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52,080 results on '"Choy, A"'

1. Simultaneous stable determination of quasilinear terms for parabolic equations

Author

Choy, Jason and Kian, Yavar

Subjects
Mathematics - Analysis of PDEs
Abstract

In this work, we consider the inverse problem of simultaneously recovering two classes of quasilinear terms appearing in a parabolic equation from boundary measurements. It is motivated by several industrial and scientific applications, including the problems of heat conduction and population dynamics, and we study the issue of stability. More precisely, we derive simultaneous Lipschitz and H\"older stability estimates for two separate classes of quasilinear terms. The analysis combines different arguments including the linearization technique with a novel construction of singular solutions and properties of solutions of parabolic equations with nonsmooth boundary conditions. These stability results may be useful for deriving the convergence rate of numerical reconstruction schemes.

Published
2024

2. Commentary on Peierls's 'In defence of measurement' in Physics World, January 1991

Author

Choy, Tuck C

Subjects
Physics - History and Philosophy of Physics, High Energy Physics - Theory, Quantum Physics
Abstract

This paper is a commentary on Rudolph Peierls's "In defence of measurement" Physics World 19-20, January 1991 to accompany the reprint of this paper for the special volume dedicated to the celebration of the centenary of the discovery of quantum mechanics by Werner Heisenberg, to be published by World Scientific in July 2025.

Published
2024

3. Digital Parenting and Its Impact on Early Childhood Development: A Scoping Review

Author

Yun Nga Choy, Eva Yi Hung Lau, and Dandan Wu

Abstract

Digital parenting refers to the parenting practices that maximize the benefits and minimize potential risks of children's interactions with digital media and online spaces. Balancing the pros and cons of early digital usage is a challenge for many caregivers. This scoping review synthesizes evidence regarding digital parenting practices and their impact on children's digital use and development, drawing from 40 studies published in international peer-reviewed journals between 2010 and 2023. Four themes have emerged from this scoping review. Firstly, parental perspectives on early digital use diverged into positive views (as 'educational aids'), negative views (as 'distractions'), and cultural differences. Secondly, children's digital use was influenced by digital parenting practices, specifically parental modeling, parenting style, parental mediation and the intended purpose of children's digital use. Thirdly, a correlation was noted between varying results of digital parenting and children's digital use, with outcomes manifested in children's digital literacy, parent-child relationships, social-emotional and language development, behavioral issues, and emergent literacy. Fourthly, influential factors were child ages, parental and family-related factors (including gender, socioeconomic status, ethnicity, family structure, religion, and parents' digital literacy), and the type of digital resources. The review suggests that future research should concentrate on training programs to enhance parental digital literacy skills and employ monitoring tools to better assess children's digital use.

Published
2024
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4. Probing the Content of Affective Semantic Memory Following Caregiving-Related Early Adversity

Author

Anna Vannucci, Andrea Fields, Paul A. Bloom, Nicolas L. Camacho, Tricia Choy, Amaesha Durazi, Syntia Hadis, Chelsea Harmon, Charlotte Heleniak, Michelle VanTieghem, Mary Dozier, Michael P. Milham, Simona Ghetti, and Nim Tottenham

Abstract

Cognitive science has demonstrated that we construct knowledge about the world by abstracting patterns from routinely encountered experiences and storing them as semantic memories. This preregistered study tested the hypothesis that caregiving-related early adversities (crEAs) shape affective semantic memories to reflect the content of those adverse interpersonal-affective experiences. We also tested the hypothesis that because affective semantic memories may continue to evolve in response to later-occurring positive experiences, child-perceived attachment security will inform their content. The sample comprised 160 children (ages 6-12 at Visit 1; 87F/73 M), 66% of whom experienced crEAs (n = 105). At Visit 1, crEA exposure prior to study enrollment was operationalized as parental-reports endorsing a history of crEAs (abuse/neglect, permanent/significant parent-child separation); while child-reports assessed concurrent attachment security. A false memory task was administered online [approximately]2.5 years later (Visit 2) to probe the content of affective semantic memories--specifically attachment schemas. Results showed that crEA exposure (vs. no exposure) was associated with a higher likelihood of falsely endorsing insecure (vs. secure) schema scenes. Attachment security moderated the association between crEA exposure and insecure schema-based false recognition. Findings suggest that interpersonal-affective semantic schemas include representations of parent-child interactions that may capture the quality of one's own attachment experiences and that these representations shape how children remember attachment-relevant narrative events. Findings are also consistent with the hypothesis that these affective semantic memories can be modified by later experiences. Moving forward, the approach taken in this study provides a means of operationalizing Bowlby's notion of internal working models within a cognitive neuroscience framework.

Published
2024
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5. Exploring the design space of deep-learning-based weather forecasting systems

Author

Siddiqui, Shoaib Ahmed, Kossaifi, Jean, Bonev, Boris, Choy, Christopher, Kautz, Jan, Krueger, David, and Azizzadenesheli, Kamyar

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Despite tremendous progress in developing deep-learning-based weather forecasting systems, their design space, including the impact of different design choices, is yet to be well understood. This paper aims to fill this knowledge gap by systematically analyzing these choices including architecture, problem formulation, pretraining scheme, use of image-based pretrained models, loss functions, noise injection, multi-step inputs, additional static masks, multi-step finetuning (including larger stride models), as well as training on a larger dataset. We study fixed-grid architectures such as UNet, fully convolutional architectures, and transformer-based models, along with grid-invariant architectures, including graph-based and operator-based models. Our results show that fixed-grid architectures outperform grid-invariant architectures, indicating a need for further architectural developments in grid-invariant models such as neural operators. We therefore propose a hybrid system that combines the strong performance of fixed-grid models with the flexibility of grid-invariant architectures. We further show that multi-step fine-tuning is essential for most deep-learning models to work well in practice, which has been a common practice in the past. Pretraining objectives degrade performance in comparison to supervised training, while image-based pretrained models provide useful inductive biases in some cases in comparison to training the model from scratch. Interestingly, we see a strong positive effect of using a larger dataset when training a smaller model as compared to training on a smaller dataset for longer. Larger models, on the other hand, primarily benefit from just an increase in the computational budget. We believe that these results will aid in the design of better weather forecasting systems in the future.

Published
2024

6. Entangled two-photon absorption for the continuous generation of excited state populations in plasma

Author

Smith, David R., Beuting, Matthias, Hartog, Daniel J. Den, Geiger, Benedikt, Sanders, Scott T., Yang, Xuting, and Choy, Jennifer T.

Subjects
Quantum Physics, Physics - Plasma Physics
Abstract

Entangled two-photon absorption (ETPA) may be a viable technique to continuously drive an excited state population in plasma for high-bandwidth spectroscopy measurements of localized plasma turbulence or impurity density. Classical two-photon absorption commonly requires a high-intensity, pulsed laser, but entangled photons with short entanglement time and high time correlation may allow for ETPA using a lower intensity, continuous-wave laser. Notably, ETPA with non-collinear entangled photon generation allows for cross-beam spatial localization of the absorption or fluorescence signal using a single laser source. Entangled photon generation, the ETPA cross-section, candidate transitions for an Ar-II species, and plans for a proof-of-principle measurement in a helicon plasma are discussed.

Published
2024

7. Sub-nanometer-thick native sp2 carbon on oxidized diamond surfaces

Author

Vidrio, Ricardo, Saucedo, Cesar, Lordi, Vincenzo, Kolkowitz, Shimon, Ray, Keith G., Hamers, Robert J., and Choy, Jennifer T.

Subjects
Condensed Matter - Materials Science
Abstract

Oxygen-terminated diamond has a wide breadth of applications, which include stabilizing near-surface color centers, semiconductor devices, and biological sensors. Despite the vast literature on characterizing functionalization groups on diamond, the chemical composition on the shallowest portion of the surface (< 1 nm) is challenging to probe with conventional techniques like XPS and FTIR. In this work, we demonstrate the use of angle-resolved XPS to probe the first ten nanometers of (100) single-crystalline diamond, showing the changes of the oxygen functional groups and the allotropes of carbon with respect to depth. With the use of consistent peak-fitting methods, the peak identities and relative peak binding energies were identified for sp2 carbon, ether, hydroxyl, carbonyl, and C-H groups. For the oxygen-terminated sample, we also quantified the thickness of the sp2 carbon layer situated on top of the bulk sp3 diamond bonded carbon to be 0.4 $\pm$ 0.1 nm, based on the analysis of the Auger electron spectra and D-parameter calculations. These results indicate that the majority of the oxygen is bonded to the sp2 carbon layer on the diamond, and not directly on the sp3 diamond bonded carbon.

Published
2024

8. Broadband light extraction from near-surface NV centers using crystalline-silicon antennas

Author

Kim, Minjeong, Zahedian, Maryam, Wu, Wenxin, Fang, Chengyu, Yu, Zhaoning, Wambold, Raymond A., Yin, Shenwei, Czaplewski, David A., Choy, Jennifer T., and Kats, Mikhail A.

Subjects
Physics - Optics, Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science, Physics - Applied Physics, Quantum Physics
Abstract

We use crystalline silicon (Si) antennas to efficiently extract broadband single-photon fluorescence from shallow nitrogen-vacancy (NV) centers in diamond into free space. Our design features relatively easy-to-pattern high-index Si resonators on the diamond surface to boost photon extraction by overcoming total internal reflection and Fresnel reflection at the diamond-air interface, and providing modest Purcell enhancement, without etching or otherwise damaging the diamond surface. In simulations, ~20 times more single photons are collected from a single NV center compared to the case without the antenna; in experiments, we observe an enhancement of ~4 times, limited by spatial alignment between the NV and the antenna. Our approach can be readily applied to other color centers in diamond, and more generally to the extraction of light from quantum emitters in wide-bandgap materials., Comment: Main text + supplementary

Published
2024

9. Potential Fraction Concept Images Afforded in Textbooks: A Comparison of Northern Ireland and Singapore

Author

Mathematics Education Research Group of Australasia (MERGA), Ban Heng Choy, and Pamela Moffett

Abstract

Fractions are among the most problematic concepts that children encounter in their primary school years because of the many different conceptions of fractions. Textbook analyses have tried to provide insights into how fractions are introduced, focusing on the different concepts and representations of fractions. In this paper, we contribute to these efforts by investigating the way fractions are first introduced using the notion of potential concept images as afforded by the textbooks. Analyses of two textbooks, one from Northern Ireland and the other from Singapore, will be presented to highlight these potential concept images and their implications for practice.

Published
2024

10. EWS-WT1 fusion isoforms establish oncogenic programs and therapeutic vulnerabilities in desmoplastic small round cell tumors.

Author

Boulay, Gaylor, Broye, Liliane, Dong, Rui, Iyer, Sowmya, Sanalkumar, Rajendran, Xing, Yu-Hang, Buisson, Rémi, Rengarajan, Shruthi, Naigles, Beverly, Duc, Benoît, Volorio, Angela, Awad, Mary, Renella, Raffaele, Chebib, Ivan, Nielsen, G, Choy, Edwin, Cote, Gregory, Zou, Lee, Letovanec, Igor, Stamenkovic, Ivan, Rivera, Miguel, and Riggi, Nicolò

Subjects
Humans, Desmoplastic Small Round Cell Tumor, Oncogene Proteins, Fusion, Animals, RNA-Binding Protein EWS, Gene Expression Regulation, Neoplastic, Pyridines, Protein Isoforms, Cyclin D1, Mice, Cell Line, Tumor, Piperazines, WT1 Proteins, Chromatin, Xenograft Model Antitumor Assays, Gene Regulatory Networks, Female
Abstract

EWS fusion oncoproteins underlie several human malignancies including Desmoplastic Small Round Cell Tumor (DSRCT), an aggressive cancer driven by EWS-WT1 fusion proteins. Here we combine chromatin occupancy and 3D profiles to identify EWS-WT1-dependent gene regulation networks and target genes. We show that EWS-WT1 is a powerful chromatin activator controlling an oncogenic gene expression program that characterizes primary tumors. Similar to wild type WT1, EWS-WT1 has two isoforms that differ in their DNA binding domain and we find that they have distinct DNA binding profiles and are both required to generate viable tumors that resemble primary DSRCT. Finally, we identify candidate EWS-WT1 target genes with potential therapeutic implications, including CCND1, whose inhibition by the clinically-approved drug Palbociclib leads to marked tumor burden decrease in DSRCT PDXs in vivo. Taken together, our studies identify gene regulation programs and therapeutic vulnerabilities in DSRCT and provide a mechanistic understanding of the complex oncogenic activity of EWS-WT1.

Published
2024

11. CodeGemma: Open Code Models Based on Gemma

Author

CodeGemma Team, Zhao, Heri, Hui, Jeffrey, Howland, Joshua, Nguyen, Nam, Zuo, Siqi, Hu, Andrea, Choquette-Choo, Christopher A., Shen, Jingyue, Kelley, Joe, Bansal, Kshitij, Vilnis, Luke, Wirth, Mateo, Michel, Paul, Choy, Peter, Joshi, Pratik, Kumar, Ravin, Hashmi, Sarmad, Agrawal, Shubham, Gong, Zhitao, Fine, Jane, Warkentin, Tris, Hartman, Ale Jakse, Ni, Bin, Korevec, Kathy, Schaefer, Kelly, and Huffman, Scott

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

This paper introduces CodeGemma, a collection of specialized open code models built on top of Gemma, capable of a variety of code and natural language generation tasks. We release three model variants. CodeGemma 7B pretrained (PT) and instruction-tuned (IT) variants have remarkably resilient natural language understanding, excel in mathematical reasoning, and match code capabilities of other open models. CodeGemma 2B is a state-of-the-art code completion model designed for fast code infilling and open-ended generation in latency-sensitive settings., Comment: v1: 11 pages, 4 figures, 5 tables. v2: Update metadata

Published
2024

12. Adjoint Sensitivity Analysis on Multi-Scale Bioprocess Stochastic Reaction Network

Author

Choy, Keilung and Xie, Wei

Subjects
Quantitative Biology - Molecular Networks, Statistics - Machine Learning
Abstract

Motivated by the pressing challenges in the digital twin development for biomanufacturing systems, we introduce an adjoint sensitivity analysis (SA) approach to expedite the learning of mechanistic model parameters. In this paper, we consider enzymatic stochastic reaction networks representing a multi-scale bioprocess mechanistic model that allows us to integrate disparate data from diverse production processes and leverage the information from existing macro-kinetic and genome-scale models. To support forward prediction and backward reasoning, we develop a convergent adjoint SA algorithm studying how the perturbations of model parameters and inputs (e.g., initial state) propagate through enzymatic reaction networks and impact on output trajectory predictions. This SA can provide a sample efficient and interpretable way to assess the sensitivities between inputs and outputs accounting for their causal dependencies. Our empirical study underscores the resilience of these sensitivities and illuminates a deeper comprehension of the regulatory mechanisms behind bioprocess through sensitivities., Comment: 11 pages, 2 figures

Published
2024

18. Antibiotic-mediated selection of randomly mutagenized and cytokine-expressing oncolytic viruses

Author

Rezaei, Reza, Boulton, Stephen, Ahmadi, Mahsa, Petryk, Julia, Da Silva, Miles, Kooshki Zamani, Nika, Singaravelu, Ragunath, St-Laurent, Gabriel, Daniel, Lauren, Sadeghipour, Arezoo, Pelin, Adrian, Poutou, Joanna, Munoz Zuniga, Abril Ixchel, Choy, Clarence, Gilchrist, Victoria H., Khalid, Zumama, Austin, Bradley, Onsu, Kemal Alper, Marius, Ricardo, Ameli, Zahra, Mohammadi, Fazel, Mancinelli, Valeria, Wang, Emily, Nik-Akhtar, Abolfazl, Alwithenani, Akram, Panahi Arasi, Fatemeh, Ferguson, Stephen S. G., Hobman, Tom C., Alain, Tommy, Tai, Lee-Hwa, Ilkow, Carolina S., Diallo, Jean-Simon, Bell, John C., and Azad, Taha

Published
2024
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21. Enabling a Smooth Migration Towards Post-Quantum Security for Ethereum

Author

Fan, Xinxin, Tan, Teik Guan, Ho, Nicholas, Choy, Shi Hong, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Feng, Jun, editor, He, Songlin, editor, and Zhang, Liang-Jie, editor

Published
2025
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22. Atomic magnetometry using a metasurface polarizing beamsplitter in silicon on sapphire

Author

Yang, Xuting, Mukherjee, Pritha, Kim, Minjeong, Mei, Hongyan, Fang, Chengyu, Choi, Soyeon, Tong, Yuhan, Perlowski, Sarah, Czaplewski, David A., Dibos, Alan M., Kats, Mikhail A., and Choy, Jennifer T.

Subjects
Physics - Optics, Physics - Atomic Physics, Quantum Physics
Abstract

We demonstrate atomic magnetometry using a metasurface polarizing beamsplitter fabricated on a silicon-on-sapphire (SOS) platform. The metasurface splits a beam that is near-resonant with the rubidium atoms (795 nm) into orthogonal linear polarizations, enabling measurement of magnetically sensitive circular birefringence in a rubidium vapor through balanced polarimetry. We incorporated the metasurface into an atomic magnetometer based on nonlinear magneto-optical rotation and measured sub-nanotesla sensitivity, which is limited by low-frequency technical noise and transmission loss through the metasurface. To our knowledge, this work represents the first demonstration of SOS nanophotonics for atom-based sensing and paves the way for highly integrated, miniaturized atomic sensors with enhanced sensitivity and portability.

Published
2024

23. Improving Distant 3D Object Detection Using 2D Box Supervision

Author

Yang, Zetong, Yu, Zhiding, Choy, Chris, Wang, Renhao, Anandkumar, Anima, and Alvarez, Jose M.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Improving the detection of distant 3d objects is an important yet challenging task. For camera-based 3D perception, the annotation of 3d bounding relies heavily on LiDAR for accurate depth information. As such, the distance of annotation is often limited due to the sparsity of LiDAR points on distant objects, which hampers the capability of existing detectors for long-range scenarios. We address this challenge by considering only 2D box supervision for distant objects since they are easy to annotate. We propose LR3D, a framework that learns to recover the missing depth of distant objects. LR3D adopts an implicit projection head to learn the generation of mapping between 2D boxes and depth using the 3D supervision on close objects. This mapping allows the depth estimation of distant objects conditioned on their 2D boxes, making long-range 3D detection with 2D supervision feasible. Experiments show that without distant 3D annotations, LR3D allows camera-based methods to detect distant objects (over 200m) with comparable accuracy to full 3D supervision. Our framework is general, and could widely benefit 3D detection methods to a large extent., Comment: Accepted by CVPR 2024

Published
2024

24. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Author

Gemini Team, Georgiev, Petko, Lei, Ving Ian, Burnell, Ryan, Bai, Libin, Gulati, Anmol, Tanzer, Garrett, Vincent, Damien, Pan, Zhufeng, Wang, Shibo, Mariooryad, Soroosh, Ding, Yifan, Geng, Xinyang, Alcober, Fred, Frostig, Roy, Omernick, Mark, Walker, Lexi, Paduraru, Cosmin, Sorokin, Christina, Tacchetti, Andrea, Gaffney, Colin, Daruki, Samira, Sercinoglu, Olcan, Gleicher, Zach, Love, Juliette, Voigtlaender, Paul, Jain, Rohan, Surita, Gabriela, Mohamed, Kareem, Blevins, Rory, Ahn, Junwhan, Zhu, Tao, Kawintiranon, Kornraphop, Firat, Orhan, Gu, Yiming, Zhang, Yujing, Rahtz, Matthew, Faruqui, Manaal, Clay, Natalie, Gilmer, Justin, Co-Reyes, JD, Penchev, Ivo, Zhu, Rui, Morioka, Nobuyuki, Hui, Kevin, Haridasan, Krishna, Campos, Victor, Mahdieh, Mahdis, Guo, Mandy, Hassan, Samer, Kilgour, Kevin, Vezer, Arpi, Cheng, Heng-Tze, de Liedekerke, Raoul, Goyal, Siddharth, Barham, Paul, Strouse, DJ, Noury, Seb, Adler, Jonas, Sundararajan, Mukund, Vikram, Sharad, Lepikhin, Dmitry, Paganini, Michela, Garcia, Xavier, Yang, Fan, Valter, Dasha, Trebacz, Maja, Vodrahalli, Kiran, Asawaroengchai, Chulayuth, Ring, Roman, Kalb, Norbert, Soares, Livio Baldini, Brahma, Siddhartha, Steiner, David, Yu, Tianhe, Mentzer, Fabian, He, Antoine, Gonzalez, Lucas, Xu, Bibo, Kaufman, Raphael Lopez, Shafey, Laurent El, Oh, Junhyuk, Hennigan, Tom, Driessche, George van den, Odoom, Seth, Lucic, Mario, Roelofs, Becca, Lall, Sid, Marathe, Amit, Chan, Betty, Ontanon, Santiago, He, Luheng, Teplyashin, Denis, Lai, Jonathan, Crone, Phil, Damoc, Bogdan, Ho, Lewis, Riedel, Sebastian, Lenc, Karel, Yeh, Chih-Kuan, Chowdhery, Aakanksha, Xu, Yang, Kazemi, Mehran, Amid, Ehsan, Petrushkina, Anastasia, Swersky, Kevin, Khodaei, Ali, Chen, Gowoon, Larkin, Chris, Pinto, Mario, Yan, Geng, Badia, Adria Puigdomenech, Patil, Piyush, Hansen, Steven, Orr, Dave, Arnold, Sebastien M. R., Grimstad, Jordan, Dai, Andrew, Douglas, Sholto, Sinha, Rishika, Yadav, Vikas, Chen, Xi, Gribovskaya, Elena, Austin, Jacob, Zhao, Jeffrey, Patel, Kaushal, Komarek, Paul, Austin, Sophia, Borgeaud, Sebastian, Friso, Linda, Goyal, Abhimanyu, Caine, Ben, Cao, Kris, Chung, Da-Woon, Lamm, Matthew, Barth-Maron, Gabe, Kagohara, Thais, Olszewska, Kate, Chen, Mia, Shivakumar, Kaushik, Agarwal, Rishabh, Godhia, Harshal, Rajwar, Ravi, Snaider, Javier, Dotiwalla, Xerxes, Liu, Yuan, Barua, Aditya, Ungureanu, Victor, Zhang, Yuan, Batsaikhan, Bat-Orgil, Wirth, Mateo, Qin, James, Danihelka, Ivo, Doshi, Tulsee, Chadwick, Martin, Chen, Jilin, Jain, Sanil, Le, Quoc, Kar, Arjun, Gurumurthy, Madhu, Li, Cheng, Sang, Ruoxin, Liu, Fangyu, Lamprou, Lampros, Munoz, Rich, Lintz, Nathan, Mehta, Harsh, Howard, Heidi, Reynolds, Malcolm, Aroyo, Lora, Wang, Quan, Blanco, Lorenzo, Cassirer, Albin, Griffith, Jordan, Das, Dipanjan, Lee, Stephan, Sygnowski, Jakub, Fisher, Zach, Besley, James, Powell, Richard, Ahmed, Zafarali, Paulus, Dominik, Reitter, David, Borsos, Zalan, Joshi, Rishabh, Pope, Aedan, Hand, Steven, Selo, Vittorio, Jain, Vihan, Sethi, Nikhil, Goel, Megha, Makino, Takaki, May, Rhys, Yang, Zhen, Schalkwyk, Johan, Butterfield, Christina, Hauth, Anja, Goldin, Alex, Hawkins, Will, Senter, Evan, Brin, Sergey, Woodman, Oliver, Ritter, Marvin, Noland, Eric, Giang, Minh, Bolina, Vijay, Lee, Lisa, Blyth, Tim, Mackinnon, Ian, Reid, Machel, Sarvana, Obaid, Silver, David, Chen, Alexander, Wang, Lily, Maggiore, Loren, Chang, Oscar, Attaluri, Nithya, Thornton, Gregory, Chiu, Chung-Cheng, Bunyan, Oskar, Levine, Nir, Chung, Timothy, Eltyshev, Evgenii, Si, Xiance, Lillicrap, Timothy, Brady, Demetra, Aggarwal, Vaibhav, Wu, Boxi, Xu, Yuanzhong, McIlroy, Ross, Badola, Kartikeya, Sandhu, Paramjit, Moreira, Erica, Stokowiec, Wojciech, Hemsley, Ross, Li, Dong, Tudor, Alex, Shyam, Pranav, Rahimtoroghi, Elahe, Haykal, Salem, Sprechmann, Pablo, Zhou, Xiang, Mincu, Diana, Li, Yujia, Addanki, Ravi, Krishna, Kalpesh, Wu, Xiao, Frechette, Alexandre, Eyal, Matan, Dafoe, Allan, Lacey, Dave, Whang, Jay, Avrahami, Thi, Zhang, Ye, Taropa, Emanuel, Lin, Hanzhao, Toyama, Daniel, Rutherford, Eliza, Sano, Motoki, Choe, HyunJeong, Tomala, Alex, Safranek-Shrader, Chalence, Kassner, Nora, Pajarskas, Mantas, Harvey, Matt, Sechrist, Sean, Fortunato, Meire, Lyu, Christina, Elsayed, Gamaleldin, Kuang, Chenkai, Lottes, James, Chu, Eric, Jia, Chao, Chen, Chih-Wei, Humphreys, Peter, Baumli, Kate, Tao, Connie, Samuel, Rajkumar, Santos, Cicero Nogueira dos, Andreassen, Anders, Rakićević, Nemanja, Grewe, Dominik, Kumar, Aviral, Winkler, Stephanie, Caton, Jonathan, Brock, Andrew, Dalmia, Sid, Sheahan, Hannah, Barr, Iain, Miao, Yingjie, Natsev, Paul, Devlin, Jacob, Behbahani, Feryal, Prost, Flavien, Sun, Yanhua, Myaskovsky, Artiom, Pillai, Thanumalayan Sankaranarayana, Hurt, Dan, Lazaridou, Angeliki, Xiong, Xi, Zheng, Ce, Pardo, Fabio, Li, Xiaowei, Horgan, Dan, Stanton, Joe, Ambar, Moran, Xia, Fei, Lince, Alejandro, Wang, Mingqiu, Mustafa, Basil, Webson, Albert, Lee, Hyo, Anil, Rohan, Wicke, Martin, Dozat, Timothy, Sinha, Abhishek, Piqueras, Enrique, Dabir, Elahe, Upadhyay, Shyam, Boral, Anudhyan, Hendricks, Lisa Anne, Fry, Corey, Djolonga, Josip, Su, Yi, Walker, Jake, Labanowski, Jane, Huang, Ronny, Misra, Vedant, Chen, Jeremy, Skerry-Ryan, RJ, Singh, Avi, Rijhwani, Shruti, Yu, Dian, Castro-Ros, Alex, Changpinyo, Beer, Datta, Romina, Bagri, Sumit, Hrafnkelsson, Arnar Mar, Maggioni, Marcello, Zheng, Daniel, Sulsky, Yury, Hou, Shaobo, Paine, Tom Le, Yang, Antoine, Riesa, Jason, Rogozinska, Dominika, Marcus, Dror, Badawy, Dalia El, Zhang, Qiao, Wang, Luyu, Miller, Helen, Greer, Jeremy, Sjos, Lars Lowe, Nova, Azade, Zen, Heiga, Chaabouni, Rahma, Rosca, Mihaela, Jiang, Jiepu, Chen, Charlie, Liu, Ruibo, Sainath, Tara, Krikun, Maxim, Polozov, Alex, Lespiau, Jean-Baptiste, Newlan, Josh, Cankara, Zeyncep, Kwak, Soo, Xu, Yunhan, Chen, Phil, Coenen, Andy, Meyer, Clemens, Tsihlas, Katerina, Ma, Ada, Gottweis, Juraj, Xing, Jinwei, Gu, Chenjie, Miao, Jin, Frank, Christian, Cankara, Zeynep, Ganapathy, Sanjay, Dasgupta, Ishita, Hughes-Fitt, Steph, Chen, Heng, Reid, David, Rong, Keran, Fan, Hongmin, van Amersfoort, Joost, Zhuang, Vincent, Cohen, Aaron, Gu, Shixiang Shane, Mohananey, Anhad, Ilic, Anastasija, Tobin, Taylor, Wieting, John, Bortsova, Anna, Thacker, Phoebe, Wang, Emma, Caveness, Emily, Chiu, Justin, Sezener, Eren, Kaskasoli, Alex, Baker, Steven, Millican, Katie, Elhawaty, Mohamed, Aisopos, Kostas, Lebsack, Carl, Byrd, Nathan, Dai, Hanjun, Jia, Wenhao, Wiethoff, Matthew, Davoodi, Elnaz, Weston, Albert, Yagati, Lakshman, Ahuja, Arun, Gao, Isabel, Pundak, Golan, Zhang, Susan, Azzam, Michael, Sim, Khe Chai, Caelles, Sergi, Keeling, James, Sharma, Abhanshu, Swing, Andy, Li, YaGuang, Liu, Chenxi, Bostock, Carrie Grimes, Bansal, Yamini, Nado, Zachary, Anand, Ankesh, Lipschultz, Josh, Karmarkar, Abhijit, Proleev, Lev, Ittycheriah, Abe, Yeganeh, Soheil Hassas, Polovets, George, Faust, Aleksandra, Sun, Jiao, Rrustemi, Alban, Li, Pen, Shivanna, Rakesh, Liu, Jeremiah, Welty, Chris, Lebron, Federico, Baddepudi, Anirudh, Krause, Sebastian, Parisotto, Emilio, Soricut, Radu, Xu, Zheng, Bloxwich, Dawn, Johnson, Melvin, Neyshabur, Behnam, Mao-Jones, Justin, Wang, Renshen, Ramasesh, Vinay, Abbas, Zaheer, Guez, Arthur, Segal, Constant, Nguyen, Duc Dung, Svensson, James, Hou, Le, York, Sarah, Milan, Kieran, Bridgers, Sophie, Gworek, Wiktor, Tagliasacchi, Marco, Lee-Thorp, James, Chang, Michael, Guseynov, Alexey, Hartman, Ale Jakse, Kwong, Michael, Zhao, Ruizhe, Kashem, Sheleem, Cole, Elizabeth, Miech, Antoine, Tanburn, Richard, Phuong, Mary, Pavetic, Filip, Cevey, Sebastien, Comanescu, Ramona, Ives, Richard, Yang, Sherry, Du, Cosmo, Li, Bo, Zhang, Zizhao, Iinuma, Mariko, Hu, Clara Huiyi, Roy, Aurko, Bijwadia, Shaan, Zhu, Zhenkai, Martins, Danilo, Saputro, Rachel, Gergely, Anita, Zheng, Steven, Jia, Dawei, Antonoglou, Ioannis, Sadovsky, Adam, Gu, Shane, Bi, Yingying, Andreev, Alek, Samangooei, Sina, Khan, Mina, Kocisky, Tomas, Filos, Angelos, Kumar, Chintu, Bishop, Colton, Yu, Adams, Hodkinson, Sarah, Mittal, Sid, Shah, Premal, Moufarek, Alexandre, Cheng, Yong, Bloniarz, Adam, Lee, Jaehoon, Pejman, Pedram, Michel, Paul, Spencer, Stephen, Feinberg, Vladimir, Xiong, Xuehan, Savinov, Nikolay, Smith, Charlotte, Shakeri, Siamak, Tran, Dustin, Chesus, Mary, Bohnet, Bernd, Tucker, George, von Glehn, Tamara, Muir, Carrie, Mao, Yiran, Kazawa, Hideto, Slone, Ambrose, Soparkar, Kedar, Shrivastava, Disha, Cobon-Kerr, James, Sharman, Michael, Pavagadhi, Jay, Araya, Carlos, Misiunas, Karolis, Ghelani, Nimesh, Laskin, Michael, Barker, David, Li, Qiujia, Briukhov, Anton, Houlsby, Neil, Glaese, Mia, Lakshminarayanan, Balaji, Schucher, Nathan, Tang, Yunhao, Collins, Eli, Lim, Hyeontaek, Feng, Fangxiaoyu, Recasens, Adria, Lai, Guangda, Magni, Alberto, De Cao, Nicola, Siddhant, Aditya, Ashwood, Zoe, Orbay, Jordi, Dehghani, Mostafa, Brennan, Jenny, He, Yifan, Xu, Kelvin, Gao, Yang, Saroufim, Carl, Molloy, James, Wu, Xinyi, Arnold, Seb, Chang, Solomon, Schrittwieser, Julian, Buchatskaya, Elena, Radpour, Soroush, Polacek, Martin, Giordano, Skye, Bapna, Ankur, Tokumine, Simon, Hellendoorn, Vincent, Sottiaux, Thibault, Cogan, Sarah, Severyn, Aliaksei, Saleh, Mohammad, Thakoor, Shantanu, Shefey, Laurent, Qiao, Siyuan, Gaba, Meenu, Chang, Shuo-yiin, Swanson, Craig, Zhang, Biao, Lee, Benjamin, Rubenstein, Paul Kishan, Song, Gan, Kwiatkowski, Tom, Koop, Anna, Kannan, Ajay, Kao, David, Schuh, Parker, Stjerngren, Axel, Ghiasi, Golnaz, Gibson, Gena, Vilnis, Luke, Yuan, Ye, Ferreira, Felipe Tiengo, Kamath, Aishwarya, Klimenko, Ted, Franko, Ken, Xiao, Kefan, Bhattacharya, Indro, Patel, Miteyan, Wang, Rui, Morris, Alex, Strudel, Robin, Sharma, Vivek, Choy, Peter, Hashemi, Sayed Hadi, Landon, Jessica, Finkelstein, Mara, Jhakra, Priya, Frye, Justin, Barnes, Megan, Mauger, Matthew, Daun, Dennis, Baatarsukh, Khuslen, Tung, Matthew, Farhan, Wael, Michalewski, Henryk, Viola, Fabio, Quitry, Felix de Chaumont, Lan, Charline Le, Hudson, Tom, Wang, Qingze, Fischer, Felix, Zheng, Ivy, White, Elspeth, Dragan, Anca, Alayrac, Jean-baptiste, Ni, Eric, Pritzel, Alexander, Iwanicki, Adam, Isard, Michael, Bulanova, Anna, Zilka, Lukas, Dyer, Ethan, Sachan, Devendra, Srinivasan, Srivatsan, Muckenhirn, Hannah, Cai, Honglong, Mandhane, Amol, Tariq, Mukarram, Rae, Jack W., Wang, Gary, Ayoub, Kareem, FitzGerald, Nicholas, Zhao, Yao, Han, Woohyun, Alberti, Chris, Garrette, Dan, Krishnakumar, Kashyap, Gimenez, Mai, Levskaya, Anselm, Sohn, Daniel, Matak, Josip, Iturrate, Inaki, Chang, Michael B., Xiang, Jackie, Cao, Yuan, Ranka, Nishant, Brown, Geoff, Hutter, Adrian, Mirrokni, Vahab, Chen, Nanxin, Yao, Kaisheng, Egyed, Zoltan, Galilee, Francois, Liechty, Tyler, Kallakuri, Praveen, Palmer, Evan, Ghemawat, Sanjay, Liu, Jasmine, Tao, David, Thornton, Chloe, Green, Tim, Jasarevic, Mimi, Lin, Sharon, Cotruta, Victor, Tan, Yi-Xuan, Fiedel, Noah, Yu, Hongkun, Chi, Ed, Neitz, Alexander, Heitkaemper, Jens, Sinha, Anu, Zhou, Denny, Sun, Yi, Kaed, Charbel, Hulse, Brice, Mishra, Swaroop, Georgaki, Maria, Kudugunta, Sneha, Farabet, Clement, Shafran, Izhak, Vlasic, Daniel, Tsitsulin, Anton, Ananthanarayanan, Rajagopal, Carin, Alen, Su, Guolong, Sun, Pei, V, Shashank, Carvajal, Gabriel, Broder, Josef, Comsa, Iulia, Repina, Alena, Wong, William, Chen, Warren Weilun, Hawkins, Peter, Filonov, Egor, Loher, Lucia, Hirnschall, Christoph, Wang, Weiyi, Ye, Jingchen, Burns, Andrea, Cate, Hardie, Wright, Diana Gage, Piccinini, Federico, Zhang, Lei, Lin, Chu-Cheng, Gog, Ionel, Kulizhskaya, Yana, Sreevatsa, Ashwin, Song, Shuang, Cobo, Luis C., Iyer, Anand, Tekur, Chetan, Garrido, Guillermo, Xiao, Zhuyun, Kemp, Rupert, Zheng, Huaixiu Steven, Li, Hui, Agarwal, Ananth, Ngani, Christel, Goshvadi, Kati, Santamaria-Fernandez, Rebeca, Fica, Wojciech, Chen, Xinyun, Gorgolewski, Chris, Sun, Sean, Garg, Roopal, Ye, Xinyu, Eslami, S. M. Ali, Hua, Nan, Simon, Jon, Joshi, Pratik, Kim, Yelin, Tenney, Ian, Potluri, Sahitya, Thiet, Lam Nguyen, Yuan, Quan, Luisier, Florian, Chronopoulou, Alexandra, Scellato, Salvatore, Srinivasan, Praveen, Chen, Minmin, Koverkathu, Vinod, Dalibard, Valentin, Xu, Yaming, Saeta, Brennan, Anderson, Keith, Sellam, Thibault, Fernando, Nick, Huot, Fantine, Jung, Junehyuk, Varadarajan, Mani, Quinn, Michael, Raul, Amit, Le, Maigo, Habalov, Ruslan, Clark, Jon, Jalan, Komal, Bullard, Kalesha, Singhal, Achintya, Luong, Thang, Wang, Boyu, Rajayogam, Sujeevan, Eisenschlos, Julian, Jia, Johnson, Finchelstein, Daniel, Yakubovich, Alex, Balle, Daniel, Fink, Michael, Agarwal, Sameer, Li, Jing, Dvijotham, Dj, Pal, Shalini, Kang, Kai, Konzelmann, Jaclyn, Beattie, Jennifer, Dousse, Olivier, Wu, Diane, Crocker, Remi, Elkind, Chen, Jonnalagadda, Siddhartha Reddy, Lee, Jong, Holtmann-Rice, Dan, Kallarackal, Krystal, Liu, Rosanne, Vnukov, Denis, Vats, Neera, Invernizzi, Luca, Jafari, Mohsen, Zhou, Huanjie, Taylor, Lilly, Prendki, Jennifer, Wu, Marcus, Eccles, Tom, Liu, Tianqi, Kopparapu, Kavya, Beaufays, Francoise, Angermueller, Christof, Marzoca, Andreea, Sarcar, Shourya, Dib, Hilal, Stanway, Jeff, Perbet, Frank, Trdin, Nejc, Sterneck, Rachel, Khorlin, Andrey, Li, Dinghua, Wu, Xihui, Goenka, Sonam, Madras, David, Goldshtein, Sasha, Gierke, Willi, Zhou, Tong, Liu, Yaxin, Liang, Yannie, White, Anais, Li, Yunjie, Singh, Shreya, Bahargam, Sanaz, Epstein, Mark, Basu, Sujoy, Lao, Li, Ozturel, Adnan, Crous, Carl, Zhai, Alex, Lu, Han, Tung, Zora, Gaur, Neeraj, Walton, Alanna, Dixon, Lucas, Zhang, Ming, Globerson, Amir, Uy, Grant, Bolt, Andrew, Wiles, Olivia, Nasr, Milad, Shumailov, Ilia, Selvi, Marco, Piccinno, Francesco, Aguilar, Ricardo, McCarthy, Sara, Khalman, Misha, Shukla, Mrinal, Galic, Vlado, Carpenter, John, Villela, Kevin, Zhang, Haibin, Richardson, Harry, Martens, James, Bosnjak, Matko, Belle, Shreyas Rammohan, Seibert, Jeff, Alnahlawi, Mahmoud, McWilliams, Brian, Singh, Sankalp, Louis, Annie, Ding, Wen, Popovici, Dan, Simicich, Lenin, Knight, Laura, Mehta, Pulkit, Gupta, Nishesh, Shi, Chongyang, Fatehi, Saaber, Mitrovic, Jovana, Grills, Alex, Pagadora, Joseph, Munkhdalai, Tsendsuren, Petrova, Dessie, Eisenbud, Danielle, Zhang, Zhishuai, Yates, Damion, Mittal, Bhavishya, Tripuraneni, Nilesh, Assael, Yannis, Brovelli, Thomas, Jain, Prateek, Velimirovic, Mihajlo, Akbulut, Canfer, Mu, Jiaqi, Macherey, Wolfgang, Kumar, Ravin, Xu, Jun, Qureshi, Haroon, Comanici, Gheorghe, Wiesner, Jeremy, Gong, Zhitao, Ruddock, Anton, Bauer, Matthias, Felt, Nick, GP, Anirudh, Arnab, Anurag, Zelle, Dustin, Rothfuss, Jonas, Rosgen, Bill, Shenoy, Ashish, Seybold, Bryan, Li, Xinjian, Mudigonda, Jayaram, Erdogan, Goker, Xia, Jiawei, Simsa, Jiri, Michi, Andrea, Yao, Yi, Yew, Christopher, Kan, Steven, Caswell, Isaac, Radebaugh, Carey, Elisseeff, Andre, Valenzuela, Pedro, McKinney, Kay, Paterson, Kim, Cui, Albert, Latorre-Chimoto, Eri, Kim, Solomon, Zeng, William, Durden, Ken, Ponnapalli, Priya, Sosea, Tiberiu, Choquette-Choo, Christopher A., Manyika, James, Robenek, Brona, Vashisht, Harsha, Pereira, Sebastien, Lam, Hoi, Velic, Marko, Owusu-Afriyie, Denese, Lee, Katherine, Bolukbasi, Tolga, Parrish, Alicia, Lu, Shawn, Park, Jane, Venkatraman, Balaji, Talbert, Alice, Rosique, Lambert, Cheng, Yuchung, Sozanschi, Andrei, Paszke, Adam, Kumar, Praveen, Austin, Jessica, Li, Lu, Salama, Khalid, Perz, Bartek, Kim, Wooyeol, Dukkipati, Nandita, Baryshnikov, Anthony, Kaplanis, Christos, Sheng, XiangHai, Chervonyi, Yuri, Unlu, Caglar, Casas, Diego de Las, Askham, Harry, Tunyasuvunakool, Kathryn, Gimeno, Felix, Poder, Siim, Kwak, Chester, Miecnikowski, Matt, Dimitriev, Alek, Parisi, Aaron, Liu, Dangyi, Tsai, Tomy, Shevlane, Toby, Kouridi, Christina, Garmon, Drew, Goedeckemeyer, Adrian, Brown, Adam R., Vijayakumar, Anitha, Elqursh, Ali, Jazayeri, Sadegh, Huang, Jin, Carthy, Sara Mc, Hoover, Jay, Kim, Lucy, Kumar, Sandeep, Chen, Wei, Biles, Courtney, Bingham, Garrett, Rosen, Evan, Wang, Lisa, Tan, Qijun, Engel, David, Pongetti, Francesco, de Cesare, Dario, Hwang, Dongseong, Yu, Lily, Pullman, Jennifer, Narayanan, Srini, Levin, Kyle, Gopal, Siddharth, Li, Megan, Aharoni, Asaf, Trinh, Trieu, Lo, Jessica, Casagrande, Norman, Vij, Roopali, Matthey, Loic, Ramadhana, Bramandia, Matthews, Austin, Carey, CJ, Johnson, Matthew, Goranova, Kremena, Shah, Rohin, Ashraf, Shereen, Dasgupta, Kingshuk, Larsen, Rasmus, Wang, Yicheng, Vuyyuru, Manish Reddy, Jiang, Chong, Ijazi, Joana, Osawa, Kazuki, Smith, Celine, Boppana, Ramya Sree, Bilal, Taylan, Koizumi, Yuma, Xu, Ying, Altun, Yasemin, Shabat, Nir, Bariach, Ben, Korchemniy, Alex, Choo, Kiam, Ronneberger, Olaf, Iwuanyanwu, Chimezie, Zhao, Shubin, Soergel, David, Hsieh, Cho-Jui, Cai, Irene, Iqbal, Shariq, Sundermeyer, Martin, Chen, Zhe, Bursztein, Elie, Malaviya, Chaitanya, Biadsy, Fadi, Shroff, Prakash, Dhillon, Inderjit, Latkar, Tejasi, Dyer, Chris, Forbes, Hannah, Nicosia, Massimo, Nikolaev, Vitaly, Greene, Somer, Georgiev, Marin, Wang, Pidong, Martin, Nina, Sedghi, Hanie, Zhang, John, Banzal, Praseem, Fritz, Doug, Rao, Vikram, Wang, Xuezhi, Zhang, Jiageng, Patraucean, Viorica, Du, Dayou, Mordatch, Igor, Jurin, Ivan, Liu, Lewis, Dubey, Ayush, Mohan, Abhi, Nowakowski, Janek, Ion, Vlad-Doru, Wei, Nan, Tojo, Reiko, Raad, Maria Abi, Hudson, Drew A., Keshava, Vaishakh, Agrawal, Shubham, Ramirez, Kevin, Wu, Zhichun, Nguyen, Hoang, Liu, Ji, Sewak, Madhavi, Petrini, Bryce, Choi, DongHyun, Philips, Ivan, Wang, Ziyue, Bica, Ioana, Garg, Ankush, Wilkiewicz, Jarek, Agrawal, Priyanka, Guo, Danhao, Xue, Emily, Shaik, Naseer, Leach, Andrew, Khan, Sadh MNM, Wiesinger, Julia, Jerome, Sammy, Chakladar, Abhishek, Wang, Alek Wenjiao, Ornduff, Tina, Abu, Folake, Ghaffarkhah, Alireza, Wainwright, Marcus, Cortes, Mario, Liu, Frederick, Maynez, Joshua, Terzis, Andreas, Samangouei, Pouya, Mansour, Riham, Kępa, Tomasz, Aubet, François-Xavier, Algymr, Anton, Banica, Dan, Weisz, Agoston, Orban, Andras, Senges, Alexandre, Andrejczuk, Ewa, Geller, Mark, Santo, Niccolo Dal, Anklin, Valentin, Merey, Majd Al, Baeuml, Martin, Strohman, Trevor, Bai, Junwen, Petrov, Slav, Wu, Yonghui, Hassabis, Demis, Kavukcuoglu, Koray, Dean, Jeff, and Vinyals, Oriol

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

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.

Published
2024

25. Spatial Coherence Loss: All Objects Matter in Salient and Camouflaged Object Detection

Author

Yang, Ziyun, Choy, Kevin, and Farsiu, Sina

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Generic object detection is a category-independent task that relies on accurate modeling of objectness. We show that for accurate semantic analysis, the network needs to learn all object-level predictions that appear at any stage of learning, including the pre-defined ground truth (GT) objects and the ambiguous decoy objects that the network misidentifies as foreground. Yet, most relevant models focused mainly on improving the learning of the GT objects. A few methods that consider decoy objects utilize loss functions that only focus on the single-response, i.e., the loss response of a single ambiguous pixel, and thus do not benefit from the wealth of information that an object-level ambiguity learning design can provide. Inspired by the human visual system, which first discerns the boundaries of ambiguous regions before delving into the semantic meaning, we propose a novel loss function, Spatial Coherence Loss (SCLoss), that incorporates the mutual response between adjacent pixels into the widely-used single-response loss functions. We demonstrate that the proposed SCLoss can gradually learn the ambiguous regions by detecting and emphasizing their boundaries in a self-adaptive manner. Through comprehensive experiments, we demonstrate that replacing popular loss functions with SCLoss can improve the performance of current state-of-the-art (SOTA) salient or camouflaged object detection (SOD or COD) models. We also demonstrate that combining SCLoss with other loss functions can further improve performance and result in SOTA outcomes for different applications.

Published
2024

26. Determination of Factors Associated with Upstage in Atypical Ductal Hyperplasia to Identify Low-Risk Patients Where Active Surveillance May be an Alternative.

Author

Greene, Alexandra, Davis, Joshua, Moon, Jessica, Dubin, Iram, Cruz, Anastasia, Gupta, Megha, Moazzez, Ashkan, Ozao-Choy, Junko, Gupta, Esha, Manchandia, Tejas, Kalantari, Babak, Rahbar, Guita, and Dauphine, Christine

Subjects
Atypical ductal hyperplasia (ADH), Low-risk cohort, Predictors of upstage, Female, Humans, Biopsy, Large-Core Needle, Breast, Breast Neoplasms, Calcinosis, Carcinoma, Ductal, Breast, Carcinoma, Intraductal, Noninfiltrating, Cross-Sectional Studies, Hyperplasia, Mammography, Retrospective Studies, Watchful Waiting
Abstract

BACKGROUND: Excision is routinely recommended for atypical ductal hyperplasia (ADH) found on core biopsy given cancer upstage rates of near 20%. Identifying a cohort at low-risk for upstage may avoid low-value surgery. Objectives were to elucidate factors predictive of upstage in ADH, specifically near-complete core sampling, to potentially define a group at low upstage risk. PATIENTS AND METHODS: This retrospective, cross-sectional, multi-institutional study from 2015 to 2019 of 221 ADH lesions in 216 patients who underwent excision or active observation (≥ 12 months imaging surveillance, mean follow-up 32.6 months) evaluated clinical, radiologic, pathologic, and procedural factors for association with upstage. Radiologists prospectively examined imaging for lesional size and sampling proportion. RESULTS: Upstage occurred in 37 (16.7%) lesions, 25 (67.6%) to ductal carcinoma in situ (DCIS) and 12 (32.4%) to invasive cancer. Factors independently predictive of upstage were lesion size ≥ 10 mm (OR 5.47, 95% CI 2.03-14.77, p < 0.001), pathologic suspicion for DCIS (OR 12.29, 95% CI 3.24-46.56, p < 0.001), and calcification distribution pattern (OR 8.08, 95% CI 2.04-32.00, p = 0.003, regional; OR 19.28, 95% CI 3.47-106.97, p < 0.001, linear). Near-complete sampling was not correlated with upstage (p = 0.64). All three significant predictors were absent in 65 (29.4%) cases, with a 1.5% upstage rate. CONCLUSIONS: The upstage rate among 221 ADH lesions was 16.7%, highest in lesions ≥ 10 mm, with pathologic suspicion of DCIS, and linear/regional calcifications on mammography. Conversely, 30% of the cohort exhibited all low-risk factors, with an upstage rate < 2%, suggesting that active surveillance may be permissible in lieu of surgery.

Published
2024

27. Factors Influencing Speech Pathology Practice in Dysphagia after Stroke: A Qualitative Focus Group Study

Author

Jacinda Choy, Fereshteh Pourkazemi, Hans Bogaardt, Caitlin Anderson, Shing Yee Chai, and Roxanna N. Pebdani

Abstract

Background: Dysphagia affects over half of adults after stroke. Dysphagia rehabilitation aims to improve swallowing and reduce negative outcomes for these adults. However, significant variability exists in dysphagia rehabilitation. Research is needed to explore the underlying clinician-specific and workplace factors that contribute to variability in dysphagia rehabilitation after stroke. Aim: To explore factors influencing speech pathology practice in dysphagia rehabilitation after stroke. Methods & Procedures: We used a phenomenological approach with an interpretivist perspective. Twenty speech pathologists working in dysphagia rehabilitation participated from different workplace settings around Australia. Five semi-structured focus groups were conducted online. Data were inductively analysed using thematic analysis with a coding reliability method. Outcomes & Results: Four themes were discussed within focus groups: (1) relationship between experienced and less experienced clinicians: 'Following what other people have done', (2) need for collaborative learning: 'A safe space to share and train', (3) variation between settings impacts on continuity of care: 'There's a difference between community and acute', and (4) working effectively with multidisciplinary teams (MDT): 'An MDT which can listen to the voice of speech pathology'. Conclusions & Implications: Relationships between senior and junior speech pathologists, within speech pathology and MDT, and across inpatient and community settings influenced speech pathology practice. Flattened hierarchies in speech pathology, collaborative learning in workplaces, mutual respect within teams and connection across inpatient and community settings could improve the quality and consistency of dysphagia rehabilitation after stroke.

Published
2024
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35. Rapid Estimation of Left Ventricular Contractility with a Physics-Informed Neural Network Inverse Modeling Approach

Author

Naghavi, Ehsan, Wang, Haifeng, Fan, Lei, Choy, Jenny S., Kassab, Ghassan, Baek, Seungik, and Lee, Lik-Chuan

Subjects
Computer Science - Computational Engineering, Finance, and Science
Abstract

Physics-based computer models based on numerical solution of the governing equations generally cannot make rapid predictions, which in turn, limits their applications in the clinic. To address this issue, we developed a physics-informed neural network (PINN) model that encodes the physics of a closed-loop blood circulation system embedding a left ventricle (LV). The PINN model is trained to satisfy a system of ordinary differential equations (ODEs) associated with a lumped parameter description of the circulatory system. The model predictions have a maximum error of less than 5% when compared to those obtained by solving the ODEs numerically. An inverse modeling approach using the PINN model is also developed to rapidly estimate model parameters (in $\sim$ 3 mins) from single-beat LV pressure and volume waveforms. Using synthetic LV pressure and volume waveforms generated by the PINN model with different model parameter values, we show that the inverse modeling approach can recover the corresponding ground truth values, which suggests that the model parameters are unique. The PINN inverse modeling approach is then applied to estimate LV contractility indexed by the end-systolic elastance $E_{es}$ using waveforms acquired from 11 swine models, including waveforms acquired before and after administration of dobutamine (an inotropic agent) in 3 animals. The estimated $E_{es}$ is about 58% to 284% higher for the data associated with dobutamine compared to those without, which implies that this approach can be used to estimate LV contractility using single-beat measurements. The PINN inverse modeling can potentially be used in the clinic to simultaneously estimate LV contractility and other physiological parameters from single-beat measurements.

Published
2024
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36. Energy Landscapes for the Quantum Approximate Optimisation Algorithm

Author

Choy, Boy and Wales, David J.

Subjects
Quantum Physics
Abstract

Variational quantum algorithms (VQAs) have demonstrated considerable potential in solving NP-hard combinatorial problems in the contemporary near intermediate-scale quantum (NISQ) era. The quantum approximate optimisation algorithm (QAOA) is one such algorithm, used in solving the maximum cut (Max-Cut) problem for a given graph by successive implementation of $L$ quantum circuit layers within a corresponding Trotterised ansatz. The challenge of exploring the cost function of VQAs arising from an exponential proliferation of local minima with increasing circuit depth has been well-documented. However, fewer studies have investigated the impact of circuit depth on QAOA performance in finding the correct Max-Cut solution. Here, we employ basin-hopping global optimisation methods to navigate the energy landscapes for QAOA ans\"atze for various graphs, and analyse QAOA performance in finding the correct Max-Cut solution. The structure of the solution space is also investigated using discrete path sampling to build databases of local minima and the transition states that connect them, providing insightful visualisations using disconnectivity graphs. We find that the corresponding landscapes generally have a single funnel organisation, which makes it relatively straightforward to locate low-lying minima with good Max-Cut solution probabilities. In some cases below the adiabatic limit the second lowest local minimum may even yield a higher solution probability than the global minimum. This important observation has motivated us to develop broader metrics in evaluating QAOA performance, based on collections of minima obtained from basin-hopping global optimisation. Hence we establish expectation thresholds in elucidating useful solution probabilities from local minima, an approach that may provide significant gains in elucidating reasonable solution probabilities from local minima.

Published
2024

37. Impact of Upadacitinib on Laboratory Parameters and Related Adverse Events in Patients with RA: Integrated Data Up to 6.5 Years.

Author

Choy, Ernest, Furst, Daniel, Vencovský, Jiří, Wilson, Anthony, Burmester, Gerd, Coombs, Derek, Penn, Sara, Khan, Nasser, Yee, Jillian, Rahawi, Kassim, McInnes, Iain, Giles, Jon, Lane, Nancy, and Charles-Schoeman, Christina

Subjects
JAK inhibitor, Laboratory parameters, Rheumatoid arthritis, Upadacitinib
Abstract

INTRODUCTION: Upadacitinib (UPA) is a Janus kinase inhibitor that has demonstrated efficacy in moderate-to-severe rheumatoid arthritis (RA) with an acceptable safety profile. We investigated laboratory parameter changes in UPA RA clinical trials. METHODS: Pooled data from six randomized trials in the SELECT phase 3 program were included. Key laboratory parameters and safety data were measured for UPA 15 and 30 mg once daily (QD), adalimumab (ADA) 40 mg every other week + methotrexate (MTX), and MTX monotherapy. Exposure-adjusted event rates (EAERs) of adverse events were calculated. RESULTS: A total of 3209 patients receiving UPA 15 mg QD (10 782.7 patient-years [PY]), 1204 patients receiving UPA 30 mg QD (3162.5 PY), 579 patients receiving ADA + MTX (1573.2 PY), and 314 patients receiving MTX monotherapy (865.1 PY) were included, representing up to 6.5 years of total exposure. Decreases in mean levels of hemoglobin, neutrophils, and lymphocytes, and increases in mean levels of liver enzymes and creatinine phosphokinase were observed with UPA, with grade 3 or 4 changes observed in some patients. Mean low- and high-density lipoprotein cholesterol ratios remained stable for patients receiving UPA 15 mg QD. EAERs of anemia and neutropenia occurred at generally consistent rates between UPA and active comparators (3.1-4.3 and 1.7-5.0 events [E]/100 PY across treatment groups, respectively). Rates of hepatic disorder were higher with MTX monotherapy, UPA 15 mg and UPA 30 mg (10.8, 9.7, and 11.0 E/100 PY, respectively) versus ADA + MTX (6.4 E/100 PY). Rates of lymphopenia were highest with MTX monotherapy (3.2 E/100 PY). Treatment discontinuations due to laboratory-related events were rare, occurring in 1.1% and 2.2% of patients treated with UPA 15 and 30 mg QD, respectively. CONCLUSIONS: The results of this integrated long-term analysis of laboratory parameters continue to support an acceptable safety profile of UPA 15 mg QD for moderate-to-severe RA.

Published
2024

38. Which psychological needs profile exhibits higher engagement and favorable attitudes toward interprofessional education? A cluster analysis among health and social care Hong Kong students

Author

Dizon, John Ian Wilzon T., He, Qing, Shen, Xiaoai, Luk, Pauline, Chong, Doris Yin Kei, Chan, Karen Man Kei, Chan, Chad Wing Nga, Chan, Sarah So Ching, Choy, Jacky Chak Pui, Lam, Chor Yin, Lee, Diana Pui Ling, Manio, Michael Magtoto, Ng, Zoe Lai Han, Ng, Terry Tin Wai, Orlu, Mine, Sit, Cecilia Tin Yan, Szeto, Grace Pui Yuk, Yuen, Jacqueline Kwan Yuk, Tipoe, George L., and Ganotice, Jr, Fraide A.

Published
2024
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41. Multiplatform metabolomic interlaboratory study of a whole human stool candidate reference material from omnivore and vegan donors

Author

Cruz, Abraham Kuri, Alves, Marina Amaral, Andresson, Thorkell, Bayless, Amanda L., Bloodsworth, Kent J., Bowden, John A., Bullock, Kevin, Burnet, Meagan C., Neto, Fausto Carnevale, Choy, Angelina, Clish, Clary B., Couvillion, Sneha P., Cumeras, Raquel, Dailey, Lucas, Dallmann, Guido, Davis, W. Clay, Deik, Amy A., Dickens, Alex M., Djukovic, Danijel, Dorrestein, Pieter C., Eder, Josie G., Fiehn, Oliver, Flores, Roberto, Gika, Helen, Hagiwara, Kehau A., Pham, Tuan Hai, Harynuk, James J., Aristizabal-Henao, Juan J., Hoyt, David W., Jean-François, Focant, Kråkström, Matilda, Kumar, Amit, Kyle, Jennifer E., Lamichhane, Santosh, Li, Yuan, Nam, Seo Lin, Mandal, Rupasri, de la Mata, A. Paulina, Meehan, Michael J., Meikopoulos, Thomas, Metz, Thomas O., Mouskeftara, Thomai, Munoz, Nathalie, Gowda, G. A. Nagana, Orešic, Matej, Panitchpakdi, Morgan, Pierre-Hugues, Stefanuto, Raftery, Daniel, Rushing, Blake, Schock, Tracey, Seifried, Harold, Servetas, Stephanie, Shen, Tong, Sumner, Susan, Carrillo, Kieran S. Tarazona, Thibaut, Dejong, Trejo, Jesse B., Van Meulebroek, Lieven, Vanhaecke, Lynn, Virgiliou, Christina, Weldon, Kelly C., Wishart, David S., Zhang, Lu, Zheng, Jiamin, and Da Silva, Sandra

Published
2024
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