Miracle-dz/UniFAR-FTUs huggingface dataset:
Backreaction of frame dragging

The backreaction on black holes due to dragging heavy, rather than test, objects is discussed. As a case study, a five-dimensional regular black Saturn system where the central black hole has vanishing intrinsic angular momentum, J{sup BH}=0, is considered. It is shown that there is a correlation between the sign of two response functions. One is interpreted as a moment of inertia of the black ring in the black Saturn system. The other measures the variation of the black ring horizon angular velocity with the central black hole mass, for fixed ring mass and angular momentum. The two different phases defined by these response functions collapse, for small central black hole mass, to the thin and fat ring phases. In the fat phase, the zero area limit of the black Saturn ring has reduced spin j{sup 2}>1, which is related to the behavior of the ring angular velocity. Using the 'gravitomagnetic clock effect', for which a universality property is exhibited, it is shown that frame dragging measured by an asymptotic observer decreases, in both phases, when the central black hole mass increases, for fixed ring mass and angular momentum. A close parallelism between the results for the fat phase andmore » those obtained recently for the double Kerr solution is drawn, considering also a regular black Saturn system with J{sup BH}{ne}0. « less
Meta-Proof for Structured Specifications

We are working on a practical, template-based approach to the formalisation of object-oriented models. In previous papers, which demonstrate the approach, we have asserted that the formal proofs required for consistency checking and analysis of the model, are also amenable to a template-based solution, using modular metaproofs. In this paper, we explain the meta-proof concept, and illustrate its use on the simplest of the Z consistency checks, the initialisation theorem.
Robust and Efficient Estimation of the Tail Index of a Single-Parameter Pareto Distribution

Abstract Estimation of the tail index parameter of a single-parameter Pareto model has wide application in actuarial and other sciences. Here we examine various estimators from the standpoint of two competing criteria: efficiency and robustness against upper outliers. With the maximum likelihood estimator (MLE) being efficient but nonrobust, we desire alternative estimators that retain a relatively high degree of efficiency while also being adequately robust. A new generalized median type estimator is introduced and compared with the MLE and several well-established estimators associated with the methods of moments, trimming, least squares, quantiles, and percentile matching. The method of moments and least squares estimators are found to be relatively deficient with respect to both criteria and should become disfavored, while the trimmed mean and generalized median estimators tend to dominate the other competitors. The generalized median type performs best overall. These findings provide a basis for revision and updating of prevailing viewpoints. Other topics discussed are applications to robust estimation of upper quantiles, tail probabilities, and actuarial quantities, such as stop-loss and excess-of-loss reinsurance premiums that arise concerning solvency of portfolios. Robust parametric methods are compared with empirical nonparametric methods, which are typically nonrobust.
Ad Exchange Optimization Algorithms on Advertising Networks

Online advertising has seen great growth over the past few years. Advertisers have gotten better results with campaigns targeted at more specific audiences. Ad networks with few visits are unable to create such campaigns and hence are moving forward towards a new model, consisting of a huge global ad exchange market. In this market millions of advertisers compete for the ad space so that their ad will be shown to users upon visiting a page. In selecting the best candidate from all possibilities algorithms able to process advertiser's requirements in tenths of seconds are needed. To face this problem we have developed algorithms using techniques such as threads, AVL trees with hash, multiple node trees or Hadoop technology. Throughout this article we will show the results gained from each algorithm, a comparative performance analysis and some conclusions. We have also proposed some future lines of work.
Tunable Low Energy, Compact and High Performance Neuromorphic Circuit for Spike-Based Synaptic Plasticity

Cortical circuits in the brain have long been recognised for their information processing capabilities and have been studied both experimentally and theoretically via spiking neural networks. Neuromorphic engineers are primarily concerned with translating the computational capabilities of biological cortical circuits, using the Spiking Neural Network (SNN) paradigm, into in silico applications that can mimic the behaviour and capabilities of real biological circuits/systems. These capabilities include low power consumption, compactness, and relevant dynamics. In this paper, we propose a new accelerated-time circuit that has several advantages over its previous neuromorphic counterparts in terms of compactness, power consumption, and capability to mimic the outcomes of biological experiments. The presented circuit simulation results demonstrate that, in comparing the new circuit to previous published synaptic plasticity circuits, reduced silicon area and lower energy consumption for processing each spike is achieved. In addition, it can be tuned in order to closely mimic the outcomes of various spike timing- and rate-based synaptic plasticity experiments. The proposed circuit is also investigated and compared to other designs in terms of tolerance to mismatch and process variation. Monte Carlo simulation results show that the proposed design is much more stable than its previous counterparts in terms of vulnerability to transistor mismatch, which is a significant challenge in analog neuromorphic design. All these features make the proposed design an ideal circuit for use in large scale SNNs, which aim at implementing neuromorphic systems with an inherent capability that can adapt to a continuously changing environment, thus leading to systems with significant learning and computational abilities.
Reduced Loading of Intracellular Ca2+ Stores and Downregulation of Capacitative Ca2+Influx in Bcl-2–Overexpressing Cells

The mechanism of action of the oncogene bcl-2, a key regulator of the apoptotic process, is still debated. We have employed organelle-targeted chimeras of the Ca2+-sensitive photoprotein, aequorin, to investigate in detail the effect of Bcl-2 overexpression on intracellular Ca2+ homeostasis. In the ER and the Golgi apparatus, Bcl-2 overexpression increases the Ca2+ leak (while leaving Ca2+ accumulation unaffected), hence reducing the steady-state [Ca2+] levels. As a direct consequence, the [Ca2+] increases caused by inositol 1,4,5 trisphosphate (IP3)-generating agonists were reduced in amplitude in both the cytosol and the mitochondria. Bcl-2 overexpression also reduced the rate of Ca2+ influx activated by Ca2+ store depletion, possibly by an adaptive downregulation of this pathway. By interfering with Ca2+-dependent events at multiple intracellular sites, these effects of Bcl-2 on intracellular Ca2+ homeostasis may contribute to the protective role of this oncogene against programed cell death.
A Low-Power Design Methodology / Flow and its Application to the Implementation of a DCS 1800-GSM / DECT Modulator / Demodulator ( ESPRIT 25256 ) Title : Low Power Methodology for Designing Multiple Filters

Techniques for power optimization and estimation of sum-of-product computational kernels at the architecture level are described. Synthesis techniques that reduce the switching activity in the inputs of the functional units are proposed. Information related to both coefficients and input data is included in the cost function that drives the synthesis procedure. Novel heuristics based on the Travelling Salesman's Problem and the Minimal Spanning Trees are used for scheduling and assignment. A novel method for the accurate calculation of the transition activity in the input and output nodes of single functional unit implementations of FIR filters is also introduced. The method is developed for input signals which can be described by a Gaussian process. The DBT model is used for modeling the bit-level transition activity of a signal word. The transition activity is calculated as a function of the signal statistics. Recent advances in the areas of wireless communications made available a large number of portable battery-operated systems such as cellular phones, pagers, wireless modems and portable videophones. Such systems make extensive use of digital signal processing. Since power consumption is the overriding issue in the design of portable systems, the development of design techniques for low power implementation of digital signal processing algorithms is of utmost importance. Architectural synthesis is used to map algorithms onto hardware structures. Low power architectural synthesis techniques are very important since they lead to the most significant power savings in comparison to those achieved by optimizations performed in the lower levels of abstraction. Power estimation techniques in the architectural level are also important since they can be used for the fast power evaluation of different candidate architectures allowing early architectural decisions. The purpose of this report is twofold: In the first step it describes novel techniques for low power architectural synthesis of FIR filters and Sum-of-Product computational structures in general. In the second step it presents novel techniques for the accurate estimation of the switching activity in the input and output nodes of single functional unit realizations of FIR filters.
Exporting data-plane energy-aware capabilities from network devices toward the control plane: The Green Abstraction Layer

Energy efficiency is well-known to have recently become one of the most important aspects for both today's and tomorrow's telecommunications infrastructures. To curb their energy requirements, next-generation hardware platforms of network devices are expected to include advanced power management capabilities, which may allow a dynamic trade-off between power consumption and network performance. At the same time, network protocols are going to evolve in order to carry energy-aware information, and to add them to classical performance indexes in network optimisation strategies. However, the question of how to map energy-aware indexes, often arising from low-level local hardware details, and the ones related to network performance is still an open issue. Starting from these considerations, we propose the Green Abstraction Layer (GAL), a device internal interface that provides a standard way of accessing and organising energy-aware information from the low-level hardware components to control processes. The GAL is specifically designed to hide the heterogeneous hardware implementation details, and to provide a simple, hierarchical, and common view of underlying power management capabilities to network control processes.
Dynamic traffic light controller using machine vision and optimization algorithms

This paper presents a fuzzy traffic controller that in an autonomous, centralized and optimal way, manages traffic flow in a group of intersections. The system obtains information from a network of cameras and through machine vision algorithms can detect the number of vehicles in each of the roads. Using this information, the fuzzy system selects the sequence of phases that optimize traffic flow globally. To evaluate the performance of the controller, a scenario was developed where it was possible to simulate through artificially created videos two adjacent intersections. System performance was compared versus fixed time controllers as they are currently the most used in the city of Bogota. As a control variable it was used the average waiting time of each vehicle. The results show that the system performance increases by about 20% over situations with heavy traffic conditions and that the controller is able to adapt smoothly to different flow changes.
Towards a model for evaluating organisational readiness for ERP and data warehousing projects

The need for an integrated enterprise-wide set of management information pronounced Data Warehousing the ‘hot topic’ of the early-to-mid 1990’s, however, it became unfashionable through the mid-to-late 1990s, with the approach of Y2K and the widespread implementation of ERP (Enterprise Resource Planning) systems. However, in recent times, the re-emergence of Data Warehousing, coupled with ERP implementations, to address the limitations and unrealised benefits of ERP systems in the area of reporting, provides researchers and managers with new challenges. This paper lays the foundation for a model of organisational prerequisites for enterprise-wide integration projects. The model is aimed at the ‘Intelligence’ phase of managerial decision making for such projects and should help managers assess their organisation’s readiness for ERP and data warehousing projects . The main objective of the paper is to present a literature-based model which lists the key prerequisites that organisations should assess prior to undertaking these expensive projects.
Novel illumination algorithms for off-line and real-time rendering

Author Janne Kontkanen Title Novel Illumination Algorithms for Off-line and Real-time Rendering This thesis presents new and efficient illumination algorithms for offline and real-time rendering. The realistic rendering of arbitrary indirect illumination is a difficult task. Assuming ray optics model of light, the rendering equation describes the propagation of light in the scene with high accuracy. However, the computation is expensive, and thus even in off-line rendering, i.e., in prerendered animations, indirect illumination is often approximated as it would otherwise constitute a bottleneck in the production pipeline. Indirect illumination can be computed using Monte Carlo integration, but when restrained to a reasonable amount of computation time, the result is often corrupted by noise. This thesis includes a method that effectively reduces the noise by applying a spatially varying filter to the noisy illumination. For real-time performance, some components of indirect illumination can be precomputed. Irradiance volume and many variations of it precompute reflections and shadowing of a static scene into a volumetric data structure. This data is then used to shade dynamic objects in real-time. The practical usage of the method is limited due to aliasing artifacts. This thesis shows that with a suitable super-sampling approach, a significant quality improvement can be obtained. Another direction is to precompute how light propagates in the scene and use the precomputed data during run-time to solve both direct and indirect illumination based on the known incident lighting. To keep the memory and precomputation costs tractable, these methods are typically restricted to infinitely distant lighting. Those that are not, require a very long precomputation time. This thesis presents an algorithm that adopts a wavelet-based hierarchical finite element method for the precomputation. A significant performance improvement over the existing techniques is obtained. When full global illumination cannot be afforded, ambient occlusion is an attractive alternative. This thesis includes two methods for real-time rendering of ambient occlusion in dynamic scenes. The first method models the shadowing of ambient light between rigid moving bodies. The second method gives a data-oriented solution for rendering approximate ambient occlusion for animated characters in real-time. Both methods achieve unprecedented efficiency. UDC 004.925, 004.925. 3
Improving Underwater Vehicle navigation state estimation using Locally Weighted Projection Regression

Navigation is instrumental in the successful deployment of Autonomous Underwater Vehicles (AUVs). Sensor hardware is installed on AUVs to support navigational accuracy. Sensors, however, may fail during deployment, thereby jeopardizing the mission. This work proposes a solution, based on an adaptive dynamic model, to accurately predict the navigation of the AUV. A hydrodynamic model, derived from simple laws of physics, is integrated with a powerful non-parametric regression method. The incremental regression method, namely the Locally Weighted Projection Regression (LWPR), is used to compensate for un-modeled dynamics, as well as for possible changes in the operating conditions of the vehicle. The augmented hydrodynamic model is used within an Extended Kalman Filter, to provide optimal estimations of the AUV's position and orientation. Experimental results demonstrate an overall improvement in the prediction of the vehicle's acceleration and velocity.
Tactile feedback for predictive text entry

Predictive text entry provides a fast way to enter text on phones and other small devices. Early work on predictive text entry highlighted that the reaction time for checking the screen dominates text entry times. Improving accuracy of predictions brings a downside: as prediction gets better, users will drop the slow operation of checking the screen and will thus miss prediction errors and system feedback/suggestions. In this note, we present an experiment into the use of vibration to alert the user when word completion is likely to aid them, using a dynamic approach based on their current typing speed, and when there are no dictionary matches to their entry. Results show significantly faster entry rates for users with vibration alerts, raising speeds from 20wpm to 23wpm once practiced.
A comparison of select trigger algorithms for automated global seismic phase and event detection

Digital algorithms for robust detection of phase arrivals in the presence of stationary and nonstationary noise have a long history in seismology and have been exploited primarily to reduce the amount of data recorded by data logging systems to manageable levels. In the present era of inexpensive digital storage, however, such algorithms are increasingly being used to flag signal segments in continuously recorded digital data streams for subsequent processing by automatic and/or expert interpretation systems. In the course of our development of an automated, near-real-time, waveform correlation event-detection and location system (WCEDS), we have surveyed the abilities of such algorithms to enhance seismic phase arrivals in teleseismic data streams. Specifically, we have considered envelopes generated by energy transient (STA/LTA), Z-statistic, frequency transient, and polarization algorithms. The WCEDS system requires a set of input data streams that have a smooth, low-amplitude response to background noise and seismic coda and that contain peaks at times corresponding to phase arrivals. The algorithm used to generate these input streams from raw seismograms must perform well under a wide range of source, path, receiver, and noise scenarios. Present computational capabilities allow the application of considerably more robust algorithms than have been historically used in real time. However, highly complex calculations can still be computationally prohibitive for current workstations when the number of data streams become large. While no algorithm was clearly optimal under all source, receiver, path, and noise conditions tested, an STA/LTA algorithm incorporating adaptive window lengths controlled by nonstationary seismogram spectral characteristics was found to provide an output that best met the requirements of a global correlation-based event-detection and location system.
Exploiting the Focus of the Document for Enhanced Entities' Sentiment Relevance Detection

A key question in sentiment analysis is whether sentiment ex-pressions, in a given text, are related to particular entities. This is an imperative question, since people are typically interested in sentiments on specific entities and not in the overall sentiment articulated in an article or a document. Sentiment relevance is aimed at addressing this precise problem. In this paper, we argue that exploiting information about the focus of the document on the entity of interest can significantly improve the task of detecting sentiment relevance and, hence, the final sentiment scores assigned for the entities. In order to assess the value of such information, we look at various methods for detecting sentiment relevance for entities. We consider both rule-based algorithms that rely on the entity's physical or syntactic proximity to the sentiment expressions as well as more sophisticated machine learning classification algorithms. We demonstrate that the focus of the document on the entities within it is, indeed, an important piece of information, which can be accurately learned with super-vised classification means. We, further, found that overall classification-based algorithms perform better than the deterministic ones in identifying sentiment relevance, with sequence-classification performing significantly better than direct classification.
Fast rate allocation schemes for JPEG2000

JPEG2000 can provide excellent rate-distortion performance. However, the conventional post compression rate-distortion (PCRD) optimum algorithm in JPEG2000 is not enough efficient for IC design. Calculation and storage of the rate-distortion slope and search of the optimum truncation points are complexity and they require a large quantity of memory. In this paper, two schemes are proposed to reduce computation and memory for the real-time rate allocation in JPEG2000. A simplification scheme is used to facilitate calculation of the rate-distortion slopes based on some approximate treatments by means of logarithm expression. The other scheme can find all the truncation points for every code-block through a round search based on the 256 quality layers which are divided in parallel with tier1-coding. Compared with PCRD, PSNR of reconstructed images using our schemes decrease very slightly which can be omitted in most applications.
UTE: A Ubiquitous Data Exploration Platform for Mobile Sensing Experiments

In this paper, we present Ubiquitous data Exploration (UTE), a mobile sensor data collection, annotation and exploration platform. Our platform facilitates rapid prototyping of data mining experiments by using a flexible and do-it-yourself approach. The platform allows researchers to quickly design and deploy applications on mobile devices in order to record sensor data and the corresponding ground-truth information. The platform is supported by a web interface for designing data collection experiments, synchronizing and storing the sensor data with the corresponding labels, and sharing data.
Effects of the Active Smarter Kids (ASK) Physical Activity School-based Intervention on Executive Functions: A Cluster-Randomized Controlled Trial

ABSTRACT The purpose of this study was to examine the effects of a seven-month curriculum prescribed physical activity (PA) intervention (the Active Smarter Kids [ASK] intervention) on executive functions in 10-year-old Norwegian children. A linear mixed model was used to analyze data from 971–1,123 fifth grade children at 28 intervention schools and 29 control schools. The intervention constituted three PA elements: PA educational lessons, PA breaks, and PA homework, adding 165 minutes of PA to the mandatory 135 minutes of PA and physical education. There was no effect of the intervention on executive functions in the intention-to-treat analyses. Per protocol analyses (n = 776–850) revealed small effects of the intervention on the composite score of executive functions, cognitive flexibility, and motor skills. Cognitively engaging and coordinative demanding activities/games seem viable options to increase executive functions and possibly improve academic performance in children.