Considerations about multistep community detection

The problem and implications of community detection in networks have raised a huge attention, for its important applications in both natural and social sciences. A number of algorithms has been developed to solve this problem, addressing either speed optimization or the quality of the partitions calculated. In this paper we propose a multi-step procedure bridging the fastest, but less accurate algorithms (coarse clustering), with the slowest, most effective ones (refinement). By adopting heuristic ranking of the nodes, and classifying a fraction of them as `critical', a refinement step can be restricted to this subset of the network, thus saving computational time. Preliminary numerical results are discussed, showing improvement of the final partition.Comment: 12 page

On the Importance of Displacement History in Soft-Body Contact Models

Two approaches are commonly used for handling frictional contact within the framework of the discrete element method (DEM). One relies on the complementarity method (CM) to enforce a nonpenetration condition and the Coulomb dry-friction model at the interface between two bodies in mutual contact. The second approach, called the penalty method (PM), invokes an elasticity argument to produce a frictional contact force that factors in the local deformation and relative motion of the bodies in contact. We give a brief presentation of a DEM-PM contact model that includes multi-time-step tangential contact displacement history. We show that its implementation in an open-source simulation capability called Chrono is capable of accurately reproducing results from physical tests typical of the field of geomechanics, i.e., direct shear tests on a monodisperse material. Keeping track of the tangential contact displacement history emerges as a key element of the model. We show that identical simulations using contact models that include either no tangential contact displacement history or only single-time-step tangential contact displacement history are unable to accurately model the direct shear test

Safety evaluation of the food enzyme pullulanase from genetically modified Bacillus subtilis strain NZYM-AK

Publisher PD

An efficient strategy for evaluating new non-invasive screening tests for colorectal cancer: the guiding principles.

New screening tests for colorectal cancer (CRC) are rapidly emerging. Conducting trials with mortality reduction as the end point supporting their adoption is challenging. We re-examined the principles underlying evaluation of new non-invasive tests in view of technological developments and identification of new biomarkers. A formal consensus approach involving a multidisciplinary expert panel revised eight previously established principles. Twelve newly stated principles emerged. Effectiveness of a new test can be evaluated by comparison with a proven comparator non-invasive test. The faecal immunochemical test is now considered the appropriate comparator, while colonoscopy remains the diagnostic standard. For a new test to be able to meet differing screening goals and regulatory requirements, flexibility to adjust its positivity threshold is desirable. A rigorous and efficient four-phased approach is proposed, commencing with small studies assessing the test's ability to discriminate between CRC and non-cancer states (phase I), followed by prospective estimation of accuracy across the continuum of neoplastic lesions in neoplasia-enriched populations (phase II). If these show promise, a provisional test positivity threshold is set before evaluation in typical screening populations. Phase III prospective studies determine single round intention-to-screen programme outcomes and confirm the test positivity threshold. Phase IV studies involve evaluation over repeated screening rounds with monitoring for missed lesions. Phases III and IV findings will provide the real-world data required to model test impact on CRC mortality and incidence. New non-invasive tests can be efficiently evaluated by a rigorous phased comparative approach, generating data from unbiased populations that inform predictions of their health impact

Multistep transition of diamond to warm dense matter state revealed by femtosecond X-ray diffraction

Diamond bulk irradiated with a free-electron laser pulse of 6100 eV photon energy, 5 fs duration, at the $\sim 19-25$ eV/atom absorbed doses, is studied theoretically on its way to warm dense matter state. Simulations with our hybrid code XTANT show disordering on sub-100 fs timescale, with the diffraction peak (220) vanishing faster than the peak (111). The warm dense matter formation proceeds as a nonthermal damage of diamond with the band gap collapse triggering atomic disordering. Short-living graphite-like state is identified during a few femtoseconds between the disappearance of (220) peak and the disappearance of (111) peak. The results obtained are compared with the data from the recent experiment at SACLA, showing qualitative agreement. Challenges remaining for the accurate modeling of the transition of solids to warm dense matter state and proposals for supplementary measurements are discussed in detail.Comment: Preprint, submitte

Communities in Networks

We survey some of the concepts, methods, and applications of community detection, which has become an increasingly important area of network science. To help ease newcomers into the field, we provide a guide to available methodology and open problems, and discuss why scientists from diverse backgrounds are interested in these problems. As a running theme, we emphasize the connections of community detection to problems in statistical physics and computational optimization.Comment: survey/review article on community structure in networks; published version is available at http://people.maths.ox.ac.uk/~porterm/papers/comnotices.pd

Predicting expected TCP throughput using genetic algorithm

Predicting the expected throughput of TCP is important for several aspects such as e.g. determining handover criteria for future multihomed mobile nodes or determining the expected throughput of a given MPTCP subflow for load-balancing reasons. However, this is challenging due to time varying behavior of the underlying network characteristics. In this paper, we present a genetic-algorithm-based prediction model for estimating TCP throughput values. Our approach tries to find the best matching combination of mathematical functions that approximate a given time series that accounts for the TCP throughput samples using genetic algorithm. Based on collected historical datapoints about measured TCP throughput samples, our algorithm estimates expected throughput over time. We evaluate the quality of the prediction using different selection and diversity strategies for creating new chromosomes. Also, we explore the use of different fitness functions in order to evaluate the goodness of a chromosome. The goal is to show how different tuning on the genetic algorithm may have an impact on the prediction. Using extensive simulations over several TCP throughput traces, we find that the genetic algorithm successfully finds reasonable matching mathematical functions that allow to describe the TCP sampled throughput values with good fidelity. We also explore the effectiveness of predicting time series throughput samples for a given prediction horizon and estimate the prediction error and confidence.Peer ReviewedPostprint (author's final draft