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1. AI-generated Image Detection: Passive or Watermark?

Author: Guo, Moyang, Hu, Yuepeng, Jiang, Zhengyuan, Li, Zeyu, Sadovnik, Amir, Daw, Arka, and Gong, Neil
Subjects: Computer Science - Cryptography and Security, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: While text-to-image models offer numerous benefits, they also pose significant societal risks. Detecting AI-generated images is crucial for mitigating these risks. Detection methods can be broadly categorized into passive and watermark-based approaches: passive detectors rely on artifacts present in AI-generated images, whereas watermark-based detectors proactively embed watermarks into such images. A key question is which type of detector performs better in terms of effectiveness, robustness, and efficiency. However, the current literature lacks a comprehensive understanding of this issue. In this work, we aim to bridge that gap by developing ImageDetectBench, the first comprehensive benchmark to compare the effectiveness, robustness, and efficiency of passive and watermark-based detectors. Our benchmark includes four datasets, each containing a mix of AI-generated and non-AI-generated images. We evaluate five passive detectors and four watermark-based detectors against eight types of common perturbations and three types of adversarial perturbations. Our benchmark results reveal several interesting findings. For instance, watermark-based detectors consistently outperform passive detectors, both in the presence and absence of perturbations. Based on these insights, we provide recommendations for detecting AI-generated images, e.g., when both types of detectors are applicable, watermark-based detectors should be the preferred choice. Our code and data are publicly available at https://github.com/moyangkuo/ImageDetectBench.git.
Published: 2024

2. Identifying Spicules in Mg II: Statistics and Comparisons with H{\alpha}

Author: Herde, Vicki L., Bose, Souvik, Chamberlin, Phillip C., Schmit, Don, Daw, Adrian, Polito, Vanessa, and Gonzalez, Gabriella
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The Sun's chromosphere is a critical region to understand when considering energy and mass deposition into the transition region and corona, but many of the smaller, faster events which transport a portion of this mass and energy are still difficult to observe, identify and model. Solar Spicules are small, spike-like events in the solar chromosphere that have the potential to transfer energy and mass to the transition region, but whose energetic origins are still being researched. Chromospheric spicule activity on-disk can be identified by observing temporary excursions in the red and blue wings of chromospheric emission lines. Researchers have demonstrated this in Hydrogen~Alpha (H{\alpha}, 6563 {\AA}), Ca II (8542 {\AA}, k 3934 {\AA}), Mg II (h 2803 {\AA}, k 2796 {\AA}), and Si IV (1394 {\AA}, 1405 {\AA}) spectral observations, with the vast majority of identification efforts focused on lower chromospheric observations of H$\alpha$ and Ca II. Because any spicules which deposit mass and energy into the transition region must necessarily pass through the upper chromosphere, observations from this region such as Mg II or Hydrogen Lyman Alpha (Ly$\alpha$ 1216 {\AA}) in enough quantity to perform proper statistics will be critical to fully characterizing spicules' impact on mass and energy transfer in the Sun. This research proposes a definition with numerical limits for how spicules appear in Mg II wavelengths, tunes an algorithm for automatically detecting spicules in Mg II spectral observations, and uses K Means Clustering to identify and display the full range of spicule spectrum shapes. This work will help allow statistical studies on spicules in the upper chromosphere to be as thorough as those of the lower chromosphere, allowing researchers to better understand the physical nature of spicules and their role in energy transfer and deposition in the solar atmosphere., Comment: Submitted an early draft, was not ready for full submission
Published: 2024

3. Receiver Noise in Axion Haloscopes

Author: Guzzetti, M., Zhang, D., Goodman, C., Hanretty, C., Sinnis, J., Rosenberg, L. J, Rybka, G., Clarke, John, Siddiqi, I., Chou, A. S., Hollister, M., Knirck, S., Sonnenschein, A., Caligiure, T. J., Gleason, J. R., Hipp, A. T., Sikivie, P., Solano, M. E., Sullivan, N. S., Tanner, D. B., Khatiwada, R., Carosi, G., Du, N., Cisneros, C., Robertson, N., Woollett, N., Duffy, L. D., Boutan, C., Braine, T., Oblath, N. S., Taubman, M. S., Lentz, E., Daw, E. J., Mostyn, C., Perry, M. G., Bartram, C., Dyson, T. A., Kuo, C. L., Ruppert, S., Withers, M. O., Yi, A. K., McAllister, B. T., Buckley, J. H., Gaikwad, C., Hoffman, J., Murch, K., Russell, J., Goryachev, M., Hartman, E., Quiskamp, A., and Tobar, M. E.
Subjects: High Energy Physics - Experiment
Abstract: Axions are a well-motivated candidate for dark matter. The preeminent method to search for axion dark matter is known as the axion haloscope, which makes use of the conversion of axions to photons in a large magnetic field. Due to the weak coupling of axions to photons however, the expected signal strength is exceptionally small. To increase signal strength, many haloscopes make use of resonant enhancement and high gain amplifiers, while also taking measures to keep receiver noise as low as possible such as the use of dilution refrigerators and ultra low-noise electronics. In this paper we derive the theoretical noise model based on the sources of noise found within a typical axion haloscope receiver chain, using the Axion Dark Matter eXperiment (ADMX) as a case study. We present examples of different noise calibration measurements at 1280~MHz using a variable temperature stage with ADMX during its most recent data taking run. The consistency between the measurements and the detailed model provide suggestions for future improvements within ADMX and other axion haloscopes to reach a lower noise temperature and to simplify the receiver chain design.
Published: 2024

4. Co-Producing Ethics Guidelines Together with People with Learning Disabilities

Author: Mary Bottomley, Jodie Bradley, Lisa Clark, Bryan Collis, Bojana Daw Srdanovic, Victoria Farnsworth, Annie Ferguson, Dan Goodley, Andrew Fox, Nikita K. Hayden, Charlotte Lawthom, Rebecca Lawthom, Claudia Magwood, Robert McLean, Ian Middleton, Alison Owen, Matty Prothero, Simon Rice, Simon Richards, Katherine Runswick-Cole, Kelly Scargill, Rohit Shankar, and Toni Ann Wood
Abstract: Background: We are a research team of clinical, academic and advocacy-based researchers with and without learning disabilities, working on the "Humanising Healthcare" (for people with learning disabilities) project. The project is dedicated to finding and sharing healthcare practices that enhance the lives of people with learning disabilities. As part of our ethics applications to access National Health Service study sites for fieldwork, we worked together to write guiding principles for co-producing research ethics with researchers with learning disabilities. In this paper, we introduce these Participatory Ethics Good Practice Guidelines and reflect on our collaboration. Methods: We reflect on developing the Participatory Ethics Good Practice Guidelines. These guidelines were developed during online co-production meetings with our full research team, including advocacy-based organisation researchers, clinical researchers and university researchers. We considered consent, understanding research, and understanding research methods during the development of these Guidelines. Findings: We present ten guidelines for co-producing research with people with learning disabilities. Conclusions: Our findings may be helpful to researchers with learning disabilities, university and clinical researchers, funders, and those who work in research governance (e.g., ethics committees and university research departments).
Published: 2024
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5. Hiding-in-Plain-Sight (HiPS) Attack on CLIP for Targetted Object Removal from Images

Author: Daw, Arka, Chung, Megan Hong-Thanh, Mahbub, Maria, and Sadovnik, Amir
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Cryptography and Security, Computer Science - Computer Vision and Pattern Recognition
Abstract: Machine learning models are known to be vulnerable to adversarial attacks, but traditional attacks have mostly focused on single-modalities. With the rise of large multi-modal models (LMMs) like CLIP, which combine vision and language capabilities, new vulnerabilities have emerged. However, prior work in multimodal targeted attacks aim to completely change the model's output to what the adversary wants. In many realistic scenarios, an adversary might seek to make only subtle modifications to the output, so that the changes go unnoticed by downstream models or even by humans. We introduce Hiding-in-Plain-Sight (HiPS) attacks, a novel class of adversarial attacks that subtly modifies model predictions by selectively concealing target object(s), as if the target object was absent from the scene. We propose two HiPS attack variants, HiPS-cls and HiPS-cap, and demonstrate their effectiveness in transferring to downstream image captioning models, such as CLIP-Cap, for targeted object removal from image captions., Comment: Published in the 3rd Workshop on New Frontiers in Adversarial Machine Learning at NeurIPS 2024. 10 pages, 7 figures, 3 tables
Published: 2024

6. A Unified Framework for Forward and Inverse Problems in Subsurface Imaging using Latent Space Translations

Author: Gupta, Naveen, Sawhney, Medha, Daw, Arka, Lin, Youzuo, and Karpatne, Anuj
Subjects: Computer Science - Machine Learning, Mathematical Physics, Physics - Geophysics
Abstract: In subsurface imaging, learning the mapping from velocity maps to seismic waveforms (forward problem) and waveforms to velocity (inverse problem) is important for several applications. While traditional techniques for solving forward and inverse problems are computationally prohibitive, there is a growing interest in leveraging recent advances in deep learning to learn the mapping between velocity maps and seismic waveform images directly from data. Despite the variety of architectures explored in previous works, several open questions still remain unanswered such as the effect of latent space sizes, the importance of manifold learning, the complexity of translation models, and the value of jointly solving forward and inverse problems. We propose a unified framework to systematically characterize prior research in this area termed the Generalized Forward-Inverse (GFI) framework, building on the assumption of manifolds and latent space translations. We show that GFI encompasses previous works in deep learning for subsurface imaging, which can be viewed as specific instantiations of GFI. We also propose two new model architectures within the framework of GFI: Latent U-Net and Invertible X-Net, leveraging the power of U-Nets for domain translation and the ability of IU-Nets to simultaneously learn forward and inverse translations, respectively. We show that our proposed models achieve state-of-the-art (SOTA) performance for forward and inverse problems on a wide range of synthetic datasets, and also investigate their zero-shot effectiveness on two real-world-like datasets.
Published: 2024

7. 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

8. Molecular Hydrogen Line Identifications in Solar Flares Observed by IRIS: Lower Atmospheric Structure from Radiometric Analysis

Author: Jaeggli, Sarah A. and Daw, Adrian N.
Subjects: Astrophysics - Solar and Stellar Astrophysics
Abstract: A rich spectrum of molecular hydrogen (H$_2$) emission lines is seen in sensitive observations from the far ultraviolet (FUV) channels of the Interface Region Imaging Spectrograph (IRIS) during flare activity in solar active region NOAA 11861. Based on this observation we have determined 37 new line identifications by comparing synthetic spectra produced using 1D modeling of H$_2$ fluorescence. To avoid misidentification of the H$_2$ lines, we have also compiled a complete list of atomic line identifications for the IRIS FUV bandpasses from previous work. We carry out analysis of the spatially resolved H$_2$ emission that occurs during the flares and find that: (1) in spatially resolved observations the H$_2$ line ratios may show optically thick line formation, contrary to previous results; (2) comparison of the spatial distribution of H$_2$ Doppler velocities with those measured from other species reveals that H$_2$ remote sensing probes an intermediate depth in the atmosphere between the photosphere and chromosphere, consistent with expectations from modeling; (3) the relationship between H$_2$ line intensity and the observed intensity of its exciter is related to the atmospheric stratification; however, (4) H$_2$ fluorescence can sometimes occur in response to radiation from distant sources many Mm away across the solar surface., Comment: 36 pages, 15 figures, 2 tables, accepted for publication in ApJ
Published: 2024

9. What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traits

Author: Manogaran, Harish Babu, Maruf, M., Daw, Arka, Mehrab, Kazi Sajeed, Charpentier, Caleb Patrick, Uyeda, Josef C., Dahdul, Wasila, Thompson, Matthew J, Campolongo, Elizabeth G, Provost, Kaiya L, Mabee, Paula M., Lapp, Hilmar, and Karpatne, Anuj
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: A grand challenge in biology is to discover evolutionary traits - features of organisms common to a group of species with a shared ancestor in the tree of life (also referred to as phylogenetic tree). With the growing availability of image repositories in biology, there is a tremendous opportunity to discover evolutionary traits directly from images in the form of a hierarchy of prototypes. However, current prototype-based methods are mostly designed to operate over a flat structure of classes and face several challenges in discovering hierarchical prototypes, including the issue of learning over-specific features at internal nodes. To overcome these challenges, we introduce the framework of Hierarchy aligned Commonality through Prototypical Networks (HComP-Net). We empirically show that HComP-Net learns prototypes that are accurate, semantically consistent, and generalizable to unseen species in comparison to baselines on birds, butterflies, and fishes datasets. The code and datasets are available at https://github.com/Imageomics/HComPNet., Comment: 34 pages, 27 figures
Published: 2024

10. VLM4Bio: A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological Images

Author: Maruf, M., Daw, Arka, Mehrab, Kazi Sajeed, Manogaran, Harish Babu, Neog, Abhilash, Sawhney, Medha, Khurana, Mridul, Balhoff, James P., Bakis, Yasin, Altintas, Bahadir, Thompson, Matthew J., Campolongo, Elizabeth G., Uyeda, Josef C., Lapp, Hilmar, Bart, Henry L., Mabee, Paula M., Su, Yu, Chao, Wei-Lun, Stewart, Charles, Berger-Wolf, Tanya, Dahdul, Wasila, and Karpatne, Anuj
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Images are increasingly becoming the currency for documenting biodiversity on the planet, providing novel opportunities for accelerating scientific discoveries in the field of organismal biology, especially with the advent of large vision-language models (VLMs). We ask if pre-trained VLMs can aid scientists in answering a range of biologically relevant questions without any additional fine-tuning. In this paper, we evaluate the effectiveness of 12 state-of-the-art (SOTA) VLMs in the field of organismal biology using a novel dataset, VLM4Bio, consisting of 469K question-answer pairs involving 30K images from three groups of organisms: fishes, birds, and butterflies, covering five biologically relevant tasks. We also explore the effects of applying prompting techniques and tests for reasoning hallucination on the performance of VLMs, shedding new light on the capabilities of current SOTA VLMs in answering biologically relevant questions using images. The code and datasets for running all the analyses reported in this paper can be found at https://github.com/sammarfy/VLM4Bio., Comment: 36 pages, 37 figures, 7 tables
Published: 2024

11. 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/cm${}^3$ made up 100% of axions.
Published: 2024

12. The Hybrid Hospital: Balancing On-Site and Remote Hospitalization

Author: Zychlinski, Noa, Mendelson, Gal, and Daw, Andrew
Subjects: Electrical Engineering and Systems Science - Systems and Control
Abstract: Hybrid hospitals offer on-site and remote hospitalization through telemedicine. These new healthcare models require novel operational policies to balance costs, efficiency, and patient well-being. Our study addresses two first-order questions: (i) how to direct patient admission and call-in based on individual characteristics and proximity and (ii) how to determine the optimal allocation of medical resources between these two hospitalization options and among different patient types. We develop a model that uses Brownian Motion to capture the patient's health evolution during remote/on-site hospitalization and during travel. Under cost-minimizing call-in policies, we find that remote hospitalization can be cost-effective for moderately distant patients, as the optimal call-in threshold is non-monotonic in the patient's travel time. Subject to scarce resources, the optimal solution structure becomes equivalent to a simultaneous, identically sized increase of remote and on-site costs under abundant resources. When limited resources must be divided among multiple patient types, the optimal thresholds shift in non-obvious ways as resource availability changes. Finally, we develop a practical and efficient policy that allows for swapping an on-site patient with a remote patient when the latter is called-in and sufficient resources are not available to treat both on-site. Contrary to the widely held view that telemedicine can mitigate rural and non-rural healthcare disparities, our research suggests that on-site care may actually be more cost-effective than remote hospitalization for patients in distant locations, due to (potentially overlooked) risks during patient travel. This finding may be of particular concern in light of the growing number of ``hospital deserts'' amid recent rural hospital closures, as these communities may in fact not be well-served through at-home care.
Published: 2024

13. Disrupted brain functional connectivity as early signature in cognitively healthy individuals with pathological CSF amyloid/tau.

Author: Al-Ezzi, Abdulhakim, Arechavala, Rebecca, Butler, Ryan, Nolty, Anne, Kang, Jimmy, Shimojo, Shinsuke, Wu, Daw-An, Fonteh, Alfred, Kleinman, Michael, Kloner, Robert, and Arakaki, Xianghong
Subjects: Humans, tau Proteins, Female, Male, Brain, Amyloid beta-Peptides, Aged, Electroencephalography, Cognition, Alzheimer Disease, Magnetic Resonance Imaging, Middle Aged
Abstract: Alterations in functional connectivity (FC) have been observed in individuals with Alzheimers disease (AD) with elevated amyloid (Aβ) and tau. However, it is not yet known whether directed FC is already influenced by Aβ and tau load in cognitively healthy (CH) individuals. A 21-channel electroencephalogram (EEG) was used from 46 CHs classified based on cerebrospinal fluid (CSF) Aβ tau ratio: pathological (CH-PAT) or normal (CH-NAT). Directed FC was estimated with Partial Directed Coherence in frontal, temporal, parietal, central, and occipital regions. We also examined the correlations between directed FC and various functional metrics, including neuropsychology, cognitive reserve, MRI volumetrics, and heart rate variability between both groups. Compared to CH-NATs, the CH-PATs showed decreased FC from the temporal regions, indicating a loss of relative functional importance of the temporal regions. In addition, frontal regions showed enhanced FC in the CH-PATs compared to CH-NATs, suggesting neural compensation for the damage caused by the pathology. Moreover, CH-PATs showed greater FC in the frontal and occipital regions than CH-NATs. Our findings provide a useful and non-invasive method for EEG-based analysis to identify alterations in brain connectivity in CHs with a pathological versus normal CSF Aβ/tau.
Published: 2024

14. Hierarchical Conditioning of Diffusion Models Using Tree-of-Life for Studying Species Evolution

Author: Khurana, Mridul, Daw, Arka, Maruf, M., Uyeda, Josef C., Dahdul, Wasila, Charpentier, Caleb, Bakış, Yasin, Bart Jr., Henry L., Mabee, Paula M., Lapp, Hilmar, Balhoff, James P., Chao, Wei-Lun, Stewart, Charles, Berger-Wolf, Tanya, and Karpatne, Anuj
Subjects: Quantitative Biology - Populations and Evolution, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: A central problem in biology is to understand how organisms evolve and adapt to their environment by acquiring variations in the observable characteristics or traits of species across the tree of life. With the growing availability of large-scale image repositories in biology and recent advances in generative modeling, there is an opportunity to accelerate the discovery of evolutionary traits automatically from images. Toward this goal, we introduce Phylo-Diffusion, a novel framework for conditioning diffusion models with phylogenetic knowledge represented in the form of HIERarchical Embeddings (HIER-Embeds). We also propose two new experiments for perturbing the embedding space of Phylo-Diffusion: trait masking and trait swapping, inspired by counterpart experiments of gene knockout and gene editing/swapping. Our work represents a novel methodological advance in generative modeling to structure the embedding space of diffusion models using tree-based knowledge. Our work also opens a new chapter of research in evolutionary biology by using generative models to visualize evolutionary changes directly from images. We empirically demonstrate the usefulness of Phylo-Diffusion in capturing meaningful trait variations for fishes and birds, revealing novel insights about the biological mechanisms of their evolution.
Published: 2024

15. Swift-BAT GUANO follow-up of gravitational-wave triggers in the third LIGO-Virgo-KAGRA observing run

Author: Raman, Gayathri, Ronchini, Samuele, Delaunay, James, Tohuvavohu, Aaron, Kennea, Jamie A., Parsotan, Tyler, Ambrosi, Elena, Bernardini, Maria Grazia, Campana, Sergio, Cusumano, Giancarlo, D'Ai, Antonino, D'Avanzo, Paolo, D'Elia, Valerio, De Pasquale, Massimiliano, Dichiara, Simone, Evans, Phil, Hartmann, Dieter, Kuin, Paul, Melandri, Andrea, O'Brien, Paul, Osborne, Julian P., Page, Kim, Palmer, David M., Sbarufatti, Boris, Tagliaferri, Gianpiero, Troja, Eleonora, Abac, A. G., Abbott, R., Abe, H., Abouelfettouh, I., Acernese, F., Ackley, K., Adamcewicz, C., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Bai, Y., Baier, J. G., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Bogaert, G., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boumerdassi, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. 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C., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhong, S., Zhou, R., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract: We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables
Published: 2024

16. The Solar eruptioN Integral Field Spectrograph

Author: Herde, Vicki L., Chamberlin, Phillip C., Schmit, Don, Daw, Adrian, Milligan, Ryan O., Polito, Vanessa, Bose, Souvik, Boyajian, Spencer, Buedel, Paris, Edgar, Will, Gebben, Alex, Gong, Qian, Jacobsen, Ross, Nell, Nicholas, Schwab, Bennet, Sims, Alan, Summers, David, Turner, Zachary, Valade, Trace, and Wallace, Joseph
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Instrumentation and Detectors, Physics - Space Physics
Abstract: The Solar eruptioN Integral Field Spectrograph (SNIFS) is a solar-gazing spectrograph scheduled to fly in the summer of 2025 on a NASA sounding rocket. Its goal is to view the solar chromosphere and transition region at a high cadence (1s) both spatially (0.5") and spectrally (33 m{\AA}) viewing wavelengths around Lyman Alpha (1216 {\AA}), Si iii (1206 {\AA}) and O v (1218 {\AA}) to observe spicules, nanoflares, and possibly a solar flare. This time cadence will provide yet-unobserved detail about fast-changing features of the Sun. The instrument is comprised of a Gregorian-style reflecting telescope combined with a spectrograph via a specialized mirrorlet array that focuses the light from each spatial location in the image so that it may be spectrally dispersed without overlap from neighboring locations. This paper discusses the driving science, detailed instrument and subsystem design, and pre-integration testing of the SNIFS instrument., Comment: 22 pages (not including references), 7 figures, submitting to Solar Physics
Published: 2024

17. Fish-Vista: A Multi-Purpose Dataset for Understanding & Identification of Traits from Images

Author: Mehrab, Kazi Sajeed, Maruf, M., Daw, Arka, Manogaran, Harish Babu, Neog, Abhilash, Khurana, Mridul, Altintas, Bahadir, Bakis, Yasin, Campolongo, Elizabeth G, Thompson, Matthew J, Wang, Xiaojun, Lapp, Hilmar, Chao, Wei-Lun, Mabee, Paula M., Bart Jr., Henry L., Dahdul, Wasila, and Karpatne, Anuj
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Fishes are integral to both ecological systems and economic sectors, and studying fish traits is crucial for understanding biodiversity patterns and macro-evolution trends. To enable the analysis of visual traits from fish images, we introduce the Fish-Visual Trait Analysis (Fish-Vista) dataset - a large, annotated collection of about 60K fish images spanning 1900 different species, supporting several challenging and biologically relevant tasks including species classification, trait identification, and trait segmentation. These images have been curated through a sophisticated data processing pipeline applied to a cumulative set of images obtained from various museum collections. Fish-Vista provides fine-grained labels of various visual traits present in each image. It also offers pixel-level annotations of 9 different traits for 2427 fish images, facilitating additional trait segmentation and localization tasks. The ultimate goal of Fish-Vista is to provide a clean, carefully curated, high-resolution dataset that can serve as a foundation for accelerating biological discoveries using advances in AI. Finally, we provide a comprehensive analysis of state-of-the-art deep learning techniques on Fish-Vista.
Published: 2024

18. Learning the boundary-to-domain mapping using Lifting Product Fourier Neural Operators for partial differential equations

Author: Kashi, Aditya, Daw, Arka, Meena, Muralikrishnan Gopalakrishnan, and Lu, Hao
Subjects: Computer Science - Machine Learning, Mathematics - Numerical Analysis, 65N99, 68T07, I.2.1, J.2
Abstract: Neural operators such as the Fourier Neural Operator (FNO) have been shown to provide resolution-independent deep learning models that can learn mappings between function spaces. For example, an initial condition can be mapped to the solution of a partial differential equation (PDE) at a future time-step using a neural operator. Despite the popularity of neural operators, their use to predict solution functions over a domain given only data over the boundary (such as a spatially varying Dirichlet boundary condition) remains unexplored. In this paper, we refer to such problems as boundary-to-domain problems; they have a wide range of applications in areas such as fluid mechanics, solid mechanics, heat transfer etc. We present a novel FNO-based architecture, named Lifting Product FNO (or LP-FNO) which can map arbitrary boundary functions defined on the lower-dimensional boundary to a solution in the entire domain. Specifically, two FNOs defined on the lower-dimensional boundary are lifted into the higher dimensional domain using our proposed lifting product layer. We demonstrate the efficacy and resolution independence of the proposed LP-FNO for the 2D Poisson equation., Comment: Accepted by ICML 2024 AI for Science Workshop
Published: 2024

19. SNP rs6543176 is associated with extreme human longevity but increased risk for cancer

Author: Gurinovich, Anastasia, Song, Zeyuan, Bae, Harold, Leshchyk, Anastasia, Li, Mengze, Lords, Hannah, Andersen, Stacy L., Nygaard, Marianne, Christensen, Kaare, Daw, E. Warwick, Arbeev, Konstantin G., Brent, Michael R., Perls, Thomas T., and Sebastiani, Paola
Published: 2025
Full Text: View/download PDF

20. On the periods of twisted moments of the Kloosterman connection

Author: Chuang, Ping-Hsun and Yu, Jeng-Daw
Published: 2024
Full Text: View/download PDF

21. Ultralight vector dark matter search using data from the KAGRA O3GK run

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abe, H., Abouelfettouh, I., Acernese, F., Ackley, K., Adamcewicz, C., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Bai, Y., Baier, J. G., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Bogaert, G., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boumerdassi, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Santoro, G. Caneva, Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, C., Chan, J. C. L., Chan, K. H. M., Chan, M., Chan, W. L., Chandra, K., Chang, R. -J., Chanial, P., Chao, S., Chapman-Bird, C., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chatziioannou, K., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, K. H., Chen, X., Chen, Yi-Ru, Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Chia, H. 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J., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tanasijczuk, A. J., Tang, D., Tanioka, S., Tanner, D. B., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, Shubhanshu, Tiwari, Srishti, Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trani, A. A., Trapananti, A., Travasso, F., Traylor, G., Trenado, J., Trevor, M., Tringali, M. C., Tripathee, A., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Ubhi, A. S., Uchiyama, T., Udall, R. P., Uehara, T., Ueno, K., Unnikrishnan, C. S., Ushiba, T., Utina, A., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Veske, D., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Walet, R. C., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Ward, R. L., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Weller, C. M., Weller, R. A., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., White, D. D., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, D., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wysocki, D. M., Xiao, L., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, M., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, L. -C., Yang, Y., Yarbrough, Z., Yeh, S. -W., Yelikar, A. B., Yeung, S. M. C., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuzurihara, H., Zadrożny, A., Zannelli, A. J., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhong, S., Zhou, R., Zhu, Z. -H., Zucker, M. E., Zweizig, J., Fujimori, T., Fujimoto, H., Fujita, T., Manita, Y., Obata, I., and Takidera, H.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, General Relativity and Quantum Cosmology, High Energy Physics - Phenomenology
Abstract: Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for $U(1)_{B-L}$ gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the $U(1)_{B-L}$ gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM., Comment: 20 pages, 5 figures
Published: 2024

22. Program-Based Strategy Induction for Reinforcement Learning

Author: Correa, Carlos G., Griffiths, Thomas L., and Daw, Nathaniel D.
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: Typical models of learning assume incremental estimation of continuously-varying decision variables like expected rewards. However, this class of models fails to capture more idiosyncratic, discrete heuristics and strategies that people and animals appear to exhibit. Despite recent advances in strategy discovery using tools like recurrent networks that generalize the classic models, the resulting strategies are often onerous to interpret, making connections to cognition difficult to establish. We use Bayesian program induction to discover strategies implemented by programs, letting the simplicity of strategies trade off against their effectiveness. Focusing on bandit tasks, we find strategies that are difficult or unexpected with classical incremental learning, like asymmetric learning from rewarded and unrewarded trials, adaptive horizon-dependent random exploration, and discrete state switching.
Published: 2024

23. Asymmetries of Service: Interdependence and Synchronicity

Author: Daw, Andrew and Yom-Tov, Galit B.
Subjects: Computer Science - Computer Science and Game Theory, Computer Science - Performance, Mathematics - Probability, 90B22, 60G55
Abstract: On many dimensions, services can be seen to exist along spectra measuring the degree of interaction between customer and agent. For instance, every interaction features some number of contributions by each of the two sides, creating a spectrum of interdependence. Additionally, each interaction is further characterized by the pacing of these contributions, implying a spectrum of synchronicity. Where a service falls on such spectra can simply be a consequence of its design, but it can also be a function of its state. As broadly evidenced empirically, an agent with several concurrent interactions will be slowed in each individual interaction, altering the service's synchronicity. Here, we study a Hawkes cluster model of the customer-agent interaction, which we show captures both of these service (a)symmetries. We find insightful connections to behavioral operations, such as proving the occurrence of non-monotonic performance (e.g., inverted-U throughput) from concurrency-driven asynchrony. Hence, we can prescribe the agent's optimal concurrency level. Furthermore, we show how the service design dictates the efficacy of these operational improvements, proving that the concurrency-optimized throughput is itself non-monotonic as a function of the interdependence. In what may be of independent interest methodologically, we establish an interpretable decomposition for Hawkes clusters via probabilistic combinatorics.
Published: 2024

24. Exposure to Traumatic Events and Shame in Adolescent Surf Lifesavers: An Australian Perspective

Author: Fien, Samantha, Lawes, Jasmin C., Ledger, Jessica, de Terte, Ian, Drummond, Murray, Simon, Pamela, Joseph, Nancy, Daw, Shane, Kelly, Sean, Hillman, Wendy, Stanton, Robert, and Best, Talitha
Published: 2024
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25. A feature-specific prediction error model explains dopaminergic heterogeneity

Author: Lee, Rachel S., Sagiv, Yotam, Engelhard, Ben, Witten, Ilana B., and Daw, Nathaniel D.
Published: 2024
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26. Axion Dark Matter eXperiment: Run 1A Analysis Details

Author: Boutan, C., LaRoque, B. H., Lentz, E., Oblath, N. S., Taubman, M. S., Tedeschi, J., Yang, J., Jones, A. M., Braine, T., Crisosto, N., Rosenberg, L. J, Rybka, G., Will, D., Zhang, D., Kimes, S., Ottens, R., Bartram, C., Bowring, D., Cervantes, R., Chou, A. S., Knirck, S., Mitchell, D. V., Sonnenschein, A., Wester, W., Khatiwada, R., Carosi, G., Du, N., Durham, S., O'Kelley, S. R., Woollett, N., Duffy, L. D., Bradley, R., Clarke, J., Siddiqi, I., Agrawal, A., Dixit, A. V., Gleason, J. R., Hipp, A. T., Jois, S., Sikivie, P., Sullivan, N. S., Tanner, D. B., Buckley, J. H., Gaikwad, C., Henriksen, E. A., Hoffman, J., Murch, K. W., Harrington, P. M., Daw, E. J., Perry, M. G., and Hilton, G. C.
Subjects: High Energy Physics - Experiment, Astrophysics - Cosmology and Nongalactic Astrophysics, Physics - Instrumentation and Detectors
Abstract: The ADMX collaboration gathered data for its Run 1A axion dark matter search from January to June 2017, scanning with an axion haloscope over the frequency range 645-680 MHz (2.66-2.81 ueV in axion mass) at DFSZ sensitivity. The resulting axion search found no axion-like signals comprising all the dark matter in the form of a virialized galactic halo over the entire frequency range, implying lower bound exclusion limits at or below DFSZ coupling at the 90% confidence level. This paper presents expanded details of the axion search analysis of Run 1A, including review of relevant experimental systems, data-taking operations, preparation and interpretation of raw data, axion search methodology, candidate handling, and final axion limits., Comment: 27 pages, 19 figures, accepted for publication in PRD
Published: 2023

27. A Criterion for Multivariate Regionalization of Spatial Data

Author: Daw, Ranadeep, Wikle, Christopher K., Bradley, Jonathan R., and Holan, Scott H.
Subjects: Statistics - Methodology
Abstract: The modifiable areal unit problem in geography or the change-of-support (COS) problem in statistics demonstrates that the interpretation of spatial (or spatio-temporal) data analysis is affected by the choice of resolutions or geographical units used in the study. The ecological fallacy is one famous example of this phenomenon. Here we investigate the ecological fallacy associated with the COS problem for multivariate spatial data with the goal of providing a data-driven discretization criterion for the domain of interest that minimizes aggregation errors. The discretization is based on a novel multiscale metric, called the Multivariate Criterion for Aggregation Error (MVCAGE). Such multi-scale representations of an underlying multivariate process are often formulated in terms of basis expansions. We show that a particularly useful basis expansion in this context is the multivariate Karhunen-Lo`eve expansion (MKLE). We use the MKLE to build the MVCAGE loss function and use it within the framework of spatial clustering algorithms to perform optimal spatial aggregation. We demonstrate the effectiveness of our approach through simulation and through regionalization of county-level income and hospital quality data over the United States and prediction of ocean color in the coastal Gulf of Alaska.
Published: 2023

28. Exploring the hierarchical structure of human plans via program generation

Author: Correa, Carlos G., Sanborn, Sophia, Ho, Mark K., Callaway, Frederick, Daw, Nathaniel D., and Griffiths, Thomas L.
Subjects: Computer Science - Artificial Intelligence
Abstract: Human behavior is often assumed to be hierarchically structured, made up of abstract actions that can be decomposed into concrete actions. However, behavior is typically measured as a sequence of actions, which makes it difficult to infer its hierarchical structure. In this paper, we explore how people form hierarchically structured plans, using an experimental paradigm with observable hierarchical representations: participants create programs that produce sequences of actions in a language with explicit hierarchical structure. This task lets us test two well-established principles of human behavior: utility maximization (i.e. using fewer actions) and minimum description length (MDL; i.e. having a shorter program). We find that humans are sensitive to both metrics, but that both accounts fail to predict a qualitative feature of human-created programs, namely that people prefer programs with reuse over and above the predictions of MDL. We formalize this preference for reuse by extending the MDL account into a generative model over programs, modeling hierarchy choice as the induction of a grammar over actions. Our account can explain the preference for reuse and provides better predictions of human behavior, going beyond simple accounts of compressibility to highlight a principle that guides hierarchical planning.
Published: 2023
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29. Non-Virialized Axion Search Sensitive to Doppler Effects in the Milky Way Halo

Author: Bartram, C., Braine, T., Cervantes, R., Crisosto, N., Du, N., Goodman, C., Guzzetti, M., Hanretty, C., Lee, S., Leum, G., Rosenberg, L. J., Rybka, G., Sinnis, J., Zhang, D., Awida, M. H., Bowring, D., Chou, A. S., Hollister, M., Knirck, S., Sonnenschein, A., Wester, W., Khatiwada, R., Brodsky, J., Carosi, G., Duffy, L. D., Goryachev, M., McAllister, B., Quiskamp, A., Thomson, C., Tobar, M. E., Boutan, C., Jones, M., LaRoque, B. H., Lentz, E., Man, N. E., Oblath, N. S., Taubman, M. S., Yang, J., Clarke, John, Siddiqi, I., Agrawal, A., Dixit, A. V., Gleason, J. R., Han, Y., Hipp, A. T., Jois, S., Sikivie, P., Sullivan, N. S., Tanner, D. B., 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 (ADMX) has previously excluded Dine-Fischler-Srednicki-Zhitnisky (DFSZ) axions between 680-790 MHz under the assumption that the dark matter is described by the isothermal halo model. However, the precise nature of the velocity distribution of dark matter is still unknown, and alternative models have been proposed. We report the results of a non-virialized axion search over the mass range 2.81-3.31 {\mu}eV, corresponding to the frequency range 680-800 MHz. This analysis marks the most sensitive search for non-virialized axions sensitive to Doppler effects in the Milky Way Halo to date. Accounting for frequency shifts due to the detector's motion through the Galaxy, we exclude cold flow relic axions with a velocity dispersion of order 10^-7 c with 95% confidence.
Published: 2023
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30. Computational processes of simultaneous learning of stochasticity and volatility in humans

Author: Piray, Payam and Daw, Nathaniel D.
Published: 2024
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31. Hodgkin lymphoma: hypodense lesions in mediastinal masses

Author: Damek, Adrian, Kurch, Lars, Franke, Friedrich Christian, Attarbaschi, Andishe, Beishuizen, Auke, Cepelova, Michaela, Ceppi, Francesco, Daw, Stephen, Dieckmann, Karin, Fernández-Teijeiro, Ana, Feuchtinger, Tobias, Flerlage, Jamie E., Fosså, Alexander, Georgi, Thomas W., Hasenclever, Dirk, Hraskova, Andrea, Karlen, Jonas, Klekawka, Tomasz, Kluge, Regine, Körholz, Dieter, Landman-Parker, Judith, Leblanc, Thierry, Mauz-Körholz, Christine, Metzler, Markus, Pears, Jane, Steglich, Jonas, Uyttebroeck, Anne, Vordermark, Dirk, Wallace, William Hamish, Wohlgemuth, Walter Alexander, and Stoevesandt, Dietrich
Published: 2024
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32. Magnetic voluntary head-fixation in transgenic rats enables lifespan imaging of hippocampal neurons

Author: Rich, P. Dylan, Thiberge, Stephan Yves, Scott, Benjamin B., Guo, Caiying, Tervo, D. Gowanlock R., Brody, Carlos D., Karpova, Alla Y., Daw, Nathaniel D., and Tank, David W.
Published: 2024
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33. Structural basis and synergism of ATP and Na+ activation in bacterial K+ uptake system KtrAB

Author: Chiang, Wesley Tien, Chang, Yao-Kai, Hui, Wei-Han, Chang, Shu-Wei, Liao, Chen-Yi, Chang, Yi-Chuan, Chen, Chun-Jung, Wang, Wei-Chen, Lai, Chien-Chen, Wang, Chun-Hsiung, Luo, Siou-Ying, Huang, Ya-Ping, Chou, Shan-Ho, Horng, Tzyy-Leng, Hou, Ming-Hon, Muench, Stephen P., Chen, Ren-Shiang, Tsai, Ming-Daw, and Hu, Nien-Jen
Published: 2024
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34. Comparison of quantitative whole body PET parameters on [68Ga]Ga-PSMA-11 PET/CT using ordered Subset Expectation Maximization (OSEM) vs. bayesian penalized likelihood (BPL) reconstruction algorithms in men with metastatic castration-resistant prostate cancer

Author: Ayati, Narjess, McIntosh, Lachlan, Buteau, James, Alipour, Ramin, Pudis, Michal, Daw, Nicholas, Jackson, Price, and Hofman, Michael S.
Published: 2024
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35. Roflumilast inhibits tumor growth and migration in STK11/LKB1 deficient pancreatic cancer

Author: Zhang, Shuman, Yun, Duo, Yang, Hao, Eckstein, Markus, Elbait, Gihan Daw, Zhou, Yaxing, Lu, Yanxi, Yang, Hai, Zhang, Jinping, Dörflein, Isabella, Britzen-Laurent, Nathalie, Pfeffer, Susanne, Stemmler, Marc P., Dahl, Andreas, Mukhopadhyay, Debabrata, Chang, David, He, Hang, Zeng, Siyuan, Lan, Bin, Frey, Benjamin, Hampel, Chuanpit, Lentsch, Eva, Gollavilli, Paradesi Naidu, Büttner, Christian, Ekici, Arif B., Biankin, Andrew, Schneider-Stock, Regine, Ceppi, Paolo, Grützmann, Robert, and Pilarsky, Christian
Published: 2024
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36. A unified view on enzyme catalysis by cryo-EM study of a DNA topoisomerase

Author: Chang, Chiung-Wen Mary, Wang, Shun-Chang, Wang, Chun-Hsiung, Pang, Allan H., Yang, Cheng-Han, Chang, Yao-Kai, Wu, Wen-Jin, and Tsai, Ming-Daw
Published: 2024
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37. Nature’s contribution to poverty alleviation, human wellbeing and the SDGs

Author: Poudyal, Mahesh, Kraft, Franziska, Wells, Geoff, Das, Anamika, Attiwilli, Suman, Schreckenberg, Kate, Lele, Sharachchandra, Daw, Tim, Torres-Vitolas, Carlos, Setty, Siddappa, Adams, Helen, Ahmad, Sate, Ryan, Casey, Fisher, Janet, Robinson, Brian, Jones, Julia P. G., Homewood, Katherine, Bluwstein, Jevgeniy, Keane, Aidan, Macamo, Celia, and Mugi, Lilian Mwihaki
Published: 2024
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38. Germline mutations of homologous recombination genes and clinical outcomes in pancreatic cancer: a multicenter study in Taiwan

Author: Cheng, Siao Muk, Su, Yung-Yeh, Chiang, Nai-Jung, Wang, Chih-Jung, Chao, Ying-Jui, Huang, Chien-Jui, Tsai, Hui-Jen, Chen, Shang-Hung, Chang, Chi-Yen, Tsai, Chia-Rung, Li, Yi-Jie, Yen, Chia-Jui, Chuang, Shih-Chang, Chang, Jeffrey Shu-Ming, Shan, Yan-Shen, Hwang, Daw-Yang, and Chen, Li-Tzong
Published: 2024
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39. Assessment Re-Think: Income-Generating and Industry-Based Assessments

Author: de Souza-Daw, Tony, Di Serio, Antony, Falah, Ahm, Fahd, Kiran, and Parvin, Sazia
Abstract: Assessments are the fundamental media between students and educators. This paper aims to evaluate how to create assessments, how students learn from them, and how to link them to the industry and entrepreneurism. The implementation plan postulates how students can generate income from income-generation assessments or business innovation assessments. In this paper, we discuss the involvement of modern industry in assessment. We examine evidence from approximately 100 assessments detailed in 32 subject outlines. We employ a descriptive, pragmatic research methodology to consider whether they can be aligned more with industry expectations and expected duties. We propose a framework to connect with industry and create student income-generating projects. This proposed income-generating assessments framework recommended industry-based assessments with which students can not only earn marks towards a subject but potentially earn an income based on it. This paper extends the idea of peer learning to expert or industry learning: an approach that did not employ in higher education. Our approach supports educators in keeping the assessment up-to-date, enabling students to add more value to their learning of industry products and procedures. Students can directly contribute to the product and procedures and learn from the strategies actively employed in the workplace.
Published: 2022

40. Glocalising Education: Reflections from an International School in Delhi, India

Author: Bianca Daw
Abstract: This article documents, describes and discusses the dynamics of an international school located in south Delhi, India. It discusses the means and ways via which the international school designs and delivers its curricular and pedagogic content that tend to shape and reinforce an element of 'internationalism' among its pupils. The international school form of education primarily embeds teaching and learning processes within the domain of the school as well as outside of it. While the school places enormous emphasis on the concept of 'global' or at times even 'international' to promote and brand itself, it is deeply rooted to the local and national conditions and cultures as well. This aspect of inter-mixing the global with the local and national is strongly emphasized by the school, whose stakeholders believe that the true spirit of internationalism lies in becoming 'glocal' and not simply global. The school as well as parents want their students and children to possess a holistic understanding of the world, and not an exclusive one. Thereby, it can be argued that there is a transition from the much-hyped era of globalisation to one of 'glocalisation'. Thus, this system of schooling is critically analysed through this article, which centrally argues that the school functions in a manner that enables students' global compatibility of body and mind, and aims to lend them a competitive edge in the global education and job market.
Published: 2024
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41. MEMTRACK: A Deep Learning-Based Approach to Microrobot Tracking in Dense and Low-Contrast Environments

Author: Sawhney, Medha, Karmarkar, Bhas, Leaman, Eric J., Daw, Arka, Karpatne, Anuj, and Behkam, Bahareh
Subjects: Computer Science - Computer Vision and Pattern Recognition, Physics - Biological Physics, Quantitative Biology - Quantitative Methods
Abstract: Tracking microrobots is challenging, considering their minute size and high speed. As the field progresses towards developing microrobots for biomedical applications and conducting mechanistic studies in physiologically relevant media (e.g., collagen), this challenge is exacerbated by the dense surrounding environments with feature size and shape comparable to microrobots. Herein, we report Motion Enhanced Multi-level Tracker (MEMTrack), a robust pipeline for detecting and tracking microrobots using synthetic motion features, deep learning-based object detection, and a modified Simple Online and Real-time Tracking (SORT) algorithm with interpolation for tracking. Our object detection approach combines different models based on the object's motion pattern. We trained and validated our model using bacterial micro-motors in collagen (tissue phantom) and tested it in collagen and aqueous media. We demonstrate that MEMTrack accurately tracks even the most challenging bacteria missed by skilled human annotators, achieving precision and recall of 77% and 48% in collagen and 94% and 35% in liquid media, respectively. Moreover, we show that MEMTrack can quantify average bacteria speed with no statistically significant difference from the laboriously-produced manual tracking data. MEMTrack represents a significant contribution to microrobot localization and tracking, and opens the potential for vision-based deep learning approaches to microrobot control in dense and low-contrast settings. All source code for training and testing MEMTrack and reproducing the results of the paper have been made publicly available https://github.com/sawhney-medha/MEMTrack.
Published: 2023

42. An Optically Thin View of the Flaring Chromosphere: Nonthermal widths in a chromospheric condensation during an X-class solar flare

Author: Kerr, Graham S., Kowalski, Adam F., Allred, Joel C., Daw, Adrian N., and Kane, Melissa R.
Subjects: Astrophysics - Solar and Stellar Astrophysics, Physics - Space Physics
Abstract: The bulk of solar flare energy is deposited in the chromosphere. Flare ribbons and footpoints in the chromosphere therefore offer great diagnostic potential of flare energy release and transport processes. High quality observations from the IRIS spacecraft have transformed our view of the Sun's atmospheric response to flares. Since most of the chromospheric lines observed by IRIS are optically thick, forward modelling is required to fully appreciate and extract the information they carry. Reproducing certain aspects of the Mg II lines remain frustratingly out of reach in state-of-the-art flare models, which are unable to satisfactorily reproduce the very broad line profiles. A commonly proposed resolution to this is to assert that very large values of `microturbulence' is present. We asses the validity of that approach by analysing optically thin lines in the flare chromosphere from the X-class flare SOL2014-10-25T17:08:00, using the derived value of nonthermal width as a constraint to our numerical models. A nonthermal width of the order 10~km~s$^{-1}$ was found within the short-lived red wing components of three spectral lines, with relatively narrow stationary components. Simulations of this flare were produced, and in the post-processing spectral synthesis we include within the downflows a microturbulence of 10~km~s$^{-1}$. While we can reproduce the O I 1355.598~\AA\ line rather well, and we can capture the general shape and properties of the Mg II line widths, the synthetic lines are still too narrow., Comment: Accepted for publication in Monthly Notices of the Royal Astronomical Society
Published: 2023

43. A Joint Fermi-GBM and Swift-BAT Analysis of Gravitational-Wave Candidates from the Third Gravitational-wave Observing Run

Author: Fletcher, C., Wood, J., Hamburg, R., Veres, P., Hui, C. M., Bissaldi, E., Briggs, M. S., Burns, E., Cleveland, W. H., Giles, M. M., Goldstein, A., Hristov, B. A., Kocevski, D., Lesage, S., Mailyan, B., Malacaria, C., Poolakkil, S., von Kienlin, A., Wilson-Hodge, C. A., Team, The Fermi Gamma-ray Burst Monitor, Crnogorčević, M., DeLaunay, J., Tohuvavohu, A., Caputo, R., Cenko, S. B., Laha, S., Parsotan, T., Abbott, R., Abe, H., Acernese, F., Ackley, K., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Allocca, A., Altin, P. A., Amato, A., Anand, C., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andríc, T., Angelova, S. V., Ansoldi, S., Antelis, J. M., Antier, S., Apostolatos, T., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Arène, M., Aritomi, N., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Asada, H., Asali, Y., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Austin, C., Babak, S., Badaracco, F., Bader, M. K. M., Badger, C., Bae, S., Bae, Y., Baer, A. M., Bagnasco, S., Bai, Y., Baird, J., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Balsamo, A., Baltus, G., Banagiri, S., Banerjee, B., Bankar, D., Barayoga, J. C., Barbieri, C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Barton, M. A., Bartos, I., Basak, S., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Bazzan, M., Becher, B. R., Becsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belahcene, I., Benedetto, V., Beniwal, D., Benjamin, M. G., Bennett, T. F., Bentley, J. D., BenYaala, M., Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bhandare, R., Bhandari, A. V., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Bini, S., Birney, R., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Biswas, B., Bitossi, M., Bizouard, M. A., Blackburn, J. K., Blair, C. D., Blair, D. G., Blair, R. M., Bobba, F., Bode, N., Boër, M., Bogaert, G., Boldrini, M., Bolingbroke, G. N., Bonavena, L. D., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., Boom, B. A., Bork, R., Boschi, V., Bose, N., Bose, S., Bossilkov, V., Boudart, V., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Bramley, A., Branch, A., Branchesi, M., Brau, J. E., Breschi, M., Briant, T., Briggs, J. H., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brooks, J., Brown, D. D., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Caesar, M., Cagnoli, G., Cahillane, C., Bustillo, J. Calderòn, Callaghan, J. D., Callister, T. A., Calloni, E., Cameron, J., Camp, J. B., Canepa, M., Canevarolo, S., Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carney, M. F., Carpinelli, M., Carrillo, G., Carullo, G., Carver, T. L., Díaz, J. Casanueva, Casentini, C., Castaldi, G., Caudill, S., Cavaglià, M., Cavalier, F., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Subrahmanya, S. Chalathadka, Champion, E., Chan, C. H., Chan, C., Chan, C. L., Chan, K., Chan, M., Chandra, K., Chang, I. P., Chanial, P., Chao, S., Chapman-Bird, C., Charlton, P., Chase, E. A., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chen, C., Chen, D., Chen, H. Y., Chen, J., Chen, K., Chen, X., Chen, Y. B., Chen, Y. R., Chen, Z., Cheng, H., Cheong, C. K., Cheung, H. Y., Chia, H. Y., Chiadini, F., Chiang, C. Y., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Choudhary, R. K., Choudhary, S., Christensen, N., Chu, Q., Chu, Y. K., Chua, S. S. Y., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Cipriano, F., Clara, F., Clark, J. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. F., Cohen, D. E., Colleoni, M., Collette, C. G., Colombo, A., Colpi, M., Compton, C. M., Constancio Jr., M., Conti, L., Cooper, S. J., Corban, P., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Corley, K. R., Cornish, N. J., Corre, D., Corsi, A., Cortese, S., Costa, C. A., Cotesta, R., Cottingham, R., Coughlin, M. W., Coulon, J. P., Countryman, S. T., Cousins, B., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Creighton, J. D. E., Creighton, T. D., Criswell, A. W., Croquette, M., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cummings, R., Cunningham, L., Cuoco, E., lo, M. Cury, Dabadie, P., Canton, T. Dal, Dall'Osso, S., Dálya, G., Dana, A., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darsow-Fromm, C., Dasgupta, A., Datrier, L. E. H., Datta, Sayak, Datta, Sayantani, Dattilo, V., Dave, I., Davier, M., Davis, D., Davis, M. C., Daw, E. J., Dean, R., DeBra, D., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., De Lillo, N., Dell'Aquila, D., Del Pozzo, W., DeMarchi, L. M., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhurandhar, S., D'iaz, M. C., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giorgio, C., Di Giovanni, F., Di Giovanni, M., Di Girolamo, T., Di Lieto, A., Di Michele, A., Ding, B., Di Pace, S., Di Palma, I., Di Renzo, F., Divakarla, A. K., Dmitriev, A., Doctor, Z., Donahue, L., D'Onofrio, L., Donovan, F., Dooley, K. L., Doravari, S., Drago, M., Driggers, J. C., Drori, Y., Ducoin, J. G., Dupej, P., Dupletsa, U., Durante, O., D'Urso, D., Duverne, P. A., Dwyer, S. E., Eassa, C., Easter, P. J., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eguchi, S., Eichholz, J., Eikenberry, S. S., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Engelby, E., Enomoto, Y., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etienne, Z., Etzel, T., Evans, M., Evans, T. M., Evstafyeva, T., Ewing, B. E., Fabrizi, F., Faedi, F., Fafone, V., Fair, H., Fairhurst, S., Fan, P. C., Farah, A. M., Farinon, S., Farr, B., Farr, W. M., Fauchon-Jones, E. J., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Fenyvesi, E., Ferguson, D. L., Fernandez-Galiana, A., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Floden, E., Fong, H. K., Font, J. A., Fornal, B., Forsyth, P. W. F., Franke, A., Frasca, S., Frasconi, F., Freed, J. P., Frei, Z., Freise, A., Freitas, O., Frey, R., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fujii, Y., Fujikawa, Y., Fujimoto, Y., Fulda, P., Fyffe, M., Gabbard, H. A., Gabella, W. E., Gadre, B. U., Gair, J. R., Gais, J., Galaudage, S., Gamba, R., Ganapathy, D., Ganguly, A., Gao, D., Gaonkar, S. G., Garaventa, B., Núñez, C. García, García-Quirós, C., Garufi, F., Gateley, B., Gayathri, V., Ge, G. G., Gemme, G., Gennai, A., George, J., Gerberding, O., Gergely, L., Gewecke, P., Ghonge, S., Ghosh, Abhirup, Ghosh, Archisman, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Tathagata, Giacomazzo, B., Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gier, C., Giesler, M., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Gleckl, A. E., Godwin, P., Goetz, E., Goetz, R., Gohlke, N., Golomb, J., Goncharov, B., Gonzalez, G., Gosselin, M., Gouaty, R., Gould, D. W., Goyal, S., Grace, B., Grado, A., Graham, V., Granata, M., Granata, V., Grant, A., Gras, S., Grassia, P., Gray, C., Gray, R., Greco, G., Green, A. C., Green, R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimes, E., Grimm, S. J., Grote, H., Grunewald, S., Gruning, P., Gruson, A. S., Guerra, D., Guidi, G. M., Guimaraes, A. R., Guixe, G., Gulati, H. K., Gunny, A. M., Guo, H. K., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, I. M., Gupta, P., Gupta, S. K., Gustafson, R., Guzman, F., Ha, S., Hadiputrawan, I. P. W., Haegel, L., Haino, S., Halim, O., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O., Hansen, H., Hansen, T. J., Hanson, J., Harder, T., Haris, K., Harms, J., Harry, G. M., Harry, I. W., Hartwig, D., Hasegawa, K., Haskell, B., Haster, C. J., Hathaway, J. S., Hattori, K., Haughian, K., Hayakawa, H., Hayama, K., Hayes, F. J., Healy, J., Heidmann, A., Heidt, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Hendry, M., Heng, I. S., Hennes, E., Hennig, J., Hennig, M. H., Henshaw, C., Hernandez, A. G., Vivanco, F. Hernandez, Heurs, M., Hewitt, A. L., Higginbotham, S., Hild, S., Hill, P., Himemoto, Y., Hines, A. S., Hirata, N., Hirose, C., Ho, T. C., Hochheim, S., Hofman, D., Hohmann, J. N., Holcomb, D. G., Holland, N. A., Hollows, I. J., Holmes, Z. J., Holt, K., Holz, D. E., Hong, Q., Hough, J., Hourihane, S., Howell, E. J., Hoy, C. G., Hoyland, D., Hreibi, A., Hsieh, B. H., Hsieh, H. F., Hsiung, C., Hsu, Y., Huang, H. Y., Huang, P., Huang, Y. C., Huang, Y. J., Huang, Yiting, Huang, Yiwen, Hübner, M. T., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Husa, S., Huttner, S. H., Huxford, R., Huynh-Dinh, T., Ide, S., Idzkowski, B., Iess, A., Inayoshi, K., Inoue, Y., Iosif, P., Isi, M., Isleif, K., Ito, K., Itoh, Y., Iyer, B. R., JaberianHamedan, V., Jacqmin, T., Jacquet, P. E., Jadhav, S. J., Jadhav, S. P., Jain, T., James, A. L., Jan, A. Z., Jani, K., Janquart, J., Janssens, K., Janthalur, N. N., Jaranowski, P., Jariwala, D., Jaume, R., Jenkins, A. C., Jenner, K., Jeon, C., Jia, W., Jiang, J., Jin, H. B., Johns, G. R., Johnston, R., Jones, A. W., Jones, D. I., Jones, P., Jones, R., Joshi, P., Ju, L., Jue, A., Jung, P., Jung, K., Junker, J., Juste, V., Kaihotsu, K., Kajita, T., Kakizaki, M., Kalaghatgi, C. V., Kalogera, V., Kamai, B., Kamiizumi, M., Kanda, N., Kandhasamy, S., Kang, G., Kanner, J. B., Kao, Y., Kapadia, S. J., Kapasi, D. P., Karathanasis, C., Karki, S., Kashyap, R., Kasprzack, M., Kastaun, W., Kato, T., Katsanevas, S., Katsavounidis, E., Katzman, W., Kaur, T., Kawabe, K., Kawaguchi, K., Kéfélian, F., Keitel, D., Key, J. S., Khadka, S., Khalili, F. Y., Khan, S., Khanam, T., Khazanov, E. A., Khetan, N., Khursheed, M., Kijbunchoo, N., Kim, A., Kim, C., Kim, J. C., Kim, J., Kim, K., Kim, W. S., Kim, Y. M., Kimball, C., Kimura, N., Kinley-Hanlon, M., Kirchhoff, R., Kissel, J. S., Klimenko, S., Klinger, T., Knee, A. M., Knowles, T. D., Knust, N., Knyazev, E., Kobayashi, Y., Koch, P., Koekoek, G., Kohri, K., Kokeyama, K., Koley, S., Kolitsidou, P., Kolstein, M., Komori, K., Kondrashov, V., Kong, A. K. H., Kontos, A., Koper, N., Korobko, M., Kovalam, M., Koyama, N., Kozak, D. B., Kozakai, C., Kringel, V., Krishnendu, N. V., Królak, A., Kuehn, G., Kuei, F., Kuijer, P., Kulkarni, S., Kumar, A., Kumar, Prayush, Kumar, Rahul, Kumar, Rakesh, Kume, J., Kuns, K., Kuromiya, Y., Kuroyanagi, S., Kwak, K., Lacaille, G., Lagabbe, P., Laghi, D., Lalande, E., Lalleman, M., Lam, T. L., Lamberts, A., Landry, M., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., La Rosa, I., Lartaux-Vollard, A., Lasky, P. D., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Leavey, S., LeBohec, S., Lecoeuche, Y. K., Lee, E., Lee, H. M., Lee, H. W., Lee, K., Lee, R., Legred, I. N., Lehmann, J., Lemaitre, A., Lenti, M., Leonardi, M., Leonova, E., Leroy, N., Letendre, N., Levesque, C., Levin, Y., Leviton, J. N., Leyde, K., Li, A. K. Y., Li, B., Li, J., Li, K. L., Li, P., Li, T. G. F., Li, X., Lin, C. Y., Lin, E. T., Lin, F. K., Lin, F. L., Lin, H. L., Lin, L. C. C., Linde, F., Linker, S. D., Linley, J. N., Littenberg, T. B., Liu, G. C., Liu, J., Liu, K., Liu, X., Llamas, F., Lo, R. K. L., Lo, T., London, L. T., Longo, A., Lopez, D., Portilla, M. Lopez, Lorenzini, M., Loriette, V., Lormand, M., Losurdo, G., Lott, T. P., Lough, J. D., Lousto, C. O., Lovelace, G., Lucaccioni, J. F., Lück, H., Lumaca, D., Lundgren, A. P., Luo, L. W., Lynam, J. E., Maarif, M., Macas, R., Machtinger, J. B., MacInnis, M., Macleod, D. M., MacMillan, I. A. O., Macquet, A., Hernandez, I. Magaña, Magazzù, C., Magee, R. M., Maggiore, R., Magnozzi, M., Mahesh, S., Majorana, E., Maksimovic, I., Maliakal, S., Malik, A., Man, N., Mandic, V., Mangano, V., Mansell, G. L., Manske, M., Mantovani, M., Mapelli, M., Marchesoni, F., Pina, D. Marín, Marion, F., Mark, Z., Márka, S., Márka, Z., Markakis, C., Markosyan, A. S., Markowitz, A., Maros, E., Marquina, A., Marsat, S., Martelli, F., Martin, I. W., Martin, R. M., Martinez, M., Martinez, V. A., Martinez, V., Martinovic, K., Martynov, D. V., Marx, E. J., Masalehdan, H., Mason, K., Massera, E., Masserot, A., Masso-Reid, M., Mastrogiovanni, S., Matas, A., Mateu-Lucena, M., Matichard, F., Matiushechkina, M., Mavalvala, N., McCann, J. J., McCarthy, R., McClelland, D. E., McClincy, P. K., McCormick, S., McCuller, L., McGhee, G. I., McGuire, S. C., McIsaac, C., McIver, J., McRae, T., McWilliams, S. T., Meacher, D., Mehmet, M., Mehta, A. K., Meijer, Q., Melatos, A., Melchor, D. A., Mendell, G., Menendez-Vazquez, A., Menoni, C. S., Mercer, R. A., Mereni, L., Merfeld, K., Merilh, E. L., Merritt, J. D., Merzougui, M., Meshkov, S., Messenger, C., Messick, C., Meyers, P. M., Meylahn, F., Mhaske, A., Miani, A., Miao, H., Michaloliakos, I., Michel, C., Michimura, Y., Middleton, H., Mihaylov, D. P., Milano, L., Miller, A. L., Miller, A., Miller, B., Millhouse, M., Mills, J. C., Milotti, E., Minenkov, Y., Mio, N., Mir, Ll. M., Miravet-Tenés, M., Mishkin, A., Mishra, C., Mishra, T., Mistry, T., Mitra, S., Mitrofanov, V. P., Mitselmakher, G., Mittleman, R., Miyakawa, O., Miyo, K., Miyoki, S., Mo, Geoffrey, Modafferi, L. M., Moguel, E., Mogushi, K., Mohapatra, S. R. P., Mohite, S. R., Molina, I., Molina-Ruiz, M., Mondin, M., Montani, M., Moore, C. J., Moragues, J., Moraru, D., Morawski, F., More, A., Moreno, C., Moreno, G., Mori, Y., Morisaki, S., Morisue, N., Moriwaki, Y., Mours, B., Mow-Lowry, C. M., Mozzon, S., Muciaccia, F., Mukherjee, Arunava, Mukherjee, D., Mukherjee, Soma, Mukherjee, Subroto, Mukherjee, Suvodip, Mukund, N., Mullavey, A., Munch, J., Muniz, E. A., Murray, P. G., Musenich, R., Muusse, S., Nadji, S. L., Nagano, K., Nagar, A., Nakamura, K., Nakano, H., Nakano, M., Nakayama, Y., Napolano, V., Nardecchia, I., Narola, H., Naticchioni, L., Nayak, B., Nayak, R. K., Neil, B. F., Neilson, J., Nelson, A., Nelson, T. J. N., Nery, M., Neubauer, P., Neunzert, A., Ng, K. Y., Ng, S. W. S., Nguyen, C., Nguyen, P., Nguyen, T., Quynh, L. Nguyen, Ni, J., Ni, W. T., Nichols, S. A., Nishimoto, T., Nishizawa, A., Nissanke, S., Nitoglia, E., Nocera, F., Norman, M., North, C., Nozaki, S., Nurbek, G., Nuttall, L. K., Obayashi, Y., Oberling, J., O'Brien, B. D., O'Dell, J., Oelker, E., Ogaki, W., Oganesyan, G., Oh, J. J., Oh, K., Oh, S. H., Ohashi, M., Ohashi, T., Ohkawa, M., Ohme, F., Ohta, H., Okada, M. A., Okutani, Y., Olivetto, C., Oohara, K., Oram, R., O'Reilly, B., Ormiston, R. G., Ormsby, N. D., O'Shaughnessy, R., O'Shea, E., Oshino, S., Ossokine, S., Osthelder, C., Otabe, S., Ottaway, D. J., Overmier, H., Pace, A. E., Pagano, G., Pagano, R., Page, M. A., Pagliaroli, G., Pai, A., Pai, S. A., Pal, S., Palamos, J. R., Palashov, O., Palomba, C., Pan, H., Pan, K. C., Panda, P. K., Pang, P. T. H., Pankow, C., Pannarale, F., Pant, B. C., Panther, F. H., Paoletti, F., Paoli, A., Paolone, A., Pappas, G., Parisi, A., Park, H., Park, J., Parker, W., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passuello, D., Patel, M., Pathak, M., Patricelli, B., Patron, A. S., Paul, S., Payne, E., Pedraza, M., Pedurand, R., Pegoraro, M., Pele, A., Arellano, F. E. Pena, Penano, S., Penn, S., Perego, A., Pereira, A., Pereira, T., Perez, C. J., Pèrigois, C., Perkins, C. C., Perreca, A., Perriés, S., Pesios, D., Petermann, J., Petterson, D., Pfeiffer, H. P., Pham, H., Pham, K. A., Phukon, K. S., Phurailatpam, H., Piccinni, O. J., Pichot, M., Piendibene, M., Piergiovanni, F., Pierini, L., Pierro, V., Pillant, G., Pillas, M., Pilo, F., Pinard, L., Pineda-Bosque, C., Pinto, I. M., Pinto, M., Piotrzkowski, B. J., Piotrzkowski, K., Pirello, M., Pitkin, M. D., Placidi, A., Placidi, E., Planas, M. L., Plastino, W., Pluchar, C., Poggiani, R., Polini, E., Pong, D. Y. T., Ponrathnam, S., Porter, E. K., Poulton, R., Poverman, A., Powell, J., Pracchia, M., Pradier, T., Prajapati, A. K., Prasai, K., Prasanna, R., Pratten, G., Principe, M., Prodi, G. A., Prokhorov, L., Prosposito, P., Prudenzi, L., Puecher, A., Punturo, M., Puosi, F., Puppo, P., Purrer, M., Qi, H., Quartey, N., Quetschke, V., Quinonez, P. J., Quitzow-James, R., Raab, F. J., Raaijmakers, G., Radkins, H., Radulesco, N., Raffai, P., Rail, S. X., Raja, S., Rajan, C., Ramirez, K. E., Ramirez, T. D., Ramos-Buades, A., Rana, J., Rapagnani, P., Ray, A., Raymond, V., Raza, N., Razzano, M., Read, J., Rees, L. A., Regimbau, T., Rei, L., Reid, S., Reid, S. W., Reitze, D. H., Relton, P., Renzini, A., Rettegno, P., Revenu, B., Reza, A., Rezac, M., Ricci, F., Richards, D., Richardson, J. W., Richardson, L., Riemenschneider, G., Riles, K., Rinaldi, S., Rink, K., Robertson, N. A., Robie, R., Robinet, F., Rocchi, A., Rodriguez, S., Rolland, L., Rollins, J. G., Romanelli, M., Romano, R., Romel, C. L., Romero, A., Romero-Shaw, I. M., Romie, J. H., Ronchini, S., Rosa, L., Rose, C. A., Rosinska, D., Ross, M. P., Rowan, S., Rowlinson, S. 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Z., Sun, L., Sunil, S., Sur, A., Suresh, J., Sutton, P. J., Suzuki, Takamasa, Suzuki, Takanori, Suzuki, Toshikazu, Swinkels, B. L., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takano, S., Takeda, H., Takeda, M., Talbot, C. J., Talbot, C., Tanaka, K., Tanaka, Taiki, Tanaka, Takahiro, Tanasijczuk, A. J., Tanioka, S., Tanner, D. B., Tao, D., Tao, L., Tapia, R. D., Martin, E. N. Tapia San, Taranto, C., Taruya, A., Tasson, J. D., Tenorio, R., Terhune, J. E. S., Terkowski, L., Thirugnanasambandam, M. P., Thomas, M., Thomas, P., Thompson, E. E., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tiwari, Shubhanshu, Tiwari, Srishti, Tiwari, V., Toivonen, A. M., Tolley, A. E., Tomaru, T., Tomura, T., Tonelli, M., Tornasi, Z., Torres-Forne, A., Torrie, C. I., Melo, I. Tosta e, Toyra, D., Trapananti, A., Travasso, F., Traylor, G., Trevor, M., Tringali, M. C., Tripathee, A., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. 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B., Ying, M., Yokoyama, J., Yokozawa, T., Yoo, J., Yoshioka, T., Yu, Hang, Yu, Haocun, Yuzurihara, H., Zadrozny, A., Zanolin, M., Zeidler, S., Zelenova, T., Zendri, J. P., Zevin, M., Zhan, M., Zhang, H., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, G., Zhao, Y., Zhao, Yue, Zhou, R., Zhou, Z., Zhu, X. J., Zhu, Z. H., Zimmerman, A. B., Zucker, M. E., Zweizig, J., Collaboration, The LIGO Scientific, Collaboration, the Virgo, and Collaboration, the KAGRA
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We present Fermi Gamma-ray Burst Monitor (Fermi-GBM) and Swift Burst Alert Telescope (Swift-BAT) searches for gamma-ray/X-ray counterparts to gravitational wave (GW) candidate events identified during the third observing run of the Advanced LIGO and Advanced Virgo detectors. Using Fermi-GBM on-board triggers and sub-threshold gamma-ray burst (GRB) candidates found in the Fermi-GBM ground analyses, the Targeted Search and the Untargeted Search, we investigate whether there are any coincident GRBs associated with the GWs. We also search the Swift-BAT rate data around the GW times to determine whether a GRB counterpart is present. No counterparts are found. Using both the Fermi-GBM Targeted Search and the Swift-BAT search, we calculate flux upper limits and present joint upper limits on the gamma-ray luminosity of each GW. Given these limits, we constrain theoretical models for the emission of gamma-rays from binary black hole mergers.
Published: 2023

44. Beyond Discriminative Regions: Saliency Maps as Alternatives to CAMs for Weakly Supervised Semantic Segmentation

Author: Maruf, M., Daw, Arka, Dutta, Amartya, Bu, Jie, and Karpatne, Anuj
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract: In recent years, several Weakly Supervised Semantic Segmentation (WS3) methods have been proposed that use class activation maps (CAMs) generated by a classifier to produce pseudo-ground truths for training segmentation models. While CAMs are good at highlighting discriminative regions (DR) of an image, they are known to disregard regions of the object that do not contribute to the classifier's prediction, termed non-discriminative regions (NDR). In contrast, attribution methods such as saliency maps provide an alternative approach for assigning a score to every pixel based on its contribution to the classification prediction. This paper provides a comprehensive comparison between saliencies and CAMs for WS3. Our study includes multiple perspectives on understanding their similarities and dissimilarities. Moreover, we provide new evaluation metrics that perform a comprehensive assessment of WS3 performance of alternative methods w.r.t. CAMs. We demonstrate the effectiveness of saliencies in addressing the limitation of CAMs through our empirical studies on benchmark datasets. Furthermore, we propose random cropping as a stochastic aggregation technique that improves the performance of saliency, making it a strong alternative to CAM for WS3., Comment: 24 pages, 13 figures, 4 tables
Published: 2023

45. Search for Eccentric Black Hole Coalescences during the Third Observing Run of LIGO and Virgo

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abe, H., Acernese, F., Ackley, K., Adamcewicz, C., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Almualla, M., Altin, P. A., Álvarez-López, S., Amato, A., Amez-Droz, L., Amorosi, A., Anand, S., Ananyeva, A., Andersen, R., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Apostolatos, T., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Babak, S., Badalyan, A., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bai, Y., Baier, J. G., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Baltus, G., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barber, J., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhandari, A. V., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bianchi, A., Bilenko, I. A., Bilicki, M., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boër, M., Bogaert, G., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonnand, R., Booker, P., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Cagnoli, G., Cahillane, C., Cain III, H. W., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chalathadka-Subrahmanya, S., Chan, C., Chan, J. C. L., Chan, K. H. M., Chan, M., Chan, W. L., Chandra, K., Chang, I. P., Chang, R. -J., Chang, W., Chanial, P., Chao, S., Chapman-Bird, C., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chastain, L., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chatziioannou, K., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, X., Chen, Y. -R., Chen, Y., Cheng, H., Chessa, P., Chia, H. Y., Chiadini, F., Chiang, C., Chiarini, G., Chiba, A., Chiba, R., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Clara, F., Clark, J. A., Clarke, T. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. -F., Colleoni, M., Collette, C. G., Collins, J., Colombo, A., Colpi, M., Compton, C. M., Conti, L., Cooper, S. J., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Cousins, B., Couvares, P., Coward, D. M., Cowart, M. J., Cowburn, B. D., Coyne, D. C., Coyne, R., Craig, K., Creighton, J. D. E., Creighton, T. D., Criswell, A. W., Crockett-Gray, J. C. G., Croquette, M., Crouch, R., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Curyło, M., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Darsow-Fromm, C., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Dave, I., Davenport, A., Davier, M., Davis, D., Davis, M. C., Daw, E. J., Dax, M., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., De Pietri, R., De Rosa, R., De Rossi, C., De Simone, R., Dhurandhar, S., Diab, R., Diamond, P. Z., Díaz, M. C., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, F., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Lieto, A., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Donahue, L., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Drori, Y., Du, H., Ducoin, J. -G., Dunn, L., Dupletsa, U., D'Urso, D., Duval, H., Duverne, P. -A., Dwyer, S. E., Eassa, C., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Einsle, H., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Engelby, E., Engl, A. J., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, C. R., Evans, M., Evans, T. M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fair, H., Fairhurst, S., Fan, P. C., Farah, A. M., Farr, B., Farr, W. M., Fauchon-Jones, E. J., Favaro, G., Favata, M., Fays, M., Feicht, J., Fejer, M. M., Fenyvesi, E., Ferguson, D. L., Ferrante, I., Ferreira, T. A., Fidecaro, F., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. S., Floden, E., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franceschetti, K., Franke, A., Frasca, S., Frasconi, F., Mascioli, A. Frattale, Frei, Z., Freise, A., Freitas, O., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fujii, S., Fukunaga, I., Fulda, P., Fyffe, M., Gabella, W. E., Gadre, B., Gair, J. R., Gais, J., Galaudage, S., Gallardo, S., Gamba, R., Ganapathy, D., Ganguly, A., Gaonkar, S. G., Garaventa, B., Garcia-Bellido, J., García-Núñez, C., García-Quirós, C., Gardner, J. W., Gardner, K. A., Gargiulo, J., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., George, J., Gerberding, O., Gergely, L., Ghadiri, N., Ghosh, Abhirup, Ghosh, Archisman, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Suprovo, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Gleckl, A. E., Glotin, F., Godfrey, J., Godwin, P., Goetz, E., Goetz, R., Golomb, J., Lopez, S. Gomez, Goncharov, B., González, G., Goodwin-Jones, A. 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J., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zevin, M., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Yanqi, Zhang, Ya, Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass $M>70$ $M_\odot$) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities $0 < e \leq 0.3$ at $0.33$ Gpc$^{-3}$ yr$^{-1}$ at 90\% confidence level., Comment: 24 pages, 5 figures
Published: 2023

46. Anxiety symptoms of major depression associated with increased willingness to exert cognitive, but not physical effort

Author: Bustamante, Laura A., Barch, Deanna, Solis, Johanne, Oshinowo, Temitope, Grahek, Ivan, Konova, Anna B, Daw, Nathaniel, and Cohen, Jonathan
Subjects: Cognitive Neuroscience, Decision making, Mood, Bayesian modeling, Clinical methods
Abstract: Reduced cognitive function in major depression (MDD) is often interpreted as a reduced ability to exert cognitive control. Here we used the Effort Foraging Task to test the hypothesis that reduced cognitive function may be due, in part, to decreased willingness to exert control in MDD because of increased cognitive effort "costs". Contrary to our predictions, neither cognitive nor physical effort costs differed with MDD diagnosis (N MDD=52, N Comparisons=27). However, we found distinct patterns of symptom relationships for cognitive and physical effort costs. In MDD, greater anxiety symptoms were selectively associated with lower cognitive, but not physical effort cost (i.e. greater willingness to exert cognitive effort), whereas greater anhedonia and behavioral apathy symptoms were selectively associated with increased physical (but not cognitive) effort costs. These findings support the measurement of both cognitive and physical effort as decision-making function markers that may inform heterogeneity of MDD.
Published: 2024

47. Temporally extended decision-making through episodic sampling

Author: Zhou, Corey Yishan, Talmi, Deborah, Daw, Nathaniel, and Mattar, Marcelo G
Subjects: Psychology, Decision making, Memory, Computer-based experiment, Statistics
Abstract: A major goal of cognitive science is to characterize how an individual's past experiences guide their present decisions in a sequential task. Various empirical evidence support a process of incremental learning, well-characterized by the framework of reinforcement learning, whereby repeated exposures to similar situations shape decisions. However, in a complex world with sparse data a more sample-efficient process is needed. Prior work has suggested that episodic memory supports decision-making in such settings. Here, we provide novel behavioral evidence that episodic memory supports decision-making in temporally extended settings. We propose that value-based decision-making and episodic memory share common mechanisms to encode and retrieve past events, which in turn shape option evaluation and ultimately choice. In two experiments, we empirically test hypotheses that relate classic dynamics of sequential episodic memory retrieval to response patterns in novel evaluation and decision tasks. We find subjects' reported value estimates are subject to biases analogous to classic episodic memory biases (Experiment 1), and their choices are best captured by an episodic recall-based model (Experiment 2). These results suggest a novel link between value-based decision-making and episodic memory, which could reflect a psychologically plausible mechanism for computing decision variables by Monte Carlo sampling.
Published: 2024

48. Dual credit assignment processes underlie dopamine signals in a complex spatial environment.

Author: Krausz, Timothy, Comrie, Alison, Kahn, Ari, Frank, Loren, Daw, Nathaniel, and Berke, Joshua
Subjects: credit assignment, decision-making, dopamine, maze, navigation, reinforcement learning, Rats, Animals, Dopamine, Decision Making, Reward, Brain
Abstract: Animals frequently make decisions based on expectations of future reward (values). Values are updated by ongoing experience: places and choices that result in reward are assigned greater value. Yet, the specific algorithms used by the brain for such credit assignment remain unclear. We monitored accumbens dopamine as rats foraged for rewards in a complex, changing environment. We observed brief dopamine pulses both at reward receipt (scaling with prediction error) and at novel path opportunities. Dopamine also ramped up as rats ran toward reward ports, in proportion to the value at each location. By examining the evolution of these dopamine place-value signals, we found evidence for two distinct update processes: progressive propagation of value along taken paths, as in temporal difference learning, and inference of value throughout the maze, using internal models. Our results demonstrate that within rich, naturalistic environments dopamine conveys place values that are updated via multiple, complementary learning algorithms.
Published: 2023

49. Pulmonary lesions in early response assessment in pediatric Hodgkin lymphoma: prevalence and possible implications for initial staging

Author: Stoevesandt, Dietrich, Ludwig, Christiane, Mauz-Körholz, Christine, Körholz, Dieter, Hasenclever, Dirk, McCarten, Kathleen, Flerlage, Jamie E., Kurch, Lars, Wohlgemuth, Walter A., Landman-Parker, Judith, Wallace, William H., Fosså, Alexander, Vordermark, Dirk, Karlén, Jonas, Cepelová, Michaela, Klekawka, Tomasz, Attarbaschi, Andishe, Hraskova, Andrea, Uyttebroeck, Anne, Beishuizen, Auke, Dieckmann, Karin, Leblanc, Thierry, Daw, Stephen, and Steglich, Jonas
Published: 2024
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50. Self-Supervised Ensemble Learning: A Universal Method for Phase Transition Classification of Many-Body Systems

Author: Ho, Chi-Ting and Wang, Daw-Wei
Subjects: Condensed Matter - Statistical Mechanics
Abstract: We develop a self-supervised ensemble learning (SSEL) method to accurately classify distinct types of phase transitions by analyzing the fluctuation properties of machine learning outputs. Employing the 2D Potts model and the 2D Clock model as benchmarks, we demonstrate the capability of SSEL in discerning first-order, second-order, and Berezinskii-Kosterlitz-Thouless transitions, using in-situ spin configurations as the input features. Furthermore, we show that the SSEL approach can also be applied to investigate quantum phase transitions in 1D Ising and 1D XXZ models upon incorporating quantum sampling. We argue that the SSEL model simulates a special state function with higher-order correlations between physical quantities, and hence provides richer information than previous machine learning methods. Consequently, our SSEL method can be generally applied to the identification/classification of phase transitions even without explicit knowledge of the underlying theoretical models.
Published: 2023
Full Text: View/download PDF

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