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7,238 results on '"Koto A"'

1. Examining the Content of Disaster-Related Information Disseminated to a Migrant Population

Author

Maho Aikawa and Koto Akiyoshi

Subjects
disaster preparedness, information dissemination, migrants in Japan, policy assumptions, Bibliography. Library science. Information resources, Communities. Classes. Races, HT51-1595
Abstract

Many local governments, especially in Japan, disseminate multilingual disaster-related information to migrants as part of their disaster education programs to make their disaster risk reduction (DRR) policies more inclusive. However, little is known about the specific content of the information disseminated through such programs and how or why that content is selected. Using an example case of Okayama Prefecture in Japan and its principal city, Okayama City, the current study identifies the content of multilingual disaster-related information, as well as the assumptions behind its selection. To this end, the content of multilingual disaster-related information disseminated by these local governments was inductively analyzed, focusing on (1) what is and is not communicated, (2) how the government views the migrant population, and (3) their goals of information dissemination targeting migrants. As a result of conducting a qualitative content analysis on information disseminated by the Okayama local governments via information booklets and a YouTube video, it was found that the content was largely concerned with calling migrants to be proactive in taking preventive and harm reduction measures, as well as acquiring prerequisite knowledge (e.g., about disasters and their management). The underlying assumptions and expectations behind this content (e.g., how a behavioral change occurs and self-sufficiency as an expectation in disaster preparedness) are discussed, as well as issues associated with these assumptions.

Published
2024
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2. High-speed metasurface modulator using perfectly absorptive bimodal plasmonic resonance

Author

Jiaqi Zhang, Yuji Kosugi, Makoto Ogasawara, Koto Ariu, Akira Otomo, Toshiki Yamada, Yoshiaki Nakano, and Takuo Tanemura

Subjects
Applied optics. Photonics, TA1501-1820
Abstract

Free-space electro-optic (EO) modulators operating at gigahertz and beyond are attractive for a wide range of emerging applications, including high-speed imaging, free-space optical communication, microwave photonics, and diffractive computing. Here, we experimentally demonstrate a high-speed plasmonic metasurface EO modulator operating in a near-infrared wavelength range with a gigahertz modulation bandwidth. To achieve efficient intensity modulation of reflected light from an ultrathin metasurface layer, we utilize the bimodal plasmonic resonance inside a subwavelength metal–insulator–metal grating, which is precisely tuned to satisfy the critical coupling condition. As a result, perfect absorption of −27 dB (99.8%) and a high quality (Q) factor of 113 are obtained at a resonant wavelength of 1650 nm. By incorporating an EO polymer inside the grating, we achieve a modulation depth of up to 9.5 dB under an applied voltage of ±30 V. The 3-dB modulation bandwidth is confirmed to be 1.25 GHz, which is primarily limited by the undesired contact resistance and the output impedance of the driver. Owing to the high electrical conductivity of metallic gratings and a compact device structure with a minimal parasitic capacitance, the demonstrated device can potentially operate at several tens of gigahertz, which opens up exciting opportunities for ultrahigh-speed active metasurface devices in various applications.

Published
2023
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3. Cracking the Code: Multi-domain LLM Evaluation on Real-World Professional Exams in Indonesia

Author

Koto, Fajri

Subjects
Computer Science - Computation and Language
Abstract

While knowledge evaluation in large language models has predominantly focused on academic subjects like math and physics, these assessments often fail to capture the practical demands of real-world professions. In this paper, we introduce IndoCareer, a dataset comprising 8,834 multiple-choice questions designed to evaluate performance in vocational and professional certification exams across various fields. With a focus on Indonesia, IndoCareer provides rich local contexts, spanning six key sectors: (1) healthcare, (2) insurance and finance, (3) creative and design, (4) tourism and hospitality, (5) education and training, and (6) law. Our comprehensive evaluation of 27 large language models shows that these models struggle particularly in fields with strong local contexts, such as insurance and finance. Additionally, while using the entire dataset, shuffling answer options generally maintains consistent evaluation results across models, but it introduces instability specifically in the insurance and finance sectors.

Published
2024

4. Decentralized Droop-based Finite-Control-Set Model Predictive Control of Inverter-based Resources in Islanded AC Microgrid

Author

Olajube, Ayobami, Omiloli, Koto, Vedula, Satish, and Anubi, Olugbenga

Subjects
Mathematics - Optimization and Control
Abstract

This paper presents an improved droop control method to ensure effective power sharing, voltage regulation, and frequency stabilization of inverter-based resources (IBRs) connected in parallel in an islanded AC microgrid. In the contemporary droop control algorithm, the distance between connected inverters affects the effectiveness of the active power-frequency and the reactive power-voltage droop characteristics which results in poor power sharing at the primary level of the microgrid. That is, high impedance emanating from long transmission lines results in instability, poor voltage tracking, and ineffective frequency regulation. Hence, in this work, we use a finite-control-set model predictive controller (FCS-MPC) in the inner loop, which gives efficient voltage tracking, good frequency regulation, and faster performance response. FCS-MPC is easy to implement in fast switching converters and does not suffer from computational burden unlike the continuous-set MPC and is also devoid of issues of multiple-loop, parameter variation, and slow response associated with conventional droop control methods. We derived the condition for bounded stability for the FCS-MPC and the proposed method is tested via a numerical simulation on three IBRs. The results show effective power sharing, capacitor voltage tracking, and efficient frequency regulation with reduced oscillations to changes in load., Comment: Accepted for Publication at MECC 2024, Chicago, IL

Published
2024

5. Fault-Ride-Through (FRT) Control of a grid-connected Fixed-Speed Wind Energy Conversion System using STATCOM

Author

Olajube, Ayobami, Vedula, Satish, Omiloli, Koto, and Anubi, Olugbenga

Subjects
Mathematics - Optimization and Control
Abstract

Wind energy conversion system (WECS) is stochastic in nature and has low inertia to grid voltage instability, poor reactive power compensation and most importantly fault susceptibility. Variable speed WECS such as a doubly-fed induction generators (DFIG) are well known to reach steady state quickly after fault occurrence without the need for an external reactive power source because of the presence of a back-to-back converter that provides independent control of the active and reactive power unlike in the fixed-speed squirrel cage induction generator (SCIG) counterpart that cant be stabilized unless ab external source of reactive power support is present. However, controlling DFIG is complicated and costly due to complete tripping unlike the fixed-speed generators which doesnt trip completely when fault occurs. Hence, in this work, a 48-pulse, 3-phase static synchronous compensator (STATCOM) is used to ensure reactive power compensation and fault-ride through (FRT) control of the SCIG against over-voltage emanating from fault occurrence in a grid-connected power system. The goal here is to guarantee voltage stability and fault-ride through control against injected faults within certain time ranges at the point of common coupling (PCC) between the AC sources, the load, and the fixed-speed WECS., Comment: Accepted for Publication at IEEE IECON-2024

Published
2024

6. SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages

Author

Lovenia, Holy, Mahendra, Rahmad, Akbar, Salsabil Maulana, Miranda, Lester James V., Santoso, Jennifer, Aco, Elyanah, Fadhilah, Akhdan, Mansurov, Jonibek, Imperial, Joseph Marvin, Kampman, Onno P., Moniz, Joel Ruben Antony, Habibi, Muhammad Ravi Shulthan, Hudi, Frederikus, Montalan, Railey, Ignatius, Ryan, Lopo, Joanito Agili, Nixon, William, Karlsson, Börje F., Jaya, James, Diandaru, Ryandito, Gao, Yuze, Amadeus, Patrick, Wang, Bin, Cruz, Jan Christian Blaise, Whitehouse, Chenxi, Parmonangan, Ivan Halim, Khelli, Maria, Zhang, Wenyu, Susanto, Lucky, Ryanda, Reynard Adha, Hermawan, Sonny Lazuardi, Velasco, Dan John, Kautsar, Muhammad Dehan Al, Hendria, Willy Fitra, Moslem, Yasmin, Flynn, Noah, Adilazuarda, Muhammad Farid, Li, Haochen, Lee, Johanes, Damanhuri, R., Sun, Shuo, Qorib, Muhammad Reza, Djanibekov, Amirbek, Leong, Wei Qi, Do, Quyet V., Muennighoff, Niklas, Pansuwan, Tanrada, Putra, Ilham Firdausi, Xu, Yan, Tai, Ngee Chia, Purwarianti, Ayu, Ruder, Sebastian, Tjhi, William, Limkonchotiwat, Peerat, Aji, Alham Fikri, Keh, Sedrick, Winata, Genta Indra, Zhang, Ruochen, Koto, Fajri, Yong, Zheng-Xin, and Cahyawijaya, Samuel

Subjects
Computer Science - Computation and Language
Abstract

Southeast Asia (SEA) is a region rich in linguistic diversity and cultural variety, with over 1,300 indigenous languages and a population of 671 million people. However, prevailing AI models suffer from a significant lack of representation of texts, images, and audio datasets from SEA, compromising the quality of AI models for SEA languages. Evaluating models for SEA languages is challenging due to the scarcity of high-quality datasets, compounded by the dominance of English training data, raising concerns about potential cultural misrepresentation. To address these challenges, we introduce SEACrowd, a collaborative initiative that consolidates a comprehensive resource hub that fills the resource gap by providing standardized corpora in nearly 1,000 SEA languages across three modalities. Through our SEACrowd benchmarks, we assess the quality of AI models on 36 indigenous languages across 13 tasks, offering valuable insights into the current AI landscape in SEA. Furthermore, we propose strategies to facilitate greater AI advancements, maximizing potential utility and resource equity for the future of AI in SEA., Comment: https://seacrowd.github.io/ Accepted in EMNLP 2024

Published
2024

7. CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark

Author

Romero, David, Lyu, Chenyang, Wibowo, Haryo Akbarianto, Lynn, Teresa, Hamed, Injy, Kishore, Aditya Nanda, Mandal, Aishik, Dragonetti, Alina, Abzaliev, Artem, Tonja, Atnafu Lambebo, Balcha, Bontu Fufa, Whitehouse, Chenxi, Salamea, Christian, Velasco, Dan John, Adelani, David Ifeoluwa, Meur, David Le, Villa-Cueva, Emilio, Koto, Fajri, Farooqui, Fauzan, Belcavello, Frederico, Batnasan, Ganzorig, Vallejo, Gisela, Caulfield, Grainne, Ivetta, Guido, Song, Haiyue, Ademtew, Henok Biadglign, Maina, Hernán, Lovenia, Holy, Azime, Israel Abebe, Cruz, Jan Christian Blaise, Gala, Jay, Geng, Jiahui, Ortiz-Barajas, Jesus-German, Baek, Jinheon, Dunstan, Jocelyn, Alemany, Laura Alonso, Nagasinghe, Kumaranage Ravindu Yasas, Benotti, Luciana, D'Haro, Luis Fernando, Viridiano, Marcelo, Estecha-Garitagoitia, Marcos, Cabrera, Maria Camila Buitrago, Rodríguez-Cantelar, Mario, Jouitteau, Mélanie, Mihaylov, Mihail, Imam, Mohamed Fazli Mohamed, Adilazuarda, Muhammad Farid, Gochoo, Munkhjargal, Otgonbold, Munkh-Erdene, Etori, Naome, Niyomugisha, Olivier, Silva, Paula Mónica, Chitale, Pranjal, Dabre, Raj, Chevi, Rendi, Zhang, Ruochen, Diandaru, Ryandito, Cahyawijaya, Samuel, Góngora, Santiago, Jeong, Soyeong, Purkayastha, Sukannya, Kuribayashi, Tatsuki, Jayakumar, Thanmay, Torrent, Tiago Timponi, Ehsan, Toqeer, Araujo, Vladimir, Kementchedjhieva, Yova, Burzo, Zara, Lim, Zheng Wei, Yong, Zheng Xin, Ignat, Oana, Nwatu, Joan, Mihalcea, Rada, Solorio, Thamar, and Aji, Alham Fikri

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

Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge present in both visual and textual data. However, most of the current VQA models use datasets that are primarily focused on English and a few major world languages, with images that are typically Western-centric. While recent efforts have tried to increase the number of languages covered on VQA datasets, they still lack diversity in low-resource languages. More importantly, although these datasets often extend their linguistic range via translation or some other approaches, they usually keep images the same, resulting in narrow cultural representation. To address these limitations, we construct CVQA, a new Culturally-diverse multilingual Visual Question Answering benchmark, designed to cover a rich set of languages and cultures, where we engage native speakers and cultural experts in the data collection process. As a result, CVQA includes culturally-driven images and questions from across 28 countries on four continents, covering 26 languages with 11 scripts, providing a total of 9k questions. We then benchmark several Multimodal Large Language Models (MLLMs) on CVQA, and show that the dataset is challenging for the current state-of-the-art models. This benchmark can serve as a probing evaluation suite for assessing the cultural capability and bias of multimodal models and hopefully encourage more research efforts toward increasing cultural awareness and linguistic diversity in this field.

Published
2024

8. Battery Degradation Heuristics for Predictive Energy Management in Shipboard Power Systems

Author

Vedula, Satish, Olajube, Ayobami, Omiloli, Koto, and Anubi, Olugbenga Moses

Subjects
Mathematics - Optimization and Control
Abstract

The presence of Pulse Power Loads (PPLs) in the Notional Shipboard Power System (SPS) presents a challenge in the form of meeting their high ramp rate requirements. Considering the ramp rate limitations on the generators, this might hinder the power flow in the grid. Failure to meet the ramp rate requirements might cause instability. Aggregating generators with energy storage elements usually addresses the ramp requirements while ensuring the power demand is achieved. This paper proposes an energy management strategy that adaptively splits the power demand between the generators and the batteries while simultaneously considering the battery degradation and the generator's efficient operation. Since it is challenging to incorporate the battery degradation model directly into the optimization problem due to its complex structure and the degradation time scale which is not practical for real-time implementation, two reasonable heuristics in terms of minimizing the absolute battery power and minimizing the battery state of charge are proposed and compared to manage the battery degradation. A model predictive energy management strategy is then developed to coordinate the power split considering the generator efficiency and minimizing the battery degradation based on the two heuristic approaches. The designed strategy is tested via a simulation of a lumped notional shipboard power system. The results show the impact of the battery degradation heuristics for energy management strategy in mitigating battery degradation and its health management., Comment: Accepted for publication at IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, Chicago, IL

Published
2024

9. First joint oscillation analysis of Super-Kamiokande atmospheric and T2K accelerator neutrino data

Author

Super-Kamiokande, collaborations, T2K, Abe, S., Abe, K., Akhlaq, N., Akutsu, R., Alarakia-Charles, H., Ali, A., Hakim, Y. I. Alj, Monsalve, S. Alonso, Amanai, S., Andreopoulos, C., Anthony, L. H. V., Antonova, M., Aoki, S., Apte, K. A., Arai, T., Arihara, T., Arimoto, S., Asada, Y., Asaka, R., Ashida, Y., Atkin, E. T., Babu, N., Barbi, M., Barker, G. J., Barr, G., Barrow, D., Bates, P., Batkiewicz-Kwasniak, M., Beauchêne, A., Berardi, V., Berns, L., Bhadra, S., Bhuiyan, N., Bian, J., Blanchet, A., Blondel, A., Bodur, B., Bolognesi, S., Bordoni, S., Boyd, S. B., Bravar, A., Bronner, C., Bubak, A., Avanzini, M. Buizza, Burton, G. T., Caballero, J. A., Calabria, N. F., Cao, S., Carabadjac, D., Carter, A. J., Cartwright, S. L., Casado, M. P., Catanesi, M. G., Cervera, A., Chakrani, J., Chalumeau, A., Chen, S., Cherdack, D., Choi, K., Chong, P. S., Chvirova, A., Cicerchia, M., Coleman, J., Collazuol, G., Cook, L., Cormier, F., Cudd, A., Dalmazzone, C., Daret, T., Dasgupta, P., Davis, C., Davydov, Yu. I., De Roeck, A., De Rosa, G., Dealtry, T., Delogu, C. C., Densham, C., Dergacheva, A., Dharmapal, R., Di Lodovico, F., Lopez, G. Diaz, Dolan, S., Douqa, D., Doyle, T. A., Drapier, O., Duffy, K. E., Dumarchez, J., Dunne, P., Dygnarowicz, K., D'ago, D., Edwards, R., Eguchi, A., Elias, J., Emery-Schrenk, S., Erofeev, G., Ershova, A., Eurin, G., Fannon, J. E. P., Fedorova, D., Fedotov, S., Feltre, M., Feng, J., Feng, L., Ferlewicz, D., Fernandez, P., Finch, A. J., Aguirre, G. A. Fiorentini, Fiorillo, G., Fitton, M. D., Patiño, J. M. Franco, Friend, M., Fujii, Y., Fujisawa, C., Fujita, S., Fukuda, Y., Furui, Y., Gao, J., Gaur, R., Giampaolo, A., Giannessi, L., Giganti, C., Glagolev, V., Goldsack, A., Gonin, M., Rosa, J. González, Goodman, E. A. G., Gorin, A., Gorshanov, K., Gousy-Leblanc, V., Grassi, M., Griskevich, N. J., Guigue, M., Hadley, D., Haigh, J. T., Han, S., Harada, M., Harris, D. A., Hartz, M., Hasegawa, T., Hassani, S., Hastings, N. C., Hayato, Y., Heitkamp, I., Henaff, D., Hill, J., Hino, Y., Hiraide, K., Hogan, M., Holeczek, J., Holin, A., Holvey, T., Van, N. T. Hong, Honjo, T., Horiuchi, S., Hosokawa, K., Hu, Z., Hu, J., Iacob, F., Ichikawa, A. K., Ieki, K., Ikeda, M., Iovine, N., Ishida, T., Ishino, H., Ishitsuka, M., Ishizuka, T., Ito, H., Itow, Y., Izmaylov, A., Izumiyama, S., Jakkapu, M., Jamieson, B., Jang, M. C., Jang, J. S., Jenkins, S. J., Jesús-Valls, C., Ji, J. Y., Jia, M., Jiang, J., Jonsson, P., Joshi, S., Jung, C. K., Jung, S., Kabirnezhad, M., Kaboth, A. C., Kajita, T., Kakuno, H., Kameda, J., Kanemura, Y., Kaneshima, R., Karpova, S., Kasetti, S. P., Kashiwagi, Y., Kasturi, V. S., Kataoka, Y., Katori, T., Kawamura, Y., Kawaue, M., Kearns, E., Khabibullin, M., Khotjantsev, A., Kikawa, T., Kim, S. B., King, S., Kiseeva, V., Kisiel, J., Kneale, L., Kobayashi, H., Kobayashi, T., Kobayashi, M., Koch, L., Kodama, S., Kolupanova, M., Konaka, A., Kormos, L. L., Koshio, Y., Koto, T., Kowalik, K., Kudenko, Y., Kudo, Y., Kuribayashi, S., Kurjata, R., Kurochka, V., Kutter, T., Kuze, M., Kwon, E., La Commara, M., Labarga, L., Lachat, M., Lachner, K., Lagoda, J., Lakshmi, S. M., LamersJames, M., Langella, A., Laporte, J. -F., Last, D., Latham, N., Laveder, M., Lavitola, L., Lawe, M., Learned, J. G., Lee, Y., Lee, S. H., Silverio, D. Leon, Levorato, S., Lewis, S., Li, X., Li, W., Lin, C., Litchfield, R. P., Liu, S. L., Liu, Y. M., Long, K. R., Longhin, A., Moreno, A. Lopez, Lu, X., Ludovici, L., Lux, T., Machado, L. N., Maekawa, Y., Magaletti, L., Mahn, K., Mahtani, K. K., Malek, M., Mandal, M., Manly, S., Marino, A. D., Martens, K., Marti, Ll., Martin, D. G. R., Martin, J. F., Martin, D., Martini, M., Maruyama, T., Matsubara, T., Matsumoto, R., Mattiazzi, M., Matveev, V., Mauger, C., Mavrokoridis, K., Mazzucato, E., McCauley, N., McElwee, J. M., McFarland, K. S., McGrew, C., McKean, J., Mefodiev, A., Megias, G. D., Mehta, P., Mellet, L., Menjo, H., Metelko, C., Mezzetto, M., Migenda, J., Mijakowski, P., Miki, S., Miller, E., Minamino, A., Mine, S., Mineev, O., Mirabito, J., Miura, M., Bueno, L. Molina, Moon, D. H., Mori, M., Moriyama, S., Morrison, P., Muñoz, A., Mueller, Th. A., Munford, D., Munteanu, L., Nagai, Y., Nagai, K., Nakadaira, T., Nakagiri, K., Nakahata, M., Nakajima, Y., Nakamura, A., Nakamura, K., Nakamura, K. D., Nakamura, T., Nakanishi, F., Nakano, Y., Nakaya, T., Nakayama, S., Nakayoshi, K., Naseby, C. E. R., Ngoc, T. V., Nguyen, V. Q., Nguyen, D. T., Nicholson, M., Niewczas, K., Ninomiya, K., Nishijima, K., Nishimori, S., Nishimura, Y., Noguchi, Y., Nosek, T., Nova, F., Novella, P., Nugent, J. C., Odagawa, T., Okazaki, R., Okazawa, H., Okinaga, W., Okumura, K., Okusawa, T., Ommura, Y., Onda, N., Ospina, N., Osu, L., Oyama, Y., O'Flaherty, M., O'Keeffe, H. M., O'Sullivan, L., Périssé, L., Paganini, P., Palladino, V., Paolone, V., Pari, M., Park, R. G., Parlone, J., Pasternak, J., Payne, D., Penn, G. C., de Perio, P., Pershey, D., Pfaff, M., Pickering, L., Pintaudi, G., Pistillo, C., Pointon, B. W., Popov, B., Yrey, A. Portocarrero, Porwit, K., Posiadala-Zezula, M., Prabhu, Y. S., Prasad, H., Pronost, G., Prouse, N. W., Pupilli, F., Quilain, B., Quyen, P. T., Raaf, J. L., Radermacher, T., Radicioni, E., Radics, B., Ramirez, M. A., Ramsden, R. M., Ratoff, P. N., Reh, M., Riccio, C., Richards, B., Rogly, R., Rondio, E., Roth, S., Roy, N., Rubbia, A., Russo, L., Rychter, A., Saenz, W., Sakai, S., Sakashita, K., Samani, S., Santos, A. D., Sato, Y., Sato, K., Schefke, T., Schloesser, C. M., Scholberg, K., Scott, M., Seiya, Y., Sekiguchi, T., Sekiya, H., Seo, J. W., Sgalaberna, D., Shaikhiev, A., Shi, W., Shiba, H., Shibayama, R., Shigeta, N., Shima, S., Shimamura, R., Shimizu, K., Shinoki, M., Shiozawa, M., Shiraishi, Y., Shvartsman, A., Skrobova, N., Skwarczynski, K., Smy, M. B., Smyczek, D., Sobczyk, J. T., Sobel, H. W., Soler, F. J. P., Sonoda, Y., Speers, A. J., Spina, R., Stroke, Y., Suslov, I. A., Suvorov, S., Suzuki, S., Suzuki, A., Suzuki, S. Y., Suzuki, Y., Sánchez, F., Tada, T., Tada, M., Tairafune, S., Takagi, Y., Takeda, A., Takemoto, Y., Takeuchi, Y., Takhistov, V., Takifuji, K., Tanaka, H., Tanaka, H. K., Tanigawa, H., Taniuchi, N., Tano, T., Tarrant, A., Tashiro, T., Teklu, A., Terada, K., Tereshchenko, V. V., Thamm, N., Thiesse, M. D., Thompson, L. F., Toki, W., Tomiya, T., Touramanis, C., Tsui, K. M., Tsukamoto, T., Tzanov, M., Uchida, Y., Vagins, M. R., Vargas, D., Varghese, M., Vasseur, G., Villa, E., Vinning, W. G. S., Virginet, U., Vladisavljevic, T., Wachala, T., Wakabayashi, D., Wallace, H. T., Walsh, J. G., Walter, C. W., Wan, L., Wang, X., Wang, Y., Wark, D., Wascko, M. O., Watanabe, E., Weber, A., Wendell, R. A., Wester, T., Wilking, M. J., Wilkinson, C., Wilson, S. T., Wilson, J. R., Wood, K., Wret, C., Wu, Y., Xia, J., Xie, Z., Xu, B. D., Xu, Y. -H., Yamamoto, K., Yamamoto, T., Yamauchi, K., Yanagisawa, C., Yang, G., Yang, B. S., Yang, J. Y., Yankelevich, A., Yano, T., Yasutome, K., Yershov, N., Yevarouskaya, U., Yokoyama, M., Yoo, J., Yoshida, T., Yoshida, S., Yoshimoto, Y., Yoshimura, N., Yoshioka, Y., Yu, M., Yu, I., Zaki, R., Zaldivar, B., Zalewska, A., Zalipska, J., Zaremba, K., Zarnecki, G., Zhang, J., Zhang, A. Q., Zhang, B., Zhao, X. Y., Zhong, H., Zhu, T., Ziembicki, M., Zimmerman, E. D., Zito, M., and Zsoldos, S.

Subjects
High Energy Physics - Experiment
Abstract

The Super-Kamiokande and T2K collaborations present a joint measurement of neutrino oscillation parameters from their atmospheric and beam neutrino data. It uses a common interaction model for events overlapping in neutrino energy and correlated detector systematic uncertainties between the two datasets, which are found to be compatible. Using 3244.4 days of atmospheric data and a beam exposure of $19.7(16.3) \times 10^{20}$ protons on target in (anti)neutrino mode, the analysis finds a 1.9$\sigma$ exclusion of CP-conservation (defined as $J_{CP}=0$) and a preference for the normal mass ordering., Comment: 12 pages, 4 figures

Published
2024

10. Cendol: Open Instruction-tuned Generative Large Language Models for Indonesian Languages

Author

Cahyawijaya, Samuel, Lovenia, Holy, Koto, Fajri, Putri, Rifki Afina, Dave, Emmanuel, Lee, Jhonson, Shadieq, Nuur, Cenggoro, Wawan, Akbar, Salsabil Maulana, Mahendra, Muhammad Ihza, Putri, Dea Annisayanti, Wilie, Bryan, Winata, Genta Indra, Aji, Alham Fikri, Purwarianti, Ayu, and Fung, Pascale

Subjects
Computer Science - Computation and Language
Abstract

Large language models (LLMs) show remarkable human-like capability in various domains and languages. However, a notable quality gap arises in low-resource languages, e.g., Indonesian indigenous languages, rendering them ineffective and inefficient in such linguistic contexts. To bridge this quality gap, we introduce Cendol, a collection of Indonesian LLMs encompassing both decoder-only and encoder-decoder architectures across a range of model sizes. We highlight Cendol's effectiveness across a diverse array of tasks, attaining 20% improvement, and demonstrate its capability to generalize to unseen tasks and indigenous languages of Indonesia. Furthermore, Cendol models showcase improved human favorability despite their limitations in capturing indigenous knowledge and cultural values in Indonesia. In addition, we discuss the shortcomings of parameter-efficient tunings, such as LoRA, for language adaptation. Alternatively, we propose the usage of vocabulary adaptation to enhance efficiency. Lastly, we evaluate the safety of Cendol and showcase that safety in pre-training in one language such as English is transferable to low-resource languages, such as Indonesian, even without RLHF and safety fine-tuning., Comment: Cendol models are released under Apache 2.0 license and will be made publicly available soon

Published
2024

11. IndoCulture: Exploring Geographically-Influenced Cultural Commonsense Reasoning Across Eleven Indonesian Provinces

Author

Koto, Fajri, Mahendra, Rahmad, Aisyah, Nurul, and Baldwin, Timothy

Subjects
Computer Science - Computation and Language
Abstract

Although commonsense reasoning is greatly shaped by cultural and geographical factors, previous studies have predominantly centered on cultures grounded in the English language, potentially resulting in an Anglocentric bias. In this paper, we introduce IndoCulture, aimed at understanding the influence of geographical factors on language model reasoning ability, with a specific emphasis on the diverse cultures found within eleven Indonesian provinces. In contrast to prior work that has relied on templates (Yin et al., 2022) and online scrapping (Fung et al., 2024), we create IndoCulture by asking local people to manually develop a cultural context and plausible options, across a set of predefined topics. Evaluation of 27 language models reveals several insights: (1) the open-weight Llama-3 is competitive with GPT-4, while other open-weight models struggle, with accuracies below 50%; (2) there is a general pattern of models generally performing better for some provinces, such as Bali and West Java, and less well for others; and (3) the inclusion of location context enhances performance, especially for larger models like GPT-4, emphasizing the significance of geographical context in commonsense reasoning., Comment: Accepted at TACL

Published
2024

13. Molecular docking and molecular dynamics simulation studies of the interaction of anti-oral cancer plant Curcuma longa derived-compounds with human epidermal growth factor receptor 2

Author

Charly, Mfutu Mana, Ngbolua, Jean-Paul Koto-Te-Nyiwa, Issouradi, Jean-Paul Sekele, Mulongo, Emmanuel Kitete, Ashande, Colette Masengo, Thambwe, Jason Kilembe, Matondo, Aristote, Hippolyte, Situakibanza Nani-Tuma, Erick, Kamangu Ntambwe, Alifi, Paul Bobe, Adelin, Nzudjom Foche, and Mpiana, Pius Tshimankinda

Published
2024
Full Text
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15. ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic

Author

Koto, Fajri, Li, Haonan, Shatnawi, Sara, Doughman, Jad, Sadallah, Abdelrahman Boda, Alraeesi, Aisha, Almubarak, Khalid, Alyafeai, Zaid, Sengupta, Neha, Shehata, Shady, Habash, Nizar, Nakov, Preslav, and Baldwin, Timothy

Subjects
Computer Science - Computation and Language
Abstract

The focus of language model evaluation has transitioned towards reasoning and knowledge-intensive tasks, driven by advancements in pretraining large models. While state-of-the-art models are partially trained on large Arabic texts, evaluating their performance in Arabic remains challenging due to the limited availability of relevant datasets. To bridge this gap, we present \datasetname{}, the first multi-task language understanding benchmark for the Arabic language, sourced from school exams across diverse educational levels in different countries spanning North Africa, the Levant, and the Gulf regions. Our data comprises 40 tasks and 14,575 multiple-choice questions in Modern Standard Arabic (MSA) and is carefully constructed by collaborating with native speakers in the region. Our comprehensive evaluations of 35 models reveal substantial room for improvement, particularly among the best open-source models. Notably, BLOOMZ, mT0, LLaMA2, and Falcon struggle to achieve a score of 50%, while even the top-performing Arabic-centric model only achieves a score of 62.3%., Comment: Findings of ACL 2024

Published
2024

16. Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon

Author

Koto, Fajri, Beck, Tilman, Talat, Zeerak, Gurevych, Iryna, and Baldwin, Timothy

Subjects
Computer Science - Computation and Language
Abstract

Improving multilingual language models capabilities in low-resource languages is generally difficult due to the scarcity of large-scale data in those languages. In this paper, we relax the reliance on texts in low-resource languages by using multilingual lexicons in pretraining to enhance multilingual capabilities. Specifically, we focus on zero-shot sentiment analysis tasks across 34 languages, including 6 high/medium-resource languages, 25 low-resource languages, and 3 code-switching datasets. We demonstrate that pretraining using multilingual lexicons, without using any sentence-level sentiment data, achieves superior zero-shot performance compared to models fine-tuned on English sentiment datasets, and large language models like GPT--3.5, BLOOMZ, and XGLM. These findings are observable for unseen low-resource languages to code-mixed scenarios involving high-resource languages., Comment: Accepted at EACL 2024

Published
2024

17. LLM360: Towards Fully Transparent Open-Source LLMs

Author

Liu, Zhengzhong, Qiao, Aurick, Neiswanger, Willie, Wang, Hongyi, Tan, Bowen, Tao, Tianhua, Li, Junbo, Wang, Yuqi, Sun, Suqi, Pangarkar, Omkar, Fan, Richard, Gu, Yi, Miller, Victor, Zhuang, Yonghao, He, Guowei, Li, Haonan, Koto, Fajri, Tang, Liping, Ranjan, Nikhil, Shen, Zhiqiang, Ren, Xuguang, Iriondo, Roberto, Mu, Cun, Hu, Zhiting, Schulze, Mark, Nakov, Preslav, Baldwin, Tim, and Xing, Eric P.

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

The recent surge in open-source Large Language Models (LLMs), such as LLaMA, Falcon, and Mistral, provides diverse options for AI practitioners and researchers. However, most LLMs have only released partial artifacts, such as the final model weights or inference code, and technical reports increasingly limit their scope to high-level design choices and surface statistics. These choices hinder progress in the field by degrading transparency into the training of LLMs and forcing teams to rediscover many details in the training process. We present LLM360, an initiative to fully open-source LLMs, which advocates for all training code and data, model checkpoints, and intermediate results to be made available to the community. The goal of LLM360 is to support open and collaborative AI research by making the end-to-end LLM training process transparent and reproducible by everyone. As a first step of LLM360, we release two 7B parameter LLMs pre-trained from scratch, Amber and CrystalCoder, including their training code, data, intermediate checkpoints, and analyses (at https://www.llm360.ai). We are committed to continually pushing the boundaries of LLMs through this open-source effort. More large-scale and stronger models are underway and will be released in the future.

Published
2023

21. A mirror theorem for non-split toric bundles

Author

Koto, Yuki

Subjects
Mathematics - Algebraic Geometry, Mathematics - Symplectic Geometry
Abstract

We construct an I-function for toric bundles obtained as a fiberwise GIT quotient of a (not necessarily split) vector bundle. This is a generalization of Brown's I-function for split toric bundles and the I-function for non-split projective bundles. In order to prove the mirror theorem, we establish a characterization of points on the Givental Lagrangian cones of toric bundles and prove a mirror theorem for the twisted Gromov-Witten theory of a fiber product of projective bundles. The former result generalizes Brown's characterization for split toric bundles to the non-split case., Comment: 46 pages; v2 added an appendix; v3 minor changes, typo corrected

Published
2023

22. Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLU

Author

Koto, Fajri, Aisyah, Nurul, Li, Haonan, and Baldwin, Timothy

Subjects
Computer Science - Computation and Language
Abstract

Although large language models (LLMs) are often pre-trained on large-scale multilingual texts, their reasoning abilities and real-world knowledge are mainly evaluated based on English datasets. Assessing LLM capabilities beyond English is increasingly vital but hindered due to the lack of suitable datasets. In this work, we introduce IndoMMLU, the first multi-task language understanding benchmark for Indonesian culture and languages, which consists of questions from primary school to university entrance exams in Indonesia. By employing professional teachers, we obtain 14,981 questions across 64 tasks and education levels, with 46% of the questions focusing on assessing proficiency in the Indonesian language and knowledge of nine local languages and cultures in Indonesia. Our empirical evaluations show that GPT-3.5 only manages to pass the Indonesian primary school level, with limited knowledge of local Indonesian languages and culture. Other smaller models such as BLOOMZ and Falcon perform at even lower levels., Comment: Accepted at EMNLP 2023

Published
2023

23. NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages

Author

Cahyawijaya, Samuel, Lovenia, Holy, Koto, Fajri, Adhista, Dea, Dave, Emmanuel, Oktavianti, Sarah, Akbar, Salsabil Maulana, Lee, Jhonson, Shadieq, Nuur, Cenggoro, Tjeng Wawan, Linuwih, Hanung Wahyuning, Wilie, Bryan, Muridan, Galih Pradipta, Winata, Genta Indra, Moeljadi, David, Aji, Alham Fikri, Purwarianti, Ayu, and Fung, Pascale

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

Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translation. While these methods have proven effective and cost-efficient, we have identified limitations in the resulting corpora, including a lack of lexical diversity and cultural relevance to local communities. To address this gap, we conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content. In addition, we present the \datasetname{} benchmark, encompassing 12 underrepresented and extremely low-resource languages spoken by millions of individuals in Indonesia. Our empirical experiment results using existing multilingual large language models conclude the need to extend these models to more underrepresented languages. We release the NusaWrites dataset at https://github.com/IndoNLP/nusa-writes.

Published
2023

24. Are Multilingual LLMs Culturally-Diverse Reasoners? An Investigation into Multicultural Proverbs and Sayings

Author

Liu, Chen Cecilia, Koto, Fajri, Baldwin, Timothy, and Gurevych, Iryna

Subjects
Computer Science - Computation and Language
Abstract

Large language models (LLMs) are highly adept at question answering and reasoning tasks, but when reasoning in a situational context, human expectations vary depending on the relevant cultural common ground. As languages are associated with diverse cultures, LLMs should also be culturally-diverse reasoners. In this paper, we study the ability of a wide range of state-of-the-art multilingual LLMs (mLLMs) to reason with proverbs and sayings in a conversational context. Our experiments reveal that: (1) mLLMs "know" limited proverbs and memorizing proverbs does not mean understanding them within a conversational context; (2) mLLMs struggle to reason with figurative proverbs and sayings, and when asked to select the wrong answer (instead of asking it to select the correct answer); and (3) there is a "culture gap" in mLLMs when reasoning about proverbs and sayings translated from other languages. We construct and release our evaluation dataset MAPS (MulticultrAl Proverbs and Sayings) for proverb understanding with conversational context for six different languages., Comment: NAACL

Published
2023

25. Measurements of the $\nu_{\mu}$ and $\bar{\nu}_{\mu}$-induced Coherent Charged Pion Production Cross Sections on $^{12}C$ by the T2K experiment

Author

Abe, K., Akhlaq, N., Akutsu, R., Ali, A., Monsalve, S. Alonso, Alt, C., Andreopoulos, C., Antonova, M., Aoki, S., Arihara, T., Asada, Y., Ashida, Y., Atkin, E. T., Barbi, M., Barker, G. J., Barr, G., Barrow, D., Batkiewicz-Kwasniak, M., Berardi, V., Berns, L., Bhadra, S., Blanchet, A., Blondel, A., Bolognesi, S., Bonus, T., Bordoni, S., Boyd, S. B., Bravar, A., Bronner, C., Bron, S., Bubak, A., Avanzini, M. Buizza, Caballero, J. A., Calabria, N. F., Cao, S., Carabadjac, D., Carter, A. J., Cartwright, S. L., Casado, M. P., Catanesi, M. G., Cervera, A., Chakrani, J., Cherdack, D., Chong, P. S., Christodoulou, G., Chvirova, A., Cicerchia, M., Coleman, J., Collazuol, G., Cook, L., Cudd, A., Dalmazzone, C., Daret, T., Davydov, Yu. I., De Roeck, A., De Rosa, G., Dealtry, T., Delogu, C. C., Densham, C., Dergacheva, A., Di Lodovico, F., Dolan, S., Douqa, D., Doyle, T. A., Drapier, O., Dumarchez, J., Dunne, P., Dygnarowicz, K., Eguchi, A., Emery-Schrenk, S., Erofeev, G., Ershova, A., Eurin, G., Fedorova, D., Fedotov, S., Feltre, M., Finch, A. J., Aguirre, G. A. Fiorentini, Fiorillo, G., Fitton, M. D., Patiño, J. M. Franco, Friend, M., Fujii, Y., Fukuda, Y., Furui, Y., Giannessi, L., Giganti, C., Glagolev, V., Gonin, M., Rosa, J. González, Goodman, E. A. G., Gorin, A., Grassi, M., Guigue, M., Hadley, D. R., Haigh, J. T., Hamacher-Baumann, P., Harris, D. A., Hartz, M., Hasegawa, T., Hassani, S., Hastings, N. C., Hayato, Y., Henaff, D., Hogan, M., Holeczek, J., Holin, A., Holvey, T., Van, N. T. Hong, Honjo, T., Ichikawa, A. K., Ikeda, M., Ishida, T., Ishitsuka, M., Israel, H. T., Izmaylov, A., Jakkapu, M., Jamieson, B., Jenkins, S. J., Jesús-Valls, C., Jiang, J. J., Ji, J. Y., Jonsson, P., Joshi, S., Jung, C. K., Jurj, P. B., Kabirnezhad, M., Kaboth, A. C., Kajita, T., Kakuno, H., Kameda, J., Kasetti, S. P., Kataoka, Y., Katori, T., Kawaue, M., Kearns, E., Khabibullin, M., Khotjantsev, A., Kikawa, T., King, S., Kiseeva, V., Kisiel, J., Kobayashi, H., Kobayashi, T., Koch, L., Kodama, S., Konaka, A., Kormos, L. L., Koshio, Y., Koto, T., Kowalik, K., Kudenko, Y., Kudo, Y., Kuribayashi, S., Kurjata, R., Kutter, T., Kuze, M., La Commara, M., Labarga, L., Lachner, K., Lagoda, J., Lakshmi, S. M., James, M. Lamers, Lamoureux, M., Langella, A., Laporte, J. -F., Last, D., Latham, N., Laveder, M., Lavitola, L., Lawe, M., Lee, Y., Lin, C., Lin, S. -K., Litchfield, R. P., Liu, S. L., Li, W., Longhin, A., Long, K. R., Moreno, A. Lopez, Ludovici, L., Lu, X., Lux, T., Machado, L. N., Magaletti, L., Mahn, K., Malek, M., Mandal, M., Manly, S., Marino, A. D., Marti-Magro, L., Martin, D. G. R., Martini, M., Martin, J. F., Maruyama, T., Matsubara, T., Matveev, V., Mauger, C., Mavrokoridis, K., Mazzucato, E., McCauley, N., McElwee, J., McFarland, K. S., McGrew, C., McKean, J., Mefodiev, A., Megias, G. D., Mehta, P., Mellet, L., Metelko, C., Mezzetto, M., Miller, E., Minamino, A., Mineev, O., Mine, S., Miura, M., Bueno, L. Molina, Moriyama, S., Morrison, P., Mueller, Th. A., Munford, D., Munteanu, L., Nagai, K., Nagai, Y., Nakadaira, T., Nakagiri, K., Nakahata, M., Nakajima, Y., Nakamura, A., Nakamura, H., Nakamura, K., Nakamura, K. D., Nakano, Y., Nakayama, S., Nakaya, T., Nakayoshi, K., Naseby, C. E. R., Ngoc, T. V., Nguyen, V. Q., Niewczas, K., Nishimori, S., Nishimura, Y., Nishizaki, K., Nosek, T., Nova, F., Novella, P., Nugent, J. C., O'Keeffe, H. M., O'Sullivan, L., Odagawa, T., Okinaga, W., Okumura, K., Okusawa, T., Ospina, N., Oyama, Y., Palladino, V., Paolone, V., Pari, M., Parlone, J., Pasternak, J., Pavin, M., Payne, D., Penn, G. C., Pershey, D., Pickering, L., Pidcott, C., Pintaudi, G., Pistillo, C., Popov, B., Porwit, K., Posiadala-Zezula, M., Prabhu, Y. S., Pupilli, F., Quilain, B., Radermacher, T., Radicioni, E., Radics, B., Ramírez, M. A., Ratoff, P. N., Reh, M., Riccio, C., Rondio, E., Roth, S., Roy, N., Rubbia, A., Ruggeri, A. C., Ruggles, C. A., Rychter, A., Sakashita, K., Sánchez, F., Schloesser, C. M., Scholberg, K., Scott, M., Seiya, Y., Sekiguchi, T., Sekiya, H., Sgalaberna, D., Shaikhiev, A., Shaker, F., Shiozawa, M., Shorrock, W., Shvartsman, A., Skrobova, N., Skwarczynski, K., Smyczek, D., Smy, M., Sobczyk, J. T., Sobel, H., Soler, F. J. P., Sonoda, Y., Speers, A. J., Spina, R., Suslov, I. A., Suvorov, S., Suzuki, A., Suzuki, S. Y., Suzuki, Y., Tada, M., Tairafune, S., Takayasu, S., Takeda, A., Takeuchi, Y., Takifuji, K., Tanaka, H. K., Tani, M., Teklu, A., Tereshchenko, V. V., Thamm, N., Thompson, L. F., Toki, W., Touramanis, C., Towstego, T., Tsui, K. M., Tsukamoto, T., Tzanov, M., Uchida, Y., Vagins, M., Vargas, D., Varghese, M., Vasseur, G., Vilela, C., Villa, E., Vinning, W. G. S., Virginet, U., Vladisavljevic, T., Wachala, T., Walsh, J. G., Wang, Y., Wan, L., Wark, D., Wascko, M. O., Weber, A., Wendell, R., Wilking, M. J., Wilkinson, C., Wilson, J. R., Wood, K., Wret, C., Xia, J., Xu, Y. -h., Yamamoto, K., Yamamoto, T., Yanagisawa, C., Yang, G., Yano, T., Yasutome, K., Yershov, N., Yevarouskaya, U., Yokoyama, M., Yoshimoto, Y., Yoshimura, N., Yu, M., Zaki, R., Zalewska, A., Zalipska, J., Zaremba, K., Zarnecki, G., Zhao, X., Zhu, T., Ziembicki, M., Zimmerman, E. D., Zito, M., and Zsoldos, S.

Subjects
High Energy Physics - Experiment
Abstract

We report an updated measurement of the $\nu_{\mu}$-induced, and the first measurement of the $\bar{\nu}_{\mu}$-induced coherent charged pion production cross section on $^{12}C$ nuclei in the T2K experiment. This is measured in a restricted region of the final-state phase space for which $p_{\mu,\pi} > 0.2$ GeV, $\cos(\theta_{\mu}) > 0.8$ and $\cos(\theta_{\pi}) > 0.6$, and at a mean (anti)neutrino energy of 0.85 GeV using the T2K near detector. The measured $\nu_{\mu}$ CC coherent pion production flux-averaged cross section on $^{12}C$ is $(2.98 \pm 0.37 (stat.) \pm 0.31 (syst.) \substack{ +0.49 \\ -0.00 } \mathrm{ (Q^2\,model)}) \times 10^{-40}~\mathrm{cm}^{2}$. The new measurement of the $\bar{\nu}_{\mu}$-induced cross section on $^{12}{C}$ is $(3.05 \pm 0.71 (stat.) \pm 0.39 (syst.) \substack{ +0.74 \\ -0.00 } \mathrm{(Q^2\,model)}) \times 10^{-40}~\mathrm{cm}^{2}$. The results are compatible with both the NEUT 5.4.0 Berger-Sehgal (2009) and GENIE 2.8.0 Rein-Sehgal (2007) model predictions.

Published
2023

26. Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models

Author

Sengupta, Neha, Sahu, Sunil Kumar, Jia, Bokang, Katipomu, Satheesh, Li, Haonan, Koto, Fajri, Marshall, William, Gosal, Gurpreet, Liu, Cynthia, Chen, Zhiming, Afzal, Osama Mohammed, Kamboj, Samta, Pandit, Onkar, Pal, Rahul, Pradhan, Lalit, Mujahid, Zain Muhammad, Baali, Massa, Han, Xudong, Bsharat, Sondos Mahmoud, Aji, Alham Fikri, Shen, Zhiqiang, Liu, Zhengzhong, Vassilieva, Natalia, Hestness, Joel, Hock, Andy, Feldman, Andrew, Lee, Jonathan, Jackson, Andrew, Ren, Hector Xuguang, Nakov, Preslav, Baldwin, Timothy, and Xing, Eric

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, 68T50, F.2.2, I.2.7
Abstract

We introduce Jais and Jais-chat, new state-of-the-art Arabic-centric foundation and instruction-tuned open generative large language models (LLMs). The models are based on the GPT-3 decoder-only architecture and are pretrained on a mixture of Arabic and English texts, including source code in various programming languages. With 13 billion parameters, they demonstrate better knowledge and reasoning capabilities in Arabic than any existing open Arabic and multilingual models by a sizable margin, based on extensive evaluation. Moreover, the models are competitive in English compared to English-centric open models of similar size, despite being trained on much less English data. We provide a detailed description of the training, the tuning, the safety alignment, and the evaluation of the models. We release two open versions of the model -- the foundation Jais model, and an instruction-tuned Jais-chat variant -- with the aim of promoting research on Arabic LLMs. Available at https://huggingface.co/inception-mbzuai/jais-13b-chat, Comment: Arabic-centric, foundation model, large-language model, LLM, generative model, instruction-tuned, Jais, Jais-chat

Published
2023

28. Quantum cohomology of projective bundles

Author

Iritani, Hiroshi and Koto, Yuki

Subjects
Mathematics - Algebraic Geometry, Mathematics - Symplectic Geometry
Abstract

We construct an I-function of the projective bundle P(V) associated with a not necessarily split vector bundle V\to B as a Fourier transform of the S^1-equivariant J-function of the total space of V and show that it lies on the Givental Lagrangian cone of P(V). Using this result, we show that the quantum cohomology D-module of P(V) splits into the direct sum of the quantum cohomology D-modules of the base space B. This has applications to the semisimplicity of big quantum cohomology., Comment: 38 pages, 2 figures; v2: Figure 2 added, v3: Introducion improved

Published
2023

29. CMMLU: Measuring massive multitask language understanding in Chinese

Author

Li, Haonan, Zhang, Yixuan, Koto, Fajri, Yang, Yifei, Zhao, Hai, Gong, Yeyun, Duan, Nan, and Baldwin, Timothy

Subjects
Computer Science - Computation and Language
Abstract

As the capabilities of large language models (LLMs) continue to advance, evaluating their performance becomes increasingly crucial and challenging. This paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese benchmark that covers various subjects, including natural science, social sciences, engineering, and humanities. We conduct a thorough evaluation of 18 advanced multilingual- and Chinese-oriented LLMs, assessing their performance across different subjects and settings. The results reveal that most existing LLMs struggle to achieve an average accuracy of 50%, even when provided with in-context examples and chain-of-thought prompts, whereas the random baseline stands at 25%. This highlights significant room for improvement in LLMs. Additionally, we conduct extensive experiments to identify factors impacting the models' performance and propose directions for enhancing LLMs. CMMLU fills the gap in evaluating the knowledge and reasoning capabilities of large language models within the Chinese context.

Published
2023

36. Reducing wait times and avoiding unnecessary use of high-cost mental health services through a Rapid Access and Stabilization Program: protocol for a program evaluation study

Author

Adu, Medard K., da Luz Dias, Raquel, Obuobi-Donkor, Gloria, Ezeanozie, Ngozi, Sridharan, Sanjana, Morrison, Jason, Simon, Patryk, Taylor, Bryanne, MacKinnon, Monica, Gossen, Shiloh, Awara, Mahmoud, White, Mattew, Shalaby, Reham, Agyapong, Belinda, Eboreime, Ejemai, Wang, JianLi, Feng, Cindy, Wozney, Lori, Koto, Prosper, Warford, Jordan, Murphy, Gail Tomblin, and Agyapong, Vincent Israel Opoku

Published
2024
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37. Preventing Respiratory Viral Illness Invisibly (PRiVII): protocol for a pragmatic cluster randomized trial evaluating far-UVC light devices in long-term care facilities to reduce infections

Author

Nix, Hayden P., Meeker, Samantha, King, Caroline E., Andrew, Melissa, Davis, Ian R. C., Koto, Prosper S., Sim, Meaghan, Murdoch, Jennifer, Patriquin, Glenn, Theriault, Chris, Reidy, Stephanie, Rockwood, Michael, Sampalli, Tara, Searle, Samuel D., and Rockwood, Kenneth

Published
2024
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40. Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank Adaptation

Author

Li, Haonan, Koto, Fajri, Wu, Minghao, Aji, Alham Fikri, and Baldwin, Timothy

Subjects
Computer Science - Computation and Language
Abstract

Instruction tuning has shown great promise in improving the performance of large language models. However, research on multilingual instruction tuning has been limited due to the scarcity of high-quality instruction-response datasets across different languages. To bridge this gap, we present Bactrian-X, a comprehensive multilingual parallel dataset of 3.4 million instruction-response pairs across 52 languages. Leveraging this dataset, we train a set of adapters using low-rank adaptation (LoRA), which are lightweight components that seamlessly integrate with large language models. These adapters have a substantially lower parameter count than the base model, making them easily replaceable and usable as plug-ins for different languages or language groups. Extensive experiments in various multilingual evaluation settings demonstrate that models derived from LoRA-based training over Bactrian-X outperform both the vanilla models and existing instruction-tuned models. The code and models are publicly available at https://github.com/mbzuai-nlp/bactrian-x

Published
2023

41. Updated T2K measurements of muon neutrino and antineutrino disappearance using 3.6 $\times$ 10$^{21}$ protons on target

Author

Abe, K., Akhlaq, N., Akutsu, R., Alarakia-Charles, H., Ali, A., Hakim, Y. I. Alj, Monsalve, S. Alonso, Alt, C., Andreopoulos, C., Antonova, M., Aoki, S., Arihara, T., Asada, Y., Ashida, Y., Atkin, E. T., Barbi, M., Barker, G. J., Barr, G., Barrow, D., Batkiewicz-Kwasniak, M., Bench, F., Berardi, V., Berns, L., Bhadra, S., Blanchet, A., Blondel, A., Bolognesi, S., Bonus, T., Bordoni, S., Boyd, S. B., Bravar, A., Bronner, C., Bron, S., Bubak, A., Avanzini, M. Buizza, Caballero, J. A., Calabria, N. F., Cao, S., Carabadjac, D., Carter, A. J., Cartwright, S. L., Casado, M. P., Catanesi, M. G., Cervera, A., Chakrani, J., Cherdack, D., Chong, P. S., Christodoulou, G., Chvirova, A., Cicerchia, M., Coleman, J., Collazuol, G., Cook, L., Cudd, A., Dalmazzone, C., Daret, T., Dasgupta, P., Davydov, Yu. I., De Roeck, A., De Rosa, G., Dealtry, T., Delogu, C. C., Densham, C., Dergacheva, A., Di Lodovico, F., Dolan, S., Douqa, D., Doyle, T. A., Drapier, O., Dumarchez, J., Dunne, P., Dygnarowicz, K., Eguchi, A., Emery-Schrenk, S., Erofeev, G., Ershova, A., Eurin, G., Fedorova, D., Fedotov, S., Feltre, M., Finch, A. J., Aguirre, G. A. Fiorentini, Fiorillo, G., Fitton, M. D., Patiño, J. M. Franco, Friend, M., Fujii, Y., Fukuda, Y., Furui, Y., Fusshoeller, K., Giannessi, L., Giganti, C., Glagolev, V., Gonin, M., Rosa, J. González, Goodman, E. A. G., Gorin, A., Grassi, M., Guigue, M., Hadley, D. R., Haigh, J. T., Hamacher-Baumann, P., Harris, D. A., Hartz, M., Hasegawa, T., Hassani, S., Hastings, N. C., Hayato, Y., Henaff, D., Hiramoto, A., Hogan, M., Holeczek, J., Holin, A., Holvey, T., Van, N. T. Hong, Honjo, T., Iacob, F., Ichikawa, A. K., Ikeda, M., Ishida, T., Ishitsuka, M., Israel, H. T., Iwamoto, K., Izmaylov, A., Izumi, N., Jakkapu, M., Jamieson, B., Jenkins, S. J., Jesús-Valls, C., Jiang, J. J., Ji, J. Y., Jonsson, P., Joshi, S., Jung, C. K., Jurj, P. B., Kabirnezhad, M., Kaboth, A. C., Kajita, T., Kakuno, H., Kameda, J., Kasetti, S. P., Kataoka, Y., Katayama, Y., Katori, T., Kawaue, M., Kearns, E., Khabibullin, M., Khotjantsev, A., Kikawa, T., Kikutani, H., King, S., Kiseeva, V., Kisiel, J., Kobata, T., Kobayashi, H., Kobayashi, T., Koch, L., Kodama, S., Konaka, A., Kormos, L. L., Koshio, Y., Kostin, A., Koto, T., Kowalik, K., Kudenko, Y., Kudo, Y., Kuribayashi, S., Kurjata, R., Kutter, T., Kuze, M., La Commara, M., Labarga, L., Lachner, K., Lagoda, J., Lakshmi, S. M., James, M. Lamers, Lamoureux, M., Langella, A., Laporte, J. -F., Last, D., Latham, N., Laveder, M., Lavitola, L., Lawe, M., Lee, Y., Lin, C., Lin, S. -K., Litchfield, R. P., Liu, S. L., Li, W., Longhin, A., Long, K. R., Moreno, A. Lopez, Ludovici, L., Lu, X., Lux, T., Machado, L. N., Magaletti, L., Mahn, K., Malek, M., Mandal, M., Manly, S., Marino, A. D., Marti-Magro, L., Martin, D. G. R., Martini, M., Martin, J. F., Maruyama, T., Matsubara, T., Matveev, V., Mauger, C., Mavrokoridis, K., Mazzucato, E., McCauley, N., McElwee, J., McFarland, K. S., McGrew, C., McKean, J., Mefodiev, A., Megias, G. D., Mehta, P., Mellet, L., Metelko, C., Mezzetto, M., Miller, E., Minamino, A., Mineev, O., Mine, S., Miura, M., Bueno, L. Molina, Moriyama, S., Morrison, P., Mueller, Th. A., Munford, D., Munteanu, L., Nagai, K., Nagai, Y., Nakadaira, T., Nakagiri, K., Nakahata, M., Nakajima, Y., Nakamura, A., Nakamura, H., Nakamura, K., Nakamura, K. D., Nakano, Y., Nakayama, S., Nakaya, T., Nakayoshi, K., Naseby, C. E. R., Ngoc, T. V., Nguyen, V. Q., Niewczas, K., Nishimori, S., Nishimura, Y., Nishizaki, K., Nosek, T., Nova, F., Novella, P., Nugent, J. C., O'Keeffe, H. M., O'Sullivan, L., Odagawa, T., Ogawa, T., Okada, R., Okinaga, W., Okumura, K., Okusawa, T., Ospina, N., Osu, L., Owen, R. A., Oyama, Y., Palladino, V., Paolone, V., Pari, M., Parlone, J., Parsa, S., Pasternak, J., Pavin, M., Payne, D., Penn, G. C., Pershey, D., Pickering, L., Pidcott, C., Pintaudi, G., Pistillo, C., Popov, B., Yrey, A. J. Portocarrero, Porwit, K., Posiadala-Zezula, M., Prabhu, Y. S., Pupilli, F., Quilain, B., Radermacher, T., Radicioni, E., Radics, B., Ramírez, M. A., Ratoff, P. N., Reh, M., Riccio, C., Rondio, E., Roth, S., Roy, N., Rubbia, A., Ruggeri, A. C., Ruggles, C. A., Rychter, A., Sakashita, K., Sánchez, F., Santucci, G., Schefke, T., Schloesser, C. M., Scholberg, K., Scott, M., Seiya, Y., Sekiguchi, T., Sekiya, H., Sgalaberna, D., Shaikhiev, A., Shaker, F., Shiozawa, M., Shorrock, W., Shvartsman, A., Skrobova, N., Skwarczynski, K., Smyczek, D., Smy, M., Sobczyk, J. T., Sobel, H., Soler, F. J. P., Sonoda, Y., Speers, A. J., Spina, R., Suslov, I. A., Suvorov, S., Suzuki, A., Suzuki, S. Y., Suzuki, Y., Sztuc, A. A., Tada, M., Tairafune, S., Takayasu, S., Takeda, A., Takeuchi, Y., Takifuji, K., Tanaka, H. K., Tanigawa, H., Tanihara, Y., Tani, M., Teklu, A., Tereshchenko, V. V., Teshima, N., Thamm, N., Thompson, L. F., Toki, W., Touramanis, C., Towstego, T., Tsui, K. M., Tsukamoto, T., Tzanov, M., Uchida, Y., Vagins, M., Vargas, D., Varghese, M., Vasseur, G., Vilela, C., Villa, E., Vinning, W. G. S., Virginet, U., Vladisavljevic, T., Wachala, T., Wakabayashi, D., Walsh, J. G., Wang, Y., Wan, L., Wark, D., Wascko, M. O., Weber, A., Wendell, R., Wilking, M. J., Wilkinson, C., Wilson, J. R., Wood, K., Wret, C., Xia, J., Xu, Y. -h., Yamamoto, K., Yamamoto, T., Yanagisawa, C., Yang, G., Yano, T., Yasutome, K., Yershov, N., Yevarouskaya, U., Yokoyama, M., Yoshimoto, Y., Yoshimura, N., Yu, M., Zaki, R., Zalewska, A., Zalipska, J., Zaremba, K., Zarnecki, G., Zhao, X., Zhu, T., Ziembicki, M., Zimmerman, E. D., Zito, M., and Zsoldos, S.

Subjects
High Energy Physics - Experiment
Abstract

Muon neutrino and antineutrino disappearance probabilities are identical in the standard three-flavor neutrino oscillation framework, but CPT violation and non-standard interactions can violate this symmetry. In this work we report the measurements of $\sin^{2} \theta_{23}$ and $\Delta m_{32}^2$ independently for neutrinos and antineutrinos. The aforementioned symmetry violation would manifest as an inconsistency in the neutrino and antineutrino oscillation parameters. The analysis discussed here uses a total of 1.97$\times$10$^{21}$ and 1.63$\times$10$^{21}$ protons on target taken with a neutrino and antineutrino beam respectively, and benefits from improved flux and cross-section models, new near detector samples and more than double the data reducing the overall uncertainty of the result. No significant deviation is observed, consistent with the standard neutrino oscillation picture.

Published
2023

42. First measurement of muon neutrino charged-current interactions on hydrocarbon without pions in the final state using multiple detectors with correlated energy spectra at T2K

Author

Abe, K., Akhlaq, N., Akutsu, R., Alarakia-Charles, H., Ali, A., Hakim, Y. I. Alj, Monsalve, S. Alonso, Alt, C., Andreopoulos, C., Antonova, M., Aoki, S., Arihara, T., Asada, Y., Ashida, Y., Atkin, E. T., Barbi, M., Barker, G. J., Barr, G., Barrow, D., Batkiewicz-Kwasniak, M., Bench, F., Berardi, V., Berns, L., Bhadra, S., Blanchet, A., Blondel, A., Bolognesi, S., Bonus, T., Bordoni, S., Boyd, S. B., Bravar, A., Bronner, C., Bron, S., Bubak, A., Avanzini, M. Buizza, Caballero, J. A., Calabria, N. F., Cao, S., Carabadjac, D., Carter, A. J., Cartwright, S. L., Casado, M. P., Catanesi, M. G., Cervera, A., Chakrani, J., Cherdack, D., Chong, P. S., Christodoulou, G., Chvirova, A., Cicerchia, M., Coleman, J., Collazuol, G., Cook, L., Cudd, A., Dalmazzone, C., Daret, T., Dasgupta, P., Davydov, Yu. I., De Roeck, A., De Rosa, G., Dealtry, T., Delogu, C. C., Densham, C., Dergacheva, A., Di Lodovico, F., Dolan, S., Douqa, D., Doyle, T. A., Drapier, O., Dumarchez, J., Dunne, P., Dygnarowicz, K., Eguchi, A., Emery-Schrenk, S., Erofeev, G., Ershova, A., Eurin, G., Fedorova, D., Fedotov, S., Feltre, M., Finch, A. J., Aguirre, G. A. Fiorentini, Fiorillo, G., Fitton, M. D., Patiño, J. M. Franco, Friend, M., Fujii, Y., Fukuda, Y., Furui, Y., Fusshoeller, K., Giannessi, L., Giganti, C., Glagolev, V., Gonin, M., Rosa, J. González, Goodman, E. A. G., Gorin, A., Grassi, M., Guigue, M., Hadley, D. R., Haigh, J. T., Hamacher-Baumann, P., Harris, D. A., Hartz, M., Hasegawa, T., Hassani, S., Hastings, N. C., Hayato, Y., Henaff, D., Hiramoto, A., Hogan, M., Holeczek, J., Holin, A., Holvey, T., Van, N. T. Hong, Honjo, T., Iacob, F., Ichikawa, A. K., Ikeda, M., Ishida, T., Ishitsuka, M., Israel, H. T., Izmaylov, A., Izumi, N., Jakkapu, M., Jamieson, B., Jenkins, S. J., Jesús-Valls, C., Jiang, J. J., Ji, J. Y., Jonsson, P., Joshi, S., Jung, C. K., Jurj, P. B., Kabirnezhad, M., Kaboth, A. C., Kajita, T., Kakuno, H., Kameda, J., Kasetti, S. P., Kataoka, Y., Katori, T., Kawaue, M., Kearns, E., Khabibullin, M., Khotjantsev, A., Kikawa, T., King, S., Kiseeva, V., Kisiel, J., Kobata, T., Kobayashi, H., Kobayashi, T., Koch, L., Kodama, S., Konaka, A., Kormos, L. L., Koshio, Y., Kostin, A., Koto, T., Kowalik, K., Kudenko, Y., Kudo, Y., Kuribayashi, S., Kurjata, R., Kutter, T., Kuze, M., La Commara, M., Labarga, L., Lachner, K., Lagoda, J., Lakshmi, S. M., James, M. Lamers, Lamoureux, M., Langella, A., Laporte, J. -F., Last, D., Latham, N., Laveder, M., Lavitola, L., Lawe, M., Lee, Y., Lin, C., Lin, S. -K., Litchfield, R. P., Liu, S. L., Li, W., Longhin, A., Long, K. R., Moreno, A. Lopez, Ludovici, L., Lu, X., Lux, T., Machado, L. N., Magaletti, L., Mahn, K., Malek, M., Mandal, M., Manly, S., Marino, A. D., Marti-Magro, L., Martin, D. G. R., Martini, M., Martin, J. F., Maruyama, T., Matsubara, T., Matveev, V., Mauger, C., Mavrokoridis, K., Mazzucato, E., McCauley, N., McElwee, J., McFarland, K. S., McGrew, C., McKean, J., Mefodiev, A., Megias, G. D., Mehta, P., Mellet, L., Metelko, C., Mezzetto, M., Miller, E., Minamino, A., Mineev, O., Mine, S., Miura, M., Bueno, L. Molina, Moriyama, S., Morrison, P., Mueller, Th. A., Munford, D., Munteanu, L., Nagai, K., Nagai, Y., Nakadaira, T., Nakagiri, K., Nakahata, M., Nakajima, Y., Nakamura, A., Nakamura, H., Nakamura, K., Nakamura, K. D., Nakano, Y., Nakayama, S., Nakaya, T., Nakayoshi, K., Naseby, C. E. R., Ngoc, T. V., Nguyen, V. Q., Niewczas, K., Nishimori, S., Nishimura, Y., Nishizaki, K., Nosek, T., Nova, F., Novella, P., Nugent, J. C., O'Keeffe, H. M., O'Sullivan, L., Odagawa, T., Ogawa, T., Okinaga, W., Okumura, K., Okusawa, T., Ospina, N., Osu, L., Owen, R. A., Oyama, Y., Palladino, V., Paolone, V., Pari, M., Parlone, J., Parsa, S., Pasternak, J., Pavin, M., Payne, D., Penn, G. C., Pershey, D., Pickering, L., Pidcott, C., Pintaudi, G., Pistillo, C., Popov, B., Yrey, A. J. Portocarrero, Porwit, K., Posiadala-Zezula, M., Prabhu, Y. S., Pupilli, F., Quilain, B., Radermacher, T., Radicioni, E., Radics, B., Ramírez, M. A., Ratoff, P. N., Reh, M., Riccio, C., Rondio, E., Roth, S., Roy, N., Rubbia, A., Ruggeri, A. C., Ruggles, C. A., Rychter, A., Sakashita, K., Sánchez, F., Santucci, G., Schefke, T., Schloesser, C. M., Scholberg, K., Scott, M., Seiya, Y., Sekiguchi, T., Sekiya, H., Sgalaberna, D., Shaikhiev, A., Shaker, F., Shiozawa, M., Shorrock, W., Shvartsman, A., Skrobova, N., Skwarczynski, K., Smyczek, D., Smy, M., Sobczyk, J. T., Sobel, H., Soler, F. J. P., Sonoda, Y., Speers, A. J., Spina, R., Suslov, I. A., Suvorov, S., Suzuki, A., Suzuki, S. Y., Suzuki, Y., Sztuc, A. A., Tada, M., Tairafune, S., Takayasu, S., Takeda, A., Takeuchi, Y., Takifuji, K., Tanaka, H. K., Tanigawa, H., Tani, M., Teklu, A., Tereshchenko, V. V., Teshima, N., Thamm, N., Thompson, L. F., Toki, W., Touramanis, C., Towstego, T., Tsui, K. M., Tsukamoto, T., Tzanov, M., Uchida, Y., Vagins, M., Vargas, D., Varghese, M., Vasseur, G., Vilela, C., Villa, E., Vinning, W. G. S., Virginet, U., Vladisavljevic, T., Wachala, T., Wakabayashi, D., Walsh, J. G., Wang, Y., Wan, L., Wark, D., Wascko, M. O., Weber, A., Wendell, R., Wilking, M. J., Wilkinson, C., Wilson, J. R., Wood, K., Wret, C., Xia, J., Xu, Y. -h., Yamamoto, K., Yamamoto, T., Yanagisawa, C., Yang, G., Yano, T., Yasutome, K., Yershov, N., Yevarouskaya, U., Yokoyama, M., Yoshimoto, Y., Yoshimura, N., Yu, M., Zaki, R., Zalewska, A., Zalipska, J., Zaremba, K., Zarnecki, G., Zhao, X., Zhu, T., Ziembicki, M., Zimmerman, E. D., Zito, M., and Zsoldos, S.

Subjects
High Energy Physics - Experiment
Abstract

This paper reports the first measurement of muon neutrino charged-current interactions without pions in the final state using multiple detectors with correlated energy spectra at T2K. The data was collected on hydrocarbon targets using the off-axis T2K near detector (ND280) and the on-axis T2K near detector (INGRID) with neutrino energy spectra peaked at 0.6 GeV and 1.1 GeV respectively. The correlated neutrino flux presents an opportunity to reduce the impact of the flux uncertainty and to study the energy dependence of neutrino interactions. The extracted double-differential cross sections are compared to several Monte Carlo neutrino-nucleus interaction event generators showing the agreement between both detectors individually and with the correlated result., Comment: Updated discussion in Sec. V-A; Updated author list

Published
2023
Full Text
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43. Measurements of neutrino oscillation parameters from the T2K experiment using $3.6\times10^{21}$ protons on target

Author

The T2K Collaboration, Abe, K., Akhlaq, N., Akutsu, R., Ali, A., Monsalve, S. Alonso, Alt, C., Andreopoulos, C., Antonova, M., Aoki, S., Arihara, T., Asada, Y., Ashida, Y., Atkin, E. T., Barbi, M., Barker, G. J., Barr, G., Barrow, D., Batkiewicz-Kwasniak, M., Bench, F., Berardi, V., Berns, L., Bhadra, S., Blanchet, A., Blondel, A., Bolognesi, S., Bonus, T., Bordoni, S., Boyd, S. B., Bravar, A., Bronner, C., Bron, S., Bubak, A., Avanzini, M. Buizza, Caballero, J. A., Calabria, N. F., Cao, S., Carabadjac, D., Carter, A. J., Cartwright, S. L., Catanesi, M. G., Cervera, A., Chakrani, J., Cherdack, D., Chong, P. S., Christodoulou, G., Chvirova, A., Cicerchia, M., Coleman, J., Collazuol, G., Cook, L., Cudd, A., Dalmazzone, C., Daret, T., Davydov, Yu. I., De Roeck, A., De Rosa, G., Dealtry, T., Delogu, C. C., Densham, C., Dergacheva, A., Di Lodovico, F., Dolan, S., Douqa, D., Doyle, T. A., Drapier, O., Dumarchez, J., Dunne, P., Dygnarowicz, K., Eguchi, A., Emery-Schrenk, S., Erofeev, G., Ershova, A., Eurin, G., Fedorova, D., Fedotov, S., Feltre, M., Finch, A. J., Aguirre, G. A. Fiorentini, Fiorillo, G., Fitton, M. D., Patiño, J. M. Franco, Friend, M., Fujii, Y., Fukuda, Y., Fusshoeller, K., Giannessi, L., Giganti, C., Glagolev, V., Gonin, M., Rosa, J. González, Goodman, E. A. G., Gorin, A., Grassi, M., Guigue, M., Hadley, D. R., Haigh, J. T., Hamacher-Baumann, P., Harris, D. A., Hartz, M., Hasegawa, T., Hassani, S., Hastings, N. C., Hayato, Y., Henaff, D., Hiramoto, A., Hogan, M., Holeczek, J., Holin, A., Holvey, T., Van, N. T. Hong, Honjo, T., Iacob, F., Ichikawa, A. K., Ikeda, M., Ishida, T., Ishitsuka, M., Israel, H. T., Iwamoto, K., Izmaylov, A., Izumi, N., Jakkapu, M., Jamieson, B., Jenkins, S. J., Jesús-Valls, C., Jiang, J. J., Jonsson, P., Joshi, S., Jung, C. K., Jurj, P. B., Kabirnezhad, M., Kaboth, A. C., Kajita, T., Kakuno, H., Kameda, J., Kasetti, S. P., Kataoka, Y., Katayama, Y., Katori, T., Kawaue, M., Kearns, E., Khabibullin, M., Khotjantsev, A., Kikawa, T., Kikutani, H., King, S., Kiseeva, V., Kisiel, J., Kobata, T., Kobayashi, H., Kobayashi, T., Koch, L., Kodama, S., Konaka, A., Kormos, L. L., Koshio, Y., Kostin, A., Koto, T., Kowalik, K., Kudenko, Y., Kudo, Y., Kuribayashi, S., Kurjata, R., Kutter, T., Kuze, M., La Commara, M., Labarga, L., Lachner, K., Lagoda, J., Lakshmi, S. M., James, M. Lamers, Lamoureux, M., Langella, A., Laporte, J. -F., Last, D., Latham, N., Laveder, M., Lavitola, L., Lawe, M., Lee, Y., Lin, C., Lin, S. -K., Litchfield, R. P., Liu, S. L., Li, W., Longhin, A., Long, K. R., Moreno, A. Lopez, Ludovici, L., Lu, X., Lux, T., Machado, L. N., Magaletti, L., Mahn, K., Malek, M., Mandal, M., Manly, S., Marino, A. D., Marti-Magro, L., Martin, D. G. R., Martini, M., Martin, J. F., Maruyama, T., Matsubara, T., Matveev, V., Mauger, C., Mavrokoridis, K., Mazzucato, E., McCauley, N., McElwee, J., McFarland, K. S., McGrew, C., McKean, J., Mefodiev, A., Megias, G. D., Mehta, P., Mellet, L., Metelko, C., Mezzetto, M., Miller, E., Minamino, A., Mineev, O., Mine, S., Miura, M., Bueno, L. Molina, Moriyama, S., Morrison, P., Mueller, Th. A., Munford, D., Munteanu, L., Nagai, K., Nagai, Y., Nakadaira, T., Nakagiri, K., Nakahata, M., Nakajima, Y., Nakamura, A., Nakamura, H., Nakamura, K., Nakamura, K. D., Nakano, Y., Nakayama, S., Nakaya, T., Nakayoshi, K., Naseby, C. E. R., Ngoc, T. V., Nguyen, V. Q., Niewczas, K., Nishimori, S., Nishimura, Y., Nishizaki, K., Nosek, T., Nova, F., Novella, P., Nugent, J. C., O'Keeffe, H. M., O'Sullivan, L., Odagawa, T., Ogawa, T., Okada, R., Okinaga, W., Okumura, K., Okusawa, T., Ospina, N., Owen, R. A., Oyama, Y., Palladino, V., Paolone, V., Pari, M., Parlone, J., Parsa, S., Pasternak, J., Pavin, M., Payne, D., Penn, G. C., Pershey, D., Pickering, L., Pidcott, C., Pintaudi, G., Pistillo, C., Popov, B., Porwit, K., Posiadala-Zezula, M., Prabhu, Y. S., Pupilli, F., Quilain, B., Radermacher, T., Radicioni, E., Radics, B., Ramírez, M. A., Ratoff, P. N., Reh, M., Riccio, C., Rondio, E., Roth, S., Roy, N., Rubbia, A., Ruggeri, A. C., Ruggles, C. A., Rychter, A., Sakashita, K., Sánchez, F., Santucci, G., Schloesser, C. M., Scholberg, K., Scott, M., Seiya, Y., Sekiguchi, T., Sekiya, H., Sgalaberna, D., Shaikhiev, A., Shaker, F., Shaykina, A., Shiozawa, M., Shorrock, W., Shvartsman, A., Skrobova, N., Skwarczynski, K., Smyczek, D., Smy, M., Sobczyk, J. T., Sobel, H., Soler, F. J. P., Sonoda, Y., Speers, A. J., Spina, R., Suslov, I. A., Suvorov, S., Suzuki, A., Suzuki, S. Y., Suzuki, Y., Sztuc, A. A., Tada, M., Tairafune, S., Takayasu, S., Takeda, A., Takeuchi, Y., Takifuji, K., Tanaka, H. K., Tanihara, Y., Tani, M., Teklu, A., Tereshchenko, V. V., Teshima, N., Thamm, N., Thompson, L. F., Toki, W., Touramanis, C., Towstego, T., Tsui, K. M., Tsukamoto, T., Tzanov, M., Uchida, Y., Vagins, M., Vargas, D., Varghese, M., Vasseur, G., Vilela, C., Villa, E., Vinning, W. G. S., Virginet, U., Vladisavljevic, T., Wachala, T., Walsh, J. G., Wang, Y., Wan, L., Wark, D., Wascko, M. O., Weber, A., Wendell, R., Wilking, M. J., Wilkinson, C., Wilson, J. R., Wood, K., Wret, C., Xia, J., Xu, Y. -h., Yamamoto, K., Yamamoto, T., Yanagisawa, C., Yang, G., Yano, T., Yasutome, K., Yershov, N., Yevarouskaya, U., Yokoyama, M., Yoshimoto, Y., Yoshimura, N., Yu, M., Zaki, R., Zalewska, A., Zalipska, J., Zaremba, K., Zarnecki, G., Zhao, X., Zhu, T., Ziembicki, M., Zimmerman, E. D., Zito, M., and Zsoldos, S.

Subjects
High Energy Physics - Experiment
Abstract

The T2K experiment presents new measurements of neutrino oscillation parameters using $19.7(16.3)\times10^{20}$ protons on target (POT) in (anti-)neutrino mode at the far detector (FD). Compared to the previous analysis, an additional $4.7\times10^{20}$ POT neutrino data was collected at the FD. Significant improvements were made to the analysis methodology, with the near-detector analysis introducing new selections and using more than double the data. Additionally, this is the first T2K oscillation analysis to use NA61/SHINE data on a replica of the T2K target to tune the neutrino flux model, and the neutrino interaction model was improved to include new nuclear effects and calculations. Frequentist and Bayesian analyses are presented, including results on $\sin^2\theta_{13}$ and the impact of priors on the $\delta_\mathrm{CP}$ measurement. Both analyses prefer the normal mass ordering and upper octant of $\sin^2\theta_{23}$ with a nearly maximally CP-violating phase. Assuming the normal ordering and using the constraint on $\sin^2\theta_{13}$ from reactors, $\sin^2\theta_{23}=0.561^{+0.021}_{-0.032}$ using Feldman--Cousins corrected intervals, and $\Delta{}m^2_{32}=2.494_{-0.058}^{+0.041}\times10^{-3}~\mathrm{eV^2}$ using constant $\Delta\chi^{2}$ intervals. The CP-violating phase is constrained to $\delta_\mathrm{CP}=-1.97_{-0.70}^{+0.97}$ using Feldman--Cousins corrected intervals, and $\delta_\mathrm{CP}=0,\pi$ is excluded at more than 90% confidence level. A Jarlskog invariant of zero is excluded at more than $2\sigma$ credible level using a flat prior in $\delta_\mathrm{CP}$, and just below $2\sigma$ using a flat prior in $\sin\delta_\mathrm{CP}$. When the external constraint on $\sin^2\theta_{13}$ is removed, $\sin^2\theta_{13}=28.0^{+2.8}_{-6.5}\times10^{-3}$, in agreement with measurements from reactor experiments. These results are consistent with previous T2K analyses.

Published
2023
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44. Teachers' Ability in Writing Mathematical Literacy Module Based on Local Context

Author

Susanta, Agus, Koto, Irwan, and Susanto, Edi

Abstract

One problem in elementary schools in facilitating students' literacy skills is the limitation of teaching materials. This study aims to explore the ability of elementary school teachers to write mathematical literacy modules based on the Bengkulu context. This research is descriptive research with a quantitative approach. Subjects consisted of two groups taken from 40 postgraduate students of elementary school teachers' education FKIP UNIB. Data were collected using ability tests, document analysis, and interviews. The results showed that: (1) there were differences in students' abilities to understand teaching materials before and after developing the module; (2) the fresh graduate students are better at choosing local context problems, designing modules, and displaying modules; (3) students from elementary school teachers tend to be better at developing indicators, presenting material, and examples of reinforcement; and (4) the literacy mathematics module-based local context has met the criteria valid in terms of construction, material, language, and readability.

Published
2022

45. NusaCrowd: Open Source Initiative for Indonesian NLP Resources

Author

Cahyawijaya, Samuel, Lovenia, Holy, Aji, Alham Fikri, Winata, Genta Indra, Wilie, Bryan, Mahendra, Rahmad, Wibisono, Christian, Romadhony, Ade, Vincentio, Karissa, Koto, Fajri, Santoso, Jennifer, Moeljadi, David, Wirawan, Cahya, Hudi, Frederikus, Parmonangan, Ivan Halim, Alfina, Ika, Wicaksono, Muhammad Satrio, Putra, Ilham Firdausi, Rahmadani, Samsul, Oenang, Yulianti, Septiandri, Ali Akbar, Jaya, James, Dhole, Kaustubh D., Suryani, Arie Ardiyanti, Putri, Rifki Afina, Su, Dan, Stevens, Keith, Nityasya, Made Nindyatama, Adilazuarda, Muhammad Farid, Ignatius, Ryan, Diandaru, Ryandito, Yu, Tiezheng, Ghifari, Vito, Dai, Wenliang, Xu, Yan, Damapuspita, Dyah, Tho, Cuk, Karo, Ichwanul Muslim Karo, Fatyanosa, Tirana Noor, Ji, Ziwei, Fung, Pascale, Neubig, Graham, Baldwin, Timothy, Ruder, Sebastian, Sujaini, Herry, Sakti, Sakriani, and Purwarianti, Ayu

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

We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have brought together 137 datasets and 118 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their value is demonstrated through multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and the local languages of Indonesia. Our work strives to advance natural language processing (NLP) research for languages that are under-represented despite being widely spoken.

Published
2022

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