Self-Organising Networks for Classification: developing Applications to Science Analysis for Astroparticle Physics

Physics analysis in astroparticle experiments requires the capability of recognizing new phenomena; in order to establish what is new, it is important to develop tools for automatic classification, able to compare the final result with data from different detectors. A typical example is the problem of Gamma Ray Burst detection, classification, and possible association to known sources: for this task physicists will need in the next years tools to associate data from optical databases, from satellite experiments (EGRET, GLAST), and from Cherenkov telescopes (MAGIC, HESS, CANGAROO, VERITAS)

Region of Interest Growing Neural Gas for Real-Time Point Cloud Processing

This paper proposes a real-time topological structure learning method based on concentrated/distributed sensing for a 2D/3D point cloud. First of all, we explain a modified Growing Neural Gas with Utility (GNG-U2) that can learn the topological structure of 3D space environment and color information simultaneously by using a weight vector. Next, we propose a Region Of Interest Growing Neural Gas (ROI-GNG) for realizing concentrated/distributed sensing in real-time. In ROI-GNG, the discount rates of the accumulated error and utility value are variable according to the situation. We show experimental results of the proposed method and discuss the effectiveness of the proposed method

Unsupervised Learning with Normalised Data and Non-Euclidean Norms

Peer reviewe

Obfuscating Against Side-Channel Power Analysis Using Hiding Techniques for AES

The transfer of information has always been an integral part of military and civilian operations, and remains so today. Because not all information we share is public, it is important to secure our data from unwanted parties. Message encryption serves to prevent all but the sender and recipient from viewing any encrypted information as long as the key stays hidden. The Advanced Encryption Standard (AES) is the current industry and military standard for symmetric-key encryption. While AES remains computationally infeasible to break the encrypted message stream, it is susceptible to side-channel attacks if an adversary has access to the appropriate hardware. The most common and effective side-channel attack on AES is Differential Power Analysis (DPA). Thus, countermeasures to DPA are crucial to data security. This research attempts to evaluate and combine two hiding DPA countermeasures in an attempt to further hinder side-channel analysis of AES encryption. Analysis of DPA attack success before and after the countermeasures is used to determine effectiveness of the protection techniques. The results are measured by evaluating the number of traces required to attack the circuit and by measuring the signal-to-noise ratios

Avoiding that accidents turn into disaster: Barrier management and emergency preparedness in the oil and gas industry - on the importance of having a good link between the facility-specific risk picture and the set-up of emergency preparedness, including training and exercise

Facility-specific competence is crucial in emergency preparedness organisations on offshore oil and gas facilities to prevent emerging situations from developing into full-blown disasters. However, the Petroleum Safety Authority (PSA) of Norway has, over a more extended period, raised concerns regarding possible decreasing levels of facility-specific competence amongst personnel. The concerns stem from the development in the industry where critical analyses that should be used to identify and include the specific challenges are moved away from the facilities and operators and are rather conducted by external companies. As such, this study investigates the research problem: How to ensure that facility-specific competence is present in an emergency preparedness organisation? This study approaches the challenge of including facility-specific challenges and measures to handle these in the establishment of emergency preparedness through semi-structured interviews with various companies and the PSA. The interviews uncover that operating and consultant companies often identify facility-specific challenges through the use of qualitative risk analyses and emergency preparedness analyses. Furthermore, the associated roles and competence needed to handle the challenges are identified in the emergency preparedness plans or as part of an organisational barrier element. The interviews also bring forward that the analyses and methodologies used in the industry today, in many cases, are too generic and technical to identify the necessary information needed in the mapping of necessary facility-specific competence. Building on the theory of emergency preparedness and answers from the interviews, it becomes evident that the analyses and methodologies used in the industry today need to be properly adapted to each facility in order to secure a red line between the generic analyses and the specific conditions at each facility. It is further suggested and emphasised to increase the focus on ‘de-academising’ the information in the QRA, include relevant personnel in the establishment of the emergency preparedness analyses and plans, establish clear and specific competence requirements linked to the various roles, and map the availability of personnel with necessary competence to better secure that personnel with facility-specific competence is present in an emergency preparedness situation

Computational Models of Adult Neurogenesis

Experimental results in recent years have shown that adult neurogenesis is a significant phenomenon in the mammalian brain. Little is known, however, about the functional role played by the generation and destruction of neurons in the context of and adult brain. Here we propose two models where new projection neurons are incorporated. We show that in both models, using incorporation and removal of neurons as a computational tool, it is possible to achieve a higher computational efficiency that in purely static, synapse-learning driven networks. We also discuss the implication for understanding the role of adult neurogenesis in specific brain areas.Comment: To appear Physica A, 7 page

Hierarchical growing neural gas

“The original publication is available at www.springerlink.com”. Copyright Springer.This paper describes TreeGNG, a top-down unsupervised learning method that produces hierarchical classification schemes. TreeGNG is an extension to the Growing Neural Gas algorithm that maintains a time history of the learned topological mapping. TreeGNG is able to correct poor decisions made during the early phases of the construction of the tree, and provides the novel ability to influence the general shape and form of the learned hierarchy