Improving random number generators by chaotic iterations. Application in data hiding

In this paper, a new pseudo-random number generator (PRNG) based on chaotic iterations is proposed. This method also combines the digits of two XORshifts PRNGs. The statistical properties of this new generator are improved: the generated sequences can pass all the DieHARD statistical test suite. In addition, this generator behaves chaotically, as defined by Devaney. This makes our generator suitable for cryptographic applications. An illustration in the field of data hiding is presented and the robustness of the obtained data hiding algorithm against attacks is evaluated.Comment: 6 pages, 8 figures, In ICCASM 2010, Int. Conf. on Computer Application and System Modeling, Taiyuan, China, pages ***--***, October 201

Voronoi-Like grid systems for tall buildings

In the context of innovative patterns for tall buildings, Voronoi tessellation is certainly worthy of interest. It is an irregular biomimetic pattern based on the Voronoi diagram, which derives from the direct observation of natural structures. The paper is mainly focused on the application of this nature-inspired typology to load-resisting systems for tall buildings, investigating the potential of non-regular grids on the global mechanical response of the structure. In particular, the study concentrates on the periodic and non-periodic Voronoi tessellation, describing the procedure for generating irregular patterns through parametric modeling and illustrates the homogenization-based approach proposed in the literature for dealing with unconventional patterns. To appreciate the consistency of preliminary design equations, numerical and analytical results are compared. Moreover, since the mechanical response of the building strongly depends on the parameters of the microstructure, the paper focuses on the influence of the grid arrangement on the global lateral stiffness, therefore on the displacement constraint, which is an essential requirement in the design of tall buildings. To this end, five case studies, accounting for different levels of irregularity and relative density, are generated and analyzed through static and modal analysis in the elastic field. In addition, the paper focuses on the mechanical response of a pattern with gradual rarefying density to evaluate its applicability to tall buildings. Displacement based optimizations are carried out to assess the adequate member cross sections that provide the maximum contribution in restraining deflection with the minimum material weight. The results obtained for all the models generated are compared and discussed to outline a final evaluation of the Voronoi structures. In addition to the wind loading scenario, the efficiency of the building model with varying density Voronoi pattern, is tested for seismic ground motion through a response spectrum analysis. The potential applications of Voronoi tessellation for tall buildings is demonstrated for both regions with high wind load conditions and areas of high seismicity

The challenge of detecting intracluster filaments with Faraday Rotation

The detection of filaments in the cosmic web will be crucial to distinguish between the possible magnetogenesis scenarios and future large polarization surveys will be able to shed light on their magnetization level. In this work, we use numerical simulations of galaxy clusters to investigate their possible detection. We compute the Faraday Rotation signal in intracluster filaments and compare it to its surrounding environment. We find that the expected big improvement in sensitivity with the SKA-MID will in principle allow the detection of a large fraction of filaments surrounding galaxy clusters. However, the contamination of the intrinsic Faraday Rotation of background polarized sources will represent a big limiter to the number of objects that can be significantly detected. We discuss possible strategies to minimize this effect and increase the chances of detection of the cosmic web with the large statistics expected from future surveys.Comment: 16 pages, accepted to Galaxie

The optimal sequence compression

This paper presents the optimal compression for sequences with undefined values. Let we have $(N-m)$ undefined and $m$ defined positions in the boolean sequence $vv V$ of length $N$. The sequence code length can\u27t be less then $m$ in general case, otherwise at least two sequences will have the same code. We present the coding algorithm which generates codes of almost $m$ length, i.e. almost equal to the lower bound. The paper presents the decoding circuit too. The circuit has low complexity which depends from the inverse density of defined values $D(vv V) = frac{N}{m}$. The decoding circuit includes RAM and random logic. It performs sequential decoding. The total RAM size is proportional to the $$logleft(D(vv V) ight) ,$$ the number of random logic cells is proportional to $$log logleft(D(vv V) ight) * left(log log logleft(D(vv V) ight) ight)^2 .$$ So the decoding circuit will be small enough even for the very low density sequences. The decoder complexity doesn\u27t depend of the sequence length at all

Entropy-scaling search of massive biological data

Many datasets exhibit a well-defined structure that can be exploited to design faster search tools, but it is not always clear when such acceleration is possible. Here, we introduce a framework for similarity search based on characterizing a dataset's entropy and fractal dimension. We prove that searching scales in time with metric entropy (number of covering hyperspheres), if the fractal dimension of the dataset is low, and scales in space with the sum of metric entropy and information-theoretic entropy (randomness of the data). Using these ideas, we present accelerated versions of standard tools, with no loss in specificity and little loss in sensitivity, for use in three domains---high-throughput drug screening (Ammolite, 150x speedup), metagenomics (MICA, 3.5x speedup of DIAMOND [3,700x BLASTX]), and protein structure search (esFragBag, 10x speedup of FragBag). Our framework can be used to achieve "compressive omics," and the general theory can be readily applied to data science problems outside of biology.Comment: Including supplement: 41 pages, 6 figures, 4 tables, 1 bo

Une nouvelle approche pour l’identification des états dynamiques de la parcellisation fonctionnelle cérébrale individuelle

Les parcellations cérébrales sont appliquées en neuroimagerie pour aider les chercheurs à ré- duire la haute dimensionnalité des données d’IRM fonctionnelle. L’objectif principal est une meilleure compréhension de l’organisation fonctionnelle du cerveau tant chez les sujets sains que chez les sujets souffrant de troubles neurologiques, dont la maladie d’Alzheimer. Malgré la vague d’approches de parcellations précédentes, les mesures de performance doivent en- core être améliorées pour générer des parcellations fiables, même avec de longues acquisitions. Autrement dit, une reproductibilité plus élevée qui permet aux chercheurs de reproduire des parcellations et de comparer leurs études. Il est également important de minimiser la perte d’informations entre les données compressées et les données brutes pour représenter avec précision l’organisation d’un cerveau individuel. Dans cette thèse, j’ai développé une nou- velle approche pour parcellaire le cerveau en reconfigurations spatiales distinctes appelées «états dynamiques de parcellations». J’ai utilisé une méthode d’agrégation de cluster simple DYPAC1.0 de parcelles basées sur des semences sur plusieurs fenêtres de temps. J’ai émis l’hypothèse que cette nouvelle façon de formaliser le problème de parcellisation améliorera les mesures de performance par rapport aux parcellations statiques. Le premier chapitre de ce document est une introduction générale au contexte des réseaux à grande échelle du cerveau humain. Je montre également l’importance des parcellations pour une meilleure compréhension du cerveau humain à l’aide de connectomes fonctionnels afin de prédire les schémas de progression de la maladie. Ensuite, j’explique pourquoi le problème de parcelli- sation cérébrale est difficile et les différentes questions de recherche ouvertes associées à ce domaine. Mes contributions à la recherche sont subdivisées en deux articles. Les deuxième et troisième chapitres sont consacrés au premier article principal et à son supplément publié dans Network Neuroscience Journal. Le quatrième chapitre représente le deuxième document en préparation. Le cinquième chapitre conclut mes contributions et ses implications dans le domaine de la neuroimagerie, ainsi que des orientations de recherche ouvertes. En un mot, la principale conclusion de ce travail est l’existence de reconfigurations spatiales distinctes dans tout le cerveau avec des scores de reproductibilité presque parfaits sur les données de test-retest (jusqu’à 0,9 coefficient de corrélation de Pearson). Un algorithme d’agrégation de cluster simple et évolutif appelé DYPAC 1.0 est expliqué pour identifier ces reconfigu- rations ou «états dynamiques de parcellations» pour des sous-réseaux de départ spécifiques (deuxième chapitre). L’analyse de ces états a montré l’existence d’un répertoire plus riche «d’états dynamiques» dans le cas des cortex hétéromodaux (ex: cortex cingulaire posté- rieur et cortex cingulaire antérieur dorsal) par rapport aux cortex unimodaux (ex: cortex visuel). En outre, les résultats de l’analyse de reproductibilité ont montré que DYPAC 1.0 a de meilleurs résultats de reproductibilité (en termes de corrélation de Pearson) par rapport aux parcelles statiques (deuxième chapitre). Plusieurs analyses démontrent que DYPAC 1.0 est robuste au choix de ses paramètres (troisième chapitre). Ces résultats et l’évolutivité de DYPAC 1.0 ont motivé une analyse complète du niveau cérébral. Je présente DYPAC 2.0 comme une approche au niveau cérébral complet pour fragmenter le cerveau en «états dynamiques de parcellations». Des reconfigurations spatiales distinctes et se chevauchant ou «états dynamiques» sont identifiées pour différentes régions du cerveau (quatrième chapitre). Ces états ont des scores de compression prometteurs qui montrent une faible perte d’infor- mations entre les cartes de stabilité d’état réduit et les données d’origine dans les cortex cérébraux, c’est-à-dire jusqu’à seulement 20% de perte de la variance expliquée. Cette thèse présente ainsi de nouvelles contributions dans le domaine de la parcellisation fonctionnelle qui pourraient avoir un impact sur la manière dont les chercheurs modélisent les interactions riches et dynamiques entre les réseaux cérébraux dans la santé et la maladie.Brain parcellations are applied in neuroimaging to help researchers reduce the high dimen- sionality of the functional MRI data. The main objective is a better understanding of the brain functional organization in both healthy subjects and subjects having neurological dis- orders, including Alzheimer disease. Despite the flurry of previous parcellation approaches, the performance measures still need improvement to generate reliable parcellations even with long acquisitions. That is, a higher reproducibility that allows researchers to replicate par- cellations and compare their studies. It is also important to minimize the information loss between the compressed data and the raw data to accurately represent the organization of an individual brain. In this thesis, I developed a new approach to parcellate the brain into distinct spatial reconfigurations called “dynamic states of parcellations”. I used a simple cluster aggregation method DYPAC1.0 of seed based parcels over multiple time windows. I hypothesized this new way to formalize the parcellation problem will improve performance measures over static parcellations. The first chapter of this document is a general context introduction to the human brain large scale networks. I also show the importance of par- cellations for a better understanding of the human brain using functional connectomes in order to predict patterns of disease progression. Then, I explain why the brain parcellation problem is hard and the different open research questions associated with this field. My research contributions are subdivided into two papers. The second and the third chapters are dedicated to the first main paper and its supplementary published in Network Neuro- science Journal. The fourth chapter represents the second paper under preparation. The fifth chapter concludes my contributions and its implications in the neuroimaging field, along with open research directions. In a nutshell, the main finding of this work is the existence of distinct spatial reconfigurations throughout the brain with near perfect reproducibility scores across test-retest data (up to .9 Pearson correlation coefficient). A simple and scalable clus- ter aggregation algorithm called DYPAC 1.0 is explained to identify these reconfigurations or “dynamic states of parcellations” for specific seed subnetworks (second chapter). The analysis of these states showed the existence of a richer repertoire of “dynamic states” in the case of heteromodal cortices (e.g., posterior cingulate cortex and the dorsal anterior cingulate cortex) compared to unimodal cortices (e.g., visual cortex). Also, the reproducibility analysis results showed that DYPAC 1.0 has better reproducibility results (in terms of Pearson corre- lation) compared to static parcels (second chapter). Several analyses demonstrate DYPAC 1.0 is robust to the choice of its parameters (third chapter). These findings and the scalabil- ity of DYPAC 1.0 motivated a full brain level analysis. I present DYPAC 2.0 as the full brain level approach to parcellate the brain into “dynamic states of parcellations”. Distinct and overlapping spatial reconfigurations or “dynamic states” are identified for different regions throughout the brain (fourth chapter). These states have promising compression scores that show low information loss between the reduced state stability maps and the original data throughout the cerebral cortices, i.e. up to only 20% loss in explained variance. This thesis thus presents new contributions in the functional parcellation field that may impact how researchers model the rich and dynamic interactions between brain networks in health and disease