Article
Computer Science, Information Systems
Dongyang Zhang, Jie Shao, Zhenwen Liang, Lianli Gao, Heng Tao Shen
Summary: This study introduces a cascaded super-resolution convolutional neural network (CSRCNN) to address the aliasing artifacts and high computational costs caused by existing methods that use interpolation during the beginning stage. Experimental results show that the proposed network achieves superior performance, especially with an 8x upsampling factor.
IEEE TRANSACTIONS ON MULTIMEDIA
(2021)
Article
Computer Science, Artificial Intelligence
Quanshi Zhang, Xin Wang, Ying Nian Wu, Huilin Zhou, Song-Chun Zhu
Summary: This paper introduces a generic method to learn interpretable convolutional filters in a deep CNN without additional annotations, using the same training data as traditional CNNs. The experiments demonstrate that the interpretable filters are more semantically meaningful than traditional filters.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Sebastien Herbreteau, Charles Kervrann
Summary: This study addresses the problem of image denoising, focusing on the DCT image denoising algorithm and its combination with deep convolutional neural networks (CNN). By tuning the linear transform of DCT through gradient descent, its performance is improved, and a hybrid solution that combines DCT and DCT2net is proposed to deal with remaining artifacts.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Quanshi Zhang, Jie Ren, Ge Huang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu
Summary: This paper presents a method for mining object-part patterns from a pre-trained CNN, organizing the mined patterns using an And-Or graph to semanticize CNN representations. The method exhibits high learning efficiency in experiments and achieves similar or better part-localization performance compared to fast-RCNN methods.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Computer Science, Information Systems
Shih-Wei Hu, Gang-Xuan Lin, Chun-Shien Lu
Summary: A learning-based method termed GPX-ADMM-Net is proposed for solving image compressive sensing problems, achieving high performance and adaptivity to measurement rates and cross-task tasks, such as other image inverse problems.
Article
Computer Science, Artificial Intelligence
Daniela Szwarcman, Daniel Civitarese, Marley Vellasco
Summary: This paper introduces the Q-NAS algorithm, which can automatically generate network architectures that outperform hand-designed models on CIFAR-10 and CIFAR-100. Compared to other neural architecture search methods, Q-NAS achieves a promising balance between performance, runtime efficiency, and automation.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Jianjun Chu, Xiaoshan Yu, Shangshang Yang, Jianfeng Qiu, Qijun Wang
Summary: This paper proposes an evolutionary algorithm for neural network architecture search, introduces a novel indicator to measure population diversity, and suggests effective sampling and encoding strategies to optimize the search process. Experimental results demonstrate that the proposed method outperforms existing comparison methods.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jie Chen, Licheng Jiao, Xu Liu, Fang Liu, Lingling Li, Shuyuan Yang
Summary: This article proposes a novel approach for modeling contextual relationships in images using a multiresolution interpretable contourlet graph network (MICGNet), which balances graph representation learning with the geometric features of images. Experimental analysis shows that MICGNet is significantly more effective and efficient than other recent algorithms.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoyu He, Yong Wang, Xiaojing Wang, Weihong Huang, Shuang Zhao, Xiang Chen
Summary: This paper introduces a genetic algorithm with a simple encoding scheme (SEECNN) for evolving CNNs to address image classification problems, and achieves effective results through a stable search strategy to update the population.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Information Systems
Gean T. Pereira, Iury B. A. Santos, Luis P. F. Garcia, Thierry Urruty, Muriel Visani, Andre C. P. L. F. de Carvalho
Summary: This paper proposes a Prediction-based and interpretable Meta-Learning method called MbML-NAS, which can generalize to different search spaces and datasets using less data. The method uses interpretable meta-features extracted from neural architectures and regression models as meta-predictors to infer Convolutional Networks performances.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Fahui Miao, Li Yao, Xiaojie Zhao
Summary: CNN design usually requires manual iterations and adjustments, which are time-consuming and labor-intensive. Our proposed sosCNN algorithm automates the construction of strong CNN architectures.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Xin Deng, Enpeng Liu, Shengxi Li, Yiping Duan, Mai Xu
Summary: Multi-modal image registration aims to align two images from different modalities by separating alignment features from non-alignment features. The proposed DCSC model and InMIR-Net use deep learning to improve registration accuracy and efficiency. The accompanying guidance network further enhances feature extraction. Extensive experiments demonstrate the effectiveness of the method on various multi-modal image datasets.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Article
Environmental Sciences
Zhen Zhang, Shanghao Liu, Yang Zhang, Wenbo Chen
Summary: This article proposes a new paradigm for automatically designing a suitable CNN architecture for remote sensing scene classification. The more efficient RS-DARTS search framework is adopted to find the optimal network architecture, with new strategies introduced in the search phase, noise added to suppress skip connections, and sampling to reduce redundancy in exploring the network space. Extensive experiments demonstrate the effectiveness of the proposed method in classification performance and search cost compared to other methods.
Article
Computer Science, Artificial Intelligence
Wei Wang, Xianpeng Wang, Xiangman Song
Summary: This research proposes a sparse convex surrogate model to guide the evolutionary process of CNN design and a balance strategy between computational resources and accuracy in the selection of network architectures. Experimental results show that the proposed method is competitive in the segmentation of steel microstructure images.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Debanjan Konar, Siddhartha Bhattacharyya, Tapan K. Gandhi, Bijaya K. Panigrahi, Richard Jiang
Summary: This article presents a new shallow 3D self-supervised tensor neural network called 3D quantum-inspired self-supervised tensor neural network (3D-QNet) for volumetric segmentation of medical images. The network consists of input, intermediate, and output layers interconnected using a third-order neighborhood-based topology. Each layer contains quantum neurons designated by qubits or quantum bits. The network incorporates tensor decomposition in quantum formalism for faster convergence and achieves promising results in semantic segmentation.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Physics, Multidisciplinary
J. Knoerzer, T. Shi, E. Demler, J. Cirac
Summary: By studying trapped-ion quantum systems, we can gain insights into generalized Holstein models and benchmark expensive numerical calculations. Our focus is on simulating many-electron systems and examining the competition between charge-density wave order, fermion pairing, and phase separation.
PHYSICAL REVIEW LETTERS
(2022)
Article
Physics, Multidisciplinary
A. von Hoegen, M. Fechner, M. Foerst, N. Taherian, E. Rowe, A. Ribak, J. Porras, B. Keimer, M. Michael, E. Demler, A. Cavalleri
Summary: In this study, it is shown that certain lattice vibrations in cuprate high-T-c superconductors can induce transient terahertz reflectivity features suggestive of nonequilibrium superconductivity above the critical temperature. Time-resolved measurements reveal a three-order-of-magnitude amplification of a 2.5-THz electronic mode in driven YBa2Cu3O6+x. Theoretical analysis explains these observations by proposing an amplification mechanism for finite-momentum Josephson plasma polaritons. The study also emphasizes the significance of nonlinear mode mixing in amplifying fluctuating modes above the transition temperature in a wide range of materials.
Article
Multidisciplinary Sciences
Sarah Hirthe, Thomas Chalopin, Dominik Bourgund, Petar Bojovic, Annabelle Bohrdt, Eugene Demler, Fabian Grusdt, Immanuel Bloch, Timon A. Hilker
Summary: In this study, the experimental method of quantum gas of ultracold atoms was used to observe hole pairing phenomenon caused by magnetic correlations in a doped antiferromagnetic ladder system with mixed-dimensional couplings. The results showed that magnetic correlations can significantly increase the binding energy of holes and reduce the pair size, allowing holes to predominantly occupy the same rung of the ladder. It was also found that spatial structures in the pair distribution appeared with increased doping, indicating repulsion between bound hole pairs. By engineering a configuration to enhance binding, a strategy to increase the critical temperature for superconductivity was outlined.
Article
Physics, Multidisciplinary
F. A. Palm, M. Kurttutan, A. Bohrdt, U. Schollwoeck, F. Grusdt
Summary: Strongly interacting fermionic systems can exhibit interesting quantum many-body states with exotic excitations. This study focuses on the interplay between strong interactions and the Pauli exclusion principle in the Hofstadter-Fermi-Hubbard model. The researchers discover a lattice analog of the quantum Hall ferromagnet at magnetic filling factor nu = 1, and observe spin-singlet states with spin-spin correlations similar to skyrmions. They also predict the breakdown of flat-band ferromagnetism at large fields. This work opens up possibilities for experimental studies of lattice QH ferromagnetism and its relation to high-Tc superconductivity.
NEW JOURNAL OF PHYSICS
(2023)
Article
Physics, Multidisciplinary
Yuri D. Lensky, Kostyantyn Kechedzhi, Igor Aleiner, Eun-Ah Kim
Summary: Stabilizer codes allow for non-local encoding and processing of quantum information. Deformations of stabilizer surface codes introduce new and non-trivial geometry, leading to emergence of long sought after objects known as projective Ising non-Abelian anyons. We present a simple and systematic approach to construct effective unitary protocols for braiding, manipulation and readout of non-Abelian anyons.
Article
Chemistry, Multidisciplinary
Valerie Hsieh, Dorri Halbertal, Nathan R. . Finney, Ziyan Zhu, Eli Gerber, Michele Pizzochero, Emine Kucukbenli, Gabriel R. Schleder, Mattia Angeli, Kenji Watanabe, Takashi Taniguchi, Eun-Ah Kim, Efthimios Kaxiras, James Hone, Cory R. Dean, D. N. Basov
Summary: Twisted van der Waals multilayers are regarded as a rich platform for accessing novel electronic phases. This study proposes that naturally formed stacking domains due to relative twist between layers can act as an additional control knob. The researchers observe selective adhesion of metallic nanoparticles and liquid water at domains with specific stacking configurations and demonstrate the manipulation of nanoparticles can locally reconfigure the moire superlattice.
Article
Multidisciplinary Sciences
T. I. Andersen, Y. D. Lensky, K. Kechedzhi, I. K. Drozdov, A. Bengtsson, S. Hong, A. Morvan, X. Mi, A. Opremcak, R. Acharya, R. Allen, M. Ansmann, F. Arute, K. Arya, A. Asfaw, J. Atalaya, R. Babbush, D. Bacon, J. C. Bardin, G. Bortoli, A. Bourassa, J. Bovaird, L. Brill, M. Broughton, B. B. Buckley, D. A. Buell, T. Burger, B. Burkett, N. Bushnell, Z. Chen, B. Chiaro, D. Chik, C. Chou, J. Cogan, R. Collins, P. Conner, W. Courtney, A. L. Crook, B. Curtin, D. M. Debroy, A. Del Toro Barba, S. Demura, A. Dunsworth, D. Eppens, C. Erickson, L. Faoro, E. Farhi, R. Fatemi, V. S. Ferreira, L. F. Burgos, E. Forati, A. G. Fowler, B. Foxen, W. Giang, C. Gidney, D. Gilboa, M. Giustina, R. Gosula, A. G. Dau, J. A. Gross, S. Habegger, M. C. Hamilton, M. Hansen, M. P. Harrigan, S. D. Harrington, P. Heu, J. Hilton, M. R. Hoffmann, T. Huang, A. Huff, W. J. Huggins, L. B. Ioffe, S. V. Isakov, J. Iveland, E. Jeffrey, Z. Jiang, C. Jones, P. Juhas, D. Kafri, T. Khattar, M. Khezri, M. Kieferova, S. Kim, A. Kitaev, P. V. Klimov, A. R. Klots, A. N. Korotkov, F. Kostritsa, J. M. Kreikebaum, D. Landhuis, P. Laptev, K. -M. Lau, L. Laws, J. Lee, K. W. Lee, B. J. Lester, A. T. Lill, W. Liu, A. Locharla, E. Lucero, F. D. Malone, O. Martin, J. R. McClean, T. McCourt, M. McEwen, K. C. Miao, A. Mieszala, M. Mohseni, S. Montazeri, E. Mount, R. Movassagh, W. Mruczkiewicz, O. Naaman, M. Neeley, C. Neill, A. Nersisyan, M. Newman, J. H. Ng, A. Nguyen, M. Nguyen, M. Y. Niu, T. E. O'Brien, S. Omonije, A. Petukhov, R. Potter, L. P. Pryadko, C. Quintana, C. Rocque, N. C. Rubin, N. Saei, D. Sank, K. Sankaragomathi, K. J. Satzinger, H. F. Schurkus, C. Schuster, M. J. Shearn, A. Shorter, N. Shutty, V. Shvarts, J. Skruzny, W. C. Smith, R. Somma, G. Sterling, D. Strain, M. Szalay, A. Torres, G. Vidal, B. Villalonga, C. V. Heidweiller, T. White, B. W. K. Woo, C. Xing, Z. J. Yao, P. Yeh, J. Yoo, G. Young, A. Zalcman, Y. Zhang, N. Zhu, N. Zobrist, H. Neven, S. Boixo, A. Megrant, J. Kelly, Y. Chen, V. Smelyanskiy, E. -A. Kim, I. Aleiner, P. Roushan
Summary: Indistinguishability of particles is a fundamental principle in quantum mechanics. While braiding of Abelian anyons leaves the system unchanged, braiding of non-Abelian anyons can change the observables of the system without violating the principle of indistinguishability. Experimental observation of non-Abelian anyons' exchange statistics has remained elusive, but using quantum processors, it is now possible to manipulate and braid them, allowing for the verification of their fusion rules and statistics. This work provides insights into non-Abelian braiding and its potential application in fault-tolerant quantum computing with the inclusion of error correction.
Correction
Multidisciplinary Sciences
Michael Matty, Eun-Ah Kim
NATURE COMMUNICATIONS
(2023)
Article
Materials Science, Multidisciplinary
Linus Kautzsch, Brenden R. Ortiz, Krishnanand Mallayya, Jayden Plumb, Ganesh Pokharel, Jacob P. C. Ruff, Zahirul Islam, Eun-Ah Kim, Ram Seshadri, Stephen D. Wilson
Summary: The structural ground states and temperature-dependent evolution of the kagome superconductors KV3Sb5, RbV3Sb5, and CsV3Sb5 have been investigated. KV3Sb5 and RbV3Sb5 exhibit 2 x 2 x 2 superstructures with staggered trihexagonal deformation of vanadium layers in the Fmmm space group. CsV3Sb5 displays more complex structural evolution, with a staged progression of ordering into a 2 x 2 x 4 supercell exhibiting trihexagonal and Star of David patterns of deformations. Diffraction under pulsed magnetic fields suggests that the CDW state of CsV3Sb5 is insensitive to external magnetic fields.
PHYSICAL REVIEW MATERIALS
(2023)
Article
Multidisciplinary Sciences
Muqing Xu, Lev Haldar Kendrick, Anant Kale, Youqi Gang, Geoffrey Ji, Richard T. T. Scalettar, Martin Lebrat, Markus Greiner
Summary: Geometrical frustration in strongly correlated systems can lead to the emergence of novel ordered states and magnetic phases, such as quantum spin liquids. This study investigates the effects of frustration and doping on the local spin order in a controllable Hubbard model. The results show that frustration reduces the range of magnetic correlations and induces a transition from a collinear Neel antiferromagnet to a short-range correlated 120 degrees spiral phase. The triangular lattice exhibits enhanced antiferromagnetic correlations on the hole-doped side and a reversal to ferromagnetic correlations at high particle dopings.
Article
Materials Science, Multidisciplinary
Ganesh Pokharel, Brenden R. Ortiz, Linus Kautzsch, S. J. Gomez Alvarado, Krishnanand Mallayya, Guang Wu, Eun-Ah Kim, Jacob P. C. Ruff, Suchismita Sarker, Stephen D. Wilson
Summary: In this study, the stability of charge order in the kagome metal ScV6Sn6 is investigated. Short-range and long-range charge correlations at different wave vectors are observed, which are both quenched upon the introduction of larger Y ions. The results validate the theoretical prediction of the primary lattice instability and provide insights into the frustration of charge order in this compound.
PHYSICAL REVIEW MATERIALS
(2023)
Article
Physics, Multidisciplinary
Cole Miles, Rhine Samajdar, Sepehr Ebadi, Tout T. Wang, Hannes Pichler, Subir Sachdev, Mikhail D. Lukin, Markus Greiner, Kilian Q. Weinberger, Eun-Ah Kim
Summary: Machine learning is a promising approach for studying complex phenomena with rich datasets. This study introduces a hybrid-correlation convolutional neural network (hybrid-CCNN) and applies it to experimental data generated by a programmable quantum simulator. The hybrid-CCNN is able to discover and identify new quantum phases on square lattices with programmable interactions. This combination of programmable quantum simulators with machine learning provides a powerful tool for exploring correlated quantum states of matter.
PHYSICAL REVIEW RESEARCH
(2023)
Article
Optics
Anant Kale, Jakob Hendrik Huhn, Muqing Xu, Lev Haldar Kendrick, Martin Lebrat, Christie Chiu, Geoffrey Ji, Fabian Grusdt, Annabelle Bohrdt, Markus Greiner
Summary: In strongly interacting systems with a separation of energy scales, low-energy effective Hamiltonians provide insights into the physics at low temperatures. Virtual excitations mediate the interactions in the effective model, making it advantageous to consider the effective model for interpreting experimental results. By performing measurements in a rotated basis, quantum simulators allow more direct access to the effective model. A proposed protocol involving a linear ramp of the optical lattice depth enables the preparation of approximate t-J-3s model states by eliminating virtual excitations.
Article
Computer Science, Artificial Intelligence
Peter Cha, Paul Ginsparg, Felix Wu, Juan Carrasquilla, Peter L. McMahon, Eun-Ah Kim
Summary: In this study, an attention mechanism-based generative network model is proposed for quantum state reconstruction of noisy quantum states. The research demonstrates that the model outperforms previous neural-network-based methods on identical tasks and accurately reconstructs the density matrix of a noisy quantum state realized experimentally.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2022)