Towards End-User Driven Power Saving Control in Android devices

Abstract. During the last decade mobile communications increasingly became part of people's daily routine. Such usage raises new challenges regarding devices' battery lifetime management when using most popular wireless access technologies, such as IEEE 802.11. This paper investigates the energy/delay trade-off of using an end-user driven power saving approach, when compared with the standard IEEE 802.11 power saving algorithms. The assessment was conducted in a real testbed using an Android mobile phone and high-precision energy measurement hardware. The results show clear energy benefits of employing user-driven power saving techniques, when compared with other standard approaches.

SLA-Driven Simulation of Multi-Tenant Scalable Cloud-Distributed Enterprise Information Systems

Cloud Computing is an enabler for delivering large-scale, distributed enterprise applications with strict requirements in terms of performance. It is often the case that such applications have complex scaling and Service Level Agreement (SLA) management requirements. In this paper we present a simulation approach for validating and comparing SLA-aware scaling policies using the CloudSim simulator, using data from an actual Distributed Enterprise Information System (dEIS). We extend CloudSim with concurrent and multi-tenant task simulation capabilities. We then show how different scaling policies can be used for simulating multiple dEIS applications. We present multiple experiments depicting the impact of VM scaling on both datacenter energy consumption and dEIS performance indicators.

Towards a data-driven analysis of hadronic light-by-light scattering

The hadronic light-by-light contribution to the anomalous magnetic moment of the muon was recently analyzed in the framework of dispersion theory, providing a systematic formalism where all input quantities are expressed in terms of on-shell form factors and scattering amplitudes that are in principle accessible in experiment. We briefly review the main ideas behind this framework and discuss the various experimental ingredients needed for the evaluation of one- and two-pion intermediate states. In particular, we identify processes that in the absence of data for doubly-virtual pion–photon interactions can help constrain parameters in the dispersive reconstruction of the relevant input quantities, the pion transition form factor and the helicity partial waves for γ⁎γ⁎→ππ.

Real-time simulation of large open quantum spin systems driven by dissipation

We consider a large quantum system with spins 12 whose dynamics is driven entirely by measurements of the total spin of spin pairs. This gives rise to a dissipative coupling to the environment. When one averages over the measurement results, the corresponding real-time path integral does not suffer from a sign problem. Using an efficient cluster algorithm, we study the real-time evolution from an initial antiferromagnetic state of the two-dimensional Heisenberg model, which is driven to a disordered phase, not by a Hamiltonian, but by sporadic measurements or by continuous Lindblad evolution.

Data-driven healthcare: from patterns to actions

The era of big data opens up new opportunities in personalised medicine, preventive care, chronic disease management and in telemonitoring and managing of patients with implanted devices. The rich data accumulating within online services and internet companies provide a microscope to study human behaviour at scale, and to ask completely new questions about the interplay between behavioural patterns and health. In this paper, we shed light on a particular aspect of data-driven healthcare: autonomous decision-making. We first look at three examples where we can expect data-driven decisions to be taken autonomously by technology, with no or limited human intervention. We then discuss some of the technical and practical challenges that can be expected, and sketch the research agenda to address them.

Distinct characteristics of e13a2 versus e14a2 BCR-ABL1 driven chronic myeloid leukemia under first-line therapy with imatinib

The vast majority of chronic myeloid leukemia patients express a BCR-ABL1 fusion gene mRNA encoding a 210 kDa tyrosine kinase which promotes leukemic transformation. A possible differential impact of the corresponding BCR-ABL1 transcript variants e13a2 ("b2a2") and e14a2 ("b3a2") on disease phenotype and outcome is still a subject of debate. A total of 1105 newly diagnosed imatinib-treated patients were analyzed according to transcript type at diagnosis (e13a2, n=451; e14a2, n=496; e13a2+e14a2, n=158). No differences regarding age, sex, or Euro risk score were observed. A significant difference was found between e13a2 and e14a2 when comparing white blood cells (88 vs. 65 × 10(9)/L, respectively; P<0.001) and platelets (296 vs. 430 × 10(9)/L, respectively; P<0.001) at diagnosis, indicating a distinct disease phenotype. No significant difference was observed regarding other hematologic features, including spleen size and hematologic adverse events, during imatinib-based therapies. Cumulative molecular response was inferior in e13a2 patients (P=0.002 for major molecular response; P<0.001 for MR4). No difference was observed with regard to cytogenetic response and overall survival. In conclusion, e13a2 and e14a2 chronic myeloid leukemia seem to represent distinct biological entities. However, clinical outcome under imatinib treatment was comparable and no risk prediction can be made according to e13a2 versus e14a2 BCR-ABL1 transcript type at diagnosis. (clinicaltrials.gov identifier:00055874).

The role of climate-driven chemical weathering on soil production

Climate plays an important role in controlling rates of weathering and weathered regolith production. Regolith production functions, however, seldom take climate parameters into account. Based on a climate-dependent weathered regolith production model, at low denudation rates, relative regolith thicknesses are less sensitive to changes in precipitation rates, while at high denudation rates, small changes in climatic parameters can result in complete stripping of hillslopes. This pattern is compounded by the long residence times and system response times associated with low denudation rates, and vice versa. As others have shown, the transition between regolith-mantled and bedrock slopes is dependent on the ratio of denudation to production. Here, we further suggest that this is itself a function of precipitation rate and temperature. We suggest that climatic parameters can be easily incorporated into existing soil production models and that such additions improve the predictive power of soil production models. (C) 2013 Elsevier B.V. All rights reserved.

3D Intervertebral Disc Localization and Segmentation from MR Images by Data-Driven Regression and Classification

In this paper we propose a new fully-automatic method for localizing and segmenting 3D intervertebral discs from MR images, where the two problems are solved in a unified data-driven regression and classification framework. We estimate the output (image displacements for localization, or fg/bg labels for segmentation) of image points by exploiting both training data and geometric constraints simultaneously. The problem is formulated in a unified objective function which is then solved globally and efficiently. We validate our method on MR images of 25 patients. Taking manually labeled data as the ground truth, our method achieves a mean localization error of 1.3 mm, a mean Dice metric of 87%, and a mean surface distance of 1.3 mm. Our method can be applied to other localization and segmentation tasks.

Automatic X-ray landmark detection and shape segmentation via data-driven joint estimation of image displacements

In this paper, we propose a new method for fully-automatic landmark detection and shape segmentation in X-ray images. To detect landmarks, we estimate the displacements from some randomly sampled image patches to the (unknown) landmark positions, and then we integrate these predictions via a voting scheme. Our key contribution is a new algorithm for estimating these displacements. Different from other methods where each image patch independently predicts its displacement, we jointly estimate the displacements from all patches together in a data driven way, by considering not only the training data but also geometric constraints on the test image. The displacements estimation is formulated as a convex optimization problem that can be solved efficiently. Finally, we use the sparse shape composition model as the a priori information to regularize the landmark positions and thus generate the segmented shape contour. We validate our method on X-ray image datasets of three different anatomical structures: complete femur, proximal femur and pelvis. Experiments show that our method is accurate and robust in landmark detection, and, combined with the shape model, gives a better or comparable performance in shape segmentation compared to state-of-the art methods. Finally, a preliminary study using CT data shows the extensibility of our method to 3D data.