Helsinki University of Technology

About: Helsinki University of Technology is a based out in . It is known for research contribution in the topics: Artificial neural network & Finite element method. The organization has 8962 authors who have published 20136 publications receiving 723787 citations. The organization is also known as: TKK & Teknillinen korkeakoulu.

Vortex formation in neutron-irradiated superfluid 3He as an analogue of cosmological defect formation

Abstract: TOPOLOGICAL defects formed during a rapid symmetry-breaking phase transition in the early Universe1,2 could be responsible for seeding large-scale structure, for the anisotropy of the microwave background radiation, and for the predominance of matter over antimatter3,4. The theory describing this cosmological phase transition is formally analogous to that describing the transition to the superfluid state in liquid 3He, so that in principle the process of cosmological defect formation can be modelled in the laboratory. Here we report the results of an experiment in which the 'primordial fireball' is mimicked using a neutron-induced nuclear reaction (n + 3He → p + 3He + 0.76 MeV) to heat small regions of superfluid 3He above the superfluid transition temperature. These bubbles of normal liquid cool extremely rapidly, and we find that their transition back to the superfluid state is accompanied by the formation of a random network of vortices (the superfluid analogue of cosmic strings). We monitor the evolution of this defect state by rotating the superfluid sample, allowing vortices to escape from the network and thus be probed individually. Our results provide clear confirmation of the idea that topological defects form at a rapid second-order phase transition, and give quantitative support to the Kibble–Zurek mechanism5,6 of cosmological defect formation.

On private scalar product computation for privacy-preserving data mining

Abstract: In mining and integrating data from multiple sources, there are many privacy and security issues. In several different contexts, the security of the full privacy-preserving data mining protocol depends on the security of the underlying private scalar product protocol. We show that two of the private scalar product protocols, one of which was proposed in a leading data mining conference, are insecure. We then describe a provably private scalar product protocol that is based on homomorphic encryption and improve its efficiency so that it can also be used on massive datasets.

DOS-Resistant Authentication with Client Puzzles

Abstract: Denial of service by server resource exhaustion has become a major security threat in open communications networks. Public-key authentication does not completely protect against the attacks because the authentication protocols often leave ways for an unauthenticated client to consume a server's memory space and computational resources by initiating a large number of protocol runs and inducing the server to perform expensive cryptographic computations. We show how stateless authentication protocols and the client puzzles of Juels and Brainard can be used to prevent such attacks.

Point-defect complexes and broadband luminescence in GaN and AlN

Abstract: We have employed the plane-wave pseudopotential method to study point defect complexes in GaN and AlN. The results reveal that defect complexes consisting of dominant donors bound to cation vacancies are likely to be formed in both materials. The position of the electronic levels in the band gap due to these defect complexes is shown to correlate well with the experimentally commonly observed broadband luminescence both in GaN and in AlN. The origin of the large bandwidth of the luminescence spectrum is discussed.

Sparse code shrinkage: denoising of nongaussian data by maximum likelihood estimation

Abstract: Sparse coding is a method for finding a representation of data in which each of the components of the representation is only rarely significantly active. Such a representation is closely related to redundancy reduction and independent component analysis, and has some neurophysiological plausibility. In this article, we show how sparse coding can be used for denoising. Using maximum likelihood estimation of nongaussian variables corrupted by gaussian noise, we show how to apply a soft-thresholding (shrinkage) operator on the components of sparse coding so as to reduce noise. Our method is closely related to the method of wavelet shrinkage, but it has the important benefit over wavelet methods that the representation is determined solely by the statistical properties of the data. The wavelet representation, on the other hand, relies heavily on certain mathematical properties (like self-similarity) that may be only weakly related to the properties of natural data.