Use of neural networks for predictions using time series: Illustration with the El Niño Southern oscillation phenomenon

This paper introduces the use of sets of multiple networks (bundled networks) to manage the variability due to different initialization parameters. This method makes it statistically impossible for the networks to be trapped in the same local minimum, and therefore allows better control of the confidence of the prediction eventually given. The spread of the forecasts given by these different networks can be used for prediction reliability purposes. An illustration of this usage is given with the El Niño phenomenon.     

Using retina modelling to characterize blinking: comparison between EOG and video analysis

A large number of car crashes are caused by drowsiness every year. The analysis of eye blinks provides reliable information about drowsiness. This paper proposes to study the relation between electrooculogram (EOG) and video analysis for blink detection and characterization. An original method to detect and characterize blinks from a video analysis is presented here. The method uses different filters based on the human retina modelling. A illumination robust filter is first used to normalize illumination variations of the video input. Then, Outer and an Inner Plexiform Layer filters are used to extract energy signals from eye area. The eye detection is processed mixing gradient and projection methods which makes it able to detect even closed eyes. The illumination robust filter makes it possible to detect the eyes even in night conditions, without using external lighting. The video analysis extracts two signals from the eye area measuring the quantity of static edges and moving edges. Blinks are then detected and characterized from these two signals. A comparison between the features extracted from the EOG and the same features extracted from the video analysis is then performed on a database of 14 different people. This study shows that some blink features extracted from the video are highly correlated with their EOG equivalent: the duration, the duration at 50%, the frequency, the percentage of eye closure at 80% and the amplitude velocity ratio. The frame rate influence on the accuracy of the different features extracted is also studied and enlightens on the need of a high frame rate camera to detect and characterize accurately blinks from a video analysis.     

Personality Organizations, Psychiatric Severity, and Self and Object Representations.

This study examined the hypothesis that psychotic, borderline, and neurotic personality organizations (POs) present a progressive differentiation between self and object representations and an increasing integration of their bad and good aspects. Fifty patients participated in the study. Measures included scales of self and object representations (S. J. Blatt, S. A. Bers, & C. E. Schaffer, 1993; S. J. Blatt, H. Wiseman, E. Prince-Gibson, & C. Gatt, 1991), as well as the Personality Organization Diagnostic Form (L. Diguer & L. Normandin, 1997) and estimations of psychiatric severity. Results showed that PO groups differed in terms of the integration of the object and its valence. It was also observed that although object and self representations were closely intertwined, the latter showed more discrimination between POs than the former. Psychiatric severity was shown to correlate more with object representations than self representations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Energy Efficient Cloud Service Provisioning: Keeping Data Center Granularity in Perspective

The cost of power and its associated delivery are becoming significant factors in the total expenditure of large-scale data centers. Numerous techniques have been proposed to address the energy efficiency issue in cloud systems. Recently, some efforts have been made to decentralize the cloud via distributing data centers in diverse geographical positions, at different scales. In this paper, we elaborate on the energy effectiveness of service provisioning on different cloud architectures, from a mega-data center to a nano data center, which provides the extreme decentralization in terms of cloud architecture, as well as P2P-clouds or community network clouds. We study the energy consumption through an analytical and simulation framework for video streaming and MapReduce applications.     

A Linear Stochastic Programming Model for Optimal Leveraged Portfolio Selection

The literature of portfolio optimization is extensive and covers several important aspects of the asset allocation problem. However, previous works consider simplified linear borrowing cost functions that leads to suboptimal allocations. This paper aims at efficiently solving the leveraged portfolio selection problem with a thorough borrowing cost representation comprising a number lenders with different rates and credit limits. We propose a two-stage stochastic programming model for asset and debt allocation considering a CVaR-based risk constraint and a convex piecewise-linear borrowing cost function. We compare our model to its counterpart with the fixed borrowing rate approximation used in literature. Numerical results show our model significantly improves performance in terms of risk-return trade-off.     

Voicing of animated GIF by data hiding

GIF animations are silent image sequences widely used on the web thanks to their wide support and portability. In this work, we propose an original technique based on data hiding, to add sound tracks in GIF animations. Data hiding is usually used to embed security codes in a host medium to prevent from illegal copying or to protect copyrights (watermarking) or to send secret messages to a dedicated receiver (steganography). We propose to use host GIF images as a “transmission channel” to convey “hidden” sound bits with lowest perceptual image distortion and without altering the wide portability of the GIF format, by means of data hiding. The inserted bits are neither secret nor intended for security issues. They are intended to be played by an audio player synchronously with the GIF player to add sound to the GIF animation. The embedding process is a low complexity, luminance based steganography algorithm, that slightly modifies the pixels colors of the GIF images to insert the sound bits. The extraction of the inserted audio is completely blind: the audio is directly extracted from the pixels of each cover image. The proposed GIF voicing was tested with different GIF sequences (cartoons and real scenes) and no audio degradation was reported while a slight, most imperceptible, color modification was noticed in case of an important amount of inserted data. The cover images have undergone objective quality criteria and informal subjective evaluation and has proved to be of good quality.     

Statistical analysis of achievable resolution limit in the near field source localization context

In this fast communication, we derive the statistical resolution limit (SRL), characterizing the minimal parameter separation, to resolve two closely spaced known near-field sources impinging on a linear array. Toward this goal, we conduct on the first-order Taylor expansion of the observation model a Generalized Likelihood Ratio Test (GLRT) based on a Constrained Maximum Likelihood Estimator (CMLE) of the SRL. More precisely, the minimum separation between two near-field sources, that is detectable for a given probability of false alarm and a given probability of detection, is derived herein. Finally, numerical simulations are done to quantify the impact of the array geometry of the signal sources power distribution and of the array aperture on the statistical resolution limit.     

The ECHORD project proposals analysis – Research profiles,collaboration patterns and research topic trends

This paper investigates the research profiles, collaboration patterns and research topic trends which can be identified in the proposals submitted to the ECHORD (European Clearing House for Open Robotics Development) FP7 project. On a country level, clusters were identified and characterized by patterns of proposal production per inhabitant, score and international cooperation. Belgium and Sweden constitute a cluster characterized by high proposal production, with very high scores and extensive international collaboration. Belgium also excels from another cluster analysis, being as the only country where 100% of proposals involve industry–academia cooperation and obtain scores above 10. Other findings show that single partner proposals have significantly lower quality than multi-partner proposals but, on the other hand, the number of countries involved shows no influence on the quality of the proposals. Despite the high number of industrial participants present on the proposals, it is observed that they play secondary roles in the proposals, with a very low number projects leaded by companies. Also, it is observed that partnerships between research institutions (non-universities) are the most successful. Concerning topics of the proposals, the technology human–robot interface and the product vision robot for small-scale manufacturing are the most significant. Finally, the paper shows clusters of institutions extracted from the giant network of relations obtained from the ECHORD set of proposals.