National Technical University of Athens

Although geospatial question answering systems have received increasing attention in recent years, existing prototype systems struggle to properly answer qualitative spatial questions. In this work, we propose a unique framework for answering qualitative spatial questions, which comprises three main components: a geoparser that takes the input questions and extracts place semantic information from text, a reasoning system which is embedded with a crisp reasoner, and finally, answer extraction, which refines the solution space and generates final answers. We present an experimental design to evaluate our framework for point-based cardinal direction calculus (CDC) relations by developing an automated approach for generating three types of synthetic qualitative spatial questions. The initial evaluations of generated answers in our system are promising because a high proportion of answers were labelled correct.

This clearly written , mathematically rigorous text includes a novel algorithmic exposition of the simplex method and also discusses the Soviet ellipsoid algorithm for linear programming; efficient algorithms for network flow, matching, spanning trees, and matroids; the theory of NP-complete problems; approximation algorithms, local search heuristics for NPcomplete problems, more. All chapters are supplemented by thoughtprovoking problems. A useful work for graduate-level students with backgrounds in computer science, operations research, and electrical engineering. Mathematicians wishing a self-contained introduction need look no further.—American Mathematical Monthly. 1982 ed.

Geant4 is a software toolkit for the simulation of the passage of particles through matter. It is used by a large number of experiments and projects in a variety of application domains, including high energy physics, astrophysics and space science, medical physics and radiation protection. Over the past several years, major changes have been made to the toolkit in order to accommodate the needs of these user communities, and to efficiently exploit the growth of computing power made available by advances in technology. The adaptation of Geant4 to multithreading, advances in physics, detector modeling and visualization, extensions to the toolkit, including biasing and reverse Monte Carlo, and tools for physics and release validation are discussed here.

Author(s): Collaboration, The ATLAS; Aad, G; Abat, E; Abdallah, J; Abdelalim, AA; Abdesselam, A; Abdinov, O; Abi, BA; Abolins, M; Abramowicz, H; Acerbi, E; Acharya, BS; Achenbach, R; Ackers, M; Adams, DL; Adamyan, F; Addy, TN; Aderholz, M; Adorisio, C; Adragna, P; Aharrouche, M; Ahlen, SP; Ahles, F; Ahmad, A; Ahmed, H; Aielli, G; Åkesson, PF; Åkesson, TPA; Akimov, AV; Alam, SM; Albert, J; Albrand, S; Aleksa, M; Aleksandrov, IN; Aleppo, M; Alessandria, F; Alexa, C; Alexander, G; Alexopoulos, T; Alimonti, G; Aliyev, M; Allport, PP; Allwood-Spiers, SE; Aloisio, A; Alonso, J; Alves, R; Alviggi, MG; Amako, K; Amaral, P; Amaral, SP; Ambrosini, G; Ambrosio, G; Amelung, C; Ammosov, VV; Amorim, A; Amram, N; Anastopoulos, C; Anderson, B; Anderson, KJ; Anderssen, EC; Andreazza, A; Andrei, V; Andricek, L; Andrieux, M-L; Anduaga, XS; Anghinolfi, F; Antonaki, A; Antonelli, M; Antonelli, S; Apsimon, R; Arabidze, G; Aracena, I; Arai, Y; Arce, ATH; Archambault, JP; Arguin, J-F; Arik, E; Arik, M; Arms, KE; Armstrong, SR; Arnaud, M; Arnault, C; Artamonov, A; Asai, S; Ask, S

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.

The increasing interest in integrating intermittent renewable energy sources into microgrids presents major challenges from the viewpoints of reliable operation and control. In this paper, the major issues and challenges in microgrid control are discussed, and a review of state-of-the-art control strategies and trends is presented; a general overview of the main control principles (e.g., droop control, model predictive control, multi-agent systems) is also included. The paper classifies microgrid control strategies into three levels: primary, secondary, and tertiary, where primary and secondary levels are associated with the operation of the microgrid itself, and tertiary level pertains to the coordinated operation of the microgrid and the host grid. Each control level is discussed in detail in view of the relevant existing technical literature.

Two channels have been distinguished in human interaction: one transmits explicit messages, which may be about anything or nothing; the other transmits implicit messages about the speakers themselves. Both linguistics and technology have invested enormous efforts in understanding the first, explicit channel, but the second is not as well understood. Understanding the other party's emotions is one of the key tasks associated with the second, implicit channel. To tackle that task, signal processing and analysis techniques have to be developed, while, at the same time, consolidating psychological and linguistic analyses of emotion. This article examines basic issues in those areas. It is motivated by the PKYSTA project, in which we aim to develop a hybrid system capable of using information from faces and voices to recognize people's emotions.

The Large Hadron Collider (LHC) at CERN will extend the frontiers of particle physics with its
\nunprecedented high energy and luminosity. Inside the LHC, bunches of up to 1011 protons (p)
\nwill collide 40 million times per second to provide 14 TeV proton-proton collisions at a design
\nluminosity of 1034 cm􀀀2s􀀀1. The LHC will also collide heavy ions (A), in particular lead nuclei, at
\n5.5 TeV per nucleon pair, at a design luminosity of 1027 cm􀀀2s􀀀1.
\nThe high interaction rates, radiation doses, particle multiplicities and energies, as well as the
\nrequirements for precision measurements have set new standards for the design of particle detectors.
\nTwo general purpose detectors, ATLAS (A Toroidal LHC ApparatuS) and CMS (Compact
\nMuon Solenoid) have been built for probing p-p and A-A collisions.
\nThis paper presents a comprehensive overview of the ATLAS detector prior to the first LHC
\ncollisions, written as the installation of the ATLAS detector is nearing completion. This detector
\nrepresents the work of a large collaboration of several thousand physicists, engineers, technicians,
\nand students over a period of fifteen years of dedicated design, development, fabrication, and installation.

The GPS‐derived velocity field (1988–2005) for the zone of interaction of the Arabian, African (Nubian, Somalian), and Eurasian plates indicates counterclockwise rotation of a broad area of the Earth's surface including the Arabian plate, adjacent parts of the Zagros and central Iran, Turkey, and the Aegean/Peloponnesus relative to Eurasia at rates in the range of 20–30 mm/yr. This relatively rapid motion occurs within the framework of the slow‐moving (∼5 mm/yr relative motions) Eurasian, Nubian, and Somalian plates. The circulatory pattern of motion increases in rate toward the Hellenic trench system. We develop an elastic block model to constrain present‐day plate motions (relative Euler vectors), regional deformation within the interplate zone, and slip rates for major faults. Substantial areas of continental lithosphere within the region of plate interaction show coherent motion with internal deformations below ∼1–2 mm/yr, including central and eastern Anatolia (Turkey), the southwestern Aegean/Peloponnesus, the Lesser Caucasus, and Central Iran. Geodetic slip rates for major block‐bounding structures are mostly comparable to geologic rates estimated for the most recent geological period (∼3–5 Myr). We find that the convergence of Arabia with Eurasia is accommodated in large part by lateral transport within the interior part of the collision zone and lithospheric shortening along the Caucasus and Zagros mountain belts around the periphery of the collision zone. In addition, we find that the principal boundary between the westerly moving Anatolian plate and Arabia (East Anatolian fault) is presently characterized by pure left‐lateral strike slip with no fault‐normal convergence. This implies that “extrusion” is not presently inducing westward motion of Anatolia. On the basis of the observed kinematics, we hypothesize that deformation in the Africa‐Arabia‐Eurasia collision zone is driven in large part by rollback of the subducting African lithosphere beneath the Hellenic and Cyprus trenches aided by slab pull on the southeastern side of the subducting Arabian plate along the Makran subduction zone. We further suggest that the separation of Arabia from Africa is a response to plate motions induced by active subduction.

Additive manufacturing (AM) is fundamentally different from traditional formative or subtractive manufacturing in that it is the closest to the 'bottom up' manufacturing where a structure can be built into its designed shape using a 'layer-by-layer' approach rather than casting or forming by technologies such as forging or machining. AM is versatile, flexible, highly customizable and, as such, can suite most sectors of industrial production. Materials to make these parts/objects can be of a widely varying type. These include metallic, ceramic and polymeric materials along with combinations in the form of composites, hybrid, or functionally graded materials (FGMs). The challenge remains, however, to transfer this 'making' shapes and structures into obtaining objects that are functional. A great deal of work is needed in AM in addressing the challenges related to its two key enabling technologies namely 'materials' and 'metrology' to achieve this functionality in a predictive and reproductive ways. The good news is that there is a significant interest in industry for taking up AM as one of the main production engineering route. Additive Manufacturing, in our opinion, is definitely at the cross-road from where this new, much-hyped but somewhat unproven manufacturing process must move towards a technology that can demonstrate the ability to produce real, innovative, complex and robust products.

There is extensive experimental evidence that oxidative damage permanently occurs to lipids of cellular membranes, proteins, and DNA. In nuclear and mitochondrial DNA, 8-hydroxy-2' -deoxyguanosine (8-OHdG) or 8-oxo-7,8-dihydro-2' -deoxyguanosine (8-oxodG) is one of the predominant forms of free radical-induced oxidative lesions, and has therefore been widely used as a biomarker for oxidative stress and carcinogenesis. Studies showed that urinary 8-OHdG is a good biomarker for risk assessment of various cancers and degenerative diseases. The most widely used method of quantitative analysis is high-performance liquid chromatography (HPLC) with electrochemical detection (EC), gas chromatography-mass spectrometry (GC-MS), and HPLC tandem mass spectrometry. In order to resolve the methodological problems encountered in measuring quantitatively 8-OHdG, the European Standards Committee for Oxidative DNA Damage was set up in 1997 to resolve the artifactual oxidation problems during the procedures of isolation and purification of oxidative DNA products. The biomarker 8-OHdG or 8-oxodG has been a pivotal marker for measuring the effect of endogenous oxidative damage to DNA and as a factor of initiation and promotion of carcinogenesis. The biomarker has been used to estimate the DNA damage in humans after exposure to cancer-causing agents, such as tobacco smoke, asbestos fibers, heavy metals, and polycyclic aromatic hydrocarbons. In recent years, 8-OHdG has been used widely in many studies not only as a biomarker for the measurement of endogenous oxidative DNA damage but also as a risk factor for many diseases including cancer.

As the field of transportation moves toward the "total quality management" paradigm, performance-based outcomes and quantitative measures have become increasingly important. Measuring performance in the field depends heavily on modeling trends and data, which in turn requires powerful, and flexible analytical tools. To date, however, transportation

Cancer immune evasion is a major stumbling block in designing effective anticancer therapeutic strategies. Although considerable progress has been made in understanding how cancers evade destructive immunity, measures to counteract tumor escape have not kept pace. There are a number of factors that contribute to tumor persistence despite having a normal host immune system. Immune editing is one of the key aspects why tumors evade surveillance causing the tumors to lie dormant in patients for years through "equilibrium" and "senescence" before re-emerging. In addition, tumors exploit several immunological processes such as targeting the regulatory T cell function or their secretions, antigen presentation, modifying the production of immune suppressive mediators, tolerance and immune deviation. Besides these, tumor heterogeneity and metastasis also play a critical role in tumor growth. A number of potential targets like promoting Th1, NK cell, γδ T cell responses, inhibiting Treg functionality, induction of IL-12, use of drugs including phytochemicals have been designed to counter tumor progression with much success. Some natural agents and phytochemicals merit further study. For example, use of certain key polysaccharide components from mushrooms and plants have shown to possess therapeutic impact on tumor-imposed genetic instability, anti-growth signaling, replicative immortality, dysregulated metabolism etc. In this review, we will discuss the advances made toward understanding the basis of cancer immune evasion and summarize the efficacy of various therapeutic measures and targets that have been developed or are being investigated to enhance tumor rejection.

Environmental exposure to active pharmaceutical ingredients (APIs) can have negative effects on the health of ecosystems and humans. While numerous studies have monitored APIs in rivers, these employ different analytical methods, measure different APIs, and have ignored many of the countries of the world. This makes it difficult to quantify the scale of the problem from a global perspective. Furthermore, comparison of the existing data, generated for different studies/regions/continents, is challenging due to the vast differences between the analytical methodologies employed. Here, we present a global-scale study of API pollution in 258 of the world's rivers, representing the environmental influence of 471.4 million people across 137 geographic regions. Samples were obtained from 1,052 locations in 104 countries (representing all continents and 36 countries not previously studied for API contamination) and analyzed for 61 APIs. Highest cumulative API concentrations were observed in sub-Saharan Africa, south Asia, and South America. The most contaminated sites were in low- to middle-income countries and were associated with areas with poor wastewater and waste management infrastructure and pharmaceutical manufacturing. The most frequently detected APIs were carbamazepine, metformin, and caffeine (a compound also arising from lifestyle use), which were detected at over half of the sites monitored. Concentrations of at least one API at 25.7% of the sampling sites were greater than concentrations considered safe for aquatic organisms, or which are of concern in terms of selection for antimicrobial resistance. Therefore, pharmaceutical pollution poses a global threat to environmental and human health, as well as to delivery of the United Nations Sustainable Development Goals.

Over recent decades, the role of torsion in gravity has been extensively investigated along the main direction of bringing gravity closer to its gauge formulation and incorporating spin in a geometric description. Here we review various torsional constructions, from teleparallel, to Einstein-Cartan, and metric-affine gauge theories, resulting in extending torsional gravity in the paradigm of f (T) gravity, where f (T) is an arbitrary function of the torsion scalar. Based on this theory, we further review the corresponding cosmological and astrophysical applications. In particular, we study cosmological solutions arising from f (T) gravity, both at the background and perturbation levels, in different eras along the cosmic expansion. The f (T) gravity construction can provide a theoretical interpretation of the late-time universe acceleration, alternative to a cosmological constant, and it can easily accommodate with the regular thermal expanding history including the radiation and cold dark matter dominated phases. Furthermore, if one traces back to very early times, for a certain class of f (T) models, a sufficiently long period of inflation can be achieved and hence can be investigated by cosmic microwave background observations-or, alternatively, the Big Bang singularity can be avoided at even earlier moments due to the appearance of non-singular bounces. Various observational constraints, especially the bounds coming from the large-scale structure data in the case of f (T) cosmology, as well as the behavior of gravitational waves, are described in detail. Moreover, the spherically symmetric and black hole solutions of the theory are reviewed. Additionally, we discuss various extensions of the f (T) paradigm. Finally, we consider the relation with other modified gravitational theories, such as those based on curvature, like f (R) gravity, trying to illuminate the subject of which formulation, or combination of formulations, might be more suitable for quantization ventures and cosmological applications.

Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions.

This paper provides an overview of grid code technical requirements regarding the connection of large wind farms to the electric power systems. The grid codes examined are generally compiled by transmission system operators (TSOs) of countries or regions with high wind penetration and therefore incorporate the accumulated experience after several years of system operation at significant wind penetration levels. The paper focuses on the most important technical requirements for wind farms, included in most grid codes, such as active and reactive power regulation, voltage and frequency operating limits and wind farm behaviour during grid disturbances. The paper also includes a review of modern wind turbine technologies, regarding their capability of satisfying the requirements set by the codes, demonstrating that recent developments in wind turbine technology provide wind farms with stability and regulation capabilities directly comparable to those of conventional generating plants.

A measurement of the Higgs boson mass is presented based on the combined data samples of the ATLAS and CMS experiments at the CERN LHC in the H→γγ and H→ZZ→4ℓ decay channels. The results are obtained from a simultaneous fit to the reconstructed invariant mass peaks in the two channels and for the two experiments. The measured masses from the individual channels and the two experiments are found to be consistent among themselves. The combined measured mass of the Higgs boson is m_{H}=125.09±0.21 (stat)±0.11 (syst) GeV.

This article provides a detailed discussion of wireless resource and channel allocation schemes. The authors provide a survey of a large number of published papers in the area of fixed, dynamic, and hybrid allocation schemes and compare their trade-offs in terms of complexity and performance. We also investigate these channel allocation schemes based on other factors such as distributed/centralized control and adaptability to traffic conditions. Moreover, we provide a detailed discussion on reuse partitioning schemes, the effect of handoffs, and prioritization schemes. Finally, we discuss other important issues in resource allocation such as overlay cells, frequency planning, and power control.

DIANA-TarBase v8 (http://www.microrna.gr/tarbase) is a reference database devoted to the indexing of experimentally supported microRNA (miRNA) targets. Its eighth version is the first database indexing >1 million entries, corresponding to ∼670 000 unique miRNA-target pairs. The interactions are supported by >33 experimental methodologies, applied to ∼600 cell types/tissues under ∼451 experimental conditions. It integrates information on cell-type specific miRNA-gene regulation, while hundreds of thousands of miRNA-binding locations are reported. TarBase is coming of age, with more than a decade of continuous support in the non-coding RNA field. A new module has been implemented that enables the browsing of interactions through different filtering combinations. It permits easy retrieval of positive and negative miRNA targets per species, methodology, cell type and tissue. An incorporated ranking system is utilized for the display of interactions based on the robustness of their supporting methodologies. Statistics, pie-charts and interactive bar-plots depicting the database content are available through a dedicated result page. An intuitive interface is introduced, providing a user-friendly application with flexible options to different queries.