An Open Framework for Extensible Multi-Stage Bioinformatics Software

In research labs, there is often a need to customise software at every step in a given bioinformatics workflow, but traditionally it has been difficult to obtain both a high degree of customisability and good performance. Performance-sensitive tools are often highly monolithic, which can make research difficult. We present a novel set of software development principles and a bioinformatics framework, Friedrich, which is currently in early development. Friedrich applications support both early stage experimentation and late stage batch processing, since they simultaneously allow for good performance and a high degree of flexibility and customisability. These benefits are obtained in large part by basing Friedrich on the multiparadigm programming language Scala. We present a case study in the form of a basic genome assembler and its extension with new functionality. Our architecture has the potential to greatly increase the overall productivity of software developers and researchers in bioinformatics.Comment: 12 pages, 1 figure, to appear in proceedings of PRIB 201

Why ecosystem characteristics predicted from remotely sensed data are unbiased and biased at the same time – And how this affects applications

Remotely sensed data are frequently used for predicting and mapping ecosystem characteristics, and spatially explicit wall-to-wall information is sometimes proposed as the best possible source of information for decisionmaking. However, wall-to-wall information typically relies on model-based prediction, and several features of model-based prediction should be understood before extensively relying on this type of information. One such feature is that model-based predictors can be considered both unbiased and biased at the same time, which has important implications in several areas of application. In this discussion paper, we first describe the conventional model-unbiasedness paradigm that underpins most prediction techniques using remotely sensed (or other) auxiliary data. From this point of view, model-based predictors are typically unbiased. Secondly, we show that for specific domains, identified based on their true values, the same model-based predictors can be considered biased, and sometimes severely so. We suggest distinguishing between conventional model-bias, defined in the statistical literature as the difference between the expected value of a predictor and the expected value of the quantity being predicted, and design-bias of model-based estimators, defined as the difference between the expected value of a model-based estimator and the true value of the quantity being predicted. We show that model-based estimators (or predictors) are typically design-biased, and that there is a trend in the design-bias from overestimating small true values to underestimating large true values. Further, we give examples of applications where this is important to acknowledge and to potentially make adjustments to correct for the design-bias trend. We argue that relying entirely on conventional model-unbiasedness may lead to mistakes in several areas of application that use predictions from remotely sensed data

Poplar: Java Composition with Labels and AI Planning

ABSTRACT Class evolution in object-oriented programming often causes so-called breaking changes, largely because of the rigidity of component interconnections in the form of explicit method calls and field accesses. We present a Java extension, Poplar, which we are currently developing. In Poplar, inter-component dependencies are expressed using declarative queries; concrete linking code, generated using a planning algorithm, replaces these at compile time. We show how Poplar can enable fully automatic integration of Java components through evolvable and statically checkable integration links, pointing the way to a new general composition method for objectoriented languages

Second marker

Object oriented languages are virtual, that is, they execute methods indirectly through method pointers. The extent to which code is virtualised depends on the language; a common paradigm is to devirtualise methods in order to gain performance. However, some languages, such as Haskell compiled with GHC, are fully virtual. It has been shown by Cheadle et al[2] that it is possible to exploit the virtualisation in GHC to construct a relatively cheap read barrier for an incremental garbage collector. This is done through the equivalent of method specialisation in Haskell. The widespread programming language Java is only partly virtual, and indeeed conventional wisdom for optimising it at run-time has been to devirtualise methods. We now ask: if Java were to be fully virtual, could the cost of the added virtualisation be traded for something beneficial that might potentially offset it, such incremental garbage collection similar to what was done with GHC? This project makes several contributions. We explore some prior work in exploiting virtual dispatch and introduce Jikes RVM, an extremely popular platform for programming language research. We develop a general framework that allows method specialisation at low cost. This framework has three components: transforms that transform all classes loaded into the Java VM, generic extensions to the Jikes RVM, and extensions to the inlining subsystem. We evaluate this framework for the purposes of using it as a read barrier in an incremental garbage collector, though its use is by no means limited to garbage collection. i Acknowledgements I would like to thank my supervisor Andrew Cheadle and Dr Tony Field for the plentiful and concise help, encouragement and guidance they have given me. I also want to thank my friends in JMC for helping me to stay on track and fo

Service-oriented middleware for dynamic management of heterogeneous sensing devices

In Proceedings of the 7th ACM International Conference on Pervasive Services (ICPS'10)International audienceno abstrac

Service-oriented middleware for dynamic management of heterogeneous sensing devices

In Proceedings of the 7th ACM International Conference on Pervasive Services (ICPS'10)International audienceno abstrac

Plug&Manage Heterogeneous Sensing Devices

International audienceWith the emergence of sensors in applications in which the quality of service requirements are high (e.g., industrial, medical, domotic), management of these sensing devices gains an increasing importance. However, management issues are still little explored in this context. This demonstration presents our solution for dynamically managing networked heteroge- neous sensing devices. The solution is based on a service oriented middleware that provides generic management op- erations for configuration, software management and perfor- mance monitoring of sensing devices

Improved Prediction of Forest Variables Using Data Assimilation of Interferometric Synthetic Aperture Radar Data

The statistical framework of data assimilation provides methods for utilizing new data for obtaining up-to-date forest data: existing forest data are forecasted and combined with each new remote sensing data set. This new paradigm for updating forest database, well known from other fields of study, will provide a framework for utilizing all available remote sensing data in proportion to their quality to improve prediction. It also solves the problem that not all remote sensing data sets provide information for the entire area of interest, since areas with no remote sensing data can be forecasted until new remote sensing data become available. In this study, extended Kalman filtering was used for assimilating data from 19 TanDEM-X InSAR images on 137 sample plots, each of 10-meter radius at a test site in southern Sweden over a period of 4 years. At almost all time points data assimilation resulted in predictions closer to the reference value than predictions based on data from that single time point. For the study variables Lorey's mean height, basal area, and stem volume, the median reduction in root mean square error was 0.4 m, 0.9 m2/ha, and 15.3 m3/ha (2, 3, and 6 percentage points), respectively