Local supersymmetry in supergravity

We have studied the local supersymmetry in two D = 4 supergravity models, with N = 1 and N = 2, given the Lagrangians. We have used a simple method based on the differential of the action H, which provides an alternative systematic derivation of the gauge field variations in the first order formalism. This method may be used to find non-relativistic limits of supergravity models.Departamento de Física Teórica, Atómica y ÓpticaMáster en Físic

A Full Non-Monotonic Transition System for Unrestricted Non-Projective Parsing

Restricted non-monotonicity has been shown beneficial for the projective arc-eager dependency parser in previous research, as posterior decisions can repair mistakes made in previous states due to the lack of information. In this paper, we propose a novel, fully non-monotonic transition system based on the non-projective Covington algorithm. As a non-monotonic system requires exploration of erroneous actions during the training process, we develop several non-monotonic variants of the recently defined dynamic oracle for the Covington parser, based on tight approximations of the loss. Experiments on datasets from the CoNLL-X and CoNLL-XI shared tasks show that a non-monotonic dynamic oracle outperforms the monotonic version in the majority of languages.Comment: 11 pages. Accepted for publication at ACL 201

Parsing as Reduction

We reduce phrase-representation parsing to dependency parsing. Our reduction is grounded on a new intermediate representation, "head-ordered dependency trees", shown to be isomorphic to constituent trees. By encoding order information in the dependency labels, we show that any off-the-shelf, trainable dependency parser can be used to produce constituents. When this parser is non-projective, we can perform discontinuous parsing in a very natural manner. Despite the simplicity of our approach, experiments show that the resulting parsers are on par with strong baselines, such as the Berkeley parser for English and the best single system in the SPMRL-2014 shared task. Results are particularly striking for discontinuous parsing of German, where we surpass the current state of the art by a wide margin

Transition-based Semantic Role Labeling with Pointer Networks

Semantic role labeling (SRL) focuses on recognizing the predicate-argument structure of a sentence and plays a critical role in many natural language processing tasks such as machine translation and question answering. Practically all available methods do not perform full SRL, since they rely on pre-identified predicates, and most of them follow a pipeline strategy, using specific models for undertaking one or several SRL subtasks. In addition, previous approaches have a strong dependence on syntactic information to achieve state-of-the-art performance, despite being syntactic trees equally hard to produce. These simplifications and requirements make the majority of SRL systems impractical for real-world applications. In this article, we propose the first transition-based SRL approach that is capable of completely processing an input sentence in a single left-to-right pass, with neither leveraging syntactic information nor resorting to additional modules. Thanks to our implementation based on Pointer Networks, full SRL can be accurately and efficiently done in $O(n^2)$, achieving the best performance to date on the majority of languages from the CoNLL-2009 shared task.Comment: Final peer-reviewed manuscript accepted for publication in Knowledge-Based System

Chaotic image encryption using hopfield and hindmarsh–rose neurons implemented on FPGA

Chaotic systems implemented by artificial neural networks are good candidates for data encryption. In this manner, this paper introduces the cryptographic application of the Hopfield and the Hindmarsh–Rose neurons. The contribution is focused on finding suitable coefficient values of the neurons to generate robust random binary sequences that can be used in image encryption. This task is performed by evaluating the bifurcation diagrams from which one chooses appropriate coefficient values of the mathematical models that produce high positive Lyapunov exponent and Kaplan–Yorke dimension values, which are computed using TISEAN. The randomness of both the Hopfield and the Hindmarsh–Rose neurons is evaluated from chaotic time series data by performing National Institute of Standard and Technology (NIST) tests. The implementation of both neurons is done using field-programmable gate arrays whose architectures are used to develop an encryption system for RGB images. The success of the encryption system is confirmed by performing correlation, histogram, variance, entropy, and Number of Pixel Change Rate (NPCR) tests

Design of a test platform for studying radiation effects on SPI FRAM and I² C CMOS non-volatile memories

Electronic components are often exposed to external radiation that may interfere with their normal operation. This becomes particularly relevant for high-scale of integration parts and when said components are used in harsh environments such as in satellites and space ships, aviation, military applications or in heavy and automotive industries. Several design and construction techniques have been developed to overcome, or at least minimize, the impact of external factors on electronic components, so ESD (Electro-Static Discharge), EMI (Electro-Magnetic Interference) or RFI (Radio-Frequency Interference) protection is usually built-in into mission-critical electronic components like microcontrollers, FPGAs or memory chips. The Computer Architecture Department at FDI is carrying out the READAR-II project with the aim of developing techniques to improve the resistance of different types of components used in space missions. In particular, and as a first step, the study on semiconductor-based memories. This BSc. Thesis goal lies within the READAR-II research project objective and focuses on building a test platform to evaluate the radiation effects on non-volatile memories