Near-infrared identification of the counterpart to X1908+075: a new OB-supergiant X-ray binary

We report the near-infrared (IR) identification of the likely counterpart to X1908+075, a highly-absorbed Galactic X-ray source recently suspected to belong to the rare class of OB supergiant-neutron star binary systems. Our JHKs-band imaging of the field reveals the existence within the X-ray error boxes of a near-IR source consistent with an early-type star lying at d=7 kpc and suffering A(V)=16 mag of extinction, the latter value being in good agreement with the hydrogen column density derived from a modelling of the X-ray spectrum. Our follow-up, near-IR spectroscopic observations confirm the nature of this candidate and lead to a late O-type supergiant classification, thereby supporting the identification of a new Galactic OB-supergiant X-ray binary.Comment: Accepted for publication in MNRAS, 7 pages, 3 figure

Asynchrony in image analysis: using the luminance-to-response-latency relationship to improve segmentation

We deal with the probiem of segmenting static images, a procedure known to be difficult in the case of very noisy patterns, The proposed approach rests on the transformation of a static image into a data flow in which the first image points to be processed are the brighter ones. This solution, inspired by human perception, in which strong luminances elicit reactions from the visual system before weaker ones, has led to the notion of asynchronous processing. The asynchronous processing of image points has required the design of a specific architecture that exploits time differences in the processing of information. The results otained when very noisy images are segmented demonstrate the strengths of this architecture; they also suggest extensions of the approach to other computer vision problem

Racial Beliefs, Location and the Causes of Crime

This paper provides a unified explanation for why blacks commit more crime, are located in poorer neighborhoods and receive lower wages than whites. If everybody believes that blacks are more criminal than whites - even if there is no basis for this - then blacks are offered lower wages and, as a result, locate further away from jobs. Distant residence increases even more the black-white wage gap because of more tiredness and higher commuting costs. Blacks have thus a lower opportunity cost of committing crime and become indeed more criminal than whites. Therefore beliefs are self-fulfilling.Self-Fulfilling Prejudies; Urban Black Ghettos; Crime

Organized Crime, Corruption and Punishment

We analyze an oligopoly model in which differentiated criminal organizations globally compete on criminal activities and engage in local corruption to avoid punishment. When law enforcers are sufficiently well-paid, difficult to bribe and corruption detection highly probable, we show that increasing policing or sanctions effectively deters crime. However, when bribing costs are low, that is badly-paid and dishonest law enforcers work in a weak governance environment, and the rents from criminal activity relative to legal activity are sufficiently high, we find that increasing policing and sanctions can generate higher crime rates. In particular, the relationship between the traditional instruments of deterrence, namely intensification of policing and increment of sanctions, and crime is nonmonotonic. Beyond a threshold, increases in expected punishment induce organized crime to corruption, and ensuing impunity leads too higher rather than lower crime.Deterrence; Organized Crime; Corruption; Oligopoly; Free Entry

Graphic Symbol Recognition using Graph Based Signature and Bayesian Network Classifier

We present a new approach for recognition of complex graphic symbols in technical documents. Graphic symbol recognition is a well known challenge in the field of document image analysis and is at heart of most graphic recognition systems. Our method uses structural approach for symbol representation and statistical classifier for symbol recognition. In our system we represent symbols by their graph based signatures: a graphic symbol is vectorized and is converted to an attributed relational graph, which is used for computing a feature vector for the symbol. This signature corresponds to geometry and topology of the symbol. We learn a Bayesian network to encode joint probability distribution of symbol signatures and use it in a supervised learning scenario for graphic symbol recognition. We have evaluated our method on synthetically deformed and degraded images of pre-segmented 2D architectural and electronic symbols from GREC databases and have obtained encouraging recognition rates.Comment: 5 pages, 8 figures, Tenth International Conference on Document Analysis and Recognition (ICDAR), IEEE Computer Society, 2009, volume 10, 1325-132

Organised crime, corruption and punishment

We analyse an oligopoly model in which differentiated criminal organisations globally compete on criminal activities and engage in local corruption to avoid punishment. When law enforcers are sufficiently well-paid, difficult to bribe and corruption detection highly probable, we show that increasing policing, or sanctions, effectively deters crime. However, when bribing costs are low, that is badly-paid and dishonest law enforcers work in a weak governance environment, and the rents from criminal activity relative to legal activity are sufficiently high, we find that increasing policing and sanctions can generate higher crime rates. In particular, the relationship between the traditional instruments of deterrence, namely intensification of policing and sanctions, and the crime rate is nonmonotonic. Beyond a threshold, further increases in intended expected punishment create incentives for organised crime extending corruption rings, and ensuing impunity results in a fall of actual expected punishment that yields more rather than less crime. Keywords; intended deterrence, organised crime, weak governance, corruption

Analysis and Diversion of Duqu's Driver

The propagation techniques and the payload of Duqu have been closely studied over the past year and it has been said that Duqu shared functionalities with Stuxnet. We focused on the driver used by Duqu during the infection, our contribution consists in reverse-engineering the driver: we rebuilt its source code and analyzed the mechanisms it uses to execute the payload while avoiding detection. Then we diverted the driver into a defensive version capable of detecting injections in Windows binaries, thus preventing further attacks. We specifically show how Duqu's modified driver would have detected Duqu.Comment: Malware 2013 - 8th International Conference on Malicious and Unwanted Software (2013

Experiments in Clustering Homogeneous XML Documents to Validate an Existing Typology

This paper presents some experiments in clustering homogeneous XMLdocuments to validate an existing classification or more generally anorganisational structure. Our approach integrates techniques for extracting knowledge from documents with unsupervised classification (clustering) of documents. We focus on the feature selection used for representing documents and its impact on the emerging classification. We mix the selection of structured features with fine textual selection based on syntactic characteristics.We illustrate and evaluate this approach with a collection of Inria activity reports for the year 2003. The objective is to cluster projects into larger groups (Themes), based on the keywords or different chapters of these activity reports. We then compare the results of clustering using different feature selections, with the official theme structure used by Inria.Comment: (postprint); This version corrects a couple of errors in authors' names in the bibliograph