1,002 results on '"Johnson, Daniel P."'
Search Results
2. Title, Copyright, Dedication
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Johnson, Daniel P.
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- 2022
3. NOTES
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Johnson, Daniel P.
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- 2022
4. 6. POLITE SPACES AND NURSERIES OF VICE: Place, Disorder, and Cultural Practice
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Johnson, Daniel P.
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- 2022
5. 2. A GREAT NUMBER OF HANDS: Property, Empire, and Unfree Labor
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Johnson, Daniel P.
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- 2022
6. ACKNOWLEDGMENTS
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Johnson, Daniel P.
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- 2022
7. Cover
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Johnson, Daniel P.
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8. INDEX
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Johnson, Daniel P.
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9. BIBLIOGRAPHY
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Johnson, Daniel P.
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- 2022
10. 4. A GROWING EVIL IN THE CITY: Law, Crime, and the Atlantic Diaspora
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Johnson, Daniel P.
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- 2022
11. PART III. SPACES OF PLEASURE AND DANGER
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Johnson, Daniel P.
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- 2022
12. 5. THE URBAN BATTLE OF IDEAS: Order, the People, and the Press
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Johnson, Daniel P.
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- 2022
13. PART II. LAW AND DISORDER
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Johnson, Daniel P.
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- 2022
14. 3. UNINTELLIGIBLE STUFF CALLED LAW: Cultural Legalism and Authority in the City
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Johnson, Daniel P.
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- 2022
15. 1. NOTHING WILL SATISFY YOU BUT MONEY: Community, Credit, and the Politics of Money
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Johnson, Daniel P.
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- 2022
16. PART I. LABOR AND ECONOMY
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Johnson, Daniel P.
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- 2022
17. Penzai + Treescope: A Toolkit for Interpreting, Visualizing, and Editing Models As Data
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Johnson, Daniel D.
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Computer Science - Machine Learning - Abstract
Much of today's machine learning research involves interpreting, modifying or visualizing models after they are trained. I present Penzai, a neural network library designed to simplify model manipulation by representing models as simple data structures, and Treescope, an interactive pretty-printer and array visualizer that can visualize both model inputs/outputs and the models themselves. Penzai models are built using declarative combinators that expose the model forward pass in the structure of the model object itself, and use named axes to ensure each operation is semantically meaningful. With Penzai's tree-editing selector system, users can both insert and replace model components, allowing them to intervene on intermediate values or make other edits to the model structure. Users can then get immediate feedback by visualizing the modified model with Treescope. I describe the motivation and main features of Penzai and Treescope, and discuss how treating the model as data enables a variety of analyses and interventions to be implemented as data-structure transformations, without requiring model designers to add explicit hooks., Comment: Presented at the ICML 2024 Mechanistic Interpretability workshop (Spotlight). 5 pages
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- 2024
18. snompy: a package for modelling scattering-type scanning near-field optical microscopy
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Vincent, Tom, Liu, Xinyun, Johnson, Daniel, Mester, Lars, Huang, Nathaniel, Kazakova, Olga, Hillenbrand, Rainer, and Boland, Jessica Louise
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Condensed Matter - Materials Science ,Physics - Optics - Abstract
Scattering-type scanning near-field optical microscopy (s-SNOM) is a powerful technique for extreme subwavelength imaging and spectroscopy, with around 20 nm spatial resolution. But quantitative relationships between experiment and material properties requires modelling, which can be computationally and conceptually challenging. In this work, we present snompy an open-source Python library which contains implementations of two of the most common s-SNOM models, the finite dipole model (FDM) and the point dipole model (PDM). We show a series of typical uses for this package with demonstrations including simulating nano-Fourier transform infrared (FTIR) spectra and recovering permittivity from experimental s-SNOM data. We also discuss the challenges faced with this sort of modelling, such as competing descriptions of the models in literature, and finite size effects. We hope that snompy will make quantitative s-SNOM modelling more accessible to the wider research community, which will further empower the use of s-SNOM for investigating nanoscale material properties., Comment: 26 pages, 8 figures
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- 2024
19. Technical design report for the CODEX-$\beta$ demonstrator
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collaboration, CODEX-b, Aielli, Giulio, Alimena, Juliette, Beacham, James, Haim, Eli Ben, Burucs, Andras, Cardarelli, Roberto, Charles, Matthew, Vidal, Xabier Cid, De Roeck, Albert, Dey, Biplab, Dobrescu, Silviu, Durmus, Ozgur, Elashri, Mohamed, Gligorov, Vladimir, Suarez, Rebeca Gonzalez, Gorordo, Thomas, Gray, Zarria, Henderson, Conor, Henry, Louis, Ilten, Philip, Johnson, Daniel, Kautz, Jacob, Knapen, Simon, Liu, Bingxuan, Liu, Yang, Solino, Saul Lopez, Mombacher, Titus, Nachman, Benjamin, Northacker, David, Nowak, Gabriel, Papucci, Michele, Pasztor, Gabriella, Rial, Eloi Pazos, Pfaller, Jake, Pizzimento, Luca, Casasus, Maximo Plo, Rassati, Gian Andrea, Robinson, Dean, Fernandez, Emilio Xose Rodriguez, Sahoo, Debashis, Simsek, Sinem, Sokoloff, Michael, Suresh, Aditya, Swallow, Paul, Swanson, James, Vari, Riccardo, Sierra, Carlos Vazquez, Veres, Gabor, Watson, Nigel, Wilkinson, Michael, and Williams, Michael
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Physics - Instrumentation and Detectors ,High Energy Physics - Experiment - Abstract
The CODEX-$\beta$ apparatus is a demonstrator for the proposed future CODEX-b experiment, a long-lived-particle detector foreseen for operation at IP8 during HL-LHC data-taking. The demonstrator project, intended to collect data in 2025, is described, with a particular focus on the design, construction, and installation of the new apparatus.
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- 2024
20. Foreground biases in strong gravitational lensing
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Johnson, Daniel, Fleury, Pierre, Larena, Julien, and Marchetti, Lucia
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Astrophysics - Cosmology and Nongalactic Astrophysics ,General Relativity and Quantum Cosmology - Abstract
Strong gravitational lensing is a competitive tool to probe the dark matter and energy content of the universe. However, significant uncertainties can arise from the choice of lens model, and in particular the parameterisation of the line of sight. In this work, we consider the consequences of ignoring the contribution of foreground perturbers in lens modelling. We derive the explicit form of the degeneracy between the foreground shear and the ellipticity of a power law lens, which renders both quantities effectively unmeasurable from strong lensing observables. Nonetheless, we demonstrate that this degeneracy does not affect measurements of the Einstein radius. Foreground tidal effects are also not expected to bias the slope of the potential, and any biases in this slope should not affect the recovery of the Hubble constant. The foreground convergence term adds an additional uncertainty to the measurement of $H_0$, and we show that this uncertainty will be on the order of $1\%$ for lensing systems located along random lines of sight. There is evidence to indicate that the probability of strong lensing is higher towards overdense lines of sight, and this could result in a small systematic bias towards overestimations of $H_0$., Comment: 13 + 8 pages, 2 figures
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- 2024
21. Exotic Hadrons at LHCb
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Johnson, Daniel, Polyakov, Ivan, Skwarnicki, Tomasz, and Wang, Mengzhen
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High Energy Physics - Experiment ,High Energy Physics - Phenomenology - Abstract
It has been five years since the data sample from the LHCb detector, the first experiment optimized for heavy-flavor physics studies at a hadronic collider, was completed. These data led to many major discoveries in exotic hadron spectroscopy, which we review in this article. We supplement the experimental results with a selection of phenomenological interpretations. As the upgraded LHCb detector is expected to collect a larger data sample starting in 2024, the near- and further-future potential of the LHCb program in exotic hadron physics is also discussed.
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- 2024
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22. An Eye Gaze Heatmap Analysis of Uncertainty Head-Up Display Designs for Conditional Automated Driving
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Gerber, Michael A., Schroeter, Ronald, Johnson, Daniel, Janssen, Christian P., Rakotonirainy, Andry, Kuo, Jonny, and Lenne, Mike G.
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Computer Science - Human-Computer Interaction - Abstract
This paper reports results from a high-fidelity driving simulator study (N=215) about a head-up display (HUD) that conveys a conditional automated vehicle's dynamic "uncertainty" about the current situation while fallback drivers watch entertaining videos. We compared (between-group) three design interventions: display (a bar visualisation of uncertainty close to the video), interruption (interrupting the video during uncertain situations), and combination (a combination of both), against a baseline (video-only). We visualised eye-tracking data to conduct a heatmap analysis of the four groups' gaze behaviour over time. We found interruptions initiated a phase during which participants interleaved their attention between monitoring and entertainment. This improved monitoring behaviour was more pronounced in combination compared to interruption, suggesting pre-warning interruptions have positive effects. The same addition had negative effects without interruptions (comparing baseline & display). Intermittent interruptions may have safety benefits over placing additional peripheral displays without compromising usability., Comment: Accepted for publication at the 2024 ACM Conference on Human Factors in Computing Systems (CHI'24)
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- 2024
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23. Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs
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Johnson, Daniel D., Tarlow, Daniel, Duvenaud, David, and Maddison, Chris J.
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Computer Science - Machine Learning - Abstract
Identifying how much a model ${\widehat{p}}_{\theta}(Y|X)$ knows about the stochastic real-world process $p(Y|X)$ it was trained on is important to ensure it avoids producing incorrect or "hallucinated" answers or taking unsafe actions. But this is difficult for generative models because probabilistic predictions do not distinguish between per-response noise (aleatoric uncertainty) and lack of knowledge about the process (epistemic uncertainty), and existing epistemic uncertainty quantification techniques tend to be overconfident when the model underfits. We propose a general strategy for teaching a model to both approximate $p(Y|X)$ and also estimate the remaining gaps between ${\widehat{p}}_{\theta}(Y|X)$ and $p(Y|X)$: train it to predict pairs of independent responses drawn from the true conditional distribution, allow it to "cheat" by observing one response while predicting the other, then measure how much it cheats. Remarkably, we prove that being good at cheating (i.e. cheating whenever it improves your prediction) is equivalent to being second-order calibrated, a principled extension of ordinary calibration that allows us to construct provably-correct frequentist confidence intervals for $p(Y|X)$ and detect incorrect responses with high probability. We demonstrate empirically that our approach accurately estimates how much models don't know across ambiguous image classification, (synthetic) language modeling, and partially-observable navigation tasks, outperforming existing techniques., Comment: Accepted at ICML 2024. 9 pages, 6 figures
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- 2024
24. Latitudinal patterns in stabilizing density dependence of forest communities.
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Holík, Jan, Howe, Robert, Hubbell, Stephen, Itoh, Akira, Johnson, Daniel, Kenfack, David, Král, Kamil, Larson, Andrew, Lutz, James, Makana, Jean-Remy, Malhi, Yadvinder, McMahon, Sean, McShea, William, Mohamad, Mohizah, Nasardin, Musalmah, Nathalang, Anuttara, Norden, Natalia, Oliveira, Alexandre, Parmigiani, Renan, Perez, Rolando, Phillips, Richard, Pongpattananurak, Nantachai, Sun, I-Fang, Swanson, Mark, Tan, Sylvester, Thomas, Duncan, Thompson, Jill, Uriarte, Maria, Wolf, Amy, Yao, Tze, Zimmerman, Jess, Zuleta, Daniel, Hartig, Florian, Hülsmann, Lisa, Chisholm, Ryan, Comita, Liza, Visser, Marco, de Souza Leite, Melina, Aguilar, Salomon, Anderson-Teixeira, Kristina, Bourg, Norman, Brockelman, Warren, Bunyavejchewin, Sarayudh, Castaño, Nicolas, Chang-Yang, Chia-Hao, Chuyong, George, Clay, Keith, Davies, Stuart, Duque, Alvaro, Ediriweera, Sisira, Ewango, Corneille, and Gilbert, Gregory|Greg
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Seedlings ,Tropical Climate ,Forests ,Trees ,Biodiversity ,Ecosystem - Abstract
Numerous studies have shown reduced performance in plants that are surrounded by neighbours of the same species1,2, a phenomenon known as conspecific negative density dependence (CNDD)3. A long-held ecological hypothesis posits that CNDD is more pronounced in tropical than in temperate forests4,5, which increases community stabilization, species coexistence and the diversity of local tree species6,7. Previous analyses supporting such a latitudinal gradient in CNDD8,9 have suffered from methodological limitations related to the use of static data10-12. Here we present a comprehensive assessment of latitudinal CNDD patterns using dynamic mortality data to estimate species-site-specific CNDD across 23 sites. Averaged across species, we found that stabilizing CNDD was present at all except one site, but that average stabilizing CNDD was not stronger toward the tropics. However, in tropical tree communities, rare and intermediate abundant species experienced stronger stabilizing CNDD than did common species. This pattern was absent in temperate forests, which suggests that CNDD influences species abundances more strongly in tropical forests than it does in temperate ones13. We also found that interspecific variation in CNDD, which might attenuate its stabilizing effect on species diversity14,15, was high but not significantly different across latitudes. Although the consequences of these patterns for latitudinal diversity gradients are difficult to evaluate, we speculate that a more effective regulation of population abundances could translate into greater stabilization of tropical tree communities and thus contribute to the high local diversity of tropical forests.
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- 2024
25. Towards a Unified Naming Scheme for Thermo-Active Soft Actuators: A Review of Materials, Working Principles, and Applications
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Exley, Trevor, Hays, Emilly, Johnson, Daniel, Moridani, Arian, Motati, Ramya, and Jafari, Amir
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Computer Science - Robotics - Abstract
Soft robotics is a rapidly growing field that spans the fields of chemistry, materials science, and engineering. Due to the diverse background of the field, there have been contrasting naming schemes such as 'intelligent', 'smart' and 'adaptive' materials which add vagueness to the broad innovation among literature. Therefore, a clear, functional and descriptive naming scheme is proposed in which a previously vague name -- Soft Material for Soft Actuators -- can remain clear and concise -- Phase-Change Elastomers for Artificial Muscles. By synthesizing the working principle, material, and application into a naming scheme, the searchability of soft robotics can be enhanced and applied to other fields. The field of thermo-active soft actuators spans multiple domains and requires added clarity. Thermo-active actuators have potential for a variety of applications spanning virtual reality haptics to assistive devices. This review offers a comprehensive guide to selecting the type of thermo-active actuator when one has an application in mind. Additionally, it discusses future directions and improvements that are necessary for implementation., Comment: 16 pages, 10 figures, accepted to Robotics Reports
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- 2023
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26. A density estimation perspective on learning from pairwise human preferences
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Dumoulin, Vincent, Johnson, Daniel D., Castro, Pablo Samuel, Larochelle, Hugo, and Dauphin, Yann
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language - Abstract
Learning from human feedback (LHF) -- and in particular learning from pairwise preferences -- has recently become a crucial ingredient in training large language models (LLMs), and has been the subject of much research. Most recent works frame it as a reinforcement learning problem, where a reward function is learned from pairwise preference data and the LLM is treated as a policy which is adapted to maximize the rewards, often under additional regularization constraints. We propose an alternative interpretation which centers on the generative process for pairwise preferences and treats LHF as a density estimation problem. We provide theoretical and empirical results showing that for a family of generative processes defined via preference behavior distribution equations, training a reward function on pairwise preferences effectively models an annotator's implicit preference distribution. Finally, we discuss and present findings on "annotator misspecification" -- failure cases where wrong modeling assumptions are made about annotator behavior, resulting in poorly-adapted models -- suggesting that approaches that learn from pairwise human preferences could have trouble learning from a population of annotators with diverse viewpoints.
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- 2023
27. Oaks enhance early life stage longleaf pine growth and density in a subtropical xeric savanna
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Magee, Lukas, Lapalikar, Sairandhri, Cayetano, Denver T., Machado, Siddarth, Pandit, Karun, Trentin, Bruna, Wood, Derek, Leite, Rodrigo V., Cosenza, Diogo N., Mintz, Jeffrey, Valle, Denis, Crandall, Raelene M., Lichstein, Jeremy W., Montero, Nicolle, Cherro, Caitlyn, Barreto, Ross, Bohlman, Stephanie, and Johnson, Daniel J.
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- 2024
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28. Abundance and Distribution of Blue Swimmer Crab in Response to Environmental Variation Across Two Contrasting Estuaries
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Hanamseth, Roshan, Schilling, Hayden T., Johnson, Daniel. D., Suthers, Iain M., and Taylor, Matthew D.
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- 2024
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29. Selection dictates the distance pattern of similarity in trees and soil fungi across forest ecosystems
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Hu, Yue-Hua, Johnson, Daniel J., Sun, Zhen-Hua, Gao, Lian-Ming, Wen, Han-Dong, Xu, Kun, Huang, Hua, Liu, Wei-Wei, Cao, Min, Song, Ze-Wei, and Kennedy, Peter G.
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- 2024
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30. An Op-Ed Writing Curriculum for Medical Students to Engage in Advocacy Through Public Writing
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Krishnamoorthi, V. Ram, Johnson, Daniel Y., Asay, Spencer, Beem, Alexandra, Vuppaladhadiam, Lahari, Keegan, Grace E., Zietowski, Maeson L., Chen, Samuel, Jain, Shikha, and Arora, Vineet M.
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- 2024
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31. US Medical-Legal Partnerships to Address Health-Harming Legal Needs: Closing the Health Injustice Gap
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Johnson, Daniel Y., Asay, Spencer, Keegan, Grace, Wu, Lisa, Zietowski, Maeson L., Zakrison, Tanya L., Muntz, Nathan, Pillai, Rhea, and Tung, Elizabeth L.
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- 2024
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32. Addressing Discontinuous Root-Finding for Subsequent Differentiability in Machine Learning, Inverse Problems, and Control
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Johnson, Daniel and Fedkiw, Ronald
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Computer Science - Machine Learning - Abstract
There are many physical processes that have inherent discontinuities in their mathematical formulations. This paper is motivated by the specific case of collisions between two rigid or deformable bodies and the intrinsic nature of that discontinuity. The impulse response to a collision is discontinuous with the lack of any response when no collision occurs, which causes difficulties for numerical approaches that require differentiability which are typical in machine learning, inverse problems, and control. We theoretically and numerically demonstrate that the derivative of the collision time with respect to the parameters becomes infinite as one approaches the barrier separating colliding from not colliding, and use lifting to complexify the solution space so that solutions on the other side of the barrier are directly attainable as precise values. Subsequently, we mollify the barrier posed by the unbounded derivatives, so that one can tunnel back and forth in a smooth and reliable fashion facilitating the use of standard numerical approaches. Moreover, we illustrate that standard approaches fail in numerous ways mostly due to a lack of understanding of the mathematical nature of the problem (e.g. typical backpropagation utilizes many rules of differentiation, but ignores L'Hopital's rule).
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- 2023
33. Software-based Automatic Differentiation is Flawed
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Johnson, Daniel, Maxfield, Trevor, Jin, Yongxu, and Fedkiw, Ronald
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Computer Science - Machine Learning - Abstract
Various software efforts embrace the idea that object oriented programming enables a convenient implementation of the chain rule, facilitating so-called automatic differentiation via backpropagation. Such frameworks have no mechanism for simplifying the expressions (obtained via the chain rule) before evaluating them. As we illustrate below, the resulting errors tend to be unbounded.
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- 2023
34. Mycorrhizal feedbacks influence global forest structure and diversity.
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Delavaux, Camille, LaManna, Joseph, Myers, Jonathan, Phillips, Richard, Aguilar, Salomón, Allen, David, Alonso, Alfonso, Anderson-Teixeira, Kristina, Baker, Matthew, Baltzer, Jennifer, Bissiengou, Pulchérie, Bonfim, Mariana, Bourg, Norman, Brockelman, Warren, Burslem, David, Chang, Li-Wan, Chen, Yang, Chiang, Jyh-Min, Chu, Chengjin, Clay, Keith, Cordell, Susan, Cortese, Mary, den Ouden, Jan, Dick, Christopher, Ediriweera, Sisira, Ellis, Erle, Feistner, Anna, Freestone, Amy, Giambelluca, Thomas, Giardina, Christian, He, Fangliang, Holík, Jan, Howe, Robert, Huaraca Huasca, Walter, Hubbell, Stephen, Inman, Faith, Jansen, Patrick, Johnson, Daniel, Kral, Kamil, Larson, Andrew, Litton, Creighton, Lutz, James, Malhi, Yadvinder, McGuire, Krista, McMahon, Sean, McShea, William, Memiaghe, Hervé, Nathalang, Anuttara, Norden, Natalia, Novotny, Vojtech, OBrien, Michael, Orwig, David, Ostertag, Rebecca, Parker, Geoffrey, Pérez, Rolando, Reynolds, Glen, Russo, Sabrina, Sack, Lawren, Šamonil, Pavel, Sun, I-Fang, Swanson, Mark, Thompson, Jill, Uriarte, Maria, Vandermeer, John, Wang, Xihua, Ware, Ian, Weiblen, George, Wolf, Amy, Wu, Shu-Hui, Zimmerman, Jess, Lauber, Thomas, Maynard, Daniel, Crowther, Thomas, Averill, Colin, and Gilbert, Gregory|Greg
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Mycorrhizae ,Feedback ,Symbiosis ,Plants ,Soil - Abstract
One mechanism proposed to explain high species diversity in tropical systems is strong negative conspecific density dependence (CDD), which reduces recruitment of juveniles in proximity to conspecific adult plants. Although evidence shows that plant-specific soil pathogens can drive negative CDD, trees also form key mutualisms with mycorrhizal fungi, which may counteract these effects. Across 43 large-scale forest plots worldwide, we tested whether ectomycorrhizal tree species exhibit weaker negative CDD than arbuscular mycorrhizal tree species. We further tested for conmycorrhizal density dependence (CMDD) to test for benefit from shared mutualists. We found that the strength of CDD varies systematically with mycorrhizal type, with ectomycorrhizal tree species exhibiting higher sapling densities with increasing adult densities than arbuscular mycorrhizal tree species. Moreover, we found evidence of positive CMDD for tree species of both mycorrhizal types. Collectively, these findings indicate that mycorrhizal interactions likely play a foundational role in global forest diversity patterns and structure.
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- 2023
35. To Tip or Not to Tip: A New Combination for Precision Medicine in Head and Neck Cancer.
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Lee, Rex H, Johnson, Daniel E, and Grandis, Jennifer R
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Biomedical and Clinical Sciences ,Oncology and Carcinogenesis ,Dental/Oral and Craniofacial Disease ,Cancer ,Rare Diseases ,Good Health and Well Being ,Humans ,Squamous Cell Carcinoma of Head and Neck ,Carcinoma ,Squamous Cell ,Precision Medicine ,Cell Line ,Tumor ,Head and Neck Neoplasms ,TOR Serine-Threonine Kinases ,Class I Phosphatidylinositol 3-Kinases ,Proto-Oncogene Proteins p21(ras) ,Oncology & Carcinogenesis ,Biochemistry and cell biology ,Oncology and carcinogenesis - Abstract
Meaningful advances in targeted therapy for head and neck squamous cell carcinoma (HNSCC) have been hampered by limited availability of robust preclinical models for translational research. Using an impressive array of in vitro and in vivo preclinical HNSCC models, Smith and colleagues demonstrated the efficacy of alpelisib and tipifarnib combination therapy through sustained mTOR inhibition in PIK3CA/HRAS-dysregulated HNSCC, including preliminary evidence of robust antitumor activity in a patient enrolled in a precision medicine trial. This study in this issue of Cancer Research illustrates the value of preclinical avatars for informing biomarker-driven clinical trials to advance precision medicine in HNSCC and other cancers. See related article by Smith et al., p. 3252.
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- 2023
36. Columnar Cell Thyroid Carcinoma: A Heterogeneous Entity Demonstrating Overlap Between Papillary Thyroid Carcinoma and Follicular Neoplasms
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Higgins, Kathleen E., Sadow, Peter M., Johnson, Daniel N., Wang, Peng, Wanjari, Pankhuri, and Cipriani, Nicole A.
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- 2024
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37. Latitudinal patterns in stabilizing density dependence of forest communities
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Hülsmann, Lisa, Chisholm, Ryan A., Comita, Liza, Visser, Marco D., de Souza Leite, Melina, Aguilar, Salomon, Anderson-Teixeira, Kristina J., Bourg, Norman A., Brockelman, Warren Y., Bunyavejchewin, Sarayudh, Castaño, Nicolas, Chang-Yang, Chia-Hao, Chuyong, George B., Clay, Keith, Davies, Stuart J., Duque, Alvaro, Ediriweera, Sisira, Ewango, Corneille, Gilbert, Gregory S., Holík, Jan, Howe, Robert W., Hubbell, Stephen P., Itoh, Akira, Johnson, Daniel J., Kenfack, David, Král, Kamil, Larson, Andrew J., Lutz, James A., Makana, Jean-Remy, Malhi, Yadvinder, McMahon, Sean M., McShea, William J., Mohamad, Mohizah, Nasardin, Musalmah, Nathalang, Anuttara, Norden, Natalia, Oliveira, Alexandre A., Parmigiani, Renan, Perez, Rolando, Phillips, Richard P., Pongpattananurak, Nantachai, Sun, I-Fang, Swanson, Mark E., Tan, Sylvester, Thomas, Duncan, Thompson, Jill, Uriarte, Maria, Wolf, Amy T., Yao, Tze Leong, Zimmerman, Jess K., Zuleta, Daniel, and Hartig, Florian
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- 2024
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38. Efficient Multi-stage Inference on Tabular Data
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Johnson, Daniel S and Markov, Igor L
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Computer Science - Machine Learning - Abstract
Many ML applications and products train on medium amounts of input data but get bottlenecked in real-time inference. When implementing ML systems, conventional wisdom favors segregating ML code into services queried by product code via Remote Procedure Call (RPC) APIs. This approach clarifies the overall software architecture and simplifies product code by abstracting away ML internals. However, the separation adds network latency and entails additional CPU overhead. Hence, we simplify inference algorithms and embed them into the product code to reduce network communication. For public datasets and a high-performance real-time platform that deals with tabular data, we show that over half of the inputs are often amenable to such optimization, while the remainder can be handled by the original model. By applying our optimization with AutoML to both training and inference, we reduce inference latency by 1.3x, CPU resources by 30%, and network communication between application front-end and ML back-end by about 50% for a commercial end-to-end ML platform that serves millions of real-time decisions per second.
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- 2023
39. R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents
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Johnson, Daniel D., Tarlow, Daniel, and Walder, Christian
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Computer Science - Machine Learning ,Computer Science - Software Engineering - Abstract
Large language models show impressive results at predicting structured text such as code, but also commonly introduce errors and hallucinations in their output. When used to assist software developers, these models may make mistakes that users must go back and fix, or worse, introduce subtle bugs that users may miss entirely. We propose Randomized Utility-driven Synthesis of Uncertain REgions (R-U-SURE), an approach for building uncertainty-aware suggestions based on a decision-theoretic model of goal-conditioned utility, using random samples from a generative model as a proxy for the unobserved possible intents of the end user. Our technique combines minimum-Bayes-risk decoding, dual decomposition, and decision diagrams in order to efficiently produce structured uncertainty summaries, given only sample access to an arbitrary generative model of code and an optional AST parser. We demonstrate R-U-SURE on three developer-assistance tasks, and show that it can be applied different user interaction patterns without retraining the model and leads to more accurate uncertainty estimates than token-probability baselines. We also release our implementation as an open-source library at https://github.com/google-research/r_u_sure., Comment: To appear at ICML 2023. 9 pages, 6 figures
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- 2023
40. Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions
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Johnson, Daniel D., Hanchi, Ayoub El, and Maddison, Chris J.
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Computer Science - Machine Learning - Abstract
Contrastive learning is a powerful framework for learning self-supervised representations that generalize well to downstream supervised tasks. We show that multiple existing contrastive learning methods can be reinterpreted as learning kernel functions that approximate a fixed positive-pair kernel. We then prove that a simple representation obtained by combining this kernel with PCA provably minimizes the worst-case approximation error of linear predictors, under a straightforward assumption that positive pairs have similar labels. Our analysis is based on a decomposition of the target function in terms of the eigenfunctions of a positive-pair Markov chain, and a surprising equivalence between these eigenfunctions and the output of Kernel PCA. We give generalization bounds for downstream linear prediction using our Kernel PCA representation, and show empirically on a set of synthetic tasks that applying Kernel PCA to contrastive learning models can indeed approximately recover the Markov chain eigenfunctions, although the accuracy depends on the kernel parameterization as well as on the augmentation strength., Comment: Published at ICLR 2023
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- 2022
41. Report of the Topical Group on Physics Beyond the Standard Model at Energy Frontier for Snowmass 2021
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Bose, Tulika, Boveia, Antonio, Doglioni, Caterina, Griso, Simone Pagan, Hirschauer, James, Lipeles, Elliot, Liu, Zhen, Shah, Nausheen R., Wang, Lian-Tao, Agashe, Kaustubh, Alimena, Juliette, Baum, Sebastian, Berkat, Mohamed, Black, Kevin, Gardner, Gwen, Gherghetta, Tony, Greaves, Josh, Haehn, Maxx, Harris, Phil C., Harris, Robert, Hogan, Julie, Jayawardana, Suneth, Kahn, Abraham, Kalinowski, Jan, Knapen, Simon, Lewis, Ian M., Narain, Meenakshi, Pachal, Katherine, Reece, Matthew, Reina, Laura, Robens, Tania, Tricoli, Alessandro, Wagner, Carlos E. M., Xu, Riley, Yu, Felix, Zarnecki, Filip, Aboubrahim, Amin, Albert, Andreas, Albrow, Michael, Altmannshofer, Wolfgang, Andonian, Gerard, Apresyan, Artur, Assamagan, Kétévi Adikle, Azzi, Patrizia, Baer, Howard, Baker, Michael J., Banerjee, Avik, Barger, Vernon, Batell, Brian, Bauer, Martin, Beauchesne, Hugues, Bein, Samuel, Belyaev, Alexander, Beniwal, Ankit, Berggren, Mikael, Bhattiprolu, Prudhvi N., Blinov, Nikita, Blondel, Alain, Brandt, Oleg, Cacciapaglia, Giacomo, Capdevilla, Rodolfo, Carena, Marcela, Cazzaniga, Cesare, Celiberto, Francesco Giovanni, Cesarotti, Cari, Chekanov, Sergei V., Cheng, Hsin-Chia, Chen, Thomas Y., Chen, Yuze, Chivukula, R. Sekhar, Citron, Matthew, Cline, James, Cohen, Tim, Collins, Jack H., Corrigan, Eric, Craig, Nathaniel, Craik, Daniel, Crivellin, Andreas, Curtin, David, Darmora, Smita, Das, Arindam, Dasu, Sridhara, de Cosa, Annapaola, Deandrea, Aldo, Delgado, Antonio, Demiragli, Zeynep, d'Enterria, David, Deppisch, Frank F., Dermisek, Radovan, Desai, Nishita, Deshpande, Abhay, de Vries, Jordy, Dickinson, Jennet, Dienes, Keith R., Di Petrillo, Karri Folan, Dolan, Matthew J., Dong, Peter, Draper, Patrick, Drewes, Marco, Dreyer, Etienne, Du, Peizhi, Eble, Florian, Ekhterachian, Majid, Endo, Motoi, Essig, Rouven, Farr, Jesse N., Fassi, Farida, Feng, Jonathan L., Ferretti, Gabriele, Filipetto, Daniele, Flacke, Thomas, Franceschini, Roberto, Franzosi, Diogo Buarque, Fujii, Keisuke, Fuks, Benjamin, Gadam, Sri Aditya, Gao, Boyu, Garcia-Bellido, Aran, Garcia, Isabel Garcia, Garzelli, Maria Vittoria, Gedney, Stephen, Genest, Marie-Hélène, Ghosh, Tathagata, Golkowski, Mark, di Cortona, Giovanni Grilli, Guler, Emine Gurpinar, Guler, Yalcin, Guo, C., Graf, Nate, Haisch, Ulrich, Hajer, Jan, Hamaguchi, Koichi, Han, Tao, Harris, Philip, Heinemeyer, Sven, Hill, Christopher S., Hiltbrand, Joshua, Holmes, Tova Ray, Homiller, Samuel, Hong, Sungwoo, Hopkins, Walter, Hsu, Shih-Chieh, Ilten, Phil, Islam, Wasikul, Iwamoto, Sho, Jeans, Daniel, Jeanty, Laura, Jia, Haoyi, Jindariani, Sergo, Johnson, Daniel, Kahlhoefer, Felix, Kahn, Yonatan, Karchin, Paul, Katsouleas, Thomas, Kawada, Shin-ichi, Kawamura, Junichiro, Kelso, Chris, Khoda, Elham E, Khoze, Valery, Kim, Doojin, Kitahara, Teppei, Klaric, Juraj, Klasen, Michael, Kong, Kyoungchul, Kotlarski, Wojciech, Kotwal, Ashutosh V., Kozaczuk, Jonathan, Kriske, Richard, Kulkarni, Suchita, Kumar, Jason, Kunkel, Manuel, Landsberg, Greg, Lane, Kenneth, Lange, Clemens, Lee, Lawrence, Liao, Jiajun, Lillard, Benjamin, Li, Lingfeng, Li, Shuailong, Li, Shu, List, Jenny, Li, Tong, Liu, Hongkai, Liu, Jia, Long, Jonathan D, Lunghi, Enrico, Lyu, Kun-Feng, Marfatia, Danny, Martinez, Dakotah, Martin, Stephen P., McGinnis, Navin, McGinty, Karrick, Mękała, Krzysztof, Meloni, Federico, Mikulenko, Oleksii, Huang, Ming, Mishra, Rashmish K., Mitra, Manimala, Mitsou, Vasiliki A., Moon, Chang-Seong, Moreno, Alexander, Moroi, Takeo, Mourou, Gerard, Mrowietz, Malte, Muggli, Patric, Nakajima, Jurina, Nath, Pran, Nelson, J., Neubert, Matthias, Nosler, Laura, de Vera, Maria Teresa Núñez Pardo, Okada, Nobuchika, Okada, Satomi, Okorokov, Vitalii A., Onel, Yasar, Ou, Tong, Ovchynnikov, Maksym, Padhan, Rojalin, Pani, Priscilla, Panizzi, Luca, Papaefstathiou, Andreas, Pedro, Kevin, Peña, Cristián, Piazza, Federica, Pinfold, James, Pinna, Deborah, Porod, Werner, Potter, Chris, Prim, Markus Tobias, Profumo, Stefano, Proudfoot, James, Rai, Mudit, Rajec, Filip, Ramos, Reese, Ramsey-Musolf, Michael J., Resta-Lopez, Javier, Reuter, Jürgen, Ringwald, Andreas, Rizzi, Chiara, Rizzo, Thomas G., Rossi, Giancarlo, Ruiz, Richard, Rygaard, L., Sahai, Aakash A., Salam, Shadman, Sandick, Pearl, Sathyan, Deepak, Scherb, Christiane, Schwaller, Pedro, Schwarze, Leonard, Scott, Pat, Sekmen, Sezen, Sengupta, Dibyashree, Sen, S., Sfyrla, Anna, Shackelford, Eric, Sharma, T., Sharma, Varun, Shelton, Jessie, Shepherd, William, Shin, Seodong, Simmons, Elizabeth H., Sloneker, Zoie, Sierra, Carlos Vázquez, Sjöstrand, Torbjörn, Snyder, Scott, Song, Huayang, Stark, Giordon, Stengel, Patrick, Stohr, Joachim, Stolarski, Daniel, Strassler, Matt, Strobbe, Nadja, Gonski, Julia, Suarez, Rebeca Gonzalez, Suehara, Taikan, Su, Shufang, Su, Wei, Syed, Raza M., Tait, Tim M. P., Tajima, Toshiki, Tang, Andy, Tata, Xerxes, Tchalokov, Teodor, Thamm, Andrea, Thomas, Brooks, Toro, Natalia, Tran, Nhan V., Truong, Loan, Tsai, Yu-Dai, Tuecke, Eva, Venkatasubramanian, Nikhilesh, Verhaaren, Chris B., Vuosalo, Carl, Wang, Xiao-Ping, Wang, Xing, Wang, Yikun, Wang, Zhen, Weber, Christian, White, Glen, White, Martin, Williams, Anthony G., Williams, Brady, Williams, Mike, Willocq, Stephane, Woodcock, Alex, Wu, Yongcheng, Xie, Ke-Pan, Xie, Keping, Xie, Si, Yeh, C. -H., Yonamine, Ryo, Yu, David, Yu, S. -S., Zaazoua, Mohamed, Żarnecki, Aleksander Filip, Zembaczynski, Kamil, Zhang, Danyi, Zhang, Jinlong, Zimmermann, Frank, and Zurita, Jose
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High Energy Physics - Phenomenology ,High Energy Physics - Experiment - Abstract
This is the Snowmass2021 Energy Frontier (EF) Beyond the Standard Model (BSM) report. It combines the EF topical group reports of EF08 (Model-specific explorations), EF09 (More general explorations), and EF10 (Dark Matter at Colliders). The report includes a general introduction to BSM motivations and the comparative prospects for proposed future experiments for a broad range of potential BSM models and signatures, including compositeness, SUSY, leptoquarks, more general new bosons and fermions, long-lived particles, dark matter, charged-lepton flavor violation, and anomaly detection., Comment: 108 pages + 38 pages references and appendix, 37 figures, Report of the Topical Group on Beyond the Standard Model Physics at Energy Frontier for Snowmass 2021. The first nine authors are the Conveners, with Contributions from the other authors
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- 2022
42. A Library for Representing Python Programs as Graphs for Machine Learning
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Bieber, David, Shi, Kensen, Maniatis, Petros, Sutton, Charles, Hellendoorn, Vincent, Johnson, Daniel, and Tarlow, Daniel
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Computer Science - Machine Learning ,Computer Science - Programming Languages ,Computer Science - Software Engineering - Abstract
Graph representations of programs are commonly a central element of machine learning for code research. We introduce an open source Python library python_graphs that applies static analysis to construct graph representations of Python programs suitable for training machine learning models. Our library admits the construction of control-flow graphs, data-flow graphs, and composite ``program graphs'' that combine control-flow, data-flow, syntactic, and lexical information about a program. We present the capabilities and limitations of the library, perform a case study applying the library to millions of competitive programming submissions, and showcase the library's utility for machine learning research., Comment: 21 pages, 14 figures
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- 2022
43. Transcription Factors and Cancer
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Shiah, Jamie V, Johnson, Daniel E, and Grandis, Jennifer R
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Biomedical and Clinical Sciences ,Oncology and Carcinogenesis ,Cancer ,Genetics ,Rare Diseases ,2.1 Biological and endogenous factors ,Aetiology ,Humans ,Transcription Factors ,Neoplasms ,Gene Expression Regulation ,Cell Transformation ,Neoplastic ,Signal Transduction ,therapeutics ,transcription factors ,Oncology & Carcinogenesis ,Oncology and carcinogenesis - Abstract
AbstractCancer is defined by the presence of uncontrollable cell growth, whereby improper proliferative signaling has overcome regulation by cellular mechanisms. Transcription factors are uniquely situated at the helm of signaling, merging extracellular stimuli with intracellular responses. Therefore, this class of proteins plays a pivotal role in coordinating the correct gene expression levels for maintaining normal cellular functions. Dysregulation of transcription factor activity unsurprisingly drives tumorigenesis and oncogenic transformation. Although this imparts considerable therapeutic potential to targeting transcription factors, their lack of enzymatic activity renders intervention challenging and has contributed to a sense that transcription factors are "undruggable." Yet, enduring efforts to elucidate strategies for targeting transcription factors as well as a deeper understanding of their interactions with binding partners have led to advancements that are emerging to counter this narrative. Here, we highlight some of these approaches, focusing primarily on therapeutics that have advanced to the clinic.
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- 2023
44. Rare antibody phage isolation and discrimination (RAPID) biopanning enables identification of high-affinity antibodies against challenging targets
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Chung, Dong hee, Kong, Sophie, Young, Nicholas J, Chuo, Shih-Wei, Shiah, Jamie V, Connelly, Emily J, Rohweder, Peter J, Born, Alexandra, Manglik, Aashish, Grandis, Jennifer R, Johnson, Daniel E, and Craik, Charles S
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Biochemistry and Cell Biology ,Biological Sciences ,Vaccine Related ,Immunization ,Prevention ,Biotechnology ,Inflammatory and immune system ,Peptide Library ,Bioprospecting ,Bacteriophages ,Antibodies ,Antigens ,Biological sciences ,Biomedical and clinical sciences - Abstract
In vitro biopanning platforms using synthetic phage display antibody libraries have enabled the identification of antibodies against antigens that were once thought to be beyond the scope of immunization. Applying these methods against challenging targets remains a critical challenge. Here, we present a new biopanning pipeline, RAPID (Rare Antibody Phage Isolation and Discrimination), for the identification of rare high-affinity antibodies against challenging targets. RAPID biopanning uses fluorescent labeled phage displayed fragment antigen-binding (Fab) antibody libraries for the isolation of high-affinity binders with fluorescent activated sorting. Subsequently, discriminatory hit screening is performed with a biolayer interferometry (BLI) method, BIAS (Biolayer Interferometry Antibody Screen), where candidate binders are ranked and prioritized according to their estimated kinetic off rates. Previously reported antibodies were used to develop the methodology, and the RAPID biopanning pipeline was applied to three challenging targets (CHIP, Gαq, and CS3D), enabling the identification of high-affinity antibodies.
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- 2023
45. The mutational profiles and corresponding therapeutic implications of PI3K mutations in cancer
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VanLandingham, Nathan K, Nazarenko, Andrew, Grandis, Jennifer R, and Johnson, Daniel E
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Biochemistry and Cell Biology ,Biological Sciences ,Cancer ,Aetiology ,2.1 Biological and endogenous factors ,Humans ,Phosphatidylinositol 3-Kinases ,Mutation ,Signal Transduction ,Neoplasms ,Phosphoinositide-3 Kinase Inhibitors ,Class I Phosphatidylinositol 3-Kinases ,PIK3CA ,Phosphoinositide 3-kinase alpha ,p110alpha ,Alpelisib - Abstract
Genetic alterations of the PIK3CA gene, encoding the p110α catalytic subunit of PI3Kα enzyme, are found in a broad spectrum of human cancers. Many cancer-associated PIK3CA mutations occur at 3 hotspot locations and are termed canonical mutations. Canonical mutations result in hyperactivation of PI3K and promote oncogenesis via the PI3K/AKT/mTOR and PI3K/COX-2/PGE2 signaling pathways. These mutations also may serve as predictive biomarkers of response to PI3K inhibitors, as well as NSAID therapy. A large number of non-canonical PIK3CA mutations have also been identified in human tumors, but their functional properties are poorly understood. Here we review the landscape of PIK3CA mutations in different cancers and efforts underway to define the functional properties of non-canonical PIK3CA mutations. In addition, we summarize what has been learned from clinical trials of PI3K inhibitors as well as current trials incorporating these molecular targeting agents.
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- 2023
46. Therapeutic implications of transcriptomics in head and neck cancer patient-derived xenografts
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Lee, Rex H, Roy, Ritu, Li, Hua, Hechmer, Aaron, Zhu, Tian Ran, Izgutdina, Adila, Olshen, Adam B, Johnson, Daniel E, and Grandis, Jennifer R
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Biological Sciences ,Biomedical and Clinical Sciences ,Bioinformatics and Computational Biology ,Genetics ,Oncology and Carcinogenesis ,Cancer ,Biotechnology ,Infectious Diseases ,Cervical Cancer ,Precision Medicine ,Sexually Transmitted Infections ,Rare Diseases ,Orphan Drug ,Dental/Oral and Craniofacial Disease ,Cancer Genomics ,Women's Health ,Human Genome ,Digestive Diseases ,Good Health and Well Being ,Humans ,Animals ,Mice ,Squamous Cell Carcinoma of Head and Neck ,Transcriptome ,Heterografts ,Cisplatin ,Papillomavirus Infections ,Head and Neck Neoplasms ,Human Papillomavirus Viruses ,Disease Models ,Animal ,General Science & Technology - Abstract
There are currently no clinical strategies utilizing tumor gene expression to inform therapeutic selection for patients with head and neck squamous cell carcinoma (HNSCC). One of the challenges in developing predictive biomarkers is the limited characterization of preclinical HNSCC models. Patient-derived xenografts (PDXs) are increasingly recognized as translationally relevant preclinical avatars for human tumors; however, the overall transcriptomic concordance of HNSCC PDXs with primary human HNSCC is understudied, especially in human papillomavirus-associated (HPV+) disease. Here, we characterized 64 HNSCC PDXs (16 HPV+ and 48 HPV-) at the transcriptomic level using RNA-sequencing. The range of human-specific reads per PDX varied from 64.6%-96.5%, with a comparison of the most differentially expressed genes before and after removal of mouse transcripts revealing no significant benefit to filtering out mouse mRNA reads in this cohort. We demonstrate that four previously established HNSCC molecular subtypes found in The Cancer Genome Atlas (TCGA) are also clearly recapitulated in HNSCC PDXs. Unsupervised hierarchical clustering yielded a striking natural division of HNSCC PDXs by HPV status, with C19orf57 (BRME1), a gene previously correlated with positive response to cisplatin in cervical cancer, among the most significantly differentially expressed genes between HPV+ and HPV- PDXs. In vivo experiments demonstrated a possible relationship between increased C19orf57 expression and superior anti-tumor responses of PDXs to cisplatin, which should be investigated further. These findings highlight the value of PDXs as models for HPV+ and HPV- HNSCC, providing a resource for future discovery of predictive biomarkers to guide treatment selection in HNSCC.
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- 2023
47. Flipping Tickborne Illnesses with Infographics
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Johnson, Daniel
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- 2023
48. STAT3 Activation as a Predictive Biomarker for Ruxolitinib Response in Head and Neck Cancer.
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Qureshy, Zoya, Li, Hua, Zeng, Yan, Rivera, Jose, Cheng, Ning, Peterson, Christopher N, Kim, Mi-Ok, Ryan, William R, Ha, Patrick K, Bauman, Julie E, Wang, Steven J, Long, Steven R, Johnson, Daniel E, and Grandis, Jennifer R
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Biomedical and Clinical Sciences ,Oncology and Carcinogenesis ,Cancer ,Dental/Oral and Craniofacial Disease ,Rare Diseases ,Orphan Drug ,Clinical Research ,Good Health and Well Being ,Humans ,Mice ,Animals ,Squamous Cell Carcinoma of Head and Neck ,Carcinoma ,Squamous Cell ,Xenograft Model Antitumor Assays ,Head and Neck Neoplasms ,STAT3 Transcription Factor ,Biomarkers ,Cell Line ,Tumor ,Oncology & Carcinogenesis ,Clinical sciences ,Oncology and carcinogenesis - Abstract
PurposeIncreased activity of STAT3 is associated with progression of head and neck squamous cell carcinoma (HNSCC). Upstream activators of STAT3, such as JAKs, represent potential targets for therapy of solid tumors, including HNSCC. In this study, we investigated the anticancer effects of ruxolitinib, a clinical JAK1/2 inhibitor, in HNSCC preclinical models, including patient-derived xenografts (PDX) from patients treated on a window-of-opportunity trial.Experimental designHNSCC cell lines were treated with ruxolitinib, and the impact on activated STAT3 levels, cell growth, and colony formation was assessed. PDXs were generated from patients with HNSCC who received a brief course of neoadjuvant ruxolitinib on a clinical trial. The impact of ruxolitinib on tumor growth and STAT3 activation was assessed.ResultsRuxolitinib inhibited STAT3 activation, cellular growth, and colony formation of HNSCC cell lines. Ruxolitinib treatment of mice bearing an HNSCC cell line-derived xenograft significantly inhibited tumor growth compared with vehicle-treated controls. The response of HNSCC PDXs derived from patients on the clinical trial mirrored the responses seen in the neoadjuvant setting. Baseline active STAT3 (pSTAT3) and total STAT3 levels were lower, and ruxolitinib inhibited STAT3 activation in a PDX from a patient whose disease was stable on ruxolitinib, compared with a PDX from a patient whose disease progressed on ruxolitinib and where ruxolitinib treatment had minimal impact on STAT3 activation.ConclusionsRuxolitinib exhibits antitumor effects in HNSCC preclinical models. Baseline pSTAT3 or total STAT3 levels in the tumor may serve as predictive biomarkers to identify patients most likely to respond to ruxolitinib.
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- 2022
49. Freire's Hope in Radically Changing Times: A Dialogue for Curriculum Integration from Science Education to Face the Climate Crisis
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Salinas, Iván, Fernández, M. Beatriz, Johnson, Daniel, and Bastías, Nataly
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This article advances a dialogue for understanding curriculum integration as a form of radical pedagogy, starting from science education in times of climate crisis. The paper weaves Paulo Freire's work about a radical form of emancipatory pedagogy, bell hooks's proposal to transgress boundaries in teaching, and the landscape of identities for science persons in order to embrace a radical pedagogy for facing the climate crisis: an anti-oppressive curriculum integration practice. The issue of climate change education is discussed in its challenges as we present some findings about the role of policy in incorporating climate change in education in Chile and the experience of a teacher, Nataly, coauthor in this work, integrating curriculum as an action-research project. We propose that an anti-oppressive curriculum integration emerges from converging two approaches: curriculum design intended for sustaining democratic societies and thematic investigations proposed for liberatory practices of the oppressed.
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- 2023
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50. The Road Ahead for CODEX-b
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Aielli, Giulio, Alimena, Juliette, Beacham, James, Ben-Haim, Eli, Borsato, Martino, Charles, Matthew John, Vidal, Xabier Cid, Coco, Victor, De Roeck, Albert, Dey, Biplab, Dumps, Raphael, Gligorov, Vladimir V., Suarez, Rebeca Gonzalez, Gorordo, Thomas, Henry, Louis, Ilten, Philip, Johnson, Daniel, Knapen, Simon, Dortz, Olivier Le, Soliño, Saul López, Mombächer, Titus, Nachman, Benjamin, Northacker, David T., Papucci, Michele, Pásztor, Gabriella, Pizzimento, Luca, Polci, Francesco, Robinson, Dean J., Schindler, Heinrich, Sokoloff, Michael D., Suresh, Aditya, Swallow, Paul, Vari, Riccardo, Veres, Gábor, Sierra, Carlos Vázquez, Watson, Nigel, Wilkinson, Michael K., Williams, Michael, and Fernández, Emilio Xosé Rodríguez
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High Energy Physics - Experiment ,High Energy Physics - Phenomenology - Abstract
In this Snowmass contribution we present a comprehensive status update on the progress and plans for the proposed CODEX-b detector, intended to search for long-lived particles beyond the Standard Model. We review the physics case for the proposal and present recent progress on optimization strategies for the detector and shielding design, as well as the development of new fast and full simulation frameworks. A summary of the technical design for a smaller demonstrator detector (CODEX-$\beta$) for the upcoming Run~3 of the LHC is also discussed, alongside the road towards realization of the full experiment at the High-Luminosity LHC., Comment: 13 pages, 13 figures, Snowmass contribution
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- 2022
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