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14,490 results on '"Huynh P."'

1. Development of shear strength prediction model for RC beams strengthened with FRP strips based on a novel ensemble learning model

Author

Huynh P. Nam, Nguyen D. D. Nam, Nguyen H. Thinh, Phan H. Nam, Ho M. Hung, Do V. Hai, and Phan D. Thao

Subjects
shears strengthening, rc beam, frp strips, ensemble learning, shear strength, Technology
Abstract

The use of fiber-reinforced polymer (FRP) strips as an external strengthening method has gained widespread acceptance for enhancing the capacity of aging reinforced concrete (RC) beams. Despite extensive research and practical applications, there exist unresolved issues necessitating further investigation, including interface bonding, failure mechanisms, and the development of accurate predictive models for shear strength, considering diverse parameters. This paper systematically reviews shear strength formulas applied in the strengthening of RC beams using FRP strips. An extensive experimental dataset is compiled from diverse sources to support the analysis. Utilizing this dataset, a novel ensemble learning model is developed for predicting the shear strength contributed by FRP strips. Following the training of this model, the important score of each input parameter is also assessed. The findings presented herein contribute to a deeper understanding of the strengthening effectiveness of FRP strips on RC beams.

Published
2024
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2. Vocabulary in English Textbooks for Vietnamese Upper-Secondary Students: A Comparative Analysis of Reading Passages

Author

Nam Nhat Lien, Nhi Hoa Mai, and Nguyen Huynh Trang

Abstract

In EFL countries where English is rarely practiced outside the classroom, textbooks have become the major input source for learners. Particularly in Vietnam, multiple textbook series are available simultaneously for the same grade. Thus, it is important to examine if their vocabulary is appropriate and of similar difficulty. This study aims to investigate and compare the lexical demands, sophistication, diversity, and lengths of reading passages in the eight latest series for Vietnamese 10th graders with 53,360 tokens in total. The results revealed that the most frequent 1,000, 2,000-3,000, and roughly 4,000-word families in the BNC/COCA wordlist, plus proper nouns, marginal words, transparent compounds, and acronyms, were respectively needed for 85%, 95%, and 98% coverage. Additionally, pairwise comparisons uncovered that the passages differ significantly in length yet insignificantly in lexical sophistication and diversity. Therefore, the series appear to be well-suited to co-implementation and facilitative to vocabulary development despite not being optimized for independent learning. The study still calls for simplifying the eight textbook series to promote meaning-focused output. Finally, implications for exploiting and revising these textbook series are discussed.

Published
2024

3. Exploring Vietnamese EFL Learners' Perceptions Towards Online Learning at University Education

Author

Ngoc Tai Huynh and Tuyet-Nhung Thi Nguyen

Abstract

This study investigates the perceptions of Vietnamese English-majored students towards online learning courses during the COVID-19 pandemic. While online learning has been shown to have numerous benefits, few studies have focused on exploring perceptions of Vietnamese learners of English (EFL) towards online learning course at university level in the Vietnamese context. To address this gap, the present study surveyed 60 Vietnamese EFL learners who have experience with online courses offered by a university in southern Vietnam, using the Technology Acceptance Model (TAM) developed (Davis, Bagozzi, & Warshaw, 1989). The findings reveal various challenges and benefits of the online learning mode, which are reported by learners through their perceived usefulness and perceived ease of use when taking online courses. The learners' own experiences during online courses were also revealed. The outcomes of this study provide insights into the unique experiences of Vietnamese EFL learners with e-learning and offer suggestions for optimizing online education for this population in university settings.

Published
2024

4. Evaluating Large Language Model Capability in Vietnamese Fact-Checking Data Generation

Author

To, Long Truong, Le, Hung Tuan, Nguyen, Dat Van-Thanh, Nguyen, Manh Trong, Nguyen, Tri Thien, Van Huynh, Tin, and Van Nguyen, Kiet

Subjects
Computer Science - Computation and Language
Abstract

Large Language Models (LLMs), with gradually improving reading comprehension and reasoning capabilities, are being applied to a range of complex language tasks, including the automatic generation of language data for various purposes. However, research on applying LLMs for automatic data generation in low-resource languages like Vietnamese is still underdeveloped and lacks comprehensive evaluation. In this paper, we explore the use of LLMs for automatic data generation for the Vietnamese fact-checking task, which faces significant data limitations. Specifically, we focus on fact-checking data where claims are synthesized from multiple evidence sentences to assess the information synthesis capabilities of LLMs. We develop an automatic data construction process using simple prompt techniques on LLMs and explore several methods to improve the quality of the generated data. To evaluate the quality of the data generated by LLMs, we conduct both manual quality assessments and performance evaluations using language models. Experimental results and manual evaluations illustrate that while the quality of the generated data has significantly improved through fine-tuning techniques, LLMs still cannot match the data quality produced by humans.

Published
2024

5. Multi-Scale Temporal Analysis for Failure Prediction in Energy Systems

Author

Le, Anh, Huynh, Phat K., Yadav, Om P., Le, Chau, Pirim, Harun, and Le, Trung Q.

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Many existing models struggle to predict nonlinear behavior during extreme weather conditions. This study proposes a multi-scale temporal analysis for failure prediction in energy systems using PMU data. The model integrates multi-scale analysis with machine learning to capture both short-term and long-term behavior. PMU data lacks labeled states despite logged failure records, making it difficult to distinguish between normal and disturbance conditions. We address this through: (1) Extracting domain features from PMU time series data; (2) Applying multi-scale windows (30s, 60s, 180s) for pattern detection; (3) Using Recursive Feature Elimination to identify key features; (4) Training multiple machine learning models. Key contributions: Identifying significant features across multi-scale windows; Demonstrating LightGBM's superior performance (0.896 precision); Showing multi-scale analysis outperforms single-window models (0.841). Our work focuses on weather-related failures, with plans to extend to equipment failure and lightning events., Comment: 6 pages, 3 figures, RAMS 2025

Published
2024

6. SoftCTRL: Soft conservative KL-control of Transformer Reinforcement Learning for Autonomous Driving

Author

Huynh, Minh Tri and Nguyen, Duc Dung

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

In recent years, motion planning for urban self-driving cars (SDV) has become a popular problem due to its complex interaction of road components. To tackle this, many methods have relied on large-scale, human-sampled data processed through Imitation learning (IL). Although effective, IL alone cannot adequately handle safety and reliability concerns. Combining IL with Reinforcement learning (RL) by adding KL divergence between RL and IL policy to the RL loss can alleviate IL's weakness but suffer from over-conservation caused by covariate shift of IL. To address this limitation, we introduce a method that combines IL with RL using an implicit entropy-KL control that offers a simple way to reduce the over-conservation characteristic. In particular, we validate different challenging simulated urban scenarios from the unseen dataset, indicating that although IL can perform well in imitation tasks, our proposed method significantly improves robustness (over 17\% reduction in failures) and generates human-like driving behavior., Comment: submitted to IEEE Open Journal of Intelligent Transportation Systems

Published
2024

7. A Big Data-empowered System for Real-time Detection of Regional Discriminatory Comments on Vietnamese Social Media

Author

Huynh, An Nghiep, Do, Thanh Dat, and Do, Trong Hop

Subjects
Computer Science - Computation and Language, Computer Science - Computers and Society
Abstract

Regional discrimination is a persistent social issue in Vietnam. While existing research has explored hate speech in the Vietnamese language, the specific issue of regional discrimination remains under-addressed. Previous studies primarily focused on model development without considering practical system implementation. In this work, we propose a task called Detection of Regional Discriminatory Comments on Vietnamese Social Media, leveraging the power of machine learning and transfer learning models. We have built the ViRDC (Vietnamese Regional Discrimination Comments) dataset, which contains comments from social media platforms, providing a valuable resource for further research and development. Our approach integrates streaming capabilities to process real-time data from social media networks, ensuring the system's scalability and responsiveness. We developed the system on the Apache Spark framework to efficiently handle increasing data inputs during streaming. Our system offers a comprehensive solution for the real-time detection of regional discrimination in Vietnam., Comment: accepted by 2024 International Conference on Advanced Technologies for Communications (ATC) Program

Published
2024

8. SimpsonsVQA: Enhancing Inquiry-Based Learning with a Tailored Dataset

Author

Huynh, Ngoc Dung, Bouadjenek, Mohamed Reda, Aryal, Sunil, Razzak, Imran, and Hacid, Hakim

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Visual Question Answering (VQA) has emerged as a promising area of research to develop AI-based systems for enabling interactive and immersive learning. Numerous VQA datasets have been introduced to facilitate various tasks, such as answering questions or identifying unanswerable ones. However, most of these datasets are constructed using real-world images, leaving the performance of existing models on cartoon images largely unexplored. Hence, in this paper, we present "SimpsonsVQA", a novel dataset for VQA derived from The Simpsons TV show, designed to promote inquiry-based learning. Our dataset is specifically designed to address not only the traditional VQA task but also to identify irrelevant questions related to images, as well as the reverse scenario where a user provides an answer to a question that the system must evaluate (e.g., as correct, incorrect, or ambiguous). It aims to cater to various visual applications, harnessing the visual content of "The Simpsons" to create engaging and informative interactive systems. SimpsonsVQA contains approximately 23K images, 166K QA pairs, and 500K judgments (https://simpsonsvqa.org). Our experiments show that current large vision-language models like ChatGPT4o underperform in zero-shot settings across all three tasks, highlighting the dataset's value for improving model performance on cartoon images. We anticipate that SimpsonsVQA will inspire further research, innovation, and advancements in inquiry-based learning VQA.

Published
2024

9. The Local Ultraviolet to Infrared Treasury I. Survey Overview of the Broadband Imaging

Author

Gilbert, Karoline M., Choi, Yumi, Boyer, Martha L., Williams, Benjamin F., Weisz, Daniel R., Bell, Eric F., Dalcanton, Julianne J., McQuinn, Kristen B. W., Skillman, Evan D., Costa, Guglielmo, Fouesneau, Morgan, Girardi, Léo, Goldman, Steven R., Gordon, Karl D., Guhathakurta, Puragra, Gull, Maude, Hagen, Lea, Huynh, Ky, Lindberg, Christina W., Marigo, Paola, Murray, Claire E., Pastorelli, Giada, and Merica-Jones, Petia Yanchulova

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

The Local Ultraviolet to Infrared Treasury (LUVIT) is a Hubble Space Telescope program that combines newly acquired data in the near ultraviolet (NUV), optical, and near infrared (NIR) with archival optical and NIR imaging to produce multiband panchromatic resolved stellar catalogs for 23 pointings in 22 low-mass, star-forming galaxies ranging in distance from the outskirts of the Local Group to ~3.8 Mpc. We describe the survey design, detail the LUVIT broadband filter observations and the archival datasets included in the LUVIT reductions, and summarize the simultaneous multiband data reduction steps. The spatial distributions and color-magnitude diagrams (CMDs) from the resulting stellar catalogs are presented for each target, from the NUV to the NIR. We demonstrate in which regions of the CMDs stars with NUV and optical, optical and NIR, and NUV through NIR detections reside. For each target, we use the results from artificial star tests to measure representative completeness, bias, and total photometric uncertainty as a function of magnitude in each broadband filter. We also assess which LUVIT targets have significant spatial variation in the fraction of stars recovered at a given magnitude. The panchromatic LUVIT stellar catalogs will provide a rich legacy dataset for a host of resolved stellar population studies., Comment: 48 pages, 14 figures, 8 tables, accepted for publication in ApJS

Published
2024

10. Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning

Author

Van Huynh, Nguyen, Zhang, Bolun, Tran, Dinh-Hieu, Hoang, Dinh Thai, Nguyen, Diep N., Zheng, Gan, Niyato, Dusit, and Pham, Quoc-Viet

Subjects
Computer Science - Networking and Internet Architecture, Computer Science - Artificial Intelligence
Abstract

Spectrum access is an essential problem in device-to-device (D2D) communications. However, with the recent growth in the number of mobile devices, the wireless spectrum is becoming scarce, resulting in low spectral efficiency for D2D communications. To address this problem, this paper aims to integrate the ambient backscatter communication technology into D2D devices to allow them to backscatter ambient RF signals to transmit their data when the shared spectrum is occupied by mobile users. To obtain the optimal spectrum access policy, i.e., stay idle or access the shared spectrum and perform active transmissions or backscattering ambient RF signals for transmissions, to maximize the average throughput for D2D users, deep reinforcement learning (DRL) can be adopted. However, DRL-based solutions may require long training time due to the curse of dimensionality issue as well as complex deep neural network architectures. For that, we develop a novel quantum reinforcement learning (RL) algorithm that can achieve a faster convergence rate with fewer training parameters compared to DRL thanks to the quantum superposition and quantum entanglement principles. Specifically, instead of using conventional deep neural networks, the proposed quantum RL algorithm uses a parametrized quantum circuit to approximate an optimal policy. Extensive simulations then demonstrate that the proposed solution not only can significantly improve the average throughput of D2D devices when the shared spectrum is busy but also can achieve much better performance in terms of convergence rate and learning complexity compared to existing DRL-based methods., Comment: 12 pages, 7 figures

Published
2024

11. Search for gravitational waves emitted from SN 2023ixf

Author

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abouelfettouh, I., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Agarwal, D., Agathos, M., Abchouyeh, M. Aghaei, Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Argianas, L., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Attadio, F., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Baier, J. G., Baiotti, L., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartoletti, A. M., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bates, D. E., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Baynard II, P. A., Bazzan, M., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blagg, L. A., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boudon, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brandt, J., Braun, I., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, B. C., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cáceres-Barbosa, V., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannon, K. C., Cao, H., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, J. C. L., Chan, M., Chandra, K., Chang, R. -J., Chao, S., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, Y., Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Cheung, S. Y., Chiadini, F., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chugh, P., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciolfi, R., Clara, F., Clark, J. A., Clarke, J., Clarke, T. A., Clearwater, P., Clesse, S., Coccia, E., Codazzo, E., Cohadon, P. -F., Colace, S., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Connolly, G., Conti, L., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Crook, S., Crouch, R., Csizmazia, J., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Pra, S. Dal, Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Davari, N., Dave, I., Davenport, A., Davier, M., Davies, T. F., Davis, D., Davis, L., Davis, M. C., Davis, P. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., DePergola, N., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhani, A., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Dominguez, D., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Ducoin, J. -G., Dunn, L., Dupletsa, U., D'Urso, D., Duval, H., Duverne, P. -A., Dwyer, S. E., Eassa, C., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Einsle, H., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Eleveld, R. M., Emma, M., Endo, K., Engl, A. J., Enloe, E., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fairhurst, S., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Felicetti, R., Fenyvesi, E., Ferguson, D. L., Ferraiuolo, S., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. S., Floden, E., Foley, E. M., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franceschetti, K., Franchini, N., Frasca, S., Frasconi, F., Mascioli, A. Frattale, Frei, Z., Freise, A., Freitas, O., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fuentes-Garcia, M., Fujii, S., Fujimori, T., Fulda, P., Fyffe, M., Gadre, B., Gair, J. R., Galaudage, S., Galdi, V., Gallagher, H., Gallardo, S., Gallego, B., Gamba, R., Gamboa, A., Ganapathy, D., Ganguly, A., Garaventa, B., García-Bellido, J., Núñez, C. García, García-Quirós, C., Gardner, J. W., Gardner, K. A., Gargiulo, J., Garron, A., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., Gennari, V., George, J., George, R., Gerberding, O., Gergely, L., Ghosh, Archisman, Ghosh, Sayantan, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Suprovo, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gibson, D. T., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Glotin, F., Godfrey, J., Godwin, P., Goebbels, N. L., Goetz, E., Golomb, J., Lopez, S. Gomez, Goncharov, B., Gong, Y., González, G., Goodarzi, P., Goode, S., Goodwin-Jones, A. W., Gosselin, M., Göttel, A. S., Gouaty, R., Gould, D. W., Govorkova, K., Goyal, S., Grace, B., Grado, A., Graham, V., Granados, A. E., Granata, M., Granata, V., Gras, S., Grassia, P., Gray, A., Gray, C., Gray, R., Greco, G., Green, A. C., Green, S. M., Green, S. R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimaud, C., Grote, H., Guerra, D., Guetta, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H., Guo, W., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, Ish, Gupta, N. C., Gupta, P., Gupta, S. K., Gupta, T., Gupte, N., Gurs, J., Gutierrez, N., Guzman, F., H, H. -Y., Haba, D., Haberland, M., Haino, S., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hanselman, A. G., Hansen, H., Hanson, J., Harada, R., Hardison, A. R., Haris, K., Harmark, T., Harms, J., Harry, G. M., Harry, I. W., Hart, J., Haskell, B., Haster, C. -J., Hathaway, J. S., Haughian, K., Hayakawa, H., Hayama, K., Hayes, R., Heffernan, A., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Henderson-Sapir, O., Hendry, M., Heng, I. S., Hennes, E., Henshaw, C., Hertog, T., Heurs, M., Hewitt, A. L., Heyns, J., Higginbotham, S., Hild, S., Hill, S., Himemoto, Y., Hirata, N., Hirose, C., Hoang, S., Hochheim, S., Hofman, D., Holland, N. A., Holley-Bockelmann, K., Holmes, Z. J., Holz, D. E., Honet, L., Hong, C., Hornung, J., Hoshino, S., Hough, J., Hourihane, S., Howell, E. J., Hoy, C. G., Hrishikesh, C. A., Hsieh, H. -F., Hsiung, C., Hsu, H. C., Hsu, W. -F., Hu, P., Hu, Q., Huang, H. Y., Huang, Y. -J., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Husa, S., Huxford, R., Huynh-Dinh, T., Iampieri, L., Iandolo, G. A., Ianni, M., Iess, A., Imafuku, H., Inayoshi, K., Inoue, Y., Iorio, G., Iqbal, M. H., Irwin, J., Ishikawa, R., Isi, M., Ismail, M. A., Itoh, Y., Iwanaga, H., Iwaya, M., Iyer, B. R., JaberianHamedan, V., Jacquet, C., Jacquet, P. -E., Jadhav, S. J., Jadhav, S. P., Jain, T., James, A. L., James, P. A., Jamshidi, R., Janquart, J., Janssens, K., Janthalur, N. N., Jaraba, S., Jaranowski, P., Jaume, R., Javed, W., Jennings, A., Jia, W., Jiang, J., Kubisz, J., Johanson, C., Johns, G. R., Johnson, N. A., Johnston, M. C., Johnston, R., Johny, N., Jones, D. H., Jones, D. I., Jones, R., Jose, S., Joshi, P., Ju, L., Jung, K., Junker, J., Juste, V., Kajita, T., Kaku, I., Kalaghatgi, C., Kalogera, V., Kamiizumi, M., Kanda, N., Kandhasamy, S., Kang, G., Kanner, J. B., Kapadia, S. J., Kapasi, D. 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Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19th, during the LIGO-Virgo-KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered $\sim 14\%$ of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz where we assume the GW emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy $1 \times 10^{-5} M_{\odot} c^2$ and luminosity $4 \times 10^{-5} M_{\odot} c^2/\text{s}$ for a source emitting at 50 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as $1.04$, at frequencies above $1200$ Hz, surpassing results from SN 2019ejj., Comment: Main paper: 6 pages, 4 figures and 1 table. Total with appendices: 20 pages, 4 figures, and 1 table

Published
2024

12. SPHERE: Scaling Personalized Feedback in Programming Classrooms with Structured Review of LLM Outputs

Author

Tang, Xiaohang, Wong, Sam, Huynh, Marcus, He, Zicheng, Yang, Yalong, and Chen, Yan

Subjects
Computer Science - Human-Computer Interaction
Abstract

Effective personalized feedback is crucial for learning programming. However, providing personalized, real-time feedback in large programming classrooms poses significant challenges for instructors. This paper introduces SPHERE, an interactive system that leverages Large Language Models (LLMs) and structured LLM output review to scale personalized feedback for in-class coding activities. SPHERE employs two key components: an Issue Recommendation Component that identifies critical patterns in students' code and discussion, and a Feedback Review Component that uses a ``strategy-detail-verify'' approach for efficient feedback creation and verification. An in-lab, between-subject study demonstrates SPHERE's effectiveness in improving feedback quality and the overall feedback review process compared to a baseline system using off-the-shelf LLM outputs. This work contributes a novel approach to scaling personalized feedback in programming education, addressing the challenges of real-time response, issue prioritization, and large-scale personalization.

Published
2024

13. Whither Bias Goes, I Will Go: An Integrative, Systematic Review of Algorithmic Bias Mitigation

Author

Hickman, Louis, Huynh, Christopher, Gass, Jessica, Booth, Brandon, Kuruzovich, Jason, and Tay, Louis

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

Machine learning (ML) models are increasingly used for personnel assessment and selection (e.g., resume screeners, automatically scored interviews). However, concerns have been raised throughout society that ML assessments may be biased and perpetuate or exacerbate inequality. Although organizational researchers have begun investigating ML assessments from traditional psychometric and legal perspectives, there is a need to understand, clarify, and integrate fairness operationalizations and algorithmic bias mitigation methods from the computer science, data science, and organizational research literatures. We present a four-stage model of developing ML assessments and applying bias mitigation methods, including 1) generating the training data, 2) training the model, 3) testing the model, and 4) deploying the model. When introducing the four-stage model, we describe potential sources of bias and unfairness at each stage. Then, we systematically review definitions and operationalizations of algorithmic bias, legal requirements governing personnel selection from the United States and Europe, and research on algorithmic bias mitigation across multiple domains and integrate these findings into our framework. Our review provides insights for both research and practice by elucidating possible mechanisms of algorithmic bias while identifying which bias mitigation methods are legal and effective. This integrative framework also reveals gaps in the knowledge of algorithmic bias mitigation that should be addressed by future collaborative research between organizational researchers, computer scientists, and data scientists. We provide recommendations for developing and deploying ML assessments, as well as recommendations for future research into algorithmic bias and fairness., Comment: forthcoming in Journal of Applied Psychology

Published
2024
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14. Towards Designing Scalable Quantum-Enhanced Generative Networks for Neutrino Physics Experiments with Liquid Argon Time Projection Chambers

Author

Delgado, Andrea, Venegas-Vargas, Diego, Huynh, Adam, and Carroll, Kevon

Subjects
Quantum Physics, High Energy Physics - Experiment
Abstract

Generative modeling for high-resolution images in Liquid Argon Time Projection Chambers (LArTPC), used in neutrino physics experiments, presents significant challenges due to the complexity and sparsity of the data. This work explores the application of quantum-enhanced generative networks to address these challenges, focusing on the scaling of models to handle larger image sizes and avoid the often encountered problem of mode collapse. To counteract mode collapse, regularization methods were introduced and proved to be successful on small-scale images, demonstrating improvements in stabilizing the training process. Although mode collapse persisted in higher-resolution settings, the introduction of these techniques significantly enhanced the model's performance in lower-dimensional cases, providing a strong foundation for further exploration. These findings highlight the potential for quantum-enhanced generative models in LArTPC data generation and offer valuable insights for the future development of scalable hybrid quantum-classical solutions in nuclear and high-energy physics., Comment: 10 pages, 8 figures

Published
2024

15. Modal analysis of blood flows in saccular aneurysms

Author

Nguyen, Thien-Tam, Kasperski, Davina, Huynh, Phat Kim, Le, Trung Quoc, and Le, Trung Bao

Subjects
Physics - Fluid Dynamics
Abstract

Currently, it is challenging to investigate aneurismal hemodynamics based on current in-vivo data such as Magnetic Resonance Imaging or Computed Tomography due to the limitations in both spatial and temporal resolutions. In this work, we investigate the use of modal analysis at various resolutions to examine its usefulness for analyzing blood flows in brain aneurysms. Two variants of Dynamic Mode Decomposition (DMD): (i) Hankel-DMD; and (ii) Optimized-DMD, are used to extract the time-dependent dynamics of blood flows during one cardiac cycle. First, high-resolution hemodynamic data in patient-specific aneurysms are obtained using Computational Fluid Dynamics. Second, the dynamics modes, along with their spatial amplitudes and temporal magnitudes are calculated using the DMD analysis. Third, an examination of DMD analyses using a range of spatial and temporal resolutions of hemodynamic data to validate the applicability of DMD for low-resolution data, similar to ones in clinical practices. Our results show that DMD is able to characterize the inflow jet dynamics by separating large-scale structures and flow instabilities even at low spatial and temporal resolutions. Its robustness in quantifying the flow dynamics using the energy spectrum is demonstrated across different resolutions in all aneurysms in our study population. Our work indicates that DMD can be used for analyzing blood flow patterns of brain aneurysms and is a promising tool to be explored in in-vivo.

Published
2024

16. A search using GEO600 for gravitational waves coincident with fast radio bursts from SGR 1935+2154

Author

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abouelfettouh, I., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Agarwal, D., Agathos, M., Abchouyeh, M. Aghaei, Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Argianas, L., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Attadio, F., Aubin, F., AultONeal, K., Avallone, G., Azrad, D., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Baier, J. G., Baiotti, L., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartoletti, A. M., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bates, D. E., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Baynard II, P. A., Bazzan, M., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blagg, L. A., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boudon, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brandt, J., Braun, I., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, B. C., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cáceres-Barbosa, V., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannon, K. C., Cao, H., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, J. C. L., Chan, M., Chandra, K., Chang, R. -J., Chao, S., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, Y., Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Cheung, S. Y., Chiadini, F., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chugh, P., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciolfi, R., Clara, F., Clark, J. A., Clarke, J., Clarke, T. A., Clearwater, P., Clesse, S., Coccia, E., Codazzo, E., Cohadon, P. -F., Colace, S., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Connolly, G., Conti, L., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Crook, S., Crouch, R., Csizmazia, J., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Pra, S. Dal, Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Davari, N., Dave, I., Davenport, A., Davier, M., Davies, T. F., Davis, D., Davis, L., Davis, M. C., Davis, P. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., DePergola, N., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhani, A., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Dominguez, D., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Ducoin, J. -G., Dunn, L., Dupletsa, U., D'Urso, D., Duval, H., Duverne, P. -A., Dwyer, S. E., Eassa, C., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Einsle, H., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Eleveld, R. M., Emma, M., Endo, K., Engl, A. J., Enloe, E., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fairhurst, S., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Felicetti, R. ., Fenyvesi, E., Ferguson, D. L., Ferraiuolo, S., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. S., Floden, E., Foley, E. M., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franceschetti, K., Franchini, N., Frasca, S., Frasconi, F., Mascioli, A. Frattale, Frei, Z., Freise, A., Freitas, O., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fuentes-Garcia, M., Fujii, S., Fujimori, T., Fulda, P., Fyffe, M., Gadre, B., Gair, J. R., Galaudage, S., Galdi, V., Gallagher, H., Gallardo, S., Gallego, B., Gamba, R., Gamboa, A., Ganapathy, D., Ganguly, A., Garaventa, B., García-Bellido, J., Núñez, C. García, García-Quirós, C., Gardner, J. W., Gardner, K. A., Gargiulo, J., Garron, A., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., Gennari, V., George, J., George, R., Gerberding, O., Gergely, L., Ghonge, S., Ghosh, Archisman, Ghosh, Sayantan, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Suprovo, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gibson, D. T., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Glotin, F., Godfrey, J., Godwin, P., Goebbels, N. L., Goetz, E., Golomb, J., Lopez, S. Gomez, Goncharov, B., Gong, Y., González, G., Goodarzi, P., Goode, S., Goodwin-Jones, A. W., Gosselin, M., Göttel, A. S., Gouaty, R., Gould, D. W., Govorkova, K., Goyal, S., Grace, B., Grado, A., Graham, V., Granados, A. E., Granata, M., Granata, V., Gras, S., Grassia, P., Gray, A., Gray, C., Gray, R., Greco, G., Green, A. C., Green, S. M., Green, S. R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimaud, C., Grote, H., Guerra, D., Guetta, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H., Guo, W., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, Ish, Gupta, N. C., Gupta, P., Gupta, S. K., Gupta, T., Gupte, N., Gurs, J., Gutierrez, N., Guzman, F., H, H. -Y., Haba, D., Haberland, M., Haino, S., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hanselman, A. 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Y., Hui, V., Husa, S., Huxford, R., Huynh-Dinh, T., Iampieri, L., Iandolo, G. A., Ianni, M., Iess, A., Imafuku, H., Inayoshi, K., Inoue, Y., Iorio, G., Iqbal, M. H., Irwin, J., Ishikawa, R., Isi, M., Ismail, M. A., Itoh, Y., Iwanaga, H., Iwaya, M., Iyer, B. R., JaberianHamedan, V., Jacquet, C., Jacquet, P. -E., Jadhav, S. J., Jadhav, S. P., Jain, T., James, A. L., James, P. A., Jamshidi, R., Janquart, J., Janssens, K., Janthalur, N. N., Jaraba, S., Jaranowski, P., Jaume, R., Javed, W., Jennings, A., Jia, W., Jiang, J., Kubisz, J., Johanson, C., Johns, G. R., Johnson, N. A., Johnston, M. C., Johnston, R., Johny, N., Jones, D. H., Jones, D. I., Jones, R., Jose, S., Joshi, P., Ju, L., Jung, K., Junker, J., Juste, V., Kajita, T., Kaku, I., Kalaghatgi, C., Kalogera, V., Kamiizumi, M., Kanda, N., Kandhasamy, S., Kang, G., Kanner, J. B., Kapadia, S. J., Kapasi, D. 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M., Mirasola, L., Miravet-Tenés, M., Miritescu, C. -A., Mishra, A. K., Mishra, A., Mishra, C., Mishra, T., Mitchell, A. L., Mitchell, J. G., Mitra, S., Mitrofanov, V. P., Mittleman, R., Miyakawa, O., Miyamoto, S., Miyoki, S., Mo, G., Mobilia, L., Mohapatra, S. R. P., Mohite, S. R., Molina-Ruiz, M., Mondal, C., Mondin, M., Montani, M., Moore, C. J., Moraru, D., More, A., More, S., Moreno, G., Morgan, C., Morisaki, S., Moriwaki, Y., Morras, G., Moscatello, A., Mourier, P., Mours, B., Mow-Lowry, C. M., Muciaccia, F., Mukherjee, Arunava, Mukherjee, D., Mukherjee, Samanwaya, Mukherjee, Soma, Mukherjee, Subroto, Mukherjee, Suvodip, Mukund, N., Mullavey, A., Munch, J., Mundi, J., Mungioli, C. L., Oberg, W. R. Munn, Murakami, Y., Murakoshi, M., Murray, P. G., Muusse, S., Nabari, D., Nadji, S. L., Nagar, A., Nagarajan, N., Nagler, K. N., Nakagaki, K., Nakamura, K., Nakano, H., Nakano, M., Nandi, D., Napolano, V., Narayan, P., Nardecchia, I., Narola, H., Naticchioni, L., Nayak, R. K., Neilson, J., Nelson, A., Nelson, T. J. N., Nery, M., Neunzert, A., Ng, S., Quynh, L. Nguyen, Nichols, S. A., Nielsen, A. B., Nieradka, G., Niko, A., Nishino, Y., Nishizawa, A., Nissanke, S., Nitoglia, E., Niu, W., Nocera, F., Norman, M., North, C., Novak, J., Siles, J. F. Nuño, Nuttall, L. K., Obayashi, K., Oberling, J., O'Dell, J., Oertel, M., Offermans, A., Oganesyan, G., Oh, J. J., Oh, K., O'Hanlon, T., Ohashi, M., Ohkawa, M., Ohme, F., Oliveira, A. S., Oliveri, R., O'Neal, B., Oohara, K., O'Reilly, B., Ormsby, N. D., Orselli, M., O'Shaughnessy, R., O'Shea, S., Oshima, Y., Oshino, S., Ossokine, S., Osthelder, C., Ota, I., Ottaway, D. J., Ouzriat, A., Overmier, H., Owen, B. J., Pace, A. E., Pagano, R., Page, M. A., Pai, A., Pal, A., Pal, S., Palaia, M. A., Pálfi, M., Palma, P. P., Palomba, C., Palud, P., Pan, H., Pan, J., Pan, K. C., Panai, R., Panda, P. K., Pandey, S., Panebianco, L., Pang, P. T. H., Pannarale, F., Pannone, K. A., Pant, B. C., Panther, F. H., Paoletti, F., Paolone, A., Papalexakis, E. E., Papalini, L., Papigkiotis, G., Paquis, A., Parisi, A., Park, B. -J., Park, J., Parker, W., Pascale, G., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passenger, L., Passuello, D., Patane, O., Pathak, D., Pathak, M., Patra, A., Patricelli, B., Patron, A. S., Paul, K., Paul, S., Payne, E., Pearce, T., Pedraza, M., Pegna, R., Pele, A., Arellano, F. E. Peña, Penn, S., Penuliar, M. D., Perego, A., Pereira, Z., Perez, J. J., Périgois, C., Perna, G., Perreca, A., Perret, J., Perriès, S., Perry, J. W., Pesios, D., Petracca, S., Petrillo, C., Pfeiffer, H. P., Pham, H., Pham, K. A., Phukon, K. S., Phurailatpam, H., Piarulli, M., Piccari, L., Piccinni, O. J., Pichot, M., Piendibene, M., Piergiovanni, F., Pierini, L., Pierra, G., Pierro, V., Pietrzak, M., Pillas, M., Pilo, F., Pinard, L., Pinto, I. M., Pinto, M., Piotrzkowski, B. J., Pirello, M., Pitkin, M. D., Placidi, A., Placidi, E., Planas, M. L., Plastino, W., Poggiani, R., Polini, E., Pompili, L., Poon, J., Porcelli, E., Porter, E. K., Posnansky, C., Poulton, R., Powell, J., Pracchia, M., Pradhan, B. K., Pradier, T., Prajapati, A. K., Prasai, K., Prasanna, R., Prasia, P., Pratten, G., Principe, G., Principe, M., Prodi, G. A., Prokhorov, L., Prosposito, P., Puecher, A., Pullin, J., Punturo, M., Puppo, P., Pürrer, M., Qi, H., Qin, J., Quéméner, G., Quetschke, V., Quigley, C., Quinonez, P. J., Quitzow-James, R., Raab, F. J., Raabith, S. S., Raaijmakers, G., Raja, S., Rajan, C., Rajbhandari, B., Ramirez, K. E., Vidal, F. A. Ramis, Ramos-Buades, A., Rana, D., Ranjan, S., Ransom, K., Rapagnani, P., Ratto, B., Rawat, S., Ray, A., Raymond, V., Razzano, M., Read, J., Payo, M. Recaman, Regimbau, T., Rei, L., Reid, S., Reitze, D. H., Relton, P., Renzini, A. I., Rettegno, P., Revenu, B., Reyes, R., Rezaei, A. S., Ricci, F., Ricci, M., Ricciardone, A., Richardson, J. W., Richardson, M., Rijal, A., Riles, K., Riley, H. K., Rinaldi, S., Rittmeyer, J., Robertson, C., Robinet, F., Robinson, M., Rocchi, A., Rolland, L., Rollins, J. G., Romano, A. E., Romano, R., Romero, A., Romero-Shaw, I. M., Romie, J. H., Ronchini, S., Roocke, T. J., Rosa, L., Rosauer, T. J., Rose, C. A., Rosińska, D., Ross, M. P., Rossello, M., Rowan, S., Roy, S. K., Roy, S., Rozza, D., Ruggi, P., Ruhama, N., Morales, E. Ruiz, Ruiz-Rocha, K., Sachdev, S., Sadecki, T., Sadiq, J., Saffarieh, P., Sah, M. R., Saha, S. S., Saha, S., Sainrat, T., Menon, S. Sajith, Sakai, K., Sakellariadou, M., Sakon, S., Salafia, O. S., Salces-Carcoba, F., Salconi, L., Saleem, M., Salemi, F., Sallé, M., Salvador, S., Sanchez, A., Sanchez, E. J., Sanchez, J. H., Sanchez, L. E., Sanchis-Gual, N., Sanders, J. R., Sänger, E. M., Santoliquido, F., Saravanan, T. R., Sarin, N., Sasaoka, S., Sasli, A., Sassi, P., Sassolas, B., Satari, H., Sato, R., Sato, Y., Sauter, O., Savage, R. L., Sawada, T., Sawant, H. L., Sayah, S., Scacco, V., Schaetzl, D., Scheel, M., Schiebelbein, A., Schiworski, M. G., Schmidt, P., Schmidt, S., Schnabel, R., Schneewind, M., Schofield, R. M. S., Schouteden, K., Schulte, B. W., Schutz, B. F., Schwartz, E., Scialpi, M., Scott, J., Scott, S. M., Seetharamu, T. C., Seglar-Arroyo, M., Sekiguchi, Y., Sellers, D., Sengupta, A. S., Sentenac, D., Seo, E. G., Seo, J. W., Sequino, V., Serra, M., Servignat, G., Sevrin, A., Shaffer, T., Shah, U. S., Shaikh, M. A., Shao, L., Sharma, A. K., Sharma, P., Sharma-Chaudhary, S., Shaw, M. R., Shawhan, P., Shcheblanov, N. S., Sheridan, E., Shikano, Y., Shikauchi, M., Shimode, K., Shinkai, H., Shiota, J., Shoemaker, D. H., Shoemaker, D. M., Short, R. W., ShyamSundar, S., Sider, A., Siegel, H., Sieniawska, M., Sigg, D., Silenzi, L., Simmonds, M., Singer, L. P., Singh, A., Singh, D., Singh, M. K., Singh, S., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. E., Smith, W. J., Soldateschi, J., Somiya, K., Song, I., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Sotani, H., Soulard, R., Southgate, A., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Spoon, J. B., Sprague, C. A., Srivastava, A. K., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., StPierre, M., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Suleiman, L., Sullivan, K. D., Sun, L., Sunil, S., Suresh, J., Sutton, P. J., Suzuki, T., Suzuki, Y., Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takase, T., Takatani, K., Takeda, H., Takeshita, K., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tang, D., Tanioka, S., Tanner, D. B., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, P., Tiwari, S., Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trapananti, A., Travasso, F., Traylor, G., Trevor, M., Tringali, M. C., Tripathee, A., Troian, G., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Uchiyama, T., Udall, R. P., Uehara, T., Uematsu, M., Ueno, K., Ueno, S., Undheim, V., Ushiba, T., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Van Hove, P., VanKeuren, M., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varghese, J. J., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vives, A., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Wajid, A., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B. R., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Wilkin, A. T., Willadsen, D. J., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Winterflood, J., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wuchner, E., Wysocki, D. M., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, Y., Yarbrough, Z., Yasui, H., Yeh, S. -W., Yelikar, A. B., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuan, S., Yuzurihara, H., Zadrożny, A., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, X. -J., Zhu, Z. -H., Zucker, M. E., and Zweizig, J.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by CHIME/FRB and STARE2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations' O3 observing run. Here we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by CHIME/FRB, as well as X-ray glitches and X-ray bursts detected by NICER and NuSTAR close to the time of one of the FRBs. We do not detect any significant GW emission from any of the events. Instead, using a short-duration GW search (for bursts $\leq$ 1 s) we derive 50\% (90\%) upper limits of $10^{48}$ ($10^{49}$) erg for GWs at 300 Hz and $10^{49}$ ($10^{50}$) erg at 2 kHz, and constrain the GW-to-radio energy ratio to $\leq 10^{14} - 10^{16}$. We also derive upper limits from a long-duration search for bursts with durations between 1 and 10 s. These represent the strictest upper limits on concurrent GW emission from FRBs., Comment: 15 pages of text including references, 4 figures, 5 tables

Published
2024

17. Inferring birth versus death dynamics for ecological interactions in stochastic heterogeneous populations

Author

Beckman, Erin, Cho, Heyrim, and Huynh, Linh

Subjects
Quantitative Biology - Populations and Evolution, 92-08, 92D25
Abstract

In this paper, we study the significance of ecological interactions and separation of birth and death dynamics in heterogeneous stochastic populations. Interactions can manifest through the birth dynamics, the death dynamics, or some combination of the two. The underlying mechanisms are important but often implicit in data. We propose an inference method for disambiguating the types of interaction and the birth and death processes from time series data of a stochastic n-type heterogeneous population. The interspecies interactions considered can be competitive, antagonistic, or mutualistic. The inference method is then validated in the example of two-type Lotka-Volterra dynamics. Utilizing stochastic fluctuations enables us to estimate additional parameters in a stochastic Lotka-Volterra model, which are not identifiable in a deterministic model. We also show that different pairs of birth and death rates with the same net growth rate manifest different time series statistics and survival probabilities., Comment: 31 pages, 7 figures

Published
2024

18. Active and Passive Beamforming Designs for SER Minimization in RIS-Assisted MIMO Systems

Author

Van Chien, Trinh, Duc, Bui Trong, Luong, Ho Viet Duc, Binh, Huynh Thi Thanh, Ngo, Hien Quoc, and Chatzinotas, Symeon

Subjects
Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing
Abstract

This research exploits the applications of reconfigurable intelligent surface (RIS)-assisted multiple input multiple output (MIMO) systems, specifically addressing the enhancement of communication reliability with modulated signals. Specifically, we first derive the analytical downlink symbol error rate (SER) of each user as a multivariate function of both the phase-shift and beamforming vectors. The analytical SER enables us to obtain insights into the synergistic dynamics between the RIS and MIMO communication. We then introduce a novel average SER minimization problem subject to the practical constraints of the transmitted power budget and phase shift coefficients, which is NP-hard. By incorporating the differential evolution (DE) algorithm as a pivotal tool for optimizing the intricate active and passive beamforming variables in RIS-assisted communication systems, the non-convexity of the considered SER optimization problem can be effectively handled. Furthermore, an efficient local search is incorporated into the DE algorithm to overcome the local optimum, and hence offer low SER and high communication reliability. Monte Carlo simulations validate the analytical results and the proposed optimization framework, indicating that the joint active and passive beamforming design is superior to the other benchmarks., Comment: 16 pages, 13 figures, and 1 table . Accepted by TWC

Published
2024

19. Sparsity of Fourier mass of passively advected scalars in the Batchelor regime

Author

Blumenthal, Alex and Huynh, Manh Khang

Subjects
Mathematics - Dynamical Systems, Mathematics - Analysis of PDEs, 37A25, 76F20, 37D20
Abstract

In 1959, Batchelor gave a prediction for the power spectral density of a passive scalar advected by an incompressible fluid exhibiting shear-straining, a mechanism for the creation of small scales in the scalar [Bat59]. Recently, a `cumulative' version of this law, summing over Fourier modes below a given wavenumber $N$, was given for a broad class of passive scalars under incompressible advection, including by solutions to the stochastic Navier-Stokes equations [BBPS22c]. This paper addresses to what extent Fourier mass of such passive scalars truly saturates the predicted power law scaling due to Batchelor. Via discrete-time pulsed-diffusion models of the advection-reaction equations, we exhibit situations compatible with the cumulative law but for which the distribution of Fourier mass among wavenumbers $|k| \leq N$ is relatively \emph{sparse} and much smaller than a `mode-wise' version of Batchelor's original prediction. In the same situations we also establish an `exponential radial shell' version of Batchelor's laws via a novel application of the method of spectral distributions., Comment: 41 pages. Comments welcome, especially with regards to references!

Published
2024

20. Some line and conic arrangements and their Waldschmidt constants

Author

Huynh, Dinh Tuan, Linh, Tran N. K., and Long, Le Ngoc

Subjects
Mathematics - Combinatorics, Mathematics - Algebraic Geometry
Abstract

We study the Waldschmidt constant of some configurations in the projective plane. In the first part, we show that the Waldschmidt constant of a set $\mathbb{X}$ of $n$ points where at least $n-3$ points among them lie on a line is either equal to $1, \frac{2n-3}{n-1}, 2, \frac{16}{7}, \frac{7}{3}, \frac{17}{7},$ or $\frac{5}{2}$. Together with the Hilbert polynomials, this gives a complete geometric characterization for $\mathbb{X}$. Next, we study some specific configurations whose Waldschmidt constants are bounded from above by $\frac{5}{2}$. Under this condition, we describe all configurations of $n$ points with $n-1$ points among them lying on an irreducible conic, and we also study some specific configurations of $9$ points., Comment: 23 pages, 22 figures

Published
2024

21. Spanning disks in triangulations of surfaces

Author

Clinch, Katie, Dewar, Sean, Fuladi, Niloufar, Gorsky, Maximilian, Huynh, Tony, Kastis, Eleftherios, Nixon, Anthony, and Servatius, Brigitte

Subjects
Mathematics - Combinatorics, Computer Science - Discrete Mathematics, 57Kxx, 05C10, G.2.2
Abstract

Given a triangulation $G$ of a surface $\mathbb{D}$, a spanning disk is a disk $\mathbb{D} \subseteq \mathbb{S}$ containing all the vertices of $G$ such that the boundary of $\mathbb{D}$ is a cycle of $G$. In this paper, we consider the question of when a triangulation of a surface contains a spanning disk. We give a very short proof that every triangulation of the torus contains a spanning disk, which strengthens a theorem of Nevo and Tarabykin. For arbitrary surfaces, we prove that triangulations with sufficiently high facewidth always contain spanning disks. Finally, we exhibit triangulations which do not have spanning disks. This shows that a minimum facewidth condition is necessary. Our results are motivated by and have applications for rigidity questions in the plane., Comment: 7 pages, 2 figures

Published
2024

22. A variational quantum algorithm by Bayesian Inference with von Mises-Fisher distribution

Author

Huynh, Trung, An, Gwangil, Kim, Minsu, Jeon, Yu-Seong, and Lee, Jinhyoung

Subjects
Quantum Physics
Abstract

The variational quantum eigensolver algorithm has gained attentions due to its capability of locating the ground state and ground energy of a Hamiltonian, which is a fundamental task in many physical and chemical problems. Although it has demonstrated promising results, the use of various types of measurements remains a significant obstacle. Recently, a quantum phase estimation algorithm inspired measurement scheme has been proposed to overcome this issue by introducing an additional ancilla system that is coupled to the primary system. Based on this measurement scheme, we present a novel approach that employs Bayesian inference principles together with von Mises-Fisher distribution and theoretically demonstrates the new algorithm's capability in identifying the ground state with certain for various random Hamiltonian matrices. This also opens a new way for exploring the von Mises-Fisher distribution potential in other quantum information science problems.

Published
2024

23. FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization

Author

Nguyen, Manh Duong, Nguyen, Trung Thanh, Pham, Huy Hieu, Hoang, Trong Nghia, Nguyen, Phi Le, and Huynh, Thanh Trung

Subjects
Computer Science - Machine Learning, Computer Science - Multimedia
Abstract

Federated Learning (FL) is a method for training machine learning models using distributed data sources. It ensures privacy by allowing clients to collaboratively learn a shared global model while storing their data locally. However, a significant challenge arises when dealing with missing modalities in clients' datasets, where certain features or modalities are unavailable or incomplete, leading to heterogeneous data distribution. While previous studies have addressed the issue of complete-modality missing, they fail to tackle partial-modality missing on account of severe heterogeneity among clients at an instance level, where the pattern of missing data can vary significantly from one sample to another. To tackle this challenge, this study proposes a novel framework named FedMAC, designed to address multi-modality missing under conditions of partial-modality missing in FL. Additionally, to avoid trivial aggregation of multi-modal features, we introduce contrastive-based regularization to impose additional constraints on the latent representation space. The experimental results demonstrate the effectiveness of FedMAC across various client configurations with statistical heterogeneity, outperforming baseline methods by up to 26% in severe missing scenarios, highlighting its potential as a solution for the challenge of partially missing modalities in federated systems., Comment: The 22nd International Symposium on Network Computing and Applications (NCA 2024)

Published
2024

24. A Smart Chair for Health Monitoring in Daily Life

Author

Huong, Nguyen Thi Minh, Bao, Vo Quoc, Hau, Nguyen Trung, and Linh, Huynh Quang

Subjects
Computer Science - Human-Computer Interaction, Electrical Engineering and Systems Science - Signal Processing
Abstract

Recent research has focused on the risks associated with poor sitting posture and the impact of sitting on biological parameters, such as heart rate because prolonged sitting is common across all ages and professions. In this work, we propose a novel approach that can display simultaneously posture and heart rate in real-time. In this device, pressure sensors are embedded into a flexible separate cushion easily put on any chair to provide sitting behaviours and a smartwatch-like PPG module is worn on the user's wrist. Regarding posture classification, pressure figures of ten pressure sensors under the seat bottom are inputs of four machine learning models, giving a high accuracy of 99 per cent. Besides, the Electrocardiography recording module is illustrated with the same results as a commercial device called DFRobot. Another advantage of this smart chair is that it not only simultaneously displays both sitting postures and heart rates on external devices like laptops, mobile phones, or televisions through microcontrollers but also offers the relationship between them to help people adjust their sitting behaviours, avoiding influencing heart rate. The smart chair is expected to be useful equipment for people with a sedentary lifestyle, especially office workers.

Published
2024

25. A simple geometric construction of an ODE with undecidable blow-ups

Author

Huynh, Manh Khang

Subjects
Mathematics - Dynamical Systems, Mathematics - Classical Analysis and ODEs
Abstract

We present a simple construction of an ODE on $\mathbb{R}^{n}$ where the vector field is smooth, and finite-time blow-up is equivalent to the halting problem for a universal Turing machine., Comment: 1 figure

Published
2024

26. Hexahedral mesh of anatomical atlas for construction of computational human brain models: Applications to modeling biomechanics and bioelectric field propagation

Author

Huynh, Andy, Zwick, Benjamin, Jamshidian, Mostafa, Halle, Michael, Wittek, Adam, and Miller, Karol

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

Numerical simulations rely on constructing accurate and detailed models to produce reliable results - a task that is often challenging. This task becomes notably more difficult when the model is of the human brain. We create an anatomically comprehensive hexahedral mesh of the human brain using an open-source digital brain atlas. Digital atlases are valuable tools currently used by medical professionals, medical students, and researchers for gathering, presenting, and discovering knowledge about the human brain. We demonstrate that the atlas can be used to efficiently create an accurate and detailed hexahedral finite element mesh of the brain for scientific computing. We present two case studies. The first case study constructs a biomechanical model of the brain to compute brain deformations and predict traumatic brain injury risk due to violent impact. In the second case study, we construct a bioelectrical model of the brain to solve the electroencephalography (EEG) forward problem, a frequent simulation process used in electrophysiology to study electromagnetic fields generated by the nervous system. We demonstrate efficient and accurate model construction using the meshed anatomical brain atlas, as well as emphasize the importance of effective communication and contextual analysis of results for enabling multi-disciplinary scientific computing research.

Published
2024

27. Contrastive ground-level image and remote sensing pre-training improves representation learning for natural world imagery

Author

Huynh, Andy V., Gillespie, Lauren E., Lopez-Saucedo, Jael, Tang, Claire, Sikand, Rohan, and Expósito-Alonso, Moisés

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Multimodal image-text contrastive learning has shown that joint representations can be learned across modalities. Here, we show how leveraging multiple views of image data with contrastive learning can improve downstream fine-grained classification performance for species recognition, even when one view is absent. We propose ContRastive Image-remote Sensing Pre-training (CRISP)$\unicode{x2014}$a new pre-training task for ground-level and aerial image representation learning of the natural world$\unicode{x2014}$and introduce Nature Multi-View (NMV), a dataset of natural world imagery including $>3$ million ground-level and aerial image pairs for over 6,000 plant taxa across the ecologically diverse state of California. The NMV dataset and accompanying material are available at hf.co/datasets/andyvhuynh/NatureMultiView., Comment: Accepted to ECCV 2024

Published
2024

28. Parameter Estimation for the Reduced Fracture Model via a Direct Filter Method

Author

Huynh, Phuoc Toan, Bao, Feng, and Hoang, Thi-Thao-Phuong

Subjects
Mathematics - Numerical Analysis, Mathematics - Probability
Abstract

In this work, we present a numerical method that provides accurate real-time detection for the widths of the fractures in a fractured porous medium based on observational data on porous medium fluid mass and velocity. To achieve this task, an inverse problem is formulated by first constructing a forward formulation based on the reduced fracture model of the diffusion equation. A parameter estimation problem is then performed online by utilizing a direct filter method. Numerical experiments are carried out to demonstrate the accuracy of our method in approximating the target parameters.

Published
2024

29. Experimental observation of ballistic to diffusive transition in AlN thin films

Author

Hoque, Md Shafkat Bin, Liao, Michael E., Zare, Saman, Liu, Zeyu, Koh, Yee Rui, Huynh, Kenny, Shi, Jingjing, Graham, Samuel, Luo, Tengfei, Ahmad, Habib, Doolittle, W. Alan, Goorsky, Mark S., and Hopkins, Patrick E.

Subjects
Physics - Applied Physics
Abstract

Bulk AlN possesses high thermal conductivity due to long phonon mean-free-paths, high group velocity, and long lifetimes. However, the thermal transport scenario becomes very different in a thin AlN film due to phonon-defect and phonon-boundary scattering. Herein, we report experimental observation of ballistic to diffusive transition in a series of AlN thin films (1.6 - 2440 nm) grown on sapphire substrates. The ballistic transport is characterized by constant thermal resistance as a function of film thickness due to phonon scattering by defects and boundaries. In this transport regime, phonons possess very small group velocities and lifetimes. The lifetime of the optical phonons increases by more than an order of magnitude in the diffusive regime, however, remains nearly constant afterwards. Our study is important for understanding the details of nano and microscale thermal transport in a highly conductive material.

Published
2024

30. Spin Dynamics in Hybrid Halide Perovskites -- Effect of Dynamical and Permanent Symmetry Breaking

Author

Li, Kejun, Xu, Junqing, Huynh, Uyen N., Bodin, Rikard, Gupta, Mayank, Multunas, Christian, Simoni, Jacopo, Sundararaman, Ravishankar, Verdany, Zeev Valy, and Ping, Yuan

Subjects
Condensed Matter - Materials Science
Abstract

The hybrid organic-inorganic halide perovskite (HOIP), for example MAPbBr3, exhibits extended spin lifetime and apparent spin lifetime anisotropy in experiments. The underlying mechanisms of these phenomena remain illusive. By utilizing our first-principles densitymatrix dynamics approach with quantum scatterings including electron-phonon and electronelectron interactions and self-consistent spinorbit coupling, we present temperature- and magnetic field-dependent spin lifetimes in hybrid perovskites, in agreement with experimental observations. For centrosymmetric hybrid perovskite MAPbBr3, the experimentally observed spin lifetime anisotropy is mainly attributed to the dynamical Rashba effect arising from the interaction between organic and inorganic components and the rotation of the organic cation. For noncentrosymmetric perovskite, such as MPSnBr3, we found persistent spin helix texture at the conduction band minimum, which significantly enhances the spin lifetime anisotropy. Our study provides theoretical insight to spin dynamics in HOIP and strategies for controlling and optimizing spin transport.

Published
2024

31. SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems

Author

Islam, H M Mohaimanul, Vo, Huynh Q. N., and Ramanan, Paritosh

Subjects
Computer Science - Machine Learning, Mathematics - Optimization and Control, Statistics - Methodology
Abstract

Stochastic optimization problems in large-scale multi-stakeholder networked systems (e.g., power grids and supply chains) rely on data-driven scenarios to encapsulate complex spatiotemporal interdependencies. However, centralized aggregation of stakeholder data is challenging due to the existence of data silos resulting from computational and logistical bottlenecks. In this paper, we present SplitVAEs, a decentralized scenario generation framework that leverages variational autoencoders to generate high-quality scenarios without moving stakeholder data. With the help of experiments on distributed memory systems, we demonstrate the broad applicability of SplitVAEs in a variety of domain areas that are dominated by a large number of stakeholders. Our experiments indicate that SplitVAEs can learn spatial and temporal interdependencies in large-scale networks to generate scenarios that match the joint historical distribution of stakeholder data in a decentralized manner. Our experiments show that SplitVAEs deliver robust performance compared to centralized, state-of-the-art benchmark methods while significantly reducing data transmission costs, leading to a scalable, privacy-enhancing alternative to scenario generation., Comment: This work has been submitted to the IEEE for possible publication

Published
2024

32. The Lynchpin of In-Memory Computing: A Benchmarking Framework for Vector-Matrix Multiplication in RRAMs

Author

Chowdhury, Md Tawsif Rahman, Vo, Huynh Quang Nguyen, Ramanan, Paritosh, Yildirim, Murat, and Tutuncuoglu, Gozde

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Emerging Technologies, Electrical Engineering and Systems Science - Systems and Control
Abstract

The Von Neumann bottleneck, a fundamental challenge in conventional computer architecture, arises from the inability to execute fetch and data operations simultaneously due to a shared bus linking processing and memory units. This bottleneck significantly limits system performance, increases energy consumption, and exacerbates computational complexity. Emerging technologies such as Resistive Random Access Memories (RRAMs), leveraging crossbar arrays, offer promising alternatives for addressing the demands of data-intensive computational tasks through in-memory computing of analog vector-matrix multiplication (VMM) operations. However, the propagation of errors due to device and circuit-level imperfections remains a significant challenge. In this study, we introduce MELISO (In-Memory Linear Solver), a comprehensive end-to-end VMM benchmarking framework tailored for RRAM-based systems. MELISO evaluates the error propagation in VMM operations, analyzing the impact of RRAM device metrics on error magnitude and distribution. This paper introduces the MELISO framework and demonstrates its utility in characterizing and mitigating VMM error propagation using state-of-the-art RRAM device metrics., Comment: ICONS 2024.Copyright 2024 IEEE.Personal use of this material is permitted.Permission from IEEE must be obtained for all other uses,in any current or future media,including reprinting/republishing this material for advertising or promotional purposes,creating new collective works,for resale or redistribution to servers or lists or reuse of any copyrighted component of this work in other works

Published
2024

33. The Giant Radio Array for Neutrino Detection (GRAND) Collaboration -- Contributions to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024)

Author

Batista, Rafael Alves, Benoit-Lévy, Aurélien, Bister, Teresa, Bohacova, Martina, Bustamante, Mauricio, Carvalho, Washington, Chen, Yiren, Cheng, LingMei, Chiche, Simon, Colley, Jean-Marc, Correa, Pablo, Laurenciu, Nicoleta Cucu, Dai, Zigao, de Almeida, Rogerio M., de Errico, Beatriz, de Jong, Sijbrand, Neto, João R. T. de Mello, de Vries, Krijn D, Decoene, Valentin, Denton, Peter B., Duan, Bohao, Duan, Kaikai, Engel, Ralph, Erba, William, Fan, Yizhong, Ferrière, Arsène, Gou, QuanBu, Gu, Junhua, Guelfand, Marion, Guo, Jianhua, Guo, Yiqing, Guépin, Claire, Gülzow, Lukas, Haungs, Andreas, Havelka, Matej, He, Haoning, Hivon, Eric, Hu, Hongbo, Huang, Xiaoyuan, Huang, Yan, Huege, Tim, Jiang, Wen, Koirala, Ramesh, Kong, ChuiZheng, Kotera, Kumiko, Köhler, Jelena, Lago, Bruno L., Lai, Zhisen, Coz, Sandra Le, Legrand, François, Leisos, Antonios, Li, Rui, Li, Xingyu, Li, YiFei, Liu, Cheng, Liu, Ruoyu, Liu, Wei, Ma, Pengxiong, Macias, Oscar, Magnard, Frédéric, Marcowith, Alexandre, Martineau-Huynh, Olivier, McKinley, Thomas, Minodier, Paul, Mitra, Pragati, Mostafá, Miguel, Murase, Kohta, Niess, Valentin, Nonis, Stavros, Ogio, Shoichi, Oikonomou, Foteini, Pan, Hongwei, Papageorgiou, Konstantinos, Pierog, Tanguy, Piotrowski, Lech Wiktor, Prunet, Simon, Qian, Xiangli, Roth, Markus, Sako, Takashi, Schoorlemmer, Harm, Szálas-Motesiczky, Dániel, Sławiński, Szymon, Tian, Xishui, Timmermans, Anne, Timmermans, Charles, Tobiska, Petr, Tsirigotis, Apostolos, Tueros, Matías, Vittakis, George, Wang, Hanrui, Wang, Jiale, Wang, Shen, Wang, Xiangyu, Wang, Xu, Wei, Daming, Wei, Feng, Wu, Xiangping, Wu, Xuefeng, Xu, Xin, Xu, Xing, Yang, Fufu, Yang, Lili, Yang, Xuan, Yuan, Qiang, Zarka, Philippe, Zeng, Houdun, Zhang, Chao, Zhang, Jianli, Zhang, Kewen, Zhang, Pengfei, Zhang, Qingchi, Zhang, Songbo, Zhang, Yi, Zhou, Hao, Wissel, Stephanie, Zeolla, Andrew, Deaconu, Cosmin, Hughes, Kaeli, Martin, Zachary, Mulrey, Katharine, Cummings, Austin, Krömer, Oliver, Plant, Kathryn, and Schroeder, Frank G.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Experiment, High Energy Physics - Phenomenology
Abstract

This is an index of the contributions by the Giant Radio Array for Neutrino Detection (GRAND) Collaboration to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024, University of Chicago, June 11-14, 2024). The contributions include an overview of GRAND in its present and future incarnations, methods of radio-detection that are being developed for them, and ongoing joint work between the GRAND and BEACON experiments., Comment: Note: To access the list of contributions, please follow the "HTML" link that can be found on the arXiv page

Published
2024

34. Development of an Autonomous Detection-Unit Self-Trigger for GRAND

Author

Correa, Pablo, Colley, Jean-Marc, Huege, Tim, Kotera, Kumiko, Coz, Sandra Le, Martineau-Huynh, Olivier, Roth, Markus, and Tian, Xishui

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, High Energy Physics - Experiment
Abstract

One of the major challenges for the radio detection of extensive air showers, as encountered by the Giant Radio Array for Neutrino Detection (GRAND), is the requirement of an autonomous radio self-trigger. This work presents the current development of self-triggering techniques at the detection-unit level -- the so-called first-level trigger (FLT) -- in the context of the NUTRIG project. A second-level trigger (SLT) at the array level is described in a separate contribution. Two FLT methods are described, based on a template-fitting algorithm and a convolutional neural network (CNN). In this work, we compare the preliminary offline performance of both FLT methods in terms of signal selection efficiency and background rejection efficiency. We find that for both methods, ${\gtrsim}40\%$ of the background can be rejected if a signal selection efficiency of 90\% is required at the $5\sigma$ level., Comment: Presented at the 10th International Workshop on Acoustic and Radio EeV Neutrino Activities (ARENA2024). 8 pages, 4 figures

Published
2024

35. The Need for Values Education Programs for Both Pre-Service Teachers and In-Service Teachers in Vietnam

Author

Quynh Thi Nhu Nguyen and Son Van Huynh

Abstract

This paper investigates the perceptions of pre-service teachers and in-service teachers regarding the concept of 'values education' and the preparation of values education in professional programs in Vietnam. Furthermore, the pedagogical strategies employed by teachers in addressing values education situations in their classrooms are explored. A qualitative research design was employed, involving interviews with forty-three pre-service teachers from a university. Additionally, eighteen teachers from two different high schools were interviewed, and their teaching sessions, along with school assemblies, were observed. The findings reveal that values education is primarily understood as the instruction of living skills and moral education. There is a lack of conceptualization and knowledge regarding values education among the participants. Both preservice teachers and in-service teachers did not receive formal values education programs during their university education, and the support provided by schools in this regard was limited. Teachers tended to integrate values education into subjects randomly and subjectively, without employing advanced strategies. Inconsistencies in implementation and challenges associated with teaching values were observed. Highlighting an urgent necessity, this article emphasizes the imperative need for values education programs tailored for both pre-service and in-service teachers in Vietnam. It underscores the significance of adequately equipping teachers to effectively address values education in their classroom.

Published
2024

36. Exploring the Integration of the Happy School Model in Vietnamese Higher Education: Insights and Implications from the Perspectives of Tertiary EFL Teachers

Author

Nguyen Anh Thi, Le Thanh Thao, Phuong Hoang Yen, Pham Trut Thuy, Huynh Thi Anh Thu, and Nguyen Huong Tra

Abstract

This qualitative study explored the possibility of implementing the happy school model (HSM) in the context of Vietnamese higher education, with a focus on the socio-cultural perspectives of nine tertiary English as a foreign language (EFL) teachers at different career stages. Through semi-structured interviews, thematic analysis, and theoretical underpinning by constructivist paradigm and humanistic education theory, the study illuminated multifaceted insights. Key themes emerged, including aligning the HSM with holistic student development, recognizing challenges and potential benefits, balancing traditional Confucian values, and adapting the model to Vietnam's unique socio-cultural and economic landscape. The findings provide valuable guidance for educational innovation in Vietnam, highlighting complexities of aligning a new educational paradigm with existing practices and cultural norms. While the study's focus on a specific cultural context and limited participant pool presents certain limitations, the insights offer rich contributions to the broader global dialogue on education and human development. Future research directions and practical implications are also discussed, making this study a valuable resource for educators, policymakers, and researchers interested in the intersection of universal educational principles and specific cultural contexts like Vietnam.

Published
2024

37. Teachers' Continuous Professional Development through Lesson Study: Voices of Vietnamese EFL Teachers at University

Author

Anh-Thi Nguyen, Huong-Tra Nguyen, Hoang-Yen Phuong, Thanh-Thao Le, Trut-Thuy Pham, and Anh-Thu Huynh-Thi

Abstract

This study investigated how teachers perceived the benefits and difficulties of implementing a Lesson Study (LS) in the context of English language teaching at a university in Vietnam. The data were collected from six teachers through semi-structured interviews. To analyze the data, two addressing themes (teachers' perceived benefits and teachers' perceived difficulties) were focused on while implementing LS. The results generally showed that the teachers believed that LS brought them three benefits: encouraging teachers' exchange activities and helping them deepen knowledge of lesson content, understanding students' learning activities and working processes, and promoting teachers' motivation in pursuing their continuous professional development (CPD). Regarding teachers' perceived difficulties in LS implementation, they reported lacking confidence in actively implementing LS activities. Additionally, they had difficulties in time management and encountered problems reaching a consensus for joint work due to the influence of muti-faceted aspects. Accordingly, the study provides pedagogical implications for related stakeholders (teachers, students, and policymakers) regarding LS implementation issues contributing to the success of LS implementation in Vietnam and other similar contexts.

Published
2024

38. Imaging hot photocarrier transfer across a semiconductor heterojunction with ultrafast electron microscopy.

Author

Shaheen, Basamat, Huynh, Kenny, Quan, Yujie, Choudhry, Usama, Gnabasik, Ryan, Xiang, Zeyu, Goorsky, Mark, and Liao, Bolin

Subjects
interfacial transport, photocarrier dynamics, semiconductor heterojunction, ultrafast electron microscopy
Abstract

Semiconductor heterojunctions have gained significant attention for efficient optoelectronic devices owing to their unique interfaces and synergistic effects. Interaction between charge carriers with the heterojunction plays a crucial role in determining device performance, while its spatial-temporal mapping remains lacking. In this study, we employ scanning ultrafast electron microscopy (SUEM), an emerging technique that combines high spatial-temporal resolution and surface sensitivity, to investigate photocarrier dynamics across a Si/Ge heterojunction. Charge dynamics are selectively examined across the junction and compared to far bulk areas, through which the impact of the built-in potential, band offsets, and surface effects is directly visualized. In particular, we find that the heterojunction drastically modifies the hot photocarrier diffusivities in both Si and Ge regions due to charge trapping. These findings are further elucidated with insights from the band structure and surface potential measured by complementary techniques. This work demonstrates the tremendous effect of heterointerfaces on hot photocarrier dynamics and showcases the potential of SUEM in characterizing realistic optoelectronic devices.

Published
2024

39. Progress and gaps in climate change adaptation in coastal cities across the globe

Author

Wannewitz, Mia, Ajibade, Idowu, Mach, Katharine J, Magnan, Alexandre, Petzold, Jan, Reckien, Diana, Ulibarri, Nicola, Agopian, Armen, Chalastani, Vasiliki I, Hawxwell, Tom, Huynh, Lam TM, Kirchhoff, Christine J, Miller, Rebecca, Musah-Surugu, Justice Issah, Nagle Alverio, Gabriela, Nielsen, Miriam, Nunbogu, Abraham Marshall, Pentz, Brian, Reimuth, Andrea, Scarpa, Giulia, Seeteram, Nadia, Villaverde Canosa, Ivan, Zhou, Jingyao, and Garschagen, Matthias

Subjects
Climate Change Impacts and Adaptation, Environmental Sciences, Human Society, Climate Action
Published
2024

40. Facilitating phenotyping from clinical texts: the medkit library

Author

Neuraz, Antoine, Vaillant, Ghislain, Arias, Camila, Birot, Olivier, Huynh, Kim-Tam, Fabacher, Thibaut, Rogier, Alice, Garcelon, Nicolas, Lerner, Ivan, Rance, Bastien, and Coulet, Adrien

Subjects
Computer Science - Computation and Language, Computer Science - Information Retrieval
Abstract

Phenotyping consists in applying algorithms to identify individuals associated with a specific, potentially complex, trait or condition, typically out of a collection of Electronic Health Records (EHRs). Because a lot of the clinical information of EHRs are lying in texts, phenotyping from text takes an important role in studies that rely on the secondary use of EHRs. However, the heterogeneity and highly specialized aspect of both the content and form of clinical texts makes this task particularly tedious, and is the source of time and cost constraints in observational studies. To facilitate the development, evaluation and reproductibility of phenotyping pipelines, we developed an open-source Python library named medkit. It enables composing data processing pipelines made of easy-to-reuse software bricks, named medkit operations. In addition to the core of the library, we share the operations and pipelines we already developed and invite the phenotyping community for their reuse and enrichment. medkit is available at https://github.com/medkit-lib/medkit

Published
2024

41. Categorical data clustering: 25 years beyond K-modes

Author

Dinh, Tai, Hauchi, Wong, Fournier-Viger, Philippe, Lisik, Daniil, Ha, Minh-Quyet, Dam, Hieu-Chi, and Huynh, Van-Nam

Subjects
Computer Science - Machine Learning
Abstract

The clustering of categorical data is a common and important task in computer science, offering profound implications across a spectrum of applications. Unlike purely numerical data, categorical data often lack inherent ordering as in nominal data, or have varying levels of order as in ordinal data, thus requiring specialized methodologies for efficient organization and analysis. This review provides a comprehensive synthesis of categorical data clustering in the past twenty-five years, starting from the introduction of K-modes. It elucidates the pivotal role of categorical data clustering in diverse fields such as health sciences, natural sciences, social sciences, education, engineering and economics. Practical comparisons are conducted for algorithms having public implementations, highlighting distinguishing clustering methodologies and revealing the performance of recent algorithms on several benchmark categorical datasets. Finally, challenges and opportunities in the field are discussed.

Published
2024

42. Next-Generation Triggering: A Novel Event-Level Approach

Author

Köhler, Jelena, Benoit-Lévy, Aurélien, Correa, Pablo, Ferriere, Arsène, Huege, Tim, Kotera, Kumiko, Martineau-Huynh, Olivier, Prunet, Simon, and Roth, Markus

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Large-scale cosmic-ray detectors like the Giant Radio Array for Neutrino Detection (GRAND) are pushing the boundaries of our ability to identify air shower events. Existing trigger schemes rely solely on the timing of signals detected by individual antennas, which brings many challenges in distinguishing true air shower signals from background. This work explores novel event-level radio trigger methods specifically designed for GRAND, but also applicable to other systems, such as the Radio Detector (RD) of the Pierre Auger Observatory. In addition to an upgraded plane wave front reconstruction technique, we introduce orthogonal and complementary approaches that analyze the radio-emission footprint, the spatial distribution of signal strength across triggered antennas, to refine event selection. We test our methods on mock data sets constructed with simulated showers and real background noise measured with the GRAND prototype, to assess the performance potential in terms of sensitivity and background rejection in GRAND. Our preliminary results are a first step to identifying the most discriminating radio signal features at event-level, and optimizing the techniques for future implementation on experimental data., Comment: 8 pages, 5 figures, 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities - ARENA2024

Published
2024

43. Reconstruction of highly inclined extensive air showers in GRAND

Author

Macias, Oscar, Benoit-Lévy, Aurélien, Decoene, Valentin, Ferrière, Arsène, Guelfand, Marion, Guépin, Claire, Kotera, Kumiko, Lai, Zhisen, Martineau-Huynh, Olivier, Prunet, Simon, and Tueros, Matías

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The Giant Radio Array for Neutrino Detection (GRAND) aims to detect highly inclined extensive air showers (EAS) with down-going and up-going trajectories. Several working groups in the GRAND collaboration are developing methods to reconstruct the incoming direction, core position, primary energy, and composition of the showers. The reconstruction pipeline -- currently under development in the France/IAP working group -- relies on a model of spherical wavefront emission for arrival times, which is possible because the radio signals are generated far away from the antenna stations. The amplitude distribution of the signals at the antenna level is described by an Angular Distribution Function that considers various asymmetries in the data, including geomagnetic effects. In this contribution, we present preliminary results from testing our EAS reconstruction procedure using realistic mock observations., Comment: V2: Fixed typo in axis labels of Fig.5 and collaboration author list updated

Published
2024

44. Ensemble Modeling of Multiple Physical Indicators to Dynamically Phenotype Autism Spectrum Disorder

Author

Huynh, Marie, Kline, Aaron, Surabhi, Saimourya, Dunlap, Kaitlyn, Mutlu, Onur Cezmi, Honarmand, Mohammadmahdi, Azizian, Parnian, Washington, Peter, and Wall, Dennis P.

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

Early detection of autism, a neurodevelopmental disorder marked by social communication challenges, is crucial for timely intervention. Recent advancements have utilized naturalistic home videos captured via the mobile application GuessWhat. Through interactive games played between children and their guardians, GuessWhat has amassed over 3,000 structured videos from 382 children, both diagnosed with and without Autism Spectrum Disorder (ASD). This collection provides a robust dataset for training computer vision models to detect ASD-related phenotypic markers, including variations in emotional expression, eye contact, and head movements. We have developed a protocol to curate high-quality videos from this dataset, forming a comprehensive training set. Utilizing this set, we trained individual LSTM-based models using eye gaze, head positions, and facial landmarks as input features, achieving test AUCs of 86%, 67%, and 78%, respectively. To boost diagnostic accuracy, we applied late fusion techniques to create ensemble models, improving the overall AUC to 90%. This approach also yielded more equitable results across different genders and age groups. Our methodology offers a significant step forward in the early detection of ASD by potentially reducing the reliance on subjective assessments and making early identification more accessibly and equitable.

Published
2024

45. Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese

Author

Doan, Khang T., Huynh, Bao G., Hoang, Dung T., Pham, Thuc D., Pham, Nhat H., Nguyen, Quan T. M., Vo, Bang Q., and Hoang, Suong N.

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

In this report, we introduce Vintern-1B, a reliable 1-billion-parameters multimodal large language model (MLLM) for Vietnamese language tasks. By integrating the Qwen2-0.5B-Instruct language model with the InternViT-300M-448px visual model, Vintern-1B is optimized for a range of applications, including optical character recognition (OCR), document extraction, and general question-answering in Vietnamese context. The model is fine-tuned on an extensive dataset of over 3 million image-question-answer pairs, achieving robust performance and reliable results across multiple Vietnamese language benchmarks like OpenViVQA and ViTextVQA. Vintern-1B is small enough to fit into various on-device applications easily. Additionally, we have open-sourced several Vietnamese vision question answering (VQA) datasets for text and diagrams, created with Gemini 1.5 Flash. Our models are available at: https://huggingface.co/5CD-AI/Vintern-1B-v2.

Published
2024

46. Deep Reinforcement Learning for Network Energy Saving in 6G and Beyond Networks

Author

Tran, Dinh-Hieu, Van Huynh, Nguyen, Kaada, Soumeya, Vo, Van Nhan, Lagunas, Eva, and Chatzinotas, Symeon

Subjects
Computer Science - Networking and Internet Architecture
Abstract

Network energy saving has received great attention from operators and vendors to reduce energy consumption and CO2 emissions to the environment as well as significantly reduce costs for mobile network operators. However, the design of energy-saving networks also needs to ensure the mobile users' (MUs) QoS requirements such as throughput requirements (TR). This work considers a mobile cellular network including many ground base stations (GBSs), and some GBSs are intentionally turned off due to network energy saving (NES) or crash, so the MUs located in these outage GBSs are not served in time. Based on this observation, we propose the problem of maximizing the total achievable throughput in the network by optimizing the GBSs' antenna tilt and adaptive transmission power with a given number of served MUs satisfied. Notice that, the MU is considered successfully served if its Reference Signal Received Power (RSRP) and throughput requirement are satisfied. The formulated optimization problem becomes difficult to solve with multiple binary variables and non-convex constraints along with random throughput requirements and random placement of MUs. We propose a Deep Q-learning-based algorithm to help the network learn the uncertainty and dynamics of the transmission environment. Extensive simulation results show that our proposed algorithm achieves much better performance than the benchmark schemes., Comment: 7 pages, 4 figures

Published
2024

47. GRANDlib: A simulation pipeline for the Giant Radio Array for Neutrino Detection (GRAND)

Author

GRAND Collaboration, Batista, Rafael Alves, Benoit-Lévy, Aurélien, Bister, Teresa, Bohacova, Martina, Bustamante, Mauricio, Carvalho, Washington, Chen, Yiren, Cheng, LingMei, Chiche, Simon, Colley, Jean-Marc, Correa, Pablo, Laurenciu, Nicoleta Cucu, Dai, Zigao, de Almeida, Rogerio M., de Errico, Beatriz, de Jong, Sijbrand, Neto, João R. T. de Mello, de Vries, Krijn D., Decoene, Valentin, Denton, Peter B., Duan, Bohao, Duan, Kaikai, Engel, Ralph, Erba, William, Fan, Yizhong, Ferrière, Arsène, Gou, QuanBu, Gu, Junhua, Guelfand, Marion, Guo, Jianhua, Guo, Yiqing, Guépin, Claire, Gülzow, Lukas, Haungs, Andreas, Havelka, Matej, He, Haoning, Hivon, Eric, Hu, Hongbo, Huang, Xiaoyuan, Huang, Yan, Huege, Tim, Jiang, Wen, Koirala, Ramesh, Kong, ChuiZheng, Kotera, Kumiko, Köhler, Jelena, Lago, Bruno L., Lai, Zhisen, Coz, Sandra Le, Legrand, François, Leisos, Antonios, Li, Rui, Li, Xingyu, Li, YiFei, Liu, Cheng, Liu, Ruoyu, Liu, Wei, Ma, Pengxiong, Macias, Oscar, Magnard, Frédéric, Marcowith, Alexandre, Martineau-Huynh, Olivier, McKinley, Thomas, Minodier, Paul, Mitra, Pragati, Mostafá, Miguel, Murase, Kohta, Niess, Valentin, Nonis, Stavros, Ogio, Shoichi, Oikonomou, Foteini, Pan, Hongwei, Papageorgiou, Konstantinos, Pierog, Tanguy, Piotrowski, Lech Wiktor, Prunet, Simon, Qian, Xiangli, Roth, Markus, Sako, Takashi, Schoorlemmer, Harm, Szálas-Motesiczky, Dániel, Sławiński, Szymon, Tian, Xishui, Timmermans, Anne, Timmermans, Charles, Tobiska, Petr, Tsirigotis, Apostolos, Tueros, Matías, Vittakis, George, Wang, Hanrui, Wang, Jiale, Wang, Shen, Wang, Xiangyu, Wang, Xu, Wei, Daming, Wei, Feng, Wu, Xiangping, Wu, Xuefeng, Xu, Xin, Xu, Xing, Yang, Fufu, Yang, Lili, Yang, Xuan, Yuan, Qiang, Zarka, Philippe, Zeng, Houdun, Zhang, Chao, Zhang, Jianli, Zhang, Kewen, Zhang, Pengfei, Zhang, Qingchi, Zhang, Songbo, Zhang, Yi, and Zhou, Hao

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, High Energy Physics - Experiment, High Energy Physics - Phenomenology
Abstract

The operation of upcoming ultra-high-energy cosmic-ray, gamma-ray, and neutrino radio-detection experiments, like the Giant Radio Array for Neutrino Detection (GRAND), poses significant computational challenges involving the production of numerous simulations of particle showers and their detection, and a high data throughput. GRANDlib is an open-source software tool designed to meet these challenges. Its primary goal is to perform end-to-end simulations of the detector operation, from the interaction of ultra-high-energy particles, through -- by interfacing with external air-shower simulations -- the ensuing particle shower development and its radio emission, to its detection by antenna arrays and its processing by data-acquisition systems. Additionally, GRANDlib manages the visualization, storage, and retrieval of experimental and simulated data. We present an overview of GRANDlib to serve as the basis of future GRAND analyses., Comment: 11 pages, 9 figures, plus appendices

Published
2024

48. Imaging coupled vibrational, rotational, and electronic wave packet dynamics in a triatomic molecule

Author

Lam, Huynh Van Sa, Hoang, Van-Hung, Venkatachalam, Anbu Selvam, Bhattacharyya, Surjendu, Chen, Keyu, Jacob, Sina, Kudagama, Sanduni, Nguyen, Tu Thanh, Rolles, Daniel, Thumm, Uwe, Rudenko, Artem, and Kumarappan, Vinod

Subjects
Physics - Chemical Physics, Physics - Atomic and Molecular Clusters, Physics - Optics, Quantum Physics
Abstract

Molecular dynamics triggered by interaction with light often involve the excitation of several electronic, vibrational, and rotational states. Characterizing the resulting coupled electronic and nuclear wave packet motion represents a severe challenge, even for small polyatomic systems. In this Letter, we demonstrate how the interplay between vibrational, rotational, and electronic degrees of freedom governs the evolution of molecular wave packets in the low-lying states of strong-field-ionized sulfur dioxide. Using time-resolved Coulomb explosion imaging (CEI) in combination with quantum mechanical wave packet simulations, we directly map bending vibrations of the molecule, show how the vibrational wave packet is influenced by molecular alignment, and elucidate the role of the coupling between the two lowest electronic states of the cation. A conical intersection between these states couples the bending and asymmetric stretching coordinates, which is clearly reflected in the correlated fragment momenta. Our results suggest that multi-coincident CEI represents an efficient experimental tool for characterizing coupled electronic and nuclear motion in polyatomic molecules.

Published
2024

49. Differentiating Three-Dimensional Molecular Structures using Laser-induced Coulomb Explosion Imaging

Author

Lam, Huynh Van Sa, Venkatachalam, Anbu Selvam, Bhattacharyya, Surjendu, Chen, Keyu, Borne, Kurtis, Wang, Enliang, Boll, Rebecca, Jahnke, Till, Kumarappan, Vinod, Rudenko, Artem, and Rolles, Daniel

Subjects
Physics - Atomic and Molecular Clusters, Physics - Chemical Physics, Quantum Physics
Abstract

Coulomb explosion imaging (CEI) with x-ray free electron lasers has recently been shown to be a powerful method for obtaining detailed structural information of gas-phase planar ring molecules [R. Boll et al. Nat. Phys. 18, 423-428 (2022)]. In this Letter, we investigate the potential of CEI driven by a tabletop laser and extend this approach to differentiating three-dimensional (3D) structures. We study the static CEI patterns of planar and nonplanar organic molecules that resemble the structures of typical products formed in ring-opening reactions. Our results reveal that each molecule exhibits a well-localized and distinctive pattern in 3D fragment-ion momentum space. We find that these patterns yield direct information about the molecular structures and can be qualitatively reproduced using a classical Coulomb explosion simulation. Our findings suggest that laser-induced CEI can serve as a robust method for differentiating molecular structures of organic ring and chain molecules. As such, it holds great promise as a method for following ultrafast structural changes, e.g., during ring-opening reactions, by tracking the motion of individual atoms in pump-probe experiments.

Published
2024
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50. Low Thermal Resistance of Diamond-AlGaN Interfaces Achieved Using Carbide Interlayers

Author

Aller, Henry T., Pfeifer, Thomas W., Mamun, Abdullah, Huynh, Kenny, Tadjer, Marko, Feygelson, Tatyana, Hobart, Karl, Anderson, Travis, Pate, Bradford, Jacobs, Alan, Lundh, James Spencer, Goorsky, Mark, Khan, Asif, Hopkins, Patrick, and Graham, Samuel

Subjects
Condensed Matter - Materials Science, Physics - Atomic Physics
Abstract

This study investigates thermal transport across nanocrystalline diamond/AlGaN interfaces, crucial for enhancing thermal management in AlGaN/AlGaN-based devices. Chemical vapor deposition growth of diamond directly on AlGaN resulted in a disordered interface with a high thermal boundary resistance (TBR) of 20.6 m^2-K/GW. We employed sputtered carbide interlayers (e.g., $B_4C$, $SiC$, $B_4C/SiC$) to reduce thermal boundary resistance in diamond/AlGaN interfaces. The carbide interlayers resulted in record-low thermal boundary resistance values of 3.4 and 3.7 m^2-K/GW for Al$_{0.65}$Ga$_{0.35}$N samples with $B_4C$ and $SiC$ interlayers, respectively. STEM imaging of the interface reveals interlayer thicknesses between 1.7-2.5 nm, with an amorphous structure. Additionally, Fast-Fourier Transform (FFT) characterization of sections of the STEM images displayed sharp crystalline fringes in the AlGaN layer, confirming it was properly protected from damage from hydrogen plasma during the diamond growth. In order to accurately measure the thermal boundary resistance we develop a hybrid technique, combining time-domain thermoreflectance and steady-state thermoreflectance fitting, offering superior sensitivity to buried thermal resistances. Our findings underscore the efficacy of interlayer engineering in enhancing thermal transport and demonstrate the importance of innovative measurement techniques in accurately characterizing complex thermal interfaces. This study provides a foundation for future research in improving thermal properties of semiconductor devices through interface engineering and advanced measurement methodologies.

Published
2024

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