arXiv subjects

Florian Frank

Publications and source records attributed to Florian Frank.

At least 19 recordsLinked to original sources

arXiv2025

Learning Automata with Name Allocation

Automata over infinite alphabets have emerged as a convenient computational model for processing structures involving data, such as nonces in cryptographic protocols or data values in XML documents. We introduce active learning methods for bar automata, a species of automata that process finite data words represented as bar strings, which are words with explicit name binding letters. Bar automata have pleasant algorithmic properties. We develop a framework in which every learning algorithm for standard deterministic or nondeterministic finite automata over finite alphabets can be used to learn bar automata, with a query complexity determined by that of the chosen learner. The technical key to our approach is the algorithmic handling of $\alpha$-equivalence of bar strings, which allows bridging the gap between finite and infinite alphabets. The principles underlying our framework are generic and also apply to bar B\"uchi automata and bar tree automata, leading to the first active learning methods for data languages of infinite words and finite trees.

cs.FL↗

arXiv2024

Robust Noise Suppression and Quantum Sensing by Continuous Phased Dynamical Decoupling

We propose and demonstrate experimentally continuous phased dynamical decoupling (CPDD), where we apply a continuous field with discrete phase changes for quantum sensing and robust compensation of environmental and amplitude noise. CPDD does not use short pulses, making it particularly suitable for experiments with limited driving power or nuclear magnetic resonance at high magnetic fields. It requires control of the timing of the phase changes, offering much greater precision than the Rabi frequency control needed in standard continuous sensing schemes. We successfully apply our method to nanoscale nuclear magnetic resonance and combine it with quantum heterodyne detection, achieving microhertz uncertainty in the estimated signal frequency for a 120 s measurement. Our Letter expands significantly the applicability of dynamical decoupling and opens the door for a wide range of experiments, e.g., in nitrogen-vacancy centers, trapped ions, or trapped atoms.

quant-ph↗

arXiv2024

Alternating Nominal Automata with Name Allocation

Formal languages over infinite alphabets serve as abstractions of structures and processes carrying data. Automata models over infinite alphabets, such as classical register automata or, equivalently, nominal orbit-finite automata, tend to have computationally hard or even undecidable reasoning problems unless stringent restrictions are imposed on either the power of control or the number of registers. This has been shown to be ameliorated in automata models with name allocation such as regular nondeterministic nominal automata, which allow for deciding language inclusion in elementary complexity even with unboundedly many registers while retaining a reasonable level of expressiveness. In the present work, we demonstrate that elementary complexity survives under extending the power of control to alternation: We introduce regular alternating nominal automata (RANAs), and show that their non-emptiness and inclusion problems have elementary complexity even when the number of registers is unbounded. Moreover, we show that RANAs allow for nearly complete de-alternation, specifically de-alternation up to a single deadlocked universal state. As a corollary to our results, we improve the complexity of model checking for a flavour of Bar-$\mu$TL, a fixed-point logic with name allocation over finite data words, by one exponential level.

cs.FL↗

arXiv2023

Positive Data Languages

Positive data languages are languages over an infinite alphabet closed under possibly non-injective renamings of data values. Informally, they model properties of data words expressible by assertions about equality, but not inequality, of data values occurring in the word. We investigate the class of positive data languages recognizable by nondeterministic orbit-finite nominal automata, an abstract form of register automata introduced by Bojańczyk, Klin, and Lasota. As our main contribution we provide a number of equivalent characterizations of that class in terms of positive register automata, monadic second-order logic with positive equality tests, and finitely presentable nondeterministic automata in the categories of nominal renaming sets and of presheaves over finite sets.

cs.FL↗

arXiv2023

Real-World Chaos-Based Cryptography Using Synchronised Chua Chaotic Circuits

This work presents the hardware demonstrator of a secure encryption system based on synchronised Chua chaotic circuits. In particular, the presented encryption system comprises two Chua circuits that are synchronised using a dedicated bidirectional synchronisation line. One of them forms part of the transmitter, while the other of the receiver. Both circuits are tuned to operate in a chaotic mode. The output (chaotic) signal of the first circuit (transmitter) is digitised and then combined with the message to be encrypted, through an XOR gate. The second Chua circuit (receiver) is used for the decryption; the output chaotic signal of this circuit is similarly digitised and combined with the encrypted message to retrieve the original message. Our hardware demonstrator proves that this method can be used in order to provide extremely lightweight real-world, chaos-based cryptographic solutions.

cs.CR↗

arXiv2023

Practical Non-Invasive Probing Attacks Against Novel Carbon-Nanotube-Based Physical Unclonable Functions

As the number of devices being interconnected increases, so does also the demand for (lightweight) security. To this end, Physical Unclonable Functions (PUFs) have been proposed as hardware primitives that can act as roots of trust and security. Recently, a new type of PUF based on Carbon NanoTubes (CNTs) has been proposed. At the same time, attacks and testing based on direct electrical probing appear to be moving towards non-invasive techniques. In this context, this work attempts to examine the potential for practical non-invasive probing attacks against the CNT-PUF, a novel PUF based on CNTs. Our results indicate that direct probing might potentially compromise the security of this PUF. Nevertheless, we note that this holds true only in the case that the attacker can directly probe the wire corresponding to the secret value of each CNT-PUF cell. Thus, we can conclude that the examined CNT-PUFs are rather resilient to direct probing attacks, that non-invasive probing methods appear to be promising for testing such PUFs, and that, in order for the attacker to gain the full-length value of the secret, all the relevant channels would need to be probed. Nevertheless, as our work proves, practical non-invasive attacks against the CNT-PUF are feasible and adequate countermeasures need to be employed in order to address this issue.

cs.CR↗

arXiv2022

Estimating relative diffusion from 3D micro-CT images using CNNs

In the past several years, convolutional neural networks (CNNs) have proven their capability to predict characteristic quantities in porous media research directly from pore-space geometries. Due to the frequently observed significant reduction in computation time in comparison to classical computational methods, bulk parameter prediction via CNNs is especially compelling, e.g. for effective diffusion. While the current literature is mainly focused on fully saturated porous media, the partially saturated case is also of high interest. Due to the qualitatively different and more complex geometries of the domain available for diffusive transport present in this case, standard CNNs tend to lose robustness and accuracy with lower saturation rates. In this paper, we demonstrate the ability of CNNs to perform predictions of relative diffusion directly from full pore-space geometries. As such, our CNN conveniently fuses diffusion prediction and a well-established morphological model which describes phase distributions in partially saturated porous media.

physics.comp-ph↗

arXiv2022

Abusing Commodity DRAMs in IoT Devices to Remotely Spy on Temperature

The ubiquity and pervasiveness of modern Internet of Things (IoT) devices opens up vast possibilities for novel applications, but simultaneously also allows spying on, and collecting data from, unsuspecting users to a previously unseen extent. This paper details a new attack form in this vein, in which the decay properties of widespread, off-the-shelf DRAM modules are exploited to accurately sense the temperature in the vicinity of the DRAM-carrying device. Among others, this enables adversaries to remotely and purely digitally spy on personal behavior in users' private homes, or to collect security-critical data in server farms, cloud storage centers, or commercial production lines. We demonstrate that our attack can be performed by merely compromising the software of an IoT device and does not require hardware modifications or physical access at attack time. It can achieve temperature resolutions of up to 0.5°C over a range of 0°C to 70°C in practice. Perhaps most interestingly, it even works in devices that do not have a dedicated temperature sensor on board. To complete our work, we discuss practical attack scenarios as well as possible countermeasures against our temperature espionage attacks.

cs.CR↗

arXiv2022

On the Sustainability of Lightweight Cryptography Based on PUFs Implemented on NAND Flash Memories Using Programming Disturbances

In this work, we examine the potential of Physical Unclonable Functions (PUFs) that have been implemented on NAND Flash memories using programming disturbances to act as sustainable primitives for the purposes of lightweight cryptography. In particular, we investigate the ability of such PUFs to tolerate temperature and voltage variations, and examine the current shortcomings of existing NAND-Flash-memory PUFs that are based on programming disturbances as well as how these could potentially be addressed in order to provide more robust and more sustainable security solutions.

cs.CR↗

arXiv2022

Estimating permeability of 3D micro-CT images by physics-informed CNNs based on DNS

In recent years, convolutional neural networks (CNNs) have experienced an increasing interest in their ability to perform a fast approximation of effective hydrodynamic parameters in porous media research and applications. This paper presents a novel methodology for permeability prediction from micro-CT scans of geological rock samples. The training data set for CNNs dedicated to permeability prediction consists of permeability labels that are typically generated by classical lattice Boltzmann methods (LBM) that simulate the flow through the pore space of the segmented image data. We instead perform direct numerical simulation (DNS) by solving the stationary Stokes equation in an efficient and distributed-parallel manner. As such, we circumvent the convergence issues of LBM that frequently are observed on complex pore geometries, and therefore, improve the generality and accuracy of our training data set. Using the DNS-computed permeabilities, a physics-informed CNN PhyCNN) is trained by additionally providing a tailored characteristic quantity of the pore space. More precisely, by exploiting the connection to flow problems on a graph representation of the pore space, additional information about confined structures is provided to the network in terms of the maximum flow value, which is the key innovative component of our workflow. The robustness of this approach is reflected by very high prediction accuracy, which is observed for a variety of sandstone samples from archetypal rock formations.

cs.LG↗

arXiv2022

Coalgebraic Semantics for Nominal Automata

This paper provides a coalgebraic approach to the language semantics of two types of non-deterministic automata over nominal sets: non-deterministic orbit-finite automata (NOFAs) and regular nominal non-deterministic automata (RNNAs), which were introduced in previous work. While NOFAs are a straightforward nominal version of non-deterministic automata, RNNAs feature ordinary as well as name binding transitions. Correspondingly, words accepted by RNNAs are strings formed by ordinary letters and name binding letters. Bar languages are sets of such words modulo $α$-equivalence, and to every state of an RNNA one associates its accepted bar language. We show that the semantics of NOFAs and RNNAs, respectively, arise both as an instance of the Kleisli-style coalgebraic trace semantics as well as an instance of the coalgebraic language semantics obtained via generalized determinization. On the way we revisit coalgebraic trace semantics in general and give a new compact proof for the main result in that theory stating that an initial algebra for a functor yields the terminal coalgebra for the Kleisli extension of the functor. Our proof requires fewer assumptions on the functor than all previous ones.

cs.LO↗

arXiv2021

A Diffuse Interface Model for Cell Blebbing Including Membrane-Cortex Coupling with Linker Dynamics

The aim of this paper is to develop suitable models for the phenomenon of cell blebbing, which allow for computational predictions of mechanical effects including the crucial interaction of the cell membrane and the actin cortex. For this sake we resort to a two phase-field model that uses diffuse descriptions of both the membrane and the cortex, which in particular allows for a suitable description of the interaction via linker protein densities. Besides the detailed modelling we discuss some energetic aspects of the models and present a numerical scheme, which allows to carry out several computational studies. In those we demonstrate that several effects found in experiments can be reproduced, in particular bleb formation by cortex rupture, which was not possible by previous models without the linker dynamics.

q-bio.CB↗

arXiv2021

Searching for dark matter with an unequal delay interferometer

We propose a new type of experiment that compares the frequency of a clock (an ultra-stable optical cavity in this case) at time $t$ to its own frequency some time $t-T$ earlier, by "storing" the output signal (photons) in a fibre delay line. In ultra-light oscillating dark matter (DM) models, such an experiment is sensitive to coupling of DM to the standard model fields, through oscillations of the cavity and fibre lengths and of the fibre refractive index. Additionally, the sensitivity is significantly enhanced around the mechanical resonances of the cavity. We present experimental result of such an experiment and report no evidence of DM for masses in the [$4.1\times 10^{-11}$, $8.3\times 10^{-10}$]~eV region. In addition, we improve constraints on the involved coupling constants by one order of magnitude in a standard galactic DM model, at the mass corresponding to the resonant frequency of our cavity. Furthermore, in the model of relaxion DM, we improve on existing constraints over the whole DM mass range by about one order of magnitude, and up to six orders of magnitude at resonance.

gr-qc↗

arXiv2020

FESTUNG: A MATLAB /GNU Octave toolbox for the discontinuous Galerkin method. Part IV: Generic problem framework and model-coupling interface

This is the fourth installment in our series on implementing the discontinuous Galerkin (DG) method as an open source MATLAB /GNU Octave toolbox. Similarly to its predecessors, this part presents new features for application developers employing DG methods and follows our strategy of relying on fully vectorized constructs and supplying a comprehensive documentation. The specific focus of the current work is the newly added generic problem implementation framework and the highly customizable model-coupling interface for multi-domain and multi-physics simulation tools based on this framework. The functionality of the coupling interface in the FESTUNG toolbox is illustrated using a two-way coupled free-surface / groundwater flow system as an example application.

math.NA↗

arXiv2020

Benchmark for numerical solutions of flow in heterogeneous groundwater formations

This article presents numerical investigations on accuracy and convergence properties of several numerical approaches for simulating steady state flows in heterogeneous aquifers. Finite difference, finite element, discontinuous Galerkin, spectral, and random walk methods are tested on one- and two-dimensional benchmark flow problems. Realizations of log-normal hydraulic conductivity fields are generated by Kraichnan algorithms in closed form as finite sums of random periodic modes, which allow direct code verification by comparisons with manufactured reference solutions. The quality of the methods is assessed for increasing number of random modes and for increasing variance of the log-hydraulic conductivity fields with Gaussian and exponential correlation. Experimental orders of convergence are calculated from successive refinements of the grid. The numerical methods are further validated by comparisons between statistical inferences obtained from Monte Carlo ensembles of numerical solutions and theoretical first-order perturbation results. It is found that while for Gaussian correlation of the log-conductivity field all the methods perform well, in the exponential case their accuracy deteriorates and, for large variance and number of modes, the benchmark problems are practically not tractable with reasonably large computing resources, for all the methods considered in this study.

cs.CE↗

arXiv2020

Determination of the 3D Magnetic Field Vector Orientation with NV Centers in Diamond

Absolute knowledge about the magnetic field orientation plays a crucial role in single spin-based quantum magnetometry and the application toward spin-based quantum computation. In this paper, we reconstruct the 3D orientation of an arbitrary static magnetic field with individual nitrogen vacancy (NV) centers in diamond. We determine the polar and the azimuthal angle of the magnetic field orientation relative to the diamond lattice. Therefore, we use information from the photoluminescence anisotropy of the NV, together with a simple pulsed Optically Detected Magnetic Resonance (ODMR) experiment. Our nanoscopic magnetic field determination is generally applicable and does not rely on special prerequisites such as strongly coupled nuclear spins or particular controllable fields. Hence, our presented results open up new paths for precise NMR reconstructions and the modulation of the electron-electron spin interaction in EPR measurements by specifically tailored magnetic fields.

quant-ph↗

arXiv2019

Novel approaches to dark-matter detection using space-time separated clocks

We discuss the theoretical analysis and interpretation of space-time separated clock experiments in the context of a space-time varying scalar field that is non-universally coupled to the standard model fields. If massive, such a field is a candidate for dark matter and could be detected in laboratory experiments. We show that space-time separated experiments have the potential to probe a fundamentally different parameter space from more common co-located experiments, allowing decorrelation of previously necessarily correlated parameters. Finally, we describe such a space-time separated clock experiment currently running at the Paris Observatory, and present some preliminary results as a proof of principle.

gr-qc↗

arXiv2019

Two-Branch Fiber Link for International Clock Networks

We present our work on realizing two-branch fiber links enabling multiple-partner clock comparisons in Europe. We report in detail on the setup connecting two long haul links, and report for the relative frequency stability and accuracy. We report on a combined uptime of 90% for our dual-branch link during almost one month. We finally discuss the combined uncertainty contribution of the ensemble of the link architecture to a clock comparison. We show that the frequency transfer uncertainty is 2 x 10 --19 .

quant-ph↗