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  1. Book ; Online: Michelangelo

    Zhao, Zibo / Liu, Wen / Chen, Xin / Zeng, Xianfang / Wang, Rui / Cheng, Pei / Fu, Bin / Chen, Tao / Yu, Gang / Gao, Shenghua

    Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation

    2023  

    Abstract: ... michelangelo ...

    Abstract We present a novel alignment-before-generation approach to tackle the challenging task of generating general 3D shapes based on 2D images or texts. Directly learning a conditional generative model from images or texts to 3D shapes is prone to producing inconsistent results with the conditions because 3D shapes have an additional dimension whose distribution significantly differs from that of 2D images and texts. To bridge the domain gap among the three modalities and facilitate multi-modal-conditioned 3D shape generation, we explore representing 3D shapes in a shape-image-text-aligned space. Our framework comprises two models: a Shape-Image-Text-Aligned Variational Auto-Encoder (SITA-VAE) and a conditional Aligned Shape Latent Diffusion Model (ASLDM). The former model encodes the 3D shapes into the shape latent space aligned to the image and text and reconstructs the fine-grained 3D neural fields corresponding to given shape embeddings via the transformer-based decoder. The latter model learns a probabilistic mapping function from the image or text space to the latent shape space. Our extensive experiments demonstrate that our proposed approach can generate higher-quality and more diverse 3D shapes that better semantically conform to the visual or textural conditional inputs, validating the effectiveness of the shape-image-text-aligned space for cross-modality 3D shape generation.

    Comment: Project Website: https://neuralcarver.github.io/michelangelo
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 006 ; 004
    Publishing date 2023-06-29
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: Stability, Linear Convergence, and Robustness of the Wang-Elia Algorithm for Distributed Consensus Optimization

    Bin, Michelangelo / Notarnicola, Ivano / Parisini, Thomas

    2022  

    Abstract: We revisit an algorithm for distributed consensus optimization proposed in 2010 by J. Wang and N. Elia. By means of a Lyapunov-based analysis, we prove input-to-state stability of the algorithm relative to a closed invariant set composed of optimal ... ...

    Abstract We revisit an algorithm for distributed consensus optimization proposed in 2010 by J. Wang and N. Elia. By means of a Lyapunov-based analysis, we prove input-to-state stability of the algorithm relative to a closed invariant set composed of optimal equilibria and with respect to perturbations affecting the algorithm's dynamics. In the absence of perturbations, this result implies linear convergence of the local estimates and Lyapunov stability of the optimal steady state. Moreover, we unveil fundamental connections with the well-known Gradient Tracking and with distributed integral control. Overall, our results suggest that a control theoretic approach can have a considerable impact on (distributed) optimization, especially when robustness is considered.
    Keywords Mathematics - Optimization and Control ; Electrical Engineering and Systems Science - Systems and Control
    Publishing date 2022-04-21
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: Adaptive Nonlinear Regulation via Gaussian Process

    Gentilini, Lorenzo / Bin, Michelangelo / Marconi, Lorenzo

    2022  

    Abstract: The paper deals with the problem of output regulation of nonlinear systems by presenting a learning-based adaptive internal model-based design strategy. We borrow from the adaptive internal model design technique recently proposed in [1] and extend it by ...

    Abstract The paper deals with the problem of output regulation of nonlinear systems by presenting a learning-based adaptive internal model-based design strategy. We borrow from the adaptive internal model design technique recently proposed in [1] and extend it by means of a Gaussian process regressor. The learning-based adaptation is performed by following an "event-triggered" logic so that hybrid tools are used to analyse the resulting closed-loop system. Unlike the approach proposed in [1] where the friend is supposed to belong to a specific finite-dimensional model set, here we only require smoothness of the ideal steady-state control action. The paper also presents numerical simulations showing how the proposed method outperforms previous approaches.

    Comment: Submitted to CDC2022
    Keywords Electrical Engineering and Systems Science - Systems and Control
    Publishing date 2022-06-24
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Data-driven Output Regulation via Gaussian Processes and Luenberger Internal Models

    Gentilini, Lorenzo / Bin, Michelangelo / Marconi, Lorenzo

    2022  

    Abstract: This paper deals with the problem of adaptive output regulation for multivariable nonlinear systems by presenting a learning-based adaptive internal model-based design strategy. The approach builds on the recently proposed adaptive internal model design ... ...

    Abstract This paper deals with the problem of adaptive output regulation for multivariable nonlinear systems by presenting a learning-based adaptive internal model-based design strategy. The approach builds on the recently proposed adaptive internal model design techniques based on the theory of nonlinear Luenberger observers, and the adaptation side is approached as a probabilistic regression problem. In particular, Gaussian process priors are employed to cope with the learning problem. Unlike the previous approaches in the field, here only coarse assumptions about the friend structure are required, making the proposed approach suitable for applications where the exosystem is highly uncertain. The paper presents performance bounds on the attained regulation error and numerical simulations showing how the proposed method outperforms previous approaches.

    Comment: arXiv admin note: text overlap with arXiv:2206.12225
    Keywords Electrical Engineering and Systems Science - Systems and Control
    Subject code 006
    Publishing date 2022-10-28
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: A Distributed Methodology for Approximate Uniform Global Minimum Sharing

    Bin, Michelangelo / Parisini, Thomas

    2020  

    Abstract: The paper deals with the distributed minimum sharing problem, in which a network of decision-makers - exchanging information through a communication network - computes the minimum of some local quantities of interest in a distributed and decentralized ... ...

    Abstract The paper deals with the distributed minimum sharing problem, in which a network of decision-makers - exchanging information through a communication network - computes the minimum of some local quantities of interest in a distributed and decentralized way. The problem is equivalently cast into a cost-coupled distributed optimization problem, and an adjustable approximate (or sub-optimal) solution is presented which enjoys several properties of crucial importance in applications. In particular, the proposed solution is scalable in that the dimension of the state space does not grow with the size or topology of the communication network. Moreover, a global and uniform (both in the initial time and in the initial condition) asymptotic stability result is provided, as well as an attractiveness property towards a steady state which can be made arbitrarily close to the sought minimum. Exact asymptotic convergence is also recovered at the price, however, of loosing uniformity of the convergence with respect to the initial time.
    Keywords Electrical Engineering and Systems Science - Systems and Control
    Subject code 006
    Publishing date 2020-12-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Model Identification and Adaptive State Observation for a Class of Nonlinear Systems

    Bin, Michelangelo / Marconi, Lorenzo

    2020  

    Abstract: In this paper we consider the joint problems of state estimation and model identification for a class of continuous-time nonlinear systems in output-feedback canonical form. An adaptive observer is proposed that combines an extended high-gain observer ... ...

    Abstract In this paper we consider the joint problems of state estimation and model identification for a class of continuous-time nonlinear systems in output-feedback canonical form. An adaptive observer is proposed that combines an extended high-gain observer and a discrete-time identifier. The extended observer provides the identifier with a data set permitting the identification of the system model and the identifier adapts the extended observer according to the new estimated model. The design of the identifier is approached as a system identification problem and sufficient conditions are presented that, if satisfied, allow different identification algorithms to be used for the adaptation phase. The cases of recursive least-squares and multiresolution black-box identification via wavelet-based identifiers are specifically addressed. Stability results are provided relating the asymptotic estimation error to the prediction capabilities of the identifier. Robustness with respect to additive disturbances affecting the system equations and measurements is also established in terms of an input-to-state stability property relative to the noiseless estimates.

    Comment: This is the accepted version of https://ieeexplore.ieee.org/document/9272829
    Keywords Electrical Engineering and Systems Science - Systems and Control
    Subject code 006
    Publishing date 2020-10-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article: Hysteresis-based supervisory control with application to non-pharmaceutical containment of COVID-19.

    Bin, Michelangelo / Crisostomi, Emanuele / Ferraro, Pietro / Murray-Smith, Roderick / Parisini, Thomas / Shorten, Robert / Stein, Sebastian

    Annual reviews in control

    2021  Volume 52, Page(s) 508–522

    Abstract: The recent COVID-19 outbreak has motivated an extensive development of non-pharmaceutical intervention policies for epidemics containment. While a total lockdown is a viable solution, interesting policies are those allowing some degree of normal ... ...

    Abstract The recent COVID-19 outbreak has motivated an extensive development of non-pharmaceutical intervention policies for epidemics containment. While a total lockdown is a viable solution, interesting policies are those allowing some degree of normal functioning of the society, as this allows a continued, albeit reduced, economic activity and lessens the many societal problems associated with a prolonged lockdown. Recent studies have provided evidence that fast periodic alternation of lockdown and normal-functioning days may effectively lead to a good trade-off between outbreak abatement and economic activity. Nevertheless, the correct number of normal days to allocate within each period in such a way to guarantee the desired trade-off is a highly uncertain quantity that cannot be fixed a priori and that must rather be adapted online from measured data. This adaptation task, in turn, is still a largely open problem, and it is the subject of this work. In particular, we study a class of solutions based on hysteresis logic. First, in a rather general setting, we provide general convergence and performance guarantees on the evolution of the decision variable. Then, in a more specific context relevant for epidemic control, we derive a set of results characterizing robustness with respect to uncertainty and giving insight about how a priori knowledge about the controlled process may be used for fine-tuning the control parameters. Finally, we validate the results through numerical simulations tailored on the COVID-19 outbreak.
    Language English
    Publishing date 2021-08-13
    Publishing country England
    Document type Journal Article
    ZDB-ID 1501302-9
    ISSN 1872-9088 ; 1367-5788
    ISSN (online) 1872-9088
    ISSN 1367-5788
    DOI 10.1016/j.arcontrol.2021.07.001
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Book ; Online: Approximate Nonlinear Regulation via Identification-Based Adaptive Internal Models

    Bin, Michelangelo / Bernard, Pauline / Marconi, Lorenzo

    2019  

    Abstract: This paper concerns the problem of adaptive output regulation for multivariable nonlinear systems in normal form. We present a regulator employing an adaptive internal model of the exogenous signals based on the theory of nonlinear Luenberger observers. ... ...

    Abstract This paper concerns the problem of adaptive output regulation for multivariable nonlinear systems in normal form. We present a regulator employing an adaptive internal model of the exogenous signals based on the theory of nonlinear Luenberger observers. Adaptation is performed by means of discrete-time system identification schemes, in which every algorithm fulfilling some optimality and stability conditions can be used. Practical and approximate regulation results are given relating the prediction capabilities of the identified model to the asymptotic bound on the regulated variables, which become asymptotic whenever a "right" internal model exists in the identifier's model set. The proposed approach, moreover, does not require "high-gain" stabilization actions.

    Comment: This new version contains minor corrections and modifications in the text
    Keywords Electrical Engineering and Systems Science - Systems and Control
    Publishing date 2019-07-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: A System Theoretical Perspective to Gradient-Tracking Algorithms for Distributed Quadratic Optimization

    Bin, Michelangelo / Notarnicola, Ivano / Marconi, Lorenzo / Notarstefano, Giuseppe

    2019  

    Abstract: In this paper we consider a recently developed distributed optimization algorithm based on gradient tracking. We propose a system theory framework to analyze its structural properties on a preliminary, quadratic optimization set-up. Specifically, we ... ...

    Abstract In this paper we consider a recently developed distributed optimization algorithm based on gradient tracking. We propose a system theory framework to analyze its structural properties on a preliminary, quadratic optimization set-up. Specifically, we focus on a scenario in which agents in a static network want to cooperatively minimize the sum of quadratic cost functions. We show that the gradient tracking distributed algorithm for the investigated program can be viewed as a sparse closed-loop linear system in which the dynamic state-feedback controller includes consensus matrices and optimization (stepsize) parameters. The closed-loop system turns out to be not completely reachable and asymptotic stability can be shown restricted to a proper invariant set. Convergence to the global minimum, in turn, can be obtained only by means of a proper initialization. The proposed system interpretation of the distributed algorithm provides also additional insights on other structural properties and possible design choices that are discussed in the last part of the paper as a starting point for future developments.
    Keywords Electrical Engineering and Systems Science - Systems and Control ; Computer Science - Multiagent Systems ; Mathematics - Optimization and Control
    Subject code 006
    Publishing date 2019-11-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Kemeny-based testing for COVID-19.

    Serife Yilmaz / Ekaterina Dudkina / Michelangelo Bin / Emanuele Crisostomi / Pietro Ferraro / Roderick Murray-Smith / Thomas Parisini / Lewi Stone / Robert Shorten

    PLoS ONE, Vol 15, Iss 11, p e

    2020  Volume 0242401

    Abstract: Testing, tracking and tracing abilities have been identified as pivotal in helping countries to safely reopen activities after the first wave of the COVID-19 virus. Contact tracing apps give the unprecedented possibility to reconstruct graphs of daily ... ...

    Abstract Testing, tracking and tracing abilities have been identified as pivotal in helping countries to safely reopen activities after the first wave of the COVID-19 virus. Contact tracing apps give the unprecedented possibility to reconstruct graphs of daily contacts, so the question is: who should be tested? As human contact networks are known to exhibit community structure, in this paper we show that the Kemeny constant of a graph can be used to identify and analyze bridges between communities in a graph. Our 'Kemeny indicator' is the value of the Kemeny constant in the new graph that is obtained when a node is removed from the original graph. We show that testing individuals who are associated with large values of the Kemeny indicator can help in efficiently intercepting new virus outbreaks, when they are still in their early stage. Extensive simulations provide promising results in early identification and in blocking the possible 'super-spreaders' links that transmit disease between different communities.
    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2020-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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