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9,121 results on '"Koehler P."'

1. Stable Tree Labelling for Accelerating Distance Queries on Dynamic Road Networks

Author

Koehler, Henning, Farhan, Muhammad, and Wang, Qing

Subjects
Computer Science - Data Structures and Algorithms, Computer Science - Databases
Abstract

Finding the shortest-path distance between two arbitrary vertices is an important problem in road networks. Due to real-time traffic conditions, road networks undergo dynamic changes all the time. Current state-of-the-art methods incrementally maintain a distance labelling based on a hierarchy among vertices to support efficient distance computation. However, their labelling sizes are often large and cannot be efficiently maintained. To combat these issues, we present a simple yet efficient labelling method, namely \emph{Stable Tree Labelling} (STL), for answering distance queries on dynamic road networks. We observe that the properties of an underlying hierarchy play an important role in improving and balancing query and update performance. Thus, we introduce the notion of \emph{stable tree hierarchy} which lays the ground for developing efficient maintenance algorithms on dynamic road networks. Based on stable tree hierarchy, STL can be efficiently constructed as a 2-hop labelling. A crucial ingredient of STL is to only store distances within subgraphs in labels, rather than distances in the entire graph, which restricts the labels affected by dynamic changes. We further develop two efficient maintenance algorithms upon STL: \emph{Label Search algorithm} and \emph{Pareto Search algorithm}. Label Search algorithm identifies affected ancestors in a stable tree hierarchy and performs efficient searches to update labels from those ancestors. Pareto Search algorithm explores the interaction between search spaces of different ancestors, and combines searches from multiple ancestors into only two searches for each update, eliminating duplicate graph traversals. The experiments show that our algorithms significantly outperform state-of-the-art dynamic methods in maintaining the labelling and query processing, while requiring an order of magnitude less space.

Published
2025

2. Quasiconvex relaxation of planar Biot-type energies and the role of determinant constraints

Author

Martin, Robert J., Ghiba, Ionel-Dumitrel, Köhler, Maximilian, Balzani, Daniel, Sander, Oliver, and Neff, Patrizio

Subjects
Mathematics - Analysis of PDEs, Mathematical Physics, 74A05, 74A60, 74B20, 74G65
Abstract

We derive the quasiconvex relaxation of the Biot-type energy density $\lVert\sqrt{\operatorname{D}\varphi^T \operatorname{D}\varphi}-I_2\rVert^2$ for planar mappings $\varphi\colon\mathbb{R}^2\to \mathbb{R}^2$ in two different scenarios. First, we consider the case $\operatorname{D}\varphi\in\textrm{GL}^+(2)$, in which the energy can be expressed as the squared Euclidean distance $\operatorname{dist}^2(\operatorname{D}\varphi,\textrm{SO}(2))$ to the special orthogonal group $\textrm{SO}(2)$. We then allow for planar mappings with arbitrary $\operatorname{D}\varphi\in\mathbb{R}^{2\times 2}$; in the context of solid mechanics, this lack of determinant constraints on the deformation gradient would allow for self-interpenetration of matter. We demonstrate that the two resulting relaxations do not coincide and compare the analytical findings to numerical results for different relaxation approaches, including a rank-one sequential lamination algorithm, trust-region FEM calculations of representative microstructures and physics-informed neural networks.

Published
2025

3. Deformable Image Registration of Dark-Field Chest Radiographs for Local Lung Signal Change Assessment

Author

Drexel, Fabian, Sideri-Lampretsa, Vasiliki, Bast, Henriette, Marka, Alexander W., Koehler, Thomas, Gassert, Florian T., Pfeiffer, Daniela, Rueckert, Daniel, and Pfeiffer, Franz

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics
Abstract

Dark-field radiography of the human chest has been demonstrated to have promising potential for the analysis of the lung microstructure and the diagnosis of respiratory diseases. However, previous studies of dark-field chest radiographs evaluated the lung signal only in the inspiratory breathing state. Our work aims to add a new perspective to these previous assessments by locally comparing dark-field lung information between different respiratory states. To this end, we discuss suitable image registration methods for dark-field chest radiographs to enable consistent spatial alignment of the lung in distinct breathing states. Utilizing full inspiration and expiration scans from a clinical chronic obstructive pulmonary disease study, we assess the performance of the proposed registration framework and outline applicable evaluation approaches. Our regional characterization of lung dark-field signal changes between the breathing states provides a proof-of-principle that dynamic radiography-based lung function assessment approaches may benefit from considering registered dark-field images in addition to standard plain chest radiographs., Comment: 10 pages, 6 figures

Published
2025

4. Twin-width one

Author

Ahn, Jungho, Jacob, Hugo, Köhler, Noleen, Paul, Christophe, Reinald, Amadeus, and Wiederrecht, Sebastian

Subjects
Computer Science - Discrete Mathematics, Computer Science - Data Structures and Algorithms, Mathematics - Combinatorics
Abstract

We investigate the structure of graphs of twin-width at most $1$, and obtain the following results: - Graphs of twin-width at most $1$ are permutation graphs. In particular they have an intersection model and a linear structure. - There is always a $1$-contraction sequence closely following a given permutation diagram. - Based on a recursive decomposition theorem, we obtain a simple algorithm running in linear time that produces a $1$-contraction sequence of a graph, or guarantees that it has twin-width more than $1$. - We characterise distance-hereditary graphs based on their twin-width and deduce a linear time algorithm to compute optimal sequences on this class of graphs., Comment: Accepted to STACS 2025

Published
2025

5. Robust targeted exploration for systems with non-stochastic disturbances

Author

Venkatasubramanian, Janani, Köhler, Johannes, Cannon, Mark, and Allgöwer, Frank

Subjects
Mathematics - Optimization and Control, Electrical Engineering and Systems Science - Systems and Control
Abstract

In this paper, we introduce a novel targeted exploration strategy designed specifically for uncertain linear time-invariant systems with energy-bounded disturbances, i.e., without making any assumptions on the distribution of the disturbances. We use classical results characterizing the set of non-falsified parameters consistent with energy-bounded disturbances. We derive a semidefinite program which computes an exploration strategy that guarantees a desired accuracy of the parameter estimate. This design is based on sufficient conditions on the spectral content of the exploration data that robustly accounts for initial parametric uncertainty. Finally, we highlight the applicability of the exploration strategy through a numerical example involving an unmodeled nonlinearity., Comment: Submitted to Automatica

Published
2024

6. Adaptive Economic Model Predictive Control: Performance Guarantees for Nonlinear Systems

Author

Degner, Maximilian, Soloperto, Raffaele, Zeilinger, Melanie N., Lygeros, John, and Köhler, Johannes

Subjects
Electrical Engineering and Systems Science - Systems and Control, Mathematics - Optimization and Control
Abstract

We consider the problem of optimizing the economic performance of nonlinear constrained systems subject to uncertain time-varying parameters and bounded disturbances. In particular, we propose an adaptive economic model predictive control (MPC) framework that: (i) directly minimizes transient economic costs, (ii) addresses parametric uncertainty through online model adaptation, (iii) determines optimal setpoints online, and (iv) ensures robustness by using a tube-based approach. The proposed design ensures recursive feasibility, robust constraint satisfaction, and a transient performance bound. In case the disturbances have a finite energy and the parameter variations have a finite path length, the asymptotic average performance is (approximately) not worse than the performance obtained when operating at the best reachable steady-state. We highlight performance benefits in a numerical example involving a chemical reactor with unknown time-invariant and time-varying parameters.

Published
2024

7. Unified calibration and spatial mapping of fine particulate matter data from multiple low-cost air pollution sensor networks in Baltimore, Maryland

Author

Heffernan, Claire, Koehler, Kirsten, Gentner, Drew R., Peng, Roger D., and Datta, Abhirup

Subjects
Statistics - Applications, Statistics - Methodology
Abstract

Low-cost air pollution sensor networks are increasingly being deployed globally, supplementing sparse regulatory monitoring with localized air quality data. In some areas, like Baltimore, Maryland, there are only few regulatory (reference) devices but multiple low-cost networks. While there are many available methods to calibrate data from each network individually, separate calibration of each network leads to conflicting air quality predictions. We develop a general Bayesian spatial filtering model combining data from multiple networks and reference devices, providing dynamic calibrations (informed by the latest reference data) and unified predictions (combining information from all available sensors) for the entire region. This method accounts for network-specific bias and noise (observation models), as different networks can use different types of sensors, and uses a Gaussian process (state-space model) to capture spatial correlations. We apply the method to calibrate PM$_{2.5}$ data from Baltimore in June and July 2023 -- a period including days of hazardous concentrations due to wildfire smoke. Our method helps mitigate the effects of preferential sampling of one network in Baltimore, results in better predictions and narrower confidence intervals. Our approach can be used to calibrate low-cost air pollution sensor data in Baltimore and any other areas with multiple low-cost networks.

Published
2024

8. Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO)

Author

Köhler, Cristiano André, Grün, Sonja, and Denker, Michael

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Describing the processes involved in analyzing data from electrophysiology experiments to investigate the function of neural systems is inherently challenging. On the one hand, data can be analyzed by distinct methods that serve a similar purpose, such as different algorithms to estimate the spectral power content of a measured time series. On the other hand, different software codes can implement the same algorithm for the analysis while adopting different names to identify functions and parameters. Having reproducibility in mind, with these ambiguities the outcomes of the analysis are difficult to report, e.g., in the methods section of a manuscript or on a platform for scientific findings. Here, we illustrate how using an ontology to describe the analysis process can assist in improving clarity, rigour and comprehensibility by complementing, simplifying and classifying the details of the implementation. We implemented the Neuroelectrophysiology Analysis Ontology (NEAO) to define a unified vocabulary and to standardize the descriptions of the processes involved in analyzing data from neuroelectrophysiology experiments. Real-world examples demonstrate how the NEAO can be employed to annotate provenance information describing an analysis process. Based on such provenance, we detail how it can be used to query various types of information (e.g., using knowledge graphs) that enable researchers to find, understand and reuse prior analysis results.

Published
2024

9. CMSO-transducing tree-like graph decompositions

Author

Campbell, Rutger, Guillon, Bruno, Kanté, Mamadou Moustapha, Kim, Eun Jung, and Köhler, Noleen

Subjects
Computer Science - Logic in Computer Science, Computer Science - Computational Complexity, Computer Science - Discrete Mathematics, Computer Science - Formal Languages and Automata Theory
Abstract

We show that given a graph G we can CMSO-transduce its modular decomposition, its split decomposition and its bi-join decomposition. This improves results by Courcelle [Logical Methods in Computer Science, 2006] who gave such transductions using order-invariant MSO, a strictly more expressive logic than CMSO. Our methods more generally yield C2MSO-transductions of the canonical decomposition of weakly-partitive set systems and weakly-bipartitive systems of bipartitions.

Published
2024

10. Online convex optimization for constrained control of nonlinear systems

Author

Nonhoff, Marko, Köhler, Johannes, and Müller, Matthias A.

Subjects
Electrical Engineering and Systems Science - Systems and Control, Mathematics - Optimization and Control
Abstract

This paper investigates the problem of controlling nonlinear dynamical systems subject to state and input constraints while minimizing time-varying and a priori unknown cost functions. We propose a modular approach that combines the online convex optimization framework and reference governors to solve this problem. Our method is general in the sense that we do not limit our analysis to a specific choice of online convex optimization algorithm or reference governor. We show that the dynamic regret of the proposed framework is bounded linearly in both the dynamic regret and the path length of the chosen online convex optimization algorithm, even though the online convex optimization algorithm does not account for the underlying dynamics. We prove that a linear bound with respect to the online convex optimization algorithm's dynamic regret is optimal, i.e., cannot be improved upon. Furthermore, for a standard class of online convex optimization algorithms, our proposed framework attains a bound on its dynamic regret that is linear only in the variation of the cost functions, which is known to be an optimal bound. Finally, we demonstrate implementation and flexibility of the proposed framework by comparing different combinations of online convex optimization algorithms and reference governors to control a nonlinear chemical reactor in a numerical experiment., Comment: 17 pages

Published
2024

11. Millions of Students Are (Still) above Grade Level: Achievement and Achievement Variability in Mathematics and Reading before and during COVID-19 in the United States

Author

Karen E. Rambo-Hernandez, Matthew C. Makel, and Noah Koehler

Abstract

According to a 2017 article by Peters and colleagues, millions of students have already demonstrated they know the material slated to be taught that year. Consequently, grade-level standards are unlikely to be appropriately challenging for these students. In this paper, we conceptually replicated and extended this prior study. Using data from schools that administered the Renaissance Star assessment in Fall 2018 and Fall 2021, we quantified pre-COVID and mid-COVID average achievement and variance in achievement in mathematics and reading in fifth grade and estimated the grade level of instruction needed for students. Our results indicate that (a) achievement dropped during COVID-19 relative to pre-COVID, but the drop in mathematics was larger, and (b) achievement variability increased during COVID-19, but the variability in reading was slightly more pronounced. Further, our results replicated Peters et al.'s (2017) results showing that large numbers of students still performed above grade level, and substantial variability in achievement was present within schools.

Published
2024
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12. Seagrass wasting disease prevalence and lesion area increase with invertebrate grazing across the northeastern Pacific.

Author

Aoki, Lillian, Ritter, Carmen, Beatty, Deanna, Domke, Lia, Eckert, Ginny, Graham, Olivia, Gomes, Carla, Gross, Collin, Hawthorne, Timothy, Heery, Eliza, Hessing-Lewis, Margot, Hovel, Kevin, Koehler, Karl, Monteith, Zachary, Mueller, Ryan, Olson, Angeleen, Prentice, Carolyn, Rappazzo, Brendan, Stachowicz, John, Tomas, Fiona, Yang, Bo, Harvell, C, and Duffy, J

Subjects
Zostera marina, disease ecology, eelgrass wasting disease, epifauna, herbivory, mesograzers, plant‐herbivore interactions, plant‐pathogen interactions, plant‐pathogen‐herbivore interactions, Animals, Zosteraceae, Invertebrates, Pacific Ocean, Plant Diseases, Herbivory
Abstract

Disease is a key driver of community and ecosystem structure, especially when it strikes foundation species. In the widespread marine foundation species eelgrass (Zostera marina), outbreaks of wasting disease have caused large-scale meadow collapse in the past, and the causative pathogen, Labyrinthula zosterae, is commonly found in meadows globally. Research to date has mainly focused on abiotic environmental drivers of seagrass wasting disease, but there is strong evidence from other systems that biotic interactions such as herbivory can facilitate plant diseases. How biotic interactions influence seagrass wasting disease in the field is unknown but is potentially important for understanding dynamics of this globally valuable and declining habitat. Here, we investigated links between epifaunal grazers and seagrass wasting disease using a latitudinal field study across 32 eelgrass meadows distributed from southeastern Alaska to southern California. From 2019 to 2021, we conducted annual surveys to assess eelgrass shoot density, morphology, epifauna community, and the prevalence and lesion area of wasting disease infections. We integrated field data with satellite measurements of sea surface temperature and used structural equation modeling to test the magnitude and direction of possible drivers of wasting disease. Our results show that grazing by small invertebrates was associated with a 29% increase in prevalence of wasting disease infections and that both the prevalence and lesion area of disease increased with total epifauna abundances. Furthermore, these relationships differed among taxa; disease levels increased with snail (Lacuna spp.) and idoteid isopod abundances but were not related to abundance of ampithoid amphipods. This field study across 23° of latitude suggests a prominent role for invertebrate consumers in facilitating disease outbreaks with potentially large impacts on coastal seagrass ecosystems.

Published
2025

13. An acidic microenvironment produced by the V-type ATPase of Euprymna scolopes promotes specificity during Vibrio fischeri recruitment.

Author

Hargadon, Alexis, Viliunas, Joani, Koehler, Sabrina, Thies, Angus, Chen, Grischa, Ladinsky, Mark, Kuwabara, Jill, Avila-Magana, Viridiana, Ruby, Edward, Tresguerres, Martin, and McFall-Ngai, Margaret

Subjects
Aliivibrio fischeri, Animals, Decapodiformes, Symbiosis, Hydrogen-Ion Concentration, Vacuolar Proton-Translocating ATPases
Abstract

Animals often acquire their microbial symbionts from the environment, but the mechanisms underlying how specificity of the association is achieved are poorly understood. We demonstrate that the conserved proton pump, V-type ATPase (VHA), plays a key role in the establishment of the model light-organ symbiosis between the squid Euprymna scolopes and its bacterial partner, Vibrio fischeri. Recruitment of V. fischeri from the surrounding seawater is mediated by juvenile-specific ciliated fields on the organs surface. These epithelia produce acidic mucus containing antimicrobials with low-pH optima, creating a chemical environment fostering specific recruitment of V. fischeri. We provide evidence that this critical acidic landscape is created by activity of VHA. VHA inhibition abolished epithelial-cell acidity, resulting in increased mucus pH and inefficient symbiont colonization. Thus, VHA provides a mechanistic link between host modulation of microenvironmental acidity, immune function, and selection of microbial symbionts, a strategy for specificity that may govern other symbioses.

Published
2024

14. High Magnitude Earthquake Identification Using an Anomaly Detection Approach on HR GNSS Data

Author

Arenas, Javier Quintero, Cartaya, Claudia Quinteros, Lafarga, Andrea Padilla, Moraila, Carlos, Faber, Johannes, Koehler, Jonas, and Srivastava, Nishtha

Subjects
Physics - Geophysics
Abstract

Earthquake early warning systems are crucial for protecting areas that are subject to these natural disasters. An essential part of these systems is the detection procedure. Traditionally these systems work with seismograph data, but high rate GNSS data has become a promising alternative for the usage in large earthquake early warning systems. Besides traditional methods, deep learning approaches have gained recent popularity in this field, as they are able to leverage the large amounts of real and synthetic seismic data. Nevertheless, the usage of deep learning on GNSS data remains a comparatively new topic. This work contributes to the field of early warning systems by proposing an autoencoder based deep learning pipeline that aims to be lightweight and customizable for the detection of anomalies viz. high magnitude earthquakes in GNSS data. This model, DetEQ, is trained using the noise data recordings from nine stations located in Chile. The detection pipeline encompasses: (i) the generation of an anomaly score using the ground truth and reconstructed output from the autoencoder, (ii) the detection of relevant seismic events through an appropriate threshold, and (iii) the filtering of local events, that would lead to false positives. Robustness of the model was tested on the HR GNSS real data of 2011 Mw 6.8 Concepcion earthquake recorded at six stations. The results highlight the potential of GNSS based deep learning models for effective earthquake detection.

Published
2024

15. Investigating cosmic strings using large-volume hydrodynamical simulations in the context of JWST's massive UV-bright galaxies

Author

Koehler, Sonja M., Jiao, Hao, and Kannan, Rahul

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

Recent observations from the James Webb Space Telescope (JWST) have uncovered an unexpectedly large abundance of massive, UV-bright galaxies at high redshifts $z \gtrsim 10$, presenting a significant challenge to established galaxy formation models within the standard $\Lambda$CDM cosmological framework. Cosmic strings, predicted by a wide range of particle physics theories beyond the Standard Model, provide a promising potential explanation for these observations. They may act as additional gravitational seeds in the early universe, enhancing the process of high-redshift structure formation, potentially resulting in a more substantial population of massive, efficiently star-forming galaxies. We numerically investigate this prediction in large-volume hydrodynamical simulations using the moving-mesh code AREPO and the well-tested IllustrisTNG galaxy formation model. We evaluate the simulation results in the context of recent JWST data and find that sufficiently energetic cosmic strings produce UV luminosity and stellar mass functions that are in slightly to substantially better agreement with observations at high redshifts. Moreover, we observe that the halos seeded by cosmic strings exhibit a greater efficiency of star formation and enhanced central concentrations. Interestingly, our findings indicate that the simulations incorporating cosmic strings converge with those from a baseline $\Lambda$CDM model by redshift $z \sim 6$. This convergence suggests that the modified cosmological framework effectively replicates the successful predictions of the standard $\Lambda$CDM model at lower redshifts, where observational constraints are significantly stronger. Our results provide compelling evidence that cosmic strings may play a crucial role in explaining the galaxy properties observed by JWST at high redshifts while maintaining consistency with well-established models at later epochs., Comment: 13 pages, 7 figures, submitted to A&A

Published
2024

16. Ab-Initio Approach to Many-Body Quantum Spin Dynamics

Author

Dubey, Aditya, Zeybek, Zeki, Köhler, Fabian, Mukherjee, Rick, and Schmelcher, Peter

Subjects
Quantum Physics, Condensed Matter - Quantum Gases, Physics - Atomic Physics, Physics - Computational Physics
Abstract

A fundamental longstanding problem in studying spin models is the efficient and accurate numerical simulation of the long-time behavior of larger systems. The exponential growth of the Hilbert space and the entanglement accumulation at long times pose major challenges for current methods. To address these issues, we employ the multilayer multiconfiguration time-dependent Hartree (ML-MCTDH) framework to simulate the many-body spin dynamics of the Heisenberg model in various settings, including the Ising and XYZ limits with different interaction ranges and random couplings. Benchmarks with analytical and exact numerical approaches show that ML-MCTDH accurately captures the time evolution of one- and two-body observables in both one- and two-dimensional lattices. A comparison of ML-MCTDH with the discrete truncated Wigner approximation (DTWA) demonstrates that our approach excels in handling anisotropic models and consistently provides better results for two-point observables in all simulation instances. Our results indicate that the multilayer structure of ML-MCTDH is a promising numerical framework for handling the dynamics of generic many-body spin systems., Comment: 12 pages, 9 figures

Published
2024

17. Probabilistic Nets-within-Nets

Author

Köhler-Bußmeier, Michael

Subjects
Computer Science - Logic in Computer Science, 68Q10, F.1.1
Abstract

In this paper we study Hornets extended with firing probabilities. Hornets are a Nets-within-Nets formalism, i.e., a Petri net formalism where the tokens are Petri nets again. Each of these net-tokens has its own firing rate, independent from the rates of other net-tokens. Hornets provide algebraic operations to modify net-tokens during the firing. For our stochastic extension these operators could also modify the net-token's firing rate. We use our model to analyse self-modifying systems quantitatively. Hornets are very well suited to model self-adaptive systems performing a MAPE-like loop (monitoring-analyse-plan-execute). Here, the system net describes the loop, and the net-tokens describe the adapted model elements.

Published
2024

18. Efficiently learning and sampling multimodal distributions with data-based initialization

Author

Koehler, Frederic, Lee, Holden, and Vuong, Thuy-Duong

Subjects
Computer Science - Machine Learning, Computer Science - Data Structures and Algorithms, Mathematics - Probability, Statistics - Machine Learning
Abstract

We consider the problem of sampling a multimodal distribution with a Markov chain given a small number of samples from the stationary measure. Although mixing can be arbitrarily slow, we show that if the Markov chain has a $k$th order spectral gap, initialization from a set of $\tilde O(k/\varepsilon^2)$ samples from the stationary distribution will, with high probability over the samples, efficiently generate a sample whose conditional law is $\varepsilon$-close in TV distance to the stationary measure. In particular, this applies to mixtures of $k$ distributions satisfying a Poincar\'e inequality, with faster convergence when they satisfy a log-Sobolev inequality. Our bounds are stable to perturbations to the Markov chain, and in particular work for Langevin diffusion over $\mathbb R^d$ with score estimation error, as well as Glauber dynamics combined with approximation error from pseudolikelihood estimation. This justifies the success of data-based initialization for score matching methods despite slow mixing for the data distribution, and improves and generalizes the results of Koehler and Vuong (2023) to have linear, rather than exponential, dependence on $k$ and apply to arbitrary semigroups. As a consequence of our results, we show for the first time that a natural class of low-complexity Ising measures can be efficiently learned from samples.

Published
2024

19. APEBench: A Benchmark for Autoregressive Neural Emulators of PDEs

Author

Koehler, Felix, Niedermayr, Simon, Westermann, Rüdiger, and Thuerey, Nils

Subjects
Computer Science - Machine Learning
Abstract

We introduce the Autoregressive PDE Emulator Benchmark (APEBench), a comprehensive benchmark suite to evaluate autoregressive neural emulators for solving partial differential equations. APEBench is based on JAX and provides a seamlessly integrated differentiable simulation framework employing efficient pseudo-spectral methods, enabling 46 distinct PDEs across 1D, 2D, and 3D. Facilitating systematic analysis and comparison of learned emulators, we propose a novel taxonomy for unrolled training and introduce a unique identifier for PDE dynamics that directly relates to the stability criteria of classical numerical methods. APEBench enables the evaluation of diverse neural architectures, and unlike existing benchmarks, its tight integration of the solver enables support for differentiable physics training and neural-hybrid emulators. Moreover, APEBench emphasizes rollout metrics to understand temporal generalization, providing insights into the long-term behavior of emulating PDE dynamics. In several experiments, we highlight the similarities between neural emulators and numerical simulators., Comment: Accepted at Neurips 2024. The code is available at https://github.com/tum-pbs/apebench and APEBench can be installed via "pip install apebench"

Published
2024

20. A 'Symbolic' Representation of Object-Nets (Extended Version)

Author

Köhler-Bussmeier, Michael and Capra, Lorenzo

Subjects
Computer Science - Logic in Computer Science, 68Q10, F.1.1
Abstract

In this contribution we extend the concept of a Petri net morphism to Elementary Object Systems (EOS). EOS are a nets-within-nets formalism, i.e. we allow the tokens of a Petri net to be Petri nets again. This nested structure has the consequence that even systems defined by very small Petri nets have a quite huge reachability graph. In this contribution we use automorphism to describe symmetries of the Petri net topology. Since these symmetries carry over to markings as well this leads to a condensed state space, too.

Published
2024

21. Decoupling Semantic Similarity from Spatial Alignment for Neural Networks

Author

Wald, Tassilo, Ulrich, Constantin, Köhler, Gregor, Zimmerer, David, Denner, Stefan, Baumgartner, Michael, Isensee, Fabian, Jaini, Priyank, and Maier-Hein, Klaus H.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

What representation do deep neural networks learn? How similar are images to each other for neural networks? Despite the overwhelming success of deep learning methods key questions about their internal workings still remain largely unanswered, due to their internal high dimensionality and complexity. To address this, one approach is to measure the similarity of activation responses to various inputs. Representational Similarity Matrices (RSMs) distill this similarity into scalar values for each input pair. These matrices encapsulate the entire similarity structure of a system, indicating which input leads to similar responses. While the similarity between images is ambiguous, we argue that the spatial location of semantic objects does neither influence human perception nor deep learning classifiers. Thus this should be reflected in the definition of similarity between image responses for computer vision systems. Revisiting the established similarity calculations for RSMs we expose their sensitivity to spatial alignment. In this paper, we propose to solve this through semantic RSMs, which are invariant to spatial permutation. We measure semantic similarity between input responses by formulating it as a set-matching problem. Further, we quantify the superiority of semantic RSMs over spatio-semantic RSMs through image retrieval and by comparing the similarity between representations to the similarity between predicted class probabilities., Comment: Accepted at NeurIPS2024

Published
2024

22. Causal Data Fusion for Panel Data without Pre-Intervention Period

Author

Yang, Zou, Lee, Seung Hee, Köhler, Julia R., and Ghassami, AmirEmad

Subjects
Statistics - Methodology
Abstract

Traditional panel data causal inference frameworks, such as difference-in-differences and synthetic control methods, rely on pre-intervention data to estimate counterfactuals, which may not be available in real-world settings when interventions are implemented in response to sudden events. In this paper, we introduce two data fusion methods for causal inference from panel data in scenarios where pre-intervention data is unavailable. These methods leverage auxiliary reference domains with related panel data to estimate causal effects in the target domain, overcoming the limitations imposed by the absence of pre-intervention data. We show the efficacy of these methods by obtaining converging bounds on the absolute bias as well as through simulations, showing their robustness in a variety of panel data settings. Our findings provide a framework for applying causal inference in urgent and data-constrained environments, such as public health crises or epidemiological shocks.

Published
2024

23. First constraints on general neutrino interactions based on KATRIN data

Author

Aker, M., Batzler, D., Beglarian, A., Beisenkötter, J., Biassoni, M., Bieringer, B., Biondi, Y., Block, F., Bornschein, B., Bornschein, L., Böttcher, M., Carminati, M., Chatrabhuti, A., Chilingaryan, S., Daniel, B. A., Descher, M., Barrero, D. Díaz, Doe, P. J., Dragoun, O., Drexlin, G., Edzards, F., Eitel, K., Ellinger, E., Engel, R., Enomoto, S., Felden, A., Fengler, C., Fiorini, C., Formaggio, J. A., Forstner, C., Fränkle, F. M., Gagliardi, G., Gauda, K., Gavin, A. S., Gil, W., Glück, F., Grössle, R., Gutknecht, N., Hannen, V., Hasselmann, L., Helbing, K., Henke, H., Heyns, S., Hiller, R., Hillesheimer, D., Hinz, D., Höhn, T., Huber, A., Jansen, A., Khosonthongkee, K., Köhler, C., Köllenberger, L., Kopmann, A., Kovač, N., La Cascio, L., Lasserre, T., Lauer, J., Le, T. L., Lebeda, O., Lehnert, B., Li, G., Lokhov, A., Machatschek, M., Mark, M., Marsteller, A., McMichael, K., Melzer, C., Mertens, S., Mohanty, S., Mostafa, J., Müller, K., Nava, A., Neumann, H., Niemes, S., Onillon, A., Parno, D. S., Pavan, M., Pinsook, U., Poon, A. W. P., Poyato, J. M. L., Priester, F., Ráliš, J., Ramachandran, S., Robertson, R. G. H., Rodenbeck, C., Röllig, M., Sack, R., Saenz, A., Salomon, R., Schäfer, P., Schlösser, K., Schlösser, M., Schlüter, L., Schneidewind, S., Schrank, M., Schürmann, J., Schütz, A. K., Schwemmer, A., Schwenck, A., Seeyangnok, J., Šefčík, M., Siegmann, D., Simon, F., Songwadhana, J., Spanier, F., Spreng, D., Sreethawong, W., Steidl, M., Štorek, J., Stribl, X., Sturm, M., Suwonjandee, N., Jerome, N. Tan, Telle, H. H., Thorne, L. A., Thümmler, T., Titov, N., Tkachev, I., Urban, K., Valerius, K., Vénos, D., Weinheimer, C., Welte, S., Wendel, J., Wetter, M., Wiesinger, C., Wilkerson, J. F., Wolf, J., Wüstling, S., Wydra, J., Xu, W., Zadorozhny, S., and Zeller, G.

Subjects
Nuclear Experiment, High Energy Physics - Experiment, High Energy Physics - Phenomenology
Abstract

The precision measurement of the tritium $\beta$-decay spectrum performed by the KATRIN experiment provides a unique way to search for general neutrino interactions (GNI). All theoretical allowed GNI terms involving neutrinos are incorporated into a low-energy effective field theory, and can be identified by specific signatures in the measured tritium $\beta$-spectrum. In this paper an effective description of the impact of GNI on the $\beta$-spectrum is formulated and the first constraints on the effective GNI parameters are derived based on the 4 million electrons collected in the second measurement campaign of KATRIN in 2019. In addition, constraints on selected types of interactions are investigated, thereby exploring the potential of KATRIN to search for more specific new physics cases, including a right-handed W boson, a charged Higgs or leptoquarks.

Published
2024

24. Towards Multilingual LLM Evaluation for European Languages

Author

Thellmann, Klaudia, Stadler, Bernhard, Fromm, Michael, Buschhoff, Jasper Schulze, Jude, Alex, Barth, Fabio, Leveling, Johannes, Flores-Herr, Nicolas, Köhler, Joachim, Jäkel, René, and Ali, Mehdi

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

The rise of Large Language Models (LLMs) has revolutionized natural language processing across numerous languages and tasks. However, evaluating LLM performance in a consistent and meaningful way across multiple European languages remains challenging, especially due to the scarcity of language-parallel multilingual benchmarks. We introduce a multilingual evaluation approach tailored for European languages. We employ translated versions of five widely-used benchmarks to assess the capabilities of 40 LLMs across 21 European languages. Our contributions include examining the effectiveness of translated benchmarks, assessing the impact of different translation services, and offering a multilingual evaluation framework for LLMs that includes newly created datasets: EU20-MMLU, EU20-HellaSwag, EU20-ARC, EU20-TruthfulQA, and EU20-GSM8K. The benchmarks and results are made publicly available to encourage further research in multilingual LLM evaluation.

Published
2024

25. Data Processing for the OpenGPT-X Model Family

Author

Brandizzi, Nicolo', Abdelwahab, Hammam, Bhowmick, Anirban, Helmer, Lennard, Stein, Benny Jörg, Denisov, Pavel, Saleem, Qasid, Fromm, Michael, Ali, Mehdi, Rutmann, Richard, Naderi, Farzad, Agy, Mohamad Saif, Schwirjow, Alexander, Küch, Fabian, Hahn, Luzian, Ostendorff, Malte, Suarez, Pedro Ortiz, Rehm, Georg, Wegener, Dennis, Flores-Herr, Nicolas, Köhler, Joachim, and Leveling, Johannes

Subjects
Computer Science - Computation and Language, H.3.1, I.2.7
Abstract

This paper presents a comprehensive overview of the data preparation pipeline developed for the OpenGPT-X project, a large-scale initiative aimed at creating open and high-performance multilingual large language models (LLMs). The project goal is to deliver models that cover all major European languages, with a particular focus on real-world applications within the European Union. We explain all data processing steps, starting with the data selection and requirement definition to the preparation of the final datasets for model training. We distinguish between curated data and web data, as each of these categories is handled by distinct pipelines, with curated data undergoing minimal filtering and web data requiring extensive filtering and deduplication. This distinction guided the development of specialized algorithmic solutions for both pipelines. In addition to describing the processing methodologies, we provide an in-depth analysis of the datasets, increasing transparency and alignment with European data regulations. Finally, we share key insights and challenges faced during the project, offering recommendations for future endeavors in large-scale multilingual data preparation for LLMs.

Published
2024

26. Teuken-7B-Base & Teuken-7B-Instruct: Towards European LLMs

Author

Ali, Mehdi, Fromm, Michael, Thellmann, Klaudia, Ebert, Jan, Weber, Alexander Arno, Rutmann, Richard, Jain, Charvi, Lübbering, Max, Steinigen, Daniel, Leveling, Johannes, Klug, Katrin, Buschhoff, Jasper Schulze, Jurkschat, Lena, Abdelwahab, Hammam, Stein, Benny Jörg, Sylla, Karl-Heinz, Denisov, Pavel, Brandizzi, Nicolo', Saleem, Qasid, Bhowmick, Anirban, Helmer, Lennard, John, Chelsea, Suarez, Pedro Ortiz, Ostendorff, Malte, Jude, Alex, Manjunath, Lalith, Weinbach, Samuel, Penke, Carolin, Filatov, Oleg, Asaadi, Shima, Barth, Fabio, Sifa, Rafet, Küch, Fabian, Herten, Andreas, Jäkel, René, Rehm, Georg, Kesselheim, Stefan, Köhler, Joachim, and Flores-Herr, Nicolas

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

We present two multilingual LLMs designed to embrace Europe's linguistic diversity by supporting all 24 official languages of the European Union. Trained on a dataset comprising around 60% non-English data and utilizing a custom multilingual tokenizer, our models address the limitations of existing LLMs that predominantly focus on English or a few high-resource languages. We detail the models' development principles, i.e., data composition, tokenizer optimization, and training methodologies. The models demonstrate competitive performance across multilingual benchmarks, as evidenced by their performance on European versions of ARC, HellaSwag, MMLU, and TruthfulQA.

Published
2024

27. Open-Source Periorbital Segmentation Dataset for Ophthalmic Applications

Author

Nahass, George R., Koehler, Emma, Tomaras, Nicholas, Lopez, Danny, Cheung, Madison, Palacios, Alexander, Peterson, Jeffrey C., Hubschman, Sasha, Green, Kelsey, Purnell, Chad A., Setabutr, Pete, Tran, Ann Q., and Yi, Darvin

Subjects
Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Tissues and Organs
Abstract

Periorbital segmentation and distance prediction using deep learning allows for the objective quantification of disease state, treatment monitoring, and remote medicine. However, there are currently no reports of segmentation datasets for the purposes of training deep learning models with sub mm accuracy on the regions around the eyes. All images (n=2842) had the iris, sclera, lid, caruncle, and brow segmented by five trained annotators. Here, we validate this dataset through intra and intergrader reliability tests and show the utility of the data in training periorbital segmentation networks. All the annotations are publicly available for free download. Having access to segmentation datasets designed specifically for oculoplastic surgery will permit more rapid development of clinically useful segmentation networks which can be leveraged for periorbital distance prediction and disease classification. In addition to the annotations, we also provide an open-source toolkit for periorbital distance prediction from segmentation masks. The weights of all models have also been open-sourced and are publicly available for use by the community., Comment: 10 pages, 7 figures, 3 Tables

Published
2024

28. A hidden chemical assembly mechanism: reconstruction-by-reconstruction cycle growth in HKUST-1 MOF layer synthesis

Author

Koehler, T., Schmeink, J., Schleberger, M., and Marlow, F.

Subjects
Physics - Chemical Physics, Condensed Matter - Materials Science
Abstract

Thin metal-organic framework films grown in a layer-by-layer manner have been the subject of growing interest. Herein we investigate one of the most popular frameworks, type HKUST-1. Firstly, we show a synthesis procedure resulting in quick but optically perfect growth. This enables the synthesis of films of excellent optical quality within a short timeframe. Secondly and most importantly, we address the already known, but not fully understood observation that the expected monolayer growth is strongly exceeded in every single deposition cycle. This is an often-ignored contradiction in the literature. We offer a growth model using mid-cycle reconstruction process leading to a mathematically determined reconstruction-by-reconstruction (RbR) cycle growth with a 4-times higher growth rate representing an up-to-now hidden chemical assembly mechanism., Comment: 20 pages, 6 figures

Published
2024

29. The track-length extension fitting algorithm for energy measurement of interacting particles in liquid argon TPCs and its performance with ProtoDUNE-SP data

Author

DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Akbar, F., Alex, N. S., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alton, A., Alvarez, R., Alves, T., Amar, H., Amedo, P., Anderson, J., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antic, D., Antoniassi, M., Antonova, M., Antoshkin, A., Aranda-Fernandez, A., Arellano, L., Diaz, E. Arrieta, Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asner, D., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Azam, M. B., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baldini, W., Baldonedo, J., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barbu, D., Barenboim, G., Alzás, P. Barham, Barker, G. J., Barkhouse, W., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, D., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., Bathe-Peters, L., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bell, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernal, J., Bernardini, P., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhat, A., Bhatnagar, V., Bhatt, J., Bhattacharjee, M., Bhattacharya, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blucher, E., Bodek, A., Bogenschuetz, J., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Merlo, R. Borges, Borkum, A., Bostan, N., Bouet, R., Boza, J., Bracinik, J., Brahma, B., Brailsford, D., Bramati, F., Branca, A., Brandt, A., Bremer, J., Brew, C., Brice, S. J., Brio, V., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M., Buchanan, N., Budd, H., Buergi, J., Bundock, A., Burgardt, D., Butchart, S., V., G. Caceres, Cagnoli, I., Cai, T., Calabrese, R., Calcutt, J., Calivers, L., Calvo, E., Caminata, A., Camino, A. F., Campanelli, W., Campani, A., Benitez, A. Campos, Canci, N., Capó, J., Caracas, I., Caratelli, D., Carber, D., Carceller, J. M., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Carter, A., Casarejos, E., Casazza, D., Forero, J. F. Castaño, Castaño, F. A., Castillo, A., Castromonte, C., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavanna, F., Centro, S., Cerati, G., Cerna, C., Cervelli, A., Villanueva, A. Cervera, Chakraborty, K., Chalifour, M., Chappell, A., Charitonidis, N., Chatterjee, A., Chen, H., Chen, M., Chen, W. C., Chen, Y., Chen-Wishart, Z., Cherdack, D., Chi, C., Chiapponi, F., Chirco, R., Chitirasreemadam, N., Cho, K., Choate, S., Choi, G., Chokheli, D., Chong, P. S., Chowdhury, B., Christian, D., Chukanov, A., Chung, M., Church, E., Cicala, M. F., Cicerchia, M., Cicero, V., Ciolini, R., Clarke, P., Cline, G., Coan, T. E., Cocco, A. G., Coelho, J. A. B., Cohen, A., Collazo, J., Collot, J., Conley, E., Conrad, J. M., Convery, M., Copello, S., Cova, P., Cox, C., Cremaldi, L., Cremonesi, L., Crespo-Anadón, J. I., Crisler, M., Cristaldo, E., Crnkovic, J., Crone, G., Cross, R., Cudd, A., Cuesta, C., Cui, Y., Curciarello, F., Cussans, D., Dai, J., Dalager, O., Dallavalle, R., Dallaway, W., D'Amico, R., da Motta, H., Dar, Z. A., Darby, R., Peres, L. Da Silva, David, Q., Davies, G. S., Davini, S., Dawson, J., De Aguiar, R., De Almeida, P., Debbins, P., De Bonis, I., Decowski, M. P., de Gouvêa, A., De Holanda, P. C., Astiz, I. L. De Icaza, De Jong, P., Sanchez, P. Del Amo, De la Torre, A., De Lauretis, G., Delbart, A., Delepine, D., Delgado, M., Dell'Acqua, A., Monache, G. Delle, Delmonte, N., De Lurgio, P., Demario, R., De Matteis, G., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Denton, P., Deptuch, G. W., De Roeck, A., De Romeri, V., Detje, J. P., Devine, J., Dharmapalan, R., Dias, M., Diaz, A., Díaz, J. S., Díaz, F., Di Capua, F., Di Domenico, A., Di Domizio, S., Di Falco, S., Di Giulio, L., Ding, P., Di Noto, L., Diociaiuti, E., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Doering, D., Dolan, S., Dolek, F., Dolinski, M. J., Domenici, D., Domine, L., Donati, S., Donon, Y., Doran, S., Douglas, D., Doyle, T. A., Dragone, A., Drielsma, F., Duarte, L., Duchesneau, D., Duffy, K., Dugas, K., Dunne, P., Dutta, B., Duyang, H., Dwyer, D. A., Dyshkant, A. S., Dytman, S., Eads, M., Earle, A., Edayath, S., Edmunds, D., Eisch, J., Englezos, P., Ereditato, A., Erjavec, T., Escobar, C. O., Evans, J. J., Ewart, E., Ezeribe, A. C., Fahey, K., Fajt, L., Falcone, A., Fani', M., Farnese, C., Farrell, S., Farzan, Y., Fedoseev, D., Felix, J., Feng, Y., Fernandez-Martinez, E., Ferry, G., Fialova, E., Fields, L., Filip, P., Filkins, A., Filthaut, F., Fine, R., Fiorillo, G., Fiorini, M., Fogarty, S., Foreman, W., Fowler, J., Franc, J., Francis, K., Franco, D., Franklin, J., Freeman, J., Fried, J., Friedland, A., Fuess, S., Furic, I. K., Furman, K., Furmanski, A. P., Gaba, R., Gabrielli, A., Gago, A. M., Galizzi, F., Gallagher, H., Gallice, N., Galymov, V., Gamberini, E., Gamble, T., Ganacim, F., Gandhi, R., Ganguly, S., Gao, F., Gao, S., Garcia-Gamez, D., García-Peris, M. Á., Gardim, F., Gardiner, S., Gastler, D., Gauch, A., Gauvreau, J., Gauzzi, P., Gazzana, S., Ge, G., Geffroy, N., Gelli, B., Gent, S., Gerlach, L., Ghorbani-Moghaddam, Z., Giammaria, T., Gibin, D., Gil-Botella, I., Gilligan, S., Gioiosa, A., Giovannella, S., Girerd, C., Giri, A. K., Giugliano, C., Giusti, V., Gnani, D., Gogota, O., Gollapinni, S., Gollwitzer, K., Gomes, R. A., Bermeo, L. V. Gomez, Fajardo, L. S. Gomez, Gonnella, F., Gonzalez-Diaz, D., Gonzalez-Lopez, M., Goodman, M. C., Goswami, S., Gotti, C., Goudeau, J., Goudzovski, E., Grace, C., Gramellini, E., Gran, R., Granados, E., Granger, P., Grant, C., Gratieri, D. R., Grauso, G., Green, P., Greenberg, S., Greer, J., Griffith, W. C., Groetschla, F. T., Grzelak, K., Gu, L., Gu, W., Guarino, V., Guarise, M., Guenette, R., Guerzoni, M., Guffanti, D., Guglielmi, A., Guo, B., Guo, F. Y., Gupta, A., Gupta, V., Gurung, G., Gutierrez, D., Guzowski, P., Guzzo, M. M., Gwon, S., Habig, A., Hadavand, H., Haegel, L., Haenni, R., Hagaman, L., Hahn, A., Haiston, J., Hakenmüller, J., Hamernik, T., Hamilton, P., Hancock, J., Happacher, F., Harris, D. A., Hart, A. L., Hartnell, J., Hartnett, T., Harton, J., Hasegawa, T., Hasnip, C. M., Hatcher, R., Hayrapetyan, K., Hays, J., Hazen, E., He, M., Heavey, A., Heeger, K. M., Heise, J., Hellmuth, P., Henry, S., Herner, K., Hewes, V., Higuera, A., Hilgenberg, C., Hillier, S. J., Himmel, A., Hinkle, E., Hirsch, L. R., Ho, J., Hoff, J., Holin, A., Holvey, T., Hoppe, E., Horiuchi, S., Horton-Smith, G. A., Houdy, T., Howard, B., Howell, R., Hristova, I., Hronek, M. S., Huang, J., Huang, R. G., Hulcher, Z., Ibrahim, M., Iles, G., Ilic, N., Iliescu, A. M., Illingworth, R., Ingratta, G., Ioannisian, A., Irwin, B., Isenhower, L., Oliveira, M. Ismerio, Itay, R., Jackson, C. M., Jain, V., James, E., Jang, W., Jargowsky, B., Jena, D., Jentz, I., Ji, X., Jiang, C., Jiang, J., Jiang, L., Jipa, A., Jo, J. H., Joaquim, F. R., Johnson, W., Jollet, C., Jones, B., Jones, R., Jovancevic, N., Judah, M., Jung, C. K., Jung, K. Y., Junk, T., Jwa, Y., Kabirnezhad, M., Kaboth, A. C., Kadenko, I., Kakorin, I., Kalitkina, A., Kalra, D., Kandemir, M., Kaplan, D. M., Karagiorgi, G., Karaman, G., Karcher, A., Karyotakis, Y., Kasai, S., Kasetti, S. P., Kashur, L., Katsioulas, I., Kauther, A., Kazaryan, N., Ke, L., Kearns, E., Keener, P. T., Kelly, K. J., Kemp, E., Kemularia, O., Kermaidic, Y., Ketchum, W., Kettell, S. H., Khabibullin, M., Khan, N., Khvedelidze, A., Kim, D., Kim, J., Kim, M. J., King, B., Kirby, B., Kirby, M., Kish, A., Klein, J., Kleykamp, J., Klustova, A., Kobilarcik, T., Koch, L., Koehler, K., Koerner, L. W., Koh, D. H., Kolupaeva, L., Korablev, D., Kordosky, M., Kosc, T., Kose, U., Kostelecký, V. A., Kothekar, K., Kotler, I., Kovalcuk, M., Kozhukalov, V., Krah, W., Kralik, R., Kramer, M., Kreczko, L., Krennrich, F., Kreslo, I., Kroupova, T., Kubota, S., Kubu, M., Kudenko, Y., Kudryavtsev, V. A., Kufatty, G., Kuhlmann, S., Kulagin, S., Kumar, J., Kumar, P., Kumaran, S., Kunzmann, J., Kuravi, R., Kurita, N., Kuruppu, C., Kus, V., Kutter, T., Kvasnicka, J., Labree, T., Lackey, T., Lalău, I., Lambert, A., Land, B. J., Lane, C. E., Lane, N., Lang, K., Langford, T., Langstaff, M., Lanni, F., Lantwin, O., Larkin, J., Lasorak, P., Last, D., Laudrain, A., Laundrie, A., Laurenti, G., Lavaut, E., Laycock, P., Lazanu, I., LaZur, R., Lazzaroni, M., Le, T., Leardini, S., Learned, J., LeCompte, T., Legin, V., Miotto, G. Lehmann, Lehnert, R., de Oliveira, M. A. Leigui, Leitner, M., Silverio, D. Leon, Lepin, L. M., Li, J. -Y, Li, S. W., Li, Y., Liao, H., Lin, C. S., Lindebaum, D., Linden, S., Lineros, R. A., Lister, A., Littlejohn, B. R., Liu, H., Liu, J., Liu, Y., Lockwitz, S., Lokajicek, M., Lomidze, I., Long, K., Lopes, T. V., Lopez, J., de Rego, I. López, López-March, N., Lord, T., LoSecco, J. M., Louis, W. C., Sanchez, A. Lozano, Lu, X. -G., Luk, K. B., Lunday, B., Luo, X., Luppi, E., MacFarlane, D., Machado, A. A., Machado, P., Macias, C. T., Macier, J. R., MacMahon, M., Maddalena, A., Madera, A., Madigan, P., Magill, S., Magueur, C., Mahn, K., Maio, A., Major, A., Majumdar, K., Mameli, S., Man, M., Mandujano, R. C., Maneira, J., Manly, S., Mann, A., Manolopoulos, K., Plata, M. Manrique, Corchado, S. Manthey, Manyam, V. N., Marchan, M., Marchionni, A., Marciano, W., Marfatia, D., Mariani, C., Maricic, J., Marinho, F., Marino, A. D., Markiewicz, T., Marques, F. Das Chagas, Marquet, C., Marshak, M., Marshall, C. M., Marshall, J., Martina, L., Martín-Albo, J., Martinez, N., Caicedo, D. A. Martinez, López, F. Martínez, Miravé, P. Martínez, Martynenko, S., Mascagna, V., Massari, C., Mastbaum, A., Matichard, F., Matsuno, S., Matteucci, G., Matthews, J., Mauger, C., Mauri, N., Mavrokoridis, K., Mawby, I., Mazza, R., McAskill, T., McConkey, N., McFarland, K. S., McGrew, C., McNab, A., Meazza, L., Meddage, V. C. N., Mefodiev, A., Mehta, B., Mehta, P., Melas, P., Mena, O., Mendez, H., Mendez, P., Méndez, D. P., Menegolli, A., Meng, G., Mercuri, A. C. E. A., Meregaglia, A., Messier, M. D., Metallo, S., Metcalf, W., Mewes, M., Meyer, H., Miao, T., Micallef, J., Miccoli, A., Michna, G., Milincic, R., Miller, F., Miller, G., Miller, W., Mineev, O., Minotti, A., Miralles, L., Mironov, C., Miryala, S., Miscetti, S., Mishra, C. S., Mishra, P., Mishra, S. R., Mislivec, A., Mitchell, M., Mladenov, D., Mocioiu, I., Mogan, A., Moggi, N., Mohanta, R., Mohayai, T. A., Mokhov, N., Molina, J., Bueno, L. Molina, Montagna, E., Montanari, A., Montanari, C., Montanari, D., Montanino, D., Zetina, L. M. Montaño, Mooney, M., Moor, A. F., Moore, Z., Moreno, D., Moreno-Palacios, O., Morescalchi, L., Moretti, D., Moretti, R., Morris, C., Mossey, C., Moura, C. A., Mouster, G., Mu, W., Mualem, L., Mueller, J., Muether, M., Muheim, F., Muir, A., Mukhamejanov, Y., Mulhearn, M., Munford, D., Munteanu, L. J., Muramatsu, H., Muraz, J., Murphy, M., Murphy, T., Muse, J., Mytilinaki, A., Nachtman, J., Nagai, Y., Nagu, S., Nandakumar, R., Naples, D., Narita, S., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Nehm, A., Nelson, J. K., Neogi, O., Nesbit, J., Nessi, M., Newbold, D., Newcomer, M., Nichol, R., Nicolas-Arnaldos, F., Nikolica, A., Nikolov, J., Niner, E., Nishimura, K., Norman, A., Norrick, A., Novella, P., Nowak, A., Nowak, J. A., Oberling, M., Ochoa-Ricoux, J. P., Oh, S., Oh, S. B., Olivier, A., Olshevskiy, A., Olson, T., Onel, Y., Onishchuk, Y., Oranday, A., Osbiston, M., Vélez, J. A. Osorio, O'Sullivan, L., Ormachea, L. Otiniano, Ott, J., Pagani, L., Palacio, G., Palamara, O., Palestini, S., Paley, J. M., Pallavicini, M., Palomares, C., Pan, S., Panda, P., Vazquez, W. Panduro, Pantic, E., Paolone, V., Papaleo, R., Papanestis, A., Papoulias, D., Paramesvaran, S., Paris, A., Parke, S., Parozzi, E., Parsa, S., Parsa, Z., Parveen, S., Parvu, M., Pasciuto, D., Pascoli, S., Pasqualini, L., Pasternak, J., Patrick, C., Patrizii, L., Patterson, R. B., Patzak, T., Paudel, A., Paulucci, L., Pavlovic, Z., Pawloski, G., Payne, D., Pec, V., Pedreschi, E., Peeters, S. J. M., Pellico, W., Perez, A. Pena, Pennacchio, E., Penzo, A., Peres, O. L. G., Gonzalez, Y. F. Perez, Pérez-Molina, L., Pernas, C., Perry, J., Pershey, D., Pessina, G., Petrillo, G., Petta, C., Petti, R., Pfaff, M., Pia, V., Pickering, L., Pietropaolo, F., Pimentel, V. L., Pinaroli, G., Pincha, S., Pinchault, J., Pitts, K., Plows, K., Pollack, C., Pollman, T., Pompa, F., Pons, X., Poonthottathil, N., Popov, V., Poppi, F., Porter, J., Paixão, L. G. Porto, Potekhin, M., Potenza, R., Pozzato, M., Prakash, T., Pratt, C., Prest, M., Psihas, F., Pugnere, D., Qian, X., Queen, J., Raaf, J. L., Radeka, V., Rademacker, J., Radics, B., Raffaelli, F., Rafique, A., Raguzin, E., Rahaman, U., Rai, M., Rajagopalan, S., Rajaoalisoa, M., Rakhno, I., Rakotondravohitra, L., Ralte, L., Delgado, M. A. Ramirez, Ramson, B., Rappoldi, A., Raselli, G., Ratoff, P., Ray, R., Razafinime, H., Razakamiandra, R. F., Rea, E. M., Real, J. S., Rebel, B., Rechenmacher, R., Reichenbacher, J., Reitzner, S. D., Sfar, H. Rejeb, Renner, E., Renshaw, A., Rescia, S., Resnati, F., Restrepo, Diego, Reynolds, C., Ribas, M., Riboldi, S., Riccio, C., Riccobene, G., Ricol, J. S., Rigan, M., Rincón, E. V., Ritchie-Yates, A., Ritter, S., Rivera, D., Rivera, R., Robert, A., Rocha, J. L. Rocabado, Rochester, L., Roda, M., Rodrigues, P., Alonso, M. J. Rodriguez, Rondon, J. Rodriguez, Rosauro-Alcaraz, S., Rosier, P., Ross, D., Rossella, M., Rossi, M., Ross-Lonergan, M., Roy, N., Roy, P., Rubbia, C., Ruggeri, A., Ferreira, G. Ruiz, Russell, B., Ruterbories, D., Rybnikov, A., Sacerdoti, S., Saha, S., Sahoo, S. K., Sahu, N., Sala, P., Samios, N., Samoylov, O., Sanchez, M. C., Bravo, A. Sánchez, Sánchez-Castillo, A., Sanchez-Lucas, P., Sandberg, V., Sanders, D. A., Sanfilippo, S., Sankey, D., Santoro, D., Saoulidou, N., Sapienza, P., Sarasty, C., Sarcevic, I., Sarra, I., Savage, G., Savinov, V., Scanavini, G., Scaramelli, A., Scarff, A., Schefke, T., Schellman, H., Schifano, S., Schlabach, P., Schmitz, D., Schneider, A. W., Scholberg, K., Schukraft, A., Schuld, B., Segade, A., Segreto, E., Selyunin, A., Senadheera, D., Senise, C. R., Sensenig, J., Shaevitz, M. H., Shanahan, P., Sharma, P., Kumar, R., Poudel, S. Sharma, Shaw, K., Shaw, T., Shchablo, K., Shen, J., Shepherd-Themistocleous, C., Sheshukov, A., Shi, J., Shi, W., Shin, S., Shivakoti, S., Shoemaker, I., Shooltz, D., Shrock, R., Siddi, B., Siden, M., Silber, J., Simard, L., Sinclair, J., Sinev, G., Singh, Jaydip, Singh, J., Singh, L., Singh, P., Singh, V., Chauhan, S. Singh, Sipos, R., Sironneau, C., Sirri, G., Siyeon, K., Skarpaas, K., Smedley, J., Smith, E., Smith, J., Smith, P., Smolik, J., Smy, M., Snape, M., Snider, E. L., Snopok, P., Snowden-Ifft, D., Nunes, M. Soares, Sobel, H., Soderberg, M., Sokolov, S., Salinas, C. J. Solano, Söldner-Rembold, S., Solomey, N., Solovov, V., Sondheim, W. E., Sorel, M., Sotnikov, A., Soto-Oton, J., Sousa, A., Soustruznik, K., Spinella, F., Spitz, J., Spooner, N. J. C., Spurgeon, K., Stalder, D., Stancari, M., Stanco, L., Steenis, J., Stein, R., Steiner, H. M., Lisbôa, A. F. Steklain, Stepanova, A., Stewart, J., Stillwell, B., Stock, J., Stocker, F., Stokes, T., Strait, M., Strauss, T., Strigari, L., Stuart, A., Suarez, J. G., Subash, J., Surdo, A., Suter, L., Sutera, C. M., Sutton, K., Suvorov, Y., Svoboda, R., Swain, S. K., Szczerbinska, B., Szelc, A. M., Sztuc, A., Taffara, A., Talukdar, N., Tamara, J., Tanaka, H. A., Tang, S., Taniuchi, N., Casanova, A. M. Tapia, Oregui, B. Tapia, Tapper, A., Tariq, S., Tarpara, E., Tatar, E., Tayloe, R., Tedeschi, D., Teklu, A. M., Vidal, J. Tena, Tennessen, P., Tenti, M., Terao, K., Terranova, F., Testera, G., Thakore, T., Thea, A., Thomas, S., Thompson, A., Thorn, C., Timm, S. C., Tiras, E., Tishchenko, V., Tiwari, S., Todorović, N., Tomassetti, L., Tonazzo, A., Torbunov, D., Torti, M., Tortola, M., Tortorici, F., Tosi, N., Totani, D., Toups, M., Touramanis, C., Tran, D., Travaglini, R., Trevor, J., Triller, E., Trilov, S., Truchon, J., Truncali, D., Trzaska, W. H., Tsai, Y., Tsai, Y. -T., Tsamalaidze, Z., Tsang, K. V., Tsverava, N., Tu, S. Z., Tufanli, S., Tunnell, C., Turnberg, S., Turner, J., Tuzi, M., Tyler, J., Tyley, E., Tzanov, M., Uchida, M. A., González, J. Ureña, Urheim, J., Usher, T., Utaegbulam, H., Uzunyan, S., Vagins, M. R., Vahle, P., Valder, S., Valdiviesso, G. A., Valencia, E., Valentim, R., Vallari, Z., Vallazza, E., Valle, J. W. F., Van Berg, R., Van de Water, R. G., Forero, D. V., Vannozzi, A., Van Nuland-Troost, M., Varanini, F., Oliva, D. Vargas, Vasina, S., Vaughan, N., Vaziri, K., Vázquez-Ramos, A., Vega, J., Ventura, S., Verdugo, A., Vergani, S., Verzocchi, M., Vetter, K., Vicenzi, M., de Souza, H. Vieira, Vignoli, C., Vilela, C., Villa, E., Viola, S., Viren, B., Vizarreta, R., Hernandez, A. P. Vizcaya, Vuong, Q., Waldron, A. V., Wallbank, M., Walsh, J., Walton, T., Wang, H., Wang, J., Wang, L., Wang, M. H. L. S., Wang, X., Wang, Y., Warburton, K., Warner, D., Warsame, L., Wascko, M. O., Waters, D., Watson, A., Wawrowska, K., Weber, A., Weber, C. M., Weber, M., Wei, H., Weinstein, A., Westerdale, S., Wetstein, M., Whalen, K., White, A., Whitehead, L. H., Whittington, D., Wilhlemi, J., Wilking, M. J., Wilkinson, A., Wilkinson, C., Wilson, F., Wilson, R. J., Winter, P., Wisniewski, W., Wolcott, J., Wolfs, J., Wongjirad, T., Wood, A., Wood, K., Worcester, E., Worcester, M., Wospakrik, M., Wresilo, K., Wret, C., Wu, S., Wu, W., Wurm, M., Wyenberg, J., Xiao, Y., Xiotidis, I., Yaeggy, B., Yahlali, N., Yandel, E., Yang, J., Yang, K., Yang, T., Yankelevich, A., Yershov, N., Yonehara, K., Young, T., Yu, B., Yu, H., Yu, J., Yu, Y., Yuan, W., Zaki, R., Zalesak, J., Zambelli, L., Zamorano, B., Zani, A., Zapata, O., Zazueta, L., Zeller, G. P., Zennamo, J., Zeug, K., Zhang, C., Zhang, S., Zhao, M., Zhivun, E., Zimmerman, E. D., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract

This paper introduces a novel track-length extension fitting algorithm for measuring the kinetic energies of inelastically interacting particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe the impact of the dE/dx model on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions.

Published
2024

30. PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images

Author

Bai, Jieyun, Zhou, Zihao, Ou, Zhanhong, Koehler, Gregor, Stock, Raphael, Maier-Hein, Klaus, Elbatel, Marawan, Martí, Robert, Li, Xiaomeng, Qiu, Yaoyang, Gou, Panjie, Chen, Gongping, Zhao, Lei, Zhang, Jianxun, Dai, Yu, Wang, Fangyijie, Silvestre, Guénolé, Curran, Kathleen, Sun, Hongkun, Xu, Jing, Cai, Pengzhou, Jiang, Lu, Lan, Libin, Ni, Dong, Zhong, Mei, Chen, Gaowen, Campello, Víctor M., Lu, Yaosheng, and Lekadir, Karim

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Segmentation of the fetal and maternal structures, particularly intrapartum ultrasound imaging as advocated by the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) for monitoring labor progression, is a crucial first step for quantitative diagnosis and clinical decision-making. This requires specialized analysis by obstetrics professionals, in a task that i) is highly time- and cost-consuming and ii) often yields inconsistent results. The utility of automatic segmentation algorithms for biometry has been proven, though existing results remain suboptimal. To push forward advancements in this area, the Grand Challenge on Pubic Symphysis-Fetal Head Segmentation (PSFHS) was held alongside the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). This challenge aimed to enhance the development of automatic segmentation algorithms at an international scale, providing the largest dataset to date with 5,101 intrapartum ultrasound images collected from two ultrasound machines across three hospitals from two institutions. The scientific community's enthusiastic participation led to the selection of the top 8 out of 179 entries from 193 registrants in the initial phase to proceed to the competition's second stage. These algorithms have elevated the state-of-the-art in automatic PSFHS from intrapartum ultrasound images. A thorough analysis of the results pinpointed ongoing challenges in the field and outlined recommendations for future work. The top solutions and the complete dataset remain publicly available, fostering further advancements in automatic segmentation and biometry for intrapartum ultrasound imaging.

Published
2024

31. Stochastic Data-Driven Predictive Control: Chance-Constraint Satisfaction with Identified Multi-step Predictors

Author

Balim, Haldun, Carron, Andrea, Zeilinger, Melanie N., and Köhler, Johannes

Subjects
Electrical Engineering and Systems Science - Systems and Control, Mathematics - Optimization and Control
Abstract

We propose a novel data-driven stochastic model predictive control framework for uncertain linear systems with noisy output measurements. Our approach leverages multi-step predictors to efficiently propagate uncertainty, ensuring chance constraint satisfaction with minimal conservatism. In particular, we present a strategy to identify multi-step predictors and quantify the associated uncertainty using a surrogate (data-driven) state space model. Then, we utilize the derived distribution to formulate a constraint tightening that ensures chance constraint satisfaction. A numerical example highlights the reduced conservatism of handling parametric uncertainty in the proposed method compared to state-of-the-art solutions.

Published
2024

32. A Comparative Study of Open Source Computer Vision Models for Application on Small Data: The Case of CFRP Tape Laying

Author

Fraunholz, Thomas, Rall, Dennis, Köhler, Tim, Schuster, Alfons, Mayer, Monika, and Larsen, Lars

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, 68T05, 93A30, 74E30, I.2.6, I.4.8, J.2
Abstract

In the realm of industrial manufacturing, Artificial Intelligence (AI) is playing an increasing role, from automating existing processes to aiding in the development of new materials and techniques. However, a significant challenge arises in smaller, experimental processes characterized by limited training data availability, questioning the possibility to train AI models in such small data contexts. In this work, we explore the potential of Transfer Learning to address this challenge, specifically investigating the minimum amount of data required to develop a functional AI model. For this purpose, we consider the use case of quality control of Carbon Fiber Reinforced Polymer (CFRP) tape laying in aerospace manufacturing using optical sensors. We investigate the behavior of different open-source computer vision models with a continuous reduction of the training data. Our results show that the amount of data required to successfully train an AI model can be drastically reduced, and the use of smaller models does not necessarily lead to a loss of performance.

Published
2024

33. Towards safe and tractable Gaussian process-based MPC: Efficient sampling within a sequential quadratic programming framework

Author

Prajapat, Manish, Lahr, Amon, Köhler, Johannes, Krause, Andreas, and Zeilinger, Melanie N.

Subjects
Mathematics - Optimization and Control, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control, G.1.6
Abstract

Learning uncertain dynamics models using Gaussian process~(GP) regression has been demonstrated to enable high-performance and safety-aware control strategies for challenging real-world applications. Yet, for computational tractability, most approaches for Gaussian process-based model predictive control (GP-MPC) are based on approximations of the reachable set that are either overly conservative or impede the controller's safety guarantees. To address these challenges, we propose a robust GP-MPC formulation that guarantees constraint satisfaction with high probability. For its tractable implementation, we propose a sampling-based GP-MPC approach that iteratively generates consistent dynamics samples from the GP within a sequential quadratic programming framework. We highlight the improved reachable set approximation compared to existing methods, as well as real-time feasible computation times, using two numerical examples., Comment: to be published in 63rd IEEE Conference on Decision and Control (CDC 2024)

Published
2024

34. The Giant Radio Array for Neutrino Detection (GRAND) Collaboration -- Contributions to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024)

Author

Batista, Rafael Alves, Benoit-Lévy, Aurélien, Bister, Teresa, Bohacova, Martina, Bustamante, Mauricio, Carvalho, Washington, Chen, Yiren, Cheng, LingMei, Chiche, Simon, Colley, Jean-Marc, Correa, Pablo, Laurenciu, Nicoleta Cucu, Dai, Zigao, de Almeida, Rogerio M., de Errico, Beatriz, de Jong, Sijbrand, Neto, João R. T. de Mello, de Vries, Krijn D, Decoene, Valentin, Denton, Peter B., Duan, Bohao, Duan, Kaikai, Engel, Ralph, Erba, William, Fan, Yizhong, Ferrière, Arsène, Gou, QuanBu, Gu, Junhua, Guelfand, Marion, Guo, Jianhua, Guo, Yiqing, Guépin, Claire, Gülzow, Lukas, Haungs, Andreas, Havelka, Matej, He, Haoning, Hivon, Eric, Hu, Hongbo, Huang, Xiaoyuan, Huang, Yan, Huege, Tim, Jiang, Wen, Koirala, Ramesh, Kong, ChuiZheng, Kotera, Kumiko, Köhler, Jelena, Lago, Bruno L., Lai, Zhisen, Coz, Sandra Le, Legrand, François, Leisos, Antonios, Li, Rui, Li, Xingyu, Li, YiFei, Liu, Cheng, Liu, Ruoyu, Liu, Wei, Ma, Pengxiong, Macias, Oscar, Magnard, Frédéric, Marcowith, Alexandre, Martineau-Huynh, Olivier, McKinley, Thomas, Minodier, Paul, Mitra, Pragati, Mostafá, Miguel, Murase, Kohta, Niess, Valentin, Nonis, Stavros, Ogio, Shoichi, Oikonomou, Foteini, Pan, Hongwei, Papageorgiou, Konstantinos, Pierog, Tanguy, Piotrowski, Lech Wiktor, Prunet, Simon, Qian, Xiangli, Roth, Markus, Sako, Takashi, Schoorlemmer, Harm, Szálas-Motesiczky, Dániel, Sławiński, Szymon, Tian, Xishui, Timmermans, Anne, Timmermans, Charles, Tobiska, Petr, Tsirigotis, Apostolos, Tueros, Matías, Vittakis, George, Wang, Hanrui, Wang, Jiale, Wang, Shen, Wang, Xiangyu, Wang, Xu, Wei, Daming, Wei, Feng, Wu, Xiangping, Wu, Xuefeng, Xu, Xin, Xu, Xing, Yang, Fufu, Yang, Lili, Yang, Xuan, Yuan, Qiang, Zarka, Philippe, Zeng, Houdun, Zhang, Chao, Zhang, Jianli, Zhang, Kewen, Zhang, Pengfei, Zhang, Qingchi, Zhang, Songbo, Zhang, Yi, Zhou, Hao, Wissel, Stephanie, Zeolla, Andrew, Deaconu, Cosmin, Hughes, Kaeli, Martin, Zachary, Mulrey, Katharine, Cummings, Austin, Krömer, Oliver, Plant, Kathryn, and Schroeder, Frank G.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Experiment, High Energy Physics - Phenomenology
Abstract

This is an index of the contributions by the Giant Radio Array for Neutrino Detection (GRAND) Collaboration to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024, University of Chicago, June 11-14, 2024). The contributions include an overview of GRAND in its present and future incarnations, methods of radio-detection that are being developed for them, and ongoing joint work between the GRAND and BEACON experiments., Comment: Note: To access the list of contributions, please follow the "HTML" link that can be found on the arXiv page

Published
2024

35. Highly Accurate Real-space Electron Densities with Neural Networks

Author

Cheng, Lixue, Szabó, P. Bernát, Schätzle, Zeno, Kooi, Derk P., Köhler, Jonas, Giesbertz, Klaas J. H., Noé, Frank, Hermann, Jan, Gori-Giorgi, Paola, and Foster, Adam

Subjects
Physics - Chemical Physics, Computer Science - Machine Learning
Abstract

Variational ab-initio methods in quantum chemistry stand out among other methods in providing direct access to the wave function. This allows in principle straightforward extraction of any other observable of interest, besides the energy, but in practice this extraction is often technically difficult and computationally impractical. Here, we consider the electron density as a central observable in quantum chemistry and introduce a novel method to obtain accurate densities from real-space many-electron wave functions by representing the density with a neural network that captures known asymptotic properties and is trained from the wave function by score matching and noise-contrastive estimation. We use variational quantum Monte Carlo with deep-learning ans\"atze (deep QMC) to obtain highly accurate wave functions free of basis set errors, and from them, using our novel method, correspondingly accurate electron densities, which we demonstrate by calculating dipole moments, nuclear forces, contact densities, and other density-based properties., Comment: 12 pages, 9 figures in the main text

Published
2024
Full Text
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36. Talk of Family: How Institutional Overlap Shapes Family-Related Discourse across Social Class

Author

Jessica Halliday Hardie, Alina Arseniev-Koehler, Judith A. Seltzer, and Jacob G. Foster

Abstract

We develop a novel application of machine learning and apply it to the interview transcripts from the American Voices Project (N = 1,396), using discourse atom topic modeling to explore social class variation in the centrality of family in adults' lives. We take a two-phase approach, first analyzing transcripts at the person level and then at the line level. Our findings suggest that family, as represented by talk, is more central in the lives of those without a college degree than among the college educated. However, the degree of institutional overlap between family and other key institutions -- health, work, religion, and criminal justice -- does not vary by education. We interpret these findings in the context of debates about the deinstitutionalization of family in the contemporary United States. This demonstrates the value of a new method for analyzing qualitative interview data at scale. We address ways to expand the use of this method to shed light on educational disparities.

Published
2024

37. Instructor Leadership in Online Learning: Predictive Relationships between Servant Leadership and the Community of Inquiry Framework

Author

Sally Meech and Adrie A. Koehler

Abstract

Instructor leadership is widely recognized as essential for facilitating meaningful online learning in higher education. While previous studies have applied organizational leadership theories to the study of instructor leadership, fewer studies have investigated online instructor leadership. This predictive correlational study detailed the associations between the Community of Inquiry (CoI) framework and servant leadership (SL) theory and employed multiple regression analyses to investigate the predictive relationships of seven SL dimensions on the three CoI presences. Survey data were gathered from 148 graduate students enrolled in online courses in education, communication, and engineering master's degree programs using the CoI Survey (Arbaugh et al., 2008) and the SL-28 (Liden et al., 2008). The findings revealed a significant positive correlation between the instruments. The predictive model as a whole explained 66% of the variance in students' perceptions of a CoI. Three SL predictor variables demonstrated the most influence: helping subordinates grow and succeed, conceptual skills, and creating value for the community. Additional analyses at the CoI subscale level revealed that the SL variables accounted for 73% of the variance in teaching presence, 55% of the variance in cognitive presence, and 31% of the variance in social presence. Implications and limitations are discussed and recommendations are proposed to implement online instructor SL.

Published
2024

38. Theoretical Foundations and Limits of Word Embeddings: What Types of Meaning Can They Capture?

Author

Alina Arseniev-Koehler

Abstract

Measuring meaning is a central problem in cultural sociology and word embeddings may offer powerful new tools to do so. But like any tool, they build on and exert theoretical assumptions. In this paper, I theorize the ways in which word embeddings model three core premises of a structural linguistic theory of meaning: that meaning is coherent, relational, and may be analyzed as a static system. In certain ways, word embeddings are vulnerable to the enduring critiques of these premises. In other ways, word embeddings offer novel solutions to these critiques. More broadly, formalizing the study of meaning with word embeddings offers theoretical opportunities to clarify core concepts and debates in cultural sociology, such as the coherence of meaning. Just as network analysis specified the once vague notion of social relations, formalizing meaning with embeddings can push us to specify and reimagine meaning itself.

Published
2024
Full Text
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39. A prenatal skin atlas reveals immune regulation of human skin morphogenesis.

Author

Gopee, Nusayhah, Winheim, Elena, Olabi, Bayanne, Admane, Chloe, Foster, April, Huang, Ni, Botting, Rachel, Torabi, Fereshteh, Sumanaweera, Dinithi, Le, Anh, Kim, Jin, Verger, Luca, Stephenson, Emily, Adão, Diana, Ganier, Clarisse, Gim, Kelly, Serdy, Sara, Deakin, CiCi, Goh, Issac, Steele, Lloyd, Annusver, Karl, Miah, Mohi-Uddin, Tun, Win, Moghimi, Pejvak, Kwakwa, Kwasi, Li, Tong, Basurto Lozada, Daniela, Rumney, Ben, Tudor, Catherine, Roberts, Kenny, Chipampe, Nana-Jane, Sidhpura, Keval, Englebert, Justin, Jardine, Laura, Reynolds, Gary, Rose, Antony, Rowe, Vicky, Pritchard, Sophie, Mulas, Ilaria, Fletcher, James, Popescu, Dorin-Mirel, Poyner, Elizabeth, Dubois, Anna, Guy, Alyson, Filby, Andrew, Lisgo, Steven, Barker, Roger, Glass, Ian, Park, Jong-Eun, Vento-Tormo, Roser, Nikolova, Marina, He, Peng, Lawrence, John, Moore, Josh, Ballereau, Stephane, Hale, Christine, Shanmugiah, Vijaya, Horsfall, David, Rajan, Neil, McGrath, John, OToole, Edel, Treutlein, Barbara, Bayraktar, Omer, Kasper, Maria, Progatzky, Fränze, Mazin, Pavel, Lee, Jiyoon, Gambardella, Laure, Koehler, Karl, Teichmann, Sarah, and Haniffa, Muzlifah

Subjects
Humans, Skin, Morphogenesis, Macrophages, Hair Follicle, Organoids, Induced Pluripotent Stem Cells, Immunity, Innate, Atlases as Topic, Single-Cell Analysis, Endothelial Cells, Female, Neovascularization, Physiologic, Transcriptome, Human Embryonic Stem Cells, Wound Healing, Fetus
Abstract

Human prenatal skin is populated by innate immune cells, including macrophages, but whether they act solely in immunity or have additional functions in morphogenesis is unclear. Here we assembled a comprehensive multi-omics reference atlas of prenatal human skin (7-17 post-conception weeks), combining single-cell and spatial transcriptomics data, to characterize the microanatomical tissue niches of the skin. This atlas revealed that crosstalk between non-immune and immune cells underpins the formation of hair follicles, is implicated in scarless wound healing and is crucial for skin angiogenesis. We systematically compared a hair-bearing skin organoid (SkO) model derived from human embryonic stem cells and induced pluripotent stem cells to prenatal and adult skin1. The SkO model closely recapitulated in vivo skin epidermal and dermal cell types during hair follicle development and expression of genes implicated in the pathogenesis of genetic hair and skin disorders. However, the SkO model lacked immune cells and had markedly reduced endothelial cell heterogeneity and quantity. Our in vivo prenatal skin cell atlas indicated that macrophages and macrophage-derived growth factors have a role in driving endothelial development. Indeed, vascular network remodelling was enhanced following transfer of autologous macrophages derived from induced pluripotent stem cells into SkO cultures. Innate immune cells are therefore key players in skin morphogenesis beyond their conventional role in immunity, a function they achieve through crosstalk with non-immune cells.

Published
2024

40. Trafficking of mitochondrial double-stranded RNA from mitochondria to the cytosol.

Author

Krieger, Matthew, Abrahamian, Melania, He, Kevin, Atamdede, Sean, Hakimjavadi, Hesamedin, Momcilovic, Milica, Ostrow, Dejerianne, Maggo, Simran, Tsang, Yik, Gai, Xiaowu, Chanfreau, Guillaume, Shackelford, David, Teitell, Michael, and Koehler, Carla

Subjects
Humans, Cytosol, Mitochondria, RNA, Double-Stranded, Prohibitins, RNA, Mitochondrial, Cell Line, Tumor, Repressor Proteins, RNA Transport, Exoribonucleases, Voltage-Dependent Anion Channel 1, Carcinoma, Non-Small-Cell Lung, Mitochondrial Proteins
Abstract

In addition to mitochondrial DNA, mitochondrial double-stranded RNA (mtdsRNA) is exported from mitochondria. However, specific channels for RNA transport have not been demonstrated. Here, we begin to characterize channel candidates for mtdsRNA export from the mitochondrial matrix to the cytosol. Down-regulation of SUV3 resulted in the accumulation of mtdsRNAs in the matrix, whereas down-regulation of PNPase resulted in the export of mtdsRNAs to the cytosol. Targeting experiments show that PNPase functions in both the intermembrane space and matrix. Strand-specific sequencing of the double-stranded RNA confirms the mitochondrial origin. Inhibiting or down-regulating outer membrane proteins VDAC1/2 and BAK/BAX or inner membrane proteins PHB1/2 strongly attenuated the export of mtdsRNAs to the cytosol. The cytosolic mtdsRNAs subsequently localized to large granules containing the stress protein TIA-1 and activated the type 1 interferon stress response pathway. Abundant mtdsRNAs were detected in a subset of non-small-cell lung cancer cell lines that were glycolytic, indicating relevance in cancer biology. Thus, we propose that mtdsRNA is a new damage-associated molecular pattern that is exported from mitochondria in a regulated manner.

Published
2024

41. Transient contacts between filaments impart its elasticity to branched actin

Author

Bouzid, Mehdi, Gallardo, Cesar Valencia, Kopec, Magdalena, Koehler, Lara, Foffi, Giuseppe, Roure, Olivia du, Heuvingh, Julien, and Lenz, Martin

Subjects
Condensed Matter - Soft Condensed Matter, Physics - Biological Physics, Quantitative Biology - Subcellular Processes
Abstract

Branched actin networks exert pushing forces in eukaryotic cells, and adapt their stiffness to their environment. The physical basis for their mechanics and adaptability is however not understood. Indeed, here we show that their high density and low connectivity place them outside the scope of standard elastic network models for actin. We combine high-precision mechanical experiments, molecular dynamics simulations and a mean-field elastic theory to show that they are instead dominated by the proliferation of interfilament contacts under compression. This places branched actin in the same category as undercoordinated, fibrous materials such as sheep's wool. When the network is grown under force, filaments entangle as if knitted together and trap contacts in their structure. Trapped contacts play a similar role as crosslinkers in rigidifying the network, and are thus key to its active adaptive mechanics., Comment: 7 pages, 5 figures

Published
2024

42. Next-Generation Triggering: A Novel Event-Level Approach

Author

Köhler, Jelena, Benoit-Lévy, Aurélien, Correa, Pablo, Ferriere, Arsène, Huege, Tim, Kotera, Kumiko, Martineau-Huynh, Olivier, Prunet, Simon, and Roth, Markus

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Large-scale cosmic-ray detectors like the Giant Radio Array for Neutrino Detection (GRAND) are pushing the boundaries of our ability to identify air shower events. Existing trigger schemes rely solely on the timing of signals detected by individual antennas, which brings many challenges in distinguishing true air shower signals from background. This work explores novel event-level radio trigger methods specifically designed for GRAND, but also applicable to other systems, such as the Radio Detector (RD) of the Pierre Auger Observatory. In addition to an upgraded plane wave front reconstruction technique, we introduce orthogonal and complementary approaches that analyze the radio-emission footprint, the spatial distribution of signal strength across triggered antennas, to refine event selection. We test our methods on mock data sets constructed with simulated showers and real background noise measured with the GRAND prototype, to assess the performance potential in terms of sensitivity and background rejection in GRAND. Our preliminary results are a first step to identifying the most discriminating radio signal features at event-level, and optimizing the techniques for future implementation on experimental data., Comment: 8 pages, 5 figures, 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities - ARENA2024

Published
2024

43. A study of distributional complexity measures for Boolean functions

Author

Köhler-Schindler, Laurin and Steif, Jeffrey E.

Subjects
Mathematics - Probability, Computer Science - Computational Complexity, primary: 68Q87, 68Q15 secondary: 03D15, 60K35, 82B43
Abstract

A number of complexity measures for Boolean functions have previously been introduced. These include (1) sensitivity, (2) block sensitivity, (3) witness complexity, (4) subcube partition complexity and (5) algorithmic complexity. Each of these is concerned with "worst-case" inputs. It has been shown that there is "asymptotic separation" between these complexity measures and very recently, due to the work of Huang, it has been established that they are all "polynomially related". In this paper, we study the notion of distributional complexity where the input bits are independent and one considers all of the above notions in expectation. We obtain a number of results concerning distributional complexity measures, among others addressing the above concepts of "asymptotic separation" and being "polynomially related" in this context. We introduce a new distributional complexity measure, local witness complexity, which only makes sense in the distributional context and we also study a new version of algorithmic complexity which involves partial information. Many interesting examples are presented including some related to percolation. The latter connects a number of the recent developments in percolation theory over the last two decades with the study of complexity measures in theoretical computer science., Comment: 42 pages, 2 figures

Published
2024

44. DUNE Phase II: Scientific Opportunities, Detector Concepts, Technological Solutions

Author

DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Akbar, F., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alton, A., Alvarez, R., Alves, T., Amar, H., Amedo, P., Anderson, J., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antic, D., Antoniassi, M., Antonova, M., Antoshkin, A., Aranda-Fernandez, A., Arellano, L., Diaz, E. Arrieta, Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asner, D. M., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Azam, M. B., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baldini, W., Baldonedo, J., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barbu, D., Barenboim, G., Barham~Alzás, P., Barker, G. J., Barkhouse, W., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, D., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., Bathe-Peters, L., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bell, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernal, J., Bernardini, P., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhat, A., Bhatnagar, V., Bhatt, J., Bhattacharjee, M., Bhattacharya, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blucher, E., Bodek, A., Bogenschuetz, J., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Merlo, R. Borges, Borkum, A., Bostan, N., Bouet, R., Boza, J., Bracinik, J., Brahma, B., Brailsford, D., Bramati, F., Branca, A., Brandt, A., Bremer, J., Brew, C., Brice, S. J., Brio, V., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M., Buchanan, N., Budd, H., Buergi, J., Bundock, A., Burgardt, D., Butchart, S., V., G. Caceres, Cagnoli, I., Cai, T., Calabrese, R., Calcutt, J., Calivers, L., Calvo, E., Caminata, A., Camino, A. F., Campanelli, W., Campani, A., Benitez, A. Campos, Canci, N., Capó, J., Caracas, I., Caratelli, D., Carber, D., Carceller, J. M., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Carter, A., Casarejos, E., Casazza, D., Forero, J. F. Castaño, Castaño, F. A., Castillo, A., Castromonte, C., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavanna, F., Centro, S., Cerati, G., Cerna, C., Cervelli, A., Villanueva, A. Cervera, Chakraborty, K., Chakraborty, S., Chalifour, M., Chappell, A., Charitonidis, N., Chatterjee, A., Chen, H., Chen, M., Chen, W. C., Chen, Y., Chen-Wishart, Z., Cherdack, D., Chi, C., Chiapponi, F., Chirco, R., Chitirasreemadam, N., Cho, K., Choate, S., Chokheli, D., Chong, P. S., Chowdhury, B., Christian, D., Chukanov, A., Chung, M., Church, E., Cicala, M. F., Cicerchia, M., Cicero, V., Ciolini, R., Clarke, P., Cline, G., Coan, T. E., Cocco, A. G., Coelho, J. A. B., Cohen, A., Collazo, J., Collot, J., Conley, E., Conrad, J. M., Convery, M., Copello, S., Cortez, A. F. V., Cova, P., Cox, C., Cremaldi, L., Cremonesi, L., Crespo-Anadón, J. I., Crisler, M., Cristaldo, E., Crnkovic, J., Crone, G., Cross, R., Cudd, A., Cuesta, C., Cui, Y., Curciarello, F., Cussans, D., Dai, J., Dalager, O., Dallavalle, R., Dallaway, W., D'Amico, R., da Motta, H., Dar, Z. A., Darby, R., Peres, L. Da Silva, David, Q., Davies, G. S., Davini, S., Dawson, J., De Aguiar, R., De Almeida, P., Debbins, P., De Bonis, I., Decowski, M. P., de Gouvêa, A., De Holanda, P. C., Astiz, I. L. De Icaza, De Jong, P., Sanchez, P. Del Amo, De la Torre, A., De Lauretis, G., Delbart, A., Delepine, D., Delgado, M., Dell'Acqua, A., Monache, G. Delle, Delmonte, N., De Lurgio, P., Demario, R., De Matteis, G., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Denton, P., Deptuch, G. W., De Roeck, A., De Romeri, V., Detje, J. P., Devine, J., Dharmapalan, R., Dias, M., Diaz, A., Díaz, J. S., Díaz, F., Di Capua, F., Di Domenico, A., Di Domizio, S., Di Falco, S., Di Giulio, L., Ding, P., Di Noto, L., Diociaiuti, E., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Doering, D., Dolan, S., Dolek, F., Dolinski, M. J., Domenici, D., Domine, L., Donati, S., Donon, Y., Doran, S., Douglas, D., Doyle, T. A., Dragone, A., Drielsma, F., Duarte, L., Duchesneau, D., Duffy, K., Dugas, K., Dunne, P., Dutta, B., Duyang, H., Dwyer, D. A., Dyshkant, A. S., Dytman, S., Eads, M., Earle, A., Edayath, S., Edmunds, D., Eisch, J., Englezos, P., Ereditato, A., Erjavec, T., Escobar, C. O., Evans, J. J., Ewart, E., Ezeribe, A. C., Fahey, K., Fajt, L., Falcone, A., Fani', M., Farnese, C., Farrell, S., Farzan, Y., Fedoseev, D., Felix, J., Feng, Y., Fernandez-Martinez, E., Fernández-Posada, D., Ferry, G., Fialova, E., Fields, L., Filip, P., Filkins, A., Filthaut, F., Fine, R., Fiorillo, G., Fiorini, M., Fogarty, S., Foreman, W., Fowler, J., Franc, J., Francis, K., Franco, D., Franklin, J., Freeman, J., Fried, J., Friedland, A., Fuess, S., Furic, I. K., Furman, K., Furmanski, A. P., Gaba, R., Gabrielli, A., M~Gago, A., Galizzi, F., Gallagher, H., Gallice, N., Galymov, V., Gamberini, E., Gamble, T., Ganacim, F., Gandhi, R., Ganguly, S., Gao, F., Gao, S., Garcia-Gamez, D., García-Peris, M. Á., Gardim, F., Gardiner, S., Gastler, D., Gauch, A., Gauvreau, J., Gauzzi, P., Gazzana, S., Ge, G., Geffroy, N., Gelli, B., Gent, S., Gerlach, L., Ghorbani-Moghaddam, Z., Giammaria, T., Gibin, D., Gil-Botella, I., Gilligan, S., Gioiosa, A., Giovannella, S., Girerd, C., Giri, A. K., Giugliano, C., Giusti, V., Gnani, D., Gogota, O., Gollapinni, S., Gollwitzer, K., Gomes, R. A., Bermeo, L. V. Gomez, Fajardo, L. S. Gomez, Gonnella, F., Gonzalez-Diaz, D., Gonzalez-Lopez, M., Goodman, M. C., Goswami, S., Gotti, C., Goudeau, J., Goudzovski, E., Grace, C., Gramellini, E., Gran, R., Granados, E., Granger, P., Grant, C., Gratieri, D. R., Grauso, G., Green, P., Greenberg, S., Greer, J., Griffith, W. C., Groetschla, F. T., Grzelak, K., Gu, L., Gu, W., Guarino, V., Guarise, M., Guenette, R., Guerzoni, M., Guffanti, D., Guglielmi, A., Guo, B., Guo, F. Y., Gupta, A., Gupta, V., Gurung, G., Gutierrez, D., Guzowski, P., Guzzo, M. M., Gwon, S., Habig, A., Hadavand, H., Haegel, L., Haenni, R., Hagaman, L., Hahn, A., Haiston, J., Hakenmüller, J., Hamernik, T., Hamilton, P., Hancock, J., Happacher, F., Harris, D. A., Hart, A., Hartnell, J., Hartnett, T., Harton, J., Hasegawa, T., Hasnip, C. M., Hatcher, R., Hayrapetyan, K., Hays, J., Hazen, E., He, M., Heavey, A., Heeger, K. M., Heise, J., Hellmuth, P., Henry, S., Hernández-García, J., Herner, K., Hewes, V., Higuera, A., Hilgenberg, C., Hillier, S. J., Himmel, A., Hinkle, E., Hirsch, L. R., Ho, J., Hoff, J., Holin, A., Holvey, T., Hoppe, E., Horiuchi, S., Horton-Smith, G. A., Houdy, T., Howard, B., Howell, R., Hristova, I., Hronek, M. S., Huang, J., Huang, R. G., Hulcher, Z., Ibrahim, M., Iles, G., Ilic, N., Iliescu, A. M., Illingworth, R., Ingratta, G., Ioannisian, A., Irwin, B., Isenhower, L., Oliveira, M. Ismerio, Itay, R., Jackson, C. M., Jain, V., James, E., Jang, W., Jargowsky, B., Jena, D., Jentz, I., Ji, X., Jiang, C., Jiang, J., Jiang, L., Jipa, A., Jo, J. H., Joaquim, F. R., Johnson, W., Jollet, C., Jones, B., Jones, R., Jovancevic, N., Judah, M., Jung, C. K., Junk, T., Jwa, Y., Kabirnezhad, M., Kaboth, A. C., Kadenko, I., Kakorin, I., Kalitkina, A., Kalra, D., Kandemir, M., Kaplan, D. M., Karagiorgi, G., Karaman, G., Karcher, A., Karyotakis, Y., Kasai, S., Kasetti, S. P., Kashur, L., Katsioulas, I., Kauther, A., Kazaryan, N., Ke, L., Kearns, E., Keener, P. T., Kelly, K. J., Kemp, E., Kemularia, O., Kermaidic, Y., Ketchum, W., Kettell, S. H., Khabibullin, M., Khan, N., Khvedelidze, A., Kim, D., Kim, J., Kim, M. J., King, B., Kirby, B., Kirby, M., Kish, A., Klein, J., Kleykamp, J., Klustova, A., Kobilarcik, T., Koch, L., Koehler, K., Koerner, L. W., Koh, D. H., Kolupaeva, L., Korablev, D., Kordosky, M., Kosc, T., Kose, U., Kostelecký, V. A., Kothekar, K., Kotler, I., Kovalcuk, M., Kozhukalov, V., Krah, W., Kralik, R., Kramer, M., Kreczko, L., Krennrich, F., Kreslo, I., Kroupova, T., Kubota, S., Kubu, M., Kudenko, Y., Kudryavtsev, V. A., Kufatty, G., Kuhlmann, S., Kulagin, S., Kumar, J., Kumar, P., Kumaran, S., Kunzmann, J., Kuravi, R., Kurita, N., Kuruppu, C., Kus, V., Kutter, T., Kuźniak, M., Kvasnicka, J., Labree, T., Lackey, T., Lalău, I., Lambert, A., Land, B. J., Lane, C. E., Lane, N., Lang, K., Langford, T., Langstaff, M., Lanni, F., Lantwin, O., Larkin, J., Lasorak, P., Last, D., Laudrain, A., Laundrie, A., Laurenti, G., Lavaut, E., Laycock, P., Lazanu, I., LaZur, R., Lazzaroni, M., Le, T., Leardini, S., Learned, J., LeCompte, T., Legin, V., Miotto, G. Lehmann, Lehnert, R., de Oliveira, M. A. Leigui, Leitner, M., Silverio, D. Leon, Lepin, L. M., -Y~Li, J., Li, S. W., Li, Y., Liao, H., Lin, C. S., Lindebaum, D., Linden, S., Lineros, R. A., Lister, A., Littlejohn, B. R., Liu, H., Liu, J., Liu, Y., Lockwitz, S., Lokajicek, M., Lomidze, I., Long, K., Lopes, T. V., Lopez, J., de Rego, I. López, López-March, N., Lord, T., LoSecco, J. M., Louis, W. C., Sanchez, A. Lozano, Lu, X. -G., Luk, K. B., Lunday, B., Luo, X., Luppi, E., MacFarlane, D., Machado, A. A., Machado, P., Macias, C. T., Macier, J. R., MacMahon, M., Maddalena, A., Madera, A., Madigan, P., Magill, S., Magueur, C., Mahn, K., Maio, A., Major, A., Majumdar, K., Mameli, S., Man, M., Mandujano, R. C., Maneira, J., Manly, S., Mann, A., Manolopoulos, K., Plata, M. Manrique, Corchado, S. Manthey, Manyam, V. N., Marchan, M., Marchionni, A., Marciano, W., Marfatia, D., Mariani, C., Maricic, J., Marinho, F., Marino, A. D., Markiewicz, T., Marques, F. Das Chagas, Marquet, C., Marshak, M., Marshall, C. M., Marshall, J., Martina, L., Martín-Albo, J., Martinez, N., Caicedo, D. A. Martinez, López, F. Martínez, Miravé, P. Martínez, Martynenko, S., Mascagna, V., Massari, C., Mastbaum, A., Matichard, F., Matsuno, S., Matteucci, G., Matthews, J., Mauger, C., Mauri, N., Mavrokoridis, K., Mawby, I., Mazza, R., McAskill, T., McConkey, N., McFarland, K. S., McGrew, C., McNab, A., Meazza, L., Meddage, V. C. N., Mefodiev, A., Mehta, B., Mehta, P., Melas, P., Mena, O., Mendez, H., Mendez, P., Méndez, D. P., Menegolli, A., Meng, G., Mercuri, A. C. E. A., Meregaglia, A., Messier, M. D., Metallo, S., Metcalf, W., Mewes, M., Meyer, H., Miao, T., Micallef, J., Miccoli, A., Michna, G., Milincic, R., Miller, F., Miller, G., Miller, W., Mineev, O., Minotti, A., Miralles, L., Miranda, O. G., Mironov, C., Miryala, S., Miscetti, S., Mishra, C. S., Mishra, P., Mishra, S. R., Mislivec, A., Mitchell, M., Mladenov, D., Mocioiu, I., Mogan, A., Moggi, N., Mohanta, R., Mohayai, T. A., Mokhov, N., Molina, J., Bueno, L. Molina, Montagna, E., Montanari, A., Montanari, C., Montanari, D., Montanino, D., Zetina, L. M. Montaño, Mooney, M., Moor, A. F., Moore, Z., Moreno, D., Moreno-Palacios, O., Morescalchi, L., Moretti, D., Moretti, R., Morris, C., Mossey, C., Moura, C. A., Mouster, G., Mu, W., Mualem, L., Mueller, J., Muether, M., Muheim, F., Muir, A., Mulhearn, M., Munford, D., Munteanu, L. J., Muramatsu, H., Muraz, J., Murphy, M., Murphy, T., Muse, J., Mytilinaki, A., Nachtman, J., Nagai, Y., Nagu, S., Nandakumar, R., Naples, D., Narita, S., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Nehm, A., Nelson, J. K., Neogi, O., Nesbit, J., Nessi, M., Newbold, D., Newcomer, M., Nichol, R., Nicolas-Arnaldos, F., Nikolica, A., Nikolov, J., Niner, E., Nishimura, K., Norman, A., Norrick, A., Novella, P., Nowak, A., Nowak, J. A., Oberling, M., Ochoa-Ricoux, J. P., Oh, S., Oh, S. B., Olivier, A., Olshevskiy, A., Olson, T., Onel, Y., Onishchuk, Y., Oranday, A., Gann, G. D. Orebi, Osbiston, M., Vélez, J. A. Osorio, O'Sullivan, L., Ormachea, L. Otiniano, Ott, J., Pagani, L., Palacio, G., Palamara, O., Palestini, S., Paley, J. M., Pallavicini, M., Palomares, C., Pan, S., Panda, P., Vazquez, W. Panduro, Pantic, E., Paolone, V., Papaleo, R., Papanestis, A., Papoulias, D., Paramesvaran, S., Paris, A., Parke, S., Parozzi, E., Parsa, S., Parsa, Z., Parveen, S., Parvu, M., Pasciuto, D., Pascoli, S., Pasqualini, L., Pasternak, J., Patrick, C., Patrizii, L., Patterson, R. B., Patzak, T., Paudel, A., Paulucci, L., Pavlovic, Z., Pawloski, G., Payne, D., Pec, V., Pedreschi, E., Peeters, S. J. M., Pellico, W., Perez, A. Pena, Pennacchio, E., Penzo, A., Peres, O. L. G., Gonzalez, Y. F. Perez, Pérez-Molina, L., Pernas, C., Perry, J., Pershey, D., Pessina, G., Petrillo, G., Petta, C., Petti, R., Pfaff, M., Pia, V., Pickering, L., Pietropaolo, F., Pimentel, V. L., Pinaroli, G., Pincha, S., Pinchault, J., Pitts, K., Plows, K., Pollack, C., Pollman, T., Pompa, F., Pons, X., Poonthottathil, N., Popov, V., Poppi, F., Porter, J., Paix{ã}o, L. G. Porto, Potekhin, M., Potenza, R., Pozimski, J., Pozzato, M., Prakash, T., Pratt, C., Prest, M., Psihas, F., Pugnere, D., Qian, X., Queen, J., Raaf, J. L., Radeka, V., Rademacker, J., Radics, B., Raffaelli, F., Rafique, A., Raguzin, E., Rai, M., Rajagopalan, S., Rajaoalisoa, M., Rakhno, I., Rakotondravohitra, L., Ralte, L., Delgado, M. A. Ramirez, Ramson, B., Rappoldi, A., Raselli, G., Ratoff, P., Ray, R., Razafinime, H., Rea, E. M., Real, J. S., Rebel, B., Rechenmacher, R., Reichenbacher, J., Reitzner, S. D., Sfar, H. Rejeb, Renner, E., Renshaw, A., Rescia, S., Resnati, F., Diego~Restrepo, Reynolds, C., Ribas, M., Riboldi, S., Riccio, C., Riccobene, G., Ricol, J. S., Rigan, M., Rincón, E. V., Ritchie-Yates, A., Ritter, S., Rivera, D., Rivera, R., Robert, A., Rocha, J. L. Rocabado, Rochester, L., Roda, M., Rodrigues, P., Alonso, M. J. Rodriguez, Rondon, J. Rodriguez, Rosauro-Alcaraz, S., Rosier, P., Ross, D., Rossella, M., Rossi, M., Ross-Lonergan, M., Roy, N., Roy, P., Rubbia, C., Ruggeri, A., Ruiz, G., Russell, B., Ruterbories, D., Rybnikov, A., Sacerdoti, S., Saha, S., Sahoo, S. K., Sahu, N., Sala, P., Samios, N., Samoylov, O., Sanchez, M. C., Bravo, A. Sánchez, Sánchez-Castillo, A., Sanchez-Lucas, P., Sandberg, V., Sanders, D. A., Sanfilippo, S., Sankey, D., Santoro, D., Saoulidou, N., Sapienza, P., Sarasty, C., Sarcevic, I., Sarra, I., Savage, G., Savinov, V., Scanavini, G., Scaramelli, A., Scarff, A., Schefke, T., Schellman, H., Schifano, S., Schlabach, P., Schmitz, D., Schneider, A. W., Scholberg, K., Schukraft, A., Schuld, B., Segade, A., Segreto, E., Selyunin, A., Senadheera, D., Senise, C. R., Sensenig, J., Seo, S. H., Shaevitz, M. H., Shanahan, P., Sharma, P., Kumar, R., Poudel, S. Sharma, Shaw, K., Shaw, T., Shchablo, K., Shen, J., Shepherd-Themistocleous, C., Sheshukov, A., Shi, J., Shi, W., Shin, S., Shivakoti, S., Shoemaker, I., Shooltz, D., Shrock, R., Siddi, B., Siden, M., Silber, J., Simard, L., Sinclair, J., Sinev, G., Singh, J., Singh, L., Singh, P., Singh, V., Chauhan, S. Singh, Sipos, R., Sironneau, C., Sirri, G., Siyeon, K., Skarpaas, K., Smedley, J., Smith, E., Smith, J., Smith, P., Smolik, J., Smy, M., Snape, M., Snider, E. L., Snopok, P., Snowden-Ifft, D., Nunes, M. Soares, Sobel, H., Soderberg, M., Sokolov, S., Salinas, C. J. Solano, Söldner-Rembold, S., Solomey, N., Solovov, V., Sondheim, W. E., Sorel, M., Sotnikov, A., Soto-Oton, J., Sousa, A., Soustruznik, K., Spinella, F., Spitz, J., Spooner, N. J. C., Spurgeon, K., Stalder, D., Stancari, M., Stanco, L., Steenis, J., Stein, R., Steiner, H. M., Lisbôa, A. F. Steklain, Stepanova, A., Stewart, J., Stillwell, B., Stock, J., Stocker, F., Stokes, T., Strait, M., Strauss, T., Strigari, L., Stuart, A., Suarez, J. G., Subash, J., Surdo, A., Suter, L., Sutera, C. M., Sutton, K., Suvorov, Y., Svoboda, R., Swain, S. K., Szczerbinska, B., Szelc, A. M., Sztuc, A., Taffara, A., Talukdar, N., Tamara, J., Tanaka, H. A., Tang, S., Taniuchi, N., Casanova, A. M. Tapia, Oregui, B. Tapia, Tapper, A., Tariq, S., Tarpara, E., Tatar, E., Tayloe, R., Tedeschi, D., Teklu, A. M., Vidal, J. Tena, Tennessen, P., Tenti, M., Terao, K., Terranova, F., Testera, G., Thakore, T., Thea, A., Thomas, S., Thompson, A., Thorn, C., Timm, S. C., Tiras, E., Tishchenko, V., Todorović, N., Tomassetti, L., Tonazzo, A., Torbunov, D., Torti, M., Tortola, M., Tortorici, F., Tosi, N., Totani, D., Toups, M., Touramanis, C., Tran, D., Travaglini, R., Trevor, J., Triller, E., Trilov, S., Truchon, J., Truncali, D., Trzaska, W. H., Tsai, Y., Tsai, Y. -T., Tsamalaidze, Z., Tsang, K. V., Tsverava, N., Tu, S. Z., Tufanli, S., Tunnell, C., Turnberg, S., Turner, J., Tuzi, M., Tyler, J., Tyley, E., Tzanov, M., Uchida, M. A., González, J. Ureña, Urheim, J., Usher, T., Utaegbulam, H., Uzunyan, S., Vagins, M. R., Vahle, P., Valder, S., Valdiviesso, G. A., Valencia, E., Valentim, R., Vallari, Z., Vallazza, E., Valle, J. W. F., Van Berg, R., Van de Water, R. G., Forero, D. V., Vannozzi, A., Van Nuland-Troost, M., Varanini, F., Oliva, D. Vargas, Vasina, S., Vaughan, N., Vaziri, K., Vázquez-Ramos, A., Vega, J., Ventura, S., Verdugo, A., Vergani, S., Verzocchi, M., Vetter, K., Vicenzi, M., de Souza, H. Vieira, Vignoli, C., Vilela, C., Villa, E., Viola, S., Viren, B., Hernandez, A. P. Vizcaya, Vuong, Q., Waldron, A. V., Wallbank, M., Walsh, J., Walton, T., Wang, H., Wang, J., Wang, L., Wang, M. H. L. S., Wang, X., Wang, Y., Warburton, K., Warner, D., Warsame, L., Wascko, M. O., Waters, D., Watson, A., Wawrowska, K., Weber, A., Weber, C. M., Weber, M., Wei, H., Weinstein, A., Westerdale, S., Wetstein, M., Whalen, K., White, A., Whitehead, L. H., Whittington, D., Wilhlemi, J., Wilking, M. J., Wilkinson, A., Wilkinson, C., Wilson, F., Wilson, R. J., Winter, P., Wisniewski, W., Wolcott, J., Wolfs, J., Wongjirad, T., Wood, A., Wood, K., Worcester, E., Worcester, M., Wospakrik, M., Wresilo, K., Wret, C., Wu, S., Wu, W., Wurm, M., Wyenberg, J., Xiao, Y., Xiotidis, I., Yaeggy, B., Yahlali, N., Yandel, E., Yang, J., Yang, K., Yang, T., Yankelevich, A., Yershov, N., Yonehara, K., Young, T., Yu, B., Yu, H., Yu, J., Yu, Y., Yuan, W., Zaki, R., Zalesak, J., Zambelli, L., Zamorano, B., Zani, A., Zapata, O., Zazueta, L., Zeller, G. P., Zennamo, J., Zeug, K., Zhang, C., Zhang, S., Zhao, M., Zhivun, E., Zimmerman, E. D., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract

The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the European Strategy for Particle Physics. While the construction of the DUNE Phase I is well underway, this White Paper focuses on DUNE Phase II planning. DUNE Phase-II consists of a third and fourth far detector (FD) module, an upgraded near detector complex, and an enhanced 2.1 MW beam. The fourth FD module is conceived as a "Module of Opportunity", aimed at expanding the physics opportunities, in addition to supporting the core DUNE science program, with more advanced technologies. This document highlights the increased science opportunities offered by the DUNE Phase II near and far detectors, including long-baseline neutrino oscillation physics, neutrino astrophysics, and physics beyond the standard model. It describes the DUNE Phase II near and far detector technologies and detector design concepts that are currently under consideration. A summary of key R&D goals and prototyping phases needed to realize the Phase II detector technical designs is also provided. DUNE's Phase II detectors, along with the increased beam power, will complete the full scope of DUNE, enabling a multi-decadal program of groundbreaking science with neutrinos.

Published
2024

45. Time-Evolution Images of the Hypergiant RW Cephei During the Re-brightening Phase Following the Great Dimming

Author

Anugu, Narsireddy, Gies, Douglas R., Roettenbacher, Rachael M., Monnier, John D., Montargés, Miguel, Mérand, Antoine, Baron, Fabien, Schaefer, Gail H., Shepard, Katherine A., Kraus, Stefan, Anderson, Matthew D., Codron, Isabelle, Gardner, Tyler, Gutierrez, Mayra, Köhler, Rainer, Kubiak, Karolina, Lanthermann, Cyprien, Majoinen, Olli, Scott, Nicholas J., and Vollmann, Wolfgang

Subjects
Astrophysics - Solar and Stellar Astrophysics
Abstract

Stars with initial masses larger than 8 solar masses undergo substantial mass loss through mechanisms that remain elusive. Unraveling the origins of this mass loss is important for comprehending the evolutionary path of these stars, the type of supernova explosion and whether they become neutron stars or black hole remnants. In 2022 December, RW Cep experienced the Great Dimming in its visible brightness, presenting a unique opportunity to understand mass loss mechanisms. Our previous observations of RW Cep from the CHARA Array, taken during the dimming phase, show a compelling asymmetry in the star images, with a darker zone on the west side of the star indicating presence of dust in front of the star in our line of sight. Here, we present multi-epoch observations from CHARA while the star re-brightened in 2023. We created images using three image reconstruction methods and an analytical model fit. Comparisons of images acquired during the dimming and re-brightening phases reveal remarkable differences. Specifically, the west side of RW Cep, initially obscured during the dimming phase, reappeared during the subsequent re-brightening phase and the measured angular diameter became larger by 8%. We also observed image changes from epoch to epoch while the star is brightening indicating the time evolution of dust in front of the star. We suggest that the dimming of RW Cep was a result from a recent surface mass ejection event, generating a dust cloud that partially obstructed the stellar photosphere., Comment: Accepted by The Astrophysical Journal Letters (ApJL)

Published
2024

46. KeySpace: Public Key Infrastructure Considerations in Interplanetary Networks

Author

Smailes, Joshua, Köhler, Sebastian, Birnbach, Simon, Strohmeier, Martin, and Martinovic, Ivan

Subjects
Computer Science - Cryptography and Security, Computer Science - Networking and Internet Architecture
Abstract

As satellite networks expand to encompass megaconstellations and interplanetary communication, the need for effective Public Key Infrastructure (PKI) becomes increasingly pressing. This paper addresses the challenge of implementing PKI in these complex networks, identifying the essential goals and requirements. We develop a standardized framework for comparing PKI systems across various network topologies, enabling the evaluation of their performance and security. Our results demonstrate that terrestrial PKI techniques can be adapted for use in highly distributed interplanetary networks, achieving efficient low-latency connection establishment and minimizing the impact of attacks through effective revocation mechanisms. This result has significant implications for the design of future satellite networks, as it enables the reuse of existing PKI solutions to provide increased compatibility with terrestrial networks. We evaluate this by building the Deep Space Network Simulator (DSNS), a novel tool for efficiently simulating large space networks. Using DSNS, we conduct comprehensive simulations of connection establishment and key revocation under a range of network topologies and PKI configurations. Furthermore, we propose and evaluate two new configuration options: OCSP Hybrid, and the use of relay nodes as a firewall. Together these minimize the extent of the network an attacker can reach with a compromised key, and reduce the attacker's load on interplanetary relay links., Comment: 14 pages, 9 figures, 7 tables

Published
2024

47. GRANDlib: A simulation pipeline for the Giant Radio Array for Neutrino Detection (GRAND)

Author

GRAND Collaboration, Batista, Rafael Alves, Benoit-Lévy, Aurélien, Bister, Teresa, Bohacova, Martina, Bustamante, Mauricio, Carvalho, Washington, Chen, Yiren, Cheng, LingMei, Chiche, Simon, Colley, Jean-Marc, Correa, Pablo, Laurenciu, Nicoleta Cucu, Dai, Zigao, de Almeida, Rogerio M., de Errico, Beatriz, de Jong, Sijbrand, Neto, João R. T. de Mello, de Vries, Krijn D., Decoene, Valentin, Denton, Peter B., Duan, Bohao, Duan, Kaikai, Engel, Ralph, Erba, William, Fan, Yizhong, Ferrière, Arsène, Gou, QuanBu, Gu, Junhua, Guelfand, Marion, Guo, Jianhua, Guo, Yiqing, Guépin, Claire, Gülzow, Lukas, Haungs, Andreas, Havelka, Matej, He, Haoning, Hivon, Eric, Hu, Hongbo, Huang, Xiaoyuan, Huang, Yan, Huege, Tim, Jiang, Wen, Koirala, Ramesh, Kong, ChuiZheng, Kotera, Kumiko, Köhler, Jelena, Lago, Bruno L., Lai, Zhisen, Coz, Sandra Le, Legrand, François, Leisos, Antonios, Li, Rui, Li, Xingyu, Li, YiFei, Liu, Cheng, Liu, Ruoyu, Liu, Wei, Ma, Pengxiong, Macias, Oscar, Magnard, Frédéric, Marcowith, Alexandre, Martineau-Huynh, Olivier, McKinley, Thomas, Minodier, Paul, Mitra, Pragati, Mostafá, Miguel, Murase, Kohta, Niess, Valentin, Nonis, Stavros, Ogio, Shoichi, Oikonomou, Foteini, Pan, Hongwei, Papageorgiou, Konstantinos, Pierog, Tanguy, Piotrowski, Lech Wiktor, Prunet, Simon, Qian, Xiangli, Roth, Markus, Sako, Takashi, Schoorlemmer, Harm, Szálas-Motesiczky, Dániel, Sławiński, Szymon, Tian, Xishui, Timmermans, Anne, Timmermans, Charles, Tobiska, Petr, Tsirigotis, Apostolos, Tueros, Matías, Vittakis, George, Wang, Hanrui, Wang, Jiale, Wang, Shen, Wang, Xiangyu, Wang, Xu, Wei, Daming, Wei, Feng, Wu, Xiangping, Wu, Xuefeng, Xu, Xin, Xu, Xing, Yang, Fufu, Yang, Lili, Yang, Xuan, Yuan, Qiang, Zarka, Philippe, Zeng, Houdun, Zhang, Chao, Zhang, Jianli, Zhang, Kewen, Zhang, Pengfei, Zhang, Qingchi, Zhang, Songbo, Zhang, Yi, and Zhou, Hao

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, High Energy Physics - Experiment, High Energy Physics - Phenomenology
Abstract

The operation of upcoming ultra-high-energy cosmic-ray, gamma-ray, and neutrino radio-detection experiments, like the Giant Radio Array for Neutrino Detection (GRAND), poses significant computational challenges involving the production of numerous simulations of particle showers and their detection, and a high data throughput. GRANDlib is an open-source software tool designed to meet these challenges. Its primary goal is to perform end-to-end simulations of the detector operation, from the interaction of ultra-high-energy particles, through -- by interfacing with external air-shower simulations -- the ensuing particle shower development and its radio emission, to its detection by antenna arrays and its processing by data-acquisition systems. Additionally, GRANDlib manages the visualization, storage, and retrieval of experimental and simulated data. We present an overview of GRANDlib to serve as the basis of future GRAND analyses., Comment: 11 pages, 9 figures, plus appendices; Matches published version

Published
2024
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48. Analytical Uncertainty-Based Loss Weighting in Multi-Task Learning

Author

Kirchdorfer, Lukas, Elich, Cathrin, Kutsche, Simon, Stuckenschmidt, Heiner, Schott, Lukas, and Köhler, Jan M.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

With the rise of neural networks in various domains, multi-task learning (MTL) gained significant relevance. A key challenge in MTL is balancing individual task losses during neural network training to improve performance and efficiency through knowledge sharing across tasks. To address these challenges, we propose a novel task-weighting method by building on the most prevalent approach of Uncertainty Weighting and computing analytically optimal uncertainty-based weights, normalized by a softmax function with tunable temperature. Our approach yields comparable results to the combinatorially prohibitive, brute-force approach of Scalarization while offering a more cost-effective yet high-performing alternative. We conduct an extensive benchmark on various datasets and architectures. Our method consistently outperforms six other common weighting methods. Furthermore, we report noteworthy experimental findings for the practical application of MTL. For example, larger networks diminish the influence of weighting methods, and tuning the weight decay has a low impact compared to the learning rate.

Published
2024

49. Large-scale cosmic ray anisotropies with 19 years of data from the Pierre Auger Observatory

Author

The Pierre Auger Collaboration, Halim, A. Abdul, Abreu, P., Aglietta, M., Allekotte, I., Cheminant, K. Almeida, Almela, A., Aloisio, R., Alvarez-Muñiz, J., Ambrosone, A., Yebra, J. Ammerman, Anastasi, G. A., Anchordoqui, L., Andrada, B., Dourado, L. Andrade, Andringa, S., Apollonio, L., Aramo, C., Ferreira, P. R. Araújo, Arnone, E., Velázquez, J. C. Arteaga, Assis, P., Avila, G., Avocone, E., Bakalova, A., Barbato, F., Mocellin, A. Bartz, Bellido, J. A., Berat, C., Bertaina, M. E., Bhatta, G., Bianciotto, M., Biermann, P. L., Binet, V., Bismark, K., Bister, T., Biteau, J., Blazek, J., Bleve, C., Blümer, J., Boháčová, M., Boncioli, D., Bonifazi, C., Arbeletche, L. Bonneau, Borodai, N., Brack, J., Orchera, P. G. Brichetto, Briechle, F. L., Bueno, A., Buitink, S., Buscemi, M., Büsken, M., Bwembya, A., Caballero-Mora, K. S., Cabana-Freire, S., Caccianiga, L., Campuzano, F., Caruso, R., Castellina, A., Catalani, F., Cataldi, G., Cazon, L., Cerda, M., Čermáková, B., Cermenati, A., Chinellato, J. A., Chudoba, J., Chytka, L., Clay, R. W., Cerutti, A. C. Cobos, Colalillo, R., Conceição, R., Condorelli, A., Consolati, G., Conte, M., Convenga, F., Santos, D. Correia dos, Costa, P. J., Covault, C. E., Cristinziani, M., Sanchez, C. S. Cruz, Dasso, S., Daumiller, K., Dawson, B. R., de Almeida, R. M., de Errico, B., de Jesús, J., de Jong, S. J., Neto, J. R. T. de Mello, De Mitri, I., de Oliveira, J., Franco, D. de Oliveira, de Palma, F., de Souza, V., De Vito, E., Del Popolo, A., Deligny, O., Denner, N., Deval, L., di Matteo, A., Dobrigkeit, C., D'Olivo, J. C., Mendes, L. M. Domingues, Dorosti, Q., Anjos, J. C. dos, Anjos, R. C. dos, Ebr, J., Ellwanger, F., Emam, M., Engel, R., Epicoco, I., Erdmann, M., Etchegoyen, A., Evoli, C., Falcke, H., Farrar, G., Fauth, A. C., Fehler, T., Feldbusch, F., Fernandes, A., Fick, B., Figueira, J. M., Filip, P., Filipčič, A., Fitoussi, T., Flaggs, B., Fodran, T., Freitas, M., Fujii, T., Fuster, A., Galea, C., García, B., Gaudu, C., Ghia, P. L., Giaccari, U., Gobbi, F., Gollan, F., Golup, G., Berisso, M. Gómez, Vitale, P. F. Gómez, Gongora, J. P., González, J. M., González, N., Góra, D., Gorgi, A., Gottowik, M., Guarino, F., Guedes, G. P., Guido, E., Gülzow, L., Hahn, S., Hamal, P., Hampel, M. R., Hansen, P., Harvey, V. M., Haungs, A., Hebbeker, T., Hojvat, C., Hörandel, J. R., Horvath, P., Hrabovský, M., Huege, T., Insolia, A., Isar, P. G., Janecek, P., Jilek, V., Jurysek, J., Kampert, K. -H., Keilhauer, B., Khakurdikar, A., Covilakam, V. V. Kizakke, Klages, H. O., Kleifges, M., Knapp, F., Köhler, J., Krieger, F., Kubatova, M., Kunka, N., Lago, B. L., Langner, N., de Oliveira, M. A. Leigui, Lema-Capeans, Y., Letessier-Selvon, A., Lhenry-Yvon, I., Lopes, L., Lundquist, J. P., Payeras, A. Machado, Mandat, D., Manning, B. C., Mantsch, P., Mariani, F. M., Mariazzi, A. G., Mariş, I. C., Marsella, G., Martello, D., Martinelli, S., Bravo, O. Martínez, Martins, M. A., Mathes, H. -J., Matthews, J., Matthiae, G., Mayotte, E., Mayotte, S., Mazur, P. O., Medina-Tanco, G., Meinert, J., Melo, D., Menshikov, A., Merx, C., Michal, S., Micheletti, M. I., Miramonti, L., Mollerach, S., Montanet, F., Morejon, L., Mulrey, K., Mussa, R., Namasaka, W. M., Negi, S., Nellen, L., Nguyen, K., Nicora, G., Niechciol, M., Nitz, D., Nosek, D., Novotny, V., Nožka, L., Nucita, A., Núñez, L. A., Oliveira, C., Palatka, M., Pallotta, J., Panja, S., Parente, G., Paulsen, T., Pawlowsky, J., Pech, M., Pękala, J., Pelayo, R., Pelgrims, V., Pereira, L. A. S., Martins, E. E. Pereira, Bertolli, C. Pérez, Perrone, L., Petrera, S., Petrucci, C., Pierog, T., Pimenta, M., Platino, M., Pont, B., Pothast, M., Shahvar, M. Pourmohammad, Privitera, P., Prouza, M., Querchfeld, S., Rautenberg, J., Ravignani, D., Akim, J. V. Reginatto, Reuzki, A., Ridky, J., Riehn, F., Risse, M., Rizi, V., Rodriguez, E., Rojo, J. Rodriguez, Roncoroni, M. J., Rossoni, S., Roth, M., Roulet, E., Rovero, A. C., Saftoiu, A., Saharan, M., Salamida, F., Salazar, H., Salina, G., Sampathkumar, P., Gomez, J. D. Sanabria, Sánchez, F., Santos, E. M., Santos, E., Sarazin, F., Sarmento, R., Sato, R., Schäfer, C. M., Scherini, V., Schieler, H., Schimassek, M., Schimp, M., Schmidt, D., Scholten, O., Schoorlemmer, H., Schovánek, P., Schröder, F. G., Schulte, J., Schulz, T., Sciutto, S. J., Scornavacche, M., Sedoski, A., Segreto, A., Sehgal, S., Shivashankara, S. U., Sigl, G., Simkova, K., Simon, F., Šmída, R., Sommers, P., Squartini, R., Stadelmaier, M., Stanič, S., Stasielak, J., Stassi, P., Strähnz, S., Straub, M., Suomijärvi, T., Supanitsky, A. D., Svozilikova, Z., Szadkowski, Z., Tairli, F., Tapia, A., Taricco, C., Timmermans, C., Tkachenko, O., Tobiska, P., Peixoto, C. J. Todero, Tomé, B., Torrès, Z., Travaini, A., Travnicek, P., Tueros, M., Unger, M., Uzeiroska, R., Vaclavek, L., Vacula, M., Galicia, J. F. Valdés, Valore, L., Varela, E., Vašíčková, V., Vásquez-Ramírez, A., Veberič, D., Quispe, I. D. Vergara, Verzi, V., Vicha, J., Vink, J., Vorobiov, S., Watanabe, C., Watson, A. A., Weindl, A., Weitz, M., Wiencke, L., Wilczyński, H., Wittkowski, D., Wundheiler, B., Yue, B., Yushkov, A., Zapparrata, O., Zas, E., Zavrtanik, D., and Zavrtanik, M.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

Results are presented for the measurement of large-scale anisotropies in the arrival directions of ultra-high-energy cosmic rays detected at the Pierre Auger Observatory during 19 years of operation, prior to AugerPrime, the upgrade of the Observatory. The 3D dipole amplitude and direction are reconstructed above $4\,$EeV in four energy bins. Besides the established dipolar anisotropy in right ascension above $8\,$EeV, the Fourier amplitude of the $8$ to $16\,$EeV energy bin is now also above the $5\sigma$ discovery level. No time variation of the dipole moment above $8\,$EeV is found, setting an upper limit to the rate of change of such variations of $0.3\%$ per year at the $95\%$ confidence level. Additionally, the results for the angular power spectrum are shown, demonstrating no other statistically significant multipoles. The results for the equatorial dipole component down to $0.03\,$EeV are presented, using for the first time a data set obtained with a trigger that has been optimized for lower energies. Finally, model predictions are discussed and compared with observations, based on two source emission scenarios obtained in the combined fit of spectrum and composition above $0.6\,$EeV., Comment: Minor typo in table 4 corrected

Published
2024
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50. Measurement of the electric potential and the magnetic field in the shifted analysing plane of the KATRIN experiment

Author

Aker, M., Batzler, D., Beglarian, A., Behrens, J., Beisenkötter, J., Biassoni, M., Bieringer, B., Biondi, Y., Block, F., Bobien, S., Böttcher, M., Bornschein, B., Bornschein, L., Caldwell, T. S., Carminati, M., Chatrabhuti, A., Chilingaryan, S., Daniel, B. A., Debowski, K., Descher, M., Barrero, D. Díaz, Doe, P. J., Dragoun, O., Drexlin, G., Edzards, F., Eitel, K., Ellinger, E., Engel, R., Enomoto, S., Felden, A., Fengler, C., Fiorini, C., Formaggio, J. A., Forstner, C., Fränkle, F. M., Gauda, K., Gavin, A. S., Gil, W., Glück, F., Grössle, R., Gumbsheimer, R., Hannen, V., Hasselmann, L., Haußmann, N., Helbing, K., Heyns, S., Hickford, S., Hiller, R., Hillesheimer, D., Hinz, D., Höhn, T., Huber, A., Jansen, A., Karl, C., Kellerer, J., Khosonthongkee, K., Köhler, C., Köllenberger, L., Kopmann, A., Kovač, N., Krause, H., La Cascio, L., Lasserre, T., Lauer, J., Le, T. L., Lebeda, O., Lehnert, B., Li, G., Lokhov, A., Machatschek, M., Mark, M., Marsteller, A., Martin, E. L., McMichael, K., Melzer, C., Mertens, S., Mohanty, S., Mostafa, J., Müller, K., Nava, A., Neumann, H., Niemes, S., Parno, D. S., Pavan, M., Pinsook, U., Poon, A. W. P., Poyato, J. M. L., Pozzi, S., Priester, F., Ráliš, J., Ramachandran, S., Robertson, R. G. H., Rodenbeck, C., Röllig, M., Sack, R., Saenz, A., Salomon, R., Schäfer, P., Schlösser, M., Schlösser, K., Schlüter, L., Schneidewind, S., Schrank, M., Schürmann, J., Schütz, A. K., Schwemmer, A., Schwenck, A., Šefčík, M., Siegmann, D., Simon, F., Spanier, F., Spreng, D., Sreethawong, W., Steidl, M., Štorek, J., Stribl, X., Sturm, M., Suwonjandee, N., Jerome, N. Tan, Telle, H. H., Thorne, L. A., Thümmler, T., Titov, N., Tkachev, I., Urban, K., Valerius, K., Vénos, D., Weinheimer, C., Welte, S., Wendel, J., Wiesinger, C., Wilkerson, J. F., Wolf, J., Wüstling, S., Wydra, J., Xu, W., Zadorozhny, S., and Zeller, G.

Subjects
Physics - Instrumentation and Detectors
Abstract

The projected sensitivity of the effective electron neutrino-mass measurement with the KATRIN experiment is below 0.3 eV (90 % CL) after five years of data acquisition. The sensitivity is affected by the increased rate of the background electrons from KATRIN's main spectrometer. A special shifted-analysing-plane (SAP) configuration was developed to reduce this background by a factor of two. The complex layout of electromagnetic fields in the SAP configuration requires a robust method of estimating these fields. We present in this paper a dedicated calibration measurement of the fields using conversion electrons of gaseous $^\mathrm{83m}$Kr, which enables the neutrino-mass measurements in the SAP configuration., Comment: 19 pages, 11 figures

Published
2024

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