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  1. AU="Surana, Amit"
  2. AU="Praetzel-Wunder, Silke"
  3. AU="Nabil, Fatima Mohamed"
  4. AU="Lindh, Christian"
  5. AU="Vetkas, Artur"
  6. AU="Gorelick, Root"
  7. AU="Mezdari, Zaineb"
  8. AU=Wilkinson Beverley
  9. AU=Halbower Ann C AU=Halbower Ann C
  10. AU="Ghosh, Ananya"
  11. AU="Spoletini, Gabriele"
  12. AU="Gracefo, Sara"
  13. AU="Works, Kaitlyn R"
  14. AU="LIU Lei"
  15. AU="McLennan, John D"
  16. AU=Dickinson Gordon M AU=Dickinson Gordon M
  17. AU=Hertzler Dean A 2nd
  18. AU="Yan, Xinrui"
  19. AU="Seal, M L"
  20. AU="Seka, Devin J"
  21. AU="Nguyen, Phuong T B"

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  1. Buch ; Online: Efficient Quantum Algorithms for Nonlinear Stochastic Dynamical Systems

    Gnanasekaran, Abeynaya / Surana, Amit / Sahai, Tuhin

    2023  

    Abstract: In this paper, we propose an efficient quantum algorithm for solving nonlinear stochastic differential equations (SDE) via the associated Fokker-Planck equation (FPE). We discretize FPE in space and time using the Chang-Cooper scheme, and compute the ... ...

    Abstract In this paper, we propose an efficient quantum algorithm for solving nonlinear stochastic differential equations (SDE) via the associated Fokker-Planck equation (FPE). We discretize FPE in space and time using the Chang-Cooper scheme, and compute the solution of the resulting system of linear equations using the quantum linear systems algorithm. The Chang-Cooper scheme is second order accurate and satisfies conservativeness and positivity of the solution. We present detailed error and complexity analyses that demonstrate that our proposed quantum scheme, which we call the Quantum Linear Systems Chang-Cooper Algorithm (QLSCCA), computes the solution to the FPE within prescribed $\epsilon$ error bounds with polynomial dependence on state dimension $d$. Classical numerical methods scale exponentially with dimension, thus, our approach provides an \emph{exponential speed-up} over traditional approaches.
    Schlagwörter Mathematics - Dynamical Systems ; Computer Science - Data Structures and Algorithms ; Mathematics - Numerical Analysis ; Quantum Physics
    Thema/Rubrik (Code) 518
    Erscheinungsdatum 2023-03-04
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Buch ; Online: Observability of Hypergraphs

    Pickard, Joshua / Surana, Amit / Bloch, Anthony / Rajapakse, Indika

    2023  

    Abstract: In this paper we develop a framework to study observability for uniform hypergraphs. Hypergraphs are generalizations of graphs in which edges may connect any number of nodes, thereby representing multi-way relationships which are ubiquitous in many real- ... ...

    Abstract In this paper we develop a framework to study observability for uniform hypergraphs. Hypergraphs are generalizations of graphs in which edges may connect any number of nodes, thereby representing multi-way relationships which are ubiquitous in many real-world networks including neuroscience, social networks, and bioinformatics. We define a canonical multilinear dynamical system with linear outputs on uniform hypergraphs which captures such multi-way interactions and results in a homogeneous polynomial system. We derive a Kalman-rank-like condition for assessing the local weak observability of this resulting system and propose techniques for its efficient computation. We also propose a greedy heuristic to determine the minimum set of observable nodes, and demonstrate our approach numerically on different hypergraph topologies, and hypergraphs derived from an experimental biological dataset.

    Comment: 7 pages, 3 figures, 2 algorithms, lots of math!
    Schlagwörter Mathematics - Dynamical Systems ; Electrical Engineering and Systems Science - Systems and Control
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2023-04-10
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  3. Buch ; Online: An efficient quantum algorithm for simulating polynomial differential equations

    Surana, Amit / Gnanasekaran, Abeynaya / Sahai, Tuhin

    2022  

    Abstract: We present an efficient quantum algorithm to simulate nonlinear differential equations with polynomial vector fields of arbitrary degree on quantum platforms. Models of physical systems that are governed by ordinary differential equations (ODEs) or ... ...

    Abstract We present an efficient quantum algorithm to simulate nonlinear differential equations with polynomial vector fields of arbitrary degree on quantum platforms. Models of physical systems that are governed by ordinary differential equations (ODEs) or partial differential equation (PDEs) can be challenging to solve on classical computers due to high dimensionality, stiffness, nonlinearities, and sensitive dependence to initial conditions. For sparse $n$-dimensional linear ODEs, quantum algorithms have been developed which can produce a quantum state proportional to the solution in poly(log(nx)) time using the quantum linear systems algorithm (QLSA). Recently, this framework was extended to systems of nonlinear ODEs with quadratic polynomial vector fields by applying Carleman linearization that enables the embedding of the quadratic system into an approximate linear form. A detailed complexity analysis was conducted which showed significant computational advantage under certain conditions. We present an extension of this algorithm to deal with systems of nonlinear ODEs with $k$-th degree polynomial vector fields for arbitrary (finite) values of $k$. The steps involve: 1) mapping the $k$-th degree polynomial ODE to a higher dimensional quadratic polynomial ODE; 2) applying Carleman linearization to transform the quadratic ODE to an infinite-dimensional system of linear ODEs; 3) truncating and discretizing the linear ODE and solving using the forward Euler method and QLSA. Alternatively, one could apply Carleman linearization directly to the $k$-th degree polynomial ODE, resulting in a system of infinite-dimensional linear ODEs, and then apply step 3. This solution route can be computationally more efficient. We present detailed complexity analysis of the proposed algorithms, prove polynomial scaling of runtime on $k$ and demonstrate the framework on an example.
    Schlagwörter Mathematics - Dynamical Systems ; Computer Science - Data Structures and Algorithms ; Quantum Physics
    Thema/Rubrik (Code) 518
    Erscheinungsdatum 2022-12-21
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  4. Artikel ; Online: HAT: Hypergraph analysis toolbox.

    Pickard, Joshua / Chen, Can / Salman, Rahmy / Stansbury, Cooper / Kim, Sion / Surana, Amit / Bloch, Anthony / Rajapakse, Indika

    PLoS computational biology

    2023  Band 19, Heft 6, Seite(n) e1011190

    Abstract: Recent advances in biological technologies, such as multi-way chromosome conformation capture (3C), require development of methods for analysis of multi-way interactions. Hypergraphs are mathematically tractable objects that can be utilized to precisely ... ...

    Abstract Recent advances in biological technologies, such as multi-way chromosome conformation capture (3C), require development of methods for analysis of multi-way interactions. Hypergraphs are mathematically tractable objects that can be utilized to precisely represent and analyze multi-way interactions. Here we present the Hypergraph Analysis Toolbox (HAT), a software package for visualization and analysis of multi-way interactions in complex systems.
    Mesh-Begriff(e) Chromosomes ; Software
    Sprache Englisch
    Erscheinungsdatum 2023-06-05
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1011190
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  5. Buch ; Online: Hypergraph Dissimilarity Measures

    Surana, Amit / Chen, Can / Rajapakse, Indika

    2021  

    Abstract: In this paper, we propose two novel approaches for hypergraph comparison. The first approach transforms the hypergraph into a graph representation for use of standard graph dissimilarity measures. The second approach exploits the mathematics of tensors ... ...

    Abstract In this paper, we propose two novel approaches for hypergraph comparison. The first approach transforms the hypergraph into a graph representation for use of standard graph dissimilarity measures. The second approach exploits the mathematics of tensors to intrinsically capture multi-way relations. For each approach, we present measures that assess hypergraph dissimilarity at a specific scale or provide a more holistic multi-scale comparison. We test these measures on synthetic hypergraphs and apply them to biological datasets.
    Schlagwörter Computer Science - Machine Learning
    Erscheinungsdatum 2021-06-15
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  6. Buch ; Online: Controllability of Hypergraphs

    Chen, Can / Surana, Amit / Bloch, Anthony / Rajapakse, Indika

    2020  

    Abstract: In this paper, we develop a notion of controllability for hypergraphs via tensor algebra and polynomial control theory. Inspired by uniform hypergraphs, we propose a new tensor-based multilinear dynamical system representation, and derive a Kalman-rank- ... ...

    Abstract In this paper, we develop a notion of controllability for hypergraphs via tensor algebra and polynomial control theory. Inspired by uniform hypergraphs, we propose a new tensor-based multilinear dynamical system representation, and derive a Kalman-rank-like condition to determine the minimum number of control nodes (MCN) needed to achieve controllability of even uniform hypergraphs. We present an efficient heuristic to obtain the MCN. MCN can be used as a measure of robustness, and we show that it is related to the hypergraph degree distribution in simulated examples. Finally, we use MCN to examine robustness in real biological networks.

    Comment: 11 pages, 9 figures, 1 table
    Schlagwörter Mathematics - Optimization and Control ; Computer Science - Machine Learning ; Computer Science - Social and Information Networks ; Electrical Engineering and Systems Science - Systems and Control
    Erscheinungsdatum 2020-05-25
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    Dokumenttyp Buch ; Online
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  7. Buch ; Online: Guided Policy Search Based Control of a High Dimensional Advanced Manufacturing Process

    Surana, Amit / Reddy, Kishore / Siopis, Matthew

    2020  

    Abstract: In this paper we apply guided policy search (GPS) based reinforcement learning framework for a high dimensional optimal control problem arising in an additive manufacturing process. The problem comprises of controlling the process parameters so that ... ...

    Abstract In this paper we apply guided policy search (GPS) based reinforcement learning framework for a high dimensional optimal control problem arising in an additive manufacturing process. The problem comprises of controlling the process parameters so that layer-wise deposition of material leads to desired geometric characteristics of the resulting part surface while minimizing the material deposited. A realistic simulation model of the deposition process along with carefully selected set of guiding distributions generated based on iterative Linear Quadratic Regulator is used to train a neural network policy using GPS. A closed loop control based on the trained policy and in-situ measurement of the deposition profile is tested experimentally, and shows promising performance.
    Schlagwörter Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Systems and Control
    Erscheinungsdatum 2020-09-12
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  8. Buch ; Online: Assessment of Faster R-CNN in Man-Machine collaborative search

    Deza, Arturo / Surana, Amit / Eckstein, Miguel P.

    2019  

    Abstract: With the advent of modern expert systems driven by deep learning that supplement human experts (e.g. radiologists, dermatologists, surveillance scanners), we analyze how and when do such expert systems enhance human performance in a fine-grained small ... ...

    Abstract With the advent of modern expert systems driven by deep learning that supplement human experts (e.g. radiologists, dermatologists, surveillance scanners), we analyze how and when do such expert systems enhance human performance in a fine-grained small target visual search task. We set up a 2 session factorial experimental design in which humans visually search for a target with and without a Deep Learning (DL) expert system. We evaluate human changes of target detection performance and eye-movements in the presence of the DL system. We find that performance improvements with the DL system (computed via a Faster R-CNN with a VGG16) interacts with observer's perceptual abilities (e.g., sensitivity). The main results include: 1) The DL system reduces the False Alarm rate per Image on average across observer groups of both high/low sensitivity; 2) Only human observers with high sensitivity perform better than the DL system, while the low sensitivity group does not surpass individual DL system performance, even when aided with the DL system itself; 3) Increases in number of trials and decrease in viewing time were mainly driven by the DL system only for the low sensitivity group. 4) The DL system aids the human observer to fixate at a target by the 3rd fixation. These results provide insights of the benefits and limitations of deep learning systems that are collaborative or competitive with humans.

    Comment: To be presented at CVPR 2019 in Long Beach, CA
    Schlagwörter Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Human-Computer Interaction ; Computer Science - Robotics
    Thema/Rubrik (Code) 004
    Erscheinungsdatum 2019-04-04
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  9. Buch ; Online: Data-Guided Model Reduction for Multilinear Time Invariant Systems

    Chen, Can / Surana, Amit / Bloch, Anthony / Rajapakse, Indika

    2019  

    Abstract: In this paper, we explore the role of tensor algebra in the balanced truncation (BT) based model reduction/identification for high-dimensional multilinear/linear time invariant systems. Tensor decompositions facilitate discovery of hidden patterns/ ... ...

    Abstract In this paper, we explore the role of tensor algebra in the balanced truncation (BT) based model reduction/identification for high-dimensional multilinear/linear time invariant systems. Tensor decompositions facilitate discovery of hidden patterns/redundancies in multiway data by capturing higher-order interactions and couplings to obtain highly compressed yet accurate representations, enabling memory and computational advantages. In particular, we employ tensor train decomposition (TTD), which compared to other tensor decompositions, provides a good compromise between numerical stability and level of compression, and has an associated algebra which facilitates computations. Using TTD, we propose a new BT approach which we refer to as higher-order balanced truncation, and consider different data-guided variations including higher-order empirical gramians, higher-order balanced proper orthogonal decomposition and higher-order eigensystem realization algorithm. We perform computational and memory complexity analysis for these different flavors of TTD based BT methods, and compare with the corresponding standard BT methods, to develop insights into where the proposed framework may be beneficial. We provide numerical results on simulated and experimental datasets showing the efficacy of the proposed framework.

    Comment: 26 pages, 5 figures, 2 tables, submitted
    Schlagwörter Electrical Engineering and Systems Science - Systems and Control
    Erscheinungsdatum 2019-12-07
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    Dokumenttyp Buch ; Online
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  10. Buch ; Online: Multilinear Control Systems Theory

    Chen, Can / Surana, Amit / Bloch, Anthony / Rajapakse, Indika

    2019  

    Abstract: In this paper, we provide a system theoretic treatment of a new class of multilinear time-invariant (MLTI) systems in which the states, inputs and outputs are tensors, and the system evolution is governed by multilinear operators. The MLTI system ... ...

    Abstract In this paper, we provide a system theoretic treatment of a new class of multilinear time-invariant (MLTI) systems in which the states, inputs and outputs are tensors, and the system evolution is governed by multilinear operators. The MLTI system representation is based on the Einstein product and even-order paired tensors. There is a particular tensor unfolding which gives rise to an isomorphism from this tensor space to the general linear group, i.e. the group of invertible matrices. By leveraging this unfolding operation, one can extend classical linear time-invariant (LTI) system notions including stability, reachability and observability to MLTI systems. While the unfolding based formulation is a powerful theoretical construct, the computational advantages of MLTI systems can only be fully realized while working with the tensor form, where hidden patterns/structures can be exploited for efficient representations and computations. Along these lines, we establish new results which enable one to express tensor unfolding based stability, reachability and observability criteria in terms of more standard notions of tensor ranks/decompositions. In addition, we develop a generalized CANDECOMP/PARAFAC decomposition and tensor train decomposition based model reduction framework, which can significantly reduce the number of MLTI system parameters. We demonstrate our framework with numerical examples.

    Comment: 27 pages, 2 figures, 3 tables, SIAM Journal on Control and Optimization, accepted to appear. arXiv admin note: text overlap with arXiv:1905.07427
    Schlagwörter Mathematics - Optimization and Control ; Electrical Engineering and Systems Science - Systems and Control ; Mathematics - Numerical Analysis
    Thema/Rubrik (Code) 510
    Erscheinungsdatum 2019-05-20
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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