ArDM: a ton-scale liquid Argon experiment for direct detection of Dark Matter in the Universe

The ArDM project aims at developing and operating large noble liquid detectors to search for direct evidence of Weakly Interacting Massive Particle (WIMP) as Dark Matter in the Universe. The initial goal is to design, assemble and operate a $\approx$1 ton liquid Argon prototype to demonstrate the feasibility of a ton-scale experiment with the required performance to efficiently detect and sufficiently discriminate backgrounds for a successful WIMP detection. Our design addresses the possibility to detect independently ionization and scintillation signals. In this paper, we describe this goal and the conceptual design of the detector.Comment: 5 pages, 3 figures, Talk given at IXth international conference on Topics in Astroparticle and Underground Physics (TAUP05), Zaragoza, (Spain

Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic, and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.Peer reviewe

ICANOE and OPERA experiments at the LNGS/CNGS

We discuss two experiments ICANOE and OPERA that have been proposed within the context of long-baseline and atmospheric neutrino experiments in Europe. The joint ICANOE/OPERA program aims at further improving our understanding of the effect seen in atmospheric neutrinos. This program is based on (1) a continuation of the observation of atmospheric neutrinos with the improved technique of ICANOE/ICARUS (2) a sensitive numu->nue and numu->nutau appearance program with the accelerator neutrinos coming from CERN (CNGS) from a distance of 730 km.Comment: 8 pages; Invited talk at the XIX International Conference on Neutrino Physics and Astrophysics (Neutrino 2000), Sudbury, Canada, June 16-21, 2000; new version fix typo

Children, Humanoid Robots and Caregivers

This paper presents developmental learning on a humanoid robot from human-robot interactions. We consider in particular teaching humanoids as children during the child's Separation and Individuation developmental phase (Mahler, 1979). Cognitive development during this phase is characterized both by the child's dependence on her mother for learning while becoming awareness of her own individuality, and by self-exploration of her physical surroundings. We propose a learning framework for a humanoid robot inspired on such cognitive development

Towards machine learning applied to time series based network traffic forecasting

This TFG will explore some specific use cases of the application of Machine Learning techniques to Software-Define Networks, in particular to overlay protocols such as LISP, VXLAN, etc.The aim of this project is to implement a network traffic forecasting model using time series and improve its performance with machine learning techniques, offering a better prediction based in outlier correction. This is a project developed in the Computer Architecture Department (DAC) at the Universitat Politècnica de Catalunya (UPC). Time Series modeling methodology is able to shape a trend and take care of any existing outlier, however it does not cover outlier impact on forecasting. In order to achieve more precision and better confidence intervals, the model combines outlier detection methodology and Artificial Neural Networks to quantify and predict outliers. A study is realized over external data to find out if there is an improvement and its effect on the predictions. Machine learning techniques as Artificial Neural Networks has proven to be an improvement of the current methodology to realize forecasting using Time Series modeling. Future work will be oriented to create an improved standard of this system focused on generalize the model.El objetivo de este proyecto es implementar un modelo de previsión de tráfico de red utilizando series temporales y mejorar su rendimiento con técnicas de aprendizaje automático, generando una mejor predicción basada en la corrección de valores atípicos. Se trata de un proyecto desarrollado en el Departamento de Arquitectura de Computadores (DAC) de la Universidad Politécnica de Cataluña (UPC). La metodología de modelado de series temporales es capaz de predecir una tendencia y hacerse cargo de cualquier valor atípico ya existente, sin embargo, no cubre el impacto de estos sobre la predicción. Con el fin de lograr una mayor precisión y mejores intervalos de confianza, el modelo combina la metodología de detección de valores atípicos y redes neuronales artificiales para cuantificar y predecir los atípicos. Un estudio se realiza sobre datos externos para averiguar si hay una mejora y su efecto sobre las predicciones. Las técnicas de aprendizaje automático, como redes neuronales artificiales, han demostrado ser una mejora de la metodología actual para realizar la predicción utilizando modelos de series de tiempo. El trabajo futuro se orientará para crear un mejor nivel de este sistema se centró en generalizar el modelo.L'objectiu d'aquest projecte és implementar un model de previsió de tràfic de xarxa utilitzant sèries temporals i millorar el seu rendiment amb tècniques d'aprenentatge automàtic, generant una millor predicció basada en la correcció de valors atípics. Es tracta d'un projecte desenvolupat al Departament d'Arquitectura de Computadors (DAC) de la Universitat Politècnica de Catalunya (UPC). La metodologia de modelatge de sèries temporals és capaç de predir una tendència i fer-se càrrec de qualsevol valor atípic ja existent, però, no cobreix l'impacte d'aquests sobre la predicció. Per tal d'aconseguir una major precisió i millors intervals de confiança, el model combina la metodologia de detecció de valors atípics i xarxes neuronals artificials per quantificar i predir els atípics. Un estudi es realitza sobre dades externes per esbrinar si hi ha una millora i el seu efecte sobre les prediccions. Les tècniques d'aprenentatge automàtic, com xarxes neuronals artificials, han demostrat ser una millora de la metodologia actual per a fer predicció utilitzant models de sèries de temps. El treball futur s'orientarà per crear un millor nivell d'aquest sistema es va centrar en generalitzar el model

The LOFAR Transients Pipeline

Current and future astronomical survey facilities provide a remarkably rich opportunity for transient astronomy, combining unprecedented fields of view with high sensitivity and the ability to access previously unexplored wavelength regimes. This is particularly true of LOFAR, a recently-commissioned, low-frequency radio interferometer, based in the Netherlands and with stations across Europe. The identification of and response to transients is one of LOFAR's key science goals. However, the large data volumes which LOFAR produces, combined with the scientific requirement for rapid response, make automation essential. To support this, we have developed the LOFAR Transients Pipeline, or TraP. The TraP ingests multi-frequency image data from LOFAR or other instruments and searches it for transients and variables, providing automatic alerts of significant detections and populating a lightcurve database for further analysis by astronomers. Here, we discuss the scientific goals of the TraP and how it has been designed to meet them. We describe its implementation, including both the algorithms adopted to maximize performance as well as the development methodology used to ensure it is robust and reliable, particularly in the presence of artefacts typical of radio astronomy imaging. Finally, we report on a series of tests of the pipeline carried out using simulated LOFAR observations with a known population of transients.Comment: 30 pages, 11 figures; Accepted for publication in Astronomy & Computing; Code at https://github.com/transientskp/tk

Predictive Monitoring of Multi-level Processes

Infosüsteemide laialdane kasutamine järjest rohkemates valdkondades tekitab aina suuremaid salvestatavaid andmemahte. Organisatsioonide ja äride efektiivsuse kasvuga tekib suurem vajadus leida alternatiivseid viise konkurentsieelisteks. Järjest rohkem hakatakse antud infoajastul otsima ärilist väärtust andmetest. Protsessikaeve meetodeid kasutades üritatakse justnimelt seda teha, kuid äriprotsesside arenedes muutuvad keerukamaks ka andmed, mis neid protsesse kirjeldavad. Hetkel keskendutakse protsessikaeve uurimustes protsessidele, mida on võimalik väljendada järjestikkuste sündmuste jadana. Käesolevas magistritöös esitatakse uudne lähenemine äriprotsesside ennustava seire rakendamiseks mitmetasandilistele äriprotsessidele, mis sisaldavad paralleelseid alamprotsesse ning mida pole võimalik sündmuste järjendina väljendada. Väljapakutud meetodi suutlikkuse hindamiseks rakendatakse antud meetodit elulisel andmestikul telekommunikatsiooni tegevusalalt. Tulemusi võrreldakse lähenemisega, mida kasutatakse ühetasandiliste äriprotsesside ennustavaks seireks.The ever increasing use of Information Systems causes ever more information to be stored. As organizations and businesses become more efficient due to competition they need to gain competitive advantage over others. More and more companies and institutions have turned to Information Technology to find business value in a data-driven world. Modern Information Systems maintain records of process events, which correspond to real-life activities. As processes evolve and become more complex, so does the information that reflects them. In this thesis, we propose an approach to predictive monitoring of complex multi-level processes. In this context, a multi-level process consists of a high-level parent process which spawns multiple low-level subprocesses, which have their own life cycle and run independently of one another. The author proposes constructs called milestones, which include both parent- and subprocesses and are used for the predictive monitoring classification task. This approach has been validated on a real-life event log of the business-to-business change management process in place at Baltic's largest telecommunications company Telia Estonia