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1. Susceptibility of Commercial-Off-The-Shelf Sensors to IEMI using Pulse Modulated Signals

Author

L. Cesbron Lavau, M. Suhrke, and P. Knott

Subjects
Engineering (General). Civil engineering (General), TA1-2040
Abstract

The use of sensors has grown dramatically in recent years and many devices rely on the information they provide. The lack of proper security mechanisms available to control the use of sensors and the high degree of integration make them more vulnerable to Intentional Electromagnetic Interference (IEMI). The aim of this paper was to investigate the impact of IEMI on separate sensors with privileged access to the hardware and software to pursue a deep analysis of the effects of IEMI attacks using pulse modulated signals. Measurements were carried out in a shielded hall using an open TEM (Transverse Electromagnetic) waveguide in the 100 MHz–7.5 GHz frequency range. A variety of effects were observed and significant differences were found with pulse modulated signals compared to continuous wave signals. These results indicate weak points in the sensors hardware leading to possible hardening measures.

Published
2023
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2. Gandalf the Red: Adaptive Security for LLMs

Author

Pfister, Niklas, Volhejn, Václav, Knott, Manuel, Arias, Santiago, Bazińska, Julia, Bichurin, Mykhailo, Commike, Alan, Darling, Janet, Dienes, Peter, Fiedler, Matthew, Haber, David, Kraft, Matthias, Lancini, Marco, Mathys, Max, Pascual-Ortiz, Damián, Podolak, Jakub, Romero-López, Adrià, Shiarlis, Kyriacos, Signer, Andreas, Terek, Zsolt, Theocharis, Athanasios, Timbrell, Daniel, Trautwein, Samuel, Watts, Samuel, Wu, Yun-Han, and Rojas-Carulla, Mateo

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

Current evaluations of defenses against prompt attacks in large language model (LLM) applications often overlook two critical factors: the dynamic nature of adversarial behavior and the usability penalties imposed on legitimate users by restrictive defenses. We propose D-SEC (Dynamic Security Utility Threat Model), which explicitly separates attackers from legitimate users, models multi-step interactions, and expresses the security-utility in an optimizable form. We further address the shortcomings in existing evaluations by introducing Gandalf, a crowd-sourced, gamified red-teaming platform designed to generate realistic, adaptive attack. Using Gandalf, we collect and release a dataset of 279k prompt attacks. Complemented by benign user data, our analysis reveals the interplay between security and utility, showing that defenses integrated in the LLM (e.g., system prompts) can degrade usability even without blocking requests. We demonstrate that restricted application domains, defense-in-depth, and adaptive defenses are effective strategies for building secure and useful LLM applications., Comment: Niklas Pfister, V\'aclav Volhejn and Manuel Knott contributed equally

Published
2025

3. Membrane mechanics dictate axonal pearls-on-a-string morphology and function.

Author

Griswold, Jacqueline, Bonilla-Quintana, Mayte, Pepper, Renee, Lee, Christopher, Raychaudhuri, Sumana, Ma, Siyi, Gan, Quan, Syed, Sarah, Zhu, Cuncheng, Bell, Miriam, Suga, Mitsuo, Yamaguchi, Yuuki, Chéreau, Ronan, Nägerl, U, Knott, Graham, Rangamani, Padmini, and Watanabe, Shigeki

Subjects
Animals, Axons, Mice, Cell Membrane, Action Potentials, Mice, Inbred C57BL, Cholesterol, Male, Female, Neural Conduction
Abstract

Axons are ultrathin membrane cables that are specialized for the conduction of action potentials. Although their diameter is variable along their length, how their morphology is determined is unclear. Here, we demonstrate that unmyelinated axons of the mouse central nervous system have nonsynaptic, nanoscopic varicosities ~200 nm in diameter repeatedly along their length interspersed with a thin cable ~60 nm in diameter like pearls-on-a-string. In silico modeling suggests that this axon nanopearling can be explained by membrane mechanical properties. Treatments disrupting membrane properties, such as hyper- or hypotonic solutions, cholesterol removal and nonmuscle myosin II inhibition, alter axon nanopearling, confirming the role of membrane mechanics in determining axon morphology. Furthermore, neuronal activity modulates plasma membrane cholesterol concentration, leading to changes in axon nanopearls and causing slowing of action potential conduction velocity. These data reveal that biophysical forces dictate axon morphology and function, and modulation of membrane mechanics likely underlies unmyelinated axonal plasticity.

Published
2025

4. Weakly supervised image segmentation for defect-based grading of fresh produce

Author

Knott, Manuel, Odion, Divinefavour, Sontakke, Sameer, Karwa, Anup, and Defraeye, Thijs

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Implementing image-based machine learning in agriculture is often limited by scarce data and annotations, making it hard to achieve high-quality model predictions. This study tackles the issue of postharvest quality assessment of bananas in decentralized supply chains. We propose a method to detect and segment surface defects in banana images using panoptic segmentation to quantify defect size and number. Instead of time-consuming pixel-level annotations, we use weak supervision with coarse labels. A dataset of 476 smartphone images of bananas was collected under real-world field conditions and annotated for bruises and scars. Using the Segment Anything Model (SAM), a recently published foundation model for image segmentation, we generated dense annotations from coarse bounding boxes to train a segmentation model, significantly reducing manual effort while achieving a panoptic quality score of 77.6%. This demonstrates SAM's potential for low-effort, accurate segmentation in agricultural settings with limited data.

Published
2024

5. Characterizing Model Collapse in Large Language Models Using Semantic Networks and Next-Token Probability

Author

Gambetta, Daniele, Gezici, Gizem, Giannotti, Fosca, Pedreschi, Dino, Knott, Alistair, and Pappalardo, Luca

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

As synthetic content increasingly infiltrates the web, generative AI models may experience an autophagy process, where they are fine-tuned using their own outputs. This autophagy could lead to a phenomenon known as model collapse, which entails a degradation in the performance and diversity of generative AI models over successive generations. Recent studies have explored the emergence of model collapse across various generative AI models and types of data. However, the current characterizations of model collapse tend to be simplistic and lack comprehensive evaluation. In this article, we conduct a thorough investigation of model collapse across three text datasets, utilizing semantic networks to analyze text repetitiveness and diversity, while employing next-token probabilities to quantify the loss of diversity. We also examine how the proportions of synthetic tokens affect the severity of model collapse and perform cross-dataset evaluations to identify domain-specific variations. By proposing metrics and strategies for a more detailed assessment of model collapse, our study provides new insights for the development of robust generative AI systems.

Published
2024

6. Social perception of faces in a vision-language model

Author

Hausladen, Carina I., Knott, Manuel, Camerer, Colin F., and Perona, Pietro

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

We explore social perception of human faces in CLIP, a widely used open-source vision-language model. To this end, we compare the similarity in CLIP embeddings between different textual prompts and a set of face images. Our textual prompts are constructed from well-validated social psychology terms denoting social perception. The face images are synthetic and are systematically and independently varied along six dimensions: the legally protected attributes of age, gender, and race, as well as facial expression, lighting, and pose. Independently and systematically manipulating face attributes allows us to study the effect of each on social perception and avoids confounds that can occur in wild-collected data due to uncontrolled systematic correlations between attributes. Thus, our findings are experimental rather than observational. Our main findings are three. First, while CLIP is trained on the widest variety of images and texts, it is able to make fine-grained human-like social judgments on face images. Second, age, gender, and race do systematically impact CLIP's social perception of faces, suggesting an undesirable bias in CLIP vis-a-vis legally protected attributes. Most strikingly, we find a strong pattern of bias concerning the faces of Black women, where CLIP produces extreme values of social perception across different ages and facial expressions. Third, facial expression impacts social perception more than age and lighting as much as age. The last finding predicts that studies that do not control for unprotected visual attributes may reach the wrong conclusions on bias. Our novel method of investigation, which is founded on the social psychology literature and on the experiments involving the manipulation of individual attributes, yields sharper and more reliable observations than previous observational methods and may be applied to study biases in any vision-language model.

Published
2024

7. A Closer Look at Benchmarking Self-Supervised Pre-training with Image Classification

Author

Marks, Markus, Knott, Manuel, Kondapaneni, Neehar, Cole, Elijah, Defraeye, Thijs, Perez-Cruz, Fernando, and Perona, Pietro

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Self-supervised learning (SSL) is a machine learning approach where the data itself provides supervision, eliminating the need for external labels. The model is forced to learn about the data structure or context by solving a pretext task. With SSL, models can learn from abundant and cheap unlabeled data, significantly reducing the cost of training models where labels are expensive or inaccessible. In Computer Vision, SSL is widely used as pre-training followed by a downstream task, such as supervised transfer, few-shot learning on smaller labeled data sets, and/or unsupervised clustering. Unfortunately, it is infeasible to evaluate SSL methods on all possible downstream tasks and objectively measure the quality of the learned representation. Instead, SSL methods are evaluated using in-domain evaluation protocols, such as fine-tuning, linear probing, and k-nearest neighbors (kNN). However, it is not well understood how well these evaluation protocols estimate the representation quality of a pre-trained model for different downstream tasks under different conditions, such as dataset, metric, and model architecture. We study how classification-based evaluation protocols for SSL correlate and how well they predict downstream performance on different dataset types. Our study includes eleven common image datasets and 26 models that were pre-trained with different SSL methods or have different model backbones. We find that in-domain linear/kNN probing protocols are, on average, the best general predictors for out-of-domain performance. We further investigate the importance of batch normalization and evaluate how robust correlations are for different kinds of dataset domain shifts. We challenge assumptions about the relationship between discriminative and generative self-supervised methods, finding that most of their performance differences can be explained by changes to model backbones.

Published
2024

12. Objective Photoanalysis of Feminizing Frontal Cranioplasty Outcomes.

Author

David, Abel, House, Adrian, Targ, Sonia, DeBusk, W, Park, Andrea, Knott, P, and Seth, Rahul

Subjects
cranioplasty, facial feminization, gender affirmation surgery
Abstract

STUDY DESIGN: Retrospective analysis at a tertiary care center. OBJECTIVE: This study describes a method of analyzing postoperative results using lateral view clinical photographs to create normalized projection ratios of the glabella and radix. METHODS: We reviewed preoperative and postoperative photographs of 15 patients. All photographs were in the lateral view Frankfort horizontal plane. We calculated the distances between the (a) tragus and cornea, (b) cornea and radix, (c) cornea and glabella, and the (θ) nasofrontal angle. RESULTS: Fifteen sets of patient photographs were analyzed and found that there was a favorable 14% reduction at the radix and an even greater reduction (78.9%) at the glabella. The nasofrontal angle was improved to a more feminine range from 131.84° preoperatively to 145.86° postoperatively. CONCLUSIONS: Normalized projection ratios of the glabella and radix, along with the nasofrontal angle, can be used to objectively measure outcomes of frontal feminizing cranioplasty.

Published
2024

13. AKT and EZH2 inhibitors kill TNBCs by hijacking mechanisms of involution

Author

Schade, Amy E., Perurena, Naiara, Yang, Yoona, Rodriguez, Carrie L., Krishnan, Anjana, Gardner, Alycia, Loi, Patrick, Xu, Yilin, Nguyen, Van T. M., Mastellone, G. M., Pilla, Natalie F., Watanabe, Marina, Ota, Keiichi, Davis, Rachel A., Mattioli, Kaia, Xiang, Dongxi, Zoeller, Jason J., Lin, Jia-Ren, Morganti, Stefania, Garrido-Castro, Ana C., Tolaney, Sara M., Li, Zhe, Barbie, David A., Sorger, Peter K., Helin, Kristian, Santagata, Sandro, Knott, Simon R. V., and Cichowski, Karen

Published
2024
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16. Impacts of Extreme Heat on Labor Force Dynamics

Author

Ireland, Andrew, Johnston, David, and Knott, Rachel

Subjects
Economics - General Economics
Abstract

We use daily longitudinal data and a within-worker identification approach to examine the impacts of heat on labor force dynamics in Australia. High temperatures during 2001-2019 significantly reduced work attendance and hours worked, which were not compensated for in subsequent days and weeks. The largest reductions occurred in cooler regions and recent years, and were not solely concentrated amongst outdoor-based workers. Financial and Insurance Services was the most strongly affected industry, with temperatures above 38{\deg}C (100{\deg}F) increasing absenteeism by 15 percent. Adverse heat effects during the work commute and during outdoor work hours are shown to be key mechanisms.

Published
2024

18. Ergonomic Assessment of Septorhinoplasty Maneuvers During Simulated Pregnancy

Author

Bhethanabotla, Rohith M, Ledgister, Kaye, Soriano, Ian S, O'Sullivan, Patricia, Bigelow, Elaine, Knott, Philip Daniel, and Park, Andrea M

Subjects
Reproductive Medicine, Biomedical and Clinical Sciences, Clinical Sciences, Health Sciences, Musculoskeletal, ergonomics, pregnancy, rhinoplasty, simulation
Abstract

ObjectiveWomen represent an increasing proportion of the otolaryngology workforce. Work-related musculoskeletal disorders (WRMSD) are a little-studied yet important impediment to career completion. Scant attention has been directed to study the impact of pregnancy on surgeon posture and ergonomics. We piloted the use of a pregnancy simulation suit (Empathy Belly) to assess the risk of ergonomic compromise when performing open septorhinoplasty.Study designSurgical simulation.SettingSingle session, training simulation lab at academic medical center.MethodsMedical students and surgical residents performed the initial steps of a rhinoplasty procedure without and with a pregnancy simulation suit and were filmed with an artificial intelligence-based video analysis app from Kinetica Labs that calculates joint angles and categorizes the ergonomic risk factors. Still images from videos were taken and analyzed using validated posture-based analysis rubrics. Participants were asked to complete a qualitative questionnaire after the session.ResultsTwelve medical students and surgical residents participated in the study. Posture-based analysis indicated increased ergonomics risk factors among trainees when performing a rhinoplasty while wearing the pregnancy suit. Video analysis indicated trends of worsening back angle and shoulder postures. Trainees reported experiencing pain in the neck, suprapubic area, and lower back. They acknowledged the importance of ergonomics in otolaryngology and desired further education about workplace injury risk mitigation.ConclusionPregnancy impacts the ergonomics of performing septorhinoplasty and further investigation is required into interventions to reduce risk of WRMSDs.

Published
2024

20. Evaluation of a youth-focused suicide prevention HOPE aftercare service: protocol for a non-randomized hybrid effectiveness-implementation type I design

Author

Kartal, Dzenana, Jaeger, Tess, Lamblin, Michelle, Richards, Hannah, Witt, Katrina, Occhipinti, Jo-An, Mihalopoulos, Cathrine, Chatterton, Mary Lou, Chanen, Andrew, McGorry, Patrick, Skinner, Adam, Zbukvic, Isabel, Thompson, Andrew, Knott, Jonathan, Flego, Anna, Hamilton, Craig, Webb, Marianne, Mullen, Edward, Swingler, Natasha, Kenny, Bridget, and Robinson, Jo

Published
2024
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21. Metformin associates with higher myocardial perfusion reserve and survival in type 2 diabetes mellitus patients

Author

Sharrack, Noor, Knott, Kristopher D., Gulsin, Gaurav S., Kotecha, Tushar, Brown, Louise A. E., Yeo, Jian L., Porcari, Aldostefano, Adam, Robert D., Thirunavukarasu, Sharmaine, Chowdhary, Amrit, Levelt, Eylem, Moon, James C., McCann, Gerry P., Fontana, Marianna, Kellman, Peter, Munyombwe, Theresa, Gale, Chris P., Buckley, David L., Greenwood, John P., Swoboda, Peter P., and Plein, Sven

Published
2024
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26. Effectiveness of pharmacological treatments for severe agitation in real-world emergency settings: protocol of individual-participant-data network meta-analysis

Author

Siafis, Spyridon, Wu, Hui, Nomura, Nobuyuki, Schneider-Thoma, Johannes, Bighelli, Irene, Lorenz, Carolin, Dib, Joseph E., Tharyan, Prathap, Calver, Leonie A., Isbister, Geoffrey K., Chan, Esther W. Y., Knott, Jonathan C., Yap, Celene Y. L., Mantovani, Célia, Martel, Marc L., Barbic, David, Honer, William G., Hansen, Wulf-Peter, Huf, Gisele, Alexander, Jacob, Raveendran, Nirmal S., Coutinho, Evandro S. F., Priller, Josef, Adams, Clive E., Salanti, Georgia, and Leucht, Stefan

Published
2024
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30. Text-image Alignment for Diffusion-based Perception

Author

Kondapaneni, Neehar, Marks, Markus, Knott, Manuel, Guimaraes, Rogerio, and Perona, Pietro

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Diffusion models are generative models with impressive text-to-image synthesis capabilities and have spurred a new wave of creative methods for classical machine learning tasks. However, the best way to harness the perceptual knowledge of these generative models for visual tasks is still an open question. Specifically, it is unclear how to use the prompting interface when applying diffusion backbones to vision tasks. We find that automatically generated captions can improve text-image alignment and significantly enhance a model's cross-attention maps, leading to better perceptual performance. Our approach improves upon the current state-of-the-art (SOTA) in diffusion-based semantic segmentation on ADE20K and the current overall SOTA for depth estimation on NYUv2. Furthermore, our method generalizes to the cross-domain setting. We use model personalization and caption modifications to align our model to the target domain and find improvements over unaligned baselines. Our cross-domain object detection model, trained on Pascal VOC, achieves SOTA results on Watercolor2K. Our cross-domain segmentation method, trained on Cityscapes, achieves SOTA results on Dark Zurich-val and Nighttime Driving. Project page: https://www.vision.caltech.edu/tadp/. Code: https://github.com/damaggu/TADP., Comment: Project page: https://www.vision.caltech.edu/tadp/, Code page: github.com/damaggu/TADP

Published
2023

31. CasPEDIA Database: a functional classification system for class 2 CRISPR-Cas enzymes

Author

Adler, Benjamin A, Trinidad, Marena I, Bellieny-Rabelo, Daniel, Zhang, Elaine, Karp, Hannah M, Skopintsev, Petr, Thornton, Brittney W, Weissman, Rachel F, Yoon, Peter H, Chen, LinXing, Hessler, Tomas, Eggers, Amy R, Colognori, David, Boger, Ron, Doherty, Erin E, Tsuchida, Connor A, Tran, Ryan V, Hofman, Laura, Shi, Honglue, Wasko, Kevin M, Zhou, Zehan, Xia, Chenglong, Al-Shimary, Muntathar J, Patel, Jaymin R, Thomas, Vienna CJX, Pattali, Rithu, Kan, Matthew J, Vardapetyan, Anna, Yang, Alana, Lahiri, Arushi, Maxwell, Micaela F, Murdock, Andrew G, Ramit, Glenn C, Henderson, Hope R, Calvert, Roland W, Bamert, Rebecca S, Knott, Gavin J, Lapinaite, Audrone, Pausch, Patrick, Cofsky, Joshua C, Sontheimer, Erik J, Wiedenheft, Blake, Fineran, Peter C, Brouns, Stan JJ, Sashital, Dipali G, Thomas, Brian C, Brown, Christopher T, Goltsman, Daniela SA, Barrangou, Rodolphe, Siksnys, Virginius, Banfield, Jillian F, Savage, David F, and Doudna, Jennifer A

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Biotechnology, Genetics, Generic health relevance, CRISPR-Cas Systems, Phylogeny, CRISPR-Associated Proteins, Databases, Genetic, Endodeoxyribonucleases, Encyclopedias as Topic, Environmental Sciences, Information and Computing Sciences, Developmental Biology, Biological sciences, Chemical sciences, Environmental sciences
Abstract

CRISPR-Cas enzymes enable RNA-guided bacterial immunity and are widely used for biotechnological applications including genome editing. In particular, the Class 2 CRISPR-associated enzymes (Cas9, Cas12 and Cas13 families), have been deployed for numerous research, clinical and agricultural applications. However, the immense genetic and biochemical diversity of these proteins in the public domain poses a barrier for researchers seeking to leverage their activities. We present CasPEDIA (http://caspedia.org), the Cas Protein Effector Database of Information and Assessment, a curated encyclopedia that integrates enzymatic classification for hundreds of different Cas enzymes across 27 phylogenetic groups spanning the Cas9, Cas12 and Cas13 families, as well as evolutionarily related IscB and TnpB proteins. All enzymes in CasPEDIA were annotated with a standard workflow based on their primary nuclease activity, target requirements and guide-RNA design constraints. Our functional classification scheme, CasID, is described alongside current phylogenetic classification, allowing users to search related orthologs by enzymatic function and sequence similarity. CasPEDIA is a comprehensive data portal that summarizes and contextualizes enzymatic properties of widely used Cas enzymes, equipping users with valuable resources to foster biotechnological development. CasPEDIA complements phylogenetic Cas nomenclature and enables researchers to leverage the multi-faceted nucleic-acid targeting rules of diverse Class 2 Cas enzymes.

Published
2024

38. Human-AI Coevolution

Author

Pedreschi, Dino, Pappalardo, Luca, Ferragina, Emanuele, Baeza-Yates, Ricardo, Barabasi, Albert-Laszlo, Dignum, Frank, Dignum, Virginia, Eliassi-Rad, Tina, Giannotti, Fosca, Kertesz, Janos, Knott, Alistair, Ioannidis, Yannis, Lukowicz, Paul, Passarella, Andrea, Pentland, Alex Sandy, Shawe-Taylor, John, and Vespignani, Alessandro

Subjects
Computer Science - Artificial Intelligence
Abstract

Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices on online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often ``unintended'' social outcomes. This paper introduces Coevolution AI as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., technical, epistemological, legal and socio-political.

Published
2023

39. Coronal Heating as Determined by the Solar Flare Frequency Distribution Obtained by Aggregating Case Studies

Author

Mason, James Paul, Werth, Alexandra, West, Colin G., Youngblood, Allison A., Woodraska, Donald L., Peck, Courtney, Lacjak, Kevin, Frick, Florian G., Gabir, Moutamen, Alsinan, Reema A., Jacobsen, Thomas, Alrubaie, Mohammad, Chizmar, Kayla M., Lau, Benjamin P., Dominguez, Lizbeth Montoya, Price, David, Butler, Dylan R., Biron, Connor J., Feoktistov, Nikita, Dewey, Kai, Loomis, N. E., Bodzianowski, Michal, Kuybus, Connor, Dietrick, Henry, Wolfe, Aubrey M., Guerrero, Matt, Vinson, Jessica, Starbuck, Peter, Litton, Shelby D, Beck, M. G., Fisch, Jean-Paul, West, Ayana, Muniz, Alexis A., Chavez, Luis, Upthegrove, Zachary T., Runyon, Brenton M., Salazar, J., Kritzberg, Jake E., Murrel, Tyler, Ho, Ella, LaFemina, Quintin Y., Elbashir, Sara I., Chang, Ethan C., Hudson, Zachary A., Nussbaum, Rosemary O., Kennedy, Kellen, Kim, Kevin, Arango, Camila Villamil, Albakr, Mohammed A., Rotter, Michael, Garscadden, A. J., Salcido-Alcontar JR, Antonio, Pearl, Harrison M., Stepaniak, Tyler, Marquez, Josie A., Marsh, Lauren, Andringa, Jesse C, Osogwin, Austin, Shields, Amanda M., Brookins, Sarah, Hach, Grace K., Clausi, Alexis R., Millican, Emily B., Jaimes, Alan A, Graham, Alaina S., Burritt, John J., Perez, J. S., Ramirez, Nathaniel, Suri, Rohan, Myer, Michael S., Kresek, Zoe M., Goldsberry, C. A., Payne, Genevieve K., Jourabchi, Tara, Hu, J., Lucca, Jeffrey, Feng, Zitian, Gilpatrick, Connor B., Khan, Ibraheem A., Warble, Keenan, Sweeney, Joshua D., Dorricott, Philip, Meyer, Ethan, Kothamdi, Yash S., Sohail, Arman S., Grell, Kristyn, Floyd, Aidan, Bard, Titus, Mathieson, Randi M., Reed, Joseph, Cisneros, Alexis, Payne, Matthew P., Jarriel, J. R., Mora, Jacqueline Rodriguez, Sundell, M. E., Patel, Kajal, Alesmail, Mohammad, Alnasrallah, Yousef A, Abdullah, Jumana T., Molina-Saenz, Luis, Tayman, K. E., Brown, Gabriel T., Kerr-Layton, Liana, Berriman-Rozen, Zachary D., Hiatt, Quinn, Kalra, Etash, Ong, Jason, Vadayar, Shreenija, Shannahan, Callie D., Benke, Evan, zhang, Jinhua, Geisman, Jane, Martyr, Cara, Ameijenda, Federico, Akruwala, Ushmi H., Nehring, Molly, Kissner, Natalie, Rule, Ian C., Learned, Tyler, Smith, Alexandra N., Mazzotta, Liam, Rounsefell, Tyndall, Eyeson, Elizabeth A., Shelby, Arlee K., Moll, Tyler S, Menke, Riley, Shahba, Hannan, House Jr., Tony A., Clark, David B., Burns, Annemarie C., de La Beaujardiere, Tristan, Trautwein, Emily D., Plantz, Will, Reeves, Justin, Faber, Ian, Buxton, B. W., Highhouse, Nigel, Landrey, Kalin, Hansen, Connor M, Chen, Kevin, Hales, Ryder Buchanan, Borgerding, Luke R., Guo, Mutian, Crow, Christian J., Whittall, Lloyd C., Simmons, Conor, Folarin, Adeduni, Parkinson, Evan J., Rahn, Anna L., Blevins, Olivia, Morelock, Annalise M., Kelly, Nicholas, Parker, Nathan L., Smith, Kelly, Plzak, Audrey E., Saeb, David, Hares, Cameron T., Parker, Sasha R., McCoy, Andrew, Pham, Alexander V., Lauzon, Megan, Kennedy, Cayla J., Reyna, Andrea B., Acosta, Daniela M. Meza, Cool, Destiny J., Steinbarth, Sheen L., Mendoza-Anselmi, Patricia, Plutt, Kaitlyn E., Kipp, Isabel M, Rakhmonova, M., Brown, Cameron L., Van Anne, Gabreece, Moss, Alexander P., Golden, Olivia, Kirkpatrick, Hunter B., Colleran, Jake R., Sullivan, Brandon J, Tran, Kevin, Carpender, Michael Andrew, Mundy, Aria T., Koenig, Greta, Oudakker, Jessica, Engelhardt, Rasce, Ales, Nolan, Wexler, Ethan Benjamin, Beato, Quinn I, Chen, Lily, Cochran, Brooke, Hill, Paula, Hamilton, Sean R., Hashiro, Kyle, Khan, Usman, Martinez, Alexa M., Brockman, Jennifer L., Mallory, Macguire, Reed, Charlie, Terrile, Richard, Singh, Savi, Watson, James Adam, Creany, Joshua B., Price, Nicholas K., Miften, Aya M., Tran, Bryn, Kamenetskiy, Margaret, Martinez, Jose R., Opp, Elena N., Huang, Jianyang, Fails, Avery M., Belei, Brennan J., Slocum, Ryan, Astalos, Justin, East, Andrew, Nguyen, Lena P., Pherigo, Callie C, East, Andrew N., Li, David Y., Nelson, Maya LI, Taylor, Nicole, Odbayar, Anand, Rives, Anna Linnea, Mathur, Kabir P., Billingsley, Jacob, Polikoff, Hyden, Driscoll, Michael, Wilson, Orion K., Lahmers, Kyle, Toon, Nathaniel J., Lippincott, Sam, Musgrave, Andrew J., Gregory, Alannah H., Pitsuean-Meier, Sedique, Jesse, Trevor, Smith, Corey, Miles, Ethan J., Kainz, Sabrina J. H. T., Ji, Soo Yeun, Nguyen, Lena, Aryan, Maryam, Dinser, Alexis M., Shortman, Jadon, Bastias, Catalina S, Umbricht, Thomas D, Cage, Breonna, Randolph, Parker, Pollard, Matthew, Simone, Dylan M., Aramians, Andrew, Brecl, Ariana E., Robert, Amanda M., Zenner, Thomas, Saldi, Maxwell, Morales, Gavin, Mendez, Citlali, Syed, Konner, Vogel, Connor Maklain, Cone, Rebecca A., Berhanu, Naomi, Carpenter, Emily, Leoni, Cecilia, Bryan, Samuel, Ramachandra, Nidhi, Shaw, Timothy, Lee, E. C., Monyek, Eli, Wegner, Aidan B., Sharma, Shajesh, Lister, Barrett, White, Jamison R., Willard, John S., Sulaiman, S. A, Blandon, Guillermo, Narayan, Anoothi, Ruger, Ryan, Kelley, Morgan A., Moreno, Angel J., Balcer, Leo M, Ward-Chene, N. R. D., Shelby, Emma, Reagan, Brian D., Marsh, Toni, Sarkar, Sucheta, Kelley, Michael P., Fell, Kevin, Balaji, Sahana, Hildebrand, Annalise K., Shoha, Dominick, Nandu, Kshmya, Tucker, Julia, Cancio, Alejandro R., Wang, Jiawei, Rapaport, Sarah Grace, Maravi, Aimee S., Mayer, Victoria A., Miller, Andrew, Bence, Caden, Koke, Emily, Fauntleroy, John T, Doermer, Timothy, Al-Ghazwi, Adel, Morgan, Remy, Alahmed, Mohammed S., Mathavan, Adam Izz Khan Mohd Reduan, Silvester, H. K., Weiner, Amanda M., Liu, Nianzi, Iovan, Taro, Jensen, Alexander V., AlHarbi, Yazeed A., Jiang, Yufan, Zhang, Jiaqi, Jones, Olivia M., Huang, Chenqi, Reh, Eileen N., Alhamli, Dania, Pettine, Joshua, Zhou, Chongrui, Kriegman, Dylan, Yang, Jianing, Ash, Kevin, Savage, Carl, Kaiser, Emily, Augenstein, Dakota N., Padilla, Jacqueline, Stark, Ethan K., Hansen, Joshua A., Kokes, Thomas, Huynh, Leslie, Sanchez-Sanchez, Gustavo, Jeseritz, Luke A., Carillion, Emma L., Vepa, Aditya V., Khanal, Sapriya, Behr, Braden, Martin, Logan S., McMullan, Jesse J., Zhao, Tianwei, Williams, Abigail K., Alqabani, Emeen, Prinster, Gale H., Horne, Linda, Ruggles-Delgado, Kendall, Otto, Grant, Gomez, Angel R., Nguyen, Leonardo, Brumley, Preston J., Venegas, Nancy Ortiz, Varela, Ilian, Brownlow, Jordi, Cruz, Avril, Leiker, Linzhi, Batra, Jasleen, Hutabarat, Abigail P., Nunes-Valdes, Dario, Jameson, Connor, Naqi, Abdulaziz, Adams, Dante Q., Biediger, Blaine B., Borelli, William T, Cisne, Nicholas A., Collins, Nathaniel A., Curnow, Tyler L., Gopalakrishnan, Sean, Griffin, Nicholas F., Herrera, Emanuel, McGarvey, Meaghan V., Mellett, Sarah, Overchuk, Igor, Shaver, Nathan, Stratmeyer, Cooper N., Vess, Marcus T., Juels, Parker, Alyami, Saleh A., Gale, Skylar, Wallace, Steven P., Hunter, Samuel C, Lonergan, Mia C., Stewart, Trey, Maksimuk, Tiffany E., Lam, Antonia, Tressler, Judah, Napoletano, Elena R., Miller, Joshua B., Roy, Marc G., Chanders, Jasey, Fischer, Emmalee, Croteau, A. J., Kuiper, Nicolas A., Hoffman, Alex, DeBarros, Elyse, Curry, Riley T., Brzostowicz, A., Courtney, Jonas, Zhao, Tiannie, Szabo, Emi, Ghaith, Bandar Abu, Slyne, Colin, Beck, Lily, Quinonez, Oliver, Collins, Sarah, Madonna, Claire A., Morency, Cora, Palizzi, Mallory, Herwig, Tim, Beauprez, Jacob N., Ghiassi, Dorsa, Doran, Caroline R., Yang, Zhanchao, Padgette, Hannah M., Dicken, Cyrus A., Austin, Bryce W., Phalen, Ethan J., Xiao, Catherine, Palos, Adler, Gerhardstein, Phillip, Altenbern, Ava L., Orbidan, Dan, Dorr, Jackson A., Rivas, Guillermo A., Ewing, Calvin A, Giebner, B. C., McEntee, Kelleen, Kite, Emily R., Crocker, K. A., Haley, Mark S., Lezak, Adrienne R., McQuaid, Ella, Jeong, Jacob, Albaum, Jonathan, Hrudka, E. M., Mulcahy, Owen T., Tanguma, Nolan C., Oishi-Holder, Sean, White, Zachary, Coe, Ryan W., Boyer, Christine, Chapman, Mitchell G., Fortino, Elise, Salgado, Jose A., Hellweg, Tim, Martinez, Hazelia K., Mitchell, Alexander J., Schubert, Stephanie H., Schumacher, Grace K, Tesdahl, Corey D, Uphoff, C. H., Vassilyev, Alexandr, Witkoff, Briahn, Wolle, Jackson R., Dice, Kenzie A., Behrer, Timothy A., Bowen, Troy, Campbell, Andrew J, Clarkson, Peter C, Duong, Tien Q., Hawat, Elijah, Lopez, Christian, Olson, Nathaniel P., Osborn, Matthew, Peou, Munisettha E., Vaver, Nicholas J., Husted, Troy, Kallemeyn, Nicolas Ian, Spangler, Ava A, Mccurry, Kyle, Schultze, Courtney, Troisi, Thomas, Thomas, Daniel, Ort, Althea E., Singh, Maya A., Soon, Caitlin, Patton, Catherine, Billman, Jayce A., Jarvis, Sam, Hitt, Travis, Masri, Mirna, Albalushi, Yusef J., Schofer, Matthew J, Linnane, Katherine B., Knott, Philip Whiting, Valencia, Whitney, Arias-Robles, Brian A., Ryder, Diana, Simone, Anna, Abrams, Jonathan M., Belknap, Annelene L., Rouse, Charlotte, Reynolds, Alexander, Petric, Romeo S. L., Gomez, Angel A., Meiselman-Ashen, Jonah B., Carey, Luke, Dias, John S., Fischer-White, Jules, Forbes, Aidan E., Galarraga, Gabriela, Kennedy, Forrest, Lawlor, Rian, Murphy, Maxwell J., Norris, Cooper, Quarderer, Josh, Waller, Caroline, Weber, Robert J., Gunderson, Nicole, Boyne, Tom, Gregory, Joshua A., Propper, Henry Austin, von Peccoz, Charles B. Beck, Branch, Donovan, Clarke, Evelyn, Cutler, Libby, Dabberdt, Frederick M., Das, Swagatam, Figueirinhas, John Alfred D., Fougere, Benjamin L., Roy, Zoe A., Zhao, Noah Y., Cox, Corben L., Barnhart, Logan D. W., Craig, Wilmsen B., Moll, Hayden, Pohle, Kyle, Mueller, Alexander, Smith, Elena K., Spicer, Benjamin C., Aycock, Matthew C., Bat-Ulzii, Batchimeg, Murphy, Madalyn C., Altokhais, Abdullah, Thornally, Noah R., Kleinhaus, Olivia R., Sarfaraz, Darian, Barnes, Grant M., Beard, Sara, Banda, David J, Davis, Emma A. B., Huebsch, Tyler J., Wagoner, Michaela, Griego, Justus, Hale, Jack J. Mc, Porter, Trevor J., Abrashoff, Riley, Phan, Denise M., Smith, Samantha M., Srivastava, Ashish, Schlenker, Jared A. W., Madsen, Kasey O., Hirschmann, Anna E., Rankin, Frederick C, Akbar, Zainab A., Blouin, Ethan, Coleman-Plante, Aislinn, Hintsa, Evan, Lookhoff, Emily, Amer, Hamzi, Deng, Tianyue, Dvorak, Peter, Minimo, Josh, Plummer, William C., Ton, Kelly, Solt, Lincoln, AlAbbas, Batool H., AlAwadhi, Areej A., Cooper, Nicholas M., Corbitt, Jessica S, Dunlap, Christian, Johnson, Owen, Malone, Ryan A., Tellez, Yesica, Wallace, Logan, Ta, Michael-Tan D., Wheeler, Nicola H., Ramirez, Ariana C., Huang, Shancheng, Mehidic, Amar, Christiansen, Katherine E, Desai, Om, Domke, Emerson N., Howell, Noah H., Allsbrook, Martin, Alnaji, Teeb, England, Colin, Siles, Nathan, Burton, Nicholas David, Cruse, Zoe, Gilmartin, Dalton, Kim, Brian T., Hattendorf, Elsie, Buhamad, Maryam, Gayou, Lily, Seglem, Kasper, Alkhezzi, Tameem, Hicks, Imari R., Fife, Ryann, Pelster, Lily M., Fix, Alexander, Sur, Sohan N., Truong, Joshua K., Kubiak, Bartlomiej, Bondar, Matthew, Shi, Kyle Z., Johnston, Julia, Acevedo, Andres B., Lee, Junwon, Solorio, William J., Johnston, Braedon Y., McCormick, Tyler, Olguin, Nicholas, Pastor, Paige J., Wilson, Evan M., Trunko, Benjamin L., Sjoroos, Chris, Adams, Kalvyn N, Bell, Aislyn, Brumage-Heller, Grant, Canales, Braden P., Chiles, Bradyn, Driscoll, Kailer H., Hill, Hallie, Isert, Samuel A., Ketterer, Marilyn, Kim, Matthew M., Mewhirter, William J., Phillips, Lance, Phommatha, Krista, Quinn, Megan S., Reddy, Brooklyn J., Rippel, Matthew, Russell, Bowman, Williams, Sajan, Pixley, Andrew M., Gapin, Keala C., Peterson, B., Ruprecht, Collin, Hardie, Isabelle, Li, Isaac, Erickson, Abbey, Gersabeck, Clint, Gopalani, Mariam, Allanqawi, Nasser, Burton, Taylor, Cahn, Jackson R., Conti, Reese, White, Oliver S., Rojec, Stewart, Hogen, Blake A., Swartz, Jason R., Dick, R., Battist, Lexi, Dunn, Gabrielle M., Gasser, Rachel, Logan, Timothy W., Sinkovic, Madeline, Schaller, Marcus T., Heintz, Danielle A., Enrich, Andrew, Sanchez, Ethan S., Perez, Freddy, Flores, Fernando, Kapla, Shaun D., Shockley, Michael C., Phillips, Justin, Rumley, Madigan, Daboub, Johnston, Karsh, Brennan J., Linders, Bridget, Chen, Sam, Do, Helen C., Avula, Abhinav, French, James M., Bertuccio, Chrisanna, Hand, Tyler, Lee, Adrianna J., Neeland, Brenna K, Salazar, Violeta, Andrew, Carter, Barmore, Abby, Beatty, Thomas, Alonzi, Nicholas, Brown, Ryan, Chandler, Olivia M., Collier, Curran, Current, Hayden, Delasantos, Megan E., Bonilla, Alberto Espinosa de los Monteros, Fowler, Alexandra A., Geneser, Julianne R., Gentry, Eleanor, Gustavsson, E. R., Hansson, Jonathan, Hao, Tony Yunfei, Herrington, Robert N., Kelly, James, Kelly, Teagan, Kennedy, Abigail, Marquez, Mathew J., Meillon, Stella, Palmgren, Madeleine L., Pesce, Anneliese, Ranjan, Anurag, Robertson, Samuel M., Smith, Percy, Smith, Trevor J, Soby, Daniel A., Stratton, Grant L., Thielmann, Quinn N., Toups, Malena C., Veta, Jenna S., Young, Trenton J., Maly, Blake, Manzanares, Xander R., Beijer, Joshua, George, Jacob D., Mills, Dylan P., Ziebold, Josh J, Chambers, Paige, Montoya, Michael, Cheang, Nathan M., Anderson, Hunter J., Duncan, Sheridan J., Ehrlich, Lauren, Hudson, Nathan C., Kiechlin, Jack L., Koch, Will, Lee, Justin, Menassa, Dominic, Oakes, S. H., Petersen, Audrey J., Bunsow, J. R. Ramirez, Bay, Joshua, Ramirez, Sacha, Fenwick, Logan D., Boyle, Aidan P., Hibbard, Lea Pearl, Haubrich, Calder, Sherry, Daniel P., Jenkins, Josh, Furney, Sebastian, Velamala, Anjali A., Krueger, Davis J., Thompson, William N., Chhetri, Jenisha, Lee, Alexis Ying-Shan, Ray, Mia G. V., Recchia, John C., Lengerich, Dylan, Taulman, Kyle, Romero, Andres C., Steward, Ellie N., Russell, Sloan, Hardwick, Dillon F., Wootten, Katelynn, Nguyen, Valerie A., Quispe, Devon, Ragsdale, Cameron, Young, Isabel, Atchley-Rivers, N. S., Stribling, Jordin L., Gentile, Julia G, Boeyink, Taylor A., Kwiatkowski, Daniel, Dupeyron, Tomi Oshima, Crews, Anastasia, Shuttleworth, Mitchell, Dresdner, Danielle C., Flackett, Lydia, Haratsaris, Nicholas, Linger, Morgan I, Misener, Jay H., Patti, Samuel, Pine, Tawanchai P., Marikar, Nasreen, Matessi, Giorgio, Routledge, Allie C., Alkaabi, Suhail, Bartman, Jessica L., Bisacca, Gabrielle E., Busch, Celeste, Edwards, Bree, Staudenmier, Caitlyn, Starling, Travis, McVey, Caden, Montano, Maximus, Contizano, Charles J., Taylor, Eleanor, McIntyre, James K., Victory, Andrew, McCammon, Glen S., Kimlicko, Aspen, Sheldrake, Tucker, Shelchuk, Grace, Von Reich, Ferin J., Hicks, Andrew J., O'neill, Ian, Rossman, Beth, Taylor, Liam C., MacDonald, William, Becker, Simone E., Han, Soonhee, O'Sullivan, Cian, Wilcove, Isaac, Brennan, David J., Hanley, Luke C., Hull, Owen, Wilson, Timothy R., Kalmus, Madison H., Berv, Owen A., Harris, Logan Swous, Doan, Chris H, Londres, Nathan, Parulekar, Anish, Adam, Megan M., Angwin, Abigail, Cabbage, Carter C., Colleran, Zachary, Pietras, Alex, Seux, Octave, Oros, Ryan, Wilkinson, Blake C., Nguyen, Khoa D, Trank-Greene, Maedee, Barone, Kevin M., Snyder, G. L., Biehle, Samuel J, Billig, Brennen, Almquist, Justin Thomas, Dixon, Alyssa M., Erickson, Benjamin, Evans, Nathan, Genne, SL, Kelly, Christopher M, Marcus, Serafima M., Ogle, Caleb, Patel, Akhil, Vendetti, Evan, Courtney, Olivia, Deel, Sean, Del Foco, Leonardo, Gjini, Michael, Haines, Jessica, Hoff, Isabelle J., Jones, M. R., Killian, Dominic, Kuehl, Kirsten, Kuester, Chrisanne, Lantz, Maxwell B., Lee, Christian J, Mauer, Graham, McKemey, Finbar K., Millican, Sarah J., Rosasco, Ryan, Stewart, T. C., VanEtten, Eleanor, Derwin, Zachary, Serio, Lauren, Sickler, Molly G., Blake, Cassidy A., Patel, Neil S., Fox, Margaret, Gray, Michael J, Ziegler, Lucas J., Kumar, Aman Priyadarshi, Polly, Madelyn, Mesgina, Sarah, McMorris, Zane, Griffin, Kyle J., Haile, L. N., Bassel, Claire, Dixon, Thomas J., Beattie, Ryan, Houck, Timothy J, Rodgers, Maeve, Trofino, Tyson R., Lukianow, Dax, Smart, Korben, Hall, Jacqueline L., Bone, Lauren, Baldwin, James O., Doane, Connor, Almohsen, Yousef A., Stamos, Emily, Acha, Iker, Kim, Jake, Samour II, Antonio E., Chavali, S., Kanokthippayakun, Jeerakit, Gotlib, Nicholas, Murphy, Ryan C., Archibald, Jack. W., Brimhall, Alexander J, Boyer, Aidan, Chapman, Logan T., Chadda, Shivank, Sibrell, Lisa, Vallery, Mia M., Conroy, Thomas C., Pan, Luke J., Balajonda, Brian, Fuhrman, Bethany E. S., Alkubaisi, Mohamed, Engelstad, Jacob, Dodrill, Joshua, Fuchs, Calvin R., Bullard-Connor, Gigi, Alhuseini, Isehaq, Zygmunt, James C., Sipowicz, Leo, Hayrynen, Griffin A., McGill, Riley M., Keating, Caden J., Hart, Omer, Cyr, Aidan St., Steinsberger, Christopher H., Thoman, Gerig, Wood, Travis M., Ingram, Julia A., Dominguez, J., Georgiades, Nathaniel James, Johnson, Matthew, Johnson, Sawyer, Pedersen, Alexander J., Ralapanawe, Anoush K, Thomas, Jeffrey J., Sato, Ginn A., Reynolds, Hope, Nasser, Liebe, Mizzi, Alexander Z., Damgaard, Olivia, Baflah, Abdulrahman A., Liu, Steven Y., Salindeho, Adam D., Norden, Kelso, Gearhart, Emily E., Krajnak, Zack, Szeremeta, Philip, Amos, Meggan, Shin, Kyungeun, Muckenthaler, Brandon A., Medialdea, Melissa, Beach, Simone, Wilson, Connor B., Adams, Elena R, Aldhamen, Ahmed, Harris, Coyle M., Hesse, Troy M., Golding, Nathan T., Larter, Zachary, Hernandez, Angel, Morales, Genaro, Traxler, Robert B., Alosaimi, Meshal, Fitton, Aidan F., Aaron, James Holland, Lee, Nathaniel F., Liao, Ryan Z., Chen, Judy, French, Katherine V., Loring, Justin, Colter, Aurora, McConvey, Rowan, Colozzi, Michael, Vann, John D., Scheck, Benjamin T., Weigand, Anthony A, Alhabeeb, Abdulelah, Idoine, Yolande, Woodard, Aiden L., Medellin, Mateo M., Ratajczyk, Nicholas O, Tobin, Darien P., Collins, Jack C., Horning, Thomas M., Pellatz, Nick, Pitten, John, Lordi, Noah, Patterson, Alyx, Hoang, Thi D, Zimmermann, Ingrid H, Wang, Hongda, Steckhahn, Daniel, Aradhya, Arvind J., Oliver, Kristin A., Cai, Yijian, Wang, Chaoran, Yegovtsev, Nikolay, Wu, Mengyu, Ganesan, Koushik, Osborne, Andrew, Wickenden, Evan, Meyer, Josephine C., Chaparro, David, Visal, Aseem, Liu, Haixin, Menon, Thanmay S., Jin, Yan, Wilson, John, Erikson, James W., Luo, Zheng, Shitara, Nanako, Nelson, Emma E, Geerdts, T. R., Ortiz, Jorge L Ramirez, and Lewandowski, H. J.

Subjects
Astrophysics - Solar and Stellar Astrophysics
Abstract

Flare frequency distributions represent a key approach to addressing one of the largest problems in solar and stellar physics: determining the mechanism that counter-intuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres. It is widely accepted that the magnetic field is responsible for the heating, but there are two competing mechanisms that could explain it: nanoflares or Alfv\'en waves. To date, neither can be directly observed. Nanoflares are, by definition, extremely small, but their aggregate energy release could represent a substantial heating mechanism, presuming they are sufficiently abundant. One way to test this presumption is via the flare frequency distribution, which describes how often flares of various energies occur. If the slope of the power law fitting the flare frequency distribution is above a critical threshold, $\alpha=2$ as established in prior literature, then there should be a sufficient abundance of nanoflares to explain coronal heating. We performed $>$600 case studies of solar flares, made possible by an unprecedented number of data analysts via three semesters of an undergraduate physics laboratory course. This allowed us to include two crucial, but nontrivial, analysis methods: pre-flare baseline subtraction and computation of the flare energy, which requires determining flare start and stop times. We aggregated the results of these analyses into a statistical study to determine that $\alpha = 1.63 \pm 0.03$. This is below the critical threshold, suggesting that Alfv\'en waves are an important driver of coronal heating., Comment: 1,002 authors, 14 pages, 4 figures, 3 tables, published by The Astrophysical Journal on 2023-05-09, volume 948, page 71

Published
2023
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40. Heat and Worker Health

Author

Ireland, Andrew, Johnston, David, and Knott, Rachel

Subjects
Economics - General Economics
Abstract

Extreme heat negatively impacts cognition, learning, and task performance. With increasing global temperatures, workers may therefore be at increased risk of work-related injuries and illness. This study estimates the effects of temperature on worker health using records spanning 1985-2020 from an Australian mandatory insurance scheme. High temperatures are found to cause significantly more claims, particularly among manual workers in outdoor-based industries. These adverse effects have not diminished across time, with the largest effect observed for the 2015-2020 period, indicating increasing vulnerability to heat. Within occupations, the workers most adversely affected by heat are female, older-aged and higher-earning. Finally, results from firm-level panel analyses show that the percentage increase in claims on hot days is largest at "safer" firms.

Published
2023
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41. Cyber Security and Online Safety Education for Schools in the UK: Looking through the Lens of Twitter Data

Author

Knott, Jamie, Yuan, Haiyue, Boakes, Matthew, and Li, Shujun

Subjects
Computer Science - Computers and Society, Computer Science - Social and Information Networks
Abstract

In recent years, digital technologies have grown in many ways. As a result, many school-aged children have been exposed to the digital world a lot. Children are using more digital technologies, so schools need to teach kids more about cyber security and online safety. Because of this, there are now more school programmes and projects that teach students about cyber security and online safety and help them learn and improve their skills. Still, despite many programmes and projects, there is not much proof of how many schools have taken part and helped spread the word about them. This work shows how we can learn about the size and scope of cyber security and online safety education in schools in the UK, a country with a very active and advanced cyber security education profile, using nearly 200k public tweets from over 15k schools. By using simple techniques like descriptive statistics and visualisation as well as advanced natural language processing (NLP) techniques like sentiment analysis and topic modelling, we show some new findings and insights about how UK schools as a sector have been doing on Twitter with their cyber security and online safety education activities. Our work has led to a range of large-scale and real-world evidence that can help inform people and organisations interested in cyber security and teaching online safety in schools., Comment: This is the full edition of a 4-page poster paper published in the Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing (SAC '23), which can be accessed via the following DOI link: https://doi.org/10.1145/3555776.3577805

Published
2022

45. Association of Hepatic Steatosis with Adipose and Muscle Mass and Distribution in Children

Author

Malki, Ghattas J, Goyal, Nidhi P, Ugalde-Nicalo, Patricia, Chun, Lauren F, Zhang, Jasen, Ding, Ziyi, Wei, Yingjia, Knott, Cynthia, Batakis, Danielle, Henderson, Walter, Sirlin, Claude B, Middleton, Michael S, and Schwimmer, Jeffrey B

Subjects
Biomedical and Clinical Sciences, Nutrition and Dietetics, Biomedical Imaging, Nutrition, Obesity, Digestive Diseases, Chronic Liver Disease and Cirrhosis, Pediatric, Liver Disease, Oral and gastrointestinal, Metabolic and endocrine, Humans, Child, Adiposity, Fatty Liver, Liver, Intra-Abdominal Fat, Magnetic Resonance Imaging, Obesity, Abdominal, Muscles, hepatic steatosis, MRI-PDFF, adipose, muscle, nonalcoholic fatty liver disease, Medical Biotechnology, Clinical Sciences, Public Health and Health Services, Endocrinology & Metabolism, Medical biotechnology, Public health
Abstract

Background: Pediatric studies have shown associations between hepatic steatosis and total body fat, visceral fat, and lean mass. However, these associations have not been assessed simultaneously, leaving their relative importance unknown. Objective: To evaluate associations between hepatic steatosis and total-body adiposity, visceral adiposity, and lean mass in children. Method: In children at risk for fatty liver, hepatic steatosis, adipose, and lean mass were estimated with magnetic resonance imaging and dual-energy X-ray absorptiometry. Results: Two hundred twenty-seven children with mean age 12.1 years had mean percent body fat of 38.9% and mean liver fat of 8.4%. Liver fat was positively associated with total-body adiposity, visceral adiposity, and lean mass (P

Published
2023

46. The molecular consequences of androgen activity in the human breast

Author

Raths, Florian, Karimzadeh, Mehran, Ing, Nathan, Martinez, Andrew, Yang, Yoona, Qu, Ying, Lee, Tian-Yu, Mulligan, Brianna, Devkota, Suzanne, Tilley, Wayne T, Hickey, Theresa E, Wang, Bo, Giuliano, Armando E, Bose, Shikha, Goodarzi, Hani, Ray, Edward C, Cui, Xiaojiang, and Knott, Simon RV

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Breast Cancer, Genetics, Cancer, Sexual and Gender Minorities (SGM/LGBT*), Aetiology, 2.1 Biological and endogenous factors, androgen regulation of human breast homeostasis, breast microenvironment, cellular metabolism, chromatin regulation, hormone receptor, lactation, multiplexed immunohistochemistry by co-detection by indexing, single-cell ATAC sequencing, single-cell RNA sequencing, transcriptional control
Abstract

Estrogen and progesterone have been extensively studied in the mammary gland, but the molecular effects of androgen remain largely unexplored. Transgender men are recorded as female at birth but identify as male and may undergo gender-affirming androgen therapy to align their physical characteristics and gender identity. Here we perform single-cell-resolution transcriptome, chromatin, and spatial profiling of breast tissues from transgender men following androgen therapy. We find canonical androgen receptor gene targets are upregulated in cells expressing the androgen receptor and that paracrine signaling likely drives sex-relevant androgenic effects in other cell types. We also observe involution of the epithelium and a spatial reconfiguration of immune, fibroblast, and vascular cells, and identify a gene regulatory network associated with androgen-induced fat loss. This work elucidates the molecular consequences of androgen activity in the human breast at single-cell resolution.

Published
2023

47. Caring for Students Playbook: Getting Started with Key Terms, Challenges, and Approaches

Author

Achieving the Dream, Inc., Online Learning Consortium (OLC), Every Learner Everywhere, Adams, S., Bali, M., Eder, Z., Fladd, L., Garrett, K., Garth-McCullough, R., Gibson, A. M., Gunder, A., Iuzzini, J., Knott, J. L., Rafferty, J., and Weber, N. L.

Abstract

This resource serves as a companion piece to the "Caring for Students Playbook: Six Recommendations for Caring for Students" (ED613431) and provides a foundation for the approaches and methods offered therein. The content offered in this playbook begins by setting the groundwork for rooting instruction in diversity, equity, and inclusion before focusing on the challenges students are experiencing as they pursue their education in our current climate. It concludes with offering approaches that support engaging culture, acknowledging trauma, and meeting the needs of all learners.

Published
2021

48. Caring for Students Playbook: Six Recommendations for Caring for Students

Author

Achieving the Dream, Inc., Online Learning Consortium (OLC), Every Learner Everywhere, Adams, S., Bali, M., Eder, Z., Fladd, L., Garrett, K., Garth-McCullough, R., Gibson, A. M., Gunder, A., Iuzzini, J., Knott, J. L., Rafferty, J., and Weber, N. L.

Abstract

This playbook contains resources that can be used by individual instructors to support putting student care into action or by institutions seeking guidance for incorporating caring for students into their campus strategy and instructional development programming. While the impetus for this playbook was the COVID-19 pandemic, the information and resources provided represent high standards of student support regardless of the environment. This playbook provides detailed recommendations broken out into six sections. These recommendations include: (1) Reimagine your syllabus content; (2) Review and revise your course content; (3) Assess your students with care; (4) Develop and enact a communication plan; (5) Integrate institutional supports; and (6) Take time for self-care. Each of these Six Recommendations for Caring for Students provides concrete strategies and resources to support instructors in operationalizing equity-focused, inclusive teaching strategies that put student care into practice by acknowledging student challenges while identifying student assets. [For the companion playbook, "Caring for Students Playbook: Getting Started with Key Terms, Challenges, and Approaches," see ED613432.]

Published
2021

49. A sustainable waste-to-protein system to maximise waste resource utilisation for developing food- and feed-grade protein solutions

Author

Piercy, Ellen, Verstraete, Willy, Ellis, Peter R., Rockström, Johan, Smith, Pete, Witard, Oliver, Hallett, Jason, Hogstrand, Christer, Knott, Geoffrey, Karwati, Ai, Rasoarahona, Henintso Felamboahangy, Leslie, Andrew, He, Yiying, Banks, Mason, and Guo, Miao

Subjects
Physics - Physics and Society, Electrical Engineering and Systems Science - Systems and Control
Abstract

A waste-to-protein system that integrates a range of waste-to-protein upgrading technologies has the potential to converge innovations on zero-waste and protein security to ensure a sustainable protein future. We present a global overview of food-safe and feed-safe waste resource potential and technologies to sort and transform such waste streams with compositional quality characteristics into food-grade or feed-grade protein. The identified streams are rich in carbon and nutrients and absent of pathogens and hazardous contaminants, including food waste streams, lignocellulosic waste from agricultural residues and forestry, and contaminant-free waste from the food and drink industry. A wide range of chemical, physical, and biological treatments can be applied to extract nutrients and convert waste-carbon to fermentable sugars or other platform chemicals for subsequent conversion to protein. Our quantitative analyses suggest that the waste-to-protein system has the potential to maximise recovery of various low-value resources and catalyse the transformative solutions toward a sustainable protein future. However, novel protein regulation processes remain expensive and resource intensive in many countries, with protracted timelines for approval. This poses a significant barrier to market expansion, despite accelerated research and development in waste-to-protein technologies and novel protein sources. Thus, the waste-to-protein system is an important initiative to promote metabolic health across the lifespan and tackle the global hunger crisis., Comment: 40 pages, 5 figures. Supplementary materials can be accessed on request to corresponding author Miao Guo: miao.guo@kcl.ac.uk

Published
2022

50. Facilitated machine learning for image-based fruit quality assessment

Author

Knott, Manuel, Perez-Cruz, Fernando, and Defraeye, Thijs

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Image-based machine learning models can be used to make the sorting and grading of agricultural products more efficient. In many regions, implementing such systems can be difficult due to the lack of centralization and automation of postharvest supply chains. Stakeholders are often too small to specialize in machine learning, and large training data sets are unavailable. We propose a machine learning procedure for images based on pre-trained Vision Transformers. It is easier to implement than the current standard approach of training Convolutional Neural Networks (CNNs) as we do not (re-)train deep neural networks. We evaluate our approach based on two data sets for apple defect detection and banana ripeness estimation. Our model achieves a competitive classification accuracy equal to or less than one percent below the best-performing CNN. At the same time, it requires three times fewer training samples to achieve a 90% accuracy.

Published
2022
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