Quantum autoencoders via quantum adders with genetic algorithms

The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the inner than in the outer layers were considered. Here, we propose a useful connection between approximate quantum adders and quantum autoencoders. Specifically, this link allows us to employ optimized approximate quantum adders, obtained with genetic algorithms, for the implementation of quantum autoencoders for a variety of initial states. Furthermore, we can also directly optimize the quantum autoencoders via genetic algorithms. Our approach opens a different path for the design of quantum autoencoders in controllable quantum platforms

The pp-Daugavet property for function spaces

A natural extension of the Daugavet property for $p$-convex Banach function spaces and related classes is analysed. As an application, we extend the arguments given in the setting of the Daugavet property to show that no reflexive space falls into this class

Profiled support vector machines for antisense oligonucleotide efficacy prediction

BACKGROUND: This paper presents the use of Support Vector Machines (SVMs) for prediction and analysis of antisense oligonucleotide (AO) efficacy. The collected database comprises 315 AO molecules including 68 features each, inducing a problem well-suited to SVMs. The task of feature selection is crucial given the presence of noisy or redundant features, and the well-known problem of the curse of dimensionality. We propose a two-stage strategy to develop an optimal model: (1) feature selection using correlation analysis, mutual information, and SVM-based recursive feature elimination (SVM-RFE), and (2) AO prediction using standard and profiled SVM formulations. A profiled SVM gives different weights to different parts of the training data to focus the training on the most important regions. RESULTS: In the first stage, the SVM-RFE technique was most efficient and robust in the presence of low number of samples and high input space dimension. This method yielded an optimal subset of 14 representative features, which were all related to energy and sequence motifs. The second stage evaluated the performance of the predictors (overall correlation coefficient between observed and predicted efficacy, r; mean error, ME; and root-mean-square-error, RMSE) using 8-fold and minus-one-RNA cross-validation methods. The profiled SVM produced the best results (r = 0.44, ME = 0.022, and RMSE= 0.278) and predicted high (>75% inhibition of gene expression) and low efficacy (<25%) AOs with a success rate of 83.3% and 82.9%, respectively, which is better than by previous approaches. A web server for AO prediction is available online at . CONCLUSIONS: The SVM approach is well suited to the AO prediction problem, and yields a prediction accuracy superior to previous methods. The profiled SVM was found to perform better than the standard SVM, suggesting that it could lead to improvements in other prediction problems as well

A simulational and theoretical study of the spherical electrical double layer for a size-asymmetric electrolyte: the case of big coions

Monte Carlo simulations of a spherical macroion, surrounded by a size-asymmetric electrolyte in the primitive model, were performed. We considered 1:1 and 2:2 salts with a size ratio of 2 (i.e., with coions twice the size of counterions), for several surface charge densities of the macrosphere. The radial distribution functions, electrostatic potential at the Helmholtz surfaces, and integrated charge are reported. We compare these simulational data with original results obtained from the Ornstein-Zernike integral equation, supplemented by the hypernetted chain/hypernetted chain (HNC/HNC) and hypernetted chain/mean spherical approximation (HNC/MSA) closures, and with the corresponding calculations using the modified Gouy-Chapman and unequal-radius modified Gouy-Chapman theories. The HNC/HNC and HNC/MSA integral equations formalisms show good concordance with Monte Carlo "experiments", whereas the notable limitations of point-ion approaches are evidenced. Most importantly, the simulations confirm our previous theoretical predictions of the non-dominance of the counterions in the size-asymmetric spherical electrical double layer [J. Chem. Phys. 123, 034703 (2005)], the appearance of anomalous curvatures at the outer Helmholtz plane and the enhancement of charge reversal and screening at high colloidal surface charge densities due to the ionic size asymmetry.Comment: 11 pages, 7 figure

Mz 3, a Multipolar Nebula in the Making

The nebula Mz 3 has arguably the most complex bipolar morphology, consisting of three nested pairs of bipolar lobes and an equatorial ellipse. Its three pairs of bipolar lobes share the same axis of symmetry, but have very different opening angles and morphologies: the innermost pair of bipolar lobes shows closed lobe morphology, while the other two have open lobes with cylindrical and conical shapes, respectively. We have carried out high-dispersion spectroscopic observations of Mz 3, and detected distinct kinematic properties among the different morphological components. The expansion characteristics of the two outer pairs of lobes suggest that they originated in an explosive event, whereas the innermost pair of lobes resulted from the interaction of a fast wind with the surrounding material. The equatorial ellipse is associated with a fast equatorial outflow which is unique among bipolar nebulae. The dynamical ages of the different structures in Mz 3 suggest episodic bipolar ejections, and the distinct morphologies and kinematics among these different structures reveal fundamental changes in the system between these episodic ejections.Comment: To be published in the October issue of The Astronomical Journal. 16 pages, 10 figures. For full resolution figures, send requests to the Author [email protected]

Multiple and Precessing Collimated Outflows in the Planetary Nebula IC 4634

With its remarkable double-S shape, IC 4634 is an archetype of point-symmetric planetary nebulae (PN). In this paper, we present a detailed study of this PN using archival HST WFPC2 and ground-based narrow-band images to investigate its morphology, and long-slit spectroscopic observations to determine its kinematics and to derive its physical conditions and excitation. The data reveal new structural components, including a distant string of knots distributed along an arc-like feature 40"-60" from the center of the nebula, a skin of enhanced [O III]/H-alpha ratio enveloping the inner shell and the double-S feature, and a triple-shell structure. The spatio-kinematical study also finds an equatorial component of the main nebula that is kinematically independent from the bright inner S-shaped arc. We have investigated in detail the bow shock-like features in IC 4634 and found that their morphological, kinematical and emission properties are consistent with the interaction of a collimated outflow with surrounding material. Indeed, the morphology and kinematics of some of these features can be interpreted using a 3D numerical simulation of a collimated outflow precessing at a moderate, time-dependent velocity. Apparently, IC 4634 has experienced several episodes of point-symmetric ejections oriented at different directions with the outer S-shaped feature being related to an earlier point-symmetric ejection and the outermost arc-like string of knots being the relic of an even much earlier point-symmetric ejection. There is tantalizing evidence that the action of these collimated outflows has also taken part in the shaping of the innermost shell and inner S-shaped arc of IC 4634.Comment: 16 pages, 11 figures, accepted for publication in The Astrophysical Journa

Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks

[EN] Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was trained with five challenging and heterogeneous MR prostate datasets (and two US datasets), with segmentations from many different experts with varying segmentation criteria. The model achieves a consistently strong performance in all datasets independently (mean Dice Similarity Coefficient -DSC- above 0.91 for all datasets except for one), outperforming the inter-expert variability significantly in MR (mean DSC of 0.9099 vs. 0.8794). When evaluated on the publicly available Promise12 challenge dataset, it attains a similar performance to the best entries. In summary, the model has the potential of having a significant impact on current prostate procedures, undercutting, and even eliminating, the need of manual segmentations through improvements in terms of robustness, generalizability and output resolutionThis work has been partially supported by a doctoral grant of the Spanish Ministry of Innovation and Science, with reference FPU17/01993Pellicer-Valero, OJ.; González-Pérez, V.; Casanova Ramón-Borja, JL.; Martín García, I.; Barrios Benito, M.; Pelechano Gómez, P.; Rubio-Briones, J.... (2021). 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Deeply-supervised CNN for prostate segmentation. 2017 International Joint Conference on Neural Networks (IJCNN). doi:10.1109/ijcnn.2017.7965852To, M. N. N., Vu, D. Q., Turkbey, B., Choyke, P. L., & Kwak, J. T. (2018). Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging. International Journal of Computer Assisted Radiology and Surgery, 13(11), 1687-1696. doi:10.1007/s11548-018-1841-4Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2017.243Zhu, Y., Wei, R., Gao, G., Ding, L., Zhang, X., Wang, X., & Zhang, J. (2018). Fully automatic segmentation on prostate MR images based on cascaded fully convolution network. Journal of Magnetic Resonance Imaging, 49(4), 1149-1156. doi:10.1002/jmri.26337Wang, Y., Ni, D., Dou, H., Hu, X., Zhu, L., Yang, X., … Wang, T. (2019). Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound. IEEE Transactions on Medical Imaging, 38(12), 2768-2778. doi:10.1109/tmi.2019.2913184Lemaître, G., Martí, R., Freixenet, J., Vilanova, J. C., Walker, P. M., & Meriaudeau, F. (2015). Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review. Computers in Biology and Medicine, 60, 8-31. doi:10.1016/j.compbiomed.2015.02.009Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., … Madabhushi, A. (2014). Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge. Medical Image Analysis, 18(2), 359-373. doi:10.1016/j.media.2013.12.002Zhu, Q., Du, B., & Yan, P. (2020). Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation. IEEE Transactions on Medical Imaging, 39(3), 753-763. doi:10.1109/tmi.2019.2935018He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. 2015 IEEE International Conference on Computer Vision (ICCV). doi:10.1109/iccv.2015.123Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359. doi:10.1109/tkde.2009.191Smith, L. N. (2017). Cyclical Learning Rates for Training Neural Networks. 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). doi:10.1109/wacv.2017.58Abraham, N., & Khan, N. M. (2019). A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). doi:10.1109/isbi.2019.8759329Lei, Y., Tian, S., He, X., Wang, T., Wang, B., Patel, P., … Yang, X. (2019). Ultrasound prostate segmentation based on multidirectional deeply supervised V‐Net. Medical Physics, 46(7), 3194-3206. doi:10.1002/mp.13577Orlando, N., Gillies, D. J., Gyacskov, I., Romagnoli, C., D’Souza, D., & Fenster, A. (2020). Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images. Medical Physics, 47(6), 2413-2426. doi:10.1002/mp.14134Karimi, D., Zeng, Q., Mathur, P., Avinash, A., Mahdavi, S., Spadinger, I., … Salcudean, S. E. (2019). Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images. Medical Image Analysis, 57, 186-196. doi:10.1016/j.media.2019.07.005PROMISE12 Resultshttps://promise12.grand-challenge.org/Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2020). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203-211. doi:10.1038/s41592-020-01008-

Headache as a COVID-19 onset symptom and post-COVID-19 symptom in hospitalized COVID-19 survivors infected with the Wuhan, Alpha, or Delta SARS-CoV-2 variants

OBJECTIVE: This study looked at differences in the presence of headache as an onset symptom of coronavirus disease 2019 (COVID‐19) and as a post‐COVID‐19 symptom in individuals previously hospitalized owing to infection with the Wuhan, Alpha, or Delta variants of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). BACKGROUND: Headache can be present in up to 50% of individuals during the acute phase of SARS‐CoV‐2 infection and in 10% of subjects during the post‐COVID‐19 phase. There are no data on differences in the occurrence of headache in the acute‐ and post‐COVID‐19 phase according to the SARS‐CoV‐2 variants. METHODS: A cross‐sectional cohort study was conducted. Unvaccinated subjects previously hospitalized for COVID‐19 caused by the Wuhan (n = 201), Alpha (n = 211), or Delta (n = 202) SARS‐CoV‐2 variants were scheduled for a telephone interview 6 months after hospital discharge. Hospitalization data were collected from hospital medical records. RESULTS: The presence of headache as a COVID‐19 onset symptom at hospitalization was higher in subjects with the Delta variant (66/202, 32.7%) than in those infected with the Wuhan (42/201, 20.9%; odds ratio [OR] 1.83, 95% confidence interval [CI] 1.17–2.88) or Alpha (25/211, 11.8%; OR 3.61, 95% CI, 2.16–6.01) variants. The prevalence of post‐COVID‐19 headache 6 months after hospital discharge was higher in individuals infected with the Delta variant (26/202, 12.9%) than in those infected with the Wuhan (11/201, 5.5%; OR 2.52, 95% CI 1.22–5.31) or Alpha (eight of 211, 3.8%; OR 3.74, 95% CI 1.65–8.49) variants. The presence of headache as a COVID‐19 onset symptom was associated with post‐COVID‐19 headache in subjects infected with the Wuhan (OR 7.75, 95% CI 2.15–27.93) and Delta variants (OR 2.78, 95% CI 1.20–6.42) but not with the Alpha variant (OR 2.60, 95% CI 0.49–13.69). CONCLUSION: Headache was a common symptom in both the acute‐ and post‐COVID‐19 phase in subjects infected with the Wuhan, Alpha, and Delta variants but mostly in those infected with the Delta variant

IFE Plant Technology Overview and contribution to HiPER proposal

HiPER is the European Project for Laser Fusion that has been able to join 26 institutions and signed under formal government agreement by 6 countries inside the ESFRI Program of the European Union (EU). The project is already extended by EU for two years more (until 2013) after its first preparatory phase from 2008. A large work has been developed in different areas to arrive to a design of repetitive operation of Laser Fusion Reactor, and decisions are envisioned in the next phase of Technology Development or Risk Reduction for Engineering or Power Plant facilities (or both). Chamber design has been very much completed for Engineering phase and starting of preliminary options for Reactor Power Plant have been established and review here