This work is intended to be a contribution to the study of the morphology of the rising convective columns, for a better representation of the processes of entrainment and detrainment. We examine technical methods for the description of the interface of expanding clouds and reveal the role of \emph{fingering} instability which increases the effective length of the periphery of the cloud. Assuming Laplacian growth we give a detailed derivation of the time-dependent conformal transformation that solves the equation of the \emph{fingering} instability. For the phase of slower expansion, the evolution of complex poles with a dynamics largely controlled by the Hilbert operator (acting on the function that represents the interface position) leads to \emph{cusp} singularities but smooths out the smaller scale perturbations. We review the arguments that the rising column cannot preserve its integrity (seen as compacity in any horizontal section), because of the penetrative downdrafts or the incomplete repulsion of the static environmental air through momentum transfer. Then we propose an analytical framework which is adequate for competition of two distinct phases of the same system. The methods exmined here are formulated in a general framework and can be easily adapted to particular cases of atmospheric convection.

Land cover has been evaluated and classified on the basis of general features using reflectance or digital levels of photographic or satellite data. One of the most common methodologies based on CORINE land cover (Coordination of Information on the Environment) data, which classifies natural cover according to a small number of categories. This method produces generalizations about the inventoried areas, resulting in the loss of important floristic and structural information about vegetation types present (such as palm groves, tall dense mangroves, and dense forests). This classification forfeits relevant information on sites with high heterogeneity and diversity. Especially in the tropics, simplification of coverage types reaches its maximum level with the use of deforestation analysis, particularly when it is reduced to the two classes of forests and nonforests. As this paper demonstrates, these results have considerable consequences for political efforts to conserve the biodiversity of megadiverse countries. We designed a new methodological approach that incorporates biological distinctiveness combined with phytosociological classification of vegetation and its relation to physical features. This approach is based on parameters obtained through canonical correspondence analysis on a fuzzy logic model, which are used to construct multiple coverage maps. This tool is useful for monitoring and analyzing vegetation dynamics, since it maintains the typological integrity of a cartographic series. The methodology creates cartographic series congruent in time and scale, can be applied to multiple and varied satellite inputs, and always evaluates the same model parameters. We tested this new method in the southwestern Colombian Caribbean region and compared our results with those from what we believe are outdated tools used in other analyses of deforestation around the world.

We study large-scale spatial systems that contain exogenous variables, e.g. environmental factors that are significant predictors in spatial processes. Building predictive models for such processes is challenging because the large numbers of observations present makes it inefficient to apply full Kriging. In order to reduce computational complexity, this paper proposes Sparse Pseudo-input Local Kriging (SPLK), which utilizes hyperplanes to partition a domain into smaller subdomains and then applies a sparse approximation of the full Kriging to each subdomain. We also develop an optimization procedure to find the desired hyperplanes. To alleviate the problem of discontinuity in the global predictor, we impose continuity constraints on the boundaries of the neighboring subdomains. Furthermore, partitioning the domain into smaller subdomains makes it possible to use different parameter values for the covariance function in each region and, therefore, the heterogeneity in the data structure can be effectively captured. Numerical experiments demonstrate that SPLK outperforms, or is comparable to, the algorithms commonly applied to spatial datasets.

Recent decades have seen an interest in prediction problems for which Bayesian methodology has been used ubiquitously. Sampling from or approximating the posterior predictive distribution in a Bayesian model allows one to make inferential statements about potentially observable random quantities given observed data. The purpose of this note is to use statistical decision theory as a basis to justify the use of a posterior predictive distribution for making a point prediction.

Capturing the dependence structure of multivariate extreme events is a major concern in many fields involving the management of risks stemming from multiple sources, e.g. portfolio monitoring, insurance, environmental risk management and anomaly detection. One convenient (non-parametric) characterization of extremal dependence in the framework of multivariate Extreme Value Theory (EVT) is the angular measure, which provides direct information about the probable 'directions' of extremes, that is, the relative contribution of each feature/coordinate of the 'largest' observations. Modeling the angular measure in high dimensional problems is a major challenge for the multivariate analysis of rare events. The present paper proposes a novel methodology aiming at exhibiting a sparsity pattern within the dependence structure of extremes. This is done by estimating the amount of mass spread by the angular measure on representative sets of directions, corresponding to specific sub-cones of $R^d\_+$. This dimension reduction technique paves the way towards scaling up existing multivariate EVT methods. Beyond a non-asymptotic study providing a theoretical validity framework for our method, we propose as a direct application a --first-- anomaly detection algorithm based on multivariate EVT. This algorithm builds a sparse 'normal profile' of extreme behaviours, to be confronted with new (possibly abnormal) extreme observations. Illustrative experimental results provide strong empirical evidence of the relevance of our approach.

The structure of ecological networks, in particular food webs, determines their ability to evolve further, i.e. evolvability. The knowledge about how food web evolvability is determined by the structures of diverse ecological networks can guide human interventions purposefully to either promote or limit evolvability of ecosystems. However, the focus of prior food web studies was on stability and robustness; little is known regarding the impact of ecological network structures on their evolvability. To correlate ecosystem structure and evolvability, we adopt the NK model originally from evolutionary biology to generate and assess the ruggedness of fitness landscapes of a wide spectrum of model food webs with gradual variation in the amount of feeding loops and link density. The variation in network structures is controlled by linkage rewiring. Our results show that more feeding loops and lower trophic link density, i.e. higher autonomy of species, of food webs increase the potential for the ecosystem to generate heritable variations with improved fitness. Our findings allow the prediction of the evolvability of actual food webs according to their network structures, and provide guidance to enhancing or controlling the evolvability of specific ecosystems.

Marine stratocumulus clouds play a critical role in the Earth's climate system. They display an amazing array of complex behaviors at many different spatiotemporal scales. Precipitation in these clouds is in general very light, but it is vital for clouds' systematic evolution and organization. Here we identify areas of high liquid water path within these clouds as potentially precipitating, or pouches. They are breeding grounds for stratocumuli to change their organization form. We show, using different satellite data sets, that the size distribution of these pouches show a universal scaling. We argue that such scaling is an emergent property of the cloud system, which results from numbers interactions at the microscopic scale.

We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is far less likely to predict false detections where nothing exists. Finally, YOLO learns very general representations of objects. It outperforms all other detection methods, including DPM and R-CNN, by a wide margin when generalizing from natural images to artwork on both the Picasso Dataset and the People-Art Dataset.

Atomic clock technology is advancing rapidly, now reaching stabilities of $\Delta f/f \sim 10^{-18}$, which corresponds to resolving $1$ cm in equivalent geoid height over an integration timescale of about 7 hours. At this level of performance, ground-based atomic clock networks emerge as a tool for monitoring a variety of geophysical processes by directly measuring changes in the gravitational potential. Vertical changes of the clock's position due to magmatic, volcanic, post-seismic or tidal deformations can result in measurable variations in the clock tick rate. As an example, we discuss the geopotential change arising due to an inflating point source (Mogi model), and apply it to the Etna volcano. Its effect on an observer on the Earth's surface can be divided into two different terms: one purely due to uplift and one due to the redistribution of matter. Thus, with the centimetre-level precision of current clocks it is already possible to monitor volcanoes. The matter redistribution term is estimated to be 2-3 orders of magnitude smaller than the uplift term, and should be resolvable when clocks improve their stability to the sub-millimetre level. Additionally, clocks can be compared over distances of thousands of kilometres on a short-term basis (e.g. hourly). These clock networks will improve our ability to monitor periodic effects with long-wavelength like the solid Earth tide.

The Saigon River, which flows through the center of Ho Chi Minh City, is of critical importance for the development of the city as forms as the main water supply and drainage channel for the city. In recent years, riverbank erosion and failures have become more frequent along the Saigon River, causing flooding and damage to infrastructures near the river. A field investigation and numerical study has been undertaken by our research group to identify factors affecting the riverbank failure. In this paper, field investigation results obtained from multiple investigation points on the Saigon River are presented, followed by a comprehensive coupled finite element analysis of riverbank stability when subjected to river water level fluctuations. The river water level fluctuation has been identified as one of the main factors affecting the riverbank failure, i.e. removal of the balancing hydraulic forces acting on the riverbank during water drawdown.

We propose a novel deep architecture, SegNet, for semantic pixel wise image labelling. SegNet has several attractive properties; (i) it only requires forward evaluation of a fully learnt function to obtain smooth label predictions, (ii) with increasing depth, a larger context is considered for pixel labelling which improves accuracy, and (iii) it is easy to visualise the effect of feature activation(s) in the pixel label space at any depth. SegNet is composed of a stack of encoders followed by a corresponding decoder stack which feeds into a soft-max classification layer. The decoders help map low resolution feature maps at the output of the encoder stack to full input image size feature maps. This addresses an important drawback of recent deep learning approaches which have adopted networks designed for object categorization for pixel wise labelling. These methods lack a mechanism to map deep layer feature maps to input dimensions. They resort to ad hoc methods to upsample features, e.g. by replication. This results in noisy predictions and also restricts the number of pooling layers in order to avoid too much upsampling and thus reduces spatial context. SegNet overcomes these problems by learning to map encoder outputs to image pixel labels. We test the performance of SegNet on outdoor RGB scenes from CamVid, KITTI and indoor scenes from the NYU dataset. Our results show that SegNet achieves state-of-the-art performance even without use of additional cues such as depth, video frames or post-processing with CRF models.

Diverse classes of proteins function through large-scale conformational changes; sophisticated enhanced sampling methods have been proposed to generate these macromolecular transition paths. As such paths are curves in a high-dimensional space, they have been difficult to compare quantitatively, a prerequisite to, for instance, assess the quality of different sampling algorithms. The Path Similarity Analysis (PSA) approach alleviates these difficulties by utilizing the full information in 3N-dimensional trajectories in configuration space. PSA employs the Hausdorff or Fr\'echet path metrics---adopted from computational geometry---enabling us to quantify path (dis)similarity, while the new concept of a Hausdorff-pair map permits the extraction of atomic-scale determinants responsible for path differences. Combined with clustering techniques, PSA facilitates the comparison of many paths, including collections of transition ensembles. We use the closed-to-open transition of the enzyme adenylate kinase (AdK)---a commonly used testbed for the assessment enhanced sampling algorithms---to examine multiple microsecond equilibrium molecular dynamics (MD) transitions of AdK in its substrate-free form alongside transition ensembles from the MD-based dynamic importance sampling (DIMS-MD) and targeted MD (TMD) methods, and a geometrical targeting algorithm (FRODA). A Hausdorff pairs analysis of these ensembles revealed, for instance, that differences in DIMS-MD and FRODA paths were mediated by a set of conserved salt bridges whose charge-charge interactions are fully modeled in DIMS-MD but not in FRODA. We also demonstrate how existing trajectory analysis methods relying on pre-defined collective variables, such as native contacts or geometric quantities, can be used synergistically with PSA, as well as the application of PSA to more complex systems such as membrane transporter proteins.

There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .

In this paper we investigate the performance of different types of rectified activation functions in convolutional neural network: standard rectified linear unit (ReLU), leaky rectified linear unit (Leaky ReLU), parametric rectified linear unit (PReLU) and a new randomized leaky rectified linear units (RReLU). We evaluate these activation function on standard image classification task. Our experiments suggest that incorporating a non-zero slope for negative part in rectified activation units could consistently improve the results. Thus our findings are negative on the common belief that sparsity is the key of good performance in ReLU. Moreover, on small scale dataset, using deterministic negative slope or learning it are both prone to overfitting. They are not as effective as using their randomized counterpart. By using RReLU, we achieved 75.68\% accuracy on CIFAR-100 test set without multiple test or ensemble.

The NEMO general circulation ocean model is extended to incorporate three physical processes related to ocean surface waves, namely the surface stress (modified by growth and dissipation of the oceanic wave field), the turbulent kinetic energy flux from breaking waves, and the Stokes-Coriolis force. Experiments are done with NEMO in ocean-only (forced) mode and coupled to the ECMWF atmospheric and wave models. Ocean-only integrations are forced with fields from the ERA-Interim reanalysis. All three effects are noticeable in the extra-tropics, but the sea-state dependent turbulent kinetic energy flux yields by far the largest difference. This is partly because the control run has too vigorous deep mixing due to an empirical mixing term in NEMO. We investigate the relation between this ad hoc mixing and Langmuir turbulence and find that it is much more effective than the Langmuir parameterization used in NEMO. The biases in sea surface temperature as well as subsurface temperature are reduced, and the total ocean heat content exhibits a trend closer to that observed in a recent ocean reanalysis (ORAS4) when wave effects are included. Seasonal integrations of the coupled atmosphere-wave-ocean model consisting of NEMO, the wave model ECWAM and the atmospheric model of ECMWF similarly show that the sea surface temperature biases are greatly reduced when the mixing is controlled by the sea state and properly weighted by the thickness of the uppermost level of the ocean model. These wave-related physical processes were recently implemented in the operational coupled ensemble forecast system of ECMWF.

The presence of desiccation cracks in soils can significantly alter their mechanical and hydrological properties. In many circumstances, desiccation cracking in soils can cause significant damage to earthen or soil supported structures. For example, desiccation cracks can act as the preference path way for water flow, which can facilitate seepage flow causing internal erosion inside earth structures. Desiccation cracks can also trigger slope failures and landslides. Therefore, developing a computational procedure to predict desiccation cracking behaviour in soils is vital for dealing with key issues relevant to a range of applications in geotechnical and geo-environment engineering. In this paper, the smoothed particle hydrodynamics (SPH) method will be extended for the first time to simulate shrinkage-induced soil cracking. The main objective of this work is to examine the performance of the proposed numerical approach in simulating the strong discontinuity in material behaviour and to learn about the crack formation in soils, looking at the effects of soil thickness on the cracking patterns. Results show that the SPH is a promising numerical approach for simulating crack formation in soils

We present the temporal changes of the diffusion coefficient K of galactic cosmic rays (GCRs) at the Earth orbit calculated based on the experimental data using two different methods. The first approach is based on the Parker convection-diffusion approximation of GCR modulation [1]: i.e. K~Vr=dI where dI is the variation of the GCR intensity measured by neutron monitors (NM),V is the solar wind velocity and r is the radial distance. The second approach is based on the interplanetary magnetic field (IMF) data. It was suggested that parallel mean free path can be expressed in terms of B as in [2]-[4]. Using data of the product of the parallel mean free path and radial gradient of GCR calculated based on the GCR anisotropy data (Ahluwalia et al., this conference ICRC 2013, poster ID: 487 [5]), we estimate the temporal changes of the radial gradient of GCR at the Earth orbit. We show that the radial gradient exhibits a strong solar cycle dependence (11-year variation) and a weak solar magnetic cycle dependence (22-year variation), being in agreement with the previous other calculations and with PIONEER/VOYAGER observations.

The self-potential (SP) method is sensitive to water fluxes in saturated and partially saturated porous media, such as those associated with rainwater infiltration and groundwater recharge. We present a field-based study at the Voulund agricultural test site, Denmark, that is, to the best of our knowledge, the first to focus on the vertical self-potential distribution prior to and during a saline tracer test. A coupled hydrogeophysical modeling framework is used to simulate the SP response to precipitation and saline tracer infiltration. A layered hydrological model is first obtained by inverting dielectric and matric potential data. The resulting model that compares favorably with electrical resistance tomography models is subsequently used to predict the SP response. The electrokinetic contribution (caused by water fluxes in a charged porous soil) is modeled by an effective excess charge approach that considers both water saturation and pore water salinity. Our results suggest that the effective excess charge evolution prior to the tracer injection is better described by a recent flux-averaged model based on soil water retention functions than by a previously proposed volume-averaging model. This is the first time that raw vertically distributed SP measurements have been explained by a physically based model. The electrokinetic contribution cannot alone reproduce the SP data during the tracer test and an electro-diffusive contribution (caused by concentration gradients) is needed. The predicted amplitude of this contribution is too small to perfectly explain the data, but the shape is in accordance with the field data. This discrepancy is attributed to imperfect descriptions of electro-diffusive phenomena in partially saturated soils, unaccounted soil heterogeneity, and discrepancies between the measured and predicted electrical conductivities in the tracer infiltration area.

The exploration of geothermal system at Mount Ciremai has been started since the early 1980s and has just been studied carefully since the early 2000s. Previous studies have detected the potential of geothermal system and also the groundwater mechanism feeding the system. This paper will discuss the geothermal exploration based on regional scale surface temperature analysis with Landsat image to have a more detail interpretation of the geological setting and magneto-telluric or MT survey at prospect zones, which identified by the previous method, to have a more exact and in depth local scale structural interpretation. Both methods are directed to pin point appropriate locations for geothermal pilot hole drilling and testing. We used four scenes of Landsat Enhanced Thematic Mapper or ETM+ data to estimate the surface manifestation of a geothermal system. Temporal analysis of Land Surface Temperature or LST was applied and coupled with field temperature measurement at seven locations. By combining the TTM with NDVI threshold, the authors can identify six zones with surface temperature anomaly. Among these six zones, three zones were interpreted to have a relation with geothermal system and the other three zones were related to human activities. Then, MT survey was performed at the three geothermal prospects identified from previous remote sensing analysis, at the east flank of the volcano, to estimate the detail subsurface structures. The MT survey successfully identified four buried normal faults in the area, which positively are a part of the conduits in the geothermal system east of Mount Ciremai. From MT analysis, the author also found the locations of volcanic zone, bedrock zone, and the prospect zone. The combination of Landsat analysis on regional scale and MT measurement on a more detail scale has proven to be the reliable method to map geothermal prospect area.

Biodiversity and extinction are central issues in evolution. Dynamical balance among different species in ecosystems is often described by deterministic replicator equations with moderate success. However, fluctuations are inevitable, either caused by external environment or intrinsic random competitions in finite populations, and the evolutionary dynamics is stochastic in nature. Here we show that, after appropriate coarse-graining, random fluctuations generate dissipation towards extinction because the evolution trajectories in the phase space of all competing species possess positive curvature. As a demonstrating example, we compare the fluctuation-induced dissipative dynamics in Lotka-Volterra model with numerical simulations and find impressive agreement. Our finding is closely related to the fluctuation-dissipation theorem in statistical mechanics but the marked difference is the non-equilibrium essence of the generic evolutionary dynamics. As the evolving ecosystems are far from equilibrium, the relation between fluctuations and dissipations is often complicated and dependent on microscopic details. It is thus remarkable that the generic positivity of the trajectory curvature warrants dissipation arisen from the seemingly harmless fluctuations. The unexpected dissipative dynamics is beyond the reach of conventional replicator equations and plays a crucial role in investigating the biodiversity in ecosystems.

Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. Our key insight is to build "fully convolutional" networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet, the VGG net, and GoogLeNet) into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task. We then define a novel architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes one third of a second for a typical image.

The Dirichlet process mixture (DPM) is a ubiquitous, flexible Bayesian nonparametric statistical model. However, full probabilistic inference in this model is analytically intractable, so that computationally intensive techniques such as Gibb's sampling are required. As a result, DPM-based methods, which have considerable potential, are restricted to applications in which computational resources and time for inference is plentiful. For example, they would not be practical for digital signal processing on embedded hardware, where computational resources are at a serious premium. Here, we develop simplified yet statistically rigorous approximate maximum a-posteriori (MAP) inference algorithms for DPMs. This algorithm is as simple as K-means clustering, performs in experiments as well as Gibb's sampling, while requiring only a fraction of the computational effort. Unlike related small variance asymptotics, our algorithm is non-degenerate and so inherits the "rich get richer" property of the Dirichlet process. It also retains a non-degenerate closed-form likelihood which enables standard tools such as cross-validation to be used. This is a well-posed approximation to the MAP solution of the probabilistic DPM model.

$L_1$ regularized logistic regression has now become a workhorse of data mining and bioinformatics: it is widely used for many classification problems, particularly ones with many features. However, $L_1$ regularization typically selects too many features and that so-called false positives are unavoidable. In this paper, we demonstrate and analyze an aggregation method for sparse logistic regression in high dimensions. This approach linearly combines the estimators from a suitable set of logistic models with different underlying sparsity patterns and can balance the predictive ability and model interpretability. Numerical performance of our proposed aggregation method is then investigated using simulation studies. We also analyze a published genome-wide case-control dataset to further evaluate the usefulness of the aggregation method in multilocus association mapping.

We introduce the Locally Linear Latent Variable Model (LL-LVM), a probabilistic model for non-linear manifold discovery that describes a joint distribution over observations, their manifold coordinates and locally linear maps conditioned on a set of neighbourhood relationships. The model allows straightforward variational optimisation of the posterior distribution on coordinates and locally linear maps from the latent space to the observation space given the data. Thus, the LL-LVM encapsulates the local-geometry preserving intuitions that underlie non-probabilistic methods such as locally linear embedding (LLE). Its probabilistic semantics make it easy to evaluate the quality of hypothesised neighbourhood relationships, select the intrinsic dimensionality of the manifold, construct out-of-sample extensions and to combine the manifold model with additional probabilistic models that capture the structure of coordinates within the manifold.

Network metrics form a fundamental part of the network analysis toolbox. Used to quantitatively measure different aspects of the network, these metrics can give insights into the underlying network structure and function. In this work, we connect network metrics to modern probabilistic machine learning. We focus on the centrality metric, which is used a wide variety of applications from web search to gene-analysis. First, we formulate an eigenvector-based Bayesian centrality model for determining node importance. Compared to existing methods, our probabilistic model allows for the assimilation of multiple edge weight observations, the inclusion of priors and the extraction of uncertainties. To enable tractable inference, we develop a variational lower bound (VBC) that is demonstrated to be effective on a variety of networks (two synthetic and five real-world graphs). We then bridge this model to sparse Gaussian processes. The sparse variational Bayesian centrality Gaussian process (VBC-GP) learns a mapping between node attributes to latent centrality and hence, is capable of predicting centralities from node features and can potentially represent a large number of nodes using only a limited number of inducing inputs. Experiments show that the VBC-GP learns high-quality mappings and compares favorably to a two-step baseline, i.e., a full GP trained on the node attributes and pre-computed centralities. Finally, we present two case-studies using the VBC-GP: first, to ascertain relevant features in a taxi transport network and second, to distribute a limited number of vaccines to mitigate the severity of a viral outbreak.

A recent paper by James et al. finds that mutualistic interactions decrease the biodiversity of model ecosystems. However, this result can be reverted if we consider ecological trade-offs and choose parameters suitable for sparse mutualistic networks instead of fully connected networks.

Knowledge-based or Artificial Intelligence techniques are used increasingly as alternatives to more classical techniques to model ENVIRONMENTAL SYSTEMS. Use of Artificial Intelligence (AI) in environmental modelling has increased with recognition of its potential. In this paper we examine the DIFFERENT TECHNIQUES of Artificial intelligence with profound examples of human perception, learning and reasoning to solve complex problems. However with the increase of complexity better methods are required. Keeping in view of the above some researchers introduced the idea of hybrid mechanism in which two or more methods can be combined which seems to be a positive effort for creating a more complex; advanced and intelligent system which has the capability to in- cooperate human decisions thus driving the landscape changes.

Objective testing is a key issue in the process of revision and improvement of seismic hazard assessments. Therefore we continue the rigorous comparative analysis of past and newly available hazard maps for the territory of Italy against the seismic activity observed in reality. The final Global Seismic Hazard Assessment Program (GSHAP) results and the most recent version of Seismic Hazard Harmonization in Europe (SHARE) project maps, along with the reference hazard maps for the Italian seismic code, all obtained by probabilistic seismic hazard assessment (PSHA), are cross-compared to the three ground shaking maps based on the duly physically and mathematically rooted neo-deterministic approach (NDSHA). These eight hazard maps for Italy are tested against the available data on ground shaking. The results of comparison between predicted macroseismic intensities and those reported for past earthquakes (in the time interval from 1000 to 2014 year) show that models provide rather conservative estimates, which tend to over-estimate seismic hazard at the ground shaking levels below the MCS intensity IX. Only exception is represented by the neo-deterministic maps associated with a fixed return period of 475 or 2475 years, which provide a better fit to observations, at the cost of model consistent 10% or 2% cases of exceedance respectively. In terms of the Kolmogorov-Smirnov goodness of fit criterion, although all of the eight hazard maps differ significantly from the distribution of the observed ground shaking reported in the available Italian databases, the NDSHA approach appears to outscore significantly the PSHA one.

Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition.

A suite of products has been developed and evaluated to assess hazards presented by convective storm downbursts derived from the current generation of Geostationary Operational Environmental Satellite (GOES) (13-15). The existing suite of GOES downburst prediction products employs the GOES sounder to calculate risk based on conceptual models of favorable environmental profiles for convective downburst generation. A diagnostic nowcasting product, the Microburst Windspeed Potential Index (MWPI), is designed to infer attributes of a favorable downburst environment: 1) the presence of large convective available potential energy (CAPE), and 2) the presence of a surface-based or elevated mixed layer with a steep temperature lapse rate and vertical relative humidity gradient. These conditions foster intense convective downdrafts upon the interaction of sub-saturated air in the elevated or sub-cloud mixed layer with the storm precipitation core. This paper provides an updated assessment of the MWPI algorithm, presents recent case studies demonstrating effective operational use of the MWPI product over the Atlantic coastal region, and presents validation results for the United States Great Plains and Mid-Atlantic coastal region. In addition, an application of the brightness temperature difference (BTD) between GOES imager water vapor (6.5{\mu}m) and thermal infrared (11{\mu}m) channels that identifies regions where downbursts are likely to develop, due to mid-tropospheric dry air entrainment, will be outlined.

Oceanic dispersal and connectivity have been identified as crucial factors for structuring marine populations and designing Marine Protected Areas (MPAs). Focusing on larval dispersal by ocean currents, we propose an approach coupling Lagrangian transport and new tools from Network Theory to characterize marine connectivity in the Mediterranean basin. Larvae of different pelagic durations and seasons are modeled as passive tracers advected in a simulated oceanic surface flow from which a network of connected areas is constructed. Hydrodynamical provinces extracted from this network are delimited by frontiers which match multi-scale oceanographic features. By examining the repeated occurrence of such boundaries, we identify the spatial scales and geographic structures that would control larval dispersal across the entire seascape. Based on these hydrodynamical units, we study novel connectivity metrics for existing reserves. Our results are discussed in the context of ocean biogeography and MPAs design, having ecological and managerial implications.

The use of machine-learning in neuroimaging offers new perspectives in early diagnosis and prognosis of brain diseases. Although such multivariate methods can capture complex relationships in the data, traditional approaches provide irregular (l2 penalty) or scattered (l1 penalty) predictive pattern with a very limited relevance. A penalty like Total Variation (TV) that exploits the natural 3D structure of the images can increase the spatial coherence of the weight map. However, TV penalization leads to non-smooth optimization problems that are hard to minimize. We propose an optimization framework that minimizes any combination of l1, l2, and TV penalties while preserving the exact l1 penalty. This algorithm uses Nesterov's smoothing technique to approximate the TV penalty with a smooth function such that the loss and the penalties are minimized with an exact accelerated proximal gradient algorithm. We propose an original continuation algorithm that uses successively smaller values of the smoothing parameter to reach a prescribed precision while achieving the best possible convergence rate. This algorithm can be used with other losses or penalties. The algorithm is applied on a classification problem on the ADNI dataset. We observe that the TV penalty does not necessarily improve the prediction but provides a major breakthrough in terms of support recovery of the predictive brain regions.

The paper deals with electromagnetic effects associated with a radially symmetric system of progressive surface waves in the deep sea, induced by underwater oscillating sources or by dispersive decay of the initial localized perturbations of the sea surface.

In order to monitor the hydrological strain forces of the karst micro fissure networks and local fault activities, six capacitive extensometers were installed inside a karstic cave near the midi-fault in Belgium. From 2004 to 2008, the nearby Lomme River experienced several heavy rains, leading to flooding inside the Rochefort cave. The highest water level rose more than thirteen meters, the karstic fissure networks were filled with water, which altered the pore pressure of the cave. The strain response to the hydrological induced pore pressure changes are separately deduced from fifteen events when the water level exceeded six meters. The strain measured from the extensometer show a linear contraction during the water recharge and a nonlinear exponential extension releasing during the water discharge. The sensitivity and stability of the sensor are constrained by comparing continuously observed tidal strain waves with a theoretical model. Finally, a local fault deformation rate around $0.03 \pm 0.002$mm/yr is estimated from more than four years' records.

Edges characterize boundaries and are therefore a problem of practical importance in remote sensing.In this paper a comparative study of various edge detection techniques and band wise analysis of these algorithms in the context of object extraction with regard to remote sensing satellite images from the Indian Remote Sensing Satellite (IRS) sensors LISS 3, LISS 4 and Cartosat1 as well as Google Earth is presented.

The Atlantic Meridional Overturning Circulation (MOC) is a crucial part of the climate system because of its associated northward heat transport. The present-day MOC is sensitive to freshwater anomalies and may collapse to a state with a strongly reduced northward heat transport. A future collapse of the Atlantic MOC has been identified as one of the most dangerous tipping points in the climate system. It is therefore crucial to develop early warning indicators for such a potential collapse based on relatively short time series. So far, attempts to use indicators based on critical slowdown have been marginally successful. Based on complex climate network reconstruction, we here present a promising new indicator for the MOC collapse that efficiently monitors spatial changes in deep ocean circulation. Through our analysis of the performance of this indicator we formulate optimal locations of measurement of the MOC to provide early warning signals of a collapse. Our results imply that an increase in spatial resolution of the Atlantic MOC observations (i.e., at more sections) can improve early detection, because the spatial coherence in the deep ocean arising near the transition is better captured.

We present the current status of geo-neutrino measurements and their implications for radiogenic heating in the mantle. Earth models predict different levels of radiogenic heating and, therefore, different geo-neutrino fluxes from the mantle. Seismic tomography reveals features in the deep mantle possibly correlated with radiogenic heating and causing spatial variations in the mantle geo-neutrino flux at the Earth surface. An ocean-based observatory offers the greatest sensitivity to the mantle flux and potential for resolving Earth models and mantle features. Refinements to estimates of the geo-neutrino flux from continental crust reduce uncertainty in measurements of the mantle flux, especially measurements from land-based observatories. These refinements enable the resolution of Earth models using the combined measurements from multiple continental observatories.

An inductive probabilistic classification rule must generally obey the principles of Bayesian predictive inference, such that all observed and unobserved stochastic quantities are jointly modeled and the parameter uncertainty is fully acknowledged through the posterior predictive distribution. Several such rules have been recently considered and their asymptotic behavior has been characterized under the assumption that the observed features or variables used for building a classifier are conditionally independent given a simultaneous labeling of both the training samples and those from an unknown origin. Here we extend the theoretical results to predictive classifiers acknowledging feature dependencies either through graphical models or sparser alternatives defined as stratified graphical models. We also show through experimentation with both synthetic and real data that the predictive classifiers based on stratified graphical models have consistently best accuracy compared with the predictive classifiers based on either conditionally independent features or on ordinary graphical models.

Remote sensing image classification can be performed in many different ways to extract meaningful features. One common approach is to perform edge detection. A second approach is to try and detect whole shapes, given the fact that these shapes usually tend to have distinctive properties such as object foreground or background. To get optimal results, these two approaches can be combined. This paper adopts a combinatorial optimization method to adaptively select threshold based features to improve remote sensing image. Feature selection is an important combinatorial optimization problem in the remote sensing image classification. The feature selection method has to achieve three characteristics: first the performance issues by facilitating data collection and reducing storage space and classification time, second to perform semantics analysis helping to understand the problem, and third to improve prediction accuracy by avoiding the curse of dimensionality. The goal of this thresholding an image is to classify pixels as either dark or light and evaluation of classification results. Interactive adaptive thresholding is a form of thresholding that takes into account spatial variations in illumination of remote sensing image. We present a technique for remote sensing based adaptive thresholding using the interactive satellite image of the input. However, our solution is more robust to illumination changes in the remote sensing image. Additionally, our method is simple and easy to implement but it is effective algorithm to classify the image pixels. This technique is suitable for preprocessing the remote sensing image classification, making it a valuable tool for interactive remote based applications such as augmented reality of the classification procedure.

Silicon nanochannel field effect transistor (FET) biosensors are one of the most promising technologies in the development of highly sensitive and label-free analyte detection for cancer diagnostics. With their exceptional electrical properties and small dimensions, silicon nanochannels are ideally suited for extraordinarily high sensitivity. In fact, the high surface-to-volume ratios of these systems make single molecule detection possible. Further, FET biosensors offer the benefits of high speed, low cost, and high yield manufacturing, without sacrificing the sensitivity typical for traditional optical methods in diagnostics. Top down manufacturing methods leverage advantages in Complementary Metal Oxide Semiconductor (CMOS) technologies, making richly multiplexed sensor arrays a reality. Here, we discuss the fabrication and use of silicon nanochannel FET devices as biosensors for breast cancer diagnosis and monitoring.

Passive remote sensing techniques have become more and more popular for detection and characterization purposes. The advantage of using the Global Navigation Satellite Systems (GNSS) are the well known signals emitted and the availability in most areas on Earth. In the present paper, L-Band signals (including GNSS signals) are considered for oceanographic purposes. The main interest in this contribution is the analysis of the signal reflected by an evolving sea surface using time-frequency transforms. The features which occur in this domain are examined in relation to the physical phenomena: interaction of the electromagnetic waves with the moving sea surface.

Symmetric binary matrices representing relations among entities are commonly collected in many areas. Our focus is on dynamically evolving binary relational matrices, with interest being in inference on the relationship structure and prediction. We propose a nonparametric Bayesian dynamic model, which reduces dimensionality in characterizing the binary matrix through a lower-dimensional latent space representation, with the latent coordinates evolving in continuous time via Gaussian processes. By using a logistic mapping function from the probability matrix space to the latent relational space, we obtain a flexible and computational tractable formulation. Employing P\`olya-Gamma data augmentation, an efficient Gibbs sampler is developed for posterior computation, with the dimension of the latent space automatically inferred. We provide some theoretical results on flexibility of the model, and illustrate performance via simulation experiments. We also consider an application to co-movements in world financial markets.

Recent satellite and ground-based observations proved that in earthquake preparation period in the seismogenic area we have VLF/LF and ULF electromagnetic emissions. According to the opinion of the authors of the present paper this phenomenon is more universal and reliable than other earthquake indicators. Hypothetically, in case of availability of adequate methodological grounds, in the nearest future, earth VLF/LF electromagnetic emission might be declared as the main precursor of earthquake. In particular, permanent monitoring of frequency spectrum of earth electromagnetic emission generated in the earthquake preparation period might turn out very useful with the view of prediction of large (M 5) inland earthquakes. The present paper offers a scheme of the methodology according to which the reality of the above given hypothesis can be checked up. To prove the prediction capabilities of earth electromagnetic emission we have used avalanche-like unstable model of fault formation and an analogous model of electromagnetic contour, synthesis of which, according to our opinion, is rather harmonious.

Cell heterogeneity and the inherent complexity due to the interplay of multiple molecular processes within the cell pose difficult challenges for current single-cell biology. We introduce an approach that identifies a disease phenotype from multiparameter single-cell measurements, which is based on the concept of "supercell statistics", a single-cell-based averaging procedure followed by a machine learning classification scheme. We are able to assess the optimal tradeoff between the number of single cells averaged and the number of measurements needed to capture phenotypic differences between healthy and diseased patients, as well as between different diseases that are difficult to diagnose otherwise. We apply our approach to two kinds of single-cell datasets, addressing the diagnosis of a premature aging disorder using images of cell nuclei, as well as the phenotypes of two non-infectious uveitides (the ocular manifestations of Beh\c{c}et's disease and sarcoidosis) based on multicolor flow cytometry. In the former case, one nuclear shape measurement taken over a group of 30 cells is sufficient to classify samples as healthy or diseased, in agreement with usual laboratory practice. In the latter, our method is able to identify a minimal set of 5 markers that accurately predict Beh\c{c}et's disease and sarcoidosis. This is the first time that a quantitative phenotypic distinction between these two diseases has been achieved. To obtain this clear phenotypic signature, about one hundred CD8+ T cells need to be measured. Beyond these specific cases, the approach proposed here is applicable to datasets generated by other kinds of state-of-the-art and forthcoming single-cell technologies, such as multidimensional mass cytometry, single-cell gene expression, and single-cell full genome sequencing techniques.

We assess the level of cross correlation between P-waves generated by earthquakes in the Atlantic Ocean and measured by 22 array stations of the International Monitoring System (IMS). There are 931 events with 6,411 arrivals in 2011 and 2012. Station TORD was the most sensitive and detected 868 from 931 events. We constructed several 931 by 931 matrices of cross correlation coefficients (CCs) for individual stations and also for average and cumulative CCs. These matrices characterize the detection performance of the involved stations and the IMS. Sixty earthquakes located in the northern hemisphere were selected as master events for signal detection and building of events populating a cross correlation Standard Event List (XSEL) for the first halves of 2009 and 2012. High-quality signals (SNR>5.0) recorded by 10 most sensitive stations were used as waveform templates. In order to quantitatively estimate the gain in the completeness and resolution of the XSEL we compared it with the Reviewed Event Bulletin (REB) of the International Data Centre (IDC) for the North Atlantic (NA) and with the ISC Bulletin. Machine learning and classification algorithms were successfully applied to automatically reject invalid events in the XSEL for 2009.

The Gaussian Process Latent Variable Model (GP-LVM) is a non-linear probabilistic method of embedding a high dimensional dataset in terms low dimensional `latent' variables. In this paper we illustrate that maximum a posteriori (MAP) estimation of the latent variables and hyperparameters can be used for model selection and hence we can determine the optimal number or latent variables and the most appropriate model. This is an alternative to the variational approaches developed recently and may be useful when we want to use a non-Gaussian prior or kernel functions that don't have automatic relevance determination (ARD) parameters. Using a second order expansion of the latent variable posterior we can marginalise the latent variables and obtain an estimate for the hyperparameter posterior. Secondly, we use the GP-LVM to integrate multiple data sources by simultaneously embedding them in terms of common latent variables. We present results from synthetic data to illustrate the successful detection and retrieval of low dimensional structure from high dimensional data. We demonstrate that the integration of multiple data sources leads to more robust performance. Finally, we show that when the data are used for binary classification tasks we can attain a significant gain in prediction accuracy when the low dimensional representation is used.

In this study we used Hydrus-1D to simulate water infiltration from a ring infiltrometer. We generated water content profiles at each time step of infiltration, based on a particular value of the saturated hydraulic conductivity while knowing the other van Genuchten parameters. Water content profiles were converted to dielectric permittivity profiles using the Complex Refractive Index Method relation. We then used the GprMax suite of programs to generate radargrams and to follow the wetting front using arrival time of electromagnetic waves recorded by a Ground-Penetrating Radar (GPR). Theoretically, the depth of the inflection point of the water content profile simulated at any infiltration time step is related to the peak of the reflected amplitude recorded in the corresponding trace in the radargram. We used this relationship to invert the saturated hydraulic conductivity for constant and falling head infiltrations. We present our method on synthetic examples and on two experiments carried out on sand. We further discuss the possibility of estimating two other van Genuchten parameters, n and \alpha, in addition to the saturated hydraulic conductivity.

Self-potential (SP) data are of interest to vadose zone hydrology because of their direct sensitivity to water flow and ionic transport. There is unfortunately little consensus in the literature about how to best model SP data under partially saturated conditions and different approaches (often supported by one laboratory data set alone) have been proposed. We argue herein that this lack of agreement can largely be traced to electrode effects that have not been properly taken into account. A series of drainage and imbibition experiments are considered, in which we find that previously proposed approaches to remove electrode effects are unlikely to provide adequate corrections. Instead, we explicitly model the electrode effects together with classical SP contributions using a flow and transport model. The simulated data agree overall with the observed SP signals and allow decomposing the different signal contributions to analyze them separately. By reviewing other published experimental data, we suggest that most of them include electrode effects that have not been properly taken into account. Our results suggest that previously presented SP theory works well when considering the modeling uncertainties presently associated with electrode effects. Additional work is warranted to not only develop suitable electrodes for laboratory experiments, but also to assure that associated electrode effects that appear inevitable in longer-term experiments are predictable, such that they can be incorporated in the modeling framework.

Our view of the microbial world and its impact on human health is changing radically with the ability to sequence uncultured or unculturable microbes sampled directly from their habitats, ability made possible by fast and cheap next generation sequencing technologies. Such recent developments represents a paradigmatic shift in the analysis of habitat biodiversity, be it the human, soil or ocean microbiome. We review here some research examples and results that indicate the importance of the microbiome in our lives and then discus some of the challenges faced by metagenomic experiments and the subsequent analysis of the generated data. We then analyze the economic and social impact on genomic-medicine and research in both developing and developed countries. We support the idea that there are significant benefits in building capacities for developing high-level scientific research in metagenomics in developing countries. Indeed, the notion that developing countries should wait for developed countries to make advances in science and technology that they later import at great cost has recently been challenged.

This paper tries to determine the origin of springs on the Costa Verde beach, located in the district of Barranco, Miraflores and Magdalena, province of Lima, Peru. These springs emerge near the shoreline, from the lower layers of a 80 meter high cliff. They have survived the process of urbanization of agricultural land, started in the early 70, which decreased the water table aquifer of Lima, and wiped the water leaks from the cliffs. To identify the source of the springs, isotopic, physical, chemical and bacteriological analysis was carried out for samples from five springs. The isotopic concentrations in waters from Costa Verde springs are depleted compared to those obtained for Lima aquifer waters, which is recharged by infiltration of the Rimac River. The measured values of those concentrations suggest that water from the Costa Verde springs should come from a direct recharge in the upper and middle basin, due to infiltration of rainfall or the river at an altitude of about 3600 m. Conductivity and temperature, measured in situ, are similar to those obtained on Lima aquifers. The laboratory analysis showed no significant levels of total or fecal coliform, discarding possible leakage from Lima sewerage.