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17,044 results on '"Steck A"'

1. High-temperature $^{205}$Tl decay clarifies $^{205}$Pb dating in early Solar System

Author

Leckenby, G., Sidhu, R. S., Chen, R. J., Mancino, R., Szányi, B., Bai, M., Battino, U., Blaum, K., Brandau, C., Cristallo, S., Dickel, T., Dillmann, I., Dmytriiev, D., Faestermann, T., Forstner, O., Franczak, B., Geissel, H., Gernhäuser, R., Glorius, J., Griffin, C., Gumberidze, A., Haettner, E., Hillenbrand, P. -M., Karakas, A., Kaur, T., Korten, W., Kozhuharov, C., Kuzminchuk, N., Langanke, K., Litvinov, S., Litvinov, Y. A., Lugaro, M., Martínez-Pinedo, G., Menz, E., Meyer, B., Morgenroth, T., Neff, T., Nociforo, C., Petridis, N., Pignatari, M., Popp, U., Purushothaman, S., Reifarth, R., Sanjari, S., Scheidenberger, C., Spillmann, U., Steck, M., Stöhlker, T., Tanaka, Y. K., Trassinelli, M., Trotsenko, S., Varga, L., Vescovi, D., Wang, M., Weick, H., López, A. Yagüe, Yamaguchi, T., Zhang, Y., and Zhao, J.

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

Radioactive nuclei with lifetimes on the order of millions of years can reveal the formation history of the Sun and active nucleosynthesis occurring at the time and place of its birth. Among such nuclei whose decay signatures are found in the oldest meteorites, $^{205}$Pb is a powerful example, as it is produced exclusively by slow neutron captures (the s process), with most being synthesized in asymptotic giant branch (AGB) stars. However, making accurate abundance predictions for $^{205}$Pb has so far been impossible because the weak decay rates of $^{205}$Pb and $^{205}$Tl are very uncertain at stellar temperatures. To constrain these decay rates, we measured for the first time the bound-state $\beta^-$ decay of fully ionized $^{205}$Tl$^{81+}$, an exotic decay mode that only occurs in highly charged ions. The measured half-life is 4.7 times longer than the previous theoretical estimate and our 10% experimental uncertainty has eliminated the main nuclear-physics limitation. With new, experimentally backed decay rates, we used AGB stellar models to calculate $^{205}$Pb yields. Propagating those yields with basic galactic chemical evolution (GCE) and comparing with the $^{205}$Pb/$^{204}$Pb ratio from meteorites, we determined the isolation time of solar material inside its parent molecular cloud. We find positive isolation times that are consistent with the other s-process short-lived radioactive nuclei found in the early Solar System. Our results reaffirm the site of the Sun's birth as a long-lived, giant molecular cloud and support the use of the $^{205}$Pb--$^{205}$Tl decay system as a chronometer in the early Solar System., Comment: 21 pages and 11 figures/tables. Published in Nature (2024)

Published
2024
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2. SituationAdapt: Contextual UI Optimization in Mixed Reality with Situation Awareness via LLM Reasoning

Author

Li, Zhipeng, Gebhardt, Christoph, Inglin, Yves, Steck, Nicolas, Streli, Paul, and Holz, Christian

Subjects
Computer Science - Human-Computer Interaction
Abstract

Mixed Reality is increasingly used in mobile settings beyond controlled home and office spaces. This mobility introduces the need for user interface layouts that adapt to varying contexts. However, existing adaptive systems are designed only for static environments. In this paper, we introduce SituationAdapt, a system that adjusts Mixed Reality UIs to real-world surroundings by considering environmental and social cues in shared settings. Our system consists of perception, reasoning, and optimization modules for UI adaptation. Our perception module identifies objects and individuals around the user, while our reasoning module leverages a Vision-and-Language Model to assess the placement of interactive UI elements. This ensures that adapted layouts do not obstruct relevant environmental cues or interfere with social norms. Our optimization module then generates Mixed Reality interfaces that account for these considerations as well as temporal constraints. For evaluation, we first validate our reasoning module's capability of assessing UI contexts in comparison to human expert users. In an online user study, we then establish SituationAdapt's capability of producing context-aware layouts for Mixed Reality, where it outperformed previous adaptive layout methods. We conclude with a series of applications and scenarios to demonstrate SituationAdapt's versatility., Comment: 13 pages

Published
2024

3. Training microwave pulses using quantum machine learning

Author

Nola, Jaden, Sanchez, Uriah, Murthy, Anusha Krishna, Behrman, Elizabeth, and Steck, James

Subjects
Quantum Physics
Abstract

A gate sequence of single-qubit transformations may be condensed into a single microwave pulse that maps a qubit from an initialized state directly into the desired state of the composite transformation. Here, machine learning is used to learn the parameterized values for a single driving pulse associated with a transformation of three sequential gate operations on a qubit. This implies that future quantum circuits may contain roughly a third of the number of single-qubit operations performed, greatly reducing the problems of noise and decoherence. There is a potential for even greater condensation and efficiency using the methods of quantum machine learning.

Published
2024

4. Quantum Generative Adversarial Networks: Generating and Detecting Quantum Product States

Author

Steck, James E. and Behrman, Elizabeth C.

Subjects
Quantum Physics
Abstract

Machine learning can be used as a systematic method to non-algorithmically program quantum computers. Quantum machine learning enables us to perform computations without breaking down an algorithm into its gate building blocks, eliminating that difficult step and potentially reducing unnecessary complexity. In addition, the machine learning approach is robust to both noise and to decoherence, which is ideal for running on inherently noisy NISQ devices which are limited in the number of qubits available for error correction. Here we apply our prior work in quantum machine learning technique, to create a QGAN, a quantum analog to the classical Stylenet GANs developed by Kerras for image generation and classification. A quantum system is used as a generator and a separate quantum system is used as a discriminator. The generator Hamiltonian quantum parameters are augmented by quantum style parameters which play the role of the style parameters used by Kerras. Both generator parameters are trained in a GAN MinMax problem along with quantum parameters of the discriminator Hamiltonian. We choose a problem to demonstrate the QGAN that has purely quantum information. The task is to generate and discriminate quantum product states. Real product states are generated to be detected and separated from fake quantum product states generated by the quantum generator. The problem of detecting quantum product states is chosen to demonstrate the QGAN because is well known as purely quantum mechanical, has no classical analog and is an open problem for quantum product states of more than 2 qubits. With proper encoding of image pixels into quantum states as density matrices, the method demonstrated here is applicable to GAN image generation and detection that can be hosted on and take advantage of the nature of quantum computers., Comment: arXiv admin note: substantial text overlap with arXiv:2204.07011

Published
2024

5. Quantum Neural Network Training of a Repeater Node

Author

Fuentealba, Diego, Dahn, Jack, Steck, James, and Behrman, Elizabeth

Subjects
Quantum Physics
Abstract

The construction of robust and scalable quantum gates is a uniquely hard problem in the field of quantum computing. Real-world quantum computers suffer from many forms of noise, characterized by the decoherence and relaxation times of a quantum circuit, which make it very hard to construct efficient quantum algorithms. One example is a quantum repeater node, a circuit that swaps the states of two entangled input and output qubits. Robust quantum repeaters are a necessary building block of long-distance quantum networks. A solution exists for this problem, known as a swap gate, but its noise tolerance is poor. Machine learning may hold the key to efficient and robust quantum algorithm design, as demonstrated by its ability to learn to control other noisy and highly nonlinear systems. Here, a quantum neural network (QNN) is constructed to perform the swap operation and compare a trained QNN solution to the standard swap gate. The system of qubits and QNN is constructed in MATLAB and trained under ideal conditions before noise is artificially added to the system to test robustness. We find that the QNN easily generalizes for two qubits and can be scaled up to more qubits without additional training. We also find that as the number of qubits increases, the noise tolerance increases with it, meaning a sufficiently large system can produce extremely noise-tolerant results. This begins to explore the ability of neural networks to construct those robust systems.

Published
2024

6. First simultaneous measurement of the gamma-ray and neutron emission probabilities in inverse kinematics at a heavy-ion storage ring

Author

Sguazzin, M., Jurado, B., Pibernat, J., Swartz, J. A., Grieser, M., Glorius, J., Litvinov, Yu. A., Berthelot, C., Włoch, B., Adamczewski-Musch, J., Alfaurt, P., Ascher, P., Audouin, L., Blank, B., Blaum, K., Brückner, B., Dellmann, S., Dillmann, I., Domingo-Pardo, C., Dupuis, M., Erbacher, P., Flayol, M., Forstner, O., Freire-Fernández, D., Gerbaux, M., Giovinazzo, J., Grévy, S., Griffin, C. J., Gumberidze, A., Heil, S., Heinz, A., Hess, R., Kurtulgil, D., Kurz, N., Leckenby, G., Litvinov, S., Lorentz, B., Méot, V., Michaud, J., Pérard, S., Petridis, N., Popp, U., Ramos, D., Reifarth, R., Roche, M., Sanjari, M. S., Sidhu, R. S., Spillmann, U., Steck, M., Stöhlker, Th., Thomas, B., Thulliez, L., and Versteegen, M.

Subjects
Nuclear Experiment
Abstract

The probabilities for gamma-ray and particle emission as a function of the excitation energy of a decaying nucleus are valuable observables for constraining the ingredients of the models that describe the de-excitation of nuclei near the particle emission threshold. These models are essential in nuclear astrophysics and applications. In this work, we have for the first time simultaneously measured the gamma-ray and neutron emission probabilities of 208Pb. The measurement was performed in inverse kinematics at the Experimental Storage Ring (ESR) of the GSI/FAIR facility, where a 208Pb beam interacted through the 208Pb(p,p') reaction with a hydrogen gas jet target. Instead of detecting the gamma-rays and neutrons emitted by 208Pb, we detected the heavy beam-like residues produced after gamma and neutron emission. These heavy residues were fully separated by a dipole magnet of the ESR and were detected with outstanding efficiencies. The comparison of the measured probabilities with model calculations has allowed us to test and select different descriptions of the gamma-ray strength function and the nuclear level density available in the literature., Comment: 23 pages, 14 figures

Published
2024

7. Pathwise Differentiation of Worldline Path Integrals

Author

Mackrory, Jonathan B., Zheng, He, and Steck, Daniel A.

Subjects
Quantum Physics
Abstract

The worldline method is a powerful numerical path-integral framework for computing Casimir and Casimir-Polder energies. An important challenge arises when one desires derivatives of path-integral quantities--standard finite-difference techniques, for example, yield results of poor accuracy. In this work we present methods for computing derivatives of worldline-type path integrals of scalar fields to calculate forces, energy curvatures, and torques. In Casimir-Polder-type path integrals, which require derivatives with respect to the source point of the paths, the derivatives can be computed by a simple reweighting of the path integral. However, a partial-averaging technique is necessary to render the differentiated path integral computationally efficient. We also discuss the computation of Casimir forces, curvatures, and torques between macroscopic bodies. Here a different method is used, involving summing over the derivatives of all the intersections with a body; again, a different partial-averaging method makes the path integral efficient. To demonstrate the efficiency of the techniques, we give the results of numerical implementations of these worldline methods in atomplane and plane-plane geometries. Being quite general, the methods here should apply to path integrals outside the worldline context (e.g., financial mathematics)., Comment: 17 pages; 9 figures; updated references

Published
2024

8. Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation

Author

He, Zhankui, Xie, Zhouhang, Steck, Harald, Liang, Dawen, Jha, Rahul, Kallus, Nathan, and McAuley, Julian

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

Large language models (LLMs) are revolutionizing conversational recommender systems by adeptly indexing item content, understanding complex conversational contexts, and generating relevant item titles. However, controlling the distribution of recommended items remains a challenge. This leads to suboptimal performance due to the failure to capture rapidly changing data distributions, such as item popularity, on targeted conversational recommendation platforms. In conversational recommendation, LLMs recommend items by generating the titles (as multiple tokens) autoregressively, making it difficult to obtain and control the recommendations over all items. Thus, we propose a Reindex-Then-Adapt (RTA) framework, which converts multi-token item titles into single tokens within LLMs, and then adjusts the probability distributions over these single-token item titles accordingly. The RTA framework marries the benefits of both LLMs and traditional recommender systems (RecSys): understanding complex queries as LLMs do; while efficiently controlling the recommended item distributions in conversational recommendations as traditional RecSys do. Our framework demonstrates improved accuracy metrics across three different conversational recommendation datasets and two adaptation settings

Published
2024

10. Is Cosine-Similarity of Embeddings Really About Similarity?

Author

Steck, Harald, Ekanadham, Chaitanya, and Kallus, Nathan

Subjects
Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract

Cosine-similarity is the cosine of the angle between two vectors, or equivalently the dot product between their normalizations. A popular application is to quantify semantic similarity between high-dimensional objects by applying cosine-similarity to a learned low-dimensional feature embedding. This can work better but sometimes also worse than the unnormalized dot-product between embedded vectors in practice. To gain insight into this empirical observation, we study embeddings derived from regularized linear models, where closed-form solutions facilitate analytical insights. We derive analytically how cosine-similarity can yield arbitrary and therefore meaningless `similarities.' For some linear models the similarities are not even unique, while for others they are implicitly controlled by the regularization. We discuss implications beyond linear models: a combination of different regularizations are employed when learning deep models; these have implicit and unintended effects when taking cosine-similarities of the resulting embeddings, rendering results opaque and possibly arbitrary. Based on these insights, we caution against blindly using cosine-similarity and outline alternatives., Comment: 9 pages

Published
2024
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11. Consensus guidance for monitoring individuals with islet autoantibody-positive pre-stage 3 type 1 diabetes

Author

Phillip, Moshe, Achenbach, Peter, Addala, Ananta, Albanese-O’Neill, Anastasia, Battelino, Tadej, Bell, Kirstine J., Besser, Rachel E. J., Bonifacio, Ezio, Colhoun, Helen M., Couper, Jennifer J., Craig, Maria E., Danne, Thomas, de Beaufort, Carine, Dovc, Klemen, Driscoll, Kimberly A., Dutta, Sanjoy, Ebekozien, Osagie, Larsson, Helena Elding, Feiten, Daniel J., Frohnert, Brigitte I., Gabbay, Robert A., Gallagher, Mary P., Greenbaum, Carla J., Griffin, Kurt J., Hagopian, William, Haller, Michael J., Hendrieckx, Christel, Hendriks, Emile, Holt, Richard I. G., Hughes, Lucille, Ismail, Heba M., Jacobsen, Laura M., Johnson, Suzanne B., Kolb, Leslie E., Kordonouri, Olga, Lange, Karin, Lash, Robert W., Lernmark, Åke, Libman, Ingrid, Lundgren, Markus, Maahs, David M., Marcovecchio, M. Loredana, Mathieu, Chantal, Miller, Kellee M., O’Donnell, Holly K., Oron, Tal, Patil, Shivajirao P., Pop-Busui, Rodica, Rewers, Marian J., Rich, Stephen S., Schatz, Desmond A., Schulman-Rosenbaum, Rifka, Simmons, Kimber M., Sims, Emily K., Skyler, Jay S., Smith, Laura B., Speake, Cate, Steck, Andrea K., Thomas, Nicholas P. B., Tonyushkina, Ksenia N., Veijola, Riitta, Wentworth, John M., Wherrett, Diane K., Wood, Jamie R., Ziegler, Anette-Gabriele, and DiMeglio, Linda A.

Published
2024
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12. Debiased Learning for Remote Sensing Data

Author

Yeh, Chun-Hsiao, Wang, Xudong, Yu, Stella X., Hill, Charles, Steck, Zackery, Kangas, Scott, and Reite, Aaron

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep learning has had remarkable success at analyzing handheld imagery such as consumer photos due to the availability of large-scale human annotations (e.g., ImageNet). However, remote sensing data lacks such extensive annotation and thus potential for supervised learning. To address this, we propose a highly effective semi-supervised approach tailored specifically to remote sensing data. Our approach encompasses two key contributions. First, we adapt the FixMatch framework to remote sensing data by designing robust strong and weak augmentations suitable for this domain. Second, we develop an effective semi-supervised learning method by removing bias in imbalanced training data resulting from both actual labels and pseudo-labels predicted by the model. Our simple semi-supervised framework was validated by extensive experimentation. Using 30\% of labeled annotations, it delivers a 7.1\% accuracy gain over the supervised learning baseline and a 2.1\% gain over the supervised state-of-the-art CDS method on the remote sensing xView dataset., Comment: Accepted to CVPR 2023 MultiEarth Workshop

Published
2023

13. First measurement of the neutron-emission probability with a surrogate reaction in inverse kinematics at a heavy-ion storage ring

Author

Sguazzin, M., Jurado, B., Pibernat, J., Swartz, J. A., Grieser, M., Glorius, J., Litvinov, Yu. A., Adamczewski-Musch, J., Alfaurt, P., Ascher, P., Audouin, L., Berthelot, C., Blank, B., Blaum, K., Brückner, B., Dellmann, S., Dillmann, I., Domingo-Pardo, C., Dupuis, M., Erbacher, P., Flayol, M., Forstner, O., Freire-Fernández, D., Gerbaux, M., Giovinazzo, J., Grévy, S., Griffin, C. J., Gumberidze, A., Heil, S., Heinz, A., Hess, R., Kurtulgil, D., Kurz, N., Leckenby, G., Litvinov, S., Lorentz, B., Méot, V., Michaud, J., Pérard, S., Petridis, N., Popp, U., Ramos, D., Reifarth, R., Roche, M., Sanjari, M. S., Sidhu, R. S., Spillmann, U., Steck, M., Stöhlker, Th., Thomas, B., Thulliez, L., Versteegen, M., and Włoch, B.

Subjects
Nuclear Experiment
Abstract

Neutron-induced reaction cross sections of short-lived nuclei are imperative to understand the origin of heavy elements in stellar nucleosynthesis and for societal applications, but their measurement is extremely complicated due to the radioactivity of the targets involved. One way of overcoming this issue is to combine surrogate reactions with the unique possibilities offered by heavy-ion storage rings. In this work, we describe the first surrogate-reaction experiment in inverse kinematics, which we successfully conducted at the Experimental Storage Ring (ESR) of the GSI/FAIR facility, using the 208Pb(p,p') reaction as a surrogate for neutron capture on 207Pb. Thanks to the outstanding detection efficiencies possible at the ESR, we were able to measure for the first time the neutron-emission probability as a function of the excitation energy of 208Pb. We have used this probability to select different descriptions of the gamma-ray strength function and nuclear level density, and provide reliable results for the neutron-induced radiative capture cross section of 207Pb at energies for which no experimental data exist., Comment: 10 pages and 5 figures

Published
2023

14. Measurement of the Isolated Nuclear Two-Photon Decay in $^{72}\mathrm{Ge}$

Author

Freire-Fernández, D., Korten, W., Chen, R. J., Litvinov, S., Litvinov, Yu. A., Sanjari, M. S., Weick, H., Akinci, F. C., Albers, H. M., Armstrong, M., Banerjee, A., Blaum, K., Brandau, C., Brown, B. A., Bruno, C. G., Carroll, J. J., Chen, X., Chiara, Ch. J., Cortes, M. L., Dellmann, S. F., Dillmann, I., Dmytriiev, D., Forstner, O., Geissel, H., Glorius, J., Görgen, A., Górska, M., Griffin, C. J., Gumberidze, A., Harayama, S., Hess, R., Hubbard, N., Ide, K. E., John, Ph. R., Joseph, R., Jurado, B., Kalaydjieva, D., Kanika, K., Kondev, F. G., Koseoglou, P., Kosir, G., Kozhuharov, Ch., Kulikov, I., Leckenby, G., Lorenz, B., Marsh, J., Mistry, A., Ozawa, A., Pietralla, N., Podolyák, Zs., Polettini, M., Sguazzin, M., Sidhu, R. S., Steck, M., Stöhlker, Th., Swartz, J. A., Vesic, J., Walker, P. M., Yamaguchi, T., and Zidarova, R.

Subjects
Nuclear Experiment, Nuclear Theory
Abstract

The nuclear two-photon or double-gamma ($2\gamma$) decay is a second-order electromagnetic process whereby a nucleus in an excited state emits two gamma rays simultaneously. To be able to directly measure the $2\gamma$ decay rate in the low-energy regime below the electron-positron pair-creation threshold, we combined the isochronous mode of a storage ring with Schottky resonant cavities. The newly developed technique can be applied to isomers with excitation energies down to $\sim100$\,keV and half-lives as short as $\sim10$\,ms. The half-life for the $2\gamma$ decay of the first-excited $0^+$ state in bare $^{72}\mathrm{Ge}$ ions was determined to be $23.9\left(6\right)$\,ms, which strongly deviates from expectations., Comment: Submitted to Physical Review Letters, 8 pages, 3 figures, 2 tables, 7 equations

Published
2023

15. Longitudinal changes in DNA methylation during the onset of islet autoimmunity differentiate between reversion versus progression of islet autoimmunity.

Author

Carry, Patrick, Vanderlinden, Lauren, Johnson, Randi, Buckner, Teresa, Steck, Andrea, Kechris, Katerina, Yang, Ivana, Fingerlin, Tasha, Fiehn, Oliver, Rewers, Marian, and Norris, Jill

Subjects
DAISY, DNA methylation, islet autoimmunity, reversion, type 1 diabetes (T1D), Humans, Diabetes Mellitus, Type 1, Female, Male, Autoimmunity, Islets of Langerhans, Disease Progression, Autoantibodies, DNA Methylation, Child, Adolescent, Longitudinal Studies, Child, Preschool, Genome-Wide Association Study, Epigenesis, Genetic
Abstract

BACKGROUND: Type 1 diabetes (T1D) is preceded by a heterogenous pre-clinical phase, islet autoimmunity (IA). We aimed to identify pre vs. post-IA seroconversion (SV) changes in DNAm that differed across three IA progression phenotypes, those who lose autoantibodies (reverters), progress to clinical T1D (progressors), or maintain autoantibody levels (maintainers). METHODS: This epigenome-wide association study (EWAS) included longitudinal DNAm measurements in blood (Illumina 450K and EPIC) from participants in Diabetes Autoimmunity Study in the Young (DAISY) who developed IA, one or more islet autoantibodies on at least two consecutive visits. We compared reverters - individuals who sero-reverted, negative for all autoantibodies on at least two consecutive visits and did not develop T1D (n=41); maintainers - continued to test positive for autoantibodies but did not develop T1D (n=60); progressors - developed clinical T1D (n=42). DNAm data were measured before (pre-SV visit) and after IA (post-SV visit). Linear mixed models were used to test for differences in pre- vs post-SV changes in DNAm across the three groups. Linear mixed models were also used to test for group differences in average DNAm. Cell proportions, age, and sex were adjusted for in all models. Median follow-up across all participants was 15.5 yrs. (interquartile range (IQR): 10.8-18.7). RESULTS: The median age at the pre-SV visit was 2.2 yrs. (IQR: 0.8-5.3) in progressors, compared to 6.0 yrs. (IQR: 1.3-8.4) in reverters, and 5.7 yrs. (IQR: 1.4-9.7) in maintainers. Median time between the visits was similar in reverters 1.4 yrs. (IQR: 1-1.9), maintainers 1.3 yrs. (IQR: 1.0-2.0), and progressors 1.8 yrs. (IQR: 1.0-2.0). Changes in DNAm, pre- vs post-SV, differed across the groups at one site (cg16066195) and 11 regions. Average DNAm (mean of pre- and post-SV) differed across 22 regions. CONCLUSION: Differentially changing DNAm regions were located in genomic areas related to beta cell function, immune cell differentiation, and immune cell function.

Published
2024

19. Health Behavior and Anxiety Changes during the COVID-19 Pandemic among Students, Faculty, and Staff at a US University

Author

Etienne Thiria, Christine Pellegrini, Bezawit E. Kase, Katherine DeVivo, and Susan E. Steck

Abstract

Objective: To investigate change in health behaviors and anxiety one year after the onset of the COVID-19 pandemic. Participants: University students (n = 525) and faculty/staff (n = 136) surveyed in February-April 2021. Methods: Cross-sectional survey on health behaviors and anxiety before and during the pandemic. Comparison by time and between groups using paired t-tests and chi-square tests. Results: Diet quality of students did not differ comparing before to during the pandemic while diet quality improved among faculty/staff (p = 0.001). Physical activity decreased among students (p < 0.0001). Sedentary time and prevalence of symptomatic anxiety increased among both students and faculty/staff (all p < 0.05). Unhealthy changes in lifestyle and anxiety were more pronounced among those who reported a less healthy diet or weight gain during the pandemic. Conclusions: Negative impacts of the COVID-19 pandemic on physical activity, sedentary time, and anxiety have persisted approximately one year after the onset of the COVID-19 pandemic in a university population.

Published
2024
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20. Correction to: Consensus guidance for monitoring individuals with islet autoantibody‑positive pre‑stage 3 type 1 diabetes

Author

Phillip, Moshe, Achenbach, Peter, Addala, Ananta, Albanese‑O’Neill, Anastasia, Battelino, Tadej, Bell, Kirstine J., Besser, Rachel E. J., Bonifacio, Ezio, Colhoun, Helen M., Couper, Jennifer J., Craig, Maria E., Danne, Thomas, de Beaufort, Carine, Dovc, Klemen, Driscoll, Kimberly A., Dutta, Sanjoy, Ebekozien, Osagie, Larsson, Helena Elding, Feiten, Daniel J., Frohnert, Brigitte I., Gabbay, Robert A., Gallagher, Mary P., Greenbaum, Carla J., Griffin, Kurt J., Hagopian, William, Haller, Michael J., Hendrieckx, Christel, Hendriks, Emile, Holt, Richard I. G., Hughes, Lucille, Ismail, Heba M., Jacobsen, Laura M., Johnson, Suzanne B., Kolb, Leslie E., Kordonouri, Olga, Lange, Karin, Lash, Robert W., Lernmark, Åke, Libman, Ingrid, Lundgren, Markus, Maahs, David M., Marcovecchio, M. Loredana, Mathieu, Chantal, Miller, Kellee M., O’Donnell, Holly K., Oron, Tal, Patil, Shivajirao P., Pop‑Busui, Rodica, Rewers, Marian J., Rich, Stephen S., Schatz, Desmond A., Schulman‑Rosenbaum, Rifka, Simmons, Kimber M., Sims, Emily K., Skyler, Jay S., Smith, Laura B., Speake, Cate, Steck, Andrea K., Thomas, Nicholas P. B., Tonyushkina, Ksenia N., Veijola, Riitta, Wentworth, John M., Wherrett, Diane K., Wood, Jamie R., Ziegler, Anette‑Gabriele, and DiMeglio, Linda A.

Published
2024
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21. Large Language Models as Zero-Shot Conversational Recommenders

Author

He, Zhankui, Xie, Zhouhang, Jha, Rahul, Steck, Harald, Liang, Dawen, Feng, Yesu, Majumder, Bodhisattwa Prasad, Kallus, Nathan, and McAuley, Julian

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

In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contributions. (1) Data: To gain insights into model behavior in "in-the-wild" conversational recommendation scenarios, we construct a new dataset of recommendation-related conversations by scraping a popular discussion website. This is the largest public real-world conversational recommendation dataset to date. (2) Evaluation: On the new dataset and two existing conversational recommendation datasets, we observe that even without fine-tuning, large language models can outperform existing fine-tuned conversational recommendation models. (3) Analysis: We propose various probing tasks to investigate the mechanisms behind the remarkable performance of large language models in conversational recommendation. We analyze both the large language models' behaviors and the characteristics of the datasets, providing a holistic understanding of the models' effectiveness, limitations and suggesting directions for the design of future conversational recommenders, Comment: Accepted as CIKM 2023 long paper. Longer version is coming soon (e.g., more details about dataset)

Published
2023
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22. Disease-modifying therapies and features linked to treatment response in type 1 diabetes prevention: a systematic review.

Author

Felton, Jamie, Griffin, Kurt, Oram, Richard, Speake, Cate, Long, S, Onengut-Gumuscu, Suna, Rich, Stephen, Monaco, Gabriela, Evans-Molina, Carmella, DiMeglio, Linda, Ismail, Heba, Steck, Andrea, Dabelea, Dana, Johnson, Randi, Urazbayeva, Marzhan, Gitelman, Stephen, Wentworth, John, Redondo, Maria, and Sims, Emily

Abstract

BACKGROUND: Type 1 diabetes (T1D) results from immune-mediated destruction of insulin-producing beta cells. Prevention efforts have focused on immune modulation and supporting beta cell health before or around diagnosis; however, heterogeneity in disease progression and therapy response has limited translation to clinical practice, highlighting the need for precision medicine approaches to T1D disease modification. METHODS: To understand the state of knowledge in this area, we performed a systematic review of randomized-controlled trials with ≥50 participants cataloged in PubMed or Embase from the past 25 years testing T1D disease-modifying therapies and/or identifying features linked to treatment response, analyzing bias using a Cochrane-risk-of-bias instrument. RESULTS: We identify and summarize 75 manuscripts, 15 describing 11 prevention trials for individuals with increased risk for T1D, and 60 describing treatments aimed at preventing beta cell loss at disease onset. Seventeen interventions, mostly immunotherapies, show benefit compared to placebo (only two prior to T1D onset). Fifty-seven studies employ precision analyses to assess features linked to treatment response. Age, beta cell function measures, and immune phenotypes are most frequently tested. However, analyses are typically not prespecified, with inconsistent methods of reporting, and tend to report positive findings. CONCLUSIONS: While the quality of prevention and intervention trials is overall high, the low quality of precision analyses makes it difficult to draw meaningful conclusions that inform clinical practice. To facilitate precision medicine approaches to T1D prevention, considerations for future precision studies include the incorporation of uniform outcome measures, reproducible biomarkers, and prespecified, fully powered precision analyses into future trial design.

Published
2023

24. Adaptive local density estimation in tomography

Author

Miguel, Sergio Brenner and Steck, Janine

Subjects
Mathematics - Statistics Theory, Primary 62G07, secondary 44A12, 62C20
Abstract

We study the non-parametric estimation of a multidimensional unknown density f in a tomography problem based on independent and identically distributed observations, whose common density is proportional to the Radon transform of f. We identify the underlying statistical inverse problem and use a spectral cut-off regularisation to deduce an estimator. A fully data-driven choice of the cut-off parameter m in R+ is proposed and studied. To discuss the bias-variance trade off, we consider Sobolev spaces and show the minimax-optimality of the spectral cut-off density estimator. In a simulation study, we illustrate a reasonable behaviour of the studied fully data-driven estimator., Comment: 19 pages, 8 figures

Published
2023

26. Connectivity of graphs that do not have the edge-Erd\H{o}s-P\'{o}sa property

Author

Steck, Raphael

Subjects
Mathematics - Combinatorics
Abstract

We show that we can assume graphs that do not have the edge-Erd\H{o}s-P\'{o}sa property to be connected. Then we strengthen this result to $2$-connectivity under the additional assumptions of a minor-closed property and a generic counterexample.

Published
2023

27. Storage, Accumulation and Deceleration of Secondary Beams for Nuclear Astrophysics

Author

Glorius, J., Litvinov, Yu. A., Aliotta, M., Amjad, F., Brückner, B., Bruno, C. G., Chen, R., Davinson, T., Dellmann, S. F., Dickel, T., Dillmann, I., Erbacher, P., Forstner, O., Geissel, H., Griffin, C. J., Grisenti, R., Gumberidze, A., Haettner, E., Hess, R., Hillenbrand, P. -M., Hornung, C., Joseph, R., Jurado, B., Kazanseva, E., Knöbel, R., Kostyleva, D., Kozhuharov, C., Kuzminchuk, N., Langer, C., Leckenby, G., Lederer-Woods, C., Lestinsky, M., Litvinov, S., Löher, B., Lorenz, B., Lorenz, E., Marsh, J., Menz, E., Morgenroth, T., Mukha, I., Petridis, N., Popp, U., Psaltis, A., Purushothaman, S., Reifarth, R., Rocco, E., Roy, P., Sanjari, M. S., Scheidenberger, C., Sguazzin, M., Sidhu, R. S., Spillmann, U., Steck, M., Stöhlker, T., Swartz, J. A., Tanaka, Y. K., Törnqvist, H., Varga, L., Vescovi, D., Weick, H., Weigand, M., Woods, P. J., Yamaguchi, T., and Zhao, J.

Subjects
Physics - Instrumentation and Detectors, Nuclear Experiment
Abstract

Low-energy investigations on rare ion beams are often limited by the available intensity and purity of the ion species in focus. Here, we present the first application of a technique that combines in-flight production at relativistic energies with subsequent secondary beam storage, accumulation and finally deceleration to the energy of interest. Using the FRS and ESR facilities at GSI, this scheme was pioneered to provide a secondary beam of $^{118}$Te$^{52+}$ for the measurement of nuclear proton-capture at energies of 6 and 7 MeV/u. The technique provided stored beam intensities of about $10^6$ ions at high purity and brilliance, representing a major step towards low-energy nuclear physics studies using rare ion beams.

Published
2023
Full Text
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28. On the edge-Erd\H{o}s-P\'{o}sa property of walls

Author

Bruhn, Henning and Steck, Raphael

Subjects
Mathematics - Combinatorics
Abstract

We show that walls of size at least $6 \times 4$ do not have the edge-Erd\H{o}s-P\'{o}sa property.

Published
2023

29. The James Webb Space Telescope Mission

Author

Gardner, Jonathan P., Mather, John C., Abbott, Randy, Abell, James S., Abernathy, Mark, Abney, Faith E., Abraham, John G., Abraham, Roberto, Abul-Huda, Yasin M., Acton, Scott, Adams, Cynthia K., Adams, Evan, Adler, David S., Adriaensen, Maarten, Aguilar, Jonathan Albert, Ahmed, Mansoor, Ahmed, Nasif S., Ahmed, Tanjira, Albat, Rüdeger, Albert, Loïc, Alberts, Stacey, Aldridge, David, Allen, Mary Marsha, Allen, Shaune S., Altenburg, Martin, Altunc, Serhat, Alvarez, Jose Lorenzo, Álvarez-Márquez, Javier, de Oliveira, Catarina Alves, Ambrose, Leslie L., Anandakrishnan, Satya M., Andersen, Gregory C., Anderson, Harry James, Anderson, Jay, Anderson, Kristen, Anderson, Sara M., Aprea, Julio, Archer, Benita J., Arenberg, Jonathan W., Argyriou, Ioannis, Arribas, Santiago, Artigau, Étienne, Arvai, Amanda Rose, Atcheson, Paul, Atkinson, Charles B., Averbukh, Jesse, Aymergen, Cagatay, Bacinski, John J., Baggett, Wayne E., Bagnasco, Giorgio, Baker, Lynn L., Balzano, Vicki Ann, Banks, Kimberly A., Baran, David A., Barker, Elizabeth A., Barrett, Larry K., Barringer, Bruce O., Barto, Allison, Bast, William, Baudoz, Pierre, Baum, Stefi, Beatty, Thomas G., Beaulieu, Mathilde, Bechtold, Kathryn, Beck, Tracy, Beddard, Megan M., Beichman, Charles, Bellagama, Larry, Bely, Pierre, Berger, Timothy W., Bergeron, Louis E., Darveau-Bernier, Antoine, Bertch, Maria D., Beskow, Charlotte, Betz, Laura E., Biagetti, Carl P., Birkmann, Stephan, Bjorklund, Kurt F., Blackwood, James D., Blazek, Ronald Paul, Blossfeld, Stephen, Bluth, Marcel, Boccaletti, Anthony, Boegner Jr., Martin E., Bohlin, Ralph C., Boia, John Joseph, Böker, Torsten, Bonaventura, N., Bond, Nicholas A., Bosley, Kari Ann, Boucarut, Rene A., Bouchet, Patrice, Bouwman, Jeroen, Bower, Gary, Bowers, Ariel S., Bowers, Charles W., Boyce, Leslye A., Boyer, Christine T., Boyer, Martha L., Boyer, Michael, Boyer, Robert, Bradley, Larry D., Brady, Gregory R., Brandl, Bernhard R., Brannen, Judith L., Breda, David, Bremmer, Harold G., Brennan, David, Bresnahan, Pamela A., Bright, Stacey N., Broiles, Brian J., Bromenschenkel, Asa, Brooks, Brian H., Brooks, Keira J., Brown, Bob, Brown, Bruce, Brown, Thomas M., Bruce, Barry W., Bryson, Jonathan G., Bujanda, Edwin D., Bullock, Blake M., Bunker, A. J., Bureo, Rafael, Burt, Irving J., Bush, James Aaron, Bushouse, Howard A., Bussman, Marie C., Cabaud, Olivier, Cale, Steven, Calhoon, Charles D., Calvani, Humberto, Canipe, Alicia M., Caputo, Francis M., Cara, Mihai, Carey, Larkin, Case, Michael Eli, Cesari, Thaddeus, Cetorelli, Lee D., Chance, Don R., Chandler, Lynn, Chaney, Dave, Chapman, George N., Charlot, S., Chayer, Pierre, Cheezum, Jeffrey I., Chen, Bin, Chen, Christine H., Cherinka, Brian, Chichester, Sarah C., Chilton, Zachary S., Chittiraibalan, Dharini, Clampin, Mark, Clark, Charles R., Clark, Kerry W., Clark, Stephanie M., Claybrooks, Edward E., Cleveland, Keith A., Cohen, Andrew L., Cohen, Lester M., Colón, Knicole D., Coleman, Benee L., Colina, Luis, Comber, Brian J., Comeau, Thomas M., Comer, Thomas, Reis, Alain Conde, Connolly, Dennis C., Conroy, Kyle E., Contos, Adam R., Contreras, James, Cook, Neil J., Cooper, James L., Cooper, Rachel Aviva, Correia, Michael F., Correnti, Matteo, Cossou, Christophe, Costanza, Brian F., Coulais, Alain, Cox, Colin R., Coyle, Ray T., Cracraft, Misty M., Noriega-Crespo, Alberto, Crew, Keith A., Curtis, Gary J., Cusveller, Bianca, Maciel, Cleyciane Da Costa, Dailey, Christopher T., Daugeron, Frédéric, Davidson, Greg S., Davies, James E., Davis, Katherine Anne, Davis, Michael S., Day, Ratna, de Chambure, Daniel, de Jong, Pauline, De Marchi, Guido, Dean, Bruce H., Decker, John E., Delisa, Amy S., Dell, Lawrence C., Dellagatta, Gail, Dembinska, Franciszka, Demosthenes, Sandor, Dencheva, Nadezhda M., Deneu, Philippe, DePriest, William W., Deschenes, Jeremy, Dethienne, Nathalie, Detre, Örs Hunor, Diaz, Rosa Izela, Dicken, Daniel, DiFelice, Audrey S., Dillman, Matthew, Disharoon, Maureen O., van Dishoeck, Ewine F., Dixon, William V., Doggett, Jesse B., Dominguez, Keisha L., Donaldson, Thomas S., Doria-Warner, Cristina M., Santos, Tony Dos, Doty, Heather, Douglas Jr., Robert E., Doyon, René, Dressler, Alan, Driggers, Jennifer, Driggers, Phillip A., Dunn, Jamie L., DuPrie, Kimberly C., Dupuis, Jean, Durning, John, Dutta, Sanghamitra B., Earl, Nicholas M., Eccleston, Paul, Ecobichon, Pascal, Egami, Eiichi, Ehrenwinkler, Ralf, Eisenhamer, Jonathan D., Eisenhower, Michael, Eisenstein, Daniel J., Hamel, Zaky El, Elie, Michelle L., Elliott, James, Elliott, Kyle Wesley, Engesser, Michael, Espinoza, Néstor, Etienne, Odessa, Etxaluze, Mireya, Evans, Leah, Fabreguettes, Luce, Falcolini, Massimo, Falini, Patrick R., Fatig, Curtis, Feeney, Matthew, Feinberg, Lee D., Fels, Raymond, Ferdous, Nazma, Ferguson, Henry C., Ferrarese, Laura, Ferreira, Marie-Héléne, Ferruit, Pierre, Ferry, Malcolm, Filippazzo, Joseph Charles, Firre, Daniel, Fix, Mees, Flagey, Nicolas, Flanagan, Kathryn A., Fleming, Scott W., Florian, Michael, Flynn, James R., Foiadelli, Luca, Fontaine, Mark R., Fontanella, Erin Marie, Forshay, Peter Randolph, Fortner, Elizabeth A., Fox, Ori D., Framarini, Alexandro P., Francisco, John I., Franck, Randy, Franx, Marijn, Franz, David E., Friedman, Scott D., Friend, Katheryn E., Frost, James R., Fu, Henry, Fullerton, Alexander W., Gaillard, Lionel, Galkin, Sergey, Gallagher, Ben, Galyer, Anthony D., Marín, Macarena García, Gardner, Lisa E., Garland, Dennis, Garrett, Bruce Albert, Gasman, Danny, Gáspár, András, Gastaud, René, Gaudreau, Daniel, Gauthier, Peter Timothy, Geers, Vincent, Geithner, Paul H., Gennaro, Mario, Gerber, John, Gereau, John C., Giampaoli, Robert, Giardino, Giovanna, Gibbons, Paul C., Gilbert, Karolina, Gilman, Larry, Girard, Julien H., Giuliano, Mark E., Gkountis, Konstantinos, Glasse, Alistair, Glassmire, Kirk Zachary, Glauser, Adrian Michael, Glazer, Stuart D., Goldberg, Joshua, Golimowski, David A., Gonzaga, Shireen P., Gordon, Karl D., Gordon, Shawn J., Goudfrooij, Paul, Gough, Michael J., Graham, Adrian J., Grau, Christopher M., Green, Joel David, Greene, Gretchen R., Greene, Thomas P., Greenfield, Perry E., Greenhouse, Matthew A., Greve, Thomas R., Greville, Edgar M., Grimaldi, Stefano, Groe, Frank E., Groebner, Andrew, Grumm, David M., Grundy, Timothy, Güdel, Manuel, Guillard, Pierre, Guldalian, John, Gunn, Christopher A., Gurule, Anthony, Gutman, Irvin Meyer, Guy, Paul D., Guyot, Benjamin, Hack, Warren J., Haderlein, Peter, Hagan, James B., Hagedorn, Andria, Hainline, Kevin, Haley, Craig, Hami, Maryam, Hamilton, Forrest Clifford, Hammann, Jeffrey, Hammel, Heidi B., Hanley, Christopher J., Hansen, Carl August, Hardy, Bruce, Harnisch, Bernd, Harr, Michael Hunter, Harris, Pamela, Hart, Jessica Ann, Hartig, George F., Hasan, Hashima, Hashim, Kathleen Marie, Hashimoto, Ryan, Haskins, Sujee J., Hawkins, Robert Edward, Hayden, Brian, Hayden, William L., Healy, Mike, Hecht, Karen, Heeg, Vince J., Hejal, Reem, Helm, Kristopher A., Hengemihle, Nicholas J., Henning, Thomas, Henry, Alaina, Henry, Ronald L., Henshaw, Katherine, Hernandez, Scarlin, Herrington, Donald C., Heske, Astrid, Hesman, Brigette Emily, Hickey, David L., Hilbert, Bryan N., Hines, Dean C., Hinz, Michael R., Hirsch, Michael, Hitcho, Robert S., Hodapp, Klaus, Hodge, Philip E., Hoffman, Melissa, Holfeltz, Sherie T., Holler, Bryan Jason, Hoppa, Jennifer Rose, Horner, Scott, Howard, Joseph M., Howard, Richard J., Huber, Jean M., Hunkeler, Joseph S., Hunter, Alexander, Hunter, David Gavin, Hurd, Spencer W., Hurst, Brendan J., Hutchings, John B., Hylan, Jason E., Ignat, Luminita Ilinca, Illingworth, Garth, Irish, Sandra M., Isaacs III, John C., Jackson Jr., Wallace C., Jaffe, Daniel T., Jahic, Jasmin, Jahromi, Amir, Jakobsen, Peter, James, Bryan, James, John C., James, LeAndrea Rae, Jamieson, William Brian, Jandra, Raymond D., Jayawardhana, Ray, Jedrzejewski, Robert, Jeffers, Basil S., Jensen, Peter, Joanne, Egges, Johns, Alan T., Johnson, Carl A., Johnson, Eric L., Johnson, Patricia, Johnson, Phillip Stephen, Johnson, Thomas K., Johnson, Timothy W., Johnstone, Doug, Jollet, Delphine, Jones, Danny P., Jones, Gregory S., Jones, Olivia C., Jones, Ronald A., Jones, Vicki, Jordan, Ian J., Jordan, Margaret E., Jue, Reginald, Jurkowski, Mark H., Justis, Grant, Justtanont, Kay, Kaleida, Catherine C., Kalirai, Jason S., Kalmanson, Phillip Cabrales, Kaltenegger, Lisa, Kammerer, Jens, Kan, Samuel K., Kanarek, Graham Childs, Kao, Shaw-Hong, Karakla, Diane M., Karl, Hermann, Kassin, Susan A., Kauffman, David D., Kavanagh, Patrick, Kelley, Leigh L., Kelly, Douglas M., Kendrew, Sarah, Kennedy, Herbert V., Kenny, Deborah A., Keski-Kuha, Ritva A., Keyes, Charles D., Khan, Ali, Kidwell, Richard C., Kimble, Randy A., King, James S., King, Richard C., Kinzel, Wayne M., Kirk, Jeffrey R., Kirkpatrick, Marc E., Klaassen, Pamela, Klingemann, Lana, Klintworth, Paul U., Knapp, Bryan Adam, Knight, Scott, Knollenberg, Perry J., Knutsen, Daniel Mark, Koehler, Robert, Koekemoer, Anton M., Kofler, Earl T., Kontson, Vicki L., Kovacs, Aiden Rose, Kozhurina-Platais, Vera, Krause, Oliver, Kriss, Gerard A., Krist, John, Kristoffersen, Monica R., Krogel, Claudia, Krueger, Anthony P., Kulp, Bernard A., Kumari, Nimisha, Kwan, Sandy W., Kyprianou, Mark, Labador, Aurora Gadiano, Labiano, Álvaro, Lafrenière, David, Lagage, Pierre-Olivier, Laidler, Victoria G., Laine, Benoit, Laird, Simon, Lajoie, Charles-Philippe, Lallo, Matthew D., Lam, May Yen, LaMassa, Stephanie Marie, Lambros, Scott D., Lampenfield, Richard Joseph, Lander, Matthew Ed, Langston, James Hutton, Larson, Kirsten, Larson, Melora, LaVerghetta, Robert Joseph, Law, David R., Lawrence, Jon F., Lee, David W., Lee, Janice, Lee, Yat-Ning Paul, Leisenring, Jarron, Leveille, Michael Dunlap, Levenson, Nancy A., Levi, Joshua S., Levine, Marie B., Lewis, Dan, Lewis, Jake, Lewis, Nikole, Libralato, Mattia, Lidon, Norbert, Liebrecht, Paula Louisa, Lightsey, Paul, Lilly, Simon, Lim, Frederick C., Lim, Pey Lian, Ling, Sai-Kwong, Link, Lisa J., Link, Miranda Nicole, Lipinski, Jamie L., Liu, XiaoLi, Lo, Amy S., Lobmeyer, Lynette, Logue, Ryan M., Long, Chris A., Long, Douglas R., Long, Ilana D., Long, Knox S., López-Caniego, Marcos, Lotz, Jennifer M., Love-Pruitt, Jennifer M., Lubskiy, Michael, Luers, Edward B., Luetgens, Robert A., Luevano, Annetta J., Lui, Sarah Marie G. Flores, Lund III, James M., Lundquist, Ray A., Lunine, Jonathan, Lützgendorf, Nora, Lynch, Richard J., MacDonald, Alex J., MacDonald, Kenneth, Macias, Matthew J., Macklis, Keith I., Maghami, Peiman, Maharaja, Rishabh Y., Maiolino, Roberto, Makrygiannis, Konstantinos G., Malla, Sunita Giri, Malumuth, Eliot M., Manjavacas, Elena, Marini, Andrea, Marrione, Amanda, Marston, Anthony, Martel, André R, Martin, Didier, Martin, Peter G., Martinez, Kristin L., Maschmann, Marc, Masci, Gregory L., Masetti, Margaret E., Maszkiewicz, Michael, Matthews, Gary, Matuskey, Jacob E., McBrayer, Glen A., McCarthy, Donald W., McCaughrean, Mark J., McClare, Leslie A., McClare, Michael D., McCloskey, John C., McClurg, Taylore D., McCoy, Martin, McElwain, Michael W., McGregor, Roy D., McGuffey, Douglas B., McKay, Andrew G., McKenzie, William K., McLean, Brian, McMaster, Matthew, McNeil, Warren, De Meester, Wim, Mehalick, Kimberly L., Meixner, Margaret, Meléndez, Marcio, Menzel, Michael P., Menzel, Michael T., Merz, Matthew, Mesterharm, David D., Meyer, Michael R., Meyett, Michele L., Meza, Luis E., Midwinter, Calvin, Milam, Stefanie N., Miller, Jay Todd, Miller, William C., Miskey, Cherie L., Misselt, Karl, Mitchell, Eileen P., Mohan, Martin, Montoya, Emily E., Moran, Michael J., Morishita, Takahiro, Moro-Martín, Amaya, Morrison, Debra L., Morrison, Jane, Morse, Ernie C., Moschos, Michael, Moseley, S. H., Mosier, Gary E., Mosner, Peter, Mountain, Matt, Muckenthaler, Jason S., Mueller, Donald G., Mueller, Migo, Muhiem, Daniella, Mühlmann, Prisca, Mullally, Susan Elizabeth, Mullen, Stephanie M., Munger, Alan J, Murphy, Jess, Murray, Katherine T., Muzerolle, James C., Mycroft, Matthew, Myers, Andrew, Myers, Carey R., Myers, Fred Richard R., Myers, Richard, Myrick, Kaila, Nagle IV, Adrian F., Nayak, Omnarayani, Naylor, Bret, Neff, Susan G., Nelan, Edmund P., Nella, John, Nguyen, Duy Tuong, Nguyen, Michael N., Nickson, Bryony, Nidhiry, John Joseph, Niedner, Malcolm B., Nieto-Santisteban, Maria, Nikolov, Nikolay K., Nishisaka, Mary Ann, Nota, Antonella, O'Mara, Robyn C., Oboryshko, Michael, O'Brien, Marcus B., Ochs, William R., Offenberg, Joel D., Ogle, Patrick Michael, Ohl, Raymond G., Olmsted, Joseph Hamden, Osborne, Shannon Barbara, O'Shaughnessy, Brian Patrick, Östlin, Göran, O'Sullivan, Brian, Otor, O. Justin, Ottens, Richard, Ouellette, Nathalie N. -Q., Outlaw, Daria J., Owens, Beverly A., Pacifici, Camilla, Page, James Christophe, Paranilam, James G., Park, Sang, Parrish, Keith A., Paschal, Laura, Patapis, Polychronis, Patel, Jignasha, Patrick, Keith, Pattishall Jr., Robert A., Paul, Douglas William, Paul, Shirley J., Pauly, Tyler Andrew, Pavlovsky, Cheryl M., Peña-Guerrero, Maria, Pedder, Andrew H., Peek, Matthew Weldon, Pelham, Patricia A., Penanen, Konstantin, Perriello, Beth A., Perrin, Marshall D., Perrine, Richard F., Perrygo, Chuck, Peslier, Muriel, Petach, Michael, Peterson, Karla A., Pfarr, Tom, Pierson, James M., Pietraszkiewicz, Martin, Pilchen, Guy, Pipher, Judy L., Pirzkal, Norbert, Pitman, Joseph T., Player, Danielle M., Plesha, Rachel, Plitzke, Anja, Pohner, John A., Poletis, Karyn Konstantin, Pollizzi, Joseph A., Polster, Ethan, Pontius, James T., Pontoppidan, Klaus, Porges, Susana C., Potter, Gregg D., Prescott, Stephen, Proffitt, Charles R., Pueyo, Laurent, Neira, Irma Aracely Quispe, Radich, Armando, Rager, Reiko T., Rameau, Julien, Ramey, Deborah D., Alarcon, Rafael Ramos, Rampini, Riccardo, Rapp, Robert, Rashford, Robert A., Rauscher, Bernard J., Ravindranath, Swara, Rawle, Timothy, Rawlings, Tynika N., Ray, Tom, Regan, Michael W., Rehm, Brian, Rehm, Kenneth D., Reid, Neill, Reis, Carl A., Renk, Florian, Reoch, Tom B., Ressler, Michael, Rest, Armin W., Reynolds, Paul J., Richon, Joel G., Richon, Karen V., Ridgaway, Michael, Riedel, Adric Richard, Rieke, George H., Rieke, Marcia, Rifelli, Richard E., Rigby, Jane R., Riggs, Catherine S., Ringel, Nancy J., Ritchie, Christine E., Rix, Hans-Walter, Robberto, Massimo, Robinson, Michael S., Robinson, Orion, Rock, Frank W., Rodriguez, David R., del Pino, Bruno Rodríguez, Roellig, Thomas, Rohrbach, Scott O., Roman, Anthony J., Romelfanger, Frederick J., Romo Jr., Felipe P., Rosales, Jose J., Rose, Perry, Roteliuk, Anthony F., Roth, Marc N., Rothwell, Braden Quinn, Rouzaud, Sylvain, Rowe, Jason, Rowlands, Neil, Roy, Arpita, Royer, Pierre, Rui, Chunlei, Rumler, Peter, Rumpl, William, Russ, Melissa L., Ryan, Michael B., Ryan, Richard M., Saad, Karl, Sabata, Modhumita, Sabatino, Rick, Sabbi, Elena, Sabelhaus, Phillip A., Sabia, Stephen, Sahu, Kailash C., Saif, Babak N., Salvignol, Jean-Christophe, Samara-Ratna, Piyal, Samuelson, Bridget S., Sanders, Felicia A., Sappington, Bradley, Sargent, B. A., Sauer, Arne, Savadkin, Bruce J., Sawicki, Marcin, Schappell, Tina M., Scheffer, Caroline, Scheithauer, Silvia, Scherer, Ron, Schiff, Conrad, Schlawin, Everett, Schmeitzky, Olivier, Schmitz, Tyler S., Schmude, Donald J., Schneider, Analyn, Schreiber, Jürgen, Schroeven-Deceuninck, Hilde, Schultz, John J., Schwab, Ryan, Schwartz, Curtis H., Scoccimarro, Dario, Scott, John F., Scott, Michelle B., Seaton, Bonita L., Seely, Bruce S., Seery, Bernard, Seidleck, Mark, Sembach, Kenneth, Shanahan, Clare Elizabeth, Shaughnessy, Bryan, Shaw, Richard A., Shay, Christopher Michael, Sheehan, Even, Sheth, Kartik, Shih, Hsin-Yi, Shivaei, Irene, Siegel, Noah, Sienkiewicz, Matthew G., Simmons, Debra D., Simon, Bernard P., Sirianni, Marco, Sivaramakrishnan, Anand, Slade, Jeffrey E., Sloan, G. C., Slocum, Christine E., Slowinski, Steven E., Smith, Corbett T., Smith, Eric P., Smith, Erin C., Smith, Koby, Smith, Robert, Smith, Stephanie J., Smolik, John L., Soderblom, David R., Sohn, Sangmo Tony, Sokol, Jeff, Sonneborn, George, Sontag, Christopher D., Sooy, Peter R., Soummer, Remi, Southwood, Dana M., Spain, Kay, Sparmo, Joseph, Speer, David T., Spencer, Richard, Sprofera, Joseph D., Stallcup, Scott S., Stanley, Marcia K., Stansberry, John A., Stark, Christopher C., Starr, Carl W., Stassi, Diane Y., Steck, Jane A., Steeley, Christine D., Stephens, Matthew A., Stephenson, Ralph J., Stewart, Alphonso C., Stiavelli, Massimo, Stockman Jr., Hervey, Strada, Paolo, Straughn, Amber N., Streetman, Scott, Strickland, David Kendal, Strobele, Jingping F., Stuhlinger, Martin, Stys, Jeffrey Edward, Such, Miguel, Sukhatme, Kalyani, Sullivan, Joseph F., Sullivan, Pamela C., Sumner, Sandra M., Sun, Fengwu, Sunnquist, Benjamin Dale, Swade, Daryl Allen, Swam, Michael S., Swenton, Diane F., Swoish, Robby A., Litten, Oi In Tam, Tamas, Laszlo, Tao, Andrew, Taylor, David K., Taylor, Joanna M., Plate, Maurice te, Van Tea, Mason, Teague, Kelly K., Telfer, Randal C., Temim, Tea, Texter, Scott C., Thatte, Deepashri G., Thompson, Christopher Lee, Thompson, Linda M., Thomson, Shaun R., Thronson, Harley, Tierney, C. M., Tikkanen, Tuomo, Tinnin, Lee, Tippet, William Thomas, Todd, Connor William, Tran, Hien D., Trauger, John, Trejo, Edwin Gregorio, Truong, Justin Hoang Vinh, Tsukamoto, Christine L., Tufail, Yasir, Tumlinson, Jason, Tustain, Samuel, Tyra, Harrison, Ubeda, Leonardo, Underwood, Kelli, Uzzo, Michael A., Vaclavik, Steven, Valenduc, Frida, Valenti, Jeff A., Van Campen, Julie, van de Wetering, Inge, Van Der Marel, Roeland P., van Haarlem, Remy, Vandenbussche, Bart, Vanterpool, Dona D., Vernoy, Michael R., Costas, Maria Begoña Vila, Volk, Kevin, Voorzaat, Piet, Voyton, Mark F., Vydra, Ekaterina, Waddy, Darryl J., Waelkens, Christoffel, Wahlgren, Glenn Michael, Walker Jr., Frederick E., Wander, Michel, Warfield, Christine K., Warner, Gerald, Wasiak, Francis C., Wasiak, Matthew F., Wehner, James, Weiler, Kevin R., Weilert, Mark, Weiss, Stanley B., Wells, Martyn, Welty, Alan D., Wheate, Lauren, Wheeler, Thomas P., White, Christy L., Whitehouse, Paul, Whiteleather, Jennifer Margaret, Whitman, William Russell, Williams, Christina C., Willmer, Christopher N. A., Willott, Chris J., Willoughby, Scott P., Wilson, Andrew, Wilson, Debra, Wilson, Donna V., Windhorst, Rogier, Wislowski, Emily Christine, Wolfe, David J., Wolfe, Michael A., Wolff, Schuyler, Wondel, Amancio, Woo, Cindy, Woods, Robert T., Worden, Elaine, Workman, William, Wright, Gillian S., Wu, Carl, Wu, Chi-Rai, Wun, Dakin D., Wymer, Kristen B., Yadetie, Thomas, Yan, Isabelle C., Yang, Keith C., Yates, Kayla L., Yeager, Christopher R., Yerger, Ethan John, Young, Erick T., Young, Gary, Yu, Gene, Yu, Susan, Zak, Dean S., Zeidler, Peter, Zepp, Robert, Zhou, Julia, Zincke, Christian A., Zonak, Stephanie, and Zondag, Elisabeth

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit., Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figures

Published
2023
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30. Author Correction: Testing quantum electrodynamics in extreme fields using helium-like uranium

Author

Loetzsch, R., Beyer, H. F., Duval, L., Spillmann, U., Banaś, D., Dergham, P., Kröger, F. M., Glorius, J., Grisenti, R. E., Guerra, M., Gumberidze, A., Heß, R., Hillenbrand, P.-M., Indelicato, P., Jagodzinski, P., Lamour, E., Lorentz, B., Litvinov, S., Litvinov, Yu. A., Machado, J., Paul, N., Paulus, G. G., Petridis, N., Santos, J. P., Scheidel, M., Sidhu, R. S., Steck, M., Steydli, S., Szary, K., Trotsenko, S., Uschmann, I., Weber, G., Stöhlker, Th., and Trassinelli, M.

Published
2024
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36. On the Influence of Incorrect Idealized Joint Axes to the Design Process of Orthoses

Author

Steck, Patrick, Scherb, David, Miehling, Jörg, Wartzack, Sandro, Tavares, João Manuel R. S., Series Editor, Jorge, Renato Natal, Series Editor, Cohen, Laurent, Editorial Board Member, Doblare, Manuel, Editorial Board Member, Frangi, Alejandro, Editorial Board Member, Garcia-Aznar, Jose Manuel, Editorial Board Member, Holzapfel, Gerhard A., Editorial Board Member, Hughes, Thomas J.R., Editorial Board Member, Kamm, Roger, Editorial Board Member, Li, Shuo, Editorial Board Member, Löhner, Rainald, Editorial Board Member, Nithiarasu, Perumal, Editorial Board Member, Oñate, Eugenio, Editorial Board Member, Perales, Francisco J., Editorial Board Member, Prendergast, Patrick J., Editorial Board Member, Tamma, Kumar K., Editorial Board Member, Vilas-Boas, Joao Paulo, Editorial Board Member, Weiss, Jeffrey, Editorial Board Member, Zhang, Yongjie Jessica, Editorial Board Member, Skalli, Wafa, editor, Laporte, Sébastien, editor, and Benoit, Aurélie, editor

Published
2024
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37. Multi-Spectral Image Classification with Ultra-Lean Complex-Valued Models

Author

Singhal, Utkarsh, Yu, Stella X., Steck, Zackery, Kangas, Scott, and Reite, Aaron A.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Multi-spectral imagery is invaluable for remote sensing due to different spectral signatures exhibited by materials that often appear identical in greyscale and RGB imagery. Paired with modern deep learning methods, this modality has great potential utility in a variety of remote sensing applications, such as humanitarian assistance and disaster recovery efforts. State-of-the-art deep learning methods have greatly benefited from large-scale annotations like in ImageNet, but existing MSI image datasets lack annotations at a similar scale. As an alternative to transfer learning on such data with few annotations, we apply complex-valued co-domain symmetric models to classify real-valued MSI images. Our experiments on 8-band xView data show that our ultra-lean model trained on xView from scratch without data augmentations can outperform ResNet with data augmentation and modified transfer learning on xView. Our work is the first to demonstrate the value of complex-valued deep learning on real-valued MSI data., Comment: NeuRIPS 2022 HADR workshop submission

Published
2022

39. Testing quantum electrodynamics in extreme fields using helium-like uranium

Author

Loetzsch, R., Beyer, H. F., Duval, L., Spillmann, U., Banaś, D., Dergham, P., Kröger, F. M., Glorius, J., Grisenti, R. E., Guerra, M., Gumberidze, A., Heß, R., Hillenbrand, P.-M., Indelicato, P., Jagodzinski, P., Lamour, E., Lorentz, B., Litvinov, S., Litvinov, Yu. A., Machado, J., Paul, N., Paulus, G. G., Petridis, N., Santos, J. P., Scheidel, M., Sidhu, R. S., Steck, M., Steydli, S., Szary, K., Trotsenko, S., Uschmann, I., Weber, G., Stöhlker, Th., and Trassinelli, M.

Published
2024
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41. Point prevalence of evidence-based antimicrobial use among hospitalized patients in sub-Saharan Africa: a systematic review and meta-analysis

Author

Minyahil Tadesse Boltena, Mirkuzie Wolde, Belachew Hailu, Ziad El-Khatib, Veronika Steck, Selam Woldegerima, Yibeltal Siraneh, and Sudhakar Morankar

Subjects
Antibiotic prescribing, Antimicrobial use, Evidence-based healthcare, Hospitalized patients, Point prevalence survey, Sub-Saharan Africa, Medicine, Science
Abstract

Abstract Excessive and improper use of antibiotics causes antimicrobial resistance which is a major threat to global health security. Hospitals in sub-Saharan Africa (SSA) has the highest prevalence of antibiotic use. This systematic review and meta-analysis aimed to determine the pooled point prevalence (PPP) of evidence-based antimicrobial use among hospitalized patients in SSA. Literature was retrieved from CINAHL, EMBASE, Google Scholar, PubMed, Scopus, and Web of Science databases. Meta-analysis was conducted using STATA version 17. Forest plots using the random-effect model were used to present the findings. The heterogeneity and publication bias were assessed using the I 2 statistics and Egger’s test. The protocol was registered in PROSPERO with code CRD42023404075. The review was conducted according to PRISMA guidelines. A total of 26, 272 study participants reported by twenty-eight studies published from 10 countries in SSA were included. The pooled point prevalence of antimicrobial use in SSA were 64%. The pooled estimate of hospital wards with the highest antibiotic use were intensive care unit (89%). The pooled prevalence of the most common clinical indication for antibiotic use were community acquired infection (41%). The pooled point prevalence of antimicrobial use among hospitalized patients were higher in SSA. Higher use of antibiotics was recorded in intensive care units. Community acquired infection were most common clinical case among hospitalized patients. Health systems in SSA must design innovative digital health interventions to optimize clinicians adhere to evidence-based prescribing guidelines and improve antimicrobial stewardship.

Published
2024
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46. Brazilian Consensus on the Application of Thermal Ablation for Treatment of Thyroid Nodules: A Task Force Statement by the Brazilian Society of Interventional Radiology and Endovascular Surgery (SOBRICE), Brazilian Society of Head and Neck Surgery (SBCCP), and Brazilian Society of Endocrinology and Metabolism (SBEM)

Author

Gustavo Philippi de los Santos, Marco Aurélio Vamondes Kulcsar, Fabio de Aquino Capelli, Jose Higino Steck, Klecius Leite Fernandes, Cleo Otaviano Mesa Junior, Joaquim Mauricio da Motta-Leal-Filho, Rafael Selbach Scheffel, Fernanda Vaisman, Guilherme Lopes Pinheiro Martins, Denis Szejnfeld, Mauricio Kauark Amoedo, Marcos Roberto de Menezes, Antonio Rahal Junior, and Leandro Luongo Matos

Subjects
Thyroid, treatment, ablation techniques, radiofrequency ablation, interventional ultrasound, Medicine, Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract

ABSTRACT There is increasing interest in ultrasound-guided ablation treatments for thyroid diseases, including benign and malignant ones. Surgeons, radiologists, and endocrinologists carry out these treatments, and various organizations within these specialties have recently released multiple international consensus statements and clinical practice standards. The aim of the present consensus statement is to provide guidance, cohesion, and standardization of best practices for thermal ablation procedures of thyroid nodules. The statement includes the indications for these procedures, preprocedural evaluations, technical aspects of the procedures, posttreatment care, follow-up, complications, and training recommendations. This document was written by a panel of specialists from the Brazilian Society of Interventional Radiology and Endovascular Surgery (SOBRICE), the Brazilian Society of Head and Neck Surgery (SBCCP), and the Brazilian Society of Endocrinology and Metabolism (SBEM). The statement does not aim to provide criteria for assessing the capability of specialists to perform the procedure. Instead, it aims to promote the standardization of best practices to reduce potential adverse outcomes. Additionally, it strives to enhance the delivery of high-quality care and the widespread adoption of these technologies on a national level. The recommendations collectively serve as a guidebook for applying best practices in thyroid ablation.

Published
2024
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47. Programming Quantum Hardware via Levenberg Marquardt Machine Learning

Author

Steck, James E., Thompson, Nathan L., and Behrman, Elizabeth C.

Subjects
Quantum Physics
Abstract

Significant challenges remain with the development of macroscopic quantum computing, hardware problems of noise, decoherence, and scaling, software problems of error correction, and, most important, algorithm construction. Finding truly quantum algorithms is quite difficult, and many quantum algorithms, like Shor prime factoring or phase estimation, require extremely long circuit depth for any practical application, necessitating error correction. Machine learning can be used as a systematic method to nonalgorithmically program quantum computers. Quantum machine learning enables us to perform computations without breaking down an algorithm into its gate building blocks, eliminating that difficult step and potentially reducing unnecessary complexity. In addition, we have shown that our machine learning approach is robust to both noise and to decoherence, which is ideal for running on inherently noisy NISQ devices which are limited in the number of qubits available for error correction. We demonstrated this using a fundamentally non classical calculation, experimentally estimating the entanglement of an unknown quantum state. Results from this have been successfully ported to the IBM hardware and trained using a powerful hybrid reinforcement learning technique which is a modified Levenberg Marquardt LM method. The LM method is ideally suited to quantum machine learning as it only requires knowledge of the final measured output of the quantum computation, not intermediate quantum states which are generally not accessible. Since it processes all the learning data simultaneously, it also requires significantly fewer hits on the quantum hardware. Machine learning is demonstrated with results from simulations and runs on the IBM Qiskit hardware interface., Comment: 9 pages, 9 figures. arXiv admin note: substantial text overlap with arXiv:2007.08327

Published
2022

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