Background Modelling with Associated Confidence

Non

SUSIG: an on-line signature database, associated protocols and benchmark results

We present a new online signature database (SUSIG). The database consists of two parts that are collected using different pressure-sensitive tablets ( one with and the other without an LCD display). A total of 100 people contributed to each part, resulting in a database of more than 3,000 genuine signatures and 2,000 skilled forgeries. The genuine signatures in the database are real signatures of the contributors. In collecting skilled forgeries, forgers were shown the signing process on the monitor and were given a chance to practice. Furthermore, for a subset of the forgeries ( highly skilled forgeries), this animation was mapped onto the LCD screen of the tablet so that the forgers could trace over the mapped signature. Forgers in this group were also informed of how close they were to the reference signature, so that they could improve their forgery quality. We describe the signature acquisition process and several verification protocols for this database. We also report the performance of a state-of-the-art signature verification system using the associated protocols. The results show that the highly skilled forgery set is significantly more difficult compared to the skilled forgery set, providing researchers with challenging forgeries. The database is available through http://icproxy.sabanciuniv.edu:215

Brownian dynamics simulation of analytical ultracentrifugation experiments

<p>Abstract</p> <p>Background</p> <p>We have devised a protocol for the Brownian dynamics simulation of an analytical ultracentrifugation experiment that allows for an accurate and efficient prediction of the time-dependent concentration profiles, <it>c</it>(<it>r, t</it>) in the ultracentrifuge cell. The procedure accounts for the back-diffusion, described as a Brownian motion that superimposes to the centrifugal drift, and considers the sector-shaped geometry of the cell and the boundaries imposed by the meniscus and bottom.</p> <p>Results</p> <p>Simulations are carried out for four molecules covering a wide range of the ratio of sedimentation and diffusion coefficients. The evaluation is done by extracting the molecular parameters that were initially employed in the simulation by analyzing the profiles with an independent tool, the well-proved SEDFIT software. The code of simulation algorithm has been parallelized in order to take advantage of current multi-core computers.</p> <p>Conclusions</p> <p>Our Brownian dynamics simulation procedure may be considered as an alternative to other predictors based in numerical solutions of the Lamm equation, and its efficiency could make it useful in the most relevant, inverse problem, which is that of extracting the molecular parameters from experimentally determined concentration profiles.</p

Magnetoelectric Effect in Type-II Quantum Cone Induced by Donor Impurity

We consider a model of donor centered at the base of a type-II nanocone, in which the excessive electron, released from the donor, is located within a narrow tube-shaped shell exterior region around the cone lateral surface. By solving the one-electron Schrödinger equation we analyze the alteration of the spatial probability distribution of the electron, the period of the Aharonov-Bohm oscillations of the energy levels, and the electric and magnetic moments induced by external electric and magnetic fields, applied along the symmetry axis. We show that the diamagnetic confinement provided by the magnetic field forces the electron to climb along the cone’s border, inducing the electric polarization of the structure. Similarly, the external electric field, which pushes the electron toward cone’s bottom, changes the order of the energy levels with different magnetic momenta varying the magnetic polarization of the structure. Our theoretical analysis reveals a new possibility for the coupling between the polarization and magnetization arising from the quantum-size effect in type-II semiconductor nanocones

Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks

Biometric authentication by means of handwritten signatures is a challenging pattern recognition task, which aims to infer a writer model from only a handful of genuine signatures. In order to make it more difficult for a forger to attack the verification system, a promising strategy is to combine different writer models. In this work, we propose to complement a recent structural approach to offline signature verification based on graph edit distance with a statistical approach based on metric learning with deep neural networks. On the MCYT and GPDS benchmark datasets, we demonstrate that combining the structural and statistical models leads to significant improvements in performance, profiting from their complementary properties