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1. A short-term comparison of wheat straw and poplar wood chips used as litter in tiestalls on hygiene, milk, and behavior of lactating dairy cows

Author

M. Simoni, N. Mezzasalma, R. Pitino, C.L. Manuelian, C. Spadini, M. Iannarelli, S.L. Montanaro, A. Summer, C.S. Cabassi, M. De Marchi, and F. Righi

Subjects
milk composition, total bacterial count, bedding materials, organic, Dairy processing. Dairy products, SF250.5-275, Dairying, SF221-250
Abstract

ABSTRACT: A short-term study was conducted to compare the effect of using poplar wood chips (PWC) instead of wheat straw (WS) litter in dairy cows. A total of 38 lactating Holstein cows (204 ± 119 days in milk, 26.9 ± 6.5 kg of milk yield [MY]) were housed in a tiestall farm for a 10-d trial including 5 d of adaptation followed by 5 sampling days (from d 5 to 10). Cows were divided into 2 homogeneous groups: one group was bedded with WS, and the second with PWC. Both litter materials were provided in the amount of 7 kg/stall per d. Each group was composed of 3 subgroups of 6 or 7 cows; the subgroups were physically separated along the feeding line by wooden boards. During the sampling days, fecal composition, used litter composition, and bacterial count (Clostridium spp., Salmonella spp., Escherichia coli, Lactobacillus, and total bacterial count) were analyzed by subgroup twice a day. On d 1 and from d 5 to 10, udder hygiene score and cow cleanliness score were also evaluated individually twice a day. Meanwhile MY, milk hygiene (total bacterial count [TBC], coliform bacterial count [CBC], and spore-forming units [SFU]) and quality were measured and analyzed from 9 animals per group. Moreover, individual animal behavior (body position and behavioral traits) and subgroup dry matter intake were measured on d 9 and 10. Fecal dry matter did not differ between groups, PWC had the lowest used litter moisture and N content favoring the highest clean cow frequency, but also gave rise to the greatest used litter microbial contamination. The MY, milk quality, TBC, SFU, and CBC were similar. The lying behavior frequency was similar between groups. However, the PWC group showed the lowest sleeping frequency, the highest frequency of other behaviors (including discomfort signs), and the lowest dry matter intake. However, despite this apparent reduction in cow comfort, no biologically important differences were observed in this short-term study between cows on PWC and WS in milk production or hygiene.

Published
2023
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2. Educator Perceptions of Self-Efficacy and Preparedness to Work in High Poverty Schools

Author

Kristen Carroll, Juliann Sergi McBrayer, Marlynn Griffin, Taylor Norman, Summer Pannell, and Mary Josephine Carney

Abstract

This study examined the perceptions of educators to determine if they felt that they were adequately prepared to teach in a high poverty school setting. The participants, educators from four school districts, completed a survey based on their perceptions of their own level of self-efficacy and preparedness to work in high poverty schools. The analyses indicated that, overall, educators felt well-prepared with limited supporting evidence to work in high poverty schools in the areas of student learning and engagement, which included curriculum and pedagogy, differentiation, and assessment. Findings further indicated a need for professional learning so educators can best support students in the high poverty setting in terms of problem solving when issues arise in the classroom. The implications for practice suggest that educators need support to ensure a high level of preparedness to work in high-poverty schools, as educators need to have a high level of self-efficacy to positively impact student success. Future research could help pinpoint specific areas of need within student learning and engagement to determine how to best develop professional learning opportunities that are purposeful, collaborative, and sustainable. Additional research should be conducted to determine if teachers' levels of self-efficacy and perceptions of preparedness are correlated to leadership style.

Published
2024

3. Closing the Gap: The Relationship between School Climate and Student Achievement in the Middle School Sector

Author

Brad Boykin, Juliann Sergi McBrayer, Summer Pannell, Richard E. Cleveland, Suzanne B. Miller, and Mary Josephine Carney

Abstract

School leaders must make decisions and implement strategies to improve a school climate and student achievement, and it benefits them to understand which areas of school climate have the greatest impact on student achievement. The State of Georgia measures school climate and student achievement with its school accountability measure, the College and Career Ready Performance Index (CCRPI). This study employed a quantitative research design using archival data that was publicly available from CCRPI data for a two year span to examine the relationship between school climate and student achievement statewide in the middle grade sector. Findings revealed that all four components of school climate (i.e., Survey Score, Discipline Score, Safe & Substance-Free Learning Environment Score, and Attendance Score) were significantly related to student achievement. Additionally, the Survey Score and Discipline Score were the most significant predictors of student achievement. These findings provide school leaders with essential information to strategically make decisions involving processes and procedures that impact these areas. Future research is needed to determine whether the relationship between school climate and student achievement is similar for elementary, middle, or high schools. In addition, separating the data into rural, suburban, and urban schools and running similar analyses may also help administrators improve school climate and student achievement.

Published
2024

4. Planning In Natural Language Improves LLM Search For Code Generation

Author

Wang, Evan, Cassano, Federico, Wu, Catherine, Bai, Yunfeng, Song, Will, Nath, Vaskar, Han, Ziwen, Hendryx, Sean, Yue, Summer, and Zhang, Hugh

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

While scaling training compute has led to remarkable improvements in large language models (LLMs), scaling inference compute has not yet yielded analogous gains. We hypothesize that a core missing component is a lack of diverse LLM outputs, leading to inefficient search due to models repeatedly sampling highly similar, yet incorrect generations. We empirically demonstrate that this lack of diversity can be mitigated by searching over candidate plans for solving a problem in natural language. Based on this insight, we propose PLANSEARCH, a novel search algorithm which shows strong results across HumanEval+, MBPP+, and LiveCodeBench (a contamination-free benchmark for competitive coding). PLANSEARCH generates a diverse set of observations about the problem and then uses these observations to construct plans for solving the problem. By searching over plans in natural language rather than directly over code solutions, PLANSEARCH explores a significantly more diverse range of potential solutions compared to baseline search methods. Using PLANSEARCH on top of Claude 3.5 Sonnet achieves a state-of-the-art pass@200 of 77.0% on LiveCodeBench, outperforming both the best score achieved without search (pass@1 = 41.4%) and using standard repeated sampling (pass@200 = 60.6%). Finally, we show that, across all models, search algorithms, and benchmarks analyzed, we can accurately predict performance gains due to search as a direct function of the diversity over generated ideas.

Published
2024

5. LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yet

Author

Li, Nathaniel, Han, Ziwen, Steneker, Ian, Primack, Willow, Goodside, Riley, Zhang, Hugh, Wang, Zifan, Menghini, Cristina, and Yue, Summer

Subjects
Computer Science - Machine Learning, Computer Science - Computation and Language, Computer Science - Cryptography and Security, Computer Science - Computers and Society
Abstract

Recent large language model (LLM) defenses have greatly improved models' ability to refuse harmful queries, even when adversarially attacked. However, LLM defenses are primarily evaluated against automated adversarial attacks in a single turn of conversation, an insufficient threat model for real-world malicious use. We demonstrate that multi-turn human jailbreaks uncover significant vulnerabilities, exceeding 70% attack success rate (ASR) on HarmBench against defenses that report single-digit ASRs with automated single-turn attacks. Human jailbreaks also reveal vulnerabilities in machine unlearning defenses, successfully recovering dual-use biosecurity knowledge from unlearned models. We compile these results into Multi-Turn Human Jailbreaks (MHJ), a dataset of 2,912 prompts across 537 multi-turn jailbreaks. We publicly release MHJ alongside a compendium of jailbreak tactics developed across dozens of commercial red teaming engagements, supporting research towards stronger LLM defenses.

Published
2024

6. A method for verifying the generalized Riemann hypothesis

Author

Hiary, Ghaith, Ireland, Summer, and Kyi, Megan

Subjects
Mathematics - Number Theory, 11M06, 11Y35
Abstract

Riemann numerically approximated at least three zeta zeros. According to Edwards, Riemann even took steps to verify that the lowest zero he computed was indeed the first zeta zero. This approach to verification is developed, improved, and generalized to a large class of $L$-functions. Results of numerical calculations demonstrating the efficacy of the method are presented., Comment: 28 pages, 3 figures

Published
2024

7. Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compression

Author

Feng, Hao, Zhang, Boyuan, Ye, Fanjiang, Si, Min, Chu, Ching-Hsiang, Tian, Jiannan, Yin, Chunxing, Deng, Summer, Hao, Yuchen, Balaji, Pavan, Geng, Tong, and Tao, Dingwen

Subjects
Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

DLRM is a state-of-the-art recommendation system model that has gained widespread adoption across various industry applications. The large size of DLRM models, however, necessitates the use of multiple devices/GPUs for efficient training. A significant bottleneck in this process is the time-consuming all-to-all communication required to collect embedding data from all devices. To mitigate this, we introduce a method that employs error-bounded lossy compression to reduce the communication data size and accelerate DLRM training. We develop a novel error-bounded lossy compression algorithm, informed by an in-depth analysis of embedding data features, to achieve high compression ratios. Moreover, we introduce a dual-level adaptive strategy for error-bound adjustment, spanning both table-wise and iteration-wise aspects, to balance the compression benefits with the potential impacts on accuracy. We further optimize our compressor for PyTorch tensors on GPUs, minimizing compression overhead. Evaluation shows that our method achieves a 1.38$\times$ training speedup with a minimal accuracy impact., Comment: camera-ready version for SC '24

Published
2024

8. Not the Only Novice in the Room: Partnerships and Belongingness in a Research Immersion Program

Author

Katherine Ann Ayers, Robyn Ann Pennella, Olayinka Mohorn-Mintah, Summer Jane Jasper, and Susan Naomi Nordstrom

Abstract

Lack of access to STEMM mentors has been identified as a critical barrier to biomedical research careers, leading to a lack of diversity in this field. To address such a barrier, the National Institutes of Health invested funds to support institutions in developing research immersion programs to provide "underrepresented" students with mentored research experiences. While providing access and opportunity for research experiences is an important equity endeavor, a focus solely on broadening participation neglects the role of institutions in perpetuating hegemonic views of science. Institutions often fail to recognize how entanglements of affect and emotion shape youth experiences in these programs and work to (de)legitimize their sense of belonging in science and perpetuate the notion of science as for an exclusive few. In this paper, we describe findings from a project aimed at understanding the entanglement of emotion and affect in a research immersion program and how these entanglements shaped participants' sense of belongingness in the program and research more broadly. Drawing on a poststructural feminist framework, we come to understand how individual histories and emotional experiences with racial and gender stereotypes work at the meta-affective level to contract feelings of belongingness in science for students.

Published
2024
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9. Recreational Cannabis Legalization and Adolescent Sexual Risk Behaviors

Author

Natalie Grafft, Barbara Mendez-Campos, Danielle T. Walker, and Summer Sherburne Hawkins

Abstract

Little is known about the implications of cannabis legalization on adolescent health behaviors. This study sought to examine the relationship between recreational cannabis legalization and adolescent sexual risk behaviors. Data on 371,487 respondents who have ever had sexual intercourse from 41 states in the 2005 to 2019 Youth Risk Behavior Survey was linked with data on state-level cannabis policies. Difference-in-differences logistic regression models were conducted. Recreational cannabis legalization was associated with a reduced likelihood of respondents having 2+ sexual partners (adjusted OR [aOR] 0.63, 95% CI [0.47, 0.84]), but had no association with condom use (aOR 0.94, 95% CI [0.80, 1.11]). Changing social norms around cannabis is a potential pathway linking cannabis legalization to a reduction in sexual risk behaviors.

Published
2024
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10. An Open and Large-Scale Dataset for Multi-Modal Climate Change-aware Crop Yield Predictions

Author

Lin, Fudong, Guillot, Kaleb, Crawford, Summer, Zhang, Yihe, Yuan, Xu, and Tzeng, Nian-Feng

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

Precise crop yield predictions are of national importance for ensuring food security and sustainable agricultural practices. While AI-for-science approaches have exhibited promising achievements in solving many scientific problems such as drug discovery, precipitation nowcasting, etc., the development of deep learning models for predicting crop yields is constantly hindered by the lack of an open and large-scale deep learning-ready dataset with multiple modalities to accommodate sufficient information. To remedy this, we introduce the CropNet dataset, the first terabyte-sized, publicly available, and multi-modal dataset specifically targeting climate change-aware crop yield predictions for the contiguous United States (U.S.) continent at the county level. Our CropNet dataset is composed of three modalities of data, i.e., Sentinel-2 Imagery, WRF-HRRR Computed Dataset, and USDA Crop Dataset, for over 2200 U.S. counties spanning 6 years (2017-2022), expected to facilitate researchers in developing versatile deep learning models for timely and precisely predicting crop yields at the county-level, by accounting for the effects of both short-term growing season weather variations and long-term climate change on crop yields. Besides, we develop the CropNet package, offering three types of APIs, for facilitating researchers in downloading the CropNet data on the fly over the time and region of interest, and flexibly building their deep learning models for accurate crop yield predictions. Extensive experiments have been conducted on our CropNet dataset via employing various types of deep learning solutions, with the results validating the general applicability and the efficacy of the CropNet dataset in climate change-aware crop yield predictions., Comment: 13 pages

Published
2024
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11. A Careful Examination of Large Language Model Performance on Grade School Arithmetic

Author

Zhang, Hugh, Da, Jeff, Lee, Dean, Robinson, Vaughn, Wu, Catherine, Song, Will, Zhao, Tiffany, Raja, Pranav, Slack, Dylan, Lyu, Qin, Hendryx, Sean, Kaplan, Russell, Lunati, Michele, and Yue, Summer

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

Large language models (LLMs) have achieved impressive success on many benchmarks for mathematical reasoning. However, there is growing concern that some of this performance actually reflects dataset contamination, where data closely resembling benchmark questions leaks into the training data, instead of true reasoning ability. To investigate this claim rigorously, we commission Grade School Math 1000 (GSM1k). GSM1k is designed to mirror the style and complexity of the established GSM8k benchmark, the gold standard for measuring elementary mathematical reasoning. We ensure that the two benchmarks are comparable across important metrics such as human solve rates, number of steps in solution, answer magnitude, and more. When evaluating leading open- and closed-source LLMs on GSM1k, we observe accuracy drops of up to 13%, with several families of models (e.g., Phi and Mistral) showing evidence of systematic overfitting across almost all model sizes. At the same time, many models, especially those on the frontier, (e.g., Gemini/GPT/Claude) show minimal signs of overfitting. Further analysis suggests a positive relationship (Spearman's r^2=0.32) between a model's probability of generating an example from GSM8k and its performance gap between GSM8k and GSM1k, suggesting that many models may have partially memorized GSM8k.

Published
2024

12. The 'Assessment of Bullying Experiences Questionnaire' ('ABE') for Neurodivergent Youth: Establishing Scoring Criteria and Clinical Thresholds

Author

Hannah E. Morton, Summer B. Bottini, Alana J. McVey, Brooke E. Magnus, Jennifer M. Gillis, and Raymond G. Romanczyk

Abstract

Neurodivergent youth, such as those with diagnoses of autism spectrum disorder (ASD) or attention-deficit/hyperactivity disorder (ADHD), are frequently victimized by their peers. This bullying victimization may occur in unique ways that are not captured by existing questionnaires. The "Assessment of Bullying Experiences" ("ABE") is a questionnaire specific to neurodivergent youth with acceptable psychometric properties in autistic and non-autistic samples. This study sought to establish scoring criteria and clinical cutoffs for the "ABE." Participants were 335 parents of youth (ages 6-17) characterized as autistic, having ADHD, or from a community comparison sample. Receiver operating characteristic (ROC) analyses were used to (1) compare accuracy of the "ABE" total score in predicting bullying victimization experiences and (2) identify optimal questionnaire cutoffs. Clinical utility of these cutoffs was confirmed using ANCOVA analyses. The "ABE" had good-to-excellent discrimination for bullying victimization across groups. Bullied youth had greater symptoms of anxiety/depression compared to youth who were not bullied; this relationship was partially explained by autism characteristics. "ABE" clinical cutoffs generated bullying victimization prevalence rates consistent with existing literature (50% autism, 28% non-autism). The "ABE" is a useful measure to streamline bullying victimization assessment in neurodivergent youth and inform group-specific intervention.

Published
2024
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13. Methods of Biotechnology Biotech BioBrawl: A Competition-Based Learning Approach to Biotechnology

Author

Nicholas Skiados, Rabeea Summer Rehman, Megan Riley, and Kersten T. Schroeder

Abstract

Biotechnology students entering the workforce often struggle in their application of textbook knowledge to build the solutions that we see in science and health fields today. Some students may be naive to what a job in the biotechnology industry can encompass. Students should graduate having a firm grasp of the prospects of their field and have the confidence to begin contributing to the growth of the industry. For this, it is necessary for students to be able to start practising applications in their coursework before they graduate. A competition titled "Biotech BioBrawl" was incorporated in the University of Central Florida's Methods in Biotechnology (MCB4721C/MCB5722C) course agenda during the semester of Fall 2021. This competition challenged students to harness innovation and applied science in a group setting that led to the development and pitch of an original idea to a panel of judges with various biotechnology industry experiences.

Published
2024
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14. Comparison of S100A8 and PRAME as biomarkers for distinguishing melanoma from melanocytic naevus: a case–control analysis

Author

Hai, Josephine, Meyer, Summer N, Wong, Samantha L, Li, Yueju, Simmons, Elanee, Miglioretti, Diana, Fung, Maxwell A, and Kiuru, Maija

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer, 4.2 Evaluation of markers and technologies, Humans, Melanoma, Calgranulin A, Case-Control Studies, Diagnosis, Differential, Biomarkers, Tumor, Nevus, Pigmented, Antigens, Neoplasm, Skin Neoplasms, Immunohistochemistry, ROC Curve, Sensitivity and Specificity, Male, Female, Middle Aged, Adult, Clinical Sciences, Dermatology & Venereal Diseases, Clinical sciences
Abstract

BackgroundS100A8 is a melanoma biomarker expressed in the melanoma-associated epidermal keratinocytes, but its diagnostic utility has not been compared with other biomarkers, including PRAME.ObjectivesTo compare the utility of S100A8 and PRAME immunohistochemistry (IHC) in the differential diagnosis of melanoma and naevi in a case-control study.MethodsA previously described cohort of 209 melanomas (case samples) and naevi (control samples) dual-immunostained for S100A8 and PRAME were included. For S100A8, previously reported scores indicating the proportion of tumour-associated epidermis stained (0 = indeterminate; 1 = 0-4%; 2 = 5-25%; 3 = 26-50%; 4 = 51-75%; 5 = > 75%) were utilized. PRAME IHC was reviewed by at least two reviewers and a consensus score assigned, with score indicating the proportion of tumour stained (0 = indeterminate; 1 = 0%; 2 = 1-50%; 3 = > 50%). A positive test was defined as > 50% staining.ResultsThe area under the receiver operating characteristic curves for S100A8 (0.833) and PRAME (0.874) were not significantly different from each other (P = 0.22). The diagnostic sensitivity and specificity were 42.4% [95% confidence interval (CI) 32.6-52.8%] and 98.2% (95% CI 93.6-99.8%) for S100A8, and 79.8% (95% CI 70.5-87.2%) and 87.3% (95% CI 79.6-92.9%) for PRAME, respectively. A combined test requiring both S100A8 and PRAME IHC positivity had a sensitivity of 39.4% (95% CI 29.7-49.7%) and specificity of 99.1% (95% CI 95.0-100.0%).ConclusionsS100A8 and PRAME have utility in the diagnostic workup of melanoma, with S100A8 being more specific and PRAME being more sensitive when using this threshold. Our findings suggest that these two immunohistochemical markers may favourably complement one another to improve the detection of melanoma.

Published
2024

15. Fusing Climate Data Products using a Spatially Varying Autoencoder

Author

Johnson, Jacob A., Heaton, Matthew J., Christensen, William F., Warr, Lynsie R., and Rupper, Summer B.

Subjects
Statistics - Applications, Computer Science - Machine Learning
Abstract

Autoencoders are powerful machine learning models used to compress information from multiple data sources. However, autoencoders, like all artificial neural networks, are often unidentifiable and uninterpretable. This research focuses on creating an identifiable and interpretable autoencoder that can be used to meld and combine climate data products. The proposed autoencoder utilizes a Bayesian statistical framework, allowing for probabilistic interpretations while also varying spatially to capture useful spatial patterns across the various data products. Constraints are placed on the autoencoder as it learns patterns in the data, creating an interpretable consensus that includes the important features from each input. We demonstrate the utility of the autoencoder by combining information from multiple precipitation products in High Mountain Asia., Comment: 13 pages, 7 figures

Published
2024

16. Using Gaussian Processes to detect AGN flares

Author

McLaughlin, Summer A. J., Mullaney, James R., and Littlefair, Stuart P.

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Astrophysics of Galaxies
Abstract

A key feature of active galactic nuclei (AGN) is their variability across all wavelengths. Typically, AGN vary by a few tenths of a magnitude or more over periods lasting from hours to years. By contrast, extreme variability of AGN -- large luminosity changes that are a significant departure from the baseline variability -- are known as AGN flares. These events are rare and their timescales poorly constrained, with most of the literature focusing on individual events. It has been suggested that extreme AGN variability including flares can provide insights into the accretion processes in the disk. With surveys such as the Legacy Survey of Space and Time (LSST) promising millions of transient detections per night in the coming decade, there is a need for fast and efficient classification of AGN flares. The problem with the systematic detection of AGN flares is the requirement to detect them against a stochastically variable baseline; the ability to define a signal as a significant departure from the ever-present variability is a statistical challenge. Recently, Gaussian Processes (GPs) have revolutionised the analysis of time-series data in many areas of astronomical research. They have, however, seen limited uptake within the field of transient detection and classification. Here we investigate the efficacy of Gaussian Processes to detect AGN flares in both simulated and real optical light curves. We show that GP analysis can successfully detect AGN flares with a false-positive rate of less than seven per cent, and we present examples of AGN light curves that show extreme variability., Comment: 14 pages, 25 figures, accepted for publication in MNRAS

Published
2024
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17. The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning

Author

Li, Nathaniel, Pan, Alexander, Gopal, Anjali, Yue, Summer, Berrios, Daniel, Gatti, Alice, Li, Justin D., Dombrowski, Ann-Kathrin, Goel, Shashwat, Phan, Long, Mukobi, Gabriel, Helm-Burger, Nathan, Lababidi, Rassin, Justen, Lennart, Liu, Andrew B., Chen, Michael, Barrass, Isabelle, Zhang, Oliver, Zhu, Xiaoyuan, Tamirisa, Rishub, Bharathi, Bhrugu, Khoja, Adam, Zhao, Zhenqi, Herbert-Voss, Ariel, Breuer, Cort B., Marks, Samuel, Patel, Oam, Zou, Andy, Mazeika, Mantas, Wang, Zifan, Oswal, Palash, Lin, Weiran, Hunt, Adam A., Tienken-Harder, Justin, Shih, Kevin Y., Talley, Kemper, Guan, John, Kaplan, Russell, Steneker, Ian, Campbell, David, Jokubaitis, Brad, Levinson, Alex, Wang, Jean, Qian, William, Karmakar, Kallol Krishna, Basart, Steven, Fitz, Stephen, Levine, Mindy, Kumaraguru, Ponnurangam, Tupakula, Uday, Varadharajan, Vijay, Wang, Ruoyu, Shoshitaishvili, Yan, Ba, Jimmy, Esvelt, Kevin M., Wang, Alexandr, and Hendrycks, Dan

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Computers and Society
Abstract

The White House Executive Order on Artificial Intelligence highlights the risks of large language models (LLMs) empowering malicious actors in developing biological, cyber, and chemical weapons. To measure these risks of malicious use, government institutions and major AI labs are developing evaluations for hazardous capabilities in LLMs. However, current evaluations are private, preventing further research into mitigating risk. Furthermore, they focus on only a few, highly specific pathways for malicious use. To fill these gaps, we publicly release the Weapons of Mass Destruction Proxy (WMDP) benchmark, a dataset of 3,668 multiple-choice questions that serve as a proxy measurement of hazardous knowledge in biosecurity, cybersecurity, and chemical security. WMDP was developed by a consortium of academics and technical consultants, and was stringently filtered to eliminate sensitive information prior to public release. WMDP serves two roles: first, as an evaluation for hazardous knowledge in LLMs, and second, as a benchmark for unlearning methods to remove such hazardous knowledge. To guide progress on unlearning, we develop RMU, a state-of-the-art unlearning method based on controlling model representations. RMU reduces model performance on WMDP while maintaining general capabilities in areas such as biology and computer science, suggesting that unlearning may be a concrete path towards reducing malicious use from LLMs. We release our benchmark and code publicly at https://wmdp.ai, Comment: See the project page at https://wmdp.ai

Published
2024

18. Embedding Elites: Examining the Use of Tweets Embedded in Online News Articles across Reliable and Fringe Outlets

Author

Horne, Benjamin D., Phillips, Summer, and Koontz, Nelia

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

This study examines the use of embedded tweets in online news media. In particular, we add to the previous literature by exploring embedded tweets across reliable and unreliable news outlets. We use a mixed-method analysis to examine how the function and frequency of embedded tweets change across outlet reliability and news topic. We find that, no matter the outlet reliability, embedded tweets are most often used to relay the opinions of elites, to syndicate information from another news source, or to self-cite information an outlet previously produced. Our results also show some notable differences between reliable media and fringe media's use of tweets. Namely, fringe media embed tweets more and use those tweets as the source of news more than reliable media. Our work adds to the literature on hybrid media systems and the normalization of social media in journalism., Comment: MeLa Lab Preliminary Findings Report

Published
2024

19. A Learning-based Declarative Privacy-Preserving Framework for Federated Data Management

Author

Guan, Hong, Gautier, Summer, Gupta, Deepti, Ambrish, Rajan Hari, Wang, Yancheng, Lakamsani, Harsha, Giriyan, Dhanush, Maslanka, Saajan, Xiao, Chaowei, Yang, Yingzhen, and Zou, Jia

Subjects
Computer Science - Databases, Computer Science - Artificial Intelligence
Abstract

It is challenging to balance the privacy and accuracy for federated query processing over multiple private data silos. In this work, we will demonstrate an end-to-end workflow for automating an emerging privacy-preserving technique that uses a deep learning model trained using the Differentially-Private Stochastic Gradient Descent (DP-SGD) algorithm to replace portions of actual data to answer a query. Our proposed novel declarative privacy-preserving workflow allows users to specify "what private information to protect" rather than "how to protect". Under the hood, the system automatically chooses query-model transformation plans as well as hyper-parameters. At the same time, the proposed workflow also allows human experts to review and tune the selected privacy-preserving mechanism for audit/compliance, and optimization purposes.

Published
2024

35. Predicting Implementation of the PAX Good Behavior Game + MyTeachingPartner Interventions

Author

Summer S. Braun, Catherine P. Bradshaw, Lydia A. Beahm, Alexa C. Budavari, Jason Downer, Nicholas S. Ialongo, and Patrick H. Tolan

Abstract

Introduction: Effective classroom management is critical to creating a classroom environment in which social, emotional, and academic learning can take place. The present study investigated the association between early career, early elementary teachers' occupational health (job stress, burnout, and perceived teaching ability) and perceptions of program feasibility in relation to their implementation dosage and quality of two evidence-based classroom management programs implemented together: the PAX Good Behavior Game (GBG) and MyTeachingPartner (MTP) intervention. Methods: Teachers provided information on their occupational health at the start of the school year and were then randomized to the PAX GBG + MTP condition or control condition. Teachers' perceptions of the feasibility of the program, implementation dosage, and implementation quality of the intervention were measured at the end of the school year for the 94 intervention teachers. Results: Teachers participated in more MTP coaching cycles when they reported that the combined PAX GBG + MTP program was feasible. Although there were no main effects of occupational health on implementation, the associations between job stress and implementation quality were moderated by perceptions of feasibility. Discussion: Findings highlight the complexity of factors influencing the implementation of evidence-based programs in school settings. [This paper was published in "Frontiers in Psychology" v14 2023.]

Published
2023
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36. The Association between an Equitable School Climate and Students' Psychosocial Outcomes: The Moderating Roles of Race and Gender

Author

Summer S. Braun, Caryn R. R. Rodgers, Arielle Linsky, Charity Brown Griffin, and Catherine P. Bradshaw

Abstract

The present study examined the association between students' perceptions of an equitable school climate and several psychosocial outcomes and tested whether these associations were moderated by students' race and gender. Data from 57,027 6th-12th grade students were analyzed using three-level models. Students who perceived their school to have a more equitable climate experienced greater psychosocial outcomes: low endorsement of retaliatory aggressive beliefs, low stress, and high future aspirations. Moderation analyses consistently demonstrated that the effect of an equitable school climate on students' psychosocial outcomes was weaker for Black students than White, Latinx, and Asian students. Gender also served as a moderator for some outcomes, with the effect of equity on stress stronger for girls than boys, and the effect of equity on future aspirations stronger for boys than girls. [This paper will be published in "School Psychology Review."]

Published
2023
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37. Innovations in Intern/Mentor Relationships and Conceptions of the Technological Pedagogical Content Knowledge (TPACK) Framework

Author

Wells, Melissa Summer, Irish, Christy K., Peck, Kristina A., Davis, Janine S., and Clayton, Courtney

Abstract

The global COVID-19 pandemic has caused many adaptations to preservice teacher preparation, especially supporting completion of field-based internship experiences. This mixed methods research project utilized surveys to analyze the impact of virtual settings on (1) intern (n=14) and mentor teacher (n=5) experiences and relationships; and (2) the use of instructional technology, specifically Technological Pedagogical Content Knowledge (TPACK). Key findings indicated (1) reversals of traditional mentor/intern relationships; and (2) the role of technology as the foundation of instructional decision-making, resulting in challenges when adapting pedagogies of classroom management, assessment, and differentiation to virtual settings.

Published
2023

38. Exploration of the Relationship between Principal Preparation Program Admission Requirements and Program Performance

Author

Pannell, Summer

Abstract

Admission criteria for principal preparation programs vary from state to state and even amongst universities within the same state. Despite the recommendation nearly two decades ago to connect admission criteria to leadership standards, little research has been done to help develop such criteria. This study examined principal preparation program admissions requirements at one southern university to determine if a relationship existed between admission criteria, academic program performance, and performance on educational leadership licensure assessments. The Pearson product-moment correlation found a statistically significant correlation between, both, the GRE and GRE Writing assessments and the School Leadership Licensure Assessment for educational administrator certification.

Published
2023

39. An Exploration of the Relationship between Leaders' Cultural Competence and Academic Outcomes in Texas Public Schools

Author

Summer Pannell, Brian Uriegas, and Juliann Sergi McBrayer

Abstract

This study explored the cultural knowledge (CK) and cultural skills (CS) levels of Texas school leaders to determine if there was a relationship between cultural knowledge and cultural skill as well as between principals' CK and CS levels and school academic outcomes. In comparing within-group scores, the results indicated CK scores were higher than CS scores in all participant groups. Between-group analysis revealed participants in non-Title I schools scored higher in CK, but Title I school leaders scored higher in CS. Results further indicated school leaders who identified as a member of a marginalized group scored higher in CS but slightly lower in CK than leaders who did not identify as a member of a marginalized group. There was a statistically significant, positive correlation between CK and CS scores; however, CK scores were negatively correlated with proficiency levels in marginalized student groups and CS scores were positively correlated with proficiency levels in marginalized student groups.

Published
2023

40. The HELM Report: Washington State Higher Education & Labor Market Report

Author

Washington Student Achievement Council (WSAC), Workforce Training and Education Coordinating Board, Washington State Board for Community and Technical Colleges, Daryl Monear, Isaac Kwakye, Mark Lundgren, Rebecca Byrne, Summer Kenesson, Travis Dulany, David Wallace, Terje Gjertsen, Erika Borg, and Kristofer Johnson

Abstract

The Washington Student Achievement Council (WSAC) has prepared this analysis in collaboration with the State Board for Community and Technical Colleges (SBCTC), the Workforce Training and Education Coordinating Board (Workforce Board), and the Association of Washington Business, as part of a broad educational needs assessment outlined in RCW 28B.77.080. The report has four primary purposes: (1) to assess how well the state's postsecondary education system is aligned with the demands of the labor market, (2) to identify key drivers of employer demand in the various occupational clusters, (3) to highlight industries and occupational fields in which students and adult learners may find expanding employment opportunities, and (4) to explore factors, such as the advance of workplace automation, which may be impacting the labor market. [This report was co-produced with Association of Washington Business (AWB).]

Published
2023

41. In situ estimation of ice crystal properties at the South Pole using LED calibration data from the IceCube Neutrino Observatory

Author

Abbasi, Rasha, Ackermann, Markus, Adams, Jenni, Aggarwal, Nakul, Aguilar, Juanan, Ahlers, Markus, Ahrens, Maryon, Alameddine, Jean-Marco, Alves, Antonio Augusto, Amin, Najia Moureen Binte, Andeen, Karen, Anderson, Tyler, Anton, Gisela, Argüelles, Carlos, Ashida, Yosuke, Athanasiadou, Sofia, Axani, Spencer, Bai, Xinhua, Balagopal, Aswathi, Baricevic, Moreno, Barwick, Steve, Basu, Vedant, Bay, Ryan, Beatty, James, Becker, Karl Heinz, Tjus, Julia Becker, Beise, Jakob, Bellenghi, Chiara, Benda, Samuel, BenZvi, Segev, Berley, David, Bernardini, Elisa, Besson, Dave, Binder, Gary, Bindig, Daniel, Blaufuss, Erik, Blot, Summer, Bontempo, Federico, Book, Julia, Borowka, Jürgen, Meneguolo, Caterina Boscolo, Böser, Sebastian, Botner, Olga, Böttcher, Jakob, Bourbeau, Etienne, Braun, Jim, Brinson, Bennett, Brostean-Kaiser, Jannes, Burley, Ryan, Busse, Raffaela, Campana, Michael, Carnie-Bronca, Erin, Chen, Chujie, Chen, Zheyang, Chirkin, Dmitry, Choi, Koun, Clark, Brian, Classen, Lew, Coleman, Alan, Collin, Gabriel, Connolly, Amy, Conrad, Janet, Coppin, Paul, Correa, Pablo, Countryman, Stefan, Cowen, Doug, Cross, Robert, Dappen, Christian, Dave, Pranav, De Clercq, Catherine, DeLaunay, James, López, Diyaselis Delgado, Dembinski, Hans, Deoskar, Kunal, Desai, Abhishek, Desiati, Paolo, de Vries, Krijn, de Wasseige, Gwenhael, DeYoung, Tyce, Diaz, Alejandro, Díaz-Vélez, Juan Carlos, Dittmer, Markus, Dujmovic, Hrvoje, DuVernois, Michael, Ehrhardt, Thomas, Eller, Philipp, Engel, Ralph, Erpenbeck, Hannah, Evans, John, Evenson, Paul, Fan, Kwok Lung, Fazely, Ali, Fedynitch, Anatoli, Feigl, Nora, Fiedlschuster, Sebastian, Fienberg, Aaron, Finley, Chad, Fischer, Leander, Fox, Derek, and Franckowiak, Anna

Subjects
Earth Sciences, Geology, Oceanography, Physical Geography and Environmental Geoscience, Meteorology & Atmospheric Sciences, Physical geography and environmental geoscience
Abstract

The IceCube Neutrino Observatory instruments about 1ĝ€¯km3 of deep, glacial ice at the geographic South Pole. It uses 5160 photomultipliers to detect Cherenkov light emitted by charged relativistic particles. An unexpected light propagation effect observed by the experiment is an anisotropic attenuation, which is aligned with the local flow direction of the ice. We examine birefringent light propagation through the polycrystalline ice microstructure as a possible explanation for this effect. The predictions of a first-principles model developed for this purpose, in particular curved light trajectories resulting from asymmetric diffusion, provide a qualitatively good match to the main features of the data. This in turn allows us to deduce ice crystal properties. Since the wavelength of the detected light is short compared to the crystal size, these crystal properties include not only the crystal orientation fabric, but also the average crystal size and shape, as a function of depth. By adding small empirical corrections to this first-principles model, a quantitatively accurate description of the optical properties of the IceCube glacial ice is obtained. In this paper, we present the experimental signature of ice optical anisotropy observed in IceCube light-emitting diode (LED) calibration data, the theory and parameterization of the birefringence effect, the fitting procedures of these parameterizations to experimental data, and the inferred crystal properties.

Published
2024

42. Acne vulgaris in black pediatric patients: clinical presentation, treatment patterns, and unique needs

Author

Jordan, Kristiana M, Famisan, Demi O, Myer, Summer N, Fine, Jeffrey, Ren, Yunyi, and Tartar, Danielle M

Subjects
acne, pediatrics, pigmentation, skin of color
Abstract

Acne vulgaris is a common dermatological diagnosis observed in pediatric patients with skin of color, often resulting in scarring, keloid formation, and post-inflammatory hyperpigmentation, significantly impacting their quality of life. This exploratory retrospective chart review included 77 black pediatric patients seen at a tertiary care center for acne vulgaris between 2018 and 2023. We analyzed demographics, acne descriptors, and treatment modalities. The most common acne morphology was comedonal acne (83.6%), with 71% of the patients being female. Significant age differences were observed particularly for acne at the chin and overall face. Treatment regimens commonly prescribed included combinations of adapalene and benzoyl peroxide (22%), topical antibiotics, tretinoin, and benzoyl peroxide (34%). Given the higher risk of sequelae for patients with darker skin, it is crucial to address their unique treatment needs. This study highlights the distinctive characteristics of acne in black pediatric patients and calls for further research to enhance our understanding and treatment of this population. Limitations include the lack of direct patient interactions and reliance on chart data. Further studies are needed to compare acne presentation in skin of color of other populations, refining our knowledge of acne clinical presentation, complications, and treatment modalities for diverse patient populations.

Published
2024

43. Application of PET/MRI in Gynecologic Malignancies

Author

Ebrahimi, Sheida, Lundström, Elin, Batasin, Summer J, Hedlund, Elisabeth, Stålberg, Karin, Ehman, Eric C, Sheth, Vipul R, Iranpour, Negaur, Loubrie, Stephane, Schlein, Alexandra, and Rakow-Penner, Rebecca

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Cancer, Bioengineering, Women's Health, Clinical Research, Biomedical Imaging, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, 4.1 Discovery and preclinical testing of markers and technologies, PET/MRI, gynecological malignancy, cervical cancer, endometrial cancer, ovarian cancer, vaginal cancer, Oncology and carcinogenesis
Abstract

The diagnosis, treatment, and management of gynecologic malignancies benefit from both positron emission tomography/computed tomography (PET/CT) and MRI. PET/CT provides important information on the local extent of disease as well as diffuse metastatic involvement. MRI offers soft tissue delineation and loco-regional disease involvement. The combination of these two technologies is key in diagnosis, treatment planning, and evaluating treatment response in gynecological malignancies. This review aims to assess the performance of PET/MRI in gynecologic cancer patients and outlines the technical challenges and clinical advantages of PET/MR systems when specifically applied to gynecologic malignancies.

Published
2024

44. Infliximab-induced amicrobial pustulosis of the folds in a patient with Crohn disease

Author

Meyer, Summer N, Myers, Bridget, Caro-Chang, Leah Antoinette, Tartar, Danielle M, Wu, Peggy, Kiuru, Maija, and Toussi, Atrin

Subjects
Crohn disease, infliximab, inflammatory bowel disease, pustulosis, TNF inhibitor
Abstract

Tumor necrosis factor (TNF) inhibitors may paradoxically induce pustular eruptions, most of which are classified as pustular psoriasis. Amicrobial pustulosis of the folds (APF) is a much rarer entity that was recently recognized to occur in the setting of chronic anti-TNF therapy and inflammatory bowel disease, with 12 existing cases in the literature. Amicrobial pustulosis of the folds is a neutrophilic dermatosis characterized by aseptic pustules involving the major and minor skin folds, genital regions, and scalp. Herein, we report an additional case of paradoxical APF induced by chronic infliximab therapy in a patient with Crohn disease.

Published
2024

45. Gemini: A Family of Highly Capable Multimodal Models

Author

Gemini Team, Anil, Rohan, Borgeaud, Sebastian, Alayrac, Jean-Baptiste, Yu, Jiahui, Soricut, Radu, Schalkwyk, Johan, Dai, Andrew M., Hauth, Anja, Millican, Katie, Silver, David, Johnson, Melvin, Antonoglou, Ioannis, Schrittwieser, Julian, Glaese, Amelia, Chen, Jilin, Pitler, Emily, Lillicrap, Timothy, Lazaridou, Angeliki, Firat, Orhan, Molloy, James, Isard, Michael, Barham, Paul R., Hennigan, Tom, Lee, Benjamin, Viola, Fabio, Reynolds, Malcolm, Xu, Yuanzhong, Doherty, Ryan, Collins, Eli, Meyer, Clemens, Rutherford, Eliza, Moreira, Erica, Ayoub, Kareem, Goel, Megha, Krawczyk, Jack, Du, Cosmo, Chi, Ed, Cheng, Heng-Tze, Ni, Eric, Shah, Purvi, Kane, Patrick, Chan, Betty, Faruqui, Manaal, Severyn, Aliaksei, Lin, Hanzhao, Li, YaGuang, Cheng, Yong, Ittycheriah, Abe, Mahdieh, Mahdis, Chen, Mia, Sun, Pei, Tran, Dustin, Bagri, Sumit, Lakshminarayanan, Balaji, Liu, Jeremiah, Orban, Andras, Güra, Fabian, Zhou, Hao, Song, Xinying, Boffy, Aurelien, Ganapathy, Harish, Zheng, Steven, Choe, HyunJeong, Weisz, Ágoston, Zhu, Tao, Lu, Yifeng, Gopal, Siddharth, Kahn, Jarrod, Kula, Maciej, Pitman, Jeff, Shah, Rushin, Taropa, Emanuel, Merey, Majd Al, Baeuml, Martin, Chen, Zhifeng, Shafey, Laurent El, Zhang, Yujing, Sercinoglu, Olcan, Tucker, George, Piqueras, Enrique, Krikun, Maxim, Barr, Iain, Savinov, Nikolay, Danihelka, Ivo, Roelofs, Becca, White, Anaïs, Andreassen, Anders, von Glehn, Tamara, Yagati, Lakshman, Kazemi, Mehran, Gonzalez, Lucas, Khalman, Misha, Sygnowski, Jakub, Frechette, Alexandre, Smith, Charlotte, Culp, Laura, Proleev, Lev, Luan, Yi, Chen, Xi, Lottes, James, Schucher, Nathan, Lebron, Federico, Rrustemi, Alban, Clay, Natalie, Crone, Phil, Kocisky, Tomas, Zhao, Jeffrey, Perz, Bartek, Yu, Dian, Howard, Heidi, Bloniarz, Adam, Rae, Jack W., Lu, Han, Sifre, Laurent, Maggioni, Marcello, Alcober, Fred, Garrette, Dan, Barnes, Megan, Thakoor, Shantanu, Austin, Jacob, Barth-Maron, Gabriel, Wong, William, Joshi, Rishabh, Chaabouni, Rahma, Fatiha, Deeni, Ahuja, Arun, Tomar, Gaurav Singh, Senter, Evan, Chadwick, Martin, Kornakov, Ilya, Attaluri, Nithya, Iturrate, Iñaki, Liu, Ruibo, Li, Yunxuan, Cogan, Sarah, Chen, Jeremy, Jia, Chao, Gu, Chenjie, Zhang, Qiao, Grimstad, Jordan, Hartman, Ale Jakse, Garcia, Xavier, Pillai, Thanumalayan Sankaranarayana, Devlin, Jacob, Laskin, Michael, Casas, Diego de Las, Valter, Dasha, Tao, Connie, Blanco, Lorenzo, Badia, Adrià Puigdomènech, Reitter, David, Chen, Mianna, Brennan, Jenny, Rivera, Clara, Brin, Sergey, Iqbal, Shariq, Surita, Gabriela, Labanowski, Jane, Rao, Abhi, Winkler, Stephanie, Parisotto, Emilio, Gu, Yiming, Olszewska, Kate, Addanki, Ravi, Miech, Antoine, Louis, Annie, Teplyashin, Denis, Brown, Geoff, Catt, Elliot, Balaguer, Jan, Xiang, Jackie, Wang, Pidong, Ashwood, Zoe, Briukhov, Anton, Webson, Albert, Ganapathy, Sanjay, Sanghavi, Smit, Kannan, Ajay, Chang, Ming-Wei, Stjerngren, Axel, Djolonga, Josip, Sun, Yuting, Bapna, Ankur, Aitchison, Matthew, Pejman, Pedram, Michalewski, Henryk, Yu, Tianhe, Wang, Cindy, Love, Juliette, Ahn, Junwhan, Bloxwich, Dawn, Han, Kehang, Humphreys, Peter, Sellam, Thibault, Bradbury, James, Godbole, Varun, Samangooei, Sina, Damoc, Bogdan, Kaskasoli, Alex, Arnold, Sébastien M. R., Vasudevan, Vijay, Agrawal, Shubham, Riesa, Jason, Lepikhin, Dmitry, Tanburn, Richard, Srinivasan, Srivatsan, Lim, Hyeontaek, Hodkinson, Sarah, Shyam, Pranav, Ferret, Johan, Hand, Steven, Garg, Ankush, Paine, Tom Le, Li, Jian, Li, Yujia, Giang, Minh, Neitz, Alexander, Abbas, Zaheer, York, Sarah, Reid, Machel, Cole, Elizabeth, Chowdhery, Aakanksha, Das, Dipanjan, Rogozińska, Dominika, Nikolaev, Vitaliy, Sprechmann, Pablo, Nado, Zachary, Zilka, Lukas, Prost, Flavien, He, Luheng, Monteiro, Marianne, Mishra, Gaurav, Welty, Chris, Newlan, Josh, Jia, Dawei, Allamanis, Miltiadis, Hu, Clara Huiyi, de Liedekerke, Raoul, Gilmer, Justin, Saroufim, Carl, Rijhwani, Shruti, Hou, Shaobo, Shrivastava, Disha, Baddepudi, Anirudh, Goldin, Alex, Ozturel, Adnan, Cassirer, Albin, Xu, Yunhan, Sohn, Daniel, Sachan, Devendra, Amplayo, Reinald Kim, Swanson, Craig, Petrova, Dessie, Narayan, Shashi, Guez, Arthur, Brahma, Siddhartha, Landon, Jessica, Patel, Miteyan, Zhao, Ruizhe, Villela, Kevin, Wang, Luyu, Jia, Wenhao, Rahtz, Matthew, Giménez, Mai, Yeung, Legg, Keeling, James, Georgiev, Petko, Mincu, Diana, Wu, Boxi, Haykal, Salem, Saputro, Rachel, Vodrahalli, Kiran, Qin, James, Cankara, Zeynep, Sharma, Abhanshu, Fernando, Nick, Hawkins, Will, Neyshabur, Behnam, Kim, Solomon, Hutter, Adrian, Agrawal, Priyanka, Castro-Ros, Alex, Driessche, George van den, Wang, Tao, Yang, Fan, Chang, Shuo-yiin, Komarek, Paul, McIlroy, Ross, Lučić, Mario, Zhang, Guodong, Farhan, Wael, Sharman, Michael, Natsev, Paul, Michel, Paul, Bansal, Yamini, Qiao, Siyuan, Cao, Kris, Shakeri, Siamak, Butterfield, Christina, Chung, Justin, Rubenstein, Paul Kishan, Agrawal, Shivani, Mensch, Arthur, Soparkar, Kedar, Lenc, Karel, Chung, Timothy, Pope, Aedan, Maggiore, Loren, Kay, Jackie, Jhakra, Priya, Wang, Shibo, Maynez, Joshua, Phuong, Mary, Tobin, Taylor, Tacchetti, Andrea, Trebacz, Maja, Robinson, Kevin, Katariya, Yash, Riedel, Sebastian, Bailey, Paige, Xiao, Kefan, Ghelani, Nimesh, Aroyo, Lora, Slone, Ambrose, Houlsby, Neil, Xiong, Xuehan, Yang, Zhen, Gribovskaya, Elena, Adler, Jonas, Wirth, Mateo, Lee, Lisa, Li, Music, Kagohara, Thais, Pavagadhi, Jay, Bridgers, Sophie, Bortsova, Anna, Ghemawat, Sanjay, Ahmed, Zafarali, Liu, Tianqi, Powell, Richard, Bolina, Vijay, Iinuma, Mariko, Zablotskaia, Polina, Besley, James, Chung, Da-Woon, Dozat, Timothy, Comanescu, Ramona, Si, Xiance, Greer, Jeremy, Su, Guolong, Polacek, Martin, Kaufman, Raphaël Lopez, Tokumine, Simon, Hu, Hexiang, Buchatskaya, Elena, Miao, Yingjie, Elhawaty, Mohamed, Siddhant, Aditya, Tomasev, Nenad, Xing, Jinwei, Greer, Christina, Miller, Helen, Ashraf, Shereen, Roy, Aurko, Zhang, Zizhao, Ma, Ada, Filos, Angelos, Besta, Milos, Blevins, Rory, Klimenko, Ted, Yeh, Chih-Kuan, Changpinyo, Soravit, Mu, Jiaqi, Chang, Oscar, Pajarskas, Mantas, Muir, Carrie, Cohen, Vered, Lan, Charline Le, Haridasan, Krishna, Marathe, Amit, Hansen, Steven, Douglas, Sholto, Samuel, Rajkumar, Wang, Mingqiu, Austin, Sophia, Lan, Chang, Jiang, Jiepu, Chiu, Justin, Lorenzo, Jaime Alonso, Sjösund, Lars Lowe, Cevey, Sébastien, Gleicher, Zach, Avrahami, Thi, Boral, Anudhyan, Srinivasan, Hansa, Selo, Vittorio, May, Rhys, Aisopos, Konstantinos, Hussenot, Léonard, Soares, Livio Baldini, Baumli, Kate, Chang, Michael B., Recasens, Adrià, Caine, Ben, Pritzel, Alexander, Pavetic, Filip, Pardo, Fabio, Gergely, Anita, Frye, Justin, Ramasesh, Vinay, Horgan, Dan, Badola, Kartikeya, Kassner, Nora, Roy, Subhrajit, Dyer, Ethan, Campos, Víctor Campos, Tomala, Alex, Tang, Yunhao, Badawy, Dalia El, White, Elspeth, Mustafa, Basil, Lang, Oran, Jindal, Abhishek, Vikram, Sharad, Gong, Zhitao, Caelles, Sergi, Hemsley, Ross, Thornton, Gregory, Feng, Fangxiaoyu, Stokowiec, Wojciech, Zheng, Ce, Thacker, Phoebe, Ünlü, Çağlar, Zhang, Zhishuai, Saleh, Mohammad, Svensson, James, Bileschi, Max, Patil, Piyush, Anand, Ankesh, Ring, Roman, Tsihlas, Katerina, Vezer, Arpi, Selvi, Marco, Shevlane, Toby, Rodriguez, Mikel, Kwiatkowski, Tom, Daruki, Samira, Rong, Keran, Dafoe, Allan, FitzGerald, Nicholas, Gu-Lemberg, Keren, Khan, Mina, Hendricks, Lisa Anne, Pellat, Marie, Feinberg, Vladimir, Cobon-Kerr, James, Sainath, Tara, Rauh, Maribeth, Hashemi, Sayed Hadi, Ives, Richard, Hasson, Yana, Noland, Eric, Cao, Yuan, Byrd, Nathan, Hou, Le, Wang, Qingze, Sottiaux, Thibault, Paganini, Michela, Lespiau, Jean-Baptiste, Moufarek, Alexandre, Hassan, Samer, Shivakumar, Kaushik, van Amersfoort, Joost, Mandhane, Amol, Joshi, Pratik, Goyal, Anirudh, Tung, Matthew, Brock, Andrew, Sheahan, Hannah, Misra, Vedant, Li, Cheng, Rakićević, Nemanja, Dehghani, Mostafa, Liu, Fangyu, Mittal, Sid, Oh, Junhyuk, Noury, Seb, Sezener, Eren, Huot, Fantine, Lamm, Matthew, De Cao, Nicola, Chen, Charlie, Mudgal, Sidharth, Stella, Romina, Brooks, Kevin, Vasudevan, Gautam, Liu, Chenxi, Chain, Mainak, Melinkeri, Nivedita, Cohen, Aaron, Wang, Venus, Seymore, Kristie, Zubkov, Sergey, Goel, Rahul, Yue, Summer, Krishnakumaran, Sai, Albert, Brian, Hurley, Nate, Sano, Motoki, Mohananey, Anhad, Joughin, Jonah, Filonov, Egor, Kępa, Tomasz, Eldawy, Yomna, Lim, Jiawern, Rishi, Rahul, Badiezadegan, Shirin, Bos, Taylor, Chang, Jerry, Jain, Sanil, Padmanabhan, Sri Gayatri Sundara, Puttagunta, Subha, Krishna, Kalpesh, Baker, Leslie, Kalb, Norbert, Bedapudi, Vamsi, Kurzrok, Adam, Lei, Shuntong, Yu, Anthony, Litvin, Oren, Zhou, Xiang, Wu, Zhichun, Sobell, Sam, Siciliano, Andrea, Papir, Alan, Neale, Robby, Bragagnolo, Jonas, Toor, Tej, Chen, Tina, Anklin, Valentin, Wang, Feiran, Feng, Richie, Gholami, Milad, Ling, Kevin, Liu, Lijuan, Walter, Jules, Moghaddam, Hamid, Kishore, Arun, Adamek, Jakub, Mercado, Tyler, Mallinson, Jonathan, Wandekar, Siddhinita, Cagle, Stephen, Ofek, Eran, Garrido, Guillermo, Lombriser, Clemens, Mukha, Maksim, Sun, Botu, Mohammad, Hafeezul Rahman, Matak, Josip, Qian, Yadi, Peswani, Vikas, Janus, Pawel, Yuan, Quan, Schelin, Leif, David, Oana, Garg, Ankur, He, Yifan, Duzhyi, Oleksii, Älgmyr, Anton, Lottaz, Timothée, Li, Qi, Yadav, Vikas, Xu, Luyao, Chinien, Alex, Shivanna, Rakesh, Chuklin, Aleksandr, Li, Josie, Spadine, Carrie, Wolfe, Travis, Mohamed, Kareem, Das, Subhabrata, Dai, Zihang, He, Kyle, von Dincklage, Daniel, Upadhyay, Shyam, Maurya, Akanksha, Chi, Luyan, Krause, Sebastian, Salama, Khalid, Rabinovitch, Pam G, M, Pavan Kumar Reddy, Selvan, Aarush, Dektiarev, Mikhail, Ghiasi, Golnaz, Guven, Erdem, Gupta, Himanshu, Liu, Boyi, Sharma, Deepak, Shtacher, Idan Heimlich, Paul, Shachi, Akerlund, Oscar, Aubet, François-Xavier, Huang, Terry, Zhu, Chen, Zhu, Eric, Teixeira, Elico, Fritze, Matthew, Bertolini, Francesco, Marinescu, Liana-Eleonora, Bölle, Martin, Paulus, Dominik, Gupta, Khyatti, Latkar, Tejasi, Chang, Max, Sanders, Jason, Wilson, Roopa, Wu, Xuewei, Tan, Yi-Xuan, Thiet, Lam Nguyen, Doshi, Tulsee, Lall, Sid, Mishra, Swaroop, Chen, Wanming, Luong, Thang, Benjamin, Seth, Lee, Jasmine, Andrejczuk, Ewa, Rabiej, Dominik, Ranjan, Vipul, Styrc, Krzysztof, Yin, Pengcheng, Simon, Jon, Harriott, Malcolm Rose, Bansal, Mudit, Robsky, Alexei, Bacon, Geoff, Greene, David, Mirylenka, Daniil, Zhou, Chen, Sarvana, Obaid, Goyal, Abhimanyu, Andermatt, Samuel, Siegler, Patrick, Horn, Ben, Israel, Assaf, Pongetti, Francesco, Chen, Chih-Wei "Louis", Selvatici, Marco, Silva, Pedro, Wang, Kathie, Tolins, Jackson, Guu, Kelvin, Yogev, Roey, Cai, Xiaochen, Agostini, Alessandro, Shah, Maulik, Nguyen, Hung, Donnaile, Noah Ó, Pereira, Sébastien, Friso, Linda, Stambler, Adam, Kuang, Chenkai, Romanikhin, Yan, Geller, Mark, Yan, ZJ, Jang, Kane, Lee, Cheng-Chun, Fica, Wojciech, Malmi, Eric, Tan, Qijun, Banica, Dan, Balle, Daniel, Pham, Ryan, Huang, Yanping, Avram, Diana, Shi, Hongzhi, Singh, Jasjot, Hidey, Chris, Ahuja, Niharika, Saxena, Pranab, Dooley, Dan, Potharaju, Srividya Pranavi, O'Neill, Eileen, Gokulchandran, Anand, Foley, Ryan, Zhao, Kai, Dusenberry, Mike, Liu, Yuan, Mehta, Pulkit, Kotikalapudi, Ragha, Safranek-Shrader, Chalence, Goodman, Andrew, Kessinger, Joshua, Globen, Eran, Kolhar, Prateek, Gorgolewski, Chris, Ibrahim, Ali, Song, Yang, Eichenbaum, Ali, Brovelli, Thomas, Potluri, Sahitya, Lahoti, Preethi, Baetu, Cip, Ghorbani, Ali, Chen, Charles, Crawford, Andy, Pal, Shalini, Sridhar, Mukund, Gurita, Petru, Mujika, Asier, Petrovski, Igor, Cedoz, Pierre-Louis, Li, Chenmei, Chen, Shiyuan, Santo, Niccolò Dal, Goyal, Siddharth, Punjabi, Jitesh, Kappaganthu, Karthik, Kwak, Chester, LV, Pallavi, Velury, Sarmishta, Choudhury, Himadri, Hall, Jamie, Shah, Premal, Figueira, Ricardo, Thomas, Matt, Lu, Minjie, Zhou, Ting, Kumar, Chintu, Jurdi, Thomas, Chikkerur, Sharat, Ma, Yenai, Yu, Adams, Kwak, Soo, Ähdel, Victor, Rajayogam, Sujeevan, Choma, Travis, Liu, Fei, Barua, Aditya, Ji, Colin, Park, Ji Ho, Hellendoorn, Vincent, Bailey, Alex, Bilal, Taylan, Zhou, Huanjie, Khatir, Mehrdad, Sutton, Charles, Rzadkowski, Wojciech, Macintosh, Fiona, Shagin, Konstantin, Medina, Paul, Liang, Chen, Zhou, Jinjing, Shah, Pararth, Bi, Yingying, Dankovics, Attila, Banga, Shipra, Lehmann, Sabine, Bredesen, Marissa, Lin, Zifan, Hoffmann, John Eric, Lai, Jonathan, Chung, Raynald, Yang, Kai, Balani, Nihal, Bražinskas, Arthur, Sozanschi, Andrei, Hayes, Matthew, Alcalde, Héctor Fernández, Makarov, Peter, Chen, Will, Stella, Antonio, Snijders, Liselotte, Mandl, Michael, Kärrman, Ante, Nowak, Paweł, Wu, Xinyi, Dyck, Alex, Vaidyanathan, Krishnan, R, Raghavender, Mallet, Jessica, Rudominer, Mitch, Johnston, Eric, Mittal, Sushil, Udathu, Akhil, Christensen, Janara, Verma, Vishal, Irving, Zach, Santucci, Andreas, Elsayed, Gamaleldin, Davoodi, Elnaz, Georgiev, Marin, Tenney, Ian, Hua, Nan, Cideron, Geoffrey, Leurent, Edouard, Alnahlawi, Mahmoud, Georgescu, Ionut, Wei, Nan, Zheng, Ivy, Scandinaro, Dylan, Jiang, Heinrich, Snoek, Jasper, Sundararajan, Mukund, Wang, Xuezhi, Ontiveros, Zack, Karo, Itay, Cole, Jeremy, Rajashekhar, Vinu, Tumeh, Lara, Ben-David, Eyal, Jain, Rishub, Uesato, Jonathan, Datta, Romina, Bunyan, Oskar, Wu, Shimu, Zhang, John, Stanczyk, Piotr, Zhang, Ye, Steiner, David, Naskar, Subhajit, Azzam, Michael, Johnson, Matthew, Paszke, Adam, Chiu, Chung-Cheng, Elias, Jaume Sanchez, Mohiuddin, Afroz, Muhammad, Faizan, Miao, Jin, Lee, Andrew, Vieillard, Nino, Park, Jane, Zhang, Jiageng, Stanway, Jeff, Garmon, Drew, Karmarkar, Abhijit, Dong, Zhe, Lee, Jong, Kumar, Aviral, Zhou, Luowei, Evens, Jonathan, Isaac, William, Irving, Geoffrey, Loper, Edward, Fink, Michael, Arkatkar, Isha, Chen, Nanxin, Shafran, Izhak, Petrychenko, Ivan, Chen, Zhe, Jia, Johnson, Levskaya, Anselm, Zhu, Zhenkai, Grabowski, Peter, Mao, Yu, Magni, Alberto, Yao, Kaisheng, Snaider, Javier, Casagrande, Norman, Palmer, Evan, Suganthan, Paul, Castaño, Alfonso, Giannoumis, Irene, Kim, Wooyeol, Rybiński, Mikołaj, Sreevatsa, Ashwin, Prendki, Jennifer, Soergel, David, Goedeckemeyer, Adrian, Gierke, Willi, Jafari, Mohsen, Gaba, Meenu, Wiesner, Jeremy, Wright, Diana Gage, Wei, Yawen, Vashisht, Harsha, Kulizhskaya, Yana, Hoover, Jay, Le, Maigo, Li, Lu, Iwuanyanwu, Chimezie, Liu, Lu, Ramirez, Kevin, Khorlin, Andrey, Cui, Albert, LIN, Tian, Wu, Marcus, Aguilar, Ricardo, Pallo, Keith, Chakladar, Abhishek, Perng, Ginger, Abellan, Elena Allica, Zhang, Mingyang, Dasgupta, Ishita, Kushman, Nate, Penchev, Ivo, Repina, Alena, Wu, Xihui, van der Weide, Tom, Ponnapalli, Priya, Kaplan, Caroline, Simsa, Jiri, Li, Shuangfeng, Dousse, Olivier, Piper, Jeff, Ie, Nathan, Pasumarthi, Rama, Lintz, Nathan, Vijayakumar, Anitha, Andor, Daniel, Valenzuela, Pedro, Lui, Minnie, Paduraru, Cosmin, Peng, Daiyi, Lee, Katherine, Zhang, Shuyuan, Greene, Somer, Nguyen, Duc Dung, Kurylowicz, Paula, Hardin, Cassidy, Dixon, Lucas, Janzer, Lili, Choo, Kiam, Feng, Ziqiang, Zhang, Biao, Singhal, Achintya, Du, Dayou, McKinnon, Dan, Antropova, Natasha, Bolukbasi, Tolga, Keller, Orgad, Reid, David, Finchelstein, Daniel, Raad, Maria Abi, Crocker, Remi, Hawkins, Peter, Dadashi, Robert, Gaffney, Colin, Franko, Ken, Bulanova, Anna, Leblond, Rémi, Chung, Shirley, Askham, Harry, Cobo, Luis C., Xu, Kelvin, Fischer, Felix, Xu, Jun, Sorokin, Christina, Alberti, Chris, Lin, Chu-Cheng, Evans, Colin, Dimitriev, Alek, Forbes, Hannah, Banarse, Dylan, Tung, Zora, Omernick, Mark, Bishop, Colton, Sterneck, Rachel, Jain, Rohan, Xia, Jiawei, Amid, Ehsan, Piccinno, Francesco, Wang, Xingyu, Banzal, Praseem, Mankowitz, Daniel J., Polozov, Alex, Krakovna, Victoria, Brown, Sasha, Bateni, MohammadHossein, Duan, Dennis, Firoiu, Vlad, Thotakuri, Meghana, Natan, Tom, Geist, Matthieu, Girgin, Ser tan, Li, Hui, Ye, Jiayu, Roval, Ofir, Tojo, Reiko, Kwong, Michael, Lee-Thorp, James, Yew, Christopher, Sinopalnikov, Danila, Ramos, Sabela, Mellor, John, Sharma, Abhishek, Wu, Kathy, Miller, David, Sonnerat, Nicolas, Vnukov, Denis, Greig, Rory, Beattie, Jennifer, Caveness, Emily, Bai, Libin, Eisenschlos, Julian, Korchemniy, Alex, Tsai, Tomy, Jasarevic, Mimi, Kong, Weize, Dao, Phuong, Zheng, Zeyu, Liu, Frederick, Zhu, Rui, Teh, Tian Huey, Sanmiya, Jason, Gladchenko, Evgeny, Trdin, Nejc, Toyama, Daniel, Rosen, Evan, Tavakkol, Sasan, Xue, Linting, Elkind, Chen, Woodman, Oliver, Carpenter, John, Papamakarios, George, Kemp, Rupert, Kafle, Sushant, Grunina, Tanya, Sinha, Rishika, Talbert, Alice, Wu, Diane, Owusu-Afriyie, Denese, Thornton, Chloe, Pont-Tuset, Jordi, Narayana, Pradyumna, Li, Jing, Fatehi, Saaber, Wieting, John, Ajmeri, Omar, Uria, Benigno, Ko, Yeongil, Knight, Laura, Héliou, Amélie, Niu, Ning, Gu, Shane, Pang, Chenxi, Li, Yeqing, Levine, Nir, Stolovich, Ariel, Santamaria-Fernandez, Rebeca, Goenka, Sonam, Yustalim, Wenny, Strudel, Robin, Elqursh, Ali, Deck, Charlie, Lee, Hyo, Li, Zonglin, Levin, Kyle, Hoffmann, Raphael, Holtmann-Rice, Dan, Bachem, Olivier, Arora, Sho, Koh, Christy, Yeganeh, Soheil Hassas, Põder, Siim, Tariq, Mukarram, Sun, Yanhua, Ionita, Lucian, Seyedhosseini, Mojtaba, Tafti, Pouya, Liu, Zhiyu, Gulati, Anmol, Liu, Jasmine, Ye, Xinyu, Chrzaszcz, Bart, Wang, Lily, Sethi, Nikhil, Li, Tianrun, Brown, Ben, Singh, Shreya, Fan, Wei, Parisi, Aaron, Stanton, Joe, Koverkathu, Vinod, Choquette-Choo, Christopher A., Li, Yunjie, Lu, TJ, Shroff, Prakash, Varadarajan, Mani, Bahargam, Sanaz, Willoughby, Rob, Gaddy, David, Desjardins, Guillaume, Cornero, Marco, Robenek, Brona, Mittal, Bhavishya, Albrecht, Ben, Shenoy, Ashish, Moiseev, Fedor, Jacobsson, Henrik, Ghaffarkhah, Alireza, Rivière, Morgane, Walton, Alanna, Crepy, Clément, Parrish, Alicia, Zhou, Zongwei, Farabet, Clement, Radebaugh, Carey, Srinivasan, Praveen, van der Salm, Claudia, Fidjeland, Andreas, Scellato, Salvatore, Latorre-Chimoto, Eri, Klimczak-Plucińska, Hanna, Bridson, David, de Cesare, Dario, Hudson, Tom, Mendolicchio, Piermaria, Walker, Lexi, Morris, Alex, Mauger, Matthew, Guseynov, Alexey, Reid, Alison, Odoom, Seth, Loher, Lucia, Cotruta, Victor, Yenugula, Madhavi, Grewe, Dominik, Petrushkina, Anastasia, Duerig, Tom, Sanchez, Antonio, Yadlowsky, Steve, Shen, Amy, Globerson, Amir, Webb, Lynette, Dua, Sahil, Li, Dong, Bhupatiraju, Surya, Hurt, Dan, Qureshi, Haroon, Agarwal, Ananth, Shani, Tomer, Eyal, Matan, Khare, Anuj, Belle, Shreyas Rammohan, Wang, Lei, Tekur, Chetan, Kale, Mihir Sanjay, Wei, Jinliang, Sang, Ruoxin, Saeta, Brennan, Liechty, Tyler, Sun, Yi, Zhao, Yao, Lee, Stephan, Nayak, Pandu, Fritz, Doug, Vuyyuru, Manish Reddy, Aslanides, John, Vyas, Nidhi, Wicke, Martin, Ma, Xiao, Eltyshev, Evgenii, Martin, Nina, Cate, Hardie, Manyika, James, Amiri, Keyvan, Kim, Yelin, Xiong, Xi, Kang, Kai, Luisier, Florian, Tripuraneni, Nilesh, Madras, David, Guo, Mandy, Waters, Austin, Wang, Oliver, Ainslie, Joshua, Baldridge, Jason, Zhang, Han, Pruthi, Garima, Bauer, Jakob, Yang, Feng, Mansour, Riham, Gelman, Jason, Xu, Yang, Polovets, George, Liu, Ji, Cai, Honglong, Chen, Warren, Sheng, XiangHai, Xue, Emily, Ozair, Sherjil, Angermueller, Christof, Li, Xiaowei, Sinha, Anoop, Wang, Weiren, Wiesinger, Julia, Koukoumidis, Emmanouil, Tian, Yuan, Iyer, Anand, Gurumurthy, Madhu, Goldenson, Mark, Shah, Parashar, Blake, MK, Yu, Hongkun, Urbanowicz, Anthony, Palomaki, Jennimaria, Fernando, Chrisantha, Durden, Ken, Mehta, Harsh, Momchev, Nikola, Rahimtoroghi, Elahe, Georgaki, Maria, Raul, Amit, Ruder, Sebastian, Redshaw, Morgan, Lee, Jinhyuk, Zhou, Denny, Jalan, Komal, Li, Dinghua, Hechtman, Blake, Schuh, Parker, Nasr, Milad, Milan, Kieran, Mikulik, Vladimir, Franco, Juliana, Green, Tim, Nguyen, Nam, Kelley, Joe, Mahendru, Aroma, Hu, Andrea, Howland, Joshua, Vargas, Ben, Hui, Jeffrey, Bansal, Kshitij, Rao, Vikram, Ghiya, Rakesh, Wang, Emma, Ye, Ke, Sarr, Jean Michel, Preston, Melanie Moranski, Elish, Madeleine, Li, Steve, Kaku, Aakash, Gupta, Jigar, Pasupat, Ice, Juan, Da-Cheng, Someswar, Milan, M., Tejvi, Chen, Xinyun, Amini, Aida, Fabrikant, Alex, Chu, Eric, Dong, Xuanyi, Muthal, Amruta, Buthpitiya, Senaka, Jauhari, Sarthak, Khandelwal, Urvashi, Hitron, Ayal, Ren, Jie, Rinaldi, Larissa, Drath, Shahar, Dabush, Avigail, Jiang, Nan-Jiang, Godhia, Harshal, Sachs, Uli, Chen, Anthony, Fan, Yicheng, Taitelbaum, Hagai, Noga, Hila, Dai, Zhuyun, Wang, James, Hamer, Jenny, Ferng, Chun-Sung, Elkind, Chenel, Atias, Aviel, Lee, Paulina, Listík, Vít, Carlen, Mathias, van de Kerkhof, Jan, Pikus, Marcin, Zaher, Krunoslav, Müller, Paul, Zykova, Sasha, Stefanec, Richard, Gatsko, Vitaly, Hirnschall, Christoph, Sethi, Ashwin, Xu, Xingyu Federico, Ahuja, Chetan, Tsai, Beth, Stefanoiu, Anca, Feng, Bo, Dhandhania, Keshav, Katyal, Manish, Gupta, Akshay, Parulekar, Atharva, Pitta, Divya, Zhao, Jing, Bhatia, Vivaan, Bhavnani, Yashodha, Alhadlaq, Omar, Li, Xiaolin, Danenberg, Peter, Tu, Dennis, Pine, Alex, Filippova, Vera, Ghosh, Abhipso, Limonchik, Ben, Urala, Bhargava, Lanka, Chaitanya Krishna, Clive, Derik, Li, Edward, Wu, Hao, Hongtongsak, Kevin, Li, Ianna, Thakkar, Kalind, Omarov, Kuanysh, Majmundar, Kushal, Alverson, Michael, Kucharski, Michael, Patel, Mohak, Jain, Mudit, Zabelin, Maksim, Pelagatti, Paolo, Kohli, Rohan, Kumar, Saurabh, Kim, Joseph, Sankar, Swetha, Shah, Vineet, Ramachandruni, Lakshmi, Zeng, Xiangkai, Bariach, Ben, Weidinger, Laura, Vu, Tu, Andreev, Alek, He, Antoine, Hui, Kevin, Kashem, Sheleem, Subramanya, Amar, Hsiao, Sissie, Hassabis, Demis, Kavukcuoglu, Koray, Sadovsky, Adam, Le, Quoc, Strohman, Trevor, Wu, Yonghui, Petrov, Slav, Dean, Jeffrey, and Vinyals, Oriol

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.

Published
2023

46. Microscaling Data Formats for Deep Learning

Author

Rouhani, Bita Darvish, Zhao, Ritchie, More, Ankit, Hall, Mathew, Khodamoradi, Alireza, Deng, Summer, Choudhary, Dhruv, Cornea, Marius, Dellinger, Eric, Denolf, Kristof, Dusan, Stosic, Elango, Venmugil, Golub, Maximilian, Heinecke, Alexander, James-Roxby, Phil, Jani, Dharmesh, Kolhe, Gaurav, Langhammer, Martin, Li, Ada, Melnick, Levi, Mesmakhosroshahi, Maral, Rodriguez, Andres, Schulte, Michael, Shafipour, Rasoul, Shao, Lei, Siu, Michael, Dubey, Pradeep, Micikevicius, Paulius, Naumov, Maxim, Verrilli, Colin, Wittig, Ralph, Burger, Doug, and Chung, Eric

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

Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications. This paper evaluates Microscaling (MX) data formats that combine a per-block scaling factor with narrow floating-point and integer types for individual elements. MX formats balance the competing needs of hardware efficiency, model accuracy, and user friction. Empirical results on over two dozen benchmarks demonstrate practicality of MX data formats as a drop-in replacement for baseline FP32 for AI inference and training with low user friction. We also show the first instance of training generative language models at sub-8-bit weights, activations, and gradients with minimal accuracy loss and no modifications to the training recipe.

Published
2023

47. Rotation Curve Fitting Model

Author

Cisneros, Sophia Natalia, Ott, Richard, Crowley, Meagan, Roberts, Amy, Paz, Marcus, Brown, Zaneeyiah, Joyal, Landon, Rico, Roberto Real, Gutierrez-Gutierrez, Elizabeth, Pham, Phong, Holland, Zac, Livingston, Amanda, Castrellon, Lily, Graham, Summer, Rubin, Shanon J., Ashleya, Aaron, Battaglia, Dillon, Lopez, Daniel, and Salwa, Maya

Subjects
Astrophysics - Astrophysics of Galaxies, General Relativity and Quantum Cosmology
Abstract

One key piece of evidence for dark matter is the flat rotation curve problem: the disagreement between measured galactic rotation curves and their luminous mass. A novel solution to this problem is presented here. A model of relativistic frame effects on Doppler shifts due to the slightly curved frames of an emitting galaxy and the Milky Way is derived. This model predicts observed Doppler shifted spectra (in excess of the luminous mass) based only on the observed luminous matter profile and one free model parameter. Fits to the 175 galaxies reported in the SPARC database of galactic rotation profiles and accurate photometry measurements are compared between this novel model and dark matter and MOND (RAR) models. We find on the SPARC sample of 175 galaxies; that MOND-RAR has an average reduced chisquare of $\chi^2_r = 4.22$ for 175 galaxies fitted, the isothermal dark matter model has $\chi^2_r = 1.90$ for 165 galaxies fitted, and the new model we present has $\chi^2_r = 2.39$ for 172 galaxies fitted. Implications of this model are discussed., Comment: 19 pages, 19 figures

Published
2023

48. Examining Developmental Differences in Teachers' Observed Classroom Management Strategies across Elementary, Middle, and High School

Author

Heather L. McDaniel, Summer S. Braun, Jessika H. Bottiani, Danielle De Lucia, Patrick H. Tolan, and Catherine P. Bradshaw

Abstract

Classroom management practices are critical to the success of teachers and students, and a growing number of programs have been developed to improve these practices. However, there has been less investigation into observational tools to assess classroom management and exploration of whether it can be measured consistently by observers across elementary, middle, and high school classrooms. Moreover, there is a need to determine how classroom management practices vary as a function of school settings and classroom contexts (e.g., class size and racial composition). The current study aimed to examine classroom management practices using the Assessing School Settings: Interactions of Students and Teachers (ASSIST), an observational measure administered by trained external observers across 3,263 classrooms. A series of analyses indicated that the ASSIST demonstrated partial MI across contexts, and was particularly robust across class size and racial composition, which enabled us to contrast latent mean differences across developmental levels. Latent means of classroom practices across elementary and middle school were similar, whereas elementary school and high school classrooms differed significantly. The findings provide evidence that the ASSIST is similarly measuring classroom management across classroom contexts but is sensitive to mean differences in classroom management across classroom contexts.

Published
2024
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49. College Students' Experiences of Sexual Violence and Reasons for Seeking Care in Campus Health and Counseling Centers

Author

Jocelyn C. Anderson, Michelle D. S. Boakye, Zoe Feinstein, Summer Miller-Walfish, Kelley A. Jones, Carla D. Chugani, Alexandra Schmulevich, Reesha Jackson, and Elizabeth Miller

Abstract

Objective: Examine associations between care seeking reasons in college health and counseling centers and sexual violence (SV). Participants: College students (n = 2,084 baseline, n = 1,170 one-year follow up) participating in a cluster randomized controlled trial of an SV reduction intervention on 28 campuses. Methods: Computer-based survey data gathered during students' clinic visit and one-year follow up. Results: Despite high prevalence of SV, students almost never sought care specifically for SV (0.5% of reported visits). Gender differences emerged for reasons students sought care generally, but were not associated with differences in care seeking among those who experienced SV. At baseline and one-year, students who reported SV were more likely to state mental or sexual and reproductive health as their reason for care seeking. Conclusion: Many students seeking care have experienced SV yet present with other health needs. Providers need to recognize this and have a low threshold for providing SV resources routinely. Supplemental data for this article can be accessed online at https://doi.org/10.1080/07448481.2022.2057189

Published
2024
Full Text
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50. The Perceived Effects of the Onset of the COVID-19 Pandemic: A Focus on Educators' Perceptions of the Negative Effects on Educator Stress and Student Well-Being

Author

Catherine P. Bradshaw, Joseph M. Kush, Summer S. Braun, and Emily A. Kohler

Abstract

The COVID-19 pandemic resulted in an urgent pivot to remote learning, causing many challenges for teachers and school administrators. The current study sought to better understand the extent to which the perceived negative impacts of COVID-19 on U.S. educators and their students varied as a function of staff role (teacher vs. administrator), school level (elementary vs. secondary school), and type of school setting (public vs. private), as reported through a national survey of educators conducted in June through July of 2020. Using data from 608 educators (n = 481 teachers and n = 127 administrators; 48% elementary; 85% public school), we examined educators' perceptions of negative impacts on their personal lives, professional lives, and students' lives; major challenges; and stress in various domains. Findings suggested an overall high level of concern across domains. Investigation of educator subgroup effects suggested elementary educators and administrators were most concerned about the negative impacts on students.

Published
2024
Full Text
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