Service Deployment Model on Shared Virtual Network Functions With Flow Partition

Network operators can operate services in a flexible way with virtual network functions thanks to the network function virtualization technology. Flow partition allows aggregated traffic to be split into multiple parts, which increases the flexibility. This paper proposes a service deployment model with flow partition to minimize the service deployment cost with meeting service delay requirements. A virtual network function of a service is allowed to have several instances, each of which hosts a part of flows and can be shared among different services, to reduce the initial and proportional cost. We provide the mathematical formulation for the proposed model and transform it to a special case as a mixed integer second-order cone programming (MISOCP) problem. A heuristic algorithm, which is called a flow partition heuristic (FPH), is introduced to solve the original problem in practical time by decomposing it into several steps; each step handles a convex problem. We compare the performances of proposed model with flow partition and conventional model without flow partition. We consider the formulated MISOCP problem with adopting a strategy of even splitting to divide flows in a special case, which is called an even spitting heuristic (ESH). The performances of FPH and ESH are compared in a realistic scenario. We also consider the formulated MISOCP problem as an original problem and compare it to an FPH-based heuristic algorithm with the even-splitting strategy (FPH-ES), in both realistic and synthetic scenarios. The numerical results reveal that the proposed model saves the service deployment cost compared to the conventional one. It improves the maximum admissible traffic scale by 23% in average in our examined cases. We observe that FPH outperforms ESH and ESH outperforms FPH-ES in terms of the service deployment cost in their own focused problems, respectively

Undoped Strained Ge Quantum Well with Ultrahigh Mobility Grown by Reduce Pressure Chemical Vapor Deposition

We fabricate an undoped Ge quantum well under 30 nm Ge0.8Si0.2 shallow barrier with reverse grading technology. The under barrier is deposited by Ge0.8Si0.2 followed by Ge0.9Si0.1 so that the variation of Ge content forms a sharp interface which can suppress the threading dislocation density penetrating into undoped Ge quantum well. And the Ge0.8Si0.2 barrier introduces enough in-plane parallel strain -0.41% in the Ge quantum well. The heterostructure field-effect transistors with a shallow buried channel get a high two-dimensional hole gas (2DHG) mobility over 2E6 cm2/Vs at a low percolation density of 2.51 E-11 cm2. We also discover a tunable fractional quantum Hall effect at high densities and high magnetic fields. This approach defines strained germanium as providing the material basis for tuning the spin-orbit coupling strength for fast and coherent quantum computation.Comment: 11 pages, 5 figure

Adolescents' experience of comments about their weight – prevalence, accuracy and effects on weight misperception

<p>Abstract</p> <p>Background</p> <p>Weight comments are commonly received by adolescents, but the accuracy of the comments and their effects on weight misperception are unclear. We assessed the prevalence and accuracy of weight comments received by Chinese adolescents from different sources and their relation to weight misperception.</p> <p>Methods</p> <p>In the Hong Kong Student Obesity Surveillance (HKSOS) project 2006–07, 22612 students aged 11–18 (41.5% boys) completed a questionnaire on obesity. Students responded if family members, peers and professionals had seriously commented over the past 30 days that they were "too fat" or "too thin" in two separate questions. The accuracy of the comments was judged against the actual weight status derived from self-reported height and weight. Self-perceived weight status was also reported and any discordance with the actual weight status denoted weight misperception. Logistic regression yielded adjusted odd ratios for weight misperception by the type of weight comments received.</p> <p>Results</p> <p>One in three students received weight comments, and the mother was the most common source of weight comments. Health professional was the most accurate source of weight comments, yet less than half the comments were correct. Adolescents receiving incorrect comments had increased risk of having weight misperception in all weight status groups. Receiving conflicting comments was positively associated with weight misperception among normal weight adolescents. In contrast, underweight and overweight/obese adolescents receiving correct weight comments were less likely to have weight misperception.</p> <p>Conclusion</p> <p>Weight comments, mostly incorrect, were commonly received by Chinese adolescents in Hong Kong, and such incorrect comments were associated with weight misperception.</p

Global urban environmental change drives adaptation in white clover

Urbanization transforms environments in ways that alter biological evolution. We examined whether urban environmental change drives parallel evolution by sampling 110,019 white clover plants from 6169 populations in 160 cities globally. Plants were assayed for a Mendelian antiherbivore defense that also affects tolerance to abiotic stressors. Urban-rural gradients were associated with the evolution of clines in defense in 47% of cities throughout the world. Variation in the strength of clines was explained by environmental changes in drought stress and vegetation cover that varied among cities. Sequencing 2074 genomes from 26 cities revealed that the evolution of urban-rural clines was best explained by adaptive evolution, but the degree of parallel adaptation varied among cities. Our results demonstrate that urbanization leads to adaptation at a global scale

Measure of Information Content of Remotely Sensed Images Accounting for Spatial Correlation

A measure is proposed based on the information theory and geostatistics to evaluate information content in remotely sensed images. The method is based on the additive noise model and maximum mutual information.These factors affecting the information content have been taken into account, such as noise, spatial correlation and so on. It is suitable for measuring the information content in optical images that have robust spatial correlation with different land cover types. An experiment was performed on a Landsat TM image with three different kinds of land cover types (city, farmland and mountain). The result shows that city has the most information content. It also proves that there is a log positive correlation between information content and the variance of the images

Informational Analysis for Compressive Sampling in Radar Imaging

Compressive sampling or compressed sensing (CS) works on the assumption of the sparsity or compressibility of the underlying signal, relies on the trans-informational capability of the measurement matrix employed and the resultant measurements, operates with optimization-based algorithms for signal reconstruction and is thus able to complete data compression, while acquiring data, leading to sub-Nyquist sampling strategies that promote efficiency in data acquisition, while ensuring certain accuracy criteria. Information theory provides a framework complementary to classic CS theory for analyzing information mechanisms and for determining the necessary number of measurements in a CS environment, such as CS-radar, a radar sensor conceptualized or designed with CS principles and techniques. Despite increasing awareness of information-theoretic perspectives on CS-radar, reported research has been rare. This paper seeks to bridge the gap in the interdisciplinary area of CS, radar and information theory by analyzing information flows in CS-radar from sparse scenes to measurements and determining sub-Nyquist sampling rates necessary for scene reconstruction within certain distortion thresholds, given differing scene sparsity and average per-sample signal-to-noise ratios (SNRs). Simulated studies were performed to complement and validate the information-theoretic analysis. The combined strategy proposed in this paper is valuable for information-theoretic orientated CS-radar system analysis and performance evaluation

Integrating Logistic Regression and Geostatistics for User-Oriented and Uncertainty-Informed Accuracy Characterization in Remotely-Sensed Land Cover Change Information

Accuracy is increasingly recognized as an important dimension in geospatial information and analyses. A strategy well suited for map users who usually have limited information about map lineages is proposed for location-specific characterization of accuracy in land cover change maps. Logistic regression is used to predict the probabilities of correct change categorization based on local patterns of map classes in the focal three by three pixel neighborhood centered at individual pixels being analyzed, while kriging is performed to make corrections to regression predictions based on regression residuals at sample locations. To promote uncertainty-informed accuracy characterization and to facilitate adaptive sampling of validation data, standard errors in both regression predictions and kriging interpolation are quantified to derive error margins in the aforementioned accuracy predictions. It was found that the integration of logistic regression and kriging leads to more accurate predictions of local accuracies through proper handling of spatially-correlated binary data representing pixel-specific (in)correct classifications than kriging or logistic regression alone. Secondly, it was confirmed that pixel-specific class labels, focal dominances and focal class occurrences are significant covariates for regression predictions at individual pixels. Lastly, error measures computed of accuracy predictions can be used for adaptively and progressively locating samples to enhance sampling efficiency and to improve predictions. The proposed methods may be applied for characterizing the local accuracy of categorical maps concerned in spatial applications, either input or output

Quantifying Information Content in Multispectral Remote-Sensing Images Based on Image Transforms and Geostatistical Modelling

Quantifying information content in remote-sensing images is fundamental for information-theoretic characterization of remote sensing information processes, with the images being usually information sources. Information-theoretic methods, being complementary to conventional statistical methods, enable images and their derivatives to be described and analyzed in terms of information as defined in information theory rather than data per se. However, accurately quantifying images&rsquo; information content is nontrivial, as information redundancy due to spectral and spatial dependence needs to be properly handled. There has been little systematic research on this, hampering wide applications of information theory. This paper seeks to fill this important research niche by proposing a strategy for quantifying information content in multispectral images based on information theory, geostatistics, and image transformations, by which interband spectral dependence, intraband spatial dependence, and additive noise inherent to multispectral images are effectively dealt with. Specifically, to handle spectral dependence, independent component analysis (ICA) is performed to transform a multispectral image into one with statistically independent image bands (not spectral bands of the original image). The ICA-transformed image is further normal-transformed to facilitate computation of information content based on entropy formulas for Gaussian distributions. Normal transform facilitates straightforward incorporation of spatial dependence in entropy computation for the aforementioned double-transformed image bands with inter-pixel spatial correlation modeled via variograms. Experiments were undertaken using Landsat ETM+ and TM image subsets featuring different dominant land cover types (i.e., built-up, agricultural, and hilly). The experimental results confirm that the proposed methods provide more objective estimates of information content than otherwise when spectral dependence, spatial dependence, or non-normality is not accommodated properly. The differences in information content between image subsets obtained with ETM+ and TM were found to be about 3.6 bits/pixel, indicating the former&rsquo;s greater information content. The proposed methods can be adapted for information-theoretic analyses of remote sensing information processes