Hybrid Histories: Technologies of Memory and the Cultural Dynamics of Home Movies, 1895-2005

Doctoral thesis (2018)

This dissertation analyses the cultural dynamics of home movies in the twentieth century. It investigates how various generations have recorded their family memories on film, video and digital media, and ... [more ▼]

This dissertation analyses the cultural dynamics of home movies in the twentieth century. It investigates how various generations have recorded their family memories on film, video and digital media, and, more specifically, how changes in these “technologies of memory” have shaped new forms of home movie making and screening. Covering the period from the invention of the film camera in the late nineteenth century, the introduction of 9.5mm, 16mm, 8mm small-gauges and Super 8 film technologies for amateurs, via home video to digital media technologies, this study addresses the complex interrelations between the materiality of film, video and digital media technologies, their social usages and cultural meanings from a long-term historical perspective. Focusing on specific periods of transition, it becomes clear that different media technologies, user practices and discourses not only succeed each other in time, but also increasingly interrelate, interact or even transform each other. Maintaining both a diachronic and a synchronic perspective on media transitions, this dissertation proposes an alternative form of media historiography that rethinks media histories beyond the frameworks of change and continuity by perceiving hybridity as a constant factor in media historical development. [less ▲]

Hybrid Histories: Versuch einer kritischen Standortbestimmung der Mediengeschichte

Presentation (2017, November 23)

A Hybrid Machine Learning and Schedulability Method for the Verification of TSN Networks

in 15th IEEE International Workshop on Factory Communication Systems (WFCS2019) (2019, March)

Machine learning (ML), and supervised learning in particular, can be used to learn what makes it hard for a network to be feasible and try to predict whether a network configuration will be feasible ... [more ▼]

Machine learning (ML), and supervised learning in particular, can be used to learn what makes it hard for a network to be feasible and try to predict whether a network configuration will be feasible without executing a conventional schedulability analysis. A disadvantage of ML-based timing verification with respect to schedulability analysis is the possibility of "false positives": configurations deemed feasible while they are not. In this work, in order to minimize the rate of false positives, we propose the use of a measure of the uncertainty of the prediction to drop it when the uncertainty is too high, and rely instead on schedulability analysis. In this hybrid verification strategy, the clear-cut decisions are taken by ML, while the more difficult ones are taken by a conventional schedulability analysis. Importantly, the trade-off achieved between prediction accuracy and computing time can be controlled. We apply this hybrid verification method to Ethernet TSN networks and obtain, for instance in the case of priority scheduling with 8 traffic classes, a 99% prediction accuracy with a speedup factor of 5.7 with respect to conventional schedulability analysis and a reduction of 46% of the false positives compared to ML alone. [less ▲]

Hybrid mesh/particle meshless method for modeling geological flows with discontinuous transport properties

E-print/Working paper (2016)

In the present paper, we introduce the Finite Difference Method-Meshless Method (FDM-MM) in the context of geodynamical simulations. The proposed numerical scheme relies on the well-established FD method ... [more ▼]

In the present paper, we introduce the Finite Difference Method-Meshless Method (FDM-MM) in the context of geodynamical simulations. The proposed numerical scheme relies on the well-established FD method along with the newly developed “meshless” method and, is considered as a hybrid Eulerian/Lagrangian scheme. Mass, momentum, and energy equations are solved using an FDM method, while material properties are distributed over a set of markers (particles), which represent the spatial domain, with the solution interpolated back to the Eulerian grid. The proposed scheme is capable of solving flow equations (Stokes flow) in uniform geometries with particles, “sprinkled” in the spatial domain and is used to solve convection- diffusion problems avoiding the oscillation produced in the Eulerian approach. The resulting algebraic linear systems were solved using direct solvers. Our hybrid approach can capture sharp variations of stresses and thermal gradients in problems with a strongly variable viscosity and thermal conductivity as demonstrated through various benchmarking test cases. The present hybrid approach allows for the accurate calculation of fine thermal structures, offering local type adaptivity through the flexibility of the particle method. [less ▲]

A hybrid MGA-MSGD ANN training approach for approximate solution of linear elliptic PDEs

E-print/Working paper (2020)

We introduce a hybrid "Modified Genetic Algorithm-Multilevel Stochastic Gradient Descent" (MGA-MSGD) training algorithm that considerably improves accuracy and efficiency of solving 3D mechanical problems ... [more ▼]

We introduce a hybrid "Modified Genetic Algorithm-Multilevel Stochastic Gradient Descent" (MGA-MSGD) training algorithm that considerably improves accuracy and efficiency of solving 3D mechanical problems described, in strong-form, by PDEs via ANNs (Artificial Neural Networks). This presented approach allows the selection of a number of locations of interest at which the state variables are expected to fulfil the governing equations associated with a physical problem. Unlike classical PDE approximation methods such as finite differences or the finite element method, there is no need to establish and reconstruct the physical field quantity throughout the computational domain in order to predict the mechanical response at specific locations of interest. The basic idea of MGA-MSGD is the manipulation of the learnable parameters’ components responsible for the error explosion so that we can train the network with relatively larger learning rates which avoids trapping in local minima. The proposed training approach is less sensitive to the learning rate value, training points density and distribution, and the random initial parameters. The distance function to minimise is where we introduce the PDEs including any physical laws and conditions (so-called, Physics Informed ANN). The Genetic algorithm is modified to be suitable for this type of ANN in which a Coarse-level Stochastic Gradient Descent (CSGD) is exploited to make the decision of the offspring qualification. Employing the presented approach, a considerable improvement in both accuracy and efficiency, compared with standard training algorithms such classical SGD and Adam optimiser, is observed. The local displacement accuracy is studied and ensured by introducing the results of Finite Element Method (FEM) at sufficiently fine mesh as the reference displacements. A slightly more complex problem is solved ensuring the feasibility of the methodology [less ▲]

Hybrid MPI+OpenMP Implementation of eXtended Discrete Element Method

in Proc. of the 9th Workshop on Applications for Multi-Core Architectures (WAMCA'18), part of 30th Intl. Symp. on Computer Architecture and High Performance Computing (SBAC-PAD 2018) (2018, September)

The Extended Discrete Element Method (XDEM) is a novel and innovative numerical simulation technique that ex- tends classical Discrete Element Method (DEM) (which simulates the motion of granular material ... [more ▼]

The Extended Discrete Element Method (XDEM) is a novel and innovative numerical simulation technique that ex- tends classical Discrete Element Method (DEM) (which simulates the motion of granular material), by additional properties such as the chemical composition, thermodynamic state, stress/strain for each particle. It has been applied successfully to numerous industries involving the processing of granular materials such as sand, rock, wood or coke [16], [17]. In this context, computational simulation with (X)DEM has become a more and more essential tool for researchers and scientific engineers to set up and explore their experimental processes. However, increasing the size or the accuracy of a model requires the use of High Performance Computing (HPC) platforms over a parallelized implementation to accommodate the growing needs in terms of memory and computation time. In practice, such a parallelization is traditionally obtained using either MPI (distributed memory computing), OpenMP (shared memory computing) or hybrid approaches combining both of them. In this paper, we present the results of our effort to implement an OpenMP version of XDEM allowing hybrid MPI+OpenMP simulations (XDEM being already parallelized with MPI). Far from the basic OpenMP paradigm and recommendations (which simply summarizes by decorating the main computation loops with a set of OpenMP pragma), the OpenMP parallelization of XDEM required a fundamental code re-factoring and careful tuning in order to reach good performance. There are two main reasons for those difficulties. Firstly, XDEM is a legacy code devel- oped for more than 10 years, initially focused on accuracy rather than performance. Secondly, the particles in a DEM simulation are highly dynamic: they can be added, deleted and interaction relations can change at any timestep of the simulation. Thus this article details the multiple layers of optimization applied, such as a deep data structure profiling and reorganization, the usage of fast multithreaded memory allocators and of advanced process/thread-to-core pinning techniques. Experimental results evaluate the benefit of each optimization individually and validate the implementation using a real-world application executed on the HPC platform of the University of Luxembourg. Finally, we present our Hybrid MPI+OpenMP results with a 15%-20% performance gain and how it overcomes scalability limits (by increasing the number of compute cores without dropping of performances) of XDEM-based pure MPI simulations. [less ▲]

A hybrid particle-mesh method for incompressible active polar viscous gels

in Journal of Computational Physics (2015), 291

We present a hybrid particle-mesh method for numerically solving the hydrodynamic equations of incompressible active polar viscous gels. These equations model the dynamics of polar active agents, embedded ... [more ▼]

We present a hybrid particle-mesh method for numerically solving the hydrodynamic equations of incompressible active polar viscous gels. These equations model the dynamics of polar active agents, embedded in a viscous medium, in which stresses are induced through constant consumption of energy. The numerical method is based on Lagrangian particles and staggered Cartesian finite-difference meshes. We show that the method is second-order and first-order accurate with respect to grid and time-step sizes, respectively. Using the present method, we simulate the hydrodynamics in rectangular geometries, of a passive liquid crystal, of an active polar film and of active gels with topological defects in polarization. We show the emergence of spontaneous flow due to Fréedericksz transition, and transformation in the nature of topological defects by tuning the activity of the system. [less ▲]

Hybrid plasmonic nanostructures based on controlled integration of MoS2 flakes on metallic nanoholes

in Nanoscale (2018), 10(36), 17105-17111

Here, we propose an easy and robust strategy for the versatile preparation of hybrid plasmonic nanopores by means of controlled deposition of single flakes of MoS2 directly on top of metallic holes. The ... [more ▼]

Here, we propose an easy and robust strategy for the versatile preparation of hybrid plasmonic nanopores by means of controlled deposition of single flakes of MoS2 directly on top of metallic holes. The device is realized on silicon nitride membranes and can be further refined by TEM or FIB milling to achieve the passing of molecules or nanometric particles through a pore. Importantly, we show that the plasmonic enhancement provided by the nanohole is strongly accumulated in the 2D nanopore, thus representing an ideal system for single-molecule sensing and sequencing in a flow-through configuration. Here, we also demonstrate that the prepared 2D material can be decorated with metallic nanoparticles that can couple their resonance with the nanopore resonance to further enhance the electromagnetic field confinement at the nanoscale level. This method can be applied to any gold nanopore with a high level of reproducibility and parallelization; hence, it can pave the way to the next generation of solid-state nanopores with plasmonic functionalities. Moreover, the controlled/ordered integration of 2D materials on plasmonic nanostructures opens a pathway towards new investigation of the following: enhanced light emission; strong coupling from plasmonic hybrid structures; hot electron generation; and sensors in general based on 2D materials. [less ▲]

Hybrid Societies: Challenges and Perspectives in the Design of Collective Behavior in Self-Organizing Systems

in Frontiers in Robotics and AI (2016), 3(14),

A hybrid T-Trefftz polygonal finite element for linear elasticity

E-print/Working paper (2014)

In this paper, we construct hybrid T-Trefftz polygonal finite elements. The displacement field within the polygon is repre- sented by the homogeneous solution to the governing differential equation, also ... [more ▼]

In this paper, we construct hybrid T-Trefftz polygonal finite elements. The displacement field within the polygon is repre- sented by the homogeneous solution to the governing differential equation, also called as the T-complete set. On the boundary of the polygon, a conforming displacement field is independently defined to enforce continuity of the displacements across the element boundary. An optimal number of T-complete functions are chosen based on the number of nodes of the polygon and degrees of freedom per node. The stiffness matrix is computed by the hybrid formulation with auxiliary displacement frame. Results from the numerical studies presented for a few benchmark problems in the context of linear elasticity shows that the proposed method yield highly accurate results. [less ▲]