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Found 8 papers, 0 papers with code
Linear Hybrid Asymmetrical Load-Modulated Balanced Amplifier with Multi-Band Reconfigurability and Antenna-VSWR Resilience
Specifically, the PA's linearity and efficiency profiles can be maintained against arbitrary load mismatch through $Z_\mathrm{L}$-dependent reconfiguration of CA supply voltage ($V_\mathrm{DD, CA}$) and turning-on sequence of BA1 and BA2.
Uniformly Distributed Category Prototype-Guided Vision-Language Framework for Long-Tail Recognition
Category prototype-guided mechanism for image-text matching makes the features of different classes converge to these distinct and uniformly distributed category prototypes, which maintain a uniform distribution in the feature space, and improve class boundaries.
CropDefender: deep watermark which is more convenient to train and more robust against cropping
We found that the causes of vulnerability to cropping is not the loss of information on the edge, but the movement of watermark position.
Quantified limits of the nuclear landscape
We use microscopic nuclear mass models and Bayesian methodology to provide quantified predictions of proton and neutron separation energies as well as Bayesian probabilities of existence throughout the nuclear landscape all the way to the particle drip lines.
Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei
With the help of Bayesian methodology, we mix a family of nuclear mass models corrected with statistical emulators trained on the experimental mass measurements, in the proton-rich region of the nuclear chart.
Neutron drip line in the Ca region from Bayesian model averaging
In particular, considering the current experimental information and current global mass models, we predict that $^{68}$Ca has an average posterior probability ${p_{ex}\approx76}$% to be bound to two-neutron emission while the nucleus $^{61}$Ca is likely to decay by emitting a neutron (${p_{ex}\approx 46}$ %).
Dense Object Reconstruction from RGBD Images with Embedded Deep Shape Representations
Most problems involving simultaneous localization and mapping can nowadays be solved using one of two fundamentally different approaches.
Bayesian approach to model-based extrapolation of nuclear observables
The increase in the predictive power is quite astonishing: the resulting rms deviations from experiment on the testing dataset are similar to those of more phenomenological models.
