Search

This paper introduces Generalized Quantum-assisted Digital Signature (GQaDS), an improved version of a recently proposed scheme whose information theoretic security is inherited by adopting QKD keys for digital signature purposes. Its security against forging is computed considering a trial-and-error approach taken by the malicious forger and GQaDS parameters are optimized via an analytical approach balancing between forgery and repudiation probabilities. The hash functions of the previous implementation are replaced with Carter-Wegman Message Authentication Codes (MACs), strengthening the scheme security and reducing the signature length. For particular scenarios where the second verifier has a safe reputation, a simplified version of GQaDS, namely deterministic GQaDS, can further reduce the required signature length, keeping the desired security strength.

In this paper, we present an automated approach for segmenting multiple sclerosis (MS) lesions from multi-modal brain magnetic resonance images. Our method is based on a deep end-to-end 2D convolutional neural network (CNN) for slice-based segmentation of 3D volumetric data. The proposed CNN includes a multi-branch downsampling path, which enables the network to encode information from multiple modalities separately. Multi-scale feature fusion blocks are proposed to combine feature maps from different modalities at different stages of the network. Then, multi-scale feature upsampling blocks are introduced to upsize combined feature maps to leverage information from lesion shape and location. We trained and tested the proposed model using orthogonal plane orientations of each 3D modality to exploit the contextual information in all directions. The proposed pipeline is evaluated on two different datasets: a private dataset including 37 MS patients and a publicly available dataset known as the ISBI 2015 longitudinal MS lesion segmentation challenge dataset, consisting of 14 MS patients. Considering the ISBI challenge, at the time of submission, our method was amongst the top performing solutions. On the private dataset, using the same array of performance metrics as in the ISBI challenge, the proposed approach shows high improvements in MS lesion segmentation compared with other publicly available tools., Comment: This paper has been accepted for publication in NeuroImage

Starting from the Braginskii equations, relevant for the tokamak edge region, a complete set of nonlinear equations for the geodesic acoustic modes (GAM) has been derived which includes collisionality, plasma beta and external sources of particle, momentum and heat. Local linear analysis shows that the GAM frequency increases with collisionality at low radial wave number $k_{r}$ and decreases at high $k_{r}$. GAM frequency also decreases with plasma beta. Radial profiles of GAM frequency for two Tore Supra shots, which were part of a collisionality scan, are compared with these calculations. Discrepency between experiment and theory is observed, which seems to be explained by a finite $k_{r}$ for the GAM when flux surface averaged density $\langle n \rangle$ and temperature $\langle T \rangle$ are assumed to vanish. It is shown that this agreement is incidental and self-consistent inclusion of $\langle n \rangle$ and $\langle T \rangle$ responses enhances the disagreement more with $k_r$ at high $k_{r}$ . So the discrepancy between the linear GAM calculation, (which persist also for more "complete" linear models such as gyrokinetics) can probably not be resolved by simply adding a finite $k_{r}$.