Algorithms and cryptographic protocols using elliptic curves

En els darrers anys, la criptografia amb corbes el.líptiques ha adquirit una importància creixent, fins a arribar a formar part en la actualitat de diferents estàndards industrials. Tot i que s'han dissenyat variants amb corbes el.líptiques de criptosistemes clàssics, com el RSA, el seu màxim interès rau en la seva aplicació en criptosistemes basats en el Problema del Logaritme Discret, com els de tipus ElGamal. En aquest cas, els criptosistemes el.líptics garanteixen la mateixa seguretat que els construïts sobre el grup multiplicatiu d'un cos finit primer, però amb longituds de clau molt menor. Mostrarem, doncs, les bones propietats d'aquests criptosistemes, així com els requeriments bàsics per a que una corba sigui criptogràficament útil, estretament relacionat amb la seva cardinalitat. Revisarem alguns mètodes que permetin descartar corbes no criptogràficament útils, així com altres que permetin obtenir corbes bones a partir d'una de donada. Finalment, descriurem algunes aplicacions, com són el seu ús en Targes Intel.ligents i sistemes RFID, per concloure amb alguns avenços recents en aquest camp.The relevance of elliptic curve cryptography has grown in recent years, and today represents a cornerstone in many industrial standards. Although elliptic curve variants of classical cryptosystems such as RSA exist, the full potential of elliptic curve cryptography is displayed in cryptosystems based on the Discrete Logarithm Problem, such as ElGamal. For these, elliptic curve cryptosystems guarantee the same security levels as their finite field analogues, with the additional advantage of using significantly smaller key sizes. In this report we show the positive properties of elliptic curve cryptosystems, and the requirements a curve must meet to be useful in this context, closely related to the number of points. We survey methods to discard cryptographically uninteresting curves as well as methods to obtain other useful curves from a given one. We then describe some real world applications such as Smart Cards and RFID systems and conclude with a snapshot of recent developments in the field

Nonexistence of almost Moore digraphs of diameter four

Regular digraphs of degree d > 1, diameter k > 1 and order N(d; k) = d+ +dk will be called almost Moore (d; k)-digraphs. So far, the problem of their existence has only been solved when d = 2; 3 or k = 2; 3. In this paper we prove that almost Moore digraphs of diameter 4 do not exist for any degree dPostprint (published version

Cracking the “Sepsis” Code: Assessing Time Series Nature of EHR data, and Using Deep Learning for Early Sepsis Prediction

On a yearly basis, sepsis costs US hospitals more than any other health condition. A majority of patients who suffer from sepsis are not diagnosed at the time of admission. Early detection and antibiotic treatment of sepsis are vital to improve outcomes for these patients, as each hour of delayed treatment is associated with increased mortality. In this study our goal is to predict sepsis 12 hours before its diagnosis using vitals and blood tests routinely taken in the ICU. We have investigated the performance of several machine learning algorithms including XGBoost, CNN, CNN-LSTM and CNN-XGBoost. Contrary to our expectations, XGBoost outperforms all of the sequential models and yields the best hour-by-hour prediction, perhaps due to the way we imputed missing values, losing signal that relates to the time-series nature of the EHR data. We added feature engineering to detect change points in tests and vitals, resulting in 5% improvement in XGBoost. Our team, USF-Sepsis-Phys, achieved a utility score of 0.22 (untuned threshold) and an average of the three reported AUCs (test sets A, B, C) of 0.82. As expected with this AUC, the same model with tuned threshold (not run in the PhysioNet challenge) performed significantly better, as evaluated with 3-fold cross-validation of the entire PhyisoNet training set

Limited effects of preterm birth and the first enteral nutrition on cerebellum morphology and gene expression in piglets

Preterm pigs show many signs of immaturity that are characteristic of preterm infants. In preterm infants, the cerebellum grows particularly rapid and hypoplasia and cellular lesions are associated with motor dysfunction and cognitive deficits. We hypothesized that functional brain delays observed in preterm pigs would be paralleled by both structural and molecular differences in the cerebellum relative to term born piglets. Cerebella were collected from term (n=56) and preterm (90% gestation, n=112) pigs at 0, 5, and 26days after birth for stereological volume estimations, large-scale qPCR gene expression analyses (selected neurodevelopmental genes) and western blot protein expression analysis (Sonic Hedgehog pathway). Memory and learning was tested using a T-maze, documenting that preterm pigs showed delayed learning. Preterm pigs also showed reduced volume of both white and gray matter at all three ages but the proportion of white matter increased postnatally, relative to term pigs. Early initiation of enteral nutrition had limited structural or molecular effects. The Sonic Hedgehog pathway was unaffected by preterm birth. Few differences in expression of the selected genes were found, except consistently higher mRNA levels of Midkine, p75, and Neurotrophic factor 3 in the preterm cerebellum postnatally, probably reflecting an adaptive response to preterm birth. Pig cerebellar development appears more affected by postconceptional age than by environmental factors at birth or postnatally. Compensatory mechanisms following preterm birth may include faster white matter growth and increased expression of selected genes for neurotrophic factors and regulation of angiogenesis. While the pig cerebellum is immature in 90% gestation preterm pigs, it appears relatively mature and resilient toward environmental factor

Metal [100] Nanowires with Negative Poisson???s Ratio

When materials are under stretching, occurrence of lateral contraction of materials is commonly observed. This is because Poisson???s ratio, the quantity describes the relationship between a lateral strain and applied strain, is positive for nearly all materials. There are some reported structures and materials having negative Poisson???s ratio. However, most of them are at macroscale, and reentrant structures and rigid rotating units are the main mechanisms for their negative Poisson???s ratio behavior. Here, with numerical and theoretical evidence, we show that metal [100] nanowires with asymmetric cross-sections such as rectangle or ellipse can exhibit negative Poisson???s ratio behavior. Furthermore, the negative Poisson???s ratio behavior can be further improved by introducing a hole inside the asymmetric nanowires. We show that the surface effect inducing the asymmetric stresses inside the nanowires is a main origin of the superior property.ope

Influencia del color en la percepción de la forma de los objetos

[ES] Conocer en que prioriza la mente humana a la hora de tomar decisiones es de gran importancia. La ingeniería industrial tiene como meta crear productos y servicios para favorecer la calidad de vida de nuestra sociedad, dichos productos han de ser atractivos para que se produzca el consumo deseado, para ello debemos emplear técnicas que afecten a la percepción del consumidor así como a su sensación frente a él. La finalidad de este estudio es la de obtener información acerca de como influyen los factores de color y forma en la percepción de un objeto en diferentes individuos, así como determinar cual de estos factores es de mayor relevancia. La información obtenida en este estudio se podrá emplear para posteriores estudios científicos e I+D+I, así como aplicarlo a otras ramas como pueden ser el diseño industrial o el neuromarketing.Enguix Chiral, V. (2014). Influencia del color en la percepción de la forma de los objetos. http://hdl.handle.net/10251/173783Archivo delegad

Neonatal Resting-State Functional Magnetic Resonance Imaging: Optimization of Data Acquisition and Democratization of Data Preprocessing

RÉSUMÉ: L’extrême prématurité et les cardiopathies congénitales sont associées à un risque élevé de lésions cérébrales induisant des troubles neurocognitifs sévères. Jusqu’à récemment, l’examen de ces lésions reposait sur des techniques neuroanatomiques, offrant peu d’informations quant à leurs conséquences sur le plan fonctionnel. En ce sens, l’analyse des réseaux neuronaux en état de repos par IRM fonctionnelle constitue une approche prometteuse pour mieux comprendre l’effet de ces lésions. De plus ils commencent à apparaître dès le troisième trimestre de gestation, avec de fortes perturbations lors d’une naissance prématurée. Sur le plan clinique, l’impact des atteintes cérébrales sur les différents réseaux en état de repos, offrirait des avancées en termes de pronostic puisque ces atteintes, quoique présentes dans les premiers mois de vie, n’ont une expression clinique que plusieurs années après la naissance. Malgré les possibilités offertes par IRM fonctionnel en état de repos, son application pose divers défis méthodologiques. La qualité des images peut être altérée par le mouvement de la tête, pouvant conduire à des données inutilisables, et que l’on découvre à posteriori pendant les étapes de traitement d’images, car une inspection visuelle des données est impossible due au grand volume de données acquises. De plus, le traitement des données RS néonatales est très complexe. Des outils permettant le preprocessing des données existent chez l’adulte, mais ceci est difficilement applicable au nouveau-né à cause de l’inversion du contraste matière blanche/grise dû au processus de myélinisation ou de la constante croissance. Pour pallier à ces limitations, il est nécessaire d’acquérir des images d’excellente qualité ainsi que le développement d’outils adaptés aux besoins des images des nouveau-nés. Car, pour réussir le pretraitement des données IRM fonctionnel en état de repos néonatales, chaque bébé doit être traité de façon individuelle et avec beaucoup d’attention. Ce projet porte donc sur le développement d’un protocole optimisé pour le nouveau-né ainsi que l’évaluation du mouvement de la tête en temps réel (pendant que le bébé est encore dans l’IRM) afin de décider si les données acquises passent les critères de qualité ou si des données additionnelles doivent être collectées avant de sortir le bébé de l’IRM. Ceci permet d’acquérir des données de qualité, réduisant ainsi le nombre de sujets rejetés dans une étude. Enfin, afin de permettre aux différents centres de traiter leurs données de façon rapide et efficace, nous avons développé un outil open-source appelé NeoRS, permettant le pretraitement individuel des données néonatales. NeoRS répond aux spécificités du pretraitement des images néonatales en accordant une attention particulière au recalage, y compris à l’atlas néonatal, ainsi qu’aux paramètres tels que les différences de contraste dues à la myélinisation et de taille de la tête. Nous espérons que NeoRS permettra à un plus grand nombre de centres de pretraiter leurs ensembles de données, contribuant ainsi à produire un plus grand nombre d’études. Cela permettra, à terme, de développer des applications pour l’utilisation des réseaux en état de repos comme biomarqueur de l’intégrité cérébrale. ABSTRACT: Extreme prematurity and congenital heart disease are associated with a high risk of brain damage leading to severe neurocognitive disorders. Until recently, these lesions were examined based on neuroanatomical techniques, offering little information about their functional consequences. In this sense, the resting-state networks analysis by functional MRI is a promising approach to better understanding these lesions’ effects. Moreover, they appear as early as the third trimester of gestation, with strong disruptions during a premature birth. From a clinical point of view, the impact of brain damage on the different resting-state networks could help prognosticate the impact of the injury since this damage, although present during the first months of life, does not have a clinical expression until several years after birth. Despite the possibilities offered by resting-state functional MRI, its application poses various methodological challenges. For example, the head movement can alter the image quality, leading to unusable data, usually discovered afterward during the image preprocessing steps. Indeed, visual inspection of the data is impossible due to the large volume of acquired data. Moreover, the preprocessing of neonatal resting-state data is very complex. Image preprocessing tools exist for adults, but this is difficult to apply to neonates because of the inversion of the white matter/gray contrast due to the myelination process or the constant growth. To overcome these limitations, it is necessary to acquire excellent quality images and develop tools adapted to the needs of newborn data. To do so, each baby must be considered individually and with great care. Therefore, this project focuses on developing a protocol optimized for neonates and the evaluation of the head movement in real-time (while the baby is still in the MRI) to decide if the acquired data passes the quality criteria or if additional data has to be collected before the baby leaves the MRI room. This enables the acquisition of high-quality data with low motion, decreasing the number of discarded subjects in a study. Finally, we developed an open-source tool called NeoRS for neonatal data to allow the different centers to preprocess their data quickly and efficiently. NeoRS addresses the specificities of neonatal image preprocessing by paying particular attention to image registration, including neonatal atlas and parameters like differences in contrast due to myelination and different head sizes. We hope NeoRS will allow more centers to preprocess their datasets, contributing to producing a higher number of studies. Ultimately, this will enable the development of applications for using resting-state networks as a biomarker of brain integrity