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542,746 results on '"Nielsen, A"'

4. Kapitel 1 Alkoholliberalisme eller folkesundhedspolitik? Iscenesættelsen af en liberal dansk alkoholkultur under forbrugseskalationen i 1970’erne

Author

Vallentin-Holbech, Lotte, Thomsen, Kristine Rømer, Schrøder, Sidsel, Petersen, Margit Anne, Pedersen, Siri Mørch, Nygaard-Christensen, Maj, Nielsen, Anette Søgaard, Klausen, Søren Harnow, Hunt, Geoffrey, Ewing, Sarah W. Feldstein, Eriksen, Sidsel, Engelsen, Søren, Emiliussen, Jakob, Ekholm, Ola, Christiansen, Regina, Borup, Sofie Hector, Bogren, Alexandra, Bloomfield, Kim, Bjerge, Bagga, and Bach, Jonas Strandholdt

Published
2023

5. Bagside

Author

Vallentin-Holbech, Lotte, Thomsen, Kristine Rømer, Schrøder, Sidsel, Petersen, Margit Anne, Pedersen, Siri Mørch, Nygaard-Christensen, Maj, Nielsen, Anette Søgaard, Klausen, Søren Harnow, Hunt, Geoffrey, Ewing, Sarah W. Feldstein, Eriksen, Sidsel, Engelsen, Søren, Emiliussen, Jakob, Ekholm, Ola, Christiansen, Regina, Borup, Sofie Hector, Bogren, Alexandra, Bloomfield, Kim, Bjerge, Bagga, and Bach, Jonas Strandholdt

Published
2023

6. Kapitel 7 Unges brug af alkohol under covid-19-pandemien i Danmark

Author

Vallentin-Holbech, Lotte, Thomsen, Kristine Rømer, Schrøder, Sidsel, Petersen, Margit Anne, Pedersen, Siri Mørch, Nygaard-Christensen, Maj, Nielsen, Anette Søgaard, Klausen, Søren Harnow, Hunt, Geoffrey, Ewing, Sarah W. Feldstein, Eriksen, Sidsel, Engelsen, Søren, Emiliussen, Jakob, Ekholm, Ola, Christiansen, Regina, Borup, Sofie Hector, Bogren, Alexandra, Bloomfield, Kim, Bjerge, Bagga, and Bach, Jonas Strandholdt

Published
2023

7. Om forfatterne

Author

Vallentin-Holbech, Lotte, Thomsen, Kristine Rømer, Schrøder, Sidsel, Petersen, Margit Anne, Pedersen, Siri Mørch, Nygaard-Christensen, Maj, Nielsen, Anette Søgaard, Klausen, Søren Harnow, Hunt, Geoffrey, Ewing, Sarah W. Feldstein, Eriksen, Sidsel, Engelsen, Søren, Emiliussen, Jakob, Ekholm, Ola, Christiansen, Regina, Borup, Sofie Hector, Bogren, Alexandra, Bloomfield, Kim, Bjerge, Bagga, and Bach, Jonas Strandholdt

Published
2023

8. Titelside, Kolofon

Author

Vallentin-Holbech, Lotte, Thomsen, Kristine Rømer, Schrøder, Sidsel, Petersen, Margit Anne, Pedersen, Siri Mørch, Nygaard-Christensen, Maj, Nielsen, Anette Søgaard, Klausen, Søren Harnow, Hunt, Geoffrey, Ewing, Sarah W. Feldstein, Eriksen, Sidsel, Engelsen, Søren, Emiliussen, Jakob, Ekholm, Ola, Christiansen, Regina, Borup, Sofie Hector, Bogren, Alexandra, Bloomfield, Kim, Bjerge, Bagga, and Bach, Jonas Strandholdt

Published
2023

9. Kapitel 6 Seksuelle grænser i nattelivet

Author

Vallentin-Holbech, Lotte, Thomsen, Kristine Rømer, Schrøder, Sidsel, Petersen, Margit Anne, Pedersen, Siri Mørch, Nygaard-Christensen, Maj, Nielsen, Anette Søgaard, Klausen, Søren Harnow, Hunt, Geoffrey, Ewing, Sarah W. Feldstein, Eriksen, Sidsel, Engelsen, Søren, Emiliussen, Jakob, Ekholm, Ola, Christiansen, Regina, Borup, Sofie Hector, Bogren, Alexandra, Bloomfield, Kim, Bjerge, Bagga, and Bach, Jonas Strandholdt

Published
2023

10. Kapitel 4 Alkohol og grønlandske udsattemiljøer i Danmark

Author

Vallentin-Holbech, Lotte, Thomsen, Kristine Rømer, Schrøder, Sidsel, Petersen, Margit Anne, Pedersen, Siri Mørch, Nygaard-Christensen, Maj, Nielsen, Anette Søgaard, Klausen, Søren Harnow, Hunt, Geoffrey, Ewing, Sarah W. Feldstein, Eriksen, Sidsel, Engelsen, Søren, Emiliussen, Jakob, Ekholm, Ola, Christiansen, Regina, Borup, Sofie Hector, Bogren, Alexandra, Bloomfield, Kim, Bjerge, Bagga, and Bach, Jonas Strandholdt

Published
2023

11. Kapitel 5 Alkohol som stigma, nydelse og katalysator for fællesskab og konflikter blandt udsatte alkoholbrugere i et drikkeskur

Author

Vallentin-Holbech, Lotte, Thomsen, Kristine Rømer, Schrøder, Sidsel, Petersen, Margit Anne, Pedersen, Siri Mørch, Nygaard-Christensen, Maj, Nielsen, Anette Søgaard, Klausen, Søren Harnow, Hunt, Geoffrey, Ewing, Sarah W. Feldstein, Eriksen, Sidsel, Engelsen, Søren, Emiliussen, Jakob, Ekholm, Ola, Christiansen, Regina, Borup, Sofie Hector, Bogren, Alexandra, Bloomfield, Kim, Bjerge, Bagga, and Bach, Jonas Strandholdt

Published
2023

12. Kapitel 3 Ældrevelfærd og alkohol: En vanskelig cocktail

Author

Vallentin-Holbech, Lotte, Thomsen, Kristine Rømer, Schrøder, Sidsel, Petersen, Margit Anne, Pedersen, Siri Mørch, Nygaard-Christensen, Maj, Nielsen, Anette Søgaard, Klausen, Søren Harnow, Hunt, Geoffrey, Ewing, Sarah W. Feldstein, Eriksen, Sidsel, Engelsen, Søren, Emiliussen, Jakob, Ekholm, Ola, Christiansen, Regina, Borup, Sofie Hector, Bogren, Alexandra, Bloomfield, Kim, Bjerge, Bagga, and Bach, Jonas Strandholdt

Published
2023

13. DEL 2: ALKOHOLENS ROLLE PÅ PLEJEHJEM OG I UDSATTEMILJØER

Author

Vallentin-Holbech, Lotte, Thomsen, Kristine Rømer, Schrøder, Sidsel, Petersen, Margit Anne, Pedersen, Siri Mørch, Nygaard-Christensen, Maj, Nielsen, Anette Søgaard, Klausen, Søren Harnow, Hunt, Geoffrey, Ewing, Sarah W. Feldstein, Eriksen, Sidsel, Engelsen, Søren, Emiliussen, Jakob, Ekholm, Ola, Christiansen, Regina, Borup, Sofie Hector, Bogren, Alexandra, Bloomfield, Kim, Bjerge, Bagga, and Bach, Jonas Strandholdt

Published
2023

14. DEL 3: UNGES BRUG AF ALKOHOL

Author

Vallentin-Holbech, Lotte, Thomsen, Kristine Rømer, Schrøder, Sidsel, Petersen, Margit Anne, Pedersen, Siri Mørch, Nygaard-Christensen, Maj, Nielsen, Anette Søgaard, Klausen, Søren Harnow, Hunt, Geoffrey, Ewing, Sarah W. Feldstein, Eriksen, Sidsel, Engelsen, Søren, Emiliussen, Jakob, Ekholm, Ola, Christiansen, Regina, Borup, Sofie Hector, Bogren, Alexandra, Bloomfield, Kim, Bjerge, Bagga, and Bach, Jonas Strandholdt

Published
2023

15. Kapitel 2 I en liga for sig: Den sociale epidemiologi ved dansk alkoholbrug i et internationalt perspektiv

Author

Vallentin-Holbech, Lotte, Thomsen, Kristine Rømer, Schrøder, Sidsel, Petersen, Margit Anne, Pedersen, Siri Mørch, Nygaard-Christensen, Maj, Nielsen, Anette Søgaard, Klausen, Søren Harnow, Hunt, Geoffrey, Ewing, Sarah W. Feldstein, Eriksen, Sidsel, Engelsen, Søren, Emiliussen, Jakob, Ekholm, Ola, Christiansen, Regina, Borup, Sofie Hector, Bogren, Alexandra, Bloomfield, Kim, Bjerge, Bagga, and Bach, Jonas Strandholdt

Published
2023

16. DEL 1: ALKOHOLFORBRUG OG -KULTUR

Author

Vallentin-Holbech, Lotte, Thomsen, Kristine Rømer, Schrøder, Sidsel, Petersen, Margit Anne, Pedersen, Siri Mørch, Nygaard-Christensen, Maj, Nielsen, Anette Søgaard, Klausen, Søren Harnow, Hunt, Geoffrey, Ewing, Sarah W. Feldstein, Eriksen, Sidsel, Engelsen, Søren, Emiliussen, Jakob, Ekholm, Ola, Christiansen, Regina, Borup, Sofie Hector, Bogren, Alexandra, Bloomfield, Kim, Bjerge, Bagga, and Bach, Jonas Strandholdt

Published
2023

17. Forord

Author

Vallentin-Holbech, Lotte, Thomsen, Kristine Rømer, Schrøder, Sidsel, Petersen, Margit Anne, Pedersen, Siri Mørch, Nygaard-Christensen, Maj, Nielsen, Anette Søgaard, Klausen, Søren Harnow, Hunt, Geoffrey, Ewing, Sarah W. Feldstein, Eriksen, Sidsel, Engelsen, Søren, Emiliussen, Jakob, Ekholm, Ola, Christiansen, Regina, Borup, Sofie Hector, Bogren, Alexandra, Bloomfield, Kim, Bjerge, Bagga, and Bach, Jonas Strandholdt

Published
2023

18. Introduktion

Author

Vallentin-Holbech, Lotte, Thomsen, Kristine Rømer, Schrøder, Sidsel, Petersen, Margit Anne, Pedersen, Siri Mørch, Nygaard-Christensen, Maj, Nielsen, Anette Søgaard, Klausen, Søren Harnow, Hunt, Geoffrey, Ewing, Sarah W. Feldstein, Eriksen, Sidsel, Engelsen, Søren, Emiliussen, Jakob, Ekholm, Ola, Christiansen, Regina, Borup, Sofie Hector, Bogren, Alexandra, Bloomfield, Kim, Bjerge, Bagga, and Bach, Jonas Strandholdt

Published
2023

19. Assessing the Efficacy of Classical and Deep Neuroimaging Biomarkers in Early Alzheimer's Disease Diagnosis

Author

Nielsen, Milla E., Nielsen, Mads, and Ghazi, Mostafa Mehdipour

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

Alzheimer's disease (AD) is the leading cause of dementia, and its early detection is crucial for effective intervention, yet current diagnostic methods often fall short in sensitivity and specificity. This study aims to detect significant indicators of early AD by extracting and integrating various imaging biomarkers, including radiomics, hippocampal texture descriptors, cortical thickness measurements, and deep learning features. We analyze structural magnetic resonance imaging (MRI) scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts, utilizing comprehensive image analysis and machine learning techniques. Our results show that combining multiple biomarkers significantly improves detection accuracy. Radiomics and texture features emerged as the most effective predictors for early AD, achieving AUCs of 0.88 and 0.72 for AD and MCI detection, respectively. Although deep learning features proved to be less effective than traditional approaches, incorporating age with other biomarkers notably enhanced MCI detection performance. Additionally, our findings emphasize the continued importance of classical imaging biomarkers in the face of modern deep-learning approaches, providing a robust framework for early AD diagnosis., Comment: SPIE Medical Imaging (MI25)

Published
2024

20. Methodological Improvements to the Public Libraries Survey

Author

Institute of Museum and Library Services (IMLS), E. Nielsen, and M. Pelczar

Abstract

This research brief describes recent methodological initiatives with the Public Libraries Survey. It describes how starting with the 2022 data, Institute of Museum and Library Services (IMLS) updated geographic identifiers to better align Census Bureau geography types with the library's legal service area, with the goal of enabling data users to link PLS data with other geographical data more easily (e.g., the American Community Survey) and is assessing the feasibility of outlet-level data collection over the next year.

Published
2024

21. A trade-off between hydrodynamic performance and morphological bias limits the evolution of symmetric lattice animal wings

Author

Arnbjerg-Nielsen, Sif F., Biviano, Matthew D., Cazaubiel, Annette, Carlson, Alexander Schødt Andreas, and Jensen, Kaare H.

Subjects
Physics - Biological Physics, Physics - Fluid Dynamics
Abstract

Bristled and membranous insect wings have co-evolved despite apparently serving the same functionality. We emulate flight physics using an automated free-fall experiment to better understand how and why several distinct wing forms may have developed. Biomimetic two-dimensional lattice animals were laser cut from a continuous sheet of paper, and their descent in a settling tank was tracked using a camera. Data from 31 generic symmetric polyominos (1,692 experiments) reveal that morphology impacts the drag coefficient $C_D$ and hence flight efficiency. Some polyominos rapidly sediment while others remain suspended for longer. Positioning the search for an optimal shape within an evolutionary context, we relinquished control of the automated setup to a genetic algorithm. Hereditary information passes between generations in proportion to fitness and is augmented by rare mutations. High-performing morphologies are observed, but {experimental repeats do} not re-evolve the same shapes. Adaptation rates also differ if the selective pressure is reversed from suspension to sedimentation. Our data (50,086 experiments) reveal several physical sources of indeterminism in the simulated natural selection process, including fluid flow variability and morphological entropy. This provides experimental support for the idea that the lack of convergence in insect wing shape may partly reflect the competition between fitness and entropy, making it nearly impossible to achieve unique optimal forms.

Published
2024

22. Scholarly Wikidata: Population and Exploration of Conference Data in Wikidata using LLMs

Author

Mihindukulasooriya, Nandana, Tiwari, Sanju, Dobriy, Daniil, Nielsen, Finn Årup, Chhetri, Tek Raj, and Polleres, Axel

Subjects
Computer Science - Digital Libraries, Computer Science - Artificial Intelligence, Computer Science - Information Retrieval
Abstract

Several initiatives have been undertaken to conceptually model the domain of scholarly data using ontologies and to create respective Knowledge Graphs. Yet, the full potential seems unleashed, as automated means for automatic population of said ontologies are lacking, and respective initiatives from the Semantic Web community are not necessarily connected: we propose to make scholarly data more sustainably accessible by leveraging Wikidata's infrastructure and automating its population in a sustainable manner through LLMs by tapping into unstructured sources like conference Web sites and proceedings texts as well as already existing structured conference datasets. While an initial analysis shows that Semantic Web conferences are only minimally represented in Wikidata, we argue that our methodology can help to populate, evolve and maintain scholarly data as a community within Wikidata. Our main contributions include (a) an analysis of ontologies for representing scholarly data to identify gaps and relevant entities/properties in Wikidata, (b) semi-automated extraction -- requiring (minimal) manual validation -- of conference metadata (e.g., acceptance rates, organizer roles, programme committee members, best paper awards, keynotes, and sponsors) from websites and proceedings texts using LLMs. Finally, we discuss (c) extensions to visualization tools in the Wikidata context for data exploration of the generated scholarly data. Our study focuses on data from 105 Semantic Web-related conferences and extends/adds more than 6000 entities in Wikidata. It is important to note that the method can be more generally applicable beyond Semantic Web-related conferences for enhancing Wikidata's utility as a comprehensive scholarly resource. Source Repository: https://github.com/scholarly-wikidata/ DOI: https://doi.org/10.5281/zenodo.10989709 License: Creative Commons CC0 (Data), MIT (Code), Comment: 17 pages, accepted at EKAW-24

Published
2024

23. NGTS-33b: A Young Super-Jupiter Hosted by a Fast Rotating Massive Hot Star

Author

Alves, Douglas R., Jenkins, James S., Vines, Jose I., Battley, Matthew P., Lendl, Monika, Bouchy, François, Nielsen, Louise D., Gill, Samuel, Moyano, Maximiliano, Anderson, D. R., Burleigh, Matthew R., Casewell, Sarah L., Goad, Michael R., Hawthorn, Faith, Kendall, Alicia, McCormac, James, Osborn, Ares, Smith, Alexis M. S., Udry, Stephane, Wheatley, Peter J., Saha, Suman, Parc, Lena, Nigioni, Arianna, Apergis, Ioannis, and Ramsay, Gavin

Subjects
Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Solar and Stellar Astrophysics
Abstract

In the last few decades planet search surveys have been focusing on solar type stars, and only recently the high-mass regimes. This is mostly due to challenges arising from the lack of instrumental precision, and more importantly, the inherent active nature of fast rotating massive stars. Here we report NGTS-33b (TOI-6442b), a super-Jupiter planet with mass, radius and orbital period of 3.6 $\pm$ 0.3 M$_{\rm jup}$, 1.64 $\pm$ 0.07 R$_{\rm jup}$ and $2.827972 \pm 0.000001$ days, respectively. The host is a fast rotating ($0.6654 \pm 0.0006$ day) and hot (T$_{\rm eff}$ = 7437 $\pm$ 72 K) A9V type star, with a mass and radius of 1.60 $\pm$ 0.11 M$_{\odot}$ and 1.47 $\pm$ 0.06 R$_{\odot}$, respectively. Planet structure and Gyrochronology models shows that NGTS-33 is also very young with age limits of 10-50 Myr. In addition, membership analysis points towards the star being part of the Vela OB2 association, which has an age of $\sim$ 20-35 Myr, thus providing further evidences about the young nature of NGTS-33. Its low bulk density of 0.19$\pm$0.03 g cm$^{-3}$ is 13$\%$ smaller than expected when compared to transiting hot Jupiters with similar masses. Such cannot be solely explained by its age, where an up to 15$\%$ inflated atmosphere is expected from planet structure models. Finally, we found that its emission spectroscopy metric is similar to JWST community targets, making the planet an interesting target for atmospheric follow-up. Therefore, NGTS-33b's discovery will not only add to the scarce population of young, massive and hot Jupiters, but will also help place further strong constraints on current formation and evolution models for such planetary systems., Comment: 19 pages, 17 figures, 7 tables, accepted for publication in MNRAS

Published
2024

24. When are 1.58 bits enough? A Bottom-up Exploration of BitNet Quantization

Author

Nielsen, Jacob, Galke, Lukas, and Schneider-Kamp, Peter

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

Contemporary machine learning models, such as language models, are powerful, but come with immense resource requirements both at training and inference time. It has been shown that decoder-only language models can be trained to a competitive state with ternary weights (1.58 bits per weight), facilitating efficient inference. Here, we start our exploration with non-transformer model architectures, investigating 1.58-bit training for multi-layer perceptrons and graph neural networks. Then, we explore 1.58-bit training in other transformer-based language models, namely encoder-only and encoder-decoder models. Our results show that in all of these settings, 1.58-bit training is on par with or sometimes even better than the standard 32/16-bit models., Comment: 10 pages, 2 tables, 6 figures

Published
2024

25. Remarkable Scale Relation, Approximate SU(5), Fluctuating Lattice

Author

Nielsen, Holger Bech

Subjects
High Energy Physics - Phenomenology
Abstract

We discuss a series of 8 energy scales, some of which just speculated by ourselves, and fit the logarithms of these energies as a straight line versus a quantity related to the dimensionalities of action terms in a way to be defined in the article. These terms in the action are related to the energy scales in question. So e.g. the dimensionality of Einstein Hilbert action coefficient is one related to the Planck scale. In fact we suppose in the cases described with quantum field theory, that there is for each of our energy scales a pair of associated terms in the Lagrangian density, one "kinetic" and one "mass- or current" term. We use for our plotting of the energy scales the ratio of the dimensionality of say the "non-kinetic" to the dimensionality of the "kinetic" one The explanation for our phenomenological finding that the logarithm of the energies depend as a straight line on the dimensionality defined integer $q$, we give as an ontological - i.e. it really exists in nature in our model -"fluctuating lattice" with a very broad distribution of say the link size $a$.We take it Gaussian in the logarithm, $\ln(a)$. A fluctuating lattice is very natural in a theory with general relativity, since it corresponds to fluctuations in the gauge d.o.f. of general relativity. Intriguing are the lowest ones of our energy scales, being by us not described by quantum field theory as the other ones, but by actions for single particle or single string respectivily, because the string scale fits well with hadronic strings, and the particle scale is presumably the mass scale of Standard Model group monopoles, a bound state of a couple of which might be the dimuon resonance (or statistical fluctuation) found in LHC with mass 28 GeV.

Published
2024

26. Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning

Author

Celikkanat, Abdulkadir, Masegosa, Andres R., and Nielsen, Thomas D.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computational Engineering, Finance, and Science, Quantitative Biology - Genomics
Abstract

Obtaining effective representations of DNA sequences is crucial for genome analysis. Metagenomic binning, for instance, relies on genome representations to cluster complex mixtures of DNA fragments from biological samples with the aim of determining their microbial compositions. In this paper, we revisit k-mer-based representations of genomes and provide a theoretical analysis of their use in representation learning. Based on the analysis, we propose a lightweight and scalable model for performing metagenomic binning at the genome read level, relying only on the k-mer compositions of the DNA fragments. We compare the model to recent genome foundation models and demonstrate that while the models are comparable in performance, the proposed model is significantly more effective in terms of scalability, a crucial aspect for performing metagenomic binning of real-world datasets., Comment: Accepted to the Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024)

Published
2024

27. Decidability Issues for Petri Nets -- a survey

Author

Esparza, Javier and Nielsen, Mogens

Subjects
Computer Science - Formal Languages and Automata Theory, Computer Science - Distributed, Parallel, and Cluster Computing, F.1.1, F.1.2
Abstract

We survey 25 years of research on decidability issues for Petri nets. We collect results on the decidability of important properties, equivalence notions, and temporal logics.

Published
2024

28. Bireflectionality in the commutator subgroup of a finite orthogonal group

Author

Nielsen, Klaus

Subjects
Mathematics - Group Theory, 15A15, 15F10
Abstract

We classify the bireflections (products of 2 involutions) in the commutator subgroup G an orthogonal group O(V) over a finite field GF(q) of characteristic not 2. We show that every element of G is a bireflection if it is reversible (conjugate to its inverse in G), except when $q \equiv 3 \mod 4, \dim V \equiv 2 \mod 4$ and $V$ is hyperbolic. We also classify the reversible elements of G., Comment: 11 pages

Published
2024

29. Earthquakes big and small: same physics, different boundary conditions

Author

Nielsen, Stefan

Subjects
Physics - Geophysics
Abstract

Self-similarity indicates that large and small earthquakes share the same physics, where all variables scale with rupture length $L$. Here I show that rupture tip acceleration during the start of dynamic rupture (break-out phase) is also self-similar, scaling with $L_c$ in space and $L_c/C_{lim}$ in time (where $L_c$ is the breakout patch length and $C_{lim}$ the limiting rupture velocity in the subsonic regime). Rupture acceleration in the breakout phase is slower for larger initial breakout patches $L_c$. Because small faults cannot host large breakout patches, a large and slower initial breakout may be indicative of a potentially large final earthquake magnitude. Initial moment rate $\dot{M}_o$ also grows slower for larger $L_c$, therefore it may reflect fault dimensions and carry a probabilistic forecast of magnitude as suggested in some Early Warning studies. This result does not violate causality and is fully compatible with the shared fundamental, self-similar physics across all the magnitude spectrum., Comment: 22 pages including supplementary information at bottom of article, 12 figures

Published
2024

30. Danoliteracy of Generative, Large Language Models

Author

Holm, Søren Vejlgaard, Hansen, Lars Kai, and Nielsen, Martin Carsten

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, I.2.7
Abstract

The language technology moonshot moment of Generative, Large Language Models (GLLMs) was not limited to English: These models brought a surge of technological applications, investments and hype to low-resource languages as well. However, the capabilities of these models in languages such as Danish were until recently difficult to verify beyond qualitative demonstrations due to a lack of applicable evaluation corpora. We present a GLLM benchmark to evaluate Danoliteracy, a measure of Danish language and cultural competency, across eight diverse scenarios such Danish citizenship tests and abstractive social media question answering. This limited-size benchmark is found to produce a robust ranking that correlates to human feedback at $\rho \sim 0.8$ with GPT-4 and Claude Opus models achieving the highest rankings. Analyzing these model results across scenarios, we find one strong underlying factor explaining $95\%$ of scenario performance variance for GLLMs in Danish, suggesting a $g$ factor of model consistency in language adaption., Comment: 16 pages, 13 figures, submitted to: NoDaLiDa/Baltic-HLT 2025

Published
2024

31. Quantitative mapping of smooth topographic landscapes produced by thermal scanning-probe lithography

Author

Sørensen, Camilla H., Nielsen, Magnus V., Linde, Sander J., Nguyen, Duc Hieu, Iversen, Christoffer E., Jensen, Robert, Raza, Søren, Bøggild, Peter, Booth, Timothy J., and Lassaline, Nolan

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science
Abstract

Scanning probe microscopy (SPM) is a powerful technique for mapping nanoscale surface properties through tip-sample interactions. Thermal scanning-probe lithography (tSPL) is an advanced SPM variant that uses a silicon tip on a heated cantilever to sculpt and measure polymer films with nanometer precision. The surfaces produced by tSPL-smooth topographic landscapes-allow mathematically defined contours to be fabricated on the nanoscale, enabling sophisticated functionalities for photonic, electronic, chemical, and biological technologies. Evaluating the physical effects of a landscape requires fitting arbitrary mathematical functions to SPM datasets, however, this capability does not exist in standard analysis programs. Here, we provide an open-source software package (FunFit) to fit analytical functions to SPM data and develop a fabrication and characterization protocol based on this analysis. We demonstrate the benefit of this approach by patterning periodic and quasiperiodic landscapes in a polymer resist with tSPL, which we transfer to hexagonal boron nitride (hBN) flakes with high fidelity via reactive-ion etching. The topographic landscapes in polymers and hBN are measured with tSPL and atomic force microscopy (AFM), respectively. Within the FunFit program, the datasets are corrected for artefacts, fit with analytical functions, and compared, providing critical feedback on the fabrication procedure. Beyond application to tSPL, this protocol can improve analysis, reproducibility, and process development for a broad range of SPM experiments. The protocol can be performed within a working day by an inexperienced user, where fabrication and characterization take a few hours and software analysis takes a few minutes.

Published
2024

32. Architectural Solutions for High-Speed Data Processing Demands of CERN LHC Detectors with FPGA and High-Level Synthesis

Author

Devadze, Sergei, Nielsen, Christine Elizabeth, Mihhailov, Dmitri, and Ellervee, Peeter

Subjects
Computer Science - Hardware Architecture, Physics - Instrumentation and Detectors
Abstract

The planned high-luminosity upgrade of the Large Hadron Collider (LHC) at CERN will bring much higher data rates that are far above the capabilities of currently installed software-based data processing systems. Therefore, new methods must be used to facilitate on-the-fly extraction of scientifically significant information from the immense flow of data produced by LHC particle detectors. This paper focuses on implementation of a tau lepton triggering algorithm in FPGA. Due to the algorithm's complexity and strict technical requirements, its implementation in FPGA fabric becomes a particularly challenging task. The paper presents a study of algorithm development with the help of High-Level Synthesis (HLS) technique that can generate hardware description from C++ code. Various architectural solutions and optimizations that were tried out during the design architecture exploration process are also discussed in the paper., Comment: 7 pages, 6 figures, 2024 IEEE Nordic Circuits and Systems Conference (NORCAS)

Published
2024

33. Search for gravitational waves emitted from SN 2023ixf

Author

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abouelfettouh, I., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Agarwal, D., Agathos, M., Abchouyeh, M. Aghaei, Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Argianas, L., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Attadio, F., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Baier, J. G., Baiotti, L., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartoletti, A. M., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bates, D. E., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Baynard II, P. A., Bazzan, M., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blagg, L. A., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boudon, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brandt, J., Braun, I., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, B. C., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cáceres-Barbosa, V., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannon, K. C., Cao, H., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, J. C. L., Chan, M., Chandra, K., Chang, R. -J., Chao, S., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, Y., Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Cheung, S. Y., Chiadini, F., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chugh, P., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciolfi, R., Clara, F., Clark, J. A., Clarke, J., Clarke, T. A., Clearwater, P., Clesse, S., Coccia, E., Codazzo, E., Cohadon, P. -F., Colace, S., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Connolly, G., Conti, L., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Crook, S., Crouch, R., Csizmazia, J., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Pra, S. Dal, Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Davari, N., Dave, I., Davenport, A., Davier, M., Davies, T. F., Davis, D., Davis, L., Davis, M. C., Davis, P. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., DePergola, N., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhani, A., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Dominguez, D., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Ducoin, J. -G., Dunn, L., Dupletsa, U., D'Urso, D., Duval, H., Duverne, P. -A., Dwyer, S. E., Eassa, C., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Einsle, H., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Eleveld, R. M., Emma, M., Endo, K., Engl, A. J., Enloe, E., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fairhurst, S., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Felicetti, R., Fenyvesi, E., Ferguson, D. L., Ferraiuolo, S., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. S., Floden, E., Foley, E. M., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franceschetti, K., Franchini, N., Frasca, S., Frasconi, F., Mascioli, A. Frattale, Frei, Z., Freise, A., Freitas, O., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fuentes-Garcia, M., Fujii, S., Fujimori, T., Fulda, P., Fyffe, M., Gadre, B., Gair, J. R., Galaudage, S., Galdi, V., Gallagher, H., Gallardo, S., Gallego, B., Gamba, R., Gamboa, A., Ganapathy, D., Ganguly, A., Garaventa, B., García-Bellido, J., Núñez, C. García, García-Quirós, C., Gardner, J. W., Gardner, K. A., Gargiulo, J., Garron, A., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., Gennari, V., George, J., George, R., Gerberding, O., Gergely, L., Ghosh, Archisman, Ghosh, Sayantan, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Suprovo, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gibson, D. T., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Glotin, F., Godfrey, J., Godwin, P., Goebbels, N. L., Goetz, E., Golomb, J., Lopez, S. Gomez, Goncharov, B., Gong, Y., González, G., Goodarzi, P., Goode, S., Goodwin-Jones, A. W., Gosselin, M., Göttel, A. S., Gouaty, R., Gould, D. W., Govorkova, K., Goyal, S., Grace, B., Grado, A., Graham, V., Granados, A. E., Granata, M., Granata, V., Gras, S., Grassia, P., Gray, A., Gray, C., Gray, R., Greco, G., Green, A. C., Green, S. M., Green, S. R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimaud, C., Grote, H., Guerra, D., Guetta, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H., Guo, W., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, Ish, Gupta, N. C., Gupta, P., Gupta, S. K., Gupta, T., Gupte, N., Gurs, J., Gutierrez, N., Guzman, F., H, H. -Y., Haba, D., Haberland, M., Haino, S., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hanselman, A. G., Hansen, H., Hanson, J., Harada, R., Hardison, A. R., Haris, K., Harmark, T., Harms, J., Harry, G. M., Harry, I. W., Hart, J., Haskell, B., Haster, C. -J., Hathaway, J. S., Haughian, K., Hayakawa, H., Hayama, K., Hayes, R., Heffernan, A., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Henderson-Sapir, O., Hendry, M., Heng, I. S., Hennes, E., Henshaw, C., Hertog, T., Heurs, M., Hewitt, A. L., Heyns, J., Higginbotham, S., Hild, S., Hill, S., Himemoto, Y., Hirata, N., Hirose, C., Hoang, S., Hochheim, S., Hofman, D., Holland, N. A., Holley-Bockelmann, K., Holmes, Z. J., Holz, D. E., Honet, L., Hong, C., Hornung, J., Hoshino, S., Hough, J., Hourihane, S., Howell, E. J., Hoy, C. G., Hrishikesh, C. A., Hsieh, H. -F., Hsiung, C., Hsu, H. C., Hsu, W. -F., Hu, P., Hu, Q., Huang, H. Y., Huang, Y. -J., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Husa, S., Huxford, R., Huynh-Dinh, T., Iampieri, L., Iandolo, G. A., Ianni, M., Iess, A., Imafuku, H., Inayoshi, K., Inoue, Y., Iorio, G., Iqbal, M. H., Irwin, J., Ishikawa, R., Isi, M., Ismail, M. A., Itoh, Y., Iwanaga, H., Iwaya, M., Iyer, B. R., JaberianHamedan, V., Jacquet, C., Jacquet, P. -E., Jadhav, S. J., Jadhav, S. P., Jain, T., James, A. L., James, P. A., Jamshidi, R., Janquart, J., Janssens, K., Janthalur, N. N., Jaraba, S., Jaranowski, P., Jaume, R., Javed, W., Jennings, A., Jia, W., Jiang, J., Kubisz, J., Johanson, C., Johns, G. R., Johnson, N. A., Johnston, M. C., Johnston, R., Johny, N., Jones, D. H., Jones, D. I., Jones, R., Jose, S., Joshi, P., Ju, L., Jung, K., Junker, J., Juste, V., Kajita, T., Kaku, I., Kalaghatgi, C., Kalogera, V., Kamiizumi, M., Kanda, N., Kandhasamy, S., Kang, G., Kanner, J. B., Kapadia, S. J., Kapasi, D. P., Karat, S., Karathanasis, C., Kashyap, R., Kasprzack, M., Kastaun, W., Kato, T., Katsavounidis, E., Katzman, W., Kaushik, R., Kawabe, K., Kawamoto, R., Kazemi, A., Keitel, D., Kelley-Derzon, J., Kennington, J., Kesharwani, R., Key, J. S., Khadela, R., Khadka, S., Khalili, F. Y., Khan, F., Khan, I., Khanam, T., Khursheed, M., Khusid, N. M., Kiendrebeogo, W., Kijbunchoo, N., Kim, C., Kim, J. C., Kim, K., Kim, M. H., Kim, S., Kim, Y. -M., Kimball, C., Kinley-Hanlon, M., Kinnear, M., Kissel, J. S., Klimenko, S., Knee, A. M., Knust, N., Kobayashi, K., Obergaulinger, M., Koch, P., Koehlenbeck, S. M., Koekoek, G., Kohri, K., Kokeyama, K., Koley, S., Kolitsidou, P., Kolstein, M., Komori, K., Kong, A. K. H., Kontos, A., Korobko, M., Kossak, R. V., Kou, X., Koushik, A., Kouvatsos, N., Kovalam, M., Kozak, D. B., Kranzhoff, S. L., Kringel, V., Krishnendu, N. V., Królak, A., Kruska, K., Kuehn, G., Kuijer, P., Kulkarni, S., Ramamohan, A. Kulur, Kumar, A., Kumar, Praveen, Kumar, Prayush, Kumar, Rahul, Kumar, Rakesh, Kume, J., Kuns, K., Kuntimaddi, N., Kuroyanagi, S., Kurth, N. J., Kuwahara, S., Kwak, K., Kwan, K., Kwok, J., Lacaille, G., Lagabbe, P., Laghi, D., Lai, S., Laity, A. H., Lakkis, M. H., Lalande, E., Lalleman, M., Lalremruati, P. C., Landry, M., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., La Rana, A., La Rosa, I., Lartaux-Vollard, A., Lasky, P. D., Lawrence, J., Lawrence, M. N., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Lecoeuche, Y. K., Lee, H. M., Lee, H. W., Lee, K., Lee, R. -K., Lee, R., Lee, S., Lee, Y., Legred, I. N., Lehmann, J., Lehner, L., Jean, M. Le, Lemaître, A., Lenti, M., Leonardi, M., Lequime, M., Leroy, N., Lesovsky, M., Letendre, N., Lethuillier, M., Levin, S. E., Levin, Y., Leyde, K., Li, A. K. Y., Li, K. L., Li, T. G. F., Li, X., Li, Z., Lihos, A., Lin, C-Y., Lin, C. -Y., Lin, E. T., Lin, F., Lin, H., Lin, L. C. -C., Lin, Y. -C., Linde, F., Linker, S. D., Littenberg, T. B., Liu, A., Liu, G. C., Liu, Jian, Villarreal, F. Llamas, Llobera-Querol, J., Lo, R. K. L., Locquet, J. -P., London, L. T., Longo, A., Lopez, D., Portilla, M. Lopez, Lorenzini, M., Lorenzo-Medina, A., Loriette, V., Lormand, M., Losurdo, G., Lott IV, T. P., Lough, J. D., Loughlin, H. A., Lousto, C. O., Lowry, M. J., Lu, N., Lück, H., Lumaca, D., Lundgren, A. P., Lussier, A. W., Ma, L. -T., Ma, S., Ma'arif, M., Macas, R., Macedo, A., MacInnis, M., Maciy, R. R., Macleod, D. M., MacMillan, I. A. O., Macquet, A., Macri, D., Maeda, K., Maenaut, S., Hernandez, I. Magaña, Magare, S. S., Magazzù, C., Magee, R. M., Maggio, E., Maggiore, R., Magnozzi, M., Mahesh, M., Mahesh, S., Maini, M., Majhi, S., Majorana, E., Makarem, C. N., Makelele, E., Malaquias-Reis, J. A., Mali, U., Maliakal, S., Malik, A., Man, N., Mandic, V., Mangano, V., Mannix, B., Mansell, G. L., Mansingh, G., Manske, M., Mantovani, M., Mapelli, M., Marchesoni, F., Pina, D. Marín, Marion, F., Márka, S., Márka, Z., Markosyan, A. S., Markowitz, A., Maros, E., Marsat, S., Martelli, F., Martin, I. W., Martin, R. M., Martinez, B. B., Martinez, M., Martinez, V., Martini, A., Martinovic, K., Martins, J. C., Martynov, D. V., Marx, E. J., Massaro, L., Masserot, A., Masso-Reid, M., Mastrodicasa, M., Mastrogiovanni, S., Matcovich, T., Matiushechkina, M., Matsuyama, M., Mavalvala, N., Maxwell, N., McCarrol, G., McCarthy, R., McClelland, D. E., McCormick, S., McCuller, L., McEachin, S., McElhenny, C., McGhee, G. I., McGinn, J., McGowan, K. B. M., McIver, J., McLeod, A., McRae, T., Meacher, D., Meijer, Q., Melatos, A., Mellaerts, S., Menendez-Vazquez, A., Menoni, C. S., Mera, F., Mercer, R. A., Mereni, L., Merfeld, K., Merilh, E. L., Mérou, J. R., Merritt, J. D., Merzougui, M., Messenger, C., Messick, C., Meyer-Conde, M., Meylahn, F., Mhaske, A., Miani, A., Miao, H., Michaloliakos, I., Michel, C., Michimura, Y., Middleton, H., Miller, A. L., Miller, S., Millhouse, M., Milotti, E., Milotti, V., Minenkov, Y., Mio, N., Mir, Ll. M., Mirasola, L., Miravet-Tenés, M., Miritescu, C. -A., Mishra, A. K., Mishra, A., Mishra, C., Mishra, T., Mitchell, A. L., Mitchell, J. G., Mitra, S., Mitrofanov, V. P., Mittleman, R., Miyakawa, O., Miyamoto, S., Miyoki, S., Mo, G., Mobilia, L., Mohapatra, S. R. P., Mohite, S. R., Molina-Ruiz, M., Mondal, C., Mondin, M., Montani, M., Moore, C. J., Moraru, D., More, A., More, S., Moreno, G., Morgan, C., Morisaki, S., Moriwaki, Y., Morras, G., Moscatello, A., Mourier, P., Mours, B., Mow-Lowry, C. M., Muciaccia, F., Mukherjee, Arunava, Mukherjee, D., Mukherjee, Samanwaya, Mukherjee, Soma, Mukherjee, Subroto, Mukherjee, Suvodip, Mukund, N., Mullavey, A., Munch, J., Mundi, J., Mungioli, C. L., Oberg, W. R. Munn, Murakami, Y., Murakoshi, M., Murray, P. G., Muusse, S., Nabari, D., Nadji, S. L., Nagar, A., Nagarajan, N., Nagler, K. N., Nakagaki, K., Nakamura, K., Nakano, H., Nakano, M., Nandi, D., Napolano, V., Narayan, P., Nardecchia, I., Narikawa, T., Narola, H., Naticchioni, L., Nayak, R. K., Neilson, J., Nelson, A., Nelson, T. J. N., Nery, M., Neunzert, A., Ng, S., Quynh, L. Nguyen, Nichols, S. A., Nielsen, A. B., Nieradka, G., Niko, A., Nishino, Y., Nishizawa, A., Nissanke, S., Nitoglia, E., Niu, W., Nocera, F., Norman, M., North, C., Novak, J., Siles, J. F. Nuño, Nuttall, L. K., Obayashi, K., Oberling, J., O'Dell, J., Oertel, M., Offermans, A., Oganesyan, G., Oh, J. J., Oh, K., O'Hanlon, T., Ohashi, M., Ohkawa, M., Ohme, F., Oliveira, A. S., Oliveri, R., O'Neal, B., Oohara, K., O'Reilly, B., Ormsby, N. D., Orselli, M., O'Shaughnessy, R., O'Shea, S., Oshima, Y., Oshino, S., Ossokine, S., Osthelder, C., Ota, I., Ottaway, D. J., Ouzriat, A., Overmier, H., Owen, B. J., Pace, A. E., Pagano, R., Page, M. A., Pai, A., Pal, A., Pal, S., Palaia, M. A., Pálfi, M., Palma, P. P., Palomba, C., Palud, P., Pan, H., Pan, J., Pan, K. C., Panai, R., Panda, P. K., Pandey, S., Panebianco, L., Pang, P. T. H., Pannarale, F., Pannone, K. A., Pant, B. C., Panther, F. H., Paoletti, F., Paolone, A., Papalexakis, E. E., Papalini, L., Papigkiotis, G., Paquis, A., Parisi, A., Park, B. -J., Park, J., Parker, W., Pascale, G., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passenger, L., Passuello, D., Patane, O., Pathak, D., Pathak, M., Patra, A., Patricelli, B., Patron, A. S., Paul, K., Paul, S., Payne, E., Pearce, T., Pedraza, M., Pegna, R., Pele, A., Arellano, F. E. Peña, Penn, S., Penuliar, M. D., Perego, A., Pereira, Z., Perez, J. J., Périgois, C., Perna, G., Perreca, A., Perret, J., Perriès, S., Perry, J. W., Pesios, D., Petracca, S., Petrillo, C., Pfeiffer, H. P., Pham, H., Pham, K. A., Phukon, K. S., Phurailatpam, H., Piarulli, M., Piccari, L., Piccinni, O. J., Pichot, M., Piendibene, M., Piergiovanni, F., Pierini, L., Pierra, G., Pierro, V., Pietrzak, M., Pillas, M., Pilo, F., Pinard, L., Pinto, I. M., Pinto, M., Piotrzkowski, B. J., Pirello, M., Pitkin, M. D., Placidi, A., Placidi, E., Planas, M. L., Plastino, W., Poggiani, R., Polini, E., Pompili, L., Poon, J., Porcelli, E., Porter, E. K., Posnansky, C., Poulton, R., Powell, J., Pracchia, M., Pradhan, B. K., Pradier, T., Prajapati, A. K., Prasai, K., Prasanna, R., Prasia, P., Pratten, G., Principe, G., Principe, M., Prodi, G. A., Prokhorov, L., Prosposito, P., Puecher, A., Pullin, J., Punturo, M., Puppo, P., Pürrer, M., Qi, H., Qin, J., Quéméner, G., Quetschke, V., Quigley, C., Quinonez, P. J., Raab, F. J., Raabith, S. S., Raaijmakers, G., Raja, S., Rajan, C., Rajbhandari, B., Ramirez, K. E., Vidal, F. A. Ramis, Ramos-Buades, A., Rana, D., Ranjan, S., Ransom, K., Rapagnani, P., Ratto, B., Rawat, S., Ray, A., Raymond, V., Razzano, M., Read, J., Payo, M. 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C., Takahashi, H., Takahashi, R., Takamori, A., Takase, T., Takatani, K., Takeda, H., Takeshita, K., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tang, D., Tanioka, S., Tanner, D. B., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, P., Tiwari, S., Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trapananti, A., Travasso, F., Traylor, G., Trevor, M., Tringali, M. C., Tripathee, A., Troian, G., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. 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D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vives, A., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Wajid, A., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B. R., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Wilkin, A. T., Willadsen, D. J., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Winterflood, J., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wuchner, E., Wysocki, D. M., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, Y., Yarbrough, Z., Yasui, H., Yeh, S. -W., Yelikar, A. B., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuan, S., Yuzurihara, H., Zadrożny, A., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, X. -J., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19th, during the LIGO-Virgo-KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered $\sim 14\%$ of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz where we assume the GW emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy $1 \times 10^{-5} M_{\odot} c^2$ and luminosity $4 \times 10^{-5} M_{\odot} c^2/\text{s}$ for a source emitting at 50 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as $1.04$, at frequencies above $1200$ Hz, surpassing results from SN 2019ejj., Comment: Main paper: 6 pages, 4 figures and 1 table. Total with appendices: 20 pages, 4 figures, and 1 table

Published
2024

34. Reducing annotator bias by belief elicitation

Author

Jakobsen, Terne Sasha Thorn, Bjerre-Nielsen, Andreas, and Böhm, Robert

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Economics - General Economics, 68T50, I.2.7
Abstract

Crowdsourced annotations of data play a substantial role in the development of Artificial Intelligence (AI). It is broadly recognised that annotations of text data can contain annotator bias, where systematic disagreement in annotations can be traced back to differences in the annotators' backgrounds. Being unaware of such annotator bias can lead to representational bias against minority group perspectives and therefore several methods have been proposed for recognising bias or preserving perspectives. These methods typically require either a substantial number of annotators or annotations per data instance. In this study, we propose a simple method for handling bias in annotations without requirements on the number of annotators or instances. Instead, we ask annotators about their beliefs of other annotators' judgements of an instance, under the hypothesis that these beliefs may provide more representative and less biased labels than judgements. The method was examined in two controlled, survey-based experiments involving Democrats and Republicans (n=1,590) asked to judge statements as arguments and then report beliefs about others' judgements. The results indicate that bias, defined as systematic differences between the two groups of annotators, is consistently reduced when asking for beliefs instead of judgements. Our proposed method therefore has the potential to reduce the risk of annotator bias, thereby improving the generalisability of AI systems and preventing harm to unrepresented socio-demographic groups, and we highlight the need for further studies of this potential in other tasks and downstream applications.

Published
2024

35. What is an inductive mean?

Author

Nielsen, Frank

Subjects
Computer Science - Information Theory, Mathematics - Classical Analysis and ODEs
Abstract

An inductive mean is a mean defined as a limit of a convergence sequence of other means. Historically, this notion of inductive means obtained as limits of sequences was pioneered independently by Lagrange and Gauss for defining the arithmetic-geometric mean. In this note, we first explain several generalizations of the scalar geometric mean to symmetric positive-definite matrices, and then present several inductive mean mechanisms for sets of symmetric positive-definite matrices., Comment: 10 pages

Published
2024
Full Text
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36. Blind Equalization using a Variational Autoencoder with Second Order Volterra Channel Model

Author

Nielsen, Søren Føns, Zibar, Darko, and Schmidt, Mikkel N.

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Existing communication hardware is being exerted to its limits to accommodate for the ever increasing internet usage globally. This leads to non-linear distortion in the communication link that requires non-linear equalization techniques to operate the link at a reasonable bit error rate. This paper addresses the challenge of blind non-linear equalization using a variational autoencoder (VAE) with a second-order Volterra channel model. The VAE framework's costfunction, the evidence lower bound (ELBO), is derived for real-valued constellations and can be evaluated analytically without resorting to sampling techniques. We demonstrate the effectiveness of our approach through simulations on a synthetic Wiener-Hammerstein channel and a simulated intensity modulated direct detection (IM/DD) optical link. The results show significant improvements in equalization performance, compared to a VAE with linear channel assumptions, highlighting the importance of appropriate channel modeling in unsupervised VAE equalizer frameworks., Comment: Submitted

Published
2024

37. Experimental protocol for observing single quantum many-body scars with transmon qubits

Author

Larsen, Peter Græns, Nielsen, Anne E. B., Eckardt, André, and Petiziol, Francesco

Subjects
Quantum Physics, Condensed Matter - Quantum Gases
Abstract

Quantum many-body scars are energy eigenstates which fail to reproduce thermal expectation values of local observables in systems, where the rest of the many-body spectrum fulfils eigenstate thermalization. Experimental observation of quantum many-body scars has so far been limited to models with multiple scar states. Here we propose protocols to observe single scars in architectures of fixed-frequency, fixed-coupling superconducting qubits. We first adapt known models possessing the desired features into a form particularly suited for the experimental platform. We develop protocols for the implementation of these models, through trotterized sequences of two-qubit cross-resonance interactions, and verify the existence of the approximate scar state in the stroboscopic effective Hamiltonian. Since a single scar cannot be detected from coherent revivals in the dynamics, differently from towers of scar states, we propose and numerically investigate alternative and experimentally-accessible signatures. These include the dynamical response of the scar to local state deformations, to controlled noise, and to the resolution of the Lie-Suzuki-Trotter digitization., Comment: 16 pages, 7 figures, Submission to SciPost

Published
2024

38. Fast proxy centers for Jeffreys centroids: The Jeffreys-Fisher-Rao and the inductive Gauss-Bregman centers

Author

Nielsen, Frank

Subjects
Computer Science - Information Theory, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The symmetric Kullback-Leibler centroid also called the Jeffreys centroid of a set of mutually absolutely continuous probability distributions on a measure space provides a notion of centrality which has proven useful in many tasks including information retrieval, information fusion, and clustering in image, video and sound processing. However, the Jeffreys centroid is not available in closed-form for sets of categorical or normal distributions, two widely used statistical models, and thus need to be approximated numerically in practice. In this paper, we first propose the new Jeffreys-Fisher-Rao center defined as the Fisher-Rao midpoint of the sided Kullback-Leibler centroids as a plug-in replacement of the Jeffreys centroid. This Jeffreys-Fisher-Rao center admits a generic formula for uni-parameter exponential family distributions, and closed-form formula for categorical and normal distributions, matches exactly the Jeffreys centroid for same-mean normal distributions, and is experimentally observed in practice to be close to the Jeffreys centroid. Second, we define a new type of inductive centers generalizing the principle of Gauss arithmetic-geometric double sequence mean for pairs of densities of any given exponential family. This center is shown experimentally to approximate very well the Jeffreys centroid and is suggested to use when the Jeffreys-Fisher-Rao center is not available in closed form. Moreover, this Gauss-Bregman inductive center always converges and matches the Jeffreys centroid for sets of same-mean normal distributions. We report on our experiments demonstrating the use of the Jeffreys-Fisher-Rao and Gauss-Bregman centers instead of the Jeffreys centroid. Finally, we conclude this work by reinterpreting these fast proxy centers of Jeffreys centroids under the lens of dually flat spaces in information geometry., Comment: 35 pages, 10 figures

Published
2024

39. A search using GEO600 for gravitational waves coincident with fast radio bursts from SGR 1935+2154

Author

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abouelfettouh, I., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Agarwal, D., Agathos, M., Abchouyeh, M. Aghaei, Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Argianas, L., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Attadio, F., Aubin, F., AultONeal, K., Avallone, G., Azrad, D., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Baier, J. G., Baiotti, L., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartoletti, A. M., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bates, D. E., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Baynard II, P. A., Bazzan, M., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blagg, L. A., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boudon, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brandt, J., Braun, I., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, B. C., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cáceres-Barbosa, V., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannon, K. C., Cao, H., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, J. C. L., Chan, M., Chandra, K., Chang, R. -J., Chao, S., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, Y., Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Cheung, S. Y., Chiadini, F., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chugh, P., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciolfi, R., Clara, F., Clark, J. A., Clarke, J., Clarke, T. A., Clearwater, P., Clesse, S., Coccia, E., Codazzo, E., Cohadon, P. -F., Colace, S., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Connolly, G., Conti, L., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Crook, S., Crouch, R., Csizmazia, J., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. 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K., Rinaldi, S., Rittmeyer, J., Robertson, C., Robinet, F., Robinson, M., Rocchi, A., Rolland, L., Rollins, J. G., Romano, A. E., Romano, R., Romero, A., Romero-Shaw, I. M., Romie, J. H., Ronchini, S., Roocke, T. J., Rosa, L., Rosauer, T. J., Rose, C. A., Rosińska, D., Ross, M. P., Rossello, M., Rowan, S., Roy, S. K., Roy, S., Rozza, D., Ruggi, P., Ruhama, N., Morales, E. Ruiz, Ruiz-Rocha, K., Sachdev, S., Sadecki, T., Sadiq, J., Saffarieh, P., Sah, M. R., Saha, S. S., Saha, S., Sainrat, T., Menon, S. Sajith, Sakai, K., Sakellariadou, M., Sakon, S., Salafia, O. S., Salces-Carcoba, F., Salconi, L., Saleem, M., Salemi, F., Sallé, M., Salvador, S., Sanchez, A., Sanchez, E. J., Sanchez, J. H., Sanchez, L. E., Sanchis-Gual, N., Sanders, J. R., Sänger, E. M., Santoliquido, F., Saravanan, T. R., Sarin, N., Sasaoka, S., Sasli, A., Sassi, P., Sassolas, B., Satari, H., Sato, R., Sato, Y., Sauter, O., Savage, R. L., Sawada, T., Sawant, H. L., Sayah, S., Scacco, V., Schaetzl, D., Scheel, M., Schiebelbein, A., Schiworski, M. G., Schmidt, P., Schmidt, S., Schnabel, R., Schneewind, M., Schofield, R. M. S., Schouteden, K., Schulte, B. W., Schutz, B. F., Schwartz, E., Scialpi, M., Scott, J., Scott, S. M., Seetharamu, T. C., Seglar-Arroyo, M., Sekiguchi, Y., Sellers, D., Sengupta, A. S., Sentenac, D., Seo, E. G., Seo, J. W., Sequino, V., Serra, M., Servignat, G., Sevrin, A., Shaffer, T., Shah, U. S., Shaikh, M. A., Shao, L., Sharma, A. K., Sharma, P., Sharma-Chaudhary, S., Shaw, M. R., Shawhan, P., Shcheblanov, N. S., Sheridan, E., Shikano, Y., Shikauchi, M., Shimode, K., Shinkai, H., Shiota, J., Shoemaker, D. H., Shoemaker, D. M., Short, R. W., ShyamSundar, S., Sider, A., Siegel, H., Sieniawska, M., Sigg, D., Silenzi, L., Simmonds, M., Singer, L. P., Singh, A., Singh, D., Singh, M. K., Singh, S., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. E., Smith, W. J., Soldateschi, J., Somiya, K., Song, I., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Sotani, H., Soulard, R., Southgate, A., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Spoon, J. B., Sprague, C. A., Srivastava, A. K., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., StPierre, M., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Suleiman, L., Sullivan, K. D., Sun, L., Sunil, S., Suresh, J., Sutton, P. J., Suzuki, T., Suzuki, Y., Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takase, T., Takatani, K., Takeda, H., Takeshita, K., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tang, D., Tanioka, S., Tanner, D. B., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, P., Tiwari, S., Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trapananti, A., Travasso, F., Traylor, G., Trevor, M., Tringali, M. C., Tripathee, A., Troian, G., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Uchiyama, T., Udall, R. P., Uehara, T., Uematsu, M., Ueno, K., Ueno, S., Undheim, V., Ushiba, T., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Van Hove, P., VanKeuren, M., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varghese, J. J., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vives, A., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Wajid, A., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B. R., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Wilkin, A. T., Willadsen, D. J., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Winterflood, J., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wuchner, E., Wysocki, D. M., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, Y., Yarbrough, Z., Yasui, H., Yeh, S. -W., Yelikar, A. B., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuan, S., Yuzurihara, H., Zadrożny, A., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, X. -J., Zhu, Z. -H., Zucker, M. E., and Zweizig, J.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by CHIME/FRB and STARE2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations' O3 observing run. Here we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by CHIME/FRB, as well as X-ray glitches and X-ray bursts detected by NICER and NuSTAR close to the time of one of the FRBs. We do not detect any significant GW emission from any of the events. Instead, using a short-duration GW search (for bursts $\leq$ 1 s) we derive 50\% (90\%) upper limits of $10^{48}$ ($10^{49}$) erg for GWs at 300 Hz and $10^{49}$ ($10^{50}$) erg at 2 kHz, and constrain the GW-to-radio energy ratio to $\leq 10^{14} - 10^{16}$. We also derive upper limits from a long-duration search for bursts with durations between 1 and 10 s. These represent the strictest upper limits on concurrent GW emission from FRBs., Comment: 15 pages of text including references, 4 figures, 5 tables

Published
2024

40. A time warping model for seasonal data with application to age estimation from narwhal tusks

Author

Nielsen, Lars Reiter, Heide-Jørgensen, Mads Peter, Garde, Eva, Samson, Adeline, and Ditlevsen, Susanne

Subjects
Statistics - Applications, Statistics - Methodology
Abstract

Signals with varying periodicity frequently appear in real-world phenomena, necessitating the development of efficient modelling techniques to map the measured nonlinear timeline to linear time. Here we propose a regression model that allows for a representation of periodic and dynamic patterns observed in time series data. The model incorporates a hidden strictly increasing stochastic process that represents the instantaneous frequency, allowing the model to adapt and accurately capture varying time scales. A case study focusing on age estimation of narwhal tusks is presented, where cyclic element signals associated with annual growth layer groups are analyzed. We apply the methodology to data from one such tusk collected in West Greenland and use the fitted model to estimate the age of the narwhal. The proposed method is validated using simulated signals with known cycle counts and practical considerations and modelling challenges are discussed in detail. This research contributes to the field of time series analysis, providing a tool and valuable insights for understanding and modeling complex cyclic patterns in diverse domains.

Published
2024

41. Bireflectionality in special orthogonal groups

Author

Nielsen, Klaus

Subjects
Mathematics - Group Theory, 15A15, 15F10
Abstract

It is shown that a transformation in the special orthogonal group SO(V) of a nondefective quadratic space over a field K is bireflectional (product of 2 involutions) if and only if it is reversible (conjugate to its inverse). Furthermore, all elements of SO(V) are bireflectional if and only if dim V is odd or divisible by 4, or V is a hyperbolic plane over GF(2) or GF(3)., Comment: 11 pages

Published
2024

42. Converting the ANU 2.3 telescope to fully automated operation

Author

Price, Ian, Nielsen, Jon, Lidman, Chris, Soon, Jamie, Travouillon, Tony, and Sharp, Rob

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The operation of the ANU 2.3m telescope transitioned from classically scheduled remote observing to fully autonomous queue scheduled observing in March 2023. The instrument currently supported is WiFeS, a visible-light low-resolution image-slicing integral field spectrograph with a 25''x 38'' field of view (offering precision spectrophotometry free from aperture effects). It is highly suitable for rapid spectroscopic follow-up of astronomical transient events and regular cadence observations. The new control system implements flexible queue scheduling and supports rapid response override for Target-of-Opportunity observations. The ANU 2.3m is the largest optical telescope to have been retro-fitted for autonomous operation to date, and it remains a national facility servicing a broad range of science cases. We present an overview of the automated control system and report on the first six months of continuous operation., Comment: 6 pages, 5 figures; published in PASA

Published
2024
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43. TensorSocket: Shared Data Loading for Deep Learning Training

Author

Robroek, Ties, Nielsen, Neil Kim, and Tözün, Pınar

Subjects
Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Training deep learning models is a repetitive and resource-intensive process. Data scientists often train several models before landing on set of parameters (e.g., hyper-parameter tuning), model architecture (e.g., neural architecture search), among other things that yields the highest accuracy. The computational efficiency of these training tasks depends highly on how well we can supply the training process with training data. The repetitive nature of these tasks results in the same data processing pipelines running over and over exacerbating the need for and costs of computational resources. In this paper, we present Tensorsocket to reduce the computational needs of deep learning training by enabling simultaneous training processes to share the same data loader. Tensorsocket mitigates CPU-side bottlenecks in cases where the collocated training workloads have high throughput on GPU, but are held back by lower data-loading throughput on CPU. Tensorsocket achieves this by reducing redundant computations across collocated training processes and leveraging modern GPU-GPU interconnects. We demonstrate the hardware- and pipeline-agnostic nature of Tensorsocket and evaluate it using a variety of training scenarios. Our evaluation shows that Tensorsocket enables scenarios that are infeasible without data sharing, increases training throughput by up to $100\%$, and when utilizing cloud instances, Tensorsocket achieves cost savings of $50\%$ by reducing the hardware resource needs on the CPU side. Furthermore, Tensorsocket outperforms the state-of-the-art solutions for shared data loading such as CoorDL and Joader. It is easier to use, maintain, and deploy, and either achieves higher or matches the throughput of other solutions while requiring less CPU resources.

Published
2024

44. TOI-5005 b: A super-Neptune in the savanna near the ridge

Author

Castro-González, A., Lillo-Box, J., Armstrong, D. J., Acuña, L., Aguichine, A., Bourrier, V., Gandhi, S., Sousa, S. G., Delgado-Mena, E., Moya, A., Adibekyan, V., Correia, A. C. M., Barrado, D., Damasso, M., Winn, J. N., Santos, N. C., Barkaoui, K., Barros, S. C. C., Benkhaldoun, Z., Bouchy, F., Briceño, C., Caldwell, D. A., Collins, K. A., Essack, Z., Ghachoui, M., Gillon, M., Hounsell, R., Jehin, E., Jenkins, J. M., Keniger, M. A. F., Law, N., Mann, A. W., Nielsen, L. D., Pozuelos, F. J., Schanche, N., Seager, S., Tan, T. -G., Timmermans, M., Villaseñor, J., Watkins, C. N., and Ziegler, C.

Subjects
Astrophysics - Earth and Planetary Astrophysics
Abstract

The Neptunian desert and savanna have been recently found to be separated by a ridge, an overdensity of planets in the $\simeq$3-5 days period range. These features are thought to be shaped by dynamical and atmospheric processes. However, their relative roles are not yet well understood. We intend to confirm and characterise the super-Neptune TESS candidate TOI-5005.01, which orbits a moderately bright (V = 11.8) solar-type star (G2 V) with an orbital period of 6.3 days. We confirm TOI-5005 b to be a transiting super-Neptune with a radius of $R_{\rm p}$ = $6.25\pm 0.24$ $\rm R_{\rm \oplus}$ ($R_{\rm p}$ = $0.558\pm 0.021$ $\rm R_{\rm J}$) and a mass of $M_{\rm p}$ = $32.7\pm 5.9$ $\rm M_{\oplus}$ ($M_{\rm p}$ = $0.103\pm 0.018$ $\rm M_{\rm J}$), which corresponds to a mean density of $\rho_{\rm p}$ = $0.74 \pm 0.16$ $\rm g \, cm^{-3}$. Our internal structure modelling indicates that the overall metal mass fraction is well constrained to a value slightly lower than that of Neptune and Uranus ($Z_{\rm planet}$ = $0.76^{+0.04}_{-0.11}$). We also estimated the present-day atmospheric mass-loss rate of TOI-5005 b but found contrasting predictions depending on the choice of photoevaporation model. At a population level, we find statistical evidence ($p$-value = $0.0092^{+0.0184}_{-0.0066}$) that planets in the savanna such as TOI-5005 b tend to show lower densities than planets in the ridge, with a dividing line around 1 $\rm g \, cm^{-3}$, which supports the hypothesis of different evolutionary pathways populating both regimes. TOI-5005 b is located in a key region of the period-radius space to study the transition between the Neptunian ridge and the savanna. It orbits the brightest star of all such planets, which makes it a target of interest for atmospheric and orbital architecture observations that will bring a clearer picture of its overall evolution., Comment: Accepted for publication in A&A. Abstract shortened. 35 pages, 26 figures

Published
2024

45. A method for identifying causality in the response of nonlinear dynamical systems

Author

Massingham, Joseph, Nielsen, Ole, and Butlin, Tore

Subjects
Computer Science - Machine Learning
Abstract

Predicting the response of nonlinear dynamical systems subject to random, broadband excitation is important across a range of scientific disciplines, such as structural dynamics and neuroscience. Building data-driven models requires experimental measurements of the system input and output, but it can be difficult to determine whether inaccuracies in the model stem from modelling errors or noise. This paper presents a novel method to identify the causal component of the input-output data from measurements of a system in the presence of output noise, as a function of frequency, without needing a high fidelity model. An output prediction, calculated using an available model, is optimally combined with noisy measurements of the output to predict the input to the system. The parameters of the algorithm balance the two output signals and are utilised to calculate a nonlinear coherence metric as a measure of causality. This method is applicable to a broad class of nonlinear dynamical systems. There are currently no solutions to this problem in the absence of a complete benchmark model.

Published
2024

46. String Invention, Viable 3-3-1 Model, Dark Matter Black Holes

Author

Nielsen, Holger B.

Subjects
High Energy Physics - Phenomenology, High Energy Physics - Theory
Abstract

After very little memories we review slightly Paul Frampton's memories from the discovery of Veneziano model being indeed string theory with Y. Nambu and secondly his 3-3-1 theory.This latter is an indeed not excluded replacement for the Standard Model with triangle anomalies cancelling as they must in a truly viable theory. It even needs (essentially) 3 as the family number! Also primordial black holes as dark matter is mentioned, we end with review of a my own very speculative utterly recent idea, that for the purpose of the classical approximation, we could, using the functional integral as our rudimentary assumption taken over from quantum mechanics, obtain the equations of motion without the in our opinion very mysterious imaginary unit i, which usually occurs as a factor in the exponent of the functional integrand, which is this i times the action. The functional integral without the mysterious i leads to predicting some of the most strong features in cosmology, and also seems to argue for as little black holes as possible., Comment: 29 pages, 2 figures. Contribution to a Festscrift for the 80th birthday of Paul Frampton, which shall appear as a part of a book Several corrections mainly inspired by editors of the book and a mail from La Plata. Thanks to the contributors to improve

Published
2024

47. Variable Temperature and Carrier-Resolved Photo-Hall Measurements of High-Performance Selenium Thin-Film Solar Cells

Author

Nielsen, Rasmus Svejstrup, Gunawan, Oki, Todorov, Teodor, Møller, Clara Brendstrup, Hansen, Ole, and Vesborg, Peter Christian Kjærgaard

Subjects
Condensed Matter - Materials Science
Abstract

Selenium is an elemental semiconductor with a wide bandgap suitable for a range of optoelectronic and solar energy conversion technologies. However, developing such applications requires an in-depth understanding of the fundamental material properties. Here, we study the properties of the majority and minority charge carriers in selenium using a recently developed carrier-resolved photo-Hall technique, which enables simultaneous mapping of the mobilities and concentrations of both carriers under varying light intensities. Additionally, we perform temperature-dependent Hall measurements to extract information about the acceptor level and ionization efficiency. Our findings are compared to results from other advanced characterization techniques, and the inconsistencies are outlined. Finally, we characterize a high-performance selenium thin-film solar cell and perform device simulations to systematically address each discrepancy and accurately reproduce experimental current-voltage and external quantum efficiency measurements. These results contribute to a deeper understanding of the optoelectronic properties and carrier dynamics in selenium, which may guide future improvements and facilitate the development of higher-efficiency selenium solar cells.

Published
2024

48. Hysteresis Behind A Bottleneck With Location-Dependent Capacity

Author

Hammerl, Alexander, Seshadri, Ravi, Rasmussen, Thomas Kjær, and Nielsen, Otto Anker

Subjects
Condensed Matter - Statistical Mechanics
Abstract

Macroscopic fundamental diagrams (MFDs) and related network traffic dynamics models have received both theoretical support and empirical validation with the emergence of new data collection technologies. However, the existence of well-defined MFD curves can only be expected for traffic networks with specific topologies and is subject to various disturbances, most importantly hysteresis phenomena. This study aims to improve the understanding of hysteresis in Macroscopic Fundamental Diagrams and Network Exit Functions (NEFs) during rush hour conditions. We apply the LWR theory to a highway corridor featuring a location-dependent downstream bottleneck to identify a figure-eight hysteresis pattern, clockwise on the top and counter-clockwise on the bottom. We discuss why this general pattern is rare in practical scenarios, where a single clockwise loop is more typical. The paper discusses the impact of the road topology and demand parameters on the formation and intensity of hysteresis loops analytically. To substantiate these findings, we employ numerical simulations using the Cell Transmission Model (CTM). Our simulations show that even a slight reduction in the capacity of the homogeneous section can significantly decrease MFD hysteresis while maintaining outflow at the corridor's downstream end. These reductions can be achieved with minimal intervention through standard traffic control measures, such as dynamic speed limits or ramp metering.

Published
2024

49. End-to-End Learning of Transmitter and Receiver Filters in Bandwidth Limited Fiber Optic Communication Systems

Author

Nielsen, Søren Føns, Da Ros, Francesco, Schmidt, Mikkel N., and Zibar, Darko

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

This paper investigates the application of end-to-end (E2E) learning for joint optimization of pulse-shaper and receiver filter to reduce intersymbol interference (ISI) in bandwidth-limited communication systems. We investigate this in two numerical simulation models: 1) an additive white Gaussian noise (AWGN) channel with bandwidth limitation and 2) an intensity modulated direct detection (IM/DD) link employing an electro-absorption modulator. For both simulation models, we implement a wavelength division multiplexing (WDM) scheme to ensure that the learned filters adhere to the bandwidth constraints of the WDM channels. Our findings reveal that E2E learning greatly surpasses traditional single-sided transmitter pulse-shaper or receiver filter optimization methods, achieving significant performance gains in terms of symbol error rate with shorter filter lengths. These results suggest that E2E learning can decrease the complexity and enhance the performance of future high-speed optical communication systems., Comment: Under review

Published
2024

50. orbitize! v3: Orbit fitting for the High-contrast Imaging Community

Author

Blunt, Sarah, Wang, Jason Jinfei, Hirsch, Lea, Tejada, Roberto, Nagpal, Vighnesh, Surti, Tirth Dharmesh, Covarrubias, Sofia, McKenna, Thea, Chávez, Rodrigo Ferrer, Llop-Sayson, Jorge, Arora, Mireya, Chavez, Amanda, Cody, Devin, Choudhary, Saanika, Smith, Adam J. R. W., Balmer, William, Stolker, Tomas, Gallamore, Hannah, Ó, Clarissa R. Do, Nielsen, Eric L., and De Rosa, Robert J.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Solar and Stellar Astrophysics
Abstract

orbitize! is a package for Bayesian modeling of the orbital parameters of resolved binary objects from time series measurements. It was developed with the needs of the high-contrast imaging community in mind, and has since also become widely used in the binary star community. A generic orbitize! use case involves translating relative astrometric time series, optionally combined with radial velocity or astrometric time series, into a set of derived orbital posteriors. This paper is published alongside the release of orbitize! version 3.0, which has seen significant enhancements in functionality and accessibility since the release of version 1.0 (Blunt et al., 2020)., Comment: Published in JOSS

Published
2024

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