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10,343,443 results on '"Chen AT"'

1. Treatment Outcomes in Patients with Advanced Fibrolamellar Hepatocellular Carcinoma Under Systemic Treatment: Analysis of Clinical Characteristics, Management, and Radiomics

Author

Da Fonseca LG, Yamamoto VJ, Trinconi Cunha M, Torre GS, Araujo RL, Fonseca GM, Chen ATC, Chagas AL, Herman P, Alves VAF, and Carrilho FJ

Subjects
liver cancer, systemic treatment, radiomics, fibrolamellar carcinoma, rare tumors., Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Leonardo G Da Fonseca,1,* Victor Junji Yamamoto,1,* Mateus Trinconi Cunha,1 Giovanna Sawaya Torre,2 Raphael LC Araujo,3 Gilton Marques Fonseca,4 Andre Tsin Chih Chen,5 Aline Lopes Chagas,6 Paulo Herman,4 Venancio Avancini Ferreira Alves,7 Flair Jose Carrilho6 1Department of Medical Oncology, ICESP - Instituto do Cancer DO Estado de Sao Paulo, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil; 2Department of Radiology, ICESP - Instituto do Cancer do Estado de Sao Paulo, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil; 3Digestive Surgery Division, Department of Surgery, Universidade Federal de São Paulo, São Paulo, Brazil; 4Digestive Surgery Division, Department of Gastroenterology, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil; 5Radiation Oncology Department - Instituto do Cancer do Estado de Sao Paulo, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil; 6Division of Clinical Gastroenterology and Hepatology, Department of Gastroenterology, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil; 7Department of Pathology, LIM-14, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil*These authors contributed equally to this workCorrespondence: Leonardo G Da Fonseca, Department of Medical Oncology, ICESP - Instituto do Cancer do Estado de Sao Paulo, Hospital das Clinicas, University of São Paulo School of Medicine, Avenida Dr Arnaldo, 251, 5th Floor ZIP: 01246-000, São Paulo, Brazil, Tel/Fax +55 11 3893-2000, Email l.fonseca@fm.usp.brPurpose: Fibrolamellar hepatocellular carcinoma (FLHCC) is a rare primary liver malignancy often diagnosed at advanced stages. While there are limited data on the efficacy of specific agents, we aim to report outcomes of patients treated with systemic therapies and explore prognostic factors.Patients and Methods: Medical records of patients treated between 2010 and 2022 were reviewed. Treatments were defined after multidisciplinary assessment. Descriptive statistics were used for baseline demographics. Time-to-event outcomes were estimated using the Kaplan–Meier method, compared by log-rank and adjusted by a regression model. Radiomic features (including size, shape, and texture) of the primary lesion were extracted and dimensionality reduced. An unsupervised Gaussian Mixture Model (GMM) clustering was performed, and survival was compared between clusters.Results: We identified 23 patients: 12 males, with a median age of 23.6 years. At diagnosis, 82.6% had metastases, most frequently to the lungs (39.1%), lymph nodes (39.1%), and peritoneum (21.7%). Patients received a median of three lines (1– 8) of treatment, including different regimens. Sorafenib (39.1%), capecitabine (30.4%), and capecitabine/interferon (13%) were the most used first-line regimens. The median time-to-failure was 3.8 months (95% CI: 3.2– 8.7). Capecitabine + interferon (42.1%) and platinum combinations (39.1%) were the most used second-line regimens, with a time-to-failure of 3.5 months (95% CI: 1.5– 11.6). Median overall survival was 26.7 months (95% CI: 15.1– 40.4). A high baseline neutrophil-to-lymphocyte ratio (NLR) was associated with worse survival (p=0.02). Radiomic features identified three clusters, with one cluster (n=6) having better survival (40.4 vs 22.6 months, p=0.039). Tumor sphericity in the arterial phase was the most relevant characteristic associated with a better prognosis (accuracy=0.93).Conclusion: FLHCC has unique features compared to conventional HCC, including young onset, gender balance, and absence of hepatopathy. Systemic therapies can provide encouraging survival, but lack of uniformity precludes defining a preferable regimen. Radiomics and NLR were suggested to correlate with prognosis and warrant further validation.Keywords: liver cancer, systemic treatment, radiomics, fibrolamellar carcinoma, rare tumors

Published
2023

2. A Comparison of Students' Preferences for Face-to-Face and Online Laboratory Sessions: Insights from Students' Perception of Their Learning Experiences in an Immunology Course

Author

Chin Wen Png, Lih Ing Goh, Yuanxiang Kenneth Chen, Huimin Yeo, and Haiyan Liu

Abstract

The COVID-19 global pandemic has prompted educators in universities to reconsider their teaching methods, mainly due to the social distancing measures imposed within the classroom settings. On the other hand, the growing importance of continuing education opportunities for adult learners after graduation has seen the need to transform traditional teaching modes that primarily depend on face-to-face interaction into virtual modes, which are deemed more time- and cost-efficient. These major shifts in social and economic developments have a significant impact on the evolution of curriculum planning in higher education. Education that has scientific inquiry components inevitably comes into question, as conventional beliefs that experiments should be hands-on and will not be as effective if conducted virtually cast doubts on the move to the online space. This paper discusses the background of an impending shift in a university's approach to more online-based laboratory classes in an immunology course, as well as the exploration of the potential of conducting online laboratory experiments based on student perceptions.

Published
2024

3. Implementing Equity Policies in Illinois Higher Education Institutions: The Illinois Equity in Attainment Initiative

Author

NORC at the University of Chicago, Fordham University, Rachel Carly Feldman, Johanna S. Quinn, Alannah S. Caisey, and Carol Chen

Abstract

The Partnership for College Completion (PCC) was founded to "advance equity in higher education" by supporting colleges and universities to improve college completion for Black, Latinx, and Pell-eligible students--those groups that experience persistent disparities in completion. Simultaneously, PCC engages in state policy reform to bring about systems-wide change. Begun in 2018, the Illinois Equity in Attainment Initiative (ILEA) is its flagship initiative, designed to partner with Illinois colleges and universities to assist them in narrowing their college completion gaps. Promising new research suggests improving equity in college completion requires an explicit focus on race, attention to organizational change, commitment to equity-focused policies and plans, and cross-institutional partnerships to help manifest change. Drawing from this research, ILEA and its partner institutions developed and implemented public-facing equity plans to improve graduation rates for their Black, Latinx, and Pell-eligible students. This study examines how the ILEA collaborative institutions confronted and negotiated structural barriers inhibiting graduation for these historically marginalized groups. The report aims to: (1) Provide study partners, including the Partnership for College Completion, with program feedback; (2) Inform readers about equity plan development and implementation, policy, and practice collaboratives; (3) Identify strengths and growth opportunities about PCC's efforts to bring colleges and universities together to jointly improve college completion for Black, Latinx, and Pell-eligible students; and (4) Reflect on implementation successes and challenges.

Published
2024

4. The Impact of Preschool Teachers' Dual Learning on Creativity

Author

Yuan-Cheng Chang, Yu-Fang Chen, Ya-Ting Cheng, and Kuo-Pin Chao

Abstract

This study aims to explore the impact of dual learning on preschool teachers' thriving at work and creative performance, and whether preschool teachers' thriving at work acts as a mediating factor in the relationship between dual learning and teachers' creative work. The participants were preschool teachers from Taiwan. Convenience sampling was used, resulting in a sample size of 388 people. The study results found that preschool teachers' dual learning, which includes both exploitative and exploratory learning, can improve the novelty and usefulness of their work and creativity. Furthermore, the study found that preschool teachers' thriving at work mediates the relationship between dual learning and creativity. This study also discovered that dual learning should combine and complement both exploitative and exploratory learning to achieve balance and the most significant effect. Therefore, it is essential to first help preschool teachers engage in both exploitative and exploratory learning. Schools and kindergartens can also provide relevant courses to help teachers combine these two learning methods. By integrating and complementing their advantages and disadvantages, a balance in learning can be achieved, thereby promoting teachers' creativity in their work.

Published
2024

5. Online Teaching and Learning at Chinese Universities during COVID-19: Insiders' Perspectives

Author

Youliang Zhang, Yidan Zhu, Tongjie Chen, and Tongfei Ma

Abstract

During attempts to prevent and control the COVID-19 pandemic in China, higher education programs shifted their traditional educational models to online models. This paper aimed to explore how Chinese universities organized online teaching and learning during the pandemic. It investigated the factors affecting the implementation of online teaching and provided policy recommendations for improving the quality of education in the post-pandemic period. The primary data for this study came from in-depth interviews with nine students and five teaching and administrative staff at eight major universities in mainland China. Literature was obtained in both English and Chinese from January 2020 to September 2021. Peer-reviewed journals, policy reports, and university documents regarding online education in Chinese universities were reviewed, and their challenges and countermeasures were investigated. The paper found that the implementation of online education was affected by various sources, including technologies, teachers' teaching skills, network information literacy, and students' learning motivations and self-directed learning skills. Based on the insiders' views, the paper suggested that to promote the quality of online education in the post-COVID-19 pandemic, higher education institutes and programs could develop their infrastructure construction, improve teachers' quality of teaching, and focus on students' learning motivations.

Published
2024

6. Loss of Schooling from Tropical Cyclones: Evidence from 13 Low- and Middle-Income Countries. EdWorkingPaper No. 24-980

Author

Annenberg Institute for School Reform at Brown University, Renzhi Jing, Sam Heft-Neal, Zetianyu Wang, Jie Chen, Minghao Qiu, Isaac M. Opper, Zachary Wagner, and Eran Bendavid

Abstract

Increasing educational attainment is one of the most important and effective tools for health and economic improvements. The extent to which extreme climate events disrupt education, resulting in fewer years of schooling and reduced educational attainment, remains under-studied. Children in low- and middle-income countries may be uniquely vulnerable to loss of schooling after such disasters due to the poor physical condition of schools and the lack of resources to rebuild and mitigate unexpected household shocks. Our analysis assesses this overlooked social cost of tropical cyclones on schooling attainment. We study the education records of nearly 5.1 million people living in 13 low- and middle-income countries that were exposed to tropical cyclones between 1954-2010. We find that exposure to tropical cyclones during preschool age is associated with a 2.7 percentage point decrease in primary school enrollment on average (14.2% decrease), with larger effects from more intense storms (up to 28% decrease for the most intense storms). These effects are more pronounced among school-age girls compared to boys and are greater in areas less accustomed to experiencing tropical cyclones. We estimate that, across all LMICs, tropical cyclone exposure has resulted in more than 410,000 children not attending primary school in the last 20 years, leading to a reduction of more than 4.1 million total years of schooling. These impacts, identified among some of the world's poorest populations, may grow in importance as exposure to severe tropical cyclones is projected to increase with climate change.

Published
2024

7. Scaling Law for Post-training after Model Pruning

Author

Chen, Xiaodong, Hu, Yuxuan, Zhang, Jing, Zhang, Xiaokang, Li, Cuiping, and Chen, Hong

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

Large language models (LLMs) based on the Transformer architecture are widely employed across various domains and tasks. However, their increasing size imposes significant hardware demands, limiting practical deployment. To mitigate this, model pruning techniques have been developed to create more efficient models while maintaining high performance. Despite this, post-training after pruning is crucial for performance recovery and can be resource-intensive. This paper investigates the post-training requirements of pruned LLMs and introduces a scaling law to determine the optimal amount of post-training data. Post-training experiments with the Llama-3 and Qwen-2.5 series models, pruned using depth pruning, width pruning, and 2:4 semi-structured pruning, show that higher pruning ratios necessitate more post-training data for performance recovery, whereas larger LLMs require less. The proposed scaling law predicts a model's loss based on its parameter counts before and after pruning, as well as the post-training token counts. Furthermore, we find that the scaling law established from smaller LLMs can be reliably extrapolated to larger LLMs. This work provides valuable insights into the post-training of pruned LLMs and offers a practical scaling law for optimizing post-training data usage.

Published
2024

8. Constraints on the photon polarisation in $b \to s \gamma$ transitions using $B_s^0 \rightarrow \phi e^+e^-$ decays

Author

LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataíde, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Baldini, W., Balzani, L., Bao, H., Leite, J. Baptista de Souza, Pretel, C. Barbero, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartolini, M., Bartz, J., Basels, J. M., Bashir, S., Bassi, G., Batsukh, B., Battista, P. B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Behling, N. A., Belin, S., Bellee, V., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bondar, N., Bordelius, A., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bouchiba, S. A., Bovill, M., Bowcock, T. J. V., Boyer, A., Bozzi, C., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonaura, A., Buonincontri, L., Burke, A. T., Burr, C., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Caillet, A. C., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cervenkov, D., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chiotopoulos, X., Chobanova, V., Cholak, S., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collins, P., Colombo, T., Colonna, M., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Davidson, A., Davies, J. E., Davis, A., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. A., Debernardis, F., Decamp, D., Dedu, V., Dekkers, S., Del Buono, L., Delaney, B., Dembinski, H. -P., Deng, J., Denysenko, V., Deschamps, O., Dettori, F., Dey, B., Di Nezza, P., Diachkov, I., Didenko, S., Ding, S., Dittmann, L., Dobishuk, V., Docheva, A. D., Dong, C., Donohoe, A. M., Dordei, F., Reis, A. C. dos, Dowling, A. D., Duan, W., Duda, P., Dudek, M. W., Dufour, L., Duk, V., Durante, P., Duras, M. M., Durham, J. M., Durmus, O. D., Dziurda, A., Dzyuba, A., Easo, S., Eckstein, E., Egede, U., Egorychev, A., Egorychev, V., Eisenhardt, S., Ejopu, E., Eklund, L., Elashri, M., Ellbracht, J., Ely, S., Ene, A., Eschle, J., Esen, S., Evans, T., Fabiano, F., Falcao, L. N., Fan, Y., Fang, B., Fantini, L., Faria, M., Farmer, K., Fazzini, D., Felkowski, L., Feng, M., Feo, M., Casani, A. Fernandez, Gomez, M. Fernandez, Fernez, A. D., Ferrari, F., Rodrigues, F. Ferreira, Ferrillo, M., Ferro-Luzzi, M., Filippov, S., Fini, R. A., Fiorini, M., Firlej, M., Fischer, K. L., Fitzgerald, D. S., Fitzpatrick, C., Fiutowski, T., Fleuret, F., Fontana, M., Foreman, L. F., Forty, R., Foulds-Holt, D., Lima, V. Franco, Sevilla, M. Franco, Frank, M., Franzoso, E., Frau, G., Frei, C., Friday, D. A., Fu, J., Führing, Q., Fujii, Y., Fulghesu, T., Gabriel, E., Galati, G., Galati, M. D., Torreira, A. Gallas, Galli, D., Gambetta, S., Gandelman, M., Gandini, P., Ganie, B., Gao, H., Gao, R., Gao, T. Q., Gao, Y., Martin, L. M. Garcia, Moreno, P. Garcia, Pardiñas, J. García, Garg, K. G., Garrido, L., Gaspar, C., Geertsema, R. E., Gerken, L. L., Gersabeck, E., Gersabeck, M., Gershon, T., Ghizzo, S., Ghorbanimoghaddam, Z., Giambastiani, L., Giasemis, F. I., Gibson, V., Giemza, H. K., Gilman, A. L., Giovannetti, M., Gioventù, A., Girardey, L., Gironell, P. Gironella, Giugliano, C., Giza, M. A., Gkougkousis, E. L., Glaser, F. C., Gligorov, V. V., Göbel, C., Golobardes, E., Golubkov, D., Golutvin, A., Fernandez, S. Gomez, Gomulka, W., Abrantes, F. Goncalves, Goncerz, M., Gong, G., Gooding, J. A., Gorelov, I. V., Gotti, C., Grabowski, J. P., Cardoso, L. A. Granado, Graugés, E., Graverini, E., Grazette, L., Graziani, G., Grecu, A. T., Greeven, L. M., Grieser, N. A., Grillo, L., Gromov, S., Gu, C., Guarise, M., Guerry, L., Guittiere, M., Guliaeva, V., Günther, P. A., Guseinov, A. -K., Gushchin, E., Guz, Y., Gys, T., Habermann, K., Hadavizadeh, T., Hadjivasiliou, C., Haefeli, G., Haen, C., Haimberger, J., Hajheidari, M., Hallett, G., Halvorsen, M. M., Hamilton, P. M., Hammerich, J., Han, Q., Han, X., Hansmann-Menzemer, S., Hao, L., Harnew, N., Hartmann, M., Hashmi, S., He, J., Hemmer, F., Henderson, C., Henderson, R. D. L., Hennequin, A. M., Hennessy, K., Henry, L., Herd, J., Gascon, P. Herrero, Heuel, J., Hicheur, A., Mendizabal, G. Hijano, Hill, D., Horswill, J., Hou, R., Hou, Y., Howarth, N., Hu, J., Hu, W., Hu, X., Huang, W., Hulsbergen, W., Hunter, R. J., Hushchyn, M., Hutchcroft, D., Idzik, M., Ilin, D., Ilten, P., Inglessi, A., Iniukhin, A., Ishteev, A., Ivshin, K., Jacobsson, R., Jage, H., Elles, S. J. Jaimes, Jakobsen, S., Jans, E., Jashal, B. K., Jawahery, A., Jevtic, V., Jiang, E., Jiang, X., Jiang, Y., Jiang, Y. J., John, M., Rajan, A. John Rubesh, Johnson, D., Jones, C. R., Jones, T. P., Joshi, S., Jost, B., Castella, J. Juan, Jurik, N., Juszczak, I., Kaminaris, D., Kandybei, S., Kane, M., Kang, Y., Kar, C., Karacson, M., Karpenkov, D., Kauniskangas, A., Kautz, J. W., Kazanecki, M. K., Keizer, F., Kenzie, M., Ketel, T., Khanji, B., Kharisova, A., Kholodenko, S., Khreich, G., Kirn, T., Kirsebom, V. S., Kitouni, O., Klaver, S., Kleijne, N., Klimaszewski, K., Kmiec, M. R., Koliiev, S., Kolk, L., Konoplyannikov, A., Kopciewicz, P., Koppenburg, P., Korolev, M., Kostiuk, I., Kot, O., Kotriakhova, S., Kozachuk, A., Kravchenko, P., Kravchuk, L., Kreps, M., Krokovny, P., Krupa, W., Krzemien, W., Kshyvanskyi, O., Kubis, S., Kucharczyk, M., Kudryavtsev, V., Kulikova, E., Kupsc, A., Kutsenko, B. K., Lacarrere, D., Gonzalez, P. Laguarta, Lai, A., Lampis, A., Lancierini, D., Gomez, C. Landesa, Lane, J. J., Lane, R., Lanfranchi, G., Langenbruch, C., Langer, J., Lantwin, O., Latham, T., Lazzari, F., Lazzeroni, C., Gac, R. Le, Lee, H., Lefèvre, R., Leflat, A., Legotin, S., Lehuraux, M., Cid, E. Lemos, Leroy, O., Lesiak, T., Lesser, E. D., Leverington, B., Li, A., Li, C., Li, H., Li, K., Li, L., Li, M., Li, P., Li, P. -R., Li, Q., Li, S., Li, T., Li, Y., Lian, Z., Liang, X., Libralon, S., Lin, C., Lin, T., Lindner, R., Linton, H., Lisovskyi, V., Litvinov, R., Liu, F. L., Liu, G., Liu, K., Liu, S., Liu, W., Liu, Y., Liu, Y. L., Salvia, A. Lobo, Loi, A., Castro, J. Lomba, Long, T., Lopes, J. H., Huertas, A. Lopez, Soliño, S. López, Lu, Q., Lucarelli, C., Lucchesi, D., Martinez, M. Lucio, Lukashenko, V., Luo, Y., Lupato, A., Luppi, E., Lynch, K., Lyu, X. -R., Ma, G. M., Maccolini, S., Machefert, F., Maciuc, F., Mack, B., Mackay, I., Mackey, L. M., Mohan, L. R. Madhan, Madurai, M. J., Maevskiy, A., Magdalinski, D., Maisuzenko, D., Majewski, M. W., Malczewski, J. J., Malde, S., Malentacca, L., Malinin, A., Maltsev, T., Manca, G., Mancinelli, G., Mancuso, C., Escalero, R. Manera, Manganella, F. M., Manuzzi, D., Marangotto, D., Marchand, J. F., Marchevski, R., Marconi, U., Mariani, E., Mariani, S., Benito, C. Marin, Marks, J., Marshall, A. M., Martel, L., Martelli, G., Martellotti, G., Martinazzoli, L., Martinelli, M., Gomez, D. Martinez, Santos, D. Martinez, Vidal, F. Martinez, Massafferri, A., Matev, R., Mathad, A., Matiunin, V., Matteuzzi, C., Mattioli, K. R., Mauri, A., Maurice, E., Mauricio, J., Mayencourt, P., de Cos, J. Mazorra, Mazurek, M., McCann, M., Mcconnell, L., McGrath, T. H., McHugh, N. T., McNab, A., McNulty, R., Meadows, B., Meier, G., Melnychuk, D., Meng, F. M., Merk, M., Merli, A., Garcia, L. Meyer, Miao, D., Miao, H., Mikhasenko, M., Milanes, D. A., Minotti, A., Minucci, E., Miralles, T., Mitreska, B., Mitzel, D. S., Modak, A., Mohammed, R. A., Moise, R. D., Mokhnenko, S., Cardenas, E. F. Molina, Mombächer, T., Monk, M., Monteil, S., Gomez, A. Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., Moron, J., Morris, A. B., Morris, A. G., Mountain, R., Mu, H., Mu, Z. M., Muhammad, E., Muheim, F., Mulder, M., Müller, K., Muñoz-Rojas, F., Murta, R., Naik, P., Nakada, T., Nandakumar, R., Nanut, T., Nasteva, I., Needham, M., Neri, N., Neubert, S., Neufeld, N., Neustroev, P., Nicolini, J., Nicotra, D., Niel, E. M., Nikitin, N., Niu, Q., Nogarolli, P., Nogga, P., Normand, C., Fernandez, J. Novoa, Nowak, G., Nunez, C., Nur, H. N., Oblakowska-Mucha, A., Obraztsov, V., Oeser, T., Okamura, S., Okhotnikov, A., Okhrimenko, O., Oldeman, R., Oliva, F., Olocco, M., Onderwater, C. J. G., O'Neil, R. H., Osthues, D., Goicochea, J. M. Otalora, Owen, P., Oyanguren, A., Ozcelik, O., Paciolla, F., Padee, A., Padeken, K. O., Pagare, B., Pais, P. R., Pajero, T., Palano, A., Palutan, M., Panshin, G., Paolucci, L., Papanestis, A., Pappagallo, M., Pappalardo, L. L., Pappenheimer, C., Parkes, C., Passalacqua, B., Passaleva, G., Passaro, D., Pastore, A., Patel, M., Patoc, J., Patrignani, C., Paul, A., Pawley, C. J., Pellegrino, A., Peng, J., Altarelli, M. Pepe, Perazzini, S., Pereima, D., Da Costa, H. Pereira, Castro, A. Pereiro, Perret, P., Perrevoort, A., Perro, A., Petridis, K., Petrolini, A., Pfaller, J. P., Pham, H., Pica, L., Piccini, M., Piccolo, L., Pietrzyk, B., Pietrzyk, G., Pinci, D., Pisani, F., Pizzichemi, M., Placinta, V., Casasus, M. Plo, Poeschl, T., Polci, F., Lener, M. Poli, Poluektov, A., Polukhina, N., Polyakov, I., Polycarpo, E., Ponce, S., Popov, D., Poslavskii, S., Prasanth, K., Prouve, C., Provenzano, D., Pugatch, V., Punzi, G., Qasim, S., Qian, Q. Q., Qian, W., Qin, N., Qu, S., Quagliani, R., Trejo, R. I. Rabadan, Rademacker, J. H., Rama, M., García, M. Ramírez, De Oliveira, V. Ramos, Pernas, M. Ramos, Rangel, M. S., Ratnikov, F., Raven, G., De Miguel, M. Rebollo, Redi, F., Reich, J., Reiss, F., Ren, Z., Resmi, P. K., Ribatti, R., Ricart, G. R., Riccardi, D., Ricciardi, S., Richardson, K., Richardson-Slipper, M., Rinnert, K., Robbe, P., Robertson, G., Rodrigues, E., Alvarez, A. Rodriguez, Fernandez, E. Rodriguez, Lopez, J. A. Rodriguez, Rodriguez, E. Rodriguez, Roensch, J., Rogachev, A., Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Vidal, A. Romero, Romolini, G., Ronchetti, F., Rong, T., Rotondo, M., Roy, S. R., Rudolph, M. S., Diaz, M. Ruiz, Fernandez, R. A. Ruiz, Vidal, J. Ruiz, Ryzhikov, A., Ryzka, J., Saavedra-Arias, J. J., Silva, J. J. Saborido, Sadek, R., Sagidova, N., Sahoo, D., Sahoo, N., Saitta, B., Salomoni, M., Sanderswood, I., Santacesaria, R., Rios, C. Santamarina, Santimaria, M., Santoro, L., Santovetti, E., Saputi, A., Saranin, D., Sarnatskiy, A., Sarpis, G., Sarpis, M., Satriano, C., Satta, A., Saur, M., Savrina, D., Sazak, H., Sborzacchi, F., Smead, L. G. Scantlebury, Scarabotto, A., Schael, S., Scherl, S., Schiller, M., Schindler, H., Schmelling, M., Schmidt, B., Schmitt, S., Schmitz, H., Schneider, O., Schopper, A., Schulte, N., Schulte, S., Schune, M. H., Schwemmer, R., Schwering, G., Sciascia, B., Sciuccati, A., Sellam, S., Semennikov, A., Senger, T., Soares, M. Senghi, Sergi, A., Serra, N., Sestini, L., Seuthe, A., Shang, Y., Shangase, D. M., Shapkin, M., Sharma, R. S., Shchemerov, I., Shchutska, L., Shears, T., Shekhtman, L., Shen, Z., Sheng, S., Shevchenko, V., Shi, B., Shi, Q., Shimizu, Y., Shmanin, E., Shorkin, R., Shupperd, J. D., Coutinho, R. Silva, Simi, G., Simone, S., Skidmore, N., Skwarnicki, T., Slater, M. W., Smallwood, J. C., Smith, E., Smith, K., Smith, M., Snoch, A., Lavra, L. Soares, Sokoloff, M. D., Soler, F. J. P., Solomin, A., Solovev, A., Solovyev, I., Sommerfeld, N. S., Song, R., Song, Y., Song, Y. S., De Almeida, F. L. Souza, De Paula, B. Souza, Norella, E. Spadaro, Spedicato, E., Speer, J. G., Spiridenkov, E., Spradlin, P., Sriskaran, V., Stagni, F., Stahl, M., Stahl, S., Stanislaus, S., Stein, E. N., Steinkamp, O., Stenyakin, O., Stevens, H., Strekalina, D., Su, Y., Suljik, F., Sun, J., Sun, L., Sundfeld, D., Sutcliffe, W., Swallow, P. N., Swientek, K., Swystun, F., Szabelski, A., Szumlak, T., Tan, Y., Tat, M. D., Terentev, A., Terzuoli, F., Teubert, F., Thomas, E., Thompson, D. J. D., Tilquin, H., Tisserand, V., T'Jampens, S., Tobin, M., Tomassetti, L., Tonani, G., Tong, X., Machado, D. Torres, Toscano, L., Tou, D. Y., Trippl, C., Tuci, G., Tuning, N., Uecker, L. H., Ukleja, A., Unverzagt, D. J., Ursov, E., Usachov, A., Ustyuzhanin, A., Uwer, U., Vagnoni, V., Cadenas, V. Valcarce, Valenti, G., Canudas, N. Valls, Van Hecke, H., van Herwijnen, E., Van Hulse, C. B., Van Laak, R., van Veghel, M., Vasquez, G., Gomez, R. Vazquez, Regueiro, P. Vazquez, Sierra, C. Vázquez, Vecchi, S., Velthuis, J. J., Veltri, M., Venkateswaran, A., Verdoglia, M., Vesterinen, M., Benet, D. Vico, Villalba, P. Vidrier, Diaz, M. Vieites, Vilasis-Cardona, X., Figueras, E. Vilella, Villa, A., Vincent, P., Volle, F. C., Bruch, D. vom, Voropaev, N., Vos, K., Vouters, G., Vrahas, C., Wagner, J., Walsh, J., Walton, E. J., Wan, G., Wang, C., Wang, G., Wang, H., Wang, J., Wang, M., Wang, N. W., Wang, R., Wang, X., Wang, X. W., Wang, Y., Wang, Y. W., Wang, Z., Ward, J. A., Waterlaat, M., Watson, N. K., Websdale, D., Wei, Y., Wendel, J., Westhenry, B. D. C., White, C., Whitehead, M., Whiter, E., Wiederhold, A. R., Wiedner, D., Wilkinson, G., Wilkinson, M. K., Williams, M., Williams, M. J., Williams, M. R. J., Williams, R., Williams, Z., Wilson, F. F., Winn, M., Wislicki, W., Witek, M., Witola, L., Wormser, G., Wotton, S. A., Wu, H., Wu, J., Wu, X., Wu, Y., Wu, Z., Wyllie, K., Xian, S., Xiang, Z., Xie, Y., Xu, A., Xu, J., Xu, L., Xu, M., Xu, Z., Yang, D., Yang, K., Yang, S., Yang, X., Yang, Y., Yang, Z., Yeroshenko, V., Yeung, H., Yin, H., Yin, X., Yu, C. Y., Yu, J., Yuan, X., Yuan, Y, Zaffaroni, E., Zavertyaev, M., Zdybal, M., Zenesini, F., Zeng, C., Zeng, M., Zhang, C., Zhang, D., Zhang, J., Zhang, L., Zhang, S., Zhang, Y., Zhang, Y. Z., Zhao, Y., Zharkova, A., Zhelezov, A., Zheng, S. Z., Zheng, X. Z., Zheng, Y., Zhou, T., Zhou, X., Zhou, Y., Zhovkovska, V., Zhu, L. Z., Zhu, X., Zhukov, V., Zhuo, J., Zou, Q., Zuliani, D., and Zunica, G.

Subjects
High Energy Physics - Experiment
Abstract

An angular analysis of the $B_s^0 \rightarrow \phi e^+e^-$ decay is performed using the proton-proton collision dataset collected between 2011 and 2018 by the LHCb experiment, corresponding to an integrated luminosity of $9\,{\rm fb}^{-1}$ at centre-of-mass energies of 7, 8 and $13\,{\rm TeV}$. The analysis is performed in the very low dielectron invariant mass-squared region between $0.0009$ and $0.2615\,{\rm GeV}^2\!/c^4$. The longitudinal polarisation fraction of the $\phi$ meson is measured to be less than $11.5\%$ at $90\%$ confidence level. The $A_{\mathrm{T}}^{\mathcal{R}e C\!P}$ observable, which is related to the lepton forward-backward asymmetry, is measured to be $0.116 \pm 0.155 \pm 0.006$, where the first uncertainty is statistical and the second systematic. The transverse asymmetries, $A_{\mathrm{T}}^{(2)}$ and $A_{\mathrm{T}}^{\mathcal{I}m C\!P}$ , which are sensitive to the virtual photon polarisation, are found to be $-0.045 \pm 0.235 \pm 0.014$ and $0.002 \pm 0.247 \pm 0.016$, respectively. The results are consistent with Standard Model predictions., Comment: 21 pages, 4 figures. All figures and tables, along with any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/3433/(LHCb public pages)

Published
2024

9. Compound-QA: A Benchmark for Evaluating LLMs on Compound Questions

Author

Hou, Yutao, Luo, Yajing, Ruan, Zhiwen, Wang, Hongru, Ge, Weifeng, Chen, Yun, and Chen, Guanhua

Subjects
Computer Science - Computation and Language
Abstract

Large language models (LLMs) demonstrate remarkable performance across various tasks, prompting researchers to develop diverse evaluation benchmarks. However, existing benchmarks typically measure the ability of LLMs to respond to individual questions, neglecting the complex interactions in real-world applications. In this paper, we introduce Compound Question Synthesis (CQ-Syn) to create the Compound-QA benchmark, focusing on compound questions with multiple sub-questions. This benchmark is derived from existing QA datasets, annotated with proprietary LLMs and verified by humans for accuracy. It encompasses five categories: Factual-Statement, Cause-and-Effect, Hypothetical-Analysis, Comparison-and-Selection, and Evaluation-and-Suggestion. It evaluates the LLM capability in terms of three dimensions including understanding, reasoning, and knowledge. Our assessment of eight open-source LLMs using Compound-QA reveals distinct patterns in their responses to compound questions, which are significantly poorer than those to non-compound questions. Additionally, we investigate various methods to enhance LLMs performance on compound questions. The results indicate that these approaches significantly improve the models' comprehension and reasoning abilities on compound questions.

Published
2024

10. EyeDiff: text-to-image diffusion model improves rare eye disease diagnosis

Author

Chen, Ruoyu, Zhang, Weiyi, Liu, Bowen, Chen, Xiaolan, Xu, Pusheng, Liu, Shunming, He, Mingguang, and Shi, Danli

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

The rising prevalence of vision-threatening retinal diseases poses a significant burden on the global healthcare systems. Deep learning (DL) offers a promising solution for automatic disease screening but demands substantial data. Collecting and labeling large volumes of ophthalmic images across various modalities encounters several real-world challenges, especially for rare diseases. Here, we introduce EyeDiff, a text-to-image model designed to generate multimodal ophthalmic images from natural language prompts and evaluate its applicability in diagnosing common and rare diseases. EyeDiff is trained on eight large-scale datasets using the advanced latent diffusion model, covering 14 ophthalmic image modalities and over 80 ocular diseases, and is adapted to ten multi-country external datasets. The generated images accurately capture essential lesional characteristics, achieving high alignment with text prompts as evaluated by objective metrics and human experts. Furthermore, integrating generated images significantly enhances the accuracy of detecting minority classes and rare eye diseases, surpassing traditional oversampling methods in addressing data imbalance. EyeDiff effectively tackles the issue of data imbalance and insufficiency typically encountered in rare diseases and addresses the challenges of collecting large-scale annotated images, offering a transformative solution to enhance the development of expert-level diseases diagnosis models in ophthalmic field., Comment: 28 pages, 2 figures

Published
2024

11. On the Surprising Effectiveness of Attention Transfer for Vision Transformers

Author

Li, Alexander C., Tian, Yuandong, Chen, Beidi, Pathak, Deepak, and Chen, Xinlei

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing
Abstract

Conventional wisdom suggests that pre-training Vision Transformers (ViT) improves downstream performance by learning useful representations. Is this actually true? We investigate this question and find that the features and representations learned during pre-training are not essential. Surprisingly, using only the attention patterns from pre-training (i.e., guiding how information flows between tokens) is sufficient for models to learn high quality features from scratch and achieve comparable downstream performance. We show this by introducing a simple method called attention transfer, where only the attention patterns from a pre-trained teacher ViT are transferred to a student, either by copying or distilling the attention maps. Since attention transfer lets the student learn its own features, ensembling it with a fine-tuned teacher also further improves accuracy on ImageNet. We systematically study various aspects of our findings on the sufficiency of attention maps, including distribution shift settings where they underperform fine-tuning. We hope our exploration provides a better understanding of what pre-training accomplishes and leads to a useful alternative to the standard practice of fine-tuning, Comment: NeurIPS 2024. Code: https://github.com/alexlioralexli/attention-transfer

Published
2024

12. Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning

Author

Qi, Jun, Yang, Chao-Han, Chen, Samuel Yen-Chi, and Chen, Pin-Yu

Subjects
Quantum Physics, Computer Science - Artificial Intelligence
Abstract

Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning. It seeks to revolutionize machine learning by harnessing the unique capabilities of quantum mechanics and employs machine learning techniques to advance quantum computing research. This paper introduces quantum computing for the machine learning paradigm, where variational quantum circuits (VQC) are used to develop QML architectures on noisy intermediate-scale quantum (NISQ) devices. We discuss machine learning for the quantum computing paradigm, showcasing our recent theoretical and empirical findings. In particular, we delve into future directions for studying QML, exploring the potential industrial impacts of QML research., Comment: In submission

Published
2024

13. DarkSHINE Baseline Design Report: Physics Prospects and Detector Technologies

Author

Chen, Jing, Chen, Ji-Yuan, Chen, Jun-Feng, Chen, Xiang, Fu, Chang-Bo, Guo, Jun, Guo, Yi-Han, Khaw, Kim Siang, Li, Jia-Lin, Li, Liang, Li, Shu, Lin, Yu-ming, Liu, Dan-Ning, Liu, Kang, Liu, Kun, Liu, Qi-Bin, Liu, Zhi, Lu, Ze-Jia, Lv, Meng, Song, Si-Yuan, Sun, Tong, Tang, Jian-Nan, Wan, Wei-Shi, Wang, Dong, Wang, Xiao-Long, Wang, Yu-Feng, Wang, Zhen, Wang, Zi-Rui, Wu, Wei-Hao, Yang, Hai-Jun, Yang, Lin, Yang, Yong, Yu, Dian, Yuan, Rui, Zhang, Jun-Hua, Zhang, Yu-Lei, Zhang, Yun-Long, Zhao, Zhi-Yu, Zhou, Bai-Hong, Zhu, Chun-Xiang, Zhu, Xu-Liang, and Zhu, Yi-Fan

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract

DarkSHINE is a newly proposed fixed-target experiment initiative to search for the invisible decay of Dark Photon via missing energy/momentum signatures, based on the high repetition rate electron beam to be deployed/delivered by the Shanghai High repetition rate XFEL and Extreme light facility (SHINE). This report elaborates the baseline design of DarkSHINE experiment by introducing the physics goals, experimental setups, details of each sub-detector system technical designs, signal and backgground modelings, expected search sensitivities and future prospects, which mark an important step towards the further prototyping and technical demonstrations.

Published
2024

14. Measurement of $\phi(1020)$ meson production in fixed-target $\textit{p}$Ne collisions at $\sqrt{s_{NN}}$ = 68.5 GeV

Author

LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataíde, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Balboni, A., Baldini, W., Balzani, L., Bao, H., Leite, J. Baptista de Souza, Pretel, C. Barbero, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartolini, M., Bartz, J., Basels, J. M., Bashir, S., Bassi, G., Batsukh, B., Battista, P. B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Behling, N. A., Belin, S., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bonacci, R. B., Bondar, N., Bordelius, A., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bouchiba, S. A., Bovill, M., Bowcock, T. J. V., Boyer, A., Bozzi, C., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonaura, A., Buonincontri, L., Burke, A. T., Burr, C., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Caillet, A. C., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cervenkov, D., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chiotopoulos, X., Chobanova, V., Cholak, S., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collaviti, S., Collins, P., Colombo, T., Colonna, M. C., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Darze, G., Davidson, A., Davies, J. E., Davis, A., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. A., Debernardis, F., Decamp, D., Dedu, V., Dekkers, S., Del Buono, L., Delaney, B., Dembinski, H. -P., Deng, J., Denysenko, V., Deschamps, O., Dettori, F., Dey, B., Di Nezza, P., Diachkov, I., Didenko, S., Ding, S., Dittmann, L., Dobishuk, V., Docheva, A. D., Dong, C., Donohoe, A. M., Dordei, F., Reis, A. C. dos, Dowling, A. D., Duan, W., Duda, P., Dudek, M. W., Dufour, L., Duk, V., Durante, P., Duras, M. M., Durham, J. M., Durmus, O. D., Dziurda, A., Dzyuba, A., Easo, S., Eckstein, E., Egede, U., Egorychev, A., Egorychev, V., Eisenhardt, S., Ejopu, E., Eklund, L., Elashri, M., Ellbracht, J., Ely, S., Ene, A., Eschle, J., Esen, S., Evans, T., Fabiano, F., Falcao, L. N., Fan, Y., Fang, B., Fantini, L., Faria, M., Farmer, K., Fazzini, D., Felkowski, L., Feng, M., Feo, M., Casani, A. Fernandez, Gomez, M. Fernandez, Fernez, A. D., Ferrari, F., Rodrigues, F. Ferreira, Ferrillo, M., Ferro-Luzzi, M., Filippov, S., Fini, R. A., Fiorini, M., Firlej, M., Fischer, K. L., Fitzgerald, D. S., Fitzpatrick, C., Fiutowski, T., Fleuret, F., Fontana, M., Foreman, L. F., Forty, R., Foulds-Holt, D., Lima, V. Franco, Sevilla, M. Franco, Frank, M., Franzoso, E., Frau, G., Frei, C., Friday, D. A., Fu, J., Führing, Q., Fujii, Y., Fulghesu, T., Gabriel, E., Galati, G., Galati, M. D., Torreira, A. Gallas, Galli, D., Gambetta, S., Gandelman, M., Gandini, P., Ganie, B., Gao, H., Gao, R., Gao, T. Q., Gao, Y., Martin, L. M. Garcia, Moreno, P. Garcia, Pardiñas, J. García, Gardner, P., Garg, K. G., Garrido, L., Gaspar, C., Geertsema, R. E., Gerken, L. L., Gersabeck, E., Gersabeck, M., Gershon, T., Ghizzo, S., Ghorbanimoghaddam, Z., Giambastiani, L., Giasemis, F. I., Gibson, V., Giemza, H. K., Gilman, A. L., Giovannetti, M., Gioventù, A., Girardey, L., Gironell, P. Gironella, Giugliano, C., Giza, M. A., Gkougkousis, E. L., Glaser, F. C., Gligorov, V. V., Göbel, C., Golobardes, E., Golubkov, D., Golutvin, A., Fernandez, S. Gomez, Gomulka, W., Abrantes, F. Goncalves, Goncerz, M., Gong, G., Gooding, J. A., Gorelov, I. V., Gotti, C., Grabowski, J. P., Cardoso, L. A. Granado, Graugés, E., Graverini, E., Grazette, L., Graziani, G., Grecu, A. T., Greeven, L. M., Grieser, N. A., Grillo, L., Gromov, S., Gu, C., Guarise, M., Guerry, L., Guittiere, M., Guliaeva, V., Günther, P. A., Guseinov, A. -K., Gushchin, E., Guz, Y., Gys, T., Habermann, K., Hadavizadeh, T., Hadjivasiliou, C., Haefeli, G., Haen, C., Hajheidari, M., Hallett, G., Halvorsen, M. M., Hamilton, P. M., Hammerich, J., Han, Q., Han, X., Hansmann-Menzemer, S., Hao, L., Harnew, N., Harris, T. H., Hartmann, M., Hashmi, S., He, J., Hemmer, F., Henderson, C., Henderson, R. D. L., Hennequin, A. M., Hennessy, K., Henry, L., Herd, J., Gascon, P. Herrero, Heuel, J., Hicheur, A., Mendizabal, G. Hijano, Horswill, J., Hou, R., Hou, Y., Howarth, N., Hu, J., Hu, W., Hu, X., Huang, W., Hulsbergen, W., Hunter, R. J., Hushchyn, M., Hutchcroft, D., Idzik, M., Ilin, D., Ilten, P., Inglessi, A., Iniukhin, A., Ishteev, A., Ivshin, K., Jacobsson, R., Jage, H., Elles, S. J. Jaimes, Jakobsen, S., Jans, E., Jashal, B. K., Jawahery, A., Jevtic, V., Jiang, E., Jiang, X., Jiang, Y., Jiang, Y. J., John, M., Rajan, A. John Rubesh, Johnson, D., Jones, C. R., Jones, T. P., Joshi, S., Jost, B., Castella, J. Juan, Jurik, N., Juszczak, I., Kaminaris, D., Kandybei, S., Kane, M., Kang, Y., Kar, C., Karacson, M., Karpenkov, D., Kauniskangas, A., Kautz, J. W., Kazanecki, M. K., Keizer, F., Kenzie, M., Ketel, T., Khanji, B., Kharisova, A., Kholodenko, S., Khreich, G., Kirn, T., Kirsebom, V. S., Kitouni, O., Klaver, S., Kleijne, N., Klimaszewski, K., Kmiec, M. R., Koliiev, S., Kolk, L., Konoplyannikov, A., Kopciewicz, P., Koppenburg, P., Korolev, M., Kostiuk, I., Kot, O., Kotriakhova, S., Kozachuk, A., Kravchenko, P., Kravchuk, L., Kreps, M., Krokovny, P., Krupa, W., Krzemien, W., Kshyvanskyi, O., Kubis, S., Kucharczyk, M., Kudryavtsev, V., Kulikova, E., Kupsc, A., Kutsenko, B. K., Lacarrere, D., Gonzalez, P. Laguarta, Lai, A., Lampis, A., Lancierini, D., Gomez, C. Landesa, Lane, J. J., Lane, R., Lanfranchi, G., Langenbruch, C., Langer, J., Lantwin, O., Latham, T., Lazzari, F., Lazzeroni, C., Gac, R. Le, Lee, H., Lefèvre, R., Leflat, A., Legotin, S., Lehuraux, M., Cid, E. Lemos, Leroy, O., Lesiak, T., Lesser, E. D., Leverington, B., Li, A., Li, C., Li, H., Li, K., Li, L., Li, M., Li, P., Li, P. -R., Li, Q., Li, S., Li, T., Li, Y., Lian, Z., Liang, X., Libralon, S., Lin, C., Lin, T., Lindner, R., Linton, H., Lisovskyi, V., Litvinov, R., Liu, F. L., Liu, G., Liu, K., Liu, S., Liu, W., Liu, Y., Liu, Y. L., Salvia, A. Lobo, Loi, A., Long, T., Lopes, J. H., Huertas, A. Lopez, Soliño, S. López, Lu, Q., Lucarelli, C., Lucchesi, D., Martinez, M. Lucio, Lukashenko, V., Luo, Y., Lupato, A., Luppi, E., Lynch, K., Lyu, X. -R., Ma, G. M., Maccolini, S., Machefert, F., Maciuc, F., Mack, B., Mackay, I., Mackey, L. M., Mohan, L. R. Madhan, Madurai, M. J., Maevskiy, A., Magdalinski, D., Maisuzenko, D., Majewski, M. W., Malczewski, J. J., Malde, S., Malentacca, L., Malinin, A., Maltsev, T., Manca, G., Mancinelli, G., Mancuso, C., Escalero, R. Manera, Manganella, F. M., Manuzzi, D., Marangotto, D., Marchand, J. F., Marchevski, R., Marconi, U., Mariani, E., Mariani, S., Benito, C. Marin, Marks, J., Marshall, A. M., Martel, L., Martelli, G., Martellotti, G., Martinazzoli, L., Martinelli, M., Gomez, D. Martinez, Santos, D. Martinez, Vidal, F. Martinez, Granollers, A. Martorell i, Massafferri, A., Matev, R., Mathad, A., Matiunin, V., Matteuzzi, C., Mattioli, K. R., Mauri, A., Maurice, E., Mauricio, J., Mayencourt, P., de Cos, J. Mazorra, Mazurek, M., McCann, M., Mcconnell, L., McGrath, T. H., McHugh, N. T., McNab, A., McNulty, R., Meadows, B., Meier, G., Melnychuk, D., Meng, F. M., Merk, M., Merli, A., Garcia, L. Meyer, Miao, D., Miao, H., Mikhasenko, M., Milanes, D. A., Minotti, A., Minucci, E., Miralles, T., Mitreska, B., Mitzel, D. S., Modak, A., Mohammed, R. A., Moise, R. D., Mokhnenko, S., Cardenas, E. F. Molina, Mombächer, T., Monk, M., Monteil, S., Gomez, A. Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., Moron, J., Morren, W., Morris, A. B., Morris, A. G., Mountain, R., Mu, H., Mu, Z. M., Muhammad, E., Muheim, F., Mulder, M., Müller, K., Muñoz-Rojas, F., Murta, R., Naik, P., Nakada, T., Nandakumar, R., Nanut, T., Nasteva, I., Needham, M., Neri, N., Neubert, S., Neufeld, N., Neustroev, P., Nicolini, J., Nicotra, D., Niel, E. M., Nikitin, N., Niu, Q., Nogarolli, P., Nogga, P., Normand, C., Fernandez, J. Novoa, Nowak, G., Nunez, C., Nur, H. N., Oblakowska-Mucha, A., Obraztsov, V., Oeser, T., Okamura, S., Okhotnikov, A., Okhrimenko, O., Oldeman, R., Oliva, F., Olocco, M., Onderwater, C. J. G., O'Neil, R. H., Osthues, D., Goicochea, J. M. Otalora, Owen, P., Oyanguren, A., Ozcelik, O., Paciolla, F., Padee, A., Padeken, K. O., Pagare, B., Pais, P. R., Pajero, T., Palano, A., Palutan, M., Pan, X., Panshin, G., Paolucci, L., Papanestis, A., Pappagallo, M., Pappalardo, L. L., Pappenheimer, C., Parkes, C., Parmar, D., Passalacqua, B., Passaleva, G., Passaro, D., Pastore, A., Patel, M., Patoc, J., Patrignani, C., Paul, A., Pawley, C. J., Pellegrino, A., Peng, J., Altarelli, M. Pepe, Perazzini, S., Pereima, D., Da Costa, H. Pereira, Castro, A. Pereiro, Perret, P., Perrevoort, A., Perro, A., Peters, M. J., Petridis, K., Petrolini, A., Pfaller, J. P., Pham, H., Pica, L., Piccini, M., Piccolo, L., Pietrzyk, B., Pietrzyk, G., Pinci, D., Pisani, F., Pizzichemi, M., Placinta, V., Casasus, M. Plo, Poeschl, T., Polci, F., Lener, M. Poli, Poluektov, A., Polukhina, N., Polyakov, I., Polycarpo, E., Ponce, S., Popov, D., Poslavskii, S., Prasanth, K., Prouve, C., Provenzano, D., Pugatch, V., Punzi, G., Qasim, S., Qian, Q. Q., Qian, W., Qin, N., Qu, S., Quagliani, R., Trejo, R. I. Rabadan, Rademacker, J. H., Rama, M., García, M. Ramírez, De Oliveira, V. Ramos, Pernas, M. Ramos, Rangel, M. S., Ratnikov, F., Raven, G., De Miguel, M. Rebollo, Redi, F., Reich, J., Reiss, F., Ren, Z., Resmi, P. K., Ribatti, R., Ricart, G. R., Riccardi, D., Ricciardi, S., Richardson, K., Richardson-Slipper, M., Rinnert, K., Robbe, P., Robertson, G., Rodrigues, E., Alvarez, A. Rodriguez, Fernandez, E. Rodriguez, Lopez, J. A. Rodriguez, Rodriguez, E. Rodriguez, Roensch, J., Rogachev, A., Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Vidal, A. Romero, Romolini, G., Ronchetti, F., Rong, T., Rotondo, M., Roy, S. R., Rudolph, M. S., Diaz, M. Ruiz, Fernandez, R. A. Ruiz, Vidal, J. Ruiz, Ryzhikov, A., Ryzka, J., Saavedra-Arias, J. J., Silva, J. J. Saborido, Sadek, R., Sagidova, N., Sahoo, D., Sahoo, N., Saitta, B., Salomoni, M., Sanderswood, I., Santacesaria, R., Rios, C. Santamarina, Santimaria, M., Santoro, L., Santovetti, E., Saputi, A., Saranin, D., Sarnatskiy, A., Sarpis, G., Sarpis, M., Satriano, C., Satta, A., Saur, M., Savrina, D., Sazak, H., Sborzacchi, F., Smead, L. G. Scantlebury, Scarabotto, A., Schael, S., Scherl, S., Schiller, M., Schindler, H., Schmelling, M., Schmidt, B., Schmitt, S., Schmitz, H., Schneider, O., Schopper, A., Schulte, N., Schulte, S., Schune, M. H., Schwemmer, R., Schwering, G., Sciascia, B., Sciuccati, A., Segal, I., Sellam, S., Semennikov, A., Senger, T., Soares, M. Senghi, Sergi, A., Serra, N., Sestini, L., Seuthe, A., Shang, Y., Shangase, D. M., Shapkin, M., Sharma, R. S., Shchemerov, I., Shchutska, L., Shears, T., Shekhtman, L., Shen, Z., Sheng, S., Shevchenko, V., Shi, B., Shi, Q., Shimizu, Y., Shmanin, E., Shorkin, R., Shupperd, J. D., Coutinho, R. Silva, Simi, G., Simone, S., Skidmore, N., Skwarnicki, T., Slater, M. W., Smallwood, J. C., Smith, E., Smith, K., Smith, M., Snoch, A., Lavra, L. Soares, Sokoloff, M. D., Soler, F. J. P., Solomin, A., Solovev, A., Solovyev, I., Sommerfeld, N. S., Song, R., Song, Y., Song, Y. S., De Almeida, F. L. Souza, De Paula, B. Souza, Norella, E. Spadaro, Spedicato, E., Speer, J. G., Spiridenkov, E., Spradlin, P., Sriskaran, V., Stagni, F., Stahl, M., Stahl, S., Stanislaus, S., Stein, E. N., Steinkamp, O., Stenyakin, O., Stevens, H., Strekalina, D., Su, Y., Suljik, F., Sun, J., Sun, L., Sundfeld, D., Sutcliffe, W., Swallow, P. N., Swientek, K., Swystun, F., Szabelski, A., Szumlak, T., Tan, Y., Tang, Y., Tat, M. D., Terentev, A., Terzuoli, F., Teubert, F., Thomas, E., Thompson, D. J. D., Tilquin, H., Tisserand, V., T'Jampens, S., Tobin, M., Tomassetti, L., Tonani, G., Tong, X., Machado, D. Torres, Toscano, L., Tou, D. Y., Trippl, C., Tuci, G., Tuning, N., Uecker, L. H., Ukleja, A., Unverzagt, D. J., Urbach, B., Ursov, E., Usachov, A., Ustyuzhanin, A., Uwer, U., Vagnoni, V., Cadenas, V. Valcarce, Valenti, G., Canudas, N. Valls, Van Hecke, H., van Herwijnen, E., Van Hulse, C. B., Van Laak, R., van Veghel, M., Vasquez, G., Gomez, R. Vazquez, Regueiro, P. Vazquez, Sierra, C. Vázquez, Vecchi, S., Velthuis, J. J., Veltri, M., Venkateswaran, A., Verdoglia, M., Vesterinen, M., Benet, D. Vico, Villalba, P. Vidrier, Diaz, M. Vieites, Vilasis-Cardona, X., Figueras, E. Vilella, Villa, A., Vincent, P., Volle, F. C., Bruch, D. vom, Voropaev, N., Vos, K., Vrahas, C., Wagner, J., Walsh, J., Walton, E. J., Wan, G., Wang, C., Wang, G., Wang, H., Wang, J., Wang, M., Wang, N. W., Wang, R., Wang, X., Wang, X. W., Wang, Y., Wang, Y. W., Wang, Z., Ward, J. A., Waterlaat, M., Watson, N. K., Websdale, D., Wei, Y., Wendel, J., Westhenry, B. D. C., White, C., Whitehead, M., Whiter, E., Wiederhold, A. R., Wiedner, D., Wilkinson, G., Wilkinson, M. K., Williams, M., Williams, M. J., Williams, M. R. J., Williams, R., Williams, Z., Wilson, F. F., Winn, M., Wislicki, W., Witek, M., Witola, L., Wormser, G., Wotton, S. A., Wu, H., Wu, J., Wu, X., Wu, Y., Wu, Z., Wyllie, K., Xian, S., Xiang, Z., Xie, Y., Xu, A., Xu, J., Xu, L., Xu, M., Xu, Z., Yang, K., Yang, S., Yang, X., Yang, Y., Yang, Z., Yeroshenko, V., Yeung, H., Yin, H., Yin, X., Yu, C. Y., Yu, J., Yuan, X., Yuan, Y, Zaffaroni, E., Zavertyaev, M., Zdybal, M., Zenesini, F., Zeng, C., Zeng, M., Zhang, C., Zhang, D., Zhang, J., Zhang, L., Zhang, S., Zhang, Y., Zhang, Y. Z., Zhao, Y., Zharkova, A., Zhelezov, A., Zheng, S. Z., Zheng, X. Z., Zheng, Y., Zhou, T., Zhou, X., Zhou, Y., Zhovkovska, V., Zhu, L. Z., Zhu, X., Zhukov, V., Zhuo, J., Zou, Q., Zuliani, D., and Zunica, G.

Subjects
High Energy Physics - Experiment, Nuclear Experiment
Abstract

The first measurement of $\phi(1020)$ meson production in fixed-target $p$Ne collisions at $\sqrt{s_{NN}}=68.5$ GeV is presented. The $\phi(1020)$ mesons are reconstructed in their $K^{+}K^{-}$ decay in a data sample consisting of proton collisions on neon nuclei at rest, corresponding to an integrated luminosity of $21.7 \pm 1.4$ nb$^{-1}$, collected by the LHCb detector at CERN. The $\phi(1020)$ production cross-section in the centre-of-mass rapidity range of $-1.8

Published
2024

15. Adaptively Augmented Consistency Learning: A Semi-supervised Segmentation Framework for Remote Sensing

Author

Ye, Hui, Chen, Haodong, Chen, Xiaoming, and Chung, Vera

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Remote sensing (RS) involves the acquisition of data about objects or areas from a distance, primarily to monitor environmental changes, manage resources, and support planning and disaster response. A significant challenge in RS segmentation is the scarcity of high-quality labeled images due to the diversity and complexity of RS image, which makes pixel-level annotation difficult and hinders the development of effective supervised segmentation algorithms. To solve this problem, we propose Adaptively Augmented Consistency Learning (AACL), a semi-supervised segmentation framework designed to enhances RS segmentation accuracy under condictions of limited labeled data. AACL extracts additional information embedded in unlabeled images through the use of Uniform Strength Augmentation (USAug) and Adaptive Cut-Mix (AdaCM). Evaluations across various RS datasets demonstrate that AACL achieves competitive performance in semi-supervised segmentation, showing up to a 20% improvement in specific categories and 2% increase in overall performance compared to state-of-the-art frameworks.

Published
2024

16. Motion-Grounded Video Reasoning: Understanding and Perceiving Motion at Pixel Level

Author

Deng, Andong, Chen, Tongjia, Yu, Shoubin, Yang, Taojiannan, Spencer, Lincoln, Tian, Yapeng, Mian, Ajmal Saeed, Bansal, Mohit, and Chen, Chen

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

In this paper, we introduce Motion-Grounded Video Reasoning, a new motion understanding task that requires generating visual answers (video segmentation masks) according to the input question, and hence needs implicit spatiotemporal reasoning and grounding. This task extends existing spatiotemporal grounding work focusing on explicit action/motion grounding, to a more general format by enabling implicit reasoning via questions. To facilitate the development of the new task, we collect a large-scale dataset called GROUNDMORE, which comprises 1,715 video clips, 249K object masks that are deliberately designed with 4 question types (Causal, Sequential, Counterfactual, and Descriptive) for benchmarking deep and comprehensive motion reasoning abilities. GROUNDMORE uniquely requires models to generate visual answers, providing a more concrete and visually interpretable response than plain texts. It evaluates models on both spatiotemporal grounding and reasoning, fostering to address complex challenges in motion-related video reasoning, temporal perception, and pixel-level understanding. Furthermore, we introduce a novel baseline model named Motion-Grounded Video Reasoning Assistant (MORA). MORA incorporates the multimodal reasoning ability from the Multimodal LLM, the pixel-level perception capability from the grounding model (SAM), and the temporal perception ability from a lightweight localization head. MORA achieves respectable performance on GROUNDMORE outperforming the best existing visual grounding baseline model by an average of 21.5% relatively. We hope this novel and challenging task will pave the way for future advancements in robust and general motion understanding via video reasoning segmentation

Published
2024

17. InterFormer: Towards Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction

Author

Zeng, Zhichen, Liu, Xiaolong, Hang, Mengyue, Liu, Xiaoyi, Zhou, Qinghai, Yang, Chaofei, Liu, Yiqun, Ruan, Yichen, Chen, Laming, Chen, Yuxin, Hao, Yujia, Xu, Jiaqi, Nie, Jade, Liu, Xi, Zhang, Buyun, Wen, Wei, Yuan, Siyang, Wang, Kai, Chen, Wen-Yen, Han, Yiping, Li, Huayu, Yang, Chunzhi, Long, Bo, Yu, Philip S., Tong, Hanghang, and Yang, Jiyan

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

Click-through rate (CTR) prediction, which predicts the probability of a user clicking an ad, is a fundamental task in recommender systems. The emergence of heterogeneous information, such as user profile and behavior sequences, depicts user interests from different aspects. A mutually beneficial integration of heterogeneous information is the cornerstone towards the success of CTR prediction. However, most of the existing methods suffer from two fundamental limitations, including (1) insufficient inter-mode interaction due to the unidirectional information flow between modes, and (2) aggressive information aggregation caused by early summarization, resulting in excessive information loss. To address the above limitations, we propose a novel module named InterFormer to learn heterogeneous information interaction in an interleaving style. To achieve better interaction learning, InterFormer enables bidirectional information flow for mutually beneficial learning across different modes. To avoid aggressive information aggregation, we retain complete information in each data mode and use a separate bridging arch for effective information selection and summarization. Our proposed InterFormer achieves state-of-the-art performance on three public datasets and a large-scale industrial dataset., Comment: 10 pages, 6 figures

Published
2024

18. Polymetis:Large Language Modeling for Multiple Material Domains

Author

Huang, Chao, Xiao, Huichen, Chen, Chen, Chen, Chunyan, Zhao, Yi, Du, Shiyu, Zhang, Yiming, Sha, He, and Gu, Ruixin

Subjects
Computer Science - Artificial Intelligence
Abstract

As the application of large language models in various fields continues to expand, materials science also ushers in opportunities for AI-driven innovation. The traditional way of relying on manual search for materials science-related information is now using artificial intelligence technology as an auxiliary tool to improve the efficiency of materials science research. To accelerate researchers' knowledge acquisition and intelligent decision-making support in materials science research, this paper proposes a large language model Polymetis model for a variety of materials fields, aiming to provide highly professional knowledge answers in the field of materials, covering energy materials, functional materials, alloy materials, physical chemistry, biology, and other material directions. The model uses a dataset of about 2 million material knowledge instructions, and in the process of building the dataset, we developed the Intelligent Extraction Large Model (IELM), which is specially used to extract and form structured knowledge from scientific texts, avoiding a large number of costs that need to be manually annotated, and improving efficiency. We inject this data into the GLM4-9B model for learning to enhance its inference capabilities in a variety of material domains. In addition, we have introduced enhanced prompt strategies to ensure that the answers to the model are more organized and comprehensive, providing efficient and comprehensive intelligent support for the diverse needs of materials science exploration, and promoting the development of material science.

Published
2024

19. Constraining Axion-Like Particles from observations of AGN B2 2234+28A and 3C 454.3

Author

Chen, Yu-Chong, Chen, Siyu, Huang, Wei-Cong, Yang, Qing, and Zhang, Hong-Hao

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Cosmology and Nongalactic Astrophysics, High Energy Physics - Phenomenology
Abstract

Axion-photon oscillation effect provides a possible explanation for the presence of very-high-energy (VHE) $\gamma$-ray signals from distant sources. In this work, we propose a model-dependent method to select possible sources that may give sufficient constraints on the axion parameters. We investigate such effect in the spectra of active galactic nuclei (AGN) B2 2234+28A and 3C 454.3 based on data obtained from Fermi Large Area Telescope (Fermi-LAT) and MAGIC U.L. We utilize the Markov Chain Monte Carlo method to fit the axion parameters, yielding a result of $g_{a\gamma}=3.05^{+0.51}_{-0.31} \times 10^{-11}$ GeV$^{-1}$ for the axion-photon coupling strength and $m_{a}=5.25^{+2.35}_{-2.65} \times 10^{-8} $ eV for the axion mass. We also perform 95\% confidence level (CL) constraints to set an upper limit for $g_{a\gamma}$., Comment: 11 pages, 10 figures, 1 table

Published
2024

20. Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits

Author

Qi, Jun, Yang, Chao-Han, Chen, Samuel Yen-Chi, Chen, Pin-Yu, Zenil, Hector, and Tegner, Jesper

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

Quantum Machine Learning (QML) offers tremendous potential but is currently limited by the availability of qubits. We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC). This technique effectively separates approximation error from qubit count and removes the need for restrictive conditions, making QML more viable for real-world applications. Our method significantly improves parameter optimization for VQC while delivering notable gains in representation and generalization capabilities, as evidenced by rigorous theoretical analysis and extensive empirical testing on quantum dot classification tasks. Moreover, our results extend to applications such as human genome analysis, demonstrating the broad applicability of our approach. By addressing the constraints of current quantum hardware, our work paves the way for a new era of advanced QML applications, unlocking the full potential of quantum computing in fields such as machine learning, materials science, medicine, mimetics, and various interdisciplinary areas., Comment: In submission

Published
2024

21. Crystal Structure Generation Based On Material Properties

Author

Huang, Chao, Chen, JiaHui, Liang, HongRui, Chen, ChunYan, and Chen, Chen

Subjects
Computer Science - Artificial Intelligence, Condensed Matter - Materials Science
Abstract

The discovery of new materials is very important to the field of materials science. When researchers explore new materials, they often have expected performance requirements for their crystal structure. In recent years, data-driven methods have made great progress in the direction plane of crystal structure generation, but there is still a lack of methods that can effectively map material properties to crystal structure. In this paper, we propose a Crystal DiT model to generate the crystal structure from the expected material properties by embedding the material properties and combining the symmetry information predicted by the large language model. Experimental verification shows that our proposed method has good performance.

Published
2024

22. Material Property Prediction with Element Attribute Knowledge Graphs and Multimodal Representation Learning

Author

Huang, Chao, Chen, Chunyan, Shi, Ling, and Chen, Chen

Subjects
Computer Science - Machine Learning, Condensed Matter - Materials Science, Computer Science - Artificial Intelligence
Abstract

Machine learning has become a crucial tool for predicting the properties of crystalline materials. However, existing methods primarily represent material information by constructing multi-edge graphs of crystal structures, often overlooking the chemical and physical properties of elements (such as atomic radius, electronegativity, melting point, and ionization energy), which have a significant impact on material performance. To address this limitation, we first constructed an element property knowledge graph and utilized an embedding model to encode the element attributes within the knowledge graph. Furthermore, we propose a multimodal fusion framework, ESNet, which integrates element property features with crystal structure features to generate joint multimodal representations. This provides a more comprehensive perspective for predicting the performance of crystalline materials, enabling the model to consider both microstructural composition and chemical characteristics of the materials. We conducted experiments on the Materials Project benchmark dataset, which showed leading performance in the bandgap prediction task and achieved results on a par with existing benchmarks in the formation energy prediction task.

Published
2024

23. Study of the light scalar $a_{0}(980)$ through the decay $D^{0} \to a_{0}(980)^-e^{+} \nu_{e}$ with $a_{0}(980)^- \to \eta \pi^-$

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

Using 7.93 ${\rm fb^{-1}}$ of $e^+e^-$ collision data collected at a center-of-mass energy of 3.773 ${\rm GeV}$ with the BESIII detector, we present an analysis of the decay $D^{0} \to \eta \pi^- e^+ \nu_{e}$. The branching fraction of the decay $D^{0} \to a_{0}(980)^{-} e^+ \nu_{e}$ with $a_{0}(980)^{-} \to \eta \pi^{-}$ is measured to be $(0.86\pm0.17_{\text{stat}}\pm0.05_{\text{syst}})\times 10^{-4}$. The decay dynamics of this process is studied with a single-pole parameterization of the hadronic form factor and the Flatt\'e formula describing the $a_0(980)$ line shape in the differential decay rate. The product of the form factor $f^{ a_0}_{+}(0)$ and the Cabibbo-Kobayashi-Maskawa matrix element $|V_{cd}|$ is determined for the first time with the result $f^{ a_0}_+(0)|V_{cd}|=0.126\pm0.013_{\rm stat}\pm0.003_{\rm syst}$.

Published
2024

24. Towards Automated Model Design on Recommender Systems

Author

Zhang, Tunhou, Cheng, Dehua, He, Yuchen, Chen, Zhengxing, Dai, Xiaoliang, Xiong, Liang, Liu, Yudong, Cheng, Feng, Cao, Yufan, Yan, Feng, Li, Hai, Chen, Yiran, and Wen, Wei

Subjects
Computer Science - Information Retrieval
Abstract

The increasing popularity of deep learning models has created new opportunities for developing AI-based recommender systems. Designing recommender systems using deep neural networks requires careful architecture design, and further optimization demands extensive co-design efforts on jointly optimizing model architecture and hardware. Design automation, such as Automated Machine Learning (AutoML), is necessary to fully exploit the potential of recommender model design, including model choices and model-hardware co-design strategies. We introduce a novel paradigm that utilizes weight sharing to explore abundant solution spaces. Our paradigm creates a large supernet to search for optimal architectures and co-design strategies to address the challenges of data multi-modality and heterogeneity in the recommendation domain. From a model perspective, the supernet includes a variety of operators, dense connectivity, and dimension search options. From a co-design perspective, it encompasses versatile Processing-In-Memory (PIM) configurations to produce hardware-efficient models. Our solution space's scale, heterogeneity, and complexity pose several challenges, which we address by proposing various techniques for training and evaluating the supernet. Our crafted models show promising results on three Click-Through Rates (CTR) prediction benchmarks, outperforming both manually designed and AutoML-crafted models with state-of-the-art performance when focusing solely on architecture search. From a co-design perspective, we achieve 2x FLOPs efficiency, 1.8x energy efficiency, and 1.5x performance improvements in recommender models., Comment: Accepted in ACM Transactions on Recommender Systems. arXiv admin note: substantial text overlap with arXiv:2207.07187

Published
2024

25. The discrepancy in min-max statistics between two random matrices with finite third moments

Author

Chen, Zijun, Chen, Yiming, and Wei, Chengfu

Subjects
Mathematics - Probability, 60B20, 60E15, G.3
Abstract

We propose a novel coupling inequality of the min-max type for two random matrices with finite absolute third moments, which generalizes the quantitative versions of the well-known inequalities by Gordon. Previous results have calculated the quantitative bounds for pairs of Gaussian random matrices. Through integrating the methods utilized by Chatterjee-Meckes and Reinert-R\"ollin in adapting Stein's method of exchangeable pairs for multivariate normal approximation, this study eliminates the Gaussian restriction on random matrices, enabling us to achieve more extensive results., Comment: 11 pages

Published
2024

26. A Novel Automatic Real-time Motion Tracking Method for Magnetic Resonance Imaging-guided Radiotherapy: Leveraging the Enhanced Tracking-Learning-Detection Framework with Automatic Segmentation

Author

Chen, Shengqi, Wang, Zilin, Dai, Jianrong, Qin, Shirui, Cao, Ying, Zhao, Ruiao, Chen, Jiayun, Wu, Guohua, and Tang, Yuan

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Physics - Medical Physics, Quantitative Biology - Tissues and Organs
Abstract

Objective: Ensuring the precision in motion tracking for MRI-guided Radiotherapy (MRIgRT) is crucial for the delivery of effective treatments. This study refined the motion tracking accuracy in MRIgRT through the innovation of an automatic real-time tracking method, leveraging an enhanced Tracking-Learning-Detection (ETLD) framework coupled with automatic segmentation. Methods: We developed a novel MRIgRT motion tracking method by integrating two primary methods: the ETLD framework and an improved Chan-Vese model (ICV), named ETLD+ICV. The TLD framework was upgraded to suit real-time cine MRI, including advanced image preprocessing, no-reference image quality assessment, an enhanced median-flow tracker, and a refined detector with dynamic search region adjustments. Additionally, ICV was combined for precise coverage of the target volume, which refined the segmented region frame by frame using tracking results, with key parameters optimized. Tested on 3.5D MRI scans from 10 patients with liver metastases, our method ensures precise tracking and accurate segmentation vital for MRIgRT. Results: An evaluation of 106,000 frames across 77 treatment fractions revealed sub-millimeter tracking errors of less than 0.8mm, with over 99% precision and 98% recall for all subjects, underscoring the robustness and efficacy of the ETLD. Moreover, the ETLD+ICV yielded a dice global score of more than 82% for all subjects, demonstrating the proposed method's extensibility and precise target volume coverage. Conclusions: This study successfully developed an automatic real-time motion tracking method for MRIgRT that markedly surpasses current methods. The novel method not only delivers exceptional precision in tracking and segmentation but also demonstrates enhanced adaptability to clinical demands, positioning it as an indispensable asset in the quest to augment the efficacy of radiotherapy treatments.

Published
2024

27. Reference Signal-Based Waveform Design for Integrated Sensing and Communications System

Author

Lyu, Ming, Chen, Hao, Wang, Dan, Feng, Guangyin, Qiu, Chen, and Xu, Xiaodong

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Integrated sensing and communications (ISAC) as one of the key technologies is capable of supporting high-speed communication and high-precision sensing for the upcoming 6G. This paper studies a waveform strategy by designing the orthogonal frequency division multiplexing (OFDM)-based reference signal (RS) for sensing and communication in ISAC system. We derive the closed-form expressions of Cram\'er-Rao Bound (CRB) for the distance and velocity estimations, and obtain the communication rate under the mean square error of channel estimation. Then, a weighted sum CRB minimization problem on the distance and velocity estimations is formulated by considering communication rate requirement and RS intervals constraints, which is a mixed-integer problem due to the discrete RS interval values. To solve this problem, some numerical methods are typically adopted to obtain the optimal solutions, whose computational complexity grow exponentially with the number of symbols and subcarriers of OFDM. Therefore, we propose a relaxation and approximation method to transform the original discrete problem into a continuous convex one and obtain the sub-optimal solutions. Finally, our proposed scheme is compared with the exhaustive search method in numerical simulations, which show slight gap between the obtained sub-optimal and optimal solutions, and this gap further decreases with large weight factor., Comment: 6 pages, 4 figures

Published
2024

28. Size Growth on Short Timescales of Star-Forming Galaxies: Insights from Size Variation with Rest-Frame Wavelength with JADES

Author

Jia, Cheng, Wang, Enci, Wang, Huiyuan, Li, Hui, Yao, Yao, Song, Jie, Zhang, Hongxin, Rong, Yu, Chen, Yangyao, Yu, Haoran, Chen, Zeyu, Li, Haixin, Ma, Chengyu, and Kong, Xu

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We investigate size variation with rest-frame wavelength for star-forming galaxies based on the second JWST Advanced Deep Extragalactic Survey data release. Star-forming galaxies are typically smaller at longer wavelength from UV-to-NIR at $z<3.5$, especially for more massive galaxies, indicating the inside-out assembly with in-situ star formation if ignoring dust attenuation. The size variation with wavelength shows strong dependence on stellar mass, and shows little or no dependence on redshift, specific star formation rate and galaxy environment. This suggests that the size growth of star-forming galaxies is a self-regulated process primarily governed by stellar mass. We model size as a function of both mass and redshift simultaneously, obtaining $R_{\rm e} \propto M_*^{0.23} (1+z)^{-1.04}$ at a wavelength of 0.45 ${\mu \mathrm{m}}$, and $R_{\rm e} \propto M_*^{0.20} (1+z)^{-1.08}$ at 1.0 ${\mu \mathrm{m}}$. Based on this size evolution and the star formation main sequence from the literature, we obtain the locus of typical size growth for individual galaxies of different masses on the mass-size plane. The moving trend of galaxies on the mass-size plane, which indicates the slopes of their locus, strongly correlates with the size ratio between 0.45 ${\mu \mathrm{m}}$ and 1.0 ${\mu \mathrm{m}}$, supporting the idea that the size variation with wavelength provides important information on size growth of galaxies on short timescales., Comment: Accepted for publication in ApJ, 19 pages, 11 figures

Published
2024

29. Feature-Space Semantic Invariance: Enhanced OOD Detection for Open-Set Domain Generalization

Author

Wang, Haoliang, Zhao, Chen, and Chen, Feng

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

Open-set domain generalization addresses a real-world challenge: training a model to generalize across unseen domains (domain generalization) while also detecting samples from unknown classes not encountered during training (open-set recognition). However, most existing approaches tackle these issues separately, limiting their practical applicability. To overcome this limitation, we propose a unified framework for open-set domain generalization by introducing Feature-space Semantic Invariance (FSI). FSI maintains semantic consistency across different domains within the feature space, enabling more accurate detection of OOD instances in unseen domains. Additionally, we adopt a generative model to produce synthetic data with novel domain styles or class labels, enhancing model robustness. Initial experiments show that our method improves AUROC by 9.1% to 18.9% on ColoredMNIST, while also significantly increasing in-distribution classification accuracy., Comment: IEEE BigData 2024, Ph.D. Forum

Published
2024

30. Continual Memorization of Factoids in Large Language Models

Author

Chen, Howard, Geng, Jiayi, Bhaskar, Adithya, Friedman, Dan, and Chen, Danqi

Subjects
Computer Science - Computation and Language
Abstract

Large language models can absorb a massive amount of knowledge through pretraining, but pretraining is inefficient for acquiring long-tailed or specialized facts. Therefore, fine-tuning on specialized or new knowledge that reflects changes in the world has become popular, though it risks disrupting the model's original capabilities. We study this fragility in the context of continual memorization, where the model is trained on a small set of long-tail factoids (factual associations) and must retain these factoids after multiple stages of subsequent training on other datasets. Through extensive experiments, we show that LLMs suffer from forgetting across a wide range of subsequent tasks, and simple replay techniques do not fully prevent forgetting, especially when the factoid datasets are trained in the later stages. We posit that there are two ways to alleviate forgetting: 1) protect the memorization process as the model learns the factoids, or 2) reduce interference from training in later stages. With this insight, we develop an effective mitigation strategy: REMIX (Random and Generic Data Mixing). REMIX prevents forgetting by mixing generic data sampled from pretraining corpora or even randomly generated word sequences during each stage, despite being unrelated to the memorized factoids in the first stage. REMIX can recover performance from severe forgetting, often outperforming replay-based methods that have access to the factoids from the first stage. We then analyze how REMIX alters the learning process and find that successful forgetting prevention is associated with a pattern: the model stores factoids in earlier layers than usual and diversifies the set of layers that store these factoids. The efficacy of REMIX invites further investigation into the underlying dynamics of memorization and forgetting, opening exciting possibilities for future research.

Published
2024

31. Edify 3D: Scalable High-Quality 3D Asset Generation

Author

NVIDIA, Bala, Maciej, Cui, Yin, Ding, Yifan, Ge, Yunhao, Hao, Zekun, Hasselgren, Jon, Huffman, Jacob, Jin, Jingyi, Lewis, J. P., Li, Zhaoshuo, Lin, Chen-Hsuan, Lin, Yen-Chen, Lin, Tsung-Yi, Liu, Ming-Yu, Luo, Alice, Ma, Qianli, Munkberg, Jacob, Shi, Stella, Wei, Fangyin, Xiang, Donglai, Xu, Jiashu, Zeng, Xiaohui, and Zhang, Qinsheng

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

We introduce Edify 3D, an advanced solution designed for high-quality 3D asset generation. Our method first synthesizes RGB and surface normal images of the described object at multiple viewpoints using a diffusion model. The multi-view observations are then used to reconstruct the shape, texture, and PBR materials of the object. Our method can generate high-quality 3D assets with detailed geometry, clean shape topologies, high-resolution textures, and materials within 2 minutes of runtime., Comment: Project website: https://research.nvidia.com/labs/dir/edify-3d

Published
2024

32. Building a Taiwanese Mandarin Spoken Language Model: A First Attempt

Author

Yang, Chih-Kai, Fu, Yu-Kuan, Li, Chen-An, Lin, Yi-Cheng, Lin, Yu-Xiang, Chen, Wei-Chih, Chung, Ho Lam, Kuan, Chun-Yi, Huang, Wei-Ping, Lu, Ke-Han, Lin, Tzu-Quan, Wang, Hsiu-Hsuan, Hu, En-Pei, Hsu, Chan-Jan, Tseng, Liang-Hsuan, Chiu, I-Hsiang, Sanga, Ulin, Chen, Xuanjun, Hsu, Po-chun, Yang, Shu-wen, and Lee, Hung-yi

Subjects
Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

This technical report presents our initial attempt to build a spoken large language model (LLM) for Taiwanese Mandarin, specifically tailored to enable real-time, speech-to-speech interaction in multi-turn conversations. Our end-to-end model incorporates a decoder-only transformer architecture and aims to achieve seamless interaction while preserving the conversational flow, including full-duplex capabilities allowing simultaneous speaking and listening. The paper also details the training process, including data preparation with synthesized dialogues and adjustments for real-time interaction. We also developed a platform to evaluate conversational fluency and response coherence in multi-turn dialogues. We hope the release of the report can contribute to the future development of spoken LLMs in Taiwanese Mandarin., Comment: Work in progress

Published
2024

33. Scaling Mesh Generation via Compressive Tokenization

Author

Weng, Haohan, Zhao, Zibo, Lei, Biwen, Yang, Xianghui, Liu, Jian, Lai, Zeqiang, Chen, Zhuo, Liu, Yuhong, Jiang, Jie, Guo, Chunchao, Zhang, Tong, Gao, Shenghua, and Chen, C. L. Philip

Subjects
Computer Science - Graphics, Computer Science - Computer Vision and Pattern Recognition
Abstract

We propose a compressive yet effective mesh representation, Blocked and Patchified Tokenization (BPT), facilitating the generation of meshes exceeding 8k faces. BPT compresses mesh sequences by employing block-wise indexing and patch aggregation, reducing their length by approximately 75\% compared to the original sequences. This compression milestone unlocks the potential to utilize mesh data with significantly more faces, thereby enhancing detail richness and improving generation robustness. Empowered with the BPT, we have built a foundation mesh generative model training on scaled mesh data to support flexible control for point clouds and images. Our model demonstrates the capability to generate meshes with intricate details and accurate topology, achieving SoTA performance on mesh generation and reaching the level for direct product usage., Comment: Homepage: https://whaohan.github.io/bpt , Code: https://github.com/whaohan/bpt

Published
2024

34. UniHR: Hierarchical Representation Learning for Unified Knowledge Graph Link Prediction

Author

Liu, Zhiqiang, Chen, Mingyang, Hua, Yin, Chen, Zhuo, Liu, Ziqi, Liang, Lei, Chen, Huajun, and Zhang, Wen

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

Beyond-triple fact representations including hyper-relational facts with auxiliary key-value pairs, temporal facts with additional timestamps, and nested facts implying relationships between facts, are gaining significant attention. However, existing link prediction models are usually designed for one specific type of facts, making it difficult to generalize to other fact representations. To overcome this limitation, we propose a Unified Hierarchical Representation learning framework (UniHR) for unified knowledge graph link prediction. It consists of a unified Hierarchical Data Representation (HiDR) module and a unified Hierarchical Structure Learning (HiSL) module as graph encoder. The HiDR module unifies hyper-relational KGs, temporal KGs, and nested factual KGs into triple-based representations. Then HiSL incorporates intra-fact and inter-fact message passing, focusing on enhancing the semantic information within individual facts and enriching the structural information between facts. Experimental results across 7 datasets from 3 types of KGs demonstrate that our UniHR outperforms baselines designed for one specific kind of KG, indicating strong generalization capability of HiDR form and the effectiveness of HiSL module. Code and data are available at https://github.com/Lza12a/UniHR.

Published
2024

35. DiffSR: Learning Radar Reflectivity Synthesis via Diffusion Model from Satellite Observations

Author

He, Xuming, Zhou, Zhiwang, Zhang, Wenlong, Zhao, Xiangyu, Chen, Hao, Chen, Shiqi, and Bai, Lei

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Weather radar data synthesis can fill in data for areas where ground observations are missing. Existing methods often employ reconstruction-based approaches with MSE loss to reconstruct radar data from satellite observation. However, such methods lead to over-smoothing, which hinders the generation of high-frequency details or high-value observation areas associated with convective weather. To address this issue, we propose a two-stage diffusion-based method called DiffSR. We first pre-train a reconstruction model on global-scale data to obtain radar estimation and then synthesize radar reflectivity by combining radar estimation results with satellite data as conditions for the diffusion model. Extensive experiments show that our method achieves state-of-the-art (SOTA) results, demonstrating the ability to generate high-frequency details and high-value areas.

Published
2024

36. Region-Aware Text-to-Image Generation via Hard Binding and Soft Refinement

Author

Chen, Zhennan, Li, Yajie, Wang, Haofan, Chen, Zhibo, Jiang, Zhengkai, Li, Jun, Wang, Qian, Yang, Jian, and Tai, Ying

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In this paper, we present RAG, a Regional-Aware text-to-image Generation method conditioned on regional descriptions for precise layout composition. Regional prompting, or compositional generation, which enables fine-grained spatial control, has gained increasing attention for its practicality in real-world applications. However, previous methods either introduce additional trainable modules, thus only applicable to specific models, or manipulate on score maps within cross-attention layers using attention masks, resulting in limited control strength when the number of regions increases. To handle these limitations, we decouple the multi-region generation into two sub-tasks, the construction of individual region (Regional Hard Binding) that ensures the regional prompt is properly executed, and the overall detail refinement (Regional Soft Refinement) over regions that dismiss the visual boundaries and enhance adjacent interactions. Furthermore, RAG novelly makes repainting feasible, where users can modify specific unsatisfied regions in the last generation while keeping all other regions unchanged, without relying on additional inpainting models. Our approach is tuning-free and applicable to other frameworks as an enhancement to the prompt following property. Quantitative and qualitative experiments demonstrate that RAG achieves superior performance over attribute binding and object relationship than previous tuning-free methods.

Published
2024

37. MBL-CPDP: A Multi-objective Bilevel Method for Cross-Project Defect Prediction via Automated Machine Learning

Author

Chen, Jiaxin, Ding, Jinliang, Tan, Kay Chen, Qian, Jiancheng, and Li, Ke

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Cross-project defect prediction (CPDP) leverages machine learning (ML) techniques to proactively identify software defects, especially where project-specific data is scarce. However, developing a robust ML pipeline with optimal hyperparameters that effectively use cross-project information and yield satisfactory performance remains challenging. In this paper, we resolve this bottleneck by formulating CPDP as a multi-objective bilevel optimization (MBLO) method, dubbed MBL-CPDP. It comprises two nested problems: the upper-level, a multi-objective combinatorial optimization problem, enhances robustness and efficiency in optimizing ML pipelines, while the lower-level problem is an expensive optimization problem that focuses on tuning their optimal hyperparameters. Due to the high-dimensional search space characterized by feature redundancy and inconsistent data distributions, the upper-level problem combines feature selection, transfer learning, and classification to leverage limited and heterogeneous historical data. Meanwhile, an ensemble learning method is proposed to capture differences in cross-project distribution and generalize across diverse datasets. Finally, a MBLO algorithm is presented to solve this problem while achieving high adaptability effectively. To evaluate the performance of MBL-CPDP, we compare it with five automated ML tools and $50$ CPDP techniques across $20$ projects. Extensive empirical results show that MBL-CPDPoutperforms the comparison methods, demonstrating its superior adaptability and comprehensive performance evaluation capability., Comment: 37 pages

Published
2024

38. KMM: Key Frame Mask Mamba for Extended Motion Generation

Author

Zhang, Zeyu, Gao, Hang, Liu, Akide, Chen, Qi, Chen, Feng, Wang, Yiran, Li, Danning, and Tang, Hao

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Human motion generation is a cut-edge area of research in generative computer vision, with promising applications in video creation, game development, and robotic manipulation. The recent Mamba architecture shows promising results in efficiently modeling long and complex sequences, yet two significant challenges remain: Firstly, directly applying Mamba to extended motion generation is ineffective, as the limited capacity of the implicit memory leads to memory decay. Secondly, Mamba struggles with multimodal fusion compared to Transformers, and lack alignment with textual queries, often confusing directions (left or right) or omitting parts of longer text queries. To address these challenges, our paper presents three key contributions: Firstly, we introduce KMM, a novel architecture featuring Key frame Masking Modeling, designed to enhance Mamba's focus on key actions in motion segments. This approach addresses the memory decay problem and represents a pioneering method in customizing strategic frame-level masking in SSMs. Additionally, we designed a contrastive learning paradigm for addressing the multimodal fusion problem in Mamba and improving the motion-text alignment. Finally, we conducted extensive experiments on the go-to dataset, BABEL, achieving state-of-the-art performance with a reduction of more than 57% in FID and 70% parameters compared to previous state-of-the-art methods. See project website: https://steve-zeyu-zhang.github.io/KMM

Published
2024

39. ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?

Author

Chen, Canyu, Yu, Jian, Chen, Shan, Liu, Che, Wan, Zhongwei, Bitterman, Danielle, Wang, Fei, and Shu, Kai

Subjects
Computer Science - Computation and Language
Abstract

Large Language Models (LLMs) hold great promise to revolutionize current clinical systems for their superior capacities on medical text processing tasks and medical licensing exams. Meanwhile, traditional ML models such as SVM and XGBoost have still been mainly adopted in clinical prediction tasks. An emerging question is Can LLMs beat traditional ML models in clinical prediction? Thus, we build a new benchmark ClinicalBench to comprehensively study the clinical predictive modeling capacities of both general-purpose and medical LLMs, and compare them with traditional ML models. ClinicalBench embraces three common clinical prediction tasks, two databases, 14 general-purpose LLMs, 8 medical LLMs, and 11 traditional ML models. Through extensive empirical investigation, we discover that both general-purpose and medical LLMs, even with different model scales, diverse prompting or fine-tuning strategies, still cannot beat traditional ML models in clinical prediction yet, shedding light on their potential deficiency in clinical reasoning and decision-making. We call for caution when practitioners adopt LLMs in clinical applications. ClinicalBench can be utilized to bridge the gap between LLMs' development for healthcare and real-world clinical practice., Comment: The first two authors contributed equally. 10 pages for main paper, 66 pages including appendix. Project website: https://clinicalbench.github.io

Published
2024

40. SplatFormer: Point Transformer for Robust 3D Gaussian Splatting

Author

Chen, Yutong, Mihajlovic, Marko, Chen, Xiyi, Wang, Yiming, Prokudin, Sergey, and Tang, Siyu

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

3D Gaussian Splatting (3DGS) has recently transformed photorealistic reconstruction, achieving high visual fidelity and real-time performance. However, rendering quality significantly deteriorates when test views deviate from the camera angles used during training, posing a major challenge for applications in immersive free-viewpoint rendering and navigation. In this work, we conduct a comprehensive evaluation of 3DGS and related novel view synthesis methods under out-of-distribution (OOD) test camera scenarios. By creating diverse test cases with synthetic and real-world datasets, we demonstrate that most existing methods, including those incorporating various regularization techniques and data-driven priors, struggle to generalize effectively to OOD views. To address this limitation, we introduce SplatFormer, the first point transformer model specifically designed to operate on Gaussian splats. SplatFormer takes as input an initial 3DGS set optimized under limited training views and refines it in a single forward pass, effectively removing potential artifacts in OOD test views. To our knowledge, this is the first successful application of point transformers directly on 3DGS sets, surpassing the limitations of previous multi-scene training methods, which could handle only a restricted number of input views during inference. Our model significantly improves rendering quality under extreme novel views, achieving state-of-the-art performance in these challenging scenarios and outperforming various 3DGS regularization techniques, multi-scene models tailored for sparse view synthesis, and diffusion-based frameworks., Comment: Code and dataset: https://github.com/ChenYutongTHU/SplatFormer Project page: https://sergeyprokudin.github.io/splatformer/

Published
2024

41. Selective State Space Model for Monaural Speech Enhancement

Author

Chen, Moran, Zhang, Qiquan, Wang, Mingjiang, Zhang, Xiangyu, Liu, Hexin, Ambikairaiah, Eliathamby, and Chen, Deying

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Voice user interfaces (VUIs) have facilitated the efficient interactions between humans and machines through spoken commands. Since real-word acoustic scenes are complex, speech enhancement plays a critical role for robust VUI. Transformer and its variants, such as Conformer, have demonstrated cutting-edge results in speech enhancement. However, both of them suffers from the quadratic computational complexity with respect to the sequence length, which hampers their ability to handle long sequences. Recently a novel State Space Model called Mamba, which shows strong capability to handle long sequences with linear complexity, offers a solution to address this challenge. In this paper, we propose a novel hybrid convolution-Mamba backbone, denoted as MambaDC, for speech enhancement. Our MambaDC marries the benefits of convolutional networks to model the local interactions and Mamba's ability for modeling long-range global dependencies. We conduct comprehensive experiments within both basic and state-of-the-art (SoTA) speech enhancement frameworks, on two commonly used training targets. The results demonstrate that MambaDC outperforms Transformer, Conformer, and the standard Mamba across all training targets. Built upon the current advanced framework, the use of MambaDC backbone showcases superior results compared to existing \textcolor{black}{SoTA} systems. This sets the stage for efficient long-range global modeling in speech enhancement., Comment: Submitted to IEEE TCE

Published
2024

42. Artificial Intelligence for Collective Intelligence: A National-Scale Research Strategy

Author

Bullock, Seth, Ajmeri, Nirav, Batty, Mike, Black, Michaela, Cartlidge, John, Challen, Robert, Chen, Cangxiong, Chen, Jing, Condell, Joan, Danon, Leon, Dennett, Adam, Heppenstall, Alison, Marshall, Paul, Morgan, Phil, O'Kane, Aisling, Smith, Laura G. E., Smith, Theresa, and Williams, Hywel T. P.

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computers and Society
Abstract

Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or trans-national scale. Pressing challenges in healthcare, finance, infrastructure and sustainability, for instance, might all be productively addressed by leveraging and amplifying AI for national-scale collective intelligence. The development and deployment of this kind of AI faces distinctive challenges, both technical and socio-technical. Here, a research strategy for mobilising inter-disciplinary research to address these challenges is detailed and some of the key issues that must be faced are outlined., Comment: 25 pages, 3 figures, Accepted for publication at Knowledge Engineering Review (KER)

Published
2024

43. LSSInst: Improving Geometric Modeling in LSS-Based BEV Perception with Instance Representation

Author

Ma, Weijie, Jiang, Jingwei, Yang, Yang, Chen, Zehui, and Chen, Hao

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

With the attention gained by camera-only 3D object detection in autonomous driving, methods based on Bird-Eye-View (BEV) representation especially derived from the forward view transformation paradigm, i.e., lift-splat-shoot (LSS), have recently seen significant progress. The BEV representation formulated by the frustum based on depth distribution prediction is ideal for learning the road structure and scene layout from multi-view images. However, to retain computational efficiency, the compressed BEV representation such as in resolution and axis is inevitably weak in retaining the individual geometric details, undermining the methodological generality and applicability. With this in mind, to compensate for the missing details and utilize multi-view geometry constraints, we propose LSSInst, a two-stage object detector incorporating BEV and instance representations in tandem. The proposed detector exploits fine-grained pixel-level features that can be flexibly integrated into existing LSS-based BEV networks. Having said that, due to the inherent gap between two representation spaces, we design the instance adaptor for the BEV-to-instance semantic coherence rather than pass the proposal naively. Extensive experiments demonstrated that our proposed framework is of excellent generalization ability and performance, which boosts the performances of modern LSS-based BEV perception methods without bells and whistles and outperforms current LSS-based state-of-the-art works on the large-scale nuScenes benchmark., Comment: Accepted by 3DV 2025

Published
2024

44. State Chrono Representation for Enhancing Generalization in Reinforcement Learning

Author

Chen, Jianda, Ng, Wen Zheng Terence, Chen, Zichen, Pan, Sinno Jialin, and Zhang, Tianwei

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

In reinforcement learning with image-based inputs, it is crucial to establish a robust and generalizable state representation. Recent advancements in metric learning, such as deep bisimulation metric approaches, have shown promising results in learning structured low-dimensional representation space from pixel observations, where the distance between states is measured based on task-relevant features. However, these approaches face challenges in demanding generalization tasks and scenarios with non-informative rewards. This is because they fail to capture sufficient long-term information in the learned representations. To address these challenges, we propose a novel State Chrono Representation (SCR) approach. SCR augments state metric-based representations by incorporating extensive temporal information into the update step of bisimulation metric learning. It learns state distances within a temporal framework that considers both future dynamics and cumulative rewards over current and long-term future states. Our learning strategy effectively incorporates future behavioral information into the representation space without introducing a significant number of additional parameters for modeling dynamics. Extensive experiments conducted in DeepMind Control and Meta-World environments demonstrate that SCR achieves better performance comparing to other recent metric-based methods in demanding generalization tasks. The codes of SCR are available in https://github.com/jianda-chen/SCR.

Published
2024

45. SniffySquad: Patchiness-Aware Gas Source Localization with Multi-Robot Collaboration

Author

Cheng, Yuhan, Chen, Xuecheng, Yang, Yixuan, Wang, Haoyang, Xu, Jingao, Hong, Chaopeng, Xu, Susu, Zhang, Xiao-Ping, Liu, Yunhao, and Chen, Xinlei

Subjects
Computer Science - Robotics, Computer Science - Multiagent Systems
Abstract

Gas source localization is pivotal for the rapid mitigation of gas leakage disasters, where mobile robots emerge as a promising solution. However, existing methods predominantly schedule robots' movements based on reactive stimuli or simplified gas plume models. These approaches typically excel in idealized, simulated environments but fall short in real-world gas environments characterized by their patchy distribution. In this work, we introduce SniffySquad, a multi-robot olfaction-based system designed to address the inherent patchiness in gas source localization. SniffySquad incorporates a patchiness-aware active sensing approach that enhances the quality of data collection and estimation. Moreover, it features an innovative collaborative role adaptation strategy to boost the efficiency of source-seeking endeavors. Extensive evaluations demonstrate that our system achieves an increase in the success rate by $20\%+$ and an improvement in path efficiency by $30\%+$, outperforming state-of-the-art gas source localization solutions.

Published
2024

46. A capacity renting framework for shared energy storage considering peer-to-peer energy trading of prosumers with privacy protection

Author

Sun, Yingcong, Chen, Laijun, Chen, Yue, Tang, Mingrui, and Mei, Shengwei

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

Shared energy storage systems (ESS) present a promising solution to the temporal imbalance between energy generation from renewable distributed generators (DGs) and the power demands of prosumers. However, as DG penetration rates rise, spatial energy imbalances become increasingly significant, necessitating the integration of peer-to-peer (P2P) energy trading within the shared ESS framework. Two key challenges emerge in this context: the absence of effective mechanisms and the greater difficulty for privacy protection due to increased data communication. This research proposes a capacity renting framework for shared ESS considering P2P energy trading of prosumers. In the proposed framework, prosumers can participate in P2P energy trading and rent capacities from shared ESS. A generalized Nash game is formulated to model the trading process and the competitive interactions among prosumers, and the variational equilibrium of the game is proved to be equivalent to the optimal solution of a quadratic programming (QP) problem. To address the privacy protection concern, the problem is solved using the alternating direction method of multipliers (ADMM) with the Paillier cryptosystem. Finally, numerical simulations demonstrate the impact of P2P energy trading on the shared ESS framework and validate the effectiveness of the proposed privacy-preserving algorithm.

Published
2024

47. NeKo: Toward Post Recognition Generative Correction Large Language Models with Task-Oriented Experts

Author

Lin, Yen-Ting, Yang, Chao-Han Huck, Chen, Zhehuai, Zelasko, Piotr, Yang, Xuesong, Chen, Zih-Ching, Puvvada, Krishna C, Fu, Szu-Wei, Hu, Ke, Chiu, Jun Wei, Balam, Jagadeesh, Ginsburg, Boris, and Wang, Yu-Chiang Frank

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Multiagent Systems, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Construction of a general-purpose post-recognition error corrector poses a crucial question: how can we most effectively train a model on a large mixture of domain datasets? The answer would lie in learning dataset-specific features and digesting their knowledge in a single model. Previous methods achieve this by having separate correction language models, resulting in a significant increase in parameters. In this work, we present Mixture-of-Experts as a solution, highlighting that MoEs are much more than a scalability tool. We propose a Multi-Task Correction MoE, where we train the experts to become an ``expert'' of speech-to-text, language-to-text and vision-to-text datasets by learning to route each dataset's tokens to its mapped expert. Experiments on the Open ASR Leaderboard show that we explore a new state-of-the-art performance by achieving an average relative $5.0$% WER reduction and substantial improvements in BLEU scores for speech and translation tasks. On zero-shot evaluation, NeKo outperforms GPT-3.5 and Claude-Opus with $15.5$% to $27.6$% relative WER reduction in the Hyporadise benchmark. NeKo performs competitively on grammar and post-OCR correction as a multi-task model., Comment: NeKo work has been done in June 2024. NeKo LMs will be open source on https://huggingface.co/nvidia under the MIT license

Published
2024

48. FinDVer: Explainable Claim Verification over Long and Hybrid-Content Financial Documents

Author

Zhao, Yilun, Long, Yitao, Jiang, Yuru, Wang, Chengye, Chen, Weiyuan, Liu, Hongjun, Zhang, Yiming, Tang, Xiangru, Zhao, Chen, and Cohan, Arman

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

We introduce FinDVer, a comprehensive benchmark specifically designed to evaluate the explainable claim verification capabilities of LLMs in the context of understanding and analyzing long, hybrid-content financial documents. FinDVer contains 2,400 expert-annotated examples, divided into three subsets: information extraction, numerical reasoning, and knowledge-intensive reasoning, each addressing common scenarios encountered in real-world financial contexts. We assess a broad spectrum of LLMs under long-context and RAG settings. Our results show that even the current best-performing system, GPT-4o, still lags behind human experts. We further provide in-depth analysis on long-context and RAG setting, Chain-of-Thought reasoning, and model reasoning errors, offering insights to drive future advancements. We believe that FinDVer can serve as a valuable benchmark for evaluating LLMs in claim verification over complex, expert-domain documents., Comment: EMNLP 2024

Published
2024

49. Measurement of the $\psi(2S)$ to $J/\psi$ cross-section ratio as a function of centrality in PbPb collisions at $\sqrt{s_{\text{NN}}}$ = 5.02 TeV

Author

LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataíde, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Balboni, A., Baldini, W., Balzani, L., Bao, H., Leite, J. Baptista de Souza, Pretel, C. Barbero, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartolini, M., Bartz, J., Basels, J. M., Bashir, S., Bassi, G., Batsukh, B., Battista, P. B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Behling, N. A., Belin, S., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bonacci, R. B., Bondar, N., Bordelius, A., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bouchiba, S. A., Bovill, M., Bowcock, T. J. V., Boyer, A., Bozzi, C., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonaura, A., Buonincontri, L., Burke, A. T., Burr, C., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Caillet, A. C., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cervenkov, D., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chiotopoulos, X., Chobanova, V., Cholak, S., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collaviti, S., Collins, P., Colombo, T., Colonna, M., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Darze, G., Davidson, A., Davies, J. E., Davis, A., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. A., Debernardis, F., Decamp, D., Dedu, V., Dekkers, S., Del Buono, L., Delaney, B., Dembinski, H. -P., Deng, J., Denysenko, V., Deschamps, O., Dettori, F., Dey, B., Di Nezza, P., Diachkov, I., Didenko, S., Ding, S., Dittmann, L., Dobishuk, V., Docheva, A. D., Dong, C., Donohoe, A. M., Dordei, F., Reis, A. C. dos, Dowling, A. D., Duan, W., Duda, P., Dudek, M. W., Dufour, L., Duk, V., Durante, P., Duras, M. M., Durham, J. M., Durmus, O. D., Dziurda, A., Dzyuba, A., Easo, S., Eckstein, E., Egede, U., Egorychev, A., Egorychev, V., Eisenhardt, S., Ejopu, E., Eklund, L., Elashri, M., Ellbracht, J., Ely, S., Ene, A., Eschle, J., Esen, S., Evans, T., Fabiano, F., Falcao, L. N., Fan, Y., Fang, B., Fantini, L., Faria, M., Farmer, K., Fazzini, D., Felkowski, L., Feng, M., Feo, M., Casani, A. Fernandez, Gomez, M. Fernandez, Fernez, A. D., Ferrari, F., Rodrigues, F. Ferreira, Ferrillo, M., Ferro-Luzzi, M., Filippov, S., Fini, R. A., Fiorini, M., Firlej, M., Fischer, K. L., Fitzgerald, D. S., Fitzpatrick, C., Fiutowski, T., Fleuret, F., Fontana, M., Foreman, L. F., Forty, R., Foulds-Holt, D., Lima, V. Franco, Sevilla, M. Franco, Frank, M., Franzoso, E., Frau, G., Frei, C., Friday, D. A., Fu, J., Führing, Q., Fujii, Y., Fulghesu, T., Gabriel, E., Galati, G., Galati, M. D., Torreira, A. Gallas, Galli, D., Gambetta, S., Gandelman, M., Gandini, P., Ganie, B., Gao, H., Gao, R., Gao, T. Q., Gao, Y., Martin, L. M. Garcia, Moreno, P. Garcia, Pardiñas, J. García, Gardner, P., Garg, K. G., Garrido, L., Gaspar, C., Geertsema, R. E., Gerken, L. L., Gersabeck, E., Gersabeck, M., Gershon, T., Ghizzo, S., Ghorbanimoghaddam, Z., Giambastiani, L., Giasemis, F. I., Gibson, V., Giemza, H. K., Gilman, A. L., Giovannetti, M., Gioventù, A., Girardey, L., Gironell, P. Gironella, Giugliano, C., Giza, M. A., Gkougkousis, E. L., Glaser, F. C., Gligorov, V. V., Göbel, C., Golobardes, E., Golubkov, D., Golutvin, A., Fernandez, S. Gomez, Gomulka, W., Abrantes, F. Goncalves, Goncerz, M., Gong, G., Gooding, J. A., Gorelov, I. V., Gotti, C., Grabowski, J. P., Cardoso, L. A. Granado, Graugés, E., Graverini, E., Grazette, L., Graziani, G., Grecu, A. T., Greeven, L. M., Grieser, N. 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Subjects
Nuclear Experiment, High Energy Physics - Experiment
Abstract

The dissociation of quarkonium states with different binding energies produced in heavy-ion collisions is a powerful probe for investigating the formation and properties of the quark-gluon plasma. The ratio of production cross-sections of $\psi(2S)$ and $J/\psi$ mesons times the ratio of their branching fractions into the dimuon final state is measured as a function of centrality using data collected by the LHCb detector in PbPb collisions at $\sqrt{s_{\text{NN}}}$ = 5.02 TeV. The measured ratio shows no dependence on the collision centrality, and is compared to the latest theory predictions and to the recent measurements in literature., Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2024-041.html (LHCb public pages)

Published
2024

50. Multifunctional metalens for trapping and characterizing single atoms

Author

Chen, Guang-Jie, Zhao, Dong, Wang, Zhu-Bo, Li, Ziqin, Zhang, Ji-Zhe, Chen, Liang, Zhang, Yan-Lei, Xu, Xin-Biao, Liu, Ai-Ping, Dong, Chun-Hua, Guo, Guang-Can, Huang, Kun, and Zou, Chang-Ling

Subjects
Quantum Physics
Abstract

Precise control and manipulation of neutral atoms are essential for quantum technologies but largely dependent on conventional bulky optical setups. Here, we demonstrate a multifunctional metalens that integrates an achromatic lens with large numerical aperture, a quarter-wave plate, and a polarizer for trapping and characterizing single Rubidium atoms. The metalens simultaneously focuses a trapping beam at 852\,nm and collects single-photon fluorescence at 780\,nm. We observe a strong dependence of the trapping lifetime on an external bias magnetic field, suggests a complex interplay between the circularly polarized trapping light and the atom's internal states. Our work showcases the potential of metasurfaces in realizing compact and integrated quantum systems based on cold atoms, opening up new possibilities for studying quantum control and manipulation at the nanoscale., Comment: 10 pages, 5 figures

Published
2024

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