the framework of
classical Kolmogorovian probability theory, meaning that any physical system
(traditionally categorized as classical or quantum) that can be described in
operational terms can be described within classical Kolmogorovian probability
theory. In the second part of the paper we show that anything that can be
described in operational terms can, if we wish, be represented in the Hilbert
space quantum mechanical formalism. The outcomes of each measurement can be
represented by a system of pairwise orthogonal closed subspaces spanning the
entire Hilbert space; the states of the system can be represented by pure state
operators, and the probabilities of the outcomes can be reproduced by the usual
trace formula. Each real valued quantity can be associated with a suitable
self-adjoint operator, such that the possible measurement results are the
eigenvalues and the outcome events are represented by the eigenspaces,
according to the spectral decomposition of the operator in question. This
suggests that the basic postulates of quantum theory are in fact analytic
statements: they do not tell us anything about a physical system beyond the
fact that the system can be described in operational terms. This is almost
true. At the end of the paper we discuss a few subtle points where the
representation we obtained is not completely identical with standard quantum
mechanics.Comment: 40 page
Geometrical Spinoptics and the Optical Hall Effect
33 pages. Two subsections and new references added. To appear in the Journal of Geometry and PhysicsGeometrical optics is extended so as to provide a model for spinning light rays via the coadjoint orbits of the Euclidean group characterized by color and spin. This leads to a theory of ``geometrical spinoptics'' in refractive media. Symplectic scattering yields generalized Snell-Descartes laws that include the recently discovered optical Hall effect
Blockchain Technology Adoption: Implications and Challenges
According to the World Economic Forum, by 2025 10% of the world’s GDP (currently about $100 trillion) may be on blockchain. Blockchain technology is described as a distributed ledger technology (DLT) underpinned by five fundamental principles: decentralization, peer-to-peer transmission; transparency with pseudonymity; irreversibility of records; and computational logic. Despite blockchain’s transformative potential, it is unclear how Blockchain applications are implemented across industries and product/service categories. The purpose of the paper is to discuss the general challenges, risks, and implications related to blockchain implementation and adoption by the private and public sectors. We discuss how blockchain should overcome multiple barriers–technological, governance, organizational and social–for its widespread adoption. Mainly, the regulatory uncertainty, scalability and performance, interoperability, data privacy, security, legacy systems and the skills gap barriers to adoption are examined. Moreover, the socioeconomic implications of blockchain are discussed mainly the financial, economic, social and institutional impacts
3D Phase Retrieval at Nano-Scale via Accelerated Wirtinger Flow
Imaging 3D nano-structures at very high resolution is crucial in a variety of
scientific fields. However, due to fundamental limitations of light propagation
we can only measure the object indirectly via 2D intensity measurements of the
3D specimen through highly nonlinear projection mappings where a variety of
information (including phase) is lost. Reconstruction therefore involves
inverting highly non-linear and seemingly non-invertible mappings. In this
paper, we introduce a novel technique where the 3D object is directly
reconstructed from an accurate non-linear propagation model. Furthermore, we
characterize the ambiguities of this model and leverage a priori knowledge to
mitigate their effect and also significantly reduce the required number of
measurements and hence the acquisition time. We demonstrate the performance of
our algorithm via numerical experiments aimed at nano-scale reconstruction of
3D integrated circuits. Moreover, we provide rigorous theoretical guarantees
for convergence to stationarity
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