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825 results on '"Zhang,Yanqi"'

1. ChipExpert: The Open-Source Integrated-Circuit-Design-Specific Large Language Model

Author

Xu, Ning, Zhang, Zhaoyang, Qi, Lei, Wang, Wensuo, Zhang, Chao, Ren, Zihao, Zhang, Huaiyuan, Cheng, Xin, Zhang, Yanqi, Liu, Zhichao, Wei, Qingwen, Wu, Shiyang, Yang, Lanlan, Lu, Qianfeng, Ma, Yiqun, Zhao, Mengyao, Liu, Junbo, Song, Yufan, Geng, Xin, and Yang, Jun

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

The field of integrated circuit (IC) design is highly specialized, presenting significant barriers to entry and research and development challenges. Although large language models (LLMs) have achieved remarkable success in various domains, existing LLMs often fail to meet the specific needs of students, engineers, and researchers. Consequently, the potential of LLMs in the IC design domain remains largely unexplored. To address these issues, we introduce ChipExpert, the first open-source, instructional LLM specifically tailored for the IC design field. ChipExpert is trained on one of the current best open-source base model (Llama-3 8B). The entire training process encompasses several key stages, including data preparation, continue pre-training, instruction-guided supervised fine-tuning, preference alignment, and evaluation. In the data preparation stage, we construct multiple high-quality custom datasets through manual selection and data synthesis techniques. In the subsequent two stages, ChipExpert acquires a vast amount of IC design knowledge and learns how to respond to user queries professionally. ChipExpert also undergoes an alignment phase, using Direct Preference Optimization, to achieve a high standard of ethical performance. Finally, to mitigate the hallucinations of ChipExpert, we have developed a Retrieval-Augmented Generation (RAG) system, based on the IC design knowledge base. We also released the first IC design benchmark ChipICD-Bench, to evaluate the capabilities of LLMs across multiple IC design sub-domains. Through comprehensive experiments conducted on this benchmark, ChipExpert demonstrated a high level of expertise in IC design knowledge Question-and-Answer tasks.

Published
2024

4. Analytically-Driven Resource Management for Cloud-Native Microservices

Author

Zhang, Yanqi, Zhou, Zhuangzhuang, Elnikety, Sameh, and Delimitrou, Christina

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Artificial Intelligence
Abstract

Resource management for cloud-native microservices has attracted a lot of recent attention. Previous work has shown that machine learning (ML)-driven approaches outperform traditional techniques, such as autoscaling, in terms of both SLA maintenance and resource efficiency. However, ML-driven approaches also face challenges including lengthy data collection processes and limited scalability. We present Ursa, a lightweight resource management system for cloud-native microservices that addresses these challenges. Ursa uses an analytical model that decomposes the end-to-end SLA into per-service SLA, and maps per-service SLA to individual resource allocations per microservice tier. To speed up the exploration process and avoid prolonged SLA violations, Ursa explores each microservice individually, and swiftly stops exploration if latency exceeds its SLA. We evaluate Ursa on a set of representative and end-to-end microservice topologies, including a social network, media service and video processing pipeline, each consisting of multiple classes and priorities of requests with different SLAs, and compare it against two representative ML-driven systems, Sinan and Firm. Compared to these ML-driven approaches, Ursa provides significant advantages: It shortens the data collection process by more than 128x, and its control plane is 43x faster than ML-driven approaches. At the same time, Ursa does not sacrifice resource efficiency or SLAs. During online deployment, Ursa reduces the SLA violation rate by 9.0% up to 49.9%, and reduces CPU allocation by up to 86.2% compared to ML-driven approaches.

Published
2024

6. Search for Eccentric Black Hole Coalescences during the Third Observing Run of LIGO and Virgo

Author

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abe, H., Acernese, F., Ackley, K., Adamcewicz, C., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., 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., Almualla, M., Altin, P. A., Álvarez-López, S., Amato, A., Amez-Droz, L., Amorosi, A., Anand, S., Ananyeva, A., Andersen, R., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Apostolatos, T., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Babak, S., Badalyan, A., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bai, Y., Baier, J. G., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Baltus, G., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barber, J., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhandari, A. V., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bianchi, A., Bilenko, I. A., Bilicki, M., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boër, M., Bogaert, G., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonnand, R., Booker, P., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., 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., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Cagnoli, G., Cahillane, C., Cain III, H. W., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chalathadka-Subrahmanya, S., Chan, C., Chan, J. C. L., Chan, K. H. M., Chan, M., Chan, W. L., Chandra, K., Chang, I. P., Chang, R. -J., Chang, W., Chanial, P., Chao, S., Chapman-Bird, C., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chastain, L., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chatziioannou, K., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, X., Chen, Y. -R., Chen, Y., Cheng, H., Chessa, P., Chia, H. Y., Chiadini, F., Chiang, C., Chiarini, G., Chiba, A., Chiba, R., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Clara, F., Clark, J. A., Clarke, T. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. -F., Colleoni, M., Collette, C. G., Collins, J., Colombo, A., Colpi, M., Compton, C. M., Conti, L., Cooper, S. J., 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., Cousins, B., Couvares, P., Coward, D. M., Cowart, M. J., Cowburn, B. D., Coyne, D. C., Coyne, R., Craig, K., Creighton, J. D. E., Creighton, T. D., Criswell, A. W., Crockett-Gray, J. C. G., Croquette, M., Crouch, R., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Curyło, M., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Darsow-Fromm, C., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Dave, I., Davenport, A., Davier, M., Davis, D., Davis, M. C., Daw, E. J., Dax, M., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., De Pietri, R., De Rosa, R., De Rossi, C., De Simone, R., Dhurandhar, S., Diab, R., Diamond, P. Z., Díaz, M. C., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, F., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Lieto, A., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Donahue, L., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Drori, Y., Du, H., 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., Engelby, E., Engl, A. J., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, C. R., Evans, M., Evans, T. M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fair, H., Fairhurst, S., Fan, P. C., Farah, A. M., Farr, B., Farr, W. M., Fauchon-Jones, E. J., Favaro, G., Favata, M., Fays, M., Feicht, J., Fejer, M. M., Fenyvesi, E., Ferguson, D. L., Ferrante, I., Ferreira, T. A., Fidecaro, F., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. S., Floden, E., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franceschetti, K., Franke, A., 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., Fujii, S., Fukunaga, I., Fulda, P., Fyffe, M., Gabella, W. E., Gadre, B., Gair, J. R., Gais, J., Galaudage, S., Gallardo, S., Gamba, R., Ganapathy, D., Ganguly, A., Gaonkar, S. G., Garaventa, B., Garcia-Bellido, J., García-Núñez, C., García-Quirós, C., Gardner, J. W., Gardner, K. A., Gargiulo, J., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., George, J., Gerberding, O., Gergely, L., Ghadiri, N., Ghosh, Abhirup, Ghosh, Archisman, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Suprovo, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Gleckl, A. E., Glotin, F., Godfrey, J., Godwin, P., Goetz, E., Goetz, R., Golomb, J., Lopez, S. Gomez, Goncharov, B., González, G., Goodwin-Jones, A. W., Gosselin, M., Gouaty, R., Gould, D. W., Goyal, S., Grace, B., Grado, A., Graham, V., Granados, A. E., Granata, M., Granata, V., Gras, S., Grassia, P., 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., Gruson, A. S., Guerra, D., Guetta, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, Ish, Gupta, N. C., Gupta, P., Gupta, S. K., Gupte, N., Gurav, R., Gurs, J., Gustafson, E. K., Gutierrez, N., Guzman, F., Haba, D., Haegel, L., Hain, G., Haino, S., Halim, O., 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., Harder, T., Haris, K., Harmark, T., Harms, J., Harry, G. M., Harry, I. W., Hartwig, D., Haskell, B., Haster, C. -J., Hathaway, J. S., Haughian, K., Hayakawa, H., Hayama, K., Hayes, F. J., Healy, J., Heffernan, A., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Hendry, M., Heng, I. S., Hennes, E., Hennig, J. -S., Hennig, M., Henshaw, C., Hernandez, A., Hertog, T., Heurs, M., Hewitt, A. L., Higginbotham, S., Hild, S., Hill, P., Himemoto, Y., Hines, A. S., Hirata, N., Hirose, C., Ho, J., Hoang, S., Hochheim, S., Hofman, D., Hohmann, J. N., Holland, N. A., Holley-Bockelmann, K., Hollows, I. J., Holmes, Z. J., Holz, D. E., Hong, C., Hong, Q., Hornung, J., Hoshino, S., Hough, J., Hourihane, S., Howell, E. J., Hoy, C. G., Hoyland, D., Hsieh, H. -F., Hsiung, C., Hsu, H. C., Hsu, S. -C., Hsu, W. -F., Hu, P., Hu, Q., Huang, H. Y., Huang, Y. -J., Huang, Y., Huang, Y. T., Hübner, M. T., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Hur, R., Husa, S., Huxford, R., Huynh-Dinh, T., Hyland, J., Iakovlev, A., Iandolo, G. A., Iess, A., Inayoshi, K., Inoue, Y., Iorio, G., Iosif, P., Irwin, J., Isi, M., Ismail, M. A., Itoh, Y., Iwaya, M., Iyer, B. R., JaberianHamedan, V., Jacqmin, T., Jacquet, P. -E., Jadhav, S. J., Jadhav, S. P., Jain, D., Jain, T., James, A. L., James, P. A., Jamshidi, R., Jan, A. Z., Jani, K., Janiurek, L., Janquart, J., Janssens, K., Janthalur, N. N., Jaraba, S., Jaranowski, P., Jarov, S., Jasal, P., Jaume, R., Javed, W., Jenner, K., Jennings, A., Jia, W., Jiang, J., Jin, H. -B., Johansmeyer, K., Johns, G. R., Johnson, N. A., Johnston, R., Johny, N., Jones, D. H., Jones, D. I., Jones, R., Joshi, P., Ju, L., Jung, K., Junker, J., Juste, V., Kajita, T., Kalaghatgi, C., Kalogera, V., Kamiizumi, M., Kanda, N., Kandhasamy, S., Kang, G., Kanner, J. B., Kapadia, S. J., Kapasi, D. P., Karat, S., Karathanasis, C., Karki, S., Karydas, T., Kas-danouche, Y. A., Kashyap, R., Kasprzack, M., Kastaun, W., Kato, J., Kato, T., Katsanevas, S., Katsavounidis, E., Katsuren, J. K., Katzman, W., Kaur, T., Kawabe, K., Kéfélian, F., Keitel, D., Kelley-Derzon, J., Kemper, S. A., Kennington, J., Kesharwani, R., Key, J. S., Khadka, S., Khalili, F. Y., Khanam, T., Khazanov, E. A., Khursheed, M., Kijbunchoo, N., Kim, C., Kim, J. C., Kim, K., Kim, M. H., Kim, P., Kim, S., Kim, W. S., Kim, Y. -M., Kimball, C., Kimura, N., Kinley-Hanlon, M., Kirchhoff, R., Kissel, J. S., Kiyota, T., Klimenko, S., Klinger, T., Knee, A. M., Knust, N., Koch, P., Koehlenbeck, S. M., Koekoek, G., Kohri, K., Kokeyama, K., Koley, S., Koliadko, N. D., Kolitsidou, P., Kolstein, M., Komori, K., Kondrashov, V., Kong, A. K. H., Kontos, A., Korobko, M., Kossak, R. V., Kouvatsos, N., Kovalam, M., Koyama, N., Kozak, D. B., Kranzhoff, S. L., Kringel, V., Krishnendu, N. V., Królak, A., Kuehn, G., Kuijer, P., Kulkarni, S., Ramamohan, A. Kulur, Kumar, A., Kumar, Praveen, Kumar, Prayush, Kumar, Rahul, Kumar, Rakesh, Kume, J., Kuns, K., Kuroyanagi, S., Kuwahara, S., Kwak, K., Kwan, K., Lacaille, G., Lagabbe, P., Laghi, D., Lai, S., Lakkis, M. H., Lalande, E., Lalleman, M., Lamberts, A., Landry, M., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., La Rana, A., La Rosa, I., Lartaux-Vollard, A., Lasky, P. D., Lawrence, J., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Leavey, S., LeBohec, S., Lecoeuche, Y. K., Lee, H. M., Lee, H. W., Lee, K., Lee, R. -K., Lee, R., Lee, S., Lee, Y., Legred, I. N., Lehmann, J., Lehner, L., Lemaître, A., Lenti, M., Leonardi, M., Leonova, E., Leroy, N., Lesovsky, M., Letendre, N., Lethuillier, M., Levesque, C., Levin, Y., Leyde, K., Li, A. K. Y., Li, K. L., Li, T. G. F., Li, X., Lin, Chien-Yu, Lin, Chun-Yu, Lin, E. T., Lin, F., Lin, H., Lin, L. C. -C., Lin, Y., Linde, F., Linker, S. D., Littenberg, T. B., Liu, A., Liu, G. C., Liu, Jian, Llamas, F., Lo, R. K. L., Lo, T., Locquet, J. -P., London, L., Longo, A., Lopez, D., Portilla, M. Lopez, Lorenzini, M., Loriette, V., Lormand, M., Losurdo, G., Lott, T. P., Lough, J. D., Loughlin, H. A., Lousto, C. O., Lovelace, G., Lowry, M. J., Lück, H., Lumaca, D., Lundgren, A. P., Lussier, A. W., Lynam, J. E., Ma, L. -T., Ma, S., Ma'arif, M., Macas, R., MacInnis, M., Macleod, D. M., MacMillan, I. A. O., Macquet, A., Maeda, K., Maenaut, S., Hernandez, I. Magaña, Magazzù, C., Magee, R. M., Maggiore, R., Magnozzi, M., Mahesh, M., Mahesh, S., Maini, M., Majhi, S., Majorana, E., Makarem, C. N., Maliakal, S., Malik, A., Man, N., Mandic, V., Mangano, V., Mannix, B., Mansell, G. L., Mansingh, G., Manske, M., Mantovani, M., Mapelli, M., Marchesoni, F., Pina, D. Marín, Marion, F., Márka, S., Márka, Z., Markakis, C., Markosyan, A. S., Markowitz, A., Maros, E., Marquina, A., Marsat, S., Martelli, F., Martin, I. W., Martin, R. M., Martinez, B. B., Martinez, M., Martinez, V. A., Martinez, V., Martini, A., Martinovic, K., Martynov, D. V., Marx, E. J., Masalehdan, H., Masserot, A., Masso-Reid, M., Mastrodicasa, M., Mastrogiovanni, S., Mateu-Lucena, M., Matiushechkina, M., Matsuyama, M., Mavalvala, N., Maxwell, N., McCarrol, G., McCarthy, R., McClelland, D. E., McCormick, S., McCuller, L., McGhee, G. I., McGinn, J., Mchedlidze, M., McIsaac, C., McIver, J., McKinney, K., McLeod, A., McRae, T., McWilliams, S. T., Meacher, D., Mehmet, M., Mehta, A. K., Meijer, Q., Melatos, A., Mellaerts, S., Menendez-Vazquez, A., Menoni, C. S., Mercer, R. A., Mereni, L., Merfeld, K., Merilh, E. L., Merritt, J. D., Merzougui, M., Messenger, C., Messick, C., Meyer-Conde, M., Meylahn, F., Mhaske, A., Miani, A., Miao, H., Michaloliakos, I., Michel, C., Michimura, Y., Middleton, H., Mihaylov, D. P., Miller, A. L., Miller, A., Miller, B., Miller, S., Millhouse, M., Milotti, E., Minenkov, Y., Mio, N., Mir, Ll. M., Mirasola, L., Miravet-Tenés, M., Miritescu, C. ., Mishkin, A., Mishra, A., Mishra, C., Mishra, T., Mistry, T., Mitchell, A. L., Mitra, S., Mitrofanov, V. P., Mitselmakher, G., Mittleman, R., Miyakawa, O., Miyamoto, S., Miyoki, S., Mo, G., Mobilia, L., Modafferi, L. M., Mohapatra, S. R. P., Mohite, S. R., Molina-Ruiz, M., Mondal, C., Mondin, M., Montani, M., Moore, C. J., Morales, M., Moraru, D., Morawski, F., More, A., More, S., Moreno, C., Moreno, G., Morisaki, S., Moriwaki, Y., Morras, G., Moscatello, A., Mours, B., Mow-Lowry, C. M., Mozzon, S., Muciaccia, F., Mukherjee, Arunava, Mukherjee, D., Mukherjee, Soma, Mukherjee, Subroto, Mukherjee, Suvodip, Mukund, N., Mullavey, A., Munch, J., Muñiz, E. A., Murakoshi, M., Murray, P. G., Muusse, S., Nadji, S. L., Nagar, A., Nagar, T., Nagarajan, N., Nakamura, K., Nakano, H., Nakano, M., Napolano, V., Nardecchia, I., Narikawa, T., Narola, H., Naticchioni, L., Nayak, R. K., Neil, B. F., Neilson, J., Nelson, A., Nelson, T. J. N., Nery, M., Nesseris, S., Neunzert, A., Ng, K. Y., Ng, S. W. S., Nguyen, C., Nguyen, P., Quynh, L. Nguyen, Nichols, S. A., 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, Nurbek, G., Nuttall, L. K., Obayashi, K., Oberling, J., O'Dell, J., Oelker, E., Oertel, M., Offermans, A., Oganesyan, G., Oh, J. J., Oh, K., Oh, S. H., O'Hanlon, T., Ohashi, M., Ohkawa, M., Ohme, F., Ohta, H., Oliveira, A. S., Oliveri, R., Oloworaran, V., O'Neal, B., Oohara, K., O'Reilly, B., Ormiston, R. G., Ormsby, N. D., Orselli, M., O'Shaughnessy, R., Oshima, Y., Oshino, S., Ossokine, S., Osthelder, C., Ottaway, D. J., Ouzriat, A., Overmier, H., Pace, A. E., Pagano, R., Page, M. A., Pai, A., Pai, S. A., Pal, A., Pal, S., Palashov, O., Pálfi, M., Palomba, C., Pan, K. -C., Panda, P. K., Panebianco, L., Pang, P. T. H., Pannarale, F., Pant, B. C., Panther, F. H., Panzer, C. D., Paoletti, F., Paoli, A., Paolone, A., Papalexakis, E. E., Papalini, L., Pappas, G., Parisi, A., Park, J., Parker, W., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passuello, D., Patel, M., Pathak, D., Pathak, M., Patra, A., Patricelli, B., Patron, A. S., Paul, S., Payne, E., Pearce, T., Pedraza, M., Pegna, R., Pegoraro, M., Pele, A., Arellano, F. E. Peña, Penn, S., Perego, A., Pereira, A., Perez, C. J., Perez, J. J., Perez, L. H., Périgois, C., Perkins, C. C., Perreca, A., Perret, J., Perriès, S., Perry, J. W., Pesios, D., Petermann, J., Petrillo, C., Pfeiffer, H. P., Pham, H., Pham, K. A., Phukon, K. S., Phurailatpam, H., Piccinni, O. J., Pichot, M., Piendibene, M., Piergiovanni, F., Pierini, L., Pierra, G., Pierro, V., Pietrzak, M. ., Pillant, G., Pillas, M., Pilo, F., Pinard, L., Pineda-Bosque, C., 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., Ponrathnam, S., Poon, J., Porcelli, E., Portell, J., 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, M., Prodi, G. A., Prokhorov, L., Prosposito, P., Prudenzi, L., Puecher, A., Pullin, J., Punturo, M., Puosi, F., Puppo, P., Pürrer, M., Qi, H., Qin, J., Quetschke, V., Quinonez, P. J., Quitzow-James, R., Raab, F. J., Raaijmakers, G., Radulesco, N., Raffai, P., Rail, S. X., Raja, S., Rajan, C., Ramirez, K. E., Ramos-Buades, A., Rana, D., Randel, E., Rangnekar, P. R., Rapagnani, P., Ray, A., Raymond, V., Razzano, M., Read, J., Payo, M. Recaman, Regimbau, T., Rei, L., Reid, S., Reid, S. W., Reitze, D. H., Relton, P., Renzini, A., Rettegno, P., Revenu, B., Reza, A., Rezac, M., Rezaei, A. S., Ricci, F., Ricci, M., Richards, D., Richardson, J. W., Rijal, A., Riles, K., Riley, H. K., Rinaldi, S., Robertson, C., Robertson, N. A., Robinet, F., Robinson, M., Rocchi, A., Rolland, L., Rollins, J. G., Romanelli, M., 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., Royzman, A., Rozza, D., Ruggi, P., Morales, E. Ruiz, Ruiz-Rocha, K., Sachdev, S., Sadecki, T., Sadiq, J., Saffarieh, P., Saha, S. S., Sainrat, T., Menon, S. Sajith, Sakai, K., Sakellariadou, M., Sako, T., 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., Saravanan, T. R., Sarin, N., Sasli, A., Sassi, P., Sassolas, B., Satari, H., Sato, R., Sato, S., Sato, Y., Sauter, O., Savage, R. L., Savant, V., Sawada, T., Sawant, H. L., Sayah, S., Schaetzl, D., Scheel, M., Scherf, S. J., Scheuer, J., Schiworski, M. G., Schmidt, P., Schmidt, S., Schmitz, S. J., Schnabel, R., Schneewind, M., Schofield, R. M. S., Schönbeck, A., Schouteden, K., Schuler, H., Schulte, B. W., Schutz, B. F., Schwartz, E., 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., Servignat, G., Setyawati, Y., Shaffer, T., Shahriar, M. S., Shaikh, M. A., Shams, B., Shao, L., Sharma, P., Sharma-Chaudhary, S., Shawhan, P., Shcheblanov, N. S., Sheela, A., Shen, B., Shepard, K. G., 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., 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., Soldateschi, J., Somala, S. N., Somiya, K., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Soulard, R., Souradeep, T., Sowell, E., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Srivastava, A. K., Srivastava, V., Stachie, C., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., StPierre, M., Strang, L. C., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Suh, H. G., Sullivan, A. G., Summerscales, T. Z., Sun, L., Sunil, S., Sur, A., Suresh, J., Sutton, P. J., Suzuki, Takamasa, Suzuki, Takanori, Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takatani, K., Takeda, H., Takeda, M., Talbot, C. J., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tanasijczuk, A. J., Tanioka, S., Tanner, D. B., Tao, D., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Terkowski, L., Themann, H., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, Shubhanshu, Tiwari, Srishti, Tiwari, V., Toivonen, A. M., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tonelli, M., Toriyama, A., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trani, A. A., Trapananti, A., Travasso, F., Traylor, G., Trenado, J., Trevor, M., Tringali, M. C., Tripathee, A., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tse, M., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Ubhi, A. S., Uchikata, N., Uchiyama, T., Udall, R. P., Uehara, T., Ueno, K., Unnikrishnan, C. S., Ushiba, T., Utina, A., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., 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., van Haevermaet, H., van Heijningen, J. V., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venneberg, J., Verdier, P., Verkindt, D., Verma, P., Verma, Y., Vermeulen, S. M., Veske, D., Vetrano, F., Veutro, A., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., 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., Walet, R. C., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Ward, R. L., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watada, K., Watarai, D., Wayt, K. E., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Weller, C. M., Weller, R. A., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., White, D. D., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Willetts, K., Williams, D., Williams, M. J., Williamson, A. R., Willis, J. L., Willke, B., Wils, M., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wong, D., Wong, H. T., Wong, I. C. F., Wright, M., Wu, C., Wu, D. S., Wu, H., Wysocki, D. M., Xiao, L., Xu, V. A., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, M., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yang, F. W., Yang, K. Z., Yang, L. -C., Yang, Y. -C., Yang, Yang, Yang, Yi, Yap, M. J., Yarbrough, Z., Yeh, S. -W., Yelikar, A. B., Yeung, S. M. C., Yeung, T. Y., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuzurihara, H., Zadrożny, A., Zannelli, A. J., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zevin, M., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Yanqi, Zhang, Ya, Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass $M>70$ $M_\odot$) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities $0 < e \leq 0.3$ at $0.33$ Gpc$^{-3}$ yr$^{-1}$ at 90\% confidence level., Comment: 24 pages, 5 figures

Published
2023

9. Optical Truss Interferometer for the LISA Telescope

Author

Jersey, Kylan, Harley-Trochimczyk, Ian, Zhang, Yanqi, and Guzman, Felipe

Subjects
Physics - Optics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The LISA telescopes must exhibit an optical path length stability of $\frac{\mathrm{pm}}{\sqrt{\mathrm{Hz}}}$ in the mHz observation band to meet mission requirements. The optical truss interferometer is a proposed method to aid in the ground testing of the telescopes, as well as a risk-mitigation plan for the flight units. This consists of three Fabry-Perot cavities mounted to the telescope which are used to monitor structural displacements. We have designed and developed a fiber-based cavity injection system that integrates fiber components, mode-matching optics, and a cavity input mirror into a compact input stage. The input stages, paired with return mirror stages, can be mounted to the telescope to form the optical truss cavities. We performed a thorough sensitivity analysis using various simulation methods to support the fabrication and assembly of three first-generation prototype cavities, each of which exhibited a satisfactory performance based on our models., Comment: 9 pages, 9 figures, 13 pdf figures attached

Published
2023
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10. Diagnosis and treatment of venous adventitial cystic disease

Author

ZHANG Yanqi, YAN Bin, NING Yachan, YU Xueyuan, WANG Chunmei

Subjects
venous adventitial cystic disease, deep vein thrombosis, venous thromboembolism, vascular cyst, Medicine
Abstract

Venous adventitial cystic disease (VACD) is a rare vascular disease which often manifests as unilateral limb swelling, and so misdiagnosed as other diseases such as deep vein thrombosis. Color Doppler ultrasound, CT angiography and magnetic resonance imaging play a crucial role in the diagnosis of VACD. Surgical treatment is the main method for VACD, including cyst incision, cyst wall resection, cyst puncture, aspiration and cyst and vessel resection. The recurrence rate of VACD is relatively high, ranging from 20% to 83%. Enhancing the understanding of VACD, making accurate diagnoses and appropriate surgical operation are of great significance for improving treatment outcomes and reducing the recurrence rate.

Published
2024
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11. A Regional Farming Pig Counting System Based on Improved Instance Segmentation Algorithm

Author

ZHANG Yanqi, ZHOU Shuo, ZHANG Ning, CHAI Xiujuan, and SUN Tan

Subjects
pig counting, deep learning, wechat mini program, yolov8, instance segmentation, Agriculture (General), S1-972, Technology (General), T1-995
Abstract

ObjectiveCurrently, pig farming facilities mainly rely on manual counting for tracking slaughtered and stored pigs. This is not only time-consuming and labor-intensive, but also prone to counting errors due to pig movement and potential cheating. As breeding operations expand, the periodic live asset inventories put significant strain on human, material and financial resources. Although methods based on electronic ear tags can assist in pig counting, these ear tags are easy to break and fall off in group housing environments. Most of the existing methods for counting pigs based on computer vision require capturing images from a top-down perspective, necessitating the installation of cameras above each hogpen or even the use of drones, resulting in high installation and maintenance costs. To address the above challenges faced in the group pig counting task, a high-efficiency and low-cost pig counting method was proposed based on improved instance segmentation algorithm and WeChat public platform.MethodsFirstly, a smartphone was used to collect pig image data in the area from a human view perspective, and each pig's outline in the image was annotated to establish a pig count dataset. The training set contains 606 images and the test set contains 65 images. Secondly, an efficient global attention module was proposed by improving convolutional block attention module (CBAM). The efficient global attention module first performed a dimension permutation operation on the input feature map to obtain the interaction between its channels and spatial dimensions. The permuted features were aggregated using global average pooling (GAP). One-dimensional convolution replaced the fully connected operation in CBAM, eliminating dimensionality reduction and significantly reducing the model's parameter number. This module was integrated into the YOLOv8 single-stage instance segmentation network to build the pig counting model YOLOv8x-Ours. By adding an efficient global attention module into each C2f layer of the YOLOv8 backbone network, the dimensional dependencies and feature information in the image could be extracted more effectively, thereby achieving high-accuracy pig counting. Lastly, with a focus on user experience and outreach, a pig counting WeChat mini program was developed based on the WeChat public platform and Django Web framework. The counting model was deployed to count pigs using images captured by smartphones.Results and DiscussionsCompared with existing methods of Mask R-CNN, YOLACT(Real-time Instance Segmentation), PolarMask, SOLO and YOLOv5x, the proposed pig counting model YOLOv8x-Ours exhibited superior performance in terms of accuracy and stability. Notably, YOLOv8x-Ours achieved the highest accuracy in counting, with errors of less than 2 and 3 pigs on the test set. Specifically, 93.8% of the total test images had counting errors of less than 3 pigs. Compared with the two-stage instance segmentation algorithm Mask R-CNN and the YOLOv8x model that applies the CBAM attention mechanism, YOLOv8x-Ours showed performance improvements of 7.6% and 3%, respectively. And due to the single-stage design and anchor-free architecture of the YOLOv8 model, the processing speed of a single image was only 64 ms, 1/8 of Mask R-CNN. By embedding the model into the WeChat mini program platform, pig counting was conducted using smartphone images. In cases where the model incorrectly detected pigs, users were given the option to click on the erroneous location in the result image to adjust the statistical outcomes, thereby enhancing the accuracy of pig counting.ConclusionsThe feasibility of deep learning technology in the task of pig counting was demonstrated. The proposed method eliminates the need for installing hardware equipment in the breeding area of the pig farm, enabling pig counting to be carried out effortlessly using just a smartphone. Users can promptly spot any errors in the counting results through image segmentation visualization and easily rectify any inaccuracies. This collaborative human-machine model not only reduces the need for extensive manpower but also guarantees the precision and user-friendliness of the counting outcomes.

Published
2024
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12. QoS-Aware Resource Management for Multi-phase Serverless Workflows with Aquatope

Author

Zhou, Zhuangzhuang, Zhang, Yanqi, and Delimitrou, Christina

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Networking and Internet Architecture
Abstract

Multi-stage serverless applications, i.e., workflows with many computation and I/O stages, are becoming increasingly representative of FaaS platforms. Despite their advantages in terms of fine-grained scalability and modular development, these applications are subject to suboptimal performance, resource inefficiency, and high costs to a larger degree than previous simple serverless functions. We present Aquatope, a QoS-and-uncertainty-aware resource scheduler for end-to-end serverless workflows that takes into account the inherent uncertainty present in FaaS platforms, and improves performance predictability and resource efficiency. Aquatope uses a set of scalable and validated Bayesian models to create pre-warmed containers ahead of function invocations, and to allocate appropriate resources at function granularity to meet a complex workflow's end-to-end QoS, while minimizing resource cost. Across a diverse set of analytics and interactive multi-stage serverless workloads, Aquatope significantly outperforms prior systems, reducing QoS violations by 5x, and cost by 34% on average and up to 52% compared to other QoS-meeting methods.

Published
2022

13. Optomechanical cooling and inertial sensing at low frequencies

Author

Zhang, Yanqi, Hines, Adam, Wilson, Dalziel, and Guzman, Felipe

Subjects
Quantum Physics, Physics - Applied Physics, Physics - Optics
Abstract

An inertial sensor design is proposed in this paper to achieve high sensitivity and large dynamic range in the sub-Hz frequency regime. High acceleration sensitivity is obtained by combining optical cavity readout systems with monolithically fabricated mechanical resonators. A high-sensitivity heterodyne interferometer simultaneously monitors the test mass with an extensive dynamic range for low-stiffness resonators. The bandwidth is tuned by optical feedback cooling to the test mass via radiation pressure interaction using an intensity-modulated laser. The transfer gain of the feedback system is analyzed to optimize system parameters towards the minimum cooling temperature that can be achieved. To practically implement the inertial sensor, we propose a cascaded cooling mechanism to improve cooling efficiency while operating at low optical power levels. The overall system layout presents an integrated design that is compact and lightweight.

Published
2022

14. A characterization method for low-frequency environmental noise in LIGO

Author

Valdes, Guillermo, Hines, Adam, Nelson, Andrea, Zhang, Yanqi, and Guzman, Felipe

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, General Relativity and Quantum Cosmology, Physics - Data Analysis, Statistics and Probability, Physics - Instrumentation and Detectors
Abstract

We present a method to characterize the noise in ground-based gravitational-wave observatories such as the Laser Gravitational-Wave Observatory (LIGO). This method uses linear regression algorithms such as the least absolute shrinkage and selection operator (LASSO) to identify noise sources and analyzes the detector output versus noise witness sensors to quantify the coupling of such noise. Our method can be implemented with currently available resources at LIGO, which avoids extra coding or direct experimentation at the LIGO sites. We present two examples to validate and estimate the coupling of elevated ground motion at frequencies below 10 Hz with noise in the detector output.

Published
2022
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15. Investigation and mitigation of noise contributions in a compact heterodyne interferometer

Author

Zhang, Yanqi, Hines, Adam, Valdes, Guillermo, and Guzman, Felipe

Subjects
Physics - Instrumentation and Detectors, Physics - Applied Physics, Physics - Optics
Abstract

We present a noise estimation and subtraction algorithm capable of increasing the sensitivity of heterodyne laser interferometers by one order of magnitude. The heterodyne interferometer is specially designed for dynamic measurements of a test mass in the application of sub-Hz inertial sensing. A noise floor of 3.31E-11 m/rtHz at 100mHz is achieved after applying our noise subtraction algorithm to a benchtop prototype interferometer that showed a noise level of 2.76E-10 m/rtHz at 100mHz when tested in vacuum at levels of 3E-5 Torr. Based on the previous results, we investigated noise estimation and subtraction techniques of non-linear optical pathlength noise, laser frequency noise, and temperature fluctuations in heterodyne laser interferometers. For each noise source, we identified its contribution and removed it from the measurement by linear fitting or a spectral analysis algorithm. The noise correction algorithm we present in this article can be generally applied to heterodyne laser interferometers.

Published
2022
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16. Quasi-monolithic heterodyne laser interferometer for inertial sensing

Author

Zhang, Yanqi and Guzman, Felipe

Subjects
Physics - Instrumentation and Detectors, Physics - Applied Physics, Physics - Optics
Abstract

We present a compact heterodyne laser interferometer developed for high-sensitivity displacement sensing applications. This interferometer consists of customized prisms and waveplates assembled as a quasi-monolithic unit to realize a miniaturized system. The interferometer design adopts a common-mode rejection scheme to provide a high rejection ratio to common environmental noise. Experimental tests in vacuum show a displacement sensitivity level of 11 pm/rtHz at 100mHz, and as low as 0.6pm/rtHz above 1Hz. The prototype unit has a size of 20mm X 20mm X 10mm and weighs 4.5 grams, which allows subsequent integration into compact systems.

Published
2022
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17. Fiber-based two-wavelength heterodyne laser interferometer

Author

Zhang, Yanqi and Guzman, Felipe

Subjects
Physics - Optics, Physics - Applied Physics, Physics - Instrumentation and Detectors
Abstract

Displacement measuring interferometry is a crucial component in metrology applications. In this paper, we propose a fiber-based two-wavelength heterodyne interferometer as a compact and highly sensitive displacement sensor that can be used in inertial sensing applications. In the proposed design, two individual heterodyne interferometers are constructed using two different wavelengths, 1064 nm and 1055 nm; one of which measures the target displacement and the other monitors the common-mode noise in the fiber system. A narrow-bandwidth spectral filter separates the beam paths of the two interferometers, which are highly common and provide a high rejection ratio to the environmental noise. The preliminary test shows a sensitivity floor of 7.5pm/rtHz at 1Hz when tested in an enclosed chamber. We also investigated the effects of periodic errors due to imperfect spectral separation on the displacement measurement and propose algorithms to mitigate these effects.

Published
2022
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18. Optomechanical accelerometers for geodesy

Author

Hines, Adam, Nelson, Andrea, Zhang, Yanqi, Valdes, Guillermo, Sanjuan, Jose, Stoddart, Jeremiah, and Guzman, Felipe

Subjects
Physics - Geophysics, Physics - Applied Physics, Physics - Instrumentation and Detectors, Physics - Optics, Physics - Space Physics
Abstract

We present a novel optomechanical inertial sensor for low frequency applications and corresponding acceleration measurements. This sensor has a resonant frequency of 4.7Hz, a mechanical quality factor of 476k, a test mass of 2.6 gram, and a projected noise floor of approximately 5E-11 m s-2. per root-Hz at 1Hz. Such performance, together with its small size, low weight, reduced power consumption, and low susceptibility to environmental variables such as magnetic field or drag conditions makes it an attractive technology for future geodesy missions. In this paper, we present an experimental demonstration of low-frequency ground seismic noise detection by direct comparison with a commercial seismometer, anda data analysis algorithms for the identification, characterization, and correction of several noise sources.

Published
2022
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21. Sinan: Data Driven Resource Management for Cloud Microservices

Author

Zhang, Yanqi, Hua, Weizhe, Zhou, Zhuangzhuang, Suh, Ed, and Delimitrou, Christina

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

Cloud applications are increasingly shifting to interactive and loosely-coupled microservices. Despite their advantages, microservices complicate resource management, due to inter-tier dependencies. We present Sinan, a cluster manager for interactive microservices that leverages easily-obtainable tracing data instead of empirical decisions, to infer the impact of a resource allocation on on end-to-end performance, and allocate appropriate resources to each tier. In a preliminary evaluation of Sinan with an end-to-end social network built with microservices, we show that Sinan's data-driven approach, allows the service to always meet its QoS without sacrificing resource efficiency., Comment: arXiv admin note: substantial text overlap with arXiv:2105.13424

Published
2021

29. Sinan: Data-Driven, QoS-Aware Cluster Management for Microservices

Author

Zhang, Yanqi, Hua, Weizhe, Zhou, Zhuangzhuang, Suh, Edward, and Delimitrou, Christina

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

Cloud applications are increasingly shifting from large monolithic services, to large numbers of loosely-coupled, specialized microservices. Despite their advantages in terms of facilitating development, deployment, modularity, and isolation, microservices complicate resource management, as dependencies between them introduce backpressure effects and cascading QoS violations. We present Sinan, a data-driven cluster manager for interactive cloud microservices that is online and QoS-aware. Sinan leverages a set of scalable and validated machine learning models to determine the performance impact of dependencies between microservices, and allocate appropriate resources per tier in a way that preserves the end-to-end tail latency target. We evaluate Sinan both on dedicated local clusters and large-scale deployments on Google Compute Engine (GCE) across representative end-to-end applications built with microservices, such as social networks and hotel reservation sites. We show that Sinan always meets QoS, while also maintaining cluster utilization high, in contrast to prior work which leads to unpredictable performance or sacrifices resource efficiency. Furthermore, the techniques in Sinan are explainable, meaning that cloud operators can yield insights from the ML models on how to better deploy and design their applications to reduce unpredictable performance.

Published
2021

30. Autophagy is essential for human myelopoiesis

Author

Gu, Jiaming, Zhu, Yanling, Lin, Huaisong, Huang, Yuhua, Zhang, Yanqi, Xing, Qi, Kang, Baoqiang, Zhang, Zhishuai, Wang, Mingquan, Zhou, Tiancheng, Mai, Yuchan, Chen, Qianyu, Li, Fei, Hu, Xing, Wang, Shuoting, Peng, Jiaojiao, Guo, Xinrui, Long, Bing, Wang, Junwei, Gao, Minghui, Shan, Yongli, Cui, Yazhou, and Pan, Guangjin

Published
2024
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32. A compact high-precision periodic-error-free heterodyne interferometer

Author

Joo, Ki-Nam, Clark, Erin, Zhang, Yanqi, Ellis, Jonathan D., and Guzman, Felipe

Subjects
Physics - Instrumentation and Detectors, Physics - Applied Physics, Physics - Optics
Abstract

We present the design, bench-top setup, and experimental results of a compact heterodyne interferometer that achieves picometer-level displacement sensitivities in air over frequencies above 100 mHz. The optical configuration with spatially separated beams prevents frequency and polarization mixing, and therefore eliminates periodic errors. The interferometer is designed to maximize common-mode optical laser beam paths to obtain high rejection of environmental disturbances, such as temperature fluctuations and acoustics. The results of our experiments demonstrate the short- and long-term stabilities of the system during stationary and dynamic measurements. In addition, we provide measurements that compare our interferometer prototype with a commercial system, verifying our higher sensitivity of 3\,pm, higher thermal stability by a factor of two, and periodic-error-free performance.

Published
2020
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40. uqSim: Scalable and Validated Simulation of Cloud Microservices

Author

Zhang, Yanqi, Gan, Yu, and Delimitrou, Christina

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

Current cloud services are moving away from monolithic designs and towards graphs of many loosely-coupled, single-concerned microservices. Microservices have several advantages, including speeding up development and deployment, allowing specialization of the software infrastructure, and helping with debugging and error isolation. At the same time they introduce several hardware and software challenges. Given that most of the performance and efficiency implications of microservices happen at scales larger than what is available outside production deployments, studying such effects requires designing the right simulation infrastructures. We present uqSim, a scalable and validated queueing network simulator designed specifically for interactive microservices. uqSim provides detailed intra- and inter-microservice models that allow it to faithfully reproduce the behavior of complex, many-tier applications. uqSim is also modular, allowing reuse of individual models across microservices and end-to-end applications. We have validated uqSim both against simple and more complex microservices graphs, and have shown that it accurately captures performance in terms of throughput and tail latency. Finally, we use uqSim to model the tail at scale effects of request fanout, and the performance impact of power management in latency-sensitive microservices.

Published
2019

41. An Open-Source Benchmark Suite for Cloud and IoT Microservices

Author

Gan, Yu, Zhang, Yanqi, Cheng, Dailun, Shetty, Ankitha, Rathi, Priyal, Katarki, Nayan, Bruno, Ariana, Hu, Justin, Ritchken, Brian, Jackson, Brendon, Hu, Kelvin, Pancholi, Meghna, He, Yuan, Clancy, Brett, Colen, Chris, Wen, Fukang, Leung, Catherine, Wang, Siyuan, Zaruvinsky, Leon, Espinosa, Mateo, Lin, Rick, Liu, Zhongling, Padilla, Jake, and Delimitrou, Christina

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

Cloud services have recently started undergoing a major shift from monolithic applications, to graphs of hundreds of loosely-coupled microservices. Microservices fundamentally change a lot of assumptions current cloud systems are designed with, and present both opportunities and challenges when optimizing for quality of service (QoS) and utilization. In this paper we explore the implications microservices have across the cloud system stack. We first present DeathStarBench, a novel, open-source benchmark suite built with microservices that is representative of large end-to-end services, modular and extensible. DeathStarBench includes a social network, a media service, an e-commerce site, a banking system, and IoT applications for coordination control of UAV swarms. We then use DeathStarBench to study the architectural characteristics of microservices, their implications in networking and operating systems, their challenges with respect to cluster management, and their trade-offs in terms of application design and programming frameworks. Finally, we explore the tail at scale effects of microservices in real deployments with hundreds of users, and highlight the increased pressure they put on performance predictability.

Published
2019

42. Leveraging Deep Learning to Improve the Performance Predictability of Cloud Microservices

Author

Gan, Yu, Zhang, Yanqi, Hu, Kelvin, Cheng, Dailun, He, Yuan, Pancholi, Meghna, and Delimitrou, Christina

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

Performance unpredictability is a major roadblock towards cloud adoption, and has performance, cost, and revenue ramifications. Predictable performance is even more critical as cloud services transition from monolithic designs to microservices. Detecting QoS violations after they occur in systems with microservices results in long recovery times, as hotspots propagate and amplify across dependent services. We present Seer, an online cloud performance debugging system that leverages deep learning and the massive amount of tracing data cloud systems collect to learn spatial and temporal patterns that translate to QoS violations. Seer combines lightweight distributed RPC-level tracing, with detailed low-level hardware monitoring to signal an upcoming QoS violation, and diagnose the source of unpredictable performance. Once an imminent QoS violation is detected, Seer notifies the cluster manager to take action to avoid performance degradation altogether. We evaluate Seer both in local clusters, and in large-scale deployments of end-to-end applications built with microservices with hundreds of users. We show that Seer correctly anticipates QoS violations 91% of the time, and avoids the QoS violation to begin with in 84% of cases. Finally, we show that Seer can identify application-level design bugs, and provide insights on how to better architect microservices to achieve predictable performance.

Published
2019

48. RETRACTED: Tregs depletion aggravates activation of astrocytes by modulating IL-10/GXP4 following cerebral infarction.

Author

Wang, Shuai, Shi, Yubin, Zhang, Yanqi, Yuan, Fengyun, Mao, Mintao, and Ma, Jun

Abstract

Background: Tregs plays a critical role in the development of secondary injuries in diseases. Accumulating evidence suggests an association between ischemic stroke and renal dysfunction; however, the underlying mechanisms remain unclear. This study aimed to investigate the potential of Tregs in inhibiting the activation of astrocytes after focal cerebral infarction. Methods: This study aimed to investigate the renal consequences of focal cerebral ischemia by subjecting a mouse model to transient middle cerebral artery occlusion (tMCAO). Subsequently, we assessed renal fibrosis, renal ferroptosis, Treg infiltration, astrocyte activation, as well as the expression levels of active GPX4, FSP1, IL-10, IL-6, and IL-2 after a 2-week period. Results: In the tMCAO mouse model, depletion of tregs protected against activation of astrocyte and significantly decreased FSP1, IL-6, IL-2, and NLRP3 expression levels, while partially reversing the changes in Tregs. Mechanistically, tregs depletion attenuates renal fibrosis by modulating IL-10/GPX4 following cerebral infarction. Conclusion: Tregs depletion attenuates renal fibrosis by modulating IL-10/GPX4 following cerebral infarction. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Research on logistics management layout optimization and real-time application based on nonlinear programming

Author

Zhang Yanqi, Kou Xiaofei, Song Zhigang, Fan Yuqing, Usman Mohammed, and Jagota Vishal

Subjects
logistics management, nonlinear programming, layout optimization, general algebraic modeling system, Engineering (General). Civil engineering (General), TA1-2040
Abstract

To solve the problem of long logistics delivery time in supply chain, a Mixed Integer Non-linear Program (MINLP) model is built by using Mixed Integer nonlinear programming theory. Firstly, the General algebraic modeling system (GAMS) is used to build the model to fully integrate each parameter of logistics transportation, the total distribution time of the supply chain network, the coverage radius of the logistics base, the number of users, the total capacity of the logistics base, the mode of railway and road transportation, the nonlinear programming model is built and solved by DICOPT solver in GAMS. The cost of logistics can be decreased, transportation time can be reduced, and the logistics system's operating efficiency can be increased in the long term with the help of this algorithm. The proper operation of the logistics system is critical in encouraging the supply chain circulation of various industries and has a direct impact on the society's economic development. The optimal logistics distribution plan with 5 logistics bases covered users of 18 and railway capacity of 2. With the same railway capacity and the same total budget, the larger the number of covered users, the greater the total distribution time increases, but the larger the total budget, the growth of the total distribution time slows down significantly. Experiments show that MINLP model can solve the problem of logistics-based layout optimization in nonlinear logistics management.

Published
2022
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