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We extend our time-dependent hydrogen ionization simulations of diffuse ionized gas to include metals important for collisional cooling and diagnostic emission lines. The combination of heating from supernovae and time-dependent collisional and photoionization from midplane OB stars produces emission line intensities (and emission line ratios) that follow the trends observed in the Milky Way and other edge-on galaxies. The long recombination times in low density gas result in persistent large volumes of ions with high ionization potentials, such as O III and Ne III. In particular, the vertically extended layers of Ne III in our time-dependent simulations result in [Ne III] 15$\mu$m/[Ne II] 12$\mu$m emission line ratios in agreement with observations of the edge-on galaxy NGC 891. Simulations adopting ionization equilibrium do not allow for the persistence of ions with high ionization states and therefore cannot reproduce the observed emission lines from low density gas at high altitudes., Comment: 14 pages, 16 figures, accepted for publication in MNRAS

The growing reliance on renewable energy sources, particularly solar and wind, has introduced challenges due to their uncontrollable production. This complicates maintaining the electrical grid balance, prompting some transmission system operators in Western Europe to implement imbalance tariffs that penalize unsustainable power deviations. These tariffs create an implicit demand response framework to mitigate grid instability. Yet, several challenges limit active participation. In Belgium, for example, imbalance prices are only calculated at the end of each 15-minute settlement period, creating high risk due to price uncertainty. This risk is further amplified by the inherent volatility of imbalance prices, discouraging participation. Although transmission system operators provide minute-based price predictions, the system imbalance volatility makes accurate price predictions challenging to obtain and requires sophisticated techniques. Moreover, publishing price estimates can prompt participants to adjust their schedules, potentially affecting the system balance and the final price, adding further complexity. To address these challenges, we propose a Monte Carlo Tree Search method that publishes accurate imbalance prices while accounting for potential response actions. Our approach models the system dynamics using a neural network forecaster and a cluster of virtual batteries controlled by reinforcement learning agents. Compared to Belgium's current publication method, our technique improves price accuracy by 20.4% under ideal conditions and by 12.8% in more realistic scenarios. This research addresses an unexplored, yet crucial problem, positioning this paper as a pioneering work in analyzing the potential of more advanced imbalance price publishing techniques.

This paper proposes improvements over earlier work by Nazareth and Blei (2022) for estimating the depth of Bayesian neural networks. Here, we propose a discrete truncated normal distribution over the network depth to independently learn its mean and variance. Posterior distributions are inferred by minimizing the variational free energy, which balances the model complexity and accuracy. Our method improves test accuracy on the spiral data set and reduces the variance in posterior depth estimates., Comment: NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty. Available at https://openreview.net/forum?id=6TLRVdWGzI

With the next generation of big telescopes such as the ELT and SKA it might become possible to measure changes in the expansion rate of the Universe in real time by measuring the change of the redshifts of a large number of galaxies over a period of the order of 10 years. This phenomenon, known as 'redshift drift,' will provide a crucial direct test of cosmological models. The change in redshift is readily explained using the concept of conformal time which is the comoving distance of a galaxy in lightyears. We emphasize that the redshift drift is directly proportional to the average change in the cosmic expansion rate between the time of a galaxy's light emission and its absorption. This phenomenon is illustrated within the framework of the concordance model, the Lambda-CDM model of the universe., Comment: 12 pages, 5 figures

Augmented Reality (AR) and Virtual Reality (VR), require miniaturized displays with ultrahigh pixel densities. Here, we demonstrate an individually addressable subwavelength OLED pixel based on a nanoscale electrode capable of supporting plasmonic modes. Our approach is based on the notion that when scaling down pixel size, the 2D planar geometry of conventional organic light-emitting diodes (OLEDs) evolves into a significantly more complex 3D geometry governed by sharp nanoelectrode contours. These cause (i) spatially imbalanced charge carrier transport and recombination, resulting in a low quantum efficiency, and (ii) filament growth, leading to rapid device failure. Here, we circumvent such effects by selectively covering sharp electrode contours with an insulating layer, while utilizing a nano-aperture in flat areas of the electrode. We thereby ensure controlled charge carrier injection and recombination at the nanoscale and suppress filament growth. As a proof of principle, we first demonstrate stable and efficient hole injection from Au nanoelectrodes in hole-only devices with above 90 % pixel yield and longtime operation stability and then a complete vertical OLED pixel with an individually addressable nanoelectrode (300 x 300 nm$^{2}$), highlighting the potential to further leverage plasmonic nanoantenna effects to enhance the performance and functionality of nano-OLEDs.

The rapid scaling of AI has spurred a growing emphasis on ethical considerations in both development and practice. This has led to the formulation of increasingly sophisticated model auditing and reporting requirements, as well as governance frameworks to mitigate potential risks to individuals and society. At this critical juncture, we review the practical challenges of promoting responsible AI and transparency in informal sectors like OSS that support vital infrastructure and see widespread use. We focus on how model performance evaluation may inform or inhibit probing of model limitations, biases, and other risks. Our controlled analysis of 7903 Hugging Face projects found that risk documentation is strongly associated with evaluation practices. Yet, submissions (N=789) from the platform's most popular competitive leaderboard showed less accountability among high performers. Our findings can inform AI providers and legal scholars in designing interventions and policies that preserve open-source innovation while incentivizing ethical uptake., Comment: [Under Review][WIP]

Autonomous driving at unsignalized intersections is still considered a challenging application for machine learning due to the complications associated with handling complex multi-agent scenarios characterized by a high degree of uncertainty. Automating the decision-making process at these safety-critical environments involves comprehending multiple levels of abstractions associated with learning robust driving behaviors to enable the vehicle to navigate efficiently. In this survey, we aim at exploring the state-of-the-art techniques implemented for decision-making applications, with a focus on algorithms that combine Reinforcement Learning (RL) and deep learning for learning traversing policies at unsignalized intersections. The reviewed schemes vary in the proposed driving scenario, in the assumptions made for the used intersection model, in the tackled challenges, and in the learning algorithms that are used. We have presented comparisons for these techniques to highlight their limitations and strengths. Based on our in-depth investigation, it can be discerned that a robust decision-making scheme for navigating real-world unsignalized intersection has yet to be developed. Along with our analysis and discussion, we recommend potential research directions encouraging the interested players to tackle the highlighted challenges. By adhering to our recommendations, decision-making architectures that are both non-overcautious and safe, yet feasible, can be trained and validated in real-world unsignalized intersections environments.

Active Inference is a framework that emphasizes the interaction between agents and their environment. While the framework has seen significant advancements in the development of agents, the environmental models are often borrowed from reinforcement learning problems, which may not fully capture the complexity of multi-agent interactions or allow complex, conditional communication. This paper introduces Reactive Environments, a comprehensive paradigm that facilitates complex multi-agent communication. In this paradigm, both agents and environments are defined as entities encapsulated by boundaries with interfaces. This setup facilitates a robust framework for communication in nonequilibrium-Steady-State systems, allowing for complex interactions and information exchange. We present a Julia package RxEnvironments.jl, which is a specific implementation of Reactive Environments, where we utilize a Reactive Programming style for efficient implementation. The flexibility of this paradigm is demonstrated through its application to several complex, multi-agent environments. These case studies highlight the potential of Reactive Environments in modeling sophisticated systems of interacting agents.

Cone-beam computed tomography (CBCT) is widely used in interventional surgeries and radiation oncology. Due to the limited size of flat-panel detectors, anatomical structures might be missing outside the limited field-of-view (FOV), which restricts the clinical applications of CBCT systems. Recently, deep learning methods have been proposed to extend the FOV for multi-slice CT systems. However, in mobile CBCT system with a smaller FOV size, projection data is severely truncated and it is challenging for a network to restore all missing structures outside the FOV. In some applications, only certain structures outside the FOV are of interest, e.g., ribs in needle path planning for liver/lung cancer diagnosis. Therefore, a task-specific data preparation method is proposed in this work, which automatically let the network focus on structures of interest instead of all the structures. Our preliminary experiment shows that Pix2pixGAN with a conventional training has the risk to reconstruct false positive and false negative rib structures from severely truncated CBCT data, whereas Pix2pixGAN with the proposed task-specific training can reconstruct all the ribs reliably. The proposed method is promising to empower CBCT with more clinical applications., Comment: Published in the CT-Meeting 2024 proceeding. arXiv admin note: text overlap with arXiv:2108.13844

Cold gas and dust reprocess the X-ray emission of active galactic nuclei (AGN), producing spectro-polarimetric features in the X-ray band. The recent launch of IXPE allows for observations of this X-ray polarisation signal, encoding unique information on the circumnuclear medium of AGN. However, the models for interpreting these polarimetric data are under-explored and do not reach the same level of sophistication as the corresponding spectral models. We aim at closing the gap between the spectral and spectro-polarimetric modelling of AGN circumnuclear media by providing the tools for simulating X-ray polarisation in complex 3D transfer media alongside X-ray spectra. We lay out the framework for X-ray polarisation in 3D radiative transfer simulations and provide an implementation to the 3D radiative transfer code SKIRT, focussing on (de)polarisation due to scattering and fluorescent re-emission. As a first application, we studied a 2D toroidal reprocessor of cold gas, modelling the AGN circumnuclear medium. For the 2D torus model, we find a complex behaviour of the polarisation angle with photon energy, which we interpret as a balance between the reprocessed photon flux originating from different sky regions, with a direct link to the torus geometry. We calculated a large grid of AGN torus models and demonstrated how spatially resolved polarisation maps could form a useful tool for interpreting the geometrical information that is encoded in IXPE observations. With this work, we release high-resolution AGN torus templates that simultaneously describe X-ray spectra and spectro-polarimetry, for observational data fitting with XSPEC. The SKIRT code can now model X-ray polarisation simultaneously with X-ray spectra and provide synthetic spectro-polarimetric observations for complex 3D circumnuclear media, with all features of the established SKIRT framework available., Comment: 16 pages, 13 figures, accepted for publication in Astronomy & Astrophysics

Exchange bias is applied ubiquitously throughout the spintronics industry for pinning magnetic moment in an orientation robust against external magnetic field disturbances. Enabling efficient manipulation of the exchange-biased pinning direction is key to add or advance device functionality. Conventional methods for achieving this reorientation commonly require long heating times and assisting magnetic fields, thus limiting speed and versatility. Here, we demonstrate a field-free $180^\circ$ reorientation of perpendicular exchange bias in a thin film system integrating Co/Gd layers with IrMn. Single femtosecond laser pulses trigger a reversal of the pinning direction, while preserving the unidirectionality required for referencing. We formulate a theoretical microscopic framework involving a wide range of timescales that accurately reproduces the experimental outcomes. The understanding of the underlying mechanisms delivers guiding principles for designing reprogrammable spintronic devices., Comment: Manuscript revision for submission

We present an overview of the operations and engineering interface for Planetary Radio Interferometry and Doppler Experiment (PRIDE) radio astronomy observations as a scientific component of the ESA s Jupiter Icy Moons Explorer (JUICE) mission, as well as other prospective planetary and space science missions. The article discusses advanced scheduling and planning methods that make it possible to create observing schedules for observations of specific spacecraft in concurrence with observations of natural radio sources. In order to put this into practice and find suitable natural background calibrator sources for PRIDE of JUICE mission, we developed planning and scheduling software. The conventional scheduling software for natural celestial radio sources is not set up to include spacecraft as observation targets in the necessary control files. Therefore, difficulties already arise during observation planning. We report on the development of new and the adaptation of existing routines used in astrophysical and geodetic VLBI for satellite scheduling and planning. The analysis of the PRIDE science observations led to improved observational planning, and the mission s scheduling methodologies were studied using a systems engineering approach. In addition, we highlighted the new procedures, like finding charts for selecting calibrator radio sources over a range of frequency bands and the outcomes of those strategies for science operation planning. A simulation of the flyby of Venus during the cruise phase of the JUICE spacecraft, based on the Tudat software, is also presented, resulting in a promising opportunity to test PRIDE techniques and evaluate the improvements that PRIDE observables can make to natural bodies ephemerides., Comment: 34 pages

Generating physician letters is a time-consuming task in daily clinical practice. This study investigates local fine-tuning of large language models (LLMs), specifically LLaMA models, for physician letter generation in a privacy-preserving manner within the field of radiation oncology. Our findings demonstrate that base LLaMA models, without fine-tuning, are inadequate for effectively generating physician letters. The QLoRA algorithm provides an efficient method for local intra-institutional fine-tuning of LLMs with limited computational resources (i.e., a single 48 GB GPU workstation within the hospital). The fine-tuned LLM successfully learns radiation oncology-specific information and generates physician letters in an institution-specific style. ROUGE scores of the generated summary reports highlight the superiority of the 8B LLaMA-3 model over the 13B LLaMA-2 model. Further multidimensional physician evaluations of 10 cases reveal that, although the fine-tuned LLaMA-3 model has limited capacity to generate content beyond the provided input data, it successfully generates salutations, diagnoses and treatment histories, recommendations for further treatment, and planned schedules. Overall, clinical benefit was rated highly by the clinical experts (average score of 3.44 on a 4-point scale). With careful physician review and correction, automated LLM-based physician letter generation has significant practical value.

Wireless power transfer (WPT) has garnered increasing attention due to its potential to eliminate device-side batteries. With the advent of (distributed) multiple-input multiple-output (MIMO), radio frequency (RF) WPT has become feasible over extended distances. This study focuses on optimizing the energy delivery to Energy Receivers (ERs) while minimizing system total transmit power. Rather than continuous power delivery, we optimize the precoding weights within specified time slots to meet the energy requirements of the ERs. Both unsynchronized (non-coherent) and synchronized (coherent) systems are evaluated. Our analysis indicates that augmenting the number of antennas and transitioning from an unsynchronized to asynchronized full phase-coherent system substantially enhances system performance. This optimization ensures precise energy delivery, reducing overshoots and overall energy consumption. Experimental validation was conducted using a testbed with84 antennas, validating the trends observed in our numerical simulations., Comment: 2024 IEEE 25th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

Mixed effects models are among the most commonly used statistical methods for the exploration of multispecies data. In recent years, also Joint Species Distribution Models and Generalized Linear Latent Variale Models have gained in popularity when the goal is to incorporate residual covariation between species that cannot be explained due to measured environmental covariates. Few software implementations of such models exist that can additionally incorporate phylogenetic information, and those that exist tend to utilize Markov chain Monte Carlo methods for estimation, so that model fitting takes a long time. In this article we develop new methods for quickly and flexibly fitting phylogenetic mixed models, potentially incorporating residual covariation between species using latent variables, with the possibility to estimate the strength of phylogenetic structuring in species responses per environmental covariate, and while incorporating correlation between different covariate effects. By combining Variational approximations with a reduced rank matrix normal covariance structure, Nearest Neighbours Gaussian Processes, and parallel computation, phylogenetic mixed models can be fitted much more quickly than the current state-of-the-art. Two simulation studies demonstrate that the proposed combination of approximations is not only fast, but also enjoys high accuracy. Finally, we demonstrate use of the method with a real world dataset of wood-decaying fungi.

We included the intrinsic line profiles of the strongest fluorescent lines in the X-ray radiative transfer code SKIRT to model the cold-gas structure and kinematics based on high-resolution line observations from XRISM/Resolve and Athena/X-IFU. The intrinsic line profiles of the Ka and Kb lines of Cr, Mn, Fe, Co, Ni, and Cu were implemented based on a multi-Lorentzian parameterisation and line energies are sampled from these Lorentzian components during the radiative transfer routine. In the optically thin regime, the SKIRT results match the intrinsic line profiles as measured in the laboratory. With a more complex 3D model that also includes kinematics, we find that the intrinsic line profiles are broadened and shifted to an extent that will be detectable with XRISM/Resolve; this model also demonstrates the importance of the intrinsic line shapes for constraining kinematics. We find that observed line profiles directly trace the cold-gas kinematics, without any additional radiative transfer effects. With the advent of the first XRISM/Resolve data, this update to the X-ray radiative transfer framework of SKIRT is timely and provides a unique tool for constraining the velocity structure of cold gas from X-ray microcalorimeter spectra., Comment: 6 pages, 3 figures, accepted for publication in Astronomy & Astrophysics

Context. 3D numerical simulations of radiative transfer are crucial for understanding complex astrophysical objects. For Monte Carlo radiative transfer, the spatial grid design is critical yet complex. Common grids include hierarchical octree and unstructured Voronoi grids, each with its own strengths and weaknesses. Tetrahedral grids, widely used in ray-tracing graphics, are a potential alternative. Aims. We explore the possibilities, advantages, and limitations of tetrahedral grids for Monte Carlo radiative transfer, comparing their performance with other grid structures. Method. We integrated a tetrahedral grid structure, using the TetGen library, into the SKIRT Monte Carlo radiative transfer code. Tetrahedral grids can be imported or adaptively constructed and refined within SKIRT. We implemented an efficient grid traversal method using Pl\"ucker coordinates and Pl\"ucker products. Results. We validated the tetrahedral grid construction and traversal algorithm with 2D radiative transfer benchmarks. In a simple 3D model, we compared the performance of tetrahedral, octree, and Voronoi grids. The octree grid outperformed the others in traversal speed, while the tetrahedral grid had the lowest grid quality. Overall, tetrahedral grids performed worse than octree and Voronoi grids. Conclusion. While tetrahedral grids may not be ideal for most astrophysical simulations, they offer a viable unstructured alternative to Voronoi grids for specific applications, such as post-processing hydrodynamical simulations on tetrahedral or unstructured grids., Comment: 10 pages, 8 figures, abstract was shortened to fit the arxiv abstract requirements, article is accepted to Astronomy & Astrophysics (in production)

This letter illustrates the opinion of the molecular dynamics (MD) community on the need to adopt a new FAIR paradigm for the use of molecular simulations. It highlights the necessity of a collaborative effort to create, establish, and sustain a database that allows findability, accessibility, interoperability, and reusability of molecular dynamics simulation data. Such a development would democratize the field and significantly improve the impact of MD simulations on life science research. This will transform our working paradigm, pushing the field to a new frontier. We invite you to support our initiative at the MDDB community (https://mddbr.eu/community/)

We consider a class of chance-constrained programs in which profit needs to be maximized while enforcing that a given adverse event remains rare. Using techniques from large deviations and extreme-value theory, we show how the optimal value scales as the prescribed bound on the violation probability becomes small and how convex programs emerge in the limit. We also use our results to show that the popular CVaR approximation is asymptotically optimal under light-tail assumptions, but it is sub-optimal in a heavy-tailed setting. Then, we apply point process techniques and random set theory to study the suboptimality gap in the Monte Carlo based scenario approach., Comment: 17 pages

Recent years has witnessed an increase in technologies that use speech for the sensing of the health of the talker. This survey paper proposes a general taxonomy of the technologies and a broad overview of current progress and challenges. Vocal biomarkers are often secondary measures that are approximating a signal of another sensor or identifying an underlying mental, cognitive, or physiological state. Their measurement involve disturbances and uncertainties that may be considered as noise sources and the biomarkers are coarsely qualified in terms of the various sources of noise involved in their determination. While in some proposed biomarkers the error levels seem high, there are vocal biomarkers where the errors are expected to be low and thus are more likely to qualify as candidates for adoption in healthcare applications.

We show that for a generic Riemannian metric on a compact manifold of dimension $n\ge 3$ all geodesic loops based at a fixed point have no self-intersections. We also show that for an open and dense subset of the space of Riemannian metrics on an $n$-disc with $n \ge 3$ and with a strictly convex boundary there are $n$ geometrically distinct orthogonal geodesic chords without self-intersections. We use a perturbation result for intersecting geodesic segments of the author and a genericity statement due to Bettiol and Giamb\`o and existence results for orthogonal geodesic chords by Giamb\`o, Giannoni, and Piccione., Comment: 15 pages

We study large deviations of the size of the largest connected component in a general class of inhomogeneous random graphs with iid weights, parametrized so that the degree distribution is regularly varying. We derive a large-deviation principle with logarithmic speed: the rare event that the largest component contains linearly more vertices than expected is caused by the presence of constantly many vertices with linear degree. Conditionally on this rare event, we prove distributional limits of the weight distribution and component-size distribution., Comment: 36 pages

Single pulse all-optical switching of magnetization (AOS) in Co/Gd based synthetic ferrimagnets carries promises for hybrid spintronic-photonic integration. A crucial next step progressing towards this vision is to gain insight into AOS and multi-domain state (MDS) behavior using longer pulses, which is compatible with state-of-the-art integrated photonics. In this work, we present our studies on the AOS and MDS of [Co/Gd]n (n = 1, 2) using ps optical pulses across a large composition range. We theoretically and experimentally show that a large Gd layer thickness can enhance the AOS energy efficiency and maximum pulse duration. We have identified two augmenting roles of Gd in extending the maximum pulse duration. On the inter-atomic level, we found that more Gd offers a prolonged angular momentum supply to Co. On the micromagnetic level, a higher Gd content brings the system to be closer to magnetic compensation in the equilibrized hot state, thereby reducing the driving force for thermally assisted nucleation of domain walls, combating the formation of a MDS. Our study presents a composition overview of AOS in [Co/Gd]n and offers useful physical insights regarding AOS fundamentals as well as the projected photonic integration.

This paper unites two research lines. The first involves finding categorical models of quantum programming languages and their type systems. The second line concerns the program of quantization of mathematical structures, which amounts to finding noncommutative generalizations (also called quantum generalizations) of these structures. Using a quantization method called discrete quantization, which essentially amounts to the internalization of structures in a category of von Neumann algebras and quantum relations, we find a noncommutative generalization of $\omega$-complete partial orders (cpos), called quantum cpos. Cpos are central in domain theory, and are widely used to construct categorical models of programming languages. We show that quantum cpos have similar categorical properties to cpos and are therefore suitable for the construction of categorical models for quantum programming languages, which is illustrated with some examples. For this reason, quantum cpos may form the backbone of a future quantum domain theory., Comment: 80 pages

The Internet of Things (IoT) can support the evolution towards a digital and green future. However, the introduction of the technology clearly has in itself a direct adverse ecological impact. This paper assesses this impact at both the IoT-node and at the network side. For the nodes, we show that the electronics production of devices comes with a carbon footprint that can be much higher than during operation phase. We highlight that the inclusion of IoT support in existing cellular networks comes with a significant ecological penalty, raising overall energy consumption by more than 15%. These results call for novel design approaches for the nodes and for early consideration of the support for IoT in future networks. Raising the 'Vehicle or bandit?' question on the nature of IoT in the broader sense of sustainability, we illustrate the need for multidisciplinary cooperation to steer applications in desirable directions.

An element in the Brauer group of a general complex projective $K3$ surface $S$ defines a sublattice of the transcendental lattice of $S$. We consider those elements of prime order for which this sublattice is Hodge-isometric to the transcendental lattice of another K3 surface $X$. We recall that this defines a finite map between moduli spaces of polarized K3 surfaces and we compute its degree. We show how the Picard lattice of $X$ determines the Picard lattice of $S$ in the case that the Picard number of $X$ is two., Comment: 16 pages, submitted to Rendiconti Semin. Mat. Univ.Politecn. Torino, volume dedicated to the memory of Gianfranco Casnati

Purpose: Histology is the gold standard for sub-cellular visualization of the mouse brain. It offers excellent in-plane resolution, but a comparably low out-of-plane resolution due to physical sectioning. X-ray microtomography does not require this trade-off. Tomographic imaging of the entire mouse brain with isotropic cellular resolution produces datasets of multiple terabytes in size. These data must be processed and made accessible to domain experts who may have only limited image processing knowledge. Approach: Extended-field X-ray microtomography covering an entire mouse brain was performed. The 4,495 projections from 8 $\times$ 8 offset acquisitions were stitched to reconstruct a volume of 15,000$^3$ voxels. The microtomography volume was non-rigidly registered to the Allen Mouse Brain Common Coordinate Framework v3 based on a combination of image intensity and landmark pairs. Results: We present a 3.3 teravoxel dataset covering a full mouse brain with 0.65 $\mu$m voxel size. The data were blockwise transformed to a common coordinate system, then stored in a public repository with a hierarchical format for navigation and overlay with anatomical annotations in online viewers such as Neuroglancer or siibra-explorer. Conclusions: This study demonstrates X-ray imaging and data processing for a full mouse brain, augmenting current atlases by improving resolution in the third dimension by an order of magnitude. The data are publicly available and easily accessible for domain experts via browser-based viewers., Comment: 21 pages, 9 figures, submitted to journal

Background: This research aims to improve glioblastoma survival prediction by integrating MR images, clinical and molecular-pathologic data in a transformer-based deep learning model, addressing data heterogeneity and performance generalizability. Method: We propose and evaluate a transformer-based non-linear and non-proportional survival prediction model. The model employs self-supervised learning techniques to effectively encode the high-dimensional MRI input for integration with non-imaging data using cross-attention. To demonstrate model generalizability, the model is assessed with the time-dependent concordance index (Cdt) in two training setups using three independent public test sets: UPenn-GBM, UCSF-PDGM, and RHUH-GBM, each comprising 378, 366, and 36 cases, respectively. Results: The proposed transformer model achieved promising performance for imaging as well as non-imaging data, effectively integrating both modalities for enhanced performance (UPenn-GBM test-set, imaging Cdt 0.645, multimodal Cdt 0.707) while outperforming state-of-the-art late-fusion 3D-CNN-based models. Consistent performance was observed across the three independent multicenter test sets with Cdt values of 0.707 (UPenn-GBM, internal test set), 0.672 (UCSF-PDGM, first external test set) and 0.618 (RHUH-GBM, second external test set). The model achieved significant discrimination between patients with favorable and unfavorable survival for all three datasets (logrank p 1.9\times{10}^{-8}, 9.7\times{10}^{-3}, and 1.2\times{10}^{-2}). Conclusions: The proposed transformer-based survival prediction model integrates complementary information from diverse input modalities, contributing to improved glioblastoma survival prediction compared to state-of-the-art methods. Consistent performance was observed across institutions supporting model generalizability.

Objectives: This work aims to explore the impact of multicenter data heterogeneity on deep learning brain metastases (BM) autosegmentation performance, and assess the efficacy of an incremental transfer learning technique, namely learning without forgetting (LWF), to improve model generalizability without sharing raw data. Materials and methods: A total of six BM datasets from University Hospital Erlangen (UKER), University Hospital Zurich (USZ), Stanford, UCSF, NYU and BraTS Challenge 2023 on BM segmentation were used for this evaluation. First, the multicenter performance of a convolutional neural network (DeepMedic) for BM autosegmentation was established for exclusive single-center training and for training on pooled data, respectively. Subsequently bilateral collaboration was evaluated, where a UKER pretrained model is shared to another center for further training using transfer learning (TL) either with or without LWF. Results: For single-center training, average F1 scores of BM detection range from 0.625 (NYU) to 0.876 (UKER) on respective single-center test data. Mixed multicenter training notably improves F1 scores at Stanford and NYU, with negligible improvement at other centers. When the UKER pretrained model is applied to USZ, LWF achieves a higher average F1 score (0.839) than naive TL (0.570) and single-center training (0.688) on combined UKER and USZ test data. Naive TL improves sensitivity and contouring accuracy, but compromises precision. Conversely, LWF demonstrates commendable sensitivity, precision and contouring accuracy. When applied to Stanford, similar performance was observed. Conclusion: Data heterogeneity results in varying performance in BM autosegmentation, posing challenges to model generalizability. LWF is a promising approach to peer-to-peer privacy-preserving model training., Comment: Official published version in the Green Journal: https://doi.org/10.1016/j.radonc.2024.110419

The effectiveness of a series of optically transparent aligners for orthodontic treatments depends on the anchoring of each tooth. In contrast with roots, the crowns' positions and orientations are measurable with intraoral scans, thus avoiding any X-ray dose. Exemplified by two patients, we demonstrate that three-dimensional crown movements could be determined with micrometer precision by registering weekly intraoral scans. The data show the movement and orientation changes of the individual crowns of the upper and lower jaws as the result of the forces generated by the series of aligners. During the first weeks, the canines and incisors were more affected than the premolars and molars. We detected an overall tooth movement of up to about 1 mm during a nine-week treatment. The data on these orthodontic treatments indicate the extent to which actual tooth movement lags behind the treatment plan as represented by the aligner shapes. The proposed procedure can not only be used to quantify the clinical outcome of the therapy, but also to improve future planning of orthodontic treatments for each specific patient. The study should be treated with caution because only two cases have been investigated and the approach should be applied to a reasonably large cohort to reach strong conclusions regarding efficiency and efficacy of the therapeutic approach., Comment: 18 pages, 9 figures, accepted by MDPI journal Oral

Successful applications of a conceptually novel setup of Quantum Field Theory, that accounts for all subtheories of the Standard Model (QED, Electroweak Interaction and Higgs, Yang-Mills and QCD) and beyond (Helicity 2), call for a perspective view in a broader conceptual context. The setting is "autonomous" in the sense of being intrinsically quantum. Its principles are: Hilbert space, Poincar\'e symmetry and causality. Its free quantum fields are obtained from Wigner's unitary representations of the Poincar\'e group, with only physical and observable degrees of freedom. A "quantization" of an "underlying" classical theory is not needed. It allows renormalizable perturbation theory with interactions whose detailed structure, and in some cases even the particle content, is predicted by internal consistency. The results confirm and extend observable predictions for the interactions of the SM without assuming a "principle" of gauge invariance., Comment: v2: published version, including some updates and added references. 23 pages

Dosimetry is an essential tool to provide the best and safest radio-therapies to a patient. In this field, Monte-Carlo simulations are considered to be the golden standard for predicting accurately the deposited dose in the body. Such methods are very time-consuming and simpler dose calculation engines, like dose kernel approaches, were created for cases where a fast estimation is necessary. In those approaches, dose distribution maps (or dose kernel) are learned for simple beams geometry, then, a combination of numerous kernels is used to simulate more complex beams. However, those methods often lack personalization and oversimplify the human body, especially for the secondary interactions dose deposition. In this article, we explore the possibility of learning the dose kernel using convolutional neural networks to improve their accuracy towards each different human body. We also highlight the limits of such approaches., Comment: 16 pages, 9 figures

A continuous rise in the penetration of renewable energy sources, along with the use of the single imbalance pricing, provides a new opportunity for balance responsible parties to reduce their cost through energy arbitrage in the imbalance settlement mechanism. Model-free reinforcement learning (RL) methods are an appropriate choice for solving the energy arbitrage problem due to their outstanding performance in solving complex stochastic sequential problems. However, RL is rarely deployed in real-world applications since its learned policy does not necessarily guarantee safety during the execution phase. In this paper, we propose a new RL-based control framework for batteries to obtain a safe energy arbitrage strategy in the imbalance settlement mechanism. In our proposed control framework, the agent initially aims to optimize the arbitrage revenue. Subsequently, in the post-processing step, we correct (constrain) the learned policy following a knowledge distillation process based on properties that follow human intuition. Our post-processing step is a generic method and is not restricted to the energy arbitrage domain. We use the Belgian imbalance price of 2023 to evaluate the performance of our proposed framework. Furthermore, we deploy our proposed control framework on a real battery to show its capability in the real world., Comment: ACM e-Energy 2024

Keeping the balance between electricity generation and consumption is becoming increasingly challenging and costly, mainly due to the rising share of renewables, electric vehicles and heat pumps and electrification of industrial processes. Accurate imbalance forecasts, along with reliable uncertainty estimations, enable transmission system operators (TSOs) to dispatch appropriate reserve volumes, reducing balancing costs. Further, market parties can use these probabilistic forecasts to design strategies that exploit asset flexibility to help balance the grid, generating revenue with known risks. Despite its importance, literature regarding system imbalance (SI) forecasting is limited. Further, existing methods do not focus on situations with high imbalance magnitude, which are crucial to forecast accurately for both TSOs and market parties. Hence, we propose an ensemble of C-VSNs, which are our adaptation of variable selection networks (VSNs). Each minute, our model predicts the imbalance of the current and upcoming two quarter-hours, along with uncertainty estimations on these forecasts. We evaluate our approach by forecasting the imbalance of Belgium, where high imbalance magnitude is defined as $|$SI$| > 500\,$MW (occurs 1.3% of the time in Belgium). For high imbalance magnitude situations, our model outperforms the state-of-the-art by 23.4% (in terms of continuous ranked probability score (CRPS), which evaluates probabilistic forecasts), while also attaining a 6.5% improvement in overall CRPS. Similar improvements are achieved in terms of root-mean-squared error. Additionally, we developed a fine-tuning methodology to effectively include new inputs with limited history in our model. This work was performed in collaboration with Elia (the Belgian TSO) to further improve their imbalance forecasts, demonstrating the relevance of our work.

Widespread, high altitude, diffuse ionized gas with scale heights of around a kiloparsec is observed in the Milky Way and other star forming galaxies. Numerical radiation-magnetohydrodynamic simulations of a supernova-driven turbulent interstellar medium show that gas can be driven to high altitudes above the galactic midplane, but the degree of ionization is often less than inferred from observations. For computational expediency, ionizing radiation from massive stars is often included as a post-processing step assuming ionization equilibrium. We extend our simulations of a Milky Way-like interstellar medium to include the combined effect of supernovae and photoionization feedback from midplane OB stars and a population of hot evolved low mass stars. The diffuse ionized gas has densities below 0.1 ${\rm cm^{-3}}$, so recombination timescales can exceed millions of years. Our simulations now follow the time-dependent ionization and recombination of low density gas. The long recombination timescales result in diffuse ionized gas that persists at large altitudes long after the deaths of massive stars that produce the vast majority of the ionized gas. The diffuse ionized gas does not exhibit the large variability inherent in simulations that adopt ionization equilibrium. The vertical distribution of neutral and ionized gas is close to what is observed in the Milky Way. The volume filling factor of ionized gas increases with altitude resulting in the scale height of free electrons being larger than that inferred from H$\alpha$ emission, thus reconciling the observations of ionized gas made in H$\alpha$ and from pulsar dispersion measurements., Comment: 17 pages, 16 figures, accepted for publication in MNRAS

Race conditions are a class of bugs in software where concurrent accesses to shared resources are not protected from each other. Consequences of race conditions include privilege escalation, denial of service, and memory corruption which can potentially lead to arbitrary code execution. However, in large code bases the exact rules as to which fields should be accessed under which locks are not always clear. We propose a novel static technique that infers rules for how field accesses should be locked, and then checks the code against these rules. Traditional static analysers for detecting race conditions are based on lockset analysis. Instead, we propose an outlier-based technique enhanced with a context-sensitive mechanism that scales well. We have implemented this analysis in LLIF, and evaluated it to find incorrectly protected field accesses in Linux v5.14.11. We thoroughly evaluate its ability to find race conditions, and study the causes for false positive reports. In addition, we reported a subset of the issues and submitted patches. The maintainers confirmed 24 bugs.

Objective: Four-dimensional computed tomography (4DCT) imaging consists in reconstructing a CT acquisition into multiple phases to track internal organ and tumor motion. It is commonly used in radiotherapy treatment planning to establish planning target volumes. However, 4DCT increases protocol complexity, may not align with patient breathing during treatment, and lead to higher radiation delivery. Approach: In this study, we propose a deep synthesis method to generate pseudo respiratory CT phases from static images for motion-aware treatment planning. The model produces patient-specific deformation vector fields (DVFs) by conditioning synthesis on external patient surface-based estimation, mimicking respiratory monitoring devices. A key methodological contribution is to encourage DVF realism through supervised DVF training while using an adversarial term jointly not only on the warped image but also on the magnitude of the DVF itself. This way, we avoid excessive smoothness typically obtained through deep unsupervised learning, and encourage correlations with the respiratory amplitude. Main results: Performance is evaluated using real 4DCT acquisitions with smaller tumor volumes than previously reported. Results demonstrate for the first time that the generated pseudo-respiratory CT phases can capture organ and tumor motion with similar accuracy to repeated 4DCT scans of the same patient. Mean inter-scans tumor center-of-mass distances and Dice similarity coefficients were $1.97$mm and $0.63$, respectively, for real 4DCT phases and $2.35$mm and $0.71$ for synthetic phases, and compares favorably to a state-of-the-art technique (RMSim)., Comment: to appear in Phys. Med. Biol

Automating the current bridge visual inspection practices using drones and image processing techniques is a prominent way to make these inspections more effective, robust, and less expensive. In this paper, we investigate the development of a novel deep-learning method for the detection of fatigue cracks in high-resolution images of steel bridges. First, we present a novel and challenging dataset comprising of images of cracks in steel bridges. Secondly, we integrate the ConvNext neural network with a previous state-of-the-art encoder-decoder network for crack segmentation. We study and report, the effects of the use of background patches on the network performance when applied to high-resolution images of cracks in steel bridges. Finally, we introduce a loss function that allows the use of more background patches for the training process, which yields a significant reduction in false positive rates.