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In their inventory of offloading techniques in cellular networks **3** , Passarella et al. underline the same issues **and** conclude that non-cooperative models relying exclusively on cellular base stations or Wi-Fi access points, such as 4,5 , do not efficiently work in sparse networks where devices may be disconnected **for** **a** long time, **and** so that offloading solutions exploiting both available infrastructures **and** opportunistic device-to-device communications, **and** implementing **a** cooperative **data** diffusion, must instead be used. Opportunistic communications help tolerate the absence of end-to-end connectivity in an intermittently-connected networks 6 . The communications rely on the ”store, carry **and** forward” principle. The basic idea is to take advantage of radio contacts between devices to ex- change messages, while exploiting the mobility of these devices to carry messages between different parts of the network. Two devices can thus communicate even if there never exists any temporaneous end-to-end path between them. Recent experiments conducted in real conditions have shown that applications such as voice-messaging, e-mail, or **data** sharing can indeed perform quite satisfactorily in networks that rely on the ”store, carry **and** forward” prin- ciple 7,8,9,10 . Based on this observation, we argue that an interesting alternative **and** cost-effective solution may be to resort to new kind of **hybrid** networks combining an infrastructure part with intermittently or partially connected parts formed by mobile devices communicating in ad hoc mode. Figure 1 illustrates **a** worthwhile **hybrid** config- uration that involves 1) mobile ad hoc networks formed spontaneously by the devices carried by people, **2**) mesh routers deployed by mobile operators in locations where significant **data** transfers are expected (e.g., transportation hubs, shopping malls, campuses, homes, offices, etc.), **3**) Wi-Fi access points installed **and** operated by volunteers.

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Keywords: Stochastic system identification, Subspace **fitting**, Uncertainty bounds, Finite element **model**
1. Introduction
Linear system identification methods are of interest in mechanical engineering **for** modal analysis. Using output- only vibration measurements from structures, Operational Modal Analysis (OMA) has been successfully used as **a** complementary technique to the traditional Experimental Modal Analysis (EMA) methods [1–**3**]. With methods originating from stochastic system realization theory **for** linear systems, estimates of the modal parameters of interest (natural frequencies, damping ratios **and** observed mode shapes) can be obtained from vibration **data**. Among these methods, the stochastic subspace identification (SSI) techniques [4, 5] identify the system matrices of **a** state-space **model**, from which the modal parameters are retrieved. Subspace methods are well-suited **for** the vibration analysis

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2.3. Kinetic **Data** Structure
The generic kinetic framework that has been developed in [6,7] appears to be **a** good solution **for** our problem. The idea is to move continuously the geometry of the **data** from the initial geometry at time t = 0 to the target geometry at t = 1. In **a** kinetic framework, **a** global geometric prop- erty is maintained by constructing **and** maintaining **a** ki- netic **data** structure (KDS) throughout the time evolution. The purpose of the KDS is to track the list of geometric tests or predicates that are required to prove the global property. This **data** structure is responsible **for** ensuring that this finite set of local tests or predicates, called cer- tificates, which together prove the global geometric proper- ties, remain valid. These certificates are typically polyno- mial or rational functions in terms of the interpolation time t, that are respectively valid, degenerate or invalid when their signs are respectively positive, null or negative. Roots (**and** poles) of these certificates are called failing times, be- cause the continuous nature of the certificates ensure that the sign of **a** certificate remains constant between failing times. During the simulation, the time does not evolve con- tinuously: the KDS computes the failing times of each of its certificates **and** orders them in **a** priority queue. The inter- polation time t is then iteratively advanced to the closest failure time in the future. At that time, **a** certificate fails **and** the KDS has to make minimal changes to the topology of the object **and** has to update its internal proof accord- ingly. These updates reestablish **a** set of valid certificates, so that time can then be advanced either to the next fail- ing time of one of the certificates or to the evolution ending time t = 1.

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The relative contribution of charm **and** beauty to the final yield of electrons is not known precisely, as it suffers from large theoretical uncertainties due to the variation of the actual value of the mass of the quarks **and** unknown higher orders [40], see [51] **for** recent experimental efforts. This uncertainty is particularly relevant **for** the medium effects as we will discuss below (also [52]). Besides single-particle inclusive cross sections, we are interested in the case of two-particle correlations. The corresponding expression is similar to Eq. (3.1), but now two partons created in the hard scattering fragment independently. The fragmentation of each parton is again described by D vac k→h or D k→h med **for** the vacuum **and** the medium respectively. Notice that here we will be interested in two-particle correlations which are well separated in azimuthal angle (back-to-back correlations), so that correlations from the fragmentation of **a** single parton into two final hadrons are negligible [53, 54].

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Two principal classes of approaches have been de- veloped: Image-based [**2**, 9] **and** **Model**-based ap- proaches [5, 6]. Image-based methods extract features from images without relying on elaborate knowledge about the object of interest. Their principal quality is their quickness **and** their simplicity. However, if the **data** images are very diverse (e.g.: variation of illumina- tion, of view, of head pose) image-based approaches can become erratic **and** unsatisfactory. On the other hand **model**-based methods use models which maintain the essential characteristics of the face (position of the eyes relative to the nose **for** example), but which can deform to fit **a** range of possible facial shapes **and** expressions.

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Since our main objective is to perform fully coupled simulations, we have to consider cheaper models ie models that are somehow integrated over directions **and** frequencies. There are two categories of such models: flux-limited diffusion **and** moments models (see [12] **and** references therein **for** **a** list of the classic choices). Our choice is to select the M 1 **model**, which belongs to the category of moments models. It was introduced in [**2**] **and** has several variations among which [14], [**3**], [13] **and** [4]. To built it, the first step consists in obtaining the moments equations from (0.1). To do so, an integration over directions **and** inside each frequency group Q q is

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We can also compare our implemented FlowVizMenu with the control menu used in GraphDice (Bezerianos et al., 2010a). Both are used to transition between dimensions in **a** scatterplot, **and** both allow **for** scrubbing. However, because GraphDice’s control menu only makes use of outward motions, it contains **2** copies of every dimension: one **for** the scatterplot’s horizontal axis, **and** another **for** the vertical axis. Furthermore, the menu items in GraphDice’s control menu only cover half **a** circle. The end result is that the dimensions in our FlowVizMenu each cover an angle that is four times larger, enabling easier **and** faster selection. Our FlowVizMenu also differs in that it contains **a** visualization, whereas GraphDice’s control menu is used to control an underlying visualization. As shown in Figure 4.2, this means the user can perform brushing **and** linking **for** coordination with other views without leaving the FlowVizMenu. In our FlowVizMenu, each repeated outward-inward motion will normally replace the two dimensions in the scatterplot with new dimensions. Hence, **a** repeated ﬁgure-8 motion (Fig- ure 4.3, right) cycles between two scatterplots. However, it also occurred to us that repeated outward-inward motion could be useful **for** accumulating dimensions. In our implementation, holding down the shift key during motions causes the dimensions to be accumulated along the axes using principle component analysis (PCA). **For** example, holding down Shift, **and** moving out through dimension **A**, in through B, out through C, **and** in through D, will cause the system to compute **a** PCA projection from **A** × C to the horizontal axis, **and** **a** separate PCA projection from B × D to the vertical axis.

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3.2 **Data** Size Reduction
Reducing the number of samples have been implemented with several methods such as sampling procedures (e.g. simple random sampling **and** stratified or cluster sampling). These methods are based on statistical sampling which view **data** as expensive resources **and** assumes that it is practically impossible to collect population **data**. This approach does not suit **data** reduction in databases where population **data** is assumed to be known. Other methods are Adaptive sampling [1] **and** adaptive sampling with Genetic Algorithm [10] **and** Discernibility [9]. Moreso, other approaches such as Wavelets [11] **and** Clustering [12] **for** reducing the quantity of instances have been used. The adaptive sampling approach which employs chi-square criterion in our view is simple **and** adaptive in nature. It segments **data** into categories to ease computation; but it is intractable with very large **and** high-dimensional **data**. The approaches of [1] **and** [10] are only based on dimensionality reduction.

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The comparison amongst existing propagation models is out of the scope of this paper. However, our WPAN **model** was successfully simulated **and** tested with all of them. The aim of this scenario is to verify **data** transmission through the WPAN physical channel. In this scenario, we set two nodes; **a** sender which is **a** fixe node that send continuously 50 bytes UDP packets every 100ms (4kbps), **and** **a** destination mobile node moving away from the first node’s position in **a** constant speed (0.1m/s). The MAC sub-layer operates in beaconless mode in order to avoid any interruptions due to the association time, beacon transmissions or sleep time. We use the 2.4GHz PHY with **a** receiver sensitivity of -85dBm **and** **a** transmit power of 1mW (0dBm). Figure 9 points out the evolution of the received signal strength **for** different distances. The measured RSS continues to decrease while the receiver rolls away from the sender. The receiver continues to receive the packets until nearly 82 meters. After this distance, the RSS decreases to less than -85dBm **and** the signal is considered as undetectable. This distance represents the transmission range that can vary depending different factors such as transmitting power **and** the receiving sensitivity.

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ω **2** − ω **2** l + iωγ l
.
This method shows **a** good agreement with experimental **data** **for** noble metals, **and** has been widely imple- mented, often with four or more Lorentz poles [ HN07 ]. However, this **model** fails to properly fit materials such as transition or post-transition metals, because of the electronic correlation existing in such materi- als, introducing **a** retardation effect [ WROB13 ]. During the last decade, advanced dispersion models were designed to tackle dispersive materials more efficiently. Among them, the Critical Points (CP) **model** [ VLDC11 ] **and** the Complex-Conjugate Pole-Residue Pairs (CCPRP) **model** [ HDF06 ] have shown great improvements over the standard Drude-Lorentz **model** in **fitting** metals **and** compounds on broad frequency ranges with **a** limited amount of poles. As briefly sketched in [ VLDC11 ], these two models are mathem- atically equivalent at the continuous level. While both of these models use coupled first-order poles with complex coefficients, in [ Viq15 ] we introduced **a** generalized **model** exploiting real coefficients only:

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Table **2**: Features of the three **data** cubes. “Density” is the ratio of non-zero cells.
mart **data** delivered with Analysis Services of Microsoft SQL Server. From the Customer table (10281 records) we constructed **a** **data** cube of five dimensions: Status, Income, Child, Occupation **and** Education. Status ∈ {1, **2**} indicates whether the customer is single (value equal to 1) or not. Income takes eight possible values indicating the level of income (e.g., 1 **for** income between 10K **and** 30K, **and** 8 **for** income ≥ 150K). Child ∈ {0, 1, **2**, **3**, 4, 5} repre- sents the number of children. Occupation takes five possi- ble values indicating the customer’s occupation (e.g., 1 **for** **a** manual worker **and** 5 **for** **a** manager). Education refers to customer’s education level **and** can take five possible values (e.g., 1 **for** partial high school studies **and** 5 **for** graduate studies). The other **data** set was also extracted from Food mart **data** **and** concerns **a** **data** cube of Sales according to product category (44 values), time (in quarters) **and** coun- try (USA, Canada **and** Mexico). This cube has **a** relatively small set of dimensions but one of them has **a** large number of modalities (members).

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Accordingly, we propose **a** knowledge discovery process which is based on **a** combination of numeric-symbolic techniques **for** dierent purposes, such as noise ltration **for** avoiding overtting which occurs when the analysis describes ran- dom error or noise instead of the underlying relationships, feature selection **for** reducing dimension, **and** checking the relevance of selected features w.r.t. predic- tion. FCA [**3**] is then applied to the resulting reduced dataset **for** visualization **and** interpretation purposes. More precisely, this **hybrid** **data** mining process combines FCA with several numerical classiers including Random Forest (RF) [1], Support Vector Machine (SVM) [8], **and** Analysis of Variance (ANOVA) [**2**]. RF, SVM **and** ANOVA are used to discover discriminant biological patterns which are then organized **and** visualized thanks to FCA.

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1.1 Overview of geometric programming
First introduced in 1967 by Duffin, Peterson, **and** Zener [10], **a** geometric pro- gram (GP) 1 is **a** specific type of constrained, nonlinear optimization problem that becomes convex after **a** logarithmic change of variables. Despite signifi- cant work on early applications in structural design, network flow, **and** optimal control [**3**, 25], reliable **and** efficient numerical methods **for** solving GPs were not available until the 1990’s [21]. GP has recently undergone **a** resurgence as researchers have discovered promising applications in statistics [5], digital cir- cuit design [6], antenna optimization [**2**], communication systems [7], aircraft design [13], **and** other engineering fields.

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1. INTRODUCTION
Elaboration of real-time object tracking algorithms in image sequences is an important issue **for** numerous applications related to computer vision, robotic, etc. **For** the time-being, most of the available tracking techniques that don’t require **a** 3D **model** can be divided into two main classes: edge-based **and** texture-based tracking. The edge-based tracking relies on the high spatial gradients outlining the contour of the object or some geometrical features of its pattern (points, lines, circles, distances, splines, ...). When 2D tracking is considered, such edge points enable to defined the parame- ters of some geometrical features (such as lines, splines,...) **and** the position of the object is defined by the parameters of these features [6]. Snakes or active contours are also based on high gradients **and** can be used to outline **a** complex shape [**3**]. These edge-based techniques have proved to be very effective **for** applications that required **a** fast tracking approach. On the other hand, they have also often proved to fail in the presence of highly textured environments. Previ- ous approaches rely mainly on the analysis of intensity gra- dients in the images. When the scene is too complex, other approaches are required. Another possibility is to directly consider the image intensity **and** to perform 2D matching on **a** part of the image without any feature extraction by minimizing **a** given correlation criterion: we then refer to template-based tracking or motion estimation (according to the problem formulation). It is possible to solve the prob- lem using efficient minimization techniques that are able to consider quite complex 2D transformations [**2**, 5, 9]. Let it be noted that these methods are closely related to classi-

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[ 29 ] [ 26 ]
Ours Ours
Error (**a**) (16.8%, 10.7% ) (b) (12.2%, 5.8% ) (c) (10.0%, 7.1% ) (d) (19.6%, 11.3% )
Figure 4: Visual comparison on examples from the KITTI stereo **and** flow datasets. We initialize our **model** with [ 29 ] (rank **3** rd ) **for** stereo **and** [ 26 ] (rank 4 th ) **for** optical flow. **For** each example, we show (top row) its left-camera image, (middle row) color-coded disparity map **and** (bottom row) error map scaled between 0 (black) **and** 5 (white). We mark regions with big improvement with cyan rectangles. Note that those regions often correspond to semantic objects like cars, buildings, trees, **and** roads, where our **data**-driven regularizer can use the good matches from training examples to make better guesses.

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Abstract—Physiological **and** biophysical models have been proposed to link neuronal activity to the Blood Oxygen Level-De- pendent (BOLD) signal in functional MRI (fMRI). Those models rely on **a** set of parameter values that cannot always be extracted from the literature. In some applications, interesting insight into the brain physiology or physiopathology can be gained from an estimation of the **model** parameters from measured BOLD signals. This estimation is challenging because there are more than 10 potentially interesting parameters involved in nonlinear equations **and** whose interactions may result in identiﬁability issues. However, the availability of statistical prior knowledge about these parameters can greatly simplify the estimation task. In this work we focus on the extended Balloon **model** **and** propose the estimation of 15 parameters using two stochastic approaches: an Evolutionary Computation global search method called Differ- ential Evolution (DE) **and** **a** Markov Chain Monte Carlo version of DE. To combine both the ability to escape local optima **and** to incorporate prior knowledge, we derive the target function from Bayesian modeling. The general behavior of these algorithms is analyzed **and** compared with the de facto standard Expectation Maximization Gauss-Newton (EM/GN) approach, providing very promising results on challenging real **and** synthetic fMRI **data** sets involving rats with epileptic activity. These stochastic optimizers provided **a** better performance than EM/GN in terms of distance to the ground truth in **3** out of 6 synthetic **data** sets **and** **a** better signal ﬁtting in 11 out of 12 real **data** sets. Non-parametric statistical tests showed the existence of statistically signiﬁcant differences between the real **data** results obtained by DE **and** EM/GN. Finally, the estimates obtained from DE **for** these parameters seem both more realistic **and** more stable or at least as stable across sessions as the estimates from EM/GN.

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The solution came from applying **a** photometric constraint. **A** synthetic K magnitude was determine **for** each (T ! , T L ) in the χ **2** map, **and** the intersection of the temperature trough (see Fig. **2**) **and** of the photometry-compliant area yield the best set of temperatures **and** their uncertainties. The values of the parameters **for** this solution are summarized in Table 1.
**A** synthetic spectrum computed from the parameters fitted on the interferometric **data** was successfully compared to photometric **data** established from the measurements of Whitelock et al. 12 in J, H, K **and** L bands, after the K band was used to determine the temperatures. The same comparison is shown on Fig. **3**. The synthetic spectrum (continuous line) is computed from the parameters fitted to the interferometric **data** when assuming **a** layer optical depth of 1 at any wavelength. It is not the place here to discuss the validity of this gross simplication, nor to justify the presented modeling on an astrophysical point of view. Our aim is only to evaluate the **fitting** procedures themselves. On the same graph, we have also plotted (squares) the flux computed with the actual values of the optical depths fitted in the 4 K sub-bands.

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