La Interpretación de Enunciados y sus Consecuencias de Interfaz

En el presente trabajo, se analizan los factores que intervienen en la interpretación de enunciados asumiendo que, en tanto proceso, la interpretación supone la interacción de distintos sistemas modulares de la mente: La Facultad del Lenguaje (FdeL), y los sistemas sensorio-motriz (SM) y conceptual-intencional (CI). Este último incluye mecanismos para la elaboración de representaciones conceptuales (C) y mecanismos inferenciales involucrados más globalmente en la fijación de estados intencionales (I). Estos sistemas externos a la Facultad del Lenguaje imponen restricciones de manera tal que los núcleos de información que llegan a las interfaces sean legibles por los sistemas SM y CI (Chomsky, 1995-2008). En este sentido, entender la interpretación como proceso conlleva atender a la relación entre los aspectos puramente lingüísticos (sintaxis, semántica), los prosódicos, y los inferenciales (pragmática). En este trabajo, se busca comprender el funcionamiento de aquellos elementos del sistema lingüístico que propician la obtención de los supuestos necesarios para llevar a cabo ese proceso. ;Adoptando un paralelismo entre las categorías sintácticas y las semánticas postuladas en el relevantismo, se intenta dilucidar cómo actúa la variada evidencia lingüística que provee un H en la comunicación, de modo que un O puede arribar a alguna hipótesis del 'significado de H'. A partir del análisis de distintos enunciados, se explora el alcance de ese paralelismo y se llega a una caracterización tentativa del proceso de interpretación. Finalmente, siguiendo ideas de distintos autores, se propone concebir la interfaz FdeL-CI a partir de pares primitivos de información, que son relevantes a todos los sistemas cognitivos involucrados en la comunicación

La Interpretación de Enunciados y sus Consecuencias de Interfaz

En el presente trabajo, se analizan los factores que intervienen en la interpretación de enunciados asumiendo que, en tanto proceso, la interpretación supone la interacción de distintos sistemas modulares de la mente: La Facultad del Lenguaje (FdeL), y los sistemas sensorio-motriz (SM) y conceptual-intencional (CI). Este último incluye mecanismos para la elaboración de representaciones conceptuales (C) y mecanismos inferenciales involucrados más globalmente en la fijación de estados intencionales (I). Estos sistemas externos a la Facultad del Lenguaje imponen restricciones de manera tal que los núcleos de información que llegan a las interfaces sean legibles por los sistemas SM y CI (Chomsky, 1995-2008). En este sentido, entender la interpretación como proceso conlleva atender a la relación entre los aspectos puramente lingüísticos (sintaxis, semántica), los prosódicos, y los inferenciales (pragmática). En este trabajo, se busca comprender el funcionamiento de aquellos elementos del sistema lingüístico que propician la obtención de los supuestos necesarios para llevar a cabo ese proceso. ;Adoptando un paralelismo entre las categorías sintácticas y las semánticas postuladas en el relevantismo, se intenta dilucidar cómo actúa la variada evidencia lingüística que provee un H en la comunicación, de modo que un O puede arribar a alguna hipótesis del 'significado de H'. A partir del análisis de distintos enunciados, se explora el alcance de ese paralelismo y se llega a una caracterización tentativa del proceso de interpretación. Finalmente, siguiendo ideas de distintos autores, se propone concebir la interfaz FdeL-CI a partir de pares primitivos de información, que son relevantes a todos los sistemas cognitivos involucrados en la comunicación

Paleoclimate records from maar Monticchio

Oxygen-isotope records from Greenland ice cores indicate numerous rapid climate fluctuations during the last glacial period. North Atlantic marine sediment cores show comparable variability in sea surface temperature and the deposition of icerafted debris. In contrast, very few continental records of this time period provide the temporal resolution and environmental sensitivity necessary to reveal the extent and effects of these environmental fluctuations on the continents. Here we present high-resolution geochemical, physical and pollen data from lake sediments in Italy and from a Mediterranean sediment core, linked by a common tephrochronology. Our lacustrine sequence extends to the past 102,000 years. Many of its features correlate well with the Greenland ice-core records, demonstrating that the closely coupled ocean-atmosphere system of the Northern Hemisphere during the last glacial extended its influence at least as far as the central Mediterranean region. Numerous vegetation changes were rapid, frequently occurring in less than 200 years, showing that the terrestrial biosphere participated fully in lastglacial climate variability. Earlier than 65,000 years ago, our record shows more climate fluctuations than are apparent in the Greenland ice cores. Together, the multi-proxy data from the continental and marine records reveal differences in the seasonal character of climate during successive interstadials, and provide a step towards determining the underlying mechanisms of the centennial-millennial-scale variability.

Distributed parallelization of greedy Mobile Network Optimization algorithms

The Mobile Network Optimization (MNO) technologies have advanced at a tremendous pace in recent years. And the Dynamic Network Optimization (DNO) concept emerged years ago, aimed to continuously optimize the network in response to variations in network traffic and conditions. Yet, DNO development is still at its infancy, mainly hindered by a significant bottleneck of the lengthy optimization runtime. This paper identifies parallelism in greedy MNO algorithms and presents an advanced distributed parallel solution. The solution is designed, implemented and applied to real-life projects whose results yield a significant, highly scalable and nearly linear speedup up to 6.9 and 14.5 on distributed 8-core and 16-core systems respectively. Meanwhile, optimization outputs exhibit self-consistency and high precision compared to their sequential counterpart. This is a milestone in realizing the DNO. Further, the techniques may be applied to similar greedy optimization algorithm based applications.

El interés de la audiencia por las intervenciones televisivas de Pablo Iglesias: estrategia comunicativa de Podemos

Este artículo analiza la eclosión mediática y política del partido político Podemos durante sus primeros meses de vida tras las elecciones europeas. Comprobaremos si el interés que despierta el partido en sus inicios, en intención de voto, se traslada a las audiencias de televisión con dos hipótesis que muestran la estrategia comunicativa de Podemos y su repercusión: la H1 afirma que los programas en los que participa Pablo Iglesias obtienen mayor audiencia de la habitual y la H2 plantea que la apuesta del líder de Podemos por el medio televisivo es muy superior y con mejores resultados con respecto a los partidos clásicos. Los resultados demuestran que los programas en los que participa (N=14) crecen un 62% y baten récord de audiencia. Concluimos que la estrategia comunicativa de Podemos da importancia a la televisión, además de continuar con la estrategia en redes aprendida en el 15M.

Journalistic organisations with regard to training and professional changes. Parallelisms with academic discourse

The present paper analyses the parallelism existing between academic and professional discourse regarding three interrelated subjects: journalists´ training needs, their adaptation to multimedia profile changes and recognition of University graduates via professional regulation. This study relies on a bibliographical review and brings together the opinion of five professional groups by means of an interview using an openended questionnaire. The results show a coincidence in both discourses regarding the need for firmly grounded training of the journalists with study plans which integrate the new profiles. At the same time, it reflects the discrepancy of the journalistic groups with regard to professional regulation based on the University degree, whose vindication does not appear to be a priority in academic circles.

A Scalable General Purpose System for Large-Scale Graph Processing

Graph analytics is an important and computationally demanding class of data analytics. It is essential to balance scalability, ease-of-use and high performance in large scale graph analytics. As such, it is necessary to hide the complexity of parallelism, data distribution and memory locality behind an abstract interface. The aim of this work is to build a scalable graph analytics framework that does not demand significant parallel programming experience based on NUMA-awareness.
The realization of such a system faces two key problems:
(i)~how to develop a scale-free parallel programming framework that scales efficiently across NUMA domains; (ii)~how to efficiently apply graph partitioning in order to create separate and largely independent work items that can be distributed among threads.

FPGA-based soft-core processors for image processing applications

With security and surveillance, there is an increasing need to process image data efficiently and effectively either at source or in a large data network. Whilst a Field-Programmable Gate Array (FPGA) has been seen as a key technology for enabling this, the design process has been viewed as problematic in terms of the time and effort needed for implementation and verification. The work here proposes a different approach of using optimized FPGA-based soft-core processors which allows the user to exploit the task and data level parallelism to achieve the quality of dedicated FPGA implementations whilst reducing design time. The paper also reports some preliminary
progress on the design flow to program the structure. An implementation for a Histogram of Gradients algorithm is also reported which shows that a performance of 328 fps can be achieved with this design approach, whilst avoiding the long design time, verification and debugging steps associated with conventional FPGA implementations.

High-performance parallel systems for data-intensive computing

Thesis (Ph.D.)--University of Washington, 2016-08

Type-Aware Programming Models for Distributed Applications

Thesis (Ph.D.)--University of Washington, 2016-08

Spam campaign detection, analysis, and formalization

Les courriels Spams (courriels indésirables ou pourriels) imposent des coûts annuels extrêmement lourds en termes de temps, d’espace de stockage et d’argent aux utilisateurs privés et aux entreprises. Afin de lutter efficacement contre le problème des spams, il ne suffit pas d’arrêter les messages de spam qui sont livrés à la boîte de réception de l’utilisateur. Il est obligatoire, soit d’essayer de trouver et de persécuter les spammeurs qui, généralement, se cachent derrière des réseaux complexes de dispositifs infectés, ou d’analyser le comportement des spammeurs afin de trouver des stratégies de défense appropriées. Cependant, une telle tâche est difficile en raison des techniques de camouflage, ce qui nécessite une analyse manuelle des spams corrélés pour trouver les spammeurs. Pour faciliter une telle analyse, qui doit être effectuée sur de grandes quantités des courriels non classés, nous proposons une méthodologie de regroupement catégorique, nommé CCTree, permettant de diviser un grand volume de spams en des campagnes, et ce, en se basant sur leur similarité structurale. Nous montrons l’efficacité et l’efficience de notre algorithme de clustering proposé par plusieurs expériences. Ensuite, une approche d’auto-apprentissage est proposée pour étiqueter les campagnes de spam en se basant sur le but des spammeur, par exemple, phishing. Les campagnes de spam marquées sont utilisées afin de former un classificateur, qui peut être appliqué dans la classification des nouveaux courriels de spam. En outre, les campagnes marquées, avec un ensemble de quatre autres critères de classement, sont ordonnées selon les priorités des enquêteurs. Finalement, une structure basée sur le semiring est proposée pour la représentation abstraite de CCTree. Le schéma abstrait de CCTree, nommé CCTree terme, est appliqué pour formaliser la parallélisation du CCTree. Grâce à un certain nombre d’analyses mathématiques et de résultats expérimentaux, nous montrons l’efficience et l’efficacité du cadre proposé.

Parallel dynamic load-balancing for adaptive unstructured meshes

This chapter describes a parallel optimization technique that incorporates a distributed load-balancing algorithm and provides an extremely fast solution to the problem of load-balancing adaptive unstructured meshes. Moreover, a parallel graph contraction technique can be employed to enhance the partition quality and the resulting strategy outperforms or matches results from existing state-of-the-art static mesh partitioning algorithms. The strategy can also be applied to static partitioning problems. Dynamic procedures have been found to be much faster than static techniques, to provide partitions of similar or higher quality and, in comparison, involve the migration of a fraction of the data. The method employs a new iterative optimization technique that balances the workload and attempts to minimize the interprocessor communications overhead. Experiments on a series of adaptively refined meshes indicate that the algorithm provides partitions of an equivalent or higher quality to static partitioners (which do not reuse the existing partition) and much more quickly. The dynamic evolution of load has three major influences on possible partitioning techniques; cost, reuse, and parallelism. The unstructured mesh may be modified every few time-steps and so the load-balancing must have a low cost relative to that of the solution algorithm in between remeshing.

Dynamic Workload Parallelization and System Calibration on Single-Chip Cloud Computer

Many-core systems are emerging from the need of more computational power and power efficiency. However there are many issues which still revolve around the many-core systems. These systems need specialized software before they can be fully utilized and the hardware itself may differ from the conventional computational systems. To gain efficiency from many-core system, programs need to be parallelized. In many-core systems the cores are small and less powerful than cores used in traditional computing, so running a conventional program is not an efficient option. Also in Network-on-Chip based processors the network might get congested and the cores might work at different speeds. In this thesis is, a dynamic load balancing method is proposed and tested on Intel 48-core Single-Chip Cloud Computer by parallelizing a fault simulator. The maximum speedup is difficult to obtain due to severe bottlenecks in the system. In order to exploit all the available parallelism of the Single-Chip Cloud Computer, a runtime approach capable of dynamically balancing the load during the fault simulation process is used. The proposed dynamic fault simulation approach on the Single-Chip Cloud Computer shows up to 45X speedup compared to a serial fault simulation approach. Many-core systems can draw enormous amounts of power, and if this power is not controlled properly, the system might get damaged. One way to manage power is to set power budget for the system. But if this power is drawn by just few cores of the many, these few cores get extremely hot and might get damaged. Due to increase in power density multiple thermal sensors are deployed on the chip area to provide realtime temperature feedback for thermal management techniques. Thermal sensor accuracy is extremely prone to intra-die process variation and aging phenomena. These factors lead to a situation where thermal sensor values drift from the nominal values. This necessitates efficient calibration techniques to be applied before the sensor values are used. In addition, in modern many-core systems cores have support for dynamic voltage and frequency scaling. Thermal sensors located on cores are sensitive to the core's current voltage level, meaning that dedicated calibration is needed for each voltage level. In this thesis a general-purpose software-based auto-calibration approach is also proposed for thermal sensors to calibrate thermal sensors on different range of voltages.

Macrophage adaptation leads to parallel evolution of genetically diverseEscherichia colismall-colony variants with increased fitness in vivo and antibiotic collateral sensitivity

Small-colony variants (SCVs) are commonly observed in evolution experiments and clinical isolates, being associated with antibiotic resistance and persistent infections. We recently observed the repeated emergence of Escherichia coli SCVs during adaptation to the interaction with macrophages. To identify the genetic targets underlying the emergence of this clinically relevant morphotype, we performed whole-genome sequencing of independently evolved SCV clones. We uncovered novel mutational targets, not previously associated with SCVs (e.g. cydA, pepP) and observed widespread functional parallelism. All SCV clones had mutations in genes related to the electron-transport chain. As SCVs emerged during adaptation to macrophages, and often show increased antibiotic resistance, we measured SCV fitness inside macrophages and measured their antibiotic resistance profiles. SCVs had a fitness advantage inside macrophages and showed increased aminoglycoside resistance in vitro, but had collateral sensitivity to other antibiotics (e.g. tetracycline). Importantly, we observed similar results in vivo. SCVs had a fitness advantage upon colonization of the mouse gut, which could be tuned by antibiotic treatment: kanamycin (aminoglycoside) increased SCV fitness, but tetracycline strongly reduced it. Our results highlight the power of using experimental evolution as the basis for identifying the causes and consequences of adaptation during host-microbe interactions.

GPRM: a high performance programming framework for manycore processors

Processors with large numbers of cores are becoming commonplace. In order to utilise the available resources in such systems, the programming paradigm has to move towards increased parallelism. However, increased parallelism does not necessarily lead to better performance. Parallel programming models have to provide not only flexible ways of defining parallel tasks, but also efficient methods to manage the created tasks. Moreover, in a general-purpose system, applications residing in the system compete for the shared resources. Thread and task scheduling in such a multiprogrammed multithreaded environment is a significant challenge. In this thesis, we introduce a new task-based parallel reduction model, called the Glasgow Parallel Reduction Machine (GPRM). Our main objective is to provide high performance while maintaining ease of programming. GPRM supports native parallelism; it provides a modular way of expressing parallel tasks and the communication patterns between them. Compiling a GPRM program results in an Intermediate Representation (IR) containing useful information about tasks, their dependencies, as well as the initial mapping information. This compile-time information helps reduce the overhead of runtime task scheduling and is key to high performance. Generally speaking, the granularity and the number of tasks are major factors in achieving high performance. These factors are even more important in the case of GPRM, as it is highly dependent on tasks, rather than threads. We use three basic benchmarks to provide a detailed comparison of GPRM with Intel OpenMP, Cilk Plus, and Threading Building Blocks (TBB) on the Intel Xeon Phi, and with GNU OpenMP on the Tilera TILEPro64. GPRM shows superior performance in almost all cases, only by controlling the number of tasks. GPRM also provides a low-overhead mechanism, called “Global Sharing”, which improves performance in multiprogramming situations. We use OpenMP, as the most popular model for shared-memory parallel programming as the main GPRM competitor for solving three well-known problems on both platforms: LU factorisation of Sparse Matrices, Image Convolution, and Linked List Processing. We focus on proposing solutions that best fit into the GPRM’s model of execution. GPRM outperforms OpenMP in all cases on the TILEPro64. On the Xeon Phi, our solution for the LU Factorisation results in notable performance improvement for sparse matrices with large numbers of small blocks. We investigate the overhead of GPRM’s task creation and distribution for very short computations using the Image Convolution benchmark. We show that this overhead can be mitigated by combining smaller tasks into larger ones. As a result, GPRM can outperform OpenMP for convolving large 2D matrices on the Xeon Phi. Finally, we demonstrate that our parallel worksharing construct provides an efficient solution for Linked List processing and performs better than OpenMP implementations on the Xeon Phi. The results are very promising, as they verify that our parallel programming framework for manycore processors is flexible and scalable, and can provide high performance without sacrificing productivity.