726 results on '"Nguyen, Khai"'
Search Results
2. Confronting Privilege: The Radical Potential of Eco-communities for Urban Climate Justice
- Author
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Westman, Linda K., Stephens, Jennie, Silver, Jonathan, Shi, Linda, Schmitz, Gloria, Robin, Enora, Raditz, Vanessa Lacy, Pickerill, Jenny, Nguyen, Khai Hoan, Martinez-Lugo, Diego, Leichenko, Robin, Kumar, Ankit, Knuth, Sarah E., Huang, Ping, Goh, Kian, Foster, Sheila, Fitzgerald, Joan, Cox, Savannah, Broto, Vanesa Castan, Bouma, Dietrich Thomas, Levenda, Anthony, Long, Joshua, and Rice, Jennifer L.
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- 2023
3. Index
- Author
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Westman, Linda K., Stephens, Jennie, Silver, Jonathan, Shi, Linda, Schmitz, Gloria, Robin, Enora, Raditz, Vanessa Lacy, Pickerill, Jenny, Nguyen, Khai Hoan, Martinez-Lugo, Diego, Leichenko, Robin, Kumar, Ankit, Knuth, Sarah E., Huang, Ping, Goh, Kian, Foster, Sheila, Fitzgerald, Joan, Cox, Savannah, Broto, Vanesa Castan, Bouma, Dietrich Thomas, Levenda, Anthony, Long, Joshua, and Rice, Jennifer L.
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- 2023
4. Accounting for Climate Justice: Fiscal Fights over Climate-Changed Urban Futures
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Westman, Linda K., Stephens, Jennie, Silver, Jonathan, Shi, Linda, Schmitz, Gloria, Robin, Enora, Raditz, Vanessa Lacy, Pickerill, Jenny, Nguyen, Khai Hoan, Martinez-Lugo, Diego, Leichenko, Robin, Kumar, Ankit, Knuth, Sarah E., Huang, Ping, Goh, Kian, Foster, Sheila, Fitzgerald, Joan, Cox, Savannah, Broto, Vanesa Castan, Bouma, Dietrich Thomas, Levenda, Anthony, Long, Joshua, and Rice, Jennifer L.
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- 2023
5. Conclusion: Toward Transformative Urban Climate Justice: Abolition, Care, and Reparations
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Westman, Linda K., Stephens, Jennie, Silver, Jonathan, Shi, Linda, Schmitz, Gloria, Robin, Enora, Raditz, Vanessa Lacy, Pickerill, Jenny, Nguyen, Khai Hoan, Martinez-Lugo, Diego, Leichenko, Robin, Kumar, Ankit, Knuth, Sarah E., Huang, Ping, Goh, Kian, Foster, Sheila, Fitzgerald, Joan, Cox, Savannah, Broto, Vanesa Castan, Bouma, Dietrich Thomas, Levenda, Anthony, Long, Joshua, and Rice, Jennifer L.
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- 2023
6. Making Movements: Mobilizing for More Just Socioecological Futures in a Megacity
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Westman, Linda K., Stephens, Jennie, Silver, Jonathan, Shi, Linda, Schmitz, Gloria, Robin, Enora, Raditz, Vanessa Lacy, Pickerill, Jenny, Nguyen, Khai Hoan, Martinez-Lugo, Diego, Leichenko, Robin, Kumar, Ankit, Knuth, Sarah E., Huang, Ping, Goh, Kian, Foster, Sheila, Fitzgerald, Joan, Cox, Savannah, Broto, Vanesa Castan, Bouma, Dietrich Thomas, Levenda, Anthony, Long, Joshua, and Rice, Jennifer L.
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- 2023
7. Love in the Time of Climate Crisis: Climate Justice through a Universalism of the Oppressed
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Westman, Linda K., Stephens, Jennie, Silver, Jonathan, Shi, Linda, Schmitz, Gloria, Robin, Enora, Raditz, Vanessa Lacy, Pickerill, Jenny, Nguyen, Khai Hoan, Martinez-Lugo, Diego, Leichenko, Robin, Kumar, Ankit, Knuth, Sarah E., Huang, Ping, Goh, Kian, Foster, Sheila, Fitzgerald, Joan, Cox, Savannah, Broto, Vanesa Castan, Bouma, Dietrich Thomas, Levenda, Anthony, Long, Joshua, and Rice, Jennifer L.
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- 2023
8. PART 3. Resistance and Activism for Urban Climate Justice
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Westman, Linda K., Stephens, Jennie, Silver, Jonathan, Shi, Linda, Schmitz, Gloria, Robin, Enora, Raditz, Vanessa Lacy, Pickerill, Jenny, Nguyen, Khai Hoan, Martinez-Lugo, Diego, Leichenko, Robin, Kumar, Ankit, Knuth, Sarah E., Huang, Ping, Goh, Kian, Foster, Sheila, Fitzgerald, Joan, Cox, Savannah, Broto, Vanesa Castan, Bouma, Dietrich Thomas, Levenda, Anthony, Long, Joshua, and Rice, Jennifer L.
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- 2023
9. Beyond the Racial State, Racial Capitalism, and Settler Colonialism: Toward a Grassroots Climate Justice
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Westman, Linda K., Stephens, Jennie, Silver, Jonathan, Shi, Linda, Schmitz, Gloria, Robin, Enora, Raditz, Vanessa Lacy, Pickerill, Jenny, Nguyen, Khai Hoan, Martinez-Lugo, Diego, Leichenko, Robin, Kumar, Ankit, Knuth, Sarah E., Huang, Ping, Goh, Kian, Foster, Sheila, Fitzgerald, Joan, Cox, Savannah, Broto, Vanesa Castan, Bouma, Dietrich Thomas, Levenda, Anthony, Long, Joshua, and Rice, Jennifer L.
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- 2023
10. Leveraging Urban Climate Action for Transformative Social Justice
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Westman, Linda K., Stephens, Jennie, Silver, Jonathan, Shi, Linda, Schmitz, Gloria, Robin, Enora, Raditz, Vanessa Lacy, Pickerill, Jenny, Nguyen, Khai Hoan, Martinez-Lugo, Diego, Leichenko, Robin, Kumar, Ankit, Knuth, Sarah E., Huang, Ping, Goh, Kian, Foster, Sheila, Fitzgerald, Joan, Cox, Savannah, Broto, Vanesa Castan, Bouma, Dietrich Thomas, Levenda, Anthony, Long, Joshua, and Rice, Jennifer L.
- Published
- 2023
11. PART 2. Climate Praxis for the Just City
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Westman, Linda K., Stephens, Jennie, Silver, Jonathan, Shi, Linda, Schmitz, Gloria, Robin, Enora, Raditz, Vanessa Lacy, Pickerill, Jenny, Nguyen, Khai Hoan, Martinez-Lugo, Diego, Leichenko, Robin, Kumar, Ankit, Knuth, Sarah E., Huang, Ping, Goh, Kian, Foster, Sheila, Fitzgerald, Joan, Cox, Savannah, Broto, Vanesa Castan, Bouma, Dietrich Thomas, Levenda, Anthony, Long, Joshua, and Rice, Jennifer L.
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- 2023
12. Bringing Equity into Climate Change: Adaptation Planning in New York City
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Westman, Linda K., Stephens, Jennie, Silver, Jonathan, Shi, Linda, Schmitz, Gloria, Robin, Enora, Raditz, Vanessa Lacy, Pickerill, Jenny, Nguyen, Khai Hoan, Martinez-Lugo, Diego, Leichenko, Robin, Kumar, Ankit, Knuth, Sarah E., Huang, Ping, Goh, Kian, Foster, Sheila, Fitzgerald, Joan, Cox, Savannah, Broto, Vanesa Castan, Bouma, Dietrich Thomas, Levenda, Anthony, Long, Joshua, and Rice, Jennifer L.
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- 2023
13. Climate Urbanism as Green Structural Adjustment: Unequal Center-Periphery Relations in the Age of Climate Crisis
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Westman, Linda K., Stephens, Jennie, Silver, Jonathan, Shi, Linda, Schmitz, Gloria, Robin, Enora, Raditz, Vanessa Lacy, Pickerill, Jenny, Nguyen, Khai Hoan, Martinez-Lugo, Diego, Leichenko, Robin, Kumar, Ankit, Knuth, Sarah E., Huang, Ping, Goh, Kian, Foster, Sheila, Fitzgerald, Joan, Cox, Savannah, Broto, Vanesa Castan, Bouma, Dietrich Thomas, Levenda, Anthony, Long, Joshua, and Rice, Jennifer L.
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- 2023
14. Visibilizing Queer Resilience: Representational Justice for the Climate Movement
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Westman, Linda K., Stephens, Jennie, Silver, Jonathan, Shi, Linda, Schmitz, Gloria, Robin, Enora, Raditz, Vanessa Lacy, Pickerill, Jenny, Nguyen, Khai Hoan, Martinez-Lugo, Diego, Leichenko, Robin, Kumar, Ankit, Knuth, Sarah E., Huang, Ping, Goh, Kian, Foster, Sheila, Fitzgerald, Joan, Cox, Savannah, Broto, Vanesa Castan, Bouma, Dietrich Thomas, Levenda, Anthony, Long, Joshua, and Rice, Jennifer L.
- Published
- 2023
15. Budgeting for Climate Justice? Contested Futures of Urban Finance
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Westman, Linda K., Stephens, Jennie, Silver, Jonathan, Shi, Linda, Schmitz, Gloria, Robin, Enora, Raditz, Vanessa Lacy, Pickerill, Jenny, Nguyen, Khai Hoan, Martinez-Lugo, Diego, Leichenko, Robin, Kumar, Ankit, Knuth, Sarah E., Huang, Ping, Goh, Kian, Foster, Sheila, Fitzgerald, Joan, Cox, Savannah, Broto, Vanesa Castan, Bouma, Dietrich Thomas, Levenda, Anthony, Long, Joshua, and Rice, Jennifer L.
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- 2023
16. Introduction Realizing the Just City in the Era of Climate Change: The Urban Politics of Climate (In)security and (In)equality
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Westman, Linda K., Stephens, Jennie, Silver, Jonathan, Shi, Linda, Schmitz, Gloria, Robin, Enora, Raditz, Vanessa Lacy, Pickerill, Jenny, Nguyen, Khai Hoan, Martinez-Lugo, Diego, Leichenko, Robin, Kumar, Ankit, Knuth, Sarah E., Huang, Ping, Goh, Kian, Foster, Sheila, Fitzgerald, Joan, Cox, Savannah, Broto, Vanesa Castan, Bouma, Dietrich Thomas, Levenda, Anthony, Long, Joshua, and Rice, Jennifer L.
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- 2023
17. Just Sustainabilities in a Changing Climate
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Westman, Linda K., Stephens, Jennie, Silver, Jonathan, Shi, Linda, Schmitz, Gloria, Robin, Enora, Raditz, Vanessa Lacy, Pickerill, Jenny, Nguyen, Khai Hoan, Martinez-Lugo, Diego, Leichenko, Robin, Kumar, Ankit, Knuth, Sarah E., Huang, Ping, Goh, Kian, Foster, Sheila, Fitzgerald, Joan, Cox, Savannah, Broto, Vanesa Castan, Bouma, Dietrich Thomas, Levenda, Anthony, Long, Joshua, and Rice, Jennifer L.
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- 2023
18. Reclaiming Land Governance under Climate Change
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Westman, Linda K., Stephens, Jennie, Silver, Jonathan, Shi, Linda, Schmitz, Gloria, Robin, Enora, Raditz, Vanessa Lacy, Pickerill, Jenny, Nguyen, Khai Hoan, Martinez-Lugo, Diego, Leichenko, Robin, Kumar, Ankit, Knuth, Sarah E., Huang, Ping, Goh, Kian, Foster, Sheila, Fitzgerald, Joan, Cox, Savannah, Broto, Vanesa Castan, Bouma, Dietrich Thomas, Levenda, Anthony, Long, Joshua, and Rice, Jennifer L.
- Published
- 2023
19. PART 1. Theorizing the Just City in the Era of Climate Change
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Westman, Linda K., Stephens, Jennie, Silver, Jonathan, Shi, Linda, Schmitz, Gloria, Robin, Enora, Raditz, Vanessa Lacy, Pickerill, Jenny, Nguyen, Khai Hoan, Martinez-Lugo, Diego, Leichenko, Robin, Kumar, Ankit, Knuth, Sarah E., Huang, Ping, Goh, Kian, Foster, Sheila, Fitzgerald, Joan, Cox, Savannah, Broto, Vanesa Castan, Bouma, Dietrich Thomas, Levenda, Anthony, Long, Joshua, and Rice, Jennifer L.
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- 2023
20. Halftitle, Title Page, Copyright
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Westman, Linda K., Stephens, Jennie, Silver, Jonathan, Shi, Linda, Schmitz, Gloria, Robin, Enora, Raditz, Vanessa Lacy, Pickerill, Jenny, Nguyen, Khai Hoan, Martinez-Lugo, Diego, Leichenko, Robin, Kumar, Ankit, Knuth, Sarah E., Huang, Ping, Goh, Kian, Foster, Sheila, Fitzgerald, Joan, Cox, Savannah, Broto, Vanesa Castan, Bouma, Dietrich Thomas, Levenda, Anthony, Long, Joshua, and Rice, Jennifer L.
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- 2023
21. Optimizing electric vehicles charging through smart energy allocation and cost-saving
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Ambrosino, Luca, Calafiore, Giuseppe, Nguyen, Khai Manh, Zorgati, Riadh, Nguyen-Ngoc, Doanh, and Ghaoui, Laurent El
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Mathematics - Optimization and Control ,Electrical Engineering and Systems Science - Systems and Control - Abstract
As the global focus on combating environmental pollution intensifies, the transition to sustainable energy sources, particularly in the form of electric vehicles (EVs), has become paramount. This paper addresses the pressing need for Smart Charging for EVs by developing a comprehensive mathematical model aimed at optimizing charging station management. The model aims to efficiently allocate the power from charging sockets to EVs, prioritizing cost minimization and avoiding energy waste. Computational simulations demonstrate the efficacy of the mathematical optimization model, which can unleash its full potential when the number of EVs at the charging station is high., Comment: Paper submitted and accepted to ESCC 2024 - "11th International Conference on Energy, Sustainability and Climate Crisis August 26 - 30, 2024, Corfu, Greece"
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- 2024
22. Piecewise regular solutions to scalar balance laws with singular nonlocal sources
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Bociu, Lorena, Ftaka, Evangelia, Nguyen, Khai T., and Schino, Jacopo
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Mathematics - Analysis of PDEs - Abstract
The present paper establishes a local well-posed result for piecewise regular solutions with single shock of scalar balance laws with singular integral of convolution type kernels. In a neighborhood of the shock curve, a detailed description of the solution is provided for a general class of initial data.
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- 2024
- Full Text
- View/download PDF
23. Sentiment Reasoning for Healthcare
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Le-Duc, Khai, Nguyen, Khai-Nguyen, Tat, Bach Phan, Le, Duy, Ngo, Jerry, Vo-Dang, Long, Nguyen, Anh Totti, and Hy, Truong-Son
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning ,Computer Science - Sound ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
Transparency in AI decision-making is crucial in healthcare due to the severe consequences of errors, and this is important for building trust among AI and users in sentiment analysis task. Incorporating reasoning capabilities helps Large Language Models (LLMs) understand human emotions within broader contexts, handle nuanced and ambiguous language, and infer underlying sentiments that may not be explicitly stated. In this work, we introduce a new task - Sentiment Reasoning - for both speech and text modalities, along with our proposed multimodal multitask framework and dataset. Our study showed that rationale-augmented training enhances model performance in sentiment classification across both human transcript and ASR settings. Also, we found that the generated rationales typically exhibit different vocabularies compared to human-generated rationales, but maintain similar semantics. All code, data (English-translated and Vietnamese) and models are published online: https://github.com/leduckhai/MultiMed, Comment: Preprint, 18 pages
- Published
- 2024
24. Intimate partner violence is related to future alcohol use among a nationwide sample of LGBTQIA+ people: Results from The PRIDE Study
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Metheny, Nicholas, Tran, Nguyen Khai, Scott, Dalton, Dastur, Zubin, Lubensky, Micah E, Lunn, Mitchell R, Obedin-Maliver, Juno, and Flentje, Annesa
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Biomedical and Clinical Sciences ,Public Health ,Health Sciences ,Social Determinants of Health ,Minority Health ,Violence Against Women ,Health Disparities ,Violence Research ,Alcoholism ,Alcohol Use and Health ,Behavioral and Social Science ,Prevention ,Clinical Research ,Substance Misuse ,Women's Health ,Oral and gastrointestinal ,Peace ,Justice and Strong Institutions ,Gender Equality ,Good Health and Well Being ,Humans ,Male ,Female ,Intimate Partner Violence ,Adult ,Alcohol Drinking ,Sexual and Gender Minorities ,Longitudinal Studies ,Middle Aged ,United States ,Young Adult ,Adolescent ,Surveys and Questionnaires ,Intimate partner violence ,Alcohol use ,Sexual and gender minority people ,PRIDE study ,LGBTQIA+ ,Medical and Health Sciences ,Psychology and Cognitive Sciences ,Substance Abuse ,Biochemistry and cell biology ,Pharmacology and pharmaceutical sciences ,Epidemiology - Abstract
BackgroundLesbian, gay, bisexual, transgender, queer, intersex, aromantic and asexual (LGBTQIA+) communities in the United States experience higher rates of alcohol use than the general population. While experiencing intimate partner violence (IPV) is thought to lead to increased alcohol use in LGBTQIA+ people, little research has investigated the temporal relationship between IPV and alcohol use in this population.MethodsData from two annual questionnaires of The Population Research in Identity and Disparities for Equality Study (The PRIDE Study) longitudinal cohort (n=3,783) were included. Overall IPV and three sub-types (physical, sexual, and emotional) - measured in 2021 using the extended Hurt, Insult, Threaten, Scream (E-HITS) screening tool - was examined as a predictor of Alcohol Use Disorders Identification Test (AUDIT) score in 2022 using multivariable linear regression to assess linear and quadratic associations. Models were adjusted for sociodemographic characteristics and history of alcohol use.ResultsOne-quarter (24.7%) of respondents reported experiencing past-year IPV in 2021. The mean AUDIT score in 2022 was 3.52 (SD = 4.13). In adjusted models, both linear (B: 0.26, 95% CI: 0.14, 0.38) and quadratic (B: -0.03, 95% CI: -0.04, -0.01) terms for overall IPV were significantly associated with next-year AUDIT score. These patterns were mirrored in each IPV sub-type, were not attenuated when accounting for relationship characteristics, and were heterogeneous across gender identity groups.ConclusionsThese results provide evidence of a temporal relationship between IPV and alcohol use in LGBTQIA+ communities, suggesting that efforts to prevent and mitigate IPV may help reduce alcohol use disparities in this population.
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- 2024
25. Real-time Speech Summarization for Medical Conversations
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Le-Duc, Khai, Nguyen, Khai-Nguyen, Vo-Dang, Long, and Hy, Truong-Son
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning ,Computer Science - Sound ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
In doctor-patient conversations, identifying medically relevant information is crucial, posing the need for conversation summarization. In this work, we propose the first deployable real-time speech summarization system for real-world applications in industry, which generates a local summary after every N speech utterances within a conversation and a global summary after the end of a conversation. Our system could enhance user experience from a business standpoint, while also reducing computational costs from a technical perspective. Secondly, we present VietMed-Sum which, to our knowledge, is the first speech summarization dataset for medical conversations. Thirdly, we are the first to utilize LLM and human annotators collaboratively to create gold standard and synthetic summaries for medical conversation summarization. Finally, we present baseline results of state-of-the-art models on VietMed-Sum. All code, data (English-translated and Vietnamese) and models are available online: https://github.com/leduckhai/MultiMed, Comment: Interspeech 2024
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- 2024
26. Medical Spoken Named Entity Recognition
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Le-Duc, Khai, Thulke, David, Tran, Hung-Phong, Vo-Dang, Long, Nguyen, Khai-Nguyen, Hy, Truong-Son, and Schlüter, Ralf
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Electrical Engineering and Systems Science - Audio and Speech Processing ,Computer Science - Computation and Language ,Computer Science - Machine Learning ,Computer Science - Sound - Abstract
Spoken Named Entity Recognition (NER) aims to extracting named entities from speech and categorizing them into types like person, location, organization, etc. In this work, we present VietMed-NER - the first spoken NER dataset in the medical domain. To our best knowledge, our real-world dataset is the largest spoken NER dataset in the world in terms of the number of entity types, featuring 18 distinct types. Secondly, we present baseline results using various state-of-the-art pre-trained models: encoder-only and sequence-to-sequence. We found that pre-trained multilingual models XLM-R outperformed all monolingual models on both reference text and ASR output. Also in general, encoders perform better than sequence-to-sequence models for the NER task. By simply translating, the transcript is applicable not just to Vietnamese but to other languages as well. All code, data and models are made publicly available here: https://github.com/leduckhai/MultiMed, Comment: Preprint, 41 pages
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- 2024
27. On the structure of the value function of optimal exit time problems
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Cannarsa, Piermarco, Mazzola, Marco, and Nguyen, Khai T.
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Mathematics - Optimization and Control ,49N60, 49N05, 49J52, 49E30 - Abstract
In this paper, we study an optimal exit time problem with general running and terminal costs and a target $\mathcal{S}\subset\mathbb{R}^d$ having an inner ball property for a nonlinear control system that satisfies mild controllability assumptions. In particular, Petrov's condition at the boundary of $\mathcal{S}$ is not required and the value function $V$ may fail to be locally Lipschitz. In such a weakened set-up, we first establish a representation formula for proximal (horizontal) supergradients of $V$ by using transported proximal normal vectors. This allows us to obtain an external sphere condition for the hypograph of $V$ which yields several regularity properties. In particular, $V$ is almost everywhere twice differentiable and the Hausdorff dimension of its singularities is not greater than $d-1/2$. Furthermore, besides optimality conditions for trajectories of the optimal control problem, we extend the analysis to propagation of singularities and differentiability properties of the value function. An upper bound for the Hausdorff measure of the singular set is also studied, which implies that $V$ is a function of special bounded variation., Comment: 50 pages
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- 2024
28. A sharp quantitative estimate of critical sets
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Murdza, Andrew and Nguyen, Khai T.
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Mathematics - Functional Analysis ,46T20 - Abstract
The paper establishes a sharp quantitative estimate for the $(d-1)$-Hausdorff measure of the critical set of $\mathcal{C}^1$ vector-valued functions on $\mathbb{R}^d$. Additionally, we prove that for a generic $\mathcal{C}^2$ function where ``generic" is understood in the topological sense of Baire category, the critical set has a locally finite $(d-1)$-Hausdorff measure., Comment: arXiv admin note: substantial text overlap with arXiv:2312.17462
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- 2024
29. Borrowing Strength in Distributionally Robust Optimization via Hierarchical Dirichlet Processes
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Bariletto, Nicola, Nguyen, Khai, and Ho, Nhat
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Statistics - Machine Learning ,Computer Science - Machine Learning - Abstract
This paper presents a novel optimization framework to address key challenges presented by modern machine learning applications: High dimensionality, distributional uncertainty, and data heterogeneity. Our approach unifies regularized estimation, distributionally robust optimization (DRO), and hierarchical Bayesian modeling in a single data-driven criterion. By employing a hierarchical Dirichlet process (HDP) prior, the method effectively handles multi-source data, achieving regularization, distributional robustness, and borrowing strength across diverse yet related data-generating processes. We demonstrate the method's advantages by establishing theoretical performance guarantees and tractable Monte Carlo approximations based on Dirichlet process (DP) theory. Numerical experiments validate the framework's efficacy in improving and stabilizing both prediction and parameter estimation accuracy, showcasing its potential for application in complex data environments.
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- 2024
30. Revisiting Deep Audio-Text Retrieval Through the Lens of Transportation
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Luong, Manh, Nguyen, Khai, Ho, Nhat, Haf, Reza, Phung, Dinh, and Qu, Lizhen
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Electrical Engineering and Systems Science - Audio and Speech Processing ,Computer Science - Artificial Intelligence ,Computer Science - Sound - Abstract
The Learning-to-match (LTM) framework proves to be an effective inverse optimal transport approach for learning the underlying ground metric between two sources of data, facilitating subsequent matching. However, the conventional LTM framework faces scalability challenges, necessitating the use of the entire dataset each time the parameters of the ground metric are updated. In adapting LTM to the deep learning context, we introduce the mini-batch Learning-to-match (m-LTM) framework for audio-text retrieval problems. This framework leverages mini-batch subsampling and Mahalanobis-enhanced family of ground metrics. Moreover, to cope with misaligned training data in practice, we propose a variant using partial optimal transport to mitigate the harm of misaligned data pairs in training data. We conduct extensive experiments on audio-text matching problems using three datasets: AudioCaps, Clotho, and ESC-50. Results demonstrate that our proposed method is capable of learning rich and expressive joint embedding space, which achieves SOTA performance. Beyond this, the proposed m-LTM framework is able to close the modality gap across audio and text embedding, which surpasses both triplet and contrastive loss in the zero-shot sound event detection task on the ESC-50 dataset. Notably, our strategy of employing partial optimal transport with m-LTM demonstrates greater noise tolerance than contrastive loss, especially under varying noise ratios in training data on the AudioCaps dataset. Our code is available at https://github.com/v-manhlt3/m-LTM-Audio-Text-Retrieval
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- 2024
31. Marginal Fairness Sliced Wasserstein Barycenter
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Nguyen, Khai, Nguyen, Hai, and Ho, Nhat
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Statistics - Machine Learning ,Computer Science - Graphics ,Computer Science - Machine Learning - Abstract
The sliced Wasserstein barycenter (SWB) is a widely acknowledged method for efficiently generalizing the averaging operation within probability measure spaces. However, achieving marginal fairness SWB, ensuring approximately equal distances from the barycenter to marginals, remains unexplored. The uniform weighted SWB is not necessarily the optimal choice to obtain the desired marginal fairness barycenter due to the heterogeneous structure of marginals and the non-optimality of the optimization. As the first attempt to tackle the problem, we define the marginal fairness sliced Wasserstein barycenter (MFSWB) as a constrained SWB problem. Due to the computational disadvantages of the formal definition, we propose two hyperparameter-free and computationally tractable surrogate MFSWB problems that implicitly minimize the distances to marginals and encourage marginal fairness at the same time. To further improve the efficiency, we perform slicing distribution selection and obtain the third surrogate definition by introducing a new slicing distribution that focuses more on marginally unfair projecting directions. We discuss the relationship of the three proposed problems and their relationship to sliced multi-marginal Wasserstein distance. Finally, we conduct experiments on finding 3D point-clouds averaging, color harmonization, and training of sliced Wasserstein autoencoder with class-fairness representation to show the favorable performance of the proposed surrogate MFSWB problems., Comment: 33 pages, 14 figures, 6 tables
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- 2024
32. Hierarchical Hybrid Sliced Wasserstein: A Scalable Metric for Heterogeneous Joint Distributions
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Nguyen, Khai and Ho, Nhat
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Computer Science - Graphics ,Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
Sliced Wasserstein (SW) and Generalized Sliced Wasserstein (GSW) have been widely used in applications due to their computational and statistical scalability. However, the SW and the GSW are only defined between distributions supported on a homogeneous domain. This limitation prevents their usage in applications with heterogeneous joint distributions with marginal distributions supported on multiple different domains. Using SW and GSW directly on the joint domains cannot make a meaningful comparison since their homogeneous slicing operator i.e., Radon Transform (RT) and Generalized Radon Transform (GRT) are not expressive enough to capture the structure of the joint supports set. To address the issue, we propose two new slicing operators i.e., Partial Generalized Radon Transform (PGRT) and Hierarchical Hybrid Radon Transform (HHRT). In greater detail, PGRT is the generalization of Partial Radon Transform (PRT), which transforms a subset of function arguments non-linearly while HHRT is the composition of PRT and multiple domain-specific PGRT on marginal domain arguments. By using HHRT, we extend the SW into Hierarchical Hybrid Sliced Wasserstein (H2SW) distance which is designed specifically for comparing heterogeneous joint distributions. We then discuss the topological, statistical, and computational properties of H2SW. Finally, we demonstrate the favorable performance of H2SW in 3D mesh deformation, deep 3D mesh autoencoders, and datasets comparison., Comment: 28 pages, 11 figures, 4 tables
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- 2024
33. Generic Properties of Conjugate Points in Optimal Control Problems
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Bressan, Alberto, Mazzola, Marco, and Nguyen, Khai T.
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Mathematics - Optimization and Control ,49K05, 49L12 - Abstract
The first part of the paper studies a class of optimal control problems in Bolza form, where the dynamics is linear w.r.t.~the control function. A necessary condition is derived, for the optimality of a trajectory which starts at a conjugate point. The second part is concerned with a classical problem in the Calculus of Variations, with free terminal point. For a generic terminal cost $\psi\in \C^4(\mathbb{R}^n)$, applying the previous necessary condition we show that the set of conjugate points is contained in the image of an $(n-2)$-dimensional manifold, and has locally bounded $(n-2)$-dimensional Hausdorff measure., Comment: 13 pages
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- 2024
34. Code Generation for Conic Model-Predictive Control on Microcontrollers with TinyMPC
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Schoedel, Sam, Nguyen, Khai, Nedumaran, Elakhya, Plancher, Brian, and Manchester, Zachary
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Computer Science - Robotics ,Electrical Engineering and Systems Science - Systems and Control ,Mathematics - Optimization and Control - Abstract
Conic constraints appear in many important control applications like legged locomotion, robotic manipulation, and autonomous rocket landing. However, current solvers for conic optimization problems have relatively heavy computational demands in terms of both floating-point operations and memory footprint, making them impractical for use on small embedded devices. We extend TinyMPC, an open-source, high-speed solver targeting low-power embedded control applications, to handle second-order cone constraints. We also present code-generation software to enable deployment of TinyMPC on a variety of microcontrollers. We benchmark our generated code against state-of-the-art embedded QP and SOCP solvers, demonstrating a two-order-of-magnitude speed increase over ECOS while consuming less memory. Finally, we demonstrate TinyMPC's efficacy on the Crazyflie, a lightweight, resource-constrained quadrotor with fast dynamics. TinyMPC and its code-generation tools are publicly available at https://tinympc.org., Comment: Submitted to CDC, 2024. First two authors contributed equally
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- 2024
35. Integrating Efficient Optimal Transport and Functional Maps For Unsupervised Shape Correspondence Learning
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Le, Tung, Nguyen, Khai, Sun, Shanlin, Ho, Nhat, and Xie, Xiaohui
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
In the realm of computer vision and graphics, accurately establishing correspondences between geometric 3D shapes is pivotal for applications like object tracking, registration, texture transfer, and statistical shape analysis. Moving beyond traditional hand-crafted and data-driven feature learning methods, we incorporate spectral methods with deep learning, focusing on functional maps (FMs) and optimal transport (OT). Traditional OT-based approaches, often reliant on entropy regularization OT in learning-based framework, face computational challenges due to their quadratic cost. Our key contribution is to employ the sliced Wasserstein distance (SWD) for OT, which is a valid fast optimal transport metric in an unsupervised shape matching framework. This unsupervised framework integrates functional map regularizers with a novel OT-based loss derived from SWD, enhancing feature alignment between shapes treated as discrete probability measures. We also introduce an adaptive refinement process utilizing entropy regularized OT, further refining feature alignments for accurate point-to-point correspondences. Our method demonstrates superior performance in non-rigid shape matching, including near-isometric and non-isometric scenarios, and excels in downstream tasks like segmentation transfer. The empirical results on diverse datasets highlight our framework's effectiveness and generalization capabilities, setting new standards in non-rigid shape matching with efficient OT metrics and an adaptive refinement module., Comment: accepted by CVPR 2024
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- 2024
36. On Parameter Estimation in Deviated Gaussian Mixture of Experts
- Author
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Nguyen, Huy, Nguyen, Khai, and Ho, Nhat
- Subjects
Statistics - Machine Learning ,Computer Science - Machine Learning - Abstract
We consider the parameter estimation problem in the deviated Gaussian mixture of experts in which the data are generated from $(1 - \lambda^{\ast}) g_0(Y| X)+ \lambda^{\ast} \sum_{i = 1}^{k_{\ast}} p_{i}^{\ast} f(Y|(a_{i}^{\ast})^{\top}X+b_i^{\ast},\sigma_{i}^{\ast})$, where $X, Y$ are respectively a covariate vector and a response variable, $g_{0}(Y|X)$ is a known function, $\lambda^{\ast} \in [0, 1]$ is true but unknown mixing proportion, and $(p_{i}^{\ast}, a_{i}^{\ast}, b_{i}^{\ast}, \sigma_{i}^{\ast})$ for $1 \leq i \leq k^{\ast}$ are unknown parameters of the Gaussian mixture of experts. This problem arises from the goodness-of-fit test when we would like to test whether the data are generated from $g_{0}(Y|X)$ (null hypothesis) or they are generated from the whole mixture (alternative hypothesis). Based on the algebraic structure of the expert functions and the distinguishability between $g_0$ and the mixture part, we construct novel Voronoi-based loss functions to capture the convergence rates of maximum likelihood estimation (MLE) for our models. We further demonstrate that our proposed loss functions characterize the local convergence rates of parameter estimation more accurately than the generalized Wasserstein, a loss function being commonly used for estimating parameters in the Gaussian mixture of experts., Comment: Accepted to AISTATS 2024, 32 pages, 2 figures, 1 table
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- 2024
37. Sliced Wasserstein with Random-Path Projecting Directions
- Author
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Nguyen, Khai, Zhang, Shujian, Le, Tam, and Ho, Nhat
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Statistics - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
Slicing distribution selection has been used as an effective technique to improve the performance of parameter estimators based on minimizing sliced Wasserstein distance in applications. Previous works either utilize expensive optimization to select the slicing distribution or use slicing distributions that require expensive sampling methods. In this work, we propose an optimization-free slicing distribution that provides a fast sampling for the Monte Carlo estimation of expectation. In particular, we introduce the random-path projecting direction (RPD) which is constructed by leveraging the normalized difference between two random vectors following the two input measures. From the RPD, we derive the random-path slicing distribution (RPSD) and two variants of sliced Wasserstein, i.e., the Random-Path Projection Sliced Wasserstein (RPSW) and the Importance Weighted Random-Path Projection Sliced Wasserstein (IWRPSW). We then discuss the topological, statistical, and computational properties of RPSW and IWRPSW. Finally, we showcase the favorable performance of RPSW and IWRPSW in gradient flow and the training of denoising diffusion generative models on images., Comment: Accepted to ICML 2024, 21 pages, 5 figures, 2 tables
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- 2024
38. ELK PHOTOGRAPHY IN BENNEZETTE, PENNSYLVANIA
- Author
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Nguyen, Khai
- Subjects
Photography of animals -- Social aspects ,Moose -- Portrayals -- Environmental aspects ,Pennsylvania -- Environmental aspects - Abstract
BACKGROUND The Elk is a majestic creature. They are one of the largest species within the deer family and one of North America's largest terrestrial and native mammals. Pennsylvania's elk [...]
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- 2024
39. Asian Cohort for Alzheimer's Disease (ACAD) pilot study on genetic and non‐genetic risk factors for Alzheimer's disease among Asian Americans and Canadians
- Author
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Ho, Pei‐Chuan, Yu, Wai Haung, Tee, Boon Lead, Lee, Wan‐Ping, Li, Clara, Gu, Yian, Yokoyama, Jennifer S, Reyes‐Dumeyer, Dolly, Choi, Yun‐Beom, Yang, Hyun‐Sik, Vardarajan, Badri N, Tzuang, Marian, Lieu, Kevin, Lu, Anna, Faber, Kelley M, Potter, Zoë D, Revta, Carolyn, Kirsch, Maureen, McCallum, Jake, Mei, Diana, Booth, Briana, Cantwell, Laura B, Chen, Fangcong, Chou, Sephera, Clark, Dewi, Deng, Michelle, Hong, Ting Hei, Hwang, Ling‐Jen, Jiang, Lilly, Joo, Yoonmee, Kang, Younhee, Kim, Ellen S, Kim, Hoowon, Kim, Kyungmin, Kuzma, Amanda B, Lam, Eleanor, Lanata, Serggio C, Lee, Kunho, Li, Donghe, Li, Mingyao, Li, Xiang, Liu, Chia‐Lun, Liu, Collin, Liu, Linghsi, Lupo, Jody‐Lynn, Nguyen, Khai, Pfleuger, Shannon E, Qian, James, Qian, Winnie, Ramirez, Veronica, Russ, Kristen A, Seo, Eun Hyun, Song, Yeunjoo E, Tartaglia, Maria Carmela, Tian, Lu, Torres, Mina, Vo, Namkhue, Wong, Ellen C, Xie, Yuan, Yau, Eugene B, Yi, Isabelle, Yu, Victoria, Zeng, Xiaoyi, St George‐Hyslop, Peter, Au, Rhoda, Schellenberg, Gerard D, Dage, Jeffrey L, Varma, Rohit, Hsiung, Ging‐Yuek R, Rosen, Howard, Henderson, Victor W, Foroud, Tatiana, Kukull, Walter A, Peavy, Guerry M, Lee, Haeok, Feldman, Howard H, Mayeux, Richard, Chui, Helena, Jun, Gyungah R, Park, Van M Ta, Chow, Tiffany W, and Wang, Li‐San
- Subjects
Biomedical and Clinical Sciences ,Biological Psychology ,Clinical Sciences ,Neurosciences ,Psychology ,Clinical Trials and Supportive Activities ,Clinical Research ,Acquired Cognitive Impairment ,Minority Health ,Brain Disorders ,Dementia ,Alzheimer's Disease ,Aging ,Neurodegenerative ,Prevention ,Genetics ,Health Disparities ,Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD) ,2.4 Surveillance and distribution ,Neurological ,Humans ,Alzheimer Disease ,Pilot Projects ,Asian ,Canada ,Risk Factors ,North American People ,Alzheimer's disease ,biosamples ,community-based participatory research ,dementia ,environmental ,genetics ,Geriatrics ,Clinical sciences ,Biological psychology - Abstract
IntroductionClinical research in Alzheimer's disease (AD) lacks cohort diversity despite being a global health crisis. The Asian Cohort for Alzheimer's Disease (ACAD) was formed to address underrepresentation of Asians in research, and limited understanding of how genetics and non-genetic/lifestyle factors impact this multi-ethnic population.MethodsThe ACAD started fully recruiting in October 2021 with one central coordination site, eight recruitment sites, and two analysis sites. We developed a comprehensive study protocol for outreach and recruitment, an extensive data collection packet, and a centralized data management system, in English, Chinese, Korean, and Vietnamese.ResultsACAD has recruited 606 participants with an additional 900 expressing interest in enrollment since program inception.DiscussionACAD's traction indicates the feasibility of recruiting Asians for clinical research to enhance understanding of AD risk factors. ACAD will recruit > 5000 participants to identify genetic and non-genetic/lifestyle AD risk factors, establish blood biomarker levels for AD diagnosis, and facilitate clinical trial readiness.HighlightsThe Asian Cohort for Alzheimer's Disease (ACAD) promotes awareness of under-investment in clinical research for Asians. We are recruiting Asian Americans and Canadians for novel insights into Alzheimer's disease. We describe culturally appropriate recruitment strategies and data collection protocol. ACAD addresses challenges of recruitment from heterogeneous Asian subcommunities. We aim to implement a successful recruitment program that enrolls across three Asian subcommunities.
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- 2024
40. Hausdorff measure of zeros of polynomials
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Murdza, Andrew, Nguyen, Khai T., and Phillips, Etienne
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Mathematics - Classical Analysis and ODEs ,Mathematics - Algebraic Geometry - Abstract
The paper provides an elementary proof establishing a sharp universal bound on the $(d-1)$-Hausdorff measure of the zeros of any nontrivial multivariable polynomial $p:\mathbb{R}^d\to\mathbb{R}$ within a $d$-dimensional cube of size $r$. This bound depends solely on the parameter $r$, the dimension $d$, and the degrees of $p$.
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- 2023
41. TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers
- Author
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Nguyen, Khai, Schoedel, Sam, Alavilli, Anoushka, Plancher, Brian, and Manchester, Zachary
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Computer Science - Robotics ,Electrical Engineering and Systems Science - Systems and Control ,Mathematics - Optimization and Control - Abstract
Model-predictive control (MPC) is a powerful tool for controlling highly dynamic robotic systems subject to complex constraints. However, MPC is computationally demanding, and is often impractical to implement on small, resource-constrained robotic platforms. We present TinyMPC, a high-speed MPC solver with a low memory footprint targeting the microcontrollers common on small robots. Our approach is based on the alternating direction method of multipliers (ADMM) and leverages the structure of the MPC problem for efficiency. We demonstrate TinyMPC's effectiveness by benchmarking against the state-of-the-art solver OSQP, achieving nearly an order of magnitude speed increase, as well as through hardware experiments on a 27 gram quadrotor, demonstrating high-speed trajectory tracking and dynamic obstacle avoidance. TinyMPC is publicly available at https://tinympc.org., Comment: Accepted at ICRA 2024. Publicly available at https://tinympc.org
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- 2023
42. Diffusion Approximations of Markovian Solutions to Discontinuous ODEs
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Bressan, Alberto, Mazzola, Marco, and Nguyen, Khai T.
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- 2024
- Full Text
- View/download PDF
43. Quasi-Monte Carlo for 3D Sliced Wasserstein
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Nguyen, Khai, Bariletto, Nicola, and Ho, Nhat
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Statistics - Machine Learning ,Computer Science - Graphics ,Computer Science - Machine Learning - Abstract
Monte Carlo (MC) integration has been employed as the standard approximation method for the Sliced Wasserstein (SW) distance, whose analytical expression involves an intractable expectation. However, MC integration is not optimal in terms of absolute approximation error. To provide a better class of empirical SW, we propose quasi-sliced Wasserstein (QSW) approximations that rely on Quasi-Monte Carlo (QMC) methods. For a comprehensive investigation of QMC for SW, we focus on the 3D setting, specifically computing the SW between probability measures in three dimensions. In greater detail, we empirically evaluate various methods to construct QMC point sets on the 3D unit-hypersphere, including the Gaussian-based and equal area mappings, generalized spiral points, and optimizing discrepancy energies. Furthermore, to obtain an unbiased estimator for stochastic optimization, we extend QSW to Randomized Quasi-Sliced Wasserstein (RQSW) by introducing randomness in the discussed point sets. Theoretically, we prove the asymptotic convergence of QSW and the unbiasedness of RQSW. Finally, we conduct experiments on various 3D tasks, such as point-cloud comparison, point-cloud interpolation, image style transfer, and training deep point-cloud autoencoders, to demonstrate the favorable performance of the proposed QSW and RQSW variants., Comment: Accepted to ICLR 2024 (Spotlight), 25 pages, 13 figures, 6 tables
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- 2023
44. Diffeomorphic Mesh Deformation via Efficient Optimal Transport for Cortical Surface Reconstruction
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Le, Tung, Nguyen, Khai, Sun, Shanlin, Han, Kun, Ho, Nhat, and Xie, Xiaohui
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Mesh deformation plays a pivotal role in many 3D vision tasks including dynamic simulations, rendering, and reconstruction. However, defining an efficient discrepancy between predicted and target meshes remains an open problem. A prevalent approach in current deep learning is the set-based approach which measures the discrepancy between two surfaces by comparing two randomly sampled point-clouds from the two meshes with Chamfer pseudo-distance. Nevertheless, the set-based approach still has limitations such as lacking a theoretical guarantee for choosing the number of points in sampled point-clouds, and the pseudo-metricity and the quadratic complexity of the Chamfer divergence. To address these issues, we propose a novel metric for learning mesh deformation. The metric is defined by sliced Wasserstein distance on meshes represented as probability measures that generalize the set-based approach. By leveraging probability measure space, we gain flexibility in encoding meshes using diverse forms of probability measures, such as continuous, empirical, and discrete measures via varifold representation. After having encoded probability measures, we can compare meshes by using the sliced Wasserstein distance which is an effective optimal transport distance with linear computational complexity and can provide a fast statistical rate for approximating the surface of meshes. To the end, we employ a neural ordinary differential equation (ODE) to deform the input surface into the target shape by modeling the trajectories of the points on the surface. Our experiments on cortical surface reconstruction demonstrate that our approach surpasses other competing methods in multiple datasets and metrics., Comment: Accepted by ICLR 2024
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- 2023
45. Towards Convergence Rates for Parameter Estimation in Gaussian-gated Mixture of Experts
- Author
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Nguyen, Huy, Nguyen, TrungTin, Nguyen, Khai, and Ho, Nhat
- Subjects
Statistics - Machine Learning ,Computer Science - Machine Learning - Abstract
Originally introduced as a neural network for ensemble learning, mixture of experts (MoE) has recently become a fundamental building block of highly successful modern deep neural networks for heterogeneous data analysis in several applications of machine learning and statistics. Despite its popularity in practice, a satisfactory level of theoretical understanding of the MoE model is far from complete. To shed new light on this problem, we provide a convergence analysis for maximum likelihood estimation (MLE) in the Gaussian-gated MoE model. The main challenge of that analysis comes from the inclusion of covariates in the Gaussian gating functions and expert networks, which leads to their intrinsic interaction via some partial differential equations with respect to their parameters. We tackle these issues by designing novel Voronoi loss functions among parameters to accurately capture the heterogeneity of parameter estimation rates. Our findings reveal that the MLE has distinct behaviors under two complement settings of location parameters of the Gaussian gating functions, namely when all these parameters are non-zero versus when at least one among them vanishes. Notably, these behaviors can be characterized by the solvability of two different systems of polynomial equations. Finally, we conduct a simulation study to empirically verify our theoretical results., Comment: 32 pages, 9 figures; Huy Nguyen and TrungTin Nguyen contributed equally to this work
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- 2023
46. Sliced Wasserstein Estimation with Control Variates
- Author
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Nguyen, Khai and Ho, Nhat
- Subjects
Statistics - Machine Learning ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Graphics ,Computer Science - Machine Learning - Abstract
The sliced Wasserstein (SW) distances between two probability measures are defined as the expectation of the Wasserstein distance between two one-dimensional projections of the two measures. The randomness comes from a projecting direction that is used to project the two input measures to one dimension. Due to the intractability of the expectation, Monte Carlo integration is performed to estimate the value of the SW distance. Despite having various variants, there has been no prior work that improves the Monte Carlo estimation scheme for the SW distance in terms of controlling its variance. To bridge the literature on variance reduction and the literature on the SW distance, we propose computationally efficient control variates to reduce the variance of the empirical estimation of the SW distance. The key idea is to first find Gaussian approximations of projected one-dimensional measures, then we utilize the closed-form of the Wasserstein-2 distance between two Gaussian distributions to design the control variates. In particular, we propose using a lower bound and an upper bound of the Wasserstein-2 distance between two fitted Gaussians as two computationally efficient control variates. We empirically show that the proposed control variate estimators can help to reduce the variance considerably when comparing measures over images and point-clouds. Finally, we demonstrate the favorable performance of the proposed control variate estimators in gradient flows to interpolate between two point-clouds and in deep generative modeling on standard image datasets, such as CIFAR10 and CelebA., Comment: Accepted to ICLR2024, 20 pages, 7 figures, 4 tables
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- 2023
47. Energy-Based Sliced Wasserstein Distance
- Author
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Nguyen, Khai and Ho, Nhat
- Subjects
Statistics - Machine Learning ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Graphics ,Computer Science - Machine Learning - Abstract
The sliced Wasserstein (SW) distance has been widely recognized as a statistically effective and computationally efficient metric between two probability measures. A key component of the SW distance is the slicing distribution. There are two existing approaches for choosing this distribution. The first approach is using a fixed prior distribution. The second approach is optimizing for the best distribution which belongs to a parametric family of distributions and can maximize the expected distance. However, both approaches have their limitations. A fixed prior distribution is non-informative in terms of highlighting projecting directions that can discriminate two general probability measures. Doing optimization for the best distribution is often expensive and unstable. Moreover, designing the parametric family of the candidate distribution could be easily misspecified. To address the issues, we propose to design the slicing distribution as an energy-based distribution that is parameter-free and has the density proportional to an energy function of the projected one-dimensional Wasserstein distance. We then derive a novel sliced Wasserstein metric, energy-based sliced Waserstein (EBSW) distance, and investigate its topological, statistical, and computational properties via importance sampling, sampling importance resampling, and Markov Chain methods. Finally, we conduct experiments on point-cloud gradient flow, color transfer, and point-cloud reconstruction to show the favorable performance of the EBSW., Comment: Accepted to NeurIPS 2023, 30 pages, 8 figures, 6 tables
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- 2023
48. Cannabis use for Sleep Disturbance Among Older Patients in a Geriatrics Clinic
- Author
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Kaufmann, Christopher N, Malhotra, Atul, Yang, Kevin H, Han, Benjamin H, Nafsu, Reva, Lifset, Ella T, Nguyen, Khai, Sexton, Michelle, and Moore, Alison A
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Biological Psychology ,Psychology ,Clinical Research ,Sleep Research ,Cannabinoid Research ,Drug Abuse (NIDA only) ,Substance Misuse ,Cannabidiol Research ,Behavioral and Social Science ,Therapeutic Cannabinoid Research ,Aging ,Mental health ,Good Health and Well Being ,Humans ,Female ,Aged ,Cannabis ,Dronabinol ,Cannabidiol ,Sleep ,Geriatrics ,sleep ,older adults ,cannabis ,geriatrics ,clinical care ,Applied Mathematics ,Public Health and Health Services ,Gerontology ,Applied and developmental psychology ,Clinical and health psychology ,Social and personality psychology - Abstract
Cannabis use is growing among older adults to manage medical concerns including poor sleep. In this study, we characterized how patients seen at a geriatrics clinic use cannabis to address sleep disturbance. Specifically, we conducted an anonymous survey of 568 adults, including 83 who reported cannabis use within the past 3 years, to inquire about such use. We compared cannabis use characteristics between those using it for sleep disturbance versus all other conditions. We considered a p-value
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- 2023
49. A Learning-Based Approach for Wafer Defect Detection in Production Quality Control
- Author
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Yen, Hoang Hai, Nguyen, Nguyen Khai, Tram, Pham Bao, Vi, Tran Duc, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Nanda, Satyasai Jagannath, editor, Yadav, Rajendra Prasad, editor, Gandomi, Amir H., editor, and Saraswat, Mukesh, editor
- Published
- 2024
- Full Text
- View/download PDF
50. Author Correction: Machine learning-based approaches for identifying human blood cells harboring CRISPR-mediated fetal chromatin domain ablations
- Author
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Li, Yi, Zaheri, Shadi, Nguyen, Khai, Liu, Li, Hassanipour, Fatemeh, Pace, Betty S., and Bleris, Leonidas
- Published
- 2024
- Full Text
- View/download PDF
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