Machine Learning Science and Technology最新文献

{"title":"Learning on the correctness class for domain inverse problems of gravimetry","authors":"Yihang Chen and Wenbin Li","doi":"10.1088/2632-2153/ad72cc","DOIUrl":"https://doi.org/10.1088/2632-2153/ad72cc","url":null,"abstract":"We consider end-to-end learning approaches for inverse problems of gravimetry. Due to ill-posedness of the inverse gravimetry, the reliability of learning approaches is questionable. To deal with this problem, we propose the strategy of learning on the correctness class. The well-posedness theorems are employed when designing the neural-network architecture and constructing the training set. Given the density-contrast function as a priori information, the domain of mass can be uniquely determined under certain constrains, and the domain inverse problem is a correctness class of the inverse gravimetry. Under this correctness class, we design the neural network for learning by mimicking the level-set formulation for the inverse gravimetry. Numerical examples illustrate that the method is able to recover mass models with non-constant density contrast.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A combined modeling method for complex multi-fidelity data fusion","authors":"Lei Tang, Feng Liu, Anping Wu, Yubo Li, Wanqiu Jiang, Qingfeng Wang and Jun Huang","doi":"10.1088/2632-2153/ad718f","DOIUrl":"https://doi.org/10.1088/2632-2153/ad718f","url":null,"abstract":"Currently, mainstream methods for multi-fidelity data fusion have achieved great success in many fields, but they generally suffer from poor scalability. Therefore, this paper proposes a combination modeling method for complex multi-fidelity data fusion, devoted to solving the modeling problems with three types of multi-fidelity data fusion, and explores a general solution for any n types of multi-fidelity data fusion. Different from the traditional direct modeling method—Multi-Fidelity Deep Neural Network (MFDNN)—the method is an indirect modeling method. The experimental results on three representative benchmark functions and the prediction tasks of SG6043 airfoil aerodynamic performance show that combination modeling has the following advantages: (1) It can quickly establish the mapping relationship between high, medium, and low fidelity data. (2) It can effectively solve the data imbalance problem in multi-fidelity modeling. (3) Compared with MFDNN, it has stronger noise resistance and higher prediction accuracy. Additionally, this paper discusses the scalability problem of the method when n = 4 and n = 5, providing a reference for further research on the combined modeling method.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards a comprehensive visualisation of structure in large scale data sets","authors":"Joan Garriga and Frederic Bartumeus","doi":"10.1088/2632-2153/ad6fea","DOIUrl":"https://doi.org/10.1088/2632-2153/ad6fea","url":null,"abstract":"Dimensionality reduction methods are fundamental to the exploration and visualisation of large data sets. Basic requirements for unsupervised data exploration are flexibility and scalability. However, current methods have computational limitations that restrict our ability to explore data structures to the lower range of scales. We focus on t-SNE and propose a chunk-and-mix protocol that enables the parallel implementation of this algorithm, as well as a self-adaptive parametric scheme that facilitates its parametric configuration. As a proof of concept, we present the pt-SNE algorithm, a parallel version of Barnes-Hat-SNE (an implementation of t-SNE). In pt-SNE, a single free parameter for the size of the neighbourhood, namely the perplexity, modulates the visualisation of the data structure at different scales, from local to global. Thanks to parallelisation, the runtime of the algorithm remains almost independent of the perplexity, which extends the range of scales to be analysed. The pt-SNE converges to a good global embedding comparable to current solutions, although it adds little noise at the local scale. This noise illustrates an unavoidable trade-off between computational speed and accuracy. We expect the same approach to be applicable to faster embedding algorithms than Barnes-Hat-SNE, such as Fast-Fourier Interpolation-based t-SNE or Uniform Manifold Approximation and Projection, thus extending the state of the art and allowing a more comprehensive visualisation and analysis of data structures.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing quantum multi-category classifier from the perspective of brain processing information","authors":"Xiaodong Ding, Jinchen Xu, Zhihui Song, Yifan Hou, Zheng Shan","doi":"10.1088/2632-2153/ad7570","DOIUrl":"https://doi.org/10.1088/2632-2153/ad7570","url":null,"abstract":"In the field of machine learning, the multi-category classification problem plays a crucial role. Solving the problem has a profound impact on driving the innovation and development of machine learning techniques and addressing complex problems in the real world. In recent years, researchers have begun to focus on utilizing quantum computing to solve the multi-category classification problem. Some studies have shown that the process of processing information in the brain may be related to quantum phenomena, with different brain regions having neurons with different structures. Inspired by this, we design a quantum multi-category classifier model from this perspective for the first time. The model employs a heterogeneous population of quantum neural networks (QNNs) to simulate the cooperative work of multiple different brain regions. When processing information, these heterogeneous clusters of QNNs allow for simultaneous execution on different quantum computers, thus simulating the brain’s ability to utilize multiple brain regions working in concert to maintain the robustness of the model. By setting the number of heterogeneous QNN clusters and parameterizing the number of stacks of unit layers in the quantum circuit, the model demonstrates excellent scalability in dealing with different types of data and different numbers of classes in the classification problem. Based on the attention mechanism of the brain, we integrate the processing results of heterogeneous QNN clusters to achieve high accuracy in classification. Finally, we conducted classification simulation experiments on different datasets. The results show that our method exhibits strong robustness and scalability. Among them, on different subsets of the MNIST dataset, its classification accuracy improves by up to about 5% compared to other quantum multiclassification algorithms. This result becomes the state-of-the-art simulation result for quantum classification models and exceeds the performance of classical classifiers with a considerable number of trainable parameters on some subsets of the MNIST dataset.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Photonic modes prediction via multi-modal diffusion model","authors":"Jinyang Sun, Xi Chen, Xiumei Wang, Dandan Zhu, Xingping Zhou","doi":"10.1088/2632-2153/ad743f","DOIUrl":"https://doi.org/10.1088/2632-2153/ad743f","url":null,"abstract":"The concept of photonic modes is the cornerstone in optics and photonics, which can describe the propagation of the light. The Maxwell’s equations play the role in calculating the mode field based on the structure information, while this process needs a great deal of computations, especially in the handle with a three-dimensional model. To overcome this obstacle, we introduce the multi-modal diffusion model to predict the photonic modes in one certain structure. The Contrastive Language–Image Pre-training (CLIP) model is used to build the connections between photonic structures and the corresponding modes. Then we exemplify Stable Diffusion (SD) model to realize the function of optical fields generation from structure information. Our work introduces multi-modal deep learning to construct complex mapping between structural information and optical field as high-dimensional vectors, and generates optical field images based on this mapping.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An exponential reduction in training data sizes for machine learning derived entanglement witnesses","authors":"Aiden R Rosebush, Alexander C B Greenwood, Brian T Kirby, Li Qian","doi":"10.1088/2632-2153/ad7457","DOIUrl":"https://doi.org/10.1088/2632-2153/ad7457","url":null,"abstract":"We propose a support vector machine (SVM) based approach for generating an entanglement witness that requires exponentially less training data than previously proposed methods. SVMs generate hyperplanes represented by a weighted sum of expectation values of local observables whose coefficients are optimized to sum to a positive number for all separable states and a negative number for as many entangled states as possible near a specific target state. Previous SVM-based approaches for entanglement witness generation used large amounts of randomly generated separable states to perform training, a task with considerable computational overhead. Here, we propose a method for orienting the witness hyperplane using only the significantly smaller set of states consisting of the eigenstates of the generalized Pauli matrices and a set of entangled states near the target entangled states. With the orientation of the witness hyperplane set by the SVM, we tune the plane’s placement using a differential program that ensures perfect classification accuracy on a limited test set as well as maximal noise tolerance. For <italic toggle=\"yes\">N</italic> qubits, the SVM portion of this approach requires only <inline-formula>\u0000<tex-math><?CDATA $O(6^N)$?></tex-math><mml:math overflow=\"scroll\"><mml:mrow><mml:mi>O</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msup><mml:mn>6</mml:mn><mml:mi>N</mml:mi></mml:msup><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"mlstad7457ieqn1.gif\"></inline-graphic></inline-formula> training states, whereas an existing method needs <inline-formula>\u0000<tex-math><?CDATA $O(2^{4^N})$?></tex-math><mml:math overflow=\"scroll\"><mml:mrow><mml:mi>O</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msup><mml:mn>2</mml:mn><mml:mrow><mml:msup><mml:mn>4</mml:mn><mml:mi>N</mml:mi></mml:msup></mml:mrow></mml:msup><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"mlstad7457ieqn2.gif\"></inline-graphic></inline-formula>. We use this method to construct witnesses of 4 and 5 qubit GHZ states with coefficients agreeing with stabilizer formalism witnesses to within 3.7 percent and 1 percent, respectively. We also use the same training states to generate novel 4 and 5 qubit W state witnesses. Finally, we computationally verify these witnesses on small test sets and propose methods for further verification.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regulating the development of accurate data-driven physics-informed deformation models","authors":"Will Newman, Jamshid Ghaboussi, Michael Insana","doi":"10.1088/2632-2153/ad7192","DOIUrl":"https://doi.org/10.1088/2632-2153/ad7192","url":null,"abstract":"The challenge posed by the inverse problem associated with ultrasonic elasticity imaging is well matched to the capabilities of data-driven solutions. This report describes how data properties and the time sequence by which the data are introduced during training influence deformation-model accuracy and training times. Our goal is to image the elastic modulus of soft linear-elastic media as accurately as possible within a limited volume. To monitor progress during training, we introduce metrics describing convergence rate and stress entropy to guide data acquisition and other timing features. For example, a regularization term in the loss function may be introduced and later removed to speed and stabilize developing deformation models as well as establishing stopping rules for neural-network convergence. Images of a 14.4 cm³ volume within 3D software phantom visually indicate the quality of modulus images resulting over a range of training variables. The results show that a data-driven method constrained by the physics of a deformed solid will lead to quantitively accurate 3D elastic modulus images with minimum artifacts.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving model robustness to weight noise via consistency regularization","authors":"Yaoqi Hou, Qingtian Zhang, Namin Wang, Huaqiang Wu","doi":"10.1088/2632-2153/ad734a","DOIUrl":"https://doi.org/10.1088/2632-2153/ad734a","url":null,"abstract":"As an emerging computing architecture, the computing-in-memory (CIM) exhibits significant potential for energy efficiency and computing power in artificial intelligence applications. However, the intrinsic non-idealities of CIM devices, manifesting as random interference on the weights of neural network, may significantly impact the inference accuracy. In this paper, we propose a novel training algorithm designed to mitigate the impact of weight noise. The algorithm strategically minimizes cross-entropy loss while concurrently refining the feature representations in intermediate layers to emulate those of an ideal, noise-free network. This dual-objective approach not only preserves the accuracy of the neural network but also enhances its robustness against noise-induced degradation. Empirical validation across several benchmark datasets confirms that our algorithm sets a new benchmark for accuracy in CIM-enabled neural network applications. Compared to the most commonly used forward noise training methods, our approach yields approximately a 2% accuracy boost on the ResNet32 model with the CIFAR-10 dataset and a weight noise scale of 0.2, and achieves a minimum performance gain of 1% on ResNet18 with the ImageNet dataset under the same noise quantization conditions.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simultaneous energy and mass calibration of large-radius jets with the ATLAS detector using a deep neural network","authors":"G Aad, E Aakvaag, B Abbott, K Abeling, N J Abicht, S H Abidi, A Aboulhorma, H Abramowicz, H Abreu, Y Abulaiti, B S Acharya, C Adam Bourdarios, L Adamczyk, S V Addepalli, M J Addison, J Adelman, A Adiguzel, T Adye, A A Affolder, Y Afik, M N Agaras, J Agarwala, A Aggarwal, C Agheorghiesei, A Ahmad, F Ahmadov, W S Ahmed, S Ahuja, X Ai, G Aielli, A Aikot, M Ait Tamlihat, B Aitbenchikh, I Aizenberg, M Akbiyik, T P A Åkesson, A V Akimov, D Akiyama, N N Akolkar, S Aktas, K Al Khoury, G L Alberghi, J Albert, P Albicocco, G L Albouy, S Alderweireldt, Z L Alegria, M Aleksa, I N Aleksandrov, C Alexa, T Alexopoulos, F Alfonsi, M Algren, M Alhroob, B Ali, H M J Ali, S Ali, S W Alibocus, M Aliev, G Alimonti, W Alkakhi, C Allaire, B M M Allbrooke, J F Allen, C A Allendes Flores, P P Allport, A Aloisio, F Alonso, C Alpigiani, M Alvarez Estevez, A Alvarez Fernandez, M Alves Cardoso, M G Alviggi, M Aly, Y Amaral Coutinho, A Ambler, C Amelung, M Amerl, C G Ames, D Amidei, S P Amor Dos Santos, K R Amos, V Ananiev, C Anastopoulos, T Andeen, J K Anders, S Y Andrean, A Andreazza, S Angelidakis, A Angerami, A V Anisenkov, A Annovi, C Antel, M T Anthony, E Antipov, M Antonelli, F Anulli, M Aoki, T Aoki, J A Aparisi Pozo, M A Aparo, L Aperio Bella, C Appelt, A Apyan, S J Arbiol Val, C Arcangeletti, A T H Arce, E Arena, J-F Arguin, S Argyropoulos, J-H Arling, O Arnaez, H Arnold, G Artoni, H Asada, K Asai, S Asai, N A Asbah, K Assamagan, R Astalos, S Atashi, R J Atkin, M Atkinson, H Atmani, P A Atmasiddha, K Augsten, S Auricchio, A D Auriol, V A Austrup, G Avolio, K Axiotis, G Azuelos, D Babal, H Bachacou, K Bachas, A Bachiu, F Backman, A Badea, T M Baer, P Bagnaia, M Bahmani, D Bahner, K Bai, A J Bailey, V R Bailey, J T Baines, L Baines, O K Baker, E Bakos, D Bakshi Gupta, V Balakrishnan, R Balasubramanian, E M Baldin, P Balek, E Ballabene, F Balli, L M Baltes, W K Balunas, J Balz, E Banas, M Bandieramonte, A Bandyopadhyay, S Bansal, L Barak, M Barakat, E L Barberio, D Barberis, M Barbero, M Z Barel, K N Barends, T Barillari, M-S Barisits, T Barklow, P Baron, D A Baron Moreno, A Baroncelli, G Barone, A J Barr, J D Barr, F Barreiro, J Barreiro Guimarães da Costa, U Barron, M G Barros Teixeira, S Barsov, F Bartels, R Bartoldus, A E Barton, P Bartos, A Basan, M Baselga, A Bassalat, M J Basso, C R Basson, R L Bates, S Batlamous, B Batool, M Battaglia, D Battulga, M Bauce, M Bauer, P Bauer, L T Bazzano Hurrell, J B Beacham, T Beau, J Y Beaucamp, P H Beauchemin, P Bechtle, H P Beck, K Becker, A J Beddall, V A Bednyakov, C P Bee, L J Beemster, T A Beermann, M Begalli, M Begel, A Behera, J K Behr, J F Beirer, F Beisiegel, M Belfkir, G Bella, L Bellagamba, A Bellerive, P Bellos, K Beloborodov, D Benchekroun, F Bendebba, Y Benhammou, K C Benkendorfer, L Beresford, M Beretta, E Bergeaas Kuutmann, N Berger, B Bergmann, J Beringer, G Bernardi, C Bernius, F U Bernlochner, F Bernon, A Berrocal Guardia, T Berry, P Berta, A Berthold, S Bethke, A Betti, A J Bevan, N K Bhalla, M Bhamjee, S Bhatta, D S Bhattacharya, P Bhattarai, K D Bhide, V S Bhopatkar, R M Bianchi, G Bianco, O Biebel, R Bielski, M Biglietti, C S Billingsley, M Bindi, A Bingul, C Bini, A Biondini, C J Birch-sykes, G A Bird, M Birman, M Biros, S Biryukov, T Bisanz, E Bisceglie, J P Biswal, D Biswas, K Bjørke, I Bloch, A Blue, U Blumenschein, J Blumenthal, V S Bobrovnikov, M Boehler, B Boehm, D Bogavac, A G Bogdanchikov, C Bohm, V Boisvert, P Bokan, T Bold, M Bomben, M Bona, M Boonekamp, C D Booth, A G Borbély, I S Bordulev, H M Borecka-Bielska, G Borissov, D Bortoletto, D Boscherini, M Bosman, J D Bossio Sola, K Bouaouda, N Bouchhar, J Boudreau, E V Bouhova-Thacker, D Boumediene, R Bouquet, A Boveia, J Boyd, D Boye, I R Boyko, J Bracinik, N Brahimi, G Brandt, O Brandt, F Braren, B Brau, J E Brau, R Brener, L Brenner, R Brenner, S Bressler, D Britton, D Britzger, I Brock, G Brooijmans, E Brost, L M Brown, L E Bruce, T L Bruckler, P A Bruckman de Renstrom, B Brüers, A Bruni, G Bruni, M Bruschi, N Bruscino, T Buanes, Q Buat, D Buchin, A G Buckley, O Bulekov, B A Bullard, S Burdin, C D Burgard, A M Burger, B Burghgrave, O Burlayenko, J T P Burr, C D Burton, J C Burzynski, E L Busch, V Büscher, P J Bussey, J M Butler, C M Buttar, J M Butterworth, W Buttinger, C J Buxo Vazquez, A R Buzykaev, S Cabrera Urbán, L Cadamuro, D Caforio, H Cai, Y Cai, Y Cai, V M M Cairo, O Cakir, N Calace, P Calafiura, G Calderini, P Calfayan, G Callea, L P Caloba, D Calvet, S Calvet, M Calvetti, R Camacho Toro, S Camarda, D Camarero Munoz, P Camarri, M T Camerlingo, D Cameron, C Camincher, M Campanelli, A Camplani, V Canale, A C Canbay, J Cantero, Y Cao, F Capocasa, M Capua, A Carbone, R Cardarelli, J C J Cardenas, F Cardillo, G Carducci, T Carli, G Carlino, J I Carlotto, B T Carlson, E M Carlson, L Carminati, A Carnelli, M Carnesale, S Caron, E Carquin, S Carrá, G Carratta, A M Carroll, T M Carter, M P Casado, M Caspar, F L Castillo, L Castillo Garcia, V Castillo Gimenez, N F Castro, A Catinaccio, J R Catmore, T Cavaliere, V Cavaliere, N Cavalli, Y C Cekmecelioglu, E Celebi, F Celli, M S Centonze, V Cepaitis, K Cerny, A S Cerqueira, A Cerri, L Cerrito, F Cerutti, B Cervato, A Cervelli, G Cesarini, S A Cetin, D Chakraborty, J Chan, W Y Chan, J D Chapman, E Chapon, B Chargeishvili, D G Charlton, M Chatterjee, C Chauhan, Y Che, S Chekanov, S V Chekulaev, G A Chelkov, A Chen, B Chen, B Chen, H Chen, H Chen, J Chen, J Chen, M Chen, S Chen, S J Chen, X Chen, X Chen, Y Chen, C L Cheng, H C Cheng, S Cheong, A Cheplakov, E Cheremushkina, E Cherepanova, R Cherkaoui El Moursli, E Cheu, K Cheung, L Chevalier, V Chiarella, G Chiarelli, N Chiedde, G Chiodini, A S Chisholm, A Chitan, M Chitishvili, M V Chizhov, K Choi, Y Chou, E Y S Chow, K L Chu, M C Chu, X Chu, J Chudoba, J J Chwastowski, D Cieri, K M Ciesla, V Cindro, A Ciocio, F Cirotto, Z H Citron, M Citterio, D A Ciubotaru, A Clark, P J Clark, C Clarry, J M Clavijo Columbie, S E Clawson, C Clement, J Clercx, Y Coadou, M Cobal, A Coccaro, R F Coelho Barrue, R Coelho Lopes De Sa, S Coelli, B Cole, J Collot, P Conde Muiño, M P Connell, S H Connell, E I Conroy, F Conventi, H G Cooke, A M Cooper-Sarkar, A Cordeiro Oudot Choi, L D Corpe, M Corradi, F Corriveau, A Cortes-Gonzalez, M J Costa, F Costanza, D Costanzo, B M Cote, G Cowan, K Cranmer, D Cremonini, S Crépé-Renaudin, F Crescioli, M Cristinziani, M Cristoforetti, V Croft, J E Crosby, G Crosetti, A Cueto, T Cuhadar Donszelmann, H Cui, Z Cui, W R Cunningham, F Curcio, P Czodrowski, M M Czurylo, M J Da Cunha Sargedas De Sousa, J V Da Fonseca Pinto, C Da Via, W Dabrowski, T Dado, S Dahbi, T Dai, D Dal Santo, C Dallapiccola, M Dam, G D’amen, V D’Amico, J Damp, J R Dandoy, M Danninger, V Dao, G Darbo, S Darmora, S J Das, S D’Auria, A D’avanzo, C David, T Davidek, B Davis-Purcell, I Dawson, H A Day-hall, K De, R De Asmundis, N De Biase, S De Castro, N De Groot, P de Jong, H De la Torre, A De Maria, A De Salvo, U De Sanctis, F De Santis, A De Santo, J B De Vivie De Regie, D V Dedovich, J Degens, A M Deiana, F Del Corso, J Del Peso, F Del Rio, L Delagrange, F Deliot, C M Delitzsch, M Della Pietra, D Della Volpe, A Dell’Acqua, L Dell’Asta, M Delmastro, P A Delsart, S Demers, M Demichev, S P Denisov, L D’Eramo, D Derendarz, F Derue, P Dervan, K Desch, C Deutsch, F A Di Bello, A Di Ciaccio, L Di Ciaccio, A Di Domenico, C Di Donato, A Di Girolamo, G Di Gregorio, A Di Luca, B Di Micco, R Di Nardo, M Diamantopoulou, F A Dias, T Dias Do Vale, M A Diaz, F G Diaz Capriles, M Didenko, E B Diehl, S Díez Cornell, C Diez Pardos, C Dimitriadi, A Dimitrievska, J Dingfelder, I-M Dinu, S J Dittmeier, F Dittus, F Djama, T Djobava, C Doglioni, A Dohnalova, J Dolejsi, Z Dolezal, K M Dona, M Donadelli, B Dong, J Donini, A D’Onofrio, M D’Onofrio, J Dopke, A Doria, N Dos Santos Fernandes, P Dougan, M T Dova, A T Doyle, M A Draguet, E Dreyer, I Drivas-koulouris, M Drnevich, M Drozdova, D Du, T A du Pree, F Dubinin, M Dubovsky, E Duchovni, G Duckeck, O A Ducu, D Duda, A Dudarev, E R Duden, M D’uffizi, L Duflot, M Dührssen, A E Dumitriu, M Dunford, S Dungs, K Dunne, A Duperrin, H Duran Yildiz, M Düren, A Durglishvili, B L Dwyer, G I Dyckes, M Dyndal, B S Dziedzic, Z O Earnshaw, G H Eberwein, B Eckerova, S Eggebrecht, E Egidio Purcino De Souza, L F Ehrke, G Eigen, K Einsweiler, T Ekelof, P A Ekman, S El Farkh, Y El Ghazali, H El Jarrari, A El Moussaouy, V Ellajosyula, M Ellert, F Ellinghaus, N Ellis, J Elmsheuser, M Elsing, D Emeliyanov, Y Enari, I Ene, S Epari, P A Erland, M Errenst, M Escalier, C Escobar, E Etzion, G Evans, H Evans, L S Evans, A Ezhilov, S Ezzarqtouni, F Fabbri, L Fabbri, G Facini, V Fadeyev, R M Fakhrutdinov, D Fakoudis, S Falciano, L F Falda Ulhoa Coelho, P J Falke, J Faltova, C Fan, Y Fan, Y Fang, M Fanti, M Faraj, Z Farazpay, A Farbin, A Farilla, T Farooque, S M Farrington, F Fassi, D Fassouliotis, M Faucci Giannelli, W J Fawcett, L Fayard, P Federic, P Federicova, O L Fedin, M Feickert, L Feligioni, D E Fellers, C Feng, M Feng, Z Feng, M J Fenton, L Ferencz, R A M Ferguson, S I Fernandez Luengo, P Fernandez Martinez, M J V Fernoux, J Ferrando, A Ferrari, P Ferrari, R Ferrari, D Ferrere, C Ferretti, F Fiedler, P Fiedler, A Filipčič, E K Filmer, F Filthaut, M C N Fiolhais, L Fiorini, W C Fisher, T Fitschen, P M Fitzhugh, I Fleck, P Fleischmann, T Flick, M Flores, L R Flores Castillo, L Flores Sanz De Acedo, F M Follega, N Fomin, J H Foo, A Formica, A C Forti, E Fortin, A W Fortman, M G Foti, L Fountas, D Fournier, H Fox, P Francavilla, S Francescato, S Franchellucci, M Franchini, S Franchino, D Francis, L Franco, V Franco Lima, L Franconi, M Franklin, G Frattari, W S Freund, Y Y Frid, J Friend, N Fritzsche, A Froch, D Froidevaux, J A Frost, Y Fu, S Fuenzalida Garrido, M Fujimoto, K Y Fung, E Furtado De Simas Filho, M Furukawa, J Fuster, A Gabrielli, A Gabrielli, P Gadow, G Gagliardi, L G Gagnon, S Galantzan, E J Gallas, B J Gallop, K K Gan, S Ganguly, Y Gao, F M Garay Walls, B Garcia, C García, A Garcia Alonso, A G Garcia Caffaro, J E García Navarro, M Garcia-Sciveres, G L Gardner, R W Gardner, N Garelli, D Garg, R B Garg, J M Gargan, C A Garner, C M Garvey, P Gaspar, V K Gassmann, G Gaudio, V Gautam, P Gauzzi, I L Gavrilenko, A Gavrilyuk, C Gay, G Gaycken, E N Gazis, A A Geanta, C M Gee, A Gekow, C Gemme, M H Genest, A D Gentry, S George, W F George, T Geralis, P Gessinger-Befurt, M E Geyik, M Ghani, M Ghneimat, K Ghorbanian, A Ghosal, A Ghosh, A Ghosh, B Giacobbe, S Giagu, T Giani, P Giannetti, A Giannini, S M Gibson, M Gignac, D T Gil, A K Gilbert, B J Gilbert, D Gillberg, G Gilles, L Ginabat, D M Gingrich, M P Giordani, P F Giraud, G Giugliarelli, D Giugni, F Giuli, I Gkialas, L K Gladilin, C Glasman, G R Gledhill, G Glemža, M Glisic, I Gnesi, Y Go, M Goblirsch-Kolb, B Gocke, D Godin, B Gokturk, S Goldfarb, T Golling, M G D Gololo, D Golubkov, J P Gombas, A Gomes, G Gomes Da Silva, A J Gomez Delegido, R Gonçalo, L Gonella, A Gongadze, F Gonnella, J L Gonski, R Y González Andana, S González de la Hoz, R Gonzalez Lopez, C Gonzalez Renteria, M V Gonzalez Rodrigues, R Gonzalez Suarez, S Gonzalez-Sevilla, G R Gonzalvo Rodriguez, L Goossens, B Gorini, E Gorini, A Gorišek, T C Gosart, A T Goshaw, M I Gostkin, S Goswami, C A Gottardo, S A Gotz, M Gouighri, V Goumarre, A G Goussiou, N Govender, I Grabowska-Bold, K Graham, E Gramstad, S Grancagnolo, C M Grant, P M Gravila, F G Gravili, H M Gray, M Greco, C Grefe, I M Gregor, P Grenier, S G Grewe, A A Grillo, K Grimm, S Grinstein, J -F Grivaz, E Gross, J Grosse-Knetter, J C Grundy, L Guan, C Gubbels, J G R Guerrero Rojas, G Guerrieri, F Guescini, R Gugel, J A M Guhit, A Guida, E Guilloton, S Guindon, F Guo, J Guo, L Guo, Y Guo, R Gupta, R Gupta, S Gurbuz, S S Gurdasani, G Gustavino, M Guth, P Gutierrez, L F Gutierrez Zagazeta, M Gutsche, C Gutschow, C Gwenlan, C B Gwilliam, E S Haaland, A Haas, M Habedank, C Haber, H K Hadavand, A Hadef, S Hadzic, A I Hagan, J J Hahn, E H Haines, M Haleem, J Haley, J J Hall, G D Hallewell, L Halser, K Hamano, M Hamer, G N Hamity, E J Hampshire, J Han, K Han, L Han, L Han, S Han, Y F Han, K Hanagaki, M Hance, D A Hangal, H Hanif, M D Hank, J B Hansen, P H Hansen, K Hara, D Harada, T Harenberg, S Harkusha, M L Harris, Y T Harris, J Harrison, N M Harrison, P F Harrison, N M Hartman, N M Hartmann, Y Hasegawa, R Hauser, C M Hawkes, R J Hawkings, Y Hayashi, S Hayashida, D Hayden, C Hayes, R L Hayes, C P Hays, J M Hays, H S Hayward, F He, M He, Y He, Y He, Y He, N B Heatley, V Hedberg, A L Heggelund, N D Hehir, C Heidegger, K K Heidegger, W D Heidorn, J Heilman, S Heim, T Heim, J G Heinlein, J J Heinrich, L Heinrich, J Hejbal, A Held, S Hellesund, C M Helling, S Hellman, R C W Henderson, L Henkelmann, A M Henriques Correia, H Herde, Y Hernández Jiménez, L M Herrmann, T Herrmann, G Herten, R Hertenberger, L Hervas, M E Hesping, N P Hessey, E Hill, S J Hillier, J R Hinds, F Hinterkeuser, M Hirose, S Hirose, D Hirschbuehl, T G Hitchings, B Hiti, J Hobbs, R Hobincu, N Hod, M C Hodgkinson, B H Hodkinson, A Hoecker, D D Hofer, J Hofer, T Holm, M Holzbock, L B A H Hommels, B P Honan, J Hong, T M Hong, B H Hooberman, W H Hopkins, Y Horii, S Hou, A S Howard, J Howarth, J Hoya, M Hrabovsky, A Hrynevich, T Hryn’ova, P J Hsu, S -C Hsu, Q Hu, S Huang, X Huang, X Huang, Y Huang, Y Huang, Z Huang, Z Hubacek, M Huebner, F Huegging, T B Huffman, C A Hugli, M Huhtinen, S K Huiberts, R Hulsken, N Huseynov, J Huston, J Huth, R Hyneman, G Iacobucci, G Iakovidis, I Ibragimov, L Iconomidou-Fayard, J P Iddon, P Iengo, R Iguchi, T Iizawa, Y Ikegami, N Ilic, H Imam, M Ince Lezki, T Ingebretsen Carlson, G Introzzi, M Iodice, V Ippolito, R K Irwin, M Ishino, W Islam, C Issever, S Istin, H Ito, R Iuppa, A Ivina, J M Izen, V Izzo, P Jacka, P Jackson, B P Jaeger, C S Jagfeld, G Jain, P Jain, K Jakobs, T Jakoubek, J Jamieson, K W Janas, M Javurkova, L Jeanty, J Jejelava, P Jenni, C E Jessiman, C Jia, J Jia, X Jia, X Jia, Z Jia, S Jiggins, J Jimenez Pena, S Jin, A Jinaru, O Jinnouchi, P Johansson, K A Johns, J W Johnson, D M Jones, E Jones, P Jones, R W L Jones, T J Jones, H L Joos, R Joshi, J Jovicevic, X Ju, J J Junggeburth, T Junkermann, A Juste Rozas, M K Juzek, S Kabana, A Kaczmarska, M Kado, H Kagan, M Kagan, A Kahn, A Kahn, C Kahra, T Kaji, E Kajomovitz, N Kakati, I Kalaitzidou, C W Kalderon, N J Kang, D Kar, K Karava, M J Kareem, E Karentzos, I Karkanias, O Karkout, S N Karpov, Z M Karpova, V Kartvelishvili, A N Karyukhin, E Kasimi, J Katzy, S Kaur, K Kawade, M P Kawale, C Kawamoto, T Kawamoto, E F Kay, F I Kaya, S Kazakos, V F Kazanin, Y Ke, J M Keaveney, R Keeler, G V Kehris, J S Keller, A S Kelly, J J Kempster, P D Kennedy, O Kepka, B P Kerridge, S Kersten, B P Kerševan, S Keshri, L Keszeghova, S Ketabchi Haghighat, R A Khan, A Khanov, A G Kharlamov, T Kharlamova, E E Khoda, M Kholodenko, T J Khoo, G Khoriauli, J Khubua, Y A R Khwaira, B Kibirige, A Kilgallon, D W Kim, Y K Kim, N Kimura, M K Kingston, A Kirchhoff, C Kirfel, F Kirfel, J Kirk, A E Kiryunin, C Kitsaki, O Kivernyk, M Klassen, C Klein, L Klein, M H Klein, S B Klein, U Klein, P Klimek, A Klimentov, T Klioutchnikova, P Kluit, S Kluth, E Kneringer, T M Knight, A Knue, R Kobayashi, D Kobylianskii, S F Koch, M Kocian, P Kodyš, D M Koeck, P T Koenig, T Koffas, O Kolay, I Koletsou, T Komarek, K Köneke, A X Y Kong, T Kono, N Konstantinidis, P Kontaxakis, B Konya, R Kopeliansky, S Koperny, K Korcyl, K Kordas, A Korn, S Korn, I Korolkov, N Korotkova, B Kortman, O Kortner, S Kortner, W H Kostecka, V V Kostyukhin, A Kotsokechagia, A Kotwal, A Koulouris, A Kourkoumeli-Charalampidi, C Kourkoumelis, E Kourlitis, O Kovanda, R Kowalewski, W Kozanecki, A S Kozhin, V A Kramarenko, G Kramberger, P Kramer, M W Krasny, A Krasznahorkay, J W Kraus, J A Kremer, T Kresse, J Kretzschmar, K Kreul, P Krieger, S Krishnamurthy, M Krivos, K Krizka, K Kroeninger, H Kroha, J Kroll, J Kroll, K S Krowpman, U Kruchonak, H Krüger, N Krumnack, M C Kruse, O Kuchinskaia, S Kuday, S Kuehn, R Kuesters, T Kuhl, V Kukhtin, Y Kulchitsky, S Kuleshov, M Kumar, N Kumari, P Kumari, A Kupco, T Kupfer, A Kupich, O Kuprash, H Kurashige, L L Kurchaninov, O Kurdysh, Y A Kurochkin, A Kurova, M Kuze, A K Kvam, J Kvita, T Kwan, N G Kyriacou, L A O Laatu, C Lacasta, F Lacava, H Lacker, D Lacour, N N Lad, E Ladygin, B Laforge, T Lagouri, F Z Lahbabi, S Lai, I K Lakomiec, N Lalloue, J E Lambert, S Lammers, W Lampl, C Lampoudis, G Lamprinoudis, A N Lancaster, E Lançon, U Landgraf, M P J Landon, V S Lang, O K B Langrekken, A J Lankford, F Lanni, K Lantzsch, A Lanza, A Lapertosa, J F Laporte, T Lari, F Lasagni Manghi, M Lassnig, V Latonova, A Laudrain, A Laurier, S D Lawlor, Z Lawrence, R Lazaridou, M Lazzaroni, B Le, E M Le Boulicaut, B Leban, A Lebedev, M LeBlanc, F Ledroit-Guillon, A C A Lee, S C Lee, S Lee, T F Lee, L L Leeuw, H P Lefebvre, M Lefebvre, C Leggett, G Lehmann Miotto, M Leigh, W A Leight, W Leinonen, A Leisos, M A L Leite, C E Leitgeb, R Leitner, K J C Leney, T Lenz, S Leone, C Leonidopoulos, A Leopold, C Leroy, R Les, C G Lester, M Levchenko, J Levêque, L J Levinson, G Levrini, M P Lewicki, D J Lewis, A Li, B Li, C Li, C-Q Li, H Li, H Li, H Li, H Li, H Li, J Li, K Li, L Li, M Li, Q Y Li, S Li, S Li, T Li, X Li, Z Li, Z Li, Z Li, S Liang, Z Liang, M Liberatore, B Liberti, K Lie, J Lieber Marin, H Lien, K Lin, R E Lindley, J H Lindon, E Lipeles, A Lipniacka, A Lister, J D Little, B Liu, B X Liu, D Liu, J B Liu, J K K Liu, K Liu, M Liu, M Y Liu, P Liu, Q Liu, X Liu, X Liu, Y Liu, Y L Liu, Y W Liu, J Llorente Merino, S L Lloyd, E M Lobodzinska, P Loch, T Lohse, K Lohwasser, E Loiacono, M Lokajicek, J D Lomas, J D Long, I Longarini, L Longo, R Longo, I Lopez Paz, A Lopez Solis, N Lorenzo Martinez, A M Lory, G Löschcke Centeno, O Loseva, X Lou, X Lou, A Lounis, P A Love, G Lu, M Lu, S Lu, Y J Lu, H J Lubatti, C Luci, F L Lucio Alves, F Luehring, I Luise, O Lukianchuk, O Lundberg, B Lund-Jensen, N A Luongo, M S Lutz, A B Lux, D Lynn, R Lysak, E Lytken, V Lyubushkin, T Lyubushkina, M M Lyukova, H Ma, K Ma, L L Ma, W Ma, Y Ma, D M Mac Donell, G Maccarrone, J C MacDonald, P C Machado De Abreu Farias, R Madar, W F Mader, T Madula, J Maeda, T Maeno, H Maguire, V Maiboroda, A Maio, K Maj, O Majersky, S Majewski, N Makovec, V Maksimovic, B Malaescu, Pa Malecki, V P Maleev, F Malek, M Mali, D Malito, U Mallik, S Maltezos, S Malyukov, J Mamuzic, G Mancini, M N Mancini, G Manco, J P Mandalia, I Mandić, L Manhaes de Andrade Filho, I M Maniatis, J Manjarres Ramos, D C Mankad, A Mann, S Manzoni, L Mao, X Mapekula, A Marantis, G Marchiori, M Marcisovsky, C Marcon, M Marinescu, S Marium, M Marjanovic, E J Marshall, Z Marshall, S Marti-Garcia, T A Martin, V J Martin, B Martin dit Latour, L Martinelli, M Martinez, P Martinez Agullo, V I Martinez Outschoorn, P Martinez Suarez, S Martin-Haugh, V S Martoiu, A C Martyniuk, A Marzin, D Mascione, L Masetti, T Mashimo, J Masik, A L Maslennikov, P Massarotti, P Mastrandrea, A Mastroberardino, T Masubuchi, T Mathisen, J Matousek, N Matsuzawa, J Maurer, B Maček, D A Maximov, R Mazini, I Maznas, M Mazza, S M Mazza, E Mazzeo, C Mc Ginn, J P Mc Gowan, S P Mc Kee, C C McCracken, E F McDonald, A E McDougall, J A Mcfayden, R P McGovern, G Mchedlidze, R P Mckenzie, T C Mclachlan, D J Mclaughlin, S J McMahon, C M Mcpartland, R A McPherson, S Mehlhase, A Mehta, D Melini, B R Mellado Garcia, A H Melo, F Meloni, A M Mendes Jacques Da Costa, H Y Meng, L Meng, S Menke, M Mentink, E Meoni, G Mercado, C Merlassino, L Merola, C Meroni, J Metcalfe, A S Mete, C Meyer, J-P Meyer, R P Middleton, L Mijović, G Mikenberg, M Mikestikova, M Mikuž, H Mildner, A Milic, D W Miller, E H Miller, L S Miller, A Milov, D A Milstead, T Min, A A Minaenko, I A Minashvili, L Mince, A I Mincer, B Mindur, M Mineev, Y Mino, L M Mir, M Miralles Lopez, M Mironova, A Mishima, M C Missio, A Mitra, V A Mitsou, Y Mitsumori, O Miu, P S Miyagawa, T Mkrtchyan, M Mlinarevic, T Mlinarevic, M Mlynarikova, S Mobius, P Mogg, M H Mohamed Farook, A F Mohammed, S Mohapatra, G Mokgatitswane, L Moleri, B Mondal, S Mondal, K Mönig, E Monnier, L Monsonis Romero, J Montejo Berlingen, M Montella, F Montereali, F Monticelli, S Monzani, N Morange, A L Moreira De Carvalho, M Moreno Llácer, C Moreno Martinez, P Morettini, S Morgenstern, M Morii, M Morinaga, F Morodei, L Morvaj, P Moschovakos, B Moser, M Mosidze, T Moskalets, P Moskvitina, J Moss, A Moussa, E J W Moyse, O Mtintsilana, S Muanza, J Mueller, D Muenstermann, R Müller, G A Mullier, A J Mullin, J J Mullin, D P Mungo, D Munoz Perez, F J Munoz Sanchez, M Murin, W J Murray, M Muškinja, C Mwewa, A G Myagkov, A J Myers, G Myers, M Myska, B P Nachman, O Nackenhorst, K Nagai, K Nagano, J L Nagle, E Nagy, A M Nairz, Y Nakahama, K Nakamura, K Nakkalil, H Nanjo, R Narayan, E A Narayanan, I Naryshkin, M Naseri, S Nasri, C Nass, G Navarro, J Navarro-Gonzalez, R Nayak, A Nayaz, P Y Nechaeva, F Nechansky, L Nedic, T J Neep, A Negri, M Negrini, C Nellist, C Nelson, K Nelson, S Nemecek, M Nessi, M S Neubauer, F Neuhaus, J Neundorf, R Newhouse, P R Newman, C W Ng, Y W Y Ng, B Ngair, H D N Nguyen, R B Nickerson, R Nicolaidou, J Nielsen, M Niemeyer, J Niermann, N Nikiforou, V Nikolaenko, I Nikolic-Audit, K Nikolopoulos, P Nilsson, I Ninca, H R Nindhito, G Ninio, A Nisati, N Nishu, R Nisius, J-E Nitschke, E K Nkadimeng, T Nobe, D L Noel, T Nommensen, M B Norfolk, R R B Norisam, B J Norman, M Noury, J Novak, T Novak, L Novotny, R Novotny, L Nozka, K Ntekas, N M J Nunes De Moura Junior, J Ocariz, A Ochi, I Ochoa, S Oerdek, J T Offermann, A Ogrodnik, A Oh, C C Ohm, H Oide, R Oishi, M L Ojeda, Y Okumura, L F Oleiro Seabra, S A Olivares Pino, D Oliveira Damazio, D Oliveira Goncalves, J L Oliver, Ö O Öncel, A P O’Neill, A Onofre, P U E Onyisi, M J Oreglia, G E Orellana, D Orestano, N Orlando, R S Orr, V O’Shea, L M Osojnak, R Ospanov, G Otero y Garzon, H Otono, P S Ott, G J Ottino, M Ouchrif, F Ould-Saada, M Owen, R E Owen, K Y Oyulmaz, V E Ozcan, F Ozturk, N Ozturk, S Ozturk, H A Pacey, A Pacheco Pages, C Padilla Aranda, G Padovano, S Pagan Griso, G Palacino, A Palazzo, J Pampel, J Pan, T Pan, D K Panchal, C E Pandini, J G Panduro Vazquez, H D Pandya, H Pang, P Pani, G Panizzo, L Panwar, L Paolozzi, S Parajuli, A Paramonov, C Paraskevopoulos, D Paredes Hernandez, A Pareti, K R Park, T H Park, M A Parker, F Parodi, E W Parrish, V A Parrish, J A Parsons, U Parzefall, B Pascual Dias, L Pascual Dominguez, E Pasqualucci, S Passaggio, F Pastore, P Patel, U M Patel, J R Pater, T Pauly, C I Pazos, J Pearkes, M Pedersen, R Pedro, S V Peleganchuk, O Penc, E A Pender, G D Penn, K E Penski, M Penzin, B S Peralva, A P Pereira Peixoto, L Pereira Sanchez, D V Perepelitsa, E Perez Codina, M Perganti, H Pernegger, O Perrin, K Peters, R F Y Peters, B A Petersen, T C Petersen, E Petit, V Petousis, C Petridou, T Petru, A Petrukhin, M Pettee, N E Pettersson, A Petukhov, K Petukhova, R Pezoa, L Pezzotti, G Pezzullo, T M Pham, T Pham, P W Phillips, G Piacquadio, E Pianori, F Piazza, R Piegaia, D Pietreanu, A D Pilkington, M Pinamonti, J L Pinfold, B C Pinheiro Pereira, A E Pinto Pinoargote, L Pintucci, K M Piper, A Pirttikoski, D A Pizzi, L Pizzimento, A Pizzini, M -A Pleier, V Plesanovs, V Pleskot, E Plotnikova, G Poddar, R Poettgen, L Poggioli, I Pokharel, S Polacek, G Polesello, A Poley, A Polini, C S Pollard, Z B Pollock, E Pompa Pacchi, D Ponomarenko, L Pontecorvo, S Popa, G A Popeneciu, A Poreba, D M Portillo Quintero, S Pospisil, M A Postill, P Postolache, K Potamianos, P A Potepa, I N Potrap, C J Potter, H Potti, T Poulsen, J Poveda, M E Pozo Astigarraga, A Prades Ibanez, J Pretel, D Price, M Primavera, M A Principe Martin, R Privara, T Procter, M L Proffitt, N Proklova, K Prokofiev, G Proto, J Proudfoot, M Przybycien, W W Przygoda, A Psallidas, J E Puddefoot, D Pudzha, D Pyatiizbyantseva, J Qian, D Qichen, Y Qin, T Qiu, A Quadt, M Queitsch-Maitland, G Quetant, R P Quinn, G Rabanal Bolanos, D Rafanoharana, F Ragusa, J L Rainbolt, J A Raine, S Rajagopalan, E Ramakoti, I A Ramirez-Berend, K Ran, N P Rapheeha, H Rasheed, V Raskina, D F Rassloff, A Rastogi, S Rave, B Ravina, I Ravinovich, M Raymond, A L Read, N P Readioff, D M Rebuzzi, G Redlinger, A S Reed, K Reeves, J A Reidelsturz, D Reikher, A Rej, C Rembser, M Renda, M B Rendel, F Renner, A G Rennie, A L Rescia, S Resconi, M Ressegotti, S Rettie, J G Reyes Rivera, E Reynolds, O L Rezanova, P Reznicek, H Riani, N Ribaric, E Ricci, R Richter, S Richter, E Richter-Was, M Ridel, S Ridouani, P Rieck, P Riedler, E M Riefel, J O Rieger, M Rijssenbeek, M Rimoldi, L Rinaldi, T T Rinn, M P Rinnagel, G Ripellino, I Riu, J C Rivera Vergara, F Rizatdinova, E Rizvi, B R Roberts, S H Robertson, D Robinson, C M Robles Gajardo, M Robles Manzano, A Robson, A Rocchi, C Roda, S Rodriguez Bosca, Y Rodriguez Garcia, A Rodriguez Rodriguez, A M Rodríguez Vera, S Roe, J T Roemer, A R Roepe-Gier, J Roggel, O Røhne, R A Rojas, C P A Roland, J Roloff, A Romaniouk, E Romano, M Romano, A C Romero Hernandez, N Rompotis, L Roos, S Rosati, B J Rosser, E Rossi, E Rossi, L P Rossi, L Rossini, R Rosten, M Rotaru, B Rottler, C Rougier, D Rousseau, D Rousso, A Roy, S Roy-Garand, A Rozanov, Z M A Rozario, Y Rozen, A Rubio Jimenez, A J Ruby, V H Ruelas Rivera, T A Ruggeri, A Ruggiero, A Ruiz-Martinez, A Rummler, Z Rurikova, N A Rusakovich, H L Russell, G Russo, J P Rutherfoord, S Rutherford Colmenares, K Rybacki, M Rybar, E B Rye, A Ryzhov, J A Sabater Iglesias, P Sabatini, H F-W Sadrozinski, F Safai Tehrani, B Safarzadeh Samani, M Safdari, S Saha, M Sahinsoy, A Saibel, M Saimpert, M Saito, T Saito, D Salamani, A Salnikov, J Salt, A Salvador Salas, D Salvatore, F Salvatore, A Salzburger, D Sammel, D Sampsonidis, D Sampsonidou, J Sánchez, V Sanchez Sebastian, H Sandaker, C O Sander, J A Sandesara, M Sandhoff, C Sandoval, D P C Sankey, T Sano, A Sansoni, L Santi, C Santoni, H Santos, A Santra, K A Saoucha, J G Saraiva, J Sardain, O Sasaki, K Sato, C Sauer, F Sauerburger, E Sauvan, P Savard, R Sawada, C Sawyer, L Sawyer, I Sayago Galvan, C Sbarra, A Sbrizzi, T Scanlon, J Schaarschmidt, U Schäfer, A C Schaffer, D Schaile, R D Schamberger, C Scharf, M M Schefer, V A Schegelsky, D Scheirich, F Schenck, M Schernau, C Scheulen, C Schiavi, M Schioppa, B Schlag, K E Schleicher, S Schlenker, J Schmeing, M A Schmidt, K Schmieden, C Schmitt, N Schmitt, S Schmitt, L Schoeffel, A Schoening, P G Scholer, E Schopf, M Schott, J Schovancova, S Schramm, T Schroer, H-C Schultz-Coulon, M Schumacher, B A Schumm, Ph Schune, A J Schuy, H R Schwartz, A Schwartzman, T A Schwarz, Ph Schwemling, R Schwienhorst, A Sciandra, G Sciolla, F Scuri, C D Sebastiani, K Sedlaczek, P Seema, S C Seidel, A Seiden, B D Seidlitz, C Seitz, J M Seixas, G Sekhniaidze, L Selem, N Semprini-Cesari, D Sengupta, V Senthilkumar, L Serin, L Serkin, M Sessa, H Severini, F Sforza, A Sfyrla, Q Sha, E Shabalina, R Shaheen, J D Shahinian, D Shaked Renous, L Y Shan, M Shapiro, A Sharma, A S Sharma, P Sharma, P B Shatalov, K Shaw, S M Shaw, A Shcherbakova, Q Shen, D J Sheppard, P Sherwood, L Shi, X Shi, C O Shimmin, J D Shinner, I P J Shipsey, S Shirabe, M Shiyakova, J Shlomi, M J Shochet, J Shojaii, D R Shope, B Shrestha, S Shrestha, E M Shrif, M J Shroff, P Sicho, A M Sickles, E Sideras Haddad, A Sidoti, F Siegert, Dj Sijacki, F Sili, J M Silva, M V Silva Oliveira, S B Silverstein, S Simion, R Simoniello, E L Simpson, H Simpson, L R Simpson, N D Simpson, S Simsek, S Sindhu, P Sinervo, S Singh, S Sinha, S Sinha, M Sioli, I Siral, E Sitnikova, J Sjölin, A Skaf, E Skorda, P Skubic, M Slawinska, V Smakhtin, B H Smart, S Yu Smirnov, Y Smirnov, L N Smirnova, O Smirnova, A C Smith, E A Smith, H A Smith, J L Smith, R Smith, M Smizanska, K Smolek, A A Snesarev, S R Snider, H L Snoek, S Snyder, R Sobie, A Soffer, C A Solans Sanchez, E Yu Soldatov, U Soldevila, A A Solodkov, S Solomon, A Soloshenko, K Solovieva, O V Solovyanov, V Solovyev, P Sommer, A Sonay, W Y Song, A Sopczak, A L Sopio, F Sopkova, J D Sorenson, I R Sotarriva Alvarez, V Sothilingam, O J Soto Sandoval, S Sottocornola, R Soualah, Z Soumaimi, D South, N Soybelman, S Spagnolo, M Spalla, D Sperlich, G Spigo, S Spinali, D P Spiteri, M Spousta, E J Staats, R Stamen, A Stampekis, M Standke, E Stanecka, M V Stange, B Stanislaus, M M Stanitzki, B Stapf, E A Starchenko, G H Stark, J Stark, P Staroba, P Starovoitov, S Stärz, R Staszewski, G Stavropoulos, J Steentoft, P Steinberg, B Stelzer, H J Stelzer, O Stelzer-Chilton, H Stenzel, T J Stevenson, G A Stewart, J R Stewart, M C Stockton, G Stoicea, M Stolarski, S Stonjek, A Straessner, J Strandberg, S Strandberg, M Stratmann, M Strauss, T Strebler, P Strizenec, R Ströhmer, D M Strom, R Stroynowski, A Strubig, S A Stucci, B Stugu, J Stupak, N A Styles, D Su, S Su, W Su, X Su, D Suchy, K Sugizaki, V V Sulin, M J Sullivan, D M S Sultan, L Sultanaliyeva, S Sultansoy, T Sumida, S Sun, S Sun, O Sunneborn Gudnadottir, N Sur, M R Sutton, H Suzuki, M Svatos, M Swiatlowski, T Swirski, I Sykora, M Sykora, T Sykora, D Ta, K Tackmann, A Taffard, R Tafirout, J S Tafoya Vargas, Y Takubo, M Talby, A A Talyshev, K C Tam, N M Tamir, A Tanaka, J Tanaka, R Tanaka, M Tanasini, Z Tao, S Tapia Araya, S Tapprogge, A Tarek Abouelfadl Mohamed, S Tarem, K Tariq, G Tarna, G F Tartarelli, P Tas, M Tasevsky, E Tassi, A C Tate, G Tateno, Y Tayalati, G N Taylor, W Taylor, A S Tee, R Teixeira De Lima, P Teixeira-Dias, J J Teoh, K Terashi, J Terron, S Terzo, M Testa, R J Teuscher, A Thaler, O Theiner, N Themistokleous, T Theveneaux-Pelzer, O Thielmann, D W Thomas, J P Thomas, E A Thompson, P D Thompson, E Thomson, Y Tian, V Tikhomirov, Yu A Tikhonov, S Timoshenko, D Timoshyn, E X L Ting, P Tipton, S H Tlou, A Tnourji, K Todome, S Todorova-Nova, S Todt, M Togawa, J Tojo, S Tokár, K Tokushuku, O Toldaiev, R Tombs, M Tomoto, L Tompkins, K W Topolnicki, E Torrence, H Torres, E Torró Pastor, M Toscani, C Tosciri, M Tost, D R Tovey, A Traeet, I S Trandafir, T Trefzger, A Tricoli, I M Trigger, S Trincaz-Duvoid, D A Trischuk, B Trocmé, L Truong, M Trzebinski, A Trzupek, F Tsai, M Tsai, A Tsiamis, P V Tsiareshka, S Tsigaridas, A Tsirigotis, V Tsiskaridze, E G Tskhadadze, M Tsopoulou, Y Tsujikawa, I I Tsukerman, V Tsulaia, S Tsuno, K Tsuri, D Tsybychev, Y Tu, A Tudorache, V Tudorache, A N Tuna, S Turchikhin, I Turk Cakir, R Turra, T Turtuvshin, P M Tuts, S Tzamarias, P Tzanis, E Tzovara, F Ukegawa, P A Ulloa Poblete, E N Umaka, G Unal, M Unal, A Undrus, G Unel, J Urban, P Urquijo, P Urrejola, G Usai, R Ushioda, M Usman, Z Uysal, V Vacek, B Vachon, K O H Vadla, T Vafeiadis, A Vaitkus, C Valderanis, E Valdes Santurio, M Valente, S Valentinetti, A Valero, E Valiente Moreno, A Vallier, J A Valls Ferrer, D R Van Arneman, T R Van Daalen, A Van Der Graaf, P Van Gemmeren, M Van Rijnbach, S Van Stroud, I Van Vulpen, M Vanadia, W Vandelli, E R Vandewall, D Vannicola, L Vannoli, R Vari, E W Varnes, C Varni, T Varol, D Varouchas, L Varriale, K E Varvell, M E Vasile, L Vaslin, G A Vasquez, A Vasyukov, R Vavricka, F Vazeille, T Vazquez Schroeder, J Veatch, V Vecchio, M J Veen, I Veliscek, L M Veloce, F Veloso, S Veneziano, A Ventura, S Ventura Gonzalez, A Verbytskyi, M Verducci, C Vergis, M Verissimo De Araujo, W Verkerke, J C Vermeulen, C Vernieri, M Vessella, M C Vetterli, A Vgenopoulos, N Viaux Maira, T Vickey, O E Vickey Boeriu, G H A Viehhauser, L Vigani, M Villa, M Villaplana Perez, E M Villhauer, E Vilucchi, M G Vincter, G S Virdee, A Vishwakarma, A Visibile, C Vittori, I Vivarelli, E Voevodina, F Vogel, J C Voigt, P Vokac, Yu Volkotrub, J Von Ahnen, E Von Toerne, B Vormwald, V Vorobel, K Vorobev, M Vos, K Voss, M Vozak, L Vozdecky, N Vranjes, M Vranjes Milosavljevic, M Vreeswijk, N K Vu, R Vuillermet, O Vujinovic, I Vukotic, S Wada, C Wagner, J M Wagner, W Wagner, S Wahdan, H Wahlberg, M Wakida, J Walder, R Walker, W Walkowiak, A Wall, E J Wallin, T Wamorkar, A Z Wang, C Wang, C Wang, H Wang, J Wang, R -J Wang, R Wang, R Wang, S M Wang, S Wang, T Wang, W T Wang, W Wang, X Wang, X Wang, X Wang, Y Wang, Y Wang, Z Wang, Z Wang, Z Wang, A Warburton, R J Ward, N Warrack, S Waterhouse, A T Watson, H Watson, M F Watson, E Watton, G Watts, B M Waugh, C Weber, H A Weber, M S Weber, S M Weber, C Wei, Y Wei, A R Weidberg, E J Weik, J Weingarten, M Weirich, C Weiser, C J Wells, T Wenaus, B Wendland, T Wengler, N S Wenke, N Wermes, M Wessels, A M Wharton, A S White, A White, M J White, D Whiteson, L Wickremasinghe, W Wiedenmann, M Wielers, C Wiglesworth, D J Wilbern, H G Wilkens, D M Williams, H H Williams, S Williams, S Willocq, B J Wilson, P J Windischhofer, F I Winkel, F Winklmeier, B T Winter, J K Winter, M Wittgen, M Wobisch, Z Wolffs, J Wollrath, M W Wolter, H Wolters, E L Woodward, S D Worm, B K Wosiek, K W Woźniak, S Wozniewski, K Wraight, C Wu, M Wu, M Wu, S L Wu, X Wu, Y Wu, Z Wu, J Wuerzinger, T R Wyatt, B M Wynne, S Xella, L Xia, M Xia, J Xiang, M Xie, X Xie, S Xin, A Xiong, J Xiong, D Xu, H Xu, L Xu, R Xu, T Xu, Y Xu, Z Xu, Z Xu, B Yabsley, S Yacoob, Y Yamaguchi, E Yamashita, H Yamauchi, T Yamazaki, Y Yamazaki, J Yan, S Yan, Z Yan, H J Yang, H T Yang, S Yang, T Yang, X Yang, X Yang, Y Yang, Y Yang, Z Yang, W-M Yao, H Ye, H Ye, J Ye, S Ye, X Ye, Y Yeh, I Yeletskikh, B K Yeo, M R Yexley, P Yin, K Yorita, S Younas, C J S Young, C Young, C Yu, Y Yu, M Yuan, R Yuan, L Yue, M Zaazoua, B Zabinski, E Zaid, Z K Zak, T Zakareishvili, N Zakharchuk, S Zambito, J A Zamora Saa, J Zang, D Zanzi, O Zaplatilek, C Zeitnitz, H Zeng, J C Zeng, D T Zenger Jr, O Zenin, T Ženiš, S Zenz, S Zerradi, D Zerwas, M Zhai, D F Zhang, J Zhang, J Zhang, K Zhang, L Zhang, P Zhang, R Zhang, S Zhang, S Zhang, T Zhang, X Zhang, X Zhang, Y Zhang, Y Zhang, Y Zhang, Z Zhang, Z Zhang, H Zhao, T Zhao, Y Zhao, Z Zhao, A Zhemchugov, J Zheng, K Zheng, X Zheng, Z Zheng, D Zhong, B Zhou, H Zhou, N Zhou, Y Zhou, Y Zhou, C G Zhu, J Zhu, Y Zhu, Y Zhu, X Zhuang, K Zhukov, N I Zimine, J Zinsser, M Ziolkowski, L Živković, A Zoccoli, K Zoch, T G Zorbas, O Zormpa, W Zou, L Zwalinski, The ATLAS Collaborationgabriela.navarro@uan.edu.co","doi":"10.1088/2632-2153/ad611e","DOIUrl":"https://doi.org/10.1088/2632-2153/ad611e","url":null,"abstract":"The energy and mass measurements of jets are crucial tasks for the Large Hadron Collider experiments. This paper presents a new calibration method to simultaneously calibrate these quantities for large-radius jets measured with the ATLAS detector using a deep neural network (DNN). To address the specificities of the calibration problem, special loss functions and training procedures are employed, and a complex network architecture, which includes feature annotation and residual connection layers, is used. The DNN-based calibration is compared to the standard numerical approach in an extensive series of tests. The DNN approach is found to perform significantly better in almost all of the tests and over most of the relevant kinematic phase space. In particular, it consistently improves the energy and mass resolutions, with a 30% better energy resolution obtained for transverse momenta <inline-formula>\u0000<tex-math><?CDATA $p_{text{T}}gt 500$?></tex-math><mml:math overflow=\"scroll\"><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mrow><mml:mtext>T</mml:mtext></mml:mrow></mml:msub><mml:mo>&gt;</mml:mo><mml:mn>500</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"mlstad611eieqn1.gif\"></inline-graphic></inline-formula> GeV.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SemiH: DFT Hamiltonian neural network training with semi-supervised learning","authors":"Yucheol Cho, Guenseok Choi, Gyeongdo Ham, Mincheol Shin, Daeshik Kim","doi":"10.1088/2632-2153/ad7227","DOIUrl":"https://doi.org/10.1088/2632-2153/ad7227","url":null,"abstract":"Over the past decades, density functional theory (DFT) calculations have been utilized in various fields such as materials science and semiconductor devices. However, due to the inherent nature of DFT calculations, which rigorously consider interactions between atoms, they require significant computational cost. To address this, extensive research has recently focused on training neural networks to replace DFT calculations. However, previous methods for training neural networks necessitated an extensive number of DFT simulations to acquire the ground truth (Hamiltonians). Conversely, when dealing with a limited amount of training data, deep learning models often display increased errors in predicting Hamiltonians and band structures for testing data. This phenomenon poses the potential risk of generating inaccurate physical interpretations, including the emergence of unphysical branches within band structures. To tackle this challenge, we propose a novel deep learning-based method for calculating DFT Hamiltonians, specifically tailored to produce accurate results with limited training data. Our framework not only employs supervised learning with the calculated Hamiltonian but also generates pseudo Hamiltonians (targets for unlabeled data) and trains the neural networks on unlabeled data. Particularly, our approach, which leverages unlabeled data, is noteworthy as it marks the first attempt in the field of neural network Hamiltonians. Our framework showcases the superior performance of our framework compared to the state-of-the-art approach across various datasets, such as MoS₂, Bi₂Te₃, HfO₂, and InGaAs. Moreover, our framework demonstrates enhanced generalization performance by effectively utilizing unlabeled data, achieving noteworthy results when evaluated on data more complex than the training set, such as configurations with more atoms and temperature ranges outside the training data.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":null,"pages":null},"PeriodicalIF":6.8,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}