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We consider the problem of tracking latent time-varying parameter vectors under model misspecification. We analyze implicit and explicit score-driven (ISD and ESD) filters, which update a prediction of the parameters using the gradient of the logarithmic observation density (i.e., the score). In the ESD filter, the score is computed using the predicted parameter values, whereas in the ISD filter, the score is evaluated using the new, updated parameter values. For both filter types, we derive novel sufficient conditions for the exponential stability (i.e., invertibility) of the filtered parameter path and existence of a finite mean squared error (MSE) bound with respect to the pseudo-true parameter path. In addition, we present expressions for finite-sample and asymptotic MSE bounds. Our performance guarantees rely on mild moment conditions on the data-generating process, while our stability result is entirely agnostic about the true process. As a result, our primary conditions depend only on the characteristics of the filter; hence, they are verifiable in practice. Concavity of the postulated log density combined with simple parameter restrictions is sufficient (but not necessary) for ISD-filter stability, whereas ESD-filter stability additionally requires the score to be Lipschitz continuous. Extensive simulation studies validate our theoretical findings and demonstrate the enhanced stability and improved performance of ISD over ESD filters. An empirical application to U.S. Treasury-bill rates confirms the practical relevance of our contribution., Comment: 71 pages

This study explores the feasibility of using scintillation detectors for muon calorimetry through experiments conducted at CERN's T10 beamline. Results from organic scintillators with varying thicknesses and readout methods, as well as a lead-glass calorimeter, showed minimal correlation between muon energy and the peak or spread of the detected signal amplitudes. These findings highlight the influence of temperature on SiPMs and indicate a significant amount of contamination in the muon beam., Comment: 12 pages, 11 figures

We present a massively parallel solver that accelerates DC loadflow computations for power grid topology optimization tasks. Our approach leverages low-rank updates of the Power Transfer Distribution Factors (PTDFs) to represent substation splits, line outages, and reconfigurations without ever refactorizing the system. Furthermore, we implement the core routines on Graphics Processing Units (GPUs), thereby exploiting their high-throughput architecture for linear algebra. A two-level decomposition separates changes in branch topology from changes in nodal injections, enabling additional speed-ups by an in-the-loop brute force search over injection variations at minimal additional cost. We demonstrate billion-loadflow-per-second performance on power grids of varying sizes in workload settings which are typical for gradient-free topology optimization such as Reinforcement Learning or Quality Diversity methods. While adopting the DC approximation sacrifices some accuracy and prohibits the computation of voltage magnitudes, we show that this sacrifice unlocks new scales of computational feasibility, offering a powerful tool for large-scale grid planning and operational topology optimization.

Power grid operators face increasing difficulties in the control room as the increase in energy demand and the shift to renewable energy introduce new complexities in managing congestion and maintaining a stable supply. Effective grid topology control requires advanced tools capable of handling multi-objective trade-offs. While Reinforcement Learning (RL) offers a promising framework for tackling such challenges, existing Multi-Objective Reinforcement Learning (MORL) approaches fail to scale to the large state and action spaces inherent in real-world grid operations. Here we present a two-phase, efficient and scalable Multi-Objective Optimisation (MOO) method designed for grid topology control, combining an efficient RL learning phase with a rapid planning phase to generate day-ahead plans for unseen scenarios. We validate our approach using historical data from TenneT, a European Transmission System Operator (TSO), demonstrating minimal deployment time, generating day-ahead plans within 4-7 minutes with strong performance. These results underline the potential of our scalable method to support real-world power grid management, offering a practical, computationally efficient, and time-effective tool for operational planning. Based on current congestion costs and inefficiencies in grid operations, adopting our approach by TSOs could potentially save millions of euros annually, providing a compelling economic incentive for its integration in the control room.

Hybrid clinical trials, that borrow real-world data (RWD), are gaining interest, especially for rare diseases. They assume RWD and randomized control arm be exchangeable, but violations can bias results, inflate type I error, or reduce power. A two-step hybrid design first tests exchangeability, reducing inappropriate borrowing but potentially inflating type I error (Yuan et al., 2019). We propose four methods to better control type I error. Approach 1 estimates the variance of test statistics, rejecting the null hypothesis based on large sample normal approximation. Approach 2 uses a numerical approach for exact critical value determination. Approach 3 splits type I error rates by equivalence test outcome. Approach 4 adjusts the critical value only when equivalence is established. Simulation studies using a hypothetical ALS scenario, evaluate type I error and power under various conditions, compared to the Bayesian power prior approach (Ibrahim et al., 2015). Our methods and the Bayesian power prior control type I error, whereas Yuan et al. (2019) increases it under exchangeability. If exchangeability doesn't hold, all methods fail to control type I error. Our methods show type I error inflation of 6%-8%, compared to 10% for Yuan et al. (2019) and 16% for the Bayesian power prior.

Introducing superconductivity into group IV elements by doping has long promised a pathway to introduce quantum functionalities into well-established semiconductor technologies. The non-equilibrium hyperdoping of group III atoms into Si or Ge has successfully shown superconductivity can be achieved, however, the origin of superconductivity has been obscured by structural disorder and dopant clustering. Here, we report the epitaxial growth of hyperdoped Ga:Ge films by molecular beam epitaxy with extreme hole concentrations (n$_{h}$ = 4.15 $\times$ 10$^{21}$ cm$^{-3}$, ~17.9\% Ga substitution) that yield superconductivity with a critical temperature of T$_{C}$ = 3.5 K, and an out-of-plane critical field of 1 T at 270 mK. Synchrotron-based X-ray absorption and scattering methods reveal that Ga dopants are substitutionally incorporated within the Ge lattice, introducing a tetragonal distortion to the crystal unit cell. Our findings, corroborated by first-principles calculations, suggest that the structural order of Ga dopants creates a flat band for the emergence of superconductivity in Ge, establishing hyperdoped Ga:Ge as a low-disorder, epitaxial superconductor-semiconductor platform.

Artificially intelligent systems optimized for speech conversation are appearing at a fast pace. Such models are interesting from a healthcare perspective, as these voice-controlled assistants may support the elderly and enable remote health monitoring. The bottleneck for efficacy, however, is how well these devices work in practice and how the elderly experience them, but research on this topic is scant. We review elderly use of voice-controlled AI and highlight various user- and technology-centered issues, that need to be considered before effective speech-controlled AI for elderly care can be realized., Comment: Accepted for publication at Medical Informatics Europe 2025 conference, Glasgow. 5 pages, 1 figure, 1 table

The superior soft tissue differentiation provided by MRI may enable more accurate tumor segmentation compared to CT and PET, potentially enhancing adaptive radiotherapy treatment planning. The Head and Neck Tumor Segmentation for MR-Guided Applications challenge (HNTSMRG-24) comprises two tasks: segmentation of primary gross tumor volume (GTVp) and metastatic lymph nodes (GTVn) on T2-weighted MRI volumes obtained at (1) pre-radiotherapy (pre-RT) and (2) mid-radiotherapy (mid-RT). The training dataset consists of data from 150 patients, including MRI volumes of pre-RT, mid-RT, and pre-RT registered to the corresponding mid-RT volumes. Each MRI volume is accompanied by a label mask, generated by merging independent annotations from a minimum of three experts. For both tasks, we propose adopting the nnU-Net V2 framework by the use of a 15-fold cross-validation ensemble instead of the standard number of 5 folds for increased robustness and variability. For pre-RT segmentation, we augmented the initial training data (150 pre-RT volumes and masks) with the corresponding mid-RT data. For mid-RT segmentation, we opted for a three-channel input, which, in addition to the mid-RT MRI volume, comprises the registered pre-RT MRI volume and the corresponding mask. The mean of the aggregated Dice Similarity Coefficient for GTVp and GTVn is computed on a blind test set and determines the quality of the proposed methods. These metrics determine the final ranking of methods for both tasks separately. The final blind testing (50 patients) of the methods proposed by our team, RUG_UMCG, resulted in an aggregated Dice Similarity Coefficient of 0.81 (0.77 for GTVp and 0.85 for GTVn) for Task 1 and 0.70 (0.54 for GTVp and 0.86 for GTVn) for Task 2.

Distribution factors are indispensable tools in the design and analysis of power transmission grids. Recently, they received a renewed interest in the field of topology optimization, leading to the definition of bus merge and bus split distribution factors. In this article, we introduce a unified derivation of the most relevant distribution factors based on matrix algebraic manipulations. This approach facilitates the generalization to more complex grid modification, in particular simultaneous switching events or bus splits., Comment: 11 pages, 1 figure

We introduce CaLMFlow (Causal Language Models for Flow Matching), a novel framework that casts flow matching as a Volterra integral equation (VIE), leveraging the power of large language models (LLMs) for continuous data generation. CaLMFlow enables the direct application of LLMs to learn complex flows by formulating flow matching as a sequence modeling task, bridging discrete language modeling and continuous generative modeling. Our method implements tokenization across space and time, thereby solving a VIE over these domains. This approach enables efficient handling of high-dimensional data and outperforms ODE solver-dependent methods like conditional flow matching (CFM). We demonstrate CaLMFlow's effectiveness on synthetic and real-world data, including single-cell perturbation response prediction, showcasing its ability to incorporate textual context and generalize to unseen conditions. Our results highlight LLM-driven flow matching as a promising paradigm in generative modeling, offering improved scalability, flexibility, and context-awareness., Comment: 10 pages, 9 figures, 7 tables

We explore the emergence of intelligent behavior in artificial systems by investigating how the complexity of rule-based systems influences the capabilities of models trained to predict these rules. Our study focuses on elementary cellular automata (ECA), simple yet powerful one-dimensional systems that generate behaviors ranging from trivial to highly complex. By training distinct Large Language Models (LLMs) on different ECAs, we evaluated the relationship between the complexity of the rules' behavior and the intelligence exhibited by the LLMs, as reflected in their performance on downstream tasks. Our findings reveal that rules with higher complexity lead to models exhibiting greater intelligence, as demonstrated by their performance on reasoning and chess move prediction tasks. Both uniform and periodic systems, and often also highly chaotic systems, resulted in poorer downstream performance, highlighting a sweet spot of complexity conducive to intelligence. We conjecture that intelligence arises from the ability to predict complexity and that creating intelligence may require only exposure to complexity., Comment: 15 pages,8 Figures

The significance of Mendeleev's periodic table extends beyond the classification of elements; it lies in its remarkable predictive power for discovering new elements and properties, revealing the underlying symmetrical patterns of nature that were only fully understood with the advent of quantum mechanics. Fundamental material properties, such as electron transport and magnetism, are also governed by crystal symmetry. In particular, spin transport depends on the spin polarization of electronic states, and recently discovered materials where the electron spin polarization is independent of momentum - a property known as a persistent spin texture (PST) - promise extended spin lifetime and efficient spin accumulation. In this paper, we establish the general conditions for the existence of symmetry-protected PST in bulk crystals. By systematically analyzing all 230 crystallographic space groups, similar to elements in the periodic table, we demonstrate that PST is universally present in all nonmagnetic solids lacking inversion symmetry. Using group theory, we identify the regions within the Brillouin zone that host PST and determine the corresponding directions of spin polarization. Our findings, supported by first-principles calculations of representative materials, open the route for discovering robust spintronic materials based on PST.

Data-driven technologies have improved the efficiency, reliability and effectiveness of healthcare services, but come with an increasing demand for data, which is challenging due to privacy-related constraints on sharing data in healthcare contexts. Synthetic data has recently gained popularity as potential solution, but in the flurry of current research it can be hard to oversee its potential. This paper proposes a novel taxonomy of synthetic data in healthcare to navigate the landscape in terms of three main varieties. Data Proportion comprises different ratios of synthetic data in a dataset and associated pros and cons. Data Modality refers to the different data formats amenable to synthesis and format-specific challenges. Data Transformation concerns improving specific aspects of a dataset like its utility or privacy with synthetic data. Our taxonomy aims to help researchers in the healthcare domain interested in synthetic data to grasp what types of datasets, data modalities, and transformations are possible with synthetic data, and where the challenges and overlaps between the varieties lie., Comment: Accepted at the 23rd EFMI Special Topic Conference, Romania, November 2024

Gold-standard sleep scoring as performed by human technicians is based on a subset of PSG signals, namely the EEG, EOG, and EMG. The PSG, however, consists of many more signal derivations that could potentially be used to perform sleep staging, including cardiac and respiratory modalities. Leveraging this variety in signals would offer advantages, for example by increasing reliability, resilience to signal loss, and application to long-term non-obtrusive recordings. This paper proposes a deep generative foundation model for fully automatic sleep staging from a plurality of sensors and any combination thereof. We trained a score-based diffusion model with a transformer backbone using a dataset of 1947 expert-labeled overnight sleep recordings with 36 different signals, including neurological, cardiac, and respiratory signals. We achieve zero-shot inference on any sensor set by using a novel Bayesian factorization of the score function across the sensors, i.e., it does not require retraining on specific combinations of signals. On single-channel EEG, our method reaches the performance limit in terms of PSG inter-rater agreement (5-class accuracy 85.6%, kappa 0.791). At the same time, the method offers full flexibility to use any sensor set derived from other modalities, for example, as typically used in home recordings that include finger PPG, nasal cannula and thoracic belt (5-class accuracy 79.0%, kappa of 0.697), or by combining derivations not typically used for sleep staging such as the tibialis and sternocleidomastoid EMG (5-class accuracy 71.0%, kappa of 0.575). Additionally, we propose a novel interpretability metric in terms of information gain per sensor and show that this is linearly correlated with classification performance. Lastly, our foundation model allows for post-hoc addition of entirely new sensor modalities by merely training a score estimator on the novel input.

Cherenkov threshold detectors (XCET) are used for identifying particles in the experimental areas at CERN. These detectors observe Cherenkov light emitted by charged particles travelling inside a pressurized gas vessel. A key component of the XCET detector is the 45-degree flat mirror reflecting the Cherenkov light towards the photomultiplier (PMT). A thorough analysis and optimization was conducted on the design and materials of this mirror, along with the surface coatings and coating techniques. A suitable manufacturing process was selected, and the first mirror prototype was produced, installed, and tested in the East Area at CERN. Experimental data obtained during beam tests is presented to assess the efficiency of the new coating and materials used., Comment: 15th International Particle Accelerator Conference,Nashville, TN

Natural language has the universal properties of being compositional and grounded in reality. The emergence of linguistic properties is often investigated through simulations of emergent communication in referential games. However, these experiments have yielded mixed results compared to similar experiments addressing linguistic properties of human language. Here we address representational alignment as a potential contributing factor to these results. Specifically, we assess the representational alignment between agent image representations and between agent representations and input images. Doing so, we confirm that the emergent language does not appear to encode human-like conceptual visual features, since agent image representations drift away from inputs whilst inter-agent alignment increases. We moreover identify a strong relationship between inter-agent alignment and topographic similarity, a common metric for compositionality, and address its consequences. To address these issues, we introduce an alignment penalty that prevents representational drift but interestingly does not improve performance on a compositional discrimination task. Together, our findings emphasise the key role representational alignment plays in simulations of language emergence., Comment: Appeared at the 13th edition of the Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2024)

This paper presents initial results from the ESA-funded ``SUPPPPRESS'' project, which aims to develop high-performance liquid-crystal coronagraphs for direct imaging of Earth-like exoplanets in reflected light. The project focuses on addressing the significant challenge of polarization leakage in vector vortex coronagraphs (VVCs). We utilize newly manufactured multi-grating, liquid-crystal VVCs, consisting of a two- or three-element stack of vortex and grating patterns, to reduce this leakage to the $10^{-10}$ contrast level. We detail the experimental setups, including calibration techniques with polarization microscopes and Mueller matrix ellipsometers to enhance the direct-write accuracy of the liquid-crystal patterns. The performance testing of these coronagraph masks will be conducted on the THD2 high-contrast imaging testbed in Paris., Comment: 14 pages, 10 figures

General relativity is highly successful in explaining a wide range of gravitational phenomena including the gravitational waves emitted by binary systems and the shadows cast by supermassive black holes. From a modern perspective the theory is not fundamental though, but constitutes the lowest order term in an effective field theory description of the gravitational force. As a consequence, the gravitational dynamics should receive corrections by higher-derivative terms. This essay discusses structural aspects associated with these corrections and summarizes their imprint on static, spherically symmetric geometries. Along these lines, we critically reassess the common practice of using local field redefinitions in order to simplify the dynamics at the danger of shifting physics effects into sectors which are beyond the approximation under consideration., Comment: 10 pages. Essay received Honorable Mention at the Gravity Research Foundation 2024 Awards for Essays on Gravitation. v2: fixed typos and updated to match published version

Virtual experiments (VEs), a modern tool in metrology, can be used to help perform an uncertainty evaluation for the measurand. Current guidelines in metrology do not cover the many possibilities to incorporate VEs into an uncertainty evaluation, and it is often difficult to assess if the intended use of a VE complies with said guidelines. In recent work, it was shown that a VE can be used in conjunction with real measurement data and a Monte Carlo procedure to produce equal results to a supplement of the Guide to the Expression of Uncertainty in Measurement. However, this was shown only for linear measurement models. In this work, we extend this Monte Carlo approach to a common class of non-linear measurement models and more complex VEs, providing a reference approach for suitable uncertainty evaluations involving VEs. Numerical examples are given to show that the theoretical derivations hold in a practical scenario., Comment: Conference proceeding submitted to XXIV IMEKO World Congress 2024

In this work we challenge arguments for robot rights on metaphysical, ethical and legal grounds. Metaphysically, we argue that machines are not the kinds of things that may be denied or granted rights. Building on theories of phenomenology and post-Cartesian approaches to cognitive science, we ground our position in the lived reality of actual humans in an increasingly ubiquitously connected, controlled, digitized, and surveilled society. Ethically, we argue that, given machines current and potential harms to the most marginalized in society, limits on (rather than rights for) machines should be at the centre of current AI ethics debate. From a legal perspective, the best analogy to robot rights is not human rights but corporate rights, a highly controversial concept whose most important effect has been the undermining of worker, consumer, and voter rights by advancing the power of capital to exercise outsized influence on politics and law. The idea of robot rights, we conclude, acts as a smoke screen, allowing theorists and futurists to fantasize about benevolently sentient machines with unalterable needs and desires protected by law. While such fantasies have motivated fascinating fiction and art, once they influence legal theory and practice articulating the scope of rights claims, they threaten to immunize from legal accountability the current AI and robotics that is fuelling surveillance capitalism, accelerating environmental destruction, and entrenching injustice and human suffering., Comment: Published in First Monday special issue entitled "Ideologies of AI and the consolidation of power"

A search for hidden-charm pentaquark states decaying to a range of $\Sigma_{c}\bar{D}$ and $\Lambda_{c}\bar{D}$ final states, as well as doubly-charmed pentaquark states to $\Sigma_{c}D$ and $\Lambda_{c}^{+}D$, is made using samples of proton-proton collision data corresponding to an integrated luminosity of $5.7fb^{-1}$ recorded by the LHCb detector at $\sqrt{s} = 13Te\kern -0.1em V$. Since no significant signals are found, upper limits are set on the pentaquark yields relative to that of the $\Lambda_{c}^{+}$ baryon in the $\Lambda_{c}^{+}\to pK^{-}\pi^{+}$ decay mode. The known pentaquark states are also investigated, and their signal yields are found to be consistent with zero in all cases., Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2023-018.html (LHCb public pages)

Synthetic data from generative models emerges as the privacy-preserving data-sharing solution. Such a synthetic data set shall resemble the original data without revealing identifiable private information. The backbone technology of tabular synthesizers is rooted in image generative models, ranging from Generative Adversarial Networks (GANs) to recent diffusion models. Recent prior work sheds light on the utility-privacy tradeoff on tabular data, revealing and quantifying privacy risks on synthetic data. We first conduct an exhaustive empirical analysis, highlighting the utility-privacy tradeoff of five state-of-the-art tabular synthesizers, against eight privacy attacks, with a special focus on membership inference attacks. Motivated by the observation of high data quality but also high privacy risk in tabular diffusion, we propose DP-TLDM, Differentially Private Tabular Latent Diffusion Model, which is composed of an autoencoder network to encode the tabular data and a latent diffusion model to synthesize the latent tables. Following the emerging f-DP framework, we apply DP-SGD to train the auto-encoder in combination with batch clipping and use the separation value as the privacy metric to better capture the privacy gain from DP algorithms. Our empirical evaluation demonstrates that DP-TLDM is capable of achieving a meaningful theoretical privacy guarantee while also significantly enhancing the utility of synthetic data. Specifically, compared to other DP-protected tabular generative models, DP-TLDM improves the synthetic quality by an average of 35% in data resemblance, 15% in the utility for downstream tasks, and 50% in data discriminability, all while preserving a comparable level of privacy risk.