Quantum Corner-Transfer Matrix DMRG

We propose a new method for the calculation of thermodynamic properties of one-dimensional quantum systems by combining the TMRG approach with the corner transfer-matrix method. The corner transfer-matrix DMRG method brings reasonable advantage over TMRG for classical systems. We have modified the concept for the calculation of thermal properties of one-dimensional quantum systems. The novel QCTMRG algorithm is implemented and used to study two simple test cases, the classical Ising chain and the isotropic Heisenberg model. In a discussion, the advantages and challenges are illuminated.Comment: 17 pages, 15 figures, to appear in Int.J.Mod.Phys.

Analyzing the Gadgets Towards a Metric to Measure Gadget Quality

Current low-level exploits often rely on code-reuse, whereby short sections of code (gadgets) are chained together into a coherent exploit that can be executed without the need to inject any code. Several protection mechanisms attempt to eliminate this attack vector by applying code transformations to reduce the number of available gadgets. Nevertheless, it has emerged that the residual gadgets can still be sufficient to conduct a successful attack. Crucially, the lack of a common metric for "gadget quality" hinders the effective comparison of current mitigations. This work proposes four metrics that assign scores to a set of gadgets, measuring quality, usefulness, and practicality. We apply these metrics to binaries produced when compiling programs for architectures implementing Intel's recent MPX CPU extensions. Our results demonstrate a 17% increase in useful gadgets in MPX binaries, and a decrease in side-effects and preconditions, making them better suited for ROP attacks.Comment: International Symposium on Engineering Secure Software and Systems, Apr 2016, London, United Kingdo

Operator splitting for semi-explicit differential-algebraic equations and port-Hamiltonian DAEs

Operator splitting methods allow to split the operator describing a complex dynamical system into a sequence of simpler subsystems and treat each part independently. In the modeling of dynamical problems, systems of (possibly coupled) differential-algebraic equations (DAEs) arise. This motivates the application of operator splittings which are aware of the various structural forms of DAEs. Here, we present an approach for the splitting of coupled index-1 DAE as well as for the splitting of port-Hamiltonian DAEs, taking advantage of the energy-conservative and energy-dissipative parts. We provide numerical examples illustrating our second-order convergence results

A numerical tool for the study of the hydrodynamic recovery of the Lattice Boltzmann Method

We investigate the hydrodynamic recovery of Lattice Boltzmann Method (LBM) by analyzing exact balance relations for energy and enstrophy derived from averaging the equations of motion on sub-volumes of different sizes. In the context of 2D isotropic homogeneous turbulence, we first validate this approach on decaying turbulence by comparing the hydrodynamic recovery of an ensemble of LBM simulations against the one of an ensemble of Pseudo-Spectral (PS) simulations. We then conduct a benchmark of LBM simulations of forced turbulence with increasing Reynolds number by varying the input relaxation times of LBM. This approach can be extended to the study of implicit subgrid-scale (SGS) models, thus offering a promising route to quantify the implicit SGS models implied by existing stabilization techniques within the LBM framework

An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-oriented Systems

Deep Reinforcement Learning (Deep RL) is increasingly used to cope with the open-world assumption in service-oriented systems. Deep RL was successfully applied to problems such as dynamic service composition, job scheduling, and offloading, as well as service adaptation. While Deep RL offers many benefits, understanding the decision-making of Deep RL is challenging because its learned decision-making policy essentially appears as a black box. Yet, understanding the decision-making of Deep RL is key to help service developers perform debugging, support service providers to comply with relevant legal frameworks, and facilitate service users to build trust. We introduce Chat4XAI to facilitate the understanding of the decision-making of Deep RL by providing natural-language explanations. Compared with visual explanations, the reported benefits of natural-language explanations include better understandability for non-technical users, increased user acceptance and trust, as well as more efficient explanations. Chat4XAI leverages modern AI chatbot technology and dedicated prompt engineering. Compared to earlier work on natural-language explanations using classical software-based dialogue systems, using an AI chatbot eliminates the need for eliciting and defining potential questions and answers up-front. We prototypically realize Chat4XAI using OpenAI's ChatGPT API and evaluate the fidelity and stability of its explanations using an adaptive service exemplar.Comment: To be published at 21st Int'l Conference on Service-Oriented Computing (ICSOC 2023), Rome, Italy, November 28-December 1, 2023, ser. LNCS, F. Monti, S. Rinderle-Ma, A. Ruiz Cortes, Z. Zheng, M. Mecella, Eds., Springer, 202

Operator Splitting Based Dynamic Iteration for Linear Port-Hamiltonian Systems

A dynamic iteration scheme for linear differential-algebraic port-Hamil\-tonian systems based on Lions-Mercier-type operator splitting methods is developed. The dynamic iteration is monotone in the sense that the error is decreasing and no stability conditions are required. The developed iteration scheme is even new for linear port-Hamiltonian systems. The obtained algorithm is applied to multibody systems and electrical networks.Comment: 29 pages, 6 figure

Coupled vortex oscillations in spatially separated permalloy squares

We experimentally study the magnetization dynamics of pairs of micron-sized permalloy squares coupled via their stray fields. The trajectories of the vortex cores in the Landau-domain patterns of the squares are mapped in real space using time-resolved scanning transmission x-ray microscopy. After excitation of one of the vortex cores with a short magnetic-field pulse, the system behaves like coupled harmonic oscillators. The coupling strength depends on the separation between the squares and the configuration of the vortex-core polarizations. Considering the excitation via a rotating in-plane magnetic field, it can be understood that only a weak response of the second vortex core is observed for equal core polarizations

Parallel Programming of gradient-based iterative image reconstruction schemes for optical tomography

Summary Optical tomography (OT) is a fast developing novel imaging modality that uses near-infrared (NIR) light to obtain cross-sectional views of optical properties inside the human body. A major challenge remains the time-consuming, computationalintensive image reconstruction problem that converts NIR transmission measurements into cross-sectional images. To increase the speed of iterative image reconstruction schemes that are commonly applied for OT, we have developed and implemented several parallel algorithms on a cluster of workstations. Static process distribution as well as dynamic load balancing schemes suitable for heterogeneous clusters and varying machine performances are introduced and tested. The resulting algorithms are shown to accelerate the reconstruction process to various degrees, substantially reducing the computation times for clinically relevant problems