991 resultados para superstrings and heterotic strings


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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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[ES] El Trabajo de Fin de Grado, Monitor Web de Expresiones Regulares (MWRegEx), es una herramienta basada en tecnologías web, desarrollada usando el entorno Visual Studio. El objetivo principal de la aplicación es dar apoyo a la docencia de expresiones regulares, en el marco de la enseñanza del manejo de ristras de caracteres en las asignaturas de programación del Grado en Ingeniería Informática.  La aplicación permite obtener el dibujo de un autómata de una expresión regular, facilitando su comprensión; además, permite aplicar la expresión a diferentes ristras de caracteres, mostrando las coincidencias encontradas, y ofrece una versión de la expresión adaptada a su uso en literales string de lenguajes como Java y otros. La herramienta se ha implementado en dos partes: un servicio web, escrito en C#, donde se realizan todos los análisis de las expresiones regulares y las ristras a contrastar; y un cliente web, implementado usando tecnología asp.net, con JavaScript y JQuery, que gestiona la interfaz de usuario y muestra los resultados. Esta separación permite que el servicio web pueda ser reutilizado con otras aplicaciones cliente. El autómata que representa una expresión regular esta dibujado usando la librería Raphaël JavaScript que permite manejar los elementos SVG. Cada elemento de la expresión regular tiene un dibujo diferente y único para así diferenciarlo. Toda la interfaz gráfica de usuario está internacionalizada de manera tal que pueda adaptarse a diferentes idiomas y regiones sin la necesidad de realizar cambios de ingeniería ni en el código. Tanto el servicio web como la parte cliente están estructurados para que se puedan agregar nuevas modificaciones sin que esto genere una onda expansiva a lo largo de las diversas clases existentes.

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SQL Injection Attack (SQLIA) remains a technique used by a computer network intruder to pilfer an organisation’s confidential data. This is done by an intruder re-crafting web form’s input and query strings used in web requests with malicious intent to compromise the security of an organisation’s confidential data stored at the back-end database. The database is the most valuable data source, and thus, intruders are unrelenting in constantly evolving new techniques to bypass the signature’s solutions currently provided in Web Application Firewalls (WAF) to mitigate SQLIA. There is therefore a need for an automated scalable methodology in the pre-processing of SQLIA features fit for a supervised learning model. However, obtaining a ready-made scalable dataset that is feature engineered with numerical attributes dataset items to train Artificial Neural Network (ANN) and Machine Leaning (ML) models is a known issue in applying artificial intelligence to effectively address ever evolving novel SQLIA signatures. This proposed approach applies numerical attributes encoding ontology to encode features (both legitimate web requests and SQLIA) to numerical data items as to extract scalable dataset for input to a supervised learning model in moving towards a ML SQLIA detection and prevention model. In numerical attributes encoding of features, the proposed model explores a hybrid of static and dynamic pattern matching by implementing a Non-Deterministic Finite Automaton (NFA). This combined with proxy and SQL parser Application Programming Interface (API) to intercept and parse web requests in transition to the back-end database. In developing a solution to address SQLIA, this model allows processed web requests at the proxy deemed to contain injected query string to be excluded from reaching the target back-end database. This paper is intended for evaluating the performance metrics of a dataset obtained by numerical encoding of features ontology in Microsoft Azure Machine Learning (MAML) studio using Two-Class Support Vector Machines (TCSVM) binary classifier. This methodology then forms the subject of the empirical evaluation.

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Non-linear planar response of a string to planar narrow band random excitation is investigated in this paper. A response equation for the mean square deflection σ2 is obtained under a single mode approximation by using the equivalent linearization technique. It is shown that the response is triple valued, as in the case of harmonic excitation, if the centre frequency of excitation Ω lies in a certain specified range. The triple valued response occurs only if the excitation bandwidth β is smaller than a critical value βcrit which is a monotonically increasing function of the intensity of excitation. An approximate method of investigating the almost sure asymptotic stability of the solution is presented and regions of instability in the Ω-σ2 plane have been charted. It is shown that planar response can become unstable either due to an unbounded growth of the in-plane component of motion or due to a spontaneous appearance of an out-of-plane component.

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Motivated by the recent proposal for the S-matrix in AdS(3) x S-3 with mixed three form fluxes, we study classical folded string spinning in AdS(3) with both Ramond and Neveu-Schwarz three form fluxes. We solve the equations of motion of these strings and obtain their dispersion relation to the leading order in the Neveu-Schwarz flux b. We show that dispersion relation for the spinning strings with large spin S acquires a term given by -root lambda/2 pi b(2) log(2) S in addition to the usual root lambda/pi log S term where root lambda is proportional to the square of the radius of AdS(3). Using SO(2, 2) transformations and re-parmetrizations we show that these spinning strings can be related to light like Wilson loops in AdS(3) with Neveu-Schwarz flux b. We observe that the logarithmic divergence in the area of the light like Wilson loop is also deformed by precisely the same coefficient of the b(2) log(2) S term in the dispersion relation of the spinning string. This result indicates that the coefficient of b(2) log(2) S has a property similar to the coefficient of the log S term, known as cusp-anomalous dimension, and can possibly be determined to all orders in the coupling lambda using the recent proposal for the S-matrix.

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In this thesis, we consider two main subjects: refined, composite invariants and exceptional knot homologies of torus knots. The main technical tools are double affine Hecke algebras ("DAHA") and various insights from topological string theory.

In particular, we define and study the composite DAHA-superpolynomials of torus knots, which depend on pairs of Young diagrams and generalize the composite HOMFLY-PT polynomials from the full HOMFLY-PT skein of the annulus. We also describe a rich structure of differentials that act on homological knot invariants for exceptional groups. These follow from the physics of BPS states and the adjacencies/spectra of singularities associated with Landau-Ginzburg potentials. At the end, we construct two DAHA-hyperpolynomials which are closely related to the Deligne-Gross exceptional series of root systems.

In addition to these main themes, we also provide new results connecting DAHA-Jones polynomials to quantum torus knot invariants for Cartan types A and D, as well as the first appearance of quantum E6 knot invariants in the literature.

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Four field experiments over 2 years investigated whether wheat hybrids had higher nitrogen-use efficiency (NUE) than their parents over a range of seed rates and different N regimes. There was little heterosis for total N in the above-ground biomass (NYt), but there was high-parent heterosis for grain N yields (NYg) in two of the hybrids, Hyno Esta and Hyno Rista, associated with greater nitrogen harvest index (NHI). Overall, the hybrids did not significantly increase the total dry matter produced per unit N in the above-ground crop (NUtE(t)), but did increase the grain dry matter per unit N in the above ground crop (NUtE(g)). The improvement in NUtE(g) was at the partial detriment of grain N concentration. Heterosis for grain NYg in Hyno Esta was lower at zero-N, suggesting that it did not achieve higher yields through more efficient capture or utilization of N. The greater NHI in Hyno Esta appeared to be facilitated by both greater N uptake, and remobilization of N from vegetative tissues, after anthesis. The response of N efficiency and uptake to seed rate was dependent on N supply and season. Where N fertilizer was applied, N uptake over time was slower at the lower seed rates, but where N was withheld N capture at the lowest seed rate soon approached the N capture of the higher seed rates. During grain filling, the rate of accumulation of N into the grain increased with seed rate and the duration of N accumulation decreased with seed rate. With N applied, N yields increased to all asymptote with seed rate, when N was withheld there was little response of N yields to seed rate. In 2002, N utilization efficiency (NUtE(t) and NUtE(g)) also increased asymptotically with seed rate, but in 2003 seed rate had little effect on N utilization efficiency. When nitrogen fertilizer had not been applied, NHI consistently decreased with increasing seed rate. The timing of N application made little difference to NUE, NY, or NUtE.

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While the responsibility of States and, in more recent times, corporations, has been thoroughly discussed in relation to human right~, a new stage of evolution may be emerging in relation to the liability of the financial backers of an enterprise that is accused of human rights abuses. This article considers the basis in international law for such emerging liability and examines some of the legal avenues used in recent domestic litigation against financial institutions. The article concludes by examining some of the relevant instruments of 'soft' international law and notes that although there is little in the way of concrete legislation or judicial precedent that would hold financial institutions responsible for the actions of those they invest in, the potential for the law to evolve in that direction is clear.

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Classifying malware correctly is an important research issue for anti-malware software producers. This paper presents an effective and efficient malware classification technique based on string information using several wellknown classification algorithms. In our testing we extracted the printable strings from 1367 samples, including unpacked trojans and viruses and clean files. Information describing the printable strings contained in each sample was input to various classification algorithms, including treebased classifiers, a nearest neighbour algorithm, statistical algorithms and AdaBoost. Using k-fold cross validation on the unpacked malware and clean files, we achieved a classification accuracy of 97%. Our results reveal that strings from library code (rather than malicious code itself) can be utilised to distinguish different malware families.

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A composition written on the piano and accompanied by synth strings; a light, elegant and beautiful journey.