Characterisation of flash floods in small ungauged mountain basins of Central Spain using an integrated approach

One of the main problems of flood hazard assessment in ungauged or poorly gauged basins is the lack of runoff data. In an attempt to overcome this problem we have combined archival records, dendrogeomorphic time series and instrumental data (daily rainfall and discharge) from four ungauged and poorly gauged mountain basins in Central Spain with the aim of reconstructing and compiling information on 41 flash flood events since the end of the 19th century. Estimation of historical discharge and the incorporation of uncertainty for the at-site and regional flood frequency analysis were performed with an empirical rainfall–runoff assessment as well as stochastic and Bayesian Markov Chain Monte Carlo (MCMC) approaches. Results for each of the ungauged basins include flood frequency, severity, seasonality and triggers (synoptic meteorological situations). The reconstructed data series clearly demonstrates how uncertainty can be reduced by including historical information, but also points to the considerable influence of different approaches on quantile estimation. This uncertainty should be taken into account when these data are used for flood risk management.

Histamine reduces flash sensitivity of on ganglion cells in the primate retina.

PURPOSE. In Old World primates, the retina receives input from histaminergic neurons in the posterior hypothalamus. They are a subset of the neurons that project throughout the central nervous system and fire maximally during the day. The contribution of these neurons to vision, was examined by applying histamine to a dark-adapted, superfused baboon eye cup preparation while making extracellular recordings from peripheral retinal ganglion cells. METHODS. The stimuli were 5-ms, 560-nm, weak, full-field flashes in the low scotopic range. Ganglion cells with sustained and transient ON responses and two cell types with OFF responses were distinguished; their responses were recorded with a 16-channel microelectrode array. RESULTS. Low micromolar doses of histamine decreased the rate of maintained firing and the light sensitivity of ON ganglion cells. Both sustained and transient ON cells responded similarly to histamine. There were no statistically significant effects of histamine in a more limited study of OFF ganglion cells. The response latencies of ON cells were approximately 5 ms slower, on average, when histamine was present. Histamine also reduced the signal-to-noise ratio of ON cells, particularly in those cells with a histamine-induced increase in maintained activity. CONCLUSIONS. A major action of histamine released from retinopetal axons under dark-adapted conditions, when rod signals dominate the response, is to reduce the sensitivity of ON ganglion cells to light flashes. These findings may relate to reports that humans are less sensitive to light stimuli in the scotopic range during the day, when histamine release in the retina is expected to be at its maximum.

Misunderstanding Creativity : User Created Content in Virtual Worlds and its Constraints by Code and Law

Virtual worlds have moved from being a geek topic to one of mainstream academic interest. This transition is contingent not only on the augmented economic, societal and cultural value of these virtual realities and their effect upon real life but also on their convenience as fields for experimentation, for testing models and paradigms. User creation is however not something that has been transplanted from the real to the virtual world but a phenomenon and a dynamic process that happens from within and is defined through complex relationships between commercial and non-commercial, commodified and not commodified, individual and of the community, amateur and professional, art and not art. Accounting for this complex environment, the present paper explores user created content in virtual worlds, its dimensions and value and above all, its constraints by code and law. It puts forward suggestions for better understanding and harnessing this creativity.

Book Review: Regulating Code : Good Governance and Better Regulation in the Information Age

Digital technologies have profoundly changed not only the ways we create, distribute, access, use and re-use information but also many of the governance structures we had in place. Overall, "older" institutions at all governance levels have grappled and often failed to master the multi-faceted and multi-directional issues of the Internet. Regulatory entrepreneurs have yet to discover and fully mobilize the potential of digital technologies as an influential factor impacting upon the regulability of the environment and as a potential regulatory tool in themselves. At the same time, we have seen a deterioration of some public spaces and lower prioritization of public objectives, when strong private commercial interests are at play, such as most tellingly in the field of copyright. Less tangibly, private ordering has taken hold and captured through contracts spaces, previously regulated by public law. Code embedded in technology often replaces law. Non-state action has in general proliferated and put serious pressure upon conventional state-centered, command-and-control models. Under the conditions of this "messy" governance, the provision of key public goods, such as freedom of information, has been made difficult or is indeed jeopardized.The grand question is how can we navigate this complex multi-actor, multi-issue space and secure the attainment of fundamental public interest objectives. This is also the question that Ian Brown and Chris Marsden seek to answer with their book, Regulating Code, as recently published under the "Information Revolution and Global Politics" series of MIT Press. This book review critically assesses the bold effort by Brown and Marsden.

Combining Wireless Sensor Networks and Machine Learning for Flash Flood Nowcasting

This paper addresses an investigation with machine learning (ML) classification techniques to assist in the problem of flash flood now casting. We have been attempting to build a Wireless Sensor Network (WSN) to collect measurements from a river located in an urban area. The machine learning classification methods were investigated with the aim of allowing flash flood now casting, which in turn allows the WSN to give alerts to the local population. We have evaluated several types of ML taking account of the different now casting stages (i.e. Number of future time steps to forecast). We have also evaluated different data representation to be used as input of the ML techniques. The results show that different data representation can lead to results significantly better for different stages of now casting.

Code : oevre posthume

Alexandre Weill

Mining frequent bug-fix code changes

Detecting bugs as early as possible plays an important role in ensuring software quality before shipping. We argue that mining previous bug fixes can produce good knowledge about why bugs happen and how they are fixed. In this paper, we mine the change history of 717 open source projects to extract bug-fix patterns. We also manually inspect many of the bugs we found to get insights into the contexts and reasons behind those bugs. For instance, we found out that missing null checks and missing initializations are very recurrent and we believe that they can be automatically detected and fixed.

Scaleable Code Clone Detection

Code clone detection helps connect developers across projects, if we do it on a large scale. The cornerstones that allow clone detection to work on a large scale are: (1) bad hashing (2) lightweight parsing using regular expressions and (3) MapReduce pipelines. Bad hashing means to determine whether or not two artifacts are similar by checking whether their hashes are identical. We show a bad hashing scheme that works well on source code. Lightweight parsing using regular expressions is our technique of obtaining entire parse trees from regular expressions, robustly and efficiently. We detail the algorithm and implementation of one such regular expression engine. MapReduce pipelines are a way of expressing a computation such that it can automatically and simply be parallelized. We detail the design and implementation of one such MapReduce pipeline that is efficient and debuggable. We show a clone detector that combines these cornerstones to detect code clones across all projects, across all versions of each project.