Quotable Signatures for Authenticating Shared Quotes

Quotable signature schemes are digital signature schemes with the additional property that from the signature for a message, any party can extract signatures for (allowable) quotes from the message, without knowing the secret key or interacting with the signer of the original message. Crucially, the extracted signatures are still signed with the original secret key. We define a notion of security for quotable signature schemes and construct a concrete example of a quotable signature scheme, using Merkle trees and classical digital signature schemes. The scheme is shown to be secure, with respect to the aforementioned notion of security. Additionally, we prove bounds on the complexity of the constructed scheme and provide algorithms for signing, quoting, and verifying. Finally, concrete use cases of quotable signatures are considered, using them to combat misinformation by bolstering authentic content on social media. We consider both how quotable signatures can be used, and why using them could help mitigate the effects of fake news.Comment: 29 pages, 7 figure

Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation

Optical coherence tomography (OCT) has become the most important imaging modality in ophthalmology. A substantial amount of research has recently been devoted to the development of machine learning (ML) models for the identification and quantification of pathological features in OCT images. Among the several sources of variability the ML models have to deal with, a major factor is the acquisition device, which can limit the ML model's generalizability. In this paper, we propose to reduce the image variability across different OCT devices (Spectralis and Cirrus) by using CycleGAN, an unsupervised unpaired image transformation algorithm. The usefulness of this approach is evaluated in the setting of retinal fluid segmentation, namely intraretinal cystoid fluid (IRC) and subretinal fluid (SRF). First, we train a segmentation model on images acquired with a source OCT device. Then we evaluate the model on (1) source, (2) target and (3) transformed versions of the target OCT images. The presented transformation strategy shows an F1 score of 0.4 (0.51) for IRC (SRF) segmentations. Compared with traditional transformation approaches, this means an F1 score gain of 0.2 (0.12).Comment: * Contributed equally (order was defined by flipping a coin) --------------- Accepted for publication in the "IEEE International Symposium on Biomedical Imaging (ISBI) 2019

On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problems

The use of orthogonal projections on high-dimensional input and target data in learning frameworks is studied. First, we investigate the relations between two standard objectives in dimension reduction, preservation of variance and of pairwise relative distances. Investigations of their asymptotic correlation as well as numerical experiments show that a projection does usually not satisfy both objectives at once. In a standard classification problem we determine projections on the input data that balance the objectives and compare subsequent results. Next, we extend our application of orthogonal projections to deep learning tasks and introduce a general framework of augmented target loss functions. These loss functions integrate additional information via transformations and projections of the target data. In two supervised learning problems, clinical image segmentation and music information classification, the application of our proposed augmented target loss functions increase the accuracy

High-resolution investigations of ripple structures formed by femtosecond laser irradiation of silicon

We report on the structural investigation of self-organized periodic microstructures (ripples) generated in Si(100) targets after multishot irradiation by approximately 100-fs to 800-nm laser pulses at intensities near the single shot ablation threshold. Inspection by surface sensitive microscopy, e.g., atomic force microscopy (AFM) or scanning electron microscopy (SEM), and conventional and high-resolution transmission electron microscopy reveal complex structural modifications upon interaction with the laser: even well outside the ablated area, the target surface exhibits fine ripple-like undulations, consisting of alternating crystalline and amorphous silicon. Inside the heavily modified area, amorphous silicon is found only in the valleys but not on the crests which, instead, consist of highly distorted crystalline phases, rich in defects

Role of THBS1, WHSC1, ADAMTS1 and RBFOX2 genes in the radiation-induced Dna double strand break repair in Hela tumor cell line

It is well known that inter-individual differences of radiosensitivity have genetic causes, such as variations in the level of DNA or expression of DNA repair genes. However, differentially expressed genes which could lead to inter-individual differences in the level of DNA damage remain largely unidentified. In our study we have induced knock-out of THBS1, WHSC1, ADAMTS1 and RBFOX2 genes in HeLa cell line to clarify the effects of these genes on DNA repair and radiosensitivity

Stable registration of pathological 3D-OCT scans using retinal vessels

We propose a multiple scanner vendor registration method for pathological retinal 3D spectral domain optical coherence tomography volumes based on Myronenko’s Coherent Point Drift and our automated vessel shadow segmentation. Coherent point drift is applied to the segmented retinal vessel point sets used as landmarks to generate the registration parameters required. In contrast to other registration methods, our solution incorporates a landmark detection and extraction method that specifically limits the extraction of false positives and a registration method capable of handling any such noise in the landmark point sets. Our experiments show modified Hausdorff distance is reduced by a minimum of 91% between target and registered vessel point sets with at least 94% of bifurcations correctly overlapping based on ground truth, a significant improvement over current methods

Improving foveal avascular zone segmentation in fluorescein angiograms by leveraging manual vessel labels from public color fundus pictures

In clinical routine, ophthalmologists frequently analyze the shape and size of the foveal avascular zone (FAZ) to detect and monitor retinal diseases. In order to extract those parameters, the contours of the FAZ need to be segmented, which is normally achieved by analyzing the retinal vasculature (RV) around the macula in fluorescein angiograms (FA). Computer-aided segmentation methods based on deep learning (DL) can automate this task. However, current approaches for segmenting the FAZ are often tailored to a specific dataset or require manual initialization. Furthermore, they do not take the variability and challenges of clinical FA into account, which are often of low quality and difficult to analyze. In this paper we propose a DL-based framework to automatically segment the FAZ in challenging FA scans from clinical routine. Our approach mimics the workflow of retinal experts by using additional RV labels as a guidance during training. Hence, our model is able to produce RV segmentations simultaneously. We minimize the annotation work by using a multi-modal approach that leverages already available public datasets of color fundus pictures (CFPs) and their respective manual RV labels. Our experimental evaluation on two datasets with FA from 1) clinical routine and 2) large multicenter clinical trials shows that the addition of weak RV labels as a guidance during training improves the FAZ segmentation significantly with respect to using only manual FAZ annotations.Fil: Hofer, Dominik. Medizinische Universität Wien; AustriaFil: Schmidt Erfurth, Ursula. Medizinische Universität Wien; AustriaFil: Orlando, José Ignacio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Medizinische Universität Wien; AustriaFil: Goldbach, Felix. Medizinische Universität Wien; AustriaFil: Gerendas, Bianca S.. Medizinische Universität Wien; AustriaFil: Seeböck, Philipp. Medizinische Universität Wien; Austri