Lighting level and productivity: a field study in the electronics industry

The effect of illuminance on the speed and the quality (percentage of errors) with which workers assemble electronic devices was studied in an electronics factory in The Netherlands. For the study, the horizontal illuminance was alternated per work shift between 800 and 1200 lux. The first test was done during the summer and a second test during the winter. A significant effect of illuminance has been found. With 1200 lux at the working plane, the speed of production in the summer was 2.9% higher than with 800 lux. In the winter it was 3.1% higher with the increased illuminance. There was no significant effect of the illuminance on the percentage of errors.     

Space-time adaptation for patch-based image sequence restoration

We present a novel space-time patch-based method for image sequence restoration. We propose an adaptive statistical estimation framework based on the local analysis of the bias-variance trade-off. At each pixel, the space-time neighborhood is adapted to improve the performance of the proposed patch-based estimator. The proposed method is unsupervised and requires no motion estimation. Nevertheless, it can also be combined with motion estimation to cope with very large displacements due to camera motion. Experiments show that this method is able to drastically improve the quality of highly corrupted image sequences. Quantitative evaluations on standard artificially noise-corrupted image sequences demonstrate that our method outperforms other recent competitive methods. We also report convincing results on real noisy image sequences     

Property Testing of Regular Tree Languages

We consider the edit distance with moves on the class of words and the class of ordered trees. We first exhibit a simple tester for the class of regular languages on words and generalize it to the class of ranked and unranked regular trees. We also show that this distance problem is -complete on ordered trees. A preliminary version of this paper appeared in Proceedings of 31st International Colloquium on Automata, Languages and Programming, volume 3142 of Lecture Notes in Computer Science, pages 932–944, Springer, 2005. Work supported by ACI Sécurité Informatique: VERA of the French Ministry of research.     

Time Changes, Laplace Transforms and Path-Dependent Options

Path-dependent options have become increasingly popular over the last few years, in particular in FX markets, because of the greater precision with which they allow investors to choose or avoid exposure to well-defined sources of risk. The goal of the paper is to exhibit the power of stochastic time changes and Laplace transform techniques in the evaluation and hedging of path-dependent options in the Black–Scholes–Merton setting. We illustrate these properties in the specific case of Asian options and continuously (de-) activating double-barrier options and show that in both cases, the pricing and, just as important, the hedging results are more accurate than the ones obtained through Monte Carlo simulations.     

The Influence of Evolutionary Selection Schemes on the Iterated Prisoner's Dilemma

Many economic and social systems are essentially large multi-agent systems.By means of computational modeling, the complicated behavior of such systemscan be investigated. Modeling a multi-agent system as an evolutionary agentsystem, several important choices have to be made for evolutionary operators.Especially, it is to be expected that evolutionary dynamics substantiallydepend on the selection scheme. We therefore investigate the influence ofevolutionary selection mechanisms on a fundamental problem: the iteratedprisoner's dilemma (IPD), an elegant model for the emergence of cooperationin a multi-agent system. We observe various types of behavior, cooperationlevel, and stability, depending on the selection mechanism and the selectionintensity. Hence, our results are important for (1) the proper choice andapplication of selection schemes when modeling real economic situations and(2) assessing the validity of the conclusions drawn from computer experimentswith these models. We also conclude that the role of selection in theevolution of multi-agent systems should be investigated further, for instanceusing more detailed and complex agent interaction models.     

LARS: A learning algorithm for rewriting systems

Whereas there is a number of methods and algorithms to learn regular languages, moving up the Chomsky hierarchy is proving to be a challenging task. Indeed, several theoretical barriers make the class of context-free languages hard to learn. To tackle these barriers, we choose to change the way we represent these languages. Among the formalisms that allow the definition of classes of languages, the one of string-rewriting systems (SRS) has outstanding properties. We introduce a new type of SRS’s, called Delimited SRS (DSRS), that are expressive enough to define, in a uniform way, a noteworthy and non trivial class of languages that contains all the regular languages, , , the parenthesis languages of Dyck, the language of Lukasiewicz, and many others. Moreover, DSRS’s constitute an efficient (often linear) parsing device for strings, and are thus promising candidates in forthcoming applications of grammatical inference. In this paper, we pioneer the problem of their learnability. We propose a novel and sound algorithm (called LARS) which identifies a large subclass of them in polynomial time (but not data). We illustrate the execution of our algorithm through several examples, discuss the position of the class in the Chomsky hierarchy and finally raise some open questions and research directions. This work was supported in part by the IST Program of the European Community, under the PASCAL Network of Excellence, IST-2002-506778. This publication only reflects the authors’ views. Editor: Georgios Paliouras and Yasubumi Sakakibara     

Privacy-preserving boosting

We describe two algorithms, BiBoost (Bipartite Boosting) and MultBoost (Multiparty Boosting), that allow two or more participants to construct a boosting classifier without explicitly sharing their data sets. We analyze both the computational and the security aspects of the algorithms. The algorithms inherit the excellent generalization performance of AdaBoost. Experiments indicate that the algorithms are better than AdaBoost executed separately by the participants, and that, independently of the number of participants, they perform close to AdaBoost executed using the entire data set. Responsible Editor: Charu Aggarwal.     

Efficient and robust shot change detection

In this article, we deal with the problem of shot change detection which is of primary importance when trying to segment and abstract video sequences. Contrary to recent experiments, our aim is to elaborate a robust but very efficient (real-time even with uncompressed data) method to deal with the remaining problems related to shot change detection: illumination changes, context and data independency, and parameter settings. To do so, we have considered some adaptive threshold and derivative measures in a hue-saturation colour space. We illustrate our robust and efficient method by some experiments on news and football broadcast video sequences.

Utilizing endoscopic technology to reveal real-time proteomic alterations in response to chemoprevention

Cancer chemoprevention approaches use either pharmacological or dietary agents to impede, arrest or reverse the carcinogenic process. Although several agents have shown effectiveness against colon cancer, present intervention strategies provide only partial reduction. In this study, we utilized high-resolution endoscopy to obtain colon tumor biopsy specimens from Apc mutant mice before and after 2-wk sulindac intervention. To acquire information beyond genomics, proteome analysis using the ProteomeLab PF2D platform was implemented to generate 2-D protein expression maps from biopsies. Chromatograms produced common signature profiles between sulindac and nonsulindac treated samples, and contrasting profiles termed "fingerprints". We selected a double peak that appeared in tumor biopsies from sulindac-treated mice. Further analyses using MS sequencing identified this protein as histone H2B. The location of H2B in the 1(st) dimension strongly suggested PTM, consistent with identification of two oxidized methionines. While further studies on sulindac proteomic fingerprints are underway, this study demonstrates the feasibility and advantages of "real-time" proteomic analysis for obtaining information on biomarker discovery and drug activity that would not be revealed by a genetic assay. This approach should be broadly applicable for assessing lesion responsiveness in a wide range of translational and human clinical studies.     

Contribution of laser microdissection-based technology to proteomic analysis in hepatocellular carcinoma developing on cirrhosis

Hepatocellular carcinoma (HCC) is a major cause of cancer worldwide. Proteomic studies provide opportunities to uncover targets for the diagnosis and treatment of this disease. However, in HCC developing in a setting of cirrhosis, the detection of proteome alterations may be hampered by the increased cellular heterogeneity of tissue when analysing global liver homogenates. The aim of this study was to evaluate whether the identification of proteome alterations in these HCC cases was improved when the differential protein profile between tumour and non-tumour areas of liver was determined using hepatocytes isolated by laser microdissection (LM). Differential profiles established with LM-hepatocytes and liver section homogenates using 2-DE and MS exhibited noticeable differences: 30% of the protein spots with deregulated expression in tumorous LM-samples did not display any modification in homogenates; conversely 15% of proteins altered in tumorous homogenates were not impaired in LM-hepatocytes. These alterations resulted from the presence in cirrhotic liver of fibrotic stroma which displayed a protein pattern different from that determined in LM-hepatocytes. In conclusion, our data demonstrate the interest of LM in distinguishing between fibrotic and hepatocyte proteome alterations and thus the benefit of LM to proteome studies of HCC developing in a context of cirrhosis.