arXiv subjects

Stefan Fischer

Publications and source records attributed to Stefan Fischer.

At least 19 recordsLinked to original sources

arXiv2026

CP$^2$N$^2$: A Control Plane for Heterogeneous Physical Neural Networks

Physical neural networks (PNNs) exploit diverse physical substrates---spanning molecular, wetware, and solid-state devices---to perform embodied neural computation close to physical processes. However, their operational heterogeneity in signal modalities, timing regimes, reset requirements, calibration lifecycles, and observability prevents their integration as interchangeable execution endpoints in edge, fog, and cloud workflows. We present CP$^2$N$^2$, a substrate-aware control-plane architecture that bridges software-level orchestration and physical neural computing. CP$^2$N$^2$ introduces a three-plane model that decouples high-level orchestration, twin-state synchronisation, and substrate-specific execution under versioned Physical Neural Resource Contracts. It extends Model Context Protocol (MCP)-style discovery and invocation with explicit lifecycle management, atomic leases, telemetry freshness verification, and fail-closed safety boundaries. We evaluate a reference prototype across contrasting behavioural twins, a same-host multi-process deployment under fault injection, and the Cortical Labs SDK Simulator. In a 945-request concurrency campaign, CP$^2$N$^2$ enforces typed contention and fail-closed recovery with sub-millisecond local orchestration overhead. Furthermore, in a 160-decision Agent-to-PNN campaign with a hosted LLM planner, independent enforcement ensured that 100% of executed actions remained strictly safe, verified, and reconciled despite planner-level inconsistencies. CP$^2$N$^2$ thus provides an enforceable systems abstraction for integrating scarce, stateful physical AI substrates into agentic computing environments.

cs.DC↗

arXiv2026

Embedded DNA Inference in In-Body Nanonetworks: Detection, Delay, and Communication Trade-Offs

In-body molecular nanonetworks promise early abnormality detection close to the source of biochemical events, but their communication capabilities are severely constrained by slow diffusion-based signaling and unstable alarm traffic. We study whether simple embedded DNA-based inference at the nanonode can improve alarm transmission to an external gateway. We compare raw reporting (RR), single-marker threshold reporting (TR), and embedded inference reporting (EIR) under a communication-oriented abstraction of DNA strand-displacement-based computation with marker gating, edge-triggered alarming, hysteretic state transitions, temporally correlated marker dynamics, diffusion-based alarm transport, and leaky gateway evidence integration. The simulations identify a bounded EIR success regime in the weak-to-moderate anomaly range: EIR can improve detection relative to RR and TR while remaining competitive in event-driven communication cost, especially relative to RR. The gain does not come from uniformly lower activity, but from more stable local alarm dynamics. EIR does not dominate globally; TR often remains cheaper when abnormalities are present, and EIR incurs additional local delay. These results point to a limited operating regime in which EIR is useful, rather than to a general advantage across settings.

cs.ET↗

arXiv2026

Beyond Silicon: Materials, Mechanisms, and Methods for Physical Neural Computing

Physical implementations of neural computation now extend far beyond silicon hardware, encompassing substrates such as memristive devices, photonic circuits, mechanical metamaterials, microfluidic networks, chemical reaction systems, and living neural tissue. By exploiting intrinsic physical processes such as charge transport, wave interference, elastic deformation, mass transport, and biochemical regulation, these substrates can realize neural inference and adaptation directly in matter. As silicon GPU-centered AI faces growing energy and data-movement constraints, physical neural computation is becoming increasingly relevant as a complementary path beyond conventional digital accelerators. This trend is driven in particular by pervasive intelligence, i.e., the deployment of on-device and edge AI across large numbers of resource-constrained systems. In such settings, co-locating computation with sensing and memory can reduce data shuttling and improve efficiency. Meanwhile, physical neural approaches have emerged across disparate disciplines, yet progress remains fragmented, with limited shared terminology and few principled ways to compare platforms. This survey unifies the field by mapping neural primitives to substrate-specific mechanisms, analyzing architectural and training paradigms, and identifying key engineering constraints including scalability, precision, programmability, and I/O interfacing overhead. To enable cross-domain comparison, we introduce a first-order benchmarking scheme based on standardized static and dynamic tasks and physically interpretable performance dimensions. We show that no single substrate dominates across the considered dimensions; instead, physical neural systems occupy complementary operating regimes, enabling applications ranging from ultrafast signal processing and in-memory inference to embodied control and in-sample biochemical decision making.

cs.NE↗

arXiv2023

Towards Automatic Generation of Amplified Regression Test Oracles

Regression testing is crucial in ensuring that pure code refactoring does not adversely affect existing software functionality, but it can be expensive, accounting for half the cost of software maintenance. Automated test case generation reduces effort but may generate weak test suites. Test amplification is a promising solution that enhances tests by generating additional or improving existing ones, increasing test coverage, but it faces the test oracle problem. To address this, we propose a test oracle derivation approach that uses object state data produced during System Under Test (SUT) test execution to amplify regression test oracles. The approach monitors the object state during test execution and compares it to the previous version to detect any changes in relation to the SUT's intended behaviour. Our preliminary evaluation shows that the proposed approach can enhance the detection of behaviour changes substantially, providing initial evidence of its effectiveness.

cs.SE↗

arXiv2023

Exploring a Test Data-Driven Method for Selecting and Constraining Metamorphic Relations

Identifying and selecting high-quality Metamorphic Relations (MRs) is a challenge in Metamorphic Testing (MT). While some techniques for automatically selecting MRs have been proposed, they are either domain-specific or rely on strict assumptions about the applicability of a pre-defined MRs. This paper presents a preliminary evaluation of MetaTrimmer, a method for selecting and constraining MRs based on test data. MetaTrimmer comprises three steps: generating random test data inputs for the SUT (Step 1), performing test data transformations and logging MR violations (Step 2), and conducting manual inspections to derive constraints (Step 3). The novelty of MetaTrimmer is its avoidance of complex prediction models that require labeled datasets regarding the applicability of MRs. Moreover, MetaTrimmer facilitates the seamless integration of MT with advanced fuzzing for test data generation. In a preliminary evaluation, MetaTrimmer shows the potential to overcome existing limitations and enhance MR effectiveness.

cs.SE↗

arXiv2023

Bug or not Bug? Analysing the Reasons Behind Metamorphic Relation Violations

Metamorphic Testing (MT) is a testing technique that can effectively alleviate the oracle problem. MT uses Metamorphic Relations (MRs) to determine if a test case passes or fails. MRs specify how the outputs should vary in response to specific input changes when executing the System Under Test (SUT). If a particular MR is violated for at least one test input (and its change), there is a high probability that the SUT has a fault. On the other hand, if a particular MR is not violated, it does not guarantee that the SUT is fault free. However, deciding if the MR is being violated due to a bug or because the MR does not hold/fit for particular conditions generated by specific inputs remains a manual task and unexplored. In this paper, we develop a method for refining MRs to offer hints as to whether a violation results from a bug or arises from the MR not being matched to certain test data under specific circumstances. In our initial proof-of-concept, we derive the relevant information from rules using the Association Rule Mining (ARM) technique. In our initial proof-of-concept, we validate our method on a toy example and discuss the lessons learned from our experiments. Our proof-of-concept demonstrates that our method is applicable and that we can provide suggestions that help strengthen the test suite for regression testing purposes.

cs.SE↗

arXiv2023

Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy

Background: Deep learning (DL)-based head and neck lymph node level (HN_LNL) autodelineation is of high relevance to radiotherapy research and clinical treatment planning but still underinvestigated in academic literature. Methods: An expert-delineated cohort of 35 planning CTs was used for training of an nnU-net 3D-fullres/2D-ensemble model for autosegmentation of 20 different HN_LNL. A second cohort acquired at the same institution later in time served as the test set (n=20). In a completely blinded evaluation, 3 clinical experts rated the quality of DL autosegmentations in a head-to-head comparison with expert-created contours. For a subgroup of 10 cases, intraobserver variability was compared to the average DL autosegmentation accuracy on the original and recontoured set of expert segmentations. A postprocessing step to adjust craniocaudal boundaries of level autosegmentations to the CT slice plane was introduced and the effect on geometric accuracy and expert rating was investigated. Results: Blinded expert ratings for DL segmentations and expert-created contours were not significantly different. DL segmentations with slice plane adjustment were rated numerically higher (mean, 81.0 vs. 79.6,p=0.185) and DL segmentations without slice plane adjustment were rated numerically lower (77.2 vs. 79.6,p=0.167) than manually drawn contours. DL segmentations with CT slice plane adjustment were rated significantly better than DL contours without slice plane adjustment (81.0 vs. 77.2,p=0.004). Geometric accuracy of DL segmentations was not different from intraobserver variability (mean, 0.76 vs. 0.77, p=0.307). Conclusions: We show that a nnU-net 3D-fullres/2D-ensemble model can be used for highly accurate autodelineation of HN_LNL using only a limited training dataset that is ideally suited for large-scale standardized autodelineation of HN_LNL in the research setting.

eess.IV↗

arXiv2022

Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification

Due to data privacy constraints, data sharing among multiple centers is restricted. Continual learning, as one approach to peer-to-peer federated learning, can promote multicenter collaboration on deep learning algorithm development by sharing intermediate models instead of training data. This work aims to investigate the feasibility of continual learning for multicenter collaboration on an exemplary application of brain metastasis identification using DeepMedic. 920 T1 MRI contrast enhanced volumes are split to simulate multicenter collaboration scenarios. A continual learning algorithm, synaptic intelligence (SI), is applied to preserve important model weights for training one center after another. In a bilateral collaboration scenario, continual learning with SI achieves a sensitivity of 0.917, and naive continual learning without SI achieves a sensitivity of 0.906, while two models trained on internal data solely without continual learning achieve sensitivity of 0.853 and 0.831 only. In a seven-center multilateral collaboration scenario, the models trained on internal datasets (100 volumes each center) without continual learning obtain a mean sensitivity value of 0.699. With single-visit continual learning (i.e., the shared model visits each center only once during training), the sensitivity is improved to 0.788 and 0.849 without SI and with SI, respectively. With iterative continual learning (i.e., the shared model revisits each center multiple times during training), the sensitivity is further improved to 0.914, which is identical to the sensitivity using mixed data for training. Our experiments demonstrate that continual learning can improve brain metastasis identification performance for centers with limited data. This study demonstrates the feasibility of applying continual learning for peer-to-peer federated learning in multicenter collaboration.

cs.LG↗

arXiv2022

Using Source Code Metrics for Predicting Metamorphic Relations at Method Level

Metamorphic testing (TM) examines the relations between inputs and outputs of test runs. These relations are known as metamorphic relations (MR). Currently, MRs are handpicked and require in-depth knowledge of the System Under Test (SUT), as well as its problem domain. As a result, the identification and selection of high-quality MRs is a challenge. \citeauthor{PMR1} suggested the Predicting Metamorphic Relations (PMR) approach for automatic prediction of applicable MRs picked from a predefined list. PMR is based on a Support Vector Machine (SVM) model using features derived from the Control Flow Graphs (CFGs) of 100 Java methods. The original study of \citeauthor{PMR1} showed encouraging results, but developing classification models from CFG-related features is costly. In this paper, we aim at developing a PMR approach that is less costly without losing performance. We complement the original PMR approach by considering other than CFG-related features. We define 21 features that can be directly extracted from source code and build several classifiers, including SVM models. Our results indicate that using the original CFG-based method-level features, in particular for a SVM with random walk kernel (RWK), achieve better predictions in terms of AUC-ROC for most of the candidate MRs than our models. However, for one of the candidate MRs, using source code features achieved the best AUC-ROC result (greater than 0.8).

cs.SE↗

arXiv2021

Lifting DecPOMDPs for Nanoscale Systems -- A Work in Progress

DNA-based nanonetworks have a wide range of promising use cases, especially in the field of medicine. With a large set of agents, a partially observable stochastic environment, and noisy observations, such nanoscale systems can be modelled as a decentralised, partially observable, Markov decision process (DecPOMDP). As the agent set is a dominating factor, this paper presents (i) lifted DecPOMDPs, partitioning the agent set into sets of indistinguishable agents, reducing the worst-case space required, and (ii) a nanoscale medical system as an application. Future work turns to solving and implementing lifted DecPOMDPs.

cs.AI↗

arXiv2021

Integration of Simultaneous Resting-State Electroencephalography, Functional Magnetic Resonance Imaging, and Eye-Tracker Methods to Determine and Verify Electroencephalography Vigilance Measure

Background/Introduction: Concurrent electroencephalography and resting-state functional magnetic resonance imaging (rsfMRI) have been widely used for studying the (presumably) awake and alert human brain with high temporal/spatial resolution. Although rsfMRI scans are typically collected while individuals are instructed to focus their eyes on a fixated cross, objective and verified experimental measures to quantify degree of vigilance are not readily available. Electroencephalography (EEG) is the modality extensively used for estimating vigilance, especially during eyes-closed resting state. However, pupil size measured using an eye-tracker device could provide an indirect index of vigilance. Methods: Three 12-min resting scans (eyes open, fixating on the cross) were collected from 10 healthy control participants. We simultaneously collected EEG, fMRI, physiological, and eye-tracker data and investigated the correlation between EEG features, pupil size, and heart rate. Furthermore, we used pupil size and EEG features as regressors to find their correlations with blood-oxygen-level-dependent fMRI measures. Results: EEG frontal and occipital beta power (FOBP) correlates with pupil size changes, an indirect index for locus coeruleus activity implicated in vigilance regulation (r = 0.306, p < 0.001). Moreover, FOBP also correlated with heart rate (r = 0.255, p < 0.001), as well as several brain regions in the anticorrelated network, including the bilateral insula and inferior parietal lobule. Discussion: In this study, we investigated whether simultaneous EEG-fMRI combined with eye-tracker measurements can be used to determine EEG signal feature associated with vigilance measures during eyes-open rsfMRI. Our results support the conclusion that FOBP is an objective measure of vigilance in healthy human subjects.

eess.SP↗

arXiv2021

Dynamic lattice distortions driven by surface trapping in semiconductor nanocrystals

Nonradiative processes limit optoelectronic functionality of nanocrystals and curb their device performance. Nevertheless, the dynamic structural origins of nonradiative relaxations in nanocrystals are not understood. Here, femtosecond electron diffraction measurements corroborated by atomistic simulations uncover transient lattice deformations accompanying radiationless electronic processes in semiconductor nanocrystals. Investigation of the excitation energy dependence shows that hot carriers created by a photon energy considerably larger than the bandgap induce structural distortions at nanocrystal surfaces on few picosecond timescales associated with the localization of trapped holes. On the other hand, carriers created by a photon energy close to the bandgap result in transient lattice heating that occurs on a much longer 200 ps timescale, governed by an Auger heating mechanism. Elucidation of the structural deformations associated with the surface trapping of hot holes provides atomic-scale insights into the mechanisms deteriorating optoelectronic performance and a pathway towards minimizing these losses in nanocrystal devices.

cond-mat.mes-hall↗

arXiv2020

Towards Fault Localization via Probabilistic Software Modeling

Software testing helps developers to identify bugs. However, awareness of bugs is only the first step. Finding and correcting the faulty program components is equally hard and essential for high-quality software. Fault localization automatically pinpoints the location of an existing bug in a program. It is a hard problem, and existing methods are not yet precise enough for widespread industrial adoption. We propose fault localization via Probabilistic Software Modeling (PSM). PSM analyzes the structure and behavior of a program and synthesizes a network of Probabilistic Models (PMs). Each PM models a method with its inputs and outputs and is capable of evaluating the likelihood of runtime data. We use this likelihood evaluation to find fault locations and their impact on dependent code elements. Results indicate that PSM is a robust framework for accurate fault localization.

cs.SE↗

arXiv2018

Bright sub-20 nm cathodoluminescent nanoprobes for multicolor electron microscopy

Electron microscopy (EM) has been instrumental in our understanding of biological systems ranging from subcellular structures to complex organisms. Although EM reveals cellular morphology with nanoscale resolution, it does not provide information on the location of proteins within a cellular context. An EM-based bioimaging technology capable of localizing individual proteins and resolving protein-protein interactions with respect to cellular ultrastructure would provide important insights into the molecular biology of a cell. Here, we report on the development of luminescent nanoprobes potentially suitable for labeling biomolecules in a multicolor EM modality. In this approach, the labels are based on lanthanide-doped nanoparticles that emit light under electron excitation in a process known as cathodoluminescence (CL). Our results suggest that the optimization of nanoparticle composition, synthesis protocols and electron imaging conditions could enable high signal-to-noise localization of biomolecules with a sub-20-nm resolution, limited only by the nanoparticle size. In ensemble measurements, these luminescent labels exhibit narrow spectra of nine distinct colors that are characteristic of the corresponding rare-earth dopant type.

cond-mat.mtrl-sci↗

arXiv2017

3D DNA origami crystals

Engineering shape and interactions of nanoscopic building blocks allows for the assembly of rationally designed macroscopic three-dimensional (3D) materials with spatial accuracy inaccessible to top-down fabrication methods. Owing to its sequence-specific interaction, DNA is often used as selective binder to connect metallic nanoparticles into highly ordered lattices. Moreover, 3D crystals assembled entirely from DNA have been proposed and implemented with the declared goal to arrange guest molecules in predefined lattices. This requires design schemes that provide high rigidity and sufficiently large open guest space. We here present a DNA origami-based tensegrity triangle structure that assembles into a 3D rhombohedral crystalline lattice. We site-specifically place 10 nm and 20 nm gold particles within the lattice, demonstrating that our crystals are spacious enough to host e.g. ribosome-sized macromolecules. We validate the accurate assembly of the DNA origami lattice itself as well as the precise incorporation of gold particles by electron microscopy and small angle X-ray scattering (SAXS) experiments. Our results show that it is possible to create DNA building blocks that assemble into lattices with customized geometry. Site-specific hosting of nano objects in the transparent DNA lattice sets the stage for metamaterial and structural biology applications.

cond-mat.soft↗

arXiv2015

Upconverting Core-Shell Nanocrystals with High Quantum Yield under Low Irradiance: On the Role of Isotropic and Thick Shells

Colloidal upconverter nanocrystals (UCNCs) that convert near-infrared photons to higher energies are promising for applications ranging from life sciences to solar energy harvesting. However, practical applications of UCNCs are hindered by their low upconversion quantum yield (UCQY) and the high irradiances necessary to produce relevant upconversion luminescence. Achieving high UCQY under practically relevant irradiance remains a major challenge. The UCQY is severely limited due to non-radiative surface quenching processes. We present a rate equation model for migration of the excitation energy to show that surface quenching does not only affect the lanthanide ions directly at the surface but also many other lanthanide ions quite far away from the surface. The average migration path length is in the order of several nanometers and depends on the doping as well as the irradiance of the excitation. Using Er3+-doped beta-NaYF4 UCNCs, we show that very isotropic and thick (~10 nm) beta-NaLuF4 inert shells dramatically reduce the surface-related quenching processes, resulting in much brighter upconversion luminescence at simultaneously considerably lower irradiances. For these UCNCs embedded in PMMA, we determined an internal UCQY of 2.0+-0.2 % using an irradiance of only 0.43+-0.03 W/cm2 at 1523 nm. Normalized to the irradiance, this UCQY is 120x higher than the highest values of comparable nanomaterials in the literature. Our findings demonstrate the important role of isotropic and thick shells in achieving high UCQY at low irradiances from UCNCs. Additionally, we measured the additional short-circuit current due to upconversion in silicon solar cell devices as a proof concept and to support our findings determined using optical measurements.

cond-mat.mtrl-sci↗

arXiv2014

A Security Architecture for Mobile Wireless Sensor Networks

Wireless sensor networks increasingly become viable solutions to many challenging problems and will successively be deployed in many areas in the future. However, deploying new technology without security in mind has often proved to be unreasonably dangerous. We propose a security architecture for self-organizing mobile wireless sensor networks that prevents many attacks these networks are exposed to. Furthermore, it limits the security impact of some attacks that cannot be prevented. We analyse our security architecure and show that it provides the desired security aspects while still being a lightweight solution and thus being applicable for self-organizing mobile wireless sensor networks.

cs.CR↗

arXiv2012

Plasmon enhanced upconversion luminescence near gold nanoparticles - Simulation and analysis of the interactions

We investigate plasmon resonances in gold nanoparticles to enhance the quantum yield of upconverting materials. For this purpose, we use a rate equation model that describes the upconversion of trivalent erbium based upconverters. Changes of the optical field acting on the upconverter and the changes to the transition probabilities of the upconverter in the proximity of a gold nanoparticle are calculated using Mie theory and exact electrodynamic theory respectively. With this data, the influence on the luminescence of the upconverter is determined using the rate equation model. The results show that upconversion luminescence can be increased in the proximity of a spherical gold nanoparticle due to the change in the optical field and the modification of the transition rates.

physics.optics↗