4,905 results on '"Campbell, Andrew"'
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2. Expanding the Reach of Research: Quantitative Evaluation of a Web-Based Approach for Remote Recruitment of People Who Hear Voices
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Buck, Benjamin, Chander, Ayesha, Brian, Rachel M, Wang, Weichen, Campbell, Andrew T, and Ben-Zeev, Dror
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Medicine - Abstract
BackgroundSimilar to other populations with highly stigmatized medical or psychiatric conditions, people who hear voices (ie, experience auditory verbal hallucinations [AVH]) are often difficult to identify and reach for research. Technology-assisted remote research strategies reduce barriers to research recruitment; however, few studies have reported on the efficiency and effectiveness of these approaches. ObjectiveThis study introduces and evaluates the efficacy of technology-assisted remote research designed for people who experience AVH. MethodsOur group developed an integrated, automated and human complementary web-based recruitment and enrollment apparatus that incorporated Google Ads, web-based screening, identification verification, hybrid automation, and interaction with live staff. We examined the efficacy of that apparatus by examining the number of web-based advertisement impressions (ie, number of times the web-based advertisement was viewed); clicks on that advertisement; engagement with web-based research materials; and the extent to which it succeeded in representing a broad sample of individuals with AVH, assessed through the self-reported AVH symptom severity and demographic representativeness (relative to the US population) of the sample recruited. ResultsOver an 18-month period, our Google Ads advertisement was viewed 872,496 times and clicked on 11,183 times. A total amount of US $4429.25 was spent on Google Ads, resulting in 772 individuals who experience AVH providing consent to participate in an entirely remote research study (US $0.40 per click on the advertisement and US $5.73 per consented participant) after verifying their phone number, passing a competency screening questionnaire, and providing consent. These participants reported high levels of AVH frequency (666/756, 88.1% daily or more), distress (689/755, 91.3%), and functional interference (697/755, 92.4%). They also represented a broad sample of diversity that mirrored the US population demographics. Approximately one-third (264/756, 34.9%) of the participants had never received treatment for their AVH and, therefore, were unlikely to be identified via traditional clinic-based research recruitment strategies. ConclusionsWeb-based procedures allow for time saving, cost-efficient, and representative recruitment of individuals with AVH and can serve as a model for future studies focusing on hard-to-reach populations.
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- 2021
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3. Mental Health and Behavior of College Students During the COVID-19 Pandemic: Longitudinal Mobile Smartphone and Ecological Momentary Assessment Study, Part II
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Mack, Dante L, DaSilva, Alex W, Rogers, Courtney, Hedlund, Elin, Murphy, Eilis I, Vojdanovski, Vlado, Plomp, Jane, Wang, Weichen, Nepal, Subigya K, Holtzheimer, Paul E, Wagner, Dylan D, Jacobson, Nicholas C, Meyer, Meghan L, Campbell, Andrew T, and Huckins, Jeremy F
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Computer applications to medicine. Medical informatics ,R858-859.7 ,Public aspects of medicine ,RA1-1270 - Abstract
BackgroundSince late 2019, the lives of people across the globe have been disrupted by COVID-19. Millions of people have become infected with the disease, while billions of people have been continually asked or required by local and national governments to change their behavioral patterns. Previous research on the COVID-19 pandemic suggests that it is associated with large-scale behavioral and mental health changes; however, few studies have been able to track these changes with frequent, near real-time sampling or compare these changes to previous years of data for the same individuals. ObjectiveBy combining mobile phone sensing and self-reported mental health data in a cohort of college-aged students enrolled in a longitudinal study, we seek to understand the behavioral and mental health impacts associated with the COVID-19 pandemic, measured by interest across the United States in the search terms coronavirus and COVID fatigue. MethodsBehaviors such as the number of locations visited, distance traveled, duration of phone use, number of phone unlocks, sleep duration, and sedentary time were measured using the StudentLife mobile smartphone sensing app. Depression and anxiety were assessed using weekly self-reported ecological momentary assessments, including the Patient Health Questionnaire-4. The participants were 217 undergraduate students. Differences in behaviors and self-reported mental health collected during the Spring 2020 term, as compared to previous terms in the same cohort, were modeled using mixed linear models. ResultsLinear mixed models demonstrated differences in phone use, sleep, sedentary time and number of locations visited associated with the COVID-19 pandemic. In further models, these behaviors were strongly associated with increased interest in COVID fatigue. When mental health metrics (eg, depression and anxiety) were added to the previous measures (week of term, number of locations visited, phone use, sedentary time), both anxiety and depression (P
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- 2021
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4. Contextual AI Journaling: Integrating LLM and Time Series Behavioral Sensing Technology to Promote Self-Reflection and Well-being using the MindScape App
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Nepal, Subigya, Pillai, Arvind, Campbell, William, Massachi, Talie, Choi, Eunsol Soul, Xu, Orson, Kuc, Joanna, Huckins, Jeremy, Holden, Jason, Depp, Colin, Jacobson, Nicholas, Czerwinski, Mary, Granholm, Eric, and Campbell, Andrew T.
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Computer Science - Human-Computer Interaction ,Computer Science - Artificial Intelligence ,H.5.0 ,H.5.3 ,H.5.m ,J.0 - Abstract
MindScape aims to study the benefits of integrating time series behavioral patterns (e.g., conversational engagement, sleep, location) with Large Language Models (LLMs) to create a new form of contextual AI journaling, promoting self-reflection and well-being. We argue that integrating behavioral sensing in LLMs will likely lead to a new frontier in AI. In this Late-Breaking Work paper, we discuss the MindScape contextual journal App design that uses LLMs and behavioral sensing to generate contextual and personalized journaling prompts crafted to encourage self-reflection and emotional development. We also discuss the MindScape study of college students based on a preliminary user study and our upcoming study to assess the effectiveness of contextual AI journaling in promoting better well-being on college campuses. MindScape represents a new application class that embeds behavioral intelligence in AI.
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- 2024
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5. MoodCapture: Depression Detection Using In-the-Wild Smartphone Images
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Nepal, Subigya, Pillai, Arvind, Wang, Weichen, Griffin, Tess, Collins, Amanda C., Heinz, Michael, Lekkas, Damien, Mirjafari, Shayan, Nemesure, Matthew, Price, George, Jacobson, Nicholas C., and Campbell, Andrew T.
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Computer Science - Human-Computer Interaction ,Computer Science - Computer Vision and Pattern Recognition ,H.5.0 ,H.5.3 ,H.5.m ,J.0 - Abstract
MoodCapture presents a novel approach that assesses depression based on images automatically captured from the front-facing camera of smartphones as people go about their daily lives. We collect over 125,000 photos in the wild from N=177 participants diagnosed with major depressive disorder for 90 days. Images are captured naturalistically while participants respond to the PHQ-8 depression survey question: \textit{``I have felt down, depressed, or hopeless''}. Our analysis explores important image attributes, such as angle, dominant colors, location, objects, and lighting. We show that a random forest trained with face landmarks can classify samples as depressed or non-depressed and predict raw PHQ-8 scores effectively. Our post-hoc analysis provides several insights through an ablation study, feature importance analysis, and bias assessment. Importantly, we evaluate user concerns about using MoodCapture to detect depression based on sharing photos, providing critical insights into privacy concerns that inform the future design of in-the-wild image-based mental health assessment tools.
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- 2024
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6. Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design
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Campbell, Andrew, Yim, Jason, Barzilay, Regina, Rainforth, Tom, and Jaakkola, Tommi
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Statistics - Machine Learning ,Computer Science - Machine Learning ,Quantitative Biology - Quantitative Methods - Abstract
Combining discrete and continuous data is an important capability for generative models. We present Discrete Flow Models (DFMs), a new flow-based model of discrete data that provides the missing link in enabling flow-based generative models to be applied to multimodal continuous and discrete data problems. Our key insight is that the discrete equivalent of continuous space flow matching can be realized using Continuous Time Markov Chains. DFMs benefit from a simple derivation that includes discrete diffusion models as a specific instance while allowing improved performance over existing diffusion-based approaches. We utilize our DFMs method to build a multimodal flow-based modeling framework. We apply this capability to the task of protein co-design, wherein we learn a model for jointly generating protein structure and sequence. Our approach achieves state-of-the-art co-design performance while allowing the same multimodal model to be used for flexible generation of the sequence or structure., Comment: 60 pages, 11 figures, 6 tables; ICML 2024
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- 2024
7. Improved motif-scaffolding with SE(3) flow matching
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Yim, Jason, Campbell, Andrew, Mathieu, Emile, Foong, Andrew Y. K., Gastegger, Michael, Jiménez-Luna, José, Lewis, Sarah, Satorras, Victor Garcia, Veeling, Bastiaan S., Noé, Frank, Barzilay, Regina, and Jaakkola, Tommi S.
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Quantitative Biology - Quantitative Methods ,Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
Protein design often begins with the knowledge of a desired function from a motif which motif-scaffolding aims to construct a functional protein around. Recently, generative models have achieved breakthrough success in designing scaffolds for a range of motifs. However, generated scaffolds tend to lack structural diversity, which can hinder success in wet-lab validation. In this work, we extend FrameFlow, an SE(3) flow matching model for protein backbone generation, to perform motif-scaffolding with two complementary approaches. The first is motif amortization, in which FrameFlow is trained with the motif as input using a data augmentation strategy. The second is motif guidance, which performs scaffolding using an estimate of the conditional score from FrameFlow without additional training. On a benchmark of 24 biologically meaningful motifs, we show our method achieves 2.5 times more designable and unique motif-scaffolds compared to state-of-the-art. Code: https://github.com/microsoft/protein-frame-flow, Comment: Preprint. Code: https://github.com/ microsoft/frame-flow
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- 2024
8. Trends in pancreatic cancer mortality in the United States 1999–2020: a CDC database population-based study
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Didier, Alexander J., Nandwani, Swamroop, Fahoury, Alan M., Craig, Daniel J., Watkins, Dean, Campbell, Andrew, Spencer, Caleb T., Batten, Macelyn, Vijendra, Divya, and Sutton, Jeffrey M.
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- 2024
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9. Mental Health and Behavior of College Students During the Early Phases of the COVID-19 Pandemic: Longitudinal Smartphone and Ecological Momentary Assessment Study
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Huckins, Jeremy F, daSilva, Alex W, Wang, Weichen, Hedlund, Elin, Rogers, Courtney, Nepal, Subigya K, Wu, Jialing, Obuchi, Mikio, Murphy, Eilis I, Meyer, Meghan L, Wagner, Dylan D, Holtzheimer, Paul E, and Campbell, Andrew T
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Computer applications to medicine. Medical informatics ,R858-859.7 ,Public aspects of medicine ,RA1-1270 - Abstract
BackgroundThe vast majority of people worldwide have been impacted by coronavirus disease (COVID-19). In addition to the millions of individuals who have been infected with the disease, billions of individuals have been asked or required by local and national governments to change their behavioral patterns. Previous research on epidemics or traumatic events suggests that this can lead to profound behavioral and mental health changes; however, researchers are rarely able to track these changes with frequent, near-real-time sampling or compare their findings to previous years of data for the same individuals. ObjectiveBy combining mobile phone sensing and self-reported mental health data among college students who have been participating in a longitudinal study for the past 2 years, we sought to answer two overarching questions. First, have the behaviors and mental health of the participants changed in response to the COVID-19 pandemic compared to previous time periods? Second, are these behavior and mental health changes associated with the relative news coverage of COVID-19 in the US media? MethodsBehaviors such as the number of locations visited, distance traveled, duration of phone usage, number of phone unlocks, sleep duration, and sedentary time were measured using the StudentLife smartphone sensing app. Depression and anxiety were assessed using weekly self-reported ecological momentary assessments of the Patient Health Questionnaire-4. The participants were 217 undergraduate students, with 178 (82.0%) students providing data during the Winter 2020 term. Differences in behaviors and self-reported mental health collected during the Winter 2020 term compared to previous terms in the same cohort were modeled using mixed linear models. ResultsDuring the first academic term impacted by COVID-19 (Winter 2020), individuals were more sedentary and reported increased anxiety and depression symptoms (P
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- 2020
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10. Causal Factors of Anxiety and Depression in College Students: Longitudinal Ecological Momentary Assessment and Causal Analysis Using Peter and Clark Momentary Conditional Independence
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Huckins, Jeremy F, DaSilva, Alex W, Hedlund, Elin L, Murphy, Eilis I, Rogers, Courtney, Wang, Weichen, Obuchi, Mikio, Holtzheimer, Paul E, Wagner, Dylan D, and Campbell, Andrew T
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Psychology ,BF1-990 - Abstract
BackgroundAcross college campuses, the prevalence of clinically relevant depression or anxiety is affecting more than 27% of the college population at some point between entry to college and graduation. Stress and self-esteem have both been hypothesized to contribute to depression and anxiety levels. Although contemporaneous relationships between these variables have been well-defined, the causal relationship between these mental health factors is not well understood, as frequent sampling can be invasive, and many of the current causal techniques are not well suited to investigate correlated variables. ObjectiveThis study aims to characterize the causal and contemporaneous networks between these critical mental health factors in a cohort of first-year college students and then determine if observed results replicate in a second, distinct cohort. MethodsEcological momentary assessments of depression, anxiety, stress, and self-esteem were obtained weekly from two cohorts of first-year college students for 40 weeks (1 academic year). We used the Peter and Clark Momentary Conditional Independence algorithm to identify the contemporaneous (t) and causal (t-1) network structures between these mental health metrics. ResultsAll reported results are significant at P
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- 2020
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11. Workshop on the Development and Evaluation of Digital Therapeutics for Health Behavior Change: Science, Methods, and Projects
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Budney, Alan J, Marsch, Lisa A, Aklin, Will M, Borodovsky, Jacob T, Brunette, Mary F, Campbell, Andrew T, Dallery, Jesse, Kotz, David, Knapp, Ashley A, Lord, Sarah E, Nunes, Edward V, Scherer, Emily A, Stanger, Catherine, and Torrey, William C
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Psychology ,BF1-990 - Abstract
The health care field has integrated advances into digital technology at an accelerating pace to improve health behavior, health care delivery, and cost-effectiveness of care. The realm of behavioral science has embraced this evolution of digital health, allowing for an exciting roadmap for advancing care by addressing the many challenges to the field via technological innovations. Digital therapeutics offer the potential to extend the reach of effective interventions at reduced cost and patient burden and to increase the potency of existing interventions. Intervention models have included the use of digital tools as supplements to standard care models, as tools that can replace a portion of treatment as usual, or as stand-alone tools accessed outside of care settings or direct to the consumer. To advance the potential public health impact of this promising line of research, multiple areas warrant further development and investigation. The Center for Technology and Behavioral Health (CTBH), a P30 Center of Excellence supported by the National Institute on Drug Abuse at the National Institutes of Health, is an interdisciplinary research center at Dartmouth College focused on the goal of harnessing existing and emerging technologies to effectively develop and deliver evidence-based interventions for substance use and co-occurring disorders. The CTBH launched a series of workshops to encourage and expand multidisciplinary collaborations among Dartmouth scientists and international CTBH affiliates engaged in research related to digital technology and behavioral health (eg, addiction science, behavioral health intervention, technology development, computer science and engineering, digital security, health economics, and implementation science). This paper summarizes a workshop conducted on the Development and Evaluation of Digital Therapeutics for Behavior Change, which addressed (1) principles of behavior change, (2) methods of identifying and testing the underlying mechanisms of behavior change, (3) conceptual frameworks for optimizing applications for mental health and addictive behavior, and (4) the diversity of experimental methods and designs that are essential to the successful development and testing of digital therapeutics. Examples were presented of ongoing CTBH projects focused on identifying and improving the measurement of health behavior change mechanisms and the development and evaluation of digital therapeutics. In summary, the workshop showcased the myriad research targets that will be instrumental in promoting and accelerating progress in the field of digital health and health behavior change and illustrated how the CTBH provides a model of multidisciplinary leadership and collaboration that can facilitate innovative, science-based efforts to address the health behavior challenges afflicting our communities.
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- 2020
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12. A Customized Social Network Platform (Kids Helpline Circles) for Delivering Group Counseling to Young People Experiencing Family Discord That Impacts Their Well-Being: Exploratory Study
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Campbell, Andrew, Ridout, Brad, Amon, Krestina, Navarro, Pablo, Collyer, Brian, and Dalgleish, John
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Computer applications to medicine. Medical informatics ,R858-859.7 ,Public aspects of medicine ,RA1-1270 - Abstract
BackgroundIt has often been reported that young people are at high risk of mental health concerns, more so than at any other time in development over their life span. The situational factors that young people report as impacting their well-being are not addressed as often: specifically, family discord. Kids Helpline, a national service in Australia that provides free counseling online and by telephone to young people in distress, report that family discord and well-being issues are one of the major concerns reported by clients. In order to meet the preferences that young people seek when accessing counseling support, Kids Helpline has designed and trialed a custom-built social network platform for group counseling of young people experiencing family discord that impacts their well-being. ObjectiveIn this exploratory study, we communicate the findings of Phase 1 of an innovative study in user and online counselor experience. This will lead to an iterative design for a world-first, purpose-built social network that will do the following: (1) increase reach and quality of service by utilizing a digital tool of preference for youth to receive peer-to-peer and counselor-to-peer support in a safe online environment and (2) provide the evidence base to document the best practice for online group counseling in a social network environment. MethodsThe study utilized a participatory action research design. Young people aged 13-25 years (N=105) with mild-to-moderate depression or anxiety (not high risk) who contacted Kids Helpline were asked if they would like to trial the social networking site (SNS) for peer-to-peer and counselor-to-peer group support. Subjects were grouped into age cohorts of no more than one year above or below their reported age and assigned to groups of no more than 36 participants, in order to create a community of familiarity around age and problems experienced. Each group entered into an 8-week group counseling support program guided by counselors making regular posts and providing topic-specific content for psychoeducation and discussion. Counselors provided a weekly log of events to researchers; at 2-week intervals, subjects provided qualitative and quantitative feedback through open-ended questions and specific psychometric measures. ResultsQualitative results provided evidence of user support and benefits of the online group counseling environment. Counselors also reported benefits of the modality of therapy delivery. Psychometric scales did not report significance in changes of mood or affect. Counselors and users suggested improvements to the platform to increase user engagement. ConclusionsPhase 1 provided proof of concept for this mode of online counseling delivery. Users and counselors saw value in the model and innovation of the service. Phase 2 will address platform issues with changes to a new social network platform. Phase 2 will focus more broadly on mental health concerns raised by users and permit inclusion of a clinical population of young people experiencing depression and anxiety. Trial RegistrationAustralian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12616000518460; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370381
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- 2019
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13. Hyperspectral Lightcurve Inversion for Attitude Determination
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Marto, Simão da Graça, Vasile, Massimiliano, Campbell, Andrew, Murray, Paul, Marshall, Stephen, and Savitski, Vasili
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Electrical Engineering and Systems Science - Signal Processing ,Astrophysics - Instrumentation and Methods for Astrophysics ,Computer Science - Machine Learning - Abstract
Spectral lightcurves consisting of time series single-pixel spectral measurements of spacecraft are used to infer the spacecraft's attitude and rotation. Two methods are used. One based on numerical optimisation of a regularised least squares cost function, and another based on machine learning with a neural network model. The aim is to work with minimal information, thus no prior is available on the attitude nor on the inertia tensor. The theoretical and practical aspects of this task are investigated, and the methodology is tested on synthetic data. Results are shown based on synthetic data., Comment: 20 pages, 14 figures Accepted for presentation at SciTech 2024 in Orlando, Florida, USA
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- 2023
14. Social Isolation and Serious Mental Illness: The Role of Context-Aware Mobile Interventions
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Nepal, Subigya, Pillai, Arvind, Parrish, Emma M., Holden, Jason, Depp, Colin, Campbell, Andrew T., and Granholm, Eric
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Computer Science - Human-Computer Interaction ,Computer Science - Computers and Society ,H.5.0 ,H.5.m ,J.0 ,J.3 ,J.4 ,J.m - Abstract
Social isolation is a common problem faced by individuals with serious mental illness (SMI), and current intervention approaches have limited effectiveness. This paper presents a blended intervention approach, called mobile Social Interaction Therapy by Exposure (mSITE), to address social isolation in individuals with serious mental illness. The approach combines brief in-person cognitive-behavioral therapy (CBT) with context-triggered mobile CBT interventions that are personalized using mobile sensing data. Our approach targets social behavior and is the first context-aware intervention for improving social outcomes in serious mental illness.
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- 2023
15. Compressed and Sparse Models for Non-Convex Decentralized Learning
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Campbell, Andrew, Liu, Hang, Woldemariam, Leah, and Scaglione, Anna
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Computer Science - Machine Learning ,Computer Science - Distributed, Parallel, and Cluster Computing ,Computer Science - Data Structures and Algorithms ,Computer Science - Multiagent Systems ,Mathematics - Optimization and Control ,68W15, 68W10, 68W40, 90C06, 90C35, 90C25 ,G.1.6 ,F.2.1 ,E.4 - Abstract
Recent research highlights frequent model communication as a significant bottleneck to the efficiency of decentralized machine learning (ML), especially for large-scale and over-parameterized neural networks (NNs). To address this, we present Malcom-PSGD, a novel decentralized ML algorithm that combines gradient compression techniques with model sparsification. We promote model sparsity by adding $\ell_1$ regularization to the objective and present a decentralized proximal SGD method for training. Our approach employs vector source coding and dithering-based quantization for the compressed gradient communication of sparsified models. Our analysis demonstrates that Malcom-PSGD achieves a convergence rate of $\mathcal{O}(1/\sqrt{t})$ with respect to the iterations $t$, assuming a constant consensus and learning rate. This result is supported by our proof for the convergence of non-convex compressed Proximal SGD methods. Additionally, we conduct a bit analysis, providing a closed-form expression for the communication costs associated with Malcom-PSGD. Numerical results verify our theoretical findings and demonstrate that our method reduces communication costs by approximately $75\%$ when compared to the state-of-the-art.
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- 2023
16. Fast protein backbone generation with SE(3) flow matching
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Yim, Jason, Campbell, Andrew, Foong, Andrew Y. K., Gastegger, Michael, Jiménez-Luna, José, Lewis, Sarah, Satorras, Victor Garcia, Veeling, Bastiaan S., Barzilay, Regina, Jaakkola, Tommi, and Noé, Frank
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Quantitative Biology - Quantitative Methods - Abstract
We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching. Specifically, we adapt FrameDiff, a state-of-the-art diffusion model, to the flow-matching generative modeling paradigm. We show how flow matching can be applied on SE(3) and propose modifications during training to effectively learn the vector field. Compared to FrameDiff, FrameFlow requires five times fewer sampling timesteps while achieving two fold better designability. The ability to generate high quality protein samples at a fraction of the cost of previous methods paves the way towards more efficient generative models in de novo protein design., Comment: Preprint
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- 2023
17. Correlates of Stress in the College Environment Uncovered by the Application of Penalized Generalized Estimating Equations to Mobile Sensing Data
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DaSilva, Alex W, Huckins, Jeremy F, Wang, Rui, Wang, Weichen, Wagner, Dylan D, and Campbell, Andrew T
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Information technology ,T58.5-58.64 ,Public aspects of medicine ,RA1-1270 - Abstract
BackgroundStress levels among college students have been on the rise for the last few decades. Currently, rates of reported stress among college students are at an all-time high. Traditionally, the dominant way to assess stress levels has been through pen-and-paper surveys. ObjectiveThe aim of this study is to use passive sensing data collected via mobile phones to obtain a rich and potentially less-biased source of data that can be used to help better understand stressors in the college experience. MethodsWe used a mobile sensing app, StudentLife, in tandem with a pictorial mobile phone–based measure of stress, the Mobile Photographic Stress Meter, to investigate the situations and contexts that are more likely to precipitate stress. ResultsUsing recently developed methods for handling high-dimensional longitudinal data, penalized generalized estimating equations, we identified a set of mobile sensing features (absolute values of beta >0.001 and robust z>1.96) across the domains of social activity, movement, location, and ambient noise that were predictive of student stress levels. ConclusionsBy combining recent statistical methods and mobile phone sensing, we have been able to study stressors in the college experience in a way that is more objective, detailed, and less intrusive than past research. Future work can leverage information gained from passive sensing and use that to develop real-time, targeted interventions for students experiencing a stressful time.
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- 2019
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18. Space Object Identification and Classification from Hyperspectral Material Analysis
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Vasile, Massimiliano, Walker, Lewis, Campbell, Andrew, Marto, Simao, Murray, Paul, Marshall, Stephen, and Savitski, Vasili
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Astrophysics - Instrumentation and Methods for Astrophysics ,Computer Science - Computer Vision and Pattern Recognition ,Electrical Engineering and Systems Science - Image and Video Processing ,Physics - Space Physics ,68, 70 ,I.2.0 ,I.2.10 - Abstract
This paper presents a data processing pipeline designed to extract information from the hyperspectral signature of unknown space objects. The methodology proposed in this paper determines the material composition of space objects from single pixel images. Two techniques are used for material identification and classification: one based on machine learning and the other based on a least square match with a library of known spectra. From this information, a supervised machine learning algorithm is used to classify the object into one of several categories based on the detection of materials on the object. The behaviour of the material classification methods is investigated under non-ideal circumstances, to determine the effect of weathered materials, and the behaviour when the training library is missing a material that is present in the object being observed. Finally the paper will present some preliminary results on the identification and classification of space objects., Comment: 30 pages, 24 figures
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- 2023
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19. The fractional free convolution of $R$-diagonal elements and random polynomials under repeated differentiation
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Campbell, Andrew, O'Rourke, Sean, and Renfrew, David
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Mathematics - Probability ,Mathematics - Operator Algebras - Abstract
We extend the free convolution of Brown measures of $R$-diagonal elements introduced by K\"{o}sters and Tikhomirov [Probab. Math. Statist. 38 (2018), no. 2, 359--384] to fractional powers. We then show how this fractional free convolution arises naturally when studying the roots of random polynomials with independent coefficients under repeated differentiation. When the proportion of derivatives to the degree approaches one, we establish central limit theorem-type behavior and discuss stable distributions., Comment: 35 pages, 2 figures. Corrections and reorganized presentation. Final version
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- 2023
20. 15 Black School Leaders in Low-Income Urban Ontario Schools: Striving for Social Justice
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Butler, Alana, primary and Campbell, Andrew, additional
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- 2024
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21. Rare Life Event Detection via Mobile Sensing Using Multi-Task Learning
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Pillai, Arvind, Nepal, Subigya, and Campbell, Andrew
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Computer Science - Machine Learning ,Computer Science - Human-Computer Interaction - Abstract
Rare life events significantly impact mental health, and their detection in behavioral studies is a crucial step towards health-based interventions. We envision that mobile sensing data can be used to detect these anomalies. However, the human-centered nature of the problem, combined with the infrequency and uniqueness of these events makes it challenging for unsupervised machine learning methods. In this paper, we first investigate granger-causality between life events and human behavior using sensing data. Next, we propose a multi-task framework with an unsupervised autoencoder to capture irregular behavior, and an auxiliary sequence predictor that identifies transitions in workplace performance to contextualize events. We perform experiments using data from a mobile sensing study comprising N=126 information workers from multiple industries, spanning 10106 days with 198 rare events (<2%). Through personalized inference, we detect the exact day of a rare event with an F1 of 0.34, demonstrating that our method outperforms several baselines. Finally, we discuss the implications of our work from the context of real-world deployment., Comment: 15 pages, 4 figures, CHIL 2023 (Accepted)
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- 2023
22. Trans-Dimensional Generative Modeling via Jump Diffusion Models
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Campbell, Andrew, Harvey, William, Weilbach, Christian, De Bortoli, Valentin, Rainforth, Tom, and Doucet, Arnaud
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Statistics - Machine Learning ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
We propose a new class of generative models that naturally handle data of varying dimensionality by jointly modeling the state and dimension of each datapoint. The generative process is formulated as a jump diffusion process that makes jumps between different dimensional spaces. We first define a dimension destroying forward noising process, before deriving the dimension creating time-reversed generative process along with a novel evidence lower bound training objective for learning to approximate it. Simulating our learned approximation to the time-reversed generative process then provides an effective way of sampling data of varying dimensionality by jointly generating state values and dimensions. We demonstrate our approach on molecular and video datasets of varying dimensionality, reporting better compatibility with test-time diffusion guidance imputation tasks and improved interpolation capabilities versus fixed dimensional models that generate state values and dimensions separately., Comment: 41 pages, 11 figures, 8 tables; NeurIPS 2023
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- 2023
23. Coronal Heating as Determined by the Solar Flare Frequency Distribution Obtained by Aggregating Case Studies
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Mason, James Paul, Werth, Alexandra, West, Colin G., Youngblood, Allison A., Woodraska, Donald L., Peck, Courtney, Lacjak, Kevin, Frick, Florian G., Gabir, Moutamen, Alsinan, Reema A., Jacobsen, Thomas, Alrubaie, Mohammad, Chizmar, Kayla M., Lau, Benjamin P., Dominguez, Lizbeth Montoya, Price, David, Butler, Dylan R., Biron, Connor J., Feoktistov, Nikita, Dewey, Kai, Loomis, N. E., Bodzianowski, Michal, Kuybus, Connor, Dietrick, Henry, Wolfe, Aubrey M., Guerrero, Matt, Vinson, Jessica, Starbuck, Peter, Litton, Shelby D, Beck, M. G., Fisch, Jean-Paul, West, Ayana, Muniz, Alexis A., Chavez, Luis, Upthegrove, Zachary T., Runyon, Brenton M., Salazar, J., Kritzberg, Jake E., Murrel, Tyler, Ho, Ella, LaFemina, Quintin Y., Elbashir, Sara I., Chang, Ethan C., Hudson, Zachary A., Nussbaum, Rosemary O., Kennedy, Kellen, Kim, Kevin, Arango, Camila Villamil, Albakr, Mohammed A., Rotter, Michael, Garscadden, A. J., Salcido-Alcontar JR, Antonio, Pearl, Harrison M., Stepaniak, Tyler, Marquez, Josie A., Marsh, Lauren, Andringa, Jesse C, Osogwin, Austin, Shields, Amanda M., Brookins, Sarah, Hach, Grace K., Clausi, Alexis R., Millican, Emily B., Jaimes, Alan A, Graham, Alaina S., Burritt, John J., Perez, J. S., Ramirez, Nathaniel, Suri, Rohan, Myer, Michael S., Kresek, Zoe M., Goldsberry, C. A., Payne, Genevieve K., Jourabchi, Tara, Hu, J., Lucca, Jeffrey, Feng, Zitian, Gilpatrick, Connor B., Khan, Ibraheem A., Warble, Keenan, Sweeney, Joshua D., Dorricott, Philip, Meyer, Ethan, Kothamdi, Yash S., Sohail, Arman S., Grell, Kristyn, Floyd, Aidan, Bard, Titus, Mathieson, Randi M., Reed, Joseph, Cisneros, Alexis, Payne, Matthew P., Jarriel, J. R., Mora, Jacqueline Rodriguez, Sundell, M. E., Patel, Kajal, Alesmail, Mohammad, Alnasrallah, Yousef A, Abdullah, Jumana T., Molina-Saenz, Luis, Tayman, K. E., Brown, Gabriel T., Kerr-Layton, Liana, Berriman-Rozen, Zachary D., Hiatt, Quinn, Kalra, Etash, Ong, Jason, Vadayar, Shreenija, Shannahan, Callie D., Benke, Evan, zhang, Jinhua, Geisman, Jane, Martyr, Cara, Ameijenda, Federico, Akruwala, Ushmi H., Nehring, Molly, Kissner, Natalie, Rule, Ian C., Learned, Tyler, Smith, Alexandra N., Mazzotta, Liam, Rounsefell, Tyndall, Eyeson, Elizabeth A., Shelby, Arlee K., Moll, Tyler S, Menke, Riley, Shahba, Hannan, House Jr., Tony A., Clark, David B., Burns, Annemarie C., de La Beaujardiere, Tristan, Trautwein, Emily D., Plantz, Will, Reeves, Justin, Faber, Ian, Buxton, B. W., Highhouse, Nigel, Landrey, Kalin, Hansen, Connor M, Chen, Kevin, Hales, Ryder Buchanan, Borgerding, Luke R., Guo, Mutian, Crow, Christian J., Whittall, Lloyd C., Simmons, Conor, Folarin, Adeduni, Parkinson, Evan J., Rahn, Anna L., Blevins, Olivia, Morelock, Annalise M., Kelly, Nicholas, Parker, Nathan L., Smith, Kelly, Plzak, Audrey E., Saeb, David, Hares, Cameron T., Parker, Sasha R., McCoy, Andrew, Pham, Alexander V., Lauzon, Megan, Kennedy, Cayla J., Reyna, Andrea B., Acosta, Daniela M. Meza, Cool, Destiny J., Steinbarth, Sheen L., Mendoza-Anselmi, Patricia, Plutt, Kaitlyn E., Kipp, Isabel M, Rakhmonova, M., Brown, Cameron L., Van Anne, Gabreece, Moss, Alexander P., Golden, Olivia, Kirkpatrick, Hunter B., Colleran, Jake R., Sullivan, Brandon J, Tran, Kevin, Carpender, Michael Andrew, Mundy, Aria T., Koenig, Greta, Oudakker, Jessica, Engelhardt, Rasce, Ales, Nolan, Wexler, Ethan Benjamin, Beato, Quinn I, Chen, Lily, Cochran, Brooke, Hill, Paula, Hamilton, Sean R., Hashiro, Kyle, Khan, Usman, Martinez, Alexa M., Brockman, Jennifer L., Mallory, Macguire, Reed, Charlie, Terrile, Richard, Singh, Savi, Watson, James Adam, Creany, Joshua B., Price, Nicholas K., Miften, Aya M., Tran, Bryn, Kamenetskiy, Margaret, Martinez, Jose R., Opp, Elena N., Huang, Jianyang, Fails, Avery M., Belei, Brennan J., Slocum, Ryan, Astalos, Justin, East, Andrew, Nguyen, Lena P., Pherigo, Callie C, East, Andrew N., Li, David Y., Nelson, Maya LI, Taylor, Nicole, Odbayar, Anand, Rives, Anna Linnea, Mathur, Kabir P., Billingsley, Jacob, Polikoff, Hyden, Driscoll, Michael, Wilson, Orion K., Lahmers, Kyle, Toon, Nathaniel J., Lippincott, Sam, Musgrave, Andrew J., Gregory, Alannah H., Pitsuean-Meier, Sedique, Jesse, Trevor, Smith, Corey, Miles, Ethan J., Kainz, Sabrina J. H. T., Ji, Soo Yeun, Nguyen, Lena, Aryan, Maryam, Dinser, Alexis M., Shortman, Jadon, Bastias, Catalina S, Umbricht, Thomas D, Cage, Breonna, Randolph, Parker, Pollard, Matthew, Simone, Dylan M., Aramians, Andrew, Brecl, Ariana E., Robert, Amanda M., Zenner, Thomas, Saldi, Maxwell, Morales, Gavin, Mendez, Citlali, Syed, Konner, Vogel, Connor Maklain, Cone, Rebecca A., Berhanu, Naomi, Carpenter, Emily, Leoni, Cecilia, Bryan, Samuel, Ramachandra, Nidhi, Shaw, Timothy, Lee, E. C., Monyek, Eli, Wegner, Aidan B., Sharma, Shajesh, Lister, Barrett, White, Jamison R., Willard, John S., Sulaiman, S. A, Blandon, Guillermo, Narayan, Anoothi, Ruger, Ryan, Kelley, Morgan A., Moreno, Angel J., Balcer, Leo M, Ward-Chene, N. R. D., Shelby, Emma, Reagan, Brian D., Marsh, Toni, Sarkar, Sucheta, Kelley, Michael P., Fell, Kevin, Balaji, Sahana, Hildebrand, Annalise K., Shoha, Dominick, Nandu, Kshmya, Tucker, Julia, Cancio, Alejandro R., Wang, Jiawei, Rapaport, Sarah Grace, Maravi, Aimee S., Mayer, Victoria A., Miller, Andrew, Bence, Caden, Koke, Emily, Fauntleroy, John T, Doermer, Timothy, Al-Ghazwi, Adel, Morgan, Remy, Alahmed, Mohammed S., Mathavan, Adam Izz Khan Mohd Reduan, Silvester, H. K., Weiner, Amanda M., Liu, Nianzi, Iovan, Taro, Jensen, Alexander V., AlHarbi, Yazeed A., Jiang, Yufan, Zhang, Jiaqi, Jones, Olivia M., Huang, Chenqi, Reh, Eileen N., Alhamli, Dania, Pettine, Joshua, Zhou, Chongrui, Kriegman, Dylan, Yang, Jianing, Ash, Kevin, Savage, Carl, Kaiser, Emily, Augenstein, Dakota N., Padilla, Jacqueline, Stark, Ethan K., Hansen, Joshua A., Kokes, Thomas, Huynh, Leslie, Sanchez-Sanchez, Gustavo, Jeseritz, Luke A., Carillion, Emma L., Vepa, Aditya V., Khanal, Sapriya, Behr, Braden, Martin, Logan S., McMullan, Jesse J., Zhao, Tianwei, Williams, Abigail K., Alqabani, Emeen, Prinster, Gale H., Horne, Linda, Ruggles-Delgado, Kendall, Otto, Grant, Gomez, Angel R., Nguyen, Leonardo, Brumley, Preston J., Venegas, Nancy Ortiz, Varela, Ilian, Brownlow, Jordi, Cruz, Avril, Leiker, Linzhi, Batra, Jasleen, Hutabarat, Abigail P., Nunes-Valdes, Dario, Jameson, Connor, Naqi, Abdulaziz, Adams, Dante Q., Biediger, Blaine B., Borelli, William T, Cisne, Nicholas A., Collins, Nathaniel A., Curnow, Tyler L., Gopalakrishnan, Sean, Griffin, Nicholas F., Herrera, Emanuel, McGarvey, Meaghan V., Mellett, Sarah, Overchuk, Igor, Shaver, Nathan, Stratmeyer, Cooper N., Vess, Marcus T., Juels, Parker, Alyami, Saleh A., Gale, Skylar, Wallace, Steven P., Hunter, Samuel C, Lonergan, Mia C., Stewart, Trey, Maksimuk, Tiffany E., Lam, Antonia, Tressler, Judah, Napoletano, Elena R., Miller, Joshua B., Roy, Marc G., Chanders, Jasey, Fischer, Emmalee, Croteau, A. J., Kuiper, Nicolas A., Hoffman, Alex, DeBarros, Elyse, Curry, Riley T., Brzostowicz, A., Courtney, Jonas, Zhao, Tiannie, Szabo, Emi, Ghaith, Bandar Abu, Slyne, Colin, Beck, Lily, Quinonez, Oliver, Collins, Sarah, Madonna, Claire A., Morency, Cora, Palizzi, Mallory, Herwig, Tim, Beauprez, Jacob N., Ghiassi, Dorsa, Doran, Caroline R., Yang, Zhanchao, Padgette, Hannah M., Dicken, Cyrus A., Austin, Bryce W., Phalen, Ethan J., Xiao, Catherine, Palos, Adler, Gerhardstein, Phillip, Altenbern, Ava L., Orbidan, Dan, Dorr, Jackson A., Rivas, Guillermo A., Ewing, Calvin A, Giebner, B. C., McEntee, Kelleen, Kite, Emily R., Crocker, K. A., Haley, Mark S., Lezak, Adrienne R., McQuaid, Ella, Jeong, Jacob, Albaum, Jonathan, Hrudka, E. M., Mulcahy, Owen T., Tanguma, Nolan C., Oishi-Holder, Sean, White, Zachary, Coe, Ryan W., Boyer, Christine, Chapman, Mitchell G., Fortino, Elise, Salgado, Jose A., Hellweg, Tim, Martinez, Hazelia K., Mitchell, Alexander J., Schubert, Stephanie H., Schumacher, Grace K, Tesdahl, Corey D, Uphoff, C. H., Vassilyev, Alexandr, Witkoff, Briahn, Wolle, Jackson R., Dice, Kenzie A., Behrer, Timothy A., Bowen, Troy, Campbell, Andrew J, Clarkson, Peter C, Duong, Tien Q., Hawat, Elijah, Lopez, Christian, Olson, Nathaniel P., Osborn, Matthew, Peou, Munisettha E., Vaver, Nicholas J., Husted, Troy, Kallemeyn, Nicolas Ian, Spangler, Ava A, Mccurry, Kyle, Schultze, Courtney, Troisi, Thomas, Thomas, Daniel, Ort, Althea E., Singh, Maya A., Soon, Caitlin, Patton, Catherine, Billman, Jayce A., Jarvis, Sam, Hitt, Travis, Masri, Mirna, Albalushi, Yusef J., Schofer, Matthew J, Linnane, Katherine B., Knott, Philip Whiting, Valencia, Whitney, Arias-Robles, Brian A., Ryder, Diana, Simone, Anna, Abrams, Jonathan M., Belknap, Annelene L., Rouse, Charlotte, Reynolds, Alexander, Petric, Romeo S. L., Gomez, Angel A., Meiselman-Ashen, Jonah B., Carey, Luke, Dias, John S., Fischer-White, Jules, Forbes, Aidan E., Galarraga, Gabriela, Kennedy, Forrest, Lawlor, Rian, Murphy, Maxwell J., Norris, Cooper, Quarderer, Josh, Waller, Caroline, Weber, Robert J., Gunderson, Nicole, Boyne, Tom, Gregory, Joshua A., Propper, Henry Austin, von Peccoz, Charles B. Beck, Branch, Donovan, Clarke, Evelyn, Cutler, Libby, Dabberdt, Frederick M., Das, Swagatam, Figueirinhas, John Alfred D., Fougere, Benjamin L., Roy, Zoe A., Zhao, Noah Y., Cox, Corben L., Barnhart, Logan D. W., Craig, Wilmsen B., Moll, Hayden, Pohle, Kyle, Mueller, Alexander, Smith, Elena K., Spicer, Benjamin C., Aycock, Matthew C., Bat-Ulzii, Batchimeg, Murphy, Madalyn C., Altokhais, Abdullah, Thornally, Noah R., Kleinhaus, Olivia R., Sarfaraz, Darian, Barnes, Grant M., Beard, Sara, Banda, David J, Davis, Emma A. B., Huebsch, Tyler J., Wagoner, Michaela, Griego, Justus, Hale, Jack J. Mc, Porter, Trevor J., Abrashoff, Riley, Phan, Denise M., Smith, Samantha M., Srivastava, Ashish, Schlenker, Jared A. W., Madsen, Kasey O., Hirschmann, Anna E., Rankin, Frederick C, Akbar, Zainab A., Blouin, Ethan, Coleman-Plante, Aislinn, Hintsa, Evan, Lookhoff, Emily, Amer, Hamzi, Deng, Tianyue, Dvorak, Peter, Minimo, Josh, Plummer, William C., Ton, Kelly, Solt, Lincoln, AlAbbas, Batool H., AlAwadhi, Areej A., Cooper, Nicholas M., Corbitt, Jessica S, Dunlap, Christian, Johnson, Owen, Malone, Ryan A., Tellez, Yesica, Wallace, Logan, Ta, Michael-Tan D., Wheeler, Nicola H., Ramirez, Ariana C., Huang, Shancheng, Mehidic, Amar, Christiansen, Katherine E, Desai, Om, Domke, Emerson N., Howell, Noah H., Allsbrook, Martin, Alnaji, Teeb, England, Colin, Siles, Nathan, Burton, Nicholas David, Cruse, Zoe, Gilmartin, Dalton, Kim, Brian T., Hattendorf, Elsie, Buhamad, Maryam, Gayou, Lily, Seglem, Kasper, Alkhezzi, Tameem, Hicks, Imari R., Fife, Ryann, Pelster, Lily M., Fix, Alexander, Sur, Sohan N., Truong, Joshua K., Kubiak, Bartlomiej, Bondar, Matthew, Shi, Kyle Z., Johnston, Julia, Acevedo, Andres B., Lee, Junwon, Solorio, William J., Johnston, Braedon Y., McCormick, Tyler, Olguin, Nicholas, Pastor, Paige J., Wilson, Evan M., Trunko, Benjamin L., Sjoroos, Chris, Adams, Kalvyn N, Bell, Aislyn, Brumage-Heller, Grant, Canales, Braden P., Chiles, Bradyn, Driscoll, Kailer H., Hill, Hallie, Isert, Samuel A., Ketterer, Marilyn, Kim, Matthew M., Mewhirter, William J., Phillips, Lance, Phommatha, Krista, Quinn, Megan S., Reddy, Brooklyn J., Rippel, Matthew, Russell, Bowman, Williams, Sajan, Pixley, Andrew M., Gapin, Keala C., Peterson, B., Ruprecht, Collin, Hardie, Isabelle, Li, Isaac, Erickson, Abbey, Gersabeck, Clint, Gopalani, Mariam, Allanqawi, Nasser, Burton, Taylor, Cahn, Jackson R., Conti, Reese, White, Oliver S., Rojec, Stewart, Hogen, Blake A., Swartz, Jason R., Dick, R., Battist, Lexi, Dunn, Gabrielle M., Gasser, Rachel, Logan, Timothy W., Sinkovic, Madeline, Schaller, Marcus T., Heintz, Danielle A., Enrich, Andrew, Sanchez, Ethan S., Perez, Freddy, Flores, Fernando, Kapla, Shaun D., Shockley, Michael C., Phillips, Justin, Rumley, Madigan, Daboub, Johnston, Karsh, Brennan J., Linders, Bridget, Chen, Sam, Do, Helen C., Avula, Abhinav, French, James M., Bertuccio, Chrisanna, Hand, Tyler, Lee, Adrianna J., Neeland, Brenna K, Salazar, Violeta, Andrew, Carter, Barmore, Abby, Beatty, Thomas, Alonzi, Nicholas, Brown, Ryan, Chandler, Olivia M., Collier, Curran, Current, Hayden, Delasantos, Megan E., Bonilla, Alberto Espinosa de los Monteros, Fowler, Alexandra A., Geneser, Julianne R., Gentry, Eleanor, Gustavsson, E. R., Hansson, Jonathan, Hao, Tony Yunfei, Herrington, Robert N., Kelly, James, Kelly, Teagan, Kennedy, Abigail, Marquez, Mathew J., Meillon, Stella, Palmgren, Madeleine L., Pesce, Anneliese, Ranjan, Anurag, Robertson, Samuel M., Smith, Percy, Smith, Trevor J, Soby, Daniel A., Stratton, Grant L., Thielmann, Quinn N., Toups, Malena C., Veta, Jenna S., Young, Trenton J., Maly, Blake, Manzanares, Xander R., Beijer, Joshua, George, Jacob D., Mills, Dylan P., Ziebold, Josh J, Chambers, Paige, Montoya, Michael, Cheang, Nathan M., Anderson, Hunter J., Duncan, Sheridan J., Ehrlich, Lauren, Hudson, Nathan C., Kiechlin, Jack L., Koch, Will, Lee, Justin, Menassa, Dominic, Oakes, S. H., Petersen, Audrey J., Bunsow, J. R. Ramirez, Bay, Joshua, Ramirez, Sacha, Fenwick, Logan D., Boyle, Aidan P., Hibbard, Lea Pearl, Haubrich, Calder, Sherry, Daniel P., Jenkins, Josh, Furney, Sebastian, Velamala, Anjali A., Krueger, Davis J., Thompson, William N., Chhetri, Jenisha, Lee, Alexis Ying-Shan, Ray, Mia G. V., Recchia, John C., Lengerich, Dylan, Taulman, Kyle, Romero, Andres C., Steward, Ellie N., Russell, Sloan, Hardwick, Dillon F., Wootten, Katelynn, Nguyen, Valerie A., Quispe, Devon, Ragsdale, Cameron, Young, Isabel, Atchley-Rivers, N. S., Stribling, Jordin L., Gentile, Julia G, Boeyink, Taylor A., Kwiatkowski, Daniel, Dupeyron, Tomi Oshima, Crews, Anastasia, Shuttleworth, Mitchell, Dresdner, Danielle C., Flackett, Lydia, Haratsaris, Nicholas, Linger, Morgan I, Misener, Jay H., Patti, Samuel, Pine, Tawanchai P., Marikar, Nasreen, Matessi, Giorgio, Routledge, Allie C., Alkaabi, Suhail, Bartman, Jessica L., Bisacca, Gabrielle E., Busch, Celeste, Edwards, Bree, Staudenmier, Caitlyn, Starling, Travis, McVey, Caden, Montano, Maximus, Contizano, Charles J., Taylor, Eleanor, McIntyre, James K., Victory, Andrew, McCammon, Glen S., Kimlicko, Aspen, Sheldrake, Tucker, Shelchuk, Grace, Von Reich, Ferin J., Hicks, Andrew J., O'neill, Ian, Rossman, Beth, Taylor, Liam C., MacDonald, William, Becker, Simone E., Han, Soonhee, O'Sullivan, Cian, Wilcove, Isaac, Brennan, David J., Hanley, Luke C., Hull, Owen, Wilson, Timothy R., Kalmus, Madison H., Berv, Owen A., Harris, Logan Swous, Doan, Chris H, Londres, Nathan, Parulekar, Anish, Adam, Megan M., Angwin, Abigail, Cabbage, Carter C., Colleran, Zachary, Pietras, Alex, Seux, Octave, Oros, Ryan, Wilkinson, Blake C., Nguyen, Khoa D, Trank-Greene, Maedee, Barone, Kevin M., Snyder, G. L., Biehle, Samuel J, Billig, Brennen, Almquist, Justin Thomas, Dixon, Alyssa M., Erickson, Benjamin, Evans, Nathan, Genne, SL, Kelly, Christopher M, Marcus, Serafima M., Ogle, Caleb, Patel, Akhil, Vendetti, Evan, Courtney, Olivia, Deel, Sean, Del Foco, Leonardo, Gjini, Michael, Haines, Jessica, Hoff, Isabelle J., Jones, M. R., Killian, Dominic, Kuehl, Kirsten, Kuester, Chrisanne, Lantz, Maxwell B., Lee, Christian J, Mauer, Graham, McKemey, Finbar K., Millican, Sarah J., Rosasco, Ryan, Stewart, T. C., VanEtten, Eleanor, Derwin, Zachary, Serio, Lauren, Sickler, Molly G., Blake, Cassidy A., Patel, Neil S., Fox, Margaret, Gray, Michael J, Ziegler, Lucas J., Kumar, Aman Priyadarshi, Polly, Madelyn, Mesgina, Sarah, McMorris, Zane, Griffin, Kyle J., Haile, L. N., Bassel, Claire, Dixon, Thomas J., Beattie, Ryan, Houck, Timothy J, Rodgers, Maeve, Trofino, Tyson R., Lukianow, Dax, Smart, Korben, Hall, Jacqueline L., Bone, Lauren, Baldwin, James O., Doane, Connor, Almohsen, Yousef A., Stamos, Emily, Acha, Iker, Kim, Jake, Samour II, Antonio E., Chavali, S., Kanokthippayakun, Jeerakit, Gotlib, Nicholas, Murphy, Ryan C., Archibald, Jack. W., Brimhall, Alexander J, Boyer, Aidan, Chapman, Logan T., Chadda, Shivank, Sibrell, Lisa, Vallery, Mia M., Conroy, Thomas C., Pan, Luke J., Balajonda, Brian, Fuhrman, Bethany E. S., Alkubaisi, Mohamed, Engelstad, Jacob, Dodrill, Joshua, Fuchs, Calvin R., Bullard-Connor, Gigi, Alhuseini, Isehaq, Zygmunt, James C., Sipowicz, Leo, Hayrynen, Griffin A., McGill, Riley M., Keating, Caden J., Hart, Omer, Cyr, Aidan St., Steinsberger, Christopher H., Thoman, Gerig, Wood, Travis M., Ingram, Julia A., Dominguez, J., Georgiades, Nathaniel James, Johnson, Matthew, Johnson, Sawyer, Pedersen, Alexander J., Ralapanawe, Anoush K, Thomas, Jeffrey J., Sato, Ginn A., Reynolds, Hope, Nasser, Liebe, Mizzi, Alexander Z., Damgaard, Olivia, Baflah, Abdulrahman A., Liu, Steven Y., Salindeho, Adam D., Norden, Kelso, Gearhart, Emily E., Krajnak, Zack, Szeremeta, Philip, Amos, Meggan, Shin, Kyungeun, Muckenthaler, Brandon A., Medialdea, Melissa, Beach, Simone, Wilson, Connor B., Adams, Elena R, Aldhamen, Ahmed, Harris, Coyle M., Hesse, Troy M., Golding, Nathan T., Larter, Zachary, Hernandez, Angel, Morales, Genaro, Traxler, Robert B., Alosaimi, Meshal, Fitton, Aidan F., Aaron, James Holland, Lee, Nathaniel F., Liao, Ryan Z., Chen, Judy, French, Katherine V., Loring, Justin, Colter, Aurora, McConvey, Rowan, Colozzi, Michael, Vann, John D., Scheck, Benjamin T., Weigand, Anthony A, Alhabeeb, Abdulelah, Idoine, Yolande, Woodard, Aiden L., Medellin, Mateo M., Ratajczyk, Nicholas O, Tobin, Darien P., Collins, Jack C., Horning, Thomas M., Pellatz, Nick, Pitten, John, Lordi, Noah, Patterson, Alyx, Hoang, Thi D, Zimmermann, Ingrid H, Wang, Hongda, Steckhahn, Daniel, Aradhya, Arvind J., Oliver, Kristin A., Cai, Yijian, Wang, Chaoran, Yegovtsev, Nikolay, Wu, Mengyu, Ganesan, Koushik, Osborne, Andrew, Wickenden, Evan, Meyer, Josephine C., Chaparro, David, Visal, Aseem, Liu, Haixin, Menon, Thanmay S., Jin, Yan, Wilson, John, Erikson, James W., Luo, Zheng, Shitara, Nanako, Nelson, Emma E, Geerdts, T. R., Ortiz, Jorge L Ramirez, and Lewandowski, H. J.
- Subjects
Astrophysics - Solar and Stellar Astrophysics - Abstract
Flare frequency distributions represent a key approach to addressing one of the largest problems in solar and stellar physics: determining the mechanism that counter-intuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres. It is widely accepted that the magnetic field is responsible for the heating, but there are two competing mechanisms that could explain it: nanoflares or Alfv\'en waves. To date, neither can be directly observed. Nanoflares are, by definition, extremely small, but their aggregate energy release could represent a substantial heating mechanism, presuming they are sufficiently abundant. One way to test this presumption is via the flare frequency distribution, which describes how often flares of various energies occur. If the slope of the power law fitting the flare frequency distribution is above a critical threshold, $\alpha=2$ as established in prior literature, then there should be a sufficient abundance of nanoflares to explain coronal heating. We performed $>$600 case studies of solar flares, made possible by an unprecedented number of data analysts via three semesters of an undergraduate physics laboratory course. This allowed us to include two crucial, but nontrivial, analysis methods: pre-flare baseline subtraction and computation of the flare energy, which requires determining flare start and stop times. We aggregated the results of these analyses into a statistical study to determine that $\alpha = 1.63 \pm 0.03$. This is below the critical threshold, suggesting that Alfv\'en waves are an important driver of coronal heating., Comment: 1,002 authors, 14 pages, 4 figures, 3 tables, published by The Astrophysical Journal on 2023-05-09, volume 948, page 71
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- 2023
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24. Using Mobile Data and Deep Models to Assess Auditory Verbal Hallucinations
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Mirjafari, Shayan, Nepal, Subigya, Wang, Weichen, and Campbell, Andrew T.
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Computer Science - Sound ,Computer Science - Human-Computer Interaction ,Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
Hallucination is an apparent perception in the absence of real external sensory stimuli. An auditory hallucination is a perception of hearing sounds that are not real. A common form of auditory hallucination is hearing voices in the absence of any speakers which is known as Auditory Verbal Hallucination (AVH). AVH is fragments of the mind's creation that mostly occur in people diagnosed with mental illnesses such as bipolar disorder and schizophrenia. Assessing the valence of hallucinated voices (i.e., how negative or positive voices are) can help measure the severity of a mental illness. We study N=435 individuals, who experience hearing voices, to assess auditory verbal hallucination. Participants report the valence of voices they hear four times a day for a month through ecological momentary assessments with questions that have four answering scales from ``not at all'' to ``extremely''. We collect these self-reports as the valence supervision of AVH events via a mobile application. Using the application, participants also record audio diaries to describe the content of hallucinated voices verbally. In addition, we passively collect mobile sensing data as contextual signals. We then experiment with how predictive these linguistic and contextual cues from the audio diary and mobile sensing data are of an auditory verbal hallucination event. Finally, using transfer learning and data fusion techniques, we train a neural net model that predicts the valance of AVH with a performance of 54\% top-1 and 72\% top-2 F1 score.
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- 2023
25. Patterns and trends in melanoma mortality in the United States, 1999–2020
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Didier, Alexander J., Nandwani, Swamroop V., Watkins, Dean, Fahoury, Alan M., Campbell, Andrew, Craig, Daniel J., Vijendra, Divya, and Parquet, Nancy
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- 2024
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26. Metabolism-driven in vitro/in vivo disconnect of an oral ERɑ VHL-PROTAC
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Hayhow, Thomas G., Williamson, Beth, Lawson, Mandy, Cureton, Natalie, Braybrooke, Erin L., Campbell, Andrew, Carbajo, Rodrigo J., Cheraghchi-Bashi, Azadeh, Chiarparin, Elisabetta, Diène, Coura R., Fallan, Charlene, Fisher, David I., Goldberg, Frederick W., Hopcroft, Lorna, Hopcroft, Philip, Jackson, Anne, Kettle, Jason G., Klinowska, Teresa, Künzel, Ulrike, Lamont, Gillian, Lewis, Hilary J., Maglennon, Gareth, Martin, Scott, Gutierrez, Pablo Morentin, Morrow, Christopher J., Nikolaou, Myria, Nissink, J. Willem M., O’Shea, Patrick, Polanski, Radoslaw, Schade, Markus, Scott, James S., Smith, Aaron, Weber, Judith, Wilson, Joanne, Yang, Bin, and Crafter, Claire
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- 2024
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27. Space object identification and classification from hyperspectral material analysis
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Vasile, Massimiliano, Walker, Lewis, Campbell, Andrew, Marto, Simão, Murray, Paul, Marshall, Stephen, and Savitski, Vasili
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- 2024
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28. KRAS allelic imbalance drives tumour initiation yet suppresses metastasis in colorectal cancer in vivo
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Najumudeen, Arafath K., Fey, Sigrid K., Millett, Laura M., Ford, Catriona A., Gilroy, Kathryn, Gunduz, Nuray, Ridgway, Rachel A., Anderson, Eve, Strathdee, Douglas, Clark, William, Nixon, Colin, Morton, Jennifer P., Campbell, Andrew D., and Sansom, Owen J.
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- 2024
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29. Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer
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Malla, Sudhir B., Byrne, Ryan M., Lafarge, Maxime W., Corry, Shania M., Fisher, Natalie C., Tsantoulis, Petros K., Mills, Megan L., Ridgway, Rachel A., Lannagan, Tamsin R. M., Najumudeen, Arafath K., Gilroy, Kathryn L., Amirkhah, Raheleh, Maguire, Sarah L., Mulholland, Eoghan J., Belnoue-Davis, Hayley L., Grassi, Elena, Viviani, Marco, Rogan, Emily, Redmond, Keara L., Sakhnevych, Svetlana, McCooey, Aoife J., Bull, Courtney, Hoey, Emily, Sinevici, Nicoleta, Hall, Holly, Ahmaderaghi, Baharak, Domingo, Enric, Blake, Andrew, Richman, Susan D., Isella, Claudio, Miller, Crispin, Bertotti, Andrea, Trusolino, Livio, Loughrey, Maurice B., Kerr, Emma M., Tejpar, Sabine, Maughan, Timothy S., Lawler, Mark, Campbell, Andrew D., Leedham, Simon J., Koelzer, Viktor H., Sansom, Owen J., and Dunne, Philip D.
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- 2024
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30. Empowering Pacific Patients on the Weight Loss Surgery Pathway: A Co-designed Evaluation Study
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Taylor, Tamasin Ariana, Beban, Grant, Yi, Elaine, Veukiso, Michael, Sang-Yum, Genevieve, Dewes, Ofa, Wrapson, Wendy, Taufa, Nalei, Campbell, Andrew R. T., Siegert, Richard J., and Shepherd, Peter
- Published
- 2024
- Full Text
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31. Spectrum of Lévy–Khintchine Random Laplacian Matrices
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Campbell, Andrew and O’Rourke, Sean
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- 2024
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32. Time to surgery and outcomes following open reduction and internal fixation of both-bone forearm fractures
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Rust, Andrew, Samade, Richard, Campbell, Andrew B., McManus, Timothy, and Jain, Sonu A.
- Published
- 2024
- Full Text
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33. Diffusion Schr\'odinger Bridge Matching
- Author
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Shi, Yuyang, De Bortoli, Valentin, Campbell, Andrew, and Doucet, Arnaud
- Subjects
Statistics - Machine Learning ,Computer Science - Machine Learning - Abstract
Solving transport problems, i.e. finding a map transporting one given distribution to another, has numerous applications in machine learning. Novel mass transport methods motivated by generative modeling have recently been proposed, e.g. Denoising Diffusion Models (DDMs) and Flow Matching Models (FMMs) implement such a transport through a Stochastic Differential Equation (SDE) or an Ordinary Differential Equation (ODE). However, while it is desirable in many applications to approximate the deterministic dynamic Optimal Transport (OT) map which admits attractive properties, DDMs and FMMs are not guaranteed to provide transports close to the OT map. In contrast, Schr\"odinger bridges (SBs) compute stochastic dynamic mappings which recover entropy-regularized versions of OT. Unfortunately, existing numerical methods approximating SBs either scale poorly with dimension or accumulate errors across iterations. In this work, we introduce Iterative Markovian Fitting (IMF), a new methodology for solving SB problems, and Diffusion Schr\"odinger Bridge Matching (DSBM), a novel numerical algorithm for computing IMF iterates. DSBM significantly improves over previous SB numerics and recovers as special/limiting cases various recent transport methods. We demonstrate the performance of DSBM on a variety of problems.
- Published
- 2023
34. normflows: A PyTorch Package for Normalizing Flows
- Author
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Stimper, Vincent, Liu, David, Campbell, Andrew, Berenz, Vincent, Ryll, Lukas, Schölkopf, Bernhard, and Hernández-Lobato, José Miguel
- Subjects
Computer Science - Machine Learning - Abstract
Normalizing flows model probability distributions through an expressive tractable density. They transform a simple base distribution, such as a Gaussian, through a sequence of invertible functions, which are referred to as layers. These layers typically use neural networks to become very expressive. Flows are ubiquitous in machine learning and have been applied to image generation, text modeling, variational inference, approximating Boltzmann distributions, and many other problems. Here, we present normflows, a Python package for normalizing flows. It allows to build normalizing flow models from a suite of base distributions, flow layers, and neural networks. The package is implemented in the popular deep learning framework PyTorch, which simplifies the integration of flows in larger machine learning models or pipelines. It supports most of the common normalizing flow architectures, such as Real NVP, Glow, Masked Autoregressive Flows, Neural Spline Flows, Residual Flows, and many more. The package can be easily installed via pip and the code is publicly available on GitHub.
- Published
- 2023
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35. Rate of Convergence in Multiple SLE using Random Matrix Theory
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Campbell, Andrew, Luh, Kyle, and Margarint, Vlad
- Subjects
Mathematics - Probability ,Mathematics - Complex Variables - Abstract
We provide an order of convergence for a version of the Carath\'eodory convergence for the multiple SLE model with a Dyson Brownian motion driver towards its hydrodynamic limit, for $\beta=1$ and $\beta=2$. The result is obtained by combining techniques from the field of Schramm-Loewner Evolutions with modern techniques from random matrices. Our approach shows how one can apply modern tools used in the proof of universality in random matrix theory, in the field of Schramm-Loewner Evolutions., Comment: 19 pages, no figures
- Published
- 2023
36. The Future of California Consumer Energy Finance
- Author
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Campbell, Andrew, Lamm, Ted, and Hoff, Katherine
- Subjects
consumer energy finance ,building decarbonization ,equity - Abstract
Based on program analysis, literature review, expert interviews, and an October 2022 expert roundtable, this report identifies a set of conclusions and recommendations for California policymakers. We offer recommendations in distinct but overlapping areas:• Expanding consumer energy financing programs• Addressing the needs of lower- and moderate-income residents• Accelerating building decarbonization toward California’s 2045 goal• Ensuring equity in program revenue sources• Improving program design through learningThese recommendations all reflect a core insight developed from the research and outreach process: that the enormous size of California’s building decarbonization need calls for significant infusions of private capital, and financing programs can be a mechanism to attract some of this capital. However, consumer energy finance programs are not yet operating on a scale that matches the challenge. Even at their most robust and effective these programs will likely only fund a portion of the needed retrofits and are not always appropriate for lowerincome residents, who will require access to alternative measures involving minimal or zero repayment obligations. And effectively taking advantage of newly available federal Inflation Reduction incentives will rely on state programs that facilitate layering of funds from an array of sources.A central recommendation across this report’s sections is that state legislators and financing program administrators consider alternatives to utility ratepayer funds as the core revenue source for credit enhancement. Shifting from ratepayer funds to alternative sources including taxpayer funds, federal funds, and philanthropic sources could potentially help scale up the GoGreen Financing programs’ reach and flexibility across utility service territories, fuel sources, and eligible measures; facilitate more seamless integration with other state programs; reduce procedural barriers to rapid adaptation to market and technology developments; and advance equity by relying on a more progressive revenue source
- Published
- 2023
37. Extreme eigenvalues of Laplacian random matrices with Gaussian entries
- Author
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Campbell, Andrew, Luh, Kyle, O'Rourke, Sean, Arenas-Velilla, Santiago, and Pérez-Abreu, Victor
- Subjects
Mathematics - Probability - Abstract
A Laplacian matrix is a real symmetric matrix whose row and column sums are zero. We investigate the limiting distribution of the largest eigenvalues of a Laplacian random matrix with Gaussian entries. Unlike many classical matrix ensembles, this random matrix model contains dependent entries. Our main results show that the extreme eigenvalues of this model exhibit Poisson statistics. In particular, after properly shifting and scaling, we show that the largest eigenvalue converges to the Gumbel distribution as the dimension of the matrix tends to infinity. While the largest diagonal entry is also shown to have Gumbel fluctuations, there is a rather surprising difference between its deterministic centering term and the centering term required for the largest eigenvalues., Comment: 53 pages. Appendix by Santiago Arenas-Velilla and Victor P\'{e}rez-Abreu. Added results concerning the k largest eigenvalues as well as additional references
- Published
- 2022
38. Spectrum of L\'evy-Khintchine Random Laplacian Matrices
- Author
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Campbell, Andrew and O'Rourke, Sean
- Subjects
Mathematics - Probability ,Mathematical Physics - Abstract
We consider the spectrum of random Laplacian matrices of the form $L_n=A_n-D_n$ where $A_n$ is a real symmetric random matrix and $D_n$ is a diagonal matrix whose entries are equal to the corresponding row sums of $A_n$. If $A_n$ is a Wigner matrix with entries in the domain of attraction of a Gaussian distribution the empirical spectral measure of $L_n$ is known to converge to the free convolution of a semicircle distribution and a standard real Gaussian distribution. We consider real symmetric random matrices $A_n$ with independent entries (up to symmetry) whose row sums converge to a purely non-Gaussian infinitely divisible distribution, which fall into the class of L\'evy-Khintchine random matrices first introduced by Jung [Trans Am Math Soc, \textbf{370}, (2018)]. Our main result shows that the empirical spectral measure of $L_n$ converges almost surely to a deterministic limit. A key step in the proof is to use the purely non-Gaussian nature of the row sums to build a random operator to which $L_n$ converges in an appropriate sense. This operator leads to a recursive distributional equation uniquely describing the Stieltjes transform of the limiting empirical spectral measure., Comment: 32 pages, no figures. Minor corrections and updates
- Published
- 2022
39. A National Survey of Children's Experiences of Parental Separation and Support Needs in Australia
- Author
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Ridout, Brad, Fletcher, Jennifer, Smith-Merry, Jennifer, Collyer, Brian, Dalgleish, John, and Campbell, Andrew
- Abstract
We used a mixed-methods online survey to recruit 616 young Australians whose parents had separated, to understand their experiences and how to better support them throughout the separation process. Persistent themes included conflict, lack of communication and agency, mental health concerns, and feelings of confusion, frustration, loss, and grief. Some suggested it would have been useful to talk about reasons for the separation, their rights, opinions and feelings, with some indicating the separation process affected their ongoing mental health and relationships. There was a general preference for face-to-face counseling, closely followed by online counseling and online peer-to-peer support, indicating that a "one size fits all" approach is not suitable for young people. Young people should be offered services early in the separation process that can be extended in content to other issues such as new partners, school life and mental health, and continued beyond the timeframe of the separation process.
- Published
- 2023
- Full Text
- View/download PDF
40. A Continuous Time Framework for Discrete Denoising Models
- Author
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Campbell, Andrew, Benton, Joe, De Bortoli, Valentin, Rainforth, Tom, Deligiannidis, George, and Doucet, Arnaud
- Subjects
Statistics - Machine Learning ,Computer Science - Machine Learning - Abstract
We provide the first complete continuous time framework for denoising diffusion models of discrete data. This is achieved by formulating the forward noising process and corresponding reverse time generative process as Continuous Time Markov Chains (CTMCs). The model can be efficiently trained using a continuous time version of the ELBO. We simulate the high dimensional CTMC using techniques developed in chemical physics and exploit our continuous time framework to derive high performance samplers that we show can outperform discrete time methods for discrete data. The continuous time treatment also enables us to derive a novel theoretical result bounding the error between the generated sample distribution and the true data distribution., Comment: 44 pages, 15 figures; NeurIPS 2022
- Published
- 2022
41. A Survey of Passive Sensing in the Workplace
- Author
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Nepal, Subigya, Martinez, Gonzalo J., Pillai, Arvind, Saha, Koustuv, Mirjafari, Shayan, Swain, Vedant Das, Xu, Xuhai, Audia, Pino G., De Choudhury, Munmun, Dey, Anind K., Striegel, Aaron, and Campbell, Andrew T.
- Subjects
Computer Science - Human-Computer Interaction ,H.5.0 - Abstract
As emerging technologies increasingly integrate into all facets of our lives, the workplace stands at the forefront of potential transformative changes. A notable development in this realm is the advent of passive sensing technology, designed to enhance both cognitive and physical capabilities by monitoring human behavior. This paper reviews current research on the application of passive sensing technology in the workplace, focusing on its impact on employee wellbeing and productivity. Additionally, we explore unresolved issues and outline prospective pathways for the incorporation of passive sensing in future workplaces., Comment: Added references and other minor revisions. Also udated to include relevant works published after 2022
- Published
- 2022
42. Author Correction: Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer
- Author
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Malla, Sudhir B., Byrne, Ryan M., Lafarge, Maxime W., Corry, Shania M., Fisher, Natalie C., Tsantoulis, Petros K., Mills, Megan L., Ridgway, Rachel A., Lannagan, Tamsin R. M., Najumudeen, Arafath K., Gilroy, Kathryn L., Amirkhah, Raheleh, Maguire, Sarah L., Mulholland, Eoghan J., Belnoue-Davis, Hayley L., Grassi, Elena, Viviani, Marco, Rogan, Emily, Redmond, Keara L., Sakhnevych, Svetlana, McCooey, Aoife J., Bull, Courtney, Hoey, Emily, Sinevici, Nicoleta, Hall, Holly, Ahmaderaghi, Baharak, Domingo, Enric, Blake, Andrew, Richman, Susan D., Isella, Claudio, Miller, Crispin, Bertotti, Andrea, Trusolino, Livio, Loughrey, Maurice B., Kerr, Emma M., Tejpar, Sabine, Maughan, Timothy S., Lawler, Mark, Campbell, Andrew D., Leedham, Simon J., Koelzer, Viktor H., Sansom, Owen J., and Dunne, Philip D.
- Published
- 2024
- Full Text
- View/download PDF
43. COVID-19 mRNA vaccination responses in individuals with sickle cell disease: an ASH RC Sickle Cell Research Network Study
- Author
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Anderson, Alan R., Andemariam, Biree, Brandow, Amanda, Campbell, Andrew, Cohen, Alice, Darbari, Deepika, El Rassi, Fuad, ield, Joshua, Fung, Ellen, Gee, Beatrice, Ibrahim, Ibrahim, Idowu, Modupe, Kanter, Julie, Klings, Elizabeth S., King, Allison, Kutlar, Abdullah, Lebensburger, Jeffrey D., Leavey, Patrick, Liem, Robert I., Manwani, Deepa, Narang, Shalu, Pace, Betty, Quinn, Charles T., Rivlin, Kenneth, Strouse, John J., Thompson, Alexis A., Tubman, Venée N., Vichinsky, Elliot, Walters, Mark, Brandow, Amanda M., Vichinsky, Elliott, Leavey, Patrick J., Nero, Alecia, Ibrahim, Ibrahim F., Field, Joshua J., Baer, Amanda, Soto-Calderon, Haideliza, Vincent, Lauren, Zhao, Yan, Santos, Jefferson J. S., Hensley, Scott E., Mortier, Nicole, Lanzkron, Sophie, Neuberg, Donna, and Abrams, Charles S.
- Published
- 2024
- Full Text
- View/download PDF
44. From mood to use: Using ecological momentary assessments to examine how anhedonia and depressed mood impact cannabis use in a depressed sample
- Author
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Collins, Amanda C., Lekkas, Damien, Struble, Cara A., Trudeau, Brianna M., Jewett, Abi D., Griffin, Tess Z, Nemesure, Matthew D., Price, George D., Heinz, Michael V., Nepal, Subigya, Pillai, Arvind, Mackin, Daniel M., Campbell, Andrew T., Budney, Alan J., and Jacobson, Nicholas C.
- Published
- 2024
- Full Text
- View/download PDF
45. Metabolic profiling stratifies colorectal cancer and reveals adenosylhomocysteinase as a therapeutic target
- Author
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Vande Voorde, Johan, Steven, Rory T., Najumudeen, Arafath K., Ford, Catriona A., Dexter, Alex, Gonzalez-Fernandez, Ariadna, Nikula, Chelsea J., Xiang, Yuchen, Ford, Lauren, Maneta Stavrakaki, Stefania, Gilroy, Kathryn, Zeiger, Lucas B., Pennel, Kathryn, Hatthakarnkul, Phimmada, Elia, Efstathios A., Nasif, Ammar, Murta, Teresa, Manoli, Eftychios, Mason, Sam, Gillespie, Michael, Lannagan, Tamsin R. M., Vlahov, Nikola, Ridgway, Rachel A., Nixon, Colin, Raven, Alexander, Mills, Megan, Athineos, Dimitris, Kanellos, Georgios, Nourse, Craig, Gay, David M., Hughes, Mark, Burton, Amy, Yan, Bin, Sellers, Katherine, Wu, Vincen, De Ridder, Kobe, Shokry, Engy, Huerta Uribe, Alejandro, Clark, William, Clark, Graeme, Kirschner, Kristina, Thienpont, Bernard, Li, Vivian S. W., Maddocks, Oliver D. K., Barry, Simon T., Goodwin, Richard J. A., Kinross, James, Edwards, Joanne, Yuneva, Mariia O., Sumpton, David, Takats, Zoltan, Campbell, Andrew D., Bunch, Josephine, and Sansom, Owen J.
- Published
- 2023
- Full Text
- View/download PDF
46. Online Variational Filtering and Parameter Learning
- Author
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Campbell, Andrew, Shi, Yuyang, Rainforth, Tom, and Doucet, Arnaud
- Subjects
Statistics - Machine Learning ,Computer Science - Machine Learning ,Statistics - Computation - Abstract
We present a variational method for online state estimation and parameter learning in state-space models (SSMs), a ubiquitous class of latent variable models for sequential data. As per standard batch variational techniques, we use stochastic gradients to simultaneously optimize a lower bound on the log evidence with respect to both model parameters and a variational approximation of the states' posterior distribution. However, unlike existing approaches, our method is able to operate in an entirely online manner, such that historic observations do not require revisitation after being incorporated and the cost of updates at each time step remains constant, despite the growing dimensionality of the joint posterior distribution of the states. This is achieved by utilizing backward decompositions of this joint posterior distribution and of its variational approximation, combined with Bellman-type recursions for the evidence lower bound and its gradients. We demonstrate the performance of this methodology across several examples, including high-dimensional SSMs and sequential Variational Auto-Encoders., Comment: 27 pages, 6 figures. NeurIPS 2021 (Oral); updated references
- Published
- 2021
47. The role of borderline personality disorder traits in predicting longitudinal variability of major depressive symptoms among a sample of depressed adults
- Author
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Kline, Emily A., Lekkas, Damien, Bryan, Anastasia, Nemesure, Matthew D., Griffin, Tess Z, Collins, Amanda C., Price, George D., Heinz, Michael V., Nepal, Subigya, Pillai, Arvind, Campbell, Andrew T., and Jacobson, Nicholas C.
- Published
- 2024
- Full Text
- View/download PDF
48. Driver gene combinations dictate cutaneous squamous cell carcinoma disease continuum progression
- Author
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Bailey, Peter, Ridgway, Rachel A., Cammareri, Patrizia, Treanor-Taylor, Mairi, Bailey, Ulla-Maja, Schoenherr, Christina, Bone, Max, Schreyer, Daniel, Purdie, Karin, Thomson, Jason, Rickaby, William, Jackstadt, Rene, Campbell, Andrew D., Dimonitsas, Emmanouil, Stratigos, Alexander J., Arron, Sarah T., Wang, Jun, Blyth, Karen, Proby, Charlotte M., Harwood, Catherine A., Sansom, Owen J., Leigh, Irene M., and Inman, Gareth J.
- Published
- 2023
- Full Text
- View/download PDF
49. Sickle Cell Disease Treatment with Arginine Therapy (STArT): study protocol for a phase 3 randomized controlled trial
- Author
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Rees, Chris A., Brousseau, David C., Cohen, Daniel M., Villella, Anthony, Dampier, Carlton, Brown, Kathleen, Campbell, Andrew, Chumpitazi, Corrie E., Airewele, Gladstone, Chang, Todd, Denton, Christopher, Ellison, Angela, Thompson, Alexis, Ahmad, Fahd, Bakshi, Nitya, Coleman, Keli D., Leibovich, Sara, Leake, Deborah, Hatabah, Dunia, Wilkinson, Hagar, Robinson, Michelle, Casper, T. Charles, Vichinsky, Elliott, and Morris, Claudia R.
- Published
- 2023
- Full Text
- View/download PDF
50. Author Correction: Epithelial TGFβ engages growth-factor signalling to circumvent apoptosis and drive intestinal tumourigenesis with aggressive features
- Author
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Flanagan, Dustin J., Amirkhah, Raheleh, Vincent, David F., Gunduz, Nuray, Gentaz, Pauline, Cammareri, Patrizia, McCooey, Aoife J., McCorry, Amy M. B., Fisher, Natalie C., Davis, Hayley L., Ridgway, Rachel A., Lohuis, Jeroen, Leach, Joshua D. G., Jackstadt, Rene, Gilroy, Kathryn, Mariella, Elisa, Nixon, Colin, Clark, William, Hedley, Ann, Markert, Elke K., Strathdee, Douglas, Bartholin, Laurent, Redmond, Keara L., Kerr, Emma M., Longley, Daniel B., Ginty, Fiona, Cho, Sanghee, Coleman, Helen G., Loughrey, Maurice B., Bardelli, Alberto, Maughan, Timothy S., Campbell, Andrew D., Lawler, Mark, Leedham, Simon J., Barry, Simon T., Inman, Gareth J., van Rheenen, Jacco, Dunne, Philip D., and Sansom, Owen J.
- Published
- 2023
- Full Text
- View/download PDF
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