Changes in land surface temperatures and NDVI values over Europe between 1982 and 1999

We used land surface temperature (LST) algorithms and NDVI values to estimate changes in vegetation in the European continent between 1982 and 1999 from the Pathfinder AVHRR Land (PAL) dataset. These two parameters are monitored through HANTS (Harmonic ANalysis of Time Series) software, which allows the simultaneous observation of mean value, first harmonic amplitude and phase behaviors in the same image. These results for each complete year of data show the effect of volcanic aerosols and orbital drift on PAL data. Comparison of time series of HANTS cloud-free time series with the original time series for various land cover proves that this software is useful for LST analysis, although primarily designed for NDVI applications. Comparison of yearly averages of HANTS LST over the whole Europe with air temperature confirms the validity of the results. Maps of the evolution for both parameters between periods 1982/1986 and 1995/1999 have been elaborated: NDVI data show the well confirmed trend of increase over Europe (up to 0.1 in NDVI), Southern Europe seeing a decrease in NDVI (− 0.02). LST averages stay stable or slightly decrease (up to − 1.5 K) over the whole continent, except for southern areas for which the increase is up to 2.5 K. These results evidence that arid and semi-arid areas of Southern Europe have become more arid, the rest of Europe seeing an increase in its wood land proportion, while seasonal amplitude in Northern Europe has decreased.     

Mutational analysis of a satellite phage activator

A culture medium, named olive juice broth, which resembles the natural environment of Lactobacillus plantarum in the traditional Spanish-style green olive fermentation was obtained from green olives. In this medium, the bacteriocin-producing L. plantarum LPCO10 strain was able to produce bacteriocin throughout the incubation time (15 days). Bacteriocin purification from olive juice broth was achieved by a protocol including ammonium sulphate precipitation of cell-free, L. plantarum LPCO10 culture supernatants, and cation-exchange, hydrophobic-interaction and reversed-phase chromatographies. In a series of mixed cultures in olive juice broth, L. plantarum LPCO10 was able to dominate the bacteriocin-sensitive L. plantarum 128/2 strain, whereas the non-bacteriocin-producing, LPCO10 strain derivative, L. plantarum 55-1 strain did not show such capability. These results indicated that olive juice broth may be a valuable experimental substitute for olive fermentation brine in gaining more knowledge about the role of the bacteriocin-producing L. plantarum strains in the control of the Spanish-style green olive fermentation.     

Full-field birefringence imaging by thermal-light polarization-sensitive optical coherence tomography. I. Theory

A method for measuring birefringence by use of thermal-light polarization-sensitive optical coherence tomography is presented. The use of thermal light brings to polarization-sensitive optical coherence tomography a resolution in the micrometer range in three dimensions. The instrument is based on a Linnik interference microscope and makes use of achromatic quarter-wave plates. A mathematical representation of the instrument is presented here, and the detection scheme is described, together with a discussion of the validity domain of the equations used to evaluate the birefringence in the presence of white-light illumination.     

On the designing of densely dispersion-managed optical fiber systems for ultrafast optical communication

We present some theoretical and experimental results which suggest the possibility of constructing a non-empirical methodology of designing optical transmission systems with ultra high bit-rate per channel. Theoretically, we present an average dispersion decreasing densely dispersion-managed (A4dm) fiber system, which exhibits many advantages over the densely dispersion-managed fiber system, such as the possibility of transmitting chirp-free Gaussian pulses at 160 Gbit/s per channel over transoceanic distances, with a reduced energy and minimal intra-channel interaction. Experimentally we present generation of a 160-GHz picosecond pulse train at 1550 nm using multiple four-wave mixing temporal compression of an initial dual frequency beat signal in the anomalous-dispersion regime of a non-zero dispersion shifted fiber. A complete intensity and phase characterization of the pulse train by means of a frequency-resolved optical gating technique is achieved, showing generation of transform-limited pedestal-free Gaussian pulses.     

Performance Measurement in the Belgian Document Ordering and Delivery System Impala

This paper deals with performance measures and performance indicators in the Impala electronic document ordering and delivery system for research libraries in Belgium and compares these with some international standards as, e.g., the ProLib/PI study commissioned by the European Commission.Performance measures: Costs (clearinghouse principle) Number of ILL requests made to other libraries Number of ILL requests made to other libraries without success Number of ILL requests made to other libraries with success Number of ILL requests received from other libraries Number of ILL requests received from other libraries and not satisfied Number of ILL requests received from other libraries that were satisfied Frequency asked titlesPerformance indicators: Success rate Borrowing-lending ratio per library Response times, split into several segments of the ILL-procedureThe article concludes with some indications for quality measurement in electronic document delivery where Impala will be able to measure the real supply times as perceived by the end user.     

Stability of feature selection algorithms: a study on high-dimensional spaces

With the proliferation of extremely high-dimensional data, feature selection algorithms have become indispensable components of the learning process. Strangely, despite extensive work on the stability of learning algorithms, the stability of feature selection algorithms has been relatively neglected. This study is an attempt to fill that gap by quantifying the sensitivity of feature selection algorithms to variations in the training set. We assess the stability of feature selection algorithms based on the stability of the feature preferences that they express in the form of weights-scores, ranks, or a selected feature subset. We examine a number of measures to quantify the stability of feature preferences and propose an empirical way to estimate them. We perform a series of experiments with several feature selection algorithms on a set of proteomics datasets. The experiments allow us to explore the merits of each stability measure and create stability profiles of the feature selection algorithms. Finally, we show how stability profiles can support the choice of a feature selection algorithm. Alexandros Kalousis received the B.Sc. degree in computer science, in 1994, and the M.Sc. degree in advanced information systems, in 1997, both from the University of Athens, Greece. He received the Ph.D. degree in meta-learning for classification algorithm selection from the University of Geneva, Department of Computer Science, Geneva, in 2002. Since then he is a Senior Researcher in the same university. His research interests include relational learning with kernels and distances, stability of feature selection algorithms, and feature extraction from spectral data. Julien Prados is a Ph.D. student at the University of Geneva, Switzerland. In 1999 and 2001, he received the B.Sc. and M.Sc. degrees in computer science from the University Joseph Fourier (Grenoble, France). After a year of work in industry, he joined the Geneva Artificial Intelligence Laboratory, where he is working on bioinformatics and datamining tools for mass spectrometry data analysis. Melanie Hilario has a Ph.D. in computer science from the University of Paris VI and currently works at the University of Geneva’s Artificial Intelligence Laboratory. She has initiated and participated in several European research projects on neuro-symbolic integration, meta-learning, and biological text mining. She has served on the program committees of many conferences and workshops in machine learning, data mining, and artificial intelligence. She is currently an Associate Editor of theInternational Journal on Artificial Intelligence Toolsand a member of the Editorial Board of theIntelligent Data Analysis journal.     

Modeling adaptive behaviors in Context UNITY

Context-aware computing refers to a paradigm in which applications sense aspects of the environment and use this information to adjust their behavior in response to changing circumstances. In this paper, we present a formal model and notation (Context UNITY) for expressing quintessential aspects of context-aware computations; existential quantification, for instance, proves to be highly effective in capturing the notion of discovery in open systems. Furthermore, Context UNITY treats context in a manner that is relative to the specific needs of an individual application and promotes an approach to context maintenance that is transparent to the application. In this paper, we construct the model from first principles, introduce its proof logic, and demonstrate how the model can be used as an effective abstraction tool for context-aware applications and middleware.     

Inserting virtual pedestrians into pedestrian groups video with behavior consistency

In this paper, we propose a novel approach to integrate virtual pedestrians into a scene of real pedestrian groups with behavior consistency, and this is achieved by dynamic path planning of virtual pedestrians. Rather than accounting for the local collision avoidance only, our approach is capable of finding an optimized path for each virtual pedestrian on his way based on the current global distribution of the real groups in the scene. The big challenge is due to the information of both position and velocity of real pedestrians in the video being unavailable; also the distribution of the groups in the scene may vary dynamically. We therefore need to detect and track real pedestrians on each frame of the video to acquire their distribution and motion information. We save this information by an efficient data structure, called environment grid. During the way of a virtual pedestrian, the respective agent frequently emits the detection rays through the environment cells to find the situation of the real pedestrians ahead of him and adjust the original path if necessary. Virtual pedestrians are merged into the video finally with the occlusion between virtual characters and the real pedestrians correctly presented. Experiment results on several scenarios demonstrate the effectiveness of the proposed approach.