Parallel force measurement with a polymeric microbeam array using an optical microscope and micromanipulator

An image analysis method and its validation are presented for tracking the displacements of parallel mechanical force sensors. Force is measured using a combination of beam theory, optical microscopy, and image analysis. The primary instrument is a calibrated polymeric microbeam array mounted on a micromanipulator with the intended purpose of measuring traction forces on cell cultures or cell arrays. One application is the testing of hypotheses involving cellular mechanotransduction mechanisms. An Otsu-based image analysis code calculates displacement and force on cellular or other soft structures by using edge detection and image subtraction on digitally captured optical microscopy images. Forces as small as 250+/-50 nN and as great as 25+/-2.5 microN may be applied and measured upon as few as one or as many as hundreds of structures in parallel. A validation of the method is provided by comparing results from a rigid glass surface and a compliant polymeric surface.     

Presenting DEViSE: Data Exchange for Visualizing Security Events

The Data Exchange for Visualizing Security Events (DEViSE) is an open-source architecture designed to enable data sharing between security visualization tools. The security visualization market currently lacks interoperability between different applications, which tend to be constrained to certain log formats. DEViSE is a middleware layer that manages these interactions so one visualization tool can transfer security-related information to another application. DEViSE uses XML for all communication purposes. This allows a much greater level of freedom for application integration. To demonstrate DEViSE, the authors have created several security visualization tools that adhere to different visualization paradigms.     

An assessment of photosynthetic light use efficiency from space: Modeling the atmospheric and directional impacts on PRI reflectance

Estimation of photosynthetic light use efficiency (ε) from satellite observations is an important component of climate change research. The photochemical reflectance index, a narrow waveband index based on the reflectance at 531 and 570 nm, allows sampling of the photosynthetic activity of leaves; upscaling of these measurements to landscape and global scales, however, remains challenging. Only a few studies have used spaceborne observations of PRI so far, and research has largely focused on the MODIS sensor. Its daily global coverage and the capacity to detect a narrow reflectance band at 531 nm make it the best available choice for sensing ε from space. Previous results however, have identified a number of key issues with MODIS-based observations of PRI. First, the differences between the footprint of eddy covariance (EC) measurements and the MODIS footprint, which is determined by the sensor's observation geometry make a direct comparison between both data sources challenging and second, the PRI reflectance bands are affected by atmospheric scattering effects confounding the existing physiological signal. In this study we introduce a new approach for upscaling EC based ε measurements to MODIS. First, EC-measured ε values were “translated” into a tower-level optical PRI signal using AMSPEC, an automated multi-angular, tower-based spectroradiometer instrument. AMSPEC enabled us to adjust tower-measured PRI values to the individual viewing geometry of each MODIS overpass. Second, MODIS data were atmospherically corrected using a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, which uses a time series approach and an image-based rather than pixel-based processing for simultaneous retrievals of atmospheric aerosol and surface bidirectional reflectance (BRDF). Using this approach, we found a strong relationship between tower-based and spaceborne reflectance measurements (r² = 0.74, p < 0.01) throughout the vegetation period of 2006. Swath (non-gridded) observations yielded stronger correlations than gridded data (r² = 0.58, p < 0.01) both of which included forward and backscatter observations. Spaceborne PRI values were strongly related to canopy shadow fractions and varied with different levels of ε. We conclude that MAIAC-corrected MODIS observations were able to track the site-level physiological changes from space throughout the observation period.     

Probabilistic model checking for the quantification of DoS security threats

Secure authentication features of communication and electronic commerce protocols involve computationally expensive and memory intensive cryptographic operations that have the potential to be turned into denial-of-service (DoS) exploits. Recent proposals attempt to improve DoS resistance by implementing a trade-off between the resources required for the potential victim(s) with the resources used by a prospective attacker. Such improvements have been proposed for the Internet Key Exchange (IKE), the Just Fast Keying (JFK) key agreement protocol and the Secure Sockets Layer (SSL/TLS) protocol. In present article, we introduce probabilistic model checking as an efficient tool-assisted approach for systematically quantifying DoS security threats. We model a security protocol with a fixed network topology using probabilistic specifications for the protocol participants. We attach into the protocol model, a probabilistic attacker model which performs DoS related actions with assigned cost values. The costs for the protocol participants and the attacker reflect the level of some resource expenditure (memory, processing capacity or communication bandwidth) for the associated actions. From the developed model we obtain a Discrete Time Markov Chain (DTMC) via property preserving discrete-time semantics. The DTMC model is verified using the PRISM model checker that produces probabilistic estimates for the analyzed DoS threat. In this way, it is possible to evaluate the level of resource expenditure for the attacker, beyond which the likelihood of widespread attack is reduced and subsequently to compare alternative design considerations for optimal resistance to the analyzed DoS threat. Our approach is validated through the analysis of the Host Identity Protocol (HIP). The HIP base-exchange is seen as a cryptographic key-exchange protocol with special features related to DoS protection. We analyze a serious DoS threat, for which we provide probabilistic estimates, as well as results for the associated attacker and participants' costs.     

Mechanising a formal model of flash memory

We present second steps in the construction of formal models of NAND flash memory, based on a recently emerged open standard for such devices. The model is intended as a key part of a pilot project to develop a verified file store system based on flash memory. The project was proposed by Joshi and Holzmann as a contribution to the Grand Challenge in Verified Software, and involves constructing a highly assured flash file store for use in space-flight missions. The model is at a level of abstraction that captures the internal architecture of NAND flash devices. In this paper, we focus on mechanising the state model and its initialisation operation, where most of the conceptual complexity resides.     

Piecewise training for structured prediction

A drawback of structured prediction methods is that parameter estimation requires repeated inference, which is intractable for general structures. In this paper, we present an approximate training algorithm called piecewise training (PW) that divides the factors into tractable subgraphs, which we call pieces, that are trained independently. Piecewise training can be interpreted as approximating the exact likelihood using belief propagation, and different ways of making this interpretation yield different insights into the method. We also present an extension to piecewise training, called piecewise pseudolikelihood (PWPL), designed for when variables have large cardinality. On several real-world natural language processing tasks, piecewise training performs superior to Besag’s pseudolikelihood and sometimes comparably to exact maximum likelihood. In addition, PWPL performs similarly to PW and superior to standard pseudolikelihood, but is five to ten times more computationally efficient than batch maximum likelihood training.     

Fitness functions in evolutionary robotics: A survey and analysis

This paper surveys fitness functions used in the field of evolutionary robotics (ER). Evolutionary robotics is a field of research that applies artificial evolution to generate control systems for autonomous robots. During evolution, robots attempt to perform a given task in a given environment. The controllers in the better performing robots are selected, altered and propagated to perform the task again in an iterative process that mimics some aspects of natural evolution. A key component of this process–one might argue, the key component–is the measurement of fitness in the evolving controllers. ER is one of a host of machine learning methods that rely on interaction with, and feedback from, a complex dynamic environment to drive synthesis of controllers for autonomous agents. These methods have the potential to lead to the development of robots that can adapt to uncharacterized environments and which may be able to perform tasks that human designers do not completely understand. In order to achieve this, issues regarding fitness evaluation must be addressed. In this paper we survey current ER research and focus on work that involved real robots. The surveyed research is organized according to the degree of a priori knowledge used to formulate the various fitness functions employed during evolution. The underlying motivation for this is to identify methods that allow the development of the greatest degree of novel control, while requiring the minimum amount of a priori task knowledge from the designer.