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Charge diffusion through desoxyribonucleic acid (DNA) is a physico-chemical phenomenon that on the one hand is being explored for technological purposes, on the other hand is applied by nature for various informational processes in life. With regard to the latter, increasing experimental and theoretical evidence indicates that charge diffusion through DNA is involved in basic steps of DNA replication and repair, as well as regulation of gene expression via epigenetic mechanisms such as DNA methylation or DNA binding of proteins. From the physics point of view, DNA supports a metallic-like behavior with long-range charge mobility. Nevertheless, particularly considering a living environment, charge mobility in DNA needs to take into account omnipresent noise and disorder. Here, we analyze quantum diffusion of single charges along DNA-inspired two-dimensional tight-binding lattices in presence of different sources of intrinsic and environmental fluctuations. It is shown that double-strand lattices, parametrized according to atomistic calculations of DNA sequences, offer a complex network of pathways between sites and may give rise to long-distance coherence phenomena. These effects strongly depend on carrier type (electrons, holes), the energetic profile of the lattice (composition) as well as the type of noise and disorder. Of particular interest are spatially correlated low-frequency fluctuations which may support coherent charge transfer over distances of a few sites. Our results may trigger further experimental activities aiming at investigating charge mobility in DNA both in the native in-vivo context as well as on artificial platforms., Comment: 11 pages (10 without bibliography), 10 figures

The upcoming 6G mobile communication standard will offer a revolutionary new feature: Integrated sensing and communication (ISAC) reuses mobile communication signals to realize multi-static radar for various applications including localization. Consequently, applied ISAC propagation research necessitates to evolve from classical monostatic radar cross section (RCS) measurement of static targets on to bistatic radar reflectivity characterization of dynamic objects. Here, we introduce our "Bistatic Radar" (BIRA) and antenna measurement facility for bistatic spherical positioning with sub-millimeter accuracy on a diameter of up to 7 m and with almost continuous frequency coverage from 0.7 up to 260 GHz. Currently, BIRA is the only bistatic measurement facility capable of unrestricted ISAC research: In addition to vector network analysis, BIRA employs advanced wideband transceiver technology with an instantaneous bandwidth of up to 4 GHz. These transceivers grant BIRA the unique ability to characterize dynamic targets in both Doppler and range, while also significantly accelerating RCS measurements of static objects., Comment: 9 pages, 8 figures

The Ehrhart polynomial and Ehrhart series count lattice points in integer dilations of a lattice polytope. We introduce and study a $q$-deformation of the Ehrhart series, based on the notions of harmonic spaces and Macaulay's inverse systems for coordinate rings of finite point configurations. We conjecture that this $q$-Ehrhart series is a rational function, and introduce and study a bigraded algebra whose Hilbert series matches the $q$-Ehrhart series. Defining this algebra requires a new result on Macaulay inverse systems for Minkowski sums of point configurations., Comment: 61 pages. Version 3: This version features a streamlined proof of Theorem 1.4, based on a suggestion of Ian Cavey

Recent advances have enabled the study of human brain development using brain organoids derived from stem cells. Quantifying cellular processes like mitosis in these organoids offers insights into neurodevelopmental disorders, but the manual analysis is time-consuming, and existing datasets lack specific details for brain organoid studies. We introduce BOrg, a dataset designed to study mitotic events in the embryonic development of the brain using confocal microscopy images of brain organoids. BOrg utilizes an efficient annotation pipeline with sparse point annotations and techniques that minimize expert effort, overcoming limitations of standard deep learning approaches on sparse data. We adapt and benchmark state-of-the-art object detection and cell counting models on BOrg for detecting and analyzing mitotic cells across prophase, metaphase, anaphase, and telophase stages. Our results demonstrate these adapted models significantly improve mitosis analysis efficiency and accuracy for brain organoid research compared to existing methods. BOrg facilitates the development of automated tools to quantify statistics like mitosis rates, aiding mechanistic studies of neurodevelopmental processes and disorders. Data and code are available at https://github.com/awaisrauf/borg.

In the current era of generative AI breakthroughs, generating panoramic scenes from a single input image remains a key challenge. Most existing methods use diffusion-based iterative or simultaneous multi-view inpainting. However, the lack of global scene layout priors leads to subpar outputs with duplicated objects (e.g., multiple beds in a bedroom) or requires time-consuming human text inputs for each view. We propose L-MAGIC, a novel method leveraging large language models for guidance while diffusing multiple coherent views of 360 degree panoramic scenes. L-MAGIC harnesses pre-trained diffusion and language models without fine-tuning, ensuring zero-shot performance. The output quality is further enhanced by super-resolution and multi-view fusion techniques. Extensive experiments demonstrate that the resulting panoramic scenes feature better scene layouts and perspective view rendering quality compared to related works, with >70% preference in human evaluations. Combined with conditional diffusion models, L-MAGIC can accept various input modalities, including but not limited to text, depth maps, sketches, and colored scripts. Applying depth estimation further enables 3D point cloud generation and dynamic scene exploration with fluid camera motion. Code is available at https://github.com/IntelLabs/MMPano. The video presentation is available at https://youtu.be/XDMNEzH4-Ec?list=PLG9Zyvu7iBa0-a7ccNLO8LjcVRAoMn57s., Comment: accepted to CVPR 2024

Background & Purpose: FLASH or ultra-high dose rate (UHDR) radiation therapy (RT) has gained attention in recent years for its ability to spare normal tissues relative to conventional dose rate (CDR) RT in various preclinical trials. However, clinical implementation of this promising treatment option has been limited because of the lack of availability of accelerators capable of delivering UHDR RT. We established a framework for the acceptance, commissioning, and periodic quality assurance (QA) of electron FLASH units and present an example of commissioning. Methods: A protocol for acceptance, commissioning, and QA of UHDR linear accelerators was established by combining and adapting standards and professional recommendations for standard linear accelerators based on the experience with UHDR at four clinical centers that use different UHDR devices. Non-standard dosimetric beam parameters considered included pulse width, pulse repetition frequency, dose per pulse, and instantaneous dose rate, together with recommendations on how to acquire these measurements. Results: The 6 and 9 MeV beams of an UHDR electron device were commissioned by using this developed protocol. Measurements were acquired with a combination of ion chambers, beam current transformers (BCTs), and dose rate independent passive dosimeters. The unit was calibrated according to the concept of redundant dosimetry using a reference setup. Conclusions: This study provides detailed recommendations for the acceptance testing, commissioning, and routine QA of low-energy electron UHDR linear accelerators. The proposed framework is not limited to any specific unit, making it applicable to all existing eFLASH units in the market. Through practical insights and theoretical discourse, this document establishes a benchmark for the commissioning of UHDR devices for clinical use., Comment: 22 Pages, 8 Figures

Combining spin textures in ultra-thin films with conventional superconductors has emerged as a powerful and versatile platform for designing topologically non-trivial superconducting phases as well as spin-triplet Cooper pairs. As a consequence, two-dimensional magnet-superconductor hybrids (2D MSHs) are promising candidate systems to realize devices for topology-based quantum technologies and superconducting spintronics. So far, studies have focused mostly on systems hosting collinear ferromagnets or antiferromagnets. However, topologically non-trivial phases have been predicted to emerge in MSH systems with non-collinear spin textures as well. In this article, we present the experimental discovery of topological superconductivity in the MSH system Fe/Ta(110) where a magnetic spiral is realized in the Fe monolayer on the surface of the s-wave superconductor Ta. By combining low-temperature spin-polarized scanning tunneling microscopy measurements with theoretical modeling, we are able to conclude that the system is in a topological nodal-point superconducting phase with low-energy edge modes. Due to the non-collinear spin texture in our MSH system, these edge modes exhibit a magnetization direction-dependent dispersion. Furthermore, we identify direct signatures of Rashba spin-orbit coupling in the experimentally measured differential tunneling conductance. The present work realizes a non-collinear spin texture-based path to topological superconductivity.

Utilizing deep learning (DL) techniques for radio-based positioning of user equipment (UE) through channel state information (CSI) fingerprints has demonstrated significant potential. DL models can extract complex characteristics from the CSI fingerprints of a particular environment and accurately predict the position of a UE. Nonetheless, the effectiveness of the DL model trained on CSI fingerprints is highly dependent on the particular training environment, limiting the trained model's applicability across different environments. This paper proposes a novel DL model structure consisting of two parts, where the first part aims at identifying features that are independent from any specific environment, while the second part combines those features in an environment specific way with the goal of positioning. To train such a two-part model, we propose the multi-environment meta-learning (MEML) approach for the first part to facilitate training across various environments, while the second part of the model is trained solely on data from a specific environment. Our findings indicate that employing the MEML approach for initializing the weights of the DL model for a new unseen environment significantly boosts the accuracy of UE positioning in the new target environment as well the reliability of its uncertainty estimation. This method outperforms traditional transfer learning methods, whether direct transfer learning (DTL) between environments or completely training from scratch with data from a new environment. The proposed approach is verified with real measurements for both line-of-sight (LOS) and non-LOS (NLOS) environments., Comment: Submitted for IEEE journal publication. 10 pages, 13 figures, 7 tables

This paper presents a novel compact delay model of Ovonic Threshold Switch (OTS) devices that works efficiently for circuit simulations. The internal state variable of the two terminal devices is estimated using a delay system that uses a few electrical components related to a suggested equivalent circuit of the device. Finally, we tested the proposed model against measured data from devices fabricated by Western Digital Research.

We present the simulation of nuclear magnetic resonance (NMR) spectroscopy of small organic molecules with two promising quantum computing platforms, namely IBM's quantum processors based on superconducting qubits and IonQ's Aria trapped ion quantum computer addressed via Amazon Braket. We analyze the impact of noise on the obtained NMR spectra, and we formulate an effective decoherence rate that quantifies the threshold noise that our proposed algorithm can tolerate. Furthermore we showcase how our noise analysis allows us to improve the spectra. Our investigations pave the way to better employ such application-driven quantum tasks on current noisy quantum devices., Comment: 10 pages, 13 figures

We demonstrate algorithmic cooling on IBM-Q devices. We utilize inherent qubit noise to simulate the equilibration of an interacting spin system towards its ground state, when coupled to a dissipative auxiliary-spin bath. The steady-state correlations in the system are defined by the system Hamiltonian and are stable as long as the algorithm can be executed. In particular, we demonstrate the relaxation of system spins to ferromagnetic and antiferromagnetic ordering, controlled by the definition of the Hamiltonian. We are able to perform simulated cooling for global systems of up to three system spins and four auxiliary spins., Comment: 17 pages, 12 figures in total: 9 pages main text with 6 figures

The arithmetical hierarchy (AH) is similar to the polynomial hierarchy (PH). Unlike the PH, the AH does not collapse relative to any oracle. A language in the (k + 1)-st level of the AH is computable enumerable (c.e.) relative to the kth level. So, given an oracle in the kth level of the AH, we could use a black-box search to decide whether the input word is in the language. With very large padding arguments, i.e. the paddings grow faster than any relative to the level k of the AH computable function, we would construct a language contained in the k + 1 level of PH, if we use only a finite set of input words. From the oracle in AH, we would construct an analogue oracle at the kth level of PH. For the input words of the finite set, a word is in the language of AH, if and only if it is in the language of PH. And the input word is in the oracle set of AH, if and only if it is in the oracle of PH. As in the language of AH, we must apply a black-box search in the language of PH. So, we would also have exponentially many oracle queries in the language of PH. The PH does not collapse., Comment: 11 pages

Supersolvable hyperplane arrangements and matroids are known to give rise to certain Koszul algebras, namely their Orlik-Solomon algebras and graded Varchenko-Gel'fand algebras. We explore how this interacts with group actions, particularly for the braid arrangement and the action of the symmetric group, where the Hilbert functions of the algebras and their Koszul duals are given by Stirling numbers of the first and second kinds, respectively. The corresponding symmetric group representations exhibit branching rules that interpret Stirling number recurrences, which are shown to apply to all supersolvable arrangements. They also enjoy representation stability properties that follow from Koszul duality., Comment: References added, some typos corrected, and contact information updated

Spin qubits in silicon are strong contenders for realizing a practical quantum computer. This technology has made remarkable progress with the demonstration of single and two-qubit gates above the fault-tolerant threshold and entanglement of up to three qubits. However, maintaining high fidelity operations while executing multi-qubit algorithms has remained elusive, only being achieved for two spin qubits to date due to the small qubit size, which makes it difficult to control qubits without creating crosstalk errors. Here, we use a four-qubit silicon processor with every operation above the fault tolerant limit and demonstrate Grover's algorithm with a ~95% probability of finding the marked state, one of the most successful implementations to date. Our four-qubit processor is made of three phosphorus atoms and one electron spin precision-patterned into 1.5 nm${}^2$ isotopically pure silicon. The strong resulting confinement potential, without additional confinement gates that can increase cross-talk, leverages the benefits of having both electron and phosphorus nuclear spins. Significantly, the all-to-all connectivity of the nuclear spins provided by the hyperfine interaction not only allows for efficient multi-qubit operations, but also provides individual qubit addressability. Together with the long coherence times of the nuclear and electron spins, this results in all four single qubit fidelities above 99.9% and controlled-Z gates between all pairs of nuclear spins above 99% fidelity. The high control fidelities, combined with >99% fidelity readout of all nuclear spins, allows for the creation of a three-qubit Greenberger-Horne-Zeilinger (GHZ) state with 96.2% fidelity, the highest reported for semiconductor spin qubits so far. Such nuclear spin registers can be coupled via electron exchange, establishing a path for larger scale fault-tolerant quantum processors., Comment: 16 pages, 9 figures, 3 tables

We present Mesh2NeRF, an approach to derive ground-truth radiance fields from textured meshes for 3D generation tasks. Many 3D generative approaches represent 3D scenes as radiance fields for training. Their ground-truth radiance fields are usually fitted from multi-view renderings from a large-scale synthetic 3D dataset, which often results in artifacts due to occlusions or under-fitting issues. In Mesh2NeRF, we propose an analytic solution to directly obtain ground-truth radiance fields from 3D meshes, characterizing the density field with an occupancy function featuring a defined surface thickness, and determining view-dependent color through a reflection function considering both the mesh and environment lighting. Mesh2NeRF extracts accurate radiance fields which provides direct supervision for training generative NeRFs and single scene representation. We validate the effectiveness of Mesh2NeRF across various tasks, achieving a noteworthy 3.12dB improvement in PSNR for view synthesis in single scene representation on the ABO dataset, a 0.69 PSNR enhancement in the single-view conditional generation of ShapeNet Cars, and notably improved mesh extraction from NeRF in the unconditional generation of Objaverse Mugs., Comment: Accepted to ECCV 2024, Project page: https://terencecyj.github.io/projects/Mesh2NeRF/ Video: https://youtu.be/SsFkhSuQYGM

The analysis of wireless communication channels at the mmWave, sub-THz and THz bands gives rise to difficulties in the construction of antenna arrays due to the small maximum inter-element spacing constraints at these frequencies. Arrays with uniform spacing greater than half the wavelength for a certain carrier frequency exhibit aliasing side-lobes in the angular domain, prohibiting non-ambiguous estimates of a propagating wave-front's angle of arrival. In this paper, we present how wide-band modelling of the array response is useful in mitigating this spatial aliasing effect. This approach aims to reduce the grating lobes by exploiting the angle- and frequency-dependent phase-shifts observed in the response of the array to a planar wave-front travelling across it. Furthermore, we propose a method by which the spatial correlation characteristics of an array operating at 33 GHz carrier frequency with an instantaneous bandwidth of 1 GHz can be improved such that the angular-domain side-lobes are reduced by 5-10 dB. This method, applicable to arbitrary antenna array manifolds, makes use of a linear operator that is applied to the base-band samples of the channel transfer function measured in space and frequency domains. By means of synthetically simulated arrays, we show that when operating with a bandwidth of 1 GHz, the use of a derived linear operator applied to the array output results in the spatial correlation characteristics approaching those of the array operating at a bandwidth of 12 GHz. Hence, non-ambiguous angle estimates can be obtained in the field without the use of expensive high-bandwidth RF front-end components., Comment: 7 pages, 4 figures, 27th Workshop on Smart Antennas, 2024

Deep learning (DL) methods have been shown to improve the performance of several use cases for the fifth-generation (5G) New radio (NR) air interface. In this paper we investigate user equipment (UE) positioning using the channel state information (CSI) fingerprints between a UE and multiple base stations (BSs). In such a setup, we consider two different fusion techniques: early and late fusion. With early fusion, a single DL model can be trained for UE positioning by combining the CSI fingerprints of the multiple BSs as input. With late fusion, a separate DL model is trained at each BS using the CSI specific to that BS and the outputs of these individual models are then combined to determine the UE's position. In this work we compare these different fusion techniques and show that fusing the outputs of separate models achieves higher positioning accuracy, especially in a dynamic scenario. We also show that the combination of multiple outputs further benefits from considering the uncertainty of the output of the DL model at each BS. For a more efficient training of the DL model across BSs, we additionally propose a multi-task learning (MTL) scheme by sharing some parameters across the models while jointly training all models. This method, not only improves the accuracy of the individual models, but also of the final combined estimate. Lastly, we evaluate the reliability of the uncertainty estimation to determine which of the fusion methods provides the highest quality of uncertainty estimates., Comment: 15 Pages, 17 Figures. Published in IEEE Journal

Smart contracts codify real-world transactions and automatically execute the terms of the contract when predefined conditions are met. This paper proposes SmartML, a modeling language for smart contracts that is platform independent and easy to comprehend. We detail its formal semantics and type system with a focus on its role in addressing security vulnerabilities. We show along a case study, how SmartML contributes to the prevention of reentrancy attacks, illustrating its efficacy in reinforcing the reliability and security of smart contracts within decentralized systems.

With the upcoming multitude of commercial and public applications envisioned in the mobile 6G radio landscape using unmanned aerial vehicles (UAVs), integrated sensing and communication (ISAC) plays a key role to enable the detection and localization of passive objects with radar sensing, while optimizing the utilization of scarce resources. To explore the potential of future ISAC architectures with UAVs as mobile nodes in distributed multi-sensor networks, the system's fundamental capability to detect static and dynamic objects that reveal themselves by their bi-static back-scattering needs to be evaluated. Therefore, this paper addresses simulation- and measurement based data acquisition methods to gather knowledge about the bistatic reflectivity of single objects including their Micro-Doppler signature for object identification as well as the influence of multipath propagation in different environments on the localization accuracy and radar tracking performance. We show exemplary results from simulation models, bi-static reflectivity measurements in laboratory environment and real-flight channel sounding experiments in selected scenarios showcasing the potential of synthetic and measured data sets for development and evaluation of ISAC algorithms. The presented measurement data sets are publicly available to encourage the academic RF community to validate future algorithms using realistic scenarios alongside simulations models., Comment: 6 pages, 6 figures, 4th IEEE Symposium on Joint Communication and Sensing

Sandpile groups are a subtle graph isomorphism invariant, in the form of a finite abelian group, whose cardinality is the number of spanning trees in the graph. We study their group structure for graphs obtained by attaching a cone vertex to a tree. For example, it is shown that the number of generators of the sandpile group is at most one less than the number of leaves in the tree. For trees on a fixed number of vertices, the paths and stars are shown to provide extreme behavior, not only for the number of generators, but also for the number of spanning trees, and for Tutte polynomial evaluations that count the recurrent sandpile configurations by their numbers of chips., Comment: Version to appear in Research in the Mathematical Sciences

With the ever increasing number of active satellites in space, the rising demand for larger formations of small satellites and the commercialization of the space industry (so-called New Space), the realization of manufacturing processes in orbit comes closer to reality. Reducing launch costs and risks, allowing for faster on-demand deployment of individually configured satellites as well as the prospect for possible on-orbit servicing for satellites makes the idea of realizing an in-orbit factory promising. In this paper, we present a novel approach to an in-orbit factory of small satellites covering a digital process twin, AI-based fault detection, and teleoperated robot-control, which are being researched as part of the "AI-enabled Cyber-Physical In-Orbit Factory" project. In addition to the integration of modern automation and Industry 4.0 production approaches, the question of how artificial intelligence (AI) and learning approaches can be used to make the production process more robust, fault-tolerant and autonomous is addressed. This lays the foundation for a later realisation of satellite production in space in the form of an in-orbit factory. Central aspect is the development of a robotic AIT (Assembly, Integration and Testing) system where a small satellite could be assembled by a manipulator robot from modular subsystems. Approaches developed to improving this production process with AI include employing neural networks for optical and electrical fault detection of components. Force sensitive measuring and motion training helps to deal with uncertainties and tolerances during assembly. An AI-guided teleoperated control of the robot arm allows for human intervention while a Digital Process Twin represents process data and provides supervision during the whole production process. Approaches and results towards automated satellite production are presented in detail.

Integrated Sensing and Communication (ISAC) comprises detection and analysis of non-cooperative targets by exploiting the resources of the mobile radio system. In this context, micro-Doppler is of great importance for target classification, in order to distinguish objects with local movements. For developing algorithms for target classification, it is necessary to have a large amount of target signatures. Aiming to generate these data, this paper proposes a mathematical model for the micro-Doppler of drone rotating propellers, and validate the proposed model by comparing it to measured micro-Doppler. Results show that the proposed mathematical model can generate micro-Doppler data very similar to those from measurement data., Comment: overall: 6 pages, LaTeX; 2024-02-13 resubmission: affiliation updated, internal references corrected, external reference added, images re-generated for better quality, typo in Section III-C corrected, Tables merged for space saving

The integration of wireless communication and radar sensing is gaining the interest of researchers from wireless communication and radar societies. Sensing in Integrated Communication and Sensing (ICAS) systems differs from the traditional radar system in the configuration of transmitter-target-receiver, the operating frequency bands, and the transmitting waveform. It is necessary to understand how target electromagnetic signatures behave in this context. Therefore, this paper presents measurements and analysis of two important target signatures, reflectivity and micro-Doppler, for sensing in ICAS. These target signatures are measured in the state-of-the-art measurement system, Bistatische-Radar-Messeinrichtung (BiRa)., Comment: arXiv admin note: text overlap with arXiv:2401.14287 Submitted to International Radar Symposium 2024 (IRS 2024) Changes in 08.03.2024 version: General reformulation. The main subjects, methods, and conclusions are kept the same

The way we communicate and work has changed significantly with the rise of the Internet. While it has opened up new opportunities, it has also brought about an increase in cyber threats. One common and serious threat is phishing, where cybercriminals employ deceptive methods to steal sensitive information.This study addresses the pressing issue of phishing by introducing an advanced detection model that meticulously focuses on HTML content. Our proposed approach integrates a specialized Multi-Layer Perceptron (MLP) model for structured tabular data and two pretrained Natural Language Processing (NLP) models for analyzing textual features such as page titles and content. The embeddings from these models are harmoniously combined through a novel fusion process. The resulting fused embeddings are then input into a linear classifier. Recognizing the scarcity of recent datasets for comprehensive phishing research, our contribution extends to the creation of an up-to-date dataset, which we openly share with the community. The dataset is meticulously curated to reflect real-life phishing conditions, ensuring relevance and applicability. The research findings highlight the effectiveness of the proposed approach, with the CANINE demonstrating superior performance in analyzing page titles and the RoBERTa excelling in evaluating page content. The fusion of two NLP and one MLP model,termed MultiText-LP, achieves impressive results, yielding a 96.80 F1 score and a 97.18 accuracy score on our research dataset. Furthermore, our approach outperforms existing methods on the CatchPhish HTML dataset, showcasing its efficacies.

The escalating landscape of cyber threats, characterized by the registration of thousands of new domains daily for large-scale Internet attacks such as spam, phishing, and drive-by downloads, underscores the imperative for innovative detection methodologies. This paper introduces a cutting-edge approach for identifying suspicious domains at the onset of the registration process. The accompanying data pipeline generates crucial features by comparing new domains to registered domains, emphasizing the crucial similarity score. The proposed system analyzes semantic and numerical attributes by leveraging a novel combination of Natural Language Processing (NLP) techniques, including a pretrained CANINE model and Multilayer Perceptron (MLP) models, providing a robust solution for early threat detection. This integrated Pretrained NLP (CANINE) + MLP model showcases the outstanding performance, surpassing both individual pretrained NLP models and standalone MLP models. With an F1 score of 84.86\% and an accuracy of 84.95\% on the SecureReg dataset, it effectively detects malicious domain registrations. The findings demonstrate the effectiveness of the integrated approach and contribute to the ongoing efforts to develop proactive strategies to mitigate the risks associated with illicit online activities through the early identification of suspicious domain registrations.

The context of this work is cooperative scheduling, a concurrency paradigm, where task execution is not arbitrarily preempted. Instead, language constructs exist that let a task voluntarily yield the right to execute to another task. The inquiry is the design of provably fair schedulers and suitable notions of fairness for cooperative scheduling languages. To the best of our knowledge, this problem has not been addressed so far. Our approach is to study fairness independently from syntactic constructs or environments, purely from the point of view of the semantics of programming languages, i.e., we consider fairness criteria using the formal definition of a program execution. We develop our concepts for classic structural operational semantics (SOS) as well as for the recent locally abstract, globally concrete (LAGC) semantics. The latter is a highly modular approach to semantics ensuring the separation of concerns between local statement evaluation and scheduling decisions. The new knowledge contributed by our work is threefold: first, we show that a new fairness notion, called quiescent fairness, is needed to characterize fairness adequately in the context of cooperative scheduling; second, we define a provably fair scheduler for cooperative scheduling languages; third, a qualitative comparison between the SOS and LAGC versions yields that the latter, while taking higher initial effort, is more amenable to proving fairness and scales better under language extensions than SOS. The grounding of our work is a detailed formal proof of quiescent fairness for the scheduler defined in LAGC semantics. The importance of our work is that it provides a formal foundation for the implementation of fair schedulers for cooperative scheduling languages, an increasingly popular paradigm (for example: akka/Scala, JavaScript, async Rust). Being based solely on semantics, our ideas are widely applicable. Further, our work makes clear that the standard notion of fairness in concurrent languages needs to be adapted for cooperative scheduling and, more generally, for any language that combines atomic execution sequences with some form of preemption.