Unconformity-bounded stratigraphic units (UBSUs) in an italian alluvial-plain area: recognizing and dating

We studied the coastal zone of the Tavoliere di Puglia plain, (Puglia region, southern Italy) with the aim to recognize the main unconformities, and therefore, the unconformity-bounded stratigraphic units (UBSUs; Salvador 1987, 1994) forming its Quaternary sedimentary fill. Recognizing unconformities is particularly problematic in an alluvial plain, due to the difficulties in distinguishing the unconformities that bound the UBSUs. So far, the recognition of UBSUs in buried successions has been made mostly by using seismic profiles. Instead, in our case, the unavailability of the latter has prompted us to address the problem by developing a methodological protocol consisting of the following steps: I) geological survey in the field; II) draft of a preliminary geological setting based on the field-survey results; III) dating of 102 samples coming from a large number of boreholes and some outcropping sections by means of the amino acid racemization (AAR) method applied to ostracod shells and 14C dating, filtering of the ages and the selection of valid ages; IV) correction of the preliminary geological setting in the light of the numerical ages; definition of the final geological setting with UBSUs; identification of a ‘‘hypothetical’’ or ‘‘attributed time range’’ (HTR or ATR) for each UBSU, the former very wide and subject to a subsequent modification, the latter definitive; V) cross-checking between the numerical ages and/or other characteristics of the sedimentary bodies and/or the sea-level curves (with their effects on the sedimentary processes) in order to restrict also the hypothetical time ranges in the attributed time ranges. The successful application of AAR geochronology to ostracod shells relies on the fact that the ability of ostracods to colonize almost all environments constitutes a tool for correlation, and also allow the inclusion in the same unit of coeval sediments that differ lithologically and paleoenvironmentally. The treatment of the numerical ages obtained using the AAR method required special attention. The first filtering step was made by the laboratory (rejection criteria a and b). Then, the second filtering step was made by testing in the field the remaining ages. Among these, in fact, we never compared an age with a single preceding and/or following age; instead, we identified homogeneous groups of numerical ages consistent with their reciprocal stratigraphic position. This operation led to the rejection of further numerical ages that deviate erratically from a larger, homogeneous age population which fits well with its stratigraphic position (rejection criterion c). After all of the filtering steps, the valid ages that remained were used for the subdivision of the sedimentary sequences into UBSUs together with the lithological and paleoenvironmental criteria. The numerical ages allowed us, in the first instance, to recognize all of the age gaps between two consecutive samples. Next, we identified the level, in the sedimentary thickness that is between these two samples, that may represent the most suitable UBSU boundary based on its lithology and/or the paleoenvironment. The recognized units are: I) Coppa Nevigata sands (NEA), HTR: MIS 20–14, ATR: MIS 17–16; II) Argille subappennine (ASP), HTR: MIS 15–11, ATR: MIS 15–13; III) Coppa Nevigata synthem (NVI), HTR: MIS 13–8, ATR: MIS 12–11; IV) Sabbie di Torre Quarto (STQ), HTR: MIS 13–9.1, ATR: MIS 11; V) Amendola subsynthem (MLM1), HTR: MIS 12–10, ATR: MIS 11; VI) Undifferentiated continental unit (UCI), HTR: MIS 11–6.2, ATR: MIS 9.3–7.1; VII) Foggia synthem (TGF), ATR: MIS 6; VIII) Masseria Finamondo synthem (TPF), ATR: Upper Pleistocene; IX) Carapelle and Cervaro streams synthem (RPL), subdivided into: IXa) Incoronata subsynthem (RPL1), HTR: MIS 6–3; ATR: MIS 5–3; IXb) Marane La Pidocchiosa–Castello subsynthem (RPL3), ATR: Holocene; X) Masseria Inacquata synthem (NAQ), ATR: Holocene. The possibility of recognizing and dating Quaternary units in an alluvial plain to the scale of a marine isotope stage constitutes a clear step forward compared with similar studies regarding other alluvial-plain areas, where Quaternary units were dated almost exclusively using their stratigraphic position. As a result, they were generically associated with a geological sub-epoch. Instead, our method allowed a higher detail in the timing of the sedimentary processes: for example, MIS 11 and MIS 5.5 deposits have been recognized and characterized for the first time in the study area, highlighting their importance as phases of sedimentation.

Detección de intrusiones basada en análisis de eventos del DNS

En este proyecto se hace un análisis en profundidad de las técnicas de ataque a las redes de ordenadores conocidas como APTs (Advanced Persistent Threats), viendo cuál es el impacto que pueden llegar a tener en los equipos de una empresa y el posible robo de información y pérdida monetaria que puede llevar asociada. Para hacer esta introspección veremos qué técnicas utilizan los atacantes para introducir el malware en la red y también cómo dicho malware escala privilegios, obtiene información privilegiada y se mantiene oculto. Además, y cómo parte experimental de este proyecto se ha desarrollado una plataforma para la detección de malware de una red en base a las webs, URLs e IPs que visitan los nodos que la componen. Obtendremos esta visión gracias a la extracción de los logs y registros de DNS de consulta de la compañía, sobre los que realizaremos un análisis exhaustivo. Para poder inferir correctamente qué equipos están infectados o no se ha utilizado un algoritmo de desarrollo propio inspirado en la técnica Belief Propagation (“Propagación basada en creencia”) que ya ha sido usada antes por desarrolladores cómo los de los Álamos en Nuevo México (Estados Unidos) para fines similares a los que aquí se muestran. Además, para mejorar la velocidad de inferencia y el rendimiento del sistema se propone un algoritmo adaptado a la plataforma Hadoop de Apache, por lo que se modifica el paradigma de programación habitual y se busca un nuevo paradigma conocido como MapReduce que consiste en la división de la información en conceptos clave-valor. Por una parte, los algoritmos que existen basados en Belief Propagation para el descubrimiento de malware son propietarios y no han sido publicados completamente hasta la fecha, por otra parte, estos algoritmos aún no han sido adaptados a Hadoop ni a ningún modelo de programación distribuida aspecto que se abordará en este proyecto. No es propósito de este proyecto desarrollar una plataforma comercial o funcionalmente completa, sino estudiar el problema de las APTs y una implementación que demuestre que la plataforma mencionada es factible de implementar. Este proyecto abre, a su vez, un horizonte nuevo de investigación en el campo de la adaptación al modelo MapReduce de algoritmos del tipo Belief Propagation basados en la detección del malware mediante registros DNS. ABSTRACT. This project makes an in-depth investigation about problems related to APT in computer networks nowadays, seeing how much damage could they inflict on the hosts of a Company and how much monetary and information loss may they cause. In our investigation we will find what techniques are generally applied by attackers to inject malware into networks and how this malware escalates its privileges, extracts privileged information and stays hidden. As the main part of this Project, this paper shows how to develop and configure a platform that could detect malware from URLs and IPs visited by the hosts of the network. This information can be extracted from the logs and DNS query records of the Company, on which we will make an analysis in depth. A self-developed algorithm inspired on Belief Propagation technique has been used to infer which hosts are infected and which are not. This technique has been used before by developers of Los Alamos Lab (New Mexico, USA) for similar purposes. Moreover, this project proposes an algorithm adapted to Apache Hadoop Platform in order to improve the inference speed and system performance. This platform replaces the traditional coding paradigm by a new paradigm called MapReduce which splits and shares information among hosts and uses key-value tokens. On the one hand, existing algorithms based on Belief Propagation are part of owner software and they have not been published yet because they have been patented due to the huge economic benefits they could give. On the other hand these algorithms have neither been adapted to Hadoop nor to other distributed coding paradigms. This situation turn the challenge into a complicated problem and could lead to a dramatic increase of its installation difficulty on a client corporation. The purpose of this Project is to develop a complete and 100% functional brand platform. Herein, show a short summary of the APT problem will be presented and make an effort will be made to demonstrate the viability of an APT discovering platform. At the same time, this project opens up new horizons of investigation about adapting Belief Propagation algorithms to the MapReduce model and about malware detection with DNS records.

Methodological context of all samples from the Tara Oceans Expedition (2009-2013)

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data set is a registry of all samples collected during the Tara Oceans Expedition (2009-2013). The registry provides details about the sampling location and methodology of each sample. Uniform resource locators (URLs) offer direct links to additional contextual environmental data published at PANGAEA, and to the corresponding nucleotides data published at the European Nucleotides Archive (EBI-ENA).

Registry of all campaigns (from port to port) from the Tara Oceans Expedition (2009-2013)

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data set is a registry of all campaigns (from port to port) conducted during the Tara Oceans Expedition (2009-2013). The registry provides details about the scientific interest of each campaign, including direct links (URLs) to the corresponding (1) campaign report, (2) samples collected during the campaign, (3) environmental data published at PANGAEA, and (4) nucleotides data published at the European Nucleotides Archive (EBI-ENA).

Registry of all events from the Tara Oceans Expedition (2009-2013)

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data set is a registry of all events conducted during the Tara Oceans Expedition (2009-2013). The registry provides details about the sampling date, time, location and methodology of each event. Uniform resource locators (URLs) offer direct links to the corresponding (1) event logsheet filled on board, (2) environmental data published at PANGAEA, (3) list of samples prepared on board from each event, and (4) nucleotides data published at the European Nucleotides Archive (EBI-ENA).

Registry of selected samples from the Tara Oceans Expedition (2009-2013)

The present data set is a registry of samples from the Tara Oceans Expedition (2009-2013) that were selected for publication in a special issue of the SCIENCE journal (see related references below). The registry provides details about the sampling location and methodology of each sample. Uniform resource locators (URLs) offer direct links to additional contextual environmental data and to the corresponding sequence runs used for analysis in the related literature publications in the SCIENCE journal.

Registry of all stations from the Tara Oceans Expedition (2009-2013)

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data set is a registry of all stations conducted during the Tara Oceans Expedition (2009-2013). The registry provides details about the scientific interest of each station, including (1) the geographic context, (2) legal context, (3) the environmental features that were targeted, and (4) a detailed account of devices deployed during the station. Uniform resource locators (URLs) offer direct links to the corresponding (1) physical oceanographic context reports, (2) list of samples collected during the station, (3) environmental data published at PANGAEA, and (4) nucleotides data published at the European Nucleotides Archive (EBI-ENA).

'An Artificial Immune System as a Recommender System for Web Sites'

Artificial Immune Systems have been used successfully to build recommender systems for film databases. In this research, an attempt is made to extend this idea to web site recommendation. A collection of more than 1000 individuals' web profiles (alternatively called preferences / favourites / bookmarks file) will be used. URLs will be classified using the DMOZ (Directory Mozilla) database of the Open Directory Project as our ontology. This will then be used as the data for the Artificial Immune Systems rather than the actual addresses. The first attempt will involve using a simple classification code number coupled with the number of pages within that classification code. However, this implementation does not make use of the hierarchical tree-like structure of DMOZ. Consideration will then be given to the construction of a similarity measure for web profiles that makes use of this hierarchical information to build a better-informed Artificial Immune System.

'An Artificial Immune System as a Recommender System for Web Sites'

Artificial Immune Systems have been used successfully to build recommender systems for film databases. In this research, an attempt is made to extend this idea to web site recommendation. A collection of more than 1000 individuals' web profiles (alternatively called preferences / favourites / bookmarks file) will be used. URLs will be classified using the DMOZ (Directory Mozilla) database of the Open Directory Project as our ontology. This will then be used as the data for the Artificial Immune Systems rather than the actual addresses. The first attempt will involve using a simple classification code number coupled with the number of pages within that classification code. However, this implementation does not make use of the hierarchical tree-like structure of DMOZ. Consideration will then be given to the construction of a similarity measure for web profiles that makes use of this hierarchical information to build a better-informed Artificial Immune System.

Geospatial Data Indexing Analysis and Visualization via Web Services with Autonomic Resource Management

With the exponential growth of the usage of web-based map services, the web GIS application has become more and more popular. Spatial data index, search, analysis, visualization and the resource management of such services are becoming increasingly important to deliver user-desired Quality of Service. First, spatial indexing is typically time-consuming and is not available to end-users. To address this, we introduce TerraFly sksOpen, an open-sourced an Online Indexing and Querying System for Big Geospatial Data. Integrated with the TerraFly Geospatial database [1-9], sksOpen is an efficient indexing and query engine for processing Top-k Spatial Boolean Queries. Further, we provide ergonomic visualization of query results on interactive maps to facilitate the user’s data analysis. Second, due to the highly complex and dynamic nature of GIS systems, it is quite challenging for the end users to quickly understand and analyze the spatial data, and to efficiently share their own data and analysis results with others. Built on the TerraFly Geo spatial database, TerraFly GeoCloud is an extra layer running upon the TerraFly map and can efficiently support many different visualization functions and spatial data analysis models. Furthermore, users can create unique URLs to visualize and share the analysis results. TerraFly GeoCloud also enables the MapQL technology to customize map visualization using SQL-like statements [10]. Third, map systems often serve dynamic web workloads and involve multiple CPU and I/O intensive tiers, which make it challenging to meet the response time targets of map requests while using the resources efficiently. Virtualization facilitates the deployment of web map services and improves their resource utilization through encapsulation and consolidation. Autonomic resource management allows resources to be automatically provisioned to a map service and its internal tiers on demand. v-TerraFly are techniques to predict the demand of map workloads online and optimize resource allocations, considering both response time and data freshness as the QoS target. The proposed v-TerraFly system is prototyped on TerraFly, a production web map service, and evaluated using real TerraFly workloads. The results show that v-TerraFly can accurately predict the workload demands: 18.91% more accurate; and efficiently allocate resources to meet the QoS target: improves the QoS by 26.19% and saves resource usages by 20.83% compared to traditional peak load-based resource allocation.