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30,874 results on '"Wu Q."'

251. Generating Searchable Public-Key Ciphertexts with Hidden Structures for Fast Keyword Search

Author

Xu, P., Wu, Q., Wang, W., Susilo, W., Domingo-Ferrer, J., and Jin, H.

Subjects
Computer Science - Cryptography and Security
Abstract

Existing semantically secure public-key searchable encryption schemes take search time linear with the total number of the ciphertexts. This makes retrieval from large-scale databases prohibitive. To alleviate this problem, this paper proposes Searchable Public-Key Ciphertexts with Hidden Structures (SPCHS) for keyword search as fast as possible without sacrificing semantic security of the encrypted keywords. In SPCHS, all keyword-searchable ciphertexts are structured by hidden relations, and with the search trapdoor corresponding to a keyword, the minimum information of therelations is disclosed to a search algorithm as the guidance to find all matching ciphertexts efficiently. We construct a simple SPCHS scheme from scratch in which the ciphertexts have a hidden star-like structure. We prove our scheme to be semantically secure based on the decisional bilinear Diffie-Hellman assumption in the Random Oracle (RO) model. The search complexity of our scheme is dependent on the actual number of the ciphertexts containing the queried keyword, rather than the number of all ciphertexts. Finally, we present a generic SPCHS construction from anonymous identity-based encryption and collision-free full-identity malleable Identity-Based Key Encapsulation Mechanism (IBKEM) with anonymity. We illustrate two collision-free full-identity malleable IBKEM instances, which are semantically secure and anonymous, respectively, in the RO and standard models. The latter instance enables us to construct an SPCHS scheme with semantic security in the standard model.

Published
2015
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257. Repeatability of Radiomic Features Against Simulated Scanning Position Stochasticity Across Imaging Modalities and Cancer Subtypes: A Retrospective Multi-institutional Study on Head-and-Neck Cases

Author

Zhang, Jiang, primary, Lam, Saikit, additional, Teng, Xinzhi, additional, Zhang, Yuanpeng, additional, Ma, Zongrui, additional, Lee, Francis, additional, Au, Kwok-hung, additional, Yip, Wai Yi, additional, Chang, Tien Yee Amy, additional, Chan, Wing Chi Lawrence, additional, Lee, Victor, additional, Wu, Q. Jackie, additional, and Cai, Jing, additional

Published
2022
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258. Progress of Underground Nuclear Astrophysics Experiment JUNA in China

Author

Liu, W. P., Li, Z. H., He, J. J., Tang, X. D., Lian, G., Su, J., Shen, Y. P., An, Z., Chao, F. Q., Chang, J. J., Chen, L. H., Chen, H., Chen, X. J., Chen, Y. H., Chen, Z. J., Cui, B. Q., Du, X. C., Fang, X., Fu, C. B., Gan, L., Guo, B., Han, Z. Y., Guo, X. Y., He, G. Z., He, J. R., Heger, A., Hou, S. Q., Huang, H. X., Huang, N., Jia, B. L., Jiang, L. Y., Kubono, S., Li, J. M., Li, M. C., Li, K. A., Li, E. T., Li, T., Li, Y. J., Lugaro, M., Luo, X. B., Ma, H. Y., Ma, S. B., Mei, D. M., Nan, W., Nan, W. K., Qi, N. C., Qian, Y. Z., Qin, J. C., Ren, J., Shang, C. S., Sun, L. T., Sun, W. L., Tan, W. P., Tanihata, I., Wang, S., Wang, P., Wang, Y. B., Wu, Q., Xu, S. W., Yan, S. Q., Yang, L. T., Yang, Y., Yu, X. Q., Yue, Q., Zeng, S., Zhang, L., Zhang, H., Zhang, H. Y., Zhang, L. Y., Zhang, N. T., Zhang, P., Zhang, Q. W., Zhang, T., Zhang, X. P., Zhang, X. Z., Zhao, W., Zhou, J. F., and Zhou, Y.

Published
2022
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262. Quantum phase transition and destruction of Kondo effect in pressurized SmB6

Author

Zhou, Y, Wu, Q, Rosa, PFS, Yu, R, Guo, J, Yi, W, Zhang, S, Wang, Z, Wang, H, Cai, S, Yang, K, Li, A, Jiang, Z, Wei, X, Huang, Y, Sun, P, Yang, YF, Fisk, Z, Si, Q, Zhao, Z, and Sun, L

Subjects
Kondo insulator, Surface state, SmB6, High pressure, cond-mat.str-el
Abstract

SmB6 has been a well-known Kondo insulator for decades, but recently attracts extensive new attention as a candidate topological system. Studying SmB6 under pressure provides an opportunity to acquire the much-needed understanding about the effect of electron correlations on both the metallic surface state and bulk insulating state. Here we do so by studying the evolution of two transport gaps (low temperature gap El and high temperature gap Eh) associated with the Kondo effect by measuring the electrical resistivity under high pressure and low temperature (0.3 K) conditions. We associate the gaps with the bulk Kondo hybridization, and from their evolution with pressure we demonstrate an insulator-to-metal transition at ∼4 GPa. At the transition pressure, a large change in the Hall number and a divergence tendency of the electron-electron scattering coefficient provide evidence for a destruction of the Kondo entanglement in the ground state. Our results raise the new prospect for studying topological electronic states in quantum critical materials settings.

Published
2017

263. Spatially resolving density-dependent screening around a single charged atom in graphene

Author

Wong, D, Corsetti, F, Wang, Y, Brar, VW, Tsai, HZ, Wu, Q, Kawakami, RK, Zettl, A, Mostofi, AA, Lischner, J, and Crommie, MF

Subjects
cond-mat.mes-hall
Abstract

Electrons in two-dimensional graphene sheets behave as interacting chiral Dirac fermions and have unique screening properties due to their symmetry and reduced dimensionality. By using a combination of scanning tunneling spectroscopy measurements and theoretical modeling we have characterized how graphene's massless charge carriers screen individual charged calcium atoms. A backgated graphene device configuration has allowed us to directly visualize how the screening length for this system can be tuned with carrier density. Our results provide insight into electron-impurity and electron-electron interactions in a relativistic setting with important consequences for other graphene-based electronic devices.

Published
2017

264. Regional and global elevational patterns of microbial species richness and evenness

Author

Wang, J, Meier, S, Soininen, J, Casamayor, EO, Pan, F, Tang, X, Yang, X, Zhang, Y, Wu, Q, Zhou, J, and Shen, J

Subjects
Ecology, Ecological Applications, Environmental Science and Management
Abstract

Although elevational gradients in microbial biodiversity have attracted increasing attention recently, the generality in the patterns and underlying mechanisms are still poorly resolved. Further, previous studies focused mostly on species richness, while left understudied evenness, another important aspect of biodiversity. Here, we studied the elevational patterns in species richness and evenness of stream biofilm bacteria and diatoms in six mountains in Asia and Europe. We also reviewed published results for elevational richness patterns for soil and stream microbes in a literature analysis. Our results revealed that even within the same ecosystem type (that is, stream) or geographical region, bacteria and diatoms showed contrasting patterns in diversity. Stream microbes, including present stream data, tend to show significantly increasing or decreasing elevational patterns in richness, contrasting the findings for soil microbes that typically showed nonsignificant or significantly decreasing patterns. In all six mountains for bacteria and in four mountains for diatoms, species richness and evenness were positively correlated. The variation in bacteria and diatom richness and evenness were substantially explained by anthropogenic driven factors, such as total phosphorus (TP). However, diatom richness and evenness were also related to different main drivers as richness was mostly related to pH, while evenness was most explained by TP. Our results highlight the lack of consistent elevational biodiversity patterns of microbes and further indicate that the two facets of biodiversity may respond differently to environmental gradients.

Published
2017

265. Core 1- and 3-derived O-glycans collectively maintain the colonic mucus barrier and protect against spontaneous colitis in mice.

Author

Bergstrom, K, Fu, J, Johansson, MEV, Liu, X, Gao, N, Wu, Q, Song, J, McDaniel, JM, McGee, S, Chen, W, Braun, J, Hansson, GC, and Xia, L

Subjects
Intestinal Mucosa, Colon, Cells, Cultured, Mucus, Animals, Mice, Inbred C57BL, Mice, Knockout, Humans, Mice, Colitis, Ulcerative, Disease Models, Animal, N-Acetylglucosaminyltransferases, Galactosyltransferases, Polysaccharides, Anti-Bacterial Agents, Immunity, Mucosal, Proteolysis, Cells, Cultured, Inbred C57BL, Knockout, Colitis, Ulcerative, Disease Models, Animal, Immunity, Mucosal, Immunology, Biological Sciences, Medical and Health Sciences
Abstract

Core 1- and 3-derived mucin-type O-glycans are primary components of the mucus layer in the colon. Reduced mucus thickness and impaired O-glycosylation are observed in human ulcerative colitis. However, how both types of O-glycans maintain mucus barrier function in the colon is unclear. We found that C1galt1 expression, which synthesizes core 1 O-glycans, was detected throughout the colon, whereas C3GnT, which controls core 3 O-glycan formation, was most highly expressed in the proximal colon. Consistent with this, mice lacking intestinal core 1-derived O-glycans (IEC C1galt1-/-) developed spontaneous colitis primarily in the distal colon, whereas mice lacking both intestinal core 1- and 3-derived O-glycans (DKO) developed spontaneous colitis in both the distal and proximal colon. DKO mice showed an early onset and more severe colitis than IEC C1galt1-/- mice. Antibiotic treatment restored the mucus layer and attenuated colitis in DKO mice. Mucins from DKO mice were more susceptible to proteolysis than wild-type mucins. This study indicates that core 1- and 3-derived O-glycans collectively contribute to the mucus barrier by protecting it from bacterial protease degradation and suggests new therapeutic targets to promote mucus barrier function in colitis patients.

Published
2017

266. Advancing GIS Operational Monitoring: A Novel Voiceprint Recognition Method Using Grassmann Manifold and Multi-Kernel Functions

Author

Ji, Tianyao, Liu, Zigang, Zhuang, Xiaoliang, Li, Qiankun, Zhang, Luliang, and Wu, Q. H.

Abstract

Gas insulated switchgear (GIS) is essential for the reliability and stability of power systems, requiring precise recognition of its operating conditions, which are typically represented by voiceprint data. Traditional methods, predominantly based in Euclidean space, often struggle to differentiate specific intricate operating conditions in GIS. Addressing this gap, this paper proposes a novel method anchored in Riemannian space. Specifically, this method begins by employing Mel frequency cepstral coefficient (MFCC) and singular value decomposition (SVD) to extract features from voiceprint data. Subsequently, it projects these features within Riemannian space and maps them onto a Grassmann manifold, effectively capturing its intricate and nonlinear characteristics. A key innovation of this method is the use of three kernel functions, namely projection, Binet-Cauchy, and canonical correlation, optimized via empirical kernel alignment techniques to select the most suitable adaptive parameters, thereby enhancing classification accuracy. Further refinement is achieved through kernel linear discriminant analysis (KLDA), which integrates the aligned kernel into a lower-dimensional subspace for more efficient classification. Experiments on real data from ZF23-126 type GIS, encompassing 20 different operational conditions, show our method achieves an accuracy of 95.21%, precision of 95.38%, specificity of 99.75%, and F1-score of 95.22%, significantly outperforming all baseline methods. Moreover, the method demonstrates robust performance across various classifiers, with accuracy consistently exceeding 90%. Additional ablation experiments confirm its effectiveness and generalizability.

Published
2024
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267. You Only Look at Once for Real-Time and Generic Multi-Task

Author

Wang, Jiayuan, Jonathan Wu, Q. M., and Zhang, Ning

Abstract

High precision, lightweight, and real-time responsiveness are three essential requirements for implementing autonomous driving. In this study, we incorporate A-YOLOM, an adaptive, real-time, and lightweight multi-task model designed to concurrently address object detection, drivable area segmentation, and lane line segmentation tasks. Specifically, we develop an end-to-end multi-task model with a unified and streamlined segmentation structure. We introduce a learnable parameter that adaptively concatenates features between necks and backbone in segmentation tasks, using the same loss function for all segmentation tasks. This eliminates the need for customizations and enhances the model's generalization capabilities. We also introduce a segmentation head composed only of a series of convolutional layers, which reduces the number of parameters and inference time. We achieve competitive results on the BDD100 k dataset, particularly in visualization outcomes. The performance results show a mAP50 of 81.1% for object detection, a mIoU of 91.0% for drivable area segmentation, and an IoU of 28.8% for lane line segmentation. Additionally, we introduce real-world scenarios to evaluate our model's performance in a real scene, which significantly outperforms competitors. This demonstrates that our model not only exhibits competitive performance but is also more flexible and faster than existing multi-task models.

Published
2024
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271. Optimization of the JUNO liquid scintillator composition using a Daya Bay antineutrino detector

Author

Abusleme, A., Adam, T., Ahmad, S., Aiello, S., Akram, M., Ali, N., An, F.P., An, G.P., An, Q., Andronico, G., Anfimov, N., Antonelli, V., Antoshkina, T., Asavapibhop, B., de André, J.P.A.M., Babic, A., Balantekin, A.B., Baldini, W., Baldoncini, M., Band, H.R., Barresi, A., Baussan, E., Bellato, M., Bernieri, E., Biare, D., Birkenfeld, T., Bishai, M., Blin, S., Blum, D., Blyth, S., Bordereau, C., Brigatti, A., Brugnera, R., Budano, A., Burgbacher, P., Buscemi, M., Bussino, S., Busto, J., Butorov, I., Cabrera, A., Cai, H., Cai, X., Cai, Y.K., Cai, Z.Y., Cammi, A., Campeny, A., Cao, C.Y., Cao, G.F., Cao, J., Caruso, R., Cerna, C., Chang, J.F., Chang, Y., Chen, H.S., Chen, P.A., Chen, P.P., Chen, S.M., Chen, S.J., Chen, X.R., Chen, Y.W., Chen, Y.X., Chen, Y., Chen, Z., Cheng, J., Cheng, Y.P., Cheng, Z.K., Chepurnov, A., Cherwinka, J.J., Chiarello, F., Chiesa, D., Chimenti, P., Chu, M.C., Chukanov, A., Chuvashova, A., Clementi, C., Clerbaux, B., Di Lorenzo, S. Conforti, Corti, D., Costa, S., Dal Corso, F., Cummings, J.P., Dalager, O., De La Taille, C., Deng, F.S., Deng, J.W., Deng, Z., Deng, Z.Y., Depnering, W., Diaz, M., Ding, X.F., Ding, Y.Y., Dirgantara, B., Dmitrievsky, S., Diwan, M.V., Dohnal, T., Donchenko, G., Dong, J.M., Dornic, D., Doroshkevich, E., Dove, J., Dracos, M., Druillole, F., Du, S.X., Dusini, S., Dvorak, M., Dwyer, D.A., Enqvist, T., Enzmann, H., Fabbri, A., Fajt, L., Fan, D.H., Fan, L., Fang, C., Fang, J., Fatkina, A., Fedoseev, D., Fekete, V., Feng, L.C., Feng, Q.C., Fiorentini, G., Ford, R., Formozov, A., Fournier, A., Franke, S., Gallo, J.P., Gan, H.N., Gao, F., Garfagnini, A., Göttel, A., Genster, C., Giammarchi, M., Giaz, A., Giudice, N., Giuliani, F., Gonchar, M., Gong, G.H., Gong, H., Gorchakov, O., Gornushkin, Y., Grassi, M., Grewing, C., Gromov, M., Gromov, V., Gu, M.H., Gu, W.Q., Gu, X.F., Gu, Y., Guan, M.Y., Guardone, N., Gul, M., Guo, C., Guo, J.Y., Guo, L., Guo, W.L., Guo, X.H., Guo, Y.H., Guo, Z., Haacke, M., Hackenburg, R.W., Hackspacher, P., Hagner, C., Han, R., Han, Y., Hans, S., He, M., He, W., Heeger, K.M., Heinz, T., Heng, Y.K., Herrera, R., Higuera, A., Hong, D.J., Hor, Y.K., Hou, S.J., Hsiung, Y.B., Hu, B.Z., Hu, H., Hu, J.R., Hu, J., Hu, S.Y., Hu, T., Hu, Z.J., Huang, C.H., Huang, G.H., Huang, H.X., Huang, Q.H., Huang, W.H., Huang, X.T., Huang, Y.B., Huber, P., Hui, J.Q., Huo, L., Huo, W.J., Huss, C., Hussain, S., Insolia, A., Ioannisian, A., Ioannisyan, D., Isocrate, R., Jaffe, D.E., Jen, K.L., Ji, X.L., Ji, X.P., Ji, X.Z., Jia, H.H., Jia, J.J., Jian, S.Y., Jiang, D., Jiang, X.S., Jin, R.Y., Jing, X.P., Johnson, R.A., Jollet, C., Jones, D., Joutsenvaara, J., Jungthawan, S., Kalousis, L., Kampmann, P., Kang, L., Karagounis, M., Kazarian, N., Kettell, S.H., Khan, A., Khan, W., Khosonthongkee, K., Kinz, P., Kohn, S., Korablev, D., Kouzakov, K., Kramer, M., Krasnoperov, A., Krokhaleva, S., Krumshteyn, Z., Kruth, A., Kutovskiy, N., Kuusiniemi, P., Lachacinski, B., Lachenmaier, T., Landini, C., Langford, T.J., Lee, J., Lee, J.H.C., Lefevre, F., Lei, L., Lei, R., Leitner, R., Leung, J., Li, D.M., Li, F., Li, H.T., Li, H.L., Li, J., Li, J.J., Li, J.Q., Li, K.J., Li, M.Z., Li, N., Li, Q.J., Li, R.H., Li, S.C., Li, S.F., Li, S.J., Li, T., Li, W.D., Li, W.G., Li, X.M., Li, X.N., Li, X.L., Li, X.Q., Li, Y., Li, Y.F., Li, Z.B., Li, Z.Y., Liang, H., Liang, J.J., Liebau, D., Limphirat, A., Limpijumnong, S., Lin, C.J., Lin, G.L., Lin, S.X., Lin, T., Lin, Y.H., Ling, J.J., Link, J.M., Lippi, I., Littenberg, L., Littlejohn, B.R., Liu, F., Liu, H., Liu, H.B., Liu, H.D., Liu, H.J., Liu, H.T., Liu, J.C., Liu, J.L., Liu, M., Liu, Q., Liu, R.X., Liu, S.Y., Liu, S.B., Liu, S.L., Liu, X.W., Liu, Y., Lokhov, A., Lombardi, P., Loo, K., Lorenz, S., Lu, C., Lu, H.Q., Lu, J.B., Lu, J.G., Lu, S.X., Lu, X.X., Lubsandorzhiev, B., Lubsandorzhiev, S., Ludhova, L., Luk, K.B., Luo, F.J., Luo, G., Luo, P.W., Luo, S., Luo, W.M., Lyashuk, V., Ma, Q.M., Ma, S., Ma, X.B., Ma, X.Y., Ma, Y.Q., Malyshkin, Y., Mantovani, F., Mao, Y.J., Mari, S.M., Marini, F., Marium, S., Marshall, C., Martellini, C., Martin-Chassard, G., Caicedo, D.A. Martinez, Martini, A., Martino, J., Mayilyan, D., McDonald, K.T., McKeown, R.D., Müller, A., Meng, G., Mednieks, I., Meng, Y., Meregaglia, A., Meroni, E., Meyhöfer, D., Mezzetto, M., Miller, J., Miramonti, L., Monforte, S., Montini, P., Montuschi, M., Morozov, N., Muralidharan, P., Napolitano, J., Nastasi, M., Naumov, D.V., Naumova, E., Nemchenok, I., Nikolaev, A., Ning, F.P., Ning, Z., Nunokawa, H., Oberauer, L., Ochoa-Ricoux, J.P., Olshevskiy, A., Ortica, F., Pan, H.R., Paoloni, A., Park, J., Parkalian, N., Parmeggiano, S., Patton, S., Payupol, T., Pec, V., Pedretti, D., Pei, Y.T., Pelliccia, N., Peng, A.G., Peng, H.P., Peng, J.C., Perrot, F., Petitjean, P.A., Rico, L.F. Pineres, Popov, A., Poussot, P., Pratumwan, W., Previtali, E., Pun, C.S.J., Qi, F.Z., Qi, M., Qian, S., Qian, X., Qian, X.H., Qiao, H., Qin, Z.H., Qiu, S.K., Rajput, M., Ranucci, G., Raper, N., Re, A., Rebber, H., Rebii, A., Ren, B., Ren, J., Reveco, C.M., Rezinko, T., Ricci, B., Robens, M., Roche, M., Rodphai, N., Rohwer, L., Romani, A., Rosero, R., Roskovec, B., Roth, C., Ruan, X.C., Ruan, X.D., Rujirawat, S., Rybnikov, A., Sadovsky, A., Saggese, P., Salamanna, G., Sangka, A., Sanguansak, N., Sawangwit, U., Sawatzki, J., Sawy, F., Schever, M., Schuler, J., Schwab, C., Schweizer, K., Selivanov, D., Selyunin, A., Serafini, A., Settanta, G., Settimo, M., Shahzad, M., Shi, G., Shi, J.Y., Shi, Y.J., Shutov, V., Sidorenkov, A., Šimkovic, F., Sirignano, C., Siripak, J., Sisti, M., Slupecki, M., Smirnov, M., Smirnov, O., Sogo-Bezerra, T., Songwadhana, J., Soonthornthum, B., Sotnikov, A., Sramek, O., Sreethawong, W., Stahl, A., Stanco, L., Stankevich, K., Štefánik, D., Steiger, H., Steiner, H., Steinmann, J., Stender, M., Strati, V., Studenikin, A., Sun, G.X., Sun, L.T., Sun, J.L., Sun, S.F., Sun, X.L., Sun, Y.J., Sun, Y.Z., Suwonjandee, N., Szelezniak, M., Tang, J., Tang, Q., Tang, X., Tietzsch, A., Tkachev, I., Tmej, T., Treskov, K., Troni, G., Trzaska, W., Tse, W.-H., Tull, C.E., Tuve, C., van Waasen, S., Boom, J. van den, Vassilopoulos, N., Vedin, V., Verde, G., Vialkov, M., Viaud, B., Viren, B., Volpe, C., Vorobel, V., Votano, L., Walker, P., Wang, C., Wang, C.H., Wang, E., Wang, G.L., Wang, J., Wang, K.Y., Wang, L., Wang, M.F., Wang, M., Wang, N.Y., Wang, R.G., Wang, S.G., Wang, W., Wang, W.S., Wang, X., Wang, X.Y., Wang, Y., Wang, Y.F., Wang, Y.G., Wang, Y.M., Wang, Y.Q., Wang, Z., Wang, Z.M., Wang, Z.Y., Watcharangkool, A., Wei, H.Y., Wei, L.H., Wei, W., Wei, Y.D., Wen, L.J., Whisnant, K., White, C.G., Wiebusch, C., Wong, S.C.F., Wong, H.L.H., Wonsak, B., Worcester, E., Wu, C.H., Wu, D.R., Wu, F.L., Wu, Q., Wu, W.J., Wu, Z., Wurm, M., Wurtz, J., Wysotzki, C., Xi, Y.F., Xia, D.M., Xie, Y.G., Xie, Z.Q., Xing, Z.Z., Xu, D.L., Xu, F.R., Xu, H.K., Xu, J.L., Xu, J., Xu, M.H., Xu, T., Xu, Y., Xue, T., Yan, B.J., Yan, X.B., Yan, Y.P., Yang, A.B., Yang, C.G., Yang, H., Yang, J., Yang, L., Yang, X.Y., Yang, Y.F., Yang, Y.Z., Yao, H.F., Yasin, Z., Ye, J.X., Ye, M., Yegin, U., Yeh, M., Yermia, F., Yi, P.H., You, Z.Y., Young, B.L., Yu, B.X., Yu, C.X., Yu, C.Y., Yu, H.Z., Yu, M., Yu, X.H., Yu, Z.Y., Yuan, C.Z., Yuan, Y., Yuan, Z.X., Yuan, Z.Y., Yue, B.B., Zafar, N., Zambanini, A., Zeng, P., Zeng, S., Zeng, T.X., Zeng, Y.D., Zhan, L., Zhang, C., Zhang, F.Y., Zhang, G.Q., Zhang, H.H., Zhang, H.Q., Zhang, J., Zhang, J.B., Zhang, J.W., Zhang, P., Zhang, Q.M., Zhang, T., Zhang, X.M., Zhang, X.T., Zhang, Y., Zhang, Y.H., Zhang, Y.M., Zhang, Y.P., Zhang, Y.X., Zhang, Y.Y., Zhang, Z.J., Zhang, Z.P., Zhang, Z.Y., Zhao, F.Y., Zhao, J., Zhao, R., Zhao, S.J., Zhao, T.C., Zheng, D.Q., Zheng, H., Zheng, M.S., Zheng, Y.H., Zhong, W.R., Zhou, J., Zhou, L., Zhou, N., Zhou, S., Zhou, X., Zhu, J., Zhu, K.J., Zhuang, H.L., Zong, L., and Zou, J.H.

Published
2021
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273. Radiation Therapy for Rectal Cancer: Executive Summary of an ASTRO Clinical Practice Guideline

Author

Wo, Jennifer Y., Anker, Christopher J., Ashman, Jonathan B., Bhadkamkar, Nishin A., Bradfield, Lisa, Chang, Daniel T., Dorth, Jennifer, Garcia-Aguilar, Julio, Goff, David, Jacqmin, Dustin, Kelly, Patrick, Newman, Neil B., Olsen, Jeffrey, Raldow, Ann C., Ruiz-Garcia, Erika, Stitzenberg, Karyn B., Thomas, Charles R., Jr., Wu, Q. Jackie, and Das, Prajnan

Published
2021
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274. Landscape structure and composition affect aphid biological control in alfalfa fields, but regional differences prevail

Author

Zhang, X., Dong, Z., Wu, Q., Gagic, Vesna, Tomanovic, Z., Zalucki, M. P., Lu, Z., Zhang, X., Dong, Z., Wu, Q., Gagic, Vesna, Tomanovic, Z., Zalucki, M. P., and Lu, Z.

Abstract

Understanding how landscape composition and configuration drive the dynamics of pests and their various natural enemies is crucial to enhance biological control services (biocontrol). Geographic variability in landscape effects prompts questions about whether this is due to genuine differences or methodological differences among studies, given that most studies are conducted in a single region. This study investigated how the ecological function of parasitoids and predators responds to landscape traits in three regions encompassing an agriculture intensification gradient. The effects of landscape structure and configuration differed among regions and natural enemies. Non-crop habitats increased the abundance of parasitoids only in two less intensified regions presumably due to insufficient amount of non-crop habitats in the intensified region (typically < 10%). Nevertheless, their biological control efficiency (BCE) was consistently increased by non-crop habitats across regions. Higher maize proportion increased predator abundance and their BCE in the two non-mountainous regions. The value of maize as a source habitat for predators may have been limited in the mountainous region due to the lower aphid abundances there. Edge density positively affected abundances of predators and parasitoids, but only positively increased the BCE of all natural enemies combined. Inconsistent regional responses to the same landscape variables suggest that a uniform integrated pest management strategy for alfalfa is impractical. However, increasing edge density should be a useful first step for supporting diverse natural enemy communities and their biocontrol potential. © 2024 The authors.

Published
2024

275. Modulation of cream cheese physicochemical and functional properties with ultrafiltration and calcium reduction

Author

Wu, Q, Ong, L, Aldalur, A, Nie, S, Kentish, SE, Gras, SL, Wu, Q, Ong, L, Aldalur, A, Nie, S, Kentish, SE, and Gras, SL

Abstract

The production of cream cheese from ultrafiltered (UF) milk can reduce acid whey generation but the effect of altered protein and calcium concentration on the physicochemical properties of cream cheese is not well understood. In this study, the effect of skim milk concentration by UF (2.5 and 5 fold) was assessed both with and without calcium reduction using 2% (w/v) cation resin treatment. UF concentration increased the concentration of peptides and free amino acids and led to a more heterogeneous and porous microstructure, resulting in a softer, less viscous and less thermally stable cream cheese. Calcium reduction decreased peptide generation, increased the size of corpuscular structures, decreased porosity and increased thermal stability but did not significantly decrease cheese hardness or viscosity. The study illustrates how protein or calcium concentration, can be used to alter functional properties.

Published
2024

276. Integrated image-based deep learning and language models for primary diabetes care

Author

Li, J, Guan, Z, Wang, J, Cheung, CY, Zheng, Y, Lim, L-L, Lim, CC, Ruamviboonsuk, P, Raman, R, Corsino, L, Echouffo-Tcheugui, JB, Luk, AOY, Chen, LJ, Sun, X, Hamzah, H, Wu, Q, Wang, X, Liu, R, Wang, YX, Chen, T, Zhang, X, Yang, X, Yin, J, Wan, J, Du, W, Quek, TC, Goh, JHL, Yang, D, Hu, X, Nguyen, TX, Szeto, SKH, Chotcomwongse, P, Malek, R, Normatova, N, Ibragimova, N, Srinivasan, R, Zhong, P, Huang, W, Deng, C, Ruan, L, Zhang, C, Zhou, Y, Wu, C, Dai, R, Koh, SWC, Abdullah, A, Hee, NKY, Tan, HC, Liew, ZH, Tien, CS-Y, Kao, SL, Lim, AYL, Mok, SF, Sun, L, Gu, J, Wu, L, Li, T, Cheng, D, Wang, Z, Qin, Y, Dai, L, Meng, Z, Shu, J, Lu, Y, Jiang, N, Hu, T, Huang, S, Huang, G, Yu, S, Liu, D, Ma, W, Guo, M, Guan, X, Bascaran, C, Cleland, CR, Bao, Y, Ekinci, EI, Jenkins, A, Chan, JCN, Bee, YM, Sivaprasad, S, Shaw, JE, Simo, R, Keane, PA, Cheng, C-Y, Tan, GSW, Jia, W, Tham, Y-C, Li, H, Sheng, B, Wong, TY, Li, J, Guan, Z, Wang, J, Cheung, CY, Zheng, Y, Lim, L-L, Lim, CC, Ruamviboonsuk, P, Raman, R, Corsino, L, Echouffo-Tcheugui, JB, Luk, AOY, Chen, LJ, Sun, X, Hamzah, H, Wu, Q, Wang, X, Liu, R, Wang, YX, Chen, T, Zhang, X, Yang, X, Yin, J, Wan, J, Du, W, Quek, TC, Goh, JHL, Yang, D, Hu, X, Nguyen, TX, Szeto, SKH, Chotcomwongse, P, Malek, R, Normatova, N, Ibragimova, N, Srinivasan, R, Zhong, P, Huang, W, Deng, C, Ruan, L, Zhang, C, Zhou, Y, Wu, C, Dai, R, Koh, SWC, Abdullah, A, Hee, NKY, Tan, HC, Liew, ZH, Tien, CS-Y, Kao, SL, Lim, AYL, Mok, SF, Sun, L, Gu, J, Wu, L, Li, T, Cheng, D, Wang, Z, Qin, Y, Dai, L, Meng, Z, Shu, J, Lu, Y, Jiang, N, Hu, T, Huang, S, Huang, G, Yu, S, Liu, D, Ma, W, Guo, M, Guan, X, Bascaran, C, Cleland, CR, Bao, Y, Ekinci, EI, Jenkins, A, Chan, JCN, Bee, YM, Sivaprasad, S, Shaw, JE, Simo, R, Keane, PA, Cheng, C-Y, Tan, GSW, Jia, W, Tham, Y-C, Li, H, Sheng, B, and Wong, TY

Abstract

Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image-language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP's accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P < 0.05). For patients with referral DR, those in the PCP+DeepDR-LLM arm were more likely to adhere to DR referrals (P < 0.01). Additionally, DeepDR-LLM deployment improved the quality and empathy level of management recommendations. Given its multifaceted performance, DeepDR-LLM holds promise as a digital solution for enhancing primary diabetes care and DR screening.

Published
2024

277. Effects of message framing and risk perception on health communication for optimum cardiovascular disease primary prevention: a protocol for a multicenter randomized controlled study.

Author

Guo, Z, Wu, Q, Wang, X, Dai, Y, Ma, Y, Qiu, Y, Zhang, Y, Jin, J, Guo, Z, Wu, Q, Wang, X, Dai, Y, Ma, Y, Qiu, Y, Zhang, Y, and Jin, J

Abstract

BACKGROUND: Although several guidelines for cardiovascular disease (CVD) management have highlighted the significance of primary prevention, the execution and adherence to lifestyle modifications and preventive medication interventions are insufficient in everyday clinical practice. The utilization of effective risk communication can assist individuals in shaping their perception of CVD risk, motivating them to make lifestyle changes, and increasing their willingness to engage with preventive medication, ultimately reducing their CVD risks and potential future events. However, there is limited evidence available regarding the optimal format and content of CVD risk communication. OBJECTIVE: The pilot study aims to elucidate the most effective risk communication strategy, utilizing message framing (gain-framed, loss-framed, or no-framed), for distinct subgroups of risk perception (under-perceived, over-perceived, and correctly-perceived CVD risk) through a multi-center randomized controlled trial design. METHODS: A multi-center 3 × 3 factorial, observer-blinded experimental design was conducted. The participants will be assigned into three message-framing arms randomly in a 1:1:1 ratio and will receive an 8-week intervention online. Participants are aged 20-80 years old and have a 10-year risk of absolute CVD risk of at least 5% (moderate risk or above). We plan to enroll 240 participants based on the sample calculation. The primary outcome is the CVD prevention behaviors and CVD absolute risk value. Data collection will occur at baseline, post-intervention, and 3-month follow-up. DISCUSSION: This experimental study will expect to determine the optimal matching strategy between risk perception subgroups and risk information format, and it has the potential to offer health providers in community or clinic settings a dependable and efficient health communication information template for conducting CVD risk management.Clinical trial registration: https://www.chictr.org.c

Published
2024

278. PCL: Point Contrast and Labeling for Weakly Supervised Point Cloud Semantic Segmentation

Author

Du, A, Zhou, T, Pang, S, Wu, Q, Zhang, J, Du, A, Zhou, T, Pang, S, Wu, Q, and Zhang, J

Abstract

Point cloud semantic segmentation is a fundamental task in 3D scene understanding and has recently achieved remarkable progress. The success of existing approaches is attributed to recent advanced deep networks for point clouds and the availability of a large amount of labeled training data. However, creating such fully annotated training datasets for supervised point cloud semantic segmentation methods is a time-consuming and labor-intensive process, which increases the difficulty of extending supervised approaches to new application scenarios. To alleviate the data-hungry nature of deep learning, we propose PCL, the point contrast and labeling framework for weakly supervised point cloud semantic segmentation with small percentages of point-level annotations. The core idea of this method is to exploit contrastive learning to help learn a larger number of discriminative feature representations with limited annotations. By introducing two types of contrastive relationships, cross-sample point contrast and low-level similarity-based point contrast, our proposed framework can directly regularize the learned feature space, considering not only the low-level similarity within each point cloud but also the discriminative semantics within and across point clouds on both labeled and unlabeled points via pseudo labels. In addition, we propose a pseudo label refinery module to generate robust and reliable pseudo labels online, reducing the negative impact of incorrect pseudo labels. Our method achieves state-of-the-art performance on a diverse set of label-efficient semantic segmentation tasks.

Published
2024

279. Sensing Aided Covert Communications: Turning Interference into Allies

Author

Wang, X, Fei, Z, Liu, P, Zhang, JA, Wu, Q, Wu, N, Wang, X, Fei, Z, Liu, P, Zhang, JA, Wu, Q, and Wu, N

Abstract

In this paper, we investigate the realization of covert communication in a general radar-communication cooperation system, which includes integrated sensing and communications as a special example. We explore the possibility of utilizing the sensing ability of radar to track and jam the aerial adversary target attempting to detect the transmission. Based on the echoes from the target, the extended Kalman filtering technique is employed to predict its trajectory as well as the corresponding channels. Depending on the maneuvering altitude of adversary target, two channel state information (CSI) models are considered, with the aim of maximizing the covert transmission rate by jointly designing the radar waveform and communication transmit beamforming vector based on the constructed channels. For perfect CSI under the free-space propagation model, by decoupling the joint design, we propose an efficient algorithm to guarantee that the target cannot detect the transmission. For imperfect CSI due to the multi-path components, a robust joint transmission scheme is proposed based on the property of the Kullback-Leibler divergence. The convergence behaviour, tracking MSE, false alarm and missed detection probabilities, and covert transmission rate are evaluated. Simulation results show that the proposed algorithms achieve accurate tracking. For both channel models, the proposed sensing-assisted covert transmission design is able to guarantee the covertness, and significantly outperforms the conventional schemes.

Published
2024

280. Meta Clothing Status Calibration for Long-Term Person Re-Identification.

Author

Huang, Y, Wu, Q, Zhang, Z, Shan, C, Zhong, Y, Wang, L, Huang, Y, Wu, Q, Zhang, Z, Shan, C, Zhong, Y, and Wang, L

Abstract

Recent studies have seen significant advancements in the field of long-term person re-identification (LT-reID) through the use of clothing-irrelevant or insensitive features. This work takes the field a step further by addressing a previously unexplored issue, the Clothing Status Distribution Shift (CSDS). CSDS refers to the differing ratios of samples with clothing changes to those without clothing changes between the training and test sets, leading to a decline in LT-reID performance. We establish a connection between the performance of LT-reID and CSDS, and argue that addressing CSDS can improve LT-reID performance. To that end, we propose a novel framework called Meta Clothing Status Calibration (MCSC), which uses meta-learning to optimize the LT-reID model. Specifically, MCSC simulates CSDS between meta-train and meta-test with meta-optimization objectives, optimizing the LT-reID model and making it robust to CSDS. This framework is designed to prevent overfitting and improve the generalization ability of the LT-reID model in the presence of CSDS. Comprehensive evaluations on seven datasets demonstrate that the proposed MCSC framework effectively handles CSDS and improves current state-of-the-art LT-reID methods on several LT-reID benchmarks.

Published
2024

281. Improving Consistency of Proxy-Level Contrastive Learning for Unsupervised Person Re-Identification

Author

Liu, Y, Qi, M, Zhang, Y, Wu, Q, Wu, J, Zhuang, S, Liu, Y, Qi, M, Zhang, Y, Wu, Q, Wu, J, and Zhuang, S

Abstract

Recently, contrastive learning-based unsupervised person re-identification (Re-ID) methods have garnered significant attention due to their effectiveness. These methods rely on predicted pseudo-labels to construct contrastive pairs, optimizing the network gradually. Some methods also utilize camera labels to explore intra-camera and inter-camera contrastive relations, achieving state-of-the-art results. However, these methods fail to address the issue of inconsistency in proxy-level contrastive learning, which arises from variations in the distribution of instances belonging to the same proxy. Specifically, they are sensitive to the distribution of instances in a mini-batch used for contrastive pair construction, and uncertainty or noise in the data distribution can lead to turbulence in the contrastive loss, degrading the effectiveness of contrastive learning. In this work, we first propose a dual-branch contrastive learning (DBCL) framework. The framework comprises a dual-branch structure with an identity discrimination branch and a camera view awareness branch. These branches are mutually trained to produce a jointly optimized model with both high person identification accuracy and cross-camera robustness. Moreover, to mitigate the proxy-level contrastive inconsistency issue in the camera view awareness branch, we design intra-camera and inter-camera consistent contrastive losses. Our DBCL has been extensively evaluated on several person Re-ID datasets and has demonstrated superior performance compared to state-of-the-art methods. Notably, on the challenging MSMT17 dataset with complex scenes, our method achieved an mAP of 45.3% and Rank-1 accuracy of 75.3%. 1556-6021

Published
2024

282. GaitDAN: Cross-view Gait Recognition via Adversarial Domain Adaptation

Author

Huang, T, Ben, X, Gong, C, Xu, W, Wu, Q, Zhou, H, Huang, T, Ben, X, Gong, C, Xu, W, Wu, Q, and Zhou, H

Abstract

View change causes significant differences in the gait appearance. Consequently, recognizing gait in cross-view scenarios is highly challenging. Most recent approaches either convert the gait from the original view to the target view before recognition is carried out or extract the gait feature irrelevant to the camera view through either brute force learning or decouple learning. However, these approaches have many constraints, such as the difficulty of handling unknown camera views. This work treats the view-change issue as a domain-change issue and proposes to tackle this problem through adversarial domain adaptation. This way, gait information from different views is regarded as the data from different sub-domains. The proposed approach focuses on adapting the gait feature differences caused by such sub-domain change and, at the same time, maintaining sufficient discriminability across the different people. For this purpose, a Hierarchical Feature Aggregation (HFA) strategy is proposed for discriminative feature extraction. By incorporating HFA, the feature extractor can well aggregate the spatial-temporal feature across the various stages of the network and thereby comprehensive gait features can be obtained. Then, an Adversarial View-change Elimination (AVE) module equipped with a set of explicit models for recognizing the different gait viewpoints is proposed. Through the adversarial learning process, AVE would not be able to identify the gait viewpoint in the end, given the gait features generated by the feature extractor. That is, the adversarial domain adaptation mitigates the view change factor, and discriminative gait features that are compatible with all sub-domains are effectively extracted. Extensive experiments on three of the most popular public datasets, CASIA-B, OULP, and OUMVLP richly demonstrate the effectiveness of our approach.

Published
2024

283. Real-time monitoring for the next core-collapse supernova in JUNO

Author

Abusleme, A, Adam, T, Ahmad, S, Ahmed, R, Aiello, S, Akram, M, Aleem, A, An, F, An, Q, Andronico, G, Anfimov, N, Antonelli, V, Antoshkina, T, Asavapibhop, B, de André, J, Auguste, D, Bai, W, Balashov, N, Baldini, W, Barresi, A, Basilico, D, Baussan, E, Bellato, M, Beretta, M, Bergnoli, A, Bick, D, Bieger, L, Biktemerova, S, Birkenfeld, T, Morton-Blake, I, Blum, D, Blyth, S, Bolshakova, A, Bongrand, M, Bordereau, C, Breton, D, Brigatti, A, Brugnera, R, Bruno, R, Budano, A, Busto, J, Cabrera, A, Caccianiga, B, Cai, H, Cai, X, Cai, Y, Cai, Z, Callier, S, Cammi, A, Campeny, A, Cao, C, Cao, G, Cao, J, Caruso, R, Cerna, C, Cerrone, V, Chan, C, Chang, J, Chang, Y, Chatrabhuti, A, Chen, C, Chen, G, Chen, P, Chen, S, Chen, Y, Chen, Z, Cheng, J, Cheng, Y, Chepurnov, A, Chetverikov, A, Chiesa, D, Chimenti, P, Chin, Y, Chu, Z, Chukanov, A, Claverie, G, Clementi, C, Clerbaux, B, Molla, M, Lorenzo, S, Coppi, A, Corti, D, Csakli, S, Corso, F, Dalager, O, Datta, J, Taille, C, Deng, Z, Ding, X, Ding, Y, Dirgantara, B, Dittrich, C, Dmitrievsky, S, Dohnal, T, Dolzhikov, D, Donchenko, G, Dong, J, Doroshkevich, E, Dou, W, Dracos, M, Druillole, F, Du, R, Du, S, Dugas, K, Dusini, S, Duyang, H, Eck, J, Enqvist, T, Fabbri, A, Fahrendholz, U, Fan, L, Fang, J, Fang, W, Fargetta, M, Fedoseev, D, Fei, Z, Feng, L, Feng, Q, Ferraro, F, Fournier, A, Gan, H, Gao, F, Garfagnini, A, Gavrikov, A, Giammarchi, M, Giudice, N, Gonchar, M, Gong, G, Gong, H, Gornushkin, Y, Göttel, A, Grassi, M, Gromov, M, Gromov, V, Gu, M, Gu, X, Gu, Y, Guan, M, Guan, Y, Guardone, N, Guo, C, Guo, W, Guo, X, Hagner, C, Han, R, Han, Y, He, M, He, W, Heinz, T, Hellmuth, P, Heng, Y, Herrera, R, Hor, Y, Hou, S, Hsiung, Y, Hu, B, Hu, H, Hu, J, Hu, S, Hu, T, Hu, Y, Hu, Z, Huang, G, Huang, H, Huang, J, Huang, K, Huang, W, Huang, X, Huang, Y, Hui, J, Huo, L, Huo, W, Huss, C, Hussain, S, Imbert, L, Ioannisian, A, Isocrate, R, Jafar, A, Jelmini, B, Jeria, I, Ji, X, Jia, H, Jia, J, Jian, S, Jiang, C, Jiang, D, Jiang, W, Jiang, X, Jing, X, Jollet, C, Kampmann, P, Kang, L, Karaparambil, R, Kazarian, N, Khan, A, Khatun, A, Khosonthongkee, K, Korablev, D, Kouzakov, K, Krasnoperov, A, Kuleshov, S, Kutovskiy, N, Labit, L, Lachenmaier, T, Landini, C, Leblanc, S, Lebrin, V, Lefevre, F, Lei, R, Leitner, R, Leung, J, Li, D, Li, F, Li, G, Li, H, Li, J, Li, M, Li, N, Li, Q, Li, R, Li, S, Li, T, Li, W, Li, X, Li, Y, Li, Z, Liang, H, Liao, J, Limphirat, A, Lin, G, Lin, S, Lin, T, Ling, J, Ling, X, Lippi, I, Liu, C, Liu, F, Liu, H, Liu, J, Liu, M, Liu, Q, Liu, R, Liu, S, Liu, X, Liu, Y, Liu, Z, Lokhov, A, Lombardi, P, Lombardo, C, Loo, K, Lu, C, Lu, H, Lu, J, Lu, P, Lu, S, Lu, X, Lubsandorzhiev, B, Lubsandorzhiev, S, Ludhova, L, Lukanov, A, Luo, D, Luo, F, Luo, G, Luo, J, Luo, S, Luo, W, Luo, X, Lyashuk, V, Ma, B, Ma, Q, Ma, S, Ma, X, Maalmi, J, Magoni, M, Mai, J, Malyshkin, Y, Mandujano, R, Mantovani, F, Mao, X, Mao, Y, Mari, S, Marini, F, Martini, A, Mayer, M, Mayilyan, D, Mednieks, I, Meng, Y, Meraviglia, A, Meregaglia, A, Meroni, E, Meyhöfer, D, Miramonti, L, Mohan, N, Montuschi, M, Müller, A, Nastasi, M, Naumov, D, Naumova, E, Navas-Nicolas, D, Nemchenok, I, Thi, M, Nikolaev, A, Ning, F, Ning, Z, Nunokawa, H, Oberauer, L, Ochoa-Ricoux, J, Olshevskiy, A, Orestano, D, Ortica, F, Othegraven, R, Paoloni, A, Parmeggiano, S, Pei, Y, Pelicci, L, Peng, A, Peng, H, Peng, Y, Peng, Z, Perrot, F, Petitjean, P, Petrucci, F, Pilarczyk, O, Rico, L, Popov, A, Poussot, P, Previtali, E, Qi, F, Qi, M, Qi, X, Qian, S, Qian, X, Qian, Z, Qiao, H, Qin, Z, Qiu, S, Qu, M, Qu, Z, Ranucci, G, Rasheed, R, Re, A, Rebii, A, Redchuk, M, Ren, B, Ren, J, Ricci, B, Rientong, K, Rifai, M, Roche, M, Rodphai, N, Romani, A, Roskovec, B, Ruan, X, Rybnikov, A, Sadovsky, A, Saggese, P, Sandanayake, D, Sangka, A, Sava, G, Sawangwit, U, Schever, M, Schwab, C, Schweizer, K, Selyunin, A, Serafini, A, Settimo, M, Sharov, V, Shaydurova, A, Shi, J, Shi, Y, Shutov, V, Sidorenkov, A, Šimkovic, F, Singhal, A, Sirignano, C, Siripak, J, Sisti, M, Smirnov, M, Smirnov, O, Sogo-Bezerra, T, Sokolov, S, Songwadhana, J, Soonthornthum, B, Sotnikov, A, Šrámek, O, Sreethawong, W, Stahl, A, Stanco, L, Stankevich, K, Steiger, H, Steinmann, J, Sterr, T, Stock, M, Strati, V, Studenikin, A, Su, A, Su, J, Sun, S, Sun, X, Sun, Y, Sun, Z, Suwonjandee, N, Szelezniak, M, Takenaka, A, Tang, J, Tang, Q, Tang, X, Hariharan, V, Theisen, E, Tietzsch, A, Tkachev, I, Tmej, T, Torri, M, Tortorici, F, Treskov, K, Triossi, A, Triozzi, R, Trzaska, W, Tung, Y, Tuve, C, Ushakov, N, Vedin, V, Venettacci, C, Verde, G, Vialkov, M, Viaud, B, Vollbrecht, C, von Sturm, K, Vorobel, V, Voronin, D, Votano, L, Walker, P, Wang, C, Wang, E, Wang, G, Wang, J, Wang, L, Wang, M, Wang, R, Wang, S, Wang, W, Wang, X, Wang, Y, Wang, Z, Watcharangkool, A, Wei, W, Wei, Y, Wen, K, Wen, L, Weng, J, Wiebusch, C, Wirth, R, Wonsak, B, Wu, D, Wu, Q, Wu, Y, Wu, Z, Wurm, M, Wurtz, J, Wysotzki, C, Xi, Y, Xia, D, Xiao, F, Xiao, X, Xie, X, Xie, Y, Xie, Z, Xin, Z, Xing, Z, Xu, B, Xu, C, Xu, D, Xu, F, Xu, H, Xu, J, Xu, M, Xu, X, Xu, Y, Yan, B, Yan, Q, Yan, T, Yan, X, Yan, Y, Yang, C, Yang, J, Yang, L, Yang, X, Yang, Y, Yao, H, Ye, J, Ye, M, Ye, Z, Yermia, F, You, Z, Yu, B, Yu, C, Yu, G, Yu, H, Yu, M, Yu, X, Yu, Z, Yuan, C, Yuan, Y, Yuan, Z, Yue, B, Zafar, N, Zavadskyi, V, Zeng, F, Zeng, S, Zeng, T, Zeng, Y, Zhan, L, Zhang, A, Zhang, B, Zhang, F, Zhang, H, Zhang, J, Zhang, L, Zhang, M, Zhang, P, Zhang, Q, Zhang, S, Zhang, T, Zhang, X, Zhang, Y, Zhang, Z, Zhao, J, Zhao, R, Zhao, S, Zheng, D, Zheng, H, Zheng, Y, Zhong, W, Zhou, J, Zhou, L, Zhou, N, Zhou, S, Zhou, T, Zhou, X, Zhu, J, Zhu, K, Zhu, Z, Zhuang, B, Zhuang, H, Zong, L, Zou, J, Züfle, J, Null, N, Abusleme, Angel, Adam, Thomas, Ahmad, Shakeel, Ahmed, Rizwan, Aiello, Sebastiano, Akram, Muhammad, Aleem, Abid, An, Fengpeng, An, Qi, Andronico, Giuseppe, Anfimov, Nikolay, Antonelli, Vito, Antoshkina, Tatiana, Asavapibhop, Burin, de André, João Pedro Athayde Marcondes, Auguste, Didier, Bai, Weidong, Balashov, Nikita, Baldini, Wander, Barresi, Andrea, Basilico, Davide, Baussan, Eric, Bellato, Marco, Beretta, Marco, Bergnoli, Antonio, Bick, Daniel, Bieger, Lukas, Biktemerova, Svetlana, Birkenfeld, Thilo, Morton-Blake, Iwan, Blum, David, Blyth, Simon, Bolshakova, Anastasia, Bongrand, Mathieu, Bordereau, Clément, Breton, Dominique, Brigatti, Augusto, Brugnera, Riccardo, Bruno, Riccardo, Budano, Antonio, Busto, Jose, Cabrera, Anatael, Caccianiga, Barbara, Cai, Hao, Cai, Xiao, Cai, Yanke, Cai, Zhiyan, Callier, Stéphane, Cammi, Antonio, Campeny, Agustin, Cao, Chuanya, Cao, Guofu, Cao, Jun, Caruso, Rossella, Cerna, Cédric, Cerrone, Vanessa, Chan, Chi, Chang, Jinfan, Chang, Yun, Chatrabhuti, Auttakit, Chen, Chao, Chen, Guoming, Chen, Pingping, Chen, Shaomin, Chen, Yixue, Chen, Yu, Chen, Zhangming, Chen, Zhiyuan, Chen, Zikang, Cheng, Jie, Cheng, Yaping, Cheng, Yu Chin, Chepurnov, Alexander, Chetverikov, Alexey, Chiesa, Davide, Chimenti, Pietro, Chin, Yen-Ting, Chu, Ziliang, Chukanov, Artem, Claverie, Gérard, Clementi, Catia, Clerbaux, Barbara, Molla, Marta Colomer, Lorenzo, Selma Conforti Di, Coppi, Alberto, Corti, Daniele, Csakli, Simon, Corso, Flavio Dal, Dalager, Olivia, Datta, Jaydeep, Taille, Christophe De La, Deng, Zhi, Deng, Ziyan, Ding, Xiaoyu, Ding, Xuefeng, Ding, Yayun, Dirgantara, Bayu, Dittrich, Carsten, Dmitrievsky, Sergey, Dohnal, Tadeas, Dolzhikov, Dmitry, Donchenko, Georgy, Dong, Jianmeng, Doroshkevich, Evgeny, Dou, Wei, Dracos, Marcos, Druillole, Frédéric, Du, Ran, Du, Shuxian, Dugas, Katherine, Dusini, Stefano, Duyang, Hongyue, Eck, Jessica, Enqvist, Timo, Fabbri, Andrea, Fahrendholz, Ulrike, Fan, Lei, Fang, Jian, Fang, Wenxing, Fargetta, Marco, Fedoseev, Dmitry, Fei, Zhengyong, Feng, Li-Cheng, Feng, Qichun, Ferraro, Federico, Fournier, Amélie, Gan, Haonan, Gao, Feng, Garfagnini, Alberto, Gavrikov, Arsenii, Giammarchi, Marco, Giudice, Nunzio, Gonchar, Maxim, Gong, Guanghua, Gong, Hui, Gornushkin, Yuri, Göttel, Alexandre, Grassi, Marco, Gromov, Maxim, Gromov, Vasily, Gu, Minghao, Gu, Xiaofei, Gu, Yu, Guan, Mengyun, Guan, Yuduo, Guardone, Nunzio, Guo, Cong, Guo, Wanlei, Guo, Xinheng, Hagner, Caren, Han, Ran, Han, Yang, He, Miao, He, Wei, Heinz, Tobias, Hellmuth, Patrick, Heng, Yuekun, Herrera, Rafael, Hor, YuenKeung, Hou, Shaojing, Hsiung, Yee, Hu, Bei-Zhen, Hu, Hang, Hu, Jianrun, Hu, Jun, Hu, Shouyang, Hu, Tao, Hu, Yuxiang, Hu, Zhuojun, Huang, Guihong, Huang, Hanxiong, Huang, Jinhao, Huang, Junting, Huang, Kaixuan, Huang, Wenhao, Huang, Xin, Huang, Xingtao, Huang, Yongbo, Hui, Jiaqi, Huo, Lei, Huo, Wenju, Huss, Cédric, Hussain, Safeer, Imbert, Leonard, Ioannisian, Ara, Isocrate, Roberto, Jafar, Arshak, Jelmini, Beatrice, Jeria, Ignacio, Ji, Xiaolu, Jia, Huihui, Jia, Junji, Jian, Siyu, Jiang, Cailian, Jiang, Di, Jiang, Wei, Jiang, Xiaoshan, Jing, Xiaoping, Jollet, Cécile, Kampmann, Philipp, Kang, Li, Karaparambil, Rebin, Kazarian, Narine, Khan, Ali, Khatun, Amina, Khosonthongkee, Khanchai, Korablev, Denis, Kouzakov, Konstantin, Krasnoperov, Alexey, Kuleshov, Sergey, Kutovskiy, Nikolay, Labit, Loïc, Lachenmaier, Tobias, Landini, Cecilia, Leblanc, Sébastien, Lebrin, Victor, Lefevre, Frederic, Lei, Ruiting, Leitner, Rupert, Leung, Jason, Li, Demin, Li, Fei, Li, Fule, Li, Gaosong, Li, Huiling, Li, Jiajun, Li, Mengzhao, Li, Min, Li, Nan, Li, Qingjiang, Li, Ruhui, Li, Rui, Li, Shanfeng, Li, Tao, Li, Teng, Li, Weidong, Li, Weiguo, Li, Xiaomei, Li, Xiaonan, Li, Xinglong, Li, Yi, Li, Yichen, Li, Yufeng, Li, Zhaohan, Li, Zhibing, Li, Ziyuan, Li, Zonghai, Liang, Hao, Liao, Jiajun, Limphirat, Ayut, Lin, Guey-Lin, Lin, Shengxin, Lin, Tao, Ling, Jiajie, Ling, Xin, Lippi, Ivano, Liu, Caimei, Liu, Fang, Liu, Fengcheng, Liu, Haidong, Liu, Haotian, Liu, Hongbang, Liu, Hongjuan, Liu, Hongtao, Liu, Hui, Liu, Jianglai, Liu, Jiaxi, Liu, Jinchang, Liu, Min, Liu, Qian, Liu, Qin, Liu, Runxuan, Liu, Shenghui, Liu, Shubin, Liu, Shulin, Liu, Xiaowei, Liu, Xiwen, Liu, Xuewei, Liu, Yankai, Liu, Zhen, Lokhov, Alexey, Lombardi, Paolo, Lombardo, Claudio, Loo, Kai, Lu, Chuan, Lu, Haoqi, Lu, Jingbin, Lu, Junguang, Lu, Peizhi, Lu, Shuxiang, Lu, Xianguo, Lubsandorzhiev, Bayarto, Lubsandorzhiev, Sultim, Ludhova, Livia, Lukanov, Arslan, Luo, Daibin, Luo, Fengjiao, Luo, Guang, Luo, Jianyi, Luo, Shu, Luo, Wuming, Luo, Xiaojie, Lyashuk, Vladimir, Ma, Bangzheng, Ma, Bing, Ma, Qiumei, Ma, Si, Ma, Xiaoyan, Ma, Xubo, Maalmi, Jihane, Magoni, Marco, Mai, Jingyu, Malyshkin, Yury, Mandujano, Roberto Carlos, Mantovani, Fabio, Mao, Xin, Mao, Yajun, Mari, Stefano M., Marini, Filippo, Martini, Agnese, Mayer, Matthias, Mayilyan, Davit, Mednieks, Ints, Meng, Yue, Meraviglia, Anita, Meregaglia, Anselmo, Meroni, Emanuela, Meyhöfer, David, Miramonti, Lino, Mohan, Nikhil, Montuschi, Michele, Müller, Axel, Nastasi, Massimiliano, Naumov, Dmitry V., Naumova, Elena, Navas-Nicolas, Diana, Nemchenok, Igor, Thi, Minh Thuan Nguyen, Nikolaev, Alexey, Ning, Feipeng, Ning, Zhe, Nunokawa, Hiroshi, Oberauer, Lothar, Ochoa-Ricoux, Juan Pedro, Olshevskiy, Alexander, Orestano, Domizia, Ortica, Fausto, Othegraven, Rainer, Paoloni, Alessandro, Parmeggiano, Sergio, Pei, Yatian, Pelicci, Luca, Peng, Anguo, Peng, Haiping, Peng, Yu, Peng, Zhaoyuan, Perrot, Frédéric, Petitjean, Pierre-Alexandre, Petrucci, Fabrizio, Pilarczyk, Oliver, Rico, Luis Felipe Piñeres, Popov, Artyom, Poussot, Pascal, Previtali, Ezio, Qi, Fazhi, Qi, Ming, Qi, Xiaohui, Qian, Sen, Qian, Xiaohui, Qian, Zhen, Qiao, Hao, Qin, Zhonghua, Qiu, Shoukang, Qu, Manhao, Qu, Zhenning, Ranucci, Gioacchino, Rasheed, Reem, Re, Alessandra, Rebii, Abdel, Redchuk, Mariia, Ren, Bin, Ren, Jie, Ricci, Barbara, Rientong, Komkrit, Rifai, Mariam, Roche, Mathieu, Rodphai, Narongkiat, Romani, Aldo, Roskovec, Bedřich, Ruan, Xichao, Rybnikov, Arseniy, Sadovsky, Andrey, Saggese, Paolo, Sandanayake, Deshan, Sangka, Anut, Sava, Giuseppe, Sawangwit, Utane, Schever, Michaela, Schwab, Cédric, Schweizer, Konstantin, Selyunin, Alexandr, Serafini, Andrea, Settimo, Mariangela, Sharov, Vladislav, Shaydurova, Arina, Shi, Jingyan, Shi, Yanan, Shutov, Vitaly, Sidorenkov, Andrey, Šimkovic, Fedor, Singhal, Apeksha, Sirignano, Chiara, Siripak, Jaruchit, Sisti, Monica, Smirnov, Mikhail, Smirnov, Oleg, Sogo-Bezerra, Thiago, Sokolov, Sergey, Songwadhana, Julanan, Soonthornthum, Boonrucksar, Sotnikov, Albert, Šrámek, Ondřej, Sreethawong, Warintorn, Stahl, Achim, Stanco, Luca, Stankevich, Konstantin, Steiger, Hans, Steinmann, Jochen, Sterr, Tobias, Stock, Matthias Raphael, Strati, Virginia, Studenikin, Alexander, Su, Aoqi, Su, Jun, Sun, Shifeng, Sun, Xilei, Sun, Yongjie, Sun, Yongzhao, Sun, Zhengyang, Suwonjandee, Narumon, Szelezniak, Michal, Takenaka, Akira, Tang, Jian, Tang, Qiang, Tang, Quan, Tang, Xiao, Hariharan, Vidhya Thara, Theisen, Eric, Tietzsch, Alexander, Tkachev, Igor, Tmej, Tomas, Torri, Marco Danilo Claudio, Tortorici, Francesco, Treskov, Konstantin, Triossi, Andrea, Triozzi, Riccardo, Trzaska, Wladyslaw, Tung, Yu-Chen, Tuve, Cristina, Ushakov, Nikita, Vedin, Vadim, Venettacci, Carlo, Verde, Giuseppe, Vialkov, Maxim, Viaud, Benoit, Vollbrecht, Cornelius Moritz, von Sturm, Katharina, Vorobel, Vit, Voronin, Dmitriy, Votano, Lucia, Walker, Pablo, Wang, Caishen, Wang, Chung-Hsiang, Wang, En, Wang, Guoli, Wang, Jian, Wang, Jun, Wang, Li, Wang, Lu, Wang, Meng, Wang, Ruiguang, Wang, Siguang, Wang, Wei, Wang, Wenshuai, Wang, Xi, Wang, Xiangyue, Wang, Yangfu, Wang, Yaoguang, Wang, Yi, Wang, Yifang, Wang, Yuanqing, Wang, Yuyi, Wang, Zhe, Wang, Zheng, Wang, Zhimin, Watcharangkool, Apimook, Wei, Wei, Wei, Wenlu, Wei, Yadong, Wei, Yuehuan, Wen, Kaile, Wen, Liangjian, Weng, Jun, Wiebusch, Christopher, Wirth, Rosmarie, Wonsak, Bjoern, Wu, Diru, Wu, Qun, Wu, Yiyang, Wu, Zhi, Wurm, Michael, Wurtz, Jacques, Wysotzki, Christian, Xi, Yufei, Xia, Dongmei, Xiao, Fei, Xiao, Xiang, Xie, Xiaochuan, Xie, Yuguang, Xie, Zhangquan, Xin, Zhao, Xing, Zhizhong, Xu, Benda, Xu, Cheng, Xu, Donglian, Xu, Fanrong, Xu, Hangkun, Xu, Jilei, Xu, Jing, Xu, Meihang, Xu, Xunjie, Xu, Yin, Xu, Yu, Yan, Baojun, Yan, Qiyu, Yan, Taylor, Yan, Xiongbo, Yan, Yupeng, Yang, Changgen, Yang, Chengfeng, Yang, Jie, Yang, Lei, Yang, Xiaoyu, Yang, Yifan, Yao, Haifeng, Ye, Jiaxuan, Ye, Mei, Ye, Ziping, Yermia, Frédéric, You, Zhengyun, Yu, Boxiang, Yu, Chiye, Yu, Chunxu, Yu, Guojun, Yu, Hongzhao, Yu, Miao, Yu, Xianghui, Yu, Zeyuan, Yu, Zezhong, Yuan, Cenxi, Yuan, Chengzhuo, Yuan, Ying, Yuan, Zhenxiong, Yue, Baobiao, Zafar, Noman, Zavadskyi, Vitalii, Zeng, Fanrui, Zeng, Shan, Zeng, Tingxuan, Zeng, Yuda, Zhan, Liang, Zhang, Aiqiang, Zhang, Bin, Zhang, Binting, Zhang, Feiyang, Zhang, Haosen, Zhang, Honghao, Zhang, Jialiang, Zhang, Jiawen, Zhang, Jie, Zhang, Jingbo, Zhang, Jinnan, ZHANG, Lei, Zhang, Mohan, Zhang, Peng, Zhang, Ping, Zhang, Qingmin, Zhang, Shiqi, Zhang, Shu, Zhang, Shuihan, Zhang, Siyuan, Zhang, Tao, Zhang, Xiaomei, Zhang, Xin, Zhang, Xuantong, Zhang, Yinhong, Zhang, Yiyu, Zhang, Yongpeng, Zhang, Yu, Zhang, Yuanyuan, Zhang, Yumei, Zhang, Zhenyu, Zhang, Zhijian, Zhao, Jie, Zhao, Rong, Zhao, Runze, Zhao, Shujun, Zheng, Dongqin, Zheng, Hua, Zheng, Yangheng, Zhong, Weirong, Zhou, Jing, Zhou, Li, Zhou, Nan, Zhou, Shun, Zhou, Tong, Zhou, Xiang, Zhu, Jingsen, Zhu, Kangfu, Zhu, Kejun, Zhu, Zhihang, Zhuang, Bo, Zhuang, Honglin, Zong, Liang, Zou, Jiaheng, Züfle, Jan, null, null, Abusleme, A, Adam, T, Ahmad, S, Ahmed, R, Aiello, S, Akram, M, Aleem, A, An, F, An, Q, Andronico, G, Anfimov, N, Antonelli, V, Antoshkina, T, Asavapibhop, B, de André, J, Auguste, D, Bai, W, Balashov, N, Baldini, W, Barresi, A, Basilico, D, Baussan, E, Bellato, M, Beretta, M, Bergnoli, A, Bick, D, Bieger, L, Biktemerova, S, Birkenfeld, T, Morton-Blake, I, Blum, D, Blyth, S, Bolshakova, A, Bongrand, M, Bordereau, C, Breton, D, Brigatti, A, Brugnera, R, Bruno, R, Budano, A, Busto, J, Cabrera, A, Caccianiga, B, Cai, H, Cai, X, Cai, Y, Cai, Z, Callier, S, Cammi, A, Campeny, A, Cao, C, Cao, G, Cao, J, Caruso, R, Cerna, C, Cerrone, V, Chan, C, Chang, J, Chang, Y, Chatrabhuti, A, Chen, C, Chen, G, Chen, P, Chen, S, Chen, Y, Chen, Z, 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Federico, Fournier, Amélie, Gan, Haonan, Gao, Feng, Garfagnini, Alberto, Gavrikov, Arsenii, Giammarchi, Marco, Giudice, Nunzio, Gonchar, Maxim, Gong, Guanghua, Gong, Hui, Gornushkin, Yuri, Göttel, Alexandre, Grassi, Marco, Gromov, Maxim, Gromov, Vasily, Gu, Minghao, Gu, Xiaofei, Gu, Yu, Guan, Mengyun, Guan, Yuduo, Guardone, Nunzio, Guo, Cong, Guo, Wanlei, Guo, Xinheng, Hagner, Caren, Han, Ran, Han, Yang, He, Miao, He, Wei, Heinz, Tobias, Hellmuth, Patrick, Heng, Yuekun, Herrera, Rafael, Hor, YuenKeung, Hou, Shaojing, Hsiung, Yee, Hu, Bei-Zhen, Hu, Hang, Hu, Jianrun, Hu, Jun, Hu, Shouyang, Hu, Tao, Hu, Yuxiang, Hu, Zhuojun, Huang, Guihong, Huang, Hanxiong, Huang, Jinhao, Huang, Junting, Huang, Kaixuan, Huang, Wenhao, Huang, Xin, Huang, Xingtao, Huang, Yongbo, Hui, Jiaqi, Huo, Lei, Huo, Wenju, Huss, Cédric, Hussain, Safeer, Imbert, Leonard, Ioannisian, Ara, Isocrate, Roberto, Jafar, Arshak, Jelmini, Beatrice, Jeria, Ignacio, Ji, Xiaolu, Jia, Huihui, Jia, Junji, Jian, Siyu, Jiang, Cailian, Jiang, Di, Jiang, Wei, Jiang, Xiaoshan, Jing, Xiaoping, Jollet, Cécile, Kampmann, Philipp, Kang, Li, Karaparambil, Rebin, Kazarian, Narine, Khan, Ali, Khatun, Amina, Khosonthongkee, Khanchai, Korablev, Denis, Kouzakov, Konstantin, Krasnoperov, Alexey, Kuleshov, Sergey, Kutovskiy, Nikolay, Labit, Loïc, Lachenmaier, Tobias, Landini, Cecilia, Leblanc, Sébastien, Lebrin, Victor, Lefevre, Frederic, Lei, Ruiting, Leitner, Rupert, Leung, Jason, Li, Demin, Li, Fei, Li, Fule, Li, Gaosong, Li, Huiling, Li, Jiajun, Li, Mengzhao, Li, Min, Li, Nan, Li, Qingjiang, Li, Ruhui, Li, Rui, Li, Shanfeng, Li, Tao, Li, Teng, Li, Weidong, Li, Weiguo, Li, Xiaomei, Li, Xiaonan, Li, Xinglong, Li, Yi, Li, Yichen, Li, Yufeng, Li, Zhaohan, Li, Zhibing, Li, Ziyuan, Li, Zonghai, Liang, Hao, Liao, Jiajun, Limphirat, Ayut, Lin, Guey-Lin, Lin, Shengxin, Lin, Tao, Ling, Jiajie, Ling, Xin, Lippi, Ivano, Liu, Caimei, Liu, Fang, Liu, Fengcheng, Liu, Haidong, Liu, Haotian, Liu, Hongbang, Liu, Hongjuan, Liu, Hongtao, Liu, Hui, Liu, Jianglai, Liu, Jiaxi, Liu, Jinchang, Liu, Min, Liu, Qian, Liu, Qin, Liu, Runxuan, Liu, Shenghui, Liu, Shubin, Liu, Shulin, Liu, Xiaowei, Liu, Xiwen, Liu, Xuewei, Liu, Yankai, Liu, Zhen, Lokhov, Alexey, Lombardi, Paolo, Lombardo, Claudio, Loo, Kai, Lu, Chuan, Lu, Haoqi, Lu, Jingbin, Lu, Junguang, Lu, Peizhi, Lu, Shuxiang, Lu, Xianguo, Lubsandorzhiev, Bayarto, Lubsandorzhiev, Sultim, Ludhova, Livia, Lukanov, Arslan, Luo, Daibin, Luo, Fengjiao, Luo, Guang, Luo, Jianyi, Luo, Shu, Luo, Wuming, Luo, Xiaojie, Lyashuk, Vladimir, Ma, Bangzheng, Ma, Bing, Ma, Qiumei, Ma, Si, Ma, Xiaoyan, Ma, Xubo, Maalmi, Jihane, Magoni, Marco, Mai, Jingyu, Malyshkin, Yury, Mandujano, Roberto Carlos, Mantovani, Fabio, Mao, Xin, Mao, Yajun, Mari, Stefano M., Marini, Filippo, Martini, Agnese, Mayer, Matthias, Mayilyan, Davit, Mednieks, Ints, Meng, Yue, Meraviglia, Anita, Meregaglia, Anselmo, Meroni, Emanuela, Meyhöfer, David, Miramonti, Lino, Mohan, Nikhil, Montuschi, Michele, Müller, Axel, Nastasi, Massimiliano, Naumov, Dmitry V., Naumova, Elena, Navas-Nicolas, Diana, Nemchenok, Igor, Thi, Minh Thuan Nguyen, Nikolaev, Alexey, Ning, Feipeng, Ning, Zhe, Nunokawa, Hiroshi, Oberauer, Lothar, Ochoa-Ricoux, Juan Pedro, Olshevskiy, Alexander, Orestano, Domizia, Ortica, Fausto, Othegraven, Rainer, Paoloni, Alessandro, Parmeggiano, Sergio, Pei, Yatian, Pelicci, Luca, Peng, Anguo, Peng, Haiping, Peng, Yu, Peng, Zhaoyuan, Perrot, Frédéric, Petitjean, Pierre-Alexandre, Petrucci, Fabrizio, Pilarczyk, Oliver, Rico, Luis Felipe Piñeres, Popov, Artyom, Poussot, Pascal, Previtali, Ezio, Qi, Fazhi, Qi, Ming, Qi, Xiaohui, Qian, Sen, Qian, Xiaohui, Qian, Zhen, Qiao, Hao, Qin, Zhonghua, Qiu, Shoukang, Qu, Manhao, Qu, Zhenning, Ranucci, Gioacchino, Rasheed, Reem, Re, Alessandra, Rebii, Abdel, Redchuk, Mariia, Ren, Bin, Ren, Jie, Ricci, Barbara, Rientong, Komkrit, Rifai, Mariam, Roche, Mathieu, Rodphai, Narongkiat, Romani, Aldo, Roskovec, Bedřich, Ruan, Xichao, Rybnikov, Arseniy, Sadovsky, Andrey, Saggese, Paolo, Sandanayake, Deshan, Sangka, Anut, Sava, Giuseppe, Sawangwit, Utane, Schever, Michaela, Schwab, Cédric, Schweizer, Konstantin, Selyunin, Alexandr, Serafini, Andrea, Settimo, Mariangela, Sharov, Vladislav, Shaydurova, Arina, Shi, Jingyan, Shi, Yanan, Shutov, Vitaly, Sidorenkov, Andrey, Šimkovic, Fedor, Singhal, Apeksha, Sirignano, Chiara, Siripak, Jaruchit, Sisti, Monica, Smirnov, Mikhail, Smirnov, Oleg, Sogo-Bezerra, Thiago, Sokolov, Sergey, Songwadhana, Julanan, Soonthornthum, Boonrucksar, Sotnikov, Albert, Šrámek, Ondřej, Sreethawong, Warintorn, Stahl, Achim, Stanco, Luca, Stankevich, Konstantin, Steiger, Hans, Steinmann, Jochen, Sterr, Tobias, Stock, Matthias Raphael, Strati, Virginia, Studenikin, Alexander, Su, Aoqi, Su, Jun, Sun, Shifeng, Sun, Xilei, Sun, Yongjie, Sun, Yongzhao, Sun, Zhengyang, Suwonjandee, Narumon, Szelezniak, Michal, Takenaka, Akira, Tang, Jian, Tang, Qiang, Tang, Quan, Tang, Xiao, Hariharan, Vidhya Thara, Theisen, Eric, Tietzsch, Alexander, Tkachev, Igor, Tmej, Tomas, Torri, Marco Danilo Claudio, Tortorici, Francesco, Treskov, Konstantin, Triossi, Andrea, Triozzi, Riccardo, Trzaska, Wladyslaw, Tung, Yu-Chen, Tuve, Cristina, Ushakov, Nikita, Vedin, Vadim, Venettacci, Carlo, Verde, Giuseppe, Vialkov, Maxim, Viaud, Benoit, Vollbrecht, Cornelius Moritz, von Sturm, Katharina, Vorobel, Vit, Voronin, Dmitriy, Votano, Lucia, Walker, Pablo, Wang, Caishen, Wang, Chung-Hsiang, Wang, En, Wang, Guoli, Wang, Jian, Wang, Jun, Wang, Li, Wang, Lu, Wang, Meng, Wang, Ruiguang, Wang, Siguang, Wang, Wei, Wang, Wenshuai, Wang, Xi, Wang, Xiangyue, Wang, Yangfu, Wang, Yaoguang, Wang, Yi, Wang, Yifang, Wang, Yuanqing, Wang, Yuyi, Wang, Zhe, Wang, Zheng, Wang, Zhimin, Watcharangkool, Apimook, Wei, Wei, Wei, Wenlu, Wei, Yadong, Wei, Yuehuan, Wen, Kaile, Wen, Liangjian, Weng, Jun, Wiebusch, Christopher, Wirth, Rosmarie, Wonsak, Bjoern, Wu, Diru, Wu, Qun, Wu, Yiyang, Wu, Zhi, Wurm, Michael, Wurtz, Jacques, Wysotzki, Christian, Xi, Yufei, Xia, Dongmei, Xiao, Fei, Xiao, Xiang, Xie, Xiaochuan, Xie, Yuguang, Xie, Zhangquan, Xin, Zhao, Xing, Zhizhong, Xu, Benda, Xu, Cheng, Xu, Donglian, Xu, Fanrong, Xu, Hangkun, Xu, Jilei, Xu, Jing, Xu, Meihang, Xu, Xunjie, Xu, Yin, Xu, Yu, Yan, Baojun, Yan, Qiyu, Yan, Taylor, Yan, Xiongbo, Yan, Yupeng, Yang, Changgen, Yang, Chengfeng, Yang, Jie, Yang, Lei, Yang, Xiaoyu, Yang, Yifan, Yao, Haifeng, Ye, Jiaxuan, Ye, Mei, Ye, Ziping, Yermia, Frédéric, You, Zhengyun, Yu, Boxiang, Yu, Chiye, Yu, Chunxu, Yu, Guojun, Yu, Hongzhao, Yu, Miao, Yu, Xianghui, Yu, Zeyuan, Yu, Zezhong, Yuan, Cenxi, Yuan, Chengzhuo, Yuan, Ying, Yuan, Zhenxiong, Yue, Baobiao, Zafar, Noman, Zavadskyi, Vitalii, Zeng, Fanrui, Zeng, Shan, Zeng, Tingxuan, Zeng, Yuda, Zhan, Liang, Zhang, Aiqiang, Zhang, Bin, Zhang, Binting, Zhang, Feiyang, Zhang, Haosen, Zhang, Honghao, Zhang, Jialiang, Zhang, Jiawen, Zhang, Jie, Zhang, Jingbo, Zhang, Jinnan, ZHANG, Lei, Zhang, Mohan, Zhang, Peng, Zhang, Ping, Zhang, Qingmin, Zhang, Shiqi, Zhang, Shu, Zhang, Shuihan, Zhang, Siyuan, Zhang, Tao, Zhang, Xiaomei, Zhang, Xin, Zhang, Xuantong, Zhang, Yinhong, Zhang, Yiyu, Zhang, Yongpeng, Zhang, Yu, Zhang, Yuanyuan, Zhang, Yumei, Zhang, Zhenyu, Zhang, Zhijian, Zhao, Jie, Zhao, Rong, Zhao, Runze, Zhao, Shujun, Zheng, Dongqin, Zheng, Hua, Zheng, Yangheng, Zhong, Weirong, Zhou, Jing, Zhou, Li, Zhou, Nan, Zhou, Shun, Zhou, Tong, Zhou, Xiang, Zhu, Jingsen, Zhu, Kangfu, Zhu, Kejun, Zhu, Zhihang, Zhuang, Bo, Zhuang, Honglin, Zong, Liang, Zou, Jiaheng, Züfle, Jan, and null, null

Abstract

The core-collapse supernova (CCSN) is considered one of the most energetic astrophysical events in the universe. The early and prompt detection of neutrinos before (pre-SN) and during the supernova (SN) burst presents a unique opportunity for multi-messenger observations of CCSN events. In this study, we describe the monitoring concept and present the sensitivity of the system to pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton liquid scintillator detector currently under construction in South China. The real-time monitoring system is designed to ensure both prompt alert speed and comprehensive coverage of progenitor stars. It incorporates prompt monitors on the electronic board as well as online monitors at the data acquisition stage. Assuming a false alert rate of 1 per year, this monitoring system exhibits sensitivity to pre-SN neutrinos up to a distance of approximately 1.6 (0.9) kiloparsecs and SN neutrinos up to about 370 (360) kiloparsecs for a progenitor mass of 30 solar masses, considering both normal and inverted mass ordering scenarios. The pointing ability of the CCSN is evaluated by analyzing the accumulated event anisotropy of inverse beta decay interactions from pre-SN or SN neutrinos. This, along with the early alert, can play a crucial role in facilitating follow-up multi-messenger observations of the next galactic or nearby extragalactic CCSN.

Published
2024

284. Unveiling Pseudo-Inert Basal Plane for Electrocatalysis in 2D Semiconductors: Critical Role of Reversal-Activation Mechanism

Author

Zang, Y., Wu, Q., Wang, S., Huang, B., Dai, Y., (0000-0003-2379-6251) Heine, T., Ma, Y., Zang, Y., Wu, Q., Wang, S., Huang, B., Dai, Y., (0000-0003-2379-6251) Heine, T., and Ma, Y.

Abstract

Partially occupied orbitals play a pivotal role in enhancing the performance of electrocatalyst by facilitating electron acceptance and donation, thus enabling the activation of molecular bonds. According to this principle, the basal plane of most 2D semiconductors is inert for electrocatalysis because of the fully occupied orbitals at the surface. Here, taking monolayer CrX (X = P, As, Sb) and Cr2PY (Y = As, Sb) as examples and through first-principles calculations, it is revealed that even with fully occupied surface orbitals, the basal planes exhibit remarkable catalytic activity for the nitrogen oxide reduction reaction (NORR). This leads to the concept of the pseudo-inert electrocatalyst. The underlying physics behind such pseudo-inert character can be attributed to the reversal-activation mechanism: contrary to conventional expectations, the adsorbed NO molecule reversely triggers the activity of the inert basal plane first, and then the basal plane activates NO molecules, forming the intriguing “Reversal Activation-Transfer-Donation-Backdonation” process. This study further predicts that such pseudo-inert character can demonstrate many distinctive properties, for example, it can introduce a novel type of surface catalysis, one that selectively targets radicals possessing an inherent dipole moment such as NO. The explored phenomena and insights greatly enrich the realms of electrocatalysis and 2D materials.

Published
2024

285. Unraveling the influence of channel size and shape in 3D printed ceramic scaffolds on osteogenesis.

Author

Entezari, A, Wu, Q, Mirkhalaf, M, Lu, Z, Roohani, I, Li, Q, Dunstan, CR, Jiang, X, Zreiqat, H, Entezari, A, Wu, Q, Mirkhalaf, M, Lu, Z, Roohani, I, Li, Q, Dunstan, CR, Jiang, X, and Zreiqat, H

Abstract

Bone has the capacity to regenerate itself for relatively small defects; however, this regenerative capacity is diminished in critical-size bone defects. The development of synthetic materials has risen as a distinct strategy to address this challenge. Effective synthetic materials to have emerged in recent years are bioceramic implants, which are biocompatible and highly bioactive. Yet nothing suitable for the repair of large bone defects has made the transition from laboratory to clinic. The clinical success of bioceramics has been shown to depend not only on the scaffold's intrinsic material properties but also on its internal porous geometry. This study aimed to systematically explore the implications of varying channel size, shape, and curvature in tissue scaffolds on in vivo bone regeneration outcomes. 3D printed bioceramic scaffolds with varying channel sizes (0.3 mm to 1.5 mm), shapes (circular vs rectangular), and curvatures (concave vs convex) were implanted in rabbit femoral defects for 8 weeks, followed by histological evaluation. We demonstrated that circular channel sizes of around 0.9 mm diameter significantly enhanced bone formation, compared to channel with diameters of 0.3 mm and 1.5 mm. Interestingly, varying channel shapes (rectangular vs circular) had no significant effect on the volume of newly formed bone. Furthermore, the present study systematically demonstrated the beneficial effect of concave surfaces on bone tissue growth in vivo, reinforcing previous in silico and in vitro findings. This study demonstrates that optimizing architectural configurations within ceramic scaffolds is crucial in enhancing bone regeneration outcomes. STATEMENT OF SIGNIFICANCE: Despite the explosion of work on developing synthetic scaffolds to repair bone defects, the amount of new bone formed by scaffolds in vivo remains suboptimal. Recent studies have illuminated the pivotal role of scaffolds' internal architecture in osteogenesis. However, these investigations

Published
2024

286. Unsupervised Point Cloud Representation Learning by Clustering and Neural Rendering

Author

Mei, G, Saltori, C, Ricci, E, Sebe, N, Wu, Q, Zhang, J, Poiesi, F, Mei, G, Saltori, C, Ricci, E, Sebe, N, Wu, Q, Zhang, J, and Poiesi, F

Abstract

Data augmentation has contributed to the rapid advancement of unsupervised learning on 3D point clouds. However, we argue that data augmentation is not ideal, as it requires a careful application-dependent selection of the types of augmentations to be performed, thus potentially biasing the information learned by the network during self-training. Moreover, several unsupervised methods only focus on uni-modal information, thus potentially introducing challenges in the case of sparse and textureless point clouds. To address these issues, we propose an augmentation-free unsupervised approach for point clouds, named CluRender, to learn transferable point-level features by leveraging uni-modal information for soft clustering and cross-modal information for neural rendering. Soft clustering enables self-training through a pseudo-label prediction task, where the affiliation of points to their clusters is used as a proxy under the constraint that these pseudo-labels divide the point cloud into approximate equal partitions. This allows us to formulate a clustering loss to minimize the standard cross-entropy between pseudo and predicted labels. Neural rendering generates photorealistic renderings from various viewpoints to transfer photometric cues from 2D images to the features. The consistency between rendered and real images is then measured to form a fitting loss, combined with the cross-entropy loss to self-train networks. Experiments on downstream applications, including 3D object detection, semantic segmentation, classification, part segmentation, and few-shot learning, demonstrate the effectiveness of our framework in outperforming state-of-the-art techniques.

Published
2024

287. Enhancing Person Re-Identification Performance Through In Vivo Learning.

Author

Huang, Y, Zhang, Z, Wu, Q, Zhong, Y, Wang, L, Huang, Y, Zhang, Z, Wu, Q, Zhong, Y, and Wang, L

Abstract

This research investigates the potential of in vivo learning to enhance visual representation learning for image-based person re-identification (re-ID). Compared to traditional self-supervised learning (which require external data), the introduced in vivo learning utilizes supervisory labels generated from pedestrian images to improve re-ID accuracy without relying on external data sources. Three carefully designed in vivo learning tasks, leveraging statistical regularities within images, are proposed without the need for laborious manual annotations. These tasks enable feature extractors to learn more comprehensive and discriminative person representations by jointly modeling various aspects of human biological structure information, contributing to enhanced re-ID performance. Notably, the method seamlessly integrates with existing re-ID frameworks, requiring minimal modifications and no additional data beyond the existing training set. Extensive experiments on diverse datasets, including Market1501, CUHK03-NP, Celeb-reID, Celeb-reid-light, PRCC, and LTCC, demonstrate substantial enhancements in rank-1 precision compared to state-of-the-art methods.

Published
2024

288. Customized meta-dataset for automatic classifier accuracy evaluation

Author

Huang, Y, Zhang, Z, Wu, Q, Huang, H, Zhong, Y, Wang, L, Huang, Y, Zhang, Z, Wu, Q, Huang, H, Zhong, Y, and Wang, L

Abstract

Automatic classifier accuracy evaluation (ACAEval) on unlabeled test sets is critical for unseen real-world environments. The use of dataset-level regression on synthesized meta-datasets (comprised of many sample sets) has shown promising results for ACAEval. However, the existing meta-dataset for ACAEval is created using simple image transformations such as rotation and background substitution, which can make it difficult to ensure a reasonable distribution shift between the sample set and the test set. When the distribution shift is large, it becomes challenging to estimate the classifier accuracy on the test set using those sample sets. To ensure more robust ACAEval, this paper attempts to customize a meta-dataset in which each sample set has a reasonable distribution shift to the test set. An intra-class cycle-consistent adversarial learning (ICAL) method is introduced to transfer the style of a labeled training set to the style of the test set, by jointly considering the domain shift issue, the label flipping issue (the semantic information may be changed after style transformation), and the diversity of multiple sample sets in the meta-dataset. Experiments validate that under the same experimental setup, our method outperforms the existing ACAEval methods by a good margin, and achieves state-of-the-art performance on several standard benchmark datasets, including digit classification and natural image classification.

Published
2024

289. Light Field Salient Object Detection With Sparse Views via Complementary and Discriminative Interaction Network

Author

Chen, Y, Li, G, An, P, Liu, Z, Huang, X, Wu, Q, Chen, Y, Li, G, An, P, Liu, Z, Huang, X, and Wu, Q

Abstract

4D light field data record the scene from multiple views, thus implicitly providing beneficial depth cue for salient object detection in challenging scenes. Existing light field salient object detection (LF SOD) methods usually use a large number of views to improve the detection accuracy. However, using so many views for LF SOD brings difficulties to its practical applications. Considering that adjacent views in a light field are actually with very similar contents, in this work, we propose defining a more efficient pattern of input views, i. e., key sparse views, and design a network to effectively explore the depth cue from sparse views for LF SOD. Specifically, we firstly introduce a low rank-based statistical analysis to the existing LF SOD datasets, which allows us to conclude a fixed yet universal pattern for our key sparse views, including the number and positions of views. These views maintain the sufficient depth cue, but greatly lower the number of views to be captured and processed, facilitating practical applications. Then, we propose an effective solution with a key Complementary and Discriminative Interaction Module (CDIM) for LF SOD from key sparse views, named CDINet. The CDINet follows a two-stream structure to extract the depth cue from the light field stream (i. e., sparse views) and the appearance cue from the RGB stream (i. e., center view), generating features and initial saliency maps for each stream. The CDIM is tailored for inter-stream interaction of both these features and saliency maps, using the depth cue to complement the missing salient regions in RGB stream and discriminate the background distraction, to enhance the final saliency map further. Extensive experiments on three LF multi-view datasets demonstrate that our CDINet not only outperforms the state-of-the-art 2D methods, but also achieves competitive performance as compared with the state-of-the-art 3D and 4D methods. The code and results of our method are available at https

Published
2024

290. JUNO Conceptual Design Report

Author

Adam, T., An, F., An, G., An, Q., Anfimov, N., Antonelli, V., Baccolo, G., Baldoncini, M., Baussan, E., Bellato, M., Bezrukov, L., Bick, D., Blyth, S., Boarin, S., Brigatti, A., Brugière, T., Brugnera, R., Avanzini, M. Buizza, Busto, J., Cabrera, A., Cai, H., Cai, X., Cammi, A., Cao, D., Cao, G., Cao, J., Chang, J., Chang, Y., Chen, M., Chen, P., Chen, Q., Chen, S., Chen, X., Chen, Y., Cheng, Y., Chiesa, D., Chukanov, A., Clemenza, M., Clerbaux, B., D'Angelo, D., de Kerret, H., Deng, Z., Ding, X., Ding, Y., Djurcic, Z., Dmitrievsky, S., Dolgareva, M., Dornic, D., Doroshkevich, E., Dracos, M., Drapier, O., Dusini, S., Díaz, M. A., Enqvist, T., Fan, D., Fang, C., Fang, J., Fang, X., Favart, L., Fedoseev, D., Fiorentini, G., Ford, R., Formozov, A., Gaigher, R., Gan, H., Garfagnini, A., Gaudiot, G., Genster, C., Giammarchi, M., Giuliani, F., Gonchar, M., Gong, G., Gong, H., Gonin, M., Gornushkin, Y., Grassi, M., Grewing, C., Gromov, V., Gu, M., Guan, M., Guarino, V., Guo, W., Guo, X., Guo, Y., Göger-Neff, M., Hackspacher, P., Hagner, C., Han, R., Han, Z., Hao, J., He, M., Hellgartner, D., Heng, Y., Hong, D., Hou, S., Hsiung, Y., Hu, B., Hu, J., Hu, S., Hu, T., Hu, W., Huang, H., Huang, X., Huo, L., Huo, W., Ioannisian, A., Ioannisyan, D., Jeitler, M., Jen, K., Jetter, S., Ji, X., Jian, S., Jiang, D., Jiang, X., Jollet, C., Kaiser, M., Kan, B., Kang, L., Karagounis, M., Kazarian, N., Kettell, S., Korablev, D., Krasnoperov, A., Krokhaleva, S., Krumshteyn, Z., Kruth, A., Kuusiniemi, P., Lachenmaier, T., Lei, L., Lei, R., Lei, X., Leitner, R., Lenz, F., Li, C., Li, F., Li, J., Li, N., Li, S., Li, T., Li, W., Li, X., Li, Y., Li, Z., Liang, H., Liang, J., Licciardi, M., Lin, G., Lin, S., Lin, T., Lin, Y., Lippi, I., Liu, G., Liu, H., Liu, J., Liu, Q., Liu, S., Liu, Y., Lombardi, P., Long, Y., Lorenz, S., Lu, C., Lu, F., Lu, H., Lu, J., Lubsandorzhiev, B., Lubsandorzhiev, S., Ludhova, L., Luo, F., Luo, S., Lv, Z., Lyashuk, V., Ma, Q., Ma, S., Ma, X., Malyshkin, Y., Mantovani, F., Mao, Y., Mari, S., Mayilyan, D., McDonough, W., Meng, G., Meregaglia, A., Meroni, E., Mezzetto, M., Min, J., Miramonti, L., Montuschi, M., Morozov, N., Mueller, T., Muralidharan, P., Nastasi, M., Naumov, D., Naumova, E., Nemchenok, I., Ning, Z., Nunokawa, H., Oberauer, L., Ochoa-Ricoux, J. P., Olshevskiy, A., Ortica, F., Pan, H., Paoloni, A., Parkalian, N., Parmeggiano, S., Pec, V., Pelliccia, N., Peng, H., Poussot, P., Pozzi, S., Previtali, E., Prummer, S., Qi, F., Qi, M., Qian, S., Qian, X., Qiao, H., Qin, Z., Ranucci, G., Re, A., Ren, B., Ren, J., Rezinko, T., Ricci, B., Robens, M., Romani, A., Roskovec, B., Ruan, X., Rybnikov, A., Sadovsky, A., Saggese, P., Salamanna, G., Sawatzki, J., Schuler, J., Selyunin, A., Shi, G., Shi, J., Shi, Y., Sinev, V., Sirignano, C., Sisti, M., Smirnov, O., Soiron, M., Stahl, A., Stanco, L., Steinmann, J., Strati, V., Sun, G., Sun, X., Sun, Y., Taichenachev, D., Tang, J., Tietzsch, A., Tkachev, I., Trzaska, W. H., Tung, Y., van Waasen, S., Volpe, C., Vorobel, V., Votano, L., Wang, C., Wang, G., Wang, H., Wang, M., Wang, R., Wang, S., Wang, W., Wang, Y., Wang, Z., Wei, W., Wei, Y., Weifels, M., Wen, L., Wen, Y., Wiebusch, C., Wipperfurth, S., Wong, S. C., Wonsak, B., Wu, C., Wu, Q., Wu, Z., Wurm, M., Wurtz, J., Xi, Y., Xia, D., Xia, J., Xiao, M., Xie, Y., Xu, J., Xu, L., Xu, Y., Yan, B., Yan, X., Yang, C., Yang, H., Yang, L., Yang, M., Yang, Y., Yanovich, E., Yao, Y., Ye, M., Ye, X., Yegin, U., Yermia, F., You, Z., Yu, B., Yu, C., Yu, G., Yu, Z., Yuan, Y., Yuan, Z., Zanetti, M., Zeng, P., Zeng, S., Zeng, T., Zhan, L., Zhang, C., Zhang, F., Zhang, G., Zhang, H., Zhang, J., Zhang, K., Zhang, P., Zhang, Q., Zhang, T., Zhang, X., Zhang, Y., Zhang, Z., Zhao, J., Zhao, M., Zhao, T., Zhao, Y., Zheng, H., Zheng, M., Zheng, X., Zheng, Y., Zhong, W., Zhou, G., Zhou, J., Zhou, L., Zhou, N., Zhou, R., Zhou, S., Zhou, W., Zhou, X., Zhou, Y., Zhu, H., Zhu, K., Zhuang, H., Zong, L., and Zou, J.

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

The Jiangmen Underground Neutrino Observatory (JUNO) is proposed to determine the neutrino mass hierarchy using an underground liquid scintillator detector. It is located 53 km away from both Yangjiang and Taishan Nuclear Power Plants in Guangdong, China. The experimental hall, spanning more than 50 meters, is under a granite mountain of over 700 m overburden. Within six years of running, the detection of reactor antineutrinos can resolve the neutrino mass hierarchy at a confidence level of 3-4$\sigma$, and determine neutrino oscillation parameters $\sin^2\theta_{12}$, $\Delta m^2_{21}$, and $|\Delta m^2_{ee}|$ to an accuracy of better than 1%. The JUNO detector can be also used to study terrestrial and extra-terrestrial neutrinos and new physics beyond the Standard Model. The central detector contains 20,000 tons liquid scintillator with an acrylic sphere of 35 m in diameter. $\sim$17,000 508-mm diameter PMTs with high quantum efficiency provide $\sim$75% optical coverage. The current choice of the liquid scintillator is: linear alkyl benzene (LAB) as the solvent, plus PPO as the scintillation fluor and a wavelength-shifter (Bis-MSB). The number of detected photoelectrons per MeV is larger than 1,100 and the energy resolution is expected to be 3% at 1 MeV. The calibration system is designed to deploy multiple sources to cover the entire energy range of reactor antineutrinos, and to achieve a full-volume position coverage inside the detector. The veto system is used for muon detection, muon induced background study and reduction. It consists of a Water Cherenkov detector and a Top Tracker system. The readout system, the detector control system and the offline system insure efficient and stable data acquisition and processing., Comment: 328 pages, 211 figures

Published
2015

291. Measurement of the Reactor Antineutrino Flux and Spectrum at Daya Bay

Author

Daya Bay Collaboration, An, F. P., Balantekin, A. B., Band, H. R., Bishai, M., Blyth, S., Butorov, I., Cao, D., Cao, G. F., Cao, J., Cen, W. R., Chan, Y. L., Chang, J. F., Chang, L. C., Chang, Y., Chen, H. S., Chen, Q. Y., Chen, S. M., Chen, Y. X., Chen, Y., Cheng, J. H., Cheng, J., Cheng, Y. P., Cherwinka, J. J., Chu, M. C., Cummings, J. P., de Arcos, J., Deng, Z. Y., Ding, X. F., Ding, Y. Y., Diwan, M. V., Dove, J., Draeger, E., Dwyer, D. A., Edwards, W. R., Ely, S. R., Gill, R., Gonchar, M., Gong, G. H., Gong, H., Grassi, M., Gu, W. Q., Guan, M. Y., Guo, L., Guo, X. H., Hackenburg, R. W., Han, R., Hans, S., He, M., Heeger, K. M., Heng, Y. K., Higuera, A., Hor, Y. K., Hsiung, Y. B., Hu, B. Z., Hu, L. M., Hu, L. J., Hu, T., Hu, W., Huang, E. C., Huang, H. X., Huang, X. T., Huber, P., Hussain, G., Jaffe, D. E., Jaffke, P., Jen, K. L., Jetter, S., Ji, X. P., Ji, X. L., Jiao, J. B., Johnson, R. A., Kang, L., Kettell, S. H., Kohn, S., Kramer, M., Kwan, K. K., Kwok, M. W., Kwok, T., Langford, T. J., Lau, K., Lebanowski, L., Lee, J., Lei, R. T., Leitner, R., Leung, K. Y., Leung, J. K. C., Lewis, C. A., Li, D. J., Li, F., Li, G. S., Li, Q. J., Li, S. C., Li, W. D., Li, X. N., Li, X. Q., Li, Y. F., Li, Z. B., Liang, H., Lin, C. J., Lin, G. L., Lin, P. Y., Lin, S. K., Ling, J. J., Link, J. M., Littenberg, L., Littlejohn, B. R., Liu, D. W., Liu, H., Liu, J. L., Liu, J. C., Liu, S. S., Lu, C., Lu, H. Q., Lu, J. S., Luk, K. B., Ma, Q. M., Ma, X. Y., Ma, X. B., Ma, Y. Q., Caicedo, D. A. Martinez, McDonald, K. T., McKeown, R. D., Meng, Y., Mitchell, I., Kebwaro, J. Monari, Nakajima, Y., Napolitano, J., Naumov, D., Naumova, E., Ngai, H. Y., Ning, Z., Ochoa-Ricoux, J. P., Olshevski, A., Pan, H. -R., Park, J., Patton, S., Pec, V., Peng, J. C., Piilonen, L. E., Pinsky, L., Pun, C. S. J., Qi, F. Z., Qi, M., Qian, X., Raper, N., Ren, B., Ren, J., Rosero, R., Roskovec, B., Ruan, X. C., Shao, B. B., Steiner, H., Sun, G. X., Sun, J. L., Tang, W., Taychenachev, D., Tsang, K. V., Tull, C. E., Tung, Y. C., Viaux, N., Viren, B., Vorobel, V., Wang, C. H., Wang, M., Wang, N. Y., Wang, R. G., Wang, W., Wang, W. W., Wang, X., Wang, Y. F., Wang, Z., Wang, Z. M., Wei, H. Y., Wen, L. J., Whisnant, K., White, C. G., Whitehead, L., Wise, T., Wong, H. L. H., Wong, S. C. F., Worcester, E., Wu, Q., Xia, D. M., Xia, J. K., Xia, X., Xing, Z. Z., Xu, J. Y., Xu, J. L., Xu, J., Xu, Y., Xue, T., Yan, J., Yang, C. G., Yang, L., Yang, M. S., Yang, M. T., Ye, M., Yeh, M., Young, B. L., Yu, G. Y., Yu, Z. Y., Zang, S. L., Zhan, L., Zhang, C., Zhang, H. H., Zhang, J. W., Zhang, Q. M., Zhang, Y. M., Zhang, Y. X., Zhang, Z. J., Zhang, Z. Y., Zhang, Z. P., Zhao, J., Zhao, Q. W., Zhao, Y. F., Zhao, Y. B., Zheng, L., Zhong, W. L., Zhou, L., Zhou, N., Zhuang, H. L., and Zou, J. H.

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

This Letter reports a measurement of the flux and energy spectrum of electron antineutrinos from six 2.9~GW$_{th}$ nuclear reactors with six detectors deployed in two near (effective baselines 512~m and 561~m) and one far (1,579~m) underground experimental halls in the Daya Bay experiment. Using 217 days of data, 296,721 and 41,589 inverse beta decay (IBD) candidates were detected in the near and far halls, respectively. The measured IBD yield is (1.55 $\pm$ 0.04) $\times$ 10$^{-18}$~cm$^2$/GW/day or (5.92 $\pm$ 0.14) $\times$ 10$^{-43}$~cm$^2$/fission. This flux measurement is consistent with previous short-baseline reactor antineutrino experiments and is $0.946\pm0.022$ ($0.991\pm0.023$) relative to the flux predicted with the Huber+Mueller (ILL+Vogel) fissile antineutrino model. The measured IBD positron energy spectrum deviates from both spectral predictions by more than 2$\sigma$ over the full energy range with a local significance of up to $\sim$4$\sigma$ between 4-6 MeV. A reactor antineutrino spectrum of IBD reactions is extracted from the measured positron energy spectrum for model-independent predictions.

Published
2015
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292. The Detector System of The Daya Bay Reactor Neutrino Experiment

Author

An, F. P., Bai, J. Z., Balantekin, A. B., Band, H. R., Beavis, D., Beriguete, W., Bishai, M., Blyth, S., Brown, R. L., Butorov, I., Cao, D., Cao, G. F., Cao, J., Carr, R., Cen, W. R., Chan, W. T., Chan, Y. L., Chang, J. F., Chang, L. C., Chang, Y., Chasman, C., Chen, H. Y., Chen, H. S., Chen, M. J., Chen, Q. Y., Chen, S. J., Chen, S. M., Chen, X. C., Chen, X. H., Chen, X. S., Chen, Y. X., Chen, Y., Cheng, J. H., Cheng, J., Cheng, Y. P., Cherwinka, J. J., Chidzik, S., Chow, K., Chu, M. C., Cummings, J. P., de Arcos, J., Deng, Z. Y., Ding, X. F., Ding, Y. Y., Diwan, M. V., Dong, L., Dove, J., Draeger, E., Du, X. F., Dwyer, D. A., Edwards, W. R., Ely, S. R., Fang, S. D., Fu, J. Y., Fu, Z. W., Ge, L. Q., Ghazikhanian, V., Gill, R., Goett, J., Gonchar, M., Gong, G. H., Gong, H., Gornushkin, Y. A., Grassi, M., Greenler, L. S., Gu, W. Q., Guan, M. Y., Guo, R. P., Guo, X. H., Hackenburg, R. W., Hahn, R. L., Han, R., Hans, S., He, M., He, Q., He, W. S., Heeger, K. M., Heng, Y. K., Higuera, A., Hinrichs, P., Ho, T. H., Hoff, M., Hor, Y. K., Hsiung, Y. B., Hu, B. Z., Hu, L. M., Hu, L. J., Hu, T., Hu, W., Huang, E. C., Huang, H. Z., Huang, H. X., Huang, P. W., Huang, X., Huang, X. T., Huber, P., Hussain, G., Isvan, Z., Jaffe, D. E., Jaffke, P., Jen, K. L., Jetter, S., Ji, X. P., Ji, X. L., Jiang, H. J., Jiang, W. Q., Jiao, J. B., Johnson, R. A., Joseph, J., Kang, L., Kettell, S. H., Kohn, S., Kramer, M., Kwan, K. K., Kwok, M. W., Kwok, T., Lai, C. Y., Lai, W. C., Lai, W. H., Langford, T. J., Lau, K., Lebanowski, L., Lee, J., Lee, M. K. P., Lei, R. T., Leitner, R., Leung, J. K. C., Leung, K. Y., Lewis, C. A., Li, B., Li, C., Li, D. J., Li, F., Li, G. S., Li, J., Li, N. Y., Li, Q. J., Li, S. F., Li, S. C., Li, W. D., Li, X. B., Li, X. N., Li, X. Q., Li, Y., Li, Y. F., Li, Z. B., Liang, H., Liang, J., Lin, C. J., Lin, G. L., Lin, P. Y., Lin, S. X., Lin, S. K., Lin, Y. C., Ling, J. J., Link, J. M., Littenberg, L., Littlejohn, B. R., Liu, B. J., Liu, C., Liu, D. W., Liu, H., Liu, J. L., Liu, J. C., Liu, S., Liu, S. S., Liu, X., Liu, Y. B., Lu, C., Lu, H. Q., Lu, J. S., Luk, A., Luk, K. B., Luo, T., Luo, X. L., Ma, L. H., Ma, Q. M., Ma, X. Y., Ma, X. B., Ma, Y. Q., Mayes, B., McDonald, K. T., McFarlane, M. C., McKeown, R. D., Meng, Y., Mitchell, I., Mohapatra, D., Kebwaro, J. Monari, Morgan, J. E., Nakajima, Y., Napolitano, J., Naumov, D., Naumova, E., Newsom, C., Ngai, H. Y., Ngai, W. K., Nie, Y. B., Ning, Z., Ochoa-Ricoux, J. P., Olshevskiy, A., Pagac, A., Pan, H. -R., Patton, S., Pearson, C., Pec, V., Peng, J. C., Piilonen, L. E., Pinsky, L., Pun, C. S. J., Qi, F. Z., Qi, M., Qian, X., Raper, N., Ren, B., Ren, J., Rosero, R., Roskovec, B., Ruan, X. C., Sands III, W. R., Seilhan, B., Shao, B. B., Shih, K., Song, W. Y., Steiner, H., Stoler, P., Stuart, M., Sun, G. X., Sun, J. L., Tagg, N., Tam, Y. H., Tanaka, H. K., Tang, W., Tang, X., Taychenachev, D., Themann, H., Torun, Y., Trentalange, S., Tsai, O., Tsang, K. V., Tsang, R. H. M., Tull, C. E., Tung, Y. C., Viaux, N., Viren, B., Virostek, S., Vorobel, V., Wang, C. H., Wang, L. S., Wang, L. Y., Wang, L. Z., Wang, M., Wang, N. Y., Wang, R. G., Wang, T., Wang, W., Wang, W. W., Wang, X. T., Wang, X., Wang, Y. F., Wang, Z., Wang, Z. M., Webber, D. M., Wei, H. Y., Wei, Y. D., Wen, L. J., Wenman, D. L., Whisnant, K., White, C. G., Whitehead, L., Whitten Jr., C. A., Wilhelmi, J., Wise, T., Wong, H. C., Wong, H. L. H., Wong, J., Wong, S. C. F., Worcester, E., Wu, F. F., Wu, Q., Xia, D. M., Xia, J. K., Xiang, S. T., Xiao, Q., Xing, Z. Z., Xu, G., Xu, J. Y., Xu, J. L., Xu, J., Xu, W., Xu, Y., Xue, T., Yan, J., Yang, C. G., Yang, L., Yang, M. S., Yang, M. T., Ye, M., Yeh, M., Yeh, Y. S., Yip, K., Young, B. L., Yu, G. Y., Yu, Z. Y., Zeng, S., Zhan, L., Zhang, C., Zhang, F. H., Zhang, H. H., Zhang, J. W., Zhang, K., Zhang, Q. X., Zhang, Q. M., Zhang, S. H., Zhang, X. T., Zhang, Y. C., Zhang, Y. H., Zhang, Y. M., Zhang, Y. X., Zhang, Z. J., Zhang, Z. Y., Zhang, Z. P., Zhao, J., Zhao, Q. W., Zhao, Y. F., Zhao, Y. B., Zheng, L., Zhong, W. L., Zhou, L., Zhou, N., Zhou, Z. Y., Zhuang, H. L., Zimmerman, S., and Zou, J. H.

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

The Daya Bay experiment was the first to report simultaneous measurements of reactor antineutrinos at multiple baselines leading to the discovery of $\bar{\nu}_e$ oscillations over km-baselines. Subsequent data has provided the world's most precise measurement of $\rm{sin}^22\theta_{13}$ and the effective mass splitting $\Delta m_{ee}^2$. The experiment is located in Daya Bay, China where the cluster of six nuclear reactors is among the world's most prolific sources of electron antineutrinos. Multiple antineutrino detectors are deployed in three underground water pools at different distances from the reactor cores to search for deviations in the antineutrino rate and energy spectrum due to neutrino mixing. Instrumented with photomultiplier tubes (PMTs), the water pools serve as shielding against natural radioactivity from the surrounding rock and provide efficient muon tagging. Arrays of resistive plate chambers over the top of each pool provide additional muon detection. The antineutrino detectors were specifically designed for measurements of the antineutrino flux with minimal systematic uncertainty. Relative detector efficiencies between the near and far detectors are known to better than 0.2%. With the unblinding of the final two detectors' baselines and target masses, a complete description and comparison of the eight antineutrino detectors can now be presented. This paper describes the Daya Bay detector systems, consisting of eight antineutrino detectors in three instrumented water pools in three underground halls, and their operation through the first year of eight detector data-taking., Comment: 52 pages, 51 figures

Published
2015
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293. A new measurement of antineutrino oscillation with the full detector configuration at Daya Bay

Author

Daya Bay Collaboration, An, F. P., Balantekin, A. B., Band, H. R., Bishai, M., Blyth, S., Butorov, I., Cao, G. F., Cao, J., Cen, W. R., Chan, Y. L., Chang, J. F., Chang, L. C., Chang, Y., Chen, H. S., Chen, Q. Y., Chen, S. M., Chen, Y. X., Chen, Y., Cheng, J. H., Cheng, J., Cheng, Y. P., Cherwinka, J. J., Chu, M. C., Cummings, J. P., de Arcos, J., Deng, Z. Y., Ding, X. F., Ding, Y. Y., Diwan, M. V., Draeger, E., Dwyer, D. A., Edwards, W. R., Ely, S. R., Gill, R., Gonchar, M., Gong, G. H., Gong, H., Grassi, M., Gu, W. Q., Guan, M. Y., Guo, L., Guo, X. H., Hackenburg, R. W., Han, R., Hans, S., He, M., Heeger, K. M., Heng, Y. K., Hor, Y. K., Hsiung, Y. B., Hu, B. Z., Hu, L. M., Hu, L. J., Hu, T., Hu, W., Huang, E. C., Huang, H. X., Huang, X. T., Huber, P., Hussain, G., Jaffe, D. E., Jaffke, P., Jen, K. L., Jetter, S., Ji, X. P., Ji, X. L., Jiao, J. B., Johnson, R. A., Kang, L., Kettell, S. H., Kramer, M., Kwan, K. K., Kwok, M. W., Kwok, T., Langford, T. J., Lau, K., Lebanowski, L., Lee, J., Lei, R. T., Leitner, R., Leung, A., Leung, J. K. C., Lewis, C. A., Li, D. J., Li, F., Li, G. S., Li, Q. J., Li, S. C., Li, W. D., Li, X. N., Li, X. Q., Li, Y. F., Li, Z. B., Liang, H., Lin, C. J., Lin, G. L., Lin, P. Y., Lin, S. K., Ling, J. J., Link, J. M., Littenberg, L., Littlejohn, B. R., Liu, D. W., Liu, H., Liu, J. L., Liu, J. C., Liu, S. S., Lu, C., Lu, H. Q., Lu, J. S., Luk, K. B., Ma, Q. M., Ma, X. Y., Ma, X. B., Ma, Y. Q., McDonald, K. T., McKeown, R. D., Meng, Y., Mitchell, I., Kebwaro, J. Monari, Nakajima, Y., Napolitano, J., Naumov, D., Naumova, E., Ngai, H. Y., Ning, Z., Ochoa-Ricoux, J. P., Olshevski, A., Patton, S., Pec, V., Peng, J. C., Piilonen, L. E., Pinsky, L., Pun, C. S. J., Qi, F. Z., Qi, M., Qian, X., Raper, N., Ren, B., Ren, J., Rosero, R., Roskovec, B., Ruan, X. C., Shao, B. B., Steiner, H., Sun, G. X., Sun, J. L., Tang, W., Themann, H., Tsang, K. V., Tull, C. E., Tung, Y. C., Viaux, N., Viren, B., Vorobel, V., Wang, C. H., Wang, M., Wang, N. Y., Wang, R. G., Wang, W., Wang, W. W., Wang, X., Wang, Y. F., Wang, Z., Wang, Z. M., Wei, H. Y., Wen, L. J., Whisnant, K., White, C. G., Whitehead, L., Wise, T., Wong, H. L. H., Wong, S. C. F., Worcester, E., Wu, Q., Xia, D. M., Xia, J. K., Xia, X., Xing, Z. Z., Xu, J. Y., Xu, J. L., Xu, J., Xu, Y., Xue, T., Yan, J., Yang, C. G., Yang, L., Yang, M. S., Yang, M. T., Ye, M., Yeh, M., Yeh, Y. S., Young, B. L., Yu, G. Y., Yu, Z. Y., Zang, S. L., Zhan, L., Zhang, C., Zhang, H. H., Zhang, J. W., Zhang, Q. M., Zhang, Y. M., Zhang, Y. X., Zhang, Z. J., Zhang, Z. Y., Zhang, Z. P., Zhao, J., Zhao, Q. W., Zhao, Y. F., Zhao, Y. B., Zheng, L., Zhong, W. L., Zhou, L., Zhou, N., Zhuang, H. L., and Zou, J. H.

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

We report a new measurement of electron antineutrino disappearance using the fully-constructed Daya Bay Reactor Neutrino Experiment. The final two of eight antineutrino detectors were installed in the summer of 2012. Including the 404 days of data collected from October 2012 to November 2013 resulted in a total exposure of 6.9$\times$10$^5$ GW$_{\rm th}$-ton-days, a 3.6 times increase over our previous results. Improvements in energy calibration limited variations between detectors to 0.2%. Removal of six $^{241}$Am-$^{13}$C radioactive calibration sources reduced the background by a factor of two for the detectors in the experimental hall furthest from the reactors. Direct prediction of the antineutrino signal in the far detectors based on the measurements in the near detectors explicitly minimized the dependence of the measurement on models of reactor antineutrino emission. The uncertainties in our estimates of $\sin^{2}2\theta_{13}$ and $|\Delta m^2_{ee}|$ were halved as a result of these improvements. Analysis of the relative antineutrino rates and energy spectra between detectors gave $\sin^{2}2\theta_{13} = 0.084\pm0.005$ and $|\Delta m^{2}_{ee}|= (2.42\pm0.11) \times 10^{-3}$ eV$^2$ in the three-neutrino framework., Comment: Updated to match final published version

Published
2015
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294. SDSS/SEGUE Spectral Feature Analysis For Stellar Atmospheric Parameter Estimation

Author

Li, Xiangru, Wu, Q. M. Jonathan, Luo, Ali, Zhao, Yongheng, Lu, Yu, Zuo, Fang, Yang, Tan, and Wang, Yongjun

Subjects
Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Large-scale and deep sky survey missions are rapidly collecting a large amount of stellar spectra, which necessitate the estimation of atmospheric parameters directly from spectra and makes it feasible to statistically investigate latent principles in a large dataset. We present a technique for estimating parameters $T_{eff}$, log$~g$ and [Fe/H] from stellar spectra. With this technique, we first extract features from stellar spectra using the LASSO algorithm; then, the parameters are estimated from the extracted features using the SVR. On a subsample of 20~000 stellar spectra from SDSS with reference parameters provided by SDSS/SEGUE Pipeline SSPP, estimation consistency are 0.007458 dex for log$~T_{eff}$ (101.609921 K for $T_{eff}$), 0.189557 dex for log$~g$ and 0.182060 for [Fe/H], where the consistency is evaluated by mean absolute error. Prominent characteristics of the proposed scheme are sparseness, locality, and physical interpretability. In this work, every spectrum consists of 3821 fluxes, and 10, 19, and 14 typical wavelength positions are detected respectively for estimating $T_{eff}$, log$~g$ and [Fe/H]. It is shown that the positions are related to typical lines of stellar spectra. This characteristic is important in investigating physical indications from analysis results. Then, stellar spectra can be described by the individual fluxes on the detected positions (PD) or local integration of fluxes near them (LI). The abovementioned consistency is the result based on features described by LI. If features are described by PD, consistency are 0.009092 dex for log$~T_{eff}$ (124.545075 K for $T_{eff}$), 0.198928 dex for log$~g$, and 0.206814 dex for [Fe/H]., Comment: 22 pages, 10 figures, 15 table, ApJ

Published
2015
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295. Ultrafast Dynamics Evidence of High Temperature Superconductivity in Single Unit Cell FeSe on SrTiO3

Author

Tian, Y. C., Zhang, W. H., Li, F. S., Wu, Y. L., Wu, Q., Sun, F., Wang, Lili, Ma, Xucun, Xue, Qi-Kun, and Zhao, Jimin

Subjects
Condensed Matter - Superconductivity
Abstract

The recent observation of high-temperature superconductivity in one unit cell thick FeSe on SrTiO3 (1UC FeSe/STO)1 initiated a new pathway to realizing even higher transition temperature (Tc) superconductors and established a promising different paradigm to understand the mechanism of high temperature superconductivity2-10. However, independent confirmation on the high Tc value, the role of capping layer in reducing Tc, and the underlying mechanism of the novel superconductivity remain open questions11. Here we report a time-resolved study of the excited state ultrafast dynamics of 1UC FeSe/STO protected by 2UC FeTe capping layer, and identify the superconducting Tc to be 68 (-5/+2) K. We found a coherent acoustic phonon mode in the capping layer, which provides an additional decay channel to the gluing bosons and explains the reduced Tc value. Our investigation supports the phonon pairing scenario.

Published
2015
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296. Full-reference image quality assessment by combining global and local distortion measures

Author

Saha, Ashirbani and Wu, Q. M. Jonathan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Full-reference image quality assessment (FR-IQA) techniques compare a reference and a distorted/test image and predict the perceptual quality of the test image in terms of a scalar value representing an objective score. The evaluation of FR-IQA techniques is carried out by comparing the objective scores from the techniques with the subjective scores (obtained from human observers) provided in the image databases used for the IQA. Hence, we reasonably assume that the goal of a human observer is to rate the distortion present in the test image. The goal oriented tasks are processed by the human visual system (HVS) through top-down processing which actively searches for local distortions driven by the goal. Therefore local distortion measures in an image are important for the top-down processing. At the same time, bottom-up processing also takes place signifying spontaneous visual functions in the HVS. To account for this, global perceptual features can be used. Therefore, we hypothesize that the resulting objective score for an image can be derived from the combination of local and global distortion measures calculated from the reference and test images. We calculate the local distortion by measuring the local correlation differences from the gradient and contrast information. For global distortion, dissimilarity of the saliency maps computed from a bottom-up model of saliency is used. The motivation behind the proposed approach has been thoroughly discussed, accompanied by an intuitive analysis. Finally, experiments are conducted in six benchmark databases suggesting the effectiveness of the proposed approach that achieves competitive performance with the state-of-the-art methods providing an improvement in the overall performance., Comment: 31 pages, 8 figures, 2 tables

Published
2014

297. High Frequency Content based Stimulus for Perceptual Sharpness Assessment in Natural Images

Author

Saha, Ashirbani and Wu, Q. M. Jonathan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

A blind approach to evaluate the perceptual sharpness present in a natural image is proposed. Though the literature demonstrates a set of variegated visual cues to detect or evaluate the absence or presence of sharpness, we emphasize in the current work that high frequency content and local standard deviation can form strong features to compute perceived sharpness in any natural image, and can be considered an able alternative for the existing cues. Unsharp areas in a natural image happen to exhibit uniform intensity or lack of sharp changes between regions. Sharp region transitions in an image are caused by the presence of spatial high frequency content. Therefore, in the proposed approach, we hypothesize that using the high frequency content as the principal stimulus, the perceived sharpness can be quantified in an image. When an image is convolved with a high pass filter, higher values at any pixel location signify the presence of high frequency content at those locations. Considering these values as the stimulus, the exponent of the stimulus is weighted by local standard deviation to impart the contribution of the local contrast within the formation of the sharpness map. The sharpness map highlights the relatively sharper regions in the image and is used to calculate the perceived sharpness score of the image. The advantages of the proposed method lie in its use of simple visual cues of high frequency content and local contrast to arrive at the perceptual score, and requiring no training with the images. The promise of the proposed method is demonstrated by its ability to compute perceived sharpness for within image and across image sharpness changes and for blind evaluation of perceptual degradation resulting due to presence of blur. Experiments conducted on several databases demonstrate improved performance of the proposed method over that of the state-of-the-art techniques., Comment: 13 pages, 6 figures, 3 tables

Published
2014

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