Article
Mathematics, Applied
Tapio Helin, Nuutti Hyvonen, Juha-Pekka Puska
Summary: This work focuses on sequential edge-promoting Bayesian experimental design for linear inverse problems, specifically X-ray tomography. It interprets the computation process of total variation-type absorption reconstruction inside the imaged body using lagged diffusivity iteration in the Bayesian framework. By assuming a Gaussian additive noise model, an approximate Gaussian posterior with a covariance structure containing information about the location of edges in the posterior mean is obtained. The next projection geometry is then determined using A- or D-optimal Bayesian design, which minimizes the trace or determinant of the updated posterior covariance matrix that accounts for the new projection. Two- and three-dimensional numerical examples based on simulated data demonstrate the effectiveness of this approach.
SIAM JOURNAL ON SCIENTIFIC COMPUTING
(2022)
Correction
Optics
Joseph C. Chapman, Joseph M. Lukens, Bing Qi, Raphael C. Pooser, Nicholas A. Peters
Summary: We have corrected typographical errors in Eq. (15) in [Opt. Express 30, 15184 (2022)] [1]. These errors were not present in the actual formulas used for calculating the results of the paper, so all results remain unaffected.
Article
Engineering, Multidisciplinary
Ahmad Karimi, Leila Taghizadeh, Clemens Heitzinger
Summary: The optimal design of electronic devices such as sensors is crucial for accurate and timely output. This work focuses on optimal Bayesian inversion for electrical impedance tomography (EIT) technology to improve the quality of medical images and to optimize experimental designs for accurate estimation of unknown parameters. The proposed method shows efficiency in solving the EIT inverse problem and producing high-resolution medical images.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2021)
Article
Biochemical Research Methods
Jintao Li, Lizhi Zhang, Jia Liu, Diya Zhang, Dizhen Kang, Beilei Wang, Xiaowei He, Heng Zhang, Yizhe Zhao, Hongbo Guo, Yuqing Hou
Summary: This paper proposes a universally applicable adaptive parameter selection method based on maximizing the probability of data (MPD) strategy in fluorescent molecular tomography (FMT) regularization methods. The strategy establishes a regularization parameter model using maximum a posteriori (MAP) estimation and maximum likelihood (ML) estimation and determines the stable optimal parameters through multiple iterative estimates. Numerical simulations and in vivo experiments demonstrate that the MPD strategy can provide stable regularization parameters for both L-2 or L-1 norm-based regularization algorithms and achieve good reconstruction performance.
JOURNAL OF BIOPHOTONICS
(2023)
Article
Ecology
Kelly M. Thomasson, Alexander Franks, Henrique Teotonio, Stephen R. Proulx
Summary: Saccharomyces yeast undergoes sporulation in response to starvation, producing haploid cells enclosed in a protective ascus. Passage through insect guts selects for increased spore production, with wild-derived strains showing a more rapid and extreme shift towards sporulation. Domesticated strains exhibit a weaker response, suggesting genetic canalization of the sporulation initiation response.
Article
Computer Science, Interdisciplinary Applications
G. Peter Lepage
Summary: The VEGAS+ algorithm is more accurate than VEGAS in certain cases, especially for integrands with multiple peaks or structures aligned with diagonals of the integration volume. It can be combined with other integrators for better performance.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Article
Engineering, Electrical & Electronic
Lifeng Zhang, Li Dai
Summary: A novel ECT image reconstruction algorithm based on an adaptive support driven Bayesian reweighted (ASDBR) algorithm was proposed, which accurately extracts the main features of the flow pattern and removes redundant information by iteratively reweighting weights. This new method considerably enhances the quality of the reconstructed image by transforming the original problem into a series of subproblems and solving them using the iterative shrinkage-thresholding algorithm (ISTA).
IEEE SENSORS JOURNAL
(2021)
Article
Engineering, Multidisciplinary
Simona Dobrilla, Matteo Lunardelli, Mijo Nikolic, Dirk Lowke, Bojana Rosic
Summary: In this study, we propose Bayesian parameter estimation for a nonlinear mechanics model describing the behavior of mortar under double shear test with externally bonded carbon fiber reinforced polymer (CFRP) plates. The Bayesian approach allows us to identify material parameters of different phases of the mortar mesostructure. A novel sequential approach is used for parameter inference, eliminating the need for coupling between the finite element solver and stochastic analysis software. The model geometry and material mesostructure are learned from micro-computed tomography (mu CT) scans, while unknown boundary conditions are identified from experimental data. Mortar is modeled using a discrete lattice model with embedded discontinuities, enabling the description of material degradation stages.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Physics, Multidisciplinary
Mohammad Mehboudi, Mathias R. Jorgensen, Stella Seah, Jonatan B. Brask, Jan Kolodynski, Marti Perarnau-Llobet
Summary: In this study, we explore the limits of thermometry using quantum probes within the Bayesian approach. We investigate the possibilities of enhancing the sensitivity of the probes by engineering their interactions and utilizing adaptive protocols. The findings reveal an ultimate bound on thermometry precision in the Bayesian setting, with a quadratic scaling of the error when considering arbitrary interactions and measurement schemes. Additionally, a no-go theorem is derived for nonadaptive protocols that restricts the improvement of thermometry precision beyond linear scaling even with unlimited control over the probes.
PHYSICAL REVIEW LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Phuc Luong, Dang Nguyen, Sunil Gupta, Santu Rana, Svetha Venkatesh
Summary: The paper proposes a single-objective cost-aware BO framework, which utilizes a multi-armed bandit algorithm to quickly find a suitable strategy to deal with the cost of the optimization problem, achieving efficient optimization of expensive black-box functions.
KNOWLEDGE-BASED SYSTEMS
(2021)
Review
Respiratory System
Elizabeth G. Ryan, Dominique-Laurent Couturier, Stephane Heritier
Summary: The use of Bayesian adaptive designs in clinical trials has increased recently, especially during the COVID-19 pandemic. Bayesian adaptive designs offer a flexible and efficient framework and may provide more useful and natural results for clinicians compared to traditional approaches.
Article
Mathematics
Hassan Okasha, Yuhlong Lio, Mohammed Albassam
Summary: This study focuses on establishing the E-Bayesian estimates for the Lomax distribution shape parameter functions by utilizing the Gamma prior of the unknown shape parameter along with three distinctive joint priors of Gamma hyper-parameters, analyzing the effect of hyper-parameters' selections through mathematical propositions and conducting Monte Carlo simulations for comparison.
Article
Chemistry, Multidisciplinary
Naser Aldulaijan, Joe A. Marsden, Jamie A. Manson, Adam D. Clayton
Summary: Catalytic reactions play a crucial role in industrial processes, but complex interactions between categorical and continuous variables result in non-smooth response surfaces. In this article, a new adaptive latent Bayesian optimiser (ALaBO) algorithm is developed and benchmarked for optimization of mixed variable chemical reactions. By integrating ALaBO with a continuous flow reactor, rapid self-optimization of an exemplar Suzuki-Miyaura cross-coupling reaction was achieved.
REACTION CHEMISTRY & ENGINEERING
(2023)
Article
Engineering, Industrial
Kyeongsu Kim, Gunhak Lee, Keonhee Park, Seongho Park, Won Bo Lee
Summary: A framework for predicting corrosion defect distribution using limited observation data was proposed in this study. The model parameters were estimated through Bayesian inferences to develop a robust prediction model that could adapt to changing defect depth distributions under different scenarios. The addition of an artificial data point and updating parameters with new inspection data contributed to the model's conservative estimation and higher reliability.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Physics, Multidisciplinary
Rui-Qi Zhang, Zhibo Hou, Jun-Feng Tang, Jiangwei Shang, Huangjun Zhu, Guo-Yong Xiang, Chuan-Feng Li, Guang-Can Guo
Summary: Verifying the correct functioning of quantum gates is crucial for reliable quantum information processing. Recent theoretical breakthroughs have shown that it is possible to verify various quantum gates with optimal sample complexity using local operations only. This study proposes a variant of quantum gate verification (QGV) that is robust to practical gate imperfections and experimentally demonstrates efficient QGV on controlled-not and Toffoli gates using local state preparations and measurements.
PHYSICAL REVIEW LETTERS
(2022)
Article
Quantum Science & Technology
Adriano Macarone Palmieri, Egor Kovlakov, Federico Bianchi, Dmitry Yudin, Stanislav Straupe, Jacob D. Biamonte, Sergei Kulik
NPJ QUANTUM INFORMATION
(2020)
Article
Optics
A. D. Moiseevskiy, G. Struchalin, S. S. Straupe, S. P. Kulik
LASER PHYSICS LETTERS
(2020)
Article
Physics, Applied
O. V. Borzenkova, G. I. Struchalin, A. S. Kardashin, V. V. Krasnikov, N. N. Skryabin, S. S. Straupe, S. P. Kulik, J. D. Biamonte
Summary: This study explores the impact of noise on quantum phase transitions in the Schwinger model within a variational framework, finding that despite noise, variational quantum algorithms can detect the phase transition of the Schwinger Hamiltonian.
APPLIED PHYSICS LETTERS
(2021)
Article
Physics, Multidisciplinary
Yong Siah Teo, Seongwook Shin, Hyunseok Jeong, Yosep Kim, Yoon-Ho Kim, Gleb Struchalin, Egor Kovlakov, Stanislav S. Straupe, Sergei P. Kulik, Gerd Leuchs, Luis L. Sanchez-Soto
Summary: In this study, convolutional neural networks are trained to predict the completeness of information in quantum measurements, accelerating the characterization of quantum states. Experimental results show that trained networks can significantly reduce certification time and improve the computation yield of large-scale quantum processors.
NEW JOURNAL OF PHYSICS
(2021)
Article
Optics
S. P. Kulik, K. S. Kravtsov, S. N. Molotkov
Summary: This paper analyzes the relationship between Fock states with different numbers of photons and pure coherent states with random phases, and discusses the experimental resources required to prepare Fock states with specific photon numbers from superposition of Fock states. Optical schemes for implementing PNS attack are provided, and estimates of experimental parameters at which the attack is possible are made.
LASER PHYSICS LETTERS
(2022)
Article
Optics
P. M. Vinetskaya, K. S. Kravtsov, N. A. Borshchevskaia, A. N. Klimov, S. P. Kulik
Summary: This paper reviews possible realizations of entanglement-based QKD and assesses their feasibility in terms of implementation complexity and provided security. It also proposes a novel active basis choice approach that enables to use only one single-photon detector per user. The paper provides all necessary details including the required electro-optic crystal configurations to implement such a scheme experimentally.
LASER PHYSICS LETTERS
(2023)
Article
Optics
L. Gerasimov, R. R. Yusupov, I. B. Bobrov, D. Shchepanovich, E. Kovlakov, S. S. Straupe, S. P. Kulik, D. Kupriyanov
Summary: In this study, we theoretically investigate the coherent dynamics of a spin qubit encoded in hyperfine sublevels of an alkali-metal atom in a far off-resonant optical dipole trap. We focus on various dephasing processes and their effects on the qubit dynamics. Our fully quantum treatment of the atomic motion remains valid in the limit of close-to-ground-state cooling with a low number of vibrational excitations, supported by reasonable correspondence with an experiment without fitting parameters.
Article
Quantum Science & Technology
G. Struchalin, Ya A. Zagorovskii, E. Kovlakov, S. S. Straupe, S. P. Kulik
Summary: This study demonstrates the performance of property estimation based on classical shadows in a high-dimensional spatial photon state experiment. Experimental data shows that this method can outperform traditional full state reconstruction methods.
Proceedings Paper
Quantum Science & Technology
I. Kondratyev, I. Dyakonov, M. Yu Saygin, S. S. Straupe, S. P. Kulik
FIFTH INTERNATIONAL CONFERENCE ON QUANTUM TECHNOLOGIES (ICQT-2019)
(2020)
Proceedings Paper
Quantum Science & Technology
N. N. Skryabin, S. A. Zhuravitskii, I. V. Dyakonov, M. Yu. Saygin, S. S. Straupe, S. P. Kulik
FIFTH INTERNATIONAL CONFERENCE ON QUANTUM TECHNOLOGIES (ICQT-2019)
(2020)
Article
Optics
K. S. Kravtsov, A. K. Zhutov, S. P. Kulik
Article
Optics
F. V. Gubarev, I. V. Dyakonov, M. Yu. Saygin, G. I. Struchalin, S. S. Straupe, S. P. Kulik
