Spatial inverse query processing

Traditional spatial queries return, for a given query object q, all database objects that satisfy a given predicate, such as epsilon range and k-nearest neighbors. This paper defines and studies inverse spatial queries, which, given a subset of database objects Q and a query predicate, return all objects which, if used as query objects with the predicate, contain Q in their result. We first show a straightforward solution for answering inverse spatial queries for any query predicate. Then, we propose a filter-and-refinement framework that can be used to improve efficiency. We show how to apply this framework on a variety of inverse queries, using appropriate space pruning strategies. In particular, we propose solutions for inverse epsilon range queries, inverse k-nearest neighbor queries, and inverse skyline queries. Furthermore, we show how to relax the definition of inverse queries in order to ensure non-empty result sets. Our experiments show that our framework is significantly more efficient than naive approaches.     

A Water Balance Derived Drought Index for Pinios River Basin, Greece

This study estimates hydrological drought characteristics using a water balance derived drought index in Pinios river basin, Thessaly, Greece. The concept of hydrological management at subwatershed scale has been adopted because it encompasses the areal extent of a drought event. Fourteen (14) sub-watersheds of Pinios river basin were delineated according to the major tributaries of Pinios river using GIS. For the assessment of hydrological drought, because none of the sub-watersheds have flow gauge stations at their outlets, a six-parameter monthly conceptual water balance model (UTHBAL model), has been applied regionally to simulate runoff for the period October 1960?CSeptember 2002. The synthetic runoff was normalized through Box-Cox transformation and standardized to the mean runoff to produce the water balance derived drought index for hydrological drought assessment. The standardized precipitation index (SPI) at multiple time scales and four indices of the Palmer method (i.e. PDSI, WPLM, PHDI and the Palmer moisture anomaly Z-index) were also calculated to assess hydrological droughts. The results showed that the water balance derived drought index is a good indicator of hydrological drought in all sub-watersheds, since is capable to quantify drought severity and duration. Furthermore, the drought index provides guidance on the selection of an appropriate meteorological drought index for operational hydrological drought monitoring. Hence, SPI at 3- and 6-month timescales and the WPLM could be used along with the water balance derived drought index in risk and decision analyses at the study area.     

Innovative seismic design optimization with reliability constraints

Performance-Based Design (PBD) methodologies is the contemporary trend in designing better and more economic earthquake-resistant structures where the main objective is to achieve more predictable and reliable levels of safety and operability against natural hazards. On the other hand, reliability-based optimization (RBO) methods directly account for the variability of the design parameters into the formulation of the optimization problem. The objective of this work is to incorporate PBD methodologies under seismic loading into the framework of RBO in conjunction with innovative tools for treating computational intensive problems of real-world structural systems. Two types of random variables are considered: Those which influence the level of seismic demand and those that affect the structural capacity. Reliability analysis is required for the assessment of the probabilistic constraints within the RBO formulation. The Monte Carlo Simulation (MCS) method is considered as the most reliable method for estimating the probabilities of exceedance or other statistical quantities albeit with excessive, in many cases, computational cost. First or Second Order Reliability Methods (FORM, SORM) constitute alternative approaches which require an explicit limit-state function. This type of limit-state function is not available for complex problems. In this study, in order to find the most efficient methodology for performing reliability analysis in conjunction with performance-based optimum design under seismic loading, a Neural Network approximation of the limit-state function is proposed and is combined with either MCS or with FORM approaches for handling the uncertainties. These two methodologies are applied in RBO problems with sizing and topology design variables resulting in two orders of magnitude reduction of the computational effort.     

Magnetic and transport properties of 214 oxides: Dependence on doping

We consider how doping can be described in terms of the charge-transfer insulator concept. We discuss and compare a few models for the band structure for the doped charges. This has led us to the conclusion that the band structure stability problem is one of the main issues in any correspondence between results for thet-J model and, say, the three-band model for the slightly doped layered oxides. The stability criterion is formulated and its implications discussed. Provided a phenomenological conduction band is chosen to satisfy the criterion of stability, a detailed picture of how dopants influence the spin wave spectrum atT=0 is presented. The basic physics for the destruction of the antiferromagnetic (AF) long-range order is rather model-independent: the long-range order (atT=0) disappears due to the Cerenkov effect when the Fermi velocity first exceeds the spin wave velocity. We then discuss the overall spectrum of spin excitations and see that the spin wave attenuation for x<x _c,T= 0 due to Landau damping appears in the range of magnon momentak(x)=2m ^* s±x. We also argue that in the presence of superconductivity, the Cerenkov effect is eliminated due to the gap in the spectrum. This may restore the role of the AF fluctuations as the main source of dissipation at the lowest temperatures. A brief discussion of how interaction with magnons may affect the hole spectrum concludes the paper.     

Segmentation of historical machine-printed documents using Adaptive Run Length Smoothing and skeleton segmentation paths

In this paper, we strive towards the development of efficient techniques in order to segment document pages resulting from the digitization of historical machine-printed sources. This kind of documents often suffer from low quality and local skew, several degradations due to the old printing matrix quality or ink diffusion, and exhibit complex and dense layout. To face these problems, we introduce the following innovative aspects: (i) use of a novel Adaptive Run Length Smoothing Algorithm (ARLSA) in order to face the problem of complex and dense document layout, (ii) detection of noisy areas and punctuation marks that are usual in historical machine-printed documents, (iii) detection of possible obstacles formed from background areas in order to separate neighboring text columns or text lines, and (iv) use of skeleton segmentation paths in order to isolate possible connected characters. Comparative experiments using several historical machine-printed documents prove the efficiency of the proposed technique.     

Temporal kernel CCA and its application in multimodal neuronal data analysis

Data recorded from multiple sources sometimes exhibit non-instantaneous couplings. For simple data sets, cross-correlograms may reveal the coupling dynamics. But when dealing with high-dimensional multivariate data there is no such measure as the cross-correlogram. We propose a simple algorithm based on Kernel Canonical Correlation Analysis (kCCA) that computes a multivariate temporal filter which links one data modality to another one. The filters can be used to compute a multivariate extension of the cross-correlogram, the canonical correlogram, between data sources that have different dimensionalities and temporal resolutions. The canonical correlogram reflects the coupling dynamics between the two sources. The temporal filter reveals which features in the data give rise to these couplings and when they do so. We present results from simulations and neuroscientific experiments showing that tkCCA yields easily interpretable temporal filters and correlograms. In the experiments, we simultaneously performed electrode recordings and functional magnetic resonance imaging (fMRI) in primary visual cortex of the non-human primate. While electrode recordings reflect brain activity directly, fMRI provides only an indirect view of neural activity via the Blood Oxygen Level Dependent (BOLD) response. Thus it is crucial for our understanding and the interpretation of fMRI signals in general to relate them to direct measures of neural activity acquired with electrodes. The results computed by tkCCA confirm recent models of the hemodynamic response to neural activity and allow for a more detailed analysis of neurovascular coupling dynamics.     

Algorithm,software, and hardware optimizations for Delaunay mesh generation on simultaneous multithreaded architectures

This article focuses on the optimization of PCDM, a parallel, two-dimensional (2D) Delaunay mesh generation application, and its interaction with parallel architectures based on simultaneous multithreading (SMT) processors. We first present the step-by-step effect of a series of optimizations on performance. These optimizations improve the performance of PCDM by up to a factor of six. They target issues that very often limit the performance of scientific computing codes. We then evaluate the interaction of PCDM with a real SMT-based SMP system, using both high-level metrics, such as execution time, and low-level information from hardware performance counters.     

Indoor robot gardening: design and implementation

This paper describes the architecture and implementation of a distributed autonomous gardening system with applications in urban/indoor precision agriculture. The garden is a mesh network of robots and plants. The gardening robots are mobile manipulators with an eye-in-hand camera. They are capable of locating plants in the garden, watering them, and locating and grasping fruit. The plants are potted cherry tomatoes enhanced with sensors and computation to monitor their well-being (e.g. soil humidity, state of fruits) and with networking to communicate servicing requests to the robots. By embedding sensing, computation, and communication into the pots, task allocation in the system is de-centrally coordinated, which makes the system scalable and robust against the failure of a centralized agent. We describe the architecture of this system and present experimental results for navigation, object recognition, and manipulation as well as challenges that lie ahead toward autonomous precision agriculture with multi-robot teams.     

Utilizing bloom filters for detecting flooding attacks against SIP based services

Any application or service utilizing the Internet is exposed to both general Internet attacks and other specific ones. Most of the times the latter are exploiting a vulnerability or misconfiguration in the provided service and/or in the utilized protocol itself. Consequently, the employment of critical services, like Voice over IP (VoIP) services, over the Internet is vulnerable to such attacks and, on top of that, they offer a field for new attacks or variations of existing ones. Among the various threats–attacks that a service provider should consider are the flooding attacks, at the signaling level, which are very similar to those against TCP servers but have emerged at the application level of the Internet architecture. This paper examines flooding attacks against VoIP architectures that employ the Session Initiation Protocol (SIP) as their signaling protocol. The focus is on the design and implementation of the appropriate detection method. Specifically, a bloom filter based monitor is presented and a new metric, named session distance, is introduced in order to provide an effective protection scheme against flooding attacks. The proposed scheme is evaluated through experimental test bed architecture under different scenarios. The results of the evaluation demonstrate that the required time to detect such an attack is negligible and also that the number of false alarms is close to zero.     

A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems

In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources (servers, storage disks, and networks, and the like) is a complex problem due to the presence of heterogeneous application (e.g., content delivery networks, MapReduce, web applications, and the like) workloads having contentious allocation requirements in terms of ICT resource capacities (e.g., network bandwidth, processing speed, response time, etc.). Several recent papers have tried to address the issue of improving energy efficiency in allocating cloud resources to applications with varying degree of success. However, to the best of our knowledge there is no published literature on this subject that clearly articulates the research problem and provides research taxonomy for succinct classification of existing techniques. Hence, the main aim of this paper is to identify open challenges associated with energy efficient resource allocation. In this regard, the study, first, outlines the problem and existing hardware and software-based techniques available for this purpose. Furthermore, available techniques already presented in the literature are summarized based on the energy-efficient research dimension taxonomy. The advantages and disadvantages of the existing techniques are comprehensively analyzed against the proposed research dimension taxonomy namely: resource adaption policy, objective function, allocation method, allocation operation, and interoperability.