119,699 results on '"McCormick, A."'
Search Results
2. An Evaluation of a Cooperative Extension Internship Program
- Author
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Lisa Ellis McCormick, Karen A. Vines, and Subrato Kumar Kuri
- Abstract
The purpose of this project was to evaluate the Virginia Cooperative Extension internship program from the perspective of student participants and their supervisors. Three focus groups were conducted with internship supervisors from the summer of 2019. Student survey data was used to identify concerns from the student perspective to inform the supervisor focus group questions. In addition to the questions, a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis was also used to collect qualitative data within the supervisor focus groups. Findings suggest areas of strength that can be used in marketing as well as opportunities for program improvement.
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- 2024
3. A Generative Framework for Predictive Modeling of Multiple Chronic Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph Neural Network
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Rico, Julian Carvajal, Alaeddini, Adel, Faruqui, Syed Hasib Akhter, Fisher-Hoch, Susan P, and Mccormick, Joseph B
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Computer Science - Machine Learning - Abstract
Predicting the emergence of multiple chronic conditions (MCC) is crucial for early intervention and personalized healthcare, as MCC significantly impacts patient outcomes and healthcare costs. Graph neural networks (GNNs) are effective methods for modeling complex graph data, such as those found in MCC. However, a significant challenge with GNNs is their reliance on an existing graph structure, which is not readily available for MCC. To address this challenge, we propose a novel generative framework for GNNs that constructs a representative underlying graph structure by utilizing the distribution of the data to enhance predictive analytics for MCC. Our framework employs a graph variational autoencoder (GVAE) to capture the complex relationships in patient data. This allows for a comprehensive understanding of individual health trajectories and facilitates the creation of diverse patient stochastic similarity graphs while preserving the original feature set. These variations of patient stochastic similarity graphs, generated from the GVAE decoder, are then processed by a GNN using a novel Laplacian regularization technique to refine the graph structure over time and improves the prediction accuracy of MCC. A contextual Bandit is designed to evaluate the stochastically generated graphs and identify the best-performing graph for the GNN model iteratively until model convergence. We validate the performance of the proposed contextual Bandit algorithm against $\varepsilon$-Greedy and multi-armed Bandit algorithms on a large cohort (n = 1,592) of patients with MCC. These advancements highlight the potential of the proposed approach to transform predictive healthcare analytics, enabling a more personalized and proactive approach to MCC management.
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- 2024
4. LIGO Detector Characterization in the first half of the fourth Observing run
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Soni, S., Berger, B. K., Davis, D., Renzo, F. Di., Effler, A., Ferreira, T. A., Glanzer, J., Goetz, E., González, G., Helmling-Cornell, A., Hughey, B., Huxford, R., Mannix, B., Mo, G., Nandi, D., Neunzert, A., Nichols, S., Pham, K., Renzini, A. I., Schofield, R. M. S., Stuver, A, Trevor, M., Álvarez-López, S., Beda, R., Berry, C. P. L., Bhuiyan, S., Bruntz, R., Christensen, N., Blagg, L., Chan, M., Charlton, P., Connolly, G., Dhatri, R., Ding, J., Garg, V., Holley-Bockelmann, K., Hourihane, S., Jani, K., Janssens, K., Jarov, S., Knee, A. M., Lattal, A., Lecoeuche, Y., Littenberg, T., Liyanage, A., Lott, B., Macas, R., Malakar, D., McGowan, K., McIver, J., Millhouse, M., Nuttall, L., Nykamp, D., Ota, I., Rawcliffe, C., Scully, B., Tasson, J., Tejera, A., Thiele, S., Udall, R., Winborn, C., Yarbrough, Z., Zhang, Z., Abbott, R., Abouelfettouh, I., Adhikari, R. X., Ananyeva, A., Appert, S., Arai, K., Aritomi, N., Aston, S. M., Ball, M., Ballmer, S. W., Barker, D., Barsotti, L., Betzwieser, J., Billingsley, G., Biscans, S., Bode, N., Bonilla, E., Bossilkov, V., Branch, A., Brooks, A. F., Brown, D. D., Bryant, J., Cahillane, C., Cao, H., Capote, E., Clara, F., Collins, J., Compton, C. M., Cottingham, R., Coyne, D. C., Crouch, R., Csizmazia, J., Cullen, T. J., Dartez, L. P., Demos, N., Dohmen, E., Driggers, J. C., Dwyer, S. E., Ejlli, A., Etzel, T., Evans, M., Feicht, J., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fulda, P., Fyffe, M., Ganapathy, D., Gateley, B., Giaime, J. A., Giardina, K. D., Goetz, R., Goodwin-Jones, A. W., Gras, S., Gray, C., Griffith, D., Grote, H., Guidry, T., Hall, E. D., Hanks, J., Hanson, J., Heintze, M. C., Holland, N. A., Hoyland, D., Huang, H. Y., Inoue, Y., James, A. L., Jennings, A., Jia, W., Karat, S., Karki, S., Kasprzack, M., Kawabe, K., Kijbunchoo, N., King, P. J., Kissel, J. S., Komori, K., Kontos, A., Kumar, Rahul, Kuns, K., Landry, M., Lantz, B., Laxen, M., Lee, K., Lesovsky, M., Llamas, F., Lormand, M., Loughlin, H. A., MacInnis, M., Makarem, C. N., Mansell, G. L., Martin, R. M., Mason, K., Matichard, F., Mavalvala, N., Maxwell, N., McCarrol, G., McCarthy, R., McClelland, D. E., McCormick, S., McCuller, L., McRae, T., Mera, F., Merilh, E. L., Meylahn, F., Mittleman, R., Moraru, D., Moreno, G., Mullavey, A., Nakano, M., Nelson, T. J. N., Notte, J., Oberling, J., O'Hanlon, T., Osthelder, C., Ottaway, D. J., Overmier, H., Parker, W., Pele, A., Pham, H., Pirello, M., Quetschke, V., Ramirez, K. E., Reyes, J., Richardson, J. W., Robinson, M., Rollins, J. G., Romel, C. L., Romie, J. H., Ross, M. P., Ryan, K., Sadecki, T., Sanchez, A., Sanchez, E. J., Sanchez, L. E., Savage, R. L., Schaetzl, D., Schiworski, M. G., Schnabel, R., Schwartz, E., Sellers, D., Shaffer, T., Short, R. W., Sigg, D., Slagmolen, B. J. J., Soike, C., Srivastava, V., Sun, L., Tanner, D. B., Thomas, M., Thomas, P., Thorne, K. A., Torrie, C. I., Traylor, G., Ubhi, A. S., Vajente, G., Vanosky, J., Vecchio, A., Veitch, P. J., Vibhute, A. M., von Reis, E. R. G., Warner, J., Weaver, B., Weiss, R., Whittle, C., Willke, B., Wipf, C. C., Xu, V. A., Yamamoto, H., Zhang, L., and Zucker, M. E.
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Astrophysics - Instrumentation and Methods for Astrophysics ,General Relativity and Quantum Cosmology - Abstract
Progress in gravitational-wave astronomy depends upon having sensitive detectors with good data quality. Since the end of the LIGO-Virgo-KAGRA third Observing run in March 2020, detector-characterization efforts have lead to increased sensitivity of the detectors, swifter validation of gravitational-wave candidates and improved tools used for data-quality products. In this article, we discuss these efforts in detail and their impact on our ability to detect and study gravitational-waves. These include the multiple instrumental investigations that led to reduction in transient noise, along with the work to improve software tools used to examine the detectors data-quality. We end with a brief discussion on the role and requirements of detector characterization as the sensitivity of our detectors further improves in the future Observing runs., Comment: 35 pages, 18 figures
- Published
- 2024
5. pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy
- Author
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Iyer, Kartheik G., Yunus, Mikaeel, O'Neill, Charles, Ye, Christine, Hyk, Alina, McCormick, Kiera, Ciuca, Ioana, Wu, John F., Accomazzi, Alberto, Astarita, Simone, Chakrabarty, Rishabh, Cranney, Jesse, Field, Anjalie, Ghosal, Tirthankar, Ginolfi, Michele, Huertas-Company, Marc, Jablonska, Maja, Kruk, Sandor, Liu, Huiling, Marchidan, Gabriel, Mistry, Rohit, Naiman, J. P., Peek, J. E. G., Polimera, Mugdha, Rodriguez, Sergio J., Schawinski, Kevin, Sharma, Sanjib, Smith, Michael J., Ting, Yuan-Sen, and Walmsley, Mike
- Subjects
Astrophysics - Instrumentation and Methods for Astrophysics ,Computer Science - Digital Libraries ,Computer Science - Information Retrieval - Abstract
The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We present Pathfinder, a machine learning framework designed to enable literature review and knowledge discovery in astronomy, focusing on semantic searching with natural language instead of syntactic searches with keywords. Utilizing state-of-the-art large language models (LLMs) and a corpus of 350,000 peer-reviewed papers from the Astrophysics Data System (ADS), Pathfinder offers an innovative approach to scientific inquiry and literature exploration. Our framework couples advanced retrieval techniques with LLM-based synthesis to search astronomical literature by semantic context as a complement to currently existing methods that use keywords or citation graphs. It addresses complexities of jargon, named entities, and temporal aspects through time-based and citation-based weighting schemes. We demonstrate the tool's versatility through case studies, showcasing its application in various research scenarios. The system's performance is evaluated using custom benchmarks, including single-paper and multi-paper tasks. Beyond literature review, Pathfinder offers unique capabilities for reformatting answers in ways that are accessible to various audiences (e.g. in a different language or as simplified text), visualizing research landscapes, and tracking the impact of observatories and methodologies. This tool represents a significant advancement in applying AI to astronomical research, aiding researchers at all career stages in navigating modern astronomy literature., Comment: 25 pages, 9 figures, submitted to AAS jorunals. Comments are welcome, and the tools mentioned are available online at https://pfdr.app
- Published
- 2024
6. Swift-BAT GUANO follow-up of gravitational-wave triggers in the third LIGO-Virgo-KAGRA observing run
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Raman, Gayathri, Ronchini, Samuele, Delaunay, James, Tohuvavohu, Aaron, Kennea, Jamie A., Parsotan, Tyler, Ambrosi, Elena, Bernardini, Maria Grazia, Campana, Sergio, Cusumano, Giancarlo, D'Ai, Antonino, D'Avanzo, Paolo, D'Elia, Valerio, De Pasquale, Massimiliano, Dichiara, Simone, Evans, Phil, Hartmann, Dieter, Kuin, Paul, Melandri, Andrea, O'Brien, Paul, Osborne, Julian P., Page, Kim, Palmer, David M., Sbarufatti, Boris, Tagliaferri, Gianpiero, Troja, Eleonora, Abac, A. G., Abbott, R., Abe, H., Abouelfettouh, I., Acernese, F., Ackley, K., Adamcewicz, C., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Bai, Y., Baier, J. G., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Bogaert, G., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boumerdassi, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, C., Chan, J. C. L., Chan, K. H. M., Chan, M., Chan, W. L., Chandra, K., Chang, R. -J., Chanial, P., Chao, S., Chapman-Bird, C., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, K. H., Chen, X., Chen, Yi-Ru, Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Chia, H. Y., Chiadini, F., Chiang, C., Chiarini, G., Chiba, A., Chiba, R., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Clara, F., Clark, J. A., Clarke, T. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. -F., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Conti, L., Cooper, S. J., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Cousins, B., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Croquette, M., Crouch, R., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Davari, N., Dave, I., Davenport, A., Davier, M., Davies, T. F., Davis, D., Davis, L., Davis, M. C., Daw, E. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., DePergola, N., De Pietri, R., De Rosa, R., De Rossi, C., De Simone, R., Dhani, A., Dhurandhar, S., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, F., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Donahue, L., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Drori, Y., Ducoin, J. -G., Dunn, L., Dupletsa, U., D'Urso, D., Duval, H., Duverne, P. -A., Dwyer, S. E., Eassa, C., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Einsle, H., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Emma, M., Engelby, E., Engl, A. J., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fairhurst, S., Fan, P. C., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Fenyvesi, E., Ferguson, D. L., Ferrante, I., Ferreira, T. A., Fidecaro, F., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. S., Floden, E., Foley, E. M., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franceschetti, K., Franchini, N., Frasca, S., Frasconi, F., Mascioli, A. Frattale, Frei, Z., Freise, A., Freitas, O., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fuentes-Garcia, M., Fujii, S., Fukunaga, I., Fulda, P., Fyffe, M., Gabella, W. E., Gadre, B., Gair, J. R., Galaudage, S., Gallardo, S., Gallego, B., Gamba, R., Gamboa, A., Ganapathy, D., Ganguly, A., Gaonkar, S. G., Garaventa, B., Garcia-Bellido, J., García-Núñez, C., García-Quirós, C., Gardner, J. W., Gardner, K. A., Gargiulo, J., Garron, A., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., George, J., George, R., Gerberding, O., Gergely, L., Ghadiri, N., Ghosh, Archisman, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Suprovo, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gibson, D. T., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Gleckl, A. E., Glotin, F., Godfrey, J., Godwin, P., Goebbels, N. L., Goetz, E., Golomb, J., Lopez, S. Gomez, Goncharov, B., González, G., Goodarzi, P., Goodwin-Jones, A. W., Gosselin, M., Göttel, A. S., Gouaty, R., Gould, D. W., Goyal, S., Grace, B., Grado, A., Graham, V., Granados, A. E., Granata, M., Granata, V., Argianas, L. Granda, Gras, S., Grassia, P., Gray, C., Gray, R., Greco, G., Green, A. C., Green, S. M., Green, S. R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimaud, C., Grote, H., Gruson, A. S., Guerra, D., Guetta, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H., Guo, W., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, Ish, Gupta, N. C., Gupta, P., Gupta, S. K., Gupta, T., Gupte, N., Gurav, R., Gurs, J., Gutierrez, N., Guzman, F., Haba, D., Haberland, M., Haegel, L., Hain, G., Haino, S., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hanselman, A. G., Hansen, H., Hanson, J., Harada, R., Harder, T., Haris, K., Harmark, T., Harms, J., Harry, G. M., Harry, I. W., Haskell, B., Haster, C. -J., Hathaway, J. S., Haughian, K., Hayakawa, H., Hayama, K., Healy, J., Heffernan, A., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Hendry, M., Heng, I. S., Hennes, E., Hennig, J. -S., Hennig, M., Henshaw, C., Hernandez, A., Hertog, T., Heurs, M., Hewitt, A. L., Higginbotham, S., Hild, S., Hill, P., Hill, S., Himemoto, Y., Hines, A. S., Hirata, N., Hirose, C., Ho, J., Hoang, S., Hochheim, S., Hofman, D., Holland, N. A., Holley-Bockelmann, K., Hollows, I. J., Holmes, Z. J., Holz, D. E., Hong, C., Hornung, J., Hoshino, S., Hough, J., Hourihane, S., Howell, E. J., Hoy, C. G., Hoyland, D., Hrishikesh, C. A., Hsieh, H. -F., Hsiung, C., Hsu, H. C., Hsu, S. -C., Hsu, W. -F., Hu, P., Hu, Q., Huang, H. Y., Huang, Y. -J., Huang, Y., Huang, Y. T., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Hur, R., Husa, S., Huxford, R., Huynh-Dinh, T., Iakovlev, A., Iandolo, G. A., Iess, A., Inayoshi, K., Inoue, Y., Iorio, G., Irwin, J., Isi, M., Ismail, M. A., Itoh, Y., Iwaya, M., Iyer, B. R., JaberianHamedan, V., Jacquet, P. -E., Jadhav, S. J., Jadhav, S. P., Jain, T., James, A. L., James, P. A., Jamshidi, R., Jan, A. Z., Jani, K., Janiurek, L., Janquart, J., Janssens, K., Janthalur, N. N., Jaraba, S., Jaranowski, P., Jasal, P., Jaume, R., Javed, W., Jennings, A., Jia, W., Jiang, J., Jin, H. -B., Johansmeyer, K., Johns, G. R., Johnson, N. A., Johnston, R., Johny, N., Jones, D. H., Jones, D. I., Jones, R., Jose, S., Joshi, P., Ju, L., Jung, K., Junker, J., Juste, V., Kajita, T., Kalaghatgi, C., Kalogera, V., Kamiizumi, M., Kanda, N., Kandhasamy, S., Kang, G., Kanner, J. B., Kapadia, S. J., Kapasi, D. P., Karat, S., Karathanasis, C., Karki, S., Kashyap, R., Kasprzack, M., Kastaun, W., Kato, J., Kato, T., Katsanevas, S., Katsavounidis, E., Katzman, W., Kaur, T., Kaushik, R., Kawabe, K., Keitel, D., Kelley-Derzon, J., Kennington, J., Kesharwani, R., Key, J. S., Khadka, S., Khalili, F. Y., Khan, F., Khan, I., Khanam, T., Khazanov, E. 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C., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhong, S., Zhou, R., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
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Astrophysics - High Energy Astrophysical Phenomena ,General Relativity and Quantum Cosmology - Abstract
We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables
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- 2024
7. Cartan semigroups and twisted groupoid C*-algebras
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Bice, Tristan, Clark, Lisa Orloff, Lin, Ying-Fen, and McCormick, Kathryn
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Mathematics - Operator Algebras ,46L05, 22A22 - Abstract
We prove that twisted groupoid C*-algebras are characterised, up to isomorphism, by having Cartan semigroups, a natural generalisation of normaliser semigroups of Cartan subalgebras. This extends the classic Kumjian-Renault theory to general twisted \'etale groupoid C*-algebras, even non-reduced C*-algebras of non-effective groupoids., Comment: 39 pages
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- 2024
8. Northern Ireland public finances and the general election
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McCormick, Andrew
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- 2024
9. Travel time to cataract surgical services in Kenya, Malawi and Rwanda: demonstrating a standardised indicator of physical access to cataract surgery.
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McCormick, Ian, Nesemann, John, Zhao, Jinfeng, Mdala, Shaffi, Kitema, Gatera, Mwangi, Nyawira, Gichangi, Michael, Tang, Kevin, Burton, Matthew, and Ramke, Jacqueline
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Humans ,Cataract Extraction ,Rwanda ,Kenya ,Health Services Accessibility ,Malawi ,Middle Aged ,Travel ,Time Factors ,Aged ,Female ,Cataract ,Male - Abstract
BACKGROUND: Travel time can be used to assess health services accessibility by reflecting the proximity of services to the people they serve. We aimed to demonstrate an indicator of physical access to cataract surgery and identify subnational locations where people were more at risk of not accessing cataract surgery. METHODS: We used an open-access inventory of public health facilities plus key informants in Kenya, Malawi and Rwanda to compile a geocoded inventory of cataract facilities. For each country, gridded estimates of the population aged ≥ 50 years and a travel-time friction surface were combined and a least-cost-path algorithm applied to estimate the shortest travel time between each grid and the nearest cataract facility. We categorised continuous travel time by 1-, 2- and 3 h thresholds and calculated the proportion of the population in each category. RESULTS: At the national level, the proportion of the population aged ≥ 50 years within 2 h travel time to permanent cataract surgical services was 97.2% in Rwanda (n = 10 facilities), 93.5% in Kenya (n = 74 facilities) and 92.0% in Malawi (n = 6 facilities); this reduced to 77.5%, 84.1% and 52.4% within 1 h, respectively. The least densely populated subnational regions had the poorest access to cataract facilities in Malawi (0.0%) and Kenya (1.9%). CONCLUSION: We demonstrated an indicator of access that reflects the distribution of the population at risk of age-related cataract and identifies regions that could benefit from more accessible services. This indicator provides additional demand-side context for eye health planning and supports WHOs goal of advancing integrated people-centred eye care.
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- 2024
10. Tree species explain only half of explained spatial variability in plant water sensitivity
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Konings, Alexandra G, Rao, Krishna, McCormick, Erica L, Trugman, Anna T, Williams, A Park, Diffenbaugh, Noah S, Yebra, Marta, and Zhao, Meng
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Plant Biology ,Biological Sciences ,Ecology ,Environmental Sciences ,Water ,Trees ,United States ,Plant Transpiration ,Forests ,Species Specificity ,inter-specific variability ,intra-specific variability ,live fuel moisture content ,plant hydraulic traits ,plant-water interactions ,water stress ,inter‐specific variability ,intra‐specific variability ,plant‐water interactions ,Biological sciences ,Earth sciences ,Environmental sciences - Abstract
Spatiotemporal patterns of plant water uptake, loss, and storage exert a first-order control on photosynthesis and evapotranspiration. Many studies of plant responses to water stress have focused on differences between species because of their different stomatal closure, xylem conductance, and root traits. However, several other ecohydrological factors are also relevant, including soil hydraulics, topographically driven redistribution of water, plant adaptation to local climatic variations, and changes in vegetation density. Here, we seek to understand the relative importance of the dominant species for regional-scale variations in woody plant responses to water stress. We map plant water sensitivity (PWS) based on the response of remotely sensed live fuel moisture content to variations in hydrometeorology using an auto-regressive model. Live fuel moisture content dynamics are informative of PWS because they directly reflect vegetation water content and therefore patterns of plant water uptake and evapotranspiration. The PWS is studied using 21,455 wooded locations containing U.S. Forest Service Forest Inventory and Analysis plots across the western United States, where species cover is known and where a single species is locally dominant. Using a species-specific mean PWS value explains 23% of observed PWS variability. By contrast, a random forest driven by mean vegetation density, mean climate, soil properties, and topographic descriptors explains 43% of observed PWS variability. Thus, the dominant species explains only 53% (23% compared to 43%) of explainable variations in PWS. Mean climate and mean NDVI also exert significant influence on PWS. Our results suggest that studies of differences between species should explicitly consider the environments (climate, soil, topography) in which observations for each species are made, and whether those environments are representative of the entire species range.
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- 2024
11. What's the Weight? Estimating Controlled Outcome Differences in Complex Surveys for Health Disparities Research
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Salerno, Stephen, Roberts, Emily K., Needham, Belinda L., McCormick, Tyler H., Mukherjee, Bhramar, and Shi, Xu
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Statistics - Methodology - Abstract
A basic descriptive question in statistics often asks whether there are differences in mean outcomes between groups based on levels of a discrete covariate (e.g., racial disparities in health outcomes). However, when this categorical covariate of interest is correlated with other factors related to the outcome, direct comparisons may lead to biased estimates and invalid inferential conclusions without appropriate adjustment. Propensity score methods are broadly employed with observational data as a tool to achieve covariate balance, but how to implement them in complex surveys is less studied - in particular, when the survey weights depend on the group variable under comparison. In this work, we focus on a specific example when sample selection depends on race. We propose identification formulas to properly estimate the average controlled difference (ACD) in outcomes between Black and White individuals, with appropriate weighting for covariate imbalance across the two racial groups and generalizability. Via extensive simulation, we show that our proposed methods outperform traditional analytic approaches in terms of bias, mean squared error, and coverage. We are motivated by the interplay between race and social determinants of health when estimating racial differences in telomere length using data from the National Health and Nutrition Examination Survey. We build a propensity for race to properly adjust for other social determinants while characterizing the controlled effect of race on telomere length. We find that evidence of racial differences in telomere length between Black and White individuals attenuates after accounting for confounding by socioeconomic factors and after utilizing appropriate propensity score and survey weighting techniques. Software to implement these methods can be found in the R package svycdiff at https://github.com/salernos/svycdiff.
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- 2024
12. Design, fabrication and testing of Al/p-Si Schottky and pn junctions for radiation studies
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Villani, E. Giulio, Zhang, Dengfeng, Malik, Adnan, Vickey, Trevor, Chen, Yebo, Kurth, Matthew G., Liu, Peilian, Zhu, Hongbo, Koffas, Thomas, Klein, Christoph Thomas, Vandusen, Robert, Aiton, Rodney, Mccormick, Angela, and Tarr, Garry
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Physics - Instrumentation and Detectors ,High Energy Physics - Experiment - Abstract
Strip and pixels sensors, fabricated on high resistivity silicon substrate, normally of p-type, are used in detectors for High Energy Physics (HEP) typically in a hybrid detector assembly. Furthermore, and owing to their inherent advantages over hybrid sensors, Monolithic Active Pixel Sensors (MAPS) fabricated in CMOS technology have been increasingly implemented in HEP experiments. In all cases, their use in higher radiation areas (HL-LHC and beyond) will require options to improve their radiation hardness and time resolution. These aspects demand a deep understanding of their radiation damage and reliable models to predict their behaviours at high fluences. As a first step, we fabricated several Schottky and n-on-p diodes, to allow a comparison of results and provide a backup solution for test devices, on 6 or 4-inch p-type silicon wafers with 50 {\mu}m epitaxial thickness and of doping concentration as they are normally used in HEP detectors and CMOS MAPS devices. In this paper, details of the design and fabrication process, along with test results of the fabricated devices before irradiation, will be provided. Additional test results on irradiated devices will be provided in subsequent publications.
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- 2024
13. Model-Based Inference and Experimental Design for Interference Using Partial Network Data
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Reeves, Steven Wilkins, Lubold, Shane, Chandrasekhar, Arun G., and McCormick, Tyler H.
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Statistics - Methodology ,Computer Science - Social and Information Networks ,Economics - Econometrics ,Statistics - Machine Learning ,Statistics - Other Statistics - Abstract
The stable unit treatment value assumption states that the outcome of an individual is not affected by the treatment statuses of others, however in many real world applications, treatments can have an effect on many others beyond the immediately treated. Interference can generically be thought of as mediated through some network structure. In many empirically relevant situations however, complete network data (required to adjust for these spillover effects) are too costly or logistically infeasible to collect. Partially or indirectly observed network data (e.g., subsamples, aggregated relational data (ARD), egocentric sampling, or respondent-driven sampling) reduce the logistical and financial burden of collecting network data, but the statistical properties of treatment effect adjustments from these design strategies are only beginning to be explored. In this paper, we present a framework for the estimation and inference of treatment effect adjustments using partial network data through the lens of structural causal models. We also illustrate procedures to assign treatments using only partial network data, with the goal of either minimizing estimator variance or optimally seeding. We derive single network asymptotic results applicable to a variety of choices for an underlying graph model. We validate our approach using simulated experiments on observed graphs with applications to information diffusion in India and Malawi.
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- 2024
14. V-static metrics and the volume-renormalised mass
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McCormick, Stephen
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Mathematics - Differential Geometry ,General Relativity and Quantum Cosmology ,53C21, 53C25, 83C99 - Abstract
V-static metrics generalise the notion of static metrics, and stem from the work of Miao and Tam [arXiv:0807.2693], and Corvino, Eichmair and Miao [arXiv:1211.6168] on critical points of the volume functional over the space of compact manifolds with constant scalar curvature. In this article we show that these V-static metrics arise naturally in the context of asymptotically hyperbolic manifolds as critical points of the volume-renormalised mass, recently introduced by Dahl, Kr\"oncke and the author [arXiv:2307.06196]. In particular, we show that critical points of the volume-renormalised mass over the space of constant scalar curvature asymptotically hyperbolic manifolds without boundary, or satisfying appropriate boundary conditions, are exactly V-static metrics. This is directly analogous to the relationship between critical points of the ADM mass and static metrics for asymptotically flat manifolds., Comment: 20 pages. Comments encouraged :)
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- 2024
15. Optical and Electrical Properties of Diamond-like-Carbon Coatings Prepared by Electron Cyclotron Resonance Ion Beam Deposition Process
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Wiseman, Callum, Yaala, Marwa Ben, Gier, Chalisa, Marot, Laurent, McCormick, Christopher, Clark, Caspar, Rowan, Sheila, and Reid, Stuart
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Physics - Applied Physics ,Condensed Matter - Materials Science - Abstract
Diamond-like carbon thin films have emerged as durable, chemically stable optical coatings for many optical and optoelectronics applications due to their hardness, chemical inertness, and optical transparency. This paper presents a novel high-energy electron cyclotron resonance ion beam sputter deposition technique to fabricate pure diamond-like carbon coatings at room temperature. The chemical composition of the deposited coatings including ratios of sp2/sp3 bonding in the thin films were determined by X-ray photoelectron spectroscopy. Results indicate that the sp3 percentage ranges from 45% - 85%. The transmission and reflectance spectra of the coatings were measured from UV to IR ({\lambda}= 185 to 2500 nm) by utilizing a spectrophotometer. The measured spectra were analysed by the Tauc method to determine the optical band gap and Urbach energy and an optical fitting software, which utilizes the model modified by OJL, to extract the refractive index and extinction coefficient. By varying the ion energy, the optical properties were found to be n = 2.30 - 2.51, band gap energy = 0.4 - 0.68 eV, and the Urbach energy = 0.33 - 0.49 eV. This study provides a flexible method for tuning the structural, optical, and electronic properties of diamond-like carbon coatings by controlling the ion energy during deposition.
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- 2024
16. Ion Energy Tuning for Enhanced sp3 Carbon Fraction in Electron Cyclotron Resonance Ion Beam Deposited Diamond-Like Carbon Coatings: a Computational and Experimental Approach
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Wiseman, Callum, Gier, Marwa Ben Yaala Chalisa, Marot, Laurent, McCormick, Christopher, Rowan, Sheila, and Reid, Stuart
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Condensed Matter - Materials Science ,Physics - Plasma Physics - Abstract
A novel high-energy electron cyclotron resonance (ECR) ion beam deposition (IBD) technique was used to fabricate DLC films at different ion beam energies. The ratios of sp2/sp3 bonding in the DLC coatings were determined by Raman spectroscopy and XPS, with the confirmation of being hydrogen-free due to the lack of photoluminescence (PL) background in the Raman spectra. The results indicate that the sp3 percentage ranges from 45% - 85% for the ECR-IBD fabricated DLC films in this study. Monte-Carlo based SRIM simulation was used to extract the energy and angular distribution of the sputtered particles from the carbon target and correlate it to the highest sp3 fraction in the manufactured ECR-IBD DLCs. This study demonstrates a method of depositing DLC thin films under ambient conditions (room temperature with no post-annealing or additional bias voltage applied) which produces high-sp3 coatings (higher than those traditionally reported for other sputtering methods) suitable for applications where high quality DLC coatings are required.
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- 2024
17. Designing an Evaluation Framework for Large Language Models in Astronomy Research
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Wu, John F., Hyk, Alina, McCormick, Kiera, Ye, Christine, Astarita, Simone, Baral, Elina, Ciuca, Jo, Cranney, Jesse, Field, Anjalie, Iyer, Kartheik, Koehn, Philipp, Kotler, Jenn, Kruk, Sandor, Ntampaka, Michelle, O'Neill, Charles, Peek, Joshua E. G., Sharma, Sanjib, and Yunus, Mikaeel
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Astrophysics - Instrumentation and Methods for Astrophysics ,Computer Science - Artificial Intelligence ,Computer Science - Human-Computer Interaction ,Computer Science - Information Retrieval - Abstract
Large Language Models (LLMs) are shifting how scientific research is done. It is imperative to understand how researchers interact with these models and how scientific sub-communities like astronomy might benefit from them. However, there is currently no standard for evaluating the use of LLMs in astronomy. Therefore, we present the experimental design for an evaluation study on how astronomy researchers interact with LLMs. We deploy a Slack chatbot that can answer queries from users via Retrieval-Augmented Generation (RAG); these responses are grounded in astronomy papers from arXiv. We record and anonymize user questions and chatbot answers, user upvotes and downvotes to LLM responses, user feedback to the LLM, and retrieved documents and similarity scores with the query. Our data collection method will enable future dynamic evaluations of LLM tools for astronomy., Comment: 7 pages, 3 figures. Code available at https://github.com/jsalt2024-evaluating-llms-for-astronomy/astro-arxiv-bot
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- 2024
18. Data-adaptive exposure thresholds for the Horvitz-Thompson estimator of the Average Treatment Effect in experiments with network interference
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Thiyageswaran, Vydhourie, McCormick, Tyler, and Brennan, Jennifer
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Statistics - Methodology - Abstract
Randomized controlled trials often suffer from interference, a violation of the Stable Unit Treatment Values Assumption (SUTVA) in which a unit's treatment assignment affects the outcomes of its neighbors. This interference causes bias in naive estimators of the average treatment effect (ATE). A popular method to achieve unbiasedness is to pair the Horvitz-Thompson estimator of the ATE with a known exposure mapping: a function that identifies which units in a given randomization are not subject to interference. For example, an exposure mapping can specify that any unit with at least $h$-fraction of its neighbors having the same treatment status does not experience interference. However, this threshold $h$ is difficult to elicit from domain experts, and a misspecified threshold can induce bias. In this work, we propose a data-adaptive method to select the "$h$"-fraction threshold that minimizes the mean squared error of the Hortvitz-Thompson estimator. Our method estimates the bias and variance of the Horvitz-Thompson estimator under different thresholds using a linear dose-response model of the potential outcomes. We present simulations illustrating that our method improves upon non-adaptive choices of the threshold. We further illustrate the performance of our estimator by running experiments on a publicly-available Amazon product similarity graph. Furthermore, we demonstrate that our method is robust to deviations from the linear potential outcomes model.
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- 2024
19. Some models are useful, but for how long?: A decision theoretic approach to choosing when to refit large-scale prediction models
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Hoffman, Kentaro, Salerno, Stephen, Leek, Jeff, and McCormick, Tyler
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Statistics - Methodology ,Economics - Econometrics - Abstract
Large-scale prediction models (typically using tools from artificial intelligence, AI, or machine learning, ML) are increasingly ubiquitous across a variety of industries and scientific domains. Such methods are often paired with detailed data from sources such as electronic health records, wearable sensors, and omics data (high-throughput technology used to understand biology). Despite their utility, implementing AI and ML tools at the scale necessary to work with this data introduces two major challenges. First, it can cost tens of thousands of dollars to train a modern AI/ML model at scale. Second, once the model is trained, its predictions may become less relevant as patient and provider behavior change, and predictions made for one geographical area may be less accurate for another. These two challenges raise a fundamental question: how often should you refit the AI/ML model to optimally trade-off between cost and relevance? Our work provides a framework for making decisions about when to {\it refit} AI/ML models when the goal is to maintain valid statistical inference (e.g. estimating a treatment effect in a clinical trial). Drawing on portfolio optimization theory, we treat the decision of {\it recalibrating} versus {\it refitting} the model as a choice between ''investing'' in one of two ''assets.'' One asset, recalibrating the model based on another model, is quick and relatively inexpensive but bears uncertainty from sampling and the possibility that the other model is not relevant to current circumstances. The other asset, {\it refitting} the model, is costly but removes the irrelevance concern (though not the risk of sampling error). We explore the balancing act between these two potential investments in this paper.
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- 2024
20. Squeezing the quantum noise of a gravitational-wavedetector below the standard quantum limit
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Jia, Wenxuan, Xu, Victoria, Kuns, Kevin, Nakano, Masayuki, Barsotti, Lisa, Evans, Matthew, Mavalvala, Nergis, Abbott, Rich, Abouelfettouh, Ibrahim, Adhikari, Rana, Ananyeva, Alena, Appert, Stephen, Arai, Koji, Aritomi, Naoki, Aston, Stuart, Ball, Matthew, Ballmer, Stefan, Barker, David, Berger, Beverly, Betzwieser, Joseph, Bhattacharjee, Dripta, Billingsley, Garilynn, Bode, Nina, Bonilla, Edgard, Bossilkov, Vladimir, Branch, Adam, Brooks, Aidan, Brown, Daniel, Bryant, John, Cahillane, Craig, Cao, Huy-tuong, Capote, Elenna, Chen, Yanbei, Clara, Filiberto, Collins, Josh, Compton, Camilla, Cottingham, Robert, Coyne, Dennis, Crouch, Ryan, Csizmazia, Janos, Cullen, Torrey, Dartez, Louis, Demos, Nicholas, Dohmen, Ezekiel, Driggers, Jenne, Dwyer, Sheila, Effler, Anamaria, Ejlli, Aldo, Etzel, Todd, Feicht, Jon, Frey, Raymond, Frischhertz, William, Fritschel, Peter, Frolov, Valery, Fulda, Paul, Fyffe, Michael, Ganapathy, Dhruva, Gateley, Bubba, Giaime, Joe, Giardina, Dwayne, Glanzer, Jane, Goetz, Evan, Jones, Aaron, Gras, Slawomir, Gray, Corey, Griffith, Don, Grote, Hartmut, Guidry, Tyler, Hall, Evan, Hanks, Jonathan, Hanson, Joe, Heintze, Matthew, Helmling-cornell, Adrian, Huang, Hsiang-yu, Inoue, Yuki, James, Alasdair, Jennings, Austin, Karat, Srinath, Kasprzack, Marie, Kawabe, Keita, Kijbunchoo, Nutsinee, Kissel, Jeffrey, Kontos, Antonios, Kumar, Rahul, Landry, Michael, Lantz, Brian, Laxen, Michael, Lee, Kyung-ha, Lesovsky, Madeline, Llamas, Francisco, Lormand, Marc, Loughlin, Hudsonalexander, Macas, Ronaldas, Macinnis, Myron, Makarem, Camille, Mannix, Benjaminrobert, Mansell, Georgia, Martin, Rodica, Maxwell, Nyath, Mccarrol, Garrett, Mccarthy, Richard, Mcclelland, David, Mccormick, Scott, Mcculler, Lee, Mcrae, Terry, Mera, Fernando, Merilh, Edmond, Meylahn, Fabian, Mittleman, Richard, Moraru, Dan, Moreno, Gerardo, Mould, Matthew, Mullavey, Adam, Nelson, Timothy, Neunzert, Ansel, Oberling, Jason, Ohanlon, Timothy, Osthelder, Charles, Ottaway, David, Overmier, Harry, Parker, William, Pele, Arnaud, Pham, Huyen, Pirello, Marc, Quetschke, Volker, Ramirez, Karla, Reyes, Jonathan, Richardson, Jonathan, Robinson, Mitchell, Rollins, Jameson, Romie, Janeen, Ross, Michael, Sadecki, Travis, Sanchez, Anthony, Sanchez, Eduardo, Sanchez, Luis, Savage, Richard, Schaetzl, Dean, Schiworski, Mitchell, Schnabel, Roman, Schofield, Robert, Schwartz, Eyal, Sellers, Danny, Shaffer, Thomas, Short, Ryan, Sigg, Daniel, Slagmolen, Bram, Soni, Siddharth, Sun, Ling, Tanner, David, Thomas, Michael, Thomas, Patrick, Thorne, Keith, Torrie, Calum, Traylor, Gary, Vajente, Gabriele, Vanosky, Jordan, Vecchio, Alberto, Veitch, Peter, Vibhute, Ajay, Vonreis, Erik, Warner, Jim, Weaver, Betsy, Weiss, Rainer, Whittle, Chris, Willke, Benno, Wipf, Christopher, Yamamoto, Hiro, Yu, Haocun, Zhang, Liyuan, and Zucker, Michael
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General Relativity and Quantum Cosmology ,Astrophysics - Instrumentation and Methods for Astrophysics ,Physics - Instrumentation and Detectors ,Quantum Physics - Abstract
Precision measurements of space and time, like those made by the detectors of the Laser Interferometer Gravitational-wave Observatory (LIGO), are often confronted with fundamental limitations imposed by quantum mechanics. The Heisenberg uncertainty principle dictates that the position and momentum of an object cannot both be precisely measured, giving rise to an apparent limitation called the Standard Quantum Limit (SQL). Reducing quantum noise below the SQL in gravitational-wave detectors, where photons are used to continuously measure the positions of freely falling mirrors, has been an active area of research for decades. Here we show how the LIGO A+ upgrade reduced the detectors' quantum noise below the SQL by up to 3 dB while achieving a broadband sensitivity improvement, more than two decades after this possibility was first presented.
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- 2024
- Full Text
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21. Modulation of metastable ensemble dynamics explains optimal coding at moderate arousal in auditory cortex
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Papadopoulos, Lia, Jo, Suhyun, Zumwalt, Kevin, Wehr, Michael, McCormick, David A., and Mazzucato, Luca
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Quantitative Biology - Neurons and Cognition - Abstract
Performance during perceptual decision-making exhibits an inverted-U relationship with arousal, but the underlying network mechanisms remain unclear. Here, we recorded from auditory cortex (A1) of behaving mice during passive tone presentation, while tracking arousal via pupillometry. We found that tone discriminability in A1 ensembles was optimal at intermediate arousal, revealing a population-level neural correlate of the inverted-U relationship. We explained this arousal-dependent coding using a spiking network model with a clustered architecture. Specifically, we show that optimal stimulus discriminability is achieved near a transition between a multi-attractor phase with metastable cluster dynamics (low arousal) and a single-attractor phase (high arousal). Additional signatures of this transition include arousal-induced reductions of overall neural variability and the extent of stimulus-induced variability quenching, which we observed in the empirical data. Our results elucidate computational principles underlying interactions between pupil-linked arousal, sensory processing, and neural variability, and suggest a role for phase transitions in explaining nonlinear modulations of cortical computations.
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- 2024
22. Observation of Gravitational Waves from the Coalescence of a $2.5\text{-}4.5~M_\odot$ Compact Object and a Neutron Star
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The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abouelfettouh, I., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Agarwal, D., Agathos, M., Abchouyeh, M. Aghaei, Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akçay, S., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Argianas, L., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Attadio, F., Aubin, F., AultONeal, K., Avallone, G., Azrad, D., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Baier, J. G., Baiotti, L., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartoletti, A. M., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bates, D. E., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Baynard II, P. A., Bazzan, M., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Benoit, W., Bentara, I., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blagg, L. A., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boudon, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brandt, J., Braun, I., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, B. C., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cáceres-Barbosa, V., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannon, K. C., Cao, H., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, J. C. L., Chan, M., Chandra, K., Chang, R. -J., Chao, S., Char, P., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chattopadhyay, D., Chaturvedi, M., Chaty, S., Chatziioannou, K., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, Y., Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Cheung, S. Y., Chiadini, F., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chugh, P., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciolfi, R., Clara, F., Clark, J. A., Clarke, J., Clarke, T. A., Clearwater, P., Clesse, S., Coccia, E., Codazzo, E., Cohadon, P. -F., Colace, S., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Connolly, G., Conti, L., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Crook, S., Crouch, R., Csizmazia, J., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Pra, S. Dal, Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Davari, N., Dave, I., Davenport, A., Davier, M., Davies, T. F., Davis, D., Davis, L., Davis, M. C., Davis, P. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., DePergola, N., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhani, A., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. 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S., Sentenac, D., Seo, E. G., Seo, J. W., Sequino, V., Serra, M., Servignat, G., Sevrin, A., Shaffer, T., Shah, U. S., Shaikh, M. A., Shao, L., Sharma, A. K., Sharma, P., Sharma-Chaudhary, S., Shaw, M. R., Shawhan, P., Shcheblanov, N. S., Sheridan, E., Shikano, Y., Shikauchi, M., Shimode, K., Shinkai, H., Shiota, J., Shoemaker, D. H., Shoemaker, D. M., Short, R. W., ShyamSundar, S., Sider, A., Siegel, H., Sieniawska, M., Sigg, D., Silenzi, L., Simmonds, M., Singer, L. P., Singh, A., Singh, D., Singh, M. K., Singh, S., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. E., Smith, W. J., Soldateschi, J., Somiya, K., Song, I., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Sotani, H., Soulard, R., Southgate, A., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Spoon, J. B., Sprague, C. A., Srivastava, A. K., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., Stevenson, S., StPierre, M., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Suleiman, L., Sullivan, K. D., Sun, L., Sunil, S., Suresh, J., Sutton, P. J., Suzuki, T., Suzuki, Y., Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takase, T., Takatani, K., Takeda, H., Takeshita, K., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tang, D., Tanioka, S., Tanner, D. B., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, P., Tiwari, S., Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong, H., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trapananti, A., Travasso, F., Traylor, G., Trevor, M., Tringali, M. C., Tripathee, A., Troian, G., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Uchikata, N., Uchiyama, T., Udall, R. P., Uehara, T., Uematsu, M., Ueno, K., Ueno, S., Undheim, V., Ushiba, T., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Van Hove, P., VanKeuren, M., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varghese, J. J., Varma, V., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vives, A., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Wajid, A., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B. R., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Wilkin, A. T., Willadsen, D. J., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Winterflood, J., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wuchner, E., Wysocki, D. M., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, Y., Yarbrough, Z., Yasui, H., Yeh, S. -W., Yelikar, A. B., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuan, S., Yuzurihara, H., Zadrożny, A., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, X. -J., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
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Astrophysics - High Energy Astrophysical Phenomena ,General Relativity and Quantum Cosmology - Abstract
We report the observation of a coalescing compact binary with component masses $2.5\text{-}4.5~M_\odot$ and $1.2\text{-}2.0~M_\odot$ (all measurements quoted at the 90% credible level). The gravitational-wave signal GW230529_181500 was observed during the fourth observing run of the LIGO-Virgo-KAGRA detector network on 2023 May 29 by the LIGO Livingston Observatory. The primary component of the source has a mass less than $5~M_\odot$ at 99% credibility. We cannot definitively determine from gravitational-wave data alone whether either component of the source is a neutron star or a black hole. However, given existing estimates of the maximum neutron star mass, we find the most probable interpretation of the source to be the coalescence of a neutron star with a black hole that has a mass between the most massive neutron stars and the least massive black holes observed in the Galaxy. We provisionally estimate a merger rate density of $55^{+127}_{-47}~\text{Gpc}^{-3}\,\text{yr}^{-1}$ for compact binary coalescences with properties similar to the source of GW230529_181500; assuming that the source is a neutron star-black hole merger, GW230529_181500-like sources constitute about 60% of the total merger rate inferred for neutron star-black hole coalescences. The discovery of this system implies an increase in the expected rate of neutron star-black hole mergers with electromagnetic counterparts and provides further evidence for compact objects existing within the purported lower mass gap., Comment: 45 pages (10 pages author list, 13 pages main text, 1 page acknowledgements, 13 pages appendices, 8 pages bibliography), 17 figures, 16 tables. Update to match version published in The Astrophysical Journal Letters. Data products available from https://zenodo.org/records/10845779
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- 2024
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23. Robustly estimating heterogeneity in factorial data using Rashomon Partitions
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Venkateswaran, Aparajithan, Sankar, Anirudh, Chandrasekhar, Arun G., and McCormick, Tyler H.
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Statistics - Methodology ,Computer Science - Machine Learning ,Economics - Econometrics ,Statistics - Computation ,Statistics - Machine Learning - Abstract
Many statistical analyses, in both observational data and randomized control trials, ask: how does the outcome of interest vary with combinations of observable covariates? How do various drug combinations affect health outcomes, or how does technology adoption depend on incentives and demographics? Our goal is to partition this factorial space into "pools" of covariate combinations where the outcome differs across the pools (but not within a pool). Existing approaches (i) search for a single "optimal" partition under assumptions about the association between covariates or (ii) sample from the entire set of possible partitions. Both these approaches ignore the reality that, especially with correlation structure in covariates, many ways to partition the covariate space may be statistically indistinguishable, despite very different implications for policy or science. We develop an alternative perspective, called Rashomon Partition Sets (RPSs). Each item in the RPS partitions the space of covariates using a tree-like geometry. RPSs incorporate all partitions that have posterior values near the maximum a posteriori partition, even if they offer substantively different explanations, and do so using a prior that makes no assumptions about associations between covariates. This prior is the $\ell_0$ prior, which we show is minimax optimal. Given the RPS we calculate the posterior of any measurable function of the feature effects vector on outcomes, conditional on being in the RPS. We also characterize approximation error relative to the entire posterior and provide bounds on the size of the RPS. Simulations demonstrate this framework allows for robust conclusions relative to conventional regularization techniques. We apply our method to three empirical settings: price effects on charitable giving, chromosomal structure (telomere length), and the introduction of microfinance.
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- 2024
24. From Narratives to Numbers: Valid Inference Using Language Model Predictions from Verbal Autopsy Narratives
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Fan, Shuxian, Visokay, Adam, Hoffman, Kentaro, Salerno, Stephen, Liu, Li, Leek, Jeffrey T., and McCormick, Tyler H.
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Computer Science - Computation and Language ,Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
In settings where most deaths occur outside the healthcare system, verbal autopsies (VAs) are a common tool to monitor trends in causes of death (COD). VAs are interviews with a surviving caregiver or relative that are used to predict the decedent's COD. Turning VAs into actionable insights for researchers and policymakers requires two steps (i) predicting likely COD using the VA interview and (ii) performing inference with predicted CODs (e.g. modeling the breakdown of causes by demographic factors using a sample of deaths). In this paper, we develop a method for valid inference using outcomes (in our case COD) predicted from free-form text using state-of-the-art NLP techniques. This method, which we call multiPPI++, extends recent work in "prediction-powered inference" to multinomial classification. We leverage a suite of NLP techniques for COD prediction and, through empirical analysis of VA data, demonstrate the effectiveness of our approach in handling transportability issues. multiPPI++ recovers ground truth estimates, regardless of which NLP model produced predictions and regardless of whether they were produced by a more accurate predictor like GPT-4-32k or a less accurate predictor like KNN. Our findings demonstrate the practical importance of inference correction for public health decision-making and suggests that if inference tasks are the end goal, having a small amount of contextually relevant, high quality labeled data is essential regardless of the NLP algorithm., Comment: 12 pages, 7 figures
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- 2024
25. Thriving amidst the Pandemic: Teaching Gifted Students Online and the Role of Adaptation and Innovation
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Kimberly M. McCormick and Keri M. Guilbault
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During the COVID-19 pandemic, schools across the globe shifted to emergency remote instruction. This mixed methods study explored gifted education teachers' experiences and perspectives regarding remote instruction during the first year of the pandemic. Technology training, preparation in gifted education pedagogy, and teachers' perceptions of their effectiveness during remote instruction were examined. Qualitative and quantitative data were collected from 310 teachers across 31 states using an online survey and focus groups. Results revealed that teachers augmented traditional instruction to include strategies that facilitated student interaction, enrichment opportunities, timely feedback, and social and emotional curriculum. Teachers who reported receiving sufficient technology training felt better able to integrate creativity into their virtual lessons. Two major themes emerged describing how educators optimized their teaching practices: (a) 24/7 Learning Environment and (b) Personalization. Recommendations include the need for continued professional learning for administrators and educators to advance remote learning for gifted learners.
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- 2024
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26. Perspectives on improving photosynthesis to increase crop yield
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Croce, Roberta, Carmo-Silva, Elizabete, Cho, Young B, Ermakova, Maria, Harbinson, Jeremy, Lawson, Tracy, McCormick, Alistair J, Niyogi, Krishna K, Ort, Donald R, Patel-Tupper, Dhruv, Pesaresi, Paolo, Raines, Christine, Weber, Andreas PM, and Zhu, Xin-Guang
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Plant Biology ,Biological Sciences ,Affordable and Clean Energy ,Zero Hunger ,Biochemistry and Cell Biology ,Genetics ,Plant Biology & Botany ,Plant biology - Abstract
Improving photosynthesis, the fundamental process by which plants convert light energy into chemical energy, is a key area of research with great potential for enhancing sustainable agricultural productivity and addressing global food security challenges. This perspective delves into the latest advancements and approaches aimed at optimizing photosynthetic efficiency. Our discussion encompasses the entire process, beginning with light harvesting and its regulation and progressing through the bottleneck of electron transfer. We then delve into the carbon reactions of photosynthesis, focusing on strategies targeting the enzymes of the Calvin-Benson-Bassham (CBB) cycle. Additionally, we explore methods to increase CO2 concentration near the Rubisco, the enzyme responsible for the first step of CBB cycle, drawing inspiration from various photosynthetic organisms, and conclude this section by examining ways to enhance CO2 delivery into leaves. Moving beyond individual processes, we discuss two approaches to identifying key targets for photosynthesis improvement: systems modeling and the study of natural variation. Finally, we revisit some of the strategies mentioned above to provide a holistic view of the improvements, analyzing their impact on nitrogen use efficiency and on canopy photosynthesis.
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- 2024
27. A Top-Down Proteomic Assay to Evaluate KRAS4B-Compound Engagement.
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DIppolito, Robert, Rabara, Dana, Blanco, Maria, Alberico, Emily, Drew, Matthew, Ramakrishnan, Nitya, Sontan, Dara, Widmeyer, Stephanie, Scheidemantle, Grace, Messing, Simon, Turner, David, Nissley, Dwight, DeHart, Caroline, Maciag, Anna, Stephen, Andrew, Esposito, Dominic, Mccormick, Frank, and Arkin, Michelle
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Proteomics ,Proto-Oncogene Proteins p21(ras) ,Mutation ,Binding Sites - Abstract
Development of new targeted inhibitors for oncogenic KRAS mutants may benefit from insight into how a given mutation influences the accessibility of protein residues and how compounds interact with mutant or wild-type KRAS proteins. Targeted proteomic analysis, a key validation step in the KRAS inhibitor development process, typically involves both intact mass- and peptide-based methods to confirm compound localization or quantify binding. However, these methods may not always provide a clear picture of the compound binding affinity for KRAS, how specific the compound is to the target KRAS residue, and how experimental conditions may impact these factors. To address this, we have developed a novel top-down proteomic assay to evaluate in vitro KRAS4B-compound engagement while assessing relative quantitation in parallel. We present two applications to demonstrate the capabilities of our assay: maleimide-biotin labeling of a KRAS4BG12D cysteine mutant panel and treatment of three KRAS4B proteins (WT, G12C, and G13C) with small molecule compounds. Our results show the time- or concentration-dependence of KRAS4B-compound engagement in context of the intact protein molecule while directly mapping the compound binding site.
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- 2024
28. Bayesian analysis of verbal autopsy data using factor models with age- and sex-dependent associations between symptoms
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Kunihama, Tsuyoshi, Li, Zehang Richard, Clark, Samuel J., and McCormick, Tyler H.
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Statistics - Applications - Abstract
Verbal autopsies (VAs) are extensively used to investigate the population-level distributions of deaths by cause in low-resource settings without well-organized vital statistics systems. Computer-based methods are often adopted to assign causes of death to deceased individuals based on the interview responses of their family members or caregivers. In this article, we develop a new Bayesian approach that extracts information about cause-of-death distributions from VA data considering the age- and sex-related variation in the associations between symptoms. Its performance is compared with that of existing approaches using gold-standard data from the Population Health Metrics Research Consortium. In addition, we compute the relevance of predictors to causes of death based on information-theoretic measures.
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- 2024
29. Non-robustness of diffusion estimates on networks with measurement error
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Chandrasekhar, Arun G., Goldsmith-Pinkham, Paul, McCormick, Tyler H., Thau, Samuel, and Wei, Jerry
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Economics - Econometrics ,Computer Science - Social and Information Networks ,Statistics - Applications ,Statistics - Methodology - Abstract
Network diffusion models are used to study things like disease transmission, information spread, and technology adoption. However, small amounts of mismeasurement are extremely likely in the networks constructed to operationalize these models. We show that estimates of diffusions are highly non-robust to this measurement error. First, we show that even when measurement error is vanishingly small, such that the share of missed links is close to zero, forecasts about the extent of diffusion will greatly underestimate the truth. Second, a small mismeasurement in the identity of the initial seed generates a large shift in the locations of expected diffusion path. We show that both of these results still hold when the vanishing measurement error is only local in nature. Such non-robustness in forecasting exists even under conditions where the basic reproductive number is consistently estimable. Possible solutions, such as estimating the measurement error or implementing widespread detection efforts, still face difficulties because the number of missed links are so small. Finally, we conduct Monte Carlo simulations on simulated networks, and real networks from three settings: travel data from the COVID-19 pandemic in the western US, a mobile phone marketing campaign in rural India, and in an insurance experiment in China.
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- 2024
30. Ultralight vector dark matter search using data from the KAGRA O3GK run
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The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abe, H., Abouelfettouh, I., Acernese, F., Ackley, K., Adamcewicz, C., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Bai, Y., Baier, J. G., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Bogaert, G., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boumerdassi, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Santoro, G. Caneva, Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, C., Chan, J. C. L., Chan, K. H. M., Chan, M., Chan, W. L., Chandra, K., Chang, R. -J., Chanial, P., Chao, S., Chapman-Bird, C., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chatziioannou, K., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, K. H., Chen, X., Chen, Yi-Ru, Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Chia, H. Y., Chiadini, F., Chiang, C., Chiarini, G., Chiba, A., Chiba, R., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Clara, F., Clark, J. A., Clarke, T. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. -F., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Conti, L., Cooper, S. J., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Cousins, B., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Croquette, M., Crouch, R., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Davari, N., Dave, I., Davenport, A., Davier, M., Davies, T. F., Davis, D., Davis, L., Davis, M. C., Daw, E. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., DePergola, N., De Pietri, R., De Rosa, R., De Rossi, C., De Simone, R., Dhani, A., Dhurandhar, S., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, F., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. 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P., Singh, A., Singh, D., Singh, M. K., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. E., Smith, W. J., Soldateschi, J., Somala, S. N., Somiya, K., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Soulard, R., Souradeep, T., Southgate, A., Sowell, E., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Srivastava, A. K., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., StPierre, M., Strang, L. C., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Sullivan, A. G., Sullivan, K. D., Sun, L., Sunil, S., Sur, A., Suresh, J., Sutton, P. J., Suzuki, Takamasa, Suzuki, Takanori, Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takatani, K., Takeda, H., Takeda, M., Talbot, C. J., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tanasijczuk, A. J., Tang, D., Tanioka, S., Tanner, D. B., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, Shubhanshu, Tiwari, Srishti, Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trani, A. A., Trapananti, A., Travasso, F., Traylor, G., Trenado, J., Trevor, M., Tringali, M. C., Tripathee, A., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Ubhi, A. S., Uchiyama, T., Udall, R. P., Uehara, T., Ueno, K., Unnikrishnan, C. S., Ushiba, T., Utina, A., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Veske, D., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Walet, R. C., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Ward, R. L., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Weller, C. M., Weller, R. A., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., White, D. D., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, D., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wysocki, D. M., Xiao, L., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, M., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, L. -C., Yang, Y., Yarbrough, Z., Yeh, S. -W., Yelikar, A. B., Yeung, S. M. C., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuzurihara, H., Zadrożny, A., Zannelli, A. J., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhong, S., Zhou, R., Zhu, Z. -H., Zucker, M. E., Zweizig, J., Fujimori, T., Fujimoto, H., Fujita, T., Manita, Y., Obata, I., and Takidera, H.
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Astrophysics - Cosmology and Nongalactic Astrophysics ,General Relativity and Quantum Cosmology ,High Energy Physics - Phenomenology - Abstract
Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for $U(1)_{B-L}$ gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the $U(1)_{B-L}$ gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM., Comment: 20 pages, 5 figures
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- 2024
31. Dempster-Shafer P-values: Thoughts on an Alternative Approach for Multinomial Inference
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Hoffman, Kentaro, Zhang, Kai, McCormick, Tyler, and Hannig, Jan
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Statistics - Methodology ,Mathematics - Statistics Theory - Abstract
In this paper, we demonstrate that a new measure of evidence we developed called the Dempster-Shafer p-value which allow for insights and interpretations which retain most of the structure of the p-value while covering for some of the disadvantages that traditional p- values face. Moreover, we show through classical large-sample bounds and simulations that there exists a close connection between our form of DS hypothesis testing and the classical frequentist testing paradigm. We also demonstrate how our approach gives unique insights into the dimensionality of a hypothesis test, as well as models the effects of adversarial attacks on multinomial data. Finally, we demonstrate how these insights can be used to analyze text data for public health through an analysis of the Population Health Metrics Research Consortium dataset for verbal autopsies.
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- 2024
32. Do We Really Even Need Data?
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Hoffman, Kentaro, Salerno, Stephen, Afiaz, Awan, Leek, Jeffrey T., and McCormick, Tyler H.
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Statistics - Methodology ,Computer Science - Machine Learning - Abstract
As artificial intelligence and machine learning tools become more accessible, and scientists face new obstacles to data collection (e.g. rising costs, declining survey response rates), researchers increasingly use predictions from pre-trained algorithms as outcome variables. Though appealing for financial and logistical reasons, using standard tools for inference can misrepresent the association between independent variables and the outcome of interest when the true, unobserved outcome is replaced by a predicted value. In this paper, we characterize the statistical challenges inherent to this so-called ``inference with predicted data'' problem and elucidate three potential sources of error: (i) the relationship between predicted outcomes and their true, unobserved counterparts, (ii) robustness of the machine learning model to resampling or uncertainty about the training data, and (iii) appropriately propagating not just bias but also uncertainty from predictions into the ultimate inference procedure.
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- 2024
33. An Overview of Bartnik's Quasi-Local Mass
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McCormick, Stephen
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Mathematics - Differential Geometry ,General Relativity and Quantum Cosmology ,53C20, 83C99 (Primary) 53-02 (Secondary) - Abstract
This article provides a concise introduction to Bartnik's quasi-local mass, and surveys a selection of results pertaining to the understanding of it. The aim is to serve as both an entry point to the topic, and a quick reference of results for those already familiar with it., Comment: 33 Pages. Introductory overview of the topic. To appear in BPAM. Dedicated to the memory of Robert Bartnik
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- 2024
34. Rocky Mountain Spotted Fever Mimicking Multisystem Inflammatory Syndrome in Hospitalized Children, Sonora, Mexico
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Alvarez-Hernandez, Gerardo, Rivera-Rosas, Cristian N., Calleja-Lopez, J.R. Tadeo, McCormick, David W., Paddock, Christopher D., Alvarez-Meza, Jehan Bonizu, and Correa-Morales, Fabian
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Sonora, Mexico -- Health aspects ,Rocky Mountain spotted fever -- Diagnosis -- Complications and side effects -- Comparative analysis ,Hospital patients ,Pediatric research ,Health ,Diagnosis ,Complications and side effects ,Comparative analysis ,Health aspects - Abstract
Rocky Mountain spotted fever (RMSF), a tickbome disease caused by Rickettsia rickettsii, is the leading cause of death from rickettsial infections in the Western Hemisphere (1). The disease can progress [...]
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- 2024
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35. Young Children's Resilience in the Wake of the COVID-19 Pandemic: Evidence from Acelero Learning Head Start Programs
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MDRC, McCormick, Meghan, Goldberg, Maya, Swinth, Emily, Smith Todd, Cate, Carlis, Lydia, and Chavez, Victoria
- Abstract
There is clear evidence that the COVID-19 pandemic had significant negative effects on the learning and development of school-age children in the United States, with disproportionate impacts on children from racially, ethnically, and socioeconomically marginalized groups. There is less consistent evidence on the extent to which the pandemic affected younger, preschool-age children. Acelero Learning wanted a better understanding of whether children in their programs were exhibiting "resilience" during the pandemic recovery. That is, were their scores on assessments across a variety of domains similar to or perhaps better than those of similar populations of children attending Head Start and Acelero Learning programs in the years before the crisis? This report summarizes the initial results from a study led by MDRC that is examining post-pandemic language, literacy, math, and executive functioning skills for children enrolled in Acelero Learning programs. The study aims to answer two questions: (1) To what extent did 3- and 4-year-old children enrolled in Acelero Learning programs exhibit resilience two years after the start of the pandemic?; and (2) Did children's growth in academic and cognitive skills during this time vary by demographic group, including race and ethnicity, age, gender, and language background? [Funding for this report was provided by Acelero Learning.]
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- 2023
36. Systems Reviews: An Approach to Building Coherence, Increasing Efficiency, and Improving Workflow at State Education Agencies
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Region 15 Comprehensive Center, WestEd, Mattson, Heather, Zoffel, Jennifer, and McCormick, Malachy
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The challenges state education agencies (SEAs) must address are exceedingly complex, requiring sophisticated levels of thinking and problem-solving as well as the ability to leverage disparate points of view in finding impactful solutions. These challenges are technical, requiring specific and known solutions to achieve desired results. To provide more coherent, integrated services to the field in the face of complex challenges, SEAs need to engage in systems change. This brief from the Region 15 Comprehensive Center shares its iterative Systems Review approach that assists SEAs in considering new and more effective ways to build coherence, increase efficiency, and improve workflow--all in the service of effectively supporting local education agencies (LEAs) in implementing state priorities.
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- 2023
37. Application of a Social Vulnerability Index and Its Associations with Physical Frailty and Disability in a Cross-sectional Study of Older Kenyan Women Living with and without HIV
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Prabhu, Sandeep, Oyaro, B., Wanje, G., Aunon, F. M., Gomez Juarez, N., Flaherty, B. P., McCormick, W., Andrew, M. K., Jaoko, W., McClelland, R. S., and Graham, Susan M.
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- 2024
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38. Plant invasion down under: exploring the below-ground impact of invasive plant species on soil properties and invertebrate communities in the Central Plateau of New Zealand
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Pearson, Benjamin M., Minor, Maria A., Robertson, Alastair W., and Clavijo McCormick, Andrea L.
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- 2024
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39. Spermidine is essential for fasting-mediated autophagy and longevity
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Hofer, Sebastian J., Daskalaki, Ioanna, Bergmann, Martina, Friščić, Jasna, Zimmermann, Andreas, Mueller, Melanie I., Abdellatif, Mahmoud, Nicastro, Raffaele, Masser, Sarah, Durand, Sylvère, Nartey, Alexander, Waltenstorfer, Mara, Enzenhofer, Sarah, Faimann, Isabella, Gschiel, Verena, Bajaj, Thomas, Niemeyer, Christine, Gkikas, Ilias, Pein, Lukas, Cerrato, Giulia, Pan, Hui, Liang, YongTian, Tadic, Jelena, Jerkovic, Andrea, Aprahamian, Fanny, Robbins, Christine E., Nirmalathasan, Nitharsshini, Habisch, Hansjörg, Annerer, Elisabeth, Dethloff, Frederik, Stumpe, Michael, Grundler, Franziska, Wilhelmi de Toledo, Françoise, Heinz, Daniel E., Koppold, Daniela A., Rajput Khokhar, Anika, Michalsen, Andreas, Tripolt, Norbert J., Sourij, Harald, Pieber, Thomas R., de Cabo, Rafael, McCormick, Mark A., Magnes, Christoph, Kepp, Oliver, Dengjel, Joern, Sigrist, Stephan J., Gassen, Nils C., Sedej, Simon, Madl, Tobias, De Virgilio, Claudio, Stelzl, Ulrich, Hoffmann, Markus H., Eisenberg, Tobias, Tavernarakis, Nektarios, Kroemer, Guido, and Madeo, Frank
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- 2024
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40. Understanding the Sequelae of Invasive Meningococcal Disease in the United States
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Marshall, Gary S., McCormick, Zachary L., Johns, Jeffery S., Verduzco-Gutierrez, Monica, Herrera-Restrepo, Oscar, and Harrison, Lee H.
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- 2024
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41. Relief of Biofilm Hypoxia: A Synergistic Approach with Cyanobacteria and Chlorin e6-Loaded Nanoparticles
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Kashif, Saima, Roberts, Sam, Gopal, Ashna, Osorio, Alejandra A. Schiavon, Nenninger, Anja, Yan, Li, McCormick, Alistair J., and Chen, Xianfeng
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- 2024
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42. Evaluating polyanthranilic acid as a polymeric template for the production of Prussian blue nanoclusters
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Gilpin, Victoria, McCormick, Rachel, McMath, Regan, Smith, Robert B., Papakonstantinou, Pagona, and Davis, James
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- 2024
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43. Paralog switching facilitates diadromy: ontogenetic, microevolutionary and macroevolutionary evidence
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Colby, Rebecca S., McCormick, Stephen D., Velotta, Jonathan P., Jockusch, Elizabeth, and Schultz, Eric T.
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- 2024
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44. Prior COVID-19 infection associated with increased risk of newly diagnosed erectile dysfunction
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Hebert, Kevin J., Matta, Rano, Horns, Joshua J., Paudel, Niraj, Das, Rupam, McCormick, Benjamin J., Myers, Jeremy B., and Hotaling, James M.
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- 2024
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45. Resource use differences of two coexisting chironomid species at localized scales
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McCormick, Amanda R., Phillips, Joseph S., Botsch, Jamieson C., Ólafsson, Jón S., and Ives, Anthony R.
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- 2024
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46. Primary closure versus vertical rectus abdominis myocutaneous (VRAM) flap closure of perineal wound following abdominoperineal resection—a systematic review and meta-analysis
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Temperley, Hugo C., Shokuhi, Poorya, O’Sullivan, Niall J., Mac Curtain, Benjamin, Waters, Caitlin, Murray, Alannah, Buckley, Christina E., O’Neill, Maeve, Mehigan, Brian, McCormick, Paul H., Kelly, Michael E., and Larkin, John O.
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- 2024
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47. Comments on Artūrs Logins, Normative Reasons: Between Reasons and Explanation
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Schleifer McCormick, Miriam
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- 2024
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48. Ancient Plasmodium genomes shed light on the history of human malaria
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Michel, Megan, Skourtanioti, Eirini, Pierini, Federica, Guevara, Evelyn K., Mötsch, Angela, Kocher, Arthur, Barquera, Rodrigo, Bianco, Raffaela A., Carlhoff, Selina, Coppola Bove, Lorenza, Freilich, Suzanne, Giffin, Karen, Hermes, Taylor, Hiß, Alina, Knolle, Florian, Nelson, Elizabeth A., Neumann, Gunnar U., Papac, Luka, Penske, Sandra, Rohrlach, Adam B., Salem, Nada, Semerau, Lena, Villalba-Mouco, Vanessa, Abadie, Isabelle, Aldenderfer, Mark, Beckett, Jessica F., Brown, Matthew, Campus, Franco G. R., Chenghwa, Tsang, Cruz Berrocal, María, Damašek, Ladislav, Duffett Carlson, Kellie Sara, Durand, Raphaël, Ernée, Michal, Fântăneanu, Cristinel, Frenzel, Hannah, García Atiénzar, Gabriel, Guillén, Sonia, Hsieh, Ellen, Karwowski, Maciej, Kelvin, David, Kelvin, Nikki, Khokhlov, Alexander, Kinaston, Rebecca L., Korolev, Arkadii, Krettek, Kim-Louise, Küßner, Mario, Lai, Luca, Look, Cory, Majander, Kerttu, Mandl, Kirsten, Mazzarello, Vittorio, McCormick, Michael, de Miguel Ibáñez, Patxuka, Murphy, Reg, Németh, Rita E., Nordqvist, Kerkko, Novotny, Friederike, Obenaus, Martin, Olmo-Enciso, Lauro, Onkamo, Päivi, Orschiedt, Jörg, Patrushev, Valerii, Peltola, Sanni, Romero, Alejandro, Rubino, Salvatore, Sajantila, Antti, Salazar-García, Domingo C., Serrano, Elena, Shaydullaev, Shapulat, Sias, Emanuela, Šlaus, Mario, Stančo, Ladislav, Swanston, Treena, Teschler-Nicola, Maria, Valentin, Frederique, Van de Vijver, Katrien, Varney, Tamara L., Vigil-Escalera Guirado, Alfonso, Waters, Christopher K., Weiss-Krejci, Estella, Winter, Eduard, Lamnidis, Thiseas C., Prüfer, Kay, Nägele, Kathrin, Spyrou, Maria, Schiffels, Stephan, Stockhammer, Philipp W., Haak, Wolfgang, Posth, Cosimo, Warinner, Christina, Bos, Kirsten I., Herbig, Alexander, and Krause, Johannes
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- 2024
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49. Impacts of Home Visiting during the Pandemic: Evidence from a Randomized Controlled Trial of Child First. Working Paper
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MDRC, Xia, Samantha, Hefyan, Mervett, McCormick, Meghan, Goldberg, Maya, Swinth, Emily, and Huang, Sharon
- Abstract
Existing research has found that home visiting programs for families with young children can improve children's development and strengthen caregivers' and families' well-being. However, the pandemic created numerous challenges for home visiting programs, forcing them to deliver services online or in a hybrid format and to adapt the content of their program models to respond to pandemic-related challenges. Questions remain about the impacts of these programs when delivered at scale during this uniquely challenging time. The current study reports 12-month impacts from a randomized controlled trial of Child First--an evidence-based home visiting program that provides a psychotherapeutic, parent-child intervention embedded in a coordinated system of care--implemented across two states. After randomly assigning a racially and ethnically diverse sample of families (N = 226) from predominantly low-income backgrounds to receive the Child First services or typical community services, the research team surveyed caregivers (N = 183) about a year after program enrollment. Results from regression models with site fixed effects revealed that Child First reduced caregivers' job losses, residential mobility, and self-reported substance abuse, and increased receipt of virtual services during the pandemic. Child First also reduced parenting dysfunction (the name of the subscale within the Parenting Stress Index) among caregivers who reported clinical depression at enrollment. There were null impacts on caregivers' psychological well-being, families' involvement with the child welfare system, children's behaviors, and other indicators of economic well-being. Implications for future research and policy are discussed.
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- 2023
50. Going the Distance: Disparities in Pre-K Enrollment in Higher-Quality Schools by Geographic Proximity, Race/Ethnicity, Family Income, and Home Language
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Meghan McCormick, Mirjana Pralica, JoAnn Hsueh, Christina Weiland, Amanda Ketner Weissman, Anna Shapiro, Samantha Xia, Cullen MacDowell, Samuel Maves, Anne Taylor, and Jason Sachs
- Abstract
This study leverages six years of public prekindergarten (pre-K) and kindergarten data (N = 22,469) from the Boston Public Schools (BPS) to examine enrollment in BPS pre-K from 2012-2017 for students from different racial/ethnic, socioeconomic, and linguistic groups. The largest differences in enrollment emerged with respect to race and ethnicity--and for enrollment in programs in higher-quality schools (defined as schools scoring in the top quartile on third-grade standardized tests)--with disparities increasing over time. Although there were no differences across groups in proximity to BPS pre-K programs in general, Black students lived about a quarter of a mile farther than their White peers from the nearest program in a higher-quality school, with gaps widening over time. Closer proximity was associated with a higher likelihood of enrollment in a program in a higher-quality school. Implications for future research and policy are discussed.
- Published
- 2023
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