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1. EgoMimic: Scaling Imitation Learning via Egocentric Video

Author: Kareer, Simar, Patel, Dhruv, Punamiya, Ryan, Mathur, Pranay, Cheng, Shuo, Wang, Chen, Hoffman, Judy, and Xu, Danfei
Subjects: Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition
Abstract: The scale and diversity of demonstration data required for imitation learning is a significant challenge. We present EgoMimic, a full-stack framework which scales manipulation via human embodiment data, specifically egocentric human videos paired with 3D hand tracking. EgoMimic achieves this through: (1) a system to capture human embodiment data using the ergonomic Project Aria glasses, (2) a low-cost bimanual manipulator that minimizes the kinematic gap to human data, (3) cross-domain data alignment techniques, and (4) an imitation learning architecture that co-trains on human and robot data. Compared to prior works that only extract high-level intent from human videos, our approach treats human and robot data equally as embodied demonstration data and learns a unified policy from both data sources. EgoMimic achieves significant improvement on a diverse set of long-horizon, single-arm and bimanual manipulation tasks over state-of-the-art imitation learning methods and enables generalization to entirely new scenes. Finally, we show a favorable scaling trend for EgoMimic, where adding 1 hour of additional hand data is significantly more valuable than 1 hour of additional robot data. Videos and additional information can be found at https://egomimic.github.io/
Published: 2024

2. Genetic studies through the lens of gene networks

Author: Subirana-Granés, Marc, Hoffman, Jill, Zhang, Haoyu, Akirtava, Christina, Nandi, Sutanu, Fotso, Kevin, and Pividori, Milton
Subjects: Quantitative Biology - Molecular Networks
Abstract: Understanding the genetic basis of complex traits is a longstanding challenge in the field of genomics. Genome-wide association studies (GWAS) have identified thousands of variant-trait associations, but most of these variants are located in non-coding regions, making the link to biological function elusive. While traditional approaches, such as transcriptome-wide association studies (TWAS), have advanced our understanding by linking genetic variants to gene expression, they often overlook gene-gene interactions. Here, we review current approaches to integrate different molecular data, leveraging machine learning methods to identify gene modules based on co-expression and functional relationships. These integrative approaches, like PhenoPLIER, combine TWAS and drug-induced transcriptional profiles to effectively capture biologically meaningful gene networks. This integration provides a context-specific understanding of disease processes while highlighting both core and peripheral genes. These insights pave the way for novel therapeutic targets and enhance the interpretability of genetic studies in personalized medicine., Comment: 26 pages, 4 figures GitHub: https://github.com/pivlab/annual_review_of_biomedical_data_science HTML: https://pivlab.github.io/annual_review_of_biomedical_data_science/
Published: 2024
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3. Revisiting Reliability in Large-Scale Machine Learning Research Clusters

Author: Kokolis, Apostolos, Kuchnik, Michael, Hoffman, John, Kumar, Adithya, Malani, Parth, Ma, Faye, DeVito, Zachary, Sengupta, Shubho, Saladi, Kalyan, and Wu, Carole-Jean
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning
Abstract: Reliability is a fundamental challenge in operating large-scale machine learning (ML) infrastructures, particularly as the scale of ML models and training clusters continues to grow. Despite decades of research on infrastructure failures, the impact of job failures across different scales remains unclear. This paper presents a view of managing two large, multi-tenant ML clusters, providing quantitative analysis, operational experience, and our own perspective in understanding and addressing reliability concerns at scale. Our analysis reveals that while large jobs are most vulnerable to failures, smaller jobs make up the majority of jobs in the clusters and should be incorporated into optimization objectives. We identify key workload properties, compare them across clusters, and demonstrate essential reliability requirements for pushing the boundaries of ML training at scale. We hereby introduce a taxonomy of failures and key reliability metrics, analyze 11 months of data from two state-of-the-art ML environments with over 150 million A100 GPU hours and 4 million jobs. Building on our data, we fit a failure model to project Mean Time to Failure for various GPU scales. We further propose a method to estimate a related metric, Effective Training Time Ratio, as a function of job parameters, and we use this model to gauge the efficacy of potential software mitigations at scale. Our work provides valuable insights and future research directions for improving the reliability of AI supercomputer clusters, emphasizing the need for flexible, workload-agnostic, and reliability-aware infrastructure, system software, and algorithms.
Published: 2024

4. Model merging with SVD to tie the Knots

Author: Stoica, George, Ramesh, Pratik, Ecsedi, Boglarka, Choshen, Leshem, and Hoffman, Judy
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Recent model merging methods demonstrate that the parameters of fully-finetuned models specializing in distinct tasks can be combined into one model capable of solving all tasks without retraining. Yet, this success does not transfer well when merging LoRA finetuned models. We study this phenomenon and observe that the weights of LoRA finetuned models showcase a lower degree of alignment compared to their fully-finetuned counterparts. We hypothesize that improving this alignment is key to obtaining better LoRA model merges, and propose KnOTS to address this problem. KnOTS uses the SVD to jointly transform the weights of different LoRA models into an aligned space, where existing merging methods can be applied. In addition, we introduce a new benchmark that explicitly evaluates whether merged models are general models. Notably, KnOTS consistently improves LoRA merging by up to 4.3% across several vision and language benchmarks, including our new setting. We release our code at: https://github.com/gstoica27/KnOTS.
Published: 2024

5. Movie Gen: A Cast of Media Foundation Models

Author: Polyak, Adam, Zohar, Amit, Brown, Andrew, Tjandra, Andros, Sinha, Animesh, Lee, Ann, Vyas, Apoorv, Shi, Bowen, Ma, Chih-Yao, Chuang, Ching-Yao, Yan, David, Choudhary, Dhruv, Wang, Dingkang, Sethi, Geet, Pang, Guan, Ma, Haoyu, Misra, Ishan, Hou, Ji, Wang, Jialiang, Jagadeesh, Kiran, Li, Kunpeng, Zhang, Luxin, Singh, Mannat, Williamson, Mary, Le, Matt, Yu, Matthew, Singh, Mitesh Kumar, Zhang, Peizhao, Vajda, Peter, Duval, Quentin, Girdhar, Rohit, Sumbaly, Roshan, Rambhatla, Sai Saketh, Tsai, Sam, Azadi, Samaneh, Datta, Samyak, Chen, Sanyuan, Bell, Sean, Ramaswamy, Sharadh, Sheynin, Shelly, Bhattacharya, Siddharth, Motwani, Simran, Xu, Tao, Li, Tianhe, Hou, Tingbo, Hsu, Wei-Ning, Yin, Xi, Dai, Xiaoliang, Taigman, Yaniv, Luo, Yaqiao, Liu, Yen-Cheng, Wu, Yi-Chiao, Zhao, Yue, Kirstain, Yuval, He, Zecheng, He, Zijian, Pumarola, Albert, Thabet, Ali, Sanakoyeu, Artsiom, Mallya, Arun, Guo, Baishan, Araya, Boris, Kerr, Breena, Wood, Carleigh, Liu, Ce, Peng, Cen, Vengertsev, Dimitry, Schonfeld, Edgar, Blanchard, Elliot, Juefei-Xu, Felix, Nord, Fraylie, Liang, Jeff, Hoffman, John, Kohler, Jonas, Fire, Kaolin, Sivakumar, Karthik, Chen, Lawrence, Yu, Licheng, Gao, Luya, Georgopoulos, Markos, Moritz, Rashel, Sampson, Sara K., Li, Shikai, Parmeggiani, Simone, Fine, Steve, Fowler, Tara, Petrovic, Vladan, and Du, Yuming
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabilities such as precise instruction-based video editing and generation of personalized videos based on a user's image. Our models set a new state-of-the-art on multiple tasks: text-to-video synthesis, video personalization, video editing, video-to-audio generation, and text-to-audio generation. Our largest video generation model is a 30B parameter transformer trained with a maximum context length of 73K video tokens, corresponding to a generated video of 16 seconds at 16 frames-per-second. We show multiple technical innovations and simplifications on the architecture, latent spaces, training objectives and recipes, data curation, evaluation protocols, parallelization techniques, and inference optimizations that allow us to reap the benefits of scaling pre-training data, model size, and training compute for training large scale media generation models. We hope this paper helps the research community to accelerate progress and innovation in media generation models. All videos from this paper are available at https://go.fb.me/MovieGenResearchVideos.
Published: 2024

6. Square Functions Controlling Smoothness and Higher-Order Rectifiability

Author: Hoffman, John
Subjects: Mathematics - Classical Analysis and ODEs, 28A75, 30L99, 43A85
Abstract: We provide new characterizations of the $BMO$-Sobolev space $I_{\alpha}(BMO)$ for the range $0 < \alpha <2$. When $0 < \alpha <1$, our characterizations are in terms of square functions measuring multiscale approximation of constants, and when $1 \leq \alpha <2$ our characterizations are in terms of square functions measuring multiscale approximation by linear functions., Comment: 26 pages
Published: 2024

7. ipd: An R Package for Conducting Inference on Predicted Data

Author: Salerno, Stephen, Miao, Jiacheng, Afiaz, Awan, Hoffman, Kentaro, Neufeld, Anna, Lu, Qiongshi, McCormick, Tyler H., and Leek, Jeffrey T.
Subjects: Statistics - Methodology, Statistics - Computation
Abstract: Summary: ipd is an open-source R software package for the downstream modeling of an outcome and its associated features where a potentially sizable portion of the outcome data has been imputed by an artificial intelligence or machine learning (AI/ML) prediction algorithm. The package implements several recent proposed methods for inference on predicted data (IPD) with a single, user-friendly wrapper function, ipd. The package also provides custom print, summary, tidy, glance, and augment methods to facilitate easy model inspection. This document introduces the ipd software package and provides a demonstration of its basic usage. Availability: ipd is freely available on CRAN or as a developer version at our GitHub page: github.com/ipd-tools/ipd. Full documentation, including detailed instructions and a usage `vignette' are available at github.com/ipd-tools/ipd. Contact: jtleek@fredhutch.org and tylermc@uw.edu, Comment: 5 pages, 1 figure
Published: 2024

8. Search for non-virialized axions with 3.3-4.2 $\mu$eV mass at selected resolving powers

Author: Hipp, A. T., Quiskamp, A., Caligiure, T. J., Gleason, J. R., Han, Y., Jois, S., Sikivie, P., Solano, M. E., Sullivan, N. S., Tanner, D. B., Goryachev, M., Hartman, E., Tobar, M. E., McAllister, B. T., Duffy, L. D., Braine, T., Burns, E., Cervantes, R., Crisosto, N., Goodman, C., Guzzetti, M., Hanretty, C., Lee, S., Korandla, H., Leum, G., Mohapatra, P., Nitta, T., Rosenberg, L. J, Rybka, G., Sinnis, J., Zhang, D., Bartram, C., Dyson, T. A., Kuo, C. L., Ruppert, S., Withers, M. O., Awida, M. H., Bowring, D., Chou, A. S., Hollister, M., Knirck, S., Sonnenschein, A., Wester, W., Brodsky, J., Carosi, G., Du, N., Roberston, N., Woollett, N., Boutan, C., Jones, A. M., LaRoque, B. H., Lentz, E., Man, N. E., Oblath, N. S., Taubman, M. S., Yang, J., Khatiwada, R., Clarke, John, Siddiqi, I., Agrawal, A., Dixit, A. V., Daw, E. J., Perry, M. G., Buckley, J. H., Gaikwad, C., Hoffman, J., Murch, K. W., and Russell, J.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: The Axion Dark Matter eXperiment is sensitive to narrow axion flows, given axions compose a fraction of the dark matter with a non-negligible local density. Detecting these low-velocity dispersion flows requires a high spectral resolution and careful attention to the expected signal modulation due to Earth's motion. We report an exclusion on the local axion dark matter density in narrow flows of $\rho_a \gtrsim 0.03\,\mathrm{GeV/cm^3}$ and $\rho_a \gtrsim 0.004\,\mathrm{GeV/cm^3}$ for Dine-Fischler-Srednicki-Zhitnitski and Kim-Shifman-Vainshtein-Zakharov axion-photon couplings, respectively, over the mass range $3.3-4.2\,\mu\text{eV}$. Measurements were made at selected resolving powers to allow for a range of possible velocity dispersions., Comment: 7 pages, 3 figures
Published: 2024

9. Spectropolarimetry of SN 2023ixf reveals both circumstellar material and helium core to be aspherical

Author: Shrestha, Manisha, DeSoto, Sabrina, Sand, David J., Williams, G. Grant, Hoffman, Jennifer L., Smith, Nathan, Smith, Paul S., Milne, Peter, McCall, Callum, Maund, Justyn R., Steele, Iain A, Wiersema, Klaas, Andrews, Jennifer E., Bilinski, Christopher, Anche, Ramya M., Bostroem, K. Azalee, Hosseinzadeh, Griffin, Pearson, Jeniveve, Leonard, Douglas C., Hsu, Brian, Dong, Yize, Hoang, Emily, Janzen, Daryl, Jencson, Jacob E., Jha, Saurabh W., Lundquist, M. J., Mehta, Darshana, Retamal, Nicolas Meza, Valenti, Stefano, Farah, Joseph, Howell, D. Andrew, McCully, Curtis, Newsome, Megan, Gonzalez, Estefania Padilla, Pellegrino, Craig, and Terreran, Giacomo
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract: We present multi-epoch optical spectropolarimetric and imaging polarimetric observations of the nearby Type II supernova (SN) 2023ixf discovered in M101 at a distance of 6.85 Mpc. The first imaging polarimetric observations were taken +2.33 days (60085.08 MJD) after the explosion, while the last imaging polarimetric data points (+73.19 and +76.19 days) were acquired after the fall from the light curve plateau. At +2.33 days there is strong evidence of circumstellar material (CSM) interaction in the spectra and the light curve. A significant level of polarization $P_r = 0.88\pm 0.06 \% $ seen during this phase indicates that this CSM is aspherical. We find that the polarization evolves with time toward the interstellar polarization level ($0.35\%$) during the photospheric phase, which suggests that the recombination photosphere is spherically symmetric. There is a jump in polarization ($P_r =0.65 \pm 0.08 \% $) at +73.19 days when the light curve falls from the plateau. This is a phase where polarimetric data is sensitive to non-spherical inner ejecta or a decrease in optical depth into the single scattering regime. We also present spectropolarimetric data that reveal line (de)polarization during most of the observed epochs. In addition, at +14.50 days we see an "inverse P Cygn" profile in the H and He line polarization, which clearly indicates the presence of asymmetrically distributed material overlying the photosphere. The overall temporal evolution of polarization is typical for Type II SNe, but the high level of polarization during the rising phase has only been observed in SN 2023ixf., Comment: 14 pages, 7 figures, submitted to ApJL, comments welcome
Published: 2024

10. Motivations for Early High-Profile FRIB Experiments

Author: Brown, B. Alex, Gade, Alexandra, Stroberg, S. Ragnar, Escher, Jutta, Fossez, Kevin, Giuliani, Pablo, Hoffman, Calem R., Nazarewicz, Witold, Seng, Chien-Yeah, Sorensen, Agnieszka, Vassh, Nicole, Bazin, Daniel, Brown, Kyle W., Capri, Mark A., Crawford, Heather, Danielewic, Pawel, Drischler, Christian, Ruiz, Ronald F. Garcia, Godbey, Kyle, Grzywacz, Robert, Holt, Jeremy W., Iwasaki, Hiro, Lee, Dean, Lenzi, Silvia M., Liddick, Sean, Lubna, Rebeka, Macchiavelli, Augusto O., Pinedo, Gabriel Martinez, McCoy, Anna, Mercenne, Alexis, Minamisono, Kei, Monteagudo, Belen, Navratil, Petr, Ringle, Ryan, Sargsyan, Grigor, Schatz, Hendrik, Spieker, Mark-Christoph, Volya, Alexander, Zegers, Remco G. T., Zelevinsky, Vladimir, and Zhang, Xilin
Subjects: Nuclear Theory
Abstract: This white paper is the result of a collaboration by those that attended a workshop at the Facility for Rare Isotope Beams (FRIB), organized by the FRIB Theory Alliance (FRIB-TA), on Theoretical Justifications and Motivations for Early High-Profile FRIB Experiments. It covers a wide range of topics related to the science that will be explored at FRIB. After a brief introduction, the sections address: (II) Overview of theoretical methods, (III) Experimental capabilities, (IV) Structure, (V) Near-threshold Physics, (VI) Reaction mechanisms, (VII) Nuclear equations of state, (VIII) Nuclear astrophysics, (IX) Fundamental symmetries, and (X) Experimental design and uncertainty quantification., Comment: 227 pages, 24 figures
Published: 2024

11. Biodenoising: animal vocalization denoising without access to clean data

Author: Miron, Marius, Keen, Sara, Liu, Jen-Yu, Hoffman, Benjamin, Hagiwara, Masato, Pietquin, Olivier, Effenberger, Felix, and Cusimano, Maddie
Subjects: Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: Animal vocalization denoising is a task similar to human speech enhancement, a well-studied field of research. In contrast to the latter, it is applied to a higher diversity of sound production mechanisms and recording environments, and this higher diversity is a challenge for existing models. Adding to the challenge and in contrast to speech, we lack large and diverse datasets comprising clean vocalizations. As a solution we use as training data pseudo-clean targets, i.e. pre-denoised vocalizations, and segments of background noise without a vocalization. We propose a train set derived from bioacoustics datasets and repositories representing diverse species, acoustic environments, geographic regions. Additionally, we introduce a non-overlapping benchmark set comprising clean vocalizations from different taxa and noise samples. We show that that denoising models (demucs, CleanUNet) trained on pseudo-clean targets obtained with speech enhancement models achieve competitive results on the benchmarking set. We publish data, code, libraries, and demos https://mariusmiron.com/research/biodenoising., Comment: 5 pages, 2 tables
Published: 2024

12. MCUBench: A Benchmark of Tiny Object Detectors on MCUs

Author: Sah, Sudhakar, Ganji, Darshan C., Grimaldi, Matteo, Kumar, Ravish, Hoffman, Alexander, Rohmetra, Honnesh, and Saboori, Ehsan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: We introduce MCUBench, a benchmark featuring over 100 YOLO-based object detection models evaluated on the VOC dataset across seven different MCUs. This benchmark provides detailed data on average precision, latency, RAM, and Flash usage for various input resolutions and YOLO-based one-stage detectors. By conducting a controlled comparison with a fixed training pipeline, we collect comprehensive performance metrics. Our Pareto-optimal analysis shows that integrating modern detection heads and training techniques allows various YOLO architectures, including legacy models like YOLOv3, to achieve a highly efficient tradeoff between mean Average Precision (mAP) and latency. MCUBench serves as a valuable tool for benchmarking the MCU performance of contemporary object detectors and aids in model selection based on specific constraints., Comment: Code and data are available at https://github.com/Deeplite/deeplite-torch-zoo
Published: 2024

13. Validating Convex Optimization of Reconfigurable Intelligent Surfaces via Measurements

Author: Lang, Hans-Dieter, Nyffenegger, Michel A., Keller, Sven, Stöckli, Patrik, Hoffman, Nathan A., Mathis, Heinz, and Zhang, Xingqi
Subjects: Mathematics - Optimization and Control
Abstract: Reconfigurable Intelligent Surfaces (RISs) can be designed in various ways. A previously proposed semidefinite relaxation-based optimization method for maximizing power transfer efficiency showed promise, but earlier results were only theoretical. This paper evaluates a small RIS at 3.55GHz, the center of the 5G band "n78", for practical verification of this method. The presented results not only empirically confirm the desired performance of the optimized RIS, but also affirm the optimality of the resulting reactance values. Additionally, this paper discusses several practical aspects of RIS design and measurement, such as the operation of varactor diodes and time gating to omit the direct line-of-sight (LOS) path., Comment: 5 pages, conference
Published: 2024
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14. A Physical Layer Analysis of Entropy in Delay-Based PUFs Implemented on FPGAs

Author: Plusquellic, Jim, Howard, Jennifer, MacKinnon, Ross, Hoffman, Kristianna, Tsiropoulou, Eirini Eleni, and Chan, Calvin
Subjects: Computer Science - Cryptography and Security
Abstract: Physical Unclonable Functions (PUFs) leverage signal variations that occur within the device as a source of entropy. On-chip instrumentation is utilized by some PUF architectures to measure and digitize these variations, which are then processed into bitstrings and secret keys for use in security functions such as authentication and encryption. In many cases, the variations in the measured signals are introduced by a sequence of components in the circuit structure defined by the PUF architecture. In particular, the Hardware-Embedded deLay PUF (HELP) measures delay variations that occur in combinational logic paths on Field Programmable Gate Arrays (FPGAs), which are composed of a set of interconnecting wires (nodes) and look-up tables (LUTs). Previous investigations of variations in these path delays show that it is possible to derive high quality bitstrings, i.e., those which exhibit high levels of uniqueness and randomness across the device population. However, the underlying source and level of variations associated with the constituent components of the paths remain unknown. In this paper, we apply statistical averaging and differencing techniques to derive estimates for the delay variation associated with an FPGA's basic components, namely LUTs and nodes, as a means of fully characterizing the PUF's source of entropy. The analysis is carried out on a set of 50,015 path delay measurements collected from a set of 20 Xilinx Zynq 7020 SoC-class FPGAs, on which 25 identical instances of a functional unit are instantiated, for a total of 500 instances.
Published: 2024

15. Enhancing Health Professions Students' Attitudes and Self-Efficacy to Care for Unhoused Populations

Author: Jacob T. Greenfield, Brad Phillips, Kathryn L. Hoffman, and Gina M. Baugh
Abstract: Health professions students, including occupational therapy students, often have limited exposure to unhoused populations, which may alter their attitudes and self-efficacy to participate in their care. In turn, this could reduce access and quality of care for these marginalized groups. The purpose of this study was to evaluate the impact of a phased multimodal learning approach on interprofessional health sciences students' attitudes and self-efficacy in providing care to unhoused individuals. A pre-posttest single group design was used to evaluate a didactic presentation, poverty simulation, and street-based experiential learning experience. Findings revealed significant differences in students' (N=257) attitudes and self-efficacy in providing care, and many students reported this as an "eye-opening experience." This learning approach evoked a strong emotional response, improved attitudes and self-efficacy, and has implications for future advocacy efforts related to caring for unhoused populations.
Published: 2024

16. Educator Well-Being as a Pathway toward Investing in Education Staff: Insights from the Transformative SEL (TSEL) Adult Learning Series Pilot. The BELE Learning Series

Author: Collaborative for Academic, Social, and Emotional Learning (CASEL), Building Equitable Learning Environments (BELE) Network, Brittney V. Williams, Ally Skoog-Hoffman, Rob Jagers, and Bloodine Barthelus
Abstract: The Building Equitable Learning Environments (BELE) network is a diverse collaborative of organizations working together to advance equity in education. The BELE Network research and learning team, and are responsible for documenting the network learnings, which are grounded in seven "Essential Actions" and reflected in shared annual aims. This brief shares learnings from BELE district partnerships around the fifth Essential Action: invest in staff. To support this Essential Action, Collaborative for Academic, Social, and Emotional Learning (CASEL) developed the Transformative (tSEL) Adult Learning Series, a set of six interactive workshops that build an opportunity for adults themselves to deepen their self-awareness and discover how they can curate equitable learning environments for young people by leveraging the five focal constructs (identity, agency, belonging, collaborative problem-solving, and curiosity). Through this series, education leaders are making investments for in-service professional learning that supports educator well-being and promotes equitable learning environments for adults and young people. Investing in staff actualizes the value of continuous improvement, which fosters a culture of expansion, growth, and development among educators.
Published: 2024

17. Physical Therapy Student Learning Perspectives in a Volunteer Interprofessional Interuniversity Service-Learning Opportunity: A Case Study Report

Author: Roberto Cantu, Abigail Gaines, Jessica Hall, Kelsey Wortman, Zachary Young, Stacey J. Hoffman, and Tamara Buck
Abstract: Interdisciplinary service-learning (IDSL) has become a staple of healthcare education over the last two decades as a mechanism of training students to provide cohesive, team-based health care in a complicated and sometimes fragmented health care system. This case study describes the perceived learning of Doctor of Physical Therapy (DPT) students from [Blinded] University who attended a week-long interdisciplinary, interuniversity service-learning trip in rural [Blinded]. Qualitative data from two consecutive year teams (n = 26) were collected via surveys and focus groups and thematically analyzed for motivation to attend the trip and perceived learning. Intrinsic motivation to "give back" and desire to hone professional skills were the primary drivers for desiring to attend the trip. Three themes of perceived learning that emerged were: 1) impact on core values, 2) perceived improvement in para-clinical (soft) skills including interdisciplinary fluency and cultural competence, and 3) improvement of clinical (hard) skills. The dominant theme was the impact the trip had on students' core values. The learning occurred predominantly in Bloom's affective domain and aligned with the physical therapy profession's Core Values and Code of Ethics.
Published: 2024

18. Systematic Review of the Psychometric Evidence Supporting Use of the Depression Anxiety Stress Scales, Short Form (DASS-21) with Youth

Author: Aberdine R. Dwight, Amy M. Briesch, Jessica A. Hoffman, and Christopher Rutt
Abstract: Background: Although the Depression Anxiety Stress Scales, Short Form (DASS-21) was developed for adults, its authors noted no compelling reasons to not use the measure with youth as young as 12 years. Despite increasingly widespread use with youth, psychometric evidence in support of its use with this population needs to be investigated to fully understand its utility. Objective: The goal of this paper was to conduct a systematic review to identify and appraise evidence for the psychometric properties of the Depression Anxiety Stress Scales, Short Form (DASS-21) when used with youth populations. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this systematic review identified 18 studies that reported relevant information on the psychometrics of the DASS-21 with youth samples. Each article was coded for evidence of three indicators of particular importance to progress monitoring: reliability, validity, and sensitivity to change. Results: Studies have found varied factor structures (i.e., 1-4 factors) for the DASS-21 and validity evidence based on relations to other variables has been limited. As such, further research is needed to understand the utility of the depression, anxiety, and stress subscales. Furthermore, no information is currently available about the sensitivity to change of the measure. Conclusions: Most studies reporting psychometric properties in this review utilized the DASS-21 with mixed-gender groups of secondary school students, thus supporting its use with similar populations. Pending additional inquiry, however, both researchers and practitioners are encouraged to utilize the total score, for which stronger evidence of reliability and validity exist.
Published: 2024
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19. Integrated Learning, Integrated Lives: Highlighting Opportunities for Transformative SEL within Academic Instruction. Social and Emotional Learning Innovations Series

Author: Collaborative for Academic, Social, and Emotional Learning (CASEL), Heather N. Schwartz, Ally Skoog-Hoffman, Joe Polman, Olivia Kelly, Josefina Bañales, and Rob Jagers
Abstract: The SEL Innovations series aims to help the field imagine new, more expansive and equitable approaches to social and emotional learning (SEL) and wellness to ensure that all children, adolescents, and adults feel safe, supported, and seen so that they can thrive. This is the second report in a series exploring innovations in SEL. The purpose of the report is to highlight the importance of systemic, integrated SEL in classrooms, where the goal is to foster supportive classroom environments wherein educators teach explicit SEL and integrate SEL throughout academic instruction by weaving deep academic learning with opportunities for students to understand their own emotions, empathize with diverse perspectives, solve problems constructively, and make decisions while considering the needs of others. The authors briefly explore the Collaborative for Academic, Social, and Emotional Learning (CASEL) model for SEL before critically examining the literature around inquiry-based learning opportunities. The focus is on two inquiry-based approaches to learning, Project-Based Learning (PBL) and Youth Participatory Action Research (YPAR), which show promise as examples of integrated, systemic approaches to SEL by allowing students to be authors and co-designers in their own learning. Next, conditions necessary to enact inquiry-based approaches in the classroom are explored. Lastly, they share a set of case studies illustrating what inquiry-based learning opportunities, specifically PBL and YPAR, can look like in practice. In this way, the goal is to provide both inspiration and practical application for educators, program providers, and researchers looking to continue to move the conversation forward, integrate new strategies into their programs or practices, and expand their research agendas.
Published: 2023

20. Steam‐Assisted Selective CO2 Hydrogenation to Ethanol over Ru−In Catalysts

Author: Zhou, Chengshuang, Aitbekova, Aisulu, Liccardo, Gennaro, Oh, Jinwon, Stone, Michael L, McShane, Eric J, Werghi, Baraa, Nathan, Sindhu, Song, Chengyu, Ciston, Jim, Bustillo, Karen C, Hoffman, Adam S, Hong, Jiyun, Perez‐Aguilar, Jorge, Bare, Simon R, and Cargnello, Matteo
Subjects: Chemical Sciences, CO2 Hydrogenation, Heterogeneous Cataysis, Higher Alcohol Synthesis, Strong Metal-Support Interaction, Structure-Performance Relationship, Organic Chemistry, Chemical sciences
Abstract: Multicomponent catalysts can be designed to synergistically combine reaction intermediates at interfacial active sites, but restructuring makes systematic control and understanding of such dynamics challenging. We here unveil how reducibility and mobility of indium oxide species in Ru-based catalysts crucially control the direct, selective conversion of CO2 to ethanol. When uncontrolled, reduced indium oxide species occupy the Ru surface, leading to deactivation. With the addition of steam as a mild oxidant and using porous polymer layers to control In mobility, Ru-In2O3 interface sites are stabilized, and ethanol can be produced with superior overall selectivity (70 %, rest CO). Our work highlights how engineering of bifunctional active ensembles enables cooperativity and synergy at tailored interfaces, which unlocks unprecedented performance in heterogeneous catalysts.
Published: 2024

21. Combination and summary of ATLAS dark matter searches interpreted in a 2HDM with a pseudo-scalar mediator using 139 fb−1 of s = 13 TeV pp collision data

Author: Aad, G, Abbott, B, Abeling, K, Abicht, NJ, Abidi, SH, Aboulhorma, A, Abramowicz, H, Abreu, H, Abulaiti, Y, Hoffman, AC Abusleme, Acharya, BS, Bourdarios, C Adam, Adamczyk, L, Adamek, L, Addepalli, SV, Addison, MJ, Adelman, J, Adiguzel, A, Adye, T, Affolder, AA, Afik, Y, Agaras, MN, Agarwala, J, Aggarwal, A, Agheorghiesei, C, Ahmad, A, Ahmadov, F, Ahmed, WS, Ahuja, S, Ai, X, Aielli, G, Aikot, A, Tamlihat, M Ait, Aitbenchikh, B, Aizenberg, I, Akbiyik, M, Åkesson, TPA, Akimov, AV, Akiyama, D, Akolkar, NN, Al Khoury, K, Alberghi, GL, Albert, J, Albicocco, P, Albouy, GL, Alderweireldt, S, Aleksa, M, Aleksandrov, IN, Alexa, C, Alexopoulos, T, Alfonsi, F, Algren, M, Alhroob, M, Ali, B, Ali, HMJ, Ali, S, Alibocus, SW, Aliev, M, Alimonti, G, Alkakhi, W, Allaire, C, Allbrooke, BMM, Allen, JF, Flores, CA Allendes, Allport, PP, Aloisio, A, Alonso, F, Alpigiani, C, Estevez, M Alvarez, Fernandez, A Alvarez, Cardoso, M Alves, Alviggi, MG, Aly, M, Coutinho, Y Amaral, Ambler, A, Amelung, C, Amerl, M, Ames, CG, Amidei, D, Dos Santos, SP Amor, Amos, KR, Ananiev, V, Anastopoulos, C, Andeen, T, Anders, JK, Andrean, SY, Andreazza, A, Angelidakis, S, Angerami, A, Anisenkov, AV, Annovi, A, Antel, C, Anthony, MT, Antipov, E, Antonelli, M, Anulli, F, Aoki, M, Aoki, T, Pozo, JA Aparisi, and Aparo, MA
Subjects: Nuclear and Plasma Physics, Particle and High Energy Physics, Physical Sciences, Clinical Research, ATLAS Collaboration, Beyond Standard Model, Dark matter, High-energy physics, Proton-proton
Abstract: Results from a wide range of searches targeting different experimental signatures with and without missing transverse momentum (ETmiss) are used to constrain a Two-Higgs-Doublet Model (2HDM) with an additional pseudo-scalar mediating the interaction between ordinary and dark matter (2HDM+a). The analyses use up to 139 fb-1 of proton-proton collision data at a centre-of-mass energy s=13 TeV recorded with the ATLAS detector at the Large Hadron Collider during 2015-2018. The results from three of the most sensitive searches are combined statistically. These searches target signatures with large ETmiss and a leptonically decaying Z boson; large ETmiss and a Higgs boson decaying to bottom quarks; and production of charged Higgs bosons in final states with top and bottom quarks, respectively. Constraints are derived for several common and new benchmark scenarios in the 2HDM+a.
Published: 2024

22. High rate of uncontrolled hypertension among adults receiving integrated HIV and hypertension care with aligned multi‐month dispensing in Malawi: results from a cross‐sectional survey and retrospective chart review

Author: Whitehead, Hannah S, Phiri, Khumbo, Kalande, Pericles, van Oosterhout, Joep J, Talama, George, Phiri, Sam, Moucheraud, Corrina, Moses, Agnes, and Hoffman, Risa M
Subjects: Health Services and Systems, Biomedical and Clinical Sciences, Health Sciences, HIV/AIDS, Infectious Diseases, Cardiovascular, Clinical Research, Hypertension, Sexually Transmitted Infections, Prevention, 6.1 Pharmaceuticals, Infection, Good Health and Well Being, Humans, Cross-Sectional Studies, Malawi, Female, Male, HIV Infections, Retrospective Studies, Adult, Middle Aged, Antihypertensive Agents, Delivery of Health Care, Integrated, Young Adult, HIV, hypertension, integrated care, multi‐month dispensing, sub‐Saharan Africa, Clinical Sciences, Public Health and Health Services, Other Medical and Health Sciences, Clinical sciences, Epidemiology, Public health
Abstract: IntroductionPeople living with HIV have high rates of hypertension. Integrated HIV and hypertension care with aligned multi-month dispensing of medications (MMD) could decrease the burden of care for individuals and health systems. We sought to describe hypertension control and evaluate its association with different durations of MMD among Malawian adults receiving integrated care with aligned dispensing of antiretroviral therapy (ART) and antihypertensive medication.MethodsWe conducted a cross-sectional survey and retrospective chart review of adults (≥18 years) receiving integrated HIV and hypertension care on medications for both conditions for at least 1 year, with aligned MMD at seven clinics in Malawi. Data were collected from July 2021 to April 2022 and included socio-demographics, clinical characteristics, antihypertensive medications and up to the three most recent blood pressure measurements. Bivariate analyses were used to characterize associations with hypertension control. Uncontrolled hypertension was defined as ≥2 measurements ≥140 and/or ≥90 mmHg. Chart reviews were conducted for a random subset of participants with uncontrolled hypertension to describe antihypertensive medication adjustments in the prior year.ResultsWe surveyed 459 adults receiving integrated care with aligned dispensing (58% female; median age 54 years). Individuals most commonly received a 3-month aligned dispensing of ART and antihypertensive medications (63%), followed by every 6 months (16%) and every 4 months (15%). Hypertension control was assessed in 359 respondents, of whom only 23% had controlled hypertension; 90% of individuals in this group reported high adherence to blood pressure medications (0-1 missed days/week). Control was more common among those with longer aligned medication dispensing intervals (20% among those with 1- to 3-month dispensing vs. 28% with 4-month dispensing vs. 40% with 6-month dispensing, p = 0.011). Chart reviews were conducted for 147 individuals with uncontrolled hypertension. Most had high self-reported adherence to blood pressure medications (89% missing 0-1 days/week); however, only 10% had their antihypertensive medication regimen changed in the prior year.ConclusionsUncontrolled hypertension was common among Malawian adults receiving integrated care with aligned MMD and was associated with shorter refill intervals and few antihypertensive medication escalations. Integrated care with aligned MMD is promising, but further work is needed to understand how to optimize hypertension outcomes.
Published: 2024

23. Targeting Patient-Derived Orthotopic Gastric Cancers with a Fluorescent Humanized Anti-CEA Antibody

Author: Cox, Kristin E, Turner, Michael A, Lwin, Thinzar M, Amirfakhri, Siamak, Kelly, Kaitlyn J, Hosseini, Mojgan, Ghosh, Pradipta, Obonyo, Marygorret, Hoffman, Robert M, Yazaki, Paul J, and Bouvet, Michael
Subjects: Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer, Orphan Drug, Rare Diseases, Digestive Diseases, 5.1 Pharmaceuticals, Stomach Neoplasms, Animals, Humans, Mice, Carcinoembryonic Antigen, Adenocarcinoma, Xenograft Model Antitumor Assays, Antibodies, Monoclonal, Humanized, Fluorescent Dyes, Tumor Cells, Cultured, Female, Indoles, Optical Imaging, Gastrectomy, Mice, Nude, Cell Line, Tumor, Gastric cancer, Patient-derived orthotopic xenograft, PDOX, Fluorescence, Fluorescent antibody, CEA, Tumor targeting, Tumor labeling, Gastric cancer, Oncology & Carcinogenesis, Oncology and carcinogenesis
Abstract: BackgroundGastric cancer poses a major diagnostic and therapeutic challenge as surgical resection provides the only opportunity for a cure. Specific labeling of gastric cancer could distinguish resectable and nonresectable disease and facilitate an R0 resection, which could improve survival.MethodsTwo patient-derived gastric cancer lines, KG8 and KG10, were established from surgical specimens of two patients who underwent gastrectomy for gastric adenocarcinoma. Harvested tumor fragments were implanted into the greater curvature of the stomach to establish patient-derived orthotopic xenograft (PDOX) models. M5A (humanized anti-CEA antibody) or IgG control antibodies were conjugated with the near-infrared dye IRDye800CW. Mice received 50 µg of M5A-IR800 or 50 µg of IgG-IR800 intravenously and were imaged after 72 hr. Fluorescence imaging was performed by using the LI-COR Pearl Imaging System. A tumor-to-background ratio (TBR) was calculated by dividing the mean fluorescence intensity of the tumor versus adjacent stomach tissue.ResultsM5A-IR800 administration resulted in bright labeling of both KG8 and K10 tumors. In the KG8 PDOX models, the TBR for M5A-IR800 was 5.85 (SE ± 1.64) compared with IgG-IR800 at 0.70 (SE ± 0.17). The K10 PDOX models had a TBR of 3.71 (SE ± 0.73) for M5A-IR800 compared with 0.66 (SE ± 0.12) for IgG-IR800.ConclusionsHumanized anti-CEA (M5A) antibodies conjugated to fluorescent dyes provide bright and specific labeling of gastric cancer PDOX models. This tumor-specific fluorescent antibody is a promising potential clinical tool to detect the extent of disease for the determination of resectability as well as to visualize tumor margins during gastric cancer resection.
Published: 2024

24. Axion Dark Matter eXperiment around 3.3 {\mu}eV with Dine-Fischler-Srednicki-Zhitnitsky Discovery Ability

Author: Bartram, C., Boutan, C., Braine, T., Buckley, J. H., Caligiure, T. J., Carosi, G., Chou, A. S., Cisneros, C., Clarke, John, Daw, E. J., Du, N., Duffy, L. D., Dyson, T. A., Gaikwad, C., Gleason, J. R., Goodman, C., Goryachev, M., Guzzetti, M., Hanretty, C., Hartman, E., Hipp, A. T., Hoffman, J., Hollister, M., Khatiwada, R., Knirck, S., Kuo, C. L., Lentz, E., McAllister, B. T., Mostyn, C., Murch, K., Oblath, N. S., Perry, M. G., Quiskamp, A., Robertson, N., Rosenberg, L. J, Ruppert, S., Rybka, G., Siddiqi, I., Sikivie, P., Sinnis, J., Solano, M. E., Sonnenschein, A., Sullivan, N. S., Tanner, D. B., Taubman, M. S., Tobar, M. E., Withers, M. O., Woollett, N., and Zhang, D.
Subjects: High Energy Physics - Experiment
Abstract: We report the results of a QCD axion dark matter search with discovery ability for Dine-Fischler-Srednicki-Zhitnitsky (DFSZ) axions using an axion haloscope. Sub-Kelvin noise temperatures are reached with an ultra low-noise Josephson parametric amplifier cooled by a dilution refrigerator. This work excludes (with a 90% confidence level) DFSZ axions with masses between 3.27 to 3.34 {\mu}eV, assuming a standard halo model with a local energy density of 0.45 GeV/cc made up 100% of axions.
Published: 2024

25. Machine Learning with Physics Knowledge for Prediction: A Survey

Author: Watson, Joe, Song, Chen, Weeger, Oliver, Gruner, Theo, Le, An T., Hansel, Kay, Hendawy, Ahmed, Arenz, Oleg, Trojak, Will, Cranmer, Miles, D'Eramo, Carlo, Bülow, Fabian, Goyal, Tanmay, Peters, Jan, and Hoffman, Martin W.
Subjects: Computer Science - Machine Learning, Mathematics - Numerical Analysis, Physics - Computational Physics
Abstract: This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast, with a focus on partial differential equations. These methods have attracted significant interest due to their potential impact on advancing scientific research and industrial practices by improving predictive models with small- or large-scale datasets and expressive predictive models with useful inductive biases. The survey has two parts. The first considers incorporating physics knowledge on an architectural level through objective functions, structured predictive models, and data augmentation. The second considers data as physics knowledge, which motivates looking at multi-task, meta, and contextual learning as an alternative approach to incorporating physics knowledge in a data-driven fashion. Finally, we also provide an industrial perspective on the application of these methods and a survey of the open-source ecosystem for physics-informed machine learning., Comment: 56 pages, 8 figures, 2 tables
Published: 2024

26. QM abelian varieties, hypergeometric character sums and modular forms

Author: Hoffman, Jerome William and Tu, Fang-Ting
Subjects: Mathematics - Number Theory, 11T23, 11T24, 11S40, 11F80, 11F85, 33C05, 33C65
Abstract: This is a report on recent work, with Wen-Ching Winnie Li and Ling Long. In that work explicit formulas are given, involving hypergeometric character sums, for the traces of Hecke operators $T_p$ acting spaces of cusp forms $S_k(\Gamma)$ of weight $k$ for certain arithmetically defined Fuchsian subgroups $\Gamma\subset \mathrm{SL}_2 (\mathbf{R})$. In particular we consider the groups attached to the quaternion division algebra $B_6$ over $\mathbf{Q}$ of discriminant 6., Comment: An overview of the background of the joint paper with Wen-Ching W. Li and Ling Long on "Traces of Hecke Operators via Hypergeometric Character Sums" (arXiv: 2408.02918)
Published: 2024

27. Statistical Quality Comparison of the Bitstrings Generated by a Physical Unclonable Function across Xilinx, Altera and Microsemi Devices

Author: Jao, Jenilee, Hoffman, Kristi, Reid, Cheryl, Thomson, Ryan, Thompson, Michael, and Plusquellic, Jim
Subjects: Computer Science - Cryptography and Security
Abstract: Entropy or randomness represents a foundational security property in security-related operations, such as key generation. Key generation in turn is central to security protocols such as authentication and encryption. Physical unclonable functions (PUF) are hardware-based primitives that can serve as key generation engines in modern microelectronic devices and applications. PUFs derive entropy from manufacturing variations that exist naturally within and across otherwise identical copies of a device. However, the levels of random variations that represent entropy, which are strongly correlated to the quality of the PUF-generated bitstrings, vary from one manufacturer to another. In this paper, we evaluate entropy across a set of devices manufactured by three mainstream FPGA vendors, Xilinx, Altera and Microsemi. The devices selected for evaluation are considered low-end commercial devices to make the analysis relevant to IoT applications. The SiRF PUF is used in the evaluation, and is constructed nearly identically across the three vendor devices, setting aside minor differences that exist in certain logic element primitives used within the PUF architecture, and which have only a minor impact on our comparative analysis. The SiRF PUF uses a high-resolution time-to-digital converter (TDC) crafted from high-speed carry-chain logic embedded within each device to measure path delays in an engineered netlist of logic gates as a source of entropy. Therefore, our analysis includes an evaluation of actual path delay variation as it exists across the three device classes, as well as a statistical evaluation of the PUF-generated bitstrings. A reliablity analysis is also provided using data collected in industrial-standard temperature experiments to round out the evaluation of important statistical properties of the PUF., Comment: 15 pages, 22 figures, IEEE journal
Published: 2024

28. Traces of Hecke Operators via Hypergeometric Character Sums

Author: Hoffman, Jerome W., Li, Wen-Ching Winnie, Long, Ling, and Tu, Fang-Ting
Subjects: Mathematics - Number Theory, 11F11, 11F80, 11L05, 33C20
Abstract: In this paper we obtain explicit formulas for the traces of Hecke operators on spaces of cusp forms in certain instances related to arithmetic triangle groups. These expressions are in terms of hypergeometric character sums over finite fields, a theory developed largely by Greene, Katz, Beukers-Cohen-Mellit, and Fuselier-Long-Ramakrishna-Swisher-Tu. Our approach, in contrast to the previous works, is uniform and more geometric, and it works equally well for forms on elliptic modular curves and Shimura curves. The same method can be applied to obtain eigenvalues of Hecke operators as well.
Published: 2024

29. Gemma 2: Improving Open Language Models at a Practical Size

Author: Gemma Team, Riviere, Morgane, Pathak, Shreya, Sessa, Pier Giuseppe, Hardin, Cassidy, Bhupatiraju, Surya, Hussenot, Léonard, Mesnard, Thomas, Shahriari, Bobak, Ramé, Alexandre, Ferret, Johan, Liu, Peter, Tafti, Pouya, Friesen, Abe, Casbon, Michelle, Ramos, Sabela, Kumar, Ravin, Lan, Charline Le, Jerome, Sammy, Tsitsulin, Anton, Vieillard, Nino, Stanczyk, Piotr, Girgin, Sertan, Momchev, Nikola, Hoffman, Matt, Thakoor, Shantanu, Grill, Jean-Bastien, Neyshabur, Behnam, Bachem, Olivier, Walton, Alanna, Severyn, Aliaksei, Parrish, Alicia, Ahmad, Aliya, Hutchison, Allen, Abdagic, Alvin, Carl, Amanda, Shen, Amy, Brock, Andy, Coenen, Andy, Laforge, Anthony, Paterson, Antonia, Bastian, Ben, Piot, Bilal, Wu, Bo, Royal, Brandon, Chen, Charlie, Kumar, Chintu, Perry, Chris, Welty, Chris, Choquette-Choo, Christopher A., Sinopalnikov, Danila, Weinberger, David, Vijaykumar, Dimple, Rogozińska, Dominika, Herbison, Dustin, Bandy, Elisa, Wang, Emma, Noland, Eric, Moreira, Erica, Senter, Evan, Eltyshev, Evgenii, Visin, Francesco, Rasskin, Gabriel, Wei, Gary, Cameron, Glenn, Martins, Gus, Hashemi, Hadi, Klimczak-Plucińska, Hanna, Batra, Harleen, Dhand, Harsh, Nardini, Ivan, Mein, Jacinda, Zhou, Jack, Svensson, James, Stanway, Jeff, Chan, Jetha, Zhou, Jin Peng, Carrasqueira, Joana, Iljazi, Joana, Becker, Jocelyn, Fernandez, Joe, van Amersfoort, Joost, Gordon, Josh, Lipschultz, Josh, Newlan, Josh, Ji, Ju-yeong, Mohamed, Kareem, Badola, Kartikeya, Black, Kat, Millican, Katie, McDonell, Keelin, Nguyen, Kelvin, Sodhia, Kiranbir, Greene, Kish, Sjoesund, Lars Lowe, Usui, Lauren, Sifre, Laurent, Heuermann, Lena, Lago, Leticia, McNealus, Lilly, Soares, Livio Baldini, Kilpatrick, Logan, Dixon, Lucas, Martins, Luciano, Reid, Machel, Singh, Manvinder, Iverson, Mark, Görner, Martin, Velloso, Mat, Wirth, Mateo, Davidow, Matt, Miller, Matt, Rahtz, Matthew, Watson, Matthew, Risdal, Meg, Kazemi, Mehran, Moynihan, Michael, Zhang, Ming, Kahng, Minsuk, Park, Minwoo, Rahman, Mofi, Khatwani, Mohit, Dao, Natalie, Bardoliwalla, Nenshad, Devanathan, Nesh, Dumai, Neta, Chauhan, Nilay, Wahltinez, Oscar, Botarda, Pankil, Barnes, Parker, Barham, Paul, Michel, Paul, Jin, Pengchong, Georgiev, Petko, Culliton, Phil, Kuppala, Pradeep, Comanescu, Ramona, Merhej, Ramona, Jana, Reena, Rokni, Reza Ardeshir, Agarwal, Rishabh, Mullins, Ryan, Saadat, Samaneh, Carthy, Sara Mc, Cogan, Sarah, Perrin, Sarah, Arnold, Sébastien M. R., Krause, Sebastian, Dai, Shengyang, Garg, Shruti, Sheth, Shruti, Ronstrom, Sue, Chan, Susan, Jordan, Timothy, Yu, Ting, Eccles, Tom, Hennigan, Tom, Kocisky, Tomas, Doshi, Tulsee, Jain, Vihan, Yadav, Vikas, Meshram, Vilobh, Dharmadhikari, Vishal, Barkley, Warren, Wei, Wei, Ye, Wenming, Han, Woohyun, Kwon, Woosuk, Xu, Xiang, Shen, Zhe, Gong, Zhitao, Wei, Zichuan, Cotruta, Victor, Kirk, Phoebe, Rao, Anand, Giang, Minh, Peran, Ludovic, Warkentin, Tris, Collins, Eli, Barral, Joelle, Ghahramani, Zoubin, Hadsell, Raia, Sculley, D., Banks, Jeanine, Dragan, Anca, Petrov, Slav, Vinyals, Oriol, Dean, Jeff, Hassabis, Demis, Kavukcuoglu, Koray, Farabet, Clement, Buchatskaya, Elena, Borgeaud, Sebastian, Fiedel, Noah, Joulin, Armand, Kenealy, Kathleen, Dadashi, Robert, and Andreev, Alek
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new version, we apply several known technical modifications to the Transformer architecture, such as interleaving local-global attentions (Beltagy et al., 2020a) and group-query attention (Ainslie et al., 2023). We also train the 2B and 9B models with knowledge distillation (Hinton et al., 2015) instead of next token prediction. The resulting models deliver the best performance for their size, and even offer competitive alternatives to models that are 2-3 times bigger. We release all our models to the community.
Published: 2024

30. BOND: Aligning LLMs with Best-of-N Distillation

Author: Sessa, Pier Giuseppe, Dadashi, Robert, Hussenot, Léonard, Ferret, Johan, Vieillard, Nino, Ramé, Alexandre, Shariari, Bobak, Perrin, Sarah, Friesen, Abe, Cideron, Geoffrey, Girgin, Sertan, Stanczyk, Piotr, Michi, Andrea, Sinopalnikov, Danila, Ramos, Sabela, Héliou, Amélie, Severyn, Aliaksei, Hoffman, Matt, Momchev, Nikola, and Bachem, Olivier
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract: Reinforcement learning from human feedback (RLHF) is a key driver of quality and safety in state-of-the-art large language models. Yet, a surprisingly simple and strong inference-time strategy is Best-of-N sampling that selects the best generation among N candidates. In this paper, we propose Best-of-N Distillation (BOND), a novel RLHF algorithm that seeks to emulate Best-of-N but without its significant computational overhead at inference time. Specifically, BOND is a distribution matching algorithm that forces the distribution of generations from the policy to get closer to the Best-of-N distribution. We use the Jeffreys divergence (a linear combination of forward and backward KL) to balance between mode-covering and mode-seeking behavior, and derive an iterative formulation that utilizes a moving anchor for efficiency. We demonstrate the effectiveness of our approach and several design choices through experiments on abstractive summarization and Gemma models. Aligning Gemma policies with BOND outperforms other RLHF algorithms by improving results on several benchmarks.
Published: 2024

31. BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023

Author: Kazerooni, Anahita Fathi, Khalili, Nastaran, Liu, Xinyang, Haldar, Debanjan, Jiang, Zhifan, Zapaishchykova, Anna, Pavaine, Julija, Shah, Lubdha M., Jones, Blaise V., Sheth, Nakul, Prabhu, Sanjay P., McAllister, Aaron S., Tu, Wenxin, Nandolia, Khanak K., Rodriguez, Andres F., Shaikh, Ibraheem Salman, Montano, Mariana Sanchez, Lai, Hollie Anne, Adewole, Maruf, Albrecht, Jake, Anazodo, Udunna, Anderson, Hannah, Anwar, Syed Muhammed, Aristizabal, Alejandro, Bagheri, Sina, Baid, Ujjwal, Bergquist, Timothy, Borja, Austin J., Calabrese, Evan, Chung, Verena, Conte, Gian-Marco, Eddy, James, Ezhov, Ivan, Familiar, Ariana M., Farahani, Keyvan, Gandhi, Deep, Gottipati, Anurag, Haldar, Shuvanjan, Iglesias, Juan Eugenio, Janas, Anastasia, Elaine, Elaine, Karargyris, Alexandros, Kassem, Hasan, Khalili, Neda, Kofler, Florian, LaBella, Dominic, Van Leemput, Koen, Li, Hongwei B., Maleki, Nazanin, Meier, Zeke, Menze, Bjoern, Moawad, Ahmed W., Pati, Sarthak, Piraud, Marie, Poussaint, Tina, Reitman, Zachary J., Rudie, Jeffrey D., Saluja, Rachit, Sheller, MIcah, Shinohara, Russell Takeshi, Viswanathan, Karthik, Wang, Chunhao, Wiestler, Benedikt, Wiggins, Walter F., Davatzikos, Christos, Storm, Phillip B., Bornhorst, Miriam, Packer, Roger, Hummel, Trent, de Blank, Peter, Hoffman, Lindsey, Aboian, Mariam, Nabavizadeh, Ali, Ware, Jeffrey B., Kann, Benjamin H., Rood, Brian, Resnick, Adam, Bakas, Spyridon, Vossough, Arastoo, and Linguraru, Marius George
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTSPEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors.
Published: 2024

32. MAGNET: Improving the Multilingual Fairness of Language Models with Adaptive Gradient-Based Tokenization

Author: Ahia, Orevaoghene, Kumar, Sachin, Gonen, Hila, Hoffman, Valentin, Limisiewicz, Tomasz, Tsvetkov, Yulia, and Smith, Noah A.
Subjects: Computer Science - Computation and Language
Abstract: In multilingual settings, non-Latin scripts and low-resource languages are usually disadvantaged in terms of language models' utility, efficiency, and cost. Specifically, previous studies have reported multiple modeling biases that the current tokenization algorithms introduce to non-Latin script languages, the main one being over-segmentation. In this work, we propose MAGNET; multilingual adaptive gradient-based tokenization to reduce over-segmentation via adaptive gradient-based subword tokenization. MAGNET learns to predict segment boundaries between byte tokens in a sequence via sub-modules within the model, which act as internal boundary predictors (tokenizers). Previous gradient-based tokenization methods aimed for uniform compression across sequences by integrating a single boundary predictor during training and optimizing it end-to-end through stochastic reparameterization alongside the next token prediction objective. However, this approach still results in over-segmentation for non-Latin script languages in multilingual settings. In contrast, MAGNET offers a customizable architecture where byte-level sequences are routed through language-script-specific predictors, each optimized for its respective language script. This modularity enforces equitable segmentation granularity across different language scripts compared to previous methods. Through extensive experiments, we demonstrate that in addition to reducing segmentation disparities, MAGNET also enables faster language modelling and improves downstream utility.
Published: 2024

33. Single-Neutron Adding on $^{34}$S

Author: Kuchera, A. N., Hoffman, C. R., Ryan, G., D'Amato, I. B., Guarinello, O. M., Kielb, P. S., Aggarwal, R., Ajayi, S., Conley, A. L., Conroy, I., Cottle, P. D., Esparza, J. C., Genty, S., Hanselman, K., Heinze, M., Houlihan, D., Kelly, B., Khawaja, M. I., Lopez-Saavedra, E., McCann, G. W., Morelock, A. B., Riley, L. A., Sandrik, A., Sitaraman, V., Spieker, M., Temanson, E., Wibisono, C., and Wiedenhover, I.
Subjects: Nuclear Experiment
Abstract: Purpose: Single-neutron adding data was collected in order to determine the distribution of the single-neutron strength of the $0f_{7/2}$, $1p_{3/2}$, $1p_{1/2}$ and $0f_{5/2}$ orbitals outside of $Z=16, N=18$, $^{34}$S. Methods: The $^{34}$S($d$,$p$)$^{35}$S reaction has been measured at 8 MeV/u to investigate cross sections to excited states in $^{35}$S. Outgoing proton yields and momenta were analyzed by the Super-Enge Split-Pole Spectrograph in conjunction with the CeBrA demonstrator located at the John D. Fox Laboratory at Florida State University. Angular distributions were compared with Distorted Wave Born Approximation calculations in order to extract single-neutron spectroscopic overlaps. Results: Spectroscopic overlaps and strengths were determined for states in $^{35}$S up through 6 MeV in excitation energy. Each orbital was observed to have fragmented strength where a single level carried the majority. The single-neutron centroids of the $0f_{7/2}$, $1p_{3/2}$, $1p_{1/2}$ and $0f_{5/2}$ orbitals were determined to be $2360^{+90}_{-40}$ keV, $3280^{+80}_{-50}$ keV, $4780^{+60}_{-40}$ keV, and $\gtrsim7500$ keV, respectively. Conclusion: A previous discrepancy in the literature with respect to distribution of the neutron $1p_{1/2}$ strength was resolved. The integration of the normalized spectroscopic strengths, up to 5.1 MeV in excitation energy, revealed fully-vacant occupancies for the $0f_{7/2}$, $1p_{3/2}$, and $1p_{1/2}$ orbitals, as expected. The spacing in the single-neutron energies highlighted a reduction in the traditional $N=28$ shell-gap, relative to both the $1p$ spin-orbit energy difference ($N=32$) and the lower limit on the $N=34$ shell spacing., Comment: Updated draft
Published: 2024

34. An Upper Limit on the Photoproduction Cross Section of the Spin-Exotic $\pi_1(1600)$

Author: Afzal, F., Akondi, C. S., Albrecht, M., Amaryan, M., Arrigo, S., Arroyave, V., Asaturyan, A., Austregesilo, A., Baldwin, Z., Barbosa, F., Barlow, J., Barriga, E., Barsotti, R., Barton, D., Baturin, V., Berdnikov, V. V., Black, T., Boeglin, W., Boer, M., Briscoe, W. J., Britton, T., Cao, S., Chudakov, E., Chung, G., Cole, P. L., Cortes, O., Crede, V., Dalton, M. M., Darulis, D., Deur, A., Dobbs, S., Dolgolenko, A., Dugger, M., Dzhygadlo, R., Ebersole, D., Edo, M., Egiyan, H., Erbora, T., Eugenio, P., Fabrizi, A., Fanelli, C., Fang, S., Fitches, J., Foda, A. M., Furletov, S., Gan, L., Gao, H., Gardner, A., Gasparian, A., Glazier, D., Gleason, C., Goryachev, V. S., Grube, B., Guo, J., Guo, L., Hernandez, J., Hernandez, K., Hoffman, N. D., Hornidge, D., Hou, G., Hurck, P., Hurley, A., Imoehl, W., Ireland, D. G., Ito, M. M., Jaegle, I., Jarvis, N. S., Jeske, T., Jing, M., Jones, R. T., Kakoyan, V., Kalicy, G., Khachatryan, V., Kourkoumelis, C., LaDuke, A., Larin, I., Lawrence, D., Lersch, D. I., Li, H., Liu, B., Livingston, K., Lolos, G. J., Lorenti, L., Lyubovitskij, V., Ma, R., Mack, D., Mahmood, A., Marukyan, H., Matveev, V., McCaughan, M., McCracken, M., Meyer, C. A., Miskimen, R., Mitchell, R. E., Mizutani, K., Neelamana, V., Ng, L., Nissen, E., Orešić, S., Ostrovidov, A. I., Papandreou, Z., Paudel, C., Pedroni, R., Pentchev, L., Peters, K. J., Prather, E., Rakshit, S., Reinhold, J., Remington, A., Ritchie, B. G., Ritman, J., Rodriguez, G., Romanov, D., Saldana, K., Salgado, C., Schadmand, S., Schertz, A. M., Scheuer, K., Schick, A., Schmidt, A., Schumacher, R. A., Schwiening, J., Septian, N., Sharp, P., Shen, X., Shepherd, M. R., Sikes, J., Smith, A., Smith, E. S., Sober, D. I., Somov, A., Somov, S., Stevens, J. R., Strakovsky, I. I., Sumner, B., Suresh, K., Tarasov, V. V., Taylor, S., Teymurazyan, A., Thiel, A., Viducic, T., Whitlatch, T., Wickramaarachchi, N., Wunderlich, Y., Yu, B., Zarling, J., Zhang, Z., Zhou, X., and Zihlmann, B.
Subjects: Nuclear Experiment, High Energy Physics - Experiment
Abstract: The spin-exotic hybrid meson $\pi_{1}(1600)$ is predicted to have a large decay rate to the $\omega\pi\pi$ final state. Using 76.6~pb$^{-1}$ of data collected with the GlueX detector, we measure the cross sections for the reactions $\gamma p \to \omega \pi^+ \pi^- p$, $\gamma p \to \omega \pi^0 \pi^0 p$, and $\gamma p\to\omega\pi^-\pi^0\Delta^{++}$ in the range $E_\gamma =$ 8-10 GeV. Using isospin conservation, we set the first upper limits on the photoproduction cross sections of the $\pi^{0}_{1}(1600)$ and $\pi^{-}_{1}(1600)$. We combine these limits with lattice calculations of decay widths and find that photoproduction of $\eta'\pi$ is the most sensitive two-body system to search for the $\pi_1(1600)$., Comment: 6 pages, 3 figures plus supplemental materials
Published: 2024

35. A Pattern Language for Machine Learning Tasks

Author: Rodatz, Benjamin, Fan, Ian, Laakkonen, Tuomas, Ortega, Neil John, Hoffman, Thomas, and Wang-Mascianica, Vincent
Subjects: Computer Science - Machine Learning, Mathematics - Category Theory, 18M30, 68T01, I.2.6
Abstract: Idealised as universal approximators, learners such as neural networks can be viewed as "variable functions" that may become one of a range of concrete functions after training. In the same way that equations constrain the possible values of variables in algebra, we may view objective functions as constraints on the behaviour of learners. We extract the equivalences perfectly optimised objective functions impose, calling them "tasks". For these tasks, we develop a formal graphical language that allows us to: (1) separate the core tasks of a behaviour from its implementation details; (2) reason about and design behaviours model-agnostically; and (3) simply describe and unify approaches in machine learning across domains. As proof-of-concept, we design a novel task that enables converting classifiers into generative models we call "manipulators", which we implement by directly translating task specifications into code. The resulting models exhibit capabilities such as style transfer and interpretable latent-space editing, without the need for custom architectures, adversarial training or random sampling. We formally relate the behaviour of manipulators to GANs, and empirically demonstrate their competitive performance with VAEs. We report on experiments across vision and language domains aiming to characterise manipulators as approximate Bayesian inversions of discriminative classifiers.
Published: 2024

36. Probing the connection between IceCube neutrinos and MOJAVE AGN

Author: Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Ausborm, L., Axani, S. N., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., Benning, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Collin, G. H., Connolly, A., Conrad, J. M., Corley, R., Cowen, D. F., Dave, P., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Diaz, A., Díaz-Vélez, J. C., Dierichs, P., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Durnford, D., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Gries, O., Griffin, S., Griswold, S., Groth, K. M., Guevel, D., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Jain, S., Janik, O., Jansson, M., Japaridze, G. S., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kovacevich, M., Kowalski, M., Kozynets, T., Krishnamoorthi, J., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Latseva, S., Lauber, F., Lazar, J. P., Lee, J. W., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Y. T., Liubarska, M., Love, C., Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Micallef, J., Mitchell, J., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nagai, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Oeyen, B., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Palusova, V., Pandya, H., Park, N., Parker, G. K., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Procter-Murphy, R., Przybylski, G. T., Raab, C., Rack-Helleis, J., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Reichherzer, P., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Roellinghoff, G., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Ryckbosch, D., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlickmann, L., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Sclafani, S., Seckel, D., Seikh, M., Seo, M., Seunarine, S., Myhr, P. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Turcotte, R., Twagirayezu, J. P., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Varsi, F., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Carrasco, S. Vergara, Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, A., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, S., Yuan, T., Zhang, Z., Zhelnin, P., Zilberman, P., and Zimmerman, M.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Active Galactic Nuclei (AGN) are prime candidate sources of the high-energy, astrophysical neutrinos detected by IceCube. This is demonstrated by the real-time multi-messenger detection of the blazar TXS 0506+056 and the recent evidence of neutrino emission from NGC 1068 from a separate time-averaged study. However, the production mechanism of the astrophysical neutrinos in AGN is not well established which can be resolved via correlation studies with photon observations. For neutrinos produced due to photohadronic interactions in AGN, in addition to a correlation of neutrinos with high-energy photons, there would also be a correlation of neutrinos with photons emitted at radio wavelengths. In this work, we perform an in-depth stacking study of the correlation between 15 GHz radio observations of AGN reported in the MOJAVE XV catalog, and ten years of neutrino data from IceCube. We also use a time-dependent approach which improves the statistical power of the stacking analysis. No significant correlation was found for both analyses and upper limits are reported. When compared to the IceCube diffuse flux, at 100 TeV and for a spectral index of 2.5, the upper limits derived are $\sim3\%$ and $\sim9\%$ for the time-averaged and time-dependent case, respectively., Comment: 14 Pages 7 Figures
Published: 2024
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37. Search for a light sterile neutrino with 7.5 years of IceCube DeepCore data

Author: Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Ausborm, L., Axani, S. N., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., Benning, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Collin, G. H., Connolly, A., Conrad, J. M., Corley, R., Cowen, D. F., Dave, P., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Diaz, A., Díaz-Vélez, J. C., Dierichs, P., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Durnford, D., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Gries, O., Griffin, S., Griswold, S., Groth, K. M., Guevel, D., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Jain, S., Janik, O., Jansson, M., Japaridze, G. S., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kovacevich, M., Kowalski, M., Kozynets, T., Krishnamoorthi, J., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Latseva, S., Lauber, F., Lazar, J. P., Lee, J. W., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Y. T., Liubarska, M., Love, C., Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Micallef, J., Mitchell, J., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nagai, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Oeyen, B., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Palusova, V., Pandya, H., Park, N., Parker, G. K., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Procter-Murphy, R., Przybylski, G. T., Raab, C., Rack-Helleis, J., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Reichherzer, P., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Roellinghoff, G., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Ryckbosch, D., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlickmann, L., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Sclafani, S., Seckel, D., Seikh, M., Seo, M., Seunarine, S., Myhr, P. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Turcotte, R., Twagirayezu, J. P., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Varsi, F., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Carrasco, S. Vergara, Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, A., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, S., Yuan, T., Zhang, Z., Zhelnin, P., Zilberman, P., and Zimmerman, M.
Subjects: High Energy Physics - Experiment
Abstract: We present a search for an eV-scale sterile neutrino using 7.5 years of data from the IceCube DeepCore detector. The analysis uses a sample of 21,914 events with energies between 5 and 150 GeV to search for sterile neutrinos through atmospheric muon neutrino disappearance. Improvements in event selection and treatment of systematic uncertainties provide greater statistical power compared to previous DeepCore sterile neutrino searches. Our results are compatible with the absence of mixing between active and sterile neutrino states, and we place constraints on the mixing matrix elements $|U_{\mu 4}|^2 < 0.0534$ and $|U_{\tau 4}|^2 < 0.0574$ at 90% CL under the assumption that $\Delta m^2_{41}\geq 1\;\mathrm{eV^2}$. These null results add to the growing tension between anomalous appearance results and constraints from disappearance searches in the 3+1 sterile neutrino landscape., Comment: 11 pages, 5 figures. Version accepted by Physical Review D for publication
Published: 2024
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38. Mixed-platonic 3-manifolds

Author: Chesebro, Eric, Chu, Michelle, DeBlois, Jason, Hoffman, Neil R., Mondal, Priyadip, and Walsh, Genevieve S.
Subjects: Mathematics - Geometric Topology, 57K32, 57K10
Abstract: We introduce a class of cusped hyperbolic $3$-manifolds that we call mixed-platonic, composed of regular ideal hyperbolic polyhedra of more than one type, which includes certain previously-known examples. We establish basic facts about mixed-platonic manifolds which allow us to conclude, among other things, that there is no mixed-platonic hyperbolic knot complement with hidden symmetries., Comment: 36 pages, 17 figures. Main update to v2: ancillary file is more easily accessible
Published: 2024

39. Uncertainty-enabled machine learning for emulation of regional sea-level change caused by the Antarctic Ice Sheet

Author: Yoo, Myungsoo, Gopalan, Giri, Hoffman, Matthew J., Coulson, Sophie, Han, Holly Kyeore, Wikle, Christopher K., and Hillebrand, Trevor
Subjects: Physics - Atmospheric and Oceanic Physics, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract: Projecting sea-level change in various climate-change scenarios typically involves running forward simulations of the Earth's gravitational, rotational and deformational (GRD) response to ice mass change, which requires high computational cost and time. Here we build neural-network emulators of sea-level change at 27 coastal locations, due to the GRD effects associated with future Antarctic Ice Sheet mass change over the 21st century. The emulators are based on datasets produced using a numerical solver for the static sea-level equation and published ISMIP6-2100 ice-sheet model simulations referenced in the IPCC AR6 report. We show that the neural-network emulators have an accuracy that is competitive with baseline machine learning emulators. In order to quantify uncertainty, we derive well-calibrated prediction intervals for simulated sea-level change via a linear regression postprocessing technique that uses (nonlinear) machine learning model outputs, a technique that has previously been applied to numerical climate models. We also demonstrate substantial gains in computational efficiency: a feedforward neural-network emulator exhibits on the order of 100 times speedup in comparison to the numerical sea-level equation solver that is used for training.
Published: 2024

40. Measurement of Spin-Density Matrix Elements in $\Delta^{++}(1232)$ photoproduction

Author: Afzal, F., Akondi, C. S., Albrecht, M., Amaryan, M., Arrigo, S., Arroyave, V., Asaturyan, A., Austregesilo, A., Baldwin, Z., Barbosa, F., Barlow, J., Barriga, E., Barsotti, R., Barton, D., Baturin, V., Berdnikov, V. V., Black, T., Boeglin, W., Boer, M., Briscoe, W. J., Britton, T., Cao, S., Chudakov, E., Chung, G., Cole, P. L., Cortes, O., Crede, V., Dalton, M. M., Darulis, D., Deur, A., Dobbs, S., Dolgolenko, A., Dugger, M., Dzhygadlo, R., Ebersole, D., Edo, M., Egiyan, H., Erbora, T., Eugenio, P., Fabrizi, A., Fanelli, C., Fang, S., Fitches, J., Foda, A. M., Furletov, S., Gan, L., Gao, H., Gardner, A., Gasparian, A., Glazier, D. I., Gleason, C., Goryachev, V. S., Grube, B., Guo, J., Guo, L., Hernandez, J., Hernandez, K., Hoffman, N. D., Hornidge, D., Hou, G., Hurck, P., Hurley, A., Imoehl, W., Ireland, D. G., Ito, M. M., Jaegle, I., Jarvis, N. S., Jeske, T., Jing, M., Jones, R. T., Kakoyan, V., Kalicy, G., Khachatryan, V., Kourkoumelis, C., LaDuke, A., Larin, I., Lawrence, D., Lersch, D. I., Li, H., Liu, B., Livingston, K., Lolos, G. J., Lorenti, L., Lyubovitskij, V., Ma, R., Mack, D., Mahmood, A., Marukyan, H., Matveev, V., McCaughan, M., McCracken, M., Meyer, C. A., Miskimen, R., Mitchell, R. E., Mizutani, K., Neelamana, V., Ng, L., Nissen, E., Orešić, S., Ostrovidov, A. I., Papandreou, Z., Paudel, C., Pedroni, R., Pentchev, L., Peters, K. J., Prather, E., Rakshit, S., Reinhold, J., Remington, A., Ritchie, B. G., Ritman, J., Rodriguez, G., Romanov, D., Saldana, K., Salgado, C., Schadmand, S., Schertz, A. M., Scheuer, K., Schick, A., Schmidt, A., Schumacher, R. A., Schwiening, J., Septian, N., Sharp, P., Shen, X., Shepherd, M. R., Sikes, J., Smith, A., Smith, E. S., Sober, D. I., Somov, A., Somov, S., Stevens, J. R., Strakovsky, I. I., Sumner, B., Suresh, K., Tarasov, V. V., Taylor, S., Teymurazyan, A., Thiel, A., Viducic, T., Whitlatch, T., Wickramaarachchi, N., Wunderlich, Y., Yu, B., Zarling, J., Zhang, Z., Zhou, X., and Zihlmann, B.
Subjects: Nuclear Experiment
Abstract: We measure the spin-density matrix elements (SDMEs) of the $\Delta^{++}(1232)$ in the photoproduction reaction $\gamma p \to \pi^-\Delta^{++}(1232)$ with the GlueX experiment in Hall D at Jefferson Lab. The measurement uses a linearly--polarized photon beam with energies from $8.2$ to $8.8$~GeV and the statistical precision of the SDMEs exceeds the previous measurement by three orders of magnitude for the momentum transfer squared region below $1.4$ GeV$^2$. The data are sensitive to the previously undetermined relative sign between couplings in existing Regge-exchange models. Linear combinations of the extracted SDMEs allow for a decomposition into natural and unnatural--exchange amplitudes. We find that the unnatural exchange plays an important role in the low momentum transfer region.
Published: 2024

41. IceCube Search for Neutrino Emission from X-ray Bright Seyfert Galaxies

Author: Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Ausborm, L., Axani, S. N., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., Benning, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Collin, G. H., Connolly, A., Conrad, J. M., Coppin, P., Corley, R., Correa, P., Cowen, D. F., Dave, P., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Diaz, A., Díaz-Vélez, J. C., Dierichs, P., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glauch, T., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Gries, O., Griffin, S., Griswold, S., Groth, K. M., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Janik, O., Jansson, M., Japaridze, G. S., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kovacevich, M., Kowalski, M., Kozynets, T., Krishnamoorthi, J., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Latseva, S., Lauber, F., Lazar, J. P., Lee, J. W., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Q. R., Liu, Y. T., Liubarska, M., Lohfink, E., Love, C., Mariscal, C. J. Lozano, Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Micallef, J., Mitchell, J., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nagai, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Oeyen, B., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Pandya, H., Park, N., Parker, G. K., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Philippen, S., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Procter-Murphy, R., Przybylski, G. T., Raab, C., Rack-Helleis, J., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Reichherzer, P., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Roellinghoff, G., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Ryckbosch, D., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Sclafani, S., Seckel, D., Seikh, M., Seo, M., Seunarine, S., Myhr, P. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Turcotte, R., Twagirayezu, J. P., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Varsi, F., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, A., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, S., Yuan, T., Zhang, Z., Zhelnin, P., Zilberman, P., and Zimmerman, M.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Experiment
Abstract: The recent IceCube detection of TeV neutrino emission from the nearby active galaxy NGC 1068 suggests that active galactic nuclei (AGN) could make a sizable contribution to the diffuse flux of astrophysical neutrinos. The absence of TeV $\gamma$-rays from NGC 1068 indicates neutrino production in the vicinity of the supermassive black hole, where the high radiation density leads to $\gamma$-ray attenuation. Therefore, any potential neutrino emission from similar sources is not expected to correlate with high-energy $\gamma$-rays. Disk-corona models predict neutrino emission from Seyfert galaxies to correlate with keV X-rays, as they are tracers of coronal activity. Using through-going track events from the Northern Sky recorded by IceCube between 2011 and 2021, we report results from a search for individual and aggregated neutrino signals from 27 additional Seyfert galaxies that are contained in the BAT AGN Spectroscopic Survey (BASS). Besides the generic single power-law, we evaluate the spectra predicted by the disk-corona model. Assuming all sources to be intrinsically similar to NGC 1068, our findings constrain the collective neutrino emission from X-ray bright Seyfert galaxies in the Northern Hemisphere, but, at the same time, show excesses of neutrinos that could be associated with the objects NGC 4151 and CGCG 420-015. These excesses result in a 2.7$\sigma$ significance with respect to background expectations., Comment: 17 pages, 9 figures
Published: 2024

42. Search for neutrino emission from hard X-ray AGN with IceCube

Author: Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Ausborm, L., Axani, S. N., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., Benning, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Collin, G. H., Connolly, A., Conrad, J. M., Coppin, P., Corley, R., Correa, P., Cowen, D. F., Dave, P., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Diaz, A., Díaz-Vélez, J. C., Dierichs, P., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Gries, O., Griffin, S., Griswold, S., Groth, K. M., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Jain, S., Janik, O., Jansson, M., Japaridze, G. S., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kovacevich, M., Kowalski, M., Kozynets, T., Krishnamoorthi, J., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Latseva, S., Lauber, F., Lazar, J. P., Lee, J. W., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Y. T., Liubarska, M., Love, C., Mariscal, C. J. Lozano, Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Micallef, J., Mitchell, J., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nagai, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Oeyen, B., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Palusova, V., Pandya, H., Park, N., Parker, G. K., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Philippen, S., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Privon, G. C., Procter-Murphy, R., Przybylski, G. T., Raab, C., Rack-Helleis, J., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Reichherzer, P., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Roellinghoff, G., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Ryckbosch, D., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlickmann, L., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Sclafani, S., Seckel, D., Seikh, M., Seo, M., Seunarine, S., Myhr, P. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Turcotte, R., Twagirayezu, J. P., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Varsi, F., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, A., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, S., Yuan, T., Zhang, Z., Zhelnin, P., Zilberman, P., and Zimmerman, M.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Active Galactic Nuclei (AGN) are promising candidate sources of high-energy astrophysical neutrinos since they provide environments rich in matter and photon targets where cosmic ray interactions may lead to the production of gamma rays and neutrinos. We searched for high-energy neutrino emission from AGN using the $\textit{Swift}$-BAT Spectroscopic Survey (BASS) catalog of hard X-ray sources and 12 years of IceCube muon track data. First, upon performing a stacked search, no significant emission was found. Second, we searched for neutrinos from a list of 43 candidate sources and found an excess from the direction of two sources, Seyfert galaxies NGC 1068 and NGC 4151. We observed NGC 1068 at flux $\phi_{\nu_{\mu}+\bar{\nu}_{\mu}}$ = $4.02_{-1.52}^{+1.58} \times 10^{-11}$ TeV$^{-1}$ cm$^{-2}$ s$^{-1}$ normalized at 1 TeV, with power-law spectral index, $\gamma$ = 3.10$^{+0.26}_{-0.22}$, consistent with previous IceCube results. The observation of a neutrino excess from the direction of NGC 4151 is at a post-trial significance of 2.9$\sigma$. If interpreted as an astrophysical signal, the excess observed from NGC 4151 corresponds to a flux $\phi_{\nu_{\mu}+\bar{\nu}_{\mu}}$ = $1.51_{-0.81}^{+0.99} \times 10^{-11}$ TeV$^{-1}$ cm$^{-2}$ s$^{-1}$ normalized at 1 TeV and $\gamma$ = 2.83$^{+0.35}_{-0.28}$.
Published: 2024

43. The computational power of random quantum circuits in arbitrary geometries

Author: DeCross, Matthew, Haghshenas, Reza, Liu, Minzhao, Rinaldi, Enrico, Gray, Johnnie, Alexeev, Yuri, Baldwin, Charles H., Bartolotta, John P., Bohn, Matthew, Chertkov, Eli, Cline, Julia, Colina, Jonhas, DelVento, Davide, Dreiling, Joan M., Foltz, Cameron, Gaebler, John P., Gatterman, Thomas M., Gilbreth, Christopher N., Giles, Joshua, Gresh, Dan, Hall, Alex, Hankin, Aaron, Hansen, Azure, Hewitt, Nathan, Hoffman, Ian, Holliman, Craig, Hutson, Ross B., Jacobs, Trent, Johansen, Jacob, Lee, Patricia J., Lehman, Elliot, Lucchetti, Dominic, Lykov, Danylo, Madjarov, Ivaylo S., Mathewson, Brian, Mayer, Karl, Mills, Michael, Niroula, Pradeep, Pino, Juan M., Roman, Conrad, Schecter, Michael, Siegfried, Peter E., Tiemann, Bruce G., Volin, Curtis, Walker, James, Shaydulin, Ruslan, Pistoia, Marco, Moses, Steven. A., Hayes, David, Neyenhuis, Brian, Stutz, Russell P., and Foss-Feig, Michael
Subjects: Quantum Physics
Abstract: Empirical evidence for a gap between the computational powers of classical and quantum computers has been provided by experiments that sample the output distributions of two-dimensional quantum circuits. Many attempts to close this gap have utilized classical simulations based on tensor network techniques, and their limitations shed light on the improvements to quantum hardware required to frustrate classical simulability. In particular, quantum computers having in excess of $\sim 50$ qubits are primarily vulnerable to classical simulation due to restrictions on their gate fidelity and their connectivity, the latter determining how many gates are required (and therefore how much infidelity is suffered) in generating highly-entangled states. Here, we describe recent hardware upgrades to Quantinuum's H2 quantum computer enabling it to operate on up to $56$ qubits with arbitrary connectivity and $99.843(5)\%$ two-qubit gate fidelity. Utilizing the flexible connectivity of H2, we present data from random circuit sampling in highly connected geometries, doing so at unprecedented fidelities and a scale that appears to be beyond the capabilities of state-of-the-art classical algorithms. The considerable difficulty of classically simulating H2 is likely limited only by qubit number, demonstrating the promise and scalability of the QCCD architecture as continued progress is made towards building larger machines., Comment: Includes minor updates to the text and an updated author list to include researchers who made technical contributions in upgrading the machine to 56 qubits but were left off the original version by mistake
Published: 2024

44. The density conjecture for activated random walk

Author: Hoffman, Christopher, Johnson, Tobias, and Junge, Matthew
Subjects: Mathematics - Probability, Mathematical Physics, 60K35, 82C22
Abstract: Bak, Tang, and Wiesenfeld developed their theory of self-organized criticality in the late 1980s to explain why many real-life processes exhibit signs of critical behavior despite the absence of a tuning parameter. A decade later, Dickman, Mu\~noz, Vespignani, and Zapperi explained self-organized criticality as an external force pushing a hidden parameter toward the critical value of a traditional absorbing-state phase transition. As evidence, they observed empirically that for various sandpile models, the particle density in a finite box under driven-dissipative dynamics converges to the critical density of an infinite-volume version of the model. We give the first proof of this well-known density conjecture in any setting by establishing it for activated random walk in one dimension. We prove that two other natural versions of the model have the same critical value, further establishing activated random walk as a universal model of self-organized criticality., Comment: 77 pages; split Section 3 into two sections, improved the presentation in various ways, and added new expository figures
Published: 2024

45. Exploration of mass splitting and muon/tau mixing parameters for an eV-scale sterile neutrino with IceCube

Author: Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Ausborm, L., Axani, S. N., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., Benning, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Collin, G. H., Connolly, A., Conrad, J. M., Coppin, P., Corley, R., Correa, P., Cowen, D. F., Dave, P., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Diaz, A., Díaz-Vélez, J. C., Dierichs, P., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Gries, O., Griffin, S., Griswold, S., Groth, K. M., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Jain, S., Janik, O., Jansson, M., Japaridze, G. S., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kovacevich, M., Kowalski, M., Kozynets, T., Krishnamoorthi, J., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Latseva, S., Lauber, F., Lazar, J. P., Lee, J. W., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Y. T., Liubarska, M., Love, C., Mariscal, C. J. Lozano, Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Micallef, J., Mitchell, J., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nagai, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Oeyen, B., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Palusova, V., Pandya, H., Park, N., Parker, G. K., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Philippen, S., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Procter-Murphy, R., Przybylski, G. T., Raab, C., Rack-Helleis, J., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Reichherzer, P., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Roellinghoff, G., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Ryckbosch, D., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlickmann, L., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Sclafani, S., Seckel, D., Seikh, M., Seo, M., Seunarine, S., Myhr, P. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Turcotte, R., Twagirayezu, J. P., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Varsi, F., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, A., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, S., Yuan, T., Zhang, Z., Zhelnin, P., Zilberman, P., and Zimmerman, M.
Subjects: High Energy Physics - Experiment
Abstract: We present the first three-parameter fit to a 3+1 sterile neutrino model using 7.634 years of data from the IceCube Neutrino Observatory on $\nu_\mu+\overline{\nu}_\mu$ charged-current interactions in the energy range 500--9976 GeV. Our analysis is sensitive to the mass-squared splitting between the heaviest and lightest mass state ($\Delta m_{41}^2$), the mixing matrix element connecting muon flavor to the fourth mass state ($|U_{\mu4}|^2$), and the element connecting tau flavor to the fourth mass state ($|U_{\tau4}|^2$). Predicted propagation effects in matter enhance the signature through a resonance as atmospheric neutrinos from the Northern Hemisphere traverse the Earth to the IceCube detector at the South Pole. The remaining sterile neutrino matrix elements are left fixed, with $|U_{e4}|^2= 0$ and $\delta_{14}=0$, as they have a negligible effect, and $\delta_{24}=\pi$ is set to give the most conservative limits. The result is consistent with the no-sterile neutrino hypothesis with a probability of 4.3%. Profiling the likelihood of each parameter yields the 90\% confidence levels: $ 2.4\,\mathrm{eV}^{2} < \Delta m_{41}^2 <9.6\,\mathrm{eV}^{2} $ , $0.0081 < |U_{\mu4}|^2 < 0.10$ , and $|U_{\tau4}|^2< 0.035$, which narrows the allowed parameter-space for $|U_{\tau4}|^2$. However, the primary result of this analysis is the first map of the 3+1 parameter space exploring the interdependence of $\Delta m_{41}^2$, $|U_{\mu4}|^2$, and $|U_{\tau4}|^2$., Comment: 14 pages, 8 figures. Published in PLB
Published: 2024
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46. Modeling the refractive index profile n(z) of polar ice for ultra-high energy neutrino experiments

Author: Ali, S., Allison, P., Archambault, S., Beatty, J. J., Besson, D. Z., Bishop, A., Chen, P., Chen, Y. C., Clark, B. A., Clay, W., Connolly, A., Couberly, K., Cremonesi, L., Cummings, A., Dasgupta, P., Debolt, R., de Kockere, S., de Vries, K. D., Deaconu, C., DuVernois, M. A., Flaherty, J., Friedman, E., Gaior, R., Giri, P., Hanson, J., Harty, N., Hoffman, K. D., Huang, J. J., Huang, M. -H., Hughes, K., Ishihara, A., Karle, A., Kelley, J. L., Kim, K. -C., Kim, M. -C., Kravchenko, I., Krebs, R., Kuo, C. Y., Kurusu, K., Latif, U. A., Liu, C. H, Liu, T. C., Luszczak, W., Mase, K., Muzio, M. S., Nam, J., Nichol, R. J., Novikov, A., Nozdrina, A., Oberla, E., Pan, Y., Pfendner, C., Punsuebsay, N., Roth, J., Salcedo-Gomez, A., Seckel, D., Seikh, M. F. H., Shaio, Y. -S., Smith, D., Toscano, S., Torres, J., Touart, J., van Eijndhoven, N., Vieregg, A., Wang, M. -Z., Wang, S. -H., Wissel, S. A., Xie, C., Yoshida, S., and Young, R.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: We develop an in-situ index of refraction profile using the transit time of radio signals broadcast from an englacial transmitter to 2-5 km distant radio-frequency receivers, deployed at depths up to 200 m. Maxwell's equations generally admit two ray propagation solutions from a given transmitter, corresponding to a direct path (D) and a refracted path (R); the measured D vs. R (dt(D,R)) timing differences provide constraints on the index of refraction profile near South Pole, where the Askaryan Radio Array (ARA) neutrino observatory is located. We constrain the refractive index profile by simulating D and R ray paths via ray tracing and comparing those to measured dt(D,R) signals. Using previous ice density data as a proxy for n(z), we demonstrate that our data strongly favors a glaciologically-motivated three-phase densification model rather than a single exponential scale height model. Simulations show that the single exponential model overestimates ARA neutrino sensitivity compared to the three-phase model.
Published: 2024

47. Mistranslating the genetic code with leucine in yeast and mammalian cells

Author: Davey-Young, Josephine, Hasan, Farah, Tennakoon, Rasangi, Rozik, Peter, Moore, Henry, Hall, Peter, Cozma, Ecaterina, Genereaux, Julie, Hoffman, Kyle S, Chan, Patricia P, Lowe, Todd M, Brandl, Christopher J, and O’Donoghue, Patrick
Subjects: Biological Sciences, Genetics, Animals, Humans, Saccharomyces cerevisiae, Anticodon, Leucine, RNA, Transfer, Leu, Genetic Code, Codon, RNA, Transfer, Amino Acyl-tRNA Synthetases, Alanine, Mammals, Genetic code, mistranslation, neuroblastoma cells, protein synthesis, Transfer RNA, translation fidelity, yeast, Developmental Biology, Biochemistry and cell biology
Abstract: Translation fidelity relies on accurate aminoacylation of transfer RNAs (tRNAs) by aminoacyl-tRNA synthetases (AARSs). AARSs specific for alanine (Ala), leucine (Leu), serine, and pyrrolysine do not recognize the anticodon bases. Single nucleotide anticodon variants in their cognate tRNAs can lead to mistranslation. Human genomes include both rare and more common mistranslating tRNA variants. We investigated three rare human tRNALeu variants that mis-incorporate Leu at phenylalanine or tryptophan codons. Expression of each tRNALeu anticodon variant in neuroblastoma cells caused defects in fluorescent protein production without significantly increased cytotoxicity under normal conditions or in the context of proteasome inhibition. Using tRNA sequencing and mass spectrometry we confirmed that each tRNALeu variant was expressed and generated mistranslation with Leu. To probe the flexibility of the entire genetic code towards Leu mis-incorporation, we created 64 yeast strains to express all possible tRNALeu anticodon variants in a doxycycline-inducible system. While some variants showed mild or no growth defects, many anticodon variants, enriched with G/C at positions 35 and 36, including those replacing Leu for proline, arginine, alanine, or glycine, caused dramatic reductions in growth. Differential phenotypic defects were observed for tRNALeu mutants with synonymous anticodons and for different tRNALeu isoacceptors with the same anticodon. A comparison to tRNAAla anticodon variants demonstrates that Ala mis-incorporation is more tolerable than Leu at nearly every codon. The data show that the nature of the amino acid substitution, the tRNA gene, and the anticodon are each important factors that influence the ability of cells to tolerate mistranslating tRNAs.
Published: 2024

48. Feasibility and acceptability of chaplain decision coaching on Periviable resuscitation decision quality: A pilot study.

Author: Varner-Perez, Shelley, Hoffman, Shelley, Bhamidipalli, Sruthi, Monahan, Patrick, Tucker Edmonds, Brownsyne, Kuppermann, Miriam, and Coleman-Phox, Kimberly
Subjects: Chaplain, Decision making, Decision support tool, Patient centered, Periviable delivery, Spirituality
Abstract: OBJECTIVE: To pilot test and assess the feasibility and acceptability of chaplain-led decision coaching alongside the GOALS (Getting Optimal Alignment around Life Support) decision support tool to enhance decision-making in threatened periviable delivery. METHODS: Pregnant people admitted for threatened periviable delivery and their important other (IO) were enrolled. Decisional conflict, acceptability, and knowledge were measured before and after the intervention. Chaplains journaled their impressions of training and coaching encounters. Descriptive analysis and conventional content analysis were completed. RESULTS: Eight pregnant people and two IOs participated. Decisional conflict decreased by a mean of 6.7 (SD = 9.4) and knowledge increased by a mean of 1.4 (SD = 1.8). All rated their experience as good or excellent, and the amount of information was just right. Participants found it helpful to have someone to talk to and noted chaplains helped them reach a decision. Chaplains found the intervention a valuable use of their time and skillset. CONCLUSION: This is the first small-scale pilot study to utilize chaplains as decision coaches. Our results suggest that chaplain coaching with a decision support tool is feasible and well-accepted by parents and chaplains. INNOVATIONS: Our findings recognize chaplains as an underutilized, yet practical resource in value-laden clinical decision-making.
Published: 2024

49. Deep Generative Models for Fast Photon Shower Simulation in ATLAS

Author: Aad, G, Abbott, B, Abbott, DC, Abud, AA, Abeling, K, Abhayasinghe, DK, Abidi, SH, Aboulhorma, A, Abramowicz, H, Abreu, H, Abulaiti, Y, Hoffman, ACA, Acharya, BS, Achkar, B, Adam, L, Bourdarios, CA, Adamczyk, L, Adamek, L, Addepalli, SV, Adelman, J, Adiguzel, A, Adorni, S, Adye, T, Affolder, AA, Afik, Y, Agaras, MN, Agarwala, J, Aggarwal, A, Agheorghiesei, C, Aguilar-Saavedra, JA, Ahmad, A, Ahmadov, F, Ahmed, WS, Ai, X, Aielli, G, Aizenberg, I, Akbiyik, M, Åkesson, TPA, Akimov, AV, Khoury, KA, Alberghi, GL, Albert, J, Albicocco, P, Verzini, MJA, Alderweireldt, S, Aleksa, M, Aleksandrov, IN, Alexa, C, Alexopoulos, T, Alfonsi, A, Alfonsi, F, Alhroob, M, Ali, B, Ali, S, Aliev, M, Alimonti, G, Allaire, C, Allbrooke, BMM, Allport, PP, Aloisio, A, Alonso, F, Alpigiani, C, Camelia, EA, Estevez, MA, Alviggi, MG, Coutinho, YA, Ambler, A, Ambroz, L, Amelung, C, Amidei, D, Santos, SPAD, Amoroso, S, Amos, KR, Amrouche, CS, Ananiev, V, Anastopoulos, C, Andari, N, Andeen, T, Anders, JK, Andrean, SY, Andreazza, A, Angelidakis, S, Angerami, A, Anisenkov, AV, Annovi, A, Antel, C, Anthony, MT, Antipov, E, Antonelli, M, Antrim, DJA, Anulli, F, Aoki, M, Pozo, JAA, Aparo, MA, Bella, LA, Appelt, C, Aranzabal, N, Ferraz, VA, Arcangeletti, C, and Arce, ATH
Subjects: Nuclear and Plasma Physics, Particle and High Energy Physics, Physical Sciences
Abstract: The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques.
Published: 2024

50. chief historian's FOOTNOTE : THE COL. CHARLES YOUNG FELLOWSHIP UPDATE

Author: Hoffman, Jon T.
Published: 2024

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