5,865 research outputs found
Reducing the Tension Between the BICEP2 and the Planck Measurements: A Complete Exploration of the Parameter Space
A large inflationary tensor-to-scalar ratio is reported by the BICEP2 team based on their B-mode
polarization detection, which is outside of the confidence level of the
Planck best fit model. We explore several possible ways to reduce the tension
between the two by considering a model in which ,
, and the neutrino parameters and
are set as free parameters. Using the Markov Chain
Monte Carlo (MCMC) technique to survey the complete parameter space with and
without the BICEP2 data, we find that the resulting constraints on
are consistent with each other and the apparent tension
seems to be relaxed. Further detailed investigations on those fittings suggest
that probably plays the most important role in reducing the
tension. We also find that the results obtained from fitting without adopting
the consistency relation do not deviate much from the consistency relation.
With available Planck, WMAP, BICEP2 and BAO datasets all together, we obtain
, ,
, and
; if the consistency relation is
adopted, we get .Comment: 8 pages, 4 figures, submitted to PL
A Generalized Alternating Method for Bilevel Learning under the Polyak-{\L}ojasiewicz Condition
Bilevel optimization has recently regained interest owing to its applications
in emerging machine learning fields such as hyperparameter optimization,
meta-learning, and reinforcement learning. Recent results have shown that
simple alternating (implicit) gradient-based algorithms can match the
convergence rate of single-level gradient descent (GD) when addressing bilevel
problems with a strongly convex lower-level objective. However, it remains
unclear whether this result can be generalized to bilevel problems beyond this
basic setting. In this paper, we first introduce a stationary metric for the
considered bilevel problems, which generalizes the existing metric, for a
nonconvex lower-level objective that satisfies the Polyak-{\L}ojasiewicz (PL)
condition. We then propose a Generalized ALternating mEthod for bilevel
opTimization (GALET) tailored to BLO with convex PL LL problem and establish
that GALET achieves an -stationary point for the considered problem
within iterations, which matches the iteration
complexity of GD for single-level smooth nonconvex problems.Comment: Camera ready versio
Research on Information Anxiety in Different Epidemic Prevention and Control States of Public Health Emergency-- Based on Information Task Perspective
The Technology Study of Camshaft Treated by Laser Bionic Melting Process in Aqueous Media Cooling
Biomimetic coupling wear resistance model was created according to biomimetic coupling phenomenon and biomimetic coupling theory, utilize the preparation of aqueous media by laser cooling techniques, process biological coupling unit on the gray cast iron surface according to the organism form, make the bionic coupled unit in accordance with the laws of organism combination, form bionic coupling surface which is similar to the organism form with material surface to improve the wear resistance of the camshaft. Studies show, the region which dealt with aqueous media by laser can get small grains, and grain size is uniform, hardness and wear resistance is improved. Key words: bionic coupling; laser melting; camshaf
Structure-aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation
Morphable models are essential for the statistical modeling of 3D faces.
Previous works on morphable models mostly focus on large-scale facial geometry
but ignore facial details. This paper augments morphable models in representing
facial details by learning a Structure-aware Editable Morphable Model (SEMM).
SEMM introduces a detail structure representation based on the distance field
of wrinkle lines, jointly modeled with detail displacements to establish better
correspondences and enable intuitive manipulation of wrinkle structure.
Besides, SEMM introduces two transformation modules to translate expression
blendshape weights and age values into changes in latent space, allowing
effective semantic detail editing while maintaining identity. Extensive
experiments demonstrate that the proposed model compactly represents facial
details, outperforms previous methods in expression animation qualitatively and
quantitatively, and achieves effective age editing and wrinkle line editing of
facial details. Code and model are available at
https://github.com/gerwang/facial-detail-manipulation.Comment: ECCV 202
A Transfer Learning Approach for Malignant Prostate Lesion Detection on Multiparametric MRI
Purpose: In prostate focal therapy, it is important to accurately localize malignant lesions in order to increase biological effect of the tumor region while achieving a reduction in dose to noncancerous tissue. In this work, we proposed a transfer learning–based deep learning approach, for classification of prostate lesions in multiparametric magnetic resonance imaging images. Methods: Magnetic resonance imaging images were preprocessed to remove bias artifact and normalize the data. Two state-of-the-art deep convolutional neural network models, InceptionV3 and VGG-16, were pretrained on ImageNet data set and retuned on the multiparametric magnetic resonance imaging data set. As lesion appearances differ by the prostate zone that it resides in, separate models were trained. Ensembling was performed on each prostate zone to improve area under the curve. In addition, the predictions from lesions on each prostate zone were scaled separately to increase the area under the curve for all lesions combined. Results: The models were tuned to produce the highest area under the curve on validation data set. When it was applied to the unseen test data set, the transferred InceptionV3 model achieved an area under the curve of 0.81 and the transferred VGG-16 model achieved an area under the curve of 0.83. This was the third best score among the 72 methods from 33 participating groups in ProstateX competition. Conclusion: The transfer learning approach is a promising method for prostate cancer detection on multiparametric magnetic resonance imaging images. Features learned from ImageNet data set can be useful for medical images
Electrochemical probing of selective haemoglobin binding in hydrogel-based molecularly imprinted polymers
An electrochemical method has been developed for the probing of hydrogel-based molecularly imprinted polymers (HydroMIPs) on the surface of a glassy carbon electrode. HydroMIPs designed for bovine haemoglobin selectivity were electrochemically characterised and their rebinding properties were monitored using cyclic voltammetry. The electrochemical reduction of bovine oxyhaemoglobin (BHb) in solution was observed to occur at ?0.460 V vs (Ag/AgCl) in 150 mM phosphate buffer solution (PBS). When the protein was selectively bound to the MIP, the electrochemical reduction of oxyhaemoglobin could be observed at a similar peak potential of ?0.480 V vs (Ag/AgCl). When analysing the non-imprinted control polymer (NIP) interfaced at the electrode, which contained no protein, the peak reduction potential corresponded to that observed for dissolved oxygen in solution (?0.65 V vs (Ag/AgCl)). MIP and NIP (in the absence of protein) were interfaced at the electrode and protein allowed to diffuse through the polymers from the bulk solution end to the electrode. It was observed that whereas NIP exhibited a protein response within 10 min of protein exposure, up to 45 min of exposure time was required in the case of the MIP before a protein response could be obtained. Our results suggest that due to the selective nature of the MIP, BHb arrival at the electrode via diffusion is delayed by the MIP due to attractive selective interactions with exposed cavities, but not the NIP which is devoid of selective cavities
Neural Network Based Edge Detection for Automated Medical Diagnosis
Edge detection is an important but rather difficult task in image processing and analysis. In this research, artificial neural networks are employed for edge detection based on its adaptive learning and nonlinear mapping properties. Fuzzy sets are introduced during the training phase to improve the generalization ability of neural networks. The application of the proposed neural network approach to the edge detection of medical images for automated bladder cancer diagnosis is also investigated. Successful computer simulation results are obtained
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