SketchSnakes: Sketch-Line Initialized Snakes for Efficient Interactive Medical Image Segmentation We present an intuitive, fast and accurate 2D interactive segmentation method that combines a general subdivision-curve Snake possessing powerful editing capabilities, with a novel sketch-line user initialization process, and a pen input device. Using the pen (or a mouse), the Snake is quickly and precisely initialized with a few quick sketch lines drawn across the width of the target object. The smooth contour constructed using these lines is extremely close to the position and shape of the object boundary. This makes the Snake's task of snapping to the object boundary much simpler and hence more likely to succeed in noisy images with minimal user editing. We apply our Snake to the segmentation of several 2D medical images to demonstrate it's efficiency, accuracy and robustness. We also compare Sketch Snakes to Adobe Photoshop's Magnetic Lasso [1] as well as a recent graph-cut based image cutout tool known as Snap [2] in order to highlight SketchSnakes effectiveness. Robust, fully automatic medical image segmentation systems have proved extremely challenging to develop. Consequently, semiautomatic techniques, such as Snakes [3] and LiveWire [4] [5] [6] [7] [8] have become widely popular. Since their inception in the late 1980's, many improvements to Snakes models for medical image applications have been proposed. For example, many Snakes variants have been developed that ameliorate the well-known initialization sensitivity problem, allowing "one-click" initialization and the ability of these active contour models to "flow" into complex shapes [9] [10] [11] as well as change their connectivity [12, 13] . These improvements have proved effective for some segmentation tasks, especially involving very complex-shaped objects (e.g. blood vessels or the cortical surface of the brain) or numerous small convex-shaped objects (e.g. blood cells). However, these models often fail in noisy images that contain missing object edges and this failure results in significant user intervention. Unfortunately, the mathematical formulations and numerical algorithms that enabled the simple initialization and complex shape extraction were often unable to also provide the capability to perform interactive steering, high-level control, and intuitive editing during the segmentation process, and the ability to dynamically impose shape constraints.. The deficiencies in the "flow"-type Snakes segmentation paradigm have led us to explore an alternative research direction [14] [15] [16] , creating interactive active contour models for segmenting medical images and image sequences which exhibit a significant amount of noise. These models are designed to complement and integrate with the "flow"-type Snakes. More specifically, our goals are to minimize the time and labor required to interactively initialize a Snake in noisy images, imbue Snakes with precise, intuitive steering and editing capabilities, and create an accurate, noise-insensitive model.. In this paper, we present SketchSnakes -a general, robust, highly efficient active contour model with a simple formulation -in an effort to meet our goals. Our new active contour model is constructed using a recently published subdivision curve algorithm, providing the robustness against noise of a finite-element or B-spline Snake while remaining simple enough to easily impose constraints and maintain geometric flexibility. This new subdivision curve Snake can be quickly initialized with a few rough sketch lines drawn across the width of the target object such that the initial curve is extremely close in shape to the object ( Figure 1 ). This novel initialization process makes the Snake's task of snapping to the object boundary much simpler and hence more likely to succeed in noisy images with minimal user editing. SketchSnakes are inherently smooth and naturally multi-scale, allowing coarse to fine and other custom data-fitting "schedules" if desired. We enhance user control over the model by making use of a tablet personal computer with a pen input device to allow easy, fast, precise positioning and sketching of points and lines directly on the tablet screen. We demonstrate our sketch-line based Snake on several 2D medical images and discuss and compare its performance with Adobe Photoshop's Magnetic Lasso as well as a recent graph-cut based image cutout tool known as Snap Optimizing the performance of interactive medical image segmentation methods for noisy images (and image sequences) requires a minimal number of fast, simple and fatigue-free interactions, intuitive, flexible, and precise user control and editing capabilities, and the ability to dynamically transfer the high-level image interpretation of the human expert to the algorithm. Furthermore, all of these capabilities must be seamlessly integrated into a single work-flow and presented to the user using a consistent, intuitive interaction model. We believe the combination of sketching input lines across an object, a pen input device that can be used directly on the screen, and a powerful subdivision-curve based Snake results in a tool that meets these requirements and is effective for many segmentation tasks that cannot be processed as efficiently with other techniques.. We have recently extended the SketchSnakes paradigm to 3D and used deformable subdivision surfaces to create SketchSurfaces [35] . The sketch lines are drawn across the cross-section of the target object in several key image slices of a volume image spanning the length of the object. Our initial results are extremely promising, allowing the user to extract and reconstruct a highly-accurate analytical surface model of several brain structures in under sixty seconds. In [34] , Olabarriaga and Smeulders suggested several guiding design principles should be followed to produce efficient interactive segmentation methods that generate accurate and repeatable results:. • Integrating the computational and user interaction components of the segmentation system into one process. This guiding principle is essentially referring to the role of interactive steering and we interpret it as providing the user with sufficient control of the entire segmentation process such that manual editing in the end is not necessary.. • Using pictorial input to the computational part of the system; LiveWire, Snakes, and GraphCut methods all adhere to this principle.. • Allowing the user to initialize the segmentation process with key information which will lead the method to an accurate result more quickly. This was the primary design goal behind the sketch line initialization process. LiveWire and GraphCut methods also provide key information at initialization.. • Allowing the user to control and steer the method throughout the whole process to generate accurate results. We claim that SketchSnakes provides more flexible and precise steering and editing control over than does LiveWire and GraphCut. This issue is discussed in Section 6.4.. • Allowing the user to intuitively predict the impact of the interactions on the segmentation result. In our opinion, boundary-based techniques such as Snakes and LiveWire adhere to this principle better than region-oriented techniques such as GraphCut. With boundarybased techniques one is explicitly manipulating and repositioning the boundary shape model to lay on top of the target object boundary.. • Providing visual feedback of the effect of the interaction in real-time. All pen/mouse-based interactions and fitting steps update the Snake in real-time.. • Adding intelligent behavior to elevate the abstraction level of the interaction. We claim that shape model based techniques such as Snakes, including SketchSnakes (see [15] ), have much more potential for adding intelligent behavior than do the pixel -based LiveWire and GraphCut.. We have attempted to design SketchSnakes with these guiding principles in mind. In the following sections, we provide a discussion and validation of our design with respect to the key criteria for evaluating segmentation techniques: accuracy, efficiency, and repeatability. Since control over editing and steering also impacts each of these criterion, we include a separate section on this topic.

O O O B O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


EPIDEMIC CONTROL IN A HIERARCHICAL SOCIAL NETWORK The phenomenon of epidemic spreading in a population with a hierarchical structure of interpersonal interactions is described and investigated numerically. The SIR model with incubation time is used. In our model the localization of individuals in different social groups, the effectiveness of different interpersonal interactions and the mobility of a contemporary community are taken into account. The influence of different control methods on the spreading process is investigated as a function of different initial conditions. The cost-effectiveness of mass preventive random vaccinations, target vaccinations and sick leaves are compared. A critical range of vaccinations, sufficient for suppressing of an epidemic is calculated. The results of numerical calculations are similar to the solutions of the master equation for the spreading process. In recent years it was discovered that a structure of different biological, technical, economical and social systems has the properties of complex networks.. 1-3 The short length of the average shortest-path distance and the high value of the clustering coefficient are some of the common properties of those networks.. 3-5 Social networks, which are an important example of complex networks, also have those properties. They are successfully modelled using different approaches. [6] [7] [8] [9] In particular, small-world topology of interpersonal connections 3,10-14 and their hierarchical structure 15, 16 are taken into account, e.g., the epidemic spreading in a population with a two-level structure of interpersonal interactions was analyzed in Ref. 17 . Such a structure of a social network has a strong influence on dynamical phenomena in a population.. In recent years the spreading of epidemics was investigated by many authors, who used different models of interpersonal interactions. [18] [19] [20] [21] [22] [23] [24] This was motivated by the hazards of bio-terrorist attacks 25 and new types of pathogens observed lately. In our work, we investigate epidemic spreading in the human population, taking into account spatial localization of individuals, with a three-level hierarchical structure of interpersonal interactions on the basis of the SIR model. 26 We assume that each individual belongs to some social groups:. 4,27 from small ones (e.g., family or friends), to a large ones (e.g., the community of a whole city). Interpersonal interactions among individuals in the same group are stronger than interactions among individuals from different groups. The smaller the group, the stronger an individual's influence on the other individuals in that group. From the point of view of the spreading of an epidemic, social connections within a family, among close friends etc. are most effective; however, random contacts with unknown individuals are important, too. Such a random contact is most probable for individuals, who live (or work) in the same place, e.g., in the same building. On the other hand, contemporary communities are very mobile; therefore, there is a nonzero probability of contact between two arbitrarily chosen individuals from a population. A contact like that can occur, e.g., while commuting, in the cinema or any other public place, and it can result in an infection of a new individual. In our model, we take into account this hierarchical structure of a social network, with interpersonal connections between neighbors and contacts between random individuals related to the mobility of a community. The hierarchical structure of interpersonal interactions described in the present paper seems to be plausible for modeling real social networks.. A group of co-workers is one of the social groups defined in our model. Therefore, it is possible to investigate the influence of the probability of obtaining sick leave and the duration of sick leave on spreading phenomena.. This article is organized as follows. The model of a network of human contacts and the probabilities of infection depending on the type of social contact, as well as the master equation, are described in Sec. 2. The results, like the influence of vaccination and sick leave on the spreading process, are described in Sec. 3. The results obtained from the numerical model and the results obtained from the solution of the master equation are compared in Sec. 4, and summarized in Sec. 5. The model of the spreading of an epidemic in the population with a three-level hierarchical structure of interpersonal interactions has been described and investigated numerically. In our model, the influence of mass preventive vaccinations on the spreading of an epidemic was investigated. We found the critical value of preventively vaccinated individuals, sufficient for the suppression of an epidemic. However, the part of the population which should be vaccinated is very large and strongly increases with probability of infection. Moreover, in the case of broad dispersal of pathogens (e.g., as a result of bio-terrorist attack) the range of the epidemic remains relatively large, even if almost the whole population is vaccinated.. Contrary to mass vaccination, target vaccination can give much better results with little demand for vaccines (or antiviral agents), i.e., when only the nearest neighbors of ill individuals are vaccinated. An epidemic can be suppressed with a relatively small amount of vaccines if new ill individuals are identified quickly enough. Especially good results can be obtained if target vaccination starts just after the appearance of the very first cases of infection, i.e., in the initial stage of an epidemic. Moreover, even if the efficiency of vaccines is lower than 100% an epidemic can also be suppressed with quick enough identification of infected individuals.. In our model we also investigated the influence of sick leave (which can be treated as a simple method of isolating ill individuals from part of the population) on spreading process. It turns out that for a critical value of the probability of going on sick leave there is an abrupt decrease in the range of the epidemic. The epidemic is suppressed and the number of individuals who do not work is very low.. Mass vaccination can be effective only in the case of well-known pathogens (e.g., in the case of annual influenza epidemics). If there is a new pathogen in a susceptible population (as a result of mutation or a bio-terrorist attack), only a quick response of health services can provide good results. In such a case, the efficiency of target vaccination of the nearest neighbors of ill individuals is high. Removing interpersonal interactions with spatially distant individuals by isolating an ill individual (e.g., at home) decreases significantly the number of new sources of the epidemic and is helpful in suppressing epidemic spreading.. Our results were compared with the solutions of the master equation. The character of both solutions is similar; however, there are discrepancies between the locations of the maxima of the relations of the number of ill individuals and time. This is so because in our model we assume a hierarchical structure of interpersonal interactions in a more plausible way than in the case of the master equation. Computations were performed for different initial conditions with different numbers n of ill (IL) and randomly located individuals, and the rest of the population healthy and susceptible (S). Large values of n can be compared to, e.g., broad dispersal of pathogens during a bio-terrorist attack or to a case when counteractions of health care services are introduced some time after beginning of an epidemic. Synchronous dynamics and the size of the population N = 10 5 were used. In most computations the average sizes of social groups N HG = 4, N WG = 10 and N H = N W = 100 were used. In order to investigate the dynamics of the spreading process and the range of an epidemic we introduce two observables: the time t max when the maximal number of ill individuals is reached and the number V of individuals who have had the disease.. The time of incubation τ = 1/W IN→IL influences the rate of epidemic spreading only. The time t max increases approximately linearly with τ . On the other hand, the dynamics of spreading process and the range of an epidemic depend significantly on the value of the parameter W IL→R . Figure 2 shows that for a critical value of W IL→R = W C IL→R there is an abrupt decrease in the time t max and the range of epidemic: this indicates that a phase transition takes place at this value. This is confirmed by a significant increase in the transient times (i.e., the time before the system reaches the point attractor) for W IL→R slightly smaller than W C IL→R , which is typical behavior for a phase transition.

B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Finding a Tree Structure in a Resolution Proof is NP-complete The resolution tree problem is to decide whether a given sequencelike resolution refutation admits a tree structure. This paper shows the NP-completeness of both the resolution tree problem and a natural generalization of the resolution tree problem that does not involve resolution. In the context of propositional proof complexity, a proof system is a polynomialtime decidable relation R such that a propositional formula φ has a proof π with R(φ, π) if and only if φ is unsatisfiable. A proof system R is polynomially bounded if there is a polynomial p such that every unsatisfiable formula φ has a proof π with |π| < p(|φ|) and R(φ, π).. It is an open question whether a polynomially bounded propositional proof system exists, but it is not hard to see that such a proof system exists if and only if the complexity classes NP and coNP are equal, which is widely believed to be unlikely among experts. This relation to computational complexity was in fact a main motivation of Cook and Reckhow, who introduced the concept of a proof system [8] , and it is the origin of a research enterprise to separate NP and coNP that is often called Cook's program. It asks to find lower bounds for more and more particular proof systems until the developed techniques can be applied to prove a lower bound for all proof systems and therefore NP = coNP.. One of the most extensively studied propositional proof systems is resolution that is based on the resolution inference rule. It is common to define a resolution refutation for a set of clauses Γ as a sequence of clauses such that each clause is a member of Γ or can be derived from two earlier clauses by a resolution inference.. Alternatively, a resolution refutation can be viewed as a directed acyclic graph (dag) in which every node has either two or zero predecessors. Many well-known resolution refinements refer to the dag-description of a refutation, including unit resolution, regular resolution, linear resolution, input resolution and tree-like resolution. A refutation is, for example, tree-like if its dag is a tree.. There has been a lot of work on proving superpolynomial lower bounds on the sizes of resolution refutations and its refinements, as well as on comparing the strength of the refinements in terms of proof sizes [12, 9, 5, 4, 2] . Moreover, different conditional lower bounds for finding a resolution proof have been given [1, 3, 10] . These results became increasingly popular since resolution refinements are closely related to proof search procedures that are used in SAT solvers that are highly successful in practice [7] .. This paper proves a result that reveals an inaccuracy in some papers that define a tree-like resolution proof to be a sequence of clauses. It is based on the observation that it is not well-defined to call a sequence-like refutation treelike if its underlying dag is a tree since the underlying dag of a sequence-like resolution refutation is not unique. We show that it is in fact NP-complete to decide whether a sequence-like resolution proof admits some dag that is a tree. The NP-hardness of the problem is shown by a non-trivial reduction from the exact cover problem.. A consequence of this result is that there is no polynomial time algorithm that decides for a given set of clauses Γ and a sequence of clauses whether the sequence is a tree-like refutation for Γ unless P = NP. Hence it is doubtful whether some notions of tree-like resolution that appear in the literature define a proof system in the sense of Cook and Reckhow. Although none of the existing results on tree-like resolution are affected by the issue, authors should nevertheless be aware of the problem and define tree-like resolution in an appropriate way.. In a previous work we showed some related results: On the one hand it is also NP-complete to decide whether a sequence-like refutation admits a regular dag [6] . On the other hand it is decidable in polynomial time for a given sequence-like refutation whether it admits a dag that is a unit resolution, a linear resolution or an input resolution [6] .. The present paper also describes a generalized purely-combinatorial version of the tree-structure problem that does not involve resolution. This problem is also shown to be NP-complete.

O O O O O O B O O O O B O O O O O O O O O B O O O O O O O O O B O O O B O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O B O B O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O


Using machine vision for investigation of changes in pig group lying patterns Pig lying patterns can provide information on environmental factors affecting production 9 efficiency, health and welfare. The aim of this study was to investigate the feasibility of using

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High-level semantic image annotation based on hot Internet topics Abstract Images are complex multimedia data that contain rich semantic information. Currently, most of image annotation algorithms are only annotating the object semantics of images. There are still many challenges on high-level semantic image annotation. The major issues are the lack of effective modeling method for the highlevel semantics of images and the lack of efficient dynamic update mechanism for the training set. To address these issues, we propose a high-level semantic annotation method based on hot Internet topics in this paper. There are two independent sub tasks in our method: dynamic update of the training set based on hot Internet topics and search-based image annotation. In the first sub task, we propose to model the abstract semantics of images based on three relationships: image-to-image similarity relationship, topic-to-topic co-occurrence relationship, and image-to-topic relevance relationship. Through the complex graph clustering, the hot Internet topics are extracted for images with consistent visual and semantic contents. Then the dynamic update mechanism will update the original training set with the new topics and images. It avoids the huge computing cost in traditional update methods and does not need to re-calculate the whole mapping relationship between the semantic concepts and visual features. In the second sub task, given a query image, it first searches for similar candidates in the annotated training set via visual features. Then the hypergraph modeling and spectral clustering are exploited to filter out the images with irrelevant semantics. The keywords will be extracted for annotation from the remaining images according to an annotation probability. Extensive experiments have been conducted and the results demonstrate that our algorithm could achieve better annotation performance than the state-of-the-art algorithms. And the update mechanism could extend the training set efficiently so that the coverage of the semantics in the training set wouldn't be obsolete. In this study, we develop a new high-level semantic annotation method for images based on hot Internet topics. This method has two sub tasks: search-based image annotation and the dynamic update of the training set based on hot Internet topics. This method exploits the large-scale image resources available on the Internet for image annotation and regularly updates the training set from hot Internet topics in an efficient way. We propose a new method to model the abstract semantics for images. Three sets of relationships between topics and images are exploited. And through the complex graph clustering, the hot Internet topics are extracted from images with similar visual contents. From the experiments, we have demonstrated the effectiveness of three relationships and the complex graph clustering, which make sure that images with similar visual contents and close topics will form one cluster. The keywords from this cluster are good representatives for the hot topics. The dynamic update mechanism of the training set addresses the issue of the huge computing cost in traditional update methods, which require to re-calculate the whole relevance relationships between tags and visual features of images. The experiments also show that the updated training set can deliver better annotation results since it can reduce the impact of the semantic gap problem for visual polysemous words. The search-based image annotation can effectively filter out the semantic irrelevant images via hypergraph mechanism. We calculate the annotation probability for each keyword in the candidate cluster. The annotation probability is related with the similarity between the query image and the candidate images. So, even the tags that are used less often in the cluster can be selected as the keywords to annotate the images. The experiments show that its annotation performance is better than the state-of-the-art algorithms.. The future work of our research is to look at its feasibility on large-scale data center and evaluate the computing requirement in the Cloud. Research on image annotation technology has been conducted for many years. In general, researchers have proposed knowledge modeling methods for automatic annotation, such as classification-based methods [2, 4, 15, 23] , graphical model-based methods [36, 37] , cross-media modeling methods [9, 11, 17] , and translation modelbased [5, 7, 12] methods. The key attributes of these methods are using different machine learning algorithms on a training set and the construction of mapping relationships between the semantic concepts and low-level features. These mapping relationships are used to annotate new images. However, the size of the training sets used by these methods is limited, so their quality and performance will degrade when handling large-scale Internet data. This is because the coverage of the semantics in the training set is also limited and it cannot be updated in real-time. In most cases, the annotation results are simply the object semantics of the images.. As the wide use of the social network, more and more users could share their images with others on the Internet. They could also add comments to some images to express feelings, which is called "social tagging". For example, these websites include Flickr, Photosig, Delicious, LabelMe, Peekaboom, etc. Social tagging suffers from two drawbacks, though it is easy to perform. First, the tags provided by Flickr users actually contain many noise [13] . Second, there is ambiguity in the user tagging. Users often use some common tags for different objects. So these common tags are semantically ambiguous. This is the reason why many similar images can't be retrieved based on keyword mechanism. Wu et al. [34] proposed a tag recommendation framework taking advantage of correlations between tags and visual content. Weak rankers are learned with this multi-modality model and combined using rankboost algorithm. However, these methods usually need to build a static vocabulary according to the training set and calculate the mapping relationship among tags in the vocabulary. Once a new word or a new annotated image is added, the whole mapping relationship in the vocabulary should be re-calculated. This is a huge computing cost for a large-scale image database.. Recently, the research focus of image annotation has changed to large-scale Internet image annotation [28, 30] . Given a query image for annotation, its semantic contents can be extracted from similar images that have been annotated. Thus, if similar candidates can be retrieved from Internet, an annotation of the query image can be obtained [16, 24, 29, 31, 32] . In general, these methods require a query word when searching for similar images and they submit the query word to a textbased search engine. The key issue that affects these methods is the algorithm used to identify accurate query words. Zhang et al. [38] proposed an image annotation method that could acquire the initial query words automatically. The key concept in this method was using CBIR technique to find similar candidates and their annotations in a database. Next, the initial query words were derived and used to search for similar images from the Internet after filtering out noisy words and performing a sort process. However, these methods just used a search based algorithm to retrieve similar images from the Internet instead of the training dataset, and then derived annotations from these retrieved images. They had no capability to annotate images based on hot Internet topics.. The data found on the Internet is a type of cross-media data, so the images and their associated text may have some forms of semantic relationships. This relationship may provide an effective method for image annotation. Zhu et al. [39] selected salient terms from the associated text for image annotation. Wu et al. [33] proposed more accurate annotations by calculating the contribution of each word to its visibility model. Monay and Gatica-Perez [21] introduced a latent topic model, probabilistic Latent Semantic Analysis (pLSA), into an image annotation algorithm and trained two pLSA models based on the visual features and associated text, respectively. This method then merged the topics in both models by assigning weights according to the entropy of the visual word distribution. These methods combined the relationships of different features and improved the annotation accuracy. However, these modeling approaches mainly considered the semantic relationships between images and salient words, and the relationships between salient words. In general, these salient words represent the objects in an image. Thus, assigning the salient words as annotations still fails to describe the high-level abstract semantics.

O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I


Bad luck when joining the shortest queue A frequent observation in service systems with queues in parallel is that customers in other queues tend to be served faster than those in one's own queue. This paper quantifies the probability that one's service would have started earlier if one had joined another queue than the queue that was actually chosen, for exponential multiserver systems with queues in parallel in which customers join one of the shortest queues upon arrival and in which jockeying is not possible.Jel codes: C44, C60 Consider a service system with c ≥ 2 parallel servers. Separate queues are formed in front of each server. Throughout, queues are defined as including the customer in service, if there is one. Each queue is served in a FIFO order. Customers arrive according to a Poisson process at rate λ. They join one of the shortest queues upon arrival and stay in the queue of their choice until they have been served. Then, they leave the system. This means that jockeying (see Zhao and Grassmann, 1990 ) is not considered. An example of a parallel service system in which jockeying is hardly possible is a toll booth at an autostrada (see Conolly, 1984) . Services performed by server j have an exponentially distributed duration with a mean of 1/µ j , j = 1, . . . , c. Customers in such systems often notice that customers in other queues are being served faster than those in their own queue, and that they are overtaken by customers that arrived later. Of course, this phenomenon may be due to different skills, and hence different service rates, among the servers. If customers are aware of such differences, joining the shortest queue may not be the optimal decision. But even if the service rates of all servers are equal, this phenomenon frequently occurs. A simple explanation is found by considering the situation that a customer meets an equal number of customers n ≥ 1 in each of the queues upon arrival. Then, by the lack of memory of the exponential service time distributions and the symmetry of the system, each queue has the same probability of becoming the queue that is soonest exempted of its n customers. Hence, the arriving customer has in this situation a probability of (c − 1)/c of bad luck, in the sense that he does not join the queue in which his service would have started earliest. The aim of the present paper is to quantify the probability of bad luck for systems in which customers join one of the shortest queues upon arrival. For the computations reported in this paper we have used the power-series algorithm to compute the stationary queue length distribution as described in Blanc (1987a Blanc ( , 1987b Blanc ( , 1992 for the shortest-queue system. The efficiency of the algorithm is further enhanced in Blanc (1993) . Other approaches to shortest-queue systems can be found, among others, in Haight (1958) , Flatto and McKean (1977) , Halfin (1985) , Rao and Posner (1987) , Hanqin and Rongxin (1989) , Adan, Wessels and Zijm (1990) , Adan, Van Houtum and Van der Wal (1994) and Wu and Posner (1997) . Winston (1977) , Johri (1989) and Hordijk and Koole (1990) consider the optimality of the shortest queue discipline. The organization of the rest of this paper is as follows. Section 2 considers the probability of bad luck for symmetric shortest-queue systems. Section 3 contains a discussion of this probability for asymmetric shortest-queue systems with different service rates among the servers. Section 4 is devoted to systems with both customers who join a shortest queue and customers who are dedicated to specific servers. A conclusion can be found in Section 5. This paper has studied what we have called the probability of bad luck for shortest-queue systems. A customer is said to experience bad luck if he joined one of the shortest queues upon arrival, but his service would have started earlier if he had joined one of the other queues. In symmetric systems, the probability of bad luck may well exceed 1 2 when there are three or more servers, but this only occurs if the load of the system is very close to 1. The approach of this probability to its heavy traffic limit is very steep, so that this limit, which is easily computable, will not be a good approximation for most values of the load. Asymmetry in the service rates tends to increase this probability in light traffic, but to decrease it in moderate to heavy traffic. Dedicated background traffic decreases this probability.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


GENLIB:

an

R

package

for

the

analysis

of

genealogical

data

Background:

Founder

populations

have

an

important

role

in

the

study

of

genetic

diseases.

Access

to

detailed

genealogical

records

is

often

one

of

their

advantages.

These

genealogical

data

provide

unique

information

for

researchers

in

evolutionary

and

population

genetics,

demography

and

genetic

epidemiology.

However,

analyzing

large

genealogical

datasets

requires

specialized

methods

and

software.

The

GENLIB

software

was

developed

to

study

the

large

genealogies

of

the

French

Canadian

population

of

Quebec,

Canada.

These

genealogies

are

accessible

through

the

BALSAC

database,

which

contains

over

million

records

covering

the

whole

province

of

Quebec

over

four

centuries.

Using

this

resource,

extended

pedigrees

of

up

to

generations

can

be

constructed

from

a

sample

of

present-day

individuals.

The

GENLIB

R

package

was

developed

to

facilitate

a

broader

and

more

extensive

use

of

available

genealogical

data

from

founder

populations

across

the

world.

The

package

provides

a

user

friendly

and

flexible

environment

to

analyze

genealogical

data,

allowing

a

more

efficient

and

easier

integration

of

different

types

of

data

and

analytical

methods

and

making

it

ideal

for

further

developments.

Using

GENLIB

simply

requires

a

minimal

knowledge

of

R

and

provides

many

functions

to

manage,

describe

and

analyze

genealogical

data.

Our

description

of

a

Quebec

genealogical

sample

and

our

simulation

study

illustrated

the

use

of

GENLIB

and

further

highlighted

regional

differences

present

in

the

population.

We

also

provided

insight

on

factors

influencing

the

resulting

IBD

sharing

in

founder

populations.

GENLIB

is

an

improvement

over

existing

software

in

that

it

provides

gene-and

segmentdropping

simulations

and

other

new

functionalities

and

in

that

it

can

be

used

within

R,

which

is

a

computing

environment

familiar

to

many

statistical

geneticists.

Hence,

it

is

a

highly

valuable

tool

to

researchers

studying

extended

genealogies.

In

this

paper,

we

presented

GENLIB,

an

R

package

for

the

analysis

of

complex

genealogical

data.

GENLIB

can

handle

large

extended

human

pedigrees,

extended

genealogical

data

from

human

founder

populations

or

extended

animal

pedigrees.

We

were

able

to

read

and

describe

a

fictive

pedigree

with

a

size

over

and

a

depth

of

generations.

Some

memory

limits

may

be

encountered,

depending

on

the

computer

used,

when

trying

to

perform

calculations

(e.g.,

kinship)

for

a

large

number

of

individuals

at

the

same

time

in

complex

genealogies,

but

these

issues

can

easily

be

fixed

by

performing

calculations

in

batches

instead..

GENLIB

is

designed

to

be

easily

accessible

and

used

by

researchers

familiar

or

not

with

genealogical

information.

The

only

requirement

is

a

basic

knowledge

of

the

R

environment.

GENLIB

includes

functions

to

describe

genealogies,

to

compute

different

genealogical

indices

and

to

perform

simulations

based

on

genealogical

relationships.

Given

the

flexibility

of

the

R

environment,

users

can

also

create

their

own

functions

to

further

describe,

summarize

and

present

(e.g.,

graphs)

GENLIB

results..

We

used

genealogies

from

the

Quebec

founder

population

to

illustrate

the

utility

of

GENLIB

to

describe

and

analyze

genealogical

data.

In

addition,

we

performed

simulations

to

track

the

transmission

of

alleles

or

chromosomal

segments

through

the

genealogy.

Allele-

or

segment-dropping

simulations

that

consider

the

complete

genealogical

structure

can

help

to

predict

the

risk

of

sharing

disease

mutations

introduced

by

founders,

or

to

identify

ancestral

sources

of

variation

in

a

trait

of

interest

In

our

simulations,

we

highlighted

the

fact

that

specific

population

characteristics,

such

as

the

number

of

shared

ancestors

and

the

distance

to

these

shared

ancestors,

can

help

discriminate

among

founders

more

or

less

likely

to

have

transmitted

segments

IBD.

The

fact

that

individuals

from

Saguenay

share

more

common

ancestors

that

are

more

distant

on

average

compared

to

Acadians

explains,

in

part,

why,

for

similar

kinship

values,

we

can

observe

quite

different

IBD

segment

distributions.

Genedropping

simulations

can

also

be

used

to

predict

the

risk

of

future

loss

of

genes

contributed

by

the

various

founders..

In

addition,

distributions

of

IBD

probabilities

for

any

individuals

can

be

generated

through

simulations.

We

plan

to

continue

to

improve

existing

functions,

for

example

by

adding

more

flexible

options

for

individual

fitness

in

the

simulations.

We

also

plan

to

extend

the

range

of

functions

available.

Examples

of

planned

additions

include

implementing

more

data

importation

and

graphical

functionalities,

incorporating

information

about

probands

and

ancestors,

such

as

birth

places

and

dates,

implementing

a

function

to

flag

unrelated

components

(subpedigrees)

within

a

kindred,

and

implementing

statistical

tests

to

compare

kinship

and

inbreeding

distributions

between

different

groups.

Another

planned

addition

is

to

include

an

option

in

the

segment-dropping

simulations

to

recover

the

length

of

transmitted

segments

instead

of

only

its

status

(fully

transmitted

or

not).

With

its

flexibility,

GENLIB

provides

an

important

contribution

towards

an

improved

and

optimal

way

to

combine

information

coming

from

genealogies

and

genetic

material.

These

two

sources

of

data

are

complementary

and

using

them

together

could

improve

genetic

data

analysis

in

founder

populations,

for

example

in

the

context

of

estimating

relatedness,

imputing

genomic

data

or

haplotyping

Studying

founder

or

isolated

populations

is

a

major

asset

in

reducing

or

taking

into

account

the

heterogeneity

of

genetic

and

environmental

factors

involved

in

complex

diseases.

Indeed,

potential

advantages

of

founder

populations

include

greater

genetic

and

environmental

homogeneity

and

in

some

instances,

the

availability

of

extensive

genealogical

records

Many

founder

populations

across

the

world

are

currently

studied,

among

others

Iceland

the

Amish

the

Hutterites

the

Mormons

Finland

Newfoundland

the

Sardinians

and

the

French

Canadian

founder

population

of

Quebec

As

the

value

of

genealogical

resources

is

increasingly

recognized

efforts

have

been

made

to

obtain

extended

genealogical

information

and

build

genealogical

databases

for

use

in

genetic

studies.

To

our

knowledge,

there

are

two

available,

freestanding

packages

for

genealogical

analysis,

named

PEDSYS

and

PedHunter

However,

each

lacks

some

functionality

that

we

have

found

useful

in

our

studies

of

the

Quebec

population

and

neither

PEDSYS

nor

PedHunter

can

be

used

within

a

statistical

computing

environment

such

as

R..

The

BALSAC

database

is

an

example

of

a

large

genealogical

database

that

has

proven

very

valuable

for

genetic

and

demographic

research

in

the

Quebec

founder

population

Briefly,

the

arrival

of

French

settlers

in

the

province

of

Quebec,

Canada,

at

the

beginning

of

the

century

followed

by

the

British

Conquest

about

a

century

and

a

half

later

shaped

the

colonization

history

of

Quebec

and

led

to

successive

regional

founder

effects

settlers,

the

French

speaking

population

now

represents

about

of

the

almost

million

inhabitants

of

Quebec

This

fast

population

expansion

and

the

regional

founder

effects

have

shaped

genetic

variation

and

population

structure

in

Quebec.

The

related

demographic

information

can

be

accessed

using

the

BALSAC

database,

created

years

ago,

which

now

includes

over

million

vital

event

records

providing

information

on

million

individuals

These

records

have

been

linked

allowing

for

the

reconstruction

of

ascending

genealogies

from

present-day

individuals

going

back

over

four

centuries.

The

BALSAC

data

access

policy

and

the

procedures

to

request

access

to

the

BALSAC

genealogical

data

can

be

found

on

the

BALSAC

website

Specialized

software

is

required

in

order

to

optimally

exploit

the

full

potential

of

these

well

preserved,

exhaustive

and

detailed

genealogical

data.

The

S-Plus

library

of

functions

GENLIB

was

originally

developed

to

work

with

the

BALSAC

database

to

manage,

describe

and

visualize

genealogies

as

well

as

perform

alleledropping

simulations.

The

GENLIB

S-Plus

library

has

been

used

to

study

the

selective

advantage

to

be

on

an

expanding

wave

front

during

a

range

expansion

the

genetic

contribution

of

non-French

groups

to

the

current

population

population

structure

drug

intolerance

for

an

inherited

disorder

in

multigenerational

pedigree

and

patterns

of

genetic

sharing

relatively

to

expected

sharing

from

genealogical

information

We

have

extended

and

implemented

GENLIB

in

R

to

provide

a

freely

accessible

version

of

the

software

within

a

user-friendly

and

flexible

environment

and

to

allow

a

more

efficient

and

easier

integration

of

different

types

of

data

and

analytical

methods,

also

facilitating

future

developments.

Such

flexibility

is

especially

important

in

the

context

of

integrating

large-scale

genomic

data

and

genealogical

data.

While

other

pedigree

software

exist,

few

are

comprehensive,

free

and

specific

to

extended

genealogical

databases.

For

example,

the

PedHunter

and

PEDSYS

software

include

many

functions

to

describe

and

analyze

genealogical

data

but

do

not

provide

gene-dropping

simulation

functions.

In

this

article,

we

present

most

of

the

functions

in

the

GENLIB

package.

Using

these

functions,

we

perform

a

detailed

description

of

a

large

genealogical

corpus

including

individuals

provided

as

an

example

dataset

in

the

R

package.

Then

we

show

an

example

of

gene-dropping

simulations,

a

procedure

in

which

hypothetical

alleles,

or

chromosomal

segments,

are

assigned

to

ancestors

and

dropped

down

the

whole

genealogical

tree

Overview

GENLIB

was

originally

written

in

the

S

programming

language,

integrating

C++

functions

to

accelerate

and

extend

S

programming.

We

started

with

the

same

C++

functions

in

the

translation

of

GENLIB

into

an

R

package

and

we

extended

both

the

C++

and

R

translation

of

the

S

code.

Interaction

between

R

and

C++

is

well

supported

and

high

quality

R

libraries

are

available

to

help

implement

such

packages.

The

package

is

portable

across

Linux-like

and

Windows

operating

systems,

and

benefits

from

the

advantages

of

the

R

environment,

such

as

being

open-source,

available

online

under

the

GNU

General

Public

License,

and

offering

a

wide

range

of

complementary

functions.

All

code

has

been

tested

using

version

of

R

Version

of

the

GENLIB

package

presented

in

this

paper

includes

over

functions.

GENLIB

is

included

in

the

CRAN

packages

repository

and

is

also

available

on

the

BALSAC

website

By

installing

the

package,

users

also

have

access

to

the

genealogical

data

described

in

this

paper.

O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O


Comparative

study

between

incremental

and

ensemble

learning

on

data

streams:

Case

study

With

unlimited

growth

of

real-world

data

size

and

increasing

requirement

of

real-time

processing,

immediate

processing

of

big

stream

data

has

become

an

urgent

problem.

In

stream

data,

hidden

patterns

commonly

evolve

over

time

(i.e.,concept

drift),

where

many

dynamic

learning

strategies

have

been

proposed,

such

as

the

incremental

learning

and

ensemble

learning.

To

the

best

of

our

knowledge,

there

is

no

work

systematically

compare

these

two

methods.

In

this

paper

we

conduct

comparative

study

between

theses

two

learning

methods.

We

first

introduce

the

concept

of

"concept

drift",

and

propose

how

to

quantitatively

measure

it.

Then,

we

recall

the

history

of

incremental

learning

and

ensemble

learning,

introducing

milestones

of

their

developments.

In

experiments,

we

comprehensively

compare

and

analyze

their

performances

w.r.t.

accuracy

and

time

efficiency,

under

various

concept

drift

scenarios.

We

conclude

with

several

future

possible

research

problems.

Unlimited

growth

of

big

stream

data

and

concept

drift

has

been

two

most

difficult

problems

in

data-stream

mining.

There

are

two

mainstream

solutions

to

these

problems:

incremental

learning

and

ensemble

learning

algorithms.

Incremental

learning

algorithms

employ

a

method

of

updating

a

single

model

by

incorporating

newly

arrived

data.

While

ensemble

learning

algorithms

use

the

divide-and-conquer

method

to

cutting

up

large

data

into

small

data

chunks

and

training

classifiers

on

each

data

chunk

independently,

then

a

heuristic

algorithm

is

used

to

ensemble

these

classifiers

together.

In

incremental

algorithms,

we

talked

mostly

about

how

to

recording

previous

knowledge

and

adapting

to

new

knowledge.

In

ensemble

learning

algorithms,

we

discuss

mostly

about

how

to

making

a

weighting

policy

for

each

base

classifiers..

Both

of

these

algorithms

can

handle

big

stream

data

and

concept

drift

problems,

and

each

of

them

has

its

own

properties.

Incremental

learning

algorithms

have

better

performance

on

efficiency

and

ensemble

learning

adapts

better

to

concept

drift.

Moreover,

ensemble

learning

algorithms

are

more

stable

than

incremental

algorithms.

The

size

of

data

chunk

is

another

important

factor

in

ensemble

algorithms,

which

influences

the

algorithm

performance.

Generally,

a

better

way

to

achieving

high

accuracy

is

that

the

higher

levels

a

concept

drift

is

the

smaller

a

data

chunk

will

be..

Therefore,

in

a

case

of

loose

concept

drift

or

no

concept

drift

an

incremental

algorithm

is

recommended

and

in

a

case

of

rigorous

concept

drift

an

ensemble

algorithm

is

a

better

choice.

Otherwise,

when

efficiency

is

first

considering

factor

we

tend

to

selecting

incremental

algorithm

and

when

accuracy

is

the

most

important

factor

we

choose

an

ensemble

algorithm.

We

can

employ

different

algorithms

according

to

the

real

data-stream

distributions..

Weighting

policy,

instances

selection,

classifier

diversity

and

so

on

is

the

main

rules

discussed

in

previously

researches.

With

the

very

fast

development

of

information

industry,

we

have

to

face

the

reality

of

information

explosion.

In

that

situation,

more

and

more

classifiers

will

be

trained

and

real-time

processing

will

become

a

challenge.

Therefore,

the

next

step

is

how

to

effectively

managing

large

amount

of

classifiers.

We

can

consider

some

pruning

method

or

index

technology

on

the

classifiers.

We

can

also

consider

some

parallel

algorithms

to

organizing

the

classifiers.

We

are

now

entering

the

era

of

big

data.

In

government,

business

and

industry

domains,

big

data

are

generated

rapidly

and

steadily,

with

a

constant

growth

speed

at

a

magnitude

of

million

records

per

day.

Moreover,

these

data

are

often

related

in

temporal

and

spatial

correlations.

Typical

examples

include

the

wireless

sensor

data,

RFID

data

and

Web

traffic

data.

These

data

often

arrives

unboundedly

and

rapidly,

which

forms

a

new

class

of

data

called

"big

stream

data"..

The

focus

on

learning

from

big

stream

data

is

how

to

addressing

the

concept

drifting

challenge.

Concept

drift

was

first

introduced

by

Wdimer

and

Kubat

where

they

noticed

that

the

concept

(the

classification

boundary

or

clustering

centers)

continuously

changes

with

time

elapsing.

Based

on

the

changing

speed

of

concept,

we

formally

divide

the

concept

drifting

into

loose

concept

drift

and

rigorous

concept

drift

In

the

former,

concepts

in

adjacent

data

chunks

are

sufficiently

close

to

each

other;

in

the

latter,

genuine

concepts

in

adjacent

data

chunks

may

randomly

and

rapidly

changed..

Incremental

learning

and

ensemble

learning

are

two

fundamental

methods

in

learning

from

big

stream

data

with

concept

drift.

Incremental

learning

follows

a

machine

learning

paradigm

where

the

learning

process

taking

place

whenever

new

examples

emerge,

and

then

adjusts

to

what

has

been

learned

from

the

new

examples.

While

the

ensemble

learning

employs

multiple

base

learners

and

combines

their

predictions.

The

fundamental

principle

of

dynamic

ensemble

learning

is

to

dividing

large

data-stream

into

small

data

chunks

and

training

classifiers

on

each

data

chunk

independently.

The

most

prominent

difference

of

incremental

learning

from

traditional

machine

learning

is

that

incremental

learning

does

not

assume

the

availability

of

a

sufficient

training

set

before

the

learning

process,

but

the

training

example

appears

over

time.

Moreover,

the

biggest

difference

between

incremental

learning

and

ensemble

learning

is

that

ensemble

learning

may

discard

training

data

outdated

but

incremental

learning

may

not..

Although

these

two

types

of

methods

have

their

own

strengths

in

data

streams

mining.

However,

the

comparisons

between

them

are

rare.

A.Tsymbal

described

some

types

of

concept

drift

and

related

works

to

handle

it.

Nevertheless,

it

not

clearly

categorizes

the

incremental

and

ensemble

learning

algorithms.

In

addition,

they

did

no

experiments

on

different

learning

framework..

In

this

paper

we

comparative

study

the

incremental

learning

and

ensemble

learning

algorithms.

In

addition,

we

compare

performance

between

them

in

both

accuracy

and

efficiency.

Furthermore,

some

suggestions

are

given

for

choosing

a

better

classifier..

This

paper

is

organized

as

follows.

In

section

Incremental

learning,

review

and

summarize

the

incremental

learning

algorithms.

In

section

Ensemble

learning,

ensemble

learning

algorithms

are

learned

and

classified.

In

section

Experiment

results,

incremental

learning

and

ensemble

learning

algorithms

are

analysis

and

compare

in

a

unified

standard.

The

experiment

results

and

discussions

are

given

in

section

Conclusion

and

section

O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O B I O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O B I O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O B I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O B I B I O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O


Robust analysis of hybrid dynamical systems for 1, 3-propanediol transport mechanisms in microbial continuous fermentation A better understanding of the biological phenomena observed in cells requires the creation and analysis of mathematical models of cellular metabolism and physiology. In the study of the glycerol continuous bioconversion to 1, 3-propanediol by Klebsiella pneumoniae, there are some uncertain factors such as the mechanism about the transports of substances across cell membrane. In this paper, a nonlinear hybrid system which describes the intracellular reductive pathway is presented, according to the possible transport mechanisms of 1, 3-propanediol across cell membrane. The existence, uniqueness, and continuity of the solutions for the nonlinear hybrid system are discussed. It is difficult to measure the concentrations of intracellular substances; so, a quantitative robustness analysis method is proposed. Taking the robustness of intracellular substance concentrations as the performance index, an identification model is proposed, which is subjected to the constraint of nonlinear hybrid dynamical system. Finally, an algorithm procedure is constructed to solve the parameter identification model, and the most possible transport mechanism of 1, 3-propanediol and the optimal parameters of the corresponding system are also given. 1, 3-Propanediol (1, 3-PD) is an important chemical substance which can be used as a solvent, antifreezing agent or fine chemical engineering substance, especially as a monomer for polyesters, polyethers and polyurethanes. As a result, its microbial production has recently paid much attention due to its low cost, high production and no pollution, etc. [1] . Glycerol bioconversion to 1, 3-PD by Klebsiella pneumoniae has been widely investigated since 1980s due to its high productivity.. In 1995, Zeng and Decker [2] proposed a five-dimensional dynamical system of glycerol fermentations, in which the concentrations of biomass, glycerol and products (1, 3-PD, acetate and ethanol) in reactor were considered. Xiu et al. [3] improved the model so that it could well describe the operating conditions in a relatively wide range. Gao et al., Wang et al. and Ye et al. provided further investigations on the five-dimensional dynamical system, including parameter identification, optimal control and dynamical behavior analysis [4] [5] [6] .. In 2008, Sun et al. [7] first proposed a nonlinear dynamical system involving concentration changes of three intracellular substances (glycerol, 1, 3-PD and 3-hydroxypropionaldehyde (3-HPA)) and two key enzymes (1, 3-PD oxydoreductase (PDOR) and glycerol dehydratase (GDHt)) in glycerol fermentation to 1, 3-PD by Klebsiella pneumoniae. The intermediate substance (3-HPA) is not only the product of enzyme catalysis of GDHt but also the substrate of enzyme PDOR. 3-HPA can inhibit the growth of cells and the activity of GDHt [8] . In the model proposed by Y.Q. Sun et al., it is assumed that 1, 3-PD

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Factor analysis of Internet traffic destinations from similar source networks Purpose -This study aims to assess whether similar user populations in the Internet produce similar geographical traffic destination patterns on a per-country basis.Design/methodology/approach -We have collected a country-wide NetFlow trace, which encompasses the whole Spanish academic network, which comprises more than 350 institutions and one million users, during four months. Such trace comprises several similar campus networks in terms of population size and structure. To compare their behaviors, we propose a mixture model, which is primarily based on the Zipf-Mandelbrot power law to capture the heavy-tailed nature of the per-country traffic distribution. Then, factor analysis is performed to understand the relation between the response variable, number of bytes or packets per day, with dependent variables such as the source IP network, traffic direction, and country.Findings -Surprisingly, the results show that the geographical distribution is strongly dependent on the source IP network. Furthermore, even though there are thousands of users in a typical campus network, it turns out that the aggregation level which is required to observe a stable geographical pattern is even larger. Consequently, our results show a slow convergence rate to the domain of attraction of the model, specifically, we have found that at least 35 days worth of data are necessary to reach stability of the model's estimated parameters.Practical implications -Based on these findings, conclusions drawn for one network cannot be directly extrapolated to different ones. Therefore, ISPs' traffic measurement campaigns should include an extensive set of networks to cope with the space diversity, and also encompass a significant period of time due to the large transient time.Originality/value -Current state of the art includes some analysis of geographical patterns, but not comparisons between networks with similar populations. Such comparison can be useful for the design of Content Distribution Networks and the cost-optimization of peering agreements. The geographical characteristics of Internet traffic have a major impact on a wide range of applications, such as traffic engineering (Wasem et al., 1995) , distributed network monitoring (Hofstede and Fioreze, 2009; Puzis et al., 2008; Bass, 2000) , peering agreements with other ISPs (Lippert and Spagnolo, 2008; Laffont et al., 2001) , or the design of Content Distribution Networks (CDN) (Erman et al., 2009; Qureshi et al., 2009) . Consequently, knowledge of geographical traffic patterns has proven to be useful to perform routing and capacity planning in the Internet, even though routing is mostly driven by economic and political decisions (Weis, 2010; Schwartz, 2010) . However, once a routing and capacity planning decision is made, to which extent can such decisions be extrapolated to other scenarios? Moreover, regardless of the reasons that motivated a certain routing scheme in a network, is it possible to assure that adding new users, when the aggregation level is already very large, will not alter significantly the traffic destination distribution? In addition, an ISP may wish to know more about the geographical traffic pattern of its customers. To do so, a trace driven analysis must be performed. How long, in days worth of traffic, should it last?. It is also worth noting that two of the top questions in the CAIDA's Day in the Life of the Internet project (CAIDA, 2009) were precisely: "What are the traffic patterns and connectivity in different geographic regions?" And "for ISPs appearing in different geographic regions around the world, do peering relationships change depending on the location?". In this paper, we address the above questions by studying whether similar user populations produce the same geographical traffic pattern on a per-country basis. To do so, we have performed a spatial and temporal diversity analysis of the geographical traffic pattern of destinations from different source IP networks, which share similar characteristics (population size, access link capacity, etc.). With respect to previous works, we provide a different approach by comparing the geographical traffic pattern of similar user populations. More specifically, we perform a country-wide measurement campaign that comprises the whole Spanish academic network. Then, we analyze the geographical traffic pattern per IP campus network and focus on whether similar campus networks provide the same per-country geographical traffic pattern or not.. Our findings can be used in any capacity planning or routing problem for which knowledge of the destination pattern is important. For example, let us assume that an ISP starts business with a population base which is similar to an existing population. Then, is the traffic destination pattern different, even though the customer population looks similar? Note that if the destination pattern differs so does the routing strategy (Subramanian et al., 2002) and possible peering agreements from the ISP (Norton, 2001a,b) .. Furthermore, our study has direct application to the efficient design of content distribution mechanisms. The authors in (Erman et al., 2009 ) point out that a fundamental consideration for the performance of such mechanisms is the distance the data travels to reach the end user. From the user's point of view, increased travel distances affect the load time of any resource, such as web pages and file downloads, reducing throughput. In addition, it exerts a strong impact on the Quality of Experience (QoE) of multimedia applications, such as real-time video streaming and on-line gaming. From ISPs' viewpoint, the network miles data travel reflect the direct cost of transmitting data over their backbones. In this light, shorter distances entail lower costs, and conversely, larger distances imply an increase in the expenses. Consequently, knowledge of the geographical destinations is a key metric to dimension CDN or proxies. An ISP can decide what content to cache or where to deploy a content server taking into account, on the one hand, the popularity of the destinations, and on the other hand, the cost of delivering in "air miles", as defined in (Erman et al., 2009) . We note that not only the geographical characterization is important when performing this task, but also the comparison between similar user populations. Such comparison is useful to assess to what extent the results can be extrapolated to other scenarios. Namely, our results serve to evaluate whether the same content distribution policy can be applied to similar populations, both in size and structure.. The rest of the paper is organized as follows: next sections presents a summary of our research objectives and a review of the related work. Then, Section 4 details the measurement set. Section 5 describes the methodology applied in Section 6, which is devoted to the results, and Section 7 comprises the discussion. Finally, Section 8 concludes the paper with a summary of the main findings and the future research directions. We hypothesize that the heterogeneity in the geographical traffic pattern may be primarily due to the heavy-hitters, the P2P communities, and the malicious/unwanted traffic:. • Homogeneity in the geographical traffic pattern is expected whenever the networks population sizes are large enough. In this study, the smallest university under study has more than 30,000 students, which supports this hypothesis. Nonetheless, the Internet community has pointed out that most of the Internet traffic is generated by a small fraction of network users (Brownlee and Claffy, 2002; Papagiannaki et al., 2002) , often referred to as heavy-hitters (Feldmann et al., 2001) . A heavy-hitter is typically defined as a user whose use of network resources exerts a significant impact on the aggregated traffic of the whole network. Thus, particular traffic patterns of a heavy-hitter user may have impact on the results of the whole campus network, explaining the different behavior that the analyzed universities have shown. We have found a number of examples of heavy-hitters in our datasets. For instance, we detected that a IP address of one of the RedIRIS' universities sent 122 GBytes to a certain external IP address located in Germany in only one day. Obviously, this heavy-hitter user's behavior had an important impact on the network measurements. Additionally, we note that this also explains the slow convergence rate to the domain of attraction of our model distribution with the number of days added to the sample. We had to add more and more days to diminish the impact of such heavy-hitter users.. • Traffic volumes related to P2P applications account for a sizable fraction of the total traffic (Sandvine, 2009 ). In addition, the closed P2P communities have emerged in popularity in recent years (Torres et al., 2009) . In some of these communities, popular P2P applications have been modified to take advantage of peer locality as well as network topology. This implies that the location of the peers is not longer random, but it depends on the selection algorithm of the P2P application, which breaks with the assumption of homogeneity in the dataset. Note that the same applies to on-line games and other location-aware applications.. • There are a number of examples of how malicious traffic can exert considerable influence on the characterization of the remote host location. For instance, the authors in (Xie et al., 2008) identified 7721 botnet-based spam campaigns comprising 340,050 distinct IP addresses widespread across the Internet. Similarly, the authors in (Jhon and Tafvelin, 2007; Jin et al., 2007) showed the importance of the unwanted traffic, basically, worms, port scanning, and denial of service attacks. Specifically, in (Jhon and Tafvelin, 2007) it is shown that the 16 bit address ranges of the two universities they analyzed were scanned in their entirety (2×65,534) in a 27-hour trace (20 minutes traces, four times a day, 20 days). This traffic would be computed as incoming traffic in our results. Additionally, they detected a large fraction of P2P nonmalicious outbound connection attempts to non-existing hosts that resulted unsuccessful. This is often observed for P2P traffic, where unreliable file-sharing peers are common (Ruffo and Schifanella, 2007) . It becomes apparent that the portion of malicious traffic over the total, often via collaborating (Puzis et al., 2009) or compromised hosts, can vary from one network to another according to each institution's network configuration and ability to protect from this traffic. This breaks again with the homogeneity of the measure set.. On the other hand, we believe that the careful process of selecting similar Spanish universities out of the total set of institutions and the sizable number of users of each institution is enough to assume that other factors like cultural issues or particular "brand loyalties" do not exert a significant impact on the results.. Finally, we note that abnormal results (non-typical or small countries) on the distribution of the most popular countries can be leveraged to detect malicious traffic. In this case, network managers should only take into account stationary results (as we have shown, more than one month worth of data). Once the measurement set and methodologies have been shown, we study how to characterize the end-hosts locations. We will leverage on this characterization to compare the traffic destinations from similar campus networks. Despite of the importance of factor analysis of traffic destinations, as shown in the previous sections, it turns out that the state of the art does not feature any similar study. We believe that such lack of research effort is due to the difficulties in capturing contemporary traffic from many geographically disperse source IP networks. For instance, more than ten years ago, the authors in (Arlitt and Williamson, 1997) presented a detailed workload characterization study of Internet web servers, on attempts to find invariants (Floyd and Paxson, 2001) in the Internet behavior. One of the analyzed characteristics was the geographical distribution of document requests to several web servers. However, they only considered two possible options, whether the requests were local or remote to the web-server network, finding that most part of the requests were remote. In our case, we discriminate the per-country traffic distribution, and do not restrict the analysis to the web service only.. Similarly in (Feng et al., 2005) , the authors analyzed the traffic received by a certain online-game server. Among other characteristics, the players' location is included in the study. Their results indicate a clear geographical dispersion with only 30% of the clients placed close to the online-game server. Again, the authors focus on the online-game service exclusively, i.e., the remaining of the traffic is not considered, and there is no comparison on a per-source IP network basis.. The authors in (Zink et al., 2009 ) compared the global popularity of YouTube videos obtained from the YouTube web portal and the video popularity of a campus network. Essentially, the authors analyzed if results from the entire Internet could be useful to make YouTube video caching decisions in a single campus. Given that they did not find significant correlation between both measurements, the answer was negative. Note that the users' profiles deeply varies according to life habits, different attitude towards technology, and other intangible cultural phenomena making very difficult to find any homogeneity by comparing Internet users. In this light, this paper goes one step further and aims to compare not a university and the Internet but an extensive set of similar IP subnetworks. In addition, we are not limited to any application and we have gathered traffic measurements for several months.

B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O