Signal Detection Performance of Overlapped FFT Scheme with Additional Frames Consisting of Non-continuous Samples in Indoor Environment Abstract Overlapped FFT has been proposed as a signal detection scheme in dynamic spectrum access to reduce the variance of the noise and improve the detection probability. However, the improvement of the detection probability in the conventional overlapped FFT is bounded with the upper limit of the overlap ratio. This paper proposes a new overlapped FFT scheme using additional frames. In the proposed scheme, in addition to the original FFT frames, new frames that consist of multiple subframes with non-continuous samples are constructed and included. It can realize the increase of the number of the FFT frames and the improvement of the detection probability compared with the conventional scheme. Numerical results through computer simulation show that the proposed scheme improves the detection probability by up to 0.07. On indoor channel models the proposed scheme also improves the detection probability. In addition, it is clarified that as the delay spread increases the detection probability reduces due to the correlation between the frames. With the global spread of the Internet and the evolution of digital signal processing technologies in recent years, high-speed and large-capacity wireless communications have been demanded. On the other hand, the popularity of wireless communications causes the shortage of frequency spectrum available for commercial use. Therefore, cognitive radio (CR) has been attracting much attention among researchers of wireless communications [1] [2] [3] . Cognitive radio is a technology with which a mobile terminal recognizes the status of backbone networks and radio environments autonomously. Many kinds of spectrum sensing schemes for CR have been proposed as [4] . One of the CR technologies is dynamic spectrum access (DSA). Dynamic spectrum access promotes the effective use of frequency resource by finding a white space spectrum that is temporarily not in use by a primary wireless system [5] .. In this paper, DSA is assumed to be applied in the 2.4 GHz industry-science-medical (ISM) band. The ISM band is often used by IEEE 802.11 wireless LANs (WLANs). In this case, signal detection has to be implemented to prevent the interference to WLAN users [6] [7] [8] . As an example, CleanAir technology is used in a part of the WLAN devices [9] . In this technology, spectrum sensing is carried out in IEEE 802.11 bands (2.4 and 5 GHz band). The results of the sensing are classified into normal IEEE 802.11 WLAN and 17 types of the interference signals. If the power of interference signals exceeds a threshold, the communication channel is changed.. As one of signal detection schemes, overlapped FFT has been proposed [10, 11] . Wang et al. [12] investigates the flexibility of the overlapped FFT for the rejection of the adjacent channel interference. Tomioka et al. [13] evaluates the noise reduction effect with windowing functions in the overlapped FFT. Takai et al. [14] shows through the experiment that the overlapped FFT works effectively even in the presence of a residual direct current (DC) offset when a direct conversion scheme is employed in a receiver. It also gives the theoretical analysis for the distribution of noise outputs when the DC offset exists.. In the overlapped FFT, if no signal component exists, only the noise is squared and averaged. The variance of the squared noise reduces in proportion to the overlap ratio and the detection probability improves as the overlap ratio increases. In other words, the improvement of the detection rate in the conventional overlapped FFT is bounded with the upper limit of the overlap ratio. This paper proposes a new overlapped FFT scheme using additional frames. In the proposed scheme, in addition to the original FFT frames used in the conventional overlapped FFT scheme, new frames are constructed and included in order to increase the number of FFT outputs. To reduce the correlation to the original frames, the additional frames consist of multiple subframes with non-continuous samples.. This paper is organized as follows. Firstly, signal detection with the conventional overlapped FFT and the proposed scheme is described in Sect. 2. The probability of detection (P D ) and the probability of false alarm (P F A ) using the conventional and proposed schemes are evaluated in Sect. 3. Finally, our conclusions are presented in Sect. 4. In this paper, the overlap FFT scheme for signal sensing with the additional frames has been proposed. In the proposed scheme in addition to the original frames, the new frames are constructed to increase the total number of the FFT outputs. The additional frames are formed with the subframes that consist of the different part of the received samples to reduce the correlation to the original frames.. The variances of the power of the FFT outputs without signal are theoretically analyzed for the conventional and proposed schemes. When there is no signal, the variance of the power of the FFT outputs reduces in proportion to the overlap ratio in the conventional scheme while there is the minimum point in the proposed scheme.. By increasing the number of the frames the proposed scheme improves the probability of detection by about 0.07. Mohammad Ghavami is a professor of Telecommunications Engineering at London South Bank University, UK. He is the leading author of the book (Ultra Wideband Signals and Systems) and has published over 120 technical papers in major international journals and conferences on areas related to ultra wideband systems, smart antenna, adaptive filters and biomedical signal processing. He completed his Ph.D. degree with Great Distinction in electrical engineering in 1993. During his Ph.D. studies he was awarded the DAAD scholarship at the University of Kaiserslautern, Germany from 1990 to 1993. From 1998 to 2000 he was a JSPS postdoctoral fellow at the Yokohama National University, Japan. From 2000 to 2002 he was a researcher at the Sony Computer Science Laboratories in Tokyo, Japan. He has won two invention awards from Sony Ltd.

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Multiview Image Coding Using Depth Layers and an Optimized Bit Allocation Abstract-In this paper, we present a novel wavelet-based compression algorithm for multiview images. This method uses a layer-based representation, where the 3-D scene is approximated by a set of depth planes with their associated constant disparities. The layers are extracted from a collection of images captured at multiple viewpoints and transformed using the 3-D discrete wavelet transform (DWT). The DWT consists of the 1-D disparity compensated DWT across the viewpoints and the 2-D shapeadaptive DWT across the spatial dimensions. Finally, the wavelet coefficients are quantized and entropy coded along with the layer contours. To improve the rate-distortion performance of the entire coding method, we develop a bit allocation strategy for the distribution of the available bit budget between encoding the layer contours and the wavelet coefficients. The achieved performance of our proposed scheme outperforms the state-ofthe-art codecs for several data sets of varying complexity.

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Exclusion regions for optimization problems Branch and bound methods for finding all solutions of a global optimization problem in a box frequently have the difficulty that subboxes containing no solution cannot be easily eliminated if they are close to the global minimum. This has the effect that near each global minimum, and in the process of solving the problem also near the currently best found local minimum, many small boxes are created by repeated splitting, whose processing often dominates the total work spent on the global search. This paper discusses the reasons for the occurrence of this so-called cluster effect, and how to reduce the cluster effect by defining exclusion regions around each local minimum found, that are guaranteed to contain no other local minimum and hence can safely be discarded. In addition, we will introduce a method for verifying the existence of a feasible point close to an approximate local minimum.These exclusion regions are constructed using uniqueness tests based on the Krawczyk operator and make use of first, second and third order information on the objective and constraint functions. Branch and bound methods for solving global optimization problems frequently have the difficulty that subboxes containing no solution cannot be easily eliminated if one of the best local optima found so far lies nearby the box. This has the effect that near the best local minima found, many small boxes are created by repeated splitting, whose processing often dominates the total work spent on the global search. This paper discusses in Section 2 the reasons for the occurrence of this so-called cluster effect, and how to reduce the cluster effect by defining exclusion regions around each local minimum found, that are guaranteed to contain no other local minimum and hence can safely be discarded. Such exclusion regions in the shape of boxes are the basis for the backboxing strategy by Van Iwaarden (1996) (see also Kearfott (1997 Kearfott ( , 1996a ) that eliminates the cluster effect in most cases.. Exclusion regions for systems of equations are traditionally constructed using uniqueness tests based on the Krawczyk operator (see, e.g., Neumaier (1990, Chapter 5) ) or the Kantorovich theorem (see, e.g., Ortega & Rheinboldt (2000, Theorem 12.6 .1)). They can also be constructed by a second order method by Schichl & Neumaier (2005a) . All of these methods applied to the Karush-John first order necessary optimality conditions of an optimization problem can be used to construct exclusion regions for local optima. However, the optimality conditions often lead to a degenerate system, and thus this method often fails.. In Section ?? we review known methods for constructing exclusion regions for optimization problems. Then in Section 4 we will revise the second order method presented in Schichl & Neumaier (2005a) and extend it to more generally shaped exclusion regions based on hypernorm balls. The main result of the article will be presented in Sections 5 and 6. Numerical and analytical examples will be given in Section 7, where we will also show that the exclusion regions are optimally big in a certain sense.. In the following, the notation is as in the book Neumaier (2001) . In particular, inequalities are interpreted component-wise, I denotes the identity matrix, intervals and boxes (= interval vectors) are in bold face, and rad x = 1 2 (x−x) denotes the radius of a box x = [x, x] ∈ IR n . The interior of a set S ⊆ R n is denoted by int(S), and the interval hull by ⊓ ⊔ S.. Throughout the article we consider the global optimization problem. {excGO} where f : D ⊆ R n → R and F : D ⊆ R n → R m are three times continuously differentiable. (For some results, weaker conditions suffice; it will be clear from the arguments used that continuity and the existence of the quantities in the hypothesis of the theorems are sufficient.). We will also consider the nonlinear system of equations for a twice continuously differentiable function G :. For a Lipschitz continuous function G we can always write. for any two points x and z with a suitable matrix G[z, x] ∈ R n×n , called a slope matrix for G. While G [z, x] is not uniquely determined, we always have. Thus G[z, x] is a slope version of the Jacobian. There are recursive procedures to calculate G [z, x] given x and z, see Krawczyk & Neumaier (1985) , Rump (1996) , Kolev (1997) , and Schichl & Neumaier (2005b) ; a Matlab implementation is in Intlab Rump (1999) , also the COCONUT environment Schichl & Markót (2011) provides algorithms.. If the slope matrix G [z, x] is Lipschitz-continuous we can further write. with the second order slope tensor G[z, z ′ , x] ∈ R n×n×n . Here, as throughout this paper, we use the following notation for third order tensors.. For a third order tensor T ∈ R n×m×r , vectors u ∈ R n , v ∈ R r , w ∈ R m , and matrices A ∈ R s×n , B ∈ R r×s , and C ∈ R s×m we write. important to note is that the multiplication · binds stronger than the "standard implicitly noted multiplication".. If z = z ′ formula (5) above somewhat simplifies, because of (4), to. Throughout the article, also the notion of a hypernorm will be important. This is a joint generalization of norms and componentwise absolute values, originally introduced by Fischer (1974) , see also Schichl & Neumaier (2011) . Here, we will only need real valued hypernorms on R n .. 1.1 Definition. A mapping ν : R n → R r is called a hypernorm on R n if for all v, w ∈ R n and λ ∈ R we have. The hypernorm is called monotone if. is called compatible with ν n and ν m if for all A ∈ R m×n and all v ∈ R n we have. The concept of compatibility is analogously extended from matrices to higher order tensors.. is called a dual hypernorm for ν.. Let ν : R n → R r be a hypernorm, and let 0 ≤ u ∈ R r and x ∈ R n . The set. is called the closed hypernorm ball with center x and (generalized) radius u. A closed hypernorm ball is a nonempty convex set.. 1.2 Example. 1. Every norm on R n is a hypernorm, its dual norm is a dual hypernorm, and a compatible operator norm on R n×n is a compatible hypernorm on R n×n . The closed hypernorm balls of a norm are the closed norm balls. If the norm is monotone, it is also monotone as a hypernorm.. 2. The componentwise absolute value | | : R n → R n is a monotone hypernorm. It is dual to itself, and the componentwise absolute value on matrices is a compatible hypernorm. Its closed hypernorm balls are boxes.. 3. For two hypernorms ν 1 : R n → R r and ν 2 : R m → R s the mapping (ν 1 , ν 2 ) : R n+m → R r+s is again a hypernorm. Compatible and dual hypernorms can then be stacked accordingly. If both hypernorms are monotone, the stacked hypernorm is monotone, too.

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Indistinguishability and identifiability of kinetic models for the MurC reaction in peptidoglycan biosynthesis An important question in Systems Biology is the design of experiments that enable discrimination between two (or more) competing chemical pathway models or biological mechanisms. In this paper analysis is performed between two different models describing the kinetic mechanism of a three-substrate three-product reaction, namely the MurC reaction in the cytoplasmic phase of peptidoglycan biosynthesis. One model involves ordered substrate binding and ordered release of the three products; the competing model also assumes ordered substrate binding, but with fast release of the three products. The two versions are shown to be distinguishable; however, if standard quasi steady-state assumptions are made distinguishability can not be determined. Once model structure uniqueness is ensured the experimenter must determine if it is possible to successfully recover rate constant values given the experiment observations, a process known as structural identifiability.Structural identifiability analysis is carried out for both models to determine which of the unknown reaction parameters can be determined uniquely, or otherwise, from the ideal system outputs. This structural analysis forms an integrated step towards the modelling of the full pathway of the cytoplasmic phase of peptidoglycan biosynthesis. In the biological sciences it is becoming increasingly common to collect data in high-throughput experiments on several scales: genomic, proteomic, or metabolic (Snoep and Westerhoff [30] , Sauer et al. [27] ). These data hold the promise of identifying the mechanisms of interactions that comprise large-scale regulatory biochemical networks. An important step in this work is the design of experiments to allow discrimination between two (or more) competing pathway models or biological mechanisms. Structural indistinguishability provides a formal approach to distinguish between competing model mechanisms (Kholodenko et al. [19] ).. In Systems Biology mathematical models that are generated invariably include large numbers of state variables with numerous model parameters, many of which are unknown, or cannot be directly measured. With such highly complex systems there are often few direct measurements that can be made and limited access for input perturbation to elucidate system dynamics. These limitations cause problems when investigating the existence of hidden pathways or attempting to estimate unknown parameters. Identifiability analysis provides a formal approach to determine what additional inputs and/or measurements are necessary in order to reduce, or remove, these limitations and permit the derivation of models that can be used for practical purposes with greater confidence (Snoep and Westerhoff [30] , Sauer et al. [27] , Kholodenko et al. [19] ).. Structural indistinguishability for system models is concerned with determining the uniqueness between possible candidates for the model (or mechanism) structure (Evans et al. [13] ). The analysis is concerned with whether the underlying possibilities for the parameterised mathematical model can be distinguished using the inputs (perturbations or interventions) and observations (or measurements) available for the biological system under investigation.. In chemical kinetics it is key to characterise reaction mechanisms, however there are often several different process models that are consistent with the available data. These mechanisms may be described by the same mathematical representation (see Érdi and Tóth [9] , Espenson [10] ) but without formal analysis of the mathematical model a reaction mechanism's validity is only disproved. by showing inconsistency with available data. Whilst this problem has been recognised [5, 6] , structural indistinguishability is not routinely applied to chemical kinetics experiments and model development. For linear systems structural indistinguishability analysis is generally exhaustive with all competing mechanisms generated from a given one (see Godfrey and Chapman [16] ). For nonlinear systems, approaches are generally only for pairs of candidate models, though in some cases a parameterised family of such candidates can be generated. There has been limited application of structural indistinguishability analysis to the chemical kinetic models. In Schnell et al. [28] the issues of distinguishability with respect to biochemical kinetics was considered via application of structural indistinguishability to classical models for a single-enzyme, single-substrate reaction. In addition, simple kinetic models are incorporated into studies of structural analysis methods, often. with Michaelis-Menten type reparameterisation (see Saccomani et al. [25] , Bellu et al. [3] ; however, to the authors' knowledge this is the first time indistinguishability and identifiability have been applied to a full (three-substrate/three-product) enzyme kinetic model.. Structural identifiability arises in the inverse problem of inferring from the known, or assumed,. properties of a biological system, estimates for the corresponding rate constants and other parameters; as such it can be considered as a special case of the structural indistinguishability problem.. Structural identifiability analysis considers the uniqueness of the unknown model parameters from the input-output structure corresponding to proposed experiments to collect data for parameter estimation. This is an important, but often overlooked, prerequisite to experiment design, system identification and parameter estimation, since estimates for structurally unidentifiable parameters are effectively meaningless. If parameter estimates are to be used to inform intervention or inhibition strategies, or other critical decisions, then it is essential that the parameters be uniquely identi- [12]) and differential algebra techniques (Ljung and Glad [20] , Saccomani, Audoly, and D'Angio [26] ). Unfortunately for systems with a complex structure significant computational problems can arise even for relatively low dimensional models. At present there has been relatively little work on techniques for large-scale, highly complex systems, which are typical in Systems Biology. As shall be shown, the analytic identifiability approaches can generate computationally intractable solutions; therefore, an alternative numerical approach that uses the sensitivity of the observation functions to changes in the parameters is implemented to suggest the local identifiability of the parameters and associated model (Fisher [15] , Jacquez [18] ).. The purpose of this paper is to explore the possible effectiveness of using structural indistinguishability and identifiability techniques in model discrimination within Systems Biology networks, using MurC as a case study; to this end, a structural indistinguishability analysis is performed between two different models describing the kinetic mechanism of the MurC (Ter-ter) reaction in the cytoplasmic phase of bacterial peptidoglycan biosynthesis (BPB). One model assumes the kinetic mechanism proposed by Emanuele et al. [8] , which involves step-by-step release of the three products; the competing model assumes that the release of the products is simultaneous. A structural identifiability analysis is also carried out for both models to ensure that the model output uniquely determines the unknown parameters. A less general approach that is applicable to large complex systems is to use numerical methods 1 To determine the rank a minimum of the 10th Taylor series co-efficient is required for the fast model and 14th for the ordered release model; however, evaluating at time t = 0 results in zero initial conditions for all but the enzyme and substrates, therefore higher order co-efficients are required.. to estimate the sensitivity matrix, where the sensitivity matrix is defined as the matrix of first derivatives, with respect to the parameter vector p p p, of the observation at each measurement time-. where t r is the r th observation sample. The sensitivity matrix can be estimated using finitedifferencing methods; however, a more robust and accurate result can be obtained with Automatic. Differentiation. The local identifiability of the system can be determined from the rank of the numerical sensitivity matrix by the following rules:. • if rank(S) = number of parameters, consistently, then the model is structurally identifiable, else. • if rank(S) < number of parameters, the model is unidentifiable.. If the model is unidentifiable the unidentifiable parameters are indicated by the dependent columns of the sensitivity matrix. In this paper the application of a range of indistinguishability and identifiability analysis, both symbolic and numeric, have been applied to a kinetic reaction scheme. This analysis has highlighted the inherent difficulties in designing experiment protocols for such a complex non-linear system.. However, using the proposed models for the MurC phase of the BPB pathway as an example, the benefits of performing this analysis have been shown. Through structural indistinguishability it has been shown that it is theoretically possible to discriminate between candidate mechanisms in order to ascertain the most feasible and appropriate model to describe the chemical process.. Integral to the applicability of the analysis is the determination of necessary, and feasible, measurements/experiments required in order to achieve distinguishability. From this analysis it can be concluded that the common assumption of considering fast release of products as equivalent to a one-by-one step mechanism may not be accurate as these processes are intrinsically different (distinguishable) and in theory by observing a substrate and product simultaneously distinguishable.. Once it has been determined that the candidate models are distinguishable it is necessary to ensure the rate constants of the reaction are identifiable a priori, given the available experimental measurements. For the MurC reaction the minimal number of feasible observations required in order to identify, uniquely, the maximum number of model parameters proved to be the first substrate and one of the products of the reaction. However, using purely symbolic analysis it is not possible to determine if all the parameters of both models are uniquely identifiable, but combining numeric and symbolic methods suggests that measuring the substrate and product in isolation will yield a model that is unidentifiable, whilst measuring these simultaneously will ensure that all parameters, and therefore the model in its entirety, will be at least locally identifiable. These results have implications in experiment design as it has been shown that relatively few measurable species are needed to be able to identify a great deal of the underlying parameters involved and therefore this reduces the amount of necessary experiments that may need to be performed.. An important issue addressed in this work are the difficulties encountered when extracting rate constant information from steady-state experiments. Unlike the models generated for the pre-steady state experiments, i.e. the full fast and ordered release models, the quasi steady-state models are intractable to symbolic analysis, with respect to the models indistinguishability or identifiability. Numerical results suggests that a small number of rate constants are identifiable but without the support of the symbolic results it is difficult to confidently identify which parameters the results relate to. Experimenters should therefore realise that multi-substrate reactions cannot be adequately described by velocity equations derived from rapid equilibrium assumptions alone as the distribution of enzyme species depends on the rate constants of all steps.

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


PAC Learning with Nasty Noise We introduce a new model for learning in the presence of noise, which we call the Nasty Noise model. This model generalizes previously considered models of learning with noise. The learning process in this model, which is a variant of the PAC model, proceeds as follows: Suppose that the learning algorithm during its execution asks for m examples. The examples that the algorithm gets are generated by a nasty adversary that works according to the following steps. First, the adversary chooses m examples (independently) according to the xed (but unknown to the learning algorithm) distribution D as in the PAC-model. Then the powerful adversary, upon seeing the speci c m examples that were chosen (and using his knowledge of the target function, the distribution D and the learning algorithm), is allowed to remove a fraction of the examples at its choice, and replace these examples by the same number of arbitrary examples of its choice; the m modi ed examples are then given to the learning algorithm. The only restriction on the adversary is that the number of examples that the adversary is allowed to modify should be distributed according to a binomial distribution with parameters (the noise rate) and m.On the negative side, we prove that no algorithm can achieve accuracy of < 2 in learning any non-trivial class of functions. We also give some lower bounds on the sample complexity required to achieve accuracy = 2 + . On the positive side, we show that a polynomial (in the usual parameters, and in ? 2 ) number of examples su ce for learning any class of nite VC-dimension with accuracy > 2 . This algorithm may not be e cient; however, we also show that a fairly wide family of concept classes can be e ciently learned in the presence of nasty noise. Valiant's PAC model of learning 22] is one of the most important models for learning from examples. Although being an extremely elegant model, the PAC model has some drawbacks. In particular, it assumes that the learning algorithm has access to a perfect source of random examples. Namely, upon request, the learning algorithm can ask for random examples and in return gets pairs (x; c t (x)) where all the x's are points in the input space distributed identically and independently according to some xed probability distribution D, and c t (x) is the correct classi cation of x according to the target function c t that the algorithm tries to learn. Since Valiant's seminal work, there were several attempts to relax these assumptions, by introducing models of noise. The rst such noise model, called the Random Classi cation Noise model, was introduced in 2] and was extensively studied, e.g., in 1, 6, 9, 12, 13, 16] . In this model the adversary, before providing each example (x; c t (x)) to the learning algorithm tosses a biased coin; whenever the coin shows \H", which happens with probability , the classi cation of the example is ipped and so the algorithm is provided with the, wrongly classi ed, example (x; 1?c t (x)). Another (stronger) model, called the Malicious Noise model, was introduced in 23], revisited in 17], and was further studied in 8, 10, 11, 20] . In this model the adversary, whenever the -biased coin shows \H", can replace the example (x; c t (x)) by some arbitrary pair (x 0 ; b) where x 0 is any point in the input space and b is a boolean value. (Note that this in particular gives the adversary the power to \distort" the distribution D.). In this work, we present a new model which we call the Nasty (Sample) Noise model. In this model, the adversary gets to see the whole sample of examples requested by the learning algorithm before giving it to the algorithm and then modify E of the examples, at its choice, where E is a random variable distributed by the binomial distribution with parameters and m, where m is the size of the sample. This distribution makes the number of examples modi ed be the same as if it were determined by m independent tosses of an -biased coin. However, we allow the adversary's choice to be dependent on the sample drawn. The modi cation applied by the adversary can be arbitrary (as in the Malicious Noise model). 1 Intuitively speaking, the new adversary is more powerful than the previous ones { it can examine the whole sample and then remove from it the most \informative" examples and replace them by less useful and even misleading examples (whereas in the Malicious Noise Model for instance, the adversary also may insert to the sample misleading examples but does not have the freedom to choose which examples to remove). The relationships between the various models are shown in Table 1 .. Random Noise-Location Adversarial Noise-Location Label Noise Only Random Classi cation Noise Nasty Classi cation Noise Point and Label Noise Malicious Noise Nasty Sample Noise Table 1 : Summary of models for PAC-learning from noisy data. We argue that the newly introduced model, not only generalizes the previous noise models, including variants such as Decatur's CAM model 11] and CPCN model 12], but also, that in many real-world situations, the assumptions previous models made about the noise seem unjusti ed. For example, when training data is the result of some physical experiment, noise may tend to be stronger in boundary areas rather than being uniformly distributed over all inputs. While special models were 1 We also consider a weaker variant of this model, called the Nasty Classi cation Noise model, where the adversary may modify only the classi cation of the chosen points (as in the Random Classi cation Noise model).. devised to describe this situation in the exact-learning setting (for example, the incomplete boundary query model of Blum et al., 5] ), it may be regarded as a special case of Nasty Noise, where the adversary chooses to provide unreliable answers on sample points that are near the boundary of the target concept (or to remove such points from the sample). Another situation to which our model is related is the setting of Agnostic Learning. In this model, a concept class is not given. Instead, the learning algorithm needs to minimize the empirical error while using a hypothesis from a prede ned hypotheses class (see, for example, 18] for a de nition of the model). Assuming the best hypothesis classi es the input up to an fraction, we may alternatively see the problem as that of learning the hypotheses class under nasty noise of rate . However, we note that the success criterion in the agnostic learning literature is di erent from the one used in our PAC-based setting.. We show two types of results. Sections 3 and 4 show information theoretic results, and Sect. 5 shows algorithmic results. The rst result, presented in Section 3, is a lower bound on the quality of learning possible with a nasty adversary. This result shows that any learning algorithm cannot learn any non-trivial concept class with accuracy better than 2 when the sample contains nasty noise of rate . We further show that learning a concept class of VC dimension d with accuracy = + 2 requires ( = 2 + d= ) examples. It is complemented by a matching positive result in Section 4 that shows that any class of nite VC-dimension can be learned by using a sample of polynomial size, with any accuracy > 2 . The size of the sample required is polynomial in the usual PAC parameters and in 1= where = ? 2 is the margin between the requested accuracy and the above mentioned lower bound.. The main, quite surprising, result (presented in Section 5) is another positive result showing that e cient learning algorithms are still possible in spite of the powerful adversary. More speci cally, we present a composition theorem (analogous to 3, 8] but for the nasty-noise learning model) that shows that any concept class that is constructed by composing concept classes that are PAC-learnable from a hypothesis class of xed VC-dimension, is e ciently learnable when using a sample subject to nasty noise. This includes, for instance, the class of all concepts formed by any boolean combination of half-spaces in a constant dimension Euclidean space. The complexity here is, again, polynomial in the usual parameters and in 1= . The algorithm used in the proof of this result is an adaptation to our model of the PAC algorithm presented in 8].. Our results may be compared to similar results available for the Malicious Noise model. For this model, Cesa-Bianchi et al . 10] show that the accuracy of learning with malicious noise is lower bounded by =(1 ? ). A matching algorithm for learning classes similar to those presented here with malicious noise is presented in 8]. As for the Random Classi cation Noise model, learning with arbitrary small accuracy, even when the noise rate is close to a half, is possible. Again, the techniques presented in 8] may be used to learn the same type of classes we examine in this work with Random Classi cation Noise. We have presented the model of PAC learning with nasty noise, generalizing on previous models. We have proved a negative information-theoretic result, showing that there is no learning algorithm that can learn any non-trivial class with accuracy better than 2 , paired with a positive result showing this bound to be tight. We complemented these results with lower bounds on the sample size required for learning with accuracy 2 + . We have also shown that for a wide variety of \interesting" concept classes, an e cient learning algorithm in this model exists.. Our negative result can be generalized for the case where the learning algorithm uses randomized hypotheses, or coin rules (as de ned in 10]); in such a case we get an information theoretic lower bound of for the achievable accuracy, compared to a lower bound of =2(1 + ) proved in 10] for learning with Malicious Noise of rate .. While the partition into two separate variants: the NSN and the NCN models seem intuitive and well-motivated, it remains an open problem to come up with any results that actually separate the two models. Both the negative and positive results we presented in this work apply equally to both the NSN and the NCN models.. Finally, note that the de nition of the nasty noise model requires the learning algorithm to know in advance the sample size m (or an upper bound on it). The model however can be extended so as to deal with scenarios where no such bound is known to the learning algorithm. There are several scenarios of this kind. For example, the sample complexity may depend on certain parameters (such as the \size" of the target function) which are not known to the algorithm 3 . The adversary, who knows the learning algorithm and knows the target function (and in general can know all the parameters hidden from the learning algorithm) can thus \plan ahead" and draw in advance a sample S g of size which is su ciently large to satisfy, with high probability, all the requests the learning algorithm will make. It then modi es S g as de ned above and reorders the resulted sample S randomly 4 . Now, the learning algorithm simply asks for one example at a time (as in the PAC model) and the adversary supplies the next example in its (randomly-ordered) set S. If the sample is exhausted (which may happen in those cases where we have only an expected sample-complexity guarantee), we say that the learning algorithm has failed; however, when using a large enough sample (with respect to 1= ), this will happen with su ciently small probability.

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


Internet visual media processing: a survey with graphics and vision applications In recent years, the computer graphics and computer vision communities have devoted significant attention to research based on Internet visual media resources. The huge number of images and videos continually being uploaded by millions of people have stimulated a variety of visual media creation and editing applications, while also posing serious challenges of retrieval, organization, and utilization. This article surveys recent research as regards processing of large collections of images and video, including work on analysis, manipulation, and synthesis. It discusses the problems involved, and suggests possible future directions in this emerging research area. With the rapid development of applications and technologies built around the Internet, more and more images and videos are freely available on the Internet. We refer to these images and videos as Internet visual media, and they can be considered to form a large online database. This leads to opportunities to create various new data-driven applications, allowing non-professional users to easily create and edit visual media. However, most Internet visual media resources are unstructured and were not uploaded with applications in mind; furthermore, most are simply (and quite often inaccurately) indexed by text. Utilizing these resources poses a serious challenge. For example, if a user searches for 'jumping dog' using an Internet image search engine, the top-ranked results often contain results unrelated to those desired by the user who initiated the search. Some may contain a dog in a different pose to jumping, some may contain other animals which are jumping, some may contain cartoon dogs, and some might even contain a product whose brand name is 'jumping dog'. Users have to carefully select amongst the many retrieved results, which is a tedious and time consuming task. Moreover, users expect most applications to provide interactive performance. While this may be straightforward to achieve for a small database of images or video, it becomes a problem for a large database.. A typical pipeline for Internet visual media processing consists of three steps: content retrieval, data organization and indexing, and data-driven applications. In the first step, meaningful objects are retrieved from chosen Internet visual media resources, for example by classifying the scene in each image or video, and extracting the contours of visually salient objects. This step can provide better labeling of visual media for content aware applications, and can compensate for the lack of accurate text labeling, as well as identifying the significant content. In the second step, the underlying relationships and correspondences between visual media resources are extracted at different scales, finding for example local feature similarities, providing object level classifications, determining object level similarities and dense correspondences, and so on up to scene level similarities. This information allows the construction of an efficient indexing and querying scheme for large visual media collections. For simplicity we will refer to this as database construction; it ensures that desired visual content can be rapidly retrieved. In the third step, Internet visual media applica- tions use these data. Traditional image and video processing approaches must be modified to adapt to this kind of data, and new approaches are also needed to underpin novel applications. The methods should be (i) similarity aware, to efficiently deal with the richness of Internet visual media: for example, a computation may be replaced by look-up of an image with similar appearance to the desired result, and (ii) robust to variation, to effectively deal with variations in visual media: semantically identical objects, e.g. dogs, can have widely varying appearances. Figure 1 indicates a typical pipeline for Internet visual media processing.. This survey is structured as follows: Sect. 2 introduces basic techniques and algorithms for Internet visual media retrieval. Section 3 describes recent work on visual media database construction, indexing, and organization. Sections 4-7 cover applications for visual media synthesis, editing, reconstruction, and recognition that utilize Internet visual media in unique ways. Section 8 draws conclusions, and discusses problems and possible future directions in this emerging area. We have summarized recent research that organizes and utilizes large collections or databases of images and videos for purposes of assisting visual media analysis, manipulation, synthesis, reconstruction, and understanding. As already explained, most methods of organizing and indexing Internet visual media data are based on low level features and existing hashing techniques, with limited attempts to recover high level information and its interconnections. Utilizing such information to build databases with more semantic structure, and suitable indexing algorithms for such data are likely to become increasingly the focus of attention. The key to many applications lies in prepossessing visual media so that useful or interesting data can be rapidly found, and deciding what descriptors are best suited to a wide range of applications is an important open question.. Limitations of algorithmic efficiency also prevent full utilization of the huge amount of Internet visual media. Current methods work at most with millions of Internet images, which represent only a small portion. The more images that can be used, the better the results that can be expected. While parallelization will help, it is only part of the solution, and many core image processing techniques such as segmentation, feature extraction and classification are still potentially bottlenecks in any pipeline. Further work is needed on these topics.. There is a growing amount of work that tries to utilize all kinds of information in large scale data collections, not only visual information, but also metadata such as text tags, geotags, and temporal information. Event tags on images in social network web-sites are another potentially useful source of information, and in the longer term, it is possible that useful information can be inferred from the contextual information provided by such sites-for example, where a user lives or went on holiday can give clues to the content of a photo.. Finally, we note that work utilizing large collections of video is still scarce. Although it is natural to extend most image applications to video (see, for example, recent work that explores videos of famous scenes [108] ), several reasons limit the ability to do this. Apart from the obvious limitation of processing time, many image processing and vision algorithms give unstable results when applied to video, or at least produce results with poor temporal coherence. Temporal coherence can be enforced in optimization frameworks, for example, but at the expense of even more computationally expensive procedures than processing the data frame by frame. Even state-of-the-art video object extraction methods may work well on some examples with minimum user interaction, but may fail if applied to a large collection of video data. Further, efficient, algorithms more specifically intended for Internet scale video collections are urgently needed, as exemplified by a recent paper on efficient video composition [116] . The idea of using 'algorithm-friendly' schemes to prune away videos that cannot be processed well automatically has yet to be applied.

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


Learning user interest for image browsing on small-form-factor devices Mobile devices which can capture and view pictures are becoming increasingly common in our life. The limitation of these small-form-factor devices makes the user experience of image browsing quite different from that on desktop PCs. In this paper, we first present a user study on how users interact with a mobile image browser with basic functions. We found that on small displays, users tend to use more zooming and scrolling actions in order to view interesting regions in detail. From this fact, we designed a new method to detect user interest maps and extract user attention objects from the image browsing log. This approach is more efficient than image-analysis based methods and can better represent users' actual interest. A smart image viewer was then developed based on user interest analysis. A second experiment was carried out to study how users behave with such a viewer. Experimental results demonstrate that the new smart features can improve the browsing efficiency and are a good compliment to traditional image browsers. In recent years, mobile phones or PDAs with embedded digital cameras have undergone considerable progress. These days, a typical camera-equipped phone usually includes a 0.3M pixel digital camera, capable of taking still pictures with a 640x480 pixel resolution. It is expected that phones with 5M pixel cameras will become popular within several years, and that they will ultimately replace lowrange and mid-range digital cameras.. Image browsing has become a "must-have" feature for this type of device because people want to check the pictures immediately after capture. In addition, with the steady increase of storage size on mobile devices, keeping the images directly on the device instead of saving them elsewhere is becoming more common.. Currently, most commercial image browsers on mobile devices only offer a simplified set of features directly ported from the desktop applications. Few browsers take the characteristics of small-form-factor devices into consideration, despite the fact that the user experience on mobile devices is quite different from that of desktop PCs. The differences include input capabilities, processing power and screen characteristics. Usually it is quite inconvenient to perform complex tasks on small devices because of the restricted input capabilities, such as selecting an image folder, or scrolling or zooming a large image. Currently, due to the limited processing power, it is very timeconsuming or even impossible to open high-resolution images, browse large numbers of images at the same time, or edit large images. But the biggest difference from our point of view is the screen characteristics.. A brief overview of the current display capabilities of various mobile devices has been given in [8] , but the authors focused more on improving color dithering and palettization in order to achieve better rendering results using reduced color bit-depths. In [3] [7] , the authors agreed with us that the small display problem is most critical. They proposed an attention model based image adaptation approach to address this. Based on an automatically extracted image attention model, they designed a number of novel features to aid or automate common image browsing tasks such as viewing thumbnails, zooming and scrolling.. We expect that processing power and display quality will cease to be a problem in the near future. But the input capabilities and display size will continue to be constraints due to the portability requirements of mobile devices. It is therefore useful to know how users interact with an image viewer given these constraints, and how we can then improve the user experience by adding smart features.. The contributions of this paper include:. • We propose a new method for extracting user attention objects by analyzing how the user zooms and pans over an image.. • Based on this image attention object detection method, we develop a smart image viewer which incorporates new automatic browsing functions to facilitate browsing tasks on mobile devices.. • A user study is carried out to investigate how users behave with the new features.. The rest of the paper is organized in four parts. We first describe our user study of users' image browsing behavior with a basic mobile image viewer, and several conclusions were generated based on experimental observations. Second, we present a novel approach for analyzing the browsing log to generate an image attention model with a set of user attention objects. Then, based on this model, we design and evaluate a smart image viewer for mobile devices. Finally, we discuss two potential extensions to our work and conclude the paper with some remarks. Designing image viewers for mobile devices is a lively topic today. In our experiments, we found that mobile users tend to use more zooming and scrolling actions while browsing to view interesting regions, mainly because of the limited display size. Leveraging users' browsing interactions, we proposed an adaptive technique to detect user attention objects of an image, and in turn, used them to design a set of smart features to help users' browsing process. User study results showed that the smart features can improve the browsing efficiency and are a good compliment to traditional browsing functions.. We plan to extend the image attention model to be personalized and represent the relationship between user interest and certain image features. A better organization of photos on camera-equipped phones is another interesting problem to explore. Since people usually use phones for personal communications, it is essential that the data can be organized, searched and visualized by people or events. We will continue to investigate these directions in the future.

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


Stabilizing leader election in partial synchronous systems with crash failures This article deals with stabilization and fault-tolerance. We consider two types of stabilization: the self-and the pseudo-stabilization. Our goal is to implement the self-and/or pseudo-stabilizing leader election in systems with process crashes, weak reliability, and synchrony assumptions. We try to propose, when it is possible, communication-efficient implementations. Our approach allows to obtain algorithms that tolerate both transient and crash failures.Note that some of our solutions are adapted from existing fault-tolerant algorithms. The motivation here is not to propose new algorithms but merely to show some assumptions required to obtain stabilizing leader elections in systems with crash failures. In particular, we focus on the borderline assumptions where we go from the possibility to have self-stabilizing solutions to the possibility to only have pseudo-stabilizing ones. Self-stabilization [2] is a versatile technique to design algorithms tolerating transient failures: a self-stabilizing algorithm, regardless of the initial configuration of the system, guarantees that the system reaches in a finite time a configuration γ from which it cannot deviate from its intended behavior, i.e., every possible execution suffix starting from γ is correct.. A weaker property called pseudo-stabilization is introduced in [3] : a pseudostabilizing algorithm, regardless of the initial configuration of the system, guarantees that the system reaches in a finite time a configuration from which it does not deviate from its intended behavior. Such an algorithm can deviate from its intended behavior an arbitrary but finite number of time, hence each of its executions has a correct suffix.. Another approach is the fault-tolerance: fault-tolerance is a property that enables a system to continue operating (possibly in a degraded mode) rather than failing completely, when some components of the system crash. One goal of this article is to obtain algorithms that are both fault-tolerant and stabilizing. For this, we study to the (eventual) leader election: this problem consists in making the system converge to a configuration from which the same alive process is forever distinguished as the leader. The leader election has been extensively studied in both stabilizing (e.g., [4, 5] ) and fault-tolerant (e.g., [6, 7] ) areas.. The stabilizing algorithms are usually not designed to withstand crash failures. Some derived approaches, generally called fault-tolerant (self-or pseudo-) stabilization have been proposed in [8] [9] [10] to design algorithms that (self-or pseudo-) stabilize even when some crashes occur dynamically in the network. But, as proved in the same articles, without very strong assertions faulttolerant self-stabilization is impossible to ensure and concerning the leader election problem we prove that fault-tolerant self-stabilization is intrinsically impossible to solve. Hence we consider systems in which failures of processes are static. In these systems, we study self-stabilization in systems with static crash failures (SSSCF) and pseudo-stabilization in the same systems (PSSCF). We prove that considering only static process failures is not a restriction here because for the considered problem, PSSCF is equivalent to fault-tolerant pseudo-stabilization.. The impossibility results in [11, 9] constraints us to make assumptions on the link timeliness. So, we look for the weakest assumptions allowing to obtain SSSCF or PSSCF leader election algorithm in fully connected networks where some processes may crash.. We also consider the communication-efficiency (denoted as CE in the rest of the article): an algorithm is communication-efficient if eventually it uses only n−1 unidirectionnal links where n is the number of processes, which is optimal [12] .. We show that the notions of immediate timeliness and eventually timeliness are in some sense equivalent in the fault-tolerant stabilization. As a consequence, we only consider timeliness properties that are immediate. In the systems we study: (1) all the processes are timely and can communicate with each other but any of them can crash and (2) some links may have timeliness or reliability properties. Our starting point is the fully timely system noted S 4 . We show that the SSSCF leader election can be communication-efficiently implemented in S 4 . We then show that such a strong timeliness is required in the systems we consider to obtain a CE-SSSCF leader election. Nevertheless, we also show that a SSSCF leader election that is not communication-efficient can be obtained in a weaker system (S 3 ), e.g., any system having a timely bi-source that is a process whose all input and output links are timely [13] . More generally, we show that a system having at least one path of timely links between each pair of alive processes is required to obtain the SSSCF leader election. We then consider the PSSCF. We show that a CE-PSSCF leader election can be done in some weak systems where the SSSCF leader election cannot be done: any system having a timely source that is a process whose all ouput links are timely [14] (S 2 ). Using a previous result of Aguilera et al [14] , we then recall that communication-efficiency cannot be done if we consider systems having at least one timely source but no fair hub (a hub is a process whose all links are fair lossy) (S 1 ). However, we show that a non-communication-efficient PSSCF solution can be implemented in such systems. Finally, we conclude with the basic system where all links can be asynchronous and lossy (S 0 ): the leader election has neither SSSCF nor PSSCF solution in S 0 [14, 9] . If we do not consider transient faults, given some set I of initial configurations, we get the classical definition of fault-tolerance: an algorithm A is fault-tolerant for F with initial configurations I if and only if A satisfies F in system S dyn for I.. As usual, stabilization consider that the executions can start from any configuration and then all configurations are considered as being initial. We say that algorithm A is fault-tolerant (self-pseudo-) stabilizing for. Clearly, fault-tolerant stabilization is stronger than stabilization for systems with static crash failures Observation 1 If A is fault-tolerant (self-pseudo-) stabilizing for F in system S, then it is also (self-pseudo-) stabilizing for F in system S with static crash failures.

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


Chosen Ciphertext Security with Optimal Ciphertext Overhead Abstract. Every public-key encryption scheme has to incorporate a certain amount of randomness into its ciphertexts to provide semantic security against chosen ciphertext attacks (IND-CCA). The difference between the length of a ciphertext and the embedded message is called the ciphertext overhead. While a generic brute-force adversary running in 2 t steps gives a theoretical lower bound of t bits on the ciphertext overhead for IND-CPA security, the best known IND-CCA secure schemes demand roughly 2t bits even in the random oracle model. Is the t-bit gap essential for achieving IND-CCA security? We close the gap by proposing an IND-CCA secure scheme whose ciphertext overhead matches the generic lower bound up to a small constant. Our scheme uses a variation of a four-round Feistel network in the random oracle model and hence belongs to the family of OAEP-based schemes. Maybe of independent interest is a new efficient method to encrypt long messages exceeding the length of the permutation while retaining the minimal overhead. In Other Models. [21] constructed a simple scheme with optimal ciphertext overhead in the ideal full-domain permutation model. Looking at the construction and the security proof, however, one can see that the model is very strong and has little difference from idealizing the encryption function itself. Recently it is shown that ideal full-domain permutation can be constructed using random oracles [10] but the reduction is very costly and a tight reduction needed to retain the optimal overhead is highly unlikely. Note that [21] could only present a non-optimal scheme in the random oracle model, which shows the difficulty of achieving the optimality.. In the standard model, the IND-CCA secure schemes in [6, 17] have the shortest known ciphertext overhead consisting of two group elements which require an overhead of oh ≥ 4t + 2ε bits. It remains as a very interesting open question whether or not the optimality can be achieved without random oracles.. For Short Messages. Schemes based on general one-way permutations can never offer the optimal overhead for messages shorter than the size of the permutation. For the state of art in this issue, we refer to [2] which presents a scheme that offers non-optimal but oh ≥ 2t + ε that is currently the shortest overhead for messages of arbitrary (small) length. It is left as another open problem to construct a scheme with optimal overhead for arbitrary message size. Motivation. Ever since Goldwasser and Micali introduced the concept of "probabilistic encryption" [15] it is well understood that every public-key encryption scheme has to incorporate a certain amount of randomness into their ciphertexts in order to achieve semantic security. Thus a ciphertext c must be longer than the embedded message m and the difference oh := |c| − |m| is called the ciphertext overhead. In order to achieve stronger security properties, the ciphertext overhead tends to be even larger due to the use of extended randomness or extra integrity checking mechanisms. In this paper we are asking for the minimal possible ciphertext overhead to protect against adaptive chosen ciphertext attacks (IND-CCA security).. A Generic Lower Bound. A ciphertext overhead of oh bits means that at most oh bits of randomness can be incorporated into a ciphertext. A brute-force adversary in the IND-CPA experiment can exhaustively search for the randomness used for the challenge ciphertext. After encrypting one of the challenge messages up to 2 t times, it has an advantage of Ω(2 t /2 oh ). Requiring the advantage to be smaller than 2 −ε (and ignoring small additive constants), it must hold that oh ≥ t + ε . Accordingly, t + ε bits are a lower bound on the ciphertext overhead with respect to adversaries running in 2 t steps and having a success probability of at most 2 −ε , by counting encryption as one step. (We refer to Section 2 for a more formal treatment.) We say that the ciphertext overhead is optimal if it matches the lower bound up to a (small) constant term, i.e., if oh ≤ t + ε + O (1) . Since every IND-CPA adversary is also an IND-CCA adversary, the above lower bound also applies to IND-CCA secure schemes.. For a number of schemes the ciphertext overhead primarily depends on the size of the underlying number-theoretic primitive, which often suffers from more sophisticated attacks. For example,

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


A programmable system for artistic volumetric lighting Figure 1: Our system was used to author artistic volumetric effects for the movie Tangled. Our technique's ability to produce curving light beams is used to match the organic artistic style of the film.We present a method for generating art-directable volumetric effects, ranging from physically-accurate to non-physical results. Our system mimics the way experienced artists think about volumetric effects by using an intuitive lighting primitive, and decoupling the modeling and shading of this primitive. To accomplish this, we generalize the physically-based photon beams method to allow arbitrarily programmable simulation and shading phases. This provides an intuitive design space for artists to rapidly explore a wide range of physically-based as well as plausible, but exaggerated, volumetric effects. We integrate our approach into a real-world production pipeline and couple our volumetric effects to surface shading. Light scattering in participating media is responsible for many natural phenomena. Simulating the evolution of volumetric media over time, as well as the complex light transport within it, are difficult problems in animation (e.g. [Fedkiw et al. 2001; Hong et al. 2007] ) and rendering [Jensen and Christensen 1998; Jarosz et al. 2011] . Recent advances have made it feasible to incorporate a wider range of such effects in feature animation production; however, most of this work has focused on accelerating computation and increasing accuracy. Given physically accurate techniques, manipulating physical parameters to attain a target look is a challenging process. Previous work addresses this problem by investigating art-directable control of light transport for surface reflectance (e.g. ). However, artistic authoring and manipulation of volumetric lighting remains an unsolved problem.. Guiding accurate fluid animation using arbitrary source terms, while maintaining a principled framework, has been previously explored [McNamara et al. 2004; Treuille et al. 2003 ]. Similarly, our framework allows for programmatic and art-directed injection of source terms into physically-based volumetric light transport.. While physically accurate and art-directable rendering have seemingly conflicting goals, recent efforts to incorporate physicallybased rendering into production have shown great potential [Tabellion and Lamorlette 2004; Křivánek et al. 2010] . Such techniques are seeing increased adoption because they provide complex and subtle lighting which would otherwise take extensive manual manipulation to replicate with ad-hoc techniques. Unfortunately, physically accurate rendering is often not sufficiently expressive for the caricatured nature of animated films: though physically-based rendering may provide a great starting point, the challenge then becomes introducing controls necessary to obtain a desired artistic vision. We carefully combine these two areas and choose to generalize an existing physically-based approach for rendering volumetric lighting to art-directable shading and simulation (Section 4).. We present a system for generating target stylizations of volume effects, mimicking the way professional artists hand draw these effects. We base our approach on photon beams [Jarosz et al. 2011] , which provide physically-based rendering of participating media. We make the following contributions while generalizing photon beams to allow for artistic control of volumetric effects:. • We observe that manipulating physical parameters of participating media results in unintuitive changes to the final image. To address this we derive physically-based scattering properties to match a user-specified target appearance, providing an intuitive space for appearance modeling of participating media.. • To allow for non-physical effects, we generalize both the photon generation and radiance estimation stages of the photon beams method. We replace each stage with a procedural, programmable component. While each component could implement the physically-based approach, this provides enough programmatic flexibility for artist-driven volumetric effects.. Figure 2: Left to right: to hand draw a volumetric effect, artists first sketch a coarse high-level outline of the media, then progressively refine this sketch to include dynamics and increased levels of detail, before finally "rendering" the final image. We have presented a system for generating art-directable volumetric effects. We observe that aspects of the physically-accurate photon beams approach relate closely to the methods artists normally use to reason about, and hand draw volumetric effects. By generalizing this approach and tying it to a geometric interpretation for volumes, and the lighting within them, we are able to expose an intuitive programmable model for designing volumetric effects. Lighting and Material Control. Several works provide intuitive user controls for editing the lighting or materials in a scene to obtain a desired surface shading effect. Among these approaches, either the geometry and intensity of light sources [Pellacini et al. 2007 ], or the surface material properties [Obert et al. 2008; Song et al. 2009] , are manipulated in order to control shadows and reflectance from local lights ] and environment lights [Obert et al. 2010; Pellacini 2010] . Kerr and Pellacini [2009; 2010] recently surveyed and compared several of these techniques. We generalize photon beams to both volumetric material and lighting control. Song et al. [2009] consider editing sub-surface scattering on surfaces (using the dipole approximation). We target the control of more general volumetric effects, and thus use a different mathematical model (photon beams). Sadeghi et al. [2010] present an art-directable shading model for hair rendering in animated feature films. present BendyLights, a non-linear spotlight for controlling lighting and shadows. We also incorporate non-linear lighting, and in fact each photon beam can be considered a volumetric generalization of a BendyLight. However, since we have thousands of these lighting primitives, we provide a more suitable control mechanism (see Sections 5 and 7.1).. Artistic, Non-Photorealistic Rendering. Schmid et al. [2010] expose a programmable model for controlling motion effects. Where Schmid et al. deal with imitating hand-animated motion effects, we instead focus on replicating hand-drawing workflows for volumetric effects using virtual tools. Like our approach, their model is based on traditional hand drawn effects (see Section 3), as well as supporting both physically-based and art-directable effects.. Non-photorealistic rendering (NPR) targets stylized rendering of images which we also address with our approach. Selle et al. [2004] generate cartoon renderings of smoke. Though their goals are similar to ours, we focus on a particular type of stylization: plausible but exaggerated realism, as opposed to cartoon-like results.. Steering Physical Simulation. There has been a push to use increasingly accurate rendering approaches in production; however, the challenge is providing procedural control to obtain a desired look. Manipulation of physically-based fluid animations has demonstrated the utility of this hybridization (see e.g. Angelidis et al. [2006] ). At a high-level, these methods manipulate the simulation by injecting controlled source terms to the Navier-Stokes equation, instead of setting the initial fluid conditions. Analogously in light transport, light sources represent the initial conditions, but we add source terms locally to steer the transport of light. Using nonphysical BRDFs can also be seen as an injection of source terms. Our method applies these concepts to participating media by using beams as an intuitive and controllable volumetric lighting primitive.. Programming Models. We build on top of existing content generation tools, which offer expressive customizability to author geometry and shading effects. Domain-specific programming languages (DSL) are an essential component for providing this flexibility. The classic example of this is the RenderMan TM Shading Language (RSL) [Cook et al. 1987] . Similarly, Tessendorf's FELT scripting language [2010] enables the controllable specification of fluid simulation behavior. Both of these systems have a long history of successful application in film production. Other tools such as Maya TM and Houdini TM also expose programmability through node-based visual DSLs, which provide artist-accessible models of programming. Our generalization of photons beams provides the flexibility of a DSL for the specific domain of volumetric lighting.

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