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)

Unsupervised Learning with Normalised Data and Non-Euclidean Norms

Peer reviewe

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

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

Optimization of input parameters to a CN neuron model to simulate its activity during and between epileptic absence seizures

Peer reviewe

SMART: Unique splitting-while-merging framework for gene clustering

Copyright @ 2014 Fa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori. To address this issue, in this paper, we propose a unique splitting-while-merging clustering framework, named “splitting merging awareness tactics” (SMART), which does not require any a priori knowledge of either the number of clusters or even the possible range of this number. Unlike existing self-splitting algorithms, which over-cluster the dataset to a large number of clusters and then merge some similar clusters, our framework has the ability to split and merge clusters automatically during the process and produces the the most reliable clustering results, by intrinsically integrating many clustering techniques and tasks. The SMART framework is implemented with two distinct clustering paradigms in two algorithms: competitive learning and finite mixture model. Nevertheless, within the proposed SMART framework, many other algorithms can be derived for different clustering paradigms. The minimum message length algorithm is integrated into the framework as the clustering selection criterion. The usefulness of the SMART framework and its algorithms is tested in demonstration datasets and simulated gene expression datasets. Moreover, two real microarray gene expression datasets are studied using this approach. Based on the performance of many metrics, all numerical results show that SMART is superior to compared existing self-splitting algorithms and traditional algorithms. Three main properties of the proposed SMART framework are summarized as: (1) needing no parameters dependent on the respective dataset or a priori knowledge about the datasets, (2) extendible to many different applications, (3) offering superior performance compared with counterpart algorithms.National Institute for Health Researc

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

Breathing K-Means

The k-means++ algorithm is the de-facto standard for finding approximate solutions to the k-means problem. A widely used implementation is provided by the scikit-learn Python package for machine learning. We propose the breathing k-means algorithm, which on average significantly outperforms scikit-learn's k-means++ w.r.t. both solution quality and execution speed. The initialization step in the new method is done by k-means++ but without the usual (and costly) repetitions (ten in scikit-learn). The core of the new method is a sequence of "breathing cycles," each consisting of a "breathe in" step where the number of centroids is increased by m and a "breathe out" step where m centroids are removed. Each step is ended by a run of Lloyd's algorithm. The parameter m is decreased until zero, at which point the algorithm terminates. With the default (m = 5), breathing k-means dominates scikit-learn's k-means++. This is demonstrated via experiments on various data sets, including all those from the original k-means++ publication. By setting m to smaller or larger values, one can optionally produce faster or better solutions, respectively. For larger values of m, e.g., m = 20, breathing k-means likely is the new SOTA for the k-means problem.Comment: 55 pages, 45 figures, Relevant Changes: Algorithm is now better *and* faster than the underlying KMeans class from scikit-learn. Detailed analysis of parameter m shows that it can be used to balance SSE and CPU time; Parameter theta eliminated. Submitted to JMLR; Implementation: https://github.com/gittar/breathing-k-means ; Python package: https://pypi.org/project/bkmean

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