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80,835 results on '"Rong, P"'

201. From Novice to Expert: LLM Agent Policy Optimization via Step-wise Reinforcement Learning

Author: Deng, Zhirui, Dou, Zhicheng, Zhu, Yutao, Wen, Ji-Rong, Xiong, Ruibin, Wang, Mang, and Chen, Weipeng
Subjects: Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Human-Computer Interaction, Computer Science - Robotics
Abstract: The outstanding capabilities of large language models (LLMs) render them a crucial component in various autonomous agent systems. While traditional methods depend on the inherent knowledge of LLMs without fine-tuning, more recent approaches have shifted toward the reinforcement learning strategy to further enhance agents' ability to solve complex interactive tasks with environments and tools. However, previous approaches are constrained by the sparse reward issue, where existing datasets solely provide a final scalar reward for each multi-step reasoning chain, potentially leading to ineffectiveness and inefficiency in policy learning. In this paper, we introduce StepAgent, which utilizes step-wise reward to optimize the agent's reinforcement learning process. Inheriting the spirit of novice-to-expert theory, we first compare the actions of the expert and the agent to automatically generate intermediate rewards for fine-grained optimization. Additionally, we propose implicit-reward and inverse reinforcement learning techniques to facilitate agent reflection and policy adjustment. Further theoretical analysis demonstrates that the action distribution of the agent can converge toward the expert action distribution over multiple training cycles. Experimental results across various datasets indicate that StepAgent outperforms existing baseline methods.
Published: 2024

202. The new states $X(1910)$ and $X(2300)$ and higher light excited $J^{PC}=1^{+-}$ mesons

Author: Wang, Ya-Rong, Liu, Xiao-Hai, Pang, Cheng-Qun, and Chen, Hao
Subjects: High Energy Physics - Phenomenology
Abstract: The BESIII Collaboration recently reported the observation of two new resonances, $X(1910)$ and $X(2300)$, which have sparked our interest in studying the light hadron family with $J^{PC}=1^{+-}$ . In this work, we investigate the mass spectra and OZI-allowed two-body strong decays of $b_1$, $h_1$, and $h_1^\prime$ using the MGI model and QPC model with newly fitted parameters. We also explore the possibility of identifying $X(1910)$ and $X(2300)$ as $h_1$ or $h_1^\prime$ states. Our numerical results suggest that $X(1910)$ could be a promising candidate for the $h_1^\prime(2^1P_1)$ state with quark content $s\bar{s}$, while the structure of $X(2300)$ remains uncertain.
Published: 2024

203. Tri-meson state $\boldsymbol{\bar{B}\bar{B}^\bar{B}^}$

Author: Deng, Cheng-Rong and An, Chun-Sheng
Subjects: High Energy Physics - Phenomenology
Abstract: We systematically explore the tri-meson states $\bar{B}\bar{B}^*\bar{B}^*$ with various isospin-spin configurations in the quark model by solving exactly the six-body Schr\"{o}dinger equations with the Gaussian expansion method. The isospin-spin configuration $\left[[\bar{B} \bar{B}^*]^1_0\bar{B}^*\right]^0_{\frac{1}{2}}$ is not only approximately 10.2 MeV lower than the threshold of its constituent particles but also about 0.2 MeV below that of the compact tetraquark state $[\bar{B}\bar{B}^*]^1_0$ and $\bar{B}^* $. This configuration manifests a loose two-body bound state composed of $[\bar{B}\bar{B}^*]^1_0$ and $\bar{B}^* $, with a size of around 4.75 fm. In contrast, the isospin-spin configurations $\left[[\bar{B}\bar{B}^*]^1_1\bar{B}^*\right]^0_{\frac{1}{2}}$, $\left[\bar{B} [\bar{B}^*\bar{B}^*]^0_1\right]^0_{\frac{1}{2}}$, and $\left[[\bar{B}\bar{B}^*]^1_1\bar{B}^*\right]^1_{\frac{1}{2}}$ exhibit binding energies of less than 1 MeV relative to their constituent particles, establishing a loose three-body bound state consisting of three completely separated mesons. Among the four bound configurations, the $\sigma$-meson exchange plays a decisive role. The meson pair $[\bar{B}\bar{B}^*]^1_0$, resembling the short-range strong correlated $p$-$n$ pair in nuclear physics, prevails over other types of meson pairs. This strongly correlated meson pair not only contributes to the binding mechanisms but also influences the spatial structures of those stable tri-meson configurations., Comment: 8 pages, 2 figures, 3 tables
Published: 2024

204. Study of $D_{s1}(2460)^{+}\to D_{s}^{+}\pi^{+}\pi^{-}$ in $B\to {\bar{D}}^{(*)}D_{s}^{+}\pi^{+}\pi^{-}$ 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., 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., 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., 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., 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
Abstract: An amplitude analysis of the $D_{s1}(2460)^+\to D_{s}^{+}\pi^{+}\pi^{-}$ transition is performed simultaneously in $B^{0}\to D^{-}D_{s}^{+}\pi^{+}\pi^{-}$, $B^{+}\to{\bar{D}}^{0} D_{s}^{+}\pi^{+}\pi^{-}$, and $B^{0}\to D^{*-}D_{s}^{+}\pi^{+}\pi^{-}$ decays. The study is based on a data sample of proton-proton collisions recorded with the LHCb detector at centre-of-mass energies of $\sqrt{s}=7,8,$ and $13\,$TeV, corresponding to a total integrated luminosity of $9\,\rm{fb}^{-1}$. A clear double-peak structure is observed in the $m(\pi^{+}\pi^{-})$ spectrum of the $D_{s1}(2460)^{+}\to D_{s}^{+}\pi^{+}\pi^{-}$ decay. The data can be described either with a model including $f_0(500)$, $f_0(980)$ and $f_2(1270)$ resonances, in which the contributions of $f_0(980)$ and $f_2(1270)$ are unexpectedly large, or with a model including $f_0(500)$, a doubly charged open-charm tetraquark state $T_{c\bar{s}}^{++}$ and its isospin partner $T_{c\bar{s}}^{0}$. If the former is considered implausible, the $T_{c\bar{s}}$ states are observed with high significance, and the data are consistent with isospin symmetry. When imposing isospin constraints between the two $T_{c\bar{s}}$ states, their mass and width are determined to be $2327\pm13\pm13\,$MeV and $96\pm16\,^{+170}_{-23}\,$MeV, respectively, where the first uncertainty is statistical and the second is systematic. The mass is slightly below the $DK$ threshold, and a spin-parity of $0^+$ is favoured with high significance., Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/3280/ (LHCb public pages)
Published: 2024

205. On a kind of generilized multi-harmonic sum

Author: Wang, Jiaqi and Ma, Rong
Subjects: Mathematics - Number Theory, Mathematics - Combinatorics, Primary 11B68, Secondary 11A07
Abstract: Let $p$ be an odd prime, Jianqiang Zhao has established a curious congruence $$ \sum_{i+j+k=p \atop i,j,k > 0} \frac{1}{ijk} \equiv -2B_{p-3}\pmod p , $$ where $B_{n}$ denotes the $n-$th Bernoulli numbers. In this paper, we will generalize this problem by using congruent theory and combinatorial methods, and we get some curious congruences.
Published: 2024

206. HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems

Author: Tan, Jiejun, Dou, Zhicheng, Wang, Wen, Wang, Mang, Chen, Weipeng, and Wen, Ji-Rong
Subjects: Computer Science - Information Retrieval
Abstract: Retrieval-Augmented Generation (RAG) has been shown to improve knowledge capabilities and alleviate the hallucination problem of LLMs. The Web is a major source of external knowledge used in RAG systems, and many commercial systems such as ChatGPT and Perplexity have used Web search engines as their major retrieval systems. Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation. However, much of the structural and semantic information inherent in HTML, such as headings and table structures, is lost during this plain-text-based RAG process. To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML. However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system. To address this issue, we propose HTML cleaning, compression, and pruning strategies, to shorten the HTML while minimizing the loss of information. Specifically, we design a two-step block-tree-based pruning method that prunes useless HTML blocks and keeps only the relevant part of the HTML. Experiments on six QA datasets confirm the superiority of using HTML in RAG systems.
Published: 2024

207. Tetrahedral shape and Lambda impurity effect in $^{80}$Zr with a multidimensionally constrained relativistic Hartree-Bogoliubov model

Author: Yang, Dan and Rong, Yu-Ting
Subjects: Nuclear Theory
Abstract: This study investigates the tetrahedral structure in $^{80}$Zr and Lambda ($\Lambda$) impurity effect in $^{81}_{~\Lambda}$Zr using the multidimensionally constrained relativistic Hartree-Bogoliubov model. The ground states of both $^{80}$Zr and $^{81}_{~\Lambda}$Zr exhibit a tetrahedral configuration, accompanied by prolate and axial-octupole shape isomers. Our calculations reveal there are changes in the deformation parameters $\beta_{20}$, $\beta_{30}$, and $\beta_{32}$ upon $\Lambda$ binding to $^{80}$Zr, except for $\beta_{32}$ when $\Lambda$ occupies $p$-orbits. Compared to the two shape isomers, the $\Lambda$ particle exhibits weaker binding energy in the tetrahedral state when occupying the $1/2^+[000](\Lambda_s)$ or $1/2^-[110]$ single-particle states. In contrast, the strongest binding occurs for the $\Lambda$ particle in the $1/2^-[101]$ state with tetrahedral shape. Besides, a large $\Lambda$ separation energy may not necessarily correlate with a significant overlap between the density distributions of the $\Lambda$ particle and the nuclear core, particularly for tetrahedral hypernuclei.
Published: 2024
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208. Cohomology of Lie coalgebras

Author: Chuang, Joseph, Lazarev, Andrey, Sheng, Yunhe, and Tang, Rong
Subjects: Mathematics - K-Theory and Homology, Mathematics - Category Theory, 17B62, 17B56, 18N40
Abstract: A Koszul duality-type correspondence between coderived categories of conilpotent differential graded Lie coalgebras and their Chevalley-Eilenberg differential graded algebras is established. This gives an interpretation of Lie coalgebra cohomology as a certain kind of derived functor. A similar correspondence is proved for coderived categories of commutative cofibrant differential graded algebras and their Harrison differential graded Lie coalgebras., Comment: 23 pages
Published: 2024

209. Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent

Author: Sun, Xingwu, Chen, Yanfeng, Huang, Yiqing, Xie, Ruobing, Zhu, Jiaqi, Zhang, Kai, Li, Shuaipeng, Yang, Zhen, Han, Jonny, Shu, Xiaobo, Bu, Jiahao, Chen, Zhongzhi, Huang, Xuemeng, Lian, Fengzong, Yang, Saiyong, Yan, Jianfeng, Zeng, Yuyuan, Ren, Xiaoqin, Yu, Chao, Wu, Lulu, Mao, Yue, Xia, Jun, Yang, Tao, Zheng, Suncong, Wu, Kan, Jiao, Dian, Xue, Jinbao, Zhang, Xipeng, Wu, Decheng, Liu, Kai, Wu, Dengpeng, Xu, Guanghui, Chen, Shaohua, Chen, Shuang, Feng, Xiao, Hong, Yigeng, Zheng, Junqiang, Xu, Chengcheng, Li, Zongwei, Kuang, Xiong, Hu, Jianglu, Chen, Yiqi, Deng, Yuchi, Li, Guiyang, Liu, Ao, Zhang, Chenchen, Hu, Shihui, Zhao, Zilong, Wu, Zifan, Ding, Yao, Wang, Weichao, Liu, Han, Wang, Roberts, Fei, Hao, Yu, Peijie, Zhao, Ze, Cao, Xun, Wang, Hai, Xiang, Fusheng, Huang, Mengyuan, Xiong, Zhiyuan, Hu, Bin, Hou, Xuebin, Jiang, Lei, Ma, Jianqiang, Wu, Jiajia, Deng, Yaping, Shen, Yi, Wang, Qian, Liu, Weijie, Liu, Jie, Chen, Meng, Dong, Liang, Jia, Weiwen, Chen, Hu, Liu, Feifei, Yuan, Rui, Xu, Huilin, Yan, Zhenxiang, Cao, Tengfei, Hu, Zhichao, Feng, Xinhua, Du, Dong, Yu, Tinghao, Tao, Yangyu, Zhang, Feng, Zhu, Jianchen, Xu, Chengzhong, Li, Xirui, Zha, Chong, Ouyang, Wen, Xia, Yinben, Li, Xiang, He, Zekun, Chen, Rongpeng, Song, Jiawei, Chen, Ruibin, Jiang, Fan, Zhao, Chongqing, Wang, Bo, Gong, Hao, Gan, Rong, Hu, Winston, Kang, Zhanhui, Yang, Yong, Liu, Yuhong, Wang, Di, and Jiang, Jie
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: In this paper, we introduce Hunyuan-Large, which is currently the largest open-source Transformer-based mixture of experts model, with a total of 389 billion parameters and 52 billion activation parameters, capable of handling up to 256K tokens. We conduct a thorough evaluation of Hunyuan-Large's superior performance across various benchmarks including language understanding and generation, logical reasoning, mathematical problem-solving, coding, long-context, and aggregated tasks, where it outperforms LLama3.1-70B and exhibits comparable performance when compared to the significantly larger LLama3.1-405B model. Key practice of Hunyuan-Large include large-scale synthetic data that is orders larger than in previous literature, a mixed expert routing strategy, a key-value cache compression technique, and an expert-specific learning rate strategy. Additionally, we also investigate the scaling laws and learning rate schedule of mixture of experts models, providing valuable insights and guidances for future model development and optimization. The code and checkpoints of Hunyuan-Large are released to facilitate future innovations and applications. Codes: https://github.com/Tencent/Hunyuan-Large Models: https://huggingface.co/tencent/Tencent-Hunyuan-Large, Comment: 17 pages, 4 Figures
Published: 2024

210. MultiBalance: Multi-Objective Gradient Balancing in Industrial-Scale Multi-Task Recommendation System

Author: He, Yun, Chen, Xuxing, Xu, Jiayi, Cai, Renqin, You, Yiling, Cao, Jennifer, Huang, Minhui, Yang, Liu, Liu, Yiqun, Liu, Xiaoyi, Jin, Rong, Park, Sem, Long, Bo, and Feng, Xue
Subjects: Computer Science - Information Retrieval, Computer Science - Machine Learning, Mathematics - Optimization and Control
Abstract: In industrial recommendation systems, multi-task learning (learning multiple tasks simultaneously on a single model) is a predominant approach to save training/serving resources and improve recommendation performance via knowledge transfer between the joint learning tasks. However, multi-task learning often suffers from negative transfer: one or several tasks are less optimized than training them separately. To carefully balance the optimization, we propose a gradient balancing approach called MultiBalance, which is suitable for industrial-scale multi-task recommendation systems. It balances the per-task gradients to alleviate the negative transfer, while saving the huge cost for grid search or manual explorations for appropriate task weights. Moreover, compared with prior work that normally balance the per-task gradients of shared parameters, MultiBalance is more efficient since only requiring to access per-task gradients with respect to the shared feature representations. We conduct experiments on Meta's large-scale ads and feeds multi-task recommendation system, and observe that MultiBalance achieves significant gains (e.g., 0.738% improvement for normalized entropy (NE)) with neutral training cost in Queries Per Second (QPS), which is significantly more efficient than prior methods that balance per-task gradients of shared parameters with 70~80% QPS degradation.
Published: 2024

211. Observation of freezing phenomenon in high-dimensional quantum correlation dynamics

Author: Fu, Yue, Liu, Wenquan, Wang, Yunhan, Duan, Chang-Kui, Zhang, Bo, Wang, Yeliang, and Rong, Xing
Subjects: Quantum Physics
Abstract: Quantum information processing (QIP) based on high-dimensional quantum systems provides unique advantages and new potentials where high-dimensional quantum correlations (QCs) play vital roles. Exploring the resistance of QCs against noises is crucial as QCs are fragile due to complex and unavoidable system-environment interactions. In this study, we investigate the performance of high-dimensional QCs under local dephasing noise using a single nitrogen-vacancy center in diamond. A freezing phenomenon in the high-dimensional quantum discord dynamics was observed, showing discord is robust against local dephasing noise. Utilizing a robustness metric known as freezing index, we found that the discord of qutrits outperforms their qubits counterpart when confronted with dephasing noise. Furthermore, we developed a geometric picture to explain this intriguing freezing phenomenon phenomenon. Our findings highlight the potential of utilizing discord as a physical resource for advancing QIP in high-dimensional quantum settings.
Published: 2024

212. Fast Semi-supervised Learning on Large Graphs: An Improved Green-function Method

Author: Nie, Feiping, Song, Yitao, Chang, Wei, Wang, Rong, and Li, Xuelong
Subjects: Computer Science - Machine Learning
Abstract: In the graph-based semi-supervised learning, the Green-function method is a classical method that works by computing the Green's function in the graph space. However, when applied to large graphs, especially those sparse ones, this method performs unstably and unsatisfactorily. We make a detailed analysis on it and propose a novel method from the perspective of optimization. On fully connected graphs, the method is equivalent to the Green-function method and can be seen as another interpretation with physical meanings, while on non-fully connected graphs, it helps to explain why the Green-function method causes a mess on large sparse graphs. To solve this dilemma, we propose a workable approach to improve our proposed method. Unlike the original method, our improved method can also apply two accelerating techniques, Gaussian Elimination, and Anchored Graphs to become more efficient on large graphs. Finally, the extensive experiments prove our conclusions and the efficiency, accuracy, and stability of our improved Green's function method.
Published: 2024

213. Clustering Based on Density Propagation and Subcluster Merging

Author: Nie, Feiping, Song, Yitao, Xue, Jingjing, Wang, Rong, and Li, Xuelong
Subjects: Computer Science - Machine Learning, Statistics - Machine Learning
Abstract: We propose the DPSM method, a density-based node clustering approach that automatically determines the number of clusters and can be applied in both data space and graph space. Unlike traditional density-based clustering methods, which necessitate calculating the distance between any two nodes, our proposed technique determines density through a propagation process, thereby making it suitable for a graph space. In DPSM, nodes are partitioned into small clusters based on propagated density. The partitioning technique has been proved to be sound and complete. We then extend the concept of spectral clustering from individual nodes to these small clusters, while introducing the CluCut measure to guide cluster merging. This measure is modified in various ways to account for cluster properties, thus provides guidance on when to terminate the merging process. Various experiments have validated the effectiveness of DOSM and the accuracy of these conclusions.
Published: 2024

214. Topological surface state dominated nonlinear transverse response and microwave rectification at room temperature

Author: Shen, Qia, Chen, Jiaxin, Rong, Bin, Rong, Yaqi, Chen, Hongliang, Zhao, Tieyang, Duan, Xianfa, Guan, Dandan, Wang, Shiyong, Li, Yaoyi, Zheng, Hao, Liu, Xiaoxue, Qiu, Xuepeng, Chen, Jingsheng, Cong, Longqing, Li, Tingxin, Zhong, Ruidan, Liu, Canhua, Yang, Yumeng, Liu, Liang, and Jia, Jinfeng
Subjects: Condensed Matter - Materials Science
Abstract: Nonlinear Hall effect (NLHE) offers a novel means of uncovering symmetry and topological properties in quantum materials, holding promise for exotic (opto)electronic applications such as microwave rectification and THz detection. The BCD-independent NLHE could exhibit a robust response even at room temperature, which is highly desirable for practical applications. However, in materials with bulk inversion symmetry, the coexistence of bulk and surface conducting channels often leads to a suppressed NLHE and complex thickness-dependent behavior. Here, we report the observation of room-temperature nonlinear transverse response in 3D topological insulator Bi2Te3 thin films, whose electrical transport properties are dominated by topological surface state (TSS). By varying the thickness of Bi2Te3 epitaxial films from 7 nm to 50 nm, we found that the nonlinear transverse response increases with thickness from 7 nm to 25 nm and remains almost constant above 25 nm. This is consistent with the thickness-dependent basic transport properties, including conductance, carrier density, and mobility, indicating a pure and robust TSS-dominated linear and nonlinear transport in thick (>25 nm) Bi2Te3 films. The weaker nonlinear transverse response in Bi2Te3 below 25 nm was attributed to Te deficiency and poorer crystallinity. By utilizing the TSS-dominated electrical second harmonic generation, we successfully achieved the microwave rectification from 0.01 to 16.6 GHz in 30 nm and bulk Bi2Te3. Our work demonstrated the room temperature nonlinear transverse response in a paradigm topological insulator, addressing the tunability of the topological second harmonic response by thickness engineering.
Published: 2024

215. Promoting Math Learning in Educational Games with Virtual Companions Providing Learning Supports

Author: Xiao-Rong Guo, Si-Yang Liu, Shao-Ying Gong, Yang Cao, Jing Wang, and Yan Fang
Abstract: To enhance the effectiveness of educational games, researchers have advocated adding learning supports in educational games, but this may come at the cost of disrupting the learning experience. Embedding virtual companions to provide learning supports may be an effective solution that naturally integrates learning supports into the game. However, the effects of virtual companions providing learning supports (VCPLS) and potential influence mechanism are deficient. This study investigated the efficacy of VCPLS in enhancing learning experiences and performance in comparison to the system providing learning supports (TSPLS) and explored how VCPLS played a role in improving learning performance. A single-factor (VCPLS/TSPLS) between-subjects design was employed, and four similar-level classes (126 middle school students) were randomly assigned to the two conditions. The results revealed that compared with TSPLS, VCPLS promoted learners' basic psychological need satisfaction, positive emotions, motivation, and perceived learning performance but there was no difference in transfer performance. VCPLS also encouraged students to spend more time reading explanations and feedback, and process explanations actively after the first attempt and before the second attempt. Moreover, VCPLS promoted perceived learning performance by the mediating role of basic psychological need satisfaction and the sequential mediating roles of basic psychological need satisfaction and motivation.
Published: 2024
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216. Comprehension of Spatial Demonstratives in Mandarin-Speaking Children on the Autism Spectrum: The Roles of Theory of Mind and Executive Function

Author: Yicheng Rong
Abstract: The present study aimed to examine whether Mandarin-speaking children on the autism spectrum showed differences in comprehending spatial demonstratives ("this" and "that", and "here" and "there"), as compared to typically developing (TD) children. Another aim of this study was to investigate the roles of theory of mind (ToM) and executive functions (EF) in the comprehension of spatial demonstratives. Twenty-seven autistic children (mean age 6.86) and 27 receptive-vocabulary-matched TD children (mean age 5.82) were recruited. Demonstrative comprehension was assessed based on participants' ability to place objects in certain locations according to experimenters' instructions which involved these demonstratives in three different conditions (same-, opposite-, and spectator-perspective conditions). Four false-belief tasks were administered to measure ToM, and the word-span task and the dimensional change card sort task were used to measure two subcomponents of EF -- working memory and mental flexibility -- respectively. Children on the autism spectrum were found to score below TD children in the comprehension of spatial demonstratives. In addition, the results showed that ToM and working memory were conducive to the correct interpretation of spatial demonstratives. The two cognitive abilities mutually influenced their respective roles in spatial demonstrative comprehension in the three different conditions. The findings suggest that the comprehension of spatial demonstratives comprehension is an area of need in Mandarin-speaking children on the autism spectrum, and it might be linked to their differences in cognitive abilities.
Published: 2024
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217. Electrochemical formation of bis(fluorosulfonyl)imide-derived solid-electrolyte interphase at Li-metal potential

Author: Yu, Weilai, Lin, Kuan-Yu, Boyle, David T, Tang, Michael T, Cui, Yi, Chen, Yuelang, Yu, Zhiao, Xu, Rong, Lin, Yangju, Feng, Guangxia, Huang, Zhuojun, Michalek, Lukas, Li, Weiyu, Harris, Stephen J, Jiang, Jyh-Chiang, Abild-Pedersen, Frank, Qin, Jian, and Bao, Zhenan
Subjects: Chemical Sciences, Physical Chemistry, Organic Chemistry, Chemical sciences
Abstract: Lithium bis(fluorosulfonyl)imide-based liquid electrolytes are promising for realizing high coulombic efficiency and long cycle life in next-generation Li-metal batteries. However, the role of anions in the formation of the solid-electrolyte interphase remains unclear. Here we combine electrochemical analyses and X-ray photoelectron spectroscopy measurements, both with and without sample washing, together with computational simulations, to propose the reaction pathways of electrolyte decomposition and correlate the interphase component solubility with the efficacy of passivation. We discover that not all the products derived from interphase-forming reactions are incorporated into the resulting passivation layer, with a notable portion present in the liquid electrolyte. We also find that the high-performance electrolytes can afford a sufficiently passivating interphase with minimized electrolyte decomposition, by incorporating more anion-decomposition products. Overall, this work presents a systematic approach of coupling electrochemical and surface analyses to paint a comprehensive picture of solid-electrolyte interphase formation, while identifying the key attributes of high-performance electrolytes to guide future designs.
Published: 2024

218. Uterine fibroids with heavy menstrual bleeding stratified by race in a commercial and Medicaid database.

Author: Agarwal, Sanjay, Stokes, Michael, Chen, Rong, and Lickert, Cassandra
Subjects: black women, bulk symptoms, hormonal contraceptives, hysterectomy, pharmacotherapy, real-world evidence, surgery/procedures, treatment patterns
Abstract: BACKGROUND: Historically, the clinical characteristics and treatment pathways for patients with uterine fibroids and heavy menstrual bleeding have differed between White and Black women. OBJECTIVE: To provide a contemporary comparison of patient characteristics and treatment patterns among White and Black women with uterine fibroids and heavy menstrual bleeding in the United States. STUDY DESIGN: This retrospective cohort study included administrative claims data from 46,139 White and 17,297 Black women with uterine fibroids and heavy menstrual bleeding from the Optum Clinformatics database (January 2011-December 2020) and 7353 White and 16,776 Black women from the IBM MarketScan Multi-State Medicaid Insurance database (January 2010-December 2019). Patients were indexed at their initial uterine fibroid diagnosis claim and were required to have a claim for heavy menstrual bleeding and ≥12 months of continuous enrollment pre- and postindex. Patients were followed until the earliest of death, disenrollment, hysterectomy date, or end of study database. Outcomes were stratified by race and included patient demographics, clinical characteristics, pharmacologic treatment patterns, and surgeries/procedures. Pearsons Chi-square test for categorical variables and Students t-test for continuous data were used to evaluate differences in baseline characteristics. Descriptive statistics were used to characterize treatment pathways for hormonal contraceptive use in women with ≥24 months of follow-up. Kaplan-Meier survival analysis was used to estimate time until hysterectomy, with log-rank testing to assess between-group differences. RESULTS: The mean (standard deviation) duration of follow-up was 44.6 (27.9) and 41.0 (24.9) months in the commercial and Medicaid databases, respectively. Mean (standard deviation) age at uterine fibroid diagnosis was lower for Black than White women in both databases (commercial: 42.3 [6.5] vs 44.4 [6.3] years; P
Published: 2024

219. Room-temperature vacancy emission from the jog on edge dislocation in FCC nickel under glide force

Author: Wang, Yifan, Jian, Wu-Rong, and Cai, Wei
Subjects: Condensed Matter - Materials Science
Abstract: Jogs, atomic-scale steps on dislocations, play an important role in crystal plasticity, yet they are often ignored in discrete dislocation dynamics (DDD) simulations due to their small sizes. While jogs on screw dislocations are known to move non-conservatively (i.e. climb) accompanied by vacancy emission, jogs on edge dislocations are commonly expected to move conservatively (i.e. glide) with the dislocation under ambient conditions. Here we report unexpected findings from molecular dynamics simulations of an edge dislocation containing a pair of unit jogs in face-centered cubic nickel at 300K. While the jogs glide conservatively with the edge dislocation at low stresses, we observe that one of the jogs climbs and emits vacancies intermittently at higher stresses. This observation is unexpected at such a low temperature, as climb is typically associated with temperatures closer to the creep temperature (roughly half of the melting temperature). Our results highlight the significance of the complex interplay between point defects (i.e., vacancies) and dislocations in room-temperature plasticity, suggesting that these interactions may be more significant than previously thought.
Published: 2024

220. Spin excitations of the Shastry-Sutherland model -- altermagnetism and proximate deconfined quantum criticality

Author: Chen, Hongyu, Duan, Guijing, Liu, Changle, Cui, Yi, Yu, Weiqiang, Xie, Z. Y., and Yu, Rong
Subjects: Condensed Matter - Strongly Correlated Electrons
Abstract: Symmetry plays a crucial role in condensed matter physics. In quantum magnetism, it dictates a number of exotic phenomena, including deconfined quantum criticality and altermagnetism. Here, by studying the spin excitations of the $S=1/2$ antiferromagnetic Shastry-Sutherland model, we show that the N\'eel antiferromagnetic state in this model is an altermagnet featuring a non-relativistic splitting between two chiral magnon bands. Moreover, we identify a Higgs mode in the longitudinal excitation channel, whose gap softens when approaching the antiferromagnetic to plaquette valence bond solid transition, implying the appearance of nearly deconfined excitations. However, the splitting between the two magnon bands (Goldstone modes) remains finite at the transition. These results indicate that the transition is weakly first-order and proximate to a putative deconfined quantum critical point. We find that the altermagnetism provides a sensitive means to probe the deconfined quantum criticality., Comment: 8+7 pages, 4+7 figures
Published: 2024

221. Two plaquette-singlet phases in the Shastry-Sutherland compound SrCu2(BO3)2

Author: Cui, Yi, Du, Kefan, Wu, Zhanlong, Li, Shuo, Yang, Pengtao, Chen, Ying, Xu, Xiaoyu, Chen, Hongyu, Li, Chengchen, Liu, Juanjuan, Wang, Bosen, Hong, Wenshan, Li, Shiliang, Xie, Zhiyuan, Cheng, Jinguang, Yu, Rong, and Yu, Weiqiang
Subjects: Condensed Matter - Strongly Correlated Electrons
Abstract: The nature of the high-pressure plaquette-singlet (PS) phase of SrCu$_2$(BO$_3$)$_2$ remains enigmatic. In this work, we revisit the high-pressure $^{11}$B NMR study and identify two distinct coexisting gapped PS states within the NMR spectra. In addition to the previously reported full-plaquette phase, a second PS phase is discerned, characterized by a slightly lower resonance frequency and larger spin-lattice relaxation rates in its ordered phase. Notably, this second phase exhibits enhanced spin fluctuations in its precursor liquid state above the transition temperature. The volume fraction of this phase increases significantly with pressure, reaching approximately 70\% at 2.65~GPa. Furthermore, at 2.4~GPa, a field-induced quantum phase transition from the PS phase to an antiferromagnetic phase is observed around 5.5~T, with a scaling behavior of $1/T_1 \sim T^{0.6}$ near the transition field. This behavior suggests a continuous or nearly continuous nature for the field-induced transition. Our findings provide experimental evidence for the long-sought empty-plaquette singlet phase in SrCu$_2$(BO$_3$)$_2$ within the framework of the Shastry-Sutherland model, thus establishing a promising platform for future studies of deconfined quantum criticality in this model system., Comment: 6 pages, 5 figures
Published: 2024

222. Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales?

Author: Zhou, Zhanke, Tao, Rong, Zhu, Jianing, Luo, Yiwen, Wang, Zengmao, and Han, Bo
Subjects: Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract: This paper investigates an under-explored challenge in large language models (LLMs): chain-of-thought prompting with noisy rationales, which include irrelevant or inaccurate reasoning thoughts within examples used for in-context learning. We construct NoRa dataset that is tailored to evaluate the robustness of reasoning in the presence of noisy rationales. Our findings on NoRa dataset reveal a prevalent vulnerability to such noise among current LLMs, with existing robust methods like self-correction and self-consistency showing limited efficacy. Notably, compared to prompting with clean rationales, base LLM drops by 1.4%-19.8% in accuracy with irrelevant thoughts and more drastically by 2.2%-40.4% with inaccurate thoughts. Addressing this challenge necessitates external supervision that should be accessible in practice. Here, we propose the method of contrastive denoising with noisy chain-of-thought (CD-CoT). It enhances LLMs' denoising-reasoning capabilities by contrasting noisy rationales with only one clean rationale, which can be the minimal requirement for denoising-purpose prompting. This method follows a principle of exploration and exploitation: (1) rephrasing and selecting rationales in the input space to achieve explicit denoising and (2) exploring diverse reasoning paths and voting on answers in the output space. Empirically, CD-CoT demonstrates an average improvement of 17.8% in accuracy over the base model and shows significantly stronger denoising capabilities than baseline methods. The source code is publicly available at: https://github.com/tmlr-group/NoisyRationales., Comment: Accepted by NeurIPS 2024
Published: 2024

223. Quantum anomaly triggers the violation of scaling laws in gravitational system

Author: Hu, Ya-Peng, Zhang, Hongsheng, An, Yu-Sen, Sun, Gao-Yong, You, Wen-Long, Shi, Da-Ning, Chen, Xiaosong, and Cai, Rong-Gen
Subjects: General Relativity and Quantum Cosmology, Condensed Matter - Statistical Mechanics, High Energy Physics - Theory
Abstract: Scaling laws for critical phenomena take pivotal status in almost all branches of physics. However, as scaling laws are commonly guaranteed by the renormalization group theory, systems that violate them have rarely been found. In this letter, we demonstrate that gravitational system can break scaling laws. We derive this result through investigating phase transition and critical phenomenon in a gravitational system with quantum anomaly. For the first time, we outline the key conditions to violate the scaling laws in generic gravitational system viewed from the equation of state $P=P(T,V)$. Our results indicate that quantum effects can magnify the distinctiveness of gravity, which may be significant to understand the microscopic structure of spacetime., Comment: 7 pages, 3 figures
Published: 2024

224. Microsecond-scale Dynamic Validation of Idempotency for GPU Kernels

Author: Han, Mingcong, Shen, Weihang, Peng, Guanwen, Chen, Rong, and Chen, Haibo
Subjects: Computer Science - Operating Systems, Computer Science - Distributed, Parallel, and Cluster Computing, D.4.0
Abstract: We discovered that a GPU kernel can have both idempotent and non-idempotent instances depending on the input. These kernels, called conditionally-idempotent, are prevalent in real-world GPU applications (490 out of 547 from six applications). Consequently, prior work that classifies GPU kernels as either idempotent or non-idempotent can severely compromise the correctness or efficiency of idempotence-based systems. This paper presents PICKER, the first system for instance-level idempotency validation. PICKER dynamically validates the idempotency of GPU kernel instances before their execution, by utilizing their launch arguments. Several optimizations are proposed to significantly reduce validation latency to microsecond-scale. Evaluations using representative GPU applications (547 kernels and 18,217 instances in total) show that PICKER can identify idempotent instances with no false positives and a false-negative rate of 18.54%, and can complete the validation within 5 us for all instances. Furthermore, by integrating PICKER, a fault-tolerant system can reduce the checkpoint cost to less than 4% and a scheduling system can reduce the preemption latency by 84.2%.
Published: 2024

225. Zwitterionic Polymer Coatings with Compositional Gradient for Stable and Substrate-Independent Biofouling Deterrence via All-Dry Synthesis

Author: Chen, Pengyu, Shu, Harry, Tang, Wenjing, Yu, Christina, and Yang, Rong
Subjects: Physics - Chemical Physics
Abstract: Biofouling represents a critical challenge in marine transportation, healthcare, and food manufacturing, among other industries, as it promotes contamination and increases maintenance costs. Zwitterionic polymers, known for their exceptional antifouling properties, offer a promising solution for biofouling deterrence. Despite the rapid development of zwitterionic polymers in recent years, the design rules, especially concerning the choice of cationic moieties to optimize biofouling deterrence, remain elusive. In this study, we leveraged a versatile all-dry synthesis scheme to achieve a selection of 9 zwitterionic polymers, 5 of which are unprecedented for this synthesis paradigm, thus systematically unraveling that molecular design rule. Notably, we developed a synthesis strategy to enable nanoscale compositional gradient along the coating cross-section, which ensures the robustness of the zwitterionic polymer coatings irrespective of the choice of cation-anion combinations. That robustness is enabled by an organosilicon-based layer at the coating-substrate interface, which simultaneously enhances coating adhesion and chemical stability while ensuring high concentration of zwitterionic moieties at the polymer-liquid interface to maximize biofouling deterrence. The antifouling efficacy was assessed using biofilms of Pseudomonas aeruginosa or Bacillus subtilis. All coatings demonstrated antifouling efficacy, with a novel zwitterionic polymer comprising a combination of imidazolium and carboxyl groups achieving the greatest antibiofilm effects, which we attributed to the strong hydration. This study highlights the coating architecture, i.e., one with nanoscale gradient and varying crosslinking densities, as a valid strategy to render zwitterionic polymers robust coatings and the imidazolium-based carboxybetaine as a promising next-generation antibiofouling chemistry.
Published: 2024

226. CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation

Author: Cheng, Yiruo, Mao, Kelong, Zhao, Ziliang, Dong, Guanting, Qian, Hongjin, Wu, Yongkang, Sakai, Tetsuya, Wen, Ji-Rong, and Dou, Zhicheng
Subjects: Computer Science - Information Retrieval, Computer Science - Computation and Language
Abstract: Retrieval-Augmented Generation (RAG) has become a powerful paradigm for enhancing large language models (LLMs) through external knowledge retrieval. Despite its widespread attention, existing academic research predominantly focuses on single-turn RAG, leaving a significant gap in addressing the complexities of multi-turn conversations found in real-world applications. To bridge this gap, we introduce CORAL, a large-scale benchmark designed to assess RAG systems in realistic multi-turn conversational settings. CORAL includes diverse information-seeking conversations automatically derived from Wikipedia and tackles key challenges such as open-domain coverage, knowledge intensity, free-form responses, and topic shifts. It supports three core tasks of conversational RAG: passage retrieval, response generation, and citation labeling. We propose a unified framework to standardize various conversational RAG methods and conduct a comprehensive evaluation of these methods on CORAL, demonstrating substantial opportunities for improving existing approaches.
Published: 2024

227. Search for $\Lambda$-$\bar{\Lambda} $ oscillation in $J/\psi\rightarrow\Lambda\bar{\Lambda}$ decay

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., Chang, J. F., Che, G. R., 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, 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., Lane, J. J., 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, 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, 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., Ma, Y. M., Maas, F. E., 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, 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, 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. 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. 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, 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, 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. D., Zhang, X. M., 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., 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 $(10087\pm44)\times 10^{6}$ $J/\psi$ decays collected by the BESIII detector at the BEPCII collider, we search for baryon number violation via $\Lambda-\bar{\Lambda}$ oscillation in the decay $J/\psi \to \Lambda \bar{\Lambda}$. No evidence for $\Lambda-\bar\Lambda$ oscillation is observed. The upper limit on the time-integrated probability of $\Lambda-\bar{\Lambda}$ oscillation is estimated to be $1.4\times 10^{-6}$, corresponding to an oscillation parameter less than $2.1\times 10^{-18}~\mathrm{GeV}$ at $90\%$ confidence level., Comment: 8 pages, 2 figures
Published: 2024

228. Gaussian Derivative Change-point Detection for Early Warnings of Industrial System Failures

Author: Zhao, Hao and Pan, Rong
Subjects: Computer Science - Machine Learning
Abstract: An early warning of future system failure is essential for conducting predictive maintenance and enhancing system availability. This paper introduces a three-step framework for assessing system health to predict imminent system breakdowns. First, the Gaussian Derivative Change-Point Detection (GDCPD) algorithm is proposed for detecting changes in the high-dimensional feature space. GDCPD conducts a multivariate Change-Point Detection (CPD) by implementing Gaussian derivative processes for identifying change locations on critical system features, as these changes eventually will lead to system failure. To assess the significance of these changes, Weighted Mahalanobis Distance (WMD) is applied in both offline and online analyses. In the offline setting, WMD helps establish a threshold that determines significant system variations, while in the online setting, it facilitates real-time monitoring, issuing alarms for potential future system breakdowns. Utilizing the insights gained from the GDCPD and monitoring scheme, Long Short-Term Memory (LSTM) network is then employed to estimate the Remaining Useful Life (RUL) of the system. The experimental study of a real-world system demonstrates the effectiveness of the proposed methodology in accurately forecasting system failures well before they occur. By integrating CPD with real-time monitoring and RUL prediction, this methodology significantly advances system health monitoring and early warning capabilities.
Published: 2024

229. ProMoE: Fast MoE-based LLM Serving using Proactive Caching

Author: Song, Xiaoniu, Zhong, Zihang, and Chen, Rong
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Artificial Intelligence
Abstract: The promising applications of large language models are often constrained by the limited GPU memory capacity available on edge devices. Mixture-of-Experts (MoE) models help mitigate this issue by activating only a subset of the model's parameters during computation, allowing the unused parameters to be offloaded to host memory and reducing overall GPU memory demand. However, existing cache-based offloading solutions handle cache misses reactively and significantly impact system performance. In this paper, we propose ProMoE, a novel proactive caching system that leverages intermediate model results to predict subsequent parameter usage. By proactively fetching experts in advance, ProMoE removes the loading time from the critical path and diminishes the performance overhead of offloading. Our evaluations demonstrate that ProMoE achieves an average speedup of 2.13x and 2.84x in the prefill and decode stages respectively, compared to existing offloading solutions.
Published: 2024

230. A Foundation Model for Chemical Design and Property Prediction

Author: Cai, Feiyang, Zhu, Tianyu, Tzeng, Tzuen-Rong, Duan, Yongping, Liu, Ling, Pilla, Srikanth, Li, Gang, and Luo, Feng
Subjects: Computer Science - Computational Engineering, Finance, and Science
Abstract: Artificial intelligence (AI) has significantly advanced computational chemistry research. However, traditional AI methods often rely on task-specific model designs and training, which constrain both the scalability of model size and generalization across different tasks. Here, we introduce ChemFM, a large-scale foundation model specifically developed for chemistry, comprising up to 3 billion parameters. ChemFM is pre-trained on 178 million molecules using self-supervised causal language modeling to extract generalizable molecular representations. This model can be adapted to diverse downstream chemical applications using both full-parameter and parameter-efficient fine-tuning methods. ChemFM consistently outperforms state-of-the-art approaches across multiple chemical tasks. Notably, it achieves up to 67.48% performance improvement across 34 property prediction benchmarks, up to 33.31% reduction in mean average deviation between conditioned and actual properties of generated molecules in conditional molecular generation tasks, and up to 3.7% top-1 accuracy improvement across 4 reaction prediction datasets. Moreover, ChemFM demonstrates superior performance in predicting antibiotic activity and cytotoxicity, highlighting its potential to advance the discovery of novel antibiotics. We anticipate that ChemFM will significantly advance chemistry research by providing a foundation model capable of effectively generalizing across a broad range of tasks with minimal additional training.
Published: 2024

231. Measurement of the CKM angle $\gamma$ in $B^{\pm} \to D K^*(892)^{\pm}$ 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., Godinez, J. Castro, 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. 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., 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., Epple, E., 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., Fischer, K. L., Fitzgerald, D. S., Fitzpatrick, C., 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., Garau, M., 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. G., 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., Gomes, A., Fernandez, S. Gomez, 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., Hollitt, S. E., 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., 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. 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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., Song, R., Song, Y., Song, Y. S., De Almeida, F. L. Souza, De Paula, B. Souza, Norella, E. Spadaro, Spedicato, E., Speer, J. 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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, J., Wang, M., Wang, N. W., Wang, R., Wang, X., Wang, X. W., Wang, Y., 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. R. J., Williams, R., Williams, Z., Wilson, F. F., Wislicki, W., Witek, M., Witola, L., Wormser, G., Wotton, S. A., Wu, H., Wu, J., 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: Measurements of $CP$ observables and the CKM angle $\gamma$ are performed in $B^{\pm} \to D K^*(892)^{\pm}$ decays, where $D$ represents a superposition of $D^0$ and $\overline{D}{}^0$ states, using the LHCb dataset collected during Run 1 (2011-2012) and Run 2 (2015-2018). A comprehensive study of this channel is presented with the $D$ meson reconstructed in two-body final states $K^{\pm}\pi^{\mp}$, $K^+K^-$ and $\pi^+\pi^-$; four-body final states $K^{\pm}\pi^{\mp}\pi^{\pm}\pi^{\mp}$ and $\pi^+\pi^-\pi^+\pi^-$; and three-body final states $K^0_{S} \pi^+\pi^-$ and $K^0_{S} K^+ K^-$. This analysis includes the first observation of the suppressed $B^{\pm} \to [\pi^+K^-]_D K^{*\pm}$ and $B^{\pm} \to [\pi^+K^-\pi^+\pi^-]_D K^{*\pm}$ decays. The combined result gives $\gamma=(63\pm 13)^\circ$., Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/3180/ (LHCb public pages)
Published: 2024

232. A Globally Optimal Portfolio for m-Sparse Sharpe Ratio Maximization

Author: Lin, Yizun, Lai, Zhao-Rong, and Li, Cheng
Subjects: Mathematics - Optimization and Control, 90C26, 90C32, 65K05
Abstract: The Sharpe ratio is an important and widely-used risk-adjusted return in financial engineering. In modern portfolio management, one may require an m-sparse (no more than m active assets) portfolio to save managerial and financial costs. However, few existing methods can optimize the Sharpe ratio with the m-sparse constraint, due to the nonconvexity and the complexity of this constraint. We propose to convert the m-sparse fractional optimization problem into an equivalent m-sparse quadratic programming problem. The semi-algebraic property of the resulting objective function allows us to exploit the Kurdyka-Lojasiewicz property to develop an efficient Proximal Gradient Algorithm (PGA) that leads to a portfolio which achieves the globally optimal m-sparse Sharpe ratio under certain conditions. The convergence rates of PGA are also provided. To the best of our knowledge, this is the first proposal that achieves a globally optimal m-sparse Sharpe ratio with a theoretically-sound guarantee., Comment: 26 pages, 2 figures
Published: 2024

233. Langevin deformation for R\'enyi entropy on Wasserstein space over Riemannian manifolds

Author: Lei, Rong, Li, Songzi, and Li, Xiang-Dong
Subjects: Mathematics - Probability
Abstract: We introduce the Langevin deformation for the R\'enyi entropy on the $L^2$-Wasserstein space over $\mathbb{R}^n$ or a Riemannian manifold, which interpolates between the porous medium equation and the Benamou-Brenier geodesic flow on the $L^2$-Wasserstein space and can be regarded as the compressible Euler equations for isentropic gas with damping. We prove the $W$-entropy-information formulae and the the rigidity theorems for the Langevin deformation for the R\'enyi entropy on the Wasserstein space over complete Riemannian manifolds with non-negative Ricci curvature or CD$(0, m)$-condition. Moreover, we prove the monotonicity of the Hamiltonian and the convexity of the Lagrangian along the Langevin deformation of flows. Finally, we prove the convergence of the Langevin deformation for the R\'enyi entropy as $c\rightarrow 0$ and $c\rightarrow \infty$ respectively. Our results are new even in the case of Euclidean spaces and compact or complete Riemannian manifolds with non-negative Ricci curvature.
Published: 2024

234. DAWN-ICL: Strategic Planning of Problem-solving Trajectories for Zero-Shot In-Context Learning

Author: Tang, Xinyu, Wang, Xiaolei, Zhao, Wayne Xin, and Wen, Ji-Rong
Subjects: Computer Science - Computation and Language
Abstract: Zero-shot in-context learning (ZS-ICL) aims to conduct in-context learning (ICL) without using human-annotated demonstrations. Most ZS-ICL methods use large language models (LLMs) to generate (input, label) pairs as pseudo-demonstrations and leverage historical pseudo-demonstrations to help solve the current problem. They assume that problems are from the same task and traverse them in a random order. However, in real-world scenarios, problems usually come from diverse tasks, and only a few belong to the same task. The random traversing order may generate unreliable pseudo-demonstrations and lead to error accumulation. To address this problem, we reformulate ZS-ICL as a planning problem and propose a Demonstration-aware Monte Carlo Tree Search (MCTS) approach (DAWN-ICL), which leverages MCTS to strategically plan the problem-solving trajectories for ZS-ICL. In addition, to achieve effective and efficient Q value estimation, we propose a novel demonstration-aware Q-value function and use it to enhance the selection phase and accelerate the expansion and simulation phases in MCTS. Extensive experiments demonstrate the effectiveness and efficiency of DAWN-ICL on in-domain and cross-domain scenarios, and it even outperforms ICL using human-annotated labels. The code is available at https://github.com/RUCAIBox/MCTS4ZSICL.
Published: 2024

235. Measurement of the branching fraction of $D^+ \to \tau^+\nu_{\tau}$

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., 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, T. Y., 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., 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, J. H., 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, 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., Tian, J. Y., 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, L. W., 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: By analyzing $e^{+}e^{-}$ collision data with an integrated luminosity of 7.9~fb$^{-1}$ collected with the BESIII detector at the center-of-mass energy of 3.773~GeV, the branching fraction of $D^+\to\tau^+\nu_{\tau}$ is determined as $\mathcal{B}=(9.9\pm 1.1_\mathrm{stat}\pm 0.5_\mathrm{syst})\times10^{-4}$. Taking the most precise result $\mathcal{B}(D^+\to\mu^+\nu_{\mu})=(3.981\pm 0.079_\mathrm{stat}\pm0.040_\mathrm{syst})\times10^{-4}$, we determine $R_{\tau/\mu} = \Gamma(D^+\to\tau^+\nu_{\tau})/\Gamma(D^+\to\mu^+\nu_{\mu})= 2.49\pm0.31$, achieving a factor of two improvement in precision compared to the previous BESIII result. This measurement is in agreement with the standard model prediction of lepton flavor universality within one standard deviation.
Published: 2024

236. AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios

Author: Mou, Xinyi, Liang, Jingcong, Lin, Jiayu, Zhang, Xinnong, Liu, Xiawei, Yang, Shiyue, Ye, Rong, Chen, Lei, Kuang, Haoyu, Huang, Xuanjing, and Wei, Zhongyu
Subjects: Computer Science - Computation and Language, Computer Science - Computers and Society
Abstract: Large language models (LLMs) are increasingly leveraged to empower autonomous agents to simulate human beings in various fields of behavioral research. However, evaluating their capacity to navigate complex social interactions remains a challenge. Previous studies face limitations due to insufficient scenario diversity, complexity, and a single-perspective focus. To this end, we introduce AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios. Drawing on Dramaturgical Theory, AgentSense employs a bottom-up approach to create 1,225 diverse social scenarios constructed from extensive scripts. We evaluate LLM-driven agents through multi-turn interactions, emphasizing both goal completion and implicit reasoning. We analyze goals using ERG theory and conduct comprehensive experiments. Our findings highlight that LLMs struggle with goals in complex social scenarios, especially high-level growth needs, and even GPT-4o requires improvement in private information reasoning. Code and data are available at \url{https://github.com/ljcleo/agent_sense}.
Published: 2024

237. Conceptual Design of the Muonium-to-Antimuonium Conversion Experiment (MACE)

Author: Bai, Ai-Yu, Cai, Hanjie, Chen, Chang-Lin, Chen, Siyuan, Chen, Xurong, Chen, Yu, Cheng, Weibin, Dai, Ling-Yun, Fan, Rui-Rui, Gong, Li, Guo, Zihao, He, Yuan, Hou, Zhilong, Huang, Yinyuan, Jia, Huan, Jiang, Hao, Jing, Han-Tao, Kang, Xiaoshen, Li, Hai-Bo, Li, Jincheng, Li, Yang, Liu, Shulin, Lu, Guihao, Miao, Han, Ning, Yunsong, Niu, Jianwei, Peng, Huaxing, Petrov, Alexey A., Qin, Yuanshuai, Sun, Mingchen, Tang, Jian, Tang, Jing-Yu, Tian, Ye, Wang, Rong, Wang, Xiaodong, Wang, Zhichao, Wu, Chen, Xing, Tian-Yu, Xiong, Weizhi, Xu, Yu, Yan, Baojun, Yao, De-Liang, Yu, Tao, Yuan, Ye, Yuan, Yi, Zhang, Yao, Zhang, Yongchao, Zhang, Zhilv, Zhao, Guang, and Zhao, Shihan
Subjects: High Energy Physics - Experiment, High Energy Physics - Phenomenology, Physics - Accelerator Physics, Physics - Instrumentation and Detectors
Abstract: The spontaneous conversion of muonium to antimuonium is one of the interesting charged lepton flavor violation phenomena, offering a sensitive probe of potential new physics and serving as a tool to constrain the parameter space beyond the Standard Model. Utilizing a high-intensity muon beam, a Michel electron magnetic spectrometer and a positron transport solenoid together with a positron detection system, MACE aims to discover or constrain this rare process at the conversion probability beyond the level of $10^{-13}$. This report provides an overview of the theoretical framework and detailed experimental design in the search for the muonium-to-antimuonium conversion., Comment: 115 pages, 59 figures
Published: 2024

238. Search for $\eta_c(2S)\to p\bar{p}$ and branching fraction measurements of $\chi_{cJ} \to p\bar{p}$ via $\psi(2S)$ radiative decays

Author: BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., 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., 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, G. F., Fan, J. J., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., 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, 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, P., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., 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., Lan, W. N., 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, Lei, Li, M. H., Li, P. L., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, T., Li, T. Y., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y., 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, 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, 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, Q. M., Ma, R. Q., Ma, R. Y., 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, Y. H., 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. R., Qi, M., Qian, S., Qian, W. B., Qiao, C. F., Qiao, J. H., 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, J. L., Shi, J. Y., 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, 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., 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, Bo, Wang, C., Wang, D. Y., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, L. W., 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., 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, Lianjie, Wu, X., Wu, X. H., Wu, Y. H., Wu, Y. 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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., 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. X., Zhang, Z. Y., Zhang, Z. Z., Zhang, Zh. Zh., 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, X. R., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., 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, W. Z., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
Subjects: High Energy Physics - Experiment
Abstract: Using $(27.12\pm0.14) \times 10^{8}$ $\psi(2S)$ events collected by the BESIII detector operating at BEPCII, we search for the decay $\eta_c(2S)\to p\bar{p}$ via the process $\psi(2S)\to \gamma\eta_c(2S)$, and only find a signal with a significance of $1.7\,\sigma$. The upper limit of the product branching fraction at the 90% confidence level is determined to be $\mathcal{B}(\psi(2S)\to \gamma\eta_c(2S))\times \mathcal{B}(\eta_c(2S)\to p\bar{p})<2.4\times 10^{-7}$. The branching fractions of $\chi_{cJ}\to p\bar{p}~(J=0,1,2)$ are also measured to be $\mathcal{B}(\chi_{c0}\to p\bar{p})=(2.51\pm0.02\pm0.08)\times 10^{-4}$, $\mathcal{B}(\chi_{c1}\to p\bar{p})=(8.16\pm0.09\pm0.25)\times 10^{-4}$, and $\mathcal{B}(\chi_{c2}\to p\bar{p})=(8.33\pm0.09\pm0.22)\times 10^{-4}$, where the first uncertainty is statistical and the second systematic.
Published: 2024

239. Graph Pre-Training Models Are Strong Anomaly Detectors

Author: Cheng, Jiashun, Zheng, Zinan, Liu, Yang, Tang, Jianheng, Wang, Hongwei, Rong, Yu, Li, Jia, and Tsung, Fugee
Subjects: Computer Science - Machine Learning
Abstract: Graph Anomaly Detection (GAD) is a challenging and practical research topic where Graph Neural Networks (GNNs) have recently shown promising results. The effectiveness of existing GNNs in GAD has been mainly attributed to the simultaneous learning of node representations and the classifier in an end-to-end manner. Meanwhile, graph pre-training, the two-stage learning paradigm such as DGI and GraphMAE, has shown potential in leveraging unlabeled graph data to enhance downstream tasks, yet its impact on GAD remains under-explored. In this work, we show that graph pre-training models are strong graph anomaly detectors. Specifically, we demonstrate that pre-training is highly competitive, markedly outperforming the state-of-the-art end-to-end training models when faced with limited supervision. To understand this phenomenon, we further uncover pre-training enhances the detection of distant, under-represented, unlabeled anomalies that go beyond 2-hop neighborhoods of known anomalies, shedding light on its superior performance against end-to-end models. Moreover, we extend our examination to the potential of pre-training in graph-level anomaly detection. We envision this work to stimulate a re-evaluation of pre-training's role in GAD and offer valuable insights for future research.
Published: 2024

240. Narrow [O III] emission lines as a potential proxy for the evolutionary stage of quasars

Author: Chen, Zhi-fu, Chen, Zhe-Geng, Peng, Xing-long, and Huang, Wei-rong
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Radio spectral shape of quasars can provide insight into the ages of quasars. We have compiled data for 1804 quasars with $z\lesssim1$ from the Sloan Digital Sky Survey (SDSS). Additionally, these quasars were also mapped by the Low-Frequency Array at 144 MHz and the Very Large Array Sky Survey at 3000 MHz. The radio spectral index, designated as $\alpha^{\rm 144}_{\rm 3000}$ (with $S(\nu)\propto\nu^\alpha$), is analyzed between 144 MHz and 3000 MHz as a proxy for the ages of quasars. We measure the [O III] $\lambda$5007 emission line in the SDSS spectra. A strong correlation was found between the equivalent width of the core component of the [O III] $\lambda$5007 emission line and $\alpha^{\rm 144}_{\rm 3000}$. This relationship suggests that the core component of the [O III] $\lambda$5007 emission line could potentially serve as a surrogate for the evolutionary stage of a quasar. The quasars at an early stage of evolutions tend to show weaker [O III] $\lambda$5007 emission, while older quasars exhibit stronger [O III] $\lambda$5007 emission.
Published: 2024
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241. Holographic Entanglement Entropy for Brane-World Higher Derivative Gravity

Author: Miao, Rong-Xin and Xie, Zi-Bing
Subjects: High Energy Physics - Theory, General Relativity and Quantum Cosmology
Abstract: Due to the splitting problem, it is difficult to derive the holographic entanglement entropy for general higher derivative gravity. Inspired by double holography and renormalized entanglement entropy, we develop a method to derive the generalized gravitational entropy for the brane-world higher derivative (BWHD) gravity. Remarkably, this approach is independent of the splitting problem. The so-called BWHD gravity is an effective theory on the brane, given by the counter terms of holographic renormalization. Interestingly, all solutions to Einstein gravity are also solutions to BWHD gravity. We first verify our approach can derive the correct results for curvature-squared gravity and then derive the holographic entanglement entropy for cubic BWHD gravity, which is the main result of this paper. We also derive the entropy of quartic BWHD gravity in flat space with constant extrinsic curvatures and perform several tests on our results. Finally, we briefly comment on our results., Comment: 27 pages, 0 figure
Published: 2024

242. Measurements of $\psi{(2S)}$ and $\chi_{c1}(3872)$ production within fully reconstructed jets

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., Godinez, J. Castro, 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. 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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., Kubat, J., 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. 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Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., 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., Nogarolli, P., Nogga, P., Nolte, N. S., 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. 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R., Riccardi, D., Ricciardi, S., Richardson, K., Richardson-Slipper, M., Rinnert, K., Robbe, P., Robertson, G., Rodrigues, E., Fernandez, E. Rodriguez, Lopez, J. A. Rodriguez, Rodriguez, E. Rodriguez, Roensch, J., Rogachev, A., Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Lamas, M. Romero, 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., Gras, C. Sanchez, 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., Song, R., Song, Y., Song, Y. S., De Almeida, F. L. Souza, De Paula, B. Souza, Norella, E. Spadaro, Spedicato, E., Speer, J. 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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, J., Wang, M., Wang, N. W., Wang, R., Wang, X., Wang, X. W., Wang, Y., 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. R. J., Williams, R., Williams, Z., Wilson, F. F., Wislicki, W., Witek, M., Witola, L., Wormser, G., Wotton, S. A., Wu, H., Wu, J., 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: This paper presents the first measurement of $\psi{(2S)}$ and $\chi_{c1}(3872)$ meson production within fully reconstructed jets. Each quarkonium state (tag) is reconstructed via its decay to the $J/\psi$($\rightarrow\mu^+\mu^-$)$\pi^+\pi^-$ final state in the forward region using proton-proton collision data collected by the LHCb experiment at the center-of-mass-energy of $13 \text{TeV}$ in 2016, corresponding to an integrated luminosity of $1.64 \text{fb}^{-1}$. The fragmentation function, presented as the ratio of the quarkonium-tag transverse momentum to the full jet transverse momentum ($p_{\mathrm{T}}(\text{tag})/p_{\mathrm{T}}(\text{jet})$), is measured differentially in $p_{\mathrm{T}}(\text{jet})$ and $p_{\mathrm{T}}(\text{tag})$ bins. The distributions are separated into promptly produced quarkonia from proton-proton collisions and quarkonia produced from displaced $b$-hadron decays. While the displaced quarkonia fragmentation functions are in general well described by parton-shower predictions, the prompt quarkonium distributions differ significantly from fixed-order non-relativistic QCD (NRQCD) predictions followed by a QCD parton shower., Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/1618/ (LHCb public pages)
Published: 2024

243. Anisotropies of cosmological gravitational wave backgrounds in non-flat spacetime

Author: Cai, Rong-Gen, Wang, Shao-Jiang, Yuwen, Zi-Yan, and Zeng, Xiang-Xi
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, General Relativity and Quantum Cosmology
Abstract: Recent reports of stochastic gravitational wave background from four independent pulsar-timing-array collaborations have renewed the interest in the cosmological gravitational wave background (CGWB), which is expected to open a new window into the early Universe. Although the early Universe is supposed to be extremely flat from an inflationary point of view, the cosmic microwave background (CMB) data alone from the Planck satellite measurement prefers an enhanced lensing amplitude that can be explained by a closed Universe. In this paper, we propose an independent method to constrain the early-universe flatness from the anisotropies of CGWB. Using the generalized harmonic decompositions in the non-flat spacetime, we find CGWBs from different physical mechanisms such as cosmic inflation and phase transitions share the same integrated Sachs-Wolfe (ISW) term but possess different SW terms, which would exhibit different behaviors when including the spatial curvature since the ISW effect is more sensitive to the spatial curvature than the SW effect. Furthermore, we provide the cross-correlations between CGWB and CMB, implying a positive or negative correlation between their SW effect terms depending on the GW mechanisms, which may hint at the sign of $f_{\mathrm{NL}}$ when considering non-Gaussianity contributions to anisotropies., Comment: 29 pages, 7 figures. Published version in JCAP. We have added a new section to analyze the SNR and made our code available at the acknowledgment
Published: 2024
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244. MEC-IP: Efficient Discovery of Markov Equivalent Classes via Integer Programming

Author: Elrefaey, Abdelmonem and Pan, Rong
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract: This paper presents a novel Integer Programming (IP) approach for discovering the Markov Equivalent Class (MEC) of Bayesian Networks (BNs) through observational data. The MEC-IP algorithm utilizes a unique clique-focusing strategy and Extended Maximal Spanning Graphs (EMSG) to streamline the search for MEC, thus overcoming the computational limitations inherent in other existing algorithms. Our numerical results show that not only a remarkable reduction in computational time is achieved by our algorithm but also an improvement in causal discovery accuracy is seen across diverse datasets. These findings underscore this new algorithm's potential as a powerful tool for researchers and practitioners in causal discovery and BNSL, offering a significant leap forward toward the efficient and accurate analysis of complex data structures.
Published: 2024

245. $\textit{Ab initio}$ dynamical mean-field theory with natural orbitals renormalization group impurity solver: Formalism and applications

Author: Wang, Jia-Ming, Wang, Jing-Xuan, He, Rong-Qiang, Huang, Li, and Lu, Zhong-Yi
Subjects: Condensed Matter - Strongly Correlated Electrons, Condensed Matter - Materials Science, Physics - Computational Physics
Abstract: In this study, we introduce a novel implementation of density functional theory integrated with single-site dynamical mean-field theory to investigate the complex properties of strongly correlated materials. This comprehensive first-principles many-body computational toolkit, termed $\texttt{Zen}$, utilizes the Vienna $\textit{ab initio}$ simulation package and the $\texttt{Quantum ESPRESSO}$ code to perform density functional theory calculations and generate band structures for realistic materials. The challenges associated with correlated electron systems are addressed through two distinct yet complementary quantum impurity solvers: the natural orbitals renormalization group solver for zero temperature and the hybridization expansion continuous-time quantum Monte Carlo solver for finite temperature. Additionally, this newly developed toolkit incorporates several valuable post-processing tools, such as $\texttt{ACFlow}$, which employs the maximum entropy method and the stochastic pole expansion method for the analytic continuation of Matsubara Green's functions and self-energy functions. To validate the performance of this toolkit, we examine three representative cases: the correlated metal SrVO$_{3}$, the nickel-based unconventional superconductor La$_{3}$Ni$_{2}$O$_{7}$, and the wide-gap Mott insulator MnO. The results obtained demonstrate strong agreement with experimental findings and previously available theoretical results. Notably, we successfully elucidate the quasiparticle peak and band renormalization in SrVO$_{3}$, the dominance of Hund correlation in La$_{3}$Ni$_{2}$O$_{7}$, and the pressure-driven insulator-metal transition as well as the high-spin to low-spin transition in MnO. These findings suggest that $\texttt{Zen}$ is proficient in accurately describing the electronic structures of $d$-electron correlated materials., Comment: 14 pages, 8 figures, 1 table
Published: 2024

246. A single-phase epitaxially grown ferroelectric perovskite nitride

Author: Choi, Songhee, Jin, Qiao, Zi, Xian, Rong, Dongke, Fang, Jie, Zhang, Jinfeng, Zhang, Qinghua, Li, Wei, Xu, Shuai, Chen, Shengru, Hong, Haitao, Ting, Cui, Wang, Qianying, Tang, Gang, Ge, Chen, Wang, Can, Chen, Zhiguo, Gu, Lin, Li, Qian, Wang, Lingfei, Wang, Shanmin, Hong, Jiawang, Jin, Kuijuan, and Guo, Er-Jia
Subjects: Condensed Matter - Materials Science, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract: The integration of ferroelectrics with semiconductors is crucial for developing functional devices, such as field-effect transistors, tunnel junctions, and nonvolatile memories. However, the synthesis of high-quality single-crystalline ferroelectric nitride perovskites has been limited, hindering a comprehensive understanding of their switching dynamics and potential applications. Here we report the synthesis and characterizations of epitaxial single-phase ferroelectric cerium tantalum nitride (CeTaN3) on both oxides and semiconductors. The polar symmetry of CeTaN3 was confirmed by observing the atomic displacement of central ions relative to the center of the TaN6 octahedra, as well as through optical second harmonic generation. We observed switchable ferroelectric domains in CeTaN3 films using piezo-response force microscopy, complemented by the characterization of square-like polarization-electric field hysteresis loops. The remanent polarization of CeTaN3 reaches approximately 20 uC/cm2 at room temperature, consistent with theoretical calculations. This work establishes a vital link between ferroelectric nitride perovskites and their practical applications, paving the way for next-generation information and energy-storage devices with enhanced performance, scalability, and manufacturability., Comment: 47 pages, 4 figures
Published: 2024

247. Measurement of the branching fractions of the decays $\Lambda_{c}^{+}\rightarrow\Lambda K_{S}^{0}K^{+}$, $\Lambda_{c}^{+}\rightarrow\Lambda K_{S}^{0}\pi^{+}$ and $\Lambda_{c}^{+}\rightarrow\Lambda K^{*+}$

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., Chang, J. F., Che, G. R., 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, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. 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J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, L. Z., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., 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, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., 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. 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Subjects: High Energy Physics - Experiment
Abstract: Studies are performed of the Cabibbo-favored decay $\Lambda_{c}^{+}\to\Lambda K_{S}^{0}K^+$ and the singly Cabibbo-suppressed decay $\Lambda_{c}^{+}\to\Lambda K_{S}^{0}\pi^+$, based on a sample of $e^{+}e^{-}$ collision data, corresponding to an integrated luminosity of 4.5 fb$^{-1}$, accumulated at center-of-mass energies between $4599.53$ MeV and $4698.82$ MeV with the BESIII detector. The decay $\Lambda_{c}^{+}\to\Lambda K_{S}^{0}\pi^+$ is observed for the first time. The branching fractions of $\Lambda_{c}^{+}\to\Lambda K_{S}^{0}K^+$ and $\Lambda_{c}^{+}\to\Lambda K_{S}^{0}\pi^+$ are measured to be $(3.04\pm0.30\pm0.16)\times 10^{-3}$ and $(1.73\pm0.27\pm0.10)\times 10^{-3}$, respectively, where the first uncertainties are statistical and the second are systematic. These results correspond to the most precise measurement of these quantities for both decays. Evidence of a $K^{*+}$ contribution in the $\Lambda_{c}^{+}\to\Lambda K_{S}^{0}\pi^+$ decay is found with a statistical significance of $4.7\sigma$. The branching fraction of $\Lambda_{c}^{+}\to\Lambda K^{*+}$ is calculated under three possible interference scenarios.
Published: 2024

248. Designing a Dataset for Convolutional Neural Networks to Predict Space Groups Consistent with Extinction Laws

Author: Wang, Hao, Zhong, Jiajun, Li, Yikun, Zhang, Junrong, and Du, Rong
Subjects: Computer Science - Neural and Evolutionary Computing, Physics - Data Analysis, Statistics and Probability
Abstract: In this paper, a dataset of one-dimensional powder diffraction patterns was designed with new strategy to train Convolutional Neural Networks for predicting space groups. The diffraction pattern was calculated based on lattice parameters and Extinction Laws, instead of the traditional approach of generating it from a crystallographic database. This paper demonstrates that the new strategy is more effective than the conventional method. As a result, the model trained on the cubic and tetragonal training set from the newly designed dataset achieves prediction accuracy that matches the theoretical maximums calculated based on Extinction Laws. These results demonstrate that machine learning-based prediction can be both physically reasonable and reliable. Additionally, the model trained on our newly designed dataset shows excellent generalization capability, much better than the one trained on a traditionally designed dataset., Comment: 17 pages, 10 figures
Published: 2024

249. Assessing and improving reliability of neighbor embedding methods: a map-continuity perspective

Author: Liu, Zhexuan, Ma, Rong, and Zhong, Yiqiao
Subjects: Statistics - Methodology, Computer Science - Machine Learning, Statistics - Computation, Statistics - Machine Learning
Abstract: Visualizing high-dimensional data is an important routine for understanding biomedical data and interpreting deep learning models. Neighbor embedding methods, such as t-SNE, UMAP, and LargeVis, among others, are a family of popular visualization methods which reduce high-dimensional data to two dimensions. However, recent studies suggest that these methods often produce visual artifacts, potentially leading to incorrect scientific conclusions. Recognizing that the current limitation stems from a lack of data-independent notions of embedding maps, we introduce a novel conceptual and computational framework, LOO-map, that learns the embedding maps based on a classical statistical idea known as the leave-one-out. LOO-map extends the embedding over a discrete set of input points to the entire input space, enabling a systematic assessment of map continuity, and thus the reliability of the visualizations. We find for many neighbor embedding methods, their embedding maps can be intrinsically discontinuous. The discontinuity induces two types of observed map distortion: ``overconfidence-inducing discontinuity," which exaggerates cluster separation, and ``fracture-inducing discontinuity," which creates spurious local structures. Building upon LOO-map, we propose two diagnostic point-wise scores -- perturbation score and singularity score -- to address these limitations. These scores can help identify unreliable embedding points, detect out-of-distribution data, and guide hyperparameter selection. Our approach is flexible and works as a wrapper around many neighbor embedding algorithms. We test our methods across multiple real-world datasets from computer vision and single-cell omics to demonstrate their effectiveness in enhancing the interpretability and accuracy of visualizations., Comment: 43 pages, 15 figures
Published: 2024

250. Large language models enabled multiagent ensemble method for efficient EHR data labeling

Author: Huang, Jingwei, Nezafati, Kuroush, Villanueva-Miranda, Ismael, Gu, Zifan, Navar, Ann Marie, Wanyan, Tingyi, Zhou, Qin, Yao, Bo, Rong, Ruichen, Zhan, Xiaowei, Xiao, Guanghua, Peterson, Eric D., Yang, Donghan M., and Xie, Yang
Subjects: Computer Science - Artificial Intelligence, I.2
Abstract: This study introduces a novel multiagent ensemble method powered by LLMs to address a key challenge in ML - data labeling, particularly in large-scale EHR datasets. Manual labeling of such datasets requires domain expertise and is labor-intensive, time-consuming, expensive, and error-prone. To overcome this bottleneck, we developed an ensemble LLMs method and demonstrated its effectiveness in two real-world tasks: (1) labeling a large-scale unlabeled ECG dataset in MIMIC-IV; (2) identifying social determinants of health (SDOH) from the clinical notes of EHR. Trading off benefits and cost, we selected a pool of diverse open source LLMs with satisfactory performance. We treat each LLM's prediction as a vote and apply a mechanism of majority voting with minimal winning threshold for ensemble. We implemented an ensemble LLMs application for EHR data labeling tasks. By using the ensemble LLMs and natural language processing, we labeled MIMIC-IV ECG dataset of 623,566 ECG reports with an estimated accuracy of 98.2%. We applied the ensemble LLMs method to identify SDOH from social history sections of 1,405 EHR clinical notes, also achieving competitive performance. Our experiments show that the ensemble LLMs can outperform individual LLM even the best commercial one, and the method reduces hallucination errors. From the research, we found that (1) the ensemble LLMs method significantly reduces the time and effort required for labeling large-scale EHR data, automating the process with high accuracy and quality; (2) the method generalizes well to other text data labeling tasks, as shown by its application to SDOH identification; (3) the ensemble of a group of diverse LLMs can outperform or match the performance of the best individual LLM; and (4) the ensemble method substantially reduces hallucination errors. This approach provides a scalable and efficient solution to data-labeling challenges., Comment: 27 pages, 13 figures. Under journal review
Published: 2024

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